第5.3节_分类（感知器）.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 示例代码5-3-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 读入学习数据\n",
    "train = np.loadtxt('images1.csv', delimiter=',', skiprows=1)\n",
    "train_x = train[:,0:2]\n",
    "train_y = train[:,2]\n",
    "\n",
    "# 绘图\n",
    "plt.plot(train_x[train_y == 1, 0], train_x[train_y == 1, 1], 'o')\n",
    "plt.plot(train_x[train_y == -1, 0], train_x[train_y == -1, 1], 'x')\n",
    "plt.axis('scaled')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 示例代码5-3-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 权重的初始化\n",
    "w = np.random.rand(2)\n",
    "\n",
    "# 判别函数\n",
    "def f(x):\n",
    "    if np.dot(w, x) >= 0:\n",
    "        return 1\n",
    "    else:\n",
    "        return -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 示例代码5-3-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第1次: w = [-152.88237796 -431.07310196]\n",
      "第2次: w = [321.11762204 -47.07310196]\n",
      "第3次: w = [  88.11762204 -477.07310196]\n",
      "第4次: w = [ 484.11762204 -156.07310196]\n",
      "第5次: w = [  85.11762204 -589.07310196]\n",
      "第6次: w = [ 488.11762204 -289.07310196]\n",
      "第7次: w = [ 109.11762204 -705.07310196]\n",
      "第8次: w = [ 372.11762204 -593.07310196]\n",
      "第9次: w = [ 846.11762204 -209.07310196]\n",
      "第10次: w = [ 613.11762204 -639.07310196]\n"
     ]
    }
   ],
   "source": [
    "# 重复次数\n",
    "epoch = 10\n",
    "\n",
    "# 更新次数\n",
    "count = 0\n",
    "\n",
    "# 学习权重\n",
    "for _ in range(epoch):\n",
    "    for x, y in zip(train_x, train_y):\n",
    "        if f(x) != y:\n",
    "            w = w + y * x\n",
    "\n",
    "            # 输出日志\n",
    "            count += 1\n",
    "            print('第{}次: w = {}'.format(count, w))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 示例代码5-3-4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# 绘图确认\n",
    "x1 = np.arange(0, 500)\n",
    "plt.plot(train_x[train_y ==  1, 0], train_x[train_y ==  1, 1], 'o')\n",
    "plt.plot(train_x[train_y == -1, 0], train_x[train_y == -1, 1], 'x')\n",
    "plt.plot(x1, -w[0] / w[1] * x1, linestyle='dashed')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 示例代码5-3-5 验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 200×100的横向图片\n",
    "f([200, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 100×200的纵向图片\n",
    "f([100, 200])"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}