naive_bayes from scratch.ipynb

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    "colab": {
      "name": "Untitled14.ipynb",
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      "authorship_tag": "ABX9TyOLH33Mn8PxuEBXyA/TeJo3",
      "include_colab_link": true
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/k00sharath/hello-world/blob/master/naive_bayes%20from%20scratch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "muftaIio07lW",
        "colab_type": "text"
      },
      "source": [
        "## ***Implementing naive bayes from scratch***\n",
        "# step1 seperating the dataset by class\n",
        "# step 2 summarizing the dataset (mean,std) by particular attribute across each  class\n",
        "# step 3: calculating the probabilities\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7HnhG6Aw03__",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "fedfe30b-a365-4a02-c99b-d2fe01acfc11"
      },
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris=load_iris()\n",
        "iris_data=iris['data']\n",
        "print(len(iris_data),type(iris_data),iris_data[:,1],len(iris['data']))"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "150 <class 'numpy.ndarray'> [3.5 3.  3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 3.7 3.4 3.  3.  4.  4.4 3.9 3.5\n",
            " 3.8 3.8 3.4 3.7 3.6 3.3 3.4 3.  3.4 3.5 3.4 3.2 3.1 3.4 4.1 4.2 3.1 3.2\n",
            " 3.5 3.6 3.  3.4 3.5 2.3 3.2 3.5 3.8 3.  3.8 3.2 3.7 3.3 3.2 3.2 3.1 2.3\n",
            " 2.8 2.8 3.3 2.4 2.9 2.7 2.  3.  2.2 2.9 2.9 3.1 3.  2.7 2.2 2.5 3.2 2.8\n",
            " 2.5 2.8 2.9 3.  2.8 3.  2.9 2.6 2.4 2.4 2.7 2.7 3.  3.4 3.1 2.3 3.  2.5\n",
            " 2.6 3.  2.6 2.3 2.7 3.  2.9 2.9 2.5 2.8 3.3 2.7 3.  2.9 3.  3.  2.5 2.9\n",
            " 2.5 3.6 3.2 2.7 3.  2.5 2.8 3.2 3.  3.8 2.6 2.2 3.2 2.8 2.8 2.7 3.3 3.2\n",
            " 2.8 3.  2.8 3.  2.8 3.8 2.8 2.8 2.6 3.  3.4 3.1 3.  3.1 3.1 3.1 2.7 3.2\n",
            " 3.3 3.  2.5 3.  3.4 3. ] 150\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4vwR81KhnVS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1d442c0c-5ba7-4761-e0d5-92848a90d8c2"
      },
      "source": [
        "import random\n",
        "def crossval_split(data,targets,folds):\n",
        "    train={}\n",
        "    cross_val={}\n",
        "    train_targets={}\n",
        "    cross_val_targets={}\n",
        "    indices=[]\n",
        "    for i in range(len(data)):\n",
        "        indices.append(i)\n",
        "    indices_copy=[i for i in indices]\n",
        "\n",
        "    size=len(data)/folds\n",
        "    for i in range(folds):\n",
        "        cross_val[i]=[]\n",
        "        cross_val_targets[i]=[]\n",
        "        for j in range(int(size)):\n",
        "            indice=random.choice(indices)\n",
        "            cross_val[i].append(data[indice])\n",
        "            cross_val_targets[i].append(targets[indice])\n",
        "            indices.remove(indice)\n",
        "        train[i]=[data[i] for i in indices]\n",
        "        train_targets[i]=[targets[i] for i in indices]\n",
        "        indices=[i for i in range(len(data))]\n",
        "    return(train,train_targets,cross_val,cross_val_targets)\n",
        "\n",
