{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5CBN02aGHa4"
      },
      "source": [
        "## **K-means i Voronojevi dijagrami**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "brzmXZu8FzTF"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from google.colab import files"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xk0ObpSKGVWU"
      },
      "source": [
        "### 1. Dat je k (broj klastera)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "NVOO9sJjGYCD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8205cbbd-517c-478d-ec45-b0618442458a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unesi k broj klastera (k>=1): 3\n"
          ]
        }
      ],
      "source": [
        "k = int(input(\"Unesi k broj klastera (k>=1): \"))\n",
        "if k < 1:\n",
        "    raise ValueError(\"Broj klastera mora biti veci ili jednak 1!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "In6bWJJrGdL4"
      },
      "source": [
        "### 2. Date su tačke u 2D ravni"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "jSFrovO3GeZd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69fd512e-e056-4748-89bf-ab021d6af190"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unesi koordinate n (n>3) tacaka koje zelis da klasterujes (u obliku: x1 y1, x2 y2, ...; dozvoljene vrednosti: -10 ≤ x, y ≤ 10): -6 -5, -7 -4.5, -5 -7, -8 -6, -4 -5, -9 -8, -6.5 -7, -5 -6, -7 -9, -8 -4, -3 4, -1 3, 0 5.5, -2 2, 1 4, -4 6, -1.5 3.5, 2 5, 0 2, -3 7, 8 -3, 6 -5, 7 -4.5, 9 -6, 5 -7, 4 -3, 7.5 -5, 6 -8, 8 -4, 5 -6, -2 -1, 1 0.5, 0 -2, -1 1, 3 -1, 2 0, -3 -2.5, 1 -3, 0 1, -4 0, 4 7, 3 8, 5 6.5, 2 9, 6 5, 7 4, 3.5 6, 4 8, 5 7, 2.5 4.5\n"
          ]
        }
      ],
      "source": [
        "tacke = []\n",
        "ulaz = input(f\"Unesi koordinate n (n>{k}) tacaka koje zelis da klasterujes (u obliku: x1 y1, x2 y2, ...; dozvoljene vrednosti: -10 ≤ x, y ≤ 10): \")\n",
        "\n",
        "parovi = ulaz.split(\",\")\n",
        "\n",
        "for par in parovi:\n",
        "    x, y = map(float, par.split())\n",
        "    tacke.append([x, y])\n",
        "\n",
        "tacke = np.array(tacke)\n",
        "n = len(tacke)\n",
        "if n <= k:\n",
        "    raise ValueError(\"Broj tacaka mora biti veci od broja klastera (n>k)!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m43R3M0LHA-A"
      },
      "source": [
        "Crtanje tačaka"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "wPDswsyEG7xm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "85afcc94-0174-44ae-bd82-c13f544b3cae"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_e4d1edbe-7c21-4a2a-a401-826021f213dc\", \"tacke.png\", 12281)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "width, height = 400, 400\n",
        "grid = np.ones((height, width, 3)) #3 za RGB\n",
        "\n",
        "colors = np.random.rand(k, 3)\n",
        "\n",
        "def normalizuj_tacku(tacka):\n",
        "  return np.array([(tacka[0] + 10) / 20, (tacka[1] + 10) / 20])\n",
        "\n",
        "normalizovane_tacke = np.array([normalizuj_tacku(t) for t in tacke])\n",
        "\n",
        "def nacrtaj_tacke(tacke, color, marker):\n",
        "  plt.scatter(tacke[:, 0] * width, tacke[:, 1] * height,\n",
        "              marker = marker, color=color, s=40)\n",
        "\n",
        "def nacrtaj_ose_i_grid():\n",
        "  #x i y osa\n",
        "  plt.axhline(height/2, color='black', linewidth=1)\n",
        "  plt.axvline(width/2, color='black', linewidth=1)\n",
        "  #oznake na osi normalizovane u [-10, 10]\n",
        "  plt.xticks([0, width*0.25, width*0.5, width*0.75, width], [-10, -5, 0, 5, 10])\n",
        "  plt.yticks([0, height*0.25, height*0.5, height*0.75, height], [-10, -5, 0, 5, 10])\n",
        "  plt.grid(True)\n",
        "\n",
        "plt.figure(figsize=(5, 5))\n",
        "plt.imshow(grid, origin='lower') #lower da bi (0,0) bilo dole levo\n",
        "nacrtaj_tacke(normalizovane_tacke, 'black', 'o')\n",
        "nacrtaj_ose_i_grid()\n",
        "plt.savefig(f\"tacke.png\")\n",
