MNISTclassification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNISTclassification.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP26uZyYFjZZX0hUmqJ8rtF",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hesamcs/AI/blob/main/MNISTclassification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "39y4Ff43Qhqe"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=datasets.load_digits()\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UaI0g406RD0f",
        "outputId": "febc1a50-8a60-47de-a507-e24ab0cf571e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'DESCR': \".. _digits_dataset:\\n\\nOptical recognition of handwritten digits dataset\\n--------------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 1797\\n    :Number of Attributes: 64\\n    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\\n    :Missing Attribute Values: None\\n    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\\n    :Date: July; 1998\\n\\nThis is a copy of the test set of the UCI ML hand-written digits datasets\\nhttps://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\\n\\nThe data set contains images of hand-written digits: 10 classes where\\neach class refers to a digit.\\n\\nPreprocessing programs made available by NIST were used to extract\\nnormalized bitmaps of handwritten digits from a preprinted form. From a\\ntotal of 43 people, 30 contributed to the training set and different 13\\nto the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\\n4x4 and the number of on pixels are counted in each block. This generates\\nan input matrix of 8x8 where each element is an integer in the range\\n0..16. This reduces dimensionality and gives invariance to small\\ndistortions.\\n\\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\\nT. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\\nL. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\\n1994.\\n\\n.. topic:: References\\n\\n  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\\n    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\\n    Graduate Studies in Science and Engineering, Bogazici University.\\n  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\\n  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\\n    Linear dimensionalityreduction using relevance weighted LDA. School of\\n    Electrical and Electronic Engineering Nanyang Technological University.\\n    2005.\\n  - Claudio Gentile. A New Approximate Maximal Margin Classification\\n    Algorithm. NIPS. 2000.\\n\",\n",
              " 'data': array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],\n",
              "        [ 0.,  0.,  0., ..., 10.,  0.,  0.],\n",
              "        [ 0.,  0.,  0., ..., 16.,  9.,  0.],\n",
              "        ...,\n",
              "        [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
              "        [ 0.,  0.,  2., ..., 12.,  0.,  0.],\n",
              "        [ 0.,  0., 10., ..., 12.,  1.,  0.]]),\n",
              " 'feature_names': ['pixel_0_0',\n",
              "  'pixel_0_1',\n",
              "  'pixel_0_2',\n",
              "  'pixel_0_3',\n",
              "  'pixel_0_4',\n",
              "  'pixel_0_5',\n",
              "  'pixel_0_6',\n",
              "  'pixel_0_7',\n",
              "  'pixel_1_0',\n",
              "  'pixel_1_1',\n",
              "  'pixel_1_2',\n",
              "  'pixel_1_3',\n",
              "  'pixel_1_4',\n",
              "  'pixel_1_5',\n",
              "  'pixel_1_6',\n",
              "  'pixel_1_7',\n",
              "  'pixel_2_0',\n",
              "  'pixel_2_1',\n",
              "  'pixel_2_2',\n",
              "  'pixel_2_3',\n",
              "  'pixel_2_4',\n",
              "  'pixel_2_5',\n",
              "  'pixel_2_6',\n",
              "  'pixel_2_7',\n",
              "  'pixel_3_0',\n",
