From ecf279dca520fd09f3251b4f07287bb55bc65388 Mon Sep 17 00:00:00 2001
From: Antonis Maronikolakis <antmarakis@programmers.gr>
Date: Tue, 7 Mar 2017 17:54:11 +0200
Subject: [PATCH 1/2] Added Plurality Learner Plot Image

---
 images/pluralityLearner_plot.png | Bin 0 -> 12658 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 images/pluralityLearner_plot.png

diff --git a/images/pluralityLearner_plot.png b/images/pluralityLearner_plot.png
new file mode 100644
index 0000000000000000000000000000000000000000..50aa5dcd14c138e342d8cdca63d70b1ada73b9c3
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literal 0
HcmV?d00001


From 1e3db3d9de714145a413cac41fdab7e391f8ab8d Mon Sep 17 00:00:00 2001
From: Antonis Maronikolakis <antmarakis@programmers.gr>
Date: Tue, 7 Mar 2017 17:54:37 +0200
Subject: [PATCH 2/2] Update learning.ipynb

---
 learning.ipynb | 760 ++++++++++++++++++++++---------------------------
 1 file changed, 333 insertions(+), 427 deletions(-)

diff --git a/learning.ipynb b/learning.ipynb
index f6b4460d6..f049810f2 100644
--- a/learning.ipynb
+++ b/learning.ipynb
@@ -3,7 +3,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "deletable": true,
+    "editable": true
    },
    "source": [
     "# Learning\n",
@@ -12,46 +13,47 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "from learning import *"
+    "## Contents\n",
+    "\n",
+    "* Dataset\n",
+    "* Machine Learning Overview\n",
+    "* Plurality Learner Classifier\n",
+    "    * Overview\n",
+    "    * Implementation\n",
+    "    * Example\n",
+    "* k-Nearest Neighbours Classifier\n",
+    "    * Overview\n",
+    "    * Implementation\n",
+    "    * Example\n",
+    "* MNIST Handwritten Digits Classification\n",
+    "    * Loading and Visualising\n",
+    "    * Testing\n",
+    "        * kNN Classifier"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "## Contents\n",
+    "## Dataset\n",
     "\n",
-    "* Review\n",
-    "* Explanations of learning module\n",
-    "* Practical Machine Learning Task\n",
-    "    * MNIST handwritten digits classification\n",
-    "        * Loading and Visualising digits data\n",
-    "        * kNN classifier\n",
-    "            * Review\n",
-    "            * Native implementation from Learning module\n",
-    "            * Faster implementation using NumPy\n",
-    "        * Overfitting and how to avoid it\n",
-    "            * Train-Test split\n",
-    "            * Crossvalidation\n",
-    "            * Regularisation\n",
-    "        * Sub-sampling\n",
-    "        * Fine tuning parameters to get better results\n",
-    "        * Introduction to Scikit-Learn\n",
-    "    * Email spam detector"
+    "The dataset we will be using for the following tutorials is [Fisher's Iris](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/iris.csv). Each item represents a flower, with four measurements: the length and the width of the sepals and petals. Each item/flower is categorized into one of three species: Setosa, Versicolor and Virginica."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "## Review\n",
+    "## Machine Learning Overview\n",
     "\n",
     "In this notebook, we learn about agents that can improve their behavior through diligent study of their own experiences.\n",
     "\n",
@@ -61,7 +63,7 @@
     "\n",
     "* **Supervised Learning**:\n",
     "\n",
-    "In Supervised Learning the agent observeses some example input-output pairs and learns a function that maps from input to output.\n",
+    "In Supervised Learning the agent observes some example input-output pairs and learns a function that maps from input to output.\n",
     "\n",
     "**Example**: Let's think of an agent to classify images containing cats or dogs. If we provide an image containing a cat or a dog, this agent should output a string \"cat\" or \"dog\" for that particular image. To teach this agent, we will give a lot of input-output pairs like {cat image-\"cat\"}, {dog image-\"dog\"} to the agent. The agent then learns a function that maps from an input image to one of those strings.\n",
     "\n",
@@ -81,46 +83,272 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "deletable": true,
+    "editable": true
    },
    "source": [
-    "## Explanations of learning module goes here"
+    "## Plurality Learner Classifier\n",
+    "\n",
+    "### Overview\n",
+    "\n",
+    "The Plurality Learner is a simple algorithm, used mainly as a baseline comparison for other algorithms. It finds the most popular class in the dataset and classifies any subsequent item to that class. Essentially, it classifies every new item to the same class. For that reason, it is not used very often, instead opting for more complicated algorithms when we want accurate classification.\n",
+    "\n",
+    "![pL plot](images/pluralityLearner_plot.png)\n",
+    "\n",
+    "Let's see how the classifier works with the plot above. There are three classes named **Class A** (orange-colored dots) and **Class B** (blue-colored dots) and **Class C** (green-colored dots). Every point in this plot has two **features** (i.e. X<sub>1</sub>, X<sub>2</sub>). Now, let's say we have a new point, a red star and we want to know which class this red star belongs to. Solving this problem by predicting the class of this new red star is our current classification problem.\n",
+    "\n",
+    "The Plurality Learner will find the class most represented in the plot. ***Class A*** has four items, ***Class B*** has three and ***Class C*** has seven. The most popular class is ***Class C***. Therefore, the item will get classified in ***Class C***, despite the fact that it is closer to the other two classes."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Implementation\n",
+    "\n",
+    "Below follows the implementation of the PluralityLearner algorithm:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def PluralityLearner(dataset):\n",
+    "    \"\"\"A very dumb algorithm: always pick the result that was most popular\n",
+    "    in the training data.  Makes a baseline for comparison.\"\"\"\n",
+    "    most_popular = mode([e[dataset.target] for e in dataset.examples])\n",
+    "\n",
+    "    def predict(example):\n",
+    "        \"Always return same result: the most popular from the training set.\"\n",
+    "        return most_popular\n",
+    "    return predict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "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",
+    "The function first finds the most popular class in the dataset and then each time we call its \"predict\" function, it returns it. Note that the input (\"example\") does not matter. The function always returns the same class."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Example\n",
+    "\n",
+    "For this example, we will not use the Iris dataset, since each class is represented the same. This will throw an error. Instead (and only for this algorithm) we will use the zoo dataset, found [here](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/zoo.csv). The dataset holds different animals and their classification as \"mammal\", \"fish\", etc. The new animal we want to classify has the following measurements: 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1 (don't concern yourself with what the measurements mean)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mammal\n"
+     ]
+    }
+   ],
+   "source": [
+    "from learning import DataSet, PluralityLearner\n",
+    "\n",
+    "zoo = DataSet(name=\"zoo\")\n",
+    "\n",
+    "pL = PluralityLearner(zoo)\n",
+    "print(pL([1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "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
+   },
+   "source": [
+    "## k-Nearest Neighbours (kNN) Classifier\n",
+    "\n",
+    "### Overview\n",
+    "The k-Nearest Neighbors algorithm is a non-parametric method used for classification and regression. We are going to use this to classify Iris flowers. More about kNN on [Scholarpedia](http://www.scholarpedia.org/article/K-nearest_neighbor).\n",
+    "\n",
+    "![kNN plot](images/knn_plot.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "Let's see how kNN works with a simple plot shown in the above picture.\n",
+    "\n",
+    "We have co-ordinates (we call them **features** in Machine Learning) of this red star and we need to predict its class using the kNN algorithm. In this algorithm, the value of **k** is arbitrary. **k** is one of the **hyper parameters** for kNN algorithm. We choose this number based on our dataset and choosing a particular number is known as **hyper parameter tuning/optimising**. We learn more about this in coming topics.\n",
+    "\n",
+    "Let's put **k = 3**. It means you need to find 3-Nearest Neighbors of this red star and classify this new point into the majority class. Observe that smaller circle which contains three points other than **test point** (red star). As there are two violet points, which form the majority, we predict the class of red star as **violet- Class B**.\n",
+    "\n",
+    "Similarly if we put **k = 5**, you can observe that there are four yellow points, which form the majority. So, we classify our test point as **yellow- Class A**.\n",
+    "\n",
+    "In practical tasks, we iterate through a bunch of values for k (like [1, 3, 5, 10, 20, 50, 100]), see how it performs and select the best one. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Implementation\n",
+    "\n",
+    "Below follows the implementation of the kNN algorithm:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "def NearestNeighborLearner(dataset, k=1):\n",
+    "    \"\"\"k-NearestNeighbor: the k nearest neighbors vote.\"\"\"\n",
+    "    def predict(example):\n",
+    "        \"\"\"Find the k closest items, and have them vote for the best.\"\"\"\n",
+    "        best = heapq.nsmallest(k, ((dataset.distance(e, example), e)\n",
+    "                                   for e in dataset.examples))\n",
+    "        return mode(e[dataset.target] for (d, e) in best)\n",
+    "    return predict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
-    "# Practical Machine Learning Task\n",
+    "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",
-    "## MNIST handwritten digits calssification\n",
+    "To accomplish that, the function uses a heap-queue, where the items of the dataset are sorted according to their distance from *example* (the item to classify). We then take the k smallest elements from the heap-queue and we find the majority class. We classify the item to this class."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "### Example\n",
     "\n",
-    "The MNIST database, available from [this page](http://yann.lecun.com/exdb/mnist/) is a large database of handwritten digits that is commonly used for training & testing/validating in Machine learning.\n",
+    "We measured a new flower with the following values: 5.1, 3.0, 1.1, 0.1. We want to classify that item/flower in a class. To do that, we write the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "setosa\n"
+     ]
+    }
+   ],
+   "source": [
+    "from learning import DataSet, NearestNeighborLearner\n",
+    "\n",
+    "iris = DataSet(name=\"iris\")\n",
+    "\n",
+    "kNN = NearestNeighborLearner(iris,k=3)\n",
+    "print(kNN([5.1,3.0,1.1,0.1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "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": {},
+   "source": [
+    "## MNIST Handwritten Digits Classification\n",
+    "\n",
+    "The MNIST database, available from [this page](http://yann.lecun.com/exdb/mnist/), is a large database of handwritten digits that is commonly used for training and testing/validating in Machine learning.\n",
     "\n",
     "The dataset has **60,000 training images** each of size 28x28 pixels with labels and **10,000 testing images** of size 28x28 pixels with labels.\n",
     "\n",
-    "In this section, we will use this database to compare performances of these different learning algorithms:\n",
-    "* kNN (k-Nearest Neighbour) classifier\n",
-    "* Single-hidden-layer Neural Network classifier\n",
-    "* SVMs (Support Vector Machines)\n",
+    "In this section, we will use this database to compare performances of different learning algorithms.\n",
+    "\n",
+    "It is estimated that humans have an error rate of about **0.2%** on this problem. Let's see how our algorithms perform!\n",
     "\n",
-    "It is estimates that humans have an error rate of about **0.2%** on this problem. Let's see how our algorithms perform!"
+    "NOTE: We will be using external libraries to load and visualize the dataset smoothly ([numpy](http://www.numpy.org/) for loading and [matplotlib](http://matplotlib.org/) for visualization). You do not need previous experience of the libraries to follow along."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Loading MNIST digits data\n",
+    "### Loading MNIST digits data\n",
     "\n",
     "Let's start by loading MNIST data into numpy arrays."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -138,7 +366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "collapsed": true
    },
@@ -187,14 +415,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The function `load_MNIST()` loads MNIST data from files saved in `aima-data/MNIST`. It returns four numpy arrays that we are gonna use to train & classify hand-written digits in various learning approaches."
+    "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": 4,
+   "execution_count": 5,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -207,12 +435,12 @@
    "source": [
     "Check the shape of these NumPy arrays to make sure we have loaded the database correctly.\n",
     "\n",
-    "Each 28x28 pixel image is flattened to 784x1 array and we should have 60,000 of them in training data. Similarly we should have 10,000 of those 784x1 arrays in testing data. "
+    "Each 28x28 pixel image is flattened to a 784x1 array and we should have 60,000 of them in training data. Similarly, we should have 10,000 of those 784x1 arrays in testing data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -239,16 +467,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Visualizing MNIST digits data\n",
+    "### Visualizing MNIST digits data\n",
     "\n",
-    "To get a better understanding of the dataset, let's visualize some random images for each class from training & testing datasets."