        "files.download(f\"tacke.png\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6dDeVfGrHaxQ"
      },
      "source": [
        "### 3. Nasumično biranje k centroida"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "wDMQxgRwHbjM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7965ee0-9726-4e9f-bc0e-a07e0af3d24e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nasumicno biramo k centroida:\n",
            "centroidi =  [(np.float64(4.32), np.float64(0.32)), (np.float64(-0.96), np.float64(-0.85)), (np.float64(2.85), np.float64(0.62))]\n"
          ]
        }
      ],
      "source": [
        "print(\"Nasumicno biramo k centroida:\")\n",
        "\n",
        "centroidi = [(np.round(random.uniform(-10, 10), 2), np.round(random.uniform(-10, 10), 2)) for _ in range(k)]\n",
        "print(\"centroidi = \", centroidi)\n",
        "\n",
        "normalizovani_centroidi = np.array([normalizuj_tacku(c) for c in centroidi])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLZM8XBfHqJd"
      },
      "source": [
        "Crtanje centroida"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "c0xdfAOOHnSS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "3ed629b4-12f9-4df9-d74c-e322a35f259e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_9e847819-8c76-40e6-96f2-3bc433b5761e\", \"centroidi.png\", 12898)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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D7YXgA+bAgGGgvRB8wBwYMAy0F4IPmAMDhoH2wnAGoAYMGAbaB8EH1KDeAcOtOsMLYAKCD6jDXAOGW32GF8AEBB/goXaZ4QVoZ1zcAniEGV6A1kDwAR5hhhegNRB8gEeY4QVoDQQf4BFmeAFaA8EHeIQZXoDWQPABHmGGF6A1MJwB8BAzvADhR/ABHqp3hhcA/iP4gCaYa4YXAMHhHB8AwCgEHwDAKAQfAMAoBB8AwCgEHwDAKAQfAMAoBB8AwCiM4wN+LZfLuYPObdtWKpUKxaDzXC6noaEhSdLQ0JCWL18eirqAVsURH4xXKpW0detWrV69Wjt27NATTzyhHTt2aPXq1dq6datKpVLgde3du1eStHfv3sDrAlodR3wwXk9Pj3vn9HK5rHK57C4bHByUJGUymUDrmrzrQ6VSce/kHlRdQKvjiA9GGx8fd8OlmsmQyeVy1AW0CYIPRstmszXdNX14eNiniiaEtS6gHRB8MFo+n68pYPL5vE8VTQhrXUA7IPhgNNu2a7prum3bPlU0Iax1Ae2A4IPRkslkTQGTSqV8qmhCWOsC2gHBB6N1dXUpnU7Lsqyqyy3LUjqd9n3cXFjrAtoBwxlgvIGBAUkTQxcikYgikYgqlYoqlYrS6bS7POi6pInzepOhF1RdQKvjiA/Gi8ViymQyGhsb044dO3Tvvfdq586dGhsbUyaTUSwWC7yuLVu2SJLuvvvuwOsCWh1HfMCvJRIJ9fX1BV3GLIlEQps3b9ajjz6qu+66iz9vAg3iiA8AYBSCDwBgFIIPAGAUgg8AYBSCDwBgFIIPAGAUgg8AYBTG8QFNkMvlNDw8rHw+L9u2lUql5j3+LpfLaWhoSJI0NDSk5cuXM5YPaABHfICHSqWStm7dqtWrV2vHjh164okntGPHDq1evVpbt25VqVSa17r27t0rSdq7d++81gXgXRzxAR7q6elx75xeLpdVLpfdZYODg5KkTCZT97om79RQqVTcu6/Xsy4A7+KID/DI+Pi4G1TVTAZWLpfzdV0ApiP4AI9ks9ma7po+PDzs67oATEfwAR7J5/M1hVU+n/d1XQCmI/gAj9i2XdNd023b9nVdAKYj+ACPJJPJmsIqlUr5ui4A0xF8gEe6urqUTqdlWVbV5ZN3Tq9lDJ6X6wqlN5+RTr1RfdmpNyaWA01C8AEeGhgYcAMrGo0qFospGo26QTUwMDCvdU2e74tEIvNaV6i8+Yz0/K3S/mtnh9+pNyYef/5Wwg9Nwzg+wEOxWEyZTEa9vb3uzC3xeFzJZLLuo7Op6/ryl7+sRx99VHfffbcefPDBhmaB8WpGmXlb8gFp4Srp5PhEyF33nLRw5buhd3JcWtQ18TygCQg+oAkSiYT6+vo8W9fmzZv16KOP6q677ppXUJVKJXdAfCQSUSQSUaVSUX9/v3v0GIvFPKl3TgtXToTdZMjtv1Za9y3pwCffDb3rnpt4HtAE/KkTMMDMGWVKpZLK5bI7EL6np8ffgibDb1HXRNj94P8RevANwQe0udDOArNw5cSR3lTrvtWc0ONiGkxB8AFtLrSzwJx6Y+LPm1Md+OSZA2q+uJgGMxB8QJsL5SwwMy9kuf5f3v2zZ7WAasTMi2km1z21hoWruJjGIAQf0OZCNwvMzNC77jlp+R9OP+fnZfjNPJ+4/1rplz+eXQPnFY1B8AFtLnSzwBRekU69PjtwpgbUqdcnnucVLqbBFAQf0OZCNwtM503Sh79fPXAmA+rD3594npf8vJgGoUbwAQbwckYZT3TedObAWbjS+9CT/LuYBqHn6wD2gYEB7dmzR0eOHNEVV1yhr3/961qzZo2fJSBgoZg5xEBezigzqaXey5nnFacOmJ86ewzM4PjkqaeechYsWOB885vfdP7rv/7L6e7udi644AInn8/P+bOFQsGR5GSzWadYLPpQLWYqFovOyMjIvNu/WCw63d3djmVZTjQadWKxmBONRh3Lspzu7m7e1zmMjo46kpzR0dGgS2m99/Lk647zvS7HGdbEvydfP/vjVTTa/9GYo0ePOpKcQqHgyfp8+1PnV7/6VXV3d2vLli36nd/5HT3++OM677zz9M1vftOvEhCg0M0cgnlrufcyiItpEGq+BF+xWNRLL72k9evXv7vhSETr16/XgQMH/CgBAQrtzCGoW0u+l0FdTIPQ8uUc39GjR1Uul2eNE7JtW//93/896/mnT5/W6dOn3e9PnDghaeKcwsGDB/2bTBeuUqmksbGxebX/0NCQzjnnnLNeUh+JRPTlL39ZmzdvbrTUtvTKK69M+zcorfteLpV+fljS4bMsP3jGn26k/6Nxx48f93aFnvzBdA6/+MUvHEnOj3/842mPP/zww86aNWtmPX/79u2OJL744osvvvhyv7w6x+fLEd+yZcsUjUZnTYk0eWXZTL29vdq2bZv7/YkTJ7Ry5Ur9xV/8hT784Q/zG1cASqWSRkdHtXbt2nkd8e3du3fOo4QtW7aE7CghPF555RXdc889+sY3vqEPfCC4qbVMfS8b6f9o3PHjx7VhwwbP1udL8C1YsEBXXXWV9u/fr9tvv13SxEwR+/fv1/333z/r+R0dHero6Jj1eCKR0Jo1a+h4ASiVSjp69Oi82n/ZsmV67LHHznheSJoYRP3QQw+F93L4kPjABz4Q6BAgU9/LRvo