diff --git a/Aufgabenblätter/NNW_Aufgabenblatt3.pdf b/Aufgabenblätter/NNW_Aufgabenblatt3.pdf
new file mode 100644
index 0000000..b854e4c
Binary files /dev/null and b/Aufgabenblätter/NNW_Aufgabenblatt3.pdf differ
diff --git a/Lösungen_N/AB2.ipynb b/Lösungen_N/AB2.ipynb
index 2f1275a..f0b30c9 100644
--- a/Lösungen_N/AB2.ipynb
+++ b/Lösungen_N/AB2.ipynb
@@ -1,2874 +1,2876 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "de4f7f4b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.svm import LinearSVC\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "67b3d7af",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[5.1 3.5 1.4 0.2 0. ]\n",
-      " [4.9 3.  1.4 0.2 0. ]\n",
-      " [4.7 3.2 1.3 0.2 0. ]\n",
-      " [4.6 3.1 1.5 0.2 0. ]\n",
-      " [5.  3.6 1.4 0.2 0. ]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 1a) Ja stehen in der richtigen Dimension\n",
-    "iris_data= np.loadtxt(\"../supplement_files/iris.csv\", delimiter=\",\")\n",
-    "print(iris_data[:5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c9c0fd1e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>.sk-global {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       ".sk-global.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       ".sk-global.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       ".sk-global {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".sk-global pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip-path: inset(100%);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       ".sk-global div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       ".sk-global label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label label.sk-toggleable__label,\n",
-       ".sk-global div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       ".sk-global div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       ".sk-global div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       ".sk-global div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       ".sk-global a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       ".sk-global a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-top-container.sk-global {\n",
-       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
-       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
-       "   to allow keyboard-only users to navigate the display. */\n",
-       "  overflow-x: scroll !important;\n",
-       "  max-width: 100%;\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover td {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table :is(td, th) {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
-       "*/\n",
-       ".estimator-table table td.param {\n",
-       "    text-align: left;\n",
-       "    position: relative;\n",
-       "    padding: 0;\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".default td, .estimator-table th {\n",
-       "    color: black;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       "td.fitted-att-type {\n",
-       "    white-space: preserve nowrap;\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    Styles for parameter documentation links\n",
-       "    We need styling for visited so jupyter doesn't overwrite it\n",
-       "*/\n",
-       "a.param-doc-link,\n",
-       "a.param-doc-link:link,\n",
-       "a.param-doc-link:visited {\n",
-       "    text-decoration: underline dashed;\n",
-       "    text-underline-offset: .3em;\n",
-       "    color: inherit;\n",
-       "    display: block;\n",
-       "    padding: .5em;\n",
-       "}\n",
-       "\n",
-       "@supports(anchor-name: --doc-link) {\n",
-       "    a.param-doc-link,\n",
-       "    a.param-doc-link:link,\n",
-       "    a.param-doc-link:visited {\n",
-       "    anchor-name: --doc-link;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
-       "a.param-doc-link::before {\n",
-       "    position: absolute;\n",
-       "    content: \"\";\n",
-       "    inset: 0;\n",
-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
-       "    display: none;\n",
-       "    position: absolute;\n",
-       "    z-index: 9999;\n",
-       "    left: 0;\n",
-       "    padding: .5ex;\n",
-       "    margin-left: 1.5em;\n",
-       "    color: var(--sklearn-color-text);\n",
-       "    box-shadow: .3em .3em .4em #999;\n",
-       "    width: max-content;\n",
-       "    text-align: left;\n",
-       "    max-height: 10em;\n",
-       "    overflow-y: auto;\n",
-       "\n",
-       "    /* unfitted */\n",
-       "    background: var(--sklearn-color-unfitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       "@supports(position-area: center right) {\n",
-       "    .param-doc-description {\n",
-       "    position-area: center right;\n",
-       "    position: fixed;\n",
-       "    margin-left: 0;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
-       "    /* fitted */\n",
-       "    background: var(--sklearn-color-fitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "    border: none;\n",
-       "    padding: 0;\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon:focus-visible {\n",
-       "    outline: 1px solid currentColor;\n",
-       "    outline-offset: 1px;\n",
-       "}\n",
-       "\n",
-       ".features {\n",
-       "  font-family: monospace;\n",
-       "  cursor: pointer;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
-       "}\n",
-       "\n",
-       ".features.fitted {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features summary {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  margin-bottom: 0;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "  padding: .25em;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features.fitted details[open] > summary {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features details > summary .arrow::before {\n",
-       "  content: \"▸\";\n",
-       "  color: grey;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary .arrow::before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       ".features details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features.fitted details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features .features-container {\n",
-       "  max-height: 10em;\n",
-       "  overflow: auto;\n",
-       "  scrollbar-width: thin;\n",
-       "  padding: .25em 0.1rem;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 0 0 .5em .5em;\n",
-       "}\n",
-       "\n",
-       ".features.fitted .features-container {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features .image-container {\n",
-       "  block-size: 1em;\n",
-       "  inline-size: 1em;\n",
-       "  padding: 0;\n",
-       "  margin: 0%;\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  align-items: center;\n",
-       "}\n",
-       "\n",
-       ".features .copy-paste-icon {\n",
-       "  background-size: 1em 1em;\n",
-       "  width: 1em;\n",
-       "  height: 1em;\n",
-       "  filter: grayscale(100%) opacity(60%);\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  width: 100%;\n",
-       "  margin: 0.01em;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(odd) {\n",
-       "  background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(even) {\n",
-       "  background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:hover {\n",
-       "  background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  table-layout: inherit;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table td {\n",
-       "  text-align: left;\n",
-       "  padding: 0 0.5em;\n",
-       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "  white-space: nowrap;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".total_features {\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  margin-top: 0.5em;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-2\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy penalty to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-penalty;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%7D%2C%20default%3D%27l2%27\">\n",
-       "            penalty\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-penalty;\">\n",
-       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;}, default=&#x27;l2&#x27;<br><br>Specifies the norm used in the penalization. The &#x27;l2&#x27;<br>penalty is the standard used in SVC. The &#x27;l1&#x27; leads to ``coef_``<br>vectors that are sparse.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy loss to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('loss',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-loss;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=loss,-%7B%27hinge%27%2C%20%27squared_hinge%27%7D%2C%20default%3D%27squared_hinge%27\">\n",
-       "            loss\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-loss;\">\n",
-       "            loss: {&#x27;hinge&#x27;, &#x27;squared_hinge&#x27;}, default=&#x27;squared_hinge&#x27;<br><br>Specifies the loss function. &#x27;hinge&#x27; is the standard SVM loss<br>(used e.g. by the SVC class) while &#x27;squared_hinge&#x27; is the<br>square of the hinge loss. The combination of ``penalty=&#x27;l1&#x27;``<br>and ``loss=&#x27;hinge&#x27;`` is not supported.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;squared_hinge&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy dual to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-dual;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=dual,-%22auto%22%20or%20bool%2C%20default%3D%22auto%22\">\n",
-       "            dual\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-dual;\">\n",
-       "            dual: &quot;auto&quot; or bool, default=&quot;auto&quot;<br><br>Select the algorithm to either solve the dual or primal<br>optimization problem. Prefer dual=False when n_samples &gt; n_features.<br>`dual=&quot;auto&quot;` will choose the value of the parameter automatically,<br>based on the values of `n_samples`, `n_features`, `loss`, `multi_class`<br>and `penalty`. If `n_samples` &lt; `n_features` and optimizer supports<br>chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,<br>otherwise it will be set to False.<br><br>.. versionchanged:: 1.3<br>   The `&quot;auto&quot;` option is added in version 1.3 and will be the default<br>   in version 1.5.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy tol to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-tol;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy C to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-C;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
-       "            C\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-C;\">\n",
-       "            C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>For an intuitive visualization of the effects of scaling<br>the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy multi_class to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-multi_class;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=multi_class,-%7B%27ovr%27%2C%20%27crammer_singer%27%7D%2C%20default%3D%27ovr%27\">\n",
-       "            multi_class\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-multi_class;\">\n",
-       "            multi_class: {&#x27;ovr&#x27;, &#x27;crammer_singer&#x27;}, default=&#x27;ovr&#x27;<br><br>Determines the multi-class strategy if `y` contains more than<br>two classes.<br>``&quot;ovr&quot;`` trains n_classes one-vs-rest classifiers, while<br>``&quot;crammer_singer&quot;`` optimizes a joint objective over all classes.<br>While `crammer_singer` is interesting from a theoretical perspective<br>as it is consistent, it is seldom used in practice as it rarely leads<br>to better accuracy and is more expensive to compute.<br>If ``&quot;crammer_singer&quot;`` is chosen, the options loss, penalty and dual<br>will be ignored.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Whether or not to fit an intercept. If set to True, the feature vector<br>is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where<br>1 corresponds to the intercept. If set to False, no intercept will be<br>used in calculations (i.e. data is expected to be already centered).</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy intercept_scaling to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_scaling,-float%2C%20default%3D1.0\">\n",
-       "            intercept_scaling\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
-       "            intercept_scaling: float, default=1.0<br><br>When `fit_intercept` is True, the instance vector x becomes ``[x_1,<br>..., x_n, intercept_scaling]``, i.e. a &quot;synthetic&quot; feature with a<br>constant value equal to `intercept_scaling` is appended to the instance<br>vector. The intercept becomes intercept_scaling * synthetic feature<br>weight. Note that liblinear internally penalizes the intercept,<br>treating it like any other term in the feature vector. To reduce the<br>impact of the regularization on the intercept, the `intercept_scaling`<br>parameter can be set to a value greater than 1; the higher the value of<br>`intercept_scaling`, the lower the impact of regularization on it.<br>Then, the weights become `[w_x_1, ..., w_x_n,<br>w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent<br>the feature weights and the intercept weight is scaled by<br>`intercept_scaling`. This scaling allows the intercept term to have a<br>different regularization behavior compared to the other features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy class_weight to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
-       "            class_weight\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
-       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Set the parameter C of class i to ``class_weight[i]*C`` for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy verbose to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-verbose;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
-       "            verbose\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-verbose;\">\n",
-       "            verbose: int, default=0<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in liblinear that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy random_state to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-random_state;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
-       "            random_state\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-random_state;\">\n",
-       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>the dual coordinate descent (if ``dual=True``). When ``dual=False`` the<br>underlying implementation of :class:`LinearSVC` is not random and<br>``random_state`` has no effect on the results.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy max_iter to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=max_iter,-int%2C%20default%3D1000\">\n",
-       "            max_iter\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
-       "            max_iter: int, default=1000<br><br>The maximum number of iterations to be run.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1000</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    \n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Fitted attributes</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                    <tbody>\n",
-       "                        <tr>\n",
-       "                        <th>Name</th>\n",
-       "                        <th>Type</th>\n",
-       "                        <th>Value</th>\n",
-       "                        </tr>\n",
-       "                        \n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-classes_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
-       "            classes_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-classes_;\">\n",
-       "            classes_: ndarray of shape (n_classes,)<br><br>The unique classes labels.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
-       "           <td>[0.,1.,2.]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=coef_,-ndarray%20of%20shape%20%281%2C%20n_features%29%20if%20n_classes%20%3D%3D%202%20%20%20%20%20%20%20%20%20%20%20%20%20else%20%28n_classes%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: ndarray of shape (1, n_features) if n_classes == 2             else (n_classes, n_features)<br><br>Weights assigned to the features (coefficients in the primal<br>problem).<br><br>``coef_`` is a readonly property derived from ``raw_coef_`` that<br>follows the internal memory layout of liblinear.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3, 2)</td>\n",
-       "           <td>[[-1.54, 2.28],\n",
-       " [ 0.1 ,-0.98],\n",
-       " [ 0.66,-0.35]]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20if%20n_classes%20%3D%3D%202%20else%20%28n_classes%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: ndarray of shape (1,) if n_classes == 2 else (n_classes,)<br><br>Constants in decision function.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
-       "           <td>[ 1.2 , 2.02,-3.14]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_features_in_,-int\">\n",
-       "            n_features_in_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
-       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>2</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_iter_,-int\">\n",
-       "            n_iter_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
-       "            n_iter_: int<br><br>Maximum number of iterations run across all classes.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>8</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "                    </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
-       " SPDX-License-Identifier: BSD-3-Clause\n",
-       "*/\n",
-       "\n",
-       "function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "\n",
-       "    const parent = element.parentElement;\n",
-       "    if (!parent || !parent.nextElementSibling) {\n",
-       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    const paramName = element.parentElement.nextElementSibling\n",
-       "        .textContent.trim().split(' ')[0];\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "\n",
-       "/**\n",
-       " * Copy the list of feature names formatted as a Python list.\n",
-       " *\n",
-       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
-       " *   contain a `details` element and a table containing feature named.\n",
-       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
-       " */\n",
-       "function copyFeatureNamesToClipboard(element) {\n",
-       "    var detailsElem = element.closest('.features').querySelector('details');\n",
-       "    var wasOpen = detailsElem.open;\n",
-       "    detailsElem.open = true;\n",
-       "    var content = element.closest('.features').querySelector('tbody')\n",
-       "                  .innerText.trim();\n",
-       "    if (!wasOpen) detailsElem.open = false;\n",
-       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
-       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
-       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
-       "    const originalStyle = element.style;\n",
-       "    const copyMark = document.createElement('span');\n",
-       "    copyMark.innerHTML = '✔';\n",
-       "    copyMark.style.color = 'blue';\n",
-       "    copyMark.style.fontSize = '1em';\n",
-       "\n",
-       "    navigator.clipboard.writeText(formattedText)\n",
-       "        .then(() => {\n",
-       "            element.style.display = 'none';\n",
-       "            element.parentElement.appendChild(copyMark);\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                copyMark.remove();\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'orange';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "/**\n",
-       " * Adapted from Skrub\n",
-       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
-       " * @returns \"light\" or \"dark\"\n",
-       " */\n",
-       "function detectTheme(element) {\n",
-       "    const body = document.querySelector('body');\n",
-       "\n",
-       "    // Check VSCode theme\n",
-       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
-       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
-       "\n",
-       "    if (themeKindAttr && themeNameAttr) {\n",
-       "        const themeKind = themeKindAttr.toLowerCase();\n",
-       "        const themeName = themeNameAttr.toLowerCase();\n",
-       "\n",
-       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
-       "            return \"dark\";\n",
-       "        }\n",
-       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
-       "            return \"light\";\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    // Check Jupyter theme\n",
-       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
-       "        return 'dark';\n",
-       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
-       "        return 'light';\n",
-       "    }\n",
-       "\n",
-       "    // Guess based on a parent element's color\n",
-       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
-       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
-       "    if (match) {\n",
-       "        const [r, g, b] = [\n",
-       "            parseFloat(match[1]),\n",
-       "            parseFloat(match[2]),\n",
-       "            parseFloat(match[3])\n",
-       "        ];\n",
-       "\n",
-       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
-       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
-       "\n",
-       "        if (luma > 180) {\n",
-       "            // If the text is very bright we have a dark theme\n",
-       "            return 'dark';\n",
-       "        }\n",
-       "        if (luma < 75) {\n",
-       "            // If the text is very dark we have a light theme\n",
-       "            return 'light';\n",
-       "        }\n",
-       "        // Otherwise fall back to the next heuristic.\n",
-       "    }\n",
-       "\n",
-       "    // Fallback to system preference\n",
-       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
-       "}\n",
-       "\n",
-       "\n",
-       "function forceTheme(elementId) {\n",
-       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
-       "    if (estimatorElement === null) {\n",
-       "        console.error(`Element with id ${elementId} not found.`);\n",
-       "    } else {\n",
-       "        const theme = detectTheme(estimatorElement);\n",
-       "        estimatorElement.classList.add(theme);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "forceTheme('sk-container-id-2');</script></body>"
-      ],
-      "text/plain": [
-       "LinearSVC()"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 1b)\n",
-    "clf = LinearSVC()\n",
-    "X = iris_data[:,[0,1]]\n",
-    "T = iris_data[:,4]\n",
-    "clf.fit(X, T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "cc181a3a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "80.0 Prozent wurden richtig vorausgesagt\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 1 c+d)\n",
-    "pred = clf.predict(X)\n",
-    "correct_perc = (sum(pred == T)/len(pred))*100\n",
-    "\n",
-    "print(f\"{correct_perc} Prozent wurden richtig vorausgesagt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "16af32f1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1e)\n",
-    "import nnwplot\n",
-    "nnwplot.plotTwoFeatures(X, T, clf.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "f4bcee12",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAjutJREFUeJzt3Qd4HNXVBuBvZptWvcvqki25926wwYAbHQym996T0ElCCAkECAk/LRB6J/RmXKg2xsa9F7nJ6r33rTP/c+9qZbXdHdmrreflWSytRjuzs5Lm7L3nniPIsiyDEEIIIcRLRG/tmBBCCCGEoWCEEEIIIV5FwQghhBBCvIqCEUIIIYR4FQUjhBBCCPEqCkYIIYQQ4lUUjBBCCCHEqygYIYQQQohXqeEHJElCeXk5IiIiIAiCtw+HEEIIIQqwuqotLS1ISUmBKIr+HYywQCQ9Pd3bh0EIIYSQY1BSUoK0tDT/DkbYiAjz2dBTECr6xSETQojXNH2xBK/8N5JeAeJ1FlM71r5+Xdd13BG/uLLbp2ZYIBKm0nj7cAghxCdNmGvCXYtvRPlbeqh13j4aQo5ylWJBCayEEBIgfnnwapSv1Hv7MAgZMApGCCEkAMx+Jh2fH9R6+zAIOSZ+MU1DCCHEsTnrlmDB43F0iojfomCEEEL8WOPKS7Hg8ShvHwYhx4WCEUII8eOpmYXPUyBC/B/ljBBCiJ/6dvRsbx8CIW5BIyOEEOKPIyI/ngk87+0jIcQ9KBghhBA/QsmqJBDRNA0hhBBCvIpGRgghxE+M33UFFjwY7u3DIMTtaGSEEEL8ZAnvEgpESICikRFCCPFhlKxKggEFI4QQ4qMoWZUEC5qmIYQQQohX0cgIIYT4IEpWJcGERkYIIcSHTJhromRVEnQoGCGEEB/yy4NX4wXqN0OCDAUjhBDiQ6Minx/UevswCPE4yhkhhBBfWsK70ttHQojnUTBCCCE+kKy6kAqakSBGwQghhHh5auau1Sp6DUhQo5wRQgjxIvn561C+Uk+vAQlqNDJCCCHeGhFZfCPKH6RAhBAKRgghxFtl3ilZlRCOpmkIIYQQ4lUUjBBCiAdR8ztC+qJpGkII8RDqN0NI/2hkhBBCPBSILKFaIoT0i4IRQgjxgDXlVjrPhDhA0zSEEOKJJbzP0xJeQhyhYIQQQgYJLeElRBmapiGEkEEaEblmayidW0IUoGCEEEIGQeTjl1OZd0IGe5pm3759eP755zF58mTcdNNNDrdbsWIFvv766x73hYaG4plnnjnWXRNCiE+jJbyEeCAY6ejowMUXX4yamhrU1tY6DUa2bt2K77//Hvfff3/XfTqd7lh2SwghhJAAdEzByO9//3vMnTsXBQUFirZPTEzELbfcciy7IoQQv9K48lKqJ0LIYAcjn332GdauXYstW7ZgyZIlir6nsrIS9913H0JCQjB9+nScffbZA90tIYT4tNnPpGPhj2cCz3v7SAgJ8ATWwsJC3H777fjggw+g1ytfM5+ZmYnY2FiYTCZcc801OPPMMyFJksPtjUYjmpube9wIIcSXV85cqz7V24dBSOCPjFgsFlx66aU892PixImKd3Dttdfiz3/+c9fn1113HSZMmIC33noL119/fb/f88QTT+DRRx9VvA9CCPGmiHOG0coZQjwxMrJy5Ups27YNBw8e5Pkf7LZ7925s376df1xXV9fv96Wmpvb4fMSIEZgyZQp+++03h/t66KGH0NTU1HUrKSkZyHMihBCPrpzh0zOEkMEfGRk7diyee+65HvexgEKtVvOREq1Wq3inBoMBVqvjPg1stQ2tuCGE+DpKViXEw8FIVlZWnxUx3377LU9K7X4/u+/nn3/uqiOybNkyniNi99133/HRFJbQSggh/l3qPcrbh0FIQHB7BVa2yubNN9/s+vyrr77ioyqXX345Fi5ciHPPPRf33nsvr1NCCCH+amkLtfYixF2O67eJraxh0zTdnXXWWXz1jN1rr72G/Px8bN68mVdeff3115Genn48uyWEEK+hJbyEuJ8gy7IMH8eW9kZFRWF5znyEqTTePhxCBo023ICwIc0QBKCtKgLGZmo772vJqkseDPf2YRDiNyzGdqx+6RK+GCUyMtLhdjTOSIgPCIlpQ/Zp+xE/sgpC5+Qpe5tQfygBBT+NRHtNhLcPkRBCBg117SXEy/TxrZh0wzrEjajuCkQYNjoSM6wWk67/DeHJjd48REKjIoQMKgpGCPEqGaMWb4dKa4Wo6jtjyu4T1VaMvmgbm1T1yhES28oZmp4hZPBQMEKIF0WmNSJ8SEu/gYgdGy0JiTIgNqfao8dGjmqKoE7jhAwmCkYI8aKYnBpIVsHldmyb2NwajxwT6dlz5o3nr6ZREUIGGSWwEuJFKo3jSsS9seka4oUlvCvprBMy2GhkhBAvMjTqISjIBWHJrMYmWubrSc3zTvbo/ggJZhSMEOJF1XtSIMuup2lY8mrVzjRPHBKhhFVCPI6CEUK8yNKhRdnGLF5TxBFZAqp2psLQGOrJQwvqwmYLHo/z9mEQElQoGCHEywp+GoGqHan84+7JrPaP6w4m4dCysV47vmBKVqUuvIR4ByWwEuJtsoiDS8ejckc6UqYWITK9ARCA1vIolG/JRGMBe5euYCqHHLfPD2rpLBLiBRSMEOITBDSXxPIb8c6oyF2Lb0T5SkoSJsQbaJqGEBL05Oevo0CEEC+ikRFCSFDjoyKrVd4+DEKCGgUjhJCg7jnDV85QYTNCvIqCEUJIUGIrZxY8HuXtwyCEUM4IISRYp2YIIb6DElgJIUHnlwevxgvP06gIIb6CpmkIIUHV/O5a9akof56W8BLiSygYIYQEVxdeQojPoWkaQgghhHgVjYwQQoJnCS8hxCfRyAghJOBXzixtofddhPgyCkYIOW4ynUMfFvn45bRyhhAfR28XCDkGoQktSJlWiMSxFVDpLLB0aFC1KxXlmzNhaAijc+ojqLAZIf6BghFCBihxXBlGnLsTsixAVNlGRTShZqRMK0LK1GLs+3Qy6g8l0nn1hZUzz9PLQIg/oGkaQgYgIq2BByIQ0BWIdP0yqWQIKgmjl2yFPq6VzishhChEwQghA5A+6wgfERGE/r/O7xeA1OmFdF69hOqJEOJ/KBghRCGV1oy4EVV9RkT6/FKpZCRNLKXEVi/5dvRsb+2aEHKMKGeEEIU0YSYICsN3lUaCqLFCMtOvmMdHRajnDCF+h/5SEqKQxaBRfK5kSYBkUdG59aDxu67AwgfD6ZwT4odomoYQhSwdWjSVREOWnG8nWQXU7k8EZAeJJcTtRc3YEt4lFIgQ4rcoGCFkAMrWZ7ucqmE5I2Wbsum8ekjEOcOoqBkhfo6CEUIGoHb/EJT8NpR/3HuExP75kR9Gork4ls6rh0ZFrlWfSueaED9HOSOEDIiAgh9HoLUiEmmzjiAipbnrK80lMTxQqT+UROfUQ3558GqUP6+n802In6NghJABE1CzN4XfdFEdUOtNMLfpYGoJoXPp4VGRuw5q6ZwTEgAoGCF+S1RbkTC2HEnjS6GLNMLcoUHN3mRU7UiDxeCZi5SxSc9vxLPmrFuCBY/HASvpzBMSCCgYIX4pNL4F467YBG2EkTfNZUmlITIQkdyErLmHsPeTyWg8kuDtwySDtIR3Aa2cISSgUAIr8TtsWmT8VRuhCTfx8uv21S32j9mIydhLtiIs8Wg+Bwmcoma0hJeQwEPBCPE7yVOKoQk1QRT7L8vOgxNBRvrsfI8fGxlczfNOplNMSACiYIT4nZQpxbwZnataH/GjKqEOMXvqsMggo8JmhAQuCkaIn5GhizI47JrbOyDRRhg8cVCEEEKOAyWwEr/Dyq276pzbta2F4u2AWMK7+EaqJ0JIAKO/1MTPCGjIj+cBiTOyDBga9DA0hHrsyMjgiHz8cpSvpOXThAQyCkaI3ynflKVoZKRsUxYPXoh/j4pcs5UCSkICHQUjxO80HIlH2cbMrhGQ3liPGLZN+WbbNsR/C5tdGHoXjYoQEgQoZ4T4IQH5341GR30YX76rY4XPOlkMapRvyUDR6uGQJYq1/XnlzILHo7x9GIQQD6FghPgpAeWbs3jgEZ1ZD02YCRajGk2FcZAsKm8fHCGEkAGgYIT4N1lEY2E8gokgSogbUYW44dUQtRbeG6dqZxraqiIRKKMiLzxPoyKEBJNjHsdetmwZJk6ciPvuu8/ltps2bcJFF12EGTNm4IorrkBeXt6x7paQoBaR0ojpv1uF0Uu28yaB8SOrkDKtCFNuXouxl26GSmv2+zwRCkQICT7HFIyUl5fj1ltvhcFgQEFBgdNtd+7ciZNPPhlpaWl48sknoVarceKJJ6KoqOhYj5mQoBSa0MJ78mjDbDkybEURK/5mX1kUM6wGYy/dAggS/NXSFhqsJSQYDTgYkSQJl19+OR544AHk5OS43P6xxx7jIyLPPPMMTjnlFLz55puIj4/Hv//972M9ZkKCUtbcgxBUUldjwN7Y/VGZDYgfUQV/bID39zNuoVERQoLUgIMRFlzo9Xrcfvvtirb/+eefccYZZxzdoSjyz3/66aeB7pqQoKUJM/I8EVf1Vdiy5uRpRX43NbPwxzO9fRiEEC8a0Jjor7/+iv/+97/Yvn27ou3b2tpQX1+P5OTkHvenpKSgpKTE4fcZjUZ+s2tuplbwJLiFxrc6HBHpjm0TntQCf0JTM4QQxSMjLKhg0zOvvPIKkpKSFH2P2WxLptPpdD3uZ5/bv9afJ554AlFRUV239PR0eqVIcJOVV5KVB7Ctt43fdQVNzRBClAcja9asQWVlJR5++GG+iobd2EjJjz/+yD9mSa29RUREQKvVoq6ursf97PO4uDiH+3rooYfQ1NTUdXM2ikJIMGitilDU9I/17GkqjoG/TM8seTDc24dBCPGnaRqWfMqW6HZ311138VGOp59+miel9qZSqTBhwgT+fWz1jd369esxZcoUh/tij9l7NIWQYGY1alC1MxVJE0ud5o2wr1X4SRn8pgj6HSeEDHBkhE2X2EdE7LfIyMiu+9kICMNySubMmdP1fTfddBM+++wz7NixoyuhddWqVbjxxhuV7poQAqBw9XCY27QOOxazPj2VO1LRWOh41NGXCpvRqAghxM7ti/rZVM7u3bu7Pr/++ut5kTO2vHfIkCGorq7G3//+d5x11lnu3jUhAc3cpsP2N07AiPN2Iia7nq+cYfkhgihDtooo3ZCFwlUjfL5TMVvGe+1B25sXQghhBFnur++pMqzgmSAIyMpirdqPBiM1NTUYN25cj20bGxtRUVHBk1HDwwc2T8xW07ARmOU58xGm0tArR4KePr4VccOroNJYYWzWo2bfED6V4+smzDXhlwevpqRVQoKExdiO1S9dwvM/2WzKoIyMZGdn97mPjX6wW2/R0dH8Rohvk5A8tRjxIyshqGS010Sg8OfhsBh86518R204Smv9K/mTjYjweiLPe/tICCG+hmovE9IpYVwZRpy9C6Ja5vkXTHRmA5KnFKM2bwjyPpt4PO2cghpbObPgcd/PZSGEeAf9ZSUEQPyYMow8bycfDWFYzxd2s3+cMLoSYy/bQueKEEIGAQUjhAAYftYefh7sAUh/YobVIiK1gc7XMRQ2o1ERQogzFIyQoBc/qhxqndVpIGKXPW9/0J+vgSas/m61is4ZIcQpCkZI0IsbUd2VI+IMC1bCEv2r74u3yc9fh/KVem8fBiHEx1EwQoIeq9OhmG+X8PApVNiMEKIUBSMk6DUVxSiaomGjJ8ZGepdPCCHuRsEICXoVWzMgWQSXUzUsYClclRv050tJnsgbz1NhM0KIchSMEAIRRWtsQYajgITd31YdhvpDfQv6kZ4iH7+c8kQIIQNCRc8IAVCyNoeXVk+fnd9vQNJWFY5tr59A50rBqMg1W0PpPBFCBoSCEUI6sSZzpeuzkT0/D9FZ9RAEGYZGPQp+GomWshg6T0qrrK6kU0UIGRgKRohPCk1sQubJh6HWWdBaFY6CH0YCGPx6FawHzaGlE+AtITFtiEhu4qt2WqsieQ8avyls9qB/HCsJLDJktIj5aFeVQZBViLKOQoh8fK0HrDCiXr0TFqENWjkaMZZxEOlyOagoGCE+RZ/QhEnXbIAq5GgRspihdUibWYTGgjjsfn8GAlFoQguGLdzHn2t3TcUxyP9+FFrLqckkIb3VqbfjUMhbaFUVHr1TFpBgmYkRHTcgRE4Y0EmTYEa+7kOU6pbBKhi67tdIUcgyLkaG6TwItL5/UFACK/EZ+rhmTL1lXY9ApDt2oZ5y6y8INGFJzZh43W+IzuoZiDCRaQ2YeM0GRGbUw1dRPRHiDVXqtdge+le0ikU9vyDIqFVvxKbwe2AQahQ/ngQLdoT+HUW6L3oEIoxZbMIh/VvYH/JfPhJD3I+CEeIzJlyzkf/bXyBivy8soQ3JUwoQOGSMWrydJ88K/fw2svsEUcKoC7YDggRfzBN54fkobx8GCTJmtGJv6LP894cFH73JggST0Ix9+hcVP2apdhmfmunv8ezKdCtQp956zMdNHKNghPgEbVQrNKFml8XH2EqXzFMOI1BEZdYjNKGt30DEjn1NF2HkZet9zdIWmuklnleh/YlPqTidMREk1Gu2o10sd/l4MiQUa7+1BTfOHlIWUaxdegxHTFyhYIT4hIzZRxRVQWXbaPRmBIqYobWQrK6fONsmZqjyIefBRoXNiLdzRVwFDpwM1Kt3udzMIFTDoKpy2e6Bjbiw0ROaqnE/CkaIT1BprQhGoloalG0JCWRWwaiwT5QACSaXW0nCAN7gCBIfSSHuRcEI8QlNhbGKtmPTNHIA/R3oaAhV1KiP1zxp8I1iYrOfSceFoXdRlVXiNaFSKp8ycUmQoZeSXW6mk+L4smCXZLZtLEQPlBkINhSMEJ9QuT2TBxlK+sM0FR5fDQFfUrMnBbJVyR9VoGpnGnxB87yTvX0IJMilmubzKRNX2JLcOMtkl9upEYok82wFAY6AVNPpAzhSohQFI8RnVG5Pc9kfht32fTERgcJi0KDkt6FOgzAWpFVsyYCxWe8ToyJLqLgZ8bJI63DEmacALoKHHMOVikcxsoxLILDSW3L/8z8sUNHKUUgzLTqmYybOUTBCfMahZePRcDief9z94tz9413vT4O1Q4dAUrQ6F+WbMvnH3ZNZ7R9X705F/nej4QvLeBf+eKa3D4MQXnhsXPv9iLWM52ejx4gG+1gGcjquRqp5geKzFS5lYGLbI1AhxBaQ2P/udAYnWjkGU9oe5wEJcT9Bll0NjHtfc3MzoqKisDxnPsJUGm8fDhlkkRm1GHHOboREd/DpCdkqoO5QIvK+GAdYtQF7/lnxs+QpRYhKb+DPu6UsCuVbM32i+iorbEb1RIivYataGlS7UKr9Dm2qIj6yEWuZgDTT6QhVkCviqIZJhXYVqjRrYBZaeY5IsvlUPo2jQmC9EfIEi7Edq1+6BE1NTYiMjHS4HRUJID6nuTgem1+cC318Gy8GZmwOgbnt+P8IqHRm6GPaIUkCOurCIFv7H75lRcb0cW0QVRJvlMf61XhCW1UkDi8fB1/DlvHedTBwg0Di3yMksdYJiO1wXz8pDcKRYTqb34jnUDBCfIsgIXVGIVKnFyIk2laSmY3d1R9OQPGvOWgpHXj33JDYNmTOOYyEseUQVbaBQHOHBhVbM1CydiisJttom6ixIP2EI0ieWgRtmLlrqqR23xC+7/baCASjXx68GuXPez9fhRASuCgYIT6DjUiMvmgrYnNr+qygYcXBYofVIO+zSajdr3z4NTy5CeOv2sBrdNgDEYYVTks/IR9xw6uw8+2ZkCUB46/eiPCk5h7VUNn3xI+u5NVPd78/Hc3HEAz5M9aNlxJWCSGDjRJYic9In52P2JwaHnz0rsbKAwkBGHnBDugiOxQHN2Mu2cKneroHIke/Dj4VlHvmHt4xt3cg0n3fotrKH4v9G0zWlAfX8yWEeAeNjBCfwAIHNjXjtEcLC1AEGUMml6Bo9XCXjxk/qpL3dHFGFGW+HZsKctUfRq0386meqh3pCHRsCS9fOfO8t4+EEBIMaGSE+ISItEbeKM8VFhQkjK5Q9JhxI6uUVWsV2OiHgu1kIH5kJYKBMGOmtw+BEBJEKBghPkGtU94bQq2zKH5MZ6MdXRSWmOejIyGB06TP2ajIgscDp8otIcT3UTBCfIKpNUTRdmw6xdiibJmvqSVEUUdcNjKiJGhhj+ULVVAHexnvtepTvX0YhJAgQ8EI8QmtFZForw1z2ZuGqdyhrEdL1e7UfhNXe5MsIiwG1/M07LGqdqUq2jchhBDlKBghPkLgtTx6r6Lpjk2lmNu0qFYYELBOwM1lUU5HR1jwU7YxC6XrhzkNhNhjtFWFoyE/QdG+CSGEKEfBCPEZrAdL4apc/nH3AII3yGOBSIcWu96f3lWkzDUBez+aaqu22isvxP74NXuSUbhqOIp/HdbVqK/PvmXwSqy7P5zmsIkWIYSQY0dLe4lPKf41F40F8UiZVojY4dW8WBnL/ajYmo6KbRmwdAysLDkrI7/99RORNL4MydOKEBrfygucNRXHonxzJuoPJtqSRlijvm/HoTYvGSnTCxGdVQdBlNFRH8Y75lbtTIPVRL8uhBAyGOiva0BhxbkkfrGVJd8d9BJUtkJajnrDsCqn7qx0KllUPJBhNxdHxqdhaCqGBAMZVkgwQ4SO93ghxJsoGAkAar0JKVOLeU8Ve5Gv5tIolG3KQs3eFJ+YWmCVS5MmliB1OhudaOP3sYRVNjpRuT2dBwzENzw314olK719FGSwNKj2oFi3FDXqjbwXlCjrkGI6DemmsxAmKUsOJ8TdBFlWsn7Bu5qbmxEVFYXlOfMRplKaLxAcQmLaMOHqDdCGG21LVDvjDpYfwZar1uYlIe/zSV4dKWHdcsdfuRHhyc38865j7PzJa62MxO73ZsBioNfW2/VF7MXOlrao8cLzUV49HuJ+hdrPcVj/DgRZBVk4WupfkNnfBxUmtP8R8ZYpdOqJ21iM7Vj90iVoampCZGSkw+18dyyfuCZIGHfZZmjCTDzw6L4SxV43g1UhzTrloFfP5sjzdyB8SEufnjP2z8OTWvg2xHvmrFtCVVcDHBsJYYEI0z0QsX0uQYYFO0P/gXYhOKoME99CwYgfi8utgT6u3WktDXaxT5lWBJVWWdVSd9PHtSJueA1PBnWEfY116tXHt3r02MjRQmck8BXqPgf4CIgDAvsdtaJMt8KTh0UIR8GIH0scVwZJQRlzldaK2NxqeEPi2HKeUOuKJAl8W+J5kY9fTqc9wBmEGjSp9/PRVGfYCEm55iePHRchdhSM+DGWJyIqeAVZ/ogmzHn32sGiCTUpqqrK+sNovXSMwTwiwqZnSOAzCbZ8LSXMQsugHgsh/aFgxI+xImBKG7x5KznUYlS+X3MHJbB6MlnV2YjISSkqpCzq8NjxkMGllsMGsG3ooB4LIf2hYMSP1exNVtbgzSJ0FvfyzjEq6Q/Dtqndl+yRYyKuRbUY8faUdgpIAoReTkK4NdvlMn+2ymaI+WSPHRchdhSM+LHa/Um8g62z0RH2NdZYzmIYWOVSd2mrikRTUYzT/jDsa03F0WitpKWknl7CS4IDK2qWaTy/M0nVAdb6ADLSTGd48tAI4SgY8WOsgumeD6fBYlT3udjbe7E0l8TgyPej4U2szomxSd9v0MTuMzaHIO+zyd44tKDMEaFAJDixEY8M47m2T3qtqmEjIixkGdPxe4RLrioVE+J+VIHVz7GRh22vzkHarCMYMrGUr5xhjI16XoG1fEuGw7LrnmJqDcH2109A6sxCpEwtgibUzO83t2tQviUTZRuyvDZyE0xo1UxwY6MjuYbrEGUZiWLdV2hSH7B9QRYQb5nOR06irSO9fZgkSFEwEgDYqEP+yjE48sNIXg6ejZKw5nL2BnC+gAUbRauHo3hNDrSdJetNfIqJBuc8gWqJEHtAkmQ5kd9MQhMsQju0UiTUUJ7gSohfBiOs2rzV2rPanyAIUKmoF4nbz7VVBUOjr2bCS3xkJG1mga10PR8x0aFsYxZK12f3mDGMyq7B0HkHEJbIqrbKsJpVqNk3BAU/jOoxgsLKzCdNKOUjQuwx2YohljDLGuLZgrHBw5ZKJ08uRsLYcmj0ZpjadKjelWrLzxlgZ2FPBCLHOipywXATXlipd/sxEe/TylH8Rojf9aYpKyvDP/7xD3zzzTeora1Fbm4u7rnnHlx99dUOv+exxx7Dww8/3CP4iI6O5t+vFPWm8W+i2oKpt/+CkCgjz2Xp3puGfWxo0mHLf06GZFFjxHk7kDiuvN8eNqx42q73pqO5OA7hQ5ow7opNUOttUz7de/Kw0Za8zyah7mDSoDyfmGE1GH3RVogq1gDItm9+jDJgNaqx+8NpaClzX9dhbyerNkXosOTBcLcdEyEkeFgGozfN66+/junTp2PLli2or6/HH/7wB1x33XVYscJ5+eAZM2bAYrF03QYSiBD/N+mGddBF2kZDevemYdjXJt24DplzDyBpfLnDHjasbPz4KzdBH9eCcVdugirE3Hdb1qNHJWHUkm0IT250+3MJTWzGmIttgUj3fkC242OjNRaMu3wzdJEdPjEiQsmqhBB/MKBg5JFHHuGjIElJSdDr9bj22muRmpqKTZs2Dd4REr8WNqQJoQltPQKG3tjXQuPbkDYr32m1VntAMnLxDqh1ZofVZ+37Sp+dD3dLn3WEL490VN+FByRaK1KmF8LbIs4Z5u1DIIQQRY4pe5CNbrAhlzfeeAMNDQ0455xznG6/Y8cOHrzExMRg4cKF/HMSHLJO6czYV0Cl6TnK4QjvACy6LqIWP6IKar37msCJGgsSxla4LOLGAqbkySW2eRsvoCW8hJCAD0b27t2LkJAQnvdxxx134OWXX8akSZMcbp+SkoL33nsP1dXV2LVrFxITE3HSSSehqKjI4fcYjUaeJ9L9RvxTSJTBrY/Xe1rG6bairX+Pu2jDTIqqyTLqEAtETc/EbV8o804IIQERjIwZM4aPjLS0tOA///kPzxn56quvHG7Pvr5kyRJEREQgPT0db775Jk9iYfknjjzxxBOIiorqurHvI/6JrYRxN+Up14Dkxv0P5LmwY5QsYsBUVqU+NYSQwXTMfy3Dw8N5oHHmmWfi1VdfVfx9Go0Gw4cPx+HDhx1u89BDD/FpIPutpIQNeRN/VL0nWdFIRteKFBfYNhZD34qzfbaTgI66UBga3bcs1dymRWtlhMvmhOzYGvLj+1S59NdkVXufmjvvahqUxyeEkOP+a8mmVMRumYSSJPWpK9KdyWTC/v37eeKrIzqdjo+edL8R/1S+MYs36nMWaNhGEQS0lEcoCkiKfh3merpEAEo3Zrm58JvA66IoyVdh1W89iZJVCSFBE4ycf/75WL16NU9aLS8vx9NPP43vvvuOj5DY/e1vf0NcXFzX54sXL8bPP//Mv+fQoUO48soreQ7ITTfd5N5nQnyUiH2f23KK+gs07PflfTERez6YAatJ5XS76t0pKN8wDMW/2laKONq2bn8SKrZmwt2qdqahaneyrfdPr33bPy/dkIWGwwnwBEpWJYQEXQXW+++/nxcx27hxI59uYfkjy5Ytw6JFi7q2YaMkavXRh33ggQd4gMK+JzQ0lNcpYUuB2VQNCQ71B4Zg1/vTMOKcXbymSPdiZqwk/IFvxqHxSCK/b9Nzp2DsZZsRkdrUY3pHMoso3ZCNotUj+OeFq0agoy4MGXMOQx/X3rWdqVXLRy9Kfhvqsl36sRFw4MuJaK2IQtqsAl5+345NCZWsG4bKbekeKcVP3XcJIUFZgdVbqAJr4AhLakJsbg3/uP5QAtqq+i9HrQ4xIXF8Ga+w2loZyQOa/skIT27qKgffUhbtuX43goTI1Ca+fNjcpkNLeZRH+wF5OhhZ2qLGC89T+XBCiPsrsFKjvAARmtDCa1uExrdAsopoKoo7rj4prIR79mkHED+qki9RZRf6yu3pKFk79JjzniNSG5A9bz9C41v557E51Tjyw6g+pdNZPQ+23+isOl5AjI0+WNp0aC6N7nWxlxEzrBaJ48q6gpHavCH8dqwBSUh0G4Yu3IfI1EZeL8TYrEfhL7n9B0OyiOZS75R9d3cgcvBwJF59dzh274uBWi3jxBlVuP7yQ0hKPLo0++wIC056shW/W61CuYt+NRKsqFVvQpVmLW/IxnqgDDGfhHjLVAjouSqpWcxHufZ7tInlUEGLWMskpJhOoeZthAQRGhnxc4IoYfjZu5E0oYyv4mDJk/Y+KayXy6Fl43iew0DEjynDqPN38kRNe/8Y+79Ws4idb89Aa8VALsISxl+1EVGZDf32nGkqjsGud2bwICd6aA1GX7idl1Vnz4Edg/15NRTEYd8nk2E1aqCN6MDYy7YgPKnl6PNmrWJEwNiiw97/TUVr5cDexeecubuzWJlN9+fd0aDH1ldmQzJp4G1z1i1x22NZrQJ+99B0vPTmKKhULPmcBXEyRFGGKAD/+vtm3HVTXo8+Na6CkTaxFNtDH4VBVWVbUSRIXf/qrcmY1P4IQqUUWGHEntB/oUazEYIsQubbsUcQIEKDMe13I8lygtueKyEkQHrTEN8z/JxdfGSAsa8wsfdJEVQyRpy7C/EjKxQ/XlRGHUYt3tk1ANG99wrfh1rCxOs2QBuuvPcKCxqisxoc9pyJzmzA2MtZnkgjxl66BSqtpes5dH9e0Zn1PJ9EpTNiwlUbu0ZYup63eLSjLgt+QmLaFB9j1qn7kTLFFoh0P077vyHRHZhy86/wNjYi4k53/3kaXn5rJP/YFogwAiRJhMUq4vd/nIE33s9V/HhGoR5bwh6CUbRNxfFApNu/BrHK9nWhAbtDn0KNejO/nwcitl3zcvsSzPzrdert7nuyhBCfRcGIHwtLarY1lnPSo4W9sx+6YL/i0uS5Z+7p+l5Hj8mmL3LO2Kvo8bSR7XwqxZWYoXUYumAfBKd9X2REpTdi2IL9CIltd7i8l600Z1NL6Scq7U0j8WTU7h2F++xbAPQxBsSPtnUU9hZ3Ts0UFofjxddHQXaa6CvjgUenwGRS9qeiWPcNzELL0eCi96MJEp+2ORzyNmo1W44GK70Jttf2UMibkL1UVp8Q4jkUjPix5MnFLot/sYsoe1cfnV3n8vHUoQbo4503tbOzJ6G6Mmw+C4SUYYGGqxoekhVIGON6pIcFKixQY6MsrqTOLOTbu3reLFjJPMlxsT5PLOF1p9ffz+XTMc4JqG8IwTcrXY/IsDyRMs13jgOMroeUUKn51fVqJ0FGq6oILSrvnHNCiOdQMOLHQhNaFfVKYbkU9ikNZyJSmxVXS+X7FV1cdAAe3LiTqGKjHpKi42RTSroo19NJbHpIyZoytk9tpHt77Xiz38z+g1GQJNcnUqO2Yt+B6K5qrI6YhWZYRGWvtyxYukY/XGE5KISQwEbBiB+Trc4rm3Zh0zUKLjqslofifbP9uo5FXJZO98Q5cr2NsudtTwz2pMEs865Rs9Eg109IkgVoNEdfSEel4XuvknGXwXpcQojvoGDEjzUWxSm6OLJ39E1FsS63aypmNTpc94jh/WGMakU/Pg1H4pX3plEQuLBpKVbYTMm2bLuOhlCX21XvTVHcCZgVOwuUMu9zTqh0kS+CrsTWk0+o7HHfSSl9AwSNHIFQa4rr6RcW3EiRynr3yAKiLaNcb0cI8WsUjPgxVvfD1cWEXbQbi2LQXhvh+gElNRoLj5byd4RduCu3KVsuXLgq12WAw0urS0DVrhSXOTBseohVYnWVW8Ier3xLpqILXsOhRJg71IpGmfK/89yFkeWIDGZRsyuWHIFez3JqHD9xlShhzMgGzJrmOkdIgIB009kK9iwjw3SO66E1WeR1SUJkz5TWJ4R4DwUjfoxV/cxfOZp/3N+FlF3YrSY1rzWiVN5nk/h0jaMLM7vf0KRDwY+25aAuSWoU/GQr4e6s50zh6uEo+GkULw/vLCBhZd7L1g9F9d4hjo9RAlqrIlG6Plt5/sQXE10eY83eZLTXRPplsmp/IiPMeO3/futcyiz3G4hotRLeemGt4pGjVNMCxFjHOh4dkQXEWiYg07gYOcarHD+QLEIjh2NEB/WwIiQYUDDi51gzuP1fTICpVcc/7z4KwcqT73hzFjpqwxU/nsWgxeYXT4ahQd/VDK77raUsCpteOHlAPzql64fxEQV7/kb3JnPsvvzvR6JkbQ4Prna8eQIa8uOP7rPzzbPFoObbFfzIAhuBBw8sMGFF2GzP25Y/wwKZ6j0pvIiaZFZeYLghPxF7PprKG/X1PkY2cFC+JQP7v7A1/Askl15QgM/fWYWMtLauAETsDEzGj23Amm9XYOok1yux7Fixsoltf0GqaSEEWWUbdGGjU2zZtKxGmul0TGz/C0SokWW8ECPbb7NN2cC+ne1nJMYyBtNb/wW9nDQYT5sQ4mOoAmugECTE5tRCH9fKEzJZjkhb9fG9iw9NaEbaCQXQ6E0wNoegeE0OTK3Oy4A7JyF5ajGvKcI0HIlDxZaMfgObkGhWn6SG1wsxNulRdzARsrVvnoJKa0bciOrOcvBq1B1M4kHN8YgbWYHEsRUQVBLvi1P861A+wuMJbFRkMFbOuCJJwE9rkrEnLwYqlYwTplUrCkKu2RrqsBqrSWjmJeFZ3RGNHIkEy3SeV9Jn37CgVr0FHWIlD2bYyEmYNLCqwYQQ30S9aYKNLKL+UCLAbm7CpiQOfj3BbY/HKsJaDRre3ZZhZd3ZfbK195YyX0LLluWqNLYvqkMsMLf1DUasJg2qd6fCner2J/Obp3mzCy8rFDd/bgW/DcQFw014wUEwopUjkWKe5/IxOgxmVDRL6BCtEGURFtGEzFg2QuM7A7cmNPNCbc2qIzw3Jto6BsMMl0EN1wnShBDXqFEe8YiEMeXIOWMPNHpLV04IS0bNWbQXh1eMQfUeW0DBGv2NunA7whJbu7Zj+QysaR8bRWGN9TzWlTdIAhFvkWQJW2tXoin7DSDBCrBpHchoVllwpDEboxoeREqM54PC3vaEPINK7eoe97Wo81GiXYpM4wXIdZb7QghRhIIRMugSxpbxfjf2HIzuhdpUIRaM7OyFw7ryTrh2Pe/U23s7ATJSphdBE2rC/i9ZsqnCjEriszbWfYG2Ye92eymPrq6RI4uxL+z3ECtewJAo9432DdRu/b9QpV3j4KsyinSf8ZGSHOOVHj4yQgJL4L3FJD5FUFmRe8Zeh31f7P1zck7fi+x5+3kg4qiqLNs2cVwForLqESjsK2eCbVSkqqm6VyDSi2gFVCbkqd+Et3SgClUaR4HI0Xi4UPcZLDB57LgICUQUjJBBlTC6kud7OFsayr7GtokfUeWyvD2bukmeUoRA4K1kVV9wxLIasLoYmBWtsKb8htoW7wSfB/Vvud6os8twge4jTxwSIQGLghEyqCJSGl0WMmPYNkryFVmwwhrq+btACUTOjrD0WxrelY6IHYDKdRNDpqajBN7QolLY9VkGGtXKulgTQvpHwQjxDQNKAaGW8r6ElYZPWeS6IWEPCpvk+Y9Aez6EeBYlsJJBxep0CC7b1NtWzLACZ65GR9gISkuZrYOsvwrGlTO96ZrHoSPmgKLRkbgQ9y7dVircmgWDWOV6QwGIsiisSEwI6ReNjJBBVbM3hVc1ddWbhm1TdyjRZXdhNk3De874oWBNVu3PUNVprgMRSQVV5XQkRsbDG4YbrnO9Ea8wK2Co8TJPHBIhAYuCETKoJIuK98+xr5rpzb7K5sj3o3m/G1be3VGOCRs5qc1LQmOB62Z+xLclRydBf+Rixz1sWC0ZSY0RhuvhLaFyMuLNMxxvwH6eBSDDeDbUCPHkoREScCgYIYOuamc6Dn4zDpLZNkLCgg124x+bVTi4dCzvQNxRF46db8+Cscn2h71ru85+O5U705D3hX/WGAmUhNX+RLUY8faU9gEnss6IuRThR67mIyA8+GCra+wrbNqSMKLkGa8XPRvf8RASTDM7R0C63TqlGc/AcOMN3jxEQgIC9aYhHiNqLEgcW47wZNtFq7Uiije169vQTkbM0FrE5rLeNBbem6ZqVxr/1x8FciDSXVOEDkseVN6U0a6lvRWHWzejQ1UBAVrEy2MxLDYXoti3/L+3GFDDl/q2qgp5kTOWI5JjuBpaDH4XZ0L8GfWm8XHaiA7eME5UW2FoDOVN43jX0mMmIyqzHqHxtkZ5jUWxMDSE9btl9NBq3ghOVEloLo1B+eb+m9W5Gws6WABiH9hgH/ffWVdAw5EEfiPHx2BQYeXPqSiv1CMy3IxFp5UhPs7oU6c1IjQck0JPUbRtdXMtqg2FkCEhVpOK5OgUiP0UsTEJTahTb4NF6IBOikW8ZQpvwnesQpCA8R33K9rWINSgXr0TkmCCXkpGrGU8BPQNrCzoQJ16K0xiMzRyOOLNU/vtdcNGBRtK4tDeEM6bN8ak1SE0uh3HgzUxtJ2fduikGMRZpkAF7XE9JiHHg1bTeJgmzIjcM/YgbkQVXzliz5kwtuhQtHo4n64YqLiRlRg6Lw/62I4elU4b8uNwaPnYrqAkZlg1Ri7ewfvDMGxbVtF06II8lKwbiqLVIzBYItIaeB+aiJTmrtwRdpwtFZHIXzGGB0WByFsrZ1gX3n++MA7/fH4sGpt0ttVKsgCNxoorL8rH/z22CRERyup8+ILKpmrkaV+BNX2z7Q4ZqBKA/XUjMbTpVmTHZfO7LWjDAf1rqNT8Allg/W5ss3pqKRxZxguRaTqfj2wMBoNQh/36l3gHYr50uXPfOikOwwxXIMV8WleX4nzdByjVfQurYOzaTpS1SDUtQq7h6q7AqfJACg7/OgqGFhakdG4IGbGZNRh12i7oowa2pNqCdhzUv4EKzc+9zk8YPzfsHAk0e0+8gKZpPByITLp+HbQRxj6VRu1BRMFPw1GyLkfxYyZNKMWIc3f1W26d5VtYTWpsf/0E6GPbMPayLfz+3tvZgwM2QpK/cizcLSqjHuOu2MiX+PZeumvLBxGw+/3paCoKrMRUbwUi7PW87b6ZeOVtFlz2vfCqVBImjK3HL9+sRFiYxevTNK5UNFZhb/LvALXRVia+O776SkBWwT+RFZ+GLeEPolUsBoSjfW66SzOeiRGGm9wekBiFOmwKvxcmoQFy7313XvBzO65Hhuks7Ap9EjXqTf3XWpEFxFomYGL7X1C+ayj2/zShWxBylCBIUIeYMf3SXxUHJFYYsSXsIbSojvR/fmQgxTwfozruGLSAjQQfi7Edq1+6BE1NTYiMdDytSQmsHjR03n7o+glEugcI2acdhD6uVdHjqfUm5J6122HfF7YfldaC3LP28E643ffT375TphVDF3V8w799H1zGyMXb+w1E+JdFW42RkefvCKhCWCxPxFtLeH9YnYJX3mZ1L/q/oFitInbujsU/X3B/4OlukixjX/i/+w9EGFbDRpBRmPp3HNZ+7DQQYUp1y9Cocn+11AMhb/QfiODoy3Ao5A0Uab9BjWaj4591QeZTPEXyz9j/87ieD9CNLIuwGDTdtnGtSPeF40Ckczfl2h/49A0hnkbBiIewwCFhbDkEBb1XUqYWK3rMIZNK+IXcWd8XFpDEZNdBrbO67A/DDFuYB3eKza2GLtLotJgZ+xrbJi63GoEi4pxhXtv3f14fCbXK8QWZsUoiXn5zJEwm0a2raj59UlkgrVRpQynkuP39ByJ27MIe0oQy7QqngYhtUxEl2m/deoxGoQHVmt/6D0R6EFGk+9LxcuZuitXLXG7DApK6wkR0KEjsZlNDJdrlLs8PBuH8EKIEBSMeEpne4LIJXFfwMLRG0WNGZ9cpWuXKRk6cFR3rLjKtAe4UnVWnuDcNfz5+zhcKm/20JhkWq+tf7dr6EOw/FOXWfbOA5Fh61ThSY81z3VCPkUTIKoPLzVjAUK/ZAXdqVO1zfZFnBAlmsdH1CKAgwxxeAOiV/D4IaCh1Pb3ZLpbDLCp4XQQJDerdCvZLiHtRAquHiKKCP1adBLWybUWV89GOgeKPpaB0+0CwFTuK9z+AbX2Rr5R5N1uUv8dw58jIYGDv6N1dV0aCk1GWYyALg5QIrDIp2TskBYGnBLPi3cr8nBPiWb79lyiAtNf3v8y2vxGC9hplSYDtteGKRh1YkKEkaGGjJ6Zm91aSbK8L41NJrrBtOuqUnSPiXG52i6JzzqZysjPdO63ibmFyCiAquZCyEr8KNpMFhEopcCelj8emiARZWe0UwRwGtCtJ6BYQFtvmciu9nMj375Is8OXIhHgaBSMe0l4dieayKL56xOkLopJRsZXV/XCtcluGy6kfFmAYm3VdVUydYQFL4S+5cKfq3aku+80wbEVN1S7vNEQLpFER5pbr9isKRC44uwhxsb5Vc6S3nJgxgElBcC5IiDJMVlCrR0a68Qy4U4Q1B+HWTJe5IGyKKNF8ostjZEFDQvMZgNXVGwMZ+qg2RKe6ns7RyBFINM9WFJCkmdx7fghRgoIRDyr82VbHw1FQwC7aTSXRqD+cqOjxWiujULNviNMAh/d9+XEkKnekOR0dYcfU0RCC+gND4E6WDi1K1jlP5mT7Ztuwbf2Rt3NEerv64sMYltXiMImVTRmqNRL+dM/OQdn/2REWt+WNaDUaJBTf5nwjWUDYkSsw0nKVrbiYg6CAXYhDpVQMMc91y7F1PS4E5Biusa2NdRTwyyJiLOOQa7iWBwYOgwJZhEoORa56EeKzK53kl9iW++bMyVM8VZttvAgCm5l3cn5CpESkmGz1UAjxJApGPKixIB55n02CZBF7JJXap1qaiqOx93/TFGXb2x34agJq9yf1eByGj4RIAg4tG4OaPak49O141OQl9UlmtX9sbNJh6ytzMBiKfslFyW/ZfY7R/nHZhmwUrXbviIwnAxFfw4qZ/fTVSgzPaeqqK8LwlVeQERlhxspPfsDYUY2DdgwnpaiQsmhgBbkcGZdwImIO3WX7vWB9bOw6E1v1BRdjWuwFiJCGYlLbI1BB39VNl+u88IdJ6ZjS9hhU0MHdWIXXsR33HL3Y2wv7dU7LsEBkQtufECLHYWrbP6CVY3scm/1YtXIUprQ9Dj0SMO7MbYjPqrI9TleCrG0pM1sqP2r+DiTlVig+xnApA5Pb/ga1HNbj/NgDI72Uwo+tvyqwhAw2KnrmBeoQE5ImliI2pwYqrRUd9aG88mpTUewxJ+uFpzQieXIxQhM6y8EXxqFyWzpMrT2HesOGNGHYgn0IG2LLKzA2h6B4bQ5qdg/+FElIbBtSphQjIpVdBGW0lMWgYms6OurdXygrmIMRO6tVwIofU/HORzkoKQtDdJQJF5xdiEsXFyA8fHCTFFkBtN+tVqF8pfv6CTW0NuFwx1q0hu0EBCtC2kZimPYUJEbG96kyWqn9BdXq9bzceYgUjxTzPMRZJvVblt2dWJn1cs0PqNNs40XG2EhMmmkhoqyjehQSYwml1ZoNvFIsq0+ikaMwxHwSkswn9ihbz94sNFdGo3R3JtrqwiGqJcRl1CJlbBF0YUoSXPuywoBKzRpUa9bBLLRCJ8cjxXQq4i1TB/38kOBjUVj0jIKRAKKNMPBKqywYaa2KcND3ZSBkhA9phirEAnOrlifMOgqWEkaXITKjgRdiKv0tG1aTf065KBUsze98KRixl3tvVRXx3jRhUhq0cjQ8paHBjKqGJh5UpMRHISLy+H6/rLKEUsNOtMs1CEEcMvSToHJWkIcQP0SN8oJI+JAmZM49yLvc2uePrUYVKrano3hNLg8QBkZGyvQipM08gpDoo7UbWIBT8usw1Ow7unog58xdSJ5U2qOoWcacfBibQrD1tRNh7XD/kLi3+VKyarAwCvU4EvIhKjSrIAmdq2tkEYnmWRhmvJwHJoOltLwdB4VPIeV+DWTZRpRKLDqo8i7GuNBzEB+vHXAQst3wFhoTlgPRR1cK5VvViKxdgCm6mygoIUGHwnA/F51Vi4nXrUfMsNoeiWwqnRWp04sw8brfePVX5WSMOG8nhi3cB11UzyJSYQktGHXhDmTMOcw/H3fFBiRPLu0zWMKOg33vrLt/gkrvnrwBX+HNMu/BinXB3Rh+N8o1Px4NRBhBQo1mPTaF341m0fYz6W4Fxc3Yn32TLRBRdZvaUhthHf0hdiTeicrqga1IWic/jMYhX/ddsixa0Jy0HGuE+3jAQkgwoWDEj4kaC0ZftA2CKPXf70aUoY9tR84ZexQ/5pBJpUgaX95vbRL76EfWKQeRPvsAorPrnfa7YdtPufk3BBJvlnn3J6wS63NzrW5JYt0T+gzMQmO/5dbZfVaYsDPscbcXM7NarMhP/yOgbesZiNix+0JrsCfkGcWPubv9a5hiOiuc9v69sY9qRh3CLsNHx3PohPgdCkb8WOK4cqh0Fhd9X2QkjKqENtygaFQkdUaBy1oobBVMxpwC2+O76HfDGgNqwwNjdMTXlvD6S0ByPFrFQjSq9zrv+yJIMIp1qFVvhjvtLagEooud98VhAUn6ehSUtCh6zKqYz10XZ5OButilAztYQvwcBSN+LG54lbKqkwL4yh1XdJEGhCW2Og1uGDYKw7L6ldQ3YNtknDQ4Q+ie7jdDPK9Gs1lBITPbEtpazSa37rtOt1FhXxwVyoyu+7mYLAZAx3rTuNiQfV3bhmZTrfKDJcTPUTDix9QuRkW6yICodf0OlY2yKDWQnjjqEOV9MXwRTc14D1uG2n1JrCNsdQ3b1p1kdZvrpnZ8QxGS2O5ysw6pdUAr941se0KCBAUjfszQpFfWm0ZkJeFd95wxtdjKxisxkE7ArZUR8FeUsOpdrEgYCzRcYQGLTlbSy0U5tWGIsgKEghVai+uqyeHqSGUjmYzMtu9ZP4WQQEbBiB+r2pnmsjcNY+5Qo/5QgsvtLAYt6g4mugxwWMBibNEpDlhKfxsKf0S1RNyTN3I8peF5PxUFhbhYTom7y5hnaWf1n7jaZ+cqjEwc7XIzlaiFunm4opwRsTUTerX/FgMkZKAoGPFjjQVxvPmeq+CB9X2RrcoqK7JtWWVWx/1zAKtZhX2fTLJ9Ljufyqk7yIIgquoYzI6nV41WjkS66UznIxSyiHjzNIRLWXCnjLRwCAfP6lmCvs++Bah334DoaGUF0HI7ru/8PkePZ/tnWNt1Az1cQvwaBSN+TcDe/01Fe43tHVT3KZauvi8bswY0MsFKtOd9Pon3tenebZePcrBAxKTG7g+mo7U8Fvu/Hnf0a/30u2mtDse+j6fBXwubUYVV38Ca0CWZbX2TejSY6/w42joKY9vvGZR9zwy/Hig50fZJP31xxAOLcULqQsWPl6ofhZSK2/sPSDo/T6q8Hpl6W7BPSLCgcvABQFBZkTC6EinTihAa38oDEdaUr3xzJppLOhtyDVBIdDuSpxYjYUw5T0A1t+p451/WQ8fcfnSKJiSmFaMu3I7wIUeXNloMahSvyUHZRv+bnqGpmcGxtEWNF56POubvlyGjXr0DJdplaFLl8TyScCkb6cYzkWCZCXEQR99MFhl5R0pQk/ApkLSLj4YIZbMwpPlcjByaBJU48H5StYYS7Fe/A0P0Nl7sjE316BonYIT5aiSG2JpKEhIIqDeN18jQhJogywIsHawMu+M/VCqdmS+RZdvJkm8OUqlDDbxWSEeDHpKTfjOs8Jpab4ZkFmE1aVyeH8bcrj3mxoCDOSKC6TPR2KSFySQiLtYItVpp1qFj7e0iDh+JQnS0ERlpjldesFGlunod/5ftW3TDj0VzuxVHqswYEq3GkBjH0wlWSUKdoZWXIo8NCYPgYMmUJMkoqrGgzWhFzhAtQrTioAcjx6IVpTCqahBhHQotHO+bddE2GzRQaS1QO1l1JskS2o221T16XQhEJ0vKzGjl1WI1cgRE1snXQ1jQZhZaIMPKp7ic5dtY0AGr0AG1HA4VfLOXFGs2aBHaoJZDoYLjJHy2lsosNPNzzZ6PkhVYJAB605SWlqK2thbZ2dmIilL2B6ayshIlJSUYOnQo4uLcm/XuC1hgwcqvp0wthDbCdrE1NIXw0Ql2O9q0TubFylKmFyIy1TaPbjWpULUzFaUbsmFoCIMvSD/xMFJnFkATaua5H+ziyPrNFK4ajurdaT068abNLEDShFKoNLZ5oubSaD49VLM3uSvYUGnNSJlWjJRphdBF2spnG5t1KN+SifJNmS4CGM8YPceKV/Ouwgt3j8K+AzH8vqhII2648hD+cMtepCQPvHjb6rVJuOuhGdiTF9N1LkL1ZlxxUT5efGoD1J0/Fm1tarz05gj8541RKC61TbsNSWzHrdftx5037ucddwfqkw2N+OOPv+BIxDJAZVteHV1/Iu4cswiPXnD0NaztaMFzO3/AK3tWodZgW046LDIBd0yYh5vHnIIQte21MZll3PTuQXxc8Q2M0fts32yMwCTL+Xhp8QzMyA11mjeCu5o8EpDs1D+JGs0GXgyNkwGVHIKhhsuQaT6va7vW2ggUbR2Kyv2pkDunYKJTa5ExuQAJwyq7lq+zACSvaQ0aUj4Gojvr9TRlIL76IoyOmQWtxnZ+2GgN651ToluKFtURfh/bb4ppHjKM50IvJw3ac2ZVaMu036FE+y06VBX8Po0UgVTT6cgwnQ2tfPS816q3oEj7NRo0O/nngqzm3YIzjOchUvKNCsNNqoMo0n6Fas1vtteRBeeWKfw8xlkn9uhZVKz7BmWa72AR2/h9odY0/pxTTPM9GggSD07TrFmzBn/4wx9QX1/PI5yDBw/i2muvxQsvvACVqv8IXJIk3HrrrXjnnXcwbNgw5Ofn45577sHjjz+u+CCbm5t50LM8Zz7CVN6/aPWmCTNi4jXrERLTzq839j9i/MzKQFtNBHa9M4M3rGN9X1i5dZZ/0b1GCJtaYe/Q9nwwHc2ltguht4y9fCNih9Xx4+/+5s/+k8KCh/zvxiAqox5jL9/Eq7x2X9Vjf24V29Jw6NtxfCRk/NUbERrX2u/56agLw853ZvaY/vG0kXNkXJn/Gn5YbWsCyEa27FQqCTHRJqz6eiXGjGxU/JivvZuLm+8+ofOz7u/U2BMXkJneggMbv0RbuxqnnLuIByyS1H1bGSpRRlZGK9Z8uwLJQ5QHQw99Woynyv/e+QS6rQhhF13RihMM12PtvbNR2FyLk774Byramnr0Q7G/s5yRNBTfn3svREmDof/8H6pjfrLlaojdEpSsKgjmCHx44iO4eFa0xzv5dh0GrFgXcR1MYoP9FPfNxzDPwbiO+1BbkIid30zj98vd81D4hU9ExuR85J60D80dLdii/xPkyOKez5n9fLBVL7XjcYLwZ2i1auzR/wvV2nW2r3WrT8LyXEToMLntMURZcwfheRuwLewvaFIdsO+w23GK0MkxmNL6BELlIcjXvY+CkE9sr2G3qrb2XJyx7fcjyWL/mfUOFtDt1T8LASJkodtIVecx53RcjSzTBWgTS7Al7I98JKj7c7ElO8uItUzAhPaHfXbUJ1hYFI6MDGgQuLi4GB988AEKCgqwc+dObN68GW+88Qbee+89h9/z8ssv4+OPP8aOHTuwd+9erFq1Ck8//TS++OILBIrRS7ZCF93BL8DdL972/ixhCa08CEk74QgfFeFf63Xm2cVcpbFi7GWb+SiLt2TOPcADEaZPb5rOz1lH38RxpRhz6WaIqr59cezPjfW5SZ1RiJGLdyA0ts3h+QmJbcfIC7bDm1Mz/1A9jR9/SeFBSPdAhLFaRTQ0anH6RfP51I0SRSVhuKXfQOTo50Ul4Tjn8tNw9e1zsHd/NCSeMNx9WwFWSeTbXXDNKYrruvy0uxVPlT9mK2Pee2lqZ2nz30LewJ3v5+Psb59FZa9AxD7cz/7bXF2AW1e/i3kv/IbqmJ9th9f9osyorJC1Lbh8zROob3Fvf5iB2B76sC0QcdL3pUrzK0pMm7Br6VRbknbv6q6dnxdvG4ayvanYqnoackRp3+fMLviiDCl+DzZ1vIlC3aeo1qw7+rU+/XOM2BH6V1jgujjaQO3X/5ePJPD99i7SJkgwCQ3YEfY3VKp/tQUinff3PkY2srMn9Gm0iWXwlhbxCA9E+E9g90Ck2zEf1r+DGvUmbA/7Kyy9AxG+HTsPQL16Fw6FvOXBoyfHY0DByBVXXIGRI0d2fT527FgkJCSgrMzxD++bb76JJUuWdH3frFmzcNppp/H7A0F4SiOiMhqd1vtgIwdxw2uQfsIR571cRFsVVDbl4S2p0wtdLtdlhs7fz4MnVxVg2XRPzNA6CE7ODzt3Mdn1CEtshjc0j5qDV94Z0RkM9I8FJKXlYfhyWYaix7z74Wmdb8adzV0L+H5VCr79Lp0/viMWq4gNWxKxebuyIlj3Ldtk+wPtrHqoJOK10g+wt74MFieV7liQ8r+DG7BB9ZHzxxOtkMKq8cDn+fAGNirSqN7jvIZH50txKOKVfgK/3mTkl9RDStzhvNaIaIUpfSUKNV+5eKkl/g6+UvsL3MkoNKBCs7rvBblXoNGuKkV+yPvOl0jzL8ko1S6Ht7ApFz4q5/SlEXFY9y4MYo2LnkUyyrTf8/wd4vsGnB7X3t6OtWvX4rvvvsONN96I0NBQPlXTH6vVit27d2PKlCk97p8+fTq2b3f8TthoNPKpme43X5UwpkJRFVT2957lXyiR1Dl64mmhCc1Qh1hdlnpnX9eEmVzmnrLttOFmRVVd2Tlk59IboyLLvk+DweB6blkUJXz8pbKVDt//nKrwCARe18UVtVrCR18o2/dOcYXz5m6MKMEUXgBRUal1VvBDwTt6ScSXxZ2jAx5WofnB9vOooO+LpKtR0O9GgHnoV8p608gCJJWSEQ8BlRr3BiPVmvVOipZ0I4u2XBIXP2vs4s6mSbyBjcRVan51HmAwgoQ2dbGi6riyYEYt629EfN6As3uqqqrw4IMPorGxEUVFRfjLX/6C5GSWqNhXS0sLzGZzn4RV9jnLO3HkiSeewKOPPgp/oNErSyzk+RcKtuu60HsBz3kZhN40StlX2Xh6CW/dLh1EQYbk4o+bJImoqlGW72A0sRwqZSfJVmTORdVbGahvUJZTI7NmbAqxwXnXbHPwLokS2sV6F118dfgdOtyeNzIoUwv6eqcjDkcp/GUQZJiEY69G2x+2goTnVsDV9JjCPg9sJE6wJYJ6mgQTDx4UU9Q3SODniATgyAhbQcNGRvbs2YPffvsN//jHP/DMM8/0u62mM8vcYOjZwKqjowNareOkooceeognu9hvbBWOr7ItT3XfxZtddMxt3km4MjQ4Xg1xPL1p3H0u3V1LJD7W6DIQsY+MJCUoSyLV8SWiyk6Qq0DE/vMTF6usEZxgdJxE2puykRGFL7QkIkxyXteGBSQXDHd/0BlmPbo6yG3a4xR1DFbccEYWeqxqcQe2fFdJ756BLKFXy95Z0SdCC1EewAIFRX2DZGhkx0mTxHccVxWDcePGYdGiRVixYkW/Xw8LC+OjIL1zStjnGRmO5951Oh3Puu1+81U1e1MU9YdhuRXmdmW/aFW7lQ7xu1d7TSTMBrXLIKMrYFKwnalVo6izMDuH1XwpsOeduaAUISGue5CwkZFLL7At2XRl0WlK835cj4owFouISxcXKHrESfIZzkuYM5IIXctQRSMjKhYJmRQEqqKExZmz4Q3Jlnm2n0cFfV9UhkQFIx4yNEfOU9abRpChsiq5gMsYYpoLd0owz1JWU0OQobemuLyAs1U1yeZT4A3seSSZT4Ygu/jZlUWEWTIVjYyw4Ia1CiABFoy0tfUcvmOrgtlS3e7TMGzFzYYNG7o+nz9/PpYuXdojj2TZsmX8/kDQWhGFpqIYp3kjLGu/9kCirUeMk98fllvBqpeymiPeUr4x0+kojv34878fyUvDO8sHYY9TvDYX9flxzs+PVUDDkTi0V0d6pcx7ZIQZt167n0/VOMKW92akteK8M4oV7efff9/ceR6d/cGUsei0MpyzqJg/viNqlYQTpldh6iTbKidX/nnmdFsw4uzCI0q4OeMKjItLg9pJtMgCkcuGz8QJ0qXOH09SQWxLwlMXeKfqrgoqxFjGu8pJ5XLaboYouopcBAzLiIFYPcl53oikgq74DFv9EqcvtcjfoQ8xnwx3Yst2h7DgwckIDgswwqzpyDFc4fwCzr8k2noBeUmG8WzbSJzTl0ZCrvEqhFgTe7YH6E0WkGpaBA2o4WDABSNz587Fk08+yUdC2NJctkpm3759uP/++7u2Yatk2GiJ3cMPP8yXAd988808KLn00kt5LgmrNRIo9n02mU9xsAtz7x4t7NZWHY6DX09A6frsrkCj90WcXaxZA7o9H06D1ei9WipFv4xA/WHbqo3egZP9c17QbE8a9vxvKiSLqk+gYX9uFVvTeU2S/V9M4rVE+j0/EtBeF8b74XiCMGNmv/c/8fC2ztEMufNCdRQLFOJijFjxyQ/QaJQNybMqq689a0/m7L8JSXZmC7754Ee8/Z+1GD+mgeeO9ExmZcci8e0+e2u14ud46tgw/DHtz4Ck7nsh7WyYONt4I569PBvfnPk7pITF2EY/urHngs4ckoOX5l6F7++YheSGzjcQvasFSyoIxih8NPchRId7rynixPZHobNPEzno+zLEPBfpmmkYf85mCCoJQp9lobbPM6ccRuqYMky13gehJd32nHu8NKzOiMDrjEwLvRbZxgu7+uf0DtrYBVMt6zGp7a9Qw/01VkZ23ML789iPqefzFqGVYzGx7S9IsszGUMOlXcfU+xhZtdZx7fcjVLLV2vGGCCkbYzv+YJtA7DVCYj/m3I5rEW+Zhkntf7VNwfRZnm2rARNrmYhcwzWePHziqaJnDQ0NePHFF7Fx40aeDzJmzBhe0Cw1NbVHMPLpp5/2mLphK2pYXgkbNcnNzeXBC6vEGihFzxhWGyRlas8Ko4YGPco2Z6Jia0aPCqxs1QirwBqVbks0tBhVqNqRzi/yhkbleRuDKW1WPtJmFfBkWnsFVvZ8ClYNR+3eo693SEwbryUyZGIpVJ2ltJuKY/hzqc0b0jVXLWosXecnJNrQo0JtxRZWgVXtkVERR8EIY7EIeOejHDz/6ijs3me7qMVEG3HT1Qdw1415Ayo6Zrd2QyLuenAGduyJ7ToXYWFmXHPJITz3xKaucu/t7Sr89+0R+M/ro1BQHMHvSxnShttv2I/brtuPqMiB1575YlMTHvrhVxwKWwqoTfyPdGzDHPxu/AI8fN7R17CuoxUv7v4RL+/+GdUdth5Dw6OTcMf4ebhxzMnQqY5WYL3t/UP4X+kydMTssn2zIQrTpPPx4uLpmJaj7EI72KXhd+v/hSpW88Nep4JXYNVjmOFKZJjP6tqutS4cJduGoiIvDVJnkBaTXoOMSawCa1XXdh28Aus61Cd/DIRX8vuExmzE1yzBqNgZ0HZWqGW5G2y1TLF2KVrUh/l9bL+ppgXIMJ6DEJl1sB4cEsx8GSurwNqusk2La6RIpJnOQLrpLJ5bYlen3oYi7TeoV2+zFSGU1RhiPolXN2XBgC9oVh1CsfYbVGnW2uqNyALiLVP5McZax/dY2syec6l2BSyibQlvmDUTGcazkGyeN6g9i4gy1JvGa2TeWI79lrMpF2fjxuwCzWp1mFkPG0WJcp6nDjFBE26EsVEPyeIkYBAkaPRmPrpzNPBydH5s8/Cuzo87zVm3ZEDbN7doeIEzVnlVpSAnyBWDQURBcThiokwYkuQ4EZUFfawvDvuX7dsdq5baDVYU1VqQEKlCfKTaae+VBkM7VKKIKK3eYW8aprzejBaDhOwELbSagR+kJ3rVdKAGBlUNwqwZ0DoZqmcjexajhv8uspvD7WQJBpOJ/8TqtGwFluC07wvrTcOSQT15QWRTHFa0QxKs0PAeLY7/rrBCbFbBwPu+iPDNN3ksyGKre1S8N43jBHf2rM1CG0RZDTV84w0d8UBvGuIMC0KcrwoJT27kHXbjhlfzRnmGRj0fPWFTOL7Qo6U79lxcPR9OFhWWc2fnR+PzXXhZHok7tLSo8e4nOXjl7eEoLI6ATmfFmfNLcfv1eZg2+WgOCCsDv/yHNLz4+ihs3JrAg5FJ4+r4yAjLUzmeZn2hISqMSnN8QWRB12ffZPK+OHvyonnwNXtGNW6/IQ8LTinvERD9timBHyOro2Iyi8gd2oxbrt2Pyy88gtDQoxfy4tIwvPzWCHzw6TBevTY2xogrL87HzVcfQHpqe1evmmef16FCuwql2pXoECt4wmGcZTLPW4iyjuzZQLAwESU7stBUEcs/D49vRvrEQiTmVPSbRK5HAvRWx6MRpg4Nyvdk8JuhNYQ3yUvMLUfa+EKEx/ctlCUKIkJ1jpu1dcenY+TBKXvvKglUjTBFC3xU0EElO/6dZcXCyrU/8p43RrEOoqxDonkmH23x1AgKC5K0suvVYWyKqfvoDwnwaRpv8YdpGqXSZx9G9qkH+bsx+x9Q+ytgbA7Brndn+EyzvEBwrMGIOxwpDMdp5y/iF2bGvmqGJaSyqqqPPrgND9+7iwcDF19/Mr5ekcnzU+zVWO0fzz2xAks//AlhYQpWdgwQG4VhZe5ZAMTyU9iKoe7HeNmF+Xj7xbU8QHng0Sn414vjeAE2trqH4TkuMpA7rBk/fvkd0lLaeRG5C685BRaWB9WtsqxKlKDVSvji3Z+x8NRyvFnRiDs/eQodIpv6ONrPheUGsMJXWYYlGGa8ArJVxJ6Vk1B9MLWrd4xtQ9vHUcn1mHT+Rqh1ys9PS3Uktn0+E2ajfVWY7bVhOSTsdRp52m6kjS9CsGoVi7Et7M+ddVGOFknirw0kDDfcgAzTOd4+TOIHaJrGByWOL8XI8zrn2vvBAhRTcwi2vHwSTwwl/huIGAwqjDnxPJSUhvGLuiNvvrAWG7fE47V3RzisdcIu4ueeUYzP3laexKrUggsWYNXaIQ7L0bNg49479iA1uR2//+MMh4/DgpfhOU14779rMGvBWTBbxH6XLLPH02okbFr1Bc7Y+DuUN1U5rbg5suNWtH1/D0q2sxwzB9MigoS4zBpMOn+TkqfMR0TWv30KzEbn06MTz9uI+OxqBBsL2vBbxG22QMTJazO+7SEkWmZ59NiI/6FpGp8jI/OkQ3064XbHRkpCYjoQP7oC1bsGoYhTEHGVrDrYPv0mEwVFtkRUx2Q88uRElFeGOi26xprlffFtFg4ejsTwHPdVk9yyPY43B3R6hLLAE3ojwpxPW7GAa9+BGNz3l2n8uTiqncLut1oF3PN+OcqSWHlyZzsHjmg/hmnXc85zi2QRdYVJaKmJQESCLQHXmfK9GZ0jIs76tMgo2JgblMFIuXYVTEKji2XAAgpCPkZiKwUjxD18M2syAEWkNUIf2+EyIZEtdR0yyXcrzvoDbwcizJvv5/JpD+cElJSFO23Q133k4Z2Ph8Gd3vk4h0+5uGI0qlBb7zr/gT3f1WuTu6ZwnAUuP7euhMpVNTxWa01VD2S47ufCplcq9qVDifLdGQqKowk8N6XdR1a3eVK59gfX1eMEGS2qI3w6hxB3oGDEQ3QRCkt5i0BIlLJtie8q5kGGsl+v3nVNHCkrd28uUVl5KB+lcEWl8PjY81VSVp9vG1HMOwIrEuU6OJchwNiqLLmUJasqXcWl9DEDiUGoVbzIjSW2EuIOtJrGQwZSR8NipJfFU0t4B0tkuPL+K4pSyAUZ4eHuWeFjFxFu5oGQq4BEaYBhezet9Co2gGW9JtcVNAXITpfldsdWzZicLVPvRuljBhI1QmCB6+kuRiUHX7BGBgeNjHhIU1EsL27mCnuzaCsWRo4lYdVXLD67SME0DaAPMSsaQbFYVDj3dPcOiZ9zeonDxNXeeR4ateuLMpuCHJrV7LS0PcO+nms8TVGTPkHSAPkLFByjiIRhtoJkrrDlu30qr/Z9ROjCOxCR4N4uu/4g0XyC8zLrnVhRtUhrrkeOiQQ+CkY8hK2OqdiW4bSXi618vIDKbcrmvklP3lo5058brjjEa4P0LO/eEwtWbrn2AKZPruE5Ic4u3jnZzTjtpAq3HiPriZOc1O6yL878uWW8Roiz7djz1OsteOT+HS4DHPb1vy6cBJ1K7TwckUWkmuchOt7sPHgQJOgi2hGffbRqqjOsjoiS5oTpkwoUNXkMNGmm0133h5EFXtlVpMF14iZB+KvmPUWrhqO5NKbfgIQVvWK//Pu/mAhTEM5TH++IiK9Mz9ix0vEfvLKGT4P0dxFngciMKbX4+0Pb8dHrvyA+ztBvQMLuY9MpX773c1fpeHdhfXa+fv8n6EOs/R4j23dqSjuvM/LMY5sxdlQDX2bcG/telgjL+udcseQIfnfzXn5/70DM/vm9d+zGpac34+NFt0ElqvpPZJVFRFiHIsdwDcaevh3aMGO/AQm7j02lTGC9ZhSeH1bQbOSpuzsfoL8mNjIPbDImK+vQHGhYb5rRHXfZPunT98UWiLC+L1nGC7xyfCQwUTDi4dGRXe9NR/GvOTC19Sze1lQYh53vzkRtXrInD8nv9deF11dccHYRVn29EqfM7jmiERdrwJ/u3oUfv/iOVy3NymjFlp+W4prLDiGkW+EujcaKSy84wr82ZqStj5G7sU7Am378FheeXdQjIAnVm3HTNQew6YelPLBiFWl//XYF7rljL6I6ey/ZA4xFp5Zh7bLlvAMxm6phgcsbz6/F8GE9lyGPzG3C2//5FU89spV/flb2RKxZ/BDmpY/usZ1aCucXuqlt/+CVTEMiDJh+2RqkjiuG2G26iAUiicPLMf2yXxGZOLAlz2kTingdkaghDT3u14UbkDMnD+PP2aI4sTgQpZhPw6S2vyG6WxVcRivHYJjxckxsf9hnS8gT/0QVWL114kUJYUnNEDUS7/tibPZ86ehA4AvLeJVgVViLSsL4KATr0MsqkfanqVmDfQei+ZTdiJxmxMUevfAPtppaHQ7mR/Fqq2NHNiA83OKwoNuufTG8cmx2ZisviNYf9hzYc6mr1/GRn1HDmxwubX+rshkvv90BARpEWLMcXugsJhXa6iIgSwJCY9ug1R9/nhBbvstWzbARFpYjEoxTM850CJUwiHW8dHy4lE3N58iAUNEzHydLIlorXPdcIP4diLBuCz+X5uGNfWtwqLEKoRotzpDG47rRc5Cg79lL47ufU/Cnxyfj4OEofiHPzmzBX+/fgcVn90xcbTS24539a/H1ke1oNnUgOzIe14yag0UZ43iTu2OVEG9EQrzzIl87dsfgnr9Mw9bt8bzce1JCB/5w217cdt2BHtNIBosZnxzehI8ObkR1RzMSayNxGWbiwmHTENLZ5dZ+fn4pO4B3d/2KAyHVEKFFvGUyUkzzoZNjeuy7XrULh2PeQVt8Cc9p0EuJyDZcjCGWk/qsRmOdeKsPD+EN8EIiOpAypoRPvfQXaIRGt/ObO5jatSjbk8H76FgtKoTFtCJlbDFi0urc0vTQG/TyEOitzpPqW8Ui3l+Iddtloq2jkGpaiDBpcIs3sp+DJtUB3j+nTSzmnXliLRN4p+QQOX5Q903ci0ZGiF/ytRyR/jQY2nDesufxa8VBqAURls5kIbaKRC2q8Pa8G3DJ8Bk8X2jOmWdg/ebEXstjbR+PHtGAbauW8tGU5YU7cdHKl9BhMXXlF7KcC1azY0J8OlacfQ+GhA1ON9xr7jgR736U0+0eoesY42IM2LnmK6QkG7Czthinf/MMKtubeGdbSZa7/k0Ji8bKc+7B2Lg0HkgtXv4CD9ZYEzrWFdfeJ4Z1mx3VcQefLpAgYXvon9Gg3nN0t0dPD/TWZMxofY4vSa0riseupdN492j7xrZ+MyLC4pp5Dxs27TMYKvJSse/7ibbkWLnnvmPSanley0D65/gD1i33QMhrKNUt7+opxNg/zjQuRo7hav6KuhvrOrwn9F+o0WzssW9bnouM4YYbkWE6y+37JYMzMkIDksQvR0R8nVWScNa3z+K3ysP8c3sgwkiQYZYsuPz7V/Bd0W4sXLKgMxBhuv/Rtn3MpjpOWHQGNlTm47zlL6DDYu6x0MFePGxvXRnmff1PPirhbvc9MhXvfpTb7bjsx2n7t65Bh/EnnYeipjqc+uU/UdNhq1PBApDu/1a1N+OUL59CWUs9D9TYqIjt61K3h2bvd63Yp38ONeqN2BX6DzRo9vTcbbdTxbr9bgq/B81VUdjx1YzOQOToxiwYYNrrw7H1s1ndAhX3qT2SiL0rJ/HpI1uZ+Z77biiLxY6vpymrKeNHDoe8i1Ltcv5x9x5D9o+LdF+gQPfxoOx7T+i/UaPe3GffvJ+OIOOg/lVUaFYNyr6J+1EwQvxu5YyvT80wK4p2YX3lYYdVRu3jH/f9+iV++sVV0rKAbbvi8Icfv+XTGnzZZT9YwLOvvpxPj7gTG7l57pVRLoqaCahvCMG1H27mIx6Onje7n00z3bvuY6wu2++4CivfjYBDIW+hVu3i+QhAu1iC/fkNnRd7R31xRHQ0hvERDHdi+zy0lp0fx/tm79Yby+JRX5yAQGEUGlCs/dplnbtC3acwo9Wt+24WD6NGs8FpIz/243oo5B0e2BLfR8EI8Rve7MI7UK/sWeWy9wobIdnTWAAk7XT9gFHF2Ni4w2UJdTYd8vLun+FO/3l9JC+65vKqI5rwS8cyl8fIvv7Fka186sopQUa7qlzxcTaP/pfTLrx2pTuzFD+mov1WRaGtLtLl+WFTNqW7MhAoKjQ/OwyMu5NgQaV2jVv3Xab9HoKsct3bSKxHnXqHW/dNBgcFI8RvRJzj3kZxgymvoUJ575W4g663ibVN97jCpkMONior/qXU1l1xyjYMrYOsVfYO2CxZe0xduUVUkYKNBLQ3urfHT0ej63L19pGZtnpXnZz9BwsUWW6PK2ybdlF5UKlEm6oUsqBgxEMW3L5vMjgoGCF+wV+mZ+xCVAOowWDVud7GomCbTlrRvTkRep3Vfc9jMLHS8QqIKtnty/SVclUq35+IsvIeWu6u1CrKGtedlzmZqsT6CQpGiM/z5cJmjizIGOtymqbrj3TJia4fsHIyNJLrdvZs6oPt252uuoSNyij4y98RjdCWES5XTrCppKTQSEXnhyeDKlmIwTYrPtn1ZoKE2Azny5cHKjq1XkGvG1tiZWyme/ftTTGWCYpGJ9g2bLmtO8VZJipryiiw4xzn1n2TwUHBCPFp/lBLpD+3jD3l6AoRJ4HDZcOnQ2Ni9TScNgKBYNHjprEnubyAs6mP28efBneaNa0WSYkdCgISAX+YdJrLPAI2lXTfpM7+J84ejTW/M8+E4OpdsK2CO9Kqbnee0Ng5VZI+sRDupAszKmu+JwtIG69kKsk/JFimQytFd64eckAWEWJNcnswkmw+DQJUTn8u2M9PjHncoNc6Ie5BwQghg2B4zBA8dcJFDr/OgorU8Bg8Pfsi/PeZ9Z339veX1XbfEw9vwWMnno0RMUOcBiQPTD4D05OGwt2+eu+nzvLojo/xsguO4JEFk3Fm5niHoyPs/vOyJ+N3Exbg2TmXOc0zYKXHRxpuwpj233XfTX+7RrbxEgybXIWI+BanAUn6pCOISauHuw0/eZ/D/jn2gxx+8l7oo1hQFxjYqN7Y9ntteSP9BSSyyKu1juu4R1FuyUBo5UiM7rizcz/971sth2GU4Q637pcMHgpGiE83v/PHURG7eyefjrdOux5pYTF9pinOGzoZG5Y8jKTQKFx72WG8//Ia3hCvN32IBS8+tQH337UXUbpQ/Lr4j7h8xExeNK27RH0knj/pcvxj1oWD8lxmTKnDuhXLeMXV3lGBWiXz5njvv/IrP67Pz7gT905ehDB1zxyScI0OD0w5Ax8vupVXir1j/Dx8sOBmZEbE9QlY0mKmYFrrv6CT43iF1fHtD0Il952mYmXjcw3XYZjxMqi1VkxZ8htSRpd0y+OwHatGb0TuyXt5QDAYdOFGTL/0V8QPrerTfI9VgB2zaBsyJhcg0MRax2NK2+OIsPZNLo+y5mJq61OI6tXfxl2SzadgfPsfoZd6LY2XwRv5TWv9N0J7f434LKrASnyOv07NOCuAtqosD0eaaqBXa3Fa2iikhPcMUOy+XJaOpSszIMnAvJMqcMVF/XeOreloxvfFe9FmNiI9Ihbz0kZDo3JvkqAj6zYm4N2Pc9DRocLk8fW448Z9UPez61aTASuLd6PO0IoEfQQWZoxDmKZvkiubzmI1R/Kbanji7+QR4/H0tkSUr+zbr4kVuapRr4cMCdHWcfyCxN5/92bq0KC+KAEWkxoh4QbEZta4PXHVEUNLCBpK4iFZReij2hGTXuu3peAHolnMR4sqn4eTLBAJl9y7hNp5Sfg8tImlfOom2jIGobLz8vXEc6g3DQmKVTN78qLx/qfDUFWtR1SUCRecVYjZM6t96o9/WXkE1n98KQqKIhASYkXkKWU4a2Epb0jX2/lnlvCbMwaTBZe/cgC/tv4Kq2iA3pKIP09OwH1nHfu7QNbM7t2Ph2HP/mioRBknzqjGRecWQq/vm6B44owafnMlXBuCC3OmudyuvK0R6yvzkd9YDZ1agzCNFq9P0uMG9rVuAQkr/20SGiF0TlNZhTZY0Q4RfZfWavVmDBnpnSWdrNx88uhSBJtIaRi/eRobSYu2juY34r9oZIT45ahIY5MWl914Elb+nAa1fbmkAFgsIsaPqccX7/yMoVnurfo4UKyr7V0PzsBr7w3n+Rb2AIkdY2pyGz55czVmTXN9Ue/unv8V4v+qHwdUlp6zJQKgaknHnqv+iBGpIQOqHvrkc+Pw6FMT+XHZRw/Yx1GRJrz6f+uw5NzBSbq0SFbc/ev/8NKen7t69rCTxO4fEhqFu+bcgZ8/mcG/Vqn5BXn6l2AF6+xrm6JioyOsMdoww+XINJ0/KP1PCCGeGRmhYIT4XSDCWtjPOet07NgdC6u17xA9C05Yy/qtPy9F8hDvJAyyi/zlN5+Ej7/MtjVO60UlStBoJaxbvhyTxitLqHzqmwo8VPRH2yf9XXdZHGGIRfPtTyM8VFk62GP/Ho+/PDG5368JnbkPn7+zCued0bNzsDtc/9MbeDtvXb+ralhejSioMKn5SRiEauwO+6fTavQ5HVcjy3SB24+REHJ8qFEeCVjvfTIUW3fE9RuIMBariJq6EDz1gvfqC6zbmIiPvhjabyDCWCURZrOI+/86VfFj/mnvS7YPHLeHAfT1WPSSfXWOc5VVejz6T1avoX/2Y7/j/hmwWt076rClqgBv5a11uLyXLf+VJFtH2P36V5y3xQGQH/I+TEKzW4+REOI5tJqG+F3S6n9eH+UyJ4QFKm++n8uTLL3h5bdGQK120aPFKuKnNSk4fMR1ifCvtzRBCi91XedJBn6Tv1B0jG9+mOOyiywLSMorw7DyJ/c2l3tl72qXvWlY755m9QGYxSaXz5tN2ZRrfnTrMRJCPIeCEeJXS3hZB9k9eTEORxy6a23ToLBEWd8Qd9u8PZ7nXSixc2+sy23e3liouBIpQhoU7XfH7jhF55EFVdt3uz7GgdhcdWQAvWmUPfEWVf8rjwghvo+CEeJXXXj5iEivOg4ut/cCcQC/WfbcDGdUovufCNuvkn2z0RZ3n0dhAA+odEtKYCXEf1EwQvwKu4ax2haiguZkUZFGZGe0wBtOmFbtcpqGYcHAlAl1Lre7bU62ssZgLHBoj1d0jNMm1Tov5d0tB2f65IGt+nHlhORcl9M0dkra1LOtIq25x31chBDvoGCE+F3zuztuzIMkiS67o9541UHodN7pknrrtftdTtOwYzz9tFJkpre5fLxTx0ZA3ZKlpD0MTtEqq8J6zaWHoXIRMLGgLyu9BaedVAF3unXsKS6naVjZ+5NTRiAuNMZl0MSW+CabTnXrMRJCPIeCEeJxx1vm/dLFRzD3xAqHoyNsaW9GWhseuGsPvGXa5DrcfM1+h9MgLBAJ1Vvwr79vUfyYL59wu+2i7CggkQGxNQUr71S2Qic+zoinH93iYhoHeOX/fhvQtJMSY+PSeH8aZ4GITqXmJe5vn3W9kx4ktn9GGK6Hpp/iZ4QQ/0DBCPF4nsjx0mhkLP3wJ1xyfgG/YPKaHRprV/GzE2ZUYe2y5YiLNcKbXnxqI+6/aze0GitEQbYdY+dIxPBhzVi7fDlG5jYpfrzr58bj6Zx/AJbOqqRyz5uuaSQqb3sEarXyX+u7bsrDS0+vR0Q4e11sx6hR26quDknswLf/+xHz57p3VMTu37MvxsPTzuFBB8v30IgqqAXb6qehkQlYs/ghjItPx19HjcHD8+6GVo7iXxNkFb8xKugxqv0OpJnOGJRjJIR4BhU9Ix4fFXGnopIw/O+LoaiqDkFUpBkXnF2IcaMb4UtYqfUPP8/m5eD1egsWnlKOObOqjjkp1GKRcNN7h/B5ySpYhA5Eyol4Yf5ZuHCW7WJ9LNrbVfjk62xeXp+VqWc5L2cuKIVaPfj9XBoMbfjw4AYcaa7hgcm89NE4JZUt3z56gpoidLhzFbDrp91oUh/gS3nDrVlIMp8IFfr2uyGE+AaqwEr8fuWMr2MB0NcrMtDQpEVCnIFXKY2NOfaRH1bzY+2GRL4sWJIETBxXj1PnsOmovtsWFIVj2Q9paGtXIz2lDeeeXoKwMAs8YXdtCX4uy4PRakFuVBLOzJoA7SA36WPByO9Wq/ptnkeCmwQr6tRb0CaWQeSN8sZ6pUcO6R81yiNkkLS2qnHHgzPx4adDYZUE3liOVSi97d5ZuOnqAzwPY6CJs7+uT8TNd5+A/YeieS4MGxRgRdHYaqAX/7kBp88r66qaeuPvT8DyH9P452zfbLVLeJgZ9925G3+6e5fb8zvsDjZU4rqf38BvFYd5uXb2n1WWEB8SjsdmXoCbxs4dnB0T4kCFZhUOhbwFk9gIyGLncjIZkZZcjO64C+FSJp07P0E5I2TQBdKoCKvoOv+Chfjgk6E8CGBFw/i/EGAyq/DSm6Ow+KpTYbEon4NZvXYITjt/EQ7m25pIsZVC9lL3rGjbWZfOw9fL01FTq8OJZ5yB735O5fu179te4O2RJyfhjgdmuqyqeiwONVZi1md/x8bKI13l2lkgwtQaWnHL6nfw9LYV7t8xIQ6UaJdjb+j/8U7OnCB11SBqVuVjc/h9aBXd31OJDA4KRojPLuH1RS++PpJPo7DeMv1h0ysrfkrDh58PVfR4bETl6ttn88qy/S1XtldIvfbO2fjzPyajuDSsKwDpS8B/3xrJp3rc7Y5f3kezydAVgPTnwd8+RWFzLQZDVIsRb09px513KU/4JYHLKDTgYMirtk/6i/sFNnljQp7+RU8fGjlGFIyQQR0ROZ4lvL6GBQwvvj6K/+sMm2Z5/tVRih7zu59TUFIW7rRuCgtIGpt0eOd/OQ6bA9qx1Tqsd4875TdV44eSvU4DEYZN3byyZ5Vb901If8q1P7guhidIaFLvR6tYSCfRD1AwQgZNxDmBlURWXhnKAwdXBcpZYLFtZzxMJte/Xr9uSFJUqZXVJWHTQK6wQmur1w2BO62rOKRoOxasrC7b79Z9E9KfBhWrIaSklYGARvU+Ool+YHBT4EnQcvcSXl9gNg8sdmd5I1qt68dU0h9mIHkgZoUN+pSySFbl+x7AtoQcK0mwKG5axCZsiO+jkRHik4XNfFHKkHa+asU1GanJbdDrXf8RHD2iUVGQw6ZqBAXvBFkBuLEjlXXtVWpUTIqi7VivmfFx6RhMJ6W4Hh0igS/cmgmBr55xQZARZh3cn0niHhSMELcKpJUzvbHlutddcair0qsjbGntbdezUvCuH/Pi8woRFuq6PggrRDZvbjmfrnGGJdbeet0BuNPMIcMwKibZZVdc1mvm5kFe3ssSWT99shUpizoGdT/Et6WZFkJmq2eckYEQKQGx1vGeOixyHCgYIW4TyIGI3b2370FUlMlhUMAClfTUVtxyjbKAgBUqe/zP21xu99Dvd+GpR7ZCo5Yc9uRhxzRtUg0Wn1UEd2KVUP81+xLbx06SVxcPnYLpScpWER1vQEKCW7iUZWuM6KiBIhtEFIDhHddDoMucX6BghLhFMAQiTFpKO35ZuoL/y9iDEvtoyfCcJqz+ZiViopVPVd15Yx6e/MsW/li2QMPWbIZNubB8kgd/vwt/fWAHr8i68tMfEB1le2x7UGJPgJ09oworPvkBWq37OxWfnjkeHy26FXq1lgckQrepGWZJzjS8v+DmHiXcCRlMozpuR7LZ1qm5a8qG/+rwTkcY3f57JFpOoBfBT1BvGuIWwRKMdE9O/fb7dHz6VRbqGnRISujAZRcewfy55cdcAbWiUo/X38/Fpm0JPGF10vh63HjlQd6BuHfhtU+/ycLSlem8Gmx6WhuuvewwZk6tOeZ+N0o1Gdvx3oHf8HNpHgwWM3Kjk3DjmJN5F15PumZrKJWGJ1ybWIIy7fdoE0shQI0Yy1ikmE6jLs4+gnrTEI858d9p2J2TiqKWOoSqtZg1JAdhmuNrXsYat/22ORHt7Wpkprdi/JiGQb/QHktAsn5zAuobdEhMMGDGlBqHgQhrQHek0NYob+aUGkRE9J8nUtHWiO01RfwN3ri4NGRExCEYLN3ajI0FTQjTqXHlCfFIi9M4375FjReeP/bGgMeiQ6hGq6qI585EWIdBJ8d4dP+E+KNB601TUlKCdevWwWw2Y+rUqRg1ynmBpU2bNuG3337rcZ9Op8Ott9460F0TH1S6sBSTC3Zh15bSrvvCNTrcMPpk/H3m4gEHJW1tavzlyYl47d0RvMS53bjR9fjbg9tx7hkl8DZWNfVf/xmDZ18eg6qao43bMtJacf9du3HrtQe6AqcVP6bi4Scm8bojdqF6M0+EfeyP2xEZYe4qLPbAb5/gqyPbeKl1hj3EGZnj8cQJSzw+8uApT35bgce3f4q2mO1d9/3pcAhGG87D0utORXZS/0HJ2REWnPRkK5Y8yOq+DK4WsQCHQ95BnXrb0fkpWUSi+QTkGq6CXnZvXRdCgtGApmluueUW/PDDD5g2bRpUKhW++uor3HjjjXj22Wcdfs9jjz2G//znP7j44ou77tPr9XjiiScUH2RzczOioqKwPGc+wlTO3zERz/lmWDH+PX0Pf6fYuxqiShAwMSETq89/UHFAwgKRU89fiG074vqUW2e5E2x563/+ud7tq0UGglVfvfzmk/DJV9ldpdp7Z83deeM+PPuPTXj342G47s7ZPDBhZeK7Y/khbFnvr8tWoMxchNmfP95vuXWVIEKnUvPzODUpG4Hkrg+O4MX6x2xJiL2TciUVtC3DkHfDvQ4DEk908m1S7cfWsD9DAqtr0fMYWZ6CWg7DtLanESopW/5MSLCxDMbIyOmnn84DCxaIMGvXrsWcOXNw/vnn4+STT3b4fZmZmU4DFuJ/Ik5rxP8l7uUf91eW2SrL2FFThL9s/BL/7lyJ4cojT03sNxDh++i88N/5wEycdlIFhuc0wxve/l8OPv4y21FDDP7/F14bjUnj6nDjH07kuR99gxZbR959B6LxwN8mY/3kBx32fWH3Ga1mXLDiRRy56mmoBqslr4ftKjLixdonAVG23XoTrTBF5GPh6ytx8E9ne+MQebGsnaFP9BuIMGxpqRlt2K3/F2a0PeOVYyQkUAzoL9u5557bFYgws2fPhlarxYEDzt+pNjY24o033sAHH3zgclviH8mq7y2IcLlyggUkr+1djTazUVGOyGvvDnfYgM5OEGX89+0R8AYWWDz73zEuE1TZqMff/jWx8zPH54gFJG+tqcGuulKnfV/YeSxprceKol0IFHd/uRMQrM6raIpWHA5figPl3lnKW6PeCJPY0G8g0kWQ0KI+jGaVspL5hJD+HdfbrOXLl8NkMvHcEWfa29uxZs0afPLJJxg/fjzuvvtup9sbjUY+NdP9RnzL0oLtLhunMa1mI9ZXHna53catCWhpdVE7vfMC/s0K71RUrKrWY09eTJ8pl/6OsbA43GVTO8aU8QNUcF1VVC2qsCyAgpGNLRudX+TtVGa88ku5V+qN1Gm2QpBdvzZsuqZWvXVQj4WQQHfMwQhLZL3hhhtw9dVXY/LkyQ63O+ecc3D48GG88847+Prrr/Hdd9/hueeew5dffunwe1g+CcsRsd/S06mcr6+Y/Uw6X8LbblZeR6PD4rqEenuH8hnDgWzrTu0dAylFrnDpj6Zd2bayDIMlcMrsW4R2xaeozeS4Qu3bU9px511NGAwSTK47w3ICrKBCbIR4PBiprKzEvHnzMGHCBLzyyitOt2UjIWwqx27u3LmYNGkST4R15KGHHuLJLvYbC3yIbxBmzOT/Zkcl8KqbSmQqWJ7Klu8qwQp9ZWcq29bdkhIM0GiUNd1iRdBEJe/8G7MUNfJil8RAWuYbLaUDVmXB3egkzy7htWOlxJX8hMuQoJcTPXBEhAQu8VgCkVNOOQXZ2dl8dIMt0x0oFpywIMMR9pgs67b7jfjGqIgdK3RlX4LqiAgBE+MzMD7e9cjW2FGNmDS+zmGpcztJEnHjVQfhDax0+6WLC7oqnjrLGTl9fikkF4282AqhnPYzoFaQlCrJEq4ZNRuB4oYxcwCViyBMBtQtGbh9/tFl0Z6UbD7Ndf8TPi6iRpJpjkeOiZBANaBgpKqqCqeeeipfHcOW9YaEhPTZZsOGDXj55Ze7Ps/Ly+vx9b1792Lr1q08+ZX4VyBiHxVhLsqZjpyoxK5y4P2RIOPRGecr3sejD2znwYZtHKD/0YZhWc24+LwCeMt9d+7hx8ECif6wEu7hoRY88/dNGD2iwWlTPbbK5vE/FOOu8fOd53EKAq4aeSKyIxMQKB4+Jw36hnGA1cmUmwBcl3IF1Crnf6ZYzZHBaJwXJqUiyXSS4/4njAxkGs+lap+EeLLOyMSJE5Gfn48//elPPQKRmTNn8hvz17/+lS/jZStoGDadw0Y22NRMbW0t3n77bb4M+LPPPusxfeMM1RnxzTLvxS11mP/V0zjUVMUvmPaRElYbg821vzz3aj6CMhCvv5eLW+6ZxWtz2BNA2WgJC1JyspvxwxffITO9Z3l0T/vxl2Scd+WpMBjUvO6ILflB5sfM+saw/jDTJ9eivEKP+RcsRN7B6K7nYB85YUmwz/5jI+68cT8skhU3/fwW3t6/jgd3rPstY//43OxJvC+MLsBq7OwpNmD6uy/CEL2X1xVhq2c4FqCoLDgTd2HpHZO8WiKe5YLsDv0najWbeaKqfaTE/nGKaQFGddwKQUESMiHByKKwzsiAgpH77ruPV17tbdGiRfzGrFy5Er/88ktXUTP28Oy+jRs3IjQ0FNOnT+d5IwNBwYjv9pxh/Uk+PbwZr+5djYLmGoSpdTh36GTeSn5Y1LHNo+cXROCVd4bj6+UZaOtQIzujlU/NXHRuIUJClOVsDLbqmhC88UEuPvxsKBoabb1prrw4H1dfcrhHkzyjUcTnSzP5suVDBZEIDbHirIUlvKtv91op7PdkXcUhvLT7Z/xWeZh/PjUxC7eNOw2npo0K2AZ0rR0SHv6yCO8c/gnN2gKIkg6jxRPw2IIZOGtyhE/0q2E5IfXqnSjVLkezKp8X+Yu2jEaa6QxEWUfyzwkh/aPeNMSjze+aWzSoqg6BXm9FanK7z/WRGSx790ehtDwMQzNbkDusxduHE9SoeR4hQdSbhgQPJYHIzj0x+OfzY/Hp19mwdE6rjBreiN/dvA83XHnwmDvY+ro//n0SXnpjFJq7aqPIiI0x4oHf7cZ9d9gq0xJCCFEmQC8VxB2jIq6s/CkVM+afhU+/ORqIMPsPRfG8j0tuOJk3lQs0J5+9CE8+NwHNrd1zOATevfeBv07DxdcPLE+GuMdzc62DkshKCBl8FIwQh4XNnKms0uOCq0+B2SLCYun5Y2TrxSLg86VZePrFsQF1hh/+x0T8ut7epbV3oGX7/NOvs/Dau7keP7ZgxyqyUkBCiH+iYIT0MGfdkh5LeB15/f1cGE1iv03g7NjXnn15NMzmwBkdefG10Q6XHnf32L8neOR4CCEkEFAwQgY0NWP30RfZLnu0MNW1eqzfHBjVKQ/lR6CpheWIuHreAkrKwmAy0a8XIYQoQX8tSRelK2cYtpxVaXORxiZl9WR8XUl52AC2ZjkkgfG8CSFksFEwQviICJueGYghiSxRUFmJmsQEQ0Cc5WFZbOmu0rI8MuLjAuN5+2PeCCHEv1AwEuSUJKv256pLDiuoJSIjK70F0yfXIBCwyq+J8QYFAYmMETlNUNPCea8FJIPVyZcQMjgoGCHH5OpL8hETZeK9WBwT8ODvdwdUrZE/3b3TxRa2QOUfD2/1yPEQQkggCKDLBDne5ncDwXqwrPz0e0SEm3mvle7sn9992x6vddgdLHfetB9XLMnvDDp6j5DYPr/n9r04/8wSrxwfIYT4IwpGgjhP5FgDEbupk+qwe93XvJNtXKwtP4I1hFt4ShlWfPI9/vW3LQFZFv7dl9fi7Rd/5SXgjwYkMkYNb8KX7/6Mpx/d4uUjJIQQ/zKgRnneQo3yGPYyCT4xKuIIawqn0Ugup2XsP3GBEKiwrr1NTRpERZkDajrKG9jPhTt/Jpa2qPHC81Hue0BCyIBRb5oAEBLdjpRpRUiaUAq13gyrUY2avcko25SF9pqBdTTtbqArZ5TS6Rznj7DCZx99mY0XXxuF7bvi+H0Tx9Xj9hvycOniAmi1znJPfM+evGi8+PoofPxlNm8SGBttxBUX5eP26/cjZyg1zFOqoVGL19/LxX/fHomi4nBotVbMn1uOO27Mw7yTK44rODk7woLPF3UMSidfQoh70ciIj4rJqcaYi7axVwii6ujglWQVIAgyDnw9AdW7U30qGHGktVWNMy+Zh183DOHTOJJkG0Kwfzx7RhWWffQDIiIs8AfvfzIU1945m49Tde/Jw3Jl1CoZn729CmcuKPXqMfpLEblTz1uEiqpQPsJkH/lTqyR+Xu+4YR+ee2LTcQUk1MmXEP8YGaGBZR+kj23lgYigknoEIgz/XABGnLcTkWkNg15PxB2uv+tE/NZZhdUeiHT/eP3mBH5x9wfsWK+5Yw5vANg9EGGsVhEms4gLrjmFNwskzqf0Fi5ZgKpqfWcl36MRh/28vvj6aDz7X1Z+nxAS6CgY8UEp04v4iIijd4TsflkSkHbCkQEFIsdST+R45RdE8K6+7ELtiFUS8cW3WTh85Ninnjzl6RfGQhQc5++wfjzs4vr8q6M8fmz+5POlmSgsjugT0PX21HPjjqu30dtT2qnmCCF+gIIRnyNjyMTSPiMivbGvxw2vgkpn9tlAhPngs6F9lv72h23z/qfD4MtYbsg3K9NdXkBZF+N3PsrpStQlfbHzw6bplPQ2+vnX5OM6hSelqJCyiFUMJoT4KgpGfIyolqDSKitnLYiAJlR5cztvqKzWd44kOMe2Ydv6sprakB7TTM50dKjR1kYlWB0pqwhVfC59/eeCEHL86K+lj5EsIk9SdTUyYsdW2Hh6Ce9AREaYIStYksy2iYzw7cAqKlL58bGRHr2eeqQ4wqr3Kl2uzn6GCCGBjUZGfI6A2v1JPCBxRmb1LUqiYW7XOX80LwYizOKzivi0hStsmwvOLoIvi48zYta0apfTC2w1yLmnF0OlMKAMRheeW6holUyo3ox5J5V74pAIIV5EwYgPKt+U5XJkhE3RlG3Idjkq4m3TJtVi2qQafoF2hH1tyoRaTJ9cC1939217XU4vsJyS3928z2PH5K+9jcJCLU4DO/Y11k7geJd8s8Z5LJGV8kYI8V0UjPig5pJY5H8/smsEpDv75yXrs1GbN8TpEl5vj4ow7N3vp2+tRlJiR7+JrCwQSUzo4Nv4Q0VWNtJz7x27+ce9L6T2poH//OtmzJlV7ZXj8xest9FX7/0ErYbVZun9cyFDFGXMnlGNJx7e5rZ9XjDct6cBCQlmFIz4qLINQ7Hnf1PRVBLT4/7WqkjkfT4RBT+wYEXwqZUzjmSktWHLT0txxw15CA87Ov/PPr7thv3Y8tO3yMpohT9gAdNTj2zFh6/+golj63t8bda0GnzzwY+49469Xjs+f3LqSZXY8P0yPj3XPVBNTW7H43/eiu8++x4hIZR3Q0gwoAqsfkAbYYAmzAhLhxbGJucrC3wxGOmuo0OF/EJbPRHWaC401L8vNkUlYahv0CExwcAvouTY1DdoUVIWhhCdlZfTH4x8m6YIHX63WkXl4QnxIOpNE0BMLSH85oq3V84owVaYjB3ViECRmd7Gb/5MkmS88H0Nntm8BpXiAQhQIVecjL/Nm4Hzp0Udc02Wdz8ehg8/G4qauhAkxhtwxZJ8fusvByQ2xsRvg8mWOwJcw/KyqF8NIT6FlvYGEF8PRIjvqW+xYsz/fYyq2B+ASDWgsgUKe635uGDj/zBj9XVYd89siKLyhJ4NWxJw5sXz0Nis7apKe6QwAhs2J+DhJyZh5Sc/YOqkukF7ToQQ/0M5IwHCF1bOEP8z+dmvUBX9k+2TzkCk+8cb9W/i7Be3K368I4XhWHjhfD4ywoIQdmP4xxDQ1KzF/MUL+fQWIYTYUTASAPxheob4nm+3taA45lvARd2UFea3UNusbHntMy+P4dVnWb+h/rAeRa3taurdQwjpgYIRP+ZLS3iJ/3n0x62AVcFMrbYVD39V6HIzk0nE2x/muOzdwwKSN94fzjsfe8Nzc/07aZqQQETBCCFBqtxYCYgKLsySBvtrXRekq6vXob1Do2jfzS1aNDbZcko8jSWyfvqkfywlJyRYUDDip3x9CS/xfVpBB8gK/gQIEkLVrgOHgdYEYct4CSGEoWDET1EgQo7XvLQxykZGRCuumjbU5WYx0SZMGl/nsncP+/rMqdUICzu+Mu/Hi8rDE+I7KBjx01ERQo7X0xcOg2CIBpz12rGqEFY/BRfPilb0mHfdtM9l7x729TtvzIM3sakayh0hxHdQMOKHK2doVIS4Q3S4Co+NuN82VdNfAGFVQTBH4rNzWZkwZa68KB9Lzi2AIPRfQZXdf9mF+bj4/AJ4GwtI7ryryduHQQihYMS/0MoZ4m4PnZ2Ml8c+gfDGKT2/IAtIajoVP53+dyycEK748UQR+PDVNXj0we2IjTH0+FpcrAGP/Wkb3n3pV74dIYTYUQVWP0FTM2Sw3HxaPG4+7TZ8vaUZqw/WQS2KuHBKAmbkhh7T47G+Mn++Zxfuu2MPfvltCF9lEx9nwMknVEGrdZ5PQggJThSM+AmamiGD7dypkfzmLjqdhAWnlLvt8QghgYsGS/0AlXonZHCcHWGhvBFCfAAFI36AKqwSMrgBCS3zJcS7KBjxg3LvhBBCSCCjYMSHRZwzzNuHQAghhAw6CkZ8eFSEpmcI8Yy3p7RT7gghXkSraXw0YZUCEUI866QUFV6gk06IV9DIiI+hQIQQQkiwoWCEEEIIIV5FwYiPrZyh6RlCvIeW+BLiHRSM+AhaOUOI9xvnsURWCkgI8TwKRnwArZwhxHdcMNzk7UMgJOjQahofCESCpe+M1Spg+Q+p2LIjnn8+dWItzphfxhurEUIICV4DCkYkScLXX3+NX375BWazGdOmTcMVV1wBtdr5w5SVleGVV15BUVERcnNzcdtttyE2NhbBLphWznyzIh233TcT5ZVhUKttnVstFhHJSe146en1OPeMEm8fIiFdS3w/X9SB8pV6OiOE+OI0zezZs/H+++8jKysLI0eOxOOPP465c+fCZHI8rFlcXIzJkydj+/btmDlzJn788UdMnToV9fX17jh+4ge+Wp6B8686FRVVoV1BCLsxldV6LL76VHzxbYaXj5IQG8odIcTzBFmWFY+Rs8AiI+PoRaOwsBDZ2dn46quvcO655/b7PTfccAO2bNmCrVu3QqVSoaOjAzk5Objuuuvw97//XdF+m5ubERUVheU58xGm0iAQBMuoiNEoInXsRWho1EGWhX63EQQZ0VEmlO/9mLedJ8QXXLM1lEZHCDlOFmM7Vr90CZqamhAZGemekZHugQiTkpICjUbjdJRj+fLlWLx4MQ9EGL1ej7PPPhvLli1DsAqWQIT54ttM1DeEOAxEGPY1Fqx8vjTTo8dGCCEkAFbTvPrqq2ADK2yqpj9sFKSioqJPEJOZmYkjR444fFyj0chHQ7rfiH/asDUBGo3V5XYatRXrtyR65JgIIYQESDCydu1a3HvvvXjyySf5VE1/DAYD/zc8PLzH/exz+9f688QTT/BpGfstPT0dgSAYC5tJkjCAbQf1UAgZkOfmug6iCSFeDEY2btyIM888E3/4wx9wzz33ONyOBR2iKPaZxqmrq0N0dLTD73vooYf4/JL9VlJCKy381bhRDbCYXf+YsYTWcaMaPXJMhChNZP30yVY6WYT4YjCyadMmLFy4ELfccgsfwXCG5ZOMGjUKe/bs6XH/7t27MW7cOIffp9PpeKJL95u/C6Z6It1duvgI9HqLy+1C9FZcfmG+R46JEEKIHwcjbFXMggULeCDy1FNP9bvNZ599huuvv77r88svvxwff/wxysvL+ed5eXlYsWIFr08STAmrwRiIMBERFjz5yFaX2z3x8Fa+LSGEkOAzoGCETc1YrVa+pPeSSy7purEAxI6Ngnz++eddn9999928rsiECRP4iMqsWbOwZMkSXHnllQgGVOoduOOG/XjmsU3Qaq0QBRkqUYLIboLMk1v/9bdNuOumPG+/VIT0QVM1hPhgnZFPP/2UByO9jR07lt/swcjBgwf5ct7uWJ0RVqeEVWC1b6uUP9cZCaZlvK7UN2jx9v9ysLWzHPyUibW4+pJ8xMUavX1ohDi1tEWNF56PorNEyCDVGRlQMOIt/hqMsJUzhBD/R8EIIT5U9IwMbHqGEEIIIa5RMDJIgjVhlZBAdHaEhZb5EjKIKBgZpDwRQkjgJbOmLOrw9mEQEpAoGBkElLBKCCGEKEfByCCUeyeEBKYLhlMuGCGDgYIRNwnmwmaEBAvKHSFkcFAw4gZU2IwQQgg5dhSMuEHEOcPc8TCEEEJIUKJg5DhRhVVCCCHk+FAwQgghA0BLfAlxPwpGjgNbOUPLeAkJPm9PaceddzV5+zAICRgUjBwjKvdOCCGEuAcFI8cYiNAyXkIIIcQ9KBgZIApECCH2miM0VUOIe1AwMgBU2IwQ0jsgoX41hBw/CkYIIYQQ4lVq+AFZlvm/7ZLFa8cw64k0tIyeALRQ105CyFGmNsBitP2NIoT0ZDG197iOOyLIrrbwAaWlpUhPT/f2YRBCCCHkGJSUlCAtLc2/gxFJklBeXo6IiAgIgqDoe5qbm3kAw05AZGTkoB8jUY5eG99Fr43votfGd9Fr4xgLMVpaWpCSkgJRFP17moY9AWcRlTMsEKFgxDfRa+O76LXxXfTa+C56bfoXFRUFVyiBlRBCCCFeRcEIIYQQQrwqYIMRnU6HRx55hP9LfAu9Nr6LXhvfRa+N76LX5vj5RQIrIYQQQgJXwI6MEEIIIcQ/UDBCCCGEEK+iYIQQQgghXuUXdUaO1ZYtW3D48GGccsopSEpK8vbhBL1PPvmEF7DrbvLkyRg+fHjQnxtfsXPnThQVFWHixInIyMjw9uEEvV27dmHfvn39noeLLrrIaREp4rnfmcLCQkRHR2PatGkIDQ2lU38MAjYYYUHI6aefjtraWvzwww8UjPiAyy67DLNnz8aQIUN6FMOhYMT7KisrccEFF6CgoAAzZszAwYMHcc011+C+++7z9qEFtb179+Lrr7/ucd+aNWt4UM+CEeI9jY2NOOOMM3DkyBH+O8MCkrKyMv6m69RTT6WXZoACMhgxmUy45JJLcP/99/Mb8R0PPvggFi1a5O3DIN2wBXXnnXceQkJCeBDP3tmxi93y5cvpPHnZpZdeym92BoMBycnJuOWWW2hUxMtefvllHDhwAPn5+XxUhGGv1R/+8Ac+WkIGRgzUC96oUaN6/BIT37B792589dVX/Je195QN8Y6ffvoJGzduxP/93/91DTGz4f+zzjqLXhIf8/nnn6OpqQnXX3+9tw8l6BmNRsTExHQFIkx2djZ/M0wGLuBGRti7OXax27FjB29eRHwHa3L47rvv8lyEzZs383/ZkObQoUO9fWhBjQ37x8fHY+zYsVi1ahX/Izt+/Hje2Ir4ljfeeIPnwOXk5Hj7UILe7bffju+//55PPy9YsIDnWv3vf//Dq6++GvTnBsEejFRUVPB3DJ9++ilvWETBiG/55ptveB4Pw14bNl1z5ZVXYt26dd4+tKBWXV3NO2KzfB69Xg+1Ws1fk7/85S946KGHvH14pBPLTVi9ejU+/PBDOic+gF1jZs2axa83ra2tPGeEjYxkZWV5+9D8UkAFIyzZLjc3F6Wlpfjoo49QX1/P72e/wBqNBieffLK3DzGo2QMR+y8ye70WL17MX6fY2FivHlswY7kiLHH1z3/+M6677jp+3xdffIELL7yQv2ZsZQ3xvjfffJP/npx//vnePhQC4OGHH+bTZmzFk32qhuXysN8ZlkuiUqnoPAVrzggbZmZDy2yaht2+++47fj97l7d+/XpvHx7phQUkTE1NDZ0bLxo2bBj/l62msTv33HN53si2bdu8eGTEzmq14u2338ZVV11F/bZ8xC+//IL58+f3yBlhATxLaC0pKfHqsfkjdaAlrnbHRkjY1MCf/vQnzJs3z2vHRYCqqiokJibyvBE79u6bvdOzXwyJd5x55pn4/e9/j/379/MlisyhQ4f4BTA9PZ1eFh/A3lixZaM33HCDtw+FdGK/G3l5eT3OB/ucjYh0L19AgjAYIb5r7dq1ePrpp/k77oSEBPz444989IoNPbMcBeI9LIGYTZmxuhVsWSJ7PZ5//nn+ru+0006jl8ZHEldPPPFEjB492tuHQjqxshEnnXQS/71h+W9sqvPZZ5/l97OpTzIwAd21l+Ui3HbbbXwunE3hEO8XcGK5PCzRmCV6sSx09i/xDay41rJly3gwwkZIrrjiCpr39gFms5kn5rPaSazIFvEdLGn1nXfe4Stp2DJftqpm4cKF3j4svxTQwQghhBBCfF9AJbASQgghxP9QMEIIIYQQr6JghBBCCCFeRcEIIYQQQryKghFCCCGEeBUFI4QQQgjxKgpGCCGEEOJVFIwQQgghxKsoGCGEEEKIV1EwQgghhBCvomCEEEIIIV5FwQghhBBC4E3/D3BEqrLKMIv5AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1f)\n",
