diff --git a/learning.ipynb b/learning.ipynb
index 13d184e34..0b6bfc094 100644
--- a/learning.ipynb
+++ b/learning.ipynb
@@ -2,10 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "# Learning\n",
     "\n",
@@ -16,9 +13,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -27,10 +22,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Contents\n",
     "\n",
@@ -49,10 +41,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Machine Learning Overview\n",
     "\n",
@@ -83,10 +72,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Datasets\n",
     "\n",
@@ -99,10 +85,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "To make using the datasets easier, we have written a class, `DataSet`, in `learning.py`. The tutorials found here make use of this class.\n",
     "\n",
@@ -111,10 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Intro\n",
     "\n",
@@ -127,11 +107,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 2,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -140,10 +118,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Class Attributes\n",
     "\n",
@@ -170,10 +145,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Class Helper Functions\n",
     "\n",
@@ -188,10 +160,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Importing a Dataset\n",
     "\n",
@@ -202,11 +171,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -215,22 +182,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "To check that we imported the correct dataset, we can do the following:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -248,32 +208,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Which correctly prints the first line in the csv file and the list of attribute indexes."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "When importing a dataset, we can specify to exclude an attribute (for example, at index 1) by setting the parameter `exclude` to the attribute index or name."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -290,10 +240,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Attributes\n",
     "\n",
@@ -304,12 +251,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -325,22 +268,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Then we will print `attrs`, `attrnames`, `target`, `input`. Notice how `attrs` holds values in [0,4], but since the fourth attribute is the target, `inputs` holds values in [0,3]."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -362,22 +298,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now we will print all the possible values for the first feature/attribute."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -393,22 +322,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Finally we will print the dataset's name and source. Keep in mind that we have not set a source for the dataset, so in this case it is empty."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -426,28 +348,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "A useful combination of the above is `dataset.values[dataset.target]` which returns the possible values of the target. For classification problems, this will return all the possible classes. Let's try it:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['setosa', 'virginica', 'versicolor']\n"
+      "['versicolor', 'virginica', 'setosa']\n"
      ]
     }
    ],
@@ -457,20 +372,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Helper Functions"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "We will now take a look at the auxiliary functions found in the class.\n",
     "\n",
@@ -483,12 +392,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -506,54 +411,42 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Currently the `iris` dataset has three classes, setosa, virginica and versicolor. We want though to convert it to a binary class dataset (a dataset with two classes). The class we want to remove is \"virginica\". To accomplish that we will utilize the helper function `remove_examples`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['setosa', 'versicolor']\n"
+      "['versicolor', 'setosa']\n"
      ]
     }
    ],
    "source": [
-    "iris.remove_examples(\"virginica\")\n",
-    "print(iris.values[iris.target])"
+    "iris2 = DataSet(name=\"iris\")\n",
+    "\n",
+    "iris2.remove_examples(\"virginica\")\n",
+    "print(iris2.values[iris2.target])"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
-    "Finally we take a look at `classes_to_numbers`. For a lot of the classifiers in the module (like the Neural Network), classes should have numerical values. With this function we map string class names to numbers."
+    "We also have `classes_to_numbers`. For a lot of the classifiers in the module (like the Neural Network), classes should have numerical values. With this function we map string class names to numbers."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -565,27 +458,54 @@
     }
    ],
    "source": [
-    "print(\"Class of first example:\",iris.examples[0][iris.target])\n",
-    "iris.classes_to_numbers()\n",
-    "print(\"Class of first example:\",iris.examples[0][iris.target])"
+    "print(\"Class of first example:\",iris2.examples[0][iris2.target])\n",
+    "iris2.classes_to_numbers()\n",
+    "print(\"Class of first example:\",iris2.examples[0][iris2.target])"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "As you can see \"setosa\" was mapped to 0."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
+   "source": [
+    "Finally, we take a look at `find_means_and_deviations`. It finds the means and standard deviations of the features for each class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Setosa feature means: [5.006, 3.418, 1.464, 0.244]\n",
+      "Versicolor mean for first feature: 5.936\n",
+      "Setosa feature deviations: [0.3524896872134513, 0.38102439795469095, 0.17351115943644546, 0.10720950308167838]\n",
+      "Virginica deviation for second feature: 0.32249663817263746\n"
+     ]
+    }
+   ],
+   "source": [
+    "means, deviations = iris.find_means_and_deviations()\n",
+    "\n",
+    "print(\"Setosa feature means:\", means[\"setosa\"])\n",
+    "print(\"Versicolor mean for first feature:\", means[\"versicolor\"][0])\n",
+    "\n",
+    "print(\"Setosa feature deviations:\", deviations[\"setosa\"])\n",
+    "print(\"Virginica deviation for second feature:\",deviations[\"virginica\"][1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Distance Functions\n",
     "\n",
@@ -598,12 +518,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -624,10 +540,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Euclidean Distance (`euclidean_distance`)\n",
     "\n",
@@ -636,12 +549,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -662,10 +571,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Hamming Distance (`hamming_distance`)\n",
     "\n",
@@ -674,12 +580,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -700,10 +602,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Mean Boolean Error (`mean_boolean_error`)\n",
     "\n",
@@ -712,12 +611,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -738,10 +633,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Mean Error (`mean_error`)\n",
     "\n",
@@ -750,12 +642,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -776,10 +664,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Mean Square Error (`ms_error`)\n",
     "\n",
@@ -788,12 +673,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -814,10 +695,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Root of Mean Square Error (`rms_error`)\n",
     "\n",
@@ -826,12 +704,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -852,10 +726,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Plurality Learner Classifier\n",
     "\n",
@@ -872,10 +743,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Implementation\n",
     "\n",
@@ -884,11 +752,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 22,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -905,10 +771,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "It takes as input a dataset and returns a function. We can later call this function with the item we want to classify as the argument and it returns the class it should be classified in.\n",
     "\n",
@@ -917,10 +780,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Example\n",
     "\n",
@@ -929,12 +789,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -953,20 +809,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The output for the above code is \"mammal\", since that is the most popular and common class in the dataset."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## k-Nearest Neighbours (kNN) Classifier\n",
     "\n",
@@ -978,10 +828,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Let's see how kNN works with a simple plot shown in the above picture.\n",
     "\n",
@@ -996,10 +843,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Implementation\n",
     "\n",
@@ -1008,11 +852,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 24,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1028,10 +870,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "It takes as input a dataset and k (default value is 1) and it returns a function, which we can later use to classify a new item.\n",
     "\n",
@@ -1040,10 +879,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Example\n",
     "\n",
@@ -1052,12 +888,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1076,47 +908,371 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The output of the above code is \"setosa\", which means the flower with the above measurements is of the \"setosa\" species."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
-    "## Perceptron Classifier\n",
+    "## Naive Bayes Learner\n",
     "\n",
     "### Overview\n",
     "\n",
-    "The Perceptron is a linear classifier. It works the same way as a neural network with no hidden layers (just input and output). First it trains its weights given a dataset and then it can classify a new item by running it through the network.\n",
+    "#### Theory of Probabilities\n",
     "\n",
-    "Its input layer consists of the the item features, while the output layer consists of nodes (also called neurons). Each node in the output layer has *n* synapses (for every item feature), each with its own weight. Then, the nodes find the dot product of the item features and the synapse weights. These values then pass through an activation function (usually a sigmoid). Finally, we pick the largest of the values and we return its index.\n",
+    "The Naive Bayes algorithm is a probabilistic classifier, making use of [Bayes' Theorem](https://en.wikipedia.org/wiki/Bayes%27_theorem). The theorem states that the conditional probability of **A** given **B** equals the conditional probability of **B** given **A** multiplied by the probability of **A**, divided by the probability of **B**.\n",
     "\n",
-    "Note that in classification problems each node represents a class. The final classification is the class/node with the max output value.\n",
+    "$$P(A|B) = \\dfrac{P(B|A)*P(A)}{P(B)}$$\n",
     "\n",
-    "Below you can see a single node/neuron in the outer layer. With *f* we denote the item features, with *w* the synapse weights, then inside the node we have the dot product and the activation function, *g*."
+    "From the theory of Probabilities we have the Multiplication Rule, if the events *X* are independent the following is true:\n",
+    "\n",
+    "$$P(X_{1} \\cap X_{2} \\cap ... \\cap X_{n}) = P(X_{1})*P(X_{2})*...*P(X_{n})$$\n",
+    "\n",
+    "For conditional probabilities this becomes:\n",
+    "\n",
+    "$$P(X_{1}, X_{2}, ..., X_{n}|Y) = P(X_{1}|Y)*P(X_{2}|Y)*...*P(X_{n}|Y)$$"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![perceptron](images/perceptron.png)"
+    "#### Classifying an Item\n",
+    "\n",
+    "How can we use the above to classify an item though?\n",
+    "\n",
+    "We have a dataset with a set of classes (**C**) and we want to classify an item with a set of features (**F**). Essentially what we want to do is predict the class of an item given the features.\n",
+    "\n",
+    "For a specific class, **Class**, we will find the conditional probability given the item features:\n",
+    "\n",
+    "$$P(Class|F) = \\dfrac{P(F|Class)*P(Class)}{P(F)}$$\n",
+    "\n",
+    "We will do this for every class and we will pick the maximum. This will be the class the item is classified in.\n",
+    "\n",
+    "The features though are a vector with many elements. We need to break the probabilities up using the multiplication rule. Thus the above equation becomes:\n",
+    "\n",
+    "$$P(Class|F) = \\dfrac{P(Class)*P(F_{1}|Class)*P(F_{2}|Class)*...*P(F_{n}|Class)}{P(F_{1})*P(F_{2})*...*P(F_{n})}$$\n",
+    "\n",
+    "The calculation of the conditional probability then depends on the calculation of the following:\n",
+    "\n",
+    "*a)* The probability of **Class** in the dataset.\n",
+    "\n",
+    "*b)* The conditional probability of each feature occuring in an item classified in **Class**.\n",
+    "\n",
+    "*c)* The probabilities of each individual feature.\n",
+    "\n",
+    "For *a)*, we will count how many times **Class** occurs in the dataset (aka how many items are classified in a particular class).\n",
+    "\n",
+    "For *b)*, if the feature values are discrete ('Blue', '3', 'Tall', etc.), we will count how many times a feature value occurs in items of each class. If the feature values are not discrete, we will go a different route. We will use a distribution function to calculate the probability of values for a given class and feature. If we know the distribution function of the dataset, then great, we will use it to compute the probabilities. If we don't know the function, we can assume the dataset follows the normal (Gaussian) distribution without much loss of accuracy. In fact, it can be proven that any distribution tends to the Gaussian the larger the population gets (see [Central Limit Theorem](https://en.wikipedia.org/wiki/Central_limit_theorem)).\n",
+    "\n",
+    "*NOTE:* If the values are continuous but use the discrete approach, there might be issues if we are not lucky. For one, if we have two values, '5.0 and 5.1', with the discrete approach they will be two completely different values, despite being so close. Second, if we are trying to classify an item with a feature value of '5.15', if the value does not appear for the feature, its probability will be 0. This might lead to misclassification. Generally, the continuous approach is more accurate and more useful, despite the overhead of calculating the distribution function.\n",
+    "\n",
+    "The last one, *c)*, is tricky. If feature values are discrete, we can count how many times they occur in the dataset. But what if the feature values are continuous? Imagine a dataset with a height feature. Is it worth it to count how many times each value occurs? Most of the time it is not, since there can be miscellaneous differences in the values (for example, 1.7 meters and 1.700001 meters are practically equal, but they count as different values).\n",
+    "\n",
+    "So as we cannot calculate the feature value probabilities, what are we going to do?\n",
+    "\n",
+    "Let's take a step back and rethink exactly what we are doing. We are essentially comparing conditional probabilities of all the classes. For two classes, **A** and **B**, we want to know which one is greater:\n",
+    "\n",
+    "$$\\dfrac{P(F|A)*P(A)}{P(F)} vs. \\dfrac{P(F|B)*P(B)}{P(F)}$$\n",
+    "\n",
+    "Wait, **P(F)** is the same for both the classes! In fact, it is the same for every combination of classes. That is because **P(F)** does not depend on a class, thus being independent of the classes.\n",
+    "\n",
+    "So, for *c)*, we actually don't need to calculate it at all."
    ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Wrapping It Up\n",
+    "\n",
+    "Classifying an item to a class then becomes a matter of calculating the conditional probabilities of feature values and the probabilities of classes. This is something very desirable and computationally delicious.\n",
+    "\n",
+    "Remember though that all the above are true because we made the assumption that the features are independent. In most real-world cases that is not true though. Is that an issue here? Fret not, for the the algorithm is very efficient even with that assumption. That is why the algorithm is called **Naive** Bayes Classifier. We (naively) assume that the features are independent to make computations easier."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementation\n",
+    "\n",
+    "The implementation of the Naive Bayes Classifier is split in two; Discrete and Continuous. The user can choose between them with the argument `continuous`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Discrete\n",
+    "\n",
+    "The implementation for discrete values counts how many times each feature value occurs for each class, and how many times each class occurs. The results are stored in a `CountinProbDist` object."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the below code you can see the probabilities of the class \"Setosa\" appearing in the dataset and the probability of the first feature (at index 0) of the same class having a value of 5. Notice that the second probability is relatively small, even though if we observe the dataset we will find that a lot of values are around 5. The issue arises because the features in the Iris dataset are continuous, and we are assuming they are discrete. If the features were discrete (for example, \"Tall\", \"3\", etc.) this probably wouldn't have been the case and we would see a much nicer probability distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.3333333333333333\n",
+      "0.10588235294117647\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = iris\n",
+    "\n",
+    "target_vals = dataset.values[dataset.target]\n",
+    "target_dist = CountingProbDist(target_vals)\n",
+    "attr_dists = {(gv, attr): CountingProbDist(dataset.values[attr])\n",
+    "              for gv in target_vals\n",
+    "              for attr in dataset.inputs}\n",
+    "for example in dataset.examples:\n",
+    "        targetval = example[dataset.target]\n",
+    "        target_dist.add(targetval)\n",
+    "        for attr in dataset.inputs:\n",
+    "            attr_dists[targetval, attr].add(example[attr])\n",
+    "\n",
+    "\n",
+    "print(target_dist['setosa'])\n",
+    "print(attr_dists['setosa', 0][5.0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we found the different values for the classes (called targets here) and calculated their distribution. Next we initialized a dictionary of `CountingProbDist` objects, one for each class and feature. Finally, we iterated through the examples in the dataset and calculated the needed probabilites.\n",
+    "\n",
+    "Having calculated the different probabilities, we will move on to the predicting function. It will receive as input an item and output the most likely class. Using the above formula, it will multiply the probability of the class appearing, with the probability of each feature value appearing in the class. It will return the max result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "setosa\n"
+     ]
+    }
+   ],
+   "source": [
+    "def predict(example):\n",
+    "    def class_probability(targetval):\n",
+    "        return (target_dist[targetval] *\n",
+    "                product(attr_dists[targetval, attr][example[attr]]\n",
+    "                        for attr in dataset.inputs))\n",
+    "    return argmax(target_vals, key=class_probability)\n",
+    "\n",
+    "\n",
+    "print(predict([5, 3, 1, 0.1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can view the complete code by executing the next line:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%psource NaiveBayesDiscrete"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Continuous\n",
+    "\n",
+    "In the implementation we use the Gaussian/Normal distribution function. To make it work, we need to find the means and standard deviations of features for each class. We make use of the `find_means_and_deviations` Dataset function. On top of that, we will also calculate the class probabilities as we did with the Discrete approach."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5.006, 3.418, 1.464, 0.244]\n",
+      "[0.5161711470638634, 0.3137983233784114, 0.46991097723995795, 0.19775268000454405]\n"
+     ]
+    }
+   ],
+   "source": [
+    "means, deviations = dataset.find_means_and_deviations()\n",
+    "\n",
+    "target_vals = dataset.values[dataset.target]\n",
+    "target_dist = CountingProbDist(target_vals)\n",
+    "\n",
+    "\n",
+    "print(means[\"setosa\"])\n",
+    "print(deviations[\"versicolor\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can see the means of the features for the \"Setosa\" class and the deviations for \"Versicolor\".\n",
+    "\n",
+    "The prediction function will work similarly to the Discrete algorithm. It will multiply the probability of the class occuring with the conditional probabilities of the feature values for the class.\n",
+    "\n",
+    "Since we are using the Gaussian distribution, we will input the value for each feature into the Gaussian function, together with the mean and deviation of the feature. This will return the probability of the particular feature value for the given class. We will repeat for each class and pick the max value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "setosa\n"
+     ]
+    }
+   ],
+   "source": [
+    "def predict(example):\n",
+    "    def class_probability(targetval):\n",
+    "        prob = target_dist[targetval]\n",
+    "        for attr in dataset.inputs:\n",
+    "            prob *= gaussian(means[targetval][attr], deviations[targetval][attr], example[attr])\n",
+    "        return prob\n",
+    "\n",
+    "    return argmax(target_vals, key=class_probability)\n",
+    "\n",
+    "\n",
+    "print(predict([5, 3, 1, 0.1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The complete code of the continuous algorithm:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
    "metadata": {
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
+   "outputs": [],
+   "source": [
+    "%psource NaiveBayesContinuous"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Examples\n",
+    "\n",
+    "We will now use the Naive Bayes Classifier (Discrete and Continuous) to classify items:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Discrete Classifier\n",
+      "setosa\n",
+      "versicolor\n",
+      "versicolor\n",
+      "\n",
+      "Continuous Classifier\n",
+      "setosa\n",
+      "versicolor\n",
+      "virginica\n"
+     ]
+    }
+   ],
+   "source": [
+    "nBD = NaiveBayesLearner(iris, continuous=False)\n",
+    "print(\"Discrete Classifier\")\n",
+    "print(nBD([5, 3, 1, 0.1]))\n",
+    "print(nBD([6, 5, 3, 1.5]))\n",
+    "print(nBD([7, 3, 6.5, 2]))\n",
+    "\n",
+    "\n",
+    "nBC = NaiveBayesLearner(iris, continuous=True)\n",