/Gnfs2DFP1+fbVZ3btm3TX//1X2toaEg//elPdd999+nUqVPasmWLXyUgIKGbOQTzxnuJduDbAPY777xTv/zlL9Xf368jR47oyiuv1LPPPuvfxLgI1OTMIDPvAF6pVIKZOQTzxnuJVufrzC33339/1T9tov01Y+YQBIP3Eq3O1+ADEomE+vr6gi4DHuC9RKtikmoAgFEIPgCAUQg+AIBRCD4AgFEIPsBrbz5z5pubnnpjYjmAwBB8gJfefEZ6/taJm5vODL/Jm6E+fyvhBwSI4AO8tOQD0sJV797ZezL8pt4BfOGqiecBCATj+IBfy+Vy7oBs27aVSqXqH5A9eX+3yZDbf6207lvSgU9OfD/zZqhwedL+QA0IPhivVCq5dxWfOgVXf3+/OwVXXRMTzwy/H/y/iccJvao8b39gDvypE8ab3Ok6jqNyuaxSqaRyuezeTbynp6f+lS5cOXGkN9W6bxF6VTSl/YGzIPhgtPHxcXenW83kzjeXy9W34lNvTPx5c6oDnzzz1Z6Galr7A2dB8MFo2WxWkcjZPwaRSETDw8O1r3TqhSyLuqTr/2Xi35kXvKA57Q/MgeCD0fL5fE073nw+X9sKZ4bedc9Jy/9w4l/CbxbP2x+oAcEHo9m2rUqlctbnVCqV2u8bWXhFOvX67AtZJi94WdQ1sbzwSgNVtw/P2x+oAcEHoyWTyZp2vKlUqrYVdt4kffj71a/enAy/D39/4nnwvv2BGhB8MFpXV5fS6bQsy6q63LIspdPp+saTdd505qs3F64k9KZoSvsDc2AcH4w3MDAgSbPGkVUqFXccGZqH9offCD4YLxaLKZPJqLe31505JB6PK5lMcqThA9offiP4gF9LJBLq6+sLugxj0f7wC+f4AABGIfgAAEYh+AAARiH4AABGIfgAAEYh+AAARiH4AABGIfiAkMvlchoaGpIkDQ0NcW86oEEEHxBSpVJJW7du1erVq7V3715J0t69e7V69Wpt3bpVpVIp4AqB1sTMLUBI9fT0uHcnn7yDQaVSce9KLkmZTCbIEoGWxBEfEELj4+Nu6FUzGX782ROoH8EHhFA2m63pzuTDw8M+VQS0D4IPCKF8Pl9T8OXzeZ8qAtoHwQeEkG3bNd2Z3LZtnyoC2gfBB4RQMpmsKfhSqZRPFQHtg+ADQqirq0vpdFqWZVVdblmW0uk0N2oF5oHhDEBIDQwMSJIGBwfd832RSMQNvcnlAOrDER9aVi6X065du/TAAw9o165dbXdpfywWUyaT0djYmLZs2SJJuvvuuzU2NqZMJqNYLBZwhUBrIvjQcqbOaLJjxw498cQT2rFjR9vOaJJIJLR582ZJ0l133cWfN4EG8adOtJypM5qUy2WVy2V3GTOaAJgLR3xoKcxoAqBRBB9aCjOaAGgUwYeWwowmABpF8KGlMKMJgEYRfGgpzGgCoFEEH1oKM5oAaBTDGdByZs5oEolEVKlUVKlUmNFkDrlcTsPDw8rn87JtW6lUqi1/STDldXrJqDZzWkChUHAkOdls1ikWi0GXY6RiseiMjIyEqv3Hx8edL3zhC87999/v7Nq1yxkfHw+6pKYZHR11JDmjo6Pz+vliseh0d3c7lmU50WjUicViTjQadSzLcrq7u0P1vjaiWa8zjP3fK63QN44ePepIcgqFgifr44gPLSuRSKivry/oMlqCKYP+TXmdXjKxzTjHB7Q5Uwb9m/I6vWRqmxF8QJszZdC/Ka/TS6a2GcEHtDlTBv2b8jq9ZGqbEXxAmzNl0L8pr9NLprYZwQe0OVMG/ZvyOr1kapsRfECbM2XQvymv00umthnDGQADmDLo35TX6SUT24zgAwwQi8WUyWTU29vrzs4Rj8eVTCbb6rd5U16nl0xsM4IPMIgpg/5NeZ1eMqnNOMcHADAKwQcAMArBBwAwCsEHADAKwQcAMArBBwAwCsEHADAK4/gakMvl3AGftm0rlUq17YBPAO3NpP0ZwTcPpVLJvWvx1Cl++vv73Sl+YrFY0GUCwJxM3J8RfPMw2Ukcx1G5XFa5XHaXDQ4OSpIymUxQ5QFAzUzcn3GOr07j4+NuJ6nGcRwNDg4ql8v5XBkA1MfU/RnBV6dsNlvTHYuHh4d9qggA5sfU/RnBV6d8Pl9TR8nn8z5VBADzY+r+jOCrk23bNd2x2LZtnyoCgPkxdX9G8NUpmUzW1FFSqZRPFQHA/Ji6PyP46tTV1aV0Oi3LsqoutyxL6XS6bce/AGgfpu7PGM4wDwMDA5I0a9xLpVJxx70AQCswcX9G8M1DLBZTJpNRb2+vO9NBPB5XMpmc929GJs2aAMAbXuw3mrE/CzuCrwGJREJ9fX0NrcPEWRMANKYZ+w0v9metguALmImzJgBoDPuNxnBxS4BMnTUBwPyx32gcwRcgU2dNADB/7DcaR/AFyNRZEwDMH/uNxhF8ATJ11gQA88d+o3EEX4BMnTUBwPyx32gcwRcgU2dNADB/7Dcax3CGgJk4awKAxrDfaIwvwfepT31KQ0ND0x7bsGGDnn32WT82H2rNmjUhrDPBhLUuwA9e9f969ht85qpwfLB582bnxhtvdA4fPux+vfXWWzX/fKFQcCQ52WzWKRaLTay09RWLRae7u9uxLMuJRqNOLBZzotGoY1mW093dPe/2KxaLzsjISEM/34y6TDE6OupIckZHR4MuxUit2P/b6TN39OhRR5JTKBQ8WZ9vf+rs6OhQPB73a3PGCuuMDmGtC/BDEP2fz9yZ+XZxy3PPPaf3vve9uuyyy3Tffffp2LFjfm3aGGGd0SGsdQF+CKL/85k7O1+O+G688UZ9/OMfVyKR0NjYmD73uc/ppptu0oEDBxSNRmc9//Tp0zp9+rT7/YkTJ9z/l0olP0puSU899ZQWLlw47Te7maLRqPbt26eHH364rnVPtvt82r+ZdZliavvzGfBfq/X/dvvMed3nLedMvxLM0/DwsO699173+2eeeUbXXHPNtOeMj49r9erV+qd/+iddd911s9axY8cO7dy5c9bj2WxW5513npflAi1hbGxMDz74oL7yla9o9erVQZcD+Ortt99WMplUoVDQ4sWLG16f50d8t956q9auXet+f9FFF816TldXl5YtW6ZDhw5VDb7e3l5t27bN/f7EiRNauXKlJOn666/nNj1nsGfPHu3evXvO3/J6e3vndcT3gx/8YF7t38y6THHw4EFJ0tq1a7VmzZqAqzFPq/X/dvvMeX5qzJNLZOr0xhtvOJZlOd/73vdqej5XddZmbGzMsSzLkXTGL8uynPHx8brX3chVbc2syxRc1RmsVuv/7faZ8/qqzqZf3HLy5Ek9/PDDevHFF/Xaa69p//79uu2223TJJZdow4YNzd68UcI6o0NY6wL8EET/5zN3dk2/uCUajeo///M/NTQ0pOPHj6uzs1M33HCDvvCFL6ijo6PZmzdOWGd0CGtdgB+C6P985s7M84tbmuHEiRNasmSJstmsPvGJT3COrwZTZ2vwYiaYUqmkp59+WjfffHND7e91XaY4ePCg1q5dq9HRUc7xBaCV+387fOaOHTumZcuWhffiFoRDIpFQX19f0GXMEta6AD8E0f/5zM3G3RkAAEYh+AAARiH4AABGIfgAAEYh+AAARiH4AABGIfgAAEZhHF8Dpg4MtW1bqVSq5QaG1iKXy2nfvn26/PLLtWfPHm3atKktXydQDf2/fqHfN3oy42eThW2S6mKx6HR3dzuWZTnRaNSJxWJONBp1LMtyuru7Q1GjF6a+zkWLFjkjIyPOokWL2u51tgImqfYf/b9+zdo3ej1JNUd889DT0+Pe3bhcLk+79cfg4KAkKZPJBFWeZ2a+Tkkql8vu3Zul9nidQDX0//q1yr6Rc3x1Gh8fd9/YaiY/FLlczufKvGXK6wSqof/Xr5XajOCrUzabVSRy9maLRCIaHh72qaLmMOV1AtXQ/+vXSm1G8NUpn8/X9Obm83mfKmoOU14nUA39v36t1GYEX51s21alUjnrcyqVimzb9qmi5jDldQLV0P/r10ptRvDVKZlM1vTmplIpnypqDlNeJ1AN/b9+rdRmBF+durq6lE6nZVlW1eWWZSmdTodrzMo8mPI6gWro//VrpTZjOMM8DAwMSJq4PDcSiSgSiahSqahSqSidTrvLW93U1xmNRiVJ0WjU7cDt8jqBauj/9WuVfaPlnOna0xA5ceKElixZomw2q0984hOKxWJBlyRp+uwE8XhcyWQyFL/NeG3qzBU/+clPtHHjxrZ8nWF28OBBrV27VqOjo1qzZk3Q5RiF/l8/r/eNx44d07Jly1QoFLR48eKG6yP4UJNSqaSnn35aN998M+0fAIIvWPT/YHkdfJzjAwAYheADABiF4AMAGIXgAwAYheADABiF4AMAGIXgAwAYheBrMblcTrt27dIDDzygXbt2+XJvq1wupz179kiS9uzZE4r7aQF+of+3IU/u495khULBkeRks9l537q+1RWLRae7u9uxLMuJRqNOLBZzotGoY1mW093d3ZR2mbrNRYsWOSMjI86iRYuauk1UNzo66khyRkdHgy7FGPT/8Dh69KgjySkUCp6sj7k6W0RPT497d+NyuaxyuewuGxwclCRlMpmmblOSyuWyeyflZmwTCAv6f/viT50tYHx83P0AVjP5QfTyTzBBbBMIC/p/eyP4WkA2m63pzsbDw8MtvU0gLOj/7Y3gawH5fL6mD2E+n2/pbQJhQf9vbwRfC7Btu6Y7G9u23dLbBMKC/t/eCL4WkEwma/oQplKplt4mEBb0//ZG8LWArq4updNpWZZVdfnkHaG9vDlmENsEwoL+394YztAiBgYGJE0MXYhEIopEIqpUKqpUKkqn0+7yZm0zGo1KkqLRqPuhb8Y2gbCg/7cv7sDeYnK5nIaHh5XP5xWPx5VMJpv+W2cul9O+fft0+eWX6yc/+Yk2btzIb7o+4w7swaH/B8/rO7ATfKhJqVTS008/rZtvvpn2DwDBFyz6f7C8Dj7O8QEAjELwAQCMQvABAIxC8AEAjELwAQCMQvABAIxC8AEAjGLczC1TB4Dbtq1UKsVgVAC+YP8TDsYEX6lUcu+oPHXKr/7+fnf6IQamAmgG9j/hYkzwTXY6x3FULpdVLpfdZYODg5KkTCYTVHkA2hj7n3Ax4hzf+Pi42+mqcRxHg4ODyuVyPlcGoN2x/wkfI4Ivm83WdDfl4eFhnyoCYAr2P+FjRPDl8/maOl4+n/epIgCmYP8TPkYEn23bNd1N2bZtnyoCYAr2P+FjRPAlk8maOl4qlfKpIgCmYP8TPkYEX1dXl9LptCzLqrp88o7KjKcB4DX2P+FjzHCGgYEBSZo1jqZSqbjjaACgGdj/hIsxwReLxZTJZNTb2+vOnBCPx5VMJvlNC0BTsf8JF2OCb1IikVBfX1/QZQAwEPufcDDiHB8AAJMIPgCAUQg+AIBRCD4AgFEIPgCAUQg+AIBRCD4AgFEIPgCAUYwbwB5WuVzOndHBtm2lUilmdACAJiD4AlYqldTT0zNrDr/+/n53Dr9YLBZ0mQDQNgi+gE2GnuM4KpfLKpfL7rLBwUFJUiaTCao8AGg7nOML0Pj4uBt61TiOo8HBQeVyOZ8rA4D2RfAFKJvNKhI5+1sQiUQ0PDzsU0UA0P4IvgDl8/magi+fz/tUEQC0P4IvQLZtq1KpnPU5lUpFtm37VBEAtD+CL0DJZLKm4EulUj5VBADtj+ALUFdXl9LptCzLqrrcsiyl02nG8wGAhxjOELCBgQFJmjWOr1KpuOP4AADeIfgCFovFlMlk1Nvb687cEo/HlUwmOdIDgCYg+EIikUior68v6DIAoO1xjg8AYBSCDwBgFIIPAGAUgg8AYBSCDwBgFIIPAGAUgg8AYBSCDwBglIaD7zvf+Y5uuOEGLV26VJZl6eWXX571nHfeeUc9PT1aunSpFi1apDvuuINb7QAAAtFw8J06dUpXX321vvjFL57xOX/+53+uv//7v9e3v/1tPf/883rzzTf18Y9/vNFNAwBQt4anLPvkJz8pSXrttdeqLi8UCvrGN76hbDarP/qjP5Ik7d27V7/927+tF198UX/wB3/QaAkAANSs6XN1vvTSSyqVSlq/fr372Pvf/36tWrVKBw4cqBp8p0+f1unTp93vC4WCJOntt9/WsWPHFIvFml02ZiiVSrR/gI4fP+7+e+zYsWCLMRD9P1hvvfWWJMlxHE/W1/TgO3LkiBYsWKALLrhg2uO2bevIkSNVf2b37t3auXPnrMfT6XQzSgRaxoYNG4IuAQjMsWPHtGTJkobXU1fwDQ8P695773W/f+aZZ3TNNdc0XMRMvb292rZtm/t9pVLR//zP/+jKK6/UG2+8ocWLF3u+TZzdiRMntHLlSto/ILR/sGj/YBUKBa1atUoXXnihJ+urK/huvfVWrV271v3+oosumvNn4vG4isWijh8/Pu2ob/K+c9V0dHSoo6Nj2mORyMR1OIsXL6bjBYj2DxbtHyzaP1iTOdCouoLv/PPP1/nnn1/XBq666irFYjHt379fd9xxhyTpZz/7mV5//XWtW7eurnUBANCohs/xvfXWW3r99df15ptvSpoINWniSC8ej2vJkiW65557tG3bNl144YVavHixHnjgAa1bt44rOgEAvmv4uPH73/++PvjBD+qjH/2oJGnjxo364Ac/qMcff9x9zl/+5V/qj//4j3XHHXfoQx/6kOLxuL7zne/UtZ2Ojg5t37591p9A4Q/aP1i0f7Bo/2B53f6W49X1oQAAtADm6gQAGIXgAwAYheADABiF4AMAGCWUwcetjsLnU5/6lCzLmvZ14403Bl2WEQYGBvRbv/VbOuecc7R27VodPHgw6JKMsWPHjln9/v3vf3/QZbWtH/3oR7rlllvU2dkpy7I0MjIybbnjOOrv79eKFSt07rnnav369Xr11Vfr3k4og49bHYXTjTfeqMOHD7tf+/btC7qktve3f/u32rZtm7Zv365///d/1xVXXKENGzbof//3f4MuzRiXX375tH7/wgsvBF1S2zp16pSuuOIKDQwMVF3+pS99SV/72tf0+OOPa3R0VAsXLtSGDRv0zjvv1LchJ8RyuZwjyfmP//iPaY8fP37cicVizre//W33sZ/+9KeOJOfAgQM+V2mGzZs3O7fddlvQZRhnzZo1Tk9Pj/t9uVx2Ojs7nd27dwdYlTm2b9/uXHHFFUGXYSRJzne/+133+0ql4sTjcWfPnj3uY8ePH3c6Ojqcffv21bXuUB7xzWWuWx2hOZ577jm9973v1WWXXab77ruP2+M0WbFY1EsvvTStn0ciEa1fv55+7qNXX31VnZ2d6urqUiqV0uuvvx50SUbK5XI6cuTItM/DkiVLtHbt2ro/Dy0ZfPO51REac+ONN+pv/uZvtH//fn3xi1/U888/r5tuuknlcjno0trW0aNHVS6XZdv2tMfp5/5Zu3atnnzyST377LN67LHHlMvldM011+hXv/pV0KUZZ7LPe/F5CDz4hoeHtWjRIvfrn//5n4MuyXjV3pONGzfq1ltv1e/+7u/q9ttv1z/8wz/oX//1X/Xcc88FXS7QNDfddJP+5E/+RL/3e7+nDRs26Omnn9bx48f1d3/3d0GXhgY0/Ua0c/HrVkeoXS3vSVdXl5YtW6ZDhw7puuuu87M8YyxbtkzRaHTW1cr08+BccMEFuvTSS3Xo0KGgSzHOZJ/P5/NasWKF+3g+n9eVV15Z17oCP+I7//zzdckll7hf55577pw/M/VWR5O41ZF3anlPfv7zn+vYsWPTOiC8tWDBAl111VXT+nmlUtH+/fvp5wE5efKkxsbG6PcBSCQSisfj0z4PJ06c0OjoaN2fh8CP+KrhVkfhcvLkSe3cuVN33HGH4vG4xsbG9NnPflaXXHKJNmzYEHR5bW3btm3avHmzfv/3f19r1qzRX/3VX+nUqVPasmVL0KUZ4aGHHtItt9yiiy++WG+++aa2b9+uaDSqTZs2BV1aWzp58uS0o+lcLqeXX35ZF154oVatWqXPfOYz2rVrl973vvcpkUjokUceUWdnp26//fb6NuTVpade2rt3ryNp1tf27dvd5/zf//2f8+lPf9r5jd/4Dee8885zPvaxjzmHDx8Orug29vbbbzs33HCDs3z5cicWizkXX3yx093d7Rw5ciTo0ozw9a9/3Vm1apWzYMECZ82aNc6LL74YdEnGuPPOO50VK1Y4CxYscC666CLnzjvvdA4dOhR0WW3rhz/8YdV9/+bNmx3HmRjS8Mgjjzi2bTsdHR3Odddd5/zsZz+rezvclggAYJTAz/EBAOAngg8AYBSCDwBgFIIPAGAUgg8AYBSCDwBgFIIPAGAUgg8AYBSCDwBgFIIPAGAUgg8AYBSCDwBglP8PVYamDNVxxmAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(5, 5))\n",