-    "from sklearn.svm import SVC\n",
-    "svc = SVC()\n",
-    "svc.fit(X, T)\n",
-    "nnwplot.plotTwoFeatures(X, T, svc.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "2c8991de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1g)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "1a5e84f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#1h)\n",
-    "X_0_3 = iris_data[:,[0,3]]\n",
-    "clf.fit(X_0_3, T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, clf.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "892baf25",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X_0_3 = iris_data[:,[0,3]]\n",
-    "svc.fit(X_0_3, T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, svc.predict) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "1bad3e36",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAgmdJREFUeJzt3Qd4VGXWB/D/nZaZVNI7vfcioFJEBaSIDVHEuuqqa3ddd3XVXfXT1d21s+ra21pWxFWxIKigAor03gmkEdJ7MvV+z3knExIy5SaZPuf3bNZkcnPvnTsJ98z7nvccSZZlGYwxxhhjAaIK1IEZY4wxxggHI4wxxhgLKA5GGGOMMRZQHIwwxhhjLKA4GGGMMcZYQHEwwhhjjLGA4mCEMcYYYwHFwQhjjDHGAkqDEGCz2VBcXIy4uDhIkhTo02GMMcaYAlRXta6uDllZWVCpVKEdjFAgkpubG+jTYIwxxlgXFBQUICcnJ7SDERoRIZOvfwMaXXSgT4cxxhhjClhMjVjz2rWt9/GQDkYcUzMUiGiiOBhhjDHGQomnFAtOYGWMMcZYQHEwwhhjjLGA4mCEMcYYYwHFwQhjjDHGAoqDEcYYY4wFFAcjjDHGGAsoDkYYY4wxFlAcjDDGGGMsoDgYYYwxxlhAcTDCGGOMsYDiYIQxxhhjAcXBCGOMMcYCioMRxhhjjAUUByOMMcYYC6iQCkZuvKk20KfAGGOMsUgORmbHWbDkifpAnwZjjDHGIjUYIQl1RmTNagr0aTDGGGMsUoMR8tw0a6BPgTHGGGORHIzQ6MiK+yt4hIQxxhgLAyEZjDi8Na4Rt91eE+jTYIwxxlikBiOMMcYYC30hH4zMi7Pw6AhjjDEWwkI+GHEEJLTkl1fZMMYYY6EnLIIRR1Irr7JhjDHGQk/YBCMOPDrCGGOMhZawCkZodIRW2HBAwlhokWGDGfWwwohgJUOGBY2woEl8HuroitM1l8F1m1jgabryQ4WFhTCbzejZsyfUarXbbY8dO4aioqL2B9VoMHr0aPgKBSTLBpqw+PkEnx2DMdZ9RqkC+bovUKRbDouqQTyWYBmEXNM8pJsnQwqC90sUgBTqvkZB1JcwqsrFY9HWbHGOWabpUEOHUFKp3ob8qM9RrtkISDJUshYZ5jPR03geYm09A316LEJJsiwrDvHffPNNPPbYY2hubhZBiNFoxLPPPouFCxe6/JlHH30UTzzxBAYPHtz6WFxcHFatWqX4JGtra5GQkIDqvBcRH2dQ/HM1cVG4Y7UaxcuV/wxjzD/qVHnYHPMAzFIDINlOfENWia/TTVMxvOkuSHD/hseXjFIVNsbchybVMTE2AslxjvSJjATrIIxpeBgaRCMU5EV9hEP6/0CSVZDbXHP6mp7cyMb7kGqZENBzZOHFYmzE6hcXoqamBvHx8S6369TbjoMHD2LlypViZOTo0aN44IEHcOWVV2LXrl1uf2748OHYuHFj60dnApHu4KRWxoITTcdsiflrx0CEtHx9XPsj8qI+Dui0zLbox9CsKhEjCK2BiDhH+9c16gPYY3gBoaBU87MIREjbQMTxNU3XbI9+Ao2q4gCdIYtknQpGaFSkT58+rV/ffPPNUKlUWLdundufs9ls2LdvnwhgOjEQ4zWcQ8JYcCnR/giTVN0xEGlLAvJ1n4rchkCoUe9DrWZ/hxt3O5INx7Vr0CyVIdgdiVpqH3VyRQRbNjElxZi/dWtCdvfu3TCZTOjdu7fb7TZs2IA5c+bglFNOQWZmJj766CP4O6mVy8YzFjxKtKsddz+3KI+kUrMVgUAjM5KsZIpIEgFJMKNgiQIrt8FfywjJMe33fjsvxrodjFDeyHXXXYeJEyfi7LPPdrndmDFjxKjIoUOHUFpaijvvvBOLFi0S0zWuUC4K5Ym0/egurtTKWPAwqWhURNkoqUnq/t9/V9BxlayakSDBpAruHlmduYZmqd6n58KY14IRWklzySWXoLy8HEuXLhVTNa7MnTsXAwcOFJ9LkoR7770X/fr1w4cffujyZx5//HGRsOr4yM3NhTdMzVLzlA1jQUArx7ckgSrZNs7n5+P8uLEi0PCEAhatLTDn2JnnopRGjvHpuTDmlWCEApFLL70UO3fuFImo2dnZnd2FmKqhJFhX7rvvPpF56/goKCiAt6Zs5g80eWVfjLGuyzCfYV+d4oFaNiDJMiogl5qWFsuSkhocNqSZJyGYGeR0xFn7egwAaVWN/bVhLIiDEYvFIpbxbt26FatXrxZ1Rk5WXFwsvt92OqetyspKbNu2DUOGDHF5nKioKLEEqO2Ht3AfG8YCL8N0hn3Ew11CpSwh1zgPakQhEHpYhyHW2qdl2asLsgqplomIljMQ7HoZL3I/NdbyrVzTXL+dE2NdCkYuv/xyrFixAk8++aTI/3As1aUAxOGVV17BtGnTWr+mz59++mkxirJkyRLMmDFDTL3QSpxA4aRWxgJLAwNGN/5FBBodbvb07l0GUizj0dfouoaRr9EUzeiGBxAlJ3cMmujGLUuiSNjQxjsQCtLNU9Creb79i5Oej/01UGFY092IseUE5gRZROtUBVaaWhk0aBD+9re/tXv8hhtuEB8kKytLJK06LFu2TBRGo2XB0dHRuPDCC3HHHXeIwmeMschFBcMm1j2H/KhPUaz7DjbJXgo+xpaLnsZ5yDJPD2jBM6KXUzGh/hkU6D4XS17NqjrxOAUoNIKQazwXaugRCii4GmC8Wlx3qsBardlpf1xWIc18OnqZLkS8dUCgT5NFqE5VYA2UrlZgVWJZnYbLxjMWYFRLxCTVQAWdmL5RkjjqbzZYYabaKHSWckJQlKrvDtELSGoS1ztUAioWenxSgTUcOXJIGGOBo4IWejkFOjk+KAMRoqJJJTkZUXJiyAciRItYMfLDgQgLBqH/F+WlHBIOSFgooeWkTdJx1KuOBqwOhzdZ0IB6VT4aVcdEB99gLWHfoCpAg6owYFVhGQtXXerayxgLDLpRF2lXiDn/RnXL8nhZEs3NehsvFvkAoYQCkCNRS0QFU8cyWr0tVeRi5JrOFSMmwdBZ+EjUJyjWrYC1Ja9FY4tFjmk2ehkvFCMMjLHu4ZGRk0ZHuI8NC+ZAZKfhaew1vIhGVdGJb0gyyjUbsCHmTziuWYtQUaXeiV9jf4/j2p/a1fOg0uUH9G9hS/RDAR+BoKZxv8TeiULdl62BCLGo6kWvl19j/2DvscMY6xYORtrgJb8smOXrlol+KSKl4qR6EfZmbjbsjH5STN+EwrTM1phHRbDRoRFdy/Or0uzEQf27AQ3+tkY/CrNU57xZnmQTHX13Gp4JxOkxFlY4GHGCy8azYEPt3WkJrFuiPIeMQt1yBLti3SpY0eS+CJcko0j3NSy0XQBUqrfbp8LcNJejIKVSu0XkkTDGuo6DESe4bDwLNnXqwzCqKjw3uqWW9rofEexoakZJOXiaGqnUbEMglGrXKevaK6tQqv3ZH6fEWNjiYMQFXvLLgokFjcq3lZRvGygW6gyrcAWvRWrw9em4OG6j4q69gTpHxsIFByMeRkhW3F/BSa0s4HRyD2UbyoDOpnDbAIqyJSnu2qv4uXsZHVdZ114bdLZEv5wTY+GKgxEF3hrXiNtur/H9q8GYCzG2noix9lRwA5eQZZoe9Ncx03yW+3yRFlpbfMC69maapins2ishwzzZD2fEWPjiYEQhTmplgUTv0PsYF3jouqqCRo5GlnkGgl2aeRKibCnuO+IC6G2cD1WAyiHF2/oj0TzCQ9deCZnmM+3N9BhjXcbBiEKc1MoCLcN8Bvo2LxKfd0islKlYuR5jGh8SJdWDnRo6jG14BFo5obVLb6uWm3+2cRZ6mi5AII1o+pMYlXJ06W3V8nmiZSQGN90UuBNkLExEfKO8zqqJi8KCe7niIgucKvUuFOi+QJl2PWTJAq0tAdmmmaIiKPV3CSVUyr5I943oiGtUlYsgi6ZlqPpqsmVcUPSpoTLwx3Tfi2tO5eBpnCre2l+cY7p5quhZwxjrXqM8Dka66JpN0She7t0Owox1hn2lhw1SmNwMqZYKDdYGQwDiir1vjhTU58hYMOGuvT42f6DJ14dgzC37LTE8AhFCzyXYb/LUrTfYz5GxUMSN8rpRh2TprCYeHWHMzShCnfqQKKdOq2LibH3Fzbw7aF91qjyx71hbrl8TR41SpWjsR8FIrK2Py9wcmtapVR+ATTLDYEtHtC3L6XY22HBc8yOa1aXQ2ZKRaT4jYMm6oa5KtQfVml2isWK6aTL04ITiUMO/+d1c8rtsoAmLn0/w3ivCWBhMH1EZ9yO6T8SN1sFgTUcv43xkm8/p9OhCs1SBQ/p3UaL9UeTJtO1W3K/5SsRSkqmPUJ4I9cgp06xvXc1EuS0Z5qno13wF9HJqa2G6w/oPUCS6+54oYd/DMhR9my9HknVEaxCyW/8sjuvaNwjcIy9GmvlUDG/6I1S8tkCRIu13OKB/AxZVXetjB/Svi6TjkY33IcaW7a1fA+ZjnDPiBcvqNByQMNYSiOzVv4SiqOX2FShtY46Wr3OM52JQ828VByTU+G9D7B9hlmo6NKyjZbcqsTLnMSRYB3j9NahVHcSm2D/DBpPTY2vkOEyo/ye0cjw2xt6HetXRjr1sxOogGSMa/4hUy2n4NeYu1GvyXF4fgzUbp9W/wAGJB3m6j3FI/07Li3HSN2X7tN/E+ufFCBoLHM4ZYYz5Xal2rT0QcXaDaPm6MOoL+yiDQjujn3YaiBB6zAoTtkU/BvrMm2h/22IeE/t3dWyLVIcd0U/hgP4t54EIEY/J2Bn9FPZHvW4PRMTjJ29n/0+Tugj79a949bmEm2ZUuQ5EWh6jhOjNMQ/4+9RYF3GdES/2scmaFZjuoowFi3zdZ611QlySVSiIWqZof5QfUqPZ4zQYaCXZYFJVolzzK7ypXLOhpTmh+669tZp9KNZ963Y7x82xmDoqeyo8K1NX4++7fuIR4IDhDfsn7gbXJFo6XoUa1X5/nRbrBg5GvFgUjXJIOCBhkcqCBtRo9rm/KRPJhirNDpHo6Um5doPn4KYlh6NM6+VgRLtRYdde6UQei/sNYVOZFXReBmxSMxqlY0pPNeJUarYq3pbqw7Dgx8GIlz03zcoBCYtIVqm5k9t7DkYoYFHarM6mILjpDCuaFXXtVdx+WOr8yiHmnA0Kgrou/l6ywOBgxMu4bDyLVJTEqZK1irZVyTpo5BiP21FFWXuhMfcoYHGsavEWOray+53n8xNohEdJbCO2BQy2DIUbRx6xrFphnBhty/TDGbHu4mDERzkk3OWXRRqq8ZBhnuZxaoO+T52FlZRRTzdNgaSgAgHlbmSazoY3ZZnOdp+r0oLOL8qW7PnmKNlEc0AlOSMGWyZ0CP4eQ4GSY5qrbGREBvoYL/XDGbHu4mDERziplUWiXsYL7IXN2jaVa0u2V43taTpP0f60iEVP0zz3N3BZhVTzRK/XGomx5SLNdLr7nBUZokcN1Rtxd3OkZcAx1lwMbbqt9edc7Y8MaL62G2ce/nJN86CWDR4DuxTLeGgQ7a/TYt3AwYgfklp5lIRFCrqBj2p4UIyStLuJt3S9VSMKoxv+6rIqqTP9m69Epvks8Xm7UZeW/SdahmFY4+/hC8Oa7kSSZURrQOHg+JxGgvo3X40s89mi+FrHc7RHKDTSMabhESRbx2Bg0/Ut3zspKGn5vJ/xCqRZJvrk+YQLqi4zof5pqBBlf8DJdYyz9MfIxvsDcn6s87jomZ86/d6xWs2l41nEoNLpVIm0RLsaJqlOzPFnms9ElmkGouTETu+PEkmr1DtRqPtKlP2mr+OsfZBjmoNUy3if9uihJbnlmk0o0H2JOvVhkZ+SYB2CXOMcJFpHtkuwpaXIdI60EocKpVEQQt2U082TRSB2YrsjonJolWYnZFjE+fewDBEjIvG2/j57LuGGqt4e0r+HYu13rVVv9bY09DFegmzzjECfHgN37Q06XKWVMcZYpLEYG7H6xYWoqalBfLzrPCiepvETTmpljDHGnONGeX4OSKY+Uc9TNox1UaOqGDXqfWK5L03TUCdgZ8yoF4WxLFIT9HIyEi2jFK3eYaGHpuxq1fvQoCoS012UQ+TtZd7M9zgYCUBSKzi7m7FOqVflY5/+FVRpt7d7PM7SDwObr0Oidbj42oImHNC/iWLdd5Alc+t2OlsP9DZeglzT3E53DGbBi0r279e/iUZ14YkHZQkpllMwqOkGGOT0QJ4e6wSepgkArtLKmHKUFLoh9h6R7Nnhe+rDohlahWazqJi6KeZ+kTjbNhAhJlU19htewUH9W3zpw0SJ9gdsjf4/NKqK2n9DkkXC8a+xf0CTVBKo02OdxMFIAPCSX8aUD8Hvin7a3sfGaUdc2kLGDsOTOBT1XxGcuOuNczTqf6hW7+bLH+KoVP4uw/P2LyQnxUYkm9hmt+EFv58b6xoORgKIk1oZc4/yQ+rVRz10xJVhUdWjkBqieaiYSjVAuHFa6KOlvLQk2n3XXhuqtNtEnhELfhyMMMaCFiWhti025pKsgk3luVGeLFlRod3inZNjAe7aq6A5jUzbbvPHKbFu4mAkSMrGM8Y6stG7X6VNSDq1TxbKRMdnRXnIEr/eIYKDkSDJIaGAJGuWvYIgY8yOOq5SBVTPJGXxiCwhmrvhhrwYa46yETNJ5tc7RHAwEiQ4qZWxjtLMp7cro+6aDYmWkQpuULIoz85CW5Z5pueOyjIt6U5EkmWsv06LdQMHI0GGp20YO0EDA/o0L3R/SWQJWeYZGGC8umWExPn4PQUqBlsGMkxn8iUOcQnWAUgxT3DfUVmiJotXcbG7EMHBSJCOkqy4v4KnbRgD0Mt0EXo3X+Kkc669omqG+QwMbroJ8dYBGN1IHYN19oDEMW3T8jPUtG5cw2MiwGGhb0TjH5DSMurRoZuzLGFA03WimzILDVyBlTEW1Khian/jFcg0T0OhbrmoE0KrYuKt/ZBtmi3eJTskW8ZiSu2bOKb7Dse1P8MqNUBvS0WmaTpSLROg4n/ywoYaeoxqfFD8PhTplqNenQ+VrEGSZSSyTbO4+mqIkWRZVp6GHiC1tbVISEhAdd6LiI+LrHc112yKRvHyyHrOjDHGwgN37Q0Tb41rxG231wT6NBhjjDGf4WmaEOr2u+De2ECfCmMBQ2Xfq9W77F17JStirX1EzgB1avX9sa2i/02dOg8SVIi3DBLN+Zw13aNeOlSUyyqZxLLSVPOpTlcEmaRqlGp/hkmqgVaOQ5r5NETJSd04Rxk16j2oVu8R1yfG2hMplvEhm8BpkmpRql0nrpNWjhXXUS+nBPq0mI9wMBJSSa1GnrZhEalKvQt7DP9Co7pIJChSCEBLO3W2JAxq+i3SLZN8duxSzc/YZ3gFRlWFSKAV89p6GwzWLAxturW1YzC1sN9teBY1mn0igZKCFgoK1HI0+jZfhp6m80TwQn129hlexjHt9yKAENvBhn36V5FhniqScTWd7OxNAdpuw/NoUBe0XB9JHJu6FVNXY0ryDRU2mLFP/xqKqeEhbO2uT7p5MoY03QwNYgJ9mszLeDVNiOFpGxaJgQh15m3tMSLZWmtMmKRK7Ij5O45pV/vk2CXaH7E9+nEYpQrxtThuy7GbVMewOeZBVKp3oFF1DBtj/oga9YGWc5RFMECsUiMOGF7H4aj3xY12a8wj9t4qYl8t21GzN8kmjrc55i/2xoCdCEQ2xvwZDarCNtfHfmwaVdgZ/RSKtCsRCmywYmv0YyIh1XFdTlwfGce1a0VnZurQzMILByMhiGuRsEhB74hptIH+67w7q7241R7DC7DAuxWM6YZHozGtx+lwbLnl/J4T79otUoPbRn15+v/iSNTHqFLvcP5cxD5tqFUfQKHua0XnSGdAIyL2pnGur89ew79hRvC3nSjR/oBK7Wa314emyvKjPvf3qTEf42AkhKdtOLGVhbtK9XY0qY+7vjkRid5RG1GiW+31G6N4B+62M6yMZnUpKjQbPVYEpSmefOos7LGpiiw6C1Og4QnliIipGQ/Xh4IVWu4c7ERHZRdF69pfn68UtglgoYKDkRA2NUvNhdFYWKvW7G5f0MollUjc9PqxlfwTSUW2FDRto2DFoqpzHzgQCSLAoSkWj+dIz1lJjxbxfLx7fbyNGhjWaQ4quj4mVSWaW6bOWHjgYCQM+tlwgz0Wvjz0H2nD2++UaQomkEWYxNSUp20kq9MVPc72FvwjCZ272o68GBYeOBgJA5zUysJVjK2nwpuOjFhbL68fW9EN0lPDNgdZgkrWKtqlxhYDnZzg+RytSq+PCjFW714fb1NBC701TdH1Uct6RNmS/XFazE84GAmzpFYeJWHhhGpvaGyxCm5QErJM0716bNqfklEHChziLP085zpIMjJM0zz/syurkGOapah0PdURoeW7nq+PDdmmmQh2uaa5HnNqKPcmyzQTaupBxMIGByNhhKdtWLihd8sDmn/j/v4kA72N87tVMMyZKDkRvY0LPG5H50e1PETg4iogkSWkmieiv/FqUbirbcO/tuhxGhHJNZ2n6BypoBkd27FqxvmxgZ6m82GQ0xDssk3niM7K7q6PRo5DL+OFfj835lscjIQhziNh4STbPAODmm6CJGtauvG2dORt6c5KgUg/4+U+OXZf4yJ7x2BxTDqePbAQRc1kDQY13YBs80xR+IyatqmpI3DruZ3oMkzFuoY3/gE6OR6n1D+OaFuueFwk57bZjm7EpzQ8LgIhpaig2ZDG2yBB6+T62AORAc3XIBRQsbdxDX9DrK230+tDTQ/p+uhlnqIJN9woL0wtq9Ng8fOe55wZC6Xy4Me034kKp5TcGWvtjWzTDOjlVJ8fu1kqR5FuBerVR8Q0QoJlELLM00Vw0RYtBabCZY5y8BRc0PRIrMg/OYHOn5Ytl+h+gFmqE+XO081TkGwZo2wFjxNUR6RYR9dnj9g/5ZPQ9THI6Qg1lG5L9Vjo+lC5fI0cIwI6f5X/Z/5vlMfBSBjjgIQxxlggcddexkmtjDHGQgI3youIpFZg2UATT9sEiFGqwjHtKjSpSkT3VhqKT7KM7jAcb4UJpdq1qFHvFwPVcda+onGaGvpAnXpIssGGIu1yHNN9C4tkRJSth2hUl2izN7Rj4YWmuag3UaOqEBI0SLQMR4plQre6FVNJ/uOadaLEv07uIfJyYmzZXj1v1o1pGpPJhPfeew8//PADzGYzxo8fjxtvvBEGg8Htzx04cACLFy/G0aNHMWDAANx1113Izlb+wtbW1iIhIQHVeS8iPs79sZhrPG3j/6ZfB/RvoED3pQguHMEH1YUwWNMxovFPiLf1F49RnsFe/UuwqBpaK45SkSoKRAY0XYsc8yw/n31oqlBvxbaY/4NNMndYXRJlS8PE+qehg+t5axZaeSV5Uf9FXtRH4m+l7d+XzpaI4Y2/R5J1VKdzg7ZHP4Fazf42lX+pWZ8NKeYJYp+d7agc6SwKc0Y6lSl1+umnY82aNZg2bRpmzZqFN998E5MmTUJTU5PbQGTChAmoqKjAwoULsXfvXvF1aWlp554R6zauReLffyj3GBbbe2206c7qKFDVpCrDxtj7UK86guPaNdgZ/aS90VrLP6b2TqXU8bUZe6NfVNw4LZLVqPZhS8xfRWdcQWr/YVSVYl3cTbDBFOhTZV5wKOpdHNa/D1mydPj7olL6W2IeQpV6p+L9UaLsxtg/oU59UHzt2J+j51C5ZqPoqNz6+8UCNzJSXl6OlJSU1q+PHTsmRjiWLFmC+fPnO/2Zq666SgQg69evhyRJYkSlf//+IjD5+9//rui4PDLifTxK4lvV6r3YGPtH9xvJKiRZRqJOfUgMNburpaGSdZha+w6/K3NjbexNaFIVe+wT07P5Agw0Xut+IxbUmqQSrI27wUP9GQkxtlycWr9YUfG6A/o3cVT3mfuKujIwpOlWsZybBXBkpG0gQhITE6HRaFBf77o19TfffIPzzz9fBCJEq9Vi3rx5WL58eWcOzVhIKdR95bJwUyvJJpaAmkXzNPeb0rt5b3elDSfNKFMUiNDNpFi30k9nxXylUPeN59uXJKNBnY8a9T6P+6PRjiLtCgWl/SUURFHnZRZURc+ef/55qFQqnH322U6/39jYKKZjcnPtBX4ccnJycOQIrdd3zmg0itGQth/M+1M2t91ew5fVR2rV+z22lBdE5UzPf4ZUW6G2ZfiYdVSh3aqocy5t45gOY6GLEkwV9QSSpdZpF3eaVKUiX8sjSRZTq8HfdDCCghEa2bj//vtFQELBhauEV3Jygmt0dHTr95x5/PHHRcKq4+PkYIZ5N4eEMcYYC7lg5Pvvv8dFF12ERx99FDfccIPL7WJjY8U0TmVlZbvHKZmVpnhcue+++8T8kuOjoKCgK6fJFC795QZ73hdvHeR5moZQxpaCd3j0Tizeal95wzpKNo9W1oFehqjmyUJbgnWAohFFGsmIU/B3Y7CliYaHHsmSKFXPVWCDIBhZtWqVyPl48MEH8ac//cntthSIDBs2DFu2bGn3OH09apTrJVdRUVEi0aXtB/N9gz2etvGeXNNsz9M0IoF1NLS2OI83UhV0LR1fmTN6pMJgy/IckEjUjI2TD0MdNdSjTA+3KIHV2gsJ1kGKGjKKpFSPAY6MXOO5nTtZ5v1ghOqLnHvuuXjggQfE6IUz//nPf3DxxRe3fn311Vfjo48+Ql5envh68+bNIqmVHmfBhadtvDsykmk6200XVyrJpBMdVwc3/85911UAA5uv55U0HlANCHEh3XSv1djifNZUj/mPQc5w31GZGhlChcHUYFFRMhHQyzjfbUdl+psVf9fmM7t41sxrwciFF14oElYpKKE6I44PCkAcDh48iG+//bb169tuuw2zZ8/GiBEjMHHiREyePFkUSqOlvSz48LSNd9A/gLQEMNd0bmt3WSqi5CikZLClYlz944i19Wrp6HpP6/RB63YyoJb1GNx4M3JMXPTM4++ubSDGNDwsRpEEuf2H3paG0+teOvF9FtL6Ga9A3+ZFrd2c2/59UdXUMQ0PIdE6TPH+7B2Vn0AcTQG1+Tt0BCeplvEYK36/tD56RpGtU3VGVq5cCau1YxYx1Q2hD0cwQpVWT15hc+jQIeTn54vtOpuQynVG/K8mLgoL7o0NwJHDtxx8s+q4uBEmW8YiyTLKZTl4WilARdPirH24HHwXy8EXa79Bse5bUTSOqnHay8ErvzGxUCsH/0NLOXi118rBl2rWwczl4LuNu/aybgcjd6xWo3g5l99njDHWNdy1l3ULJ7UyxhjzF+7ayzwmtU59op6nbIKIBY04qH8HZZoNsEkm6OQE9DReiGyz8+KDwa5elS+qojaqikXZ+2TLaNEl9eRuxSapFsXab1GjoYqaNsRae4uVMXo5NWDnzuyMUgWKdCtRpz4svo63DkCWaTqiZNclHBjrcs5IoHDOSHC4ZlM0T9sEWKH2a+w1/FvUT2i3akSilSKxmFj3DAxIRyigHJndhudwXPeTSBKUxVJN+7IiNQwY3ng3Ui0TxLYFuq+wX/9am8qX9OQp50ZGL+NF6G+8skMODvNX59wPcTjqw5bXzvEa2pO4+zdfjV6mC/mliGAWX/SmYZGNa5EE1nHNGuw1vHRiDXDbrrT0Ry/V45f422BBM0LhJkadiqljsfhadDa2F6kS3YrRjG3Rf0OlejuKtCuxz/Dv1u6sjm0c3ZCPRi3FoagTK/qY/xyJWoLD+g9aXpO2ryF10bXhgOFN5FPnasY84GCEdQrXIgmcvYZX7J+4KptAN3GpGYf0wX9jrlHvQZn2F/uNyxnxuIz9htdxQP+G+2JmEt0UPxFTBcx/aNrMPiLi3iH9OyK4ZMwdDkZYl5JbV9xfgaxZTXz1/KRWdRBmqVpZV1rtiTo/wapQt1xBV2MZ9eo8ewMzj3WrZBTpgv95h5MS7SpFDeMoECnR/uiXc2Khi4MR1mU8beM/VZqdirvSWqVGBDtKdFTU1Zi4qmLbjoQGVX53T4t1Qr06X1GeDtX+oG0Zc4dX0zCvrLb5sfjEO6Sl+3Wc6OpljsqSCrdGsFP54J8ebl4WvL+T3SlAxiIDByPMK9M28+JOfD1vnAXLBppEUEK4cFr3pZlPw375VUXTNFG2JAS7RMsI1KuOKGgm6Mgf8cSGHtah3jo9pkAP6xAUScs9bidLViRYhvA1ZW5xMMJ8NmJCQQlpG5gQDk46T48URNty0agqcB+QSEBP0wUIdtmmWciP+sz9RrJK1BxpVBWhSVXqOiiR6Z13FDJMZ/jkXJlzaeZJ2Gd7BRbJTU6PLIk+MSmW8XwZmVucM8L8EphQfonjg5Nfu2Zk45/sf7JuutIarFnINc1DsIuxZYt+MS7JKmhkAwY134ChTXfap2Cc5Y6IkRNgSNMt0IBbF/gTdZ0e2nR7S6dkZ6+Nfd35sKY7eZqGecRFz1hA+9+0xY35lK2q2RzzV1hUdR2Ckh7WYRjb8H8+ycfwVa2RAt0XOBz1vlgxQzkI9BjVq0iwDBJBCAUtpFq9G7sNi9GoLhKBiiiNJtmgsyVhUNMNSLecHuinE7HKNRtEIb5mVVlL8Tp7DRiDNV10rk6yjgr0KbIA4kZ5LCQtq2t/I138fELAziWYVai3oFD3lagroreliVbqeiQjFFElVqo50qQqgUrWIskyGnG2Ph22o0ClWr0LNep9IhCJs/YWHZA5cTXwqHpupWbriXLwlgFItI4UVVhZZLMorMDKIyMsJLoHE841YYyx8AxGQmM8l0V492D75zXTrK2BiQMHKIwxFvo4GGEhGZg40EodnspxzmK14FDlXpTov4VFXwKVOQ5J9dMwMGEcDFH6dtMfter9KNR9jTp1npj3T7QOR45pNqJtWe322SyVo0j3DSo0m2GTzIix5iDbdE6HIXkrjCjR/iA+TKoa6Gw9kGmehnTzFKhxIlfIBguO6j5Foe5LmFS1Ysol3toPA5quQ4Ktf7tjN6gKxTlS/oh9mqafOMcE64B225lRj2LddyjVrhMrPfS2VNFBNtUyscv5NLXqA+LYtepD4nn2sAwTx46x5SDU0PU5pluFUu1amKV66G0pyDSfjTTzqVBB27qdDVaRD0IdlZtVpVDLBqSaJyLLPAM62fU7XMa6gqdpWNglwtLoidIRk9turwnLYKaqvhqbox+CnHgYsKkBlbUl4VUCzNHoV/g39EnuAxvM2Gl4BqW6NfYEUslevE4kIko29GleiL7Gy8QNmG7Ge/X/tu+jpT6I42cSzSMwqvF+aBAtkmy3xDwEs1Tbsi11GLZ346VlnmMaHhY5IU04jl/i7oBV1di6KkZoSczNMJ2J4c13iWCJGuEd0S8Ryasnjm0/R1rSS6s66EZartmE7dFPwAaTfUfisPafibZmY2zDI9DLqYqvIwVLewyLxc3b2fXp1Twf/Y1XhUxuRKV6G7bFPCaCxZOvj8GagTGNDyPalgmjVIktMX9FvfroiWve8vtDAd3wxnuQZjk10E+HhQDOGWERTWkiLAUjU7PUnQpggl2zyYg1uruB2GJAba/10o5NBdi0GFb8L1RmvI9j2tVuC4vRKEWUnISd0f90fVAaTbEMw5Cm2/Fr7J2wSE2tQcPJ22nkaEyofxrrY++0l653dh9vCU56NV8ErRyHg4a33RxbQpZ5uqhdsjHmjyKZ0tnzoQCCkn0n1j+neBnwHv2LYiTI3fXp13wl+hgXINjVqfLwa+wfIKOl+7GT66OTkzC+7ilsiX1A1HdxWpSuZcnuuIbHkGgd5p+TZyGLc0YYIr22Sbuv76/oEKC0bldnwfyBGiwOk2BkX/UGoH+B6xuoit7lWrBf8y7MOs8NzA7r34fGFtd+9OJkkg1V2h04IL/hOhBp2c6CRuyOftY+IuKKfSAFR6M+bTd14HxbGcXalWiSSlre7Tt/3nRjpRU7JbpVyDHNcb9PAE3ScRTplnusepsX9RFyjXPFqFAwo/OkCTR318eIChwwvIYGdYHrHbWMdNHvBQUkjHkD54ywiA1QwlV58v88b6S2wpz5k/1drody61apCVa1gg7Nsgpl2vWuAxEHyYZq9R73wY3Yjv7PBptMUwqeqFCl2aGgLY+EAt3XioIRyjux14V0/3zoFn5cuwbZ5pkIViapFqXanz2/NpDFMmuPvxcUfGp2oFEqQbSc4e3TZRGIK7AyBoipmnAhJxxR1s9FbKNgO8oZUIJudEo78dKxFadZKNlQ4f4kWYyOKNGkOqZoO0q6VbptoDRLZcpeG4mCK7PCfkBAkzq4nzcLHRyMMNayUmfJE/XImqVgBCDYWU/0AfLajT7oKU8gVbqiRoJG4V5lSJ6mkgLM41RXV/crB/fzZqGDgxHG2gQk4UBXMgWwKrjhGqnVssIRFCXxCDVFsyU471Ny0nZqWa9wnwq79ko2qGxRnjej5nvmMQoODNGkz2NX4ZZcC9q2LQpsHR/BEOBG27KhsyV6vOZ0faLkFLFyyBN6DeOt7ZdfM9ZVHIww1gY18guGm0d39LLMcb6Kpi2bCj0KL4eaki7dBA90c4qz9EOq5VTxuftZEgk9my/yHOBIMno2K+gsLAM6WzJirb3cTxXJEjS2WOSYzvUYCFHgkEvbKZBmPh1akbjrZp+yCjHWnkiwDhErs+iDGkFSYOv4oN8px/cC9bulghq5prkeR5Do+vRuvrh1CbPrDVXIMs2EGifq1TDWHRyMMHaS56Z5+Ic4yOUm9ULsoWtcb2BTQ106FiN7zMCIxrvddF1VQQU9hjXdhUFNN0Ir93AekLQkog5uuhm9zOeJ4mIub+AykGY6HX1NC5Funnri553tEyqMafwLhjfebS+U5vTYFAJJGNH0B/QzXioKoTnfzv6fXsYL0cM6FEqnNqieBlXWcHV9qHPtmWNuwcr7K0WCtKskacf3HIEJBSX+Dkx6Gi9AgnWQ28AuxzgXOebZJzoqO31tVIix5aJf8yLfnSyLOFz0jDEnrtkUHdJ1R2yyjB1la1GW9SYQU3biG2Y9YvIXYFzCBdBp7fP9lertOGB4o7XJmUOSeTQGNd/QWmW0WarAPsMrKNP80m7qJNqahf7NVyGtpXMuLd49HPUhCqKWiZU4Dmo5Gj2N56GP8dLWlvL7o15DQdSX7d+Jy0CULQWjGx9sbZhXr8rHPv2rqNJua3eOcZb+GNh8ragYSyxowgH9mzim+05UiHWgCrB9jJcgxzS30wXKqOLrPv1rqNMcbPc4FXp7eP5luLVnJrqKlpv7s+geFTs7qH8bRboVsElUGM5Oa4tHb+PF6Gk6v/X6FGm/wWH9hzCqKlq3k2QNMszTMLDpWmgR67fzZqGLi54x1k3+vlH4gsVqRUH1ETTZakShr54JfaHXOc+toMqp9ep8MRJA76CpEqczFJRUa3aKoIO2oSkKZzd4uvFRJ1ezVAetHI8ky6h2peAdbLChUPsV6tV5UCEK2abpiLP1dXrsRlUxatT7RWEz6trrajsqeV6p2SaCoSg5GYmWka0BUFfVqQ6jTn1EPNd460B89bcEr+QZUQXhBff698ZOtV4qNFtF0TmdnCheG2eJvTKsqFLvRLOqXLx2dB25FDzrDC56xhiDRq1Gn+R+J1WcNTutOBtv6y8+PNHLycgwn+FxO7p5iSkbD2gyqKf5XIogPKJeOSf3y3GG3rWnWybBmyjwaR/81CNUUYG29JaRLE/LlpOso0StNMZ8iXNGGHOB5vjp5h0uaGUHPae2SZWs8yjXg65luKy+YiwYcAVW1i31pma8t/9nvLrrBxytq4BBo8V5fcbg5hFnYWhSdlgUQ1s6qymk80fo5knBB+raPy6SLcO0UaA3yTagPC8dBdt6o640ATHv2lA38ThuuW4vzpxSAik0euQxFtQ4gZV12aGaUkz/9B/Ir6MEN0l0VyUaSQWrbMMzUxbh9lEzQv4Kh3LuCI1+eCqDTzkL4dQo0JusZjW2fj4eVfmpLZ1r7YPJGrUNFqsKly84hDcXr4FGI4dUzghjwZYzwtM0rEsazUbM+PSfKKyvEiGIIxARv3yyTXx150/vY+nBjSF/helmTsPyoTiVoKQfD0/buLZr+WhUFSTbv2izJJYCEfL+x31x7yPjuv+CMRbhOBhhXfLhgfU4UlcuRkBcoVUHD//6KWQ5FMqJhw/HtExncxrCLUemuxoqYlF6MMttXQ5ZlvCvV4egotJz9VfGmGscjLAueW3Xj1B5quYIGTsri7Ct3E078hARSr1r5g88UT8ikhsGdlemIU1Mx3hitqjw3097+/x3KxR+9xjrKg5GWJdQnoh9MsazgvrKsLjKdNPozo3eH+iGpWRqhrm+fo4prqgyA5QM6mk0NuQXxvr8dyvYf/cY6w5eTcO6JEarfFg6RtuZLrLBLZhX1yhJVo0kWZ14nWhbagPQOrVVB0RHWyDR2zUPgyM2m4SYaAVFUrqBR0VYuOOREdYlF/QbC7X4l9q9eK0ep2WET2fP7iZ7+qInSWeSVSOB43oofZ06BCItzp1ZAIvF8++41arCvHMK4Sv0HMTSbMbCGAcjrEtuGn6m518uScINw8+EQRM+IyPeSPb0ZsGxriar+gI9JwoCApEE6whAHEGI43p0ZyXU3JmFyMlqgNpN3gjllEwcV4rRI7o/Fek417bBKgeZLFJwMMK6pE98Kl496zcihZWCjg6/WJKEien98PBEBa3iQ5Szm4c/BeM75kAHRa6O7+o1clxDZz9HtUP+9873MOitTgMSCkSSkox4/5Uf4e2RN/q9WnF/RcCvJ2P+wjkjrMuuGTIZmdEJeOjXT7H++ImOrz2iovG74WfhgfHzwnJUpOPNA1g20OSxMFprJdROoJ9xlrhIuSsJdcEzLRPIfBVX0ywO9Phz06KwYLmL1UNuruO40RVYv/ILPPDYGHz2dU/YbPb3b1qtFZddlIdH79+MnCzvB4TOngtXzGXhjCuwMq/YV3UM+XWVohz8+PQ+iFLb29NHEneVWp3dMJ1tf/I7+ECMfNB5Ld1vDyKVJICe3KfFnxVdOxPgnXy9OxtAHSsxYPf+HlBJMkYNr0RSYmBWt4RyRWAWeSwKK7DyyAjzikGJmeIjktGNbeoT9S5vxCe/2237TtfTu3t/ovOaN85+k3Y34uOq501nRou6o7PBRNuVUF0ZycnMaBIfjDHv45ERxnyARgfachdk0LbBEIQQiwVQqewfrp6Lg5JzdvWz5Mdia7tgRRTzldCu8ZwjSOvq8YP9egdyZMTewsEGCVzojvkOj4wwFkCdudkF+sZYclyP2+6diM+X94TZbI9CYmMsuOrSg3j60Q3dOj93PzsvDhj2BxPOWTAMhdt7wVhvgCTZkDn2OJ5+YD9mzi5Gj3pjh5GX7gr09Q60GvU+FOi+QKl2HWySGRpbLLLMZyPXOBcGOSPQp8ciFK+mYSyC7dydgN5jFmDpst4tgQgNS0iob9DgxTeGIHfEAtTW+mY2d9+BeEweMxt5vwyCsV4vHpNlFY5vTcfCC8/AvTeOVVQBlSmXr/scG2LvwXHtGhGIEIuqHvm6Zfg57lZUqrfx5WQBwcEIYxHKZgNOm3UuTKYTQcgJ9s/LKvSYNGeO14/d3KzGzItniv1TBdO2x6YiYuSVtwfhqReGef3YoY5yX7qynLxcswn7Da+Jz2XppKkvyQYbzNga839okkq9daqMKcbBCGMR6uW3B6GhkVY9uWt4KGHX3kTsOxDn1WN//HkvFBTFtgYervxj8QiYze4bMkYa+1Jla6cDkiNRS9x2IIYkwwYLCnVfd/8kGeskDkYYi1AvvDZYpDEq8fA/x3j12O/8tx9UKs8dccsr9PjuxyyvHjsSNUvlqNbsFiMgbkk2FOu+9ddpMdaKgxHGIlRVtfJmh8Ul0V49Nu3PUUDMk+Nl9nwS1vUeSSZJeYl+s1TLl5r5HQcjjEWomGjldTYSe3h3BUpSDxMkSdmoTI/4wBQXCycaKA8mNQi+jtQs/HEwwliEuuwiKuGvLB/jD7fs9OqxF1yQp2i7mGgzzp56zKvHjsSGjQZbBmKsPSlz1e12kqxGummKF8+QMWU4GGEsQt1/93ZoNJRD4G6EQkZmeiMmTSzz6rGvuvQQ4mLNbvNGVCoZN16zD7GxwdODJ1QDEgkSehkvFEmqLsn0PxtyTXO9f5KMecDBCGMRSqezYelb37dUPHV2k5LFNj9+4f3VFQnxZnz2n+/E/qn77cnHpSmcaZOO4bH7N3v92JEq03wWso2z7V+cPEIiVtlIGNJ0K2JtvQNyfiyycTDCWASbN6sQ65Z/geFDqtoFJBQMTDntOA5u/Bj9+tT75NhnTDqODd9+gYUXHW4ZobHrlduAfz68AV/991tERXleccOUodGRwc03YVjjXe0DDhlItozGuIbHkG2ewZeTBQT3pmGMCeUVOvyyKQ1ajQ1TTi1BdLT/AoGaWi2KjkVDH2VF75717XrjMN/0rGmWymCRmqCTE8QHY77AvWkYY52SkmzCuTMLA3LVaNomIV758lPmviuxEno5VWmZGcZ8jt9/MHYS6odyvFSPgqLollLp3il/TvsrK4/ye7+VhgYN8gtjUFWtc7tdY7MVGw83Y2teMyxW74yK1NZpxbHpv263a7Ri/YFG7CowwmaT3ZawP1ZiEKMoFov7lSGVVTpxbHr+3kDHo+PS8ek8vKGuTqPo+iitPTJ/IC+DZqGpS3+lZWVl2LVrF4YMGYL09HS32x49ehR5ee2X8Wk0GkyePLkrh2bMZyjweOWdgXj+5aE4mBcvHouNMePaKw7g7pt3Ije7sdP7PHAoDk++MBzv/rcfmo32P7cRQytxx427cc1lB306HbFpazKe/NdwLF3WC5aWsuuTJx7HXTfvwoVz81u325nfjN/+dwPWS58A+mrxmLo+B7NiL8KbV41ASnzn/5n4/scMcexvVmVDliWRgzJneiHuvmUXpk0uad1u9a4G3Pr5GuzWfwZo7eXNddWDsCD9fLx+zWDotPaAgwKKf70+GC+8NgSFxTGttU9otc1dN+1Caoq9DgoFevR8n31pKNZtsP/bRAmyC87Pwx9u3YUxIys7/VzKK6Lw7MtD8e83B6Gyyl6ALTuzAbdcvxe3XrenS6t9flibjqdeGI6vVubA1nJ9Zkwrxh9u3YnpZxzr1soa3F7TqekaxkIuZ2TPnj149NFHsXr1ahQXF+Pdd9/FFVdc4fZnaPt//OMfGDt2bOtjsbGx+OKLLxSfZG1tLRISElCd9yLi47ggD/M+GrmYu3A6Vq+1t1CnG6gD3czi4sxY9dlyjBxGiZ7KrPklDbMWzIDJpG4NBhxLVumd9cXnHcEHr/4Itdr7QyVLPuuFy284Q3ze9thqlQ1Wmwp337IT/3hoI1btasDMLx+FzVAGqNo0T6NTklWIrhmBvTffgpxk5e/cn3lpKO5+cALUalu73jOOr59/4hfcev1evLG6AtdvehjQNAHqNjd0apwnAWlVZ+HwHy+DyWjAmeefgx27k1pGJKR2+8xMbxIrfnrl1uOu+yfg+VeGtj5PB0eC7EdvrMYFc04EYp7QqMWUubNFxdj2fXRk8ToOH1Itfi8SeygfkXjpjUG49U+nQq2S2782LdfnHw9tEIGTv3JHGAuGnJFOvS87cOAAZs+ejcOHqViSckOHDhUBjOOjM4EIY/7wx4fG4Yd1GSIIaRuIELph0DD6nEunw2hU9idDUyLnXjYdzcb2gQhxdKlduqw3Hn92BLxt/8F4XH7jGbDapA7Hdtyg6V35O//tjTmfPQ+b/qRAhNApqmxoTNiJSS9+pvjYq37KEIGIONbJx275+vZ7T8Vn3/bAbzc8Bmgb2gciRCWLehilid9j5r9+xnW3n46dexI7dPd17LPkuAHnX3EW3ny/vwhE2j5PB4tFBatVwqXXnYHDR2IVPRd6m0b7PdYhECGSKGe/a28P/Oa2ScouDoB1v6aKQIR+xzq8Ni1f//Gh8fj2h0x0Z3RkyRO+WQHFmK90Khg577zzxEhIVJTynhbEbDZj06ZNYmrHZOI5TRZcqmt0ePXdgS03O+foRlFcEoNPvuilaJ9vf9gfdfVat/1X6Ib03MtDvZaX4vDCG4PtBazcVNukd/X3vlcFU8J+QH1SINJuQysKEr4WuSRKPP3iMPEO3x0aabrt/UOQaUrIXbM8ScY66SP875s0t9196aZOoyaP/HO02xLzdD3oNf73W4MUPZeffk7Htp3JHYKGtui8li3vqTjAefbfNGrjfiSMrt8zLw5Dd/NHOtvVl7GwT2Ddtm0brr32WsyZMwcZGRl466233G5vNBrF1EzbD8Z85csVOTC25HO4Q9VCP/q0j6J9frC0j6JE1YpKPX782X3eVWd98HFftzdQQjfl46lfAFZl+SCPf7Pf4zaU1/H1tzluAwdC51aY9LmylRxRdUC/lR43oxt4fmGs2wCM0Lm9t6SfggMDH33Wu139E1dUkoyPP/dcKIyCzv99eSJ/x905Lv8+WyS3dsdz06wckLCQ4fNgZOLEiThy5IgISCiZ9ZFHHsF1112HdevWufyZxx9/XOSIOD5yc3N9fZosglVW68QNxRMa5SgtV9ZBtrxSr7jvS0Vl50YaPampdb9qppWhElApSL6U1ahoalRUK4SSMRWJLrdPxyggRXtOOu3M6paqGmXXp6oqCrKC/arUMiqqPL+G9Q0aj4GaAwVV1UpfRzejIxyQsFDh82BkxowZyMnJaf361ltvxaBBg/Dxxx+7/Jn77rtPJLs4PgoKCnx9miyCpSYbFd1E6d13RpqyFTVpKU2Ku9KmJiubAvF6h93GVMCm4N23ZEV6jOdpiB4JJre9ZtqpTwfcTGG1JTeketymM6uSkhOVXZ/k5GZICvZLuShKXkPqxUMF5ZSg4Dgxgae0WeQISJ2R1NRUsRrHFcpJoazbth+M+crcGQWINngeIaB3tYsuVtZt9spLDynaLj21CVNPPw5vomN7ytugoCG38ryOyaPOyCrcP2uAx82io604f3a+k14z7dHUR5+a8wHJ841ZakoEDk9X9Nr061PrMRii63L1ZQehxKL5eSLxVckoxqUXev690GplXHJBnsepH7p+82blc4NAFlG8HozQlMyaNWtav66rq2v3/dLSUmzZsgUjRnh/FQFjXREXZ8HN1+1xO5JBN7HePetw3ixly0KvWHAYSYlGD0GBLJbYajTeXdp787V7oVHbm825m3J68tpoGKqGu88bsanQv+48jOylbCrp9zfvFqt4XJPFSMJLv8mFqjENsKndbCrhTO1CLDq/SCzVdXfzPmV0OR6+d4vbhGG6HjqtDTdevU/Rc5k4rgwTx5W6Da7ovCjAUFqD5s6bdrdMKbl+bej6/f7mri/tbYunalhYBiNVVVWty3MddUfo8337TvxxU3Lqueee2/r12WefLfJEli1bhjfffBNnnnkmMjMzcfPNN3vzeTDWLdQddt459unAk99d0w2HhuGXf7RSvLtVIj7OjK//u1IUTTs5IHF8fe3lB7x202mrT696LH37e2i1HTviOo798L2bseD8fHy/8HfQNGbbp0zaPjUKKGQJCdUTsO72lk6vCkyaWIp/P7VO3PhPPjaNCNB0yuvPr8XMybX4cNqfIRkTOgYkLV/3qj4XX90yHv9+6mdMPKVM7LN9gGWv9UG9bD599zsxkvHA3VvbPc/WY6ttiNJZ8b93v1ccOFA34/+9swp9etWJ45zcSJA+JowtxytPu85/O9m40RV4819rxP5OHiGhr2mfL/7zZ0w5rRTewpVZWdgVPaPluXfffXeHx+fOnYt77rmnNRihfBBHLRHK+XjxxRexfv16REdHY8KECbjpppug1ytLBCRc9Iz5A71jf+/jvnj+lSHYvC1FPJaS3Izf/WYvbrluL9JSO5/bQSXgF786BK++MxA1tfbRBWpCd9sNezB/3lFxw/OVPfsTxNJhqv7a1KwReQhzZhTijpt24+ypJ6p85h0343cfbsO3zZ/AFmN/PKp6MC5OPw8vXTEIsYbOD6D+ujlFVEH9eFlvMdVBuRI0gkCVZ08ZU9G63ebDTbh16Uasl/4HWW8vKBdbeQqu7TcbTy7sDY3afmyq7/Lm+wPEtdyzv4d4LCeLqqDuwU3X7BO9bRxWrMrC8y8Pwdff5YgpFIPBgqsXHsQdN+zGoAGdX5lHNWZefmsg/vXaEBQU2XNnBg+oxq2/3YNrFx2EXu9mabSb6rjPvTwE//20D8xmtQiW6PeBXptTTymDt9XEReGO1WrFfWsY83fRM+7ay5gTTU1qmMwqMcLhjYCBhuZr63SiK21Xbl7dDbKo5gnlxeh0rqccqCdMcZUFOrWEtB7e6ediNkuob9CK5E1301HUC6e4yop4vQo9YtUelxDT9aQy7O5eG1pK29ikEcf2RpVbettWX68RozsxMZ0vAe+q3w29NjSCpnTUrTuu2RTNAQnzK+7ay1g3GAxW8eEtdAOj1SaBQDdiJcdWqaROlX1Xgm6wSkql0whIzxRlIzBKAwEKvHQ6711zCnwov8ibKEDrTCl5xsKVd97+MBZAVP3yeJkBCfEmDBlY49OpDwZU1gAvf21GXZMN54zR4YzRoX3Bqf8MdeOl0Ylhg6tdLhOmEv8HDseL6a7BA2pcrnah0aBdexNFK4BeOfXIzOh+JVTqIp2XHyfyXoYOqkZUVNfaBr81rhHLBpq4dw0LOhyMsJD1+de5eOyZkdiw+UQdiv59asUKlRuu3s9BiZflHZNxxt93oDDrbaClENkTP6oQ9c4cPHDKebh/kXdHVXxt5epMPPrkKPz0i705IumZUy9WvNz22z2tUzt5R2PxyD9H4YOlfWEy26eQog1m/Obyg3jw7m2tuUQ0tfePxcPx4uuDUVZhz81wdCt+8J5tItm1s7buoDL3o/D58tzWlUJJic2iW/F9d+zo0vLfqVlqLJ3VxNM1LKhwzggLSU+9MAz3/HW8WPnSdjkn/eNPSYu/WbQfrz1HqzoCepphY3++jCGvLIactr1jUz2rWlRpvU71NF69PQah4PX/DMANd50uRjnaN9WjVTIQnX2pwy8lAU89dzbq67UdyrjTip2sjEasW/4VEhOMmHHROVi/OaXD8mLazr4y53vMnVmo+By//zEDcxfOgMUqdajcSvscMbQKqz9fLvKaOoOTWVnId+1lLBis35QiAhFy8j/8jt4kb74/EG990D8g5xeOJj71g/NAhFCjPZUFr5vvxU5lNeECigKMG39/mkhIPbm7L5Xwp9+hT7/qiWf/PQQXXHGWSDB11k+GAgTq6HvlTVPw58fGOQ1EHNtREvEl105DeUWU4hU8F119FsyWjoGIY587difi7gftfweMhToORljIWfzKEI9VPmmE5JmXhilqVsfc23pARk2fD5wHIg5Um0XXgKv+fTToL+eLbwyGSsSsrofN6Pfm78+PwOGj8R47Bq9em4lX3h7osUOz0aTGm+8rC5D/s6Svx67PdF60bLuzvY2o7gjljtx2e02nfo4xX+JghIUcJZ1P6R//nXsScbRAWWt35tqfP6gANMpWfOzA90F/KT/+3PPvDwUq5RUGj2X1CU0VNjd7Tr+j5chLl3nu7kv+90UvRdtRDss332cp2paxYMbBCAspNNxNBbyUqqvnHO3uqjYqXHoqybBpg//dNuV/KKWka6/yvCQJNXXKjk0dex1Tju7JYgSlK+bFWXh0hAUNDkZYSKEVDilJyiqh0lRNRlr3l1VGur6Jcco2tKmhbcpGsMvMoHLwyubvlHTttVmVRSOiOWFWg6Jtc7MbFI3KUIBDSbRdRQFJ1iz+G2GBx8EICzm/ufyAx3+o6fuzzipCaoqydvHMted/Gwc02Mvju6WyYk6K5w67gXbt5Qdd1hJpGzgMH1LpNl+kdVu1jIx0zwEO5X/8ZpGyjsFXX3pQ0bGTk5pxzlmuO6ArMX8gF11jgcfBCAs51CeGyqq7bhcvw2aTcO+d2/18ZuEpKQEYVn6T+42o82/5YLxzh71vTDC7/or9okCeu07AFDj87YHNmD290G3gS0uDb7hqH/7yh21uE2Ip4bpv71pcdK6yBN9zzynE0EFVHhO1/3T7Drcl/pWOjix5or5b+2CsuzgYYSGnZ04DvvrvSkQbrB1uKPSPN03lUGdUb3Y+jXRbn+iLlAM3ngg8HCivgT5qemHp9HsQGx38hV1Sko1YufQbxMebWwJaud3vD03vPf/ELyIgeP+VHzBhjL1YWdvg1xGgzJuVj2ce2yCKkP3pDnvw2zaAcHT3zcpsxIqPVyiunEq/w8uXrBQdiSVR+6T9OZJbr9+Nu2/xXtdnnq5hgcRFz1jIKj5mwMtvDxLdXMvK9aL406UXHcbN1+4T5bqZd1mtMu58pRmv71+B5uzvAbUJUlU/jDVdig9vy0W/4E8XaaesPAqvvjsQr707ECXHDSK4nX/eEdx87V6MGm7vIOxouEcrcKhr7/ZdSVCpZJx6SqkYoZs3q6DdlM/a9Wn416tDsPz7bBhNKvTJrcdN1+7F1QsPdbo4maMp4Lsf9cOLbwzCwbx46LQ2nD21GLdevxfTJpd4tagfFUNbcC+vPmPexV17GWOMdcqyOg33rWFexRVYGWOMdbpvDWOBwEUYGOui+noNXnprEI6XGsRSzBuv3ge9vmNOgNUKPPHsCPy6JRUx0Wbcd+cOjBha7XSfn3+djXf+218k4F583hEsuvhI0L0+VPHz5w2poqLogL61GDnsxJTGyd1rf/o5HZXVUUhONGLKaceh0chOq51u25mEQ0fsXWknTSxFYg/nKzyou+7GLcmwyRKGD67CgH513XoudOxfNqa2dO21YOppJYiOdlNpNsxRddYV9xtxzaZobqTH/IqDEcY6qblZhXMXTceqnzLbFaaiPiHnzc7Hx2+ubs0juOTaM0TVzbbbffhJX/RIMOGHZV+3BiWv/6c/brnnNJjM9IP2bT/9qjeuudWKx+7fhHtu2x0UORZ/evgUvP/xie61ZOyocrHyZOaZxa2VRv/5r+F4+oVhrd1rSXpqk+io/Pubd7Ven6+/zcafHx2LbTuTW7ejgOTyBYfwj4c2IinRHpQcPhKLP/xlPD5f3lMEag7TJh0T250ypqLTz+fdj/ri4b+PFiXfHeJizbjh6n145N4tMBgiNyhhzN84gZWxTqBkxr5j56O4JNrJUk561y+JJZnbf/oMk+fMwS8b01of77gtsO3Hz/D9Txm46/6Jjj9Jp9vde8d2/O3BLQF7rUrL9Dht1lzkF8Z0qH9BCZ00wvDeyz/ikgvycNXNU/DB0r4uKojKuHrhQbyxeK3ov3LNLVPEShFnnW779qoTHXGpudzps+eK5nEduteqbGK05ZuPV2Dq6ccVP5/Hnh6JB/821ulrQ6tmTjulDCs/WQG9PnIDEh4dYd7ACayM+cDVt0wWzcnc1ZQgF87Nw/++dPQhcbWtjPg4E+rqqfS3++1IQ8F/AvZufdFvp2LJ571dFuKigEKrteGZR3/FLX88zeP+XnpyHW6/91TRldbV86aAZNH8w9izvwe27EhyeWwKHpKTjCjYvkRRzY1NW5Mxfvo8t9vQPh+4ezse+tNWRCpeXcO8gRNYGfMymn747//6KNhSFlMsdu6CFgm1dY4eJO63ow8KhALhWInBbSBC6DlYLCr87ZmRborRnRjNoO2sYjPXz5uO9/7Svti4NcXtsWlUpazcgP992VPR8/nXa4Oh0bg/R9ondfelkbBIzh/h2iPMXyL3L42xTtq+KxEmE+VKeCruIHkY6Wi/rbI+KTK++yEw3Vm//ylTUWlyCtaKjsW4bXtPrDYVCopiPW4ntrWqPAY3jkJgy7/LgRJfrMgVgZMn5RV68ZpHsrfGNXIzPeYXHIwwplB1ja4T18r7lUgtYkrD/5qNSpd7+ub8lBT2sslAU5Oy8zQqfj5AUzMvdWXMHzgYYUyhoYOqFXd7tW+ndFtlkpIC0/SPSpIrQSMY7vq9nJwPomTEg1gVdMVVSUDvXsrOs3dufbvy6u70ylXWZTecUe+a227nisbMtzgYYUyhtFQj+vepVRBkyGLprueRgs4FLE8+sgGBMG1SCXKz6xV1pZ09o9Bjczf6/tyZhQqmaWT0zq1DVJTnpF2LVYVrFx2AEjdcs09RsERl16kPErMHJJw/wnyJgxHGOuHpxxwBgasbsyxCkE/e+rbl3be7G7iEC+YcRabH9vO06saM+fPyA/JaUdO2R+7b4ja4opv3aaeU4smHN4jgwdWoB42cGAwWPPXIrzhlTLmHwEXCYw9sxr137HB7fWhp8aL5hzCwPwWKnl116SHkZDW47MbrGDX5yz2Ru5LGmfkDnReiY8wbOBhhrBPOnVnYZoSibbBh/5zyG9596UdMm1KGlR9/0ybfoe3N1L7tKaPL8Mk7q3Fw4yeipb3z7QC93oLDmz4O6OtEjd7+/tcN4kbd9ibu+HzsqAp8/v53GNi/TnSbjYu1tAvGHN1rKahasXQF+vWpx5cfrMTI4ZXt9uP4nLZ95rH1uGx+Hh78wzbcev0e8b22wYvj83NnFuC159Ypfi50Dt9/+o2omtuhG6+oW2LDB6/+wF2fnYyOLHlC2VQYY53FRc8Y64IDh+JExdXvfswU1Uj1UVbMnVkgApWcrKbW7Wpq1Ljy5jOw/Lvs1hUcmelN+OfDv3Yo9f7g46Px3L+Hor5BK76ONljw26v24clHNkIdJHmU+w/G46U3B+Gb77NFYit1R77pmn2YQ9MzbUq9V1Xr8PaH/fHekr4oq9CL6qtXXHJIjEokxJvbJeUu+yYXL781CPsPxcOgt2LW2YX43W/2oX/fug71QejYVGLeapMwdmSF2K6r3Wsp4fWjz3rjjf8MQH5RjAigqAT/9VfsR1bmideQncC1R1hncdEzxhhjPglI7lit5t41TBEuesYYY8wnxdC4/gjzNm6Ux1gXVVbpRAEtmpJITW7GvHMKEBdn6db1pI60v25JEc3gxoyoxOkTSp1OQWzcmiwqiVK/lv596vDA77chPr57x+7MFBUVQqOuvQP71WLGtGKR5MoiL4dk6awmHiFhXsHBCGOd1Nioxt1/GY833xsg8kUoAZKWqRr0Ftxy/R48dv9maLWduzmv35SC3919GrbuTG5dzUEl1gcPqMbiv6/H2VOPicf2HYjD9IvOEZVOT5Dw5L+GY870QpFE6uiI623UOffG35+O736kSrCyOA4FTVkZDfjbg5tFPgiLvBU2i5ef6MzMWFdxAitjnWA0qjBz/kys/TXNaZ0MCiTOn52PJW+uVjxasHZ9Gs6+8ByRzHnyPml/tHT1s/98h0H9azD0tAthFomwzjM2R4+owOZVy7z+muYdjcWEGeeKKrQdS8PbO98++7f1uP0G+6oXFjmW1Wmw+PmEQJ8GC1KcM8KYD1DztLXr010W7KLRjE+/6oUPP1HSUM/ez+XK301xGog49kejD1fdPAUXXnWm20CEbN2RjJffGghvu+1PE10EImg9n98/MB4FRdFePzYLblOzgmSpFwtpXGeEMYUocFj8yhCPNVNp2mbxa0MU7XPl6iwcyY9zW42UApLKKj127klSVNX1iedGwNujIl9/l+OxWR7ltrzyziCvHpuFRkIr1x9h3cXBCGMKFZdE40hBnAgO3KHA4tdNqYraz69em+Gxnf2JwlzKugDnF8bCm9asT/P4nAkFK9/9kOnVY7PQCUi4fw2LmGBk3Yz/BfoUWAQzm1Ve77JL+1TatC0UnrdRQQDGwhNP17DuCLl/OX6atATy+l8CfRosAlHl1GjDieqhrsnISGuEweC5wRtVMLUoudmLkQklQYuMmBgl56jcIIU9X6g8+/DB1NmYRfJ0DTfUYxERjJA1vy9A7f3vBfo0WITR6634zeUHPXalpdUvN1+3V1GJ8ksvyBON4zyhfSYlNisISCRcseAwvIlqnQzsV+NxBIc6596ooCMuC++AhLGICUbIttW6QJ8Ci0B/uGUnYmPNLju+0uM0gkI9U5SgImkP/clTd1gZd9+6E0//n+eOwdRU74kHN8KbKKj6x0Mb3R6bclqoYd1p48u8emwWep6bZuXRERY5wQjh0RHmb71yG7Dqs+VIS2lu1/HVEZz06VmP1Z8vR3KS8neId9+yCw/9aYvLjrh33rRbFFK7auFh/L1dUNC2YzAQE2PBpu8/90kl1vNmF+DtF36CTmeDqk03XscoEVWf/fC11V1qWMfCC5eLZ2Ff9Oyr/jMQo7Z3NG1rytoFATkvFrlopcwnX/TCR5/2RnmFHhnpjbj84sOYO7N999rOOFoQg1feGYj1G1NhkyWMG1WBG6/u2L225Lge9/x1PFasyhIl2VOSmsW0EAUtvqq+6lBRGYU33++Pb3/IRLNRIwqx/fbK/ThlTIVvD8xCEhdEYxZjI1a/uBA1NTWIj48P72CETH46F9LEU/1+bowxxpzjYIRZFAYjIT1N0xYntTLGWPA10+OCaCziGuVRUuuUQJ8E84oNm5Px1bc5aGjUiDyNyy46jKREU1BdXepe+/Gy3q1dey+98Ah65jR02I7KqFN5+Lz8WOijrJh1dhFOPaWM8ytYxKDlvsXcUI+5ETbTNA6jppkQ/9jlfjs35v0b/KIbzsCmbSkiOVJSybBYVNBqbLj9xt14/MHNAW9XT3kTV988GV99myuSTGnZrdUqiSqltFT3lWfWITbWIsrHP/T30aKjLuV2UKVV2oaez8hhlXj/lR8wdFBNQJ8LY/5QExeFBfd6tzIwCw0RN03TdnSECqOx0HMkPxanz56LbTuTWutWmM1qcQM3mdV46oXh+O2dpyOQ4XNtnRZnzJuFb1Zlt5ZAp3OkEvB0nks+743Zl8wQ3X3vuG8iHn1qlEj0pO/RdhSIkF17e2DS7LnYf9D1Hydj4YL71zBPwi4YceBKraHnz/83FjU1OhGEOEM39Lc+GIA1v6QhUJ57eQj2Hkhw2TSOHl+3IQ2P/HMUXnidmuVJLrerb9Dgnr+e4uMzZoyx4Be2wQjhpNbQUVqmx8ef93YZiDjQ1M1LbwxGINBUzIuvD3HbYdfhpTcHe6zUSgHJFytyUVAU7cWzZCx4R0dW3F/BBdFY5AUjhCu1hoYtO5I8BiKEtqEusoFQdCwax8sMHrejEZzqmihFz4e23bg1xUtnyFjwmz8wuBLRWXAI+2CEcKXW4GezKS/dSQXBgv0cg2G/jAXrcl9upsciMhhxJLVyt9/gNWJolcdGbIRWr4wbGZhqn9mZDeiR4LnMOz2PmGizx2kah5FDK71wdoyFjrfGNeK223klGYuwYKRtDgkHJMEpJ6sRc6YXumxA1zbP4nfXBqYzrFYr44ar93s8R3LNZQc9TtPQfs6cfAwD+rUv985YJOCCaCxigxEW3B7/yyZE6awub/bUlG7O9ALMPLMIgfL73+0SXXldjXrQuQ8eUIO/PbAJF5+XJ2qQuHouVDvlRDdcxhiLXBEXjPAKm+A1fEi16IhLN3tCRcLo5u4ITqig2JI3V/u8GZw7aanN+PGLrzFscFW7c6T/ktPGl4rnEBdnwbsv/YSrLj3Y2o237XapKc1YsXQFxo3mBnMscjnqj3AOCQu7CqxKcaXW4GWxSPhqZQ6+XJmDxkYNeubW45qFB4NqOoP+aqjeyUef9UFVlU4EF4vmH8b4sR2Di7yjsaLTbd7ROOj19nLw583KF9M+jDFuqBfOIq5rb1dxt1/GGAt8ufg7Vqu5f00Yithy8J3FSa2MMRb46Zrnpll5uiaChVXXXha45nZvfdgf+QWxYhpi7sxCnDuzABpN+0G3owUx+MNfTsGWHcni61HDK/H0/20QXXlDDY0n/rguXfSiqa7WISXFiMvnH3I6TdOZvjf/WdIXGzaniiZ7o0dU4uqFBzt0K6ZKsF9/m41l3+SioUGDnOxGsd2QgTXdqoD71gf9Rc8cysk5fcJxLJqfh5gYS5f3yRhjSnV6mmbHjh146aWXsHnzZjz88MM455xzPP7M1q1b8fTTT+Po0aMYMGAA7r33XvTv3z8opmkcOIek85qa1Lj+jtPxwSf9WpNMJYlyPlTIymjA0rdXYeK4cvH4pdedgSWf9Xa6n/nzjmDJmz8glBr6XXDlWdi+K8neidcGSCr785408TiWvrVKJLp2xmvvDhCN9ZqN6tYVOLJNgkZrw2P3b8bvb94lru2W7Um48KqzkF8Ye+LYdM2tKlw49wjeeXFNpwII+uunPjqPPTVKFJOjZFsqwWaxSoiNseDlp9fhsvl5nb5GjHXVsjoNFj+fwBcwTPhkmoaCkEWLFmHo0KFYv349ysrKPP7Mzp07MXnyZMTGxoogpKGhAaeeeioKCwsRbIXRuFKrcvTO/dLrpuG/n/Zprf9BH46utMdLDTjrglnYvisRi26YiiWf0XaS04+ly/qIYCUU0AjClLmzsXtvD/E1PV+r7cTzXr8xFWeePwv19coHHWlE4oa7JqGp2d6h2HEtKTgwmdS456/j8fSLw7DvQDymnTdLlKVvd+yWeiaff90T511xthg5Ueqhv4/Gw/8YI/ZBlWDFayj2J4lGfpffeAY+/rxXJ68SY103NUvNly8CdWpkhCIbGqEQPyhJePfdd3HFFVe4/ZlLL70URUVFWLNmjfjaarVi0KBBmDdvHp555pmgGRlpi5NaPVuxKguzFsx0uw2Nlpxx2jF8vybLZffaE2QU7/ovMtI7N6Lgb/c+Mg5PvTDMZddeQqMLTz6yAXf9brfH/TU3q5E59BLU1OrcXiOqv3LOWYX4cmWu22OTT975HhfMyfd47OJjBvQavcDD/mSx1Dp/+xKo1UGf687CBI+OhA+fjIw4ApHO+O6770Tg4aBWqzF37lx8++23CFac1OrZS28MUtSV1h6IKEMjAMHMZFLhlbcHegwGyL9eHaJon0uX9UJNbZTHYM1kVokcEU/HpgDwhdeUdTV+4/0BYprGPQnHjkfjq5XZivbJmLeqs3K5+Mji09U0NCVTUVGBrKz2NyT6mvJHXDEajWI0pO0HCy6btqUo6krreUTkhK07ExHMikuiRTdeT2iqJS8/TuTUeLJ1ZxK0WqvH7SiPRJY9X28KVqgDshLbdiaJc/WEclPoPBljLCSDEbPZLP4bFdX+H3CDwdD6PWcef/xxMQrj+MjNzYW/caVW95Q0teusQFZWVcJVaffubC+2kQNzHenYSl/HYH9tWPjh3jWRxaf/xMTFxUGr1aKysn1XUhotSU62L+905r777hPzS46PgoICBLLbL+to8qnHW0ubu2dTODoi4fTxpUF9qbMzG5Geai9V7w7d4KlcfFSU5+tz6illMFs8j6BQUqqSBn00dab0Ok4cVwYoGBmhRFk6T8b8jcvFRw6fBiOUHzJy5Ehs2LCh3eO0EmfMmDEuf45GUijRpe1HIFFAwt1+27v52n2tK0jc3RgvmH205d23u3fgtJxUxuMPbkIwowTOm6/bI5rceXLbb/co2ue8cwpEgON+hEIWy2wXXnTYY54OTZ3dcr2yY1NnYVo67A491769anHWlGOK9smYLwKS+QPb19ph4cfrwchrr72GGTNmtH593XXX4eOPP8bu3faVBbSqhpJa6fFQwkmt7Z0+oRTXXr7f5U2U3sXHx5vw1KObRI0MO2fb2h+7/cbd6NHD9dRdsLjzxt2iK6+rUQq1yj4yQUXIlKDCcK8+u1Z87vxa0mMSXvjnz3jiL5uRnNTsMiChn1944WFMP0NZ4EDF1J5+9Nc2x+kYiKgk4NVn14laJowFcrkvN9MLb50KRmiEg2qE0Ad56KGHxOeU4+FA9UPajoTcdNNNYvkvjYTQkt7p06eLaZgLLrjAm8+D+RndnF5++mfcc9tO6LRWcSOkREzHTXr08Er8vPxL9OlVj38+vBF/uHVnmxvaiZESeuzuW3bhmcfaj54FK+rG+8Oy5Zh1VpH4mp4vPW+6cdM1uOSCPHz90UpFUzQO555TiM/+8x0y0uxTQFqNVXyQ5CQj3nv5B1x5yWExTbRu+Vc4ZUx5+2O3XPvbb9iNd176qVOBA41wUTCUEG9/50n7cUy/5WQ14uslK3HmlBLlO2TMR6Mjb41r5BU2YaxTdUZoVYtjhKOttLQ09O3btzUYKSkpwSmnnNJuGyqQRt/r3bs3EhM7t2rC33VG3OFKrR1VVunw3//1wZGCWEQbLJgzvdBpWfTGRhUe+scYrPs1rXV05aE/bkF0tPIbd7CVwV/6RW/x/NNSmnHphXnIzW7sVrfiL1fk4NctKWKVy5gRFTh/dgF0uo7XhyqxinLwjRrkZjdg4YV5SEk2dvnYVO/kky96YseeRDEdNWlCKc45q4gTV1lQ4fojoYe79vrYlLULfH0IxhhjJ+GAJLRw114f46RWxhjzP84fCU9cPaAbOKmVMcb8i1fXhCcORhhjjIUULhcffjgY6Sau1MoYY4x1DwcjXsCVWhljzL+4XHx44WDEi2rvf8+bu2OMMeYhf2TF/RVcEC0McDDi5RESxhhj/vXcNCsHJCGOgxEv4yW/jDHm/xESFto4GPEBTmpljDH/4nLxoY2DER8ntXK3X8YYY8w9DkZ8rO7zQ74+BGOMMa4/EtI4GPHDCAmPjjDGmH9wQbTQxMGIH3DZeMYYY8w1Dkb8hJNaGWPMvwXRsmY18SUPERyM+BEntTLGGGMdcTDCGGMsLGuPcDG00MHBSABwDgljjPknIOH6I6GBg5EA4RwSxhjzXw4J548ENw5GApxDws31GGPM9+YPNPFlDmIcjAQYJ7Uyxpjvcf2R4MbBCGOMsYgwNUvN0zVBShPoE2AnckgmP925qyFNPJUvH2OMdWqFTRQWLOdLFmw4GAmygKQzRk07hPjHLvfZ+TDGWDgGJLfdbsXi5xMCfSqsDQ5GWESjBGLK23GY/HQujzgxFgHTNYsDfRKsHc4ZCWGc/Np11Lzwp0lL2gUihJdcMxYZoyNcLj648MhIGKAbKE/ZdC4QcTclJgKUSUswapp9KSBPhTEWngEJEB3o02AteGQkTHDNEuXTMkpzc+ia8ugTY+GLy8UHDx4ZCSOOd/SRkvdAIxzOuHruJ+eHdGW1UyRcV8YibXXNHWhC8XJDoE8nonEwEobC/cbpaZrFV1NWPB3GWPjh6ZrgwMEIC4sAxNkI0ZS1C0762a6NijjbtzORMiLFWLihZnrLBpp4uW8AcTASpsLhXTwFEXWfHxKfd2V65aeWJNSuTs109ZqTuPP6cWDCGGMKSbIsywhytbW1SEhIwFf9ZyBGrQ306YQcxwhBuI6EBCseKWEstCyr0/DoiJdZjI1Y/eJC1NTUID4+3uV2PDISAWiEgG6MJBinEU5ORKXREH+NZvhSOIxOMRZpzfSWzuJk1kDgYCRCnBhlKAiq3AbnIyChH4g4UFA16v73OCBhLETMp9wRXlnjdxyMRKBAvWMPh6mXrgYkUwJ9EowxxaMjU5+ox4J7Y/mK+REHIxHq5CqjrpIuqTaHV48Z4VNlwTIixRhzL4una/yKg5EI1y5AWH1iNciJ70VuAOFtnEPCWGjgYmj+x+XgmdMS6JE8iuFLXLafsdAJSCh/hPkHByOM+Rn3u2GMsfY4GGEsgNM23szJYYyxUMU5I4wFkLPy8pzoylhwray5Y7WaG+n5GAcjjAWZtmXlHbhwGmOBwY30/IODEeZnMtQ6q/jMalJTTVh+BZzokEAchEuDT66cG0znxhgLLRyMML9QaS3IHFuArPFHYEhqEo81VRlQ/GtvHNucC5uZfxWVjJhMfjrwN33XxeuCq7ovYyx08B2A+f6XTG/CyKvWIya9rt3j+h5N6DtzD9JHFWL7uxNhaeLlxMGgO5VyuZYKC0dcIt73uGsv87nhl21Aj37lUKmcN4iWbRKqDidj5/sT+NXwY1dmX5fnD8Vu0Yy5wh19u4a79rKgEJ1Sh6QBZW63kVQykvqXIzq1Do1lcX47t3AqNe8o6+8s0dXV8mFfF7bjEvgsnEzNUnNHXx/iaRrmU6nDj8FmlaBSOx8VcaBt0oYX48iqQfyKdEFrYNGS6OpgH/kI3PSXY9qG+h4RzidhjDnDwQjzKa1BeTllbTSXXvaGYOuMLAKl1fZzCkS3aMa8Vx5eg8XLDXxBfYArsDKfsjRrFW9r5gTWsMel8FmoF0G77faaQJ9GWOJghPlU2e5Mj1M04hdRLaNsVya/GhE0enNynRLGWOTiYIT5VMPxeFTnJYmcEFfoe9VHE8W2LHJwbx7GmAMHI8zn9nwyBs1V0ZBtgHzSIAk91lxtwJ6Px/IrEeHTNjxSwkIBT9X4BiewMp8zN0Rhy+unI3vCEWSdchS6OHuiqqleh+KNvVC0vjesRuW5JSyck27tVVyd4ZU4LJgCkqWzmrh5nhdxMML8goKN/J8GIH9Nf+hijOIxU0MUIHNvGqZsNVAwlMJnjPkGByPMv2QJpno9X3XWxSDFHqhwDxzGwgvnjLAgJUPfo1FUZdXozYE+GRZk6j4/FOhTYIwFemRk//79OHr0KAYMGIDevXu73fbgwYPYu3dvu8e0Wi3OOeecrhyahTtJRua4fGRPzEN0cmNrkmv53gwUrO2H+mMJgT5DFiSJr5PX/8LTNixg3hrXiGsAzhsJRDBisVhw+eWX4+uvv8aIESOwdetWXHPNNfjXv/4FSXI+9//hhx/iqaeewqRJk1ofi42N5WCEdSTZMGT+FqQMOd7+YRWQPOi4+Ni9ZCwq96fz1WPcIZgF3HPTrFiwPNBnEYHByHPPPYfvvvsO27dvFyMiFIyceuqpItBYtGiRy58bNGgQvvjiC2+cLwtjuacfFoGIs7iWiqLRsuChF2/Br4unwVTHeSespdR8Sz8eTm5lLEJyRt555x1ccsklrVMzo0ePFiMc9Lg7zc3N+OGHH7B+/XrU1dV174xZWJJUNmRPPOI0EGndRrJvlzEmuHqvsMDjiq4sULJmNfHF92cwQlM0u3btwqhRo9o9TgHJtm3b3P7svn378Oc//xnXXnstsrKy8MILL7jd3mg0ora2tt0HC29x2dXQxXpulEdTNtTdl7GTcUVXFojmeZQ7wgGJH4OR+vp6WK1WJCUltXs8OTkZ1dXVLn9uypQpyM/Px9q1a0Uws3jxYtx2221ipMSVxx9/HAkJCa0fubnOiyCx8NGZFTO8uoa5m7bhSq7M3+YP5I7jfgtGdDqd+G9jo32FgwN97fieM2eccQZSU1Nbv6aE12HDhuF///ufy5+57777UFNT0/pRUMDD8uHOVB+laDvKG1G6LYtMvOyXsTAORqKjo5GWlobCwsJ2j1Og0KdPn04dNDExESUlJS6/HxUVhfj4+HYfLLzRkt2myugOvWucKdnKI2VMWb8bxvxhapaap2r8mcA6a9YsfPrpp5Bb7hgmkwnLli0Tj7etQfLNN9+0fl1RUdFuH8XFxdi8eTPGjuXGaKwtCfk/9XObwGqzAZYmLY5vy+ZLxzziHBLmL5w74udg5C9/+YsoYnbZZZfh3Xffxfnnnw+bzYa77767dZv3338fl156aevXc+bMwR//+Ed88MEHIl+Eckj69++Pm266yQunz8LJ8W05IiAhNmv7qIQKn1F/mx3/mcBN9VinRklq73+Prxhj4RSM9OvXD5s2bUJmZqYYIaGVNRs2bGiXEzJw4MB2IyWrVq0SCahffvklduzYIfJB6Gd46oV1JOHIqkHY9s5EVO5Paw1ITHU65P/UH5temoL6Eq7AyjqHp20YC36S7JhzCWK0tJdW1XzVfwZi1NxqPnLIFJ9wZ1/mFaOmmRD/2OV8NZnP1MRF4Y7Vai4R34bF2IjVLy4Ui1HcDUJwozwWxCQORJjX8LJf5o/cESoRz/zUKI+xrtJGGxGTbq/C23A8DuZG58t0VToz0kcUQaO3oL4kHlWH0vx20fWJ9ciacARqrRUVB9JRuS/Db8fWGEyITa8VcVhDaRzMDbyM2dtJraOmHeIREsaCDAcjzC/0PRrR+6x9SBlSIvrMEMoJKd+TgSOrBqK5Ksb+C6k3YdjCjYjPrW63ssZiUqNgTT8UrOnvs3OMSa/GiCs3QGswtx47c2yROM+CtX1xdPUgnx07KqEJvc/ch9Rhx1qvj6NbMV2fpopYnx07UvvZTFm7INCnwhhrwdM0zOcMKfUY89s17QIR8cunlsVjY65fi+jUOhGITLh9VYdAhNAoRe8z92PAPPetB7oqNqsKY29Y1y4QcZBUMnpOOYSB5/nm2PrEBnEN2gYibbsV0/diaLSEeRXXImEseHAwwnxMxtAFm6HWWdvdaFt/AdUy1FFWDL14M4Yt2iA+d1ZrxPFYxugiJPQq9/pZjrpqfbvjODt2+qgiGJK83ehRxpD5W0SJe1fXR6WxYdglm+wJvcyruFor80XeyJIn6vnCdhIHI8ynEnpVIia13umNtvWXUCUjOrUB8dk1Hrv2kj7T93n1HHv0KYVKa1N07MEXeXd0JC6rBnFZte6vj1qGPrEJif3LvHpsxkmtzHcByW231/Dl7QQORphPJQ0o7VDAzBnKj3AXDDjQNrEZ3p2y6Dn1oKJjk5g0746MJA1Udn1om+QBpV49NrPjSq2MBR4HI8ynKNfD2yTJu9MVNIWkpNoOBSyUPxKo66PywbVk7QujMcYCg4MR5lPGWr1XgwcKGqxmNbypuTJG0ciI/diqgFwfOj9jrcGrx2YdcVIr85Z5cRbOHekEDkaYTx3frrCpnUQ3eknRCAUtB/am/cuGKx4ZKd3h3SZ9pTuyIMueIyEakeEGgf7B0zbMm7kjWbOa+IIqwMEI8ylTnQElW3NFTogr9L2SrTkoWt/H7QgFBQy07eEVQ7x6jlaTDg2l7ut4OI598KuhXj02FX07trGn22CIjlu6I7O1FgvzPa7Wyph/cTDCfO7g10NRsS9dfN42WdPxecX+dBz8ahiOfD8Ypbsy7Df+Njdnx9d0U6auvZZmndfPcfPLk2Css+/35GPbPwG2vHEaZXl4/diHVw5B2a5Ml9en6nAK9i8b6fXjMvd42S/zhvkDTXwhFeBGecxPZCT2K0fmKUcRn10tHqkt6oFjG3uh6lCKfZ6mReKAUvQ5c5995YoE2MwqUYn00IrBsDTqfXqWvc7Yh+xT86DW2YdyKACqzkvBno9HixEU35HRo08Fsuj65FaJR+qOJYjrU3mAumIrXO7DvI4rtTJvNNBbcG9kVlG2KGyUx8EIY4x5MPnpXEgTT+XrxLqEg5GF3LWXMca6i5NaGfMtbpQX5CS1FYl9y6GNMcHSpBX5Azazs5dNRlx2NaJTGiBbJdTkJ3V7KahaZ0GPvuWiVLmpPgrVh1Mg27qXZhSdUofYLHtlwvpjCWgsi3O6nT6pXkxZqKMsYpuiX3sD3Tx2KDAk14uqrJBkNJTEo6HU9bAm839S6+T1v/AICWM+wMFI0JKRO/kQck47DK3B0vqo1aRG8caeopOrbFW3VjntM32vKLve+tMyJYam4dDyYTDWGDodAPU5ex8yx+VDrT2xDMbcqEXBur4oXNe30zkMsZk16HfObiT0tOdDONQU9MCh5UNRf6yH+FrfowEjrvhVlD9vu7Km7/S9KNmWjQPLRiEcUSO8fufsQo/e7a9PbWECDq0YirrCxICdG2uf1BrP0zWsS0t81ShezrWCXOGckaAkY+D525E+ssjpUldKqqzKS8GuD05BytBjGHzhNrHag7q8ttvOKsHcrMWW106HsSZacSAyYtEG0VPm5P2JfcpAyeZcHPhyuOKAJC6nCiOvXA+V2tbxHG30ocL2dybCWBeF8bf8AEktd3jedFx6rPJgCna+PwHhhAK1UVf/AkljE316OlwfWRKriGqOJgfsHFl7nEPCumJZnQaLn0+IqItnUZjAGv7j3iEoZXAJMkY5D0QI3dBp6ib71MMYdN6O1sc6bKeWxRTLwHPt2yiRPeGoy0BE7FMCMscViNEYRSQZQy7a4jQQcZy3pLJh8PwtGL5wg9NAxHFcktS/HMmDjyF8yBh84VZIGmuHQKT1+tA1nL9FXCcWPDkk8vpfAn0ajIUNDkaCUNaEI26LhAkykHNanrhBuSsURh1fE/tVQJ/YoODIMrLGH/E44EH1L7ImHFWwPyCxXxn0PZpdBjeEvqdPaEZ0WoPHsuw0QtJ72gGEi4TelSLPR+Xh+uhiTUgedNyfp8Y84KRWxryHg5FgI9mQ0KvK7c279QYVY1Y0U0I3cKph4Ym+R5M9cPCwTxHgiP15rqFOIzje7tprSFYSWIWGxD7Krg9t06NPuV/OiSnHlVpZZ/vVcHl45zgYCTJ0o1fazp4o2lam/Xoe4ldplHeFFd1rFRxbyXED3bU3kFQaSgrpxLYs6HClVtYZb41r5IDECQ5GgozNooKpXlmlTxpNsCmIH2gUpakqWlEHWUWjGDLQXKMHFDR4axIdcZV0ofO8iePYFmP4LAJrqowWeTKe0DWka8mCc3SEu/0y1j0cjAQdCcc29fSYM0JBA/V0Uak937yNtVGoOkQlxd2zmrQo25nlOSCRIZq7ebMrrWyTYDGqPHbPFZ1zlXYCDgGlO7MgW5T9GR7fluPz82Fdx0mtjHUdByNBqHhDL5gbdbDZJNcjIhYVDn87GOX70twGLnTzPvL9IEWjGCR/bT8RGLjaJwUqVADt2OaeirvSFv7cx31XWhko+qUPjqwe2Pq1q+2sZhXyvhuEcGE1asU1d4eeN/1OmOp825eHdR9P2TClUzW33W4v/sjsOBgJQnQD3/bOqTDVRYmvHYGBveYETVNoRd2J5soY7P1kNCpbRj3ajmjY63cAh74ZguPblb+jbiqPxY73JsBq0rTrnus4B7ohUk0QS5PypnEUDNHN9ORzdHxOoyx5tM36vij8ubf9eG2P3fJfaphHNVNslvCZpiH5P/ZHwbo+Lq9PyZYcHFoxJGDnxzo3ZVN7/3t8yZhHU7O83wE8lHHRsyBGBchSh5YgbWQRdLFGWBq1KNuVheM7sk4qCS8jvmcVMsfmi063NqtKdJql6Z7OVl91UOvMSBtZjNShx6AxmEVgRNME5XvTWyu/dlZ0Wi2yxuUjLsfetbeusIc4x5NLnhuS69B35h5RrZUSZS3NWpRszsHRnwaEdUl4KpWfeUo+4nOqRA5NfXECiun6lERWkaRwMGqaCfGPXR7o02BBLFKa51m4ay9jjAUWV2plrnAw0l74vs1kjLEA48JojCkTXpPvISRzQh6yxuaL2hHNVdHY99lImOq73kRJrTNh0AXbEZ1aJ1avlO/KxNEfnCV62pA+uhApQ0rEFAh1zqWcBWd5GEkDS9DrjAPQRFlgbtDhoGho17Fhm0prQcrg46JoGiWYVh5ME7knHZkx+rpfEJ1qL1rWWBaDra+fCkDbYcuMMUeRNfEo1BormqsN2LdsOEw1HfdJjfXoHHVxzbA060SybF1Rx3OkSrVUwj46tV4k6FJXY5omcramOG1kgZgek9Q2NByPw9HVA51en5iMGuROOgStwQRTvR75P/VDU4XzLsTeZkiqR9KAMtFZmbozl+/JEHk+jLHQaZ635AngjtXcQI9wzoifpQ4twqALt4kluY7ETFrxQp83VkRj04tTKLTo1D5H/WYd4nOqW/fTuk8bcPjbQSj6xb5aI3VEkehTQ514T96OkiQPfDlSPBadVoMx1/4MVZuOvQ7mRg02LJ4Gq0nX2lm45+RDUGmt4iZP9TCorkl1XhL2fTZK3CjJmOt/QmxmXcdzlIH6klhseXVqawA0dMHmdtfHobnKgA3/ou3UUGksGH3tOsSkn+hU7EBLmbe9dVprc8C0EUXoO2OPKKlOSaGOc2w4Hot9n49s7RicMvgYBl2wDWrdSddHpuXEWdj32WjxmC62CaOv/RlRCc0djk0B1pY3ToNNXB/v08U2iyaKSf3KW5voUVBJq6sKf+6Lo6sHdLqjMvM9ziFhkdo8z8I5I8EnaVAJhl2y2W3lVGOdDuufma54n2Nv/BGxTm7IxHFDPbxiMEyNURh8wTanx3Z0xC3dmYkjqwZg/K0/ujxHscrFImHNP2ai79n7kHPqEafHpps+LU/e8tokDFv0K2LT6l0emzSUxuDwiiEYccVGl8cmpgYNfnlmOibe8b0ILlydIyXxbnj+DCQPLMOAc3e2PseOReNU2PbWqYhKaMLQBVvcHpuWUe//bCQm3vm9CNRcHdvSpMX6Z8/0+qofbYwRY65fK5KZqVKvs2OLoPKLERyQBCEOSJgzHIzY8ciIH026d7nLm1hbh1acGM1wp0efUnHzdrc/cbOnm7NF5fHYtK2pTgddnPObfFs1BQlIyHW/Tp4CkrJdKUgbUSa+drVPR0BiNUtQaz2Xw68pjEdCTq3bbWiftYUJiMuqdXrjbj1HG9BYGgd9j0aoo6wer09tQQ/E51Z73K5kazYOLBsFb+o/ZwcyxxR6rNi67e2JqDma7NVjM++ZsnYBX04WMYmsFoUjI5zA6icJfcoUBSJ0I+s55ZCifQ44d5fHbeh4NCVBUw9K+tgoCUToHONzajxWaqUgIHVYmf0c3Gzq+L6SQEQcO7tWUaVWOkdPpeipW25sRh00eveBiAMFIkqkDT8Gb6Kl1umjijwGIqKj8nhlHZUZY8GSO1If8f1qOBjxk6yxBYq70mr0FkX7jIpvUrTPtgXElBxf6TbuRhwcnOV+OONsGsXdsRVv68Xr4wiaPB3bHljZRF6Lt1DARPv0hF6ThF6VXjsu8z7uY8NYRxyM+Isq+Duuer1bcITzapdd6pLcmY7KLKjxkl/G2uNgxE8qD6Qp2k4kX1qU3empMmln3tErObbSUQx7kqj/I5KTS8R73DZQ18dGr4/3ElibymM8Nk8Ux7bRKiH/LC9m3S8dL6//hS8jA03VPDdNQQv2MMbBiJ8c39oTNiu82pX26A/9FeVYOG6OSo5tNaoV3XBpma2naRo6ZlW+8huj0uvTXGvv2eP22DLQVBkt8mU8bWesiVJ+7CqDoutTeSjFq39eVMeEujS7ap7Yeo4qe1M9Fhq4sR47OXckUnEw4kcFP/f12JWWRkUOfDlM0f6ObewDS7Pr4MHxeNmuDFEMrG2ND2fbUt2NvZ/aV4B42ue2/5yCqrxkl+/W7Y36VMj7ZoQIcDyxmtQ4+uMAz9fHCmx541TIVsnjOVKdk6Jfe7ndjq7JoRVDUbDGvnrJ3baNFQbsXjJWjLa4247qrRz4cji87cjqAfbn7fKaS6gp6IGKfelePzbz3egI5ZAwFuk4GPGjo98PFg3fOoxaOLrSWiT8+oK9qJdSVM/CMR3Qdn+OfVYdTMHeT8Yi77shKN6Y63Q7EYiUxmLTq6ejcn8GDn59okPsyfujG/G2tyfAVB2HXR+OQ0XL9BNN2ThGYIjoLPzeeFFQbN0/zoLFpHJ6XFGXw6TCur+fhYKfBqDo154uj0034o0vToWlLgabX50Eq1ntfDsbsOujsagrTMShb4aiuCUgoZt121EiWu6899ORKN9jr1ZbtL59x+C2+6Rpks0vT0HD8QTs/OAUsS/nr6EKW984FaZae8E1b2osjcf2dyfC3NIxmZYlt50uq8pLws73x4sgkDEWmqMjK+6viMiVNVxnJACogufgi7YiLquGXgFRxpvemRett4+cdEXqyAJRhMyxEqexPAZ7l45GU2X7aRJdfCP6nbMH8dnVYqUJlVo/8t0g1OSfVJdCbcKQC3YisX8ZVGobbBa1qJ1BBdRODpaoLHrGqEJE9WiCzawW+TFluzM6dPdNG3UU/WfvaV0VQqXjKfAp3dZ+WkFtaMLQ+dtbutfKsJo1KPq5NwrW2kdOTrAha3y+WMqq0ZtFcFK2MwtHfugH2Nrna1AdkYyxBYhOcZSDTxRdiK1GbYfXpu85e5CQa++ca6zRI2/VQNTkpbY/tMqGnlMOIn1EEdQ6KyxGjehAXPQLBTQqn3dzpvL7yQOPQ6WzwlSrR8m2HNQX2yvJstDE9UdYOBZC4wqsjDEWQrhCK2vrmk3RKF7e9X5lwYKLnjHGWIjljzDmQKtrImm6htt8ehF1r00bdgwxGfZS5XXFCSjbldlhuiJUxGZVi+6+migzzA1RKN2RhabKjmWLe/QtRf9Zu6GNNol8BerhclAk4ar90r02bUSx6NtCeSrUvZanK1ioomTWyU/nQppI3awZixwcjHhJxph89J25R+QPOJIbsyfI4iZNKyvKdmUhVFBl1yEXb25X8p1WnfQ64yDK96Zh36ejYDVpodaZMOH21dAY2lcazRpXiMyxhSK/pDt5MJ7Kow86fztShhxvTUwlPScdFj1p9nw8trVjMGOMseDGafdekDE2HwPn7RSBCN20qf6GowaHOsqCIfO3InVYMUKBNtqIUdf8jNhM++iO47k4qnomDyzFiMs3QFKbcOrvvxeBSNsy6W3rnvSduRfpo/N9ksA5/PINSB503P61Sm53zencR/3mZ/FcGAvF6qxcDI0l1Bnx1rjGiJmq4WCkm9Q6C/rN3CM+d1aAzFHbo//sXeImGuxyTj+MqHjnLeodRbXicqoxfNEmt833HI/T8/Y2mpaJz6l2WdCMzj0qzojcSYe9fmzG/IGLoTGHSKnMysFIN6WNKIJK6/6XhW7M2mizWI4ZzChYyqSGfp56m8hAj15ViiqWqjSyyCnxpmzqSuvp2CoZGWMKQiIAZOxkXCqetR0hiYTREQ5GuommBBw5Iu5Q7kUs1RUJYvoeTYo6BosRCYX9XGib9JFF8B4ZMem1Hsu8E3ou+sTw/yNm4Ymb6TEHmq657fbgvn9ERAKr3PIWvNHmvZbs3lJntMHQJEPl4eZos8moM9nQYDUjWFktFtQ2KtvWUUpd6TXy3vOWUduk/Nj1Fguag/iaM+ZOrckC1HFAzYDGOo1YMRhqLKbGdvfxkK7AWlhYiNxceylzxhhjjIWWgoIC5OScaIcSksGIzWZDcXEx4uLiICl8S1xbWysCGLoA8fHxPj9Hphy/NsGLX5vgxa9N8OLXxjUKMerq6pCVlQWVmymEkJimoSfgLqJyhwIRDkaCE782wYtfm+DFr03w4tfGuYQEz312OIGVMcYYYwHFwQhjjDHGAipsg5GoqCj89a9/Ff9lwYVfm+DFr03w4tcmePFr030hkcDKGGOMsfAVtiMjjDHGGAsNHIwwxhhjLKA4GGGMMcZYQIVEnZGu2rhxIw4ePIgzzzwT6enpgT6diPfRRx+JAnZtjR07FgMHDoz4axMstm3bhqNHj2L06NHo2bNnoE8n4m3fvh27d+92eh0uueQSt0WkmP/+Zo4cOYIePXpg/PjxiI6O5kvfBWEbjFAQMnv2bJSXl2PlypUcjASBRYsWYfLkycjIyGhXDIeDkcArKSnB/PnzkZeXh4kTJ2L//v245pprcM899wT61CLarl278Nlnn7V77McffxRBPQUjLHCqq6sxZ84cHD58WPzNUEBSVFQk3nSdddZZ/NJ0UlgGIyaTCQsXLsQf//hH8cGCx7333otZs2YF+jRYG7Sg7oILLoBerxdBPL2zo5vdV199xdcpwC677DLx4dDc3IzMzEzcdNNNPCoSYC+99BL27duHQ4cOiVERQq/VXXfdJUZLWOeowvWGN2TIkHZ/xCw47NixA59++qn4Yz15yoYFxnfffYf169fjmWeeaR1ipuH/c889l1+SILN06VLU1NTguuuuC/SpRDyj0YjExMTWQIT06dNHvBlmnRd2IyP0bo5udlu3bhXNi1jwoCaH77zzjshF2LBhg/gvDWn27ds30KcW0WjYPyUlBcOHD8eqVavEP7IjR44Uja1YcHn99ddFDlz//v0DfSoR75ZbbsGKFSvE9PPMmTNFrtUHH3yAV155JeKvDSI9GDl27Jh4x7BkyRLRsIiDkeDy+eefizweQq8NTddceeWVWLt2baBPLaKVlpaKjtiUz2MwGKDRaMRr8pe//AX33XdfoE+PtaDchNWrV+P999/naxIE6B5z2mmniftNfX29yBmhkZHevXsH+tRCUlgFI5RsN2DAABQWFuLDDz9EZWWleJz+gLVaLc4444xAn2JEcwQijj9ker0uuugi8TolJSUF9NwiGeWKUOLqAw88gGuvvVY89sknn+Diiy8WrxmtrGGB98Ybb4i/kwsvvDDQp8IAPPjgg2LajFY8OaZqKJeH/mYol0StVvN1itScERpmpqFlmqahj2+++UY8Tu/yfv7550CfHjsJBSSkrKyMr00A9evXT/yXVtM4nH/++SJvZPPmzQE8M+ZgtVrx1ltv4aqrruJ+W0Hihx9+wIwZM9rljFAATwmtBQUFAT23UKQJt8TVtmiEhKYG7r//fkyfPj1g58WA48ePIy0tTeSNONC7b3qn57gZssCYO3cu7rzzTuzdu1csUSQHDhwQN8Dc3Fx+WYIAvbGiZaPXX399oE+FtaC/jT179rS7HvQ1jYi0LV/AIjAYYcFrzZo1+Oc//ynecaempuLbb78Vo1c09Ew5CixwKIGYpsyobgUtS6TX4/nnnxfv+s4++2x+aYIkcXXSpEkYOnRooE+FtaCyEVOnThV/N5T/RlOdzz77rHicpj5Z54R1117KRbj55pvFXDhN4bDAF3CiXB5KNKZEL8pCp/+y4EDFtb788ksRjNAIyRVXXMHz3kHAbDaLxHyqnURFtljwoKTVt99+W6ykoWW+tKrmnHPOCfRphaSwDkYYY4wxFvzCKoGVMcYYY6GHgxHGGGOMBRQHI4wxxhgLKA5GGGOMMRZQHIwwxhhjLKA4GGGMMcZYQHEwwhhjjLGA4mCEMcYYYwHFwQhjjDHGAoqDEcYYY4wFFAcjjDHGGAsoDkYYY4wxhkD6f9YK91v0pmAdAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1i)\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "knc = KNeighborsClassifier()\n",
-    "knc.fit(X,T)\n",
-    "nnwplot.plotTwoFeatures(X, T, knc.predict)\n",
-    "knc.fit(X_0_3,T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, knc.predict)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "d79741d2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2, 0, 0, 0, 2, 2, 0, 2, 0, 0, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2,\n",
-       "       0, 2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 0, 0, 2, 0, 0, 2, 0, 0, 0, 2,\n",
-       "       2, 0, 2, 0, 2, 2, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 3, 1, 3, 3, 3, 3, 1, 3, 3, 3,\n",
-       "       3, 3, 3, 1, 1, 3, 3, 3, 3, 1, 3, 1, 3, 1, 3, 3, 1, 1, 3, 3, 3, 3,\n",
-       "       3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 1], dtype=int32)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 2a) probabilistisches Verfahren\n",
-    "from sklearn.cluster import KMeans\n",
-    "\n",
-    "X_0_2 = iris_data[:,[0,2]]\n",
-    "km = KMeans(4)\n",
-    "km.fit(X_0_2)\n",
-    "km.predict(X_0_2)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "88b3d717",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3a)\n",
-    "X = np.linspace(0, 2)\n",
-    "np.random.seed(42) # vereinfachte Variante, siehe np.random\n",
-    "X = np.sort(np.random.rand(50)*2)\n",
-    "T = np.sin(5*X) + X**2 + np.random.randn(len(X))/10\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.show()\n",
-    "# Es gibt 50 Merkmale in den Daten"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "b251a2c1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.04116899 0.06877704 0.09290083 0.11616722 0.13010319 0.19534423\n",
-      " 0.24407647 0.27898772 0.31198904 0.31203728 0.34104825 0.36364993\n",
-      " 0.36680902 0.36970891 0.39934756 0.42467822 0.51755996 0.58245828\n",
-      " 0.5842893  0.60848449 0.60922754 0.62342215 0.73272369 0.74908024\n",
-      " 0.86389004 0.88030499 0.91213997 0.99035382 1.02846888 1.04013604\n",
-      " 1.04951286 1.09342056 1.18482914 1.19731697 1.20223002 1.2150897\n",
-      " 1.22370579 1.32504457 1.36846605 1.41614516 1.46398788 1.57035192\n",
-      " 1.6167947  1.66488528 1.73235229 1.8186408  1.89777107 1.90142861\n",
-      " 1.93126407 1.9398197 ]\n",
-      "[[0.04116899]\n",
-      " [0.06877704]\n",
-      " [0.09290083]\n",
-      " [0.11616722]\n",
-      " [0.13010319]\n",
-      " [0.19534423]\n",
-      " [0.24407647]\n",
-      " [0.27898772]\n",
-      " [0.31198904]\n",
-      " [0.31203728]\n",
-      " [0.34104825]\n",
-      " [0.36364993]\n",
-      " [0.36680902]\n",
-      " [0.36970891]\n",
-      " [0.39934756]\n",
-      " [0.42467822]\n",
-      " [0.51755996]\n",
-      " [0.58245828]\n",
-      " [0.5842893 ]\n",
-      " [0.60848449]\n",
-      " [0.60922754]\n",
-      " [0.62342215]\n",
-      " [0.73272369]\n",
-      " [0.74908024]\n",
-      " [0.86389004]\n",
-      " [0.88030499]\n",
-      " [0.91213997]\n",
-      " [0.99035382]\n",
-      " [1.02846888]\n",
-      " [1.04013604]\n",
-      " [1.04951286]\n",
-      " [1.09342056]\n",
-      " [1.18482914]\n",
-      " [1.19731697]\n",
-      " [1.20223002]\n",
-      " [1.2150897 ]\n",
-      " [1.22370579]\n",
-      " [1.32504457]\n",
-      " [1.36846605]\n",
-      " [1.41614516]\n",
-      " [1.46398788]\n",
-      " [1.57035192]\n",
-      " [1.6167947 ]\n",
-      " [1.66488528]\n",
-      " [1.73235229]\n",
-      " [1.8186408 ]\n",
-      " [1.89777107]\n",
-      " [1.90142861]\n",
-      " [1.93126407]\n",
-      " [1.9398197 ]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>.sk-global {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       ".sk-global.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       ".sk-global.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       ".sk-global {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".sk-global pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip-path: inset(100%);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       ".sk-global div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       ".sk-global label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label label.sk-toggleable__label,\n",
-       ".sk-global div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       ".sk-global div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       ".sk-global div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       ".sk-global div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       ".sk-global a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       ".sk-global a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-top-container.sk-global {\n",
-       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
-       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
-       "   to allow keyboard-only users to navigate the display. */\n",
-       "  overflow-x: scroll !important;\n",
-       "  max-width: 100%;\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover td {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table :is(td, th) {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
-       "*/\n",
-       ".estimator-table table td.param {\n",
-       "    text-align: left;\n",
-       "    position: relative;\n",
-       "    padding: 0;\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".default td, .estimator-table th {\n",
-       "    color: black;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       "td.fitted-att-type {\n",
-       "    white-space: preserve nowrap;\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    Styles for parameter documentation links\n",
-       "    We need styling for visited so jupyter doesn't overwrite it\n",
-       "*/\n",
-       "a.param-doc-link,\n",
-       "a.param-doc-link:link,\n",
-       "a.param-doc-link:visited {\n",
-       "    text-decoration: underline dashed;\n",
-       "    text-underline-offset: .3em;\n",
-       "    color: inherit;\n",
-       "    display: block;\n",
-       "    padding: .5em;\n",
-       "}\n",
-       "\n",
-       "@supports(anchor-name: --doc-link) {\n",
-       "    a.param-doc-link,\n",
-       "    a.param-doc-link:link,\n",
-       "    a.param-doc-link:visited {\n",
-       "    anchor-name: --doc-link;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
-       "a.param-doc-link::before {\n",
-       "    position: absolute;\n",
-       "    content: \"\";\n",
-       "    inset: 0;\n",
-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
-       "    display: none;\n",
-       "    position: absolute;\n",
-       "    z-index: 9999;\n",
-       "    left: 0;\n",
-       "    padding: .5ex;\n",
-       "    margin-left: 1.5em;\n",
-       "    color: var(--sklearn-color-text);\n",
-       "    box-shadow: .3em .3em .4em #999;\n",
-       "    width: max-content;\n",
-       "    text-align: left;\n",
-       "    max-height: 10em;\n",
-       "    overflow-y: auto;\n",
-       "\n",
-       "    /* unfitted */\n",
-       "    background: var(--sklearn-color-unfitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       "@supports(position-area: center right) {\n",
-       "    .param-doc-description {\n",
-       "    position-area: center right;\n",
-       "    position: fixed;\n",
-       "    margin-left: 0;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
-       "    /* fitted */\n",
-       "    background: var(--sklearn-color-fitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "    border: none;\n",
-       "    padding: 0;\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon:focus-visible {\n",
-       "    outline: 1px solid currentColor;\n",
-       "    outline-offset: 1px;\n",
-       "}\n",
-       "\n",
-       ".features {\n",
-       "  font-family: monospace;\n",
-       "  cursor: pointer;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
-       "}\n",
-       "\n",
-       ".features.fitted {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features summary {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  margin-bottom: 0;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "  padding: .25em;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features.fitted details[open] > summary {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features details > summary .arrow::before {\n",
-       "  content: \"▸\";\n",
-       "  color: grey;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary .arrow::before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       ".features details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features.fitted details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features .features-container {\n",
-       "  max-height: 10em;\n",
-       "  overflow: auto;\n",
-       "  scrollbar-width: thin;\n",
-       "  padding: .25em 0.1rem;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 0 0 .5em .5em;\n",
-       "}\n",
-       "\n",
-       ".features.fitted .features-container {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features .image-container {\n",
-       "  block-size: 1em;\n",
-       "  inline-size: 1em;\n",
-       "  padding: 0;\n",
-       "  margin: 0%;\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  align-items: center;\n",
-       "}\n",
-       "\n",
-       ".features .copy-paste-icon {\n",
-       "  background-size: 1em 1em;\n",
-       "  width: 1em;\n",
-       "  height: 1em;\n",
-       "  filter: grayscale(100%) opacity(60%);\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  width: 100%;\n",
-       "  margin: 0.01em;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(odd) {\n",
-       "  background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(even) {\n",
-       "  background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:hover {\n",
-       "  background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  table-layout: inherit;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table td {\n",
-       "  text-align: left;\n",
-       "  padding: 0 0.5em;\n",
-       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "  white-space: nowrap;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".total_features {\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  margin-top: 0.5em;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-3\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy copy_X to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('copy_X',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-copy_X;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
-       "            copy_X\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-copy_X;\">\n",
-       "            copy_X: bool, default=True<br><br>If True, X will be copied; else, it may be overwritten.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy tol to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-tol;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies the convergence criterion of the underlying solver. `tol` is<br>set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. `tol` is set as `cond` of<br>:func:`scipy.linalg.lstsq` when fitting on dense training data.<br><br>.. versionadded:: 1.7<br>.. versionchanged:: 1.9<br>    Now supported on dense data, interpreted as the `cond` parameter.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1e-06</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy n_jobs to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
-       "            n_jobs\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
-       "            n_jobs: int, default=None<br><br>The number of jobs to use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets &gt; 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary &lt;n_jobs&gt;` for more details.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy positive to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('positive',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-positive;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
-       "            positive\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-positive;\">\n",
-       "            positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    \n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Fitted attributes</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                    <tbody>\n",
-       "                        <tr>\n",
-       "                        <th>Name</th>\n",
-       "                        <th>Type</th>\n",
-       "                        <th>Value</th>\n",
-       "                        </tr>\n",
-       "                        \n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=coef_,-array%20of%20shape%20%28n_features%2C%20%29%20or%20%28n_targets%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: array of shape (n_features, ) or (n_targets, n_features)<br><br>Estimated coefficients for the linear regression problem.<br>If multiple targets are passed during the fit (y 2D), this<br>is a 2D array of shape (n_targets, n_features), while if only<br>one target is passed, this is a 1D array of length n_features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[1.64]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=intercept_,-float%20or%20array%20of%20shape%20%28n_targets%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: float or array of shape (n_targets,)<br><br>Independent term in the linear model. Set to 0.0 if<br>`fit_intercept = False`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">float64</td>\n",
-       "           <td>-0.1418</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_features_in_,-int\">\n",
-       "            n_features_in_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
-       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>1</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-rank_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=rank_,-int\">\n",
-       "            rank_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-rank_;\">\n",
-       "            rank_: int<br><br>Rank of matrix `X`. Only available when `X` is dense.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int64</td>\n",
-       "           <td>np.int64(1)</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-singular_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=singular_,-array%20of%20shape%20%28min%28X%2C%20y%29%2C%29\">\n",
-       "            singular_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-singular_;\">\n",
-       "            singular_: array of shape (min(X, y),)<br><br>Singular values of `X`. Only available when `X` is dense.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[4.04]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "                    </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
-       " SPDX-License-Identifier: BSD-3-Clause\n",
-       "*/\n",
-       "\n",
-       "function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "\n",
-       "    const parent = element.parentElement;\n",
-       "    if (!parent || !parent.nextElementSibling) {\n",
-       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    const paramName = element.parentElement.nextElementSibling\n",
-       "        .textContent.trim().split(' ')[0];\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "\n",
-       "/**\n",
-       " * Copy the list of feature names formatted as a Python list.\n",
-       " *\n",
-       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
-       " *   contain a `details` element and a table containing feature named.\n",
-       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
-       " */\n",
-       "function copyFeatureNamesToClipboard(element) {\n",
-       "    var detailsElem = element.closest('.features').querySelector('details');\n",
-       "    var wasOpen = detailsElem.open;\n",
-       "    detailsElem.open = true;\n",
-       "    var content = element.closest('.features').querySelector('tbody')\n",
-       "                  .innerText.trim();\n",
-       "    if (!wasOpen) detailsElem.open = false;\n",
-       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
-       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
-       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
-       "    const originalStyle = element.style;\n",
-       "    const copyMark = document.createElement('span');\n",
-       "    copyMark.innerHTML = '✔';\n",
-       "    copyMark.style.color = 'blue';\n",
-       "    copyMark.style.fontSize = '1em';\n",
-       "\n",
-       "    navigator.clipboard.writeText(formattedText)\n",
-       "        .then(() => {\n",
-       "            element.style.display = 'none';\n",
-       "            element.parentElement.appendChild(copyMark);\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                copyMark.remove();\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'orange';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "/**\n",
-       " * Adapted from Skrub\n",
-       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
-       " * @returns \"light\" or \"dark\"\n",
-       " */\n",
-       "function detectTheme(element) {\n",
-       "    const body = document.querySelector('body');\n",
-       "\n",
-       "    // Check VSCode theme\n",
-       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
-       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
-       "\n",
-       "    if (themeKindAttr && themeNameAttr) {\n",
-       "        const themeKind = themeKindAttr.toLowerCase();\n",
-       "        const themeName = themeNameAttr.toLowerCase();\n",
-       "\n",
-       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
-       "            return \"dark\";\n",
-       "        }\n",
-       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
-       "            return \"light\";\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    // Check Jupyter theme\n",
-       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
-       "        return 'dark';\n",
-       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
-       "        return 'light';\n",
-       "    }\n",
-       "\n",
