IIS_task1_Lysov.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMudVn6CyhLe/wCa6XhhpaY",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/YLysov0017/Pproject/blob/master/IIS_task1_Lysov.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ИИС - Задание 1, Вариант №53"
      ],
      "metadata": {
        "id": "7Qf-_4YEpqBv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans"
      ],
      "metadata": {
        "id": "5GGpWNYFqEH1"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "F-TOzpvvpebF"
      },
      "outputs": [],
      "source": [
        "data = np.array([[ 4.06862817, 5.65143303],\n",
        " [ 0.28566948, 6.15080519],\n",
        " [18.96191779,20.97435219],\n",
        " [ 7.47025486, 1.28619121],\n",
        " [18.82318364,20.33041478],\n",
        " [12.74272459,10.77526927],\n",
        " [12.73783278, 8.37174629],\n",
        " [ 1.7861078 , 6.22975279],\n",
        " [21.17457326,16.8461289 ],\n",
        " [17.21694767,17.29741577],\n",
        " [12.65183807,10.52108452],\n",
        " [20.15820272,15.84984491],\n",
        " [21.79493533,16.15235561],\n",
        " [12.81894894, 6.83125738],\n",
        " [19.77728405,15.88419247],\n",
        " [ 7.76413836, 0.93831447],\n",
        " [20.73834599,15.910502 ],\n",
        " [ 7.27746927, 0.7196397 ],\n",
        " [12.82997215,12.78901709],\n",
        " [12.48235776, 9.54050997],\n",
        " [11.78971457, 9.02121329],\n",
        " [12.66767773,12.65615208],\n",
        " [ 2.37888126, 5.83304913],\n",
        " [13.27648498,10.75455707],\n",
        " [ 8.25943012, 1.90189554],\n",
        " [ 0.47880094, 5.72091809],\n",
        " [ 0.75823137, 4.87270697],\n",
        " [ 7.66447064, 1.5820944 ],\n",
        " [ 6.63788947, 0.66622021],\n",
        " [ 7.99491597, 1.53582632],\n",
        " [13.04135907, 9.33528871],\n",
        " [17.25056871,17.74790678],\n",
        " [ 2.16998297, 6.79580514],\n",
        " [22.05296729,16.44973563],\n",
        " [ 7.48322114, 0.45304823],\n",
        " [ 7.52762937, 1.00463058],\n",
        " [20.09266063,16.63830525],\n",
        " [18.41367124,17.63623363],\n",
        " [ 7.24011894, 0.8894556 ],\n",
        " [ 7.67626638, 0.74472799],\n",
        " [ 1.87056179, 6.36411128],\n",
        " [22.0569224 ,15.33537353],\n",
        " [ 2.39571916, 5.03046969],\n",
        " [ 7.24494471, 0.17575503],\n",
        " [ 0.94309924, 6.32682124],\n",
        " [13.3533457 ,11.19552927],\n",
        " [17.6463541 ,18.53975804],\n",
        " [12.09708732,10.76500138],\n",
        " [12.67051421, 9.233864 ],\n",
        " [ 2.14872397, 5.73402454],\n",
        " [ 7.3264472 , 1.18486361],\n",
        " [ 1.57850917, 5.64870692],\n",
        " [ 0.79925699, 6.23196994],\n",
        " [ 8.89552282, 1.0134367 ],\n",
        " [13.55842539,12.25374628],\n",
        " [ 2.45550882, 5.69123728]])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# список для сохранения значений инерции (сумма квадратов расстояний от точек до ближ. центроидов)\n",
        "inertia_values = []\n",
        "# Перебираем количество кластеров\n",
        "for k in range(2, 11):\n",
        " # объект KMeans с текущим количеством кластеров\n",
        " kmeans = KMeans( n_clusters = k)\n",
        " kmeans.fit( data )\n",
        " # Сохраняем значение инерции - мерой компактности кластеров.\n",
        " # Он вычисляется как сумма квадратов расстояний между каждым объектом данных и центроидом\n",
        " inertia_values.append( kmeans.inertia_ )\n",
        "\n",
