{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2026-10-03T16:46:34.175559799Z",
     "start_time": "2026-10-03T16:46:34.063922625Z"
    }
   },
   "source": [
    "from sklearn.svm import LinearSVC, SVC\n",
    "import numpy as np\n",
    "from nnwplot import plotTwoFeatures"
   ],
   "outputs": [],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-03T16:46:34.219658258Z",
     "start_time": "2026-10-03T16:46:34.190531089Z"
    }
   },
   "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": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-03T16:46:34.416357667Z",
     "start_time": "2026-10-03T16:46:34.226237363Z"
    }
   },
   "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": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-03T16:46:34.532571321Z",
     "start_time": "2026-10-03T16:46:34.444142909Z"
    }
   },
   "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-3\" 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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy penalty to clipboard\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-penalty;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%7D%2C%20default%3D%27l2%27\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-penalty;\">\n",
       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;}, default=&#x27;l2&#x27;<br><br>Specifies the norm used in the penalization. The &#x27;l2&#x27;<br>penalty is the standard used in SVC. The &#x27;l1&#x27; leads to ``coef_``<br>vectors that are sparse.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy loss to clipboard\"\n",
       "                 onclick=\"copyToClipboard('loss',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-loss;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=loss,-%7B%27hinge%27%2C%20%27squared_hinge%27%7D%2C%20default%3D%27squared_hinge%27\">\n",
       "            loss\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-loss;\">\n",
       "            loss: {&#x27;hinge&#x27;, &#x27;squared_hinge&#x27;}, default=&#x27;squared_hinge&#x27;<br><br>Specifies the loss function. &#x27;hinge&#x27; is the standard SVM loss<br>(used e.g. by the SVC class) while &#x27;squared_hinge&#x27; is the<br>square of the hinge loss. The combination of ``penalty=&#x27;l1&#x27;``<br>and ``loss=&#x27;hinge&#x27;`` is not supported.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;squared_hinge&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy dual to clipboard\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dual;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=dual,-%22auto%22%20or%20bool%2C%20default%3D%22auto%22\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dual;\">\n",
       "            dual: &quot;auto&quot; or bool, default=&quot;auto&quot;<br><br>Select the algorithm to either solve the dual or primal<br>optimization problem. Prefer dual=False when n_samples &gt; n_features.<br>`dual=&quot;auto&quot;` will choose the value of the parameter automatically,<br>based on the values of `n_samples`, `n_features`, `loss`, `multi_class`<br>and `penalty`. If `n_samples` &lt; `n_features` and optimizer supports<br>chosen `loss`, `multi_class` and `penalty`, then dual will be set to True,<br>otherwise it will be set to False.<br><br>.. versionchanged:: 1.3<br>   The `&quot;auto&quot;` option is added in version 1.3 and will be the default<br>   in version 1.5.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy tol to clipboard\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy C to clipboard\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-C;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-C;\">\n",
       "            C: float, default=1.0<br><br>Regularization parameter. The strength of the regularization is<br>inversely proportional to C. Must be strictly positive.<br>For an intuitive visualization of the effects of scaling<br>the regularization parameter C, see<br>:ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy multi_class to clipboard\"\n",
       "                 onclick=\"copyToClipboard('multi_class',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-multi_class;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=multi_class,-%7B%27ovr%27%2C%20%27crammer_singer%27%7D%2C%20default%3D%27ovr%27\">\n",
       "            multi_class\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-multi_class;\">\n",
       "            multi_class: {&#x27;ovr&#x27;, &#x27;crammer_singer&#x27;}, default=&#x27;ovr&#x27;<br><br>Determines the multi-class strategy if `y` contains more than<br>two classes.<br>``&quot;ovr&quot;`` trains n_classes one-vs-rest classifiers, while<br>``&quot;crammer_singer&quot;`` optimizes a joint objective over all classes.<br>While `crammer_singer` is interesting from a theoretical perspective<br>as it is consistent, it is seldom used in practice as it rarely leads<br>to better accuracy and is more expensive to compute.<br>If ``&quot;crammer_singer&quot;`` is chosen, the options loss, penalty and dual<br>will be ignored.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;ovr&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy fit_intercept to clipboard\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            fit_intercept: bool, default=True<br><br>Whether or not to fit an intercept. If set to True, the feature vector<br>is extended to include an intercept term: `[x_1, ..., x_n, 1]`, where<br>1 corresponds to the intercept. If set to False, no intercept will be<br>used in calculations (i.e. data is expected to be already centered).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy intercept_scaling to clipboard\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_scaling,-float%2C%20default%3D1.0\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
       "            intercept_scaling: float, default=1.0<br><br>When `fit_intercept` is True, the instance vector x becomes ``[x_1,<br>..., x_n, intercept_scaling]``, i.e. a &quot;synthetic&quot; feature with a<br>constant value equal to `intercept_scaling` is appended to the instance<br>vector. The intercept becomes intercept_scaling * synthetic feature<br>weight. Note that liblinear internally penalizes the intercept,<br>treating it like any other term in the feature vector. To reduce the<br>impact of the regularization on the intercept, the `intercept_scaling`<br>parameter can be set to a value greater than 1; the higher the value of<br>`intercept_scaling`, the lower the impact of regularization on it.<br>Then, the weights become `[w_x_1, ..., w_x_n,<br>w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent<br>the feature weights and the intercept weight is scaled by<br>`intercept_scaling`. This scaling allows the intercept term to have a<br>different regularization behavior compared to the other features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy class_weight to clipboard\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Set the parameter C of class i to ``class_weight[i]*C`` for<br>SVC. If not given, all classes are supposed to have<br>weight one.<br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy verbose to clipboard\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>Enable verbose output. Note that this setting takes advantage of a<br>per-process runtime setting in liblinear that, if enabled, may not work<br>properly in a multithreaded context.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy random_state to clipboard\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo random number generation for shuffling the data for<br>the dual coordinate descent (if ``dual=True``). When ``dual=False`` the<br>underlying implementation of :class:`LinearSVC` is not random and<br>``random_state`` has no effect on the results.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy max_iter to clipboard\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=max_iter,-int%2C%20default%3D1000\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
