{
 "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",
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       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
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       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
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       "}\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",
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       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
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       "  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",
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       "  display: flex;\n",
       "  margin-bottom: 0;\n",
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       "  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",
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       "  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",
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       "}\n",
       "\n",
       ".features details:hover > summary {\n",
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       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "\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",
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       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
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       "\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",
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       "    \n",
       "\n",
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       "            <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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      "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",
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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",
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       "                 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",
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       "    // 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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DYReBLf/TtmVZinw+PPyUbbpXfDp9+jTCw8PV66+++iolqEnPf//9h7Jly8LPzy/lvkaNGqX6GRER2a7QiFhcuB2DMv6eCPDJr9255h0gKQ4o3QKo0VvvJpKN0z3wkWBn27ZtaqJy48aN1aGsjNy5cweFCqWeuliwYEE4OTmpP0tPfHy8ejGJjIzMwdYTEVFOWbT3MsYHH4FRAZwMwJSeNdDX+yhweg3g5AJ0/hwwGHjA6bHonhL/3Xff4dChQ2qOj6+vr5q0LDO90uPq6poqiBGyr9FoVH+WnilTpsDHxyflEhgYmCvPg4iIHq+nxxT0CLn+KHg/kla/qd3RdDRQuBIPMdl+4GPi4eGhzvI6c+YMTp06le4+QUFBalKzOdOMLvlZesaPH4+IiIiUS0hISC60noiIHocMb5mCHpORzsvgEnkF8AkEWt4PgIhsNfCJi4t76L6LFy+mDF+Z6vXs27cvZRhLeoOkZ0jyeUxWrFihBk0yTJYeSYAuUKBAqgsREVkXyemR4S2TcoarGOa8UrvR8VPAzVO3tpF90S3w+fbbb/HCCy8gODgYW7Zswddff41XXnkFQ4YMQYkSJdR9oqKi0KBBAzW4EU2bNkXnzp0xcOBAdYbX7Nmz8d5776kzu7y8vPR6KkRE9JgkkVlyepzVHB4FH7vOgZshGajQAajcmceXcoxBUZQ0nYt5Q/7bxYsXY+nSpWovTsmSJdXEZrkY7ievSd2e1q1bY+LEiejSpYt6X2xsLD777DNs3LhR7c3p27evGkBZSpKbJddHhr3Y+0NEZH25PlF7F6LitjGASz7g5V2AXxm9m0VWIKe+v3ULfPTCwIeIyIrFRQDfNgCibwBt3gNaMbeHcvb722qSm4mIiLBpihb0+JUDmr3KA0I5joEPERFZh+tHgD0/atudpwEuWmV/opzEwIeIiKzD5k8BxQhU7QaUa6t3a8hOMfAhIiL93TwJnLw/fb31BL1bQ3aMgQ8REelv25fadeUuQJHKereG7BgDHyIi0n/19SOLte0Wr/NsUK5i4ENERPra/jWgJANl2wAl6vFsUK5i4ENERPqJug4cmKdttxjHM0G5joEPERHpZ+d3QHI8ULIhULo5zwTlOgY+RESkj3t3gX2/aNst3wDuL1dElJsY+BARkT72/AwkRANFqwMV2vMsUJ5g4ENERHkvPhrY/f2DmVzs7aE8wsCHiIjy3v45QGwY4FcWqNqdZ4DyDAMfIiLKW0nxwI5vtO3mYwEnZ54ByjMMfIiIKG8dnA9EXwcKlABq9uPRpzzFwIeIiPJOchKwfbq23XQ04OLGo095ioEPERHlnWPLtCUqPAoBdQfzyFOeY+BDRER5w2gEtn2hbTceCbh58shTnmPgQ0REeeP0WuDmccDNG2jwEo866YKBDxER5T5FAbZO07Ybvgjk9+VRJ10w8CEiotx3YQtwdT/gkg9o/DKPOOmGgQ8REeU+U2+PJDR7FeERJ90w8CEiotwVslfr8XFy0aawE+mIgQ8REeUu00yumn0B3yAebdIVAx8iIso9N44Bp1YDMADNxvBIk+4Y+BARUe7Zdr9Kc9WuQOGKPNKkOwY+RESUOyKvAceCHyxGSmQFGPgQEVHu2DsLMCYBQU2B4nV4lMkqMPAhIqKclxgH7J+tbTcaziNMVoOBDxER5bwji4F7dwCfQKByFx5hshoMfIiIKOeXp9j9g7bd4EXA2YVHmKyGVbwaw8LCULBgwUfupygKrl69+tD9fn5+8PDwyKXWERFRllzcBtw4Crjk1yo1E1kR3Xp8rl27hldeeUUNWsqWLQtvb2+MHTsWCQkJGT4mJiYGgYGBqFu3Lho3bpxyWbVqVZ62nYiIMmHq7anVD6GJ+bHj3G2ERsTykJFj9/hs2LABVapUwfnz5+Hr64sjR46gXbt2cHV1xWeffZbpY//66y814CEiIisTdul+wUJgjWc3jPp0I4wK4GQApvSsgb4NWLmZHLTHZ9CgQRg1apQa9IgaNWpg4MCBWLNmzSMfm5iYiDt37uRBK4mIKEv2/AQoRsQHtcKodffUoEfI9YTgo+z5Id1ZVXLz2bNnUaxYsUfu17ZtW5QpU0YNmt5++23ExcXlSfuIiCgT8dHAf3PVzfPlnk0JekySFQUXb9/jISRdWU3gs3z5cqxcuRJjxmS8louTkxM++ugjtbcnMjJS3X/27Nlq8JOR+Ph4dV/zCxER5YJDC4D4CMCvLHxrdVaHt8w5Gwwo7c+JKKQvqwh8tm3bpg5zTZw4EZ07d85wP5m59f7776NAgQLq7ebNm2P8+PGYOXMmjEZjuo+ZMmUKfHx8Ui6SHE1ERDlMPoN3/6htNxyOAF9PNadHgh0h15N7VkeAT34eetKVQZE54jrauXMnOnTogFdffRWTJk3KVk9R9+7dERoamu4wmfT4yMVEenwk+ImIiEgJoIiI6DGdWQ/83gtw8wZePw7k0z5fZTaXDG9JTw+DHnoc8v0tHRiP+/2tax2fXbt2oWPHjhg9enS6QY+pbo+pTo/06shwV9rfIQfA398/3f/D3d1dvRARUR5MYa/zbErQIyTYYcBD1kS3wGf//v1qT0+vXr0wcuRIXLlyRb1fApvixYur2xLVSe+M5PEMGTIE06dPx82bN9G1a1c1sXn16tX44osv1CEyFxerqMVIROR4bp8Bzq6TQQSg0TC9W0OUKd2ihX/++UctWijXcjGRbqxjx46lBEElSpSAp6enelt6hn744Qe88847uHXrllr4cPHixWogREREeUOGry7cjkEZf0+tN8eU21Oxo5rYTGTNdM/xsdUxQiIiR7Ro72WMDz6SUpRw2tOl0HNTeyAxBhi8HCjbWu8mkp2KtIccHyIisq2eHlPQI+T6xOrvAZcYoHAVoEwrvZtIZBvT2YmIyPrJ8JZ5UUInGDHY6W/tRqPhwP2p60TWjIEPERFZRHJ6zIsStnPaj0CnWzDmKwjU7MujSDaBgQ8REVlEEpnNixIOddF6e5zqPQe4sSIz2Qbm+BARkcVkdfWWFQvj5pn9qLXyOGBwBhq+xCNINoOBDxERZYlalPDqQu1GlacBn5I8gmQzONRFRERZE3MHOLJY2248kkePbAoDHyIiypr9s4GkOCCgNhDYiEePbAoDHyIislxyIrB3lrbdaASnsJPNYeBDRESWO7UaiLoGeBYGqvfkkSObw8CHiIgst+8X7bruYMDFnUeObA4DHyIissydc8D5zdoq7HWf41Ejm8TAh4iIstbbU+FJoGApHjWySQx8iIjo0RJjgYO/a9v1X+ARI5vFwIeIiB7t+HIgNgzwCdR6fIhsFAMfIiJ6NNMUdlmXy8mZR4xsFgMfIiLK3PUjwJU9gJMLUGcwjxbZNAY+RESUuX2ztevKXQDvojxaZNMY+BARUcbio4DDi7Tt+kN5pMjmMfAhIqKMyWKkCdFAofJAmZY8UmTzGPgQEVH6FAXY+8uD3h6DgUeKbB4DHyIiSt+VfcCNI4BLPqBWfx4lsgsMfIiIKH377k9hr9YT8PDjUSK7wMCHiIgedu8ucDRY22ZSM9kRBj5ERPSwQwuA5HigWA2gZH0eIbIbDHyIiOjhpGbTgqRMaiY7w8CHiIhSu7AFuHMWcPMGajzDo0N2hYEPERGln9Rcsw/g7s2jQ3aFgQ8REalCI2Kx98hxKCdXaXcwqZnsEAMfIiLCor2X0ezTjfh34RcwGJNwu2BtoFh1HhmyOwx8iIgcnPT0jA8+AihG9HfZqN435WZT9X4ie2MVgY8iMwjy4DFERPSwC7djYFSA1k4HUcJwB2GKF1YmN8TF2/d4uMju6Bb4hIWFYeLEiahQoQLc3d1RunRpfPLJJzAajZk+7sKFC2jfvr36GC8vLwwdOhQxMTF51m4iIntTxt8TTgbgWef16u3Fya2QZHBHaX8PvZtGZD+Bz+LFi2EwGLBmzRrcu3cPc+bMwf/+9z98/PHHGT4mMTERnTp1Qv78+XHt2jUcOHAA27Ztw/Dhw/O07URE9iTAJz++6uiH1k6H1NuLjE9gcs/q6v1E9sagWNGY0WuvvYbNmzfj0CHtzZfWX3/9hW7duuHy5csIDAxU71u4cCEGDhyIq1evolixYo/8PyIjI+Hj44OIiAgUKFAgx58DEZFN2vARsPVzhBdrhtj+Sxn0kNXJqe9vq8jxMQkNDUXBggUz/PnOnTtRtmzZlKBHtGnTRh0e27NnTx61kojIziQnAgd+Vzd9Wwxj0EN2zQVWYuPGjVi6dCnmz5+f4T43btxA4cKFU93n7++vDpnJz9ITHx+vXswjRiIiMnP6byD6OuDhD1TqxENDds0qenxkaKt379545ZVX0Ldv30z3TZv8bBqpk+AnPVOmTFG7xkwX894iIiIC8N+v2mGoMxBwceMhIbume+Bz5MgRtGvXTg14pk+fnum+AQEBuHnzZqr7bt26pQY/GeX3jB8/Xh0PNF1CQkJytP1ERDYtPAQ4s07brvuc3q0hsu/