Percetron_.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Percetron(object):\n",
    "    def __init__(self, learning_rate=0.001, n_iter=500):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.n_iter = n_iter\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        self.w_ = np.zeros(1 + X.shape[1])  # 样本特征维度再增加1列，即偏置b\n",
    "        self.errors_ = []                   # 记录每一轮误分类的样本数量\n",
    "        \n",
    "        for _ in range(self.n_iter):  # 更新多少次\n",
    "            errors = 0\n",
    "            for xi, target in zip(X, y):\n",
    "                update = self.learning_rate * (target - self.predict(xi))#调用了predict()函数\n",
    "                self.w_[1:] += update * xi\n",
    "                self.w_[0] += update\n",
    "                errors += int(update != 0.0)\n",
    "                \n",
    "            self.errors_.append(errors)\n",
    "        return self\n",
    "    \n",
    "    def net_input(self, X):\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]  # wx+b\n",
    "    \n",
    "    def predict(self, X):\n",
    "        return np.where(self.net_input(X) >= 0, 1, -1)  # 预测值，大于0则为1类，小于0则为-1类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.colors import ListedColormap\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('iris.data', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3            4\n",
       "0  5.1  3.5  1.4  0.2  Iris-setosa\n",
       "1  4.9  3.0  1.4  0.2  Iris-setosa\n",
       "2  4.7  3.2  1.3  0.2  Iris-setosa\n",
       "3  4.6  3.1  1.5  0.2  Iris-setosa\n",
       "4  5.0  3.6  1.4  0.2  Iris-setosa"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 从中抽取两类花\n",
    "y = df.iloc[0:100,4].values  # 自动转化为array类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
       "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
       "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = np.where(y == 'Iris-setosa', 1, -1)  # 转换为感知机支持的1，-1而分类问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = df.iloc[0:100, [0 ,2]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  1.4],\n",
       "       [ 4.9,  1.4],\n",
       "       [ 4.7,  1.3],\n",
       "       [ 4.6,  1.5],\n",
       "       [ 5. ,  1.4]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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G3SN9ECQSLwE9gV6Jx4GtLD8C8JqaGm9JTU2Nt7XM6tXuRx7pHqQHweOMM9w/+qj5snfc\n4W6Wuiy4V1S4z5mT/v3//nf3uXPdZ8xwv/JK99dfbzGUrJ1//vl+0EEHeUNDw+6xd9991ysrK/36\n6693d/eTTjrJhw0b5jt37kxZ9/jjj/fBgwfvfn7vvfe6mfnYsWNT3s/dvWvXrj5r1qxWY5k2bZr3\n69dv9/NHH33Uzcxnz57d4jorV650M/MZM2akjH/ve9/ziooKf/zxx3ePjRs3zk888cTdz2+77Tav\nqKjw6urq3WO7du3yz3/+837AAQf4R4lf4Lp169zMvGvXrr5p06ZWP4N7ZvuMxNvrrzf/fzT5UVcX\ndYTl7fXX3R9+WPMcd43/1gEjPI/f5XE5zbHL3d939w2Jx+ZCbqy+HiZNgtdfTx1/6CH49rebL/+7\n36V/n4YGSPdH9N/+BsccA9Onw9y5wSmRIUPgnntyjx3g3HPPZcOGDTz++OO7x37729/i7px77rls\n2bKFxx57jLPPPptt27axadOm3Y+JEyeyevVq3nnnnd3rmhmXXnppsyMMXbt2Zfny5SnLtqXxNMxV\nV13V4jIPP/wwZsbs2bNTxr/73e/i7vzxj39scd1HHnmEgw46iPPOO2/3WGVlJd/61rf46KOP+POf\n/5yy/JQpU3Q0op0YNCgotqysTB2vrAzGdVSiMDZvDv49HTwYJk8Ofg+TJsGWLVFHJsUUl2RioJm9\nbWZvmNn9ZnZoITe2ZElwSDTdudX77oOtW3N7/29/G9avD37etSt434YG+PrXg0QjV5MmTeKAAw7g\nv/7rv3aPzZ8/n2HDhtG/f3/WrFmDu/