{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7d35a349-0ab4-4a90-8123-7c4eb0465763",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         9\n",
      "           1       0.92      0.92      0.92        12\n",
      "           2       0.89      0.89      0.89         9\n",
      "\n",
      "    accuracy                           0.93        30\n",
      "   macro avg       0.94      0.94      0.94        30\n",
      "weighted avg       0.93      0.93      0.93        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Загрузка и разбиение данных\n",
    "X, y = load_iris(return_X_y=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Модель MLP — многослойный перцептрон\n",
    "clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=2500)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Отчёт о точности\n",
    "print(classification_report(y_test, clf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afff685e-6415-4da5-ba74-13c66fde512e",
   "metadata": {},
   "source": [
    "# **Индивидуальное задание** Пример инициализации K-Means++\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44409ece-512f-4df9-a564-b33e659c7098",
   "metadata": {},
   "source": [
    "## **Цель:** исследовать работу алгоритма K-Means++ — улучшенного метода инициализации центроидов для K-Means на синтетических и реальных данных."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "777e71a0-7a3c-4dc2-b037-b1f48185f5ca",
   "metadata": {},
   "source": [
    "## **Часть 1. Синтетические данные make_blobs**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aab3ff7-9e68-481e-aa26-8621365258ec",
   "metadata": {},
   "source": [
    "## **Цель задачи:** показать, как K-Means++ выбирает начальные центры кластеров, чтобы уменьшить вероятность плохой сходимости алгоритма из-за неудачного случайного выбора начальных точек."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a12c499-a06f-4296-9340-b2971fbe91ed",
   "metadata": {},
   "source": [
    "### 1. Импорт библиотек"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "63993174-b630-4309-a98f-d06b28cfaf85",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import kmeans_plusplus\n",
    "from sklearn.datasets import make_blobs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20330745-3ba9-4447-84f7-32a1870dc79c",
   "metadata": {},
   "source": [
    "### 2.Генерация тестовых данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "25c9cd9e-2b1f-4c0d-92ce-722954361c13",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 4000\n",
    "n_components = 4\n",
    "\n",
    "X, y_true = make_blobs(\n",
    "    n_samples=n_samples, centers=n_components, cluster_std=0.60, random_state=0\n",
    ")\n",
    "X = X[:, ::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e479a5f-aec4-4f3a-825a-fc7bf7052624",
   "metadata": {},
   "source": [
    "### 3.Вычисление начальных центроидов с помощью k-means++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "072cf524-8d8b-4e17-a02b-4106fdc0d26e",
   "metadata": {},
   "outputs": [],
   "source": [
    "centers_init, indices = kmeans_plusplus(X, n_clusters=4, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb1676a-1966-4de2-84a3-5713ffa76e04",
   "metadata": {},
   "source": [
    "### 4.Визуализация начальных центроидов вместе с данными"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b4a3f35f-8457-4d47-83bd-ea892ee23efb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "colors = [\"#4EACC5\", \"#FF9C34\", \"#4E9A06\", \"m\"]\n",
    "\n",
    "for k, col in enumerate(colors):\n",
    "    cluster_data = y_true == k\n",
    "    plt.scatter(X[cluster_data, 0], X[cluster_data, 1], c=col, marker=\".\", s=10)\n",
    "\n",
    "plt.scatter(centers_init[:, 0], centers_init[:, 1], c=\"b\", s=50)\n",
    "plt.title(\"Инициализация K-Means++\")\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af53d62-dc7a-410f-acc8-6b7f147f0dc1",
   "metadata": {},
   "source": [
    "## **Часть 2. Реальные данные (Diabetes из CSV)**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dacde73-4815-4c83-bc21-e1c6c9a87e63",
   "metadata": {},
   "source": [
    "### 1. Импорт библиотек"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1dd660da-3cd1-40d6-a31a-41c8ebcea640",
   "metadata": {},
   "outputs": [],
   "source": [
    "import openml\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans, kmeans_plusplus\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.ensemble import IsolationForest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3caa369-d867-46c4-9ab6-6596d4f393f9",
   "metadata": {},
   "source": [
    "### 2. Загрузка датасета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2f59c678-b142-431c-a70b-d421171659e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Загрузка данных\n",
    "dataset = openml.datasets.get_dataset(43977)\n",
    "X, y, _, _ = dataset.get_data(target=dataset.default_target_attribute)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4ff564b-3d1f-461f-990e-8b08bd1bbfaf",
   "metadata": {},
   "source": [