+    "print(\"\\nContinuous Classifier\")\n",
+    "print(nBC([5, 3, 1, 0.1]))\n",
+    "print(nBC([6, 5, 3, 1.5]))\n",
+    "print(nBC([7, 3, 6.5, 2]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how the Discrete Classifier misclassified the second item, while the Continuous one had no problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Perceptron Classifier\n",
+    "\n",
+    "### Overview\n",
+    "\n",
+    "The Perceptron is a linear classifier. It works the same way as a neural network with no hidden layers (just input and output). First it trains its weights given a dataset and then it can classify a new item by running it through the network.\n",
+    "\n",
+    "Its input layer consists of the the item features, while the output layer consists of nodes (also called neurons). Each node in the output layer has *n* synapses (for every item feature), each with its own weight. Then, the nodes find the dot product of the item features and the synapse weights. These values then pass through an activation function (usually a sigmoid). Finally, we pick the largest of the values and we return its index.\n",
+    "\n",
+    "Note that in classification problems each node represents a class. The final classification is the class/node with the max output value.\n",
+    "\n",
+    "Below you can see a single node/neuron in the outer layer. With *f* we denote the item features, with *w* the synapse weights, then inside the node we have the dot product and the activation function, *g*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![perceptron](images/perceptron.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "### Implementation\n",
     "\n",
@@ -1125,11 +1281,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 33,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1138,10 +1292,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Note that the Perceptron is a one-layer neural network, without any hidden layers. So, in `BackPropagationLearner`, we will pass no hidden layers. From that function we get our network, which is just one layer, with the weights calculated.\n",
     "\n",
@@ -1150,10 +1301,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Example\n",
     "\n",
@@ -1162,12 +1310,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 34,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1187,20 +1331,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The output is 0, which means the item is classified in the first class, \"Setosa\". This is indeed correct. Note that the Perceptron algorithm is not perfect and may produce false classifications."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## MNIST Handwritten Digits Classification\n",
     "\n",
@@ -1217,10 +1355,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Loading MNIST digits data\n",
     "\n",
@@ -1229,11 +1364,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1251,11 +1384,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1300,21 +1431,16 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "The function `load_MNIST()` loads MNIST data from files saved in `aima-data/MNIST`. It returns four numpy arrays that we are going to use to train and classify hand-written digits in various learning approaches."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1323,10 +1449,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Check the shape of these NumPy arrays to make sure we have loaded the database correctly.\n",
     "\n",
@@ -1335,12 +1458,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1362,10 +1481,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### Visualizing MNIST digits data\n",
     "\n",
@@ -1374,11 +1490,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1412,18 +1526,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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1BODrr79m5syZYW3fT2We2bNnB+Dll18G4J577mH06NEA9O7dO+LtRrPk+oUX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0bjcO1F8uTJY0J5Er4MVC4fMGCACTuI35hXyZUrF2AdHwkVSLL9559/zgMPPABEnxIlSGgvMTGR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\nr9GsWTMA3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7b88++2yqV68OOMY0QJ48eYxhcuWVVwJw7NgxT8YXjP79+wNw3nnnmW2NGjUC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\neKSBlIiIiIhHGkiJiIiIePQ/B3zVmzgAI0oAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3b11a17f60>"
+       "<matplotlib.figure.Figure at 0x7f3150035390>"
       ]
      },
      "metadata": {},
@@ -1437,18 +1547,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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UrVoVcN1dcgObO3euyVr8448/vDEwRCQcokyZMmYiKwHJ/fr1S/f+Q4cOAaknl8Fu3H5C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\n3Vi/YpPKykrf8wTdb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PorLFQ4mKNaKwBBY5/PzzzwAmVyoQUQ6l3Lxnz54ZlChB9mvmhN9YkDu3c9uaPXt2UF/A\nAwcOGFuYSZMmAbB79+6I7Bw+//zzoFy/d999NydDjhmS8xSYOC6vjR071hyzso9zYnUQb8RGRK6V\nr776asRKlCik1atXN9uSc1w6T3iBrxZSclGWENzo0aOzTTiWBOvOnTubhVPm6hGpxMiMyPSBSFKb\nHylevDjdu3fP8Jr426QKkuCbbOzevTvq3xWvqCVLlpwwUdtv3HfffUGh9FBIuKxLly7m81JN4yfy\n58/PPffck+G1xYsX58j52W9I1Zok64KzOAJCJlNLuO9EYS1ZqEhSbyx58MEHgYyFAFK5NXjwYJP6\nEC01a9ZMmoo28dqThVIgc+bMCUouDzyWc9JBIR4EVtkDfPPNNxFvQ3ynZPF98OBBc6x4iYb2FEVR\nFEVRosRXipS4A0+bNi3bz0kTUJGnJeE1EkI9fcmTsx855ZRTzNOhlPD6NTwSDRdddJFxRRcy9wDz\nK8OHDzd+QoGIL5l4lRUrVsz4DQni8N2+fXvTJ1HcePfs2ROzMecE6S3Yt29fozBl57MjjazLli1r\nPic+PRUrVgz5NJ0I+vfvzwUXXAC451inTp2MbUoqICE6Yd68eSGVbUmHEP+dQMSyQqIANWrUMMe6\nlOPHUsWTqMPq1at55plnAHjllVc82/4jjzxCwYIFAVfRyK7MPpEEupOLaigpKj///HNQKC9QaZPE\n81SlXbt2nHHGGRle69mzZ44iCFmhipSiKIqiKEqU+EqReumll074mccff5zHHnsMyNkTe6jfzSqR\n3Q9IDz1w8778amYYDeXKlTMmeL/99huQPC7RBw8ezDbnZ8yYMYBTLCAKVOvWrTN85rTTTmPTpk0A\nPPXUU4CjkPgJsacIpWCIArFt2zbzmiTely5d2rwmuYlSHr9s2TJjmpdoAs8xcdVfv359kFv/sWPH\nzD4SqwDpuyhu934kb968xkJFOHjwYFDhQ548eXjhhRcAN0dK2L59uykAkmN+zpw5RukIVcTjNVJY\nJD+9QiIiV111lVG9Iu0DmSi2bNliLCDketOuXbsscxe3bNliFEW/Eqktg9w/JNdZDHTB6YEJ8M47\n73g0uoz4aiElF7JQFU8iYU6ePNmTkEeom9QTTzyR4+16jSzu+vTpw6FDh8zfUwW5AfXv39/s97p1\n6wKpUSUVyPvvv2+8omrXrg1gfJUCw8riXL927VpPQxY5RfxXpCno4cOHgxI3a9SowZlnngkEVzB+\n++235uYrN1zxAfIDoa475cuXNxWZgUiFpYQqZYHy2GOP8f333wP+C023b9/eVNd98cUXQMbrYN68\neQFnn1x//fUht9GnT5+gh4YffvjB7GsJCSYjgSkl8mAgTe2TAblHSnj1559/zjJp3m8PaaHIXEXa\nq1cvk9YjDzCBHn733XcfkLG1mJzTUkQS+KDnJRraUxRFURRFiRJfOZuLO66UKoLr/XT++ed7No7H\nH3/cNI0VxowZk+0qPVHO5uKTsmDBAlP+KSqO1yTC2fz1118HnHJsSUAO5VjvBX5wp8+MPD316tWL\nQYMGyfcDTthFvMPCLSuP5RxLlSoFuN0AJk+eHDSuUqVKmUa4Yu0gYZKWLVt6Iq3Hao65cuUyvkmi\nrAR2OZAQ5FlnnWXCQJmTWQMRT6Vnn33W9O8MtydfLM7FMmXKGDdnUS8qVapk3pfQrShqgYi61rFj\nxyBPpqVLlxo3ftnngb3fQuGnc1HmLbYJefPmNYqxePVFQ6LmKCrUqlWrslSkcuJmHkgs5yjRGEnx\nyJcvnykCEWWpSJEiGY7hTN9pCtFk/RCNIqXO5oqiKIqiKDHEVzlSo0ePBjB98CDyXkLZIbkbHTt2\nDNqu3/KjJF9BVArwX85FTpAybHGZBUyCa7Igibjbtm2LujxanrCmTp2aYV+DYxApJrV+QIobxHE4\nq8/InOQckxL6WCV6esXRo0eNeibJqRMmTDDvB/5dVNPMSnnr1q2pUqUK4KrJPXv2pGrVqoB73IvF\nRTxp1qyZUdyksAEcWwqA1157Leh3PvvsMwDjFn7s2DEKFSoEuNemRo0amWiC9ChMJsT1Ws61uXPn\nhlTl/E6gEhX471DUr1/f98nmYlMgdivTp0839iSi5FuWZXKppk6dCmRMMhf38ljlRgmqSCmKoiiK\nokSJrxSpWCFmcWJgedppp5n3PvjgA8B9AvULUgUkq/Fdu3YxefLkRA7JM0qUKMHDDz8MuMrbr7/+\nysqVK3O03ebNm5tclEaNGuVskGHw7LPPAs6+CWxX8W+mUqVKQUZ/q1evTtBoYocoSitWrMjw+ooV\nK4zqI/363njjDdNHUo6TGTNmxGuohrS0NPP3QDVC+siJMgPudVGsDuSa2bp1a5P/FGgXIyXnUlWV\nDJQsWRJwbXckb6hdu3YJG1O