OjH/2Inj17pjyuueYaADZs2JDynocffniz7dx44428/PLL\nHHrooYwePZo5c+bw5ptvthrb2rVr6dOnD11bOffTWM8wYMCAlPHevXvTtWtX1jdOXgvrDkzzrTBk\nyBDcvdm66T6XlK/qapgwIXVswoRgXApDRa8C8UgmngWmAVXATKAf8ISZ7VuoDbb2fbhrFzT9Q/yM\nM9IvW1EBZ52VOrZ9e3Ako2miAkFC8dvfZhdrOnvvvTdf/vKX+d3vfkdDQwNvv/02Tz/99O6/1hsv\n1bzssstYunRps8eSJUuafZF36tSp2XbOPvts1q5dy89//nMOPvhgbrrpJo466igWJV93l4Ni9JBI\n97mkfHXrBgsXBsWYDz8c/HfhQl0WWigqepVGkRdgunvyN9PLZvYcsB44B2jxxMDs2bPp0qVLytjU\nqVOZmkE6/JnPtPxax45wyCGpY1/7WlBsmVyAWVERHNZrelrkk0/SJxKN63z4YZvhZeTcc89l3rx5\n/OlPf+KVV14B4JxzzgHgiCOOAGCvvfbKuXtk7969mTlzJjNnzmTjxo0MHz6c66+/nqqqqrTL9+/f\nn8WLF7N169YWj0707duXhoYGVq9ezeDBg3ePb9iwga1bt9K3b98W4+nbty9/+ctfmo03XqXR2rrS\nfgwcqNMaxaCi13irrq6muslhuW3bthVkW3E4MpHC3bcBdcCA1pa79dZbeeihh1IemSQSAGPHwrHH\nQocmqZQZfOMbsP/+qeMdO8Kjj8Ktt8Lxx8PnPgc//nFwJUiTfIZu3YL6iHR/dO/aBePGZRRimyZM\nmEC3bt144IEHmD9/Pscdd9zuL9KePXsybtw47rrrLt59991m627cuLHN929oaOCDJte+9ujRgz59\n+vDpp5+2uN5ZZ51FQ0MDc+bMaXGZyZMn4+7cdtttKeM333wzZsapp57a6rrvvvtuyime+vp6fvaz\nn7H//vszduzYtj6aSOTq6uCRR0r/L/f+/Vt/fUCr/4qHVy7zV2hTp05t9j156623FmRbkR+ZaMrM\n9iNIJOa1tWxYFRXBjnjBBdBYw9ihA1xySVAsmU6nTsFRiHQFmsnM4MYb4bTTgu00NoesqAiKwI4/\nPj+foUOHDpx55pk88MADbN++nZtvvjnl9V/84heccMIJHHPMMVx66aUcccQRvPfeeyxbtoy3336b\nF154Yfey3ni4JcmHH37IIYccwpQpUxg6dCj77bcfS5YsYcWKFdxyyy0txjVu3DguvPBCfvrTn1JX\nV8ekSZNoaGjgySefZPz48Xzzm9/k2GOP5aKLLuKXv/wlW7ZsYezYsSxfvpx58+Zx5plntpoQfP3r\nX+euu+5i2rRprFixYvelocuWLeP2229n330LdnZMJGfl1qGzseh16dLUI7KVlUGtSr6PSpTb/JWV\nfF4aEuYB/DvwRaAv8HlgCfAe0L2F5fNyaWij1avd//xn9w0b2lw0K4sXu3/+8+577eXeu7f7j37k\n/vHH+d3G0qVLvaKiwjt06OBvv/12s9fffPNNnzZtmvfp08f32WcfP/TQQ/20007zBQsW7F7m3nvv\n9YqKimZztWPHDr/88st9+PDh3qVLF99///19+PDhftddd6UsN23aND/iiCNSxhoaGvzmm2/2I488\n0jt27Oi9e/f2U0891V944YXdy9TX1/t1113n/fv393322cf79u3rP/zhD33Hjh0p7zVu3DgfP358\nytj777/v06dP9169ennHjh196NChPm/evJRl1q1b5xUVFX7LLbdkMJO6NFSKo6rKvbIy9ZLVyspg\nvFRt3hzEn/yZqqqC8Xwrx/krtkJdGmqe5q/SYjKzauAEoDvwPvAUcKW7py2TNLMRQE1NTc3uBkhN\n1dbWMnLkSFpbRiSZ9hkptLq6oM6qtddLub5g9eqgRqJQN1cr9/krlsZ/64CR7l7b1vKZivw0h7vr\nAiIRKXvlXqxY6KLXcp+/Uhe7AkwRkXIUVbFiudD8xZuSCRGJtXKp3FeHzuay+d1q/uJNyYSIxFI5\ntmlWh85A2N+t5i++Iq+ZEBFJp7U2zQsXRhNTrho7dBa6WDHuwv5uNX/xpWRCRGKnsU1zU8ltmkv5\nS6Q9d+jMx++2Pc9fXOk0h4jETiaV+1Ka9LstTzoyISKxE0Xl/qJFwf13xoyBk0/O//snq6sLvlTL\n6TB9pp8pyhbc5TbncVLWyUTjzZ9E2qJ9JV6K2ab5jTdg9GjYtGnPWPfu8Pzz0K9f/rYD5dkOOtvP\npBbcZSqf7TSL8SCDdtrr16/3zp07N7YM1UOPjB6dO3f29evXZ9qVVgqsWG2au3dP3Ubjo3v3/G7H\nvTzbQYf5TGrBHZ2ybaedrUzaaQO89dZbGd0dU6RRjx49OOyww6IOQ5ooZOX+okXBJYktWbw4f6c8\nyrEddK6fSS24i69s22kXymGHHaYvBpEyUMjK/eXLW3992bL8JRPl2A4618+kFtzlo2yTCRGRtowe\n3frrY8bkb1ulVHgY92LKTMU9vnKiS0NFpN2qqmq5CK9bt/xe1VHsdtBhukxmu07cW1zHPb5yomRC\nRNq1Y47JbjwXxWwH3VqXyXyuE/cW13GPr1yUbQGmiEhboirQi2PhYdyLKXMV9/iKRQWYIiJ5FlWB\nXhwLD+NeTJmruMdX6pRMiMRQMbv1FbJALyqZdrPMR4FeseYim+2E+VwqVpSc5LNpRTEeZNC0SqRU\nbdpUvGY+YbZVzPjCWLOmeROq7t3d165teZ2wTY2KNRdhtxPmc6nBU/krVNOqyJODrANWMiFlrJj/\nmJfjl02YbpZhuzEWay7CbifM5ypmZ0qJhjpgJqgAU8pVMYsBoyjQK7Rcu1lmU6BXrLnIx3bCFB6q\nWLF8FaoAU5eGisREMW/NHGZbcb91dCbdLFszcCCcckpmX57Fmot8bCebz5XLOtK+qQBTJCaKWQxY\njgV6pdTNMu4dJsuxKFcKS0cmRGIil259xehcGPduglVVwa3D0+nePR7dLOPeYbIYXTOlTOWzAKMY\nD1SAKWWsmMWA5Vigt3Zt9ldzhBVmLsrxdt1xL8qVVCrATFABprQHxSwGLMcCvSVLghqJtvpM5EOm\ncxH3DpPlWJQrzakDpkg7kk23vig6F8a9m+DJJxc+iWiU6VzEvcNkFF0zpXwomRApcXEvjCx3xSqm\nLHSBYzkW5UrxqABTpMTFvTCyXBWrmLJYBY7lWJQrxaNkQqQM6DbLxVes23WH2U5YYeLTviegW5CL\nlJW4F0aWi2IVU5bSLdK175UGFWCKSJviXhhZLopVTFlKt0jXvte+6TSHiEiWilV4qAJHKRVKJkTa\nubo6eOSR4DB1phYtgmuvDfo5FFqY+AqtWIWHKnCUUqFkQqSdCnOVwBtvQI8ewXJXXw0TJwbP33wz\nHvEVU7EKD1XgKKVABZgi7dSkScFVAfX1e8YqK4MvqoUL06/Towds2tR8vHt32Lgx+viiUKzCQxU4\nSj6oAFNE8qauLjhV0VR9fTC+enXzL6xFi9InEhCML1mSv66TYeKLSrEKD1XgKHGm0xwi7VAmVwk0\ntXx56+ssWxY+nqbCxCci0dGRCZF2KMxVAqNHt77OmDFtb7dYradFpLh0ZEKkHRo0CMaPB7PUcbNg\nPN0XfVUVdOuW/v26dWv9FEexWk+LSDSUTIi0Y03rr9uqxz7mmOzGGxWr9bSIRCN2yYSZ/auZNZjZ\nLVHHIlKu6urg0UfTv/boo+l7OtTVwRNPpF/niSda7gPRWEyZfFUGpBZTptOtW3DVRl0dPPxw8N+F\nC1s+OiIi0YlVMmFmnwW+DqyMOhaRchamwDFsUWSuxZQDB8Ipp+jUhkicxSaZMLP9gPuBS4CtEYcj\nUpIy7UwZpsAxbFFkFMWUYbpmxrHTpkipiE0yAfwC+L27t3DwVURakm1nyjAFjmGLIgcNghNPTP9a\nS8WeYYXpmhn3TpsipSAWyYSZnQcMA66IOhaRUjR6dPOGUps2wWc/2/I6YQocwxZFmqW/ciTfwhR6\nhllHRFJF3mfCzA4BbgMmuPvOqOMRKTVhO1M2Fjhm06Y5zDotFXu67yn2zMfRiTBdM0up06ZInEWe\nTAAjgZ5Ardnuv1UqgS+a2T8D+3iaG4jMnj2bLl26pIxNnTqVqfpzQtqZTDpTttYDIkyb5mzWyaQA\nMx9f2GG2U6zYRKJQXV1NdZPDhtu2bSvItuKQTCwFml6lfi+wCrghXSIBcOutt+pGX1J0mXZwLKZc\nO1MW+jMVqwCzmEWlIqUg3R/YSTf6yqvIaybc/e/u/mryA/g7sMndV0UdnwjEu0ivqiq4a2c63bu3\nfFSiWJ8pTLfNsNspVlGpiKSKPJloQWndF13KXtyL9J5/vnlC0b17MN6SYn+mbLtthlHMolIR2cNa\nOIsQW2Y2AqipqanRaQ4pirq64K/31l6Py1+wS5YENRJjxrReJ1HMzxTF/GVTIJrLOiKlJuk0x0h3\nr83X+8ahZkIk1kqpSO/kk1tPIhoV8zNFMX+FLioVkVRKJkTaUEpFenG8xXcpzZ+IhBPXmgmR2CiF\nIr043+K7FOZPRHKjZEIkA3Ev0ov7Lb7jPn8ikhud5hDJQJjOj8UStotjMT9TnOdPRHKnZEIkC3Es\n0su1wLGYnymO8yciudNpDpESpwJHEYla1smEme1rZteZ2TNmtsbM1iY/ChGkiLSsmLf4FhFJJ8xp\njruBscCvgXdQt0qRyDXe4ju5B10hbvEtIpJOmGTiFOBUd38638GISPaKdYtvEZGWhKmZ2AJszncg\nIhJOJgWYIiKFFCaZ+BFwrZl1zncwIpI9FWCKSNQyOs1hZi+QWhsxAHjPzNYBO5OXdXfdfUukiBo7\nTC5dGvSWaFRZGTSGausUR6YtuEVEWpJpzcR/FzQKEclJdXXQ7TK5eVVbHSY3bw46ZyavU1UVrNOt\nW+FiFZHyk1Ey4e5zCh2IiIQXpsNkay24Fy4sXKwiUn7C9JlYa2bd04x3VZ8JkWgNHAinnJLZqY1F\ni1JPi0BqC24RkUyFKcA8HKhMM74PcEhO0YhIUegKEBHJp4z7TJjZaUlPq8xsW9LzSuAk4M18BSaS\nKRUQZi/XK0A05yKSLJumVY1FmA7c1+S1ncA64Lt5iEkkIyogDC/sFSCacxFJJ+PTHO5e4e4VwFtA\nr8bnicc+7j7Y3f9QuFBFUrVWQChtq64OEodkbV0BojkXkXSybqft7v0KEYhINhoLCJtKLiDU4ffW\nZXsFiOZcRFqSdTJhZt9q4SUHPgHWAE+4e30Ly4nkLJMCQn2xZWbgwMzmSnMuIi0Jc6Ov2UBPoDPB\nfToAugGKvgjBAAAclklEQVTbgY+AXsBaMzvR3f+alyhFmlAL6eLTnItIS8JcGno58Dww0N27u3t3\nYBCwHPgOcBjwLnBr3qIUaaKxgLCyyUXKlZXBuP5Czj/NuYi0JEwy8WNgtrvvPujp7muAy4Afu/v/\nAt8Hjs9PiCLphSkglNxozkUknTCnOfq0sF4H4KDEz38D9g8blEgmwrSQltxozkUknTDJxGPAXWZ2\nibu/AGBmw4E7gUcTyxyDGlhJkWRaQCj5ozkXkWRhTnNMBzYDNWb2qZl9CqxIjE1PLPMRamAlIdTV\nwSOPZHdviDDriIhI/oTpM/EucLKZfYag8BLgdXd/PWmZx/IUn7QTYTorqhujiEg8hDkyAYC7v+bu\nDyUer7e9hkjLwnRWVDdGEZF4CNO0qhKYRnBjr140SUjcfXxeIpN2I0xnRXVjFBGJjzBHJm5PPCqB\nl4GVTR4iWQlzO2zdQltEJD7CXM1xHnCOuz+c72CkfQrTWVHdGEVE4iPMkYkdBPffEMmLMJ0V1Y1R\nRCQ+wiQTNwPfNjPLdzDSfoXprKhujCIi8RDmNMcXgBOBU8zsFWBn8ovufmY+ApP2JUxnRXVjFBGJ\nhzDJxFbgd/kORATCdVZUN0YRkWiFaVr1tUIEIiIiIqUpVNMqM+tgZhPMbIaZ7Z8Y62Nm++U3PBER\nEYm7ME2r+gILgcOAfYAlwIfA5YnnM/MZoIiIiMRb2KZVK4BuwMdJ478j6IopIiIi7UiYZOIE4N/c\nfUeT8XXAwdm+mZnNNLOVZrYt8XjGzCaFiEtEREQiECaZqCBopd3UIQSnO7L1V4JTJCOAkcCjwP+Y\n2ZAQ7yUiIiJFFiaZWAx8J+m5Jwov5wBZt9h29z+6+0J3f8Pd17j7D4GPgM+FiE0kY3V18MgjQZ8K\nEREJL0wy8V3geDN7FegI/Cd7TnFcnkswZlZhZucBnYFlubyXSEs2b4ZJk2DwYJg8OWjNPWkSbNkS\ndWQiIqUpTJ+J/zWzoQQ3/DoW2A+YC/zG3T9udeUWmNnRBMlDR4JTJWe4+2th3kukLeefD0uXpo4t\nXQpTpwYdNUVEJDthOmDi7ruA+/MYx2vAUKALMAWYZ2ZfVEIh+VZXB4sWNR+vrw/GV69WN00RkWxl\nlEyY2WmZvqG7P5RtEInkZG3i6QtmdhzwbeAbLa0ze/ZsunTpkjI2depUpk6dmu3mpR15443WX1+z\nRsmEiJSH6upqqpvc+XDbtm0F2Za5e9sLmTVk+H7u7umu9MiKmf0JWO/uF6d5bQRQU1NTw4gRI3Ld\nlLQzdXVBrURrryuZEJFyVVtby8iRIwFGunttvt43oyMT7h6q7XYmzOzHwCPAW8D+wAXAWGBiobYp\n7degQVBVFdRI1NfvGa+sDG5frkRCRCR7BUsSstALuI+gbmIpQa+Jie7+aKRRSdmqrg4Sh2QTJgTj\nIiKSvVAFmPnk7pdEHYO0L926BVdtrF4d1EgMGKAjEiIiuYg8mRCJysCBSiJERPIhDqc5REREpIQp\nmRAREZGcZNpn4oBM39DdPwgfjoiIiJSaTGsmtgJtNaSwxDI595kQERGR0pFpMnFiQaMQERGRkpVp\n06o/FzoQERERKU2hLw01s87AYcDeyePu/lKuQYmIiEjpyDqZMLOewD3AKS0sopoJERGRdiTMpaG3\nAV2B0cDHwCTgImA1kPHdRUVERKQ8hDnNMR443d1XJO4mut7dl5jZB8AVwB/zGqGIiIjEWpgjE/sC\nGxI/bwF6Jn7+C6B7gouIiLQzYZKJ14HBiZ9XAjPM7GBgJvBOvgITERGR0hDmNMftwD8kfp4DLAQu\nAHYA0/ITloiIiJSKrJMJd78/6ecaM+sLfAZ4y9035jM4ERERib+sT3OY2VWJHhMAuPt2d68F/m5m\nV+U1OhEREYm9MDUTVwP7pRnvnHhNRERE2pEwyUTjDb2aGgpszi0cERERKTUZ10yY2RaCJMKBOjNL\nTigqCY5W/N/8hiciIiJxl00B5ncIjkr8iuB0xrak13YA69x9WR5jExERkRKQcTLh7vcBmNmbwNPu\nvqtgUYmIiEjJyLpmInE78r5m9m9mVm1mvQDM7BQzOyrvEYqIiEishbk0dCxB6+zRwJnsubJjKEET\nKxEREWlHwlzNcQPwQ3c/maBWotGjwOfyEpWIiIiUjDDJxDHA79KMbwB65BaOiIiIlJowycRW9tyb\nI9lw4O3cwhEREZFSEyaZeAD4P2Z2EEHPiQozOx64CZiXz+BEREQk/sIkEz8AXgP+SlB8+SrwBPAM\n8G/5C01ERERKQZi7hu4ALjWz64CjCRKKF9x9db6DExERkfjLOplo5O5vmdlfEz+nu1eHiIiItANh\nTnNgZtPN7GXgE+ATM3vZzC7Jb2giIiJSCrI+MmFm1wL/AvwMaLwXxxjgVjM7zN2vymN8IiIiEnNh\nTnN8A7jU3auTxh4ys5cIEgwlEyIiIu1ImNMcewEr0ozXkEMNhoiIiJSmMMnErwmOTjT1deA3uYUj\nIiIipSbskYTpZjYReDbxfDRwGDDPzG5pXMjd/yXH+ERERCTmwiQTRwO1iZ/7J/67MfE4Omk5XS4q\nIiLSDoRpWnViIQIRERGR0hSqz4SIiIhIIyUTIiIikhMlEyIiIpKTyJMJM7vCzJ4zsw/M7D0z+52Z\nDYo6LhEREclM5MkEcAJB58zRwASCpliLzaxTpFGJiIhIRiLvWOnuk5Ofm9k0YAMwEngqiphEREQk\nc3E4MtFUV4IeFZujDkRERETaFqtkwswMuA14yt1fjToeERERaVvkpzmauAM4Eji+rQVnz55Nly5d\nUsamTp3K1KlTCxSaiIhI6aiurqa6ujplbNu2bQXZlrnHo+u1mf0c+BJwgru/1cpyI4CampoaRowY\nUbT4JIS6OnjjDRgwAAYOjDqa5uIen4hIntXW1jJy5EiAke5e29bymYrFaY5EInE6cGJriYSUiM2b\nYdIkGDwYJk+GQYOC51u2RB1ZIO7xiYiUmMiTCTO7A7gAOB/4u5n1Tjw6RhyahHX++bB0aerY0qUQ\nl1NQcY9PRKTERJ5MADOBA4DHgb8lPc6JMCYJq64OFi2C+vrU8fr6YHz16mjiahT3+ERESlDkyYS7\nV7h7ZZrHvKhjkxDeeKP119esKU4cLYl7fCIiJSjyZELKTP/+rb8+YEBx4mhJ3OMTESlBSiYkvwYN\ngqoqqKxMHa+sDMajvmoi7vGJiJQgJROSf9XVMGFC6tiECcF4HMQ9PhGREhO3plVSDrp1g4ULg2LG\nNWvi18ch7vGJiJQYJRNSOAMHxvtLOu7xiYiUCJ3mEBERkZzoyIS0X3Fup71oESxfDmPGwMknRx1N\nc3GeOxEpOiUT0v5s3hx0wVy0aM9YVVVQgNmtW3RxQfAFPXo0bNq0Z6x7d3j+eejXL7q4GsV57kQk\nMjrNIe1PnNtpN00kIHj+2c9GE09TcZ47EYmMkglpX+LcTnvRouaJRKNNm2DJkuLG01Sc505EIqVk\nQtqXOLfTXr689deXLStOHC2J89yJSKSUTEjb6urgkUey/8tz0SK49trC/0U9dy5ceCHce2/by8a5\nnfbo0a2/PmZMceJoSZznTkQipWRCWrZ5M0yaBIMHw+TJQSvqSZNgy5bW13vjDejRI1j26qth4sTg\n+Ztv5je+mhrYe2+45BK4/3742teC5y++2PI6gwbBiSemf238+GivTKiqCoot0+nePfqrOtSKXERa\noGRCWha22K5YRYRjxsDOnaljO3fCcce1vp5Z8Gg6FgfPP988oWi8miMO1IpcRNJQMiHphS22K1YR\n4dy5zROJRjt3tnzKo64OHn0U3FPH3YPxqIsI+/WDjRth8WKYMyf478aN8bgsFPa0Iq+rg4cfDv67\ncKEuCxVp55RMSHphi+2KVUT4+OOtv/6nP6UfL5UiwpNPhquuiv7URksGDoRTTtGpDREBlEy0P5kW\nU4Yttsu1iDDTYspx41p//aST0o/nWkQYpqg0TAFrsYpXwwpblCsi5cndS+oBjAC8pqbGJQubNrlX\nVbkHB/SDR1WV++bNLa9TVeVeWZm6TmVlMN6a7t1T12l8dO/e8jorVrjvtVfq8nvt5f7CCy2v06FD\n+u106