    "### 3. Удаление категориальных признаков и пропущенных значений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d18a8fc9-96ad-4e60-9e23-fa2c79338635",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.select_dtypes(include=['float64', 'int64'])\n",
    "X.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dc3ad39-b4a5-4a88-8e8d-7cdb356f60b6",
   "metadata": {},
   "source": [
    "### 4. Масштабирование данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f5369ecb-7e67-4c54-bbd5-92da3200864b",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1505f71-7f7c-44df-b306-8bd02f3d550d",
   "metadata": {},
   "source": [
    "### 5. Инициализация центроидов K-Means++"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "450672c8-f1f2-4462-ae92-e02a3dcd14ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_clusters = 5\n",
    "centers_init, indices = kmeans_plusplus(X_scaled, n_clusters=n_clusters, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "800ff2c0-9637-4af7-8d4c-6696782a12dd",
   "metadata": {},
   "source": [
    "### 6. Обучение K-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1ffec0b3-9107-4844-893c-a00bc6e17a65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\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",
       "  /* 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-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 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-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-container-id-6 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-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 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-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 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-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 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-6 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-6 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-6 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-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 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-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 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: 0.5em;\n",
       "  text-align: center;\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-6 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-6 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-6 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-6 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-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(init=array([[-5.56758861e-01,  1.20238181e+00, -1.07739433e+00,\n",
       "        -1.10105596e-01,  1.68193476e+00,  1.34389067e+00,\n",
       "        -1.08165089e+00,  5.89070772e-01,  8.33471712e-01,\n",
       "         3.44240943e-01,  7.71196505e-02, -1.33620879e-01,\n",
       "         8.65878020e-01,  7.36543691e-01,  6.83543059e-01,\n",
       "        -1.34349148e-01, -6.70590632e-02,  3.27172837e-02,\n",
       "         2.31624128e-01,  2.27931713e-01,  3.79983740e-01,\n",
       "         6.348521...\n",
       "        -8.63509497e-01,  8.61928592e-02, -5.85368528e-02,\n",
       "         9.59375311e-01,  1.84472089e+00,  5.18824060e-01,\n",
       "         7.79947998e-01,  5.80093860e-01,  2.90267084e-01,\n",
       "         1.05876617e-01,  5.13397170e-01, -2.29624650e-01,\n",
       "        -1.70018566e-01, -6.50472034e-01, -3.10117559e-01,\n",
       "         1.34038138e+00, -3.77133563e-01,  5.22979042e-01,\n",
       "         1.83928247e+00, -3.01523771e-02,  1.35317760e+00]]),\n",
       "       n_clusters=5, n_init=1, random_state=42)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KMeans</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(init=array([[-5.56758861e-01,  1.20238181e+00, -1.07739433e+00,\n",
       "        -1.10105596e-01,  1.68193476e+00,  1.34389067e+00,\n",
       "        -1.08165089e+00,  5.89070772e-01,  8.33471712e-01,\n",
       "         3.44240943e-01,  7.71196505e-02, -1.33620879e-01,\n",
       "         8.65878020e-01,  7.36543691e-01,  6.83543059e-01,\n",
       "        -1.34349148e-01, -6.70590632e-02,  3.27172837e-02,\n",
       "         2.31624128e-01,  2.27931713e-01,  3.79983740e-01,\n",
       "         6.348521...\n",
       "        -8.63509497e-01,  8.61928592e-02, -5.85368528e-02,\n",
       "         9.59375311e-01,  1.84472089e+00,  5.18824060e-01,\n",
       "         7.79947998e-01,  5.80093860e-01,  2.90267084e-01,\n",
       "         1.05876617e-01,  5.13397170e-01, -2.29624650e-01,\n",
       "        -1.70018566e-01, -6.50472034e-01, -3.10117559e-01,\n",
       "         1.34038138e+00, -3.77133563e-01,  5.22979042e-01,\n",
       "         1.83928247e+00, -3.01523771e-02,  1.35317760e+00]]),\n",
       "       n_clusters=5, n_init=1, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(init=array([[-5.56758861e-01,  1.20238181e+00, -1.07739433e+00,\n",
       "        -1.10105596e-01,  1.68193476e+00,  1.34389067e+00,\n",
       "        -1.08165089e+00,  5.89070772e-01,  8.33471712e-01,\n",
       "         3.44240943e-01,  7.71196505e-02, -1.33620879e-01,\n",