              "  'pixel_3_1',\n",
              "  'pixel_3_2',\n",
              "  'pixel_3_3',\n",
              "  'pixel_3_4',\n",
              "  'pixel_3_5',\n",
              "  'pixel_3_6',\n",
              "  'pixel_3_7',\n",
              "  'pixel_4_0',\n",
              "  'pixel_4_1',\n",
              "  'pixel_4_2',\n",
              "  'pixel_4_3',\n",
              "  'pixel_4_4',\n",
              "  'pixel_4_5',\n",
              "  'pixel_4_6',\n",
              "  'pixel_4_7',\n",
              "  'pixel_5_0',\n",
              "  'pixel_5_1',\n",
              "  'pixel_5_2',\n",
              "  'pixel_5_3',\n",
              "  'pixel_5_4',\n",
              "  'pixel_5_5',\n",
              "  'pixel_5_6',\n",
              "  'pixel_5_7',\n",
              "  'pixel_6_0',\n",
              "  'pixel_6_1',\n",
              "  'pixel_6_2',\n",
              "  'pixel_6_3',\n",
              "  'pixel_6_4',\n",
              "  'pixel_6_5',\n",
              "  'pixel_6_6',\n",
              "  'pixel_6_7',\n",
              "  'pixel_7_0',\n",
              "  'pixel_7_1',\n",
              "  'pixel_7_2',\n",
              "  'pixel_7_3',\n",
              "  'pixel_7_4',\n",
              "  'pixel_7_5',\n",
              "  'pixel_7_6',\n",
              "  'pixel_7_7'],\n",
              " 'frame': None,\n",
              " 'images': array([[[ 0.,  0.,  5., ...,  1.,  0.,  0.],\n",
              "         [ 0.,  0., 13., ..., 15.,  5.,  0.],\n",
              "         [ 0.,  3., 15., ..., 11.,  8.,  0.],\n",
              "         ...,\n",
              "         [ 0.,  4., 11., ..., 12.,  7.,  0.],\n",
              "         [ 0.,  2., 14., ..., 12.,  0.,  0.],\n",
              "         [ 0.,  0.,  6., ...,  0.,  0.,  0.]],\n",
              " \n",
              "        [[ 0.,  0.,  0., ...,  5.,  0.,  0.],\n",
              "         [ 0.,  0.,  0., ...,  9.,  0.,  0.],\n",
              "         [ 0.,  0.,  3., ...,  6.,  0.,  0.],\n",
              "         ...,\n",
              "         [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
              "         [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
              "         [ 0.,  0.,  0., ..., 10.,  0.,  0.]],\n",
              " \n",
              "        [[ 0.,  0.,  0., ..., 12.,  0.,  0.],\n",
              "         [ 0.,  0.,  3., ..., 14.,  0.,  0.],\n",
              "         [ 0.,  0.,  8., ..., 16.,  0.,  0.],\n",
              "         ...,\n",
              "         [ 0.,  9., 16., ...,  0.,  0.,  0.],\n",
              "         [ 0.,  3., 13., ..., 11.,  5.,  0.],\n",
              "         [ 0.,  0.,  0., ..., 16.,  9.,  0.]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[ 0.,  0.,  1., ...,  1.,  0.,  0.],\n",
              "         [ 0.,  0., 13., ...,  2.,  1.,  0.],\n",
              "         [ 0.,  0., 16., ..., 16.,  5.,  0.],\n",
              "         ...,\n",
              "         [ 0.,  0., 16., ..., 15.,  0.,  0.],\n",
              "         [ 0.,  0., 15., ..., 16.,  0.,  0.],\n",
              "         [ 0.,  0.,  2., ...,  6.,  0.,  0.]],\n",
              " \n",
              "        [[ 0.,  0.,  2., ...,  0.,  0.,  0.],\n",
              "         [ 0.,  0., 14., ..., 15.,  1.,  0.],\n",
              "         [ 0.,  4., 16., ..., 16.,  7.,  0.],\n",
              "         ...,\n",
              "         [ 0.,  0.,  0., ..., 16.,  2.,  0.],\n",
              "         [ 0.,  0.,  4., ..., 16.,  2.,  0.],\n",
              "         [ 0.,  0.,  5., ..., 12.,  0.,  0.]],\n",
              " \n",
              "        [[ 0.,  0., 10., ...,  1.,  0.,  0.],\n",
              "         [ 0.,  2., 16., ...,  1.,  0.,  0.],\n",
              "         [ 0.,  0., 15., ..., 15.,  0.,  0.],\n",
