machineLearn1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "class perceptron(object):\n",
    "    def __int__(self,eta=0.01,n_iter=10):\n",
    "        self.eta = eta;\n",
    "        self.n_iter = n_iter\n",
    "        pass\n",
    "    def fit(self,X,y):\n",
    "        self.w_=np.zero(1+X.shape[1]);\n",
    "        self.errors_=[]\n",
    "        for _ in range(self.n_iter):\n",
    "            errors = 0\n",
    "            for xi,target in zip(X,y):\n",
    "                update = self.eta*(target-self.predict(xi))\n",
    "                self.w_[1:]+=update*xi\n",
    "                self.w_[0]+=update*xi\n",
    "                errors +=int(update!=0.0)\n",
    "                self.errors_.append(errors)\n",
    "                pass\n",
    "            pass\n",
    "        \n",
    "    \n",
    "        def net_input(self,x):\n",
    "            return np.dot(X,self.w_[1:])+self.w_[0]\n",
    "            pass\n",
    "        def predict(self,X):\n",
    "            return np.where(self.net_input(X)>=0.0,1,-1)\n",
    "            pass\n",
    "        pass\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</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\n",
       "5  5.4  3.9  1.7  0.4  Iris-setosa\n",
       "6  4.6  3.4  1.4  0.3  Iris-setosa\n",
       "7  5.0  3.4  1.5  0.2  Iris-setosa\n",
       "8  4.4  2.9  1.4  0.2  Iris-setosa\n",
       "9  4.9  3.1  1.5  0.1  Iris-setosa"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = \"/Users/holyant/Downloads/iris.cvs\"\n",
    "import pandas as pd\n",
    "df = pd.read_csv(file,header=None)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NYmx6c3Ow3twcrNfU5B5dGqYJy8YmlUTYORhKsXAuHSqHeFx1yRJNmzJ3yZLc0+ua7Gvz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\nnmDCJyLyBBM+EZEnmPCJiDzx/4sHWo0q+oNmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c855e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "y = df.iloc[0:100,4].values\n",
    "y = np.where(y=='Iris-setosa',-1,1)\n",
    "\n",
    "X = df.iloc[0:100,[0,2]].values\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',marker='x',label='versicolor')\n",
    "plt.xlabel('huabanchangdu')\n",
    "plt.ylabel('huajingchangdu')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "def plot_decision_regions(X,)\n"
   ]
  }
 ],
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