Classification_MNIST_Logistic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Name: Ibrahim Odumas Odufowora <br />\n",
    "Course: Machine Learning <br />\n",
    "Project Name: Classification with MNIST <br />\n",
    "\n",
    "If the Latex formula are not showing correctly, please use chrome browser."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problems Identification:\n",
    "This project involves the implementation of efficient and effective Logistic Regression classifiers on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the needed packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "from scipy.stats import mode\n",
    "import numpy as np\n",
    "from mnist import MNIST\n",
    "from time import time\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "import random\n",
    "plt.rcdefaults()\n",
    "from IPython.display import display, HTML\n",
    "from itertools import chain\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from pylab import subplot\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from plotly.graph_objs import *\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset, having downloaded it from Yann. The train data set comprises of 60,000 samples and the test data set has 10,000 samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "__mndata__ = MNIST(os.getcwd() +'\\\\image')\n",
    "__train_image__, __train_label__ = __mndata__.load_training() #train & train_label\n",
    "__test_image__, __test_label__ = __mndata__.load_testing() #test & test_label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The train and test images were reshaped to 784 dimension long, which translate to one feature for each pixel in the image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization:\n",
    "Visualizing a random 28 by 28 image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BBAAwAoEEADACgQQAMAKBBAAwAoEEADACgQQAMAKBBAAwQlNvFwA0Runp6bbH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\nEQgkAIAR/g8oMXFNBI2kMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ae1e5ccf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.reshape(train[random.randint(1,1000)], [28,28]), cmap='Greys_r'); plt.title('Visualizing a random sample'); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Utility:\n",
    "    \"\"\"This class warehouses the data and other general functions\"\"\"\n",
    "    def __init__(self):\n",
    "        self.train = np.asarray([np.reshape(x, (784)) for x in __train_image__]).astype('float64') #change train_image to numpy array #784 means 28 by 28 pixel\n",
    "        self.train_label = np.asarray(__train_label__)\n",
    "        self.test = np.asarray([np.reshape(x, (784)) for x in __test_image__]).astype('float64') #change test_image to numpy array\n",
    "        self.test_label = np.asarray(__test_label__)\n",
    "        self.n_class = list(range(10)) #class labels\n",
    "    \n",
    "    def confusionMatrix(actual, predict, classV=list(range(10)), print_cfm=True, print_err_digit=True):\n",
    "        cfm = pd.DataFrame(confusion_matrix(actual, predict))\n",
    "        err_all = round((sum(np.diagonal(cfm)) / len(predict)) * 100, 4)\n",
    "        error_digit = []\n",
    "        for i in classV:\n",
    "            error_digit.append(round(cfm.iloc[i, i] / sum(cfm.iloc[i, :]), 4) * 100)\n",
    "\n",
    "        tab_error = pd.DataFrame(columns=['digit', 'Accuracy per digit in %'])\n",
    "        tab_error['digit'] = classV\n",
    "        tab_error['Accuracy per digit in %'] = error_digit\n",
    "\n",
    "        if print_cfm:\n",
    "            print('Testing Confusion Matrix: Actual vs. Prediction')\n",
    "            display(cfm)\n",
    "\n",
    "        if print_err_digit:\n",
    "            print('% Accuracy per digit')\n",
    "            display(tab_error)\n",
    "            print('\\nThe overall testing accuracy is {}%'.format(err_all))\n",
    "\n",
    "        return cfm, tab_error, err_all\n",
    "    \n",
    "    def mutiplePlot(yValues, legend, title, xLabel, yLabel):\n",
    "        plt.subplots(figsize=(10, 5))\n",
    "        for i in range(len(yValues)):\n",
    "            plt.semilogx(yValues[i], label=legend[i])\n",
    "        plt.xlabel(xLabel)\n",
    "        plt.ylabel(yLabel)\n",
    "        #plt.xticks(comp, comp)\n",
    "        plt.legend()\n",
    "        plt.title(title)\n",
    "        plt.show()\n",
    "        \n",
    "    def plot_(xValues, yValue, xlabel, ylabel, title, semilogx=False, semilogy=False):\n",
    "        yValue = np.round(yValue, 4)\n",
    "        if semilogx:\n",
    "            plt.semilogx(xValues, yValue, '-gD', color='black')\n",
    "        elif semilogy:\n",
    "            plt.semilogy(xValues, yValue, '-gD', color='black')\n",
    "        else:\n",
    "            plt.plot(xValues, yValue, '-gD', color='black')\n",
    "            #ax.set_xticks(xValues, xValues)\n",
    "            \n",
    "        ax = subplot(111)\n",
    "        plt.ylabel(ylabel)\n",
    "        plt.xlabel(xlabel)\n",
    "        plt.title(title)\n",
    "        plt.show()\n",
    "        \n",
    "    def images_plot(prob, imageData):\n",
    "        classes = [\"W - c=0\", \"W - c=1\", \"W - c=2\",\"W - c=3\", \"W - c=4\",\"W - c=5\",\"W - c=6\",\"W - c=7\",\"W - c=8\", \"W - c=9\"]\n",
    "        if prob:\n",
    "            classes = [\"P(x|c=0)\", \"P(x|c=1)\", \"P(x|c=2)\", \"P(x|c=3)\", \"P(x|c=4)\", \"P(x|c=5)\", \"P(x|c=6)\", \"P(x|c=7)\",\n",
    "                   \"P(x|c=8)\", \"P(x|c=9)\"]\n",
    "\n",
    "        num_classes = len(classes)\n",
    "        plt.subplots(figsize=(15, 2))\n",
    "        for y, cls in enumerate(classes):\n",
    "            plt_idx = y + 1\n",
    "            plt.subplot(1, num_classes, plt_idx)\n",
    "            if prob:\n",
    "                plt.imshow(imageData[y].reshape((28, 28)))\n",
    "            else:\n",
    "                plt.imshow(imageData[y].reshape(785, 1)[1:785].reshape((28, 28)))\n",
    "            plt.axis(\"off\")\n",
    "            plt.title(cls)\n",
    "\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "        \n",
    "#End of Class Utility\n",
    "\n",
    "class Logistic_Regr:\n",
    "    \"\"\"This class warehouses the logistic regression classifier\"\"\"\n",
    "    def normalize(self, features):\n",
    "        return features / 255\n",
    "\n",
    "    def logistic_regression(self, features, target, lambdaV, iteration):\n",
    "        weights = np.reshape(np.ones(features.shape[1]), (1, features.shape[1]))\n",
    "        iter = 1;\n",
    "        dist = 1\n",
    "        dist_all = []\n",
    "        learning_rate = (0.05 / 60000)\n",
    "        while dist > 0.0000001 and iter < iteration:\n",
    "            old_weights = weights.copy()\n",
    "            scores = np.dot(features, weights.T)\n",
    "            scores_desc = np.multiply(target.T, scores)\n",
    "            probs_desc = 1 / (1 + np.exp(-scores_desc))\n",
    "            _probs_desc_1 = (1 - probs_desc).T\n",
    "            _probs_desc_1_target = np.multiply(target, _probs_desc_1)\n",
    "            grad_desc = - np.dot(_probs_desc_1_target, features)\n",
    "            lamda_weight = (lambdaV * weights)\n",
    "            grad_desc_lamda_weight = grad_desc + lamda_weight\n",
    "            grad_desc_lamda_weight_smoth =  learning_rate * grad_desc_lamda_weight\n",
    "            weights = weights - grad_desc_lamda_weight_smoth\n",
    "            dist = np.linalg.norm(old_weights - weights)\n",
    "            dist_all.append(dist)\n",
    "            iter += 1\n",
    "\n",
    "        return weights, dist_all\n",
    "\n",
    "    def get_weights_pred(self, features, train_lb, lambdaV, test, test_lb, iteration, add_b=True):\n",
    "        sta_time = time()\n",
    "        if add_b:  #hstack ones# for w0\n",
    "            features = np.asarray(np.hstack((np.matrix(np.ones(features.shape[0])).T, features)))\n",
    "            test = np.asarray(np.hstack((np.matrix(np.ones(test.shape[0])).T, test)))\n",
    "\n",
    "        features = self.normalize(features)\n",
    "        test = self.normalize(test)\n",
    "        weights_all = []\n",
    "        dist_all = []\n",
    "        probab = pd.DataFrame(index=range(test.shape[0]), columns=range(10))\n",
    "        for c in range(10):\n",
    "            tr_label = np.reshape(np.where(train_lb == c, 1, -1), (1, train_lb.shape[0]))\n",
    "            logistic = self.logistic_regression(features, tr_label, lambdaV, iteration)\n",
    "            weights = logistic[0]\n",
    "            dist = logistic[1]\n",
    "            weights_all.append(weights)\n",
    "            dist_all.append(dist)\n",
    "            scores = np.dot(test, weights.T)\n",
    "            pred_prob = 1.0 / (1 + np.exp(-scores))\n",
    "            probab.iloc[:, c] = pred_prob\n",
    "\n",
    "        probability = np.asarray(probab)\n",
    "        overall_pred_class = np.argmax(probability, axis=1)\n",
    "        overall_err = 1 - np.sum([overall_pred_class == test_lb]) / len(test_lb)\n",
    "\n",
    "        return {\"weights\": weights_all, \"time\": time() - sta_time, \"prediction\": overall_pred_class,\n",
    "                      \"probability\": probability, \"distance_iteration\": dist_all, \"overall_err\": overall_err}\n",
    "    \n",
    "    def logistic_multlambda(self, train, train_lb, test, test_lb, lambdas, iteration):\n",
    "        legends = ['class 0', 'class 1', 'class 2', 'class 3', 'class 4', 'class 5', 'class 6', 'class 7',\n",
    "                   'class 8', 'class 9']\n",
    "        titl = 'Gradient Curves - Showing that the optimal weights were almost reached'\n",
    "        result_all = []\n",
    "        accuracy_all = []\n",
    "        weight_all = []\n",
    "        time_all = []\n",
    "        for i, lambdaV in enumerate(lambdas):\n",
    "            result = self.get_weights_pred(train, train_lb, lambdaV, test, test_lb, iteration=iteration)\n",
    "            accuracy_all.append(1 - result['overall_err'])\n",
    "            weight_all.append(result['weights'])\n",
    "            time_all.append(result['time'])\n",
    "            result_all.append(result) \n",
    "        return {'result_all':result_all, 'accuracy_all':accuracy_all, 'weight_all':weight_all, 'time_all':time_all}\n",
    "    \n",
    "    def logistic_multlambda_plots(self, lambdas, test_lb, result):\n",
    "        legends = ['class 0', 'class 1', 'class 2', 'class 3', 'class 4', 'class 5', 'class 6', 'class 7',\n",
    "                   'class 8', 'class 9']\n",
    "        titl = 'Gradient Curves - Showing that the optimal weights were almost reached'\n",
    "        for i, lambdaV in enumerate(lambdas):\n",
    "            print('\\n\\033[2;40;46m                        RESULTS FOR LAMBDA = {}                        \\033[0;40;46m'.format(lambdaV))\n",
    "            dist_all = result['result_all'][i]['distance_iteration']\n",
    "            Utility.mutiplePlot(dist_all, legends, titl, 'Epochs', 'Loss')\n",
    "            prediction = result['result_all'][i]['prediction']\n",
    "            conf_matix = Utility.confusionMatrix(test_lb, prediction)\n",
    "            print('\\nImages of each 10 trained classiers: ')\n",
    "            weights = result['result_all'][i]['weights']\n",
    "            Utility.images_plot(False, weights)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# L2 Regularized Logistic Regression: \n",
    "I implemented a regularized logistic regression classifier and presented the testing accuracy for each digit in a table.\n",
    "\n",
    "Below are the implemention explanations:\n",
    "\n",
    "$$ P(y \\ = +1 \\ | \\ X) =  \\frac{1}{1 \\ + exp(-yW^{T}X)} $$\n",
    "\n",
    "Thus, \n",
    "\n",
    "$$ P(y \\ = -1 \\ | \\ X) = 1 - P(y \\ = +1 \\ | \\ X)  = \\frac{1}{1 \\ + exp(yW^{T}X)} $$\n",
    "\n",
    "Learn parameter W by Maximum Likelihood Estimation (MLE):\n",
    "$$P(y \\ | \\ X) =  \\prod_{i=1}^{N} \\frac{1}{1 \\ + exp(-yW^{T}X)} $$\n",
    "\n",
    "Taking logarithm of likelihood:\n",
    "$$ln \\ P(y \\ | \\ X) = - \\sum_{i=1}^{N} ln \\left \\{   1 \\ + exp(-y_{i}W^{T}X_{i})\\right \\} $$\n",
    "\n",
    "$$w^{*} = \\min_{w} \\sum_{i=1}^{N} ln \\left \\{   1 \\ + exp(-y_{i}W^{T}X_{i})\\right \\} $$\n",
    "\n",
    "Thus, adding ridge regularization to the loss function yields:\n",
    "$$L(w) =  \\sum_{i=1}^{N} ln \\left \\{   1 \\ + exp(-y_{i}W^{T}X_{i})\\right \\} + \\frac{\\lambda}{2}|W|_{2}^{2}$$\n",
    "\n",
    "The regularization favors small weights, guarantee bounded norm of W, and it provides a unique solution for W.\n",
    "\n",
    "$$\\nabla L(w) =  \\sum_{i=1}^{N} \\frac{-y_{i}X_{i} \\ exp(-y_{i}W^{T}X_{i})}{1 \\ + exp(-y_{i}W^{T}X_{i})} \\ + \\ \\lambda W$$\n",
    "\n",
    "Further simplification result to:\n",
    "$$\\nabla L(w) = - \\sum_{i=1}^{N} y_{i}X_{i}(1 \\ - \\ p(y_{i}|X_{i})) \\  + \\ \\lambda W \\ \\ \\\\where \\ p(y_{i}|X_{i}) \\ is \\ the \\ classification \\ error.$$\n",
    "\n",
    "Finally, \n",
    "\n",
    "$$W_{t+1} = W_{t} \\ - \\ \\eta_{t} \\nabla L(w) \\\\where \\ \\eta_{t} \\ is \\ the \\ learning \\ rate$$\n",
    "\n",
    "In order to obtain optimal weights with minimal iterations(epoch), learning rate was set at 0.05 divided by the number of training samples.\n",
    "\n",
    "I modeled the ridge regularization logistic regression above, and various values of lambda were used. The results corresponding to each lambda value were presented, these include: overall testing accuracy, confusion matrix, accuracy per digit, gradient descent curve, and images of weights. \n",
    "\n",
    "Finally, as requested I plotted the lambda values versus overall test accuracy (not error).\n",
    "\n",
    "Note: iteration was set at 10000 because of time constraint and low processing speed of my machine. Apparently, better accuracy would be achieved with a highger number of iteration. Specifically, for lambda = 0.0001, accuracy was close to 92% with 15000 iterations, however, it almost runs forever on my machine(low processing speed). Logistic regression seem to work better on a scaled dataset, hence, the train and test set were scaled between zero and one by dividing through by 255(which is the maximum value in the dataset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logistic = Logistic_Regr()\n",