0VKhQIaSxrZyLYtchn5k9e3bS9N2T3LW6devSqlUrwD32tm3bZo5f\nyWuT6j2IX6Qp5RdStWvXNiHDwDCSWCg89NBDgOu07Bcylx/PnTvXhEySnd9//90krEp/ud69e2fp\n2VKtWjVTgCBeKcWLFzd2GRKagJz1vYuWmjVr5ngbmX2lkpU6deoY3ygJMbz44ouJHFLckWN7/fr1\ngBNyEM8judgnYiEV2LdUGjHfcsstlClTJuiztWrVAtyFYnYFIJ999pkpQEgWTjrpJNNvULoNSCj6\nm2++CUr92LRpE1dddVV8BxkBoRZRffv2ZezYsUGvgbPAiocLvdeE6pMn139ZNMq++/777+PWv1RD\ne4qiKIqiKFHiK0UqVHKcJIhLz6Px48dn291bDOfkCbBLly6UK1cu6HPSBd2vCbBieigkk2SeFWIv\ncezYMfPUIIaTq1evNpK6PDmL+nTxxRcHJV1blmW2IW7S77zzDpMmTYrxLDKOAZw+V/PmzQOCO5Zn\nhSQf9+jRA3BDKIAJDR07dsyzMuVYU7RoUYAMT8DSXf7vv/9OyJhygiSF9+vXjxYtWgCRh//FablO\nnTrmtbVr13o0wsiZO3cubdq0AVx7h6wMNMOxINm8eTMAgwcPNmbBfkfCeRMnTjSKTGaqVasWpEjF\n0yYoEsTFPBBRnTKrUYGv3XPPPaY3XzIpUqGQdYNYxQg9e/aMW59MVaQURVEURVGixFeGnPIUlN2T\n3+7du409vDBu3Diz8hw6dCjgJmln/l1wVqoSaw03hhpvc7X33nsPcBNE69SpE7YpY7TE2pBT2lKc\nc8455gnv4MGDgGPGKqpG5tYioZg3bx6PPPII4LSoAE749OH1PpQcoMBjTQwPR4wYEXQct27d2jw1\niaFjYN5KwPcDjlmpqHgyxxORKBNAyel79dVXWbNmDeB2ofeaWM6xfv36ACxfvhxwiiHEdPOnn34K\naxv58+cHMKa/w4YNMyXaknt0ovZHsToXly5dCmAMNCPl+++/N0U7EydOjGobkLjjVI7NQJVQkKTl\nd999l7lz5wLuvejo0aMRtyaLxxwDr5HZKVGZad++vVGkcqJ6+8FYVVoWidoq7afEhiSnhHUu+mkh\nJRcg8UsSH5YIvwNwD7BPPvnEnDxyAQj3phRIPA+YfPnymRCAuJnfcMMNxik5VsR6ISUeM02bNs12\nkST7UFxp9+zZwyeffALAwoULAbJtEpwVXu9DST59/vnnTcjgRIvAcBaJ4tkzfPjwiEMm8b6wSaKu\nhFfLli1rkj4zdw/wiljOUQoHpIK0cOHCxlNI0gXkGAQ3bCkL49atW5vPyYX8zz//NN42Uul5ImJ1\nLkq1U7du3QAnxBPYVFqQ404WEvLw2rZtW0+apSfqBiwVXenp6cavUPzg5IEomvtDKOK9kBIkpL5l\nyxZz78vM3XffbR50knkhdd5555k5yvpBkBSJnKLO5oqiKIqiKDHEV4qUIKXu999/v/EnCRcJf4nl\nweLFi8Pub5Ud8Vx5ly1b1kj/knAdj4TAWCtSEsbauXNn0JPUr7/+yoIFCwBXDRD/oYMHDwZ1Ao+G\nWO3DFi1amKT5zKXUIbYd8v2NGzcaD6xo1DYh3k+I0udq48aN5jUpK3/77be9+Iog4jHHJk2aAG4o\nLCvefPNNAK688kr5TvOeFIgMHjzY9AwNl3j1vaxXr54JUUuhDsDIkSMBV8Xfvn17Tr8qA4lWMq69\n9lpTIPLtt98CrnefVyRKkYqUZFSkRFm97777slS+A4/nnKCKlKIoiqIoSgzxlf2BILlAI0eONPkI\nt956a7a/I+qFPCHGy4grFtStW9d0Z8+JOuE3RFXy6knBLyxZssSYg/bp0wcI32Bz3LhxAEyaNMmz\n3IxEE9g5IFkRZ+/atWvTqVMnwE1mFUsWIMikccGCBcZYVnrXeaGIx4o1a9aYCMC/iapVqxo1Jz09\nPcGjiR5Rk+65556g4o5Ah3a5n2zdutX8zNyBIJkQh/MBAwaY/Sj9Tjt37hz38fj6DDpy5IhxB5Yb\n1L+Bk046ySSnikuy4m/ef//9DD//LUjVpSQlHz58OKTLcrIhTXjT09PNuRjYDFVJHRLp7eUVY8eO\nDataLxWRanxZXH388cdxH4OG9hRFURRFUaLE14rUvxUJCSiK3xGLilB+WIqiKLHgv//9b4afiUYV\nKUVRFEVRlChRRUpRFEX5V5Genm4KCsTFXFGixZc+Un4k0b4n8SBe3jWJQvehi87R3+i56KBz9Dc6\nRwcN7SmKoiiKokRJXBUpRVEURVGUVEIVKUVRFEVRlCjRhZSiKIqiKEqU6EJKURRFURQlSnQhpSiK\noiiKEiW6kFIURVEURYkSXUgpiqIoiqJEiS6kFEVRFEVRokQXUoqiKIqiKFES1157qW4TD6k/x1Sf\nH+gc/Y7O0SHV5wc6R7+jc3RQRUpRFEVRFCVKdCGlKIqiKIoSJXEN7SmKoijJx6WXXgrAW2+9Re7c\nzm3j4osvBmDdunUJG5ei+AFVpBRFURRFUaLEsu345YClesIZpP4cU31+oHP0OzpHh3jMr3z58gAs\nX74cgMqVK3PgwAEAChcuHPV2dR+66Bz9jSabK4qiKIqixJCUyZGqVq0aAPXr1wfg5ZdfBiA9PZ0j\nR44AMHToUADmz5+fgBFGxumnnw7Apk2bAGc+nTt3TuSQ4sawYcMy/Ltx48akpaUFfW748OGA+7Qs\nPxUlkcg1qGPHjkHvPf300wAERgI2bNgQn4FFQJMmTQB49dVXAShRogQAv/32Gy+++GLCxqXkjLPO\nOguAK6+8ktatWwNw2WWXAWBZFl988QUADz74IACvvfZaAkaZfKREaC937twsW7YMgAYNGgDw0EMP\nAfDAAw+Yz6WnpwPQsGFD/vzzz4i+I94SZsWKFQF3IbV3717uuOMOAObMmePFVwSRiHCCLJAefPDB\nkIulSGjSpEm2i6lY7kMZe+Ac5GIkY3r//feDfs/rRWA85njvvfcC0LZtW/7666+wf79FixasWrUK\nIOLzLxA/hRMkCbt48eLmtb59+wLOA0B2bN68GXDCZZlJZGivSZMmzJ49G4CSJUtmeK9Pnz5MmDAh\nx9/hp30YK/wwRzkuu3btCsAjjzwCYAoGsuLvv/8GnEXWmjVrsvycH+YYazS0pyiKoiiKEkOSWpHK\nkycPAA8//LB5ShY6dOgAwPjx4ylTpkyG97p3787UqVMBOHbsWFjflWhFCjCya+3atb34iiAS8RQs\nSmK4apSE87IK91lW1lOI1T5ctmxZjtU0cFUpCatEQyyP08ceewxwFambb76ZGTNmhP37n332mXkS\nrlGjRqRfb4j3uShjbteuHeCo3Pnz5wfgtNNOAyBv3rwRb3fv3r1ARjVLSMS5KHNasmQJDRs2zPDe\nqFGjACc94vDhwzn+LlUyXGKpLMr5eeqpp2Z47+jRo/zvf/8DXGX03XffZd68eYCrRM6ZM8fcS0OR\n6DnGA1WkFEVRFEVRYkhSJ5tnztkA+PLLLwH36X7q1Kncf//9AOTKlQuASZMmmSTKP/74I06jVQIJ\nlVOUVfL4sGHDghLQ5XVwc5ESxfDhwz1RpGQbMq9Qc04kN910U463Ub16dQ9GEjvy5csHuEm5u3bt\nolmzZgARJ1n/9ttvZhuZr0vgXqsSjVwXX3nlFYAMapSoFePGjQPwRI1SYovcD4cNG2aOZ+GFF14A\n4NFHH2XFAIIdAAAgAElEQVTjxo0Z3itUqBB79uwBXEXq0KFDMR5t+BQpUsQUXN13330AVKhQgcxR\nNZnDqFGjmD59OgA7duyI6diSciF18sknA5gFUiDr168HYOfOnYAjRRcqVAiAu+++23zu6quvBiK/\nOMYLSQpMdWTxlN2iIdwFRaKq9pYvX27CcaEWVJEu9AKT1FO1EvHcc88F4KuvvkrwSDLSvHlzwK1W\nGjVqVFiLhzfeeANwFhozZ84EMEm6P//8cyyG6gm5cuUyx65UcYG7gJJF5Pbt2+M+tnC48MILAahU\nqRLgzKFs2bKAuyhu0KCBCfnLTdeyrKAbsLB8+XKzD6dMmRKzsXtNsWLFALjlllsAZ/67d+8GoGfP\nngDMnTsXwFSygxvSnT59OmeeeWaGbcr/QyK56KKLAJg9e7ZJeRG2bNkStB9l/48cOZILLrgAINvw\npBdoaE9RFEVRFCVKklKRGjRoEJDx6X/FihUA3HPPPUGf//zzz4Neq1WrVmwG5xHydJGq5LTsPy0t\nLUjpyUmSdk7Jbj6hFDU5diXZPhRpaWkpq0i1bNkS8JcilSdPnqDk+Zo1axq1SRy9Dx06RNu2bQH4\n7rvvANi2bRsQfvGKX+jevTvjx4/P8NqPP/7IFVdcYf7uV5YsWULTpk0BOOmkYE1AQjwLFy4Ma3ti\nnZOWlmasK0Td8FuYPTOWZfHoo48CrqciuCGwWbNmZfm7V155JQDXXXedeU3sg5YuXer5WCNF/u8r\nVqxorhdjxowBYMaMGRnUNYB+/foBMHDgQKpWrQq4qlskdi2RoIqUoiiKoihKlCSdInXhhRdy6623\nBr0uruW7du0Kek/Uqm+++Qbwf8JrVkgJqzgnf/TRR4kcTkIIpeQko2qTWcHyIlk91kieSain/3B/\nPzt7ikRjWRYFCxYEYN26dYCTr/bpp58CTpFKqiFqBDgl8eA80ftZiRJV4uyzzzYqzA8//AA4ScVy\nbZDcmX/++Ses7YqFRcuWLY09zsCBAwGnG0aoyIZfyJcvn7HnEA4dOpSt4itmslJkAPD7778Drumz\nGHMmguuvvx7AqKObNm0y10kZZyhErerQoQN16tQBoFevXgA8/vjjMRlr0iykJHHw0UcfpVy5coAr\ntQ8dOpSvv/46y9+VxMmxY8cCyZFAKBfvwAvdKaecArg+Uv+mhVQov6lwEtX9RqCTe+C/kwG5MUUb\nvrJtO8sEXz9Qs2ZN83dZSKVqlZqE89LS0sw+kYfRBQsWJGxc4SAtdSZNmhSy5U60SIXaggULzA14\n8ODBgFMd5ueF1F9//cWTTz4JOL6K4CwMBwwYAGQM24HTlUAKmqRqc8eOHaYIyw8VpRKWkwe3Xbt2\nZbuAyo5wF9PRoqE9RVEURVGUKEkaReriiy8G3HJccJ2+RWlKJcT/Qp4S/42E8olKRhVKGDZsWFhW\nCIH+WX7ip59+AgjqFHAixJOmcOHC5jVRif1Eenq66QF42223AdC+fXujCktqwN69e5MuqVwoXbo0\nAK1atQIcpV9Ut5EjR4a1jbPPPhtwFYO1a9fyyy+/eD3ULLnhhhsARy2MlcIpTe9Fkbr22mt5/fXX\nY/JdXiEeUXfeeScA5cuXN8qaJKBLKLRly5bGRkjSYa666iqjxPqREiVKULRoUcDtChAKUdqqVKli\nIjuTJ0+O6dhUkVIURVEURYkS3ytSkhslSX+B+NnoTomc7PKHxNrAr4nlmZ3ac+K2nkgbh+wYMWIE\n4JpPdu7cOaxee5lNE8Epuwc3qdUPHDp0yKgtH3/8MQDlypXjgw8+yPC5gQMH8vzzzwOhi1v8zM03\n3wxk3BeBRsVZ0aZNGwAuu+wyowiVKlUKcHooSm5NPJSpeLhtSyK2EE0vxXgjFhzTpk0DnGuQ5BOH\nyiGW7h5ideAnKxLA5L+JSW6jRo3MHEXRDiw6kuumqG+WZRlj7ljnSPl+ISU+GIEhvQceeACIvIrG\nixYX8UKqm0JVOTVq1AiIvVwZT9LS0oI8lWTRNHz4cN8uoIRkTB6PlMWLF2f4d+PGjfn1118B90K9\nZMkSU/nWrVs3IPRNyK+VsxIykIqhCy+8kL59+wIY1+fHHnuMO+64A4DVq1cDmLCP3Jz8SO3atU0i\nsrB+/Xpz4xVKlizJ/PnzASc8Iq+Bm5gMboJ37dq1jZt25u0nI7lz56ZTp04ZXos2yTmelChRAoDL\nL7/8hJ+dPXs2Xbt2BeDgwYMxHVe07Nu3D3AX+pMmTaJevXqAG16Wn1kRr8WhhvYURVEURVGixIpn\nObJlWRF9WalSpcyKUkr/9+/fb3wlwi3/Fxdl6Z+VN29ennrqKQDztHkibNsOywAn0jlmhST0Soih\nfPnyQZ8RCVM8VHJKOHP0an6hwniBChR4H8aL5T4MZc8QLTLvaEJ88ThOJQlelOHMSLlyqIRsefoN\nTDyPlHifiwUKFABcZW3y5Mlm3xQvXhxwlZoNGzaYUIk4aotNSyTE4ly86KKLgq6ZU6ZMMap///79\nAScRXTo/iLeU+AlNmDDBOEkPGTLEbEeO/3DUEIj/PoyEm266ySRuS+Pphg0bRnydjcccJWH83nvv\nNc2KpbdsKN577z3ASSz3IkQaz/1YrFixDA21wWkuXrlyZQD++OMPION1ScLQs2fPjvp7w5mjKlKK\noiiKoihR4uscqRo1ahglSp7qbrnlloiMKJs2bWrKIQNzNcTUza/Ik5DYIAwZMiQoX0oS8Lt162ae\nHJOBUDYAy5cv922SdTiIiuaFIuX3PKtRo0YBTs6QuOyfccYZ5n1RokKp3c8880wcRhgZ0k9N8r0y\nIyqa/JSnXHBzNyWp95577jGl8x9++CEArVu39m2OzR133GHyvQIR80kp/w/Mj5McOMldyU4BSSZE\n9RdlB5ycP/BO9feCevXqmZ6yctxdcsklQZ9bu3atKfQQatSoAcQnYd9r9uzZk60FxcyZMzP8+9ix\nY6bfYqxRRUpRFEVRFCVKfKlIFStWDMhYGi1Pi3Pnzs32d+XpUirbRo8ebVbtwtChQ3nuuec8G28s\nkXyUPn36UKRIkQzvSc/Bp556yheW/lmRna1BMhtsBiJ5TaIaDhs2LKifXiiyy1EMzBvzE9JBvXPn\nzqaa64ILLgj6nKhUflShAE4//XQAY2/w1ltvGSPOcHnnnXcy/HvOnDnGCuCll14CHINEUcUTycGD\nB9m/fz8QWkWS3KdRo0aZiuBQdgYSHZDebpmrOZMVsYY499xz2bFjBwDjxo1L5JAyULFiRcCpEJXz\nLhDJEZLq9E8//ZTt27fHb4AJJk+ePBn+vW7dOt5+++24fLcvF1KSuCmJnOC4DmeHLKD69esHYKTP\nQESenThxYlKFwsDpfRRKvvUrgb5KoTyVsmtem9mTCdxFWLJYIiT7wjBcJGSVeUEBmITlQGRx4QfE\nC0oetGrVqmXCO9H6z+zfv5/vv/8ecBfJ4reUaL788kuzUBTLAwnTAbz55ptA1kUEgiT3tmjRIhbD\njDuy/+X/5ujRo6ajhJ/664mdSOAiSooAbrzxRt56660MrwXeP/+NrFmzJm7fpaE9RVEURVGUKPGl\nItW7d++g16SUOBB5IrrkkkvMal2S0wMRk7xFixYBxC0BzUvGjBljEgel5FWYOHGiUWzef//9uI8t\nEFGRMptrQvbu5KEMOQPJiSVAPMisomWlSIXjfB5OSDBZEaXHD+TOnfHyV6dOHdMtQcJ9o0ePNuEw\nGXuxYsWCwghCixYtaNq0acjt+wEpAx80aBAANWvWNO9dddVVgGu5Am7PObnWlixZ0ig4p512mvmc\n34t3skPUJwn1Pvfcc0yZMiWRQwpJYOL4li1bAOjQoQMQWn3JnNICsHXr1hiNLnFI95PzzjsvYWNQ\nRUpRFEVRFCVK/PfIBEFJ1eA+4Z955plGfZIYcHZ9kGbOnGnKdCWBMBmZP3++6WTdoEGDDO81bNjQ\n9C5LtCKVnaqU3XvZkV0+lR/ISk3LrEoF5otlZ3HgV9UtWgL3n5/2ZWbD0MDEf+m1FthzTVpWFCxY\nMEOrlKwQlUB6E/oJMeGcM2eOyZMSBa1OnTrmc4F/z4qZM2caM89kQlr+3HjjjYCbRC/2Hn6mdOnS\nAJxzzjlAaEVK+iMGkgotfDIjERppZyRIzl888OVCKlBaFqQCSGTYrNi0aRMAjz/+OODItMmWWJ4V\nkgwpYU5xNrdtm7Zt2wKul8bkyZPjHhrKaYJ1ZkfzZAlthVoUBYbuGjdunOXnAkm1BZQgC5RVq1aZ\nXnZ+oH379oB7wT3RoiHUA14oJBT44osvArB06dJohxgzJDG5devWpvNDdoshqQoOvDm9++67AKxc\nuTIpfYk6duwIuAn3EsbcuHFjwsaUHXfddRfgdPQQEWHq1KkA3H777Tz77LOA2ydS9msgyXJNjYSs\nrqvxbCiuoT1FURRFUZQo8aUiFan3w6xZs0x/qx9//BFwS0BTie+++w6AGTNmADBixAjznoRMxNul\nQIECcX/6EOXlRCxfvtyEIJNNfYqE7BLKM9OkSZOU+z/4888/AdffpnLlyuTPnx/wx/kpyePi+1Sv\nXj169OgBQN26dYETl5Bv27YNgC+++AJwnLAlzLt+/XrvB+0xy5YtM+MdMGBAgkcTP9LS0owCJyG9\niRMnJnJIJ0Tse2644QZjXyF2HfXq1aNevXpBvyMKsKij0fR99DuVKlVK9BBUkVIURVEURYkWXypS\n8gRbqlQpE4c///zzzftSwis5Nd9++23ITvOpiiQMihnioEGDjBu89In66aef4j6uwByfwHypVDen\nDJxfJCoUBOeFpRKiDouC2rt3b5PnN3r06ISNKzPS13LhwoUm/1Ce9OvXrx9kBtypUyczJ0lA/zc5\nSKcCnTp1Mu7u0s80ngaOOeHdd9/loosuAqBLly6AY90gRVjCJ598wpAhQ8zvpCoSqclMtWrVTIFW\nrLGya1Hh+ZdZVvy+zGNs2w6r3CjV55jq84OczTHQJypUEmSsW+L48TitWrUqACtWrDBNjjdv3hz1\n9vw4R6/Rc9HB6zlK+sHChQtNOkTt2rUBd+HvFXqcusRyjlIsIEUCUtH49NNP06tXrxxvP5w5amhP\nURRFURQlSlSRChM/rLxjjT4FO+gc/Y3O0SHV5wfez1Hsc4YNG2bCs9Lk12v0OHWJxxxfeOEFwN2f\ne/bs4corrwQcy4hoUUVKURRFURQlhvgy2VxRFEVRYomYPCupQb9+/QA499xzAccCKFTv3VigCylF\nURTlX8GSJUsAaN68OZMnT07waBQvkSp28YCLJxraUxRFURRFiZK4JpsriqIoiqKkEqpIKYqiKIqi\nRIkupBRFURRFUaJEF1KKoiiKoihRogspRVEURVGUKNGFlKIoiqIoSpToQkpRFEVRFCVKdCGlKIqi\nKIoSJbqQUhRFURRFiZK4tojRLtf+RjvOO+gc/Y3O0SHV5wc6R7+jc3RQRUpRFEVRFCVKdCGlKIqi\nKIoSJbqQUhRFURRFiRJdSCkJoVChQhQqVIijR4+aP71796Z3794ULVqUokWLJnqIiqJkg2VZWJbF\n7bffjm3b2LbNrFmzmDVrFhUqVEj08BQlbuhCSlEURVEUJUos245fMr3XmfunnnoqALNmzWL69OkA\n3HHHHQA888wzAPTo0cP8fdGiRQD88ccfEX+Xn6sTHnroIYYMGQLAW2+9BUCHDh3Yu3dvRNuJZ6VQ\noUKFANizZ0/Qex9++CEAvXr1AuDzzz/34it9vQ+9Qufokqg55s2bF4Bjx44BkD9/fu68804Aihcv\nbj43aNCgLLeRDFV7U6dOBeDWW281r/36668AtGzZki+//DLL3/X7PvQCnaNLqs8x6RZSuXPnpnDh\nwoCzgAJo2rRpWL87e/ZsALp06cLff/8d0ff68YApU6YMAOvWrTOLSqF69ep89913EW0vnhfvk08+\nGYDPPvsMgKpVqwZ+BwD79+8H4MILL+SHH37I8Xf6cR96jZ/n2K5dOzZt2gTAp59+GvV2/DTHIkWK\nACDX0TPPPNM8uO3cuROAq6++Ouj3Dhw4YK5jofDrQqpUqVJ06NABgHHjxsk42Lx5MwCXXHIJAL/9\n9lu22/HTPowViZ5j4cKFueaaawC48sorAcy+A3j99dcBuPbaa7PcRsmSJc11+J9//gl6P9FzjAdq\nf6AoiqIoihJD4mrI6QX9+/fn0Ucfjep327dvD0CBAgXo1KkTAPv27fNsbPHm9ttvBwhSo5IBeboZ\nOXIkAP/5z3+oXbs2gHlSL1iwIABNmjTxRJGKB/fccw8ANWvW5Kabbgp6/6STnGcXCftIeGfUqFFx\nGmH8kf04atQotm3bBkDjxo0BOHLkSMLGFS0lSpRgzJgxAFx00UUAHD58GHD2e2b279/P9u3bAVfF\n+eijj+IxVM+oV68eAKNHj6ZBgwYZ3luzZg29e/cGTqxEKbGnTZs2ADzwwAPUqlULcBXTwAjU+eef\nn+U2KlWqBDjhW4ls9O3bFwitTP3bUUVKURRFURQlSpImR0qSND/55BMqV66c47G89NJLgJMvFQ5+\nigXnz58fcOdw3XXXmfe+/vprAJo1a2aegsPFL3kZLVq0AOCNN94A4Mcff6RGjRpAzp6GYrkPRU1b\nu3atfFdW287wviTWN2zYMNKvDElO5ijKUceOHZk7dy4Av//+e47HJE+3GzduNPMvVaoUkByFHyVL\nlgTc86x3796cd955Mpagzy9fvhxwC162bt0asQLll3PxlFNOAWDlypUAVKlSxbz3xRdfAHDvvffy\nzjvvRLTdeO7DU089NSiPtk+fPhw4cACA5557DnCvnevWrcvpVwLxP04feughwC3SKVSoUND1JpAp\nU6YATkGWILmqy5YtA5w83Pfeew+AVq1aAXDo0CHz+XjO8ayzzuKCCy4AXNUtkHbt2gEwf/58ALZt\n28bEiRMB2LBhQ9TfG84cfR/akwWUJIp7sYiCjBeEZENOmMAFlCQESmJrpIsoPyE3og8++ACASy+9\nlOrVqwPeVfDlBLmxNmvWDHAuXBUrVszwmd9//91I4nJxkt+T94Gow9SxYPTo0QB069aNm2++GSAo\njBMNoZKtk4VTTjmFOXPmABkXuxKKleRqWfSnp6ebxbQkmycjJUqUAGDevHlAxuvl+vXrAbj88ssB\n2L17d5xHlz25czu3NamU7Ny5M3Xr1s3y840aNQIwhRBt2rQhPT09xqP0hgsvvBBwwniyP+RBOxSv\nvPIKAI888gjffvst4C6QrrzySpOMXqxYMcBZgMl1SwqEAhdSsaR8+fIAPPnkkwBcc8015MmT54S/\nF3hf7NatGwAPPvgg4KaSeI2G9hRFURRFUaLE94qUhExktX0iRMLcsmULQJBSkHm79913HwBPPPEE\nR48ezdFYY825554LYEpaA5GkXSlDTmbE1kESecHxpQF/KFLyxDNp0qSg915++WXACeuItcOrr74K\nuCXI4D7pL168OKZjjQQJ1YwfP978f/9bOfPMMwEnXCBK1E8//QQ4vkkS+khFihYtao7LOnXqZHgv\nPT3dt0oUOONt27YtAAMGDIjod8844wzAUW3kWutX5P4lYbciRYqYAhZh//799O/fH3A9vwKR0F7X\nrl2D3pOQILgWNX/++acHIw+PwoUL8/zzzwMZ7Y2kqENSIsANOwuisNWqVcuokyNGjACcdcGMGTM8\nH68qUoqiKIqiKFHi+2Tz1atXA1C/fv1sPzdw4EDATUZeunQpAG3btmXo0KEn/J7ixYtn6wTuh2Rz\nieFnVtl+++03brzxRsDNL4oGvyS4ylNwYIKu7H8/GDmKMlGuXLmg9+QJCNx4/L333mteW7FiBQCt\nW7cG3Nw2r/BijvXq1TNPfPL//vHHH0c8Fklel31WrVo186QreRd+SzaXfBnJr7n44ouNCnrXXXcB\n8Msvv0S62YhJxLko+2bSpEnGWkWQ/dS7d2+TZ5MTvN6HZcuWBZxrhuTWCH///bex3ZDcvyNHjtCk\nSRPAyS8CyJcvHwB//fUXPXv2BOCFF14I5+tDEqvjtHHjxia3UmwpLMsKKmC56667TD6bIEUS99xz\njym0CrUGEOVnxIgRRrkKVXji9RzF+mbOnDkmB1V4+eWXzbU0uxxgyeX673//a9RJ4ejRo1x66aWA\nY9sRDkmbbC4JZQ899JBJpguFSMs9evQwyeiZD4pvvvnG/D2cBZVfKV68uLkxZWbZsmU5WkD5BSkk\nkIoxYfHixUZe9gNywwmUvzPTvn37kKEFcROWG5Vc4GfOnOn1MHOEFw9YUmkpyfY7d+40VWBygZOL\ntB8oWbKkqfKRGw641UDxWEAlEtkXgS1fJEVC3K/9EFoPhSyCypcvb26yUo23adMm00IsEHlQkxt2\nWloa4CRrh3pI8gs9evQwC6hQVKtWDYDbbrvNPLhdddVVgLsfpUVXIFu3bjWVpu+++y7gXQVjuIi3\nVeAiShZwAwYMCKuISh54Qjm258qVK6yE9UjR0J6iKIqiKEqU+FKREgfVEyULiv+F9NwLxdGjR035\n8i233AJAhQoVvBhmXOnbt2+G8vlA7r///jiPxnsqV67M22+/Dbhlr6KKjBw50leFAJIELwnJd911\nFwsXLszwGdu2Q6o6TzzxhHkf3FLiQGdzeYp8+umnE+6ALdYF0YT2xA/s4MGDgJOAL2XI0tjXT1So\nUCGDEiVIGFbCtmvXrjWhj1Rg2rRpgGMTIIgSJcqH3x3Lt27dCsA555xj+qimQuFNIBK+DCxaCYVY\nV3Tv3t3cI7NTmCURfciQIZ74xnmNRJ5Evc+KAgUKAM48IGOaRaxRRUpRFEVRFCVKfKlISR+uE/H+\n+++H9TmJt2ZOQgzkoosuitidNx7IKjuZDURDIT3nxKl27ty5Zv/Ie6KC5MSVNhZInF5+rlq1Kugz\nu3bt4ssvvwRc9SW7p72TTz7Z/F9I4cAVV1xh8lXefPNNj0Z/Yo4cOWIUQMllkoTccJBiCMktEsU4\nsF/ikiVLPBmrl3zxxRecc845gJNkDk7yq1w/xCrl6quvNgaPb731FpCzIohE0r17d3O85cqVK+h9\nue74XZESZVdMJsNB1LazzjorJmPyml9//RWAGTNmBKlSWeXQZpXH+eabbzJ8+HDA7cbgV0RZKlq0\naMiCMDEgletM0aJFs9zW22+/HZSA7wW+rNpbtGgR4IYGArFt2/hLiBW+SLmhKF68uFlwhZLthQUL\nFoS0nQ/43oRU7UkT1FDJ1tLioHr16p4kwsazUujss88GMIuNQHbs2AG4iYdeXcQT1VpETnQJP4Si\nQIEC5uL47LPPAs4FQRZf4h12olCfV3OUcLGE4jp16mRC5NnRoEEDI63LhV1CY6NHjzbntDjVR1O1\n6IcK2iuuuAJwWo2A6ym2efNm42ifEwfoWJ+Lkky9atWqbFMdJCwrVWzSliqn+GEf3nbbbUBwwcOB\nAwdM1ab4wkVDPOfYpEmTkEJAVi1i6tev78ni3+s5yqJ25cqVxk9Q+P7773nqqacyvFaxYkVzfZFr\nSiik8KBfv34R+2GFM0cN7SmKoiiKokSJL0N72fH5558HeZyEQkpAW7Vqla0SJYTbvDheSIghOxVg\n7NixQPKVZZ955pnZhqruvvtuwP/hhBMRSeLmwYMHzb7+8ccfAXjnnXeMqpVdWDoWPP3004CrCs+e\nPduUUEtp9OHDh03YUvo/tmvXzvTpEsVYQkZdu3Y1IT2v/bPijRRGiOu5JGyvXbvWzF+e+KdNm8ZX\nX30FuB0IEs3kyZOBjIU348ePB5yQuihuN910U4bPHz582BMfKT/z888/50iJSgT/+c9/Ivr8Aw88\nELJDRqKR8P/FF19sroeS8lClShVzXQqXjRs3Am4BW6yuO6pIKYqiKIqiRImvFClJ6gzssRYOxYoV\nM3koomZI3FSUqcxI+bKUo0u+kV+QXk9SYh+I5HyJIpVs9OnTh9NPPz3Da4cOHTL2FGKu+m9FTPDu\nvPNOY9Q5ZswYIHuF0kv27dsHuAnWq1atMiqilCMfO3bMFAaI0vLjjz+aMnpJwhdDTtu22blzZ1zG\nHy8kCX/06NGAY8kyePBgwHWFb9q0qVGuEl1eLuqgOEiDey2U3LYDBw6YnMyOHTsCrlt0s2bNUl6R\nSibEsLpr165BeVA9evTI8H4goi77lc2bN3PZZZcB7jUoGpsfSaiPtQKuipSiKIqiKEqU+EqRkix9\nMRQLRenSpU1Ha6Fdu3amHDlcJH9Bnh79RP78+U3VSCDy5Pj4448DsGfPnriOK6dIj6Mbbrgh6L2+\nfftma6z6b+SBBx4wT5mZO7vHi08++QRw1DExBJTz85JLLjHtfKQFR6i8tubNm5u/S25RqiEK3mOP\nPWbsW2TeZ599NsWLFwcSr0iJQiEKGTjtjCCjKi82AosXLwacXFNwVLYiRYoA7pyTEcuysmwVkrky\nzI+ILc6wYcOAjDYH0iJlypQpRnmUKI9UgYNrMxSujVC8keNL7tGDBw82VX3Sj/Xnn382/Xj/+usv\nwFVPwe07GGt8tZAKh3LlypmFRKTIwbZjx44clSbHCrlBzZ071zRPDUQSdf3owRMO3bt3B7JeKEsY\nVn5mdgv/tyALFulLB24icLwRPylxP1ayJy0tLdv+oIlGFkTC6tWrTfFAIFLcIIm+QunSpWPSqyze\n5MuXL8vEZXl48DMSmmvZsiXghM2l32pgaoSU+oslh/S/BHff+nUhFQpJRg/0pJN5ZHYy37t3r7Hv\niDUa2lMURVEURYkSXylS0ln8+++/B7x385Ywyddff82LL77o6ba9QBSIUGrU5s2bTdJdsiLh1A4d\nOgS99+STTxqVUIwcd+3aZd4X2VbK7F966SVf9d/zEknqDlSkpPdZMrJs2TLz9zPOOCOBI/EesSkR\n89innnoqqCfmc8895xuLksymhZs3bzZP7RLqadq0KWlpaYCrjgoLFy5MeHgyVoiSIyXzfkYKOAIR\nU+5veUsAACAASURBVNhQ+0cUqUDkPpvsXHvttUCwM////ve/E/bn8wpVpBRFURRFUaLEV4qUtAuR\n+G+oVXQ0yNPgzz//DDjtYGbMmOHJtr0kVO6BqGhTpkzxXc+5SBGTux49egQpE3nz5jXmjpLLFqjI\nCJKr8/XXX7NmzZpYDjfuiEGeJITatm062Itam4zIk296ejrdunUD3ITeZDLmlETXCy64gEGDBgGu\nYlOqVKmgz0tbij59+hhF1W+0aNHCnEeSayKWFoFIn7dnnnkmfoOLM6L6RtpCJBFk7rUHrioaaJEi\nyeah+te+8cYbMRpdfMlcfCbEyyoGfLaQEryWjsWL59577/V0u14jDWIDEc+epUuXxns4niM99IYO\nHWpuRNn1RwqF/H/IT7/Svn170/hWJPfsHgwKFy5sPNDkRnbs2DE6deoEZEyuTDb++ecfwAkLie+S\nuJ6PHDkyIWP64Ycfgnx3wL1WnHbaaYBT+HDqqacCbjPUUqVKBfUwkwqjuXPnmurTlStXAvhqEZWe\nng7AddddBziFH6GKP6QieNy4cYDbj04adacSWTX29TMy5sCfAwcOBKBSpUqAkwYhjt4NGjTI8PvJ\n9ACTHZdeein58uXL8JpUn0o3gXigoT1FURRFUZQosUI9lcXsy8LsAC2r7LZt25qSeUl+zIrXX38d\ncHtDBSJJzDl5MoxHJ28p+//666/Na/I037NnT+PVEyti3XE+EOlHJipc/vz5zT4UiVrclTt16mTK\ntiXBPPMTVjjEsxt7u3btjAO09M5r1apVkColickvvfSS8R2S4//11183Hj/h2nXEc46RUrhwYaPS\nSCFJjx49WLRoEUDYrudezHHSpEkmzChFEOJNEw4TJ04E3DCQdBkILJDICbE6F6W33ooVKwDYtm0b\n9erVA9w5HD582JyL4tHjNYk+TvPnzx/UzUK6B3gVuYjlHMXlW4pvLMsKqbBmVk4l2vPwww8zYcKE\nSL82iETtR0mD+fzzz4OiGr179wbcczSnhDNHVaQURVEURVGixJeKVCCFChUC4PnnnwdC5xEBzJs3\nL9v3c0qiFKmffvoJgMqVK0e72bCJpyKVCOL59NSkSRPzhH/eeecBzr4U52857+T4lt6KgCnZbdOm\njVFLwiXRT/onQnpHStFArVq1jOoqruddunTJdhtezNGyLOMOLR0D8uTJYxQb6RcIsH79esC1pYDY\n9+bUc9EhVnN89tlnuf322wF3//fr1w+Ir5IB0c1R+ji+8847gON0Ho4i9eijjwJOnqoXJGo/XnLJ\nJQB88MEHQe+Jyu9VHm1Y56LfF1J+IdEnfjzQi7eDV3OUE/qFF14AHBfizBe2QCTB9/LLLwfcG3gk\nJNtxWrBgQdOuRCrDJCE6K5JtjtGg56JDrOY4d+5c2rRpA7gJ9Jk9s3JKPOYoRRHDhg0LakwMbnWs\nPARII3GvOnvEez9Ke6JNmzYBULx4cXNNlbQBaXYsjdRziob2FEVRFEVRYogqUmGS6CeoeKBPwQ5e\nz1G8XAKfGMUGQNSqLVu2mATgdevWRf1depy6pPocU31+ELs5lihRwhQGJLMilWjiPUdREaVZOrgN\n3cXnTbz3vEIVKUVRFEVRlBiiilSY6NOFQ6rPD3SOfkfn6JDq84PYzTFXrlzGpf3qq68GVJGKhnjP\nUQqyRMk/9dRTGTBgAACvvvqqF18RhCabe4ieFA6pPj/QOfodnaNDqs8PdI5+R+fooKE9RVEURVGU\nKImrIqUoiqIoipJKqCKlKIqiKIoSJbqQUhRFURRFiRJdSCmKoiiKokSJLqQURVEURVGiRBdSiqIo\niqIoUaILKUVRFEVRlCjRhZSiKIqiKEqU6EJKURRFURQlSnLH88tS3SYeUn+OqT4/0Dn6HZ2jQ6rP\nD3SOfkfn6KCKlKIoiqIoSpToQkpRFEVRFCVKdCGlxJ1hw4axbNkyli1bhm3b2LZNWlpaooelKIqi\nKBGjCylFURRFUZQosWw7fjlgqZ5wBqk/x5zMb9myZQBZqk9NmjQBYPny5dF+RbboPnTROfobvyab\nP/vss9x+++0ArF27FoAWLVrw+++/R7Qd3YcuOkd/o8nmiqIoiqIoMSSu9gfKv5MTKVHCgw8+CMRO\nkYol+fLlA2DSpEkAHDp0iDfeeAOATz75BIDt27cnZnCKEiV58uQBYP78+QC0bNkSiWKUKlUKgEKF\nCkWsSClKKpHUob2rr74agAsvvJAHHngAgJNOckS2Y8eOZfl706ZN49NPPwVgypQpYX2X3yXMJ554\nAoA77rgDgCuuuIKPPvooom3EKpwQ6hiTxVKoxZVlhfVfHTGx3If169cHYNWqVUHv/fbbb4CzoLrm\nmmsi3XREJOo4rVixIgB58+blhx9+iGobctN+4okn6N27NxD6WPDTuXjaaacBsG/fPgDKly9v/i9a\nt24NQMmSJenQoUOG35s4cSJ9+vTJcrt+Ce3dddddAIwbN06+0zwY9erVC4Cvv/464u36aR/GCj/O\nsUKFCoCzX+vWrQtArVq1AJg+fTr9+/ePaHt+nKPXaGhPURRFURQlhiRdaK9MmTJ0794dgPvuuw9w\nnmRF9RAlKjul7dZbb6VLly4AnHPOOQDcfffdsRpyzKlUqZJ5gs+d29mlrVu3jliRihWZ1afhw4cz\nbNgwIPywXzJTpkwZAFq1asWECRMAV0H86aefEjYuL2jatCkAs2fPBpwwz8yZMwHMMblnz56wtlWu\nXDnAUTriqZSfiNtuuw2AG2+80bz25ZdfAtCmTRsAduzYATjXE1HWBMuyguZz3nnnxWy8XiD79eGH\nH87w+i+//ELPnj0B+Pbbb+M+LiUybr75ZgBzvRUF9eSTTw767Omnnx63caUaqkgpiqIoiqJEie9z\npCTfYNasWQCcccYZJskxi+8AslekAjl69Cjg5Ba9+OKLWX7Oz7Hg7du3U7p0aQB27doFOHljW7Zs\niWg7icjLkCclSTQHR7EKfM8rYrkPRU158sknAahbty6FCxcGoESJEoHbBuDVV18FXNVG8qhySryP\n00aNGgGushiY0yR5Yx9//HG228iVKxcA8+bNA5zcx+nTpwPQtWvXoM/Hc46lS5fmww8/BEI/sYdz\nvdm1a5d5f8WKFQB069YtW6UukTlSJUuW5LvvvgOgePHigKNEATRu3Jgff/wxx9/hxT7MnTu3OXZC\ncd111wFQvXp1GjZsCMDKlSsB5z5y6NAhAHOetmvXLmgbY8eOBWD//v3mOFiyZInMIduxJ+qeIXlQ\nEyZM4KqrrgLcSIWwd+9eRo4cCbjn58qVKzly5EhE3xXvOcq1VObVsmVLqlatCsAFF1wAwOjRowEY\nNGiQub/nhHDm6OvQXsWKFXnttdcANyHuRMgF4Kuvvgp6r3HjxoB7cQD3Iv7ggw+aE0W24XckxFm8\neHFzwMgiJNJFlJIz5EYTmFRcrFgxwPHZARg4cCA1a9YEoG3btoBb0SehvmRDFgarV68GoEGDBhFv\nQ3yJpHgEMBf5RHPvvfdmG/I4ePAgkHGxKMfCwoULAZgzZ04MR+g9kyZNMseuLBZ69OgB4MkiKqcU\nKFAAgKVLl3LRRRdF9Luy8A9FqAKlUAUBUqF7+PDhiL47lpx22mlMmzYNgIsvvhhwrz+BfP7554CT\nZiDHqd8RkWDKlClmf8s9etGiRbz//vuAW2j29NNPA1C2bFnzICaL5lihoT1FURRFUZQo8aUiJdLk\n/PnzzRN8dhw5csSsSmUFunXr1qDPSWL54MGDg8qRK1asaEo/xULAr5QvXx5wpEtwku1F2ZDVeLIQ\nGNJLNSR0I8nXy5cv59dff83wGZGlk5HcuXObsvgaNWqY13fv3g3AH3/8ccJtnHLKKdx0000ZXps1\na1bQ/1Oi6Nu3b1AIZ/r06Sbk888//wCwcePGuI/NK0SVl+tJ27ZtzZwnT54M+MvbTVSiUGrUwYMH\njbIUSoWYMWMGAJs3b872O6T4RSwsAN577z0ge2udeCHqS8eOHQEYP368UaA2bdoEOGqq2AIVLVoU\ncO0skkGNkqKGESNGALBhwwaj5IsCDu798Kmnnsrw+506daJfv36AWwwSK1SRUhRFURRFiRJfKlLy\nFHAiNUrivX379jW5GtkhxnGSWwQZc1puvfVWwP+KlJRjS4IzwOuvv56o4XiOn55+vUASPc8666yg\n90RJTAbkKVhK94cOHWoSeoX9+/cb09FwjDmffPJJk5QueX233HILf//9t2fjjgbJpTnppJOMAiEJ\n9ZLTlSqIEhVY3PHuu+8CTl4fwJ9//hn3cWXF448/DoS21Vi7dq15PSe5rqHscMQKwosE5pxQu3Zt\ns6/kXDt69Kg5PuX+0KZNG1Os1bdvXyC0YbAfKVSokBnz0qVLAee8E5WxTp06APTv399cV9etWwfA\nN998Azi5bAcOHIjLeH1ZtSeyXVaJhBK2k0qMaBKrzz77bMD1gwkkc4UD+KNq79JLLwXchUagi3uR\nIkUAN/k1GuJZKSTSuZz8mb7Di68IIlH7UB4MXnvtNTO3/fv3A26lSbRu4JmJ1RxPOukks2gSz6hA\n3nzzTcAJ1coFLTskpPn222+bi/1jjz0GuDf2rIjlfpTE1gULFgDONUiukZJc/OGHH5pwrYQx5Zzc\nuXNnpF8ZknidixUrVuSzzz4DMhbhiN+QV9WkmfHD9TQrWrZsaarECxYsCDgLybJlywLhX2NjNcfn\nnnvOpLBIuPH+++83BQ/NmzcHnOuNiAfymtetfGI1xzJlyrBt2zYAnn/+ecCprpT7tvhgjRkzhlde\neQVwr6lyfSpevDjNmjWL5GtDos7miqIoiqIoMcRXoT3x8zj33HOz/ZzIzP+mEv/cuXMH9RMUrrnm\nmhwpUYkglZ3Mw0Ekaims8EqR8hrZTwMHDuSKK67I8N7Ro0dNmFyeAqXn3IlYtGgR4Cgikqgtx3ei\nKFiwoGk0LUphIOJY3qhRIxP6E4VRwkmrV682tgfihRWpN088qVy5cpDVwfz58z1T1pIJCTG/8sor\nRonau3cv4KjKfrnGSmQC3DBjzZo1TQJ5t27dAMifP7+xw0m2ptK7d+/mkUceAVx1dMOGDcYjSnrl\nZkeuXLki9pWMFlWkFEVRFEVRosRXipSUKsrTgOJyySWXBMV7P/jgA8CNkycLaWlpIW0P5Okp1RAT\nw3379pkyZMlHkQTWtLQ0Xxn8ST7I1KlTATjzzDNN0rV0ABgxYkREvQJz585tbAMqV64MOGqNX5J4\n8+TJYxTCSJH92rJlS1q2bAlAlSpVABgwYIA3A/QQyTERe4NAbrvtNrOv5Vos0QJwIwLxSuSNNZJf\nKon1gXN9+eWXAfda6wfGjh1rxir3hGbNmhnFLH/+/OazYmQtBtV+MFQNh0OHDuVYoS5RogR58+YF\nXJuSWOGrZHO5kJ5oTJKVH+hdEyniRTJmzJig9/yUbC433PXr15uLvMi0UnEoVQ05JV4JrsOGDQu5\nkIpVkrnghwTXoUOHAo7HC7g3qrfffts4oOcEr+YooTrxbbFtm8GDBwNuUnikXH/99aY1jjBo0KCI\ntxfL/ShJxtdff71sg/nz5wOYUENgRaGE+KSgIHP4E0JfT05ErM9FGefixYvNa1OmTAGcquZHH30U\ncJsyS3GAZVmmGbq06ZCE+0jww7koSHh64sSJ5jW5xsr8o6l2i+UcZfEn94DatWsbR/Pzzz8/6POS\niC33u7Fjx5qwZU7w034UNNlcURRFURQlifCVIiVjCeUcK87lVapUMY1hJUwQKWXLluWtt94CXLfz\nQEI1wkzUylsSCwN9smTeEgr1ingpUqGOueHDh3vepDjE9/rm6UmsPUQBOfXUU02JcjieaFnh1Rxl\nH8nPtWvXUrdu3YjGIuE76b/Xs2fPIEuT8ePHm/LlNWvWhLXdeDSfFvXtq6++MopUOG7QU6ZMCWq0\n3KRJk4j3aazPxZtvvhlwEuJFmZBr4bBhw8LyypKwV+fOnSP+fj+ci6L2i91OYJeBpk2bAqHtWcIl\n3nOcO3cu4CqFH330EZUqVQIIaqD+4YcfGrU5J10E/LAfBSnCeueddwBnHaGKlKIoiqIois/xVbK5\nKFGhFAsx6UtPT89xGWrbtm2pXr160HdJTx8/IKrYkCFDzGvSL2jSpEkJGVNOibXilEyIeZ7km7Rv\n394kePuBv/76C/g/e2ceZ1P9//HnyNZYosguZIsJhUKyRCTKEsqeEomGRISsLb6SUETWhOwiyZqR\nVCqJoizVTIQSkX29vz/O7/05d2buXHfu3OXc6f18PDxmnHPuuZ/PnHM+5/N5L6+3Xem+cuXKRqZg\n3rx5Xj8rq0CJYXQPfk1KbGwsTz/9NGDHNiStvRdKxOr0zDPP+PX5b775xlRIEMqXL58mK2MwuO++\n+wBr/JN6iSI789RTT5lxUWQQ3n//fQCOHDli4vzq1asX0jYHkty5czN79mwgeb3L6dOn8+WXX4aj\nWWlCVM4lls89DlUkBEaOHAlYz5hYguvWrQs4o4ZgWpB3pvQnULHDvqAWKUVRFEVRFD9xlEXKGyJq\nlxZrlMzKPfn/L126xLZt2/w+d6ARcTj3LCCxBDhVvPFaeMrU+6/jfj9L1o2UHwknEpsncXgtWrSg\nePHigB0/lFr+/fdffvjhhxT3i/UrkvFkVQx33UBvXLp0yVgCpZzIyZMnjbVmypQpifZ16dLFXKdI\nlj9o1aoVDz74YKJtIiPTq1cvR18zT9SqVctYZMRz4Y6