attempt3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lambda_initaliser(n): #bigger Lambda means smaller effective updates, more regularisation, more precise prior\n",
    "#     return (np.ones((n,n))+np.eye(n))*1e1\n",
    "#     return (-1*np.ones((n,n))+np.eye(n)*(n))*1\n",
    "#     return (np.ones((n,n))+np.diag(np.random.rand(n))+np.random.rand(n,n)*0.1)*1\n",
    "    return (np.eye(n))/n**1#/np.sqrt(n)\n",
    "#     return np.ones((n,n))*1e-4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialiser_function(rows,columns):\n",
    "#     return np.random.randn(rows,columns)\n",
    "#     inits= np.zeros((rows,columns))+np.random.randn(rows,columns)*0.1\n",
    "#     inits=(np.random.randn(rows,columns))*np.sqrt(6)/np.sqrt(rows+columns)\n",
    "    inits=(np.random.randn(rows,columns))*np.sqrt(6)/np.sqrt(rows+columns)\n",
    "#     inits=(np.random.randn(rows,columns))*np.sqrt(2)/np.sqrt(rows)\n",
    "\n",
    "#     inits=(np.random.randn(rows,columns))*np.sqrt(6)/np.sqrt(rows+columns)\n",
    "\n",
    "#     inits=(np.random.randn(rows,columns)*2)*np.sqrt(6)/np.sqrt(rows+columns)*0\n",
    "#     inits=(np.random.randn(rows,columns))*0.1#*np.sqrt(6)/np.sqrt(rows+columns)\n",
    "#     inits=(np.random.rand(rows,columns)*2-1)*np.sqrt(6)/np.sqrt(rows+columns)\n",
    "\n",
    "#     inits[0,:]=np.ones((1,columns))\n",
    "    return inits\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bias_initialiser(n):\n",
    "\n",
    "    return (np.random.randn(1,n)*2-1)*np.sqrt(6)/np.sqrt(n+1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NNet:\n",
    "    # this is a standard normal network using ReLU activation functions\n",
    "    # and an identity as the output activation function\n",
    "    # it is trained using standard gradient descent with gradients calculated using backprop\n",
    "    \n",
    "    def __init__(self,hidden_layer_sizes, input_dim, output_dim,initialiser_function=initialiser_function):\n",
    "        \n",
    "        self.layer_sizes=hidden_layer_sizes\n",
    "        self.input_dim=input_dim\n",
    "        self.output_dim=output_dim\n",
    "        \n",
    "        self.weights=[] #initialise weights, the bias vectors are treated as an extra row of the weights\n",
    "        self.weights.append(initialiser_function(input_dim+1,hidden_layer_sizes[0]))\n",
    "        for ii in range(len(hidden_layer_sizes)-1):\n",
    "            self.weights.append(initialiser_function(hidden_layer_sizes[ii]+1,hidden_layer_sizes[ii+1]))\n",
    "        self.weights.append(initialiser_function(hidden_layer_sizes[-1]+1,output_dim))\n",
    "        \n",
    "        #initialise variables calculated when using the network\n",
    "        self.activated=[] \n",
    "        self.z=[]\n",
    "        self.a=[]\n",
    "        self.Delta=[]\n",
    "        \n",
    "    def forward(self,x):\n",
    "        # Does a forward pass of the network, and saves relevent variables\n",
    "        \n",
    "        #clear saved variables for forward pass\n",
    "        self.z=[]\n",
    "        self.a=[]\n",
    "        self.activated=[]\n",
    "        \n",
    "        x=np.hstack([np.ones((x.shape[0],1)),x]) #append 1 to the x to account for the bias\n",
    "\n",
    "        for ii in range(len(self.weights)):\n",
    "\n",
    "            x=x @ self.weights[ii]\n",
    "\n",
    "            if not ii== len(self.weights)-1: #if not the end\n",
    "                x=np.hstack([np.ones((x.shape[0],1)),x]) # append 1 to account for the bias\n",
    "                self.activated.append(x>0) #save whether the neurons were activated\n",
    "                self.z.append(x) \n",
    "                x=x*self.activated[ii] #apply ReLU activation function\n",
    "                self.a.append(x) #save activations\n",
    "\n",
    "                if np.sum(np.sum(self.activated[ii]))==0: #if there are no activations in a layer\n",
    "                    print('No activations')\n",
    "            \n",
    "        self.z.append(x) #append the final things\n",
    "        \n",
    "        # for this case we are using the identity as the final activation function because we want to \n",
    "        # approximate functions with negative values\n",
    "        # this means that the neurons in the final layer are always activated\n",
    "        self.a.append(x)\n",
    "        self.activated.append(np.ones(x.shape,dtype=bool))\n",
    "        return x\n",
    "    \n",
    "    def gradients(self,x,Y):\n",
    "        # calculates the gradients of neural network using standard back propagation\n",
    "        \n",
    "        Delta=[] #Delta=d(loss)/dz=d(loss)/da*gradsigma(z)\n",
    "        \n",
    "        #the self.activated variable is the effectively the grad of the activation function\n",
    "\n",
    "        Delta.append((Y-self.a[-1])*self.activated[-1]) #calculate Delta for the final layer\n",
    "        for ll in range(len(self.weights)-2,-1,-1):\n",
    "            #calculate Delta for the other layers\n",
    "            Delta.insert(0,((Delta[0] @ self.weights[ll+1].T)*self.activated[ll])[:,1:])\n",
    "        \n",
    "        grad_w=[] #calculating the gradient of the error with respect to the weights\n",
    "        grad_w.append(Delta[0].T @ np.hstack([np.ones((x.shape[0],1)),x]))\n",
    "        for ll in range(len(Delta)-1):\n",
    "            grad_w.append(Delta[ll+1].T @ self.a[ll])\n",
    "            \n",
    "        return grad_w\n",
    "    \n",
    "    def train(self,xx,YY,alpha=0.01, print_error=False):\n",
    "        #trains the neural network for one epoch, one sample at a time using standard SGD\n",
    "        \n",
    "        #shuffle the data\n",
    "        inds=np.arange(xx.shape[0])\n",
    "        np.random.shuffle(inds)\n",
    "        xx=xx[inds,:]\n",
    "        YY=YY[inds,:]\n",
    "        \n",
    "        for ii in range(len(YY)):\n",
    "            # for each piece of data\n",
    "            x=xx[ii,:]\n",
    "            x=x.reshape((1,x.shape[0]))\n",
    "            Y=YY[ii,:]\n",
    "            \n",
    "            Y_out=self.forward(x) #do a forward pass\n",
    "            error_out=np.sum((Y-Y_out)**2) #calcuate the loss, in this case the square error\n",
    "            grad_w=self.gradients(x,Y) #calculate the gradients\n",
    "#             print(self.biases)\n",
    "            for ll in range(len(self.weights)):\n",
    "                # update the weights using the gradients\n",
    "                self.weights[ll]=self.weights[ll]+alpha*grad_w[ll].T\n",
    "                \n",
    "            if print_error: #print the error if you want\n",
    "                print(str(ii)+': Error: '+ str(error_out))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BNet5:\n",
    "    \n",
    "    def __init__(self,hidden_layer_sizes, input_dim, output_dim,initialiser_function=initialiser_function,lambda_initaliser=lambda_initaliser):\n",
    "        self.layer_sizes=hidden_layer_sizes\n",
    "        self.input_dim=input_dim\n",
    "        self.output_dim=output_dim\n",
    "        self.weights=[]\n",
    "        self.biases=[]\n",
    "        self.Lambda=[]\n",
    "        self.weights.append(initialiser_function(input_dim+1,hidden_layer_sizes[0]))\n",
    "        self.Lambda.append(lambda_initaliser(input_dim+1))\n",
    "        for ii in range(len(hidden_layer_sizes)-1):\n",
    "            self.weights.append(initialiser_function(hidden_layer_sizes[ii]+1,hidden_layer_sizes[ii+1]))\n",
    "            self.Lambda.append(lambda_initaliser(hidden_layer_sizes[ii]+1))\n",
    "        self.weights.append(initialiser_function(hidden_layer_sizes[-1]+1,output_dim))\n",
    "        self.Lambda.append(lambda_initaliser(hidden_layer_sizes[-1]+1))\n",
    "        self.activated=[]\n",
    "        self.z=[]\n",
    "        self.a=[]\n",
    "        self.Delta=[]\n",
    "    def forward(self,x):\n",
    "#         print(x.shape)\n",
    "        self.z=[]\n",
    "        self.a=[]\n",
    "        self.activated=[]\n",
    "#         assert len(x)==self.input_dim\n",
    "        x=np.hstack([np.ones((x.shape[0],1)),x])\n",
    "        all_activated=1\n",
    "        for ii in range(len(self.weights)):\n",
    "\n",
    "            x=x @ self.weights[ii]\n",
    "\n",
    "#             self.activated.append(x>0)\n",
    "#             self.z.append(x)\n",
    "            if not ii== len(self.weights)-1:\n",
    "                x=np.hstack([np.ones((x.shape[0],1)),x])\n",
    "                self.activated.append(x>0)\n",
    "                self.z.append(x)\n",
    "                x=x*self.activated[ii]\n",
    "                self.a.append(x)\n",
    "#             print(x)\n",
    "                if np.sum(np.sum(self.activated[ii][:,1:]))==0:\n",
    "#                     print('No activations')\n",
    "                    all_activated=0\n",
    "        self.z.append(x)\n",
    "        self.a.append(x)\n",
    "        self.activated.append(np.ones(x.shape,dtype=bool))\n",
    "#             print(self.activated)\n",
    "        return x, all_activated\n",
    "    \n",
    "    \n",
    "    \n",
    "    def train(self,xx,YY,passes=1, print_error=False,attempt_numbers=5,total_info=1):\n",
    "        c=total_info/passes\n",
    "        sample_used=np.zeros(len(xx))\n",
    "        for attempt in range(attempt_numbers):\n",
    "            \n",
    "            for ii in range(len(YY)):\n",
    "                \n",
    "                if not sample_used[ii]:\n",
    "                    x=xx[ii]\n",
    "                    x=x.reshape((1,x.shape[0]))\n",
    "                    Y=YY[ii]\n",
    "                    Y=Y.reshape((1,Y.shape[0]))\n",
    "        #             print(Y.shape)\n",
    "                    Y_out,all_activated=self.forward(x)\n",
    "#                     all_activated=1\n",
    "                    error_out1=np.sum((Y-Y_out)**2)\n",
    "                    if all_activated:\n",
    "                        sample_used[ii]=1\n",
    "\n",
    "                        for ps in range(passes):\n",
    "                            Y_out,all_activated=self.forward(x)\n",
    "\n",
    "                            for ll in range(len(self.weights)-1,-1,-1):\n",
    "            #                     print(ll)\n",
    "                                xl=self.a[ll-1]\n",
    "                                if ll==len(self.weights)-1:\n",
    "                                    Yl=Y\n",
    "                                    w=self.weights[ll]\n",
    "                                else:\n",
    "            #                         print(Yl.shape)\n",
    "            # #                         print(np.linalg.pinv(self.weights[ll+1]).shape)\n",
    "            #                         print(np.linalg.pinv(w).shape)\n",
    "\n",
    "                                    Yl=Yl @ np.linalg.pinv(w)\n",
    "                                    Yl=Yl[:,1:]/Yl[0,0] #the bias isn't an outout of the previous transformation, but we can use it to normalise\n",
    "            #                         print(Yl.shape)\n",
    "                                    Yl=Yl[:,self.activated[ll][0,1:]]\n",
    "                                    w=self.weights[ll][:,self.activated[ll][0,1:]] #this constructs a new weight mat using only the columns that lead to activations\n",
    "                                L=self.Lambda[ll]\n",
    "\n",
    "            #                     print(xl.T.shape)\n",
    "            #                     print(Yl.shape)\n",
    "            #                     print(L.shape)\n",
    "            #                     print(w.shape)\n",
    "            #                     print(w)\n",
    "            #                     print(w.shape)\n",
    "            #                     print(Yl.shape)\n",
    "\n",
    "            #                     print(xl.shape)\n",
    "            #                     print(w.shape)\n",
    "            #                     print(np.linalg.inv(c*(xl.T @ xl) + L).shape)\n",
    "            #                     print(((xl.T @ Yl)).shape)\n",
    "            #                     print(L.shape)\n",
    "                                w=np.linalg.pinv(c*(xl.T @ xl) + L) @ (c*(xl.T @ Yl) + L @ w)\n",
    "            #                     print(w)\n",
    "                                w=np.nan_to_num(w)\n",
    "                                L=c*xl.T @ xl + L\n",
    "                                if ll==len(self.weights)-1:\n",
    "                                    self.weights[ll]=w\n",
    "                                else:\n",
    "                                    self.weights[ll][:,self.activated[ll][0,1:]]=w\n",
    "                                self.Lambda[ll]=L\n",
    "            #                     print(self.weights[ll])\n",
    "                    else:\n",
    "                        print('not today')\n",
    "                    Y_out,all_activated=self.forward(x)\n",
    "\n",
    "                    error_out2=np.sum((Y-Y_out)**2)\n",
    "\n",
    "                    if print_error:\n",
    "                        print(str(ii)+': Error1: '+ str(error_out1)+', Error2: '+ str(error_out2))\n",
    "            print(sum(sample_used))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_Y(Y,w1,X0): \n",
    "    # This function finds the new Y value for the layer, by going 'backwards' through the matrix\n",
    "    # initally we had X0 @ w0 = Y, and then updated w0 -> w1, so now we solve X1 @ w1 = Y for X1\n",
    "    # this is often under determined, and so we find the X1 which is closest (L2 norm) to X0\n",
    "\n",
    "    delta=w1 @ np.linalg.inv(w1.T @ w1) @ (Y - X0 @ w1).T\n",
    "    X1 = X0+delta.T\n",
    "\n",
    "    # remember to kill bias if needed\n",
    "    return X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BNet6:\n",
    "    # Neural net that can be trained using approximate (dodgy) Bayesian updating\n",
    "    # The forward pass works just like a standard neural net\n",
    "    # For training this one does all the passes for one sample in a row, rather than running through all the data\n",
    "    \n",
    "    def __init__(self,hidden_layer_sizes, input_dim, output_dim,initialiser_function=initialiser_function,lambda_initaliser=lambda_initaliser):\n",
    "        self.layer_sizes=hidden_layer_sizes\n",
    "        self.input_dim=input_dim\n",
    "        self.output_dim=output_dim\n",
    "        \n",
    "        # initalise the weights and Lambda matrices\n",
    "        # the weights are the standard weights of the network\n",
    "        # the Lambda matrices are the (inverse of) the covariance matrices of the rows of the weight matrices\n",
    "        # the Lambdas are a measure of the 'precision' of each weight matrix\n",
    "        # we do not consider any effect of covariance between different layers (that's a task for another day...)\n",
    "        self.weights=[]\n",
    "        self.Lambda=[]\n",
    "        self.weights.append(initialiser_function(input_dim+1,hidden_layer_sizes[0]))\n",
    "        self.Lambda.append(lambda_initaliser(input_dim+1))\n",
    "        for ii in range(len(hidden_layer_sizes)-1):\n",
    "            self.weights.append(initialiser_function(hidden_layer_sizes[ii]+1,hidden_layer_sizes[ii+1]))\n",
    "            self.Lambda.append(lambda_initaliser(hidden_layer_sizes[ii]+1))\n",
    "        self.weights.append(initialiser_function(hidden_layer_sizes[-1]+1,output_dim))\n",
    "        self.Lambda.append(lambda_initaliser(hidden_layer_sizes[-1]+1))\n",
    "        \n",
    "        #initialise variables calculated when using the network\n",
    "        self.activated=[]\n",
    "        self.z=[]\n",
    "        self.a=[]\n",
    "        self.Delta=[]\n",
    "    def forward(self,x):\n",
    "        #performs a standard forward pass of the network, saving relevant variables\n",
    "        \n",
    "        self.z=[]\n",
    "        self.a=[]\n",
    "        self.activated=[]\n",
    "        x=np.hstack([np.ones((x.shape[0],1)),x]) #append 1 to account for bias\n",
    "        all_activated=1 #keep to make sure there is at least one activation in each layer\n",
    "        # if there are no activations in a layer then you can't do the back step\n",
    "        # Not sure if the bias should count towards the activation (I don't think so)\n",
    "        \n",
    "        for ii in range(len(self.weights)):\n",
    "\n",
    "            x=x @ self.weights[ii] #apply weight matrix\n",
    "\n",
    "            if not ii== len(self.weights)-1: # if it's not the final layer\n",
    "                \n",
    "                x=np.hstack([np.ones((x.shape[0],1)),x]) #append the 1 for the bias\n",
    "                self.activated.append(x>0) #which neurons are activated\n",
    "                self.z.append(x)\n",
    "                x=x*self.activated[ii] #apply the ReLU\n",
    "                self.a.append(x) #save activations\n",
    "#             print(x)\n",
    "                if np.sum(np.sum(self.activated[ii][:,1:]))==0: #test if there was at least on activation in each layer\n",
    "#                     print('No activations')\n",
    "                    all_activated=0\n",
    "        #saving variables for the last layer\n",
    "        #for this output layer we are using just the identity as the activation function (not a non linear function)\n",
    "        # this can be thought of as a ReLU that is always activated\n",
    "        self.z.append(x)\n",
    "        self.a.append(x)\n",
    "        self.activated.append(np.ones(x.shape,dtype=bool))\n",
    "        return x, all_activated\n",
    "    \n",
    "    \n",
    "    \n",
    "    def train(self,xx,YY,passes=1, print_error=False,failed_attempts=5,total_info=1):\n",
    "        # trains the network using 'Bayes'\n",
    "        # xx is the input data, YY measured variable corresponding to xx\n",
    "        # passes is the number of times you go over the data\n",
    "        # total_info is the total amount of 'information' transfered over all the passes\n",
    "        # print_error if you want to print the error each time\n",
    "        # failed_attempts is how many times to try use the same piece of data if each time it\n",
    "        # means that there are no neurons in a layer and thus can't be used to train\n",
    "        \n",
    "        #shuffle the data\n",
    "        inds=np.arange(xx.shape[0])\n",
    "        np.random.shuffle(inds)\n",
    "        xx=xx[inds,:]\n",
    "        YY=YY[inds,:]\n",
    "        \n",
    "        c=total_info/passes #this is analogous to alpha in SGD\n",
    "        sample_used=np.zeros(len(xx)) # number of times the sample has been used, corresponds to 'passes'\n",
    "        sample_attempted= np.zeros(len(xx)) # number of times the sample has been attempted and failed, corresponds to 'failed_attempts'\n",
    "        \n",
    "        \n",
    "        still_going= True # keeps going if we haven't had the right number of passes or failed attempts\n",
    "        \n",
    "        while still_going:\n",
    "            \n",
    "            for ii in range(len(YY)): #for each sample\n",
    "\n",
    "                if sample_used[ii]<passes: #if this data hasn't already been used too many times\n",
    "                    x=xx[ii,:]\n",
    "                    x=x.reshape((1,x.shape[0]))                    \n",
    "                    Y=YY[ii,:]\n",
    "                    Y=Y.reshape((1,Y.shape[0]))\n",
    "\n",
    "                    Y_out,all_activated=self.forward(x) #do a forward pass\n",
    "                    # Y_out is just used for printing the error\n",
    "                    # all_activated = 1 if all layers have at least one activated neuron\n",
    "    #                     all_activated=1\n",
    "                    \n",
    "                    error_out1=np.sum((Y-Y_out)**2) #calculate MSE\n",
    "                    if all_activated:\n",
    "                        sample_used[ii]+=1 #count the times this sample has been used\n",
    "\n",
    "                        for ps in range(passes):\n",
    "                            Y_out,all_activated=self.forward(x) #do a forward pass each time to calculate new values of self.a etc\n",
    "\n",
    "                            for ll in range(len(self.weights)-1,-1,-1):\n",
    "                                #updating the weight matrices, starting from the final one\n",
    "\n",
    "                                xl=self.a[ll-1] #set xl to the 'input' of the weight matrix\n",
    "                                \n",
    "                                if ll==len(self.weights)-1: \n",
    "                                    # if we are updating the final weight matrix (which we do first)\n",
