initialIris.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets start by defining a so called perceptron Machine Learning (ML) classifier, you can find more info at https://en.wikipedia.org/wiki/Perceptron\n",
    "This exists in most ML software packages, but it is good to run throught the defining code.\n",
    "\n",
    "In essence we're trying to find a simple criteria for the separation of our classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Perceptron(object):\n",
    "    \"\"\"Perceptron classifier.\n",
    "    Parameters\n",
    "    ------------\n",
    "    eta : float\n",
    "        Learning rate (between 0.0 and 1.0)\n",
    "    n_iter : int\n",
    "        Passes over the training dataset.\n",
    "    Attributes\n",
    "    -----------\n",
    "    w_ : 1d-array\n",
    "        Weights after fitting.\n",
    "    errors_ : list\n",
    "        Number of misclassifications in every epoch.\n",
    "    \"\"\"\n",
    "    def __init__(self, eta=0.01, n_iter=10):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "    def fit(self, X, y):\n",
    "           self.w_ = np.zeros(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\n",
    "                   errors += int(update != 0.0)\n",
    "               self.errors_.append(errors)\n",
    "           return self\n",
    "    def net_input(self, X):\n",
    "           \"\"\"Calculate net input\"\"\"\n",
    "           return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "    def predict(self, X):\n",
    "           \"\"\"Return class label after unit step\"\"\"\n",
    "           return np.where(self.net_input(X) >= 0.0, 1, -1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, download the CSV file with the iris data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)\n",
    "#df= pd.read_csv('iris.data',header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the end few lines with .tail() or we could look at the start .head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3               4\n",
       "145  6.7  3.0  5.2  2.3  Iris-virginica\n",
       "146  6.3  2.5  5.0  1.9  Iris-virginica\n",
       "147  6.5  3.0  5.2  2.0  Iris-virginica\n",
       "148  6.2  3.4  5.4  2.3  Iris-virginica\n",
       "149  5.9  3.0  5.1  1.8  Iris-virginica"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()\n",
    "# Sepal length, sepal width, petal length, petal width"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the flower information for the first 100 entries, i.e the 4th column from above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = df.iloc[0:100, 4].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the values of Iris-setosa to 1, otherwise (Iris-virginica) to -1 \n",
