1. ANN/1.1 Introduction - Deep Learning and ANN.ipynb

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   "source": [
    "# Introduction to Deep Learning"
   ]
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   "source": [
    "Deep learning allows computational models that are composed of multiple processing **layers** to learn representations of data with multiple levels of abstraction."
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   "source": [
    "These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. "
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   "source": [
    "**Deep learning** is one of the leading tools in data analysis these days and one of the most common frameworks for deep learning is **Keras**. "
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   "source": [
    "The Tutorial will provide an introduction to deep learning using `keras` with practical code examples."
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   "source": [
    "## This Section will cover:\n",
    "\n",
    "* Getting a conceptual understanding of multi-layer neural networks\n",
    "* Training neural networks for image classification\n",
    "* Implementing the powerful backpropagation algorithm\n",
    "* Debugging neural network implementations"
   ]
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   "source": [
    "# Building Blocks: Artificial Neural Networks (ANN)"
   ]
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   "source": [
    "In machine learning and cognitive science, an artificial neural network (ANN) is a network inspired by biological neural networks which are used to estimate or approximate functions that can depend on a large number of inputs that are generally unknown"
   ]
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   "source": [
    "An ANN is built from nodes (neurons) stacked in layers between the feature vector and the target vector. "
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   "source": [
    "A node in a neural network is built from Weights and Activation function"
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   "source": [
    "An early version of ANN built from one node was called the **Perceptron**"
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   "source": [
    "<img src=\"../imgs/Perceptron.png\" width=\"45%\">"
   ]
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   "source": [
    "The Perceptron is an algorithm for supervised learning of binary classifiers. functions that can decide whether an input (represented by a vector of numbers) belongs to one class or another.\n",
    "\n",
    "Much like logistic regression, the weights in a neural net are being multiplied by the input vector summed up and feeded into the activation function's input."
   ]
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   "source": [
    "A Perceptron Network can be designed to have *multiple layers*, leading to the **Multi-Layer Perceptron** (aka `MLP`)"
   ]
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   "source": [
    "<img src=\"../imgs/MLP.png\" width=\"45%\">"
   ]
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   "source": [
    "# Single Layer Neural Network"
   ]
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   "source": [
    "<img src=\"../imgs/single_layer.png\" width=\"65%\" />\n",
    "\n",
    "_(Source: Python Machine Learning, S. Raschka)_"
   ]
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   "source": [
    "### Weights Update Rule"
   ]
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    "- We use a **gradient descent** optimization algorithm to learn the _Weights Coefficients_ of the model.\n",
    "<br><br>\n",
    "- In every **epoch** (pass over the training set), we update the weight vector $w$ using the following update rule:\n",
    "\n",
    "$$\n",
    "w = w + \\Delta w, \\text{where } \\Delta w = - \\eta \\nabla J(w)\n",
    "$$\n",
    "\n",
    "<br><br>\n",
    "\n",
    "In other words, we computed the gradient based on the whole training set and updated the weights of the model by taking a step into the **opposite direction** of the gradient $ \\nabla J(w)$. \n",
    "\n",
    "In order to fin the **optimal weights of the model**, we optimized an objective function (e.g. the Sum of Squared Errors (SSE)) cost function $J(w)$. \n",
    "\n",
    "Furthermore, we multiply the gradient by a factor, the learning rate $\\eta$ , which we choose carefully to balance the **speed of learning** against the risk of overshooting the global minimum of the cost function."
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   "source": [
    "### Gradient Descent"
   ]
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   "source": [
    "In **gradient descent optimization**, we update all the **weights simultaneously** after each epoch, and we define the _partial derivative_ for each weight $w_j$ in the weight vector $w$ as follows:\n",
    "\n",
    "$$\n",
    "\\frac{\\partial}{\\partial w_j} J(w) = \\sum_{i} ( y^{(i)} - a^{(i)} )  x^{(i)}_j\n",
    "$$\n",
    "\n",
    "**Note**: _The superscript $(i)$ refers to the i-th sample. The subscript $j$ refers to the j-th dimension/feature_\n",
    "\n",
    "\n",
    "Here $y^{(i)}$ is the target class label of a particular sample $x^{(i)}$ , and $a^{(i)}$ is the **activation** of the neuron \n",
    "\n",
    "(which is a linear function in the special case of _Perceptron_)."
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    "We define the **activation function** $\\phi(\\cdot)$ as follows:\n",
    "\n",
    "$$\n",
    "\\phi(z) = z = a = \\sum_{j} w_j x_j = \\mathbf{w}^T \\mathbf{x}\n",
    "$$"
   ]
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   "source": [
    "### Binary Classification"
   ]
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   "source": [
    "While we used the **activation** $\\phi(z)$ to compute the gradient update, we may use a **threshold function** _(Heaviside function)_ to squash the continuous-valued output into binary class labels for prediction:\n",
    "\n",
    "$$\n",
    "\\hat{y} = \n",
    "\\begin{cases}\n",
    "    1 & \\text{if } \\phi(z) \\geq 0 \\\\\n",
    "    0 & \\text{otherwise}\n",
    "\\end{cases}\n",
    "$$"
   ]
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   "source": [
    "## Building Neural Nets from scratch \n"
   ]
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   "source": [
    "### Idea:\n",
    "\n",
    "We will build the neural networks from first principles. \n",
    "We will create a very simple model and understand how it works. We will also be implementing backpropagation algorithm. \n",
    "\n",
    "**Please note that this code is not optimized and not to be used in production**. \n",
    "\n",
    "This is for instructive purpose - for us to understand how ANN works. \n",
    "\n",
    "Libraries like `theano` have highly optimized code."
   ]
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   "source": [
    "### Perceptron and Adaline Models\n",
    "\n",
    "Take a look at this notebook : <a href=\"1.1.1 Perceptron and Adaline.ipynb\" target=\"_blank_\"> Perceptron and Adaline </a>"
   ]
  },
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   "source": [
    "If you want a sneak peek of alternate (production ready) implementation of _Perceptron_ for instance try:\n",
    "```python\n",
    "from sklearn.linear_model import Perceptron\n",
    "```"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Introducing the multi-layer neural network architecture"
   ]
  },
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   "source": [
    "<img src=\"../imgs/multi-layers-1.png\" width=\"50%\" />\n",
    "\n",
    "_(Source: Python Machine Learning, S. Raschka)_"
   ]
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   "source": [
    "Now we will see how to connect **multiple single neurons** to a **multi-layer feedforward neural network**; this special type of network is also called a **multi-layer perceptron** (MLP). \n",
    "\n",
    "The figure shows the concept of an **MLP** consisting of three layers: one _input_ layer, one _hidden_ layer, and one _output_ layer. \n",
    "\n",
    "The units in the hidden layer are fully connected to the input layer, and the output layer is fully connected to the hidden layer, respectively. \n",
    "\n",
    "If such a network has **more than one hidden layer**, we also call it a **deep artificial neural network**.\n"
   ]
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   "metadata": {},
   "source": [
    "### Notation"
   ]
  },