    "                                    # then set Yl to the actual value of Y, and w to the weight matrix\n",
    "                                    Yl=Y\n",
    "                                    w=self.weights[ll] #this is a tempory variable for the weights which get updated\n",
    "                                    # because we are using the identity as the final acivation function \n",
    "                                    # this is just the weight matrix, we don't need to modify it\n",
    "                                else:\n",
    "\n",
    "                                    # various options for doing the 'inversion' step\n",
    "                                    # can simply pseudoinvert the weights, and the normalise to the bias\n",
    "#                                     Yl=Yl @ np.linalg.pinv(w)\n",
    "#                                     Yl=Yl[:,1:]/Yl[0,0] #the bias isn't an outout of the previous transformation, but we can use it to normalise\n",
    "                                    \n",
    "                                    # can use our fancy invert method to find the closest Yl to a_l\n",
    "                                    # here we just entirely leave out the bias\n",
    "#                                     Yl=find_Y(Yl,w[1:,:],self.a[ll][:,1:])\n",
    "                                    \n",
    "                                    # or can use our fancy method, but include the bias and normalise to it\n",
    "                                    Yl= find_Y(Yl, w, self.a[ll])\n",
    "                                    Yl=Yl[:,1:]/Yl[0,0]\n",
    "                                    \n",
    "                                    # I think there may be a better way to do this, which *forces* the first element of\n",
    "                                    # Yl to be one (which we would then remove)\n",
    "                                    # and makes the rest of Yl as close to a_l as possible\n",
    "                                \n",
    "    \n",
    "                                    Yl=Yl[:,self.activated[ll][0,1:]] # select only the elements of Yl which correspond to activations\n",
    "                \n",
    "                                    w=self.weights[ll][:,self.activated[ll][0,1:]] #this constructs a new weight mat using only the columns that lead to activations\n",
    "                \n",
    "                \n",
    "                                L=self.Lambda[ll] # make a tempory variable for Lambda \n",
    "                                \n",
    "                                # now we have the actual updating!\n",
    "                                # update the weight matrix\n",
    "                                w=np.linalg.inv(c*(xl.T @ xl) + L) @ (c*(xl.T @ Yl) + L @ w)\n",
    "                                \n",
    "#                                 w=np.nan_to_num(w)\n",
    "                                # update the Lambda\n",
    "                                L=c*xl.T @ xl + L\n",
    "#                                 L=L*np.eye(L.shape[0])\n",
    "\n",
    "                                if ll==len(self.weights)-1: #if updating the final layer, just set it equal to w\n",
    "                                    self.weights[ll]=w\n",
    "                                else: #otherwise just update the elements which were updated\n",
    "                                    self.weights[ll][:,self.activated[ll][0,1:]]=w\n",
    "                                self.Lambda[ll]=L # set Lambda to the new Lambda\n",
    "                            \n",
    "                    else: # if it failed because there were no activations\n",
    "#                         print('not today')\n",
    "                        sample_attempted[ii]+=1 # count fails for this sample\n",
    "                    Y_out,all_activated=self.forward(x) # do another forward pass to see how the error changes\n",
    "\n",
    "                    error_out2=np.sum((Y-Y_out)**2) # calculate the new error\n",
    "\n",
    "                    if print_error:\n",
    "                        print(str(ii)+': Error1: '+ str(error_out1)+', Error2: '+ str(error_out2)\n",
    "                              +' \\nTotal fails (no activations): ' +str(int(sum(sample_attempted))), end=\"\\n\\r\", flush=True)\n",
    "\n",
    "            if all([(sample_used[jj]>=passes or sample_attempted[jj]>= failed_attempts) for jj in range(len(YY))]):\n",
    "                # if for each sample, it has either been used the correct number of times\n",
    "                # or been attempted and failed the max number of times \n",
    "                # then end the training process\n",
    "                still_going=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BNet7:\n",
    "    # Neural net that can be trained using approximate (dodgy) Bayesian updating\n",
    "    # The forward pass works just like a standard neural net\n",
    "    \n",
    "    def __init__(self,hidden_layer_sizes, input_dim, output_dim,initialiser_function=initialiser_function,lambda_initaliser=lambda_initaliser):\n",
    "        self.layer_sizes=hidden_layer_sizes\n",
    "        self.input_dim=input_dim\n",
    "        self.output_dim=output_dim\n",
    "        \n",
    "        # initalise the weights and Lambda matrices\n",
    "        # the weights are the standard weights of the network\n",
    "        # the Lambda matrices are the (inverse of) the covariance matrices of the rows of the weight matrices\n",
    "        # the Lambdas are a measure of the 'precision' of each weight matrix\n",
    "        # we do not consider any effect of covariance between different layers (that's a task for another day...)\n",
    "        self.weights=[]\n",
    "        self.Lambda=[]\n",
    "        self.weights.append(initialiser_function(input_dim+1,hidden_layer_sizes[0]))\n",
    "        self.Lambda.append(lambda_initaliser(input_dim+1))\n",
    "        for ii in range(len(hidden_layer_sizes)-1):\n",
    "            self.weights.append(initialiser_function(hidden_layer_sizes[ii]+1,hidden_layer_sizes[ii+1]))\n",
    "            self.Lambda.append(lambda_initaliser(hidden_layer_sizes[ii]+1))\n",
    "        self.weights.append(initialiser_function(hidden_layer_sizes[-1]+1,output_dim))\n",
    "        self.Lambda.append(lambda_initaliser(hidden_layer_sizes[-1]+1))\n",
    "        \n",
    "        #initialise variables calculated when using the network\n",
    "        self.activated=[]\n",
    "        self.z=[]\n",
    "        self.a=[]\n",
    "        self.Delta=[]\n",
    "    def forward(self,x):\n",
    "        #performs a standard forward pass of the network, saving relevant variables\n",
    "        \n",
    "        self.z=[]\n",
    "        self.a=[]\n",
    "        self.activated=[]\n",
    "        x=np.hstack([np.ones((x.shape[0],1)),x]) #append 1 to account for bias\n",
    "        all_activated=1 #keep to make sure there is at least one activation in each layer\n",
    "        # if there are no activations in a layer then you can't do the back step\n",
    "        # Not sure if the bias should count towards the activation (I don't think so)\n",
    "        \n",
    "        for ii in range(len(self.weights)):\n",
    "\n",
    "            x=x @ self.weights[ii] #apply weight matrix\n",
    "\n",
    "            if not ii== len(self.weights)-1: # if it's not the final layer\n",
    "                \n",
    "                x=np.hstack([np.ones((x.shape[0],1)),x]) #append the 1 for the bias\n",
    "                self.activated.append(x>0) #which neurons are activated\n",
    "                self.z.append(x)\n",
    "                x=x*self.activated[ii] #apply the ReLU\n",
    "                self.a.append(x) #save activations\n",
    "#             print(x)\n",
    "                if np.sum(np.sum(self.activated[ii][:,1:]))==0: #test if there was at least on activation in each layer\n",
    "#                     print('No activations')\n",
    "                    all_activated=0\n",
    "        #saving variables for the last layer\n",
    "        #for this output layer we are using just the identity as the activation function (not a non linear function)\n",
    "        # this can be thought of as a ReLU that is always activated\n",
    "        self.z.append(x)\n",
    "        self.a.append(x)\n",
    "        self.activated.append(np.ones(x.shape,dtype=bool))\n",
    "        return x, all_activated\n",
    "    \n",
    "    \n",
    "    \n",
    "    def train(self,xx,YY,passes=1, print_error=False,return_error=False,failed_attempts=5,total_info=1):\n",
    "        # trains the network using 'Bayes'\n",
    "        # xx is the input data, YY measured variable corresponding to xx\n",
    "        # passes is the number of times you go over the data\n",
    "        # total_info is the total amount of 'information' transfered over all the passes\n",
    "        # print_error if you want to print the error each time\n",
    "        # failed_attempts is how many times to try use the same piece of data if each time it\n",
    "        # means that there are no neurons in a layer and thus can't be used to train\n",
    "        \n",
    "        if return_error:\n",
    "            error1=[] # error1 is the error on the sample, before updating\n",
    "            error2=[] # error2 is the error on the sample, after updating\n",
    "        \n",
    "        #shuffle the data\n",
    "        inds=np.arange(xx.shape[0])\n",
    "        np.random.shuffle(inds)\n",
    "        xx=xx[inds,:]\n",
    "        YY=YY[inds,:]\n",
    "        \n",
    "        c=total_info/passes #this is analogous to alpha in SGD\n",
    "        sample_used=np.zeros(len(xx)) # number of times the sample has been used, corresponds to 'passes'\n",
    "        sample_attempted= np.zeros(len(xx)) # number of times the sample has been attempted and failed, corresponds to 'failed_attempts'\n",
    "        \n",
    "        \n",
    "        still_going= True # keeps going if we haven't had the right number of passes or failed attempts\n",
    "        \n",
    "        while still_going:\n",
    "            \n",
    "            for ii in range(len(YY)): #for each sample\n",
    "                if sample_used[ii]<passes and sample_attempted[ii]< failed_attempts: #if this data hasn't already been used too many times\n",
    "                    x=xx[ii,:]\n",
    "                    x=x.reshape((1,x.shape[0]))                    \n",
    "                    Y=YY[ii,:]\n",
    "                    Y=Y.reshape((1,Y.shape[0]))\n",
    "\n",
    "                    Y_out,all_activated=self.forward(x) #do a forward pass\n",
    "                    # Y_out is just used for printing the error\n",
    "                    # all_activated = 1 if all layers have at least one activated neuron\n",
    "    #                     all_activated=1\n",
    "                    \n",
    "                    error_out1=np.sum((Y-Y_out)**2) #calculate MSE\n",
    "                    if all_activated:\n",
    "                        sample_used[ii]+=1 #count the times this sample has been used\n",
    "\n",
    "#                         Y_out,all_activated=self.forward(x) #do a forward pass each time to calculate new values of self.a etc\n",
    "\n",
    "                        for ll in range(len(self.weights)-1,-1,-1):\n",
    "                            #updating the weight matrices, starting from the final one\n",
    "                            \n",
    "                            if ll==0:\n",
    "                                xl=np.hstack([np.ones((x.shape[0],1)),x])\n",
    "                            else:\n",
    "                                xl=self.a[ll-1] #set xl to the 'input' of the weight matrix\n",
    "\n",
    "                            if ll==len(self.weights)-1: \n",
    "                                # if we are updating the final weight matrix (which we do first)\n",
    "                                # then set Yl to the actual value of Y, and w to the weight matrix\n",
    "                                Yl=Y\n",
    "                                w=self.weights[ll] #this is a tempory variable for the weights which get updated\n",
    "                                # because we are using the identity as the final acivation function \n",
    "                                # this is just the weight matrix, we don't need to modify it\n",
    "                            else:\n",
    "\n",
    "                                # various options for doing the 'inversion' step\n",
    "                                # can simply pseudoinvert the weights, and the normalise to the bias\n",
    "#                                     Yl=Yl @ np.linalg.pinv(w)\n",
    "#                                     Yl=Yl[:,1:]/Yl[0,0] #the bias isn't an outout of the previous transformation, but we can use it to normalise\n",
    "\n",
    "                                # can use our fancy invert method to find the closest Yl to a_l\n",
    "                                # here we just entirely leave out the bias\n",
    "#                                     Yl=find_Y(Yl,w[1:,:],self.a[ll][:,1:])\n",
    "\n",
    "                                # or can use our fancy method, but include the bias and normalise to it\n",
    "                                Yl= find_Y(Yl, w, self.a[ll])\n",
    "                                Yl=Yl[:,1:]/Yl[0,0]\n",
    "\n",
    "                                # I think there may be a better way to do this, which *forces* the first element of\n",
    "                                # Yl to be one (which we would then remove)\n",
    "                                # and makes the rest of Yl as close to a_l as possible\n",
    "\n",
    "\n",
    "                                Yl=Yl[:,self.activated[ll][0,1:]] # select only the elements of Yl which correspond to activations\n",
    "\n",
    "                                w=self.weights[ll][:,self.activated[ll][0,1:]] #this constructs a new weight mat using only the columns that lead to activations\n",
    "\n",
    "\n",
    "                            L=self.Lambda[ll] # make a tempory variable for Lambda \n",
    "\n",
    "                            # now we have the actual updating!\n",
    "                            # update the weight matrix\n",
    "\n",
    "                            w=np.linalg.inv(c*(xl.T @ xl) + L) @ (c*(xl.T @ Yl) + L @ w)\n",
    "\n",
    "#                                 w=np.nan_to_num(w)\n",
    "                            # update the Lambda\n",
    "                            L=c*xl.T @ xl + L\n",
    "#                                 L=L*np.eye(L.shape[0])\n",
    "\n",
    "                            if ll==len(self.weights)-1: #if updating the final layer, just set it equal to w\n",
    "                                self.weights[ll]=w\n",
    "                            else: #otherwise just update the elements which were updated\n",
    "                                self.weights[ll][:,self.activated[ll][0,1:]]=w\n",
    "                            self.Lambda[ll]=L # set Lambda to the new Lambda\n",
    "                            \n",
    "                        Y_out,all_activated=self.forward(x) # do another forward pass to see how the error changes\n",
    "                        error_out2=np.sum((Y-Y_out)**2) # calculate the new error\n",
    "                        if return_error:\n",
    "                            error1.append(error_out1)\n",
    "                            error2.append(error_out2)\n",
    "                    else: # if it failed because there were no activations\n",
    "#                         print('not today')\n",
    "                        sample_attempted[ii]+=1 # count fails for this sample\n",
    "\n",
    "\n",
    "                    if print_error:\n",
    "                        print(str(ii)+': Error1: '+ str(error_out1)+', Error2: '+ str(error_out2)\n",
    "                              +' \\nTotal fails (no activations): ' +str(int(sum(sample_attempted))), end=\"\\n\\r\", flush=True)\n",
    "\n",
    "            if all([(sample_used[jj]>=passes or sample_attempted[jj]>= failed_attempts) for jj in range(len(YY))]):\n",
    "                # if for each sample, it has either been used the correct number of times\n",
    "                # or been attempted and failed the max number of times \n",
    "                # then end the training process\n",
    "                still_going=False\n",
    "        if return_error:\n",
    "            return error1, error2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: Error1: 2.898126710136263, Error2: 2.1784634023220894e-06 \n",
      "Total fails (no activations): 0\n",
      "1: Error1: 0.5910171365321435, Error2: 0.00024891058290945223 \n",
      "Total fails (no activations): 0\n",
      "2: Error1: 0.04891435182300399, Error2: 0.004561612631070931 \n",
      "Total fails (no activations): 0\n",
      "3: Error1: 0.020090952608358227, Error2: 0.00040660382896136603 \n",
      "Total fails (no activations): 0\n",
      "4: Error1: 0.03858116864140366, Error2: 9.470089700932067e-05 \n",
      "Total fails (no activations): 0\n",
      "5: Error1: 1.6263678261707515, Error2: 0.011095116901283633 \n",
      "Total fails (no activations): 0\n",
      "6: Error1: 0.009163741198307436, Error2: 0.0012037194792417515 \n",
      "Total fails (no activations): 0\n",
      "7: Error1: 0.04900357302503612, Error2: 0.011008942682508942 \n",
      "Total fails (no activations): 0\n",
      "8: Error1: 0.0011467125544335243, Error2: 0.0001116721194333622 \n",
      "Total fails (no activations): 0\n",
      "9: Error1: 0.38969113488605917, Error2: 0.0015663065604769437 \n",
      "Total fails (no activations): 0\n",
      "10: Error1: 0.14700010208642628, Error2: 0.010961227967174103 \n",
      "Total fails (no activations): 0\n",
      "11: Error1: 0.0076341023672129235, Error2: 0.0022315615168332337 \n",
      "Total fails (no activations): 0\n",
      "12: Error1: 5.951822670976046e-05, Error2: 1.3680283048929053e-05 \n",
      "Total fails (no activations): 0\n",
      "13: Error1: 1.4146285527018319, Error2: 5.6825723049717815e-05 \n",
      "Total fails (no activations): 0\n",
      "14: Error1: 0.0995581634759507, Error2: 0.02505608187622545 \n",
      "Total fails (no activations): 0\n",
      "15: Error1: 0.043626822615662544, Error2: 0.006580186225173245 \n",
      "Total fails (no activations): 0\n",
      "16: Error1: 1.5810523639463985, Error2: 0.11007032791454567 \n",
      "Total fails (no activations): 0\n",
      "17: Error1: 0.0028231206665902103, Error2: 0.0001709321794120989 \n",
      "Total fails (no activations): 0\n",
      "18: Error1: 0.0005764556231383557, Error2: 0.00028018852625379847 \n",
      "Total fails (no activations): 0\n",
      "19: Error1: 0.20911760450521513, Error2: 0.11855401815526762 \n",
      "Total fails (no activations): 0\n",
      "20: Error1: 0.003995362681018279, Error2: 0.0010932423340179963 \n",
      "Total fails (no activations): 0\n",
      "21: Error1: 0.023718982248708692, Error2: 0.0024454815024229815 \n",
      "Total fails (no activations): 0\n",
      "22: Error1: 0.031131504129082936, Error2: 0.011641112260651417 \n",
      "Total fails (no activations): 0\n",
      "23: Error1: 0.7724432330325015, Error2: 0.005042828213891469 \n",
      "Total fails (no activations): 0\n",
      "24: Error1: 0.012633381030017151, Error2: 0.0008038472242941309 \n",
      "Total fails (no activations): 0\n",
      "25: Error1: 0.002216188399503864, Error2: 0.0003498340960572811 \n",
      "Total fails (no activations): 0\n",
      "26: Error1: 0.01267430431692753, Error2: 0.0012905574830603043 \n",
      "Total fails (no activations): 0\n",
      "27: Error1: 2.6438039829428604e-05, Error2: 1.1912835367641457e-05 \n",
      "Total fails (no activations): 0\n",
      "28: Error1: 0.19489544981601847, Error2: 0.07475555777391672 \n",
      "Total fails (no activations): 0\n",
      "29: Error1: 0.1841542523578459, Error2: 0.05248947760908403 \n",
      "Total fails (no activations): 0\n",
      "30: Error1: 0.18284799331824036, Error2: 0.06722413285419578 \n",
      "Total fails (no activations): 0\n",
      "31: Error1: 0.03356461579748502, Error2: 0.0063703788068602794 \n",
      "Total fails (no activations): 0\n",
      "32: Error1: 0.005929585004563296, Error2: 0.002057280602927446 \n",
      "Total fails (no activations): 0\n",
      "33: Error1: 0.004632807754596716, Error2: 0.0012465367755604344 \n",
      "Total fails (no activations): 0\n",
      "34: Error1: 0.023557379131901687, Error2: 0.010132398614991182 \n",
      "Total fails (no activations): 0\n",
      "35: Error1: 0.020400972720217212, Error2: 0.0076753794655674825 \n",
      "Total fails (no activations): 0\n",