        "crosssval_split(iris['data'],iris['target'],3)\n",
        "                   \n"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   array([5.5, 2.4, 3.7, 1. ]),\n",
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              "   array([5. , 2.3, 3.3, 1. ]),\n",
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              "  2: [2,\n",
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              "   0,\n",
              "   2,\n",
              "   2,\n",
              "   2]})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMeLtq7-20uK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5fd880a0-94be-40ad-f4a8-506574d5fc4c"
      },
      "source": [
        "print(iris_data[:,1])\n",
        "print(iris)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3.5 3.  3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 3.7 3.4 3.  3.  4.  4.4 3.9 3.5\n",
            " 3.8 3.8 3.4 3.7 3.6 3.3 3.4 3.  3.4 3.5 3.4 3.2 3.1 3.4 4.1 4.2 3.1 3.2\n",
            " 3.5 3.6 3.  3.4 3.5 2.3 3.2 3.5 3.8 3.  3.8 3.2 3.7 3.3 3.2 3.2 3.1 2.3\n",
            " 2.8 2.8 3.3 2.4 2.9 2.7 2.  3.  2.2 2.9 2.9 3.1 3.  2.7 2.2 2.5 3.2 2.8\n",
            " 2.5 2.8 2.9 3.  2.8 3.  2.9 2.6 2.4 2.4 2.7 2.7 3.  3.4 3.1 2.3 3.  2.5\n",
            " 2.6 3.  2.6 2.3 2.7 3.  2.9 2.9 2.5 2.8 3.3 2.7 3.  2.9 3.  3.  2.5 2.9\n",
            " 2.5 3.6 3.2 2.7 3.  2.5 2.8 3.2 3.  3.8 2.6 2.2 3.2 2.8 2.8 2.7 3.3 3.2\n",
            " 2.8 3.  2.8 3.  2.8 3.8 2.8 2.8 2.6 3.  3.4 3.1 3.  3.1 3.1 3.1 2.7 3.2\n",
            " 3.3 3.  2.5 3.  3.4 3. ]\n",
            "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
            "       [4.9, 3. , 1.4, 0.2],\n",
            "       [4.7, 3.2, 1.3, 0.2],\n",
            "       [4.6, 3.1, 1.5, 0.2],\n",
            "       [5. , 3.6, 1.4, 0.2],\n",
            "       [5.4, 3.9, 1.7, 0.4],\n",
            "       [4.6, 3.4, 1.4, 0.3],\n",
            "       [5. , 3.4, 1.5, 0.2],\n",
            "       [4.4, 2.9, 1.4, 0.2],\n",
            "       [4.9, 3.1, 1.5, 0.1],\n",
            "       [5.4, 3.7, 1.5, 0.2],\n",
            "       [4.8, 3.4, 1.6, 0.2],\n",
            "       [4.8, 3. , 1.4, 0.1],\n",
            "       [4.3, 3. , 1.1, 0.1],\n",
            "       [5.8, 4. , 1.2, 0.2],\n",
            "       [5.7, 4.4, 1.5, 0.4],\n",
            "       [5.4, 3.9, 1.3, 0.4],\n",
            "       [5.1, 3.5, 1.4, 0.3],\n",
            "       [5.7, 3.8, 1.7, 0.3],\n",
            "       [5.1, 3.8, 1.5, 0.3],\n",
            "       [5.4, 3.4, 1.7, 0.2],\n",
            "       [5.1, 3.7, 1.5, 0.4],\n",
            "       [4.6, 3.6, 1. , 0.2],\n",
            "       [5.1, 3.3, 1.7, 0.5],\n",
            "       [4.8, 3.4, 1.9, 0.2],\n",
            "       [5. , 3. , 1.6, 0.2],\n",
            "       [5. , 3.4, 1.6, 0.4],\n",
            "       [5.2, 3.5, 1.5, 0.2],\n",
            "       [5.2, 3.4, 1.4, 0.2],\n",
            "       [4.7, 3.2, 1.6, 0.2],\n",
            "       [4.8, 3.1, 1.6, 0.2],\n",
            "       [5.4, 3.4, 1.5, 0.4],\n",
            "       [5.2, 4.1, 1.5, 0.1],\n",
            "       [5.5, 4.2, 1.4, 0.2],\n",
            "       [4.9, 3.1, 1.5, 0.2],\n",
            "       [5. , 3.2, 1.2, 0.2],\n",
