6 - KMeans (animação).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "6 - KMeans (animação).ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lauramoraes/Analise_de_Clusters/blob/master/6%20-%20KMeans%20(anima%C3%A7%C3%A3o).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adUM93BeCtJz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from matplotlib.animation import FuncAnimation\n",
        "from matplotlib import animation, rc, cm\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5LZFxJKCvPz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "NPOINTS = 200\n",
        "NCLUSTERS = 5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TFFBM8pDG0c",
        "colab_type": "code",
        "outputId": "27da11d1-46f7-4459-da52-e22f10e1cdd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "data = np.random.rand(NPOINTS, 2)\n",
        "rc('animation', html='html5')\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots(1, 1, figsize=(12, 12))\n",
        "ln, = plt.plot([], [], 'ro')\n",
        "\n",
        "def init():\n",
        "    sns.set(style=\"darkgrid\")\n",
        "    ax.set_xlim(0, 1)\n",
        "    ax.set_ylim(0, 1)\n",
        "\n",
        "    return ln,\n",
        "\n",
        "def update(frame):\n",
        "\n",
        "  ln = plot_kmeans(frame[0], frame[1])\n",
        "  return ln,\n",
        "    \n",
        "\n",
        "def plot_kmeans(kmeans, data):\n",
        "  global ax\n",
        "  ax.clear()\n",
        "  # C0\n",
        "  color=iter(cm.rainbow(np.linspace(0, 1, NCLUSTERS)))\n",
        "  for cluster in range(NCLUSTERS):\n",
        "    c = next(color)\n",
        "    idx = np.where(kmeans.labels_ == cluster)[0]\n",
        "    ax.scatter(data[idx].T[0], data[idx].T[1], color=c)\n",
        "    ax.scatter(kmeans.cluster_centers_[cluster][0],\n",
        "                kmeans.cluster_centers_[cluster][1],\n",
        "                color=c, s=2000, marker='^')\n",
        "\n",
        "  return ln\n",
        "\n",
        "\n",
        "def create_frames(data):\n",
        "    #First frame\n",
        "    frames = []\n",
        "    initial_centroids = np.random.rand(NCLUSTERS, 2)\n",
        "    kmeans = KMeans(n_clusters=NCLUSTERS, init=initial_centroids,\n",
        "                    max_iter=1, n_init=1,\n",
        "                    algorithm='full',\n",
        "                    precompute_distances=False,\n",
        "                    verbose=0)\n",
        "    kmeans.fit(data)\n",
        "    frames.append((\n",
        "        kmeans,\n",
        "        data\n",
        "    ))\n",
        "    for _ in range(100):\n",
        "        kmeans = KMeans(n_clusters=NCLUSTERS, \n",
        "                        init=kmeans.cluster_centers_,\n",
        "                        max_iter=1,\n",
        "                        n_init=1,\n",
        "                        algorithm='full',\n",
        "                        precompute_distances=False,\n",
        "                        verbose=0)\n",
        "        kmeans.fit(data)\n",
        "        frames.append((\n",
        "            kmeans,\n",
        "            data\n",
        "        ))\n",
        "    return frames\n",
        "  \n",
        "ani = FuncAnimation(fig, update, frames=create_frames(data),\n",
        "                    init_func=init, interval=1000);\n",
        "\n",
        "ani"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  Your browser does not support the video tag.\n",
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            ],
            "text/plain": [
              "<matplotlib.animation.FuncAnimation at 0x7f338f4b5160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
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Zo0qvL8uRlF5fVs+ZowRoQ5B20LR6vzjpwd5eI73q4VWdPfQ+2yTq/Q70XQPBys3PyClt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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MHY9_rMJPfPt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}