      "36: Error1: 0.015589019395618048, Error2: 0.007012459848285665 \n",
      "Total fails (no activations): 0\n",
      "37: Error1: 0.22685825407320004, Error2: 0.09471752522018109 \n",
      "Total fails (no activations): 0\n",
      "38: Error1: 0.008604775169621824, Error2: 0.004376388903509353 \n",
      "Total fails (no activations): 0\n",
      "39: Error1: 0.00043590404537342776, Error2: 0.000199690222583837 \n",
      "Total fails (no activations): 0\n",
      "40: Error1: 0.000648019318286018, Error2: 0.00015320992325717825 \n",
      "Total fails (no activations): 0\n",
      "41: Error1: 0.14285820794244453, Error2: 0.08772273268539942 \n",
      "Total fails (no activations): 0\n",
      "42: Error1: 0.030558979468605335, Error2: 0.013660550599796168 \n",
      "Total fails (no activations): 0\n",
      "43: Error1: 0.08103644201946965, Error2: 0.020469393408557208 \n",
      "Total fails (no activations): 0\n",
      "44: Error1: 0.10143594701339465, Error2: 0.05305145014554018 \n",
      "Total fails (no activations): 0\n",
      "45: Error1: 0.04912752251269923, Error2: 0.029592362006795644 \n",
      "Total fails (no activations): 0\n",
      "46: Error1: 0.0004203618428623272, Error2: 0.00024928183845376433 \n",
      "Total fails (no activations): 0\n",
      "47: Error1: 0.06543041596910043, Error2: 0.043672260590716334 \n",
      "Total fails (no activations): 0\n",
      "48: Error1: 0.02482645782156556, Error2: 0.014440758279294967 \n",
      "Total fails (no activations): 0\n",
      "49: Error1: 0.13415406462886234, Error2: 0.09269652006022072 \n",
      "Total fails (no activations): 0\n",
      "50: Error1: 0.18694317183576617, Error2: 0.03930550750243686 \n",
      "Total fails (no activations): 0\n",
      "51: Error1: 0.016310459156535288, Error2: 0.009840031399609338 \n",
      "Total fails (no activations): 0\n",
      "52: Error1: 0.013325941576466256, Error2: 0.007804988493696955 \n",
      "Total fails (no activations): 0\n",
      "53: Error1: 0.0003137549324960071, Error2: 0.00019824984918429916 \n",
      "Total fails (no activations): 0\n",
      "54: Error1: 0.002151005585323647, Error2: 0.001397444303187186 \n",
      "Total fails (no activations): 0\n",
      "55: Error1: 0.0807005788662747, Error2: 0.057793722496578576 \n",
      "Total fails (no activations): 0\n",
      "56: Error1: 0.00828475269118206, Error2: 0.005848665457893271 \n",
      "Total fails (no activations): 0\n",
      "57: Error1: 0.04901565396022264, Error2: 0.026914636241141893 \n",
      "Total fails (no activations): 0\n",
      "58: Error1: 0.01447288890081365, Error2: 0.009757919741446519 \n",
      "Total fails (no activations): 0\n",
      "59: Error1: 0.039385033882028374, Error2: 0.021398687689332875 \n",
      "Total fails (no activations): 0\n",
      "60: Error1: 0.09252512937256427, Error2: 0.05556380551312884 \n",
      "Total fails (no activations): 0\n",
      "61: Error1: 0.05475305244002562, Error2: 0.02804894699012119 \n",
      "Total fails (no activations): 0\n",
      "62: Error1: 1.6618344418430586e-05, Error2: 1.1584076969549176e-05 \n",
      "Total fails (no activations): 0\n",
      "63: Error1: 0.07717274144725343, Error2: 0.053133860541387044 \n",
      "Total fails (no activations): 0\n",
      "64: Error1: 0.16604352223112773, Error2: 0.05349716351277134 \n",
      "Total fails (no activations): 0\n",
      "65: Error1: 0.01855070555004267, Error2: 0.010734034341327177 \n",
      "Total fails (no activations): 0\n",
      "66: Error1: 0.02532569288889887, Error2: 0.019310505907005983 \n",
      "Total fails (no activations): 0\n",
      "67: Error1: 0.07310110344942834, Error2: 0.04731206347350963 \n",
      "Total fails (no activations): 0\n",
      "68: Error1: 0.0654305357805854, Error2: 0.04294605086335635 \n",
      "Total fails (no activations): 0\n",
      "69: Error1: 0.03189024725626898, Error2: 0.024253415868883674 \n",
      "Total fails (no activations): 0\n",
      "70: Error1: 0.048544828614658427, Error2: 0.033891561573781134 \n",
      "Total fails (no activations): 0\n",
      "71: Error1: 0.014874197238936416, Error2: 0.00837214990862121 \n",
      "Total fails (no activations): 0\n",
      "72: Error1: 0.014141861407313324, Error2: 0.009201604301332711 \n",
      "Total fails (no activations): 0\n",
      "73: Error1: 0.01753445780414305, Error2: 0.012416445631053949 \n",
      "Total fails (no activations): 0\n",
      "74: Error1: 0.0007501477580178263, Error2: 0.00028705848472233153 \n",
      "Total fails (no activations): 0\n",
      "75: Error1: 0.052821580836191415, Error2: 0.041398995429273514 \n",
      "Total fails (no activations): 0\n",
      "76: Error1: 3.2212357019133e-07, Error2: 2.2463954631518194e-07 \n",
      "Total fails (no activations): 0\n",
      "77: Error1: 0.00022683764248657213, Error2: 0.00017318059532396058 \n",
      "Total fails (no activations): 0\n",
      "78: Error1: 0.01311196183086279, Error2: 0.00875330396047842 \n",
      "Total fails (no activations): 0\n",
      "79: Error1: 0.05371480408425765, Error2: 0.03757841352871725 \n",
      "Total fails (no activations): 0\n",
      "80: Error1: 0.004413687295248813, Error2: 0.0035407675044095274 \n",
      "Total fails (no activations): 0\n",
      "81: Error1: 0.07174065896377145, Error2: 0.051699776470483814 \n",
      "Total fails (no activations): 0\n",
      "82: Error1: 0.03561306766629266, Error2: 0.02847255613844307 \n",
      "Total fails (no activations): 0\n",
      "83: Error1: 0.00014256819847119725, Error2: 8.974521180337946e-05 \n",
      "Total fails (no activations): 0\n",
      "84: Error1: 0.03781532941834184, Error2: 0.029152729292652058 \n",
      "Total fails (no activations): 0\n",
      "85: Error1: 0.056944230566174266, Error2: 0.036295795615610166 \n",
      "Total fails (no activations): 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86: Error1: 0.06662327512115596, Error2: 0.027075731909018373 \n",
      "Total fails (no activations): 0\n",
      "87: Error1: 0.01079877282091641, Error2: 0.007118803536294816 \n",
      "Total fails (no activations): 0\n",
      "88: Error1: 0.1093691768586404, Error2: 0.08260165777393774 \n",
      "Total fails (no activations): 0\n",
      "89: Error1: 0.001158994467566464, Error2: 0.0008061659762450228 \n",
      "Total fails (no activations): 0\n",
      "90: Error1: 0.0022868670030285637, Error2: 0.001624270682166165 \n",
      "Total fails (no activations): 0\n",
      "91: Error1: 0.034196075711972494, Error2: 0.025569514303234123 \n",
      "Total fails (no activations): 0\n",
      "92: Error1: 0.02425504138963155, Error2: 0.018025646380443076 \n",
      "Total fails (no activations): 0\n",
      "93: Error1: 0.01389277953169497, Error2: 0.010141237477872423 \n",
      "Total fails (no activations): 0\n",
      "94: Error1: 0.009206385776192273, Error2: 0.007186211040604273 \n",
      "Total fails (no activations): 0\n",
      "95: Error1: 0.08660316673027808, Error2: 0.055194763531018015 \n",
      "Total fails (no activations): 0\n",
      "96: Error1: 0.013358894298410865, Error2: 0.009470434288755216 \n",
      "Total fails (no activations): 0\n",
      "97: Error1: 0.05812330325071221, Error2: 0.04327681535927185 \n",
      "Total fails (no activations): 0\n",
      "98: Error1: 0.1351688881010129, Error2: 0.08984064204178865 \n",
      "Total fails (no activations): 0\n",
      "99: Error1: 0.008828992267729669, Error2: 0.007252477910877085 \n",
      "Total fails (no activations): 0\n",
      "100: Error1: 0.003931774003983575, Error2: 0.0029694781660277894 \n",
      "Total fails (no activations): 0\n",
      "0: Error1: 0.5800095346832377, Error2: 6.199012387900068e-08 \n",
      "Total fails (no activations): 0\n",
      "1: Error1: 0.04773387782990589, Error2: 4.6622027964652845e-07 \n",
      "Total fails (no activations): 0\n",
      "2: Error1: 0.03293218449538829, Error2: 0.0007192930316937904 \n",
      "Total fails (no activations): 0\n",
      "3: Error1: 2.378863399381191, Error2: 1.2747351988179172e-05 \n",
      "Total fails (no activations): 0\n",
      "4: Error1: 1.1563850182405333, Error2: 0.00016842817561709176 \n",
      "Total fails (no activations): 0\n",
      "5: Error1: 0.001996703127381062, Error2: 0.00019636988862936937 \n",
      "Total fails (no activations): 0\n",
      "6: Error1: 0.020151853800166004, Error2: 0.004223384520718138 \n",
      "Total fails (no activations): 0\n",
      "7: Error1: 0.0022217924767808945, Error2: 0.00017438512832903565 \n",
      "Total fails (no activations): 0\n",
      "8: Error1: 0.18282097993789903, Error2: 7.151862180221874e-05 \n",
      "Total fails (no activations): 0\n",
      "9: Error1: 2.7273872912071664e-05, Error2: 1.1894442160183322e-06 \n",
      "Total fails (no activations): 0\n",
      "10: Error1: 3.5051354104208388, Error2: 7.868048121468907e-05 \n",
      "Total fails (no activations): 0\n",
      "11: Error1: 0.013615061114095177, Error2: 0.0011340384033226318 \n",
      "Total fails (no activations): 0\n",
      "12: Error1: 0.028721154111189917, Error2: 1.4537308986370658e-06 \n",
      "Total fails (no activations): 0\n",
      "13: Error1: 0.07168931865684233, Error2: 0.005360240632696958 \n",
      "Total fails (no activations): 0\n",
      "14: Error1: 0.16029441275870945, Error2: 1.3065109205043857e-07 \n",
      "Total fails (no activations): 0\n",
      "15: Error1: 0.07189970236770059, Error2: 0.009185100594315668 \n",
      "Total fails (no activations): 0\n",
      "16: Error1: 0.0023804917178205684, Error2: 0.00019870303492267263 \n",
      "Total fails (no activations): 0\n",
      "17: Error1: 0.9231057192766776, Error2: 0.0025590511093324915 \n",
      "Total fails (no activations): 0\n",
      "18: Error1: 0.0007006174117079059, Error2: 7.351047678769505e-05 \n",
      "Total fails (no activations): 0\n",
      "19: Error1: 0.01435913610744303, Error2: 0.0024971825029341146 \n",
      "Total fails (no activations): 0\n",
      "20: Error1: 0.00157900702703891, Error2: 8.486290573329636e-05 \n",
      "Total fails (no activations): 0\n",
      "21: Error1: 0.047180717972218636, Error2: 0.004750688542913692 \n",
      "Total fails (no activations): 0\n",
      "22: Error1: 0.012167773408322955, Error2: 0.005070707143174543 \n",
      "Total fails (no activations): 0\n",
      "23: Error1: 0.013107136701235356, Error2: 0.001911532248336262 \n",
      "Total fails (no activations): 0\n",
      "24: Error1: 0.022775951703463363, Error2: 0.0035178398504067186 \n",
      "Total fails (no activations): 0\n",
      "25: Error1: 0.00043896994208547364, Error2: 0.00013753330536280886 \n",
      "Total fails (no activations): 0\n",
      "26: Error1: 0.06318979371818946, Error2: 0.011737319110029834 \n",
      "Total fails (no activations): 0\n",
      "27: Error1: 0.018211794618956455, Error2: 0.0062671495457234175 \n",
      "Total fails (no activations): 0\n",
      "28: Error1: 0.03154877173296852, Error2: 0.009136900471203543 \n",
      "Total fails (no activations): 0\n",
      "29: Error1: 0.0023638158909331597, Error2: 0.0009658504432342518 \n",
      "Total fails (no activations): 0\n",
      "30: Error1: 0.022073618712771133, Error2: 0.006483034217441765 \n",
      "Total fails (no activations): 0\n",
      "31: Error1: 0.0006856249048337606, Error2: 0.00014139539028235336 \n",
      "Total fails (no activations): 0\n",
      "32: Error1: 0.0021593369839167276, Error2: 0.0009544803923990288 \n",
      "Total fails (no activations): 0\n",
      "33: Error1: 0.001451695197431402, Error2: 0.00031195484331784175 \n",
      "Total fails (no activations): 0\n",
      "34: Error1: 0.009649187131709728, Error2: 0.001010457689606226 \n",
      "Total fails (no activations): 0\n",
      "35: Error1: 0.036807897910382734, Error2: 0.01451293719597669 \n",
      "Total fails (no activations): 0\n",
      "36: Error1: 0.04527888702499554, Error2: 0.015396000665774418 \n",
      "Total fails (no activations): 0\n",
      "37: Error1: 0.00486552078917482, Error2: 0.0009512462230215683 \n",
      "Total fails (no activations): 0\n",
      "38: Error1: 0.020482930095774666, Error2: 0.0071520312263965245 \n",
      "Total fails (no activations): 0\n",
      "39: Error1: 0.005126916614900365, Error2: 0.0015078134001129061 \n",
      "Total fails (no activations): 0\n",
      "40: Error1: 0.024379781709847413, Error2: 0.004999312930066178 \n",
      "Total fails (no activations): 0\n",
      "41: Error1: 0.00424645970980959, Error2: 0.001596367541656185 \n",
      "Total fails (no activations): 0\n",
      "42: Error1: 0.0002639585860406882, Error2: 3.889032292923996e-05 \n",
      "Total fails (no activations): 0\n",
      "43: Error1: 0.031622436641534565, Error2: 0.01065294328036651 \n",
      "Total fails (no activations): 0\n",
      "44: Error1: 0.008706177476508994, Error2: 0.0037190659574877875 \n",
      "Total fails (no activations): 0\n",
      "45: Error1: 0.023347166743130147, Error2: 0.012408777327352353 \n",
      "Total fails (no activations): 0\n",
      "46: Error1: 0.0021659976427345962, Error2: 0.00042897248757527243 \n",
      "Total fails (no activations): 0\n",
      "47: Error1: 0.035582015028033175, Error2: 0.009374559057715346 \n",
      "Total fails (no activations): 0\n",
      "48: Error1: 0.009433906969774532, Error2: 0.002640354235954649 \n",
      "Total fails (no activations): 0\n",
      "49: Error1: 0.011353445252743791, Error2: 0.005905328971597043 \n",
      "Total fails (no activations): 0\n",
      "50: Error1: 0.003219283781389275, Error2: 0.0013994705411346819 \n",
      "Total fails (no activations): 0\n",
      "51: Error1: 0.004501262007508651, Error2: 0.0018664542155349773 \n",
      "Total fails (no activations): 0\n",
      "52: Error1: 8.897743356546842e-05, Error2: 4.444128997818343e-05 \n",
      "Total fails (no activations): 0\n",
      "53: Error1: 0.016531549803913127, Error2: 0.008963277154467795 \n",
      "Total fails (no activations): 0\n",
      "54: Error1: 0.050093358321077246, Error2: 0.022297729329681926 \n",
      "Total fails (no activations): 0\n",
      "55: Error1: 0.00013912717137275774, Error2: 4.866042539686673e-05 \n",
      "Total fails (no activations): 0\n",
      "56: Error1: 0.0038695859533352268, Error2: 0.001404962739032709 \n",
      "Total fails (no activations): 0\n",
      "57: Error1: 0.007328632232094854, Error2: 0.002168370389362976 \n",
      "Total fails (no activations): 0\n",
      "58: Error1: 0.035473491529999464, Error2: 0.01622990115464511 \n",
      "Total fails (no activations): 0\n",
      "59: Error1: 0.0035233947960134652, Error2: 0.002179291300505358 \n",
      "Total fails (no activations): 0\n",
      "60: Error1: 0.02107209498571916, Error2: 0.009378127907836193 \n",
      "Total fails (no activations): 0\n",
      "61: Error1: 0.007827890382286385, Error2: 0.004359800125895834 \n",
      "Total fails (no activations): 0\n",
      "62: Error1: 0.007029809855030686, Error2: 0.0029697506425989677 \n",
      "Total fails (no activations): 0\n",
      "63: Error1: 6.39199190740177e-06, Error2: 3.561269143987253e-06 \n",
      "Total fails (no activations): 0\n",
      "64: Error1: 0.002851544272863406, Error2: 0.0011596827432093968 \n",
      "Total fails (no activations): 0\n",
      "65: Error1: 0.0001487309122555678, Error2: 9.75418796654428e-05 \n",
      "Total fails (no activations): 0\n",
      "66: Error1: 0.00027326000711915736, Error2: 0.00010570213385067215 \n",
      "Total fails (no activations): 0\n",
      "67: Error1: 0.0008004569282283455, Error2: 0.00035942268944985206 \n",
      "Total fails (no activations): 0\n",
      "68: Error1: 0.014418063230253556, Error2: 0.00868662143671122 \n",
      "Total fails (no activations): 0\n",
      "69: Error1: 0.008220258827373641, Error2: 0.004075791600712517 \n",
      "Total fails (no activations): 0\n",
      "70: Error1: 0.003508203320651224, Error2: 0.0016234111893910362 \n",
      "Total fails (no activations): 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71: Error1: 6.101680683268724e-05, Error2: 3.793142077480511e-05 \n",
      "Total fails (no activations): 0\n",
      "72: Error1: 0.03058432140429534, Error2: 0.020230335287673335 \n",
      "Total fails (no activations): 0\n",
      "73: Error1: 0.006633137664559315, Error2: 0.003605023049333188 \n",
      "Total fails (no activations): 0\n",
      "74: Error1: 0.0006178730288026363, Error2: 0.00024848983363758936 \n",
      "Total fails (no activations): 0\n",
      "75: Error1: 0.024860192481300177, Error2: 0.013929054277910032 \n",
      "Total fails (no activations): 0\n",
      "76: Error1: 0.007672297355209604, Error2: 0.00346229150528348 \n",
      "Total fails (no activations): 0\n",
      "77: Error1: 1.1367493794453375e-05, Error2: 6.653830999607892e-06 \n",
      "Total fails (no activations): 0\n",
      "78: Error1: 0.0037987933564247412, Error2: 0.0016716139340795268 \n",
      "Total fails (no activations): 0\n",
      "79: Error1: 0.018555968118886476, Error2: 0.00820570877812052 \n",
      "Total fails (no activations): 0\n",
      "80: Error1: 1.592621687616608e-05, Error2: 9.588638174159148e-06 \n",
      "Total fails (no activations): 0\n",
      "81: Error1: 0.0013242071858977484, Error2: 0.0007032627959666877 \n",
      "Total fails (no activations): 0\n",
      "82: Error1: 0.0015924749059571355, Error2: 0.0008239362254186266 \n",
      "Total fails (no activations): 0\n",
      "83: Error1: 9.370651503557165e-05, Error2: 5.309684928823365e-05 \n",
      "Total fails (no activations): 0\n",
      "84: Error1: 0.05706080543728495, Error2: 0.03247197388993983 \n",
      "Total fails (no activations): 0\n",
      "85: Error1: 0.029039962471927704, Error2: 0.017794795434737453 \n",
      "Total fails (no activations): 0\n",
      "86: Error1: 0.012569937464838699, Error2: 0.007355117577697328 \n",
      "Total fails (no activations): 0\n",
      "87: Error1: 7.669903770289771e-08, Error2: 4.6710930874164946e-08 \n",
      "Total fails (no activations): 0\n",
      "88: Error1: 0.01845032251365826, Error2: 0.012080140219721185 \n",
      "Total fails (no activations): 0\n",
      "89: Error1: 0.0010221009516745802, Error2: 0.0006879169284076 \n",
      "Total fails (no activations): 0\n",
      "90: Error1: 0.07151376717893282, Error2: 0.02957262290272507 \n",
      "Total fails (no activations): 0\n",
      "91: Error1: 0.019376186581944944, Error2: 0.009636512287770663 \n",
      "Total fails (no activations): 0\n",
      "92: Error1: 0.0031936633630712404, Error2: 0.0021579422181617273 \n",
      "Total fails (no activations): 0\n",
      "93: Error1: 0.003617578383062291, Error2: 0.002161588798703422 \n",
      "Total fails (no activations): 0\n",
      "94: Error1: 9.629169311925473e-05, Error2: 5.981557421551093e-05 \n",
      "Total fails (no activations): 0\n",
      "95: Error1: 0.004054460361780323, Error2: 0.0022125530308762173 \n",
      "Total fails (no activations): 0\n",
      "96: Error1: 0.019863125623138314, Error2: 0.012566656137626678 \n",
      "Total fails (no activations): 0\n",
      "97: Error1: 0.0034096356373649727, Error2: 0.002246097929705994 \n",
      "Total fails (no activations): 0\n",
      "98: Error1: 0.007953708168859748, Error2: 0.005308639189212823 \n",
      "Total fails (no activations): 0\n",
      "99: Error1: 0.011267148194957322, Error2: 0.005786692095246562 \n",
      "Total fails (no activations): 0\n",
      "100: Error1: 0.008438123709771962, Error2: 0.006032319313106751 \n",
      "Total fails (no activations): 0\n",
      "0: Error1: 0.01817449158647787, Error2: 3.815918331219615e-09 \n",
      "Total fails (no activations): 0\n",
      "1: Error1: 0.2776498562937852, Error2: 0.00010983765719291101 \n",
      "Total fails (no activations): 0\n",
      "2: Error1: 0.04780439554267759, Error2: 0.002059626975426103 \n",
      "Total fails (no activations): 0\n",
      "3: Error1: 2.8712869086222357, Error2: 3.114247674008241e-06 \n",
      "Total fails (no activations): 0\n",
      "4: Error1: 0.4823951674782907, Error2: 0.0027217826880334755 \n",
      "Total fails (no activations): 0\n",
      "5: Error1: 1.6118313226775587, Error2: 0.0001680014299110666 \n",
      "Total fails (no activations): 0\n",
      "6: Error1: 0.047967731656240825, Error2: 0.0010586784609129952 \n",
      "Total fails (no activations): 0\n",
      "7: Error1: 0.015791933275610515, Error2: 0.002141913268366841 \n",
      "Total fails (no activations): 0\n",
      "8: Error1: 0.006381089915220148, Error2: 0.002052898014133983 \n",
      "Total fails (no activations): 0\n",
      "9: Error1: 0.0021373834298987673, Error2: 0.00025672750346604544 \n",
      "Total fails (no activations): 0\n",
      "10: Error1: 0.06645266672693842, Error2: 0.000881069362155976 \n",
      "Total fails (no activations): 0\n",
      "11: Error1: 0.012477340511636282, Error2: 0.0005230087066986695 \n",