            "       [5.5, 3.5, 1.3, 0.2],\n",
            "       [4.9, 3.6, 1.4, 0.1],\n",
            "       [4.4, 3. , 1.3, 0.2],\n",
            "       [5.1, 3.4, 1.5, 0.2],\n",
            "       [5. , 3.5, 1.3, 0.3],\n",
            "       [4.5, 2.3, 1.3, 0.3],\n",
            "       [4.4, 3.2, 1.3, 0.2],\n",
            "       [5. , 3.5, 1.6, 0.6],\n",
            "       [5.1, 3.8, 1.9, 0.4],\n",
            "       [4.8, 3. , 1.4, 0.3],\n",
            "       [5.1, 3.8, 1.6, 0.2],\n",
            "       [4.6, 3.2, 1.4, 0.2],\n",
            "       [5.3, 3.7, 1.5, 0.2],\n",
            "       [5. , 3.3, 1.4, 0.2],\n",
            "       [7. , 3.2, 4.7, 1.4],\n",
            "       [6.4, 3.2, 4.5, 1.5],\n",
            "       [6.9, 3.1, 4.9, 1.5],\n",
            "       [5.5, 2.3, 4. , 1.3],\n",
            "       [6.5, 2.8, 4.6, 1.5],\n",
            "       [5.7, 2.8, 4.5, 1.3],\n",
            "       [6.3, 3.3, 4.7, 1.6],\n",
            "       [4.9, 2.4, 3.3, 1. ],\n",
            "       [6.6, 2.9, 4.6, 1.3],\n",
            "       [5.2, 2.7, 3.9, 1.4],\n",
            "       [5. , 2. , 3.5, 1. ],\n",
            "       [5.9, 3. , 4.2, 1.5],\n",
            "       [6. , 2.2, 4. , 1. ],\n",
            "       [6.1, 2.9, 4.7, 1.4],\n",
            "       [5.6, 2.9, 3.6, 1.3],\n",
            "       [6.7, 3.1, 4.4, 1.4],\n",
            "       [5.6, 3. , 4.5, 1.5],\n",
            "       [5.8, 2.7, 4.1, 1. ],\n",
            "       [6.2, 2.2, 4.5, 1.5],\n",
            "       [5.6, 2.5, 3.9, 1.1],\n",
            "       [5.9, 3.2, 4.8, 1.8],\n",
            "       [6.1, 2.8, 4. , 1.3],\n",
            "       [6.3, 2.5, 4.9, 1.5],\n",
            "       [6.1, 2.8, 4.7, 1.2],\n",
            "       [6.4, 2.9, 4.3, 1.3],\n",
            "       [6.6, 3. , 4.4, 1.4],\n",
            "       [6.8, 2.8, 4.8, 1.4],\n",
            "       [6.7, 3. , 5. , 1.7],\n",
            "       [6. , 2.9, 4.5, 1.5],\n",
            "       [5.7, 2.6, 3.5, 1. ],\n",
            "       [5.5, 2.4, 3.8, 1.1],\n",
            "       [5.5, 2.4, 3.7, 1. ],\n",
            "       [5.8, 2.7, 3.9, 1.2],\n",
            "       [6. , 2.7, 5.1, 1.6],\n",
            "       [5.4, 3. , 4.5, 1.5],\n",
            "       [6. , 3.4, 4.5, 1.6],\n",
            "       [6.7, 3.1, 4.7, 1.5],\n",
            "       [6.3, 2.3, 4.4, 1.3],\n",
            "       [5.6, 3. , 4.1, 1.3],\n",
            "       [5.5, 2.5, 4. , 1.3],\n",
            "       [5.5, 2.6, 4.4, 1.2],\n",
            "       [6.1, 3. , 4.6, 1.4],\n",
            "       [5.8, 2.6, 4. , 1.2],\n",
            "       [5. , 2.3, 3.3, 1. ],\n",
            "       [5.6, 2.7, 4.2, 1.3],\n",
            "       [5.7, 3. , 4.2, 1.2],\n",
            "       [5.7, 2.9, 4.2, 1.3],\n",
            "       [6.2, 2.9, 4.3, 1.3],\n",
            "       [5.1, 2.5, 3. , 1.1],\n",
            "       [5.7, 2.8, 4.1, 1.3],\n",
            "       [6.3, 3.3, 6. , 2.5],\n",
            "       [5.8, 2.7, 5.1, 1.9],\n",
            "       [7.1, 3. , 5.9, 2.1],\n",
            "       [6.3, 2.9, 5.6, 1.8],\n",
            "       [6.5, 3. , 5.8, 2.2],\n",
            "       [7.6, 3. , 6.6, 2.1],\n",
            "       [4.9, 2.5, 4.5, 1.7],\n",
