{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2026-10-04T09:28:26.125908200Z",
     "start_time": "2026-10-04T09:28:26.087555100Z"
    }
   },
   "source": [
    "from sklearn.svm import LinearSVC, SVC\n",
    "import numpy as np\n",
    "from nnwplot import plotTwoFeatures\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.cluster import KMeans, DBSCAN"
   ],
   "outputs": [],
   "execution_count": 28
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-04T08:55:13.379546900Z",
     "start_time": "2026-10-04T08:55:13.341972400Z"
    }
   },
   "cell_type": "code",
   "source": [
    "flower_data = np.loadtxt('data/iris.csv', delimiter=',')\n",
    "inverse = flower_data.T\n",
    "X = inverse[:-1]\n",
    "T = inverse[-1]"
   ],
   "id": "b4e863de5bc956",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-04T08:55:13.492273800Z",
     "start_time": "2026-10-04T08:55:13.386609600Z"
    }
   },
   "cell_type": "code",
   "source": [
    "two_features = X[:2].T\n",
    "print(two_features.shape)"
   ],
   "id": "bcd23c414c49a88e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 2)\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-04T08:55:13.811777Z",
     "start_time": "2026-10-04T08:55:13.514275500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "clf = LinearSVC()\n",
    "\n",
    "clf.fit(two_features,T.T)"
   ],
   "id": "dcb75df28cbf865f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC()"
      ],
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "    border: none;\n",
       "    padding: 0;\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".copy-paste-icon:focus-visible {\n",
       "    outline: 1px solid currentColor;\n",
       "    outline-offset: 1px;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy penalty to clipboard\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-penalty;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%7D%2C%20default%3D%27l2%27\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-penalty;\">\n",
       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;}, default=&#x27;l2&#x27;<br><br>Specifies the norm used in the penalization. The &#x27;l2&#x27;<br>penalty is the standard used in SVC. The &#x27;l1&#x27; leads to ``coef_``<br>vectors that are sparse.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy loss to clipboard\"\n",
       "                 onclick=\"copyToClipboard('loss',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-loss;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=loss,-%7B%27hinge%27%2C%20%27squared_hinge%27%7D%2C%20default%3D%27squared_hinge%27\">\n",
       "            loss\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-loss;\">\n",
       "            loss: {&#x27;hinge&#x27;, &#x27;squared_hinge&#x27;}, default=&#x27;squared_hinge&#x27;<br><br>Specifies the loss function. &#x27;hinge&#x27; is the standard SVM loss<br>(used e.g. by the SVC class) while &#x27;squared_hinge&#x27; is the<br>square of the hinge loss. The combination of ``penalty=&#x27;l1&#x27;``<br>and ``loss=&#x27;hinge&#x27;`` is not supported.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;squared_hinge&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy dual to clipboard\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dual;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=dual,-%22auto%22%20or%20bool%2C%20default%3D%22auto%22\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dual;\">\n",
       "            dual: &quot;auto&quot; or bool, default=&quot;auto&quot;<br><br>Select the algorithm to either solve the dual or primal<br>optimization problem. Prefer dual=False when n_samples &gt; n_features.<br>`dual=&quot;auto&quot;` will choose the value of the parameter automatically,<br>based on the values of `n_samples`, `n_features`, `loss`, `multi_class`<br>and `penalty`. If `n_samples` &lt; `n_features` and optimizer supports<br>chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,<br>otherwise it will be set to False.<br><br>.. versionchanged:: 1.3<br>   The `&quot;auto&quot;` option is added in version 1.3 and will be the default<br>   in version 1.5.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy tol to clipboard\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy C to clipboard\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-C;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-C;\">\n",