      "Total fails (no activations): 0\n",
      "12: Error1: 0.003662899157611562, Error2: 0.0011631314494692732 \n",
      "Total fails (no activations): 0\n",
      "13: Error1: 0.09989719214614398, Error2: 0.0016411727382409235 \n",
      "Total fails (no activations): 0\n",
      "14: Error1: 0.005264827870564097, Error2: 0.0023858137452854966 \n",
      "Total fails (no activations): 0\n",
      "15: Error1: 0.010876859920361524, Error2: 0.0027395418939095656 \n",
      "Total fails (no activations): 0\n",
      "16: Error1: 0.3199351528526424, Error2: 0.014726696535506185 \n",
      "Total fails (no activations): 0\n",
      "17: Error1: 0.010101948031395401, Error2: 0.002530485541116741 \n",
      "Total fails (no activations): 0\n",
      "18: Error1: 0.007610488638044477, Error2: 0.0021405053180248534 \n",
      "Total fails (no activations): 0\n",
      "19: Error1: 0.0003405930666526371, Error2: 0.00015442389740534586 \n",
      "Total fails (no activations): 0\n",
      "20: Error1: 0.004533619300287475, Error2: 0.0014942295685774149 \n",
      "Total fails (no activations): 0\n",
      "21: Error1: 0.06029342559160325, Error2: 0.026190889499999814 \n",
      "Total fails (no activations): 0\n",
      "22: Error1: 1.8782780996390376e-06, Error2: 1.074245811432951e-06 \n",
      "Total fails (no activations): 0\n",
      "23: Error1: 0.05527870388553582, Error2: 0.020750362180326495 \n",
      "Total fails (no activations): 0\n",
      "24: Error1: 0.0005077402712401589, Error2: 0.0002469818717385757 \n",
      "Total fails (no activations): 0\n",
      "25: Error1: 0.007124116879538502, Error2: 0.00372892018936207 \n",
      "Total fails (no activations): 0\n",
      "26: Error1: 0.8235427433659032, Error2: 0.008656313686779013 \n",
      "Total fails (no activations): 0\n",
      "27: Error1: 0.08657340719896033, Error2: 0.030147509042604374 \n",
      "Total fails (no activations): 0\n",
      "28: Error1: 0.022903025702743503, Error2: 0.012327000309437963 \n",
      "Total fails (no activations): 0\n",
      "29: Error1: 0.004404453692558703, Error2: 0.0023806765586874886 \n",
      "Total fails (no activations): 0\n",
      "30: Error1: 0.17510073418311992, Error2: 0.029030533960892765 \n",
      "Total fails (no activations): 0\n",
      "31: Error1: 0.001697128137631072, Error2: 0.0010036621351959016 \n",
      "Total fails (no activations): 0\n",
      "32: Error1: 0.011891951303505883, Error2: 0.006389786551217566 \n",
      "Total fails (no activations): 0\n",
      "33: Error1: 0.011906166046285594, Error2: 0.007514447902077771 \n",
      "Total fails (no activations): 0\n",
      "34: Error1: 0.026413613598482925, Error2: 0.01655001401430484 \n",
      "Total fails (no activations): 0\n",
      "35: Error1: 0.008398500654147349, Error2: 0.003938104357787709 \n",
      "Total fails (no activations): 0\n",
      "36: Error1: 0.00010040620522929909, Error2: 5.2583538065011655e-05 \n",
      "Total fails (no activations): 0\n",
      "37: Error1: 0.00020218630513906178, Error2: 0.0001435017522823316 \n",
      "Total fails (no activations): 0\n",
      "38: Error1: 0.0015289021710534767, Error2: 0.0010672746547002853 \n",
      "Total fails (no activations): 0\n",
      "39: Error1: 0.01230652403908408, Error2: 0.007028354133078301 \n",
      "Total fails (no activations): 0\n",
      "40: Error1: 0.023896856735164486, Error2: 0.007301132831362809 \n",
      "Total fails (no activations): 0\n",
      "41: Error1: 0.003085491891274383, Error2: 0.002306005021124851 \n",
      "Total fails (no activations): 0\n",
      "42: Error1: 0.0020161820496364426, Error2: 0.00112075190120277 \n",
      "Total fails (no activations): 0\n",
      "43: Error1: 0.008059784073405187, Error2: 0.005196908681022666 \n",
      "Total fails (no activations): 0\n",
      "44: Error1: 0.009287671575225826, Error2: 0.004532461979670601 \n",
      "Total fails (no activations): 0\n",
      "45: Error1: 0.00783235472936805, Error2: 0.004029838362970213 \n",
      "Total fails (no activations): 0\n",
      "46: Error1: 0.04337627396840285, Error2: 0.02522924014584898 \n",
      "Total fails (no activations): 0\n",
      "47: Error1: 0.029187284391291705, Error2: 0.020375530862358864 \n",
      "Total fails (no activations): 0\n",
      "48: Error1: 0.013293107631128901, Error2: 0.006872091532090522 \n",
      "Total fails (no activations): 0\n",
      "49: Error1: 0.0035914307684736073, Error2: 0.001950205568816991 \n",
      "Total fails (no activations): 0\n",
      "50: Error1: 0.0728274636104882, Error2: 0.046798848487001776 \n",
      "Total fails (no activations): 0\n",
      "51: Error1: 0.0326064615637282, Error2: 0.015440848811761963 \n",
      "Total fails (no activations): 0\n",
      "52: Error1: 0.01403266894606355, Error2: 0.009866490987948914 \n",
      "Total fails (no activations): 0\n",
      "53: Error1: 0.010511571485143987, Error2: 0.005705865349280558 \n",
      "Total fails (no activations): 0\n",
      "54: Error1: 0.012430299941596147, Error2: 0.009383049311825676 \n",
      "Total fails (no activations): 0\n",
      "55: Error1: 0.002572531441105838, Error2: 0.0017150557063783697 \n",
      "Total fails (no activations): 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56: Error1: 0.016770146818783683, Error2: 0.008972774220867705 \n",
      "Total fails (no activations): 0\n",
      "57: Error1: 0.018471305292177755, Error2: 0.010748550547260856 \n",
      "Total fails (no activations): 0\n",
      "58: Error1: 0.0021084155840294934, Error2: 0.0015279201511867596 \n",
      "Total fails (no activations): 0\n",
      "59: Error1: 0.009225904316341856, Error2: 0.003852470917171681 \n",
      "Total fails (no activations): 0\n",
      "60: Error1: 0.046326934125230104, Error2: 0.027702816678313886 \n",
      "Total fails (no activations): 0\n",
      "61: Error1: 6.664656431191932e-05, Error2: 3.609920184190104e-05 \n",
      "Total fails (no activations): 0\n",
      "62: Error1: 0.0037419120900460444, Error2: 0.0027461980214545548 \n",
      "Total fails (no activations): 0\n",
      "63: Error1: 0.0008861179202946403, Error2: 0.0005498253346838857 \n",
      "Total fails (no activations): 0\n",
      "64: Error1: 1.0208238491213195e-06, Error2: 6.323719856785194e-07 \n",
      "Total fails (no activations): 0\n",
      "65: Error1: 5.5174069282664635e-06, Error2: 3.538684410464014e-06 \n",
      "Total fails (no activations): 0\n",
      "66: Error1: 0.014496627698078917, Error2: 0.009663417489893085 \n",
      "Total fails (no activations): 0\n",
      "67: Error1: 0.018182728758933932, Error2: 0.013358675905033042 \n",
      "Total fails (no activations): 0\n",
      "68: Error1: 0.02101281355936349, Error2: 0.016634912235760527 \n",
      "Total fails (no activations): 0\n",
      "69: Error1: 0.013921640734251154, Error2: 0.008551161333223216 \n",
      "Total fails (no activations): 0\n",
      "70: Error1: 0.10276828976296692, Error2: 0.05838252986937219 \n",
      "Total fails (no activations): 0\n",
      "71: Error1: 0.004242768571395866, Error2: 0.0029245905864260222 \n",
      "Total fails (no activations): 0\n",
      "72: Error1: 0.007513604327210243, Error2: 0.00574236734848612 \n",
      "Total fails (no activations): 0\n",
      "73: Error1: 0.0024958768342494904, Error2: 0.0016983530722975955 \n",
      "Total fails (no activations): 0\n",
      "74: Error1: 0.0011424520364370526, Error2: 0.0008461173066658715 \n",
      "Total fails (no activations): 0\n",
      "75: Error1: 2.3650220992899218e-05, Error2: 1.7854171511552118e-05 \n",
      "Total fails (no activations): 0\n",
      "76: Error1: 3.63111145529078e-05, Error2: 2.6879933415383755e-05 \n",
      "Total fails (no activations): 0\n",
      "77: Error1: 0.00012211292213739293, Error2: 9.547234995875589e-05 \n",
      "Total fails (no activations): 0\n",
      "78: Error1: 3.063788477247876e-05, Error2: 2.1142107577000796e-05 \n",
      "Total fails (no activations): 0\n",
      "79: Error1: 0.007869111674844338, Error2: 0.0051201406929942865 \n",
      "Total fails (no activations): 0\n",
      "80: Error1: 0.0008603142926821666, Error2: 0.000665483804321277 \n",
      "Total fails (no activations): 0\n",
      "81: Error1: 0.042435519750904205, Error2: 0.03191762832963346 \n",
      "Total fails (no activations): 0\n",
      "82: Error1: 0.01680247934725518, Error2: 0.012857224838434246 \n",
      "Total fails (no activations): 0\n",
      "83: Error1: 0.027240486129031925, Error2: 0.01820862557714683 \n",
      "Total fails (no activations): 0\n",
      "84: Error1: 0.00072385872166653, Error2: 0.0005629999038725114 \n",
      "Total fails (no activations): 0\n",
      "85: Error1: 0.024881915328981558, Error2: 0.016992468831325585 \n",
      "Total fails (no activations): 0\n",
      "86: Error1: 0.006517656152365453, Error2: 0.004840332488651143 \n",
      "Total fails (no activations): 0\n",
      "87: Error1: 0.024216540338320897, Error2: 0.019226432951766053 \n",
      "Total fails (no activations): 0\n",
      "88: Error1: 0.006840492519742903, Error2: 0.005315563404019837 \n",
      "Total fails (no activations): 0\n",
      "89: Error1: 0.04020452536665904, Error2: 0.028416566431494653 \n",
      "Total fails (no activations): 0\n",
      "90: Error1: 0.000183501754000135, Error2: 0.00013711303613334343 \n",
      "Total fails (no activations): 0\n",
      "91: Error1: 0.02537671217970514, Error2: 0.018200363747637244 \n",
      "Total fails (no activations): 0\n",
      "92: Error1: 0.028133561545978934, Error2: 0.020798096599607314 \n",
      "Total fails (no activations): 0\n",
      "93: Error1: 0.0066192978182712725, Error2: 0.0045110824287969715 \n",
      "Total fails (no activations): 0\n",
      "94: Error1: 0.013478465955001686, Error2: 0.00888368789113031 \n",
      "Total fails (no activations): 0\n",
      "95: Error1: 0.002311989470373543, Error2: 0.0018285778648516567 \n",
      "Total fails (no activations): 0\n",
      "96: Error1: 0.0030535648839706523, Error2: 0.0021686831029233498 \n",
      "Total fails (no activations): 0\n",
      "97: Error1: 0.002427859196774211, Error2: 0.001831823276963224 \n",
      "Total fails (no activations): 0\n",
      "98: Error1: 0.026179496254638217, Error2: 0.02063359566240381 \n",
      "Total fails (no activations): 0\n",
      "99: Error1: 0.04129126672093661, Error2: 0.026304897935586765 \n",
      "Total fails (no activations): 0\n",
      "100: Error1: 0.0006958291821691214, Error2: 0.0005788673831499822 \n",
      "Total fails (no activations): 0\n",
      "0: Error1: 1.14534174014456, Error2: 7.128015413922469e-06 \n",
      "Total fails (no activations): 0\n",
      "1: Error1: 0.429227456031056, Error2: 0.00018162128562602324 \n",
      "Total fails (no activations): 0\n",
      "2: Error1: 0.20117290649222852, Error2: 0.02551826160322583 \n",
      "Total fails (no activations): 0\n",
      "3: Error1: 0.07504107597744777, Error2: 0.001889743052394936 \n",
      "Total fails (no activations): 0\n",
      "4: Error1: 0.45956387217910033, Error2: 0.015368714920940621 \n",
      "Total fails (no activations): 0\n",
      "5: Error1: 1.1879562162636779e-05, Error2: 3.0906339945831796e-06 \n",
      "Total fails (no activations): 0\n",
      "6: Error1: 0.09427696131526501, Error2: 0.0069551269720803865 \n",
      "Total fails (no activations): 0\n",
      "7: Error1: 0.00492198861487593, Error2: 0.0006897492491969891 \n",
      "Total fails (no activations): 0\n",
      "8: Error1: 0.07810868935362814, Error2: 0.03255579686305648 \n",
      "Total fails (no activations): 0\n",
      "9: Error1: 0.511689950146365, Error2: 0.0805743747002633 \n",
      "Total fails (no activations): 0\n",
      "10: Error1: 0.05586099168138067, Error2: 0.010499329270012544 \n",
      "Total fails (no activations): 0\n",
      "11: Error1: 0.05909264987226591, Error2: 0.007877579450791773 \n",
      "Total fails (no activations): 0\n",
      "12: Error1: 0.1249989417545778, Error2: 0.0006594354666224373 \n",
      "Total fails (no activations): 0\n",
      "13: Error1: 0.1283104915729953, Error2: 0.02044163936949543 \n",
      "Total fails (no activations): 0\n",
      "14: Error1: 0.008937361883773169, Error2: 0.003679454949879672 \n",
      "Total fails (no activations): 0\n",
      "15: Error1: 0.0027750353851849685, Error2: 0.000291769906514754 \n",
      "Total fails (no activations): 0\n",
      "16: Error1: 0.03002979360712271, Error2: 0.0242319726777964 \n",
      "Total fails (no activations): 0\n",
      "17: Error1: 0.008287180720604559, Error2: 0.00251843449882906 \n",
      "Total fails (no activations): 0\n",
      "18: Error1: 0.09757882887224541, Error2: 0.036494855186606646 \n",
      "Total fails (no activations): 0\n",
      "19: Error1: 1.9982068806804707, Error2: 0.013911018605384401 \n",
      "Total fails (no activations): 0\n",
      "20: Error1: 0.02972435911259211, Error2: 0.011355456282920893 \n",
      "Total fails (no activations): 0\n",
      "21: Error1: 0.174773482739685, Error2: 0.026148901486607267 \n",
      "Total fails (no activations): 0\n",
      "22: Error1: 0.00803684548358606, Error2: 0.0033175837191969423 \n",
      "Total fails (no activations): 0\n",
      "23: Error1: 0.0011092112119227603, Error2: 0.0003856893808528264 \n",
      "Total fails (no activations): 0\n",
      "24: Error1: 0.009520567234203619, Error2: 0.0031425689366874846 \n",
      "Total fails (no activations): 0\n",
      "25: Error1: 0.011960089453476857, Error2: 0.005870187931437375 \n",
      "Total fails (no activations): 0\n",
      "26: Error1: 0.007442899199045068, Error2: 0.0038763527991977935 \n",
      "Total fails (no activations): 0\n",
      "27: Error1: 0.12125152333127863, Error2: 0.052352790513842305 \n",
      "Total fails (no activations): 0\n",
      "28: Error1: 5.21962792824464e-05, Error2: 2.5806529613183678e-05 \n",
      "Total fails (no activations): 0\n",
      "29: Error1: 0.004757263800050485, Error2: 0.0017535274599264862 \n",
      "Total fails (no activations): 0\n",
      "30: Error1: 0.0040172506502374715, Error2: 0.001068466786558801 \n",
      "Total fails (no activations): 0\n",
      "31: Error1: 0.04279474934636295, Error2: 0.010671432645863605 \n",
      "Total fails (no activations): 0\n",
      "32: Error1: 0.00027778834590303853, Error2: 8.070423100197644e-05 \n",
      "Total fails (no activations): 0\n",
      "33: Error1: 0.027966119335681484, Error2: 0.013988089664668208 \n",
      "Total fails (no activations): 0\n",
      "34: Error1: 0.054407501479485317, Error2: 0.03140179240317662 \n",
      "Total fails (no activations): 0\n",
      "35: Error1: 0.017832140773054603, Error2: 0.010107789956834114 \n",
      "Total fails (no activations): 0\n",
      "36: Error1: 0.0017773210603828124, Error2: 0.000886236823599845 \n",
      "Total fails (no activations): 0\n",
      "37: Error1: 6.329148145931301e-05, Error2: 3.7922367199841836e-05 \n",
      "Total fails (no activations): 0\n",
      "38: Error1: 5.019456625503567e-05, Error2: 2.9922799866186e-05 \n",
      "Total fails (no activations): 0\n",
      "39: Error1: 1.674789742492121e-06, Error2: 7.419565858929538e-07 \n",
      "Total fails (no activations): 0\n",
      "40: Error1: 0.026193345308649935, Error2: 0.010375747230159134 \n",
      "Total fails (no activations): 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41: Error1: 0.006359463175224315, Error2: 0.003182308368920002 \n",
      "Total fails (no activations): 0\n",
      "42: Error1: 0.0027962967495125286, Error2: 0.001635922287387599 \n",
      "Total fails (no activations): 0\n",
      "43: Error1: 0.06423854978735366, Error2: 0.03451026297771372 \n",
      "Total fails (no activations): 0\n",
      "44: Error1: 0.0027779865822850055, Error2: 0.0016982591448960107 \n",
      "Total fails (no activations): 0\n",
      "45: Error1: 0.6484335215163922, Error2: 0.068973030019055 \n",
      "Total fails (no activations): 0\n",
      "46: Error1: 0.019137777612802425, Error2: 0.010321341442985492 \n",
      "Total fails (no activations): 0\n",
      "47: Error1: 0.015174698285352945, Error2: 0.00925798946806674 \n",
      "Total fails (no activations): 0\n",
      "48: Error1: 0.0019302222733494155, Error2: 0.0011465144617152537 \n",
      "Total fails (no activations): 0\n",
      "49: Error1: 0.00013678580826804434, Error2: 4.7100405696064416e-05 \n",
      "Total fails (no activations): 0\n",
      "50: Error1: 0.026435392551777313, Error2: 0.008649124006842517 \n",
      "Total fails (no activations): 0\n",
      "51: Error1: 0.013979754366573578, Error2: 0.00883434904852187 \n",
      "Total fails (no activations): 0\n",
      "52: Error1: 0.010599138029148887, Error2: 0.006456109762354918 \n",
      "Total fails (no activations): 0\n",
      "53: Error1: 0.0009851185639193761, Error2: 0.000329222455302947 \n",
      "Total fails (no activations): 0\n",
      "54: Error1: 0.0050734288581583626, Error2: 0.0032734307654513762 \n",
      "Total fails (no activations): 0\n",
      "55: Error1: 0.07858272751323224, Error2: 0.03821624831335784 \n",
      "Total fails (no activations): 0\n",
      "56: Error1: 0.009265728283595875, Error2: 0.005139810068298555 \n",
      "Total fails (no activations): 0\n",
      "57: Error1: 0.0038372233990580937, Error2: 0.0025437215570977777 \n",
      "Total fails (no activations): 0\n",
      "58: Error1: 0.007322131475322674, Error2: 0.0040128141832130254 \n",
      "Total fails (no activations): 0\n",
      "59: Error1: 0.0019978139453285925, Error2: 0.0011872650343954477 \n",
      "Total fails (no activations): 0\n",
      "60: Error1: 0.04698542033915734, Error2: 0.030339172552951706 \n",
      "Total fails (no activations): 0\n",
      "61: Error1: 0.0014968328005651454, Error2: 0.001075476692526491 \n",
      "Total fails (no activations): 0\n",
      "62: Error1: 3.5614176064927836e-05, Error2: 1.91821367866926e-05 \n",
      "Total fails (no activations): 0\n",
      "63: Error1: 0.0007856762754236208, Error2: 0.000536618661633762 \n",
      "Total fails (no activations): 0\n",
      "64: Error1: 0.0032907765009736302, Error2: 0.002279936384508356 \n",
      "Total fails (no activations): 0\n",
      "65: Error1: 0.06379866259926471, Error2: 0.04311317554401877 \n",
      "Total fails (no activations): 0\n",
      "66: Error1: 0.024897334118256256, Error2: 0.011588510079335673 \n",
      "Total fails (no activations): 0\n",
      "67: Error1: 0.005804518982627264, Error2: 0.0041202017635055205 \n",
      "Total fails (no activations): 0\n",
      "68: Error1: 0.002523813430990231, Error2: 0.0017684320564894907 \n",
      "Total fails (no activations): 0\n",
      "69: Error1: 0.00027214397437174754, Error2: 8.206078625157184e-05 \n",
      "Total fails (no activations): 0\n",
      "70: Error1: 0.0013839074918380477, Error2: 0.0008844398718025847 \n",
      "Total fails (no activations): 0\n",
      "71: Error1: 0.030908962160564192, Error2: 0.01965985569004168 \n",
      "Total fails (no activations): 0\n",
      "72: Error1: 0.030045241552895473, Error2: 0.020448852529076782 \n",
      "Total fails (no activations): 0\n",
      "73: Error1: 0.018483528485784287, Error2: 0.013640512967532189 \n",
      "Total fails (no activations): 0\n",
      "74: Error1: 0.006638844485089258, Error2: 0.004633012662726841 \n",
      "Total fails (no activations): 0\n",
      "75: Error1: 0.026675551224617247, Error2: 0.019472528368628454 \n",
      "Total fails (no activations): 0\n",
      "76: Error1: 0.013991894386850185, Error2: 0.008862463264653097 \n",
      "Total fails (no activations): 0\n",
      "77: Error1: 0.30801673096522403, Error2: 0.09319984214825787 \n",
      "Total fails (no activations): 0\n",
      "78: Error1: 0.0003879343160116622, Error2: 0.000261305115811446 \n",
      "Total fails (no activations): 0\n",
      "79: Error1: 0.002853974209097655, Error2: 0.0016468368294787396 \n",
      "Total fails (no activations): 0\n",
      "80: Error1: 0.005621097151415684, Error2: 0.001773263994461216 \n",
      "Total fails (no activations): 0\n",
      "81: Error1: 0.008976293506423172, Error2: 0.0069646377757524485 \n",
      "Total fails (no activations): 0\n",
      "82: Error1: 0.002151589436763813, Error2: 0.0014101982451924652 \n",
      "Total fails (no activations): 0\n",
      "83: Error1: 0.0007296375198264822, Error2: 0.0004955485786516018 \n",
      "Total fails (no activations): 0\n",
      "84: Error1: 0.008909761196612067, Error2: 0.004840807781043412 \n",
      "Total fails (no activations): 0\n",
      "85: Error1: 0.00998508603952505, Error2: 0.007617733284641914 \n",
      "Total fails (no activations): 0\n",
      "86: Error1: 0.007622533076839937, Error2: 0.005484778853545086 \n",
      "Total fails (no activations): 0\n",
      "87: Error1: 0.01214683758230717, Error2: 0.00754452928427366 \n",
      "Total fails (no activations): 0\n",
      "88: Error1: 0.010067360955457858, Error2: 0.005330728592453803 \n",