-       "    // Guess based on a parent element's color\n",
-       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
-       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
-       "    if (match) {\n",
-       "        const [r, g, b] = [\n",
-       "            parseFloat(match[1]),\n",
-       "            parseFloat(match[2]),\n",
-       "            parseFloat(match[3])\n",
-       "        ];\n",
-       "\n",
-       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
-       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
-       "\n",
-       "        if (luma > 180) {\n",
-       "            // If the text is very bright we have a dark theme\n",
-       "            return 'dark';\n",
-       "        }\n",
-       "        if (luma < 75) {\n",
-       "            // If the text is very dark we have a light theme\n",
-       "            return 'light';\n",
-       "        }\n",
-       "        // Otherwise fall back to the next heuristic.\n",
-       "    }\n",
-       "\n",
-       "    // Fallback to system preference\n",
-       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
-       "}\n",
-       "\n",
-       "\n",
-       "function forceTheme(elementId) {\n",
-       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
-       "    if (estimatorElement === null) {\n",
-       "        console.error(`Element with id ${elementId} not found.`);\n",
-       "    } else {\n",
-       "        const theme = detectTheme(estimatorElement);\n",
-       "        estimatorElement.classList.add(theme);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "forceTheme('sk-container-id-3');</script></body>"
-      ],
-      "text/plain": [
-       "LinearRegression()"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 3b)\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "reg = LinearRegression()\n",
-    "print(X)\n",
-    "print(X.reshape(-1,1)) # transponiert den Merkmalsraum in einen Spaltenvektor\n",
-    "reg.fit(X.reshape(-1,1),T)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "98476c93",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.07437258 -0.0291456   0.0103735   0.04848806  0.07131768  0.17819428\n",
-      "  0.25802648  0.31521741  0.36927952  0.36935855  0.41688375  0.45390939\n",
-      "  0.45908454  0.46383508  0.51238855  0.55388473  0.70604178  0.81235693\n",
-      "  0.81535647  0.85499255  0.85620981  0.87946314  1.05851877  1.08531376\n",
-      "  1.27339292  1.30028357  1.35243501  1.48056341  1.54300275  1.56211567\n",
-      "  1.57747659  1.64940532  1.79914906  1.81960638  1.82765485  1.84872133\n",
-      "  1.86283603  2.02884723  2.09997946  2.17808643  2.25646145  2.43070493\n",
-      "  2.50678657  2.58556763  2.69609076  2.83744691  2.96707657  2.97306828\n",
-      "  3.02194413  3.03595981]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 3c)\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,reg.predict(X.reshape(-1,1)))\n",
-    "plt.show()\n",
-    "print(reg.predict(X.reshape(-1,1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "94ab4bd3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3d) Decision Tree Regressor\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "dtr = DecisionTreeRegressor()\n",
-    "dtr.fit(X.reshape(-1,1),T)\n",
-    "\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,dtr.predict(X.reshape(-1,1)))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "475aa6e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3d) SV Regressor\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "svr = SVR()\n",
-    "svr.fit(X.reshape(-1,1),T)\n",
-    "\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,svr.predict(X.reshape(-1,1)))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b787c0a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3e) -- TODO warum sieht es anders aus? \n",
-    "svr_iris = SVR()\n",
-    "X = iris_data[:,[0,1]]\n",
-    "T = iris_data[:,4]\n",
-    "svr_iris.fit(X,T)\n",
-    "nnwplot.plotTwoFeatures(X, T, svr_iris.predict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "ef499321",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3f)\n",
-    "decision = lambda x : np.round(svr_iris.predict(x)) # kurze Funktionsdefinition\n",
-    "y = decision(X)\n",
-    "nnwplot.plotTwoFeatures(X, T, decision)"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de4f7f4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.svm import LinearSVC\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67b3d7af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[5.1 3.5 1.4 0.2 0. ]\n",
+      " [4.9 3.  1.4 0.2 0. ]\n",
+      " [4.7 3.2 1.3 0.2 0. ]\n",
+      " [4.6 3.1 1.5 0.2 0. ]\n",
+      " [5.  3.6 1.4 0.2 0. ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1a) Ja stehen in der richtigen Dimension\n",
+    "iris_data= np.loadtxt(\"../supplement_files/iris.csv\", delimiter=\",\")\n",
+    "print(iris_data[:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9c0fd1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.sk-global {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       ".sk-global.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       ".sk-global.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       ".sk-global {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       ".sk-global pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip-path: inset(100%);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       ".sk-global div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       ".sk-global div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       ".sk-global div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       ".sk-global label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".sk-global label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       ".sk-global label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-label label.sk-toggleable__label,\n",
+       ".sk-global div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       ".sk-global div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       ".sk-global div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       ".sk-global div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       ".sk-global a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       ".sk-global a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       ".sk-global a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       ".sk-global a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-top-container.sk-global {\n",
+       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
+       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
+       "   to allow keyboard-only users to navigate the display. */\n",
+       "  overflow-x: scroll !important;\n",
+       "  max-width: 100%;\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover td {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table :is(td, th) {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td, .estimator-table th {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "td.fitted-att-type {\n",
+       "    white-space: preserve nowrap;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "@supports(anchor-name: --doc-link) {\n",
+       "    a.param-doc-link,\n",
+       "    a.param-doc-link:link,\n",
+       "    a.param-doc-link:visited {\n",
+       "    anchor-name: --doc-link;\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "@supports(position-area: center right) {\n",
+       "    .param-doc-description {\n",
+       "    position-area: center right;\n",
+       "    position: fixed;\n",
+       "    margin-left: 0;\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "    border: none;\n",
+       "    padding: 0;\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon:focus-visible {\n",
+       "    outline: 1px solid currentColor;\n",
+       "    outline-offset: 1px;\n",
+       "}\n",
+       "\n",
+       ".features {\n",
+       "  font-family: monospace;\n",
+       "  cursor: pointer;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: .20em;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  font-size: inherit; /* Needed for jupyter */\n",
+       "}\n",
+       "\n",
+       ".features.fitted {\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".features summary {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  margin-bottom: 0;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "  padding: .25em;\n",
+       "}\n",
+       "\n",
+       ".features details[open] > summary {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "  border-radius: .20em 0 0 0;\n",
+       "}\n",
+       "\n",
+       ".features.fitted details[open] > summary {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "  border-radius: .20em 0 0 0;\n",
+       "}\n",
+       "\n",
+       ".features details > summary .arrow::before {\n",
+       "  content: \"▸\";\n",
+       "  color: grey;\n",
+       "}\n",
+       "\n",
+       ".features details[open] > summary .arrow::before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       ".features details:hover > summary {\n",
+       "  margin: 0;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".features.fitted details:hover > summary {\n",
+       "  margin: 0;\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".features .features-container {\n",
+       "  max-height: 10em;\n",
+       "  overflow: auto;\n",
+       "  scrollbar-width: thin;\n",
+       "  padding: .25em 0.1rem;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 0 0 .5em .5em;\n",
+       "}\n",
+       "\n",
+       ".features.fitted .features-container {\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".features .image-container {\n",
+       "  block-size: 1em;\n",
+       "  inline-size: 1em;\n",
+       "  padding: 0;\n",
+       "  margin: 0%;\n",
+       "  display: flex;\n",
+       "  justify-content: center;\n",
+       "  align-items: center;\n",
+       "}\n",
+       "\n",
+       ".features .copy-paste-icon {\n",
+       "  background-size: 1em 1em;\n",
+       "  width: 1em;\n",
+       "  height: 1em;\n",
+       "  filter: grayscale(100%) opacity(60%);\n",
+       "}\n",
+       "\n",
+       ".features .features-container table {\n",
+       "  width: 100%;\n",
+       "  margin: 0.01em;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:nth-child(odd) {\n",
+       "  background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:nth-child(even) {\n",
+       "  background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:hover {\n",
+       "  background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table {\n",
+       "  table-layout: inherit;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table td {\n",
+       "  text-align: left;\n",
+       "  padding: 0 0.5em;\n",
+       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "  white-space: nowrap;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       ".total_features {\n",
+       "  display: flex;\n",
+       "  justify-content: center;\n",
+       "  margin-top: 0.5em;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-13\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy penalty to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('penalty',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-penalty;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%7D%2C%20default%3D%27l2%27\">\n",
+       "            penalty\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-penalty;\">\n",
+       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;}, default=&#x27;l2&#x27;<br><br>Specifies the norm used in the penalization. The &#x27;l2&#x27;<br>penalty is the standard used in SVC. The &#x27;l1&#x27; leads to ``coef_``<br>vectors that are sparse.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy loss to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('loss',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-loss;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=loss,-%7B%27hinge%27%2C%20%27squared_hinge%27%7D%2C%20default%3D%27squared_hinge%27\">\n",
+       "            loss\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-loss;\">\n",
+       "            loss: {&#x27;hinge&#x27;, &#x27;squared_hinge&#x27;}, default=&#x27;squared_hinge&#x27;<br><br>Specifies the loss function. &#x27;hinge&#x27; is the standard SVM loss<br>(used e.g. by the SVC class) while &#x27;squared_hinge&#x27; is the<br>square of the hinge loss. The combination of ``penalty=&#x27;l1&#x27;``<br>and ``loss=&#x27;hinge&#x27;`` is not supported.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;squared_hinge&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy dual to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('dual',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-dual;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=dual,-%22auto%22%20or%20bool%2C%20default%3D%22auto%22\">\n",
+       "            dual\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-dual;\">\n",
+       "            dual: &quot;auto&quot; or bool, default=&quot;auto&quot;<br><br>Select the algorithm to either solve the dual or primal<br>optimization problem. Prefer dual=False when n_samples &gt; n_features.<br>`dual=&quot;auto&quot;` will choose the value of the parameter automatically,<br>based on the values of `n_samples`, `n_features`, `loss`, `multi_class`<br>and `penalty`. If `n_samples` &lt; `n_features` and optimizer supports<br>chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,<br>otherwise it will be set to False.<br><br>.. versionchanged:: 1.3<br>   The `&quot;auto&quot;` option is added in version 1.3 and will be the default<br>   in version 1.5.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy tol to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-tol;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-tol;\">\n",
+       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0001</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy C to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('C',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-C;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
+       "            C\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-C;\">\n",
+       "            C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>For an intuitive visualization of the effects of scaling<br>the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy multi_class to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('multi_class',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-multi_class;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=multi_class,-%7B%27ovr%27%2C%20%27crammer_singer%27%7D%2C%20default%3D%27ovr%27\">\n",
+       "            multi_class\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-multi_class;\">\n",
+       "            multi_class: {&#x27;ovr&#x27;, &#x27;crammer_singer&#x27;}, default=&#x27;ovr&#x27;<br><br>Determines the multi-class strategy if `y` contains more than<br>two classes.<br>``&quot;ovr&quot;`` trains n_classes one-vs-rest classifiers, while<br>``&quot;crammer_singer&quot;`` optimizes a joint objective over all classes.<br>While `crammer_singer` is interesting from a theoretical perspective<br>as it is consistent, it is seldom used in practice as it rarely leads<br>to better accuracy and is more expensive to compute.<br>If ``&quot;crammer_singer&quot;`` is chosen, the options loss, penalty and dual<br>will be ignored.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
+       "            fit_intercept: bool, default=True<br><br>Whether or not to fit an intercept. If set to True, the feature vector<br>is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where<br>1 corresponds to the intercept. If set to False, no intercept will be<br>used in calculations (i.e. data is expected to be already centered).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy intercept_scaling to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_scaling,-float%2C%20default%3D1.0\">\n",
+       "            intercept_scaling\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
+       "            intercept_scaling: float, default=1.0<br><br>When `fit_intercept` is True, the instance vector x becomes ``[x_1,<br>..., x_n, intercept_scaling]``, i.e. a &quot;synthetic&quot; feature with a<br>constant value equal to `intercept_scaling` is appended to the instance<br>vector. The intercept becomes intercept_scaling * synthetic feature<br>weight. Note that liblinear internally penalizes the intercept,<br>treating it like any other term in the feature vector. To reduce the<br>impact of the regularization on the intercept, the `intercept_scaling`<br>parameter can be set to a value greater than 1; the higher the value of<br>`intercept_scaling`, the lower the impact of regularization on it.<br>Then, the weights become `[w_x_1, ..., w_x_n,<br>w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent<br>the feature weights and the intercept weight is scaled by<br>`intercept_scaling`. This scaling allows the intercept term to have a<br>different regularization behavior compared to the other features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy class_weight to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('class_weight',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
+       "            class_weight\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
+       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Set the parameter C of class i to ``class_weight[i]*C`` for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy verbose to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-verbose;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-verbose;\">\n",
+       "            verbose: int, default=0<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in liblinear that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy random_state to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-random_state;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-random_state;\">\n",
+       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>the dual coordinate descent (if ``dual=True``). When ``dual=False`` the<br>underlying implementation of :class:`LinearSVC` is not random and<br>``random_state`` has no effect on the results.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy max_iter to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('max_iter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=max_iter,-int%2C%20default%3D1000\">\n",
+       "            max_iter\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
+       "            max_iter: int, default=1000<br><br>The maximum number of iterations to be run.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1000</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    \n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Fitted attributes</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                    <tbody>\n",
+       "                        <tr>\n",
+       "                        <th>Name</th>\n",
+       "                        <th>Type</th>\n",
+       "                        <th>Value</th>\n",
+       "                        </tr>\n",
+       "                        \n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-classes_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
+       "            classes_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-classes_;\">\n",
+       "            classes_: ndarray of shape (n_classes,)<br><br>The unique classes labels.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
+       "           <td>[0.,1.,2.]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-coef_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=coef_,-ndarray%20of%20shape%20%281%2C%20n_features%29%20if%20n_classes%20%3D%3D%202%20%20%20%20%20%20%20%20%20%20%20%20%20else%20%28n_classes%2C%20n_features%29\">\n",
+       "            coef_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-coef_;\">\n",
+       "            coef_: ndarray of shape (1, n_features) if n_classes == 2             else (n_classes, n_features)<br><br>Weights assigned to the features (coefficients in the primal<br>problem).<br><br>``coef_`` is a readonly property derived from ``raw_coef_`` that<br>follows the internal memory layout of liblinear.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ndarray[float64](3, 2)</td>\n",
+       "           <td>[[-1.54, 2.28],\n",
+       " [ 0.1 ,-0.98],\n",
+       " [ 0.66,-0.35]]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20if%20n_classes%20%3D%3D%202%20else%20%28n_classes%2C%29\">\n",
+       "            intercept_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
+       "            intercept_: ndarray of shape (1,) if n_classes == 2 else (n_classes,)<br><br>Constants in decision function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
+       "           <td>[ 1.2 , 2.02,-3.14]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_features_in_,-int\">\n",
+       "            n_features_in_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
+       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">int</td>\n",
+       "           <td>2</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_iter_,-int\">\n",
+       "            n_iter_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
+       "            n_iter_: int<br><br>Maximum number of iterations run across all classes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">int</td>\n",
+       "           <td>8</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "                    </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
+       " SPDX-License-Identifier: BSD-3-Clause\n",
+       "*/\n",
+       "\n",
+       "function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "\n",
+       "    const parent = element.parentElement;\n",
+       "    if (!parent || !parent.nextElementSibling) {\n",
+       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "/**\n",
+       " * Copy the list of feature names formatted as a Python list.\n",
+       " *\n",
+       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
+       " *   contain a `details` element and a table containing feature named.\n",
+       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
+       " */\n",
+       "function copyFeatureNamesToClipboard(element) {\n",
+       "    var detailsElem = element.closest('.features').querySelector('details');\n",
+       "    var wasOpen = detailsElem.open;\n",
+       "    detailsElem.open = true;\n",
+       "    var content = element.closest('.features').querySelector('tbody')\n",
+       "                  .innerText.trim();\n",
+       "    if (!wasOpen) detailsElem.open = false;\n",
+       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
+       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
+       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
+       "    const originalStyle = element.style;\n",
+       "    const copyMark = document.createElement('span');\n",
+       "    copyMark.innerHTML = '✔';\n",
+       "    copyMark.style.color = 'blue';\n",
+       "    copyMark.style.fontSize = '1em';\n",
+       "\n",
+       "    navigator.clipboard.writeText(formattedText)\n",
+       "        .then(() => {\n",
+       "            element.style.display = 'none';\n",
+       "            element.parentElement.appendChild(copyMark);\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                copyMark.remove();\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 1000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'orange';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 1000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-13');</script></body>"
+      ],
+      "text/plain": [
+       "LinearSVC()"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 1b)\n",
+    "clf = LinearSVC()\n",
+    "X = iris_data[:,[0,1]]\n",
+    "T = iris_data[:,4]\n",
+    "clf.fit(X, T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc181a3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "80.0 Prozent wurden richtig vorausgesagt\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1 c+d)\n",
+    "pred = clf.predict(X)\n",
+    "correct_perc = (sum(pred == T)/len(pred))*100\n",
+    "\n",
+    "print(f\"{correct_perc} Prozent wurden richtig vorausgesagt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16af32f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1e)\n",
+    "import nnwplot\n",
+    "nnwplot.plotTwoFeatures(X, T, clf.predict) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4bcee12",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1f)\n",
+    "from sklearn.svm import SVC\n",
+    "svc = SVC()\n",
+    "svc.fit(X, T)\n",
+    "nnwplot.plotTwoFeatures(X, T, svc.predict) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c8991de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a5e84f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAf/9JREFUeJzt3Xd4HNXV+PHvzGxTt2RZtmRJ7r0bsOm9GNMDAUIIEMgLhEAKCQReIIH84CWFEEIIJIQWSIBgIPRiik3HuPduWVaXZfW2Zeb+/lhpbVnS7sheaVfy+TyPH9Dqau7ZXUlzNHPvOZpSSiGEEEIIESN6rAMQQgghxKFNkhEhhBBCxJQkI0IIIYSIKUlGhBBCCBFTkowIIYQQIqYkGRFCCCFETEkyIoQQQoiYkmRECCGEEDHliHUAdliWRWlpKSkpKWiaFutwhBBCCGGDUoqGhgZycnLQ9e6vf/SLZKS0tJS8vLxYhyGEEEKIA1BUVERubm63n+8XyUhKSgoAx/7gKRyuxBhHI4QQQgg7Ar5mPn/i6tB5vDv9IhlpvzXjcCXicEsyIoQQQvQnkZZYyAJWIYQQQsSUJCNCCCGEiClJRoQQQggRU5KMCCGEECKmJBkRQgghRExJMiKEEEKImJJkRAghhBAxJcmIEEIIIWJKkhEhhBBCxJQkI0IIIYSIKUlGhBBCCBFTkowIIYQQIqYkGRFCCCFETEkyIoQQQoiYkmRECCGEEDElyYgQQgghYkqSESGEEELElCQjQgghhIgpSUaEEEIIEVOSjAghhBAipiQZEUIIIURMSTIihBBCiJiSZEQIIYQQMSXJiBBCCCFiSpIRIYQQQsSUJCNCiJhTWPhpxMQb61C6pVAEaCZACwoV63AOmoUfP40ozFiHIgSOA/mi4uJi/H4/+fn5GIYRdmxZWRklJSUdJ3U4mDlz5oFMLYQYQLzaHna53qLE9R4BvQmAtMAE8nznMNR/LFoc/L0UoJli17sUud/Gq1cBkGgOJ893Djm+UzFwxTjCnqk2VrPL/QZVjmWgKXTlZJj/JPK955Js5cc6PHGI6lEy8vTTT3PffffR2tqKYRh4vV4eeughLr300m6/5sknn+S3v/0tEydODD2WkpLCokWLDjxqIUS/16AXsCLpTvxaE2hW6PE6Yyt1iQ+w2/cNU1t+hkb4P3h6k1erYVnS7bToZbDP1ZBmvZTNnr9R7lzErKZ7cJAYsxh7osD9Ets9/0JTOmjB52NpfsqcH1Lm/IjpzbczJDAnxlGKQ1GPkpFt27bxwQcfMGrUKAAeeeQRvve97zFt2jSmTJnS7ddNnTqVr7/++uAiFUIMGCZeVib9ulMiAoQ+rnB+SpKVx2jvJTGIMHhbZnXifbTq5aETd0jbx3XGVjYm/JVpLbfEIMKeqXR8xXbPvwBQ+73mSrNAwZrE33JU4yMkWjmxCFEcwnp0DfS+++4LJSIAN9xwA7qu8+WXX4b9Osuy2Lx5M4WFhSjV/++1CiEOTrnzU3xabedEZF8a7HK9hoW/z+LaV52xmXrHlk4n7g40iwrn57Rqu/susAO00/0KqDC/8jUAi2LXu30VkhAhB3VDdsOGDfh8PkaOHBl23NKlS5k/fz6HH3442dnZvPTSSwczrRCinyt3Lqbt7BdWQG+i2rGqt8PpUoXzUzRl5xaRRoXz816P52C0arupd2wJn/wRvEJS5vy4j6ISYq8DWsAK0NrayjXXXMPcuXM55ZRTuh03a9YsNm/ezPjx41FK8bvf/Y7LLruM0aNHc/jhh3f5NV6vF69376r6+vr6Aw1TCBGHfHpt51sf3Y3VYvPz79Pqbe2a0dDw6XV9ENGB68lr6NcaezESIbp2QFdG/H4/F198MVVVVbzyyivoeveHOeussxg/fjwAmqZx2223MWbMGF588cVuv+b+++8nLS0t9C8vL+9AwhRCxCmnSgUV+cpIcGxKL0fT3bzJaDau3igUTis2MdrlVMm2xzpUUi9GIkTXepyM+P1+LrnkEtatW8eiRYsYPnx4jyfNzs6muLi428/ffvvt1NXVhf4VFRX1eA4hRPwa5j8BbFx1MFQCGYEZvR9QF4b6j0VpdmpwWGT5j+n1eA5GghpKijk6YgKoKb3tvRGib/UoGQkEAlx66aWsWrWKxYsXk5/feU96aWkpq1atCn3c2tra4fPV1dWsXr2aSZMmdTuP2+0mNTW1wz8hxMAxzHdC8IpHuAWVSiPPew4G7r4LbB+DzCkkm6OC22C7o3SGBOaSqIb1XWAHaIT3W+FvjbV9Ks93Vt8EJMQ+epSMfPe732XhwoU88MADVFZWsmzZMpYtW0ZpaWlozOOPP86JJ54Y+vjEE0/kwQcfZNGiRSxYsIDTTjuNtLQ0brjhhqg9CSFE/+IggZnNv8LA3flkrzRQkBk4gtHe7msY9TYNjZlNd+JWgzsnTQpQGslWPpObfxKT+HpqqP84RrReGPxgv+cTfA90prT8nCQrt++DE4c8TfVgr+0xxxzTYWFpu2uvvZZrr70WCCYjL7zwQqio2e7du3nooYdYsmQJiYmJzJkzh5/85CekpNi/x1pfX09aWhon3vAiDnf/KC4khIisWStnl/s1Sl0fYWnB3y1JZj753nPI8Z8a04Jn7XxaPUWuNyh2vYtfbwDAbQ0mz3cWed6zMfDEOMKeqXR8zS73G9Q61gHBRCTLfzQjfBeQao6LcXRioAl4m1n86KXU1dWFvcvRo2QkViQZEWJgs/Dj0+rQceFUKbYWjvY1CxO/VgvouFRaXJSqPxh+GjG1Fpwqpd8lVKL/sJuMHPDWXiGEiBYdJx6VGeswwtIxgrdsBggnyT3aZSNEb5JkRIh+SKFo1SoxtVZcKh2X6t+LvAM00arvQcdJgjU0Lq86mHhp1SsBjQRrKDrOWIckxIAhyYgQ/YjCosS5kF3uN2g22rbHK40hgTmM9F5EmjkhtgH2UKO+i53uBVQ4Pw9to/VYQ8jznk2e7+y4OOF7tT3sdL9KqWshZtu6FoeVTK7vTEZ4L8CJXF0Q4mBJMiJEP6GwWJfwIBXOT+lQSl1TVDmWstuxlGnNtzA0EN81L9rVGOtYmXQ3ikCH/i+t2m62ep6hyrGMWc13xzQhadZLWZr0SwJaQ4cYA3ojO92vUOH8giOafotLDYpZjEIMBPF3LVQI0aVdrjeDiYhGp3oRwROlxbrEB2jRKmISX08EaGJV0r1Y+Ds3omt7fjWOdWzzPBeT+CCY/K1KvBf/folIiGbRqpezLuFPfR+cEAOMJCNC9AMKk13u18IP0oJrSYpd7/VJTAej1LUIk5bwRbg0RYnrXQK09F1g+6g21gRvhYVpLqc0i2rnSpr07itKCyEik2REiH6gwdiBV98TudGtZlHh+rRPYjoYFc7PsFMO3tS8VDtW935AXah0fmmva6/SqXR+1fsBCTGASTIiRD8QoNn+WM3+2FgJaI2RE6vQ2KbeDabbeZttd+2NVYxCDBSSjAjRD9heIKnAZdkcG0NuK8N2195YLQ51qUE2u/ZauKz0PohIiIFLkhEh+oEkK58kM9/GCVwjx3dqn8R0MLL9J4dfL9LGaaXGrGtvtu9Em117NYb5j+31eIQYyCQZEaIf0NAY5f12hK6rOg6VSI7/tL4L7ABl+Y/BbWWG74gLjPReiB6jCgSp1ljS/dMidO3VyPafNKAqswoRC5KMCNFPDPOfwOjWywA6L6xUOgYeZjXf3S+qsRq4mN30G5wqLdSlN6Tt5D/cO4983/kxia/dtJZfkmTlh7r0hrT9f3pgOhNbro9NcEIMIFL0TIh+ZLT3UtID0yhyvcVu5xKUFsBppTHcdzq5vjPjvr/LvpKsXI5s/Aslrvcpdr2LV6tCUwYZgRnk+c5mcOCwmDfMc6lUjmj8A2WujylyvUWTXgRopJpjyfOdzVD/8ehx0FlYiP5OkhEh+pl0cwrpLVNQLQqw0PrxydClUhnl/TajvN9GYQJ6zBOQ/Rm4yfWdSa7vTBQWwZtm8RWjEP2dJCNC9FPBE2L/TUT21x+Sqnhs4CfEQCDJiBCiVygsGozt+LUGnFYqKdbogz6Z+7UGGvQCFBbJVl6fLhz1atU06rvQ0Ei2RnW7NsfES72xFUvzk2ANJdHK6XKchUWF41NajUpc1mCy/SfEbLFuf1ejb6TWsR4dJ0N9x+JBFhT3N/KdL4SIKkWwjPtO16u0GpWhxxPMoYzwXshw/xk9vs3Rqu1hu+c5yp2forRA20TBbsVjWr9HspUfzafQQZNexDbPc+x2LAntZtKUwTD/8YxpvRyPGgIEC9Pt8LxAiWshpra3hP2gwGRGt36XDHMaEExCNngeosL1WYetwxvVX8jyH8nUllvR5QqMLSXOj9jqeYqA3hB6bKvnSZKsfKY3306SNTyG0Yme0JRSkTf7x1h9fT1paWmceMOLONyJsQ5HCNENhWKT5zFK3O8Fd6BoHT4JGuR6z2ZC6//YTkhatAqWJt+KX6vr1LBOUzo6LmY33UeaOS5qz6Ndvb6N5cn/i4Wvy7kdKoU5jX/AqVJZlnw7jXph5142SgcU05pvZUjgKL5J+hmNjoJuX58EczhHNf5VEpIIClwvs93zbPCD/b+VVPC239zGh0m28vo8NrFXwNvM4kcvpa6ujtTU7nf6yXe7ECJqKp1fBBMR6HyCaPu42P1W8CqDTesSH+wyEYFgozoTH6sT78PCToEy+yxMVifdh9lFItI+d0BrYG3iH9nqeabrRATaHlOsS/wjW9xPBhMR6Pb1aTFK2OJ5PKrPZaBppab7RKTtMYXJiqQ7+zQuceAkGRFCRM0u1+uhOiHdUjpF7jdtHa9BL6DOsbHLZCBEs/Dp1VQ5vulBpJFVOZa2NScM37W33rGZUteHYce1nxxLXe9F7g+ooNT18YEFfYjYmvBU8H/CXVzTwKfVUKdv6ZOYxMGRZEQIERUBmqhzbA5/UgbQLGocazHxRjxmlXNp5OSG4BqO3c4oJyPOZTa79mp717GEH4il+210XgZLa6VZK7MT5iGp2rHK9tgi11u9F4iIGklGhBBRYWqtPRwfORkx8dpuVmfZSG56wqTVVtde2+2He1iaxK81RB50iLKwkdS16en3pYgNSUaEEFHhVKnoymlrrK5cOFRSxHEeldlWaCw8DS20qyVaPCrT5vkucnxA8AqP3e0CChKsYTYHH3pcKtXea6lBopXd6/GIgyfJiBAiKnScDPOfGPHWhqYMcnyn2iqjPtR3HJqNCgRKs8j2nWI7VjtyfKeEX6vSRsOB2xoc+eSoWbitTFtrRhKsbFzEf4+hWMn1nWXvyoiCUd5Lej0ecfAkGRFCRM0I7/nBwmaqmzOF0tAwyPeda+t4TpLJ950T/gSudIb450a91kiSlUeW7+jwa1YU5PnOZkzr5WFPjprSSTLzmNxyU+jrujsewLjWqw8o5kNFnu8cDJUQMbHLDByBAykH0R9IMiKEiJokK48ZTXeh4+x4Em/remvgZmbTr7utStqVsa3fI9t/MrBft+K246cHpjCl+eZohN/JlJafkhGY1jb33ufT/v/D/CcytvVKcvynMKb1e13EGMxQEqxsZjX9hsHmLMa3/KDtc+zXrTj4nzHey8kKzO2V5zNQ6OjMaXwQHXfwgS5ex5TAWKY339HnsYkDI0XPhBBR59WqKXEtpNy5GJ/WgEulku0/iRzfabhVeo+Pp1DUGOsodr1DrWM9CkWKOYpc33yGBI7o1b42CpMqx3KKXG/TYOxAQyPNnESedz7p5vQOC2wb9AKKXe9Q5VyGhY8EK5tc35kM9R+L0X7iBBr0nWz1PEWNYx2KABoGgwKTGNd6NanW2F57LgNNgGa2e/5NqfOjUNVbj5XFKO/FDPefFuPoBNgveibJiBBCCCF6hVRgFUIIIUS/II3yhBD9RrNeSp2xGYVFijmKFGt0l+P8NFLtWEVAa8GjBpMemGFr947ofxSKemMzTXoJGgbpgSlR3+Ytep8kI0KIuNeo72Kz53FqnGs6PJ4SGMP41mtIN6cCEKCFrZ6nKXV9hNL8oXEuaxAjvReT5zurxx2DRfyqcixli+dpmo3ivQ8qjczA4UxouZYENTR2wYkekds0Qoi41qAXsDT5Fmoc6zp/ztjBiqQ72eNYgUkry5PuoMS1sEMiAuDTa9mS8DjbPM/0UdSit5U7P2FV4v+jWS/p+AlNUeVYzjfJv6BFK49NcKLHJBkRQsQthWJ94oPBPjZddsRVKBRrEx5gu/s/NBg7wvbGKXT/l1pjQy9GLPqCX2tgfcLDwQ+0LvZgaBZ+rYENCX/t28DEAZNkRAgRt+qMzTQahRE64ioCeiPFrrciNunTlCGN0waAUudHKAIRuvZa1DhX06yX9llc4sBJMiKEiFvVjlUdio11S+lYeuRGeUoz2eNcGYXIRCwFu/baqEqhoNqxurfDEVEgyYgQIm5ZBLDdhKRHxxT9mal5bXbt1eT97ickGRFCxK1EKxuFaWOkZi8fURqJ0g2330syc+1dMdOUvN/9hCQjQoi4leU/ukMZ9e5ZpAem2zhBKXJ9Z0YjNBFDOf7TI3dUVuCy0skIzO6boMRBkWRECBG3HCQwqvXS8IOURo7/NMZ5ryR4haTr6/ea0kmwhjHMd1L0AxV9Ks0cR6Z/TviOyhqMbb1Cit31E5KMCCHi2gjftxjZejGwf+fc4ElmmP8EJrZcT6o5jpnNd6HjCiYk7bdt2r4mwcrmsKb7cJDQp/GL3jGt+Rdktl316NTNWWmMa7mGHP8pMYpO9JRUYBVCxDUNjbHey8n2n0ix6z1qjQ0ozSTVHMNw35mkmeNCYwcHZnNc/dOUuT6iwvkVptaExxpCtu9UhgTmoMuvvAHDwMOM5ruoNTZQ4nqPRmMXunKQEZjOcN88qb7az8hPphCiX0iycpnQ+oOI45wkk+87j3zfeX0QlYglDY10cwrpLVNiHYo4SHKbRgghhBAxJVdGhBD9gkJRa6wPdu3VTJLNUWQGZqP1wQJFhckexwoajAI0dFIDE0g3p3bZdK9BL6DasQpT85FoDWOI/8gudwT5tFoqnV/h0+pwqhSy/EfhVhkHEaOizthIrbERpZkkmflkBo7otws4fVo9lc4v8Wm1OFUyQ/xH4lGZsQ5L9BJJRoQQca/GWM/GhEdoNkpA6WiA0ixcVgYTWv6HoYFjem3uSsdXbE54HK++B03pwXWxHosEM4fJLTeGOgY36SVsSHiIOsdmUBoaOkozMVQio1u/Q77vXDQ0TLxsTvg7Zc6PUajgOCw2e/7BMP/xTGy5HgeJPYqxztjMhoSHaTKK2l4fDaWZuKxBjG+9hmH+E6L/wvQSCz+bPU9Q6lqIwurw+gz1H8uklhtwkBTrMEWUyW0aIURcqzHWsyLpzr09RjQrVGPCp1WzNul3lDkX98rc5c5PWZN4P15tDxBMgNr737ToZaxIuotqYy3NehnLkm6lztjaFqNCacFibabWzNaEJ9nhfh4LP6uSfhPsraJZe8dpCjSLcuenrEj6VbAxoE11xmaWJf0vTXpx29xWaG6fVsu6xD9S4vwgSq9I77IwWZV4HyWu90Kvy97XR1Hh/ILlSXdg0hrrUEWUSTIihIhbCosNCQ+hsLrpzgoo2JjwVwK0RHVuk1Y2Jjyyd55Oc6u2+P7MZs8/CGhNYRv1FXj+w073y9QYa7t+LgCaRb2xlWLXu7ZiVCg2JDzc1jSu+9dnU8Lf8NNo65ixVO78hGrnirCvT4NRwC73G30bmOh1kowIIeJWtbGGFqOi+5MTgAYWXspdi6M6d7nzk+Bf4GE7wypajUr2OJZFrAiqKZ1drreI3FRFUeR6C2Wjvn2dsTF4aybC66MIUOb6KOLxYq3I9Va3Rev2UhS53rHZJkD0F5KMCCHiVq1jQ8eCVt3SqTU2Rn9uO78ilW6raZvSLAJ6Q/jEAUCDVqMSn1YbOUZjY/gqpPuOdUT39Yk2iwANjm22Xh+fXk1r260zMTBIMiKEiGMR+o/sI9p/KSusHvQCjj5l47krzexyR09XR4v/Kwk9e7Xb18WIgUGSESFE3Eqy8m2edBTJ1oioz23rBBmpYVs7paErp61DOqwkXCot4rgk0+7ro5NkRvf1iTYdJx4zy9brYygPbmtw7wcl+owkI0KIuJXlPwqHlWzjBKWR4zs1qnPn+E61ddXBYSWREhgTea2DphjmO5GIv3aVTq5vnq3S9ZmBI3BZg2y8PhbDfadHPF6s5fnOItI9L03p5PhOx8DVN0GJPiHJiBAibuk4Gdf6/fDnJwUjvRceVMGwrrhVOiO93444blzr9xnfek0wcekuIVEaQ/xzGeu9Eo/K7NDwb1+a0nGpNPJ859qKUcdgfOs1oV0zXc8N+b7zSFBZto4ZS8N9Z5BgDQv7+jhUCiO8F/RxZKK3STIihIhrw/2nMaHlejTlaOvG29aRt60760jvhYzxfrdX5h7tvSzYMVhpbfMRikFTDia0XMtw/+mkm1OZ0XwXBgn7xLa3y/BQ/7FMbf4FLpXK4Y33k2jlAW3dZvcZl2AN4/Cm+3GrdNsxDvOfwKTmm9BwdvH6BBORca1XRfeF6SUOEjms6f9ItkYCnV8fjzWEw5vux6PkFs1AoymlYrlGy5b6+nrS0tI48YYXcbh7VplQCDEw+LR6ypwfUefYjMIi2RzJcN9peNSQXp+7VauixLWQRmMnoJEWmECO/1RcKrXDOJNWyp2fhsrBJ1jDGO47nWQrv8M4hUW1sYZy1yf4tQacKpmh/uMYHJhlbwdPF/w0Uur6iDrHRhQWSWY+w32n9cvutQpFjbGWctcn+LQ6HCqJof5j+6z8v4iegLeZxY9eSl1dHampqd2Ok2RECCGEEL3CbjIit2mEEEIIEVPSKE+IXubVaihzLqJFL8fAzeDALDICMztdjjfxUen8gjpjC6BIMUczzH88Bp7YBN5PWViUON+jzPUhAc2L2xrE6NbvkG5NjXVoohf4tQbKnItp1ovRcJAemEpmYM5BdSuuN7ZS4fiSgNaESw1imP8EkqzhUYxa7K9HyYjP5+Pf//43n3zyCX6/nyOOOILrrruOhISEsF+3detW/vKXv1BYWMi4ceP42c9+xvDh8saKgc3CZKvnKYpcb0Nbd1aAXe7XSTCHMq35l6RaY4FgQ7ZNnscI6E2hiqMKky0JTzCu5Wpy/fNi9TT6lT3GKlYn/T8szR/aXdKsF7E8eS1uK4u5jQ/iovtLxaL/UCgK3P+hwP0SCjP081XkfhOXlc7U5pvJMGf06JitWhVrEn9LvWPLPpV/FQWeF8n0z2Fq88097qgs7OnRbZqjjz6azz//nBNPPJF58+bx9NNPc8wxx9DS0n2Dqq1btzJnzhz27NnDpZdeyqZNm5gzZw6VlZUHHbwQ8Uqh2Jjwl2CvjX26s7YXqGrRd7Ms+XYa9Z1UOD9nXeIDwUZrsHecBqbWyqbER203TjuU1embWZn0ayz8wQe0jv+8eiVfplyPhS+GUYpo2e5+jh2e51FaoNPPl0+rZWXS3dQY62wfz6fVsSz5lzQY24C9P4ftPYeqHMtYkfSrvd9fIqp6tIC1qqqKzMzM0MdlZWUMHz6cBQsWcOGFF3b5NVdccQWbNm1iyZIlaJqG3+9n7NixXHrppfzud7+zNa8sYBX9Ta2xiWXJt4YfpHQyAtNpMLbj1xrC1tLQlYvj65+Vv8rC+CL5elr00oh9YvJbz2e89+q+CUr0ihatnC9Sro1Qf0YjycrjyMa/2Cpet9XzNIWu18NX1FUwqeVGhvvjv4BcvOiVBaz7JiIA6enpOBwOGhu7b039/vvvc95556FpwW8Gp9PJOeecw3vvvdeTqYXoV4pd73RbuClEs6h2rMKvh09EACx8Ue9KO5C0sttWIoKCUtcHfRKT6D3FrveJePrSFE3GLuqMzRGPZ+GnxLnQRml/jSL3W7bjFPYd1ALWhx9+GF3XOeWUU7r8fHNzM5WVleTl5XV4PDc3l507d3Z7XK/Xi9frDX1cX19/MGEK0efqjS0RW8oDbZUz9Yi/BDUM6tsuH4vO9jhX2eqciwYBmno7HNHL6o2t9noCKY0GYxuDzIlhh7XolQR0G98XmqJR39m2RkXqnUTTAW/tfe+997jjjjt4+OGHyc3N7XKMzxe8N7v/AtfExMTQ57py//33k5aWFvq3fzIjhBBCiIHjgJKRjz/+mG9961vce++9XHvttd2OS05OxuFwUF1d3eHxPXv2kJ7efbnj22+/nbq6utC/oqKiAwlTiJhJNSdEvk0DwR0fNv7CU5ikmmMPPrABarB/pr0O9AocKqm3wxG9LM0cF7yiGImmSLHxc5NgZeGwbHxfKI1ka6RcFekFPU5GFi1axDnnnMNdd93FL3/5y7BjHQ4HU6ZMYeXKlR0eX7lyJTNmdL/lyu12k5qa2uGfEP1Jnu/MyLdplE5GYCZOKyXiiVTH1dbxVXTFwxASrJzICYlGv+heK8Ib7jsDiPTzpZFkjiDNnBDxeDrO4KLUiAmOIs97tu04hX09SkY++eQTzj77bO68805uv/32Lsf861//4qKLLgp9fOWVV/LSSy9RUFAAwIoVK3j//fe58sorDyJsIeJbqjmBbN8pYbq46hi4GN96DRNbfxi+6yowvvUHspMmgqnNNwNa2O61Diul15rqib6ToIaF76isNDR0JrZcb2snDcAI74VhOyqj9ODPtf+kA4hYRNKjBawXXHABuq7zySef8Mknn4Qev/zyy7n88ssB2LZtGx9++GHoczfddBPLli1j2rRpTJkyhbVr13Lddddx6aWXRukpCBF/NDQmtdyIQyV2KnqmNJMEawjTmm8j2RpBsjUC1azY5HmUgNax6JmBR4qe2ZRmjWdW0z2sTro3WEtkv6TEY2Uxp/FBdFyxCVBE1Rjv5eg4OxU9U5qJSw1iavPNpJtTbB8v2FH5t6xJ/B31js0dip4pzWJI4AimNP8MHWcvPBvRozojH3zwAaZpdnp87NixjB0bvC+3bds2CgsLO+2w2b59O7t27WLs2LE9XpAqdUZEf9ZeDr5Vr0DHxeDAbDICM7otB19vbEWhSDFHSTn4A2BhUep8n1LXh5haKy4rva0cvP0Tk+g/guXgP2krB29ErRx8peNL/FIO/qBJ114hhBBCxJR07RVCCCFEvyBde4XoZwI0s83zLLsdS7E0Hy6VRr73Aob7uy4+GO8a9V2Uuj6gWS9FVy4GB2YyzH9Cp9tTPq2eUueH1Dk2AxbJ5kiG+07Ho4bEJnAR4tX2UOL6gAZjBwCp5jhyfKfiVt2XcBBiX3KbRoh+pNj5LpsS/gaa6rhAUwOHlczchj+RwNCYxdcTJj42JPyZCtdnaEpHYdG+rcggganNP2dIYA4ARa532OJ5AkX7mjVF8MKuYoT3W4z1fq/TGhzR+4Kdc19kh/tFgu9d+3sYXMQ9tvVKRvguiGWIIsbkNo0QA0yF43M2JTxGKAvZtystENAa+Tr1JgK0xihC+xSKdYkPUOH8PPixZrU9FxXsVkwrqxP/j2pjDSXOD9ic8LdQd9b2Me3dkAvdr7Dd/a+YPp9D1U73AnZ4Xmh7T/Z9D4M7ULYmPM0ul/RyEZFJMiJEP7Ep4fHg/3RXNkEDU2tluyf+T8x1xkZ2O78Onri6oilAsSXhSbZ6ngpfzEyDne5X8Wp7eiNU0Q2fVt92RSS87Z5nMftBgixiS5IRIfqBen0bfq3WXlda54cRBsVeses9G12NFY1GQbCBWcS6VYoSV/w/74Gk3Llon9tm3TNppdz5aR9EJPozSUaE6AdqHOtsd6U1teZej+dgNRg77HU1hu6r2Hag0aTvOqiYRM80GrtsrdPRMGg05L0R4UkyIkQ/sLcapK3RvRZHtOi9sJFPmpf1rZ58Tx5MATJxaJBkRIh+IMt/lO2utG4ro9fjOVjpgWk96Gps54lbDDInH2xYogcGmZNQWuTbNEozSQtM6oOIRH8myYgQ/YCHTBKtPFtdafN95/dFSAdluG+era7GgwOzSTCHhr9Vo0DHzTDfCdENUoSV5T8Gh5UU/ntSabisdDIDR/RZXKJ/kmREiH5ievMvAT1sV9oEM4c83zl9GdYBSbKGM7r1O90PUDoOlcCE1muZ3PLT4C2YrhISBWgwqeVHOEjotXhFZwYuJrf8mGCn5K7em+C+8yktP5XbNCIiSUaE6CeSrXzmND6AQ6UEH1Ad/w0yp3BU4yPo/eTHepT3Usa3/E/wr2va1iC03bpJM8dxRNMDJFo5pJtTOKzpXhKtnOAXKj10i8elMpjWdBvZ/hNj8RQOeVmBo5jZfCcelQkQfF/a3psEK4vZTfcwODArliGKfkIqsArRD+0xVlLsegdTa8VjZTG69TI8DI51WAfExMdu59e06OXoyklGYCYp1qhO4xSKWmM9dcZmlGaRYo5kcGC2LFyNAwqLaseqveXgA+NIN6ej9YPF1KJ32a3AKr1phOiHBpuzGNwyMP7iNHAxzH98xHEaGunmVNLNqX0QlegJjeD6nsGB2bEORfRT/eN6rhBCCCEGLLkyIsQAFTADbK/eRLnnQwKecnR/ChmNJzI+7TAS3Hs74ioU9cYWil3v0mAUoCmddHMqub4z967TaNOqVVHiep89jhVYmp8kM5fhvjM6XZI38VLu/IRy5yf49Dpc1iCy/Scy1H8cBu7QOIsAha7XKHa9jU+vR8Mg1RzDuJZrSLPGdpi7SS+m2PUutcaGtts0Y8j1nUmaOa7DOD+NlLo+otL5JQGtCY81hBzfqQwJzD3g+ib1xlaKXe9Sb2xHQ2NQYAq5vjNJsnIP6Hix5KeRMtciKp1f4Nca8ViZZPtPIct/JDrO0DgLkyrHUkpdH9CqV2KoBIb455LjPw2X6v5yuxAHQtaMCDEA1TTWsiLxblT6DrAM0M22XTga+BMZU/x/jBo8Cgs/6xL+RKXrczRlhOpGaEpHaRajWi9ltPc7aGgUu95lk+dvwWO0bctt/5p0/zRmNN+Bg0Tq9W2sTLobv1bfNla17axQuNQgZjXdQ4o1ihYq+DrlJ5h6c2hXDBDaLTTMdxJTW3+GQrHd/S92ehYEF0eG5g7GOMx3ApNbfoyOkyrHctYk/hYLX/BAGqGvSTSHM7vpN3jUENuvo0WAjQl/ocy1qMvXZ0TrhYz1XtFv1kZUG6tZnXQfJl72f30SzGHMar6HRCsbr1bNyqRf02gU7n3N275/dBxMbb6FrMCRsX0yol+Qrr1CHKJafV6WJ9+JSm0rwa23FaZq76jqaGF7/q2U1VWwMeERKp1fAHQoYNVeA6TA8yK7XG9Q7vws2DG4vTtraFzwa2oc61mdeB/NWgUrku7CrzXunQ9CnXZ9Wj3Lk+6gWSsPJiLtpev3PZe3/X+5exFb3c9Q6Ho1mIjAfnMH/7/c+SmbEv5GnbGV1Yn3BhOR9s6++3xNi17G8qQ7CNBi+7Xc7HmcMufibl+fQs8r7HS/bPt4sdSgF7Ay6TfBRKSL16dVr2R50h20UsOKpLto0os6fL79/bQIsCbxt9QY6/v8OYiBS5IRIQaYzbVLIbUIjEDXA3QLtABbHM9R5loUscLpDs/zbHX/M0LnXIsa51q2ep4ioLV0SBr2HxfQmtmQ+FDwikiYDsQoKHS/xg5PhM6wmqLU+QHb3M8Ev6ib56M0ixa9nHLXovDHa9OiVVDiei/i61PgfokA8d8PqMD9EmCGfX282h62JjxBk1HUfVG6tq/f4Xm+lyIVhyJJRoQYYKoG/zfyIMPEn/2ZrSZ0ptaC16i00TFYZ7dzSfeJSDvNotbYaKuaLJqFhTdijKBT41hro/meRpHrXRvHg1LXR9j5FWnhpcL5ua1jxopPq6fS+ZWN10ex2/l15O8LzaLGsZZmrTxqMYpDmyQjQgwwKm2nvX4uWnvFtEgHtPlrQrMiJyL7zm17mYWdgTaPpyladHsn0Ba9zNY4DcP22Fhp1Xbbe280sPDb7AcELUZ8P2/Rf0gyIsRAY7p6MNjmiT7u2V9AandHjYbD5lEV2j67UOKR3kvx6Sq+n7foPyQZEWKAcZUfB6aNE643BVuJhqZsdgzWcFlpkS/xKw1DeWx3IbZ3lcdCt9yRhymdwX57xeIGB2bauK0RXGsxODDT1jFjJdEajstKj/iaa0rHrTKDpfkjMJSHVHNsxHFC2CHJiBADzIjA/O4Xr7azdAYVfxeDxLDJg6Z0UgJjGBI4MtQPpksqWCE1v/Vb2Djjkd96fvgxbcd0WYNJNkeEv1WkNBxWMrm+syMmQkqzyPOdHXluIMt/NE4rJULHYJ0kM580c5KtY8aKjkGe7ywiXUFSmsXI1os67BzqeqBOju90DDzhxwlhkyQjQgwweRkjSN5+VfcDLAOjcjbTB53GtOaf033XVR0dD1NafsaElutwqkFdJyRtNUImttzACP+5DAnM7f4EriDLdzSjfZcytL0EfFe5iwLQmdX8K6Y2/zxYKK3LuTU0NKa1/IIx3ktIMcd0My74nxHeCxhkTu46tv3oOJnafAsaerevj4GLqc0/7xd1RvK955NmTgib2OV6zyLXf+bejspdvjc6SVYeY1ov651AxSFJip4JMQBZSrF29xfsznkaknbv/YTfQ9Kub3NY2vm4nMH7/dXGGrYmPBVqctYuwz+TCa3XhqqMtmp72JzwOLsdX3e4dZJo5jC29QqyAkcH5ybADveLFLnfxNT21vQwVCL53nMZ5b0k1FJ+i/sJitxvd/xLXIHbymRm812hhnmN+i42e/5BjXN1hxhTAmMZ33p1qF9NgBa2ep6mzPURluYPjXNZgxjlvZhc31k9ThxqjQ1s9jxBg2Nbh8fT/dOY0HotydaIHh0vlky8bPP8kxLXQizNF3rcaaUy0nsR+b7zQq9PifN9dnhexKvvCY3TlINh/hMZ33I1TpL7PH7R/9gteibJiBADWMA0KardSYtVh4ME8tNG43F1vbaiXt9Go7ELDZ00cwKJVnaX41q1PdQ61mERINHKJs2c1OUJ3sRLtWMVfq0Bp0olIzCjQyn4dhYWxc53aDQK0HEz3HcqKdboLudu1kupM7agCHbt7W6cn0aqHasxtRbcajDpgemhBOhANeg7aDB2oqGRao4nyRp+UMeLpQDN7HGswtSacal0MgIzulzYqzCpMdbRqldh4CY9MF1KwYsekWRECCGEEDEl5eCFEEII0S9I115xUAK0UO76hBLX+7Rqlei4GOKfS65vPslWfqzDE+KgKQuqCoZStHokDZVpaJoiLbuGvJkFpOftQYv/tatC9LqceV33fPI1tcCjkb9ekhFxwJr1MlYk3UWrVkmoOytQ4nqPYtc7jG/9H/J958Q2SCEOguk3WPXGEdTsGtLWuTZ4Mblqx1B2b89m2MQiJp+xGl2P+7vdQvSam35cxzkpXZcTqG9oIUJ3KUCSEXGATLysSLwLr1bVVrpg7y/j9kJRWxL+gdsazNC2XRZC9Dfr35tJTdHg4Af7bIlVbf9fvikXV6KP8SdsiEV4QvSqnHktPHNY3zSBlGREHJBy56e06hGapymNHZ4XyGo8ql/UYRBiX017kqnclhNhlEbRqlGMnLMVV4I/wlgh4seC3zZGHJPWYKdJZXRIMiIOSKlrIaE+793RFE1GIY16QbdbMIWIV6Ub8tA0K3QVpDvK0qjYkkPejMI+ikyI7t3047qIY85JCUBDHwTTA5KMiAPSou+23dmzVa+SZET0O60NHlvtczRd0Vqf0OvxiENXd4tD9/fnE03SGiK0gohTkoyIA2Io+z0pDBW5gZkQ8cZwmmgaRKrEpFRwrBA9ZSfJCCYYNm+XxNnVjp6QZEQckKzAkRTqrwV3GIRhqATSzIl9E5QQUZQ5uoLSdTZKvSudIaMrej8gMaAs+G2jvSSjHycYPSHJiDggud55FLpeCzVJ65LSyPXN67IEuBDxLnNUJe7kFrxN3TTpA9AsUofWkpJV37fBibh104/rOD4ncuuBvlwc2h9IMiIOSIIaxuSWm9iQ8OdgR9P9148onTRzPKOls6fop3RdMePcpSxfcDRWgE4LWTXNwpHgZ9r8FTGKUPSlnHktXDjeF3FccHFo/1y3EUuSjIgDluM/BbdKZ7v7Beodm0OPO6wkcn3zGeW9WK6KiH4tdWgdc77zGdu+nMju7cOCiTeg6RbDJpQw5phNeFJaYxylOFB2Ewyg26JeIjokGREHZXBgNoMDs2nSi2nVd2MoN6nmOHScsQ5NiKhIGtzIjHOW4W1001SdApoiZUg9To/UFYlXdhaGXjjeJwlGHJFkRERFkpVLkpUb6zCE6DXuZC/uZLnPH+/6smqoiB5JRoQQIVbb5ig9Bv28lUWwxZEU6+0TCgVYaERebBkPcua18OcTI2+hloWh/ZMkI0Ic4lobXWxeNI2qHcNQVjATMJwBsicXM/74dei9+FvC1+KiZM0IiteMwNuYgKZZpOdXkT+zgMGjKiUx6QV1xmaKXG9R6fwSS/PjsJLJ8Z9CnvcsEtSwmMRkZwdKWoP3kNnmeiiSZESIQ1jD7mS+ef54lNV+KSR49jf9DopXj6JiSw7HfP9jHO7o31tvqk5i+YKj8bW4Q10FlNKp2ZVJdWEWw6ftZOIpayUhiaJdrjfYkvAEmjJQWvAqQ0BvZJfrTYpd7zKz6S4yzBlRmctOWXKA43OMYNVQ2YFySJNkRIhDlGXBsv8c15aI7H/GD37sb3HxzYvHcPSVn0R1bjOgs+LVI/G3uEI7VNq1b6EtWTuSxEFNjDh8R1TnPlRVOZazJeEJgFAiEqJZWMrPqqT/x1ENj5Kgsro9jp3FoT1asyFJiECSESEOWSVr8zH9kX4FaDRXp9BUnURSRlPU5q7cmo23ITHiuJ3LxpI3qwDdsNcHSXRvp3tBsHhbd1WTNYWlAhS73mWc98ouh9iuGipED0kyIsQhqnjVKMKX0N1rx9fjmTZ/ZdTmLtuQGzwpRuiI629xU12USebI3VGb+1DUqlVR69gQeaBm0ZTxAQt+cmGnT8maDdGbJBkR4hDl97psj/U22m+MaO94CRETkXa+Jimcd6Dad6CsLi/jxKftfU11S4Nc/RB9TpIRIQ5RhiMANivkRrvAl8Pjw+5VGYcUF+vA7sJQaC9NDsMD9n/Vpzijm3gKYYckI0IcooZNLKFgyQRbY/MP2x7VuYeOL6WuNCPiOMMZICOvKqpzxyv77eR7vuBzdOoQpmQMZ0N1aVt9ka45NJ2Lx83p8fGFOFiSjAhxiBo1Zws7vxmHUhphWi/jSmwlfXhNVOfOmVzM9i8nYvqNMLdrFMOnF+JwRS501d/d9OM6e6XJD3DNhqZp/HzWPK7+6Mmw40yluHHaKQc2iRAHQZIRIQ5RugOmnbOUNW/MoetbJgpNtzj8ki+jPrfDHWDmeUtZ+epclGXt1xE3+Jd7el4VY4/eFPW5+1L7mo14WINx5cRjWFqxg8fWLUJHw9rnComh6VhK8Y+Tr2JaZl4MoxSHKk0pFfd75urr60lLS+PEG17E4Y68HVAIYV9dWRobPphJ054U9iYkikHD9zD1zJW92pW2sSqZncvGUrF5eKjwmielmbxZBeTNjO8tvbarhsYRpRT/3vIVf1z5HqurioDgO35G/jR+edh8Thg+MbYBigGnvqGFQaNuoK6ujtTU1G7HSTIihADA1+ykriwdzbAYlFONw9VNPYpeEPA6aG30oBsWCWnNMam62pN28sGqofGVaPRUUcMe6n2tZCWmMCSh+5OEEAfDbjIit2mEEAC4Ev0MGVMZk7kd7gDJ7sZeO75UDe0sL2VwrEMQIkSSESH2oxT4ml0oS8eV6I3KrQIzoONvcaEbFs4EX5/+5W/6DfwtLgxXIOwW3YBp0uRtQQeSEhLRtYNv3RvwOgh4nTjc/rD9bQIBkyZfE4buINGdgN7NC6RUsO6IQsOV6CV3fvcJhLfehb/VwcNnNZNjHXxL+UBAo2J3ArqmGJrVEpXOxg0NDmrq3AxK85GaIluYxaHrgJKR3bt3s379eiZNmsTQoUPDji0sLKSgoKDjpA4Hxx577IFMLUSvsUyNkrUj2LVyFC21yUBwa2nO1F2MOGz7Aa2daK5JonD5GMo25GKZwfUFyZn15M3aQc6Uol5NSuor0ihcNobKrdmhBaJpOXsYcdgOssaWh8Y1tDSxsfFj6ocvgIzati/OI7PiYianH43L6ezx3NW7BlO4bCx7CocQXJWgyBxVQf5hO8jI27N3XGMtm70Lacp7BZzBqxfanokMrb6IyZmHoevB18z0GxStGknRqlHBgmlAcpqXOdpmfnb9eoZkBm+ZKAWvvDmChx6bzJdLg7+b3jAsvn1eAb+4cT2zplf3+LlU7XHz0N8n87enJ1BdE6zBMTy7iR/9YBM3XrOR5OSeXyX55Iuh/PGvU3nng1wspaFpitNOLOUXN67j1BPKenw8Ifq7Hq0Z2bhxI/feey+LFy+mtLSU5557jssvvzzs19x77738/ve/Z/bs2aHHkpOTeeutt2wHKWtGRG8zAzqrXptLTVH7peu9WYKmWRiuAId9+0tShtjfW1lbksGKV+eiTL3L3SJZ48qYNn85UbgA0UnFlmzWvRP8meswd1sJ9hGHbWPscRupaaplRdqtkLgb9H220CpA6ehV0zlauxOPy3611sIVo9n6yZTO5d7bPp5w0lryZu6kpKaUjdm3gKMFjH1O6JYGGgypPYVVv7kSf6uHs087hQ3rBmFZsO97YxgW2UNb+PStdxmR18jP7pjDw49PxtAtTGvv3A5HcP3LS08t5vz5u2w/l13FSRx31pmUlidimh3fQ11XTJ1Uy6LX3yN9kL21JgCPPTWBG395JIauCOxzTMOwME2d39+9lF/cuN728YSIZ3bXjPTo1+DWrVs588wz2bGjZ100J0+ezOLFi0P/epKICNEXtn42iZriwQRPdJ27yAZ8Dlb9dy5WwN6PjL/VycrX5mB1SkQIzVG5NZuCpeOiEX4HTTVJrHt3Nkppnedu+7hw+VhKN2azMuFeSNgvEWkPUbewMteytOlZ23NXFw0OJiL7zLX/3JsXTeOIGbBx6O3gbOqYiADoCjTF7kEf8e37F/Ozqw9n4/o0LKvze2OaOuUVCZx3+ck8/fxYHn58cvBxq+PcgYCOaWpccs0J7NiZbOu5KAXnXX4yZZ0SEQANy9JZv2kQ37/pGFvHA/jymyHc+MsjUUrrkIi0PxeAW+8+gg8/ybZ9TCEGgh4lI+eeey6XX345bnfPekX4/X6WL1/O+vXr8fns/wUhRF/wtzooXTuiUyv7DpSOtymBym3DbB2zbEMups8Rof+KRtGK0VhmdO/VFK8a2XbxJdxxFdtKS1EZW8AIU1RMN/Hmv0Vdc9eLS3PmtXDTj+tC//Q9+ehG+F04DsPiuVe3gKcW9DBjNcWX2kv89/2sLpKBvQKmztoNGfzmDzPRtO4v9CqlYVkaf3vGXtXZz74ayup1gzslDfsyTZ0338u3neA89LfJGHr4i9GGYfGnR6fYOp4QA0UvXCDubPXq1Vx99dXMnz+fYcOG8cwzz4Qd7/V6qa+v7/BPiN5SVTA0tJ4jLE1RsSXH1jHLNw23Nc7f6mq7IhM95ZuGd3E1Zn8a/lGvg2lv2VhF0kpy5rV0+HfTj+t45rBmzkkJcE5KgJN1WLF4OFaYkzcEk4fijDcIU5V8L3cDjPkg4jDDsNhVnNxWTbZ7pqnz7wVjbEwML70+MnR7JxxdU7z8xsiI43w+nf++PSJsctMe43sfD6ehQfYXiENHr3+3z507l507d5KbmwvAI488wjXXXMP48eM5+uiju/ya+++/n3vuuae3QxMCCN5SsdW0TWn4mu1dFfS3uCIfLzS//fUYdgR8NhecJlSDbmPxpTIY6amPuPW1rt6JFSEZCEmsCt6OsUFLrI6Yt1g9KIlSU2fv9a6pcaNsHFc3FHtqIn9fNDY5wl7h2ZdSGrX1LlLslIgXYgDo9Ssjp512WigRAbjxxhuZMGECL7/8crdfc/vtt1NXVxf6V1RU1NthikOYK9GHrcRBs3Al2St05Uz0Yu9Pf3AlRLd4lsNtc4to8xCwbPw9opkMTYp8G2JQmg893G2XfTUOBcvmiblpSMQxPdlmOzjd3us9eHCrrcXFpqkxZHDknVYpyX6cNq60QPBqS3qa3NIWh44+uU2zvyFDhlBaWtrt591uN6mpqR3+CdFbMkdVoDvsXCHQGTaxxNYxsycX2xrnSmxlUG7Pt5u223/Nxk0/ruOsy7dHXLeh6xZ51ed2XjzaFaVzx7zIC20TE03OO3MXjkhrRhwWo+rOC+6uiUBrSYcdp0YcZ5o6Y0bVR0yGDMPiyu9si3g8gMsuLCBgY8GyUhqXXFAQcZzTqbj4/IKIt34chsU583Yd0JZhIfqrqN+m2blzJ8XFxaE6Ig0NDaSkpIQ+X1lZycqVKzn99NOjPbUQB8ThMsmbsZPC5WPo9gqJZuFJaWXI6PKuP7+f7Ikl7PhyAn6vM2xX2hGHbUfv5nbFTT+uizhPV51ep16/gXf/OQGfpbpdQ2FZOg9cnchVa6bSkrqp+6TE0hnbcC7TR9i7PXXzDRt47Z0RYUYoTFPjse/nMf+LLKyEPZ138oSGapzkvJRh55Xwn/+O6rRDpp3DsJg5rZqf/nA9l193Qrcza5rC5bS47srNtp7L3MN2M/ewSpavyux2nYehW1x03k7yhtsrqvbT6zfwwqujCHdb0LQ0br5BtvaKQ0uPkpGamhpWr14d+njjxo0sXryY7OxsJkwIrlB/5plneOihh6itrQXglFNO4eyzz2bWrFlUVVXxwAMPkJ2dzQ033BC9ZyHEQRpzzCaaapKp2jEMNNVxZ41m4UrwMetbX9uuxupwB5h1wRJWvHoUAR8dEhJNt1CWztgzCjjyp+u7vBUQ7PR6YH8ZjxrRyCv//JhvXXkylql1WcvinttW8O3zdpE/+Ycc9/LvCSQVBa9UtD/ttlofabVz+PKnZ9qe+5i5lfztj19y/c+P7lRHw+GwsCyNJ/78BacfW8+Lzv/lkk9/g3LXdUxILAN0kxG1Z/PObUfg837FzqJkvlqaBbBPgqXQdRiZ38hrz31ETnYLm7akce8fZ4aeZ2huw8LhsPjvcx/bThw0Df777CJOOGce23emdqhx0r5rZ87sKh5/0H5X48Nm7uHpRz7n+zcei66rDldeHA4L09R49A9fcdxRsSnLL0Ss9Kjo2fLly/n5z3/e6fGzzjqLW265BQgmIy+//HKolkhdXR2PPvooS5YsITExkTlz5nD99dfj8XhsBylFz0RfUBaUbcqlaOUoGioHAeBM8JI7fSd5M3e2rS3pmdYGD0UrR1GybgQBb3Bh6XFHlnPTtRu58JzCXq3AunFLGn/++2Se+88YWlod6Jpi/mnF/OT6DZxy/N4qnwUVfn744mo+bH0VKyn4uLt2IhcNPZfHLp9AckLP7+Z+syKThx6bzMtvjiQQ0HE6LC4+v4CfXLeBw2ftrcC6YkcLN76yjCXaf1GeGgCSqw/n6jFn8sClI3EYwbm9Xp2nnx/HX/4xiY1bBgGQm9PEj36wkeuv2kxa6t51MgsX5fDw3yfx7ke5KKWRkBDgyku38ZNrNzBhXM935tU3OPn7M+N55IlJFJUE185MHFfLjf+zkasv24bHE2ZrdDeWrxrMn/8+if+8Ngq/38BhWFx4TiE/uX4DRx6+u8fHEyJeSddeIQ6CGdBRpo7hCthKGHLmtfDnE7s/KVkWNNQ7yfK3HNDJ62CYpkZDo5PEhACuMJ14LUtRWhPAZWhkDYrOHVy/X6OxyUlKsh+Ho/tfNQHTorTGJNWjMyg5/DbrpiYHlgXJyeHfG59Pp7nFQUqyHyMK/YWUgsZGB7oOSUnRWc8RCATfm+QkP05n3P8qFqLHpGuvEAfBcFjcdHMNx+fYqD8CwXbyESrFpxtesHe4qDIMxSAbOzN0XSN3cM/70ITjdCpbpdIdhk5+pr0rMHYTAZfLwuWK3o4UTSPqW20dDnuvjxADnSQjot9rrk3E1+zG4faTlNEY8UpGzrwWLhwf/gRwfI4RXLMxAFrFR1t1Hfz9XT8NLRZnzHJxwsw+bEHcC3YVJ1FSlkhykp8pE2u73SZcU+ti645UdE0xcVxdt7td/H6N9ZvSafUajMhtJHtYy0HHWFHpoWBXCm6XyeQJtbjdPSisIkQ/IMmI6Ld2bx9KwZJx1Fekhx5LyWlgykWbGTd/R5dJyYXjfV3uQOlEkpBOCsoUJ/xuLcU5/4TE4Hbk336q4352Pncefi53XBbdqyq97YPF2dz7wAw++3pvif/83EZ+ev0GbvqfjaFbOwWFyfzmDzN44ZXR+PzBS1uJCX6+/91t3PXz1WQNCdYYaWkx+P1fpvLokxPZvSfYWVjTFPNPLeauW1YzZ3ZVj2NctTaD3/xhBm+8l4fVtpsoI72V667azO0/WSvbf8WAIWtGRL9UuHw0Wz/t3BlW04LbWb9/2Rae+POXvbpA9FCyZZdi0uN/QWWt6bwV1zRAGVyjP8g/fpwUmwB76Ml/jePanx2Nrqn9tgwrNA3On7+Ll55azMYtaRx/9pk0Njo7be81DIucYc18+d47pKd5Oe1bZ7BkRWYoadh3XHBnzsecdbq9+jMAH386jLMuPY2AqXWq3GoYFtMm17D4jfdITbFZ5E6IGJAFrGLAWPDbxuCajDZLlmdy1BlnR/y6Jx/+nO9fZq/AlQgv/SeLqRvzr+5rglg6+BNZ862/MHVU38bWUxu3pDHt2PO67ALcTtMUv797KY89NZHC4uRuy7g7DItjj6xg+tQaHvnHxE6JyL7H87hNClcvIHNw5Aqw9Q1O8qd/m8YmR7fHNAyLq76zjX88ZH9rsRB9TRawirgVaefJvrpaGPqXxyfhMKywDcc0TfGnx6Zw1Xe2ydWRg7Rqq6Ju1AvdJyIQ7L7rauKKvxWy4nfhip7F3qNPTUTXwArTAkAp+N3D06hqu93SnYCps/iLbL5ePqTbpCF4PA2vz+Dp58dyy02RC5r9a8FoGhqdYRv/mabOc/8Zw29/tZzBGdFtKSBEX5NkRESV7aqhEXaehGOn86lSGus2plNYlMzI/MYDn0zwvy/sgQx7Oz7W8jHw/d4N6CC9/Ebk7x/QqNqT0Kl4Wld03aK1NfKvUsuCV94caSsZ+e9b9hI6n9/g/Y9zuOyiyOXohYhnkowIW3LmRd4RcDBVQ+0yTY0WG7/42zU0yrf4war12tx6qiksZ+RkNNYaG+0vtLXTtdf+lTeNugZ7c9fWu8JeFdlL0dCD5yNEvJLf1Ic4+0mGjcvAB3G1wy7DUGRmtFJVHbmCr6YphmUd/LbKQ93o9BS+tjPQMnC2DO/tcA5a9rBmtu1IxU6nZk0HItxRtEx72YiuW+TlNNkamze8iVVrMyJelQGNnGH2ytsLEc8kGTmE3fTjOpvbXHs/lp74/ne38uCjU8L+ojYMi9NPLGVIptxLP1gP/08Kz/8tE5IibE3VTeZnRu6wG2tXf3cbd943u63XTNd03WLyhFrWbcyIeDzdUAzJbKG8IoFwCY5l6bYXVF95yTZeezvyrZrBGa2ccXL3HdCF6C8kGRlgbvpxXQ+qhvbPGgU/umYTjz45kZZWulk0qLAsjdt+uqbPYxuIMtJgStX1rE+6t/tBpgNqxvLsrYP6LK4D9YPLt/CHv0ylvt7ZbSdgy9L5vztX8NjTE1m4KKfbxFfXFNdesZlpk2u54Zajup3TYVjk5zXyrbMLbcV49hnFTJ5Qw5ZtaWHXt/zyx2vDlvgXor+QZKSfkKqhe+XnNvHOfz7grEtPo6WFDicUh2GhgCcf/kI6n0bRqt+OJvvm66ga9/dg4mG0fY+1r2uoG8Erp95CcmL8b13KHOzlg1fe57QLz6Cu3tlhi6/DsDAtjT/fv4SzzyjmuKMqmH/xaXy1LAtdt0LJb/vC1nPm7eJP9y3F6bQoLE7id3+e3mGnV3t335zsZha+vNB25VTDULy34ANOPm8e2wtSQNvbrbj9+Df+YAM//1HkxbBC9AdSZyTGIq3ZaE9AbN1OOcSUliXw939O4Onnx7G7ykNqip9LvrWDG67ezMRx8b+Qsr8xTcVPH2/lyS0LaR3+MRg+tJoxzPZdwos35TEm/peLdLC7ys0/nhvPE8+Np7wigcQEkwvP3ckNV29ixtSa0DifT+flN0bwyBOTWLM+A11XHHl4JT+6ZhPnzCvqUD7+iyVZPPKPSbz38XC8Pp1ReY1cf/Umrrx0+wEVJ2tqcvDcS2N49KkJbCtIxeW0OOX4Um78wSZOPLZctq2LuCdFz+KAncWhzxwmi8+EEEIMTFL0LIbai3rZ2oEihBBCHOIkGbHpYKuGioGnsdHBY89MoKIygbzhTVx35WY8ns5rAkwTfvvQNL5ZOYSkRD+3/3Qt0ybXdnnMN94dzrP/GYtlaVx07k4uu2hn7z6JA7Cn2s1XS4fg9RmMG13P9Ck1XY7z+zU++2oo1bVuBqd7Oe6oChyOzhdilYLV6zLYvjPYlfaYuZWkD+p6fVRJWSLLVg7GUhpTJ9YwbszB/aApBV8vG9LWtTfA8UeVk5ho7+dcCBE9cpuGHlQNFQJobdU5+7JTWfRZdofCVLpuce6Zu3j56cWhdQQXX30Cr7w5cr8CVopBaT4+efPdUFLy5L/G8qNbjsLn19l3e6jDYXLfHcu55aYNvf/EIthd5eaX9xzO8y/v7V4LMHtGFf935wpOPym4xdSy4A+PTOXBv04Jda8FGDqkhZ//aB0337A+9Pq8++Fw/vfe2axeNzg0zu0y+e63t/P7u5eRkR5MSnbsTOYXvzqCN97Lb1twGnTiMWX8/u5lHD5rT4+fz3Mvjeae381kR+HeS8cpyX6uvXIzv7ltJQkJkpQIcbBkzQiSZIjo8/l0Rs++kNLyRDrXlFCAxuQJNaz57HWOnT+fr5dlhR7vPBZWf/o6H382jJ/dMbft8a7H3faTNfzfXSuj+VR6pHK3h6PmncWu4qRO21x1XaEU/Pvvn3Lx+QVcccNxvPDK6G4qiCquvHQbT/3lC/61YDRX/eg4NE112el29IgGvnzvHar2uDn6zLOob3B27l6rWzgcivdfXsjxR1fYfj73PTidu/5vNl29N7pucdThu/ng1YV4PJKQCHEwBnQyEtWqoUL0wJU/Opbn/jOGSNU7LzirgP++PbLto+7GKlJTfDQ0ugj+FHY/DqCp6F8x+2v9sv85ngVvjOy23oamKZxOiz/d+w0/urX7ehvtHnvgS35825H4A913zjUMi8su3MHGLYNYGaYaqa5bDM7wUrRmga2aG8tXDeaIU88JO0bXLe78+Rru/uWqiMcTQnRvwCYjP7vFL1czRExYFiTmfg+fr+OtlM4UmkaEBGPvWDtlyQEuOreAl576xNbYaCorTyB/xrdtNIxTZA9tpqwiIWwHW0O3yMlupqQsMew4wFajunYv/GMxl1ywM+K47994DP9+eQyBQPjjZg5upXjtS1JUTIiDYDcZsfdTHif+eU+jJCIiZtasT8fnM4icPGg2E5H2MXb+HlB89EmOjXHR9/Fn2bYSAsuCkrKkiAmGaekUlSRHHAdgmjq6HjkZcBgW732UG3EcwFsL8yImIgBVezysWZ9u65hCiIPTr5KRtEab3UOF6AW1da4ejI5+NapAIDYVrlq99toL9MZzBntdcS0FLS324vTafj7Q0mp/rBDiwPWrZESIWJo8oRZ7VzFoGxfdO6AZGbFZAzUyv9HWOF23MGxcxYDg7Rc7VzwATBtdcXUNRo6wF+fIvMZQmfZIRuTZ67IrhDg4kowIYVPWEC9jR9UTOckIbt21t17EfsLywG+W2h4bTSceU07e8EYixWpZOmeeVozDCJ9kOAyLs04vtnGbRjEyrwG3O/Ki3YCpc/VlWyOOA7j2qs0RxxhGsOx6fq4kI0L0BUlGhOiBB+9rTwi6OzErNODVZz5s++s73Alc4/z5hWQPbY4wTpGa4ufCc3YdQMQHzzAUv7l9JeGSK8OwOOrwSh64Zylut9ntVQ9Dt0hICPDH33zD4bOqIiQuGvfduYLbfrKWcK+Prisuu3A748fW23o+V1yyndycJoxu5m6/avKrW1bZOp4Q4uBJMiJED5x9evE+Vyj2TTaC/69p8Nxjn3Licbv54OX391nvsO/JNDj28Jm7efXZxWxb9ippqb5uxoHHE2DH8pd75fnYdeWl2/ndr5eiaarDSbz9/2fP2MMbz3/E+LENvLfgA1KSAx2SMU1TaFowqVr4ykLGjGrk7Rc+YPrU6g7Haf9/TVP86b4lfOfCAu76xWpu/MFGgA7JS/v/n316EU/8+UvbzyU1xc/Hr71P3vDgVY99E6dg3RKLF/7xiXR9FqIP9autvbUFj5KakhD5C4ToZVu3p/Dzu47go0+z8fkNPG6Ts04v4oHfLCU3Z28dnLo6g+/dcALvfTQ8tIMje2gLf7jnm06l3u+6fyZ//ttkGpucACQmBPifKzbzwG+WYcTJOsot21J57OkJvP/xcFq9BhPH1XH9VZuZf1pxh1LvNbUu/vniWP69YDS793gYOqSFyy/ezhWXbCctdW/32kBA48338/j7MxPYsj2VBI/JvFOK+eH3NzN2dMdS78tXDeaxpyfw2VdDMS2N2dP38MPvbz7g7rUtLQYvvT6Sp/41jl0lSaQkB7jo3J384PIt5GRHrmUkhIhsQNYZkWRECCGEiF/1d/y748e+APlPL5auvUIIIYSIDrXk624/9/nNRUDHEghNNnbDgSQjQhyw6hoXby3Mo6bWxZDBrZxzRhEpB1mUb9nKwXyzMhPL0pg1rZqj51R2eQti2arBPPLEROobnIwd1cCdN68mNbVvCgJu3Z7Cx59l4/UZjB9Tz2knlmIYcX+BVQhxgNoTkGCy0TskGRGih5qbDX7+qyN4+t/j8PkNdN3CsnQSPAF+9ION3HfHCpzOnp2clyzP5Ic/P4pV6waHdnMopTFxXC1/+d0STjm+DIDNW1M49VtnUFKWtM9XazzwyFTmn1rMG89/FOqIG207diZz3c1H89GnOYBC18GyNHKGNfF/d63giku2987EQog+U3/Hv1m9uCcFHqND1owI0QNer87pF57OF99kdVknQ9MU5525iwVPL7Z9teCLJVmccsEZBAJap2NqmkLXFa//6yMmjK1j8lEX4A903xtn5rQ9rFj0Zo+fVyQFhcnMOe1sautcXZSGD/bXeej/lvDjazdGfW4hRPT0dbLRZPqZv+0DWTMiRDQ9+tREvlgyFEt1nQwopfHaOyN48dVRfPfbOyIez7Lgez88rstEpP14lgVX3HAcOcOawiYiAKvWDubvz4znuqu22H5Odtz0y7ndJCKE4rn5ziO44KxC8oY3R3VuIYR9+y8g3VcwCen7qx52SDIihE2WBX95fFLEmqm6bvGXJybZSkY+WJzDzl0pYccopVFd46G6xo2dqq6//fO0qCYjBYXJvPtRLqqbBKydpsHjz07g/92+MmpzCyE66ukC0v5CkhEhbCotT2RnUfjEAYJl0b9ZPgSfT4/Yfn7xF8NwOKyIXWTb16VEprGrONnGOPs+X5IVMRGBYIfdjz7JlmREiCjoKunozQWksSbJiBA2+f09WxkaCGi4IvyR4vfrtpu2xUpPnrfXJ0WdhTgYasnXAzrp6I4kI0LYlD20hcQEP80tzggjFcOyWkhIiNzgbeK4OgJ2TvZKo32haKS5k5L8Ecb0zASbPV8chsXUibVRnVuIgeZQTTYikWRECJs8HpPvf3cbf396AoEuF3IG6brihms22SpRfsn5Bfz0f+dETHB0XTEo1Ut1jYfwCYnG5TbWqvTE0XMqGT+mjq07UsPergmYOtfZ6IgrxECmlnxNwxvdb3OPxbbZ/kCSESF64Bc/Wse/F4ymodHZ5c4Sw7AYltXCD79v76SckhLg7l+u4ta7jwgzSvHzG9cxaVwtV914HN1fIVF4PCa/vWuZrbnt0jT4/d3LuOCKk7udW9ct5p9awlFH7I7q3ELEG3vJhiQcPSXJiBA9MCKviUWvv8f8S06jrCIxtLDUMCxMU2dUfiPvvvQBgzO8to/58x+tp7nFwT2/n4muq1CS037Mn16/gfvuWIGuQ0VVAr+8+3D2dvfde/smKSnANx+82SuVWM89s4h//vUzfvDTYwj4dSwVnNthWARMnXPOKOLff//0gBrWCRGPulpA2vDGdkk2eokUPRPiAPh8Oq++NYKXXhtJ1R4Pw4Y2892LdnDW6R271/ZEYVESjz87niXLhmApjcNm7OG6Kzt3ry2v8HDLr49g4aIcvD6DzIxWbrhmEz+9fkOvVV9tt6fazdPPj+XDT7Jp9TqYMLaO//neFg6ftad3JxaiD8WqCulAZLfomSQjQgghDimSbPQdqcAqhBDikBQ52ZBEJN5IMiLi0tIVg3nnw1yamh2MyGviO9/aQUa6L9ZhdbB1ewovvzky1LX3kgt2kp/b1GlcbZ2LF18dRcGuZDxuk3mnlHDk4btlfYUQB6i/ljwX3ZPbNCKubN2ewmXXnsDy1Zk4DAtNVwQCOk6HxY+v28D9d62Iebv6PdVurrzhWN75MA/DsNoWnWoopXHJ+QU8/qcvSU4OYFlw9+9m8sAjU/H6DBwOC6U0AgGd6VOqef7xT5g8oS6mz0WIeNVd2XOp0dG/yG0a0e/s3JXM0WeeRV1d8K+agKlDW90wn9/gj3+dStUeD08+/EXMrirUNzg54Zx5bN6WBgRLoJv71DZb8MZIikqS+PC/7/OLXx3BX5+cSPtWWL/fCI1bv2kQx5x5FksWvsV4m0XFhDhUSGGwQ48kIyJu/O//m01dnavbgmJKaTzzwji+f9lWjjuqso+jC/rz3yexaWtat31iTFPny6VZ/OYPM/jrk5O6PY5p6jQ2Objl14fz+r8/7q1whYhLkmyI/UkyIuJC5W4PL78xMmxlUwiWHH/sqYkxSUZMU+PRJyfZalj32NMTQzU4uj+ezlsL8ygqSSRveHM0QxUipiTZED0lyYiICyvXZkRMRCB46+bzJVl9EFFnJWWJVOyOvGZJKY3aOretYyqlsWxVJnnDdx1seEL0GSl5LqJNkhERFyzL/iIQy0Y7+97Qkxjj4bhCHCypQir6iiQjIi5Mm1yDpqmwjdggWCL9sOmxqfY5PLuJQWneiFc9NE2RmBDA6zVsXe2ZPrk6WiEKERXhb7NIEiKiT5IRERdyc5qZf2ox7308vMsGdO1MU+eHV8emM6zTqbj2yi388a9TwsYIcNV3toVdwArBxOr4oyoYN6Yh7DghesNnxyyIdQhChEgyIuLG/b9azqLPh+H10eXJXtct5p1cwuknlcQguqCbf7ie518eTXlFQpdXPQzDYvyYev7vzuVU7Pbw6lsju7wNo+sWTofF7++OboddIdpJsiH6E0lGRNyYOqmWRa+/x4VXnUxxaVJbkbDg50xT55LzC3jiz1/2ejO4cLKGtPLpW+9ywfdOYvX6waEYNQ0CAZ2jjqjk5acXk5IS4LnHPiM5KcA/XxyLrgefSPu4IZmtLHhqMYfNlAZz4sBErkIqRP8hFVhF3AkENN75IJe3P8iludlBfl4jV126La5uZygFn3+dxUuvj6KmxsWQzFYuu3AHR8zunFwUFCbz9PNjKShMweMJloM/d94unM64/9ETMSZVSEV/J117hRAiznWXbMC+u1aE6L+kHLwQQsQx6SwrxF6SjIiDtnV7Cs+8OJZdRcl4PCZnnV7M2acX4XB0vOhWWJTEL351OCvXDgZgxtRqHvx/SxmR17nTbbxTCj79cigL3hhJba2LzEwv371we5e3aeyqb3DyrwWjWbpiCJYFM6dVc+Wl2zp1KzZNjXc/HM6b7+fR1OQgd3gzV166jUnjD7zpXuVuD8+8MJb1mwah63D0nAouu7CApKTAAR/zUCfJhhD29fg2zdq1a3nsscdYsWIF99xzD2eccUbEr1m1ahUPPvgghYWFjBs3jttuu42xY8fanlNu08SnlhaDH/zkaF54dQyGYQF7F2jmDGvilX8uYu5hVQBccs0JLHh9ZJfHufCcnSx4+pO+Cvug7dyVzPnfO5k16zOCC1gt0PTg8z5mbgWvPLOIrCGtPTrmE8+N4ye3z6XVa4QWuypLw+G0uO+OFdx8w3o0DVauyeCCK05mV3Hy3rm1YGXaC87aybOPft6jBEIp+M0fZnDfH2dgKQ1NU2hAwNRITgrw9we/5DsXFvTouRwqZAGpEJH1ypqRxx57jEcffZTrrruOm266ieeee47LL7887NesW7eOI488kiuuuIJzzjmHZ599lg8++IBVq1aRm5tra15JRuKPZcH5l5/COx8O77JXi6FbuNwWX733Nr/98zRefHV02ON9+7wC/vNk/Ccklbs9HHbyOVRUdr2112FYjBtTz5KFb5GcbC8peOaFsVx907GAor3D7/7+cM9Szj69iDmnnU1zi6PLrc+GbnH8MRUsfHkhhmHvx/rXv53J/3tgZjefDcbz0lOLuOjcQlvHG4i6Sjok2RDCnl5JRurq6khLC7ZO1zTNVjJyySWXUFJSwueffw6AaZpMmDCBc845hz/96U+25pVkJP4sXJTDvG+fHnaMYViccFQZH3+eQ3cn2b0Upev/w7ChPbui0Ndu+81hEYueaZrigd8s5Wc/3BDxeK2tBtmTL6au3kW418jtMjnj5GLe/iAvYsG1V5/9mPPnR+51U1qWwIiZ345wPEX20BZ2rVlgO8Hpb8ItIpVdK0IcnF5ZwNqeiPTERx99xC233BL62DAMzjrrLD788MMeH0vEj8eemmCrK20wEbHnll8fwXN/+ywa4fUKn0/n8X+Oj5gMADzyj0m2kpFX3hxBXX3kpno+v86b7+ehVPi5DcPir09MtJWMPPX8OCL/KaJRVpHIOx8M55x5xRGP2Z9IZ1kh4kevLmBtampiz5495OR0PCHl5ORQWNj9ZV+v14vX6w19XF9f32sxigOzfHWmrb4rwb/27f1FvWpd+kHF1NtKyxNtdeNVSqNgVwotLQYJCWbYsavWZeB0mvj9Rthxuq5sJUGmqbNybUbEcQCr12VE7AUE4HBYrFqX0e+SEUk2hOg/ejUZ8fv9ALjdHX+BJyQkhD7Xlfvvv5977rmnN0MTB0nTon/JPpaVVe1oX1gazfG6ruzmaj2Y1+44Zas5YU+OGQuRd60IIeJdryYjKSkpOJ1Oqqs7diXds2cPgwcP7vbrbr/9dm6++ebQx/X19eTl5fVanKLnjj2yggWvJxIIRDpLWYC9KyhHH1EZhch6z/DsZoYOaaFid/h1S5qmmDyhFrfbinjMIw/fjT8Q/qoIBLfzGoYV8eqIw7Bsv45zD9vNK2+MjDguENA58vDdto7ZG8LtWgFZTCrEQNCryYhhGEyfPp2lS5dy/fXXhx5fsmQJs2bN6vbr3G53p6spIr7ccPVmXnhlTNgxDsPi7NMLef29kW1rE7r7Czy4nfT+u5ZHOcroMgzFDdds5J7fz+xyB9G+bvqfjbaOec4ZRQwd0kJllSfMFQpFclKA8+YX8p9XR4e9PRYwdX70A3tzX/WdbfzvvYfh83WfDOm6xci8Rk4+rszWMQ+EVCEVQkT94usTTzzBaaedFvr4mmuu4eWXX2bDhuBivs8//5yPPvqIa665JtpTiz509JxKrv7ulm5v1xiGRWqqjz/eu5ybb1jf9mhXY4OP/fi6DQwa1P2tu3jx0+s2MHFcXaiuyv4MPXhl4spLt9k6nsOh+MdDXwDd3foKbq/96x++4re/WsHgjFYc3cytaYpLL9jBqSfYSxwy0n08eO83+8zTka5b6Br846Ev0SLfyTkg9Xf8m89vLur2nyQiQhwaenRlZOnSpdx0002hj++++24eeeQRzjvvPG6//XYAiouLWbp0aWjM9ddfz7p165g1axYjR46ksLCQ22+/nfPPPz86z0DEhKbB3x/8iszBXh56bDL+gI7DYWFZGqapM3NqNc8//gmjRjTyh3uWoWmKP/51atsVkvYTn4amwc03rOcP9yyL4bOxLyUlwCdvvsdVPzqWtz/IwzCstsWlGkppXHx+AX9/8Ctbt2janX1GMa//6yOuu/loyioScTqCi179AYPBGV4evn9JqPDYl++9w3evO56vl2XtnTugYzgsbrh6U9trbf/53HD1Ztwui1/86nDq6t04nSZKaQQCOrk5zTz58BecdFx5j16jfUkVUiGEHT2qM1JfXx+6wrGvrKwsRo8OFrUqLi6mvLycww8/vMOY3bt3U1xczMiRI0lP79muCakzEt+qa1z857+j2FmUTGJCgPmnFndZFr25Wefu38/iy2+ygODVlbtvXUliov0TdzzZuj2FV94aSXWNi6zMVi65oIC84c0HfLxAQOPthbl8szITpTRmTdvDeWcW4XJ1fn1WrskIloNvdpA3vIlLLyggc7C3i6Pa09pq8Opb+azdmI5hKI6ZU8kZJ5fYWrgqu1aEEN2Rrr1CiKhQS76m4Y3t3X5ebqUIIbojXXuFELaEW0AK7VVIJeEQQvQeSUaEOITJLRYhRDyQZESIAUySDSFEfyDJiBADgFQhFUL0Z5KMCNEPhKtCGkxCJBERQvRfkowIEQcit7GXZEMIMXBJMiJEH4icbAghxKFLkhEhetlnxyyIdQhCCBHXJBkRIgpk14oQQhw4SUaEsEGqkAohRO+RZEQI7CYbknAIIURvkGREHBLCLSBteGO7JBtCCBFDkoyIAU/a2AshRHyTZEQMGN3vWpFkQwgh4pkkI6LfiFyFVAghRH8kyYiIG1LyXAghDk2SjIg+IyXPhRBCdEWSEdHrpCCYEEKIcCQZEVEjbeyFEEIcCElGhG2Rr3BIIiKEEKLnJBkRIVLyXAghRCxIMnIIkSqkQggh4pEkI4cIqUIqhBAiXkkyMoCEX9MhyYYQQoj4JMlIP9N9yXMhhBCif5JkJM5IyXMhhBCHGklG+phUIRVCCCE6kmSkF3SXcEgVUiGEEKIzSUaiTKqQCiGEED0jyUgPyRZZIYQQIrokGdmPtLEXQggh+tYhl4zIAlIhhBAivgzYZKSrpEMWkAohhBDxZ8AlI5E7ywohhBAinvS7ZESSDSGEEGJg6VfJSP1vXmL1l4mxDkMIIYQQUaTHOoCeWPeZM9YhCCGEECLK+lUyIoQQQoiBR5IRIYQQQsSUJCNCCCGEiClJRoQQQggRU5KMCCGEECKm+tXWXjEQKAyXCYDpMwAttuEIIYSIOUlGRJ/QnQGyZxeRc8ROEjJaAGipSaD0m5GUrcjD8su3ohBCHKrkDCB6ncPjY/oVS0ga2tDhcc+gFkafvpGhM4pZ89xcAi3SpFAIIQ5FsmZE9LqJF6wmMasRTQNtn7sy7R8nZTUy8YJVMYtPCCFEbEkyInpVYmYDGeN2o+uq2zGarsgYW0XikIZuxwghhBi4JBkRvWrI1DIsM/IiVcvUyJpa2gcRCSGEiDeSjIhe5Uzw2R+baH+sEEKIgUOSEdGrAq32mxv6ZQGrEEIckiQZEb1q94ZsdKP79SLtdEOxe312H0QkhBAi3kgyInpVU0UqtQUZYdeNWKZGbWE6TRWpfRiZEEKIeCHJiOh1G1+dRWtNIsoCtd9FEmVBa20CG1+eHZvghBBCxJwUPRO9zt/kZuWTRzN8zk5yDi/ElRJcqOprdFG6bAQlS0Zieu2vLRFCCDGwSDIi+oTpdbLrs3Hs+nwsriQvAL4mNyjpTSOEEIc6SUZE31IavkZPrKMQQggRRyQZEXFK4RnUgu408TV4erRFWAghRP9yQMnIli1bKCwsZNy4cYwcOTLs2G3btrFp06YOjzmdTs4444wDmVoMdJoi+7BdDJ9bQOLgZiC4yLVq0zCKvhhDY1lajAMUQggRbT1KRgKBAN/97nd59913mTZtGqtWreKqq67ikUceQdO6vvf/4osv8sc//pFjjjkm9FhycrIkI6IzzWLShSvJnFTR8WEdBk+oYPCECjYsmE31lqExClAIIURv6FEy8uc//5mPPvqINWvWMHLkSFatWsWRRx7JMcccw2WXXdbt102YMIG33nrroIMVA1ve0TvInFRBV3mtbiiUgskXreSbv5yIr0HWnQghxEDRozojzz77LBdffHHo1szMmTM544wzePbZZ8N+XWtrK5988glLliyhoUE6s4rONN1i+NydXSYioTFacNywWUV9F5gQQoheZzsZCQQCrF+/nhkzZnR4fObMmaxevTrs127evJn//d//5eqrryYnJ4e//vWvYcd7vV7q6+s7/BMDW8rwWlzJkRvlaTrS3VcIIQYY27dpGhsbMU2TjIyMDo8PHjyY2trabr/uuOOOY9euXQwZMgSAZ555hquvvpqpU6dywgkndPk1999/P/fcc4/d0MQA4PD4e2WsEEKI+Gf7yojLFeyo2tzc3OHx5ubm0Oe6csIJJ4QSEYCrrrqKKVOm8N///rfbr7n99tupq6sL/SsqksvyA52v0W1rnFL2xwohhOgfbF8ZSUxMJCsri+Li4g6PFxUVMWrUqB5Nmp6eTnl5ebefd7vduN1ywjmUNJal0VKdiCe9Oey6EYDyVXl9E5QQQog+0aMFrPPmzeO1115DtXU78/l8vPnmm8ybNy80ZsuWLbz//vuhj/fs2dPhGKWlpaxYsYLZs6UxmtiXxq7PxoRNRCwLAi1OKlYP77uwhBBC9Loebe391a9+xeGHH853vvMdzjrrLJ5//nksy+LnP/95aMzzzz/PQw89FFpHMn/+fE444QRmzZpFVVUVDz30EGPHjuX666+P6hMR/V/F6lwSMprJP247lqmhG3tb/Cor2N9m7b/mSFM9IYQYYHp0ZWTMmDEsX76c7OxsXnvtNWbMmMHSpUs7rAkZP358hyslixYtIi8vj7fffpu1a9dy++23s3TpUlJTU6P3LMQAobFz0QRWPzuX6i1ZWGbwMomvwcWuz8ay/LHjaCyXCqxCCDHQaKr9nkscq6+vJy0tjXfGnkaSIX8VHzoUaEhnXyGE6KeaTD/zt31AXV1d2IsQ0ihPxDEN4j5VFkIIcbAkGRF9ypnoJWlosApvU0UK/uaud03pLj9Dp5Xg8ARoLE+lZntWn8XoSW8kZ85ODKfJnq1Dqd48rM/mdiT4SB5aDxo0Vabgb5JdZUKIgU+SEdEnPIOaGXnyZjInlYcWplqmRtXGYexcNJ7WmiQAHB4fUy5dRmpebYedNQGfQdHnYyj6fGyvxZg0tJZp31uKM8Efmjt7dgmWqVH0xWgKF0/otbndaS2MPGkzQ6aUhV6f9m7FOxeNp2VPcq/NLYQQsSbJiOh1CZmNzPz+lxgus8MOGd1QZE4qJ33MblY/cxS+BjdzfrwIw2122uJrOE1GnrQFT3oTW9+cQbQl59Qw65qvADrNremK/OO2405tZcsb0Z/bk97EzKu/wuHxd3h92rsVt78+TRWy6FsIMTD1aDeNED2nmPztFZ0SkXa6oTDcJpMvWsGUy5Z2mYjA3gRh2MwS0kZURT3KGVcs6TBPV3MPnVFCQka0Gz0qJl24slMi0k43FLrDYsrFy5EFNEKIgUqSEdGr0kZUkzSkscsTbTtdVyQOaSJ1eF3Err0Ao07dHNUYB42qRHdatuae+K3wTSF7KiWnjpSc+vCvj6HwpLeQPnZ3VOcWQoh4IcmI6FUZ4ypD9ULCUVbXVyX2p2mQPCy6XZzzj99ma26ApKzoXhnJGG/v9bFMjcHjKqM6txBCxAtJRkSvMpxm1I+padG9XWG4TOxU29G04PqRqM7dg9dH74XXUggh4oEkI6JXees9UU0elALTb0TteACt1Um2rowE547uj4zd10fTwFufENW5hRAiXkgyInpVxRqbTe00MP2arSsUVRujW/djy5tTbV8ZqVwb3SZ9lWtzUDYqzGq6kgaBQogBS5IR0at8DQmUr8pDWd2PURaUr8qlZMmosFcolAqO3bFwUlRjNH0umirD1/Fon3vbO5OjOre/2U3ZsvywyZCyoHJtdqgWixBCDDSSjIhet+3dyezZPBSgw2LN9v/fs2Uo296Zws6PJ1K5fljwxL/Pybn9Y2XB2n/NIdDqinqMK/5+DN4GV2i+fecO/g+sfOooILq3iAB2fDCJ3euzga5fn5odmWx5c3rU5xVCiHghjfJEH1Gkj6ki+/BCUofXAlBfMoiyZSOo2Z5JsCNeUPq4SkadtDm4c0UDy69TtWkY2xdOJNDs6dUoR5ywmeFHFmC4gpdylAW1BZlsfHkmpi/6SdBeikGj9pBzeCGpeTUANJSlUbZsBNVbh7Dv6yOEEP2F3UZ5kowIIYQQolfYTUbkNo0QQgghYkp608Q5zTBJH12FM8lHoMVJzY5MLH9Xb5siZXgtiZlNKFOjblfGQW8FNVwBBo2uwuHx42t0U7sjE2UdXP6amNlAck4dAI1laTTvTulynCejkZzDCzHcAZp3p1DyzUg4yLn7g4TBjaTk1IGmaCpPpalS+tEIIQY+SUbiliLv2O3kHrUDZ0Ig9KjpMyhdls/OReNRZnAxZca4SkaduomkIY17v1rBni1ZbH9vCt66niUlmmEy6pTNZB+2C8O5dxuMv9lJ0ZejKf5yND1dw5CcXceYMzaQll/T4fG6okFsf28yjWWDAPAMamLa5d/gSW/psLNm9KmbKF89vFea5MWDpKH1jDljPYNGdnx96ovT2L5wMg3F6TGKTAghep+sGYlLivHnrWHo9JIut7oqC2oKMln/wuFkTi5j4gWrQQW7vHYYZ2r4W52sfOJovHWJtmbWDJNply0lbUR1p+NBMMkpX5HH1renYjchScmtYfr3lqAbVucYLVCWzppn5+JtcHPEjz5BM1Sn561UsM5H9bZM1j0/x9a8/UVydh0zrvwazWGh71fhVVmglMbaf82hrnBwjCIUQogDI2tG+rHMieUMm9F1IgLBpCN9dBXDj9zBhHPXhh7rNM5QODx+xp+91vbcw+cUdpuIQDAhyD6siAy7fVI0xaRvrewyEWmPW9MtJl64kqmXLu0yEWmfFyBjbBWDJ5bZm7tfUEy8YBWaw+yUiEDb66MFO/tqephiLUII0Y9JMhKHcubsDFskDAAFuUcVoOnhu83qhiJ9zB486U02ZlbkHLEz4gUPy9TImVNo43iQPmY3nkGt3SY3EDzhetJaScxqiliWXSkYeeJWW3P3B2kjq0nMbEKP8Pq4kn0MnlDRd4EJIUQfkmQk3mgWaSNqwp68oe0EleS3dadEKRg0ak/EcZ5BLcHEIcIxdUORPmoPEPkOX/roqqh37U0YbCex6h/SR9l7fSxTY9Coqj6ISAgh+p4kI3FG7+Y2RXdsjVWgG5Ev8esO+11hNV3ZSoTszNtT0e7aG0u6w7KT0+0dK4QQA5AkI3HGCuj4Gu1V+lQWWDbyB02HlprIC1i99R57VzEUtNZ5wEaDt5bqJHvJg80ETCkIeAfOJrCW6kQ0w07XXkVLtfSmEUIMTJKMxB2NsuX5EdeMWKbGni1D0SO0SlEKvPVuarYPiTiz6XOye11O5IREQdmy/IjHA/tdaZWlEfDqEbvnahpU2u0E3A9UrstBBez9GFaszu3laIQQIjYkGYlDpUtH4G92YVldn8SVFbyCsuPDiVRtzgqbuGga7Px4gq2rGAC7vhiDsrRuj2mZGr5GN2Ur7CUj/mY3xV+NCt+VVkHJ16PYuXh86OPuxpl+nYKPJtiauz8wvU52fTEm7Bilgt8Tvobe7csjhBCxIslIHPI3u1n97JH4GtwAocQgWHMCAl4na/81h9bqJDa9OpPqtqse+17RCNbvgO3vT6Jijf2/qFuqkln77zmYPkeH7rntMfgaPKx5di6BFvtN43Z+PIHSpSM6xdj+/2XL8in4eAKlS0ZT/NXI4Hz7zt32X8uvs/KJo7ECA+c2DcCuT8dS9OUooOvXp3xlLtsXTopJbEII0Rek6Fkc0wyTIZPLyZpegivZS6DZye71OVSszdmvJLwiNb+G7Nm7SMpqwDJ1agsyKVue3+Pqq+0Ml5+s6aUMmVyGI8GPr8FNxepcqjYNDVV+7anErHpyDttFSm4tAA3Fgyhbnt+p5HnC4AZGn76RtPwaNF0RaHVSviKXws/GDeiS8ImZDWQfvovU3BrQoLE0jdLl+TSVp8U6NCGEOCDStVcIIYQQMSUVWIUQQgjRLwysm+/9SPacAnJm70J3WLTWJLL59en4Gg+8y67h8jHh/DUkDmlAKY2q9dkUftLVQk+LoTOLyZxUjqYrGsvS2PXp2C7XYWSML2fECVtxuAP4m1xse28yjWWdG7bpzgCZEyvwDGrB9OtUb8uipSq5i7n9zLzmaxKHBIuWNe9OYtWTRwKdr3YNm1VIztxCDIdJa20Cm9+ciq+u8zE9g5oYccJWXCmtBFpdFH81ioaSzjFqukXGuEoShzSirGBX44biQXS1pzhrehFDJpejGRZNFSkULh7f5euTNKyOvGO240zw4Wv0sOuzMbTs6boLcbQlZDSSMW43hiuAtz6Bqo3DMH3y4yyE6J/kNk0fGzK5hAkXrEY39i7M1LTg/zfvSWT5o8cBPVuTMeP7X5KaWxs6TuiYFuz4cAIlXwd3awyZVsL4s9diOK1O48pX5rL17ekAJGbVMevqr9CdnbfU+JsdLP3LiZg+F+2dhfOP3Y7uNFGWhqYpNB1qCzLY/PoMvPXBBGvWDz4jObuhc4wKGsuTWfmP44FgAjT52ys6vD7tWmsSWPrI8YCB7ggw8+ovSRrayP689W5WP3NUqDlg1rQSRp+2EVeyD8vcG2NTRTKb35ge6hicObGMCeevxnBZnWKsXJPD5tdnAuBKbmHm1V/hTmvtNHfz7iRWPnUUls/+At+ecCW3Mv68NWSMqQo10dN0hRXQKf5qNIWLx9HTjspCCNFbZM1IHMqYUM6Ui1cA3VdO9Ta4WPKnU20fc/Z1n5LcxQkZ9p7MdyyciK/ZzcTzV3c5d3tH3Mp12excNI4jbvy02xiVAhXQ+Pz3pzP6lM3kHrmzy7ktU8Pf7GLlE8cw5bJvSM5q7HZugKbKJHYsnMS0y5d1OzeAr8nB1386lbk/+RhXsq/bGC1TZ+nDJzB4/G7Gnb0u9Bw7jLOC41Y/cyTutBYmf3tl2LmrNmex5fXpzP3px+jOrnsCKQWBFidLHjop6rt+nEleZv3gC1zJXvQuCqUp1ZZUvjUNSUiEEPFAkpE4dMxt73V7EtvX9oV7r2aEM2hUJdMuXxb2eEoBKliXJNLcSoGvwYUrpeuT/L7qitJIy6sLO8YyNXavzyRr2m6g+5N8+3eg6dcwnJHL4dcVp5KWWx92jFJQX5xGSk59lyfuUIwWNFem4BnUjOE2I74+9UWDSM2rjTiufNVwtr45I/wT6aGx89eSPas4YsXW1f+cS13h4KjOLYQQB0IWsMaZtFG7bSUiSkH+cdttHXPc2esjjtG0YDl4wxV5bsBWIqIUpObWRazUqhuKIVN2B2MIM7T983YSEaUgdXi9rUqtqbl1EUvR6zokD2vA4QmfiLRLzauNPAjImlpma5xdhsvP0BklERMRy9TIOcJeR2UhhIgXkoz0kZzZRba70jo8AVvHdKe22DrmvgXE7Mxvd0y4Kw7tulr70ZWubqOEm9v22Ci+Pu1JU6S5g4mVhe6w9z7akTysAaOLNTz70w1F2ojqqM0rhBB9QZKRvqLHf8fVqHcLPsRFtcuubv9uqtaDsUIIEQ8kGekj1VuzbI1TCqyAvTN9oNXZo7/o7cxt9ypGcJFo32ck+5eIjzg2Vq+PBYHW6C1gbalKitg8EYLzNlX0zfZiIYSIFklG+kjFqnwsM/LJrCddaQs/GWtrjUX7ydHO3KbXsHXCba1JiHibRllQs8v+idHu69Na7454LKWgpToRLcJ3uFLgrXPbn7smwdbrU709k2j+ePkaPezZMrTb5ontNJ1QHyAhhOgvJBnpQ0VfjQbCd6W1Ahpb355i63hly0YRaO0+eWh/fPf6YRR/NapDjY+uxjZVJLPptRkRYwRY/a/DqSkY3O1f68FGfToF70/D9Eaum2L6DAo/HRdxbsuElU8diTK1iDFufn0GJd+MCDtO02D7wskUfT4m4tzNexLYsGA2hLlCEkz8NLa+PbXrAQdh5+Jxwefd7WuuUVc0iD2bh0Z9biGE6E2SjPShwo8nUr5ibwfd0FWL9q60AY1v/hos6mXXkodOCt0O2Pd47ces2ZbJpldnU/DRJEqX5XU5Tiloqkxm+T+OpnrLMLa9u7dD7P7HQ8Hqf87BV5vC+hcPY0/b7SerLTloP1EGvE7W/vsIGssG8eXvTybg07ucVykI+HS+/N3JFH02jpJv8rudW5kayx49nkBDEiv+cQym3+h6nAXrX5pNQ3E629+fTGlbQqKsvTGqtu3Om16bTtXGYLXakiUju30dW6qSWPH342iqSGPdC4ej2q5QdH4PdVY9dSS++kTb76FdzZWprHluLv62jslW+/Nou11WU5DBuuePQA3gZoJCiIFJ6ozEgCu5hYnfWkVKTh1oCtPnoOjzMZQsGX3AxxwyvYjRp2wO7cRprkpi0yszaanueJvEldrMmDM2kjq8FjRorU1g50cTqNu1X10Kw8ek89eRPnY3umFhBQzKVw1nx8KJ7J8sJQ2rY9iMYtyDWrD8BtVbs9i9YVin7r5ZMwoZe+bG0K4Q06+z7d1JVK7ueFvBSGhh8oVr2rrXKky/g5KvRlL0xbj9nrVFzhG7yDmiEIfHj+k32L0uh52fjAGr43oNz6Bmhs0uIjGzvRx8OhWrczG9Hb+fXMktjD5jI2l5NaCBt85DwaLx1BUM6Ti1bpF/3DaGTivBcJkEvA7KludT8vVIejvH1wyTzIkVDB5fge4y8dV7KF+dS2PpoF6dVwghekqKngkhhBAipqTomRBCCCH6BWnzGUW6M0DWlDKShgVLlTeUprF7fXan2xX9RXJOLZmTynG4/fib3FSuzaGlunPn3EGjKxk7bwPORB/K0qnanMW2t6fQ04Z/ByIho5GsaaU4k7wEvE6qNg6T2xVCCNHPyG2aKBk2axejT9+I4TJDixt1QxFodbD17ansXp8T4wjtc6e2MOmiFR1KvgfLyiuqNmWx+bUZmD4nhsvHnB8vxpHQsdJo+66dHQsnHtQ6mHAMl58J560hc1JFaGEqBF/z+uI0Nr48O9QxWAghRGzIbZo+NGz2Lsafsw7DFexvohsqVIPDcAeYdOEqhkwpjXGU9jgTvcy46iuSs4NXd9qfS3tVz8HjK5n23aVoho8jb/4YR0KgQ5n0feuejD59E0Nn7op6jJphMvW7Sxk8oSL4sa46vObJ2fXM+P5XOBO9UZ9bCCFE9EkycpAMV4Axp28Euq7i2X6VYOyZ69EMs4+j67nco3fgTu26RT0Ei2ql5NYy9bLlYZvvtT8+9szIzfx6KmtaKam5td0WNNMNhTvFS94xO6I+txBCiOiTZOQgZU0rQXeGTzI0DZyJfjInVvRRVAdGM0yyZxdF7m2iYNCIGlsVS3WHYtDoyugFCQw/ojBimXdNVwybVdQvEkAhhDjUSTJykJKz60NrRMKxTI3knLo+iOjAeQa12OoYrOmAzX4umgZDp5ccfHAhiqSh9RHLvEOw+7EnvSWKcwshhOgN/WI3Tfsa22Yrei3Zo6XBa5HQotAjnBwtS9Hgs2gy/X0T2AEwAwHqm+2NbS+lbkeDN5rPW1HfYn/uxkCA1jh+zYUQYiBrP29H2ivTL3bTFBcXk5eXF+swhBBCCHEAioqKyM3N7fbz/SIZsSyL0tJSUlJS0Gz+SVxfX09eXh5FRUVhtxOJvifvTfyS9yZ+yXsTv+S96Z5SioaGBnJyctDD3ELoF7dpdF0Pm1GFk5qaKt8ccUrem/gl7038kvcmfsl707W0tLSIY2QBqxBCCCFiSpIRIYQQQsTUgE1G3G43v/71r3G73bEORexH3pv4Je9N/JL3Jn7Je3Pw+sUCViGEEEIMXAP2yogQQggh+gdJRoQQQggRU5KMCCGEECKm+kWdkQO1bNkytm3bxkknncTQoUNjHc4h76WXXsKyrA6PzZ49m/Hjx8coIrG/1atXU1hYyMyZM8nPz491OIe8NWvWsGHDhi4/d/HFF4ctIiX6xurVq9m5cyeDBg3iiCOOIDExMdYh9UsDNhnZtm0bZ555JlVVVXzwwQeSjMSByy67jGOPPZZhw4aFHktLS5NkJA6Ul5dz4YUXUlBQwNy5c9myZQtXXXUVt9xyS6xDO6StX7+e119/vcNjn376KZZlcfHFF8coKgFQW1vL/Pnz2bFjB3PnzmXnzp2UlJTw0ksvcfLJJ8c6vH5nQCYjPp+PSy+9lFtvvZVbb7011uGIfdx2223Mmzcv1mGIfSilOP/88/F4PGzbto3ExEQsy+Kdd96JdWiHvO985zt85zvfCX3c2tpKdnY2119/vVwVibHHHnuMzZs3s337dgYNGgQE36+f/exnrF69OrbB9UMD8rv5tttuY9KkSR1+iEV8WLt2La+99hqrV6/udMtGxMZHH33EkiVL+NOf/hS6xKzrOmeffXaMIxP7e+WVV6irq+Oaa66JdSiHPK/XS3p6eigRARg1ahQ+ny92QfVjA+7KyDvvvMNrr73GqlWrqK+vj3U4Yh+apvHss8+Sn5/P0qVLyc/P56WXXmL06NGxDu2Q9umnn5KZmcnUqVNZtGgRXq+X6dOnk5OTE+vQxH6efPJJTjrpJMaOHRvrUA55P/rRj1i4cCGXXXYZp59+OoWFhbzwwgs8/vjjsQ6tXxpQyUhZWRnXXHMNCxYsIDU1VZKROPPGG29w5plnAsEul/PmzeN73/seX3zxRYwjO7RVVlaSkpLCscceS0JCAg6Hgy+++IJf/epX3H777bEOT7TZsWMHixcv5vnnn491KIJgU7yjjjqKBQsW0NjYyM6dOxk1ahQjR46MdWj90oBKRm655RbGjRtHcXExL774ItXV1QAsXrwYp9PJCSecEOMID23tiQgEf5BvueUWvvWtb1FdXU1GRkYMIzu0eTweCgoKuPPOO7n66qsBePXVV7nooos488wzmTlzZmwDFAA89dRTZGRkcMEFF8Q6FAHcddddvPLKK6xZsyZ0q+b666/nzDPPZPPmzRiGEdsA+5kBtWZk6tSp5OTk8Nprr/Haa6/x/vvvA/DFF1/w1VdfxTg6sb/2Vtu7d++OcSSHtjFjxgBw4YUXhh4777zz0HWdFStWxCossQ/TNHnmmWe44oorpP9JnPjkk0847bTTOqwZueiii9i+fTtFRUWxC6yfGlBXRm677bYOHxcXF/PGG29wxx13cOqpp8YoKgFQUVFBVlYWmqaFHnv11VfJyMgInQxFbJx11ln89Kc/ZdOmTcydOxeArVu3YpomeXl5MY5OALz//vuUlJTwgx/8INahiDZ5eXls3Lixw2MbN27EMIwO5QuEPQMqGRHx6/PPP+cPf/gD5513HkOGDOHDDz/ktdde46mnnsLhkG/DWBo9ejS33HILF198MT/72c9wOBw8/PDDnHbaaZxyyimxDk8QXLh6zDHHMHny5FiHItrceuutHH/88Vx88cXMmzePgoICHnroIW699VY8Hk+sw+t3BnTX3urqam644QbuvPNOpk6dGutwDnnr16/nxRdfpKysjFGjRnHZZZcxatSoWIcl2rz++uu8/fbbOBwO5s6dy+WXXy73veOA3+/nmmuu4dJLL2X+/PmxDkfsY+fOnfzzn/+ksLCQ9PR0Tj/9dM4444xYh9UvDehkRAghhBDxb0AtYBVCCCFE/yPJiBBCCCFiSpIRIYQQQsSUJCNCCCGEiClJRoQQQggRU5KMCCGEECKmJBkRQgghRExJMiKEEEKImJJkRAghhBAxJcmIEEIIIWJKkhEhhBBCxJQkI0IIIYSIqf8PkkNp3L1yt4AAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#1h)\n",
