{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing a perceptron learning algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading in a dataset\n",
    "\n",
    "First let's load in a dataset into a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('iris.data', header=None)\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas dataframe objects are like a SQL table and provide efficient queries. They are built upon and designed to work with numpy arrays.\n",
    "\n",
    "We want to work with just 2 dimensions of this dataset for now so it's easier to visualize and reason about. We'll use the sepal length and petal length from columns 0 and 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.1  1.4]\n",
      " [ 4.9  1.4]\n",
      " [ 4.7  1.3]\n",
      " [ 4.6  1.5]\n",
      " [ 5.   1.4]]\n",
      "[[ 7.   4.7]\n",
      " [ 6.4  4.5]\n",
      " [ 6.9  4.9]\n",
      " [ 5.5  4. ]\n",
      " [ 6.5  4.6]]\n"
     ]
    }
   ],
   "source": [
    "# Get the first 100 rows, selecting columns 0 and 2 (sepal and petal length)\n",
    "sepal_petal_rows = df.iloc[0:100, [0, 2]].values\n",
    "\n",
    "# in the dataset the first 50 rows are iris setosa, the next 50 are iris versicolor\n",
    "setosa_rows = sepal_petal_rows[:50]\n",
    "versicolor_rows = sepal_petal_rows[50:]\n",
    "\n",
    "print(setosa_rows[:5])\n",
    "print(versicolor_rows[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing 2 classes \n",
    "\n",
    "Plotting the two classes on 2 dimensions (sepal and petal length), we can see that they are linearly separable, so our single perceptron model should be able to handle it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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+/d//nR/84AcAXHLJJQzu4PWgp556KuPHjwfg5JNP5sknn2zY9+STT3LCCSdQ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\nJEmSJKkkAylJkiRJKun/A7HcGdpXjC2KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107f6e9b0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 425
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "plt.scatter(setosa_rows[:,0], setosa_rows[:,1],\n",
    "            color='red', marker='o', label='setosa')\n",
    "plt.scatter(versicolor_rows[:, 0], versicolor_rows[:, 1],\n",
    "            color='blue', marker='x', label='versicolor')\n",
    "\n",
    "plt.xlabel('sepal length [cm]')\n",
    "plt.ylabel('petal length [cm]')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model\n",
    "\n",
    "Let's implement the perceptron. We want to see how the weights and error rate progresses during iterations of training so our training function will also provide insight into that.\n",
    "\n",
    "Note: I diverged from the author's implementation to a perhaps cleaner solution using a higher order function instead of a class, and also instrumented it to return a log of the weights across iterations so we can visualize how the fit improved with each iteration. I also break once there are no errors found instead of continuing to iterate mindlessly after converging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %load perceptron.py\n",
    "\"\"\"\n",
    "Implementation of perceptron algorithm from Chapter 2 of \"Python Machine Learning\"\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def train_perceptron(observations, labels, learning_rate=0.1, max_training_iterations=10):\n",
    "    \"\"\"\n",
    "    Trains a (binary) perceptron, returning a function that can predict / classify\n",
    "    given a new observations, as well as insight into how the training progressed via\n",
    "    a log of the weights and number of errors for each iteration.\n",
    "\n",
    "    :param observations: array of rows\n",
    "    :param labels: correct label classification for each row: [1, -1, 1, 1, ...]\n",
    "    :param learning_rate: how fast to update weights\n",
    "    :param max_training_iterations: max number of times to iterate through observations\n",
    "    :return: (prediction_fn, weights_log, errors_log)\n",
    "    \"\"\"\n",
    "    the_weights = np.zeros(1 + observations.shape[1])\n",
    "    weights_log = []\n",
    "    errors_log = []\n",
    "\n",
    "    def net_input(observation, weights):\n",
    "        return np.dot(observation, weights[1:]) + weights[0]\n",
    "\n",
    "    def predict(observation, weights=the_weights):\n",
    "        return np.where(net_input(observation, weights) >= 0.0, 1, -1)\n",
    "\n",
    "    for _ in range(max_training_iterations):\n",
    "        errors = 0\n",
    "        weights_log.append(np.copy(the_weights))\n",
    "        for observation, correct_output in zip(observations, labels):\n",
    "            weight_delta = learning_rate * (correct_output - predict(observation))\n",
    "            the_weights[1:] += weight_delta * observation\n",
    "            the_weights[0] += weight_delta\n",
    "            errors += int(weight_delta != 0.0)\n",
    "        errors_log.append(errors)\n",
    "        if errors == 0:\n",
    "            break\n",
    "\n",
    "    return predict, weights_log, errors_log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use this function to train a perceptron using our data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YbLO0zK9PH/jyy7yrkqT6K6sgtQRYazXHtCg/TlIRuPtuOO20dP/WW+G44/Kt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\nGtrfA5+NiHsp2lT/L0WziYOAXSkWKDy9tOok6VXHAudHxD3AcmA1MJz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10942ae10>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 425