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   "source": [
    "we denote the `ith` activation unit in the `lth` layer as $a_i^{(l)}$ , and the activation units $a_0^{(1)}$ and \n",
    "$a_0^{(2)}$ are the **bias units**, respectively, which we set equal to $1$. \n",
    "<br><br>\n",
    "The _activation_ of the units in the **input layer** is just its input plus the bias unit:\n",
    "\n",
    "$$\n",
    "\\mathbf{a}^{(1)} = [a_0^{(1)}, a_1^{(1)}, \\ldots, a_m^{(1)}]^T = [1, x_1^{(i)}, \\ldots, x_m^{(i)}]^T\n",
    "$$\n",
    "<br><br>\n",
    "**Note**: $x_j^{(i)}$ refers to the jth feature/dimension of the ith sample"
   ]
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   "source": [
    "### Notes on Notation (usually) Adopted\n",
    "\n",
    "The terminology around the indices (subscripts and superscripts) may look a little bit confusing at first. \n",
    "<br><br>\n",
    "\n",
    "You may wonder why we wrote $w_{j,k}^{(l)}$ and not $w_{k,j}^{(l)}$ to refer to \n",
    "the **weight coefficient** that connects the *kth* unit in layer $l$ to the jth unit in layer $l+1$. \n",
    "<br><br>\n",
    "\n",
    "What may seem a little bit quirky at first will make much more sense later when we **vectorize** the neural network representation. \n",
    "<br><br>\n",
    "\n",
    "For example, we will summarize the weights that connect the input and hidden layer by a matrix \n",
    "$$ W^{(1)} \\in \\mathbb{R}^{h×[m+1]}$$\n",
    "\n",
    "where $h$ is the number of hidden units and $m + 1$ is the number of hidden units plus bias unit. "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../imgs/multi-layers-2.png\" width=\"50%\" />\n",
    "\n",
    "_(Source: Python Machine Learning, S. Raschka)_"
   ]
  },
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   "source": [
    "## Forward Propagation"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Starting at the input layer, we forward propagate the patterns of the training data through the network to generate an output.\n",
    "\n",
    "* Based on the network's output, we calculate the error that we want to minimize using a cost function that we will describe later.\n",
    "\n",
    "* We backpropagate the error, find its derivative with respect to each weight in the network, and update the model."
   ]
  },
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   "metadata": {},
   "source": [
    "### Sigmoid Activation"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../imgs/logistic_function.png\" width=\"50%\" />\n",
    "\n",
    "_(Source: Python Machine Learning, S. Raschka)_"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../imgs/fwd_step.png\" width=\"50%\" />\n",
    "\n",
    "_(Source: Python Machine Learning, S. Raschka)_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../imgs/fwd_step_net.png\" width=\"50%\" />\n",
    "\n",
    "_(Source: Python Machine Learning, S. Raschka)_"
   ]
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   "source": [
    "## Backward Propagation"
   ]
  },
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   "source": [
    "The weights of each neuron are learned by **gradient descent**, where each neuron's error is derived with respect to it's weight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../imgs/bkwd_step_net.png\" width=\"50%\" />\n",
    "\n",
    "_(Source: Python Machine Learning, S. Raschka)_"
   ]
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   "source": [
    "Optimization is done for each layer with respect to the previous layer in a technique known as **BackPropagation**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../imgs/backprop.png\" width=\"50%\">"
   ]
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   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   "source": [
    "(*The following code is inspired from [these](https://github.com/dennybritz/nn-from-scratch) terrific notebooks*)"
   ]
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   "source": [
    "# Import the required packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Display plots in notebook \n",
    "%matplotlib inline\n",
    "# Define plot's default figure size\n",
    "matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)"
   ]
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "#read the datasets\n",
    "train = pd.read_csv(\"../data/intro_to_ann.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "source": [
    "X, y = np.array(train.ix[:,0:2]), np.array(train.ix[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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     "execution_count": 5,
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   "source": [
    "X.shape"
   ]
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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       "(500,)"
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     "execution_count": 6,
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     "output_type": "execute_result"
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   "source": [
    "y.shape"
   ]
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WfIUMY4z9BydbjJUhhYWFSExMRM2aNaGnpydXmzZt2mCQtzcGDBosKQu7dAmDv/NCYmJi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\noG/fvrC3t0efPn3Qp08f+O/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C8HYWrGwQBIHKUjxMsV6+fIlNmzbh2vXrMDYywsiRI2Fn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\nDX9u7LtHVykAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df84c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's plot the dataset and see how it is\n",
    "plt.scatter(X[:,0], X[:,1], s=40, c=y, cmap=plt.cm.BuGn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Start Building our MLP building blocks\n",
    "\n",
    "Note: This process will eventually result in our own Neural Networks class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### A look at the details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../imgs/mlp_details.png\" width=\"65%\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import random\n",
    "random.seed(123)\n",
    "\n",
    "# calculate a random number where:  a <= rand < b\n",
    "def rand(a, b):\n",
    "    return (b-a)*random.random() + a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "##### Function to generate a random number, given two numbers\n",
    "\n",
    "**Where will it be used?**: When we initialize the neural networks, the weights have to be randomly assigned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Make a matrix \n",
    "def makeMatrix(I, J, fill=0.0):\n",
    "    return np.zeros([I,J])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Define our activation function. Let's use sigmoid function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# our sigmoid function\n",
    "def sigmoid(x):\n",
    "    #return math.tanh(x)\n",
    "    return 1/(1+np.exp(-x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Derivative of our activation function. \n",
    "\n",
    "Note: We need this when we run the backpropagation algorithm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# derivative of our sigmoid function, in terms of the output (i.e. y)\n",
    "def dsigmoid(y):\n",
    "    return y - y**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Our neural networks class\n",
    "\n",
    "When we first create a neural networks architecture, we need to know the number of inputs, number of hidden layers and number of outputs.\n",
    "\n",
    "The weights have to be randomly initialized."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "```python\n",
    "class MLP:\n",
    "    def __init__(self, ni, nh, no):\n",
    "        # number of input, hidden, and output nodes\n",
    "        self.ni = ni + 1 # +1 for bias node\n",
    "        self.nh = nh\n",
    "        self.no = no\n",
    "\n",
    "        # activations for nodes\n",
    "        self.ai = [1.0]*self.ni\n",
    "        self.ah = [1.0]*self.nh\n",
    "        self.ao = [1.0]*self.no\n",
    "        \n",
    "        # create weights\n",
    "        self.wi = makeMatrix(self.ni, self.nh)\n",
    "        self.wo = makeMatrix(self.nh, self.no)\n",
    "        \n",
    "        # set them to random vaules\n",
    "        self.wi = rand(-0.2, 0.2, size=self.wi.shape)\n",
    "        self.wo = rand(-2.0, 2.0, size=self.wo.shape)\n",
    "\n",
    "        # last change in weights for momentum   \n",
    "        self.ci = makeMatrix(self.ni, self.nh)\n",
    "        self.co = makeMatrix(self.nh, self.no)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Activation Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "def activate(self, inputs):\n",