UW5E6fNu2bWPSpEmA9bcAO15TST0RM5FK\nCzK4yY0ibj13XnvtNVMvzAlI4LHgcrlMEF16ZOjQoWailRY9qUhzH7oHcKc2mDuYSL28du3aAVYg\nstyTUrTYE1OnTjVSAKJ1IyxYsMCoLqdXPE2kROHciRw+fNi8ZIVy5col2ybB18OHDzdyDuLqjSRu\nuukmAHr27Gm2HTlyBLCV9cWtHUnkz58/VcevWLHCuHR1IpV21LWnKIqiKIriJxFjkXJfrftSZ0eI\njY01gaMlS5ZMtl8UT7///vs0tjBwPPTQQ8lW8xMmTEgknheJSBDgtVKK0+IClM/Kd8XFxfl9rrQi\nCtcSdH3gwAFj6ZGUc1HEBntVKT9lpewEdu/ebdw73siVKxebN29OtE0CXOWn02jWrBkAa9as8dsa\n0bdvXwAef/xxs02CeZ2IBFN36NDBCP2KsObhw4dNuny+fPkASzAWEgtyiuUxkhDpGPd6rrNmzQLs\nxI//Avny5TNyAUWLFg1zayIftUgpiqIoiqL4iaMsUgcPHgRsUTx33nrrLcAKWO3Tpw+AxxWyVKsX\nn/4999xj6kq5IytPCTCUiu1OYOLEiSYOQSwX4s+OZMQ6VLduXWM5kusVaOT84bRI/fvvv4AVfydI\ncLnU1XO3SH300UeAsyxRviKWi6eeesoEvf7555+ALRQoCSNOQ8pF1alTh969e6fqsxKUL+fImDEj\n+/btS7TNiUjNNZfLZWLfpERT+/btTekRuYYih7B161aeffZZwL/SMOFCAqvF+gi2EKXUk4xkVqxY\nwTfffAPYYqvTpk1LscbcLbfcYp5TeT4V/3HUREr0ZyR7zpP+TM6cOZk2bVqK55DBwJti+++//27U\n0d3rK4UbeRm5T/z27NkDQHx8fDiaFBTi4uLMBMc9OLx27dpA6idXci5Rv3cKTZo0SbYte/bsQHLN\nllOnTjFhwoSQtCsYyDVz12Lr3LkzYGm/ORkZK7p162ayCqdPn+71MzL5WLVqVaJzuFwucx8eP348\nKO0NBOLa++WXX0ytQ/kZFRWVTNm+R48egBWQHEkTKLDceKK+Hh0dDViTKKmjlx7G1vPnz5vsy9df\nfx2wMr5lcSbI+7Fdu3ZmUfdfcmkGC3XtKYqiKIqi+Imjau0J48aNA6yq3J7q3l3jOwDPFilR6W3Z\nsmWqq3mHoqaQWGeGDBnCrl27ANtKFwp3T6hq7YWLUNeFEpmAHTt2uJ9b2pLo2P79+zNmzJg0f2eo\n+yj12aR2ZYECBYyLQVb8gb53A93HypUrA5Z1KWfOnICtn7R8+XLzLIqFfODAgSaoXFzwcj1ffvll\n48pNya3iC6F6FnPkyGGSAB555BHAstCMHj0asK29p06dSutXJSIU96nUlVu8eLGxdgvr169PJjET\naEL9LMq9u3fvXsBK6pEqAVeuXAHsuoizZs1i5cqVgJXc5C9OqrWXKVMmwH7uFi5cyGOPPZbm82qt\nPUVRFEVRlCDiSIuU0KlTJ6OifMsttwBc00KVdMV/+vRpEw81efJkwLPy67UI5sy7Vq1agJ1inDlz\nZiOSFspAQLVIWQSqj5kzZwYwwblNmjQxK2O5P+fMmQNYQdoXL15M83eGuo8zZswA7LT/nTt3UqlS\npUCcOkWC1cfKlSubVXrevHnlHF7jLSVpRcapKVOmpMkSJeizaJGWPso96R7vJgHmDz30UNAlb8Jl\nrRGPTq9evUy8sYwtIiM0c+ZMUzMxLRZjJ1uknnzyyYCI4fr0LDp5IuWOZCJUqVKF7t27J9p34cIF\nE+SadCI1ceJETp8+7e/XGoJ5wzzwwAOAHbjatWtX8/CH8vro4G2hffSN6Oho82ISt0KrVq2CPvkP\nZh8LFy4M2GWkBg8e7PEZFI0occcHWuVbn0WLtPRRrlHr1q3NNgnETqrTFwzCNd6IS7N79+7cfPPN\nAJQoUQKw1dsDNYl00pgqumYffvghYD3LMj6lBXXtKYqiKIqiBJGIsUiFGyfNvIOFroIttI/ORvto\nkd77B4G3SEmA+fr16/09rc/ofWoTij6KV2rw4MEAZMuWLSB1E9UipSiKoiiKEkQcJcipKIqiKIHk\nyJEjRsT5t99+C3NrlGCRJ08eACPdEYjkHV9R156POMmEGSzUnWChfXQ22keL9N4/0D46He2jhbr2\nFEVRFEVR/CSkFilFURRFUZT0hFqkFEVRFEVR/EQnUoqiKIqiKH6iEylFURRFURQ/0YmUoiiKoiiK\nn+hESlEURVEUxU90IqUoiqIoiuInOpFSFEVRFEXxE51IKYqiKIqi+ElIa+2ld5l4SP99TO/9A+2j\n09E+WqT3/oH20eloHy3UIqUoiqIoiuInOpFSFEVRFEXxE51IKYqiKIqi+IlOpBRFURSfKVy4MIUL\nFyYuLo64uDgqVKgQ7iYpSljRiZSiKIqiKIqfhDRrT1GEm2++GYANGzYAkCtXLubNmwfApEmTAEhI\nSAhP4xRFSZFXX30VgHvvvReAtm3bsnPnznA2SVHCSpTLFbqsRH9SIKOirMzDnj17AjB48GDy5MmT\n6JjnnnuO9957D4A6deoAULRoUQB27tzJpk2b/G6z4OQ0z5kzZ3LTTTcB8PDDD/t9nlClXEdFRTF9\n+nQAHn/88WT79+3bB0CDBg2AwE2onHQNY2JiAOjatSsAjz32mLmv5Z4fOHAgo0aNAsDX59RJffSX\nu+66i927dwNw+vTpZPtD0cdixYoBkD9//mT7vvrqK39P6zNOlT945JFHWLBgAQA//PADAPfccw9n\nz55N1XnSw316LULRxxw5cgDQpUsXxo4dC8DVq1fN/hMnTgBw//33A/Ddd9/5+1Ue0etooa49RVEU\nRVEUP3G8Reqll14CYOjQod7Oa6wWsqqPjo4G4O2336Z3796pbmtSnDjzrlevHgCffPIJ48ePB6Bf\nv35+ny9Uq+DevXub1dPBgwcBeOONNxg5ciQA2bNnB+CDDz4AoEOHDolWWf4S7mtYqVIlnnvuOcCy\nQAFkzOjdu541a1YALl265NN3hLuPAAULFgQs6wXAO++8A8Dly5c9Hp8hQ4ZEx8+ZM4e1a9cC8NBD\nDyU7PhR9/OOPPwDIly9fsn1ff/0127ZtA2zr6eHDh83+v//+G4CNGzf6+/WOs0iJBfWTTz4hU6ZM\nAPTv3x/AeANSgxPu02ATzD6KB2L27NkANGzY0FiyPb3T+/btC8C4ceNS+1VeccJ1LFGiBABPP/00\nAC1btgTglltuMcc0adIEsO7f1KIWKUVRFEVRlCDi6GDzQoUKeYyh8YTERCUlV65cZgXl66re6WTJ\nkgWw42uuu+46+vTpA6TNIhUqRo4cycWLFwHb0jhz5kyuXLkCwIQJEwBo06YNACNGjGDPnj1haGlg\nqFu3LgDLly8nW7ZsgH0vfvTRR4Bl2bjhhhsAePLJJ8PQysBQoEABPv30UwBKly4NwMmTJwF79exO\n3rx5adiwYaL9Z86c4cCBA6FobjLEeiaxUZ5W93fffTd33XWXx89HRUVx4cIFALZs2QLAqlWrePPN\nN4PR3KAjSSGyki9YsCBdunQB/LNEhZJ77rkHgBYtWphnSyhdurS5PxctWgTAxIkTAfj5559D2Er/\nKFWqFIB5dtwRa2mWLFmMJdFXqlWrBkDTpk0BK7Hg1KlTaWlqUMidOzdgXdt3330XSP6suv9/8eLF\nALRr144PP/ww4O1x9ETqzJkzxmUnZrrFixcb050vdOjQwfxB//e//wGR8aB4QwIM5e+wcuVKfv/9\n93A2ySfEnRUdHW2CqGfOnGn2v//++wDG/VW8eHEAhgwZQrt27ULZ1IAyf/58wAr8XLNmDQDt27cH\nMC9dwAwIwubNm83kMlKIjY01LyhviDvvueee48UXXwTsgW/YsGG88cYbwWtkClSqVIm2bdsmap8n\nl7K4UDwRFRVlFjr33Xef+Sn9kT7Onz+fhQsXAtYE26nImCnu2tdff93jhDhcyHW68cYbAahatapx\nOdaqVQtIOVFDruMzzzwDQMeOHQFrXF23bl3wGh0ApK3udOvWDYC5c+cCULFiRT7//HPAMihci06d\nOjFjxgzA/pt9+OGHbN26NSBtDgTi0pSEB0kuA/jxxx+BxG722rVrA3aIxJAhQ4IykVLXnqIoiqIo\nip842iJ14sQJY27dsWMHYM22y5cvD8Btt93m03k6dOgAwKFDhwAYNGhQoJsaUho3bpzo//nyYXLQ\nAAAAIABJREFU5eP5558PU2t8RxIHoqKiTECuO+ICkoBICaB/5JFHeO211wB71RFJiBuzffv2fPbZ\nZx6P6dmzJ507dwbsYOUBAwYEJMg+FEiCQMWKFc02sabFxcUlO14scgMGDDCrXwnwfvvtt4PZ1BTp\n3bu3cb3K3z0la4a3JB1f9j366KMUKFAAcJ5FKmPGjEyePBmwLR+vv/46YMnPOMlKKkHUMj544uLF\ni8mCjCVJAOwgZbmHp0+fTqVKlQA4fvx4QNsbKNyfM6Fs2bIAnDt3Ltm+wYMHA5a1NylirRKJIafS\nvHlzcx+KPAnAlClTAPu9/s8//5h98s73lDQSSNQipSiKoiiK4ieOtkgBLFmyJNHPLVu2JLNEJSQk\nsGvXLgCqVKkCWEGsSZGA7KioKAYOHBi0NgcbSROXuKj69et7FC50ChLwWKhQIcAKtPZmdRDLlJA5\nc2bj645Ei5Tck3/++afZJpaPV155BYDu3bsbUUNZPYZC+DEtZMyY0cTOSHxXgwYNzIpQYp/kPs2Q\nIQOdOnUCbHVswDy7opTtHjcWCiTu4u677062b+nSpUaqIy1IXKMEse/bty9RLIcTkDiSt99+21hH\nJXB5wIABYWtXSlSsWDFZcs3FixdNHKLEYe7du9erZUksoZIQUKhQIePFEKu40xAPhCQ0uG+T2LzG\njRub96an96E8ux9//DEAFSpUMDFnEoPkhPioRo0aAVYyyvXXXw9gBHsffvhh4uPjfT6XWFoDjeMn\nUhIQ+MQTTwBw++23Jztm5syZRoNIlL2XLl2a7DjJ3nvhhRciciJ15513ArZKbebMmQFrouLkl271\n6tUByJkzJ2BNir1lUEow6+jRowE7cyhScZ9ACZKZ9+yzzwKWC0HU3qdOnRq6xqWBmjVrmgw9d8TE\nnjR4vn///mbi6I64+USFOdRIEoRkQrlz5swZc/+KKnQgKiU4EQnS7ty5M7/99htgL9qcyOrVq02Q\nuTw7kyZN4vvvv0/VeaQ0lXtmZWqz3UKN3ItSMHrRokWUKVMm0balS5eaLFR5H0oiyOLFi83kSn66\nXC6TiOWEibMsNocPHw5YSUpybWWx6W0SVblyZbOAkQli9+7dk41LgUBde4qiKIqiKH7ieIuUIAFl\n7og2hFijABPMK2rmKZlmRTpAzhEJJFV5jo2NBQJfPymQZM+ePVkgvK/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YAGDeD+K+jYqK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\nY7pw7U2ZM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2/U2dkpSfOmN/ORza7LBwMDAy64tQ/v8vLyQH92fHxcHz9+lOSdzpDmLzYP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\nEoEUAABASARSAAAAIf0HBztPDx0KfCcAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3b1086e860>"
+       "<matplotlib.figure.Figure at 0x7f3144b93320>"
       ]
      },
      "metadata": {},
@@ -1462,21 +1568,16 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Let's have a look at the average of all the images of training and testing data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -1513,12 +1614,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1541,7 +1638,7 @@
      "data": {