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select setosa and versicolor\n",
    "y = df.iloc[0:100, 4].values\n",
    "y = np.where(y == 'Iris-setosa', -1, 1)\n",
    "\n",
    "ppn, weights_log, errors_log = train_perceptron(sepal_petal_rows, y)\n",
    "\n",
    "plt.plot(range(1, len(errors_log) + 1), errors_log, marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Number of misclassifications')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the training converges and has zero errors by the last iteration.\n",
    "\n",
    "Let's visualize the decision regions of the final solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %load decision_region_plot.py\n",
    "\"\"\"\n",
    "Helper function for plotting decision regions from Chapter 2 of Python Machine Learning.\n",
    "\"\"\"\n",
    "\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "\n",
    "def plot_decision_regions(plt, observations, labels, predict_fn, weights, resolution=0.02,\n",
    "                          xlabel='sepal length [cm]', ylabel='petal length [cm]'):\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(labels))])\n",
    "\n",
    "    # plot the decision surface\n",
    "    x1_min, x1_max = observations[:, 0].min() - 1, observations[:, 0].max() + 1\n",
    "    x2_min, x2_max = observations[:, 1].min() - 1, observations[:, 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 = predict_fn(np.array([xx1.ravel(), xx2.ravel()]).T, weights)\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",
    "\n",
    "    # plot class samples\n",
    "    for idx, cl in enumerate(np.unique(labels)):\n",
    "        plt.scatter(x=observations[labels == cl, 0], y=observations[labels == cl, 1],\n",
    "                    alpha=0.8, c=cmap(idx),\n",
    "                    marker=markers[idx], label=cl)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.legend(loc='upper left')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pJgYpSZIkSWqSQUqSEqjtXVSZyagtWKDu4PspSZqJQUqSWlRbnOCCC7Kf6nLb\nfvnuHr6fkqTZSFpsIoRwKnAqsBRYWO+YGOOlyS44RxabkJRKqZT1FapXza32C/nb324j16Lz/ZSk\n/tJKsYnDUgwghPALwFeA187i8I4HKUlKpdK76M4783sXbdzYuHeRisP3U5I0W0lmpEIIG4ALgO8A\n1wA/Bw7UOzbG+I8tX7BFzkhJSq3V3kUqFt9PSeoPHZ+RAt4EfB/49Rjj04nOKUldo9EXa790dx/f\nT0nSTFIVm3gm8E1DlCRJkqR+kCpI/QR4TqJzSZIkSVKhpQpSnwF+K4RwTKLzSdK8KpXmti/V4yVJ\nUneZU5AKIbyg+ge4BfgG8E8hhPNDCCfVHlN1rCQVypYtWcnrer2BJiayfVu2tO/xkiSp+8x1RmoH\nsL38u/Lv3wZeAPwV8K9V+3bUHCdJhVEqZaWu6zVare4bdOed9WeWWn28JEnqTnMqfx5CuHaO14sx\nxrfP8bHJWP5cUrXaRqvnnpttr91W3VMo5eMlSVJntFL+PEkfqW5jkJJUqzoMVQLPxMTsQ1Crj5ck\nSfOvlSCVqtiEJHW1xYunAs/ERPZTva3dj5ckSd0lSZAKIRwMIXxkhmM+FEKwz5QkSZKkrjffM1JN\nT5lJ0nyo3JpXmUmqzCzVFpBo1+MlSVJ3mc8gtRTYP4/Xk6RZqS0WccEF2c+yZfWr8aV+vCRJ6j5z\nDlIhhJXln9PLm1ZUbav+eW0I4TzgLcCPUgxaklIplepX16usb6oOQ3nlz1t5vCRJ6k6HtfDYf6z5\n+7zyT56DwB+1cD1JSm5gAE46KevzVFsYohKGNm7MjhkYSP94SZLUneZc/jyEcHHVnx8Bvl3+qfU0\n8AjwzRjjPXO6WGKWP5dUq1TKDzqN9qV6vCRJmn+tlD+f84xUjPHiyr/Lt+7dGGP81FzPJ0md1Cjo\nzCYEtfp4SZLUXVq5tW9SjHFFivNIkiRJUruNjcHmza2dI0mQkiRJkqQiGh2F8fGajfffxyDj7Gjh\nvEmCVAjhW8BMC44OAruBHwJfiTFuTXFtSZIkSao1Ogrj/74bHnuM4UXfY+WiOyb3jSy/AYaHCRvn\nfv45F5uYdpIQDjb5kAhsiDG+q+WLz4HFJiRJkqTuNTYGm2/ZDXv25B4zyDirl2xi5LhbYfXquseE\nNWuAeS42UeNI4IvAi4GPApuBh4DnAL8GfBj4AfAHwAnAZcA7QwhbY4xXJxqDJEmSpC434/qlPVmA\nGh64netqaEFTAAAgAElEQVSXr4Xh4fxjV64E6oeoVqUKUh8CXgm8LMb4eNX2nwI/DSHcDPwb8Hsx\nxg+HEP4F+DHZszJISZIkSX1uMkCV1y+tXrIp99iRkyuzTOvmbXy1UgWpc4G/qQlRk2KMj4cQ/qZ8\n3IdjjI+EEL4GnJno+pIkSZK6xOT6pf37pjaWDkyfZVq5ssEZ2jPL1IxUQWo58NQMx5TKx1X8nOyW\nQEmSJEk9YmwMtm3L3z9+90Ow/0nWHX0lIyfcOn1nh2eZmpEqSN0PrAohrIsxHqjdGUIYAFaVj6tY\nBjyW6PqSJEmSOmhylqm8finP6kV3ZAFq9WqKMLM0V6mC1OeBjwD/EEL4EPDdGOPBEMICYBj4GDBE\nVoii4tXA3XO5WAjh42Rrsn4ZeDawH/gZcBPw5zHGh+b6RCRJkiRNN7l+ac/u/IPKZcavP/my3Cp5\nmRV0c4CqSBWk1pMFm/8bGAOeDiE8CjwLWFg+5hbgTwFCCM8D7gT+eo7Xey/wPeDrwDiwmCyYfRAY\nCSEMxxgbTChKkiRJqmdsbPrflTLjwwO3c/2ytfkPPG6w62eZmpEkSMUYnwwhrALeCrwNeDnZTNFu\n4F+Az8cYP191/APAf23hkktijIesyQohfIwsTF1Iv7yDknpGqQQDA83vkyQphdHR8vql0gEGB3ZN\nbh9mW1YAYt06umX90nxINSNFzDr7Xlf+aat6IapsE1mQWp6zX5IKacsWuPNOOPdcWLx4+r6JCdi4\nEU46CU47rTPjkyR1t8n1S3n275teAGJoKNs+WTnPAFUrWZAqiFXl3//YyUFIUjNKpSxE7dyZBabq\nMFUJUTt3ZseccoozU5Kk6Wpvxau2bVs5QFXWL+XdmreEqpLj3tg1GyGbSEp4whCOApYytTZqmhjj\nvQmv9UfAM4Bnkq3RehVwLfD7McZSg8dFgA0b0j53SZqr6sC0bFkWpuDQbbWzVZKk/jU2NrV+qfpW\nvGlKJYYGdnD9CTMVgOhPYc0aAGKMoenHpgpSIYTfBT4AvBjIG0iMMdYNWHO85gPAc6o2bQYuijF+\nc4bHGaQkFU51mKqekTJESZJGR2F8vGrDnpoCEMPD+Q9u2Ni2v7USpJLc2hdCOA+4Gnga+CeyUuSH\n9JMCkiaXGOPzytdfRlZm/TLgGyGE82KM16e8liS12+LFWWC66qosQFVvM0RJUn+aLABRXr9UbeTk\nW7uqgW2vSTIjFUL4N7ICD8Mxxh+2fMK5j+MFwI+Bx2OMz2lw3IxP+swzL2LVqosTjk6SZjYxcWiQ\nuuACg5Qk9aLJAhD79+UeM1i6n9VLNjHyG/c6szRHF990E5fcfHPDYzp2a18IYT9wbYzxnS2frPWx\nfB84CVie15jXW/skFZG39klS7xgbywo95KnMMg0v+l62finP0JABqo06fmsfsAt4MtG5WrWc7BbC\nJzo9EEmarZmKTdRW85MkFVN1AYjhgdtzj1u96I6szHgfNbDtNamC1E3Aa0IIIaYuA1gjhDAEjMcY\nH6/ZvgD4KLAM+EaMcaKd45CkVEql/Op85547PUy9/e2WP5ekoqjbm6lSZnyygW2eFRiguluqILWO\nrGLehhDC2hhjO2eDfhNYH0L4DrADeISsct/pwLHAT4GO32IoSbM1MJA1263XkLdSbKLSkNcQJUnz\nY3Q0f9/4OLDzIQZL97NuySZGjrt1+gEWgOgLqdZIfQs4GvgVYALYBjxW79gY46+3eK0TyILSrwHH\nlK+7B7iHbGbsz2cKcq6RklREpVJ+UGq0T5KUTnWVvOFF38s9buWiOywA0QOKsEbq9Kp/LwZOTnTe\nQ8QY7wb+sF3nl6ROaRSUDFGS1LrJ9Ut59u+bLDM+tX4pz4