    "since the ML algorithm requires numerical values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = np.where(y == 'Iris-setosa', -1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the first 100 values from column 0 and 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df.iloc[0:100, [0, 2]].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data, using different colors and markers depending on the label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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3R+/sm5uj5SOPPDDph7qtn0i5lPBFYjKDQYWx6bNmRcuzZkXLgwb1Pro0ThNWjU2pibhz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1156f1450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:50, 0], X[:50, 1],\n",
    "...             color='red', marker='o', label='setosa')\n",
    "plt.scatter(X[50:100, 0], X[50:100, 1],\n",
    "...             color='blue', marker='x', label='versicolor')\n",
    "plt.xlabel('petal length')\n",
    "plt.ylabel('sepal length')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initiate the perceptron algorithm with initial values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ppn = Perceptron(eta=0.1, n_iter=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the X, column 0 and 2 of the data and y the classification to train our algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Perceptron at 0x1154d9550>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppn.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the error in the model, The number of misclassifications, as a function of epocs, \n",
    "itirations of training in our Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Su6ZwBfBBArKJRO6M/p0Ynt+ZJ+Ys56ozcmnTMtS8VCJNVn0thb8LllbA58C5\nxMYy2gRoCElJGXePLWDr7v08+/aqqKOIxF2df/a4uwaoEwEG53Zg9HFdeWrOcq4e2pf2WZlRRxKJ\nmzC9j/qZ2UQzm9rIQ2eLNBl3jS1gx94DPP3myqijiMRVmBOk04Cnid3FrNs7JSV9vVd7Lvh6d6a8\nuZLrhuXRIbtF1JFE4iJM76O97j7J3We7+5yqJe7JRJLMXWML2L3/AJPnrog6ikjchCkKj5jZ/WZ2\nppmdWrXEPZlIkino1pZvnNyTZ95axaad+6KOIxIXYYrCicAPgAeI3cj2EPCzeIYSSVbjR+ez70Al\nT85ZHnUUkbgIc03hCqB/zeGzRVJV/y5tuOzU3vzm3dX8YHh/urdvFXUkkUYVpqWwGMiJdxCRpmL8\n6HwOHnQen13a8MYiTUyYopADfGpm09UlVQT6dGzNt0/vwwsffMbaL/ZEHUekUYU5fXR/3FOINDG3\njxrIi/PW8ujMUv778pOijiPSaMLMp6DupyKH6dE+i38Ykstv3l3NLSMGkNc5O+pIIo0izB3NO81s\nR7DsNbNKM9uRiHAiyewfRw4gM914ZOayqKOINJoGi4K7t3X3du7ejthAeN8CfhH3ZCJJrmvbVlx7\nZh7TFpRRulGjyUvzEOZCczWPmQaMC7O9mZ1vZkvNrNTM7q3l/RFmtt3MFgTLvx5JHpGo3XTuAFpn\npvPwDLUWpHlo8JpCjXkVIFZECoG9IT6XDjwOjAXWAh+Y2Uvu/vFhm85194vDRxZJHh2zW3D92f14\ndFYpt47YwfE920UdSeSYhGkp/F2NZRyxWdcuCfG5IUCpu68Ibnx7IeTnRJqU75/dn7atMnh4RknU\nUUSOWZjeR0c7r0IvoOaM52uBM2rZbpiZLQTKgH9y9yVHeTyRSLRvnckPhvdnYlEJH63Zxsl9dK+n\nNF31TcdZ3/l9d/f/aITjzwdy3X2XmV1IbJju/Fqy3AjcCJCbm9sIhxVpXNedlcev31rJxKISnr1+\nSNRxRI5afaePdteyANwA/DDEvsuAPjVe9w7WVXP3He6+K3j+CpBpZp0P35G7T3b3Qncv7NKlS4hD\niyRW21aZ3HTuAOaUbKJ41dao44gctTqLgrs/VLUAk4l1R72O2LWB/iH2/QGQH8zc1gK4EjhkeAwz\n625mFjwfEuTZclQ/iUjErjmzL53btOSh13RtQZquei80m1lHM/sJsJDYqaZT3f2H7r6xoR27+wHg\nNmA68AmSEn6GAAAJd0lEQVTwR3dfYmY3m9nNwWaXA4vN7CNgEnClu/sx/DwikWndIoN/HDGAd1Zs\n4e3SzVHHETkqVtfvYDN7ELiMWCvh8arTPFErLCz04uLiqGOI1GpvRSUjHnydXh2yePHmMwkawiKR\nM7N57l7Y0Hb1tRQmAD2B/wesqzHUxU4NcyFSu1aZ6dw2aiDzVn/BnJJNUccROWL1XVNIc/esmsNc\nBEvbYMgLEanFtwv70LtDFhOLStDZUGlqjmiYCxFpWIuMNO4Ync/Ctdsp+vjzqOOIHBEVBZE4uGxw\