+    "To get a better understanding of the dataset, let's visualize some random images for each class from training and testing datasets."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -282,16 +510,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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hwwZjPfHTTz8BTil8OKK69e7dO99tjQezZ88GyFF1AvfaIwnmkicUNPr165fFIFmut9n1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\n2y4r2tatW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WvXAnAfUWY+apiNM9kagxGnoaEhk/fJz4y2traMc0CDVlFRYdpEJLZI6e7u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\nRMQDTZ5EREREPNDkSURERMSD/wFvutcO9t8bawAAAABJRU5ErkJggg==\n",
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ffRXx9oaCrMgh/P7776neky1bNnPtycRNJjwAe/bsAdyB5+HDh30rqhdkIBzoZSbPSAmO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A4ePAjYOyu3IMmMsgN0Ijk2I0aMMDH+Dz74APBNRPd//Y033uiqhHpBEurz5cvH4sWLk33dZZdd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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x108cd3828>"
+       "<matplotlib.figure.Figure at 0x7f7403b82780>"
       ]
      },
      "metadata": {},
@@ -299,22 +527,22 @@
     }
    ],
    "source": [
-    "# takes 5-10 secs. to execute the cell\n",
+    "# takes 5-10 seconds to execute this\n",
     "show_MNIST(\"training\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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SYFwmQlkh2+3Zs8fU1kvE8QciCSnxRN12iqIoiqIoEeAb5UnkRnHb\n/fjjj8ZlIam1kt4ur7IdON2mpS+YFE30Qpn4ryGBtJUqVTK93eS4BfYsk4KD8iquWAhdITY97733\nXnQMziGibkqphBtuuMG4tJYuXQq4Lo+UlBTTO6tz584A9O7d23RDFzfXH3/8YfYlrulEUpyEOXPm\nAGkL0PqdQOVaXMqikoZCCrlOnDgxpnbFijZt2phCwUL37t0BR32IR4q318jzpGXLlua5I1W6Ewlx\nv8m11rNnT1NEeevWrYAz1uuuuw5w+mqCmyy1atUqU4A3UShbtmzIMkZenLeqPCmKoiiKokSAb5Qn\nQWbMQ4YMMW07ZNV+5plnAk7w6htvvAG4JeqTxVf/9ttvA84KV+K//Iys0kuVKmVUqEiDL/2iKoXD\nyJEjAVdVGzp0qDlPpdCeKFH58+cPSv3eu3evUd0mT54MJE9wrhTSBFfFkQJ+gZ/5iYcffhhw2uFI\njF5mSIuLcALo/cisWbOMQn/33XcDbiyp9FVMdi688ELAKTshRRU/+eQTL03KFqL0dujQAYBu3bqZ\neF8hV65cmSbiJBpVq1YNWYTZi3hg302ehGPHjhmXnATR/hcQd821115rmnRKRVjJSPQT0qNOXpMd\nyeyTnm3z58/n9ttvB1x3pdyc//rrLzOxkkaV0kQ42ZEaOuKiDKdvoRfIeXv55Zeb+0zPnj3TbDNv\n3jyGDx8OuLXFEpX9+/ebRAWp9ySVp/v16+eZXfFEqoqD09g70ZGaefXr1zd964TM3Odedy7IDqGy\nj3fs2GF6acYTddspiqIoiqJEgBXrwE7LshIjcjQDbNvOMuc62ceY6OOD5B+jH85TceG+/PLLprdd\nNLu2x3qMUkssfZmQI0eOhFU/Jxok+3kK3o9RQiOuuOIKU0vwrrvuitr+vboWU1NTjbdC6sVZlmXS\n+CUZRVyUixcvznaNvHiPUZIc1q9fzxlnnCH7B2Dt2rXUrVs3Wj9lyGqMqjwpiqIoiqJEgG9jnhRF\nSSykDEWVKlU8tiR7SGBtIvU3U8JH+mMG9rSLl6IYDzZu3JgUvRdDIQkoefPmDfps1KhR8TYHUOVJ\nURRFURQlIlR5UhRFUZKejh07Am4LsL///ptJkyZ5aJESLlIGxk/Kmk6eFEVRlKQnvYtu0aJFCVuv\nS/EeddspiqIoiqJEQMxLFSiKoiiKoiQTqjwpiqIoiqJEgE6eFEVRFEVRIkAnT4qiKIqiKBGgkydF\nURRFUZQI0MmToiiKoihKBOjkSVEURVEUJQJ08qQoiqIoihIBOnlSFEVRFEWJAJ08KYqiKIqiREDM\ne9tZlpXQJcxt27ay2ibZx5jo44PkH6Oepw7JPsZEHx8k/xj1PHVI9jGq8qQoiqIoihIBOnlSFEX5\nD9KgQQMaNGjAiRMnOHHiBCNHjvTaJEVJGHTypCiKoiiKEgExj3lSlEKFCtG2bVsA6tSpE/T5mjVr\nAJg9e3Zc7VKU/zLdu3cHwLad0JSzzjrLS3MUJaFQ5UlRFEVRFCUCkkZ5qlWrFu+88w4Ap512WprP\nFi5cSLt27bwwK2K+//57ADZu3Ej79u09tiZnlChRAoA33niDhg0bAu4qNxSXXnopAA899BAA+/bt\ni7GFivLf47LLLgPg9ttvB9xrctmyZZ7ZpCiJhipPiqIoiqIoEZCwylOhQoUAeOKJJwDo2LEjZcuW\nBYLVjeLFi1O3bl3Aja/xK2J727Zt6devHwBjx4710qSIufrqqwEYPHgwADVr1gzre7fccguAOY5X\nXXVVDKyLDk2bNgWc1fr+/fsBmD59OgBffPEFAKtXrzbbr1+/Pr4GRpm8efMC8O+//wJw4sSJTLfv\n1q0bADNmzABg3LhxADzyyCPm76V4Q8WKFdP8f8+ePQBMmTLFC3MUJSFJyMlTSkoKjz/+OAB9+vQB\nwLKsDF1C9evXZ/ny5QD06NEDgLfeeisOlmYfy7LMZEICqX///XcvTcqSRx99FIA777wTgMKFC5vP\nJk+eDMCECRPSfKdWrVrmpl2wYEEAateuDTju1+3bt8fW6Gyya9cuANauXcsFF1wAuG6QUHz44YdA\naLflzJkzAXj++eejbWZU6Natm5kIL126FIC77rorrO/KJKtv374A5MqVy5wfijdI8oYgoQLHjh3z\nwpyYULp0aQBuvvnmoM+aNGkCQIsWLTL8/osvvsjo0aMB2LRpUwwsjA1ybLt27WreK1euHOCGRXz9\n9dcAzJs3j0mTJgHu/SxRkXvKww8/DLjHf/78+XTq1Ckmv6luO0VRFEVRlAiwMgvgjcoPxKBE+0sv\nvcS1116b/ncyDUYWJKj8qquu4ujRo1luH+8y9Bs3bgSgWrVq5r0BAwYA8PTTT0frZ9IQjXYJrVu3\nNgpZgQIF0nx27rnnsmHDhgy/KyrgFVdcIfaYfb733ntZ/XRYxKolRIECBWjTpg3grvZk/OIqBtdt\nV7NmTeNyFldYrlzOGuaXX36hVatWAGzevDkiO2JxnubOnRuA1157jc6dOwPuCrVZs2YAGR5Xcdu9\n9tprad6fMGFCtpUnP7WEyJ8/v/n7HD58GHCPp23bWFZaU/PkcUX+f/75ByDk/SfWrUsaN27MihUr\nZF8AJplmwYIFOdl12MRqjA8++CD3338/4F5Toman27/Yken+JPzgzTffjMgOr87TW2+91XhkihUr\nFvhbYlfQd/766y8AqlatCsDu3bvD+i0/XIsSOjFkyBAaNGgAuPcs4dixY8YzkNkzKBTankVRFEVR\nFCWKJFTM0wMPPAC4xd2yQ8uWLQHIly9fWMpTvJG4mblz55pUf/Hbz5w507cxQLNnzw5a5Ukgalb+\n9JUrVwLuCjirYGQ/cfjwYebNmwdgXrOiRo0aAJxyyikAPPvss+b9008/HYhceYoFElgsqhPAkSNH\nAPj7778z/a4oaMlA/vz5TaykKG4tWrQwcRVLliwBoHr16oCjKIm6KHFEVapUMfvbsmULAJ988gng\nKK+zZs2K9TAAaN68uVEgtm7dCsCqVavi8tux5oUXXqBMmTKAG2MnrFq1ysR2hVJipCxMoGLjd4oW\nLQo4zwpwlJj0yktWFClSBHA9MlOnTjWxl36MgcudOzc33ngjAE8++STgqIuicK9btw5wnu8AI0aM\nMCpktEmIyVNqaioAvXv3BsjwjyEXTPoHz5gxY9K4wfyMBLYPGDCA//3vf4A7/tTUVN9Onpo0aWIm\nBcLChQuB8Os1yaQp1q5krxH5uHz58oCTDeonxMUkrjeA48ePAzBx4kTAnQBkRKhK8omCBNbKpLFN\nmzZUqlQpw+3FfSBZlpUrV+aVV14B4JprrgEc9+Vvv/0W8vvp3dyxZOfOnebfcgwTPVhY2LVrFw8+\n+CDgnqfCzp07M70PiVtdJk/79u3z7b1WJk2ffvopAGeffTbg3DcPHToEOLX1wDknZZIu1+SFF14I\nQK9evcw+zz//fMA5F6dOnQr4c/I0duxY7rjjjjTvtWjRwtQok0nTDz/8AMCiRYv49ttvY2KLuu0U\nRVEURVEiwNfKk7jpRHE688wzM9z2oYceMvJl+pVUrly5fF+aID2hVkm1atUyypTfWLt2LWvXrvXa\njISgZ8+egJtWK6nEGzZs4Mcff/TMLkHciSNGjDDvSbr2yJEjs/x+4cKFSUlJiY1xMUQSM4YMGQK4\nteQ2bdpkVNSXX34ZcJS4Rx55BIB77rkHIOS1KdXy/UL6Gk/JhgTvi4suK6TkRnplccmSJXz++efR\nNS7KlCpVKs3/d+/ebcJSxH0ViIxHarUFKk9C9erVTYcOP9yL5BqU665Dhw5GEZMEo8DK+OJ9qlCh\nAoBR0WKBKk+KoiiKoigR4FvlqWbNmmYlWLJkyaDPpUqxzJ5lZRgu9erVS7heTl26dGH8+PFemxFV\nzjzzTFMMVBCV47PPPvPCpJhy0003mWMoqe2ywmvVqhXbtm3zxC4JND377LPp2LFjms+OHTsW0Sq0\nR48eJvVZECV1zpw5ObQ0NkycOJHrrrsOcGM9hg4dCsBTTz3FwYMHg77z7rvvApg4k2REelKeddZZ\nJmZLCqUmOh06dDCp/RIrI+p5uEVgvUDuG6eeeirgBsAPHz48pOIkSvL//d//AaHHlr60hl8QlT6w\n24TMC+T5nTdvXhN3mD5RoF27dkYhjjaqPCmKoiiKokSA75QnSfd9++23QypO4KhOolaEozhJVlMg\nkkbvV3bs2MGBAwcAN7siGXnllVeCYtmklYkUcEtUihUrZgq0dejQAXD698nKUZBSDV6pTgBnnHEG\nAF999ZV5TxSYxYsXB6lRmZFeSQSnTQK4Y/UbDRo0IH/+/IDbS1KUp4xIRMXJsqywVAbJWnvssceA\ntBmwUohy1KhRMbAwftSuXdsoToLESoWbIewFUmRVbJSSNu+//37Qtp07d+aFF14A3HZZobKZJbN0\n+fLl/Pnnn9E3OkLkmSC9a4X77ruPadOmAW6Wa+/evU0pkfRklRWcE3w3eRo0aBCAqXcTikWLFoVd\nUwdCB23+/PPPkRsXRz777DOTbimVqs8991wuuugiIPFrs4gMe/HFF5v3pHbQM88844lN0UIaOvfv\n399M3DOr8nvDDTcAziRZHtiRVsPNLlL2Q+oXBSI36SVLlnDeeecBaSdXGTFz5kzTn1CQVOjChQuH\ndIF5zZYtW6hVqxaAqVYsNXASfRIfiG3bmZYCkfuvTJBk271795rg3Ztuuglw08ElLT5RkAetjBHc\nYP/+/ft7YlMkyORmzJgxgNtTdPbs2aZe1TnnnAM491I5j+VYyqJo/PjxpqaTH4LDAxHBIL1wsGXL\nFmOr1FkLVdtKJpaBSS/RRt12iqIoiqIoEeA75UlWwoHSsqxUpfCcBIxlhbhMihYtavYnio0fC4Cl\nR4pkSnGzIkWKGAUgUZUnSQkeN24c4KyGpLSEyLHxUl2ijaTFSgX8SKv9durUybi14vU3EBdi+qKC\n4PYFmzhxolEFQwXxy3X566+/AgS5QgC+/PJLAF+qTuC4VUWhltW7XGNPPPGESZVOdiSYWI69VJ7u\n3LmzqRovx1vU/0jPc6+RY2nbtin+Kp0dwu3t5gdEVZGK26mpqXz88ceAq8oEItenlEh59dVX42Fm\ntpDnsySGSSHhUJX4d+3aZYLiBUnmWLNmTcxsVOVJURRFURQlAqxYt8IIt7OylMWfPXs2AJdddpn5\nTGbMEtSaFeLjleA/6XcEbvxTOMX+wNvu0fXr1wfc1V+hQoXMTFpK7EeDWHdyB1eNkD5uEucDTio4\npI1BiDbxGKN0X2/dujXgrMjF9y7pst988w2rV68GMPFrknKbK1cus7IKbI0SDtk9T+W4xDr4Wfq/\nDRgwgI0bN2ZrH/G6FkV1kcK6zZo1M/E9Xbt2BUIXIYwGsT5PzzvvPJOOLzFt119/PeCs8mXM4gEQ\npTuwxYUUERUFo2/fviGVy4yIx