ryj/pVkiAVY1yQ4jySJElSCmNjU//e\ntq28lqmyfmnZ2voPWgIMDloAQrOSakZKkiRJ6rj168nWL7FzamOpxPDADq4/5TLXLykZg5QkSZK6\nwuhoudBDnnIBiMkGttVWrsRZJqWULEiFEBYCvw+cC7wEWBxjXFje93LgAuCTMcYfp7qmJEmSel91\nAYh1R1+Ze9zIshtgeLgcmlbM2/jUn5IEqRDC4cAtwGvIypHvAZ5RdcgO4HzgYeAjKa4pSZKk7jfZ\ni2n/vtxjJsuMz1gAwlv2NH9SzUi9nyxEXQJ8DPgwVYEpxrir3Pfp9RikJEmS+sLYWLnQQ97teHuq\nCkCccFn+iYaGvDVPhZMqSJ0LfDfGeAlACHXLsG8Hzkx0PUmSJBXUZJnxPXsYHrid1YvuyD125JRb\nrZKnrpQqSB0LfHWGYx4Fnp3oepIkSSqAyfVL1UoHGB64neuXr4V162i8XskApe6UKkg9CRw9wzHP\nBx5LdD1JkiS12eho/r7xcSar5K1bsomR426dfoBlxtXjUgWp7wOvDyEcEWN8snZnCOGZwBuAf050\nPUmSJLXJZAGI8vqleoaAlYvuYOSse12/pL6UKkj9JbAR2BhCOL96RwhhKXA18Cwgv16lJEmS2m5y\n/VKe/fsmy4xPrV/KswLLjKtfJQlSMcYbQghnAOcBqyjfwhdC2Aq8DDgc+GyMcaZ1VJIkSUpkbGzq\n39u2lWeZygUgrl+2tv6DlgCDgxaAkGaQrCFvjPH8EMIY8B7gV8qbXwHcDVwRY7wm1bUkSZKUb/16\nsvVL7JzaWCoxPLCD60++zPVLUgLJghRAjPFa4NoQwlHAUuDxGOMTKa8hSZLUz0ZHG/RlgskCEKuX\nbGLkN+6dvs+1TFIySYNURYxxL7C3HeeWJEnqZdW341Xbtq1cZry8finPyLIbYHi4HJpWtGWMktoU\npCRJkjR7k1Xy9uxhcGBX/YNKpazM+AkzFYDwlj1pPswpSIUQtgNxLo+NMb5wLo+TJEnqBevX12zY\nM1Vm/PqTL4OhofwHe2ueVBhznZEK5Z9mzSl8SZIkdbOxMdi8mWnrl6pNlRk3JEndYk5BKsa4IvE4\nJEmSutboaHn9Up7SAQYZ57blZ1etX6pmgJK6jWukJEmSGhgdzd83Pg7snCoAMXLcrfkHDw3BStcv\nSb3CICVJklTHZAGI8vqleoaAlYvuYOSse12/JPUZg5QkSeprk+uX9uye2rh/39Qs0ykzVclbgWXG\npf5jkJIkST0try8TlAPU/fdNrV+qWAIMDloAQlIug5QkSepJ69eTVcljJ5RKdY8ZHtjB9cvXlgtA\nuH5J0uwZpCRJUteZXL+UZ/++yTLjI79xb50qedUMUJKaZ5CSJEmFVXtb3rZt5TLjlfVLS26o/8Al\nVJUZX9HmUUrqRwYpSZJUOJMFIO6/j8GBXVM7SiXWLdnEyAmVAhDOJknqDIOUJEmad+vXN9i5Zzfs\n2TO9gW01y4xLKoA5BakQwkEgNvswIMYYF87lmpIkqftNFoAor1/KM7L8Bli3DmecJBXVXGekGhQS\nbajZ8CVJkrrE6Gh5/VKe0gEGGc8KQJxlAQhJ3W1OQSrG+JrE45AkSV1ibCwr+lBtWgGI427Nf/DQ\nkAUgJPUE10hJkqRZmVYAgnGGBnZM7lu96I6qWSbXL0nqfQYpSZIETK1fylU6wPDA7VkD23W1t96t\nwFkmSf0kaZAKISwHXgcsB46od0yM8dKU15QkSTMbHc3fNz7O5CzT6iWbGt+aZ8lxSQISBqkQwqXA\nhbM4p0FKkqR5MlkAYv+TDC/6Xt1jhiCbZZpsYOuteZI0kyRBKoRwLvAh4JvAZ4H/CXwO+AZwOvAO\nYBNwZYrrSZKk6SbXL+3ZPbVx/z4GS/dnDWytkidJSaWakfo94D7gjTHGUggBYHuM8QbghhDCV4D/\nH7gh0fUkSeorYw0aj2y+JWtgOzxwO9cvWzu1YwlVs0wr2jxCSeovqYLUicAXY4ylqm2TjXdjjF8P\nIXwd+CPg7xJdU5KknjatSt7ArtzjhtlWVQDCmSVJmg+pgtQA8HDV3/uAZ9Yc82/AOxNdT5Kkrjc6\nWi70kKdcAOK25WdnM0t5Vq7EACVJ8ytVkHoQeF7V3z8DTqo55nnAgUTXkySpa1XKjFfWL+UZWX5D\n+dY8Q5IkFU2qIPV94GVVf98KrAkh/C7w18Brgd8GvpvoepIkFd7YWHn90v5907YPlu7PyoxbAEKS\nulaqIHUT8NkQwrExxu3Ax4E3A9cC1wABeIqssp8kSV1vcv1Snj01BSAGB6f2DQ1ZAEKSulySIBVj\nvJYsNFX+vjeE8H8Ca4Hjge3AZ2OMd6W4niRJnTKtAES5gW2ekZNvtYGtJPWoZA15a8UY/wP4g3ad\nX5KkdqisX8pVOpDNMk1rYJvHxraS1KtSNeS9CPhWjDG3y0UI4T8Br40xXprimpIktWJsDLZtm/p7\nfJzJAhCrl2xi5Lhb8x/sLJMk9b1UM1IXARFo0C6Q08vHGaQkSR0zOgrj/z61fqliCLK1TJOzTM4m\nSZLyte3WvjoGyMKWJEltMbl+ac/u/IMee4zhRd/j+pMvK88sVXOWSZI0O/MZpF7O9Ka9kiQ1ZazB\nfQ+bb6mpkpfnuMFygHLGSZI0d3MOUiGEbzF9hum8EMJr6hy6EHg+WY3XG+Z6PUlSf5qcZdr5EIPs\nzD1umG1ZAYh163BmSZLUbq3MSJ1e8/cK6jfEiMAjwBeB97ZwPUlSjxsdLRd9qFYuM37b8rOz9Ut5\nVq7EACVJmi9zDlIxxgWVf4cQDgKXxBgvSTIqSVLPaXRb3rZt5QIQ5fVLKxfdAcDIkhtgOeUCEIYk\nSVJxpFojdT7w/UTnkiT1kLGxqfVLgwO76h9UKrFuySZGTqk0sF1R3mF4kiQVU5IgFWO8NsV5JEnd\nZXL9Up49VQUgKg1s81hyXJLURZJV7QshLAR+HzgXeAmwOMa4sLzv5cAFwCdjjD9OdU1JUmdMBqjy\n+qXVSzblHjty8q02sJUk9ZwkQSqEcDhwC/AassISe4BnVB2yg+z2v4eBj6S4piRpfkw2sN2/b2pj\n6cD0AhArVzY4g7NMkqTek2pG6v1kIeoS4GPAh6kKTDHGXSGE7wCvxyAlSYUxNpYVesgzfvdDsP9J\n1h19JSMn3Dp9p7NMkqQ+lipInQt8t1K1L4RQ75jtwJmJridJasHkLFN5/VKe1YvuyAKUDWwlSZom\nVZA6FvjqDMc8Cjw70fUkSTkm1y/t2Z1/ULnM+PUnX1YOSXlWYICSJOlQqYLUk8DRMxzzfOCxRNeT\npL7VqB/TZJnxyvqlPMcNOsskSVILUgWp7wOvDyEcEWN8snZnCOGZwBuAf050PUnqK5OzTDsfYrB0\nP0MDO+oeNwxZmfF163D9kiRJ7ZMqSP0lsBHYGEI4v3pHCGEpcDXwLODKRNeTpJ41uX6pWvUs04xV\n8gxQkiS1W6qGvDeEEM4AzgNWUb6FL4SwFXgZcDjw2RjjTOuoJKmnNbotb9u2coCqrF9atnZq5xLK\nAcqQJElSESRryBtjPD+EMAa8B/iV8uZXAHcDV8QYr0l1LUnqNmNjVeuXBnbVP6hUYnhgB9efcpml\nxSVJKrhkQQogxngtcG0I4ShgKfB4jPGJlNcACCE8Czgb+E3gRGA58BRwF3ANcE2MMaa+riTVM7l+\nKc+eqTLj1y9fm80s5Vm5EgtASJJUfEmDVEWMcS+wtx3nLjsH+CxwP/At4F7guWTh6q+ANwL/pY3X\nl6SpAHX/fQwyzuolm3KPHVl+gwUgJEnqIUmDVAhhCfCfgZOBZwKPk1X0+0rimakfAatq11yFED4I\n3A78PyGEs2OMf5PwmpL62GQBiP37pjaWDlgAQpKkPpUsSIUQziGrylevn9SnQghrYoz5/7m2CTHG\nb+VsfyiEcCXwJ8DpgEFK0ozGxrJCD3mmFYA44bLpO13LJElSX0oSpMoV+74AHAQ+B3wbeJDsdrvX\nAOcCXwghPBZj/PsU12zgQM1vSaqrugDE8MDtucetXnQHI6fcagNbSZI0KdWM1EfIij38pxjj92r2\nXRtC+AzwnfJxbQtSIYTDgN8t/3lLu64jqfiqG9jm2v9kNss02cA2zwoMUJIkqVqqIPVy4Et1QhQA\nMcatIYQvAb+d6Hp5LgNOAL46DzNfkgpidHT63+PjTBaAuG352TA4mP9gb82TJElzkCpIPUVWQa+R\nB8rHtUUI4d3AWuCHwO+06zqSimN0FMbvfmhyZqliCKpmmQxJkiQpvZCi3VII4Ubg6Bjjaxoc821g\nV4zxrJYveOi5/wD4NFnz39fFGMdnOH7GJ33mmRexatXFaQYoqWmT65fy7N8H+59k3dFXMnJcZf2S\nJEnSdBffdBOX3Hxzw2NijKHZ86YKUi8GvgtcBVwaY5yo2vcMsrVR7wBeHWP8UcsXnH7t9wJXkDXj\nfV2M8eFZPCYCbNhgz16pE8bG8vdt21ZTJW/Z2vyDBwcNUJIkac7CmjXA3IJUqlv7PgDcCbwfuCCE\n8C/AQ8BzgFeQlUQfAz4QwvQxxhjPn+tFQwgfANaT9ao6I8b46FzPJan9pjWwHdhV/6BSieGBHVx/\nymWuX5IkSYWVakbq4FwfG2NcMMdrfhi4BNgKvD7G+FgTj3VGSmqz0dFy0YeKPVNlxq9ftjZrYJun\nYWPb6d7zpS+x+Sc/aXjM8PHH86k3v3nW5+yEXnkekiR1kyLMSL0w0XlmJYTwNrIQ9TTwT8B7a2e6\ngO0xxs/N57gkTS8Ase7oK6ftG1l+Q/ICEJt/8hN48MHGxyS7Wvv0yvOQJKlfJAlSMcYdKc7ThBXl\n3wuA9+Yc849kzYElJTI6Wl6/tH8flOr3vB5knHVLNjFyQr0CEO27TW/rc59bd/srZwgnRdMrz0OS\npF6XakZqXsUYLyGbkZKUyOT6pTx7qgpAnHAZDA3lH7tyJTawlSRJvawrg5SkdCbLjJfXL61cdEfu\nsSOnVGaZDEmSJKm/GaSkPjK5fqla6UBWAGKyge2KBmcwQEmSJIFBSuoZo6P5+8bHgZ0PMVi6P1u/\ndNyt0w+wzLgkSVJTDFJSl5ssAFFev1TPELBy0R2MnHWv65ckSZISMEhJBTa5finP/n2TZcan1i/l\nWUHj2/a6W69UteuV5yFJUq8zSEkFMTY29e9t28qzTNUNbOtZAgwO9nUBiOHjj5+xv9Lw8cfPy1ha\n0SvPQ5KkfhFijJ0ew7wLIUSADRv677mreNavJ1u/xM6pjaUSQwM7sjLjDWeZJEmSNFdhzRoAYoyh\n2cc6IyW10ehoudDDnpzb8/bvY7B0P6uXbGLkN+6dvs+1TJIkSYVlkJLmqPpWvFrbtpXLjFfWLy25\nof6BS4Dh4XJoWtGGUUqSJKkdDFJSkyar5O3Zw+DArvoHlUpZmfETbrW0uCRJUg8ySEkNrF9fs2HP\nVJnx60++DIaG8h/srXmSJEk9yyAl1TFZAKK8fqnaVJlxQ5IkSVK/Mkip74yOltcv5SkdYJBxblt+\ndtX6pWoGKKX3ni99ic0/+UnDY4aPP55PvfnN8zSi5p380Y+ybXy84TFDg4P864c/PE8jmpteeC8k\nSe1nkFJPGR3N3zc+DuysKgBx3K35Bw8NwUrXNWn+bP7JT2CGZrwz9ZnqtG3j4xz71FMzHlN0vfBe\nSJLazyClnjBZAKK8fqmeIWDlojsYOete1y+psLY+97l1t79yhi/2RfJvhx9ed/vLZghZRdML74Uk\nqX0MUuo669czvS/T/n2w/8msAMQpMzWwXYFlxiVJktQqg5QKZWws68FUz/g4cP99U+uXKpYAg4MW\ngJAkSdK8MUipECpV8iozS/UMAdcvX1suAOH6JUmSJHWOQUptN7l+Kc/+fZNlxqfWL+UxQEmSJKnz\nDFJKamxs6t/btpXLjFfWLy1bW/9BS6gqM75iHkapImm11HQRSm4ve9/7eHRiouExz1q8mJ2XX567\n/2ePPsoTpVJuIYMflko849FHWxqnJElKxyCllo2NwebNZOuXBnZN7SiVWLdkEyMnVBrYOpukQ7Va\naroIJbcfnZjgxTE2POaeGYLWE089xbExsj/nuRwLbO+SqnfdVp0vj9X5JEmNGKQ0o/XrZzigugDE\n8PD0fZYZ1yy1Wmq6CCW37w6h7vYTZghZAM844ggolfjywEDd/eeUStkxBTY0ODir2cGiGz7++Bn7\nRA0ff/y8jEWSVFwGKeWqFICorF/KM7L8BgtASC16/tKl8OSTvDQnUC568MHsmAJr5+2T8ynvNlJJ\nkqoZpPrU6Gh5/VKe0gEGGbcAhCRJklSHQarH1evLVCkAse7oKxk57tb8Bw8NWQBCkiRJqsMg1aOm\nFYBgnKGBHZP7Vi+6o6oAhOuXJEmSpGYZpLrUZAPbPKUDDA/cXtXAtvrWvBUYoCRJkqS5M0gV0Oho\n/r7xcSZnmVYv2dT41jxLjqvNWu0BVa3VUtOtVOdrtQ/UwXJVvkbV+Q7OULnvf/385wwdPMhL7703\n95htP/957r4U/bR6oadXCik/15Kk3mWQKpDJAhDlBrb1DEHNLJMzS+qcVntAQeulplOU3G61D9TC\nBQv4wcGDDR+/cMGChvsPHDzI9oZHZMfkSdFPqxd6eqWQ4nMtSep9BqkOmFy/tGf31Mb9+xgs3V/T\nwDaPs0wqllZ6QLX6X/VTzm7MtQ/UycccAw8+2Ph1yNlXsSAEXhhjwzHck7OvWop+Wr3Q0yuFVl8H\nSVJvM0i1wdhY/r7Nt+yGPXuy9UvL1k7tWIKzTJIkSVKXMEglMjnLtPMhBtmZe9ww27Jb89atw5kl\nSZIkqTsZpGZpdLRc6CFPuQDEbcvPzmaW8qxciQFKkiRJ6m4GqRlUyoxX1i/lGVl+Q/nWPEOSJEmS\n1Ov6OkiNjsL4v2drlvJMlhk/696aXky1DFCSJElSv+jrIDX+vXtZd/SVjJx8KwwN5R+4ciVZE1up\nOIrQ6+YHDzzAk6USCxv0PjrigQfadn1I07uo1T5QP3jgAWKpxJENXocww+vQ6hieOnCAg8BLG1TG\ne+rAgYZjqOhkT68ifK4rrM4nSWqkr4PU9lP+S7nMuFXy1H2K0Oum9PTTvHiGY3789NNtHUOK3kWt\n9oEqPf00Df5TTDaGWbwOP5jxiHwDCxeyfYbnMLBwYcP9RejpVYTPdauvgySpP/R1kGrcq0nqDp3s\ndXP4YYcRnnqqYd+gww+bn/8z00rvolb7QB1+2GEsaPF1OOrwwzl2hnNsz9kH8NLnPa/lXlZF6unV\nyc/1fMx2SZK6X/5/YpUkSZIk1WWQkiRJkqQmGaQkSZIkqUkGKUmSJElqkkFKkiRJkprU31X7pC6W\noodTqp49Regb1MoYKuZaES5lD6dWn0cnex+leC9/9uijPFEqsfhnP8s95hmPPjrnMUqSlIpBSupS\nKXo4tdqzpwh9g1KModW+QSl6OLX6PIrQ+yhJD6gQOHaGQ3aG0MywJElqC4OU1KVS9nCaa8+eIvQN\nSjGGVvsGpejh1OrzKFLvo1Z6QD1/6VJ48smG53j+0qUtjU+SpBRcIyVJkiRJTTJISZIkSVKTDFKS\nJEmS1CSDlCRJkiQ1ySAlSZIkSU2yap/U5TrZPymlIoxhrn62axfLSiVe1qD30c5du+ZxRJ2V4r3s\n5s8DpOuPJkkqLoOU1KWK0D8phSKMoWUxsn2GQ54R47wMpZNSvJc98XkgUU8tSVKhGaSkLlWE/kkp\nFGEMrXr+s54FTz3VuH/Ss541z6Oafyney174PFRrpaeWJKnYXCMlSZIkSU0ySEmSJElSkwxSkiRJ\nktQkg5QkSZIkNckgJUmSJElNsmqf1AH2mOlNVmJTLT8TktS7DFJSB9hjprf0Su8jpeNnQpJ6n0FK\n6iB7zPQGZw5Vy8+EJPU+10hJkiRJUpMMUpIkSZLUpK4MUiGE3w4h/HkI4TshhN0hhIMhhOs6PS5J\nkiRJ/aFb10h9CDgJ2AP8HHgxEDs6IkmSJEl9o1uD1HuBn8UY/z2EcDrwrU4PSOpHlnGXJEn9qiuD\nVIzxH6v+DJ0ah9Sqbq/OZxl3SZLUr7oySEndrtd6zFjGXZIk9RuDlNQB3uomSZLU3bqyap8kSZIk\ndVJfz0iFNWty91105plcvGrVPI5GkiRJUmoX33QTl9x8c/Lz9nWQihs2dHoIkiRJktro4lWrcidI\nGk2szMRb+yRJkiSpSX09IyUpDavzSZKkfmOQkjRnvVbGXZIkaba6MkiFEM4Czir/WWlg86shhGvL\n/94ZY3z/vA9M6jOWcZckSf2qK4MU8CvA7wKx/HcEjgVeWP57B2CQkiRJktQWXVlsIsZ4SYxxQYxx\nYc3PgvLPC2c+iyRJkiTNTVcGKUmSJEnqJIOUJEmSJDXJICVJkiRJTTJISZIkSVKTDFKSJEmS1CSD\nlCRJkiQ1ySAlSZIkSU0ySEmSJElSkwxSkiRJktQkg5QkSZIkNckgJUmSJElNMkhJkiRJUpMMUpIk\nSZLUJIOUJEmSJDXJICVJkiRJTTJISZIkSVKTDFKSJEmS1CSDlCRJkiQ1ySAlSZIkSU0ySEmSJElS\nkwxSkiRJktQkg5QkSZIkNckgJUmSJElNMkhJkiRJUpMMUpIkSZLUJIOUJEmSJDXJICVJkiRJTTJI\nSZIkSVKTDFKSJEmS1CSDlCRJkiQ1ySAlSZIkSU0ySEmSJElSkwxSkiRJktQkg5QkSZIkNckgJUmS\nJElNMkhJkiRJUpMMUpIkSZLUJIOUJEmSJDXJICVJkiRJTTJISZIkSVKTDFKSJEmS1CSDlCRJkiQ1\nySAlSZIkSU0ySEmSJElSkwxSkiRJktQkg5QkSZIkNckgJUmSJElNMkhJkiRJUpMMUpIkSZLUJIOU\nJEmSJDXJICVJkiRJTTJISZIkSVKTDFKSJEmS1CSDlCRJkiQ1ySAlSZIkSU0ySEmSJElSkwxSkiRJ\nktQkg5QkSZIkNckgJUmSJElNMkhJkiRJUpMMUpIkSZLUJIOUJEmSJDXJICVJkiRJTeraIBVCOCaE\ncHUI4f4Qwv4QwvYQwp+FEI7u9Ng0/y6+6aZOD0Gaxs+kisjPpYrGz6S6WVcGqRDCccD3gPOALcAV\nwA4kcFgAAA7/SURBVH8A7wH+OYTwrM6NTp1wyc03d3oI0jR+JlVEfi5VNH4m1c26MkgBnwWWAX8Y\nYzw7xvjBGOPrgD8DXgT8SUdHJ0mSJKmndV2QKs9GnQFsjzH+j5rdFwF7gbeGEI6a98FJkiRJ6gtd\nF6SA15Z/f6N2R4zxCWAzsBg4bT4HJUmSJKl/dGOQelH5949z9m8r/x6ah7FIkiRJ6kPdGKSeWf79\neM7+ynar90mSJElqixBj7PQYmhJC+EvgHcA7YoxX19n/J8A6YF2M8eM55+iuJy1JkiSpbWKModnH\ndOOMVGXG6Zk5+yvbH5uHsUiSJEnqQ4d1egBzcE/594ty9lfWRuWtoZpT4pQkSZKkim68te+FwE+A\n7cDxseoJhBCWAA8AERiMMe7rzCglSZIk9bKuu7UvxvgfZKXPjwV+v2b3JcBRwHWGKEmSJEnt0nUz\nUjA5K/VdYBD4W7Lb/V4FvAb4EfCrMcZdHRugJEmSpJ7WlUEKIIRwDHAp8BvAs4H7ga8Al8QY80qj\nS5IkSVLLujZISZIkSVKndN0aKUmSJEnqNIOUJEmSJDXJIKWeEUJ4awjhYPlndafHo/4SQthR9fmr\n/Xmg0+NTfwshvC6E8JUQwoMh/O/27j3YrrK84/j3JyikMJNIlYsCBqkMMRAQyAhqbmBATCdTUJQq\nlkOLcag1OOJlaCtERkemXqCSqYICQnDUOgyEGBNyISQBAopNGBV6oYAJQYFgTgSSQ0nO0z/ed8Nm\nufbhrHNb2Wf/PjNr3px3vWutZ62dOdlP3stSj6TNkpZKOr3u2KxzSOrq4/dkY9tZd5xm/dWOL+Q1\n+xOSDgHmA88B+5LeJWY20rqBK0vqnxvpQMwaJP0L8FlgE3ArsIW06u1xwDRgSX3RWYdZD8xrsW8q\ncDLwsxGLxmyQnEhZ25Mk4HrgadLKjZ+tNyLrYN0RcVndQZg1SPo46Xfi94E5EbGzsN/fA2zERMQD\nwANl+ySty3+8ZuQiMhscD+2z0WAuMAM4D9hecyxmZrsFSXsBXwF+S0kSBVBWZzbSJB1Neh/o48Di\nmsMx6zf/T5S1NUkTgMuBKyPiLknvrTsm62h7SzoHOBR4nvQ/r2siorfesKxDzQTeACwAQtIs4Cig\nB7gvIu6tMzizJnNyeW34vTzWRpxIWdvKQ1IWAI8B/1hvNGYEcCBwY6H+UUnnRcSaGmKyzjY5ly8A\nG4CJzTslrQE+GBFbRjowswZJY4BzgJ3A92oOx6wSD+2zdnYJcCzQFREv1B2MdbzrSROlDwD+DDga\nuBoYDyyRNKm+0KxD7Z/LzwG7gPeQFuOZBCwjTe7/ST2hmb3kQ8BYYGlEbK47GLMqnEhZW5L0TuBi\n4GsRcV/d8ZhFxGURcWdEPB0RPRHxm4i4APgmMIbWK1WZDZfGv/EvArMj4p6I2B4RvwbOIM1HmSbp\nxNoiNHt5WN/VtUZhNgBOpKzt5CF9NwL/BVzaqtnIRWTWp+/kckqtUVgn6s7l+ojY2LwjInYAt+cf\nJ2NWA0kTgZNIS/N72XNrO06krB3tC7wNeDvQ0/wiP9JwP4Dv5roraovSLGnMP9mn1iisE/1nLrtb\n7G/UjxmBWMzKeJEJa2tebMLaUQ9wLeUv3T0eeAewltRjdc8IxmVWpjFs6pFao7BOtJL0e/LtklTy\nRfWoXD46smGZgaS9gY+RFpm4tuZwzAbEiZS1nYjoAT5etk/SPFIidUNEXDeScVnnknQksCkini/U\njwfm5x9vGuGwrMNFxEZJi4DZwIXAlY19kk4FTgO2AkvridA63FnAOGCRF5mwduVEysxs8M4GLpK0\nGtgIPAscDswC9iK9YPLr9YVnHeyTpP9c+mZ+j9QG4DDgr0iLUJwfEc/WGJ91rsawvmtqjcJsEJxI\n2WgTlA/5MxtOdwBHkL6wvps0H2orsAZYEBHujbJaRMRmSceT5o/OJi15vg1YCHw1Iu6vMz7rTJIm\nkH5XepEJa2vy3D4zMzMzM7NqvGqfmZmZmZlZRU6kzMzMzMzMKnIiZWZmZmZmVpETKTMzMzMzs4qc\nSJmZmZmZmVXkRMrMzMzMzKwiJ1JmZmZmZmYVOZEyMzMzMzOryImUmZmZmZlZRU6kzMzMzMzMKnIi\nZWZmZmZmVpETKTMzMzMzs4qcSJmZmZmZmVXkRMrMzIadpOmSeiVd2s/2Xbn9ucMd23CTND7fy/VD\neK7GtmMoYhwKkuYXYuvXZ21m1q72rDsAMzPrKDHM7WshqRdYHREz+mg2lPeyAbgV2DmE5xysxcBT\nwGHAubTJZ2dmNlBOpMzMzIbGSCYOGyLishG83quKiCXAEknTSYmUmdmo5qF9ZmZmZmZmFTmRMjNr\nE5JmS1op6XeSeiRtlnSnpAtK2u4n6auSHpK0XVK3pBWSZpa0fWk+kqRZku6R9JykP0j6iaS/KDnm\nCEmXS7pf0tM5nsckXS3pzcP1DPK1D87zcR7J190iaaGkE0razsv3Nk3SByX9XNLzkp6R9ENJb2px\njcmSlkl6VtI2Scslndh0vqm5XVce1gfQmAfWco5QnuP0oxzzDkm/kDRrSB9Qus6pkhZJeio/o42S\nbpV0SlObl+atSTpB0tL892SrpJslHZLbHS7px/lz3i5plaRJQx2zmVm7cSJlZtYGJM0hzYk5ElgI\nfJ00J2UM0FVo+xbgl8AXgCeBbwM/BiYASyWd3+IyZwK3ABuBK4F1wAeAeyUdUdL2E8BvgR8A3wIe\nBM4HftEqQRksSceR5gddADyUr7sImArcJen0Fof+PbAAeASYD/wa+DCwQtLrCteYCqwFpgE/Ba4C\ndgCrgMmF864HvpT//Bgwr2lbVWg7HrgPOBS4gfSZHAUszMPhhoSkLwFLSc9kCenvykrS5//RkkMm\nA2uAXuCaHOMZwEpJE4GfAwcB3yf9nZsKLJe0z1DFbGbWliLCmzdv3rzt5hspMdoBvKFk336Fn+8k\nLULwoUL9WNIX/+3A/k31XaQv0b3A+wvHzM31Kwr1bwJeWxLLzHztfyvUT8/nuaSf99uI6W+a6vYE\nHs7xTym0Pwh4HHgCeF1T/bx8nm5gYuGYH+R9ZzXVvQb4H2AXcFqh/Sdy+13A1MK+XuCOFvcyvun5\nfrGw79Rcv7ifz6Vxruta7G+c72HgoJL9by75THqBvy60+17Tc7u4sO+f8765LWKo9Fl78+bNW7tu\n7pEyM2sfuyhZpS0i/tD4s6RjSD0GN0fEvxfabSMlFnuTepqKVkbEzwp180m9OCdLOrTpXE9ExIsl\nsSwn9Uyd1s97qmIW8FbgqohYW7ju74CvAQcCp5Qc+62I+E2h7ru5bO5lehdwOLAqIm4vtL8G+G9A\nAwufx4AvN1dExDJgE3/a0zVQn8rlRfmZvEJEbC45Zm1E/LBQd0MunwEuL+y7MZfHDDhKM7NRwKv2\nmZm1h5uAbwAPSvoRaSjW3RHxdKHdSbkcJ2leyXnemMsJJftWFysiolfSXaQE5ljSsD8AJJ1D6jk6\nBhgH7NF06Auvcj8D0bi38S3u7W25nEAa0tbs/pL2j+fy9U1178jlXcXGERGS1gHFYY79tSEiylb2\n2wS8c4DnLDqR1Bu0tMIxZc+mkYSVxfxELg+uGJuZ2ajiRMrMrA1ExBWStpDm+swFPg2EpNXA5yLi\nl7npn+dyZt5KTweUzW95skX73+dybKNC0hXAhaQv1UuAzaShhwDnkeYBDbXGvZ3VR5tW99ZdUtfo\n3WtOABv32OpZtKrvj7IYGnEM1QiRccDWiKiSyG4rqdvZal9E7JQE8Nrq4ZmZjR5OpMzM2kRELAAW\nSBpLGoJ2BvC3wO2SjoyILbz8xXduRMyveIkDWtQfmMttAJL2JyVzvwLeFRHPNzeWVLagwVBo3Nvs\niPjpMF3jj7ls9Sxa1e8uuoHXS9o7InrqDsbMbDTzHCkzszYTEdsiYklEzCGtpLYfMCXvXpfLqQM4\n9fRihaQ9gPeQenrW5+q3kuYJLStJog7O+4fDYO6tv/4jl1OKOyS9hpTAlgle2bNVl3Wkf9vfV3cg\nZmajnRMpM7M2IGlGi12NHpLtAHmI31rgTEnntTjX0ZLeWLLr5JJ3Gv0DKTFaFRGbct2juZySk4vG\nefclLeAwXAnFQuB/gU+2WuZc0kmSxgziGnfna8yQVExG5pDmYZXNc3oGOGQQ1x0qV+XyG2VL0A/X\nsvRmZp3IQ/vMzNrDLZKeBe4lvbtJpF6TE0iLBaxoavsR4A7gWklzSe8B6iYtDjAJmEhalKC4UMWi\nfJ1bSMnEsaSejWdIc7MAiIgn84IXZwMbJC0nzS2aSUroNuRjh1Sem3MmcDuwWNI9wAP5moeQVr47\njDQUcUfLE/V9jcjv2VoK3CbpZtKqhZOA95Lmg51OWtCh2QrgbEm3kXruXgRWF1cX7MNAVwIsxr9c\n0pdJS5Q/JOlW0qIaB5B6FteR5rCZmdkgOZEyM2sPXyAtKX4c8H6gh7Sc9ueBb0fErkbDiNgs6XjS\nUtgfICVWe5BWYnsQ+FfSC2mLbiYt8f1PpKXG/y/XXRwRDxfa/h0pwfgwKcl6CrgNuDQfU9ZrU0WU\nnSMifpWXeP8M8Je8/L6pJ0jv2voiKfHr8zx9XjhitaRppKXKGz1095KGPn4s//zHwmEX5uucQvp8\nRHpRb38Sqcox9nmyiEvy6oJzSc9oH9IiGffz8rLmZmY2SCpfidXMzDqFpC7gOqArIm58leYdTdLd\npJ6vsRExoF6vQV5/PCmBvSEidsueJUnTST2i8yLisprDMTMbNp4jZWZm1kTSGEnjSuq7SO+yWlZH\nElVwrqReSXXH8RJJ8yX1kpIoM7NRz0P7zMzMXuktwHpJy0hzxfYkvaj33cBW4KIaY9tKGjLYGE6y\ns4+2I20xaYhnw501xWFmNiKcSJmZ2ZDO0RkFfg/cBEwDZgB7keaXXQd8JSIe7ePYYRUR20iJ1G4n\nIpaQFuMwM+sIniNlZmZmZmZWkedImZmZmZmZVeREyszMzMzMrCInUmZmZmZmZhU5kTIzMzMzM6vI\niZSZmZmZmVlFTqTMzMzMzMwqciJlZmZmZmZWkRMpMzMzMzOzipxImZmZmZmZVeREyszMzMzMrCIn\nUmZmZmZmZhU5kTIzMzMzM6vIiZSZmZmZmVlF/w/2l2PCb/NYgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1094e0eb8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 425
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_regions(plt, sepal_petal_rows, y, ppn, weights_log[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see how the decision region improved over the iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 0 iteration there are 2 errors using these weights:\n",
      "[ 0.  0.  0.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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K0vGmJL+U5FMNHQ+AEdW9Jqpbra9bvU+IAqBNGmnIm+SdSU5K8rZSyolJPpvk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\n/wDihwuGdeoAxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10949c5f8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 425
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 1 iteration there are 2 errors using these weights:\n",
      "[ 0.    0.38  0.66]\n"
     ]
    },
    {
     "data": {
      "image/png": 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K0vGmJL+U5FMNHQ+AEdW9Jqpbra9bvU+IAqBNGmnIm+SdSU5K8rZSyolJPpvk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\n/wDihwuGdeoAxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b5e5198>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 425
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 2 iteration there are 3 errors using these weights:\n",
      "[ 0.    0.76  1.32]\n"
     ]
    },
    {
     "data": {