NB6fGPGpF9vzJiW11mzpvnn6t7dfe3altcJM+dhtlNMYT6TiMRGTU2NAw6M8Dx+N+vIRHsR\nppgybLFdmCLCMMWUo0ZlN54cXzbjEK6oNMycqwOmiJQgJRPtQdhiyrDFdtkWEYYppqyrg2efTb/O\ns8+2/JnmzoVdu9K/tmtX+m2FKSoNM+fqgCkiJUrJRHuQa9Fh2GK7TIsIwxRThv1MYbYVpqg0THzq\ngCkiJUrJRFwUsqAtqs6FmRYRhimmDPuZwmwrTFFpmPjy0QGzHPcjEYm/fBZgFONBuRVgFqug7Qtf\nSF90eMIJ+d2Oe7giwjDFlCecEO4zhdnWgQemX+fAA1teJ0wBa5jiVffi7Udhi3JFJBZUgFmuilXQ\n9uqr6cdfeSW/24FwRYRhiilbir2tz9S5c3bjAEcdld04hCtgDdsBs1j7kTpgikgaSiaiVKyCtkWL\ngvtspLN5c34L+8IWK2ZbTBn2My1aBB98kP61Dz5oOb4nn0y/zpNP5reANUwHzGIWRqoDpoikoWQi\nSsUqaCtmYV/cixWLFV+yMAWs2XTAjKIwUh0wRSRJh6gDaNdyLWirqwu+SAYMaP0f9VwL+zLdTtht\nFbNYsVjx5SqbOVdhpIhELZ8FGMV4UG4FmGEKI8MU24Up7Atb1BdmW2EK+8IURRYzvjDCzvmJJ6b/\nTOPH5zc+ESlpKsAsV2EKI8MU24Up7Atb1BdmW2EK+8IURRYzvjDCznmcb6suImXPPPhrv2SY2Qig\npqamhhEjRkQdTm4WLYJJk1p+ffHi5ufM6+pg8OCW16mra/2w+JIlQV3AmDGtn4/PdTvZbCvZ6tXB\nOf62Du/HPb4wwn6mfMyFiLQLtbW1jBw5EmCku9fm631VMxGlTIoBm37JZVJs19oXx8kn56+or60v\nqEy3lWzgwMy++OIeXxhhP1M+5kJEJAc6zRGlOBcDxr2oL+7xhRH2M5XjXIhISVEyEaWqqubn7ht1\n757+r+ZBg4L1KitTxysrg/F8/QVarO2EFff4wgj7mUppLgrZ7ltEIqNkImpxLgaMe7fDO+6Arl1T\nx7p2hTvvjCaefAg753H/XW3eHNQHDR4MkycHCdCkSbBlS9SRiUgeqAAzLuJWDBjFdrI1aVJwpUNy\n58fKyuBLdOHC6OLKh7Bzrt+ViLSiUAWYSiakNOkKhtKh35VIbBQqmdBpDilNUbSQlnD0uxIpe7o0\nNN+yaYMs4ekKhtKh35VI2dORiXxRgVlxldIVDO2dflciZU/JRL6EbYMs4cX9CgbZQ78rkbIWi9Mc\nZnYC8D1gJPAPwJfd/aFoo8pCXV3QGrup+vpgfPVq/fVVCN26BVcCxPUKBtlDvyuRshaLZALYF3gR\nmAssiDiW7KmdcbQK2eJa8ku/K5GyFItkwt0XAgsBzErwVoelVGCmAlEREckz1UzkQykUmKlAVERE\nCkTJRL7EvcBMBaIiIlIgsTjNURbiXGCmAlERESmgkk0mZs+eTZcuXVLGpk6dytSo/9KOY4GZCkRF\nRNqd6upqqpscHd+2bVtBthW7e3OYWQOtXBqqe3OEoHsjiIgIZX5vDjPb18yGmtmwxNARieeHRhpY\nuSiFAlERESlZsUgmgFHAC0AN4MDNQC0wJ8qgykrcC0RFRKRkxaJmwt3/THwSm/IU5wJREREpabFI\nJqSI4lggKiIiJU1HA0RERCQnSiZEREQkJ0omREREJCdKJkRERCQnSiZEREQkJ0omREREJCdKJkRE\nRCQnSiZEREQkJ0omREREJCdKJkRERCQnSiZEREQkJ0omREREJCdKJkRERCQnSiZEREQkJ0omRERE\nJCdKJkRERCQnSiZEREQkJ0omREREJCdKJkRERCQnSiZEREQkJ0omREREJCdKJkRERCQnSiZEREQk\nJ0omREREJCdKJkRERCQnSiZEREQkJ0omREREJCdKJkRERCQnSiZEREQkJ0omREREJCdKJkRERCQn\nSiZEREQkJ0omREREJCdKJkRERCQnSiZEREQkJ0omREREJCdKJkRERCQnSiZEREQkJ0omREREJCdK\nJkRERCQnsUkmzOyfzOxNM/vYzJ41s89GHVMpqK6ujjqEWNA87KG5CGge9tBcBDQPhROLZMLMzgVu\nBq4GhgMrgUVm1iPSwEqA/ucIaB720FwENA97aC4CmofCiUUyAcwG7nL3ee7+GjAT2A5cHG1YIiIi\n0pbIkwkz2wsYCfypcczdHVgKjIkqLhEREclM5MkE0AOoBN5rMv4ecFDxwxEREZFsdIg6gBA6Aqxa\ntSrqOGJh27Zt1NbWRh1G5DQPe2guApqHPTQXAc1Dyndnx3y+rwVnFKKTOM2xHTjL3R9KGr8X6OLu\nZzRZ/nzgN0UNUkREpLxc4O7/ma83i/zIhLvvNLMa4CTgIQAzs8Tzn6ZZZRFwAbAO+KRIYYqIiJSD\njsDhBN+leRP5kQkAMzsHuJfgKo7nCK7umAJ8xt3fjzA0ERERaUPkRyYA3H1+oqfEtUBv4EWgSomE\niIhI/MXiyISIiIiUrjhcGioiIiIlTMmEiIiI5CTWyYSZ/auZNZjZLW0sN87MaszsEzOrM7OLihVj\nMWQyD2Y2NrFM8qPezHoVM9Z8M7Or03yuV9tYpyz3h2znolz3CQAz62NmvzazjWa23cxWmtmINtYp\nu/0i23ko130icZPIpp+rwcx+1so65bg/ZDUP+dwfYlGAmU7irqFfJ7jpV2vLHQ78AbgDOB+YANxt\nZn9z9yUFDrPgMp2HBAcGAR/uHnDfUKDQiullgkuFLfF8V0sLlvv+QBZzkVB2+4SZdQWeJmjBXwVs\nBAYCW1pZ53DKbL8IMw8JZbdPAKMIOik3OgZYDMxPt3A57g8JWc1DQl72h1gmE2a2H3A/cAnwozYW\n/waw1t2/n3j+upl9geDy0lLeKbKdh0bvu/sHhYsqEruyuLKnbPeHhGzmolG57RP/Crzl7pckja1v\nY51y3C/CzEOjston3H1T8nMz+xLwhrs/2cIq5bg/hJmHRjnvD3E9zfEL4Pfu/mgGy36O4KZgyRZR\nHjcJy2YeIPhr9UUz+5uZLTazzxcwtmIaaGZvm9kbZna/mR3ayrLlvD9AdnMB5blPfAlYYWbzzew9\nM6s1s0vaWKcc94sw8wDluU/