        "plt.plot(range(2, 11), inertia_values, marker='o')\n",
        "plt.xlabel(\"Количество кластеров\")\n",
        "plt.ylabel(\"Сумма квадратов расстояний\")\n",
        "plt.title(\"Метод локтя\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "PVulj8K8qQ2w",
        "outputId": "073aaed9-4818-417f-c591-458b35bf888b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Сумма квадратов расстояний перестает существенно меняться при числе кластеров больше 4. 4 кластера можно считать достаточным.*"
      ],
      "metadata": {
        "id": "qUq4qq3kqhm6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans( n_clusters = 4 )\n",
        "# Производим кластеризацию данных\n",
        "kmeans.fit( data )\n",
        "# метки кластеров для каждого объекта\n",
        "labels = kmeans.labels_\n",
        "# координаты центроидов\n",
        "centroids = kmeans.cluster_centers_\n",
        "print(\"Метки кластеров:\")\n",
        "print(labels)\n",
        "print(\"Координаты центроидов кластеров:\")\n",
        "print(centroids)\n",
        "labelNum = np.unique(labels)\n",
        "for v in labelNum:\n",
        " print(f\"В кластере с меткой {v} содержится {np.sum(labels == v)} точек\" )\n",
        "plt.scatter(data[:,0], data[:,1], c = labels )\n",
        "plt.scatter(centroids[:,0], centroids[:,1], c = \"r\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "id": "7VQdcQcSq4cY",
        "outputId": "1ec4ded7-e4d0-4340-b7f9-ed04db723004"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Метки кластеров:\n",
            "[1 1 2 3 2 0 0 1 2 2 0 2 2 0 2 3 2 3 0 0 0 0 1 0 3 1 1 3 3 3 0 2 1 2 3 3 2\n",
            " 2 3 3 1 2 1 3 1 0 2 0 0 1 3 1 1 3 0 1]\n",
            "Координаты центроидов кластеров:\n",
            "[[12.76559166 10.28887404]\n",
            " [ 1.72269151  5.87727223]\n",
            " [19.72560963 17.25660853]\n",
            " [ 7.60447995  1.00686426]]\n",
            "В кластере с меткой 0 содержится 14 точек\n",
            "В кластере с меткой 1 содержится 14 точек\n",
            "В кластере с меткой 2 содержится 14 точек\n",
            "В кластере с меткой 3 содержится 14 точек\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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7Ki0m0fVaHtv8iZ7bsaTqzzU9AnwsR0YTU4Y0ZlnHIXwAABAA7cJjNcHlD/GjHSrJ1393fl6v11iSLux8ijpF+Td5NVAIHwAA1ENBeYkOleQrLjRS8Se4r0pWUbY+yvhGB0vy1C48VpNShyopIr5BfS3K+k5OHfM7pIrAYSSFWh5d3HWUbuo9sUHXOxGEDwAA/JBeeFhPbp2v+RnrVG4cWZJGte+tX/Uar/7xnSRJewsP6cP0NTpYkq8OEXE6J/UnNW4wVzk34/kdS2VJsi1LjjF6fPMnuqL76bqh19n1Xvgrt6xIto8Fzypd1nWMesUla3SHvkHblI7wAQBAHXYXHNTVKx5T/lHrZRhJKw5s0aqDW/X3YVdq8b6NemXXctmWJVsVYeLJLZ/qirTTdGPvCdXCxLzti6vmZhhVTPr88dwiRYeEa3r30+tVY2pU2zqDhyXp8rQx6hAR3F3jWWQMAIA6PLTx3WrBo5IjI68xunPNf/XKkT1cHGNUbhw5MjIyenbHYj2/Y2nVa4rKS+tcVXTe9kUq9vre1fZYpyf2V0xIRK3Pt3gsW6Pa9w568JAIHwAA+JRVlK3lBzbXOqpgZFTgLfHZx7zti1RyJEx8eWibirylPtsXlJfo62OWQ69LuCdUvx1wgYx03CO2tmUpwhOqW/qeU68+GwvhAwAAH3YVHvTjgVXf8suLtebwTkkVq476o8DPdkebkDJED590hXoc9ZivJWlku156euQN6haEx39rwpwPAAB8iA4JD0g/RUfCRLfoDn617xbjX7tjjUnsq9Ed+mhXwQHllBUpJbJNk7jVcjTCBwAAPvSJS1VSRLyyinNOqJ/KUYc+canqHZuirXmZxy3JLlXsB9MnLlW9YlMafC3LstS1geHFDdx2AQDAB49l61c9x9d63pIUHxolu5apnrYsDW7TtWrFU8uydM+gaQr3hB63I67HshXhCdPdg6YGrP6miPABAEAdzu80TP/TZ7I8li1LlkIsT1XYmNL5FP395CtrDRNRIeH6/cALqx3vE5eqZ0bdoNMT+1X1Y8vSmUkDNG/UDSc06uHL/uJcrTywRWsP71SZU94o1/CHZYw50Xk0AZWbm6v4+Hjl5OQoLq5p3aMCALRuh0vz9XH6N8ooylabsGhNSBmsjlEJkqSd+fv0z60LtTDrW3mNoxDLowkpg3Rtz3E+ly/PLy9WTmmh4sOiFBMS0Sh17yvO0d82vKsl+zZW3eqJD43S9O6n6/JuY+q9oFlN6vP5TfgAACCAispLlVtWESYiPGHBLkeHSvI1fflcHSjJq/Fx4Z93GxOQR3Dr8/nNbRcAAAIoMiRMSZFtmkTwkKTndyzR/uLcWtcpeWHn5/ohf7+rNRE+AABooRzj6O09X9b4VE0lj2Xr3b2rXayK8AEAQItV7C1Tfh2LlRljlFmc7U5BRxA+AABoocI9oQqzfS/pZVmW2oZFu1RRhXqHjyVLluj8889XamqqLMvSW2+9Ve38lVdeKcuyqn1NmjQpUPUCAAA/eSxbk1KGHPcI8NG8xtHk1J+4WFUDwkdBQYGGDBmiuXPn1tpm0qRJysjIqPp68cUXT6hIAADQMNO7n6FwO7TGRdBsWTojsb/6x3dytaZ6L68+efJkTZ482Web8PBwJScnN7goAAAQGJ2j2+mpEdfp7m9e1s6C/bIkGVUEj/M6nqQ7+v/U9ZoaZW+XRYsWKTExUW3bttVZZ52lBx54QO3a1bzASklJiUpKfpwMk5ub2xglAQDQavWOS9XLY27RN9k/aGtepsLsEJ3avrfaB2nDuYCHj0mTJmnq1KlKS0vTtm3b9Lvf/U6TJ0/W8uXL5fF4jms/Z84czZ49O9BlAACAo1iWpaFtu2lo227BLuXEVji1LEtvvvmmpkyZUmub7du3q0ePHvr00081bty4487XNPLRuXNnVjgFAKAZaVIrnHbv3l3t27fX1q1bazwfHh6uuLi4al8AAKDlavTwsWfPHh08eFApKY2zQx8AAGhe6j3nIz8/v9ooxo4dO7R27VolJCQoISFBs2fP1rRp05ScnKxt27bpzjvvVM+ePTVx4sSAFg4AAJqneoePr776SmeeeWbVn2fOnClJmj59uh5//HGtW7dOzz77rLKzs5WamqoJEybo/vvvV3h4eOCqBgAAzdYJTThtDPWZsAIAAJqGJjXhFAAA4GiEDwAA4CrCBwAAcBXhAwAAuIrwAQAAXEX4AAAAriJ8AAAAVxE+AACAqwgfAADAVYQPAADgKsIHAABwFeEDAAC4ivABAABcRfgAAACuInwAAABXET4AAICrCB8AAMBVhA8AAOAqwgcAAHAV4QMAALiK8AEAAFxF+AAAAK4ifAAAAFcRPgC0OiVFJcrYnqXs/TnBLgVolUKCXQAAuCV7f46eu+9VfTLvM5UUlUqSBo7pq1/O+plOGjcoyNUBrQcjHwBahez9OfrNqN/r/afmVwUPSdqwbJN+O+F+LXr5iyBWB7QuhA8ALdrerRl69MZ/6rJOv1bG9iw5XqfaeccxMjJ66NrHVZRfFKQqgdaF8AGgxfp+1RZd/5M79P4/P1V5mbf2hkYqLizRZy8tc684oBUjfABokbxer2Zf9JBKi8uOG+2oSUiIR3s27XWhMgBMOAXQIq36YI0O7Dnod3vHMYqMjWzEigBUYuQDQIu0ZfV2eUI8frd3vI5Ou2hkI1YEoBIjHwBapJCwEBlj/Gpre2yN+unJ6tq/cyNXBUBi5ANAC3XKOT/xa66HJI06/2T99vnf1NnOW+7Vsre/1H8feF2vPvSu9mzJONEygVaJkQ8ALVLPoWkaeuZArVuyodYQMuLck3TNnMuVNrBLnf2tW7JBf7zsER3KOCxPiEfGcfTUHc9p7LSRuuOZGxUZw3wRwF+MfABose55ZaZ6Du0mqeLWiiR5Qip+veTOC3T/O7/1K3jsWP+DfjvpAWVnZUuqGAFxnIpbOl+8tUp/uPhhv2/xAGDkA0ALFtcuVo+u+JO+/HCtFr38hfIOF6hjz2Sdc914dRvg//yOF+a8US1wHM3xOvrq47XauHKL+o/sHcjygRaL8AGgRfN4PBp53jCNPG9Yg15fXlaupa+tkFNe+/wRT4hHn734OeED8BO3XQDAh5LCEnl9BA9JMsYoP7vApYqA5o/wAQA+RMZGKqZNdJ3tUrsnu1AN0DIQPgDAB9u2de6vxldNWK2JcYwmXnWGazUBzR3hAwDqcMn/TlFK9yTZITX/lTn9D5cosUsHl6sCmi/CBwDUIbZtjP7+xQM6+5enKyTsx3n6yWmJuu3fN+ry308LYnVA82OZJvZwem5uruLj45WTk6O4uLhglwMA1eRnFyh9W6bCI8PUuW9H2Tb/hgOk+n1+86gtANRDTJto9R7WI9hlAM0akR0AALiK8AEAAFxF+AAAAK4ifAAAAFcRPgAAgKt42gUAauEt92r1/HU6sOeg2iTF6+SJQxUWHhrssoBmj/ABADVY/Opy/ePmfyt7X07Vsdi20br2L7/UOdeOC2JlQPNH+ACAY3z+5ko9cMnDxx3PO1yg//erJ2RZ0uRrCCBAQzHnAwCO4jiOnrjtWcmqvc0///c/Ki0pc68ooIUhfADAUb5fuUVZO/dLPjaeyDuUr9WffONeUUALQ/gAgKNk78v1s11O3Y0A1IjwAQBHad8pwc927Rq5EqDlYsIpgGanpKhEK95drYPph9U2uY1Gnj9MkdERPl9TXFiiRS99oe9XbZUnxNbwST/R8MlD5fF4qrXrdVJ3denXUbs3pcs4Nd97SUhpo5PGDQrY9wO0NoQPAM3Kh/9eoCdue1aFuUWybUuOYxQZE6Gr//RzTblpco2vWbNwvWZf9DcVZBfKE1oRNt557GN16p2iP33we6V0T6pqW5RfrOS0RO3auPf4jo5MQr3p0WvkCfEcfx6AX7jtAqDZmP/cYj183RMqzC2SJDlHRiaK8os19zdP690nPjnuNbs37dXvz5tT9RpvmVfeMq8kKWN7lu4YN1slRSWSpNLiUv3vhD/oq49rnkwaHhmuWa/frrHTRgb8ewNaE8IHgGbBW+7Vv+76r882z9z94nGPwL7xyPtyyr013kLxljvK+mG/Fr28TFJFuPl+1VY5XqfG/ksKSxQaxgqnwIkifABoFr79/Hsdyjjss03eoXyt+XRdtWOLX10mb3nNYUKSLNvS0jdWSpLe/+ensnws8GF7bH34rwX1qBpATQgfAJqFnAP+PQKbcyCv6veZO/cp/3Chz/bGMSrOL5Yk7fthv4ypfYEPx+soY3uWX3UAqB3hA0CzkNQt0c92HSRJBbmFuu2MWT7DhCR5QmylDeoiSYprH+ezrW1bapsU71cdAGpH+ADQLPQe1l1d+3eSZdd8W8SyLCV166BBY/tJkj6Zt0j7dx+ss19vuaPzfn22JGnilWfW2r9UMcH17CvOqH/xAKohfABoFizL0s1zr5Vt28cFBMu2ZNmW/ufxX8m2K/5aW/DCUplj1ki3jdFgs09nml0abPbJNkZX3n+puvbvLEk691fjldi5vTwhx//VaHts9RjSVWMv4kkX4EQRPgA0G0NOH6C/LZylXid1r3a8+6Au+vPHd2v4xKFVx/IO5lfbn2WM2av/6AM9pCX6nVbpIS3Rf+0PdXm/H4NMTJtoPbx4tvqe0qviwFEZ5+SJQ/Tgp7MUFs7TLsCJskxdN0Rdlpubq/j4eOXk5Cguzvf9VwCt1+5Ne3Vg7yElJLepGrk42u/P+5O++vgbOV5HY8xe3avlkqpvVmtUMaKi116Tpk6t9vpt3+zUhmWbZNm2hp45QJ16pzbidwM0f/X5/CZ8AGiRlr3zpWZNeVC2MfqPPlB7FdX8EK1lSZ06STt2SB5WLQUaqj6f39x2AdAijTxvmMZMHaFB1gF1qC14SJIx0u7d0tKlbpYHtGqEDwAtkm3buvulW3XBtCH+vSAjo3ELAlCl3uFjyZIlOv/885WamirLsvTWW29VO2+M0b333quUlBRFRkZq/Pjx2rJlS6DqBQC/eUI8GjvjQv8ap6Q0bjEAqtQ7fBQUFGjIkCGaO3dujecffPBBPfroo3riiSe0cuVKRUdHa+LEiSouLj7hYgGg3saOrZjTYdVy48WypM6dK9oBcEVIfV8wefJkTZ5c87bVxhg98sgjuvvuu3XBBRdIkp577jklJSXprbfe0qWXXnpi1QJAfXk80t//Ll10UUXQOHqOfWUgeeQRJpsCLgronI8dO3YoMzNT48ePrzoWHx+vESNGaPny5TW+pqSkRLm5udW+ACCgpk6teJy2Y8fqxzt1qvExWwCNq94jH75kZmZKkpKSkqodT0pKqjp3rDlz5mj27NmBLAMAjjd1qnTBBRVPtWRkVMzxGDuWEQ8gCAIaPhrirrvu0syZM6v+nJubq86dj18wCABOmMcjnXFGsKsAWr2Aho/k5GRJUlZWllKOmjmelZWloUOH1via8PBwhYeHB7IMAK3c1rU7NP/ZxTqUeVgJyW119vTT1XNoWrDLAnBEQMNHWlqakpOTtWDBgqqwkZubq5UrV+qGG24I5KUA4Djecq8euvZxzX9usTwhthzHyLYtvfH393X2Fafrtn/dIE8It1mAYKt3+MjPz9fWrVur/rxjxw6tXbtWCQkJ6tKli2655RY98MAD6tWrl9LS0nTPPfcoNTVVU6ZMCWTdAHCcp3//ouY/v1iS5C13Kn51Kp5umf/8YrVNaqPr/vKLoNUHoEK993ZZtGiRzjzzzOOOT58+XfPmzZMxRrNmzdJTTz2l7OxsjRkzRo899ph69+7tV//s7QKgIQpyCvSzlOtUWlxWa5uwiFC9kvkvRcdFVTuecyBX65dulON11Gd4TyV17dDY5QItDhvLAWh1lr39pWZd+GCd7Wa/eadOvWC4JKmkqESP3fKMPpm3SOVl3ooGljTqvJN16z+vV9vE+MYsGWhR2FgOQKtTWlzqV7t3Hv9YW9fskNfr1T0//Ys++vfCH4OHJBlp1Ydfa+Zp96ggt7CRqgVaN8IHgBYhbXBXv9qtWbBeNwy7U/f+9C9as2C9HOf4wV9vuaO9WzP1wVOfBrpMACJ8AGghuvbrpEFj+8n2+P5rzfFWTERd9eEaWbXt9yLJOEYf/ntBQGsEUIHwAaDJKi0p07efb9TXC9br8L6cOtvf/vSNik2IkR3i319tdU15O5hx2K9+ANRP0Fc4BYBjOY6jl/78ll596B3lHy6QJNkeW6ddPFIz/n612nSoeSJoao9kPfH1g3r5wbf1/pPzVVZafkJ1JCS3PaHXA6gZIx8Ampz/m/EvPXP3i1XBQ6q4XbLk1RX6n9F3K+9wfq2vbd+xnWb8/WpdPefnsuzab6vUxbItTb7mrAa/HkDtCB8AmpSta3bovSfn13jO8TrK3LFPbz36YZ399B7WQ6aGyaRHsyzVOO/DDrGV0j1J5/5qfA2vAnCiCB8AmpSPnl4oj485G47X0XtPfVJnP4PG9lPnvh1rnYBqe2yddflpmnjVGdWXXLekkycM1SNL71d0fHR9ywfgB+Z8AGhS9u0+ULU0em0OZWTLcRzZdu0hxbIs3fPyrZp5xiwV5hZVPeUiVdxS6dwnVTc+cqXiEmJ19Z8u1/olG+Qtd9R3RE+lpCUF7PsBcDzCB4AmJb5drDwhts8AEh0f5TN4VEob1FVPrvmrXnv4Pc1/brEKcgrUvmM7nfvrszXl5slVy6y3TYzXaReNCtj3AMA3wgeAJuWsy8fqo2c+q/W8HWJrwvQz/O4vsUsH3fjIVbrxkatkjPG5tgcAdzDnA0CTMvTMgRp29uAaRzZsj63o2EhdNPO8BvVN8ACaBsIHgCbFsizNeuMOnXHpqVVhofLXLv066uEl9yuxC7vOAs0Zu9oCaLL27T6grz5aq7LScvU6KU39RvZm9AJoourz+c2cDwBNVmLn9jrnusCstbHtm51674lPtOXr7QqPCtfYqSN19hWn8TgtEASMfABo8f77wOuad+9LPz5FY0mWLMV3iNNfF8xStwGdg10i0OzV5/ObOR8AWrTP31ypefe+JEk/Pr5rKjaVyz2Yp7smPaCy0rIgVgi0PoQPAC3aK397p9Y9XhyvowN7D2np6ytdrgpo3QgfAFqskqISbVy+2eceL54QW2s+XediVQAIHwBaLKeOjeUkyRjJ6/W9nDuAwCJ8AGixIqMj1KVfR/l6OtdxHPUf1ce9ogAQPgC0bNNuOU+1PdNn2ZaiYiM17vIx7hYFtHKEDwAt2qRrztLZV5wuqWJ59kqeEFuhYSG67407FBkTGazygFaJRcYAtGi2beuOZ2Zo1Pkn6+25H2n7NzsVFhmmsdNGasrNk9WxZ0qwSwRaHRYZAwAAJ4xFxgAAQJNF+AAAAK4ifAAAAFcRPgAAgKsIHwAAwFWEDwAA4CrCBwAAcBXhAwAAuIrwAQAAXEX4AAAAriJ8AAAAVxE+AACAqwgfAADAVYQPAADgqpBgFwBpf3a+NvyQJdu2NLRHqmKjIoJdEtAiFeYV6eNnPtNHTy/UocxsdeiUoMnXjteE6acrPDI82OUBrYZljDHBLuJoubm5io+PV05OjuLi4oJdTqM6nF+kOS8u0MI1W+Uc+TGEhng0dfRA3TLtNIWHkg2BQDm8L0e3nX6v9mzOkCQZY2RZkpHUY3BX/e2z2YppEx3cIoFmrD6f39x2CZKC4lJd+9Ar+mztj8FDksrKvXp1yTrd/uS7cpwmlQuBZu1vV81V+rZMGWNU+W8uYyQZace3u/XojH8Ft0CgFSF8BMmbn6/XzqxD8tYQMBxj9MV3O7V84w9BqAxoedK3ZWrVh2vkLXdqPO94HS1+ZZkOZR52uTKgdSJ8BMkbn6+XrxteHtvS28u+da8goAXbsHxznW0cr6PvV211oRoAhI8g2Z9T4PO81zHKPJTnUjVAy2bblp/t+CsRcAPvtCBJiI30ed62LXWIZ/IbEAgDx/aTZfkOICFhIeo/qrdLFQGtG+HDRZv37NfHX23S0vXbde6IfrJ9/GXoOEY/HTXAxeqAliuxc3uddvFI2Z6a/8qzbUsTrzxDce1iXa4MaJ14lrMWxhit+n6X3lm+Qfuy89WhTbTOHzlAI/p28XsIt9LmPfs1+/lPtHHXvqpjUeGhio4MU0FRiY6dc2pblob2TNWYQWmB+FYASLr1yV8rc+d+bVq1VbZtyXGMbI8tx+to0Gn9df3DVwa7RKDVYJ2PGuzel617n/1Y32xPl8e25HVM1a+n9u+qv/36p4oI8y+37cg8pF/8+QWVlJZXe6S2UkpCrDKOmtthW5YmDu+j3182TlERYQH7ngBIZaVl+vyNVfpk3mc6kH5IiV3aa9LV43TqT0+WJ8QT7PKAZq0+n9+Ej6Ns3JWlR95Yqi837a61jW1ZmjJ6oO6+fHyd/ZV5vbrsj//R9oxDPvt74pZpyjyUJ49t6+TendShTUyD6gcAIFjq8/ndKm67fLlpt15atEbfbMtQiMfWaYO669Izh6p7SruqNt/uzNS1D72icm/N6wBUcozR28u+04wLRqttTO2TRr2Oo5mPv+MzeFRde0emrpw43P9vCACAZqzFTzh9/N1l+vUjr2nJuu06lFeofdn5evOL9brkgf9o4dofn+n/0wsLVO51arw1ciyv4+jrzXt8tlmwZou++G5nnX3ZtqX9Ofl1tgMAoKVo0eHji+926p8frJSkaiuJeh0jx3F017/e1/7sfG3es1/f797nV/CoVNcIyauL18mfaamOY9SeR2oBAK1Iiw4fLyz8Wp5ankwxqgghb37xrfYcyKl33/26Jvk8/0PWYfkbZc45pV+9rw8AQHPVosPH2q17a9w7pZJjjL7eskdxUf5vpe2xLY3q11VdEtv4bBdzpE/bcTRs71ZN3PK1hu3dKtupPmLyy7OHKaktawsAAFqPFj3htK4VDaWKORdDe3RUQmykDuUV+e5PUmKbGM26YkKd/Z4zvK82Pfqkbvv8DSUX/Diykhkdr7+NvlALuw/WaYPS9JspY6q9rqikTB+s2qgFa7aosKRMvTt20LSxg9Snc2Kd1wQAoDlo0eFjRL8uWrJue62jH5YljejbVSEeWzN+Olr3//fTWvuKjQzXL8cP08WnD1F8dESd17708HZFf/zMcbdeEgty9OAn8/THqddr5tUzZFmWDucV6nB+kQpLynTHU+8q63C+LKtiu+/vdmbqtaXr9KtzR+r680bV59sHAKBJatHrfKzZulfXPPSKzzbREaEaO7C7Lh93ktZtz9Cjby1VSZlXHtuW4zgKCfHoV+eM1NWThvs1kiJJ8nqlbt1k9uypcdKpI8np2FFbP/9Sc99boWXf7fRrfsicq8/RxOF9/KsBAAAXscjYUV5Z/I3+8tJC2UdWKK2Jx7ZkjDR7+kSdPri7Pl2zRfuz89UuLlrjf9JLcX6MdEgVS7J/vWWvvv7Xf3TdX++qs/2NU27Sl6k9fM5LOVp0RJg+mnOdol1a+fRwfpHe/uJbzf96c9UtoItPH6xhvTr5H8QAAK0C4eMY2zMO6tUl67R0/XalH8yttZ3HtvTO/VcrJaH+13Ucowf++6neWvatJm9boz/Of77O1/x+/C/1Yc+f1Os6Q3uk6t+3/azRP/w379mv6x95TbmFJVWPIFcuMX/pGUN1x8/OIIAAAKrU5/O7RT/tUql7Sjv97yVnKrVdvM9N4YykN5aub9A1Xlj4td5a9q0kaV+Ef0+v7Ius/1Mua7el+1z+PRDKyr26+R9vKq+opNraJ5UjNC8tWqt3lm9o1BoAAC1Xqwgflb7dmSHH16O3jtG6HRn17tfrOHru09VVf16T0l2Z0fGqbRkyI0uZ0W20JqV7va/lsS199OWmer+uPj77Zqv25xT4nKj73Pyv1MQGzQAAzUSrCh8eu+5vN8RT//8ku/Zl60BOQdWfHdvW30ZfWPH7YxtblmRJfxs9RY4f9RzLcYxyCovr/br6WL15j0J81GZMxW69uYUljVoHAKBlalXhY8zAbrWueCpVrOMxZkBavft1nOPHOBZ2H6w7J1ypfdHx1U906qTSF1/S8n7D6n0dqWJdkk7t4+tueAL8HdBg5AMA0BCtKnxcPm5Yrfu32JalmKhwnTey/kudd+7QRrE1rJK6sPtgnXf5Pbru/Bt117hf6NtnXpR27FD4JT/TNZNOqfd1pIp5FxecOqBBr/XXT3p2VHkNgaqSJalLYhu/1jsBAOBYrSp8DOyWrNnTJ8q2rWoTTy1J0ZFhmnvzVMVG1f8DNSw0RJecPlR2DU9/OLattZ176btTz1T/Ky6RPB5J0lUTh+vaySMqarEshXhsWZbksSy1jYmsdYTmirOHqXtKu3rXWB/jftJT7eKiavx+pIqJub8cP4ynXQAADdIqHrU9VvrBXL3x+Xqt356hkBBbowd00/kj+zcoeFQqLSvXLY+/rRUbd8m2rKoRFtuyFBsVrn/NvFg9Utsf97oDOQX6+KtNOphboMQ2MZp4ch/Ztq1H3liiD1ZuVNmR3XM7xEfrqkmn6JLTh7jyob/hhyxd//fXVVhcetyjtheOHqi7Lx9P+AAAVGGdjyAp9zr6cNVGvbZknXbtz1ZMRJjOGdFPF502WB3iY+rdX15hsXZmHVZYiEc9O7b3a8JsIO3PydfrS9dr/urNKiopU6+O7fWz04fo1AHdCB4AgGoIH82Y4xgt27BTb3y+XnsP5KhtbJTOG9FPZw/rrfDQFr0VDwCgGQvqImP33XefLMuq9tW3b99AX6ZFKiv36van3tVv5r6lpeu3a8veA/pq027d++zH+uWfX9DhvMJglwgAwAlrlHH8AQMGKCMjo+rr888/b4zLNIrMQ3n6avNufb97n88FyRrDE+8t1+J12yT9uJpo5XyLHZmH9PtnPnS1HgAAGkOjjOOHhIQoOTm5MbpuNLv2HdZfX1lUbYfZ1HZxuumC0Zo0vPFHbopKy/TyorW1rrHhdYxWbNyl7RkHG/1pFwAAGlOjjHxs2bJFqamp6t69uy6//HLt2rWrMS4TMLv3Z+uKv7yoFRt/qLa1ffrBXP3u6Q/16uJvGr2GTbv3qbCkzGcbS2r0fV0AAGhsAQ8fI0aM0Lx58/TRRx/p8ccf144dOzR27Fjl5eXV2L6kpES5ubnVvtz2j7e/UEFxaa17mTz02mLlFTXuUuJ+3eKxWFUUAND8BTx8TJ48WRdffLEGDx6siRMn6oMPPlB2drZeeeWVGtvPmTNH8fHxVV+dO3cOdEk+5RYUa8GaLbUGD6liIujHXzXuZm69OnVQWIjHZxtjpCE9Uhu1DgAAGlujLxzRpk0b9e7dW1u3bq3x/F133aWcnJyqr9273b2tsD8nv85RB4/HVsbBxh2RiY0M1wWnDqx1VVGPbWlgt2T165LUqHUAANDYGj185Ofna9u2bUpJSanxfHh4uOLi4qp9uSnOj/1JHMeoTUxko9dyy9SxGpRWMVH36AxiWZbax8foz9ee2+g1AADQ2AL+tMvtt9+u888/X127dlV6erpmzZolj8ejyy67LNCXCogO8TEa1quT1mzdW+umc0ZGZ5/Uu9FriQwP1ZO3XKQPVn2v15eu096DuWobE6nzR/bX1DGD/ApKAAA0dQEPH3v27NFll12mgwcPqkOHDhozZoxWrFihDh06BPpSAXPjT0/Vr/7fq7J0/HbylqRLTh+q5IRYV2oJCw3RlNEDNWX0QFeuBwCA21he/YhlG3Zq1rMf62BuYdXGcB7b1s/P+ol+c+EY1/dVAQCgOWFvlwYq9zpatmGndu2r2BTu9MHd1TY2ytUaAABojurz+c1OZUcJ8dg6bVD3YJcBAECLxr0EAADgKsIHAABwFeEDAAC4ivABAABcRfgAAACuInwAAABXET4AAICrCB8AAMBVhA8AAOAqwgcAAHAV4QMAALiK8AEAAFxF+AAAAK4ifAAAAFcRPgAAgKsIHwAAwFWEDwAA4CrCBwAAcBXhAwAAuIrwAQAAXEX4AAAAriJ8AAAAVxE+AACAqwgfAADAVYQPAADgKsIHAABwFeEDAAC4ivABAABcRfgAAACuInwAAABXET4AAICrCB8AAMBVhA8AAOAqwgcAAHAV4QMAALiK8AEAAFwVEuwCgNbKOIVS+QZJRgrpJ8uOCXZJAOAKwgfgMmNKZfL/LhX+VzKFR46Gy0RdJCvmDll2VFDrA4DGRvgAXGSMI5N9s1SySJI56kyJVPiiTNn3UsKzsqywIFUIAI2POR+Am0oWSSWfqXrwqORIZaulonddLgoA3EX4AFxkil6R5PHRwpIpetmtcgAgKAgfQIAYUy7jPSjjFNTeyLtbktdXL5J3T6BLA4AmhTkfwAkyTp5MwZNS4UuSyZVkyYSNlhVzo6ywk2WMkcrWSN50yfga9TjCbtvoNQNAMBE+gBNgnDyZQ5dK5dskOZVHpdLlMoeWyURfLxW/J3l3+dmjJStyaiNVCwBNA+EDOAEm/x9S+Xb9GDwqHbm1UvCYJMvP3jySnSRFXhy4AgGgCWLOB9BAxpRKRa/I9xwOqeYnW452JJyEDpbV7gVZdlwAqgOApouRD6CBTOkqyfiYXOqPyEtkhfSSwk6WFdo/MIUBQBNH+ADqyZRtksm9Rypbe8J9WaH9ZUVdduJFAUAzQvgA/GTK98gUPCUVvaq6b7X4ye4QmH4AoBkhfAB+MAX/lsl7UHXP36gHK14KPy1w/QFAM8GEU6AOpvgjmby/yP/gceRt5elz5Pc1P+1ixf4ve7gAaJUY+QB8MMbI5M9VRYCoJXx4jbSySMrySkkeafRwWW3vlUKHSqWLZXL/UH3VUrudrNg7ZUVeWL9aynfLFD4vFX8smWIptK+sqF9I4eNlWf4+zgsAwUf4AHxx9kvlm2o//36+rHv2y8oorzpkUg/JzCmSfjpAVvg4qd0HssrXS94MyW4nhY2QZdXvrWdKv5Q5dI2kMlXNNyldJVO6XIq4SIp/QJbFQCaA5oHwAfhiSms/936+rOsyjh8QySiSdeXHMv9cL3Puu5Knm5QwT1bY8IaV4BTKHL5eUqmqL2Z2JIQUvyaFDZWiftag/gHAbfxTCfDBFL1d8wmvkXXPfskcP6PDOhJGrHv3VdyS8e6WOXS1jGngEzLF70omT8evolp1RZmCeQ3rGwCCgPAB1MKUrJQK/l7zyZVFsjLKa1043TKSlV5eMRdEXsm7XSpZ0rA6ytZI8rUhnZG8W2Wc/Ab1DwBuI3yg1THGyJStkyn+TKZsY8WuszW1K3xWtX7oZ/k5ilHVziNT8lm9a63g59vU8mPHXABoApjzgVbFFC+QyZtTfZfZkN5S3L2ywk6p3rj0K9W6mFiSnx/01dr5mD/igxV2qkzRaz5a2FLIQFlWZIP6BwC3MfKBVsMUfyyTfaPk3V39RPlWmUPTK26zVOPj8dURkTIpITK1NDGWZFJDpBGVgcCRFdLAvVsiJkh2omq/9eLIirm2YX0DQBAQPtAqGFMukztbFY+mHHubxZFkZPLur34LJuzU2jv0WDL3VyyNfmwAqfyz+UMHyVN5Mlyq57oelSwrTFbbf0tWnKoHoiNhJPomWRGTGtQ3AAQD4QOtQ+kyyTngo4EjlW+Wyjf+eChisu8+z42R+WeKlHzM3cuUkIrj58ZUHbLaPCzLjq1/3ZWvD+0jq8MnsmJ/K4UOk0L6SxEXymr3uuzY3zS4XwAIBuZ8oHXwZvjZLlM6srW9FXZS3QuqnxsjMyla5ugVTkdEHjXiYUlhp8qKGN/QyqtYdrwUfZWs6KtOuC8ACCbCB1oHu12921me9jKhJ0lla+RzXxePJZ0aVctJIyv6er/LBIDWgNsuaB3Cxx6ZM1EbS/J0lkIHVT8ac/OP5+vlSPvIy6Rjn6IBgFaO8IFWwbLCZcXe7qOFObLLbPW3hBU+Wlb8w5Lqufusp6OsuPsqvtj0DQCq4bYLWg0r6lJJksn765Hlyo+w28mKvUdWxITjXmOcXJniT1SvNTo8PWS1f5+N3gCgFoQPtCpW1KUVj7yWLJacQ5KdJIWPkWWFHtfWmFKZQ1dJ5Rvkc85HNR4p/AyCBwD4QPhAq2NZ4RULd9Wl+EOpfH39+4+6rAFVAUDr0Wj/PJs7d666deumiIgIjRgxQqtWrWqsSwGNwhS9Lv/fIh5Jnor1PEK6NGJVAND8NUr4ePnllzVz5kzNmjVLX3/9tYYMGaKJEydq3759jXE5oHF496n2bewreSRPmhR1uaz2H8iqa2EyAEDjhI+HH35Y1113na666ir1799fTzzxhKKiovT00083xuWAxuFJlu+3iCWFDpDd4WPZcXfLCklzqzIAaNYCHj5KS0u1evVqjR//44qOtm1r/PjxWr58+XHtS0pKlJubW+0LaAqsyIvle+TDHGkDAKiPgIePAwcOyOv1KikpqdrxpKQkZWZmHtd+zpw5io+Pr/rq3LlzoEsCGiZiQsU+KjW+TTwV+6tEXuB2VQDQ7AX9ecC77rpLOTk5VV+7d++u+0WACywrtGI32Yipqv5gmC1FnCMr4XlZVkSwygOAZivgj9q2b99eHo9HWVlZ1Y5nZWUpOTn5uPbh4eEKDw8PdBlAQFh2lKw2f5JxbpdKj+zxEjpElqdDsEsDgGYr4CMfYWFhGjZsmBYsWFB1zHEcLViwQKNGjQr05QBXWHaCrIhxsiLGEzwA4AQ1yiJjM2fO1PTp03XyySfrlFNO0SOPPKKCggJddRVbgQMA0No1Svi45JJLtH//ft17773KzMzU0KFD9dFHHx03CRUAALQ+ljHG300rXJGbm6v4+Hjl5OQoLs7XFugAAKCpqM/nd9CfdgEAAK0L4QMAALiK8AEAAFxF+AAAAK4ifAAAAFcRPgAAgKsaZZ2PE1H55C+72wIA0HxUfm77s4JHkwsfeXl5ksTutgAANEN5eXmKj4/32abJLTLmOI7S09MVGxsry7Ia1Edubq46d+6s3bt3s1BZE8TPp+niZ9O08fNpuvjZVIx45OXlKTU1Vbbte1ZHkxv5sG1bnTp1CkhfcXFxrfZ/guaAn0/Txc+maePn03S19p9NXSMelZhwCgAAXEX4AAAArmqR4SM8PFyzZs1SeHh4sEtBDfj5NF38bJo2fj5NFz+b+mlyE04BAEDL1iJHPgAAQNNF+AAAAK4ifAAAAFcRPgAAgKtaXPiYO3euunXrpoiICI0YMUKrVq0KdkmQdN9998myrGpfffv2DXZZrdaSJUt0/vnnKzU1VZZl6a233qp23hije++9VykpKYqMjNT48eO1ZcuW4BTbytT1s7nyyiuPey9NmjQpOMW2MnPmzNHw4cMVGxurxMRETZkyRZs2barWpri4WDNmzFC7du0UExOjadOmKSsrK0gVN10tKny8/PLLmjlzpmbNmqWvv/5aQ4YM0cSJE7Vv375glwZJAwYMUEZGRtXX559/HuySWq