7UYCikguWjRIrHDpMLPmDEjar8T72eGBIQH3lMD+f333wG37VA0\n4pviNUYpeSJlFsC9V0lR7JYtW3LfffcBmH61EjMd2I4oUrIao2/cdhLgFjhpEsKtEiqTJsk+CJw0\nSd80PzYDzgjpSfT1118DcMkll3hpTo64++67gdAXeLK4RCZMmAC4E/O8efOaDLrMbliygJk1a1bc\na1uJ20Im6o0aNTLZjqGQ6sNS9b9kyZJpgv4zQlw+559/vnENyQM5u5OpWCE356uvvhpw3HaSQSj9\nxBo0aJCQFfU3bdrEN998AzgJKOBmFf70009BfUND9QWbOXMm4E6e+vfvz/Tp0wF8kakVinbt2pnA\ndhnj+PHjozpp8gpZAIXKopwzZ46Z8Ccict8M9dyQe9GUKVPM2GWukJNJU7io205RFEVRFCUCfKM8\nZYa4ObLio48+AjApx4Fs3boVcGu4JCpVqlQB3PIFsQyIixZ169Y1q9T0XHbZZXz33Xdxtig2iHs1\nXAoUKJDm/40bN2b06NHRNClLRJGV4OhwExFkFV+4cOGgauKDBw82QdfpKVOmjHFNPvnkk4BbU8hv\niIpy++23GzeduJ1nzZpFamoq4PYKSwQOHz5syhGIu0eOX+BxXLBgQZb78mtV6kCkttE999xjFF45\n50VFTDTy5HEe2wMHDgScRBMIXQZlxYoVcbMrXkiSiyQunHnmmfz000+AW74hHqjypCiKoiiKEgG+\nVp4kkDbU7FmKZz333HMANGnSxPT6CbUfCYpMdCSeq2bNmoC/lSdRVhYuXGhWgLLqk9VvdldGxYoV\nM/s/++yzAbeHkyQdxBL5+xcuXDjbZSPSn5N79uzx9fEMxcGDB4OCp1955ZUMladEZfLkyYDTPwwc\nBbhLly5AdION44GoSlJcUfq7SRFMcDvWSyzc999/H9SVQZSO8uXLm/uSX2KeJAFJ0vEbNWpkPpMi\nn6HS3hMB6TcopQoyK8ArccDJhJyvUtj3xIkTJtkonsU+VXlSFEVRFEWJAF8rT5KBJ6s+WZW3aNHC\nKE8yC7csK2jmLb3hRo0aZdI1ExEZRyDi212/fj3gTwXqtttuA6BUqVJGcZJjJKvY0aNHm/5D9erV\nS7NNKGSVdckll2RYuiIeypOk/V599dXm3+Fyxx13AHD55ZdH3S4/IFlcgQwePBhwVvtS5FYKZ3pJ\nhQoVADcmMit69eoFOGqq9PtLNOVJENX+gw8+AJyspfQlUCSO9OjRo6bchKjIwqFDh0zWpl+QrG0p\nGwLuvTLc7G2/klGR6OnTp5tMUVHXBgwYYGILk4ESJUoEZQ8OGzYsonZt0cLXkydxw0kvooya/6Xn\nyJEjgNuMVW4OiYpcCMuWLaNatWoApmmyn913gW6A9EhafyCZyc+RbBMPJIU70j58lStXZtiwYUBw\n36ZrrrkmOsZ5zJVXXhn0XmAVcqnm7AeWLl0KOKUUNm/enOX2kgJtWVaaUiiJjJzLTZo0MaU10jdl\nT0lJCXlcwak672VT60DERllwC19//bVxRSYyRYsWpXr16mnemzNnDuBUGpdedUIy9WUEx+Us/UL3\n7t0LhF+zMdqo205RFEVRFCUCfK08RYJlWUYFEBdBssy6ZVX3yy+/GOVJlJcHHngAgJdeeskb4zJB\n3ALlypXj1ltvjfr+JaFg+/btAEyaNCnqv5EVtm2blOFQZQakqnHlypUBeOyxx0wwqxxDqbAeqiBh\nIjJ27FjTp1Do168f4KSM+4n8+fMDTi8s6REmrqpAZLX72muvAc6xS1R3XUYcO3bMVA+Xis6i9C5Y\nsMD0pdyxYwfgFhyW5A+vKVasmOkgIb3QhClTppj7RKIjx0RepU/o8ePHgz5LhHIS4SDPPSnRA5hn\nir0n4z0AACAASURBVFeFr1V5UhRFURRFiQDfKE/SgkRiEEK1aQmFqDLXXHONSZmWDvDJxogRI4yS\nISsK6U/lR6SvW58+fUzQYiTxLvPmzQuKQwmMeZKO29KBO56IXYsXL+axxx4D4KqrrgLcjt7lypUz\n5QiksJ1t20ZxGjVqFOAqpcnCwYMHg95LH2TsF6Sg7rx580xvQkluCERaYMg4VqxY4Uu1N6eIWiyv\nicTTTz+dRpkATAHexYsXe2FSTJD7h7xKAeLhw4fToEGDNJ+J4p2oSFyotP/Jmzevie/1OpbZN5On\nwMw4cCZAGdWKWb16tXELSB8uyaT4ryD1S6TWh9+RyUYsm//GExlPv379jCtHGv3Kayh27dplgjql\nWfB/AcnAK1y4cMjJlVeILS1btqRGjRqAW7lZJr5///23qaguge+rVq2KeTNlJTzq1KkDEDIgXBZt\n4SQDJCqStduiRYugz6QuWaIhyUbSuPmCCy4AnFAcqa+2e/dub4w7ibrtFEVRFEVRIsCKdcq3ZVne\n5pTnENu2s4y4S/YxJvr4ILZjFJeOBENL0OqHH35oei5JWu2UKVPCrikUCX46T2vUqMHy5csBKF26\ndJrPnn32We6+++5s7TdeYxTXsFSwP3HihCl/Emv0Wox8jOIul5R9cF2vUpdr5syZkRmZA2J9ntau\nXRuA+fPnA65rLvBZLpXee/ToEROXZazHKIpTetf4008/nWGdq2iT1RhVeVIURVEURYkAVZ6ywE8r\n+lihq93EH6Oepw7JPsZEHx9Ef4ySxj5w4EBTbkLK1sRLpQgkXudpp06dALdI5O7du00w9fjx4wHY\nuHFjTn8mJLEeo5Rv6d+/P+DGrKWmpsYtQUiVJ0VRFEVRlCiiylMW6Go38ccHyT9GPU8dkn2MiT4+\nSP4x6nnqkOxjVOVJURRFURQlAnTypCiKoiiKEgExd9spiqIoiqIkE6o8KYqiKIqiRIBOnhRFURRF\nUSJAJ0+KoiiKoigRoJMnRVEURVGUCNDJk6IoiqIoSgTo5ElRFEVRFCUCdPKkKIqiKIoSATp5UhRF\nURRFiQCdPCmKoiiKokRAnlj/QLI3B4TkH2Oijw+Sf4x6njok+xgTfXyQ/GPU89Qh2ceoypOiKIqi\nKEoE6ORJURRFoWfPnti2jW3bnDhxghMnTtCuXTvatWvntWmK4jt08qQoiqIoihIBMY95UhRFUfxL\n8eLFAbjttts4ceJEms9sO6HDVhQlZqjypCiKoiiKEgGqPCkxp2XLltx7770ANGvWLMPtLMtJbnjz\nzTcB+OSTTxg7diwAx44di7GVSiw47bTTAPj1118BKF++PDt37vTSpKjRtGnTNK9NmjQB4IMPPjD/\nls8CWbFiBQCXXnpprE3MlKuuugqAm2++GYCLL77YfPbNN98A8O2338bfMEVJAFR5UhRFURRFiQAr\n1j7tWNR6KFCgAPfffz8AhQoVAqBVq1bUqFEj5PbDhg1j6NCh2fotP9Wz6NWrFw8//DAAb7/9NgD3\n3HNPjvcbq7orlStXBmD9+vXkz58/O7tg7969ADzxxBMAbNiwAYDFixdHtJ941JYpV64cACNHjgSg\nS5cupKSkALBgwQIAxowZw/Lly3P6U0H46TwN5NZbbwVg0qRJAFx00UV8/vnn2dqXl2NMrzI98sgj\nOd6nKK2BxOM8LVasGACvvvoqAG3atAnapmLFigD8/vvvOf25ILTOU2zGWLBgQe677z7AvRf17t07\naLtcuRzN5O233+ahhx4CIlcY/Xq/iSZZjTEh3HYFChQAoEWLFgAMHDiQ+vXrA+4NyLZt/vrrLwD2\n7dsHuDeAjh07Znvy5AcGDx4MODfsHTt2APDbb795aVKmnHfeeQDMnTsXINsTJ4CSJUsC8OSTTwJw\n6NAhAO69917zQPYLt9xyCwAVKlQAnBtSnjzOJXbFFVcAzjn8wAMPABiXZDKSO3duIHhyv3nzZg+s\nyRnLly8P6X7LLl6664oVK8aECROA4EnT8ePHGTNmDAC7d++Ou21KZMjiVCZIzZo146KLLgLSPhfT\nI0kBbdq0Mc/Wtm3bAnD06NGY2pxMqNtOURRFURQlAhLCbTds2DAAIzGm2z/gzLAfe+wxACZOnAjA\n9ddfD8APP/xgVJBI8VKeFKVNXFS5cuVizZo1AFx44YVR+51oy+gSePrxxx8D8O+//xq75Rj+888/\nQd+TlGnZpnz58kbFSc/mzZvN7+zatStLm7xwFaSkpBiJvFGjRgBMnjzZjKlMmTKAq5TmhHifp+Iu\nP3LkCP/++2/Q57Vr1wbgyy+/TPN+mTJlsh0wHu8xins1GqrT0KFDGTJkSJbbxfo87dmzJ9OmTQv5\n2ZYtWzjzzDNzsvuwULddzsZYrVo1wD0/5T4SyNdffw04yQmzZ89O89mpp54KwPz58817L774IuCE\nSTz33HMAbN26NUMb1G2nypOiKIqiKEpE+DrmqXHjxgD06dMnrO3vuusuANauXQvA448/HhvD4oTE\ny4h6AYkxpk2bNgFO0T2ASpUqmUD3cJDg6tTUVO6++27AjScSKleubOKIMlpJe01geYX33nsPgG3b\ntplYhcsuuwyAOXPmxN227CKxEU899RQAn376qVF4A5G4t0QkfXB4uEgJgg8++CAslckL6tSpk+Fn\ncq0lClJq4dprrwWgQ4cOQbE+U6ZMyfD7jRs3pnr16iG3Gzt2LBs3boy6zTmlWrVq5l5StmxZwB3r\njBkzGDFiBODcZwATBxyIxAIHInFTlmXxxRdfAGTbWxNNJF5W7pVz587lzz//BDCK2uHDh41ytmXL\nFsBRxGONrydPCxcuBNyA8awoXLgwAK+88grgZDtB5JlZfmXnzp388MMPXpuRJZIhl9mNKxw2btzo\nyxvYf5FatWoBMHXqVABKly4NwMyZM8P6vgSpJkLF6nCyIVesWMEHH3wA4NuJEmCyPQcMGADAHXfc\nEbTNypUrAaeuWiIh7v0LLrgASHtuyb+lhpVt20ETK8uyQm4HziSzXr16sR5CxPz5559Bi2px0d13\n331s3749y33IojMw2/PgwYMATJ8+3ReTpqpVqwLuMb7hhhvMZ+J27Nu3r3lP6gief/75AKxbty7m\nNqrbTlEURVEUJQJ8qzy1bt3aKEnp+y2Bm7IuFCxYMOjfkoqbLMrTDz/88J+q+HvppZea+k6JTqVK\nlQBHwRG3pigXfid//vzMmDEDcBUnCQQXN0F6JEBeEDUnnOB+rwhHQZIyA+Ki8ztnn302AMOHD89w\nm+effx6APXv2xMWmaCHlFJ555hmAHKnU8vcpVapUzg2LIfPnzzcB4nItiZKUleokLncJCLdt2xzz\njh07Am6Sj5ekpqYa12RGCUMZceONNwKuCzqWSrcqT4qiKIqiKBHgO+WpRIkSALz00ksZxkns27fP\nBAtKsUwpUxDI7bffDsCiRYuSQn0aN26c1ybEBekLds8995iYjfRs3LjRxLb5mdNPPx2Ar776CoCi\nRYsycOBAgITp8ZaammpinoS33noLCN1zsHDhwkEFGMOJxUgEEkVxCgcpATNr1iyPLckeonp+9NFH\nOd7Xo48+CrjPmu+++y7H+4wFmzZtMoUwixQpAriKUlZFg0NVkpcOAH5QnMqXLw/A+++/b3pipmfg\nwIEmyF/i1AK58847AUwh4vQeqmiiypOiKIqiKEoE+E55ktTEwPT89Dz33HNmtbF69WrASWVs1qxZ\nyO0HDx6ckMpT+hINyd4yoXXr1oC7EpZCjIF89tlngJOmHKo4o5+oUaOG8d0XLVoUcDKbEu1cDNVO\nZNWqVRlun5KSErRyfP/996NuV7QRVSmzvnUSF+XnDLuskAK10uopVExpZkgx28C2S5I+fvjw4WiY\nGBbRUJykBMopp5wCuMpTr169crzvWBCo4Ioq37x5cwDefPPNIIW3bdu2DBo0CHAz0SSzrkOHDr5Q\nnATxOoVSnSTLfNWqVSazLjOk2GssY4R9N3mSgx/KHSAEplJKL573338/w8mTuE4SDanjkcyUKlXK\nVISXm0CoSZPcKOWhlQgur2XLlpngTrH3qquuikpFca+RSWEo2rdvH/SeuPn8jEyepA9mqEmUvNek\nSRNPe9TlhJ9++gnI2s2THqmjd9NNNwFQs2ZN85kseLp37x4NE+OOTJr8Xkpj2rRp5vqSZAAJ9s6X\nL19QKYrRo0dz1llnAW7pnyuvvDJe5kaETOJPnDgRJJ7IGBYvXmwSyWTxfOzYsaByRh06dABiO3lS\nt52iKIqiKEoE+E55ygxJ7Q6Vkrp48eIM03ELFSpEjRo1ANiwYUPsDIwiDRs2JDU1FUhbzCxZkKrA\n/fr1M3Jyenbs2MHrr78OYIKs4+kWyCkvvPCCCWqUwm6vv/66cRVIyYJERNRBcdcEImMNRFaLiZAO\nL+qmJC6EqjTetGlTo1IkWvmCSJC/wUsvvUS5cuUAyJ07d9B2UpBYzulEcWvK9Sn32FCJR35i06ZN\nRvGTkBW5f7Zp04ZffvkFgB9//BFwik3OmzcPgOuuuy7e5kaEPJtXrlyZYXX/woULmyDwMWPGADB+\n/HjjghYkED6zEh05RZUnRVEURVGUCPCd8iS9l4oUKWL8nuILPX78OOAGPAby1VdfsWzZMsDtgyMc\nOnQoYRQn4d577zWre+nVF4+S87EgV65cZtUqKzspNRFY3FTYv38/4KwsRo8eHScro8/DDz9sCtKN\nGjUKgB49epj3WrRo4ZltkRBKIZO2CQ8++GBQwHGomCfpPZX+2vQzoig1bdo005Yt8llgzFSiqlAS\nOzJ+/HgAWrZsCWCu34wQ5UaCfhOBU045xRR9FRVRlO5EoGHDhoDbCzQwBk9ihObNm2f61iWKan/n\nnXeawq2XXHJJms+OHz9uWrVIb7tQSnc88N3kSSLt8+bNG1TnKbNgvkqVKhnXXPrt/B4EGIozzjjD\n/FsmE4kSaCyTXgl4HzRokJFRQyGTYhlfq1atALc2