      "Total fails (no activations): 0\n",
      "89: Error1: 0.05968321388223455, Error2: 0.04219965658509538 \n",
      "Total fails (no activations): 0\n",
      "90: Error1: 0.0022087240067233224, Error2: 3.4459985045774742e-06 \n",
      "Total fails (no activations): 0\n",
      "91: Error1: 0.0009451348160426096, Error2: 0.0006724634700151244 \n",
      "Total fails (no activations): 0\n",
      "92: Error1: 0.002377531879989224, Error2: 0.0012906860227179968 \n",
      "Total fails (no activations): 0\n",
      "93: Error1: 0.0017348343346392733, Error2: 0.0011286849616734403 \n",
      "Total fails (no activations): 0\n",
      "94: Error1: 0.0009472417254918867, Error2: 0.0005555026411934442 \n",
      "Total fails (no activations): 0\n",
      "95: Error1: 0.09340425105699268, Error2: 0.06432311214361307 \n",
      "Total fails (no activations): 0\n",
      "96: Error1: 0.034889414209827156, Error2: 0.022416516822847468 \n",
      "Total fails (no activations): 0\n",
      "97: Error1: 0.069631436880661, Error2: 0.04891891944764813 \n",
      "Total fails (no activations): 0\n",
      "98: Error1: 0.0004644100146196631, Error2: 1.53514046136258e-05 \n",
      "Total fails (no activations): 0\n",
      "99: Error1: 0.007260095022804511, Error2: 0.005441813712286354 \n",
      "Total fails (no activations): 0\n",
      "100: Error1: 0.041376066070404546, Error2: 0.02949012761827609 \n",
      "Total fails (no activations): 0\n",
      "\r"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "n_data=101\n",
    "# hidden_layers=[16,32,256,32,16]\n",
    "hidden_layer_options=[[16,16,16,16],[128,128,128],[256],[16,32,128,32,16]]\n",
    "# hidden_layers=[128,128,128]\n",
    "# hidden_layers=[16,32,128,32,16]\n",
    "x=np.random.rand(n_data,1)*4-2\n",
    "y= 0.8*np.sin(x*3)+np.random.randn(x.shape[0],x.shape[1])*0.1\n",
    "x_test=np.linspace(-2,2,1000).reshape(1000,1)\n",
    "plt.plot(x,y,'.')\n",
    "y_test=[]\n",
    "for ii in range(len(hidden_layer_options)):\n",
    "    hidden_layers=hidden_layer_options[ii]\n",
    "    net=BNet7(hidden_layer_sizes=hidden_layers,input_dim=1,output_dim=1,lambda_initaliser=lambda_initaliser,initialiser_function=initialiser_function)\n",
    "    net.train(x,y,print_error=False,passes=1,total_info=1,failed_attempts=10)\n",
    "    y_test.append(net.forward(x_test)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(16,9))\n",
    "for ii in range(4):\n",
    "    fig.add_subplot(2,2,ii+1)\n",
    "    plt.plot(x,y,'.')\n",
    "    plt.plot(x_test,y_test[ii])\n",
    "    plt.title('Layers = ' +str(hidden_layer_options[ii]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "n_data=101\n",
    "hidden_layers=[128,128,256,128]\n",
    "x=np.random.rand(n_data,1)*4-2\n",
    "y= 0.8*np.sin(x*3)+np.random.randn(x.shape[0],x.shape[1])*0.1\n",
    "x_test=np.linspace(-2,2,1000).reshape(1000,1)\n",
    "plt.plot(x,y,'.')\n",
    "for ii in range(20):\n",
    "    net=BNet7(hidden_layer_sizes=hidden_layers,input_dim=1,output_dim=1,lambda_initaliser=lambda_initaliser,initialiser_function=initialiser_function)\n",
    "    e1,e2=net.train(x,y,print_error=False,return_error=True, passes=1,total_info=1,failed_attempts=10)\n",
    "    plt.plot(x_test,net.forward(x_test)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_fun_2d(x):\n",
    "    # used for testing 2D data, generates the value of a radial cosine at (x[0],x[1]) for all the x\n",
    "    y=0.8*np.cos(np.sqrt(x[:,0]**2+x[:,1]**2)*5)\n",
    "    y=y.reshape((y.shape[0],1))+np.random.randn(y.shape[0],1)*0.1\n",
    "\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_fun_classification(n):\n",
    "    # super basic classification test\n",
    "    # for n samples, generates a random 0 or 1 for y, \n",
    "    # if y=0, then x= [1,1,0,0], if y=1 then x=[0,0,1,1]\n",
    "    # there is some noise added to the x\n",
    "    # returns x, a one-hot vector for y, and y\n",
    "    \n",
    "    y=np.random.randint(2,size=(n,1))\n",
    "    x=np.zeros((n,4))\n",
    "    y_hot=np.zeros((n,2))\n",
    "    for ii,yi in enumerate(y):\n",
    "        \n",
    "        if yi==0:\n",
    "            y_hot[ii,0]=1\n",
    "            x[ii,:]=np.array([1,1,0,0])+np.random.randn(4)*0.01\n",
    "        elif yi==1:\n",
    "            y_hot[ii,1]=1\n",
    "            x[ii,:]=np.array([0,0,1,1])+np.random.randn(4)*0.01\n",
    "\n",
    "        else:\n",
    "            print('SOMETHING BAD')\n",
    "    return x, y_hot, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "n_data=1001\n",
    "# hidden_layers=[16,32,256,32,16]\n",
    "hidden_layers=[16,16,16,16]\n",
    "hidden_layers=[128,128,128]\n",
    "hidden_layers=[32,256,256,32]\n",
    "hidden_layers=[128,256,256,128]\n",
    "\n",
    "\n",
    "n1=int(np.sqrt(n_data))\n",
    "\n",
    "#for making x data in a grid\n",
    "x1,x2=np.meshgrid(np.linspace(-2,2,n1),np.linspace(-2,2,n1))\n",
    "x=np.hstack([x1.reshape((n1*n1,1)),x2.reshape(n1*n1,1)])\n",
    "\n",
    "#for x just random sampled points\n",
    "x=np.random.rand(n_data,2)*4-2\n",
    "\n",
    "y=test_fun_2d(x) #generate y values\n",
    "x_test=np.linspace(-2,2,1000).reshape(1000,1)\n",
    "\n",
    "for ii in range(1):\n",
    "    net=BNet7(hidden_layer_sizes=hidden_layers,input_dim=2,output_dim=1,lambda_initaliser=lambda_initaliser,initialiser_function=initialiser_function)\n",
    "    net.train(x,y,print_error=False,passes=1,total_info=1,failed_attempts=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6MAAAFpCAYAAABpiv7nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOy9d3hkSXku/p7UauU80oxSK0yQNIqjCesFk9YYMMEXsA0sCxvYIML6eu0F1vbPvj/utReMDQ82ZpeFDWBjbGxjgsEXk2HDzM5I08qaGYVWGkmjnFrdfdL94/R0fXWk1gRJMxpU7/PoUZ0+VXXqVNWpqq/q/b5Psm0bAgICAgICAgICAgICAgI3EvLNLoCAgICAgICAgICAgIDA7oMQRgUEBAQEBAQEBAQEBARuOIQwKiAgICAgICAgICAgIHDDIYRRAQEBAQEBAQEBAQEBgRsOIYwKCAgICAgICAgICAgI3HAIYVRAQEBAQEBAQEBAQEDghmPTwqgkSUWSJP1UkqRuSZK6JEn6/XXiSJIk/a0kSX2SJLVLktS42ecKCAgICAgIrA8xNwsICAgI3ApQtyAPA8Af2rbdKklSKoAWSZJ+aNt2N4nzRgD7o3/HATwR/S8gICAgICCw9RBzs4CAgIDAjsemT0Zt2x63bbs1Gl4C0AOgwBXtbQC+ajs4CSBDkqS9m322gICAgICAwFqIuVlAQEBA4FbAluqMSpLkA9AA4JTrVgGAEXI9irWTooCAgICAgMAWQ8zNAgICAgI7FVtB0wUASJKUAuDfAfxP27YXN5HPAwAeAAApwXNE25u7Jo6iWrGwVzFIWjsWlkkYALxyJBa2IbHfJZ1/PmwSZlhd1pCwYiCcrEJLMeGd0pE0ydLqeTKMPQqmjRSkTYeQPrUau7e4x4ulHC+Xd8jS2PuQsgYN9jsA2DYrhaaY8K7qSAxGsKAlIej1sIgWSWSxNBKrHtju1pbJuxosDakqWHxxoIRIcpOlt1QJ8SCRstlk+0MNmlw8WyZ50LDNt6VksgwtTWFJDOf3TH0ZOfqSkxTAjJqKWW/aumW4nGY92BIpQ/zXg6ST8iSQ8pDfbcWVAclb0lk9mEl8I0km/+6x/OT4BbJYEaCEWXp3Gbj209g9SwUS9QhK5qYgwYYNCUNZuQhqHtDPSiLNZySSfPlPChbpplxfUF3tqrBrRVm/XRSJ/z1RYQ+zSCNFLL4evaRQKUo4Fjbp9yXx/dEAq0iLxEuVWfqg7fpeSRmCJntxw1a4ePQ9VnQWj45h2oKN5HAIKwleBBM8iIyMTtu2vXZA3AR+8zXJ9syseeWIcdDSHv6Bbdtv2MIiCWwxtmNu1hKVI1mladx3AQDrj1aATO6orm+YfgsymcgUKV5u/HdLv9Ow67u3yGBP51/3+sBdpljeJL37XemzDJPEs8gEY/Bp6BAjk7nZ/XjumoQlMheuSUPmRRjkQVL8uQcWHZBJHPf8IrN3onPPmnlIWj9su8tAo9F3MkiYvo9u0CSwzesfs7YKkkb6msr6oE3ras28HyezOG0MAJJFrmnQlTe95sKkO9ru50sb3IsD7tOh64H4SymuDK5PlF+XkrW9RsIehW9/j8zan87bdPyQEP8bV0kaNe6oBZhxGsydprNDF3PzJrElwqgkSRqcye5rtm1/c50oYwCKyHVh9Lc1sG37KQBPAUBCaaG99xMfXjPDZWSvxML7M6djYdph6SIVAKqTL8bCIdL7q7wXuXgakd6UaMcu+PocDv3ZJCTLhrmo4Gf/cAAA8Nr3nXMEDk1C/xPZCB5JwHOzt6OkbRoffPBnUAwTpqrgmb8+hpG6bGjkAzq3nBcLp6psceuf5jelQzor6+tGevH4I/8BTTcRUSO48+P34ez+YgCAFSFC0AoLJ8ywUSCc7bxPw2AAJy7042RtKVorSgAA6jR7TsoQ+wBXWTEBABkXWGMkLLD3CWXwi206+GhBlsZIZHln+ee5NGYyW5SbXlYeWec/SHWeCfqrRUzI9E4GAQCVyyP45PmvQrVNGJKCx0regfaiAyRvUj9TQVJovqNZXiZorJlUCLTJJVae0kyW9yTL20hP4NLYGqughNGFWHiuMYeL51kigyspnp4Yn9QQymL30gfYzoKeyreRZ5719WA+e9fVbBn3nvkRPnTyv6Daznrq89VN+PLxO7gFVMICK9BMLfs96SJfVytFZIG5yspm5PLfqJbCypqZGsR6SPeucteHM8ZZuU32DkMrWVy8Q2mTsfCJlH5WNou1S77G98cZIyUWDpJ4r0zqi4X94UIujU6ETv9KcSx8KZzKxcvS2Pu9OOmLhb2qU8HV3WP43KPfgGYY0NVVvPeDD+Gbn/u7IWwxpmdNnPpB4ZUjxoG2tz/nyrEEbha2a27Or86y3/dPr8Mq2XABAN1ef1xKIAPHHs8Sdy+F7HCmyiycTDZ9AEAmi8klk+1+TRpsDhhazebSrBjsu1XJ/JuiRLh4Gdr6482MnhwL0/EFAPoXWdefXiLxlslYP8fXj2eejPtz7HdtmX8unTOVEBk/I0SoX+XnRW2WjY3yLKlj1bXMI4ITVsnuMhV6vK75KpHMzSnsnpHs2oyjgg+Z40wt/vyp6Oy5nlnW5uosW+fZk9NcGnOeH6tvBtTc/FjYzk6Pha1kVj96Gt/+ZgKpIFIlyirZkIm41jtLrE7o5rSRxreRns6eFU5nbayTNZfFFwcmycLilwccpDhCJ9nXhbrqWqjT9UoyK0OI/0QRziFrnByWYV422zfzpc1yaYoS2cezx8Pi0fHDvbmcrbKPLFdhabJJGoAXQJdIhdENrAyZHz8OFI2LuXmT2LQwKkmSBOBpAD22bX8mTrTvAPiwJEn/DMc4woJt2+Nx4u4opLcGUfXnE5DM6Nihm9hzagndD+3DT756EHtOLSH59jCCR9hXPVSXgy988dWobB3HYFMORuqy4+Z/Laj1j0LTTSiWDc0wcKJnICaMXi0aBgP42ue/6Cxyf6DizkfvjwmkvyroSSnCY2XvRe3KENqTS9CTfP0f9K2K2okAjoz3odcuQXfGtbfvmYIK6IoKmAZ0RcXpoopNl6lhMIBf6xnAqbIKnC3xbTq/X2U0to1AMwyotg2YBk709WM9SUJAIB5+1edmAQEBAYFfDWzFyejtAO4C0CFJkj/62x8DKAYA27afBPB9AG8C0AcgCOCeq87dlpCexW8bNuwZjYWTToZQ3zYKf10h1FewOEVeficlT2OnT11Bdvr4k4VKLl5Z4lQsHLQ8uO35fsByBFEbgCQDF49lQLcVjNdnYLw+w6EGRDd0jOj2Un9NHvbcvoQ0hFAd3Wj+9lRDLO/xIDspCelsd3ElzG9dUfrPd3Mb8C7lDDQY0FUFrQ3FSEhyTpdCq6wpJXN9emnqgIxXvTzAFrmGgdf8OIDB+QqOmhsi+ylJGyxLJLKjmd7Pn1it5jHhPGGWUCnJbql7Z49CW2C7VfIKv0MeLmS7kEl9pJ0JdafXykZvYrbTdkur8AQJNTOH1T2l1IRzvdxzkrrYy+sljIEhuam95ESVUl+pRrY2y++86zlsJ91MZbv8GZ38jq+VwPqGlcjaOHGY1Xc4zznBq5ofQv3cAJasBHww8H1olgldVvHIsfvRlVkCzyJPdQllE6o4af/UMRODKML/vP0BHA4OomVfBXpSfPAsgutPqznsIsfP3jucwT0GDUODePupFvzOS6ehWBZ0RcX972jGC7e7NlImWZ1MFbJt2rQMVndD0/yJJ6XO0VPS/ESejTivszr+8igbKF6deyEWHtf5gu8lJ6WHEljeLSF2kBR20XQjhHVRnzwcC//f0GHEgy+d7fIuRym7vU35MP9RhmyYMBUZva/LBr4XN4tNwIZpb8CvEriVsW1zswwLSUoElsvshGytfzJKTxWWTH6cTSAU+mmDjc2zUjIXj6rU6OQ7CxNdEjdNd5mcjIZMdo/S/ABgQk7DeljUWfpVlwrNzDIrXzjE5m3KUlJM/kSQO6zhTpviUzNpUSmNFe7PVqanbvSI0nViRemvlD67AfuHKxtRP1GD/JxC53ebzoW0X7iovTQ/jgVHy+ZOoxLW0kaU3S0c29S9+dy1nctYUEY669ORdFa2SCr/PVhxTog9RM3JfUpuh1m/tUlb2qorb3qySdckVFXL1cZSnK7gZiPKJB5Vw6Gnoa6DSJgeliE9gXV9/rC9LPOEBFbYJI0/faSg3/mlCPt2F+SkWDhH4xkYXosVXJNYvCWLL1DIJqeh5COjTA2vxPf77cHumps3LYzatv08NtSoA2znC/rQZp/lRnn7JfzRo/8NTTehawqeePLVCNRt7cn08NEsmB4ZUsSCLQMDn8jGVEPqlRNeA2p6RtHYPozW2mKcLCuLG6+1ogR3Pno/TpwbQOuRQvgPFsWNS1E/EsCxQB9asytwupCdeBmKijMFmz/xupVRuTyCusVBtKWVwp+7/2YX57pRNT+Ev2n5MjTLgCVJkG0bCmzYloH62QF0ZZbE4tXNDaItsxStmRu3fVe2D6cPlm+qXLXjATz1H08hQdchITpQmAaaRvvwAq7tVH83Ir42y9blb237UwRuBm7m3CwgICAgcP3YbXPzlhkw2m7U9o6iqWMIZ2pKgKge46HWiRht1dZNVLRc2nJhdKw+E19/+hhe5T+HhRNeLB/xAvE3bK4Z1d0X8dnHvgFVN2FoCu79/9+HtkPxhczWihK0VpTAmx6KG4eifiSAZ7/yBLQo3fL+tzfj/rc34+hoH1rzK9Ce79uiN7n1ULk0jE/1PgfNNqFfVPBIxgMxoe1WQ/3cADTLcARQ24YtyTBswJBV+LOcDY6q+SH8devT0CwDuqzi4bQH0Znt29ZyNY32QTMMyHAGVxOArqg4U7i7N0GuhFr/KBTTgmIDtmmhsX0Yz2zTs6w1RywCAgICAgICNxO7aW6+JYTR2t5RPPUn/wjNMKGrCv7mC69Hf+0e9DbmQ9cU2FFBru/Inm15/lh9JsZuz7hyxOtAvX8Eqm5CtWxAN3G0M7ChMHqtOBbog2Yy3bOjo314+tgdaN/ng7KFQvWtiLrFADTbjApwJn5zrAX1MwPwZ5fdckKpP7MMuqzCtgwYsoIv+N6EdCOIln0HYu9SNzfIBFbLQMOl/m0XRs8UVkBXnZN4U1bwrcqj+G7VUbTt82Etz0zgMtrrC2FoChAd21prxSmygICAgICAwK8edrQwKsk2jo304UPf/Dk8ugHFBmCYyHtpET8uPYRAaRasv5dR2TKOniN7UXr7FHLg8MR1lx+TfJXpfx3NYm7VdJc968cvvjEW/kDeL2Lhn68cioUHg7wF51yXdcDL+I9Ljdz1dIjx1EcvOfoG/11chfdrL8UWnS9X+zgVDz1ErMrOs3BkkVn6BADvPHHNQlQsX/atNURz+ZU9S+xByeNMMk0ZI3ofLh0Hi+gcmFGLrtUzAZzo70Z7io8ZC9pDlATIC3kHZ2Lh4AH+FJtaBzRSGG/f49IZpVYDEWThlQZmqCj5pX5wSGF1r6c6ebfk78d7xn4G2zJhSTLeMHwaKmzokPFY6pvQUX88lmbmMNM5zG1xtbfOdBEShlk/0/OZLoO8yusYmB5WxyrReZEu8brOdinzP08t6emZrDyyYaE3pQiP1t+HuvkBnCo7gM4cHwDgWEcv3j/2Q7Sl+XA2txz64GWBVcX5sQSkX3Isw64cJ/Rw0uTJk/F1IzzLjpLK4akAGif70ZrnUHrru/txZm8FOvJ86E734cG3NuPNvacBAN+tviyIArbi1pMi703cEy2MMh3h5L28Qs3cKmvX/5ysiYUr9l7i4jVkMj1zatmz0MPqezDMf9f9Iba5tWCy5xwn1nR/tMTrgr4zvSUWfnGV1elni/6Ti/eZ6dtj4bfm+mPh86tRvaRXAH/46XfG9OEHqrfUanwMNmyYbp0yAYErQJZsJMkRmC7ruarEFNeoO6MElws1igWDfVvUzUvI5VdMIZtXJtFVpfpjbpdO1FUa1fmcN3nToVSndZXYcAgTa/a6zqcxyLW5wtLIxGK4HHa504o3nLo+Qap/R8d9apOAc30Cl/sU6nbErVNJv3eFvBPnymwDVyw0P9f70N5Ax3C672i73MBxerAE1F6CnMbr9CqJxJfYRuMX1bEkloPtILNDYEVcu/JE31bNYTYK7Ay+DHoWK0MkjdV3KItYsk3m35UaZKaHAbLBnkmtJwOAmbC+mVv371QflObNuVVxueqj+p90Kez2qkRtiqih9fVR17hsIcWjVnwtjyvzBKIz6ll/nLBc48xEiNgNId4o0lTWxkkKv26cNZiOt1tvPR6oSzg6NrndPG0HdtvcvKOF0aRQBF/5xHPQdAOyDZiSBENT4K9jQkd/zR701ziLxlJMxctqx+LsgWJ8+PF3x3RG20q37lQUAPxFPtzz/mZHZzSnIiYIbBWqZwL43PNPOTRgScFj5Xdtm/XaQ6tjqA0Oo80qQ0/K5uupO6MEH62+B3ULg9gTnsebJlucU0NYqNXH0bEFZb6ZODwdwKd6nosaMlLwyLEH8Mix+1E/OwB/VhkGBzZ/Mnl4KoDP//hJqKYBU5YhQYJsmzBkFQ/9VjM68nwAgLecOwPNNPCWc2dw/zubt7wf/iqiu3ofuqv3bftztlMvRZKkZwC8GcAl27bXWHGKWnz9HBwjOkEAd9u23Rq9934AfxqN+n9s2/7KthVUQEBAQEBgB2E3zc07WhhNDoWhGWbM1+GZBh++/N5XoH+bTgluFjqrCtBZFbXwu7px3OuBv8gHf5EPxKDwlqFhegCqacSorrXLgW0RRg+tjuHx4a87+p3TL+Dj++9aVyCtnh1C/Uw/evUE9Gr56+TEoye1GD2pxahcGsZvTJ6FDQsGZLRre6+YdiehamEYn/Y7+qDvHfopPvKaB9F4qR+aFaUhWybqZwfwtfLXxGi7SRjc9HMbJ/uhmgZU2JAsx0OXDACWgabxPnTk+dA0xlPFm0b7hDC6Q+Do8W7r7utzAD4P4Ktx7r8RwP7o33EATwA4LklSFoA/B9AULWaLJEnfsW17Lk4+AgICAgICvxLYbXPzjhZG7TQJ1ooM07BgqTJO/34pMurC2Bdibhuos+pZk1FXqxJ4v91jOjPD/dOlqljYbf6ZOsb+70W2WUBdOCS6lC1HQyxvakp+YoW3uru4yqgBNjH5PjXPym2PJ3JpKGVSXWEXqktopebAKd3ROxy/M6tBFjG4h71fwjyrAznMn55RskTi4BzOrWbDkBQgehrWuu8g9PREpPYy+iN1YxL2MdqL9xL/EvIiu9bziPsVj4ra+VFOv7NuMYDexEJAY+U+PB3AZ059CaplwICCx3Lehl5PVKhMZHWfMMb6j+11PoFz2l58/MD7ULs8hPaUEvSkFEGbYPHySdhKdVE8iFNxzjm4l/FU1OkVLomXUI8lSsVwUYEUF0U59jtxLyMZJhpnu5k+qGngtu5utKf6iB6pii6tEAlzLD+d6CZTirRFTMZ7L/Lfh57NKHWRVAUt+eUwOqM6oZIMSWIno6255ZB1Gy8drMD9ZxhVvDWvAkoESO/laUbh6GdUPxJA0y/7cKq8HGdLfBzFJzjMf1Mr6YQnFGblHlB4CnjfBNvAOlbC/FO/tMisBWdofH+kY8MdKd2xcGuI6W9SGg8ATJmsr88Tau+/LR3i4s3qLN6PZtl4dDx9IBbO8m7DztQ62M7dV9u2fyFJkm+DKG8D8NWoVdeTkiRlSJK0F8CrAfzQtu1ZAJAk6YcA3gDg69tWWIGrhgoTOeoSZImfHyjl1u10/jIUV5qgyT5wnXD73FQ4SselFOAwcdmyrPPuwig1l7pQC+n80idCriPElYZF1GRst5sWndAiQ4TaGYw/T1MVGs6txhqaLqHjEhor5/LFRW+VLJKhsr6LHQCATsZMdX2arq2uTw11ynOVjBrizkUiFFJLdrkkSSD3yHyuEjcxcLkxgXV1ZeDqMczGaonUgRxyzbGU4uxl/cnMTOKihTMInTud0MbT2ftEXB6DuHYm3t7IshFqAv+uEun3tP0tzwZUas5VkBTnd0AySB+mv7s+XZm48aP36CfqYtKCfNacaxcrgS+DQty5eBSWuUn6z2SQV0vTSLwI8WlDVXBmdH6tsCyxdRul5bvHIzpu5ahsDUBVEkL2jRGddtPcvKOF0Uiiis89+TpUt46jvykXQ1tsKVdg8+hNLMBjhe9CtXYJbRll6E7fHkMr7ckl0CUFtm3CkBS0p/jWxKmfGYBqOad0gIna8BgTRq8CPSlFa05bD62OoXZ1BO2JRehNLIiT8uajPbGYr59UH3pSivAHv/YA6mf64c8uR7+Rt+XP7cz14UN3PITjgxdiVnurIwG05JejY48PANBW6MO9dzbj2FAfzmZXoH2vL25+9SMBPPsPUevPqor3PfAQWvbHj7/VKG6bQdmZKQw05SLY5LlyAoHNogDACLkejf4W73cBAQEBAQGB7cUNnZt3tDAKAIO1uZhs2B5LtjsZDYEATvT142RFOc6W+m52cTZEb2IB2guqrhxxE+hJLsRjZe/F6+baEc91nj+7DIasAtGT0faEzX0fh1bH8PjYvzjUYEnBYwW/t2MF0subArWrw2jLrogJ1V1ZJejKcmi53ktX5w7oWtGZ60OfzKjZp/PW+iZtK/ShrdAHzxWo4seGCKXXMHC8v/+GCaPFbTP4wP2/hKKbMDUF//rsEYzXb93Yk392AQWn5zF2NMMhvuwA2MBmjSTkSJJ0hlw/Zdv2U5srlYCAgICAwO7Fbpubd7QwatoylnTvGrrObenMUmqJZzoWDhEqbbLMUy/o9RvT2mLhKZPnUYx4smPhJELHLVAZHfrfLzZwabwKowYsEZrQctjD+Uc9d5id0kWSGLUgNMfoA94lCfXDATz33JPQTAOmouDfjhzFt5qacF4ujcXTgnwntYiFOkqXoNbbZBf1wjvN6iSYT2isU8TqnMdlQTB5/S7j6ZvkrpeamHCSeorRIq29rH7loMuKHbHSp/VPxMKRA44RF0Px4o7BDmiWgTvm2vDxg+9HRzGzWDowtYLHit+N2uAw2tNKOd1VI529H6XuqDOM32xm8DScWn2MowYfVi+hI78SRjJvkk6mVvVSWf0kXmL1axCqcuXiMBovnkN7UjF6kgqh5xKatsz3dQ+hB5vpxIJuiPU5I8P5vRMH0YmDkIM6EH3H5A7WLhEfq3uAp1upC6wtZEJngouSpQTZvfRzLBzKZ2XzzvEdzbPMOqSexN7vyMBA7NS2K6sE+pSMHplsKMgqOr28P9LESZ4LFCR0Mi2H9VuPhzfzuLLA2j/fy+qUUnL2nFyEoptQLOe9k16IoO+AYxwtT2VS9GiEUc0PeMe556TKrAw1XrZ5uPfsPKrvnYAcsWF5JKjPWphscMYeShOi1F75Bjm83qQZq2nbtps2kX4MAKUjFEZ/G4NDB6K//2wTzxHYQmiSiXxtHpZrY5BavaTWcCn91m0ll97byDKuTuh4ITKxUYu5SxGeprsSIRRgg6SP8GWIBMn1Kosnh+JbxqUUR2pVVCX0SzdNVyLxOOvhrv1VSueU41B211iRJfFsjb3Dmq1be30KLkfNVeJTQLnnugYPm1BwLaKmQtcNhpcfw+NZcbWIxXkljkVZJ9H6eQE8vVSmqi3UQnGIV7WgdWcTunMoh2fKhDPYvUgaoeaS/Us91UVJDVHKLPvd9LLfjUR33ZM+SMptua0SxxvIaf266bdkjuGsH2/Q/PQ59FN2l4fe42i6Xr6gCRorFP3G51bZmsKy+D7j0dY3Sy1LjJq7ZPDtqpKCU/UCaoEXADKJ6h+12k3li0Xr6qzxbha7aW7e0cLorQ63f9QPf/Ld6Ki8snEf6htUMQy869RLeHvrGTzwtuYNKY63Ig4FRx3hMakYverGNNK6+QGmF2mbqF0KoCOvjIvTk1SInqRCQNtg8rpKcNRgWYU/vfTKia6AysVhfLr9GWhW1PpwyXvQgYObznc7UUnaqCt17ann9aBmKoDPvviUo98rq/iDX3sA/qRSdOb48JHXPIjaxQG07KtAe74Pa/webBN6G/NhKjIk24IpSzh/ZOtozeknQ5AjNiQLkHQbe1+ejwmjNxM27O02knAlfAfAhyVJ+mc4RhIWbNselyTpBwD+UpKkywr5rwfw2M0qpICAgICAwI3CbpubhTC6jWjqGHKsAVs2YJhobB++KmH0sm9QydAhw1Es1wwDTWOOf8OmsT74s8tjbjPcqJ0IoOliH87sq0B3esnWvdAW41BwlFnIlRQ8tu930euN78qiLaOMGeSJ6kUCjiXZuoVBdNn5jiC6RehJLsRjpXeidmUIrfkH0JO2eX3Y+vlBXqAODu9oYbQyOIrHh/4p1kYfS7obPambd6vTONHP9HstwzkhLXSE/c4cH1oqyq6Qw/ZAcv3fKiyc8MLySJB0G7YmYfzYDlE9sLkDlS2HJElfh7OLmiNJ0igcK3waANi2/SSA78MxHd8Hx6THPdF7s5Ik/W8Ap6NZfeKywQQBAQEBAYFfaeyyufmWEUZL26dwoGXSOa24jf2e2bqC7FMrmDmejPEdplt6pqYEuqoA0ZPR1tr1hZnG/iGcODeAkwfL0J1YAX+xD3ff3Yzf9p/G28++DMWyoKsq5r3JeOpbjnEXQ1bR/MaH1giktRMBPPm9J6CaBgxFxQdf/1DMkMx6qFwcRv3CIE5pB9GZHT/edqA2OMzRYGtXRzYURrvTi/Fo3X1ouHQhZqCnamHtSeNWC6Q9yYUw0hKvHPkq4M8o5QXqpO0x+LRVcLdR3eLgNQuj1TMBNE71ozW3HP4kR+BszS/n6Lj+7K05cd0MDrVOQDYtyDZgWzYOtExisHZr3EgtH/Gi62v5SD8ZwsIJLyarb/6p6I2AbdvvvsJ9G8CH4tx7BsAz21EuAQEBAQGB3YqdNjfvaGFUt2RcDKbhd0ZbcG/zCzHDIt95tg4TDenIP7uA4/cGoEQsmB4Z33u2Ni71rdrDdBC/Pn8sFl40eO73wSQW76uBE7Hw24qYnml+Eu/uomua+bMMhoiOSq2Chz/1LjS2D6O1thg/zqoCLjn3PKMOib5+OICvPP0lx3qoouK+dzejrRqQbYcAACAASURBVMCHzhwfOu/w4b9KjqJprA9nCipwbJj313h8sA/nExxhRl112OW/1nceqhE9cTIM3N7Zg5FCR+9NT+N1Hg4FR/HpjmehWQbuHP4pPlp9D3pSi6FMM5260H5+MZ7wUi+7yN8TC5oFvD5i6mmmLxc5yARMdYHx81sLDuHdMy/GXI+0ZVfASnZ05vRSlh91xdKHTJx55Rtj1zUtAe6k8bB1EZ3p+2FrvI5BJJ3oGOUyCm9mD9t6WvbxAqd3htWXHLHWDQOAh7htWdnL3Pyoq+yZWlQnszexEB+tuhs1oWH4M8vQnVECdYXoNrh1dVZZfUleVh5phf1uZfB9WJ1i+s1WHiuPW683VMl0mGm7UJ2Z9lQf9KnnY1Z6z+7ZDzPJea9IGhs+UrqZ7nakkG0KVS0M49NtT8c2R/7gxAPoyirBBbUIzW96CEfG+9Gy17G82zAyiMbJfrTmlePl3DLUjgfQNNaHU8sVaCvwAQASp/itQoPoXkfA6sEKuszj5zH93R8EmJuV6j2sTrQ6A2/R2gDdgqHKHE33R3PVsXBjGtOBntTTuefsUdjYkCQxPdwZMwUz9SlAvXMdDDMlmlmD6RPflnKBZcaaDgDwb9h62Ni0XorALoRHMlGgrLVGRt0fmGD9elFnY6vu0v+ieqYG0RmjLlsAwKD6qJw7FzYuBnV+jlsNszGYuorQQ66lT5BdK0tEV5Ho+Lk8unG6czK5R709Uf1RwOVqxKa/46pAXXNIpuW6R5X5WNhK4PVjJYnqLZI0VFdScs1DxLWKTeYoe42bFqInmsjCehJpY69Lt5A0BacrS12jJcTXM+V0Rl3aObQt3G5NLkMJ8fVjE91HqgcZynStKVLXd+ESSSd6nQl8G9G8JZPUCVl6yLrbLQ9Zexjr65w61+S5pB5oXbnthlA1UepCzc0Opd2B6oJu5NqF0xn1kn7r4QtuGCwhdY1okfpxd0eD9Aeq96qbxIaEwr+sKhM9UY2tBxIVXreUjmFUn33JZI3kudoPdhPYbXPzjhZGL6P0zDQxLGKi4PQ8JhrSUXB6HkrEgmwB0K0do4dF0VlVgM6qqAXWibX3jw/yAubR4b7YwhsA2vN9Ub05Z2Clp0mtuWupjP7sMhiK49PRUFS0baDnWDdHKKOWibqFQfSkbs1J3aHwOGrDo2hPKEQ78teN051ejEdr70XdwiDa0ktxHteuo+fPItRdWUFb2ub1OrcbPanF6Nh7808CrwaXrRjXrgyhPbnkml331M0PQDV5Ou5l674de3yxU/uaSwH83U++GBNaP2X8Nj76y29BMw08cFrFfe9q5r6L7cD5w/n4xN++BVWtF9HduA8ztSkxRkZHQyH6a/ZcOZNbDhLMLSclCwgICAgICFw/dtfcfEsIo4NNOTA1h+5qqorjGgHA2NEMmB4Z0C2Ymrxz9LA2QGPfEE70DOJM9kH4i304VerohyJ6Mnq6uCJu2o48Hx76rWY0jfehPbkUXevQarsyS/D7tz+AhukBnM0pw4CxviAIAG2Zpbwgdx0Gei774fR7DsQE2cqlYTw+/R8xPcNHF7LjCjHd6cWxe+pCeN04G6E7owSPHP0A6mcH0JlQvGXCtADDZary9aAtI7o5cgU67pHxfk5ovaO/bcNNmu3C+cP5OH/Y+Waa2gfxB80/gqKbeJPWgU/9/Rt/5QRSG0CcAwMBAQEBAQGBm4DdNjfvaGE0UdFxOGMcl3LT8Y9fPoGSMzMYasrGcE02YADTNalI/koI+acWMXE8DcFGD1Lh0A0XTN5Nxw+WGc2uwsuoef4VXnjpXGF+JA9mXoqFJ8KMjres8+bj5yaZOWkthfF1Jnv4hettbUN45h+ejlJyf4L7fs857Xnorc0xKm53ug8a8zYCbYX1Ru+ciX6pEP37CteY0dZTGJ2gVy5Bb5pz+qQuMQoCRwcFcM5TgI9W3Y26xUGczWXGjminSBhd5NKgiFFuDxrjMT+cd80+j88dfCu+V3gch6dGoBI9w8aL53DByAHAU0DVJV74lEYcNxmV+iRqplZieqGRPSlcvKRxls5MVNGd6UN3po+jiSz6eOpN4hQjPHjnWHjyOKOSJbkpoImEcpbBasX08HVvElP1SZOsjpf3sTJkXVrh0ljZpA8FCe1J4bkuBqE/RzIYj8ZLTtkjGfy7aumsP1pedk9OdtGQL0yxC/JcK4ell2eXaRJoxDy+Z4xRVexEQmkOsd97EwrwR/X3oW5uEG2ZpehJKYrRnDN7WDueDxdwQutJTyWOSIOA7Vx3JZQhdcTCaq6LfkssvKcMEsouz56FNc/Kt0qar3eafaMV2dM0CQ60THKuXg62TODc4Xy8OBd/w+i1hedi4RGDFcICX25K/8nRGLV3xmB9fZ82H/c5AgI3EzKAJNlEhs3zUFdUNq4tcG6K2LixYvLjkEm4fhxl18X7o65eDEK5jRDKbkh3uYMh1yahA9omn7e8yq61ZVYelQzbLjYfx6HjKLsknrbiotLGWVyuce1Ck8VJI+luziWl6ZLkLsvynNsX4qbFojRdl5qLpbqpo9F4LrUSi9An9URKQyWuS1w0Xer2g74rrSvTRXemLtBoN9mIukq5njS9mxZLqb50naXzyxDoqSScQuoxmbSLxhfIIn2YUlf1Dei31LUL7U+yy7sJrQeuXSjF1tWMpkbXHiSJq2tx9R2HIu0+xONovwpph7BrjUP4vLZJ64Fk6KI7U8b8EilQhNB0FZn/cNK9zM9SispycLuOpO6lFgw2hikqK0PuriLQ3hjsaGGUYrQ+C6P1WWt+n25IxXSDMyooN9cM8hVxNOCi5I44pz2Uinuj0ZPqnCaaidfeFWqXh4hxGxsPn/sOBlPy0ZZZytGJ25Ov3qJvpT6Jxxe/B23Rgi4pji/R5JtjWVVga9CdXrKhVefKpWHULQ7iXw68AgfnxvCT4lr8JKEegeQ81C8M4qX9B9GZ67txBY7i/JG8KCPD0SHtbtzL3d/fMYHqs+PoatiLCzXxGQg7HbuJCiQgICAgIHArYDfNzbeMMHqroOH8MI53DeJUdSlaUg9x9077eEruvDcZHzj5I/hzKm6aMLoZtKeUwIYEGzYkAJJtoW5uEF8vfTUeOXY/6mcH4M8qw0Bw7SZCPNQa49BgbehLVOBXB5VLw/ir7mehWSZk2DAB1E8NYvxwFnrSitGTVozl3BvjYNqNwdpcfPaJO3CgZRL++iL01TCd5v0dE/j/Hv5PqLoFQ5Pxv//2zVclkGa2riDn1DKWGry4WJ95xfjbDRu7a8ITEBAQEBDY6dhtc/OOFkYtyFgxEzCv85Tb6pSLsXDYZkf8IZOF9yfw1oLaDHYyM0/oQ+1zvCuRg+mMmnt2sgDrwXBRfBSvw5douDCMr/7Fc9AMA7qq4s6HH0BrmS8Wr6XCh/ff14zjg32Y15Lxxz/4VsyK7v3vaEb7Ph9nlQ/gaSKWtj7tBQCyfjwYC9s5THeWWruTgzwt1lYJpSFIuSmMoxEq4g1CJQ4zC4qdeeX4nPUWPNz/XUi2BUNS0R3JRcLwPHpKmP6mojE+yWoeEyyUsMsqbXIJWhclvLvDH7Oweza3AjOHeVoXpacokfVPwzMu8BW5XMD6RtIlk4RZPYbTnbqiflovbxIoYfYcM4GveyOR0ExINUYIrWeuhueNGiSPVEolkl20J4V9omqQWEgktFjPIs/X0XMZ9ZjSfmUvb2nSyGdtqxAKt+VhL7FayxuVSmkZjYUj+5nw5Tk/HgtLSbwFZm2e0WNsheW9eCAFhzrHoFrR03VEByTTwOHwMNoyHDqsm45EkRYgVKdkVncmz6SHtszqYUVl7bVCuGDDGq9zPhdKRG9xHv6r+DAkyQailL0s7yoOtU5A1S0olg3JMPHb5/wYT0/DpW+kYPhoFsbqMxEm9KMHMk9Da4kg+64ZSDqwX7uEbz7TiImGdBRpM7F4jWTcuqDfGB14N01JQOBKuKzPpNv8EoLSbBNkN691fSzrbE6Qyfe4hqZLKHjue7HfXXOzESHUU0LHtA0XTZcaNCfTpLZCw/xcIxvsmtIiqcVcLeian+x4tFHEjcdZ4CVqEjS8BtT6rYen6VoeopJB1hRmwvp1Crgs6G5gQZXSObnxmFBzXQ4MuLGadieat3vO5UhwtEpd5aZUT1puSoW1UlypyHOpRViDX4by1NwkQp9NZp3Jtvi8bWX9PkOZ67p7SUOulcj69Fsgfn251YooKJWWpldcXSvO58b9brmkCc6Kb5iWwZXZCjX9S/LjqLl8GmpI2iLUXmqZNyXZtd6l6gCk8lZNfl1EoRG+spcMEsnytds3uR7sprl5RwujtxpOdA9CMxgN98SFAU4YBQB/sQ/+Yh8e+vGPeMruaB/a9/nWzXcn47/2HkUgOQ+NY71oTyxGb+L6AvzVoietGI/W3IP6hQD8GaXoSbuxBolqJwL44neZn9YH39J8S55a3ypozSuHoaiQTAMybBjAjvE7uhG6GvfB0BRIhgFLkxHKUPDGu7sgR91Mff3pYxio5XXGE16KQNIdfRwFFgpPz2GiIT3OE24Mdtvuq4CAgICAwE7HbpubhTB6lajtHcXRjgBO1/jQun99AelkVSl0ldFwT+6PTy99ucRlRbcwvlGUnY6etGL0rW4d5bAnrRg9G+gYbieaLvY5Vl2jmwRNF/uEMLqN6Mz14cOvewiNk/1YMb1IjwThzy6LuX/ZCLXjAZzo70NLfnnMRcyNwvnD+fhff/sWvONcK8aPpWHvy4uQo26mbN1C8enZNcJo+DYPUqI77ZYiIfViCPlnF4Bj6zxAQEBAQEBAQGAXYEcLo2nKKn4jowsrFs+5q/cOx8IdYeZyoiSBWcP82VIllyZdZVTBQCgnFk5SeSpRmhqKhRNUhx9Y0zOKv//Tf47Rb99/TzP8xb5YPMnjcAv8yQfwgd9pxtGRPpwuqsBg0IfMTpZ3iBlGRT9K8KHffAhv6jsDW3Es9SlhrLGcR2moaog47W2fibpVGUZ7YjE6Sw7G7slh9k5ykNFV7QlGQQYAaR+jYJoeVsdSLqMHUlouAETymAk5ZUVH5fII6hYH4c8s49yq6KmMehHKIVYQCV3HTnNZ+VNYGdQVRo9IH+DbiFJuqTU46nja1HjqxZLP+V8/HMCvLTvt01bg42hYZgJwqrQCH2iJ+mmVVbTkVziUHsqOcVmao1b2KFUlkVjnDafF3+GaqWbvo4T4e7YC1F4M4OhoH/xZTOhKWGQPCqfzeXtnWT9ZzWZ1nJTK13fyILOUvLSfndAljzLThd5pvkOae1knNohTck8y4zBF0vm6Tzw3x9Lns02LxCmnXftRgP68AiSMLwEJAJYBNZFVskL6vSfaHavmh/CZ01+Gajkn2B95zYPoyvLF4gX3uuh65DJxjJXbmmL8qBJ9FCd6BnCysgytFSXI943F7g3MsPe+TPd5qbwML5Q7J7jVvos4rI3BEz0pnTmejCTCuX9s7I1APlD++Uu4/XsX8KrvXcDhfx1D5bfH8b1na2P+kU8TOtJohAwYAIBebDVsSDDdtCkBgSvABhCBvGbnPkL0FKjFaArd4n+PkOsQsYyrusyKUmoupexSSG7qIqHwYZWlkQ0+okzoj1IcNRDPMl8ebZmNURa1XkrD1gbUXkp3dVmvjWsZlYvjsixOwlYCm1OMZJeF4WRWD5Q+qxPVnzUWXWlZNzisoTRdSnGldFC3CgWlvxqE+srRTl0WeClLUopD2d3wHm0i3hi967mEiutic1pkjoKXhTUv60BGhK97Q6XqPuu/q7uCqeoWXe+YrnLTl6LvtJG1YbpeiWfpGeDVj7h+u4EEQdVrVOIxwArz78dZTU6gvxM6uckXjn7XlKYrE4u3tut96PgRNFgFuamwMqkkqjZgkvQaXIvAbcBum5t3tDC6nShpm0bFmUtQqg7g3OG9G8Y90j7s0G8tGzAMHB/s44RRirZ9PrRF6bbayrpROLy57wxUy8Bbzp/Bg29tRnve+vm6cWh1DI+P/nPMl+dH0++54T42K5dH8Kne56DZJt4z/nN8tGptGapnAqifG0BrXjk6c3xb+vyayQCOTPShZV/FVdVb/XAAX336CWdTQVFx3+81ozvDSVc7EUDjZB/OFFbg/nc2o2m0D/7sazcsVTcWiG1GnPds/nS39mIAX/rmE9CiwnHzGx+64aeAOxH1swPQLMOxoG0ZaLzUzwmj15zfcADPffXLTN/7Yx9A2Hf1w2NX1T78wad/F+8ffAljxzIw2ZCO9ear/po9qGwZh2ywU9S9L8/HhNGbgd2klyIgICAgIHArYDfNzbtKGN3nn0PR6VlMpKTjf3z6LBTdxG9oPfiTz75tQ4G0pbbYod9GF6qnSreGUntkoh+qRSihY31XLYzWrg4Ttyom6hYHb7gwWrc4yMpgrS1D9UwAn3v+qZj+5Ude+yD8haVb8uyayQCe/P4TzsmYrOKhNzfjTJpvwzTHB/o4nd6jI33ozvA5eqLffoIZk3pnM54+dsdVbSZQ1F4M4Ev/zvL50Os3LzgeHSXugCwDR8b7hTAKwJ9VBl1WYUf9kLbu2ZyO6bFAH7fhdKJnAD+/48A15dFVtQ+tr3c2IPLOLqD61BhGjmatsZrbc2QvTI8MW7dgaTLGj90YQ0XrYbfppQgICAgICOx07La5+ZYXRnPPLiHv1CImj6cBR+PH2+efw+/ddxpKxIIlSZAtG7IN2LqJ1/6gFzX+MXTUFwC3r03bUVmIu/70HhzvHsSpqlK0K77rLm/9aABHh/rQmVSOlvxy5o9TUXGm4OqF3PbEYuiSAts2YUgK2tKuT8g7tDqG2uAw/NJ+9KQUXVPatrRS6BejZZDXlqFhesDRv7x8ejXZv2XCaNN4HxPkLQNHLvbhzH7fhmlOlVWwTQVFxekip76bxnj/r02jfbHT7WsBJziaBo5MbF5wPF3IdIsNWUXL3p1t2OdGoTujBI8c/QBqVgNo3eOcuq91Fn71eNlXwW04nay8fndCeWcX8NZ72qBEjRn9y9NH0VXBrHb31+zBN55pQtHLsxg5loXlhpvjusaBxNGPBAQEBAQEBG42dtfcvKOFUdOWsGIlYMniF2tjhnOSkNm6gjve3x1b9H332TqHHgfgDWntXJrpF1OZgRHJhiXLsG0bpiLjju/3QjYtGJqCD3ziLrQdcoQyk5jlPl1QjtMFjiAgRXj+XdIII9RrTN0Oaognrf/6LwbwuRfYSeFDb2rGQ29qxon+CzibU4Z+uwiJM/yKOnVgORaWQhFUBkdRuzKMdikPj+W9A7WhUbR7CzmDP/ICK4SdwBQd9Mb9XN61Fzrx+MVvQLNNvHvmBXzs0N3oSS2CtMSUFKwkJ33l8ghqlwJ4vqw6JmD5Uw7gkawHUT/Tj7PZ5ZzRGWXVQntqqSNsk9MrzqS6xu/6UH2IoryLODrch9PFa6myphd44XAF7vMzIc2fVQ6iFhzTEb0MbQnoyvTh3nc1o2HhAk4eKENrWRG0BeCXZgXub2GCyAuHKxDKAVZKWGHVZWrDnM/7slrBi5UVeOAUM0r186YKLBQ775TIq+vCIKbcw8QNa+IkH+/0YR/uTmvGsUAfzuZUxIRkI4nVlbrq0qegJtqJzlMok9ezmtvPHpwyTvVXiLn/RD6NRNwCeKdYhVtp7IU88y6z5x7WB6kOsyfk8tlCnkvdD3DPJ7ojPWklaNnPNnCoPlbKiCtrYi+funFYLnDqsSPXh7sefAjH+/txqrwcbRk+mOeI3moqSzS+wCi1WSnkgwcwFs5E+UuXYmMNdAsVZy5hpjGFi+c5ZmDyWBo8MLAQZkpTGQrL72pdYwgI3AxYtuTQ5ONAj6M/6qaeGRZxu6SzscKIoxd6vZCInqi64nLPRnT1qa4b/QRll88NdZndlHSiq0b0P936nqaXLbmorpwU4ScV6tItnv6oneCqH+LWxiL6/FRHFADCGSyeTuYRc6M9sTgbfbLbBUg83VJOD5NPo6cSvbw0VvkS0a+MePl3UEj7cZuQLlcq9B6tb6oraauu+ZPqR1JXLAmul42jJ5pAXNlZLldDkof0E5noo5K5T3K5HaJuWgzmtS2uuxU34qjKOveoLQzS79e4aZHXD9NvxV0e+k3RdnC7jeFMwpDCcu6SXDrels0yMdX1O51l8QUK6+yl5I0UZOOAunbRsbVjk8AOF0avhOxTK1AiltO5dQsFL8/HhFE3zh/Jg6kpgGHBUGU8+/u3I3UhhKSxMN7y/XaHnqebaOocigmjWw33SWHTeB+erb8DferVPa8yOIrHB/8ppif6WN478I2M6zfFWbs6sg7Ndm1ZKpdH8MnzX3V0Qyd/iQ++gdFPuzJL0JVZsq5Rg67MEjxy7H7UBAdjp1dXg8MzAXzuF1+M0V0/8LvNABDTxWwt88Ff6MO9723GK/19aMkrR2fu1eXdVujDS7fx7+gv8eF99zXj+EAfTpVVxNUHvhL8xT7c967mmBB9vfmsl6+/2AfP/JZkJxAHZ30+nPX5Np3PYFMOTE2BZJgwNRljN5GGeyXYcPw5CwgICAgICOwM7La5+ZYWRmeOJ8P0yIBucYu+vLMLKGydw8KJRCw1Ottwg7W5+OwTd+BAyyTO1Ppw/nA+ACDrzCLe+MMuQDdhaArOHN4+lyJnc8pgEMrlmb3Xpntau8LridaGRtHr3cfFqVwcRsNcD9oTHYGrdukiFuREpFuraE04hO50ptPZnljEqL7r0Gxjz10KEKH12uinXZklOOu7Nspjw6V+ju761q7TeGvXmZhweu97m+EvdATSAWNr2stf4rsu4bF+OIDjg3142ecIn20Fzt+NhGPIqd9xcXKVOscC24eRumw886XbUX92hBkzuvaN2BuG3aSXIiAgICAgcCtgN83NO1oYTZYjOOIdwnOzvCJnkTYDALjUkAb/PxYh82QQcyeScHp/GYpfnsFb7m+HqpswPTK+/Uw9JhrScVtaP/AKYPEViTg/lR/L61z1Xjzy6d9BvX8E/voi/DLpEDDl3JNC7CiedomEWReNgtBOdGKmPNHlFqMjrxQPv+pBNFzqR2t+Oc4lF8OzbEEJM8pB8jBP+6O26tsyyqBfej4mPPr3VcJMdQRwKWJy1m1NyLABqFHj0BaAd089j8dK70RPsuMOpzu3Ah9Pugu1y0M4u6cCPWmOoGrmEi6ILOGsfQDvmfgl7KixoLb0MmjLNiRCiwxnuahA6ayOKI2U0ksNF11ndY+T5heJ+3F3749idFdLAyecHgv0xQS+yV9nVBl5hVDBvC7aUwIrjxxhYSOZ0GbSXLTRZfZ5RDLYc6R0h67RcGEYX/nK09D0qJB8ZzNefGUBew5xJeA2w295SJ1kEFpPAv9JJo2tb679MrW3fjiAL/zfJ7lT5NYqXywepQe7dSpTx9gP1F3NbDVr/+yOJS6NvMQouEY26+zqPKPsSi6b6os1zJWStkLctMzxfmwI8waJA7Psdy978WAJb3XWO8vqziRtnHmO5V21MIzq8BBac8vQle3DYjFrF4UwiqmpfQBI7mNtsVJJaEGk/4wv834KhtOdhhk+kIXWSp/z4xJQ6+IN96yyfmKR0SVks3ct0Oaw3bDt3aWXIrA1uGxcY8X2XDGuG5bbHQxx7RIxiMsqg59TKG3XInRMSvt1u9KwCeVRJmnc7rmoiodCXGkoHL3ftaNELuVVMpCECU1XdY37GpkT4rhscedNVRYMumRLci3fyFqBUjspLRcAIsTNGHVLRl2suJlOtL44erFryozHfuTVc/h7dK5WkliGiUmsIUIJfD8zPITuHKEcYL7gSpjOn4Ryq22wO8i53CFl8/Iv6yHXHkLN1VRWWREXJ9X2MqqnqZO+QJ8Zir++jMShHQPgqNRyHMqtew1A423k7oaCa3PaFzbIm86z7rzpe8jkk9+o3FTFi+tbpP0pLRfg1e4oEl3uHbU4xieWSEOE1A0qaIuw2+bmHS2MXg0WGhOx0BjVV1sCys5MQdHNmL5Wwel5TMSh7l5Gd9U+dFdFTxgDa+83DgRw4kI/Tu4vR2uZb1Pl7cz2oTPbhzgu2DZET0oRPn7gfahdCqAto2wNpZZat5WifiVkOOOFAjinqStDMWEUAHqSi9CTXAQzNf6ioju9GI/W34e6+QG87DuAzmzftRf+GtBW6MM9dzXj2FAfXi6pAGTgt/1nANOAKSvYuzCHutEA2gq3txzx0HBhGCd6BrBvep6zzntsqI8TRm8Ejg/yRpOOjvShtdx3Q8uwGVQuOX5q29JK0ZO89fT4qoVhfNr/dMwf6cOvfAAvFQsjUAICAgICAgICOwG3vDDqxkBTLq+vdXRz+lqNAwF87e++yPwPfuRBdGVcv6XNzaInpQg9KUVrdlwB3rqtFT0ZVWBCgePy0JAUtCdfH621O70Y3enFWM2+9p3w64G/yAd/kc+5kIF73t+Mt7WdxtvPvox3+k/ibR1ncO+dzXippHDDfLYaTn94GpphwJRlmLIM2BZ0RXUE5xuMU6XM2i61EHwroHIpepJvmdBlBR87ePc1W3S+EurmiT9S00Dj1ABeghBGKdwnVQICAgICAgI3F7tpbt7RwmjEVjBmpqM+eZj7XSOckXkribs3XJeNL3/plSg7M4WBplwMV2cDEWDFYHS6wALz+6fIPNdBm2VVYqnAbb0D3OnXbecG0FvLC6MpF1kelM4STuMFxsxzQRKP0DRT2JG/PL3ApUESs1JqZBBa5DRPn8RqCOeRhMdy/gdqlemY0FkljWNBS0K6HkSXUsAv9gmdMpTD6odSbJOmeT4Tpfis5rByR1L54iQssvByEUujMePAWDrA560Qi7X1wwGc6OvHyYpytNQX4mRxARrnz0NpsWLuXBrmL+AlD9OZtRIIZVd10XRTWBmSUhlnJLTKhGtz1U17YvWTuNdxOvqKF3uZP0pY+Nc7jmAkPRsnD5Widf9epKUz56RLM4zu6pvu4AAAIABJREFUqntc1A+dvauySPpcEh9vmXQ1dYEdpxtpzru+nFWEOx9+kJzcF0MJU8u4hKbkej0voZtTGo1nkfSLvESaBGYh64Mpg6wPXra6DAB6Gk9d9c4wGoxnjFlhqlsdhGYxA1r10xdw3nYovXoe61D0W0nq5604LR9k3zKlvZlRaltrbjneG/gpbNuAKSnI0edx9NxgTOc5lMXSeC+56GxkHytxgL1fKJdQt/JWaRL8spdZrH7loQux8Avz/CbB4dSLsbBOeEtVCez3eZOv++2AQ7fcPVQgga2BCRnzViLmzWTu91mT8T7DpF/T+Tdk8hQ3ndBvIyS8GuY3PimtkdLxKBUXEb4vy4TyqARZGsVl8Jsy8+QIrg5x1olSkGUuqS4rsETlwCLWcE2XZVzLs3YsAwAzY31qJ8BbM6U0Rp1vIp6aS+7pqaQS3PMVsWZKLZu6qaKSvj59klq5tVzqEEhk87Y3kVV+RhIbWyMevlGWPIwyGV5ldWrrLoortcqu0UbewMYseSmVpKFlA3g6rlclVoBJesPjooCSQ4QwKUOE9GGqwuOUlZaNldVtbN1NPY/lRz63Ne1FqoR+lhtRbrl7pOo2Ks9GbFMunrr+72uovZQ2Tr55S6GUf/6hdhIroJVA1iQyX3GrpCKWTTZuJSmsz61Y/BpnO7Db5uYtEUYlSXoGwJsBXLJt+/A6918N4NsABqM/fdO27U9sxbPXw3BdNobrsrckr5MV5Y7/wejJ08mK+KcqNZcCODLef0XrrpWLw6ifG0Bbeil6Uovjxrse9CbsRU/Wwdi1P/9QLOyZWV0vyY5DQyCAf/jik+w0+tH70VpRgpMHy/i2OMBvCjT2DeFEzyBOVpbCv00WkU8f9kFXFcAwoasKvvXqepwqvLknba1lvk3Tx28G2tJKocsKbMvRgW5P2tpvAWD+SF8358eb+17Gb58/iTf3n8EHf/OhTfuA3WoUtc1ib8cCFk94sXJk+yc7B7tLL2W3YafNzQICAgICV4PdNTdv1cnocwA+D+CrG8T5pW3bb96i520JanpG0dQxjNbaYnRWra/rd9bnw3ubH8KJfueU7qzPB++ltfFqLgXw9//9ZMyH6IfueAj+orXWaSsXh/HpjmehWQZ0WcFHq+9BR4qgDVIc7+9np9GGgRPnBtBaUYLWihLc+ej9ONE1GPUT6gOiurGNfUP42l89HRNg3/vH9+Ls/q0XbgDgW6+uhw3g26+uh/9gEbByxSQC66AntQgfO3R3TGf0vJVz5UTXge6MElRHhqBYVsyt0rVYhL4RKGqbxd33Pw9Nt7BXk3Dun/IwX39jTkZ3k/n4XYjncAvOzQICAgK7Gbttbt4SYdS27V9IkuTbirw2Qu7ZJeSdWsTqbSrmGpOvnGAD1PSM4guPfR2q4bh0+fDj795QID1b6tswv8bJfs6H6JHJ/nWF0fqFwZgOm22ZqFsYRMdeIYxSnCqPnkZHBcuTB9kJaGtFCVrXqde3v9AKj65DseEIsN2DWy6M1p8bwTN//hVo0VPRb7+6/qrSNfYN4UT3AE4eKEdr+fa5DroV0ZNaFDPEpSxs38l9a36541YpahG6JX9nfXP13xmGGrYg2YAMG2knQxi7uu4lIBAXN2puFhAQEBAQuF7cSJ3R2yRJagNwEcAf2bbdtV4kSZIeAPAAAGTvS8CEnoFxPQMF/jn8xge6oUQs6H+v4C8//yb01eThXbmnYmmnI0wZYmSFN1xEeeGqYuFo5xBUw3R0/3QTB09O4id5h2Cls3hUfzSFqK1qQZ54r67aaMsgPkQlFR3JpZxZeMAx8+7PKoc+/DPHTYqkoD3ZB8800yW1U3kdWGlihoXTGGfdyOGVNNVRpqdiJjOKnxxZ30w1AIT3MIE+kkr0RC8xBYH5/TxZn7qr4XRRXGoXq7nkFinCUhm70GZ5PRkz6maldX8J3vOHD+DEuX6cPFiO9pp9UMDKlLuP6SpOzaeg4fwwfueXZyDbUZ69IuPUYR8kheh8JjN9j6rcyVh4dJlZWs7wxheGipLm8YYfdcBjGFAsAIaJXz9/AYMNuUhKZwqyI9NMh7G6fBSHOsfxF3/zLai66VCOP3YfWveXYE8xc9vha5nC8e4ATlX50FbC04v1EDFhn01csYSJ7tAK30bSCmvLSCbVH3X1W6pPRbyshDKJPpbCtxHVHVk8kLbu795pl27NHKvXcAmrn4RhXv/TTmQ6YrJO9DKHGRVhtYY3WJUwx3Q/Vvew9J5Z53uoXHas9fozy/DRSucU9uTBQ+hJL4EatuGdjdLrJ/rRpfnQnck2C0ZeR3R5iZ6Uto+4X3Lp4KRlsWPy3tk9sXBhmksXnEA+ZaLhW8OQov3XkGV87dBx7Lcm46bZSphu5TOB3YZrnpv37FOh2wrn7gAApnU2L82QCWKJ6IwuRngKelBn322YuGYxwi49yjBZrlC9ReJKRVnl+zLVb6RjnFsnjrqrUCJEZ5C4JaO662sgxfmGwrwinRxh6wuTuGaxVT69kcTeSU8krmuIazTTxeTndUZJ2GVzkLpUM1JIRaSxsqoJvBsTm/M9QvQ/Xa5ULJMUgrooobqcKl+PsofoXnrYc73E5UZGAj83JxJdzBmF9bNQyKVc6CX9RCbzJym25JoXFRJPJa5ZvBrflh5OZ5Td04mroiRX3dN6pK5GdPIcy8vrMHL60TLt6y79aFo88kqc5wZXF+b0ScljlavUR6X61WrYtd6lXYaUQXa5A6K2Hji7FqSfuvVRlRDRnSXfuEl0ls1UvtBUl1cmFbGs842kkvZPJBVhaay+RyNbowZ4JeymuflGCaOtAEps216WJOlNAL4FYP96EW3bfgrAUwDgO5wS6zElp2egRCzIFqAaFqpax9FXk3f9BaothqEpQFRIOFm19rTtWtCZ48PDr3oQRy72wZ9djq6s9U/ALrtJaZi84LizSCmCZMb50ncxWstLYqeIKtiA0HB+GK8d7OWo1ce7B6FYjt0xSwL++w1VOHtg41PRA50TqGq9iJ8dOhD3RNyN80fyYWoKbN2CoSlorb3yyWuNfwyabkKxopTj3kG07md9o6ZnFJ//xNdjp613/6+7HervLYTD044w15pbji7i9qdqYRh18wOO4azkG2v1GADnd/c94z/HR6vuwT8XvArLOYz+WnMpgC/8IEqvl1U8cvR+TiC9FtSfG8bt5/pxusaH9kNX/75VreOQTTvWf198azkGa3OxH9svjDpOoHYPFUhgDa5rbj5Qk7iBZCYgICAgsBnstrn5hgijtm0vkvD3JUn6giRJObZtT19tHkNHs2F6ZEcQUGV0N+7dVJk6qwrwkU++CwdeuoSTVaVbQunszPGhN/nK+XSnF+Ocsrny70Y0nB/GP/zFM9AMI0at/um+gzhVVRqj9Rqagh/+ZuWG+RzonMCfPfxdqLqFd2it+Mgn33VVAulAbS4+84XXI/WXQbTWFqOj8soCR0d9AXRNgX150+MQv+nR2D4M7fIJvWHiWNfgLSWMHp4O4O9++sWYrvTDv/4g+rHP8e/Z9rSjGy0peKz8rhsukFK/u7Zlom7RsdFS2TWK1rxydOb4cGSCp9fXzw5clzBaf24YX/nEc7FNhfv/z104dzj/qtJ2N+6FocmAYcFUZZz8rRvrOsraRUYSBHhsxdwsICAgILD12E1z8w0RRiVJygcwadu2LUnSMTjGqmeukAweyUSRNoOg5cHykQR8+5l6FJyex4u15ZitTUUWgvjG1LFY/L1eRoVrC+7j8tqTzHyKjAecI/bxpGz86BXRxo6WxjvNGp+aPafUHbeLDC1IKIVBdsqZuszzEegxv2oRekxXH4vTcIgmgZLCaLtyiORn8xvTq9XsfROmgqhcHsEdM22IpGv4r9ImdOb44EniaU+UdrSyj9EBloqJqXTXt0BdhXDuXFxsAur+IrWI+HkZYtROoyhEkyA1nVFxZEKd2ZvqpP+tn7RHabIOtbrWP4rWg8UYOJKL5r98D+568RRg28jxBrEnk3d9k6Cwuiv69iwU3YJi2bB1E7/eewHTR9PQkD7Cpfn3QB0rq+VUxGBZFuRyp2yZcOiak8tORdT2jOItZ9twqsoH/8FiyJKN8zX5+NPPvg1VZ8fRXl+IleoEHMQE9nid/jh9eyr0f2LWeXuO7ENmMquH7BxG++wZZcJNWgajilouF0KhLOI+gJi9xzLf/rT9KBuEMO1gXeIbViPVqkSA+tkBJsyZBhqm+tFR40PV7BDUy7rRMHEgaQInK6uRdoFlsFqWxeWdGGDUZWWW9JkIOxnXFl20t1V2nbrEqOpGigdncyvwnvFfOJR4WcVMejr+quu5qPEwFY/W3IPTvgrc1+bokuqKiudrD2A+36mzjPPsOUEiV1rdxH1FrvO9N512TsAdo1smak+P4fkc9i3X5TKXLQDwsyl2+GSVS/ijv34n6tpGkfraMLwNBioxjikjDduN3WY+XoDH9c7NNiREbHVN31klnNCpEBtIpkNsHlt10eKWQ+w6EiZjV4Qfryjtk9JvZX39MACohFGvBBEXlJZI1WvkMJvHKMUWAOQgSySFSAZ0bvby72oTfqipUSouX486oePqxC1ZhAwJLoY0N1dzlF2XKxWLum1JJrRYosqSncpb5TOtqxsjghHWfiESjnCqJK42Ii7ZKEVyI2QS2u5KJL7vc5lQMxVl/bwTXfRbStONGGyx56bpJmvr+wCiz6TrDjdWdZa3TurAnYJSe7lmcLv2CVHuMaEnU9cprjS0D9G5XXIVgrqEoflphMauhPh+ZiaQh1HXMC5OBVUfomtrSt9VXUYiKeWevgNHO3a79yO06CDpM25fnrQPpmmsQBNhptK1aLg+vm3Abpubt8q1y9cBvBpAjiRJowD+HIAGALZtPwngnQCaJUkyAKwCeJdt29dM85loSMdEQzoCwVzu94qOSVS2TkDOsZA8H0F/Uy6Quy7TaFtQPTeE+pl++LPL0Ztw4+mIblQuj+Cvzn8Fmm0C08BvDZ7Gh1/bjPOe7T9xqx8J4FigD8/Xl8V1N9IwGHD8YjaWXNOJ9EJaIixJgi3Z0FUFZ2r4E6xXfO8CVN3CK77fh5FPZsQ97WytK4pRtE1NQVfD1dF0N0Jtzyi++Cf/GDsZe/+f3Y3V/8fel4dHctXXntpaau37vrSWmdEy2jUj2QYbEjazQ14IYBtsg8czrA8SAv5ekkdCEggh5AUwNoYY7NiPPYDJw2ADAa+jGe3rLFpaGu372lJ3be+PavX93ZI00oylsQbV+b755rb63upbVbfq1q/u+Z2Tat2wzh9NR0fJ5uOitywVJ/7hdtR2DKKxLBcXS3a2mrZf0LQuDBRcGW1JsYSBmlN5waDmlGsvGNQdm4NPld+NioUBNGUcQuVMHwuQDQ2VCwO4P+1WnHzzKdSM9eJsdiHa0zxX9VunDxPbIVnG6eLtVzeLu0ZR0TqM1opsdJdmoLs0A29Nb7+q33fgYDNcq7nZgQMHDhw4uFrslprue7b5/muw5OV3HYUdE/jMR56EHNAtARsRVj7fP2btiEb5UlE24cWXX3wIcvCh+y+qPoDu2JdXMbV8yQvZ1EPve2RDR/VkHy5kWcGoFTz3ozU+D91xu9fXyktefPvRB6DoGk49K+O2j967ISD9s+dfxOd++BOIponAUzJu+8wHdhSQFnWO4eTXnoFoGDBEEf98z+vQXpwVSkSv6RiEHFzthGagun1oy2C0qyQTH/unP0N12yV465K2pFOWnxsOBYnjtXGb1inuGsObGtqRNrXA0W3rur343c1Fm7axo70oK5Rj6N7wXnR/oyPFgw+9/iSODfWiJaUAncGc0c4kDz76R/eieqIPzSkWJZaiePkSShZH0ZqQf9U5msVLQ6ic6UN7VC56Ijd/0dIdm4Pu2ByoMdYrU01kAXJrrEWZbk/zoD3Ns4HxcCVozvfgtv95AvXeizhdnG+NaXXr+sVdo/jin/8nFFWHqpzBp770J+guzdi6wR7BhHCgRBIOGl7OudmBAwcOHFwdDtrcfC3VdK8Ya6aCbn8mEqRl7u9HIsZC5YyWeciqDimoRCkalhLnjT39GKxivoWza7xK7TpEv00NjjA+XIQpSOk1UeMsYLih92LIqgWGhoqFAXQleAAAyixPQw0kMOEUqoqGyiOhojS1CA6q9VtF/jGUr0yhPSIH5yKysJoTy1Wj6nstaYdx+9jvrZVRAJoooTm1AL4UGWWTXnz5zDeZH+rrTlp+i4TRQKkOpo0ppRHrw0ACoVeEGahuvQhFD/qD6hpuON+P1pw8eOJnAQBHOsfwuR/9BHJQbMilaXjzWDtW/siSBRyaZUqrhUksZckl6ahpH7KEgEzANE0k+5bhVlRkRFnHa/TGeOjfY3l3kzfGoDCWbYNSMdySCuMmGY035aG4cxQ3frcPF2tTMFTBFNI8bdN4+K8fhRQwoLtEfPOhm0PfzwYs/nZ++xQ++cmnIKkGdEmAIQnQYdGvZm+J4uget6Z0csfxB8M1oXKsm42Tkrhxrt6UPxqHO8dR2jyKnup0XAgGzt1TTLxLlnj6mDrDKCRiDKMSGeBPpnSI8XLWfOyki+NMplGN5K8Pjv4VVIA8nZ6HFk8e+bv1/5nkfJwpzkfEJKE9JbhRMj+IL1z8D8imFRSefPMptKd6EDZNqC+E3maGs/MiD1jXflFgHJ+ffQIKdCsnNe+96IkgL5+iealJZUnDBSUTn6r4AMqXBqwgOC4XrmVKM+JpPe5JRvtdSWf3D3+CVa+634sbX+hHQ0EBWnI9aE/OR2eddV4UaMhIZ2zHc/NMWRcAPt71WyZspeq4qasf07UxnBqpZJf83CPspZeZIAhvAPBvACQA3zJN8wu27/8VwKuDHyMApJimGRf8TgfQEfxuyDTNt+5ZRx1cEQKmhFE1HiP+eO7vY2uMRzrmI8q6S0S53c8rnupEadUMECrumu1+RWiInJomYUtKNuakyC5hKITqJ2o2BVXSTlkmaqo+NtfLC37aBOIaaeQjaq8yUTqP4r2CtWh2X9MJNZeq5AI2mi55dKEK9ppNLdR2e2dw8fcRkSjlhrnZGzNKzc2NnuPbEPVROpdqhi31g3yeIc9cy252P14L8I+dVNlW2oKmG7D9jkYmIkqzddnmQkqTVch3lEobo/DnlW573s/mJPu2I4iCrsFRacnYtHFSNYP2m/VN09n+2dV9qQKuRlV3Jf446uSZUiD7IBJ6ql1ZeSuFadE2tCg1VyZ0XJmkqNnptyCHlaf52irSFCHyHKuQuVne8BxK6O7ksYFLK1P5eU0jSstU9dl+jlQX+7HpNXbBDevsmdunbk0N300cpLl5XwejO8G6wilUHaIB6IIAVZbQVnFt6LKtiQVQRTmUl9Yav/viI0X+MXx+5qdQzOCDd8570ILYLet3x+bgz6vuwWvHm6G5RTyZXxtambILtlRP9FnB6C6AoypKMmYjI3Dq6d9gwuXG+aPpKGsdgRAMRE1YAX575c7OU1d1BrSgENBmSrYXjqbh6994FQobJ9Fbm4Leou2VlvPbJ/HRD/0WUpCue/+Dr4a3wnqBUdg0GVJvNlUD+Y1TXLAKAIebxiGpOtbnh1+/pQjTadHors6wgsZdILsd7hzHZz/2c8iqDk0R8XdfeUsoIL1eUTnXz73AqRntRXuq54q2UR4YgYKgOJGpo3xliA9Gt0B3bA46k6/stzZDdb8Xj3/1G1CCXrjvO3ESLbmbb7ekexTlrcNoqchGV3C1vqeGiRbthiDb1cI0AX2PRBIEQZAA3A/gtQCGAZwVBOEJ0zS72e+bnyD1Pwqgimxi1TRNx23VgQMHDhwcKBy0ufm6D0bXFU4PN42jQ0hH3NIqGstyMVoav33jXUBXfC7+ouoDqJgbQNsu017XUR4YYaqgpo5y3xBaUHrZNuv0RF8ae4NTNulF6vKc9QbNNKxcvtTdy+ULURUv9GMuLBJ/85OfQdE06L+W8L/+9W1BZVkZUDWYooj7/+er0FO6s4fwC0fT8NmvvAWlzaP4fdGhTSm4gxVJGAwGk/Bv+HoDjjRNsGBSM1DYNBkKRntrUkLqzYYior82eUN79iLEgKaIeOaNR3Y9UCxtHrVW/g0TUA2UNI9e98Foa3y+9QInuDLalFF4xdtod2VChQQTetCv96WrYV8J6i/2QdGCLABNQ11f36bBaEn3KL78qR8GXyZI+PgX34Wukkz0laXgn+6/FcVNY+ioysTFl2BTtY9xHECvaZr9ACAIwvcAvA1A9xb13wMrp9GBAwcOHDhwsDfYd3PzdR+MAlZA2l+ejIZx9kCahJXLtNgZKoa9OD7YizO5hbigeLas1x2bu6d5ou2uTKiCBNMMPnhHXPmD99FpL776W8uCQxcl/PRQPZ4sqN21VdF1NOd70JzvwYf/67/Zw7qqo6x1BD+6vRb3ErGeiWNbr+5uhgtH03DhaBpGl3dHZfR8TaoVTAapvb01jErprUjCDx6uRfaZWVw6noCh0o0mx+svQrLPzLDV0F0GXRHWFRHd1dc+r3C30R2Xiz+v+SCKMIimjMIrXhUFgHOuNNyX8FaUK7Noj8zZ0arobuL0oYKQnZAqy2go2PylTmXrJY6OW9V2KbQ62leWgr6ylA0UtGsLYYOa4BUiSRCERvL5oaAfJQBkAqAS1cMA6jbthSDkAsgD8Fvy5/DgtjUAXzBN86cvpaMOHDhw4MDB9YGDNTfv62BUNSVMBmLQo/KrZwPLLDAYW2SBSXQ4Ww6bXonk2ix6iQANPb+2c+0KusNUjHjx799/IJRbec87T6E93WM1seWbiBrjzasRjJeuR/C5MeHDzHoGJN/ESCReGhL/YOo/koE2ZOAvFpJQvuRFa0I+uuJz4Vrgk2PWkthvSSTXxh+0/SjvIxYcpo7JiHg05bM8Pyptrx1luYSBST7XNkAWnCMyWC7vyixfr+3mVKhPr1uWyPh5ZgW6R9KRXTWLoSrr/K34eN79W/JZXmWYyPIp+n1JXL1/OfKDUPmB8T8KlYd8zCqkLGaEa5Plmg2VhwNWvcBxBT/5dhWyzs5h+Fg8am8YQC0GQvW6qzPREwz+EnT2ciMvbIpt+CagpcYDAMjFLGIkljtEbyTJMp8LfFNyf6g8RTxW/DYVHboiPH1DFHwVYcjCHLQkEUc6x1HaMoLz1Wm4WMYCYS2B5Sp2jLJrJ7GQtw4cG2HHSyC2L3oSGwx6JD8e5WWS07FEpNJJHO6e4JpgLYG1cQXbd6bmoTXMorS7Z6yElNkydhzI6UfCk8xjJVDKXsS0IxM9U8HzYgBGJMtLcg3y7hRrh9jKdngfOw6mwMaWZrM+UmPZ+Iwg+yTqIs6583H3e0+heqoXpwsL0OLxWNuYZjli3mk3fplUgTukBiiwqOu/Si3DpaEkiLnsHvLK5D72myRJu33hpas8bwcTL5kKNG2aZu0udOXdAH5kmibNVso1TXNEEIR8AL8VBKHDNM2+Ldo7uIZQTQljahxG/fwLxclVZnu0uMqSudZWSA64z5Yz6Cf5W9SyxablRvNEqdUDzfeUbIwYajdBbSjEAD+HU+s2eYUNQYnYqQmarUMaGapUA8JF8kIj+TkuEMv2fTWB3HP5FHdOt4Hm+RnhpN/RfH9kkgtqktxESeETAKPc7CDFhrP5KsXNDqrHzd8/Y2VWTyfzms/W8XmS4Bols99ZDmf1Jn1RXBuV5EtGELsUmstnz9GjuaW0Xmw4798TRyxgZJIU6SaDJsw20HZq2xEubZ4zSiHb8v7pM8GazsaCQPbBr/HXh0byG1WSMxqw3bZ1gYwnnf0Op/thTx2iCbv0O1vqLpcnSq8jegnY87DpBg1WtkshGArrQ9gC2yC1dglE262PWJluT14l+cwiP58b5N5iRLL+BDS+3gyJHSYNNlZVYsWj2W2n9gAHbW7e18EoRV77FA43TeBCTSoG8jeuUu02jl3q5XIra4d7Q8Hoy4Hu2Fx0pORtX3ELNKUXclYbTWl7a7XRVpSNez53B2o7B/Fc4SG0HL62NMqdYt0uKK1lAVlfn8NCvRtL1S/NQyqrdRa5jTMYqE3CcGXC9g22wfqKcE4EC6iPdI7jbz/+RCiX9HNfeTMXkDp46aCWTaczr+56ac734LZPnEBdbx9OH8lHc+HLq7S9GfbQy2wEAJU5zgr+bTO8G8CH6R9M0xwJ/t8vCMLvYOWsOMGoAwcOHDj4g8dBmpuvi2A0r30Knzj165DQzNiXY3eca3i1OJvNB2+NWVee13Y5FK2NonztEtrDs9GNI9s3eIloT/Pgw689iZrxPjSlFeyKiMt2aCvKRltRNny+sO0rv4xIa1nAO+9uhhwwYLgEdDyecdUBaVbrLO6450VIAQM3u0Q88s0bdyUgteOWJ89BCWgQTQCqgZufvIDSljF0VaWj5+j1T+V9uVE6w1s2nUw6xXmQVgx78fDjlo2RKsu4/dTJ0OqoHc35HjQd2Z8vY0wIW77V3wWcBXBIEIQ8WBPduwG8115JEIQiAPEAXiR/iwfgM03TLwhCEoCbAHxxrzrqwIEDBw4c7BcctLl5XwejAkwooo6kF5YgqQakoLJpfccAJqotetCKm9E3JuYYZVddte2awpblXTMSKoe8qBvoRUNeIVpzPKHv1tkoXQkefPg1J1Ez0Yem1AKMDycjZdiisaxk8XLtdLyED/M2NByC1NyitVF8fvyHIXXcv0y8Cz3R1sOqmcDTi2Ufo4IEYtnvroXxwZKyzFbQV5PYvlMp+GduzsMzYKurlJ3iIgxi/wLbthTP8550Qk/QCL2hqGCUq5cdOR8qX1hgFMnYMKYlTq1XAKAsglHYVZPtQ7Gb3/aMzqgTOW62WphCvHgOhfEWKZcCbDW91D0cKhumiOKmMUgBw6J7qMDo7xLw24JihAns2L8quof9js1qaFpj9NJb2s+HVHihGjjaPILAMRkugadKUXql1uxDAAAgAElEQVRbTfQg29eOEaQ0LGOyLgqz1VF4YpoJmGWGWXL76a3zeM0vzkEI2hkZAvDq/3ceom5CU0T86Ns1GKu0aOnjK6xvMWG81VBSITveF6bYOZIJFcin8C8SqOy5obBz5JonVgTRXBMESIqv7COUrGn+mAjz7HfD5gkFKpet+LqGecsBSmsX/YRrHmajyA+x8WjEMo4PpbtrEez6qprmFX/rBi7ivNu6RvVwEfUXe5mNkabhxq4+dCbmQSXUIn4e4d9wziyzC7MzjL08WCZ0tDen8XZAT+D6gmmamiAIHwHwK1jy8Q+bptklCMLfAWg0TXN9l94N4HumaVKeVzGAbwiCYMA6eF+gSn8OXl4YELGshyNgSyuglEtKLzSJvYTdckFaZZ9lwrLcQLn1b1EmFEK7tYu8Sm1aCLXTRikUyNCT/IQqSOi3ZjhPFTXpPUZn9w6d2LeoUfx9KBBDKJfkPqnbnCKoXYUaQ2xV3MSeROb5jhFuQnEl9iDU+gQAEgiVNcXNUnJSw1h5fa5ZR5zE2oQL7HfsKzfDZJ4dk9gcR9MPKH0XAKb9/DPPOqgNih1rKjuuLpk9yITLPOU22832I1xkxyGKeJos6/yzFKX9GoQmebnAwEUsZCjtd6O1CzsOcS7WBxfhu1L6LgCopA3tw5Jtbl5zsWPiJ+dfJ2lb0tJl6KWkq6LNfoW7dsi0Te3QaLqa1VlWpNeXIfPnVSHXG7V9WfcGB+xzKXtOt4NSdgWdb6RGkXMpsmO8ZrsfrZEfE1SSNkDL2p4FidcE+3Fu3tfB6DraKrOgEluPl2qDUDnkxSMPPxCyZXj/3ae4gHQdncme0ApizKJvw/dXi/K1S5w6bsXiQCgYdXDtMVkXDcMlwFQBXRbRt4ly7k4xV++GxyXAUE3oioiR43HbNwoirWUBt9x9EWLARLFLwO8fPQRsMiyyz8xC1K3sE0MABouSkNczbYnkaAayz8yGgtHS7hFUt11Cc0U2LlXtPb39DwUtSfmcZVNzCk/TbcwqhCoxG6MzebvLnLiW2EMqEEzT/AWAX9j+9je2z5/dpN0LAMr2rGMOHDhw4MDBPsZBmpuvi2C0pzQDf/kv70RF6zDaKrMwVXZlKqzVvYOo7xjE6cP5aM73oG6gl7dlGOjdNBjdK7SHZ3PquG0xV58L6uClY6YqCr979AgCzyroq01m9jBXgcVqN1oez0L86VW0VmdjomrnYzXz7DzEgBlcVTWR0rC8aTB66XgCpwL8zFsPI7t3NuRZeem4RQtOb53HRz7925CtyH1ffgfOHU1HUecYyluH0VVlrciVtY7gF56jaD2SvfHHtkGV14v63j40pRaiNdtzxe33K7oSPfhUxQdQMd+Ptrj8kE/vOtrTPTjxzlOonOnFmbzrd99N8G//HThw4MCBAwcvLw7a3Lyvg1ETAtYMBQlhq5iojsdT1ZaUa9MQs3GQCVWFKpKZwaX36r5BPP6lf7dWQSUZ77/rFLqFAmiilQ+qSjJaEgoRFhSPI+wNhC0S6kQKo0TEnuXzfNcOMY9AwaA8AX4pf101txtHcF/kHShf9qI9yoMzFUWhOvFnJ7k2q/ks31AhKn9igKdEjNcxqglVT9MIA0ZZAofVdIPUY311xzEOxOo8T0muOeJlfXWx1WLdxqMYWGL9zotmVNpZP6NIukWePrRksN+SCMdDMm30D4IIwsui1NxXhPHqtU8SGlUiodlOaRaHdKYqCkq1hkzMIhOz3HcAUEZ4zP+5zOf41kYwZdw1U8FalYLJqhj4NBeiYR3L2CYfkhqWMV0XhdnqSCSSYxchWvswXRcJwyUCQW/TS8cT8FcZ7MXVL1dKrHpV0Wj/vxmIP72KuXo3fCUu/LysApln5zFyLA59ZamADqQ1LIZsRQRVx6u7LyArYh4f/eRvIak6NEmCAEDSDbxbacRf/5+34fzRNI4eFZ7M054uTLNV40PnZvDY1x8KMQz+/k1vR/zqCl4oyUdLnidUzz3GtudLZeNEUm3qtW5CjyGkEC2S3ab8R3gOcFwno98Kq2wsLJXxq9vh04TCFiBqmbNMQTJ6lreDai3OR2uOpfgrqoQ+FGSJNx3y4EJYLuAH4nqt71eT2L7qhKG3kstTkin6Z9mKdWwEu/a8a9diJVvg1DEdONgJVEPC2FoMptd4iuXcKkkloWrpREFXWuXHG1XGVUgGhGJzZ1NWiDKubwv6rW1eFHTyncrKVKkTAExCHdTDCK2RUAi1KJ5Lq4exNlSJm6qDqhH87wRiiEIoSV/YoKYbRmiWUWTej2JzZlwsz9ZKj2JzHk815ftAqbm5JM0lx8UUdBNlPhUlWWLbjiTpKysmT0Om6SiKwOaONVIvSeG3fUFgz090PFHq6rJtH+jzBlWstavXRpMHOqqor5B+Tgn8nCJi82NnDwzofTOCKOu6ReKUYOv3MjnRqsK2F6Wwfko2ai/tzwrhc4+KvMXdnMCerej5XxXYbxp+fh/M1c3v/YJ9uiLPstz8uaZv+nc76PUl2VSp6Smj1x5F+Dy/bY1ce5JKnxu2SpPhr3mDPnuY/G/Sx1KaykZOK2ynaI9wsObmfR2M7gbqz/ezVVDdWgX9Qdofh/JBXygqRFuG55r3qycyCz2R19Yb0cHeIbbZh4TTPkzXR2G+mre5SWpZwive3wsxYMJwCXju0ULg8MZtTFXF4KlHSpDasIiJuhhMVUWjADMIbwog4sUA0ioXQsq/8S1WILpY7QbWmCowAMv5CcBgbSI0xQpuNUXExdoUHGqchKTqkAwApnWrE03AVHUcbRnB+SvwSq2/QK4tVcVnn/gxRNPEx58W8Nd/+g58/6Ybru5gOrhmOGhvXx04cODAgYP9joM2N//BB6NzkREwBQGaaUKVZDTkFQKrLB/Ul3L1bx4sRdxhNM6Xoztu/1k27CaqewdRf74fc6obF64gYDkIiG324djtQxACJgpc02h4zMMFpGkNixz9NqlhedNgFACmqqIxVcXe1IY3BZD9nhkIKvBOpRm//8xh3PKFC5ACBjwuAS2PZwElm29ruDIBf/eVt6CkeRTd1RlYLLf6tE7x1UTRCkZ1A7oiobPqynwtTx/OhypbeZMmBIiGAQmAaJr43A9/ggsZ6dwK6X5G0eoIyleH0O7OwTn33vt7OnDgwIEDBw4cONjnwahqSJjwRyO/YxKlzaPoqs7AhaNpMIhKn5+ou5oLjMIgCCaq+73439/7OUTThCGJ+Ns73oQzr0hH3FlCSZ22q+qxcvgkoU7ME0qMoqBodQSfn/gxFFPHe5rO4FPld6M7NgegqmE2RU9pgW3DV8horJGjTF1uqZTlK5bOeFE+0YHmtAJ0JHtAhQvtNCNuHwi1QHMTM+VUnh7himG8A4Gq74UxnoKYYKLi3CX8+5cehaLpMP5LxP0PvhreiiRM+lnQtKzyPKNbkntDZWr0/MYEpowbsNEjFnQWwBWETYXKio0zQum8sZIPaS0LyDw7j8FjiSHhnnCBpwB7FKbc++vl0lD55shzoXLbGp+g6fUzqucLMqMpvYG0AQCpwQ/Rb0IwASNgQnwe8B5NDlGNB48lodw1AlM1YSoChBtNTjm4JIwdk/4ATy8deyYG2YEZCIZFXS58ejKk1muoJlwvGFBK+eNDqUkJN2uYuzkS6VhAmKZjpTYcj32rHrmNM2iutPb3cOMEmipycaksAREIcAqZVI0XAPzE+LnvlnicSL0NtR2DWIwNx2e+/quQsJJgGqgb7EXT0Wz4SGynLJJrd9EaF+XjXtSO9qIlqQAdKR4AwFocUbEkwyTpDKNaAUAglSkrK3OsonuCV2yUCQU3kMLa6OkxKFkYwud7vw/F0KAKEu7LeBfOuTMRNkeVrBWUzlq+o23D+eiOt14++VL4Wyil3oUTsWi7+p5viV0v1KA+nChfDq/G41rgIFGBHOwOAoaES8txWFzjlUh9q4QSuMLmP2mZjXFliR9vNH3EtUzLPOVSIZ+VZTaXSctkvgrwc5ypbE77M20pNKZC523WhtIGTdmuzimRMlHJJSkvdpVcld16oBECDaXlAoARTtKPItg+ucJZOcLFSwfHEHVWmkIjgd92GFF+paqyEuFLUsVc+zbCyXwcbpubVwx2/hNkG886iCWbeu2am40Tn8YOWICoyEYoNplk2A5sELK4NVWUKvpSdWBVkrasR8t2yi0Vl6H7Sp9XVHPrbVP1Yjpnh9nSl/wGOz7jRIV/2cU/cwWICi+l6aqEkqq6+f6Yy5vPs5qNXk5YyJzitWCYm5YBwHBRKixJoRNtxzGc1CNfif6tafUKSYczCN2ZKt7a1bjpUOWGiS0LjMYAlKbLDfVrNGUepLl5XwejAFDQPolPfeypkAjLZ7/yFrRE74zeWn/RohFKpgnTBBKWttCDvgqUrxJFXENDxcKAFYzuEkpnvPi35x+CrGvQJBkfft1JtKV7dm37V4JjnV4omg7ZMKFrBgqbJuF9CSI/u4m0lgW87e5WSAEDtS4vfvwwszW5VlDjRSBosyIYwGoc/xJioioG5x5PRczpNSzWh2O5Jhywz61bYOR4HPRgHqmuiOh9bQoym+aDeaUCJuuit9+IDcOVCRiuTMB4MHIaKE/GXCBim1YMleeHUNflRWtVFtqLrH/rdjCf+fqvIBgGVFnB6SP5226rfNyLb/z8AWucizI+9IaToYB0t1HkG0a5bwjNYUXctVqxMBCycTFNHeWrlzasjpbODuJfie/oJ4/dEwpIr2eYpnCgqEAOHDhw4MDBfsdBm5v3fTBa1DwOOSjCYqo6SptHgVt21vb0IUYjVGUZp4t3T7W23c0UcQ1BRMraPEoWhnARuxOkVU33Q9Y1yEGfw+rxvpctGD171ANVlgBNhyGL6K1JeVn6sRkyz85zvp5ZZ+aueTCqzBmAaAWihgi459UNdZZrgkHoFWKiKhZPfLsCmWesld/xqljMHI5CcdMYJuuiMVMVBWz9MnjXUXl+CI/87XegaDrUH0k48Q+3o73Iejn001ur0OdJRvHzkzh9JB/NhdsHa7WjvdY4N61xXjPed0XBaMn8ICrmBtAWn4eLSN2yXpFvGJ8f+q7FZJh5gTEZALTF5jEbF0FCu3ujqnDlTB9kg12PlbP9fxDBKADoB2jCc+DAgQMHDq4HHKS5ed8Ho+eq06AFPUZ1RUJXdcb2jYJozvfgto+dQP3FfpyuyN3Rw/GO++XOxH2Zf4Y/XuzEa5c68aaxs3jdRAvu87x3V4SJWpLyoUmW4q8mymhOK9i+0RZYt994ocKD5oIrPwZtRdn4wN+9D8c6vcAfi/tmVRQARo7xK4fDx68NtZFioT4chkuAcBXeojvBRFUsJqpiQxSf8apY4Niu/sSOUdfFVsmh6ajtGAwFowDQUZyF/44rvcwWeDRmFFrjPLgy2nSZcV424cXNkx1oj8xFT2QWileG8fmu/2vRa0UZnzl0B3qiNrenKfcNbclk6I7NwafK70b1UDfa3dmb5oy2JjIFbk2U0Zqw/aovRdWAdQ2eLiy4bvJoHThw4MCBAwcO9hr7OhhVDQnPFh5Cy33ZqO8ewOmSPLRE5cDQyNsCIhlPyd7SmlWnLSMfbRn5UBYFhAcdOKiUtKTyPPe4FpboZbrYtrnck+Dfe9x5KNdGIS8aoYfc8tEu9ARzIMScdG7bWhJLGFlLYNuOGmZLW0YwL6UjNQ8fu+VeVMz1h3JGwxZZX+cO23j3JH2Actsrp/vw2APfhKJp+MivJbzvr+9Cy2HrIVxdY31ISGTJOqlRLJfhcHTQaiYH8L4uBZ3z6UDQTeNz+T8N1XvBd4jrT6rMrFDCSQ7EErFYeUNkP9fmaZ8nVF4zSe6IzdplnuSWTlVG4wcP1yL7zCym6qIxWxWJMKjoD/Crt2+O7AuV3xXbHCp/d742VB5e4wPZj6b+JlT+3tzxUPm8nz+vhRUT8P+VjOxfzqPnUDoiXwwAKiDVsX7PGLwFAs2tCSdJvp2r/IuMW2PaQmXVJDkhJNFhSuOpukkkv5Xm19qR5mL1bo1rD5V71lgw1gCeTdBSkQ31R9YquSrLeLbwEJZXwjlbpbBRnqbsT2fnXyX1/CsSziZ4cFfMKbyt4yyEgGWFEIgWsELeOSV2mTg65cX9Tz/I0dYLJ6YYvdbQcDQwjI7oQgBAIJa/tbUsHsF7Zl4IrX52BZLgGp4DXFZfe5GAixmv5NoIOutrd3wuvlr6Vtwy3o7fp5ShK8F6qWO/f0SOknyx4CkuH/PiGz/7BhTdspK653+cQsM7WW6wOsnG+lIUWz0flXjp/r2ACT6n24GDnUDTRcwsR2LVZ7M7IfOxuEbsF9ZILtca1wQy+az42PVELVsAQF7d3NpMIPloMPg2VDqA5p0JtpxRXWH91mgOG2mjK/Y5l33W3LTM6qi2LAqNkGO0SJITF87TWwSSw6q42fwQE8EOVqyLP5DU1iSKJMxF2ZLnIshnOk9TO5dokd82tWaZMdi+JtrqUWuXSNGWtLcFaL5lhptZdU0HojarDoDP3zS3sHkB+JWlNZJ7SedfO2h+LM2jNWz5nzQ31J7nuQ7V4K8PmstJrWZiSQ6r3drFR7ZBc05H13gP8ygXO96SyPqtk/O14OfnRZ3kkKrEdsg+JfC5mKwNPdyiy7aKR76j+dbGZfRORI3koNI8U5uVCrcNei2TepItFYqeIq7fNmYZl1tK5ndaz9g8FX1XcdDm5n0djK6j5VAOWg7tXj7mbqI9MheqKME0dGiihHYlfftGO0RnogetWS+NWsxZ22g66roGQsHoHwrGKuMwVhkX8uu81khoXkbV3w9D9JtIfW4JpghoioRfPlKKyaq9DyiuJdqKsnHP5+5AbecgnjtUuGtj6W3tjVB0DW8914gT7ziFF212SzXjfYy2rqv4YNuv8N855SGqvCZIaIvberWyJyYHf1l6FyoWB9CpJeNc+M4ZFgBQOjeIj3Y9AdnQUD7jxUB0Grp2SNOtHemFojN7qdrhXjQgefuG1wTCgaICOXDgwIEDB/sfB2tuvi6C0f2MnsgsfLr4TlQsetEW48H50e3bXEucPkLzZiU0lO5e3qwDCykNy5Z1S1DESDQASdORfmbhDy4YBayAtK0oG77VzRUNrxTHBzcGay/WeLg6TWkFQTqvCgnA8dGLqJoYwIPpr0Wsvor2yNxtBcR6YnLQE5MDeXz+svU2w2Y5ozsNRhszC6EGqciqJKMxq/CKf3+vYHmZHZy3rw4cOHDgwMF+x0Gbm/d1MOqWVVTEjWB8yeK6lJ8bRm3HIJ4/XIDWI9aDZ2CVrZeLhL6rLPInMcZLaIQLbI3etcDTK9ZyWL6fa4ao7xLKXiCZVx69MJyMC1HJgAHoJYyOKc35uHrUXsKVzGgi/nhGH6Gy8AAvsb2aSGhGNmqBP5X9gVpc9BxJwd3BfM/mihycL0pDRFDKNSCz41CRPBIqr+msP3aqzMdyfwsAiG9ewdAPktBdnY6LZamoj+Mpt9SmJVxcwGa4qPLbTib0UolwPCiNFQC+N1UXKv9xfE+oPBRIDJUpBQYAvr1QGSrfGMFsZyjt5U8Tz3BtfrxQEypPBRjfqj6ql6vXVZOJItc4EDBCIkaGIuK5skPwrlj5tW0mHyjVRg+Eyr8iVjPpLj5QovQoSk/OVuZC5fywSa7NuMrGMD2myzZJ/ddHdYbKXo0du2lyXmrjvFybtyczivMPxlni6sUpttLnz+OpW5GRjD7km2TXhy/bGrPPHs/DyedlQLOCtRdLCjlKzUqqgIbUPHwg4RQ+9NwvUT90MUTNjYgy8Z2CWwHw0vJRnfwxMSPJvi+z69BMZfst2GwhhAC7pnrWUqAJEmDq0EQZ7TEeSKoB1c3fQmMG2er8Yo4VrHfFe3D7h06ynFFPLpRz7FoWo1i/1xbZfSEjbRzXAtSiwIGDncAwBaytKTDW+PEv+tj9VFollgvk0hL5y2zD53XYLSAMmW2P0lgFYvVm589Rqj0t221aQH7LcFHKLdu27tp6blbJIwG9zdqtXXRCzUUksWyJ5J9DZJnde6LC2f0zMYLdu1LcS1ybOIU9r4SRg5pA6LcAOAZRDOFMx4k+UuYptiMauy+tkBQau20MtWtbJAfCIPcYu+0a7d+Swtr4icWYnX5LH9KpBUzAdv7pNnyEmiuSseCyPV9ES+w4UmsNH8Js9dix24qSTG1ZAH5+zyBz+OUpzZsr5tsDlShif2M/XutYC9hSaAiFVxXId7Zt89cvuVZkdryVFZtdErksdTuFl9s2uS45mi6rY9htlci1R6nvXAqe/ZCS7VEKsP1QUXovpenSesY1ipwO0ty8r4NRivJzw3jofz0GRdNxQn4W7/+bO0MBqYPLo/VINlqPZEORd0d2Nb55BTfe0QchAGiKiH/46puAV25eN61lAUeaxjFZF4XZ6q1zQF4qklsWkfPiLLy1iRiuTNi+wS5irDIOP364Blln5qDHCwifV7EWp6CwyQqI9pPg035Ec74Ht33iBF7RNIAznkK0Zns2rdeW4cGDx9+A6tGBUP7llQoJXS16IrJwX+57Ue4bQlNOEbrj2Kpo2aQXNWN9aEovgBcbxY8AoMXjQYvHc0366sCBAwcOHDhwcL3guglGazsGORXPum6vE4y+TEhap6UG7VRKmscw8cqNdFTqAVrsEvD7Rw9hqWJrQZ2rRXLLIm69swtiwIDuEvHIN28Ejm9dP6Z5FbGnV7FQ7wYObV3vSsDyVv1IbVnEm+5qt/qjSLj/wVejv3x37HASmles43+T9cYu5vQa1OMi5qt37hO6FeKD275UloChisTtG1wBKs5dwvEuL57NKN5U0bk53wPXjITjXmvVuXmLwK093YMTbz+F2pFeNGYWYmhl8+BvL9ATkYWeiCwE4tib6rJJLx548sGQ/+if13yQC1T3O0wIB4oK5MCBAwcOHOx3HLS5eV8Ho4uBcPzqUhHWAgpeKCrACflZS8VTkvFCcjHKnpvCjZ19OJNnraZohO5mV+zTwonimo8ogy3zlBE1jQVLRhihiYwxeoWQwAdUWjwLBMQVtsavx/H1DJeEkoUhVMz3o3n6UCjnbCWRLcUveWzUi0ukb2SBTY3h6THSMtuGlsV2XiSD+XU557k2z4wxG41FlfU1Smb8hsORE1ybQ8oU3DdpwNcAXQUMRYD2ShFpMk/FLQkbQ3LrEmTVoq5CNZFxZgHTJIeywcfbeFS5vaFyy6onVLbTjN6UyJRfl/RwpDQsQVz3GvUbuOG/evEf5TdxbdYv6py2GZSeGIMUtAr6/r+JOH80DQDQPGfZghR1jqGsdQSJr1jGYHBVs5yciP8z+Bpu23+UciFU7lrJwK3PdkAMGJAMQNB0HG8fwEQ1r3yXrcyEyvkKo5T+aI73bDm7yI7Dnw404hXv77OUJL9q/U3QgVRlEb//zGG4F1QMH4tH5DF2/sSzJjLPzGM1TsGfrA0CN7qBWutc/8cSs0EpaJvADXcMQAiYKHRN4jvffAUuVSTgNVHdXH8afGwlMjOCUIqJHs/ECk8rqu/rwxc/+59QVB0n5Wdx26c/aNksEUpfdb8X3370ISia5Ql820fvDVmgSH42tt3TJs5F5eLcEevaMck7EGWVXRO+V/BCYgln2fH2l7GXWFSVz32Op8WaCYxSbEQzitY6/RYAjjf2cn7AR9e8aE6wxjVlocf28tf1Yh7rq+EmyqBL7J5zcebarKgbB4gK5GCXoAvQfQqwyo8dMUBVN8nfOVVbflNUfZbSAwXDRhXUaZoKoQCrhL5rU8mFuTld0ZT5flMaoRpFFEaJSq5uE2Dl6LjkO42j7PLztBFBFFij2LNChJsX36NqqG4XO3guIunplvhnlySFzZNUnZWWAZ4SGi0ySiql3IbbuItUJZfSXSd1/l5P00omNDbn0RQRu/JsLKHFUiikD4rMP9BpVCWXpBWJNiVaml5DQem3duFDqpJL03gibLxPSuelbajSfarCPxdFkGMfR85LpMD6QI8hwKvpUkGbGMX2kEvgIrxamYwl8I8hmCNsuUWdKP7beKiCTq8romRM/qy7bBc2bUIpsrZLkt4PqOI13bZhp8hvIYbM0W/t6QBbEAMpTRiwqe5Smr9By9cmSDxIc/O+DkYpWo9k487P3onjXQN4IakEAPDY1x+0Hl4lGXfeeQqNxft7RaJkYQj/3PbvUAwNtw/+Nz55w4kdi6DsJ6zWuDD43SQM/T4el44lYLQyHrmY3lBv5QYXTEWACROmImCmLnKTrb10jB2PgyELEAImBBM48pMJ5LxpZtPVvYLGKUiqDskAoOkobRkJBaOAFYj+wyd/CkXVoT8q4hsP3RIKSHeK8zWp0BUJgqbDkAREj64hp23z/lwJUhsWQ0G3GbxhCiYgmQZe/ffnIRgmdJeI3z96GDNVUUhsWcYtd12A5Des/AsRgEuA+cPMUEC6jsSGFQjB1W5JNZB3dhqXKnaH7lzROgwloEEyAUXVUN/Tv8Hzt/4CUX3WNdRf7Lsu/DibUwt4P+AU9oKlfNSLY8O9OJtViK4kz8vXycvANAH9AL19deDAgQMHDvY7DtrcfN0EowDLfVQvReHU07/hHl6PD/Tu+2C0Yr6f80WsnOnb18FoQcckbuzoxeCxRIxU8h6cqzUuNBwu2KKlBV9NGAa+m4jA8xJm6iIxVx3JeU/tFiaqYnD+HWko+cEYBNMSJ8hvnNo0+OurTYauWD6Zuiyhq4qneZa1jkBRdUiGCWgGChqnrjgY7S9PwZe+/jq8+al2lPxkFEd/OIKin47jW9985UsKSCfqYmC4REA1gPW3iTpgCgIE3VLzhWogpWEJM1VRSGlYghQwQiq/ggGYqgm8sLohGJ2pi4TpEmCoJnRFxMCx3VuVW4gJD/VBMk3MRm+kFJ8+TFSfJRmnD11+bO0n/CKvBjCBJ/Nr0ZnoAQAcnfbiK79j3qIn33oK7Wmel7WfW5S4qMAAACAASURBVOEgUYEcOHDgwIGD6wEHaW6+roJRitOFBdbDa3BldC4iEid/8xs0FBS8bEIhxUtDqJzuQ3tULnoiszd83xaXD1WUYQZXUVoT9+8Dd0HHJD794SetFUKXiMe/Vb8hIL0cIpr8iHwxgJUbXOg9tTv5kpfDxben4vDPJiCpBnRFRH/t5j6OQxWJeOihm1HQOIW+2mScL0jjvu+ozISqSDBVHYYsom+L7WyH/vIULHWGQ9TMkNXLVgHyTjFVFY2nHilBasMiEl5pUXwiXvSjIyITt3zhAhDc98k6i5o0WRdt0c+CKr+mCEARLKquDXPVkXjxP/KQ2LCCZyuO7NqqKADELq7BEADJBDQBSFjybajTnO/BbR+9F/UX+3D6UMF1sSpaNunFV3/7Dci6Bk2S8WR+bei76sk+3q5mpHffBqMOHDhw4MCBAwcvF/Z1MKprIhZmIyGF8WRvQQVaszy448RJ3HC+D7MRkfirX/wUim4FeSffdGqDImf0JZJ3QexcxFU+DyBsmnG0A3GMqy+uMYpp2IUxvqOahqLAOD4/+wQU6FBFCZ8uuhMdSVawWTI/iIq5AXS6svHpI+9HxeIAni8vQUeK1UfNLaB83IvakV68aBSiNYv1faaK5CKoRFLbRs9PPzTF6pG8CZp7csnHB5NvzOrCZoiS1nC8sx+KqgcpoQayzs7BW5GE3/kOh+qtEdny4eficahxEhdrrcDznaeaIQUFhc4+nIexSstuhOavFIczOxkA8JksEaBtKStUznbPcfU0kmQwuBIMmnKAhn/LQ2nzKLqqM7BQGgEEcwby2qeQ9MIS2iuzcO5oOgYPJeLZQ9Z+0HyROJeG2WNR+NL9r0NR8zieKSlET04GMAeMhLNki7gwPlejdYG9eDBIokRLZQ6OKV5IwVXYjqpMLGrhyGufgtAqhFacaQ6NakuoSnaxPKAuXyZwBMARUs8D5ITNYvpwNLLOzGH4eDwK6iYRi1WgDvjZw5XIPDuP1VgZ0Yt+jB2PxeThGGCR3/dZPQqzFVHoqwBuCO/DDegDYLOdaZ1H6rOL6KlJR19ZChSShOHT2Fg4Es/bqnRWZUJ1yTBVHaosoaUmExExq5Dj+WXyJjkHTcVWPqdAJNX9SaysRvFvCmP7WNm1xPqjhfH1Amksd0iZZ9e8P4kE5i7eh0HQWf/UWDY2Y/ut9jcNnoeiW0wH6Bpu6OnB+Sqr/+3R+dAEGTA1aIKMs1mFXA5M3Hn2YTmHnXNqBbG0uPtiX3ZYIgkHJy/FwS7BFAC/aMsl40HTzgRaNraux+VearacUZobSvK8DGobYct1oxYeNP3KsOW36eFEc4HkgtKUQ8OWp0ZzQzU3+x2dlBHFJ67J5FkmOpLNI9Eu/jmE5kSKJOGOrpTM+LdWp6c5sIrNB47mjNL5ZtHkrb8o5g22syMqe46w24XR/MZJcvAWiCaFPdeVWtJEEbEPasvit51XjTwA0eOzrPIniX5eJbml0STf0m5P4ybeHlzOqC23lLajNi3hZD4P2ObzGJKjG72Fncui7ZiukIFH7WSSw3gtDb/OjtEqeTaLJhog9jxTOoerGuvrqm0sqOT4C2QMUssV2Zb6y6XYcjmjtjxqu83SehOaS2qzebKdshDo9W5LH+buO2KAPCOrfMWt7KDobxphe79iedDm5n0djG6HFo8H7Wl5uPf3v2arEIaG2rHeLe0h9grlgREo0IMUXB0ViwPoyChAyfwgvtRs5YmqghWkfi/jZsynsImkfNyLb/zsASi6hnsaZdx9+ykAwLHBXjxXk4fm/Gu7LwBw6XgCdJcIUzVgKCK8tRtX9LJaZ5HbOANfrILX/1N3SBSo4c15Fj00qLabdWYuFIzuJS4cTcOFYP5nKix/zbz2KXz85G8sT0hFwn1ffgdGqy6/6tdXnoK+8hQMr7y0PtNV2M7qDAyUJ4f6I5MV5+ldsLxZV/MFgAJMbvh+9nAU+mv4FeqkliWkNCxhsi562/OT3jqPd93dCDFgQlNE/NP9t+54lffc0XTc9+V3oLx1GE/lF6OtaCNr4HqExXSQYBo6NFFCW0xe6LuuhFx88tgHUTnbj9aE/G1XRSuHvDju7cXpQ4Voyb183d2GjoNDBXLgwIEDBw6uBxykufm6DkbX0ZBXCFWy8s00UUZjeuE170O7KxMqJAA6IAhYkK03iRVzAyxP1LSC1J4o/mG8dqSXo/S9tf0s3t7eCEXXcOo5Gbd9/MSGgLS63xuiNO4kWC3tHsXN3b3oqkrHxbK0beuPVcbhBw/XIrVhaVPvztSWBbz1njZIAQOmSHIWNev1kx7MbdQVEcPHd0bvXbcWma6LAnYpXjncOBESLDJVHeWtw9sGo7uJoYpEDFUkYjn4yn+9P+srzrlnZ3YlGN0MkU3+kLWO7hLx5HeOYjKoZpzSsog/uvM8xIABwyXiqUdKMFW1ufIgAGSfmWUvGDQDxU1jV0Q5Pnc03VqVnt051bu634v6C/1oyCvcMW23dG4QlTN9aEwrQFfiztpcLbpjc/DpojtRsTiAtpg89ETzg7Y7LndHNi+VQ1585zvWy6gP/V7G+z546poFpCYOVl6KAwcOHDhwsN9x0Obm6yAYFWCO8nS1iEn+BHXHefDBd51CzVgvzuZaNNe4Xp4LJK8yOgKVj17N4bWu3cNLoXJ4gFFsTBc5VBpPvfEfzUYbsnH/sIGPnX8Cgmng1KVfYiA6DR0ROWT1REZjzhH448MRd34l1P7Fw4W4pzEo3iLLMCVwwWm9txfNZdkh7Z/q3kE8/tVvMRuMj9yL1gRmZZGRzCw36pO8yGmbwYlPPRNaufzXB16DgfJkxMusD0Vho6Gyvs5nqgdeKLMC+3UqzKsiLBuTxJalUHBiwIQpCdBNE4YiYPCdiTj/9jR4GmfgrU1Eat0CCmHZZrT4PKEV1cSbl7BUbdFBopvXUHrHWChwGns4DuNV1rl5arKEO97hMjv+lKIz4WNB3Tpt9Hclh/F6pQumqkNTJDSX50Am1JTFAKOjjC/zwVicm/FOeueYoM+aykuv5ycw25AAoQ+NrLHVxotzVu6pv0Bk/ZEkPJJTh9V5Nr7jXeycADw9KYzItae4FkPlsQA/hr0uK0gsen6cW6HOPjMLRdCQ1rCIiNEAs8MJrl6vn4sOPxN1elt0BwAg/FUB4EHAUAFTERD5Kj9eH98RqpeYxChDv1lk1F4AGF9lxzU1hl1fG+nO7DgcOzuOR77NrF5uP3USLR4PbAr/HK01TxzGl08/BNnQ8L6LMj52872hgFQjdg2uGXb+pVVyLWs8nU1cYTwjI4GNE9ci60R3vAfd8R7WnzBC849hvxk7yN8zAlGs3rF5/mXUseGLaCzJgakeHIqOg+sMJgBdgLTB2oWUtS3KPEtzA2039BO2VBTCPOTmcIPYtAiybWPy5teQ3doFwubUQ8qYtN1moUWx3zLDWFl0s51Vwvjrnlq4UGpuopvPow8nVFZKSaX0XdnmVbFC/C7mREarDbcdcPqZo+yC/Y7dsuVSgL3AHSZlO312mlCHZ/2sD/N+dm8Pl/n+JIQRmi6hkQbItn0an0IR0Flf1wg9dWqZf7GrG4SuTGxMYsLZ77ht/aH2OeESO3/xLv4cUYorTT/SQSnSNn8RglmdHZ8ZcryXbDRdiag+UmsY+vwGAEsCa+c22TjbyqoG4FO6KM131M+fV11j++RX2PWhLJBrz2adsuV1bbNfonRae/rZOqSA7Q+Ufa9tTqW1U3kpPZi2Ef18R7nvNPadobB9NcwteMIOrhrXQTDKUOX1or63D61xhWjL9HDftWV6XhY6K0WMugrBNK2DqmuoWBjA97JuwV+W3oWKhQE0ZR7eVD23LcuDu997CseHevHiYSv4e0droyXOJMs4XZzP1a/v2WiD0XosfcN218HbmRg43DSBgfKrE+ZZh++GMJiuZRiqZdvy9GeK4Z5XQ1YvPsMVWk1NBfPaymqdxR33vGitqH5DQMfjGViqDkfs6VWe2nt2LhSMvhSsU0RLW0bRVpGF7tIMpGFp+4Z7gLKeYVS3D+HL974GMQtraCzPQUdxFgoxs2n9vPYplDSPobcmBd4rVPQFgMm6KBQHV6gNRcRavIzXvv+ctRoqCzBlAYZuwiCiR1thrcaFke8lYvKZSEzUxWCqKhoR2DznZTdQN9DLxrimob63b1thstqxXsgGuy6qp/r2fHV0N9CQX8iJsV1bJeGDlZfiwIEDBw4c7H8crLl5V4JRQRAeBvBmAJOmaR7d5HsBwL8BeCMAH4A7TdNsvpLfqPJ68dgDzFf0g+86tSEgfbnRFp9neQ4G1TXbYq0csp7oHPRE53CCSBvaZnnQluUJiSK8/65TqBvoxfP1uRs8GU8XX5kNBrMzMaDLIi7UpL60HYVl7bKuvjpTF4n2kpwdtcttnGErqqqJ2NOrWKoOx0K9m6f2Hts5nXM7nDuajvbirO0r7iHKeoZx/33fhRxcoT35j+9Fx2X6lNc+hU+c+nVoNfv+B1+Nsaory2GdrY7Ck98pRfqZRYwdj0HWmTm2GqqbOPenaVjJCMPY8VisVm09NtexVuNCZ0nmtvV2Aw15JECTZZwu3D5Aa0wvhCZanp+6ICLFN4fSGe++D0hbcj143z2nUNffixdK8q+5krBxgPJSDhquxdzswIEDBw52Hwdpbt6tldHvAPgagEe3+P5WAIeC/+oAPBD8//IwATMgInxKwCva+rjVwJqxXjTnexDGGKlwkQUv1yLPGaBUnvBxtlKnjNg4Qy7GBRJWGJVj8tUZoXJMNB94qUEKYGt0AT4ecQJV0/1oScpHZ0oeV28pi1BsVtnvKKTfASulD8351kqvmqyGVGEjEywqyLnqFNz7j7ehtmMQjWW56C+OQ7TJqC5hEk8NWRfSqf+vfpgCkKwsI7lnGTe09Yb8P0tcTA3uR4tlobJk41r8Ypk9z+RXTYZyEJN0frWR0lMeGrklVL6pvA+vVC5CCgbG3y+qQ/90MpAD1H7Ti7yz0xg4loT448vIAK+iu46pVaZsTGk447MxobI/hqewTM+wlT81nXFBphYZrWfNZwvISGrp1DgJBG1UsPMaEwbKjGcDsnOK5eaeaH8WsqpDNkxA1VHePIKGPGvFe5ZQmEZWrH2ofG4QomoEc10NRD+/iidyylHSNYrKtmGM1seht8x6qfDWpBauP00rbNxJxQZQbJXLjGEcdY2GAv7od6tQagTEYQovrHpCbdJktg+/Wi7itn1L5IVQ+b+WykPlJZntA6UpAcDROKY+TRUNV3X+eCvhbMx0vCEBd+e9D8c6vXj+UCFaDqcBWAPmecr+KnmvcibGg7tiT+FtHWfxjvazeOtAA9442ISTbzyFAY0F0Xok+11lioyTcL4/Zhjra/gEqyeOz4bKRh6ffx0+RWhvMaz9WjzPP/IlsUkmYhw4H+bB+WIPVpMBOch41m108L3AQTPWPoD4DvZibgYg6jz9FgBkwmSUtqLs2qZc+lkkypaSnc5LpnRKpRXJ7cauuklVcyk117DRdHXX5tRcndxu9Ej+vi/Esh0Mj2CdDQ9jZbdiU44NZ/N0mpulWtgVZmNIPgKl1c6pjNppp1xGkG2IhNop2uZw+l20yH5HJ6swsyZPd10glNIJP5tL13T+HkXVa0eWGLPJt8burbLEP5sthZMUCHlzWiud5wFgldwb6bYDS7Y5fIU84spsbM272f5FxPAysOEu1odYcr5EmzwrVd2Nleg22EWwhq3v4VMae16h59Jv8G1SFfa8Gi6Qc2zrD1X7pc9fPqLGu6Dx8ycdZ7EuVl62HZNliY0ZbZadLzqF6za16R3T7+lncvna7y0U9HKh17xA6LP23+fuMxq9z9iujwD7TNV0aWqAjSG/Jzhoc/OuBKOmaT4jCILnMlXeBuBR0zRNAKcFQYgTBCHdNM2xy7ThcCaHiRSpkoyzuXsnUlTkG0a5bwjtrkycC8/Y8H3J/CAq5/rRGp+/QaCkK9FzTVZi2ouzrni1r+LnlyAFDFT9dAiAAFEzYLoEvPgfecANe9NPO/rLk/Hlr78Oh5vGcaEmDf2ELnypIiHkbxmP5a02cV2iuTwHmiIBwZXRs0c9l63fUpHN1W+ryEJJ1yj+5VM/gqLq0B4T8YWv3RoKSHeCsco4/PjhmpAFTE3N8EvcKyCzdQ45Z2exVB+GqaqYy9bNbZtG7tkZXKhJ3RFNvPVINlqPZMO/tvOgrC3Lg+NDvZBMnVPXHki+Niu61yMOEhXooOFazM0OHDhw4GD3cZDm5muVM5oJ4BL5PBz824YJTxCEEwBOAICUwFaj2rI8uPs9Vl7lmTzei3M38fq5Fnx4/CmIMKAKMu7LeBcXkJZNePEvTd+yrFpEGX9e80G0ReVfZosbUT7qRZ23F03phdtaPuwWChqnSE6mCcBSvzVUE4kNK8AN1+4NTH95MheEHgR0FGfhw59/D6rbh9BcnoNWz+XlgrtKMvHxL74LxzoGQ7mu7378LBRVh2SYlqJt8/gVBaMAbwFjXYZXj8zWObznA2cgBQwYD4p48julWwakuW3T+NC9v+NEtLpLryxArLowhJueu4QzuYWXtW46k8PouiF17cu8ZXXg4ADj6ubm+N1Lo3DgwIEDBwcb+07AyDTNhwA8BABhniyOh7CeV2lf5t8tFK2N4iPjT0EKMrVNU0P56iUuGK0d6w1ZtcBQ8brRZrRl7TwYLR/14pv/adk4aKKMk28+hZYcD8rHvKgd6cVz2t74DPbVJod8Qy26gQDoBkxFwExdJKLg23Yb2yG5ZTGUnzhfHbF9gwOGjuIslidqM4gu7hpFReswzpR50BXMy+wqyeSCzdaKLKiKBFPVocsieqq3t+jZS+ScneVEp9LPLG4ZjBY2Tm4Q0bqSYLTqwhAe+8eHoagWM+KuO05tGZC2ZXlw8o2nUDvWi8b0QnSkehBlp+M7ALBurH1wqEAOrh7c3JyT7chJOnDgwMEe4aDNzdcqGB0B7xyZFfzb5WEKEDQB/kR+3oscZidIXmXfhc8xrrc/nt+1iDHGpzciGNF9rYC94S3u7YQA0wpEAZiCiJbMYmgxcaGH2W7ZAw0ixGBm3K2jjfjlVC26Eiy6rhq59bJ6+JyJG3p74dKsYFYwNJSt9GLFMPHQzyxxphNnZdx55ym05nisvhKZ+KRolrc2sxzJbfuW7N5Q+fS4J1RezysYrkxA02PZSDjtw2y9FSgqL5iWMmpFNCbXWHSUIDOKrGKTGVdNdlw7V9kpre/p5XwrH/tWPUYqrWNbGD0VqpfuYvkPhWHj3LY/dfZPQ+WxQpZvMuXb2odzmeSLSCSvYa6TV58109j5H7rAAjzTRXJrfPxbjimV9cE1yqiiaiyfY0DdRgaGWYBlkBwVQedvKmYcC47iulbwT5/7TyiajvfKZ3DiH25He5EVtCoRLDmhrTgbH//iu1DVdglnSj3oyMwCZoEE12Fu25MkpyfTzY736Xn+pQnNRenysX5HR7Gx8L4YNq6kRj9GnzUxVx+BhWo33DcFYD4owFAtS5/lehfipBWUujeuuEqv1GF+S4QWMKHLEp4vLeCk8QGgIGU6VB6YZh6m8XErePXAeT5nfKIXZ4/mQl5hx5W+pGrP8KA9wxP67E9g41ZeIzZPSew6UkZJAjoAU+THfgiR7EWLPLnIfRXIYGNG0Oj55+9hIhkPUaNsPIXPsr/7Uq7NRLSXIgmCILwBlkCOBOBbpml+wfb9nQD+GWw++Jppmt8Kfvd+AH8V/Pvfm6b5yJ519ODiKudmQFAF2J0raJ6XTF64UcsXKcBfC/LaFpYLGl9PIPmkNF2OS52z5YxqUey+TfNMtQj+Xu+PZfM2ScuEGkVyxqL5nY2MZvnhMW42CySEsxe7ETbbEGoPEkNsTGIkW94iSXCjxvfxCmuv2nwwaN4htQFbts3hlPpHrV0SJfZ8sWhrM62yOXjUx+5xi36+nqazbS8tsfxEzcfuv3YN9tUwlmwoiOQck1Np2OdPkgsq+NlvSgG+Xhi5n1IXGi2SHSuf7bXKmovND2sB1si0BQY0F5fmXvoMoklg8zuZ1dl847MnWW4BcQtrl3mdf+FP80Sp1Uy0xPpmzx/2m+z6SHFv7TJwyWAsxUU3uabI8dY027PvFo/CduYp/UzvHybt6mXacN/Rw22b1q4mrqO/w83h+rV5F3eQ5uZrFYw+AeAjgiB8D5Y4wsJu5qSUTXhRM96Hjsi8l5SvuaBEWm8jYMKAiK/mvQU9MbxYUVd8Lp5OqMQbZ5sgAhBNA5UzfaFgdNvfcEVAhAkTgGiamI2IRF0fL8503NsbCkZ3EwvVEVggK5bj5Vemzno5pDQscb6VuWdnQsGog8ujrmsAihYUN9J01HYMhoJRO7pKMtFVkgm/em1JDVKjHxF/NoWCgAnTJaDpsRxMVMeEFJUnjsdgtjpyy/bTVdF4+pEiTP4mBh2VmTh/NB2pV2Cx01ieA022cmhVWUZDwbW0P/nDxV4aawuCIAG4H8BrYdE/zwqC8IRpmt22qt83TfMjtrYJAP43gNpgN5uCbTdXNXNwtdjTudmBAwcOHFw5DtrcvFvWLt8F8CoASYIgDMPqqAIApmk+COAXsKTje2FJjd21k+1G+P340C9/izPZhVt6DJZNePHAkw9CDuaHffwVJ64qIC2dG8RHu56AAAM6RNyfeSueTD+2ad3fJJTjNXNtkE0dmiChNXHnD8axAR90WAdeA5DgW8HpIwWcz+AZz96JM+0VJuuiYRBPy8Fjids32seoGvCifugiThflb7DWecnbvdiH01W5aD5kbbehNA+qLAGaDlWW0Fi2e7+3G5Aa/XB9eQEImCFLnvjTPkxUxWCu2lJjtis7bobpqmj8KL12y++LOsdQ3jqM9sosDKTx46ejOAsnP/9elP5mGg0FBdt6jjrYFzgOoNc0zX4ACAY8bwNgn/A2w+sBPG2a5myw7dMA3gDgu3vU1z9I7NXc7MCBAwcOrlvsu7l5t9R037PN9yaAD1/pdvMmpvHJJ34JVZbxvntOhXIpKYOkerKPM7qvHenFBVf2BroslWVezWArOO5LFiW1dqInmAsKmDARu7oIaZFRZ1yGieKlIVQsDqChsAgfyzmJqqk+tCQXoCueBQ+riex3/baFQTUS+F1EIe7uUEKBZ2NmIVqyPXjfB6nPYA6wTs0wWL8pNbc6jadCJiqMYvO3RU+Eyk0+ZvNhnBWQ1rCI8boYTFdFY9CfhKzWWeQ2zuDFsnz0lVsWJcdivKE2sxpPkS0Mn2D7QwKQ08WFGP5mAnIbZzBYm4jhigTrnQkA7woLLKb9bHvf6r6R27aLSOI3T7KVwZU1Xq49MMqOgyuD7bc6xlZ9BZvCe03jKOp7+3C6sAC9YMfElNg+qMHNVg578fBj34ASVG6+552ncKbcw9rIPEUj6Xfsx6ZrCe1XJRShcAPVfYN4/GsPWV65v5Jx28dOoDnfg6bIItx+8hTqe/vwYnUumpNzgRmr3SrZd3WNXa7JSWxV8dwiL2KkERn8/AhGfc2LnObqXVhjOafUZuWFZUb7zWt7AeW3TUH0m5bVkgiYioC5+ghOmp6CUr8Aiz6U0LyCpIZlCNXAaHDFvGuZ5WIXdEziLz/5SyiqDlWR4PuiEsqdXZfx7yjOwjPyup2MdQ5UOjzJJW8o7NhXXvLihsFeNGYXoi3DA0Ni9KiwBcbrWU3hj6PkZ+fSPcKo62o6o6mJqzx1T4smdjBjbGzKqzwlSyYKwVo4uTcRyxebCv+eYQ8V+zYTx9nMNuRPBEG4GcAFAJ8wTfPSFm0dSeQrxF7NzVZj3m4FAEfbpWWFpNPI/5+9Nw+v5CjPxd/ezpGO9n2XjpZZNJrRPpsXTACz5BLbGCfEG/HY2J4BYhKDiSe5XJY8v5hAQi5kMbaJsUOCLyE4BAhgCDEGmxmNNFqO9v1om5FG+y6d3n5/9NGpr1rSrJJGE/X7PPNM6XRVd3VVdVV/Xe/3fou2UAo0nAt55miIhWBl166GxMav7uY/itGhvRKCDQCWYvkxrzLPBtAlj871gsTXRxLZ3woJV0JDbkQqS1yZWEKzpfkUWxwLN6Hp0udTvcgrG6XmLhKq6LLOlxkm56OhPrLd46H0rMHTbwcX2MvMyBxrrNk5GwVYZec2Z9gcJy+tP8cYCgm/wx0goTRsLv/K/DrHbEOEsp9dJB8ZZjBlXq1dJxTuBUI9HRf5k7tJGJoZN2uH8zpbHxZtoW8iSDiYCJmtkxIZC8kK7/pBw+rQtD20C6V2p8rMBYdSPXXbXM+FIRJjsB7mVTKeCCtrmVQhIPHjTHez60rLZIfP1kectw7pF3GducT+t0Tmj/XCt9j/vpgLjaDRwUEPkOtsFU13B63N207AiEKAaYWG0jQc7ulaU9jnLAl0rwky6uOuTNl2Bb7IHKiCBDO42+kL59VOC2f78aWWb0IxdNw/9AaeuO1xfKvwnVY9r2Bg1uVYhudNrV04k1OAhqAqcF2OF3U5XuiedYIzXSMy6ydw+6NtEAMGDrhE/PzlvVADMh589BSkgIFblE58+e/fEzJIrwaDpfEYLI2/dMYtRlmvH//8rOWTq8oyHrvrBHxp3nXzH+zrgqKzDxwHB7s4Y9SOAxf8qDjfjbNp+Xgd2evmO9LB07GPdPagNs86b53XizqvF0b89RXa8fpGUVg7jO7KJPSVJCLm9CLEgAnBBAwRmLg5Aj2fSMR0eThwmVWNr53HLR/ughgwscc1gu/848GQQbqCwrPnQ0rBpqqjrGEgZIxeC0oH/HjppWfZh4XfPYEOZXvtPF9XmNcskpAoCEIN+fv5oNDN5eKHAF4xTXNZEITHAbwM4B3XUiEHDhw4cODghsYOW5u3tTFqQoAmwPIRy1ubuupL9eL4+0+g4nwX2sycVXE/LxetEVl4uuBBlIx3wReetSq+aMlMLxRDhwQTpqGh1eWckwAAIABJREFUbLQbTYneq7pWXY4XzfFXV/Zq4a0Z53w6U6tm4NKNkBqqrBnYWzt8TcbodsWRLmIEahoqB7suaoxW59hi2mauT5su7/Hj2Z88H6KJ37f3sZCBacfp3UE6dvC8p3dd3YeTjURm/QS8NeNoKLV2ov/w+H+Hwq889/xtmD4SDsMlQFBNmIrADNErQGLVHMQgxddUDWRVT6wyRlsr0kJKwZoioa7k4qFvLheHevkPC5UDXejIu7w5omiiD6Xj3ahPyEcPbmza+Xowcc0iCWOmaa7Hvb6kOI5pmuPkz28A+BIp+3Zb2V9eS0UdOHDgwIGDGwE7bW3e1sZob3IS/qayErVJBWiO88IVZC+EXyBb7IYlKtQclwPPGNvvD5vk+UPu84z6sJhFFC8NthPZFp6B9uigqI+uc6q7jUtZZOdURkNsXohSJBJ1wKgBuv3PN69BGBsLaey6UgKja+Sl8FTKRKLM1zbOYnMuBGmVu5uGUVR7Dk1lGWjfb9EuqYLbb0W2AgCi37YI4zkRCBgwBQHzsS64CwMwn2NqqPotIkqjBuAiHIjKiB6uPj+YKAulPxB/NpRuWeIFd/5tkOVboVkCwNzi+gpygWWWL9DLqECGYqNbEPrr8iijrSjzIsnD8tek8cblm2UFmA9uukVSskEQ9ZlePPzACRxt60JNRgGakr2gzBn3OKPxvO1ML6OJGxre/mYvepYsH+JFYte7e0V0IR+PfuAEDg524a2iAjTFeuGa4McFJnhaj6awvqRKg+NTjEs2MWNT1SNl2tyMiruk8eMxpWYKT5/8MWRNx9td7ei4KxmyqlsfLDQdBTUX8B+PlePsC154a8bhr0xAejnzUacT5T43m8femN/LXSf60AL2ukZC/sRjhyMRKS1xyn69xYn4q79/N/bWDqOtPBVz+z3IgXWttgnWkHG7Jrhzjw+TZ3mOUPSCUoxncvm+r8kqgKEw0bO6xHw0JXmtdptj9Ska9+NvfvMcFFOHKkg4GfFetMoWjVfOTmOXmWFUXADwzDFa3mIOEwgzXPyiIs8TRV+B1dtFxpluo5pvFjZRPr4awC5BEHJhLWC/D+A+mkEQhDQimHMHgNZg+jUAfyEIwspXi3cDOLlZFXVwZRAMS7nUTp+k1DqqmisRPh8d+wAgL7BCIlG6FlQ+n0BoutTtRo9wrfk7AKiRbM5TIwSS5usdIB4HWiSbBwyiZi5J/DpE1VUv9xmiri0eIbBuPonwFakaKlVMXTL5tWKMcI2nVPbBcFblqbQLGis3YLKPgn43++Bmv5/zC2tTc/VpfpKiyrbKHFG5pWxluzIqkc0lLFaemmmn367HyrHlo+KxlFJO6+Oa4u9VX2R9FCAqvvMyz1qbdLF1t0NIJmVYeTtFOpyoK0e72HtfJKHszml8m1I6rofceJZ7nMuXIDFXEhe52QAZc7tcfAQDFWQ8EslruwbEgoe9t9F7GidU6oCNxqyL9N5Zp8sLfHvTvuDSZCysnmfWoebq9P17/TKSStwBlvmMYsBWMAja+4K8afRZ/po7aG3e1sbogsuFZ9/1ToSNX/9YOys7p8VzfThTUHjZ6rmbid1Nw/jcEz+EHNxN+uxX7wgZpHbMlIfj9J96cfQLvRAME0f+wo/T3/KG1FB95dZHksrn/Rg/HIEL68SLvB4Iif7sykddrveKy9dne/HoB0+gcrALNZmXF8e1PtOL1shL56vJtBk7GRcXn1oJObK0DTbaKnz9kIMqvkbQ78IIxqLVZQndldbHD0q/TseVC6atKOnm/fsoFxrIju7iZLYzv0Fs9fpsLx56KPhhIegzWtney4mefez242hK8qJo3I+ysR7UJeahbKwHihlkQpg6irXhkDHq4PJgmqYmCMLHYS1eEoAXTdNsFgThCwBqTNP8AYAnBEG4A5ae2wSAh4JlJwRB+HNYiyYAfGFFMMGBAwcOHDhwcHXYjmvztjZGtxtaI7LQGpGFhfj1415uJYpqz0EmfnZFdUPrGqMAEDalQzDMEFU3oWoeXSeSMVkeAVQBdz9cCykYJ/THL+2/KoM0rX4KmWcm0epN3hCfvw+dOoXPv/oqRNNEQJZx/8cfR0Nm7qUL2tCQ7kUDiTu5UWhItwzdg4NdqM4sCO2y3Qg4uxIuRdMBRUDXXcnouisZylsGuiuT0F+SAA/W/3p/pcj/vkUVL/zeMH71mV2ofp93w859MdRne9ESw65VMdzN7WZXDHcDAL761vOQdQ2aJOOrB+4IMSEMQUSSMYdCbWTDDdJ9U30oHvSjNinvmsJSXS02Uz4eAEzT/DEsxVb62/8h6ZNY56uqaZovAnhx0yrnwIEDBw4cbEPstLV5Wxujgg64ZoRVuySBGNZB7ilC/5ljvA5XH09hWCpgFFdPL9ndEWydLbMmkc4RYz+M0RTmU/n4nDJRClxKILSF9cXJgOi1eSaUlgsAXVNsC+192a2hdJprCsptOoyXmT+f6zYdb09oD6mZJtfNIL92FDNHwjBf4cbCUQXGPwhAkJY7cCges4ZF59lTMxzyHzVVAzGnF9F2IB0pRJUNACJlZpycmt8VSv/nQBEOtA7ixMnXIWs6KmU/PvrMvWgszIREuDJuhfXRrrMXcKS1B6cL81C3KxvSEKP/yAuW+MznX30VsmERQl2ahpuae9ASw3wt45tES0BouBujsRGIXZpHTWYB2sP5nev5NNYvt73ei4rhbpxNzUfVAWbYeka4IlAWKB2clY8Y5mkcHVE56AiGf1HmyHgkXbmYZAvGPcXSlGayHMfnM+YZ3YpSOBfLmUygNs7TsMIzmNLuTICN256RRC7fUpmMp/76gyhpGMTirW707bWO1yQGXQnGgD/O/S+uzJl5FsZohQIOAFGE4pPj4p891ZSQXDUX8lk2DRO3fqET7bmpGChh45uqQc7o7L6HXOxBmlxc31/VCGfjTDXtRxmt5