       "            max_iter: int, default=1000<br><br>The maximum number of iterations to be run.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1000</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes,)<br><br>The unique classes labels.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
       "           <td>[0.,1.,2.]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-coef_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=coef_,-ndarray%20of%20shape%20%281%2C%20n_features%29%20if%20n_classes%20%3D%3D%202%20%20%20%20%20%20%20%20%20%20%20%20%20else%20%28n_classes%2C%20n_features%29\">\n",
       "            coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef_;\">\n",
       "            coef_: ndarray of shape (1, n_features) if n_classes == 2             else (n_classes, n_features)<br><br>Weights assigned to the features (coefficients in the primal<br>problem).<br><br>``coef_`` is a readonly property derived from ``raw_coef_`` that<br>follows the internal memory layout of liblinear.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](3, 2)</td>\n",
       "           <td>[[-1.54, 2.28],\n",
       " [ 0.1 ,-0.98],\n",
       " [ 0.66,-0.35]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20if%20n_classes%20%3D%3D%202%20else%20%28n_classes%2C%29\">\n",
       "            intercept_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
       "            intercept_: ndarray of shape (1,) if n_classes == 2 else (n_classes,)<br><br>Constants in decision function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
       "           <td>[ 1.2 , 2.02,-3.14]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>2</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.svm.LinearSVC.html#:~:text=n_iter_,-int\">\n",
       "            n_iter_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
       "            n_iter_: int<br><br>Maximum number of iterations run across all classes.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>8</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-03T16:46:34.610116848Z",
     "start_time": "2026-10-03T16:46:34.553528744Z"
    }
   },
   "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": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-03T16:46:34.662634196Z",
     "start_time": "2026-10-03T16:46:34.613846003Z"
    }
   },
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-03T16:46:34.821724933Z",
     "start_time": "2026-10-03T16:46:34.666199151Z"
    }
   },
   "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": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-03T16:47:02.178218052Z",
     "start_time": "2026-10-03T16:47:01.792237192Z"
    }
   },
   "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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xNtKmr79aOvEKIawnI0ctWEBGRgbf/953+f73vgtAbm4uSinefWcJV19zHRUVna9VNzU14fF4SElJ6XR7akoKtXW13Z7rwYf+wqN/e6zj87i4ONasXmk1VCEGnaaKBEy/jm7reXGqGdCoL0zpcUy0OG7ZJSyWREQIQS+SkWWffcZZZ5/T6bZf/fKXeL0efvXre6ipqTnke0zTZPPmLcyaOZOnnvpXx+1z5sxmw/oN3Z7L6/UeMpsixFAW8NipWJfNsJnFPa4b0Q1F2QApg1+f4Ix0CEKIKGF5AWtjYyObNm3u9K+xqZGGttt9Ph8A11xzNf99+aWO7/v3v//NWWedyZQpkwE49thjOOaYY/j3M8+E+a4IMbjtXToeX7Oj247FSkH52mzq9qb1c2S9V7fkCrk8I4ToEPaVY5kZGUycOKHj82f+8yxjx43j9ddepaqqirS0dH73+z/w3nvvh/vUQgxqvmYna/5+DBPOX0fKqBqUGVwfoukKFdAp/mIkez+cQLR3Kl54fy5f2e4IPVAIMWRoI7JyQvdi70b7mpHi4uKO2zIyMkhLS2Pr1q2dxiYmJpKZmUlpaSktLS29Ok98fDzbt21h2ZyLCTS3Hm64QgwarvQm0sZXYNgDeBpcVG0eTsAT/ZVXZyzy8tFd10k9ESGGiFinjVd+ewbjJ0yiqamp23FHNDNSVFR0yG1VVVVUVVUdcntDQwMNDQ1Hcjoh+oHJiLmFpE8sRzMULVUJ7P1gPH53dL2Tb90XT/G+gXWZY+H9ucF6Ig9EOhIhRLSRDf5CtMmYVsKEc9aj2xSqbb4wOb+WEXMK2bdlOFtenMlhdlAY8oI9Z6J/LYsQIjLkmVUIIH1KCRPPX4fWtlNF04L/2j/OmFzO1Ctle7kQQvQFSUaEAMafvRHYn4B0JWXMPhKyu6+PI7o2ff3VMisihOiRJCNiyEufVIrNGegxEWk36pStoQeJDjMWebl9qRHpMIQQUU6SETHkpU2o7Fgj0hNNg7jMgdX3JdLUAzdQusQV6TCEEFFOkhEx5Gl6L3a3R3cJj6gihc2EEFZJMiKGvPqCFEuXaJQCT528yxdCiHCTZEQMeWWr8jD9WshLNZoGez8c1z9BDWAzFnn5+wNS2EwIYZ0kI0KgU/BxMMnoLiFRCpor46jZMbwf4xqYEu+5StaJCCF6RYqeCQEUfToWwx4gd+GuLhOS5op4Vj9+TP8HNsDMWOTl+lWxkQ5DCDHASDIiRJu9H06g+PNRjDp1C8kja9A0hbvOxZ73J9JYkhLp8KJeR5XVJZGORAgx0EgyIqJSbGY9+SfsxOb001QRz553JwJ9X6/C73aw47UZfX6e7sSkNJMwoh40aKpIpHWA9J+Zvv5qFsvOGREBCkWjvosWowRNGSQFJhGjjqzIXgAPNbZ1+LVmHCqZFP80dHm57FPy6Iqo4sqoZ9b1X2DE7C9CljK6mpyjCqjbk8aGpxdENsA+EpvRyJjTNpMyurrT7fWFKex6ZxJNpcmRCUyIKFZtW8OOmH/SZOzdf6PSyPAfxYTWm4hRGb06nomPXc5nKHa+QUBzd9xuN5MY6bmQPO/5aLK/v0/IAlYRNVxpDcz9+rJOiciBUkZXM+cbH/V/YH0sblgDM2/4jOSR1Yd8LTGnlpnXf0FiXk0EIrNG6omISKiwfcqa2F/QpBd0/oKm2Gdbzpfxd+LWDu0g3x0TP2tjf0WB8+VOiQiAT69nh+ufbI35K4pe1CUSlkkyIqLGjOuXA133h2m/LS6jmRFz9vRjVH1NMenCNRj2AFoXf42aDppuMumiNaCZ/R9eCMctu0S28Ip+56OJTbF/AhRohyYHSjPxag1sdj1k+ZjFjjeosa3r8njtSpxvUW1bdRgRi1AkGRFRwZHUhD3WF7L4mFKQf+LO/gmqHyTl1xCb0dxlItJO08GZ4CFtQmX/BWbRa41ypVf0vzLH+5j4eq6IrJnU2NfQopeGPJ7CpNDxOoSY9dCUTqHjtd4FKyyRZEREhbyFuy1VQdU0sLt8fR9QP0kZvQ8zEPqOmwGNlNHWp5z7mhQ2E5FUbVtDqMQBgkNqbOtDDnNrlbiNipDtHpRmUmNbJ5dq+oAkIyIqGI5ApEOICN1m/dJLb8YKMZgFNI/FPlEaJt6Qo0ytF29wNBOF/C2GmyQjIirU7021NE4pUIPoeaC1NtZSoz5NU7hro6OY2ML7c7k49japsioiJtbMRlMWXr40hcscEXKY00xDUxZKByhwmqno/VBmYKiRZEREhfI1+Siz+3Ls7TQN6vceWQ2BaFK1MQsVsPKkChXrcvo+IAsaTjkh0iGIIS7beyrKwoJuu5lEmn92yHE2YhnmW2ghwdHI9p5hMUrRG5KMiKhRvib4YttTfxilYPPLM/svqD7md9sp+mx0j0mYMqFsZR6ehsjPRCy8P1e28YqISwyMJ803B0IkD2Pd11iexRjpuQQNG6iur/9oSsehksjxnt7reEVokoyIqLHjjenU7kwHOickB368/ul5BFqd/RxZ3ypYOo7SL/MBOi1mbf+4ckM2u96eHJHYDnTcsks47b2zIh2GEGhoTGv5Pqn+6cHPD0xKlA4KxrZeR7ZvseVjxpt5zGz+OQYxwYSk/XmnLTlxqBTmNN+DQ8mi7b4g+/JEVNn4n/kk5u1jwrkbiEluBQ1UQGPfjky2vDwNAo5Ih9gHNHa9PYXytbmMmFNAUm4taNBYkkTpqvyoqL5at+QKFt8jT8IiethwMavlbmqN9RQ73qbZKEDDRqp/BjneM4i1sFbkYKmBaSxs+Dtljg+psH+MT2vCaaYywncSw3wLMRhcb4SiiSQjIuo0FKaz4qFFuNKbMewBPA0x+JqP/EnAcPpwpbRgmhqt1XGoQNfTt5pu4kprRjdM3HUu/O7+SYCaKxLZ+ea0fjlXb8xY5OW27YMxCRQDnYZGamAGqa3h6ydlJ5487znkec8J2zFFaJKMiOiimWQv2Ev2/L3EJAdLMisFNTszKPxkLI3Fve+eG5PaTP5xO8mYWopuBOdefa12ylblUfTpaAJeOwC63U/uMbsZMbcAR1xwq58Z0Ni3eTiFn4ylZV9CmO7kwPLRXddR+kDk16sIIQYvSUZE1NB0k8mXriJ1XOfiXpoWLA6WOqaKLS/OYt9W69Ov8SPqmX7tF+g2syMRgWDhtNxjdpE2voJ1TxyFMjWmX7ec+GENnaqh6oYifXI5aRMq2fD0fBoOIxkayKavv1oWrAoh+pwsYBVRI3fhLlLHVqFph/an0Q0FGky8aC3OxFZLx9N0kymXr8SwBzolIvu/Dq70ZsadtZExp20+JBE58Ny6LcCUy1ei24ZWcbaPS4fW/RVCRIbMjIiooOkm2fP39tyjRQM0xfDZRRQsHR/ymOmTynEmeHoco+uK9EnlKEXI/jA2l4+MqaVUrM0Nee6BbuH9ucGdMw9EOhIhxFAgMyMiKiTk1GGPDV2SWdMhY3KZpWOmTaywVq1VA91KKQIF6RPLLZ17oNMWHBXpEIQQQ4gkIyIq2JzWe0PYnH7Lx+xptqODxRLzmg62mMHTpK87C+/PZfE9g6fKrRAi+kkyIqKCtynG0jilwNNobZuvtzHGUkdctJ4v0bQzA1pUVEHtSzMWefmK7aRIhyGEGGIkGRFRoakskZZ9cSF70wCUr7XWo6ViQ3aXC1cPZvp1/O7Q12l0Q1GxPtvSuYUQQlgnyYiIEhqFn4w9ZBfNgZQJvmYHlRYTgvq9qTSUJPU4O6IUlCwfSfHnY3pMhMyARnNFPLW7MiydWwghhHWSjIioUbkhm70fjgM692hRbWs6fK0O1j89v6NIWWgam56dG6y2etC6kPbjV20cwd4Px1P4yZiORn2HnFuBu87FhmfmddtESwghxOGTrb0iqhR+Mo66PelkzdtL6vhKdJuJtzGGslW5lK3Ow9/au7LkvmYnax4/lmHTSxgxr4DY9CaUqVFfmErpinxqtmcCwQRjx+vT2LdlBFnz95I8shpNV7TWxFG2Mo+KdTkEvPLnIoQQfUGeXQcVhW4zUaaGMqN30kszgoW0uusN01CcEtZKp6bfoGx1HmWr80JFRu2uDLkUI4YERQATHzpONGTGT0SWJCODgM3lJWtuISPmFnQU+WooTqLky5FUbcqKiksLui3AsJlFZM8vIDa9GYCWfXGUrsinfE0upt9KoQ/RH/68KMAlSyIdhegrtcZGCp2vUWVbDpqJrpxkeU8m13s2caa1xeFChJskIwNcTEozM677Ake8hwPf3CRk1TPpwnVkTCpny0uzIjpTYjh9TL9mOfEjGjrd7kprZszpmxk2s5gN/1qA3211LYjoCwvvzw0WO2v0cOttAR58ICnSIYkw2+t4iZ2uJ9GUAVpwEZWpeShxLKHE8S4zWn5Eun9OhKMUQ1H0zuWL0DSTaVeuwB7nRdM793Npr5uRNrGCkSduj0x8bSZesJb44Y2H9Jxp/zx+WCMTL1gbsfgEHLfsEqm6OshV2Zaz0/UkAErr3HNIaSYKP+tif0OLNjSqDIvoIsnIAJY2rgpXWkuPtTQ0DbLmFWA4rFUtDTdXWhNp46vQ9B5i1BWp46pwpTf1Y2Si3YxF3kiHIPrBXudLoHpqwKSAACXOt/otJiHaSTIygGVOK8G0UMbccARIHVfZ9wF1IXNqKcoMvWbFNDUyp5b2Q0TiYIn3XBXpEEQfc2tV1Nu2dlya6Y7STErt7/dTVELsJ8nIAOaI96Bb+AkqE+xxPXev7Sv2WK+lqqoocEQoxqFqxiIvxy27JNJhiH7g1RpCD2rj0xr7MBIhuibJyADma3VYbvAWqcWhfo/18/paZQFrf1l4f26PMyLHZxlknd7ajxGJvmRTcb0YG9uHkQjRNUlGBrCqTSOsNXjza23Fvfpf1aYRlvrD6IZi3+YR/RCRsCKp0cMTc1okIRkkXGoY8YFRIbf5a8pguO+EfopKiP0kGRnA9m0dhqfR2ePsiDKDjeX87t5VLg2X5opE6gtSeuwPYwY06guTaSqXraT9oWMLrxgyNDTyPRe0LVLthgKFIsd7Zv8FJkQbSUYGMBUw2PjMPPwe2yEv9u29WBqKUtj9zuQIRRi05aVZeOpdXSZNygRPQwxbXpzd/4ENMe1rRCQRGZqG+04gz3Ne8JODdtVoygA0prR+m3gzVKViIcJPip4NcM0Viaz+23HkHL2b4TOLMRzB+gGeOhclX46kdGVet2XX+4u3KYY1jx9D9lF7yZpbgD3WB4CvxU7pynxKvhgZsZmboUR2zQxtGhrj3DeQ5J9IofN/1Nu2Bb+gNNL988n3XEByYGJkgxRDliQjg4Cn3sWuJVPY/e5EnAkezICGtzEGoqjfhN/toGDpeAo/HoujrWS9t9EZ1T10BhOpJSIgmJAM8x/LMP+xeLV6/FoLDjMRG9YXuArRF/olGTGMzu/MlVKYVgpkiF5RAQN3XbSuhDfJPmovOUftCZauB7xNTkqWj6T481EceMUwaVQVo0/ZRlxmI5qmCPgMqjYPZ8+7kzrNoBhOH8NmFDN8ZjGOeA9+t52qTSMoW53Xloz1HXuchxGzC8mYWord5cPb7KRyfXZwfU4vOwv3tRmLvIc9K3LReC8PLnGFOSIRDRwqCYeSdVoiOmgjsnKsVIEAYPjw4dx26y0sXnwqKSmp7Nmzh0f/9jdeeOHFbr/n9ttv4wff/x5+//4KoA0NDUydNsNykPHx8WzftoVlcy4m0Cyr+wca3eZn7rc+IibJg1L7S8K3f+yud7LyLydg+m1MOH8tmdOCxc8OHAegTI31/5pPQ2Ea8cPrmXb1l9hcvs5jTVCmzpYXZ1G9fVif3J+UMVVMvnQVumFCW0l7pQAFAY+NDc/Mo7EkfF2Hj0Q4FqvWJzi55K74MEUkhBhKYp02XvntGYyfMImmpu6rbPdqjvzKKy5nzdq1nH7GWUyZOo3HHnuc+//vD5x44qIev2/V6tXk5Y/q+NebREQMfLNuWoYzMTgbcnBvGgBnoodZNy8jf9E2hk0v7baHjaYrpl/zJa60RqZd8yVGjO/QsTpohsmkS1YTP6Iu7PclNrOBKZcFE5ED+wEF4wPD6WfaVStwJkY+aZ6xyCuLVYUQA0KvkpH7//gnXnjhRfbt24fb7ea555+nvLycWTNn9lF4YqCLG15PbEZzp4ThYJoGsenN5By9q8dqre0JycQL12Jz+rqtPtt+rtyFuw4/8G7kHr0bNNVtfRdND5bfz5q/N+zn7q2Ec8dEOgQhhLDksFYPGoZBQkICV1x+GUlJSbzzzrs9jp8yeTK7du5g86YNPPPvp5kyJbJbTUX/GXniNstjDTs9Ji3t4oc3hiz2phuK9AkV2FzhW7ip2/1kTC0LWcRN0xUjZhcBlq+AhpVs4RVCDDS9XsA6fvx43nv3bWw2G62tbr73/e+zcdOmbsdXlFdw2+3f5sMPl5KUlMhdd/2Al196kZNOPpWSkpIuv8fhcOBw7F8EGBcnK70Hqpgkd1iPZyVZ6RirB/v3hGtBqSPOa6maLIAtxo9uD2D6+nfDmhQ0E0IMRL1+pty+fTt5+aOIjY3l3HPO4f7/+wMtzS0sefvtLsc/+9xzHR83Nzdzxx3f5YsvPuPKK6/g97//Q5ffc+st3+LOO+/obWgiCgV84a9xcuAi2FDMMJ6/N/dFKTD9/bttuS8TkazTWymVXTVCiD5y2M+WLS0tPPvcc7z//gdcddWVlr/P7/eze/duRo0c2e2YBx/6C+MnTOr4N2v23MMNU0RY5cYRlhKHjh0pISgFfvehFWcPGWdCa3Us7rrwvYD6mh00lSeEbE5oBjRqd6UfUuWyL/XlYtX2PjW33lbfJ8cXQogjfrZ0OByYBzw7a5qG3kNfe7vdztgxYykvL+92jNfrpampqeNfc3PzkYYpIqR0+UhMv9ZjohGcRdBoLE2wlJAUfDIm9OUSDYqXjyS8hd80SpaPtLRepeTLkWE8b2iyWFUIMZD1Khn5++OPcfTRR5GUlMSwYcP4xte/xqJFJ/Dss/svxdzxnW+zaeP6js8ff+xvHHvsMSQlJTFq1EgefODPJCTE8/S//x2+eyGimM7ml2YBXc98tN+25eWZbPz3AgJeo8dxlRuyKP1iDIWfjOnxmNVbh1G2Kj8cd6CTinU5VGwYEez9c9C52z8v/mIktTszwn7urshiVSHEYNCrNSMPP/IIt99+G7NnzcLn87F9+3auufZ6li5d2jHGNE0CgUDH5395+GG+851vM3vWLFpbW1mzdi1nnnUOu3fvCdudENGtZttw1j89jwnnrseZ6OlUzMzb6GTbq9Oo250JwJd/PpGpV64gIbu+0+Ud06dT/MUoCpZOAGDvhxNorY4j77iduNJaOsZ5mxyULB9J0WejQ7ZLPzwa2/47k6ayJHKO3oOzrbQ9gLvORdGyMZSvzqU/SvHLYlUhxGDRqwqskSIVWAePuGH1pI6rAqBmRwbNFV2Xo7bFeMmcXoLN5aOpPJGabcO7OaIifkR9Rzn4xpLk/ut3o5kkZtdjc3nxNTtpLE2iP/sB9Xcy8lqjjQcfkPLhQgjrrFZglUZ5g0RsRiMjZhcRm96IGdCpL0g7oj4pus3PqJO3kT6pHN0ewO+2U74ml6JPR3O4S40SsmsZdcpWYtODv5CpYyvZ/e6kQ0qn63Y/6ZPKSR5ZjeEI4Ezw4G920lCcTOcXe0XKmH1kTivpSEb2bRnOvi3DDzshiUluZvRpm0nMrkPTFZ4GF3s/Gtd1MqR0GoojU/Y93InI9p2J/O2p8WzYnILNpjh2QQU3XrWDYZn7t2afk+Dn+HubuH2pEXJnjUmAfbYvqbB/ilerx6GSGO47nnT/XDQ670pq0HdR6niHZr0UAwep/llkeU+U5m1CDCEyMzLAabrJ+HM2MGxGCWZAQzdUR58UZWrseGMaFetyenXM9CklTLpgHZq+fxtt+/8DPp11Tyygqaw3L8Im069dTlJ+bTDmg3rO1BemsP7JBYBO8ugqJl+8BsPpBxWsFdJ+v2r3pLH5+dkEPHYcCa1MvXIl8cMa999vMzje0+hk03/m0lTeu3fxY8/a0FasjI442+93a62LVY8uxPTae3XMvnDcskvCdqxAQOP2H87n4X9MwjBMAgEdUOi6QtfgD79awW1f3dIxvj7BGTIZadaLWRN7N26jIrijSDM7/u8KjGBWy8+JNbMI4GFj7B+osi9HUzpKM9vqxGno2JnScgfD/MeE7b4KIfpfn/SmEdFn/LnryZwWLB7XvsOkvU+KZigmnLee9Illlo+XlFfNpAvXdUxAaAf9X7eZzLzhCxzx1pPCqVeuJHlkbbc9Z5Lza5l61QoSsuuYesVKDIe/4z4ceL+S82uYeuUKDKeHGdcu75hh6bjfbePtcR6mX7ucmBTru7BGnrSVrDlFneI68H7HJLcy52ufWD5eX1l4f25Yj3fHT+bxyD8nArQlIgAapqnjD+h8+0cL+PvT4ywfz6PVsDLuh3j0qrZDmZ3+79Yrgl/XatkQex9VthUAwUQkeGrQFCY+NsTeR7VtzZHeRSHEACDJyAAWN6wh2Fiuhx4tSsHoxVuxWpp83FkbO763u2NqumLsmd1X3T2QI7GFlDH7Qo5LGV3N6MWb0Xrs+6JIyq1jzOKtxKS2dLu9V9dBtwfIPdZqbxqTnKP39FhMTdPAleImfXKpxWP2jXBemtlbGM9Dj09C9bjQV/GDu+fg9Vp7qih0vopPa9yfXBx8NM3Eq9WzM+YJ9tlX7k9WDqYFf7Y7Yv6BilBZfSFE/5FkZAAbMbswZPEvTQu+q08eVR3yeLZYN670npvatWtfhBrKmFO3WhoHkJRbF7KGhxmAjCmhZ3p0QzFseimGwx9ybPZRe9ENFfJ+KwX5x+8Meby+0L6FN5wef3ocuh7qhV6jpjaGV5eEnpExCVBif7v7BKPjkCbl9k9C73bSFE1GAY1GZB5zIUT/kWRkAIvNaLLUK0WZdFzS6ElCdoPlaqm6oUAP8aIDuNLDW7BON0C3m5bi1G0mzqTQl5MSsussFVvTNHAkhrfXjhUL788l8Z6rwn7crduTMM3QD6TdFmDztmQgWI21Oz6tAb9u7eetNH/H7EcozXqxpXFCiIFLkpEBTAV6rmzaQQsuZg3F9Fn/dVAKCJ2LhCyd3tdUiJmj4Bhr97t9YXB/6ssy73abQrOQEJhKw27f/4PsrjT8wbtkwqWvjiuEiB6SjAxgdQVpll4cNQ3qC1JDjqsvTEaZoXvEKAV+jw0rvz61u9Ot96axkLiYAQ1vk8PSWG+Tg9ba2JDjKjdlWW6811TWv3U2+rLM+3HHlIdYLxIUCOiccEzn9g3HZx2aINhVArGBrNCXX5SG3Uy01rtHaST7J4UeJ4QY0CQZGcDK1+SGfDFRJtQVpNCyLyH0AU0bdXvTQg7TNChfbW278N4Px4VMcJQKxlmxPivkGhjdUBR/MSrk2hJlQunKfEsveLU7MvG12izNMu16u/9eGPu6zPvVl+zG5fLTU0Zr6CZTJtZy9LzQa4Q0NHK951g4syLPey4hp9aUTrp/LjGqf0rrCyEiR5KRAczX7GTXkslA1y/2ZkAj4LWx441plo+55cVZmD692xdmpcBd72TPexOtHdC0sef9Cd3G2H7b3qXj2fP+JLyNzh4TkqLPRlPy+WgqNw3vPkYTmioSKf58lLUYga0vzwwZY9WmEbRUJVo+5uHqi8WqXUlM8PHYHz9r28p86B03dBOHw+SfD35qeeYo27uYlMDU7mdHlEaqfwb5ngsZ67m2+wMpHbuKZ0LrV62dWAgxoEkyMsCVrcpn68sz8DY5ATrNQjSWJrH2H0fTui/e8vH8bgcrHjoBd62roxncgf8aS5L48sET6M2vTvHnY9j19qSO9RsHNplTAY1d70yk6NOx+JqdrP3HMdTuSt9/TrM9Lhu73pnInvcmABpbX55J0WejCbStc1FmcP2MGdCo3JjF+icXYPqsFxiu3ZXJxmfnEvAah8SIgtKVeWx9eZbl4w0UV1y0h5ee/JC8nODCU0M30dsSk+lTa/n49beYOyv0Tqx2OnZmNv+MbO9paMoITrooPVjATtnI8Z7BzJafoWNjpOdiJrZ8M3jJhvZxwd+RFP8U5jf9AZcaFtb7K4SITlKBdbDQTFLH7sOV1oQK6NQXpNJceWTv4mMzGsg5Zg92lxdPQwyFH4/F29RzGfCemYyYW0jK6OCLW+3uNMpW5tFVYhOT3ELKmCp0ewBPvYvq7ZmowKHrFAyHj7QJlW3l4G1Ubx+Gr9l5BDFC2sQyMqeWoRkmTeWJFH4yGsz+6ZwwY5G3T3bOhGKa8P7HI9i4JQXDUBwzr9JSEnL9qthuq7F6tQb22b7EpzViV4lk+OdjV4deLjTxs8+2kla9HB07qf4ZxJm9qxoshIhO0ptmqFE6NTsyYUdm2A7ZUpXI9ldmhO14mqEIuO2464IvXgGPHc1QqMDBIxWORDfOpFYMe/CLthg/vuZDk5GA107lhuywxQhQvXUE1VtHhPWYVkSyC6+uw6mLyjh1kfVqvQAXjffyYDfJiEMlkuU7JeQxWt0+yhpMWvUAutLx617yU030UAuD+pGXBnbGPEGDsRsNjeTAFMa4r8RG6AXSQojQJBkR/SJjSiljz9yI3eXvWBOiG4qxp29i51tTqNwYTChi0xuZdPEa4jKbOsZpmmLUydsoW5nH7ncn9V9X3n4UyUQkUkxlsmrfEupH/R0yAqAMQNFg+NldN4pJtXeRldL/SeHBNsbcT7ljaafbGm27KHK8Rr7nIsb1tPZFCGGJJCOiz2VMLWHShes61mAcWKjNiPEzsa0XTkNxMjO+8jmGI3DIOA1F1vwC7LFetv53Jp2794qBaHn1yzSPeeqAH+X+3TUqsZDNcd9GL3uQ4Unhm+3rrQ2uP1Dh+LibryoKnC+ioTHWc02/xiXEYDP43mKKqKIZAcaduanbvi/t/XPGnrGJUadsxXAEuq0qq2mQOa2MpJE1fRx1/2nfOTPUZkUq6isPSkQOogfA8LLF9o9+jetArVRQYe8uEaEj9r3OF/Hj7Z+ghBikJBkRfSpjcjm2GH+PW0M1LbgmJH1CRcjy9mZAY8ScgjBHGRmRWqwaDXb7l0IgxMSsHiCQ9Rn7GiOTfG53/TP0oLYuw3ucz/Z5PEIMZpKMiD6VkFUXspAZBJMMK+sVdSPYuXegGyyJyDkJ/i5Lw4fSmrAWjNBNDAGqWot6ffxwaDQsdn1WUGez1sVaCNE1SUZEdOjVEpCo340+pByfZZB1ei+33FtskjdwDLb7I0T/kgWsok81lSeihWxTH9wxo0xCzo6YAY3GkuTwBBchQ3HnzMGcDdNoTdlmaXYkLSa8W7etig+MxK1XhB6oQZLfYkViIUSXZGZE9KmqTVkEvEbI3jQBr0H1jsyQ3YV1QwV7zgxAQ3WxaldGGyeHTkRMA6N8PpmJ6f0T1EHGu28IPUgBSmO058o+j0eIwUySEdGnTL/BriWTO3bNHKx9l83udyaz572JBHx6t2tMlAn7tgyjbk/oZn4iuo1IHoZr92Xd97AxdTBtTHDf2L+BHSBWjSDdt6D7AQrQIM9zDjZi+i0uIQYjSUZEn6tYl8v2V6dh+oyO/jFmoK2XjM9g+2tTKV+TS2t1POueOBpPffCJvWNcW7+d8nU5bHl5JgOxxshgWbDalaRGD0/Maen1QtYFKVcQv/s6MI1g8hGw7d9h0zyMCUX3R7zo2fTWH5LhPaptBuSAf21yPGcy3nNThKITYvCQNSOiX5SvzaVy0wgyp5YSPyL4otVUlkTlxqxODe2aKxJZ8dAiUkbvI3VcFbrdj6feRcX6HDz1R9IXJ3IGcyJyoOOzDB7sxXibrnNU+oU0VixmZ9MKWo0yNBykq6mMSR2Hnnpo+f/+pqMzo/VHuFur2O76J03GXjQ0kvwTGeu+Dgd938VZiKFAkpEIcSS0kjK6Gt0WwF0XS+3utGDX0sOmSMqvITY92CivriAVd21clyOTR1eSObUM3TBpKE6hdEXXzerCzfTZaCpL6pjYaCpL6qazrkbt7gxqd2f0eUyDndttsOSDbErLXSTG+zj95BLS0zyRDquThNh4ZsWeaGlsZcM+Kt17UZik2rMZkZyF3kURG69WT7VtNX6tFaeZSrp/Djr2w44xhgymt37f0li3VkWNbR2m5sVljiDVPx2NQxMrP61U21bh1Ruwq3jSfXO77HWjFNQWpdFSG49mmKTkVBOb3HLY9wWCTQyDj08LTjOFNP8cDBxHdEwhjoQkI/3MHudh3JkbSZtQgabvXzPhaXRSsHQ85Wtye33MtInljD5lC67U1k6VTmt3pbHjzakdSUnKmEomXrgWuyu4cFCpYEXT0Yu3ULRsNAVLJ4Ttfh4sIaeWsadvIiGroWPtiKZBY1kiu96aQkNxSp+dO5IitXPGNOF3D07jdw9Mpa7eGdytpDTs9gDXXLqLP/76SxISrNX5iAbl9ZVscTxKIHdF8AYFFRpsrZ7I6PpvMCptFAB+mtnmeoxy+0coLdCxrsNmxjPSczH53gvQ+ugyn1urZqvrYfbZVga3Lred22mmMcZ9NVm+k4Fgl+Jdzn9T7HydgObpGKcrB9ne0xnnvq4jcSrflsXOTybhboylYyCK1PwqJp28HldS77ZU+2lhu+vvlNk/OOjxiSPfewEjPRejydV7EQHaiKycqN8gHx8fz/ZtW1g252ICzb2sZxBF7HEeZt24DEeC55BKo+1JxJ73x1O0bKzlYw6bUcyE89Z3WW7dDGgEvDbWPH4MrtRmpl65Ejh0XHtyULoij11Lpvb6foWSlFfDtKuXo+nqkK27wfUgGhuenk99weBamBqpREQp+Ob3juLRJybQ1foawzCZMbWGj15dQlxc+BKS+gQnl9wVH7bjtSurq2DTiNvB5gmWiT+QqQEaI/f8jpHpOayMv4smvRA0s8tj5XjOYoL7q2FPSDxaNV/GfxevVos6+NxtL/jjWm8kz3s262Pvpcr2Zde1VpRGqn8GM1t+Run60Wx9f8b+AxxA00xsMT7mX/GJ5YQkgIeVcT+k0djd9eOjIMt3KpNab+mzhE0MPbFOG6/89gzGT5hEU1NTt+MkBe5Ho0/ZirOLRAT2JwijTt6OK637H9iBbC4v487e0G3fF91QGA4/487eyKSL13Q6T1fnzppXiDPpyKZ/Dz24YuKFa7pMRCBYV0TTFBMvWDuoCmHNWOSN2Bbed5dm8egTE+luoW8goLNuQyq/ezD8iWe4mUqxOf7/uk5EAHQFmmJv9q/Y6Xiux0QEoNj5BnVG+Kulbov5e9eJCHT8GHbE/J0Cx6tU2Zd3/7uuKWps6yhQH7D1g2mdD3AApXT8bvsBY0IrcL7cfSLSdppSx7tU21ZbPqYQ4SLJSD+xubxkTC1Fs9B7JWtuoaVjDp9VhKapHvu+6IYiZVQ1NmcgZH8YgDGnbbF0bqtSx1XiTPT0WMxM08GZ6CFtXGVYzx1JCeeOidi5//L4RGxG9y/IAAFT55F/TMTrDd9TQFKjhxfutZZIW1VcW4xK29p1ItJOUxBTT4njrR4TkeBQnSLH62GN0aPVUmn/rOtEpBOdAud/u9/OfIBC2xshxyilU703k1YLC7tN/BQ53gz5+NAHj48QVkgy0k8Sc2tDNoGDtuRhdJWlYyaPqra0y1Wprmt8dCUxp9baQIuSR1Zb7k2TPKo6rOeOhGgobPb+xyPwB0L/ae+riWHrjqSwnjup0XNYvWq6UxXYErqhHoCpowx3yGFKM6mxrz3ywA5QZ2wO/SIPoJn49LrQM4Cawhe/B1xW/h40aotDX95s0Uvx6RZ+LppJrW2DhfMKEV6ygLWf6LqFJ6s2ms3aWN3oebajtzSN4LR3GOkh3qF3On8vxkajaCnz7vNbf48RzpmRvmDiJ9x1ZUx6mGU5DErro4XAhtfK2TEtJJ4mPsunVQychc1i8IjuZ6JBpKWm6222BzMDGi1V1hYBtuyLtzTroGldrxU5mFLgbQhvJcmW6jg0C2tBNE3RWm3tMRI9Gzeq0dJjbjNMRuWH97JKuMWpLNCtvJBq1nrVKY1YM+tIw+rE6vE0paMpa7VTNF8ctFhZ0K0Rl9occpRLZaJZKR2gNFxmZAvNiaFJkpF+0lKZSENJEirEm3/dUJStyrN0zPLVeSEv/SgFngZnRxXTnmga7P1onKVzW1W5ITtkvxkI7qipWB+ZhmjhEC2zIgBfv2FryDE2w+SicwpIS42umiMHG5syBbwWknPNJMk920KtHkWu58ywxNYuITCW+EB+yLUgSjPJ9B0bMkZN6WQ0nAmBUG8MFK6kZpKzQ1/OsasEMn0LLSUkOd7wPj5CWCHJSD/a+0Gwjkd3SYEyNeqLkqnZmWnpeE3lSVRtHt5jgqNpsPu9iZSvzelxdkQpaK2NoWbbcEvntsrf6qBoWc+LOZWComVj8LcOzKJLkV4jcrDrLtvJmJGN3S5i1XUTm93kx3eu65Pzn5PgD9u6EYfdTkbhN3sepDTidl/NRP+1weJi3SQFmtKJNbMZ7lsUltg6jovGWPf1gOp+dkbppPinMc79FewqofukQOkYKpZxttNJH1Xew/qS4HbfscdtsXypdpTnUjRsPT4+MWYmWd6TrR1QiDCSZKQf1e1JZ8uLszD9eqdFpe2XWuoLk9n0n3mWVtu32/a/GezbOqzTcaCtfoepseONKVRtzGbH69Op2jLskMWs7R976p2sevS4I7uD3Sj4aBxFn406JMb2j0u+GEXB0vDOyPSX45ZdEukQDpGQ4Of9/y1h/NhgQmC0JSWaptBQJCb4WPL8u0ydVNdnMRyfZZB1enhqAk3LOJaUHbcF/y7MAy5ztC1sde25jHmpF5FgjmZW888xcHV00wU6ZiLizFzmNP8aA2dY4jpQun8OU1vv3P9i317Yr+2yTIp/GjOaf0yMSmNu829wqNROsbXH6lBJzGm+BxcZTDtrNekjK4LH6VggG9zKrOmKSaeuZdi4Mssxxpt5zG7+JTYV1+nxaU+MXGYWc5t/02UVWCH6mhQ9iwBbjJdhM4tJHVuF4QjQWhNL+Zpc6gtSOdzFevFZdYyYXUhsRls5+L1plK/OxdvUeao3bng9YxZvJm54cF2BpyGGwk/HUrWh7y+RxKQ2kzWnkITsOkDRWJJC2apcWmvCXyirv0RjMtIuENB4671snnx2LEUlcSQnebnonL1cceEe4uP7dpFifYKT25calC4JXz+h2qZ6drZ+SlPcOtACxDRPZIzjRDIT0zuN89NCueMjKm2f49daiDHTyfKdQpp/Vpdl2cPJqzVQan+XavtqAniINbPJ8Z5GUmBSp0JiJj4q7V9Qbv8Ir1aLXSUx3Hc8w3zHdipbrxQ0lCdTvCGf5up4dJtJWt4+sqYW4IyzssD1UAHclNs/ptK+DJ/WhFOlk+U9iXT/3D5/fMTQY7XomSQjg4gjwY0rtRkV0GmqSOim70tvKOKHN2DE+PE1OWjZF093yVLG5BIS82rxu+0UfzaKgHdgXnKxaqg0vztcfZGMQLDce5NRgMIkzszBoZLDevye1Nb6qKitR0MjKz2JhMQj+/sKKJNi9zpaVBUxpJHnmoXRU0EeIQYgq8mIbO0dBOKH15O/aDup46o6rh8HPAZla3Ip/HgcfndvG4QpsuYXkHPUbmKS99duaKpIoOiTMVRt3r97YOxZ6xkxq7hTUbO843bhqY9h1WPHEmgN/5R4pEXTYtWhwqPVsDvmGcrsH2JqbbtrlE6m72jGeK4izszps3MXl7awXXsBc9wrMDI4o1Tkd2JsuYxpseeSnt67xDugTNa4/0ldxpuQvH+n0K6AjcR9i5nj/KokJWLIkd/4AS555D5m3vA5KWP2dVrIZjgDZM8vYOYNn2Fz9WY6VzHh/HWMOW0zzqTORaTiMhqZdPFa8o7bCcC0q79gxOziQyZLNA2cSW6OvuN9DNfgmsmKZJn3ocqtVbE8/g5K7e/tT0QANJMq++d8GX8HDfrOPjn3nsIGto76ajARMQ64tGXzEJj8DGszb6W8snc7kpapn1I3/JVDtyzrfhqGvcnH2vcIhNp2J8QgI8nIAKbb/Uy+dDWabnbd70ZXuFJbGHvmRsvHHD6rmGHTS7usTdL+Zm3kidvJXbiN5FE1wdu76Xej6TDna59ZPvdAEMky7wNJUqOHPy8KhGUR68bY+/FpdV2WW1eaSQAv6+LuCXsxs4A/wK7cH4GjuXMi0s7wQ2wVG2Put3zMDS2v4E1pq3B68N9N+6xm0g7Wu589vKCFGKAkGRnAMqeVYjj9Ifq+KDImleOID10qGxTZC/aErIViBjTyjtsTPH6IfjfOBA+O+MExOxJtW3ijXXtCciSa9L3U2Tb13PdFM/Ho1eyzrTiicx1s055ySC7suS+O4Yfcz9lT1GjpmBUpL4UuzqagOvU164EKMQhIMjKApY2vsFZ1UoPUsaH73TgT3cRlNvWY3ECwMJtuMy3VN9A0yDu+b6bQ+0t7vxnR/6rsKywUMgtuod1n/zKs5652LrfYF8egxBO6n4vX7wZnXegNcxrgaKbBu89KmEIMCpKMDGC2ELMiHRTojtDvUA2n9e2evemJY4ux3hcjGsmlmcgJ4O60JbY7CpMAVmb/rFO25tBN7QCUjqm3hBzWajb1aue+x4zuUv1ChJMkIwOYu95lrTeNDh4LPWe8jc6Ql2ja9aYTcFN5grWBUUgWrEZWjEpDEfqXUkPDqaz0crHO5h5urQChFsDhD101Od6WaG0mE0BBvC099DghBglJRgawinU5IXvTAPhabdTsyAg5zu92UL09M2SCo0zwNIbestuesBR/Njrk2GgktUSOXFKj54hKw2f6FloqxKU0M+xlzEc6ju564eohJzeYmDk55DBDd2BrGG9pzYjelI/LNnCLAQrRW5KMDGB1e9JoKEkKmTwULRuDClirrFi0bAyapnronwMBn8Hm52cFP+/hiVXToHp7BkhVxyHtSHrVOFQiud6zep6hUDrpvnnEmyMPL8Bu5OXEo20/u3MJ+kPOrWHbcBPJydZKNo1rvbHt+7o7XvB/Y5pvsB6oEIOAJCMDmsam/8ylpSr4DurASywdfV+Wj+zVzERjSQpbXpqFMrVO3XaVaktEvDY2/Hs+TaWpbH1l2v6vddHvpqkyns3PzTvM+xZZC+/PlVmRKDHWfT3DfMG+SZ0azLV9nByYxNSWO/vk3EfF3whFxwY/6aIvjr7tQo7JPs3y8bJdk8gq+1bwk4MTkrbPh5XfSL5r1mFGLMTAJBVYBzhfi5M1fz+GjMnlZM0rIDa9CTOgUbcnndIV+TQUpfb6mPu2jGDlX5IYMbeQjCml2GJ8+JqclK/NoXxNLr6W4CWaqvW5NBalMOniNcQPb+xIQvxuG4Ufj6Vk+cC7PCOXZqKPjsHU1jvJ8p1MkeMN6o0tKEzizVHkes4iw38Ueh/NvsXFGRzvv5MtKy6lKuMFGLYelIZWcjTDG85j4uhhGHrv+klNjjuNzMrJbLU9iTt5Neh+UAbOuhlM8F1HZuyoPrkvQkQzSUbCTmGP9aKUhr/VTk/L5w2nD91m4m+1o8zDn6RSAYPKDdlUhrHZnbsulj3vTaTos5E4Ezy01rowu+g3466NZ81jx6HpJjaXD9OnE/D2VH4++PgA+FocHG5jwL6ScO4YlIK6egder05aqgeb7cjbN7W06OzcnURysoe8nO53XigF1TVOlIK0VA96GOYuG1oC7K7wMTzZxvCU7v/kA6ZJtbsJQ9NJjYlD62bLlGkqCqr8NHsCjB3uIMbR9xOsGhpp/lmk+a3PGDRRjMeoIiEwGgdJ3Y4z/To+tx3D4cfWxa4zh01jxvg8TPUdWhqCu3tcGTHomd3/7vpowtR82FUCehdPs+kxuSzkJ3DghhmDI76iqVD4tEYUARwqscf1Nn5aCWit2FQ8BtHZSyqAB7/WjE3FYtD9InyTAD6tAR0bNhVvaQeWiC6HlYyMGDGc1NRUCguLaGy0VuwnIyODrKwsCgsLqK2tO5zTRjXD6SN7fgFZc/fiSAi+2LrrYyhdkU/pivwDmtYpMqeVkjV/L4nZwevoAa9Bxbpsir8Yhbs2