+    "X_0_3 = iris_data[:,[0,3]]\n",
+    "clf.fit(X_0_3, T)\n",
+    "nnwplot.plotTwoFeatures(X_0_3, T, clf.predict) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "892baf25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_0_3 = iris_data[:,[0,3]]\n",
+    "svc.fit(X_0_3, T)\n",
+    "nnwplot.plotTwoFeatures(X_0_3, T, svc.predict) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bad3e36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1i)\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "knc = KNeighborsClassifier()\n",
+    "knc.fit(X,T)\n",
+    "nnwplot.plotTwoFeatures(X, T, knc.predict)\n",
+    "knc.fit(X_0_3,T)\n",
+    "nnwplot.plotTwoFeatures(X_0_3, T, knc.predict)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d79741d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 3, 3, 3, 1, 3, 1, 3, 1, 3, 1, 1, 1, 1, 3, 1, 3,\n",
+       "       1, 1, 3, 1, 3, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 3, 1, 1, 3, 3,\n",
+       "       1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 2, 3, 3, 2, 1, 2, 3, 2,\n",
+       "       3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 2, 3, 3, 3, 2, 2, 2,\n",
+       "       3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3], dtype=int32)"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 2a) probabilistisches Verfahren\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "X_0_2 = iris_data[:,[0,2]]\n",
+    "km = KMeans(4)\n",
+    "km.fit(X_0_2)\n",
+    "km.predict(X_0_2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88b3d717",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 3a)\n",
+    "X = np.linspace(0, 2)\n",
+    "np.random.seed(42) # vereinfachte Variante, siehe np.random\n",
+    "X = np.sort(np.random.rand(50)*2)\n",
+    "T = np.sin(5*X) + X**2 + np.random.randn(len(X))/10\n",
+    "plt.plot(X, T, '.')\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('t')\n",
+    "plt.show()\n",
+    "# Es gibt 50 Merkmale in den Daten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b251a2c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.04116899 0.06877704 0.09290083 0.11616722 0.13010319 0.19534423\n",
+      " 0.24407647 0.27898772 0.31198904 0.31203728 0.34104825 0.36364993\n",
+      " 0.36680902 0.36970891 0.39934756 0.42467822 0.51755996 0.58245828\n",
+      " 0.5842893  0.60848449 0.60922754 0.62342215 0.73272369 0.74908024\n",
+      " 0.86389004 0.88030499 0.91213997 0.99035382 1.02846888 1.04013604\n",
+      " 1.04951286 1.09342056 1.18482914 1.19731697 1.20223002 1.2150897\n",
+      " 1.22370579 1.32504457 1.36846605 1.41614516 1.46398788 1.57035192\n",
+      " 1.6167947  1.66488528 1.73235229 1.8186408  1.89777107 1.90142861\n",
+      " 1.93126407 1.9398197 ]\n",
+      "[[0.04116899]\n",
+      " [0.06877704]\n",
+      " [0.09290083]\n",
+      " [0.11616722]\n",
+      " [0.13010319]\n",
+      " [0.19534423]\n",
+      " [0.24407647]\n",
+      " [0.27898772]\n",
+      " [0.31198904]\n",
+      " [0.31203728]\n",
+      " [0.34104825]\n",
+      " [0.36364993]\n",
+      " [0.36680902]\n",
+      " [0.36970891]\n",
+      " [0.39934756]\n",
+      " [0.42467822]\n",
+      " [0.51755996]\n",
+      " [0.58245828]\n",
+      " [0.5842893 ]\n",
+      " [0.60848449]\n",
+      " [0.60922754]\n",
+      " [0.62342215]\n",
+      " [0.73272369]\n",
+      " [0.74908024]\n",
+      " [0.86389004]\n",
+      " [0.88030499]\n",
+      " [0.91213997]\n",
+      " [0.99035382]\n",
+      " [1.02846888]\n",
+      " [1.04013604]\n",
+      " [1.04951286]\n",
+      " [1.09342056]\n",
+      " [1.18482914]\n",
+      " [1.19731697]\n",
+      " [1.20223002]\n",
+      " [1.2150897 ]\n",
+      " [1.22370579]\n",
+      " [1.32504457]\n",
+      " [1.36846605]\n",
+      " [1.41614516]\n",
+      " [1.46398788]\n",
+      " [1.57035192]\n",
+      " [1.6167947 ]\n",
+      " [1.66488528]\n",
+      " [1.73235229]\n",
+      " [1.8186408 ]\n",
+      " [1.89777107]\n",
+      " [1.90142861]\n",
+      " [1.93126407]\n",
+      " [1.9398197 ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>.sk-global {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       ".sk-global.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       ".sk-global.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       ".sk-global {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       ".sk-global pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip-path: inset(100%);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       ".sk-global div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       ".sk-global div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       ".sk-global div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       ".sk-global label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".sk-global label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       ".sk-global label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-label label.sk-toggleable__label,\n",
+       ".sk-global div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       ".sk-global div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       ".sk-global div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       ".sk-global div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       ".sk-global a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       ".sk-global a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       ".sk-global a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       ".sk-global a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-top-container.sk-global {\n",
+       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
+       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
+       "   to allow keyboard-only users to navigate the display. */\n",
+       "  overflow-x: scroll !important;\n",
+       "  max-width: 100%;\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover td {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table :is(td, th) {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td, .estimator-table th {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "td.fitted-att-type {\n",
+       "    white-space: preserve nowrap;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "@supports(anchor-name: --doc-link) {\n",
+       "    a.param-doc-link,\n",
+       "    a.param-doc-link:link,\n",
+       "    a.param-doc-link:visited {\n",
+       "    anchor-name: --doc-link;\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "@supports(position-area: center right) {\n",
+       "    .param-doc-description {\n",
+       "    position-area: center right;\n",
+       "    position: fixed;\n",
+       "    margin-left: 0;\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "    border: none;\n",
+       "    padding: 0;\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon:focus-visible {\n",
+       "    outline: 1px solid currentColor;\n",
+       "    outline-offset: 1px;\n",
+       "}\n",
+       "\n",
+       ".features {\n",
+       "  font-family: monospace;\n",
+       "  cursor: pointer;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: .20em;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  font-size: inherit; /* Needed for jupyter */\n",
+       "}\n",
+       "\n",
+       ".features.fitted {\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".features summary {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  margin-bottom: 0;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "  padding: .25em;\n",
+       "}\n",
+       "\n",
+       ".features details[open] > summary {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "  border-radius: .20em 0 0 0;\n",
+       "}\n",
+       "\n",
+       ".features.fitted details[open] > summary {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "  border-radius: .20em 0 0 0;\n",
+       "}\n",
+       "\n",
+       ".features details > summary .arrow::before {\n",
+       "  content: \"▸\";\n",
+       "  color: grey;\n",
+       "}\n",
+       "\n",
+       ".features details[open] > summary .arrow::before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       ".features details:hover > summary {\n",
+       "  margin: 0;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".features.fitted details:hover > summary {\n",
+       "  margin: 0;\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".features .features-container {\n",
+       "  max-height: 10em;\n",
+       "  overflow: auto;\n",
+       "  scrollbar-width: thin;\n",
+       "  padding: .25em 0.1rem;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 0 0 .5em .5em;\n",
+       "}\n",
+       "\n",
+       ".features.fitted .features-container {\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".features .image-container {\n",
+       "  block-size: 1em;\n",
+       "  inline-size: 1em;\n",
+       "  padding: 0;\n",
+       "  margin: 0%;\n",
+       "  display: flex;\n",
+       "  justify-content: center;\n",
+       "  align-items: center;\n",
+       "}\n",
+       "\n",
+       ".features .copy-paste-icon {\n",
+       "  background-size: 1em 1em;\n",
+       "  width: 1em;\n",
+       "  height: 1em;\n",
+       "  filter: grayscale(100%) opacity(60%);\n",
+       "}\n",
+       "\n",
+       ".features .features-container table {\n",
+       "  width: 100%;\n",
+       "  margin: 0.01em;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:nth-child(odd) {\n",
+       "  background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:nth-child(even) {\n",
+       "  background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:hover {\n",
+       "  background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table {\n",
+       "  table-layout: inherit;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table td {\n",
+       "  text-align: left;\n",
+       "  padding: 0 0.5em;\n",
+       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "  white-space: nowrap;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       ".total_features {\n",
+       "  display: flex;\n",
+       "  justify-content: center;\n",
+       "  margin-top: 0.5em;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
+       "            fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy copy_X to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-copy_X;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
+       "            copy_X\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-copy_X;\">\n",
+       "            copy_X: bool, default=True<br><br>If True, X will be copied; else, it may be overwritten.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy tol to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-tol;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-tol;\">\n",
+       "            tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies the convergence criterion of the underlying solver. `tol` is<br>set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. `tol` is set as `cond` of<br>:func:`scipy.linalg.lstsq` when fitting on dense training data.<br><br>.. versionadded:: 1.7<br>.. versionchanged:: 1.9<br>    Now supported on dense data, interpreted as the `cond` parameter.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1e-06</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy n_jobs to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
+       "            n_jobs: int, default=None<br><br>The number of jobs to use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets &gt; 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary &lt;n_jobs&gt;` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
+       "                 aria-label=\"Copy positive to clipboard\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></button></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-positive;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
+       "            positive\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-positive;\">\n",
+       "            positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    \n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Fitted attributes</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                    <tbody>\n",
+       "                        <tr>\n",
+       "                        <th>Name</th>\n",
+       "                        <th>Type</th>\n",
+       "                        <th>Value</th>\n",
+       "                        </tr>\n",
+       "                        \n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-coef_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=coef_,-array%20of%20shape%20%28n_features%2C%20%29%20or%20%28n_targets%2C%20n_features%29\">\n",
+       "            coef_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-coef_;\">\n",
+       "            coef_: array of shape (n_features, ) or (n_targets, n_features)<br><br>Estimated coefficients for the linear regression problem.<br>If multiple targets are passed during the fit (y 2D), this<br>is a 2D array of shape (n_targets, n_features), while if only<br>one target is passed, this is a 1D array of length n_features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
+       "           <td>[1.64]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=intercept_,-float%20or%20array%20of%20shape%20%28n_targets%2C%29\">\n",
+       "            intercept_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
+       "            intercept_: float or array of shape (n_targets,)<br><br>Independent term in the linear model. Set to 0.0 if<br>`fit_intercept = False`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">float64</td>\n",
+       "           <td>-0.1418</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_features_in_,-int\">\n",
+       "            n_features_in_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
+       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">int</td>\n",
+       "           <td>1</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-rank_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=rank_,-int\">\n",
+       "            rank_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-rank_;\">\n",
+       "            rank_: int<br><br>Rank of matrix `X`. Only available when `X` is dense.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">int64</td>\n",
+       "           <td>np.int64(1)</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-singular_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=singular_,-array%20of%20shape%20%28min%28X%2C%20y%29%2C%29\">\n",
+       "            singular_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-singular_;\">\n",
+       "            singular_: array of shape (min(X, y),)<br><br>Singular values of `X`. Only available when `X` is dense.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
+       "           <td>[4.04]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "                    </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
+       " SPDX-License-Identifier: BSD-3-Clause\n",
+       "*/\n",
+       "\n",
+       "function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "\n",
+       "    const parent = element.parentElement;\n",
+       "    if (!parent || !parent.nextElementSibling) {\n",
+       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "/**\n",
+       " * Copy the list of feature names formatted as a Python list.\n",
+       " *\n",
+       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
+       " *   contain a `details` element and a table containing feature named.\n",
+       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
+       " */\n",
+       "function copyFeatureNamesToClipboard(element) {\n",
+       "    var detailsElem = element.closest('.features').querySelector('details');\n",
+       "    var wasOpen = detailsElem.open;\n",
+       "    detailsElem.open = true;\n",
+       "    var content = element.closest('.features').querySelector('tbody')\n",
+       "                  .innerText.trim();\n",
+       "    if (!wasOpen) detailsElem.open = false;\n",
+       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
+       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
+       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
+       "    const originalStyle = element.style;\n",
+       "    const copyMark = document.createElement('span');\n",
+       "    copyMark.innerHTML = '✔';\n",
+       "    copyMark.style.color = 'blue';\n",
+       "    copyMark.style.fontSize = '1em';\n",
+       "\n",
+       "    navigator.clipboard.writeText(formattedText)\n",
+       "        .then(() => {\n",
+       "            element.style.display = 'none';\n",
+       "            element.parentElement.appendChild(copyMark);\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                copyMark.remove();\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 1000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'orange';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 1000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-3');</script></body>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 3b)\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "reg = LinearRegression()\n",
+    "print(X)\n",
+    "print(X.reshape(-1,1)) # transponiert den Merkmalsraum in einen Spaltenvektor\n",
+    "reg.fit(X.reshape(-1,1),T)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "98476c93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.07437258 -0.0291456   0.0103735   0.04848806  0.07131768  0.17819428\n",
+      "  0.25802648  0.31521741  0.36927952  0.36935855  0.41688375  0.45390939\n",
+      "  0.45908454  0.46383508  0.51238855  0.55388473  0.70604178  0.81235693\n",
+      "  0.81535647  0.85499255  0.85620981  0.87946314  1.05851877  1.08531376\n",
+      "  1.27339292  1.30028357  1.35243501  1.48056341  1.54300275  1.56211567\n",
+      "  1.57747659  1.64940532  1.79914906  1.81960638  1.82765485  1.84872133\n",
+      "  1.86283603  2.02884723  2.09997946  2.17808643  2.25646145  2.43070493\n",
+      "  2.50678657  2.58556763  2.69609076  2.83744691  2.96707657  2.97306828\n",
+      "  3.02194413  3.03595981]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 3c)\n",
+    "plt.plot(X, T, '.')\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('t')\n",
+    "plt.plot(X,reg.predict(X.reshape(-1,1)))\n",
+    "plt.show()\n",
+    "print(reg.predict(X.reshape(-1,1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94ab4bd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 3d) Decision Tree Regressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.svm import SVR\n",
+    "\n",
+    "dtr = DecisionTreeRegressor()\n",
+    "dtr.fit(X.reshape(-1,1),T)\n",
+    "\n",
+    "plt.plot(X, T, '.')\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('t')\n",
+    "plt.plot(X,dtr.predict(X.reshape(-1,1)))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "475aa6e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 3d) SV Regressor\n",
+    "from sklearn.svm import SVR\n",
+    "\n",
+    "svr = SVR()\n",
+    "svr.fit(X.reshape(-1,1),T)\n",
+    "\n",
+    "plt.plot(X, T, '.')\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('t')\n",
+    "plt.plot(X,svr.predict(X.reshape(-1,1)))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b787c0a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 3e) -- TODO warum sieht es anders aus? \n",
+    "svr_iris = SVR()\n",
+    "X = iris_data[:,[0,1]]\n",
+    "T = iris_data[:,4]\n",
+    "svr_iris.fit(X,T)\n",
+    "nnwplot.plotTwoFeatures(X, T, svr_iris.predict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "ef499321",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 3f)\n",
+    "decision = lambda x : np.round(svr_iris.predict(x)) # kurze Funktionsdefinition\n",
+    "y = decision(X)\n",
+    "nnwplot.plotTwoFeatures(X, T, decision)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nnw (3.14.x)",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Lösungen_N/AB2.ipynb.orig b/Lösungen_N/AB2.ipynb.orig
deleted file mode 100644
index 45be331..0000000
--- a/Lösungen_N/AB2.ipynb.orig
+++ /dev/null
@@ -1,1894 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 73,
-   "id": "29e20c6b",
-=======
-   "execution_count": 2,
-   "id": "de4f7f4b",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-<<<<<<< HEAD
-    "from sklearn.svm import LinearSVC"
-=======
-    "from sklearn.svm import LinearSVC\n",
-    "import matplotlib.pyplot as plt"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 74,
-   "id": "67b3d7af",
-=======
-   "execution_count": 3,
-   "id": "88b3d717",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3a)\n",
-    "X = np.linspace(0, 2)\n",
-    "np.random.seed(42) # vereinfachte Variante, siehe np.random\n",
-    "X = np.sort(np.random.rand(50)*2)\n",
-    "T = np.sin(5*X) + X**2 + np.random.randn(len(X))/10\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.show()\n",
-    "# Es gibt 50 Merkmale in den Daten"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b251a2c1",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-<<<<<<< HEAD
-      "[[5.1 3.5 1.4 0.2 0. ]\n",
-      " [4.9 3.  1.4 0.2 0. ]\n",
-      " [4.7 3.2 1.3 0.2 0. ]\n",
-      " [4.6 3.1 1.5 0.2 0. ]\n",
-      " [5.  3.6 1.4 0.2 0. ]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 1a) Ja stehen in der richtigen Dimension\n",
-    "iris_data= np.loadtxt(\"../supplement_files/iris.csv\", delimiter=\",\")\n",
-    "print(iris_data[:5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "c9c0fd1e",
-   "metadata": {},
-   "outputs": [
-=======
-      "[0.04116899 0.06877704 0.09290083 0.11616722 0.13010319 0.19534423\n",
-      " 0.24407647 0.27898772 0.31198904 0.31203728 0.34104825 0.36364993\n",
-      " 0.36680902 0.36970891 0.39934756 0.42467822 0.51755996 0.58245828\n",
-      " 0.5842893  0.60848449 0.60922754 0.62342215 0.73272369 0.74908024\n",
-      " 0.86389004 0.88030499 0.91213997 0.99035382 1.02846888 1.04013604\n",
-      " 1.04951286 1.09342056 1.18482914 1.19731697 1.20223002 1.2150897\n",
-      " 1.22370579 1.32504457 1.36846605 1.41614516 1.46398788 1.57035192\n",
-      " 1.6167947  1.66488528 1.73235229 1.8186408  1.89777107 1.90142861\n",
-      " 1.93126407 1.9398197 ]\n",
-      "[[0.04116899]\n",
-      " [0.06877704]\n",
-      " [0.09290083]\n",
-      " [0.11616722]\n",
-      " [0.13010319]\n",
-      " [0.19534423]\n",
-      " [0.24407647]\n",
-      " [0.27898772]\n",
-      " [0.31198904]\n",
-      " [0.31203728]\n",
-      " [0.34104825]\n",
-      " [0.36364993]\n",
-      " [0.36680902]\n",
-      " [0.36970891]\n",
-      " [0.39934756]\n",
-      " [0.42467822]\n",
-      " [0.51755996]\n",
-      " [0.58245828]\n",
-      " [0.5842893 ]\n",
-      " [0.60848449]\n",
-      " [0.60922754]\n",
-      " [0.62342215]\n",
-      " [0.73272369]\n",
-      " [0.74908024]\n",
-      " [0.86389004]\n",
-      " [0.88030499]\n",
-      " [0.91213997]\n",
-      " [0.99035382]\n",
-      " [1.02846888]\n",
-      " [1.04013604]\n",
-      " [1.04951286]\n",
-      " [1.09342056]\n",
-      " [1.18482914]\n",
-      " [1.19731697]\n",
-      " [1.20223002]\n",
-      " [1.2150897 ]\n",
-      " [1.22370579]\n",
-      " [1.32504457]\n",
-      " [1.36846605]\n",
-      " [1.41614516]\n",
-      " [1.46398788]\n",
-      " [1.57035192]\n",
-      " [1.6167947 ]\n",
-      " [1.66488528]\n",
-      " [1.73235229]\n",
-      " [1.8186408 ]\n",
-      " [1.89777107]\n",
-      " [1.90142861]\n",
-      " [1.93126407]\n",
-      " [1.9398197 ]]\n"
-     ]
-    },
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-    {
-     "data": {
-      "text/html": [
-       "<style>.sk-global {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       ".sk-global.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       ".sk-global.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       ".sk-global {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".sk-global pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip-path: inset(100%);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       ".sk-global div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       ".sk-global label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label label.sk-toggleable__label,\n",
-       ".sk-global div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       ".sk-global div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       ".sk-global div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       ".sk-global div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       ".sk-global a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       ".sk-global a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-top-container.sk-global {\n",
-       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
-       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
-       "   to allow keyboard-only users to navigate the display. */\n",
-       "  overflow-x: scroll !important;\n",
-       "  max-width: 100%;\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover td {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table :is(td, th) {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
-       "*/\n",
-       ".estimator-table table td.param {\n",
-       "    text-align: left;\n",
-       "    position: relative;\n",
-       "    padding: 0;\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".default td, .estimator-table th {\n",
-       "    color: black;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       "td.fitted-att-type {\n",
-       "    white-space: preserve nowrap;\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    Styles for parameter documentation links\n",
-       "    We need styling for visited so jupyter doesn't overwrite it\n",
-       "*/\n",
-       "a.param-doc-link,\n",
-       "a.param-doc-link:link,\n",
-       "a.param-doc-link:visited {\n",
-       "    text-decoration: underline dashed;\n",
-       "    text-underline-offset: .3em;\n",
-       "    color: inherit;\n",
-       "    display: block;\n",
-       "    padding: .5em;\n",
-       "}\n",
-       "\n",
-       "@supports(anchor-name: --doc-link) {\n",
-       "    a.param-doc-link,\n",
-       "    a.param-doc-link:link,\n",
-       "    a.param-doc-link:visited {\n",
-       "    anchor-name: --doc-link;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
-       "a.param-doc-link::before {\n",
-       "    position: absolute;\n",
-       "    content: \"\";\n",
-       "    inset: 0;\n",
-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
-       "    display: none;\n",
-       "    position: absolute;\n",
-       "    z-index: 9999;\n",
-       "    left: 0;\n",
-       "    padding: .5ex;\n",
-       "    margin-left: 1.5em;\n",
-       "    color: var(--sklearn-color-text);\n",
-       "    box-shadow: .3em .3em .4em #999;\n",
-       "    width: max-content;\n",
-       "    text-align: left;\n",
-       "    max-height: 10em;\n",
-       "    overflow-y: auto;\n",
-       "\n",
-       "    /* unfitted */\n",
-       "    background: var(--sklearn-color-unfitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       "@supports(position-area: center right) {\n",
-       "    .param-doc-description {\n",
-       "    position-area: center right;\n",
-       "    position: fixed;\n",
-       "    margin-left: 0;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
-       "    /* fitted */\n",
-       "    background: var(--sklearn-color-fitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "    border: none;\n",
-       "    padding: 0;\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon:focus-visible {\n",
-       "    outline: 1px solid currentColor;\n",
-       "    outline-offset: 1px;\n",
-       "}\n",
-       "\n",
-       ".features {\n",
-       "  font-family: monospace;\n",
-       "  cursor: pointer;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
-       "}\n",
-       "\n",
-       ".features.fitted {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features summary {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  margin-bottom: 0;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "  padding: .25em;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features.fitted details[open] > summary {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features details > summary .arrow::before {\n",
-       "  content: \"▸\";\n",
-       "  color: grey;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary .arrow::before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       ".features details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features.fitted details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features .features-container {\n",
-       "  max-height: 10em;\n",
-       "  overflow: auto;\n",
-       "  scrollbar-width: thin;\n",
-       "  padding: .25em 0.1rem;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 0 0 .5em .5em;\n",
-       "}\n",
-       "\n",
-       ".features.fitted .features-container {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features .image-container {\n",
-       "  block-size: 1em;\n",
-       "  inline-size: 1em;\n",
-       "  padding: 0;\n",
-       "  margin: 0%;\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  align-items: center;\n",
-       "}\n",
-       "\n",
-       ".features .copy-paste-icon {\n",
-       "  background-size: 1em 1em;\n",
-       "  width: 1em;\n",
-       "  height: 1em;\n",
-       "  filter: grayscale(100%) opacity(60%);\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  width: 100%;\n",
-       "  margin: 0.01em;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(odd) {\n",
-       "  background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(even) {\n",
-       "  background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:hover {\n",
-       "  background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  table-layout: inherit;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table td {\n",
-       "  text-align: left;\n",
-       "  padding: 0 0.5em;\n",
-       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "  white-space: nowrap;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".total_features {\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  margin-top: 0.5em;\n",
-       "}\n",
-<<<<<<< HEAD
-       "</style><body><div id=\"sk-container-id-13\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-=======
-       "</style><body><div id=\"sk-container-id-2\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-<<<<<<< HEAD
-       "                 aria-label=\"Copy penalty to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-=======
-       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-penalty;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%7D%2C%20default%3D%27l2%27\">\n",
-       "            penalty\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-penalty;\">\n",
-       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;}, default=&#x27;l2&#x27;<br><br>Specifies the norm used in the penalization. The &#x27;l2&#x27;<br>penalty is the standard used in SVC. The &#x27;l1&#x27; leads to ``coef_``<br>vectors that are sparse.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-<<<<<<< HEAD
-       "                 aria-label=\"Copy loss to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('loss',\n",
-=======
-       "                 aria-label=\"Copy copy_X to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('copy_X',\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-loss;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=loss,-%7B%27hinge%27%2C%20%27squared_hinge%27%7D%2C%20default%3D%27squared_hinge%27\">\n",
-       "            loss\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-loss;\">\n",
-       "            loss: {&#x27;hinge&#x27;, &#x27;squared_hinge&#x27;}, default=&#x27;squared_hinge&#x27;<br><br>Specifies the loss function. &#x27;hinge&#x27; is the standard SVM loss<br>(used e.g. by the SVC class) while &#x27;squared_hinge&#x27; is the<br>square of the hinge loss. The combination of ``penalty=&#x27;l1&#x27;``<br>and ``loss=&#x27;hinge&#x27;`` is not supported.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;squared_hinge&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy dual to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-dual;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=dual,-%22auto%22%20or%20bool%2C%20default%3D%22auto%22\">\n",
-       "            dual\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-dual;\">\n",
-       "            dual: &quot;auto&quot; or bool, default=&quot;auto&quot;<br><br>Select the algorithm to either solve the dual or primal<br>optimization problem. Prefer dual=False when n_samples &gt; n_features.<br>`dual=&quot;auto&quot;` will choose the value of the parameter automatically,<br>based on the values of `n_samples`, `n_features`, `loss`, `multi_class`<br>and `penalty`. If `n_samples` &lt; `n_features` and optimizer supports<br>chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,<br>otherwise it will be set to False.<br><br>.. versionchanged:: 1.3<br>   The `&quot;auto&quot;` option is added in version 1.3 and will be the default<br>   in version 1.5.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-copy_X;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
-       "            copy_X\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-copy_X;\">\n",
-       "            copy_X: bool, default=True<br><br>If True, X will be copied; else, it may be overwritten.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy tol to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-tol;\"\n",
-<<<<<<< HEAD
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-=======
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies the convergence criterion of the underlying solver. `tol` is<br>set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. `tol` is set as `cond` of<br>:func:`scipy.linalg.lstsq` when fitting on dense training data.<br><br>.. versionadded:: 1.7<br>.. versionchanged:: 1.9<br>    Now supported on dense data, interpreted as the `cond` parameter.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1e-06</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-<<<<<<< HEAD
-       "                 aria-label=\"Copy C to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-=======
-       "                 aria-label=\"Copy n_jobs to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-C;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
-       "            C\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-C;\">\n",
-       "            C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>For an intuitive visualization of the effects of scaling<br>the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy multi_class to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-multi_class;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=multi_class,-%7B%27ovr%27%2C%20%27crammer_singer%27%7D%2C%20default%3D%27ovr%27\">\n",
-       "            multi_class\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-multi_class;\">\n",
-       "            multi_class: {&#x27;ovr&#x27;, &#x27;crammer_singer&#x27;}, default=&#x27;ovr&#x27;<br><br>Determines the multi-class strategy if `y` contains more than<br>two classes.<br>``&quot;ovr&quot;`` trains n_classes one-vs-rest classifiers, while<br>``&quot;crammer_singer&quot;`` optimizes a joint objective over all classes.<br>While `crammer_singer` is interesting from a theoretical perspective<br>as it is consistent, it is seldom used in practice as it rarely leads<br>to better accuracy and is more expensive to compute.<br>If ``&quot;crammer_singer&quot;`` is chosen, the options loss, penalty and dual<br>will be ignored.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Whether or not to fit an intercept. If set to True, the feature vector<br>is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where<br>1 corresponds to the intercept. If set to False, no intercept will be<br>used in calculations (i.e. data is expected to be already centered).</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy intercept_scaling to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_scaling,-float%2C%20default%3D1.0\">\n",
-       "            intercept_scaling\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
-       "            intercept_scaling: float, default=1.0<br><br>When `fit_intercept` is True, the instance vector x becomes ``[x_1,<br>..., x_n, intercept_scaling]``, i.e. a &quot;synthetic&quot; feature with a<br>constant value equal to `intercept_scaling` is appended to the instance<br>vector. The intercept becomes intercept_scaling * synthetic feature<br>weight. Note that liblinear internally penalizes the intercept,<br>treating it like any other term in the feature vector. To reduce the<br>impact of the regularization on the intercept, the `intercept_scaling`<br>parameter can be set to a value greater than 1; the higher the value of<br>`intercept_scaling`, the lower the impact of regularization on it.<br>Then, the weights become `[w_x_1, ..., w_x_n,<br>w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent<br>the feature weights and the intercept weight is scaled by<br>`intercept_scaling`. This scaling allows the intercept term to have a<br>different regularization behavior compared to the other features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy class_weight to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
-       "            class_weight\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
-       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Set the parameter C of class i to ``class_weight[i]*C`` for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
-=======
-       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
-       "            n_jobs\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
-       "            n_jobs: int, default=None<br><br>The number of jobs to use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets &gt; 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary &lt;n_jobs&gt;` for more details.</span>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-<<<<<<< HEAD
-       "                 aria-label=\"Copy verbose to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-=======
-       "                 aria-label=\"Copy positive to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('positive',\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-verbose;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
-       "            verbose\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-verbose;\">\n",
-       "            verbose: int, default=0<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in liblinear that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy random_state to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-random_state;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
-       "            random_state\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-random_state;\">\n",
-       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>the dual coordinate descent (if ``dual=True``). When ``dual=False`` the<br>underlying implementation of :class:`LinearSVC` is not random and<br>``random_state`` has no effect on the results.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy max_iter to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=max_iter,-int%2C%20default%3D1000\">\n",
-       "            max_iter\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
-       "            max_iter: int, default=1000<br><br>The maximum number of iterations to be run.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1000</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-positive;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
-       "            positive\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-positive;\">\n",
-       "            positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    \n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Fitted attributes</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                    <tbody>\n",
-       "                        <tr>\n",
-       "                        <th>Name</th>\n",
-       "                        <th>Type</th>\n",
-       "                        <th>Value</th>\n",
-       "                        </tr>\n",
-       "                        \n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-classes_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
-       "            classes_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-classes_;\">\n",
-       "            classes_: ndarray of shape (n_classes,)<br><br>The unique classes labels.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
-       "           <td>[0.,1.,2.]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=coef_,-ndarray%20of%20shape%20%281%2C%20n_features%29%20if%20n_classes%20%3D%3D%202%20%20%20%20%20%20%20%20%20%20%20%20%20else%20%28n_classes%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: ndarray of shape (1, n_features) if n_classes == 2             else (n_classes, n_features)<br><br>Weights assigned to the features (coefficients in the primal<br>problem).<br><br>``coef_`` is a readonly property derived from ``raw_coef_`` that<br>follows the internal memory layout of liblinear.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3, 2)</td>\n",
-       "           <td>[[-1.54, 2.28],\n",
-       " [ 0.1 ,-0.98],\n",
-       " [ 0.66,-0.35]]</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=coef_,-array%20of%20shape%20%28n_features%2C%20%29%20or%20%28n_targets%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: array of shape (n_features, ) or (n_targets, n_features)<br><br>Estimated coefficients for the linear regression problem.<br>If multiple targets are passed during the fit (y 2D), this<br>is a 2D array of shape (n_targets, n_features), while if only<br>one target is passed, this is a 1D array of length n_features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[1.64]</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
-<<<<<<< HEAD
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20if%20n_classes%20%3D%3D%202%20else%20%28n_classes%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: ndarray of shape (1,) if n_classes == 2 else (n_classes,)<br><br>Constants in decision function.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
-       "           <td>[ 1.2 , 2.02,-3.14]</td>\n",
-=======
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=intercept_,-float%20or%20array%20of%20shape%20%28n_targets%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: float or array of shape (n_targets,)<br><br>Independent term in the linear model. Set to 0.0 if<br>`fit_intercept = False`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">float64</td>\n",
-       "           <td>-0.1418</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
-<<<<<<< HEAD
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_features_in_,-int\">\n",
-=======
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_features_in_,-int\">\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "            n_features_in_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
-       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-<<<<<<< HEAD
-       "           <td>2</td>\n",
-=======
-       "           <td>1</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_iter_,-int\">\n",
-       "            n_iter_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
-       "            n_iter_: int<br><br>Maximum number of iterations run across all classes.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>8</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-rank_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=rank_,-int\">\n",
-       "            rank_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-rank_;\">\n",
-       "            rank_: int<br><br>Rank of matrix `X`. Only available when `X` is dense.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int64</td>\n",
-       "           <td>np.int64(1)</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-singular_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=singular_,-array%20of%20shape%20%28min%28X%2C%20y%29%2C%29\">\n",
-       "            singular_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-singular_;\">\n",
-       "            singular_: array of shape (min(X, y),)<br><br>Singular values of `X`. Only available when `X` is dense.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[4.04]</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "                    </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
-       " SPDX-License-Identifier: BSD-3-Clause\n",
-       "*/\n",
-       "\n",
-       "function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "\n",
-       "    const parent = element.parentElement;\n",
-       "    if (!parent || !parent.nextElementSibling) {\n",
-       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    const paramName = element.parentElement.nextElementSibling\n",
-       "        .textContent.trim().split(' ')[0];\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "\n",
-       "/**\n",
-       " * Copy the list of feature names formatted as a Python list.\n",
-       " *\n",
-       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
-       " *   contain a `details` element and a table containing feature named.\n",
-       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
-       " */\n",
-       "function copyFeatureNamesToClipboard(element) {\n",
-       "    var detailsElem = element.closest('.features').querySelector('details');\n",
-       "    var wasOpen = detailsElem.open;\n",
-       "    detailsElem.open = true;\n",
-       "    var content = element.closest('.features').querySelector('tbody')\n",
-       "                  .innerText.trim();\n",
-       "    if (!wasOpen) detailsElem.open = false;\n",
-       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
-       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
-<<<<<<< HEAD
-       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
-       "    const originalStyle = element.style;\n",
-       "    const copyMark = document.createElement('span');\n",
-       "    copyMark.innerHTML = '✔';\n",
-=======
-       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
-       "    const originalStyle = element.style;\n",
-       "    const copyMark = document.createElement('span');\n",
-       "    copyMark.innerHTML = '✔';\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "    copyMark.style.color = 'blue';\n",
-       "    copyMark.style.fontSize = '1em';\n",
-       "\n",
-       "    navigator.clipboard.writeText(formattedText)\n",
-       "        .then(() => {\n",
-       "            element.style.display = 'none';\n",
-       "            element.parentElement.appendChild(copyMark);\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                copyMark.remove();\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'orange';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "/**\n",
-       " * Adapted from Skrub\n",
-       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
-       " * @returns \"light\" or \"dark\"\n",
-       " */\n",
-       "function detectTheme(element) {\n",
-       "    const body = document.querySelector('body');\n",
-       "\n",
-       "    // Check VSCode theme\n",
-       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
-       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
-       "\n",
-       "    if (themeKindAttr && themeNameAttr) {\n",
-       "        const themeKind = themeKindAttr.toLowerCase();\n",
-       "        const themeName = themeNameAttr.toLowerCase();\n",
-       "\n",
-       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
-       "            return \"dark\";\n",
-       "        }\n",
-       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
-       "            return \"light\";\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    // Check Jupyter theme\n",
-       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
-       "        return 'dark';\n",
-       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
-       "        return 'light';\n",
-       "    }\n",
-       "\n",
-       "    // Guess based on a parent element's color\n",
-       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
-       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
-       "    if (match) {\n",
-       "        const [r, g, b] = [\n",
-       "            parseFloat(match[1]),\n",
-       "            parseFloat(match[2]),\n",
-       "            parseFloat(match[3])\n",
-       "        ];\n",
-       "\n",
-       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
-       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
-       "\n",
-       "        if (luma > 180) {\n",
-       "            // If the text is very bright we have a dark theme\n",
-       "            return 'dark';\n",
-       "        }\n",
-       "        if (luma < 75) {\n",
-       "            // If the text is very dark we have a light theme\n",
-       "            return 'light';\n",
-       "        }\n",
-       "        // Otherwise fall back to the next heuristic.\n",
-       "    }\n",
-       "\n",
-       "    // Fallback to system preference\n",
-       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
-       "}\n",
-       "\n",
-       "\n",
-       "function forceTheme(elementId) {\n",
-       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
-       "    if (estimatorElement === null) {\n",
-       "        console.error(`Element with id ${elementId} not found.`);\n",
-       "    } else {\n",
-       "        const theme = detectTheme(estimatorElement);\n",
-       "        estimatorElement.classList.add(theme);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-<<<<<<< HEAD
-       "forceTheme('sk-container-id-13');</script></body>"
-      ],
-      "text/plain": [
-       "LinearSVC()"
-      ]
-     },
-     "execution_count": 75,
-=======
-       "forceTheme('sk-container-id-2');</script></body>"
-      ],
-      "text/plain": [
-       "LinearRegression()"
-      ]
-     },
-     "execution_count": 12,
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-<<<<<<< HEAD
-    "# 1b)\n",
-    "X = iris_data[:,[0,1]]\n",
-    "T = iris_data[:,4]\n",
-    "clf.fit(X, T)"
-=======
-    "# 3b)\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "reg = LinearRegression()\n",
-    "print(X)\n",
-    "print(X.reshape(-1,1)) # transponiert den Merkmalsraum in einen Spaltenvektor\n",
-    "reg.fit(X.reshape(-1,1),T)\n"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 76,
-   "id": "cc181a3a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "80.0 Prozent wurden richtig vorausgesagt\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 1 c+d)\n",
-    "pred = clf.predict(X)\n",
-    "correct_perc = (sum(pred == T)/len(pred))*100\n",
-    "\n",
-    "print(f\"{correct_perc} Prozent wurden richtig vorausgesagt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "16af32f1",
-=======
-   "execution_count": 17,
-   "id": "98476c93",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-<<<<<<< HEAD
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1e)\n",
-    "import nnwplot\n",
-    "nnwplot.plotTwoFeatures(X, T, clf.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "f4bcee12",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1f)\n",
-    "from sklearn.svm import SVC\n",
-    "svc = SVC()\n",
-    "svc.fit(X, T)\n",
-    "nnwplot.plotTwoFeatures(X, T, svc.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "2c8991de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1g)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "1a5e84f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#1h)\n",
-    "X_0_3 = iris_data[:,[0,3]]\n",
-    "clf.fit(X_0_3, T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, clf.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "892baf25",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X_0_3 = iris_data[:,[0,3]]\n",
-    "svc.fit(X_0_3, T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, svc.predict) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "1bad3e36",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-=======
-      "image/png": "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",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-<<<<<<< HEAD
-     "data": {
-      "image/png": "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",
-=======
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.07437258 -0.0291456   0.0103735   0.04848806  0.07131768  0.17819428\n",
-      "  0.25802648  0.31521741  0.36927952  0.36935855  0.41688375  0.45390939\n",
-      "  0.45908454  0.46383508  0.51238855  0.55388473  0.70604178  0.81235693\n",
-      "  0.81535647  0.85499255  0.85620981  0.87946314  1.05851877  1.08531376\n",
-      "  1.27339292  1.30028357  1.35243501  1.48056341  1.54300275  1.56211567\n",
-      "  1.57747659  1.64940532  1.79914906  1.81960638  1.82765485  1.84872133\n",
-      "  1.86283603  2.02884723  2.09997946  2.17808643  2.25646145  2.43070493\n",
-      "  2.50678657  2.58556763  2.69609076  2.83744691  2.96707657  2.97306828\n",
-      "  3.02194413  3.03595981]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 3c)\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,reg.predict(X.reshape(-1,1)))\n",
-    "plt.show()\n",
-    "print(reg.predict(X.reshape(-1,1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "94ab4bd3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-<<<<<<< HEAD
-    "# 1i)\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "knc = KNeighborsClassifier()\n",
-    "knc.fit(X,T)\n",
-    "nnwplot.plotTwoFeatures(X, T, knc.predict)\n",
-    "knc.fit(X_0_3,T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, knc.predict)\n",
-    "\n"
-=======
-    "# 3d) Decision Tree Regressor\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "dtr = DecisionTreeRegressor()\n",
-    "dtr.fit(X.reshape(-1,1),T)\n",
-    "\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,dtr.predict(X.reshape(-1,1)))\n",
-    "plt.show()"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 101,
-   "id": "d79741d2",
-=======
-   "execution_count": 20,
-   "id": "475aa6e4",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-<<<<<<< HEAD
-      "text/plain": [
-       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 3, 3, 3, 1, 3, 1, 3, 1, 3, 1, 1, 1, 1, 3, 1, 3,\n",
-       "       1, 1, 3, 1, 3, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 3, 1, 1, 3, 3,\n",
-       "       1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 2, 3, 3, 2, 1, 2, 3, 2,\n",
-       "       3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 2, 3, 3, 3, 2, 2, 2,\n",
-       "       3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3], dtype=int32)"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 2a) probabilistisches Verfahren\n",
-    "from sklearn.cluster import KMeans\n",
-    "\n",
-    "X_0_2 = iris_data[:,[0,2]]\n",
-    "km = KMeans(4)\n",
-    "km.fit(X_0_2)\n",
-    "km.predict(X_0_2)\n"
-=======
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj4AAAGwCAYAAACpYG+ZAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAW8NJREFUeJzt3Xd4FOXexvHvbhKWJKQQQgkQeu8dBEVBBAUUKYqCFRWxoNgFjx7bEXs/6lE58AqiHBREQFSqCNKlg4LUAKGnkJCEJDvvH2MWliSQwCaz5f5c116ZnZ3Z/Q2bzd4888zz2AzDMBAREREJAHarCxAREREpLQo+IiIiEjAUfERERCRgKPiIiIhIwFDwERERkYCh4CMiIiIBQ8FHREREAkaw1QWUNqfTyYEDB4iIiMBms1ldjoiIiBSBYRicOHGCqlWrYrdfeLtNwAWfAwcOEB8fb3UZIiIicgESEhKoXr36Be8fcMEnIiICMP/hIiMjLa5GREREiiI1NZX4+HjX9/iFCrjgk3d6KzIyUsFHRETEx1xsNxV1bhYREZGAoeAjIiIiAUPBR0RERAKGgo+IiIgEDAUfERERCRgKPiIiIhIwFHxEREQkYCj4iIiISMBQ8BEREZGAoeAjIiIiAUPBR0RERAKGgo+IiEghElMy+G3HURJTMqwuRTwk4CYpFRERKYopq/YyetpGnAbYbTB2QHMGt69hdVlykdTiIyIicpbElAxX6AFwGjBm2ia1/PgBBR8REZGz7Dqa7go9eXINg91HT1pTkHiMgo+IiMhZaseGY7e5rwuy2agVG2ZNQeIx6uMjIiJylrioUMYOaM6YaZvINQyCbDZeGdCMuKjQknnBk8dh07dwYC2Uqwzla0J0TfNnVDwEhZTM6wYgBR8REZECDG5fg64NKrL76ElqxYZ5PvTk5sCO+bDuS/hzDuSeKng7mx0iq50OQq6fNczliDiw6wROUSn4iIiIFCIuKtTzgefQFjPsbPgfpB8+vb5yc2h4NWQkQdIeSN4DyXshJxNSEszbniX5ny+ojNkq5BaKzvgZVgFstvz7BSgFHxERkZKWfgw2fQPrJkPiutPrw2KhxY3Q8maIa5F/P8OAtMNmCEraA8m7T4eipD2Qss9sKTq+w7wVpGpruPpVqNGpJI7M59gMwzDOv5n/SE1NJSoqipSUFCIjI60uR0RE/FVuNmyfa7bubPsJnNnmenuI2bLTcgjUv+ri+u/k5kDq/tOtQ2eGouQ9cCLx9LbNBsFVL0JUtQt6qcSUDHYdTad2bHjJ9XU6B099f6vFR0RExJMSN8D6r8xTWSePnl4f1wpaDTEDSHgFz7xWULB5Sqt8zYIfTzsMC16C3yeaLU5//gCXPgqdH4SQoocXfxrMUS0+IiIiHnD89+9w/Poq4UlbT68MrwQtB5utO5WbnHP/Em1RObAO5jwFCcvN+9E1oOfL0Pi68/b/SUzJoMurC9zGNQqy2VjydLdSbflRi4+IiIg3yM1h6+QnaLzjvwBkGcEcqtqdGt3uhrpXmq0y51HiLSpVW8GwH81L5uc+Z54W+99tUOsyuOY1qNy00F3PNZijFae8LpaufxMREblQ6UfJmtDPFXrG5VxDh6yP6Lb7ThIrdy1S6CnJ6THcJlm12aD5IHhwFXR9EoLLwu5f4ZNLYfZj5lhCBfC3wRwVfERERC7EvjXwn8txJCwh3XDw4KmRvJRzKymUK9b0FiU1PcaUVXvp8uoChny2gi6vLmDKqr3mA2XCofsz8MBKaNIPDCes+hzebw0rPjU7TJ8hbzDHoL9PiZX4YI4lTKe6REREisMwYM0EmPMk5J4ip3xdBhy6lz+d1V2bFKdFJK9F5ew+NBfTolJYK1LXBhVPB5byNeHGL2DXYpjzNBzeDHOegDXjzcvf61zuer68wRzX7E4CG7StWf6Ca7OaWnxERESKKjsDvn8QZo0yx89p1JfgexcyrP81F9wiUhItKsVqRardFe5dDH3egtDycHgLfHEdfD0Ukna7Nlu87QgPfb2WByevdW9B8jG6qktERKQokvbA/26FxPXmNBJXPgddRrmuikpMybio6S0udv+zn+uCrsQ6eRwWjYVV48DIhSAHdH6Qgy3up/PbK4r0fCV1dZqnvr8tDz7Hjx9ny5YtREdH07hxY4KCggrdNjc3l7lz5+Zb37x5c6pVK9qATAo+IiJSbH/Ng2/vNqeTCKsAA8dB3W5WV3VOU1btzTfJapGvFDu0BX58Gnb9AkBWaGWeTBnIDGcX4HRP56/u6cQldU+PSVSSV6f5fPBJSUnhoYce4ueff6ZBgwbs2rWL4OBgvvrqKzp27FjgPmlpaURERNCpUyeioqJc6x977DGuuuqqIr2ugo+IiBSZ0wm/vgUL/wUYULWN2S8mOt7qyorkolqRDAP+mA0/jTFHgQYW5bZkVPb9JBORr8WnpMf78flxfJKTk+nXrx8TJkzAZrPhdDq56aabuOuuu9i0adM5933nnXfo1ElzjoiISPEV+VRMThZMvRP+nG3eb3sHXP0ahJQtlTo94aImWbXZoHFfqNcDlv+bnIWvcQXr+c72HMNznuCu/r3cnttXxvuxLPjUrFmTmjVPD7Ftt9tp27Ytv/zyy3n33b59O1lZWdStW5fq1aufd3sREREoxqkYpxOmjzBDT5DD7Pjb5tbSL9gbhJSFyx4juH4vcibfRK3UBH4s9wL26NrA6X+7krg6rSRYflXX6tWr+eGHH3j33Xd5//33ef3118+7z3PPPcfTTz9Nw4YN6du3L0ePHi1026ysLFJTU91uIiISeIo1UODcZ2HzNHNC0SFTAjf0nKlKM4LvXQQ1OmM/dQIm3wDLPjJPieE74/1YPo7Pt99+y8qVK/njjz+oX79+of17AIKDg5k2bRr9+/cHYP/+/XTv3p0RI0bwzTffFLjP2LFjeeGFF0qkdhER8R1FPhXz24ew7ENz+fqPvL4Tc6kKj4XbZsDsR2DtJPhpNBzZCr3fguAyrvF+PHV1Wkmw/KquPDk5OYwYMYKff/6Z7du343A4irTf559/zv333096ejohISH5Hs/KyiIrK8t1PzU1lfj4eHVuFhEJMEXqfLvxG/j2LnP5qhehy8OlX6gvMAxY9m+zZcxwQs1LzU7fnpp1vgCe6txs+amuPMHBwQwfPpyEhAS2bdtW5P1iYmLIzs7m2LFjBT7ucDiIjIx0u4mISOA576mYXYvhu/vM5Y4joPNDFlXqA2w26Pwg3DwFykTAniXk/OcK1q5Z5pE5xkqSpVd1RUdHu63bsmULNpuNSpUqAWYr0Lx581zj9CQlJVG+vPsw2bNnzyYuLo7KlSuXVukiIuKjCj0Vc2izOVJx7ilz/qper7gGJpRzaNAT7p5L2viBlEvdS+3vB3JP9uMM6j/Is7PLe5BlwWfixInMmzePPn36EBsby7p163j33XcZNWqUK8SkpaVxzTXXMH78eO644w6+/vprZs+ezXXXXUd0dDQ//PADX331Ff/3f/+HTb+gIiJSBPku8U5OgEkDISsVanSG/p+CvfDBdMVdoqMWfZOf5bOQN2lj/4uJIa8w6rsTdG3wpFf28bEs+IwcOZJmzZrxzTffcPDgQapXr87MmTO5/PLTk6KFhITQq1cv16jM9913H40aNWLq1KkcOXKEOnXqsHHjRho0aGDVYYiIiC/LSIIvB8GJRKjYCG6e7FPj9HiDXUfTOWZEMuTUM3wY8j49gtZyX9B37D58n1cGH6/p3FxaNHKziIgAkJ0JE/vD3t8gIg7umuszIzJ7kzM7jQeRyyPB3zAx92q+e7q/V87V5TWdm0VEREqNMxemDzdDjyMShn6j0HOBzuw0nksQ7+TexKMDLvXK1h7wgnF8RERESpVhwI+jYcsMc4DCm76EKs2srsqn+cL4PXkUfEREJLD89j6s/I+53P8TqN3V2nr8xEXNC1aKdKpLREQCx97lMPc5c7nnv6D5IGvrkVKn4CMiIoEhNwdmPWoutxxiDsAnAUfBR0REAsPK/8DhzRBaHnq+bHU1YhEFHxER8X+pB2DhK+ZyjxdKdE4p8W4KPiIi4v9+egZOpUH19tD6VqurEQsp+IiIiH/bsQA2TwObHfq8DXZ99QUyvfsiIuK/crJg9uPmcofhENfC2nrEcgo+IiLiv5a+D8d3QLnK0G2M1dWIF1DwERER/5S0G35901zu9QqUjbK0HPEOCj4iIuJ/DAN+eBJyMs2RmZsNtLoi8RIKPiIi4n/+/AG2/2TOxdX7LbDZrK5IvISCj4iI+JdT6TDnKXO580io2MDaesSrKPiIiIh/WfwmpCRAVA3o+oTV1YiXUfARERH/ceRP+O0Dc/ma16BMmLX1iNdR8BEREf9gGDD7MXBmQ4OroVFvqysSL6TgIyIi/mHjN7D7Vwgua7b2iBRAwUdERHxfZgr89PcAhV0fh/K1LC1HvJeCj4iI+L6Fr0D6YahQDzo/ZHU14sUUfERExLclroeVn5rLvd+EYIe19YhXU/ARERHf5XTCrEfBcELTAVC3m9UViZdT8BEREd+19gvYvxrKRJjzcYmch4KPiIj4pvRjMO95c7nbGIiMs7Qc8Q0KPiIi4pvm/RMykqByM+gw3OpqxEco+IiIiO/ZuwLWTjSX+7wNQcHW1iM+Q8FHRER8S24OzH7UXG59C9ToaG094lMUfERExLes+gwObYLQ8tDjRaurER+j4CMiIr4jNREW/Mtc7vE8hFewtBzxPQo+IiLiO35+Bk6dgGrtoPVtVlcjPsjy4JOdnc0ff/zBwYMHi7xPbm4uW7duZdeuXSVYmYiIeJUdC2HTt2CzQ9+3wW75V5j4IMt+a9LS0njssceoXLkyAwcOpFGjRrRt25bNmzefc7+lS5dSq1YtrrzySlq1akWHDh3Yv39/KVUtIiKWyMmCHx43l9vfA3Etra1HfJZlwefQoUM0bNiQxMRENm/ezMGDB6lSpQq33VZ402VaWhoDBgxg0KBBHDhwgMOHD+NwOLj11ltLsXIRESl1v30Ax/6C8ErQ/RmrqxEfZlnwqVu3LsOHD8fhMCeTK1u2LN26dWPPnj2F7jNjxgyOHz/Os88+C4DD4WDMmDEsXLiQnTt3lkrdIiJSypJ2w+I3zOVer0DZKEvLEd9m+YhP27ZtIzk5mW3btvHee++5Qk1Bfv/9d+rUqUNMTIxrXceOHd0eExER35WYksGuo+nUjg0nLirUXDnnacjJhFqXQfNB1hYoPs/y4PPee++xZMkSdu3aRadOnRgwYECh2x47dowKFdwvXSxfvjx2u51jx44VuE9WVhZZWVmu+6mpqZ4pXEREPGrKqr2MnrYRpwF2G4wd0JzBEZtg2xywB0Oft8Bms7pM8XGWd4n/97//zfr16zl48CDR0dFceeWVZGdnF7htSEiIW4gB86owp9NJSEhIgfuMHTuWqKgo1y0+Pt7jxyAiIhcnMSXDFXoAnAa8OG0NOT88Ya7oPBIqNrSuQPEblgefPGFhYTz22GNs376dP//8s8BtatSowYEDB9zW5V3RVaNGjQL3GT16NCkpKa5bQkKCZwsXEZGLtutouiv05LkvaDrBqfsgKh66PmFNYeJ3LAs+mZmZ+dbt3r0bME9fgTlez+rVq12nsa688koOHjzI77//7tpn5syZhIWF0alTpwJfx+FwEBkZ6XYTERHvUjs2HPsZZ7Hq2vYzPGiWeefqV6FMuDWFid+xLPh8+OGH3HXXXUybNo3Fixfz/vvv8+CDD3LHHXdQrVo1AE6cOEH79u2ZOXMmAJ07d6ZPnz4MHTqUGTNmMH78eP7xj38wZswYypUrZ9WhiIjIRYqLCmXsgOYE2WyAwUshEyhjy4X6vaBRH6vLEz9iMwzDOP9mnmcYBlOnTuXbb7/l4MGDVK9enQEDBjBgwABsf3deS0tL44orruD555+nb9++AGRkZPD666+zYMECHA4HgwcP5q677iry66amphIVFUVKSopaf0REvExiSgYnVn1NgyWjILgs3L8cYmpbXZZ4AU99f1sWfKyi4CMi4sUyU+DD9pB2CLr9Ay5X3x4xeer722s6N4uIiLBwrBl6YupCl4esrkb8kIKPiIh4h4MbYeV/zOU+b0Kww9p6xC8p+IiIiHdY9CoYTmjSD+p2t7oa8VMKPiIiYr3Df8Aff1++fsUYa2sRv6bgIyIi1lvyjvmzUV+o1MjaWsSvKfiIiIi1knbDxqnm8mWPWlqK+D8FHxERsdbS98HIhTrdoFpbq6sRP6fgIyIi1jlxENZOMpcve8zaWiQgKPiIiIh1lv0bcrOgegeodanV1UgAUPARERFrnDwOq/9rLnd9HGy2c28v4gEKPiIiYo2Vn8GpNKjcDOr3tLoaCRAKPiIiUvqy0mDFx+byZY+qtUdKjYKPiIiUvjUTICMJYupAk+utrkYCiIKPiIiUrpws+O0Dc/nSR8AeZG09ElAUfEREpHStmwxpByGyGrS4yepqJMAo+IiISOnJzYGl75rLnUdCcBlLy5HAo+AjIiKlZ/N0c4qKsArQ5jarq5EApOAjIiKlw+mEJW+by53ugzLh1tYjAUnBR0RESse2H+HwFigTAe3vsboaCVAKPiIiUvIMA35901zucDeERltajgQuBR8RESl5uxbD/jUQXBY63W91NRLAFHxERKTk5bX2tLkNylWythYJaAo+IiJSshJWmS0+9mDzEnYRCyn4iIhIycq7kqvFYIiuYW0tEvAUfEREpOQc2gx//gDYoMsoq6sRUfAREZEStORd82eT66BiA0tLEQEFHxERKSmpB2DzNHP50kesrUXkbwo+IiJSMlaNA2cO1OgMVVtbXY0IoOAjIiIlITsT1ow3lzvea20tImdQ8BEREc/bOBVOHoOoeGjU1+pqRFwUfERExLMMA1Z8Yi63vxuCgq2tR+QMXvHbmJSURPny5c+7nWEY7N+/P9/6mJgYwsLCSqI0EREprt1L4NAmCA41R2oW8SKWtfgcOHCABx98kJiYGOrUqUNERASPPPIIp06dKnSf9PR04uPjadOmDZ06dXLdZs+eXYqVi4jIOeW19rS8icTsUH7bcZTElAxraxL5m2UtPvPnz6dx48bs3LmT6OhoNm7cSI8ePQgJCeH1118/577ff/89nTp1KqVKRUSkyJL2/D1gIcwJ78cDry7AaYDdBmMHNGdwe43cLNayrMXn1ltv5YEHHiA6OhqA5s2bM3ToUObMmXPefbOzszl27FgJVygiIsW28lMwnGTVuJwH5p7EaZirnQaMmbZJLT9iOa/q3PzXX39RpUqV827XvXt3ateuTXR0NE899RSZmZmlUJ2IiJxTVhr8PhGAnXVvcYWePLmGwe6jJy0oTOQ0rwk+M2bMYNasWYwaNarQbex2Oy+++CLHjh0jNTWVWbNmMX78eJ566qlC98nKyiI1NdXtJiIiJWD9V5CVAjF1iG7ZB7vN/eEgm41asboQRazlFcFnyZIlDB06lOeff54+ffoUul1YWBjPPvsskZGRAFx66aWMHj2azz//HKfTWeA+Y8eOJSoqynWLj48vkWMQEQloTies+I+53OFe4qLDGTugOUE2M/0E2Wy8MqAZcVGhFhYpAjbDMIzzb1Zyli1bRq9evXjooYd4+eWXi73/jBkzuP7660lMTCzwNFlWVhZZWVmu+6mpqcTHx5OSkuIKUCIicpG2z4MvB0KZCHh0C5Q1/74mpmSw++hJasWGKfTIRUlNTSUqKuqiv78tHcdn+fLlXH311YwcObLA0JM3bk/eOD1OpxO73Z7vOSIjI4mNjS3wNRwOBw6Ho0TqFxGRv+Vdwt76FlfoAYiLClXgEa9iWfBZs2YNvXr1YuDAgdx3333s27cPMPvxVK1aFYCUlBTi4+MZP348d9xxB++++y6HDx/muuuuIzo6mh9++IG3336b559/nuBgrxiLUUQk8BzdDn/NBWzQcbjV1Yick2Vp4eeffyYiIoKff/6Zn3/+2bU+KiqKzZs3A2YIqlatGuHh4QCMHDmSTz75hKeffpojR45Qp04dpk6dynXXXWfJMYiIBKLElAx2HU2ndmy42ZqT17enwdUQU8fa4kTOw/I+PqXNU+cIRUQC0ZRVexk9baNrUMI3r63JgIU9ITsdbpsBda6wukTxU37Rx0dERHxHYkqGK/SAOSjh1h8+huB0qNgYal9ubYEiReAVl7OLiIj323U03W1QQjtObrP/ZN7peC/YbAXvKOJFFHxERKRIaseGuw1K2MO+hnj7EZxly0OLwdYVJlIMCj4iIlIkcVGhboMSDgs2W3vsbW+HMhqRWXyD+viIiEiRDW5fg64NKnJ4+xpaztoCtiDocI/VZYkUmYKPiIgUS1xUKHH7vzbvNL4WoqpbW5BIMehUl4iIFE/6Mdg41VzudJ+1tYgUk4KPiIgUz5rxkJMJca0gvqPV1YgUi4KPiIgUXW42rBpnLnccoUvYxeco+IiISNH9+QOcOADhFaHZAKurESk2BR8RESm61f81f7a5DYId1tYicgEUfEREpGiO7YCdiwAbtLnd6mpELoiCj4iIFE1ea0/9q6B8TWtrEblACj4iInJ+2Rmw7ktzud1d1tYichEUfERE5Py2zICMJIiKN1t8RHyUgo+IiJxf3iXsbW8He5C1tYhcBAUfERE5t4MbYd9KsAdD69usrkbkoij4iIjIua0eb/5s1BciKltbi8hFUvAREZHCZZ2ADVPM5XbDrK1FxAMUfEREpHAbp8KpNKhQD2p3tboakYum4CMiIgUzDFj199g97YZpXi7xCwo+IiJSsH2r4dBGCC4LLW+2uhoRj1DwERGRgq3++xL2pgMgLMbaWkQ8RMFHRETyO3kcNk0zl9WpWfyIgo+IiOS3/ivIzYIqzaF6O6urEfEYBR8REXFnGKcnJFWnZvEzCj4iIuJu12I49heUiYDmN1hdjYhHKfiIiIi7vE7NLW4ER4S1tYh4mIKPiIgAkJiSwaqNWzD+mG2uUKdm8UMKPiIiwpRVe+ny6gJ++fptbM4cjpZvBVWaWV2WiMcp+IiIBLjElAxGT9sIhpObgxcAMPZwZxJTMiyuTMTzvCL4GIZRKvuIiEh+u46m4zTgCvs6qtmOkWSUY1ZuB3YfPWl1aSIeZ1nwSUpK4vnnn6d+/fo4HA5q1arFv/71L5xO5zn327VrFz179sThcFCuXDmGDRtGenp6KVUtIuJ/aseGY7fBLUHzAJiaezk5Nge1YsMsrkzE8ywLPlOnTsVmszFnzhxOnjzJhAkTeOONN3jppZcK3Sc7O5vevXsTGhrKgQMHWLt2LUuWLOHee+8txcpFRPxLXFQo710dwxX29QBMcV7JKwOaERcVanFlIp5nM7zonNHDDz/MokWLWL9+fYGPf//99/Tr14+9e/cSHx8PwNdff83QoUPZv38/VapUOe9rpKamEhUVRUpKCpGRkR6tX0TEZ81/EX59i+QqXci4+VuFHvE6nvr+9oo+PnkSExMpX758oY8vW7aMOnXquEIPQLdu3XA6naxcubI0ShQR8T+52bD2SwCiLxuu0CN+LdjqAvIsWLCAb7/9lsmTJxe6zaFDh6hYsaLbutjYWGw2G4cOHSpwn6ysLLKyslz3U1NTPVOwiIi/2PYTpB2EsFho2NvqakRKlFe0+Kxfv55Bgwbx4IMPMnjw4HNue3bn57wzdbZC5pIZO3YsUVFRrtuZrUUiIgL8/n/mz9ZDIbiMtbWIlDDLg8/GjRvp0aMHgwcP5t133z3ntnFxcRw+fNht3ZEjRzAMo9D+PaNHjyYlJcV1S0hI8FTpIiK+LzkBts81l9vcbm0tIqXA0uCzadMmrrzySgYOHMhHH31UYKtNZmYmubm5AFx66aXs2bOHnTt3uh6fP38+QUFBdOrUqcDXcDgcREZGut1ERORvaycCBtS6DCrUtboakRJnWfDZunUrV155JX379uWdd94hKyuLzMxMt/44ycnJhIaGMnHiRAB69uxJq1atGD58ODt27GDVqlU888wzDBs2jNjYWKsORUTEN+XmwNpJ5nLbOywtRaS0WBZ8Jk+eTEpKCpMnT6Z8+fJER0cTHR1NzZo1XdvYbDYcDgdBQUEABAUFMXv2bMqXL0+HDh3o168fAwYM4IMPPrDqMEREfNdf8yB1P4TGQONrra5GpFR41Tg+pUHj+IiI/G3yTbBtDlzyIPT6l9XViJyTX47jIyIipST1AGz/yVxWp2YJIAo+IiKBaO0kMJxQswtUbGB1NSKlRsFHRCTQOHPh9y/MZbX2SIBR8BERCTQ7FkJKApSNhibXWV2NSKlS8BERCTRrxps/W94EIZqXSwKLgo+ISCA5cRC2/Wgu6zSXBCAFHxGRQLLuS3DmQHxHqNzE6mpESp2Cj4hIoHA6Yc3fE5KqtUcClIKPiEig2PULJO8BRxQ07W91NSKWKHbwWbp0KT/++GOxHxMREYutmWD+bHEjlAmztBQRqxQ7+Cxbtox58+YV+tj8+fMvuigREfGwtCPwx2xzua1Oc0ng8tipLsMw2Lx5MxUrVvTUU4qIiKesnwzObKjWFqo0t7oaEcsEF3XDjz76iOeee46MjAwMw2DChAluj588eRKbzcaaNWs8XaOIiFwMwzh9mqvtHVZWImK5IgefK6+8kpiYGGbNmkVKSgpDhw51ezwyMpJ27dpRqVIljxcpIiIXYfevcHwnlCkHTQdYXY2IpYocfBo2bEjDhg3p3LkzWVlZ1K9fvyTrEhERT8m7hL35DeAoZ20tIhYrcvDJU6NGjZKoQ0RESkL6Mdj6vbmsTs0iGsdHRMSvrf8Kck9BXEuo2trqakQsp+AjIuKvDAN+//s0lzo1iwAKPiIi/mvvMji6DULCoNkgq6sR8QoKPiIi/iqvU3OzgVA20tpaRLyEgo+IiD86eRw2TzeX295pbS0iXkTBR0TEH234H+RmQeVmUK2N1dWIeA0FHxERf3N2p2abzdJyRLyJgo+IiL/ZtwoOb4HgUHPQQhFxUfAREfE3efNyNe0PodFWViLidRR8RET8SWYKbJpmLmvsHpF8FHxERPzJhv9BTgZUbAzxHayuRsTrKPiIiPgLwzg9dk/b29WpWaQACj4iIv7iwO9waCMEOaDFYKurEfFKCj4iIv4ir1Nzk34QFmNpKSLeSsFHRMQfZJ2Ajd+ay+rULFIoBR8REX+w8RvITocK9aFmZ6urEfFalgafP/74g1GjRhEXF0e3bt3Ou316ejrR0dH5bpMnTy6FakVEvFjeaS51ahY5p2CrXjgrK4v+/ftzzz330LNnTzZv3nzefQzDICUlhblz59KuXTvX+rCwsJIsVUTEux1YB4nrIKgMtBxidTUiXs2y4ONwONi6dSsAo0aNKta+5cqVIzo62vNFiYj4orx5uRpfC+EVrK1FxMv5ZB+fm266ibi4OC699FImTpxodTkiItbJSoMNU83lNrdbW4uID7CsxedC3XjjjTzyyCPExcUxZ84c7r33Xg4fPsxjjz1W4PZZWVlkZWW57qemppZWqSIiJW/zdDh1AmLqQK3LrK5GxOv5VPApV64cU6ZMcd0fMWIEBw4c4OWXXy40+IwdO5YXXnihtEoUESldeZ2a29wOdp9sxBcpVT7/KWnXrh3JyckcPny4wMdHjx5NSkqK65aQkFDKFYqIlJCDm2D/arAHQyt1ahYpCp9q8SnIH3/8gcPhIDIyssDHHQ4HDoejlKsSESkFeZ2aG/WBcpWsrUXER3h1i09KSorbOD3//e9/mTRpEikpKeTm5vLjjz/y6quvcuedd1K2bFmLqxURKUWnTsL6v0/9q1OzSJFZ2uLTuXNntmzZQkZGBrm5ua5L1Pfv3094eLhr3J5Tp04B0Lt3b/75z38yatQo0tLSqFy5Mo8//jiPP/64hUchImKBLTMgKwWia0Cd8w8AKyImm2EYhlUvfuLECXJzc/OtzwtAecEnLCyMMmXKuG2TnZ1NSEhIsV8zNTWVqKgoUlJSCj09JiLi9cb1goTl0P1Z6Kr//In/89T3t6UtPhEREed83GazFTpQ4YWEHhERv3B4qxl6bEHQ+harqxHxKV7dx0dERArw+xfmz4bXQEQVa2sR8TEKPiIiviQ7E9b9PTFz2zssLUXEF/n85ewiIv4uMSWDXUfTqR0bTtyemZCZDFHxULe71aWJ+BwFHxERLzZl1V5GT9uI0wAbsKjiR9QEaH0r2IMsrk7E9+hUl4iIl0pMyXCFHoDatgPUPLGWXMPG93Zdwi5yIRR8RES81K6j6a7QA3BT0EIAFjpb8cicoySmZFhUmYjvUvAREfFStWPDsdvM5TJkMzBoMQBf5XYn1zDYffSkhdWJ+CYFHxERL5KYksFvO8zWnLioUMYOaI4d6GlfTQXbCQ4a5VnkbEWQzUat2DCryxXxOercLCLiJc7syGy3wdgBzRncvgZdG1Qk6/O34ARMyb0CbMG8MqAZcVGhVpcs4nMUfEREvMDZHZmdBoyZtomuDSoSdyoBTqzBsNm59MZHubFmfYUekQuk4CMi4gXO7sgMuPrxxG2fAICtfi/atmhR+sWJ+BH18RER8QJndmTOE2SzUSvaBuv/Hqm53bDSL0zEzyj4iIh4gbyOzEE2M/0E2WxmP559P0NGEkTVgHpXWlyliO/TqS4RES+R15F599GT1IoNM/vxfH6X+WDb2zRSs4gHKPiIiHiRuKjQ0x2XD26EfSvBHgytb7O2MBE/oVNdIiLeatU482fjayGisrW1iPgJBR8REW+UmQob/mcut7vL2lpE/IiCj4iIN9owBbLTIbYh1LrU6mpE/IaCjxTZmUPp+/NriljOMGD1f83ldsPAZjv39iJSZOrcLEVS2FD6npCYksGuo+nUjg13G422JF9TxKvtXQ6Ht0BIGLS8yepqRPyKWnzkvAobSt8TrTBTVu2ly6sLGPLZCrq8uoApq/aW+GuKeL3Vf3dqbjYQQqMtLUXE3yj4yHmdayj9oirolNW5wo0nXlPEJ6Udgc3fmcvt77a0FBF/pFNdcl55Q+mfGUSCbDZqxYa5bVfcU1bnCjfne83CXkvE5639ApzZUK0dVG1ldTUifkctPnJehQ6lf1a4Ke4pq0LnJvp7xNrCXrOw1xLxec5cWD3eXG6vS9hFSoJafKRIChxK/2+FhZuuDSqes1XnkroVGDugOWOmbSLXMPIFqoJe81yvpZYf8XnbfoSUBAiNgaYDrK5GxC8p+EiRuQ2lf4aLOWV1rkBV0Gue67UUfMTnrfzM/NnmVggpa20tIn5Kp7rkol3oKas8cVGhXFK3QpGCy7leS8SnHf0Ldi4EbBqpWaQEqcVHLlpeuCnOKSu4sA7K53stEZ+16nPzZ4OroXxNa2sR8WM2wzCM82/mP1JTU4mKiiIlJYXIyEiry/EriSkZhZ6yOtvFDk5YnNcS8XpZafB2Y8hKhVu+hXo9rK5IxOt46vtbLT7iMYX1ATqbJzooF/W1RHxBysovicpKJSe6NsF1ultdjohfUx8fKXUanFDktCkrd3N47nsAvHq0C1PW7LO4IhH/ZmmLz8GDB/n888+ZMmUK9erVY/r06efdJzU1leeff5758+dTtmxZBg8ezKhRo7DbleG81snjcHAjnDwKudk0SUvnlqBNBJGLDYNkoxw7jTg2763NJXUrWF2tSKlJTMlg/oz/Y3DIflKNML7OuYIMDc8gUqIsCz6nTp2iQ4cO3HLLLTRp0oQdO3YUab/rr7+e5ORk3nvvPZKSkrj77rs5evQor7zySglXLEVy4iAkrj/jtgFS3AcYjAZeDsm/a86if5K9uREh1dtAXEvzVqUZlAkvldJFStuuI2ncGzQTgEm5PUgjDDQ8g0iJsiz4hISEsGPHDkJCQhg1alSRgs+iRYtYuHAhmzZtomnTpgAcPnyYhx9+mKefflqdla1y4iCsmwxrJ8HxQt7H8rUgsjoElwF7CMcyDX7bnYoNiCGVhvYEKthOwNEt5m3dJHM/mx1qXAKthkKTfuAoV1pHJVLiGmRtIta+nSwjhPE5VwMankGkpFkWfGw2GyEhBfy3/xwWLVpEtWrVXKEHoHfv3owYMYJly5bRq1cvT5cphcnNgb/mwu9fwLafwMg119vsENsQ4lr83WLTAqo0zzfD9KmUDB5+dcEZfX0MqtuS+P6GcsQkbzndYpR2EPYsNW9znoSm10PrWyG+I9jOGtBHxMfErvsYgGnOrhwhWsMziJQCn7qqKyEhgSpVqrity7u/b1/BHQKzsrLIyspy3U9NTS25AgPBsR1my866yWYoyRPfyRxttkk/cESc92nyj8djZ+SAy4lpUwPof3rD5L2w4X/maybtMn+unQQV6sGlj0KrIQpA4psObYHtPwE2rrzrJb7KraLhGURKgU8FH6fTma+VKCgoCLvdTm5uboH7jB07lhdeeKE0yvNvx3bAz/+AP384vS4sFlrdbLbAVGxY7Kc833QVAETXgK6Pw2WPwZ7fzNCz5Ts49hfMuB92zIe+70JZneYUH7PUvJKLJtdRqVZTKllbjUjA8KngExsby7Fjx9zWJSUl4XQ6iY2NLXCf0aNH8+ijj7rup6amEh8fX6J1+qoCR1LOOgGL34BlH4EzG7CZg6u1uRUaXGP22bkIRR6Px2aDWl3MW+/XYcUnsHAsbPoW9v8ON4yHqq0vqhaRUpOcAJu+MZe7jLK0FJFA41PBp3379rz99tscPnyYSpXM/x8tWbIEgHbt2hW4j8PhwOFwlFqNvirfSMr9mzK4zDKY909IO2RuVK8H9BoLFRtYW6wjAro+AbW6wrd3mafAPr8Ker4MHe/VqS/xfsv+Dc4cqN0VqrWxuhqRgOLVg9+cOHGCevXqMW3aNAD69u1LXFwcY8aMIScnh5SUFF5++WV69+5NjRpFn+5A3J09knIzdtBg1kD4boQZemLqwM1TYOg31oeeM9XoCPcuhkZ9zdaoH5+Cr4ea4waJeKuTx+H3/zOX1dojUuosbfHp3bs327Zt4+jRo2RmZlKvXj0ANmzYQFhYGLm5uezYscPVITksLIyZM2cydOhQYmJiyMrKomvXrkyYMMHCo/B9eSMpVySZJ4KncGPwLwDkBocTdMUT0Ol+CD5/q9mFTDp60cJiYPAkWPnp332QZsMn62HQf81gJOJtVn4G2SfNKx7rFjw9hSWfJZEAYekkpQkJCW5XXOWpW7cuNpsNp9PJzp07qVy5MhER7lcKJSYm4nA4iImJKdZrapLS/BJTMnj29Td5K/gjomzmtBHTci+jy4gPqFytdpGe42InHfWIA+vgmzvh+E6wBUH3f5j/o9ao3uItTp2Ed5vByWMwcBw0H5RvE6/4LIl4IU99f2t29kDnzIVFr8Li1wHY6KzF8znDuLF//yL/sU1MyaCL25g85iBsS57uVvr/W81MhVmPnO44Wrc79P8PlNM1M+IFVnwKc56A6Jow8ncIcm9096rPkoiX0ezscvFOHodv7zYvCQfSWw0jvcmTfFg5ulh/ZM816Wip/7EuGwkDP4c6l8MPT8KOBfDJpTDgM3OdiFVyc2DZB+Zy55H5Qg942WdJxE/pHECgOrAOPr3cDD3BodD/U8Kvf4dODeKK/Qe2dmw49rMupLJ02H2bDdrcBsMXQsVGZgftL/rBgn+ZXz4iVtg83RyQMywWWt9S4CZe91kS8UMKPoFo7SQY19P8I1y+Ntw9D1oOvuCnyxuFOejvy8i9Ztj9So3hnoVmCMIwT+d9cR2kHrC2Lgk8hnF6wMKOIyCk4M+G136WRPyI+vgEkpwsc76rNRPM+w2ugf6f5JtH60IlpmScexRmK238BmY+DKfSIDTGPO4GmttNSsn2efDlQAgJh0c2mVcjnoNXf5ZELKI+PlI8yQnwv9vgwO+ADbo/A5c+5tErnoo8CrMVmg8yR3aeegcc3ACTb4RLHoQr/3nRo0+LnNfSd82fbe84b+gBL/8sifg4neoKBDsWwn+6mqEntDzc8o058nGgXeZdoa55Wq/Dveb9ZR/C+KshabelZYmf27cGdv8K9mC45H6rqxEJeAH2zRdgnE749S2YNAAyjkNcKxj+izn1RKAKdphzfQ3+EspGwf418ElX2DLD6srEXy19x/zZ/EaIqm5tLSKi4OO3crNh2j0w/0UwnOYM6sN+gvI1ra7MOzTuCyOWQPUOkJVingac9ShkZ1pdmfiTo9th6yxzucvD1tYiIoCCj3/KyTL7smz6Bqc9hOQeb0K/DyGkrNWVeZfoGnDnD3DpI+b91ePg8x7ml5WIJ/z2PmCYFxJUamR1NSKCgo//yc4wJ+r8YxZZRgjDMkfRZnZVpqzaa3Vl3ikoBHo8D7d8a46vcmgj/LeXwo9cvBMHYf3X5vKloywtRUROU/DxJ1lp8OUN8NdcThoO7sx+gkXO1jgNGDNtE4kpGVZX6L3q9YD7lpr9oE4eg4kDIDXR6qrEly3/CHJPQXwnqNHJ6mpE5G8KPv4iMwUmDYTdv5ITUo7bTz3Fb85mrofzhr2Xc4ioYrb8xNSFlL1/dwpPsroq8UWZKbB6vLms1h4Rr6Lg4w9OHocvroeE5VA2iuRB/2MN7v0JNOx9EYXHwq3ToVwVOLwFvrrZPH0oUhyr/wtZqeaUKfU1UKaIN1Hw8XVpR+D/rjXH6AmrALfPIrZhFw17fzHK1zRbfhxRsHcZfDNMc3xJ0WVnwvKPzeUuDwfeeFkiXk4jN/uy1ERz8s2jf0K5ynDbDHN+KmBw+xp0bVBRw95fqCrN4OavYGJ/+PMHmPUwXPehOQGqyLms/8qcGDeyGjQbZHU1InIW/VfEVyUnwITeZuiJrAZ3/OAKPXniokK5pG4FhZ5iSEzJ4LcdR82O4LW6wA3jwWY3J3ad/6LV5Ym3c+b+fQk7cMkDmg5FxAupxccXHd8J/9fP7IAbXRNu/x7K17K6Kp83ZdVeRk/biNMAuw3GDmjO4PZ9oO+7MPMhWPI2lKsEne6zulTxVltnmp/PstHQ5narqxGRAqjFx9cc2Qbje5uhp0I9uHOOQo8HJKZkuEIP4D4EQNvbofuz5gM/Pg0bplpXqHgvwzg9GWmHe8BRztJyRKRgCj6+5NBm8/TWiUSo2Ng8vRVVzeqq/MKuo+mu0JPHbQiAyx6DjiPM5e9GwF/zSrdA8X67FsOBtRBc9vREuCLidRR8fMWBtTChD6QfgSot4I7ZEFHZ6qr8Ru3YcOxn9Vt2GwLAZoNeY83Oqs4cmHKbOeu2SJ681p7Wt0C5ipaWIiKFU/DxBQkrzT49GUlQrZ3Zpye8gtVV+ZW4qNDzDwFgt8P1H0Pd7pCdDl8OMk89iiSuhx0LzI7wlzxodTUicg7q3Oztdv0KkwebX7Q1u8CQKeCIsLoqv1SkIQCCy8CNE0+PnTRpgDnrvU45Bral75k/mw6AmNrW1iIi56QWH2+261dz7q3sdKhzBQz9RqGnhBVpCABHORg61excnpJgThVy8njpFSne5fgu2DzdXO7ysLW1iMh5Kfh4q73LzZaenAyo3xNungJlNOWE18ib2iIiDo5sha9uglOaCy0QuI31BLDsQzCcUPdKiGthbXEicl461eWN9q2BSYPMlp663c1TKyFlra5KzhZdA26ZBuOvhoQV8M2dMHgSBIVYXZmUkLPHenq7T1WuXzvJfFCTkYr4BLX4eJvEDTCpP5w6AbUug8FfKvR4s8pNzNa44LKw7UdOfns/v/115HRrgPiNgsZ62vfje5CTCVXbmJ9XEfF6Cj7e5PAfMPF6yEyB+I5w89c6veULal4Cg8bjtAURtuV/rBj/FF1eXcCUVXutrkw86OyxnsLI5Jagn807l47SPG4iPkLBx1sc2wFfXAcnj0HV1mbnWY386jMS47ox5tQwAB4J+ZZethWukZ/z9QkRn3T2WE83By0g2pZOTnQdaNTXusJEpFgUfLxB8l74v+vMGZ0rNTX7jZSNsroqKYZdR9P5Orcbn+X0BuDtkI9pxC7GL9lNl1cXMOSzFWoF8nFnjvUUQg53B/8AQPBlD4M9yOLqRKSoFHyslnrAHBMmdR9UqA+3fQdhMVZXJcWU1xowNmcIi3JbEmo7xWdl3uS7Jb8XPP+X+KTB7Wuw5OluzOl+iDjbcShXGVrcZHVZIlIMCj5WSjsCX/SDpN2nZ1kvV0mnRnxQXFQoT13TCGx2RmaP5C9nVarajvNJyNs4OOXazm3+L/FJcREO6m0fZ97pdJ8uPhDxMZZezp6VlcV7773H/PnzKVu2LIMHD2bIkCGFbp+RkcFVV12Vb/0zzzzDNddcU5Klet7J4zCxPxzdBpHVzNATWTXf5bJjBzRncPsaVlcr5zFl1V5em/MHTgPSCGPVJR9RZ90dtMn6i1dCxvFY9gjA5j7/l/im7T/BkT/AEQnthlldjYgUk6XBZ8iQIWzYsIGXXnqJpKQk7rnnHvbv388TTzxR4Pa5ubksXbqU//znPzRp0sS1vkGDBqVVsmdkpphTHRzaCOGV4LbvoXytAi+XHTNtE10bVDz3SMJiqbPfNwP4x+IMeg75jPLTbmJg0K/86azOOOd1+ef/Et+z5F3zZ7s71RdPxAdZFnxWrFjBtGnTWLFiBR06dAAgMzOT5557jgceeICwsML/V9yiRQs6depUWqV6VlaaOQ3FgbUQGgO3zYDYekD+y2Xh9KkRfVl6r8Let21hbbnkmtfgh8cZHfI1I/r1Jqa1Wu982p5lkLAcgspAp/utrkZELoBlfXzmzp1L5cqVXaEHoF+/fqSlpbFs2bJz7jtmzBiuuuoqRowYwZo1a0q6VM/JzjCnNkhYYf5P8bbvzAHw/nb25bKATo34gHO+b+3vhnbDsGEQ8+MDcORPa4qUi+d0ws//MJdb3gwRVaytR0QuiGXBZ8+ePVSr5j6jdd79vXsLv+S3devWDBkyhFGjRhEcHEynTp345ptvCt0+KyuL1NRUt5slcrJgyi2w+1coE2Fesh7X0m2TMy+XBfPL88mrG7LraLo6Onuxgt431yktmw2ufg1qdIasVPjqZshIsrhiuSAb/wf7V0OZctBtjNXViMgFsuxUV3Z2NmXLul8NUaZMGex2O6dOnSpwn7CwMJYvX06ZMmUA6NOnD7m5uTzyyCMMGjSowH3Gjh3LCy+84Nniiys3G6beCX/Ng5AwGPo/qN6uwE0Ht69B1wYV2X30JBv2J7s6zKqjs3c7832rFRvmfmoyuAzc+AV81g2O74Bv7jIHqNTYL74jKw3mPW8uX/aYWntEfJhlLT4xMTEcO3bMbV1ycjJOp5OYmILHsbHb7a7Qk6dnz57s27cv33PlGT16NCkpKa5bQkKCZw6gqHJzYNo98OdsCHLATZOhZudz7hIXFUqt2DBX6AGNAeML4qJCuaRuhYL7Y5WraL73waGwYz7Mfa70C5QLt+QdOJEI5Wupb4+Ij7Ms+LRu3ZqdO3eSnJzsWrdy5UrXY0V16NAh7HY7DoejwMcdDgeRkZFut1KTmwPTh8Pm6WAPgcEToW4318PnGq/nXB2dxUfFtYDrPzKXl30Iq8dbW48UTdJu+O0Dc7nnvzRuj4iPsyz49OvXj8jISMaOHQuYp75ee+01LrvsMurVM69ySktL49JLL2XOnDkAzJw5k7Vr17qeY+fOnbz++uv06dOHcuW8bF4rZy58NwI2fWuGnhu/gAa9XA9PWbX3nFMZqKOzn2o2AK4YbS7Pfgy2/WxtPXJ+c5+D3Cyo3RUa9bG6GhG5SJYFn8jISKZOncqECROoXbs2VatW5ejRo0ycONG1TU5ODkuXLuXQoUMA1KpVi5EjR1KjRg1atGhBkyZN6NSpE+PHe9n/nJ258N19sHEq2IPhhgnQqLfr4cLG6zmz5eecHWbF57i17l3+FLQaCkYuTL0DDqyzujwpzO4lsGUG2Oxw9auagV3ED1g6gGG3bt1ISEhg8+bNOBwOGjdujO2MPywRERH8+uuvrgEKmzdvzpIlSzhw4ABHjhyhdu3apXvqqiicuTDjAdgwBWxBMGg8NHafubmo4/Wcs8Os+IwCR+O+9j1I3Q87F8HkG+Hu+RAdb3WpciZnLsx52lxuNwwqN7W2HhHxCEuDD5hXchXWpycoKIhLL7003/qqVatStWrVki6t+JxO+H4krP/q79DzX2hyXb7N8k5jnRl+CjuNFRcVqsDjwwpq3Rv97UbCHcG0u/pTqnzTHw5vNge1HPYjhEZbWq+c4fcvzNHVy0bBFbp8XcRfaJJST3E6YeZIWPelGXoGfg5Nry9wU53GChwFte45gQcnr6XzO6v5vtm7EBEHR7aa4zzlFDyUg5SyjGRY8JK5fMUYCK9gaTki4jmWt/j4jcVvwNpJZl+AAZ+anVjPQaexAkNBrXt5nAY8MucolwybSMWp15uDW34/Evp/or4kVlv8Bpw8BrENof1dVlcjIh6kFh9PaXcnVG4G/T+F5u6DKRZ22fo5x30Rv3B2697Zcg2Dv+x14Mb/M1sKN3wNi8aWcpXi5uh2WPGJuXz1KxAUYm09IuJRavHxlHKVYPgvEOT+T1pgx1aNvhxQ8lr31uxO4qGv1xbctyuqB1z7rtni88trEBUPbW61rOaA9tMYcOZAg6uhXg+rqxERD1OLjyedFXqKctm6BIa4qFD6tqx67r5dbW6Dyx43l2eNgh0LrCk2kG2fC9t/Nsfe6vkvq6sRkRKgFp8SVNTL1iVwnLdvV/d/QEqCORzClNvMK72qNLOm2ECTm2229gB0vBdi61lbj4iUCLX4lCCNviwFOWffLpsNrvsQal0Gp06Yl7mn7C/9IgPRqs/h6DYIi4XLn7S6GhEpIQo+JUiXrcsFCS5jzusW2xBOHDAHOMxMtboq/5Z+FBb+3an8yufMsXtExC/ZDMMo4EJb/5WamkpUVBQpKSmlNupzYkqGLluX4kveC5/3gLRDULc7DPmfrjAqKTNHwZrxUKW5eZGCPcjqikTkLJ76/laLjwfpsnXxqOgaMGQKhISZHZ1njYLA+n9K6dizzAw9wLHLXlToEfFzCj4ecr7Z1kUuSNXW5nxvNrs5QObiN62uyL9kZ5D6vxEAfJ1zBe0nZeizK+LnFHw8QJetS4lqeDX0/jvwLHwZ1k+xth4/kvbTS0Sm7+agUZ5XcobqsysSABR8POBcl62LeET7u6DLw+byjAdg5y/W1uMPElYRvuZjAP6RPYxUwgF9dkX8nYKPB+iydSkVVz4PTQeAMxum3AqHt1pdke86lQ7Th2MznHyX24V5zrauh/TZFfFvCj4eoMvWpVTY7XD9x1DjEshKMcf4OXHQ6qp809zn4PhOiKiK85rX9dkVCSC6nN2DdNm6lIqTx2HcVXDsL4hrCXf8AI5yVlflO/6aB5MGmsu3fgd1u+mzK+IDdDm7F9Jl61IqwmJg6DfmCMOJ6+GbOyE3x+qqfEPKPphuXsVFh3uhbjdAn12RQKLgI+KLYmqbY/wEh5qTas55QmP8nE92Bnw9FNKPkF6+EYkdnrK6IhGxgIKPiK+q3g4GjQNssPq/sPRdqyvyXoYBMx+GxHUcN8rR6+B9dHlrucbsEQlACj4ivqxRH7j6VXN53vOw8RtLy/Fayz+CDVPIMew8kP0w+4yKGrNHJEAp+Ij4uk4joNMD5vJ398Ge36ytx9vsWAg//wOAl3NuYZmzqeshjdkjEngUfET8Qc+XofG1kHsKvroZjmyzuiLvcHyX2fnbcHKyyY184ezl9rAdOJaepVYfkQCi4CPiD+x2GPAZVG8Pmcnw5SBIO2x1VdbKSoOvh0BGElRrS1j/Dxg7oIVrzB4bYAAPTl6r+fVEAoiCj4i/CAmFm7+G8rUheQ9MHmyOUByIDMM87Xd4C5SrDIMnQUhZBrevwZKnu/Hhza2x2czgA5pfTySQKPiI+JPwWLjlWwiNgQO/wzfDICfL6qpKTWJKBr/tOMqJuWNh6/dgD4EbJ0JkVdc2cVGhxJQro/n1RAKUgo+Iv6lQ12z5CXLAth/Nlp+sNKurKnFTVu2ly6sLGDfuIyJ+e81c2ectqNEx37aaX08kcCn4iPijGh1h6P8gJBx2LoSJ15t9XfxUYkoGo6dtpDb7eTfkIwAm5l5FYr0bC9xe8+uJBK5gqwsQkRJS5wq4/XtzXqp9q2B8H7h1OkRUtroyj9t1NJ0Oti28E/IREbYMVjgb8UL2rdQ7erLQMDO4fQ26NqioObpEAoxafET8WfV2cOccKFcFDm+GjzrCknf8q9NzbjYt/niPySH/Is52nB3OOO4/9TCGLeS8p640R5dI4FHwEfF3lZvAsDlQsbF5umve8/BeS1j2b3P+Kl92bAeM60m5Ve9jtxlMye1G31P/ItkWrVNXIlIgm2EE1syGnprWXsTn5ObAxqnwy6uQtNtcFxEHlz0GbW6DYIel5RWLYcD6r+CHJ+BUGpSNhuveJ7FaT526EvFTnvr+tjT4OJ1OJkyYwPz58ylbtiw33ngjvXr18vg+Z1LwkYCXmw3rJsPiNyAlwVwXFQ9dn4BWQyAoxNr6zicjGWY9ApunmfdrXgoD/gNR1S0tS0RKlqe+vy091XX33Xfz7LPPcskll1CrVi2uu+46Pv30U4/vIyJnCAqBtrfDyDXQ+02z/09KAsx8CD5sB+u+Ameu1VUWbM8y+ORSM/TYgqD7s2YHboUeESkiy1p81q9fT6tWrVi0aBGXX345AP/617946623SExMxOHI3+x+IfucTS0+ImfJzoDV/zU7PacfMddVqA9XPA1NB5jTYVgtNwcWv262UhlOKF8LBo4zO2+LSEDw+RafOXPmEBsbS9euXV3rbrjhBpKSkli+fLnH9hGR8wgJhUsegIfXQ4/nIbQ8HNsO395ltq5snWn2qbFK0m4Yfw388poZeloOgRFLFHpE5IJYFnx27dpF9erVsdlOD59ao0YN12Oe2icrK4vU1FS3m4gUoEw4XPoIPLwBrhgDjkjzEvgpt8Cnl8O2n0o/AG2YCp9cBvtWmvUMHAf9PwZHROnWISJ+w7Lgk5WVRViY+xgbZcuWJSgoiMzMTI/tM3bsWKKioly3+Ph4zxyAiL8qGwlXPAWjNsBlj0OZcpC4HibfCJ/3gB0LSj4AZabCtOEw7W7ISoX4TmYrT/NBJfu6IuL3LAs+UVFRJCW5D6GfnJxMbm4u5cuX99g+o0ePJiUlxXVLSEjwzAGI+LvQ8nDls2YLUOeHIDgU9q+Gif1hQh/YvbTIT5U3eWiRZj9PWGWeYtswBWx2uGI03DEbyte8iIMRETFZFnxatmzJzp07OXHihGvdunXrAGjRooXH9nE4HERGRrrdRKQYwitAz5fMPkAdR0BQGdizFCb0hi/6mUHlHPImDx3y2Qq6vLqAKav2FryhMxd+eQP+2wuS90BUDXPU6SuehiDNriMinmFZ8OnXrx9lypThgw8+AMzxed566y3atm1L48aNAUhPT+f6669nwYIFRd5HREzFamUpiojKcM1r8NA6aDcM7MGwcxGM6wFf3gAH1hZYw+hpG3H+fWbMacCYaZvy15ScABP6wsKXwciFZoPgviVQo5NnahcR+Ztl/42qUKECEydO5NZbb2X69OkkJydz6tQp5syZ49omOzubGTNmcP311xd5HxExW1nyAofdBmMHNGdw+xqeefKoatD3HejysNlCs/4r2P6zeWvUF7qNgcpNAXPyUOdZ3YFyDYM9h5KIS91knjrbtxq2z4WsFCgTAX3ehBaD4YyLGEREPMXyKSuSk5NZtWoVDoeDTp06UaZMGddj2dnZzJ49mzZt2riu3jrfPuejcXzE3yWmZNDl1QVugSPIZmPJ091KZhqHYztg0avmdBgYgI2MBtexo+YNOByhPD1tI4YBNWyHaWX/i9b2HbQI3ovNme3+PNXawcDPIaa252sUEZ/nF1NWWEHBR/zdbzuOMuSzFfnWf3VPJy6pW6HkXvjwH7BoLGz5rmjbh1WA6u3NwFO9HdS6TH15RKRQnvr+1l8ZET9TOzYcu418LT61YsMK38kTKjUisdcn3L2uAw8EfUcz2y4MbGCDKpFlOeWogD2+HeF1OkG1tuboyzqdJSKlTMFHxM/ERYUydkBzxkzbRK5hEGSz8cqAZoDZGlQ7NrzEZi7fdTSdzc5a3O8c5bb+q+tLuLVJRKSIFHxE/NDg9jXo2qAiu4+epFZsGIu3HXH1+/F4Z+czWNbaJCJSRF4w+6CIlIS4qFBXK0uRLin30GuOHdCcoL9PYeW1NpVUC5OISHGpxUfEzxV2SfnuoydLJJCc3dpUlNdITMlg19H0Ej0NJyICCj4ifs+K009xUaFFDjAlOuaQiMhZdKpLxM958+mnIo/sLCLiIWrxEQkAF3L6qTSU9mk4EREFH5EAUZzTT6VFV4GJSGnTqS4RsYw3n4YTEf+kFh8RsZS3noYTEf+k4CMilvPG03Ai4p90qktEREQChoKPiIiIBAwFHxEREQkYCj4iIiISMBR8REREJGAo+IiIiEjAUPARERGRgKHgIyIiIgFDwUdEREQChoKPiIiIBAwFHxEREQkYATdXl2EYAKSmplpciYiIiBRV3vd23vf4hQq44HPixAkA4uPjLa5EREREiuvEiRNERUVd8P4242Kjk49xOp0cOHCAiIgIbDZbkfZJTU0lPj6ehIQEIiMjS7hCawXSsUJgHa+O1X8F0vEG0rFCYB3v+Y7VMAxOnDhB1apVsdsvvKdOwLX42O12qlevfkH7RkZG+v0vXp5AOlYIrOPVsfqvQDreQDpWCKzjPdexXkxLTx51bhYREZGAoeAjIiIiAUPBpwgcDgf//Oc/cTgcVpdS4gLpWCGwjlfH6r8C6XgD6VghsI63tI414Do3i4iISOBSi4+IiIgEDAUfERERCRgKPiIiIhIwAm4cn8zMTN5++23mz59P2bJlGTx4MLfddttF73Mhz1safvzxRz777DOOHz9Ou3btGD16NDExMYVun5iYyMcff8zKlSsJCwuje/fuDB8+nDJlyri2GTNmDAsWLHDbr02bNnz00UcldhxFsW/fPl555RW2bNlCXFwcI0eOpHPnzoVuv3jxYp588sl862fOnEnFihUv+HlLQ25uLh999BGzZs0CoG/fvtx///0EBQUVuP3SpUt57LHHCnzso48+ok2bNgBcddVVrtHN84wYMYI77rjDc8VfgFWrVvHxxx+zZcsW3n33XTp16nTefX777Tc++OADEhMTadKkCWPGjMk3hldRtiltR44c4b///S8zZ86kW7duvPTSS+fc3jAMvvvuO6ZPn86BAweoV68eI0eOpGnTpq5ttm/fzq233ppv308//ZQWLVp4/BiKKjc3l1mzZjFu3DgOHz7M4sWL3f7WFOS2225j27Ztbuv69evH6NGj3dZNmjSJr7/+moyMDLp168Zjjz1GaGiox4+hOLZu3conn3zCypUrefLJJ+nfv/85t7/zzjvZunVrvvVn/r2dMmUK77zzjtvjwcHBLFmyxHOFX4B58+bx1VdfsXv3bmrWrMm9995Lx44dz7nPyZMnefPNN1m0aBHh4eHcfPPNDBkypNjbnJcRYPr162c0aNDAmDp1qvHpp58a5cqVM8aOHXvR+1zI85a0b7/91ggODjbGjh1rfP/990aXLl2M5s2bG1lZWQVuf/ToUaNOnTrGSy+9ZMyZM8f48ssvjdq1axt9+/Y1nE6na7uBAwca/fv3N5YtW+a6bd68ubQOq0DHjx83qlevblx77bXG7NmzjSeeeMIICQkxli5dWug+06dPN0JDQ92OY9myZW7/PhfyvKXh/vvvN6pUqWJMmjTJ+PLLL41KlSoZDzzwQKHbJyUl5TvOm266yYiMjDTS0tJc20VFRRmvvPKK23b79u0rjUMq1Isvvmi0bdvWePPNNw3AmDNnznn3Wbp0qRESEmI8+eSTxuzZs42+ffsa8fHxxvHjx4u1TWn766+/jGrVqhmPP/640aFDB2Pw4MHn3efRRx81BgwYYHzxxRfGvHnzjPvuu88oU6aMsXz5ctc2a9euNQDjxx9/dHtvU1NTS/Jwzqtnz57Gtddeazz00EMGYGRkZJx3n5YtWxojR450O44dO3a4bfP6668b4eHhxieffGJ88803RsOGDY0+ffqU1GEUyRdffGE0atTIePvtt43Q0FDj448/Pu8+GzdudDvOn376ybDb7cZLL73k2uadd94x6tSp47bdme+9Fd58802jR48exrhx44z58+cbY8aMMex2uzFjxoxz7terVy+jSZMmxrfffmt89NFHRmhoqPHOO+8Ue5vzCajg89tvvxmAsWrVKte6d955xwgPD3f741/cfS7keUtDw4YN3b4MDx8+bISEhBgTJkwocPvs7GwjMzPTbd38+fMNwPjzzz9d6wYOHGjce++9JVP0BXrxxReNihUruoWWPn36GD169Ch0n+nTpxvh4eEef96Stnv3bsNutxvTp093rZs6dapht9uNvXv3Fuk5cnJyjGrVqhkjRoxwWx8VFWVMnTrVk+VetOTkZMMwDOPIkSNFDj7du3c3+vXr57qfmZlpVKhQwXj55ZeLtU1py8jIcH0G+/TpU6Tgc+LEiXzrLrnkEuPWW2913c8LPkeOHPFcsR6Q995OnTq1WMHnjTfeKPTxkydPGuXKlXP7Mly1apUBGEuWLLnomi9USkqKazk8PLxIwedsn3zyiREUFOT2n5F33nnHaNq0qUdq9JSCfidvuOEGo1u3boXus2DBAgMwNm7c6Fo3duxYIyoqyvWZKMo2RRFQfXzmz59PlSpVaNeunWtdv379SE9PZ/ny5Re8z4U8b0nbt28ff/75J9dee61rXcWKFbnkkkuYN29egfsEBwfnGz+hXLlyAJw6dcpt/fz587niiiu44YYb+Pjjj8nNzfXwERTP/Pnz6dWrl1szeb9+/fjll1/Izs4udL+srCz69OlDr169ePzxx0lMTPTI85akhQsXYrfbufrqq13r+vbti81my3cKsjA//fQT+/fv55577sn32JtvvskVV1zB7bffzs8//+yxui9UcYeoz87O5tdff3X73Xc4HPTq1cv1u1+UbaxQtmzZYo9hkvcZPXvd2Z9ZgFtuuYUePXrwwAMP5DtdZIULnX5g4sSJXH755QwZMoQpU6a4PbZixQrS0tLc3tt27dpRtWpVS99bT0w3MW7cOHr37k21atXc1u/fv5+ePXvSp08f/vnPf5KSknLRr3UxivM7mWf+/PnUrFmTZs2audb169ePlJQUVq9eXeRtiiKggs+ePXuoWrWq27q8X6A9e/Zc8D4X8rwlLe91z66ratWqxarplVdeoUGDBjRu3Ni1Ljo6mltvvZVnnnmGHj168PLLL9OvXz/PFH6BCnoPqlatSnZ2dr4wk8dmszFo0CDuuece7rvvPjZt2kSTJk3YtWvXRT1vSduzZw8xMTGULVvWta5s2bKUL1++yO/tuHHjaNOmjatvT55GjRpx55138swzz1CjRg2uu+66fP0HvN2BAwfIzs4+5+9+UbbxVcuXL2f+/Plcf/31but79uzJnXfeySOPPEJycjItWrRgxYoV1hR5EapVq8Ydd9zBs88+S+vWrbn33nsZNWqU63FP/e3zNhs3bmTVqlX5/rMSFBTEzTffzKhRo7j99tuZM2cOLVu2JDk52ZpCC/DXX38xZcqUfL+TZ9qzZ0++QJf3Hp75XXu+bYoioDo3Z2dn5/vfVEhICHa7vdD/vRdlnwt53pKW97pn1xUaGlrkmp577jnmzp3LL7/84tZp9t///rfb87Zt25b27dszb948evTo4YHqi6+g9yCvI2Nhx3v11Ve7Bba+ffvSqlUrXnjhBSZMmHDBz1vSCqoJiv7eHjlyhJkzZ/L+++/ne+yXX35xPfdVV11FeHg4Y8aM4b777nMLWt6sKL/7nvh8eKPdu3czcOBAbrrpJm666SbX+saNG/PTTz+57vfp04djx47x1FNPsWjRIgsqvXDTpk1zvW89evQgPj6eIUOG8NBDD1GnTh3X+3d2J2lff2/HjRtH1apV6d27t9v64cOHu/0eX3311dSrV493332X559/vpSrzO/YsWNcd911dOrUyS2gnq0of2s99fc4oFp8YmJiOH78uNu65ORknE4nFSpUuOB9LuR5S1relVtn13Xs2LEi1TR27FjeeustZs2a5XYKD/J/WbRr146oqCjWr19/kVVfuILeg2PHjrkeK8jZxxEcHMwVV1zhdhwX8rwlraCaoOjv7RdffEFISEiBV0Kc/W/So0cPMjMz+fPPPy+84FJWlN/9i/18eKO9e/fSvXt3OnbsyP/93/+5PVZQUO7Ro4eln9kLVdDvqGEYbNy4ETj93iYlJblt58vv7alTp5g0aRLDhg3Ld+Xm2f8ekZGRdOjQwSve2+PHj3PVVVdRoUIFZsyYQXBw4W0t5/pbe67v2rO3KYqACj5t2rRhx44dbh+IvKbe1q1bX/A+F/K8Ja1hw4aEh4ezatUq1zrDMFi9evV5a3rttdd46aWXXJfTnk96ejppaWmEh4dfdN0Xqk2bNm7HCuZ7UKtWLcqXL1/k5zl48KDbcXjqeT2pTZs2pKenu13mumnTJk6ePFmk37dx48YxePDgIvU5OHjwIICl721xxcTEULNmzQLft7x/n6Js40sSEhLo1q0bLVq0YMqUKef8gslz9u+6rzr7dzTv9O2Z721SUhLbt2/3yfcWYPr06Rw/fpy77rqrSNt7w3ublJTEVVddRVhYGHPmzCmw38+Z2rRpwx9//EFaWpprXd73aKtWrYq8TZEUuRu0H0hNTTViY2ONxx57zDAMw8jKyjIuv/xy4/LLL3dtc+LECaNjx47GrFmzirxPUbaxwvDhw40GDRoYx44dMwzDvCIgODjY2LZtm2ubJ5980rjvvvtc99944w0jLCzMmD9/foHPeeTIEePDDz80cnJyDMMwr4QZNmyYERYWZullz4sXLzZsNpvxww8/GIZhGLt27TIqVqzodtnnggULjI4dOxqHDx82DMMwPvzwQyMxMdH1+Pfff28EBQUZH3zwQbGet7Tl5OQYDRo0MG6++WbD6XQaTqfTuPHGG40GDRq43hfDMIxu3boZ48ePd9t36dKlBmD89ttv+Z534cKFxrx581z39+/fb7Rq1cpo165diR1LcZzrqq4333zTGDRokOv+iy++aFSqVMnYvXu3YRiGMXPmTMNmsxm//vprsbaxUmFXdW3dutXo2LGjsX79esMwDGPfvn1G3bp1jeuvv944depUgc81YcIEY/v27a77y5cvNyIiIlx/s6x2rqu6hgwZYrzyyiuGYRjGhg0bjClTprgeS0pKMnr27GlUr17dbd/u3bsbXbt2dV3p8/jjjxsxMTGuq8isVthVXV9++aXRpUuXfOuvuuoqo2fPngU+1xtvvOF2XB9//LEBGDNnzvRcwcWUnJxstG3b1ujSpUuBV3gZhmEcO3bM6NixozF37lzDMMyhQ6Kjo41nnnnGMAzzCsdLLrnE6NWrl2ufomxTFAEVfAzDMH755RcjLi7OqF69ulG+fHmjVatWxp49e1yPJyUlGYDbF8b59inqNqUtNTXV6NWrlxEWFmbUrVvXiIiIML788ku3bfr16+cKaDt27DAAo2LFikbHjh3dbr/88othGGbQefTRR42YmBijefPmRnR0tNG0aVNj8eLFpX14+eSNj9GgQQPD4XAYQ4cOdfsiyPvjmpCQYBiGYUybNs2oW7euUb9+faNWrVpGRESE8corr7iNWVSU57XChg0bjHr16hmVK1c2KlWqZNSrV8/YsGGD2zbh4eH5AtqwYcMKvfR1z549Rt++fY2KFSsaTZs2NRwOh3Httde6/r2sMnfuXKNjx45G27ZtDcBo1KiR0bFjR+Pf//63a5sHHnjAqFu3ruv+qVOnjKFDhxoOh8OoX7++UbZs2XxjfRRlGyt07drV6NixoxEdHW1UqFDB6Nixo9G7d2/X43mXZucFtJtvvtkAjDZt2rh9Zu+44w7XPr/88ovRokULo2bNmq5jffTRRwsd06u0vPbaa0bHjh2N+vXrG4DRoUMHo2PHjsayZctc2zRt2tS46667DMMwvyxvu+0219+f8PBw47LLLss3jlhCQoLRpk0bIzo62oiPjzcqV65sLFiwoFSP7WybNm1yvTd2u92oXbu20bFjR+Mf//iHa5s33njDCAoKctsvb/iKwoaZ+M9//mNUrVrVaNy4sVG1alWjYsWKxueff16ix3I+TzzxhAEYzZo1c/udPPP3ODEx0QCMr776yrVu7ty5RqVKlYwaNWoYUVFRRvv27Y39+/e7PXdRtjmfgJydPScnh61bt+JwOGjQoIHbY7m5uaxatYq6deu6jd57rn2Ks40V9u7dy/Hjx2nQoAFhYWFuj23bto2cnByaNGlCZmYm69atK/A5GjZs6HZqJzMzk7/++ovY2FiqVKlSkuUXS2pqKjt27KBKlSrExcW5PXb8+HG2bdtGmzZtXB0fDcNg165dOJ1OatWqVegpgnM9r1WcTid//PEHYF6NZbe7n7letWoVVatWdbsKYv369URGRlK7du1CnzcpKYl9+/ZRs2ZNj1yCe7GOHTvG9u3b862vVq0a8fHxgNmpNzk5OV9zd2JiIgcPHqRu3bqFHktRtilNK1euxOl0uq0rU6aM6xROeno6GzdupGnTpkRERLB9+3ZXP4czlStXzu2yXzBPiaWnp1O7du1iXzZfEnbt2sWhQ4fyrW/cuLHrUvcNGzZQrlw56tSp43o8LS2NnTt3Uq1atXP27di+fTsZGRk0btyYkJAQzx9AMaSlpbFp06Z862NjY6lXrx5g/i7u3bvXbYTjvN//tm3bFnoMTqeT7du343A4qFGjRr6/BaVtz549BV71eubvcXZ2NmvWrKF+/fpu72F2djZbt24lLCzM9e9ytqJscy4BGXxEREQkMAVU52YREREJbAo+IiIiEjAUfERERCRgKPiIiIhIwFDwERERkYCh4CMiIiIBQ8FHREREAoaCj4iIiAQMBR8REREJGAo+IuLTsrOzmTJlCtu2bXNbv3DhQhYtWmRNUSLitRR8RMSnhYSEsHTpUnr37s2JEycA+PXXX+nZsyc5OTkWVyci3kZzdYmIz8vMzKR9+/a0bduWd999l5YtW3LDDTfw5ptvWl2aiHgZBR8R8QsbN26kQ4cO1K9fn6CgIFasWEGZMmWsLktEvIxOdYmIX2jevDk33ngjGzdu5PXXX1foEZECqcVHRPzC6tWr6dy5M/Xq1aNixYosXLgQu13/txMRd/qrICI+Lz09naFDh3LPPfcwf/58tmzZwmuvvWZ1WSLihdTiIyI+b/jw4fz666/8/vvvhIaGMmPGDG644QaWLVtG27ZtrS5PRLyIWnxExKft3r2bEydOMHnyZEJDQwHo168fL774IjNnzrS4OhHxNmrxERERkYChFh8REREJGAo+IiIiEjAUfERERCRgKPiIiIhIwFDwERERkYCh4CMiIiIBQ8FHREREAoaCj4iIiAQMBR8REREJGAo+IiIiEjAUfERERCRgKPiIiIhIwPh/23JSguoESZIAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3d) SV Regressor\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "svr = SVR()\n",
-    "svr.fit(X.reshape(-1,1),T)\n",
-    "\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,svr.predict(X.reshape(-1,1)))\n",
-    "plt.show()"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-<<<<<<< HEAD
-   "display_name": "nnw (3.14.0)",
-=======
-   "display_name": "nnw (3.14.8)",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-<<<<<<< HEAD
-   "version": "3.14.0"
-=======
-   "version": "3.14.8"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Lösungen_N/AB2_BACKUP_1208.ipynb b/Lösungen_N/AB2_BACKUP_1208.ipynb
deleted file mode 100644
index 45be331..0000000
--- a/Lösungen_N/AB2_BACKUP_1208.ipynb
+++ /dev/null
@@ -1,1894 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 73,
-   "id": "29e20c6b",
-=======
-   "execution_count": 2,
-   "id": "de4f7f4b",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-<<<<<<< HEAD
-    "from sklearn.svm import LinearSVC"
-=======
-    "from sklearn.svm import LinearSVC\n",
-    "import matplotlib.pyplot as plt"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 74,
-   "id": "67b3d7af",
-=======
-   "execution_count": 3,
-   "id": "88b3d717",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj4AAAGwCAYAAACpYG+ZAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAOhVJREFUeJzt3Xl4VNXh//HPJIQhCWRCCEsCYScICCpbUKyKslhAo7ig8NO6fEWtYlGsFazWrcatorVV22qldeVBoYgWKwRBQdBoLbuCECBAkC2ZQEhCSM7vD5opQybJJJnJnZn7fj3PPI9z7zl3znVmMh/OOfdchzHGCAAAwAairG4AAABAUyH4AAAA2yD4AAAA2yD4AAAA2yD4AAAA2yD4AAAA2yD4AAAA22hmdQOaWmVlpfbs2aNWrVrJ4XBY3RwAAOAHY4wOHz6s1NRURUU1vN/GdsFnz549SktLs7oZAACgAfLy8tSpU6cG17dd8GnVqpWkE//jEhISLG4NAADwR1FRkdLS0jy/4w1lu+BTNbyVkJBA8AEAIMw0dpoKk5sBAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAKhBvrtEX2w9oHx3idVNQYDY7ialAAD4Y07OTs2Yt06VRopySFkT+mvikM5WNwuNRI8PAACnyHeXeEKPJFUaaea89fT8RACCDwAAp8g9UOwJPVUqjNH2A0etaRAChuADAMApuiXHK8rhvS3a4VDX5DhrGoSAIfgAAHCKFFessib0V7TjRPqJdjj0xITTleKKtbhlaCwmNwMA4MPEIZ11XnpbbT9wVF2T4wg9EYLgAwBADVJcsQSeCMNQFwAAsA2CDwAAqFOkLObIUBcAAKhVJC3mSI8PAAAB0NgekVDtUYm0xRzp8QEAoJEa2yMSyj0qtS3mGI4Tv+nxAQCgERrbIxLMHpVA9CJF2mKOBB8AABqhsbe3CNbtMebk7NTwJ5dq0l++1PAnl2pOzs4GHSfSFnNkqAsAgEao6hE5ObzUp0eksfV9qakX6bz0tg0KLFWLOX6zvUBySIO6tG5w26xGjw8AAI3Q2B6RYPSoBKMX6bPN+3XXu9/qzre/bVQPktXo8QEAoJEae3uLQN8eI9C9SPXpQcp3lyj3QLG6JceH5HCY5cHn0KFD2rhxoxITE9WnTx9FR0fXWLaiokKLFy+utr1///7q2LFjMJsJAECtGnt7i0DeHqOqF2nmvPWqMKbRvUj+XtkVylenVbEs+Ljdbt1111365JNPlJ6ertzcXDVr1kzvvPOOMjIyfNYpKSnRT3/6Uw0bNkwul8uzffr06QQfAABOEsheJH96kAI9ryhYLAs+hYWFyszM1OzZs+VwOFRZWalrrrlGN998s9avX19r3VmzZmnYsGFN1FIAQCQJ9aGYQApUL5I/PUjhst6PZcGnS5cu6tKli+d5VFSUBg0apOXLl9dZd8uWLSorK1OPHj3UqVOnYDYTABBBwmEoJlTV1YMUjKvTgsHyOT5ff/219u3bp82bN+v3v/+9nn766TrrPPTQQ+rQoYPWrl2rESNGaPbs2UpOTvZZtqysTGVlZZ7nRUVFAWs7ACB8hMtQTCirrQcp0POKgsXy4PP+++/rq6++0nfffadevXrVOL9Hkpo1a6Z58+bp8ssvlyTt3r1bF154oW677Ta99957PutkZWXpkUceCUrbAQDhI1yGYsJZoK9OCwaHMcbUXSz4jh8/rttuu02ffPKJtmzZIqfT6Ve9V199VT//+c9VXFysmJiYavt99fikpaXJ7XYrISEhYO0HAIS2fHeJhj+5tNpQzIr7R4TkDzS8FRUVyeVyNfr3O2QWMGzWrJmmTJmivLw8bd682e96SUlJKi8v18GDB33udzqdSkhI8HoAAOwn0m69EGpC9e7yp7L0qq7ExESvbRs3bpTD4VC7du0knegFWrJkiWednoKCArVu7b1M9kcffaSUlBS1b9++qZoOAAhT4TAUE47CadK4ZcHnjTfe0JIlSzRu3DglJyfrP//5j55//nlNmzbNE2KOHDmin/70p3r99dd1ww036N1339VHH32kSy+9VImJifrnP/+pd955R3/729/kcDjqeEUAAAK7UCDCb9K4ZcFn6tSpOv300/Xee+9p79696tSpkxYuXKjzzz/fUyYmJkZjxozxLE54++2367TTTtPcuXO1f/9+de/eXevWrVN6erpVpwEAgK2F26TxkJnc3FQCNTkKAAA03aTxiJvcDAAAwk+4TRq3fB0fAAAQ3sJp0jjBBwAANFq4TBpnqAsAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANiG5cGnvLxc3333nfbu3et3nYqKCm3atEm5ublBbBkAAIg0lgWfI0eOaPr06Wrfvr2uuOIKnXbaaRo0aJA2bNhQa72VK1eqa9euuuiii3TmmWdq6NCh2r17dxO1GgAAhDPLgs+PP/6o3r17Kz8/Xxs2bNDevXvVoUMHXX/99TXWOXLkiCZMmKArr7xSe/bs0b59++R0OnXdddc1YcsBAEC4siz49OjRQ1OmTJHT6ZQktWjRQiNGjNCOHTtqrLNgwQIdOnRIDz74oCTJ6XRq5syZ+vTTT7Vt27YmaTcAAAhfzaxuwObNm1VYWKjNmzfrhRde8IQaX/7973+re/fuSkpK8mzLyMjw2gcACF/57hLlHihWt+R4pbhirW4OIpDlweeFF17QihUrlJubq2HDhmnChAk1lj148KDatGnjta1169aKiorSwYMHfdYpKytTWVmZ53lRUVFgGg4ACKg5OTs1Y946VRopyiFlTeiviUM6W90sRBjLr+r64x//qDVr1mjv3r1KTEzURRddpPLycp9lY2JivEKMdOKqsMrKSsXExPisk5WVJZfL5XmkpaUF/BwAAI2T7y7xhB5JqjTSzHnrle8usbZhiDiWB58qcXFxmj59urZs2aLvv//eZ5nOnTtrz549Xtuqrujq3Nn3vwpmzJght9vteeTl5QW24QCARss9UOwJPVUqjNH2A0etaRAilmXBp7S0tNq27du3SzoxfCWdWK/n66+/9gxjXXTRRdq7d6/+/e9/e+osXLhQcXFxGjZsmM/XcTqdSkhI8HoAAEJLt+R4RTm8t0U7HOqaHGdNgxCxLAs+f/jDH3TzzTdr3rx5+uyzz/T73/9ed955p2644QZ17NhRknT48GENGTJECxculCSdc845GjdunCZPnqwFCxbo9ddf169//WvNnDlTLVu2tOpUAACNlOKKVdaE/op2nEg/0Q6HnphwOhOcEXAOY4ypu1jgGWM0d+5cvf/++9q7d686deqkCRMmaMKECXL894N/5MgRXXDBBXr44Yc1fvx4SVJJSYmefvppLV26VE6nUxMnTtTNN9/s9+sWFRXJ5XLJ7XbT+wMAISbfXaLtB46qa3IcoQdeAvX7bVnwsQrBBwCA8BOo3++QmdwMAAAQbAQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgG82sboAkFRQUqHXr1nWWM8Zo9+7d1bYnJSUpLi4uGE0DAAARxLIenz179ujOO+9UUlKSunfvrlatWunuu+/WsWPHaqxTXFystLQ0DRw4UMOGDfM8PvrooyZsOQDAX/nuEn2x9YDy3SVWNwWQZGGPT3Z2tvr06aNt27YpMTFR69at08iRIxUTE6Onn3661roffPCBhg0b1kQtBQA0xJycnZoxb50qjRTlkLIm9NfEIZ2tbhZszrIen+uuu0533HGHEhMTJUn9+/fX5MmTtWjRojrrlpeX6+DBg0FuIQCgofLdJZ7QI0mVRpo5bz09P7BcSE1u/uGHH9ShQ4c6y1144YXq1q2bEhMT9atf/UqlpaVN0DoAgL9yDxR7Qk+VCmO0/cBRaxoE/FdITG6WpAULFujDDz/UwoULaywTFRWlRx99VL/4xS+UkJCgFStWaMKECSotLdULL7zgs05ZWZnKyso8z4uKigLedgCAt27J8YpyyCv8RDsc6prMhSiwVkj0+KxYsUKTJ0/Www8/rHHjxtVYLi4uTg8++KASEhIkSeeee65mzJihV199VZWVlT7rZGVlyeVyeR5paWlBOQcAwP+kuGKVNaG/oh0OSSdCzxMTTleKK9bilsHuHMYYU3ex4Fm1apXGjBmju+66S48//ni96y9YsECXXXaZ8vPzfQ6T+erxSUtLk9vt9gQoAEBw5LtLtP3AUXVNjiP0oFGKiorkcrka/ftt6VDX6tWrdfHFF2vq1Kk+Q0/Vuj1V6/RUVlYqKiqq2jESEhKUnJzs8zWcTqecTmdQ2g8AqF2KK5bAg5BiWfD55ptvNGbMGF1xxRW6/fbbtWvXLkkn5vGkpqZKktxut9LS0vT666/rhhtu0PPPP699+/bp0ksvVWJiov75z3/queee08MPP6xmzUJmuhIAAAhRlqWFTz75RK1atdInn3yiTz75xLPd5XJpw4YNkk6EoI4dOyo+Pl6SNHXqVL3yyiu6//77tX//fnXv3l1z587VpZdeask5AIAd5btLlHugWN2S4+nNQdixfI5PUwvUGCEA2BGLEsIqgfr9DomrugAAoY9FCREJCD4AAL+wKCEiAcEHAOCXqkUJT8aihAg3BB8AgF9YlBCRgGvAAQB+mziks85Lb8uihAhbBB8AQL2wKCHCGUNdAADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AABJUr67RF9sPaB8d4nVTQGCppnVDQAAWG9Ozk7NmLdOlUaKckhZE/pr4pDOVjcLCDh6fADA5vLdJZ7QI0mVRpo5bz09P4hIIRF8jDFNUgcAUF3ugWJP6KlSYYy2HzhqTYOAILIs+BQUFOjhhx9Wr1695HQ61bVrV/32t79VZWVlrfVyc3M1evRoOZ1OtWzZUjfddJOKi4ubqNUAEHm6JccryuG9LdrhUNfkOGsaBASRZcFn7ty5cjgcWrRokY4eParZs2frmWee0WOPPVZjnfLyco0dO1axsbHas2ePvv32W61YsUK33nprE7YcACJLiitWWRP6K9pxIv1EOxx6YsLpSnHFWtwyIPAcJoTGjH7xi19o2bJlWrNmjc/9H3zwgTIzM7Vz506lpaVJkt59911NnjxZu3fvVocOHep8jaKiIrlcLrndbiUkJAS0/QAQzvLdJdp+4Ki6JscRehByAvX7HRJzfKrk5+erdevWNe5ftWqVunfv7gk9kjRixAhVVlbqq6++aoomAkDESnHF6uwebQg9iGghczn70qVL9f777+vtt9+uscyPP/6otm3bem1LTk6Ww+HQjz/+6LNOWVmZysrKPM+LiooC02AAABB2QqLHZ82aNbryyit15513auLEibWWPXXyc9VIncPh8FVcWVlZcrlcnsfJvUUAAMBeLA8+69at08iRIzVx4kQ9//zztZZNSUnRvn37vLbt379fxpga5/fMmDFDbrfb88jLywtU0wEAQJixNPisX79eF110ka644gq99NJLPnttSktLVVFRIUk699xztWPHDm3bts2zPzs7W9HR0Ro2bJjP13A6nUpISPB6AAAAe7Is+GzatEkXXXSRxo8fr1mzZqmsrEylpaVe83EKCwsVGxurN954Q5I0evRonXnmmZoyZYq2bt2qnJwcPfDAA7rpppuUnJxs1akAAIAwYVnwefvtt+V2u/X222+rdevWSkxMVGJiorp06eIp43A45HQ6FR0dLUmKjo7WRx99pNatW2vo0KHKzMzUhAkT9OKLL1p1GgAAIIyE1Do+TYF1fAAACD8RuY4PAABAMBF8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbdQ7+KxcuVIff/xxvfcBAABYrd7BZ9WqVVqyZEmN+7KzsxvdKAAAgGAI2FCXMUYbNmxQ27ZtA3VIAACAgGrmb8GXXnpJDz30kEpKSmSM0ezZs732Hz16VA6HQ998802g2wgAABAQfgefiy66SElJSfrwww/ldrs1efJkr/0JCQkaPHiw2rVrF/BGAgAABILfwad3797q3bu3zjnnHJWVlalXr17BbBcAAEDA+R18qnTu3DkY7QAAAAg61vEBAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2YWnw+e677zRt2jSlpKRoxIgRdZYvLi5WYmJitcfbb7/dBK0FAADhrplVL1xWVqbLL79ct9xyi0aPHq0NGzbUWccYI7fbrcWLF2vw4MGe7XFxccFsKgAAiBCWBR+n06lNmzZJkqZNm1avui1btlRiYmLgGwUAACJaWM7xueaaa5SSkqJzzz1Xb7zxhtXNAQAAYcKyHp+Guvrqq3X33XcrJSVFixYt0q233qp9+/Zp+vTpPsuXlZWprKzM87yoqKipmgoAAEJMWAWfli1bas6cOZ7nt912m/bs2aPHH3+8xuCTlZWlRx55pKmaCAAAQlhYDnWdbPDgwSosLNS+fft87p8xY4bcbrfnkZeX18QtBAAAoSKsenx8+e677+R0OpWQkOBzv9PplNPpbOJWAQCAUBTSPT5ut9trnZ6//vWvevPNN+V2u1VRUaGPP/5YTz75pG688Ua1aNHC4tYCAIBQZ2mPzznnnKONGzeqpKREFRUVnkvUd+/erfj4eM+6PceOHZMkjR07Vr/5zW80bdo0HTlyRO3bt9e9996re++918KzAAAA4cJhjDFWvfjhw4dVUVFRbXtVAKoKPnFxcWrevLlXmfLycsXExNT7NYuKiuRyueR2u2scHgMAAKElUL/flvb4tGrVqtb9DoejxoUKGxJ6AACAvYX0HB8AAIBAIvgAAADbIPgAQIjLd5foi60HlO8usbopQNgL+3V8ACCSzcnZqRnz1qnSSA5Jt/ykm248t5tSXLFWNw0IS/T4AECIyneXeEKPJBlJf/48V8OfXKo5OTstbRsQrgg+ABCicg8Ue0LPySqNNHPeeoa+gAYg+ABAiOqWHK8oh+99FcZo+4GjTdsgIAIQfAAghJw8kTnFFausCf19/qGOdjjUNTmuydsHhDsmNwNAiDh5InOUQ8qa0F8Th3TWeelt9fqK7Xp1xTZVmhOh54kJpzPBGWgAS29ZYQVuWQEgFOW7SzT8yaVec3qiHQ6tuH+EJ+Dku0u0/cBRdU2OI/TAdiLilhUAgBN8TWSumsdTFXJSXLEEHqCRmOMDACHA10Rm5vEAgUfwAYAQUDWROdpxIv0wjwcIDoa6ACBEVE1kZh4PEDwEHwAIIczjAYKLoS4AAGAbBB8AAGAbBB8AAGAbBB/47eSl9CP5NQEAkYvJzfBLTUvpB0K+u0S5B4rVLTnea1JnMF8TAGBP9PigTvnuEk8AkaRKI82ctz4gvTBzcnZq+JNLNekvX2r4k0s1J2dn0F8TAGBfBB/Uqbal9P3la8iqtnATiNcEAOBUDHWhTlVL6Z9688RTl9Kv75BVbeGmrtes6bUAAKgNPT6okz9L6TdkyKq2exPV9po1vRYAAHWhxwd+qW0p/ZrCzXnpbWvt1Tm7RxtlTeivmfPWq8KYaoHK12vW9lr0/AAA6kLwgd9qWkq/MUNWdd2b6NTXrO21CD4AgLow1IVGa+iQVZUUV6zO7tHGr+BS22sBAFAXenzQaFXhpj5DVlLDJijX9VoAANTGYYwxdReLHEVFRXK5XHK73UpISLC6OREl311S45DVqRq7OGF9XgsAEP4C9ftNjw8CpqY5QKcKxARlf18LCAcszwA0HYIPmhwTlIH/4dYsQNOydHLz3r179fjjj6t///66/PLL/apTVFSke+65R2eccYYyMjL03HPPqbKyMsgtRSD5mqAsSWt3FzZ5WwArcWsWoOlZFnyOHTumoUOH6ujRo+rbt6/y8vL8qnfZZZdp2bJleuGFF3T//ffrt7/9rX79618HubUIpBRXrH518WnVtj+96Hv+4MNWuDUL0PQsG+qKiYnR1q1bFRMTo2nTpmnr1q111lm2bJk+/fRTrV+/Xv369ZMk7du3T7/4xS90//33M1k5jPTv5Kq2jeEu2I2/t4MBEDiW9fg4HA7FxMTUq86yZcvUsWNHT+iRpLFjx6qsrEyrVq0KdBMRRKzHA/h3OxgAgRVWk5vz8vLUoUMHr21Vz3ft2uWzTllZmcrKyjzPi4qKgtdA+I31eIAT6lq9HEBghVXwqaysrNZLFB0draioKFVUVPisk5WVpUceeaQpmod64g8+cALLMwBNJ6xuWZGcnKyDBw96bSsoKFBlZaWSk5N91pkxY4bcbrfn4e8kajvKd5foi60HmnSCcX1uVwEAQGOFVY/PkCFD9Nxzz2nfvn1q166dJGnFihWSpMGDB/us43Q65XQ6m6yN4Yq1RAAAdhDSPT6HDx9Wz549NW/ePEnS+PHjlZKSopkzZ+r48eNyu916/PHHNXbsWHXuzI90Q7GWCADALizt8Rk7dqw2b96sAwcOqLS0VD179pQkrV27VnFxcaqoqNDWrVs9E5Lj4uK0cOFCTZ48WUlJSSorK9N5552n2bNnW3gW4S9QKymz7D4QGHyXgOCxNPj86U9/8rriqkps7IkvekJCgrZs2aL27dt79p111lnauHGj8vPz5XQ6lZSU1GTtjVSBWEuEoTIgMPguAcHF3dkh6cQf21MvLff3j22+u0TDn1xaLTituH8E/1oF6oHvElAz7s6OgGrMpeXcdBQIDL5LQPARfODR0LVEWHYfCAy+S0DwhfRVXQgPLLsPBAbfJSD4mOODgMl3l7AKMxAAfJeA6pjjg5DDsvtAYPBdAoKHoS4AAGAbBB8AAGAbBB8AAGAbBJ8IZsXd1gEACGVMbo5QLHsPAEB19PhEIO62DgCAbwSfCFTbsvcAANgZwScCVS17fzKWvQcAgOATkVj2HgAA35jcHKEac7d1AAAiFcEngrHsff3lu0uUe6BY3ZLj+X8HABGI4AP8F0sAAEDkY44PIJYAAAC7IPgAYgkAALALgg8glgAAALsg+ABiCQAAsAsmNwP/xRIAABD5CD7ASVgCAAAiG0NdAFAP+e4SfbH1AFf8AWGKHh8A8BNrPQHhjx4fIIDoDYhcrPUERAZ6fIAAoTcgstW21hPzwoDwQY8PEAC19QbQCxQZWOsJiAz0+AABUFNvwOsrtuvVFdvoBYoAVWs9zZy3XhXGsNYTEKYIPkAAVPUGnBx+oiRP6JH+1wt0XnpbfizDFGs9AeGPoa4QxNBI+ElxxepXPz3NMxQS7XDo/37Sjft/RaAUV6zO7tGG0AOEKUt7fMrKyvTCCy8oOztbLVq00MSJEzVp0qQay5eUlGjUqFHVtj/wwAP66U9/GsymNhkmyIanOTk79dSi71RpJIek+y7urUvPTNWrK3K9wg9zQgDAWpYGn0mTJmnt2rV67LHHVFBQoFtuuUW7d+/WL3/5S5/lKyoqtHLlSv3pT39S3759PdvT09ObqslBVdMEWYZGQtup75uR9PTH3+vSM1OZEwIAIcay4PPll19q3rx5+vLLLzV06FBJUmlpqR566CHdcccdiour+V/FAwYM0LBhw5qqqU2Gy2XDU23vG3NCACC0WDbHZ/HixWrfvr0n9EhSZmamjhw5olWrVtVad+bMmRo1apRuu+02ffPNN8FuapPhctnwVNf7xpwQAAgdlgWfHTt2qGPHjl7bqp7v3LmzxnpnnXWWJk2apGnTpqlZs2YaNmyY3nvvvRrLl5WVqaioyOsRqqoul412nPgVjXY4dN/FvZV7oJiJziHM1/vGkBYAhCbLhrrKy8vVokULr23NmzdXVFSUjh075rNOXFycVq9erebNm0uSxo0bp4qKCt1999268sorfdbJysrSI488EtjGB9HJQyNrdxd6Jswy0Tm0MaQFAOHBsh6fpKQkHTx40GtbYWGhKisrlZSU5LNOVFSUJ/RUGT16tHbt2lXtWFVmzJght9vteeTl5QXmBIIoxRWrrslxntAjcV+gcMCQFgCEPsuCz1lnnaVt27apsLDQs+2rr77y7PPXjz/+qKioKDmdTp/7nU6nEhISvB6horb1emqbMAsAABrGsuCTmZmphIQEZWVlSTox9PXUU0/pJz/5iXr27ClJOnLkiM4991wtWrRIkrRw4UJ9++23nmNs27ZNTz/9tMaNG6eWLVs2/Uk0wpycnRr+5FJN+suXGv7kUs3J8Z7XxERnAAACz7Lgk5CQoLlz52r27Nnq1q2bUlNTdeDAAb3xxhueMsePH9fKlSv1448/SpK6du2qqVOnqnPnzhowYID69u2rYcOG6fXXX7fqNBqkthtaVmHCbGRhNW4ACA2WLmA4YsQI5eXlacOGDXI6nerTp48cjv91c7Rq1Uqff/65Z4HC/v37a8WKFdqzZ4/279+vbt26hdTQlb/8Xa+HCbORgdW4ASB0WH6T0ubNm9c4pyc6Olrnnntute2pqalKTU0NdtOCxtcNLWsaxkpxxRJ4wpiv3r0Z769TvLOZBnVpzXsLAE2Mm5RagGEs+/DVu1cp6c63v/U5twsAEFyW9/jYFcNY9uCrd68K92IDgKZHj08TqGliK+u+RL5Te/dOxRIFANC06PEJMia2oqp375vtBbrr3W/9mtsFAAgOenyCyJ/L1mEPKa5YjT8jlbldAGAxenyCyN/L1mEfzO0CAGsRfIKoPpetwz5YogAArMNQVxBx2ToAAKGFHp8gY2gDAIDQQfAJoHx3iXIPFKtbcrxXwGFoAwhtNX13AUQegk+AcNk6EJ747gL2whyfAOCydSA88d0F7IfgEwC1XbYOIHTx3QXsh+ATAFWXrZ+My9aB0Md3F7Afgk8AcNk6EJ747gL24zDG+LhvdOQqKiqSy+WS2+1WQkJCQI+d7y7hsnUgDPHdBUJfoH6/uaorgLhsHQhPfHcB+2CoC4Ct5LtL9MXWA1y5BdgUPT4AbIM1ewDQ4wPAFlizB4BE8AFgE6zZA0Ai+ACwCV9r9kRJOlhcRq8PYCMEHwC2cOqaPQ5JRtKdb3+r4U8u1ZycnZa2D0DTIPgAsI2JQzprxf0j9Idrz5LDcSL4SMz3AeyE4AMgYvhzqXqKK1ZJLZsz3wewKS5nBxAR6nOpetV8n5PDD/foAuyBHh8AYa++l6pzjy7AvujxARD2artUvaYwM3FIZ52X3pZ7dAE2Q/ABEPYaOnTFPboA+2GoC0DYY+gKgL/o8QEQERi6AuAPS4NPZWWlZs+erezsbLVo0UJXX321xowZE/A6AOyBoSsAdbF0qOv//u//9OCDD+rss89W165ddemll+rPf/5zwOsAAABIFvb4rFmzRq+//rqWLVum888/X5IUFRWl+++/Xz/72c/kdDoDUgcAAKCKZT0+ixYtUnJyss477zzPtquuukoFBQVavXp1wOoAAABUsSz45ObmqlOnTnI4/ne75M6dO3v2BapOWVmZioqKvB4AAMCeLAs+ZWVliovzXmOjRYsWio6OVmlpacDqZGVlyeVyeR5paWmBOQEAABB2LAs+LpdLBQUFXtsKCwtVUVGh1q1bB6zOjBkz5Ha7PY+8vLzAnAAAv/lz81AAaAqWBZ8zzjhD27Zt0+HDhz3b/vOf/0iSBgwYELA6TqdTCQkJXg8ATWdOzk4Nf3KpJv3lSw1/cqnm5Oy0ukkAbMyy4JOZmanmzZvrxRdflHRifZ7f/e53GjRokPr06SNJKi4u1mWXXaalS5f6XQfACaHQy1Lfm4cCQLBZdjl7mzZt9MYbb+i6667T/PnzVVhYqGPHjmnRokWeMuXl5VqwYIEuu+wyv+sAONHLUhU4ohxS1oT+mjikc5O3oyE3DwWAYLJ05ebMzEzt3LlTOTk5cjqdGjZsmJo3b+7ZHx8fr/nz52vgwIF+1wHsrqZelvPS2zZZ2Mh3lyj3QLHim0c36OahABAslt+rKzExUaNGjfK5LyYmxtPb428dwO6s7mU5tbfp8rM66h/f7lGFMdw8FIDlLA8+AAKrW3K8Zb0svnqb/vHtHs37+dk6eqySm4cCsJyl9+oCEHgprlhlTeiv6P8u9FnVyyIp6JOda+ptOnqsUmf3aEPoAWA5enyACDRxSGedl95W2w8cVdfkOH22eb+GP7k06JOdrextAgB/0OMDRKgUV6zO7tFGkprskvKaepvo6QEQKujxASJcU092PrW3yZ/XqLoKrFtyPCEJQFARfIAIZ8XwU4or1u8AEyprDgGwB4a6gAgXysNPrOwMoKnR4wPYQEOGn5qC1WsOAbAfgg9gE/UZfmoqXAUGoKkx1AXAMqE8DAcgMtHjA8BSoToMByAyEXwAWC4Uh+EARCaGugAAgG0QfAAAgG0QfAAAgG0QfAAAgG0QfAAAgG0QfAAAgG0QfAAAgG0QfAAAgG0QfAAAgG0QfAAAgG0QfAAAgG3Y7l5dxhhJUlFRkcUtAQAA/qr63a76HW8o2wWfw4cPS5LS0tIsbgkAAKivw4cPy+VyNbi+wzQ2OoWZyspK7dmzR61atZLD4fCrTlFRkdLS0pSXl6eEhIQgt9BadjpXyV7ny7lGLjudr53OVbLX+dZ1rsYYHT58WKmpqYqKavhMHdv1+ERFRalTp04NqpuQkBDxH7wqdjpXyV7ny7lGLjudr53OVbLX+dZ2ro3p6anC5GYAAGAbBB8AAGAbBB8/OJ1O/eY3v5HT6bS6KUFnp3OV7HW+nGvkstP52ulcJXudb1Odq+0mNwMAAPuixwcAANgGwQcAANgGwQcAANiG7dbxKS0t1XPPPafs7Gy1aNFCEydO1PXXX9/oOg05blP4+OOP9Ze//EWHDh3S4MGDNWPGDCUlJdVYPj8/Xy+//LK++uorxcXF6cILL9SUKVPUvHlzT5mZM2dq6dKlXvUGDhyol156KWjn4Y9du3bpiSee0MaNG5WSkqKpU6fqnHPOqbH8Z599pvvuu6/a9oULF6pt27YNPm5TqKio0EsvvaQPP/xQkjR+/Hj9/Oc/V3R0tM/yK1eu1PTp033ue+mllzRw4EBJ0qhRozyrm1e57bbbdMMNNwSu8Q2Qk5Ojl19+WRs3btTzzz+vYcOG1Vnniy++0Isvvqj8/Hz17dtXM2fOrLaGlz9lmtr+/fv117/+VQsXLtSIESP02GOP1VreGKN//OMfmj9/vvbs2aOePXtq6tSp6tevn6fMli1bdN1111Wr++c//1kDBgwI+Dn4q6KiQh9++KFee+017du3T5999pnX3xpfrr/+em3evNlrW2ZmpmbMmOG17c0339S7776rkpISjRgxQtOnT1dsbGzAz6E+Nm3apFdeeUVfffWV7rvvPl1++eW1lr/xxhu1adOmattP/ns7Z84czZo1y2t/s2bNtGLFisA1vAGWLFmid955R9u3b1eXLl106623KiMjo9Y6R48e1bPPPqtly5YpPj5e1157rSZNmlTvMnUyNpOZmWnS09PN3LlzzZ///GfTsmVLk5WV1eg6DTlusL3//vumWbNmJisry3zwwQdm+PDhpn///qasrMxn+QMHDpju3bubxx57zCxatMi89dZbplu3bmb8+PGmsrLSU+6KK64wl19+uVm1apXnsWHDhqY6LZ8OHTpkOnXqZC655BLz0UcfmV/+8pcmJibGrFy5ssY68+fPN7GxsV7nsWrVKq//Pw05blP4+c9/bjp06GDefPNN89Zbb5l27dqZO+64o8byBQUF1c7zmmuuMQkJCebIkSOeci6XyzzxxBNe5Xbt2tUUp1SjRx991AwaNMg8++yzRpJZtGhRnXVWrlxpYmJizH333Wc++ugjM378eJOWlmYOHTpUrzJN7YcffjAdO3Y09957rxk6dKiZOHFinXXuueceM2HCBPP3v//dLFmyxNx+++2mefPmZvXq1Z4y3377rZFkPv74Y6/3tqioKJinU6fRo0ebSy65xNx1111GkikpKamzzhlnnGGmTp3qdR5bt271KvP000+b+Ph488orr5j33nvP9O7d24wbNy5Yp+GXv//97+a0004zzz33nImNjTUvv/xynXXWrVvndZ7/+te/TFRUlHnsscc8ZWbNmmW6d+/uVe7k994Kzz77rBk5cqR57bXXTHZ2tpk5c6aJiooyCxYsqLXemDFjTN++fc37779vXnrpJRMbG2tmzZpV7zJ1sVXw+eKLL4wkk5OT49k2a9YsEx8f7/XHv751GnLcptC7d2+vH8N9+/aZmJgYM3v2bJ/ly8vLTWlpqde27OxsI8l8//33nm1XXHGFufXWW4PT6AZ69NFHTdu2bb1Cy7hx48zIkSNrrDN//nwTHx8f8OMG2/bt201UVJSZP3++Z9vcuXNNVFSU2blzp1/HOH78uOnYsaO57bbbvLa7XC4zd+7cQDa30QoLC40xxuzfv9/v4HPhhReazMxMz/PS0lLTpk0b8/jjj9erTFMrKSnxfAfHjRvnV/A5fPhwtW1nn322ue666zzPq4LP/v37A9fYAKh6b+fOnVuv4PPMM8/UuP/o0aOmZcuWXj+GOTk5RpJZsWJFo9vcUG632/Pf8fHxfgWfU73yyismOjra6x8js2bNMv369QtIGwPF12fyqquuMiNGjKixztKlS40ks27dOs+2rKws43K5PN8Jf8r4w1ZzfLKzs9WhQwcNHjzYsy0zM1PFxcVavXp1g+s05LjBtmvXLn3//fe65JJLPNvatm2rs88+W0uWLPFZp1mzZtXWT2jZsqUk6dixY17bs7OzdcEFF+iqq67Syy+/rIqKigCfQf1kZ2drzJgxXt3kmZmZWr58ucrLy2usV1ZWpnHjxmnMmDG69957lZ+fH5DjBtOnn36qqKgoXXzxxZ5t48ePl8PhqDYEWZN//etf2r17t2655ZZq+5599lldcMEF+tnPfqZPPvkkYO1uqPouUV9eXq7PP//c67PvdDo1ZswYz2ffnzJWaNGiRb3XMKn6jp667dTvrCT9v//3/zRy5Ejdcccd1YaLrNDQ2w+88cYbOv/88zVp0iTNmTPHa9+XX36pI0eOeL23gwcPVmpqqqXvbSBuN/Haa69p7Nix6tixo9f23bt3a/To0Ro3bpx+85vfyO12N/q1GqM+n8kq2dnZ6tKli04//XTPtszMTLndbn399dd+l/GHrYLPjh07lJqa6rWt6gO0Y8eOBtdpyHGDrep1T21Xampqvdr0xBNPKD09XX369PFsS0xM1HXXXacHHnhAI0eO1OOPP67MzMzANLyBfL0HqampKi8vrxZmqjgcDl155ZW65ZZbdPvtt2v9+vXq27evcnNzG3XcYNuxY4eSkpLUokULz7YWLVqodevWfr+3r732mgYOHOiZ21PltNNO04033qgHHnhAnTt31qWXXlpt/kCo27Nnj8rLy2v97PtTJlytXr1a2dnZuuyyy7y2jx49WjfeeKPuvvtuFRYWasCAAfryyy+taWQjdOzYUTfccIMefPBBnXXWWbr11ls1bdo0z/5A/e0LNevWrVNOTk61f6xER0fr2muv1bRp0/Szn/1MixYt0hlnnKHCwkJrGurDDz/8oDlz5lT7TJ5sx44d1QJd1Xt48m9tXWX8YavJzeXl5dX+NRUTE6OoqKga//XuT52GHDfYql731HbFxsb63aaHHnpIixcv1vLly70mzf7xj3/0Ou6gQYM0ZMgQLVmyRCNHjgxA6+vP13tQNZGxpvO9+OKLvQLb+PHjdeaZZ+qRRx7R7NmzG3zcYPPVJsn/93b//v1auHChfv/731fbt3z5cs+xR40apfj4eM2cOVO33367V9AKZf589gPx/QhF27dv1xVXXKFrrrlG11xzjWd7nz599K9//cvzfNy4cTp48KB+9atfadmyZRa0tOHmzZvned9GjhyptLQ0TZo0SXfddZe6d+/uef9OnSQd7u/ta6+9ptTUVI0dO9Zr+5QpU7w+xxdffLF69uyp559/Xg8//HATt7K6gwcP6tJLL9WwYcO8Auqp/PlbG6i/x7bq8UlKStKhQ4e8thUWFqqyslJt2rRpcJ2GHDfYqq7cOrVdBw8e9KtNWVlZ+t3vfqcPP/zQawhPqv5jMXjwYLlcLq1Zs6aRrW44X+/BwYMHPft8OfU8mjVrpgsuuMDrPBpy3GDz1SbJ//f273//u2JiYnxeCXHq/5ORI0eqtLRU33//fcMb3MT8+ew39vsRinbu3KkLL7xQGRkZ+tvf/ua1z1dQHjlypKXf2Yby9Rk1xmjdunWS/vfeFhQUeJUL5/f22LFjevPNN3XTTTdVu3Lz1P8fCQkJGjp0aEi8t4cOHdKoUaPUpk0bLViwQM2a1dzXUtvf2tp+a08t4w9bBZ+BAwdq69atXl+Iqq7es846q8F1GnLcYOvdu7fi4+OVk5Pj2WaM0ddff11nm5566ik99thjnstp61JcXKwjR44oPj6+0e1uqIEDB3qdq3TiPejatatat27t93H27t3rdR6BOm4gDRw4UMXFxV6Xua5fv15Hjx716/P22muvaeLEiX7NOdi7d68kWfre1ldSUpK6dOni832r+v/jT5lwkpeXpxEjRmjAgAGaM2dOrT8wVU79rIerUz+jVcO3J7+3BQUF2rJlS1i+t5I0f/58HTp0SDfffLNf5UPhvS0oKNCoUaMUFxenRYsW+Zz3c7KBAwfqu+++05EjRzzbqn5HzzzzTL/L+MXvadARoKioyCQnJ5vp06cbY4wpKysz559/vjn//PM9ZQ4fPmwyMjLMhx9+6Hcdf8pYYcqUKSY9Pd0cPHjQGHPiioBmzZqZzZs3e8rcd9995vbbb/c8f+aZZ0xcXJzJzs72ecz9+/ebP/zhD+b48ePGmBNXwtx0000mLi7O0sueP/vsM+NwOMw///lPY4wxubm5pm3btl6XfS5dutRkZGSYffv2GWOM+cMf/mDy8/M9+z/44AMTHR1tXnzxxXodt6kdP37cpKenm2uvvdZUVlaayspKc/XVV5v09HTP+2KMMSNGjDCvv/66V92VK1caSeaLL76odtxPP/3ULFmyxPN89+7d5swzzzSDBw8O2rnUR21XdT377LPmyiuv9Dx/9NFHTbt27cz27duNMcYsXLjQOBwO8/nnn9erjJVquqpr06ZNJiMjw6xZs8YYY8yuXbtMjx49zGWXXWaOHTvm81izZ882W7Zs8TxfvXq1adWqledvltVqu6pr0qRJ5oknnjDGGLN27VozZ84cz76CggIzevRo06lTJ6+6F154oTnvvPM8V/rce++9JikpyXMVmdVquqrrrbfeMsOHD6+2fdSoUWb06NE+j/XMM894ndfLL79sJJmFCxcGrsH1VFhYaAYNGmSGDx/u8wovY4w5ePCgycjIMIsXLzbGnFg6JDEx0TzwwAPGmBNXOJ599tlmzJgxnjr+lPGHrYKPMcYsX77cpKSkmE6dOpnWrVubM8880+zYscOzv6CgwEjy+sGoq46/ZZpaUVGRGTNmjImLizM9evQwrVq1Mm+99ZZXmczMTE9A27p1q5Fk2rZtazIyMrwey5cvN8acCDr33HOPSUpKMv379zeJiYmmX79+5rPPPmvq06uman2M9PR043Q6zeTJk71+CKr+uObl5RljjJk3b57p0aOH6dWrl+natatp1aqVeeKJJ7zWLPLnuFZYu3at6dmzp2nfvr1p166d6dmzp1m7dq1Xmfj4+GoB7aabbqrx0tcdO3aY8ePHm7Zt25p+/foZp9NpLrnkEs//L6ssXrzYZGRkmEGDBhlJ5rTTTjMZGRnmj3/8o6fMHXfcYXr06OF5fuzYMTN58mTjdDpNr169TIsWLaqt9eFPGSucd955JiMjwyQmJpo2bdqYjIwMM3bsWM/+qkuzqwLatddeaySZgQMHen1nb7jhBk+d5cuXmwEDBpguXbp4zvWee+6pcU2vpvLUU0+ZjIwM06tXLyPJDB061GRkZJhVq1Z5yvTr18/cfPPNxpgTP5bXX3+95+9PfHy8+clPflJtHbG8vDwzcOBAk5iYaNLS0kz79u3N0qVLm/TcTrV+/XrPexMVFWW6detmMjIyzK9//WtPmWeeecZER0d71atavqKmZSb+9Kc/mdTUVNOnTx+Tmppq2rZta1599dWgnktdfvnLXxpJ5vTTT/f6TJ78Oc7PzzeSzDvvvOPZtnjxYtOuXTvTuXNn43K5zJAhQ8zu3bu9ju1PmbrY8u7sx48f16ZNm+R0OpWenu61r6KiQjk5OerRo4fX6r211alPGSvs3LlThw4dUnp6uuLi4rz2bd68WcePH1ffvn1VWlqq//znPz6P0bt3b6+hndLSUv3www9KTk5Whw4dgtn8eikqKtLWrVvVoUMHpaSkeO07dOiQNm/erIEDB3omPhpjlJubq8rKSnXt2rXGIYLajmuVyspKfffdd5JOXI0VFeU9cp2Tk6PU1FSvqyDWrFmjhIQEdevWrcbjFhQUaNeuXerSpUtALsFtrIMHD2rLli3Vtnfs2FFpaWmSTkzqLSwsrNbdnZ+fr71796pHjx41nos/ZZrSV199pcrKSq9tzZs39wzhFBcXa926derXr59atWqlLVu2eOY5nKxly5Zel/1KJ4bEiouL1a1bt3pfNh8Mubm5+vHHH6tt79Onj+dS97Vr16ply5bq3r27Z/+RI0e0bds2dezYsda5HVu2bFFJSYn69OmjmJiYwJ9APRw5ckTr16+vtj05OVk9e/aUdOKzuHPnTq8Vjqs+/4MGDarxHCorK7VlyxY5nU517ty52t+CprZjxw6fV72e/DkuLy/XN998o169enm9h+Xl5dq0aZPi4uI8/19O5U+Z2tgy+AAAAHuy1eRmAABgbwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAGGtvLxcc+bM0ebNm722f/rpp1q2bJk1jQIQsgg+AMJaTEyMVq5cqbFjx+rw4cOSpM8//1yjR4/W8ePHLW4dgFDDvboAhL3S0lINGTJEgwYN0vPPP68zzjhDV111lZ599lmrmwYgxBB8AESEdevWaejQoerVq5eio6P15Zdfqnnz5lY3C0CIYagLQETo37+/rr76aq1bt05PP/00oQeAT/T4AIgIX3/9tc455xz17NlTbdu21aeffqqoKP5tB8AbfxUAhL3i4mJNnjxZt9xyi7Kzs7Vx40Y99dRTVjcLQAiixwdA2JsyZYo+//xz/fvf/1ZsbKwWLFigq666SqtWrdKgQYOsbh6AEEKPD4Cwtn37dh0+fFhvv/22YmNjJUmZmZl69NFHtXDhQotbByDU0OMDAABsgx4fAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgG/8fX7XSYVr5DM8AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3a)\n",
-    "X = np.linspace(0, 2)\n",
-    "np.random.seed(42) # vereinfachte Variante, siehe np.random\n",
-    "X = np.sort(np.random.rand(50)*2)\n",
-    "T = np.sin(5*X) + X**2 + np.random.randn(len(X))/10\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.show()\n",
-    "# Es gibt 50 Merkmale in den Daten"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b251a2c1",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-<<<<<<< HEAD
-      "[[5.1 3.5 1.4 0.2 0. ]\n",
-      " [4.9 3.  1.4 0.2 0. ]\n",
-      " [4.7 3.2 1.3 0.2 0. ]\n",
-      " [4.6 3.1 1.5 0.2 0. ]\n",
-      " [5.  3.6 1.4 0.2 0. ]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 1a) Ja stehen in der richtigen Dimension\n",
-    "iris_data= np.loadtxt(\"../supplement_files/iris.csv\", delimiter=\",\")\n",
-    "print(iris_data[:5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "c9c0fd1e",
-   "metadata": {},
-   "outputs": [
-=======
-      "[0.04116899 0.06877704 0.09290083 0.11616722 0.13010319 0.19534423\n",
-      " 0.24407647 0.27898772 0.31198904 0.31203728 0.34104825 0.36364993\n",
-      " 0.36680902 0.36970891 0.39934756 0.42467822 0.51755996 0.58245828\n",
-      " 0.5842893  0.60848449 0.60922754 0.62342215 0.73272369 0.74908024\n",
-      " 0.86389004 0.88030499 0.91213997 0.99035382 1.02846888 1.04013604\n",
-      " 1.04951286 1.09342056 1.18482914 1.19731697 1.20223002 1.2150897\n",
-      " 1.22370579 1.32504457 1.36846605 1.41614516 1.46398788 1.57035192\n",
-      " 1.6167947  1.66488528 1.73235229 1.8186408  1.89777107 1.90142861\n",
-      " 1.93126407 1.9398197 ]\n",
-      "[[0.04116899]\n",
-      " [0.06877704]\n",
-      " [0.09290083]\n",
-      " [0.11616722]\n",
-      " [0.13010319]\n",
-      " [0.19534423]\n",
-      " [0.24407647]\n",
-      " [0.27898772]\n",
-      " [0.31198904]\n",
-      " [0.31203728]\n",
-      " [0.34104825]\n",
-      " [0.36364993]\n",
-      " [0.36680902]\n",
-      " [0.36970891]\n",
-      " [0.39934756]\n",
-      " [0.42467822]\n",
-      " [0.51755996]\n",
-      " [0.58245828]\n",
-      " [0.5842893 ]\n",
-      " [0.60848449]\n",
-      " [0.60922754]\n",
-      " [0.62342215]\n",
-      " [0.73272369]\n",
-      " [0.74908024]\n",
-      " [0.86389004]\n",
-      " [0.88030499]\n",
-      " [0.91213997]\n",
-      " [0.99035382]\n",
-      " [1.02846888]\n",
-      " [1.04013604]\n",
-      " [1.04951286]\n",
-      " [1.09342056]\n",
-      " [1.18482914]\n",
-      " [1.19731697]\n",
-      " [1.20223002]\n",
-      " [1.2150897 ]\n",
-      " [1.22370579]\n",
-      " [1.32504457]\n",
-      " [1.36846605]\n",
-      " [1.41614516]\n",
-      " [1.46398788]\n",
-      " [1.57035192]\n",
-      " [1.6167947 ]\n",
-      " [1.66488528]\n",
-      " [1.73235229]\n",
-      " [1.8186408 ]\n",
-      " [1.89777107]\n",
-      " [1.90142861]\n",
-      " [1.93126407]\n",
-      " [1.9398197 ]]\n"
-     ]
-    },
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-    {
-     "data": {
-      "text/html": [
-       "<style>.sk-global {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       ".sk-global.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       ".sk-global.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       ".sk-global {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".sk-global pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip-path: inset(100%);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       ".sk-global div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       ".sk-global label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label label.sk-toggleable__label,\n",
-       ".sk-global div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       ".sk-global div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       ".sk-global div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       ".sk-global div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       ".sk-global a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       ".sk-global a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-top-container.sk-global {\n",
-       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
-       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
-       "   to allow keyboard-only users to navigate the display. */\n",
-       "  overflow-x: scroll !important;\n",
-       "  max-width: 100%;\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover td {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table :is(td, th) {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
-       "*/\n",
-       ".estimator-table table td.param {\n",
-       "    text-align: left;\n",
-       "    position: relative;\n",
-       "    padding: 0;\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".default td, .estimator-table th {\n",
-       "    color: black;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       "td.fitted-att-type {\n",
-       "    white-space: preserve nowrap;\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    Styles for parameter documentation links\n",
-       "    We need styling for visited so jupyter doesn't overwrite it\n",
-       "*/\n",
-       "a.param-doc-link,\n",
-       "a.param-doc-link:link,\n",
-       "a.param-doc-link:visited {\n",
-       "    text-decoration: underline dashed;\n",
-       "    text-underline-offset: .3em;\n",
-       "    color: inherit;\n",
-       "    display: block;\n",
-       "    padding: .5em;\n",
-       "}\n",
-       "\n",
-       "@supports(anchor-name: --doc-link) {\n",
-       "    a.param-doc-link,\n",
-       "    a.param-doc-link:link,\n",
-       "    a.param-doc-link:visited {\n",
-       "    anchor-name: --doc-link;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
-       "a.param-doc-link::before {\n",
-       "    position: absolute;\n",
-       "    content: \"\";\n",
-       "    inset: 0;\n",
-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
-       "    display: none;\n",
-       "    position: absolute;\n",
-       "    z-index: 9999;\n",
-       "    left: 0;\n",
-       "    padding: .5ex;\n",
-       "    margin-left: 1.5em;\n",
-       "    color: var(--sklearn-color-text);\n",
-       "    box-shadow: .3em .3em .4em #999;\n",
-       "    width: max-content;\n",
-       "    text-align: left;\n",
-       "    max-height: 10em;\n",
-       "    overflow-y: auto;\n",
-       "\n",
-       "    /* unfitted */\n",
-       "    background: var(--sklearn-color-unfitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       "@supports(position-area: center right) {\n",
-       "    .param-doc-description {\n",
-       "    position-area: center right;\n",
-       "    position: fixed;\n",
-       "    margin-left: 0;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
-       "    /* fitted */\n",
-       "    background: var(--sklearn-color-fitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "    border: none;\n",
-       "    padding: 0;\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon:focus-visible {\n",
-       "    outline: 1px solid currentColor;\n",
-       "    outline-offset: 1px;\n",
-       "}\n",
-       "\n",
-       ".features {\n",
-       "  font-family: monospace;\n",
-       "  cursor: pointer;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
-       "}\n",
-       "\n",
-       ".features.fitted {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features summary {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  margin-bottom: 0;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "  padding: .25em;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features.fitted details[open] > summary {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features details > summary .arrow::before {\n",
-       "  content: \"▸\";\n",
-       "  color: grey;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary .arrow::before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       ".features details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features.fitted details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features .features-container {\n",
-       "  max-height: 10em;\n",
-       "  overflow: auto;\n",
-       "  scrollbar-width: thin;\n",
-       "  padding: .25em 0.1rem;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 0 0 .5em .5em;\n",
-       "}\n",
-       "\n",
-       ".features.fitted .features-container {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features .image-container {\n",
-       "  block-size: 1em;\n",
-       "  inline-size: 1em;\n",
-       "  padding: 0;\n",
-       "  margin: 0%;\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  align-items: center;\n",
-       "}\n",
-       "\n",
-       ".features .copy-paste-icon {\n",
-       "  background-size: 1em 1em;\n",
-       "  width: 1em;\n",
-       "  height: 1em;\n",
-       "  filter: grayscale(100%) opacity(60%);\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  width: 100%;\n",
-       "  margin: 0.01em;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(odd) {\n",
-       "  background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(even) {\n",
-       "  background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:hover {\n",
-       "  background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  table-layout: inherit;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table td {\n",
-       "  text-align: left;\n",
-       "  padding: 0 0.5em;\n",
-       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "  white-space: nowrap;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".total_features {\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  margin-top: 0.5em;\n",
-       "}\n",
-<<<<<<< HEAD
-       "</style><body><div id=\"sk-container-id-13\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-=======
-       "</style><body><div id=\"sk-container-id-2\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-<<<<<<< HEAD
-       "                 aria-label=\"Copy penalty to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-=======
-       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-penalty;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%7D%2C%20default%3D%27l2%27\">\n",
-       "            penalty\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-penalty;\">\n",
-       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;}, default=&#x27;l2&#x27;<br><br>Specifies the norm used in the penalization. The &#x27;l2&#x27;<br>penalty is the standard used in SVC. The &#x27;l1&#x27; leads to ``coef_``<br>vectors that are sparse.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-<<<<<<< HEAD
-       "                 aria-label=\"Copy loss to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('loss',\n",
-=======
-       "                 aria-label=\"Copy copy_X to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('copy_X',\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-loss;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=loss,-%7B%27hinge%27%2C%20%27squared_hinge%27%7D%2C%20default%3D%27squared_hinge%27\">\n",
-       "            loss\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-loss;\">\n",
-       "            loss: {&#x27;hinge&#x27;, &#x27;squared_hinge&#x27;}, default=&#x27;squared_hinge&#x27;<br><br>Specifies the loss function. &#x27;hinge&#x27; is the standard SVM loss<br>(used e.g. by the SVC class) while &#x27;squared_hinge&#x27; is the<br>square of the hinge loss. The combination of ``penalty=&#x27;l1&#x27;``<br>and ``loss=&#x27;hinge&#x27;`` is not supported.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;squared_hinge&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy dual to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-dual;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=dual,-%22auto%22%20or%20bool%2C%20default%3D%22auto%22\">\n",
-       "            dual\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-dual;\">\n",
-       "            dual: &quot;auto&quot; or bool, default=&quot;auto&quot;<br><br>Select the algorithm to either solve the dual or primal<br>optimization problem. Prefer dual=False when n_samples &gt; n_features.<br>`dual=&quot;auto&quot;` will choose the value of the parameter automatically,<br>based on the values of `n_samples`, `n_features`, `loss`, `multi_class`<br>and `penalty`. If `n_samples` &lt; `n_features` and optimizer supports<br>chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,<br>otherwise it will be set to False.<br><br>.. versionchanged:: 1.3<br>   The `&quot;auto&quot;` option is added in version 1.3 and will be the default<br>   in version 1.5.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-copy_X;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
-       "            copy_X\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-copy_X;\">\n",
-       "            copy_X: bool, default=True<br><br>If True, X will be copied; else, it may be overwritten.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy tol to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-tol;\"\n",
-<<<<<<< HEAD
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-=======
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies the convergence criterion of the underlying solver. `tol` is<br>set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. `tol` is set as `cond` of<br>:func:`scipy.linalg.lstsq` when fitting on dense training data.<br><br>.. versionadded:: 1.7<br>.. versionchanged:: 1.9<br>    Now supported on dense data, interpreted as the `cond` parameter.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1e-06</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-<<<<<<< HEAD
-       "                 aria-label=\"Copy C to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-=======
-       "                 aria-label=\"Copy n_jobs to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-C;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
-       "            C\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-C;\">\n",
-       "            C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>For an intuitive visualization of the effects of scaling<br>the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy multi_class to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-multi_class;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=multi_class,-%7B%27ovr%27%2C%20%27crammer_singer%27%7D%2C%20default%3D%27ovr%27\">\n",
-       "            multi_class\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-multi_class;\">\n",
-       "            multi_class: {&#x27;ovr&#x27;, &#x27;crammer_singer&#x27;}, default=&#x27;ovr&#x27;<br><br>Determines the multi-class strategy if `y` contains more than<br>two classes.<br>``&quot;ovr&quot;`` trains n_classes one-vs-rest classifiers, while<br>``&quot;crammer_singer&quot;`` optimizes a joint objective over all classes.<br>While `crammer_singer` is interesting from a theoretical perspective<br>as it is consistent, it is seldom used in practice as it rarely leads<br>to better accuracy and is more expensive to compute.<br>If ``&quot;crammer_singer&quot;`` is chosen, the options loss, penalty and dual<br>will be ignored.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Whether or not to fit an intercept. If set to True, the feature vector<br>is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where<br>1 corresponds to the intercept. If set to False, no intercept will be<br>used in calculations (i.e. data is expected to be already centered).</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy intercept_scaling to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_scaling,-float%2C%20default%3D1.0\">\n",
-       "            intercept_scaling\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
-       "            intercept_scaling: float, default=1.0<br><br>When `fit_intercept` is True, the instance vector x becomes ``[x_1,<br>..., x_n, intercept_scaling]``, i.e. a &quot;synthetic&quot; feature with a<br>constant value equal to `intercept_scaling` is appended to the instance<br>vector. The intercept becomes intercept_scaling * synthetic feature<br>weight. Note that liblinear internally penalizes the intercept,<br>treating it like any other term in the feature vector. To reduce the<br>impact of the regularization on the intercept, the `intercept_scaling`<br>parameter can be set to a value greater than 1; the higher the value of<br>`intercept_scaling`, the lower the impact of regularization on it.<br>Then, the weights become `[w_x_1, ..., w_x_n,<br>w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent<br>the feature weights and the intercept weight is scaled by<br>`intercept_scaling`. This scaling allows the intercept term to have a<br>different regularization behavior compared to the other features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy class_weight to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
-       "            class_weight\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
-       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Set the parameter C of class i to ``class_weight[i]*C`` for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
-=======
-       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
-       "            n_jobs\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
-       "            n_jobs: int, default=None<br><br>The number of jobs to use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets &gt; 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary &lt;n_jobs&gt;` for more details.</span>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-<<<<<<< HEAD
-       "                 aria-label=\"Copy verbose to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-=======
-       "                 aria-label=\"Copy positive to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('positive',\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-verbose;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
-       "            verbose\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-verbose;\">\n",
-       "            verbose: int, default=0<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in liblinear that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy random_state to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-random_state;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
-       "            random_state\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-random_state;\">\n",
-       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>the dual coordinate descent (if ``dual=True``). When ``dual=False`` the<br>underlying implementation of :class:`LinearSVC` is not random and<br>``random_state`` has no effect on the results.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy max_iter to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=max_iter,-int%2C%20default%3D1000\">\n",
-       "            max_iter\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
-       "            max_iter: int, default=1000<br><br>The maximum number of iterations to be run.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1000</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-positive;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
-       "            positive\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-positive;\">\n",
-       "            positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    \n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Fitted attributes</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                    <tbody>\n",
-       "                        <tr>\n",
-       "                        <th>Name</th>\n",
-       "                        <th>Type</th>\n",
-       "                        <th>Value</th>\n",
-       "                        </tr>\n",
-       "                        \n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-classes_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
-       "            classes_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-classes_;\">\n",
-       "            classes_: ndarray of shape (n_classes,)<br><br>The unique classes labels.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
-       "           <td>[0.,1.,2.]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=coef_,-ndarray%20of%20shape%20%281%2C%20n_features%29%20if%20n_classes%20%3D%3D%202%20%20%20%20%20%20%20%20%20%20%20%20%20else%20%28n_classes%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: ndarray of shape (1, n_features) if n_classes == 2             else (n_classes, n_features)<br><br>Weights assigned to the features (coefficients in the primal<br>problem).<br><br>``coef_`` is a readonly property derived from ``raw_coef_`` that<br>follows the internal memory layout of liblinear.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3, 2)</td>\n",
-       "           <td>[[-1.54, 2.28],\n",
-       " [ 0.1 ,-0.98],\n",
-       " [ 0.66,-0.35]]</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=coef_,-array%20of%20shape%20%28n_features%2C%20%29%20or%20%28n_targets%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: array of shape (n_features, ) or (n_targets, n_features)<br><br>Estimated coefficients for the linear regression problem.<br>If multiple targets are passed during the fit (y 2D), this<br>is a 2D array of shape (n_targets, n_features), while if only<br>one target is passed, this is a 1D array of length n_features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[1.64]</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
-<<<<<<< HEAD
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20if%20n_classes%20%3D%3D%202%20else%20%28n_classes%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: ndarray of shape (1,) if n_classes == 2 else (n_classes,)<br><br>Constants in decision function.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
-       "           <td>[ 1.2 , 2.02,-3.14]</td>\n",
-=======
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=intercept_,-float%20or%20array%20of%20shape%20%28n_targets%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: float or array of shape (n_targets,)<br><br>Independent term in the linear model. Set to 0.0 if<br>`fit_intercept = False`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">float64</td>\n",
-       "           <td>-0.1418</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
-<<<<<<< HEAD
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_features_in_,-int\">\n",
-=======
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_features_in_,-int\">\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "            n_features_in_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
-       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-<<<<<<< HEAD
-       "           <td>2</td>\n",
-=======
-       "           <td>1</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_iter_,-int\">\n",
-       "            n_iter_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
-       "            n_iter_: int<br><br>Maximum number of iterations run across all classes.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>8</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-rank_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=rank_,-int\">\n",
-       "            rank_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-rank_;\">\n",
-       "            rank_: int<br><br>Rank of matrix `X`. Only available when `X` is dense.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int64</td>\n",
-       "           <td>np.int64(1)</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-singular_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=singular_,-array%20of%20shape%20%28min%28X%2C%20y%29%2C%29\">\n",
-       "            singular_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-singular_;\">\n",
-       "            singular_: array of shape (min(X, y),)<br><br>Singular values of `X`. Only available when `X` is dense.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[4.04]</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "                    </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
-       " SPDX-License-Identifier: BSD-3-Clause\n",
-       "*/\n",
-       "\n",
-       "function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "\n",
-       "    const parent = element.parentElement;\n",
-       "    if (!parent || !parent.nextElementSibling) {\n",
-       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    const paramName = element.parentElement.nextElementSibling\n",
-       "        .textContent.trim().split(' ')[0];\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "\n",
-       "/**\n",
-       " * Copy the list of feature names formatted as a Python list.\n",
-       " *\n",
-       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
-       " *   contain a `details` element and a table containing feature named.\n",
-       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
-       " */\n",
-       "function copyFeatureNamesToClipboard(element) {\n",
-       "    var detailsElem = element.closest('.features').querySelector('details');\n",
-       "    var wasOpen = detailsElem.open;\n",
-       "    detailsElem.open = true;\n",
-       "    var content = element.closest('.features').querySelector('tbody')\n",
-       "                  .innerText.trim();\n",
-       "    if (!wasOpen) detailsElem.open = false;\n",
-       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
-       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
-<<<<<<< HEAD
-       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
-       "    const originalStyle = element.style;\n",
-       "    const copyMark = document.createElement('span');\n",
-       "    copyMark.innerHTML = '✔';\n",
-=======
-       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
-       "    const originalStyle = element.style;\n",
-       "    const copyMark = document.createElement('span');\n",
-       "    copyMark.innerHTML = '✔';\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "    copyMark.style.color = 'blue';\n",
-       "    copyMark.style.fontSize = '1em';\n",
-       "\n",
-       "    navigator.clipboard.writeText(formattedText)\n",
-       "        .then(() => {\n",
-       "            element.style.display = 'none';\n",
-       "            element.parentElement.appendChild(copyMark);\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                copyMark.remove();\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'orange';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "/**\n",
-       " * Adapted from Skrub\n",
-       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
-       " * @returns \"light\" or \"dark\"\n",
-       " */\n",
-       "function detectTheme(element) {\n",
-       "    const body = document.querySelector('body');\n",
-       "\n",
-       "    // Check VSCode theme\n",
-       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
-       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
-       "\n",
-       "    if (themeKindAttr && themeNameAttr) {\n",
-       "        const themeKind = themeKindAttr.toLowerCase();\n",
-       "        const themeName = themeNameAttr.toLowerCase();\n",
-       "\n",
-       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
-       "            return \"dark\";\n",
-       "        }\n",
-       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