      "image/png": 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K0vGmJL+U5FMNHQ+AEdW9Jqpbra9bvU+IAqBNGmnIm+SdSU5K8rZSyolJPpvk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\n/wDihwuGdeoAxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b65e2b0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 425
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 3 iteration there are 2 errors using these weights:\n",
      "[-0.2   0.22  1.68]\n"
     ]
    },
    {
     "data": {
      "image/png": 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K0vGmJL+U5FMNHQ+AEdW9Jqpbra9bvU+IAqBNGmnIm+SdSU5K8rZSyolJPpvk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\n/wDihwuGdeoAxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bbf38d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 425
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 4 iteration there are 1 errors using these weights:\n",
      "[-0.2   0.34  2.1 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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K0vGmJL+U5FMNHQ+AEdW9Jqpbra9bvU+IAqBNGmnIm+SdSU5K8rZSyolJPpvk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\n/wDihwuGdeoAxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be3fef0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 425
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 5 iteration there are 0 errors using these weights:\n",
      "[-0.4  -0.68  1.82]\n"
     ]
    },
    {
     "data": {
      "image/png": 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pJgYpSZIkSWqSQUqSEqjtXVSZyagtWKDu4PspSZqJQUqSWlRbnOCCC7Kf6nLb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c0748d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 425
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, (weights, num_errors) in enumerate(zip(weights_log, errors_log)):\n",
    "    print(\"after %d iteration there are %d errors using these weights:\\n%s\" % (i, num_errors, weights))\n",
    "    plot_decision_regions(plt, sepal_petal_rows, y, ppn, weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm confused as to why the decision regions are plotted 100% red until the solution converges, I would expect that it would show where the weights are recognizing correctly and not. However, I don't want to get bogged down, so moving on.\n",
    "\n",
    "Update: after digging in a bit, snapshotting the weights each loop doesn't really work since they are updated iteratively in the inner loop as the observations are evaluated, so the number of errors won't be reproduced with the weights snapshot. Below, when we use batch gradient descent with Adeline, the weights log will allow us to plot the improving decision regions.\n",
    "\n",
    "## Adaptive learning and gradient descent\n",
    "\n",
    "We can do better by training based on the output before it has been quantized, and doing so in batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %load adeline.py\n",
    "\"\"\"\n",
    "Implementation of ADAptive LInear NEuron (Adaline) algorithm from Chapter 2 of\n",
    "\"Python Machine Learning\"\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def train_adeline(observations, labels, learning_rate=0.0001, max_training_iterations=100):\n",
    "    \"\"\"\n",
    "    Trains a (binary) perceptron, returning a function that can predict / classify\n",
    "    given a new observations, as well as insight into how the training progressed via\n",
    "    a log of the weights and squared errors for each iteration.\n",
    "\n",
    "    :param observations: array of rows\n",
    "    :param labels: correct label classification for each row: [1, -1, 1, 1, ...]\n",
    "    :param learning_rate: how fast to update weights\n",
    "    :param max_training_iterations: max number of times to iterate through observations\n",
    "    :return: (prediction_fn, weights_log, errors_log)\n",
    "    \"\"\"\n",
    "    the_weights = np.zeros(1 + observations.shape[1])\n",
    "    weights_log = []\n",
    "    num_errors_log = []\n",
    "    squared_error_log = []\n",
    "\n",
    "    def net_input(observations, weights):\n",
    "        return np.dot(observations, weights[1:]) + weights[0]\n",
    "\n",
    "    def quantized_output(output):\n",
    "        return np.where(output >= 0.0, 1, -1)\n",
    "\n",
    "    def predict(observations, weights=the_weights):\n",
    "        return quantized_output(net_input(observations, weights))\n",
    "\n",
    "    for _ in range(max_training_iterations):\n",
    "        weights_log.append(np.copy(the_weights))\n",
    "        raw_outputs = net_input(observations, the_weights)\n",
    "        errors = labels - raw_outputs\n",
    "        weight_deltas = learning_rate * np.dot(observations.transpose(), errors)\n",
    "        the_weights[1:] += weight_deltas\n",
    "        the_weights[0] += learning_rate * np.sum(errors)\n",
    "\n",
    "        squared_errors = (errors ** 2).sum() / 2.0\n",
    "        num_errors = (quantized_output(raw_outputs) != labels).sum()\n",
    "        squared_error_log.append(squared_errors)\n",
    "        num_errors_log.append(num_errors)\n",
    "        if num_errors == 0:\n",
    "            break\n",
    "\n",
    "    return predict, weights_log, squared_error_log, num_errors_log\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uuQbOOguOOQbmz6+6GknSYLXc1r52Z5CSpJGlpwde8Ypideqpp6DTB3hIUkto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\ndwaLMBcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c2f4748>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 425