    "        \n",
    "    if len(inputs) != self.ni-1:\n",
    "        print(inputs)\n",
    "        raise ValueError('wrong number of inputs')\n",
    "\n",
    "    # input activations\n",
    "    for i in range(self.ni-1):\n",
    "        self.ai[i] = inputs[i]\n",
    "\n",
    "    # hidden activations\n",
    "    for j in range(self.nh):\n",
    "        sum_h = 0.0\n",
    "        for i in range(self.ni):\n",
    "            sum_h += self.ai[i] * self.wi[i][j]\n",
    "        self.ah[j] = sigmoid(sum_h)\n",
    "\n",
    "    # output activations\n",
    "    for k in range(self.no):\n",
    "        sum_o = 0.0\n",
    "        for j in range(self.nh):\n",
    "            sum_o += self.ah[j] * self.wo[j][k]\n",
    "        self.ao[k] = sigmoid(sum_o)\n",
    "\n",
    "    return self.ao[:]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### BackPropagation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "def backPropagate(self, targets, N, M):\n",
    "        \n",
    "    if len(targets) != self.no:\n",
    "        print(targets)\n",
    "        raise ValueError('wrong number of target values')\n",
    "\n",
    "    # calculate error terms for output\n",
    "    output_deltas = np.zeros(self.no)\n",
    "    for k in range(self.no):\n",
    "        error = targets[k]-self.ao[k]\n",
    "        output_deltas[k] = dsigmoid(self.ao[k]) * error\n",
    "\n",
    "    # calculate error terms for hidden\n",
    "    hidden_deltas = np.zeros(self.nh)\n",
    "    for j in range(self.nh):\n",
    "        error = 0.0\n",
    "        for k in range(self.no):\n",
    "            error += output_deltas[k]*self.wo[j][k]\n",
    "        hidden_deltas[j] = dsigmoid(self.ah[j]) * error\n",
    "\n",
    "    # update output weights\n",
    "    for j in range(self.nh):\n",
    "        for k in range(self.no):\n",
    "            change = output_deltas[k] * self.ah[j]\n",
    "            self.wo[j][k] += N*change + \n",
    "                             M*self.co[j][k]\n",
    "            self.co[j][k] = change\n",
    "\n",
    "    # update input weights\n",
    "    for i in range(self.ni):\n",
    "        for j in range(self.nh):\n",
    "            change = hidden_deltas[j]*self.ai[i]\n",
    "            self.wi[i][j] += N*change + \n",
    "                             M*self.ci[i][j]\n",
    "            self.ci[i][j] = change\n",
    "\n",
    "    # calculate error\n",
    "    error = 0.0\n",
    "    for k in range(len(targets)):\n",
    "        error += 0.5*(targets[k]-self.ao[k])**2\n",
    "    return error\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Putting all together\n",
    "\n",
    "class MLP:\n",
    "    def __init__(self, ni, nh, no):\n",
    "        # number of input, hidden, and output nodes\n",
    "        self.ni = ni + 1 # +1 for bias node\n",
    "        self.nh = nh\n",
    "        self.no = no\n",
    "\n",
    "        # activations for nodes\n",
    "        self.ai = [1.0]*self.ni\n",
    "        self.ah = [1.0]*self.nh\n",
    "        self.ao = [1.0]*self.no\n",
    "        \n",
    "        # create weights\n",
    "        self.wi = makeMatrix(self.ni, self.nh)\n",
    "        self.wo = makeMatrix(self.nh, self.no)\n",
    "        \n",
    "        # set them to random vaules\n",
    "        for i in range(self.ni):\n",
    "            for j in range(self.nh):\n",
    "                self.wi[i][j] = rand(-0.2, 0.2)\n",
    "        for j in range(self.nh):\n",
    "            for k in range(self.no):\n",
    "                self.wo[j][k] = rand(-2.0, 2.0)\n",
    "\n",
    "        # last change in weights for momentum   \n",
    "        self.ci = makeMatrix(self.ni, self.nh)\n",
    "        self.co = makeMatrix(self.nh, self.no)\n",
    "        \n",
    "\n",
    "    def backPropagate(self, targets, N, M):\n",
    "        \n",
    "        if len(targets) != self.no:\n",
    "            print(targets)\n",
    "            raise ValueError('wrong number of target values')\n",
    "\n",
    "        # calculate error terms for output\n",
    "        output_deltas = np.zeros(self.no)\n",
    "        for k in range(self.no):\n",
    "            error = targets[k]-self.ao[k]\n",
    "            output_deltas[k] = dsigmoid(self.ao[k]) * error\n",
    "\n",
    "        # calculate error terms for hidden\n",
    "        hidden_deltas = np.zeros(self.nh)\n",
    "        for j in range(self.nh):\n",
    "            error = 0.0\n",
    "            for k in range(self.no):\n",
    "                error += output_deltas[k]*self.wo[j][k]\n",
    "            hidden_deltas[j] = dsigmoid(self.ah[j]) * error\n",
    "\n",
    "        # update output weights\n",
    "        for j in range(self.nh):\n",
    "            for k in range(self.no):\n",
    "                change = output_deltas[k] * self.ah[j]\n",
    "                self.wo[j][k] += N*change + M*self.co[j][k]\n",
    "                self.co[j][k] = change\n",
    "\n",
    "        # update input weights\n",
    "        for i in range(self.ni):\n",
    "            for j in range(self.nh):\n",
    "                change = hidden_deltas[j]*self.ai[i]\n",
    "                self.wi[i][j] += N*change + M*self.ci[i][j]\n",
    "                self.ci[i][j] = change\n",
    "\n",
    "        # calculate error\n",
    "        error = 0.0\n",
    "        for k in range(len(targets)):\n",
    "            error += 0.5*(targets[k]-self.ao[k])**2\n",
    "        return error\n",
    "\n",
    "\n",
    "    def test(self, patterns):\n",
    "        self.predict = np.empty([len(patterns), self.no])\n",
    "        for i, p in enumerate(patterns):\n",
    "            self.predict[i] = self.activate(p)\n",
    "            #self.predict[i] = self.activate(p[0])\n",
    "            \n",
    "    def activate(self, inputs):\n",
    "        \n",
    "        if len(inputs) != self.ni-1:\n",
    "            print(inputs)\n",
    "            raise ValueError('wrong number of inputs')\n",
    "\n",
    "        # input activations\n",
    "        for i in range(self.ni-1):\n",
    "            self.ai[i] = inputs[i]\n",
    "\n",
    "        # hidden activations\n",
    "        for j in range(self.nh):\n",
    "            sum_h = 0.0\n",
    "            for i in range(self.ni):\n",
    "                sum_h += self.ai[i] * self.wi[i][j]\n",
    "            self.ah[j] = sigmoid(sum_h)\n",
    "\n",
    "        # output activations\n",
    "        for k in range(self.no):\n",
    "            sum_o = 0.0\n",
    "            for j in range(self.nh):\n",
    "                sum_o += self.ah[j] * self.wo[j][k]\n",
    "            self.ao[k] = sigmoid(sum_o)\n",
    "\n",
    "        return self.ao[:]\n",
    "    \n",
    "\n",
    "    def train(self, patterns, iterations=1000, N=0.5, M=0.1):\n",
    "        # N: learning rate\n",
    "        # M: momentum factor\n",
    "        patterns = list(patterns)\n",
    "        for i in range(iterations):\n",
    "            error = 0.0\n",
    "            for p in patterns:\n",
    "                inputs = p[0]\n",
    "                targets = p[1]\n",
    "                self.activate(inputs)\n",
    "                error += self.backPropagate([targets], N, M)\n",
    "            if i % 5 == 0:\n",
    "                print('error in interation %d : %-.5f' % (i,error))\n",
    "            print('Final training error: %-.5f' % error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Running the model on our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error in interation 0 : 53.62995\n",
      "Final training error: 53.62995\n",
      "Final training error: 47.35136\n",
      "1 loop, best of 1: 36.7 ms per loop\n"
     ]
    }
   ],
   "source": [
    "# create a network with two inputs, one hidden, and one output nodes\n",
    "ann = MLP(2, 1, 1)\n",
    "\n",
    "%timeit -n 1 -r 1 ann.train(zip(X,y), iterations=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Predicting on training dataset and measuring in-sample accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 1: 11.8 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 1 -r 1 ann.test(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.491100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.495469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.097362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.400006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.489664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   actual  prediction\n",