            "       [7.3, 2.9, 6.3, 1.8],\n",
            "       [6.7, 2.5, 5.8, 1.8],\n",
            "       [7.2, 3.6, 6.1, 2.5],\n",
            "       [6.5, 3.2, 5.1, 2. ],\n",
            "       [6.4, 2.7, 5.3, 1.9],\n",
            "       [6.8, 3. , 5.5, 2.1],\n",
            "       [5.7, 2.5, 5. , 2. ],\n",
            "       [5.8, 2.8, 5.1, 2.4],\n",
            "       [6.4, 3.2, 5.3, 2.3],\n",
            "       [6.5, 3. , 5.5, 1.8],\n",
            "       [7.7, 3.8, 6.7, 2.2],\n",
            "       [7.7, 2.6, 6.9, 2.3],\n",
            "       [6. , 2.2, 5. , 1.5],\n",
            "       [6.9, 3.2, 5.7, 2.3],\n",
            "       [5.6, 2.8, 4.9, 2. ],\n",
            "       [7.7, 2.8, 6.7, 2. ],\n",
            "       [6.3, 2.7, 4.9, 1.8],\n",
            "       [6.7, 3.3, 5.7, 2.1],\n",
            "       [7.2, 3.2, 6. , 1.8],\n",
            "       [6.2, 2.8, 4.8, 1.8],\n",
            "       [6.1, 3. , 4.9, 1.8],\n",
            "       [6.4, 2.8, 5.6, 2.1],\n",
            "       [7.2, 3. , 5.8, 1.6],\n",
            "       [7.4, 2.8, 6.1, 1.9],\n",
            "       [7.9, 3.8, 6.4, 2. ],\n",
            "       [6.4, 2.8, 5.6, 2.2],\n",
            "       [6.3, 2.8, 5.1, 1.5],\n",
            "       [6.1, 2.6, 5.6, 1.4],\n",
            "       [7.7, 3. , 6.1, 2.3],\n",
            "       [6.3, 3.4, 5.6, 2.4],\n",
            "       [6.4, 3.1, 5.5, 1.8],\n",
            "       [6. , 3. , 4.8, 1.8],\n",
            "       [6.9, 3.1, 5.4, 2.1],\n",
            "       [6.7, 3.1, 5.6, 2.4],\n",
            "       [6.9, 3.1, 5.1, 2.3],\n",
            "       [5.8, 2.7, 5.1, 1.9],\n",
            "       [6.8, 3.2, 5.9, 2.3],\n",
            "       [6.7, 3.3, 5.7, 2.5],\n",
            "       [6.7, 3. , 5.2, 2.3],\n",
            "       [6.3, 2.5, 5. , 1.9],\n",
            "       [6.5, 3. , 5.2, 2. ],\n",
            "       [6.2, 3.4, 5.4, 2.3],\n",
            "       [5.9, 3. , 5.1, 1.8]]), 'target': array([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, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 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,\n",
            "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
            "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
            "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], 'filename': '/usr/local/lib/python3.6/dist-packages/sklearn/datasets/data/iris.csv'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVlLN1mp27j6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def seperate_by_class(data,target):\n",
        "    class_wise_data={}\n",
        "    for i in range(len(data)):\n",
        "        if target[i] not in class_wise_data:\n",
        "            class_wise_data[target[i]]=[]\n",
        "        class_wise_data[target[i]].append(data[i])\n",
        "    return(class_wise_data)\n",
        "\n"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pe_Kho5N6I34",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def mean(column_wise_data):\n",
        "    sum=0\n",
        "    for i in range(len(column_wise_data)):\n",
        "        sum=sum+column_wise_data[i]\n",
        "    return(sum/(len(column_wise_data)))\n",
        "\n",