UiLzxx9/AK574NxzzzWJDXKzC6eXmpcsXbrU\nPFRk0nvfffcBToPmRYsWAe5EWK7DQGTylIisWLHCTA7kWGXkypNXP7vaS5YsCUDdunUBWLNmDQDF\nihUzyTihkm82b94MwNtvvw24NXUSlTp16pjK/+IKimVdoGgjyVLdunUDYMmSJUEhEE2bNqVr166A\nE0aQCHz77bdmkSULsfXr1wPw+++/B2WdB4ojoe49sULddoqiKIqiKBHgO+VJJMi9e/eaNG9BXHqX\nXnpp0Gq9RIkSGVYlTXReffVVr02IiAcffBBwKxhnxs8//2yUJkmhTkaOHDkCOB3QzznnHMDtE+d3\njh49apSmiy++GHAV4i5duphg4VBI6REJWk10RC0cMmRIpvWg/IzUNJLUdalp1Ldv3wzLvfz6669G\nBRCFMVB5EhUko16HfqRDhw5B7rqc9MfzigkTJgBOJ4Bt27YBULFiRcApBSMqTqIoT+AqgNJTMzMO\nHjxoShKo8qQoiqIoiuJTfKc8CRs2bAhSnsRXP2fOHNPfRlKf4znjjCVSiTpfvnwmQFxiDPxMamoq\nt912GxBcGT2QKVOmAG4vqX379mU7zkBUADkXIi365wWrVq0yMQqffvqpx9aEj6Q+N27cGHDjLDp0\n6GAUYYlRO++888z33nzzTSDzordeEBifBE5Kfkbp0YFIAc1E4ddffwXcNPWJEyca5UiUT4lZy4zp\n06eTL18+IHQvPFFwEqF4rShvjRs3NrFpiRQoLki/SVGDFy5caK7LwDIG8rlsH8vCkX5B5g5NmzaN\nWfKGKk+KoiiKoigR4FvlqX379hw4cCDkZ8WLF08z2wY3QyvRkXGcddZZLFmyBPB3T7tx48YB0LVr\n17BieCQVWo5fZjRv3pzLL788zXs///wz4MRYiAoiheESQXnq0qWLySLdunWrx9ZEjihQUnxu5MiR\nJj1dVFPpQwXu8fITWZUeyIxEi3OSDFaJm7Qsi2nTpkW8nwce+H/2zjzApvr9468ZKox9aZFQKVsJ\nKcouslSyJqmoiJIlqq8sSbQoayJLZWuTJSmltIwlkhYJUVIUydZiT8zvj/N7PufOvXdm7pm5595z\nbs/rn2Hu9vnMPcvn836e5/0MMJ3qw+GH3oWC5GSWL1/eVGn5UXl6//33AUx/OulhB3YZf7Vq1ShV\nqhSQvhdhoiE9TyX/8rTTTgPctc7w7OLp8OHDLF++HLBDBYFIryy5YYpjdTgkRKREn/vuuw+I3A5C\nvqfMvq/MCLRwEP8rr/ZqCkS8SWrWrGk8TBKBEydOmFBNOM8uKW/3Em5ZQwT3xvMi77zzjtmgySIq\npz00Dx48aLzLvIz45ol3VVJSEgsXLoznkLKF9AKV700WSrKxgfQLKfFpC9zUJBoSpgymWbNmri2M\nNWynKIqiKIriAM8qT2A5NANmd1C4cOGQ50iScjjlQxIhZaXuN/xg2CaqgyRhRhsJO0jJu4TopkyZ\nYhKU5TGvkS9fPrMD7NevH2AVOIwbNy6ew4opXnQ1Dmd1khNEafJDP7c//viDpUuXAtCmTRvATiTu\n27evo8IbCRtNnDjRpBh4GTF4lQKTvXv3mgIWv5AvXz4ThpO0lty57dt4gQIFAFtlqlatmrFJCe4E\nkEhIcZWobBdffDFgO627gSpPiqIoiqIoDvC08rRy5UoAnn76aQAeeOABwLYsyAhRIh555BEXR+c+\nTz31VLyHkCViwNa9e3fat28P2Ml6ohSmpaU5Sno/efIkAIMGDTKmdZIQ6Aek7cVLL71kdvXSj7Fz\n584JbQYqyHxFnfASqampxuZCEsAjsSkIfo9ly5YB/lCcwrFmzZp0P/3cQicSZs2aBdhRildeecVY\nOfiF6667jubNmwOWcgb2tbFz58706dMHsO1C0tLSTC7ewYMHYzza2HHo0CHAaiUFtvLkJp5ePAmy\nePrll18AmD17dtjnyaJJnHD92kgXLInVDzKreIb06tXLOA5LE9FbbrkFsDx+pCovkZEbspzASUlJ\npqKua9euACZkkohICHfJkiUmKbd69eqAfYP2CnJDCfSAiWQR5NeF0n+dChUqhPRIXbBgQTyHlC0+\n/vhjU10njYFXrFgBELbDxv79+40D+X8BKSKKBRq2UxRFURRFcYAvlCdBdgq7d+82ibjiELt27VrT\nB8fPipMoOS+++GJMV9HRZNeuXQCMGjUqziOJLZIcvWrVKgC+/PJL4ynjxcTpaCMFDhJW8BuqKiUe\nZcqUAawCk+RkSyuQPouSFuIn9u/fHxJqDKc4iT3PpEmTfOH6Hi0kzUNSP9xElSdFURRFURQHJEVq\nbpjtD0hKcvcDXCYtLS0pq+ck+hz9Pj9I/DnqcWqR6HP0+/wgtnOUvotr1qwxfVCvuOIKANeSxfU4\ntYjnHKVQpWLFilx55ZWAFbFyQlZzVOVJURRFURTFAao8ZYHXV9jRQHe7/p+jHqcWiT5Hv88PYjtH\n6V1XrFgx6tevD9h5MW6hx6lFPOcYWIkodjEbN2509B5ZzdFXCeOKoiiKkhXiJl6sWDHAKmJwe9Gk\neAfxvho9erRrn6FhO0VRFEVRFAe4HrZTFEVRFEVJJFR5UhRFURRFcYAunhRFURRFURygiydFURRF\nURQH6OJJURRFURTFAbp4UhRFURRFcYAunhRFURRFURygiydFURRFURQH6OJJURRFURTFAbp4UhRF\nURRFcYDrve20AaL30Wak/p+jHqcWiT5Hv88PEn+OepxaJPoctTGwoiiKEpbLLrsMgK+++opNmzYB\ncPXVVwNw8ODBuI1LUeKNLp4URVGUsNSoUQOAtLQ0Dhw4AMCJEyfiOSRF8QSa86QoiqIoiuIAVZ4U\nRVGUsJQpUwaA/fv3M3ToUACOHTsWzyEpiidQ5UlRFEVRFMUBvlKeGjRoYH7Wr18/3e+GDRtGamoq\ngPmpKG4xfvx4AO677z7zu6QkqzgjLc0qMpk0aRKTJ08GYOvWrQAcP348lsNUlGwxbdo0AFq3bg3A\n2rVr9bqqKAGo8qQoiqIoiuKAJNklu/YBUfB6ePTRRwFMzD1Shg0bBlhKVHZ3TfH0syhatCgAU6dO\nBeDIkSPcfvvtUf+cePqupKSkmN1tnTp10j02btw4Nm/eHJXPifYcd+/eDUDx4sUD30M+K+T5M2bM\nAKBr165OPiZi/OS7cv/99wMwZswYAKpVq8a6deuyfJ3bc6xSpQoARYoUAaBVq1YAFC5cONPXlStX\nDoAXX3wRgNy5czN37lwA/vrrL0djiLcHklTXrVmzBoA9e/YA0Lx584i+o0iI9xzdxgvn4nnnnQdA\nsWLFGDx4MGDdPwD69+8PwN69e7P9/l6Yo1C4cGG6d+8OwPXXXw/AE088AcB7772X7ffN8jj18uJJ\nQnKffPJJjschi6eGDRs6el08D5LatWsDsHLlSgC2b99O2bJlo/45sbyYlShRAoBZs2YBULp0acqX\nLy+fI+MB4OjRo1xxxRUAOV5ERXuOP/30E2BdpI4ePQrA4cOH5bPM8woWLAjAGWecAVgL4XvvvdfJ\nR0WEly5mmVGhQgVzY05OtoTvatWqmbBmZrgxR/leJk2axM033wxA3rx5nbxFWB566CEARo0a5eh1\n8VxY5M2bl3nz5gHQokULwA5P9+3bN2qfo4sn9+bYtm1bwN6s5cuXz1yP5Ly76667gJxdU71wvZFN\nzaJFi8y9UtiyZQsAlSpVyvb7ZzVHDdspiqIoiqI4wNMJ49FQnIRgFcupAhUPqlWrlu7/Z555JhUq\nVAByrsTEkjJlyhilqW7duoCtziQlJfHdd98BsGPHDsAOhV1++eXMnz8fgMqVK8d0zFlx3XXXAdC+\nfXsz3l69eoU8r1atWgDceeedAHTs2JE333wTgKVLl8ZiqK4j4S45Jv/5558Mn9uzZ08KFCgAwGuv\nvQYQkerkFjfddBMAd9xxR1TfV3b3TpWneNKjRw+aNm0KwA8//ABYoXOvIiFwcUEPh8znwgsvzDSs\nLqGtJ598MtrDjBmtW7fmpZdeAmz19LPPPmPJkiUAPPPMM4D/rSZEzZ8wYQJg3V/atWsHwCOPPALY\n1yQ3UeVJURRFURTFAZ5VnqKpOgUSqEB5XX2qWLFiuv/v2bPHV4qTULx4cROTll2f/HziiSfMbk8S\nGiVxfNmyZSYfymtIn69Ro0aZXl/h+OyzzwBbRbzrrrt44403ALjgggsA+OOPP9wcqqu8/fbbNGvW\nDLATp7dv3x7yPCkK6NSpk9n5ivIUTwYMGJDhYy+//DIAK1asMPkiQs2aNc2/RTmV7xrCqxteRZJr\n7733XvPdXXvttQD8/PPP8RpWljRp0gSw83ySkpIy/LsH/j7cc4YPHw7Y3+XChQujOlY3SUlJAazz\n6dSpU4B1ngEsWLDAKG7ymJ9JTk4256zMcdq0aUbNl1zDWOC5xZNU1skixy0aNGjgqxCen9mxY0dI\nkvSCBQsA2LdvX8jzJdlPTnovc/jw4UzDb3Jhk5tRcnKykZ1z5/bc6Zcl8p1IAnGLFi1M8ny4RrHy\n/C5dugBWkqfcqN555x23h5slsoB77LHHzO9+++03AB588EEAfv/995DXrV+/PgajcwcJ6fTo0QPA\nFGXs2bOH5s2bA95eNGXEoUOHzPf51ltvAfDFF19k+PwaNWowcuRIwE4sHjRoEOCPxZOkC0hoOHfu\n3Mafa86cOeZ5UkEplWhy/p08eTJmY40W1apV43//+x9gf7eBXnuxRMN2iqIoiqIoDvDc1lecwyMl\nEguCTz75JKyS5ba6pVjs3bvXeFVFgiSXp6Wl8fjjj7s1rJgwYsQIAG644QbAks79FNIJRhIxR48e\nDVghOlHVDhw4EPJ8CenJ/E+ePMnatWtjMdSI+Pjjj4H0ypMk3YZTnBKBQoUKAXYCsdC3b19+/PHH\neAwpKlSvXt3R+N977z1KliwJwJQpUwDLFwkshfTPP/+M/iCjSM+ePQG49dZbAdi2bZtRZQIRhUbC\n5VIk4YWweaSIgj9ixAh27doF2PM4ceIEZ599NmAVBkBsohaqPCmKoiiKojjAM8qT5B9FqgaJe7jk\nSGXGsmXLMn1feUx7N8WXyy+/HLDzg5KSksKqGV4nT548APTp04fGjRuHPD5kyBDA6lTvF8SQ7oMP\nPkj3+4YNG4bNjzn99NMBOxlZGD58uCdynQRJ8p49eza33XYbANdccw1gf0+JREpKipmX7M6lTP+5\n556L27iiQXZUs9dffx3AKDaiXHTq1ImJEydGb3BRJFeuXIBtZCqFNrVq1eLvv//O8HUbNmwAoF+/\nfoC/lKeWLVsC0KhRI+6++24gfV6emEeLchgLdV+VJ0VRFEVRFAd4Rnlykn+UmpoakeIk75lVTzxV\nnrxF4K5BLAG8zLnnngtAmzZtAKhXrx5gl+cHIztFqbbLzFTSC6SkpPDqq68CdnsdqWYSY9NA6tSp\nQ4cOHYBQA0OxafAKUr4daAFStWpVwLZeiKeJZ7S55557TJXdl19+CditPDKjQIECxlpDeqJJ7kk8\nyJs3r1GJcpKbJC2VpA2NGC/WrVvXs8pTsLoiFcuRqvTbtm0DrH6FOen9FgtExX/44YcBq1XZzJkz\nQ54jjwuffvqp62PzxOIpkoVQIJFaC0TqFeX08xV3GDhwIGCHE3755Re++uqreA4pBLmhisdM06ZN\nTbhRkk+zkowl2VqaWUpDWfm912jUqJFJ/D5+/Dhg32zGjRtH+/btATtUV7BgQRNaEL755hvAuwuR\nwBCAXLAvuugiwLtjzg5t27Y1vmI33ngjYFszBCI9/8SDrW7dusarTLyg5Dvt2bNn2Pdwk6NHj5om\n6dHYfMgi2g/FHNKsWTyNJNxapkyZsB5rgpT0i5t++fLlPb94kqRw6TARaEsg15shQ4YYGwZpxC1h\nPjfRsJ2iKIqiKIoDPKE8RRu1IIgP0ncvOGz13XffmbCISP7iCBv4vFatWgH27u/+++8Pa6IZQYt5\nUgAAIABJREFUT8TgUozpRKUAywAT7F3sli1bwhrRyXuIe/rTTz8NWDspKZn2QqL8JZdcAsC8efPM\n70SRkBL/QKQU+sSJE0Z5ku9SrAD+/fdf9wacA5YuXWpMPqX3XqNGjQCrpD1fvnwAXHzxxeY1koDr\n1TkFIgnR1apVY+XKlUCo4nTBBReYTgCiagR3OQBL4QA7STclJcX0kIslfgjpu8ny5csBWLx4MQDv\nvvuuUYh/+eUXwCpaEVVGOjecdtppAHz//fcxHW92EFVf+Pbbb42zuCin4jAPGJPQWNhMqPKkKIqi\nKIriAE8oT1kldAtZJXRn1+7Aq0gyox+4/PLLeffddwE7qVhUh6ZNm5p/B3c2X7lypVGs5DFRmwLV\nKa8gcfbAeUiJtCgXkrs0b948Tpw4EfIe8veRXZW0ARk+fLiJ1ctPUerigSiBslMF+3uTvm9ffPGF\nOe9EeVqwYIFRrZ566inAm99lIPv27WPVqlUARkWRnJrGjRubliaBytPGjRsBe27Scmj9+vWe6yN2\n8803A1aRguzYBVGQlixZYpLCg8/TefPmheTTyHVWjmclPkiy9M6dOxkzZgxgt25p0KCBKVDZuXMn\nAM8//zzgjfZIWSHqqByPorYF/i4tLY0ffvgBIKxJqFt4YvEUKdlxEc+ISCv24olUNMnN1cuMGTPG\nVIGIK7iE6urUqWPk/7p16wL2RblOnTohzYLlp/h5BL5X4EJLnifvKY9Jry43EF+gRx55BIB8+fIx\nffp0wF70HT16NNP3kAXRkiVLADsE1r17d7PYECm+YcOGcVtEy8X1zDPPNBK/uBUHNsEVpC/a+eef\nbxYPH374YSyGGhUk1CHIoiCjxYEkscpP8Uu68sorM+2pFg/kRnP06FFzPMmiWI5fqV4D+yYkG6Jw\nITKpRsusMbZfkZCsH5AQedOmTc05KEydOtV8v+HOWa8j6RGrV68GoHfv3mYDI+ddWlqauZbGEg3b\nKYqiKIqiOMATylNqaqrjJG9RjaQXntPXL1u2zNHzlcypWLGi2d1u2bIFsNWizZs3h9gQCIH/l3/L\nTn/y5MkZhvuSk5ONuhGcqB0LZEcUDaTUesKECeTPnx+wO5+XK1fOlITHmnXr1gHWbi8SxLk6JSXF\nJJlHahfiBSTk0aVLF8D24QI7JPn2228D8Pnnn5vHJMQqCuiAAQNo166d6+N1gpw3P//8s1GcJHwn\n4165cqVJuJWwbDjEC0n8ouJ1fEYTUc3lWrJixYp4DiciRCkUp/Dq1asb6wFRDKtWrepLxUmQYgy5\nX6elpRlVXvj111/jkoKjypOiKIqiKIoDPKE8DRs2LCLlSNSmSBPMwyFJ517Pd/IbzZs3NzsCcYAN\nVI0yymtasWKF6X9WqVIlwC7hr1evnvm3IK87depUun+DnWvlZxYtWgTYytNrr71m/i5eRb6jSy+9\nFLAKHSQnzE+IUirHo+Qafvjhh+aaI2pcIGPHjgXsHLY6deqYLu+7d+92d9AOqVixoulPKIqT8NNP\nPxnFSSwpRNkvU6aMKQkXK5JE4MwzzwSgW7dugF3iHs9CjUi58sorAfu8q1WrFl9//TWAsUh54403\nqF69OoDnDIedIHldHTt2NLYhQocOHeJi7aLKk6IoiqIoigOS3LajT0pKiugD3B6HKE6RtnYR0tLS\nkrJ6TqRzdIoY1omp3aFDh0yLhGi2jMhqjpHOb9CgQYBlzAZ2HsGRI0fS9Q4De3cfqxL2aM3RTcqV\nK2fK3aWS5NChQ2bnmFnX+Hgep1IJU7NmTQDmzJlDx44do/458ZxjJMyZMweA9u3bm3NB2ptEilvH\nqdhOzJ492+zcg6+5f/75p6k4lHyvQJNM6Ykmlg5icdC1a1dH1yMvnYsS8ZCq0O+++w6w1ZzsEKvj\nVM4xyT0TlTCQkiVLsnTpUoAQA82cEOtzUSqvxWYB7N6MtWvXDmsJk1OymqMnwnZgL2rcSDBNTU11\nvGjyKpLQ6EUkbDZ79mzA9hoJt3jyO+IsXrx4cfNvpwtaSTS+4YYbAEt+TklJAewb2/r16zNdNMWb\nChUqhDT/9WqPPrcQ7y+54SYlJZmFhVdYuHAhYLmIS0hVnMJloX748GET0pNFhVgulChRwiQmiyO+\n4Cc/ukRCfLfCucALu3btMgUf0ifOT+dn4cKFAbjtttsA69wSvyqZjxsLp0jw7p1YURRFURTFg3hG\neZKwmpQc5iQpXJD38mtyuCQtikNs/vz5Tb8tL/cl2rFjR7qfiYgkMM6ZM8e4aUs5uyQOb9q0KV3v\nO4B7773XqEoS1gy2bwhEwptepWXLlmaOa9euBaz+U4lGoMu6cMsttwAYWwIxaU1LSwvb09AL7N27\nl169esV7GJ5BnNf9iIRPJVm6atWqYQsaPvroI8C+lkjoS+4rXkQUp2effRawjVi3b99O9+7dAct2\nI56o8qQoiqIoiuIAzyhPgqhEqampjvOfgtWrrHrheZ2CBQsCdtkw2PkJSnyRPI/+/fsbc8XzzjsP\nwHT9DkegbUNmSBK2tG7xKv379zf/luT/48ePx2s4rlCjRg2zyw80zsyI1NRU7r33XreHpUSBIkWK\nALb66wdzzGCkoOTZZ5+lb9++gFVoEowoxI0bNwZsWxQvcu211wL2tXT//v0A3HPPPSYvL954bvEk\npKammkVQZi7igQsmvy+WgpFkTXFXlZCd4h0+++wzrr/+esBe5IpHU5cuXUJ6ZGVWxbNhwwbTz1Cc\nnr2+EJGbD/irj50TihcvbsK0mSEeO+K0rnibli1bUqpUKcAu0PBjyFnCb6NGjTKVzjKPQoUKmQbX\nUsQgG3AvL56kiEaYMWMGQFx62GWEhu0URVEURVEc4BmfJ6/iBW8ZSWrs1auX8Y+JpsrmJd8Vt0j0\nOcb6OC1dujRgeU+JjC6J00ePHo3Wx6Qjnuei7ITl/BN3Z7BtACSRNSfu1Il+nIJ35rh7925jpyKI\n0/j06dOz/b7xPE7F2f6qq64CYN68eUZVk/6iU6dOBWw39ezg5hzLli1rwqcHDx4E4IorrgBia4uR\n1RxVeVIURVEURXGAKk9Z4AXlyW28shN0k0Sfox6nFok+R7/PD7wzx6+++ooqVaoAGPW0RYsWOX5f\nPU4tEn2OqjwpiqIoiqI4QBdPiqIoyn+OwP6LP/74o6fbICneQ8N2WaDypP/nB4k/Rz1OLRJ9jn6f\nHyT+HPU4tUj0OarypCiKoiiK4gDXlSdFURRFUZREQpUnRVEURVEUB+jiSVEURVEUxQG6eFIURVEU\nRXGALp4URVEURVEcoIsnRVEURVEUB+jiSVEURVEUxQG6eFIURVEURXGALp4URVEURVEcoIsnRVEU\nRVEUB+R2+wMSvb8NJP4c/T4/SPw56nFqkehz9Pv8IPHnqMepRaLPUZUnRVEURVEUB+jiSVEURVEU\nxQG6eFIURVEURXGA6zlPipIVAwYMAKBNmzYA1KhRwzz2ww8/ADBo0CAA5s2bF+PRKcp/gxtvvJGF\nCxcCMHDgQACefPLJeA5JUTyLKk+KoiiKoigOUOVJiQspKSmAtcN98MEHAVi+fDkATZs2BeCvv/7i\nxRdfBGDWrFkAlC1bFoBRo0bFcriOuPjiiwFYtWoVRYsWBSAtzS482bVrFwAdO3YEYOXKlTEeoaKE\nR47TESNGAJArV650/1e8R/78+QFo165dyGO1a9fmzjvvBDCq4rRp0wBYsmRJjEaYmKjypCiKoiiK\n4oCkwB2xKx8QBa+HChUqAPDBBx8AcNZZZ5nHXnrpJQBWr15t1Ilo4lU/i9mzZwPQtm1bAC655BK2\nbduWrfeKh+/Ka6+9BkCHDh246667AJg+fXqGz//5558B2LdvH5A+LyoSYjFHGdPUqVMBqFKlCklJ\nSfL5Gb5OdoyyM8wOXjhOJS+tUKFCJmfm33//jdr7e2GObhNPD6QvvviC6tWrp/udKLwPPfRQ1D5H\nfZ6iM0dRnMaMGQNAo0aN+O677wA4cOCAeV7x4sUBqFmzZrrX9+nTh1deeSVbn63noofCdlWrVgXg\n/fffByBfvnx069YNgFOnTgFQqlSpkNd1794dgG7dujFkyBAAJk6cCFhhH8j8puw3ZOHYrFkzAHLn\ntr5COZG8zs033wxYyakAo0ePjuj7eeqppwB47rnnAGvRNWfOHJdGmT1kIVulSpWQx44fPw7A33//\nzemnnw5YiwyAcePGAbB582Y2b94ci6FGlVatWgHw2GOPAdZC8fvvvwfghRdeiNu4vETlypUzfGzr\n1q3m+IgHEj4O3JQK7733XqyHE1UkzH/hhRdy9tlnA9CkSRPACkl26tQp3fNbtGgB+COkVa5cOQA2\nbNgAwN13353p84cOHQrA4MGDAZgxY0a2F09eQELKM2bM4NZbbwXszXXjxo0B+Oabb1z7fA3bKYqi\nKIqiOMAzYTtZAcsuCODll18GYPz48YCtSm3dupUrr7wyy/cUxWr69Olmd79x48ZIhw54T5585pln\nALjuuusAeOedd4CcyeqxlNElNCVJ1ZUqVYrodbVr1wZgxYoVALz11lu0bt064s+NxRx3794N2DI5\nwNGjRwG4/fbbAXjzzTcpUaIEYIVJwFZU586da5Q5p8TzOF27di2ACfmkpaXx9NNPA3bJezTw2rmY\nEXny5OGGG24AbKW1VatWGYZuZ8yYQdeuXYH4hLRE7ZRzEmDChAmAfV2JpjIW7TmWL18egDfeeIMi\nRYqke6xAgQKArfJmhYTAHnjgASdDSIdXj9PChQsDthpTsmRJLr30UgDHine85picnGwKjLp06QLY\n338gMp/q1atz7NixbH2WtmdRFEVRFEWJIp7IeXrxxRdDdtzff/89jzzyCGAnC1esWBGAY8eOmRyf\nMmXKANC1a1cTyz7vvPMAa5UKcNdddxmlRp7jVIHyAsnJyRQsWBCATZs2AfD666/Hc0iO6dOnD+A8\n4fvTTz8FMLk0kojtdSQW/9Zbb5nf7d27F7DynwJp2rSp2Tn/8ccfMRphzqhcubLJvQhEvudFixYB\n8Nlnn8V0XDlBVIozzjgDsJLeAxNwAU4//XR69OgBYOwoWrZsCVhKovwuM+T7l9LxWHPBBRcAmGtK\nIJJPGM9crEjJmzcvYOX0SG6kjPvXX38FYN26deb58ndfuHChUbRF0U9kGjRoAKRX4YoVKxan0ThD\n7uV33313iHHryZMnGTZsGGCfg3J/adasWY4KcTLDE4unW2+91dwMxVG6WbNmZtEkyE0H4ODBgwD8\n9ttvgHVxPvPMMwGMr4Uk0JUtW9YkCy5duhSwFlF+W0D169eP888/H8AcLF999VU8h+SY7du3p/vp\nlLlz5wJWdaHXkBNXQlXTp09Pt2gSxOMqT548gL0QLFiwoCkA8AslSpQwoZFAZOERrsjDq0hVr6QH\nnHvuuYB1rZFK31WrVgFWcrWEtCKpqFy2bJm5tsk1SBaUcoOPNXJ9lGsj2KHn/fv3x2VM2UEWRuvW\nrXN8owwMsSc6UpQl5+vKlSvNptSryCZE/P6k0Ajgyy+/BKwCsRkzZqR7viyeAo/taKNhO0VRFEVR\nFAd4bpsru/dg1SkS9uzZA9hl7RIyeOedd0zJaqACJeWMEgLzKhLK6datG8uWLQPw/I7BLaRQ4MiR\nI3EeSSiff/45YJfuh6Nw4cJ8/PHHAEZFFMUiNTWVP//80+VRRgcJl0+dOtUoLyKtS6EGwL333gv4\noyfh8OHDAVtxEgoWLGhsKMT9fuvWreZxCbHKMVmyZEmjVInyOGXKFBdH7gwptLj//vtDHhPPtS1b\ntsR0TPGiQ4cO8R6CI/LkyWP81OrUqQOkVzwlmTrw+xNF9bbbbgPs81PUHC8iNgSPP/44kF5xEvuM\nnj17AunXCvXq1YvRCFV5UhRFURRFcURclScpMUxOTjbu2NL3KxqIonT99debkv5ABapNmzbpnudV\npOz7wgsvpHnz5nEeTXwQd9xGjRoBtkWDFxETzB49epgdk5gkJicnZ5hMvGPHDk6cOBGbQeYQKRO+\n4IILzM5XVLNjx44ZOwa3rVCiiexaRUkTRalBgwYmKV7yoYoWLWpUcrHPELNCryPn0mmnnRbymDiK\ny/c7duxYwF8J/5GSJ08eU0gkePm6AtZ9S64Rn3zyCWDboJQpU8Z8TxJ1SUpKMrYZgig1bhpI5pSb\nbroJsE2whRdeeMEopocPHza/l2tusNFroEIcbVR5UhRFURRFcUBclSdZXebKlcvYqp88eTLqn7Np\n0yZj+jZ69Oiov79bSJn7LbfcAlir7uz2r/M7UoUmsXCxLPASUgL85ptvApaSEUklluQeSC6Dl5EK\nwUCTWjECFZXt0UcfNcqTqBvy/UWz1120ke9Ifkr/xQ0bNoSoSjt37uT555+P7QCjwLnnnmsMOcMh\ndjDyU9pAbdiwweRtudFDNB4kJycb40hRTX/66ad4DilLfv75Z9MGSZg/fz5gmUnLfEQVTU5OTpeD\nCHael5eVJ+n3KUieU79+/dIpToKsJSRfUSpY3VRM47p4kt51YDX2BfcSgaWRbuDiST5/xIgRrnxm\nTpGbcb58+QA7Ef6/iNhPHDp0CLD7F3oJKc93mrT49ddfA+mtOLyKbEIkcRrsxf3y5ctDnn/11VcD\ntnu1l0Pk4pQuoXEJXT300ENmgeh3Fi5cmKkHldxQ5TwTKleubM6566+/HrDTCcQp328E9rWT7z67\nFirxRCx3GjdubBa2l112WYbPl02OV21uihQpYoq55JoiC77g4xKsEHrdunXT/U56aoZ7frTQsJ2i\nKIqiKIoD4qo8xTuZVJzIvYqYmgnZsW+IJyIhN2zYkIsuugiw/+alS5cGLEVCbBdEfv7oo48A+Oef\nf8x7SQ8msQPYsWOH28N3jISkxMC1YMGCIeX7P/74IxdeeGG618lzvIyoacE7vF27dpldu7Bhw4aY\nlgxHC0myFeVJnKu7dOniyxBdOMQeIxDpUrBz506jwv/111/pntOyZUtTNi4hFUm2bt26tbFm8BOB\n11dR0fyIFEH9+OOPIY/NmjXLmPKK3cbgwYMB2LZtGzNnzozNIB3QrFkzY+QpRpiSEF62bFmzbpC0\nlp49e4aYYQZ3BHAD71+1FUVRFEVRPERclSeJSw4bNswYCw4dOhQI7fuVUzp37hzV94sFsqPwg8Fg\nINKn8LnnngMIm2MhCYCbN282+TCLFy8GbHVpzJgxxoxR7Pa9vEOU3Y4kL4rhINhmiYsWLTIJu5J/\nJ8mdXszjEuS7FAVR2Lp1q2npcc455wBW+5ng3oPffvstAL179/bsPOU7ClaZRPVMNFJTUwG7TUtm\n+SGLFi0yz5dcIfkeBw0aZBQCP7V1qVatmvm35B36CVFe7rnnHsBSt0W1f+KJJwArZ0iKPEQRF2uD\neEd+MiJQiRdjT7mnFC1a1Ixb5hWO7777zsURWiS5/QdMSkrK8APkprlx40ZTRdW3b18Ann322Rx/\ntkh9AwcONIsnuRkDvPLKK4D9BYUjLS0tyw60mc0xu5x22mmmAlGkdLeaV2Y1x0jmV7JkSbMYaN++\nPQC//PILYFVfBfuniIfOqVOnTEWW9CYcMGAAYJ0w0rhSFlTXXHMNQNiKi8yIxhyjhRyX0pNLPIQa\nNGhgeqc5xe3jVKpgg68Xf/31l0kyloWVLKLCcd111xmvJKfE6lzs1asXAOPGjQOsSizxRnLTNwbc\nP0737dtnNjPi/yOblkiRkIqEfFq1amU6HzRs2DDL18f7XJQQz/r1601vO6kOjcbiL1bHqZyTkhKw\nbNkyE5oLDruCLUxI2G7RokXm+U5xc465c+c218FwDeSl0lqKwEqXLm2KvySdQ3qf5iRhPKs5athO\nURRFURTFAXEN28kK8tSpU0Z5qlWrFgCTJk3KtieMlGmKdBnOlTstLS3bu/xYcO2111KwYEEgfl3X\nnTBs2DBTTipd40WByioEK465O3fuBOydf6lSpUxZrfjOiOQcrwRecQo/fvw4kD0lQqwnxNpAjv1w\njs9eINDTKZhChQqFOHOHU7MlqTy7qlMskR2tqOBly5Y1Pfr69esXt3FFGznfnCLhH/lbtGrVyuz0\n/UCpUqUAKF68OAsWLABsJdwPBEcg5P7Qtm3bsIpTRqxcuTKq44oW//77r7E/kXuIsHnzZt59913A\nvgZLIQPY0Q43LQoEVZ4URVEURVEcEFflSZg3bx4dO3YE7MSw8ePHG7fXPXv2hLymZMmSgJ0/0qVL\nF1MCLjvhcFYEsiueNGmSp8uPW7RoEe8hRISoY9dee63J4RG16NixY47eS5QXKYVu1qwZTz75JGD3\nQRwzZgxg5UeJM3eslLmiRYua3Zqoor179zZO1JFQr149pk+fDqTPv/Myl19+eUTPk7yX4sWLU6lS\npXSPzZgxI9rDcg1xmw407JXrjV/Jnz8/kD4Zt3///kDmOZ/hkPM00GRS1FSJHHi5F95DDz0EWOew\nRCeCXbi9jOSBClOnTgXC5zkBJq9LClMELyf3i6Iv1/9wyDHXunVr87slS5a4O7AAVHlSFEVRFEVx\ngCeUp5EjR9KkSRPAXiWvXr3amH6Fa/sgBm1SoZUZSUlJZjf56quvAnZejR+Q7tleRHKRSpYsaez+\ns6s43XfffYBdwj9nzpyQ1jmi2gwdOtQcK7H6LnPnzm2UNiGrHB4pp7322msBq6xbLCgEMQN1WkEY\nK+bMmWMUW8n5ku8h8LuW1isFCxY0PRjFKNWP7Nq1C4BKlSoZRUW+/2hbqbiNqETS8glCc+7C9RWV\nc7NQoULmPeRvITmOp06dMrkmXlacJC9LcmCPHz/u2RYlGXH22Web6lxRETPL3S1XrpyJBIgpqFiL\nhDPV9BNSKVihQgXzu1jmMXti8bR+/XoeeeQRwPYOSUpKMmG4YEdmpxw8eJD7778f8E/4oHDhwsbx\nOFzY0iusWbMGgMmTJ5uDWS5O4uUUDil3btSoEQ888AAAtWvXBmDu3LmA5c0V6DIOdsL4q6++GlOJ\nVghOhh45ciQDBw4E7N50sqCvX7++mVtg6Cv4PUaNGgV4t0fYgQMHmDx5csTP//vvv43NRpEiRQA7\nlC7NZf3AwoULAatnmCQZi+WJ9PjzC/J3f/zxx41VgYTHJZk/8LsRSxBZKD344IMh7ynH8ZQpU+jZ\ns6dLI48ecpMVx+2dO3eajYwsJLds2RKXsUXKsWPHTMK+hBoDffRkQyksWrTIWIiID534Q0lnB78i\ni0iwNzPS5y8WaNhOURRFURTFAZ5QngCzs5XdzODBg02SZrBbcSDS7TzQ1kB2vWK0+eGHH8Z0RZoT\nZCfUqlUrevfuDfgjmXHu3LlGHhbFTIwt9+zZY2wpBNnR1q1bl/Xr1wN2abgcC8GqU+Dv5DNiyb59\n+4wqJiW0d955J23atAFsy4XAUEdm5fsSjhZn60RCkuiHDBkC2CGeoUOHum406QRxZ966dasJj8u1\nRMIiycnJYXf5fuTdd981RTm5c1uX/5EjR6b7GY60tDRzfIvCOnz4cMBOWPY6kigupKamGiNUUeG8\nzp9//snvv/+e7nfTpk0DoEqVKiFJ4eXLlzfHrqilXk4DiQQpfpDjGOx0nFhGaVR5UhRFURRFcYBn\nlCdB4u5Tpkyhe/fuACFJuoHMmTMHsG3Z/Y7YzOfLl8+U/vuB5cuXm4REaWUhikzXrl1DcgmkrPbh\nhx82eWjBOyqvcerUKaMGXn311YBluBeYhJsVH330kdkpSid6vyUf54QmTZp4SnmSQpUJEybw8ccf\nA/ZOXnJDTp06ZZRDL409O9x+++3G7mPQoEEAmbbokOvqsGHDTJGAH7nmmmu44oorAFsF7tChgzFj\nzK4hczwIvpbK9UfargSye/duunTpAtiRAL8j1gSBfTbF1iiWeG7xFIifkktzivgYNW7cGLD6avmt\nEkQSEiVRXH7KgjARkJBFo0aNAHjsscdMSCqYDRs2sGLFCsAOzaWmpvrqQh1tZMHoFSRU/M8//5jQ\njXy3gUgloSSR+xnZlAW7NycypUqVCgmdz58/33dN18EOr0rVnMzrxhtvNP1ixYX8xx9/9H1ieDDn\nn39+uv//9ddfJmwXSzRspyiKoiiK4gBPK0//JaQEP2/evACMGDEibJKx4g3EI6VTp07pnJYVCwnx\nSFhEQrNe85aRsNQDDzxgksGDlafPP//cWJ141YtLyZxWrVqZf0vx0J133hmv4USFmTNnpvv/rFmz\n4jSS+PLaa69lu09jTlDlSVEURVEUxQFJbqsbSUlJvpZP0tLSMvZJ+H8SfY5+nx8k/hz1OLXIyRyl\ndF/yRoTdu3ebfD63SfTjFOIzx//973/06dMHsPNKJY8t2ui5aOHWHMXmRnqbvvzyy5n2wMsuWc1R\nlSdFURRFURQHqPKUBbqL8P/8IPHnqMepRaLP0e/zg8Sfox6nFok+R1WeFEVRFEVRHKCLJ0VRFEVR\nFAe4HrZTFEVRFEVJJFR5UhRFURRFcYAunhRFURRFURygiydFURRFURQH6OJJURRFURTFAbp4UhRF\nURRFcYAunhRFURRFURygiydFURRFURQH6OJJURRFURTFAbp4UhRFURRFcUButz8g0ZsDQuLP0e/z\ng8Sfox6nFok+R7/PDxJ/jnqcWiT6HFV5UhRFURRFcYDrypOiKIrib0qWLMnHH38MQLFixQBo2LAh\nABs2bIjbuBQlXqjypCiKoiiK4gBVnhRFUZSwlCpVCoAPPviAiy++GIDvv/8egE2bNsVtXIoSb1R5\nUhRFURRFcUBCKU916tQB4N133wXgzDPPBODYsWNxG1O0efTRRwEYOnQoAElJWRY9xI2iRYsC0KFD\nBwYOHAhYuRPBrFy5EoCFCxcCMGHCBAD+/fffWAxTUZQgOnToAMDgwYMBqFChgnls+PDhAJw6dSr2\nA1MUj6DKk6IoiqIoigOS0tLctWKIpdfD/fffD8Do0aMB6NWrFwATJ07M9nt6zc8i+PvUCGRZAAAg\nAElEQVRKTU0F7MqXbL5nVH1XatWqBcDYsWMBuPLKK0PGHfT+Mg4AU9Vz55138uuvvzr56Axxy1um\nadOmPPjggwBcc8018lkA/PDDDzzxxBMAzJw5MztvHzHxPE6D1dBA5LiU4zQneO1clO+7bdu2AOTL\nl888Jqrr9ddfL+Myx8WgQYMAePLJJ0PeM94eSA8//DAAw4YNAyB3bjs4cdtttwEwb948AI4fP56t\nz4j3HN3GzeP0sssuo169egAUL14csNXB5OTkEDVw/vz55v63bNmy7HxkWLx2LrpBVnP0fdiuUqVK\nAOTPn58zzjgDsG9eZ599dtzG5QaffPJJyO8aNGgAWDcwuYnFgzx58jBgwAAAHnroIQBOP/10AE6e\nPMmrr74KYBYagcjN5PbbbwegUaNGALz33nvm33v37nVx9JHTvHlzwJ5HzZo1yZMnDxAaxihXrhxT\np04F7AvcjTfemFCJto8++mjYRZMgx2w0F1HxRM63p59+mho1akT8urS0NH7//XcAtm3b5sbQcszD\nDz9sriGBiyaAl156ifnz5wPZXzQpzklJSQEwC6bp06ebRZMg97tTp06FbFLbtGlD48aNAXvxdPfd\ndwPeuaYGkydPHnO9yJ8/PwCXX345AOeeey633norAC+88AIA+/fvZ8WKFQAsXrw4ZuPUsJ2iKIqi\nKIoDfBm2q1KlCldeeSUAo0aNAqBgwYImxHPuuecCmP+XKVMm25/lJXkys+9q2LBh2VaeciKj16xZ\nE4AxY8aYfwtr164FrHDOBx98kOU4pCy6X79+APTu3du8rkWLFlm+PjNyMsemTZsCMGDAAKM2SIjm\n33//NeP96KOPQl7bv39/wApBAuzatcvsBLds2eJsEpkQ6+NUFJhwamg4YhFeBnfOxdy5c5tzb/ny\n5QBcddVVGT7/+PHjptjhnXfeAWDdunXMmDEDwChQ4YhHSEuU30ceeYTTTjst3WMvvvgiYJ2LR48e\njcrnRXuOUoQiSe6BtGzZEoBvvvmG0qVLA5b6C+nDXM8++ywAf/31FwBDhgwhOdnSFm655RYAXnvt\ntYjGE63jNCUlhWeeeQaw1SKAw4cPA9a1BOww6k033cQbb7wBQLt27czzxWJCjuEuXboA8PLLL2c1\nhAxx41yU72fy5Mnmmhspcr5JsZGEnQ8ePOjofQLR9iyKoiiKoihRxFfKU968eQFYsmQJdevWTffY\n4sWLzQ6kWrVqAPzyyy+A/5WnzBJyhYYNG2Y7nyQnO0FZ6d9zzz3md7IjEnVw9+7djsZToEABAL74\n4gsuvPBCwE5WjXT3F0x25ig5XLKLOXTokFGLZs+eDVg72lWrVmX4vhKzX7BgAWAlGe/YsQOAypUr\nA3DkyBEHMwlPrI5Tp4pTmDFk+7NjfS5KLtvcuXPNtUdy8AL5/PPPAbtQZfXq1dkudIil8iTXRTl+\nzznnHPOYKE59+vQBonOMCtGaoyTjT5s2DYASJUoEvod8VmafE1Ehi8y9fv36fPXVV1mOK1rH6bRp\n07jjjjtCft+tWzfAyn+KBFGj2rRpA1iFLAAVK1aM6PXhiOa5WKhQIcA+j8qVK5ftcQmPPPIIAM89\n95xRE52SEAnjuXLlAuwbZ926dfntt98Au9Llyy+/ND5PS5cujcMo3aN+/foZPiY39ngl4s6dOxew\nviMJ00V6UmeESK2NGjUyJ9Rzzz0H2KHArVu35ugzIuGBBx4AMOGKefPm0b17d0fvcejQIQAjv9eo\nUcPI03Jc+4nsLprkOPUTEppt0qSJKX44cOAAYB2HkrgqN9dohbXcRhZNb7/9NpB+0SRJuFK5HM1F\nU7SRBW3gosnNz+nfvz+dOnVy9bPAqqgDuOGGG0Iea9++PW+++aaj97vpppsAO02gcOHCgFWNLgtE\nqQbO7kIjuxQqVIhZs2YBkS2a/vzzT/NvmUc4HnvsMcA6TyNJGckOGrZTFEVRFEVxgC+UJynTlOS/\nffv2mYSywI7esisOLhnPlSsXJ0+ejMVQo4qESORnOOJpTwB2Aq38jCY7d+40O32xpLj33nsBO6nc\nTSSxW3Y4OVH3RA2dNWuW8R/zE5kdg4nKP//8A8CePXtMMcOQIUMA6/vcv39/3MaWXUqXLm0Up0su\nuSTdY8uWLTOKkyQlKzb16tUzqtA333zj6ucAIZYEQJaqk6hwgTYa48ePB+C8884DMEUBY8eONcpT\n3759AThx4gQjRowA7NQENylfvrwJv4ZDuoOIIjphwgRTECZRj2LFimX4+i5duqjypCiKoiiK4gU8\nrTyJQZgkSouiNH369HSKUzCya5KV9k033ZTtRON4ktlu3+9mg5EiOU6iPIXrjecW69ati9p7SVKk\nuK/7iQYNGmRarJCoiOmuqE5gK4ixyLlzg4ULF4YoTnv27AEsFTuRFSfJlxHVSBS4QKQwRRSmQH7+\n+WdXFSdh0aJFANxxxx1UqVIl3WOLFy/miy++AMIXEEnyfKCak1nyvBzHcm/dt2+fK1GE7CLml99/\n/z0AU6ZMMcdvZoqTIP1t3UCVJ0VRFEVRFAd4WnmSaieJAW/cuBGA//3vf5m+TiqzxPzNj2S1249m\nnyIvI8pT586d4zySrKlatSoA77//PmCrTYEEmxD6gaFDh2aqgkZSFu531qxZA3i3tUpWSC5ToOok\n7TnEXDLcNUWeX7ly5UwrTUXFmTNnDmDblbiJ5LyIGp2amppjZUjMT6tWrWpMMkWVCddayg22b98O\nQOvWrY3JqtgKNG3alAoVKgD29UVsUJKSkkIsfAKRXKZ9+/aZ38m90qs0adIk3U8v4enFk/jgCC+9\n9FJEr5MeTNJPrE2bNr4L22VUEi7hungniscL8X3Kmzev50rDxVIiXKJnOMSPzEsyeSCRFCxEih+P\nV+nh9uuvv5oQgfh2/f3333EblxPEhkBunLlz5zY3z/bt2wPhj7/rrrsOwJSRFylSJNPPkWNE+lNK\nRwCxlHETSYjOCeKnJONOS0sziyZZGEbi8RRNtm/fzqWXXgrYjuF16tQxf2MpPJGf4RoDL1++PEeu\n/krGaNhOURRFURTFAZ5Vnpo0aWKsCSQc8OOPP0b0WkmCk9dFw7E0VmS1y/+vhOsEkc7lZ/Xq1QFL\nAfCa8vTqq68CULt2bcDqt5gZEuKQHlOxCgtESqSGmJGE60R58pMCJUUKpUqVMt+RXxQnQVQKMXoE\nOyFZFCex4nj66ac566yzAIzhsChOP/30E1OmTAFg8+bN6T6jaNGiJiogidbvvfceYIeyvYooilKq\nH/h3ktDj+vXrAdu6Ih5I/7p58+bx5ZdfAqFmxKdOnTLnooRkxSHeqxw5csR0XBDzYL+gypOiKIqi\nKIoDPKs8FS9e3LREkFiz7JgSmaxKwv8rFgXCBRdcANhJmzL/QJt+ryC7PWmHkBWjRo0CMG0+ChQo\nYIohYt0mIZDstmDJDDmuhw4danIwvH4sSzuL/fv3G1NeUVKiaWPhJpIMHsiSJUsATOKxtA6SPCfA\nqAEjR44ErNynjHpU5s2bl44dOwJ2Yq+8t9cR9SY4vxbscv9Y2BM4ITDhOyPELPOVV14x/Rjl+uQl\nNmzYQM2aNQHL0BKsv7vYDIkaJf34xFYD7P6L/fv3D/v9uY1nF0/33Xef+ffixYsdvTbYwVkqtryM\nhDOy8nby