diff --git a/Lösungen_N/AB2.ipynb b/Lösungen_N/AB2.ipynb
index f0b30c9..2079587 100644
--- a/Lösungen_N/AB2.ipynb
+++ b/Lösungen_N/AB2.ipynb
@@ -1,2876 +1,2880 @@
-{
- "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",
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-       "\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",
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-       "    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",
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-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
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-       "    position: absolute;\n",
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-       "    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",
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-       "    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",
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-       "    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",
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-       "  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",
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-       "\n",
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-       "});\n",
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-       "/**\n",
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-       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
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-       "\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*$/i);\n",
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-       "        const [r, g, b] = [\n",
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-       "            parseFloat(match[2]),\n",
-       "            parseFloat(match[3])\n",
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-       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
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-       "\n",
-       "\n",
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-       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
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-       "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": 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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": 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",
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-       "    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",
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-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
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-       "\n",
-       ".features.fitted {\n",
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-       "\n",
-       ".features summary {\n",
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-       "\n",
-       ".features.fitted details[open] > summary {\n",
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-       "\n",
-       ".total_features {\n",
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-       "  margin-top: 0.5em;\n",
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-       "</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",
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-       "                 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",
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-       "    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
-}
+{
+ "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",
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+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
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+       "            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",
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+       "            // If the text is very bright we have a dark theme\n",
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+       "\n",
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+       "    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": 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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": 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"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.14.-1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Lösungen_N/AB3.ipynb b/Lösungen_N/AB3.ipynb
index eb9cd90..da03c0f 100644
--- a/Lösungen_N/AB3.ipynb
+++ b/Lösungen_N/AB3.ipynb
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 23,
    "id": "2d574c72",
    "metadata": {},
    "outputs": [],
@@ -48,13 +48,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "id": "d21f1852",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -64,7 +64,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -77,10 +77,9 @@
     "# 1b) + c)\n",
     "from nnwplot import plotTwoFeatures\n",
     "\n",
-    "classification = neuron(X,3)\n",
     "plt.figure(figsize=(6, 4), dpi=100)\n",
-    "plt.title(\"Aktivierungsschwelle = 3\")\n",
-    "plotTwoFeatures(X,T,neuron)\n",
+    "plt.title(\"Aktivierungsschwelle = 2\")\n",
+    "plotTwoFeatures(X,T,lambda X: neuron(X))\n",
     "\n",
     "plt.figure(figsize=(6, 4), dpi=100)\n",
     "plt.title(\"Aktivierungsschwelle = 1.5\")\n",
@@ -89,13 +88,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 25,
    "id": "7d7af336",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -105,7 +104,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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UQgghhCQEQgghhJCEQAghhBBIQiCEEEIIJCEQQgghBJIQCCGEEAJJCIQQQgiBJARCCCGEQBICIYQQQiAJgRBCCCGQhEAIIYQQSEIghBBCCCQhEEIIIQSSEAghhBACSQiEEEIIgSQEQgghhEASAiGEEEIgCYEQQgghkIRACCGEEEhCIIQQQggkIRBCCCEEkhAIIYQQAkkIhBBCCIEkBEIIIYRAEgIhhBBCIAmBEEIIIZCEQAghhBCcRULw8ccfk5yczM9//nOf7f75z3+SnJxc76d///5n2q0QQgghmoD5TB506NAhHnnkEWJjYyksLPTZtqysjI4dO/LNN994tqmqnJgQQgghWpNGJwQOh4MbbriBZ599lk8//dTQY6xWK8nJyY0OTgghhBDNo9Ff1R999FFSU1O55ZZbDD9mz5499O3bl6FDh/Lzn/+cnJycxnYrhBBCiCbUqDMEP/zwA3PnzmXbtm2GHxMeHs7jjz/OzJkzKSkp4cknn2T48OHs3LmTuLi4Bh9js9mw2Wye22VlZY0JUwghhBCNZDghyM3N5Y477uCTTz4hOjracAcPPfQQiqJ4bn/11Vd0796d1157jccff7zBx8yePZsnn3zScB9CCCGEODuGE4K1a9dSWFjIzTff7NlWWFiIoigkJyezdu3aBscJ1E0GwH3GYMCAAezZs8drX4899hi//vWvPbfLyspISUkxGqoQQgghGslwQjBz5kzS09Prbbv99tsJCgri9ddfp1OnTob2o2kaGRkZDBgwwGuboKAggoKCjIYmhBBCiLNkeFBhcHDwafUEgoODCQkJITk5GZPJBMBLL71Ur87AQw89xOHDhwGorq7mN7/5DVlZWdx2220BfipCCCGEOFNnVIfAl7KyMrKzsz23x48fzyWXXEJ2djY1NTUMGDCA+fPnM2LEiEB3LUSrougaaRVbSavYhlmzU2LtwI6oCZRZO7R0aEIIcRpF13X9TB9cO4YgNjbWs62srIyKigqSkpLqtS0vLyc4OBiLxdLofsrKyoiKiuJPK94kODz0TMMVotl0qj7EFZn/IMpZiAsToKMACjrbos9nfqc70JSA5+NCCHGamooqnppwD6WlpURGRnptd1Z/kRqaNhgZGdlghxEREWfTlRBtRoeao9yU8RdMuhMAE6569w8qWUaQq4qvkh+EUwbdCiFES5EawkIE2OTjH2HSnahoDd6votO3fD0pVXubOTIhhPBOEgIhAijKnkdq5Q6vyUAtFyrDin9qpqiEEMI/SQiECKCOtqMYuQhgQqNT9aEmj0cIIYyShECIANINpQO1ZPyAEKL1kIRAiAA6HtzNUFLgQiUrtFczRCSEEMZIQiBEAJVb4jgQPhTNz6+WCY3NMRc0U1RCCOGfJARCBNiShBuxq0G4vPx66cDW6EnkhPRo3sCEEMIHSQiECLCioE78t9sTFAW5i3O5MOHChIaCCxPrYi/mx053SQ0CIUSrIqXShGgCBcHJvJX6V5Kr95NasQ2LZqfUEs+uqPOoNkuRLiFE6yMJgRBNRVHICu1NVmjvlo5ECCH8kksGQgghhJCEQAghhBCSEAghhBACSQiEEEIIgSQEQgghhEASAiGEEEIgCYEQQgghkDoEQpwm1pZDYk0GOgq5Id0ptia2dEhCCNHkJCEQ4oTE6nSmHv8vXar21dueEdqfhYm3UBCc0kKRCSFE05NLBkIAyVX7uCXjCZKrDpx2X5eqPdyW/mcSqtNbIDIhhGgekhCIdk/RXVye9S9U3YWKdtr9Khpm3cHl2a+ArrdAhEII0fQkIRDtXo/yLUQ4i1HxfrBX0Yi159K1anczRiaEEM1HEgLR7nWv3IELk992Lkx0q9jRDBEJIUTzk4RAtHtm3dEkbYUQoi2RhEC0eyWWjig+LhfUUtEosSY0Q0RCCNH8JCEQ7d6O6AlgICFwKSZ2RY1r+oCEEKIFSEIg2r1ySxybYy5AR/HaRgfWxV1MjSm8+QITQohmJIWJRKtmdVUTZ89B0XUKgzphM4U1ST8/Jd6CVathUOkKXKiYTkw/rP335phpLO9wdZP0LYQQrYEkBKJVCnMUM77gSwaWLMdyYiCfUzGzK3IcqzpcSam1Q0D70xUT33X+PzbHTmNo0SKSag6io5Ad0ovNsVPJC+4W0P6EEKK1kYRAtDpR9nxuyXiCMGdZvUJBZt3JwNKV9KzYzJxuf6IwqHPA+z4WksaxzmkB368QQrR2MoZAtDqXZf2L0FOSgVoqGkGuKq7K/LtUDRRCiACShEC0KonVh+lcc8hzDb8hJjTi7MekaqAQQgSQJASiVelZvhmXgY+lCxO9yjc2Q0RCCNE+SEIgWhWrVg0+pv+dpGN11TR1OEII0W5IQiBalQpzDIqPywW1FKDcEtP0AQkhRDshCYFoVYxWAlTR2Bk1vomjEUKI9kMSAtGqVFhi2R59PpqPywYaKnsjRlAUlNSMkQkhxLlNEgLR6ixIvI3D4YMB98G/Vm2SkBnai28739sisTUVRdcIdZYS4iyX6ZRCiBZxxoWJampq2LhxI/Hx8fTp08dv+9zcXDIzM0lNTSUuLu5MuxXtgEu1MjflYXqWb2J40UI61RxC0XWOB3dlU+x09kWORFPOjZpaIc5yRhTNZ1jxT4S6ygEotnRkU+x0tsRMxalaWzhCIUR7ccZ/VR988EHefvttrrjiCubOneu1naZp3Hvvvbz//vukpaVx6NAhHn74YZ555pkz7Vq0A7qisj9yJPsjR7Z0KE0m0p7PzRlPE+EsrleEKdqRx9TjH9K3dA2fdH0MuymkBaMUQrQXZ3TJ4LPPPmPjxo1MnTrVb9vXXnuNTz/9lK1bt7Jr1y6WLFnC888/zxdffHEmXQtxbtB1rsl8gfBTkgFwz6BQ0OlUk87MY2+3THxCiHan0QlBeno6Dz30EB9++CFWq//Tme+88w7XXHON57LC2LFjmTp1Ku+8807joxXiHNG1chcdbVk+KzKqaPQtW0u4o6gZIxNCtFeNSgicTic33HADjz/+OH379vXb3uVysWPHDoYPH15v+6hRo9iyZYvXx9lsNsrKyur9CHEu6Vu21lBFRoA+ZeubOBohhGhkQvCHP/yB+Ph47rvvPkPty8vLcTgcpw0ijIuLo6jI+7ee2bNnExUV5flJSUlpTJhCtHohrooGF286lY5KiKuiGSISQrR3hhOCjRs38o9//IM777yTlStXsnLlSoqLiyksLGTlypXYbLbTHmOxWAD3jIS6qqurfV5ueOyxxygtLfX8ZGZmGg1TiDah2hReb0qlNwoa1abwZohICNHeGZ5lUF1dzYgRI3jppZc82/bs2YOqqjz66KPMnTuXxMTEeo8JCwsjLi6O7Ozsetuzs7Pp0qWL176CgoIICgoyGpoQbc7uqLEMLVliqO3eyNFNHI0QQjQiIZgwYQIrV66st23WrFkEBwfXm3Z49OhRcnJyGDNmDADTpk1j3rx5PProo4B7XMF3333HZZddFoj4hWiTjob243hQF+J9DCzUUNgdNY4KWbNBCNEMAl6p8J133mHmzJme23/84x/Ztm0bP//5z5k3bx433HAD5eXlPPzww4HuWoi2Q1GY2+Vhyi2xp5Vp1k/85IT04MdOd7ZIeEKI9uesyr3179//tLEAXbp0YezYsZ7b/fr1Y82aNbz00ku8/PLL9OzZk7Vr19K5c+ez6VqIRulfspLx+Z8T48gD3CWRj4T2Y2HirRQFt8xnscwSz7upzzC8aAHDin4i3FUKQLE1kY2x09kaPRmXVCoUQjQTRddbf+H0srIyoqKi+NOKNwkOD23pcEQbc9XRF+lVsRkdPN/F637ov+p8P3sNrrLYZHSNEFclmqJiU0NB8b64kxBCNEZNRRVPTbiH0tJSIiMjvbaTxY3EOW3S8Y/pVbEZoN6JeaXO7cuz/02UPa+5Q6tPUak2R2AzhUkyIIRoEZIQiHPaiKL5+DoFVnvovSB3TnOEI4QQrda5sWScEA1IK9+MRXcYaptasa2JoxFCiNZNzhCIc1a8Ldt/I9xnCUw4mzYYIYRo5SQhEOesKrP3wTOn0uVXQQjRzslfQXHO2hU5Fg3F5xgCcM84OB7ctTlCEkKIVksSAnHO0lQr6WEDfbapTRaWdLy+6QMSQohWTBICcU6bm/IrKk1Rnup/ddXe3hI9lSPhA5o5MiGEaF1kloFodkmV+5iV8yYxjlwUdDRUskJ782XSA1RbowPal6Za+XfPl7ki6xV6VGxBqbNuQI0axsoOV7Ax7sKA9lkrwlHI0OLF9CjfjEW3UWxJZGvMZA5EDENXTE3SpxCtSVFlHuszFrE/bxsOl50O4UmM6jaFXglDUJXGfR+1OarZkrWKrZkrqbCXEWaNYEjyeQxNGU+wRQrWBYJUKhTNalbWvxlQttpzWwFPBUEd+DbxZ+yMPb9pOtec9KzYQoirkrygZHJDezRNP8DAkmVclPMWAOqJJERDRUXjeFAXPu36OyrN0U3WvxAtbc3hBXy74wNQFHTd/TugKCq6rpEck8btYx4h1Gpsae+s4kO8u+ZvVDsqTrsv2BLG7WN+S5fYngGN/1wilQpFqzMu73NPMlC3UmDdunyzct8gvvpI0wSgmjkQOZLtMZOaNBnoUb6Zi3PeQEHzJANwMjGIt2Vx3ZHnUHWZ6ijOTduy1jBvx/vo6J5kAPD8O6cknffXPo+mN7zSZ13FVfm8vXo2NY7KBu+3Oap4d81fKaps4Wqj5wBJCESzOa/gGwC8Feat3X5Z9r+bJZ4moetMzPsMHcXr8zShkWA7Sq/yTc0amhDNQdM1Fuz51G+bzOKDHMzf6Xd/qw/9iMNlQ/cyX0hHx+Gys+rQD2cUrzhJEgLRLDpX7sWE0+tBsq4OdmMFhVqjjrYjJNiOovqZ7KihMqR4cTNFJUTzOVK4j+KqfL/tVEVlQ4bv3wGX5mLj0aV+zyRousamo8twaXLW7WxIQiCaRdfKXYaSgba+rE+03f8fQnBfPoix5zZxNEI0v6IqY6fuNV2joML370CNowqbs8bQ/uwuG1X208cYCOMkIRDNwmZqH4NBnYqlEW2tTRiJEC3DYjL+ubaYfbc1m4z/Pp1Je1GfJASiWWyNntRgLYBT6TTuoNraZIf2wmEgfg2VgxFDmyEiIZpX97h+hqYUKij0Thjis02QOZjk6DQfI3JO7ispqhshlrDGhCpOIQmBaBYuUwjFlo5+2ynA1ujJTR9QE7GZQtkRPRHNz6+Wgs7WmCnNFJUQzSciOIoBSaP9JgWKojCyq//f9XFpM7wOKKylozMudWaj4hSnk4RANJsPuj6B5mW4Xe22clM0P3W6rTnDCrilHa+jyJrYYFJQ+zwXJt5KsTWxeQMTopnMGngLUSFxXpIC97f9K4fcQ2RwjN99Deo8lsGdx/lsMzBpDENSzjuTUEUdUphINKsQewl3p/+eMFdpvZOAOnAsqDvvd3sSTI2r4tep+hCDSpYT4SjAoQZzMGIYeyNG4VJPnro3aQ76lK+nR/lmrFoNZZY4tkefz7GQtMA8sVMEuSqZcvwjBpSuxFyn3kChNZHlHa9lb+ToJulXiNaiwlbG9zs/ZHv2GjTd5dmeEJHMjH7X0SdxmOF9abrG8gPzWHno+3oDB0Ms4ZyXNpNJvS5rdOXD9sRoYSJJCESzSqhO55rMF4lwFqPhPkVV+9+skB7MTXmYaoPLFge5Krki6590r9yJCxUTGhoKKjqVpgi+SPkVWaG96Vy1j6sy/06Yq9xzf2379LD+fJn8EDZT01x7DHZVklK1B7PmoNQST05ID1Da+lwKIYyrsJVxpHAfLt1JbGgCnaO7o5zh74BTc5JesIcqezmh1nC6x/fDrEoFfn8kIRCtTowtlzvSH8ei2epV8KvlQqUwqDPvd38Kp+p79LGqO7kp4y8kVR9qcF8aCi7FzLyke7kk5zVMurPBixUaKjkhaXzY7XE0Rf6wCCHOPVK6WLQ65xV86TUZAHcFvw62TPqXrvK7r17lm0iuPuB1Xyo6Jt3F9Nz3MOkur4WCVDSSqw9I1UAhRLsnCYFoFkGuSvqVrvF6AK+lozC8aIHf/Q0rWojmZyqSikaYq8xvnxoKwwz0KYQQ5zJJCESziLHnYcLlt52KTpw9x2+7DrZMv+WBwVjlQxWdjrYsAy2FEOLcJQmBaBZaI0YA6wY+lkbaNIa/ugFCCHGuk7+ColkUWpOoUf0PCNVQyQzt7bfdkbC+uAwlDv65UDkS1tdASyGEOHdJQiCahUu1sCVmit9v4ioam2Kn+93f5phpmPyODVApsCb5TRxMaGyOmea3TyGEOJdJQiCazZr4SymyJnhNCnRgd+QYDoYP8buvzNA+bI6e4vUMgIZKuSWGL5MfosIS4zUp0IEt0ZPJDO1j6DkIIcS5SiZet0GKrpFWsZUulXtQ0Si0dmJ31LhWsaJgqLOU/qWriXLk41QsHA4fxNHQfqAo2ExhzOn2Zy489pZnmp+7UJCGS7GwMXY6SzteB0bGGygK8zvdQaUlmtEF32HRbWioKOio6KSHDeC7pJ9RaYnh/W5PcnHOG6RVbkdDQT/Rp0Oxsi5uFis7XCHFgkSr5HDZ2ZG9jmOlGaAoJEen0r/TSFnVTzQJKUzUxnSp3M0l2a8S6SzGhbvEr4oLp2JhRYerWBc3q0UOboruYvLxTxhRNB8FzTPoz4SLQmsnvkx+kPzgLp72kfZ8elZsweqqpsISzf6IkWec0Fi0GnqXbSDCUYRDDeJQxJAG1wmIseeSVr4Vi2aj3BLLvsiRONTgM3vCQjSxzUdXMG/H+9ic1aiK+3dd012EWMK4bPCdDOo8poUjFG2FVCo8ByVX7ePGjGdQvC4RBCs6XMnKDlc1b2C6zkU5bzCodHmD0/w0VByqlfe6P01RUFLzxiZEG7Tp6DI+3/KGzzbXj/iFJAXCEKlUeK7RdWYce8dnMgAwPv9LIh0FzRgYJFUfZLCXZADcAwUtmp0pxz9s1riEaIvszhrmbf/Ab7uvt72LU3P6bSeEUZIQtBFJ1QfpaMvyW4xHR2FI8eJmisptWPFPfkfyq2j0qNjW7MmKEG3Ntuw12F01fttVOyrYlbOhGSIS7YUkBG1Ep5rD6Abq7qloJFUfaoaITupcdcDvFEAABZ2E6oymD0iINiyr+LBnzIAvqmIiu6R5f9fFuU0SgjZC0XVDRXZagtJqIxPi3Ca/eSKQJCFoI3JDuhuq3a+hkhvcvRkiOiknJM1w1cC84K5NH5AQbVhSVFc03f+6H5ruIimqW9MHJNoNSQjaiKyQXhRaO/ld4U9BY0vMlGaKym1zzAWGqgYeDhtEqbVDM0UlRNs0OHkcFpPVb7tgSygDkkY1Q0SivZCEoK04UYjHfcj3nhSsjbuEUmvH5osLyArtza7IsV7HOGioOBUzSxJuaNa4hGiLgi2hXNT/Jr/tLhl4q6HEQQijpFJhG3IkrD+fdfkNl2S/Rqir3FOYqLYQ0Jr4S1nR3DUIABSFbzv/HzY1lCEli+rdpQLlltjTChN1qDnCqMIfCHZVUWqJZ2WHy6kxnzI/VtfpVHP4xCBJnePB3cgK6dW0hZd0neTq/STUZAAKx4JTyQlJk0qGolmN7n4BAN/v+hCHy16vMJHVFMylg25jaMqElgxRnIManRDs3buXb7/9loKCAnr16sW1115LeHi41/bz589n3rx59baFhobyt7/9rfHRCg6HD+ZfvV6hd9kGUqr2ououCoOS2Bk1gWpzRIvFpekqwa4KFEDh5GAnHTBrNs9ZjUh7PjdlPEOUM9/zWAUYUTyfoyF9+KjrY6Ca6Vy1nxnH3iXBdtRz5kFBp9DaifmJt3MkfEDAn0PXip1Mz32feHtOvT6PB3Vhfqc7yA7tFfA+hfBmdPcLGJJ8HluzV5NTkoGiQHJ0GoM6j8FqlgqbIvAaVanw+eef59NPP2XatGlERkby5Zdfkpuby5o1a+jcuXODj/nLX/7C22+/zcMPP+zZFhQUxD333GM4SKlU2PrdmP40Xav3Nnifjrs+wscpj3Bt1kuYdUeDFxd0oNiSwPed7uaGo39tsAiTdiLlmJvyMIcihgQs/tTyrVyT+SKcWAvh1D51VD7u+hiZskyyEKKNaZLSxQcOHKBnz56e2zabjc6dO/PII4/wyCOPNPiYv/zlL3z77besXbu2EeHXJwlB69azbCNXZ/3dZxsdcGHGhNNvNYUaNQSrVuN1VoWGQo0pjH/1+jeacvZXvVTdyS/230+wq9Jnn+XmWF7t+bKxxZeEEKKVaJLSxXWTAYDq6mocDgdxcXE+H3f8+HEee+wxnnzySX744YfGdCnagIn5c/1OiFTAUDKgA8Fatc8plio6oa4K+pStb2SkDetdtoFQV4XfPqOchaRVbA9In0II0do0+qvOsWPHeOCBB7jjjjsYPXo09913H7fddpvPx3Tu3JnQ0FDKysq48cYbueyyy9A079PUbDYbZWVl9X5E6xVnyzFQQxHDbYycsnJhIqVqj4GW/nWp2uMZoNlcfQohRGvT6IQgKCiIPn36kJaWRkREBIsXLyY/P99r+9tuu42VK1fyxz/+kRdffJHVq1fz448/8v7773t9zOzZs4mKivL8pKSkNDZM0axapl6ayUDxFiNU3YXR5xCoPoUQorVpdEIQGxvLAw88wOOPP86aNWuoqKjgiSee