sZkFX5QuAua0sVo77Q4oM5wHytD/ELpkws/OAYcAVGa5yEOlvEnaA\nme2Tz9iKKcQ8vAPMAM4CzgT+CjxuZsMKE2HRPAtMIziMOxPoBzxhZvu2sHxZ7g8J2c5Fue4TRxD8\nZfk6MBG4E/ipmV3YyjrluF+EmYdy3SeSnQF0Ae5rZZly3B+aymQe8rY/xOo0h5kdAtwGTHD3nVHH\nE5Uw8+DudUBd0tCzZtaf4LBdyRYWuXtyy9eXzew5gkO55wD3RBNVNLKdi3LdJwj+CHrO3RtP/a00\ns6MJEqxfRxdW0WU9D2W8TyS7GHjE3d+NOpCItTkP+dwf4nZkYiTQE6g1s51mthMYC3zbzHaYmaVZ\n512CrpnJegMfuPunhQ23YMLMQzrPAQMKFWQU3H0bwc7f0ucqx/0hrQzmIp1y2CfeAZreNngVcFgr\n65TjfhFmHtIph30CADM7jKCY8j/aWLQc94fdspiHdELtD3FLJpYSVJ8OA4YmHisIihCHevp2ncsI\nqtuTTUyMl6ow85DOMIJ/cMpGoih1AC1/rnLcH9LKYC7SKYd94mlgcJOxwbRefFiO+0WYeUinHPaJ\nRhcTnK54uI3lynF/SJbpPKQTbn9w91g/gMeAW5Ke/xi4L+n54QSXtPwfgv+RvgnsIDhFEHn8RZyH\nbwOnAf2BowhOk+wExkUde46f+9+BLwJ9gc8TVFq/B3Rvb/tDiLko131iFPApQT1Rf4JL+z4Ezkta\npuz3i5DzUJb7ROKzGcHdpK9P81rZ7w8h5yFv+0OsaiZa0PSv8H8Adlewu/s6MzsVuBX4FvC/wHR3\nb1qpW+panQdgb+BmoA+wHXgJOMndnyhOeAVzCPCfQHfgfeAp4HO+5xKo9rQ/ZDUXlOk+4e4rzOwM\n4AaCS6bfBL7t7g8kLVb2+0WYeaBM94mECQSfNV0tVdnvD0kyngfyuD/oRl8iIiKSk7jVTIiIiEiJ\nUTIhIiIiOVEyISIiIjlRMiEiIiI5UTIhIiIiOVEyISIiIjlRMiEiIiI5UTIhIiIiOVEyISIiIjlR\nMiEiWTGze8xsQSuvX2RmW4oZU2vM7E0z+1bUcYiUMyUTIlIIRe/TH7ckRqQ9UTIhIuXCiCCJEREl\nEyIlxcymmNlLZrbdzDaa2WIz65T0+iVm9qqZfZz47zeSXutrZg1mdq6ZPZ1Y5i9m9sWkZSrM7G4z\nW5vYxmv5OEVgZqebWU1im2vM7Cozq0x6vcHMppvZAjP7u5nVmdmXmrzHaYnx7YnPfWFivQPMbCzw\nK6BLYqzezK5KWn1fM5trZh+Y2XozuzTXzyQieyiZECkRZnYQwS3I7wY+A4wFFhD8RY6ZXQBcA1yR\neP0HwLVmdmGTt7oR+HdgGLAMeMjMuiVeqwD+CpwFDAHmANeb2ZQc4j4BuI/gds+fAWYAFyXiS3YV\n8ABwDPAw8Bsz65p4j37AbxOfd2hiDn7MniMRzwDfAT4AehPcavmmpPf+F+D5xGe+A7jTzAaG/Uwi\n0oS766GHHiXwAIYD9cChLby+Gji3ydiVwNOJn/sCDcBlSa9XAm8lj6V5358B85Oe3wMsaGX5i4DN\nSc+XAJc3WeYC4O2k5w3ANUnPOyfGJiae3wCsbPIe1yXm44B0201a7k3g3iZj7wJfj/p3qoce5fLo\nkL+0REQKbCXwJ+BlM1sELAb+n7tvNbPOQH9grpndnbROJbC1yfs82/iDu9eb2QqCoxAAmNk/AV8D\nDgM6AXsDL+QQ91Dg82b2wyZx7W1mHd39k8TYX5Li2m5mHwC9EkODCI4sJHsuixj+0uT5u0nvLSI5\nUjIhUiLcvQGYaGZjgInALIJTEMcBHycWu4TmX7L1mW7DzM4jOAUymyDp+BD4PnBcDqHvR3AKo9nl\npEmJBMDOpi+Tv1OxhXxvkXZPyYRIiXH3ZcAyM7sOWA+c4e63mdnfgP7u/kAbb/E54CmARBHkSOCn\nidc+T3Ba5K7Ghc2sf44h1wKD3X1tDu/xOnBKk7GmCc4OgiMeIlJkSiZESkTiCMRJBKc3NhAkBT2A\nVxOLXA3cnjg9sBDYBxgFdHX325Le6p/MbA2wiqAwsStBHQQEdRcXmtlEglqDC4HPArkkAtcCvzez\nvwL/j6AWYihwtLv/KMP3uAuYbWY3AHMJ6kcuSrzWWIS5DtjPzMYTnBLa7u4fN30jEck/HeYTKR0f\nAF8E/kjwl/q1wL+4+2IAd59LcJrja8BLwOMEX7hvNnmff008XiQ4EvEld9+ceO0ugtMRDxCc5jgQ\n+EUuQSfi+0fgZIJTMMsIrrxYl7xYulWT3mMdMAU4gyBRmAFcn3j508Qyy4D/C/wXQbL1vUzeW0Ry\nZ+76f0qkPTCzvgRHGIa7+0tRx5MrM7uS4IqMvlHHItLe6TSHSPtiUQcQVqIB1/PAJuALwGXsqfUQ\nkQgpmRBpX0r5UORA4IdAN4LeGP9O0H9CRCKm0xwiIiKSExVgioiISE6UTIiIiEhOlEyIiIhITpRM\niIiISE6UTIiIiEhOlEyIiIhITpRMiIiISE6UTIiIiEhO/j8kgQqxttbVhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20c85f15d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画散点图\n",
    "# ax = plt.figure().add_subplot(111, projection = '3d')  \n",
    "plt.scatter(x[:50, 0], x[:50, 1], color='red', marker='o', label='setosa')\n",
    "plt.scatter(x[50:100, 0], x[50:100, 1], color='blue', label='versicolor')\n",
    "plt.xlabel('sepal length')  \n",
    "plt.ylabel('petal length')  \n",