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adeline, weights_log, squared_error_log, num_errors_log = train_adeline(sepal_petal_rows, y)\n",
    "\n",
    "plt.plot(range(1, len(num_errors_log) + 1), num_errors_log, marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Number of misclassifications')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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funuT76ckaS4GKUmSJElqkkFKkhKY3buospIxu2CBeoPvpyRpLgYpSWrR7OIE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c305438>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 425
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_regions(plt, sepal_petal_rows, y, adeline, weights_log[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine how the learning rate for gradient descent influences convergance: with a learning rate of 0.01, the error increases, whereas with a learning rate of .0001 it doesn't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Kvx0qjBQR/ZBRXMqhHWpDcSiHNkB5tENtiJ8KI/mnfKJ4lEMboDzaoTYUh3Jo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\nBzmG1X1st5VdZz9jeBvwbfJU22fIbw6d5M7g54CNyq5xBP4fr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c581470>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 280,
       "width": 803
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = [.01, .001, .0001]\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=len(learning_rates), figsize=(4.5*len(learning_rates), 4))\n",
    "\n",
    "for i, learning_rate in enumerate(learning_rates):\n",
    "    adeline, weights_log, squared_error_log, num_errors_log = train_adeline(\n",
    "        sepal_petal_rows, y, \n",
    "        learning_rate=learning_rate, \n",
    "        max_training_iterations=10)\n",
    "    ax[i].plot(range(1, len(squared_error_log) + 1), np.log10(squared_error_log), marker='o')\n",
    "    ax[i].set_xlabel('Epochs')\n",
    "    ax[i].set_ylabel('log(Sum-squared-error)')\n",
    "    ax[i].set_title('Adaline - Learning rate %s' % learning_rate)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature scaling to increase learning speed\n",
    "\n",
    "It's kind of a bummer to have to hunt and peck for a learning rate that will converge. If we scale the features before training, we can get away with a learning rate of 0.01, and get convergence after just 3 iterations!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7MPArir/MN9Y9Ary5S+f0CIZfrNxYHqCYy7Z9RB+/BCweUf6w0Y73iP0OoJjv1NzG/eXP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\nZuq4oVrHoqpEdbs/pYzD6jZO5SHKeJ2+iYh5EbFPl+kbUpLtpDpPKg8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c305f98>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 425
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
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zZCHj7y1benXr1StbkahjR7j++qz3QJIkScXQJnUBK+lj4Frq2aCMrDdgK2AmWRCYVbp+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\nklRyhgJJkiSp5AwFkiRJUsn9f1gIxR/t+Dd2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be56f28>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 270,
       "width": 386
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sepal_petal_rows_std = np.copy(sepal_petal_rows)\n",
    "for i in range(sepal_petal_rows_std.shape[1]):\n",
    "    sepal_petal_rows_std[:, i] = (sepal_petal_rows[:,i] - sepal_petal_rows[:,i].mean()) / sepal_petal_rows[:,i].std()\n",
    "\n",
    "adeline, weights_log, squared_error_log, num_errors_log = train_adeline(sepal_petal_rows_std, y, learning_rate=0.01)\n",
    "plt.title('Adaline - Gradient Descent')\n",
    "plot_decision_regions(\n",
    "    plt, sepal_petal_rows_std, y, adeline, weights_log[-1],\n",
    "    xlabel = 'sepal length [standardized]',\n",
    "    ylabel = 'petal length [standardized]')\n",
    "\n",
    "plt.plot(range(1, len(num_errors_log) + 1), num_errors_log, marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Num misclassifications')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, as promised above, we can plot how the decision boundary improves with each iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 0 iteration there are 50 errors using these weights:\n",
      "[ 0.  0.  0.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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s4F+wbs1GFsztZ0dfFzv6ulhQvb+SIUqS1GwTPbXvUuDXJrHfyT5fkiRJkppm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c5022b0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 425
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 1 iteration there are 4 errors using these weights:\n",
      "[ 0.          0.72829015  0.96995527]\n"
     ]
    },
    {
     "data": {
      "image/png": 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0R+fpk0SnPIDixeODv+7puN1b0LdrhZJJFvQFAADB46pH6ruS/rMxpsHR+QCE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\nBplISZIkSVKDTKQkSZIkqUH/H2NcG4pjFgW9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c502c50>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 425
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after 2 iteration there are 0 errors using these weights:\n",
      "[  1.49658064e-15  -5.96910515e-02   3.78300208e-01]\n"
     ]
    },
    {