nL/p1zmZiUQkHD6q1IE2HioJIHGSkpzF+dD6fbtjJq4s3RB1HJDQVBZE4+buTe5LftQ0PzyihUq0F\naSJUFETiJD3NuGtsAaUbd/HSR2UNf0AkCagoiMTR+Sd05/ge7fj5jGVUVB6MOo5Ig1QUROIoLc24\ne2wBq7fsYer8tVHHEWmQioJInI3+WldO7pPDpJml7DtQGXUckXqpKIjEmZkxYWwBZdvK+eMHaxr+\ngEiEVBREEmB4fmdOz+vAY7NL2Vuh1oIkLxUFkQQwMyacN4jPd+zjt++ujjqOSJ1UFEQSZGj/Tpw1\nsBNPzlnOnv0Hoo4jUisVBZEEunvsIDbv2s+zb6u1IMlJRUEkgU7r24GRg7rw1BvL2bm3Iuo4Il+h\noiCSYHePHcS2PRVMeXNV1FFEvkJFQSTBTuzdnnEndONXc1ewbc/+qOOIHEJFQSQCd40tYNf+A/xy\n7oqoo4gcQkVBJALHdW/HRSf24NdvrWLLrn1RxxGppqIgEpE7xxSwt6KSJ+csjzqKSDUVBZGIDOza\nhksH9+K5d1azccfeqOOIACoKIpEaPzqfyoPOL15Xa0GSg4qCSIT6dsrmisLePP/eZ5RtK486jkh8\ni4KZnW9mS82s1MzureV9M7NJwfsLzezUeOYRSUa3jcoH4LFZpREnEYGMeO3YzNKBx4GxwFrgAzN7\nyd0/rrHZBUB+sJwBPBE8iqSMXjlZXDWkD8+9s5pZn37Oxh376JmTxT3jBnHp4F4JzTLtwzIenL6U\nddvKI8ugHNHmiFtRAIYApe6+AsDMXgAuAWoWhUuA59zdgXfNLMfMerj7+jjmEkk6A7pk48DnO2Ld\nU8u2lXPf1EUACfslNO3DMu6buojyYGjvKDIoR/Q5LPb7uPGZ2eXA+e7+/eD1d4Ez3P22Gtu8DDzg\n7m8Gr2cCP3T34rr2W1hY6MXFdb4t0iSd9cCsWq8pZKQZ/TpnJyTDys27OXDwq78PEplBOcLn6JWT\nxVv3jgq9HzOb5+6FDW0Xz5ZCozGzG4EbAXJzcyNOI9L41tVxkfnAQSe/W5uEZFi2cVfkGZQjfI66\n/p85VvEsCmVAnxqvewfrjnQb3H0yMBliLYXGjSkSvZ45WbW2FHrlZPGL75yWkAx1tVYSmUE5wufo\nmZMVl+PFs/fRB0C+mfUzsxbAlcBLh23zEnBN0AtpKLBd1xMkFd0zbhBZmemHrMvKTOeecYNSKoNy\nRJ8jbi0Fdz9gZrcB04F0YIq7LzGzm4P3nwReAS4ESoE9wHXxyiOSzKouGEbZ0yUZMihH9DnidqE5\nXnShWUTkyIW90Kw7mkVEpJqKgoiIVFNREBGRaioKIiJSTUVBRESqNbneR2a2CVgddY5j1BnYHHWI\nJKLv41D6Pr6k7+JQx/J99HX3Lg1t1OSKQnNgZsVhuoalCn0fh9L38SV9F4dKxPeh00ciIlJNRUFE\nRKqpKERjctQBkoy+j0Pp+/iSvotDxf370DUFERGpppaCiIhUU1FIIDPrY2azzexjM1tiZuOjzhQ1\nM0s3sw+DWfhSWjAd7Ytm9qmZfWJmZ0adKUpmdlfw72Sxmf3ezFpFnSmRzGyKmW00s8U11nU0syIz\nWxY8dmjs46ooJNYBYIK7Hw8MBW41s+MjzhS18cAnUYdIEo8Af3P344CTSeHvxcx6AXcAhe7+dWLD\n718ZbaqEewY4/7B19wIz3T0fmBm8blQqCgnk7uvdfX7wfCexf/SJHZw9iZhZb+Ai4FdRZ4mambUH\nzgGeBnD3/e6+LdpUkcsAsswsA2gNrIs4T0K5+xvA1sNWXwI8Gzx/Fri0sY+rohARM8sDBgPvRZsk\nUj8H/hk4GHWQJNAP2AT8Ojid9iszS9zs8EnG3cuAnwGfAeuJzcr4WrSpkkK3GrNTbgC6NfYBVBQi\nYGZtgD8Dd7r7jqjzRMHMLgY2uvu8qLMkiQzgVOAJdx8M7CYOpwaaiuBc+SXEimVPINvMro42VXLx\nWNfRRu8+qqKQYGaWSawg/M7dp0adJ0JnAd8ws1XAC8AoM/tttJEitRZY6+5VLccXiRWJVDUGWOnu\nm9y9ApgKDIs4UzL43Mx6AASPGxv7ACoKCWRmRuyc8SfuPjHqPFFy9/vcvbe75xG7gDjL3VP2L0F3\n3wCsMbOq2dhHAx9HGClqnwFDzax18O9mNCl84b2Gl4Brg+fXAv/b2AdQUUiss4DvEvureEGwXBh1\nKEkatwO/M7OFwCnATyPOE5mgxfQiMB9YROx3VUrd3WxmvwfeAQaZ2VozuwF4ABhrZsuItaYeaPTj\n6o5mERGpopaCiIhUU1EQEZFqKgoiIlJNRUFERKqpKIiISDUVBZGAmVXW6Cq8wMwa7Y5iM8urOdql\nSLLKiDqASBIpd/dTog4hEiW1FEQaYGarzOx/zGyRmb1vZgOD9XlmNsvMFprZTDPLDdZ3M7O/mNlH\nwVI1PEO6mf0ymCPgNTPLCra/I5hjY6GZvRDRjykCqCiI1JR12Omjv6/x3nZ3PxF4jNjorgCPAs+6\n+0nA74BJwfpJwBx3P5nY+EVLgvX5wOPufgKwDfhWsP5eYHCwn5vj9cOJhKE7mkUCZrbL3dvUsn4V\nMMrdVwQDGm5w905mthno4e4Vwfr17t7ZzDYBvd19X4195AFFweQomNkPgUx3/4mZ/Q3YBUwDprn7\nrjj/qCJ1UktBJByv4/mR2FfjeSVfXtO7CHicWKvig2BSGZFIqCiIhPP3NR7fCZ6/zZdTRH4HmBs8\nnwncAtVzULeva6dmlgb0cffZwA+B9sBXWisiiaK/SES+lGVmC2q8/pu7V3VL7RCMXroPuCpYdzux\nmdLuITZr2nXB+vHA5GBUy0piBWI9tUsHfhsUDgMmaRpOiZKuKYg0ILimUOjum6POIhJvOn0kIiLV\n1FIQEZFqaimIiEg1FQUREammoiAiItVUFEREpJqKgoiIVFNREBGRav8fOHAT9dmpSG4AAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1154d9ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_,marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Number of misclassifications')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets now define a way of plotting the decision regions from any algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "def plot_decision_regions(X, y, classifier, resolution=0.02):\n",