        "                                                                                 #across a particular class\n"
      ],
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bCZDrIlM6Jr8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def std(column_wise_data):\n",
        "    std=0\n",
        "    avg=mean(column_wise_data)\n",
        "    for i in range(len(column_wise_data)):\n",
        "        std+=((column_wise_data[i]-avg)**2/float((len(column_wise_data)-1)))\n",
        "    return(avg,std,len(column_wise_data))\n",
        "\n",
        "\n"
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n2NIOGfs059b",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lKtn2jP04QKi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def summarize_by_attributes(class_wise_data): \n",
        "    summary_class_wise={}\n",
        "    for i in class_wise_data:\n",
        "        if i not in summary_class_wise:\n",
        "            summary_class_wise[i]=[]\n",
        "        for j in range(len(class_wise_data[0][0])):\n",
        "            column_wise_data=np.array(class_wise_data[i])\n",
        "\n",
        "            summary_class_wise[i].append(std(column_wise_data[:,j]))\n",
        "    return summary_class_wise\n",
        "\n",
        "\n",
        "                                                                                #across a particular class"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ufIpZUbaCM-O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "b7fe984c-5898-401b-98f4-b292f8e924e3"
      },
      "source": [
        "import numpy as np\n",
        "class_wise_data=seperate_by_class(iris['data'],iris['target'])\n",
        "column_wise_data=np.array(class_wise_data[0])\n",
        "print(column_wise_data[:,1])\n",
        "print(class_wise_data)\n",
        "print(summarize_by_attribute(class_wise_data))"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3.5 3.  3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 3.7 3.4 3.  3.  4.  4.4 3.9 3.5\n",
            " 3.8 3.8 3.4 3.7 3.6 3.3 3.4 3.  3.4 3.5 3.4 3.2 3.1 3.4 4.1 4.2 3.1 3.2\n",
            " 3.5 3.6 3.  3.4 3.5 2.3 3.2 3.5 3.8 3.  3.8 3.2 3.7 3.3]\n",
            "{0: [array([5.1, 3.5, 1.4, 0.2]), array([4.9, 3. , 1.4, 0.2]), array([4.7, 3.2, 1.3, 0.2]), array([4.6, 3.1, 1.5, 0.2]), array([5. , 3.6, 1.4, 0.2]), array([5.4, 3.9, 1.7, 0.4]), array([4.6, 3.4, 1.4, 0.3]), array([5. , 3.4, 1.5, 0.2]), array([4.4, 2.9, 1.4, 0.2]), array([4.9, 3.1, 1.5, 0.1]), array([5.4, 3.7, 1.5, 0.2]), array([4.8, 3.4, 1.6, 0.2]), array([4.8, 3. , 1.4, 0.1]), array([4.3, 3. , 1.1, 0.1]), array([5.8, 4. , 1.2, 0.2]), array([5.7, 4.4, 1.5, 0.4]), array([5.4, 3.9, 1.3, 0.4]), array([5.1, 3.5, 1.4, 0.3]), array([5.7, 3.8, 1.7, 0.3]), array([5.1, 3.8, 1.5, 0.3]), array([5.4, 3.4, 1.7, 0.2]), array([5.1, 3.7, 1.5, 0.4]), array([4.6, 3.6, 1. , 0.2]), array([5.1, 3.3, 1.7, 0.5]), array([4.8, 3.4, 1.9, 0.2]), array([5. , 3. , 1.6, 0.2]), array([5. , 3.4, 1.6, 0.4]), array([5.2, 3.5, 1.5, 0.2]), array([5.2, 3.4, 1.4, 0.2]), array([4.7, 3.2, 1.6, 0.2]), array([4.8, 3.1, 1.6, 0.2]), array([5.4, 3.4, 1.5, 0.4]), array([5.2, 4.1, 1.5, 0.1]), array([5.5, 4.2, 1.4, 0.2]), array([4.9, 3.1, 1.5, 0.2]), array([5. , 3.2, 1.2, 0.2]), array([5.5, 3.5, 