8Nr+1IN53759GT58OCtXrvT6mMcee4zS0lLPT2ZmZmPDFM2o2hQesJTA6H4UNAqtgVlKudCaZKj8sop7RocQQpyLzmp0lMViYciQIezfv79Rj7PZbDid3pftDAoKIjIyst6PaL02x0wzNDag9scf3VAZZJUd0YGZh70zeryhPp2Kld2RYwPSpxBCtDaNSggWLFhQ73Z+fj5Llixh+PDhnm3ff/99vRkH8+fPr/eYRYsWsWXLFmbMmHEm8YpWaE38pdSoIV4P9rXbt0ZNqne7oXYuTKyOv9RnfzqwNn4WVeaoxgfbgCpzFGvjZ/lNVlZ0uBK7KSQgfQohRGvTqDlbc+bM4Xe/+x2DBg2iurqahQsXMnr0aP74xz962qxfv5433njDU3jok08+4ZFHHmHIkCEUFBSwaNEifvnLX3LDDVLG9mwEuypIqj7kLkxkTaI4KPGM92XWahhcvJQwVykl5g5sj54IqvGPhqaaeSv1r9x1+PeEaJXonD44cFX8ZazoeC3HQtK4MPdtOHG/Uue/TsXCf7v9mePB3dAUExPyv0AHz+h/DQUVnfWxF7G8w9WNfp7BzgoGlSwlWKukwJrs/ravunPi5R2uxqQ7GV34HToq6om1GTRUFHRWdriCdXGzGt1nS6gpt5C5Mw6nzURcl3I6pp75oFxN18gsOkClvZxQawRdYnuiNjDtsspeQVbxIVy6iw7hScSHn/nnUQjRMhpVhwBg//79rF+/HovFQv/+/RkwoH7FuPXr17Nt27Z6hYdqHxMaGsqwYcPo1q1bo4KUOgQnhTpLmXT8E/qXrcasn7zscjS0D0s7Xteoanqq5uTqzBfpXrmj3pQ7DZXdkWOYl3Sv54BpxPjc/zG2aB7mOgsd6UC+pTOfdHuMSksMqu7k/OP/Y0Tx/HrxV6nhLEq4gZ0xkwCIsedywbEPSKvc5rkcoQNHQvuyMPF2CoKTDccV7Kzg2qN/I6nmUL1LG07MbIydxpLEmz3bou3HGVK8mMTqdFAgJ6QHW6MnU9YGFmWqKrWy4JXBbJ7XHaftZEKXMrCAafdvo8fo44b3pekaaw7PZ/nB7yivKfZsjwiOYUKPixmXOgNVUSmvKWX+7k/YlrUaV533s1tcH2b0vZaucb0D8+SEEGesSQoTtRRJCNzCHcXcmv5nIpzFnm+wtdw1/RTmpvyawwYq+Kmak3sP/pIIZ/Fp1/9rv7EfC+7Ge6nPGIrtouzXGVS6HDh9eqGO+9v/62kvcNGxt+hWufO0Of+1fS7vcBV7I0dxa/qTWLSa01ZRdC+UZOHDbo+TG5LqN65gZwX3HXgIq17j9XnuCx/OF11+beh5tlZVJVb+c/t0CrPC0V31kzhF1QCF62avYtD0o373pes6X2x9i01Hl3ptM6zLRKb1uYbXVzxBWU0xml7/fVJQURS4ZfTD9E4YcgbPSAgRKE1SmEi0rJnH3ia8gWQAQEVDQePy7H9hdVX73dfFx95oMBmAkwf0TjUZjMv/0u++Olfu85oM1G4z6w5uS/8T3RtIBuo+bmL+51x99AWsDSQD4H6eZt3BlVkvo+i+l1wGuDrzxQaTgbp99qrYRM+yDX731Zp9+8IwihpIBgB0TUXX4bPHx1JRFOR3Xzty1vlMBgA2H13Of9e92GAyAKCjoek6H2/4JzaH/8+jEKLlSULQRkTZ8+hRsaXBg2QtFR2rVkP/0lW+d6Zp9Cld57dPHRhRON9vuynHPwJ8Fx5SgHBXKf7mGWioxDryG0x6aqloRDkKSa3Y5nNfVmcVydX7DRVEOj9/roFWrVNFURDbf+yG1kAy4KEraC6VTV+l+d3f6kM/ovj506CgkFOa0WAyUKdT7C4bW7K8TzEWQrQekhC0Ed0rdxhqp6OQVrHFZ5vEmsOYDZQRVoBQrRw071NE3fvLMFyF0F87Fc1wpcK0iq0+2/QtW2s4rjhbtoGWrdPhDQm+k4ETdA12L2t4EbJaNkc1R4sPoPtIyAD0RlSe2Hd8q+G2QoiWIwlBG2HR7J4leX1xnyWw+2wTbOCSQi0FsGo2P238n7oPPB2z7vDZIkirMrw3I4WJWitHjdEZIQqOat9tHZrv1/RM2J2+Pz9CiNZBEoI2otQS73PxnVouVEr8jIjPD042fPjTAbs5zGcbuxrcApUKocziZ1Gt4G6G+3QqFoM9tz7RSRWG2ikmjdhk321DLKFYTNZAhAWAqqjEhnUM2P6EEE1HEoI24lD4EKpNvg/MACY0tkVP8tmm0hJDqTne78FXB7JCevppBTujxhuqVKjhHiPgu51i6EyIgsb2qIk+2xwJH4BNMZas7IsYaaBV69R9eB5RCZX4S6d0l8rIKw75bGNSzQzvcn6DtQbqUhUVk+o/idJ0jRFdzvfbTgjR8iQhaCNcqoWV8Vf6bKOhkhHaj2wDB/HFCTcCvqsGAixMvNXvvpYmXIcTs8/DkQLsijzvRJwNH/BdqFSZItkSPcXnvjQUtkdNNFQbYHX8ZYDv56mjsijxJr/7aq1UFabdtx1fIzRUk0bnfoX0HHfM7/7OS7sQs2pB8bI/BQWTamZi2kU+96MoKqnx/egSa7w2hhCi5UhC0IZsjJ3B6jh3WV9Xnbeu9lt3dkgPvkj5JSj+v2HvixrNsg7XAPXXGKj773lJ93LcwFx/pxrMB93/jOtE4cu6B9/a/e2OGM23yffxZfKDaIqp3pkC7cQV/GpzBB93/T0LO93Gjqjxpz3P2n8fiBjO/E53+o0LYG2HS9kaPcVrXBoqH3d9lCpztKH9tVbDLk1nxoPuwaSq6eSYDncNAkjoUcJt/1qKavJ/viQuLIHbx/4Oqzn4tKRAQcFqDuL2MY9wQd9rmNTTnXDVPaNQO0MhJSaNm0b9EsXA57EpFVXmsStnAwfzduL0M0BWiPZMChO1QR1rMhhW9JN7Tr/uIj8omS2xUzkYPhRdMTVqX/E1mUzNnUOX6r2ouguXYuZQ+GAWJdzc6Op8yRV7ufjY68Q48jyHEadiZlvU+SzsdJs7Nl1nZNGPjC34hjCXu6SuDhRYk1iUcBPptUWVdJ3k6v0MK1pIcvUB0HWOhaSyOeYCjoT1N5T01Iutci+T8z6hU/VhFDQcShB7I0exOOEGasznzuJZxw9Fsu6znuxflYTTbiK+axmjrzlAv0lZmCyN+1Wvspez6ehytmSupNJeRpg1gqEpExjeZSKh1ggAKiqcvD/vO7It34Clxv1ATSGiYixXjb6JXr2iA/wMjduWtZofdn1MWU2RZ5uqqPRJGMq1wx/Aag7cWAkhWjOpVCiaVWrFNq7KfAlF1+rVSqj9cB0KG8znKb9ieu77DC1Z4lmXoFbtmgHfJd3Djmi55twWlJU6ePGLl3FEbwdFq3/FwmUGZwhXp/2FYcPjmz22Jfu/YuGez7zeH2qN4JFpL2M1BzdjVEK0DKlUKJpNiLOMKzNfRtVdpxVOqq09kFa5nSuz/sHQkiUAp82YcBci0rko503ia7KaJ3BxVt6YOw9H9A5QtdOHL5icYKli7v5nqKl2NWtcx8uzfSYD4D778e6a55opIiHaBkkIxFkbXLIUk+7wOS1SQSetYqvfgYc6CsOLFwY8RhFYx3NrKIr5GlQfB3vVBaF5zF90sPkCA77f+aGhdkeK9lNlr2ziaIRoOyQhEGetX+kaQ4V9VAMTCk1o9C1dE5jARJNZue4wqAYG6Gkqu477L5MdSOkFuw23XZ+xqAkjEaJtkYRAnLVgrcpQiWCjrJoshtPaVdVUgW7gz4eq4aC06QOqw6Ubv0RRaStrwkiEaFskIRBnrdwc7bW2wJmoOodG/Z+rosIi3WMH/HGZCKZxs1XOlkU1PnsgRqooCuEhCYE4azuiJ/q9ZKADTsx+Vz3QUNkuswxavckTU8Huv3ImJhcjuo1r+oDq6NdpuKF2Cgqjuk1p4miEaDskIRBnbVfUeVSaonyWJVaAbTGTfX7gNBScioUtMVMDHqMIrIhIM11qbgTdx5khlxlTSR/On5TcfIEBF/W/yWuVxboGdB6DWTW6MJQQ5z5JCJqTrmPW7Kh6YKqlqboTs2YHX6UkNI1gZxlmrSYgfTbEoQbzSddHqTGFnXbpoLa64Nq4i1mQeBsLE26pt90TJipOxcJnXX5DuZ9Fi9ozXQd7tQmtJRaYPMWd100kqmCW+4ZWpyCWroCuoFYmc8+khzGb6lSl1HQqKpzYbb6v82u6ht1Zg6Y3/omGB0dx48iH8FXKuXNUd64bdl+DfforzeJ0OXC4fK8o6nmfXIG5lOZ0OXC6Ar8SpRB1SWGiZhDsqmBY0U8MK/6JCGcxADnBqWyMm8nuyLHofhaSqUvRNfqVrWFE4Y8k1RwGoNwcw+aYC9gcewE1pnDAXYFwxrH3SK7e55kOaFNC2BozicUdr4cm+GbUofoI03PfJ6V6Pwo6OlClRrAxbgar4y/3VBfsVH2I4UXz6VO2AYtup8oUzrboSWyOuaDR1RHbi+OHolj9UW+2fNcNp82MatboPyWT827cR5fBBS0Wl9OlsWjREdZl/kBNzDpQXahl3ekTfDmXTB9IVLT7ev7x3Bq+/mk9GaYvIDQfgKDC0YzodBEzpqd6kobMooOsOvwjO3PWo+kuzKqFIcnnMS5tJomRKY2K7Xh5NvO2v096we4Tn0YID4rivNQLOb/XJZ526QV7WHX4R/bkbkbXNSwmK8NSJnJe2kziwzsB4HDZ2Xx0OWsOLyCvIhuA2LAExnafzsiukzwFjopzwljzcS82fJWGrcKKouj0HJfDuBv308vAOhJ12Z02Nh5Zwur0BRRVHgegQ3gSY1NnMLzLxICuSinObVKpsJWIsudxc8bThDuL663jV1upb3/4ML5MeQhN8X+AVnUnV2T+g14Vm+tV+nPX5VcoN8fyYbfHibdlcXXmS6etG6jj/s5UbOnAG2kvoAUwKUioTueGI88SpNWcKDJU+zxVVDQ2xVzAgsTbTy85rGvQiISoPdq1OJmPH3Gv7aC5Tr5WqklD0xQueWQTY6/f31LheWiajqbr9c4IAOzdXcoH2/4MwUX16xZoJlBdRBdcxq9vu4aNmT/xzfb3UBW13pkB9zoJCtePeIABSaPOKDan5kRFRVXrx7bswDzm7/6kwT4VReWW0Q/TNaYn76z5K5nFB6mtlnGSQkJEMnef9wcK9nTn3fsn47CZ0Ou8T4pJQ3epTLx9FzMe3Gao6naVvZy3Vj1LblnmiS31/0wnR6dx57hHCba0rb+HomVIQtAKKLrGPYd+S7Q977QKfrV0FNbHXshiA6vtTcn9kFFFP3gdwKehUmLpQLTjuKdCYMN9wpHQfnzc7Q/GnogfVlcV9x78FcGuqnrJwKkWJtzCxriZAemzvTh+KJJ/XX+R+9Szj+v1d/5nET1GH2/GyIypqXbx9BePoYfkgsnLZQJdIbH6EnJDv/G5L1Ux8YtJz5IQGZgxCbuPbWTO+r/7aKFgVs2kxvfjQN4OdC+fbVVRSYnsS+7jm7FXm9E17wnuVU+uYfil6X5je2f1bA4X7PZ6yURRVPomDOPm0b/yuy8hpHRxK5BWsZU4e67XZADcFfyGFf+E1VXlc19WVxXDihf6HM2vohHrOI77+5R3CtC1ajdWZ2CqtA0oXUWIq8JnMqADYwq/RTmDa8Lt2ZpPep84BeT9HVVMGive79t8QTXC/EUH0cOzvScDAIpObtD3+P7UAuisSZ8fsNiWHZjnZ/ChjktzsT9vm9dkANxjD46U7sIWuctnMoCis+yd/j6H/AAcKz3KwfydPsdP6LrG7tyNFFa2viRQtF2SEDSh/qWrfY68r2XR7fQs3+yzTc/yzVh0/4OK6i5f7IsCjC763kBL/waWrDDUX4SzmOSqfQHpsz3Qddjybfd6lwkabOdSObC2E1Ulre+a8o7c5eDrIFnL5MTfJ1fTNbZkrvQ76M+I4qp8MosPesYWeOMrEagfnBkG+CmZrCsUHInk2P5on822Za2ut5y0N4qisi1rtbH4hDBAEoImFOYs8fmtuZaGQqjLd8W0UFeZoeI/vi4VnCrSUWiwpW9hrlLDffp7nuIkl0PFUWNwnIeuUFkS1LQBnQG7ucBYASODHC673xH+RlTaygMQTR06EJZvrO8i3yssVtrLMPJbrKBIpUURUJIQNKFqU7jBg7hOjcl3kZcaU5ih9QKMniEAqDRHGWzpW7UaZrjPGtVAMRsBgMmiYbIaL8MbHNH6pqWZXdGgBa6KpUk1B2R0fYg1wJ9DBaiONdZ3pO+EJsTwQEGdEGu4wbZC+CcJQRPaGzna5wqAtTRMHAwf5rPNwfBhaJh8tgHjZwd0YH3cRQZb+7Yr6jxDPVeZwskM6xOQPtsDRYGB046gmnx/w1ZUja5D8oiIa7paE2eqb+wEUAyki5q/kS/uwXsDk0ajGBmm70dsaEcSIlP8FjBSjP6JVJ2w52o/jXSiEipJ6lvks9XApDFoBtZj0HSNgUmjjcUnhAGSEDShfZEjKTPH+BxHoKGwPXoC1eYIn/uqNkewPXqCzzMOGirl5mjA91kCHTgWnEpVgM4Q7IieiF0N8hmbDmyInWloeqU46bwb96H7+YatayoTbt3bTBE1zoUX9Iaa2PqFi06lqcRWTcLfuS1d1xmXOiMgcSmKwsQeF/sdQ4ACXWJ6+LymryoqCWGpqLmj/SY/E27dg+rnr25yTBrJ0al++0yN7xewGRdCgCQETUpTzPyvyyPY1JDTkoLa2gE5IT1YlHiLof0tSryFnJAe6A1cPNBQsakhfNrld3zf6R5PH6f3CdVqOB8GaMohuJOVuSm/xqWYG6hA6I51X8RI1sRfGrA+24vO/Yq5/PH1oOinnSlQTlybn3zPDvpNzmqJ8PwKj7BwZY8/gyMUXKckg5p7KmVI0XgeuPJ2pvS+Ejj9W3ntgfGywXeSHJMWsNiGJI/nvNSZJ/qsn3SpioqCwrXD7uXm0Q8TG5bQ4NkERVGJCI7htnEPcfPzK1FNmtf3aegl6YwxUC9CURRuGvVLIoNjURpIChRUYkI7cv2IBww/VyGMkDoEzSDSUcDowu8YVLwMq24DoNQSz8bY6WyKmYarEauzmTQ7U45/xKCS5Z59aShkhA3gx053UGpNAKBrxU6m5b5PvD3H82fMhYn9EcP5tvPPcarugU0RjkKGFi+iX+lqgl1VVJoj2RE9kW3Rk6iuXXXQ5WJa3gcMLlmG+cRMBw2VQ+GD+arz/bhMIQDE2bIYXfAd/ctWYz5Rnjnf2pkNcTPZHj2pURUZRX3pmzqw4oO+7FvRGf3EFMRuw44z4Za99J2U3cLR+bd3XxFfbpxDeeh6UE/8ybGHk6pczq2XTMca5D6DsCd3MysPfkd64ckzHr0ThjChxyxS4xs3tbIoX+PTD4+SGfw5RKWDM4TgYzOYNHgiE2e4f+d0XWdnznpWHvr+ROEhd3LQr9MIJvS4mC6xPQGorKng7cX/Ide+9eRZAB3iTf25Y/K9xITHAJCzL5oV7/dlx4KuntkhnXoXcd5N+xg6K91QUaJaVfZyVh78gXUZP1HtcE8RDrNGMLr7BZyXdiEhFhmPI4yRwkStkEmzE+4sQVNMlJtjGl+hT9e54PgcRhb9iAsF04lzALVVC48Fd+PTLo/Wu/wQ7Cwj3paNQw3ieFA36p6v7Fm+kcuz/oWqa57ZELVnLhxqMJ92eYTcoG48uP9egvUaT6XD2nbKib7fSP0rxcEnT11atBrCnGU4FAuV5ujTqxOKM1ZdbqGqJIjgcAdhMbaWDseQsppi3lk9m7zybBRPUWs81QEn97qcC/pcXW9sQJW9nGpHFaHW8DM68O3Y4OTjnU9CzGF3DYfaJMRlBpOTDgf+wK9+06/eYypsZdic1YRZI+pVAKyutjH7mz/gDD5GQ78Eqj2Gh6c/R0zUyThtVWYqi4KwhLjOemyHS3NSVuMueR4ZHItJ9T+WSIi6JCE4B40umMeUvE+83q+hkh3Sgznd/uT3IJxQnc5t6X9GxdXglX8NBYcahF0JItzHtEId95mH5/t9YPh5iPZD0zVeWfoH8sqzfBbauWzQHYzufkFA+qwo03n28z9D1JET9Q0alpb1PHfdn+R3f09/+gTVQQd8lv4025J46rrnzzBiIZqWVCo8x5g0O+MK/JR2RSOlej8pVf4HmI0t+AZOWeug/r50LFqNz2QA3H8jzbgYnf+13z5F+7Pv+FZyy476XbVw8b4vcWnGp1j68skHORB7yGcygK5wKOQNXC7f34cOHz3uOxkAUMAZnMPmXYfOLGAhWglJCNqIHhVbCNZ8lzcG97LCg0qW+WwT5Kqid/lGnyWVwfiHQwfGBKjqoTi3bDqyrMGBcacqt5VwuGBXQPpM17/1Xx1R0SHuAIvm+T6d/816g4muDgt3fWkwQiFaJ0kI2ohIR5GhIkcmNKL8VCAMc5YaqqAIxusaWLS2cT1bNK/iqnx0g+tXlFb7np9vlB5x1HB1xGO5visWVrqMVR8EqNYDU/lTiJYiCUEb4VCDDFUq1FCwqb5Lo9pV4yVujQ4wMbJmg2h/gsy+P4t1WUwBKr3sMF69LzjI4vN+s2I8JhOtr3S0EI0hf8XbiEPhgzFW31znYITvqocV5hjyglL8nnHQDPXobnM0rHWutidaVr9OI/xWAwT30sY9Ow4ISJ9RxdN9F0KqZQ9j2izfxbmGJk82/EvQJ3aCsQCFaKUkIWgjyi1x7I8Y7rfqoU0NYXfUWN87UxTWx13ot6yyysnCQt7UFjv6pvO9vvsU7dKwLhMxqb6rUyqKyuDkcYRafVfrNOrS6UNAV32f3tJMhKbfTGwH338Cp44eCs4Q/6U/NTOXTT7/DKIVovWQhKAN+bHTnZRaOjSYFGioaIqJL1J+hcPPJQOAHVET2BE1vsHFkGpvr4q/jLnJv6q3raF2q2MvwW6SRVbE6UKt4dww8sETlf8aqLqnqHQIT2LWQGPVOo3oO8REj9zZgNLw4ELNBLnDuO+O8X73ZTKpXN3nMfe+fPwSzEh+GKtZynKLtk3qEJylMGcJg4uXkFqxA7NupyAoiW3Rk8kM7XOyFoCuk1R9kCHFi+lgy0JTTGSEDWBrzGTKLXGN6i/EWc6E/LkMLFmOVXevmqYDh8MGs7zjNeSGdDe+M11jTME3nFfwtWdf4F69cHHC9WyPmQJAl/LtXJ31MlbdVq8miwsTixNuYFPchZ79davczeCSJcTYj+NQgzgYPoTt0ZP8rtXQ1KrLLGyel8rupZ2xVVqISapk+GWH6TXuGKqpcb8C9hqVxa8PZPO87tgqrZisLnqOOcZFv95MVMLJUeuVxUFs+jqVvSuScFSbiU2pYOQVB0kdddxvPftT1dg0flp0iG05K7CbCjBrUfSJPY8LL+hNeMTJ6+DlNSVsOLKEA3k7cLrsxEckMarrZLrF9fEU/tE0nU0b81m+cxVlyiEU3Uwny1AunDiSLl2b5vfrSNF+fto7l0P5J2cSWM3BjOw6mam9r/QUAiquKuDTja+QVXIITdcxKSppHfpzzfD7Cauzsl9RdhgbPu/B4U0d0V0KnfoUM+qqg3TuW+xp88WcEjZWvwod68xecAQTdvhW7rtzIjHx7tfD5qhmS9YqduWsp9pRSVRIHMNSJtAncZinCNCu/Uf535ZXcQRl1itMZLZ14tJ+P2fEwJ6Nej2qHZVsPrqCPbmbqHFUERPageFdzqdXwuB6axgcL8tiXcYisooPoSjQJaYno7pNpUOE//oJTcmpOdl9bCNbM1dSVlNMqDWcgZ3HMLjzWKx1xo009Hkc2XUy3et8HkXTa5LCRBUVFfzrX//i66+/pqCggF69evHLX/6S6dOn+3zcDz/8wOzZszly5Ag9e/bkqaeeYty4cYafTGtNCAYVL2XmsbdRTqwuoOCe9mdC43DYQL5MfghNMXFp9r/pXb7Rcx/UDsLTWZRwMxvjZja6b6urmoSaDFTdRVFQp0YnFgADS5Zxcc4bntsKJwuxuVD5LOW3pIcPZFzB10zM/wydk6eUXIAJ2Bl1Ht8l/QyrVsPVR18kpXq/53nWVj3UFBPzku5lb9SYRscYCPtWJPHRI+Nx2EwnvtEpKCYN3aXSqXcRt7+ylIh4Y9Xkjm6L4817LsDlMHHy1Tr5K3Thrzcz4ZZ97FiYwv8eH4fmUNFP9KmaNDSXSpfB+dz6j2WERvleBrfWgf3lvL/uWbTIo+5vt6rLvQ6AokNNLFem/YkRIzuw6cgyvtz2NrqunVYNsEeHAdw08pe47Fb+8cmHlMfP91TtA05cc9fp5/gVN1/rewzK2Siuyqew8jgm1UznqO5YzScH4i3Y/RlLD3zl9bFXDrmH4V0mseydfiz492AURUc/cQag9rUdclE6Vz6xjtLSal755j/Y4taffM103JcSNDPjQn7PrIt6crhgD3PWvUiNs9rTj6Ko6LpGfHgn7hj7O2JCO3juyzpWyKa9u9HRGdyjD91TOjb6Ndh3fCsfbfgnDped2s9ObZ+dIrtw+9jfERYUybc7PmBt+kLPewgn388JPS5mZr8bWuSgWlCRy7tr/kpxVb6n8mTtf0Ms4dw25jd0ie3JpqPL+HJrw5/HtA79uWnkL+tVhBRNp0kSgocffpjY2FhmzpxJZGQkn3zyCU8++SRLly5l/PiGT7+tWrWKSZMm8cwzz3DJJZfw7rvv8sorr7B582b69DG2FG5rTAh6lW3gqqyX61UyrUtD5UhYP2xqCL3KN/q8Xv9t0s/ZET2xyWJtSNeKndxwdDbQcPy1B/M1cZdwXqH3gkg6ClujJtLRlkWnmvQGpzOe+JPHJ10eJSM8MAPHjDq6PY437pyGdmIhnVOpJo34bmXc/+GPWIJ8T1Uryw/mbxdejuZS8P6qweR7drL0rQHuW176TOpbxM/fXYjJ7PvXryDfxt8X/h49OB9MDRTu0UzgDGFK95tZnP0fr/tRFJXU+L7k7elCeex8n9PyhvMHrrqsn9f7m8KWzJV8tvk1v+3GVb3D6r/d4fV+RdEZcskBdnW9EXvMVncicKoT78nYyF+yofIVXJqzwVUPVUUlKiSOX0x6NmAHrqNFB3hj5dMnDvAN9xkf3omeHQax6vAPPvc1tc9VTD2xIFRzqbKX848lj1FpK22w2JSCgsUUxMz+N/DN9ne97kdRVLrH9eWucY/JmYJm0CSVCl944QX+8Ic/MHz4cHr27Mkf//hHkpKSWLRokdfHzJ49mwsuuIBHHnmEvn378re//Y0ePXrw4osvNqbr1kXXmZz3iddkANxVA7tX7qRP+QafyYAOnJ/3KYrBudqBMv3Ye4D3+N2HPJ0xhd/63I+CztDSZXSuOeS1tkHtd+jz8z4903DP2E+vDnJ/Q2/gwAyguVTyDkWzc2EXv/v69m/DfSQDeLYve/fEwdRHn1k749m3wv9p368XbkIPyWs4GQD3Ac9Uw9Ijc3zuR9c1DuXvojz+B99z9HXYXPk2dnvzfh7nbX/fULs19qd93q/rClv27sMet6nhZADcZ1Z0hbUF76DpLq9LIGu6RnFVAZszVxiKzYif9n5+oj/vfeaVZ7P68I9+97Vs/zeeRY+ay/qMxVTUlHitPKmj43DZmL/be4l1cH8eDxfs4nDB7qYIU5yhRiUEp2Zy8+fPJy8vj8mTJ3t9zIoVK5g2bVq9bTNmzGD58uWN6bpVSa7eT6w91+9spIa/A9SnABHOElIrtgcmOANCnaXEOY4Zmk3lr5ohnBhk7WdvKjpJNYfpUHPUWJABUJwTysF1nTynlb1RVI21n/m/BrxnmZG15xU0p8mzIqHXViaN9Z/38Lu3w/oXoPh5D0xONHOF/9B0xX2pwWdgoIflsnxFpv/9BUhxVQE1Tv9VOAH0qHT/jUa8Bi4/0w5VDT2o4W+5p1qXvtBQbP6UVBVwMH+HoUJN3pKUupyak21ZqwMRmmFr0xf6jU1Hx1bnEow3qqKy/sjiQIUmAqDRw2L37dvH+eefT2VlJU6nk7fffpuJExs+3V1eXk5ZWRkJCQn1tickJJCTk+O1D5vNhs12svJdWVlZY8NsUjH2XEPtjGZbGgqx9mMcYsgZx9QYHauPGq4vYJS/KYy1YuzHyQ/2/208EIqyjA1k1DWVggzvp9FquRwqjXtVfPTpUsk77HsOfI1NQw8/FpD+APc3YyPhayrHCvKBroHr24f0/EZ8S1SA0FyoSvTeJn6v9zMqjaZTWJkXkD0VVh4PyH5qqYpKQYWxv0WBUHfVxUDQdI388ta/dHd70uhph2lpaWzdupUNGzbw2GOPcffdd7N06dIG29YOTzCfMh3HbDbjcnn/hZ09ezZRUVGen5SUlMaG2aQ0xXgeZeQwqaDjVHxXTAskeyMqwgV6CoqmNN/SrSaL8dPejWkbKGar74OW2YzXyw5nzNAHUsdsar7PY7C1Ecsb64DNdyKFK7AVAwO13LDZTz2G1rJPbxRFNVRkqjHMavN9zoR/jU4IzGYziYmJ9OnThz/96U9MmjSJv//97w22DQ8PJzg4mIKCgnrb8/Pz6djR++jcxx57jNLSUs9PZmbznb404mhoH0PrChhpA+4vPUfCmm8QV05wKi4D3+l9jZE4lZHn6lTMZIX2MrjHs5fUpwhriMNvO9Wk0XOc/2/i4XHVGDui+m/j7tP3tzuzSSW4cIz/qnveL0mf0s7gu6noDO3XzVjbAOjRcaDxxpoJXCG+2xyc4Z5B4Y+B10xVVNLiAzMQtlNUt3pT8rwxetDVdBepHfqfbViGqYrqnr4aoPI1iqI27r0XTe6s39ng4GBqahqesqWqKiNGjGDlypX1ti9fvpxRo0Z53WdQUBCRkZH1flqTckscB/xUDazlUsw+/+64UDkS2peioGacV6yaORQ+1G8zBahQI3w+Tx1wKFa/6YWGyu7IsdQ0YwEja4iLkVceQvGz0I3mUhl73X6/+xt/y178p0g6YbE1qCb/fY6++oDfPkcmX+R9cJynS5VwLa3e/PWGKArgsvo+ELpMBBeMpU8/P9/CA8hqspIQYWR8BgQdu8D3+6nomLff4/8100wEV/f1e/DVdI2xqb6nVRtlNQcxsutkv6s/uqfvhfmMTUEhOrQDPTo076ydcakz0P2MK1JQSIhI8ft5RNcZ1W1qAKMTZ6tRCcH999/PwYMHAXC5XHz88cd89913XHvttZ42L7zwAr179/bcfuCBB/jyyy9ZsGABuq7z8ccfs3r1au6///4APYWWsTDxVirNkQ0eLGv/3i5IvJ3vk35Wb1tdGio2Uyg/JN3ddIF68XXyfdjUUK/HBh0oMXfgky6P4FCtXqojuqsvfJ38AIs7Xu953OntVMoscSxOuDFg8Rs19ec76NCtDMXHAfr8O3eR3N//Snvjb9lLx7QSHy10LMEu7n5rIVGJVV6SAvcrNOPBLXRM9T82Zsa07sQUXO79272mElw0mrsmP0CYNdLnH+FLBt3O6KBHcY8cbGB/LhPYI7lhwm1+4wq0u8f/0e+B0qJaufuCn2MNdjWcFCg6igLX/7aQ3jUPurc19IF0mVErOnPnxF/QOTrV54F3VLepAT3oTu19JR3Ck3w+1/N7XsKNIx/yeopeQcGkmrl++P3+D7oB1q/TCIYke6/wqKDQNa43t4z6NWFBUX4+j7cRHdL4+imi6TSqDsHnn3/OU089RXp6Ona7nW7duvGb3/yGu+8+eUB74oknePnllykpKfFse/7553nmmWew2WxERETw17/+lTvvvNNwkK2xDgFAhKOQ6cfeo2fFlnoV/0ss8SzteD17Tqwp0KN8ExfkziHGcXJwkg5khA7gx6Q7KbEmnLrrZmF1VnLTkWdIsB2pV5RIB46G9uWTLo+iqWbia7KYnvseXav21Ht8XlAyixJu8dQWGFiyjIl5nxHpPDnwSENlb+RIFibeRpW5+b511lVdZuHb54ez7YduaC53QShQCIutYcrPdjDm2gMYnQqtafDfh85n38okOOVVi+9Wxj1vLiIivoaKoiC+fnYEuxanuIvhnBAWW83MB7cx/LLDhuN3OnXe/WQV6dYPIajsZJeOYBIrruHuay8gNNRMaXUhX219n315m6l7JIy0duSiQdcyqLP787jgpyMsz34HLfrgyU50hZCCidxw/g306OkejKnrcHRbPNt+7EpVaRChUTYGzThK1yH59V4vh8vO9qw1pBfuRdNdxId3YniX84kKia33PIpzQtn0dRqFmRGYrS7SRh1nwAVHMVvdB/ey6hL+ufRRquynL0kcHRLPL6c+j9Vk5fihSL5+ZhQZW+pfdozvVsolv9tEzzHuSzGff72bzRXvoofXGcCsmYgsnMHtF19FYqdgbM4avt/5IZszl+PSnJ5mIZZwzu85iwk9ZgV8nny1o5Jvd3zAtqw1aPrJMxlh1kim9L6CMd2noSgKR4r28+32D8gurT+zIiWmB5cMvI3kmFTPtmOlR9hyompgkDmEfp2G07PjoCZJGDRdY8n+r1h18Id6s0PMqoWR3aYws9/1WExWSqsL+Wb7++zN3VxvZkJ0SDwz+l3P4GQ/a66IgGmSwkS1qqqqsFgsWCynDwipqKigqqrqtDECLpfLc2BXG1m3tbUmBLUiHQV0qdyDSXdQbE3kaGgfOPGLqOgak/I+YVThdydaKyf+X8emhvBt0v9xIHJEC0XuFm4vZHTh94S6yiizxLEubhY15tNP7cfacuhcfQBV18gPSiEnJI1Tj6SKrtG1chfRjnycioWMsAFUWGKa66n4VFEUxMG1nbBXm4hKqKLH6FxMlsZ9/KvLLXzyu/M4sCbJPR3Q83CV2ORybnl5OQlppRzZGs+Hv5lARWGI+9us7q4sqGsqgy9K58o/rfNbCOlUVZUuVqw6SklFOWHBoUwY14WoaKvn/gNrE/nk0fOo5jhK6mJ01QbFPSB9EqOuPMwlv9uIosL8fw1mxQd9IGWle0S+ZoYjEwmyd+Hqp9bSf0oWpXkhzPnVRLJ3x6GaNHTd/VZrLpWkvkXc8vdlRCVUs/vYRj7b/B9szmpUpbYqo/tFOS/tQmb2vwHdaeLb54ezfm5P90wHTu4rJMrGdc+upledMRx5ZVl8v+sjquzlRIXEMmvgbfWSi63fd+Orv4zEXm327A9dQVF1Jt+9i6n/t8PzsXQ6dVatyuZ4YTFWs4WRw7rSOfn0MQjV9kr2523H5qwmIjianh0GNvnAygpbGQfzdmB32YgKiaVHhwENLgSVU5JBTmkGAMkxqSRGnpylU+2o5JONr3Agb7vn9VcU90E7NrQjt4x+mIRIY5djGsvhsnMgbzvltlJCLGH06jiowQJOJVUFHC7Yg0tzEBuWQPf4vs1+ZqO9a9KEoLm19oTAlwuOfcCI4vk+6top/C/lNxyOGNKscYnGc9pV3rjrArJ3xzZY20AxaQSHObjm6TV89NsJuJxKw+1UnT4Ts7j5pRWGz0z4k7GlA2/dMxVdA11voE9FZ8jF6YRG2Vn1YW8aHAuhuMtvXzd7FQteGUzJsbATZ1TqU00aUQlVzPzXbD7ZMdvnvPSx3adj+/xVNs9LbfAyhaLoKKrOXa8vovvwfL/Pc8fCFD5+ZAK+hrxOvnsn0+5vvroeLcWpOXlj5VNkl6Q3WNtAVVSCzCHcf/5fiA1rfIllce5okkqFonFibLmM9JIMwMkTztNyP4DWn5e1e9t/7ErWznivhY50l4qt0sKXfxnlNRkA0DWFPUtTOLwhcJeKvnthGLrecDIAJyr4fZvKqg/74HVgpO6+8PX1syMpzmk4GQD3N/vi3BC+2vJfvwP116QvYPMql9cxELquoOvw3QvD/ewJXE6Fec+NwN/8l6Xv9Kcsz89MhHPA9qw1ZBUf8lroSNM1bM5qluz/qnkDE22WJARNaEjJYr8zEVR0Yh3HSana20xRiTO15tNehmYslOeH+q2OqJo01n3mv1KhETn7osneHee/IqNiYH6irlBdFoTur6JhykqqzZkG9meCEa/7bqKp5OyNJXuP70tL+1cmUVEYgpGZHhu/SvPTpu1bm77A0CyJrZmrqHEYqwQp2jdJCJpQQnWG1/r+dWkoJNQcaYaIxNnIPRDt96BrlOZSyd4TmBHWufuMjdFwl1M2eo3CT7vEbWDktVBckLDVUI+5B6J93n9sf4zf6ZwA6ArH9vve17kgtyzTUIljl+5s1oqGou2ShKAJ6QYHziiNaCtajqIG9rJOoPYX6LgM0dWTA/r8tjVW6c/feArDz/PEuIRzXWOqBsqKgsIIOQo1oeyQnoaKFynoZIcE5vSxaDopAwv8XjJwM1apsNuQwNTITxlYaKidoUsGHn7aZY02lhDoKmSPMdRjysACv/d7G9dQr0tdoYvB16QtS4ntYahqoNUURIfwZix8JtosSQia0LaYSfj7w6qhkBvcldyQVJ/tRMsbe/1+/9fpTRqxyeXGKhVee9BnG6Piu5aTOjLXb7Ki64rfKwGKqhHZodL/t/VjI4h09fb7LVVRdNj4M799dht2nA7dTq8/UFfaqOPEJpefSGy8M5k1hl1qvM5DWzW2+3S/VQNVRWV410lYzYFd30GcmyQhaELlljiWdbzO6/0aCppi4sdOdzVjVOJM9ZuUTZ+JWV4PSKpJI6pjFdfNXk1QmMNHUqAz6pr9pAwI3LfYWY9swuKtgt8J59+xi5kPbvV6v6JqqCad62avJqlfkdd9KapGpz5FXDvuFlTV5DMpmN73WiZdU+KzT0uQi0sf3ei1jaetAlf8aR2KqnuJzf2+XPybzYRG2f3ur63r22k4fRKGeX39VUUlMjiGyb0ub97ARJsldQiamq4zvHgB5+f9D6tWg4YJ0DGhUWRJ4NvO95Id2rOloxQGOe0q3/5tOBsWRKL3/Qwis6EmCvZcQWr3KK59Zg2RHavJz4jg08fOI2dvLHXPEqkWjfNv28PUe7dTW5/LqTnZc2wTuWWZqIpCl9hepHXo3+jiLTn7ovnf78eRdzjanYwoOppTxRLsYso9O5l4x24UBdZ80osf/zkIR3Vt4R0dUIlOKuf6Z9fQZXABNeUWvnhqNDs3AP3mQlg+VMXDrqvpP1zhqifWERzh4GjRAf636VWKqvJQPPU6dSymIGb0u46x3acDCsvf68tP/03ANf5P0GGve02FHdcTV3wNN8xeQ1LvEs/zKMoKY9fiFKrLrITF2hg47QiRHU6ul3JofQJz/zyG0tywE88TNKdKcISNi361hRFXNP7sQH5GBHuWJlNTYSEivoaB048QHmvz/8AW5nQ5+Hbnf9l4ZAm6rqMoKjo6uq7RPa4v1w2/n8iQk4NOs0vS2X98Gw7NTkxoBwYmjW6wmFCg6LpOZvFBDubvxKk5iAtLYGDSaEOLPInAMVqHoPnWzmyvFIVNsTPYFj2JPmXribPloCkmjob24UhYf/8jqUTrYrajXno/yqDF6JqGgsl92vaCx1A6DECJvBeIpiQnjPwjEac9XHOopG/pgKaBqsLmoyv4ftccquwVqIo7WdR0jZjQDlw19GekxhtfBTOpdwkPzf2eI1s7cHBdIi6HSlxyOQOnHyUo7GRZ3oyt8SeSgbqll6E8L5S89Ei6DC5ACS5Due46GL8OdE4+z+m/RU0aBcF3A6EoinoibrfaPaqKiqqoKIqCpmlk9L0L10Nb6l9B676EUvUBCsPvJYnRVJVa+fyJ0exZluwpWKS7VL5/cShDL87g0sc2YA1xlzz+7Xdfc3BNJ45si0fXFBLSSuk/NdNTBtmo8oJgPvvTGA6uSUJRNU+f370wjJFXHeTi32zG3AJLYxtlNlm4fPCdTO19FTuy11BWU0KwJYS+icPrVSgsqMjlf5v+TVbJYc8aCZruYt7295nYcxZTel8Z8OqBuWVH+d+m18gtO3pi3yf7nNL7iiYpCy3OjpwhEMIgTdf4cP3Lp9Vmr6UqKlEhcVwc9Rpz/u/KEwe/hmtUJvYsYezzj/Dl1rca7EtBQVFU7hz3aKOSAn8+eGgie5d7K2Xrfk6XP7GUjTH3kF2c3uA1agWVpOhuXDzgZt5ZPRuX5vQ6/W1mvxvYe3wLGYW+62xcN+g3LH3kD+SlR6I3MHDQPc4gjztfXdLoctPeVJVYefWWGV4rMiqKTp+J2dz04gpUU6v/M+lVUWUery7/IzWOKjQvRYzGdJ/GpYNuD1ifeeXZvLb8T9hddq+Fkyb1uozpfa9t8D4RWFKpUIgA23NsE3tyN3k9+Gm6Rkl1IZ/+sNRHMuDenpup8PXW9732VXva94stbxKonD17Twx7l3f20cId7zcLt5BVfNjrgDUdjZySdD7Z+IrPZABg/u5P/CYDAHO3/oe8ww0nA+AuXpS+MYGt33f3uy+jlr7T32dFRl1X2LMsmd1Lfb1mrd8Puz72mQwArE1fSFbxoYD1OW/H+zh8JAMAS/d/TV55jtf7RfOThEAIg9akL/C/lr2u4ej7Pliqfe9s8HtoutNnEx2doqo8DhXsamyoDfr+paGG2mnDX/XbRkenrKbIb2EcI4VzAFxqBXr3hT7bKKrO6o97GdqfPw6byoYv0vzPGlE11nwSmD5bQllNMbtzN/pMBsB9dmtt+k8B6bOwIpdD+bsM9bk+Y1FA+hSBIQmBEAZlFh30+Y3HI6gc4vb7bpOy1mt9/7pUReVo0QGDEfp2/GC0/0bBJRB7COP1CgKo7+c+79Y1lWP7YnE5zv66c0FGJLZKq992uqaSub3DWffXUnK8LHx0Kk3XDJ3JMSLT4JmGQPYpAkMSAiEM0gyUofZQXH7uN7ovxfC3bH/c6xP4KzDQggPoDBV9qi3BfHYas4/WP8rKO3/f0s+0bWvrUwSGJARCGJQYkWKsXKzTCkV+Kk8eH2ioT013kRiRYqitP7HJ5fj95l8TAxWBW4WxUdIn+75f0YnpXN7omQQNiU0ux2Txk7ThvmSQkFZ61v21lMTILobaqYp7oGhA+owy9nlVFZWkqMD0KQJDEgIhDBqTOs3vt3VVUVH33AA27yN5Adhyl6F1hsKskfRJHNaIKL2b/sB2/HaqKygb7zWQ+CiEWML8788gVQtC2X2D33bjbvBzKcag4HAnQy5O91tRUtcUxl4fmD5bQmxYR9I69Pc79kXTNcZ0mxaQPpOiutE5qruhlRjHdL8gIH2KwJCEQAiDBnceR+fo7l7/uCqKitUczMwhF5/Y4i150Am3xDCp16V++5w18FZMqrHFgfzpOTaXxJ7FPuMCGN9nClGh8V7npauKSnRIHJcNut1vOjC62zRiQvxfg5+SdiNh0TavB2jVpNGhWxkjLg/cSPgp9+zyWVFSNWkk9S1i0MyMgPXZEmb2uwGT4r2ipIJCn4ShpHYI3PTWiwbejKIoPvscnHweyTHn/jLVbYkkBKJVyzscyeZ53dn0TfcWX9LWbLJw59jHPHUBFEzub9S4D9jRIXH8bPyfGD/Lwazfbazz5Vmv8wNRiZX88vPvmNb3aib3ugJPwzprD1lMVq4e+nMGJ49tdJyleSFs/b4bG79KJX1Th3rXwO+bM5+kvkWnxHSy4/Nu3suFP8/kZ+P/6DndrJ44mNQmCAkRyfxswp8YlDyOG0Y+RNCJqnPuP/6K53/jUmdyyaBbeWjKX30urjO191VMGXQBP393IXFd3OsZqCYNRdE8B+vO/Qu5+81F9Qosna2YpEp+9s5PRCVWNdhnt2F53PnaEixBbfs6d+fo7tw57jFCre5CWeqJwkS17+fAzmO4YeSDAS1M1D2uD7eN+S1BJ6ogntrnsC7nc9VQ32tciOYnhYlEq5SzL5p5z43gyJaO9bZ37l/IJb/dRJfBvlfGayq6Dqvm9GHxPBs1aZ9CRA7YIlEPXMaIAT246FfbsYa4r03XVJn56ZVB7F3RGZdDJbJDFVP+bwe9zzu5Nv0Hn2xkr/lVsJxSJrcmislxv2PaBV0Nx1aWF8I3zw1nz9KUEwMI3WKTy5nx4FYGTsv0bDu6I475/xxMUVYEqkmn29A8LvzVZsJjT64BoOs6GYV72Z69lkp7OaHWcAZ3Hku3uD6eCnNFlXl8te1tDubvrBdLbGgCswbeQp9E91THnTnrmbftfcrtJfXa9eo4hCuG3EVUSCwAmgaH1yew46cuVJdZCY+1MeSidFIGFjZZUU/NpXBgdSd2LU6mptJCZIdqhl6cTud+xU3TYQtxak52H9vI/uNbcbjcpYuHdzmfDhFNtxKiw2VnZ846Dubv8pQuHtFlErFhHf0/WASM0cJEkhCIVidrVyxv3nUBTod62jxxRdVQVZ3bX11C2sjALB9slK7Dt88PZ83HvRu8X1E1Ovcr4p43F2EJ9j9g7Y05y8kIf/3Eg0/tzP2fiSFPMXOG/9OqpXkhvHbLDCoKg08vtKPooCtc/vg6Rl0VuFPu7gp4f6LGUdnAaHF3EePrht+P3VnDl9vebnAfqqISFhTFfROf8iQFQojAkkqFok3SNPjk0fMaTAbAPS9c0xQ+fey8gMxHb4zDGxK8JgPgji17dxzL3+/rd195x2u8JwN1ti0vn20otm//NrzhZABO1DvQ+frZkZTmhRjanxFfbH3LSzIAtRnN3C2v89W2d7zuQ9M1Km2lzNvuvWqjEKJ5SEIgWpXD6xMoyorwWUFO11QqCkPYvdRbTf6mseaTXoZGpa/9tJffZOW/3/7g/oevZgpgqWbu174rFZbmhbB7SYrXErx1O9rwuZ/pkAYVVBzjcIH/anT+ShuDOynYk7uJ0urALQcthGg8SQhEq3JofSKq2f8gLtWscWh9YjNEdNKBtYl+DrpulcXB5KVH+WxTZF1vrFMd9uSv8tkkfVPHemMGvO5KU9m/upOxfv04mB+Ycsq1dHTSC6RqnRAtSRIC0ao47SYUI5X5dHA5mvfjqzmN9+c3NtX4aHlNsfu8vzGvg9MemCmMLs1prEhTIzg1R0D3J4RoHEkIRKsS16Ucl4Fv4boOcSnlzRDRSbGdK9wD9PxQVJ3oTpU+21hsBr+pKxCldPPZJC6lwtCuaufyB0JcWELASip79hnevGd8hBD1SUIgWpXBF2ZgMnDJAGDYJelNHE19o6/xv8iQatLoNzmT8Fibz3aTUq81VuRPh5tn+a4g13VIPrEp5X6TFc2lMuqqgwY69a9nx0GEB/m+LNIYsaEd6RbrfcCmEKLpSUIgWpWQCAeT7vJzfVrRGXv9fiI7+lliOMCGXXqYmE6VXgcWKoqGoupMvmdng/fXNWlSMmppmt+lBcILphPfMchnG0WBmQ9t8cwmaLCNqpE6MpfUkcf9xmaESTUxo991vuNCoW/icFLj/ZfOndH/ek9tAyFEy5CEQLQ6U362k/G37AGod/Ct/ffIKw5y0a+3NFn/lcVBpG/qQPrmDlSXWTzbg8Od3P3mImI6u0/RK7Wr8yk6KDqWEBe3/WspSb1LPI9x2lUyd8RxeGNHirLD6vXzyOV/Qik/sfhM3eP4iX8HFYzlkdtvMRTzgKlZXPHHdagm/WRcnHzNug/P4+aXltcr7lNWXczhgj0cKdyHzVljqJ+6hnc5n4