+g0nHNddd51xvJWfV1xxhXlcqrUWLlyY7ueXX35pEjgFuaifOHHC3UHHgMmTJwN2D6tu\n3bqZ5FSnx3w0cdtNXN7f68eyhIpTUlJMk2C/0Lp1ayC8X5EU0gR7CB0+fJjhw4cD9qIikhvuOeec\nw/nnn5/ud1IJ52Xq169vKrnD4bVFkyANfoNZvny5qcqTopWKFSsah21Z2Eay+IolsiB6+umnzc9I\nFk9CjRo14rJ40rCdoiiKoiiKAzyrPIm7ONjhjUgJ9tdxIwwRbaTMPTP8WnI6duxYE34LhyhPd911\nV7qf4YiFf0yskMKGAQMGANZuvVu3boC9OxR/oVgiipBbCpSE8ORzvKpAibr5+++/U6ZMGQAOHToU\nzyFFjIxXzq1AghUn6R/Wo0cPXnnllSzfW7zKypcvD1jWMFKUIwnLq1atyubI3UeutampqSGl/RLm\nlARtLyLjD/5uGzZsaIocxB+qTJkyJtT8+++/A7biPXXqVM+qa7/88ku6n5khNg6xRpUnRVEURVEU\nB3hWeQokeHeQFf369QPgjz/+ALyd8xSJEaFXd+ZZ8dZbbwGWsaXsSMVeYMaMGeZ5RYsWBcIntwYj\nJpmJhHRKf/LJJxk4cCBgl8THQ3kShVMUW7dzoLyKqExLliwx7tqSi+H13nbjxo0D7ITvzJztpUjh\n7bffNrYFuXNbt4Y777wTsPJppC+jJPjK+ZqUlGRyUeT4lWRer1CsWDGTxyV5ToGl/fv37wcsQ1Sv\nI2OWn2KACrBp0yYAWrVqBcDjjz9O2bJlAdulXI7ltm3bmmRyeZ0SOao8KYqiKIqiOMAXylOkSB8m\niQWLaaGXd4mZ5WOJ4uTXXCfJc0pKSjIGp1LSLHkWYO8E5af0qgok2CSzdOnSpvIuURg2bBiXX345\nANdee22cRxP5cZeZSWa4/Klhw4ale8zrSNk3eLPcOzPeffddAG688cYMnyPtTfr06WPaz0TSjV7O\n4TfeeMPkpW7YsCFH44020nalb9++YSuyRHES9fezzz6L3eBcRIw9b7jhBmOU+vDDDwP29bV48eI8\n9NBDACbX0o9VzJmZl+bKlYtcuXIBcPLkyah+rmcXT3PnzjWJjeK8HM6dWG42derUMRK18OGHH7o7\nSJdJFDfxtLQ0s5AKF4YKbiwb6JIrfwPpwyXvM3z4cF80C5YwyDXXXANAs2bNzIUqmH///dec4BLK\n7NatG9OmTYvBSN1Bvj8/bgBkwS6JtmC5jYP3FgkZ8emnnwK2Z5HcSMIRrqhDzsXAm+rrr78O2KXl\nXgz5iHfas88+C6R3Dg9EvIXEEd0PSO9WWfiI9cSGDRvCJrpLf0L5KTYoTZs25bbbbgMwFhWRdvHw\nEuPGjcuwqKxevXqmh+gXX3wR1c/VsJ2iKIqiKIoDPKs8LVu2zCQXSxKi9JcqXLiwSQqXHVX+/PmN\nkVanTp2A6K80o01WSeJ+6gMWjgMHDjh6vvRskp3V1KlTjZO4qIgTJkwA4JZbbjG2BUOGDAEs5cYL\nSMhj8uTJpoRbfsqxnBWiEATbbvgNrx7D0sNNlIl8+fKFPEeSrANDqF5OAQiH7MhXrFgBWEaJooJK\nCDKw1Pv5558H4LfffgPsUvGZM2fGZsBRQsabWUi5f//+5u/iJ2TMkugv3HfffSxduhTIvEOBKP1J\nSUlGXZUCAzHs9RPxGrMqT4qiKIqiKA7wrPK0cuVKE7eW7vOrV68G4IwzzjCJjcKCBQvo0aMH4D37\n+ewgSbV+RszcIjUxk/yYcEm5L7zwAmC3HRgxYoTJhZs9ezbgndwLUUhlhw92cm39+vVNXlO4pHBp\nXyNWBQsWLHB1rP9VpBRf2orI3z0rREH0mwIluYZr1qxJV9qeaIwePRqw89XC2dz0798fsNUWvyHX\nSTG7FOuBunXr8vXXXwPpc9REqRJFv1ixYoClyjm1AfIiYsEQa5IykzWj8gFJSTn+gMceewywZfRa\ntWqZqghxxP3pp584fPhwTj8qhLS0tFCL3iByMseM/v7hnIHdIqs5RuM7jBZSbTdp0iRTNXLllVcC\nZNi4FGI7x8cffxyAe+65x3HYTRZNsliUBWJWuH2cZkSDBg0yrRiN5nHsxhw3b94MwMUXX5zp844f\nPw7YF+qff/7ZycdEjJ/Oxezi1hxLlixpNlAFChSQzzKPS1K4VPy65RYfq3NRunCMHTsWgJYtW5rN\nZdBnybhCHpPw3i233ALA+++/H9Fnx+t6E47ffvst0+pQ8SVzmsaT1Rw1bKcoiqIoiuIAXyhP8cRL\nK2y30N2uO3MsW7ZsSMgyd+7cxtVXup0HImG6bdu2OfqseB6n4ZzI3fAoc2OO8v3069fPOGiHQ2wx\nJETsFnouZn+O5513Hj/99JO8h3wWAAcPHqRNmzaA+71O43UuXnbZZdStWxewQ3kVK1bMVHmSa9Hy\n5csdfZaX7ouqPCmKoiiKovgAVZ6ywEsrbLfQ3a7/56jHqUWiz9Hv8wN3c542btwIQMGCBQFMHmzv\n3r3T9dN0Ez1OLWI1x8KFC9O4cWMAYygszvI7duwwbutOrWxUeVIURVEURYkiqjxlgZdW2G6hu13/\nz1GPU4tEn6Pf5wfuzlFMQe+//37A6rsHdoVdLNDj1CLR56iLpyzQg8T/84PEn6MepxaJPke/zw8S\nf456nFok+hw1bKcoiqIoiuIA15UnRVEURVGUREKVJ0VRFEVRFAfo4klRFEVRFMUBunhSFEVRFEVx\ngC6eFEVRFEVRHKCLJ0VRFEVRFAfo4klRFEVRFMUBunhSFEVRFEVxgC6eFEVRFEVRHKCLJ0VRFEVR\nFAfkdvsDEr2/DST+HP0+P0j8OepxapHoc/T7/CDx56jHqUWiz1GVJ0VRFEVRFAfo4klRFEVRFMUB\nunhSFEVRFEVxgOs5T4oSyOmnnw5A7969AbjuuuuoX78+AGlpoSHy3bt3AzBixAgApk6dCsDJkydd\nH6ui/Fc57bTTAHj++ecBuPPOO3n44YcBGDlyZNzGpSheQZUnRVEURVEUBySM8lS/fn1SU1MBOHXq\nVLrH5s+fz8SJEwFYtmxZrIcWNT7//HPefvttAIYPHx7n0TgjV65cAIwePRqAe+65xzwmilM45ems\ns84CYMKECQA0b94cgB49erBr1y73BqxEBTnv5PtOTtb9mh8YM2YMAHfccQcAhw4d4u+//47nkBTF\nU+iVTFEURVEUxQG+V57Kli0LwIIFC4ziFKxgtGnThsaNGwNwyy23ALBkyZLYDTKHVKhQAYCLL744\nziPJPjVq1ADSK07ZoUWLFoClQL344os5HpfiLj/99BMQXlVUvEfTpk0BuP322wFYs2YNAM888wxv\nvvlm3MalZI8CBQpw1VVXpfvdrbfeCkC1atWoXLlyusfmzZvHbbfdBsDx48djM0if4tvFkyyaHnro\nIQAKFSqU6fPlcXn+ihUrOHz4sHsDjCIy9oIFC8Z5JNmnUaNGYX//0UcfsWjRIgCmT5+e7rGrr76a\n1q1bA9C9e/d0j/Xq1YvZs2cD8M8//0R7uFFHFsB9+/Y1v9uyZQsA5cuXB6BevXpmkbF582YAWrVq\nxdlnnw3A3r17YzbeYPLmzQvA0aNH4zYGxV3KlCljFki5c1u3hkGDBgHwySefxG1cSubIuVmlShXa\ntm0LwDnnnANAs2bNKFq0aLrnHzp0CLDO5eBNTbt27Uxqi4TclfBo2E5RFEVRFMUBvlKekpIst/R6\n9eqxYMECIGvFKZh69eoBMHbsWO6+++7oDtAlgncOfkQsB0S5GDZsGACTJ082O6FgPvzwQ9atWwfA\nJZdcAkDt2rXN/2WX9dprr7k38BwiO/cBAwYAkC9fPrPbk+M58P/yb1Gq0tLSmDVrFmAny8eaxo0b\nM3jwYAAaNGiQ4/f68MMPozCq2NGjR4+Q68zMmTPNMR2OVq1aAbaqGIh8j4HhlDPOOCMaQ80WhQsX\nBuCDDz4gT548AMyYMQPwn+LUq1cvwJ6TcPfdd3PuueeGPD/4HPzzzz8BeOyxxxg3bpybQ80WuXLl\n4oorrgDsKEqlSpUAuOiii8zzAue1f/9+AP7991/A/m5ffvllOnfunO7969Spw9atW92bgEPkO5N0\nm549e1KmTBkgfSqAFBT16dMnZmNT5UlRFEVRFMUBvlKexo4dC1i7i3AJqLL6lLySEiVKAFZyeNWq\nVdM9t27dum4ONarIqhvgggsuiONIso+oJ2vXrgVgw4YNEb1u3759AHz88ceArTwB3HTTTYC3ladO\nnToBluIE1o5Q8plKly4NYPJMtm/fzsCBAwG7pP/UqVPcf//9MR1zMJ07d85xsYLshP2gOl122WUA\nvPfee4BllyHjF0qUKMGvv/4K2OX8geemKDhi0ZEZq1evzvmgc0DPnj0BS7kQ1SHex5wT5Nq+dOnS\nTFX6cPeM4N+Jwjh06FBjfSPqtxcYOHAgjz76KBCqmoF9fn399deAlVP62WefAXDw4MGQ9/vf//7n\n5nCzRf78+Wnfvj1gW2ZIvu/HH3/M448/DtjFKI8//rhR0ETpzyiaEU08vXiSxY/I3FIFEIgkffft\n29d4IAmSYNu0aVN+//33dI+lpKQY+W/79u3RHXiUkJNDkjcBzjzzzHgNJ0eII3ikiyahVq1aAMbd\nOJAvvvgi5wNzAUlyHzhwoAnbBF7gNm3aBGDCjrKYGj58uHmeVI5u2rTJPB4vrrnmGr788ktHr5Ek\nVkl291O13dVXXw3YYw9Hv379wt68MiIwJHvixAkAXnjhBQDeeeedHI03u8h1dejQoYA1DxnLX3/9\nFZcxZQcJf2a2cNq+fbs5htu0aZPlexYsWNBce7y0eGrbtq057g4cOABYi0aw/AznzZsXt7HlFCkC\nmzx5Mk2aNAFg5cqVgO1r+Mknn4R0l0hJSTFpPLKQlte5iYbtFEVRFEVRHOBp5Ul2Ri+99FKGz1m/\nfj0QWuYeiIR+AilevLgJ3XlVeZKkxw4dOpjfiQT7X6Bs2bIm3BeovoH1vWf2nceSlJQUwE7ynj9/\nPmDt5GWXKKG5wYMHZ6gkDRo0yKilzz77LICRqOOB7ARTUlLMnCJFwnx+Cv9IH7eOHTtm+dydO3fy\n/vvvR/zeCxcuNMqHKFCZJZy7iYRARPmS0OL27dvp169fXMaUEzZu3AhYioWEeSTxWzhx4oQpVgm0\nfKlYsSLgn84TaWlp5vgR5T2S4zUcJUuWZMiQIYCVIA/w22+/RWGUzpBiIFHQihYtygMPPADA+PHj\ngdCuIYEE2qdIMr0qT4qiKIqiKB7D08qTJH+FQ/JGxC3VKdu3b+fll1/O1msVd2nYsCFglYhnlCA/\nceJET/S2GzRokEnoD85vSktLM07NojwdOXLEvFZMPgOfL7H7eCpOQo8ePQArgfOrr77K0Xv5IVFc\ncpwyM6NdvHgxAMuXL+eZZ56JybiijRTUyHz37NkDYLow+I3ly5en+5kVUroPZNiv78iRI3HPNQzH\nyJEjTSRGcr0kP0iUm6yQiM7zzz/PBx98AMRHcQJL1RYFV6ILXbt2NdfGSNizZ49RpmJpJK3Kk6Io\niqIoigM8pzxJjsgbb7xBuXLlwj7n22+/NbukcPlMwYwfPz5d6TdAkSJFTEnyN998k+NxK9mnfv36\nAMaIsU6dOgCcdtppIc9dtWoVYB0f8URygFq1ahVSdSXq0u233x7SD6xEiRLGnFVsDOR148eP54kn\nnnB/8BFSsmRJgJAyfSfIa+OV3xMpFSpUoFmzZmEfW7x4MU8++SSAyVvya9+vs846K6TVkezyt23b\nFvJ8uc4OGDDAGHmKOirWMX4moxyvP//801gVeInXX3/dWJw89dRTAEydOhWA6tWr88cff2T4Wvku\nJdftjDPOMBYw8aJ27dpGAZVIkxPVCeCHH34wBqBiNSH2IbNnzzaPRRvPLZ4kga9169YhJcASqmvc\nuHFEiyZJ5C1dunRI0+DAhHGvLp7OOuuseA8hR4jXTYMGDUzDUUHKhUuVKhWysA1EblJyIWvXrh2Q\nPvwVD8RBOvAYlZuKJGEGyv6STD527FiTRN27d2/AtuAQCd0rVKtWDbCSo7Mr6weGJL3M/v37M0xK\nLVeunLkui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iKEqY6ERKURRFURQlTHQipSiKoiiKEiY6kVIURVEURQkTnUgpiqIoiqKEiU6k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TQ5fvy4qWiWG+qKFSt6Nc0uXLiwSVBPyaleWLBgQUh8szS0pyiKoiiKEiCuUqRk1Swrypw5c5oS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\nrkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x105b1d710>"
+       "<matplotlib.figure.Figure at 0x7f7400a4b1d0>"
       ]
      },
      "metadata": {},
@@ -322,7 +550,7 @@
     }
    ],
    "source": [
-    "# takes 5-10 secs. to execute the cell\n",
+    "# takes 5-10 seconds to execute this\n",
     "show_MNIST(\"testing\")"
    ]
   },
@@ -330,14 +558,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's have a look at average of all the images of training and testing data."