1.3, 0.2]), array([4.9, 3.6, 1.4, 0.1]), array([4.4, 3. , 1.3, 0.2]), array([5.1, 3.4, 1.5, 0.2]), array([5. , 3.5, 1.3, 0.3]), array([4.5, 2.3, 1.3, 0.3]), array([4.4, 3.2, 1.3, 0.2]), array([5. , 3.5, 1.6, 0.6]), array([5.1, 3.8, 1.9, 0.4]), array([4.8, 3. , 1.4, 0.3]), array([5.1, 3.8, 1.6, 0.2]), array([4.6, 3.2, 1.4, 0.2]), array([5.3, 3.7, 1.5, 0.2]), array([5. , 3.3, 1.4, 0.2])], 1: [array([7. , 3.2, 4.7, 1.4]), array([6.4, 3.2, 4.5, 1.5]), array([6.9, 3.1, 4.9, 1.5]), array([5.5, 2.3, 4. , 1.3]), array([6.5, 2.8, 4.6, 1.5]), array([5.7, 2.8, 4.5, 1.3]), array([6.3, 3.3, 4.7, 1.6]), array([4.9, 2.4, 3.3, 1. ]), array([6.6, 2.9, 4.6, 1.3]), array([5.2, 2.7, 3.9, 1.4]), array([5. , 2. , 3.5, 1. ]), array([5.9, 3. , 4.2, 1.5]), array([6. , 2.2, 4. , 1. ]), array([6.1, 2.9, 4.7, 1.4]), array([5.6, 2.9, 3.6, 1.3]), array([6.7, 3.1, 4.4, 1.4]), array([5.6, 3. , 4.5, 1.5]), array([5.8, 2.7, 4.1, 1. ]), array([6.2, 2.2, 4.5, 1.5]), array([5.6, 2.5, 3.9, 1.1]), array([5.9, 3.2, 4.8, 1.8]), array([6.1, 2.8, 4. , 1.3]), array([6.3, 2.5, 4.9, 1.5]), array([6.1, 2.8, 4.7, 1.2]), array([6.4, 2.9, 4.3, 1.3]), array([6.6, 3. , 4.4, 1.4]), array([6.8, 2.8, 4.8, 1.4]), array([6.7, 3. , 5. , 1.7]), array([6. , 2.9, 4.5, 1.5]), array([5.7, 2.6, 3.5, 1. ]), array([5.5, 2.4, 3.8, 1.1]), array([5.5, 2.4, 3.7, 1. ]), array([5.8, 2.7, 3.9, 1.2]), array([6. , 2.7, 5.1, 1.6]), array([5.4, 3. , 4.5, 1.5]), array([6. , 3.4, 4.5, 1.6]), array([6.7, 3.1, 4.7, 1.5]), array([6.3, 2.3, 4.4, 1.3]), array([5.6, 3. , 4.1, 1.3]), array([5.5, 2.5, 4. , 1.3]), array([5.5, 2.6, 4.4, 1.2]), array([6.1, 3. , 4.6, 1.4]), array([5.8, 2.6, 4. , 1.2]), array([5. , 2.3, 3.3, 1. ]), array([5.6, 2.7, 4.2, 1.3]), array([5.7, 3. , 4.2, 1.2]), array([5.7, 2.9, 4.2, 1.3]), array([6.2, 2.9, 4.3, 1.3]), array([5.1, 2.5, 3. , 1.1]), array([5.7, 2.8, 4.1, 1.3])], 2: [array([6.3, 3.3, 6. , 2.5]), array([5.8, 2.7, 5.1, 1.9]), array([7.1, 3. , 5.9, 2.1]), array([6.3, 2.9, 5.6, 1.8]), array([6.5, 3. , 5.8, 2.2]), array([7.6, 3. , 6.6, 2.1]), array([4.9, 2.5, 4.5, 1.7]), array([7.3, 2.9, 6.3, 1.8]), array([6.7, 2.5, 5.8, 1.8]), array([7.2, 3.6, 6.1, 2.5]), array([6.5, 3.2, 5.1, 2. ]), array([6.4, 2.7, 5.3, 1.9]), array([6.8, 3. , 5.5, 2.1]), array([5.7, 2.5, 5. , 2. ]), array([5.8, 2.8, 5.1, 2.4]), array([6.4, 3.2, 5.3, 2.3]), array([6.5, 3. , 5.5, 1.8]), array([7.7, 3.8, 6.7, 2.2]), array([7.7, 2.6, 6.9, 2.3]), array([6. , 2.2, 5. , 1.5]), array([6.9, 3.2, 5.7, 2.3]), array([5.6, 2.8, 4.9, 2. ]), array([7.7, 2.8, 6.7, 2. ]), array([6.3, 2.7, 4.9, 1.8]), array([6.7, 3.3, 5.7, 2.1]), array([7.2, 3.2, 6. , 1.8]), array([6.2, 2.8, 4.8, 1.8]), array([6.1, 3. , 4.9, 1.8]), array([6.4, 2.8, 5.6, 2.1]), array([7.2, 3. , 5.8, 1.6]), array([7.4, 2.8, 6.1, 1.9]), array([7.9, 3.8, 6.4, 2. ]), array([6.4, 2.8, 5.6, 2.2]), array([6.3, 2.8, 5.1, 1.5]), array([6.1, 2.6, 5.6, 1.4]), array([7.7, 3. , 6.1, 2.3]), array([6.3, 3.4, 5.6, 2.4]), array([6.4, 3.1, 5.5, 1.8]), array([6. , 3. , 4.8, 1.8]), array([6.9, 3.1, 5.4, 2.1]), array([6.7, 3.1, 5.6, 2.4]), array([6.9, 3.1, 5.1, 2.3]), array([5.8, 2.7, 5.1, 1.9]), array([6.8, 3.2, 5.9, 2.3]), array([6.7, 3.3, 5.7, 2.5]), array([6.7, 3. , 5.2, 2.3]), array([6.3, 2.5, 5. , 1.9]), array([6.5, 3. , 5.2, 2. ]), array([6.2, 3.4, 5.4, 2.3]), array([5.9, 3. , 5.1, 1.8])]}\n",