    "    # setup marker generator and color map\n",
    "    markers = ('s', 'x', 'o', '^', 'v')\n",
    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "    # plot the decision 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",
    "    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",
    "    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n",
    "    plt.xlim(xx1.min(), xx1.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "    # plot class samples\n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],\n",
    "                    alpha=0.8, c=cmap(idx),\n",
    "                    marker=markers[idx], label=cl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the training data and the regions which are produced in the Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vJp5+P5Xpd1Pe4UnfmGbAfwE4tGB7briviLuvBlanOI6WM7O17r6o3ePoRPrd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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118c6e790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_regions(X, y, classifier=ppn)\n",
    "plt.xlabel('sepal length [cm]')\n",
    "plt.ylabel('petal length [cm]')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets now define a ADAptive LInear NEuron classifier,\n",
    "you can find more info at https://en.wikipedia.org/wiki/ADALINE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class AdalineGD(object):\n",
    "    \"\"\"ADAptive LInear NEuron classifier.\n",
    "    Parameters\n",
    "    ------------\n",
    "    eta : float\n",
    "        Learning rate (between 0.0 and 1.0)\n",
    "    n_iter : int\n",
    "    Passes over the training dataset.\n",
    "    Attributes\n",
    "    -----------\n",
    "    w_ : 1d-array\n",
    "        Weights after fitting.\n",
    "    errors_ : list\n",
    "        Number of misclassifications in every epoch.\n",
    "    \"\"\"\n",
    "    def __init__(self, eta=0.01, n_iter=50):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "    def fit(self, X, y):\n",
    "        \"\"\" Fit training data.\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array-like}, shape = [n_samples, n_features]\n",
    "            Training vectors,\n",
    "            where n_samples is the number of samples and\n",
    "            n_features is the number of features.\n",
    "        y : array-like, shape = [n_samples]\n",
    "            Target values.\n",
    "        Returns\n",
    "        -------\n",
    "        self : object\n",
    "        \"\"\"\n",
    "        self.w_ = np.zeros(1 + X.shape[1])\n",