2CggINGTJEc+fOrfH8gw8+qEcffVRPPPGEVq5cqejoaE2cOFHFxcUuV9r61PWzkaRJkyZVey+9+OKLLlbYei1evFgzZszQihUrNH/+fJWVlWnChAkqKCioanPrrbfq3Xff1auvvqrFixcrPT1dU6dODWLVTZRpQU455RQzY8aMqj97vV6Tmppq5syZE8SqYIwxs2bNMkOGDAl2GaiBJPPmm29W/dlxHJOcnGz++te/Vh3Lzs424eHh5sUXXwxCha3XsT8bY4yZPn26ueCCC4JSD6rbt2+fkWQWL15sjKl4n4SGhppXX321qs3GjRuNJLN8+fJgldkktZiRj9LSUq1evVrjx4+vOmbbtsaPH6/ly5cHsTJU2rJli1JTU9W9e3ddfvnl2rVrV7BLQg127NihzMzMau+l+Ph4jRgxgvdSE7Fo0SIlJiaqT58+uuGGG3Tw4MFgl9Qq5eTkSJISEhIkSatXr1ZZWVm1907fvn3VpUsX3jvHaDHh48CBA/J6vUpKSqp2PCkpSZmZmUGqCpVGjBihefPm6aOPPtLjjz+uHTt2aOzYscrLywt2aThG5fuF91LTNGnSJD333HNasGCB/vKXv2jx4sWaPHmyvF5vsEtrVRzH0S233KLRo0dr4MCBkireO2FhYWrTpk21trx3jtfkdrVFyzR58uSq3w8ePFgjRoxQ165d9corr+iaa64JYmVA83LppZdW/X7QoEEaPHiwevTooUWLFmncuHFBrKx1mTFjhr799lvmrjVQixn5aN++vTwez3GzirOyspScnBykqlCbNm3aqHfv3tq6dWuwS8ExKt8vvJeah+7du6t9+/a8l1x000036b333tNnn32mTp06VR1PTk5WaWmpsrOzq7XnvXO8FhM+wsLCNGzYMC1YsKDqmOM4WrBggUaNGhXEylCT/Px8bdu2TSkpKcEuBcdIS0tTcnJytfdSbm6uVq5cyXupCdqzZ48OHjzIe8kFxhjddNNNevPNN7Vw4UKlpaVVOz9s2DCFhoZWe+9s2rRJu3bt4r1zjBZ122XmzJmaPn26Tj75ZJ1yyil65JFHVFBQoKuuuirYpbV6t99+u84//3x17dpV6enpmjVrljwejy677LJgl9Yq5efnV/uX8o4dO7R27VolJCSoS5cuuuWWW/TAAw+oV69eSktL0z333KPU1FRNmTIleEW3Er5+NgkJCZo9e7amTZum5ORkbdu2TXfeead69uypiRMnBrHq1mHGjBl64YUX9Pbbbys2NrZqHkd8fLwiIyMVHx+va665RjNnzlRCQoLi4uJ08803a9SoURo5cmSQq29igv24TaD93//9n+nSpYsJCwszp5xyilmxYkWwS4Ix5pJLLjEpKSkmLCzMdOzY0VxyySVm69atwS6r1frss8+MpOO+pk+fboypeNz2nnvuMUlJSSY8PNyMGzfObNq0KbhFtxK+fjaFhYVmwoQJpkOHDiY0NNR07drVXHfddSYzMzPYZbcKNf1cJJlnnnmmqk1RUZG58cYbTdu2bU1UVJS58MILTUZGRvCKbqIsY4xxP/IAAIDWqsXM+QAAAM0D4QMAALiK8AEAAFxF+AAAAK4ifAAAAFcRPgAAgKsIHwAAwFWEDwAA4CrCBwAAcBXhAwAAuIrwAQAAXEX4AAAArvr/vbbwZH5yJRUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Кластеризация с random_state"
      ],
      "metadata": {
        "id": "5WSLJbTMQl06"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1"
      ],
      "metadata": {
        "id": "23KOUfXvRB2p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans( n_clusters = 4, random_state=10 )\n",
        "# Производим кластеризацию данных\n",
        "kmeans.fit( data )\n",
        "# метки кластеров для каждого объекта\n",
        "labels = kmeans.labels_\n",
        "# координаты центроидов\n",
        "centroids = kmeans.cluster_centers_\n",
        "print(\"Метки кластеров:\")\n",
        "print(labels)\n",
        "print(\"Координаты центроидов кластеров:\")\n",
        "print(centroids)\n",
        "labelNum = np.unique(labels)\n",
        "for v in labelNum:\n",
        " print(f\"В кластере с меткой {v} содержится {np.sum(labels == v)} точек\" )\n",
        "plt.scatter(data[:,0], data[:,1], c = labels )\n",
        "plt.scatter(centroids[:,0], centroids[:,1], c = \"r\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "JjT9U1_dQlCf",
        "outputId": "45a8407f-8b24-4ddc-b5df-84e39aab6ea2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Метки кластеров:\n",
            "[3 3 1 0 1 2 2 3 1 1 2 1 1 2 1 0 1 0 2 2 2 2 3 2 0 3 3 0 0 0 2 1 3 1 0 0 1\n",
            " 1 0 0 3 1 3 0 3 2 1 2 2 3 0 3 3 0 2 3]\n",
            "Координаты центроидов кластеров:\n",
            "[[ 7.60447995  1.00686426]\n",
            " [19.72560963 17.25660853]\n",
            " [12.76559166 10.28887404]\n",
            " [ 1.72269151  5.87727223]]\n",
            "В кластере с меткой 0 содержится 14 точек\n",
            "В кластере с меткой 1 содержится 14 точек\n",
            "В кластере с меткой 2 содержится 14 точек\n",
            "В кластере с меткой 3 содержится 14 точек\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2"