-       "            return \"light\";\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    // Check Jupyter theme\n",
-       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
-       "        return 'dark';\n",
-       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
-       "        return 'light';\n",
-       "    }\n",
-       "\n",
-       "    // Guess based on a parent element's color\n",
-       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
-       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
-       "    if (match) {\n",
-       "        const [r, g, b] = [\n",
-       "            parseFloat(match[1]),\n",
-       "            parseFloat(match[2]),\n",
-       "            parseFloat(match[3])\n",
-       "        ];\n",
-       "\n",
-       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
-       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
-       "\n",
-       "        if (luma > 180) {\n",
-       "            // If the text is very bright we have a dark theme\n",
-       "            return 'dark';\n",
-       "        }\n",
-       "        if (luma < 75) {\n",
-       "            // If the text is very dark we have a light theme\n",
-       "            return 'light';\n",
-       "        }\n",
-       "        // Otherwise fall back to the next heuristic.\n",
-       "    }\n",
-       "\n",
-       "    // Fallback to system preference\n",
-       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
-       "}\n",
-       "\n",
-       "\n",
-       "function forceTheme(elementId) {\n",
-       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
-       "    if (estimatorElement === null) {\n",
-       "        console.error(`Element with id ${elementId} not found.`);\n",
-       "    } else {\n",
-       "        const theme = detectTheme(estimatorElement);\n",
-       "        estimatorElement.classList.add(theme);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-<<<<<<< HEAD
-       "forceTheme('sk-container-id-13');</script></body>"
-      ],
-      "text/plain": [
-       "LinearSVC()"
-      ]
-     },
-     "execution_count": 75,
-=======
-       "forceTheme('sk-container-id-2');</script></body>"
-      ],
-      "text/plain": [
-       "LinearRegression()"
-      ]
-     },
-     "execution_count": 12,
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-<<<<<<< HEAD
-    "# 1b)\n",
-    "X = iris_data[:,[0,1]]\n",
-    "T = iris_data[:,4]\n",
-    "clf.fit(X, T)"
-=======
-    "# 3b)\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "reg = LinearRegression()\n",
-    "print(X)\n",
-    "print(X.reshape(-1,1)) # transponiert den Merkmalsraum in einen Spaltenvektor\n",
-    "reg.fit(X.reshape(-1,1),T)\n"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 76,
-   "id": "cc181a3a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "80.0 Prozent wurden richtig vorausgesagt\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 1 c+d)\n",
-    "pred = clf.predict(X)\n",
-    "correct_perc = (sum(pred == T)/len(pred))*100\n",
-    "\n",
-    "print(f\"{correct_perc} Prozent wurden richtig vorausgesagt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "16af32f1",
-=======
-   "execution_count": 17,
-   "id": "98476c93",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-<<<<<<< HEAD
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1e)\n",
-    "import nnwplot\n",
-    "nnwplot.plotTwoFeatures(X, T, clf.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "f4bcee12",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1f)\n",
-    "from sklearn.svm import SVC\n",
-    "svc = SVC()\n",
-    "svc.fit(X, T)\n",
-    "nnwplot.plotTwoFeatures(X, T, svc.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "2c8991de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1g)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "1a5e84f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#1h)\n",
-    "X_0_3 = iris_data[:,[0,3]]\n",
-    "clf.fit(X_0_3, T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, clf.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "892baf25",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X_0_3 = iris_data[:,[0,3]]\n",
-    "svc.fit(X_0_3, T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, svc.predict) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "1bad3e36",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-=======
-      "image/png": "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",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-<<<<<<< HEAD
-     "data": {
-      "image/png": "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",
-=======
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.07437258 -0.0291456   0.0103735   0.04848806  0.07131768  0.17819428\n",
-      "  0.25802648  0.31521741  0.36927952  0.36935855  0.41688375  0.45390939\n",
-      "  0.45908454  0.46383508  0.51238855  0.55388473  0.70604178  0.81235693\n",
-      "  0.81535647  0.85499255  0.85620981  0.87946314  1.05851877  1.08531376\n",
-      "  1.27339292  1.30028357  1.35243501  1.48056341  1.54300275  1.56211567\n",
-      "  1.57747659  1.64940532  1.79914906  1.81960638  1.82765485  1.84872133\n",
-      "  1.86283603  2.02884723  2.09997946  2.17808643  2.25646145  2.43070493\n",
-      "  2.50678657  2.58556763  2.69609076  2.83744691  2.96707657  2.97306828\n",
-      "  3.02194413  3.03595981]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 3c)\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,reg.predict(X.reshape(-1,1)))\n",
-    "plt.show()\n",
-    "print(reg.predict(X.reshape(-1,1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "94ab4bd3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-<<<<<<< HEAD
-    "# 1i)\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "knc = KNeighborsClassifier()\n",
-    "knc.fit(X,T)\n",
-    "nnwplot.plotTwoFeatures(X, T, knc.predict)\n",
-    "knc.fit(X_0_3,T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, knc.predict)\n",
-    "\n"
-=======
-    "# 3d) Decision Tree Regressor\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "dtr = DecisionTreeRegressor()\n",
-    "dtr.fit(X.reshape(-1,1),T)\n",
-    "\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,dtr.predict(X.reshape(-1,1)))\n",
-    "plt.show()"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 101,
-   "id": "d79741d2",
-=======
-   "execution_count": 20,
-   "id": "475aa6e4",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-<<<<<<< HEAD
-      "text/plain": [
-       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 3, 3, 3, 1, 3, 1, 3, 1, 3, 1, 1, 1, 1, 3, 1, 3,\n",
-       "       1, 1, 3, 1, 3, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 3, 1, 1, 3, 3,\n",
-       "       1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 2, 3, 3, 2, 1, 2, 3, 2,\n",
-       "       3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 2, 3, 3, 3, 2, 2, 2,\n",
-       "       3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3], dtype=int32)"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 2a) probabilistisches Verfahren\n",
-    "from sklearn.cluster import KMeans\n",
-    "\n",
-    "X_0_2 = iris_data[:,[0,2]]\n",
-    "km = KMeans(4)\n",
-    "km.fit(X_0_2)\n",
-    "km.predict(X_0_2)\n"
-=======
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3d) SV Regressor\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "svr = SVR()\n",
-    "svr.fit(X.reshape(-1,1),T)\n",
-    "\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,svr.predict(X.reshape(-1,1)))\n",
-    "plt.show()"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-<<<<<<< HEAD
-   "display_name": "nnw (3.14.0)",
-=======
-   "display_name": "nnw (3.14.8)",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-<<<<<<< HEAD
-   "version": "3.14.0"
-=======
-   "version": "3.14.8"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Lösungen_N/AB2_BACKUP_715.ipynb b/Lösungen_N/AB2_BACKUP_715.ipynb
deleted file mode 100644
index 45be331..0000000
--- a/Lösungen_N/AB2_BACKUP_715.ipynb
+++ /dev/null
@@ -1,1894 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 73,
-   "id": "29e20c6b",
-=======
-   "execution_count": 2,
-   "id": "de4f7f4b",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-<<<<<<< HEAD
-    "from sklearn.svm import LinearSVC"
-=======
-    "from sklearn.svm import LinearSVC\n",
-    "import matplotlib.pyplot as plt"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 74,
-   "id": "67b3d7af",
-=======
-   "execution_count": 3,
-   "id": "88b3d717",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj4AAAGwCAYAAACpYG+ZAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAOhVJREFUeJzt3Xl4VNXh//HPJIQhCWRCCEsCYScICCpbUKyKslhAo7ig8NO6fEWtYlGsFazWrcatorVV22qldeVBoYgWKwRBQdBoLbuCECBAkC2ZQEhCSM7vD5opQybJJJnJnZn7fj3PPI9z7zl3znVmMh/OOfdchzHGCAAAwAairG4AAABAUyH4AAAA2yD4AAAA2yD4AAAA2yD4AAAA2yD4AAAA2yD4AAAA22hmdQOaWmVlpfbs2aNWrVrJ4XBY3RwAAOAHY4wOHz6s1NRURUU1vN/GdsFnz549SktLs7oZAACgAfLy8tSpU6cG17dd8GnVqpWkE//jEhISLG4NAADwR1FRkdLS0jy/4w1lu+BTNbyVkJBA8AEAIMw0dpoKk5sBAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAIBtEHwAAKhBvrtEX2w9oHx3idVNQYDY7ialAAD4Y07OTs2Yt06VRopySFkT+mvikM5WNwuNRI8PAACnyHeXeEKPJFUaaea89fT8RACCDwAAp8g9UOwJPVUqjNH2A0etaRAChuADAMApuiXHK8rhvS3a4VDX5DhrGoSAIfgAAHCKFFessib0V7TjRPqJdjj0xITTleKKtbhlaCwmNwMA4MPEIZ11XnpbbT9wVF2T4wg9EYLgAwBADVJcsQSeCMNQFwAAsA2CDwAAqFOkLObIUBcAAKhVJC3mSI8PAAAB0NgekVDtUYm0xRzp8QEAoJEa2yMSyj0qtS3mGI4Tv+nxAQCgERrbIxLMHpVA9CJF2mKOBB8AABqhsbe3CNbtMebk7NTwJ5dq0l++1PAnl2pOzs4GHSfSFnNkqAsAgEao6hE5ObzUp0eksfV9qakX6bz0tg0KLFWLOX6zvUBySIO6tG5w26xGjw8AAI3Q2B6RYPSoBKMX6bPN+3XXu9/qzre/bVQPktXo8QEAoJEae3uLQN8eI9C9SPXpQcp3lyj3QLG6JceH5HCY5cHn0KFD2rhxoxITE9WnTx9FR0fXWLaiokKLFy+utr1///7q2LFjMJsJAECtGnt7i0DeHqOqF2nmvPWqMKbRvUj+XtkVylenVbEs+Ljdbt1111365JNPlJ6ertzcXDVr1kzvvPOOMjIyfNYpKSnRT3/6Uw0bNkwul8uzffr06QQfAABOEsheJH96kAI9ryhYLAs+hYWFyszM1OzZs+VwOFRZWalrrrlGN998s9avX19r3VmzZmnYsGFN1FIAQCQJ9aGYQApUL5I/PUjhst6PZcGnS5cu6tKli+d5VFSUBg0apOXLl9dZd8uWLSorK1OPHj3UqVOnYDYTABBBwmEoJlTV1YMUjKvTgsHyOT5ff/219u3bp82bN+v3v/+9nn766TrrPPTQQ+rQoYPWrl2rESNGaPbs2UpOTvZZtqysTGVlZZ7nRUVFAWs7ACB8hMtQTCirrQcp0POKgsXy4PP+++/rq6++0nfffadevXrVOL9Hkpo1a6Z58+bp8ssvlyTt3r1bF154oW677Ta99957PutkZWXpkUceCUrbAQDhI1yGYsJZoK9OCwaHMcbUXSz4jh8/rttuu02ffPKJtmzZIqfT6Ve9V199VT//+c9VXFysmJiYavt99fikpaXJ7XYrISEhYO0HAIS2fHeJhj+5tNpQzIr7R4TkDzS8FRUVyeVyNfr3O2QWMGzWrJmmTJmivLw8bd682e96SUlJKi8v18GDB33udzqdSkhI8HoAAOwn0m69EGpC9e7yp7L0qq7ExESvbRs3bpTD4VC7du0knegFWrJkiWednoKCArVu7b1M9kcffaSUlBS1b9++qZoOAAhT4TAUE47CadK4ZcHnjTfe0JIlSzRu3DglJyfrP//5j55//nlNmzbNE2KOHDmin/70p3r99dd1ww036N1339VHH32kSy+9VImJifrnP/+pd955R3/729/kcDjqeEUAAAK7UCDCb9K4ZcFn6tSpOv300/Xee+9p79696tSpkxYuXKjzzz/fUyYmJkZjxozxLE54++2367TTTtPcuXO1f/9+de/eXevWrVN6erpVpwEAgK2F26TxkJnc3FQCNTkKAAA03aTxiJvcDAAAwk+4TRq3fB0fAAAQ3sJp0jjBBwAANFq4TBpnqAsAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANgGwQcAANiG5cGnvLxc3333nfbu3et3nYqKCm3atEm5ublBbBkAAIg0lgWfI0eOaPr06Wrfvr2uuOIKnXbaaRo0aJA2bNhQa72VK1eqa9euuuiii3TmmWdq6NCh2r17dxO1GgAAhDPLgs+PP/6o3r17Kz8/Xxs2bNDevXvVoUMHXX/99TXWOXLkiCZMmKArr7xSe/bs0b59++R0OnXdddc1YcsBAEC4siz49OjRQ1OmTJHT6ZQktWjRQiNGjNCOHTtqrLNgwQIdOnRIDz74oCTJ6XRq5syZ+vTTT7Vt27YmaTcAAAhfzaxuwObNm1VYWKjNmzfrhRde8IQaX/7973+re/fuSkpK8mzLyMjw2gcACF/57hLlHihWt+R4pbhirW4OIpDlweeFF17QihUrlJubq2HDhmnChAk1lj148KDatGnjta1169aKiorSwYMHfdYpKytTWVmZ53lRUVFgGg4ACKg5OTs1Y946VRopyiFlTeiviUM6W90sRBjLr+r64x//qDVr1mjv3r1KTEzURRddpPLycp9lY2JivEKMdOKqsMrKSsXExPisk5WVJZfL5XmkpaUF/BwAAI2T7y7xhB5JqjTSzHnrle8usbZhiDiWB58qcXFxmj59urZs2aLvv//eZ5nOnTtrz549Xtuqrujq3Nn3vwpmzJght9vteeTl5QW24QCARss9UOwJPVUqjNH2A0etaRAilmXBp7S0tNq27du3SzoxfCWdWK/n66+/9gxjXXTRRdq7d6/+/e9/e+osXLhQcXFxGjZsmM/XcTqdSkhI8HoAAEJLt+R4RTm8t0U7HOqaHGdNgxCxLAs+f/jDH3TzzTdr3rx5+uyzz/T73/9ed955p2644QZ17NhRknT48GENGTJECxculCSdc845GjdunCZPnqwFCxbo9ddf169//WvNnDlTLVu2tOpUAACNlOKKVdaE/op2nEg/0Q6HnphwOhOcEXAOY4ypu1jgGWM0d+5cvf/++9q7d686deqkCRMmaMKECXL894N/5MgRXXDBBXr44Yc1fvx4SVJJSYmefvppLV26VE6nUxMnTtTNN9/s9+sWFRXJ5XLJ7XbT+wMAISbfXaLtB46qa3IcoQdeAvX7bVnwsQrBBwCA8BOo3++QmdwMAAAQbAQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgG82sboAkFRQUqHXr1nWWM8Zo9+7d1bYnJSUpLi4uGE0DAAARxLIenz179ujOO+9UUlKSunfvrlatWunuu+/WsWPHaqxTXFystLQ0DRw4UMOGDfM8PvrooyZsOQDAX/nuEn2x9YDy3SVWNwWQZGGPT3Z2tvr06aNt27YpMTFR69at08iRIxUTE6Onn3661roffPCBhg0b1kQtBQA0xJycnZoxb50qjRTlkLIm9NfEIZ2tbhZszrIen+uuu0533HGHEhMTJUn9+/fX5MmTtWjRojrrlpeX6+DBg0FuIQCgofLdJZ7QI0mVRpo5bz09P7BcSE1u/uGHH9ShQ4c6y1144YXq1q2bEhMT9atf/UqlpaVN0DoAgL9yDxR7Qk+VCmO0/cBRaxoE/FdITG6WpAULFujDDz/UwoULaywTFRWlRx99VL/4xS+UkJCgFStWaMKECSotLdULL7zgs05ZWZnKyso8z4uKigLedgCAt27J8YpyyCv8RDsc6prMhSiwVkj0+KxYsUKTJ0/Www8/rHHjxtVYLi4uTg8++KASEhIkSeeee65mzJihV199VZWVlT7rZGVlyeVyeR5paWlBOQcAwP+kuGKVNaG/oh0OSSdCzxMTTleKK9bilsHuHMYYU3ex4Fm1apXGjBmju+66S48//ni96y9YsECXXXaZ8vPzfQ6T+erxSUtLk9vt9gQoAEBw5LtLtP3AUXVNjiP0oFGKiorkcrka/ftt6VDX6tWrdfHFF2vq1Kk+Q0/Vuj1V6/RUVlYqKiqq2jESEhKUnJzs8zWcTqecTmdQ2g8AqF2KK5bAg5BiWfD55ptvNGbMGF1xxRW6/fbbtWvXLkkn5vGkpqZKktxut9LS0vT666/rhhtu0PPPP699+/bp0ksvVWJiov75z3/queee08MPP6xmzUJmuhIAAAhRlqWFTz75RK1atdInn3yiTz75xLPd5XJpw4YNkk6EoI4dOyo+Pl6SNHXqVL3yyiu6//77tX//fnXv3l1z587VpZdeask5AIAd5btLlHugWN2S4+nNQdixfI5PUwvUGCEA2BGLEsIqgfr9DomrugAAoY9FCREJCD4AAL+wKCEiAcEHAOCXqkUJT8aihAg3BB8AgF9YlBCRgGvAAQB+mziks85Lb8uihAhbBB8AQL2wKCHCGUNdAADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AADANgg+AABJUr67RF9sPaB8d4nVTQGCppnVDQAAWG9Ozk7NmLdOlUaKckhZE/pr4pDOVjcLCDh6fADA5vLdJZ7QI0mVRpo5bz09P4hIIRF8jDFNUgcAUF3ugWJP6KlSYYy2HzhqTYOAILIs+BQUFOjhhx9Wr1695HQ61bVrV/32t79VZWVlrfVyc3M1evRoOZ1OtWzZUjfddJOKi4ubqNUAEHm6JccryuG9LdrhUNfkOGsaBASRZcFn7ty5cjgcWrRokY4eParZs2frmWee0WOPPVZjnfLyco0dO1axsbHas2ePvv32W61YsUK33nprE7YcACJLiitWWRP6K9pxIv1EOxx6YsLpSnHFWtwyIPAcJoTGjH7xi19o2bJlWrNmjc/9H3zwgTIzM7Vz506lpaVJkt59911NnjxZu3fvVocOHep8jaKiIrlcLrndbiUkJAS0/QAQzvLdJdp+4Ki6JscRehByAvX7HRJzfKrk5+erdevWNe5ftWqVunfv7gk9kjRixAhVVlbqq6++aoomAkDESnHF6uwebQg9iGghczn70qVL9f777+vtt9+uscyPP/6otm3bem1LTk6Ww+HQjz/+6LNOWVmZysrKPM+LiooC02AAABB2QqLHZ82aNbryyit15513auLEibWWPXXyc9VIncPh8FVcWVlZcrlcnsfJvUUAAMBeLA8+69at08iRIzVx4kQ9//zztZZNSUnRvn37vLbt379fxpga5/fMmDFDbrfb88jLywtU0wEAQJixNPisX79eF110ka644gq99NJLPnttSktLVVFRIUk699xztWPHDm3bts2zPzs7W9HR0Ro2bJjP13A6nUpISPB6AAAAe7Is+GzatEkXXXSRxo8fr1mzZqmsrEylpaVe83EKCwsVGxurN954Q5I0evRonXnmmZoyZYq2bt2qnJwcPfDAA7rpppuUnJxs1akAAIAwYVnwefvtt+V2u/X222+rdevWSkxMVGJiorp06eIp43A45HQ6FR0dLUmKjo7WRx99pNatW2vo0KHKzMzUhAkT9OKLL1p1GgAAIIyE1Do+TYF1fAAACD8RuY4PAABAMBF8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbRB8AACAbdQ7+KxcuVIff/xxvfcBAABYrd7BZ9WqVVqyZEmN+7KzsxvdKAAAgGAI2FCXMUYbNmxQ27ZtA3VIAACAgGrmb8GXXnpJDz30kEpKSmSM0ezZs732Hz16VA6HQ998802g2wgAABAQfgefiy66SElJSfrwww/ldrs1efJkr/0JCQkaPHiw2rVrF/BGAgAABILfwad3797q3bu3zjnnHJWVlalXr17BbBcAAEDA+R18qnTu3DkY7QAAAAg61vEBAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2QfABAAC2YWnw+e677zRt2jSlpKRoxIgRdZYvLi5WYmJitcfbb7/dBK0FAADhrplVL1xWVqbLL79ct9xyi0aPHq0NGzbUWccYI7fbrcWLF2vw4MGe7XFxccFsKgAAiBCWBR+n06lNmzZJkqZNm1avui1btlRiYmLgGwUAACJaWM7xueaaa5SSkqJzzz1Xb7zxhtXNAQAAYcKyHp+Guvrqq3X33XcrJSVFixYt0q233qp9+/Zp+vTpPsuXlZWprKzM87yoqKipmgoAAEJMWAWfli1bas6cOZ7nt912m/bs2aPHH3+8xuCTlZWlRx55pKmaCAAAQlhYDnWdbPDgwSosLNS+fft87p8xY4bcbrfnkZeX18QtBAAAoSKsenx8+e677+R0OpWQkOBzv9PplNPpbOJWAQCAUBTSPT5ut9trnZ6//vWvevPNN+V2u1VRUaGPP/5YTz75pG688Ua1aNHC4tYCAIBQZ2mPzznnnKONGzeqpKREFRUVnkvUd+/erfj4eM+6PceOHZMkjR07Vr/5zW80bdo0HTlyRO3bt9e9996re++918KzAAAA4cJhjDFWvfjhw4dVUVFRbXtVAKoKPnFxcWrevLlXmfLycsXExNT7NYuKiuRyueR2u2scHgMAAKElUL/flvb4tGrVqtb9DoejxoUKGxJ6AACAvYX0HB8AAIBAIvgAAADbIPgAQIjLd5foi60HlO8usbopQNgL+3V8ACCSzcnZqRnz1qnSSA5Jt/ykm248t5tSXLFWNw0IS/T4AECIyneXeEKPJBlJf/48V8OfXKo5OTstbRsQrgg+ABCicg8Ue0LPySqNNHPeeoa+gAYg+ABAiOqWHK8oh+99FcZo+4GjTdsgIAIQfAAghJw8kTnFFausCf19/qGOdjjUNTmuydsHhDsmNwNAiDh5InOUQ8qa0F8Th3TWeelt9fqK7Xp1xTZVmhOh54kJpzPBGWgAS29ZYQVuWQEgFOW7SzT8yaVec3qiHQ6tuH+EJ+Dku0u0/cBRdU2OI/TAdiLilhUAgBN8TWSumsdTFXJSXLEEHqCRmOMDACHA10Rm5vEAgUfwAYAQUDWROdpxIv0wjwcIDoa6ACBEVE1kZh4PEDwEHwAIIczjAYKLoS4AAGAbBB8AAGAbBB8AAGAbBB/47eSl9CP5NQEAkYvJzfBLTUvpB0K+u0S5B4rVLTnea1JnMF8TAGBP9PigTvnuEk8AkaRKI82ctz4gvTBzcnZq+JNLNekvX2r4k0s1J2dn0F8TAGBfBB/Uqbal9P3la8iqtnATiNcEAOBUDHWhTlVL6Z9688RTl9Kv75BVbeGmrtes6bUAAKgNPT6okz9L6TdkyKq2exPV9po1vRYAAHWhxwd+qW0p/ZrCzXnpbWvt1Tm7RxtlTeivmfPWq8KYaoHK12vW9lr0/AAA6kLwgd9qWkq/MUNWdd2b6NTXrO21CD4AgLow1IVGa+iQVZUUV6zO7tHGr+BS22sBAFAXenzQaFXhpj5DVlLDJijX9VoAANTGYYwxdReLHEVFRXK5XHK73UpISLC6OREl311S45DVqRq7OGF9XgsAEP4C9ftNjw8CpqY5QKcKxARlf18LCAcszwA0HYIPmhwTlIH/4dYsQNOydHLz3r179fjjj6t///66/PLL/apTVFSke+65R2eccYYyMjL03HPPqbKyMsgtRSD5mqAsSWt3FzZ5WwArcWsWoOlZFnyOHTumoUOH6ujRo+rbt6/y8vL8qnfZZZdp2bJleuGFF3T//ffrt7/9rX79618HubUIpBRXrH518WnVtj+96Hv+4MNWuDUL0PQsG+qKiYnR1q1bFRMTo2nTpmnr1q111lm2bJk+/fRTrV+/Xv369ZMk7du3T7/4xS90//33M1k5jPTv5Kq2jeEu2I2/t4MBEDiW9fg4HA7FxMTUq86yZcvUsWNHT+iRpLFjx6qsrEyrVq0KdBMRRKzHA/h3OxgAgRVWk5vz8vLUoUMHr21Vz3ft2uWzTllZmcrKyjzPi4qKgtdA+I31eIAT6lq9HEBghVXwqaysrNZLFB0draioKFVUVPisk5WVpUceeaQpmod64g8+cALLMwBNJ6xuWZGcnKyDBw96bSsoKFBlZaWSk5N91pkxY4bcbrfn4e8kajvKd5foi60HmnSCcX1uVwEAQGOFVY/PkCFD9Nxzz2nfvn1q166dJGnFihWSpMGDB/us43Q65XQ6m6yN4Yq1RAAAdhDSPT6HDx9Wz549NW/ePEnS+PHjlZKSopkzZ+r48eNyu916/PHHNXbsWHXuzI90Q7GWCADALizt8Rk7dqw2b96sAwcOqLS0VD179pQkrV27VnFxcaqoqNDWrVs9E5Lj4uK0cOFCTZ48WUlJSSorK9N5552n2bNnW3gW4S9QKymz7D4QGHyXgOCxNPj86U9/8rriqkps7IkvekJCgrZs2aL27dt79p111lnauHGj8vPz5XQ6lZSU1GTtjVSBWEuEoTIgMPguAcHF3dkh6cQf21MvLff3j22+u0TDn1xaLTituH8E/1oF6oHvElAz7s6OgGrMpeXcdBQIDL5LQPARfODR0LVEWHYfCAy+S0DwhfRVXQgPLLsPBAbfJSD4mOODgMl3l7AKMxAAfJeA6pjjg5DDsvtAYPBdAoKHoS4AAGAbBB8AAGAbBB8AAGAbBJ8IZsXd1gEACGVMbo5QLHsPAEB19PhEIO62DgCAbwSfCFTbsvcAANgZwScCVS17fzKWvQcAgOATkVj2HgAA35jcHKEac7d1AAAiFcEngrHsff3lu0uUe6BY3ZLj+X8HABGI4AP8F0sAAEDkY44PIJYAAAC7IPgAYgkAALALgg8glgAAALsg+ABiCQAAsAsmNwP/xRIAABD5CD7ASVgCAAAiG0NdAFAP+e4SfbH1AFf8AWGKHh8A8BNrPQHhjx4fIIDoDYhcrPUERAZ6fIAAoTcgstW21hPzwoDwQY8PEAC19QbQCxQZWOsJiAz0+AABUFNvwOsrtuvVFdvoBYoAVWs9zZy3XhXGsNYTEKYIPkAAVPUGnBx+oiRP6JH+1wt0XnpbfizDFGs9AeGPoa4QxNBI+ElxxepXPz3NMxQS7XDo/37Sjft/RaAUV6zO7tGG0AOEKUt7fMrKyvTCCy8oOztbLVq00MSJEzVp0qQay5eUlGjUqFHVtj/wwAP66U9/GsymNhkmyIanOTk79dSi71RpJIek+y7urUvPTNWrK3K9wg9zQgDAWpYGn0mTJmnt2rV67LHHVFBQoFtuuUW7d+/WL3/5S5/lKyoqtHLlSv3pT39S3759PdvT09ObqslBVdMEWYZGQtup75uR9PTH3+vSM1OZEwIAIcay4PPll19q3rx5+vLLLzV06FBJUmlpqR566CHdcccdiour+V/FAwYM0LBhw5qqqU2Gy2XDU23vG3NCACC0WDbHZ/HixWrfvr0n9EhSZmamjhw5olWrVtVad+bMmRo1apRuu+02ffPNN8FuapPhctnwVNf7xpwQAAgdlgWfHTt2qGPHjl7bqp7v3LmzxnpnnXWWJk2apGnTpqlZs2YaNmyY3nvvvRrLl5WVqaioyOsRqqoul412nPgVjXY4dN/FvZV7oJiJziHM1/vGkBYAhCbLhrrKy8vVokULr23NmzdXVFSUjh075rNOXFycVq9erebNm0uSxo0bp4qKCt1999268sorfdbJysrSI488EtjGB9HJQyNrdxd6Jswy0Tm0MaQFAOHBsh6fpKQkHTx40GtbYWGhKisrlZSU5LNOVFSUJ/RUGT16tHbt2lXtWFVmzJght9vteeTl5QXmBIIoxRWrrslxntAjcV+gcMCQFgCEPsuCz1lnnaVt27apsLDQs+2rr77y7PPXjz/+qKioKDmdTp/7nU6nEhISvB6horb1emqbMAsAABrGsuCTmZmphIQEZWVlSTox9PXUU0/pJz/5iXr27ClJOnLkiM4991wtWrRIkrRw4UJ9++23nmNs27ZNTz/9tMaNG6eWLVs2/Uk0wpycnRr+5FJN+suXGv7kUs3J8Z7XxERnAAACz7Lgk5CQoLlz52r27Nnq1q2bUlNTdeDAAb3xxhueMsePH9fKlSv1448/SpK6du2qqVOnqnPnzhowYID69u2rYcOG6fXXX7fqNBqkthtaVmHCbGRhNW4ACA2WLmA4YsQI5eXlacOGDXI6nerTp48cjv91c7Rq1Uqff/65Z4HC/v37a8WKFdqzZ4/279+vbt26hdTQlb/8Xa+HCbORgdW4ASB0WH6T0ubNm9c4pyc6Olrnnntute2pqalKTU0NdtOCxtcNLWsaxkpxxRJ4wpiv3r0Z769TvLOZBnVpzXsLAE2Mm5RagGEs+/DVu1cp6c63v/U5twsAEFyW9/jYFcNY9uCrd68K92IDgKZHj08TqGliK+u+RL5Te/dOxRIFANC06PEJMia2oqp375vtBbrr3W/9mtsFAAgOenyCyJ/L1mEPKa5YjT8jlbldAGAxenyCyN/L1mEfzO0CAGsRfIKoPpetwz5YogAArMNQVxBx2ToAAKGFHp8gY2gDAIDQQfAJoHx3iXIPFKtbcrxXwGFoAwhtNX13AUQegk+AcNk6EJ747gL2whyfAOCydSA88d0F7IfgEwC1XbYOIHTx3QXsh+ATAFWXrZ+My9aB0Md3F7Afgk8AcNk6EJ747gL24zDG+LhvdOQqKiqSy+WS2+1WQkJCQI+d7y7hsnUgDPHdBUJfoH6/uaorgLhsHQhPfHcB+2CoC4Ct5LtL9MXWA1y5BdgUPT4AbIM1ewDQ4wPAFlizB4BE8AFgE6zZA0Ai+ACwCV9r9kRJOlhcRq8PYCMEHwC2cOqaPQ5JRtKdb3+r4U8u1ZycnZa2D0DTIPgAsI2JQzprxf0j9Idrz5LDcSL4SMz3AeyE4AMgYvhzqXqKK1ZJLZsz3wewKS5nBxAR6nOpetV8n5PDD/foAuyBHh8AYa++l6pzjy7AvujxARD2artUvaYwM3FIZ52X3pZ7dAE2Q/ABEPYaOnTFPboA+2GoC0DYY+gKgL/o8QEQERi6AuAPS4NPZWWlZs+erezsbLVo0UJXX321xowZE/A6AOyBoSsAdbF0qOv//u//9OCDD+rss89W165ddemll+rPf/5zwOsAAABIFvb4rFmzRq+//rqWLVum888/X5IUFRWl+++/Xz/72c/kdDoDUgcAAKCKZT0+ixYtUnJyss477zzPtquuukoFBQVavXp1wOoAAABUsSz45ObmqlOnTnI4/ne75M6dO3v2BapOWVmZioqKvB4AAMCeLAs+ZWVliovzXmOjRYsWio6OVmlpacDqZGVlyeVyeR5paWmBOQEAABB2LAs+LpdLBQUFXtsKCwtVUVGh1q1bB6zOjBkz5Ha7PY+8vLzAnAAAv/lz81AAaAqWBZ8zzjhD27Zt0+HDhz3b/vOf/0iSBgwYELA6TqdTCQkJXg8ATWdOzk4Nf3KpJv3lSw1/cqnm5Oy0ukkAbMyy4JOZmanmzZvrxRdflHRifZ7f/e53GjRokPr06SNJKi4u1mWXXaalS5f6XQfACaHQy1Lfm4cCQLBZdjl7mzZt9MYbb+i6667T/PnzVVhYqGPHjmnRokWeMuXl5VqwYIEuu+wyv+sAONHLUhU4ohxS1oT+mjikc5O3oyE3DwWAYLJ05ebMzEzt3LlTOTk5cjqdGjZsmJo3b+7ZHx8fr/nz52vgwIF+1wHsrqZelvPS2zZZ2Mh3lyj3QLHim0c36OahABAslt+rKzExUaNGjfK5LyYmxtPb428dwO6s7mU5tbfp8rM66h/f7lGFMdw8FIDlLA8+AAKrW3K8Zb0svnqb/vHtHs37+dk6eqySm4cCsJyl9+oCEHgprlhlTeiv6P8u9FnVyyIp6JOda+ptOnqsUmf3aEPoAWA5enyACDRxSGedl95W2w8cVdfkOH22eb+GP7k06JOdrextAgB/0OMDRKgUV6zO7tFGkprskvKaepvo6QEQKujxASJcU092PrW3yZ/XqLoKrFtyPCEJQFARfIAIZ8XwU4or1u8AEyprDgGwB4a6gAgXysNPrOwMoKnR4wPYQEOGn5qC1WsOAbAfgg9gE/UZfmoqXAUGoKkx1AXAMqE8DAcgMtHjA8BSoToMByAyEXwAWC4Uh+EARCaGugAAgG0QfAAAgG0QfAAAgG0QfAAAgG0QfAAAgG0QfAAAgG0QfAAAgG0QfAAAgG0QfAAAgG0QfAAAgG0QfAAAgG3Y7l5dxhhJUlFRkcUtAQAA/qr63a76HW8o2wWfw4cPS5LS0tIsbgkAAKivw4cPy+VyNbi+wzQ2OoWZyspK7dmzR61atZLD4fCrTlFRkdLS0pSXl6eEhIQgt9BadjpXyV7ny7lGLjudr53OVbLX+dZ1rsYYHT58WKmpqYqKavhMHdv1+ERFRalTp04NqpuQkBDxH7wqdjpXyV7ny7lGLjudr53OVbLX+dZ2ro3p6anC5GYAAGAbBB8AAGAbBB8/OJ1O/eY3v5HT6bS6KUFnp3OV7HW+nGvkstP52ulcJXudb1Odq+0mNwMAAPuixwcAANgGwQcAANgGwQcAANiG7dbxKS0t1XPPPafs7Gy1aNFCEydO1PXXX9/oOg05blP4+OOP9Ze//EWHDh3S4MGDNWPGDCUlJdVYPj8/Xy+//LK++uorxcXF6cILL9SUKVPUvHlzT5mZM2dq6dKlXvUGDhyol156KWjn4Y9du3bpiSee0MaNG5WSkqKpU6fqnHPOqbH8Z599pvvuu6/a9oULF6pt27YNPm5TqKio0EsvvaQPP/xQkjR+/Hj9/Oc/V3R0tM/yK1eu1PTp033ue+mllzRw4EBJ0qhRozyrm1e57bbbdMMNNwSu8Q2Qk5Ojl19+WRs3btTzzz+vYcOG1Vnniy++0Isvvqj8/Hz17dtXM2fOrLaGlz9lmtr+/fv117/+VQsXLtSIESP02GOP1VreGKN//OMfmj9/vvbs2aOePXtq6tSp6tevn6fMli1bdN1111Wr++c//1kDBgwI+Dn4q6KiQh9++KFee+017du3T5999pnX3xpfrr/+em3evNlrW2ZmpmbMmOG17c0339S7776rkpISjRgxQtOnT1dsbGzAz6E+Nm3apFdeeUVfffWV7rvvPl1++eW1lr/xxhu1adOmattP/ns7Z84czZo1y2t/s2bNtGLFisA1vAGWLFmid955R9u3b1eXLl106623KiMjo9Y6R48e1bPPPqtly5YpPj5e1157rSZNmlTvMnUyNpOZmWnS09PN3LlzzZ///GfTsmVLk5WV1eg6DTlusL3//vumWbNmJisry3zwwQdm+PDhpn///qasrMxn+QMHDpju3bubxx57zCxatMi89dZbplu3bmb8+PGmsrLSU+6KK64wl19+uVm1apXnsWHDhqY6LZ8OHTpkOnXqZC655BLz0UcfmV/+8pcmJibGrFy5ssY68+fPN7GxsV7nsWrVKq//Pw05blP4+c9/bjp06GDefPNN89Zbb5l27dqZO+64o8byBQUF1c7zmmuuMQkJCebIkSOeci6XyzzxxBNe5Xbt2tUUp1SjRx991AwaNMg8++yzRpJZtGhRnXVWrlxpYmJizH333Wc++ugjM378eJOWlmYOHTpUrzJN7YcffjAdO3Y09957rxk6dKiZOHFinXXuueceM2HCBPP3v//dLFmyxNx+++2mefPmZvXq1Z4y3377rZFkPv74Y6/3tqioKJinU6fRo0ebSy65xNx1111GkikpKamzzhlnnGGmTp3qdR5bt271KvP000+b+Ph488orr5j33nvP9O7d24wbNy5Yp+GXv//97+a0004zzz33nImNjTUvv/xynXXWrVvndZ7/+te/TFRUlHnsscc8ZWbNmmW6d+/uVe7k994Kzz77rBk5cqR57bXXTHZ2tpk5c6aJiooyCxYsqLXemDFjTN++fc37779vXnrpJRMbG2tmzZpV7zJ1sVXw+eKLL4wkk5OT49k2a9YsEx8f7/XHv751GnLcptC7d2+vH8N9+/aZmJgYM3v2bJ/ly8vLTWlpqde27OxsI8l8//33nm1XXHGFufXWW4PT6AZ69NFHTdu2bb1Cy7hx48zIkSNrrDN//nwTHx8f8OMG2/bt201UVJSZP3++Z9vcuXNNVFSU2blzp1/HOH78uOnYsaO57bbbvLa7XC4zd+7cQDa30QoLC40xxuzfv9/v4HPhhReazMxMz/PS0lLTpk0b8/jjj9erTFMrKSnxfAfHjRvnV/A5fPhwtW1nn322ue666zzPq4LP/v37A9fYAKh6b+fOnVuv4PPMM8/UuP/o0aOmZcuWXj+GOTk5RpJZsWJFo9vcUG632/Pf8fHxfgWfU73yyismOjra6x8js2bNMv369QtIGwPF12fyqquuMiNGjKixztKlS40ks27dOs+2rKws43K5PN8Jf8r4w1ZzfLKzs9WhQwcNHjzYsy0zM1PFxcVavXp1g+s05LjBtmvXLn3//fe65JJLPNvatm2rs88+W0uWLPFZp1mzZtXWT2jZsqUk6dixY17bs7OzdcEFF+iqq67Syy+/rIqKigCfQf1kZ2drzJgxXt3kmZmZWr58ucrLy2usV1ZWpnHjxmnMmDG69957lZ+fH5DjBtOnn36qqKgoXXzxxZ5t48ePl8PhqDYEWZN//etf2r17t2655ZZq+5599lldcMEF+tnPfqZPPvkkYO1uqPouUV9eXq7PP//c67PvdDo1ZswYz2ffnzJWaNGiRb3XMKn6jp667dTvrCT9v//3/zRy5Ejdcccd1YaLrNDQ2w+88cYbOv/88zVp0iTNmTPHa9+XX36pI0eOeL23gwcPVmpqqqXvbSBuN/Haa69p7Nix6tixo9f23bt3a/To0Ro3bpx+85vfyO12N/q1GqM+n8kq2dnZ6tKli04//XTPtszMTLndbn399dd+l/GHrYLPjh07lJqa6rWt6gO0Y8eOBtdpyHGDrep1T21Xampqvdr0xBNPKD09XX369PFsS0xM1HXXXacHHnhAI0eO1OOPP67MzMzANLyBfL0HqampKi8vrxZmqjgcDl155ZW65ZZbdPvtt2v9+vXq27evcnNzG3XcYNuxY4eSkpLUokULz7YWLVqodevWfr+3r732mgYOHOiZ21PltNNO04033qgHHnhAnTt31qWXXlpt/kCo27Nnj8rLy2v97PtTJlytXr1a2dnZuuyyy7y2jx49WjfeeKPuvvtuFRYWasCAAfryyy+taWQjdOzYUTfccIMefPBBnXXWWbr11ls1bdo0z/5A/e0LNevWrVNOTk61f6xER0fr2muv1bRp0/Szn/1MixYt0hlnnKHCwkJrGurDDz/8oDlz5lT7TJ5sx44d1QJd1Xt48m9tXWX8YavJzeXl5dX+NRUTE6OoqKga//XuT52GHDfYql731HbFxsb63aaHHnpIixcv1vLly70mzf7xj3/0Ou6gQYM0ZMgQLVmyRCNHjgxA6+vP13tQNZGxpvO9+OKLvQLb+PHjdeaZZ+qRRx7R7NmzG3zcYPPVJsn/93b//v1auHChfv/731fbt3z5cs+xR40apfj4eM2cOVO33367V9AKZf589gPx/QhF27dv1xVXXKFrrrlG11xzjWd7nz599K9//cvzfNy4cTp48KB+9atfadmyZRa0tOHmzZvned9GjhyptLQ0TZo0SXfddZe6d+/uef9OnSQd7u/ta6+9ptTUVI0dO9Zr+5QpU7w+xxdffLF69uyp559/Xg8//HATt7K6gwcP6tJLL9WwYcO8Auqp/PlbG6i/x7bq8UlKStKhQ4e8thUWFqqyslJt2rRpcJ2GHDfYqq7cOrVdBw8e9KtNWVlZ+t3vfqcPP/zQawhPqv5jMXjwYLlcLq1Zs6aRrW44X+/BwYMHPft8OfU8mjVrpgsuuMDrPBpy3GDz1SbJ//f273//u2JiYnxeCXHq/5ORI0eqtLRU33//fcMb3MT8+ew39vsRinbu3KkLL7xQGRkZ+tvf/ua1z1dQHjlypKXf2Yby9Rk1xmjdunWS/vfeFhQUeJUL5/f22LFjevPNN3XTTTdVu3Lz1P8fCQkJGjp0aEi8t4cOHdKoUaPUpk0bLViwQM2a1dzXUtvf2tp+a08t4w9bBZ+BAwdq69atXl+Iqq7es846q8F1GnLcYOvdu7fi4+OVk5Pj2WaM0ddff11nm5566ik99thjnstp61JcXKwjR44oPj6+0e1uqIEDB3qdq3TiPejatatat27t93H27t3rdR6BOm4gDRw4UMXFxV6Xua5fv15Hjx716/P22muvaeLEiX7NOdi7d68kWfre1ldSUpK6dOni832r+v/jT5lwkpeXpxEjRmjAgAGaM2dOrT8wVU79rIerUz+jVcO3J7+3BQUF2rJlS1i+t5I0f/58HTp0SDfffLNf5UPhvS0oKNCoUaMUFxenRYsW+Zz3c7KBAwfqu+++05EjRzzbqn5HzzzzTL/L+MXvadARoKioyCQnJ5vp06cbY4wpKysz559/vjn//PM9ZQ4fPmwyMjLMhx9+6Hcdf8pYYcqUKSY9Pd0cPHjQGHPiioBmzZqZzZs3e8rcd9995vbbb/c8f+aZZ0xcXJzJzs72ecz9+/ebP/zhD+b48ePGmBNXwtx0000mLi7O0sueP/vsM+NwOMw///lPY4wxubm5pm3btl6XfS5dutRkZGSYffv2GWOM+cMf/mDy8/M9+z/44AMTHR1tXnzxxXodt6kdP37cpKenm2uvvdZUVlaayspKc/XVV5v09HTP+2KMMSNGjDCvv/66V92VK1caSeaLL76odtxPP/3ULFmyxPN89+7d5swzzzSDBw8O2rnUR21XdT377LPmyiuv9Dx/9NFHTbt27cz27duNMcYsXLjQOBwO8/nnn9erjJVquqpr06ZNJiMjw6xZs8YYY8yuXbtMjx49zGWXXWaOHTvm81izZ882W7Zs8TxfvXq1adWqledvltVqu6pr0qRJ5oknnjDGGLN27VozZ84cz76CggIzevRo06lTJ6+6F154oTnvvPM8V/rce++9JikpyXMVmdVquqrrrbfeMsOHD6+2fdSoUWb06NE+j/XMM894ndfLL79sJJmFCxcGrsH1VFhYaAYNGmSGDx/u8wovY4w5ePCgycjIMIsXLzbGnFg6JDEx0TzwwAPGmBNXOJ599tlmzJgxnjr+lPGHrYKPMcYsX77cpKSkmE6dOpnWrVubM8880+zYscOzv6CgwEjy+sGoq46/ZZpaUVGRGTNmjImLizM9evQwrVq1Mm+99ZZXmczMTE9A27p1q5Fk2rZtazIyMrwey5cvN8acCDr33HOPSUpKMv379zeJiYmmX79+5rPPPmvq06uman2M9PR043Q6zeTJk71+CKr+uObl5RljjJk3b57p0aOH6dWrl+natatp1aqVeeKJJ7zWLPLnuFZYu3at6dmzp2nfvr1p166d6dmzp1m7dq1Xmfj4+GoB7aabbqrx0tcdO3aY8ePHm7Zt25p+/foZp9NpLrnkEs//L6ssXrzYZGRkmEGDBhlJ5rTTTjMZGRnmj3/8o6fMHXfcYXr06OF5fuzYMTN58mTjdDpNr169TIsWLaqt9eFPGSucd955JiMjwyQmJpo2bdqYjIwMM3bsWM/+qkuzqwLatddeaySZgQMHen1nb7jhBk+d5cuXmwEDBpguXbp4zvWee+6pcU2vpvLUU0+ZjIwM06tXLyPJDB061GRkZJhVq1Z5yvTr18/cfPPNxpgTP5bXX3+95+9PfHy8+clPflJtHbG8vDwzcOBAk5iYaNLS0kz79u3N0qVLm/TcTrV+/XrPexMVFWW6detmMjIyzK9//WtPmWeeecZER0d71atavqKmZSb+9Kc/mdTUVNOnTx+Tmppq2rZta1599dWgnktdfvnLXxpJ5vTTT/f6TJ78Oc7PzzeSzDvvvOPZtnjxYtOuXTvTuXNn43K5zJAhQ8zu3bu9ju1PmbrY8u7sx48f16ZNm+R0OpWenu61r6KiQjk5OerRo4fX6r211alPGSvs3LlThw4dUnp6uuLi4rz2bd68WcePH1ffvn1VWlqq//znPz6P0bt3b6+hndLSUv3www9KTk5Whw4dgtn8eikqKtLWrVvVoUMHpaSkeO07dOiQNm/erIEDB3omPhpjlJubq8rKSnXt2rXGIYLajmuVyspKfffdd5JOXI0VFeU9cp2Tk6PU1FSvqyDWrFmjhIQEdevWrcbjFhQUaNeuXerSpUtALsFtrIMHD2rLli3Vtnfs2FFpaWmSTkzqLSwsrNbdnZ+fr71796pHjx41nos/ZZrSV199pcrKSq9tzZs39wzhFBcXa926derXr59atWqlLVu2eOY5nKxly5Zel/1KJ4bEiouL1a1bt3pfNh8Mubm5+vHHH6tt79Onj+dS97Vr16ply5bq3r27Z/+RI0e0bds2dezYsda5HVu2bFFJSYn69OmjmJiYwJ9APRw5ckTr16+vtj05OVk9e/aUdOKzuHPnTq8Vjqs+/4MGDarxHCorK7VlyxY5nU517ty52t+CprZjxw6fV72e/DkuLy/XN998o169enm9h+Xl5dq0aZPi4uI8/19O5U+Z2tgy+AAAAHuy1eRmAABgbwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAGGtvLxcc+bM0ebNm722f/rpp1q2bJk1jQIQsgg+AMJaTEyMVq5cqbFjx+rw4cOSpM8//1yjR4/W8ePHLW4dgFDDvboAhL3S0lINGTJEgwYN0vPPP68zzjhDV111lZ599lmrmwYgxBB8AESEdevWaejQoerVq5eio6P15Zdfqnnz5lY3C0CIYagLQETo37+/rr76aq1bt05PP/00oQeAT/T4AIgIX3/9tc455xz17NlTbdu21aeffqqoKP5tB8AbfxUAhL3i4mJNnjxZt9xyi7Kzs7Vx40Y99dRTVjcLQAiixwdA2JsyZYo+//xz/fvf/1ZsbKwWLFigq666SqtWrdKgQYOsbh6AEEKPD4Cwtn37dh0+fFhvv/22YmNjJUmZmZl69NFHtXDhQotbByDU0OMDAABsgx4fAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgGwQfAABgG/8fX7XSYVr5DM8AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3a)\n",
-    "X = np.linspace(0, 2)\n",
-    "np.random.seed(42) # vereinfachte Variante, siehe np.random\n",
-    "X = np.sort(np.random.rand(50)*2)\n",
-    "T = np.sin(5*X) + X**2 + np.random.randn(len(X))/10\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.show()\n",
-    "# Es gibt 50 Merkmale in den Daten"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b251a2c1",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-<<<<<<< HEAD
-      "[[5.1 3.5 1.4 0.2 0. ]\n",
-      " [4.9 3.  1.4 0.2 0. ]\n",
-      " [4.7 3.2 1.3 0.2 0. ]\n",
-      " [4.6 3.1 1.5 0.2 0. ]\n",
-      " [5.  3.6 1.4 0.2 0. ]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 1a) Ja stehen in der richtigen Dimension\n",
-    "iris_data= np.loadtxt(\"../supplement_files/iris.csv\", delimiter=\",\")\n",
-    "print(iris_data[:5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "c9c0fd1e",
-   "metadata": {},
-   "outputs": [
-=======
-      "[0.04116899 0.06877704 0.09290083 0.11616722 0.13010319 0.19534423\n",
-      " 0.24407647 0.27898772 0.31198904 0.31203728 0.34104825 0.36364993\n",
-      " 0.36680902 0.36970891 0.39934756 0.42467822 0.51755996 0.58245828\n",
-      " 0.5842893  0.60848449 0.60922754 0.62342215 0.73272369 0.74908024\n",
-      " 0.86389004 0.88030499 0.91213997 0.99035382 1.02846888 1.04013604\n",
-      " 1.04951286 1.09342056 1.18482914 1.19731697 1.20223002 1.2150897\n",
-      " 1.22370579 1.32504457 1.36846605 1.41614516 1.46398788 1.57035192\n",
-      " 1.6167947  1.66488528 1.73235229 1.8186408  1.89777107 1.90142861\n",
-      " 1.93126407 1.9398197 ]\n",
-      "[[0.04116899]\n",
-      " [0.06877704]\n",
-      " [0.09290083]\n",
-      " [0.11616722]\n",
-      " [0.13010319]\n",
-      " [0.19534423]\n",
-      " [0.24407647]\n",
-      " [0.27898772]\n",
-      " [0.31198904]\n",
-      " [0.31203728]\n",
-      " [0.34104825]\n",
-      " [0.36364993]\n",
-      " [0.36680902]\n",
-      " [0.36970891]\n",
-      " [0.39934756]\n",
-      " [0.42467822]\n",
-      " [0.51755996]\n",
-      " [0.58245828]\n",
-      " [0.5842893 ]\n",
-      " [0.60848449]\n",
-      " [0.60922754]\n",
-      " [0.62342215]\n",
-      " [0.73272369]\n",
-      " [0.74908024]\n",
-      " [0.86389004]\n",
-      " [0.88030499]\n",
-      " [0.91213997]\n",
-      " [0.99035382]\n",
-      " [1.02846888]\n",
-      " [1.04013604]\n",
-      " [1.04951286]\n",
-      " [1.09342056]\n",
-      " [1.18482914]\n",
-      " [1.19731697]\n",
-      " [1.20223002]\n",
-      " [1.2150897 ]\n",
-      " [1.22370579]\n",
-      " [1.32504457]\n",
-      " [1.36846605]\n",
-      " [1.41614516]\n",
-      " [1.46398788]\n",
-      " [1.57035192]\n",
-      " [1.6167947 ]\n",
-      " [1.66488528]\n",
-      " [1.73235229]\n",
-      " [1.8186408 ]\n",
-      " [1.89777107]\n",
-      " [1.90142861]\n",
-      " [1.93126407]\n",
-      " [1.9398197 ]]\n"
-     ]
-    },
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-    {
-     "data": {
-      "text/html": [
-       "<style>.sk-global {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       ".sk-global.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       ".sk-global.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       ".sk-global {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".sk-global pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip-path: inset(100%);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       ".sk-global div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       ".sk-global label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label label.sk-toggleable__label,\n",
-       ".sk-global div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       ".sk-global div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       ".sk-global div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       ".sk-global div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       ".sk-global a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       ".sk-global a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-top-container.sk-global {\n",
-       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
-       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
-       "   to allow keyboard-only users to navigate the display. */\n",
-       "  overflow-x: scroll !important;\n",
-       "  max-width: 100%;\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover td {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table :is(td, th) {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
-       "*/\n",
-       ".estimator-table table td.param {\n",
-       "    text-align: left;\n",
-       "    position: relative;\n",
-       "    padding: 0;\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".default td, .estimator-table th {\n",
-       "    color: black;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       "td.fitted-att-type {\n",
-       "    white-space: preserve nowrap;\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    Styles for parameter documentation links\n",
-       "    We need styling for visited so jupyter doesn't overwrite it\n",
-       "*/\n",
-       "a.param-doc-link,\n",
-       "a.param-doc-link:link,\n",
-       "a.param-doc-link:visited {\n",
-       "    text-decoration: underline dashed;\n",
-       "    text-underline-offset: .3em;\n",
-       "    color: inherit;\n",
-       "    display: block;\n",
-       "    padding: .5em;\n",
-       "}\n",
-       "\n",
-       "@supports(anchor-name: --doc-link) {\n",
-       "    a.param-doc-link,\n",
-       "    a.param-doc-link:link,\n",
-       "    a.param-doc-link:visited {\n",
-       "    anchor-name: --doc-link;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
-       "a.param-doc-link::before {\n",
-       "    position: absolute;\n",
-       "    content: \"\";\n",
-       "    inset: 0;\n",
-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
-       "    display: none;\n",
-       "    position: absolute;\n",
-       "    z-index: 9999;\n",
-       "    left: 0;\n",
-       "    padding: .5ex;\n",
-       "    margin-left: 1.5em;\n",
-       "    color: var(--sklearn-color-text);\n",
-       "    box-shadow: .3em .3em .4em #999;\n",
-       "    width: max-content;\n",
-       "    text-align: left;\n",
-       "    max-height: 10em;\n",
-       "    overflow-y: auto;\n",
-       "\n",
-       "    /* unfitted */\n",
-       "    background: var(--sklearn-color-unfitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       "@supports(position-area: center right) {\n",
-       "    .param-doc-description {\n",
-       "    position-area: center right;\n",
-       "    position: fixed;\n",
-       "    margin-left: 0;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
-       "    /* fitted */\n",
-       "    background: var(--sklearn-color-fitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "    border: none;\n",
-       "    padding: 0;\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon:focus-visible {\n",
-       "    outline: 1px solid currentColor;\n",
-       "    outline-offset: 1px;\n",
-       "}\n",
-       "\n",
-       ".features {\n",
-       "  font-family: monospace;\n",
-       "  cursor: pointer;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
-       "}\n",
-       "\n",
-       ".features.fitted {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features summary {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  margin-bottom: 0;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "  padding: .25em;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features.fitted details[open] > summary {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features details > summary .arrow::before {\n",
-       "  content: \"▸\";\n",
-       "  color: grey;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary .arrow::before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       ".features details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features.fitted details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features .features-container {\n",
-       "  max-height: 10em;\n",
-       "  overflow: auto;\n",
-       "  scrollbar-width: thin;\n",
-       "  padding: .25em 0.1rem;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 0 0 .5em .5em;\n",
-       "}\n",
-       "\n",
-       ".features.fitted .features-container {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features .image-container {\n",
-       "  block-size: 1em;\n",
-       "  inline-size: 1em;\n",
-       "  padding: 0;\n",
-       "  margin: 0%;\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  align-items: center;\n",
-       "}\n",
-       "\n",
-       ".features .copy-paste-icon {\n",
-       "  background-size: 1em 1em;\n",
-       "  width: 1em;\n",
-       "  height: 1em;\n",
-       "  filter: grayscale(100%) opacity(60%);\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  width: 100%;\n",
-       "  margin: 0.01em;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(odd) {\n",
-       "  background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(even) {\n",
-       "  background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:hover {\n",
-       "  background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  table-layout: inherit;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table td {\n",
-       "  text-align: left;\n",
-       "  padding: 0 0.5em;\n",
-       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "  white-space: nowrap;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".total_features {\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  margin-top: 0.5em;\n",
-       "}\n",
-<<<<<<< HEAD
-       "</style><body><div id=\"sk-container-id-13\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-=======
-       "</style><body><div id=\"sk-container-id-2\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-<<<<<<< HEAD
-       "                 aria-label=\"Copy penalty to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-=======
-       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-penalty;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%7D%2C%20default%3D%27l2%27\">\n",
-       "            penalty\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-penalty;\">\n",
-       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;}, default=&#x27;l2&#x27;<br><br>Specifies the norm used in the penalization. The &#x27;l2&#x27;<br>penalty is the standard used in SVC. The &#x27;l1&#x27; leads to ``coef_``<br>vectors that are sparse.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-<<<<<<< HEAD
-       "                 aria-label=\"Copy loss to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('loss',\n",
-=======
-       "                 aria-label=\"Copy copy_X to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('copy_X',\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-loss;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=loss,-%7B%27hinge%27%2C%20%27squared_hinge%27%7D%2C%20default%3D%27squared_hinge%27\">\n",
-       "            loss\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-loss;\">\n",
-       "            loss: {&#x27;hinge&#x27;, &#x27;squared_hinge&#x27;}, default=&#x27;squared_hinge&#x27;<br><br>Specifies the loss function. &#x27;hinge&#x27; is the standard SVM loss<br>(used e.g. by the SVC class) while &#x27;squared_hinge&#x27; is the<br>square of the hinge loss. The combination of ``penalty=&#x27;l1&#x27;``<br>and ``loss=&#x27;hinge&#x27;`` is not supported.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;squared_hinge&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy dual to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-dual;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=dual,-%22auto%22%20or%20bool%2C%20default%3D%22auto%22\">\n",
-       "            dual\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-dual;\">\n",
-       "            dual: &quot;auto&quot; or bool, default=&quot;auto&quot;<br><br>Select the algorithm to either solve the dual or primal<br>optimization problem. Prefer dual=False when n_samples &gt; n_features.<br>`dual=&quot;auto&quot;` will choose the value of the parameter automatically,<br>based on the values of `n_samples`, `n_features`, `loss`, `multi_class`<br>and `penalty`. If `n_samples` &lt; `n_features` and optimizer supports<br>chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,<br>otherwise it will be set to False.<br><br>.. versionchanged:: 1.3<br>   The `&quot;auto&quot;` option is added in version 1.3 and will be the default<br>   in version 1.5.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-copy_X;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
-       "            copy_X\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-copy_X;\">\n",
-       "            copy_X: bool, default=True<br><br>If True, X will be copied; else, it may be overwritten.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy tol to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-tol;\"\n",
-<<<<<<< HEAD
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-=======
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies the convergence criterion of the underlying solver. `tol` is<br>set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. `tol` is set as `cond` of<br>:func:`scipy.linalg.lstsq` when fitting on dense training data.<br><br>.. versionadded:: 1.7<br>.. versionchanged:: 1.9<br>    Now supported on dense data, interpreted as the `cond` parameter.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1e-06</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-<<<<<<< HEAD
-       "                 aria-label=\"Copy C to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-=======
-       "                 aria-label=\"Copy n_jobs to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-C;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
-       "            C\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-C;\">\n",
-       "            C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>For an intuitive visualization of the effects of scaling<br>the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy multi_class to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-multi_class;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=multi_class,-%7B%27ovr%27%2C%20%27crammer_singer%27%7D%2C%20default%3D%27ovr%27\">\n",
-       "            multi_class\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-multi_class;\">\n",
-       "            multi_class: {&#x27;ovr&#x27;, &#x27;crammer_singer&#x27;}, default=&#x27;ovr&#x27;<br><br>Determines the multi-class strategy if `y` contains more than<br>two classes.<br>``&quot;ovr&quot;`` trains n_classes one-vs-rest classifiers, while<br>``&quot;crammer_singer&quot;`` optimizes a joint objective over all classes.<br>While `crammer_singer` is interesting from a theoretical perspective<br>as it is consistent, it is seldom used in practice as it rarely leads<br>to better accuracy and is more expensive to compute.<br>If ``&quot;crammer_singer&quot;`` is chosen, the options loss, penalty and dual<br>will be ignored.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Whether or not to fit an intercept. If set to True, the feature vector<br>is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where<br>1 corresponds to the intercept. If set to False, no intercept will be<br>used in calculations (i.e. data is expected to be already centered).</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy intercept_scaling to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_scaling,-float%2C%20default%3D1.0\">\n",
-       "            intercept_scaling\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
-       "            intercept_scaling: float, default=1.0<br><br>When `fit_intercept` is True, the instance vector x becomes ``[x_1,<br>..., x_n, intercept_scaling]``, i.e. a &quot;synthetic&quot; feature with a<br>constant value equal to `intercept_scaling` is appended to the instance<br>vector. The intercept becomes intercept_scaling * synthetic feature<br>weight. Note that liblinear internally penalizes the intercept,<br>treating it like any other term in the feature vector. To reduce the<br>impact of the regularization on the intercept, the `intercept_scaling`<br>parameter can be set to a value greater than 1; the higher the value of<br>`intercept_scaling`, the lower the impact of regularization on it.<br>Then, the weights become `[w_x_1, ..., w_x_n,<br>w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent<br>the feature weights and the intercept weight is scaled by<br>`intercept_scaling`. This scaling allows the intercept term to have a<br>different regularization behavior compared to the other features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy class_weight to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
-       "            class_weight\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
-       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Set the parameter C of class i to ``class_weight[i]*C`` for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
-=======
-       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
-       "            n_jobs\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
-       "            n_jobs: int, default=None<br><br>The number of jobs to use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets &gt; 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary &lt;n_jobs&gt;` for more details.</span>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-<<<<<<< HEAD
-       "                 aria-label=\"Copy verbose to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-=======
-       "                 aria-label=\"Copy positive to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('positive',\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-verbose;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
-       "            verbose\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-verbose;\">\n",
-       "            verbose: int, default=0<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in liblinear that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy random_state to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-random_state;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
-       "            random_state\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-random_state;\">\n",
-       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>the dual coordinate descent (if ``dual=True``). When ``dual=False`` the<br>underlying implementation of :class:`LinearSVC` is not random and<br>``random_state`` has no effect on the results.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy max_iter to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=max_iter,-int%2C%20default%3D1000\">\n",
-       "            max_iter\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
-       "            max_iter: int, default=1000<br><br>The maximum number of iterations to be run.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1000</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-positive;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
-       "            positive\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-positive;\">\n",
-       "            positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    \n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Fitted attributes</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                    <tbody>\n",
-       "                        <tr>\n",
-       "                        <th>Name</th>\n",
-       "                        <th>Type</th>\n",
-       "                        <th>Value</th>\n",
-       "                        </tr>\n",
-       "                        \n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-classes_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
-       "            classes_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-classes_;\">\n",
-       "            classes_: ndarray of shape (n_classes,)<br><br>The unique classes labels.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
-       "           <td>[0.,1.,2.]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=coef_,-ndarray%20of%20shape%20%281%2C%20n_features%29%20if%20n_classes%20%3D%3D%202%20%20%20%20%20%20%20%20%20%20%20%20%20else%20%28n_classes%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: ndarray of shape (1, n_features) if n_classes == 2             else (n_classes, n_features)<br><br>Weights assigned to the features (coefficients in the primal<br>problem).<br><br>``coef_`` is a readonly property derived from ``raw_coef_`` that<br>follows the internal memory layout of liblinear.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3, 2)</td>\n",
-       "           <td>[[-1.54, 2.28],\n",
-       " [ 0.1 ,-0.98],\n",
-       " [ 0.66,-0.35]]</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=coef_,-array%20of%20shape%20%28n_features%2C%20%29%20or%20%28n_targets%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: array of shape (n_features, ) or (n_targets, n_features)<br><br>Estimated coefficients for the linear regression problem.<br>If multiple targets are passed during the fit (y 2D), this<br>is a 2D array of shape (n_targets, n_features), while if only<br>one target is passed, this is a 1D array of length n_features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[1.64]</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
-<<<<<<< HEAD
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20if%20n_classes%20%3D%3D%202%20else%20%28n_classes%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: ndarray of shape (1,) if n_classes == 2 else (n_classes,)<br><br>Constants in decision function.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
-       "           <td>[ 1.2 , 2.02,-3.14]</td>\n",
-=======
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=intercept_,-float%20or%20array%20of%20shape%20%28n_targets%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: float or array of shape (n_targets,)<br><br>Independent term in the linear model. Set to 0.0 if<br>`fit_intercept = False`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">float64</td>\n",
-       "           <td>-0.1418</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
-<<<<<<< HEAD
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_features_in_,-int\">\n",
-=======
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_features_in_,-int\">\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "            n_features_in_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
-       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-<<<<<<< HEAD
-       "           <td>2</td>\n",
-=======
-       "           <td>1</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-<<<<<<< HEAD
-       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_iter_,-int\">\n",
-       "            n_iter_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
-       "            n_iter_: int<br><br>Maximum number of iterations run across all classes.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>8</td>\n",
-=======
-       "            style=\"anchor-name: --doc-link-rank_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=rank_,-int\">\n",
-       "            rank_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-rank_;\">\n",
-       "            rank_: int<br><br>Rank of matrix `X`. Only available when `X` is dense.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int64</td>\n",
-       "           <td>np.int64(1)</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-singular_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=singular_,-array%20of%20shape%20%28min%28X%2C%20y%29%2C%29\">\n",
-       "            singular_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-singular_;\">\n",
-       "            singular_: array of shape (min(X, y),)<br><br>Singular values of `X`. Only available when `X` is dense.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[4.04]</td>\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "                    </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
-       " SPDX-License-Identifier: BSD-3-Clause\n",
-       "*/\n",
-       "\n",
-       "function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "\n",
-       "    const parent = element.parentElement;\n",
-       "    if (!parent || !parent.nextElementSibling) {\n",
-       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    const paramName = element.parentElement.nextElementSibling\n",
-       "        .textContent.trim().split(' ')[0];\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "\n",
-       "/**\n",
-       " * Copy the list of feature names formatted as a Python list.\n",
-       " *\n",
-       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
-       " *   contain a `details` element and a table containing feature named.\n",
-       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
-       " */\n",
-       "function copyFeatureNamesToClipboard(element) {\n",
-       "    var detailsElem = element.closest('.features').querySelector('details');\n",
-       "    var wasOpen = detailsElem.open;\n",
-       "    detailsElem.open = true;\n",
-       "    var content = element.closest('.features').querySelector('tbody')\n",
-       "                  .innerText.trim();\n",
-       "    if (!wasOpen) detailsElem.open = false;\n",
-       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
-       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
-<<<<<<< HEAD
-       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
-       "    const originalStyle = element.style;\n",
-       "    const copyMark = document.createElement('span');\n",
-       "    copyMark.innerHTML = '✔';\n",
-=======
-       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
-       "    const originalStyle = element.style;\n",
-       "    const copyMark = document.createElement('span');\n",
-       "    copyMark.innerHTML = '✔';\n",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-       "    copyMark.style.color = 'blue';\n",
-       "    copyMark.style.fontSize = '1em';\n",
-       "\n",
-       "    navigator.clipboard.writeText(formattedText)\n",
-       "        .then(() => {\n",
-       "            element.style.display = 'none';\n",
-       "            element.parentElement.appendChild(copyMark);\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                copyMark.remove();\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'orange';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "/**\n",
-       " * Adapted from Skrub\n",
-       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
-       " * @returns \"light\" or \"dark\"\n",
-       " */\n",
-       "function detectTheme(element) {\n",
-       "    const body = document.querySelector('body');\n",
-       "\n",
-       "    // Check VSCode theme\n",
-       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
-       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
-       "\n",
-       "    if (themeKindAttr && themeNameAttr) {\n",
-       "        const themeKind = themeKindAttr.toLowerCase();\n",
-       "        const themeName = themeNameAttr.toLowerCase();\n",
-       "\n",
-       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
-       "            return \"dark\";\n",
-       "        }\n",
-       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
-       "            return \"light\";\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    // Check Jupyter theme\n",
-       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
-       "        return 'dark';\n",
-       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
-       "        return 'light';\n",
-       "    }\n",
-       "\n",
-       "    // Guess based on a parent element's color\n",
-       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
-       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
-       "    if (match) {\n",
-       "        const [r, g, b] = [\n",
-       "            parseFloat(match[1]),\n",
-       "            parseFloat(match[2]),\n",
-       "            parseFloat(match[3])\n",
-       "        ];\n",
-       "\n",
-       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
-       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
-       "\n",
-       "        if (luma > 180) {\n",
-       "            // If the text is very bright we have a dark theme\n",
-       "            return 'dark';\n",
-       "        }\n",
-       "        if (luma < 75) {\n",
-       "            // If the text is very dark we have a light theme\n",
-       "            return 'light';\n",
-       "        }\n",
-       "        // Otherwise fall back to the next heuristic.\n",
-       "    }\n",
-       "\n",
-       "    // Fallback to system preference\n",
-       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
-       "}\n",
-       "\n",
-       "\n",
-       "function forceTheme(elementId) {\n",
-       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
-       "    if (estimatorElement === null) {\n",
-       "        console.error(`Element with id ${elementId} not found.`);\n",
-       "    } else {\n",
-       "        const theme = detectTheme(estimatorElement);\n",
-       "        estimatorElement.classList.add(theme);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-<<<<<<< HEAD
-       "forceTheme('sk-container-id-13');</script></body>"
-      ],
-      "text/plain": [
-       "LinearSVC()"
-      ]
-     },
-     "execution_count": 75,
-=======
-       "forceTheme('sk-container-id-2');</script></body>"
-      ],
-      "text/plain": [
-       "LinearRegression()"
-      ]
-     },
-     "execution_count": 12,
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-<<<<<<< HEAD
-    "# 1b)\n",
-    "X = iris_data[:,[0,1]]\n",
-    "T = iris_data[:,4]\n",
-    "clf.fit(X, T)"
-=======
-    "# 3b)\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "reg = LinearRegression()\n",
-    "print(X)\n",
-    "print(X.reshape(-1,1)) # transponiert den Merkmalsraum in einen Spaltenvektor\n",
-    "reg.fit(X.reshape(-1,1),T)\n"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 76,
-   "id": "cc181a3a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "80.0 Prozent wurden richtig vorausgesagt\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 1 c+d)\n",
-    "pred = clf.predict(X)\n",
-    "correct_perc = (sum(pred == T)/len(pred))*100\n",
-    "\n",
-    "print(f\"{correct_perc} Prozent wurden richtig vorausgesagt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "16af32f1",
-=======
-   "execution_count": 17,
-   "id": "98476c93",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-<<<<<<< HEAD
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1e)\n",
-    "import nnwplot\n",
-    "nnwplot.plotTwoFeatures(X, T, clf.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "f4bcee12",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1f)\n",
-    "from sklearn.svm import SVC\n",
-    "svc = SVC()\n",
-    "svc.fit(X, T)\n",
