P17-手写数字识别.ipynb · fengbingmax/ant-exercises-sklearn

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P17-手写数字识别.ipynb 14.79 KB

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fa311218",
   "metadata": {},
   "source": [
    "### P083 手写数字识别-加载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e35c0187",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6568667c",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "data, target = load_digits(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "346c752a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da29eda2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "813e4756",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  2., 13.,  0.,  0.,  0.,  0.,  0.,  0.,  8., 15.,\n",
       "        0.,  0.,  0.,  0.,  0.,  5., 16.,  5.,  2.,  0.,  0.,  0.,  0.,\n",
       "       15., 12.,  1., 16.,  4.,  0.,  0.,  4., 16.,  2.,  9., 16.,  8.,\n",
       "        0.,  0.,  0., 10., 14., 16., 16.,  4.,  0.,  0.,  0.,  0.,  0.,\n",
       "       13.,  8.,  0.,  0.,  0.,  0.,  0.,  0., 13.,  6.,  0.,  0.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f225522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.,  2., 13.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  8., 15.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  5., 16.,  5.,  2.,  0.,  0.],\n",
       "       [ 0.,  0., 15., 12.,  1., 16.,  4.,  0.],\n",
       "       [ 0.,  4., 16.,  2.,  9., 16.,  8.,  0.],\n",
       "       [ 0.,  0., 10., 14., 16., 16.,  4.,  0.],\n",
       "       [ 0.,  0.,  0.,  0., 13.,  8.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0., 13.,  6.,  0.,  0.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[100].reshape(8, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "194a08a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "455e600a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x26420efec40>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(data[100].reshape(8, 8), cmap='gray_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a04c7960",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3484bcb8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b83f5415",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bacc20c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8a10b884",
   "metadata": {},
   "source": [
    "### P084 手写数字识别-训练测试集拆分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "737b82e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "29cdfb3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(data, target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f6c80837",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1347, 64), (450, 64), (1347,), (450,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "094a7737",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2004ad75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce5b662e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f8f61bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb45d00f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a080920",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aeb6d48d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2521e97",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36879276",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "948dd2a8",
   "metadata": {},
   "source": [
    "### P085 手写数字识别-K近邻算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "21e04c24",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9282d4fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8e548865",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f7635826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9933333333333333"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4c9f21b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9888641425389755"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d5b2123",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "096f8d5b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80a8eea2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b07f165",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "877d33d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cd36bd86",
   "metadata": {},
   "source": [
    "### P086 手写数字识别-逻辑回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "54f806a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3f5169bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = LogisticRegression(max_iter=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c934e64e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=10000)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ff8910a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9733333333333334"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "28223bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4176de52",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1d1e6a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfd89578",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b723bffe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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   "pygments_lexer": "ipython3",
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 },
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authored 2022-01-02 20:44 . 'xx'

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https://gitee.com/fengbingmax/ant-exercises-sklearn.git

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ant-exercises-sklearn

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fengbingmax/ant-exercises-sklearn

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