              "         ...,\n",
              "         [ 0.,  4., 16., ..., 16.,  6.,  0.],\n",
              "         [ 0.,  8., 16., ..., 16.,  8.,  0.],\n",
              "         [ 0.,  1.,  8., ..., 12.,  1.,  0.]]]),\n",
              " 'target': array([0, 1, 2, ..., 8, 9, 8]),\n",
              " 'target_names': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(data.target)) #tedad lable ha\n",
        "print(data.target) #lable\n",
        "print(len(data.images)) # tedad ax ha\n",
        "data.images[0] # ax aval ama hex mibashad"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDO_PEJxRh96",
        "outputId": "22f7630b-79af-4518-8cc6-cb7801d983f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1797\n",
            "[0 1 2 ... 8 9 8]\n",
            "1797\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.],\n",
              "       [ 0.,  0., 13., 15., 10., 15.,  5.,  0.],\n",
              "       [ 0.,  3., 15.,  2.,  0., 11.,  8.,  0.],\n",
              "       [ 0.,  4., 12.,  0.,  0.,  8.,  8.,  0.],\n",
              "       [ 0.,  5.,  8.,  0.,  0.,  9.,  8.,  0.],\n",
              "       [ 0.,  4., 11.,  0.,  1., 12.,  7.,  0.],\n",
              "       [ 0.,  2., 14.,  5., 10., 12.,  0.,  0.],\n",
              "       [ 0.,  0.,  6., 13., 10.,  0.,  0.,  0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot()\n",
        "plt.imshow(data.images[2],cmap=plt.cm.gray_r)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "nKcVFia8Rn7V",
        "outputId": "2b47a3f3-cac8-4840-d82c-f141547afd71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f96b2fbd710>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_and_lables=list(zip(data.images,data.target))\n",
        "for i ,(image,lable) in enumerate(image_and_lables[0:10]):\n",
        "  plt.subplot(2,5,i+1)\n",
        "  plt.imshow(image,cmap=plt.cm.gray_r)\n",
        "  plt.title(lable)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "m2tApUrmcrQ2",
        "outputId": "a253b743-83fb-439e-97fe-a377fd64245a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Preprocessing"
      ],
      "metadata": {
        "id": "9GxULJa8lJky"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.shape(data.images)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-RBeNFHtlljN",
        "outputId": "c6548b8b-5165-41cf-a3ca-07e906288831"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1797, 8, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=data.images.reshape(len(data.images),-1)"
      ],
      "metadata": {
        "id": "7ff4NOf2lIBR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.shape(x))\n",
        "y=data.target\n",
        "len(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iWH7C4xMlZ0z",
        "outputId": "6b0e727c-09a1-4c20-9dd5-37881047f24d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1797, 64)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1797"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#suport vector machine first algorithm"
      ],
      "metadata": {
        "id": "mkzyLAY7mjEp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#initial library"
      ],
      "metadata": {
        "id": "6nF7yIY1xkSr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "SVC #chon SVC yek class mibashad ma az in class yek instance migirim"
      ],
      "metadata": {
        "id": "xMK6Ij88miTN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ab36061-b5e5-46c3-f710-e90a48969c7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.svm._classes.SVC"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc_classifire=SVC()\n",
        "svc_classifire.fit(x,y) #warning should len(x)=len(y) yani yadgiri migirad va dar svc_classifire"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mWzc6kgux2zr",