LkL3oLPcY3eSfdQe7LE+NC34Iumpj2Hvh+Op3LC/D0hMajM5R+1h2IxiDHvwOlFDcTIly0dStWkE7cmG4fCRNa+QrHl7cSYGy2d7GpyUrsyn9Mv8EAlM/5h8XIC/bbmWB++YxOZtKQAkJXq46ZodfOfrm8ga0fvibUs/HcZtP1zAxi0ptD8WsS4fV1+6i4fu+wJb269Fc7ONh/8xgb/8fRKFxcHLbsMzW/jGDVu59eatJCf1vkPr81/U8aP3PmJ3whtgBLdXJ9ccy61TTufui/b/DPe1NvLnde/y6MYP2ecOvjqOSczglhmn8LUpJxJjC/5svD7FV5/aznNlr+JJ3hz8Zk8Cs/wX8PCFC1gwrvvW8+ck+OG2eh58oPukIFzWue6lyv4FtBdKU2CoGEa7ryTfd37HuKZ9CRSsGk351mxU2yWY5Ox95M3eQ8aY8o7t6y0eN1vqP6Y26zlIbqvXU59HeuWlTE45Gofd3nYakzL7hxQ5X6PR2A0Ez5vlPYU8z3m41LA+u88BvJQ43qbI8TqtRhkAdjOBbO8Z5HnPwaH2P+77bCspcLxCrX0dAJqyMcx3LHme80k0o2Mbe72xnQLH/6i0fxb8OSpI888hz3MeaYGZHeM8Wg2Fzlcpsb+NX28GIDaQQ573HLK8p3aZCIro1KuuvQsWLODuX/yM5ORkGpuaGD1qNM89/zw/+clPMc2ut99pmsa99/6GSy6+mIKCAvLz83n0b49x332/sxxktHfttcd5mHn958SktMABZdSVAhQ0VyWw/skF+N12Jpy/jmHTS1EmnWqEmAEN06+z8d/zaShOicj9aDf1quWkjqlGqc71RNovw5R+mc+ut6eQlFfD1Ku+RNNVp1097fetbHUOO16fhj3Wy/TrlhOb1tTl49NaHce6J4/quPwTCROPU1yz6zHeXZrVFtv+O24YJinJXj58ZQlTJtZZPuZjT43ja3cc0/bZge/UFKCRn9vItuX/pbnFxonnnc7GLSkE/4y0jnGGrhiZ18THr7/FiOHWf/d/+EIh95X+qu0OHLAjxDRAD3CM+0Y+/e5C9jbs4/iXf0NZc32nfijt7ywXDBvNO+d9F920M/p3/6Ey5f3gWg39gL/3gIHmS+CZY3/OZUcndxtTX3Xy7QiDAMsSbsCr17Y/xPu1r8fwHce01u+xb08m616dBwrUgetQNBOUTt7sXYw7fjMNrY2sdP0YlVjY+T4rLbjrZd90jtF+gsNhY6PrD1Q6lgW/dkB9Ek3p6DiZ3fxrkgLj+uB+u1kd9zPqjW3tJzwgTh2nSmFO02+JVcPZ5XyaPTHPB3+GB1S1bV+LM7Xl+wzzH0Mkldk/ZJPrT2joKO2Amaq2mMe2XsdI70U060WsjPsRPq2x030JLnZWpPpnMKPlp1E76zNUWO3a26v51ezsLG655TaOOvpYTj31NM4862yuuPwyLr74om6/57prr+Xcc85h8eLTOfGkU7jk0sv4xte/xhlnnN6bU0e1yZeswpnciqZ3fvFu788Sl9HEhPPXkXPMbjKnlQa/dtAjrxsKwx5g6pUrMJyRKxKWv2gbqWOqgS5607R9njW/gMxpxUy5YgW6cWhfnPb7NnxWMdkL9jLxwrXEpjZ3+/jEpLYw8aI1fXWXQlp4fy6/MX7Pex9loZTWKREBCAR0auscnHHpqXi91v5kCori+HqXicj+zwuK4jn3qpO57lvHsWlrMqapHTRWI2DqFBTFc9H1J1qu6/L+hibuK/11sIz5wVtT20qbfxbzd259ehfnvP4nyg9KRCA43a9QrKjcwzeWPsUpD35GZcoHwfD0g954GAGUo5GrPv4tNY3h7Q/TG2tifxpMRKDbvi8V9k8o8n7J+tfmBhdpH1zdte3zwtVjKNmUzSrj96iE4kPvs6ZAV5jpG/my9R/sdb5ApX3Z/q8deEjNJICHtbG/wE/o4mi9tdX1V+qN7cHzHlykTTPxarWsjfsl5bZPgolI2+0Hx6gw2Rj7e5r1krDHaFWjvptNrj8BqnMiAh0x73Q9SZXtS9bE/QL/wYkItD0OUGNbz46Yf/ZL3OLI9SoZefnl/7Jz166Oz7dt20Z1TQ3Dhw/v9nsuv/xSXn/9jY7vW7VqNZ9+uozLL7/sMEOOLvFZdSTl1fVY70PTFWnjq8g9ZnfPvVz0YBXUYTOK+yBSa7Ln7w25XRdg9KlbMeyBkBVgc4/dScroarQeHh/dUKSMqiEus+EwIj5yDZOO49EnJ7QlA10LBHSKS+P47xt5lo55x0/ntb0Z7+natcY7H2bx+tu5BALdP5D+gM4XKzNZscZaEazvvfFl8Am6p+qhps5jxf9mU00J/h4q3QWUyX+2f8EXxrM9H08PYMZV8oOXdnU/pg8FCFBn29hzDY+2H8WOhEe7SPwOpthVVIOZubbnWiN6AG/uEvba/xfiR23i0xopd3zUw6De82i1lNmXHvqCfAClmbQYxeyKebrnLdIagKLY8WZYY+yNQuerwVm5Hn80OjudT+HWq0L0LFKUON7Bh1SyHQh6vfLMFRPD/HnzOOGEE/j97+6jtbWV5557vuuD6zoTJ05k/YbOfRvWrl3L1ClTuz2Hw+EgPj6+419cXHSso+hKxpQyS1VQlQn2WGszHsPaZk/6W2xGA7aYQMhS75oG9jhvyLWnmgaOeJ+lqq5mQCNjSpn1YMNk4f25vPFODm536GvLum7y3H+t7XR45wOri4k1NAslx202k2dftnbudfpbPTd3A9BNvPF70C2VWgccFt7Rmzr/LVxmKcZwK7O/G/x9tND3xXRWWeh3o+Eb/T9rvWmUhmlYmfHQKLeHNxmptH+OpbKuSg+uJQnxu6a04LqXSFAoyu2f9JxgAGgmzbZCS9VxleZjnz28DRRF3+j16p70jAx+9KO7SExMIicnmz/+8U9UVFR0OTY+Ph6Hw0FtbW2n22tra0lOTu72HLfe8i3uvPOO3oYWEXaXtYWFSlnbN9LxQh8BMSnWp5B705vGqvZdNv3hwC281eud6JrCDPHkZpo6FVXW1jt4vAZWdwoFi8yFqHqroKbW2poa5ayzNA7AtNpp0co43aRFr+n2y8Euvk5upzXs60b65NKCq6bHGYf9LP4xaAqvdvjVaLvi0xqCaysIdXnMYp8HwK81H1lQh8nEi9J6cYnaUt8gDZ8WmRlX0Tu9TkaKioo4/4LgGpGJEyfy8ksvYCrFo4/+7ZCxPl/wF8vp7PwkGhMTg8/X/QvPgw/9hUf/9ljH53FxcaxZvbK3ofaL4PbU0Ky+eCsFvubILLhy13a/G+Jg7ZdywpmUWH0sj9TBtUTSUz0hExEIzowMy7C2iNTpCOD3W3mrTshEBIKPc1qqtUZwmicZFWut46uOFjIhUVYbqpg6cWbPdW2SGj1cNN7Gg2FORuICOaEH9VZLWtsMSqgXcouPj9I67WoJB4dKtNS7x3JCCdhUZGaidRzoyo5pNSE5aKFwlzSFXSUeeXCizx1RgYCtW7eydOlHnHTioi6/3traSk1NDSMOWlMyfPhwSkq6vxTh9Xppamrq+NfcHJlM3YqqTVmW+sNoOvharG1frQhjvZDeaKlKxOe2hVwo2ZEwWRjnbbJb6iysG4rKTSOsBxtGZy0uJiYmdA8S09S54qLdlo55+slW1/2EnhUB8Pt1rrhwj6UjzlJn9lzCHMDUcTaOtjQzYmgaeC0kqrrJhfkLLcUYbiP8pwR/Hy30fTHcmRZmPBT23edb602jKYyAlRdwxXDvIgvjrMvwHW2tpoamcAWyQl7a0JTOCN+JYYqudzQ0hvlOQFMhfneVTpw/39LMiK7spPsGZhXooaZXyYjLdei7mfz8/E51Q7Kzspg9e39hoo8//oRTTz1l/wl1nZNPPpmPP/nkMMKNPk1lSdQXpPS4bkSZGvu2ZQZ7xPTw96PMYPXSinWRSUYASpfn9zjb0R7/rncmEvDaelwPomlQ+Ok4anal9fz4BDRqd6fRUtn372C6KvOemODjG1/Zit7Dk5thmOTlNHH+mYWWzvN/v1rR9jj29ISpOP3kEs49vRDD6P6BtBkmx8yvYO6sakvn/t1Z84PJSE8vPLrJ1/KuZlpaDrYeskVD07hy/FEcY17R8/FMA715GPddFJmquwYGKf7podakAjC2+WvoeqjMRWNMXgp65aye142YBs7CM4P1S3r8UevYVSLDfSf0MKj3nCqF4b4Te1wDoymduEAuY91X9/wCrgD0YC+gCMnznBOcievxR2MyznMtMYHMzu0BDqY0sr2nY0caDg4EvUpGXnrxBW751jc58cRFnHHG6fztb39l/Phx/OXhRzrGXH75Zfz76X91fP7HP/2ZyZMnc999v+XUU0/h4YcfIj4+rsvLOgPV5hdn466NRZmH9mhRCpor49n+ygyKPx/VkWgc/CJuBjQCPoONz8wj4IlcAbCCjyZQszO4a+PgxKn985LlI6namMPG/8zF9BuHJBrt961sVS6lX+az9eVZtFbHdf34mNBSHceWl/qnF4e24Kgub//tT1e3zWaotheq/QzDJC3Fw1vPv4vdbm2qOy+nhcf+1L6Ys+smJKPyG3n13+/xxF8+ZfqUWjRNHbSYVaHrJqPyG3nxn0stnRfgpKlx/CjnJ2DaDn0hbWuYuNBzM3+6ahSvnnU7WXEpwdmPA7RfYDpq+FgeXnQt79xyNCNqTw1+8eBqwaaB5kni2UU/JDk+ck0RZ7bcjbP9MlE3fV+G+xaRa5/H9HNXoBkm2iHbQoOf58/ZSfaUEuYGvofWmBu8z51+NBooDX3fdObFfoVRnos7+uccnLRpSsemXMxq/gU2wl9jZWLr10kOTOqIqROl41CpzGz+GcP8CxntvqIjpoNj1DCY1vJ9Ys2ssMdoVYI5iqmt3wH0Q2ZI2mMe1/oV0v3zmNXyi+AlmEO2ZwdrwKT6ZzLOfX3/BC6OWK+KniUlJfGVr1zPrFkz8fv8bNu+naee+hfl5eUdYy6/7DLOPvssrr7m2o7bJk6cyNe+ejNZ2Vns2bOHhx/+K4WF1t5hQvQXPYNgBdasuZ0rjLprXZSsyKdsVV6nCqwZU8rImr+XpNw6APweg4q1uZQsH4m7zvq6jb6Uc/Quco7egz3O21GB1V3rYs+H49m3af/MTUxKM9kL9jJ8ZjFGWynt+sIUSpaPZN+W4bS/VdXt/o7HJyY5uPahvUJt2cp8At6+r5S48P7cbpMRAL9f48lnx/LA3yaxYXPwRS0l2cNXr9vGbTdv6VXRsXaffpHJbXctYO3GVNofi7g4H9dfvoM///bLjnLvLS0Gf31iAn95fBJ7ChMAyBrezLdu2so3b9hKUmLva8+8/GU9P3z3E3bEvQY2LyiN1NrjuH36Yn56/v6fYXVrEw9teI9HNnxAZWuwovL45GHcMv0Ubp5yAk5jfwXWbz69g/8Uv0FryvrgN7uTmGdewEMXzmfeWGsvtK812vq0EusG1x+osC+D9joVCgzlYoz7GvJ8Z3eMa6qOp2j1aMq25GC2JWkpuVXkzdpDxpj9i/JbPW621C+jZsRzEB98rtPqRpFedQmTUhfgsO2vwFpu/4hCx2s02nYCwfNmexeT5zmXGJXRZ/fZxEeJ4x2KHK/TYgQX89rNRHK8Z5LrPRvHAesmqm2rKXC8So1tdbAIobIx3Hc8eZ7zSDCjoy9Og7GDQserVNg/DdYbURrp/rnkec4jNTC9Y5xHq6XI8TrFjrfw68EtvHGBfPI8ZzPCd0qf9SwS1lktetarZCRSBkIysp/CFuMDNPxuGz3NG+t2P4Y9gK/VbmGrYWTYYrzY4z146lyY/h4SBs3E7vIR8BkHJF5dUdja1meEenzC6bhll/RqfEOjHa9XJyXZi2FhTVAobrfOnsJ4UpK8DB/W/ULU9r44SkFKsjcsC4Rb3AEK9vnJSDRIT+z+Z2Mqk1p3C4auk+RwddubBqC0xkej22RUhgOHvfdB9nVCAtBKFW6jirhAHo4epurNgIbfY8ewBzDs3e9KMZWJ2+tFA5wOJ3oPj4+fVkzNh03F9esLokIRoAVTC2BX8Wg9TH4H8BDQ3NhULDqRb8fQFRMffq0ZQ8X2WElVEcCnNaMrGzai4w2dCLKajEjh/rDT8Lt73hUSP6KOrHkFpI2vRLeZuOtclK3Ko2JddlT0aDmQ3+0IeX8AULrFcu4afnf/3cfD7cKbmBCeKriNjTaeen4sjz4xnr2FCTidAc46tZhv3biFebP3rwExTXjz3RweenwSy1dloBTMmlbNt27ayvlnFh5Rs77YGINJOd2/IHq9Oi++ms9f/j6JjVuSMQzFwgWVfOumLSw+sbRTQvTZlxk89Pgk3vkgG69PZ9zoBr7+la1cdfFuYmP3v5AXFsfxyD8n8O8XxlBb5yA1xcM1l+3ia9dtIze7paNXzZ8ecFLm+JBixxJa9TJ0ZSfNP5tc71kkBSZ2HE8pqN6bSdHakdSXpaIUxKc3kDtzL5ljy7pcRO4iA1eg+9kIb6ud0o15lG7Mw90Ug80RIHNcKTnT9xKffuiTpq7pxDq7b9Z2IBsuUH1T9r4nGho24ixtnDFwYqju/2Z9NFHqeI8Sx9t49Gp05STTdxQ53jP7bQZFx45DJYccp2F0mv0RA4/MjPSz3IU7GXXSdsyA1vEE2r6OwtMQw/qnFkRNs7zB4HCTkXDYvTeeky84ncLi4M+zfdeMzTDxB3Tuvms1P/3uerxenctuPIFX3srHMMyOaqztHy86tozXnnmfuDgLOzt6qa4+WOZ++aoMdN3EbFsP0h7jlRfv4omHPsUwFD+4ew5/eGgaNpuJ3x8cp2nBxYbjxjTw3n/fJierhTfeyeHi60/EH9A6VZY1dBOHw+Tlpz7gtJNK+UdZHbc+fx+tejlt1wuCx1Q6SjMZ6b6EMZ6rUQGdjUtmUbk9u6N3THBg8OOkETXMumA5Nqf1x6exMpHVLx2Fz9O+K0xruz8mSmlMPHkDOdMLjvjxHaia9EJWx/2krS7K/iJJmtJRmIx330Se99yIxigGBrlME4Uypxcz8fz13X7dDGh4G2JY+cjxmH651nmkIpmIuN0GU449n6LiOPw9lHr/x4OfsnxlOo89NaHbWieGbnLemYW8+MTSsMe5+KLFfPjp8G7L0Wua4ru3bCR7RAvf/tGCbo9jM0zGj63nX3/9mKMXn43Pr3e5ZVnTFA67yZcfvsyZy2+ntL6ix4qbE1u/QfM7d1K0ZjTdXtLTTNLyq5h1wZc93td23lY7nz9xIj5Pz5dHZ56/nPRRlZaOOZj4aeazhG8GE5EefjbTm39Ipv/ofoxMDERymSbqKPKP33FIJ9wD6YYiJqWV9MllVK7vgyJOQ0ioxap97YVX89lTkBBilOLn986ktDy2x6JrAVPn5ddHsn1nIuPHhq+a5Mo1abz3Uc87J5TSeOBvk0iI6/mylT+gs3lbCt/72TzMLpoNHni8QEDjzqdLKRlWFnIr7m7Hc3jX/5keByqd6r3DaKxKICGjscc4AUo35bXNiPTUp0WxZ/m4IZmMlDo+xKvVhdgGrLEn5jkymyQZEeERnasmB6GEnDpcqa0hFyQqE4bPKuqfoAapSCciAP94ehz6wZ1eD6FRVBLfY4O+djbD5MnnxoQnuDZPPjcWmy109U6Px2BfTej1D7pusvTTER2XcLrjD+h80LQEI1Q1PA28Rg3khe7nomkmZZtzQ44DKN2QZ6E4mkZ9WSotUbK7rT+VOt4l5AOkKRqN3TTp1ndFCtETSUb6iTPBYilvHWKSrI0V0auwJL5j/UUoB9c16U5JaXjXEpWUxhKw0OTRsBifaeqWyuoDmAmFBKx0UARICp2cKzQ8TdYWl7qbYrC6i8vqMQcTt7bP8iY3j26tEJ8Qochlmn7Smzoafo/8WA5Xb7fw9pXEeOtN/0KV3wdAU8THh2eHT7uEeB+6rkImJFYTjOC7aauvYr3Y1usNXUFTQ/W4LfdANkcAb0/b1A9g9ZiDiY0Y/IS+3AVgqKGXrIm+ITMj/aS+IBW/J/SiVGXSVixM9NaMRZHpdtyVC88psHCZBlwxPkszKH6/wXlnhHdK/NwzirpduHogpTTsttAvypoGo0c29FjaHoK7hMZ5Tka3kLhoph12LbYQo07GmPKQ4wAyx5UeWnn10CPijG8lISO8XXYHgkzfMT2XWW9jNxNJDIzrh4jEUCDJSD8x/QZlq/N67OUSLB+vUb7a2rVv0Vmkds505aard2CzHVzevTNdN/n6V7Yxf3YVth5ewA3DZOyoBk4+viysMZ57eiEjhrWE7Itz6qISrrlsV4/jNE3hcvn5+ffXhkxwAgGdX5w2C6dh6zkdUTrZvlNITvf1nDxoJs6EFtJHVXQ/5gA50/daak6YO2uPpSaPg02O94zQ/WGURq73bHSZXBdhMgT/1CKn4MPxNBSndJmQmCagYOvLM/EOwevUR2LGIm/UXJ5pN2J4K/9+9GN0XXX5Iq7rJgvm7ONXP1zDs49/RHqau8uExGaYJMT7+O+/PugoHR8udrvilaffxxUT6DJGm2GSndXCEw99yv2/XsHUSbUYXcz2GIaJzWby4j+XcvUlu7n9a5sADknE2j//7i0buOKMBp47/ZsYutH1QlalkxAYzVj39Uw9Yw2OOE+XCYmmmRj2ADPOXWE5cYhPb2LiSRvaDtBVExtF+qgK8mZb69A82MSaWUxuvS34ySF9Xwi2FfDPZKTnon6PTQxekoz0I9NvsP5f8yn8ZCze5s5VSOv3prHuqaPYt2VEhKIbmLrqwhstLjqngA9fWcKJCzvPaKSluvnxHet57+W3iY0NMDKviZXvv8b1V+4g5oDCXXZ7gCsu2s3K919jysS6Polx7qxqvnzvdS4+p6BTQhLr8vHV67fx5buvMWJ4K4kJPj55/S3uvGUTSW29lyCYYJx+UgmfvvEmp59cgqbB/b9ewd8f+JTxYzpvQ544rp4n/vIJ9/18FQBnj5rJxxf+kFNyJ3caZzPjGem5iLnNv8GGi5gEN/Ov/JjsaYXoB1wu0jSTzPGlzL/yExIze7flOWdGATPPX07S8NpOtzvj3Yw9bgvTz11peWHxYJTlO5lZzb8k+YAquAAOlcIYz1XMbPlp1JaQFwOTFD2LEE03iRvWgG438dS58DT0f+nowSAatvFaUVgcR0FRHK6YANOn1OJwdH3Zob7BzuZtySgFE8Y2kJbq6XJcX6ja52T7riQMQzF1Yi3x8V1XNHW7DdZvTsHr1RmV30T2iJYuxykFm7clU13jJD3NzaTx9d1ubf9neQOPPNGKhp2EwMhuX+j8XoPm6gSUqRGb2ozDdeTrhFrqYvE0xWDYAyRk1A/JSzM9adXKcevVGMpJvDlKms+JXpGiZ1FOmTpNZcmRDmNAGwiJiFKKD4q38PfNH7OjroJYu4MzzencMPk4Mlyde2m8/UEWP75nNtt3JqEUjMpv5BffX8uF53ReuFrnaeHJrZ/yyu41NHhbGZWYzvWTjuP0vGkYR3AtJyPdQ0Z6z0W+1m5I4c6fzWPVmnT8AY1hGa1855ub+OYN2zpdRnL7fTy/80ue3b6cytYGMvclciVHcfGYecTY9icaSik+KtnGU+s/YVtMJToO0v2zyfKeilOldDp3jbGenSlP0pxehELhMjMZ5b6M4f7jO43ze2yUbcmhcudw/B47MQmtZE0pIn1URZeJRmxyC7HJXSdUveVtcVCyMY/qvZkE/AZxKU1kTS0kJac6LE0PI8GlhuMK9LyovkkvoNixhAZjBwDJgUlke08jzuzb4o0KRb2xjRLH2zTrhWjYSPXPINu7mBiV3qfnFuElMyNiQIq2NSJdqXU3c/4bD/BJ2XZsmo6/bbGQjoZNN3jilJu4fPwCTBOOO+tMPl+RSeftscGPJ0+oZfWHr+FwmLy5dx2XLnmYVr+3Y32hoekElMmM9FzeOudOhsf1TTfc6285lqeeHXvALVpHjGkpbtZ9/D+yRrhZt6+QM169n/KWenRNw1Sq4/9ZccksOfdOpqbl0OBt5cI3H+SD4i3omo6pzI4+MRo6k1pvIct3MiYma2J/Qq1t4/7T7n94cAVGsKDpz9iIobognfWvzSPgMzpiDPab0YlLa2DWBcuJsVjzp7fKtmSz+Z2ZwcWx7fej7dwpOfuYce6KXvXPGQgUAbbFPEax882OnkKwv79QvudCxrqvQ+uD7twBPGyM/QNV9uWdzh1c56IY776ZPO/ZYT+v6B2rMyMyISkGnIX3R/9uo4Bpcvbrf+Kz8p0AHYkIgInCZ/q56p1HebtgA6ddsrgtEYHOdTqCH2/elswxp5/JF+W7OP/NB2n1+zptdGgvHrapuoRTXvkdbn9465EAfO/nc3nq2fZtnNoBcQb/X13rZPrx51NQX81J//0dVa3BOhVmWxGV9v9XtDRw4n/vo6SxhvPfeICPSra1fd084NAKRYDNrj9TZVvO+tjfUGvf2Pm0+09Nq17Gl/F30lCRxNr/LWhLRPYPVm2LMFtq4ln14tEHJCrhs293JpuWzEKZWluZ+c7nri1JZe0r86zVlBlAdsY8RbHjTYBOPYbaPy5wvswe53N9cu6Nsf9HlW3FIedGM0FTbHf9jTL7h31ybhF+koyIAWXGIm/UX5oBeKtgPZ+X7+y2ymj7/Mf3Pvkv738UatGyxur1aXznvddRSgW3XXbBr0w215Ty/E5rDeOsMk3486OToMeiZho1tTF85ZkVNHhbu73fAWVS52nhu8ueY2nJ1u6rsGrB/+yI+Sf7bCHujwYtehFbd9W2vdh31xdHp7UujrIt2T0fr5eUgh2fTtofTJeDdOpK0qkpzAjruSPJo9VS6HglZJ27vc4X8NH9O+LD0aDvpMr+RY+N/FCwI+ZJFEOvcN1AJMmIGDAi2YW3tx7d+GHI3ismio11e2DYutAHTCpked3akCXUdU3jkQ0f9CbUkP7y+ET8/vbZhp5O7uWj1jdCxhhQJi/vXoUtZG8aRYtRajnOhsl/6LELb7vidSMtH9PSeSuSaK5OJNTjo2kmxevzwnruSCqzf9BtYnwgEz/ljo/Deu4SxztoKsQMlwZevYZq29qwnlv0DUlGxICRcG54G8X1pS21ZdZ7r6RtDz0mdaelQ5lKsb3OWvEvq1atT7M2MLYa5bD2DthnBjpdugqLpAILgzRa6sLb46e1LnS5egjOzDTXhOrkPHC0GKVoFl5CNHRadOtJpRXNRjFKszDjobSwn1v0DUlGxIAwUC7PtIsxelGDIeAMPcZvYUwbhx7eNREup8Vpbiv3oy+Z1h5z3Qjvwg3NQtn/dqFK5Q8kurK+GTPclVp1ZQ/deRkAJVViBwhJRkTUi+bCZt1ZnDc15GUaaHuSLjo29AHLZ2M3Q7ezt2k6i/OmWgnRsmsv34mlZ/7WZGIbJ4TcOaFrGsNiEy09PijNWu89DbTCE0IP00xS83revtxbydk1FnrdAJpJan54zx1JKf4ZlmYnlBYg1T8jrOdO88/E0i+GBin+aWE9t+gbkoyIqDYQaol05etTT9y/Q6QbNk3nyvHzsXtTCNEIBM3v4qtTjw/5Au5XJt+afnLvA+7B0fP2MSyzNUSMABrfmXVyyHUEplJ8b9YZIcdpSifDdxRaqHfBwQru5FR8q+cFjQQvleTO3NvjmN5yxnmsNd9TGjnTrVxKGhgy/PNxmMltu4e6oXRiAsPCnoyM8J2MhtHj74WmdFJ80/q81okID0lGhOgD41OGc98xl3b7dUPTyY5P4fcLL+Wv93/edmtXz6zB237705X8+thzmJAyvMeE5Aezz2T+sNFHEHnX/vev99vKo3cf45UX7ebni2dzVv70bmdHNDTOHzWb22cs5k/HXdnt+TR0HCqFie6vMqXl9gNP09WpGeW5nDGzK0hIb+wxIcmdtZuUnJpuv364xp+wudv+Oe1Bjj9hE66kwVMnScfG1JbvBteNdJWQKB0dg2mtd1paW9IbDpXI5NZb287T9bltKo5J7lvCel7RdyQZEVGpvfndQJwVaffd2Wfwz5NvJCeucyVRXdM4f/RsvrjkpwyLTeIrV+7k6Uc+JiH+0Pogrhg/D933Bd+/bRNJzlg+ufBHXDXhKGwHrQvJdCXywPFX8ZujL+6T+7JgTjXL3nqDYRmHzpDYDMXtX9vE049+gk03eOnMW/nu7NOJs3VeQxJvd/KDOWfy3OnfwNB1bpl+Cv9e/DXyEzovkNXQyEmZw7ymP+BUaQz3H8/0lrsw1KGXqXTsjHPfwBjPldgcAeZc8hlZk4sOWMcRjNXu8jDuhE2MP2FT2B6TAznjPcy/4hPSR1cc0nwvJqGVKaevJm/2nj45dySlBqYzp/keEgKHLi5PCoxjbtN9JB3U3yZcRvhOZHrLj3CZB22NV5Dqn8m8pv8j9uCviaglFVhF1Bmol2a6EzBNPizZwu76Klw2ByfnTCIrPqXLsf99I5fXluRhKjjl+DKuvrTrzrFVrQ28U7iJZp+H3IRUTsmZjN3on4V6y5Zn8NRzY2ltNZg9vYZbbt6MrYtTN3ndLCncQLW7iQxXAqflTSPOfugiV1OZLC3Zyq76KmIMO7MnTOf3qzMpXXJov6Yq2wqqbJ+jMEkOTGOE70T0Lt5TeVvt1BRk4PfaiIl3k5pfFfaFq91xN8ZQW5SOGdBxJbWQkrtvwJaC740GfReNxi5AIykwjnhzZL+cN1gSfgvNejEaBsn+KcSqnsvXi/4jvWnEgNTbXTMbtyTz9AtjqKh0kZTk5aKz97LwqMqoevIvKU3g8+euYE9BAjExARJPLOHs04oxunhxvOCsIi44q6jH47m9fq56dBufNH1CQHfj8mfyk9kZfO/sw38XWF3j5KnnxrBxazKGrjh2QSWXnrcXl+vQBYrHLqji2AVVIY8Z74jh4rHzQo4rba7j8/Jd7KqrxGmzE2d38PgsFzdBp4QkgAevVofWdpkqoDUToAWdQ7fWOlw+hk+MzJbOmAQ3IyYXR+TckZRojiHR7P/t9xoayYHJJAcmhx4sopbMjIioYnVWpK7ewZU3H8+SD3KwGftLifv9OtOn1PDykx8wemR4qz72lterc9tdC3jsX+PRddWRIPn9Otkjmnn+H0s5el7oF/UD3fmfvfyx8h4w/J2vlmhgNOay8dofMSE7xvLxlIJ7/zyNu++bid+vd8we+P06SYle/vbHZVxyXt8suvSbAe745D88vDFYpE1HA03DbwYYHpvEbcfdwgfPLwCg3P4RW1wPE6A1uHARUJho2Bjjvop87wV90v9ECHFkpDeNGHCsJiJut8GpFy3m3Y+yAPAH9OA/f/DXefPWZBaeeSZl5YdO8/cXpeD6Wxby2L/Go5RGoC2+9hjLK1ycfMFprFmfavmY971axh/33Q16W7M17YB/QCC+iEnP/JimFuu1LO65fzo//vUcvD4DU2mdYmxotHP5TYv435t9UzX0ax8+wV82fICpFKZS+JWJ3wzOxFS2NvCzd++j3thOhe1TNsb+HwFaQQtuFVVaINjDRvOx0/UEBY6X+yRGIUT/kGREDDj/en40q9amEQh0/evrD+hUVcdw34ORqy+wbHkmz748OtjBtQsBU8fn0/n+L+ZaPuaPNz0c/KD79jDgquH0hz/vZkBn5RUu7v7dzG6/3h77Ld9fQCAQ3lmHlRV7+OeWT7vd3msqhWkGO8JudT3ac1scYFfM03i1hrDGKIToP5KMiKjQm0Wrf3l8Usg1IYGAzj+eHkdra/g7tFrxyD8nYLOF6NES0Hn/4yx27g5dIvyVlfWY8cWh6zwp+ExZmyX4xzNjQ3aRVUqjtDyOJe+Ht7nco5uWhuxNY6JosG3Dp9eHvN8Kk1L7e2GMUAjRnyQZERHV2y28pgkbt6R0O+NwoKZmO3uLrPUNCbcVa9I7LneEsm5T6Es1Tyzfa7kSKTG1ls67dkOapcfRZjNZs8H65SQrVlTs7kVvGmt3vNHoeueRECL6STIiIuZwuvBqGofUcQg5PgL0XvxlaRbuj6GH/45omrJ0blT4H0etFwe0OlIWsAoxcEkyIgYUTYPZ02vQLTQnS0r0MCqvsR+iOtQx8ypDXqaBYEIwZ0Z1yHHfPG6UtcZgCrSWdAsDYd6sfT2X8m7jD+jMn927XT+hHDNiXMjLNO2stKkHRWJg3JEFJYSIGElGREQcSfO7W27egmn2/KtrGCY3X7sdpzMyXVK/8ZWtIS/TGIbJGScXk5/bHPJ4J01NwNY40kp7GE50WKvCev0VOzFCJEy6bjIyt5GTjy+zdEyrvjH1xJCXaQxN54SsCaTFpoRMmjRsjPCeFM4QhRD9SJIR0e+OtMz7FRfuZtGxZd3OjtgMk7ycZn5w28bDPseRmje7mq9dv7XbyyCGYRLr8vOHX620fMxHjvlW8EW5u4REgd6UxZJbre3QSU/z8Pu7uz9/8DIOPPrHz3p12cmKqWk53D5jcbdfNzQdp2HjgeOv4ltH3xi8scseJMH/TXDfiL2