A5+jRo3jiiSfQq1cvzJgxI91em7i4OCQnJ6vbzZs3x6VLl3D+/PmUn2/YsAHOzs5o3Lhxuv+HTHuXJCjzCxER3XdgrvSdA6VbAIXK8bCQ3dMt8Dlx4oQa9HTp0gVffvmlmocjQY55Pk54eLg6dX3uXHljQq3fU7t2bQwbNgznzp3D3r178e6776q1fCTXh4iIsiA5CTgwT9uuN4SHjhyCboGPJDHL0JNcy0wuX19f9VKqVKmUfaQHSHpspEdHyPWqVavU/Rs2bKhObe/Zsye++eYbvZ4GEZHtOrseiLwK5PcDqjytd2uIHK+OT15gHR8iovvm9wNOrwGavAJ0+ISHhayaXdbxISKiPBJ5DTjzt7bNpGZyIAx8iIgckeT2KEagVDOgcEW9W0OUZxj4EBE5GmMy8N9v2jZ7e8jBMPAhInI05zYBESFAPl+gale9W0OUpxj4EBE5mv2zteta/QBXrsBOjoWBDxGRI4m6Dpxeq21zmIscEAMfIiJHcvB3wJgEBDYCilbVuzVEeY6BDxGRo5BFnvffX5CUvT3koBj4EBE5igv/AuGXAHcfoFoPvVtDZBuBz/bt27F27dos/4yIiHS2f452XbMP4Oahd2uIbCPw2blzJ9avX5/hz2S1dCIisjLRt4CTq7Ttes/p3Roi2x/qkiW/jh07hsKFC+fUryQiopxyaD5gTARK1AOK1eBxJYflYumOM2bMwAcffIDY2Fg1yJkz536X6X337t1TV1Pfv39/brSTiIiyS9aiNg1z1RvC40gOzeLA54knnoCfnx9Wrlyprow6cODAVD+XlVLr16+PIkWK5EY7iYgouy5uBe6eB9y8gGo9eRzJoVkc+FSqVEm9NG3aFPHx8ahQoULutoyIiHKGaQp7jWcAdy8eVXJoFgc+JkFBQbnTEiIiynkxd4ATf2nbTGomYh0fIiK7dmgBkJwABNQCitfRuzVEumMBQyIieyVJzf/dH+ZiUjORioEPEZG9urwTuH0acPUAqvfWuzVEVoGBDxGRvSc1V+8F5Cugd2uIrAIDHyIie3TvLnBsmbZd73m9W0NkNRj4EBHZo8N/AMnxQNHqQIm6ereGyGow8CEisvekZoNB7xYRWQ0GPkRE9ubKXuDmccAlv1a0kIhSMPAhIrI3pnW5qvUA8vvq3Roiq8LAh4jInsRFAEeDtW3W7iF6CAMfIiJ7S2pOigUKVwECG+rdGiKrw8CHiMiekppNtXtkXS4mNRM9hIEPEZG9uPYfcOMI4OwO1Oyrd2uIrBIDHyIie0tqrtoN8PDTuzVEVomBDxGRPYiPAo4s1baZ1EyUIQY+RET24MgSIDEGKFQBKNVU79YQWS1dA5+TJ09izJgxCAgIQJs2bR65f0xMDHx9fR+6zJ8/P0/aS0Rk9cNcTGomypQLdBIfH48ePXrgpZdeQvv27XHs2LFHPkZRFERERGDdunWoX79+yv0eHh653FoiIit27SAQehBwdgNqDdC7NURWTbfAx93dHSdOnFC3pdcnK7y8vNSeHiIiwoN1uao8DXgW4iEhsrccn379+qnDY82bN8fcuXP1bg4RkX7io4HDi7Xtus/xTBBZa49PdvXp0wdjx45VA581a9Zg+PDhuHnzJsaNG5fhkJpcTCIjI/OwtUREuezYMiAhCvArC5RuwcNNZE+BjwxxLVq0KOX2iBEjcO3aNUyaNCnDwGfKlCn48MMP87CVREQ6JDVLb4+TTXbiE+Upm3+XSJJzeHi42uuTnvHjx6sJ0aZLSEhInreRiChXXD8KXN0HOLkAtZnUTGR3PT4ZTYmXROkCBQqk+3P5mVyIiOw2qblyZ8CriN6tIbIJVt3jIz005nV6fvnlF8ybN0+9Pzk5GWvXrsWnn36K559/Hvny5dO7uUREeSfhHnDo/tA/k5qJbKPHp2nTpjh+/DhiY2PVQMY0Rf3q1avw9PRMqduTkJCg3t+pUyf83//9nzr9PTo6GkWLFsUbb7yhXoiIHMrx5UB8BOAbBJR9dAFYItIYFIkudBIVFaUGPGmZAiBT4CMFCt3c3FLtk5iYCFdX1yz/nzKry8fHR/29GQ2PERFZvVkdgJBdQNv3gZb844/sX2QOfX/r2uPj7e2d6c8NBkOGhQqzE/QQEdmFmye0oMfgDNR5Vu/WENkUq87xISKidPz3m3Zd6SnAuxgPEVEWMPAhIrIliXHAwfsLM9cbondriGyOzU9nJyKyd6ERsbhwOwZl/D0RcGkFEBcO+AQC5drq3TQim8PAh4jIii3aexnjg4/AqAAGAJsLz0Ap+UGdQYCTs97NI7I5HOoiIrLinh5T0CPKGK6hVNQBJCsG/OXEKexE2cHAh4jISsnwlinoEf2cN6nXm4y1MXbNbTUwIqKsYeBDRGSlJKfHSca3ALghEb2ct6jbC5LbIllRcPH2PX0bSGSDGPgQEVkR6cXZcU7rzQnwyY8pPWuoH9TtnfahkCEK15WC2GysDWeDAaX9PfRuLpHNYXIzEZEVJjJLT48EPX0bBKFlxcKIn/k5EAUsSm4NGFwwuWd1NTAioqxh4ENEZIWJzHI9IfioGvQEJIQAUfuhGJzQvM/r6FOqAoMeomxi4ENEZIWJzMKUxxNwZo5621ChA+rVrKlPA4nsBHN8iIisLJHZRM3j8TUAh+5Xaq4/VJe2EdkTBj5ERFbAlMgswY6QazWP58o/QGwY4BMElH9C72YS2TwOdRERWQlTIrMMb8mMLTV5eeYL2g/rDWalZqIcwMCHiMiKSLCTMlvr+hHgyh7AyQWoM1jvphHZBQ51ERFZq72ztOsqTwPeRfVuDZFdYOBDRGSN4iKBw39o2/XvD3cR0WNj4ENEZI0OLwISYwD/SkDp5nq3hshuMPChbJXSt+f/k0h3igLs++XBFPb7M72I6PExuZkeq5R+TpCgRoq3SR0T8xL8ufl/Elm1y7uAm8cBVw+gVj+9W0NkV9jjQ9kupZ8TvTAS3DT7dCMG/LxbvZbbuf1/Elm9ffeTmqv3AvL76t0aIrvCwIceq5T+4wxZZRbc5MT/SWSTom8Bx/7Uthu8qHdriOwOh7rI4lL65oGIWkrf3+OxhqwyC24e9X9m9H8R2bwDvwHGRKBEfaB4bb1bQ2R32OND2S+lnya4yeqQVYZrE92vWJvR/5nR/0Vk84zJwL7Z2nYDTmEnyg3s8aHsl9K/L6PgRvbPrFenSblCanAj+8p9aQOq9P7PzP4v9vyQzTu9FogIAfL7AdV66t0aIrvEwIeyV0rfzOMMWWUWUKX3f2b2fzHwIZu352ftuu4gwDWf3q0hsksc6qLHlt0hKxPZlt4fSwKXzP4vIpt2+yxwfhMAAys1E+Ui9vjQYzMFN1kZsspugvKj/i8im7V3pnZdsSNQsJTerSGyWwZFkRKhjiMyMhI+Pj6IiIhAgQIF9G6OXZFAJqMhq7QetzhhVv4vIqsXHw18UQWIjwSeXQqUb6d3i4js9vubPT6U6zlAaeVEgrKl/xeRLYjY8zt84iOR5FsGLmXb6t0cIrvGHB/KcyxOSPTAoj0XcXPdV+r2p7ebYdH+Kzw8RLlI1x6f69evY+bMmVi0aBHKly+PZcuWWdTVNXHiRGzYsAH58uVD3759MWbMGDg5MYazWvfuAtePAPduA8mJqBodg2edj8IZyTBAQbjihfNKAI5dLqMmORM5Cun93LD8V/R1vYpIxQMLk1ojluUZiOwz8ElISEDDhg3x7LPPomrVqjh37pxFj+vevTvCw8Px1VdfISwsDC+++CJu376NyZMn53qbyQJR14HQQ2aXw0BE6gKDsvLQJNeHH5q0+f+QeKwyXEvWBQJqaZdi1QE3Tx56sksXbkVjuPMKdXtecjtEw0NdmZ3lGYjsMPBxdXVVgx25lh4bSwKfzZs3Y9OmTTh69CiqVaum3nfz5k289tpreOedd5isrGewc3A+cGAecDeD81iwNFCgJODiBji54k6cgh0XI2XiLvwQiUpOIShkiAJuH9cuB+dpjzM4AUFNgNoDgardAHevPH1qRLmpYvxR+DudQbziitlJHdX7WJ6ByE4DH4PBoAY9WSGBT4kSJVKCHtGpUyeMGDECO3fuRIcOHXKhpZSu5CTg7Drgv9+A038DSvKDQMW/EhBQ836PTU2gWI2HVphOiIjFa59uNCtGqKCkIQx/PeMFv/DjD3qMoq8Dl7ZrlzVvAdW6A3UGAYGN5EXEk0M2zf/g9+p1sLElbsGX5RmI8oBNzeoKCQlBsWLFUt1nun3lSvoJgfHx8erFPEeIHsOdc1rPjvTwSFBiEthYqzar9sp4Z6MejxNG92wFv7oypb3Hgx3DLwOH/9D+z7AL2rVcCpUHmr8O1B7AAIhs043jwJm/1YKFT7zwMRYkF2N5BqI8YFOBj9FofKiXyNnZWU1sTk6+3+OQxpQpU/Dhhx/mUQvtPOD55z3g1OoH93n4A7X7az0whStl+Vc+arkKlW8Q0PINoMU44NIOLeg5/idw5yyw/GXg3Aagy3QgH2sykY3Zrs3kQtWuKFK6Goro3R4iB2FTgY+/vz/u3LmT6j5JcJaASH6WnvHjx+P1119P1eMTGBiY6221RelWUo6PArb8D9g5AzAmauX0pbia9O5UfErL2XkMFtfjkWGt0s20S6fPgN0/AJumAEeXAlf/A56ZDRSv81htIcoz4SHA0SXadrMxPPBEecimAp8GDRrgiy++UBOaixTR/j7atm2bel2/fv10H+Pu7q5eCFmrpNyjGvq67QTW/x8QfUPbSQKeDlOAwhX1PZwylNbyTaB0S2DpC9oQ2MwngfaTgEbDOfRF1m/nd4AxCSjTEihRV+/WEDkUqy5+ExUVpdb3CQ4OVm936dIFAQEBmDBhApKSktSy1ZMmTVITnIOCLF/ugDKvpFwd51BxZS/gzxFa0ONXFui/CBi4RP+gx1xQI2D4FqByF603au3bwMKBWt0gImslr8//ftW22dtD5Fg9PhKwnD59Wq3DExcXpwY54vDhw/Dw8FDzdmSauykhWe5bsWIFBg4cCD8/PzVpuWXLlpgzZ46eT8NuKikXRjjedFmEPi7/qvcnu3jCufWbQOOXAZdH95plZ9HRx+bhB/SdB+z56X4O0irgh0NA71+0wIjI2uz5GUi8p814LNfWet5LRA5C10VKZZaW+Ywrk3LlyqnT3SV35/z58yhatCi8vVPPFAoNDVWHsCQAygouUpr+h+z7n03D5y4z4GO4p94XnNwCzUZ8g6Ilylh0XB930dEcce0gsOR54O55wOAMtH1P+4uaVb3JWiTcA6ZXB+7dAXrNAmr0ts73EpEVyqnvb67O7uiMycDmT4Etn6k3jxhLY2LSUPTp0cPiD1sJnJqlqsmjFWHb9k6bvP9rNS4SWDn2QeKo/EXd40fAi3NmyArs/glY8ybgWwoY/R/g7GK97yUiK8PV2Slncg2WvqhNCQcQU3soYqq+hW+L+mbpQzazRUfz/MNaprX3mgmUbQWsfgs4txH4oTnQ82ftPiI9i37u/Ebbbjr6oaDH6t5LRHbKqpObKZeHhX5qpQU9LvmBHj/Bs/uXaFwxIMsfsJKHIF3y5nQtuy9T3+sOBoZtAgpX1hK0f+sGbPxE+/Ih0sOxZVpBTql/VedZ23gvEdkhBj6OSIoAzmqvfQgXLAO8uB6o1Tfbv85UhVk+oIVcT+5ZXf+/UItUAV7apAVBULThvN+6ApHX9G0XOR5JpTQVLGw0AnDNb1vvJSI7whwfR5IUr613tf/+LDgpQNjjh4fW0couyU/ItAqzno4sAVa8BiREA/n9tOddkWu7UR45sx74vRfg6gmMParNRrTV9xKRTpjjQ1mvFPvHYODaf1r15bbvAs3H5eiMJ4urMOtBZs9IZefFQ4Drh4H5fYAmrwBP/N9jV58meqTt07XrekMeGfRY/XuJyMZxqMsRnNsE/NhSC3ryFwSeXaJVPna0ad6FymnDeg2Ha7d3fgvM7giEXdS7ZWTPruwHLm4FnFyAJi/r3Roih+dg33wOxmgEtn4OzOsJxN4FAmoDw/7Vlp5wVFKIUdb66vs7kM8HuLof+KElcHy53i0je7X9S+26Rh/Ap6TerSFyeAx87FVyIhD8ErDhI0AxaiuoD/0bKFhK75ZZhypdgBHbgJINgfgIbRhw5etAYpzeLSN7cvsMcGKltt3sNb1bQ0Qc6rLjJGbJZTm6BEYnV4S3mwZ0+xZwzad3y6yLbxDw/Gqg+Vjt9r5ZwMx22pcVUU7Y8bU2o1AmEhSpzGNKZAXY42NvEmO1hTpPrkS84oqhcWNQd1VxtQw+pcPZFWg3EXh2qVZf5cYR4JcODH7o8UVdBw4t1Labj+ERJbISDHzsSXw08PszwNl1uKe44/nEN7HZWEetBDsh+Kg6RZYyIHlPI7dreVCyjtLcnkBkKA8XZd+uGUByAhDYGAhqzCNJZCUY+NiLuAhgXi919kiSqxeeS3gbO4zVHyp7T5nwLqb1/PiVAyIu308KD+Mho+y9H/fN1rbZ20NkVRj42MuaW791B0J2qTOVwnv/gf1InU/AsvcW8vQHBi0DvIoBN48DC/prw4dEWbHvFyA+UlsypQILZRJZEwY+ti76FvDr01qNHo9CwHMr4V+pGcvePw6Z+SY9P+4+wOWdwJKhXOOLLCczA3d9/2Aml6PVyyKycg8vD0y2Q3JQZPHN26cAr6LA4OXa+lQA+jYIQsuKhVn2PruKVQf6LwDm9gBOrQZWvgZ0/VZbAJUoM4cWaAvjFigBVO/NY0VkZfiniC0vQTGnkxb0yAfskNUpQY+JlLxvUq4QS99ngSSA7zh3W0sEL90MeGY2YHDSFnaVmkhEmTEm35/CDqDJKC6HQmSF2ONji+6eB37tpiXg+pYCnvsLKFha71bZPJnyPz74iDoLzskAdbiwb4POQJfpwIpXgW1fAF5FgMYj9W4qWasTK7T3Zz5foO5zereGiNLBHh9bc+s0MLuTFvQUKg88v4ZBTw6QHh5T0CNSlQCo9xzQ9n3tB2vfAQ4vzon/kuyNojxYjLThS4C7l94tIqJ0MPCxJTeOacNbUaFA4Sra8JZPCb1bZRcu3I5JCXrSLQHQYhzQaIS2/ecI4Oz6vG8kWbcLW4BrBwCXfA8WwiUiq8PAx1bIB+qczkDMLaBYTWDIKsC7qN6tshtl/D3V4a0MSwBIUnOHKVqyqjEJWDRYW3WbyMTU21PnWcCrMI8LkZVi4GMLQvZoOT1STK9EfS2nx7OQ3q2yK5IILjk9EuwIuZ7cs3rqxHCZltz9e6BcWyAxBvi9tzb0SBR6CDi3UUuEb/IKjweRFWNys7W7sBWY31f7oi3VDBiwCHD31rtVdsmiEgAubkCfuQ9qJ0l1Z1n1nkOOjm37V9p1tZ6AXxm9W0NEmWCPj7UHPbL2lgQ9ZVsDA5cw6MllFpUAkKTVgYu15PKIEG2pEKmeTY7p7gXg2LIHBQuJyKox8LFWl3dpPT1JsUCF9kD/RYDb/XwTsp6lLbwDgFsngAX9gASuheZwtZ7Ezm8BxQiUewIIqKl384joETjUZY0kaXZeb62nR/JJZGjFNZ/eraK0fIOAZ4OB2R2BkN3AkueBvvMAZ1ceKwep9fRF5+LoLsUtBRcjJbIJ7PGxNqGHgXk9gIQooHQLoO/vDHqsWdGqWm+cTGE+vRb3lr6MHWdvPegNILuu9XRl7VdAUhxQvK72fiUiq8fAx5rcPAnM7Q7ERQCBjYD+Czm8ZQtKNQF6z4bR4AyP439g9+y30ezTjWrvANlvrScPxOFZ538e9PZwHTcim8DAx1rcOQf81hW4dwcoXkdLnmXlV5sRGtAGExKGqttjXZeig2F3SuXnh3JCyC5qPfV33ghfQwySfMsClbvo2TQiygIGPtYg/DLwa1dtReci1bS8kXw+ereKstgbsDC5DX5O6qTe/sL1e1TGBczedlHt/Rnw8272AtlRrSdXJOFFl9Xq/S4tXgOcnPVuHhFZiIGP3iKvaTVhIq8AhSoAg/8EPPz0bhVlszdgStIAbE6uhfyGBPzsNg1/bvsv/fW/yGZrPW17pw3WtL2BAMNdwKsoULOf3s0ioixg4KOn6FvAb92AsIsPVln3KsKhERvtDXj7qcpq5d7RiaNx1lgcxQ138YPrF3BHQvrrf5FNCvB2R/kzs7QbjUdy8gGRjdF1Ont8fDy++uorbNiwAfny5UPfvn0xYMCADPePjY3Fk08++dD97777Lp566inYFCl4N7cHcPs0UKCEFvQUKP7QdFnpWpe/Msm6yXmbuuaket6i4YG9TWag7MEhqBt/FpNdZ2Fcoixwaki9/hfZpjN/A7dOAu4FgPpaXhcR2Q5dAx8Jcg4fPoyPP/4YYWFheOmll3D16lW8+eab6e6fnJyM7du348cff0TVqlVT7q9YsSJsiszakqUObhwBPIsAg/8CCpZOd7qsDI3IMgqZVhImXaU9b3L13pZYtB/wMwoG90Mv5604ZSyJWcauD6//RbZn2/3FSOs/z1w8IhukW+Cze/duBAcHq9cNGzZU74uLi8MHH3yAUaNGwcMj47+Ka9asicaNG8MmxUdry1DIauv5/YDBywH/8ulOlzUfGuGXpfXK6Lyd9qiHJk9NBVa/gfGuCzGiWyf41WHvnU27tBMI2QU4uwGNX9a7NURkSzk+69atQ9GiRVOCHtGtWzdER0dj586dmT52woQJ6pDXiBEjsH//ftiMxFhtaQOp8iuztiSRWQrgZTBdVnBoxPplet4avKgOhxigwG/tKODWKb2aSY/LaAT+eU/brtUf8C7GY0pkg3QLfC5duoQSJUqkus90+/LljAu/1alTRx0iGzNmDFxcXNSenyVLlmSaRxQZGZnqooukeGDRs8DFrYCbtzZlPaBWhtNlhVy/1bGS2qPAmUDWK73zljKkJfd1nAoENQXiI4EF/YHYML2bTNlx5A/g6j7AzQtoM4HHkMhG6TbUlZiYqCY0m3Nzc4OTkxMSEh7MgjEnw1+7du1S9xOdO3dW837Gjh2L3r17p/uYKVOm4MMPP4SukhOBxc8DZ9cDrh7AwD+AkvXT3VUSmSWnR4a3Dl8NT0mYZaKzdTM/b9LTk2po0sUN6PMb8HMb4O45YMkLWoFK1n6xrSHq9RO17Rbj2NtDZMN06/Hx8/PDnTt3Ut0XHh4Oo9Go/iw9EhSZgh6T9u3b48qVKw/9LpPx48cjIiIi5RISEoI8lZwEBL8EnFoFOLsD/eYDpZpm+hD50pQvT1PQI1gDxvrJeWtSrlD6+VhehbVz75IfOLcBWPeBHk2k7Nr2JRAVqk5CYG4PkW3TLfCRIavz58+rwY7Jnj17Un5mqRs3bqgBkbu7e7o/l/sLFCiQ6pKnQc+yYcCxZYCTK9B3LlCuTcqPM1vKILNEZ7JRATWB7jO07Z3fAvtm690isoTU2drxjbbd/hPW7SGycboFPpLILEGIDEWZhr6mTp2KFi1aoHx5bZaTJDo3b94ca9asUW+vWLECBw4cSPkdEjh99tln6pCXl5cXrIoxGfhzBHB0qRb0yFBHxQ6p6r5ktpQBE53tVPWeQOvx2vaqccDp+4tckvWS3rnkeKBMS6ByZ71bQ0S2GvhI0LN48WLMmTMHZcqUQfHixXH79m3MnTs3ZZ+kpCS1bo/06ojSpUtj9OjRCAoKUqe0Sy0fSW6ePXu2FQY9I4EjksfhAjwzB6isreEkMqrXY97zk2nCLNmcVL17rd4Gag8ElGRg8RDg2kG9m0cZubgNOL5crciNjp9yBXYiO6BrAcM2bdqoOTfHjh1Th6SqVKkCw/0veuHt7Y2tW7emFCisUaMGtm3bhmvXruHWrVtqwJSnQ1eWBj3LRwGHFwEGZ6D3bKBK6pWbLa3Xk2nCLNmMdKtxP/0VEHkVOL8ZmN8HeHED4Buod1Mp7Xt5zTvatlRoLlqNx4fIDuga+AhJVs4op8fZ2Vkd6kpLeofkYpV1Pv4aDRxacD/o+QWo2vWh3UzDWObBT0b1eiTYYcBju9Lr3Ru/9Ag83V1Qv+NPKLakB3DzmFbUcuhaIL+v3k0mk/9+06qrS82t1py+TmQvuEhpTgY9K0YDB3/Xgp5eM4Fq3dPdlcNYjiO93j0jgFfmH0DTL/fhr+rTAe8A4NYJrc5TUvqlHCiPxYYDGz/WtiXo8SzEU0BkJ3Tv8bEbW/4HHJin5QL0/ElLYs0Eh7EcQ3q9eyZy39g1t9Fk6FwUXtxdK24pPYY9fmAuiTW8n+/dAfwrAQ1e0Ls1RJSD2OOTU2TBwqLVgR4/ATVSF1PMaNp6pnVfyC6k7d1LS3K7zjqVBfr8qvUUHl4IbNZmOpJObp8Bdv+gbXecDDi78lQQ2RH2+OQUryLAsH8BZ5dHJ7Y24EKVjsTUu7f/YhheXXgg/dwun3bA09O1Hp9/pwI+gUDdQXo223H9PQEwJgEVOwLl2+ndGiLKYezxyUlpgh5Lpq2T4/T8dKlVPPMSBXUHAy3e0LZXjgHObdSxxQ7qzDrgzD9a7S0pVkhEdoc9PrnI0mnr5DgemdvV9j0gIkQrh7BosDbTq1h1vZrrWGRNPentEY2GA/5aIVUisi/s8clFrL5M6ck0t0t6g7p+C5RuASREadPcI67yQOaFvTOB26cBD3+g1Vs85kR2ioFPLuK0dcoWWc1d1nWTGUVR17QCh3GRPJi5KeY2sOl+UvkTH2i1e4jILhkURUlnoq39ioyMhI+Pj7pSe15VfZacHlZfpiwLvwzMbAdE3wDKtQUG/MEZRrllxRhg/2ygWA1tkoKTc679V0Sk7/c3e3xyEKetU47yDQIGLAJcPbREZ0l4dqy/U/LGpZ1a0APgTouPGPQQ2TkGPjnkUautE2VL8Traem9SGFMKZG6ZxgOZkxJjEfnHCHVzYVJrNJgXy/cukZ1j4JMDOG2dclWljkCn+wHPpknAoUU84Dkk+u+PUSDmIq4rBTE5aSBLThA5AAY+uTxtnShHyLIJzV7TtpePAs7/ywP7uEL2wnP/9+rme4lDEQlPdZvvXSL7xsAnB3DaOuWJJyYC1XoCxkRg0SDg5gke+OxKiAGWDYNBMeLP5GZYb6z3cDVtIrJLDHxyAKetU55wcgK6fw8ENQHiI7QaP1HXefCzY90HwN3zgHdxGJ/6LONq2kRkdzidPQdx2jrliXt3gVlPAnfOAgG1gCGrAXcvHnxLnV0PzOulbQ/6EyjXhu9dIhvA6exWiKutU57w8AMGLtEqDIceApY8DyQn8eBbIuIKsEybxYWGw9WgR/C9S+Q4ONRFZIv8ymg1flzya4tqrnmTNX4eJTEWWDgQiLmFmIKVEdrw7Tw5VURkXRj4ENmqkvWB3rNkxBrY9wuwfbreLbJeUvhxxWtA6EHcVbzQ4fpINPt8F2v2EDkgBj5EtqxyZ6Djp9r2+onAkSV6t8g67ZqhrnifpDhhVOJruKIUZs0eIgfFwIfI1jUeATQepW3/ORK4tEPvFlmXc5uAf95TNyclPYudxmopP2LNHiLHw8CHyB60nwRUeRpITgAW9Adunda7Rdbh7gUt+Vsx4l7VPvjN2OGhD8A7MfHqrC4icgwMfIjspcZPz5+Bkg2AuHDg995A9E04tPhoYOEAIDYMKFEPHj2+wZSeNVNq9si/UnD9lfkHuL4ekQNh4ENkL1zzA/0XAgXLAOGXgPl9tQrFjprMLMN+N48DXkWBvvMA13zo2yAI295pg2/714HEP6aVZmTJmQnBR9nzQ+QAGPgQ2RNPf+DZpUB+P+Daf8CSoUBSPByFDFntOHcbUeumACf+ApxcgT5zgQLFU/aRmj1+Xm5cX4/IQTHwIbI3hcppPT/O7sDptVrPjwz72LlFey+rQ1azZs2A946p2p2dPweCGj20L9fXI3JcDHyI7JF82Q/8A3D1BM5vAuZ213Jd7LinZ3zwEZTBVUx3naHeNzf5SYSW75Pu/lxfj8hxuejdACLKJWVbA8/9pa1LdWUvMLszMGgZ4F3U7g75hdsxaGg4ji9dZ8DbEIvdxsr4MHEQyt++l+GCo5Lv07JiYVy8fU9djZ0LkxI5Bvb4ENl7defn1wBexYCbx4AZjYBtX9pX0nNyImqe/ArzXT9BgOEuzhkD8HLCa1AMrmpAkxmu0UXkeBj4ENm7olWBoWuAwlW04S6p8PxVLWDnd9r6VbbszjlgVnt47f0aTgYFi5LboEvCJwg3+GJyz+rsxSGihxgUReZ9Oo6cWtaeyObICu5HFgP/fgqEXdTu8w4AWowD6g4GXNxhM+Rj69ACYPWbQEI0kM8X6Po1Qku059AVkZ2KzKHvb10DH6PRiDlz5mDDhg3Ily8f+vTpgw4dOuT4Y8wx8CGHl5wIHJwPbPkfEBGiHQ6fQKDlm0DtAYCzq3UfothwYOVY4FiwdrtUc6Dnj4BPSb1bRkS5KKe+v3Ud6nrxxRfx/vvvo0mTJihdujS6du2Kn376KccfQ0RmJLCp9xwwej/QaZqW/yMB0IpXgW/rAwcXAMZk6zxkl3YCPzTXgh6DM9D2fS2Bm0EPEVl7j8+hQ4dQu3ZtbN68Ga1atVLv++STT/D5558jNDQU7u7uOfKYtNjjQ5SG5Pns+0VLeo65pd1XqALQ+h2gWk9tOQxrGKbb8pnWS6UYgYKlgV6ztORtInIIkbbe47NmzRr4+/ujZcuWKfc988wzCAsLw65du3LsMURkwVIXTUYBrx0C2k0E8hcE7pwBlr6g9a6cWKHl1OhF8pFmPwX8O1ULemoNAEZsY9BDRLYV+Fy4cAElS5aE4f6CgSIoKCjlZzn1mPj4eDVKNL8QUTrcPIHmY4HXDgOtJwDuBbQp8IueBX5qBZz+O+8DoMOLgR9aAFf2aO2RXp4e3wPu3jyFRGRbgY8EJB4eqWtsSLKys7Mz4uLicuwxU6ZMUbvGTJfAwMAcfBZEdihfAaD128CYw0CLNwA3LyD0EDC/DzCzHXBuY+4HQHGRQPAwIPhFID4SCGys9fLU6J27/y8R2T3dAh8JQmSIylx4eDiSk5NRsGDBHHvM+PHj1fFA0yUk5P4sFiLKnAx5PfG+1gPU9FXAJT9wdR8wtwcwpzNwcXuWFw+V60cK2asNsR1eBBicgNbjgSGrgIKleMaIyHYDn1q1auH8+fOIiopKue/gwYPqdc2aNXPsMZLwLElQ5hciygLPQkD7j7UcoEYjAGc34NJ2YE4n4LduWqBiweKhA37erV7L7XTJTLJ//wf80gEIvwT4BGlVpyXJ2pmr6xCRjQc+3bp1g5ubG7755puU+jwyO6tevXqoUqWKel9MTAy6d++OjRs3WvwYIspGL4slZI2vp6YCrx4E6g8FnFyA85uBWe2A358Brh3IcPFQ4/2RMbmeEHz04TaFhwBzugCbJgFKMlC9NzByGxDUmKeTiHKUbn9GFSpUCHPnzsWgQYOwbNkydcgqISFBnbllkpiYiOXLl6vBj6WPISKtl8UUcDgZgCk9a6iLcuYInxJAly+BZq9pPTRSQfnMP9qlchegzQSgaLWUxUNNQY9JsqLg0o0wBEQe1YbOruwDzqwD4iMAN2+g8zSgZl/