        "plt.imshow(grid, origin='lower') #lower da bi (0,0) bilo dole levo\n",
        "nacrtaj_tacke(normalizovane_tacke, 'black', 'o')\n",
        "nacrtaj_tacke(normalizovani_centroidi, 'orange', 'x')\n",
        "nacrtaj_ose_i_grid()\n",
        "plt.savefig(f\"centroidi.png\")\n",
        "files.download(f\"centroidi.png\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c5BHbqtbId5r"
      },
      "source": [
        "\n",
        "### 4. Dodeljivanje tačaka najbližem centroidu\n",
        "Računanje Euklidske distance svakog centroida i svake tačke i za svaku tačku se određuje koji je njen najbliži centroid (bojenje klastera)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "w7jix2m3PVSP"
      },
      "outputs": [],
      "source": [
        "def dodeli_tacke_najblizem_centroidu():\n",
        "  klasteri = {k: [] for k in centroidi}\n",
        "\n",
        "  for tacka in tacke:\n",
        "      x, y = tacka\n",
        "      min_dist = float('inf')\n",
        "      min_c = (float('inf'), float('inf'))\n",
        "\n",
        "      for c in klasteri.keys():  #trazimo najblizi centroid\n",
        "        dist = math.sqrt((c[0] - tacka[0])**2 + (c[1] - tacka[1])**2) #racunanje Euklidske distance sqrt( (x2-x1)^2 + (y2-y1)^2 )\n",
        "\n",
        "        if dist < min_dist:\n",
        "          min_dist = dist\n",
        "          min_c = c\n",
        "\n",
        "      klasteri[min_c].append(tacka)\n",
        "\n",
        "  return klasteri"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "dsY_XwvGbNor"
      },
      "outputs": [],
      "source": [
        "def oboji_klastere():\n",
        "  grid = np.ones((height, width, 3))\n",
        "  plt.figure(figsize=(5, 5))\n",
        "  plt.imshow(grid, origin='lower') #lower da bi (0,0) bilo dole levo\n",
        "  nacrtaj_tacke(normalizovani_centroidi, 'orange', 'x')\n",
        "\n",
        "  cmap = plt.colormaps.get_cmap('tab10') #tab10 ima 10 razlicitih boja\n",
        "\n",
        "  for i, (k, v) in enumerate(klasteri.items()):\n",
        "      v_copy = v.copy()\n",
        "      for l in range(len(v_copy)):\n",
        "         v_copy[l] = normalizuj_tacku(v_copy[l])\n",
        "      if v_copy: #ako nije prazan\n",
        "        nacrtaj_tacke(np.array(v_copy), cmap(i % cmap.N), 'o')\n",
        "\n",
        "  nacrtaj_ose_i_grid()\n",
        "  plt.savefig(f\"dodeljivanje_tacaka_it{iteracija}.png\")\n",
        "  files.download(f\"dodeljivanje_tacaka_it{iteracija}.png\")\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2zDYCdQxGea"
      },
      "source": [
        "Generisanje Voronojevog dijagrama nad centroidima (za p=1, p=2, p=2.5, p=3 i p=inf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "dIIDv5pOSm6h"
      },
      "outputs": [],
      "source": [
        "def izgenerisi_voronojev_dijagram():\n",
        "   p_vrednosti = [1, 2, 2.5, 3, np.inf]\n",
        "   generatori = np.array(centroidi)\n",
        "\n",
        "   #rastojanje tacaka od generatora racunamo preko distance Minkovskog za razlicite vrednosti parametra p\n",
        "   def rastojanje(A, B, p):\n",
        "      if p == np.inf:\n",
        "          return max(abs(A[0]-B[0]), abs(A[1]-B[1]))\n",
        "      return ( (abs(A[0]-B[0])**p) + (abs(A[1]-B[1])**p) )**(1/p)   #n u sumi je ovde 2 (radimo sa 2D prostorom)\n",
        "\n",
        "   colors = np.random.rand(len(generatori), 3)\n",
        "\n",
        "   for p in p_vrednosti:\n",
        "      print(f\"\\n\\nGenerisemo Voronojev dijagram nad centroidima za p = {p}\\n\")\n",
        "      normalizovani_generatori = np.array(    #normalizovanje tacaka nam je potrebno zbog grafickog prikaza; nije kljucno za razumevanje glavne logike\n",
        "          [normalizuj_tacku(g) for g in generatori])\n",