    "#plt.set_zlabel('petal length') \n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ppn = Percetron(learning_rate=0.1, n_iter=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Percetron at 0x20c85f8dda0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppn.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YoMzMSqrZWy2fBL4BfBoI4GejHBfAaq0EEBH7Nr6XdBDwADADuHKU0z4M/CUijkzf3yjp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20c85f990f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, len(ppn.errors_)+1), ppn.errors_, marker='o')\n",
    "plt.xlabel('Epoches')\n",
    "plt.ylabel('Number of Wrong Classifications')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 画分界线超平面\n",
    "def plot_decision_region(X, y, classifier, resolution=0.02):\n",
    "    markers=('s','x','o','^','v')\n",
    "    colors=('red','blue','lightgreen','gray','cyan')\n",
    "    cmap=ListedColormap(colors[:len(np.unique(y))])\n",
    "     \n",
    "    #plot the desicion surface\n",
    "    x1_min,x1_max=X[:,0].min()-1,X[:,0].max()+1\n",
    "    x2_min,x2_max=X[:,1].min()-1,X[:,1].max()+1  \n",
    "    # x3_min,x3_max=X[:,2].min()-1,X[:,2].max()+1\n",
    "     \n",
    "    xx1,xx2=np.meshgrid(np.arange(x1_min,x1_max,resolution),\n",
    "                        np.arange(x2_min,x2_max,resolution))\n",
    "    Z=classifier.predict(np.array([xx1.ravel(),xx2.ravel()],).T)\n",
    "    Z=Z.reshape(xx1.shape)\n",
    "     \n",
    "    plt.contour(xx1,xx2,Z,alpha=0.4,cmap=cmap)\n",
    "    plt.xlim(xx1.min(),xx1.max())\n",
    "    plt.ylim(xx2.min(),xx2.max())\n",
    "     \n",
    "    #绘画样本点\n",
    "    for idx,cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(x=X[y==cl,0],y=X[y==cl,1],alpha=0.8,c=cmap(idx), marker=markers[idx],label=cl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x1_min,x1_max=x[:,0].min()-1,x[:,0].max()+1\n",
    "x2_min,x2_max=x[:,1].min()-1,x[:,1].max()+1  \n",
    "x3_min,x3_max=x[:,2].min()-1,x[:,2].max()+1\n",
    "     \n",
    "xx1,xx2=np.meshgrid(np.arange(x1_min,x1_max,resolution),\n",
    "                    np.arange(x2_min,x2_max,resolution))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305, 235)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.3 ,  3.32,  3.34, ...,  7.94,  7.96,  7.98],\n",
       "       [ 3.3 ,  3.32,  3.34, ...,  7.94,  7.96,  7.98],\n",
       "       [ 3.3 ,  3.32,  3.34, ...,  7.94,  7.96,  7.98],\n",
       "       ..., \n",
       "       [ 3.3 ,  3.32,  3.34, ...,  7.94,  7.96,  7.98],\n",
       "       [ 3.3 ,  3.32,  3.34, ...,  7.94,  7.96,  7.98],\n",
       "       [ 3.3 ,  3.32,  3.34, ...,  7.94,  7.96,  7.98]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(71675,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx1.ravel().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n",
      "E:\\Anaconda3\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n",
      "E:\\Anaconda3\\lib\\site-packages\\matplotlib\\legend.py:325: UserWarning: Unrecognized location \"upperleft\". Falling back on \"best\"; valid locations are\n",
      "\tupper right\n",
      "\tlower left\n",
      "\tupper center\n",
      "\tcenter left\n",
      "\tbest\n",
      "\tcenter\n",
      "\tlower center\n",
      "\tright\n",
      "\tupper left\n",
      "\tcenter right\n",
      "\tlower right\n",
      "\n",
      "  six.iterkeys(self.codes))))\n"