       "image/png": 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7J8+LKVOGYRiGYRgtcCSVKR14DUTrofQQP/CBDwBoeIxTU1NiIdODLxaLYkHzs+iVDA4O\nispFz2NlZUU8LSoKaSpTRCtU9CpZj8nJSfEaWGYdiB4GDnZTmdJb3tkOu7u7sl5NL4dxI/V6XTwk\n/t3JkyclNoUqAe9BLpcTpUTHQ3RTtanX63Jv2Y8ymYx4dVQjSL1el/LTi56ZmZF4K3qRbCMdXPm0\noNpOkBQzxftdKBRks0AYUK9VJXrt3nu5F3w/29V7L+OMMTm1Wi0Wl9HpuJswZqpQKIiXnpQmgOXR\nSWTDVCT8XaveOgCdP3N8dDLGKElVDWOlWN+JiQm5D2yT+fl5UWY4T7Lu1Wq1a6krWI8w3mtyclLm\nCG78uHjxYtPmD74PiMZakjIVzlmcr27evIkbN240laFarTalEAA6E0ekVUWWWccgsm5UZvh8KJfL\n8jykCrOxsSH9i89WrbKGCurIyEjsPnVyDtLPftZxenpaNph9/OMfBwB89KMfBRDFpbLMnINLpVKs\nHai6zszMyIYPPXbDTT3POwaPnDGVyWTkBrHznzt3LhbYy0mgVqtJp2en0bvz2Bm4FHjp0iUZhDrf\n0fvvvw+gEVjJSTFNdOOyHjpQlMGBYS6tnZ2dWIbwbk14/K4we/njx4+lTdi+rJ/OgcVBe/r0aXnI\nMeCQE0B/f3/T7o1uondpUT7n5LuxsSGTQTjpZLNZ6Xec+MbGxpp2vQGNCXlvb0/unW7LbmfM1nI7\ny0zDghMxy7S8vNzUpkDUF8LxzMmtVCrJ5Mb+sre3F1ui7pYxxTE2OjoqZaWRwT4INJwXPqxWV1el\nL3AJm8bU3t6evKZz+vA1PakDnRmnSctiejlZXwuFgvQ37uq6f/++/Bxu0Onr64vtkOpUcHZSXjeg\nOYcdx9aFCxdk9zD7m94pzP7JazablT7O97HflkolWd7Tc9APcyD086Idm3A5+sSJE7GgcY6Z9fV1\neabduXMHQNTXWH46cVpk0AH6rFc3guy1wcg25XPuwoULeO211wBArmzP+fl5vPvuuwCi3YhAVP9w\n84h2YlnfcFNCO7BlPsMwDMMwjBY4cspUf3+/LINQObp06ZJ4GbQ6yeLiolintMQfPHggHhQtXf49\n0AgmpZV65syZxKDStNHeAOvBZYczZ86IDE+vmAHoOii024oUEHmm9G5Zh93dXbm3oUdHbwpotMmJ\nEyfk88L8Inp5SG9V7qT3xM/m95ZKJfmZqoLeoh3m8ZmYmBDv+cUXXwQAXL58Wfq43koPRCpPKElX\nKpWuprjQOaV0HhuqvPSKWb5arSZyO1VInUqBihbH8NLSkihSfP/Ozo681sm+q5dO6fHroGyOM9aV\nS5Q7OztNy19AtL2cY49jke2olTa9uYV9h+3O8dLuOmvljW2Zz+ebFG6g0SYDAwOyXZ5Le7qdwpxh\nerOPVnvC3GCt9tunLS/prPkci5ubm1IP9kmWb2trK5bOYXh4WAKcw+dDJpOJjX/dht3O+cb+qkMB\niA494IoN+6tWidmfOYYzmUxsLO7u7kq/7ORytE47Q3WQc+WVK1ekPWgXMD/kt771LXz7298G0FiS\nLRQKsjmNY5jtWCgUZD4OQyjagSlThmEYhmEYLXBklClaiIODg+I1MVD84sWLYmVyvZzxCm+//Tbe\nfPNNAI2tkMvLy2Kh63VyILJWw1QKo6OjEhMRBg6nibb+aZUzwHJ8fFy8Bt4Lesf7+/tdVaSI9t7C\nAHi9zT58P9C472yTfD4vbUdvWCtZOiYF6Ezm6KSy6qDocDt8f3+/eFZUYxgEe/XqVdnGyySzZ86c\nkc/lmj89rIWFBYmJo8pRrVa7ErtAdAyDTpTH8cOxqJNSsszsm6OjoxLjwBgG3ptHjx6JQkBFZ39/\nv6tZlnUsCtWW8fFxKSvVCo4/HdzKdi+Xy9LPqYhTDdjZ2ZE+yvfr0wjCayeyn7N+VKZOnjwpbUJl\nivXb3NyU+CgqowcHBzIn61QnhG3Nfrq9vS19KYzdfF50/I6OWwSie8yyso71el3mRd53vkcnGuVn\nTU5O4mMf+xiAhmrDOu7u7kr/pMqVFLjcSbz3MXWsUqk0KWVAYwwPDQ2J6kRFNJ/PS8wx1XHOu4uL\ni9LuVPSS0j90Ishex/Rxzmf/nJ2dlfbgmHr99dcBAN/85jdFpaLSNDU1JSor48I432xubjadixrW\np9V2NGXKMAzDMAyjBY6MMkXrdGRkpOncPSDyaOnx01JmfNSbb76Jt99+G0DDot7b24tZ8fQsNjY2\nxHukFayPa9FHfaRFuC6dyWQktoaecrVaFSWKKQb0duU0j8M5ODiIbRvu6+uTn5MSdFKRoqd8+vRp\n8UjYJvQwt7e3m9b1+X+scyd22YSfpZU/XQ96VEyexx0oH/7wh2Xtn+/JZDKiRIXHGZVKpa6mCNDo\nmCmOEbbL3Nyc9MXwSJ+1tTVpdyoY09PT4gVT7aGnXCqVYsly9X3t5G6ppJgprcKxvvRu+X/lclk8\nX5Y9m82Khx+eGVmr1aRuWskMlahOnpeZFBOmk3QCjTG2tbUl8wr/7vz58/I+qh38v3K5LGoNlY2+\nvr6YitSOPszP5Fih6rW9vR07o61cLsvP7J8s3+rqqrRJUows25p9eW1trUmtAaJnTKePVAEa90sf\neaaPYOK957xBBfns2bPS3nyOjo2NSX9mP+UuxTt37si5oNzRvr6+HlMWOxErpWPBwmSyVEyBRloO\nqvhbW1tNKRSAaN69fv06gIb6xvtVqVSkL3Rih/SRMaY4GAqFgiwj6LP2OJAY9MlMpjdv3pTG14cV\nhzlJ9GGHYboA730suK8b216flXw+L3m1KLc/fPiwKRsv0PkDYX8QSQ8GfV85cXGy4oN6dHRUDGhu\nFLh48WJTpmygIbGvr6/LhM9J5ODgIDY4O7F0kvRZ7LsnTpyQejA3Co2qy5cvS31YvlKpJBM2Jzca\nkGNjY7EMxpVKpSvLt/oEAk7ONITm5uakD3JC4n0/ceJELG/Wq6++ig9+8IMAGgYZ+61e+tFBtfw5\nXFrtBM65WL/ROX3CpelcLiftyHLlcrmmvgw0DBedOkDPQd061y3JmJqcnJQAX5aXhuHW1pbcBz6M\n5ubmpO04drWTyuU0fo9ehg8zaLdS3yednVcqlWLGlN4gwgcol6CLxaJ8ln7u0FHlGOTfPXr0SIwp\nvRmkG2hjKlxK1kHm3EDFZ+fs7KzMQWR0dFTuCTdtvfPOOwCicBkaKZxbS6VSU8ZzXZ52kpT+Qacu\nYN/hfMN+fOnSJZlnGE5x/fp1Mabo9NFI1Jt69AkF7aqTLfMZhmEYhmG0wJFRpmiRTkxMiHVNb2h4\neFgsSlrP7733HoBoaY+KlD4jKTyPSqsi/C56OPpU8KOgRBGdwJKKDV9bX1+XZGysfxpB55qkbL3a\nY6d0S2VDJ3Gk8sG2n56eFg8kzCC9vr7epEjxu8N2JZ3I/q4VDfaxvr4+8ajCLNn37t0TD55or4if\nwfpfvHhRAi5ZV61MdbKttTIVZsjWZ7fRU6SCMTIyIp4vl22vXbsmgfdsTy636PMX2U/08ok+6w3o\njMKo02zwPq+trYl6xjKzP+/t7cXG29DQkNwT3jt9zluoziQltezGMh/v8fj4eGx5Ty9FUl3lysDc\n3Jz8Ld/HMZnJZKQuvH+rq6uyItDOzNlJyUGBaFywL2plKlw6Z9mBuBJ89epV2RjCPsnUEPPz84nq\nf3gyQSefHfV6XerLemxtbYkyxWcl31MoFKT92HcrlYooUVRruMLz4MGDpk0gQPJydCfRmwy06qjr\nBDROQanX600pW4BoSZP9l32B8+69e/eaVDd+hilThmEYhmEYR4Ajo0zRej516lRsu269Xo8pUzro\nOty2qc8Uo4fMNeXx8XHxmvTW2nALaJro8+mASKWgB0Ur/d69exKQ141jN54FfXYey8u2nJmZkTVs\nXqnCzMzMiDfBNs9ms01xHEBDmSoWi+LBsJ2HhoZiZ2uFnmw70N6oPqcPiDw6ekGMSWDZ8/l8LDZn\naGhI+iXvEz2tubm52Bb13d3dWAxDOwk97cHBwVjskP5exlNReaJCpesxMzMjqlYYiF2v1xPjlbp1\nBiHLoI8DAqIUK+xXVCf0lvvwCCCdPJH3i/emWCzK57IvdCsWhWVMOn6F7cNysxz5fL5pWzoQ9dMw\nQbDeWMLPpaKjjybR5WgX4biuVqux79OKWXhUld6Cz00hH/nIR0Qd5zOA6uT8/LzMRd0IOn8SYX2q\n1Wos/lfPwWFKmbW1NUklwLrpVCZpHD2mv0+n1OGYWV5elrZiP+PzA2jUV19ZX34GV3Dm5+eb4qqB\n9o67I2NMcbIaGxsTOU8bPXyg8AGTdNCmXp7gZ4S74M6cOdO0cwWIGoyThZaCu43OEQI059rgRECZ\n8r333ms6TDZN9H0HIoNVHzgKRAGCbAO9Yw+I2iTcJVQqlWIPXz0p6iULIDKqwkNmdcBqyzlEgizs\n+mBUfvbe3p70Tw5aLkfrw591X+f94WTA/nry5EmpGw3MlZUVWbLoxGQQLjkdHBzI9zEAd2RkRMYK\nH9K6DKwb+0J/f784LVySoCO0sLAgn6XzhbXr4NGnoZdow5xIDx48kPZjP9PnwnF8sl30nEWDgu/R\n+dL42sDAgHxepw9U10a/DnUIzybj7zpLNO/HwsKCOG5sSzoBk5OTiTtdO/lgDo0pvXuYc4Qen0ln\nE9JwYrDytWvX5P5wzHIpbHFxUeZa3Te7Pe+GYQV6eZnLtpwz6vV6LAP80tKSzE+cW3WIgj70GYjq\n2knHJmzHSqUi445lz+fz4ngw5IDzjs5Cr8+apG1Ag1EH1iftkLZlPsMwDMMwjCPAkVGm9JlP9OB0\nzhCdfwdoDjympaq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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3b0d96dcc0>"
+       "<matplotlib.figure.Figure at 0x7f3143051d30>"
       ]
      },
      "metadata": {},
@@ -1568,7 +1665,7 @@
      "data": {
       "image/png": 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ShfrMZz4DAHjuuefw+OOPA6i3cTablX7LjUuvvPIKAODHP/4xrl27BqCusK6vr0sdW1Wo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\nAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3b0d1ec7f0>"
+       "<matplotlib.figure.Figure at 0x7f31434f97b8>"
       ]
      },
      "metadata": {},
@@ -1582,10 +1679,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "## Testing\n",
     "\n",
@@ -1594,12 +1688,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1619,22 +1709,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Now, we will initialize a DataSet with our training examples, so we can use it in our algorithms."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# takes ~8 seconds to execute this\n",
@@ -1643,20 +1726,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Moving forward we can use `MNIST_DataSet` to test our algorithms."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "### k-Nearest Neighbors\n",
     "\n",
@@ -1667,12 +1744,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1692,22 +1765,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "To make sure that the output we got is correct, let's plot that image along with its label."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1719,10 +1785,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f3b0df19390>"
+       "<matplotlib.image.AxesImage at 0x7f314374b198>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1730,7 +1796,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdgAAAHVCAYAAABSR+pHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngcACQAOBBYAG/webxyRlyxBmMwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3b0e46b438>"
+       "<matplotlib.figure.Figure at 0x7f3143f85400>"
       ]
      },
      "metadata": {},
@@ -1744,10 +1810,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "deletable": true,
-    "editable": true
-   },
+   "metadata": {},
    "source": [
     "Hurray! We've got it correct. Don't worry if our algorithm predicted a wrong class. With this techinique we have only ~97% accuracy on this dataset. Let's try with a different test image and hope we get it this time.\n",
     "\n",
@@ -1771,7 +1834,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.5.2+"
   }
  },
  "nbformat": 4,
diff --git a/tests/test_learning.py b/tests/test_learning.py
index ec2cf18bd..5e998b6f5 100644
--- a/tests/test_learning.py
+++ b/tests/test_learning.py
@@ -60,10 +60,10 @@ def test_naive_bayes():
     iris = DataSet(name="iris")
 
     # Discrete
-    nBD = NaiveBayesLearner(iris)
+    nBD = NaiveBayesLearner(iris, continuous=False)
     assert nBD([5, 3, 1, 0.1]) == "setosa"
-    assert nBD([6, 5, 3, 1.5]) == "versicolor"
-    assert nBD([7, 3, 6.5, 2]) == "virginica"
+    assert nBD([6, 3, 4, 1.1]) == "versicolor"
+    assert nBD([7.7, 3, 6, 2]) == "virginica"
 
     # Continuous
     nBC = NaiveBayesLearner(iris, continuous=True)