KMA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4ff96049-b0c5-4270-8a2d-96c7dac79516",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.datasets import load_digits\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.decomposition import PCA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "09d76ffa-3fee-4844-8cd5-982e47fc57f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_digits()\n",
    "X = data.data\n",
    "y = data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9c9e41db-aa37-4815-8dff-d1344e846420",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X,y , test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b7bb7ac8-3f9b-411b-a743-9d659cc44d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KMeans(n_clusters=10, init=\"random\", n_init=20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1be0783b-74cb-4475-8f7b-76c0b8a7de29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 336\n"
     ]
    }
   ],
   "source": [
    "# true = 0\n",
    "# false = 0\n",
    "# for i in range(len(x_test)):\n",
    "#     if predications[i]==y_test[i]:\n",
    "#         true+=1\n",
    "#     else:\n",
    "#         false+=1\n",
    "# print(true, false)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "39fd861d-3d00-4d03-8958-d8c458ae117b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 178, 1: 182, 2: 177, 3: 183, 4: 181, 5: 182, 6: 181, 7: 179, 8: 174, 9: 180}\n"
     ]
    }
   ],
   "source": [
    "# samples ={}\n",
    "\n",
    "# for i in data.target:\n",
    "#     if i not in samples:\n",
    "#         samples[i]=1\n",
    "#     else:\n",
    "#         samples[i]+=1\n",
    "# print(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d9d61b2c-6062-4f18-b286-44675fd1c004",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bench_k_means(estimator, name, data):\n",
    "    estimator.fit(data)\n",
    "    print('%-9s\\t%i\\t%.3f\\t%.3f\\t%.3f\\t%.3f\\t%.3f\\t%.3f'\n",
    "          % (name, estimator.inertia_,\n",
    "             metrics.homogeneity_score(y, estimator.labels_),\n",
    "             metrics.completeness_score(y, estimator.labels_),\n",
    "             metrics.v_measure_score(y, estimator.labels_),\n",
    "             metrics.adjusted_rand_score(y, estimator.labels_),\n",
    "             metrics.adjusted_mutual_info_score(y,  estimator.labels_),\n",
    "             metrics.silhouette_score(data, estimator.labels_, metric='euclidean')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f836851c-d62c-44f6-a870-ebf6725254f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1        \t1165165\t0.737\t0.745\t0.741\t0.665\t0.738\t0.182\n"
     ]
    }
   ],
   "source": [
    "bench_k_means(model, \"1\", X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5233835e-73f8-42ac-af86-d13971618f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_data = PCA(n_components=2).fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ad4e87b2-ec6a-42db-92ae-ab3f453925d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\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",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-container-id-4 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-container-id-4 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-container-id-4 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-container-id-4 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-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 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-container-id-4 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-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 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-container-id-4 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-container-id-4 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-container-id-4 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-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 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-container-id-4 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-container-id-4 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-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 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-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\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",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\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",
       "  color: var(--sklearn-color-background);\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",
       "  color: var(--sklearn-color-background);\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-container-id-4 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-background);\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-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\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-container-id-4 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-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=10, n_init=4)</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\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=10, n_init=4)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=10, n_init=4)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(init=\"k-means++\", n_clusters=10, n_init=4)\n",
    "kmeans.fit(reduced_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "9bb9cc2e-b74c-4335-a81d-1af40b02ab93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step size of the mesh. Decrease to increase the quality of the VQ.\n",
    "h = 0.02  # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\n",
    "y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "# Obtain labels for each point in mesh. Use last trained model.\n",
    "Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1)\n",
    "plt.clf()\n",
    "plt.imshow(\n",
    "    Z,\n",
    "    interpolation=\"nearest\",\n",
    "    extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n",
    "    cmap=plt.cm.Paired,\n",
    "    aspect=\"auto\",\n",
    "    origin=\"lower\",\n",
    ")\n",
    "\n",
    "plt.plot(reduced_data[:, 0], reduced_data[:, 1], \"k.\", markersize=2)\n",
    "# Plot the centroids as a white X\n",
    "centroids = kmeans.cluster_centers_\n",
    "plt.scatter(\n",
    "    centroids[:, 0],\n",
    "    centroids[:, 1],\n",
    "    marker=\"x\",\n",
    "    s=169,\n",
    "    linewidths=3,\n",
    "    color=\"w\",\n",
    "    zorder=10,\n",
    ")\n",
    "plt.title(\n",
    "    \"K-means clustering on the digits dataset (PCA-reduced data)\\n\"\n",
    "    \"Centroids are marked with white cross\"\n",
    ")\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cea49322-2f1b-47ca-a636-25147630b532",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}