        "outputId": "bce97fef-e2be-4a83-f9bc-577a86a1d893"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#predict"
      ],
      "metadata": {
        "id": "JNQZruZAyuTS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svc_classifire.predict([x[200],x[190]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E5zjzkRtyxRY",
        "outputId": "a37a8911-e6bc-4da9-c7b5-73ac7f0984f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#test"
      ],
      "metadata": {
        "id": "ymdiDeBH0JsN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot()\n",
        "plt.imshow(data.images[200],cmap=plt.cm.gray_r)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "OfTHfkWEzt6U",
        "outputId": "bbd404a0-13b2-4179-ed18-569c88ecedc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f96b215a6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#generate new data for predict"
      ],
      "metadata": {
        "id": "8BST5_5I0kdG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test=data.images[190]\n",
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UuNZEdE30X9t",
        "outputId": "057829cb-8167-4a15-f0fa-e7b912dc6aa3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.,  2., 15., 15.,  6.,  0.,  0.,  0.],\n",
              "       [ 0.,  0., 10., 13., 16.,  5.,  0.,  0.],\n",
              "       [ 0.,  0.,  0.,  2., 16.,  9.,  0.,  0.],\n",
              "       [ 0.,  0.,  0.,  3., 16., 11.,  0.,  0.],\n",
              "       [ 0.,  0.,  0.,  0., 13., 14.,  1.,  0.],\n",
              "       [ 0.,  0.,  0.,  0.,  7., 16.,  5.,  0.],\n",
              "       [ 0.,  1.,  4.,  6., 13., 15.,  1.,  0.],\n",
              "       [ 0.,  3., 15., 14., 11.,  2.,  0.,  0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test[0,1]=10\n",
        "test[3,2]=16\n",
        "test[6,3]=8\n",
        "test[6,1]=0\n",
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Dg2sClt0vXO",
        "outputId": "b6cd68c9-a897-4c13-d58d-c8b8e9410469"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0., 10., 15., 15.,  6.,  0.,  0.,  0.],\n",
              "       [ 0.,  0., 10., 13., 16.,  5.,  0.,  0.],\n",
              "       [ 0.,  0.,  0.,  2., 16.,  9.,  0.,  0.],\n",
              "       [ 0.,  0., 16.,  3., 16., 11.,  0.,  0.],\n",
              "       [ 0.,  0.,  0.,  0., 13., 14.,  1.,  0.],\n",
              "       [ 0.,  0.,  0.,  0.,  7., 16.,  5.,  0.],\n",
              "       [ 0.,  0.,  4.,  8., 13., 15.,  1.,  0.],\n",
              "       [ 0.,  3., 15., 14., 11.,  2.,  0.,  0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot()\n",
        "plt.imshow(test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "xbNBgGuO1Uhx",
        "outputId": "9cca29df-d8a9-4eeb-8a06-e7aa24f5c0e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f96b2c90ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test=test.reshape(-1)\n",
        "svc_classifire.predict([test])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0KmdG8WD1tj4",
        "outputId": "2052a0b0-d176-4105-820d-3cfef0971643"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " # evaluation\n",
        " bayad baray tak tak class ha ya lable ha bedast avard"
      ],
      "metadata": {
        "id": "0X8GTGZD38dQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "matrix confution for eny class"
      ],
      "metadata": {
        "id": "T-wyBauQ4ZyI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.unique(y)"
      ],
      "metadata": {
        "id": "50Y-L-F_37iE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4de13abf-281d-4260-fdab-d2ef1e793b38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "computing accuracy recall precision "
      ],
      "metadata": {
        "id": "cQyi1mUO-9eO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svm_new=SVC()\n",
