5.1 机器学习算法.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4ea499ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b38adfd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x235d8294bb0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HVYHjatuSXpJ0LiKe3/VWpY7pQeOs4jE9DFehDGH7L7V91i1J10j6TUR0Smxpqmy/IumUtpfhvCDpaUn/KelVSWuSNiU9FBFJfwF4wDhPaftX7ZC0IemRnXniVNn+qaTfSfpE0qWi/JS254crc0wPGefDqtgxPQwBDgCJYgoFABJFgANAoghwAEgUAQ4AiSLAASBRBDgAJIoAB4BE/QnjDOwxq9x9HwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_val = np.random.uniform(low = 0, high = 30, size = 10)\n",
    "y_val = x_val + 3 * np.random.normal(size=len(x_val))\n",
    "plt.scatter(x_val, y_val, c='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a2f06191",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x235d94e8040>]"
      ]
     },
     "execution_count": 12,
     "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.scatter(x_val, y_val, c='black')\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_val.reshape(-1,1), y_val)\n",
    "\n",
    "x_lf = np.linspace(0, 30, 50)\n",
    "y_lf = lr.predict(x_lf.reshape(-1,1))\n",
    "plt.plot(x_lf, y_lf, 'b--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e6500b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.   7.5 15.  22.5 30. ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0. ],\n",
       "       [ 7.5],\n",
       "       [15. ],\n",
       "       [22.5],\n",
       "       [30. ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(0, 30, 5)\n",
    "print(x)\n",
    "x.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a884f424",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coef =  [1.06299225] intercept =  -1.118126746241602\n",
      "coef = 1.06 intercept = -1.12\n"
     ]
    }
   ],
   "source": [
    "coef = lr.coef_\n",
    "intercept = lr.intercept_\n",
    "\n",
    "print('coef = ', coef, 'intercept = ', intercept)\n",
    "print('coef = %.2f'%coef[0], 'intercept = %.2f'%intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37e37dfa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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