L4mdCiIFBkhHRr2Ys8h7xMex2xWvPvM/lF+xB0xSGbmK3BzqKnx2zoIJP33iTtFTPEZ/rSDx033K+f9sGHPYAuqaCMbbNRIwf08Cnb77JxHH1lo9346J0fj/2N+Bvq5+iOv9z1k+k/Js/x2az/md921e38PDvPych3gsEY7TbgrU+hme28vp/3uPUReGdFWn3fwsv46fzzsVp2NDQsOsGNi24+2l0YgYfX/hDpqXn8otJU/jpKXfgUEkAaMpAU8FxBi4mtdxCjvfMPolRCNE/pAKr6FfHLbskrMcrKIrjPy+PpqIyhqREHxeds5dpk+vCeo4jVV3j5JmXRrGnIAGXy89pJ5Zy3NEVh70o1O83+eq/dvBS0Yf4tVYSVSYPnno2Fx+ddNgxtrQYPP/KKDZuScYwFMfMq+SsxcXYbH3/9FDrbuaZ7V+wu6EKp2HjlNzJnJg9qdMi1/oEJ7d+COvf30C9bRsKk/jASIb5jsXg0H43QojoYLUCqyQjol8czs6ZaFdRGcMrb+VRW+8gI83N+WcWkppy+DM/SsGnX2SyYk06pqkxc1oNJx1X1uUlkj0F8bzxbg7NLTZys5o574wi4uL8R3BvrNuwr4gPSrbgCfgZlzSMs0bOwNHHTfrqE5zcvtTosnmeGNpMAlTbVtKsl6BjkOyfGpEeOaJr0ihPiD7S1GTjlruO4pkXRhMwNQxdEQhofPO7R/PV67bx+7tX9nrh7CefZ/K1O45h645kdN1E04JF0UblNfLQ777gjFNKgGDV1Ju/fQxvvpcDgKEr/AGd+Dgf37t1Az++Y33Y13e0215bzg0f/J3PynaiaxoaGgFlkh4Tz6+PuoivTl3UNycWohtl9g/ZEfNPvHodKJ3gdjJFon8ck1tvI97Mj3SIwiJZMyL63GCaFWltNTj1otP49/Oj8Qd0lNKC/0fD6zN4+B+TuPDak/D7rV+DWfrpcE6+4HS270oEwDT1jlL3e4viOfuKU3jlzVyq9jk59swzefuDbJTSOs4NwQJvP793Frf84KiQVVUPx466co5+8VcsLw8WFjOVItC2G2afu4mvL32S369+K/wnFqIbRY432RT7R7xaXfAGzeyoQdRg7GJF/Pdo0gsjF6DoFUlGRJ86ki280eihxyeyYk06gW62Fpumxlvv5/DMS6MtHS8Q0LjuWwsxTbrcrtxeIfUrty7kJ7+ZTWFxXEcCciiNv/5zIp9+kWnp3L1xy0dP0+B1dyQgXbnrsxfY27Av7OcGSGr08MScFm69zfqCXzF4ebRatsf8LfhJV3m/ZhLAyxbXQ/0alzh8koyIPjNjkfeItvBGG9OEhx6fhBniCoyumzzwt0mWjvn2B1kUlcT3WDdFKY26eidP/mdst80B29lsJn953Nq5rdpVX8m7RZt6TEQAdE3j0Y0fhvXcQnSl1PFu6GJ4mkm9bStN+t5+iUkcGUlGRJ9JOHdwLSIrLY+lqCSeUAXKTVNn9bp0vN7Qf16ffDHMUqVWwzDx+kI3/fP7dZYuGx5yXG8sK9thaVxAmSwt2RrWcwvRlVpjI5ZKEiuNOtvmPo9HHDlZwCr6RLi38EYDn693ubvfr+FwhD6mlf4wvVkH4rPYoM8qvxmwfu5ejBXicJma33LTIhP5nRwIZGZEhF04CptFo6zhLcTH+SyMVGSPaMblCv0kOHlCnaUkRykNzcI7QUM3mTrRWtdeqyalZFkaZ9N0pqflhvXcBzs+K/TskBj84gP5aMrCy5emiAv07e+kCA9JRkRYDaadMwdzOk1uuHpHR6XX7ug6fPPGrZaKml12/l7iYkPXBzEMxSmLSjFCnDtg6nzjhm2hT9wLRw0fw6SUESG74vqVydf6eHtvUqOHF+5tIut0qTc0lOV4T0NpIS5vKogxM0gNTO+foMQRkWREhM1gTkTaffdbG0lK8nabFNgMk9zsJr5+vbWEIC7Ozz0/WR1y3A+/vZ77fr4Ku83stiePYZjMm1XFhWcXWDq3VZqm8YeFlwc/7maMrmlcOHoO84dZ20V0JJIaI1vmX0RevDky2BixuwaKCtBgfOuNaPIyNyDIT0mExVBIRAByslr46LW3yMlqAehIStpnS8aPrWfpq0tISbZ+qerWm7dw789WYhjtiUaw2Yyhm2ia4q5vr+cXP1jLzGk1LHnhXZKTgsduT0raF8AuXFDBW8+/i8MR/k7FZ+RP59nTv4HL5kBjf1Ji04JPIZeMncfTi7/WqYS7EH1pUuu3GOELdmruuGSjAKWhYWdyy7fJ9B8TuQBFr8gCViF6afKEenaseInX38nlhf+NpLrWybCMVq68eDenLirtdQVUTYPv37aRay7dxeNPj+PL1RkoBbOm13DzNdvJy2nuGHv8MRUUrX+BF14dyWtLcmlqspGb08xXrtzJUXOrDrvfjRWXjJ3H4twp/GvbZ3xQvAW338e45GHcPOUEpqbl9N2JheiCjp0prbcz0nMhJY53aNaL0bCR4p9Klvdk6eI8wEhvGnHEjv2/HDaMzaagsZpYm4Ojh48lzn5kzctaWgw+W5FJS4uN/Nwmpk+p7dMX2sPh92t8viKDmlonmRluFsyp6jYR2bglmd17g43yjppTRUJC1+tEyprrWFNVgAKmpeWQl5DWd3cgiry2qoHle+qJc9q45ph0ctLsPY9vtPHgA4ffGPBwtGqVNBkFaGgkBMbgVCn9en4hBqI+602TlTWCeXPnYbPbWLduPTt37uxx/MyZM5k7d06n27xeL0899a/enlpEoeLTipm9Zz3rVxZ33BZvd3LT5BP41VEX9jopaW628bN7Z/LYUxNoat7/gjRtcg2/vGsN551ZFLbYD1cgoPGHv0zhT49MoaJqf+O2vJwmvn/bBr7xlW0didNb72Xz09/OYvW69I5xsS4fN1y9g1//aA2JCcHdObvqK/nBZ8/zv92rMdv28WrAmfnT+e0xlwzamYd7Xy/jnjUv0JyypuO2H++MYbL7fF674SRGDes6KTknwc/x9zZxyV19/+63Ud/Dzpgnqbat3n99Sulk+o5hnPtaXCq8dV2EGIp6NTNy772/4fjjjmfdunUEAgFOP/00/v3MM/z853d3+z23334b1193La++9lrHbe5WN7+99z7LQcrMSHR6dUwh/zd/I8FNp51/jQxNY2ZGPksvuMtyQtLcbOOkC05j9dq0Q8qta5pCKY2//O7zsO8W6Q3ThKu+djzP/29UR6n2/YKr5m69eTN/+s2XPPXcGG64dSGaFiwTfyDDMJk8oY5P3niLEl8BC1+6p8ty64am4zRsLL3gLuYOG9W3d66f3fbv3TxU8+vgIsSDF+WaBo7GMWy56bvdJiT90cm33tjKqrifYOIP9j45gKZ0bCqOec2/J9a0tv1ZiKGmT2ZGPvxgKT/60U8w2+phz583j//972Xeeuttvvjii26/r7ikpMeERQw8CSfX8cfMTQBdlmUOKMXaqgJ+tvy//F/bToxQfn7fzC4TEdjfo+XWHxzFyceXMX5swxFEf/ie+M9YnvvvKLppiAHAg49NZta0am7+zrEoRRdJS7Aj7+Ztyfzgl7P5fPZd3fZ9CSgTT8DHRW89xO5rf4/RVy15+9n6Ag8P7bsXdBX8dzA9gDdhF6c9voTtPz6n/wMkWCxrXexvu0xEAJRm4qOZDa4/sKD5/ghEKMTg0atntrffeacjEQH4csUKPB4PY8b0vJ0vKTGRKy6/jAsuOD/kWBH9Zizy8q/FCSF3TgSU4rFNS2n2hd6K2dJi8NhT47ttQNdO0xV/fWJCr+INF6XgT3+dEnKBqmGY/PIPM9s+6/4xCgR0/vlxFeuri3vs+xJQiqKmGt4qWN/7oKPUHf9dB1qg5yqaeoCd8a+xrTQyW3mrbMvx6rVdJiIdNJNG204aDGsl84UQXTuit1knnXQiTqeT9et6fpJ0uVwsOGoB555zDu+9+w4///nPehzvcDiIj4/v+BcXF3ckYYo+8NqeNSEbpwE0+Tx8Xt7zuiKA5asyaGwKUTud4Av4q29FpqJiRaWLjVtSDrnkcrBAQGdvYXzIpnYA3rx3MQhdVdSmG7wxiJKR5Y3Le36Rb2f4ePSj0i6/1Nf1Rqrtq9BU6J+NpnT22Vb1aSxCDHaHvbU3K2sEf/j973nu+RfYsHFjt+PeeecdHn74EXy+4EK9o48+iuefe5bly79kyZIlXX7Prbd8izvvvONwQxN9aOH9uWgLjqLlHystf0+rP3QJ9ZZW67+KvRkbTi2tvSlFbnHrj73F2lilcPsHT5l9v9Zi+SFq9nZfofaJOS28Nt7bJztrTLyhO8MCoBFACrEJcSQOa2YkIyOD5579D5s3b+YHP7irx7FbtmztSEQAPv/8CzZu3MTxxx/X7fc8+NBfGD9hUse/WbPnHk6Yog9oC44CYFRSBrrFvbb5Fran5ud2v7DpQLpuMirf2thwG5bhxm631nTLZpjoVt7514201MhLwaDa5pts5kLAWnI3eVj/buFtF2NmWMqXFCYuldnn8QgxmPU6GcnIyODFF56jsLCIG2+6Ga+39+/WfD4viQkJ3X7d6/XS1NTU8a+5ubnbsaL/LLx//+WRm6ec0LEFtTs6GjPT85ieHvqyytRJdcyaXt1tqfN2pqlz87XbrQUcZnFxfq64cE9HxdPuGIbJGacWY4Zo5KVpirEtZ2KzsCjVVCbXT1rYq3ij2U1TjgMjRBKmwNaYx7dOTe95XB8Z4Ts5dP8TQMPGMG/3b66EEKH1KhlJT0/nheefpbi4hBtuvAmP59CpydmzZ3Httdd0fD527NhOXx8/fjzTpk3jyxUrDjNkEQntl2faXTp2PmOTMjvKgXfFRHH3ggssn+PuH6zBNHXoZmrcZpiMGdnAZefvsXzMcPverRuxGcEy7V0xdJP4WD/3/+pLJk+o7bGpnlIa93ynkNumn9rzOk5N49qJxzIqMeMIo48ePz03B1ftNAj0cMlNgxuyrsZm9Pw0dU6Cv08a58WZ2QzzHt99/xMABfme86TapxBHqFfJyH+eeZqsrCw+//xzrrnmam666UZuuulGZs+e1THmxEWL+OFdP+j4/J5f/5LHH/sb3/727fzy7l/w6iv/5aOPPuY//3k2fPdC9JkZi7wct+ySTokIQIzNznvnf7/jBfLASzaGpqNrGo+eeD3njJpp+Vxnn1bM3/64DF1XnRrRtc+WjMxr4r3/vo3LZe1SSV+YMrGOV595H5fLj64He8gEKTRNkZjo4+2X3mHMqCbeefEdxo0JbkE+cMbHaEtm/vzbL7jkvAJ+e8zFXDfxWIBOyV37x+eMnMlfT7yuX+5ff3HYNZZf901iGtt2RpkHXLJpS1DO4jb+et04S8d7Yk5LnyQkk1tvJd0fvEx8YMv69o+zfIsZ4xn8PZmE6Gu9Knr205/8GJv90HcyH374EUuXLgVg0aJFHH3Ugk5FzU48cRGzZ82itbWVNWvX8vnn3dck6YoUPYucUA3w3H4fL+xcwd82LWVPQxVxNifnjZ7N16YuYkzS4V1H37UngUefHM8rb+bR3GpjVF4TN1+7nUvP20tMTOQSkQNVVsXw93+P45kXR1NbF+xNc81lu7ju8p2dmuR5PDovvZbPY0+NZ8eeRGJjApx9WhFfv35bp1opSimWle3g4Q0f8Fn5TpRSzM0cyTenncxJOZMGbQO6plaTn/63gCd3vk+DYw+66WSyfgy/XryAs2d3fym3K9eviu2TAmgKkxrbOoodb9Jg7EJDI9k/mRzvmSQFJqJZXYkrxBBkteiZ9KYR3epNJ96GRjsVlTG4XAGyR7REXR+ZvrJpaxLFpXGMzm9k3JjGSIczpPVVMiKEOHx91ptGDB1WEpF1G1P43QNTeeGVUfjb6mpMGl/H7V/bzE3XbO91B9uB4ke/msXDf59EQ0dtFEVqiocf3L6B792yKaKxCSHEQDNIXyrEkZqxKPQuqSXvZ7Pg1LN54dX9iQjA1h1JfP3Oo7n8phMIBAbfFMkJ55zOvX+eQUPTgT1TNGpqnfzgF/O47MYTIhbbUPbnRYE+WTcihOh7koyIQyy8PzfkrEh5hYuLrjsRn1/H7+/8axTsxaLx0msj+f1DU/sw0v7309/M5JPP27u0HpxoBT9/4ZWRPPaUtYWXInySGj2SkAgxQEkyIjrpaudMVx5/ehwer95lE7h2Smn86ZHJ+HyDZ3bkoccm093W4wP9+v9m9H0wQggxSEgyIjpYuTTT7tmXR4Xs0QJQuc/F5ysGR3XKHbsSqG90ELqOuUZRSRxer/x5CSGEFfJsKTpY3TkDUFvnxGpzkbr60A3wBoKi0t40bNSoqR0c91sIIfqaJCOio7BZbwzPbMXK5QqAzAz3YUQVfcaMbMTqfQZFetrguN8DSfu6ESHEwCLJyBBnZbFqV669fKeFWiKKkbmNzJ9ddVixRZv83GYy092ETkgUE8bWY5ON8xGR1Ojh1tvqIx2GEKIXJBkRh+W6y3eRkuTF6LGxncZd394wqGqN/PiOdSFGBBOV3/x0Vd8HI4QQg8QgepkQvXVw87veSE7ysuSFd0iI93XqIwN0fH7HNzdGrMNuX7n1q1u5+pJdBJOOg2dIgp/f+a1NXHBWUX+HJoQQA5YkI0PUjEXew05E2s2dVc2GZa/wvVs3kpYaXB+h6yannVjCW8+/wx9+uXJQloV/6pFPeeKhTxidf+AaEsWk8fX896kP+P3dKyMZnhBCDDjSm2bAUFjdvWLFkcyKdMfj0bHbzZCXZVTbb9xgSFRME+rr7SQl+QbV5ahIUCq8vxOvNdp48IGk8B1QCNFr0ptmEIhJbiFrXgHDZhRjc/kIeGxUbRpByZcjaanqXUfTA/V254xVTmf360d8Po1n/zuKhx6bxJr1aQDMnFbDt27awhUX7sHh6GntSfTZuCWZhx6fxHP/HUVDo53UZA9XX7qLb924lbGjpWGeVbV1Dh7/1zj++sRECgrjcTgCnLqolFtu3sIpJ5QdUXJyToKfl05vleZ5QgwAMjMSpVLGVjLl0tWgKXRj/4/IDGhommLbKzOo3JB9WMfuq2SkO01NNs66/BQ++WI4um5imsEphPaPFy6o4I1n3yUhwd+vcR2up58fzVduXYgGnXryGIaJzVC8+MSHnLW4OHIBDhA7diVw0vmnU1YRi2lC+8yfzTDxB3RuuWkzf/7tl0eUkEgnXyEiy+rMiEwsRyFXahNTLl2NZpidEhEg+LkGE85fR2JOba+Oezj1