       "0     1.0    0.491100\n",
       "1     1.0    0.495469\n",
       "2     0.0    0.097362\n",
       "3     0.0    0.400006\n",
       "4     1.0    0.489664"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = pd.DataFrame(data=np.array([y, np.ravel(ann.predict)]).T, \n",
    "                          columns=[\"actual\", \"prediction\"])\n",
    "prediction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.076553078113180129"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(prediction.prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Let's visualize and observe the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Helper function to plot a decision boundary.\n",
    "# This generates the contour plot to show the decision boundary visually\n",
    "def plot_decision_boundary(nn_model):\n",
    "    # Set min and max values and give it some padding\n",
    "    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "    h = 0.01\n",
    "    # Generate a grid of points with distance h between them\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), \n",
    "                         np.arange(y_min, y_max, h))\n",
    "    # Predict the function value for the whole gid\n",
    "    nn_model.test(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = nn_model.predict\n",
    "    Z[Z>=0.5] = 1\n",
    "    Z[Z<0.5] = 0\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    # Plot the contour and training examples\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n",
    "    plt.scatter(X[:, 0], X[:, 1], s=40,  c=y, cmap=plt.cm.BuGn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x110bafd68>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RSxcv8vr1a/5YvoyJ40bz86DaiV2eEELPZJsG8d3zeRNA2rRpSJUqlU67ubk56dKlxedN\nAOnTmX3TGO/efcD7TQCWWdKSPPk//2ymzj/E2AlT6NCpMwBp0qRh0/ZdFMpjza27TyiaAIFnaG8H\n6v00iWxWVtStVx9/f3+mTZ6IFhZAzapF9D6e0BUYGMyxU3cJCQ2jepVCpE2TMt59l89tx7zfjtC9\nY0tee/lRoWw+9m/sR+kSMS8dCiG+byqp3d6tlNISsibNZ3WCnVskjpCQMKyLj2D/EScKFS4c3X7v\n7l0cHezwvD5TJxR9jcDAYAaN3cqWXRdJZW5OcHAQIwc4MqCHA0opLIsM5fQFZ7Jly6bTr3vn9lQu\nbkTXdnbf9L3F5dipO4yctItHbi+IiIiggWMJ5k9pQQaLVF/u/C8EB4eyZfdlTp1zIW3qFLRvWS5B\nwmNSt/uAM10HrqWYTTFMTEw4f/4CU0Y3onfnaoldmhAiFip9x4QfQyk0TVOftssMlvjuJU9uyM9D\n6tGqWSPmL/qNsuXLc+nCBQb268XPQ+r+63AF0HnAWjSjrNx+4IqFhQX3792jbatmmJgY0bOjPZky\npMHNxSVGwHJ1eUTzWuW/9VuLk4NdYS4fK4Tf20BMjI1IkSJ5go3l/+4DDk3nkTJVJpo2b82L58+o\n0XQBE0fWp0eHhAmQSZHnE2+6DlzL7gNHKFmqFAAe7u7UsK+ETeHsVCybN5ErFEIkJTKDJX4Yf249\nz5wlx3jo+ox8uS0Z3Ls67VpU+Nfn83jsTZma03jo/oQUKVJEt1+6eJFObZricmUav648wdptd9l9\n4Ahp0qQBYNOGDYwfPYRHl6diZPT9/z/Mz9N24vYiBSvXrkepyP9Jc3N1pWLZktw7N5lMGVMncoX/\njcm/7OXlu0zMXbBYp33R/HncvrKH1Ys7JVJlQoi4yAyWEHrQtnl52jbX36zRA5cXFCtWVCdcAZQu\nUwbPJ68JCQmjZ0d7Hrp6UThfLipXrsSTJ0/we+PFnvV9f4hwBbBj33VWrNsWHa4AcuXOTc1atdh7\n+HqCXQZNal55vSNX4cox2nPnycvxgwl3I4UQ4vv0Y/wFECIB5MmZidu37hAcHIyxsXF0+/Vr18hm\naYGRkQFKKeZPbcmQ3g6cv+yKRbpC2FUsgIHBj3ODbkRERPTdmR8zNDQkIiJpzYAnpHIlrVm5ZQd9\n+vXTCZv79+6ifKkciViZECIp+nH+CgihZ7lzZqR8mVz0790Df39/ADw9POjTowuDejro/JHNbpme\nFo3KUK1Kof8kXLl5vGbA6I1UrDubll2Xc/LMvQQbq0HtYiz/Tfey2PPnzzl44AB1a9ok2LhJTfOG\npfH1fsqAPr1wd3PjxYsXTJk4nqOH99Ozo31ilyeESGIkYAnxGWsXdyLsvTv5c1lhWzgfFUoXp4lj\nPvp1q55oNd2884QKjjMwSWvDpOlLqFKjHR37reO3VScTZLwR/R05/9cxWjZtyNYtm1kwdw72Fcow\naoAjllnSJsiYSZGxsRHHdw7GWPOkaqWylCxakCePTnN67zAyZkiYuzeFEN8vWeQuRDx4+7zjlZc/\nOa0sMDU1/nKHeAoMDOa3VSfZceAmoGhUuwh9ulT77Bh1Wy+iVv1O9OjdO7rN5dEjqlQog+e1mZiZ\nmeitvr+9e/eB1ZvO4nTWlXRpU9ChZTkqlfv23cfdPF5z/rIrGSzMqVa5IIaGBnqoVgghIiXmIncJ\nWEIkkpCQMByaziNNemt69R2AUorflizE+6ULJ3YOxtjYKEafiIgITLJ259UbP0xNTXVeq1m1EqP7\nladWtaL/1bfwr4WHR9B7+AZ27nfG3t4OTw8PvL1esmtd7//L/bWEEAlD7iIU4v/Q1t2XUUZp2Lxj\nN8mSRV6tt69WjTo1qrJ51yXat6wYo49SCmPj5Lx79y5GwHr37h2mKfQ3u6YPd+4/w9X9NQXzZSFv\n7szR7QuWHeWe2wfuuXhgZha5y/7GP/+kYdvhPLw0VWayhBDfPVmDJUQiOeJ0n1Y/tY8OVxAZoFq3\n7cDhk/dj7aOUomXjcsyYOomPZ3oPHTjAG5/XVCiTJ8Hrjg9fv/fUar6A2i0WsWzDPSrX+4VmnZYS\nGBgMwB9/nmPK9NnR4Qqgddu2ZMyclWOn7iZW2UIIoTcygyUE8PipD1dveJAlUxrKlsylc4fg5zx0\necn8Zce5dvsp2bOmpU/nKthVLBCvvilNk+Pr6xuj/c2bN5iljHsmaua4Jjg0mYuDXUVq1q7Hvbs3\nOX70KDvX9k4y20N0GbiWXAXKsuPgQgwNDQkODqZ75w4MHreVpb+05bWXHzlzxXz+Xrbs1rx8/TYR\nKhZCCP1KGr+NhUgkYWHh9Bz6JyWqTWbVVhc6DdhIiaqTcff0+mLfK9fcqVxvFumzlWPaL8uxq9mO\n9n3W8vu60/Eau23zMiz/dRGvX7+ObvPy8mLpkoW0aVo6zn7p05lx6ehoBnYtyQefC1S0Tc79C5OT\nzKNaXrz049TZ+0yb+QsGBgZs27qFhnXrcs35Oht3XOKysxvlSudl357dOv0CAwM5dvQ4O/dfJ6mt\nDRVCiK8lM1ji/9rsxYd44BHCfVdPzM3N0TSNRfPn0bj9r1xzGvvZmawRk3cxZcZs2neMfERKhYoV\nqWxnR/UqFWjTtOwX7zasUCYvXdqUp7RNEZq2aIlSim2bN9GjQ2WqVMj/2b5GRoY0a1CaZg3iDmKJ\n5eXrt1haZsHU1JTpU6awdctmxk2cSL58+dm/dy91Ws1izqTmDBgxDKUU9Rs2wsPdnbGjR1O3fgMu\nXzzLhm0X+OuiK863/pkZrFq5YGJ/a0IIEW9yF6H4v5azxEg27ziAja1tdJumaRQvkp+V81tRvnTs\na5pCQsIwz9EL77fvSJ5c90HLVSuWYerI6vEOBA9dXrLzwFU0DRrVKUGBvFn+/TeUBLx/H0wO2+Hs\nP3ICxxoOXLt9h8yZ/1ngvvKP39m7bTlePv4YJE/Pg/v3yZAxIx07d2HAoEEM6NuH7Vs202/gQKo5\n1OTenTvMmDqRMYNr0b39/8djeYQQ+iF3EQqRSF68fEO+/LqzRUop8ubNy4tXfnH2MzBIhqGhAe/e\nvSN9+vTR7Zqm8dbfn5SfWUP1qXx5MjOif92vLz6JSpnSmEG9avJTi6aUKl1aJ1wBNG/ZikH9++Fg\nb0PrTgNp1ryFzuunnJyYNXde9Mxg+QoVqFSlClUrlaNN03Jf9d4KIURikTVY4v9aqeJ5OHhgv05b\nQEAA586dp6SNdZz9DAyS0axBWWZOm6KzXmj3rp2EBr+nlG3cff8fjB5Uh/o18/PY01Onff/evTRp\nUJ8UKUx56/+eGVMmEhQUFP26u7s77m5utPqpjU6/fPnzky9/Pi45u/0n9QshxLeSgCX+r40fVpfB\n/XqzY/s2Pnz4wM0bN2jeqD5N6pUkR3aLz/b9ZWJTnI7uwcGuItMmT6ZNy6YM6N2dP5d20dl64f+R\nUoo5k1oSEuTPrp07AFiyaBHDhw6he69enDh9hkbNu/LkyTOK5M/N1EkTGTV8KHbly2BkZBj97Me/\naZrG27dvMTVNHttwP4THT304cfouj5/6JHYpQgg9kDVY4v/eUac7TJy9n8vOj8iSOT3d21dmeL/a\n8drsMjQ0jN0Hr3H99hOyZ01LqyZlSZ3K9Iv9/l9cueZOg7aLKVK0GOcvXOHSVWdy5c4d/fq2rVuY\nPWU0taoVJIWJES0alWbWoiOYZyjG7Lnzo28y2LljO+NGDuT+hck/XHgNDAym66B1HDl5myJFCnHn\nzj0c7Arxx7z2cjlUiG8kj8r5iAQsIX4sgYHBTJu3j0OnnnL20lWd18LCwkifygxf18WkSBE5O+Xl\n7U/NZvMxNc9A9RqO3Ll9nbNnTrN3fV9Kl4i5d9b3rvvgdbwNSsfSP1ZhampKYGAgvbt3IaWhF3/M\nb5/Y5QnxXZOA9REJWELET2BgMI/cXpE5Y2oyZUyd2OV81rWbnjTvupLbD9x0tr54/fo1hfLmwtd1\nkc6MYWhoGHsPX+farcdkz5qOlo3L/Gczgx6PvZm16DCnzz8iTeqUdGhVhi5tKifIzNlb/0Csi4/g\n7iN30qVLF93u6+tLwTzWuDvPJE1qmREV4t9KzID1Y821C/F/QNM0ps07gJXNcNr0Xk/BCmNp3nkZ\nvn7vE7u0ONkWtcLURLF21croNk3TmDz+Z1o2LhfjcqyRkSFN6pVi8qgmdO9g/5+FKzeP11RwnE7q\nzKVYtX4nw8bMZMXGW/QeviFBxnvt5U+6dGl1whVA2rRpsbBIzyvZ1V6I75bMYAmRiO49fM6psw9I\nk9qU+rVs47Xm5teVJ/hjwzU2bdtNDmtr3r9/z5gRQ/F4dJmDm/v/B1X/O3fuP6NOqwXky1+IQkVs\ncDpxFFOTCA5s7EfaNCkTuzwAug1aS2brivw8fmJ0W0BAAEXy5ebk7sF636MsKCgUK5vhnDp3SWdt\nmrubG1XKl8bz+szoS6dCiK8nM1hC/J8JD4+g26C1VGs0j4t3NNZsf0TOEiNw+iv2hzx/bN5vx1mw\nZDk5rK0BSJkyJbPnLeTW3WfcffAsXuPfffCMLgPWULzqFBq0XcLhE7diHKNpGn9deMjwCVsZNXkb\nzjc8dF5/9+4DQUGh8RoPoHABSx5enEq31oXInvYlcybU5ez+4UkmXAGcOH2fFq1+0mkzMzPDsW4d\nTp65p/fxTEyMGNy7Bm1aNuXG9esA3Lxxg7atmjKoVw0JV0J8x2SjUSG+QNM0Vm44w4JlTri6P6dw\nwRyM6FeDpvVL/etzLl97ijsuAdx55EbKlJEB49TJk7Ro1RS3qzMwMzOJsxZX9+eULKU7tpGREcVs\niuHq7kWh/JafHfviVVfq/7SY/oOG0LV/Le7dvUPPYWMZ2tuLPl2qRY/TffA6Tp51o027joSGhtCo\n/TLatShD3RpFGDZhJ9dvuaMU1KtVgu7tKvH7n39x4YobGSxS0a1dBbq2rRLjUUPGxka0aFTmq9+v\niIgILlxx5Y3ve8qUyEXGDKni3ff5C198fAPIlzszxsZGcR539boHEZrG69evY2w+6/X6NanKWH11\n3fExor8jKUyS07yRI17eb7FIn4rBvWowoIdDgownhPhvyCVCIb5g1sJDrNt+g7kLllCiVCnOnjnD\ngL49mTDckQ4tK/yrc5atNYMJUxdSzUH3j2izhnVoVS8HbZqXj7NvkUoTWPDraipVqRLdFhwcTD7r\n7Py1fxh5cmX67NhVG82hbefBtG3fIbrN1cWFyuVL43ltJmZmJuw64My4WcdwOnsxOgD6+PhQ2qYw\nwcEhzF20hKbNmvP+/XuGDhrAzu3bGTt+AvUbNsLN1ZUJY0dRuUwW5k5ugc+bAK5cdyddmpSUKp7z\ns893jM3dB89o1mkZysCEbNmycfnyFXp1qsqU0Y0+e65Xr9/SZeA6LlxxIUMGC974+DBuWH16drTn\n5eu3pE1tiqmpMeHhEXTqt5rT512xss5FRAQcOHIEY+PIy7Xnzp6lReP6uF2djrl5iq+q/WtomsaH\nDyGkSJH8q98jIUTs5FE5QiRRgYHBzFp0kLOXnLHOmROAWo6OrNu4lfatGtO2WTkMDL7+SruvbwBZ\nLWPONFlms8LHNwAAnzcB/PHnaS45PyFLJnO6tq2EbVErhvWrQe8eXfhz0zaK2djg5eXFsEH9qVQu\nzxfDVWhoGGcv3GfPkdY67bnz5KFgwQJcdHajepVCbN7lTO9+A6PDFUD69OnJZpUTxzp1aNkqsn/q\n1Knx8/Vj6oyZ9OjVG4BcuXNTqkwZCufLRUR4OGu3nMfW1oZnz55hYqSxZUV38sdzLVNoaBj1Wi9i\n1NgptOvYCaUUXl5eNHB0IHfOv+j8U+VY+2maRoN2v2JXvQHrd5zGxMSEB/fvU9uhKpNm7yNZMgM+\nBH2gdZNyFMibCbdnoVy/+xBDQ0M6d2hP0YIFqO3oiNer55w+dYoNy7slaLiCyF/SX3pAuBDi+yFr\nsIT4jAcuL8lqmSU6XP2tdJkyvA8M4bWXfxw9P69y+bzs3L5Vpy0oKIgD+/ZiVyE/nk+8KVl9Mnfc\njWjadiAZc1TCseVC1m45R4eWFRnYvRJN6tciV7bMFC2Qh1TGPqxZ3OmL4xoYJMPY2Ag/P93nLGqa\nxps3bzCLnBU6AAAgAElEQVSPujQZEhJOCtOYd+69efOGqtV1Z91OnzpF00+eJ5gmTRrKV6jIzoN3\nuHb7PgePneLG3Ud07zOMuq0XERYWHq/36fCJ22SxtKJ9p87RszoZMmRg8rTZLF39V5z9zl58hH9A\nOJOmzsDEJPJ7unL5Eqam5uw5eBSP56+4ee8RPu/TMGvRUUaOHkeKFCkwMjJi3YaNbNyylYsXzmGe\n3AfXq9OpWbVIvOoVQoi/yQyWEJ+RMUMqXrx4RVBQUPQfagBvb2+CQ4JJ9S9nNUb2r0WV+rNJlsyA\nZi1a8uLFC6ZMHEuV8nmwKWJF254raNexu87dbPUbNsbBriKNatvSrV0VurWrEnWpK2W8d/xOliwZ\nLRuXY8qEcSxY8lt0aNm2dQtv3/qRI1vkdgF1axRkzYrltGjZKnr/p5CQEN4HvOPmjeuUK//PJcy0\n6dLx4vlzLCx0Hy3k5uZBl249ox/2rJSia4+erF+3kiMnb1Onhs0X6332wpf8BQrGaM9XoADPX7yJ\ns5+L+2tKliqlc6ltzuzZLPvjD2yLFwcgU6ZM/L5qLVZZMhMUHKzTv2SpUlSqXBlrC2/ZmV8I8a/I\nDJYQn3j/PpjHT30ICwvHMktaypXKzcRxPxMREQFE7j4+athgmjcsoxNsvLz9mTBzN9Uaz6Npx6Xs\nP3IjzjHy5s7MqT3DcL1zjDo1qjCkX2caVM/G6sUdAdhzyJkevfvq9ClUuDD5CxYmY4EBmGbrQd3W\ni/HyfvdVj1Px8vbH44k3mzZtokSxoowdPZq6tWrSv3dvbG1LUqTSBI6fvkubZuXRwnxpWKcmu3bu\nYMvmTdSqVoW8OdMxa9pkbt28CUTOfJUoUZIRQ4cQEhISPc7uXTvx9PSkRevWMWooULAwT5/7xqve\n0sVzcuLYMUJDde9WPHLwIKWK54yjFxTMl4UL5y9E/8w0TePe3btUqFRJ5zgTExOKlyjJujVrdNo/\nfPjA3l07cbArHK86hRDiUxKwxHcvKCiUZWucaNjuV5p3Xsa2PZej/7B+jcDAYHoO/ZNsxYZSwXEW\nOWxHsGTFCVYuaM/lc4cpkj83bVo0pkAuK968usfcSc2j+z5/4UvZmtN54pOGIaNmUrtRNwaP38O4\n6bvjHC9fnsysWdIJj2szuO40lkG9akZvuJksmSIsLCxGn/DwCH5fuQYvP38atexJ7RYLcHV/He/v\nsVnn5RQr6YDH0+fkyZuPI0cO07L1T7h4PmbH3gNs3LqTtj1XAHBwU3+a1M7OmmUz2LpuPj3b2XB8\n5xCmjGlA3ZpVqVDahsL5cnL/7mUMIrwpmj83Pbp0xsHejoH9+mFra4vTieM644eFhXHy+HFK2ljH\nq94SNtbYFMlK+59a4uriQnBwMJs2bmDS+DGMHlg7zn5lSuQiWxYzBvTtjZ+fH0opsmXPjvNV3Uf1\nhIaG8vDhAy5dvMCIoUNxvnqVwwcPUqdmNapWyott0YS5c1AI8ePTy12ESqkVQD3glaZpxeI4ZiHg\nCLwHOmqadj2O4+QuQhFvHz6EULP5fIxNM5K/UFFev3rFdefLVCxtxapFHT97N9bzF778ttqJqzee\nkS1rajwee5E2Y37mLFhMhgwZuH3rFm1bNWNo7yp0/qkSV6974ObpRaH8WSlSMJvOufqN3IiBWUFm\n/jI3us3b2xubQvlwPjEWq2zpv+r76jJgDWkyl2DazNnRbVevXKFRvbo8dPcgRYrIS5MTxo7hvbcz\nC6fHnCn61LWbnjTp9Ad3HrphYGCAtWVWjp86Te48eXSOc7CrQP/OJTh93pV1W87y/n0QFhZpqVA6\nJ9PGNCZ/3iwEB4fifNMT0xTJKVY4O0opnG94ULvFfEaOmUCX7t25fu0aLZo0Zt6iRTRs1Jhnz54x\nZsRQPvh7sG9Dv3i/Fx8+hDBh1h5WbzyLzxt/KpUvxOSR9alcPt9n+73xDaBIpYm8C/iAeapUhIWG\nkiVLVrbt3o2VlRWBgYGMHjkCNxcXfvv9DxbMncvJE8d59fIl3duVY8KIRv/qBgYhRNLxI2w0ugqo\n9ZnBHYHcmqblBXoAS/U0rvg/t2rjX0Qoc+7ee4ibiwtZs1qS3NiUvUduceTE7Tj73Xv4nFIOU3gT\nZEm3vuOwLujAJWdPatSuQ4YMGQAoUrQovy5fyexFR1BKUap4Tlo0KhMjXAEcOHqLTl266bRZWFjg\nWKcOh47rbuJ56PgtKjjOxDhLN3KWGMmMBQcID9edcZs6uhH7d2+mReP6rPzjd4YPGUTdWjVZvHRp\ndLgCcKhRC+dbT+P1Xrk/9qJYsaIYGETOkn348IFUqWM+wzBVqlRMmLUP/5BMXL5+m5c+bxg5Zjwn\nzjykQp0ZXLvpibGxEeVLR64X+zvElrCxpmD+7OTIaY2JiQnlypdn3YaNLFm4iFQpTChjWxSrDB/Y\n8kf3z9YZEhKG01/3OXH6LkFBoaRIkZzu7aswqJcDY4c1YM7Epl8MVwDunt6YpzLH9fETTp87j/vT\nZzRu2pSyJYpTIE9usmfOxKMHD1izfgOWlpbMmjOHbbt2Ex4WzMgBdSVcCSG+iV5+g2ia9hfwuUUV\nDYG1UcdeBFIrpT5/P7kQ8bDn0G08PJ/SrWdP6tSrR7Xq1blw5So1atZi/Ky9cfYbPnEHQ4aPYe6C\nxdSpV48hw0ZwzOkUo4cP58OHD9HHla9QgUeuz2IEoE+ZmBgREBAQo/19wDtMTP7Z3PLQ8Vt07r+O\nAcMn89r3LZt3HODQ6Rf0G7lRp1/mTKm5enwsjnYWXDmzmUd3TlGpUgUaNmqsc9yd27fJnjXtZ2v7\nW5EC2bh08TLBUQu6a9SqFWPt0dOnTzl9+i+MU6Rh2YpVWFlZYWZmRs/evenTrz/FbEowdkbc72uP\n9hWY8PMoXr16BYBd1ar06tuHzJnS8uLuXOZMbvHZrQgOHrtJzhIjGT75ED/PPEEO2+F06ruC8o4z\neOxjwbvw3DTt9Du9hv7Jl2a6/fwDyZQpE6lTpyZ79uwYGhoy6uefuefiytMnTxgz2JHHnm7cvH6d\n0NBQLl64QMsmDRjcu9ZXrWsTQojY/Fd3EVoCTz76+llU26v/aHzxg/J7G0BIcDCb1q+nip0df65d\nx/ChQ1iydBlN6teNtU94eASHj19nzZaTOu1FixUjT968XDx/HvtqkTuaO1+9So7smb44m9GyUUl+\nmTmN9Zu3Rc8Q3b51CyenU6z4ZXr0cRNm72fBr0tp0LARADa2tmzduZcCuXMwepAj2bL+89DflCmN\n6dmxKj07gq/fewqU+5l9e/dQr34DAB7cv88vM6eyfumXt2eAyDVfFcvmpmvHdsyau4Cx4yfgUNWe\nZ0+f0LhJU1xdXZg5bTLlS+WiZMX6MS6v1nJ05MD+fZy7HfOxOn9r3bQcj9y8sC2cnzJlSvPy5UsC\n/H3Zt6H/Fx/74vHYm/a9V7Jp+24qVY7c3+qaszO1qldjw5atONSoAcCon8dRvUp5tu+9QrMGpeM8\nXylba+7cWcbjx4+xsvpnLdXRI4cpWyofowbWI7tleoYO6Mbd+57kyZWVAT2q0bOj/ZfeSiGE+KL/\nKmDFthAmzv/9nDBhQvTn9vb22Nvb678i8UMICw2nZu3a/LFqdfR2AosXLmTU8GF8CApC07QYQUEp\nMDAwICQkRGcjTQA/Pz9CoxaXe3p60q9XNwb1qv7FOob2qUXdnxZTpXwpGjVpybOnnmzdsplfZ7eJ\nftaepmlccX6EYx3d4JcqVSpKlCjJwuVHGT2oPmlSx9wWIG2alOxa14e2PXsxcewozM3NefjgIdPH\nNYnX5bK/rV3SieETt2NbOD8GBgaYmZrg/uAvxo06QeYM5vw6sxmeT3w4fPZOjL6PHj4gffr0mKVM\nQWBgML5vA8mcMbVO+FRKMX54A3p3tuf8ZVfSpC5DxbJ543W5beX6v2jdtl10uAIoXqIE/QYO5MD+\nfdEBy8zMjP6DhrFh+9LPBqzUqUwZ2d+R+rUdmDh1BoULF+HYkcNMnzKRrSt7ANC2eXnafmbXfCGE\n+JSTkxNOTk5fPE5vj8pRSuUA9sa2yF0ptRQ4qWna5qiv7wN2mqbFmMGSRe4ivkJDw7DIO4DbD13J\nmDFjdHt4eDh5rHNgnS0V5w6OjLVvm55/kD1PJSZOmRbddvL4cVq3aApoWFpm5eWLlwzsWYMxg+vG\n69El4eERHDx2k1PnHpIubUraNCsXY3F79mLD2H3gOIWL/LNxZUREBPlz5cTKypK7d+8ye2JzurSJ\nfYfy8PAILjm78eFDCOVK5f6qnb8jIiI4cvIOTmcfYGaWnLoONtgUyR4dTP/21j+QfGXGsGzFWhzr\nRobBu3fu0KCOI1ZW2QkK9MLN4xUmxiYYGipGDaxN8aJW7DpwnWTJoGm9kp/dQiEunfuvoXSVVnTu\nqruWbcf2bWzeuJHN27ZHt+3bu4ffF0/i0Ob+Xzzv9r1XWLziNE+f+VC8WA6G963xr+oTQnx/fpRH\n5Shin6kC2AP0ATYrpcoBfrGFKyG+RlBQKGHhETE2uDQwMCBNmjR0bB33Q4Vnj29K1UZzuH3zOtVr\n1uH+3Vvs3L6Nbat6UrRgNl6+fktu64xftRbHwCAZ9WrZUq+WbZzHdG9fheGDB7B5x27MzMzQNI35\nc+eQIWNGjp06i8ujR9Swr0TJYjli3SLg8IlbTJ13mBu33bHKlpG+Xe3o1anqFwNgUFAoDdou4fWb\nUBo1acnTF0+p0Wwei2e2plXjsjrHpk5lys61vWnWqRPmqdMTGhLCi5cvscxqye3bd7CvWpWdB86Q\nKVMmrl+7RsO6jiRPbkTHzl0JDw+jaaffadW4JDPHN433ewdQ0iYbRw8fiBGw9u3ZQzGbf95TTdP4\nc80qatkXAMDpr/vMXnKMuw+ekcs6IwO7V6V+7X+Ob1q/1Dc9mFsIIf4NvQQspdQGwB5Ir5R6DIwH\nkgOapmnLNU07oJSqo5RyIXKbhvgtGhHiM8zMTCiQ15LDBw9Gz7QAHD54EA93V969K8aLl35kyZwm\nRt+sWdJy7eRYNu64iPPNfVhnTc11p3FkzRK5YDxjhlQJUvOogXV4PGw9+XNZUbx4SVxd3UiTNi2b\ntm1HKUXefPno0bsvKzecZeF03YC1Y/9VBozaypyFS6hW3YHbt24xbFA/nr/0Z8roRp8dd97SIxin\nzMq5w7uj14h169mHGlUrU9O+MOnSmukcX6FMXh7fmEmvoeu4eseP46fPEBIcTOUK5Vm7YWP0rvav\nXr4kTdp0/HXhIubm5gD0GziYCqWLU79WUSqVi//ly3YtKjD310lMnjCOfgMHY2BgwPKlv7F/724K\nFS6Ira0tpqamrFqxHA+XW6ydN4wd+6/Sb8RmJk+fxdxKlbl69QoDRg7j+au39OhgF+dYd+4/Y/22\ni/i/C6J6lfzUr2UbvQeZEELog94uEeqLXCIUX3Lu0iN+XXkaz2e+WKRJwV8XXZg26xcq29kzqH8/\nLl+8SMtWrQgIeMu+vXuZN7Ul7VtUSOyydXg+8aZDn5VUsG/MuImTdGagNq7/k0O7lrHp939mcjRN\nw8ZuEjPmLKV61FokgFevXmFbOD8ul6fFCEkfs7GbzILf1lC+gu770KZlU+rapaNTLA9N1jSNHLYj\n2LH3CEWLFePQgQP8tmQxu/cfiD6ma6eOlC5Tlh69eun0nT1zBi9cT7JkVpv4vynAk2c+jJi0k537\nIjeLrVurBFNGNeDMhUds3nWNkNAw6tUoRO/O1TA3M6FQhfEs+HUVdlWrRp/j7p07ODrY4Xl9JsbG\nRjHG+HXlSSbN3k+Hzl2wyJCJtatX8vTJU1KnMqVpvaJMHtVEHrosxA/iR7lEKESCW7flHKMm72bI\n8FF0LGbDyRPHOHPxEWt+/4VRwwaTKbMlD9zcMTOLDBsP7t+nWpUKVK2Yn+yWX7fZZ0LKkd2CFo1K\ncejM9RiX9/bs2o59aWudtg8fQnjk+pxqDroPWs6UKROFChXk5p2n2FcqEOd4QUEh0TNMHzNPlYrD\nJ26zadc1tAgN0xTJeOQW+ZigOjWK8PyFD0WKFgUgZ65c3Lx5k9DQUIyMIoNLcHAwKVPGXJRvZmbO\ntVtPYrTHrCuU2/eekjpVCvLmzkx2y/RsWNaViIjOANHrwwrlt6RHB3udvi9e+uHjG0CVT26CKVS4\nMBYZMnDv4YsYl1mfPn/D2Om7OH/5GjmsrQHo068fTRs1xNjYmNWbT7P70E1uOE2QrRqEEN9EdtIT\n342goFCGjtvKrv2H6d2vP5Xt7Bg3cTIzf5mHkZERpUvkYcz48dHhCiB/gQI0bdacTTsuJWLlsWvf\nogIuD24xZGB/3Fxd8XB3Z/iQQdy5eZWOrXWfmWdsbESKFMY8eaIbWsLCwnBxcWPmosNMn78fL2//\nWMeqXb0Qa1ev1Gl78+YNO7bt5JmXAZ16juLZ6xBInp3lqzezfutegshBypQpOXb0KBD5XtrY2DBk\n4EACAwMByJ0nL78uXkJ4eHj0eUNDQ1mzehX3Hz777COLfl93mhy2w+k6ZCtVG82jguNMXNwil2Ym\nS5YsxuL7T5mlNCY4OAR/f93vOTQ0FK/X3qRNEzP47dh3lQYNG0WHK4hcszd4yFCePX3G7n378X7z\ngWVrnD47thBCfIkELPHduHrDAysrK4oW071RtUWr1ly4/ADft4FksMgQo196i4z4B3yI0Z7YzMxM\ncNo9FC3wAdWrlKNqpTKEvL3DqT3DSGWeQudYA4NkdPqpEsMHD4jeKFTTNGZMnYqZWSpadxrKw6em\nFK86mYcuL2OMNbK/I3t3bqZ39y6cOHaM9evWYl+xHBkyWnDk5GnCw8NJmzYdm7Ztp1Tp0hQtVoz5\ni3/FoaYDXTu258b1yCdbTZ81m6NHDpEja2bKFC/M4gVzefb0CQ3qOLJr5w62bd2CY40aWFpa8i7g\nAzWazufsxUcx6jl47CbT5h3h8IkzXHS+zUOPpzRr3R3HlgsIDY35DMbYmJunoE4NGyZPGKuz6ej8\nObMpXNCSHNktYvQJC4uIXj/2seTGxoSFhVK6TBksLCzYvOtqjGO+FweP3aS840ySZ+6KdfGRTJ+/\nn7Cw8C93FELolazBEt+Nq9c9aNN7HdfvPNS5rObn50eu7Fnp19WB52/T8/vKVdGvhYSEUNq2ML/P\naflV+0UlRUFBobTvs5Iz510oX7E8ly9fw9zcnL0HD5EtW+TjexbMncOZ45vZu75vjP5e3v4sWXGS\nk2ddSJvGFC/vt3TpNZI27dozsF9f8uTNR9/+utse7N61k1lTRvHylS8fgkIJCgomd87MDOtTgyIF\nLTFLaUzZWtP4efxkjh4+RLJkyWjUpAl58uajS4f2jBk3jtHDh7BrXW/Kl/7nmYe1WyykdafBtGr9\nk854NewrMqhbKRrXLRnre/DWP5DVG89y/oonGS1S0riuLaOm7OL9B0XFSpVxvnqFwIA3HNjUX2eL\nDE3TCA4Ow+OJF/YN5uJ8+x7p06ePfq17l85kz27FmHHjsM5mScE8GTm1d+i/+0ElooPHbtJ14J/M\nX7KUWrUdefjgAcMG96OAtQm//fJ16+GE+BHIGiwh4qF4MSuUFsKunTto3OSfLQDmzp5JA8eSnL3i\nxr2H5+nTsycdOnXirZ8fkydNJGc2cyqVy5uIleuHiYkRW1b04P6jF2zdfYnbtwxxvnlLJ2x269mL\n8WN/Jjg4NMYC7wwWqZgwoiETor5u33tV9KW+NGnS8uL58xhjvnj2nLy5MhAeFkaGzDlp3a4D/n5v\nmTR3Li0bFmPK6MY0qVeKE0cPsui3ZWTPnp3r167RsV1bho8aRdv2HQgPD2fynN84sOmfBzx7PPam\nePESMcazLVEKd0/vWL//Fy/9sGswG5sSZanbuAcebq781H0xU8Y0JJeVBXcfPqdh1arUrFokemPT\n0NAwJv+yn6WrnXjrH0De3JZULJcb+4pl6T9oKBkyZmTThg24urgwa85c/li+HAV0bF021hqSuom/\nHGD+kn+eFFC0WDG27Pj7SQG1k9Q6RCF+dDKDJb4rl53dqN9mMXZVq1G0WAlOHj/Mi2fuTBxej4lz\njzPzlwWMGTWS58+fk8LEhOzZs1Mwd3J+n9fhP6nP6a/7rNxwjjd+H6hcLifd29tF7+SuT+cvu9Br\nxE4uOus+tub9+/dYZrTAz21xrHfQfezA0RsMm3SAU+cu8eL5cxzs7Thx+gx58kaG0VevXmFfsSxV\nyloRapCdFWv+jA5zPj4+lChSkNN7h2JtZcHoqTv5fc0pUMlIlTo1I0aNomv3Hiil8PLywqZgXnxc\nFkaP3bTjUqo5dqBrjx7RbZqmUaG0LTPG1KJm1SJ8qseQdaRMb8P0Wb9Etz188AC7imVxd55B6lQx\n11x1HbiWJ6+SMXfhEnLnyYPTiRN079yedi1Kce/Ra/664EqKFClp1qIFN65f49Kli5QvlYv9G/t/\nd9s2aJqGUaau+L0PjL4J4W+N69WiZ5tCNHAsnkjVCZE4EnMGS9Zgie9K6RK5uHd+MlVKpuDdq7N0\naVmAayfH4uLxmvwFCtOlYwdat2nL1h076TdwEI8ePeLYqXv/SW0zFx6k04D1lKjQjA49RnHTxYAy\nNaby8tVbvY9VtGA2vL1ec+bUKZ32Zb/9Ss1qxb4YrgAcHYphXz4HpW2LsGnDekqWKkXp4ra0bNqY\nrh3bUqJoQbr8VJZb91/RtUdvnZmy9OnT07hpU/Yevk7y5Ib8MrE5G5Z3I1dOKx64utGtR8/o410e\nPSJzJt0HUg/t48DkCT9z+OBBIiIi8PPzY8TQQRgQhINdoVjr3bXfmZ59+um05cufn3LlynLUKeaj\nfZ4882HXAWfWb9lOnrx5UUpRtXp1fvt9FfuP3GHH6l48vTmDfl3Kcemv3RhEvGb9b505uHnAdxeu\nIPKXfJbM6Xj44IFOe0REBC4uLmSNZT84IUTCkUuE4rvx7IUvqzb8hedTP2wKZ2H0oLrRsxYZLcw5\nc3ovq9b+iUPNmgCUK18eGxsbWjVvQkRExBfvSvsWz1/4MnPhQa7evEvWrFkBaNCwEcMGD2Ta/AMs\nnN5aL+MEBAQxZPw2Nu24QESERuMG9enUpTO2xUtw4thhTjsd58TOIfE6l1KKxTNbc8nZjd0Hr1Oi\noCFDu/XD44k3oaHhTBs2luyW6dl96CZhoaEx+oeEhGBo+M97Wrt6UQaO2cz6tWtp3ylyL2F/f39+\nHjWMbu0q6vQtXzoPqxZ1YOSIfnRo601YWBj1a5fg4Ob+cf6clFIQy+x2bM+bBLh55yklS5aIsT1F\nNQcHbt31ICIiguTJDRna15GhfR2//IZ9B3p0qMKwQf3ZsnNP9JMC5s2ZTWozQ0raWid2eUL8X5FL\nhCJJCg+P4H1gMOZmJiilOHX2Ps07L6Nx02Z4+/hy2skJ/3f+lLTJzdTRDchgYU7l+nN44eWt88dW\n0zTyWmfj+PYB5MuTOcHqXbn+NEfOvWPN+s067ffv3aNxXQfcnKfrZZx6Py0iTYb8TJ89lwwZMrBt\n82YG9O1NCZsc1K5WkE4/VfrshqOfCgkJ4/a9p5ibmZA3d+zvz+xFBzlzNYDNO3ZHh58nT55QrqQN\nzifG6iwmv/vgGY3b/0ZK87TkyJGDM2f+om6NYhgYJGPf4RsYGCSjSb0STBzRAIv05miahs+bAExT\nJP/i5p69hv6JUapCzJ47P7rt3t27VLeriLvzjBh3Xt66+4T6bZZy18Ujevd6gNu3btGorgNPbs6O\n9/v0vQgLC6f38A3s2HeVcuXK8ujRI1KlNGDbqh6x3lUpxI9OFrkLESUkJIzxM/fw+9pTBAWHkDVz\nekYPdmTy7H38sfpPDh48wOtXr9m1bz/58ufn4IH9tO4+gF9ntUKLCCMkJARj43/+UIeFhRH0IQhT\n0+QJWreRkSEhwUEx2oODgzEy0s8/sxu3H3PzznPuuZ7H0DDynM1btSK5iTELZo1lSJ/aX3W+NZvP\nMXLidiwyZMD3jS+WWdOwdnFH8ufNonNc367VOXB8EdUql6NF6/Z4e71i1R/LGTukXoyHWRfKb8m9\n85M4fe4hr739mTh4MI3b/0bTlu04d2UN4WFhLJj3C1UbzuHikVGYmhpjkT7mBqixmTiiAXYNZtPc\n3ZV6DRrj7ubKyj+WM2dS8xjhCqBooexYZUvDpPFjGTthEoaGhvj6+jJ4QB/6dKkaywj/jfuPXrD4\nj5Pcd3lN3pwW9O1alcIFLPVybkNDA5bPbcfPg+vgfNMTy8xlKVU8Z7weVi4STkBAEIdO3CIoKBQH\nu8JkzpQ6sUsS/wGZwRJJSpcBa3j5xpi5C5eQw9qa8+fO0a51c1KkMOXQcSfKFLflvqsbqVL986zA\nbVu3sGzhFIyTG1LNsRWDhw6Pfm3JwgXs2voHp/cOS9C6ff3ek7vUKE6eOU+BggWByNmzLh3aYp3p\nPVPHNPnmMdZvPc+ek96s3bBVp93Pz4/8uazwc1sc73OdPHOPDn3Xsn3PAYrZ2BAeHs6K5cuYO2sK\n985PxsREdw1XaGgYuw9e49jpB6QyM6ZNs7LYFIn5MOpPzV50kOuPkrFy7Xqd9sb1atO0djY6t6nM\nhSuunD7/gPRpzWjWoDRpUsdcrP63gIAg1m75H3tnGRfV1sXhZ+hSQkQQsQNFRbHFVgwUsRMLsbu7\nu7s7wRY7uLZggigooYSUCtIMDDAz7wfuHR0HE7z6Xuf5/fzgPnuvs88Zzpw1e6/1X564nnzEi6Bo\nsrKyUVdTxaVPI+ZPdURDQ96ZffM2iZ5DdvIyJJay5cri6/s0p+bhgm6yTMN/kxt3Aug2cBtDR4yi\nbj1bHj64x+YN69i/eQCtmlX51+ej5Odz5qIPvYZtQ9VAE6mKgOxYIdPGtmPWhPa/emp/BL9yBUvp\nYCn55cS8SWTV5itc8PAnMvo9oZHRcmrs2zZvZv3aNSxZsYJ9u3dzwv2M3Pj09HSKGBkQcG8xdp3X\nUMagOC8AACAASURBVKZcRerWb8TD+3d54f+UK8fHfnb760dITknn6OkHREYnYFO1BG1bWqOqqsKB\no15MmHWM3n37UcyiBKdPHCFLFM/VY2MpkMsKy/fi+SAY57FuCjpgN65dY+r4IfjcmPXNtjr220Jr\nRxf6Ow+Ua3do3ZwBXSvQs3PdPM8XoFP/rXRxGkfnLl3l2ndu38bjO0dITE7n6fO3tGvfgajI19y4\ndo1D21xyzSL8hx0HbrF66232HHCluo0N4eHhjBk+hOKmEratcsp1zIugaKJiEqhSsRhFTH7N6oFU\nKqVa4/nMWrgah/aOsnaPK1eYMHoQL7zmK1ea/mNExSRQvu400isVBP2/V9FFYnSeJXFyx/Av/p0r\nyR+UWYRK/lhi3iRS334pItXSuAwdh03NWnLOFcAAFxdiYmJ49/YdISEhfOqAh7x6RRETQ0qVKIzf\nnXn07VQWccpjejiU4Lnn/Hx1rh75hFKhzkzO34gjU92KRetvU7fVEuITUunTrR6eF6dQQCWUEL8L\njOxfjVtnJuWLcwU5geEF9VSZP2cWmZmZAISHhzNp/GhGDW7yXbZCwuOolqsOVS1CwmPzY7oAmBjr\nERoSotAeGvKK1xFxCDML4uMXwLKVqznodpwjJ8/Qe8hO0tJEudqTSqUsXXuJ7bv3U90mZ/4lSpRg\n3+EjHHN/8NmMzYrli9KisdUvc64gpw7i29gU2rZzkGtvbmdHhkhC8Ku3v2hmSn4WB496Iims+cG5\nAtBURWimybqdV3/dxJT8KygdLCW/lFWbr9DOsQsr16ynuZ0dL4ODyc6WL5USHBSEtpYa82ZNIyU5\nmS2bNsqcLKFQyNSJ4xjctyGQI8bZu2s9Fk7vRN/utmhrf1vsVUJiGrOXnKZakwXUtlvC8vUXSU/P\nlOsjkUjoNWQnazZuxe2EO3PmL+Cm50Nq1m3O1AWnAChbugjzp3Vg0/JedO9YR2HLKi8IBALc9w/n\n4d0LlCtRDNva1ahXsxq9OlZmwCe1C79GZcui3LklL/EglUq5c+sGlfMpHghgYK/6bF6/hlcvX8ra\nnj19yv49u3kdncj02fPQ0PjwGdk2aECt2rU4e/lJrvbS0kS8eZdArdq15dr19fWpXNmKgOCYfJt7\nfiEWS9i8+xrtnbaQnpHJYGdngoOCZMclEgkZGSI0NZUhsf813sQmI8pN8UNLlZh3+S/fouT3Qulg\nKfmleNwKpEevPgBYVqxIhQqWzJw2TbZCExcXx5iRQ5g0qg03zkykqW0pliyYh3UlS3p0caRC6eKY\nFxYzZfSPp9mnpKTTpP1Kwt7psX7rAZas2sYd71Tse26Qq+F2/3EImtoF6NDxQzyVQCBg2qw5uJ7w\n+mxh47fvkjh0zItj7g9JSclbTUQzUwOunhiL16UpbFriSLjPMqaOsf/uraXxQ5uzfMlCzp89i0Qi\nISUlhdkzppKWHEvbltZ5muPH1LIpzZzJbbGtU4NODq1xaN2cVs0asX5pd0SiLAoXVqwdaVzYhOTP\n3CcdHQ0K6GkraD1lZGQQGBBECYvfT6l86MSDHDodxNJVW7l7/wFly5ejRZPGsmvYv2c3JUsY55rl\n9y42maf+EQiFua/oKfm9aVy/AnopEgV5EY2ELFo2svpFs1Lyb6GMwVLyS2nYbiUTpy+ltb09ALGx\nsbj078fDBw8oVboEIa9e4dy7Ictmd5YFJUskEu7cCybmbSI1q5WiTCmTPM1h/farXH+QguuxUzJH\nRSKR0LRBHSYPr0endjUB8Ljpz7zVt/G46Sk3XiQSUahgAdKjtikIVK7ceIlFa87TrFlThEIhD+4/\nYMe6fnT6TK29f5OrN/yZPPckYRGxiMViWjWryoYlPX5KhlNikpCTZx9x7XYASalZFDc3JDImnmq1\n2zJ91hxZv+TkZKzKl8br4tTPfq4LVp7Fw/MNh46cpHDhwqSnpzNlwljeRDzB/cCIfJ97XngRFE3z\njmvxC3qFru4HRf/5c+dy+OABNNTViIuLZf3ibvTuWl92PDFJyJAJB7l6ww8zM1PevnnLhBGtmDqm\njTJO6/+I7GwxtVrOJyAhAZG5NqgKUHmTjkGSBL+bC5XZhP8CSpkGJX8sTl1rsnzpAho3bYq2tjaF\nCxdm/qLFtGzWmNljG2FbZyCFjORjslRUVGhUv0K+zeGv2y/p0X+C3ItLRUWFrj364HHzvMzBqmNT\nmufPtxPy6hWly5SR9T3iepgmDSsrOFd/3XrOpj13efz0OebmOdtuPt7etGvVgprWJRUkDv5t7JpY\n4X29Eu/jU9HSVEdPT+uL/T0fBLNw9SUePH6JmakRg/vaMmJgs28ScI1PSGXuinM0t2tDr25tCQ4M\n4PjZddz2XE1GRgYdO3clMjKCpQvn0rNT7S86zdPHtSUp5QRVK5ajXNkyhISG0bBeefZtHPC9t+CH\nyMrKZq/bXY6deUJWlhiHllYM6dcEXV1FHa9bnoG0atNazrlKSEjg1InjlCxVir79+hETE8O0het4\n8y6FCSNaAdB76C7MS1YnOOwSenp6hIWG0qNLB/QLajPc+ddJTCj5PtTUVLnlPpW5K9w5cMyTzMxs\n7O2sWTy9s9K5+gNQrmAp+aWIxRKcR+/jpudLHDt1IS72LZcuXmTrKie6tq8l65eZmY26uupP+fXu\nNHQXtRv3YMiwYXLtc2ZOB6EfS2d3kbVt2vUXKzffYPqsuVSsZIXHlUts3rCOC26jqVm9lNz4XkN2\nUq9JTwZ/YnfsqOEUM4xlxnj5YOffmTv3gujcfysLl66gtX1bXgYHM2PqBGpVMWLd4h65jhGLJbyP\nT0W/oDZ9R+zByqYNU6bPkB33e/aMZo1s6dTWhsfPIjEy0MW5V136dKv/TZ9zfEIqgS/fUKyo4TcX\nMQ57HYdIlEW5MkV+SNlfIpHQqf9W4lPUGDV2IpoamuzasZU3UYFcPzVBQSz16OkH7D4aiPuFDwHN\nc2bOJDo6iu27dsuuMyoqilrVKvP01lxSUjNo0WktASGv5WoKPrh/n4F9uhL0YOF3z/tjMjKy8HsR\niaGBbp5Xf5Uo+d1RyjR8hNLB+jN5/CQMj1v+FNDTpotDTUwK5+hcHXV/yIJVFwgIjKCQUUGG9m/C\nzAlt87VW3JXrfoyYeoKbng8oVCjnRR0eHk7jerW4emIslSsWU+i/ec/tv2UaLBg/rAWWn4hzAth1\nWceoCQtp1UY+PmzNqlW8CfmLtYtyd0x+R1p0XoOT8wR69v4gg5CUlETFsiXxvTmHYkWN5PrvOHCL\nRasvkCYUIRaLycwU4xcYhJmZ/H1qULcOIS8D8Dg5kepVS/y0+fu9iGTg2AO8johHS0sTdTVYv6Q7\nrZt/n/bURY+nTFt8mTv3vVFVVeXGtWsEBATgenAf/bpaMWJgM7n+QqGIUjZT2XfoKE2bNwegWmUr\n9uw/IMuC/AeX/k7YWqtTrKghmw/4c+rcZbnjEokEXQ11xLG7f/iHxta9N5iz1B1TM1PiYt9TqoQx\n+zf1p3RJpaOl5L+JcotQyb9KbFwy+494EhGdSPUqxejmWPubs+1+FjWqlVSolXbszEMmzz3N1p17\nadKsGS+Dgxk3ajgjprh+Vu/oe5BIJAgEAuyaWNHdMZjqVpZ06NSJzMxMzrifZv5URwXnCqBl08rf\npF9jW6skZ91PyjlYUqmUs6ePM9alZp7n/2/ieT+QI+4d5Nr09fWxta3P/cchcg7WXre7rNh0A9fj\nZ7GpUYPo6GiGDXJh4rixHHKTLyWkrq6By9CR9B66C/+7837KCmVSspBWXdcya+5iWY3EqZMm0WvI\nbgoW0KSJbQWmjWmtoGCfGxf/8qdH7/7Ex8fT0aEdYrGYuvXqkSHKYv6K83S0r05RM0O5Z8y5d336\n9u5GzZo1MS9mwds3MbkmROTUyxRgWc4M78cHEYlEclUJPO/exbK8xQ/fo3OXn7Bsw19cuX4Hy4oV\nyc7OZtP6dbTpvg7/u/P+LwtcK1HyO6PMIvzDuHMvCCvbOTx9pU7Rsna4nQ2netP5RMck5MmuWCzh\n2q3nuJ64x6vQd/ky10WrLrJ5+26aNm+OQCCgXPnyuB4/xfEzD4nKw3zvPXpF805r0DAdhGGZUYya\n6srkUa25c34Slhap2FQQ43tzjsJqxPcy3LkpVy+fY+6sGYSHh/Pi+XMGDehLdmYCHeyr58n2j3D4\nhBdl60xB3XQgJWwmsvPATQVNsc9R2Fif8LAwuTapVEp4eDgmxgXl2petu8yWHXuwqZETyF+0aFFc\njx3H4+pVwsPDZf3u37vHq5fBTJ85C7FUDW/fcH4Gh47fo36DRgxwcUFVVZXxY0bz4P59Drq6cfbS\ndcpY2dG4/QpeBEV/1Za2ljrJSQmMGj6MZs1bcO/RY9Zt3MQDbx8GuAzGZdxB7twLonKDD8+YzwsR\nBgW1adfUlGrlsujmaMOm9Wvk7n14eDgXL1zAoVU1ypYuQoO6ZRkysD9xcXFAznbqiCEDmTSq5Q/f\nh3U7brBgyXJZpQE1NTXGjJ+AsUlRLlx9+sN2lShRkjvKLcI/CLFYQvnaM1i1fjtt2raVtc+ZOZ3w\noNu4bh/0Q3ZfBEXToc9mCugbUapUaW7evEX71tXYurL3D/8qFoslaJi6kCrKVIiVadeqGRMG1/ju\n7R3IKQDcvONqlq5cQ9fuPYiNjWXerOmEBntz88zEfF9BeR35nvkrz3P+ii+amup0c6zBzAntcq2d\n9zPZuvc6ExceQ1hKBww0IUmEdqiQacPbMHPc12PB5q84i9eTFNxOnEZLKycYft/uXaxZMR//u/Nk\nn1F2thitooNJFWUq3MsmDWyRAn369sXvmV9OiaMdO2nr4ECT+rVYOsOOxraWebrON2+TCAl/R5mS\nJjJR0XEz3DAt04Kx48fzMjiYZo0a4h8UTIECH2ogrlqxHP/H5zi01eWL9n2ehmPfYwPCjCxCIyLl\ngtczMjIobWGGnq4m6zbtUnjGXgff4fA2F5KShbTotAaDQsXo3K0nMdFR7Ny2hfHDmpOalsGR096k\nCTMw1Ncl9PU7ChbQQyLJZsb4tnkKcC9bcwbuF69Rtlw5ufbxY0ZSvmgiY4b8uPOmRMnvilLJXcm/\nwkOfUHT09OW++AHGTZyM+4XHZGVlf2bk5xGLJXTos5kJU+Zw98ETDh45ScCrcEIixazafOWH56qi\nIsC0iBGBAQGfnE9MUGAQxYoa/pDd5RuuMmHKdHr36YuGhgbm5uZs3bmHhGQx12+/+OH5fo7ixQqx\nc21fYp6vIsxnKcvn5l6YOK+IxRLOX/Fl7dYrXPrrGWJxzhbU+/hUHnqHMG3hcYQV9KCQFsRmoBuW\nRbZQzKKV55kw6yiZmV/+7KeOaYNRASEVy5Skv1MPGtaxYcWSOZzcN0zOAVZTU8W8qDFPfX3lxmdl\nZREeFk7LVq14/OgR1zyuMnvuXNo6OBAYEMDLl6+o9UmSwPeQnp5JvxF7sGowm/FzLlKx/iwGjtlH\nRkYWluVNuXc3R1T1zu1b2LVqJedcAXTp1p0bdwJyMy1H9aol6NOtDupqanLOFYCWlhb6BfXR1NLN\n9Rk7ff4R2dli9AvqcPvcZHo5lsbr2iGS3tzn9P6hnPfw40mQhJ37j3Hu8g1atOmEfkFt3A8MJcxn\naZ6zB60qmnP7c+Kyf2+Fh4S947ZXEO/jU/N0LiVKlChjsP4oMjOz0dFWfLlraWkhkUiQSL5/5fCW\nZyC6BQzp91FNO11dXRYtW0nfHh1+WABUIBAw3Lkp40YPw+24O/r6+oS8esX6NaspYWGYa2zUt+D9\n9DXjZrSSa1NRUaG5XWse+4bTrFGl77aZkpLOlj03OO/xHC0tdbo7VqNvd9t/LaYlMjqehu2XEp+e\njkhXFc1UMYX1dKhfswJnL/tQvLgFogwJWrFiMlLSKByvievJU9S3teV1eDhjRw1n8PgD7P2MzIFI\nlMWh414I07OoXtUCY914+k9pQYvGlXItmDx6UDNGDx+M2wl3zMzMSE9PZ/rUKVS1rsqMWbMBaNms\nKSkpKezdvYslC+aycEZHhQy872HMjCOkZRciMOQ1BQoUIDk5mUED+jJxzjGWzOzI4jVz2bhuLUXM\nzHgTo6j2HhMdjaGBXi6WPxD3PoVj7g8x0NdGQ13APS8v6tarJzvu4+2NMD0Ni0IWCmO1tLQQi8Wy\nZ0xLS50BvRoyoFdOBYILV31JShVw/q8TMod10dLlpCQncfrCE2ysS/7orZExaUQLujpPp1gxC1q0\nbElSUhIL5s5CS0NMlYrm2PfYgLdvOKXLlCIgIJCBTo1YOqvTLymKrUTJfwHlk/MHUdumNGFhYQqr\nCwf37aOxrRWamuqfGfl53sYmU7p0aYX20mXK8DY28YfnCjBldGusymhTrqQFJc3NsK1Tm5MnjvMu\nNvmrqw2h4bFs2X2NXQdvEfc+RdZe1MyAFy+eK/QPeO6HuZnBd88xJSWdxu1X8vB5NpNnrmDg8Bns\nOuJP76G7vjm+Ka/0GLKVSPVMUqrok1laj5QqBQmVpnPxxgueB4fywMePV68jqFu6GnrRYvbtO4Bt\ngwYIBAJKlCzJQbdjnLviS3hEnIJtkSgL+54bOHAqmI49R9Oh+3DuPn7L4RMPUFHJfTt13DA7WjQo\nhk2VilSvXImS5kWJioxk78FDQE48kffjRxx33cXlMzvZta43Q/o1/uHrT0wScsz9ARu2bJetTBUs\nWJANW7Zz+MQ9AK4eH8f503sZNWwwXp6eeFz5sLoqEolYOHcW/brX+ew5Tl/wpkLdmdzyzuBdWjGy\nxdC1oyOuhw8RFhrK0SNu9OjsyKLpjrx+HZ7rM9a0YZXPlk665RVM+w5dFbbDO3buxk2vVz90Xz6l\nQd3y7Fzbh2kTh1G0sBHlSlqQ8MaXC66j6D10N+UrNyQ4PJLrd+7zLOAlXj7vWbnp8tcNK1GiJFeU\nK1h/EFpa6qxe2A1H+5aMmTCZypWrcO2vqxzav4fLx8b+kM06NUozapobqampckWaz51xp27Ncl8Y\n+XXU1FSZO9mB42cesWjpMnr2dkJFRYWL58/TbWA/7l6YolDIWSqVMnupO1v33qRtu3YIhUlMmjuD\n9Ut64tS1HiOcGzFl9gzq1K2HhYUFUqmUY0eO4PfMl467usnsxMYls23fTR74RGBWpCCDnGwVdK4A\ntu+/ScmyVTjgelQWc9S6jT11a1Tlxp0AmjasmKd78DVi3iTy2Dcccd2PyqwIBEhL6pHiFS9z8oyM\njNi77yAVSpfCpqZ8BqOuri41atjgHxClUK7l4DEvUDXkwuW/ZC//rt17ULeGNTfvBtKkgWLMlIqK\nCgund2DSyJZ4PXjFiKmHQZrNWffTvHoZzJ6d29m2ug+9OtfNn3vwNhETk8IYGclLRZiYmKCvr8+7\nuBTKlzXl2qnxvItN5s79IJz79qRGzZoUL1GKi+fPUb92KcYOtcvVfkJiGgPH7OPcJQ9Z4P68hYtp\nVK82a5bPYW6SkHJlTNmyojttWlRFU1OdDm1bMWb8JKwqV+EvjyscPrD3i8+YkYEOLyMVg/yjoiIp\nZKSby4gfo21La+ztqhL3PgUdbU10dTV5ERTNi6A3nL68HDW1nFeCsbEx6zZuxdG+BZNHtVaqxytR\n8gMoV7D+MHp3qYf7gWEEPrnAmmVTUckI4P6V6VhXLv5D9kqVKEzHtjZ0dmzLo4cPiY+P5+D+fUyf\nPIE5k9p+3cBX2Od2l5at2+DUtx+qqjlCo/bt2uE8aDBb9txU6H/R4ylHzzzjiX8g23bt44DrMa7d\n8mTcjKOEhsfSwd4Gl161qF29Cq2bN6KmdSUWzpnMucOjZFIVoeGx1Gi+kJA3BegzaColLFvQ3mkz\nuw/fUTjfpeuB9O0/UO4FpKmpSfeefbj4l1+er/9rJKWko66pCp+uJqkIUNNUJyU5WdZUpEgR9PX1\nuX3rllzX7OxsXjx/kauy/JnLfjgPlo+z0tbWxqmfM+4XfRX6f4x+QR1at6iC7405tGpgxO2r+xGn\nPOPGmYn55lwBlChWiLi490RERMi1h4aEkJqSgrnZh3g9k8IF6dSuJiGPl+LUoTRWJYWc3j8E1+2D\nUFNTlas9+Q8nzz2mWfPmMucKcrb8lq1cg5amBmE+S7l6fCxtWlQFwKlrPU7vH0rA38+Yqijwq89Y\nry51OXn8OL5PPhS5jouLY8XSRQzokX/3CnK23wsbF5Qpz4dHvKdipYoy5+ofrCpX5s3b+FzviRIl\nSr6OcgXrD6SWTWlq2Shu6/0oW1b0Zs2WKzg7deZtbCJ1apTj5L5h1K9dlqs3/HE9+QhheiatmlrS\nq3Pd79qKDAqJo1adjorXUKcee7ZcU2jf63af8ZOmYmz8YSWmYqVKdO/Vi0PH7zFzggOTRrVmUN9G\nnLnkw+VrUiJjNFi+8QpD+zWkUf0KzFjsjvOg4UybOVtmo51De5o2rEdXhxoU+ChIXUdbnaTkJIV5\nJCUlUlD7+7dcv5eypUzQUFGF5Ewo+JGWWVIm2praWBT/8FKPiIhAKExj6cJ51Klbl8KFC5OZmcmM\nqZPR1hIwed5pdLTV6dmpBp3a1UAgEKCmqkrW34W3PyZTJEJN7dt+n+npaTHcuRnDnfN8ubmio6PJ\nyIHN6NuzK1t37qWCpSUBL14waIATY4faoaWl+Dno6mrSq0tO/FRWVjZzl7mzZc8N4t4nYVrEkK6O\nNVg8ozM6Opokp6RjXFhRiLOQsfFni3d/7zNWrKgRW1c70bpFExo1aohegYJcuniR4QOa0q5V/hXf\nzg0ry6L4+OxSWIW+ffMmluUtUFdXviaUKPkRlCtYSvKMqqoKE0e2JvD+QhJDNnL52Bhs65RjyrwT\njJh6giq1OtCy/SAOnAymRec1CIWib7ZtWdaEe563Fdrved6lYjkTMjOz8XwQzCOfUCQSCQlJ6ZiZ\nFVXob1bUgvgEoez/kdHxTJ57ArNS9Zgxby11G/fAadhetu27ydlL3gwaOlxufAVLS6pXr8aNu4Fy\n7T071mD96uUIhR9sR0REcPjAfrp3rP3N1/mjqKmpsnZBT3QCUiBGCGlZEJ2Gml8ihYz0ef237lRo\nSAj9nXowZnALmtQzo2rFcjRrWJeyxYtxxPUQ5StWo//QabTpOJgFa64zdOJBALq2r8aWjWsRiT58\nZvHx8ezfs5Oujr++YPU/zJnsgEOLkrRq1hAzY0PatGhE17YVmD7O/qtjh006zM0H7zG3KEWVqtZ0\n79WfJwGZlKw+FZ+n4bRoXImzp0+RlpYmN87t8EHsmny/rERUTALPA6MUsna7OOSsrHVpbU7jGlo8\n/msm86c5/vTtOQvzQrRvXZ2+PbsRHhaGVCrFy9OToYMGMG1sq68bUKJESa4odbCU/BR8/V7Ttucm\nHj19jqFhzhaNRCKhW0cHmtczZOzQb9PciU9IpUrDucyYvZB+zs6oqKjgfvoUo4cNZtZEexavuURR\n86IIhemIs9Jp1qA86RRj++59MhtisZjG9Wsxe1wTHFpXA6C90yaatnJi+KhRsn4vg4NpVL82YrEY\nH78AihaVd9TsmtgybWQ92VbQP9c0aNwBrt99RbcevUlLS+WI6yGmj23z2Zien4HHTX/mrT5D8Ku3\nVCxvxswxDty+95JNu66hrq5OVlYWI12aMXNCO1RVVXgfn4p/QBQXPZ4RGKHG4aMnZC/y1NRUalat\nxNGdzthYl8Rp2C6eBcTh1G8gGenp7N21nR4dbVg6u/O/dn3filgsISlZiH5BnW/Kfnsd+R6bZgto\n36EzKioqbNyyVXYfjh5xY9GcKTz3nIfT0F0EhqYxc858TIoU4dgRV04eO4znxakKZYI+xdfvNWcu\nPSElNQOvh2EEvIzBwECfdGEai2d1pFkDSy5cfYq6uioOraphXKjAF+19jEiUhbq66g/VVfyYzMxs\nZi91Z+eBW2RmZWNqYsiMCfb0614/T3aVKPnVKGsRfoTSwfpvMH+FO0nZpVm8bIVc+5VLl1i1ZCo3\n3Md/sy2/F5EMnXiYgOBoVFVUKVbUkBHOjZix+Awnz17EpkYNpFIp58+dZejAfhQsoEPrdp1wdhlM\nWloaq1csJel9KB4nxqGqqoJUKkXTbBBv3icoaBm1atYQLbVUKlRpwvJVa2TtD+7fp3P7Nrz2XaGw\n5SSVSnngHcL5K0/R1FSjm2MtheD7fxOxWMKKjZfYuvcWkVGxVK1cijmT7HFsY6PQt4njaiZOX4Zd\nK/mVijkzZ6CR9Zz50zoikUjwuPmcs5efoa6mQrcONalbs8y/dTk/lTMXfdhy0J+7d+/z9EWAXK1E\nqVRKjaoVad2kDHvdPLGqXJWoqCgS4hMoX9qY43uHfdG5kkqlTJp7HLeTj+jcrQdH3Y4weNgwJk6e\ngoaGBt6PH+PQuiViiZi27doiEon4y8ODVQu6MaBngy/O+8p1P2YuOcuTpyHo6mrRr0cDFs/okCep\nC8gRik0TiihYQFsZ2K7kP4GyFqGS/xwCgSDXemtisZjv/d6uXLEYd85PJuZNItliMcWKGjFo3AFG\njZsoCzwWCAS0c2iPfTsHSpqkkJwSQq8u7dDQVKNHhxqMHzZKbkVDU1OD1NRUBQcrNTWFMWMaM2PR\nKToGvqB1W0eCg17gduggu9b1yzWeRyAQUKdGGerU+D2cjtHT3PB/lc6x0xepYGnJlcuXGD58CGqq\nqrRtKR/Po66uQnqGYhxReroQPZ0cHS8VFZVvrr/4/4RUKiUuPgVv7ycIhUJZoe9/EAgEGBkZscf1\nLve9n1L873i2hIQEmtjW4dnzyC86WFeu+3H2SgCPnj7H6+5dHtx/yPSZs2THU5KT0dbV4869+5ia\n5jjkwUFBNGtYn/o1y3y2NuItz0D6Dt/Lxm07sG/bjujoaGZMmUg3l+2cOzwq1zHfipqaKvoFdfJk\n43dALJbwPDAKDXU1ypc1VTqLSn4JSgdLyTeTlCwkNi4FC3Ojrwaqd2pXA7vOa5kweSqFCxcGnH+t\nDgAAIABJREFUcpyrzRvW0sXhx4J2zUw/6FSFRybQoaeincpVbYgMusy6xT1Zs6h7rnYEAgHdO9Zl\n+ZJFrFyzTvbl63HlClGRUTi0qkarppVxPXmfB49OYWZSgIceMylZ3DhXe78TkdHxuJ26z4uXYejr\n55SKcWifE8czb8EUBQerm2N1NqxdSes29mho5ATJR0dH43boILfOTvzX5/8zkUqluJ64x5a9d4iM\niicjMwtdXV2qWFfjwb37dHBox5nzF2TZdOFhYTx58oyBgwbJnCsAQ0NDRo2dwIFjh+S2iz/l0PGH\njBg9HkNDQ16+fEnN2rXkju/ft5fxEyfJnCuAcuXL06dff0ZMPkS5MmZUtSqKU5e6cokVS9ZdZtGy\nFTi0dwTAwsKC3fsPYVWuFN6+YfkiSvr/zIWrvjiP3YMwMwuJWIJp4YIc2TZMoZj8P4S9jmP7/huE\nRsTRsE55+nStJ3e/lSj5UZRB7kq+ilAoYvD4A5SsPoWW3TZS3Hoyy9df+qKQppWlOYP7NqRBbRtW\nLF3C9q1baN6oHtKsOFycGuV5TtaVzLh+zUOh/cZfl7G2Mv/q+KWzOnL+zHEa1K3DimXLcO7XD6de\nPcnKEnPR4xk6OpoMdGrEtlVOzJ3imK/O1evI9yxafZaxM9w45v7wh0oUfY4nz15Tu3YtmXP1D63b\n2PPYJ1jhMxvQswHGBcXY1q7OyuXLmD1jGra1qjNxhN1nV1D+X5m/4ixLNtxi3JTFnL18nQmTp5Oc\nLGT23PmERkaRmppKJ8f2PPX1xc31MPYtm1G9agmKmisqsxcyNiYlVT678s69IAaO2UeHvltYtekS\nCUnp6BvkfA6WlpZ4eXrK3f/EhATMiyn+rZpblCA2UYCljSNX7iZg3Xg+Ya8/iMD6PA2jWQv5+D41\nNTUaN22Kz7PXebpH/+/4B0TRbdAW3pmrk1rDEGEtI0K0s2jeeXmu5X8uXPWlcqOZrD5zhyPPA5m8\nwZ0K9acTGR2f73N7H5/KLc9AQsLe5bttJb8nSgdLyVcZOHY/yaJC+AeF8OJlONdu38PV3Z9NuxRl\nEj5m7pT2uG4fwNuwWzzxOs74wXW44DbqhxTjP2WkSzMO7d/Dnp07yczMJDk5mYXz5hAU8IweHT+v\nyP0PerpapKZm0L1HTxLi47GpUQO/gED2Hz7CtIWnv0uFXSKRcPCoJ627r6dBuxXMW+5OfELutdxO\nnHtEjWYLiUo0waxMC9btekyDditIShbm2v97MStiQHBwsML2bFBgIGamhRS2StTUVDm+Zwgr57Ql\nLvwWqqLnXD42mkmjWufLfH4X4t6nsG67B+cu/0VbBwfKlS/PqDFjWbJ8OfNmz0ZPT4/1Gzfx8P49\n+vfqiNveNaxd0IEJw1pwzO0AYvEHLSipVIrbof3YNf4gpLt68xV6D92LZbW2dO83gQf+2fg8DWPP\njm1IpVKa29mRnZXF7JkzSEtLQyqVUqyYBYcPHJCbp1Qq5egRNyZPn8HQ4cNxO36a/i7DGDfrmKyP\nhbkxL/z9Fa7xuZ8fFuZfDrj/r7N662VEplpg9HcsmkAApjpkGaiz101exy4zM5tew7YjtCxAZmk9\nMNdFWKEAsTpSxsw4nG9zEosljJx6EAvrCTgO2UzlxrNp5LhErsKEkv8myiB3JV/kdeR7ajRfSHBY\nJNof1TF84uND905tCfVe8q/EN2RkZLH78G3cL/mhIhDQuV1VrK0smL74DHe8XqCiIqBtSxtWze+M\nhbmiYOanPH4ShvO4I5y9dI07t2+hp6tH0+bNUVdXx9zEiACvBRQ2LvhNc3MZux/fF/GMnzwdI0Mj\nDh/cx33P69y9MJVCRh90hVJS0illM5Vzl/+iuk1OwLlUKmWwcz9M9RNYPrfrj92cj5BKpdRpuZj2\nXfozcfJUBAIBqampdO/kSOPaRsya6JDnc/w/cu7yEzbsfcqZi/KrnhkZGRjrFyQlQ0RaWhoWpiYI\nI7fKjovFEtp0X4+aVhFGj5uIhqYmu7Zvwd/3HnfOT0FPT4uYN4lYNZjNwyd+FCv2oUbm+DEjOXv6\nBDY16zB46AjevXvHnJkziIuLw0BfDy1NNbKzxdi378igIcMRiUQsW7KYt2/ecOXaddmWbWpqKham\nJsS/3ICWljq7D99m3Y57uF+4gpmZGRKJhK2bNrJt00r8787/o2sH1mu7iPuiBDDWkj/wOhXnOtbs\nXPOh3ua1W8/pNHwryZU/ec6zJKh5vkMUvSNfvtsWrjrD0t1Xc4qta6iCRIp6WCo1CpvgeX5Gnu0r\n+TLKIHclvy3BIW+pXNlKzrkCqFa9Om/fJZCRkSVTQP9ZiERZtO6+Di1dUwaNnEV2djZbNq7F/dIz\nLh0ZTWZmNioqgu9aGdMvqE1ERAzVrCrRoFEj3se9Z+jgQWzZvoOsrGx0tL8tG+vxkzA8bgbi7Rcg\nC5hv0qwZwwY5s26bB/OndZD1vXTNj1q1a8mcK8h5MCdMnoajffMfcrDexSbj6x9BUVMDrCzNEQgE\nHN8zlM79t7J/z07Kly/P/fsP6GBfnWljv64JlZ88fhKG99NwLMyNsGti9Utf/Ab6Orx58wapVCr3\n0oyJjsbAwACBQMCF8+eoXUO+vJOqqgpnDo5gy57rzJs5muwsMQ6tKrN54ST09HJe4peuPcOuZUs5\n5wpg8NARnD9zEttq2iyZNwFVVQGTRjSic7uaZGZlY2FuRNz7VJatv0T3jm2QSiFdJObpiyCZcwWg\nrp7zd/3PquSAng2IiEygemVLqlatQmRkFIYF1TnvOvqPdq4AalYtweNrsWR/sqOvmy7FpkoJuTaJ\nVAq5+U+CnB8qn/6t/AhSqZTVW68grKCb41wBqAjIKqnH04cRvAiKpmJ5Rd0+Jf8NlA6Wki9StpQJ\n/v7PSU9Pl3OyfJ88waSwQa5ZdT/KQ+8QFqy+iOf9IAob6zOwd33GDrXD9eR9BOqGnDp3Sab349De\nkcb1a3Hu8hMc7RXlB77G0+cRGBgYcP2OpyzI+Orly/Tu2YPWzavKyoh8jcvX/ejYpbtCNqJTP2em\njhskc7DS0zPxuOGPiuqHDC2pVIpYLEZbWxuRKOu75i+RSJg45zh7Xe9gbV2VkFchFC9miNsOF4oX\nK8SDq9N55BNK9JtENi9umWsZnJ+FUCiim8t2/APe0rhpUwLcrjNm+hHOHR75y+Qr6tcuS4YwmSOu\nrvTo1QvISbqYNWM63Xv25OgRNyaNHcXhbQMVxmppqTNuWEvGDctdu01NTZXsLEW1+6ysLNTVVJk0\nqg2TRrXJdaxJ4YKsWtCNVQu6kZWVTcnqU/H386NO3Q/lcQ7s3YttHUuZBINAIGDO5PaMGtQM76fh\nFC5UgKpWFspMOWDckJbsdbtLtrYKFNEGCahEC9HKkOLUtZ5cX9va5ZCkZkFKJhT44NCqRAlp0bRy\nnrXFIEf2IilRCLryMZGoCFDX1+J15Hulg/UfRulgKfkiJSyMadrAkhFDXFi1biOGhoaEhoQwbNAA\nxg+zy7cv9Uc+obTtuYE5CxazcVcHwsPCmDV9Mi+CD5KSKqJv/yFyX3iqqqq0bd+ZE+c9fsjB2n7A\nk/mLl8hlcNm1aoVtA1vq1zJQ6C+RSHgXm0IBPS0550tHW4PkmASF/slJSejo5Hxpv4tNplnHVRQu\nUpzHj2/z+vVrjh05wuaNG4iJjsbUzBTLcrk7HlExCSSnpFOudBHU1FRl7as3X+G+bzz+QSEYGRkh\nFotZtnghnQdsxetiztZgfpZD+h5mLD6Njn4p/IK8ZBl52zZvorvLWh5fm/lLHAEVFRWO7hqMQ+/x\n7N+znfKWlbh04QLJySkIhUL8fG5weNtAmjWq9N2229pVZcx0NwIDAqhgmaPsLpVKWbd6BZ0dvv63\n+U9IhLq6GhuW9aRbRweGjRxNlarVuHHdg6Ouh2SFoqVSKXfuBRMR9R4b65K0aGz13fP9L1O6pAlX\nj03EZfweXt59B1IpNW1KsWe/MwU/yQzU1tZg+6r+DBy/h0xTLcTaqmilZKOTImHDoV75Mh91dTWK\nWRgRkZD5IS4MIFtCxnshVpZfT8hR8v+LMgZLyVdJSxMxapobp84/wti4EIkJiYwbZse0sfb59rJ0\n7LOZlg4DcBk85KPzplGpbCnq1ixNi3b9GTRkKAB/Xb3KlEkTiYqMJFMkopFtRbas6EkJi2/P9Kve\ndCGbdhymRs2acu0zp03BQD2EGeM/xCq5nbrPrMVnSEwWkpmZRad2NVm7qBv6BXWIjI6nWpP53Lhz\nj3LlywMgEolo17o5vTuUZ0i/JgwefwAtAytWrF7L2tWrWbV8GaVKl2bT1m1UrlKF2zdvMsSlP3Mm\ntZYpZ4dHxNFjyFae+EWgrqmKhooq6xf1ktXPK20zjcPHz8ptN0okEqpYluHI9gGfTUn/2UilUozK\njuaBzzM5aYN/5nZ0x4BfKiMgEmVx9vITomISqFW9FPVqlc2Xv+G9bneZMvckfQc4U8yiBKdPHiEt\n6Q1XT4z7rK6Uf0AU0xae5pKHD1pamvToVJclMzsS/SaBLXtuER6ZiLWVGcMGNMbCvBDhEXE4Om0m\nW6pOpUqVuHP7Lg3rlWX/pgH5kjjyXyM2Lhk1NVUMDXS/2O+pfwQbd/9FyOs4GtUpx7D+Tb85/vJb\nOHTMkyHTDiIsqwv6GpAhRjs0Dcf6lTm8dcjXDSjJE0ol949QOli/LwmJabyLTaZ4sUL5HndVuPxY\nHj97QZEiReTa+zv1oKhhEhevh3LL6yGhISG0a92KbTt30drenoyMDDauW8O+XZt5dnveN29ZDpt4\nEGOLesycM0/WJhaLqWNThbUL2tP875WMC1d9GTrRjT0H3bBt0ID4+HhmTZtM+CtvPE6MA2D34TtM\nnnucLl27YVSoMMePulK9simHtg5ETU0Vo7KjeeTrj7m5OfHx8ViWKY1/ULBMHwzgnpcXLn27EfRg\nIdnZYsrWnkq0VjZiC11QEUBSJjqBKZzdN5qmDSuiXsSF98kpaGrKb2V2cbRnYPfydPiBVb38ICsr\nG51iQ0kSpqOqqip3rHXzRkwfVQ+7Jv/NVZeA4Bj2H/HifYKQhnVK09Wx1mcdn/CIOOq0XMyU6XPo\n5+xMSkoKyxYvwOvWZR5cna5QYFkqlVKn1RI6dnNm3IRJCAQCRCIRTj26UqmU4LcsW6TkA/vc7jBt\n8Qnex6WioaHK4L5NWDKzCxoayk2kn82vdLD+7IhIJd+FoYEuFcqZ/ZSg9sLG+oSHhSm0h4eF0LxR\nJZrZlqSWtRXDBg9izLjxtGnbFoFAgLa2NpOmTqdUmQqcOPvom883YbgdO7ZuYsPaNcTFxREUGMiA\nPr0obKROs4YVZf2WbbjK8jXradCwIQKBgEKFCrFhy3ZCXyfyyCcUAOdeDXjkMZNSRRJQFT1j15oe\nuO0YJNvSy8rKljlCL54/p2IlKznnCqBO3bq8i0siOSWd81efkpiVibiEXo5zBaCvgbCYFgvWngWg\nunUZ/rp6Vc6GUCjk3r37VKtcXK49OSWd1NSMb743eUFdXY0a1cty9oy7XHtMTAy+vk+pYV3iMyP/\n/7EsZ8bimZ3YtsoJp271v7iqtG7bXzj1c2b4qFHo6upiamrK6nUbKWBgyukLPgr9n/pHEBefwdjx\nE2UrbpqamixftZadB259l6yIkn+ffj0aEOW7mtjA9SS+2syq+T2UztUfgNLBUvJTSElJZ/Pua/Qd\nsYfJc48REBzzxf4uTvWZNX0yaWlpsrajbm6EhYSwZtt1dh28iVCYRnTka2wbNlQYX8+2CS++co6P\nKVu6CB4nx3Pv5jEqlStFmxaNKFkknbMHR8ptGT0PiMS2gfz5VFVVqVe/Hn4BUbK2ksWNmTK6LQum\ndaJhvfJyNhxa27BzW07qv7m5OaEhrxCJRHI2X79+jbqaKro6mgS/eku6di7bVgU1CHr1FoCZ41oz\nevhgPK5cQSKR8OrlS5y6d8G+RVWZKKrP03BqtZqPcYVRFCo/kqYdl/Eq9OeLHC6c5sCY4UPYv3cP\nUVFReFy5gqN9S8YMboGRod7XDfwBPPKNoGUr+cB3gUBAy9btePQkTKH/29hkSpYsoRB4XbxECZKS\n08jOFiuMUfJ7IRAIKFhA+4/P9PyTUH7SSvKdmDeJ1GyxiKueiTS064uKXmUaO6zgyOkHnx0zZogd\nZS3UqVimJP16d6OJbS2mTBhFZraE7n1GE/k2Fo+bnujoFeDRA0U7jx96UbaUyXfNs3LFYhzfM5Sk\n0E1EPlvBsjldFLIHS5UswhNvb7k2qVTKEx8fSpeQX4X6HIumO7Jr+0ZGDHHh2bOnmBQpwpRJE8nK\nyskcTEtLY/yo4Qx0aoSamiqW5czQTles40hSJlYVcjKO2repzrolXZkxaTj6Oto0sa1D9Yqa7FjT\nB8jRL2vSYRmP0xPJblCELNsi3H4bQz37hfkmago5cS5BL9/IveBbNLbixN6huB/dQoPa1Zk3cwzj\nhtRj9qQ/U4MrN8zNDAgMCFBof+Ljg+eDVwpK4tWrFMfH5wmxsbFy7ZcuXKBalVIKW4pKvkx6eib7\n3O4wevohNmy/SkJi2tcH/UtIJBIePwnD6+FLMjPzr8qDkn8fZQyWknzHZex+CppUY8nylbK2p76+\ntGnRhPAny78ogRD86g1ej15hYlyQHQfu0rBFL4aNHCk77uXpSYd2bTl19hz1bW3Jzs5m947trFy2\ngBee82Wp7PmF64l7zFvlwckzFyhTtmyOGOTihVw5f5QHV6d/c4D0u9hktu69wQOfCAz1NXkZGkdE\nVAKVrCrh7e2DQ6tqbFvlhIZGjvhk+XrTiFDJRFxcJ2ebMCETneAULrmOp0Hd8nK2MzOzUVdXlZvL\nxDlH2HjRK0eh+iN0g1JYMsyBkYNa5Om+vItNZvCEg9y8G4ChoT6ZogwWTHdkQM8GebL7DyJRFqfO\ne/PsRQSlS5jQvUNtme7Uf4GbdwPoO2I/Fz1uUKZsWSCnFmY/p9507tqNI66HWL2wK869PqyeTp1/\ngute0SxduZZKVlZ4XL3CpLGj2bm2N/Z2P1bf808kIuo99ewXkYyYVG3QyQS1pCw8jk+iZvVSv3Ru\nng+C6eqyhZQMESpqKggyJWxf1Z+ujrW+PlhJriiFRpX8pzh57hGPfPfJtVW1tsa6mjXXbr/AoXW1\nz44tV8ZUppU0ZMJBFq9pK3e8Xv361Kpdk55dOqBXQI+0VCGlShhz+djYfHeuAHp2rsvb2BQa169N\nUXMz3sS8w7qyBe4Hhn9X9plJ4YLMntReru1FUDThEe+xsmwnpz6vpqbKnTPTcBqxA0+vl6iqq6Bf\nQJuNG1wUnCsg11iOB76hZBZQVWhP0xHw8GnYN887N6RSKe16b6RhUwf2uF1DR0eHJz4+9OjsSCED\nXdq3qZ4n+1ExCdS3X0SCOJNULdDNEjB5/lFunJ5ClUqKdQG/xNlLT9i85xaRMQnUqFqcSSNb/hap\n8Y1tLZk6xg7b2jUoW94SqVTK27dvcTt2nIaNG6OiImDagmPExqUyZXTOVuKSWZ3YvPsaIwY58Try\nHQULFEBLS5VrtwOpXqWEXDF0JZ/HZfxe3mpLEJcsAIAQ4K2QLi6bCX20/Jfpib2LTaZ1j9WkltSB\nwgY5ZX6SMhkwdhdlS5lQvep/N37xv4rSwVKS70il0lxF+lRUVBRq5H0J0yIGBAcFUbKU/K/KrMwM\nNq3oiWVZM3R1NL+rELNYLOH0BW9OnvdFIICO9tZ0sLf5YlzE2KF2DO7biBdBMRgX0vsuOYgvUbF8\n0c+KDBY1M+Taycm8j08lNS0DC3Oj7xI+tCxjitfdN4g/2cXUyoAKpYvkPugbue0VhDBDwKKlH15G\n1apXZ+mqtaxaMzfPDtbAcXuI1sxGXDInVT4NSIsR0nngZgI9F3/zC3D15its2efFzLkLqFixElev\nXKap43LOuY6i9i/SCPuYYQOaEhEZR3CUDoOHDqN+gwYy3bBuPXpyz8uLZesvMrhvIwwNdBEIBAx3\nbobnw1AKmRRl8tRZFDI25vDBfdRvsxSvS9MwLaL/lbP+eVy79Zw5K915ERRN8WKFeOYXgbjBJ8+A\niTZxr+N59jySqlbf58TnF/uP3EVspAEmH+l16WuQYarF6m1XOLBp0C+Zl5IfR+lgKcl3HNvYsHXT\nBuYtXCxre+7vj7e3D812dv9mO0P6NWD2jClUr1EDY2NjpFIpR1xdCQ0JxqHlgO/OwhGLJfQYtIOw\nqHQGDh4OwJINmzjq7o3rdpcvOjA6Opq/RFuqkJGeXD3Db2XMIDsOn7iH0EAdDDRBKoXYDNTiRTj3\napSnOQW+fEPtOnUVHJ3adeoy7tWbPNlOTc3g+u0XiOt94hmaahP9KJ6A4JhvUr5OShaycPU57j32\npUSJnF/+1tWqYWJiwozFG7h6fGye5plfmBYxICpBhUZNmsi1x8REU7hwYYwL6eP54CVtW+ZsAT70\nCcXr0Wt8/ALQ0srZMq1RsyZSqZQ1W6+ybE6Xf/sSfmtOnH1E39E7SS+uAxV0iU9IBrHkQ3buPwgE\nCDOziYiKV3CwJBIJPk9fI5ZIsKlaQk7wNz8JCn1LuobijweJrhrBYW9/yjmV/FzyJchdIBC0FggE\nAQKBIEggEEzJ5Xg/gUDwTiAQeP/9zzk/zqvk92ThdEeOue6jX+/uuLkeZvGCedjbNWXtou4U+ERN\n+UsM6NmAVo1LUsWyLB3atqRqxXLMnjaeMwdH/lCK85lLPoREpHHt9j0GDHShbr36TJo2i2cBsZy7\n7Pvd9n5nrCzNcds2lEJhIgo8SUTPOxHzeLjkNj5PqxxisYSwiDhuXL+pIA1w/54X5cua5WnemVnZ\nOeXhVBVfgKrqqqSliXIbpsC9R6+wtq4qc67+oXvPXty47YdY/O0rqT+T7h1qc+7sGXw+SqRISkpi\nxdKlOPXrR1xcnJwCucfN53To1FXmXMns9HTC41bgvzbv/wekUiljZh4mvVwBMNMBbTUoqgN66vA2\nXb5zUiZkibnhKZ94cMszEHPrCTTtvgK73mswtRrLuctPfsp8a1crha5QMf5YPTmbOtV+/Yqrku8n\nzytYAoFABdgINAeigYcCgcBdKpV+miLjJpVKR+f1fEp+f4oVNeLxtVnsOXyHCye2YGpSgMvHxnz3\n0rtAIGDJrE6UKGbIzCXumJmZIZGI6T10F/s2flmtPC1NhNup+zx9HkVJCyP6dKvP6QtPGeAylLS0\nNDo7tsff3x9ra2vevn3PpHknqWFdgrd/C6kaFyqQx7vw62nXqhoxfmvw9YtATU0lz/XqpFIp3Qdt\n4ZLXcxCrMmnCeOYtWIiuri4+3t5MmziO9YvztoJiZKhH6dImBLzLyKkl9w/JmaiIwbryt/0NFdDT\nIi4uTqFg7/v379HR0UTl0xWMX0QRE312ruuHXdPGNGrcBFOzopw/e4auPXqgoaFBcuJ76tcuK+tf\nsIAWAa8VVwnjYmMVSsH86bx5m0R8YhpYfrKlb2kAj+MgLQuMtHJqEYangrkutx4Ey7pFxSTQtvda\n0sroQqG/Y6ISRfQYspWHV2bnew3Bnp3qMnv5aTLCUhEX+zu5JSYdzTgR44bkXgdTye9Nfqxg1QaC\npVJpuFQqzQLcAMdc+v0e32hK/hUM9HUYN6wlR3YOYt3iHj8c13D/8SsWrLrIuUsePPL1xy8whKmz\nl9Ku1wYSk3KXG3gd+R7rxvM4fTWGYuVb4ROkgpXtbN7HpyCRSBk2eBBlypUj8FUIp86eIzAkFBVV\nXSrUnYnzuKOUrzODQeP2k5HxfQWYf0fU1FSpUa0k1pWL5zl49869YC7f9kdY2QBhZV32nDpIMbMi\nWJiZ4tDGjjmT23wxgeFb2bqsLzqhaaiEp0KiCEFEGjovktm0pPc3yxHUrVkGUXoKJ44fk7VJpVIW\nzJlJ7y71f3ogs1Qq5ebdAGYvOcXKjRcVZBc+pqO9Dc895/My8AmXLpzFvl1bQl8GMn7UUI7sHCwX\nH9i1fS3OnzvHs6dPZW1CoZAVSxfQp2vN3Mz/sejpaiIRS0H8yapQAXUQSEEkhtBkSM0G60IINFUp\n8VGyyc6DN8k21gRjrRznCsBAE5GpFut3euT7fHV1Nbl3cSYtShZH7e471G6/pZauITdPT/2uOFMl\nvw95lmkQCASdgVZSqXTw3/93Amp/vFolEAj6AYuBWCAIGC+VSiM/Y08p06BERt/hu7Gu04GRY+Rj\nZvr07EqTWroMd26mMKZjvy1Y12rDtJmzZW2nT51k8rgRaOsUIDYugZdh4ejo5NSImzppEs/9/di9\n/wDGxsYkJiYy1GUARQzS2LrSKdd5paZmIJFK/6hVgynzj7Li3B0o/VGdtkwxxGZQTVMfb4+5+XYu\n/4Aolqw/j/ez15QrbcKUEW2oX7vcd9nw9g2jXa8N2NSohWWlynhcuYiuloQLbqM+Wx8wP8jKyqab\ny3YCXsbTsXN3Yt+94dTJE6xf0oNenet+dpxUKuXa7Rc89AmlqKkBndvVzFXS5Kj7Q4ZOOEAbe3uM\nChXmzKkTNG9Unp1r+35XIsSfQId+G7j4/CVZpfVkTpJqeCrSsBQk1kY58YkAwmx0/JK44jZe9nfW\na+g23PwCoNgntQzfpdPEsAjXTkz+afPO+B97Zx0W1dbF4XeooUMBxcQOUFERULG7u7v12t2Bce3O\na3d3N3YiYivSIS0xMAOT3x/jBUdQMbh6vzvv89znyp5z9t7nMJyz9tpr/VaqDIVC+UVJGy3Z498u\n05DVUvBTC+kksFelUskEAsEgYAfqLcUsmTVrVvq/a9euTe1PAkC1/H+jVCq5dO0Fpy8+565XID0G\nZvaKlHdyJjj0VqZ2sTiNi1efsnmPZhmZVq3bMH3yeGxz6yOVWaQbV2KxmB3btvLo6TOsrdWrREtL\nS9Zt3IJDiaLMn9ZGo1jsW/9IRk49xI07LwFwqVyC5XPaU+GT8jRfQ6VScfD4A9Zsv0qFL88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0Nbvv4SwREqXvkHpV/Xwf37adNzLL4P5uZYHb6/6dLKhYX7rpJqaZBhaMmU6MWk/XAR8exS2cke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\nOV/pujv+Iv/ZbQ/fnakMKm35fs1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dfca160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(ann)\n",
    "plt.title(\"Our initial model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Exercise**: \n",
    "\n",
    "Create Neural networks with 10 hidden nodes on the above code. \n",
    "\n",
    "What's the impact on accuracy?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Put your code here \n",
    "#(or load the solution if you wanna cheat :-)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# %load ../solutions/sol_111.py\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "Train the neural networks by increasing the epochs. \n",
    "\n",
    "What's the impact on accuracy?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Put your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# %load ../solutions/sol_112.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Addendum\n",
    "\n",
    "There is an additional notebook in the repo, i.e. [MLP and MNIST](1.1.2 MLP and MNIST.ipynb) for a more complete (but still *naive* implementation) of **SGD** and **MLP** applied on **MNIST** dataset.\n",
    "\n",
    "Another terrific reference to start is the online book http://neuralnetworksanddeeplearning.com/. Highly recommended!  "
   ]
  }
 ],
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   "pygments_lexer": "ipython3",
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