gausi.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "gausi.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "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/shub29shub/Student_clustering_GMM/blob/master/gausi.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "3sqfXpmmLotf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4HP0pd-ZMhat",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from pandas import DataFrame\n",
        "from sklearn import datasets\n",
        "from sklearn.mixture import GaussianMixture\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pG_Fth6-NBdx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "iris = datasets.load_iris()\n",
        "# print(iris)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TerHinBwOw_Q",
        "colab_type": "code",
        "outputId": "37fe9f73-c701-4973-c6a4-4eeda614e390",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1453
        }
      },
      "cell_type": "code",
      "source": [
        "X = iris.data[:] \n",
        "# print(X)\n",
        "d = pd.DataFrame(X) \n",
        "print(d)\n",
        "plt.scatter(d[0], d[1],d[2],d[3]) "
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       0    1    2    3\n",
            "0    5.1  3.5  1.4  0.2\n",
            "1    4.9  3.0  1.4  0.2\n",
            "2    4.7  3.2  1.3  0.2\n",
            "3    4.6  3.1  1.5  0.2\n",
            "4    5.0  3.6  1.4  0.2\n",
            "5    5.4  3.9  1.7  0.4\n",
            "6    4.6  3.4  1.4  0.3\n",
            "7    5.0  3.4  1.5  0.2\n",
            "8    4.4  2.9  1.4  0.2\n",
            "9    4.9  3.1  1.5  0.1\n",
            "10   5.4  3.7  1.5  0.2\n",
            "11   4.8  3.4  1.6  0.2\n",
            "12   4.8  3.0  1.4  0.1\n",
            "13   4.3  3.0  1.1  0.1\n",
            "14   5.8  4.0  1.2  0.2\n",
            "15   5.7  4.4  1.5  0.4\n",
            "16   5.4  3.9  1.3  0.4\n",
            "17   5.1  3.5  1.4  0.3\n",
            "18   5.7  3.8  1.7  0.3\n",
            "19   5.1  3.8  1.5  0.3\n",
            "20   5.4  3.4  1.7  0.2\n",
            "21   5.1  3.7  1.5  0.4\n",
            "22   4.6  3.6  1.0  0.2\n",
            "23   5.1  3.3  1.7  0.5\n",
            "24   4.8  3.4  1.9  0.2\n",
            "25   5.0  3.0  1.6  0.2\n",
            "26   5.0  3.4  1.6  0.4\n",
            "27   5.2  3.5  1.5  0.2\n",
            "28   5.2  3.4  1.4  0.2\n",
            "29   4.7  3.2  1.6  0.2\n",
            "..   ...  ...  ...  ...\n",
            "120  6.9  3.2  5.7  2.3\n",
            "121  5.6  2.8  4.9  2.0\n",
            "122  7.7  2.8  6.7  2.0\n",
            "123  6.3  2.7  4.9  1.8\n",
            "124  6.7  3.3  5.7  2.1\n",
            "125  7.2  3.2  6.0  1.8\n",
            "126  6.2  2.8  4.8  1.8\n",
            "127  6.1  3.0  4.9  1.8\n",
            "128  6.4  2.8  5.6  2.1\n",
            "129  7.2  3.0  5.8  1.6\n",
            "130  7.4  2.8  6.1  1.9\n",
            "131  7.9  3.8  6.4  2.0\n",
            "132  6.4  2.8  5.6  2.2\n",
            "133  6.3  2.8  5.1  1.5\n",
            "134  6.1  2.6  5.6  1.4\n",
            "135  7.7  3.0  6.1  2.3\n",
            "136  6.3  3.4  5.6  2.4\n",
            "137  6.4  3.1  5.5  1.8\n",
            "138  6.0  3.0  4.8  1.8\n",
            "139  6.9  3.1  5.4  2.1\n",
            "140  6.7  3.1  5.6  2.4\n",
            "141  6.9  3.1  5.1  2.3\n",
            "142  5.8  2.7  5.1  1.9\n",
            "143  6.8  3.2  5.9  2.3\n",
            "144  6.7  3.3  5.7  2.5\n",
            "145  6.7  3.0  5.2  2.3\n",
            "146  6.3  2.5  5.0  1.9\n",
            "147  6.5  3.0  5.2  2.0\n",
            "148  6.2  3.4  5.4  2.3\n",
            "149  5.9  3.0  5.1  1.8\n",
            "\n",
            "[150 rows x 4 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f33800d3780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1X31VoKqqY9q798uQa0tLj6i6ukpHjhyxo9QWR49W6siREtXW1srn+8bWvWE/1/z58+d3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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "t2pUdNiGPBbj",
        "colab_type": "code",
        "outputId": "35affded-f367-479e-9b57-5dcd9c307733",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "gmm = GaussianMixture(n_components = 3) \n",
        "gmm.fit(d) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianMixture(covariance_type='full', init_params='kmeans', max_iter=100,\n",
              "        means_init=None, n_components=3, n_init=1, precisions_init=None,\n",
              "        random_state=None, reg_covar=1e-06, tol=0.001, verbose=0,\n",
              "        verbose_interval=10, warm_start=False, weights_init=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "id": "dHSW_HchPspD",
        "colab_type": "code",
        "outputId": "8f446913-05d3-465b-f09e-19a0a6f2407c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "cell_type": "code",
      "source": [
        "labels = gmm.predict(d)\n",
        "print(labels)\n",
        "d['labels']= labels\n",
        "d0 = d[d['labels']== 0] \n",
        "d1 = d[d['labels']== 1] \n",
        "d2 = d[d['labels']== 2] \n",
        "  \n",
        "# plot three clusters in same plot \n",
        "plt.scatter(d0[0], d0[1],d0[2], c ='r') \n",
        "plt.scatter(d1[0], d1[1],d1[2], c ='yellow') \n",
        "plt.scatter(d2[0], d2[1],d2[2], c ='g') \n",
        "\n",
        "print(gmm.lower_bound_) \n",
        "print(gmm.n_iter_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 1 2\n",
            " 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1]\n",
            "-1.2014746139064725\n",
            "17\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fbnfqHGZt/ZpFCG73XIhiIoWqpgaIx6vR338YwMQcZm1zEkZV1RI4HO0AYgAk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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}