RMLhxtuO5bO2KqzticiBH3++IoOv3Lqw3+M6HJ+vyOD6W44jENA6JSIAgYCO16dz0fUnsnWHXB7oicejc9oli6modLVV8t2fcbQ/rg89Ppk//XVyhCIUQvQnSUaiUNb8AtBUt+8INQ2UqZFzzG7Lx5yxyHtY9USO1K49Cbzw6igCge5/1QKmzsuvj2Tn7sO/9NRffv/gVHSt+/U7SmmYpsYDf5vUv4ENMC+9ls/ewoRDErqD3ffnaUfU2+iJOS1Sc0SIAUCSkaijGD6z+JAZkYPphiJtfAWG0xfyiJFKRAD+/eLoQ7b+dsUwTJ5+YUw/RHT4GhrtvLokN+QLqN+v8+SzYzsW6opDPfnsWPQea9QEVe5z8cEnI47oXMdnGdLJV4goJ8lIlNFtJobDWjlrTQd7rPXmdpFQXulqm0noma4pyiuj+9p+1b6YTpeZetLaaqO5WdaHd6ekLNbyYxntvxdCiCMnz5ZRxvTrmAEt5MxIu4Cn5x9hX2zh7Y3EBB/KwpZkhUZiQnQnVkmJ1uMzDBOXS3qkdCclyYvV7eqJCaFn/4QQA5vMjEQdjX1bh2EGen6SVibUFyXja3H2fLQIJiIAF55dgN8f+tfM79e56JyCfojo8KWneTh6XmXIyws2w+S8MwoxLCaUQ9HF5+21tEsm1uXjlONL+z4gIURESTIShUq/HBlyZkTToeSLUT2OWXh/bjjDOizzZu1j3qwqbD2sG7EZJnNm7GP+7H39GNnhueObm0JeXvAHdG7/2uZ+imhguu7yXcTF+ntM7HTd5OZrtx/xlu+kRg9PzGmRdSNCRDFJRqJQQ1Equ96ZCARnQA7U/nnR56PYt2V4l9/fvoU30rMiENz588I/lzIss7XLhaw2wyQzo5UX/rl0QFRkvfDsAr57ywaAQ15I25sG/u4XKzju6Mp+j20gSU7y8r9/vY/DbnaRqCp0XbFwQSW//enqsJ3zovHRfRlQiKFMkpEoVfLFaDb+Zy71RSmdbm+qSGTLSzPZ8+5EurreHsmdM93Jy2lm5fuvcctNW4iP23/9Pz7Oxzdv2srK919nZF73xXCiiabBfT9fxTN/+4iZU2s6fe3oeVW8+u/3+O4tmyIU3cBy0vHlfPHOG1x0TkGnRDV7RAv3/GQVb7/4DjExsu5GiKFAKrAOAI4EN/Y4D/5WB576nncWRGMycqDWVoNde4P1REbnNxIbO7BfbAqK4qipdZKZ4SZ7REukwxmwamodFJXEEeMMMHZ0Y5+st6lPcHL7UkMqsgrRj6Q3zSDibYzB2xgTclykd85Y4XIFmDqpLtJhhE1+bjP5uc2RDuOImKbiwXequH/Fx5Tr29AwGKfP5penLOCCeYdXSbah0c5Tz43hmRdHU1UdQ2a6m6sv2cXVl+zqcg1IaoqX1JS+vYwSXDsC14MkJEJEGUlGBpFoT0RE9KlpDDDlj89RkfouJNrACCYKmwK7uGj5f1iw9AaW3bkQXbe+oOeLlRmcddkp1DU4gGBV2t17E/hiRQY//e0sljz/LnNnVffJ/RFCDEyyZmSQiIadM2Lgmf2n/1GR/H7wE+OAGYu2j5e7/sE5D62xfLzde+M57eJTaWi0o5SGUsEkRikNhUZ9g4NTLzyNgqK4sN0HIcTAJ8nIIDAQLs+I6PP66kYKU16HEHVT3vL9k30N1rbX3v/IFFpbbQS62f4cCOg0tdikd48QohNJRgawaNrCKwaeu99bBQELV2odTfz0f3tDDvN6dZ54ZmzI3j2BgM7fnx5PIERhv77y50UDe9G0EIORJCNCDFGlnnLQLbwwm3a27gtdkK66xklLq93SuRsaHdTVOyyNDbekRg8v3DswtpILMVRIMjJARfsWXhH9HJoTlIWnAM0k1hY6cehtTZAYp8xQCCGCJBkZoCQREUfqlJwp1mZG9ADXzhsdclhKspdZ06tD9u7RdZOj5lYSF3dkZd6PlJSHFyJ6SDIyAM1YJGWtxZH7/cVj0NzJ0FOvnYBBXM0cLjs62dIxb/vq5pC9e0xT59abt1gPtA8kNXpk7YgQUUSSkQFm4f25MisiwiI53uDXE74fvFTTVQIRMNB8ibx43vWWj3nNpbu45Lw9aFrXFVQ1TXHlxbu47II9hxl1+CQ1erj1tvpIhyGEQJKRAUV2zohw++E5I3hk6m+Jr5vT+QtKY1j9Sbx/xq84bUa85ePpOjzzt4+5+641pKa4O30tLdXNr3+8mqce/gRdnnmEEAeQCqwDhFyaEX3layen87WTv8krKxtYur0am65z8ZwMFoyLPazjGYbiJ3eu53u3bOSjz4ZTXeMkPc3NCcdU4HD0vJ5ECDE0STIyQMilGdHXzpubyHlzE8N2PKfTZPGJpWE7nhBi8JLJ0gFASr0L0TfOSfDLuhEhooAkIwOArBMRou+ck+CXbb5CRJgkI1Gsvdy7EEIIMZhJMhLFEs4dE+kQhBBCiD4nyUiUmrHIK5dnhOgnT8xpkbUjQkSQ7KaJQgvvz5VERIh+dnyWwYORDkKIIUpmRqKMJCJCCCGGGklGhBBCCBFRkoxEifadMzIrIkTkyBZfISJDkpEoITtnhIispEYPT8xpkYREiAiQZCQKyM4ZIaLHReOlD5QQ/U1200TYjEXeIdN3JhDQePPdbFauTQdg7sx9nHlqCYbRdbt5IYQQQ0OvkhFN0zjttNM4+ugF2G121q5bx0svvUwgEOjx+4YPH841V19Fdk42e/bs5cknn6Kuru5I4h4UhtLOmVffyuWb3zuK0vI4bLZg51a/X2fEsBYe/v3nnHdmUYQjFCLo+CyDl05vpXSJK9KhCDFk9OoyzSv/e5mLLryAoqJidu7cyW233cpLLz6P3W7v9nuys7J4e8mbTJ06hdWr13DcwoW89ebrJCcnH2nsYoD435t5XHDtSZRVBFvS+/06fn/wV6+80sWF153Ey6/nRTJEITrI2hEh+l+vZka+8Y1vUVK6vyX4O+++x5fLP+ekE0/k7Xfe6fJ7vvOdb1NZWclXbrgJ0zR5/vkXWPbpJ9x88038/vd/OLLoB7ChMivi8ejcdPsxACilHfJ1pTQ0TXHzt4/lrFOLcTrN/g5RCCFEhPVqZuTARASgoqICr9fb4yzHSSedxJtvvoVpBl9k3G437773LqecfFLvox0khkoiAvDy6/nU1MZ0mYi0U0qjts7JS6/l92NkQgghosUR7aa5+qor0bT/b+/eg6K6DjCAf8tTs6AoCqILCiIPpygIqBiJRCsLKJhJ0eB0mrYmrVpj/ojRsalMTGcidoxW5S3WGkFN4gNR8ZEaEQzqWHHtECNbEjEgdBhfgLsJ4C70D8wOCMhzPXd3v9/M/sHdu7MfHle/OXvuuTJcuny5y+eHDBmCMWNcO5WYu3er4eHR/bS8nZ0dHBwcDA+5XD6QmCTQlZLRsLV9/poiALC10ePyNZcXkIiIiKSm31fTTA8NRWLiBmxK2oyqqq4XH9rb2wMAtFpth+M/arWG57qy+p1VWLPmvf5GkyxLunLmZy0t3c+IdD7XiEGI+mhHhB6Lz4hOQWQZ+jUzEhQUiH379iIrazcyM3d1e55Wq4Ver+/0Nc6IESPQ0NDQ7euSU1Lh4+tveARNC+lPTJKAAP9H0D3p+a+ZTmeFAP864wci6qXhj5twaLNGdAwii9DnMhIYGIgD+3OQnZ2DpM1/e+65Op0O5eXfwc/Xt8NxPz8/lJWVdfu65uZmaDQaw+PZmRVTZImzIgCw9PXbGDpU1+N5Q4bq8ev4719AIiIikpo+lZEpU6bg4IG2IvLxpqQuz1mwIAZbP9li+Plobi7i4mLh6uoKAPD29sbcua/iyNHcAcQ2LbO3uVtkEQEAR0cdNn9Y0uN5SYklcHTsubQQEZH56dOakex9e2FtbQ13d3ekp6Uajp/Mz0d+/ikAgJ+vL2JiorHm/bUAgF27sjB9eijO/essSku/QVBQIE6cPInDh48M4q8hXdzqHXjn7TLodFZY/9dg6J5YQSZrRSsAtMpgbdOCpMQSvPvHW6JjEnXS9lUNsHi9g+goRGZN5jZW0eu9uBcuXABrK+tOx8vUaqjVagCAr68vvLw8cfp0x5VfAQEBGDduHCoqKgzn9paDgwP+q76F4uB46LWmtRGRJV3G25OHj+yw96A3Sp5uBx8ceB+/TfgeziObBCcjer4Tj22QvHO46BhEJuclexvkJUXDx9cfGk33a7D6NDNy8mR+j+eo2xWT9kpLS1FaWtqXtzN54cWLRUeQlJEjmvHen74VHYOIiCSGd+01kqkRvPMnERFRb7CMGImlLlglMkexjjpe5ktkRCwjRjB7m7voCEQ0yIY/buLN84iMhGXECLhglYiIqPdYRgbR1IhmLlolMmO/8uFaMCJjYBkZJJa8sRmRpeDaESLjYBkZBNzYjIiIqP9YRgaBY9xE0RGIiIhMFsvIAHGHVSIiooFhGSEi6gNe4ks0+FhGBiC8eDFnRYgs0N7gH7H63XrRMYjMBstIP3G7dyIiosHBMtIPUyOaeRkvERHRIGEZ6SMWESIC2vYc4Vc1RIODZaQPuLEZEbUX66jjYlaiQcAyQkRERELZiA7QF9byocLeOyxJAVlIqLD3JyJpGmJtg5fsTeqfUqIXZmgvPxsyt7GKViNnGTBXV1eorl8THYOIiIj6IWhaCGpra7t93iTKCNBWSLRaba/Pl8vlUF2/hqBpIX16HRkfx0a6ODbSxbGRLo7N88nl8ucWEcCEvqbp6RfpjlarhUbDu2xKEcdGujg20sWxkS6OTdd682fCBaxEREQkFMsIERERCWW2ZaS5uRlbt25DczO3bZcajo10cWyki2MjXRybgTOZBaxERERknsx2ZoSIiIhMA8sIERERCcUyQkREREKZzD4j/TFlyhR4TpiA4kuXcP/+fdFxLF5s7EJYyTr239JvSnH7doWgRPSsyZP9oRinwM2bN1FdUyM6jsXz9/eDzySfLp87fuIEWlu55E+0yZP94a5wR31DA/5z4wZ+amwUHckkmW0ZmTBhAvbn7IOzszPeSFiKixe/Fh3J4qWmJOPq1X/j3r17hmMNjxtYRiRg9OjR2J2VCXd3D6hUKnh5eeKLLw4hPSNTdDSL5uvjC6UyssOxGTNmwMpKhuMnTghKRQAwbNgw5GR/Cg8PD6hUN6BwV8BtzBgsX7ESxcWXRMczOWZZRmxtbZGeloq0tHQkJm4QHYfaSUlNw4ULF0THoGfs2bMbTY1NePnl2fipsREymQzz5s0VHcviHcvLw7G8PMPP9vb2UF2/huzsHM6KCPbmm7+B18SJmDVrNhoaGgAAaakp2LjxQ8yfrxSczvSY5ZqRDz74M8q/K+/wISZp8PfzRZRSicmT/SGTyUTHIQDh4bMRPG0aNm78yDDF3NrainPnvhKcjJ4VExONYcOG4eBnn4uOYvHs7exQX1dvKCIAUFlVBTtbW4GpTJfZzYzMnfsqopSRmB8ZBUdHB9FxqJ3W1lbEx8ejuqYagVOnorq6BstXrERlZaXoaBZt5owZePDgAcrUasyaFQY7O3vcunWr3/eDIuNZujQBxZcu4c6dO6KjWLx/7v0Uc+a8gtSUZBQWFUExbhxeWxSHdevWi45mksyqjLi4uGDrJ1uwfPlKaDQalhGJ+d3vl6Gg4AIAwMHBAQf2ZyN553Yseu11scEsnPMoZ2g0GhzLPYrGxkbo9TqEhITi79u3IyUlVXQ8esrDwwOzwsKwatVq0VEIbTd/u1ZyHbELF0Aul0OhUKCyqgpVd6tERzNJZlVGEjf8BRUVd+Dm5oZFcXFwGuEEAAgLC8OTJzpcuXJFbEAL93MRAdo+yGnpmdjzjyw4OTmhrq5OWC5L19TUhPHjx2PHjmR89nnb9H90dBSydmWioKAAN29+KzghAUBCwhuoq6vD6TNnREchAOvWvo8FMdGY98tIw1c1mzdvQk52NsJfmYOWlhbBCU2LWa0ZUavVqK2tRVSUElFRSkTMmQMACA0NQUjwNMHp6FkazWMAgLPzSMFJLNsPd34AAOSfOmU4dvbsl2hpaUHALwJExaJ2rKyssGTJYhw6fIT3P5GImTNnoqjoYoc1I/n5p+DpOQFjx44VmMw0mdXMSEpqWoef3dzGQKmMxM6dyby0V7BRo0Z12uslJjoaDx89wp2n/xmSGOe+Oo+PPtoIb++JUKluAAC8PD1hY2ODGu41IgkREXMw1s0NBw4cFB2Fnqr5Xw0mTfLucGyStzd0Ol2H7Quod8yqjJB0TZ8eipUrV+Ds2S/x8MEDhIeHQ6mMxJo1a6HX60XHs2iVlZVIT89AZkY6dmXthl6nw7K3lqGwsAgXv2aJl4KlCQm4evUqysvLRUehp9LSMnD0yCFkZKThQkEh3D3c8Ye330JaegaamppExzM5Zn3XXicnJyRt+hjbd+yEWq0WHcfi+fj4YNGiOLi6uKCyqgq5ucdQVcXFXlKhjIzEvHlzodProVKpcOTIUX7vLQE2NjbYunUL8vKO4/z5AtFxqB2FQoEli+OhUChQV1+PwsIiFBYWio5lksy6jBAREZH0mdUCViIiIjI9LCNEREQkFMsIERERCcUyQkREREKxjBAREZFQLCNEREQkFMsIERERCcUyQkREREKxjBAREZFQLCNEREQkFMsIERERCcUyQkREREL9H/gRIGma4OMtAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 27
  },
  {
   "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": "2026-10-03T16:54:06.644022467Z",
     "start_time": "2026-10-03T16:54:06.262419785Z"
    }
   },
   "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": 40
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-10-03T16:54:47.089822572Z",
     "start_time": "2026-10-03T16:54:46.963575914Z"
    }
   },
   "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": 42
  }
 ],
 "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
}