sG3OyuS1jngF9bja53whCuG34/N4/6JalxfevdB3ged/nguxiYNLrR/Yu2weGyc7ToAIcLdlNS1TIFxYQxUphItFp5hyNZN7cnR7fFo+uQ3L+I0VcfoFOdA24gFWWFsfDVQexY0NUzm8BkcTH4wgwuuHcH0SdK3LocCnuWJbN5XndKcsMIDrczcFomQy9OJzjCPTDOUWNi6dv9WftZT6pLT37D7z78OFP/bwepI/I8z/G9t45QkvpPiMoEVMjvQ/Lxh7nlwSAi4hp3oC7LC2HDl2nsX90Jp81Eh25ljLzqIKkj8jzJQE5pBj/tmcu+41s94wCspiCGdzmfqX2uItQa3qg+i6vy2ZCxhEMFO3FpLjpGJDO6+1S6xPT0fOvXdZ30wj2sz1hMfkUOZtVCz46DGNV1CpEhMY3qT7QNNmcNi/d9wfqMxdicJ8/m9egwgAv6XE2X2J4tGF37IpUKhWiE3INRvHHnNOxV5tOmFqomjZBIO//3/gJDA/js1SbeuXcKmTviT5sO6P72rnDtM6uJSynnrXum4rSbGuwzIr6a/3t/AVEJgbs0crhgD++teQ6X7kI/pYaAoqjEhnbg5xOeIDzIz2qNQvhQ46jizZVPk1uedfrnDBVFgZtG/Yq+AVrJU/gmlQqFMEjT4MOHJ2CrPD0ZAPdgvOoyKx//bjxG0uefXhtE5o64BmsD6JqKrsH/Hh/LBw+ej8N2ejJQ22d5QQif/bHxixt5Y3famLP+JXexoAYKCum6RnFVPl/7qCwohBHf7/qowWQAQEdD0zU+3vBPquzNO1NI+CYJgWj3Dq1PpPBopM/qiJpLJWdPLFm74nzuy15tYv3cHj73BQq6BhVFIX77PLwhkbzDgfm2vj17DTWOKp/TBTVdY/exjVI1UJyxanslW46uaDAZqMulOdl0dHkzRSWMkIRAtHt7l3X2W5IY3Kfx9yzr7LPNka0dsFdbfLYBQFfxO8UA9yWGvSt892nUntzNhooJ6ejsO74tIH2K9udgwU5cuv/CWzo6u3I2NENEwihJCES7Z682G2qnKDr2Kt9tbZUGkoFGUFT89mmUzVltqJiQoqjYz2DWgRAAdofxz86ZzG4RTUcSAtHuRXasMtRO01SiEny3jUo0ti8jZwcANJfit0+jokPi603780bXNSJlKWJxhqJCfF9Wq6UoKjGh8U0cjWgMSQhEuzdsVrqhRYtAZ/CFGT5bJPcvJK5LmYESxwpGShWaLRoDph01EJt/w7pM8Ls6IUCQOURGf4szltqhHxHB/qeS6rrG8C7nN0NEwihJCES7F9elgoHTj9Qr6HMqRdEZecUhIjv4PsWpKDD1/3acqBrYMNWkEZNUQY8xx3z2iaIz7sa9hEQEZtGf7nF9SYnp4fcswfk9L8FisgakT9H+qIrKlF6X+23TMaIzfSTxbFUkIRCGabpGWXUxJdWFuDTjq/V5U1VqpTgnDFuArpGfjaueWEvaqFyAegdp5cRgw76Tspj1u031HmOrMlOcE0ZVaf2D55ALjzDjwS0nHu+CsDyIOgKWKlB0IjtWcdfri7jxbyvpMqjgtD5rBzgOuSiD6Q9sD9hzVBSFW0f/hoTIFPftOmcoapOE0d2mcX7PSxu9b03Tycmu5mhGJTU2Y2tRNBeHy05xVT4VtlLaQNmVc8KoblOZfCIpOLU6JbjXrrhj7O8wqYFZfVMERqMLE7lcLg4cOIDD4aBHjx6EhIT4bH/kyBHS0+svQmM2mxk/frzhPqUwUcuyO22sTV/AmvQFlFYXARBsDmVUtymcl3YREcHGF7nRddi1OJlVc/pwZGtHwH0wHDA1kwm37SG5f1GTPAcjKoqsfPPXEexekoLmdP+hMllcDJx2lEt+t4GQSHcSlLUrlhXv92XnohTPtMGuQ/I476Z99J+aiaK4k6fF6zexJv0HqkP2uTtwWenCDC6ZMJnOHRLcmxwKu5cms/bTXhzbH4Oi6nQbmsfY6/aTNvo4TVHe3+lysCNnHWvTF5JfnoOqqqTF92dM6nS6xfZu1JoCNdUu5s7bxl7b52iRGe6N9nA6VV7J5VPGk9IlzOfjm1J+eQ4rDn7HlqyVngQ2ITKF81JnMqzLREPjKcTZySw+xJrDCziQtw2X5iQuLJHR3S9gUOexWM2+1+gQgdMklQpfeeUVnnvuOcLCwjCZTOTk5PDCCy9w1113eX3MX/7yF/72t78xbNjJU0Ph4eF8++23RruVhKAF1TiqeGvVMxwrPXLaCHVFUQm3RvKzCX8iLizB7750HX74+1BW/rcviqrVm4OvmjR0Ha6bvZpB0wNzzbwxSvNCeOPOaZQcCz2lNoCOourEdy3nnrd+In1TRz559Dz3Qb/OuIPa53PezXuY+atN/G/Tv9mRsxYFpd7rpioqJtXMbWN+S2p8v2Z8hoFXUeHkxU9fwxa3zr2h7rgJzQS2KG4a8BT9BzR/aeL0gj28t/ZvuDRnvXETte/HgKRRXD/iF5IUiHahSSoVlpWVsWHDBvbu3cuuXbt46aWX+NnPfsa2bb7nLPfr14+lS5d6fhqTDIiW9eXWtzhWdrTB6Wq6rlFpL+ODtS8YGqy27YdurPyve5GbUwvyaC4VXVP43+/HkZ8REZjgDdJ1d6XC0txTkwFwFxFSKTwawZxfT+DT35+HriunDUKsfdyqOX359LuV7MhZ695+yuum6RpOl4MP1r7Y5qu0vfm/H7DFrvcs7lSP6gJrKR9tm93slxCq7BV8sO4FnC7HaZ/L2vdjZ856lu3/plnjEqK1a1RC8Pvf/57ExETP7dtvvx2z2cy6det8Ps7hcLBp0yZ27dqF3W4/s0hFsyupLmRnznqfFcc0XSO/IodD+b6X/NV1WPZuXxSfo+/dp6rX/q/XmYR7xrJ2xpG1M97nTAPNpXJ0W0d0DZ8DBjHZ2VU112d/OjoOl61NV2kryLeRH/UF+BoUaXKhh2fz06LDzRcYsPnocmxOm9+aC6sO/4gzAGNhhDhXnNX5sq1bt2K32+nZ0/eqVdu2bePOO+/koosuIjExkffee89ne5vNRllZWb0f0fyMVhFTFZXt2Wt9tinKDOf4wRh0XwdT3Afebd93MxpiQOxY0MVQpULQ/ZQkBlJWoQf7L/uro7M1a5WxAFuh5asPg9lAcq+p7MhZ0/QB1bEtazVG6jxU2cvJKNzb9AEJ0UaccUJQWVnJHXfcwcSJE5k0aZLXdqNHjyYjI4Nt27Zx5MgRnnrqKe666y5Wr17t9TGzZ88mKirK85OSknKmYYqzUO2oqLfOvTearlHtqPS9r3Lj09hqKgJb7c+fxsTmV4jxQZFVdv8rJ7ZWlTWV4C85AlA17EpJk8dTV2UjLsVU231/boVoT84oIaipqeHyyy/Hbrfz2Wef+RyVPG3aNJKTkz23H3jgAXr37s3cud5Pqz722GOUlpZ6fjIzM88kTHGWwqyRfhcoAVAVE2FW3wvwhMXYDPcbGmW8bSCERgewv6oOhptGBBmfndHaRIRG+L5cUEszEYSxynWBEt6IWS9hQc07XkWI1qzRCYHNZuPyyy8nMzOTxYsX07Fjx0Z32qFDB3JycrzeHxQURGRkZL0f0fwGJI3CyLw3TXcxOHmczzYxSZUk9y/wXYgH97z/oZek+2wTaIMvzDBYqVDxfxDMGotanei7zYl9DU2ZYCS8VmnS+FRwBPtvqLoYmhK4JZyNGJpsbEpzRFA0XWN7N3E0QrQdjUoIapOBjIwMlixZUm+AYa2MjAxWrlzpuV1eXv/0XV5eHlu2bGHgwIFnGLJoLhHB0QxNGe9zhTxVUUmK6kb3uD5+9zfxjt2+r8ErOiaTzuhrDpxJuGcsqXcJqSNyfY4jUFSNHmOOYTY3MKK+LpeFIVHX+OxPQSHEEsbQFOO1OFqb6BgLSRXX+b5s4DKhlqYyeVLX5gsMGJoynlBrOIqfP28Tes6SwjhC1NGohODaa69l5cqVPPHEE+zbt88zjTAjI8PT5r333mPWrFme21OnTuWpp55i3rx5vPvuu0yePJlOnTpx3333BexJiKZz6cDb6Rbn/hZ1amKgoBAdEs8to39tqJjNgKlZTLvfPUVVOeXgq5o0TGaNm15cTmzn5r+ue/1zq4jvVuaeBVH3gH/idlLfYm7820puenE5JrN2WvJQ+3wuuG8bV80czaiuU9zbT/kVUxQVqzmYO8Y+QrClbdfUuPuGCwgrnOy+cWpioKko1QncPvYRrNbmnesfbAnl9jG/I8gcfNoYmNr3Y0TXyZyXOrNZ4xKitWtUYaLp06c3OG3w9ttv5/bbbwfcCcHcuXM9tQZKS0t59dVXWbduHaGhoYwaNYr/+7//IzjYwOnGE6QwUcuyO1x899MOthbMwxHpHpWtVnWit/UKLp02nKhI4+8lwKH1Caz6sDf7VnRG1xXMQU6GXZLOuBv30bF7y80oKcsLZs7DE8naFXdyaqGi03VIPje/tIywaPeaAnnpkaz+qDdbvu2Oo8aMouj0npDNeTftI23UcQB0XWf3sY2sPjyf9MI9gHvRoFHdpjA2dQbRBleEa+2cTp153+9ja+E3OGJPJHuVHemmXcXl00fSoWPLVaMrqS5kzeH5bMhYQo3TvWJk97g+jE2dSf9OIxpVkVGItqxJKhW2FEkIWo7TofLxb8ezZ1myuxqfanMXnXGEgq6Q2LOEO/+zmPDYxg/MczkUHDVmrKFOVFPLfgxLjoXw8lWzsFfXrqtQe7DQAYXgCBu//noe4TEnE2LNpWCvMmMJdmKyeI/f6XLg1BxYzcHndGW8qionTodOeIQZVW09B1tN17A7azCrFsym5p3BIkRr0CSVCkX78/2LQ9m7vDNwohqfMwTs4aCrgELe4Sg+fHgCZ5JWmiw6wRGOFk8GAP5988wTycCpyxK7/11TbuXVm2fUe4xqcsfvKxkAMJssBFtCz+lkACA01ExklKVVJQPgHucSbAmVZEAIP87tv1DirFQWB7H+854+iwlpLpUjWzuSuaPtngLfvTSJyqIQ8DF4EhRKcsI5uq3tPk8hhPBFEgLh1c6fUtBc/r/tqSaNLd92b4aImsayd/tjpLIdwKLXZXaMEOLcJAmB8Kq8IMTQ6XxNUygv9L0MdmtWVWJ84FtFUeMGUAohRFshCYHwKjjcga4ZOEOg6gSHOZohoqZhDTG+wI01tO0+TyGE8EUSAuFV30lZhgYLai6V/lPbbnnp4Zcdwvf4gVoKY67b39ThCCFEi5CEQHgVl1JB7/HZPiv4qSaN6KQKeo/3Xoq6tRtz3QHMVie+xxHoBIXZGTyj7SY+QgjhiyQEwqern1xHbEp5g2sQqCaNoDAHt/5jWauYOnimVBVu+9fSE8s2NPQ8dBRV5643FjVvYEII0YzM/puI5hLsqmRgyXIGlK4kxFlOtTmCnVHj2RE9kRpTWIvEFBZj494PFrD8vX6sn9uD6jL3ADyz1cXQSw4z6c7dxCS5Sw07HSq7FqWw4Ys0ijIjMAc76Tsxm9HXHCA2uXUvM5s2Ko97P/yRTx4ZT1FWOHULE8V3LeOml5aTkGp8Wd32JPdAFOs+68nBdYm4HCoJPUoZffUBep13rE0nikK0BQ8v+dxvm7IaB08Z2JdUKmwlOlft49qjzxOk1QA6CrXfVRVsajD/6/JbskNbdmU2p12lMDMCzaUQ27mCoLCTg/HK8kJ4597J5B2Odlc0PFHbXjFpoCtc+ugGRl9zsKVC98vpUPnfH8ayc2FXULQThZfw/HvYZYe48o/r5QBXh67Dov8MZPEbA1FNmmfFyNp/dxuax63/WEZwhAzEFKKxjBzojSqrcdDh9/P8ViqUMwStQLT9ONcfeQ6zbkepc8ra/R1Vx6rVcP2R53g7bTYl1oSWChOzVSMhrfS07U6Hyjv3TqbgiPuDVndFQ/3EQeLrZ0cREV9Dv8lZzRNsI30zewS7furivqHXuZJ24t+bv0klNNLORb/e0gLRtU5rP+3F4jfcdRnqLh9d++8j2+L58DfjufM/S4ysoi1EuxHIg30gSULQCowq/AGT7kD1MqhNRcesOxhZ+CMLO93WzNH5t3txMnmHo322URSdha8Oou+krFZ3cCjOCWXTV2k+KzKiK6z+qDfn37GbsJjGr9twrnE6VBb9Z4DPNrqmcmh9J45ui6frkIJmikyIltFaD/KNIQlBC1N1JwNLlmHC+0h+ABWNQSVLWZR4E5rSut629Z/3qHeZoCG6rnD8YDQ5e2Po3Le4GaPzb8u3qe5ljn0lBICuKWz9oRvn3bivmSJrvfavTKKq1H+RJtWksfGrNEkIRJt2LhzsjWhdR5Z2KNhViVU/fUnphlh1O8GuSqrMUU0cVeMUZYX7TAbqKs4Ob3UJQXFOGIriv3ixYtIozm6ZwZ2tTXFOmN8kENyXD9yDNIVondrLwd4ISQhamFNp3ApsjW3fHCzBLuNtg4y3bS6WYJexukS60qjnei4zB7kMVbFE0RtVCVKIQJEDfeNJQtDC7KZQsoPT6FRz2OsYAgANhWMhadhNrW+WRd/zsyg4EuH326I5yEnXoXnNFJVxvcblsPbTXn7baS6VXue13QJMgdRz7DHqTIXxqVcbLlolWhc5yDctSQhagY1xM7ks+98+26jobIyd0UwRNc6oqw+y4oN+uI8ODX9rVFSN4ZceJji89X1b7HXeMaISKinLD/Ga1KgmjfiuZXQbmt/M0bVOsZ0r6T0+hwOrO9WbYVCPomMJcjH04vTmDU60SXKwb3mSELQCuyPHklqxjQGlK087nNZ+AdsRNYHdkWObOzRDYjtXcvkf1vPl06NRFP200fqKqtExtZQZD25tmQD9UE06N724gjfvvgCn4+RUyZP3a1hCnFz/3KpWN0OiJV3++Hr+c+t0ygtCTksKFFVDUeD6v65qlUmgaF5ysG8bJCFoDRSFb5N+Tl5QF0YXfke46+Rc/0pTFOviLmZ93IW05qPRyCsPER5bw4J/D+L4wRjPdkuQk+GXH2b6A1tb9YEhuX8R934wnx//MYT9q5M8Mw4URaf3+Gxm/nIrHbq1/kqFxUV2vv9pJxll29AUGxFqCpOHjWHw4LhG78vlVNi3Iont87tSURRMWIyNwTOP0HtCNiazTlTHau79r/s12z6/G5rzZFLQZVAB0x/YRvfhckblXCUH+XOPVCpsZRTdRXLVfkJd5VSZIsgK7YWumFo6LMN0HY7ti6EoOwxLkIuuQ/JbdSLQkOKcMI7ti0ZRIKlvEVEJ1S0dkiHf/XCQVZV/BXONe4Oig8sMJicR+TP5xfU3EB5u7DtAwZEI3ntgEkVZEZ7ZBLX/jelcwe2vLKmXIFUWB5G5Mw7NqRLftYyOqWVN8RRFM5GD/bnFaKVCSQiEOAcsXpLJT6WPuW8oDfxKayrhhVN59M7bUFXfZ5rKC4P513UXUlUS1OD4ANWkERpt4xef/EBEfE0gwhfNSA727Y+ULhaindA0nSVH34VIQPWS36saFR0WsmH9hYwe47v89ZqPe1FZHOR1gKXmUqksDmL1x72Z8YttZxm9CAQ5yItAkIRAiDZu86YCXNEGqie6zCzftYLRY6722kRzKayb29PvFFJdU1k/twfT7tsuCz41ITnQi+YkCYEQbdyRHIMD90xOyk0HfDapLrNSXRpkaHfVZUFUlVoJj5W1Hc6EHOxFayMJgRBtnNlkAoMFFBXNd6VL1ex7TY3T2svZgQbJwV60RZIQCNHGDe7fmXXblIYHE9almUg0D/PZJDjcQYfupeRnRPpe7EnR6dC1jJBIY+twnAvkIC/OdZIQCNHGdeseTtiiKVTGLgWTr1MFGjPGj/K5L0WBcTfu4+tnRvruVIexN+xrzaUxGkUO9kJIQiDEOeGGydfw1oatYC3xmhT0tj1Iaqr/lQdHXHaInQtTOLwh8bSqkwCKqtN9+HFGXHH4LKNuenKgF8I4SQiEOAekpkVwl/4MHy57n5r4Ne7T/boKqguqOjA0+G6uuX6AoX2ZLDq3/nMZ3784jI1fpeFyqCiqjq4pmCwawy8/xMUPb8Zsadx4g0CSA70QgSeFiYQ4x+zdXcrmXek4NScJMXFMPr8L1qAzq3ZZVWplz9JkqkqthEbZ6Xt+FqHRTTduQA70QgSeFCYSop3q0y+KPv2GBGRfoVF2hl8WmEsDcrAXonWThEAIccbkIC/EuUMSAiFEg+RgL0T7IgmBEO2QHOyFEKeShECIc4Qc5IUQZ0MSAiHaOEkEhBCBIAmBEK2YHOyFEM1FEgIhWogc7IUQrYkkBCKgHDaVPcuSKckJw2x10XPcMTp0K2/psJqVHOiFEG1RoxOCJUuWsGzZMhwOByNHjuSyyy5D8bPCSWFhIe+//z5HjhyhZ8+e3H777YSH+6+pLtoOXYdVH/Zm8esDqamwopo0dE1B1xXSRh3jqifWEd2pqqXDPCtyoBdCnMsaVbr4ggsuQNd1JkyYgMlk4vXXX2fgwIF8++23mEwNl0bNzc1l1KhRpKamMmPGDD7//HNqampYs2YNERERhvqV0sWt34JXBrH07YZr5asmjdBoG/d/+CNRCdXNHJl/cqAXQpzLmqR08T/+8Q/69+/vuX3ttdfSp08ffvzxRy6++OIGH/P0008THh7OggULsFqt3H///fTo0YN//OMfPP74443pXrRSuQeivCYDAJpLpaokiB/+PpTr/7q6GSOTg70QQhjVqISgbjIAkJaWhtVqJTc31+tjvvnmG+644w6sVisAkZGRXHrppXzzzTeSEJwj1v6vF6pJQ3OpXttoLpWdP3WhomgT4bG2s+pPDvJCCBF4ZzWo8IMPPsDpdDJhwoQG76+pqSErK4vu3bvX2969e3e++OILr/u12WzYbCcPGmVlZWcTpmhihzck+EwGamkulcwd8fQ9P9trGznYCyFEyzjjhGDz5s08+OCDPP744/Tq1avBNtXV7uvFp44ViIyMpKrK+wCz2bNn8+STT55paKKZuRz+k4Fas7au43JtXRNGI4QQ4kwY/0tex44dO5gxYwa33nqrzwN3eHg4qqpSUlJSb3txcbHPgQ2PPfYYpaWlnp/MzMwzCVM0g4eXfM55Mbsxqy5D7fskZDVxREIIIc5Eo88Q7Ny5k6lTp3LVVVfx73//22dbi8VCz5492b17d73tu3fvPm08Ql1BQUEEBQU1NjQRQI05df+zcfP5dtcon21UxcXorvvpk+D9coEQQoiW06iEYNeuXUyZMoWrrrqKV199tcH6A99++y2LFy/mpZdeAuD666/nzTff5Pe//z3x8fGkp6fz3Xff8cILLwTmGYhGC/R1+mm9tzG553aWH+yPSz99+qmqaKiKzrOX/Deg/QohhAicRtUhSElJoaSkhBtvvLFeMjBr1ixmzZoFwBNPPMHLL7/suUxQVVXFzJkzOXLkCGPGjGHZsmWMHj2azz//HLPZWD4idQj8a+nBeBW2YG7576/4fvcIzKoTp2ZGQUNHISKomjm3vsTMvltaNEYhhGiPmqQOwZ///GecTudp2xMTEz3/njVrFl27dvXcDg0NZcmSJSxdupSjR4/yi1/8gvHjxzem23avpQ/2RoQH1fDl3bPZnJnKu+umkl6YQKjVxow+W7h+2ArCgs5uqqEQQoim1agzBC3lXD5D0BYO9kIIIdquJjlDIIyTA70QQoi2RBKCRpIDvRBCiHORJAQnyIFeCCFEe9YuEgI52AshhBC+tdmEQA7yQgghROC0qYTgFyu+ITLY0tJhCCGEEOecM1rLQAghhBDnFkkIhBBCCCEJgRBCCCEkIRBCCCEEkhAIIYQQAkkIhBBCCEEbmXZYu/5SeY2jhSMRQggh2pbaY6e/tQzbxGqHWVlZpKSktHQYQgghRJuVmZlJcnKy1/vbREKgaRo5OTlERESgKIrf9mVlZaSkpJCZmelzqUfRdOQ9aFny+rc8eQ9alrz+J+m6Tnl5OUlJSaiq95ECbeKSgaqqPrMabyIjI9v9B6GlyXvQsuT1b3nyHrQsef3doqKi/LaRQYVCCCGEkIRACCGEEOdoQhAUFMSf//xngoKCWjqUdkveg5Ylr3/Lk/egZcnr33htYlChEEIIIZrWOXmGQAghhBCNIwmBEEIIISQhEEIIIUQbqUNwJg4cOMCmTZsYNmwYvXr1aulw2oW8vDwWL1582vYLL7zQ0BxYETjZ2dls3ryZpKQkhg8f3tLhtBtr1qzhyJEjp20PCQnhsssua4GI2qf8/Hy2bt2K3W6nb9++pKamtnRIbcI5mRCUl5cza9YsDh06xAsvvCAJQTPZvXs3N9xwA9dee229ipLjx4+XhKCZaJrGr371K9566y0mTpxIZWUl4eHhfP3111gslpYO75y3YcMGVq9eXW/bDz/8QO/evSUhaCb//Oc/efTRRxk1ahRhYWEsXbqU66+/nrfeestQpdt2TT8H3XDDDfof/vAHPSoqSv/73//e0uG0G0uWLNEB3eFwtHQo7daTTz6px8TE6Hv27PFsW7hwoV5ZWdmCUbVfWVlZuslk0v/zn/+0dCjtQllZmW4ymfR//vOfnm1r1qzRAX3x4sUtGFnbcM6NIXjnnXc4cOAAf/7zn1s6lHZr8eLFfP/996Snp7d0KO2KzWbjpZde4le/+hV9+vTxbL/gggsIDQ1twcjar/fee4/g4GBuuOGGlg6lXbDb7ei6Trdu3Tzbai8X2O32Foqq7TinLhns3buXxx57jJUrV8rp0RZisVh4+umnsVgsrFmzhquuuop33nkHq9Xa0qGd87Zt20ZpaSnTp09n+/btZGRkkJqayoABA1o6tHZJ13XeffddrrvuOqml30zi4uJ47rnneOSRR0hPTyckJIT33nuP2267jenTp7d0eK3eOZMQ2Gw2rrvuOv7yl7/Qs2fPlg6nXUpJSWHnzp2eMRt79+5lzJgxPPPMMzz55JMtHN25Ly8vD4C///3vbN++nbS0NFatWsWoUaP46quvCA4ObuEI25elS5dy6NAh5syZ09KhtCsDBw5EVVXmzp1LWFgY2dnZ3HTTTTJ+wIBzJiH4+9//TllZGREREXzyyScAOBwOtmzZwjfffMOll17awhGe+9LS0urd7tOnDzfddBPz5s2ThKAZ1B7wnU4nO3fuRFVVjh07xoABA3jxxRf5wx/+0MIRti9vv/02AwYMYMyYMS0dSruRkZHBpZdeyn/+8x/uuOMOALZv386IESPo3LmzDOz045xJCFJSUhg9ejRfffWVZ5vD4WDHjh2Eh4dLQtBCIiMjyc/Pb+kw2oXahOyKK67wrHneqVMnxo0bx6ZNm1oytHanpKSEL774gr/+9a8tHUq7smbNGux2O9dee61n26BBg+jVqxdLliyRhMCPcyYhuOmmm7jpppvqbYuOjubWW2/ll7/8ZcsE1c4cO3aMTp06eW47HA7mzZvHyJEjWzCq9qN79+4MGDCAvXv3era5XC4OHDjAjBkzWjCy9uejjz5C13Vuvvnmlg6lXUlJSQFgz549jBgxAnBPQ8/KyiI5ObklQ2sTzpmEQLS8Z599lmPHjnH++ecD8N///peioiKeffbZFo6s/fjHP/7B5ZdfjsPhoGfPnnz55ZcUFhby8MMPt3Ro7crbb7/NVVddRWxsbEuH0q6MGzeOqVOnctVVV/HQQw8RFhbGu+++S2RkpOcSgvDunF7t8K677uLqq6/mwgsvbOlQ2o0ffviB+fPnY7fb6devH7fffjvh4eEtHVa7snPnTt577z2Kioro1asX99xzD3FxcS0dVrtRUFDAL37xC37961/L2bEW4HQ6+eijj1i/fr2nUuFdd90lMz0MOKcTAiGEEEIYc84VJhJCCCFE40lCIIQQQghJCIQQQgghCYEQQgghkIRACCGEEEhCIIQQQggkIRBCCCEEkhAIIYQQAkkIhBBCCIEkBEIIIYRAEgIhhBBCIAmBEEIIIYD/B5zJYH0mt6gtAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -115,7 +114,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -128,7 +127,7 @@
     "# 1d)\n",
     "W=[-0.2,1]\n",
     "W2=[-0.1,1]\n",
-    "W3=[0,1]\n",
+    "W3=[0,-1]\n",
     "plt.figure(figsize=(6, 4), dpi=100)\n",
     "plt.title(\"Aktivierungsschwelle = 2\")\n",
     "plotTwoFeatures(X,T,lambda X: neuron(X,2,W))\n",
@@ -136,8 +135,8 @@
     "plt.title(\"Aktivierungsschwelle = 1.5\")\n",
     "plotTwoFeatures(X,T,lambda X: neuron(X,1.5,W2))\n",
     "plt.figure(figsize=(6, 4), dpi=100)\n",
-    "plt.title(\"Aktivierungsschwelle = 2\")\n",
-    "plotTwoFeatures(X,T,lambda X: neuron(X,3,W3))\n",
+    "plt.title(\"Aktivierungsschwelle = 3\")\n",
+    "plotTwoFeatures(X,T,lambda X: neuron(X,-3,W3))\n",
     "\n",
     "# Antwort: Der Winkel der Trennlinie ändert sich"
    ]
@@ -145,12 +144,95 @@
   {
    "cell_type": "code",
    "execution_count": 6,
+   "id": "18f65fc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import numpy as np\n",
+    "# x=np.array([1.,2,3]) # Der Punkt bewirkt Datentyp float.\n",
+    "# W=np.array([[1.,2,3],[4,3,2]]) # Der Punkt bewirkt Datentyp float.\n",
+    "# W # auf der Console werden Variablen ohne Zuweisung ausgegeben\n",
+    "# # in Skripten müssen Sie print(W) für Ausgaben schreiben\n",
+    "# x.shape\n",
+    "# W.shape\n",
+    "# W.T\n",
+    "# x.T # seltsam ...? Logik: 1D bleibt 1D. x ist *kein* Spalten-Vektor!\n",
+    "# y=x[np.newaxis].T # erzeugt 2D Matrix 1x3 (=Zeilenvektor!) und transponiert sie.\n",
+    "# y\n",
+    "# y.shape\n",
+    "# # numpy-Arrays können beliebig viele Dimensionen haben:\n",
+    "# M=np.ones( (2,3,4,5) )\n",
+    "# M.ndim\n",
+    "# M.shape\n",
+    "# M.T.shape # Transponieren bei >2 Dimensionen ... wie ändert sich der shape?\n",
+    "\n",
+    "# class myclass:\n",
+    "#     def __init__(self,W): # Konstruktor, self entspricht this in Java/C++\n",
+    "#         self._W = W # Konvention: private Variablen beginnen mit _\n",
+    "#         # self muss beim Zugriff angegeben werden, sonst\n",
+    "#         # ist es eine lokale Variable\n",
+    "#     def f(self): # self muss immer explizit übergeben werden, sonst \"static\"\n",
+    "#         return self._W*2\n",
+    "# m=myclass(np.array([-1,1]))\n",
+    "# m._W # Zugriff ist auch auf private Variablen erlaubt\n",