        "\n",
        "      #prolazimo kroz mrezu, da bismo videli sta cemo da bojimo\n",
        "      for i in range(height):\n",
        "          for j in range(width):\n",
        "              pos = np.array([j/width, i/height]) #normalizovana pozicija i,j na gridu\n",
        "\n",
        "              rastojanja = []\n",
        "              #racunamo rastojanje trenutne tacke (i,j) (pos) od generatora (za svaki generator)\n",
        "              for g in normalizovani_generatori:\n",
        "                  d = rastojanje(g, pos, p)\n",
        "                  rastojanja.append(d)\n",
        "\n",
        "              najblizi_generator = np.argmin(rastojanja)\n",
        "              grid[i, j] = colors[najblizi_generator] #ovime su sve tacke koje pripadaju istom generatoru obojene u istu boju\n",
        "\n",
        "      #crtanje\n",
        "      plt.figure(figsize=(5, 5))\n",
        "      plt.imshow(grid, origin='lower') #lower da bi (0,0) bilo dole levo\n",
        "      nacrtaj_tacke(normalizovani_generatori, 'orange', 'x') #prikaz generatora\n",
        "\n",
        "      cmap = plt.colormaps.get_cmap('tab10') #tab10 ima 10 razlicitih boja\n",
        "\n",
        "      for i, (k, v) in enumerate(klasteri.items()):\n",
        "        v_copy = v.copy()\n",
        "        for l in range(len(v_copy)):\n",
        "          v_copy[l] = normalizuj_tacku(v_copy[l])\n",
        "        if v_copy: #ako nije prazan\n",
        "          nacrtaj_tacke(np.array(v_copy), cmap(i % cmap.N), 'o')\n",
        "\n",
        "      nacrtaj_ose_i_grid()\n",
        "      plt.savefig(f\"Voronojev_dijagram_p{p}.png\")\n",
        "      files.download(f\"Voronojev_dijagram_p{p}.png\")\n",
        "      plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2h_BeHQxj1R"
      },
      "source": [
        "### 5. Ažuriranje centroida\n",
        "Pronalaženje srednje vrednosti svakog klastera i pomeranje centroida na to mesto."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "zFebuZtUUGbi"
      },
      "outputs": [],
      "source": [
        "def azuriraj_centroide():\n",
        "  postoji_pomeren_centroid = False\n",
        "  novi_klasteri = {}\n",
        "\n",
        "  for i, (k, v) in enumerate(klasteri.items()):\n",
        "      if not v:\n",
        "        novi_klasteri[(k[0], k[1])] = v\n",
        "        continue\n",
        "\n",
        "      x_suma = 0\n",
        "      y_suma = 0\n",
        "      for x, y in v: #za svaku tacku (x,y) iz v\n",
        "        x_suma += x\n",
        "        y_suma += y\n",
        "\n",
        "      srednja_x = x_suma / len(v)\n",
        "      srednja_y = y_suma / len(v)\n",
        "\n",
        "      novi_klasteri[(srednja_x, srednja_y)] = v\n",
        "\n",
        "      if srednja_x != k[0] or srednja_y != k[1]:\n",
        "          postoji_pomeren_centroid = True\n",
        "\n",
        "  return novi_klasteri, postoji_pomeren_centroid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "FNj8EXa3f6Cb"
      },
      "outputs": [],
      "source": [
        "def nacrtaj_pomerene_centroide():\n",
        "  grid = np.ones((height, width, 3))\n",
        "  plt.figure(figsize=(5, 5))\n",
        "  plt.imshow(grid, origin='lower') #lower da bi (0,0) bilo dole levo\n",
        "  normalizovani_centroidi = np.array([normalizuj_tacku(c) for c in centroidi])\n",
        "  nacrtaj_tacke(normalizovani_centroidi, 'orange', 'x')\n",
        "\n",
        "  cmap = plt.colormaps.get_cmap('tab10') #tab10 ima 10 razlicitih boja\n",
        "\n",
        "  for i, (k, v) in enumerate(novi_klasteri.items()):\n",
        "      v_copy = v.copy()\n",
        "      for l in range(len(v_copy)):\n",
        "         v_copy[l] = normalizuj_tacku(v_copy[l])\n",
        "      if v_copy: #ako nije prazan\n",
        "        nacrtaj_tacke(np.array(v_copy), cmap(i % cmap.N), 'o')\n",
        "\n",
        "  nacrtaj_ose_i_grid()\n",
        "  plt.savefig(f\"pomeranje_centroida_it{iteracija}.png\")\n",
