c6_实现简单线性回归.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1., 2, 3, 4, 5])\n",
    "y = np.array([1., 3, 2, 3, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27f7157b320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.axis([0,6, 0,6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $a = \\frac{\\sum_{i=1}^m(x^{(i)} - \\overline{x})(y^{(i)} - \\overline{y})}{\\sum_{i=1}^m(x^{(i)} - \\overline{x})^2}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $b = \\overline{y} - a\\overline{x}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_mean = np.mean(x)\n",
    "y_mean = np.mean(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "num = 0.0  # 分子\n",
    "deno = 0.0 # 分母\n",
    "for x_i, y_i in zip(x, y):\n",
    "    num += (x_i - x_mean) * (y_i - y_mean)\n",
    "    deno += (x_i - x_mean)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8 0.39999999999999947\n"
     ]
    }
   ],
   "source": [
    "a = num/deno\n",
    "b = y_mean - a*x_mean\n",
    "print(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27f71645cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat = a*x + b\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x, y_hat, color='red')\n",
    "plt.axis([0,6, 0,6])  # 设置坐标轴范围\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用自己编写的SimpleLinearRegression1类来训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from c6_SimpleLinearRegression import SimpleLinearRegression1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Simple Linear Regression1()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1 = SimpleLinearRegression1()\n",
    "reg1.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.56])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1.predict(np.array([5.2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8 0.39999999999999947\n"
     ]
    }
   ],
   "source": [
    "print(reg1.a_, reg1.b_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat1 = reg1.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27f7165cf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(x, y_hat1, color='red')\n",
    "plt.axis([0,6, 0,6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 向量化实现SimpleLinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from c6_SimpleLinearRegression import SimpleLinearRegression2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Simple Linear Regression2()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg2 = SimpleLinearRegression2()\n",
    "reg2.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8 0.39999999999999947\n"
     ]
    }
   ],
   "source": [
    "print(reg2.a_, reg2.b_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 向量化实现性能对比测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = 1000000\n",
    "big_x = np.random.random(size=m)\n",
    "big_y = big_x * 2.0 + 3.0 + np.random.normal(size=m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.52 s ± 19.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "30 ms ± 1.19 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit reg1.fit(big_x, big_y)\n",
    "%timeit reg2.fit(big_x, big_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9909272694164883 3.0064488413998864 1.9909272694163427 3.0064488413999593\n"
     ]
    }
   ],
   "source": [
    "print(reg1.a_, reg1.b_, reg2.a_, reg2.b_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}