+    "Let's have a look at the average of all the images of training and testing data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -374,7 +602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -398,9 +626,9 @@
     },
     {
      "data": {
-      "image/png": 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V6WMf+xgA4OWXX8bzzz8PoKkQ9/f3S59kKMS3v/1tAMCrr76KN998E0AzjGdnZyd2Tz4I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\n",
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7J8+LKVOGYRiGYRgtcCSVKR14DUTrofQQP/CBDwBoeIxTU1NiIdODLxaLYkHzs+iVDA4O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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1199f49b0>"
+       "<matplotlib.figure.Figure at 0x7f740390c7b8>"
       ]
      },
      "metadata": {},
@@ -425,9 +653,9 @@
     },
     {
      "data": {
-      "image/png": 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SfjrinGO899LX2H+dc9I3aSPwOSwtLSVuu9q2nWEYhmEYRsYcCuWpu7tbvBgd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\nwzCMFjDjyTAMwzAMowX+P+71/Wk0f7yTAAAAAElFTkSuQmCC\n",
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ShfrMZz4DAHjuuefw+OOPA6i3cTablX7LjUuvvPIKAODHP/4xrl27BqCusK6vr0sdW1Wo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\nAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ae08b38>"
+       "<matplotlib.figure.Figure at 0x7f740369a048>"
       ]
      },
      "metadata": {},
@@ -443,86 +671,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## k-Nearest Neighbours (kNN) classifier\n",
-    "\n",
-    "### Review\n",
-    "k-Nearest Neighbors algorithm is a non-parametric method used for classification and regression. We are gonna use this to classify MNIST handwritten digits. More about kNN on [Scholarpedia](http://www.scholarpedia.org/article/K-nearest_neighbor).\n",
+    "## Testing\n",
     "\n",
-    "![kNN plot](images/knn_plot.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see how kNN works with a simple plot shown in the above picture. There are two classes named **Class A** yellow color dots and **Class B** violet color dots. Every point in this plot has two **features** i.e. (X<sub>2</sub>, X<sub>1</sub>) values of that particular point which we used to plot. Now, let's say we have a new point, a red star and we want to know which class this red star belongs. Solving this problem by predicting the class of this new red star is out current classification problem.\n",
-    "\n",
-    "We have co-ordinates (we call them **features** in ML) of this red star and we need to predict its class using kNN algorithm. In this algorithm, the value of **k** is arbitary. **k** is one of the **hyper parameters** for kNN algorithm. We choose this number based on our dataset and choosing a particular number is known as **hyper parameter tuning/optimising**. We learn more about this in coming topics.\n",
-    "\n",
-    "Let's put **k = 3**. It means you need to find 3-Nearest Neighbors of this red star and classify this new point into majority class. Observe that smaller circle which containg 3 points other that **test point** (red star). As there are two violet points, which is majority, we predict the class of red star as **violet- Class B**.\n",
-    "\n",
-    "Similarly if we put **k = 5**, you can observe that there are 4 yellow points, which is majority. So, we classify our test point as **yellow- Class A**.\n",
-    "\n",
-    "In practical tasks, we iterate through a bunch of values for k (like [1, 2, 5, 10, 20, 50, 100]) and see how it performs and select the best one. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Native implementations from Learning module\n",
-    "\n",
-    "Let's classify MNIST data in this method. Similar to these points, our images in MNIST data also have **features**. These points have two features as (2, 3) which represents co-ordinates of the point in 2-dimentional plane. Our images have 28x28 pixel values and we treat them as **features** for this particular task. \n",
-    "\n",
-    "Next couple of cells help you understand some useful definitions from learning module."
+    "Now, let us convert this raw data into `DataSet.examples` to run our algorithms defined in `learning.py`. Every image is represented by 784 numbers (28x28 pixels) and we append them with its label or class to make them work with our implementations in learning module."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%psource DataSet"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "class DataSet explanation goes here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%psource NearestNeighborLearner"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Nearest NeighborLearner explanation goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, let us convert this raw data into `Dataset.examples` to run our `NearestNeighborLearner(dataset, k=1)` defined in `learning.py`. Every image is represented by 784 numbers (28x28 pixels) and we append them with its label or class to make them work with our implementations in learning module."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -547,42 +703,44 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, we will initialize DataSet with our training examples. Call NearestNeighbor Learner on this dataset. Predict the class of a test image."
+    "Now, we will initialize a DataSet with our training examples, so we can use it in our algorithms."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "# takes ~8 Secs. to execute this cell\n",
+    "from learning import DataSet, manhattan_distance\n",
+    "\n",
+    "# takes ~8 seconds to execute this\n",
     "MNIST_DataSet = DataSet(examples=training_examples, distance=manhattan_distance)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "kNN_Learner = NearestNeighborLearner(MNIST_DataSet)"
+    "Moving forward we can use `MNIST_DataSet` to test our algorithms."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Choose a number from 0 to 9999 for `test_img_choice` and we are going to predict the class of that test image."
+    "### k-Nearest Neighbors\n",
+    "\n",
+    "We will now try to classify a random image from the dataset using the kNN classifier.\n",
+    "\n",
+    "First, we choose a number from 0 to 9999 for `test_img_choice` and we are going to predict the class of that test image."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -591,15 +749,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Predicted class of test image: 2\n"
+      "5\n"
      ]
     }
    ],
    "source": [
-    "# takes ~20 Secs. to execute this cell\n",
-    "test_img_choice = 2311\n",
-    "predicted_class = kNN_Learner(test_img[test_img_choice])\n",
-    "print(\"Predicted class of test image:\", predicted_class)"
+    "from learning import NearestNeighborLearner\n",
+    "\n",
+    "# takes ~20 Secs. to execute this\n",
+    "kNN = NearestNeighborLearner(MNIST_DataSet,k=3)\n",
+    "print(kNN(test_img[211]))"
    ]
   },
   {
@@ -611,7 +770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
@@ -620,24 +779,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Actual class of test image: 2\n"
+      "Actual class of test image: 5\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x119835518>"
+       "<matplotlib.image.AxesImage at 0x7f74036d6c18>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdgAAAHVCAYAAABSR+pHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngcACQAOBBYAG/webxyRlyxBmMwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ae1a2e8>"
+       "<matplotlib.figure.Figure at 0x7f7400d4b908>"
       ]
      },
      "metadata": {},
@@ -645,267 +804,18 @@
     }
    ],
    "source": [
-    "print(\"Actual class of test image:\", test_lbl[test_img_choice])\n",
-    "plt.imshow(test_img[test_img_choice].reshape((28,28)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "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",
-    "You might have recognized that our algorithm took ~20 seconds to predict a single image. How would we even predict all 10,000 test images? Yeah, the implementations we have in our learning module are not optimized to run on this particular dataset. We will have an optimised version below in NumPy which is nearly ~50-100 times faster than our native implementation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Faster implementation using NumPy\n",
-    "\n",
-    "Here we calculate manhattan distance between two images faster than our native implementation. Which in turn make predicting labels for test images far efficient."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "class kNN_learner:\n",
-    "    \"Simple kNN learner with manhattan distance\"\n",
-    "    def __init__(self):\n",
-    "        pass\n",
-    "    \n",
-    "    def train(self, train_img, train_lbl):\n",
-    "        self.train_img = train_img\n",
-    "        self.train_lbl = train_lbl\n",
-    "\n",
-    "    def predict_labels(self, test_img, k=1, distance=\"manhattan\"):\n",
-    "        if distance == \"manhattan\":    \n",
-    "            distances = self.compute_manhattan_distances(test_img)\n",
-    "        num_test = distances.shape[0]\n",
-    "        predictions = np.zeros(num_test, dtype=np.uint8)\n",
-    "        \n",
-    "        for i in range(num_test):\n",
-    "            k_best_labels = self.train_lbl[np.argsort(distances[i])].flatten()[:k]\n",
-    "            predictions[i] = mode(k_best_labels)\n",
-    "            \n",
-    "        return predictions\n",
-    "    \n",
-    "    def compute_manhattan_distances(self, test_img):\n",
-    "        num_test = test_img.shape[0]\n",
-    "        num_train = self.train_img.shape[0]\n",
-    "#         print(num_test, num_train)\n",
-    "        \n",
-    "        dists = np.zeros((num_test, num_train))\n",
-    "        \n",
-    "        for i in range(num_test):\n",
-    "            dists[i] = np.sum(abs(self.train_img - test_img[i]), axis = 1)\n",
-    "            \n",
-    "        return(dists)\n",
-    "        "
+    "print(\"Actual class of test image:\", test_lbl[211])\n",
+    "plt.imshow(test_img[211].reshape((28,28)))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's print the shapes of data to make sure everything's on track."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training images size: (60000, 784)\n",
-      "Training labels size: (60000,)\n",
-      "Testing images size: (10000, 784)\n",
-      "Training labels size: (10000,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Training images size:\", train_img.shape)\n",
-    "print(\"Training labels size:\", train_lbl.shape)\n",
-    "print(\"Testing images size:\", test_img.shape)\n",
-    "print(\"Training labels size:\", test_lbl.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "learner = kNN_learner()\n",
-    "learner.train(train_img, train_lbl)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us predict the classes of first 100 test images."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# takes ~17 Secs. to execute this cell\n",
-    "num_test = 100\n",
-    "predictions = learner.predict_labels(test_img[:num_test], k=3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's compare the performances of both implementations. It took 20 Secs. to predict one image using our native implementations and 17 Secs. to predict 100 images in faster implementations. That's 110 times faster.\n",
-    "\n",
-    "Now, test the accuracy of our predictions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy of predictions: 98.0 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "# print(predictions)\n",
-    "# print(test_lbl[:num_test])\n",
-    "\n",
-    "num_correct = np.sum([predictions == test_lbl[:num_test]])\n",
-    "num_accuracy = (float(num_correct) / num_test) * 100\n",
-    "print(\"Accuracy of predictions:\", num_accuracy, \"%\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Introduction to Scikit-Learn\n",
-    "\n",
-    "In this section we will solve this MNIST problem using Scikit-Learn. Learn more about Scikit-Learn [here](http://scikit-learn.org/stable/index.html). As we are using this library, we don't need to define our own functions (kNN or Support Vector Machines aka SVMs) to classify digits.\n",
+    "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",
-    "Let's start by importing necessary modules for kNN and SVM."
+    "You might have recognized that our algorithm took ~20 seconds to predict a single image. How would we even predict all 10,000 test images? Yeah, the implementations we have in our learning module are not optimized to run on this particular dataset, as they are written with readability in mind, instead of efficiency."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.neighbors import NearestNeighbors\n",
-    "from sklearn import svm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
-       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
-       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
-       "     verbose=0)"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# takes ~3 mins to execute the cell\n",
-    "SVMclf = svm.LinearSVC()\n",
-    "SVMclf.fit(train_img, train_lbl)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "predictions = SVMclf.predict(test_img)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy of predictions: 88.25 %\n"
-     ]
-    }
-   ],
-   "source": [
-    "num_correct = np.sum(predictions == test_lbl)\n",
-    "num_accuracy = (float(num_correct)/len(test_lbl)) * 100\n",
-    "print(\"Accuracy of predictions:\", num_accuracy, \"%\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You might observe that this accuracy is far less than what we got using native kNN implementation. But we can tweak the parameters to get higher accuracy on this problem which we are going to explain in coming sections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -924,13 +834,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
-  },
-  "widgets": {
-   "state": {},
-   "version": "1.1.1"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 2
 }

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