    "        self.cost_ = []\n",
    "        for i in range(self.n_iter):\n",
    "            output = self.net_input(X)\n",
    "            errors = (y - output)\n",
    "            self.w_[1:] += self.eta * X.T.dot(errors)\n",
    "            self.w_[0] += self.eta * errors.sum()\n",
    "            cost = (errors**2).sum() / 2.0\n",
    "            self.cost_.append(cost)\n",
    "        return self\n",
    "    def net_input(self, X):\n",
    "        \"\"\"Calculate net input\"\"\"\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "    def activation(self, X):\n",
    "        \"\"\"Compute linear activation\"\"\"\n",
    "        return self.net_input(X)\n",
    "    def predict(self, X):\n",
    "        \"\"\"Return class label after unit step\"\"\"\n",
    "        return np.where(self.activation(X) >= 0.0, 1, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets now, in a slightly different way train our algorithm and check the errors as depending on the epochs. \n",
    "Does the learning rate have an effect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2CbrKSG1VfRfIA1DVHCC3+E3MmRh+YXN+e2lL3knaylOfrMGpe2WM/xR7RC4i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\nmZlZhf0LztRPg/RR1KEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118c4b250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
    "ada1 = AdalineGD(n_iter=10, eta=0.01).fit(X, y)\n",
    "ax[0].plot(range(1, len(ada1.cost_) + 1),np.log10(ada1.cost_), marker='o')\n",
    "ax[0].set_xlabel('Epochs')\n",
    "ax[0].set_ylabel('log(Sum-squared-error)')\n",
    "ax[0].set_title('Adaline - Learning rate 0.01')\n",
    "ada2 = AdalineGD(n_iter=10, eta=0.0001).fit(X, y)\n",
    "ax[1].plot(range(1, len(ada2.cost_) + 1),ada2.cost_, marker='o')\n",
    "ax[1].set_xlabel('Epochs')\n",
    "ax[1].set_ylabel('Sum-squared-error')\n",
    "ax[1].set_title('Adaline - Learning rate 0.0001')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now rerun the same thing as we did for the Perceptron and visualise our regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lM7OkVk/l+wEk/SIiPl79mqRfAB8f8I1WGs/UZWatVOQewuuqF/KgdG9uTjij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\n9995ZR+F3p8T3UzSL+ZTJX0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11936f890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uxaOXdKZwZwmDXpzCxU9PJm9pXJYhkyhRIRCRQ5KUZJzXOZsxt/fhLwOOZsmG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157710d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ada = AdalineGD(n_iter=15, eta=0.01)\n",
    "ada =AdalineGD(n_iter=10, eta=0.0001).fit(X, y)\n",
    "plot_decision_regions(X_std, y, classifier=ada)\n",
    "plt.title('Adaline - Gradient Descent')\n",
    "plt.xlabel('sepal length [standardized]')\n",
    "plt.ylabel('petal length [standardized]')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()\n",
    "plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Sum-squared-error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}