-    "nnwplot.plotTwoFeatures(X, T, svc.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "2c8991de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1g)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "1a5e84f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAeoxJREFUeJzt3Qd4HNX1NvB3tq+q1a3m3ruNC2B6x6abAAECBAgQSkIJCQRIIB/8IQkhtEDoBBIgGAi92IBNMxj33i2rWs3qbdvM99y72rVkaXdH9kq7K72/5xFIq9HM7KzkOXvvuecomqZpICIiIooQQ6QOTERERCQwGCEiIqKIYjBCREREEcVghIiIiCKKwQgRERFFFIMRIiIiiigGI0RERBRRDEaIiIgookyIAaqqoqysDImJiVAUJdKnQ0RERDqIuqqNjY3IycmBwWCI7WBEBCL5+fmRPg0iIiI6CMXFxcjLy4vtYESMiAhHXf0iTJa4SJ8OERER6eB2tuDb56/038djOhjxTc2IQMRkZTBCREQUS0KlWDCBlYiIiCKKwQgRERFFFIMRIiIiiigGI0RERBRRDEaIiIgoohiMEBERUUQxGCEiIqKIYjBCREREEcVghIiIiCKKwQgRERFFFIMRIiIiiigGI0RERBRRDEaIiIgoohiMEBERUUQxGCEiIqKIYjBCREREEcVghIiIiCKKwQgRERFFFIMRIiIiiigGI0RERBRRDEaIiIgoohiMEBERUUQxGCEiIqKIYjBCREREEcVghIiIiCKKwQgRRZwGFS40wQMHopUGDW60wI1W+XmsE1dcXHMNnkifChFMB3MNSkpK4HK5MGTIEBiNxqDb7t27F6WlpZ0eM5lMmDZtGi8/0QDnUPahyPIhSi2fwm1olo8lu8ci33kmslxHQYmC90siACmxfIJi60dwGKrlY3GeXHmOOc6TYIQFsaTGuA5F1vdRbVoJKBoMmhmDXcdjiOMsJKhDIn16NED1KBh56aWX8MADD6CtrU0GIQ6HA48++iguuuiigD/zwgsv4KGHHsK4ceP8jyUmJmLJkiWHduZEFNMaDQVYHX83XEozoKj+x+uNO1Af9zCqnD9iUustUBD8DU9vcii1WBl/J1oNe+XYiE+LoQzbbP9EuXkJpjffBxPiEAsKrG9il+3fUDSDDEQEVXFhr/lz7DV/gSktdyLDPTvSp0kDUI+CkZ07d2Lx4sUYPny4/PrJJ5/Ez372M0yePBkTJ04M+HOTJk3CDz/8cOhnS0T9gpiOWRP/xy6BiNT+dYX5a8Sr+RjhuDAi5yimYtbFPYA2Q7n/xr3/HDV/4LTF/g9Mbr0d0a7S9L0MRATtgGsuv9aA9XEP4YimJxGn5kToLGmg6tEYqBgV8QUiwvXXXw+DwYBly5YF/TlVVbFt2zYUFhZC02J/rpWIDk25+Ws4lbqugUhHClBkeVfmNkRCvXEbGkzbu9y4O1FUVJi/RZtShWi3x/o2IEZEAlHEf1Q5JUXU1w5pQnbz5s1wOp0YNmxY0O1WrFiBefPmYebMmcjOzsabb755KIclohhXbl7qu/sFJfJIakxrEQliZEbR9EwRKTIgiWYiWBKBVdDgr32EZK/5yz47L6JDSmAVRN7IVVddhTlz5uDEE08MuN306dPlqMiYMWPkqMif//xnXHzxxRgxYoQMTrojclHEh09DQ8PBniYRRSGnQYyK6BsldSqR+fsXx9WzakaBAqehHtGsJ9fQpTT16rkQhW1kRKykueCCC1BdXY23335bTtUEMn/+fBmICIqi4I477sDIkSPxxhtvBPyZBx98EMnJyf6P/Pz8gzlNIopSZi1JvA3XuW1ir59P98dNkIFGKCJgMauROceePBe9TFp8r54LUViCERGIXHjhhdi4caNcEZObm9vTXcipGrE8OJA777wT9fX1/o/i4uIeH4OIotdg17GdVqcEYtTsSHVPRSSIpcWaoqcGh4pM11xEM7uWhUTPiJABoFhl431tiKI4GHG73XIZ79q1a7F06VJZZ+RAZWVl8vsdp3M6qqmpwbp16zB+/PiAx7FarUhKSur0QUT9x2Dnsd4Rj2AJlZqCfMeZMMKKSBjkmYgEz3DvMthANAMy3HMQpw1GtBvqOC/41Fj7t/Kd8/vsnIgOKhi55JJLsGjRIjz88MOorKzEypUr5YcIQHyeffZZHHfccf6vxeePPPKIHEVZuHAhTj75ZDn1IlbiENHAZIId01r+IAONLjd78e5dA9LdszDCEbiGUW8TUzTTmu+GVUvrGjSJG7emyCJhE1p+jViQ5ToaQ9sWeL844Pl4XwMDJrbehng1LzInSAOaovVgre3cuXM7JZb6XHPNNfLDF4y8/vrr/qJmVVVVsjDa8uXLERcXh9mzZ+PXv/61LHyml0hgFQHMcde/AZM1NooLEVFoLUo5iqzvoszyBVTF+29LvGcIhjjORI7rpIgWPOuY/FlseV8ueXUZGuVjVjVNjiDkO86AETbEkkrTD7ICa51poz8QyXQdiaHOc5HkGR3p06N+xu1owdKnLpIpF8FmOXoUjEQKgxGi/k3UEnEq9TDAIqdv9CSO9jUVHrhEbRRxllpyVJSqPxSyF5DSKq93rAVU1P+CkYNe2ktEFC4GmGHT0qP6ghrEpJKYsuknzEjo0Sobot7EYIQoBonlpG1KJTxKGyxaCixiqWwMc6MZbYZ9Miixq1lROeogSti3GSplNok4R3GuRBQeDEaIYogGFaXmRXLOv8XYvjxeU2Rzs2GO85HsGYtY0mQowh7rQlnB1LeM1qZmyFyMfOcZUXHDF52F91jfQZllETzteS0mNQF5ztMx1HGuHGEgokPDYIQohgKRjfZHZJnyTqXUFQ3VphWoMq3A5JbbkeWO7poXPrXGjVgTfy80uDv1fxGly3fYXpYt7qe33BvRgER0510R/zu4lcZO5+g2NMleLxXm7zCr+SFYtEERO0ei/iD6xkKJqFtFlg+8gYiIQw6oF+G9UarYGPcwWpWKmJiWWRt/v0xc7dKIrv351Zo2Yqft1YgGf2vj7ofrgEDET1FlR9+N9r9H4vSI+hUGI0QxQINHLoENSpbn0FBi+RTRrsyyBB60Bi/CpWgotXwCt9guAmqM671TYUGay4kgpca8Bs2GwBWliSg0BiNEMaDRuBsOw77QjW5FS3uLmMaJbhXmb3SVgxc5GjWmdYiESvMyfV17NQMqzd/3xSkR9VsMRohigBst+rdV9G8bKW7RGVZnKRG30tzbpxPguC26u/ZG6hyJ+gsGI0QxQHeCpAZY1OhPprSqqbq79kYqOVQcV1/XXhUWNaVPzomov2IwQhQD4tUhskx66Bu4ghznSYh22a4TgueLtDOrSRHr2pvtPE5n114Fg11H9cEZEfVfDEaIYoB4hz7c8ZMQXVcNMGlxyHGdjGiX6ZoLq5oevCMugGGOBTBEqAJBkjoKKa7JIbr2Ksh2Hd+vKrMSRQKDEaIYMdh1LEa0XSw/75JYqYli5TZZlyMWqrEaYcGM5j/BrCX7u/T6td/8cx2nYYjzHETS5NbfyVEpX5dev/bPU9xTMK71usidIFE/waJnRDFkhOMipLgno9jyIarMy6EpbpjVZOQ6T5EVQaO9v0tHolX94U1PoNTymeyI61CqZZAlpmVE9dU092ERb5gnArtZTX/FXsuX8po3G4rlOFWSZ5Q8xyzXMbJnDREdGgYjRDEmxTMRKa0TobWKt+sqlBi+GYqbvZh+Eh+ilooYrI10AHIgI6wy0BMfIlnVO2kWXedIFOsYjBDFKO8NMXYDkQPFQlAVjQ38iPoDBiNE1CvEKEKjcZcspy5WxSSqIw75Zi721WgokPtOUPP7NHHUodTIxn4iCExQhwfMzRHdfRuMO6AqLtndN07N6XY7FSoqTF+jzVgJi5qGbNexEUvWjXW1hi2oM22SfYyynEfBBiYUxxr+5hNRWIlCYaKM+x7LO/JG62P3ZGGoYwFyXaf2eJqjTdmHXbZXUW7+WubJdOxWPLLtZ0gQSaa9ROSJiB45Vabl/tVMIrdlsOsYjGy7FDYtw1+YbrftdZTK7r77S9gPck/AiLZLkOqZ7A9CNtseRYXlm05Lh7doTyDTdTgmtf4WBo7A6FJq/gI7bC/CbWj0P7bD9oJMOp7Scifi1dxw/RpQL1M0TQu92D/CGhoakJycjOOufwMma1ykT4eIggQiW21Po9T6qXcFSseYo/3rPMcZGNv2C90BiWj8tyLht3Ap9V0a1olltwa5MucBJHtGh/11aTDsxKqE30OFs9tjm7REzG76K8xaElYm3IkmQ2HXXjZydZCGyS2/RYb7CPwYfwuaTAUBr4/dk4sjmv7BgCSEAstb2GV7pf3FOOCbmnfab07T43IEjSLH7WjB0qcuQn19PZKSAq/04wQoEYVNpfk7byDS3Q2i/esS64feUQadNsY90m0gIojHPHBiXdwDEJ+Fk9jfuvgH5P4DHdutNGJD3N+ww/Zy94GIIB/TsDHub9hufcEbiMjHD9zO+79WYym2254N63Ppb9pQGzgQaX9MJESvjr+7r0+NDhKDESIKmyLLe/46IQFpBhRbP9C1P5EfUm/a0m0w4KeocBpqUG36EeFUbVrR3pwweNfeBtM2lFk+D7qd7+ZYJjoqhxqL1kRX4y8P/sQHgB32F72fBBtcUwCnUot6w/a+Oi06BAxGiCgs3GhGvWlb8JuyoKioNW2QiZ6hVJtXhA5u2nM4qsxhDkbMK3V27VX257EE3xCqwaWj8zKgKm1oUfbqPdUBp8a0Vve2oj4MRT8GI0QUFh6lrYfbhw5GRMCit1mdqiO46QkP2nR17dXdfljp+coh6p4KHUHdQf5eUmQwGCGisBBJnAbNrO8fHs0CkxYfcjtRUdZbaCw4EbD4VrWEizi2vvtd6POTxAiP3uUCGmBXB+vceOCRy6p1xolxanYfnBEdKgYjRBQWosbDYNdxIac2xPdFZ2E9ZdSznEdD0VGBQORuZDtPRDjlOE8MnqvSTpyfVU0LfXNUVNkcUE/OiF3NhgXR32MoUvKc8/WNjGjAcMeFfXBGdKgYjBBR2Ax1nOMtbNaxqVxHmhjDMGKI8yxd+zMjAUOcZwa/gWsGZLjmhL3WSLyaj0znkcFzVjTIHjWi3kiwm6NYBhzvyceE1pv8Pxdof8LotisP4cz7v3znmTBq9pCBXbp7FkxgOYhYwGCEiMJ6A5/afI8cJel0E2/veiv6vExr/mPAqqTdGdX2M2S7TpCfdxp1ad9/insiJrbc2iuv4sTWm5HqnuwPKHx8n4uRoFFtlyPHdaIsvtb1HL0RihjpmN78J6R5pmNM69Xt3zsgKGn/fKTjUmS65/TK8+kvRHWZ2U2PwACr94FurmOiexSmtNwVkfOjnmPRMyLqldLpohJpuXkpnEqjnOPPdh2PHOfJsGopPd6fSCStNW5EieVjWfZbfJ3oGY485zxkuGf1al8bsSS32rQKxZaP0GjcLfNTkj3jke+YhxTPlE4JtmIpsjhHsRJHFEoTQYhosJflOkoGYvu32yMrh9aaNkKDW57/IPd4OSKSpI7qtefS34iqt7ts/0GZ+Qt/1VubmonhjguQ6zo50qdH0F/0jMEIERER9QpWYCUiIqKYwEZ5RBQzWgxlqDduk8t9xTSN6ATcHReaZGEst9IKm5aGFPdUXat3KPaIKbsG4zY0G0rldJfIIQr3Mm/qfQxGiCjqNRmKsM32LGrN6zs9nugeiTFtVyHFM0l+7UYrdtheQpnlC2iKy7+dRR2EYY4LkO+c3+OOwRS9RMn+7baX0GIs2f+gpiDdPRNjW6+BXcuK5OlRD3A1DRFFNZEUuiLhdpns2eV7xt2yGdo+02pZMXVV/F0ycbZjICI4DXXYbn8WO20v9+GZU28qN3+FtXH/Dy2G0s7fUDSZcPxjwm/QqpTzRYgRDEaIKKqH4DfFPeLtY9NtR1yxhYYN9oexy/pfGZwE641TaP0f6oybe/ekqdeJUvmb7I97v1C6KTaiqHKbzfZ/8NWIEQxGiChqifyQJmNhiI64GtyGJpSIhmghKqaKGiBsnBb7xFJesSQ6eNdeFbXmdTLPiKIfgxEiiloiCbVjsbGANANUQ+hGeZriwT7zmvCcHEW4a6+O5jSa2HZdX5wSHSIGI0QUtVTx7ldvE5Ie7ZNimez4rCsPWeHrHSMYjBBR1BIdV0UF1NAUffGIpiCO3XBjXrwnT9+ImaLx9Y4RDEaIKGpluo7sVEY9MBUp7ik6blCaLM9OsS3HdUrojsqaWNKdglT3jL46LToEDEaIKGqZYMfwtouCb6QpyHGdjNGOy9tHSLofvxeBil0djMHO43vnZKnPJHtGI901O3hHZUU0WbyMxe5iBIMRIopqQ53nYVjbBd10zvVWVB3sOhbjWq9Dkmc0prWIjsEWb0Dim7Zp/xnRtO6w5gdkgEOxb3LLb5DePurRpZuzpmB061WymzLFBlZgJaKoJiqmjnJcimzXcSixfCrrhIhVMUmekch1ni7fJfukuWfg6IaXsNfyBSrM38OjNMOmZiDbeRIy3LNh4D95/YYRNkxtuUf+PpRaPkWTsQgGzYRU9xTkOk9j9dUYw2CEiGJCvJqHsW1Xh9zOjAQMcZ4tP6j/B6opnolIaZ0Y6VOhQ8RpGiIiIooojowQUUwQZd/rjJu8XXsVDxI8w2XOgOjU2vvH9sj+N43GAigwIMk9Vjbn667pnuilI4pyeRSnXFaa4Tq82xVBTqUOlebv4VTqYdYSkek6AlYt9RDOUUO9cQvqjFvk9Yn3DEG6e1bMJnA6lQZUmpfJ62TWEuR1tGnpkT4t6iUMRogo6tUaN2GL/Um0GEtlgqIIAcTSTouairGtv0CWe26vHbvS9D222Z+Fw7BPJtDKvFibCrsnBxNab/R3DBYt7DfbH0W9aZtMoBRBiwgKjFocRrT9FEOcZ8ngRfTZ2WZ/BnvNX8oAQm4HFdtsz2Gw6xiZjGtCXI/OUQRom+2Po9lY3H59FHls0a1YdDUWSb6xQoUL22zPo0w0PITa6fpkuY7C+NbrYUJ8pE+TwozTNEQU9YGI6Mzr7zGiqP4aE06lBhvi/4y95qW9cuxy89dYH/cgHMo++bU8bvuxWw17sTr+HtQYN6DFsBcr43+LeuOO9nPUZDAgeJQW7LC/gN3W1+SNdm38n7y9VeS+2rcTzd4UVR5vdfwfvI0BexCIrIz/PZoNJR2uj/fYYlRhY9zfUGpejFigwoO1cQ/IhFTfddl/fTRUmL+TnZlFh2bqXxiMEFHUEu+IxWiD+H/33Vm9xa222P8BN1rDemxxwxOjMf7jdDm21n5+j8l37W6lOWijvgLbf7HH+hZqjRu6fy5ynyoajDtQYvlE1zmKMxAjIt6mcYGvz1b7P+FCE6Jdufkr1JhXB70+YqqsyPp+X58a9TIGI0QUtWqM69FqrAh8cxIU8Y7agXLL0rDfGOU78KCdYTW0GSuxz7QyZEVQMcVTJDoLh2yqosnOwiLQCEXkiMipmRDXRwQrYrlztJMdlQMUret8fT7W2SaAYgWDESKKWnWmzZ0LWgVkkImbYT+2nn8iRZEtHU3bRLDiNjQGDxwEBTLAEVMsIc9RPGc9PVrk8wnv9Qk30cCw0bRT1/VxGmrQ1j51Rv0DgxEiimIh+o90EO53ymIKRn8v4PCTU1OhtlE83a7o6W5v0T+S0LOr7cuLof6BwQgRRa14dYjOm46GBHVo2I+t6wYZqmGbj6bAoJl17dKkxsOiJYc+R4/e62NAvCe81yfcDDDD5snUdX2Mmg1WNa0vTov6CIMRIopaovaGSU3QcYNSkOM8KazHFvvTM+ogAodE98jQuQ6KhsHO40L/s6sZkOc8TVfpelFHRCzfDX19VOQ6T0G0y3fOD5lTI3JvcpynwCh6EFG/wWCEiKL63fLotp8Hvz9pwDDHgkMqGNYdq5aCYY6fhNxOnJ+o5SEDl0ABiaYgwzUHoxyXy8JdHRv+dSQeFyMi+c6zdJ2jKGgmju1bNdP9sSFL49u1TES7XOepsrNysOtj0hIx1HFun58b9S4GI0QU1XJdJ2Ns63VQNFN7N972jrzt3VlFIDLScUmvHHuE42Jvx2B5THE8b2Ahi5ppJoxtvQa5rlNk4TPRtM0oOgL7z21/l2FRrGtSy29g0ZIws+lBxKn58nGZnNthO3Ejntn8oAyE9BIFzca33AQF5m6ujzcQGd12BWKBKPZ2WPP/IUEd1u31EU0PxfWxaZyi6W8UTdMimaOlS0NDA5KTk3Hc9W/AZO1ZZUIi6h9EefC95i9khVOR3JngGYZc58mwaRm9fuw2pRqllkVoMu6R0wjJ7rHIcZ0kg4uOxFJgUbjMVw5eBBdieiRB5p/sJ85fLFsut3wFl9Ioy51nuY5Gmnu6vhU83RB1RMos4vpskfsX+STi+ti1LMQakW4r6rGI6yPK5Zu0eBnQ9VX5fwoft6MFS5+6CPX19UhK6vz30hGDESIiIopoMMJpGiIiIoooNsoj6mUOpRZ7zUvQaiiX3VvFUHyqe1qX4XgPnKg0f4d643Y5UJ3oGSEbpxlh42vUAypUlJo/xV7L53ArDljVQbJRXYrqbWhH/YuY5hK9iVoMJVBgQop7EtLdsw+pW7EoyV9hWiZL/Fu0QTIvJ17NDet50yEEI06nE//5z3/w1VdfweVyYdasWbj22mtht9uD/tyOHTvwxBNPoLCwEKNHj8Ytt9yC3Fy+sNS/iaZfO2wvotjykQwufMFHkfU92D1ZmNzyOySpo+RjIs9gq+1puA3N/oqjokjVdvvzGN16JfJcp0X0ucSKfca1WBf//6AqLv/qkhZDMVYlbIBVzcScpkdgQeChYoqtvJIC639RYH1T/q34/r6KrR/AoqZgUsutSPVM7XFu0Pq4h9Bg2t6h8q+GAtsbSHfNlvvsaUdl0qdH0zRHHnkkvv32Wxx33HE47bTT8NJLL2Hu3LlobW0NGojMnj0b+/btw0UXXYStW7fKrysrK3tyaKKY+4dyi/0Jb6+NDt1ZfQWqWg1VWJlwJ5oMe1Bh/hYb4x72Nlprryzp7VQqOr62YWvcU7obpw1k9YZtWBP/R9kZV1I6fzgMlViWeB1UOCN9qhQGu6yvYrftNWiKu8vflyilvyb+XtQaN+ren0iUXZnwOzQad8qvffvz9RyqNq2UHZX9v18UVj1KYK2urkZ6err/671798oRjoULF2LBggXd/sxll10mA5Dly5dDURQ5ojJq1CgZmPz5z3/WdVyupqFYU2fcipUJvw2+kWZAqnsKGo275FBzsFoaBs2CYxpe4buyIL5LuA6thrKQfWKGtJ2DMY4rg29EUa1VKcd3ideEqD+jIF7Nx+FNT+gqXrfD9hIKLe8Fr6irAeNbb5TLuSmCCawdAxEhJSUFJpMJTU2BW1N/9tlnOPvss2UgIpjNZpx55pn49NNPe3JoophSYvk4YOEmP0WVS0Bdsnla8E3Fu/lwd6XtT9pQpSsQETeTMsviPjor6i0lls9C374UDc3GItQbt4XcnxjtKDUv0lHaX0GxVXRepqhKYH388cdhMBhw4okndvv9lpYWOR2Tn+8t8OOTl5eHPXvEev3uORwO+dFxZIQoljQYt4dsKS8pvq6vIdrPw4iG9uFj6mqfea2uzrliGze802EUu0SCqa6eQJoip10GecYF3azVUCnztUJSNDm16s1RYb2TcDropb1iZOOuu+6SAYkILgIlvAoHJrjGxcX5v9edBx98UBY5830cGMwQERHRAA9GvvzyS5x33nm4//77cc011wTcLiEhQU7j1NTUdHpcJLOKKZ5A7rzzTjm/5PsoLi4+mNMkipgkz9jQ0zSCyNjS8Q5PvBNL8nhX3lBXaa5p+jrQa5DVPCm2JXtGe0cUQ1HEEvnQfzd2NVM2PAxJU2Speo6KREEwsmTJEpnzcc899+B3v/td0G1FIDJx4kSsWbOm0+Pi66lTAy+5slqtMtGl4wdRLMl3nh56mkYmsE6DWU0MeSM1wNLe8ZW6Y0MG7GpO6IBEEc3YmHwY60RDPZHpEZRIYPUMRbJnrK6GjDIpNWSAoyHfcUbPTpbCH4yI+iJnnHEG7r77bjl60Z1///vfOP/88/1fX3755XjzzTdRUFAgv169erVMahWPE/XnkZFs54lBuriKkkwW2XF1XNsvg3ddBTCm7WqupAlB1ICQFzJI91qTmthrTfWo79i1wcE7KotGhjBgnGiwqCuZCBjqWBC0o7L4m5V/167jD/KsKWwJrOeee65MWBVBifjwufTSS+WHsHPnTnz++ef+7910001YuXIlJk+eLEdJNmzYIAuliaW9RP2V+AdQLAE0aXFdip6J2gV2NQOTW+5AgjpUfmgtGrbanpK1RjoWPRPVV1n0TJ9kdQymN9+HdfH3e2uJHBCU2NRMzG56RI4yUewb6bhUjmgcWPRM/H2JqqkiOE3xTNS9P29H5YewPu7PaDBt61T0TIxyZrhnYWLLLfKYFOE6I4sXL4bH4y0q05GoGyI+fMGIqLR64AqbXbt2oaioSG7X04RU1hmh/lAOvs1QIW+Eae4ZSHVPDVgOXqwUEEXTEj3DWQ7+IMvBl5k/Q5nlc1k0TlTj9JaD139jolgrB/9Vezl4Y9jKwVealsHFcvCHjF17iYiIKKLYtZeIiIhiArv2EsUYN1qw0/YKqkwroCpOWLRkDHGci1xX98UHo12ToUhWRW0xlMmy92nuabJL6oHdip1KA8rMn6PeJCpqqkjwDJMrY2xaRsTOnbwcyj6UWhaj0bhbfp3kGY0c50mwaoFLOBAddM5IpDBnhMirxPwJttr/KesndErQVMRKkQTMafw77MiKicslcmQ22x9DheUbuYJBk0s1vcuKjLBjUsttyHDPltsWWz7GdtvzMlHRSzx5kXOjYajjPIxy/KxLDg71VefcN7Db+kb7a+d7Db1J3KPaLsdQ57l8KQYwd2/0piGiyKkwfYut9qf3rwHu2JVW/NErTfgh6Sa40RYTNzHRqVh0LJZfy87G3iJVslsx2rAu7v9QY1yPUvNibLP/09+d1beNrxtyofVt7LL+O9JPaUDaY12I3bbX21+Tjq+hdwXKDvtLKBKdq4lCYDBCFCO22p/1fhKobIK4iStt2GWL/htzvXELqsw/eG9c3ZGPa9hufwE7bC8GL2amiJviO3KqgPqOmDbzjogEt8v2igwuiYJhMEIUAxoMO+FS6vR1pTXvr/MTrUosn+roaqyhyVjgbWAWsm6VhlJL9D/v/qTcvKTDtFlgIhApN3/dJ+dEsYvBCFEMqDVt1N2V1qO0INqJREddXY2FQFVsO1HQbCg61NOiHmgyFunK0xG1P8S2RMEwGCGKAfurQeraGtHO0AsL+di8LHp/Jw+lABkNDAxGiGJApusI3V1prWoqol2Ke3IPuhrreeIqBnkmhOPUSKdBnvGy9HooYptk93heVwqKwQhRDLAhHXFqvq6utEOc5yDa5TpP09XVWJTOt3uygk/VaOIfMisGO48N+3lSYJmuuTCp8cF/JzVFluNPd8/ipaSgGIwQxYgpLb/z/skG6Upr9+Qg33kmol28miv7xQSkGWDS7Bjbdg0mtN7snYLpLiCRIyfA+NYbYIK9V8+ZOhNdpye0/qq9U3J3r4133fnE1ps5TUMhMRghihEJ6hDMbnoYJi3R+4DW+WOQZyKOaHoShhj5sx7uuAhjWn/hfXfty0Fon7pJ9ozGrOaHEafmyM6rhzXfLz+XNIN/iseipWJy8x3Idh0XuScygGW6j8C0lrth09Ll1/J1aX9t7GomZjTfhzT39AifJcUCVmAlikH7jGtQYvlY1hWxqZkY0XYxbEhDLBKVWEXNkVZDOQyaGanuaUhUh3dbKK3OuAn1xm1yiifRM0xO4zBxNfJE9dwa09r95eDdo5HimSKrsNLA5tZZgZW9aYhiUJpnOtJap/eb4f7BrmNCbidubCmeSfKDootY4isCQ/FBdDBiYzyXiIiI+i2OjBD1U26PG7tqtqLc9jnctnIYXIlIbToOY5IPg91q6zT90WDcjhLLJ2g0Fsh5fzH6kOc8fX+eRrs2pRqlls+wz7QaquJCvCcPuc5TuwzJe+BAufkr+eE01MOiDpJ5HVmuo2GE1b+dCjcKLe+ixPIRnIYGOeWS5BmJ0a1XIVkd1enYzYYSeY51xs3t0zQj5TmK/JKOXGhCmeULVJqXwa00w6ZmyA6yGe45B13fpMG4Qx67wbhLPs9B7ony2PFqHmKNuD57LUtQaf4OLqUJNjUd2a4Tkek6HAaY/dup8KDatEJ2VG4zVMKo2ZHhmoMc18mwaIGH24kOBnNGiPqh2qY6rI67F1rKbkA1AgZP+yocBXDFYWTJ/2F42nCocGGj/e+otHwrE0h9dSNkF11FxfC2izDC8VN5AxY34622f3r30b4s1/czKa7JmNpyF0yIk6Xr18TfC5fS0L6t6DDs7cZr0QZhevN9MiekFRX4IfHX8Bha/KtipPbVQoOdx2NS2y0yWBKN8PbYFnqTI/3H9p6jWNIrVnWIG2m1aRXWxz0EFU7vjuRhvT8T58nFjOY/waZl6L6OIljaYn9C3ry7uz5D2xZglOOymMmNqDGuw7r4B2SweOD1sXsGY3rLfYhTs+FQarAm/o9oMhbuv+btvz8ioJvUcjsy3YdH+ulQDGDXXqIBqs3pwKqEu6EltZfgFoGI4OuoamrFriG/xd76CmyxPynfIQsdC1j5aoAU2N5AkeV9lJu/8XYM9nVn9W/n/Zla0yasi3sALUoFVsffI99x+48nj+298Ynmaqvi70KLUu4NRHyl6zvey9s/L7cuwQ7ryyi0vOMNROT3Oh7b+7noe7LV/k/UG3dgXdz93kDE19m3w8+0GvbKY7vRqvtabrM9i73mpQGvT6HtbeyxvoVY0GgowJr4P3kDkW6ujxj9ENenDbXyNWw2FHf6vu/1FAGaCPhqjZsi9EyoP2LOCFE/s61uBZBUDBjd3W9gEK3e3dhuelW+4w9V4XS37TXssP4rROdcFbXmDbLDrltp7RQ0HLidW2nB5rhHvSMiQToQi+MVWt/FbluIzrCKhjLzYuy0vtz+br/7ExUBhFixUy6esw6tSgVKLZ+GvD4F1jfhRvT3AxLnKSbQgl0f0fl4h/15NBuLAxela/958XtBFC4MRoj6meq0/4XeyOiBK/sbXU3oPEorHMZKHR2DDagyLw8ciPgoKuqMW3RVkxXbqnJKIRQDak0bdDTfU1Bs+UTH/iDzTvT8EynOr8L8LaKZGJGqNH+v4/pocpl1yN8LEXyaNsgRLqJwYDBC1M9oyXv09XOR2+jYTk8PGbk/MeKisxNvx2mC0Bvr2Ebn/hRNjo7oIaZ19BBJt3q3jZQ2pUrfa6OI4Mqlsx8Q0GqM7udNsYPBCFF/47H0YGOdN/qopz+BVO+KGgUmnXvVoHRYhRKNOq6SCet+teh+3hQ7GIwQ9TOW8qMBj44brkOUldc5gqKrY7BoipYceohfU2DUbLq7EOsb5VFhUK2hNxPN91z6isWluafpmNbw5lqIbaNZnJorG9aFuubi+li1dG9p/hDEa5jk6bz8muhgMRgh6meGuucFTl71UQ0YVHIJjIgLGjyIm1OieyQy3If7+8EEniVRMKTtvNABjqJhSJuOzsIaYFHTkOAZGnyqSFNgUhOQ5zwjZCAkAod8sZ0Oma4jYVYTQ3QMNiDeMwTJnvGIZgYYke+cH3IESVyfYW3nd1o51P2GBuQ4T4ER++vVEB0KBiNE/Ux+6lAk7Loi8AaqEcbKGZgy6GRMbrktSNdV0XLPhomtt2Bs67Uwa4O6D0jaa4SMa70eQ11nyeJiAW/gGpDpPBIjnBchy1cCvrvYRT5mwPSWP2BSy23eQmndHluEQAomt/4GIx0XykJo3W/n/d9Qx7kY5JkAvVMbop6GqKwR6PqIUvbi/GKhzsgQxzlI9owNGtjlOeYjz3X6/o7K3b42BsSr+RjZdnHvnSwNOCx6RtQPqZqGDVXfoSrnJSC+av83XDbEF/0EhyWfA4vZO99fY1yPHfYX/U3OfFJd0zC27Rp/ldE2ZR+22Z9FlemHTlMncZ4cjGq7DJnuI73Hhhu7rW+g2PqBXInjY9TiMMRxFoY7LvS3lN9ufR7F1o86vxPXAKuajmkt9/gb5jUZirDN9hxqzes6nWOiexTGtF3p71cjaojssL2EvZYvZIVYH1EBdrjjAuQ55/c4cBAVX7fZnkejaWenx0WhN3F9EtShiBWixshO279QalkEVRGF4bzMahKGOc7HEOfZ/utTav5MLqt2GPb5t1M0Ewa7jsOY1ithRkJEngP1z6JnDEaI+jG3x4Piuj1oVethgh1DkkfAZuk+t0JUTm0yFsmRAPEOWlTi7I4ISupMG2XQIbYRUxTd3eDFjU90cnUpjTBrSUh1T+1UCt5HLN4tMX+MJmMBDLAi13kSEtUR3R67xVCGeuN22SVWdO0NtJ0oeV5jWieDIauWhhT3FH8AdLAaDbvRaNwjn2uSZwzi1VzEKlEXZZ9prSw6Z9FS5GvTXWKvBg9qjRvRZqiWr524jiwFTz3BYISIiIgiiuXgiYiIKCaway8dEjFHX275SnZybVMqYYBFdvbMc85DgjqEV5dinqYC1QVZKF43DI2VyVAUDcnZtcifVoCU/H1Qoj93lajX5ZzWfc8nZ3Mr8FTon2cwQgetxbBXNtQSQYi/O6tIfLN8ihLLxxjT9gsMcZ7JK0wxy+MyYu37s1BblNHeuda7EqV6dxaqdmVj8LhiTDh1HQyGWCgMR9Q7bvpVPc5M7L6cQENjK0J0l5IYjNBBEcmJq+PugUOpbi9dsP8fY1+hqO3252BV05DVvsqCKNZs+nQaaovTvF90WBKrtX9evjUPljgnxhy7OVKnSNSrox0vH9Y3TSAZjNBBEW3bRcvxoKskNQW7ba8js+mImKjDQNRR874EVO7MCXFRFBSvHY5hs3fAYt+/lJgo2i18qCnkNsmNeppUhgeDETooZZZF+/u8B6JoaDYWoslQEHAJJlG0KtucD0VR/aMggWiqgortOcifWthn50YUbMokFDml0oiowmCEDkqrQXQB1TdPLmoUMBihWNPWaNPVPkcxaGhrsPfBGdFAlRMgOfRAjx3nQXJjiFYQUYrBCB0U2ehM97ahG5gRRRuj2SNXymghIhLxfbEtUW8EGY/JAEPndEmUjXb0BIMROiiZ7sNRaHjXu8IgCKNmR7JnHK8yxZz0ERUo26ij1LtmQMaIir44JepnORu6goxGDAgMRuig5DlOQ6HlXX+TtG5pCvKcp3VbApwo2qUPr4Q1oRWO5gBN+gRFRVJWHRIzG/r69CiKczaOyTFGVXJoLGAwQgfFrg3GhNabsNn+mLej6YH5I5robzIGI9jZk2KUqB0y9awVWLXwSKju/ct5fURyq8nuwuR5qyN2jtS3UyoLxuxvLhg8OTQ28zYiicEIHbQc14mwainYZX0dDaZt+3+p1HhZgVV0SeWoCMWypKx6zP7pN9i5bByqdg32Bt4yaVXF4LGlGDl3K2yJbZE+TerlAEMIVNSLwoPBCB2SNPcM+dFsKEGboUomqyZ5RsMAb3t6olgXn9aEqWeuhKPJiuaaRDkKmJjRALONdUViOTFUBCEMMKIHgxEKi3g1T34Q9VfWBIf8oOjWl1VDKXwYjBCRn9q+OMoQvM5XrzWkky2OWKy3b663zD5XoSB0smW0BBlimWsoTAyNTQxGiAa4tiYLti2ZjOrdg2U1UcFodiN7QgnGHLMRhl78V8LZakHp+qEoWT8Ujia7TApNGVKNIdMKkDa8koFJL6g3bkOx5UNUmpdBVVwwqQky/yvfMV8mpkfrChQZZAyQZa4DEYMRogGssSoBP752DDTVNxTiDUY8LhNK1g2XZc7n/vxLmKzhT95rromXK1WcrWLprPcxsWKltigdNYWZyJ28B+NO3MCAJIyKLO9ju/15KJoRmuIdZXAbmlBk+QAllk8wrfkepHqm9llZckEEIbJqKFegDGgMRogG8JTMyv8e3R6IHDg34v3a1WrBj2/MxZGXfxXWY3vcBqx+53C5f98KFR/fEtrSDcMQN6gZQ2fuDuuxB6pq0yoZiAi+QMRPUaFqLqyN/384ovEp2LXMQ0oO7VHOBoMQYjBCNHCVbhgiR0CCU9BSkyhHMeJTm8N27Mod2XA0xoXcbs/KUcifXgCDUV8fJApyLa0LvcXbAlVNVjSomluOkIx2XH5oVUOJeogjI0QDVMna4e1dl0NnjO7+YQwmz1sTtmPv3ZznvSmG6IjrarWipjgd6cOqwnbsgahNqUadaXPoDRUVzamLsfDXC7p8izkb1JsYjBANUC6HRfe2jib9jRH17c8eMhDxcYpy7HRIK1DWle/FcS/p+5ma1kaOflCfYzBCNEAZTSIpVd+NPtwFvkw2p+5RGROLix1UYuj+0uRArlv/P/WJ5vAGnkR6MBghGqAGjytFwfKxurYdctiusB47a0wZ6stSQ24nlhin5ldjINDfTr7nK5tGJGVgYmouNteUtdcX6Z5JMeCC0bN7vH+iQ8VghGiAGj57O/b8OBqaXM0SsPUyLHFtSMmtDeuxcyaUYNeycfC4jEGmazTkTimEyRK60FV/GO3QVZr8IOtsKIqC26afhiu/eCHodh5Nw42TTzy4gxAdAgYjRAOUKGY2+cwVWP/+7ABTJppsCDfzwmVhP7aoWzLt7BVY884caKp6QEdc7zv3lPxqjDpyK/pDzkY0rEC5fNxcrKjYjac3LoEBCtQOIyRGxQBV0/DcCVdgcnp+RM+TBiZF07SoXzPX0NCA5ORkHHf9GzBZQy8HJCL96vcmY/PiaWjel9ghINEwKHcfJp2+ple70jZVJ8jluxXbcv2F12yJLXI5b/606F7Sq7tqaBQR/9z/Z/v3+NuaT7Guulg+Jl7xU4dMxu8Om4djc8dF+hSpn2lobMWg4dejvr4eSUlJAbdjMEJEkrPFjPq9KVCMKgbl1MBkCVCPohe4HSa0NdlgMKqwJ7dEpOpqT9rJe6uGRleg0VPFjfvQ4GxDZlwiMuyBbxJEfRGMcJqGiCRLnAsZIysjcjXEtE2CtanX9s+qoV3lJ6b12vUm6ikGI0QHEBOXzhaLnDawxDnCMlUgyp+L0ufinb/Z7uzTd/4iSVQc22hxB12i6/Z40OxohZgsibfHwaAYwjLi4XaYYbK6gva3cbs9aHY2w2gwIc5qhyHABZKvTbNVZLPI1yZvXuCy444GC1xtJjw+vwU56qG3lHe7FVRUiXPTkJXZGpbOxo2NJtTWWzEo2YmkxPAunybq98FIVVUVNm3ahPHjxyMrKyvotoWFhSgoKOh8UJMJRx111MEcmqjXqB4FpRuGomjNcLTWJfiXluZMKsLQw3YdVO5ES208CleNlBVHVY83vyAhvQH503cjZ2JxrwYlDRXJKFw5UpZe9yWIJufsw9DDdiNzVLl/u8bWZmxp+hINuQuB1Lr2H85HesUFmJByJCxmc4+PXVOUhsKVo7CvMKM9K0FD+vAKDDlsN1Lz9+3frqkO2xyL0Jz/NmD2Lm1V9o1DVs35mJB+GAwGoz+gKl47DMVrh3sLponrmOzAbGUbbrluEzLSHf5g5e0PhuLRpydg2Qrvv03vG1X85OwC/ObGTZg+pabHz6V6nxWPPjMB/3xpLGpqvTU4crObccPVW3HjVVuQkNDzpbZffZeFv/1jEj5enAdVU6AoGk4+rgy/uXEjTjp2b4/3RxTrepQzsmXLFtx///1YunQpysrK8Oqrr+LSSy8N+jNi+7/85S+YMWOG/7GEhAR8+OGHuk+SCazU28TIxdp356C22Dd0vT9KEG3txajCYT9ZhsQM/Wsr60pTsVqsFvEYul0tkjl6LybPW4UwDEB0UbE9Gxs/9v7NdTp2ewn2oYftxKijt6C2uQ6rk38LxFUBBk/nU9QMMFRPwZHK3bBZ9FdrLVw9Aju+mti13Hv712OP34D8aXtQWluGLdm3A6ZWwNjhhq4q8vJn1J2ItX+6HK42G844+URs3jhINvfr+NoYjSqys1rx9YefYGh+E265azYef3YCjAYVHn8nYvEGyJv/8uaLS3HOvCLdz6WoJB5Hzz8dZeVx8Hg6v4YGg4ZJ4+uw5L1PkTJIX66J8PSLY3Hj7w6H0aDB3WGf4rmIY/zl3hUycCIaSDkjPfpncMeOHTj99NOxe3fPumhOmDBBBjC+j54EIkR9Ycc341FbIgKRrjU3xM3c7TRh7f/mQHXr+5NxtZmx5t3ZULsEIvAfQ4xYFKwYjXBrro3Hxk9myPohXY7d/nXhqlEo25KNNfb7AfsBgYjvFA0q1PQNWNH8iu5j1xSneQORDsc68NjblkzGrKnAlqw7AXNz50BEMGiyaVvVoC/wkweX4pYrZ2LLpmSoatfXRty8yyvsOPvSE/DSa6NkICIf7xCICG63AR6PgguvOha793hHvUIRb9PEfvd2CUQEBapqwKatg/Dzm+ZCr2U/ZshARLw2HQMR33MRfnvvLHz+VbbufRL1Bz0KRs466yw5EmK19qxXhMvlwqpVq+TUjtOp/x0EUV8QeQVlG4Z2aWXfiWaAo9mOyp2Dde1TTMt4nKYQ/VcUFK8eIaeHwqlk7bD2wZdg+9Wws6wMWup2wBikqJjBA8eQD1Hf0hQwMVQscfV9GPYNkXkxwZiMKl59Zztgq5MBT0CKhmXKm/jfZ5ndBAP7iZv6hs2p+NNfp8npjkBEACACmn++rK/q7DffZ2HdxrQuQUNH4rw++HSI7gDn0X+KUZvgg9FihOTvT7UHdEQDRC8MEHe1bt06XHnllZg3bx4GDx6Ml19+Oej2DodDTs10/CDqLdUFWf58jqAUDRXbc3Tts3xrrq7tXG2W9hGZ8BHH7joacyAFruHvAR59aWMV8Wtk4NHxQwQfLx/WIiuHio8TDMDqpblyNCgYcXMvSX3fN1sVnLURGLk45GbiBl5UktBeTRZBg4f/LByp48DAm+8N80/vBCMSWt96f1jI7ZxOA/730dCgwY3vHD/9MlcmtxINFL3+2z5nzhzs2bMHeXl58usnn3wSV111FcaMGYMjjzyy25958MEHcd999/X2qRH5p1R0NW3TFDhb9I0KitUreprAeY+vPx9DD7dTZ8KpvQYw6Ei+1IwYZmuQgUcw9Q1mmYypS1y1dzpGByWuJmTc4s0l0ae2Xt/1rq21QtOxX7Haal9t6N+LpmZT0BGejkRQVddgQaKeEvFE/UCvj4ycfPLJ/kBEuPHGGzF27Fi89dZbAX/mzjvvlMkuvo/iYm+lQKLeYIkTU4c6bqKKCku8vkJX5jixnb6brcUe3uJZYhmtLi0ZgKrj/YjiQVZ86GkIsTzVEGzapaOmLOCAvI5AtGaxIie4niyzTUvRd73T0tp0JReLXJSMtNArrRITXDDrGGnxjbakJHNKmwaOPpmmOVBGRoZcjROIyEkRWbcdP4h6i1hyajDpGSEwyE63emRPKNG1nWhCNyiv58tNA+VsiI/5l+4Kmbchgob8mrO6Jo92RzPgrtNCJ9rGxXlw9ulFMickGDH1Mbz+bO/qmhCU1hRg90khtxMjDiOHN4QMhsR0zuU/3Qk9Ll5QIBNf9YxiXHhu5/IF3TGbNVxwTkHIqR9x/c48reiglgwTxaqwT9OIKZmSkhJ/HZHGxkYkJoqeF16VlZVYs2YNTjnllHAfmuigiK6w+VP3yHogAUdIFFXWGckYsb8+RzDZ40qxe9lYuBzmoF1pRf0SsUS0OyKwCKW7Tq+TrtuMT/41Fk5VC5hDIVaCPHxlHK5YPwmtSVsDByWqAaMaz8KUofqmp269fjPe/XhokC00OZLw9M/zMe+7TKj2fV1X8vg3VXC8+SIMPrsU//3f8C4rZDrevKdNrsHNv9yES689NuCRRXKrxazi2su36Xoucw6rwpzDKrFqbXrAPA+xhPj8s/cgP1dfUbWbr9uM198ZHnRa0KMquPV6Lu2lgaVHwUhtba1MRu1Yd0Qs1c3OzpZTL4JITn300UdRV+ctnnTiiSfijDPOwPTp01FdXY2HH35Ybn/99deH+7kQHbSRc7eiuTYB1bsHy0TVTitrxPSM3Ynp5/2guxqrqDY6/dzlWP3OEXCL0fYOAYnohCuqu446tQCH37yp26kAb6fXg3tnPHxoE97+15c47/IT5Eqd7mpZ3HfHavzk7CIMmfBLHP3WX+COL/aOVCida30k183GsptP133suXMq8c+/LcN1tx3ZpY6GGBEQq1mef+w7nHJUA94w/x4Xfv0naNb6zgGJapRfD607Ax/fMQtOx/fYU5yA71dkym/vD7BErQ9g2JAmvPvqF8jJbsXW7cm4/2/T/M/Tf2yjKo//v1e/1B04iIJ0/3tlCY498zTs2pPUqcaJb9XO7BnVePYR/V2ND5u2Dy89+S1+fuNRMgjtOPIizk8Eak/99XscfURkyvITxUTRM7E897bbbuvy+Pz583H77bf7gxGRD+KrJSJyPp566iksX74ccXFxmD17Nq677jrYbN5Khnqw6Bn1BZGsuHdrHorXDEdj5SD5mNnuQN6UPbJIlze3pGfaGm1yf6Ubh8qy6MLRh5fjpmu2YMGZhb1agXXL9mQ89swEvPrfkWhtM8k8hHknl+DX123Gicfsr/JZUOHCL99Yh8/b3oEa733cWjcO52edhacvHYsEe89nc39cnS6roL71wTB5wxW5EmKK4tfXbsbM6fsrsK7e3Yob316J5cr/oNlq5WMJNTNx5cjT8fBFw2Ayeo/tcBjw0muj8cRz47Flu/e1ycsRVVC34LortiE5aX+ezKIlOXj8mfH45Is8GbjY7W5cftFO/PqazRg7uucr8xoazXjm5TF48vnxKC715s6MG12HG3+xBVdevBM2W5Cl0QGsWpuGx54Zj/++Oxwul1EGS+L3Qbw2h8+s6vH+iKIVu/YSHWJFVlE5VVRe1RMwiNwNMZoRiHhX3dhgRqar9aBuXodCvNtubDIjzu6GJUgnXlXVUFbrhsWoIHNQeGZwXS4FTc1mmbxpMgV+3+P2qCir9SDJZsCghODLrJubTfJ6ipyKYK+NWErb0mqSxzaGob+QeNvW1GSSozHx8eHJ5xD9bsRrkxDvkjklRP0Nu/YSHQKjScVNt9bKVvF6yHbyISrFpxgdgL7dhZW4EYuVLqEYDAry0nrehyYYcYPVUypdjIAMSdc3AqM3EBCBl8USvhUpIvAJ91JbEaD1pJQ8UX/FqjoU81rq4mT9D7GkNT61KeRIhhjFWDAm+A1ABCEyZ+Mg8zb6s5p64JlPXGhsVXHqdAuOndaHLYh7geg/U7o3To5OTBxXF3CZcG2dBTt2J8nprnGj6wOudhGjQZu2pqDNYcTQvCZkD/Y2ADwUFZU2FBQlwmrxYMLYOlitPSisQhQDGIxQzKralYWC5aPRUJHifywxpxETz9+G0fN2dxuUiCCkuxUoXTAI6aJgr4Zj/7wBJTn/AuK8y5Ef+toA6yvzcPfMs3DXxeEdVelti5dm4/6Hp+KbH/aX+B+S1yRXvNz0iy3+qZ2CwgT86a9T8frbI+B0eYe24uwu/PySnbjntnXIzPDWGGltNeIvT0zCUy+MQ9U+uz/Rdd5JJbjn9nUy2bWn1m4QZe6n4v1P8+UKKCE1pQ3XXrENd/56A5f/0sBMYI0UJrDSgQpXjcCOr7t2hhX/+IukxZ9fvB3PP7asVxNEB5LtRRrGP/sEtMz1XZfiilL6mhFXGR7Bc7+KRyx44d+jcc0tR8pRjs5LhjX5OyM6+4oOvyIJ+JgzTkdTk7nL8l6xYidncAuWffoxUpIdOPm8U7F8dbo/aOi4nXdlzpeYf4q++jPCl18PxvyLTobbo3Sp3Cr2OXlCLZa+/ymSEnUWuSOKACawUr+x8KEmb05Gu+Wr0nHEqWeE/LkXHv8WP79YX4ErCi7l10tRP/LfgWuCiBuwKw7rz3sCk0QZjSgmAozJR53dbRfgjkHtX+5dgadfHIfCkoSAZdzFKpijDq/AlEm1ePK5cV0CkY77s1k9KFy3EOlpDl0reIZM+YksIR9onyIgueKnO/Hco/qXFhP1NSawUtQKtfIkVGLoE8+OlzeBYA3HxD/+f396ovzHmqMjh2btDg31w18PHIgIovKppRmX/bMQq/8crOhZ5D314jgYFEAN0gJAjBf/+fHJqG6fbglE/A4u/S4bP6zKCBg0ePenwOE04qXXRuH2m0IXNPv3whFylU2wxn8iQBLLth/6wyqkpYa3pQBRX2POCIWV7qqhIVaeBKOn86n4R3zjlhQUFifIolh08H7/+j4gVd+Kjw34EsDPo/pyv/V+6N8fMWIiApEDi6d1R5Sgb2sL/U+pWI789gfDdAUj//tQX0Anclg++zIHF58fuhw9UTRjMEK6RzNCOZSqoT2pmSEKeOnV2MRf8UNV59C59FTRoJpDB6ORJvI/9NLTtVf/yJuC+kZ9xxYde4ONiuynyREUoljHf6kHOP1Bho5h4EMY7dBLrHBIT21DdU3oCr5iqmZw5qEvqxzoRqQk4gc9G6pGmFtzEe2yB7dg527RfDP0zV6W6g8xoyhK7ushmxPmNOvaNj+3Wa6kCTUqI56DSKIlinUMRgb4lIq+Za6IKj+/ZAceeWpi0H+oxfD6KceVISOdc+mH6vFfJOK1f6YD8SGWpho8mJceusNupF15yU7c/cCM9l4zgQMHUc9j45bUkPsT/Yoy0ltRXiHySwIHJiKnRG9C9eUX7sS7H4WeqklLbcOpJwTugE4UKxiM9MMAQ3/V0Ngs6HXDVVtlLYfWNu8/8F1pcqXEHTevj8DZ9T+pycDE6uuwKf7+wBt5TEDtKLzyW2/fmGh29aXb8dcnJqGhwRywE7D4vfq/u1fj6ZfGyV43gQJfsTT4msu2YfKEOlx/+xEBjykSrofkN+G8Mwp1neMZp5ZgwthabN+ZHDS/5Xe/2hC0xD9RrGAwEiNYNXS/IXnN+Pi/i2UNhtZW0XK9c3dWUTjnhce/Y+fTMFr70Ahk33otqkc/4w08jO2BrC+voX4o3j7pdiTERX9hF7G0dvHbn+HkBaeivsHcaYmv+P3xqAoee3C5DAiOPqIC8y44Gd+vzJSjJb7g15fYeuZpRfj7AytgNqsoLInHnx+b0mmll6+7b052Cxa9tUh35VQxHfnpwsU44ezTsKsgUZ6eL4fEt/8br96M224InQxLFAtY9CzKczZ8Zct1TacMMGV77XjmX2NlN9eqapss/nThebtx/ZXbZLluCi+PR8PNz7bhhe2L0Jb7JWB0QqkdiRnOC/HGTfkYGf3pIp1UVVvx3Ktj8PyrY+QUS5zdgwVn7cH1V27F1EneDsK+hntiBY7o2rt+UyoMBg2Hz6yUI3RnnlbcqXz8d8sz8eRz4/Hpl7lwOA0Ynt+E667cissv2nVQxclEU8BX3xyJp14ci50FSbCYVZx4TBluvHorjjuqnMvWKeqx6FmMJIe+fBiTz4iIqH9i0bMoKOqlawUKERHRAMeckT6qGkr9T1OTCU+/PBYVlXa5FPPay7fBZuuaE+DxAA89Ohk/rslAfJwLd968QSY8duf9T3Lxyn9HyTyG88/ag4vP34Nos6/Giu9XZMiKoqNHNGDKxP1TGgd2r/3m+yzU1FmRluKQ+Rcmk9ZttdN1G1Oxa4+3K+3cOZVIGdR9bRPRXXflmjSomoJJ42oxeuSh/aGJY/+wMqO9a68bxxxRjrg4fX/nRBQ+zBnpSdVQIgBtbQaccfFJWPJNdqfCVCLB8azTi/DWS0v9eQQXXHmsrLrZuYCVhkHJTnz1wSf+oOSFf4/CDbcfAadL/OD+bU0mDx64axVuv2lzVORY/O6+mXjtrf3da4UZU6vlypNTjvcuMRVLZv/65CQ88o+J/u61QlZGK267YSNuvX6T//p88nkufn//DKzbmObfTgQkl/xkF/5y70qkpniDkt17EvCbP8zC+58OaU849Tpu7l653czp+3r8fF59cwTu+/M07C4UNUe8EhNcuObybfjTHWtgtzMoITpUzBlhkEG9QCQzjpixAGXlcd3UlBDv+hW5JHP9N+/hqHnz8MPKTP/jXbcF1n39Hr78ZjBuuWtO++Pdb3fHr9fj/+5ZE7HXtLLKhiNOm4+ikvguy1xFQqcYYfjPM1/jgnMKcNn1R+P1t0cEqCCq4fKLduLFJ76T/VeuuOFoueKku063I4Y2yo641fusOPL0+bJ5XJfutQZVjrZ89tYiHHNkhe7n88AjU3DP/83o9rURQeURM6uw+J1FsNkYkBAdin4djIS1aihRD1x+w1GyOVmo6p3nzi/A/z4a1v5VoG01JCU60dgkSn8H305oLv53xN6tX/yLY7Dw/WEB622IgEIsb/37/T/iht8Grrfh8/TDy/CrOw6Hyx24c64ISC5esBtbtg/CmiDVSEXwIBrFFa9fqKvmxqq1aZh10plBtxH7vPu29bj3d2tD7o+IBmAwcsvtLk6ZUESI6Ye4vJ/J0ZHgwYgml1wGDzD2b6unLLlw/lkFePPFr3pyymGxt9yOIVN/oqNhnIbsrBbsrbAH7WArRjNE3Q2RpxFsO7mtjkZ1Pq8/txQXnhs6x+bnN87Ff94aCbc7+H7T09pQsuFNFhUj6oNgRN9feZT4131NDEQoYtZvSoHTadQRPCg6AxHfNnreD2j44qscRMKX32TrCghEsFa6Nz5kgCGK1BWXJoTcTm7rMchRilBEIbBPv8iDHh8uyg8ZiAjV+2zyNSei3hdTwUhyk87uoUS9oK7e0oOtw1+J1C2nNPpem8MYsecs96pjt6oGtLbqO0+H7ucDtLbp35aIBkgwQhRJonGavlEMtG8X3hnQ1NTI5EANG9KkazsxgiGmYPQQ0y96RjwEj46uuAYFGDZU33kOy2/yl2kPZWi+vi67RHRoGIwQ6ZSZ4cCo4Q06ggzv0l19+SL6A5aH/7QCkXDc3HLk5zaFPFcx7XL6ySVyyiQY8f35p5TomKbRMCy/EVZr6KRd0avlyot3QI9rrtimK1gSZddFHyQi6n0MRoh64JEHfAFBoBuzJkOQd17+vP3dd7AbuIJz5hXKpM/g24lVNy4sOLMoIq+VaNr2pzvXBA2uxM37iJmVePi+FTJ4CDTqIUZO7HY3/vanHzFzenWIwEXBA3evxh2/3hD0+ojE2YsX7MKYUSJQDO2yC3chL6dZnnO3R20fNfnD7VxJQ9RXGIwQ9cAZp5R0GKHoGGx4Pxf5Da8+/TWOO7oKi9/6rEO+Q8ebqXfbmdOq8M4rS7Fz5TtITnIG2A6w2dzYveqtiL5OotHbn/+4Qt6oO97EfZ/PmLoP77/2BcaMapTdZhMT3J2CMfG5+BBB1aK3F2Hk8CZ89PpiTJlU02k/vs/Ftn9/YDl+uqAA9/xmHW68eov8Xsfgxff5GacU4/nHlul+LuIcvnz3M1k1V+gYOHnrlqh4/bmv2PWZqA/F1NLeuoKnkJS4v6IjUaTs2JWI2+6ZhS++zpbVSG1WD+afUiwDlbyc/XVw6uuN+Nn1x+LTL3L9Kziys1rx1/t+7FLq/Z4Hp+Gxf05AU7NZfh1nd+MXl23Dw39aCWOU5FFu35mEp18ai8++zJWJraI78nVXbMM8MT3TodR7bZ0F/3pjFP6zcASq9tlk9dVLL9glRyWSk1ydknI/+Cwfz7w8Ftt3JcFu8+C0E0vwy59vw6gRjV3qg4hjixLzHlXBjCn75HYH271WJLy++d4wvPjv0SgqjZcBlCjBf/Wl25GTHbqWEREN0DojDEaIiIiiV8Nd/+n8tdONIS8tDRmMsFEeERER6aIt/yHg9769tRhA5xIIzTpWwwkMRogOUk2tRRbQElMSGWltOPPUYiQeYkNF0ZH2xzXpshnc9Mk1OHJ2ZbdTECvXpuHJ58fJfi2jhjfi7lvXISnJ3WdTVKIQmujaO2ZkA04+rkwmuRJR/w5AvpXBRu9gMELUQy0tRtz2h1l46T+jZb6ISIAUy1TtNjduuHoLHrhrNczmnt2cl69Kxy9vOwJrN6b5V3OIRnPjRtfhiT8vx4nH7JWPbduRiJPOO1VWOt1PwcNPTsK8k0pkEqmvI264ic651956JL74WlSC1eRxRNCUM7gZ/3fPapkPQkSxP82ybmlPCjyGB3NGiHrA4TDglAWn4LsfM7utkyECibNPL8LCl5bqHi34bnkmTjz3VJnMeeA+xf7E0tX3/v0Fxo6qx4QjzoVLJsJ2P/Q5bfI+rF7yQdhf04LCBMw++QxZhbZraXhvf51H/285fnWNd9ULEUWnhj4ONpo9LszbuZg5I0Th9NSL4/Dd8iyoWvfBgBjNePfjoXjjneG45Ce7dfVz+dkvj+42EPHtT2xz2fVHyxGIYIGIsHZDGp55eQyuvWI7wumm380JEIjAfz633j0L584vRH6uqJtCRNGQQNqRNwjp+1EPPThNQ6STCAqeeHZ8yJqpYtrmiefH6wpGFi/NwZ6ixKDbiICkptaGmlqrrqquDz02OazBiBgV+eSLPHkewYjclmdfGYv/JwukEVE0JJDGCgYjRDqVlcdhT3HwwEEQIxw/rsqA02kI2X5+6XeDZZGtUF1kfXkpoSkoKklAOH27PDNkICKIUZMvvspmMELUS0HHt72YQBppDEaIdHK5epYZKqZeLJbQ+9TbtC0WnrfDyaLORIcahHzbj4OOQBiMEOkkKqfG2V1oafVWSA1Mw+DMVtjtoRu8iQqmbj03ezky4U0UDXXs+Pj9FU7DYazOni+iPPukcaKzMREFMlCDjVAYjBDpZLN58PNLduKZl8bKLrGBiNUv11+1VVeJ8gvPKcDNv58dMsAR+xyU5JC5I8EDEgWX6shV6QlR62TMyHrs2J0UdLpGXJNrdXTEJervwUbj+4GXuUdi2WwsYDBC1AO/uWGj7LfS2GTudmWJaPImRkVEzxQ9RJG0e3+3Fr+9d1aQrTTcduNGjB9dhytuPDrICIkmA6aH7lmJcBJB1V/uXYlzLzsh4LFFTsu8k0pxxKyqsB6bKDaDDQYcPcVghKgHhuY3Y8l7n2LehSdjb0WcP7FUBCEiOBk+pAmfvLkYaakO3fu87YZNaGk14b6/TJMjIL4gx7fPm6/bLAupiSJjFdV2/O7emR26++6fvomPd+PHxR/0SiXWs04vxr/+8Q2uvnmunFZS5eEVOTUjRkRE9dn/PPP1QTWsI4qVBFIRhDDY6B0sekZ0EMRKmXc+HIo33x2G6n02DM5qwSXn78b8Uzp3r+2JwuJ4PPvKGCxfmSHrmBw2dR+uvbxr99ryChtu/+MsLFqSI0uyp6e2yWkhEbT0VvVVn301Vrz02ih8/lU22hwmWYjtFz/bjpnT9/XugYkGQBXS/khv0TMGI0RENKAw2Og7rMBKREQDUuhgg6Me0YY5IxSVVqxOw8ef56G5xSTzNH563m6kpjgRTUT32rc+GObv2nvhuXswJK+5y3aijLooD19QlACb1YPTTizF4TOrmF9BNMBKnlNgnKahqLvBX3zNsVi1Ll0mRyoGTVYnNZtU/OrazXjwntURb1cv8iYuv/4ofPx5vkwy9SadKnLZq1iq++zflyEhwS3Lx9/752myo67I7RCVVsU24vlMmViD1579ChPG1kf0uRDFWtlz1uiILZymoZizpygBR54+H/X13nc1spZHe90wp8uIv/1jkkwWfeHx7yI2qtDQaMaxZ56GbTuT5dditYunQ22zhe8PQ3FpPD7/32f4zR9m4R8vjPMvhXW5jP7tNm0dhLmnz8fyRR9ijM6iYkQDBQuDDTycpqGo8fv/N0MGIoEKiolRhZdfH42fX7wDRx9RiUh47Jnx2LojOWCfGBGcLFuRiT/9dSr+8cL4gPsR2zU1m3D7H2fivf982YtnTBR9GGzQgRiMUFSorLLhrfeHBa1sKoipm6dfHBeRYERMxTz1wnhdDeuefmmcvwZH4P0Z8OGifBSXxiE/tyXMZ0sUOQw2qKcYjFBUWLMhNWQgIohtRBfZSCjdG4eKKnvI7cQITl29Vdc+xbYr16YjP7coDGdI1DdY8pzCjcEIRQVV1Z8EIgqCRfs5RsN+iQ4Vq5BSX2EwQlFh8oRaKIoWtBGbIFavHDYlMtU+c7ObMSjZEXLUQzyPOLsbDodR12jPlAk1YTxLot6eZuGyWQo/BiMUFfJyWjDvpBJ8+mVutw3ofMT3fnllZDrDms0arrl8O/72j4lBz1G44qc7gyaw+gKrY46owOiRncu9E/WFb+Yu5IWmqMFghKLGg39YhSXfDobD6Q06DiSa0p12QilOOb4UkXLrLzfhtbdGoLzC3u2ohwgwxoxswP/dvQoVVTa88+GwbqdhxHMRtVNEN1yi3sBgg2IJgxGKGpPG18mOuAuuOAElZfHtRcK83xPBiSgo9vxjy3q9GVwwmRlt+PrDT3Duz47Huk1p/nMUdU9EMbMjZlXirZeWIjHRjVef/gYJ8W78641RsjCa4NsuI70NC19cisOmscEc9VYVUqLYwQqsFHXcbgUfL87DR4vz0NJiwpD8Jlxx0c6oms4QAci3P2TizfeGo7bWIoOLixfsxqwZXYOLgsIE2em2oDARNpu3HPxZpxXJaR+ioL9nrEJKMY5de4mIYjTYEBrf38URDop5LAdPRBTF2FmWaD/mjFBYmtu9/MYoFBUnyGmI+aeU4IxTimEydZ6GKCyOx2/+MBNrNqTJr6dOqsEj/2+F7Moba8Q0zdfLsmQvmro6C9LTHbhkwa5up2l60vfm3wtHYMXqDNlkb9rkGlx+0c4u3YpFJdhPPs/FB5/lo7nZhLzcFrnd+DH1h1QB9+XXR8meOSIn58jZFbh4QQHi490Hvc+BjsEGUS/mjGzYsAFPP/00Vq9ejfvuuw+nnnpqyJ9Zu3YtHnnkERQWFmL06NG44447MGrUKN3HbGhoQHJyMuoKnkJSYugKmNQ3WluNuPrXR+L1d0bKVSQdEzRzBjfj7X8twZzDquXjF151LBa+N6zb/Sw4cw8WvvRVTDX0O+dnJ2D9plRvAqsKKAbv8547pwJvv7xEJrr2xPOvjsav75yDNofRn+yqqQpMZhUP3LUat16/SV7bNetTce5lJ6CoJGH/scU19xhw7vw9eOWpb3sUQIi/ftFH54G/TZXF5ESNFLH2x+1RZPLtM48sw08XFPT4Gg0ETCAlilDOiAhCnnrqKVx77bW46aab8Oqrr+LSSy8N+jMbN27E4YcfjssuuwxnnnkmXnnlFSxevFgGKHl5ebqOy2Ak+oh37udceiI+/jy3214tRoMKi1XF959+hIcem4w33hkRdH8/ObsA/30h+gMSMYJw2AlnoqKy+6W9oh/N6JENshtvQoK+oECMSFx501EiNPB3+D3QX+9bIUebZp98BlpaTd0ufRbX/Ji5FVj01iIYjfr+rP/40DT8v4enBfiu93zefHEJzj+rEANVd0EHV6sQRTAYETsTIxTyBxVFVzBy4YUXorS0FN9++6382uPxYOzYsTIw+fvf/67ruAxGos+iJTk47SenBN1GjJYce8RefPltTsCb7H4ayjb9F4Ozejai0Nfu+NNhIYueidGFh/+0Arf8cnPI/bW1GZE94QLUN1iCXiOrxYNTTyjBR4vzQxZce+eVL3HOvNC9bsr22jF02k9C7E9DdlYritYv1B3g9Kck0sBVSIkoYgmsvkCkJ7744gvcfvvt/q+NRiPmz5+Pzz//vMf7oujx9ItjdXWl9QYi+tz+x1l49Z/fIFo5nQY8+68xIYMB4cnnxusKRt7+YCjqG0I31XO6DDJHRNMMIQPAfzw/Tlcw8uJro/11XAJTsLciDh8vzsWZp5WgP2FnWaIBksDa3NyMffv2ISen8w1JfC3yRwJxOBzyo+PICEWXVevSdfVd8b7b1/eOeu3GFESzsvI4Xd14RX+dgqJEmVNjt3uCbrt2YyrMZg9cLmPQ7UQeiZ4gSGwjOiDrsW5jasheQILITRHnGWvBCIMNotjRq8GIy+WS/7daO/8Dbrfb/d/rzoMPPiiTYyl6iamIcItkZVU9fIml4dxebqNF5jqKY+tpTtiTfUbnqhUiGtDBSGJiIsxmM2pqOnclFaMlaWne5Z3dufPOO3Hrrbd2GhnJz8/vzVOlHjrq8AosfC9OriAJTqyy0TeCcuSsyqh+HXKzW5CV0YqKquArusQNfsLYOlit3hVGwRw+swoud/BREd9yXjEFE2p0REyd6b2Ocw6rwtvvd7/CqSPxGovzjMZVKwIDEaLY16vBiMgPmTJlClasWIHrrrvO//jy5csxffr0gD8nRlIOHE2h6HL9ldvw+tsjQ94YzzilEO99Oqw9NyHQO3DvctIH71mFaCYSOK+/agvu+8u0blcQdXTTL7bo2ueZpxbLAKey2hZkhEKTy2zPnleI/74zIuj0mPjeDVfrO7boLPz7+w+D0xk4GBIN/YblN+GEo/eit7AKKRGFffD1+eefx8knn+z/+qqrrsJbb72FzZu9yXxiVY1IahWPU+w6cnYlrrxke8DpGvEuPinJib/dv0rWyPDqblvvY7+6djMGDQo8dRctbr52M8aNrvfXVeluea0YmRBFyPQQheGee/Q7+Xn319K7vPYff/0eD/1hNdJS22SQ1x3x8xeduxsnHasvcBDF1B65/8cOx+kaiBgU4LlHl8laJr016iFWrAT64KgH0cDQo5ERMcIh6ov43HvvvXjyySdx9tlny6kVoaSkRG7nI0ZERK0RMRIybNgwmbgqtj3nnHPC+Tyoj4mb0zOPfI/0NAcefXoCXG6DTHRUVUVOJUybVIPXnv0Kw4c24a/3rZQ3yr/9Y1L7CInvxieKbEEGK2KbWCC68X71wae44oaj5DJbEZR4k0sVObJxwTkF8rromaLxOePUErz37y9w7a1HypUrZpM36VVM36SlOvD4g8v9hceWffoxLrn2GPywMnP/sd0GGE0qrr9ya/u17tkIl9Wiysq4YlWPSKYVz0NMzeTltOCFx7/D8UeX42CxCikR6dGjOiMid8M3wtFRZmYmRowY4Q9GysvLMXPmzE7bVFVVye+JgCQlpWerJlhnJLrV1Frw3/8Nx57iBMTZ3Zh3Ukm3ZdFbWgy49y/TsezHTP/oyr2/XYO4OP037mgrg//2h8Pk889Mb8OF5xYgP7flkLoVf7QoDz+uSZcBwfTJ+3D26cWwWLpeH1GJVZaDbzEhP7cZF51bIAPDgyXqnbzz4RBs2JIip6Pmzq7EqSeU6kpc5aoVIgqEXXuJKCxEsCE6yAbCqRQiCoRde4nokBNI91ch5dJZIuo97NpLNIBxioWIogGDEaJ+jMEGEcUCBiNE/QCrkBJRLGMwQhQDglUh9SaQMqeDiGIXgxGimGhjz2CDiPovBiNEURFsEBENXAxGiHrZN3MX8hoTEQXBYIQoDLhqhYjo4DEYIdKBVUiJiHoPgxEi3cEGk0iJiHoDgxHCQE8gFUEIgw0ioshhMEL9HtvYExFFNwYjNABWrXB6hYgomjEYoX5UhZSIiGIRgxGKGix5TkQ0MDEYoT7DkudERNQdBiPU61gQjIiIgmEwQmHDNvZERHQwGIxQGEc4mERKREQ9x2CE/FjynIiIIoHByADCKqRERBSNGIwMEKxCSkRE0YrByIDJ6WA+BxERRScGI/2m5DkREVFsYjASZVjynIiIBhoGI32MVUiJiIg6YzDShwFH8BodREREAxODkTBjFVIiIqKeYTDSQ1wiS0REFF4MRg7ANvZERER9a8AFI0wgJSIiii6mgRR0MIGUiIgo+pgGXmdZIiIiiiYxF4ww2CAiIupfYioYafjTm1i3LC7Sp0FERERhZEAM2fiNOdKnQERERAM5GCEiIqL+h8EIERERRRSDESIiIoooBiNEREQUUQxGiIiIKKJiamkv9QcajBaP/MzjNAJQIn1CREQUYQxGqE8YzG5kzyhGzqw9sKe2ysdaa+0o+3EY9q7Oh+riryIR0UDFOwD1/i+ZzYkply1HfFZjp8dtg1ox4pQtyJpagvWvzoG71cJXg4hoAGLOCPW6ceeuQ1xmExQF8sPH93V8ZhPGnbuWrwQR0QDFYIR6VVx6I1JHV8Fg0AJuoxg0pI6qRlxG55ETIiIaGBiMUK/KmLQXqid0kqrYJnNSGV8NIqIBiMEI9Sqz3al/2zj92xIRUf/BYIR6lbtNf3NDFxNYiYgGJAYj1KuqNmfDYNRC/yIaNVRtyuarQUQ0ADEYoV7VXJGEuoLUoHkj4nt1hSlyWyIiGngYjFCv2/LOdLTVxkFTAe2AQRLxWFudHVvemsFXgohogGLRM+p1rmYr1rxwJHJn70HOzEJYEr2Jqs4mC8pWDkXp8mHwOPTnlhARUf/CYIT6hAg2ir4ZjaJvR8ES75CPOZutgMbeNEREAx2DEepbmgJnk41XnYiI/BiMUJTSZO8ag9kDZ6OtR0uEiYhoAAQj27dvR2FhIUaPHo1hw4YF3Xbnzp3YunVrp8fMZjNOPfXUgzk09XeKhuzDipA7pwBxaS3+JNfqrYNR/N1INO1NjvQZEhFRJIMRt9uNSy65BJ988gkmT56MtWvX4oorrsCTTz4JpWMHtA7eeOMN/O1vf8PcuXP9jyUkJDAYoa4UFeMXrEH6+IrODxuAtLEV8mPzwhmo2Z7Fq0dENFCDkcceewxffPEF1q9fL0dERDBy+OGHy0Dj4osvDvhzY8eOxYcffhiO86V+LP/I3TIQ6S6uFUXRxLLgCeevwY9PHCenboiIaADWGXnllVdwwQUX+Kdmpk2bJkc4xOPBtLW14auvvsLy5cvR2MjOrNSVYlCRO2dPt4GIfxvFu93g6cW8hEREAzEYEVM0mzZtwtSpUzs9LgKSdevWBf3Zbdu24fe//z2uvPJK5OTk4B//+EfQ7R0OBxoaGjp9UP+WmFsHS0LoRnliyobdfYmIBug0TVNTEzweD1JTUzs9npaWhrq6uoA/d/TRR6OoqAgZGRny65dfflkGJZMmTcKxxx7b7c88+OCDuO+++/Q/C4p5JpurV7YlIqJ+NDJisVjk/1tavCscfMTXvu91RwQcvkBEEAmvEydOxP/+97+AP3PnnXeivr7e/1FczGH5/s7ZZNW1ncgb0bstERH1s5GRuLg4ZGZmoqSkpNPjIlAYPnx4jw6akpKC8vLygN+3Wq3ygwYOsWS3tSYOtpSWoHkjQvna/L46LSIiirYE1tNOOw3vvvsutPZuZ06nEx988IF8vGMNks8++8z/9b59+zrto6ysDKtXr8aMGWyMRh0pKPpmZNBARFUBd6sZFetyeemIiAbq0t4//OEPmDlzJn76059i/vz5eO2116CqKm677Tb/NuKxRx991J9HMm/ePDlVM336dFRXV8vvjRo1Ctddd134nw3FtIp1ebCntmDI0bugehS5nNdHFD4T/W02/Hs2m+oREQ3kkZGRI0di1apVyM7OliMkYmXNihUrOuWEjBkzptNIyZIlS5Cfn4+PPvoIGzZskPkg4meSkpLC+0yoH1CwZ8lYrHtlDmq2Z8qARHA2WlD0zSisevpoNJWzAisRUX+jaL45lygmlvYmJyfj41EnI97IHiUDhybiE3b2JSKKUc0eF+btXCwXowQbhGCjPIpiioxHiIiof2MwQn3KHOdAfJa3Cm9zRSJcLd2vmjJYXMiaXAqTzY2m8iTU7srss3O0pTQhZ/YeGM0e7NuRhZptg/vs2Ca7EwlZDTIOa65MhKuZq8qIqP9jMEJ9wjaoBcNO2Ib08eX+xFSRE1K9ZTD2LBmDttp47y+kzYmJF61EUn5dp5U1bqcRxd+ORPG3o3rtHOOz6jD5Zytgtrv8x86eUSrPs/i7EShcOrbXjm1NbsWw47chY+Je//XxdSsW16d1X0KvHZuIKNIYjFCvs6c3YdrPl8Fo8XRaISM+F8FJysgqrHv5CDgbrZj9qyUwWj1dlviKUYphx2+HLaUZOz7o3JIgHBJyajH9qu/l5wceWzFocoWPNakN298P/7HFc5p25feysmzH6+PrVuy7Ps0VTPomov6pR6tpiHpOw4SfrO4SiPh/AY2aDD4mnL8aEy9e0W0gIvgeGzytFMlDq8P+Qky9bHmn43R37KyppbCnhrvRo4bxC9Z0CUR8xGMGk4qJF6zyJvQSEfVDDEaoVyUPrUF8RlO3N1r/L6FBQ1xGM5Jy60N27RWGn7QtrOc4aHglDGZV17HHnRe8KWRPJebUIzGnIfj1MWqwpbQiZVRVWI9NRBQtGIxQr0odXemvFxKMyI8IVQZeENskDA5vF+chx+zUdWwhPjO8IyOpY/RdH7FN2ujKsB6biChaMBihXiVyPcJNUcI7XSGmkPRU2xEBi8gfidT1MfTCtSQiigYMRqhXORpsYQ0eRNDgcRkRTm018bpGRrzHNkTk+ojzczTYw3psIqJowWCEelXFep1N7RRxo1d0jVCI5cDhtP2DSbpHRio3hLdJX+WGHGha6EhIjMiwQSAR9VcMRqhXORvtKF+bL3NCAhHfK1+bh9Llw4OOUIiAQWy7e9H4sJ6jx2lBc2XwOh6+Y+/8eEJYjy2Kvu1dOSRoMCSOW7kh21+LhYiov2EwQr1u5ycTsG9blvy8Y7Km7/N927Ow8+OJ2PPlOFRuGuy98Xe4Ofu+Fjdl0bXX3WYJ+zmufmYuHI3e/R54bO8nwJoXjxBZHmE/9u7F41G1KTvg9andnY7tH0wJ+3GJiKIFG+VRH9GQMrIa2TMLkZRbJx9pKB2EvSuHonZXuneepl3K6EoMP36bd+WKAqgug6xEumvROLhbbL16lkOP3YbcwwtgtHiHckQAVFeQji1vTZMjKL1Hw6Dh+5Ajrk9+rXykcW+yvD41O0RXbJ3LfYiIYrBRHoMRIiIiimgwwmkaIiIiiij2polyitGDlBHVMMc74W41y/wB1dXdy6YhMbcOcenN0DwK6otSD3kpqNHixqAR1bJUubPJirrd6dDUQ4tf49IbkZBTLz9v2puMlqrEbrezpTbJKQuj1S23Kf1xGHCIx44F9rQmWZUViobm8iQ0V7IfDRH1fwxGopaG/KN2Ie+I3TDb3f5HPU4jylYOkZ1cNY/RX+V0+ElbZdl1/09rIjE0E7s+nQhHvb3HAdDwE7ch+7AiGM37l8G4WswoXjYCJctG9DiHISG7HiNP3YzkId58CJ/64kHY9ekENO0dJL+2DWrG5Et/lOXPO66sGXHSVpSvy+2VJnnRID6rASNP3YRBwzpfn4aSZOxaNAGNJSkROzciot7GnJGopGHM2euRNaW026WuIqmytiAdm16fifQJezHu3HVytYfo8tppO48CV5sZa54/Eo76ON2ByOSLV8ieMgfuT+5TA8pX52PHR5N0BySJebWY8rPlMBjVrueoig8D1r8yB45GK2bd8BUUo9bleYvjisdqdqZj42uz0Z+IQG3q5T9AMamyT0+X66MpchVRfWFaxM6RiOhgMGckhqWPK8fgqd0HIoK4oYupm9zDd2PsWRv8j3XZzqjJKZYxZ3i30SN3dmHAQETuUwGyDyuWozG6KBrGn7em20DEd96KQcW4BWsw6aIV3QYivuMKqaOqkTZuL/oPDePOXQvF5OkSiPivj7iGC9bI60RE1B/1/0n4GJQze0/QImGSBuQdUSBvUMEKhYmOrykj98GW0qzjyBpyZu0JOeAh6l/kzC7UsT8gZWQVbIPaAgY3gvieLbkNcZnNIcuyixGSYcftQH+RPKxG5vkYQlwfS4ITaWMr+vLUiIj6DIORaKOoSB5aG/Tm7b9Bxbt0zZSIG7ioYRGKbVCrN3AIsU8Z4Mj9ha6hLkZwwt21156mJ7CKDSnD9V0fsc2g4dV9ck5ERH2NwUiUETd6ve3sBV3bamK/oYf4DSb9XWFl91odx9Zz3Eh37Y0kg0kkhfRgWyKifojBSJRR3QY4m/RV+hSjCaqO+EGMorTWxunqIKtrFEMD2uptgI4Gb62yI66eLnShN/Ed2+3oP4vAWmviZJ5MKOIaimtJRNQfMRiJOgr2rhoSMmdEBA2ip4vBGPrm7WiwonaXKCkenMdpRtXGnNABiQbZ3C2cXWk1VYHbYQjZPVd2ztXbCTgGVG7MgebW92dYsS6v18+HiCgSGIxEobIVQ+FqsUBVlcAjIm4Ddn8+DtXbMoMGLuLmvefLsbpGMYSi70bKwCDQPkWgIgqg7V09RHdX2pLvhwfvSqsBpT8Mx56lY/xfB9rO4zKg4Iux6C88DrO85sGI5y1+J5yNvduXh4goUhiMRCFxA1/3yuFwNlrl177AwFtzQkxTmGXdibaaeGx9Zxpq2kc9Oo5oeOt3ALs+G4+K9frfUbdWJ2DDf2bD4zR16p7rOwdxQxQ1Qdyt+pvGiWBI3EwPPEff52KUpUBss3wESr4f5j1ex2O3/180zBM1U1R3/5mmEYq+HoXiZcMDXp/yNXnYtWh8xM6PiKi3sehZFBMFyDImlCNzSiksCQ64W8yo2pSDig05B5SE15A0pBbZM4pkp1vVY5CdZsV0T0+rr/oYLS5kTilDxoS9MNldMjAS0wTVW7P8lV97Ki6zATmHFSExz9u1t7FkkDzHA0ue29MaMeKULbJaq0iUdbeZUb46D4XfjO7XJeFFqfzsmUVIyquVOTRNZckoE9enPDnSp0ZEdFDYtZeIiIgiihVYiYiIKCb0r8n3GJI9uwA5M4pk7Yi22jhse28KnE0H32XXaHFi7DnrEZfRKFevVG/KRuFX3SV6qsiaVoL08eVyCkR0zhU5C93lYaSOKcfQY3fAZHXD1WzBTtnQrmvDNoPZjfRxFbJomkgwrdmZKXNPunJh2lU/IC7DW7SspSoea184HIC5y5aDpxciZ04hjCYP2urs2PbBJDjru+5TNNYT52hJbIO7zSKTZRtLu56jqFQrStjHZTTJBF3R1VhME3W3pjhzSrGcHlOMKporElG4dEy31yd+cD3y5+6C2e6Es8mGom9GonVf912Iw82e2oTU0VWys7Lozly9ZbDM8yEiikXMGeljGRNKMfbcdXJJri8xU6x4EZ+37IvDqqeOFqFFj/Y59efLkJRX59+Pf58qsPvzsSj9wbtaI2NyqexTIzrxHridSJLc8dEU+VhcZj2mX/k9DB069vq4WkxY8cRx8Dgt/s7CQ47aBYPZI2/yoh6GqGtSV5CKbe9NlTdKYfrV3yAhu7HrOWpAU3kC1jx3jD8AmvCT1Z2uj09brR0rnhTbGWEwuTHtymWIz9rfqdhHLGVe9/IR/uaAmZNLMeLkLbKkukgK9Z1jc0UCtr0/xd8xOH3cXow9Zx2MlgOujyaWE+dg23vT5GOWhFZMu/J7WJPbuhxbBFhrXjwCqrw+4WdJaJNNFFNHVvub6ImgUqyuKvl+BAqXju5xR2Uiot7CnJEolDq2HBMvWB20cqqj0YLlfz9J9z5nXPs1Erq5IQu+G+ruRePgbLFi3Dnruj22ryNu5cZs7FkyGrNu/DrgOcpVLm4F3/7lFIw4cRvyDt/T7bHFTV8sT17z/FxMvPhHJGQ2BTy20FwZj92LxmPypSsDHltwNpvww99PwpxffymDi0DnKJJ4Vzx+LNLGVGH0GRv9z7Fr0TgD1r18OKzJrZjwkzVBjy2WUW9/bwrm3PylDNQCHdvdasbyR48P+6ofc7wD06/+TiYzi0q93R1bBpUfTmZAQkRRgcFIFJp7x6cBb2Id7Vq0fzQjmEHDK+XNO9j+5M1e3JzdhpDHFts6Gy2wJHZ/k++ovjgZyfn1QbcRAUnVpnRkTq6SXwfapy8g8bgUGM2hy+HXlyQhOa8h6DZinw0lyUjMaej2xu0/RxVoqUyEbVALjFZPyOvTUDwISfl1IbcrX5uLHR9MRTiNmrcB2dNLQlZsXfevOagvTAvrsYmIDgYTWKNM8vAqXYGIuJENOXqXrn2OPmNTyG3E8cSUhJh60NPHRk8gIs4xKa8+ZKVWEQRkTKzynkOQTX3f1xOIyGPnNuiq1CrOMVQpetEtN2FwI0y24IGIjwhE9MictBfhJJZaZ00tDRmIyI7Ks/R1VCYiihb9t2hDlMmZUay7K63J5ta1T2tSq659diwgpuf4ercJNuLg013uR3e6m0YJdmzd24bx+viCplDH9gZWqsxrCRcRMIl9hiJek+ShNWE7LhFRX2Aw0lcM0d9xNezdgge4sHbZFV2Se9JRmYgohjAY6SM1OzJ1bSeTL9367vSiMmlP3tHrObbeUQxvkmjfRyQHlogPuW2kro8qXp/wJbC2VseHbJ4oj62KVUJ9s7yYiChcGIz0kYq1Q6B6ENautIVfjdKVY+G7Oeo5tsdh1HXDFctsQ03TiGPWFum/Meq9Pm0N3p49QY+tAa01cTJfJtR2jnqr/mPX2nVdn5pd6WH98xJ1TESX5kDNE/3naPA21SMiiiUMRvpQ8fcjQnalFaMiOz6aqGt/e1cOh7stcPDge7xq02BZDKxjjY/uthV1N7a+610BEmqf6/49E7UFaQHfrXsb9RlQ8NlkGeCE4nEaUfj16NDXxwOsefFwaB4l5DmKOielPw4Nup24JrsWTUDxt97VS8G2bdlnx+aFM+RoS7DtRL2VHR9NQrjtWTra+7wDXnMF9cWDsG9bVtiPTUTUmxiM9KHCL8fJhm9dRi18XWndCn78h7eol16inoVvOqDj/nz7rN2Zjq3vzEDBF+NRtjK/2+1kIFKZgFXPHYma7YOx85P9HWIP3J+4Ea/712w46xKx6Y3DsK99+klM2fhGYATZWfg/s2RBsWV/OQFup6Hb48q6HE4Dlv35BBR/MxqlPw4JeGxxI1751DFwN8Zj9XNz4XEZu99OBTa9OQONJSnY9dkElLUHJOJm3XGUSCx33vruFFRv8VarLV3euWNwx32KaZLVzxyN5opkbHx9ptxX96+hAWtfPBzOBm/BtXBqqUzC+lfnwNXeMVksS+44XVZbkIqNr82SQSARUSxhBdYIEBU8x523Fok59eIVkGW8xTvz0uXekZODkTGlWBYh863EaamOx9a3p6G1pvM0iSWpBSNP3YKk3Dq50kSUWt/zxVjUFx1Ql8LoxPhzNiJlVBUMRhWq2yhrZ4gCagcGS6Is+uCpJbAOaoXqMsr8mKrNg7t0982cWohRp2/xrwoRpeNF4FO5rvO0gtHeigkL1rd3r9XgcZlQ+v0wFH/nHTnZT0XOrCK5lNVkc8ngpGpjDvZ8NRJQO+driDoig2cUIy7dVw4+RXYh9jjMXV6bEaduQXK+t3Ouo96GgiVjUF+Q0fnQBhVDjt6JrMmlMFo8cDtMsgNx6Q8ioDH0ejdnUX4/bUwFDBYPnA02lK/LQ1OZt5IsEVG0YNEzIiIiiigWPSMiIqKYwDafYSS612ZO3Iv4wd5S5Y1lyajalN1luiJWJOTUye6+JqsLrmYrKjfkoLWma+fcQSMqMeq0zTDHOWW+gujhslMm4Rr7pHtt5uQy2bdF5KmI7rWcriAiii3MGQmTwdOLMOKULTJ/wJfcKJa+iuRSsbKialMOYoWo7Dr+/NWdSr57y8prqN6aiW3vToXHaYbR4sTsXy2Fyd650qhv1Y7ILzmUPJhQ5dHHnr0e6eMr/ImpvmsuetJseWuGv2MwERFFBqdp+tDgGUUYc+ZGGYiIG7G4IfpqcBitboxfsBYZE8sQC8xxDky94nskZHtHd3zPxVfVM21MJSZfsgKK0YnDb/1SBiIdy6R3rHsy4pStyJpW1CsJnJMuWYG0sRXerw1ap2suzn3qz7+Xz4WIiKIf1wAeIqPFjZGnbJGfd1eAzDdKMOr0TfImGu3yjtwNa1L3Lep9RbUS8+ow6eJVQZvv+R4XzzvcxLRMUl5dwIJm4tytiQ7kz90d9mMTEVH4MRg5RJmTS2EwBw8yxI3ZHOeSyzGjmQiWskVDv1C9TTRg0NBaXRVLDSZN5pSEU67oShvq2AYNg6cXx0QASEQ00DEYOURiSsCXIxKMyL1IEHVFophtUKuujsFyREJnPxexTdaUUoSPhvishpBl3gXxXGwprWE8NhERDdjVNFr7W/AWNXwt2cOl0aHC3qrBEOLmqKoaGp2qTOaJVh63Gw0t+rb1lVLXe43C97w1NLTqP3aT2422KL7mRET9me++7buPx/RqmpKSEuTne0uZExERUWwpLi5GXt7+digxGYyoqoqysjIkJiZC0fmWuKGhQQYw4gIkJSX1+jmSfnxtohdfm+jF1yZ68bUJTIQYjY2NyMnJgSHIFEJMTNOIJxAsogpGBCIMRqITX5voxdcmevG1iV58bbqXnJyMUJjASkRERBHFYISIiIgiqt8GI1arFX/84x/l/ym68LWJXnxtohdfm+jF1+bQxUQCKxEREfVf/XZkhIiIiGIDgxEiIiKKKAYjREREFFExUWfkYK1cuRI7d+7E8ccfj6ysrEifzoD35ptvygJ2Hc2YMQNjxowZ8NcmWqxbtw6FhYWYNm0ahgwZEunTGfDWr1+PzZs3d3sdLrjggqBFpKjv/mb27NmDQYMGYdasWYiLi+OlPwj9NhgRQcjpp5+O6upqLF68mMFIFLj44otx1FFHYfDgwZ2K4TAYibzy8nIsWLAABQUFmDNnDrZv344rrrgCt99+e6RPbUDbtGkT3nvvvU6Pff311zKoF8EIRU5dXR3mzZuH3bt3y78ZEZCUlpbKN10nnHACX5oe6pfBiNPpxEUXXYTf/va38oOixx133IHTTjst0qdBHYgFdeeccw5sNpsM4sU7O3Gz+/jjj3mdIuynP/2p/PBpa2tDdnY2rrvuOo6KRNjTTz+Nbdu2YdeuXXJURBCv1S233CJHS6hnDP31hjd+/PhOf8QUHTZs2IB3331X/rEeOGVDkfHFF19g+fLl+Pvf/+4fYhbD/2eccQZfkijz9ttvo76+HldddVWkT2XAczgcSElJ8QciwvDhw+WbYeq5fjcyIt7NiZvd2rVrZfMiih6iyeErr7wicxFWrFgh/y+GNEeMGBHpUxvQxLB/eno6Jk2ahCVLlsh/ZKdMmSIbW1F0eeGFF2QO3KhRoyJ9KgPeDTfcgEWLFsnp51NOOUXmWr3++ut49tlnB/y1wUAPRvbu3SvfMSxcuFA2LGIwEl3ef/99mccjiNdGTNf87Gc/w3fffRfpUxvQKisrZUdskc9jt9thMpnka/KHP/wBd955Z6RPj9qJ3ISlS5fitdde4zWJAuIec8QRR8j7TVNTk8wZESMjw4YNi/SpxaR+FYyIZLvRo0ejpKQEb7zxBmpqauTj4g/YbDbj2GOPjfQpDmi+QMT3hyxer/POO0++TqmpqRE9t4FM5IqIxNW7774bV155pXzsnXfewfnnny9fM7GyhiLvxRdflH8n5557bqRPhQDcc889ctpMrHjyTdWIXB7xNyNySYxGI6/TQM0ZEcPMYmhZTNOIj88++0w+Lt7lff/995E+PTqACEiEqqoqXpsIGjlypPy/WE3jc/bZZ8u8kdWrV0fwzMjH4/Hg5ZdfxmWXXcZ+W1Hiq6++wsknn9wpZ0QE8CKhtbi4OKLnFotM/S1xtSMxQiKmBu666y6cdNJJETsvAioqKpCZmSnzRnzEu2/xTs93M6TImD9/Pm6++WZs3bpVLlEUduzYIW+A+fn5fFmigHhjJZaNXn311ZE+FWon/ja2bNnS6XqIr8WISMfyBTQAgxGKXt9++y3++te/ynfcGRkZ+Pzzz+XolRh6FjkKFDkigVhMmYm6FWJZong9Hn/8cfmu78QTT+RLEyWJq3PnzsWECRMifSrUTpSNOOaYY+Tfjch/E1Odjz76qHxcTH1Sz/Trrr0iF+H666+Xc+FiCociX8BJ5PKIRGOR6CWy0MX/KTqI4lofffSRDEbECMmll17Kee8o4HK5ZGK+qJ0kimxR9BBJq//617/kShqxzFesqjn11FMjfVoxqV8HI0RERBT9+lUCKxEREcUeBiNEREQUUQxGiIiIKKIYjBAREVFEMRghIiKiiGIwQkRERBHFYISIiIgiisEIERERRRSDESIiIoooBiNEREQUUQxGiIiIKKIYjBAREREi6f8DkkNp3LtDk6YAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#1h)\n",
-    "X_0_3 = iris_data[:,[0,3]]\n",
-    "clf.fit(X_0_3, T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, clf.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "892baf25",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X_0_3 = iris_data[:,[0,3]]\n",
-    "svc.fit(X_0_3, T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, svc.predict) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "1bad3e36",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-=======
-      "image/png": "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",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-<<<<<<< HEAD
-     "data": {
-      "image/png": "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",
-=======
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.07437258 -0.0291456   0.0103735   0.04848806  0.07131768  0.17819428\n",
-      "  0.25802648  0.31521741  0.36927952  0.36935855  0.41688375  0.45390939\n",
-      "  0.45908454  0.46383508  0.51238855  0.55388473  0.70604178  0.81235693\n",
-      "  0.81535647  0.85499255  0.85620981  0.87946314  1.05851877  1.08531376\n",
-      "  1.27339292  1.30028357  1.35243501  1.48056341  1.54300275  1.56211567\n",
-      "  1.57747659  1.64940532  1.79914906  1.81960638  1.82765485  1.84872133\n",
-      "  1.86283603  2.02884723  2.09997946  2.17808643  2.25646145  2.43070493\n",
-      "  2.50678657  2.58556763  2.69609076  2.83744691  2.96707657  2.97306828\n",
-      "  3.02194413  3.03595981]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 3c)\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,reg.predict(X.reshape(-1,1)))\n",
-    "plt.show()\n",
-    "print(reg.predict(X.reshape(-1,1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "94ab4bd3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-<<<<<<< HEAD
-    "# 1i)\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "knc = KNeighborsClassifier()\n",
-    "knc.fit(X,T)\n",
-    "nnwplot.plotTwoFeatures(X, T, knc.predict)\n",
-    "knc.fit(X_0_3,T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, knc.predict)\n",
-    "\n"
-=======
-    "# 3d) Decision Tree Regressor\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "dtr = DecisionTreeRegressor()\n",
-    "dtr.fit(X.reshape(-1,1),T)\n",
-    "\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,dtr.predict(X.reshape(-1,1)))\n",
-    "plt.show()"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   ]
-  },
-  {
-   "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 101,
-   "id": "d79741d2",
-=======
-   "execution_count": 20,
-   "id": "475aa6e4",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-<<<<<<< HEAD
-      "text/plain": [
-       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 3, 3, 3, 1, 3, 1, 3, 1, 3, 1, 1, 1, 1, 3, 1, 3,\n",
-       "       1, 1, 3, 1, 3, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 3, 1, 1, 3, 3,\n",
-       "       1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 2, 3, 3, 2, 1, 2, 3, 2,\n",
-       "       3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 2, 3, 3, 3, 2, 2, 2,\n",
-       "       3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3], dtype=int32)"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 2a) probabilistisches Verfahren\n",
-    "from sklearn.cluster import KMeans\n",
-    "\n",
-    "X_0_2 = iris_data[:,[0,2]]\n",
-    "km = KMeans(4)\n",
-    "km.fit(X_0_2)\n",
-    "km.predict(X_0_2)\n"
-=======
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3d) SV Regressor\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "svr = SVR()\n",
-    "svr.fit(X.reshape(-1,1),T)\n",
-    "\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,svr.predict(X.reshape(-1,1)))\n",
-    "plt.show()"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-<<<<<<< HEAD
-   "display_name": "nnw (3.14.0)",
-=======
-   "display_name": "nnw (3.14.8)",
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-<<<<<<< HEAD
-   "version": "3.14.0"
-=======
-   "version": "3.14.8"
->>>>>>> 81a61b91621992cca635346d6ee1433a01c31f30
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Lösungen_N/AB2_BASE_1208.ipynb b/Lösungen_N/AB2_BASE_1208.ipynb
deleted file mode 100644
index e69de29..0000000
diff --git a/Lösungen_N/AB2_BASE_715.ipynb b/Lösungen_N/AB2_BASE_715.ipynb
deleted file mode 100644
index e69de29..0000000
diff --git a/Lösungen_N/AB2_LOCAL_1208.ipynb b/Lösungen_N/AB2_LOCAL_1208.ipynb
deleted file mode 100644
index bfe7dbf..0000000
--- a/Lösungen_N/AB2_LOCAL_1208.ipynb
+++ /dev/null
@@ -1,1517 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "29e20c6b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.svm import LinearSVC"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "67b3d7af",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[5.1 3.5 1.4 0.2 0. ]\n",
-      " [4.9 3.  1.4 0.2 0. ]\n",
-      " [4.7 3.2 1.3 0.2 0. ]\n",
-      " [4.6 3.1 1.5 0.2 0. ]\n",
-      " [5.  3.6 1.4 0.2 0. ]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 1a) Ja stehen in der richtigen Dimension\n",
-    "iris_data= np.loadtxt(\"../supplement_files/iris.csv\", delimiter=\",\")\n",
-    "print(iris_data[:5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "c9c0fd1e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>.sk-global {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       ".sk-global.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       ".sk-global.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       ".sk-global {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".sk-global pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip-path: inset(100%);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       ".sk-global div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       ".sk-global label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label label.sk-toggleable__label,\n",
-       ".sk-global div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       ".sk-global div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       ".sk-global div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       ".sk-global div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       ".sk-global a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       ".sk-global a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-top-container.sk-global {\n",
-       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
-       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
-       "   to allow keyboard-only users to navigate the display. */\n",
-       "  overflow-x: scroll !important;\n",
-       "  max-width: 100%;\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover td {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table :is(td, th) {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
-       "*/\n",
-       ".estimator-table table td.param {\n",
-       "    text-align: left;\n",
-       "    position: relative;\n",
-       "    padding: 0;\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".default td, .estimator-table th {\n",
-       "    color: black;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       "td.fitted-att-type {\n",
-       "    white-space: preserve nowrap;\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    Styles for parameter documentation links\n",
-       "    We need styling for visited so jupyter doesn't overwrite it\n",
-       "*/\n",
-       "a.param-doc-link,\n",
-       "a.param-doc-link:link,\n",
-       "a.param-doc-link:visited {\n",
-       "    text-decoration: underline dashed;\n",
-       "    text-underline-offset: .3em;\n",
-       "    color: inherit;\n",
-       "    display: block;\n",
-       "    padding: .5em;\n",
-       "}\n",
-       "\n",
-       "@supports(anchor-name: --doc-link) {\n",
-       "    a.param-doc-link,\n",
-       "    a.param-doc-link:link,\n",
-       "    a.param-doc-link:visited {\n",
-       "    anchor-name: --doc-link;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
-       "a.param-doc-link::before {\n",
-       "    position: absolute;\n",
-       "    content: \"\";\n",
-       "    inset: 0;\n",
-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
-       "    display: none;\n",
-       "    position: absolute;\n",
-       "    z-index: 9999;\n",
-       "    left: 0;\n",
-       "    padding: .5ex;\n",
-       "    margin-left: 1.5em;\n",
-       "    color: var(--sklearn-color-text);\n",
-       "    box-shadow: .3em .3em .4em #999;\n",
-       "    width: max-content;\n",
-       "    text-align: left;\n",
-       "    max-height: 10em;\n",
-       "    overflow-y: auto;\n",
-       "\n",
-       "    /* unfitted */\n",
-       "    background: var(--sklearn-color-unfitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       "@supports(position-area: center right) {\n",
-       "    .param-doc-description {\n",
-       "    position-area: center right;\n",
-       "    position: fixed;\n",
-       "    margin-left: 0;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
-       "    /* fitted */\n",
-       "    background: var(--sklearn-color-fitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "    border: none;\n",
-       "    padding: 0;\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon:focus-visible {\n",
-       "    outline: 1px solid currentColor;\n",
-       "    outline-offset: 1px;\n",
-       "}\n",
-       "\n",
-       ".features {\n",
-       "  font-family: monospace;\n",
-       "  cursor: pointer;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
-       "}\n",
-       "\n",
-       ".features.fitted {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features summary {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  margin-bottom: 0;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "  padding: .25em;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features.fitted details[open] > summary {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features details > summary .arrow::before {\n",
-       "  content: \"▸\";\n",
-       "  color: grey;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary .arrow::before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       ".features details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features.fitted details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features .features-container {\n",
-       "  max-height: 10em;\n",
-       "  overflow: auto;\n",
-       "  scrollbar-width: thin;\n",
-       "  padding: .25em 0.1rem;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 0 0 .5em .5em;\n",
-       "}\n",
-       "\n",
-       ".features.fitted .features-container {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features .image-container {\n",
-       "  block-size: 1em;\n",
-       "  inline-size: 1em;\n",
-       "  padding: 0;\n",
-       "  margin: 0%;\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  align-items: center;\n",
-       "}\n",
-       "\n",
-       ".features .copy-paste-icon {\n",
-       "  background-size: 1em 1em;\n",
-       "  width: 1em;\n",
-       "  height: 1em;\n",
-       "  filter: grayscale(100%) opacity(60%);\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  width: 100%;\n",
-       "  margin: 0.01em;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(odd) {\n",
-       "  background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(even) {\n",
-       "  background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:hover {\n",
-       "  background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  table-layout: inherit;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table td {\n",
-       "  text-align: left;\n",
-       "  padding: 0 0.5em;\n",
-       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "  white-space: nowrap;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".total_features {\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  margin-top: 0.5em;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-13\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy penalty to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-penalty;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%7D%2C%20default%3D%27l2%27\">\n",
-       "            penalty\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-penalty;\">\n",
-       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;}, default=&#x27;l2&#x27;<br><br>Specifies the norm used in the penalization. The &#x27;l2&#x27;<br>penalty is the standard used in SVC. The &#x27;l1&#x27; leads to ``coef_``<br>vectors that are sparse.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy loss to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('loss',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-loss;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=loss,-%7B%27hinge%27%2C%20%27squared_hinge%27%7D%2C%20default%3D%27squared_hinge%27\">\n",
-       "            loss\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-loss;\">\n",
-       "            loss: {&#x27;hinge&#x27;, &#x27;squared_hinge&#x27;}, default=&#x27;squared_hinge&#x27;<br><br>Specifies the loss function. &#x27;hinge&#x27; is the standard SVM loss<br>(used e.g. by the SVC class) while &#x27;squared_hinge&#x27; is the<br>square of the hinge loss. The combination of ``penalty=&#x27;l1&#x27;``<br>and ``loss=&#x27;hinge&#x27;`` is not supported.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;squared_hinge&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy dual to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-dual;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=dual,-%22auto%22%20or%20bool%2C%20default%3D%22auto%22\">\n",
-       "            dual\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-dual;\">\n",
-       "            dual: &quot;auto&quot; or bool, default=&quot;auto&quot;<br><br>Select the algorithm to either solve the dual or primal<br>optimization problem. Prefer dual=False when n_samples &gt; n_features.<br>`dual=&quot;auto&quot;` will choose the value of the parameter automatically,<br>based on the values of `n_samples`, `n_features`, `loss`, `multi_class`<br>and `penalty`. If `n_samples` &lt; `n_features` and optimizer supports<br>chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,<br>otherwise it will be set to False.<br><br>.. versionchanged:: 1.3<br>   The `&quot;auto&quot;` option is added in version 1.3 and will be the default<br>   in version 1.5.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy tol to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-tol;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy C to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-C;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
-       "            C\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-C;\">\n",
-       "            C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>For an intuitive visualization of the effects of scaling<br>the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy multi_class to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-multi_class;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=multi_class,-%7B%27ovr%27%2C%20%27crammer_singer%27%7D%2C%20default%3D%27ovr%27\">\n",
-       "            multi_class\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-multi_class;\">\n",
-       "            multi_class: {&#x27;ovr&#x27;, &#x27;crammer_singer&#x27;}, default=&#x27;ovr&#x27;<br><br>Determines the multi-class strategy if `y` contains more than<br>two classes.<br>``&quot;ovr&quot;`` trains n_classes one-vs-rest classifiers, while<br>``&quot;crammer_singer&quot;`` optimizes a joint objective over all classes.<br>While `crammer_singer` is interesting from a theoretical perspective<br>as it is consistent, it is seldom used in practice as it rarely leads<br>to better accuracy and is more expensive to compute.<br>If ``&quot;crammer_singer&quot;`` is chosen, the options loss, penalty and dual<br>will be ignored.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Whether or not to fit an intercept. If set to True, the feature vector<br>is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where<br>1 corresponds to the intercept. If set to False, no intercept will be<br>used in calculations (i.e. data is expected to be already centered).</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy intercept_scaling to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_scaling,-float%2C%20default%3D1.0\">\n",
-       "            intercept_scaling\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
-       "            intercept_scaling: float, default=1.0<br><br>When `fit_intercept` is True, the instance vector x becomes ``[x_1,<br>..., x_n, intercept_scaling]``, i.e. a &quot;synthetic&quot; feature with a<br>constant value equal to `intercept_scaling` is appended to the instance<br>vector. The intercept becomes intercept_scaling * synthetic feature<br>weight. Note that liblinear internally penalizes the intercept,<br>treating it like any other term in the feature vector. To reduce the<br>impact of the regularization on the intercept, the `intercept_scaling`<br>parameter can be set to a value greater than 1; the higher the value of<br>`intercept_scaling`, the lower the impact of regularization on it.<br>Then, the weights become `[w_x_1, ..., w_x_n,<br>w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent<br>the feature weights and the intercept weight is scaled by<br>`intercept_scaling`. This scaling allows the intercept term to have a<br>different regularization behavior compared to the other features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy class_weight to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
-       "            class_weight\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
-       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Set the parameter C of class i to ``class_weight[i]*C`` for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy verbose to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-verbose;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
-       "            verbose\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-verbose;\">\n",
-       "            verbose: int, default=0<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in liblinear that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy random_state to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-random_state;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
-       "            random_state\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-random_state;\">\n",
-       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>the dual coordinate descent (if ``dual=True``). When ``dual=False`` the<br>underlying implementation of :class:`LinearSVC` is not random and<br>``random_state`` has no effect on the results.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy max_iter to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=max_iter,-int%2C%20default%3D1000\">\n",
-       "            max_iter\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
-       "            max_iter: int, default=1000<br><br>The maximum number of iterations to be run.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1000</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    \n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Fitted attributes</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                    <tbody>\n",
-       "                        <tr>\n",
-       "                        <th>Name</th>\n",
-       "                        <th>Type</th>\n",
-       "                        <th>Value</th>\n",
-       "                        </tr>\n",
-       "                        \n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-classes_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
-       "            classes_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-classes_;\">\n",
-       "            classes_: ndarray of shape (n_classes,)<br><br>The unique classes labels.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
-       "           <td>[0.,1.,2.]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=coef_,-ndarray%20of%20shape%20%281%2C%20n_features%29%20if%20n_classes%20%3D%3D%202%20%20%20%20%20%20%20%20%20%20%20%20%20else%20%28n_classes%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: ndarray of shape (1, n_features) if n_classes == 2             else (n_classes, n_features)<br><br>Weights assigned to the features (coefficients in the primal<br>problem).<br><br>``coef_`` is a readonly property derived from ``raw_coef_`` that<br>follows the internal memory layout of liblinear.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3, 2)</td>\n",
-       "           <td>[[-1.54, 2.28],\n",
-       " [ 0.1 ,-0.98],\n",
-       " [ 0.66,-0.35]]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20if%20n_classes%20%3D%3D%202%20else%20%28n_classes%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: ndarray of shape (1,) if n_classes == 2 else (n_classes,)<br><br>Constants in decision function.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
-       "           <td>[ 1.2 , 2.02,-3.14]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_features_in_,-int\">\n",
-       "            n_features_in_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
-       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>2</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_iter_,-int\">\n",
-       "            n_iter_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
-       "            n_iter_: int<br><br>Maximum number of iterations run across all classes.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>8</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "                    </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
-       " SPDX-License-Identifier: BSD-3-Clause\n",
-       "*/\n",
-       "\n",
-       "function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "\n",
-       "    const parent = element.parentElement;\n",
-       "    if (!parent || !parent.nextElementSibling) {\n",
-       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    const paramName = element.parentElement.nextElementSibling\n",
-       "        .textContent.trim().split(' ')[0];\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "\n",
-       "/**\n",
-       " * Copy the list of feature names formatted as a Python list.\n",
-       " *\n",
-       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
-       " *   contain a `details` element and a table containing feature named.\n",
-       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
-       " */\n",
-       "function copyFeatureNamesToClipboard(element) {\n",
-       "    var detailsElem = element.closest('.features').querySelector('details');\n",
-       "    var wasOpen = detailsElem.open;\n",
-       "    detailsElem.open = true;\n",
-       "    var content = element.closest('.features').querySelector('tbody')\n",
-       "                  .innerText.trim();\n",
-       "    if (!wasOpen) detailsElem.open = false;\n",
-       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
-       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
-       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
-       "    const originalStyle = element.style;\n",
-       "    const copyMark = document.createElement('span');\n",
-       "    copyMark.innerHTML = '✔';\n",
-       "    copyMark.style.color = 'blue';\n",
-       "    copyMark.style.fontSize = '1em';\n",
-       "\n",
-       "    navigator.clipboard.writeText(formattedText)\n",
-       "        .then(() => {\n",
-       "            element.style.display = 'none';\n",
-       "            element.parentElement.appendChild(copyMark);\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                copyMark.remove();\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'orange';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "/**\n",
-       " * Adapted from Skrub\n",
-       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
-       " * @returns \"light\" or \"dark\"\n",
-       " */\n",
-       "function detectTheme(element) {\n",
-       "    const body = document.querySelector('body');\n",
-       "\n",
-       "    // Check VSCode theme\n",
-       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
-       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
-       "\n",
-       "    if (themeKindAttr && themeNameAttr) {\n",
-       "        const themeKind = themeKindAttr.toLowerCase();\n",
-       "        const themeName = themeNameAttr.toLowerCase();\n",
-       "\n",
-       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
-       "            return \"dark\";\n",
-       "        }\n",
-       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
-       "            return \"light\";\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    // Check Jupyter theme\n",
-       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
-       "        return 'dark';\n",
-       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
-       "        return 'light';\n",
-       "    }\n",
-       "\n",
-       "    // Guess based on a parent element's color\n",
-       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
-       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
-       "    if (match) {\n",
-       "        const [r, g, b] = [\n",
-       "            parseFloat(match[1]),\n",
-       "            parseFloat(match[2]),\n",
-       "            parseFloat(match[3])\n",
-       "        ];\n",
-       "\n",
-       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
-       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
-       "\n",
-       "        if (luma > 180) {\n",
-       "            // If the text is very bright we have a dark theme\n",
-       "            return 'dark';\n",
-       "        }\n",
-       "        if (luma < 75) {\n",
-       "            // If the text is very dark we have a light theme\n",
-       "            return 'light';\n",
-       "        }\n",
-       "        // Otherwise fall back to the next heuristic.\n",
-       "    }\n",
-       "\n",
-       "    // Fallback to system preference\n",
-       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
-       "}\n",
-       "\n",
-       "\n",
-       "function forceTheme(elementId) {\n",
-       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
-       "    if (estimatorElement === null) {\n",
-       "        console.error(`Element with id ${elementId} not found.`);\n",
-       "    } else {\n",
-       "        const theme = detectTheme(estimatorElement);\n",
-       "        estimatorElement.classList.add(theme);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "forceTheme('sk-container-id-13');</script></body>"
-      ],
-      "text/plain": [
-       "LinearSVC()"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 1b)\n",
-    "X = iris_data[:,[0,1]]\n",
-    "T = iris_data[:,4]\n",
-    "clf.fit(X, T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "cc181a3a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "80.0 Prozent wurden richtig vorausgesagt\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 1 c+d)\n",
-    "pred = clf.predict(X)\n",
-    "correct_perc = (sum(pred == T)/len(pred))*100\n",
-    "\n",
-    "print(f\"{correct_perc} Prozent wurden richtig vorausgesagt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "16af32f1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1e)\n",
-    "import nnwplot\n",
-    "nnwplot.plotTwoFeatures(X, T, clf.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "f4bcee12",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1f)\n",
-    "from sklearn.svm import SVC\n",
-    "svc = SVC()\n",
-    "svc.fit(X, T)\n",
-    "nnwplot.plotTwoFeatures(X, T, svc.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "2c8991de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1g)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "1a5e84f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#1h)\n",
-    "X_0_3 = iris_data[:,[0,3]]\n",
-    "clf.fit(X_0_3, T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, clf.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "892baf25",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X_0_3 = iris_data[:,[0,3]]\n",
-    "svc.fit(X_0_3, T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, svc.predict) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "1bad3e36",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1i)\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "knc = KNeighborsClassifier()\n",
-    "knc.fit(X,T)\n",
-    "nnwplot.plotTwoFeatures(X, T, knc.predict)\n",
-    "knc.fit(X_0_3,T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, knc.predict)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "d79741d2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 3, 3, 3, 1, 3, 1, 3, 1, 3, 1, 1, 1, 1, 3, 1, 3,\n",
-       "       1, 1, 3, 1, 3, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 3, 1, 1, 3, 3,\n",
-       "       1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 2, 3, 3, 2, 1, 2, 3, 2,\n",
-       "       3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 2, 3, 3, 3, 2, 2, 2,\n",
-       "       3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3], dtype=int32)"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 2a) probabilistisches Verfahren\n",
-    "from sklearn.cluster import KMeans\n",
-    "\n",
-    "X_0_2 = iris_data[:,[0,2]]\n",
-    "km = KMeans(4)\n",
-    "km.fit(X_0_2)\n",
-    "km.predict(X_0_2)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "nnw (3.14.0)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.14.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Lösungen_N/AB2_LOCAL_715.ipynb b/Lösungen_N/AB2_LOCAL_715.ipynb
deleted file mode 100644
index bfe7dbf..0000000
--- a/Lösungen_N/AB2_LOCAL_715.ipynb
+++ /dev/null
@@ -1,1517 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "29e20c6b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.svm import LinearSVC"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "id": "67b3d7af",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[5.1 3.5 1.4 0.2 0. ]\n",
-      " [4.9 3.  1.4 0.2 0. ]\n",
-      " [4.7 3.2 1.3 0.2 0. ]\n",
-      " [4.6 3.1 1.5 0.2 0. ]\n",
-      " [5.  3.6 1.4 0.2 0. ]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 1a) Ja stehen in der richtigen Dimension\n",
-    "iris_data= np.loadtxt(\"../supplement_files/iris.csv\", delimiter=\",\")\n",
-    "print(iris_data[:5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "c9c0fd1e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>.sk-global {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       ".sk-global.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       ".sk-global.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       ".sk-global {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".sk-global pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip-path: inset(100%);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       ".sk-global div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       ".sk-global label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label label.sk-toggleable__label,\n",
-       ".sk-global div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       ".sk-global div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       ".sk-global div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       ".sk-global div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       ".sk-global a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       ".sk-global a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-top-container.sk-global {\n",
-       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
-       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
-       "   to allow keyboard-only users to navigate the display. */\n",
-       "  overflow-x: scroll !important;\n",
-       "  max-width: 100%;\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover td {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table :is(td, th) {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
-       "*/\n",
-       ".estimator-table table td.param {\n",
-       "    text-align: left;\n",
-       "    position: relative;\n",
-       "    padding: 0;\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".default td, .estimator-table th {\n",
-       "    color: black;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       "td.fitted-att-type {\n",
-       "    white-space: preserve nowrap;\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    Styles for parameter documentation links\n",
-       "    We need styling for visited so jupyter doesn't overwrite it\n",
-       "*/\n",
-       "a.param-doc-link,\n",
-       "a.param-doc-link:link,\n",
-       "a.param-doc-link:visited {\n",
-       "    text-decoration: underline dashed;\n",
-       "    text-underline-offset: .3em;\n",
-       "    color: inherit;\n",
-       "    display: block;\n",
-       "    padding: .5em;\n",
-       "}\n",
-       "\n",
-       "@supports(anchor-name: --doc-link) {\n",
-       "    a.param-doc-link,\n",
-       "    a.param-doc-link:link,\n",
-       "    a.param-doc-link:visited {\n",
-       "    anchor-name: --doc-link;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
-       "a.param-doc-link::before {\n",
-       "    position: absolute;\n",
-       "    content: \"\";\n",
-       "    inset: 0;\n",
-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
-       "    display: none;\n",
-       "    position: absolute;\n",
-       "    z-index: 9999;\n",
-       "    left: 0;\n",
-       "    padding: .5ex;\n",
-       "    margin-left: 1.5em;\n",
-       "    color: var(--sklearn-color-text);\n",
-       "    box-shadow: .3em .3em .4em #999;\n",
-       "    width: max-content;\n",
-       "    text-align: left;\n",
-       "    max-height: 10em;\n",
-       "    overflow-y: auto;\n",
-       "\n",
-       "    /* unfitted */\n",
-       "    background: var(--sklearn-color-unfitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       "@supports(position-area: center right) {\n",
-       "    .param-doc-description {\n",
-       "    position-area: center right;\n",
-       "    position: fixed;\n",
-       "    margin-left: 0;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
-       "    /* fitted */\n",