       "            C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>For an intuitive visualization of the effects of scaling<br>the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy multi_class to clipboard\"\n",
       "                 onclick=\"copyToClipboard('multi_class',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-multi_class;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=multi_class,-%7B%27ovr%27%2C%20%27crammer_singer%27%7D%2C%20default%3D%27ovr%27\">\n",
       "            multi_class\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-multi_class;\">\n",
       "            multi_class: {&#x27;ovr&#x27;, &#x27;crammer_singer&#x27;}, default=&#x27;ovr&#x27;<br><br>Determines the multi-class strategy if `y` contains more than<br>two classes.<br>``&quot;ovr&quot;`` trains n_classes one-vs-rest classifiers, while<br>``&quot;crammer_singer&quot;`` optimizes a joint objective over all classes.<br>While `crammer_singer` is interesting from a theoretical perspective<br>as it is consistent, it is seldom used in practice as it rarely leads<br>to better accuracy and is more expensive to compute.<br>If ``&quot;crammer_singer&quot;`` is chosen, the options loss, penalty and dual<br>will be ignored.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            fit_intercept: bool, default=True<br><br>Whether or not to fit an intercept. If set to True, the feature vector<br>is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where<br>1 corresponds to the intercept. If set to False, no intercept will be<br>used in calculations (i.e. data is expected to be already centered).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy intercept_scaling to clipboard\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_scaling,-float%2C%20default%3D1.0\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
       "            intercept_scaling: float, default=1.0<br><br>When `fit_intercept` is True, the instance vector x becomes ``[x_1,<br>..., x_n, intercept_scaling]``, i.e. a &quot;synthetic&quot; feature with a<br>constant value equal to `intercept_scaling` is appended to the instance<br>vector. The intercept becomes intercept_scaling * synthetic feature<br>weight. Note that liblinear internally penalizes the intercept,<br>treating it like any other term in the feature vector. To reduce the<br>impact of the regularization on the intercept, the `intercept_scaling`<br>parameter can be set to a value greater than 1; the higher the value of<br>`intercept_scaling`, the lower the impact of regularization on it.<br>Then, the weights become `[w_x_1, ..., w_x_n,<br>w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent<br>the feature weights and the intercept weight is scaled by<br>`intercept_scaling`. This scaling allows the intercept term to have a<br>different regularization behavior compared to the other features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy class_weight to clipboard\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Set the parameter C of class i to ``class_weight[i]*C`` for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy verbose to clipboard\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in liblinear that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy random_state to clipboard\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>the dual coordinate descent (if ``dual=True``). When ``dual=False`` the<br>underlying implementation of :class:`LinearSVC` is not random and<br>``random_state`` has no effect on the results.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
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       "                 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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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-04T08:55:13.946278900Z",
     "start_time": "2026-10-04T08:55:13.820778100Z"
    }
   },
   "cell_type": "code",
   "source": [
    "y = clf.predict(two_features)\n",
    "print(y)"
   ],
   "id": "2e0e66f9f3a60d87",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 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. 1. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 2. 2. 2. 1. 2. 1. 2. 1. 2. 1. 1. 2. 1. 2. 1. 2. 1. 1. 1. 1. 2. 1.\n",
      " 1. 1. 2. 2. 2. 2. 2. 1. 1. 1. 1. 1. 0. 0. 2. 1. 1. 1. 1. 2. 1. 1. 1. 1.\n",