     "data": {
      "image/png": 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G6f5NLOGOIgg1a9Girj81b6ayBKmIOBj4EnDyNFY3SEmSJEkz1NQ9lXbsZAnr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\npDnHQEqS5riq+nSS+2lrfZYBHwIqyRrgI1X13a7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be3f630>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 425
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, (weights, num_errors) in enumerate(zip(weights_log, num_errors_log)):\n",
    "    print(\"after %d iteration there are %d errors using these weights:\\n%s\" % (i, num_errors, weights))\n",
    "    plot_decision_regions(plt, sepal_petal_rows_std, y, adeline, weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochaistic Gradient Descent\n",
    "\n",
    "As the book states, sometimes updating the weights iteratively, training sample by training sample, can be more pragmatic than in batch, as it allows you to update the weights \"online\" and may converge faster with more frequent updates in the weights. \n",
    "\n",
    "Here's my implementation. My more functional approach admittedly is limited as it doesn't have an object to store the state in, and thus is missing the author's \"partial_fit\" method.\n",
    "\n",
    "Note that the implementation does converge after a few iterations over the data set even without regularizing the parameters and there was no need to futz with the training rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %load adeline_sgd.py\n",
    "\"\"\"\n",
    "Implementation of ADAptive LInear NEuron (Adaline) algorithm from Chapter 2 of\n",
    "\"Python Machine Learning\" adapted to use stochastic gradient descent instead of batch.\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def train_adeline_sgd(observations, labels,\n",
    "                      learning_rate=0.01, max_training_iterations=100,\n",
    "                      shuffle=True):\n",
    "    \"\"\"\n",
    "    Trains a (binary) perceptron, returning a function that can predict / classify\n",
    "    given a new observations, as well as insight into how the training progressed via\n",
    "    a log of the weights and squared errors for each iteration.\n",
    "\n",
    "    :param observations: array of rows\n",
    "    :param labels: correct label classification for each row: [1, -1, 1, 1, ...]\n",
    "    :param learning_rate: how fast to update weights\n",
    "    :param max_training_iterations: max number of times to iterate through observations\n",
    "    :return: (prediction_fn, num_errors_log, final_weights)\n",
    "    \"\"\"\n",
    "    the_weights = np.zeros(1 + observations.shape[1])\n",
    "    num_errors_log = []\n",
    "\n",
    "    def net_input(observations, weights):\n",
    "        return np.dot(observations, weights[1:]) + weights[0]\n",
    "\n",
    "    def quantized_output(output):\n",
    "        return np.where(output >= 0.0, 1, -1)\n",
    "\n",
    "    def predict(observations, weights=the_weights):\n",
    "        return quantized_output(net_input(observations, weights))\n",
    "\n",
    "    idxes = list(range(len(observations)))\n",
    "\n",
    "    for _ in range(max_training_iterations):\n",
    "        num_errors = 0\n",
    "        if shuffle:\n",
    "            np.random.shuffle(idxes)\n",
    "        # for observation, correct_output in zip(observations, labels):\n",
    "        for idx in idxes:\n",
    "            observation = observations[idx]\n",
    "            correct_output = labels[idx]\n",
    "            raw_output = net_input(observation, the_weights)\n",
    "            if quantized_output(raw_output) != correct_output:\n",
    "                num_errors += 1\n",
    "                weight_delta = learning_rate * (correct_output - raw_output)\n",
    "                the_weights[1:] += weight_delta * observation\n",
    "                the_weights[0] += weight_delta\n",
    "\n",
    "        num_errors_log.append(num_errors)\n",
    "        if num_errors == 0:\n",
    "            break\n",
    "\n",
    "    return predict, num_errors_log, the_weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rll1ERJoG1UjVkWqkRESqp6ws+j2deSb88gu0aROvTz4ZWrdOu3R18+ST8Oc/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\nAMAkUgAAAJNIAQAATCIFAAAwBYCvkYU/EjzCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1097040b8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 425
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adeline_sgd, num_errors_log, final_weights = train_adeline_sgd(\n",
    "    sepal_petal_rows, y, shuffle=True)\n",
    "\n",
    "plt.title('Adaline - Stochastic Gradient Descent')\n",
    "plt.plot(range(1, len(num_errors_log) + 1), num_errors_log, marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Number of misclassifications')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EGF/Q8Qk7ZLMJScqfUgk+/vGkHGd8HF7/+vorUp/+NOzcmdz7/Y532PVOGhaT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\nS26sDYkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be2d898>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 281,
       "width": 425
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_regions(plt, sepal_petal_rows, y, adeline_sgd, final_weights)"
   ]
  }
 ],
 "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": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}