            "{0: [(5.005999999999999, 0.12424897959183677, 50), (3.428000000000001, 0.14368979591836736, 50), (1.4620000000000002, 0.030159183673469387, 50), (0.2459999999999999, 0.011106122448979594, 50)], 1: [(5.936, 0.2664326530612246, 50), (2.7700000000000005, 0.09846938775510201, 50), (4.26, 0.22081632653061226, 50), (1.3259999999999998, 0.039106122448979576, 50)], 2: [(6.587999999999998, 0.40434285714285745, 50), (2.9739999999999998, 0.10400408163265314, 50), (5.552, 0.3045877551020409, 50), (2.026, 0.07543265306122451, 50)]}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f_SR-ZL9XofM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from math import exp\n",
        "from math import sqrt\n",
        "from math import pi\n",
        "def calculate_probabilities(x_column,mean,std):\n",
        "    gaussian_probability=(exp(-(x_column-mean)**2/(2*std**2)))*sqrt(1/(2*pi))*(1/std)\n",
        "    return gaussian_probability\n",
        "def predict_class(test_example,summary):\n",
        "    probabilities={}\n",
        "    for i in summary:\n",
        "        prob=(summary[i][1][2])/(sum(summary[i][1][2] for i in summary))\n",
        "        for j,c in enumerate(summary[i]):\n",
        "            prob=prob*calculate_probabilities(test_example[j],c[0],c[1])\n",
        "        probabilities[i]=prob\n",
        "    return probabilities\n",
        "def predictions(test_data,summary):\n",
        "    predictions=[]\n",
        "    for i in range(len(test_data)):\n",
        "        probs=predict_class(test_data[i],summary)\n",
        "        predictions.append(max(probs,key=probs.get))\n",
        "    return predictions\n",
        "\n",
        "    \n",
        "def accuracy(predictions,targets):\n",
        "    correct=0\n",
        "    for i in range(len(targets)):\n",
        "        if targets[i]==predictions[i]:\n",
        "            correct=correct+1\n",
        "    return((correct/len(targets))*100)\n",
        "         \n",
        "    \n",
        "        \n",
        "\n",
        "\n"
      ],
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7u3p5oPC-8b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "a09daaee-15a4-467f-aeee-ea0936f1c29a"
      },
      "source": [
        "def naive_bayes_model(cross_val,cross_val_targets,train,train_targets):\n",
        "    for i in train:\n",
        "        seperated_data=seperate_by_class(train[i],train_targets[i])\n",
        "        summary=summarize_by_attributes(seperated_data)\n",
        "        predictions_from_model=predictions(cross_val[i],summary)\n",
        "        accuracy_from_model=accuracy(predictions_from_model,cross_val_targets[i])\n",
        "        print('Accuracy for fold',i,'is',accuracy_from_model)\n",
        "train,train_targets,cross_val,cross_val_targets=crossval_split(iris['data'],iris['target'],3)\n",
        "\n",
        "naive_bayes_model(cross_val,cross_val_targets,train,train_targets)       \n",
        "\n",
        "\n"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy for fold 0 is 96.0\n",
            "Accuracy for fold 1 is 92.0\n",
            "Accuracy for fold 2 is 88.0\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}