      " 1. 2. 1. 1. 2. 1. 2. 2. 2. 2. 1. 2. 2. 2. 2. 2. 2. 1. 1. 2. 2. 2. 2. 1.\n",
      " 2. 1. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 1. 2. 2. 2. 2. 2. 2. 2. 1. 2.\n",
      " 2. 2. 1. 2. 2. 2.]\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-04T08:55:14.023890200Z",
     "start_time": "2026-10-04T08:55:13.971850600Z"
    }
   },
   "cell_type": "code",
   "source": [
    "correct_predictions = y==T\n",
    "np.unique(correct_predictions, return_counts=True)"
   ],
   "id": "ea068a1b9d64787d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([False,  True]), array([ 30, 120]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-04T08:55:14.495601900Z",
     "start_time": "2026-10-04T08:55:14.058897500Z"
    }
   },
   "cell_type": "code",
   "source": "plotTwoFeatures(two_features, T, clf.predict)",
   "id": "f8fd7c58418b5620",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-04T08:55:15.873613800Z",
     "start_time": "2026-10-04T08:55:14.523608500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "svc = SVC()\n",
    "svc.fit(two_features,T.T)\n",
    "plotTwoFeatures(two_features, T, svc.predict)\n"
   ],
   "id": "8a07d38630992f1a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Bei dem nicht linearen SVC machen die Linien, um die Instanzen zu unterscheiden eine Kurve.",
   "id": "43a14df4e6ea0c1e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-04T08:55:16.941403Z",
     "start_time": "2026-10-04T08:55:15.973212400Z"
    }
   },
   "cell_type": "code",
   "source": [
    "outer_two_features = X[[0,3]].T\n",
    "svc2 = SVC()\n",
    "svc2.fit(outer_two_features,T.T)\n",
    "plotTwoFeatures(outer_two_features, T, svc.predict)"
   ],
   "id": "49e4d07679dfcda1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-04T08:55:17.258691100Z",
     "start_time": "2026-10-04T08:55:17.028545100Z"
    }
   },
   "cell_type": "code",
   "source": [
    "clf2 = LinearSVC()\n",
    "clf2.fit(outer_two_features,T.T)\n",
    "plotTwoFeatures(outer_two_features, T, clf2.predict)"
   ],
   "id": "7a443af9dd889b98",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-04T08:55:17.769742300Z",
     "start_time": "2026-10-04T08:55:17.267703Z"
    }
   },
   "cell_type": "code",
   "source": [
    "knc = KNeighborsClassifier()\n",
    "knc.fit(two_features,T.T)\n",
    "plotTwoFeatures(two_features, T, knc.predict)"
   ],
   "id": "73b46d9b02b26659",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-04T08:56:16.313402600Z",
     "start_time": "2026-10-04T08:56:15.860970Z"
    }
   },
   "cell_type": "code",
   "source": [
    "knc2 = KNeighborsClassifier()\n",
    "knc2.fit(outer_two_features,T.T)\n",
    "plotTwoFeatures(outer_two_features, T, knc2.predict)"
   ],
   "id": "990d0912d50e221b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-04T09:23:39.594811800Z",
     "start_time": "2026-10-04T09:23:39.247401700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "second_two_features = X[[1,3]].T\n",
    "km = KMeans(3)\n",
    "km.fit(second_two_features)\n",
    "plotTwoFeatures(second_two_features, T, km.predict)"
   ],
   "id": "d58f7531a1924618",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Die bei Aufgabe 1d falsch zugeordneten Instanzen sind wahrscheinlich die Punkte, die hier in dem \"falschen\" Bereich liegen. Wobei das hier nicht genau die gleichen sind, weil wir bei 1d die Merkmale 0 und 1 betrachten. Hier haben wir 1 und 3.\n",
    "\n",
    "Es kommen bei mehreren Ausführungen leicht unterschiedliche Ergebnisse heraus. Manchmal sogar unterscheiden sie sich sogar sehr stark\n",
    "\n",
    "DIe Farben haben beim Training (fit) keine Bedeutung, werden ja auch nicht mitgegeben. Die tauchen nur auf, weil sie in der Funktion plotTwoFeatures verwendet werden."
   ],
   "id": "260fd8e5a52015b9"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-04T09:32:52.840087700Z",
     "start_time": "2026-10-04T09:32:52.445334800Z"
    }
   },
   "cell_type": "code",
   "source": [
    "clustering = DBSCAN()\n",
    "y = clustering.fit_predict(two_features)\n",
    "knc3 = KNeighborsClassifier(1)\n",
    "knc3.fit(two_features, y) # Kein Tippfehler, wirklich y - warum?\n",
    "plotTwoFeatures(two_features, T, knc3.predict)"
   ],
   "id": "8d8426139ecbe953",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 35
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}