-       "    background: var(--sklearn-color-fitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "    border: none;\n",
-       "    padding: 0;\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon:focus-visible {\n",
-       "    outline: 1px solid currentColor;\n",
-       "    outline-offset: 1px;\n",
-       "}\n",
-       "\n",
-       ".features {\n",
-       "  font-family: monospace;\n",
-       "  cursor: pointer;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
-       "}\n",
-       "\n",
-       ".features.fitted {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features summary {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  margin-bottom: 0;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "  padding: .25em;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features.fitted details[open] > summary {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features details > summary .arrow::before {\n",
-       "  content: \"▸\";\n",
-       "  color: grey;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary .arrow::before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       ".features details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features.fitted details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features .features-container {\n",
-       "  max-height: 10em;\n",
-       "  overflow: auto;\n",
-       "  scrollbar-width: thin;\n",
-       "  padding: .25em 0.1rem;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 0 0 .5em .5em;\n",
-       "}\n",
-       "\n",
-       ".features.fitted .features-container {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features .image-container {\n",
-       "  block-size: 1em;\n",
-       "  inline-size: 1em;\n",
-       "  padding: 0;\n",
-       "  margin: 0%;\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  align-items: center;\n",
-       "}\n",
-       "\n",
-       ".features .copy-paste-icon {\n",
-       "  background-size: 1em 1em;\n",
-       "  width: 1em;\n",
-       "  height: 1em;\n",
-       "  filter: grayscale(100%) opacity(60%);\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  width: 100%;\n",
-       "  margin: 0.01em;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(odd) {\n",
-       "  background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(even) {\n",
-       "  background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:hover {\n",
-       "  background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  table-layout: inherit;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table td {\n",
-       "  text-align: left;\n",
-       "  padding: 0 0.5em;\n",
-       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "  white-space: nowrap;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".total_features {\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  margin-top: 0.5em;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-13\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy penalty to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-penalty;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%7D%2C%20default%3D%27l2%27\">\n",
-       "            penalty\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-penalty;\">\n",
-       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;}, default=&#x27;l2&#x27;<br><br>Specifies the norm used in the penalization. The &#x27;l2&#x27;<br>penalty is the standard used in SVC. The &#x27;l1&#x27; leads to ``coef_``<br>vectors that are sparse.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy loss to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('loss',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-loss;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=loss,-%7B%27hinge%27%2C%20%27squared_hinge%27%7D%2C%20default%3D%27squared_hinge%27\">\n",
-       "            loss\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-loss;\">\n",
-       "            loss: {&#x27;hinge&#x27;, &#x27;squared_hinge&#x27;}, default=&#x27;squared_hinge&#x27;<br><br>Specifies the loss function. &#x27;hinge&#x27; is the standard SVM loss<br>(used e.g. by the SVC class) while &#x27;squared_hinge&#x27; is the<br>square of the hinge loss. The combination of ``penalty=&#x27;l1&#x27;``<br>and ``loss=&#x27;hinge&#x27;`` is not supported.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;squared_hinge&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy dual to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-dual;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=dual,-%22auto%22%20or%20bool%2C%20default%3D%22auto%22\">\n",
-       "            dual\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-dual;\">\n",
-       "            dual: &quot;auto&quot; or bool, default=&quot;auto&quot;<br><br>Select the algorithm to either solve the dual or primal<br>optimization problem. Prefer dual=False when n_samples &gt; n_features.<br>`dual=&quot;auto&quot;` will choose the value of the parameter automatically,<br>based on the values of `n_samples`, `n_features`, `loss`, `multi_class`<br>and `penalty`. If `n_samples` &lt; `n_features` and optimizer supports<br>chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,<br>otherwise it will be set to False.<br><br>.. versionchanged:: 1.3<br>   The `&quot;auto&quot;` option is added in version 1.3 and will be the default<br>   in version 1.5.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy tol to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-tol;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy C to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-C;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
-       "            C\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-C;\">\n",
-       "            C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>For an intuitive visualization of the effects of scaling<br>the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy multi_class to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('multi_class',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-multi_class;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=multi_class,-%7B%27ovr%27%2C%20%27crammer_singer%27%7D%2C%20default%3D%27ovr%27\">\n",
-       "            multi_class\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-multi_class;\">\n",
-       "            multi_class: {&#x27;ovr&#x27;, &#x27;crammer_singer&#x27;}, default=&#x27;ovr&#x27;<br><br>Determines the multi-class strategy if `y` contains more than<br>two classes.<br>``&quot;ovr&quot;`` trains n_classes one-vs-rest classifiers, while<br>``&quot;crammer_singer&quot;`` optimizes a joint objective over all classes.<br>While `crammer_singer` is interesting from a theoretical perspective<br>as it is consistent, it is seldom used in practice as it rarely leads<br>to better accuracy and is more expensive to compute.<br>If ``&quot;crammer_singer&quot;`` is chosen, the options loss, penalty and dual<br>will be ignored.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Whether or not to fit an intercept. If set to True, the feature vector<br>is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where<br>1 corresponds to the intercept. If set to False, no intercept will be<br>used in calculations (i.e. data is expected to be already centered).</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy intercept_scaling to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_scaling,-float%2C%20default%3D1.0\">\n",
-       "            intercept_scaling\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
-       "            intercept_scaling: float, default=1.0<br><br>When `fit_intercept` is True, the instance vector x becomes ``[x_1,<br>..., x_n, intercept_scaling]``, i.e. a &quot;synthetic&quot; feature with a<br>constant value equal to `intercept_scaling` is appended to the instance<br>vector. The intercept becomes intercept_scaling * synthetic feature<br>weight. Note that liblinear internally penalizes the intercept,<br>treating it like any other term in the feature vector. To reduce the<br>impact of the regularization on the intercept, the `intercept_scaling`<br>parameter can be set to a value greater than 1; the higher the value of<br>`intercept_scaling`, the lower the impact of regularization on it.<br>Then, the weights become `[w_x_1, ..., w_x_n,<br>w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent<br>the feature weights and the intercept weight is scaled by<br>`intercept_scaling`. This scaling allows the intercept term to have a<br>different regularization behavior compared to the other features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy class_weight to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
-       "            class_weight\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
-       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Set the parameter C of class i to ``class_weight[i]*C`` for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy verbose to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-verbose;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
-       "            verbose\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-verbose;\">\n",
-       "            verbose: int, default=0<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in liblinear that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy random_state to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-random_state;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
-       "            random_state\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-random_state;\">\n",
-       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>the dual coordinate descent (if ``dual=True``). When ``dual=False`` the<br>underlying implementation of :class:`LinearSVC` is not random and<br>``random_state`` has no effect on the results.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy max_iter to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=max_iter,-int%2C%20default%3D1000\">\n",
-       "            max_iter\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
-       "            max_iter: int, default=1000<br><br>The maximum number of iterations to be run.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1000</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    \n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Fitted attributes</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                    <tbody>\n",
-       "                        <tr>\n",
-       "                        <th>Name</th>\n",
-       "                        <th>Type</th>\n",
-       "                        <th>Value</th>\n",
-       "                        </tr>\n",
-       "                        \n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-classes_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
-       "            classes_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-classes_;\">\n",
-       "            classes_: ndarray of shape (n_classes,)<br><br>The unique classes labels.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
-       "           <td>[0.,1.,2.]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=coef_,-ndarray%20of%20shape%20%281%2C%20n_features%29%20if%20n_classes%20%3D%3D%202%20%20%20%20%20%20%20%20%20%20%20%20%20else%20%28n_classes%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: ndarray of shape (1, n_features) if n_classes == 2             else (n_classes, n_features)<br><br>Weights assigned to the features (coefficients in the primal<br>problem).<br><br>``coef_`` is a readonly property derived from ``raw_coef_`` that<br>follows the internal memory layout of liblinear.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3, 2)</td>\n",
-       "           <td>[[-1.54, 2.28],\n",
-       " [ 0.1 ,-0.98],\n",
-       " [ 0.66,-0.35]]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20if%20n_classes%20%3D%3D%202%20else%20%28n_classes%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: ndarray of shape (1,) if n_classes == 2 else (n_classes,)<br><br>Constants in decision function.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
-       "           <td>[ 1.2 , 2.02,-3.14]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_features_in_,-int\">\n",
-       "            n_features_in_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
-       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>2</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_iter_,-int\">\n",
-       "            n_iter_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
-       "            n_iter_: int<br><br>Maximum number of iterations run across all classes.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>8</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "                    </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
-       " SPDX-License-Identifier: BSD-3-Clause\n",
-       "*/\n",
-       "\n",
-       "function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "\n",
-       "    const parent = element.parentElement;\n",
-       "    if (!parent || !parent.nextElementSibling) {\n",
-       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    const paramName = element.parentElement.nextElementSibling\n",
-       "        .textContent.trim().split(' ')[0];\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "\n",
-       "/**\n",
-       " * Copy the list of feature names formatted as a Python list.\n",
-       " *\n",
-       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
-       " *   contain a `details` element and a table containing feature named.\n",
-       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
-       " */\n",
-       "function copyFeatureNamesToClipboard(element) {\n",
-       "    var detailsElem = element.closest('.features').querySelector('details');\n",
-       "    var wasOpen = detailsElem.open;\n",
-       "    detailsElem.open = true;\n",
-       "    var content = element.closest('.features').querySelector('tbody')\n",
-       "                  .innerText.trim();\n",
-       "    if (!wasOpen) detailsElem.open = false;\n",
-       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
-       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
-       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
-       "    const originalStyle = element.style;\n",
-       "    const copyMark = document.createElement('span');\n",
-       "    copyMark.innerHTML = '✔';\n",
-       "    copyMark.style.color = 'blue';\n",
-       "    copyMark.style.fontSize = '1em';\n",
-       "\n",
-       "    navigator.clipboard.writeText(formattedText)\n",
-       "        .then(() => {\n",
-       "            element.style.display = 'none';\n",
-       "            element.parentElement.appendChild(copyMark);\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                copyMark.remove();\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'orange';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "/**\n",
-       " * Adapted from Skrub\n",
-       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
-       " * @returns \"light\" or \"dark\"\n",
-       " */\n",
-       "function detectTheme(element) {\n",
-       "    const body = document.querySelector('body');\n",
-       "\n",
-       "    // Check VSCode theme\n",
-       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
-       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
-       "\n",
-       "    if (themeKindAttr && themeNameAttr) {\n",
-       "        const themeKind = themeKindAttr.toLowerCase();\n",
-       "        const themeName = themeNameAttr.toLowerCase();\n",
-       "\n",
-       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
-       "            return \"dark\";\n",
-       "        }\n",
-       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
-       "            return \"light\";\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    // Check Jupyter theme\n",
-       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
-       "        return 'dark';\n",
-       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
-       "        return 'light';\n",
-       "    }\n",
-       "\n",
-       "    // Guess based on a parent element's color\n",
-       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
-       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
-       "    if (match) {\n",
-       "        const [r, g, b] = [\n",
-       "            parseFloat(match[1]),\n",
-       "            parseFloat(match[2]),\n",
-       "            parseFloat(match[3])\n",
-       "        ];\n",
-       "\n",
-       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
-       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
-       "\n",
-       "        if (luma > 180) {\n",
-       "            // If the text is very bright we have a dark theme\n",
-       "            return 'dark';\n",
-       "        }\n",
-       "        if (luma < 75) {\n",
-       "            // If the text is very dark we have a light theme\n",
-       "            return 'light';\n",
-       "        }\n",
-       "        // Otherwise fall back to the next heuristic.\n",
-       "    }\n",
-       "\n",
-       "    // Fallback to system preference\n",
-       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
-       "}\n",
-       "\n",
-       "\n",
-       "function forceTheme(elementId) {\n",
-       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
-       "    if (estimatorElement === null) {\n",
-       "        console.error(`Element with id ${elementId} not found.`);\n",
-       "    } else {\n",
-       "        const theme = detectTheme(estimatorElement);\n",
-       "        estimatorElement.classList.add(theme);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "forceTheme('sk-container-id-13');</script></body>"
-      ],
-      "text/plain": [
-       "LinearSVC()"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 1b)\n",
-    "X = iris_data[:,[0,1]]\n",
-    "T = iris_data[:,4]\n",
-    "clf.fit(X, T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "cc181a3a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "80.0 Prozent wurden richtig vorausgesagt\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 1 c+d)\n",
-    "pred = clf.predict(X)\n",
-    "correct_perc = (sum(pred == T)/len(pred))*100\n",
-    "\n",
-    "print(f\"{correct_perc} Prozent wurden richtig vorausgesagt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "16af32f1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1e)\n",
-    "import nnwplot\n",
-    "nnwplot.plotTwoFeatures(X, T, clf.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "f4bcee12",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1f)\n",
-    "from sklearn.svm import SVC\n",
-    "svc = SVC()\n",
-    "svc.fit(X, T)\n",
-    "nnwplot.plotTwoFeatures(X, T, svc.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "2c8991de",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1g)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "1a5e84f6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#1h)\n",
-    "X_0_3 = iris_data[:,[0,3]]\n",
-    "clf.fit(X_0_3, T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, clf.predict) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "892baf25",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X_0_3 = iris_data[:,[0,3]]\n",
-    "svc.fit(X_0_3, T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, svc.predict) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "1bad3e36",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1i)\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "knc = KNeighborsClassifier()\n",
-    "knc.fit(X,T)\n",
-    "nnwplot.plotTwoFeatures(X, T, knc.predict)\n",
-    "knc.fit(X_0_3,T)\n",
-    "nnwplot.plotTwoFeatures(X_0_3, T, knc.predict)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "d79741d2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 3, 3, 3, 1, 3, 1, 3, 1, 3, 1, 1, 1, 1, 3, 1, 3,\n",
-       "       1, 1, 3, 1, 3, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 3, 1, 1, 3, 3,\n",
-       "       1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 2, 3, 3, 2, 1, 2, 3, 2,\n",
-       "       3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 2, 3, 3, 3, 2, 2, 2,\n",
-       "       3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3], dtype=int32)"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 2a) probabilistisches Verfahren\n",
-    "from sklearn.cluster import KMeans\n",
-    "\n",
-    "X_0_2 = iris_data[:,[0,2]]\n",
-    "km = KMeans(4)\n",
-    "km.fit(X_0_2)\n",
-    "km.predict(X_0_2)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "nnw (3.14.0)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.14.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Lösungen_N/AB2_REMOTE_1208.ipynb b/Lösungen_N/AB2_REMOTE_1208.ipynb
deleted file mode 100644
index df84f5a..0000000
--- a/Lösungen_N/AB2_REMOTE_1208.ipynb
+++ /dev/null
@@ -1,1356 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "de4f7f4b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.svm import LinearSVC\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "88b3d717",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3a)\n",
-    "X = np.linspace(0, 2)\n",
-    "np.random.seed(42) # vereinfachte Variante, siehe np.random\n",
-    "X = np.sort(np.random.rand(50)*2)\n",
-    "T = np.sin(5*X) + X**2 + np.random.randn(len(X))/10\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.show()\n",
-    "# Es gibt 50 Merkmale in den Daten"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b251a2c1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.04116899 0.06877704 0.09290083 0.11616722 0.13010319 0.19534423\n",
-      " 0.24407647 0.27898772 0.31198904 0.31203728 0.34104825 0.36364993\n",
-      " 0.36680902 0.36970891 0.39934756 0.42467822 0.51755996 0.58245828\n",
-      " 0.5842893  0.60848449 0.60922754 0.62342215 0.73272369 0.74908024\n",
-      " 0.86389004 0.88030499 0.91213997 0.99035382 1.02846888 1.04013604\n",
-      " 1.04951286 1.09342056 1.18482914 1.19731697 1.20223002 1.2150897\n",
-      " 1.22370579 1.32504457 1.36846605 1.41614516 1.46398788 1.57035192\n",
-      " 1.6167947  1.66488528 1.73235229 1.8186408  1.89777107 1.90142861\n",
-      " 1.93126407 1.9398197 ]\n",
-      "[[0.04116899]\n",
-      " [0.06877704]\n",
-      " [0.09290083]\n",
-      " [0.11616722]\n",
-      " [0.13010319]\n",
-      " [0.19534423]\n",
-      " [0.24407647]\n",
-      " [0.27898772]\n",
-      " [0.31198904]\n",
-      " [0.31203728]\n",
-      " [0.34104825]\n",
-      " [0.36364993]\n",
-      " [0.36680902]\n",
-      " [0.36970891]\n",
-      " [0.39934756]\n",
-      " [0.42467822]\n",
-      " [0.51755996]\n",
-      " [0.58245828]\n",
-      " [0.5842893 ]\n",
-      " [0.60848449]\n",
-      " [0.60922754]\n",
-      " [0.62342215]\n",
-      " [0.73272369]\n",
-      " [0.74908024]\n",
-      " [0.86389004]\n",
-      " [0.88030499]\n",
-      " [0.91213997]\n",
-      " [0.99035382]\n",
-      " [1.02846888]\n",
-      " [1.04013604]\n",
-      " [1.04951286]\n",
-      " [1.09342056]\n",
-      " [1.18482914]\n",
-      " [1.19731697]\n",
-      " [1.20223002]\n",
-      " [1.2150897 ]\n",
-      " [1.22370579]\n",
-      " [1.32504457]\n",
-      " [1.36846605]\n",
-      " [1.41614516]\n",
-      " [1.46398788]\n",
-      " [1.57035192]\n",
-      " [1.6167947 ]\n",
-      " [1.66488528]\n",
-      " [1.73235229]\n",
-      " [1.8186408 ]\n",
-      " [1.89777107]\n",
-      " [1.90142861]\n",
-      " [1.93126407]\n",
-      " [1.9398197 ]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>.sk-global {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       ".sk-global.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       ".sk-global.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       ".sk-global {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".sk-global pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip-path: inset(100%);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       ".sk-global div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       ".sk-global label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label label.sk-toggleable__label,\n",
-       ".sk-global div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       ".sk-global div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       ".sk-global div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       ".sk-global div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       ".sk-global a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       ".sk-global a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-top-container.sk-global {\n",
-       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
-       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
-       "   to allow keyboard-only users to navigate the display. */\n",
-       "  overflow-x: scroll !important;\n",
-       "  max-width: 100%;\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover td {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table :is(td, th) {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
-       "*/\n",
-       ".estimator-table table td.param {\n",
-       "    text-align: left;\n",
-       "    position: relative;\n",
-       "    padding: 0;\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".default td, .estimator-table th {\n",
-       "    color: black;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       "td.fitted-att-type {\n",
-       "    white-space: preserve nowrap;\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    Styles for parameter documentation links\n",
-       "    We need styling for visited so jupyter doesn't overwrite it\n",
-       "*/\n",
-       "a.param-doc-link,\n",
-       "a.param-doc-link:link,\n",
-       "a.param-doc-link:visited {\n",
-       "    text-decoration: underline dashed;\n",
-       "    text-underline-offset: .3em;\n",
-       "    color: inherit;\n",
-       "    display: block;\n",
-       "    padding: .5em;\n",
-       "}\n",
-       "\n",
-       "@supports(anchor-name: --doc-link) {\n",
-       "    a.param-doc-link,\n",
-       "    a.param-doc-link:link,\n",
-       "    a.param-doc-link:visited {\n",
-       "    anchor-name: --doc-link;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
-       "a.param-doc-link::before {\n",
-       "    position: absolute;\n",
-       "    content: \"\";\n",
-       "    inset: 0;\n",
-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
-       "    display: none;\n",
-       "    position: absolute;\n",
-       "    z-index: 9999;\n",
-       "    left: 0;\n",
-       "    padding: .5ex;\n",
-       "    margin-left: 1.5em;\n",
-       "    color: var(--sklearn-color-text);\n",
-       "    box-shadow: .3em .3em .4em #999;\n",
-       "    width: max-content;\n",
-       "    text-align: left;\n",
-       "    max-height: 10em;\n",
-       "    overflow-y: auto;\n",
-       "\n",
-       "    /* unfitted */\n",
-       "    background: var(--sklearn-color-unfitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       "@supports(position-area: center right) {\n",
-       "    .param-doc-description {\n",
-       "    position-area: center right;\n",
-       "    position: fixed;\n",
-       "    margin-left: 0;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
-       "    /* fitted */\n",
-       "    background: var(--sklearn-color-fitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "    border: none;\n",
-       "    padding: 0;\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon:focus-visible {\n",
-       "    outline: 1px solid currentColor;\n",
-       "    outline-offset: 1px;\n",
-       "}\n",
-       "\n",
-       ".features {\n",
-       "  font-family: monospace;\n",
-       "  cursor: pointer;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
-       "}\n",
-       "\n",
-       ".features.fitted {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features summary {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  margin-bottom: 0;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "  padding: .25em;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features.fitted details[open] > summary {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features details > summary .arrow::before {\n",
-       "  content: \"▸\";\n",
-       "  color: grey;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary .arrow::before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       ".features details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features.fitted details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features .features-container {\n",
-       "  max-height: 10em;\n",
-       "  overflow: auto;\n",
-       "  scrollbar-width: thin;\n",
-       "  padding: .25em 0.1rem;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 0 0 .5em .5em;\n",
-       "}\n",
-       "\n",
-       ".features.fitted .features-container {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features .image-container {\n",
-       "  block-size: 1em;\n",
-       "  inline-size: 1em;\n",
-       "  padding: 0;\n",
-       "  margin: 0%;\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  align-items: center;\n",
-       "}\n",
-       "\n",
-       ".features .copy-paste-icon {\n",
-       "  background-size: 1em 1em;\n",
-       "  width: 1em;\n",
-       "  height: 1em;\n",
-       "  filter: grayscale(100%) opacity(60%);\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  width: 100%;\n",
-       "  margin: 0.01em;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(odd) {\n",
-       "  background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(even) {\n",
-       "  background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:hover {\n",
-       "  background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  table-layout: inherit;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table td {\n",
-       "  text-align: left;\n",
-       "  padding: 0 0.5em;\n",
-       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "  white-space: nowrap;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".total_features {\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  margin-top: 0.5em;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-2\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy copy_X to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('copy_X',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-copy_X;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
-       "            copy_X\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-copy_X;\">\n",
-       "            copy_X: bool, default=True<br><br>If True, X will be copied; else, it may be overwritten.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy tol to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-tol;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies the convergence criterion of the underlying solver. `tol` is<br>set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. `tol` is set as `cond` of<br>:func:`scipy.linalg.lstsq` when fitting on dense training data.<br><br>.. versionadded:: 1.7<br>.. versionchanged:: 1.9<br>    Now supported on dense data, interpreted as the `cond` parameter.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1e-06</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy n_jobs to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
-       "            n_jobs\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
-       "            n_jobs: int, default=None<br><br>The number of jobs to use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets &gt; 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary &lt;n_jobs&gt;` for more details.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy positive to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('positive',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-positive;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
-       "            positive\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-positive;\">\n",
-       "            positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    \n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Fitted attributes</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                    <tbody>\n",
-       "                        <tr>\n",
-       "                        <th>Name</th>\n",
-       "                        <th>Type</th>\n",
-       "                        <th>Value</th>\n",
-       "                        </tr>\n",
-       "                        \n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=coef_,-array%20of%20shape%20%28n_features%2C%20%29%20or%20%28n_targets%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: array of shape (n_features, ) or (n_targets, n_features)<br><br>Estimated coefficients for the linear regression problem.<br>If multiple targets are passed during the fit (y 2D), this<br>is a 2D array of shape (n_targets, n_features), while if only<br>one target is passed, this is a 1D array of length n_features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[1.64]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=intercept_,-float%20or%20array%20of%20shape%20%28n_targets%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: float or array of shape (n_targets,)<br><br>Independent term in the linear model. Set to 0.0 if<br>`fit_intercept = False`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">float64</td>\n",
-       "           <td>-0.1418</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_features_in_,-int\">\n",
-       "            n_features_in_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
-       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>1</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-rank_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=rank_,-int\">\n",
-       "            rank_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-rank_;\">\n",
-       "            rank_: int<br><br>Rank of matrix `X`. Only available when `X` is dense.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int64</td>\n",
-       "           <td>np.int64(1)</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-singular_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=singular_,-array%20of%20shape%20%28min%28X%2C%20y%29%2C%29\">\n",
-       "            singular_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-singular_;\">\n",
-       "            singular_: array of shape (min(X, y),)<br><br>Singular values of `X`. Only available when `X` is dense.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[4.04]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "                    </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
-       " SPDX-License-Identifier: BSD-3-Clause\n",
-       "*/\n",
-       "\n",
-       "function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "\n",
-       "    const parent = element.parentElement;\n",
-       "    if (!parent || !parent.nextElementSibling) {\n",
-       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    const paramName = element.parentElement.nextElementSibling\n",
-       "        .textContent.trim().split(' ')[0];\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "\n",
-       "/**\n",
-       " * Copy the list of feature names formatted as a Python list.\n",
-       " *\n",
-       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
-       " *   contain a `details` element and a table containing feature named.\n",
-       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
-       " */\n",
-       "function copyFeatureNamesToClipboard(element) {\n",
-       "    var detailsElem = element.closest('.features').querySelector('details');\n",
-       "    var wasOpen = detailsElem.open;\n",
-       "    detailsElem.open = true;\n",
-       "    var content = element.closest('.features').querySelector('tbody')\n",
-       "                  .innerText.trim();\n",
-       "    if (!wasOpen) detailsElem.open = false;\n",
-       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
-       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
-       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
-       "    const originalStyle = element.style;\n",
-       "    const copyMark = document.createElement('span');\n",
-       "    copyMark.innerHTML = '✔';\n",
-       "    copyMark.style.color = 'blue';\n",
-       "    copyMark.style.fontSize = '1em';\n",
-       "\n",
-       "    navigator.clipboard.writeText(formattedText)\n",
-       "        .then(() => {\n",
-       "            element.style.display = 'none';\n",
-       "            element.parentElement.appendChild(copyMark);\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                copyMark.remove();\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'orange';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "/**\n",
-       " * Adapted from Skrub\n",
-       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
-       " * @returns \"light\" or \"dark\"\n",
-       " */\n",
-       "function detectTheme(element) {\n",
-       "    const body = document.querySelector('body');\n",
-       "\n",
-       "    // Check VSCode theme\n",
-       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
-       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
-       "\n",
-       "    if (themeKindAttr && themeNameAttr) {\n",
-       "        const themeKind = themeKindAttr.toLowerCase();\n",
-       "        const themeName = themeNameAttr.toLowerCase();\n",
-       "\n",
-       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
-       "            return \"dark\";\n",
-       "        }\n",
-       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
-       "            return \"light\";\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    // Check Jupyter theme\n",
-       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
-       "        return 'dark';\n",
-       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
-       "        return 'light';\n",
-       "    }\n",
-       "\n",
-       "    // Guess based on a parent element's color\n",
-       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
-       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
-       "    if (match) {\n",
-       "        const [r, g, b] = [\n",
-       "            parseFloat(match[1]),\n",
-       "            parseFloat(match[2]),\n",
-       "            parseFloat(match[3])\n",
-       "        ];\n",
-       "\n",
-       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
-       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
-       "\n",
-       "        if (luma > 180) {\n",
-       "            // If the text is very bright we have a dark theme\n",
-       "            return 'dark';\n",
-       "        }\n",
-       "        if (luma < 75) {\n",
-       "            // If the text is very dark we have a light theme\n",
-       "            return 'light';\n",
-       "        }\n",
-       "        // Otherwise fall back to the next heuristic.\n",
-       "    }\n",
-       "\n",
-       "    // Fallback to system preference\n",
-       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
-       "}\n",
-       "\n",
-       "\n",
-       "function forceTheme(elementId) {\n",
-       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
-       "    if (estimatorElement === null) {\n",
-       "        console.error(`Element with id ${elementId} not found.`);\n",
-       "    } else {\n",
-       "        const theme = detectTheme(estimatorElement);\n",
-       "        estimatorElement.classList.add(theme);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "forceTheme('sk-container-id-2');</script></body>"
-      ],
-      "text/plain": [
-       "LinearRegression()"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 3b)\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "reg = LinearRegression()\n",
-    "print(X)\n",
-    "print(X.reshape(-1,1)) # transponiert den Merkmalsraum in einen Spaltenvektor\n",
-    "reg.fit(X.reshape(-1,1),T)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "98476c93",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.07437258 -0.0291456   0.0103735   0.04848806  0.07131768  0.17819428\n",
-      "  0.25802648  0.31521741  0.36927952  0.36935855  0.41688375  0.45390939\n",
-      "  0.45908454  0.46383508  0.51238855  0.55388473  0.70604178  0.81235693\n",
-      "  0.81535647  0.85499255  0.85620981  0.87946314  1.05851877  1.08531376\n",
-      "  1.27339292  1.30028357  1.35243501  1.48056341  1.54300275  1.56211567\n",
-      "  1.57747659  1.64940532  1.79914906  1.81960638  1.82765485  1.84872133\n",
-      "  1.86283603  2.02884723  2.09997946  2.17808643  2.25646145  2.43070493\n",
-      "  2.50678657  2.58556763  2.69609076  2.83744691  2.96707657  2.97306828\n",
-      "  3.02194413  3.03595981]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 3c)\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,reg.predict(X.reshape(-1,1)))\n",
-    "plt.show()\n",
-    "print(reg.predict(X.reshape(-1,1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "94ab4bd3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3d) Decision Tree Regressor\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "dtr = DecisionTreeRegressor()\n",
-    "dtr.fit(X.reshape(-1,1),T)\n",
-    "\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,dtr.predict(X.reshape(-1,1)))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "475aa6e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3d) SV Regressor\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "svr = SVR()\n",
-    "svr.fit(X.reshape(-1,1),T)\n",
-    "\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,svr.predict(X.reshape(-1,1)))\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "nnw (3.14.8)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.14.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Lösungen_N/AB2_REMOTE_715.ipynb b/Lösungen_N/AB2_REMOTE_715.ipynb
deleted file mode 100644
index df84f5a..0000000
--- a/Lösungen_N/AB2_REMOTE_715.ipynb
+++ /dev/null
@@ -1,1356 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "de4f7f4b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.svm import LinearSVC\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "88b3d717",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3a)\n",
-    "X = np.linspace(0, 2)\n",
-    "np.random.seed(42) # vereinfachte Variante, siehe np.random\n",
-    "X = np.sort(np.random.rand(50)*2)\n",
-    "T = np.sin(5*X) + X**2 + np.random.randn(len(X))/10\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.show()\n",
-    "# Es gibt 50 Merkmale in den Daten"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b251a2c1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.04116899 0.06877704 0.09290083 0.11616722 0.13010319 0.19534423\n",
-      " 0.24407647 0.27898772 0.31198904 0.31203728 0.34104825 0.36364993\n",
-      " 0.36680902 0.36970891 0.39934756 0.42467822 0.51755996 0.58245828\n",
-      " 0.5842893  0.60848449 0.60922754 0.62342215 0.73272369 0.74908024\n",
-      " 0.86389004 0.88030499 0.91213997 0.99035382 1.02846888 1.04013604\n",
-      " 1.04951286 1.09342056 1.18482914 1.19731697 1.20223002 1.2150897\n",
-      " 1.22370579 1.32504457 1.36846605 1.41614516 1.46398788 1.57035192\n",
-      " 1.6167947  1.66488528 1.73235229 1.8186408  1.89777107 1.90142861\n",
-      " 1.93126407 1.9398197 ]\n",
-      "[[0.04116899]\n",
-      " [0.06877704]\n",
-      " [0.09290083]\n",
-      " [0.11616722]\n",
-      " [0.13010319]\n",
-      " [0.19534423]\n",
-      " [0.24407647]\n",
-      " [0.27898772]\n",
-      " [0.31198904]\n",
-      " [0.31203728]\n",
-      " [0.34104825]\n",
-      " [0.36364993]\n",
-      " [0.36680902]\n",
-      " [0.36970891]\n",
-      " [0.39934756]\n",
-      " [0.42467822]\n",
-      " [0.51755996]\n",
-      " [0.58245828]\n",
-      " [0.5842893 ]\n",
-      " [0.60848449]\n",
-      " [0.60922754]\n",
-      " [0.62342215]\n",
-      " [0.73272369]\n",
-      " [0.74908024]\n",
-      " [0.86389004]\n",
-      " [0.88030499]\n",
-      " [0.91213997]\n",
-      " [0.99035382]\n",
-      " [1.02846888]\n",
-      " [1.04013604]\n",
-      " [1.04951286]\n",
-      " [1.09342056]\n",
-      " [1.18482914]\n",
-      " [1.19731697]\n",
-      " [1.20223002]\n",
-      " [1.2150897 ]\n",
-      " [1.22370579]\n",
-      " [1.32504457]\n",
-      " [1.36846605]\n",
-      " [1.41614516]\n",
-      " [1.46398788]\n",
-      " [1.57035192]\n",
-      " [1.6167947 ]\n",
-      " [1.66488528]\n",
-      " [1.73235229]\n",
-      " [1.8186408 ]\n",
-      " [1.89777107]\n",
-      " [1.90142861]\n",
-      " [1.93126407]\n",
-      " [1.9398197 ]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>.sk-global {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       ".sk-global.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       ".sk-global.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       ".sk-global {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".sk-global pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip-path: inset(100%);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       ".sk-global div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       ".sk-global label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label label.sk-toggleable__label,\n",
-       ".sk-global div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       ".sk-global div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       ".sk-global div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       ".sk-global div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       ".sk-global a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       ".sk-global a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-top-container.sk-global {\n",
-       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
-       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
-       "   to allow keyboard-only users to navigate the display. */\n",
-       "  overflow-x: scroll !important;\n",
-       "  max-width: 100%;\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover td {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table :is(td, th) {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
-       "*/\n",
-       ".estimator-table table td.param {\n",
-       "    text-align: left;\n",
-       "    position: relative;\n",
-       "    padding: 0;\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".default td, .estimator-table th {\n",
-       "    color: black;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       "td.fitted-att-type {\n",
-       "    white-space: preserve nowrap;\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    Styles for parameter documentation links\n",
-       "    We need styling for visited so jupyter doesn't overwrite it\n",
-       "*/\n",
-       "a.param-doc-link,\n",
-       "a.param-doc-link:link,\n",
-       "a.param-doc-link:visited {\n",
-       "    text-decoration: underline dashed;\n",
-       "    text-underline-offset: .3em;\n",
-       "    color: inherit;\n",
-       "    display: block;\n",
-       "    padding: .5em;\n",
-       "}\n",
-       "\n",
-       "@supports(anchor-name: --doc-link) {\n",
-       "    a.param-doc-link,\n",
-       "    a.param-doc-link:link,\n",
-       "    a.param-doc-link:visited {\n",
-       "    anchor-name: --doc-link;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
-       "a.param-doc-link::before {\n",
-       "    position: absolute;\n",
-       "    content: \"\";\n",
-       "    inset: 0;\n",
-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
-       "    display: none;\n",
-       "    position: absolute;\n",
-       "    z-index: 9999;\n",
-       "    left: 0;\n",
-       "    padding: .5ex;\n",
-       "    margin-left: 1.5em;\n",
-       "    color: var(--sklearn-color-text);\n",
-       "    box-shadow: .3em .3em .4em #999;\n",
-       "    width: max-content;\n",
-       "    text-align: left;\n",
-       "    max-height: 10em;\n",
-       "    overflow-y: auto;\n",
-       "\n",
-       "    /* unfitted */\n",
-       "    background: var(--sklearn-color-unfitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       "@supports(position-area: center right) {\n",
-       "    .param-doc-description {\n",
-       "    position-area: center right;\n",
-       "    position: fixed;\n",
-       "    margin-left: 0;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
-       "    /* fitted */\n",
-       "    background: var(--sklearn-color-fitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "    border: none;\n",
-       "    padding: 0;\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon:focus-visible {\n",
-       "    outline: 1px solid currentColor;\n",
-       "    outline-offset: 1px;\n",
-       "}\n",
-       "\n",
-       ".features {\n",
-       "  font-family: monospace;\n",
-       "  cursor: pointer;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
-       "}\n",
-       "\n",
-       ".features.fitted {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features summary {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  margin-bottom: 0;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "  padding: .25em;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features.fitted details[open] > summary {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features details > summary .arrow::before {\n",
-       "  content: \"▸\";\n",
-       "  color: grey;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary .arrow::before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       ".features details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features.fitted details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features .features-container {\n",
-       "  max-height: 10em;\n",
-       "  overflow: auto;\n",
-       "  scrollbar-width: thin;\n",
-       "  padding: .25em 0.1rem;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 0 0 .5em .5em;\n",
-       "}\n",
-       "\n",
-       ".features.fitted .features-container {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features .image-container {\n",
-       "  block-size: 1em;\n",
-       "  inline-size: 1em;\n",
-       "  padding: 0;\n",
-       "  margin: 0%;\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  align-items: center;\n",
-       "}\n",
-       "\n",
-       ".features .copy-paste-icon {\n",
-       "  background-size: 1em 1em;\n",
-       "  width: 1em;\n",
-       "  height: 1em;\n",
-       "  filter: grayscale(100%) opacity(60%);\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  width: 100%;\n",
-       "  margin: 0.01em;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(odd) {\n",
-       "  background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(even) {\n",
-       "  background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:hover {\n",
-       "  background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  table-layout: inherit;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table td {\n",
-       "  text-align: left;\n",
-       "  padding: 0 0.5em;\n",
-       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "  white-space: nowrap;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".total_features {\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  margin-top: 0.5em;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-2\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy copy_X to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('copy_X',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-copy_X;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
-       "            copy_X\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-copy_X;\">\n",
-       "            copy_X: bool, default=True<br><br>If True, X will be copied; else, it may be overwritten.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy tol to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-tol;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies the convergence criterion of the underlying solver. `tol` is<br>set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. `tol` is set as `cond` of<br>:func:`scipy.linalg.lstsq` when fitting on dense training data.<br><br>.. versionadded:: 1.7<br>.. versionchanged:: 1.9<br>    Now supported on dense data, interpreted as the `cond` parameter.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1e-06</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy n_jobs to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
-       "            n_jobs\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
-       "            n_jobs: int, default=None<br><br>The number of jobs to use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets &gt; 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary &lt;n_jobs&gt;` for more details.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
-       "                 aria-label=\"Copy positive to clipboard\"\n",
-       "                 onclick=\"copyToClipboard('positive',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></button></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-positive;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
-       "            positive\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-positive;\">\n",
-       "            positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    \n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Fitted attributes</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                    <tbody>\n",
-       "                        <tr>\n",
-       "                        <th>Name</th>\n",
-       "                        <th>Type</th>\n",
-       "                        <th>Value</th>\n",
-       "                        </tr>\n",
-       "                        \n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=coef_,-array%20of%20shape%20%28n_features%2C%20%29%20or%20%28n_targets%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: array of shape (n_features, ) or (n_targets, n_features)<br><br>Estimated coefficients for the linear regression problem.<br>If multiple targets are passed during the fit (y 2D), this<br>is a 2D array of shape (n_targets, n_features), while if only<br>one target is passed, this is a 1D array of length n_features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[1.64]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=intercept_,-float%20or%20array%20of%20shape%20%28n_targets%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: float or array of shape (n_targets,)<br><br>Independent term in the linear model. Set to 0.0 if<br>`fit_intercept = False`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">float64</td>\n",
-       "           <td>-0.1418</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_features_in_,-int\">\n",
-       "            n_features_in_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
-       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>1</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-rank_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=rank_,-int\">\n",
-       "            rank_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-rank_;\">\n",
-       "            rank_: int<br><br>Rank of matrix `X`. Only available when `X` is dense.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int64</td>\n",
-       "           <td>np.int64(1)</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-singular_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=singular_,-array%20of%20shape%20%28min%28X%2C%20y%29%2C%29\">\n",
-       "            singular_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-singular_;\">\n",
-       "            singular_: array of shape (min(X, y),)<br><br>Singular values of `X`. Only available when `X` is dense.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[4.04]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "                    </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
-       " SPDX-License-Identifier: BSD-3-Clause\n",
-       "*/\n",
-       "\n",
-       "function copyToClipboard(text, element) {\n",
-       "    // Get the parameter prefix from the closest toggleable content\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
-       "\n",
-       "    const originalStyle = element.style;\n",
-       "    const computedStyle = window.getComputedStyle(element);\n",
-       "    const originalWidth = computedStyle.width;\n",
-       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
-       "\n",
-       "    navigator.clipboard.writeText(fullParamName)\n",
-       "        .then(() => {\n",
-       "            element.style.width = originalWidth;\n",
-       "            element.style.color = 'green';\n",
-       "            element.innerHTML = \"Copied!\";\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'red';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 2000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
-       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
-       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
-       "\n",
-       "    const parent = element.parentElement;\n",
-       "    if (!parent || !parent.nextElementSibling) {\n",
-       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    const paramName = element.parentElement.nextElementSibling\n",
-       "        .textContent.trim().split(' ')[0];\n",
-       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
-       "\n",
-       "    element.setAttribute('title', fullParamName);\n",
-       "});\n",
-       "\n",
-       "/**\n",
-       " * Copy the list of feature names formatted as a Python list.\n",
-       " *\n",
-       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
-       " *   contain a `details` element and a table containing feature named.\n",
-       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
-       " */\n",
-       "function copyFeatureNamesToClipboard(element) {\n",
-       "    var detailsElem = element.closest('.features').querySelector('details');\n",
-       "    var wasOpen = detailsElem.open;\n",
-       "    detailsElem.open = true;\n",
-       "    var content = element.closest('.features').querySelector('tbody')\n",
-       "                  .innerText.trim();\n",
-       "    if (!wasOpen) detailsElem.open = false;\n",
-       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
-       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
-       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
-       "    const originalStyle = element.style;\n",
-       "    const copyMark = document.createElement('span');\n",
-       "    copyMark.innerHTML = '✔';\n",
-       "    copyMark.style.color = 'blue';\n",
-       "    copyMark.style.fontSize = '1em';\n",
-       "\n",
-       "    navigator.clipboard.writeText(formattedText)\n",
-       "        .then(() => {\n",
-       "            element.style.display = 'none';\n",
-       "            element.parentElement.appendChild(copyMark);\n",
-       "\n",
-       "            setTimeout(() => {\n",
-       "                copyMark.remove();\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        })\n",
-       "        .catch(err => {\n",
-       "            console.error('Failed to copy:', err);\n",
-       "            element.style.color = 'orange';\n",
-       "            element.innerHTML = \"Failed!\";\n",
-       "            setTimeout(() => {\n",
-       "                element.innerHTML = originalHTML;\n",
-       "                element.style = originalStyle;\n",
-       "            }, 1000);\n",
-       "        });\n",
-       "    return false;\n",
-       "}\n",
-       "/**\n",
-       " * Adapted from Skrub\n",
-       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
-       " * @returns \"light\" or \"dark\"\n",
-       " */\n",
-       "function detectTheme(element) {\n",
-       "    const body = document.querySelector('body');\n",
-       "\n",
-       "    // Check VSCode theme\n",
-       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
-       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
-       "\n",
-       "    if (themeKindAttr && themeNameAttr) {\n",
-       "        const themeKind = themeKindAttr.toLowerCase();\n",
-       "        const themeName = themeNameAttr.toLowerCase();\n",
-       "\n",
-       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
-       "            return \"dark\";\n",
-       "        }\n",
-       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
-       "            return \"light\";\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    // Check Jupyter theme\n",
-       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
-       "        return 'dark';\n",
-       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
-       "        return 'light';\n",
-       "    }\n",
-       "\n",
-       "    // Guess based on a parent element's color\n",
-       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
-       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
-       "    if (match) {\n",
-       "        const [r, g, b] = [\n",
-       "            parseFloat(match[1]),\n",
-       "            parseFloat(match[2]),\n",
-       "            parseFloat(match[3])\n",
-       "        ];\n",
-       "\n",
-       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
-       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
-       "\n",
-       "        if (luma > 180) {\n",
-       "            // If the text is very bright we have a dark theme\n",
-       "            return 'dark';\n",
-       "        }\n",
-       "        if (luma < 75) {\n",
-       "            // If the text is very dark we have a light theme\n",
-       "            return 'light';\n",
-       "        }\n",
-       "        // Otherwise fall back to the next heuristic.\n",
-       "    }\n",
-       "\n",
-       "    // Fallback to system preference\n",
-       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
-       "}\n",
-       "\n",
-       "\n",
-       "function forceTheme(elementId) {\n",
-       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
-       "    if (estimatorElement === null) {\n",
-       "        console.error(`Element with id ${elementId} not found.`);\n",
-       "    } else {\n",
-       "        const theme = detectTheme(estimatorElement);\n",
-       "        estimatorElement.classList.add(theme);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "forceTheme('sk-container-id-2');</script></body>"
-      ],
-      "text/plain": [
-       "LinearRegression()"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 3b)\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "reg = LinearRegression()\n",
-    "print(X)\n",
-    "print(X.reshape(-1,1)) # transponiert den Merkmalsraum in einen Spaltenvektor\n",
-    "reg.fit(X.reshape(-1,1),T)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "98476c93",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.07437258 -0.0291456   0.0103735   0.04848806  0.07131768  0.17819428\n",
-      "  0.25802648  0.31521741  0.36927952  0.36935855  0.41688375  0.45390939\n",
-      "  0.45908454  0.46383508  0.51238855  0.55388473  0.70604178  0.81235693\n",
-      "  0.81535647  0.85499255  0.85620981  0.87946314  1.05851877  1.08531376\n",
-      "  1.27339292  1.30028357  1.35243501  1.48056341  1.54300275  1.56211567\n",
-      "  1.57747659  1.64940532  1.79914906  1.81960638  1.82765485  1.84872133\n",
-      "  1.86283603  2.02884723  2.09997946  2.17808643  2.25646145  2.43070493\n",
-      "  2.50678657  2.58556763  2.69609076  2.83744691  2.96707657  2.97306828\n",
-      "  3.02194413  3.03595981]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 3c)\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,reg.predict(X.reshape(-1,1)))\n",
-    "plt.show()\n",
-    "print(reg.predict(X.reshape(-1,1)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "94ab4bd3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3d) Decision Tree Regressor\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "dtr = DecisionTreeRegressor()\n",
-    "dtr.fit(X.reshape(-1,1),T)\n",
-    "\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,dtr.predict(X.reshape(-1,1)))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "475aa6e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 3d) SV Regressor\n",
-    "from sklearn.svm import SVR\n",
-    "\n",
-    "svr = SVR()\n",
-    "svr.fit(X.reshape(-1,1),T)\n",
-    "\n",
-    "plt.plot(X, T, '.')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('t')\n",
-    "plt.plot(X,svr.predict(X.reshape(-1,1)))\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "nnw (3.14.8)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.14.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Lösungen_N/AB3.ipynb b/Lösungen_N/AB3.ipynb
new file mode 100644
index 0000000..8755ea1
--- /dev/null
+++ b/Lösungen_N/AB3.ipynb
@@ -0,0 +1,19 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b99095f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/supplement_files/NNW_P3_A1_Vorlage.py b/supplement_files/NNW_P3_A1_Vorlage.py
new file mode 100644
index 0000000..82b64ad
--- /dev/null
+++ b/supplement_files/NNW_P3_A1_Vorlage.py
@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+
+#%% 
+# AG1a
+import numpy as np
+import nnwplot
+
+def neuron(X):
+    N = X.shape[...]
+    net = np.zeros(N)
+    W = [....]
+    b = # Bias ... ergibt sich aus Aktivierungsschwelle
+    for n in range(0,N):
+        for j in range ...:
+            net[..]+=
+    b verwenden ...
+    return net ... # threshold ....
+
+#%% 
+iris = np.loadtxt("iris.csv",delimiter=',')
+
+X=iris[...]
+T=iris[...]
+
+print(X)
+print(T)
+
+#%% 
+# AG1b
+nnwplot.plotTwoFeatures(X[...], T, neuron)
diff --git a/supplement_files/SLN_Vorlage.py b/supplement_files/SLN_Vorlage.py
new file mode 100644
index 0000000..a09ab08
--- /dev/null
+++ b/supplement_files/SLN_Vorlage.py
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+# %%
+import numpy as np
+import nnwplot
+import matplotlib.pyplot as plt
+
+# %% ErrorRate
+def ErrorRate(Y,T):
+    if Y.ndim==1 or Y.shape[0]==1:
+        errors=Y!=T
+        return errors.sum()/Y.size
+    else: # für mehrere Ausgaben in one-hot Kodierung:
+        errors=Y.argmax(0)!=T.argmax(0)
+        return errors.sum()/Y.shape[1]
+
+# %%
+class SLN:
+    def __init__(...): # Konstruktor
+        np.random.seed(42)
+        self._b=np.zeros((AAA,1))
+        self._W=np.random.randn(BBB,CCC)/np.sqrt(DDD)
+
+    # def DeltaTrain(self, X, T, eta, maxIter, maxErrorRate):
+    #     N=X.shape[1]    # Anzahl Trainingsdaten
+    #     plt.ion() # interactive mode on
+    #     for it in range(maxIter):
+    #         Y = ... # nur ein Aufruf
+    #         err = ErrorRate(..., ...)
+    #         if (it%20) == 0:
+    #             print('#{} err:{}\n{}\n{}'.format(it,err,self._W,self._b))
+    #             nnwplot.plotTwoFeatures(...)
+    #             plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht)
+    #         if err <= maxErrorRate:
+    #             break
+    #         # update _W
+    #         ...
+    #         # update _b 
+    #         ...
+    #     print('#{} err:{}\n{}\n{}'.format(it,err,self._W,self._b))
+    #     nnwplot.plotTwoFeatures(...)
+    #     plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht)
+    #
+    #     input("Press Enter to continue...") # wenn gewünscht ...
\ No newline at end of file