        "svm_new.fit(x[:1000],y[:1000])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "181j0O3c-6zW",
        "outputId": "5e4da128-481b-45f1-abce-fe01cbb7cd44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svm_new.predict(x[1000:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MeIu5Y6jAY4R",
        "outputId": "6ace2de2-ff3b-4dd1-dff6-a9a79ce7d30b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 4, 0, 5, 3, 6, 9, 6, 1, 7, 5, 4, 4, 7, 2, 8, 2, 2, 5, 7, 9, 5,\n",
              "       4, 4, 9, 0, 8, 9, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4,\n",
              "       5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 9, 5, 5, 6, 5, 0,\n",
              "       9, 8, 9, 8, 4, 1, 7, 7, 3, 5, 1, 0, 0, 7, 8, 2, 0, 1, 2, 6, 3, 3,\n",
              "       7, 3, 3, 4, 6, 6, 6, 9, 9, 1, 5, 0, 9, 5, 2, 8, 2, 0, 0, 1, 7, 6,\n",
              "       3, 2, 1, 5, 4, 6, 3, 1, 7, 9, 1, 7, 6, 8, 4, 3, 1, 4, 0, 5, 3, 6,\n",
              "       9, 6, 1, 7, 5, 4, 4, 7, 2, 8, 2, 2, 5, 7, 9, 5, 4, 8, 8, 4, 9, 0,\n",
              "       8, 9, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8,\n",
              "       9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 9, 5, 5, 6, 5, 0, 9, 8, 9, 5,\n",
              "       4, 1, 7, 7, 3, 5, 1, 0, 0, 2, 2, 7, 8, 2, 0, 1, 2, 6, 3, 3, 7, 3,\n",
              "       3, 4, 6, 6, 6, 4, 9, 1, 5, 0, 9, 5, 2, 8, 2, 0, 0, 1, 7, 6, 3, 2,\n",
              "       1, 7, 4, 6, 3, 1, 3, 9, 1, 7, 6, 8, 4, 3, 1, 4, 0, 5, 3, 6, 9, 6,\n",
              "       8, 7, 5, 4, 4, 7, 2, 8, 2, 2, 5, 7, 9, 5, 4, 8, 8, 4, 9, 0, 8, 9,\n",
              "       8, 0, 9, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0,\n",
              "       1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 9, 5, 5, 6, 5, 0, 9, 8, 9, 8, 4, 1,\n",
              "       7, 7, 3, 5, 1, 0, 0, 2, 2, 7, 8, 2, 0, 1, 2, 6, 3, 3, 7, 3, 3, 4,\n",
              "       6, 6, 6, 4, 9, 1, 5, 0, 9, 6, 2, 8, 3, 0, 0, 1, 7, 6, 3, 2, 1, 7,\n",
              "       4, 6, 3, 1, 3, 9, 1, 7, 6, 8, 4, 3, 1, 4, 0, 5, 3, 6, 9, 6, 1, 7,\n",
              "       5, 4, 4, 7, 2, 8, 2, 2, 5, 7, 9, 5, 4, 8, 8, 4, 9, 0, 8, 0, 1, 2,\n",
              "       3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4,\n",
              "       5, 6, 7, 8, 9, 0, 9, 5, 5, 6, 5, 0, 9, 8, 9, 8, 4, 1, 7, 7, 3, 5,\n",
              "       1, 0, 0, 2, 2, 7, 9, 2, 0, 1, 2, 6, 3, 3, 7, 3, 3, 4, 6, 6, 6, 4,\n",
              "       9, 1, 5, 0, 9, 5, 2, 8, 2, 0, 0, 9, 7, 6, 3, 2, 1, 7, 4, 6, 3, 1,\n",
              "       3, 9, 1, 7, 6, 8, 4, 3, 1, 4, 0, 5, 3, 6, 9, 6, 1, 7, 5, 4, 4, 7,\n",
              "       2, 8, 2, 2, 5, 7, 9, 5, 4, 8, 8, 4, 9, 0, 8, 9, 8, 0, 1, 2, 3, 4,\n",
              "       5, 1, 7, 1, 9, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 8,\n",
              "       9, 4, 9, 5, 5, 6, 5, 0, 9, 8, 5, 8, 4, 1, 7, 7, 3, 5, 1, 0, 0, 0,\n",
              "       2, 7, 8, 2, 0, 1, 2, 6, 8, 7, 7, 7, 3, 4, 6, 6, 6, 9, 9, 1, 5, 0,\n",
              "       9, 5, 2, 8, 0, 1, 7, 6, 3, 2, 1, 7, 9, 6, 3, 1, 3, 9, 1, 7, 6, 8,\n",
              "       4, 3, 1, 4, 0, 5, 3, 6, 9, 6, 1, 7, 5, 4, 4, 7, 2, 2, 5, 7, 3, 5,\n",
              "       9, 4, 5, 0, 8, 9, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 8, 4,\n",
              "       5, 6, 7, 8, 9, 0, 1, 2, 5, 4, 5, 6, 7, 8, 9, 0, 9, 5, 5, 6, 5, 0,\n",
              "       9, 8, 9, 8, 4, 1, 7, 7, 7, 5, 1, 0, 0, 2, 2, 7, 8, 2, 0, 1, 2, 6,\n",
              "       8, 8, 7, 5, 8, 4, 6, 6, 6, 4, 9, 1, 5, 0, 9, 5, 2, 8, 2, 0, 0, 1,\n",
              "       7, 6, 3, 2, 1, 7, 4, 6, 3, 1, 3, 9, 1, 7, 6, 8, 4, 5, 1, 4, 0, 5,\n",
              "       3, 6, 9, 6, 1, 7, 5, 4, 4, 7, 2, 8, 2, 2, 5, 7, 9, 5, 4, 8, 8, 4,\n",
              "       9, 0, 8, 9, 8])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    }
  ]
}