        "  files.download(f\"pomeranje_centroida_it{iteracija}.png\")\n",
        "  plt.show()\n",
        "  return normalizovani_centroidi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aUQyPxpwxYrg"
      },
      "source": [
        "### 4. i 5. korak se ponavljaju sve dok postoji pomeren centroid."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "QNiTy_01IeRM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b1f353ea-00ac-486d-e20d-aa962d72e955"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "ITERACIJA 1 --------------------------------------------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "4. Dodeljivanje tacaka najblizem centroidu (bojenje klastera)\n",
            "\n"
          ]
        },
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "5. Azuriranje centroida\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_e7309c95-cb0a-4785-8e14-f24a241b33de\", \"pomeranje_centroida_it1.png\", 14583)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "ITERACIJA 2 --------------------------------------------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "4. Dodeljivanje tacaka najblizem centroidu (bojenje klastera)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_7f8efa01-bb9a-47c7-a31c-fff528caeac2\", \"dodeljivanje_tacaka_it2.png\", 13714)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Generisemo Voronojev dijagram nad centroidima za p = 1\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_ce547838-b1df-4557-b933-ea8a52f8ec4c\", \"Voronojev_dijagram_p1.png\", 16423)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Generisemo Voronojev dijagram nad centroidima za p = 2\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_e30a03a9-0d91-4090-80b4-89d08e93e79a\", \"Voronojev_dijagram_p2.png\", 18251)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Generisemo Voronojev dijagram nad centroidima za p = 2.5\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_24e82364-555b-46ce-a81d-9ec7c20e421e\", \"Voronojev_dijagram_p2.5.png\", 18148)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Generisemo Voronojev dijagram nad centroidima za p = 3\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_05b990d1-a5ee-46c7-acfc-edb033c69e67\", \"Voronojev_dijagram_p3.png\", 18203)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Generisemo Voronojev dijagram nad centroidima za p = inf\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_5bd1c481-5fad-4470-bbfc-89c4a713cd07\", \"Voronojev_dijagram_pinf.png\", 18531)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "5. Azuriranje centroida\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_a58a75d3-d552-41f9-be9d-2f3cd371c187\", \"pomeranje_centroida_it2.png\", 13825)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "ITERACIJA 3 --------------------------------------------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "4. Dodeljivanje tacaka najblizem centroidu (bojenje klastera)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_3d0845a0-bb9c-41a1-a9e4-ac4f3942ca42\", \"dodeljivanje_tacaka_it3.png\", 13619)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "5. Azuriranje centroida\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_0eb8637c-5706-4fc7-97cf-7d54cf391dbd\", \"pomeranje_centroida_it3.png\", 13603)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "ITERACIJA 4 --------------------------------------------------------------------------------------------------------------------------------------\n",
            "\n",
            "\n",
            "4. Dodeljivanje tacaka najblizem centroidu (bojenje klastera)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_1355c2b3-ed3d-46a0-b583-4af38f6891f5\", \"dodeljivanje_tacaka_it4.png\", 13603)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "5. Azuriranje centroida\n",
            "\n",
            "\n",
            "Nijedan centroid se nije pomerio. Kraj algoritma.\n"
          ]
        }
      ],
      "source": [
        "iteracija = 0\n",
        "postoji_pomeren_centroid = True\n",
        "while postoji_pomeren_centroid:\n",