    "util = Utility()\n",
    "lambdas = [0.0001, 1, 10, 100, 1000, 10000, 100000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lambdas values of 0.0001, 1, 10, 100, 1000, 10000, & 100000 were applied to the classifier, and the results corresponding to each of them are presented below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "result_mult_lamdas = logistic.logistic_multlambda(util.train, util.train_label, util.test, util.test_label, lambdas, 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[2;40;46m                        RESULTS FOR LAMBDA = 0.0001                        \u001b[0;40;46m\n"
     ]
    },
    {
     "data": {
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aa6/h/fffh7u7Ow4ePIjDhw8DqL+NayOTyeDi4oIffvgBQUFBsLGxgZ2dHayt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VdcQU9PD//yL//CtddeS3l5ORdffPGQ6/P7/dx4441s27aNUCjEGWecwf33\n35+DIxcRkeFKow6KiMgHmmEYPPTQQ1xwwQX5LkVERI4jukdLREREREQkxxS0REREREREckz3aImI\nyAearpAXEZF80BktERERERGRHFPQEhERERERyTEFLRERERERkRxT0BIREREREckxBS0REREREZEc\nU9ASERERERHJMQUtERERERGRHFPQEhERERERyTEFLRERERERkRz7/3mzzIMrrT3CAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0bfe52c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Confusion Matrix: Actual vs. Prediction\n"
     ]
    },
    {
     "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",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>961</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1095</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>911</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>918</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>900</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>735</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>906</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>934</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>866</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0     1    2    3    4    5    6    7    8    9\n",
       "0  961     0    1    3    0    0    8    1    6    0\n",
       "1    0  1095    3    3    0    2    4    1   26    1\n",
       "2    6     1  911   18   11    1   14   18   46    6\n",
       "3    3     0   22  918    2   22    4    9   22    8\n",
       "4    2     1    8    0  900    0    9    1   16   45\n",
       "5   17     5    4   43   10  735   19    8   41   10\n",
       "6   11     3    3    3   10   11  906    0   11    0\n",
       "7    5     9   28    7    7    1    4  934    5   28\n",
       "8    8     4    6   21   12   28   12   11  866    6\n",
       "9    8     7    5   16   39   11    0   26   16  881"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% Accuracy per digit\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>digit</th>\n",
       "      <th>Accuracy per digit in %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>98.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>96.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>88.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>90.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>91.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>82.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>94.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>90.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>88.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>87.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   digit  Accuracy per digit in %\n",
       "0      0                    98.06\n",
       "1      1                    96.48\n",
       "2      2                    88.28\n",
       "3      3                    90.89\n",
       "4      4                    91.65\n",
       "5      5                    82.40\n",
       "6      6                    94.57\n",
       "7      7                    90.86\n",
       "8      8                    88.91\n",
       "9      9                    87.31"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The overall testing accuracy is 91.07%\n",
      "\n",
      "Images of each 10 trained classiers: \n"
     ]
    },
    {
     "data": {
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qnVHtZpK39ZEUVb+l2rPSvFqLYdy2Q2kOY6/dj8/Dwg92drb/YgzXWvrEGa+F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\nQ7aMe6q4XlHRw35vI9/1NkO/SQghhBBCCCGEEEIIIYQ8J/g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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0af515518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[2;40;46m                        RESULTS FOR LAMBDA = 1                        \u001b[0;40;46m\n"
     ]
    },
    {
     "data": {
      "image/png": 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169bhjTfewIcffghXV1ccO3YMJ06cAFB7H9dELpfDyckJP/zwA/z8/GBlZQUb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\nImta5DDzw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je+wfHHH084HOacc87hhz/8Yc+xLrroIlKpFD/60Y+44oorqKys5NOf/vSA\n6/P7/VxzzTVs3ryZUCjEggULuPvuuwtw5iIiMljpqYMiIvKhZhgG9913H0uWLCl2KSIichTRPVoi\nIiIiIiIFpqAlIiIiIiJSYLpHS0REPtR0hbyIiBSDRrREREREREQKTEFLRERERESkwBS0RERERERE\nCkxBS0REREREpMAUtERERERERApMQUtERERERKTAFLREREREREQKTEFLRERERESkwBS0RERERERE\nCuz/A7SE46Ec4q1sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0babbc550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Confusion Matrix: Actual vs. Prediction\n"
     ]
    },
    {
     "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",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>961</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1095</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>911</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>918</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>900</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>735</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>906</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>934</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>866</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0     1    2    3    4    5    6    7    8    9\n",
       "0  961     0    1    3    0    0    8    1    6    0\n",
       "1    0  1095    3    3    0    2    4    1   26    1\n",
       "2    6     1  911   18   11    1   14   18   46    6\n",
       "3    3     0   22  918    2   22    4    9   22    8\n",
       "4    2     1    8    0  900    0    9    1   16   45\n",
       "5   17     5    4   43   10  735   19    8   41   10\n",
       "6   11     3    3    3   10   11  906    0   11    0\n",
       "7    5     9   28    7    7    1    4  934    5   28\n",
       "8    8     4    6   21   12   28   12   11  866    6\n",
       "9    8     7    5   16   39   11    0   26   16  881"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% Accuracy per digit\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>digit</th>\n",
       "      <th>Accuracy per digit in %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>98.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>96.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>88.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>90.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>91.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>82.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>94.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>90.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>88.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>87.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   digit  Accuracy per digit in %\n",
       "0      0                    98.06\n",
       "1      1                    96.48\n",
       "2      2                    88.28\n",
       "3      3                    90.89\n",
       "4      4                    91.65\n",
       "5      5                    82.40\n",
       "6      6                    94.57\n",
       "7      7                    90.86\n",
       "8      8                    88.91\n",
       "9      9                    87.31"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The overall testing accuracy is 91.07%\n",
      "\n",
      "Images of each 10 trained classiers: \n"
     ]
    },
    {
     "data": {
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BMdM999yjlFJq48aNh1xPcugkOaaeJZVKtXQx8/Pz6jOf+Yy68MIL1ZYtWw65nkeFY/r/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\nKZZ1sN9byWe9tdBvEkIIIYQQQgghhBBCCCHPC75EJ4QQQgg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0aec3e898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[2;40;46m                        RESULTS FOR LAMBDA = 10                        \u001b[0;40;46m\n"
     ]
    },
    {
     "data": {
      "image/png": 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WOu/FKlWJVlhYmM7yDz/8kABQZmYmERGlp6eTqalpteSjoKCAmjdvTsHBwbW2/+qrr5Jc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\nNgW7+NmzH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ERGvFgstt+lfO/FsiwAjDH97wfrE4lEhrS9QCCw33d93wfglFNOYcuWLfz+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\njRGt0zo45b0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0af24cf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Confusion Matrix: Actual vs. Prediction\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<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",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>961</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1095</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>911</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>918</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>901</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>735</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>906</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>934</td>\n",
       "      <td>5</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>865</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0     1    2    3    4    5    6    7    8    9\n",
       "0  961     0    1    3    0    0    8    1    6    0\n",
       "1    0  1095    3    3    0    2    4    1   26    1\n",
       "2    6     1  911   18   11    1   14   18   46    6\n",
       "3    3     0   22  918    2   22    4    9   22    8\n",
       "4    2     1    8    0  901    0    9    1   15   45\n",
       "5   17     5    4   43   10  735   19    8   41   10\n",
       "6   11     3    3    3   10   11  906    0   11    0\n",
       "7    5     9   28    7    7    1    4  934    5   28\n",
       "8    8     4    6   21   12   28   12   12  865    6\n",
       "9    8     7    5   16   40   10    0   26   16  881"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% Accuracy per digit\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>digit</th>\n",
       "      <th>Accuracy per digit in %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>98.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>96.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>88.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>90.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>91.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>82.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>94.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>90.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>88.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>87.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   digit  Accuracy per digit in %\n",
       "0      0                    98.06\n",
       "1      1                    96.48\n",
       "2      2                    88.28\n",
       "3      3                    90.89\n",
       "4      4                    91.75\n",
       "5      5                    82.40\n",
       "6      6                    94.57\n",
       "7      7                    90.86\n",
       "8      8                    88.81\n",
       "9      9                    87.31"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The overall testing accuracy is 91.07%\n",
      "\n",
      "Images of each 10 trained classiers: \n"
     ]
    },
    {
     "data": {
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DNM9hID1D2VqH74kGttd3lI+D9LcmTm5tPzaC66kl9mGMpKbNNe6ZW7kfiBJm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\npHzWa39vzTdwXZrwIM+YccC+GnL9q5QYmNhjgmA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0ba795b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[2;40;46m                        RESULTS FOR LAMBDA = 100                        \u001b[0;40;46m\n"
     ]
    },
    {
     "data": {
      "image/png": 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ghRdewJtvvomQkBDs378fBw8eBFD1MW6Oi4sLAgIC8N1336FXr17w9PSEt7c3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\ngqcKT2AY4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/zlL59ajSL7cyjj98orr3SGDBniVFdXOyUlJU44HHauvfbaw1bzwdCMVj+V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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0ab7a8828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Confusion Matrix: Actual vs. Prediction\n"
     ]
    },
    {
     "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",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>961</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1101</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>905</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>915</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>906</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>43</td>\n",
       "      <td>14</td>\n",
       "      <td>727</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>910</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>931</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>858</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>12</td>\n",
       "      <td>881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0     1    2    3    4    5    6    7    8    9\n",
       "0  961     0    1    1    0    0   10    1    6    0\n",
       "1    0  1101    3    2    0    1    4    2   22    0\n",
       "2    6     4  905   20   11    1   14   18   46    7\n",
       "3    3     0   22  915    1   23    5    9   23    9\n",
       "4    1     1    7    0  906    0    9    1   12   45\n",
       "5   17     4    3   43   14  727   19   10   43   12\n",
       "6   10     3    4    2   10   10  910    2    7    0\n",
       "7    4    14   27    6    7    1    2  931    4   32\n",
       "8    9     9    8   19   10   24   14   15  858    8\n",
       "9   10    10    5   18   40    7    0   26   12  881"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% Accuracy per digit\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>digit</th>\n",
       "      <th>Accuracy per digit in %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>98.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>97.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>87.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>90.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>92.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>81.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>94.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>90.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>88.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>87.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   digit  Accuracy per digit in %\n",
       "0      0                    98.06\n",
       "1      1                    97.00\n",
       "2      2                    87.69\n",
       "3      3                    90.59\n",
       "4      4                    92.26\n",
       "5      5                    81.50\n",
       "6      6                    94.99\n",
       "7      7                    90.56\n",
       "8      8                    88.09\n",
       "9      9                    87.31"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The overall testing accuracy is 90.95%\n",
      "\n",
      "Images of each 10 trained classiers: \n"
     ]
    },
    {
     "data": {
      "image/png": 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4x9TzhGHY0cUsLy/Lpz/9abn22mtlx44dJ9zOU8KG/j/4l5HLLrvMeJ5ngiAwy8vLUPfr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\nWcOYSqjQDOrdnfaappM7qkOYaKpDmEwCpx9ZOlO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0ba742320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[2;40;46m                        RESULTS FOR LAMBDA = 1000                        \u001b[0;40;46m\n"
     ]
    },
    {
     "data": {
      "image/png": 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jx449anfw4ME89NBDeL1eBgwYANifqiguLmbJkiWceuqpSd8i+sMf/sALL7zA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\nHQAnn3wy77//PlVVVbS2trJ69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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0adbd6710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Confusion Matrix: Actual vs. Prediction\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
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       "        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",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>956</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1104</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>869</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>54</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>902</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>63</td>\n",
       "      <td>20</td>\n",
       "      <td>671</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>902</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>917</td>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>32</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>828</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0     1    2    3    4    5    6    7    8    9\n",
       "0  956     0    2    2    0    0   11    1    8    0\n",
       "1    0  1104    2    3    1    0    4    1   20    0\n",
       "2   11    14  869   21   17    0   19   19   54    8\n",
       "3    6     1   19  902    1   23    8   14   24   12\n",
       "4    1     7    4    1  888    1   13    1   14   52\n",
       "5   18     8    3   63   20  671   22   13   59   15\n",
       "6   15     3    4    1    9   12  902    1   11    0\n",
       "7    2    26   24    2   10    0    4  917    6   37\n",
       "8   10    14    9   32   10   26   15   16  828   14\n",
       "9   14    10    8   15   43   10    1   35   12  861"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% Accuracy per digit\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>digit</th>\n",
       "      <th>Accuracy per digit in %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>97.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>97.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>84.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>89.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>90.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>75.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>94.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>89.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>85.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>85.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   digit  Accuracy per digit in %\n",
       "0      0                    97.55\n",
       "1      1                    97.27\n",
       "2      2                    84.21\n",
       "3      3                    89.31\n",
       "4      4                    90.43\n",
       "5      5                    75.22\n",
       "6      6                    94.15\n",
       "7      7                    89.20\n",
       "8      8                    85.01\n",
       "9      9                    85.33"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The overall testing accuracy is 88.98%\n",
      "\n",
      "Images of each 10 trained classiers: \n"
     ]
    },
    {
     "data": {
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EwoY/EWrHalttIeJhpXOZtI1eoWTTEWh8VlXMV+bs+FPuiA2+/Lxqs6FtikAx\no+9LAGNgSRn6jFprPGgd86fDdb/P2JbpNPaxItCwRCqmsJ3Dsav8vD9w9vCS3fnogkmmCzYdZP23\nXjhpdbCtLRMm3Zz0MdccBSUoKIOaQ0rlPWSyOsbPXLh+9RL/Ep0QQgghhBBCCCGEEEII6QIH0Qkh\nhBBCCCGEEEIIIYSQLnAQnRBCCCGEEEIIIYQQQgjpwknvREenWaocTnFqnXT1ZndvWVoDCTU49IKS\nl5EVylYutGXUes73Ds+1t9FHOxFZX+2WrPUJjYVehrWYWgfVD+pbTDoKtra35+tlk7dUsw7aStVL\nRJvg1MuAZyqXSVXegDnNng6t5AJXFHo1tcMutwIeqUUrtMskPj8u2WfSCm086jDHMqB7PWyCE005\n01p5cPBnIa1cpWEDnG2rNq19WxgmLahZ3AD/ug7dihr0Cw9FVn43FPp0Cr/zPJxYJ/V8CiIwBfqs\n59S+C4mtO9Ie6zSIiMw2vad1tmbPmc1aSVui0gHEQAscga6pvJ+xjf8kZ384VGLsXo7mfiULHsrR\nvHeZby7Y9mhbzrYxWSXS2561vjrd/oiIlFDGqNiVnTPpU/Ij7e2pnC3DUmK9+vOwvsfDw94FvFSx\n+zZq3V3TSQ3aslUbN4Fq15tFu2+cs/viPe1netVJSOf7BW5P5e9tpeCzRM+5cqS3oH1BoXVh3p+3\nfNg+m0xiy7Syw55o5VTvXWxuscctFWyjXVJrxGwp2rjF/ttK4vtVR6u23ux1T4/nfvcLHX0l5Tnv\ncHeCI137yfHWpbZrK7Fa56WVs3HRHLc/nF327Qg60CP7qDtjN++PnYK3tThipa/DeV9vTuZtn//c\n8kGT3pJdbG9/p7zb5P1d61yTXp7x7W2QDlinCgNBP4LE3otMhxNdeWQ74tKmdb8X9+1IqxjK1sEx\nW4V6sgblP+LbtSDtvZ6CXuoD3evQtEqi1vpIS1Af5yGtDozr0AwC+H2rxxNCWK8I+wc5lc4U7ftf\nh++5+rBPV0bsgh71on2ALoh0wuRpF7ZIpw9brxXSskuqSQpp7QXGRx9D/7vVUt+Q+B7mbXuqHemD\ntqJa0NFY+c2Oegac6KEKIVh+TFq2u2zatfpm25AFJVgfRj07fM5ZcKBjTy9R9WoAdWo8ap9uK1Lr\n3aBzGtfc6TGmEuAaD3rNk3V0WZ804L1Tj6zTc27TuWU1HoVr8sX2XrrQn6c2aZ/18imw5uJW1Y+a\nGjF5pZKt35LErpNWm/XBXDxgjzs0bctUWPAXm1u2cY7ta1T3x6rBPWuFkM77tHuW64IMWC+MEEII\nIYQQQgghhBBCCDl2OIhOCCFfs1zNAAAgAElEQVSEEEIIIYQQQgghhHSBg+iEEEIIIYQQQgghhBBC\nSBdOOic6+rgw3VQyqXoCXjJwoqfKwSspeIfK1r9TKHn50Gi5ZvKKkZVd1ZTfTjs0RURKKNEGJkIv\nZiwF1kc7Gw2b9GjOS7RWYnsedF1pCVXShPsSg4+7qNyVA+gbRjGY8XfBbUVHXaTSUc06qMI6eF2r\nPm4yDXCTRfZ5tiIfq4HDc9rzBOD6zKjYRu9nhyVNOwFBh4wOLe0ya0XgtwZP48lX06wfWEfpdwrf\nr2Jo65LZ5tDTbot0vvP6WHicJdh3qeEf0HLTOsxWYD2FFNyrxlHdgucOPrtMQTnN8rZMLmf3rav6\nOAXHaTqATmENXj/GVEE97yxUUugjX1XizEdrUyavBm7WihJp5kAi3BGrdR+fLfDqF6CNrCf2PNpL\n7cDzWRhZhX19G3po2fr4FjP2HQkqvuJpNa1ostc9DQex3VNoh3cLnJVYHxhfasvmOexXKa9mbtHu\nO7Tf3vOxR33/Jvu/HrFlWF426eL5Z9lCynh7azm0sbaUwTUgfBmxL4f182LTv0srNVtvxtCvivR6\nEIMQTz3cnSK2P4EOdNxXu33jIXvv0rLdORrz9UE2tMHaXLBtWai8maHVGEsI/Zs4C2VUcZ5p2Nit\nLdvzzCjPZj609ebOoq2jfmLo/vb2WbkZk/fElnGTvmfuDJ/ovlxFf4KvkHb9Qr9DO9BFbF91Lc+5\nOQ/qeSFO9doMmBektsDlRZvOL/oT5xfgu7AFsTji67DGmK1nGmO2kLVNqj6GGHbZ7v39joWN+hCs\nz0N4+Dp/JGcriM15u17GZNYLiDdlbV4Z1pLJKDv4Y43NJu+B1a0m/e+T3iG8csjKsLHeTIfA0z7u\ny4xrf0yAuzyrnO/V2LaROCaQJGq9Imj/mwmsLaOO2/f9KLw8HDvQdRR+K0V258aY306HcG0Z+PYu\n+jYlA07nFqwTFC36dG7BliGGcQYcOygox3YIS24lRVh/RLnZo2HbjwphPKqlYihetP2osAp9SBkw\nsB8F7ZP2oGdtN1jyS3Zn7RTPNCGmwBNen1RO8U3Qppxl68JLz3y4vf2WrV80eefnbEz9G4x7/f5j\nV7S3H2laX3pUw7/rhsUB9L4Ve9xQXV8G1nTIQNvcWseg4l+iE0IIIYQQQgghhBBCCCFd4CA6IYQQ\nQgghhBBCCCGEENKFk06ygNPY45b9s/1K008/r9RzJq/ZsJdr1APw64Qg0/3v/VELs5qx0yRqaqr6\namKnquAU8omoYtL748mu+4YwsWVCTSdbydnz4BRSPU3t6KKdl+MGXJWwJlrnssZUUJPumH5q/yPT\n9M/IgaIF1Sl6enNzxAZrdbNN55ZxTpnfxCnL0azdNWwq1QhM7cKprS31OoExQtI8TElSp+33cMP3\nKcVpfIHS60AArUB9sdz0U6Pma3ZqZwOmXI4W/ZQrnLa6BIqWStWfp0ORBSoVV4cpVXqKIkyblixM\nLVTxloT2OB11rCqzi0Hnkg7e73v13UlBkxGn9l7WlYZlvml1FRhT+1f8vNE5aAviVdtmav2GwJR5\nCeH5KXWHy9u41tOMRUSGSjadU9M9R/M2b0vJzlmcyFXb2xG2vQ3bNjd17OJM9n6viAB9vXjtrdax\n34sUpm87rfVqdK8rRESyyz6eImvF62ibWpGKvbyN4UzZxngyahugtKCuFepCrFsaKmZmVq0yby60\nda5WXdVr9l3B9r6lpsuG+K70CyqOcOZ+ACqfjG4r8F7Z19YoXNJxUOwU7NxcrcrBqe0C/bPIVx2S\nW8E+l903W4V41CoMrI9Bh1BR5z0U2vI+krcKreaoPxZOhT6jfMSk/zV8ngwKbq1XppfOBRUt+rjY\nDmDXQj9miEs8bqLUl6g/xH546bANsOKjc+3tdPqQycuUbL0TbfcKkCC1bXYrC23eiNIOQf+sYxp7\nn1ZL3cjCvP5SZBUnw1n/ELfll0zensJRk35B4fH29iToW1LobNSdbxf35Oxxzi5Om/S5wwfb20un\noRfTsilrVXdDylG1lNgY0ho/EZFFlX+gOmbz6va8+vshxvZ/wPpRmgCvvUe908rZly0BZYtWCGdA\n31Iu2z6x/h6oVaAfAkQVX8aRffacWVDOpjlbGWpd1ep2+OaYAsWMapc3jVu90akj8yY9U/P9rH3p\npMlrgV7U9RiL6xt0PwpiCA3Qoeo3Yx8lqoOuSql+O5S7oHNJUfWl963asYZvz3gNy2+t/LzJw2/T\nmXmr3Mw85uuWoTl7zgiuR9lEpT5mj5uD69H9sxaMbJ/IKmrwRiYIIYQQQgghhBBCCCGEkGOEg+iE\nEEIIIYQQQgghhBBCSBc4iE4IIYQQQgghhBBCCCGEdOHkd6KDf0f7uuIY/JzobNTeKRARhWF3uRWe\nExnOeT+a9qOLiDywsNWkp8FFtru80N4+o3TY5JXAu7Yjv9jezme6O9BFRGaK3kv0rXSHyUOnWWbQ\nRHnPAvQpaq9UWrCZrbyNm1ZBvX4B+KnAKV6f9OnK86wkCx2hjcetV3PIq/skD57G7KqNc+1AcxDm\nSRE81VmfbuGrBvdlgLV5kkGVdKZ73XJwddSkDyufWGvOuuLwV6ArZe84dOAiDir2AWUaKh+fTQHe\n/6KNr0wWZfkKeNDaPxzXbXOTieA+aC8c5HW4bgccvBvanb/QsC7MuYpNLxz2MVU4YNun4cP2yMV5\n/xxyy/a51yfs86xs9c96dbctX26L/dktQ9blqcG2K8S0Ek8212iLnYrdsGDLkAVXMZ73ZOd4XKVY\nX+h+Vkdes0e/CtZTQFex3hcUrbKyy/5sUvRCxE3Ltt+UlmzsHXmBdbhWdvkYScE9ii5/7XhfWrVt\npwP3daquHd3qQc7GE7Ggp1KTQpuTjvh3tTBq+70ZaD9b6hkNw1oLdXDpN0f8ebJ2qQXJL8I3ADj6\nc8rnnxRsXDdHbbqx4mN3qWHb9G/BWiYfK7ysvf1/jP+byVtJMR5VGzlofarjqJ47+p9qrRasFoOa\n/Y9E9TWwSQDNtMnPLdrjlGdsPBUO2TZPe9BdA4TqYzZm9LdCKwtr1kAHs9d7NoifdrpdxzVUtANd\nxHrQt+WsE70Q2O+u2dR7nRdTGxiLLZueT7zHfji0ddSOaMGk9wx7Z3o5sH2WYXC6746sH3+15Y/9\nlbpd30OvtyYi8kjde/bjgm3TE2j3KmodOAyhCMZK9P3utz6ViNgb0LHGAK5/pbZzcJ+GYY2Eko+v\nsZJdMCYf2ThY1I76nH12TfBXZ5WefOQRuwZf5oF9Jh1stet1VM7a1N7G/lqrZGOxPOzfpV3DiyZv\nW8G+SzoupnO2rmtkcSEKtd2H4dQBjlOiIx3WbtHoNTpERFqRv5dpDsZO7bJCpg8TD4NrfRl8+Icm\n2tvzVn8vUcX+7CSkA7XYSQo6f1xbLx5WnnPY16yZJGLGQ3D8rNe6J88W/iU6IYQQQgghhBBCCCGE\nENIFDqITQgghhBBCCCGEEEIIIV3gIDohhBBCCCGEEEIIIYQQ0oWTwomOHvReeanr7gzMgAszk/Ny\nIXTuohNdexgnwFc1lLNetUbib+uhpRGTV5kDqR74juZ2+/yJbdZftTs/Z9Kn570zfW/e/j5kIrT+\nPe1DO1SzZVqoWxFR0I8es3UCQ9GF4KBS7kX0MqXgncs0fZxkYhsI6DlsjvvjvvTsR0zeprx91n8/\nf6FJR3UfG7kl6zELm3BedT1JCdYbKKGz1m93OKh6q4oHCnyf4tTfx8Mr1lu4smjrh/w+L6JDhyt6\nvQIlpg/r4L6v4L4+H/2ujTH7nFdPt8cqT/iDlfPWE1lvWp9dpeZFZnHN5rXA3ad/pRvle/urBx1s\nEVeaPk6WquCHPmLld6Un/H0f+wE4Qh+xgrvwsPJ1Zu3zqv2H7Sa9/Hzfnj7/eQdM3ovGnzDpU/Oz\nJv1Yw7sYD9btOiENWHDhkVW/7/4F2LdixXlB1l9foWBlgrlocGPKOUxD/a3cyy1wf0uMFY/ahj6W\ny9t7HKu6Jsb+GK6R8ICP4/qUdUOv7LSxuHymrS/KW30dVa/ZmGiBpz2t+/hK12q4UvWz6DjN2vSg\n96PQQ9nKdfdSp2XwkQ/551mENqaUs+/xSN57gLcUbf01N2TT3y9taW/XG7Ze1P0kEZHcIXueoYeV\n17UFXvaSjbHmuI/X5qiN1fq4bfP/9syXtLf/9dy9Jq8ALlwTf4MdXoaOT8SsvTkt/a5i7LW6/y1Z\nh0t1FIS06rsxt2DrqNwiPLsE+t7b/ToPrbJts+Mxe6zaFt++18dtofR6SSIisfL+t/IQJAP+Z3O4\n3lcEwmG9tlgKgXKgaZ3iD9W3tbcP1a3XeSWxAmntGB/P2fGD5w8dNGn9Xf940zqqt2etP333kP0o\nyAa+/SrDGmqYHlcfBasgvEZ3vP4cxb54jn3zNtjk67UYsG+UzYHvvujbsXLWtnktiMUxtS/23SLo\n1zaO+PGeIIV2awX6+7ttn766yceTXmdGRGRos/2oHFFlGs1a7z/SVH36jvWu4D6JXpNnANYBWavb\nqLuozRHoy4L3XL/W8Sj0zeG+p2UfN0HTNhTFGZseftzHwtA0xGpky1DZZvtGsWrqcC2Z5hi02yoW\nQnsaCWEtE+OKxzVRTuB41IA3qYQQQgghhBBCCCGEEEJIdziITgghhBBCCCGEEEIIIYR04aTQuTxT\ncFpLoWSnmAyraaKN1P69P06RGc77qVDlyM4rOFqz00LnVnw6/cGQyYNZ7R06kJl4U3v7XpjKiXqX\nstLIbA/ttJxJmBJTDnyZd5QWTV4jtWGQGfBpyB3oR4RTlGGaSCbsnodTToPET4nJVOxUu+yKnQKT\nlPwz+rWtXzZ5Ky07FfQf4xeZdG5FnSeF6TIw9SYe8oVujKK+BdQ1qogta+ronAY7wGBdEqu6ZuWo\nrTvKP7DPfexhX4cV5uxU4kzD1m/Rsq/fgmXwt4C/wQ0rbQxM8Vt6gZ1CWjkVplwpDRHqXHaMLpn0\ntPhprour9trCFfuC6On+mVFbJlRvDJoqAevkFGKqEftnUl2GaeVH7X0uHfLHKs3YeieAmGpN+qmg\ny2dZdcrhy2z7dPWLvtLePr0wY/LOyh0y6SxMo246X37UuUxXID3vYyqetu8PzgxtjWtl22DpNvD6\ndD2EdVLHveih0ENVQqA0LPmiraNGy3bq+u4RPx19a8FORb9/cZtJH35gZ3u7OWxjePUUW6Tznm87\nVoXQl+M7B+0U5UbNTl0PlM7FRWvEhLpPAepocAq8CsZ+jzUR6dSLBN37Gg5iyBVsvYP3UoNT3fcM\nzbe3Lx5+1B4HfIl/H17Q3v7W9PNMXnoElXTwjqg2tTV71O5bt98WWeWSzJesom1kyNZZwwd8MB+u\nbDF59VNs/dyhUiJPEnafBi4iIvq9xjaiBDoKVfeFw7Y+O2WLVWoeXPBtUX7R1ith3R539ewJk26W\nfScZ+9aYbipbSGMTKBmKth0O8j7fJWvEywCEk1a4RJne6pGq+qiZg+/6+aZNH6379JFV+52v+2Mi\nIi2lcxmGcQjUq97e9HXUgZlxk7d767xJF/Z+3qSnVLs3ndiffbSx2ZZR9bkWY/sNudywfci6up4c\njEtg33Qg2rqnQL0rvk/6VUWNVGL7NDpmluBAqC3W+46DXvjFux406S+XTmtvzz9iY2Cs8AKTPnI2\nxIGyi4Vb7HlGizaOtVZtrmHbvP3Qh9fvS6sFNwbrbn3tgxBaePkwlpKoaigeBlXsMHzjjPu+Uq5g\n39skhgGqhk9HSzYvP2/PU5j39SiqiBfOsDG0sseeJi0orV8eNH7jtr+Ty/v6rLpojxvN2kGn3HL3\nOMF72Ovz5njhUBchhBBCCCGEEEIIIYQQ0gUOohNCCCGEEEIIIYQQQgghXeAgOiGEEEIIIYQQQggh\nhBDShZPCia6dW+jfCsGdGKk0+np3DVsXeKS8rItN69tBxnLeBzVbt/6zxar92eQRn7/167Z8pYPW\nK5UW7CMIUu/V25e3/qqvFPaa9ES02t7eG1kX8ebQlrHS8vtOZq0veX/GutMG3omOviStU0QHGmil\nWtqjia64FsTuinKKzVrXXa5s/Ypu2G//WNF6/W5eHDXp/FH7u7HsivdMoRM9HrLx1xxWnsaSvdjU\nKq1FKfU6/O+D4FrsBjoB4bFLrHx22SPW66Ud6CIiww/69zpTtQ46t2Lf49aC9w27xPrPol07xe6s\nvGTTh01WePak/Vlwlzci70tciaB+y1qHqHb3BRUba7kFG6etvC9Ta8rm5UJwdasXEf3gg0DasvdH\n32dXt88rqtr7o+uA+oR9qVtbbL1Tm/Q/u3iufda/99J/NOnXlB9qb3+ltsvk3d+wXuqt0F49WN3a\n3n5gwXqBZ+atMzQ96suYW7b3Ic3Du5f68mOUdLg8ZXDo8KXj1atkBt5xdCtODPt66JThBZN3zrB1\n4b9y6Htdy/Tf668y6QMlX8bGKDiCN9ky7ClbV7H216L/XRIbM047ONHHiah8vC8ZFPLrcwxCHbVG\n30j3h9Zyz6epf0YddR2s4bMl59365+SnTV45sHHyL9FZvjhr+aKxTJtUP2uz7XNhvyqoKrfnEetP\nby3auq/4uP8uKe/aZPKaY7Z/4NaKz36mR7+8gw4nun9XAzwOtgPqZ8/Ybtf22Dtk65lH9/v1Y8aX\n7HFWTrFe6cUzu68phJfSAlesm/DfspOTqyYvD47qRL0vi6v227RRwU58/9dLLRU4CXyoJCDLXUn8\nMzsC3/kHVqzXebXu+yG1qr2vbs72o6KKL8NCxh53KbHvfFlVYTsP2X7vgUu2mvQXJs4z6R15X5dM\nN2x5K4ktk2amavtYSzUbu7HyJ2ehL45jMH0/fmDUy1h3wL7q1gQ1+N6p2Hu8NOPTAS4JArdUt13x\n6fakP3n6t016OPTfjZ/ebvvWtc3WXb56qq1L9LUGTfgOTGw7vCS+/Afqtn1cWbDnkVjdC6yr0ZE+\nYKy1nlys+sXxOLyLI/b7u1C0Y6CaDhd90/c1ohX8HrDPqLLNP/vlPTYOFs6zZcqC51z3v/PwLTE1\nYtu2uoqxWmjfF/wTcH3fAlz64gSGFP8SnRBCCCGEEEIIIYQQQgjpAgfRCSGEEEIIIYQQQgghhJAu\ncBCdEEIIIYQQQgghhBBCCOnCSeFE16BvC9O5yMtwdgxZ9+AZQ0dMOqvEObPNYZNXDK1LSDt4D4E/\nrHKkbNJbvuvLNPx96wgNlqzzJyxbZ93wkPcR17ZYH+L3xq0P7VTlAT0vf8Dk7XRWChT3kAL1vcNs\nPenwKR77j4ZNKzoLFlfa28lR61rM7LTusnHlQTySWhf2nfNnmXRpxj7P/FHv4U+LNqbSCZtuqRph\nTV2iOg2G0ACoFo+ZDNwL7RcsLNnMqAoyPOXYbw3Dug2YPsXHTG2nddAt7rVVffmwP8/whK2/mkP2\nd6voFwuUz64ya8/z2Cq4/Kv+vIU5cPXbMJY4o92Vdl/0fsapv4eu1f+/C0aHNb5gxrmMu4KnsTnk\n922M2Uxc96CxyR/snOc/YfLeMHrQpL+sHKJ/vu8VJq8F5X3dju+a9MOrfv2PuRUbj0kNuinKp5yU\n4X3pWLRCvT+slNqgFzgAn3eoXvpCwXoW947b9TsuGPN9j1PzsyYP+yUvyvsAeyy2fSF8PtoZHA/b\nvACcwQkEeT7j64t83pbfWVWsZLP+WpsNaA/TXu8Z40mDt6NDYa39quAjzyzZd7xV88+zAgfaNGQb\njlAJZKst2/5Uxaa/PevXZigctQfOL2JdYpPNKd/erm63cdIYw2P5Hx57yDqQoyPLJt0a8q7PjrVl\n0I3bIfTuX/BSj+t1g/pM129RLu2aJyKyadTXS2eM2G/Gow37LLOHfSXVtM2WLJ1h060ddk2b1qqK\nIXjOGXDbjo+qdSdGbf07mrXHXVDre+m1UkREmujuVud1ffoZqNuVJrxgKzGsAZP6Z3K4Yr/z55dt\nXzde8T8bzdv7XDxigzWn6oPcKnyfLdl4LP37/va2i20c5M8+06QfXLLfiQeyvnGrwLVNFqDDrcB1\nJnCdIb3eR0dfFBjofha2gaqdw/V7cnZoSHLLar0VUJOHsb3nKkxlaa896Y+COvrzS/6dx2+Byo7e\nTu3oB74uCRs2RhZHbHy1CqqOhUa70IAbo25FPGQrvxT79Jo+raPMAAo0fC3b1TBrh0mux70SkXo9\n2zWvBWsDBSqdlO2NXrXNnoTqeSYFu292wrZHRfh+WF31AYptznzF1rHVZb9vZta2XblFe5+U+t+s\n1yfS2Y1aT/p/9IEQQgghhBBCCCGEEEIIeYZwEJ0QQgghhBBCCCGEEEII6cJJp3NBUEVSUNP+te5E\npHOq8WTkp+1VCnZqSiGwUxD2xxPt7XvjvXbfg/Y2juyr+sQRWwYH8xcCmLoRqPwApvS0WnbfWM3N\nmU7sHOVUrMqm2hpWefzdyfFgpiHD9JOghWnXPS+xP+wSNZUK4qC+xU5r2T4y3d6+s7rT5H3viNX8\nTE3b2M0c9jHodmyyeVAmPY2sQ+MBU8Hc8YTRAM/wQ3SdhfewNhVB2r/XCdhbkoK9qY1Jf9zW86om\nb/uknZY8/e1t/udGbKwlJXvc5qStiMKKKnQDgmLZll/HU2RneXVqR3rEUzZjg1FPGW2kOAe+P9BP\noUO5lbGVi55ui1PZW9DKx8pchlMFcRpfPObv+0TeTgf+TtM+0Hc+/Ivt7QPft9OMM5saJv3vw7YO\nm636ufDFvFWpFadsWqt8tBpJRKSxBHNZo+4T+XDasb7H/V5d4ZTsDLx7uax/cbcOr5i8l048atKX\nlB9sbw9n7LMK4SXXCpeH43GTN5y1MZIM+dhrNO1zLgzZfSfADRVHfv9zpmZMXhlUfbNK0bB/0faj\nah16F3+jMH5cR9pvZzInckLpBqLjCK8fp7arW5Bb6j21XVPdbt/po2XrzXhsyPdpquCjWkxAa/YD\n34/fdGQNLUEe+uY1v388BG3kqP3ZVP1sVLcNd6EE6oSij9Wk3P17QASazAHXMAbqeyiIIfYimKpe\n8HUJ6luGSrYdmyp21148OL/ZpHNKx1ezWZJusXVUqWTrneq8j9Ug7d3iFFV9XIrsccqRPY/WuXSo\nNXpo4QbBFJSC+q+a2PpC36+Vuh0TiCt233DZv7f5eXvzSodtfV+c8/EX1aA/vWyfZ3LosM87/VST\n1xyzz68YgX5DtTOnjRw1eacU7FjEdMO3dXgfUKdYbfp2MIT3B/um+h72vSq2w1kG2eq9jqy9Toan\n7TdN+YDXroZzducgsfuunO/71+PD9lvv/mbNpP/1yGntbezfC2jV0hXb35n8gd9/aNrGaWPctmNx\nSfWN4LvCQczo9rOVBX0JfNeanx2AOgq/g/HeaYJV0OBVcZBGbUa930XdP9PffSJiFJoiIpnD/ry5\nZRi7Omj7XMvjto7SYwZJxdax6O4rr6r3x4Z5x3iajg0cwziRcDSVEEIIIYQQQgghhBBCCOkCB9EJ\nIYQQQgghhBBCCCGEkC5wEJ0QQgghhBBCCCGEEEII6cJJ70RH59tQzvvhdufnTd4kSKn2RN4RlgcB\ndA6E1oup9/zMzg+bvMnHrZsn+8ih9nZy1HrIMsP2Z2XSekEbo94X1JiwZdgM7qtQfP4D9R0mbz47\nZNJZdX2VxHqI0H8W9au/cx3ocKKDOkqrmzGv41gl7/pE993SXisV26J8sQ/Wt5m86rR91oXDyybt\nqt6RFiTgUm6C6yr26Qw4+V0Ivk4VNg51W73SA+A100ShDYRi0bvllk+x72J9s30XtcfM5e2z055P\nEZFtmxfb26/c+rDJC6E++8weXw9Va7ZOam6yxx3Zap3Iy0d8vAV1W94M6M+0tzQuQx443ptjvoyj\nUNdN5G36aN0fLBOc9M3YmuArk4U6Op/1N74KcZKU8B1XHlSsv5rwjiun48OLUybv7Y0rTfrgfd7T\nWD5k46ISWefmfbN2HQftkz5twraZO0uL0o2DtRGTfrxo29PVqq9j0QOODut+dwzr68drR2d3qNLo\nXR0KrUNYe9DLsJDL92O7Bse+po+hFsgfc9Dg5Cb9eTJT9tn8+KnfN+mLy4+Y9P54sr29KWvrr2pq\n69yZuq//Ojz5GYwZlU6groY1a4LQ38OOWBtAAuVfzdtXXEb222efafj7HDZt/b4cWQH5Xcu+IYly\n9jjxin3Wk/f7MqDjtTkCftHIPrNkzOc3oRuPHlNdlTTBn54UbF2owxEU7h1e+YEGq2dVZWG75XLd\nbxy2A9o3LiKSC3163+qkyTs6Y9sbrRiOR6CuAM95ZcZ2gPKLPmhSWMojhbqkqNr3PKwPsxjbjtRC\nwwdRM7YxjXWUDjBcr6sfSfBFhU/dWuK/u2p1EDtj/a76UaHV0nd02OKyP29StO9/NmvLVDj7ee3t\n+Qtt/AWnWF//lqL91ptUa4PsyNuFJkK42KXQx81Iznq0V2JbbzYSH0foOUfPPL5f5EkwRqIqfIvX\n/DuODvR0wn7jr+zwz6ME9/vu6hkmXVE+e1xnD13X0aJ1ao897OMpXLEXENZspZUM+/PEZXuc5rCN\nkbhXiDB8DB3LYal6J1q1FQ3GWEtVNUkR1i7IQXul0yGs3Qjf+UNPqO2D9vtg8TRbby5Htg0Ka/5Y\nxSO2/Pl5iMe6f0fwPuCacAm0oRpcn/C41vNbA/4lOiGEEEIIIYQQQgghhBDSBQ6iE0IIIYQQQggh\nhBBCCCFd4CA6IYQQQgghhBBCCCGEENKFk14miw5L7dUcDa0/bCK0TvSS2ncqY48zC/6zo4mXILpF\n6zQrH7ZOoFZF+XsDcGyOWJlibY91uC6d5n+vkdtlXZ7bytZ/VlPCo0NN64mst8DnpkCHHn2dvTEu\nJnArZUDupfSwEkJeC9x38fYJvz1qY6q2uXtcz4KQs3jQ+seCJojPsv41Ryd60AL3lcru8L/3SJ9I\n59TJBr5Phcg+j/HRpWAzev4AACAASURBVPb2aNH6hUfyNh2n/tmiz3EUPIbblR9xS9bWFci4co4f\n3mplYmecfsikTxs5atIPDm1ub+8/OmbLuwq+12HljSzb+4DuxIkRX6ZzN9ky7C7a9S0WmyBU73Oy\n6NUHT3VexZj2gIuIxDnbFqTq1qFTD/TRklv28bd0eIvJW7FKYdn2kC9jfsHG8Wxsy7QMa3Zs3eWf\n7wvH9pu8Dqe1avfGstaVj+xzvo5Fd2cvl2c4YF7PFtyLuvJooh91umH7LPuy/h4XAhuX/6u6x6Tv\nW9nuz7lGv+PUKS/OvnDcxsTPjH3DpCvO1juziXcXL7WsaPq7y9tNembVt6e1mj1O0rRtqyZAXzr0\n9XQ7MJCe2I5rVm0BrsXSsOnCUV9/ZKv2M6Uwb+OxOeLTadbWM5nEHnfooK8ngxT7Z/b5ocu8Me7T\nHetMgP9aN9UxHKdlQ8y4PPG4iL6lbtAWlwH0eh24Fourw/NQKvNs1ralYwXbjxqOvFh2NrVtZ1Cz\n9YF+zlHNnjPaB+5r+/kpLXWo5rgt09CILVNeedr1Gg4i1uMtInJ01bvXm3X4xG9AfZYZrHoJ25wE\n/o4w1flrtE+tgr938TDsG9jjhnW/L34rBZtsnCyf6tcRWTzLPp9zt82YdBYOtqoWWNhXt+uRVGAt\nkANV33dfadq8JfBd675SMWfPifd0oPpOa1yq+UaGfdO8jZHaDt8nbjzfjues7II1hs7yddSFY0dM\nXhYWOlpc9PXB0Kx9VmkR+v/wPdCY8HFhI0TERbZMuv1sQbXTgmpHf4N0jBXg2gN67bVBiC28Rni/\nAjWuFNlmoqONSdR9xnU3HHjPXVHFDRQhe9Q+pNJRv2922Ta+maS7A/3JMvrryS1Dv28JB5LUJsZJ\noXv9/FwOaQ7wUBchhBBCCCGEEEIIIYQQ0hsOohNCCCGEEEIIIYQQQgghXeAgOiGEEEIIIYQQQggh\nhBDShZPeiY5eU00LfkeQE+uKKij3UDaw0qasWH9v2kPy3ByxP1vc7t2xUXPC5NVOs56yo+daH1r9\nNO+62jJkfa/VxO67b9UfeyRnHbQC/qMl5UFfBj8tAVC7pdLoXoxAyRsZ9509UFq0MdSY8M+hPgZO\n2m02/rRn7jvz1umKHiyXs691puiffSvsLYsyDrcU8sAhGqh1BEAB2Kn16nnW/qYA/uqzRw63t3fk\nF03epsi6zKut/NNui3Sue6B9iLGzdVIG3IlbSj5oxs+wYrXXbr5feqHrj4WSdZMvxfa85WFfL20f\nsde2rWTTZ5X9fTmzYJ3o+5q23kx61Pv9SAb98fkq5PvnO1e0DuiFgo2bVD0jdKKXD9k4GTroxefR\ncsPkZWqwFsgjj/u8vD1nccc5Jr2ctec5Z9y7PodD25YNZ2x8LiWlrvtOFWxleDDybsnlqn1f8J5G\n4J3vZ9DRncB7qz2nh5esg/ebwW6TXlbixWJoRfkPLG816Udm/Xucgkd6ctSuYXP+5EG/XbJO9P2J\n7VfdV9tp0t9YOKW9/fiidbgvHrbXE9T9tffQeIsIeCPzEC/hILdyTwOIKbWDszEO68OU7bs5fMA/\nk+Ksjanhx23dF674esllbRynxe5rAyVl209KwLFZ2Q6O9NHuzzdjQ1d005zA8h0ugnev7NPoKQ3i\nHv21QfDDanBNHtVFDhvdnfQiIomKxa0jdo2NM4etZ/o/DP+gvf1gfZvJe2TbpEk3K162np/vvb4I\ndMmkvs1fUHG7bbd2qnVzRGx9vAr+6vmqDbDKiu+fuVUb40GPd3IQQZ+3Hk/oWCss7f4uxsPwTsM7\nr3356ItOC/CzU75fdcpOux7RZN5WNA8vT5m09uPjtc2v2n5hs9l9+Af7Rrm8f9nwuLivZq11T/oO\n9N2rNN6KuGz/Y/kU/+yWz4Fvxuc9YdPqG/JVo/Z7bTG1z1lmfX2hxydEREoHYU0AWK9jdYePkXgI\n1l5LMeb99aQ5e224Log+D9aLHeuryYCBDnS4ARm13kcITvSOMRpVZ8FyRZKBvkVLrfcRNGF9D/hO\nTJX/vrbFtkeNMfhZGCPTn2wda5lgfzvU28Ea+6r/wLwTWA0N1kgEIYQQQgghhBBCCCGEEHIccBCd\nEEIIIYQQQgghhBBCCOnCSa9zaaZ2Lkg99VNiDjbtNN5Sxk5Hnwj9dOF8kIU8mD4f+el22Sk7h2L5\nlCGTdhmlHoBpBCu77O8tKqfYKcHZop/fUG3YuTWNGKbmqXkeLZgahLqD1dhPuUA1TWbwJswcF3p6\nUdiEaW4VO/coVNOlHLxdTZgOpac/1aYgUGCq+APzXhE0MzNq8sZj0MaUbNwEcdmXKW/j3GVw3ov6\nOZhWlUlgVx1GcK2DNotPs9Z0R61wuaT8oMnbGto6Ss/OmkntHNG5VtmkK0r3MpuMmDxUYrx04lF1\nDpjSB5VWA7Qx9bR7szEyYs+za8xf64vG7JTEHyk/atIX5ef8OZy9Z/9etQqJeo9pq/0IxtCWvFXh\nTGb9NF/dBoqI3Ne06Zrz0z0TmAaf5mEqYewrgUzVahVk3k45dw0VuyM2/lDfsHWr1RjtLC60t/Mw\nxw81Rg1VseZBu1ZJ7L4V1YbGMH05Gw2OvgXpmKoOtJq+rarW7H17aNnq4B7Jq/4OHLe1YNui4rQ/\nLpoEDu+xx01U3+5QzbZ5etq6iMjBRRtvtQNe2VI4amOvDFNIW6pZbsF05o6p9vnuU0aDjG0wdf8M\n9TmDQIfSTWlMGuNwXzfZh1Lb6p9vadrGxfC0fW/LT6j7DOdsjtkHmpR9LDSGQaE3CX3oMs6j9umw\n1vv9SUp+3zRvj9MqQMdK611aUP8mPaYwD0C7Z8B7o9IdmkXYN1bKphA6tueVD5j0pUWvkjsvb7Vy\nB3ePmfQ/zJ3vj1u3dRLqf+JNtq0a3+rb8D1j8yYP2/CjVd/Xw+/CypJ9P4KKr6/DOtTHRXTiDF69\npMF2ME59ndACfUvQsPWFVi11vIqgqNB1SVK28RfBeMJ52460t8dy1oXw6IrVCU0ftfGYNPyzd3VQ\nOtZt+XWZXc6WqVW0dWyg6j4Hqg7sf2ehHRwkAowZVS+ltmsq9Qn7PKrb/T0+/bTDJu9ntn7LpHdl\n/bdSDMH23x/9jyY99n1fppHHbUW5stPWM41xaAML3WMc1VBaU9SKUOdif7aVU/XOGlWQG/A6qpci\nKNOh3LXp3IrqszTweaHaq/tx8kv2Gejz1kdtHIPRUSIwTWdVmaIajF3lu5cRFW04vmbVLzYP++rr\n2e7xL9EJIYQQQgghhBBCCCGEkC5wEJ0QQgghhBBCCCGEEEII6QIH0QkhhBBCCCGEEEIIIYSQLpx0\nTnT0b2G6Gntf3OGm9WSiCy80MibrvpsCV/Tz8t5R9cKd1qH3tdqpJt2YVM46UO8kw+AaG7UCIa0C\nroFjD6VU+YL3W602rXCrDs7QWDnS0VlWiEAoOOCgLkmn0QuO3vCgpTz1GXBNg+8pLvk0aKclnLP/\nMdOYaG9nF6zwqcMDOg5OdBVUacG+8q0cis6kK+hE044q9FUNMh2exRa4CVXQFEA+Npqx+w4F/r1G\nr/kpzqYfTrx4bj6xErrv17ab9GzTO4MXmta1jnXqEuQv1ryDM58Fz2fBlun80en29kvLD5u8C/PW\ni70p9N7PbzesG34utv73QQPbLu1AFxE5p+jv83BoJXR6PQwRkX1quxbaZ7sC7Uaa8/nFOXucbNX6\nOMP6rvZ2ZdzWM5WdtpLanLPPV6/LsZpax2sdKsdq6uu3udTG+b7lCZOu1UByTZ4eaESccgoH4FaN\nlm1lH636fbNW4Sr5BXvc4lFfX6DTMLdkz7O06P2vc0N2fRv0RIbgey0s+zJFUCbEqbDW3m4REYfi\ndpXOQF4mM+DuTgRvh2pX0pJ9gMOTtj4LlPq3fop9/1cPlUy6cMS3ZT3XbRGRjOpuo0cbPZpRFb41\nmtrPCU53cOenQ/760Dcc5LuvxeBqx/FJ1ueuWPcsLq/Dw3rU39fHx20b8cC47Rv9RPmx9vYZWdvv\n+NHR75v0N3b6Nu/okG2LRodtxXPK6IJJ50IfBwt1G9NHVu2xKqodi1dsO5xZtYGr3bctdMP2EBAH\nA6bYF+ns6+q+u0vBgY5rRKn6Ax3C+B2lfdiuZHeeGls1aV2m6YrtY+0/YmMX1xwJK77MEazbgHWW\n7lbhukgtqLN024bre+B6Pbr8mNfvYBxklCMdnej4zZ8qV34Ma/19r2rrqH+Nn9fe/srjdvwp+rat\nO3Z9069dlKnZRq8+aeOpOQZOd/X40gLEE9QtOoTw2uJhjD0VT61jH4MgvZv9Dh+5co5HDRucURXG\nQ1V+x1p5QCvU+bbPEqF7PbRpvY4g9s8aJaiHdBxhnQpjV+qzsLPdO4ExxaEvQgghhBBCCCGEEEII\nIaQLHEQnhBBCCCGEEEIIIYQQQrrAQXRCCCGEEEIIIYQQQgghpAsnnRMdHVsh+L0T5f6eb1qfXQIS\npxXlXl1pWQ/rOflpk57MeL/dS8YeNXkLu6zP7pHcpvZ2WrW3ONPDhygikia+jK3Y/o4jE9lrTZWz\nrRb3fpShcpppF59I5z0dNI/Z8dCC29ws22cUKk8TOptiGyaSKh0x+jkLs/a4bi7Tdd8gtc+rOWzj\n3GV8bAcgmkT3FXrP7Yls0vis1nJODZDnDJ2BSEOJvg4nwyZve2g94aHy6mcDGxNLLfsef1c5qe9Z\neJ7NO7zNpKvzKhjj3g8nKFvRbK7oA/CUrYdN3ovH95n0j5R8XXlBbtnkaQe6iEjD+ePuSyZN3pGG\nvU/osux30LOPjvQx1T4Ng5d+fsLe50i1mQdyoyZvuWQrqcUxL3JcrYEjFNsnFUdJCRzB26wDHdvt\nA3XvvC5Hdt9aagWLoXq/HlneZPJml6wPUrehUQ6cgGu0g4NEhw/XyDBtVgiu1fyS2l6wz1X7D588\nrD9unIe1IqBdK02rtrRo903RJ1ro7nuNbUh0uDzTvFrHBI9T6O6zzkAMD6JTuBcd68UkytUMSxXE\n0H/dOubbiqlN1hlc3WJ/+OCyX/totWrFs+lh9Ker9rRin3UAdWwT/sSopeq0tAhxUoS1jvL+4rHf\nnsvb9rRR88EapOgxFvIU2P9UaxI4ePlC24RIYc7n1zO2QvisO8+ks0ps/NqRfzd5e7JHTfqaU7/S\n3l5K7foiDRADH2naPswDS1vb2zMrtkyri/CxsOLfj6gC7S6sA6C/UVo56O9ncTGJwW3zRHo70YMQ\nKzBIqlc+k8BxQrzvai2NNcYAZlZ9nCxX7LhEumxjKqrC92dd+cjhHWhl4QJUmXT5REQy2FeKfDob\n9i7/QIN+b3VbW3n89oZdVTvx+ONTJu/xfTat+96l/bZDU5jt/k63StDwwq65ZWjX1KGTke4OahHb\nr8L+GH4PmL4BlAHd/cSiq6wO9zfcuqjhb3R22b632QW7cEimroTq0J66LPS/h3w/KwPjT1Eex5+g\njOrQzSG7b1KE86q2DD/5sQ+p2z1cb+lExhS7aIQQQgghhBBCCCGEEEJIFziITgghhBBCCCGEEEII\nIYR04aTTuSB5mFqkp2etxvmueSIijTTqmlcI7NxiPY1vT85O6dszPGfSq7GfZ1At2zkHKU4ZbdpH\nEDd8OgNT7wKYkqCnnjUTe5xcBAoGdZ9yGU5jN6xx+XrKTBOmNCV2BqdRGnROKYG0OlTxqC0ETjVu\nhX7n2NoZJC3YMtXhvImaCh82e1+sVtCk9vWRVoRTFp9+W2SwpyHj+4R1y+N1ryrRU4dFRKrO3nSt\n6ai7MZM3l9opwN9Y3tPefnjeai6qszZo8jM+SHJLJktgFrJUt9uHWRzzZdpZsvqZswtWgzWW8WqR\nEObDHk0rJv09FdhfW91r8pabMK11wHQuLbh3ODV8Lu9jYTK0+oNziwdMuqrmwRVC2849lrEanZWs\nb0d02yQiEsCU5V6TfIdLdm6xbntFRKarXiuzqWDLjxyp+WvXKgcRq0PDMkZZmM6I07UHGFRQ6b4H\nKgFaMPVbtwtJCab8wtRO3abEw3ZfVKXpKb84bTUeRu0KpPV0dFQWRJBWxcA+Fqo4QhUzmRB1LoPd\nj8LLxypaJyNQAtWP2o7UrL7PcOBiZOusobyfhoz94CrGquqOY18O4y8edZBWMQV98xB0LvmCL1ME\ncVJv2AbWKW8MKnA6buoAgXoknJKt66UOvU7V/nBU9flaEyUi0lyxbemnFl7S3r53r+2HvGLzwya9\nJes7T1XoMN+/YhV6D89ZJcPyio/51gpoOpZBddWUruC0dq1ScPne+hYqqCz6XUX9WxPecaNehc5P\nR5tZ8jsUQOe0UrdxU1NKqqSKH5G9z6OVaK081m/QX1PvjINrQ+WUHk/oML8Nch2FjRyO0ShdKn4j\n43dWpuZ3iCr2uJkGnsdvxkOgc7VdYqlt9n3rLHSt4fOzY8wiUVapxhhcG37z6/jCIMF6R+uPULXB\nOsnSQ6PbMcbUMQ6jx6Ogbz5kG45WSWvlbLvRAp2Ljr+gBTqXmq07Or8BMmob6ijUNKpuIb5qHddq\nxqOeuzppgIe6CCGEEEIIIYQQQgghhJDecBCdEEIIIYQQQgghhBBCCOkCB9EJIYQQQgghhBBCCCGE\nkC6c9E70LDjRa7GX6tTBu1qKrFhOuxZT+H3CbGLFUqWMd7puDa1E+MfH7jfpc8sH29vfWd1p8p5Y\nHTfpI6vWa6wddUkMXjxwKzqtoAIvWS4CR6NymoUZe5yBd6Kja6lHflICtxfqvNLuQi/0j2kXdWHB\nPpPcInillNe8OWzjumFV2R3uKxW6EoErEh3pTrnX0WuOzlDtr+pwoA+w1wzfxbRlb85h5XRebFoX\n7KM5682M1PoFK7H1gmuPtIjI43MT7e36ot03u2DrkvyC384tQ3nBhR+DO3al4o99tGHrr/tqu0y6\nmvcHm0utkG82tXXstyu729sPr2w2eavN3utb9Dt4vbNNe98fb3oHfgz+uuGwZtLnl/a3txOIzeHI\nusvnGt5T3wQxdS2xAru68hEvrpZMXgyu8qOr1tE/VPDnRV86sljz8YdrioTQ7mWVBz2ftS5lfE8H\nK6Is6MbV7u9W3t7TZNjGTKOlnOhFeyBsH3XdkubRH2r31Y5NdL/KmH2W6LsvFXw+9hFDcHDWYx9D\nDt6zNLWFsn0u6cmguWJ7OdBFwFMJ/aTcvK0fqmrNh8dGbVtWGLJ1lH5mrRbE34jt89ez/lkHNZBq\ngjPYlWwfTD/vbNHGX9jRN/c7V6u27Upw0ZqYf8t0TKBvWPm+sY+C/fDckk9nV8CxD+sRjT7m46J6\n73aT97djO0w61aHZ4bOHJOTr5SM6fMkQItpf3bFWEXjPnV4HAN2w6B8ecPDbN1JtxUi5bvJWYN9k\nSK2pBs82gFdaf49jHVWD+iFt+mAIYO0FyYGruGEDJw58OhPjWib2UNqDHhbAgZ6zab1+TJTpvZbM\nII0nOFz/CtcNU22K8YBL55pCYd3nowMdxw7qk/4ZjO+dN3kvmDpo0pXUu6/vP7LV5K3OwPhTE8qv\n6pYAvPl6LQ8REdFxjeMiCfSjtL8a6ySOJVjw+rUTfQ3PfmKc43bnpGhvtGmfcH0baI8yDb9D2ID1\nYeq27miVYN3Hsj9vbD8Tn2Z9D5XXYy2jDrA+PoFVEntvhBBCCCGEEEIIIYQQQkgXOIhOCCGEEEII\nIYQQQgghhHSBg+iEEEIIIYQQQgghhBBCSBdOOic6+rayGetp0k6qFCSb8w0r4KkmXsBTSayMZyWx\nHsaZ2Pt7R6OqPSec54jyOS6Bx3ipYdO1hpUYJdodC5JJBy61KOevvZC1HqI8eEAjJTwaJGfZuhB0\n2RaRFBxUgZJJocesw3OmHoPL2LxkyPqr0pyKMfjVl/YlinR6sTLqUAHEVCvXXWSKjtMO77nOG3Rv\nWQ8aqX2Whyq+LmmCK7retA+vVvG+xGDe1lG5xQyk/fbwgo2JwqINxtyyry8w9uqTtgzaYSYiUi36\nOuxbkV3z4cCoFfSPFU5tb49krWMygYBabHg/fB182+mAB1gMMTTfsE7xJ3Leh5/PWF/vWGjbqx2R\nF+KnZXtf51PrSDyU989zumaf7XRinfw6lhtV+/yw7bKWdpF6ye8/VLJx0kxsN0V70DPgU4zAiZ5T\na4HgOiFsBz3o79brr4TgYU2HbF+jqRz8Qdzb5aldi+gtRmewdmw6cMNmwYFeKFr3dTnv00M5m4fr\nC+QiH0/Vhq1jOxzbg10NHR89+gvow0dfbKj6SumKff9rdRCB6joAnw++42pfV7ZxLOBtFfANi/LF\nxqtQJ6G7WB8KXbgt7FgJeSboNRPKtj5oZMFfrfyvOVi3wc3Zw5Zm/bMszNq2KKzYtlX0GkKRjZ94\nxNYljVEbT40xv399EtZiKLiu6RbkOXTFatcyOtGJAfsAJbVuSpSzay9Mle2aPnqNtdUYRPVArNaT\nWajadZBq0Obo9T0KORtvGfCRN2Lbz2qUfTqpwfAOrnui1jpBBzp6zwdtfY9nDLxvejwqa7vhEtWg\nHlLJ1A4TSQprsblC97GfM8ozJn12Ybq9fW/pDJP3+OYJk56t2f7/cl2tPwTfqrU6jF2pfjn60l0K\n8QNjFGbfQY81uH6HL666tbh2Rgzfc/oTuwn9jtA2bZJJ/Hmjuuua9zRFNKQFWLNvHMY4xn2hmnZZ\nNIlHcH02lYbuWcf4mvr26CgfneiEEEIIIYQQQgghhBBCyHMPB9EJIYQQQgghhBBCCCGEkC6cdDoX\nBKcdifipLWnL/o4AFQGzFT8lHqdU6SnjIiKpntqJ0z5xylyvqQMd0wxgqqeaFhrl7TQdnLJcUlO9\ncqBvyUI67LhPpCs4JVjPjmysMSVGzb6LwFkQVe3Dzy/5Z5Jdtc8nbNp0pOauBDCHJ6raeEwKODXU\nb6PqBacDGXDWca+p7Jzm3hWsh5LUpxuxfQD1mq2HXFWpK2D2OdYl+lk2JuwDaUzY87jM/9/evfXG\nbZxhAN6zJFtO0gQICrQoCvR/9Hf2j/WqVwmCFi3iuo5P2nMvAoTfvPRwIduJneh5rkSTWnGXw5nh\neOedYfvY/slRGRlNJSzTh7c/tNNYv4spfv+5HqYH3ly1U1OvYhpinTK6fOhT+kJGULyJtuzbV7/7\n6edXh/aaPL9pY8z+uBnmrz9etG3KbNZOWV5uhutwiHL8v107LbmW87vbdir0qFzH1MJdiX/578v2\n2MUm2rbSLm6iDGVky6q0gzl1+yFXWTk9+5z9kLK5zBicxxHvUqYWnyOi5bRrp3LOt6UMxXTM5Zt+\nzEVGv+xftGVk/6a9H14uhjL/NPpR65i6Xt97fg5palr7g5/yfmEa8tTHcx5Ng584OF+oxqXk14JG\nfbnhHxZ37cHL1xGXmNPta9GNOJrTOqav1/MfxeI98HLyjkZRSqUcZARAfsaH1VBnHW/ba7WNKJWX\nfxr2r163bWckpbXb+RgYaUDHSPw4lSosIxmzfJ1LO3xexbNcPn/Wt5dt3kNu9N4in4uvlkPb8Nmm\nfbj7+upFs/3n66Ef9eWq7TetI2vgrnSqvz88afa9iOyOR6VP9mjZ9qNeRyH6x+uvm+1vXw79wH//\n0P6d4zHquxLZluMFq2X7uYi+6xjVSe3mqdy3x5s49q5/M9bfm83G8U7z/XAt//nNV82+v/3rr832\n+noo06uIwcv+zjH7Wa+GSmqe9U62a80YWbb9GV0z1b73d5H9kHbfKZ7l959NfJh5OcszWY41ZNRi\nfWzMNjGjiLZfttd6/0V5Xrhqy+N80x+nPL9pG9RFbJ/LOf+S1ZVvogMAAAAAQIdBdAAAAAAA6DCI\nDgAAAAAAHb/6TPRUs7tOozy4dntZcvNGGaGR2drkoEdu1Gwf2/W/JjL/cB1ZY5FRVTOrriND+Pa6\nzUerOeiZDb9eRI5peX/yzd5dRN2NchDr/oxXzfzx/eOhoByup/8/a34q5XqV+WJxcLzUuclInPwz\n7bGjk5j+Xd5uej2CzAVv996V+uO4iXUanmQBG7bnWX+FJjM0MzVjez7aHt7PIsrLIrMVS45e5nrv\nM6OxnpM1HCZtD21Z+P44ZM8/27Y5rjUnczabzZaLv/z08ybaiVPc5DUHPXPYX+3aAL6Xb4aAvt22\nPTazsTOPr1pct+eUa4HU3PPM7lwt++2e6qtvKs877/G8dPNzaZsyA32XudPD9vpFrC/Sdm+adu38\nPPIP5/F34qRqO3Z40pbTN7ftwedHQ5lZRkZj5sEvs66kbyIjfXQvZj/qUNuyOHYbdUktKNnnj756\n7Z9l5vl4vaI4p/LzaH2SvEdq2b30lFVfWPH6MEbPXcOP52gjjuvIBb4dtvf5rBf9/6Z85bW7kJdc\n+2DnXANgqk826u9PrdPQ3cVb1D7q7tjWM9t4gFuUiun3q+fNvi8Wr5vtY7loj6/b5/pdVB7LUpCW\nUaientq+3TIqx+PEmh7ZX6tr2GS/ab3oZ6JnX8F4wmCU9V3qncNtuyvzq+uaMFfP2uu4/qY9trZd\nq8hWX8Rz1eFqOInt57EvzmkRbdWiZG6P1gGJ7dnEOiCTnW911LSJ+yvX3Rg99pfLnevM5Npn55pH\nHm3KPJ7JzrWM5fhnbK5uYxzz0bZ36Gx/aN9QfY7Mum2+7/ftfkm+iQ4AAAAAAB0G0QEAAAAAoMMg\nOgAAAAAAdPymM9EzJzzzh2clkypzi6/W7bHH2+H/G2qW2Gw2m50nIsEyk26R2eWRRVbzXjOX7GrV\nnlPNQ8tcskvb3MNE7GGGOtWcs8ycOt5EJnS9vJm/mUW3yf2MXRkRmpno9fj83ftkl/FB1Lom78tl\nZJFtyj1/fBT1TrzuseSUnePC5vaUrKMWi35dMqpn4nfr+8ljp3KYuZ+a5XmKLM/M9qzHZlt2iDzF\nXclePxzi2MivO9X9Ec6Xa38sIvO1Zk+v19kmtu3euuSgT683oDr7EEYZqNm+nMu6B6vIG79qjz3U\nNWs27QtlpmHmLTPb6AAAA7hJREFUWTevG+Unq7fzuuQNb9pzml9FHbUpmejL6bqveR311zvL6zXP\nr/OUyzBa3yNu+XoZRsdO9KtyjZpzXutRhnXZyAz0yBvNPlm7M8u1cvS+LmV/n+tnnNcmy155oLuU\nZ9/0qy5dxslc4Omy12xfqHfkoPddeg7en4bC8WJ/3ey7O7YPdM92Qz7539d/aPaN1iQrhWOV69BE\nffDV+tVPP2fm+T4qlmf7NiO9erLedvfNZu26OjlWkv2qeo7GEiZMrHswy/GAaG9OZW2GHCvYfd7+\n7mJX1gw5tMeOxg5qFZUZ2us4h2wTV/1jsw9W69FRNnxSR72zprrIvlAcW8vJamJ9mB+PHS5gVFGz\n46YtvLUvlONcWf5OsZbb3fpm2JflLddfq7fPLs4/hnM/VrXkm+gAAAAAANBhEB0AAAAAADp+c3Eu\nVU472kR0Sp3C9Hi9a/adrvvzTXL6VUYlnMqkijx2FH8QcwDnU1EJplF9fJemjU7sv9fVe59LbarU\nr0ZGAqyyjpqaFh7uE9nyoYg0+PRlqViWa7aM8pbxYjebffd1p9rB3HdJbdtG8SETZUxV9/O4z329\nWtXyFOXlZtY1FYP34/55+bndt7hw4afOP+MO7vNe1XfvYeKzG8fKlantOW38ff5k3b5UACfjN+Jl\nVUSftMmIk3vc0+Mi8/PUByJZPr7sw2yPq+7207vHzb5Lz/lVtil1nCJ/73Shx3Oa6IPlOWU87BRj\nD++oRqnkZ5ixeKUfdYoYvNHL3idG6j6XbiJGSkTLp+E+t2KNEzpnoxKjvzWKeFRmJmLxRvFBYbGP\neqg8IoyihzOauKYYf6JVkG+iAwAAAABAh0F0AAAAAADoMIgOAAAAAAAd8/P5UjAgAAAAAAA8TL6J\nDgAAAAAAHQbRAQAAAACgwyA6AAAAAAB0GEQHAAAAAIAOg+gAAAAAANBhEB0AAAAAADoMogMAAAAA\nQIdBdAAAAAAA6DCIDgAAAAAAHQbRAQAAAACgwyA6AAAAAAB0GEQHAAAAAIAOg+gAAAAAANBhEB0A\nAAAAADoMogMAAAAAQIdBdAAAAAAA6DCIDgAAAAAAHQbRAQAAAACgwyA6AAAAAAB0GEQHAAAAAIAO\ng+gAAAAAANBhEB0AAAAAADoMogMAAAAAQIdBdAAAAAAA6DCIDgAAAAAAHQbRAQAAAACgwyA6AAAA\nAAB0GEQHAAAAAIAOg+gAAAAAANBhEB0AAAAAADoMogMAAAAAQIdBdAAAAAAA6DCIDgAAAAAAHf8H\ndy3TuYbaNQcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0ba5969e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[2;40;46m                        RESULTS FOR LAMBDA = 10000                        \u001b[0;40;46m\n"