     ]
    },
    {
     "data": {
      "image/png": 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lkkWkZ+ncYhjpnFuVyoZmtiHDeESkQLQAvyPxj0jHhb9DeXk5FcB3ga3j2pcBV0afh+wv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20c87268438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_region(x,y,classifier=ppn)\n",
    "plt.xlabel('sepal length [cm]')\n",
    "plt.ylabel('petal length [cm]')\n",
    "plt.legend(loc='upperleft')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2010: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Percetron at 0x20c87c7b0b8>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = Percetron(learning_rate=0.1, n_iter=10)\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * np.mean(clf.predict(x_test) == y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n",
      "E:\\Anaconda3\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n"
     ]
    },
    {
     "data": {
      "image/png": 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e+PLtLLp3P7oLyYuvy14+hGWdFxDqaxlXk9yWnQDvubieq66u97q2JGlUSEeY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20c87c7b3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_region(x_test, y_test, clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def loadDataSet_x(path):   #读取数据（这里只有两个特征）\n",
    "    dataMat = []\n",
    "    #labelMat = []\n",
    "    fr = open(path)\n",
    "    for line in fr.readlines():\n",
    "        lineArr = line.strip().split()\n",
    "        dataMat.append([1.0, float(lineArr[0]), float(lineArr[1]), float(lineArr[2])])   #前面的1，表示方程的常量。比如两个特征X1,X2，共需要三个参数，W1+W2*X1+W3*X2\n",
    "        #labelMat.append(int(lineArr[2]))\n",
    "    return dataMat\n",
    "def loadDataSet_y(path):   #读取数据（这里只有两个特征）\n",
    "    #dataMat = []\n",
    "    labelMat = []\n",
    "    fr = open(path)\n",
    "    for line in fr.readlines():\n",
    "        lineArr = line.strip().split()\n",
    "        #dataMat.append([1.0, float(lineArr[0]), float(lineArr[1]), float(liArr[2])])   #前面的1，表示方程的常量。比如两个特征X1,X2，共需要三个参数，W1+W2*X1+W3*X2\n",
    "        labelMat.append(int(lineArr[0]))\n",
    "    return labelMat\n",
    "\n",
    "titanic_file_path = './data/'\n",
    "x = loadDataSet_x(titanic_file_path + 'titanic_train_data.asc')\n",
    "y = loadDataSet_y(titanic_file_path + 'titanic_train_label.asc')\n",
    "x_test = loadDataSet_x(titanic_file_path + 'titanic_test_data.asc')\n",
    "y_test = loadDataSet_y(titanic_file_path + 'titanic_test_label.asc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_test = np.array(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10255, 4)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Percetron at 0x17caa1d7278>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = np.array(x_test)\n",
    "clf = Percetron()\n",
    "clf.fit(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = clf.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.792\n"
     ]
    }
   ],
   "source": [
    "count = 0.0\n",
    "for i in range(len(y_pred)):\n",
    "    if(y_pred[i] == y[i]):\n",
    "        count += 1\n",
    "count /= len(y_pred)\n",
    "print(count)"
   ]
  }
 ],
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