        "\n",
        "  iteracija += 1\n",
        "  print(f\"\\n\\nITERACIJA {iteracija} --------------------------------------------------------------------------------------------------------------------------------------\")\n",
        "\n",
        "  print(\"\\n\\n4. Dodeljivanje tacaka najblizem centroidu (bojenje klastera)\\n\")\n",
        "  klasteri = dodeli_tacke_najblizem_centroidu()\n",
        "  oboji_klastere()\n",
        "\n",
        "  if iteracija == 2:\n",
        "    izgenerisi_voronojev_dijagram()\n",
        "\n",
        "  print(\"\\n\\n5. Azuriranje centroida\\n\")\n",
        "  novi_klasteri, postoji_pomeren_centroid = azuriraj_centroide()\n",
        "\n",
        "  if postoji_pomeren_centroid == False:\n",
        "      print(\"\\nNijedan centroid se nije pomerio. Kraj algoritma.\")\n",
        "      continue\n",
        "\n",
        "  centroidi = list(novi_klasteri.keys())\n",
        "  normalizovani_centroidi = nacrtaj_pomerene_centroide()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HrjvFoc8JhQU"
      },
      "source": [
        "**Primeri ulaza za tačke:**\n",
        "\n",
        "30 tačaka:\n",
        "\n",
        "-7 7, -6 8, -5 6, -7 5, -6 6, -5 7, -8 6, -6 5, 5 7, 6 8, 7 6, 6 6, 8 7, 5 6, 7 5, -6 -4, -5 -6, -7 -5, -4 -7, -5 -5, -6 -6, -4 -5, 5 -4, 6 -6, 7 -5, 8 -6, 6 -5, 5 -6, 7 -7, 8 -5\n",
        "\n",
        "\n",
        "50 tačaka:\n",
        "\n",
        "-6 -5, -7 -4.5, -5 -7, -8 -6, -4 -5, -9 -8, -6.5 -7, -5 -6, -7 -9, -8 -4, -3 4, -1 3, 0 5.5, -2 2, 1 4, -4 6, -1.5 3.5, 2 5, 0 2, -3 7, 8 -3, 6 -5, 7 -4.5, 9 -6, 5 -7, 4 -3, 7.5 -5, 6 -8, 8 -4, 5 -6, -2 -1, 1 0.5, 0 -2, -1 1, 3 -1, 2 0, -3 -2.5, 1 -3, 0 1, -4 0, 4 7, 3 8, 5 6.5, 2 9, 6 5, 7 4, 3.5 6, 4 8, 5 7, 2.5 4.5\n",
        "\n",
        "Sve celobrojne tačke na mreži 20x20:\n",
        "\n",
        "-9 -9, -9 -8, -9 -7, -9 -6, -9 -5, -9 -4, -9 -3, -9 -2, -9 -1, -9 0, -9 1, -9 2, -9 3, -9 4, -9 5, -9 6, -9 7, -9 8, -9 9, -8 -9, -8 -8, -8 -7, -8 -6, -8 -5, -8 -4, -8 -3, -8 -2, -8 -1, -8 0, -8 1, -8 2, -8 3, -8 4, -8 5, -8 6, -8 7, -8 8, -8 9, -7 -9, -7 -8, -7 -7, -7 -6, -7 -5, -7 -4, -7 -3, -7 -2, -7 -1, -7 0, -7 1, -7 2, -7 3, -7 4, -7 5, -7 6, -7 7, -7 8, -7 9, -6 -9, -6 -8, -6 -7, -6 -6, -6 -5, -6 -4, -6 -3, -6 -2, -6 -1, -6 0, -6 1, -6 2, -6 3, -6 4, -6 5, -6 6, -6 7, -6 8, -6 9, -5 -9, -5 -8, -5 -7, -5 -6, -5 -5, -5 -4, -5 -3, -5 -2, -5 -1, -5 0, -5 1, -5 2, -5 3, -5 4, -5 5, -5 6, -5 7, -5 8, -5 9, -4 -9, -4 -8, -4 -7, -4 -6, -4 -5, -4 -4, -4 -3, -4 -2, -4 -1, -4 0, -4 1, -4 2, -4 3, -4 4, -4 5, -4 6, -4 7, -4 8, -4 9, -3 -9, -3 -8, -3 -7, -3 -6, -3 -5, -3 -4, -3 -3, -3 -2, -3 -1, -3 0, -3 1, -3 2, -3 3, -3 4, -3 5, -3 6, -3 7, -3 8, -3 9, -2 -9, -2 -8, -2 -7, -2 -6, -2 -5, -2 -4, -2 -3, -2 -2, -2 -1, -2 0, -2 1, -2 2, -2 3, -2 4, -2 5, -2 6, -2 7, -2 8, -2 9, -1 -9, -1 -8, -1 -7, -1 -6, -1 -5, -1 -4, -1 -3, -1 -2, -1 -1, -1 0, -1 1, -1 2, -1 3, -1 4, -1 5, -1 6, -1 7, -1 8, -1 9, 0 -9, 0 -8, 0 -7, 0 -6, 0 -5, 0 -4, 0 -3, 0 -2, 0 -1, 0 0, 0 1, 0 2, 0 3, 0 4, 0 5, 0 6, 0 7, 0 8, 0 9, 1 -9, 1 -8, 1 -7, 1 -6, 1 -5, 1 -4, 1 -3, 1 -2, 1 -1, 1 0, 1 1, 1 2, 1 3, 1 4, 1 5, 1 6, 1 7, 1 8, 1 9, 2 -9, 2 -8, 2 -7, 2 -6, 2 -5, 2 -4, 2 -3, 2 -2, 2 -1, 2 0, 2 1, 2 2, 2 3, 2 4, 2 5, 2 6, 2 7, 2 8, 2 9, 3 -9, 3 -8, 3 -7, 3 -6, 3 -5, 3 -4, 3 -3, 3 -2, 3 -1, 3 0, 3 1, 3 2, 3 3, 3 4, 3 5, 3 6, 3 7, 3 8, 3 9, 4 -9, 4 -8, 4 -7, 4 -6, 4 -5, 4 -4, 4 -3, 4 -2, 4 -1, 4 0, 4 1, 4 2, 4 3, 4 4, 4 5, 4 6, 4 7, 4 8, 4 9, 5 -9, 5 -8, 5 -7, 5 -6, 5 -5, 5 -4, 5 -3, 5 -2, 5 -1, 5 0, 5 1, 5 2, 5 3, 5 4, 5 5, 5 6, 5 7, 5 8, 5 9, 6 -9, 6 -8, 6 -7, 6 -6, 6 -5, 6 -4, 6 -3, 6 -2, 6 -1, 6 0, 6 1, 6 2, 6 3, 6 4, 6 5, 6 6, 6 7, 6 8, 6 9, 7 -9, 7 -8, 7 -7, 7 -6, 7 -5, 7 -4, 7 -3, 7 -2, 7 -1, 7 0, 7 1, 7 2, 7 3, 7 4, 7 5, 7 6, 7 7, 7 8, 7 9, 8 -9, 8 -8, 8 -7, 8 -6, 8 -5, 8 -4, 8 -3, 8 -2, 8 -1, 8 0, 8 1, 8 2, 8 3, 8 4, 8 5, 8 6, 8 7, 8 8, 8 9, 9 -9, 9 -8, 9 -7, 9 -6, 9 -5, 9 -4, 9 -3, 9 -2, 9 -1, 9 0, 9 1, 9 2, 9 3, 9 4, 9 5, 9 6, 9 7, 9 8, 9 9\n",
        "\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}