     ]
    },
    {
     "data": {
      "image/png": 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B7733Hi6//HLcc889GDhwIGQyGY4fP47PP/8cV111Fa655pqQ85o4cSKee+45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\ngesjhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0af4edbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Confusion Matrix: Actual vs. Prediction\n"
     ]
    },
    {
     "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",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>947</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1086</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>820</td>\n",
       "      <td>33</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>843</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>160</td>\n",
       "      <td>26</td>\n",
       "      <td>504</td>\n",
       "      <td>37</td>\n",
       "      <td>16</td>\n",
       "      <td>58</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>888</td>\n",
       "      <td>11</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>785</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>56</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>46</td>\n",
       "      <td>14</td>\n",
       "      <td>819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0     1    2    3    4    5    6    7    8    9\n",
       "0  947     0    2    4    0    1   16    1    9    0\n",
       "1    0  1086    6    3    1    0    4    1   34    0\n",
       "2   18    38  820   33   17    0   33   23   45    5\n",
       "3    8     4   24  893    1   10    8   19   31   12\n",
       "4    3    12    5    1  843    0   21    2   20   75\n",
       "5   40    25    8  160   26  504   37   16   58   18\n",
       "6   23     5   12    2    9   14  886    0    7    0\n",
       "7    6    42   26    0   13    0    4  888   11   38\n",
       "8   14    32   12   60   10   10   18   16  785   17\n",
       "9   20    15   13   16   56    5    5   46   14  819"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% Accuracy per digit\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>digit</th>\n",
       "      <th>Accuracy per digit in %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>96.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>95.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>79.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>88.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>85.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>56.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>92.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>86.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>80.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>81.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   digit  Accuracy per digit in %\n",
       "0      0                    96.63\n",
       "1      1                    95.68\n",
       "2      2                    79.46\n",
       "3      3                    88.42\n",
       "4      4                    85.85\n",
       "5      5                    56.50\n",
       "6      6                    92.48\n",
       "7      7                    86.38\n",
       "8      8                    80.60\n",
       "9      9                    81.17"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The overall testing accuracy is 84.71%\n",
      "\n",
      "Images of each 10 trained classiers: \n"
     ]
    },
    {
     "data": {
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8UuhdLYXaJzYQgSM9AlGjYLLRo8qh59xCc3Xtp5K+d2OMqQtfJ/rS0QXre7F4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\n4raYHwhfrJeHXLyoHfdyPYW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jHijLcgu6XJoUGuBdOn/xqzrficsuNzqwrM/rg7Huzw7G\n7jwH/Xo/6SL0DxP6edTGu1y/VN6rB7LaiB6Xl8fctVMf0t8N7yFjX2huitBXY1/O2GtJAtftwrI+\nWHuMy6kzuDfHvq+nz53f2om6/4ozeJ7wmDv3IXymV9X7XYD8bb/vnl/dNzGm6uKf60DpmRbXzypa\nvKTsAieFZ1dHMo/iL9EJIYQQQgghhBBCCCGEkBbwITohhBBCCCGEEEIIIYQQ0gI+RCeEEEIIIYQQ\nQgghhBBCWtBxTvRC4NxjYzntXd0aaW/mltC5o5etdqvubQyr8nzVeX3y0/pvD6WD2nUXTLr9Jova\nWYaOM6+kvWvxRue3qoxpcc8guPuk620X+M9iqz/nQOwctei2RWec36FuvLWgyXOOnnBx6FDFlwXw\nN6u8Ow9ZD/j3wGk9mnOOtF0N7euv7NWubG/iEV0WMWcjcLyCn0/6rj0d1saAw5ph8uSgGxevp1D4\nL+sl7R6rbQCnsHBWL4L/LMvp/TY2uBM2tEn7hv1Ev3fWOC9Zo1fLxepaZ22qW1u7PKXz/MnK0oOO\nazykBd3+rOiOSyGv3X25ULdBe7I7U5p3OP1wIF3Nvu6UbAAe1Nba8CZvvTfl+qh/vf9UVfcvS2eo\n8uj33Y5H9uvzt7BVdx4zOe2+yx3rzu9Zo4+pumKg9/VQcXTl9SOzepxeruqxrZ1PEa/Tbu7Q8DBJ\nd25PpB28GyKdVw2JoMl7eiyqZPrcFTxXPqf3YVWXwm86bhX9Xa2m4+fUsUlVPndgl2nFYKgd6Pth\nTZHF2PV/eM35Pg7w5CmjPOE64vJ6GRcTLbrzkJvXcVGpaHf+V8bPEm/U5yua1HEz9kNX37tb59Nx\nr+47qhv0extiLK6NwloSRfCri3EQxz3sf+U4iH11Z45sh8hqa8uIai/BZBvK4jrOF3WftKms+7O8\n78aiRxaG9G5mdUzIJqUlbB/kfdM6BwvqbvuG/pgmp6scA/tAsr8M93O6CV0dQauC976NzJ2j5YY+\n12kM6/2I8+fjImT4OVHrbb1Y3wOYvBuPvJN2qKrZk3VMnTu0X5ULYuf7IZEfr2p39kLNPdNIs6f+\ne0p0LftNN8Xdi+wC/BjujXRaZQK5gAwcwqzYeizqgRzloVjnxAdq7ryn4Kv2q/rcFQ/qcs/Dbi2G\nYF6Pl3l4npGURDwVYHyEslwfDvsori3TniwRx72uz2cKC41NVVy5Duv7DBRrqjza485vWtJjzP5p\nnTPnhau8f7fuv+ZgXar57dqJvlh341X8MKwx8qAqmrDmYjsGd359EMY28QitKXUwRw7+Ep0QQggh\nhBBCCCGEEEIIaQEfohNCCCGEEEIIIYQQQgghLeBDdEIIIYQQQgghhBBCCCGkBR3nRM8JJ/pQuKTq\nxsJ5VR4NnMuzkGmvTwziwkrNeXxyWhdkghoIrIQryi+XVZXfq52hyTbtt5492Yl9Ktu1PO3Ufu05\nlt7z3fGoqlvOtOd4X8N5JJcTOtEPB3k4VnOiqzIom9K8/ptVPOI89pUN+pwsH6vjcUPOufXHY+26\nKx4Az3mkvVj+gPNZZb5ugwc6Ptn+pu/WJizQvEjfsAO9uqXQHXR/BA7yiC7K44jXZT84zY7pcWLZ\nTQXd1+2pao/sD5dd/7BQ0n1FMKYdwhv7dHlmwcVtPKN9Zyh89cSuU/DGZr3ac14edB3rSA/69/S2\n8xl8bofjg9UtQFezGMkD8OqCCs/YUNbr6zTUh930LYix7AEdJ7174fz9yK3FkC3otUD6zzlJlZe3\n6H2ZY93LrQU9zg0FulGJ8HdWYCybML0tt/Wph20J9s5J2voYy1zCGGN+FGxeeb0n1X7hyWQMyu78\nzKd6HZdqqset4bLrd8oDWtb/fw1pB/pQoHO98di1ER3o989CmxZcThY32qfEsi8PQn0Neh6Wu3fM\ne1LEWNaUd0A5rLpjl5/Xx7Uwqy/k9BHXwfnQ2RVm9I7Lj4hxsa5jyvZrMXUMjuuq8KA3BmDdiTY/\nR7KB3g+uZZKURDloHzMyH+j4+FrN5y3z8hidu3rTVIx5gz06nzmurNfKkk70h32dkOmx05hE3N4F\ncF9YnID1YurwXuFwhSW4TF9BC5N7AleuZHCvAP1zJXb9aALXA97rSWez1wUGfvz+SEOstaPX3THG\ngqdexoJcu8gYYyqjukMIe1w5hDhIi/qZQDBy2srrg8/XedKWU8dVGXOlfcKD/tCCfiYwsag/R64z\n4vut120yxhhPLJCF9yEZxA1/memQMeOnq8Re2R259Dk6j61s0Ed14VzX2bxp8w9VnVx3xhhjHp51\nfVh+H6wZBP0k+vprYy5HyxV0bpQW4Ppo9/VwuQg5zkEjbIcPa6vS9DAFymJtBnTahxXINRbcOVps\n6G1r/Trf7i275wk1WA/C7tG5+uBDrj8ojuuBb/44Hbt1WMutVnEx2DsOa3/M6HtKK27a0twq45Oo\nborFIxhT7O8IIYQQQgghhBBCCCGEkBbwITohhBBCCCGEEEIIIYQQ0gI+RCeEEEIIIYQQQgghhBBC\nWnDUO9HRvRwKL2UBBIWBCsgAAB70SURBVE9lT/t2hnz59XUdvlf6BzM4ao1+/R/RZudWDHq1E70+\not1Cc8dr59ncie5zBsa0V7Y31J68ibhv5fVMoj8Hne4TdbdtLT3qT/uRBf1JyokO7kUdNsbL3MYW\nJLxJWf/Nqj4ovNTH6rryljlVLvnO3/lfs9tVXagVj8b2a/edF0lhcnuvlNKTtTkOj5elhEpXomut\nmzVnUaD9gkNFd8IGYIEFdJn3iGs+D8EWoLS+DdgfbBpxn1OF/uv4Qe0IbbdGwkFwCCfwZ1mv4Jy0\npT7tcB/t1R7jTSXnU+6N9LaTNR3T8/XucqIjBXDEy3Ihp8euWqTjJM25kxTAUJBb0Oe6MOvKxUnt\nEM7t07GajB9o2d6grt3EGFKFyLU/AkEyjsWh8FL35XScLOX1eFoBt58E/ahBi+26AcyjGokLjAOL\n2nH43WSnKv84d8zKa/SjztWKqrxYc+enAb5e9PdKT+vYgHat761rL/ujNe2zfmBuo9t2Sq8hkk3r\nGJE+ZXQe2zy4r0suTj3wyPr8SUp7xEWf5vWxqw1DXiXWMsjP6XMgfenGGFOccv1FuKz7xXBR58ze\nshtvbVHHQVrU8VcfgGtiQNwDFFC6rYtySaIUtm2KsYL4fmH7Mb3jPejtwK8uHMN+vEpeK67VjSV9\nX3Vmz6OqvDVy+Q/mPl+d131hY9nlJaVl3QYcSyEFM3JpieImnQvt7JtSZTnmzdT1vd7Bim7Tsli/\nC9d4sBhessndHFv/QyoWN8gy6NCbnOjudaMPPOfg7xVKe+MnGKt4X+j2FZ2ic6xzRner8sGGPvd3\nT7uxeHxarwWSLEEuJJrhF3S/6eGNrWqtbVvuZjCPstKDDtceLG1gKhtdHNQ26GM6dPpBVf7rnf+2\n8voVJd1X3Ly8UZWnd7mOZnA/rscB64vAUkWL21yQF3phvasU13gQ/mrYD473NnJl9PF3Pe2esxhj\njOg/gho8/1xqvTZIWNPXfzKrx4b5yD2bDMCt3v+YbkJhyt0Lxn06kBv9EBdVWKNvUqwvCWMkro0j\nl0nKoPvC8VSVMaaO4HIfTPsJIYQQQgghhBBCCCGEkBbwITohhBBCCCGEEEIIIYQQ0oKj3uuRWL9l\neTnTc0pq8Pt/+c5eT+9nJNRT/spFNx9rYUCrBZY36vdmgZvC7GV6OnN1RG+7tBWmHWx209P7i3qq\n+nKqp03sq7lpyjilKobjstBw7Whk3Txx/fCRU2JwllugDQcGzAOKBupcxHTh2kY912sMzv0DlTH3\nenKDqstBm9JeHfdB5vaNipl201xWm93Zrh5nIHUzciquMcaMFNzU3ef17lV1Jxb2q/JY4DQGKZys\nxawAZXeNH4j1VM5apOdC7eh3UwCXYh0vIWhiKonud1I51b4Iqo1eHbdb+t101FP7x3UbipOqPBA4\nzc3+WCsYpmEKc4pTbTsQqRsJIIZ6Iq0pKAbuPFR79Lmu1PT5i2N37NIGjF0hTAdM3ed6cXvVgN/r\nphZ7BR2by0O6DfVBva/+vIsbVA8tpnpf8rhgrOK1pt/XsqrradK5NNw5qFb0uZud1vmPmuZe1/EU\nLehzGc27bdFG5WnTnakPuA0ehSmhE6BVQG1BKpQt+SnI+2DMltOqkxJMRUX1RpuBzeOYp8HrTRyf\ntFfP202HdBIT94pp5JM6poqTesdBQyj0QFeXlnTcWM/FTdoDfVKf/hyckq40LDDtGBOetCiCO4AD\nATHlSYULxhD7rBWarj3R7+A0cCwn9db5wvZI5yEvLLhth/3vq7rHjtEaqTtmTlx53QBdRtyj21sf\n0R1e4TiX252zWStlBiLtaNxXdfnQ3iWdG2FfWBd9pdJJGGM8iEWlUujCpD3D5weZ1LnA8UhbHx8L\nT1Iy0FcYcd8Vw/UfQy7Uu8XFxRkb96m6KjwDuHt6iyrvG3fx6c/qeAyT1n1UFurjYNnvPCVsu5jB\nKhhf4h530PtO1UrNq3d+S5UvKjvd68OJvgf70IMvVeWRH7pz27NPJz+L23Q81UZAQSsULWBkbNIS\nxWVXTsC22XQ9yH6o+7qdwwPNJEpjpuvweVRYEdocHVJNY6RUTkUVGKtm9I79hntzbas+2R70k/64\nri8ecPXRMqpGIf7EWJwUdF1bvcsz+Hig859EEEIIIYQQQgghhBBCCCFPET5EJ4QQQgghhBBCCCGE\nEEJawIfohBBCCCGEEEIIIYQQQkgLjjoneraKt62WOlHOVKxdcfsi7ZIb8g+uvI5gt8Phkipv7XNu\n3x9v0PutZOCc7XF/mwDlmmkMaMFRtkG7bXtLrozfdbKqXaSR77xE6IJNwBmcioagPx19u10PxpjU\nd6HHtY2LEXeTFKEsNM9ZTu94ekE7oO+o7Vh5XT2g4yCfQEzlwQEbuXKW03XoQJbxirGLWJ9es0PB\nB3l8X+iu8c3RrKo7KZpS5WNCEOcJZtJ5Vb5P+NH22GFVN9nQfdZEpc/VLetYayR6WIhjHTPS+9dT\n1g70E4a1X/RFgw+tvP6Fwm5VNxTo984I9zU60RvZUTdUrSnYv/dF+thtzDmPZl9YVXUZXJyPinBs\npLpTCqrgsF52nUAIXmov0/1QUHKx2hjUHrz54+D8bdBtlOsExHCuZ+BzpBe0lupt66luv3Tn50IU\nGZMnQAdqJt2wFX2Mg0WIkUW3bbSgqkx+Tu84Py/OAYwZtUE94FSFxziu6H6wAf0ijsP5ivDm61Az\noNxX41wWtXF3GmN8UVY+YWOMt9oiIh1Ok7O6Ta7ul7UDfXhQ59vpqHvv7Ii+/mNY8yUpuvjMz0Fs\ngtszFGULuU+ab5/E+LGrx+HI5vXneEX3/TyMkzZ5lYX80jKxcuDl1SYvD6uQ1866E4ZrCv3H0HNU\n+RfzP1t5fVKkY+1lQ/eq8u7tLs/a36NzllxeC2tPGJ5R5e09rpzCtXL/wpgq719w+drCol48IoUx\nW61RgT5+LEu6sP/C3Ej2Yc39mS7K9bHQTew3Wl+3aUHvKBzWA9SGXtcXyucZxhjz0NyoKh/Ypx39\n0aTbPoBrAL3U7fq7INAXVBS4AZbPC1rT9HxK+KHxfjqF85EMuGO8pU8nUjlIcP6l4sbEv9n163o/\n3xlR5Y3/6Z5zmVTvJ+nR8RSXYWATX0f60Y1p/j6J6JaSMsRaEcbHSKy1xCGuPW3GPcyxsB+KRFpV\nmNPnPg+e83BmeeW1V9XPJfEkpUMu/rxUN7B0oP04kp9z596HZ1cYY7FI/eBWtakfzXJybRzsrNs2\n6WnBX6ITQgghhBBCCCGEEEIIIS3gQ3RCCCGEEEIIIYQQQgghpAV8iE4IIYQQQgghhBBCCCGEtOCo\nE82iXxhpCCfq/nq/qusNxnDzFTYEi6pc8LRc6LT+/SuvpzZph/B4qD8nXhQeM3DxeOCCLJS0l0h+\nv6W6dq2H7TxlqxwX6THL+XTDPlWaNHnoVw1a16GDTno1g2W9cVzXMRaL05tf0H/7avZKwWUtXNMJ\nOM+SAriuRMg1eT+xLL+rAbrQr3io+ELgmcLfMfGvmqFxBzkAmWpqtLdsd8P57e6c26HqfnxgsypX\n9zvZWDQHbYjBfVfS5zIecn1Yz5B2953eu0+VzyruWnm9I9R9XQVEzAuZi9PZWMf/cqz7wqwLwqvd\nWBeCI3EkcuPXcfmDqq4v1P703mjLyusHc9oPW/G1f9h4Lv6wXwk3tE4fasM6hpaO0+3dPKJ9/j0i\nNpZS7aHF8kzDyRfnG1qU10hgPQhxDNHluVouQf6HDL2LuhxW3OvcPDrQD91JHVbhvUIhHC7DOKWV\n+waWpVHgWiTohk2KrpyV0G2t49YXOZdPN+zhkbUWU/YXdB91THlu5fXyoD65Dw/q9T5mRlz+nTuo\n+6TiQd0f5MVY58fgbYUYasrfhIPT9uo8PixAOXJxgusNIInos+xqC9F0Mxg+ooyHDd2w+Sm3cSPt\nU3XXJS9U5fkT3fjyir4fq7qxcE6Vf3XzPSuv9w1rJzqSQSP3Vtz2j83r987Pa+95tuzuKT3of9Ep\nbIVvuOn+k2PeIdPUv+OxFL7roAbnRHcHanzKwOWbj/QYs9RwGx9c1PnYwpTOi8Mp7UwPK/Ki0G3A\na8SKdbjCvG5DIacvoJwc9yCGfFyQoIto6tvbrANiQ/SEwwbifBxY0mtYfXb8Bao8UXH1Uz/YqOo2\n/ww6vxmXa3tl/aE4BgYNuNcru++zWh4l0/S0DDEBeZQXyj4K15Yx5BBpcn9D/IV1V5+b1fff0bge\ny7LJafe6Ac8l+/SY6fW58akwrTs7uY6WMcZYH557iQUo45Kuqw/o90onelLC60eXrVzPCMe5Izju\nMWMjhBBCCCGEEEIIIYQQQlrAh+iEEEIIIYQQQgghhBBCSAuOOp0LglOLrJjOMNvQU+J2eaOqXMvc\nVKhjctOqbiCoqPLOwsTK6+qonkL1EzE93hhjJsQUrDTVf6cIQMmCM1eSzG2PU+8sTNWQZZyqHkI5\nEvvC/XJaO4DHXcwvapoKnnott8WTC1YCI2fB5WfbT5lvR6bD0dQG9TzksORiKs3p/cY9upyI6TXY\n3jQHx0V+DP45rounZOH1lcJ1O1V3/cOjuRFV1+tXVXkmc1OuauDT+Vl9pyp//eDzVl7fs1vrW/IP\na//B8B7XxuKsnmqXRrq9S5t1PCU9Yko8fNfI11O7KpkLokdBO3QAplXfV3P96O6KnrJfS/R3x76w\n08ng+9bBtRSLi3E4XFJ1Y6FWpxybn1p5/dPyMarurvKxqrx/yKkSKvPad4DTyo0ME9CWDQ5rXdrm\nHt2mOHNvnmzoqazzsY7d2bob1xfrupPKMlATiXEQx0hObXfgFFp53NJIH7cs0P2BnCbepMDI4Rz4\n1mMRlpV1DqYZpwWcIgrjspj6idPn5TR2Y4wx4vsFUBeEum+U+RseM8bTKojjkzV0oKC2YLiwvPJ6\nQ173Z4URPV19V+T6mvGSVisu53TfIfMbvwG5jzYlmNooqHyG3RTnchGmRgetFYkJ9ElxjA7A1mMZ\nY8rRdD8k9AgZ5KYZqKJyYrjJ61nsJj6oY++Le5ze5Wvbz1B124ZmVbkQuNibb+hYm17W959L8+BD\nENrPoAL3iRBOckY83oNkBejPxBR/L2ivJCEaeR3nI53DVAv6pGQ5dx3jPZgHCgOpDLOwH3xGMD3n\n4jFe1jv2F3XehyYVOQ5imxJQbEi1bAH6syJ8d6mA5fMCR5N6BI9NIPOQQ9fezMzpPml2Xg9OSd3F\nHprsFrbBvUHZqT1toBtcGdGxV9e3XabR59qf9kKnhCoR2e/4oInJgc5Fvm7SuTC+FBhjUqMLT3BR\nayhz86bDCi4iL3Q78wIdF15Jj21WvDea1yq+aEE3uDGk31sfcJ9THdaf09Dpm87jcdzLtx73mmIT\nWcNxkL9EJ4QQQgghhBBCCCGEEEJawIfohBBCCCGEEEIIIYQQQkgL+BCdEEIIIYQQQgghhBBCCGnB\nUe9ED0AKlgoJ0FKsfano8orFthWQCY1E2uFa8JyHcSyvfa7pgP5bxHDBuX4nq9pttVDTfqBarE+B\n9KM1uaHQp55JJ7reFJ3ooThOdJodJuLYoifcgvsrzbeRLaF3VmjoIq39NGEV9is81bFWSZv6APr3\ndDmoy/bq96aF1uUmBzo49pQL12dMtSJOtQN1ouqcz4nVTuq99UFVzgnH+P6qFobdP7lRlSu7XWD0\n7tYnuu8x7aQrjTv3ul/T/sN4EBxm/eDrXHZl9H7+9/w2VZ5PXH3J197F2US/d0/VffeJivZiN5Kj\nfqg6bKQHHZ3oi+AJn4rd8RoKdGdyQm5ClbcUHl15vSN3UNWd2fOoKt+/0bn199UGVJ10kxtjTDVx\nHUQ91ecLncEL4I+VY7X0oxtjTB3OvfTjp5k+LiGMkbnQxXYEYyLHQQfmGoFwCqZ5cLiWwfEs/NYe\nrhEC+mdZj3UZrMUgtf847jb6wYHeD/7qHpevReDjjKLW/urV8NqsLdPtNDmrMRVSYmddtQi+6Hvt\n2MrrkZ5lVVeOWvvI+3r1miILemkQs9zn+qimWC3quCj01nU552IKfckxeI3lmI8O9CSBNQXEYWNM\ntQHXIBBrGaRlWOsnab3GUKTDyQw8rM/lyD0uDmygx6lKUa9/tRS1zvd7wD9cgvw5Fn7+uBfXKoJt\ne4RTG/JyE6LbtrWbeFWHc5fhG1zTR/QlBX39x736upVhVC+inBg+SKy1EZR0vGWQwySyv4BOFD3A\n6LhWdbBOiAefWyy771fOoxNdrzsh+1g8ZsThQ46ZiT4Kx0MPvc3iWs2g//Lgvr3c7zzU/Zv0Og3R\n2boNi3X3bKta18+50McfwhowvaKM360e6w4tSdy+MKbb0by2zCG/tTuA42Flbg5jgVzzxRhj6n1i\n3BvU595v6AdJfkHUw0lIy7D2VCjWRUsw5nVM1YZ037i82dXXh6D9sG6DWr8If/L9LHnmxF+iE0II\nIYQQQgghhBBCCCEt4EN0QgghhBBCCCGEEEIIIaQFfIhOCCGEEEIIIYQQQgghhLTgqBfNNntNhScv\n038jmK1ph6v0sk7WtIQuF4yqsnSKo5+2kmjX0FS17D6jpl1C9br2SGXgpJJOxCDUfqAAfK+BcALl\nwDmLDlq5X7pgVwEdVNK9BHUpel1FKDQ50EDFGqVtfHZNHizxGXDVJiXwp2u9qPHrYmew3yzCsnAv\nwp/Y0GGrfIr0mLUkgWt8rur6HVwj4aFU9zvLVRdQybQ+scW9+oSMPub6h95HK6oumtYiUK/mHIi2\npNuQoWcRitKxvzRVVnU/zrSE9tGy85yXwLOI/ah0XaMDHd3X3UYKF+NSoseV8Zrz5eM6IQH099uj\nyZXXfZ72fj4nd0CVe33nGH400rH5MJQfW3bneramY7UK7nx0Jvqij8Wxqx3oQMf3Sg964KOolDxB\nsxPdHasQHOJxjz53DVFMc0HbbWXfsZpaVYZ80gPuxAHdprBPO117ys4ZWgLfK/YkicgTG+CrxhyS\nmdNh0OSAlX5YTHAgp15y/dv+hh4LMA+WsZvB+QrA8er1iTPYlKBBHgX5mRyLlysg6Qfk2GZx7GIQ\nHRpwPjwMKHl/BDlvjHlu4K7rprUXIn3N9+x3n1uY0HlUYV6XvUzkywV9HxgP69yoMqbr630uVmF5\nGOVAN8aYrCSuHVijwkMnurg+0KVMND7kSjlxvMJ8TdUVQp2/xr1u7RkcJ3A8zQnXegK5XAXc0hXh\nrcb9Yt6ETut2bcC1QKQHvcmB7sMaI+r5AfOoVvjgafaFEx2HATw/0pGOY1w+r8/Ppl63Zt+5I4+o\nuheWH1TlPt/F8b11vabDI3Wdw8v7CGOMmay752JzkNNbGD+tdeO0Bx2PbTPm0YEONB0PdOeLmryu\na/ThgXY7SwuwttSQfuYZ1tx45SerJefiE+DkNmDNpIXjdLm6xa3NEPTr3DwH+Zrs7zC3s7imjegL\nn8mci0MsIYQQQgghhBBCCCGEENICPkQnhBBCCCGEEEIIIYQQQlrQgToXB046ijM9bW9JTJuaiPXU\nhlpDT7GKxZTSNMFpBVCWUwlwyg5O94GpeHL6aRQlqq6U01N6pMIlH+ptcUo/FS5Pg3bTjWBKkzzs\ngZ6pYoKa3jYUho2wCnGiT6eaMmNBt+HD3FXUvYhZVkrXgnXG6Cn0ONsZY5kKl0MDp73JKZqoNGnA\n1PWk3rqLRsXB8ha33/qgnh/sJTDfWSkYdBtwanGTSqHcui+pVfSU5WnRNy7AdNLQbz0tn7SnDrqb\nSePGr2qqx67ZWJ/QB6Oxldf9QdW0YzF1qp/pWE9P31sZUOWpqmgDjJ8Y1zi9U07pTAM9nkYwxS8v\nxkXUmDGm1gZ53HAaOE5ZjoVWISvpc1ev6ZzLa7h6L4GxEy0+MibKcF6LeoDE86y1LLpP8iFGZP9M\nfcvagefEihOKeXA7UlDs4FRdHF/bIacAZw3w08WgTsDpwvL7QN6OZfX94LuyT3qKtNO7gPrSFEHN\nI+orMN28PqTjYPkYN3YFVa03CEB3oG6zUPUIxp9UW/NMItqYQnuzAnwfoYVop295snboOsZeO3KB\nG1dCuIcuF/QN3WjOKTW25GdV3ZZIlwd8pwFqgBdzMStC2QXKXKpzt6m4V5X31XQOJrW0S432yimp\nvsPnBZhH+RwJDwlUk6CWRdFm3MIcCzWFDfEs64GljaoO8/1IJFYLcB84Xu3T7wVly3zFldupg4xp\nr2xBqHB5GojYsDkdX2mgT4K4JTO1sfaKE4s5TTvEx3qYJxV0rPaOzqvyCUJFhM8tl2Odqy/WXR+2\nWNEDaAwPr6xQq7W7ttYa/hKdEEIIIYQQQgghhBBCCGkBH6ITQgghhBBCCCGEEEIIIS3gQ3RCCCGE\nEEIIIYQQQgghpAVHvRMdaef+RseZ9KmiCzMDD6vyLqLrMWjtPET3IzqyQvgc6b7KQV0B/EHSW7aa\nA51O9DVilcMoqy38iSrTmmDlnk4L4IdFX5Woxv2i1xy959KRju/FP6NZGa/0lq0J6EBt95dL9A/L\n9yaRvsbjsnYrNjZKiTD6z9qcTPTTYn/WVHbtCKEugD5L9nd4HNAHL48LvbEaPFYZXJxZ4jqXaqI7\nmumadpm3owHrhjRSV26Ah70OruJElFfzJ6LzUZbzsBYIluW4h+7OwG/joCSHTLvrr53nM4Yyxq3a\nKwxGHrquRTX6021Fx2JchXLg3IrVHPRJ0I/KPgu/G8Yp+6Wnjjx2FpMLzHdE/5HBmi8p9FFqrIMY\nQl+n3NZHBz+CXZjMo/ASOBwhLFlzmhy7kJdIeTner6WQL6e9wt3fFD/wOe3yKuwrmvJ2UY/tBce7\nzMGwD/LaDbXsr9rS7r4Y6/K+zkNGoqWV18fnDqq650TTetvA9VklT3t/Y7usyhXrRtEZ6Gd2x9qB\nfk+4VZV3BaMrrw+Gep23RqrHSJlDovPc95hHrQU6f9DHNEtbr8FRW9b+5/r/39699LZtRGEApWVZ\nfqQNUAToov//bwXooosug9Yx7Famsovu3MEdSnIcP3DOSgQlkhbHw+GF+M1/bc75l4f9e//6/49m\n3ah2MG9S7SrPkdb1Q6HfuUxz46zzWGkOr/O4aeJUo+8u37vn60ZYn8e9V9ftHA+/3eznx/p4ed+s\n++XioVm+D/eYX7dtf5Z9umr7tw9hssCvj+1n873rYxjPzWls181ZM79MI/NLdAAAAAAAKCiiAwAA\nAABAQREdAAAAAAAK7y4TPeryzlJeakzjWaWsqJiXPk3TdHO5z/HJ+enHyJmtOcUnZrxepGNYyj0/\ndB1P0MUu5UyxkOGUM9HXKSfsOq48/Ri6WMbBcv/enB15xHFwkphp2fUkqX9YbUL+c8o7mzdtRmOc\ntyHnh40iW3NeXZfhPsgFXsoMjpte6pPkDZ8uZ08363J+enhvvpY9pu3E9XOOzs/nK2RL57k+8ntX\nXZb5fn1/3Uu5+zF7UZt5dkv/l/FcrtOIMkcIzyFzfN6ktpczDUe7zfM05H4orM855+dHZXlqX88h\nf69LGemHbzhtJme8hvO7S3PUdBfjUab1QvuL+9GGnkn8XtN1q8vgHZyPro00ExstjKNGmehZHhs1\ng6OFfidOybW0S+3tYKNxU54f5p/tZbP85/2n76/v5jbb9/P692b519U+b/jDqs0izi7O9uP6u7nd\n55fHNhv738c2OztmmV+d5xlKWttBHSOPq0bfE4fJY4t8038W/rHPbtp7u/kyjeGvw7i8m/9qdBDH\nzX+1Cst5zN6NueJcgJrLz7FwPdpt9+1k+9D2Z7e37QDodm7nUGh0bTdYqCl9Tlnsq1gHW7hWxZzz\n+LdM0zTtcrt/IX6JDgAAAAAABUV0AAAAAAAovOs4lyw/orQKj41fpAePr9fto1DDx+V/4KNOIlre\nmMGjLN3Zap+mOfmJZd6P/Nhujq6IES3nKeYit6dTLfVfT+l3PMr++sTzuUptKiccjIzaze7Ia2IT\nEaTNvGpdnzWITpkGkVPZeF0+hoWDbN4rouW1685JaFNnC9e5o/qap5z6wW60qRe2FBXX5KHkdaPP\nHn5eR5F50/QDIw60tWczGtPcbTfl8t/3H4fbbcZcqU11dYkQyZLvB3LcXrYNETSjGL+8X3EtP18X\n7xKWzxfyXZf6mvpzOfbq8A2JaHmD4vldjAELr7uYmLw82liKlEmxK83iIHbtrfBLdAAAAAAAKCii\nAwAAAABAQREdAAAAAAAKZ7vdqelKAAAAAADwvvklOgAAAAAAFBTRAQAAAACgoIgOAAAAAAAFRXQA\nAAAAACgoogMAAAAAQEERHQAAAAAACoroAAAAAABQUEQHAAAAAICCIjoAAAAAABQU0QEAAAAAoKCI\nDgAAAAAABUV0AAAAAAAoKKIDAAAAAEBBER0AAAAAAAqK6AAAAAAAUFBEBwAAAACAgiI6AAAAAAAU\nFNEBAAAAAKCgiA4AAAAAAAVFdAAAAAAAKCiiAwAAAABAQREdAAAAAAAKiugAAAAAAFBQRAcAAAAA\ngIIiOgAAAAAAFBTRAQAAAACgoIgOAAAAAAAFRXQAAAAAACgoogMAAAAAQEERHQAAAAAACoroAAAA\nAABQUEQHAAAAAICCIjoAAAAAABS+AZ4oHtHWHMTgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0aec460f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[2;40;46m                        RESULTS FOR LAMBDA = 100000                        \u001b[0;40;46m\n"
     ]
    },
    {
     "data": {
      "image/png": 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kCaytrXHy5EmcPn0aQP11XB6RSAQLCwv88ccf8PT0hL6+PgwNDaGnp/ev2ilP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\nAG74/1lJ1WaKMLJZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0baa715f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Confusion Matrix: Actual vs. Prediction\n"
     ]
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       "      <td>57</td>\n",
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       "      <td>780</td>\n",
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       "      <td>37</td>\n",
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       "      <th>3</th>\n",
       "      <td>25</td>\n",
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       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
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       "      <td>12</td>\n",
       "      <td>15</td>\n",
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       "      <td>769</td>\n",
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       "      <td>44</td>\n",
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       "      <td>105</td>\n",
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       "      <th>5</th>\n",
       "      <td>188</td>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
       "      <td>283</td>\n",
       "      <td>17</td>\n",
       "      <td>148</td>\n",
       "      <td>47</td>\n",
       "      <td>36</td>\n",
       "      <td>93</td>\n",
       "      <td>30</td>\n",
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       "      <th>6</th>\n",
       "      <td>72</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>844</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20</td>\n",
       "      <td>51</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>883</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>124</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>704</td>\n",
       "      <td>14</td>\n",
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       "      <th>9</th>\n",
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       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "      <td>24</td>\n",
       "      <td>762</td>\n",
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      ],
      "text/plain": [
       "     0     1    2    3    4    5    6    7    8    9\n",
       "0  954     0    2    4    0    0   12    1    7    0\n",
       "1    0  1074   14    8    0    0    4    1   34    0\n",
       "2   57    39  780   48   12    0   37   20   38    1\n",
       "3   25     6   21  894    0    0    9   19   28    8\n",
       "4   12    15    6    4  769    0   44    3   24  105\n",
       "5  188    36   14  283   17  148   47   36   93   30\n",
       "6   72    14   15    2    4    2  844    0    5    0\n",
       "7   20    51   29    1    5    0    3  883   13   23\n",
       "8   40    30   16  124    4    0   21   21  704   14\n",
       "9   39    21   13   20   53    0    5   72   24  762"
      ]
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    {
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     "output_type": "stream",
     "text": [
      "% Accuracy per digit\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>digit</th>\n",
       "      <th>Accuracy per digit in %</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>97.35</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>94.63</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>75.58</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>88.51</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>78.31</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>16.59</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>88.10</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>85.89</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>72.28</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>75.52</td>\n",
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      ],
      "text/plain": [
       "   digit  Accuracy per digit in %\n",
       "0      0                    97.35\n",
       "1      1                    94.63\n",
       "2      2                    75.58\n",
       "3      3                    88.51\n",
       "4      4                    78.31\n",
       "5      5                    16.59\n",
       "6      6                    88.10\n",
       "7      7                    85.89\n",
       "8      8                    72.28\n",
       "9      9                    75.52"
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The overall testing accuracy is 78.12%\n",
      "\n",
      "Images of each 10 trained classiers: \n"
     ]
    },
    {
     "data": {
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meJqYmDDf/OY3zcGDB2HMdO+99xpjjNm6deuq20lWTzfH1JMEQbCsi1lYWDAf+chHzMUX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0af08c470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic = Logistic_Regr()\n",
    "plot_mult_lambdas = logistic.logistic_multlambda_plots(lambdas, util.test_label, result_mult_lamdas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finally, below is the plot of overall test accuracy vs lambda [0.0001, 1, 10, 100, 1000, 10000, 100000]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pz549sX37djx48ABjxoyBra0tWrZs+UKHw/7333/YtWsX3N3dYWlpibt37+Lu3bsYMmQI\ngP8dYQYAGRkZMDMzK3GXd0ZGBgAUOzxaVppew+DgYEybNg2dOnXCzp07Va9ht27d1F7DjIwM1KtX\n77k/5u+99x6MjIxUw1pr1qyBubm52g9pcby9vXH//n3Va7F3715kZmaqDVmUx+tnaWmJU6dO4eTJ\nk9i9ezfc3d0RHh6OlStXqvX7999/kZ6eXuQ9ZmxsjDt37qjeY4Xzh54dDlIoFKpQpwtHjx5F3759\nIZfLERYWhuPHj+PUqVP46KOPAKDIZ1DT61+S2NhYvPvuu3jvvfcwd+5ctftK+77RRuHzpqlOW1vb\nIvOyLCwsisylNDExKfXjT5gwAWlpaao/UMLDw1GjRg3V57bQ+++/j6CgIIwaNQoxMTGqbXVyctLZ\n99y///6L/Px8WFhYFHlv/fnnn0W+j/Ud5wzpEUNDQ3Tt2hWxsbG4fv26xh/G69evIyEhAX379lU7\nKmj8+PGYMmUKkpKSkJycjLS0NLUflNq1awN4Mqnx2Q9+oWfn+2j6EYyIiICXlxeCgoLU2jMzM1Gj\nRo3Sb6wO1K5dGzY2Nli7dq3G+58+amXo0KEYOnQoHj16hOPHj2PJkiUYMWIETp8+XaYjayIiIpCb\nm4ujR49qDDnbtm3DqlWrYG5uDisrKzx8+BB37twpNhBZWVkBePL6Fv6/JgqFArm5uUXai3v+i3sN\ne/fujVWrVqm1P7tXwcrKqlQT42vVqoXRo0dj3bp1mDp1KjZv3oxx48YVmSyqSeGctvDwcLzzzjsI\nDw+Ho6MjunTpotZP16+fkZGRasI08CRwtWrVCvPmzcOoUaNQp04dAE/eY7a2tti9e7fG9VhaWqqe\nA+DJD9zTh40/evQI2dnZassoFArk5+ejoKBANWEXKPrHiCZbtmxBtWrVsHfvXhgbG6vaCyc5P0ub\nvVIJCQkYPnw4evfujZCQkCL3l/Z9o43C50rT3r6bN2+qvrd0ZeTIkZg+fTo2bNgAV1dX7N+/H5Mn\nT1ab0F5QUIDvv/8ekyZNwsKFC9WWz8jIQKNGjUp8jJI+o0+rXbs2jI2NcezYMbX3QSFTU1Mttqzq\n454hPePn5wchBKZMmaLalVtIqVTi/fffhxACvr6+avd5enpCoVBg48aN2LhxI+rVqwcPDw/V/Y0b\nN4aTkxPOnTuH1q1ba7wVN7nyaYUnqXtadHQ0bty48QJbXTb9+/fHpUuXUKdOHY3bo2kCtEKhQLdu\n3RAUFAQhBM6ePQsAqm0q7V99GzZsQM2aNREfH49Dhw6p3ZYsWYIHDx6ohi0Ld+uHhoYWu75evXqp\njiIsiYODg9rRgcCTE79pM8la02t4+vRpnDlzRq2tT58+yMrKwvfff//cdU6fPh03b97E8OHDcf/+\nfa2G+8aPH4+ff/4Zx48fR2xsLMaNG1fsj3hxr9+LMjU1xZdffokHDx4gODhY1d6/f3+kpaWpwtOz\nt8LJxZ07dwYAtaFq4ElIKRzWLuTg4AClUom//vpL1SaEQFRU1HPrlMlkMDY2VvvxvH//fqleo5Ik\nJyejX79+aNq0KSIjIzWefqG07xttPkvu7u6Qy+WIiIhQa79y5QqOHTtW5KCQF1WtWjWMHDkSUVFR\n+Pzzz6FUKosMkQGatzUyMhJ379597mM4ODggNTVVre/Dhw+LDPn1798fjx8/RmZmpsb31uuvv17G\nrayipJmqRFJatWqVMDAwEO3atRMRERHi559/FhEREaJ9+/bCwMBArFq1SuNynp6ewtraWsjlcvHJ\nJ58UuT8+Pl6YmJgIDw8P8f3334sjR46IH3/8UQQFBYlhw4ap+hVO+jx16lSRdXh5eQkTExOxYsUK\ncfDgQfHZZ58JKysr8eqrr4rOnTur+r2MCdRZWVnC2dlZ2NvbixUrVoi4uDgRGxsrvv76azFkyBCR\nmJgohBBizpw5YuLEieK7774Thw8fFjt37hSdOnUSJiYm4uLFi0IIIe7evSvkcrno0qWLOHz4sDh1\n6pTaRNannTp1SgBQmzD+tMKJwe3atVO1jRgxQhgYGIgpU6aIvXv3itjYWBEUFCRCQ0NVfT766CMB\nQHh6eoqdO3eKuLg4sWLFCrWJlOvXrxcAxLRp08SBAwfEN998Ixo1aiRsbGw0TqDeu3dvkfrmzJkj\nDAwMRGBgoDh48KBYvXq1sLa2Fo6OjqJx48aqfrm5uaJjx47CxMRE+Pn5idjYWBEVFSXmzp0rduzY\nUWS9Xbt2FQBEjx49ND4vxcnIyBByuVy8+uqrQiaTiatXrxap93mv36NHj4ShoaHo27fvcx+vcAK1\nJt26dRMmJibi+vXrQognE8S7desmrKysxOLFi0VsbKyIi4sT4eHhYsyYMWoTqwcPHiyMjIyEv7+/\niIuLE8uWLROvvfaaqFatmnj//fdV/W7fvi0sLCyEi4uL2L17t9i9e7cYOHCgcHR0fO4E6piYGAFA\njB49WsTFxYnvv/9etGzZUjg5ORVZ1sbGRgwdOlTjdj49gTo/P184OTmJ6tWri927d4sTJ06o3TIz\nM1WvQ2neN0IIUadOHeHs7Czi4uLEqVOnxLVr14QQmieJz58/XwAQ3t7eYt++fWLTpk3CwcFB1KxZ\nU+27orjXrbgJ6cU5fvy4ACBkMplo1aqVxj5DhgwR1apVE2vWrBEHDhwQS5YsEVZWVqJOnTqiX79+\nqn6aJlD/9ddfwsDAQPTq1Uvs27dPbNu2TbRv3144ODgU+S4bPXq0MDc3F5988omIjo4W8fHxYvPm\nzeLdd99V++4kIRiG9NSJEyfEsGHDhI2NjTAyMhLW1tZiyJAh4vjx48UuU3jUEgDVj8Szzp07J0aM\nGCGsra2FsbGxqFOnjujWrZtYu3atqk9JYejOnTtiwoQJwtraWpiZmQl3d3dx9OhR0blz55cehoQQ\nIjs7W/j5+YlGjRoJuVwuLC0tRYsWLcSsWbNERkaGEEKIXbt2iV69eglbW1shl8uFjY2NGDBggDhx\n4oTaujZt2iQaNWokjIyMij0aRgghJk+eLACIpKSkYuvy8fERAMRff/0lhHjyg/P555+LZs2aCblc\nLmrUqCE6duwoYmNj1ZYLCwsTrq6uQqFQCHNzc+Hm5iYiIiJU9yuVSrFkyRLh6OgoFAqFaNu2rTh6\n9GixR5NpCkMPHz4UPj4+om7dukKhUIjWrVuL6OhoMXLkyCI/ag8fPhSffPKJaNCggTA2Nha1atUS\nPXr00PjeWLt2rQAgdu3aVezzUpwhQ4YIABqP6inN65eTkyMAlHjkVaGSwtDp06eFTCZTCy+5ubli\n6dKlonnz5kKhUIjq1auLpk2biilTpojk5GRVv4cPH4oPP/xQ1K5dWygUCtGxY0dx8uRJYWpqKvz8\n/NQe55dffhFt27YVZmZmws7OTixevFh89dVXpTqabO3atcLJyUmYmJiIBg0aiM8//1yEhISUOQwV\nPnfF3QqPsNTmfbNv3z7RokULIZfL1Y6m0xSGCtudnZ1Vn40hQ4aICxcuqPXRVRgSQohmzZoJAMX+\nYZmRkSHGjh0rateuLapVqya6dOkiTp48Kdzc3J4bhoQQ4scff1S9X5ycnMT69euLHE0mhBAFBQUi\nNDRUtG7dWpiZmQkzMzPh5OQkxo8fX+QoWX0nE+KZfaxERBVQv379cP78eVy+fJlnuf5/8fHx6N69\nO3bs2FHsXD0iej5OoCaiCuvRo0dITEzEsWPHEBMTg9DQUL0NQjExMUhMTISLiwtMTEyQmJiITz/9\nFK+//joGDBggdXlElRrDEBFVWFevXkWHDh1gaWmJDz/8EO+9957UJUnGwsIC0dHR+OKLL3D//n1Y\nWVlh4MCBCA4OVjvyi4i0x2EyIiIi0ms8tJ6IiIj0GsMQERER6TWGISIiItJrnECtQUFBAW7evAlz\nc3OdXwSRiIiIyocQAvfu3YOtra3Gy5AUh2FIg5s3b8LOzk7qMoiIiKgMUlNTtbowNcOQBoXX0EpN\nTdXpFaGJiIio/GRnZ8POzq5U18J8GsOQBoVDYxYWFgxDRERElYy2U1w4gZqIiIj0GsMQERER6TWG\nISIiItJrDENERESk1xiGiIiISK8xDBEREZFeYxgiIiIivcYw9JIFBgbCwMAAgYGBUpdCVOFUlc9H\nVdkOIn3Bky6+RIGBgZg/fz4AqP7r7+8vZUlEFUZV+XxUle0g0ifcM/SSPP0FWWj+/Pn8y5EIVefz\nUVW2g0jfMAy9BJq+IAvxi5L0XVX5fFSV7Xgah/tIbwgqIisrSwAQWVlZL7yugIAAAeC5t4CAAB1U\nTlS5VJXPR1XZjqc9u02VqXbSX2X9/ZYJIUT5R67KJTs7G5aWlsjKynqhC7WW9JeiJg0bNkSDBg3K\n/HhElck///yDy5cvl7p/Rf18aLsdI0aMwMSJE1GjRg3VzdLSEnK5vByr1E5x310BAQGc/0QVWll/\nvxmGNNBVGDIwMACfXiIqDVNTU7WAVNzN0tJSY7uJiYlO6njeH3EMRFSRlfX3m0eTlaNFixZptWdo\nyJAhGDRoUDlWRFRx7Nq1Czt37ix1/4r6+dB2O+zt7WFpaYm7d+/i7t27yM7OBgDk5OQgJycHaWlp\nZapDoVCUKjQVF64UCgUWL1783O8sHiFHVRH3DGmgqz1DQOmHyvjXFumjqvL5eJHtUCqVyM7OVoWj\nrKws1f8/75aVlYWsrCyd7IE2NDSEUqksdf+K/pqQfuIwmQ7pMgwB3O1MVJKq8vmQajsKCgpw7969\n54amku4rKCjQ+nFlMlmZliMqT2X+/dbpNO4qQpdHkxUq7mgTHqFBVHU+H5VxO5RKpcjKyhIzZ84s\n1RFxlWGbSH+V9febYUiD8ghDQvBQVaKSVJXPR2Xejqp4igDSLwxDOlReYUiIJ182MpmMXyZEGlSV\nz0dl3o7nBaL69euLhw8fSl0mkUY8z5AO6XrOEBFRZVLc/Ce5XI68vDz06tULu3btgkKhkKA6ouKV\n9febl+MgIiI1/v7+CAgIUGsLCAjAgQMHYGZmhp9++gnDhg1Dbm6uRBUS6RbDEBERFVEYiGQymepI\nuE6dOiEqKgqmpqaIjo7GyJEj8fjxY6lLJXphHCbTgMNkRETFO3DgAPr374/c3FwMHToUW7duhZER\nz+FL0uMwGRERvRQ9evTArl27IJfLsWPHDowdOxb5+flSl0VUZgxDRESktd69e2PHjh0wNjbG1q1b\nMX78eK3OYE1UkTAMERFRmfTv3x8//PADjIyMEBERgYkTJ/Ks1FQpMQwREVGZDRo0CFu2bIGhoSE2\nbtyIyZMnMxBRpcMwREREL2TYsGGIiIiAgYEBvvnmG3zwwQc6uXgs0cvCMERERC9s1KhR2LRpE2Qy\nGUJDQ+Hj48NARJUGwxAREenEmDFjEBYWBgBYtWoVPvroIwYiqhQYhoiISGfGjx+PdevWAQCWLVuG\nTz75hIGIKjyGISIi0qn33nsPa9asAQAsXboUCxYskLgiopIxDBERkc5NnToVK1euBPDkwq+BgYES\nV0RUPIYhIiIqF9OnT8fnn38OAJg/fz6WLl0qcUVEmjEMERFRuZk9ezaCgoIAAH5+fli2bJnEFREV\nxTBERETlys/PD4sWLQLwJBx9+eWXEldEpK5ChKGQkBA4OjpCoVDAzc0NR48eLbbv48ePERAQgAYN\nGkChUKBly5aIjY19oXUSEVH5mj9/PubNmwcA8PHxQUhIiMQVEf2P5GEoMjISPj4+mDt3LhITE9Gp\nUyf06dMHKSkpGvvPmzcP69atw+rVq3H+/HlMnjwZgwcPRmJiYpnXSURE5S8gIAAff/wxgCcTrL/5\n5huJKyJ6QiYkPgFE27Zt4erqitDQUFVb06ZNMWjQIAQHBxfpb2tri7lz52Lq1KmqtkGDBqF69eqI\niIgo0zqflZ2dDUtLS2RlZcHCwuJFNo+IiJ4ihMDs2bOxfPlyyGQybNiwAePGjZO6LKoiyvr7Leme\noby8PCQkJMDDw0Ot3cPDA8ePH9e4TG5uLhQKhVqbqakpjh079kLrzM7OVrsREZHuyWQyfPHFF5g2\nbRqEEPD29lb9IUskFUnDUGZmJpRKJWxsbNTabWxscOvWLY3L9OrVC8uXL8elS5dQUFCAuLg47N69\nG2lpaWVeZ3BwMCwtLVU3Ozs7HWwdERFpIpPJsHLlSrz//vsQQuCdd97B1q1bpS6L9Jjkc4aAJx+M\npwkhirQV+vLLL+Hk5IQmTZqG/u3HAAAgAElEQVRALpfjgw8+wPjx42FoaFjmdfr5+SErK0t1S01N\nfYGtISKi55HJZFizZg0mTpyIgoICjBkzBtu3b5e6LNJTkoah2rVrw9DQsMgem/T09CJ7dgpZWVlh\n165dePDgAa5du4a///4b1atXh6OjY5nXaWJiAgsLC7UbERGVLwMDA6xbtw7vvPMOlEolPD09sXv3\nbqnLIj0kaRiSy+Vwc3NDXFycWntcXBw6dOhQ4rIKhQL16tVDfn4+duzYgbfeeuuF10lERC+XgYEB\nwsLC8PbbbyM/Px/Dhw9HVFSU1GWRnjGSuoCZM2di7NixaN26Ndq3b4+vv/4aKSkpmDx5MgDAy8sL\n9erVUx0F9ttvv+HGjRto1aoVbty4gYULF6KgoABz5swp9TqJiKjiMDQ0xMaNG5Gfn4/IyEgMHToU\nu3fvRu/evaUujfSE5GFo5MiRuH37NgICApCWlgZnZ2fExMTA3t4eAJCSkgIDg//twHr06BHmzZuH\n5ORkVK9eHX379sXmzZtRo0aNUq+TiIgqFiMjI2zevFm1t3/QoEGIiopCjx49pC6N9IDk5xmqiHie\nISIiaeTl5WH48OHYs2cPTE1NERMTgy5dukhdFlUSlfI8Q0RERE+Ty+X44Ycf0LdvX+Tk5KBfv368\nnBKVO4YhIiKqUExMTLBjxw54eHjg4cOH6Nu3b7EnzSXSBYYhIiKqcBQKBXbt2oXu3bvj/v376N27\nN06ePCl1WVRFMQwREVGFZGpqij179qBz5864d+8ePDw8kJCQIHVZVAUxDBERUYVlZmaGqKgouLu7\nIysrCz179sTZs2elLouqGIYhIiKq0KpXr46YmBi0a9cOd+7cQY8ePfDnn39KXRZVIQxDRERU4Zmb\nmyM2NhatW7fG7du30b17dyQlJUldFlURDENERFQpWFpaYv/+/XBxcUF6ejq6deuGCxcuSF0WVQEM\nQ0REVGm88soriIuLQ4sWLXDr1i1069YNly9flrosquQYhoiIqFKpVasWDhw4gNdffx03b95Et27d\ncOXKFanLokqMYYiIiCodKysrHDx4EE2aNEFqaiq6du2Ka9euSV0WVVIMQ0REVCnZ2NggPj4eTk5O\nuHbtGrp164br169LXRZVQgxDRERUadWtWxfx8fGoX78+kpOT0bVrV9y8eVPqsqiSYRgiIqJK7dVX\nX8WhQ4fg4OCAy5cvo1u3brh165bUZVElwjBERESV3muvvYZDhw7Bzs4OFy5cQPfu3ZGeng4ACAwM\nhIGBAQIDAyWukioqI6kLICIi0gUHBwccOnQInTt3xvnz59GjRw/069cPS5cuBQDMnz8fAODv7y9l\nmVQByYQQQuoiKprs7GxYWloiKysLFhYWUpdDRERauHjxIrp06YK0tDSN9wcEBDAQVVFl/f3mMBkR\nEVUpjRo1wrBhw4q9f/78+RwyIzUMQ0REVKUEBgZi9erVJfZhIKKnMQwREVGVERgYqJob9DwMRFSI\nc4Y04JwhIqLKycDAANr8rMlkMhQUFJRjRfQycc4QERHpvUWLFpVrf6qaGIaIiKjK8Pf3R0BAQKn6\n8qgyKsQwREREVUppAhGDED2NYYiIiKqckgJRvXr1MG/evJdcEVVkDENERFQlaQpEhoaGuHHjBo4d\nOyZRVVQRMQwREVGVVRiIZDIZAgICMGHCBADAF198IXFlVJHw0HoNeGg9EVHV9Pfff6Np06aq/2/c\nuLHEFZEu8dB6IiKi52jSpAkGDBgAAFixYoXE1VBFwTBERER6Zfbs2QCATZs2IT09XeJqqCJgGCIi\nIr3SqVMntGnTBo8ePUJISIjU5VAFwDBERER6RSaTqfYOffXVV8jJyZG4IpIawxAREemdIUOGwN7e\nHpmZmfj222+lLockxjBERER6x8jICDNmzAAALFu2jBdr1XMMQ0REpJe8vb1Ro0YNXLp0CXv37pW6\nHJIQwxAREeklc3NzTJ48GQBPwqjvGIaIiEhvffjhhzA2NsaxY8fw22+/SV0OSYRhiIiI9JatrS1G\njx4N4MncIdJPDENERKTXZs2aBQDYsWMHkpOTJa6GpMAwREREeq158+bo1asXCgoKsHLlSqnLIQkw\nDBERkd4rPAljWFgY/vvvP4mroZdN8jAUEhICR0dHKBQKuLm54ejRoyX2X7lyJRo3bgxTU1PY2dlh\nxowZePToker+/Px8zJs3D46OjjA1NUX9+vUREBDAc0gQEVGxunfvjhYtWuDhw4dYt26d1OXQSyZp\nGIqMjISPjw/mzp2LxMREdOrUCX369EFKSorG/t999x18fX2xYMECJCUlISwsDJGRkfDz81P1+fTT\nT7F27VqsWbMGSUlJ+Oyzz/D5559j9erVL2uziIioknn6Eh2rVq1Cbm6uxBXRyyQTQgipHrxt27Zw\ndXVFaGioqq1p06YYNGgQgoODi/T/4IMPkJSUhIMHD6raZs2ahZMnT6r2KPXv3x82NjYICwtT9Rk6\ndCjMzMywefPmUtWVnZ0NS0tLZGVlwcLCoqybR0RElUheXh7q16+PGzduYMOGDRg/frzUJZGWyvr7\nLdmeoby8PCQkJMDDw0Ot3cPDA8ePH9e4jLu7OxISEnDy5EkAQHJyMmJiYtCvXz+1PgcPHsTFixcB\nAOfOncOxY8fQt2/fYmvJzc1Fdna22o2IiPSLXC7H9OnTATw5zF7CfQX0kkkWhjIzM6FUKmFjY6PW\nbmNjg1u3bmlcZtSoUQgMDIS7uzuMjY3RoEEDdO3aFb6+vqo+H3/8MTw9PdGkSRMYGxvDxcUFPj4+\n8PT0LLaW4OBgWFpaqm52dna62UgiIqpU3n33XVSvXh1//fUXfvrpJ6nLoZdE8gnUMplM7d9CiCJt\nhQ4fPowlS5YgJCQEZ86cwc6dOxEVFYXAwEBVn8jISEREROD777/HmTNnsGnTJnzxxRfYtGlTsTX4\n+fkhKytLdUtNTdXNxhERUaVSo0YNvPvuuwB4iQ59Itmcoby8PJiZmWHbtm0YPHiwqn369Ok4e/Ys\njhw5UmSZTp06oV27dvj8889VbREREXjvvfdw//59GBgYwM7ODr6+vpg6daqqz+LFixEREYG///67\nVLVxzhARkf66du0aGjRoAKVSiTNnzsDFxUXqkqiUKt2cIblcDjc3N8TFxam1x8XFoUOHDhqXefjw\nIQwM1Es2NDSEEEI1tltcHx5aT0REpWFvb48RI0YA4CU69IWkw2QzZ87E+vXrsWHDBiQlJWHGjBlI\nSUlRXUXYy8tL7bD5AQMGIDQ0FFu3bsWVK1cQFxcHf39/DBw4EIaGhqo+S5YsQXR0NK5evYoff/wR\ny5cvV9v7REREVJLCS3Rs3bqVUyf0gJGUDz5y5Ejcvn0bAQEBSEtLg7OzM2JiYmBvbw8ASElJUdvL\nM2/ePMhkMsybNw83btyAlZWVKvwUWr16Nfz9/TFlyhSkp6fD1tYWkyZNwvz581/69hERUeXk5uaG\nLl264PDhw1i1apXa9AyqeiQ9z1BFxTlDREQUHR2N/v37w9zcHKmpqbC0tJS6JHqOSjdniIiIqCLr\n06cPmjZtinv37mH9+vVSl0PliGGIiIhIAwMDA9XcoZUrV+Lx48cSV0TlhWGIiIioGG+//Tasra1x\n/fp1bNu2TepyqJwwDBERERVDoVDgww8/BPDkJIycZls1MQwRERGV4P3334epqSkSExNx6NAhqcuh\ncsAwREREVIJatWrB29sbAC/RUVUxDBERET2Hj48PZDIZ9u3bh7/++kvqckjHGIaIiIieo2HDhqor\nGSxfvlziakjXGIaIiIhKYfbs2QCeXCA8LS1N4mpIlxiGiIiISqF9+/bo0KED8vLysGbNGqnLIR1i\nGCIiIiqlwpMwhoaG4sGDBxJXQ7rCMERERFRKb731Fho0aIA7d+4gPDxc6nJIRxiGiIiISsnQ0BAz\nZ84E8GQitVKplLgi0gWGISIiIi2MGzcOtWrVwpUrV/Djjz9KXQ7pAMMQERGRFszMzDBlyhQAvERH\nVcEwREREpKWpU6fCxMQEv/32G44fPy51OfSCGIaIiIi0ZGNjg7FjxwLgJTqqAoYhIiKiMiicSL17\n925cvHhR4mroRTAMERERlUHTpk3Rv39/CCGwYsUKqcuhF8AwREREVEaFJ2HcuHEjMjIyJK6Gyoph\niIiIqIw6d+4MNzc3PHr0CKGhoVKXQ2XEMERERFRGMplMdQHXNWvWICcnR+KKqCwYhoiIiF7AsGHD\n8NprryEjIwObN2+WuhwqA4YhIiKiF2BkZAQfHx8AwLJly1BQUCBxRaQthiEiIqIXNHHiRFhaWuLi\nxYuIjo6WuhzSEsMQERHRCzI3N8ekSZMA8CSMlRHDEBERkQ5MmzYNRkZG+Pnnn3Hy5EmpyyEtMAwR\nERHpQL169TB69GgAT+YOUeXBMERERKQjhZfo2L59O65evSptMVRqDENEREQ60rJlS/Ts2RMFBQVY\nuXKl1OVQKTEMERER6VDhSRjXr1+PO3fuSFwNlQbDEBERkQ717NkTzZs3x4MHD7Bu3Tqpy6FSYBgi\nIiLSIZlMprqA66pVq5CXlydxRfQ8DENEREQ65unpCVtbW6SlpWHLli1Sl0PPwTBERESkY3K5HNOm\nTQPw5CSMQgiJK6KSMAwRERGVg0mTJqF69er4888/sX//fqnLoRIwDBEREZWDGjVqYMKECQB4iY6K\njmGIiIionPj4+MDAwAAHDhzAuXPnpC6HisEwREREVE4cHBwwfPhwALxER0XGMERERFSOCk/CuGXL\nFly/fl3iakgThiEiIqJy1Lp1a3Tu3Bn5+flYtWqV1OWQBpKHoZCQEDg6OkKhUMDNzQ1Hjx4tsf/K\nlSvRuHFjmJqaws7ODjNmzMCjR4/U+ty4cQNjxoxBrVq1YGZmhlatWiEhIaE8N4OIiKhYhSdhXLdu\nHbKzsyWuhp4laRiKjIyEj48P5s6di8TERHTq1Al9+vRBSkqKxv7fffcdfH19sWDBAiQlJSEsLAyR\nkZHw8/NT9blz5w46duwIY2Nj7Nu3D+fPn8eyZctQo0aNl7VZREREavr164fGjRsjOzsbYWFhUpdD\nz5AJCc8E1bZtW7i6uiI0NFTV1rRpUwwaNAjBwcFF+n/wwQdISkrCwYMHVW2zZs3CyZMnVXuUfH19\n8csvvzx3D1NJsrOzYWlpiaysLFhYWJR5PURERIW++eYbvPfee3jttddw+fJlGBsbS11SlVPW32/J\n9gzl5eUhISEBHh4eau0eHh44fvy4xmXc3d2RkJCAkydPAgCSk5MRExODfv36qfrs2bMHrVu3xvDh\nw2FtbQ0XFxd88803JdaSm5uL7OxstRsREZEujR07FtbW1khJScH27dulLoeeIlkYyszMhFKphI2N\njVq7jY0Nbt26pXGZUaNGITAwEO7u7jA2NkaDBg3QtWtX+Pr6qvokJycjNDQUTk5O+OmnnzB58mRM\nmzYN3377bbG1BAcHw9LSUnWzs7PTzUYSERH9P4VCgalTpwLgJToqGsknUMtkMrV/CyGKtBU6fPgw\nlixZgpCQEJw5cwY7d+5EVFQUAgMDVX0KCgrg6uqKoKAguLi4YNKkSXj33XfVhuKe5efnh6ysLNUt\nNTVVNxtHRET0lClTpkChUODMmTM4cuSI1OXQ/5MsDNWuXRuGhoZF9gKlp6cX2VtUyN/fH2PHjsXE\niRPRvHlzDB48GEFBQQgODkZBQQEAoG7dumjWrJnack2bNi12UjYAmJiYwMLCQu1GRESka7Vr18b4\n8eMB8BIdFYlkYUgul8PNzQ1xcXFq7XFxcejQoYPGZR4+fAgDA/WSDQ0NIYRQ7W7s2LEjLly4oNbn\n4sWLsLe312H1REREZTNjxgzIZDJER0fj/PnzUpdDkHiYbObMmVi/fj02bNiApKQkzJgxAykpKZg8\neTIAwMvLS+2w+QEDBiA0NBRbt27FlStXEBcXB39/fwwcOBCGhoYAnrzJfv31VwQFBeHy5cv4/vvv\n8fXXX6vGaYmIiKTk5OSEQYMGAQCWL18ucTUEABAS++qrr4S9vb2Qy+XC1dVVHDlyRHVf586dxTvv\nvKP69+PHj8XChQtFgwYNhEKhEHZ2dmLKlCnizp07auvcu3evcHZ2FiYmJqJJkybi66+/1qqmrKws\nAUBkZWW90LYRERFpcuzYMQFAyOVykZaWJnU5VUZZf78lPc9QRcXzDBERUXkSQqBDhw749ddfMW/e\nPLUDgajsXtp5hq5cuaLtIkRERPQUmUymuoBrSEgIHjx4IHFF+k3rMNSwYUN07doVERERRa4JRkRE\nRKUzaNAg1K9fH//99x82btwodTl6TeswdO7cObi4uGDWrFmoU6cOJk2apDojNBEREZWOoaEhZsyY\nAQBYsWIFlEqlxBXpL63DkLOzM5YvX44bN24gPDwct27dgru7O15//XUsX74cGRkZ5VEnERFRlTN+\n/Hi88sor+Oeff7B7926py9FbZT603sjICIMHD8YPP/yATz/9FP/88w9mz56NV199FV5eXkhLS9Nl\nnURERFVOtWrVMGXKFAA8CaOUyhyGTp8+jSlTpqBu3bpYvnw5Zs+ejX/++Qfx8fG4ceMG3nrrLV3W\nSUREVCV98MEHkMvlOHHiRLEXKqfypXUYWr58OZo3b44OHTrg5s2b+Pbbb3Ht2jUsXrwYjo6O6Nix\nI9atW4czZ86UR71ERERVSp06dTBmzBgA3DskFa3PM+Tk5ARvb2+MHz8ederU0dgnLy8PW7ZswTvv\nvKOTIl82nmeIiIhepr/++gvOzs6QyWS4ePEiGjZsKHVJlVJZf7950kUNGIaIiOhl69evH2JiYjBl\nyhR89dVXUpdTKb20ky6Gh4dj27ZtRdq3bduGTZs2abs6IiIiAlQnYQwPD0dmZqbE1egXrcPQ0qVL\nUbt27SLt1tbWCAoK0klRRERE+qZLly5wdXVFTk4OQkNDpS5Hr2gdhq5duwZHR8ci7fb29khJSdFJ\nUURERPpGJpNh1qxZAIA1a9bwKg8vkdZhyNraGr///nuR9nPnzqFWrVo6KYqIiEgfDR8+HHZ2dkhP\nT0dERITU5egNrcPQqFGjMG3aNBw6dAhKpRJKpRLx8fGYPn06Ro0aVR41EhER6QVjY2P4+PgAAJYt\nW4aCggKJK9IPWh9NlpeXh7Fjx2Lbtm0wMjICABQUFMDLywtr166FXC4vl0JfJh5NRkREUsnOzoad\nnR2ys7Oxd+9e9O/fX+qSKo2Xfmj9xYsXce7cOZiamqJ58+awt7cvy2oqJIYhIiKS0kcffYQvvvgC\nXbp0waFDh6Qup9LgeYZ0iGGIiIiklJqaivr16yM/Px+nTp1C69atpS6pUijr77dRWR7s+vXr2LNn\nD1JSUpCXl6d23/Lly8uySiIiIvp/dnZ2GDVqFCIiIrBs2TJs2bJF6pKqNK33DB08eBADBw6Eo6Mj\nLly4AGdnZ1y9ehVCCLi6uiI+Pr68an1puGeIiIikdvbsWbi4uMDQ0BCXL1+Gg4OD1CVVeC/tDNR+\nfn6YNWsW/vzzTygUCuzYsQOpqano3Lkzhg8fru3qiIiISINWrVqhe/fuUCqV+PLLL6Uup0rTOgwl\nJSWpLsBqZGSEnJwcVK9eHQEBAfj00091XiAREZG+KrxEx/r163H37l0EBgbCwMAAgYGBEldWtWgd\nhqpVq4bc3FwAgK2tLf755x/VfbyWChERke706tULzs7OuH//PkaMGIH58+dDCIH58+czEOmQ1mGo\nXbt2+OWXXwA8ucLurFmzsGTJEnh7e6Ndu3Y6L5CIiEhfPX2Jjri4OLX7GIh0R+ujyZYvX4779+8D\nABYuXIj79+8jMjISDRs2xIoVK3ReIBERkT67cuVKsffNnz8fAODv7/+yyqmStApDSqUSqampaNGi\nBQDAzMwMISEh5VIYERGRvgsMDERAQECJfRiIXpxWw2SGhobo1asX7t69W171EBEREZ4EocKg8zwc\nMnsxWs8Zat68OZKTk8ujFiIiIvp/CxYsKNf+9D9ah6ElS5Zg9uzZiIqKQlpaGrKzs9VuRERE9OIW\nLVpUrv3pf7Q+A7WBwf/yk0wmU/2/EAIymQxKpVJ31UmEZ6AmIqKKoLRDZQEBAZwzhJd4bTJePZeI\niOjlKAw4JQUiBqEXp3UY6ty5c3nUQURERBqUFIgYhHRD6zD0888/l3j/m2++WeZiiIiIqChNgWjW\nrFkMQjqidRjq0qVLkban5w5VhTlDREREFc2zgahRo0ZSllOlaH002Z07d9Ru6enpiI2NRZs2bbB/\n//7yqJGIiIjwJBAVnk8oOjpa4mqqDq2PJivOzz//jBkzZiAhIUEXq5MUjyYjIqKKKjExEa6urjAz\nM8Pt27ehUCikLqnCKOvvt9Z7hopjZWWFCxcu6Gp1REREpEGrVq1ga2uLhw8f4siRI1KXUyVoPWfo\n999/V/u3EAJpaWlYunQpWrZsqbPCiIiIqCiZTIa+ffti/fr1iI6ORq9evaQuqdIr00kXZTIZnl2s\nXbt22LBhA5o0aaLTAqXAYTIiIqrIdu3ahcGDB6N+/fq4fPmy2oFM+uylnXTxypUrav82MDCAlZUV\nxyyJiIhekh49ekAulyM5ORkXLlyoEjsipKR1GLK3ty+POoiIiKiUqlevjs6dOyMuLg7R0dEMQy9I\n6wnU06ZNw6pVq4q0r1mzBj4+PjopioiIiErWr18/ADzEXhe0DkM7duxAx44di7R36NAB27dvL1MR\nISEhcHR0hEKhgJubG44ePVpi/5UrV6Jx48YwNTWFnZ0dZsyYgUePHmnsGxwcDJlMxqBGRERVSmEY\nOnr0KLKysiSupnLTOgzdvn0blpaWRdotLCyQmZmpdQGRkZHw8fHB3LlzkZiYiE6dOqFPnz5ISUnR\n2P+7776Dr68vFixYgKSkJISFhSEyMhJ+fn5F+p46dQpff/01WrRooXVdREREFVnDhg3RqFEj5Ofn\nIy4uTupyKjWtw1DDhg0RGxtbpH3fvn2oX7++1gUsX74cEyZMwMSJE9G0aVOsXLkSdnZ2CA0N1dj/\nxIkT6NixI0aPHg0HBwd4eHjA09MTp0+fVut3//59vP322/jmm2/wyiuvaF0XERFRRcehMt3QOgzN\nnDkTc+bMwYIFC3DkyBEcOXIE8+fPh6+vL2bMmKHVuvLy8pCQkAAPDw+1dg8PDxw/flzjMu7u7khI\nSMDJkycBAMnJyYiJiVG9IQpNnToV/fr1Q48ePZ5bR25uLrKzs9VuREREFV3hb19MTAwKCgokrqby\n0vpoMm9vb+Tm5mLJkiWq66M4ODggNDQUXl5eWq0rMzMTSqUSNjY2au02Nja4deuWxmVGjRqFjIwM\nuLu7QwiB/Px8vP/++/D19VX12bp1KxISEorsLSpOcHAwFi1apFXtREREUuvUqRPMzc2Rnp6OhIQE\ntGnTRuqSKqUyXY7j/fffx/Xr1/Hvv/8iOzsbycnJWgehpz17sighRLEnkDp8+DCWLFmCkJAQnDlz\nBjt37kRUVJQqmKWmpmL69On47rvvSn3uIz8/P2RlZaluqampZd4WIiKil0Uul6Nnz54AOFT2IrQO\nQ1euXMGlS5cAPLkeWfXq1QEAly5dwtWrV7VaV+3atWFoaFhkL1B6enqRvUWF/P39MXbsWEycOBHN\nmzfH4MGDERQUhODgYBQUFCAhIQHp6elwc3ODkZERjIyMcOTIEaxatQpGRkZQKpVF1mliYgILCwu1\nGxERUWXAeUMvTuswNG7cOI3zeX777TeMGzdOq3XJ5XK4ubkVmQUfFxeHDh06aFzm4cOHMDBQL9vQ\n0BBCCAgh0L17d/zxxx84e/as6ta6dWu8/fbbOHv2LAwNDbWqkYiIqCLr27cvAOD06dPFTjGhkmkd\nhhITEzWeZ6hdu3Y4e/as1gXMnDkT69evx4YNG5CUlIQZM2YgJSUFkydPBgB4eXmpHTY/YMAAhIaG\nYuvWrbhy5Qri4uLg7++PgQMHwtDQEObm5nB2dla7VatWDbVq1YKzs7PW9REREVVkderUgZubG4An\nR3aT9rSeQC2TyXDv3r0i7VlZWRqHoJ5n5MiRuH37NgICApCWlgZnZ2fExMSoLvuRkpKitido3rx5\nkMlkmDdvHm7cuAErKysMGDAAS5Ys0fqxiYiIqoJ+/fohISEB0dHRGD9+vNTlVDpaX7W+f//+MDMz\nw5YtW1RDTkqlEiNHjsSDBw+qRCrlVeuJiKgyOXnyJNq2bQtzc3NkZmZCLpdLXZIkXtpV6z/77DO8\n+eabaNy4MTp16gTgf6cCP3TokLarIyIiohfUunVrWFtbIz09HceOHUO3bt2kLqlS0XrOULNmzfD7\n779jxIgRSE9Px7179+Dl5YWLFy8iPz+/PGokIiKiEhgYGKBPnz4AeFRZWZTpPEO2trYICgpCdHQ0\n1q9fj1q1aqFHjx6qCVxERET0cvEQ+7IrUxgCgPj4eIwZMwa2trZYs2YN+vTpU+ozPhMREZFueXh4\nwMjICBcuXMA///wjdTmVilZh6Pr161i8eDHq168PT09PvPLKK3j8+DF27NiBxYsXw8XFpbzqJCIi\nohJYWlrC3d0dAPcOaavUYahv375o1qwZzp8/j9WrV+PmzZtYvXp1edZGREREWuBQWdmUOgzt378f\nEydOxKJFi9CvXz+eyZmIiKiCKQxDhw8fxv379yWupvIodRg6evQo7t27h9atW6Nt27ZYs2YNMjIy\nyrM2IiIi0kKTJk3g6OiIvLw8HDx4UOpyKo1Sh6H27dvjm2++QVpaGiZNmoStW7eiXr16KCgoQFxc\nnMazUhMREdHLI5PJOFRWBlofTWZmZgZvb28cO3YMf/zxB2bNmoWlS5fC2toaAwcOLI8aiYiIqJQK\nw1BMTAy0vMiE3irzofUA0LhxY3z22We4fv06tmzZoquaiIiIqIy6dOkCMzMz3LhxA+fOnZO6nErh\nhcJQIUNDQwwaNAh79iD6ZmYAACAASURBVOzRxeqIiIiojBQKBbp37w6AQ2WlpZMwRERERBUH5w1p\nh2GIiIioiunbty8A4Ndff0VmZqbE1VR8DENERERVjJ2dHVq0aAEhBGJjY6Uup8JjGCIiIqqCOFRW\negxDREREVVBhGIqNjUV+fr7E1VRsDENERERVULt27VCzZk3cvXsXJ06ckLqcCo1hiIiIqAoyNDRE\n7969AXCo7HkYhoiIiKoozhsqHYYhIiKiKqp3794wMDDAn3/+iZSUFKnLqbAYhoiIiKqomjVrokOH\nDgC4d6gkDENERERVGIfKno9hiIiIqAorDEPx8fHIycmRuJqKiWGIiIioCnN2doadnR1ycnJw6NAh\nqcupkBiGiIiIqjCZTMahsudgGCIiIqring5DQgiJq6l4GIaIiIiquG7dukGhUODatWs4f/681OVU\nOAxDREREVZyZmRm6du0KgENlmjAMERER6QHOGyoewxAREZEeKAxDv/zyC+7cuSNxNRULwxAREZEe\ncHBwQLNmzaBUKrF//36py6lQGIaIiIj0BIfKNGMYIiIi0hOFYWjfvn1QKpUSV1NxMAwRERHpiQ4d\nOsDS0hKZmZk4deqU1OVUGAxDREREesLY2Bi9evUCAERFRUlcTcXBMERERKRHOG+oKIYhIiIiPdKn\nTx/IZDKcPXsWN27ckLqcCoFhiIiISI9YWVnhjTfeAADExMRIXE3FwDBERESkZzhUpo5hiIiISM8U\nhqEDBw4gNzdX4mqkVyHCUEhICBwdHaFQKODm5oajR4+W2H/lypVo3LgxTE1NYWdnhxkzZuDRo0eq\n+4ODg9GmTRuYm5vD2toagwYNwoULF8p7M4iIiCoFFxcX1K1bFw8ePMCRI0ekLkdykoehyMhI+Pj4\nYO7cuUhMTESnTp3Qp08fpKSkaOz/3XffwdfXFwsWLEBSUhLCwsIQGRkJPz8/VZ8jR45g6tSp+PXX\nXxEXF4f8/Hx4eHjgwYMHL2uziIiIKiyZTIa+ffsC4FAZAMiEEELKAtq2bQtXV1eEhoaq2po2bYpB\ngwYhODi4SP8PPvgASUlJOHjwoKpt1qxZOHnyZLF7lDIyMmBtbY0jR47gzTfffG5N2dnZsLS0RFZW\nFiwsLMqwVURERBXbjz/+iCFDhqBBgwa4dOkSZDKZ1CW9sLL+fku6ZygvLw8JCQnw8PBQa/fw8MDx\n48c1LuPu7o6EhAScPHkSAJCcnIyYmBjV+Kcm/9fevUdFWe3/A38PIyA3QcDGCxcNzVBElLAEVPQo\nCEVqmpolWpJyCo+iJ5WjZEIuVlbeKrRS0aMWmgfK40ERBQS8HA3FdInHS+h4Q9MMBBS57N8f/Ziv\n4wyIMMMzMO/XWs9azZ5nnuez2Ta81957huLiYgCAvb291ucrKipQUlKidhAREbVmw4cPh6mpKS5e\nvIhz585JXY6kJA1Dt2/fRnV1NRQKhVq7QqFAUVGR1tdMnDgRcXFx8Pf3h6mpKdzc3DB06FAsWLBA\n6/lCCMyZMwf+/v7w8PDQek58fDxsbW1Vh7Ozc9M6RkREZOBsbGwwZMgQAFwqk3zPEACNqTkhRJ3T\ndVlZWVi6dCkSEhJw/PhxJCcnY9euXYiLi9N6fmRkJH755Rd8//33dd4/OjoaxcXFquPKlSuN7wwR\nEVELwY/Y/6mNlDd3dHSEXC7XmAW6deuWxmxRrZiYGEyePBnh4eEAgD59+qCsrAzTp0/HwoULYWLy\nf/lu5syZ2LlzJ7Kzs+Hk5FRnHebm5jA3N9dBj4iIiFqOl19+GVFRUcjOzkZJSYnR7pOVdGbIzMwM\n3t7eSE9PV2tPT0+Hr6+v1teUl5erBR4AkMvlEEKgdi+4EAKRkZFITk5GRkYGunXrpp8OEBERtWA9\nevRAjx49UFVVpfG72JhIvkw2Z84crFu3Dhs2bEBBQQGioqKgVCoREREBAAgLC1P72HxoaCjWrFmD\npKQkFBYWIj09HTExMXj11Vchl8sBAO+//z62bNmC7777DjY2NigqKkJRURHu378vSR+JiIgMFZfK\nJF4mA4AJEybgzp07iI2NxY0bN+Dh4YHU1FS4uroCAJRKpdpM0KJFiyCTybBo0SJcu3YNHTp0QGho\nKJYuXao6p/Zj+gEBAWr3SkxMxNSpU/XeJyIiopbi5ZdfxsqVK5GamoqamhqN1RdjIPn3DBkifs8Q\nEREZi4cPH8LBwQGlpaU4duwYXnjhBalLarQW+T1DREREJC0zMzOMGDECgPEulTEMERERGTlj3zfE\nMERERGTkav9O2bFjx3Dz5k2Jq2l+DENERERGrlOnTujfvz8AYPfu3RJX0/wYhoiIiMiol8oYhoiI\niEgVhvbu3YvKykqJq2leDENEREQEHx8fdOjQASUlJcjNzZW6nGbFMEREREQwMTFBcHAwAONbKmMY\nIiIiIgDGu2+IYYiIiIgAAIGBgZDL5Th79ix+/fVXqctpNgxDREREBACws7ODv78/AOOaHWIYIiIi\nIhVjXCpjGCIiIiKV2jCUlZWFsrIyiatpHgxDREREpOLu7o6uXbuioqIC+/fvl7qcZsEwRERERCoy\nmczolsoYhoiIiEhNbRhKTU2FEELiavSPYYiIiIjUBAQEwMLCAlevXsUvv/widTl6xzBEREREaiws\nLPCXv/wFgHEslTEMERERkQZj2jfEMEREREQaQkJCAABHjhzBnTt3JK5GvxiGiIiISIOLiwv69OmD\nmpoa7NmzR+py9IphiIiIiLQylqUyhiEiIiLSqjYM7dmzB1VVVRJXoz8MQ0RERKTVSy+9hPbt2+Pu\n3bs4cuSI1OXoDcMQERERadWmTRuMHDkSQOteKmMYIiIiojoZw74hhiEiIiKq08iRI2FiYoJTp05B\nqVRKXY5eMAwRERFRnRwcHPDSSy8B+PNvlbVGDENERERUr9a+VMYwRERERPWqDUP79+/H/fv3Ja5G\n9xiGiIiIqF6enp5wcnLC/fv3kZWVJXU5OscwRERERPWSyWSteqmMYYiIiIie6NEwJISQuBrdYhgi\nIiKiJxo2bBjMzc1x6dIlFBQUSF2OTjEMERER0RNZWVlh6NChAFrfUhnDEBERETVIa903xDBERERE\nDVIbhnJzc/HHH39IXI3uMAwRERFRg3Tr1g3u7u6orq7G3r17pS5HZxiGiIiIqMFa41IZwxARERE1\nWG0Y2r17N2pqaiSuRjcYhoiIiKjB/Pz8YGtri99++w3Hjh2TuhydMIgwlJCQgG7duqFt27bw9vZG\nTk5OveevXLkSPXv2hIWFBZydnREVFYUHDx406ZpERET0ZKampggMDATQepbKJA9D27Ztw+zZs7Fw\n4UKcOHECgwYNQnBwMJRKpdbzt27digULFmDx4sUoKCjA+vXrsW3bNkRHRzf6mkRERNRwrW3fkExI\n/J3aL774Ivr37481a9ao2tzd3TF69GjEx8drnB8ZGYmCggLs379f1TZ37lwcPXpUNfvztNd8XElJ\nCWxtbVFcXIx27do1pXtEREStzq1bt9CxY0cIIXD9+nV06tRJ6pIANP73t6QzQw8fPkReXp5quq1W\nYGAgDh06pPU1/v7+yMvLw9GjRwEAv/76K1JTU1UptTHXrKioQElJidpBRERE2j3zzDPw8fEBAKSm\npkpcTdNJGoZu376N6upqKBQKtXaFQoGioiKtr5k4cSLi4uLg7+8PU1NTuLm5YejQoViwYEGjrxkf\nHw9bW1vV4ezsrIPeERERtV6taalM8j1DACCTydQeCyE02mplZWVh6dKlSEhIwPHjx5GcnIxdu3Yh\nLi6u0deMjo5GcXGx6rhy5UoTekNERNT61Yah9PR0VFRUSFxN07SR8uaOjo6Qy+UaMza3bt3SmNmp\nFRMTg8mTJyM8PBwA0KdPH5SVlWH69OlYuHBho65pbm4Oc3NzHfSIiIjIOPTr1w8dO3ZEUVERcnJy\nMHz4cKlLajRJZ4bMzMzg7e2N9PR0tfb09HT4+vpqfU15eTlMTNTLlsvlEEJACNGoaxIREdHTMTEx\nQUhICICWv1Qm+TLZnDlzsG7dOmzYsAEFBQWIioqCUqlEREQEACAsLEztY/OhoaFYs2YNkpKSUFhY\niPT0dMTExODVV1+FXC5v0DWJiIio6VrLviFJl8kAYMKECbhz5w5iY2Nx48YNeHh4IDU1Fa6urgAA\npVKpNhO0aNEiyGQyLFq0CNeuXUOHDh0QGhqKpUuXNviaRERE1HQjRoyAqakpzp8/j/Pnz6NHjx5S\nl9Qokn/PkCHi9wwRERE1zPDhw7F//36sWLECs2fPlrSWFvk9Q0RERNSytYalMoYhIiIiarTaMHTg\nwAHcu3dP4moah2GIiIiIGu25555D9+7dUVlZiX379kldTqMwDBEREVGTtPSlMoYhIiIiapLaMJSa\nmoqW+LkshiEiIiJqksGDB8PKygo3btzAiRMnpC7nqTEMERERUZOYm5tjxIgRAFrmUhnDEBERETVZ\nS943xDBERERETVb7d8qOHj2K3377TeJqng7DEBERETVZ586d0a9fPwghsHv3bqnLeSoMQ0RERKQT\nLXWpjGGIiIiIdKI2DKWlpaGyslLiahqOYYiIiIh0wsfHB46OjiguLsahQ4ekLqfBGIaIiIhIJ+Ry\nOYKDgwG0rKUyhiEiIiLSmZa4b4hhiIiIiHQmKCgIcrkcZ86cwaVLl6Qup0EYhoiIiEhn7Ozs4Ofn\nB6DlzA4xDBEREZFOtbSlMoYhIiIi0qnaMJSZmYny8nKJq3kyhiEiIiLSqV69esHV1RUPHjxARkaG\n1OU8EcMQERER6ZRMJmtRS2UMQ0RERKRzj4YhIYTE1dSPYYiIiIh0bujQobCwsMCVK1dw+vRpqcup\nF8MQERER6ZyFhQWGDRsGwPCXyhiGiIiISC9ayr4hhiEiIiLSi9owdOjQIfz+++8SV1M3hiEiIiLS\nCxcXF3h4eKCmpgZpaWlSl1MnhiEiIiLSm5awVMYwRERERHpTG4b27NmD6upqiavRjmGIiIiI9Gbg\nwIFo37497ty5g//+979Sl6MVwxARERHpTZs2bRAUFATAcJfKGIaIiIhIrwx93xDDEBEREenVyJEj\nIZPJcPLkSVy9elXqcjQwDBEREZFeOTo64qWXXgIApKamSlyNJoYhIiIi0jtDXipjGCIiIiK9qw1D\n+/btw+LFi2FiYoK4uDiJq/qTTAghpC7C0JSUlMDW1hbFxcVo166d1OUQERG1eEIIODs749q1a2rt\nsbGxiImJ0ck9Gvv7mzNDREREpHcymQwdO3bUaP/www8lnyFiGCIiIiK9i4uLQ15entbnpA5EDENE\nRESkV3Fxcfjwww/rPUfKQMQwRERERHrTkCBUS6pAZBBhKCEhAd26dUPbtm3h7e2NnJycOs8NCAiA\nTCbTOGp3qQNAaWkpIiMj4eTkBAsLC7i7u2PNmjXN0RUiIiJ6xOLFi/V6vi5IHoa2bduG2bNnY+HC\nhThx4gQGDRqE4OBgKJVKrecnJyfjxo0bquP06dOQy+V4/fXXVedERUVhz5492LJlCwoKChAVFYWZ\nM2fip59+aq5uEREREYAlS5bo9XxdkDwMLV++HNOmTUN4eDjc3d2xcuVKODs71zmTY29vj44dO6qO\n9PR0WFpaqoWhw4cPY8qUKQgICEDXrl0xffp09O3bFz///LPWa1ZUVKCkpETtICIioqaLiYlBbGxs\ng87V5cfsn4akYejhw4fIy8tDYGCgWntgYCAOHTrUoGusX78eEydOhJWVlarN398fO3fuxLVr1yCE\nQGZmJs6dO6f6q7mPi4+Ph62trepwdnZufKeIiIhITUMCkVRBCJA4DN2+fRvV1dVQKBRq7QqFAkVF\nRU98/dGjR3H69GmEh4erta9evRq9evWCk5MTzMzMMHLkSCQkJMDf31/rdaKjo1FcXKw6rly50vhO\nERERkYb6ApGUQQgA2kh250fIZDK1x0IIjTZt1q9fDw8PDwwYMECtffXq1Thy5Ah27twJV1dXZGdn\n47333kOnTp0wfPhwjeuYm5vD3Ny8aZ0gIiKietUGnkc/XSZ1EAIkDkOOjo6Qy+Uas0C3bt3SmC16\nXHl5OZKSkjRS5v379/GPf/wDKSkpqk+YeXp6Ij8/H5999pnWMERERETNozb4LF68GEuWLJE8CAES\nL5OZmZnB29sb6enpau3p6enw9fWt97Xbt29HRUUF3nrrLbX2yspKVFZWwsREvWtyuRw1NTW6KZyI\niIgaLSYmBjU1NQYRhAADWCabM2cOJk+ejBdeeAEDBw7EN998A6VSiYiICABAWFgYunTpgvj4eLXX\nrV+/HqNHj4aDg4Nae7t27TBkyBB88MEHsLCwgKurKw4cOIB//vOfWL58ebP1i4iIiFoGycPQhAkT\ncOfOHcTGxuLGjRvw8PBAamoqXF1dAQBKpVJjlufcuXPIzc3F3r17tV4zKSkJ0dHRePPNN/H777/D\n1dUVS5cuVQUsIiIioloyIYSQughDU1JSAltbWxQXF6Ndu3ZSl0NEREQN0Njf35J/6SIRERGRlBiG\niIiIyKgxDBEREZFRYxgiIiIio8YwREREREZN8o/WG6LaD9jxr9cTERG1HLW/t5/2g/IMQ1rcu3cP\nAPjX64mIiFqge/fuwdbWtsHn83uGtKipqcH169dhY2PToD8Ya4xKSkrg7OyMK1eu8LuYDADHw7Bw\nPAwLx8Pw6GtMhBC4d+8eOnfurPGFzfXhzJAWJiYmcHJykrqMFqFdu3Z8czEgHA/DwvEwLBwPw6OP\nMXmaGaFa3EBNRERERo1hiIiIiIya/KOPPvpI6iKoZZLL5QgICECbNlxtNQQcD8PC8TAsHA/DY0hj\nwg3UREREZNS4TEZERERGjWGIiIiIjBrDEBERERk1hiEiIiIyagxDREREZNQYhkiv7t27Bx8fH3h5\neaFPnz749ttvpS7J6I0ZMwbt27fHuHHjpC7FaHEMDAffowxPmzZt4OXlBS8vL4SHhzfLPfnRetKr\n6upqVFRUwNLSEuXl5fDw8MCxY8fg4OAgdWlGKzMzE6Wlpdi0aRN27NghdTlGiWNgOPgeZXgcHR1x\n+/btZr0nZ4ZIr+RyOSwtLQEADx48QHV1NZi/pTV06FDY2NhIXYZR4xgYDr5HEcAwZPSys7MRGhqK\nzp07QyaT4ccff9Q4JyEhAd26dUPbtm3h7e2NnJycp7rHH3/8gb59+8LJyQnz5s2Do6OjrspvdZpj\nPKhpOEaGRRfjwfco3dHFeJSUlMDb2xv+/v44cOBAs9TNMGTkysrK0LdvX3z55Zdan9+2bRtmz56N\nhQsX4sSJExg0aBCCg4OhVCpV53h7e8PDw0PjuH79OgDAzs4OJ0+eRGFhIb777jvcvHmzWfrWEjXH\neFDT6GKMSHd0MR58j9IdXYzHpUuXkJeXh7Vr1yIsLAwlJSX6L1wQ/X8AREpKilrbgAEDREREhFrb\n888/LxYsWNCoe0RERIjt27c3ukZjos/xyMzMFGPHjm1yjcauKWPEMdA9Xfw/w/co3dHFeIwcOVIc\nO3ZMbzXW4swQ1enhw4fIy8tDYGCgWntgYCAOHTrUoGvcvHlTlepLSkqQnZ2Nnj176rxWY6CL8SD9\n4hgZloaMB9+jmk9DxuPu3buoqKgAAFy9ehVnzpzBs88+q/fapP9TsWSwbt++jerqaigUCrV2hUKB\noqKiBl3j6tWrmDZtGoQQEEIgMjISnp6e+ii31dPFeABAUFAQjh8/jrKyMjg5OSElJQU+Pj66Ltco\nNXSMOAbNoyHjwfeo5tOQ8SgoKMCMGTNgYmICmUyGVatWwd7eXu+1MQzRE8lkMrXHQgiNtrp4e3sj\nPz9fH2UZraaMBwCkpaXpuiR6zJPGiGPQvOobD75HNb/6xsPX1xenTp1q9pq4TEZ1cnR0hFwu15h1\nuHXrlkayJ/3jeBg+jpFh4XgYFkMeD4YhqpOZmRm8vb2Rnp6u1p6eng5fX1+JqjJeHA/DxzEyLBwP\nw2LI48FlMiNXWlqKCxcuqB4XFhYiPz8f9vb2cHFxwZw5czB58mS88MILGDhwIL755hsolUpERERI\nWHXrxfEwfBwjw8LxMCwtdjz0/nk1MmiZmZkCgMYxZcoU1TlfffWVcHV1FWZmZqJ///7iwIED0hXc\nynE8DB/HyLBwPAxLSx0P/m0yIiIiMmrcM0RERERGjWGIiIiIjBrDEBERERk1hiEiIiIyagxDRERE\nZNQYhoiIiMioMQwRERGRUWMYIiIiIqPGMERERERGjWGIqBXo2rUrVq5c+dSvk8lk+PHHH/VQkWHT\nVb8DAgIwe/ZsHVRUt40bN8LOzk6v99Alf39//P3vf5e6DKKnwjBEpENTp07F6NGjpS7D4AQEBEAm\nk0Emk8HMzAxubm6Ijo5GRUWF1KU1SXJyMuLi4nR2PW2hdsKECTh37pzO7qFNnz59EB4ervW577//\nHqamprh586ZeayCSEsMQETWLd999Fzdu3MCFCxewbNkyfPXVV/joo4+kLqtRKisrAQD29vawsbHR\n670sLCzwzDPP6PUe06ZNw/bt21FeXq7x3IYNG/DKK69AoVDotQYiKTEMETWj5cuXo0+fPrCysoKz\nszPee+89lJaWqp6vXRLZtWsXevbsCUtLS4wbNw5lZWXYtGkTunbtivbt22PmzJmorq5Wu/a9e/cw\nadIkWFtbo3Pnzvjiiy/Unj9//jwGDx6Mtm3bolevXkhPT9eob/78+XjuuedgaWmJZ599FjExMapf\n/E1laWmJjh07wsXFBWPHjsWIESOwd+9etXOuXbuGCRMmoH379nBwcMCoUaNw6dIl1fNVVVX429/+\nBjs7Ozg4OGD+/PmYMmWK2mycttkVLy+veoPXk/r90UcfwcvLCxs2bMCzzz4Lc3NzCCHUlsmysrJU\ns1+PHlOnTgUAXLx4EaNGjYJCoYC1tTV8fHywb98+1T0CAgJw+fJlREVFqV4LaF8mW7NmDdzc3GBm\nZoaePXti8+bNas/LZDKsW7cOY8aMgaWlJXr06IGdO3fW2f/JkyejoqICP/zwg1q7UqlERkYGpk2b\nBgD47bffMHHiRHTp0gWWlpbw9PTE9u3b67xuVVUVZDIZdu3apdZubW2NLVu2qB5fuXIF48ePV43r\n6NGjoVQq67wuka4xDBE1IxMTE6xevRqnT5/Gpk2bkJGRgXnz5qmdU15ejtWrVyMpKQl79uxBVlYW\nXnvtNaSmpiI1NRWbN2/GN998gx07dqi97tNPP4WnpyeOHz+O6OhoREVFqQJPTU0NXnvtNcjlchw5\ncgRr167F/PnzNeqzsbHBxo0bcebMGaxatQrffvstVqxYofOfw8mTJ3Hw4EGYmpqq9Xvo0KGwtrZG\ndnY2cnNzYW1tjZEjR+Lhw4cAgE8++QRbt25FYmIiDh48iJKSEp3s/WlIvy9cuIDt27fjX//6F/Lz\n8zWu4evrixs3bqiOjIwMtG3bFoMHDwYAlJaWIiQkBPv27cOJEycQFBSE0NBQ1S/95ORkODk5ITY2\nVnUNbVJSUjBr1izMnTsXp0+fxowZM/D2228jMzNT7bwlS5Zg/Pjx+OWXXxASEoI333wTv//+u9Zr\n1gbPxMREtfbExEQoFAoEBwcDAO7fv48BAwbgP//5D06dOoVp06Zh0qRJyMvLa8BPWbvS0lIEBATA\nzs4OOTk5yMnJQdu2bREcHIyqqqpGX5foqQgi0pkpU6aIUaNGNfj87du3CwcHB9XjxMREAUBcuHBB\n1TZjxgxhaWkp7t27p2oLCgoSM2bMUD12dXUVI0eOVLv2hAkTRHBwsBBCiLS0NCGXy8WVK1dUz+/e\nvVsAECkpKXXWt2zZMuHt7d3g/tRlyJAhwtTUVFhZWQkzMzMBQJiYmIgdO3aozlm/fr3o2bOnqKmp\nUbVVVFQICwsLkZaWJoQQQqFQiE8//VT1fFVVlXBxcVH7mbu6uooVK1ao3b9v375i8eLFqsdP2+/F\nixcLU1NTcevWLY1+zZo1S+P1t2/fFm5ubuK9996r8x5CCNGrVy/xxRdf1Ft7YmKisLW1VT329fUV\n7777rto5r7/+uggJCVHr36JFi1SPS0tLhUwmE7t3766zlt27dwuZTCYuXrwohBCipqZGdO3aVURH\nR9fbh8DAQDF//nzVYz8/PzF37lwhhBCVlZUCgPj3v/+t9horKyuxefNmIYQQX3/9tejdu7fa8w8e\nPBDm5uZi//799d6bSFc4M0TUjDIzMzFixAh06dIFNjY2CAsLw507d1BWVqY6x9LSEm5ubqrHCoUC\nXbt2hbW1tVrbrVu31K49cOBAjccFBQUAgIKCAri4uMDJyanO8wFgx44d8Pf3R8eOHWFtbY2YmJh6\nlyuCg4NhbW0Na2tr9O7du96+v/nmm8jPz8fhw4cxfvx4vPPOOxg7dqzq+by8PFy4cAE2Njaqa9rb\n2+PBgwe4ePEiiouLcfPmTQwYMED1GrlcDm9v73rv2xAN6berqys6dOjwxGtVVlZi7NixcHFxwapV\nq1TtZWVlmDdvHnr16gU7OztYW1vj7NmzT70cVFBQAD8/P7U2Pz8/1VjX8vT0VP23lZUVbGxsNP7N\nPCowMBBOTk6q2aGMjAxcunQJb7/9tuqcqqoqfPzxx/D09IS9vT2sra2RkZHRpCWtvLw8nD17VjXm\n1tbWcHBwwMOHD3Hx4sVGX5foabSRugAiY3H58mWEhIQgIiICcXFxsLe3R25uLqZNm6a2P+XRpSPg\nz/0f2tpqamqeeM/afSdCiDqfq3XkyBFMnDgRS5YsQVBQEGxtbZGUlITPP/+8zuuvW7cO9+/f11r3\n42xtbdG9e3cAwJYtW9C7d2+sX79etR+lpqYG3t7e2Lp1q8ZrHw0hj9f9eN9MTEw02urb99TQfltZ\nWdXbv1p//etfoVQqcezYMbRp839vsR988AHS0tLw2WefoXv37rCwsMC4ceNUS4BPQ9vP4PG2p/03\nY2JigqlTp2LjpNjFzwAABRVJREFUxo1YsmQJEhMTMXjwYPTo0UN1zrJly7B69WqsXLkSvXv3hpWV\nFSIjI+vsg7Z/f0IIteWvmpoavPjii9i0aZPG6xsSPol0gWGIqJn8/PPPqKqqwueffw4Tkz8nZevb\nfPq0jhw5ovH4+eefBwD06tULSqUS169fR+fOnQEAhw8fVjv/4MGDcHV1xcKFC1Vtly9frveeXbp0\naVStpqam+Mc//oHo6Gi88cYbsLS0RP/+/bFt2zY888wzaNeundbXKRQKHD16FIMGDQIAVFdX48SJ\nE/Dy8lKd06FDB7X9NiUlJSgsLKyzlsb0uy7Lly/Htm3bcPjwYTg4OKg9l5OTg6lTp2LMmDEA/twr\n8+jmcAAwMzPT2Bj/OHd3d+Tm5iIsLEzVdujQIbi7uzeq5ke9/fbb+Pjjj5GcnIzk5GSsXbtWow9j\nxozBpEmTAPz58z9//jz69eun9XpyuRz29vZq43H27Fm1r1To378/fvzxRygUCr1/Mo+oLlwmI9Kx\n4uJi5Ofnqx1KpRJubm6oqqrCF198gV9//RWbN2/W+GXTFAcPHsSyZctw7tw5fPXVV/jhhx8wa9Ys\nAMDw4cPRs2dPhIWF4eTJk8jJyVH75Q8A3bt3h1KpRFJSEi5evIjVq1cjJSVFZ/U9btKkSZDJZEhI\nSADw5zKao6MjRo0ahZycHBQWFuLAgQOYNWsWrl69CgCYOXMm4uPj8dNPP+F///sfZs2ahbt376rN\nigwbNgybN29GTk4OTp8+jSlTpkAul9dZh676vW/fPsybNw+fffYZHB0dUVRUhKKiIhQXF6vuk5yc\njPz8fJw8eRKTJk3SmKnp2rUrsrOzce3aNdy+fVvrfT744ANs3LgRa9euxfnz57F8+XIkJyfr5IsO\nu3XrhmHDhmH69OkwNTXFuHHj1J7v3r070tLScPjwYZw5cwbvvvtunXXWGjZsGFavXo38/HwcO3YM\n77//vtqM2eTJk2Fra4vRo0cjNzcXhYWFyMrKwsyZM+vcRE6kawxDRDqWlZWFfv36qR0ffvghvLy8\nsHz5cnzyySfw8PDA1q1bER8fr7P7zp07F3l5eejXrx/i4uLw+eefIygoCMCfSyApKSmoqKjAgAED\nEB4ejqVLl6q9ftSoUYiKikJkZCS8vLxw6NAhxMTE6Ky+x5mZmSEyMhLLli1DaWkpLC0tkZ2dDRcX\nF7z22mtwd3fHO++8g/v376tmiubPn4833ngDYWFhGDhwIKytrREUFIS2bduqrhsdHY3BgwfjlVde\nQUhICEaPHq22B+txuup3bm4uqqurERERgU6dOqmO2kC6YsUKtG/fHr6+vggNDUVQUBD69++vdo3Y\n2FhcunQJbm5udS4RjR49GqtWrcKnn36K3r174+uvv0ZiYiICAgKeumZtpk2bhrt372LixImwtLRU\ne27x4sXw9PTEiBEjMGzYMLi4uCA0NLTe661YsQKdO3eGn58f3nrrLSxYsADm5uaq52s/PdilSxeM\nGTMG7u7uCA8PR2Vlpdo+OSJ9kgltmwmIiFqAmpoauLu7Y/z48Tr9JmgiMi7cM0RELcbly5exd+9e\nDBkyBBUVFfjyyy9RWFio2sNCRNQYXCYjohbDxMQEGzduhI+PD/z8/HDq1Cns27dPJ5uHich4cZmM\niIiIjBpnhoiIiMioMQwRERGRUWMYIiIiIqPGMERERERGjWGIiIiIjBrDEBERERk1hiEiIiIyagxD\nREREZNT+H0tHIi4eDGD9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f082430be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Utility.plot_(lambdas, result_mult_lamdas['accuracy_all'], 'Lambda - Regularization Value', 'Accuracy', 'Overall Test Accuracy vs. Regularization Value', True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results above are for lambdas = [0.0001, 1, 10, 100, 1000, 10000, 100000], I discovered that at a very high iteration, a relatively small lambda value works best. I simulated this for 5000 iterations and a high lambda value was better. However, high iteration implies reaching the bottom of the gradient descent and it also gives optimal performance.\n",
    "\n",
    "Thus, I chose to use an iteration of 10000, with the lambda values above. It can be noticed that the result are almost the same for lambdas = 0.00001, 1, 10, this is mainly attributed to the effect of the high iteration, the same optimal values of weights were reached. The accuracy starts degrading as lambda goes beyond 10, this could be because the optimal values of weights could not be achieved. Thus, lambda values of 0.0001, 1, or 10 are preferable with an iteration of 10000 and a learning of 0.05 divided by the number of samples in the train set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Looking at the case were lambda is 0.0001 - best lamda value:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The overall testing accuracy is 91.07%. The classier almost perfectly predict digits 0 and 1 with an accuracy of 98.06% and 96.48% respectively. However, the classifier doesn't match same performance in predicting digits 5, 2, 9 & 8. Most digit 5 are either classified as 3 or 8, this might be because digit 5 has a shape that looks like 8 and 3.\n",
    "\n",
    "A key observation is that, in all the results of the various lambdas, digit 5 has the lowest accuracy. Thus, it seems that logistic regression doesn't effectively classify digit 5.\n",
    "\n",
    "The gradient descent curves show that the optimal values of weights were almost achieved.\n",
    "\n",
    "The sharpness of the images reduces as lambda goes beyond its optimal value. The images for lambda 0.0001 are brighter than those of lambda 100000. The classifier practically model the class image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Future work:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the effectiveness of PCA on regularized logistic regression."
   ]
  }
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