       "         8.65878020e-01,  7.36543691e-01,  6.83543059e-01,\n",
       "        -1.34349148e-01, -6.70590632e-02,  3.27172837e-02,\n",
       "         2.31624128e-01,  2.27931713e-01,  3.79983740e-01,\n",
       "         6.348521...\n",
       "        -8.63509497e-01,  8.61928592e-02, -5.85368528e-02,\n",
       "         9.59375311e-01,  1.84472089e+00,  5.18824060e-01,\n",
       "         7.79947998e-01,  5.80093860e-01,  2.90267084e-01,\n",
       "         1.05876617e-01,  5.13397170e-01, -2.29624650e-01,\n",
       "        -1.70018566e-01, -6.50472034e-01, -3.10117559e-01,\n",
       "         1.34038138e+00, -3.77133563e-01,  5.22979042e-01,\n",
       "         1.83928247e+00, -3.01523771e-02,  1.35317760e+00]]),\n",
       "       n_clusters=5, n_init=1, random_state=42)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=n_clusters, init=centers_init, n_init=1, random_state=42)\n",
    "kmeans.fit(X_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a455b1c-c9b2-4a61-83c6-3d082cdf6684",
   "metadata": {},
   "source": [
    "### 7.Применение PCA для визуализации в 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "23cb4621-f654-4220-972a-3f07146e6786",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X_scaled)  # Преобразование данных"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "238d233a-9637-4886-9655-903cf1352ed9",
   "metadata": {},
   "source": [
    "### 8.Преобразование центроидов в пространство PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "bb216811-5c56-498b-852b-0ad169f48eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "centers_pca = pca.transform(centers_init)  # Начальные центроиды\n",
    "final_centers_pca = pca.transform(kmeans.cluster_centers_)  # Финальные центроиды"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f853ff7e-e44c-419b-bbf4-3ab3117e8e3d",
   "metadata": {},
   "source": [
    "### 9.Проверка данных после PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "da755ab2-63ce-4773-86db-3523c6fcdf59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Форма данных после PCA: (57580, 2)\n",
      "Примеры данных PCA:\n",
      " [[-0.2692536  -0.35551055]\n",
      " [-1.0299265  -0.23488426]\n",
      " [-3.19821991 -0.83107891]\n",
      " [ 3.52627805  0.54952882]\n",
      " [ 3.96929096 -2.13247353]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Форма данных после PCA:\", X_pca.shape)  # Теперь X_pca определена\n",
    "print(\"Примеры данных PCA:\\n\", X_pca[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2986dcf0-d110-43d5-af55-73f10846b528",
   "metadata": {},
   "source": [
    "### 10. Визуализация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d164fa13-8cc9-4f8c-a424-7f311c629f71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Визуализация\n",
    "plt.figure(figsize=(10, 6))\n",
    "scatter = plt.scatter(X_pca[:, 0], X_pca[:, 1], c=kmeans.labels_, cmap='viridis', s=50, alpha=0.6)\n",
    "\n",
    "# Отображение центроидов\n",
    "plt.scatter(centers_pca[:, 0], centers_pca[:, 1], c='red', s=200, marker='X', label=\"Инициализация K-Means++\")\n",
    "plt.scatter(final_centers_pca[:, 0], final_centers_pca[:, 1], c='black', s=100, alpha=0.8, label=\"Финальные центры\")\n",
    "\n",
    "plt.xlabel(\"PCA Component 1\")\n",
    "plt.ylabel(\"PCA Component 2\")\n",
    "plt.title(\"Кластеризация оптовых клиентов (K-Means++)\")\n",
    "plt.legend()\n",
    "plt.colorbar(scatter, label=\"Кластер\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fbb1454-2e93-4800-b84f-7462d4ce8e7f",
   "metadata": {},
   "source": [
    "### 9. Анализ результатов"
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    "Вывод по пункту 2 (Реальные данные — Wholesale Customers Dataset)\n",
    "1. Анализ данных после PCA\n",
    "Форма данных: (57580, 2) — 57,580 объектов, спроецированных на две главные компоненты.\n",
    "\n",
    "Примеры значений PCA:\n",
    "[[-0.27, -0.36], [-1.03, -0.23], [-3.20, -0.83], [3.53, 0.55], [3.97, -2.13]]\n",
    "\n",
    "Значения показывают высокую вариативность, что характерно для масштабированных данных.\n",
    "Экстремальные значения (например, 3.97 и -3.20) указывают на возможные выбросы или кластеры с уникальными характеристиками.\n",
    "2. Начальные центроиды\n",
    "[[-0.25, -0.64], [0.41, 2.21], [-2.40, 0.72], [0.55, 0.68], [-2.03, -0.41]] - Форма данных: Алгоритм выбрал центроиды, максимально удаленные друг от друга, что снижает риск попадания в локальные минимумы. - Расположение: Все центроиды находятся в зонах с высокой плотностью данных, что подтверждает эффективность K-Means++.\n",
    "\n",
    "3. Финальные центроиды\n",
    "[[-1.81, -1.50], [2.89, 0.10], [-0.60, 2.61], [10.25, -3.19], [-1.38, -0.29]]\n",
    "\n",
    "Смещение: четвертый центроид ([10.25, -3.19]) сильно удален от остальных. Это может быть вызвано:\n",
    "\n",
    "-Выбросами в данных.\n",
    "\n",
    "-Наличием отдельного кластера с уникальными признаками (например, клиенты с аномальными расходами).\n",
    "\n",
    "-Первый и пятый центроиды сместились ближе к зонам высокой плотности данных."
   ]
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