+    "# m.f() # m wird automatisch als \"self\" übergeben"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "id": "369a2d08",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#0 err:0.25\n",
+      "[[ 0.28677805 -0.07982693]]\n",
+      "[[0.]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/niki/Documents/Masterstudium/1.Semester/NNW/Lösungen_N/nnwplot.py:47: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
+      "  plt.scatter(X[0,:], X[1,:], c=T, s=50,\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
+      "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": [
+      "#6 err:0.0\n",
+      "[[0.16177805 0.17017307]]\n",
+      "[[0.25]]\n"
+     ]
+    },
+    {
+     "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 600x400 with 1 Axes>"
       ]
@@ -160,24 +242,88 @@
     }
    ],
    "source": [
-    "# 3a) \n",
+    "# 3 a-c) \n",
     "from SLN_Vorlage import SLN\n",
     "\n",
     "\n",
     "sln = SLN(2, 1)\n",
-    "sln._W = [-0.3,1]\n",
-    "sln._b = -1.5\n",
+    "sln._W = np.array([[-0.3,1]])\n",
+    "sln._b = 2\n",
     "sln.neuron(X)\n",
     "\n",
     "plt.figure(figsize=(6, 4), dpi=100)\n",
-    "plt.title(\"Aktivierungsschwelle = 3\")\n",
-    "plotTwoFeatures(X,T,sln.neuron)"
+    "plt.title(\"Aktivierungsschwelle = 2\")\n",
+    "plotTwoFeatures(X,T,lambda X: sln.neuron(X))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "54363e85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3e)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "53351dac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#0 err:0.25\n",
+      "[[ 0.28677805 -0.07982693]]\n",
+      "[[0.]]\n"
+     ]
+    },
+    {
+     "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": [
+      "#6 err:0.0\n",
+      "[[0.16177805 0.17017307]]\n",
+      "[[0.25]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 3f)\n",
+    "X_AND = np.array([[0,0], [1,0], [0,1], [1,1]]).T\n",
+    "T_AND = np.array([[0],[0],[0],[1]]).T\n",
+    "\n",
+    "sln = SLN(2, 1)\n",
+    "sln.DeltaTrain(X_AND, T_AND,0.5,10,0)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "nnw (3.14.0)",
+   "display_name": "nnw (3.14.x)",
    "language": "python",
    "name": "python3"
   },
@@ -191,7 +337,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.14.0rc3"
+   "version": "3.14.-1"
   }
  },
  "nbformat": 4,
diff --git a/Lösungen_N/AB4.ipynb b/Lösungen_N/AB4.ipynb
new file mode 100644
index 0000000..4d23e30
--- /dev/null
+++ b/Lösungen_N/AB4.ipynb
@@ -0,0 +1,64 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a35b6bf3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ea7529b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris = np.loadtxt(\"../supplement_files/iris.csv\",delimiter=',')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3166860",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 4d)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f9a70f7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nnw (3.14.x)",
+   "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/SLN_Blatt2.py b/Lösungen_N/SLN_Blatt2.py
new file mode 100644
index 0000000..860a49b
--- /dev/null
+++ b/Lösungen_N/SLN_Blatt2.py
@@ -0,0 +1,65 @@
+# -*- 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 # gibt boolsche Liste zurück
+        return errors.sum()/Y.size # summiert True als 1 in errors auf 
+    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__(self, dIn, cOut): # Konstruktor
+        np.random.seed(42)
+        self._b=np.zeros((cOut,1))
+        self._W=np.random.randn(cOut,dIn)/np.sqrt(dIn+1)
+
+    def neuron(self, X):
+        N = X.shape[1]
+        net = np.zeros(N)
+
+        net = self._W @ X 
+        # b verwenden ...
+        net = net - self._b
+        return net > 0 # threshold ....
+    
+    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 = self.neuron(X) # nur ein Aufruf
+            err = ErrorRate(Y, T)
+            if (it%20) == 0:
+                print('#{} err:{}\n{}\n{}'.format(it,err,self._W,self._b))
+                nnwplot.plotTwoFeatures(X, T, self.neuron)
+                plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht)
+            if err <= maxErrorRate:
+                break
+            Y = Y.astype(float)
+            # update _W
+            # X.shape = (2,4), T.shape = (1,4), Y.shape = (1,4)
+            self._W +=  eta * (T - Y) @ X.T  / N
+            # update _b 
+            self._b -= eta * (T - Y) @ np.ones((1,N)).T / N
+            
+        print('#{} err:{}\n{}\n{}'.format(it,err,self._W,self._b))
+        nnwplot.plotTwoFeatures(X, T, self.neuron)
+        plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht)
+
+        input("Press Enter to continue...") # wenn gewünscht ...
+# %%
+# 3f)
+X_AND = np.array([[0,0], [1,0], [0,1], [1,1]]).T
+T_AND = np.array([[0],[0],[0],[1]]).T
+
+
+sln = SLN(2, 1)
+sln.DeltaTrain(X_AND, T_AND,0.5,10,0)
+
+# %%
\ No newline at end of file
diff --git a/Lösungen_N/SLN_Vorlage.py b/Lösungen_N/SLN_Vorlage.py
index ca80422..d8508ed 100644
--- a/Lösungen_N/SLN_Vorlage.py
+++ b/Lösungen_N/SLN_Vorlage.py
@@ -7,8 +7,8 @@ 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
+        errors=Y!=T # gibt boolsche Liste zurück
+        return errors.sum()/Y.size # summiert True als 1 in errors auf 
     else: # für mehrere Ausgaben in one-hot Kodierung:
         errors=Y.argmax(0)!=T.argmax(0)
         return errors.sum()/Y.shape[1]
@@ -24,37 +24,45 @@ class SLN:
         N = X.shape[1]
         net = np.zeros(N)
 
-        for n in range(0,N):
-            for j in range(0,X.shape[0]):
+        for n in range(N):
+            for j in range(X.shape[0]):
                 # print(self._W)
-                net[n]+= X[j,n] * self._W[j]
+                net[n]+= X[j,n] * self._W[0, j]
                 
         # b verwenden ...
-        net += self._b
+        net = net - self._b
         return net > 0 # threshold ....
     
-    # 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 ...
-# %%
+    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 = self.neuron(X) # nur ein Aufruf
+            err = ErrorRate(Y, T)
+            if (it%20) == 0:
+                print('#{} err:{}\n{}\n{}'.format(it,err,self._W,self._b))
+                nnwplot.plotTwoFeatures(X, T, self.neuron)
+                plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht)
+            if err <= maxErrorRate:
+                break
+            Y = Y.astype(float)
+            # update _W
+            # X.shape = (2,4), T.shape = (1,4), Y.shape = (1,4)
+            self._W +=  eta * (T - Y) @ X.T  / N
+            # update _b 
+            self._b -= eta * (T - Y) @ np.ones((1,N)).T / N
+            
+        print('#{} err:{}\n{}\n{}'.format(it,err,self._W,self._b))
+        nnwplot.plotTwoFeatures(X, T, self.neuron)
+        plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht)
 
+        input("Press Enter to continue...") # wenn gewünscht ...
+# %%
+# 3f)
+X_AND = np.array([[0,0], [1,0], [0,1], [1,1]]).T
+T_AND = np.array([[0],[0],[0],[1]]).T
+
+sln = SLN(2, 1)
+sln.DeltaTrain(X_AND, T_AND,0.5,10,0)
 
 # %%
\ No newline at end of file