      ],
      "metadata": {
        "id": "FvE0HXK6RGe3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans( n_clusters = 4, random_state=122435 )\n",
        "# Производим кластеризацию данных\n",
        "kmeans.fit( data )\n",
        "# метки кластеров для каждого объекта\n",
        "labels = kmeans.labels_\n",
        "# координаты центроидов\n",
        "centroids = kmeans.cluster_centers_\n",
        "print(\"Метки кластеров:\")\n",
        "print(labels)\n",
        "print(\"Координаты центроидов кластеров:\")\n",
        "print(centroids)\n",
        "labelNum = np.unique(labels)\n",
        "for v in labelNum:\n",
        " print(f\"В кластере с меткой {v} содержится {np.sum(labels == v)} точек\" )\n",
        "plt.scatter(data[:,0], data[:,1], c = labels )\n",
        "plt.scatter(centroids[:,0], centroids[:,1], c = \"r\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "1oUL6hLpRILC",
        "outputId": "e2cbd81f-6bb7-47d0-8453-e6e2bbdad78f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Метки кластеров:\n",
            "[2 2 1 0 1 3 3 2 1 1 3 1 1 3 1 0 1 0 3 3 3 3 2 3 0 2 2 0 0 0 3 1 2 1 0 0 1\n",
            " 1 0 0 2 1 2 0 2 3 1 3 3 2 0 2 2 0 3 2]\n",
            "Координаты центроидов кластеров:\n",
            "[[ 7.60447995  1.00686426]\n",
            " [19.72560963 17.25660853]\n",
            " [ 1.72269151  5.87727223]\n",
            " [12.76559166 10.28887404]]\n",
            "В кластере с меткой 0 содержится 14 точек\n",
            "В кластере с меткой 1 содержится 14 точек\n",
            "В кластере с меткой 2 содержится 14 точек\n",
            "В кластере с меткой 3 содержится 14 точек\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3"
      ],
      "metadata": {
        "id": "-KeZMF-RRoHS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy.random import RandomState\n",
        "kmeans = KMeans( n_clusters = 4, random_state=RandomState(131242) )\n",
        "# Производим кластеризацию данных\n",
        "kmeans.fit( data )\n",
        "# метки кластеров для каждого объекта\n",
        "labels = kmeans.labels_\n",
        "# координаты центроидов\n",
        "centroids = kmeans.cluster_centers_\n",
        "print(\"Метки кластеров:\")\n",
        "print(labels)\n",
        "print(\"Координаты центроидов кластеров:\")\n",
        "print(centroids)\n",
        "labelNum = np.unique(labels)\n",
        "for v in labelNum:\n",
        " print(f\"В кластере с меткой {v} содержится {np.sum(labels == v)} точек\" )\n",
        "plt.scatter(data[:,0], data[:,1], c = labels )\n",
        "plt.scatter(centroids[:,0], centroids[:,1], c = \"r\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "cKgPZwv1RbQO",
        "outputId": "f1634de8-7b96-4a3c-8657-88a636cdead2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Метки кластеров:\n",
            "[0 0 1 3 1 2 2 0 1 1 2 1 1 2 1 3 1 3 2 2 2 2 0 2 3 0 0 3 3 3 2 1 0 1 3 3 1\n",
            " 1 3 3 0 1 0 3 0 2 1 2 2 0 3 0 0 3 2 0]\n",
            "Координаты центроидов кластеров:\n",
            "[[ 1.72269151  5.87727223]\n",
            " [19.72560963 17.25660853]\n",
            " [12.76559166 10.28887404]\n",
            " [ 7.60447995  1.00686426]]\n",
            "В кластере с меткой 0 содержится 14 точек\n",
            "В кластере с меткой 1 содержится 14 точек\n",
            "В кластере с меткой 2 содержится 14 точек\n",
            "В кластере с меткой 3 содержится 14 точек\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}