AbBIDEZHdLFkhwcvevXvVIanGjRurPTrmgc+qVatQt27dlNlbj3rMo7COD9k76U2RISXzgMPZYMC2d9rkztpVsl7W5k+15TAg/6kBsRW74lypZ+Dunh/vBB9Rc6GDDDdR2+ks6jidQ02XyzAYE1P/nhL1gV4zAb8yOd9GIrJ5kfawZIUeGPiQvZPhLcmnSWvBS43RpFyh3PuPb54ENk8Bjv9p2f4ehYCSDbSARwoRlm7BXB4iyvXvb2YMEtmZMv6e6vBW2h6f0v6pS0HkuCKVEdrhB7x4sCFGOf+J6oYLUGCQDiAUK5APCe6F4BRYH55lGwMl6mnVlzmcRUR5jIEPkZ2R4SzJ6ZEkYsmnkaBncs/qKb1BEhjlypDX/ZyeY8bSeNk4JtX9C7rncm8TEZGFGPgQ2SFJZG5ZsTAu3r6n9vRsOX0rJe8nx5OdraG3iYjIQlaw+iAR5Qbp1TH1slg0pTyH/k8JqiTYEabeptzqYSIiyir2+BDZuYymlEtvUG4EJGl7myz5PyQIk3bm5jAcEZFg4ENk5/QYfpLgxdIAJldrDhERpcGhLiI7Z83DTxZXdiYiyiHs8SFyANkZfrLHYTgiIgY+RA4iK8NPeYWzwIgor3Goi4h0Y83DcERkn9jjQ0S6stZhOCKyTwx8iEh31jgMR0T2iUNdRERE5DAY+BAREZHDYOBDREREDoOBDxERETkMBj5ERETkMBj4EBERkcNg4ENEREQOg4EPEREROQwGPkREROQwGPgQERGRw2DgQ0RERA7D4dbqUhRFvY6MjNS7KURERGQh0/e26Xs8uxwu8ImKilKvAwMD9W4KERERZeN73MfHB9llUB43dLIxRqMR165dg7e3NwwGg8VRpgRKISEhKFCgAOyZIz1XR3u+fK72i+fWfvHcPiDhigQ9xYsXh5NT9jN1HK7HRw5WyZIls/VY+WK09y9HR3yujvZ8+VztF8+t/eK51TxOT48Jk5uJiIjIYTDwISIiIofBwMcC7u7u+L//+z/12t450nN1tOfL52q/eG7tF89tznO45GYiIiJyXOzxISIiIofBwIeIiIgcBgMfIiIichgOV8cnLi4OX3zxBTZs2IB8+fKhb9++GDx48GM/Jju/Ny+sXbsWP//8M+7evYv69etj/Pjx8PPzy3D/0NBQfP/999izZw88PDzQtm1bDBs2DG5ubin7TJgwARs3bkz1uLp162LGjBnQ05UrVzB58mQcP34cAQEBGD16NJo2bZrh/lu2bMFbb7310P0rVqxA4cKFs/1780JycrJ6vFeuXKne7tKlC15++WU4Ozunu//27dsxbty4dH8mv0fOn3jyySdTqpubjBgxAkOGDIGe9u7dq74u5RxMnz4djRs3fuRjduzYgW+++UZ9TVetWlV93aat4WXJPnnt1q1b+OWXX9TXYZs2bfDxxx9nur+kaf75559YtmyZWpy1fPny6mu0WrVqKfucOXMGgwYNeuixP/30E2rWrAk9X8fyGp41axZu3rypvifNP2vSI5+rp0+fTnVft27d1M82c/PmzcPChQsRGxurHkd5/efPnx96OnHiBH744Qf181U+e3r06JHp/s8//7z6mLTMP28XLVqEL7/8MtXPXVxcsG3bNuhp/fr1WLBgAS5evIhSpUph+PDhaNSoUaaPuXfvHqZNm4bNmzfD09MT/fv3x4ABA7K8zyMpDqZbt25KxYoVlcWLFys//fST4uXlpUyZMuWxH5Od35vbli5dqri4uKjt+Ouvv5RmzZopNWrUUOLj49Pd//bt20rZsmWVjz/+WFmzZo3y+++/K2XKlFG6dOmiGI3GlP169eql9OjRQ9m5c2fK5dixY4qe7t69q5QsWVJ5+umnlVWrVilvvvmm4urqqmzfvj3DxyxbtkzJnz9/quchF/Pjk53fmxdefvllpVixYsq8efPU81SkSBFl1KhRGe4fFhb20PPs16+fUqBAASU6OjplPx8fH2Xy5Mmp9rty5Yqip48++kipV6+eMm3aNJmIob42H0XOj5ynt956Sz1v8hoODAxUz2dW9slrZ8+eVUqUKKG88cYbSsOGDZW+ffs+8jGvv/660rNnT+W3335T1q9fr4wcOVJxc3NTdu3albLPgQMH1GO3du3aVOc2MjJS0VP79u3V99arr76qti82NvaRj6lVq5YyevToVM/j3Llzqfb57LPPFE9PT+WHH35QlixZolSqVEnp3Lmzoic5P5UrV1a++OIL9XPn+++/f+Rjjhw5kup5/v3334qTk5P6GW3y5Zdfqp/b5vuZn3s9TJs2TWnXrp0ya9YsZcOGDcqECRPUdi9fvjzTx3Xo0EGpWrWq+t01Y8YM9TjJ88vqPo/iUIHPjh071DfX3r17U+6TAyZvEPMP/6w+Jju/Ny/Im938y/DmzZvqB/2cOXPS3T8xMVGJi4tLdZ+8aOW5nTp1KlXgM3z4cMWayJdj4cKFUwUt8kEnb77MAh85Rzn9e3PbxYsX1Q8Rab+JBNxy3+XLly36HUlJSeoX7IgRI1LdL4GP/C5rEh4erl7funXL4sCnbdu26h8jJvK6LlSokDJp0qQs7ZPX5Ivf9B6U15klgU9UVNRD9zVp0kQZNGjQQ4GPHENrPLfymstK4PO///0vw5/fu3dP/cPT/MtQPpvl92/btk3RS0RERMq2fO5YEvikJYGcs7Nzqj9G5HlWq1ZNsSZR6bwmn3nmGaVNmzYZPmbjxo3qOZJgz0T+aJfPJNN7wpJ9LOFQOT4yDFWsWDF1yMe8izQmJga7du3K9mOy83tzmwzPnDp1Ck8//XTKfTJ806RJE7ULMj3SPZq2no2Xl5d6nZCQkOp+ec6tW7fGM888ow5BSJe1nqQ9HTp0SNVNLufg33//RWJiYoaPi4+PR+fOndXHvvHGG+qQR0783ty0adMmdemVjh07ptwnQ12y9lzaIciM/P3337h69Speeumlh34m3chybp977jn8888/0FtWS9TLedm6dWuq1768ruU8ml77luyjBxkmz2pNKdN7NO19ad+z4tlnn0W7du0watSoh4aL9JDd5Qfmzp2LVq1aqUMcMtRjbvfu3YiOjk51buWzWdZ30vPc5sSSODIk2KlTJ5QoUSLV/fJebt++vfpZJnXJIiIioCevLLwmzT9rZUisevXqqT5r5bns27fP4n0s4VCBz6VLl9QXvznTC0h+lt3HZOf35jbT/5u2XXI7K22S3JaKFSuiSpUqKff5+vqq+QLvvvuu+iE6adIk9cWnp/TOgdyWL7i0wYyJBAq9e/dWv/xHjhyJo0ePqnkeFy5ceKzfm9ukTZKnJV+SJrJdsGBBi8+tfIBKnoApt8ekcuXKal6BnNugoCB07dr1ofwBayd5LnJ+MnvtW7KPrZI/tuQLonv37qnuly9GObdjx45FeHi4mtsjQYKtkc9WyTl7//33UadOHTV3ZMyYMTn+2Wdtjhw5oua6pf1jRfL6JM9FjoH8sbJmzRrUqlVLPcfW4uzZs2qAmvY1aU7OTdqAznQOzb9rH7WPJRwquVk+6NL+NeXq6qr+9ZzRX++WPCY7vze3mf7ftO2S5D5L2/TBBx9g3bp1au+GedLsd999l+r31qtXDw0aNFD/mpJASA/pnQNTImNGz1d6TMwDNuk1qV27Nj788EPMmTMn2783t6XXpqycW0melcTZr7/++qGfybk2/W5JdJbkQUn4lcDQPNCyZpa89nPi/WGNJJG0V69e6Nevn3oxkT9cpJfPRHoG7ty5g7fffltNErUlwcHBKedNPm8CAwPVnp9XX30VZcuWTTl/aZOkbf3cyh8r8iUvPT7mZPKJ+etYPtckwV0mAUycOBF6u3PnjvoHlExIMA9Q07LkszanPo8dqsdH/kqW2U3mJCo2Go0oVKhQth+Tnd+b20wzt9K2S16ElrRpypQp+Pzzz9UZF+ZDeCLtC09+Ll3Whw4dgl7SOwfyXE0/S0/a5yFDfTLEY/48svN7c1t6bcrKuf3tt9/UwDy9mRBpj4l8sciMRRk2tRWWvPYf9/1hjS5fvqzOwpSZM7/++muqn6UXKMu51fM9m13pvUYlX1V6RMzPbVhYmN2cWxkikllqQ4cOfWjmZtrjIUNqDRs2tIpze/fuXfUPKDnuy5cvVz9jM5LZZ21m37Vp97GEQwU+0q1/7ty5VG8IU1evdJlm9zHZ+b25rVKlSupf69I1aiIfDjIO+qg2TZ06VZ1Ca5pO+yiSyyRj6vL/6UXOgflzNZ2D0qVLq0NAlrp+/Xqq55FTvzcnSZvkmJtPc5VhOpnmacnrTf5ylHILluQcyPEQep7brJIPR8kDSO+8mY6PJfvYkpCQEPW9KsNXMqSQ2RdMRq91W5X2NWoavjU/t/LZLFP6bfHcCilVIF/4L7zwgkX7W8O5DQsLU4MeKYsiw2/p5f2Yk/N28uRJ9bsk7feo9MRbuo9FFAciUzf9/f2VcePGqbdlpk6rVq3Ui3k2eqNGjZSVK1da/BhL9tHDsGHD1Cn2d+7cSZkRINPbT58+nbKPTOWV6a8mMlvCw8NDnc2VHpkV8u2336qzgoRk0g8dOlR9jJ7Tnrds2aIYDAZl9erV6u0LFy6os7HMp33KjAA5tzK7TcjzCA0NTfm5TPmXGRPffPNNln5vXpNjL+e1f//+apkBufTp00e9z3RehMygmD17dqrHyhRuedvLTMS0Nm3apE6HNrl69apSu3ZtpX79+oo1yGxWl0yf7d27d6rZeDLFX2bAiRUrVqjncevWrVnaR08Zzeo6ceKE+jo+dOiQelved+XKlVO6d++uJCQkpPu7ZCbnmTNnUm7LdGdvb++Uzyy9ZTara8CAAWqJBXH48GFl0aJFqco0yJR4KTlh/liZsdeyZcuUmT5SHsDPzy9lFpneMprVJaUppOxIWk8++aT6PNMjn9nmz0t+rxxLeT3rJTw8XC1BIc8lvRleQr6X5HW8bt069baUkfD19VXeffdd9bacT5mdKNPXTSzZxxIOFfiIf//9VwkICFDfKAULFlQ/2C9dupTqjSQvGvMvjEc9xtJ98poEZPKCkKBEPhjlg07eWOZkOq8pQJNaGPLc5YtdXpDmF3l+Qj5IpG6IfIhITSB5EcpUSgkQ9GaqjyEBgLu7uzJw4MBUXwSmD9eQkBD1dnBwsHpcKlSooJQuXVo9PvIBa16zyJLfqwf5AihfvrxStGhR9ctbtuW+tB+uaQM0CVIzmvoqr1epZSPnX/aR5yo1VkzHSy/ywSivQfkglfMntVDk9nfffZeyj5RtkHNpIudHzpM8Bzm/+fLle6jWhyX76EG+sOX5yXtLptfLdqdOnR6amm0K0CQAltt169ZN9Z4dMmRIymPk/VuzZk2lVKlSKc9V3scZ1fTKK1OnTlXbKm2S5yC1i+S21KIxkdfiCy+8kPJlOXjw4JTPH3mNt2jR4qE6YvKaleMhx1BqM8n7RP7w0dPRo0dTzo2UnpAaabL93nvvpQpi5I+v9MpXZFRm4scff1SKFy+uVKlSRb2W9+/MmTMVPb355pvq+axevXqq16T561j+6JR9FixYkOq9Lp9nQUFB6hT1Bg0aqH+AmbNkn0dxyNXZk5KS1GECGRuVGUvmZFq2dJGWK1cuVfXezB6TlX30GvuXblJpk3Q7mpMprdJumc0kuRwHDx7McOjMfGhH9pVMfX9/f3Uqv7WIjIxUhx2lTVJl2ZwcA3m+0l1qSnyUl7/M4pJ8LBm+ymiIILPfqxdps3T7mmZjSTK9OXkdSzKk+SwIGfeXIa4yZcpk2kUt5RBkKCgnpuA+LhnDl2GKtOR5SWKrKalX8urSdnfLzDvp9pf3c0bPxZJ98pJU9ZVza05er6YhHBnmlHwWqczs7e2tHhtTnoM5GVown/ZrGhKTx8v5z+q0+dwg770bN248dL8kY5umuh8+fFh9LpK4bCJDHefPn1dfA5nldsixkcrN8vskr01P0mYZkk5LPkMlGdn0WpTPa/MKx6bXv0wiyeg5yOtF9pFzKrMx034W5LVLly6lO+vV/HUsycj79+9HhQoVUp1DuV++R+W7ynRc0rJkn8w4ZOBDREREjsmhkpuJiIjIsTHwISIiIofBwIeIiIgcBgMfIiIichgMfIiIiMhhMPAhIiIih8HAh4iIiBwGAx8iIiJyGAx8iIiIyGEw8CEimybl62VFclmOxNymTZuwefNm3dpFRNaJgQ8R2TRZv2j79u3o1KkToqKi1Pu2bt2K9u3bq+vQERGZ41pdRGTzZNHcBg0aqAs5Tp8+HbVq1cIzzzyDadOm6d00IrIyDHyIyC7IiuUNGzZUV3t2dnbG7t271dWgiYjMcaiLiOxCjRo10KdPHzUA+uyzzxj0EFG62ONDRHZh3759aNq0KcqXL4/ChQuryc1OTvzbjohS46cCEdm8mJgYDBw4EC+99BI2bNiA48ePY+rUqXo3i4isEHt8iMjmDRs2TJ3J9d9//yF//vxYvny5mty8c+dONeGZiMiEPT5EZNMuXryoTmOfP3++GvSIbt264aOPPsKKFSv0bh4RWRn2+BAREZHDYI8PEREROQwGPkREROQwGPgQERGRw2DgQ0RERA6DgQ8RERE5DAY+RERE5DAY+BAREZHDYOBDREREDoOBDxERETkMBj5ERETkMBj4EBERkcNg4ENERERwFP8P23JSgvpIhL4AAAAASUVORK5CYII=",
      "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
}