jeFBXik3trN1gQZTeFeHO7phKJrkGD5lsbPzOGpso/g9uFavOf8Wbwv0I53qV14eteDaExg9CzZRtcx3IwCp4exY64p/nkXl/WQKJps6NAka4f2bBpVel73VjcUm7ngOfifCUEHXFP8HAcALjL/Udq7MsfmTHmRnz+lBfZsCMskbafLiWScEgVWkVDu1Ch+LFOl6kAkofbyng2cQr8RRlxowtmcJCu2ehN1XUqllMX1aR2aQR5qqvRr8K9iC2DzNqVPxsvMLWBUi8J6mCdUz7El/maXCE2Xum70jLO5WFX5ycfQiWLxIisjLvPzn0hUUyUiqk7TdoFQKgqrMKYpT820TVFUgdkg1Gy7yqkhs2N0PpWJyq5NlBakuTml5oCbp0VPSKxdZxbYANKIAq9p2NqHjBlZZuPJE8YuGu22KTCTvzPD2YuDXWmVKiArOhu3XoWtx7qNIx0Glo9SwOmYA4AE9xzWAlWUnpD5cbYosjGsE3VnacnWmeso6NL+X6V4S+nXBlXJJXWzqXFTpW6JzEHiEr82c2q6BLTlTOWGp+luO2xrY9TBxTFcFoNXXyxHZvUkBg/GYbiMvbQn183gtx9qgqQaSFMEtH87BefKY/Gbb+UjsWoOY4cjMVbKFrORw9FcnNCBg1euiktpn9B0VPj60ViYuWbekrYBvPDFfw4p3N7/9CPweXZzeQ71dUEIGqImAEMQVglZHbjgxz/89OuQdcv7wYCAgCzjY+8+jsZk7+rrDvrx9z//emgH7JH4E2jIWJ1vp6ClKB0tRekoix+6dOZrwMjhaEAUYBomBFj+X7k1Y5wxulVoyPDikXtP4GB/FzoD3lBoJlWUYQapuw1xuWiNzkbJVA9k02B03bk+NGLXJa5weaCiaCs7tGcLrnzX/1pg4poV+xw4cODAgQMHG4idtjY7xug1oHjYj4rzXahPyEfjBtEzdzUOo6juPJrL0tB5YH3K7QqGy2I4I3QFaWemIQYMCAYgwkT06SWgBJgsj7BouQD3RWq0LAo/e3kfUqpm0FqRtkrt9HJAaZ+aLOFs8fphTiqb+iyFW8NSuD3S2gNfBW+MnskpgCorgKbCFEV87o4Poj7byzm2hyiXsAxWCSYU3XqxX8sYPdTfBVnXIAcNgIMDXTvaGN0qjJZF4fRnc3Hoc70QTBO6IqG3MvHSBS8T5V19ONzag9N78lGbf2l/7oYMLxoyvEj0WV9Fm+Ny8KmyR1Ay2YuGuFy0xORAME00xOYxI1WQ4IvcOF/xhuhcqGJQFE2UcTY1/9KFNgHXqNjnwIEDBw4cONhg7KS12TFGrxLFw358/UfPMhGUdx+/ZoO0rKMfn/mLH0FWDWiKiD//2vvRlXV1O0fnD8XAcIkQVAOGImDmSNgly4yWRWG0LAoj6sX4xeujsTATH33mXlT4+nG2OHvdXVEAqNmfY4U5Ce6Mni5cHeakPsuLYw+eQOVQF6ryClCf7V2V52xqvhVnNrgzqgsCVGn9F/sz2QXQJCsurS5ISJuZRMmQ3zFItwCdH0rBr9L3YPfZYXRUpGK25NJj8nJQ3tWHf/nyC6Fd9vs++Rga0q58h7ElJgctMTm237LxVOkjKBvugC8yB62RGxNyBgBao7Lx6X3HsFcewtnU/BvK39iBAwcOHDhw4GAjsK2NUcEExGUg4jzPF3fPrM3p1sOIU4DEc7qVWbadpkcxvzMxoHH5pkuZ8ec5z8pIi8SXRQcqh7qYCIqu4uHm1/D1I+9FreJl17GFZgikkHOQ3yMiLJ+AW7s6IKlGUJDIQPyv5nDn042hfP8xcCCUPpZ7ijv3PVFtofS/ze5Ff0kCvv9iKdKqpy0Kb2EM1GXeDyTHNYb42jkkV83BV5GJ86WWL2yMxByBnh+6jSsT72bH6uaZQFG8x/p9qCIeQxXx6B1LAIJZtQGmo28G5fHfCi/C/Z94DEc6enB6dx5q07wQl1g/R3Za/3eJOah+94qBYB33jLDWqyrKxcOxJ3BwoAtzpuUzeja9AE0JvFER22WNmT5k4xO3PIb39p/Fb/dV456G07izsQafKn8EjUm8ATOfxh6PhCbmZMKNMxtmM9mxqAHml+CetsKFlI/2oDYpD1XFzFj2XGDjeTmeH7cxPaxNqM+TeZ741/JDGAsd7GNCZyLz44hJ5P0+Lswymna7zKTpDyX2hdI/mTzAlQkn0vJRIvNl+dK594bSR2O7uTJZ9eNIOzOD84eiMXWTBwM3xSMcAcgm74N1OJyV++F0GdaCW+bLZGaN4d1vNnKxRN95vhn1Rbx4lnzOaq+SIT/uaLZE4P7jwEG0kpiw0f18Q3r8lpNuJxLRUMk+llDmjLjM+xHJfRdC6YgF5luue/jJYDnJ8qepT9qF+iDt1z2tw5DZ+LGHqdgUmDvLL8XBxkAwLP8+6lcP8H6irlniJzrL5g1pgfdHo36imCfOfIZtnY9i64geyea/QCx7tgLR/NxMfUZpCC3N9h1MJ/7mCGf1VsLYnED1DgAgOozNfwlE6yHWxdLhthgkbon45RGKT6TE+wnS8Gw0hEsEmPMlnX8BYJn4k8642A1OLPO+fLNLzJdvZoocmye+oEv8OkR98RRjbb9QAFxoFepPTN0bDX7KhEimdOrXKZKx5Z7hx5m8sE74DRdfb9r/upvMc+R0ps3vmebj6u2yjS2DtaNG/IxB2k4I8PUxderDzBo1EMEaRYzh75X6jNLxM6byQpoekXVGgsSHHFuBC3y76eRNNJqMwSjJPrbY2KDrxRIJ87Ks8uaEppEQOcTP2LS9m9tD/ayA+oKKtu6mYVuoz+/FylDfckEjIaTsPqLr+IyC+MfafVg3BTtsbd7Wxuh2Rk1aAbcjd6S/E+XnevHIh67eB7H6gBdqkOaqyhKq93tRiAuXLrgOhstiMFTG023T6qeQdWYCA4fiES/P4bYPd0IMmNjrGsa/vlgZMki3ArV5XtQSY+BqsUK55ESBtPUni+YEL8rGeiAZQV9AQ0PJZO8qY3QjUTTux9d+zdRaH006AV+ad9Out12QUjeN9x1rhhhUaf7ei+41aeXXgrqSLGiKBKgWPbxmHXp4yZAf33zl7+HSrYXoA75qPHrXR3dEP6yHzVbsc+DAgQMHDhxcGXba2rztjdGSQT9uae7C2dT8NX0ArxcaU7w4/tsn8HjtT3FoqDPkq3gtPoi+vZl4+PN/gINNflTv96J+T9Y1GaN2pNVP4fceroEUMKC7RPR/IB5iwAwp6GadmdhUY7TM78fh7q6rjhe6kahLzLPoujoTrNlMlI/2MF9VXUPlYNeOMIIyzkyFVHShGsisntxwY7R5XwY+8aXfw4Hac6hZoYePr853sL8Lss6+BSu6hsqhndEPF8NOWvAcOHDgwIGDGwE7aW3e1sZouBrAi688CyVoMBz/XyfQmOKFSSTe5SW2dR52ntEQtWQ+RqY8Mo29S+dQvDiAhqRdzPfLRgWK7Gf0BDWKUSfUaNZUcpBO2hqTg2/sfw9KR3qh6BpUSUZNZkGINuAmEWQAQKUUTInt3CVFsnobt8mouq0AuxqHccfPGiEuGaGXd0rNtUt01waY+u1vk5Abr0xXhNK/X38GsmqJGgmqAQNCSEFXlyU0lWdiTI1CWyAtVCbDQ7YbAbzWui+U9qaPhdIDk7wRGxhm9B/XgoDSfj9efvHrIb++B04cR1s4bwDGdjHqxIyXUTyiedZnqP0BIIKEEggbJzRo08S+6X6UTPWgITYPLdFst0wPk9Aeno0nKx9FxfkONMTkot2VsSrkQHwjGwt6JBsLWjjf9p4BxvMRVUYBDxtl5X2RXmsnPehj3KrkIrrfup6usPHsOc/fKydvTjZ7o/oI/cz2FM+VsuuGdTO61rTA03pMld3HiItdaGSe0XeLE89xZWJkFl/mtbn9ofT/TmfhqvwaGQs3G9CfDY4xRYRys4Z9bku5N1YilDwA1YtsPBR5mLrvVAwbSyNLrG57moaxq2YE9SWZaNmfjv9KCY7NGUAYYxQqAFhMMfFmaT4++pYEMbgzqkoyqvIKQpS90RK+IeNd7D7CR1ibUll52DbgjRTGRDBd7HyFs/0omelFQ3QuWqOy4eliz9XsPvbshk2wMWiKWxTbxYGDK4SoA+5pgwtrBvD0SXmeUQopNVdYsHE7A+vEMraHXaN/k6ThYn9QWiYAUCajFkHTthAgEcSlIpzNhWFkXgy30XQVwgOMlNk9JbnYek6puADgFtd2c6C0XPvfCyQsxhSJSUNpmQBPuZxV2bw/MseHgJmZYA0hj7BzU1qtjaXJiQbSqB92mi6l4NK1i9wCtLWj2lnnI0sCXefttFw3WVsFlR3TYvh5X4hmFVI42jBZP23TLE/nZWukqPJjKxBLqaek3pT2a1sfdFI9w03ovJQCahv2AZ2dfGiRfch12XioGeSFk4Zm4d8V1w87REO7RNnWZjoGZzQSbo7UwWVzoRFEdvMm97zyjaKT51ek4yfAfrfTYk2yVNO+pO1t2NhxBgnDJhKqMF2nAUAwyQAVSb4wQlWWd46RuFXY1sZoRGCJ+YIZGirPd6ExxXtV59q7dA7PnPtXKKYOdfIUnt794Q0RI2lK8uLxOy2RnZqMgg0RwtnVOIzPPGEJGRkvCXj1xfIN2U1aOOqGqVgxKE0F6P1AIno/kIjkqln8pqQA/ZsYZuNwb5elnmuaEFQVd1fX4C/etnm7kfum+/Hl+n+EYmhQRRlPlTyClhievtkSl4NOKW2dM2wsWmJz8OTBj6B0ogf18XlojvduyXWvN6bLPfjBN0uQcWYKQ4diEV2+eOlCl4E9TcP48z/6D8iqDlWR8Mkv34P+5IuHI6rzenHfxz6KD56qAQB8v+wgWqK9G1Kfi6Fwth9/2foSFEOHKkr49L5j6EDSpQtuAXaafLwDBw4cOHCw3bHT1uZtbYzOu8KgSoshKmVNWsGlC62D4sUBKKbO4gXO+i9qjBbO9GP/hX7Ux+ehJe7i4Rx8qV74Ur1XXTc7iurOQw4KGQmquWHUxsUKF/peSYTn1DIWjroxtt/6YjpWFoX+hc2N91iVWwBdFCHp1ne6e6qr8Z/5BzdNxbZkqgeKoTGf0KmeVcboRqBoog+HzrVZO16X+LjREpuDltiNCw1yo2CkLAYjwfEbjY0xRvfXDUFW9aDYl47ShkH89Pb9lyxX5/WiMZl9BHFNXSTzBqFkxh+KJ2oaOkpmetERuT2MUQAwd9CC58CBAwcOHNwI2Elr87Y2RhdlFx793RO4ubkTtSn5aI/IhmvO4GgUlD6ynMLoJ2ED09y5fLF5UCdPBWP6SahPyIfhliHZ1HQFTUfh7AD+sm1lJ0PGkwc/gtOlLND9YjJXhKejkBaltCAA8KQw+k6Yi1033s1e0HM84xg/GgH9myKg6dBlCQ1lWTgfiEU8Ucs77OG5q36VxWxMEFm+e6JrQ+k2NRkohvUPgG+OGU/FngEAQGLdLGZ/GY72ihT0FCdDEXhaR2VefyjdOcmuqdqU1KCwcoFYE2dis/Ddowdx35unIQKQdB2He7rQnOAN5VtMYnQU9ySh307xdaC02EACUZUliskNUV4WH1KUUZ+QBzNIrXBNMoqPNMPSE7a4l1F9hCY0zfJpERb3q2iyD1859by1+3pexlMHjqFtjLWpaefbELjHGb9pNofRXmK77fKEDMuEguQi6oKz2fx1wtpZmyzkkPGt2epDaDSzi+zclekDoXT3DP+RYjrA6poTMRFK/3COqe6WhPVzZSRCDdpLrD+/yj8gtbPMUD9CuNkiGYOF0cMAgLlbwqC/LMJUDWiKhOoDOShIHQ3l65VZvUvb+1FZNWSpNud5YRJVaUofC7P5mS4kEVqPxtpHIq4B0jJPTZJm2bO8otLX6MqEKgTjiQoSmvRkaInk3k0y1sfYGJ7L4FUwNws7KZaZg42BoJtQ5g0o8/z4l6fZ+BUX2VwmLJFF0rRxF4lSJ+UommG87KpJFT3dRHWa0PRMmzInfZejVEq7gqcgE+VWsjanRs2G0tEKPzd7ZHav0TJbH0TiT7Fkl44lkAinkNJ3AUAllR3TGM12mlAkl23+GfNkjmqbYC8pUxP8PCtOE5cjQil1MQ8MKLzwOqQAqyt9JVglFEiVTcnQ0MJpR1zefCOSpctQ+DKmQiiuJl2n+T6iCriU6mkSmqXu5geDvEzOR2jorlm+DssLrBzXzaR97N2vh5M/aJOQNqG0XAC4MM+45kOzbFMiLWqGy6cSJelZovSbtI6yLgC4SGUjBDae4yV+AMyT88kiqw8d65LIv6cZOukjoiIs2N5DqPsRfS7p82rvf668QU9Aqb18v1JWs65Tni9/PpGMJ4NQeO1KzVuBnbQ2b2tjFAAa0r3oxrXvaLVGZOJk7v0onu9DXfIutEatf86SmV6yk6GhdKIHp7Fr3fwbjf6SBDz//NuQXzOK3oOJm0qfpUism8U7PtwOIWBAVyT81T+8G+dL4y5d8DLx6uEKfLDqbMhvtCbz6ne6L4XW6Gw8deAYSqd7UR+Tuyk7kqXj3ZDJ7mvpdC/aPBsXh3KzUN7dhyPtPTi9Jw+1BTfmTm1/SQK+8cKtEF83UFuSheZ9GYjEakO+tL0fL3/+JSiqDlWWcf8nHkNjwtoxaDcLrRGZOJn+eyheHIAvPAtt4Rlr5isa9+PwYIe1yx698bv4a8HcYfLxDhw4cODAwXbHTlubt70xupFojchEa0Qm9Kiwi+ZriM6FKkowDd3aVYvPu2j+zUB/SQL6SxK4L0+bjeSqWaZ8qhnYc3ZkQ43R2jwvHjx+HIe7u1GVn49O0bth514LrdHZm/JSXzTZh9LxbkwrEZwoUX3M5irybgTKu/vw7b96IfRB4L5PPXpDG6S/Tr24YXm42Q9F00MxSI909KDx6NYaowDQFp6xrhEKsNA/im75OH+66Bh+jb1bWEMHDhw4cODAgYOtx44yRi8XrVFZ+JO9D6FIHbB8RneIn9+Fw1EwXCJM1YQui2ivSNnwa9R5vajzegEAkf0Xz7sdUTg3gC/W/zPkoAH6t0V3IH52BvUx1m6WcJH4ptsBR9p7QkJS0DUcae+5YY3Ry0FVEYndK8k4vXvrPyzZsXdxCPv7G1Afy3ZAV0L/hHycZ3q3zBjdSX4pDhw4cODAwY2AnbQ2b2tjVF4GYrv1VT4mlPvtHmO+GiaVax4epUVg7Gb0SUElMtqzC1w+adlyVOhAAs5UspdB1ywrs5TIc8ep6xsXioN3p8HCFHMYSMsZCaWp7+XALB8i5UTeG6H0OdU6llE/CVeNjqGDsSFho9sj2kL5Ooj/6E9mi0PpbBfvFBdDHEZ+Nb0byAPq/iEbnjeWcfZADnypmZho5GXhszNZOJepcXbj4oSLy+cZXSf0CGk66hcKAJ4LzEmE+gWHjfL0S4GE43GPMR89w02k1kX+IXZ3s3praayN9RjWJ/G/6OXKBHans2oPW/6RJbOtUAwVEgAYGhLaevCdot8BAEizARjhzElEmiO+qfP8PQSSWdtFDDNfDWWCF/iZy4sm+WjYD3Z/4aM2Hwwy7qRl1iZaOFAXsxuq9AtAt3ZGzxTnQArTsDjHfELe6mYG255MNk4BYEFj90fDrCwTH4xpjfd1zHWzZ3GA+Dz9bPYAl+/OeObfPKqx+6Z+y+2zKdjTNIyiuiE0l2UgtpC11xKpQ0ac5Zs6eiQan/qre5D3oxlU5RagKc7LXZP6pWg2wkRsNxuP8iKrgzxLQghpvJ+MmsD61dU9gr3L51G8PAhfhBdtYemWqvfwdyGbOu6XZDzxtsfRvpwNX2wet8tenb0bUYO8P/vmYGcp9jnYIJiAtGyuihRhkvAJ1MdToIu2bT03FTJvEx8tU+F953QPO6YTn1HqT2ZKNt9CcgrqT2qKfB1oaA1JIs86mXuSwma5Mh5JJfnY/SUqfD4KGrLFIBW6EOBD0fkXmWvOhUXmo7dI5t+FAL/mzhG//8AcO0Z9RAFAmWbXpb6hnP/oLN+xrjnyzqRSn3k+HxfIoEURAAAgAElEQVQyheuXtcOgAOuHyQhEst8ViX/n0kioNdc4mydNN39yGj5FWtLXTIsqfw8iPUY0RbRoPmyMvMjqsBTHrkPfXXTbmsKNQZld19DYgfkF/joa0Tig4VIUW2gX/xJ770uVmWZKgsg6Nsz2Uuoh5/OY7D1kyuDXcDpupctk64mSsc4Be3ka94X8SrLZlyiR+P8K61xm1Tgjfwsu6s/Kjy3DxZ4dPWxt33TeT3WzsLPW5m1tjF4P7F0cQvFiP3zh2Ti9TcIvUGTUT+LeR85AChjQXSL+48XSDVHaXUFvcRLOpPzP3Sm7VvjcGVBnJZgwoEGET0rdkusWjftRfqEbtUl5nOjTlaI+y4tjD57Aob4uvPW2LNTt2hrfxI3CnqZhfP4TP4Cs6tAUCX/6lbvQtv/i4XlaitLx89lDW1RDhr3L5/HM2L9b4aRmq3Ey9XdRvMRUvaFrKB/tRntENprjcvDk0cdQMtGNusR8NMfnYKsY+jvp66sDBw4cOHBwI2Anrc03tDH6/r4q/FZ/PX4dvw8/STl4zefbu3QOz4x8z3p5FCT84VgsmhK9117RDUBS3SxyTo0j+twipBW/TtVARvXUhhqjDi6ONlcang57N4r1YfikVLRKV/7BonB+EPt6z6Mh7vKElYrG/fjar56DrGvQJBlP3PoYmpJW+6cWD/tROdSFs6kFFw01VJ/lRX2WF4FdGxNmZStRZAvpcqB+6JLG6PVC8fIgH05qaQC+sCymrCvJqE1i/qvNcTloTNzaD0EmdpZIggMHDhw4cLDdsdPW5m1tjOoKMJcuQeNZC4jt0fB+fxU+1fg9AEDFdDdgAK/FlITyqMX8yzrdVpenCLUzkRlyB877IJOXx8rBLrSHWztHS7FUIpyvj0KUs3XCnAlk2dQ9ZxmtQzcYNSA3loXIKIkZ5Ip0LKYiq2Ec73q0FbKqw5AFGLIAQTdhKgJibl3AYU8PYgjTYMlk10lTWCiNLIWn6f58Yl8oXRDBqJRJEYy7Mz0bzpWZWSK8ExoiQ+G3cVRCsUloZsdEIhEfiOIfND2M3YRMQgYEYmwU4C5GgwpkMsqt6zyTOp/fFc+VETKZEJPSzyi7eir7PbAnnSvjGiLhgcLZfde94+2o4+rD+m8pk1GqPMOrg1iu0DRXPniczHuAi38qLgS4/NHN4zg61ggl6E8IXUP5WA9njEYM6yga9+Orbz4POUi/fezuE/ClebmxafDNCGGY9KWHUIY8rO1H5nia9iQJE0DD/PQsM1rPh9M7uDJ9AUYfagET8flkQg2X7zfL7FkcDLD+ywln/ZX4jlmYLwswVBNQBLSUp0MOUux2kTHcPcuH6QmkEf48kZZXUwnlu59voKkCGSXn/Kgc6IIvJh+NyV4AgBRgz1fEOf4Zl+bYdaoLy3BvdU0ovFCtdz9aYrLxVGoMys+3wxeRg/7xWLiW2DmWk9hkZ6eaO3Cw7WAPpUJCbhhQ1sxnp7ZTaq/hZmXsoRQoZU7zkDI0SoM9+oKw9jFT4tcriYR2cUn6mumLvRhGScwlI0ok7hk2DqFOKjFrsvlXtXEKl3TWDrPLLN/ELJtnl6f5FyNxgZ1DXiRrqe2bo0KWNRdhFNMQarItZI8yz+iq4jJJL/IhaUD61vSw+VRaZumlRH6eJZ4bILfNh4OxIYxSOEU6fvhXWpWMk0AUS4cRaq88z7tDyOOEuxwgLjTL/LsQwNZ6zcP6grpt2cP7GWSc0f4ySeiTwBI/FoQApb6TcGxhPAf4wjJbq0fDWJq+99kFMRWT0G/tnHtabzL2dZKmv9vHepiLjY3lcOKCFeDj3UhLpJ8pY5d0pci/Fq2PiyyZXAgo2g62dZZzAXDRsFFbTdPdWdjWxuhaKDnnx60dHbjlfBMAa+yZAG6dbOGM0auBL8oLTZQhGBogCJhWIi5daAuQWzMGSdWt3VDdROMHM5DgncfMkTDMV7gvWX6jUdw6iMrGPvwybR9q89ffySnt9+NwTxfakI+mJO/WVXAbg9I0TVNH8XwfZ4yuBZ8ne93dtBWUjVkCOHLQYK0c7IIvzbtJd3H9MFYWhZ+/vBepVTMYPhyN9vS1adKFzedQUj+IhtJMtBalr5nnUig558cL330Wim4Zkx997/GQQXoxFM4NoGSmFzWZe/DkwUdROtGDxkhvqJ9bYrLRqSde4ixbBHN12EcHDhw4cODAwXXEDlubbxhjtOScH7/TUo27Wqoh6TpMwfpatNJXv47bt37hy0RrZBb+tvB38Ect34dgGnii6QfojU5Fc/z19aHsrUyErkgQNB26IqL1rnTk33zhutSluHUQz/3ZP0PRdDwqvYn7n3xsTYO0tN+Pf/rHZ6Fo1ov8x995fEsM0r2LQyhe6MeZhP0b3m97l86heHEA1ZMH0Bx3defmaJqCBF/Epc/T5snEyex7sSdqDLVJ+Wv6jNYl5kGTZEuYSNrcGK7XG2NlURgrC375HVl9PNc3ik988hdWbFFFwqf/+m50eq6cyls50AVFDyoPGxoqhrsvaYwWzg3gL9tegmLquO/8r/DkwUfx7fzf4gQztht2UmBtBw4cOHDg4EbATlqbt7cxagKCBlSc8+PrP3wWiq5ChLUbqpkG3owrRLgewK/j9uEnyZWQphgnURZtfB2qsieQrXeZz5cwMQXRNC21VFND0WIvqhPzOGU42L5WLGQQaoqbpV0RPIUlJ5u9OUe7GDXvw6m/CaUbFvhdsuMJp4B3AJP/GoOFN4HpI+HIKJ/AsGZT31MZ9aFjib14RxL6UGeA30WKczGVtR/27w+lF5eJIqxNEe3A6xegqMG4jaaGW6p70RKWv4q6/I63OqGowZ06Q0PZeDcaMrwwSD9EDvHtQ2m60hKjdSx4eXpM+Dm2G0zVZ3eLY3hm4BUoho57x97C07s/jNZIS0V5OYnRm4xdLGSNa5S1QSCOp72YhPJT4J7GM7XfhWJouPf0KZzMvQ+tnkzrfNGsfp4ORolZzme7X64L1thsjcrHSc8DKDLOoSE2Dy0x2RADrPGEAE8Z0oPnborbjdp4S31WXjIATk3ORHtYFp6s/AhKJ3pwas9utEbmQJk1OXVJz3nbwCUUG2GEUVMCcRJK+/w43NuFU0d5kSMjwPKNEJXH96c1hdJdy3xIoAsBRhm6jag+/3iB37EsdTOK+jnXZCjdvMCovcsGT/F5ewKjBLuDUtbljX2cX2nu6THgdnLvhPZ0sPocDvd0oSqvAI22jyX1CQUhhVtVknGqoADLsQJiell/KWPzXJnieT/b+TY0HOzwoTc2HFoqr5KtxbCxJs+wuUAkz958On+vmwETO0skwcHGwRQAQ7bzYhlE8lnfNMmrhm2xMIiCLqXmGjZlVEoB5hRL3ZRKt7qOax6T7TRdVqcwha1LkTJ7NsMlfr2iCrpuIqNPlUcVgZ/Pp4lKKaXmztikvJc01iaLKktzKrkLfPtIhJrLqeTOcdngIn+75sn7yjSh39oVZskaJVJleIGfO4RlxqcUVHY+ZYn9Lqq8UutyArun5Rjq+sPy2BV3KU1bDBDFeI+dpru2uq9EqLB2mi6IWr85zXjMgm2bSopkfWYKxL3iIm/VokrWXELNNQOUArp+eZ3wk9UAf6E5ldVhRGUuL6Muosgv8K5aOuk/Oh7Fi1B26fgOI89EmMy3o5s8U+5I1v/LNvVaVacPKekjMswkm8ebSNqL0tDpnGMfM3TaEckxLdwmu0vpuCRUH+1/qua7Wdhpa/P2NkaDqDjXBdnQICHo1AtAEyV8N/MWtIVlbui1fJE5bOdKlHE2bTUl8nogUOHCYPH1pw1X5edDlWVAs17Qz3jX3oGrS8q3duqC/nJnUze/HUtmeqEYhAI76w8Zo9d87sleKIZG6LX9IWP0StEakQlf/O4NqRdFS2wOWmJzMJd8bUZMaR/b1f7Y6zIe+NOHbyjV3aFDsShZiS0qS6je710zX3mPH//0wvNQNMvP9tiDJ1CfxfL6Urw4/v4TqDhnGau+9LXPQ9EQkwtVlGAawZ3vsNXjr3C2HyVz/lBsWgcOHDhw4MCBg52KG8IYPZvOdih0QcTPksrx8+RStEZlczGvNgKtEVl4uuBBFM/14dcVRZflI7aTUOf14sHjx3G4uxtnUwu4l3eKpkQvnrjtcZSNdqM6u2BL2rEh2mYIRG3cNRvicqGKsiVGI0jwRVwfI6Josg+l492oj827aqrwpXC4twuKFqSnahqOtPRumTEafjaAmO8t4CatC113JeNCWfSlC9kwUhaDR77wYdzxesNF8x3p6OHu85C/a9V49qV44UvxrhKiWA+tUdn4dNExlEz3oklNQlsY2/0tnO3H7aN1eM+FOkimAVWU8dSBY+hEykXOuNnYWbHMHDhw4MCBg+2PnbU2b2tjVIBFWeibT8cnyz+C0ske1MfloT2M0fZ0qtg2xtRUxfFpeioslLAy7jFGXYVtu30xKxK1KEQtCmEoQkhhbjaTbeWvCmRMlGSj0xitw+OyKc0RpIex+s0b64sQ/dNUBQArvuh7fU2YP+rCQoUbLcsZXD67Gt8KqLLfvwzxsRbDJEarSItibde9yOil6jI/RCRNQH1mLuozc0EYwHDxzY35FBFVKXmoQh5iu1R4hq1rLaSw84Wd44OD64TuKs0w7kVMB0/rEsaYSm1gN6Mkd/hjcDLFiuVYl7mP23Wi1xKWWL+Y4YT2FOA/bARSGA21u1/DyYwPoXixH7WZhdy5pTnGIdESWBmTUGD0KH7QuIbYPZhhbCfTrjRpEGpaeVU1i1spyvjjo4+hOT4H8izfPrFdrGMWUtjYWorn6TFhTKSWo0SdySmALkoQdA26KOHUnnwYmjW+MrMYzYeq5y2RE9j9HKJlVp//3XtXKH1nKm8sJrQsIflDE0AAiMMC9r46jIHvJCCpmPXdqTl+J36SWImt08yoizQWcefrDVA0HXe+3oA/+OxDqN9j9Znusu7lTFk21B8Hd/llGacLCnhqGEmHjxLKIWnG6SKmxgwAYRMaGiPz0ZiWD9cYo4DvH+nEM73fhsvUrHkNgGloKO9uQMvBO0P5XNOMzqRGrv1MbzR2kkiCgw2CAJiKAGj84KGKk5RmS38XbeqVNJ8WtraSJQDohMKrEWoupWLa12a6tBou8gzb1HRlQikMl9n6EEFoum6RpyHGyAtYC9O6Z83f7TDIRKLZ1m86hy4H1ma62OMQU8VRkdAayfQLAFAWWEFpeW1ao7TEr0N0XRLIO9Oqd2WF0rHJQYlGI+DPLS2Sc0dSyWMylmxK8Etx5HykHXSFr5BrlqjEk/FEKbtaBP+OI5L3EGGSKOJr/DpLle/DJthA0wnt3F4f2l6UjisShXe7GxhdTimlVYvjzz0TYHWgtO9xjb2TpMu8wj+ltVKlXcMuk01AnwP6fMzZwl54FNY+GnHBMg2+3lQod1lh/SotsnzKPF9GYa+rMLjH8soVb+20aG6sk3PQja+tUtPdSWvztjZGKVYoiADvT3i1KJztR8mMHw2R3g2jcl4NvL5R7Gs6hwuHiSiLDRn1k7j/I6chqwZMRUDvKwlA4RZX9AZBW1g62sLSoUXHXTrzlZ47PANt4RlQo6NRONOP0qle1MfmokPcmp0tLm6loaF0vHsTxbVM2/+Xj8z6CXhrxuGvTMBgafylCwThPhUAVLKcqIDnVAAovuIq4FBzLxRNh2yYgKbjcLM/ZIyuoG53Nh78s4dxuKUXVfty4ZO8V36hy0TxfD8UU4cI4moACT7l6pR+NxI7yS/FgQMHDhw4uBGwk9bmG8YY3UgUzvbjL1tfgmJYsR7/ZO9Dm2aQ7m8ZQrmvH7XF2egrS+COeX2j+MPj/w1Z1QFRQPXnstF152rDJqd6HFLACH7BMRFxKuAYo9cRhTP9+LLvRSiGBlWU8XTBA1vyQcPnzuT8mesTNscP93BvFyTDsHy0DQNHWntQW8CM3qKWIZQ3DKCxLB0ttrApmfUTePDRU5ACBnSXiJdfuAl9JZcXxmT5qAtQADP4qdRUgIWj1ifxuNp5JFbNYag4Dv0lCavK5jSMYf+bA2gszUDb/jScKcqFSvxGq4q8a16zbnc26nZbRqrUfVnVXBf7x/w45O9EfXxe6MPZCnwRLDyPDhE/D9uDX4TvQZtr7dA0WwXT3FkLngMHDhw4cLDdsdPW5h1pjJbM+Dmhm5KZ3k0xJva3DOHvTr4CWdWhBUNM0JiHu2ouQA7oEE3ANExUfq4fzTmZq3aT+g4mQHeJEII7o/NHXfZLOVgHhTP9KJnpRZOcgdaIjRG7Kp0iYkaGhuK5vi0xRtvcaTiZ+AEULw/izIHyTdsVrcotYCJVsozThXmhY0UtQ/jan3zHGtPflvDJL9/DGaQ5NdaHE9EATNWAt2b8so3RQIULF74bD8+/LUIzBMzcE46lChfiquZx04PdEAMmCpRRfOOFWzmDNKdhDI8/9gYk1YCqSPizr9yFH+05gIc+9xAONffiTFEu6ndvrs/r/jE//vb156Do1geKT1Z+BF1ICh1v9WTiZO59KJ7vhy8iG23z11+MzIEDBw4cOHDg4HpjQ4xRQRDeC+CrACQA3zBN84u24w8B+DKAoeBPf2ea5jcueWLTklAWbX50NJyCHk2cRGTioxJu4683s7Aq9bG5uG8oKHQjyqhLKoAelARfimfnKB3oRdmFbtQl5+PNPeyFfCmN9x1QJliZjH3MeXJv9XnIapAqqOp4e0snlg5b9Y1VFjB8JAbmcwJMzbSoiYaJotohqAfZ+cqa+hBXv4DOzyRjcdyFkcPRGC2Kwsgyk+4GgFESPkMWWf2GlllIiXTPDAoaR1BYO4zW8lTU7mXGTOtICkrb+3G42Y/w1L2ozfMCWM2ndxGXg7I+Pw4OdKE6qwCtUV4UD/tRcb4LZ9MKMDjJdnh14hNEfTjMngHu3FIeM+iEBRLuwh6mh/iluDqH2e+k/+W+C9i7fJ75WAoSnt7zB2iNzIIaz3x6lhKZYR/p5zXwdQ/z1ZkrZMaPryObixVal7wLWtRqv19OSn6Wd9wJpLP+o7RzYdk+tkjomfwU+JACH0qhjM7DE/RnMaJ5R6nFFPY3lSD3XODPvRTH2nUxmdW1Ojkbn7/7DryvoRE/21OCJvcuuIJdVVA1ATkQDO2j6sh5Ywr/GVmGuWXr/v3ZsbhZ7oKs6dAkCf+cdQhRKvPBqohnfa7YQjycXkoHigAUAa6VYyqQ8psZiAETogHImo5bGzrQdpO1oxgmqChovABZ1SEaADQNt7e34kJFDJAB1L/DCxdMlOjnQtdpvsDGptbE+kGLsvm/ER8qNZKlA9FsnMW1WeP0kL8Tis4+UFQMdaAj2zLSC2f7UTbWBV9EDr6TcgsAwEhgY0up7wmlF25lKsvywsaKs62HnSSSsNOwWWuzKQrQXSJE0fbMEF9MLiyCRP3/1veFNsmcqbv5eV8Lo76ha/uJqrZvPPQY9RkVFP7ZkkkIM6qlQMO5eKhTpu1vqtlwPsDmFI/El6EhYHTiMxou8voSInGPEKhT5EUeVZqNvAJAXuT7SCHhXKRFErJFXbvvAF7/QIsjYdIUvo/keXYfNEyZEUbD9/D9z4VpIR5YdO3SbSFkaDtoZJzYQ25IAeLfStIq8U1VI+whctg7gRJGBxffR1S7Ipz6XmpRJM37+9J1hLpYUpdhWwQhGGu70SIQ4Os9v47P6Bipjz0kYLbMQqi5sH4sbOpPGkbGKvUfpWGQAEAOJ+NMZGlZ5J+9WfLsLbtZe+kC0YNZtvlUU20HOmZI2v7uSscG9Xu22xfUN5SGOKK+znZtj83CTlqbr9kYFQRBAvD3AG4HMAigWhCEH5im2WLL+h3TND9+rdfbCFDFy/r4fLTErN41OXDBj6+98RxkXYMmyTiWcQINmd4ruk6VjSrYUcFT8gZK4vHDPy3B+/+/BgimCV2RcP4QMx5T6mZQcawfQsCE6RLw2stFGF3Hr/RyUNA4gqc//hPIqgFNEfGZ/3sX2vdbdSpt78fLn38Jiqbjo9IbuP8PHw8ZpGuhZNCPb/zrs9ZOkCTjr47ehad+833IwVAuTx14mBP52TfVh9LJHlRnFqA5Yf3zbiQ4H8sNDPXSGpmFP9n7EEpmetEQnbvm+LmRUdbrx2df/QEUTcOh7l50pqShPtsLADi9KxjaJ9jvp8lHGgBoLMzE8b+4HxWNfTh7IAe+wkzcjN5rqs/QoVhLvEQ1YCgCLhyO5I6PH46A6RKgqyZ0WUJPZdI6Z9o8rFJbDio5F87240st3wy5BJzMe2DDdug3CjtJJGEn4UZcmx04cODAgYWdtDZvxM7oIQBdpmn2AIAgCP8PwJ0A7AvepqBwdiBkFHTg8sVSWqOy0RqVHfqyt2+6HyXTvTjl2YPGZC/KR7oh6xpkmICu4VB/1xUbo/W7s/Hh/3MMh5t7UVWUi7ji5VV5zt7jRWduMnbXjKCjMgXRZWwHLe3MFITgjpChmkipmrkmY7SwdhiyakAyTEAzsL9uKGSMHm72M8EXaDjS2X1RY/RQfxdcmrUTJOga3tnbANkIhskwNJRO9YaM0X1Tffjrs9+AYmh4sPe/8cStj22JQcr5WG5wqJfWyKzrKnx1KRRN9KFsrBu1yflX3NZHOru5kCeHe7tCxmhtnhf3/+HjONLZjdMHcjlf0hX4CjPhK9w4g2ukLAY/+GYJMs5MYf6ogoly3hidLI/AqW/lYu7X4eipTLIovIvrnGyT0BKTg0+VPYLy4U74opgo2qrYt/N9IWO0cM6auxo1D1rl6xfeZTP9Ui5jZ+5JAB8BoAEYBfCwaZp9wWM6gMZg1n7TNO/YtIr+z8R1XZsdOHDgwMHVYyetzRthjGYAoHzLQQCH18j3QUEQ3gagA8Afm6Y5sEYeCILwGIDHAMDliYMyZ2IxkfeRDASVUosm+vCXb70cEpL5dNGxkAEkdw5yZbR8FgqFGqDFc35MKx58vP1HUAwND/S9jj8++hiawnOhSdYOkCbJOJNbEKJSuEd4ykDModFQutXPQo24IwPci/kRoy90LNM1EUrvevsw8HagAn5ULTBRmvbKVFS4+oM7QiKkm3XkKRcArA7lsifsfCj94wkmP9o3y1RlJ7LC8TtyPRRNhypJ+EluETpGk1DaPoCsyQnoogjAsPwES3OAKBXKAE8/XWFoTLkiIMKECUsevMuTjnKx12ovUcbZtAKokdbwOjDmh7ziY6lrKB/tQXOCF+pRXoXJ3TIUSmu5bBdZXOSpTgKRiddT2E6yHs64G67hGbRF5uOk514UL/TjrbJiNCV5rWMkFIq0zOgW89k8x8s9yegoYaPsQ0IggafFzmax60b1s7qahJoWSObP7RphlGCTUFOMCH6sy8OMCrS8i92rPMvqY6c9VTa34pnub1n05HYZf3TLY2iO90Jc5qkly7GsXEwPO1YXtwuq9F/W7qcsoyqvgNGiRBO1BTmWEerRsaK2OzXHqFt5ySRmDICeOUZxLo9jj32SPMPla11iz+g90bWhtD+QiMHSeAyWxmOv+xyiiaUZKwbTB4GXM2+y0gtAnIsPu3CqzxtKR0awtluMJW1i+wq5FE/CURDq1OFGRkfvk5i/bHO8F+0udg+mLKA+Lg/q4BtsxzTSCwgCivqa8czoq6EwPStzl6eH9fcuZQIlUz1oiM3btN13E8KmLXiXuTNXB6DSNM0FQRBOAPgSgA8Fjy2aplm6KZXbGdi8tTk8FjBNLhwIAOu1JgiRzH+UFmcPSUKHn0HK2KOVGeRtRSfLkhZO0raoKrqHuIWEEdqgi58L3TLj97kIxzVSYnNFlMS7WsRKbI6hoa0WJFa5MBv91kNirixg/ZBuE0vsRjSNuB/RECC2EBmURUyrSqmqAE/NVWZZ/WiIMthosVo0q6tBwm/YqdQ0TI9IXU4ozTfcTrkklFkPzXd585JM1jVB5e+VhuCg7FA6btUI/h5c02vTyI25ef7vRbYOSQssHbbMxpKo8u5UgRg2Tuh16X0LNqoxrStH553mX9/HPSQUncJ0GhTCVxVt3NVZgz08GYSyK4LPFyasTVePU9gzINoe7HnCQ6bH7DRdRWLjZMxk70nLEnkXsg0Fc53IM1x/26jmNFyRTOnpNtcokfQfDf0oGFtDzV3BTlubt0rA6IcAXjFNc1kQhMcBvAzgHWtlNE3zeQDPA0BkfNZFN6nLxro5IZmSmV6OGnox7JtmiqimIEAwV9RDrZAZ/7LrHXjitsdDPqP1V7gruhEYKo3Dz1/ei9SqGQwfjkZk+eqd1StB/Z4sHCOiLg17MlHaPoAXP/syFE2HKQpoKUjDt28+tOaOF0Xc0jx0WANIA7DgCscTb3sc5aPdqE3KR3u4tTNUNNmHlIVJGKIEzdChSTJqk/Iuduo1UTg3gOK5Pvgic9CO1Wqq62ElHMtk0sZRN/dN9qF0osdSTY3brNAqV4/iOT8XAqZsrAfN8d7LLt+Q6cXD957Aof4unNpbENoV3Sko6/Xj7jM1kJYFfL/0IOqzvKFjpYN+fP1Hz4bo6J8s/8gq5VyK1uhsPHXgGMpHOuGL9IZ2Re1heujcVbgwiHdONuLdUz5IpgFVlPFUySP47029603BJXfmTNN8neQ/DeCBLa2hg6tbm+MuvjY7cODAgYNti223Nm+EMToEgPIVM8HEEAAApmmOkz+/AcvCvmbUJeYzPy1RRkN07mWXLZlmiqi6aQIQoMHkQmY0JXjRtEX+jethrIzFH43E2sZoct0MvNXjuHA4CuNlkWvmWUH9nizU77G6S4CJg02MnmsaJoo6z+Gz/v9ER2bqRQ3S6qwCqLIS8h2sTbLooCuUUHlBR9FkH75S9QJkQ4Muivih9zB+4q24Ytpo4dwAvtj5rZAQ0cn030Nb2PWJz1g4248vNb8U2o1/8uCjqMoquC51AYD3jZ7FzT3t+FVSEX6SdggA4Iv0ciFg6hKv3PhvyPSiIdML7RjaXxkAACAASURBVOLDaV0UNp9Hcf0gfKWZ6Nx//SioV4qyXj9e+dqzcOnW19K766rx4WMfhS/VCwA42NfF09Eney5qjAKWQdoJvg3sYXpW5q7ChUE80/ttuEwNAqwPwqahoWSqZ/WJNwjXaFUkCoJQQ/5+Pmi0AJe/M7eCRwD8hPwdFjy3BuCLpml+/9qquuNw3dZmBw4cOHBwbdhJa/NGGKPVAHYJgpALa6H7fQD30QyCIKSZprnCI70DQOvlnFjUTISNq6t+VyYsSkQvEnAy6/dRvNAPnyeb3xWN5n0rpXlmyKnRkahNzscDfa/DNFRrRxQmTEHEM++8CzNJEu4e+CXOphXAl+YNlmHDQkvh6zQ5yyg1ETGMH5MeM83lCxiMY0EpP0smo25M23hGx2LqQ+lGotI3q1v0irT6Kbzv4SaIAQN7FQnPP/82uPcxmkGEwigVsRGMSrK/ZQilDQOYjA+HJksQVA2iCUgmoKgabqruhy9qF9RoG51pwuJH+FK9+Pi7jqNiuBtnU/PRGsW/kLumAqgY6ggZ/DAMTCAaA50iojv7rUwKP/woNVeaZvSP4tk+TojogDGMlijLAJRmWDuaRE0XGk+9cM2wv6kaKlUQXIrluR+Upju9y+qXPY1DjHJsati/5MevYpgxGilatIqiyT6UTPegLjEfzfE5UMZ4J0bDwygoVJktEM2r78mjRIl4itB/hifwnvkmfGL6lwCAyqkuSJMLeC2mBC1RWXi64EEUz/WhNmM3OlyZUOY0uLt5+uxSIjPodRejg7hmWZ5VxihRyzR1omipsbbLqp3AF0++Ggpp9Lmv3hHyTR4NsBN+b6GSO/XNsV2h9C8W9oTSb/e0h9LzJt8+/z5dEUpTKtCizueryGK0/aYLhAKeyOYFfdKFI93dkHU9xAqSdQ2V57swOGm907eZudBEGQh+AKvOLODGE4VE6D+Usg0ATfkH8OnkGCvs0EIsOifckCdGcMCwdkxFWAuRAUATZdSm7wJ6f7bmda4J1x7LbMw0zcpLZ7s4BEF4AEAlgNvIzzmmaQ4JgpAH4L8FQWg0TfMao8HuKGza2izAUmw17ZxbAkqlM8X1xxhH06Vqugpfhj5DnMIoefxMxaYCSxR0EUbcM2R+fZCIKnCkwtYUD6Hpxsu82nqWwuz4eYNREiVChQyY679iUWrv8BKvcjo1z+iTgWly7lmyHszw7SMTzwRSbdgEfTlVUUrNFSaY24QZvf5XSD2MNb7hstF0ieKKGUnovGR9saucUjpuIGZttdlVyqhE1Z9Scyn90ipH+p/UjY4zOwWUox5rRBF4cX0hAn2avetJhJLsso17aZG935nprI+lZZLPplpD21uLYPnkBf7cgXHWYCMu9v6rENq5auO3Toex+lCF6lSZf3eNkti9G6TB7LRfCqoES6+7pPPPxByhq9M7osrchu0xos88rQId69Iy347yvEaOEZruos3GUNkxgY5nuobbIzxsBnbY2nzNxqhpmpogCB8H8Bosj5EXTdNsFgThCwBqTNP8AYAnBEG4A5YVPQHgoWu97graPJlo81y5WEpLbA6ePPgRHOv4OconuyHBhGGa2HdhEE+//v2QSuxjd58IGaTbEZlnJkNxHaHpyK8ZhW+f9eKc5xvFTac70Viagbb9zJeVi38qS/jy47cjr20M97xeC8kwoEoyqvLz17kiQ1OSN+SHKS2tfilpiMmFKrIQOvWxuZYb9BXCF5nDCxFFbgw1dv+o5ftXl5SPpkTvZZWpTcm3fImDxkhtyup2Kprsw1fOvBCicv7RzY+h9wqoxWth7+IQ9vnr0RCXi5YY6/5vWbSefQGW4XLLfCdeiylB4TyjNF9q124zUO7r50IaFRGhrO2OiUgPTEGAGVyENFnG6YJ867shgOYEL46//0TIZ7RTvnoRqxURNbn/Qug3OtZ1QcRPsw/itcwKNG8mHXzzCJeX3JkDAEEQ3gXgzwDcZppm6O3YNM2h4P89giD8EkAZAMcYvUxc77XZgQMHDhxcA3bQ2rwhPqOmaf4YwI9tv/0fkj4J4ORGXGsj0RKbg3/KfRcOTPeFqL4AoOhBGp6uoXKwa1sbo4OH4qC7RJiqAV2W0B0Ma5HnG8WTH/0ZJNWAqkj4s6/chbfyrR08zljQdMTNLOJzj96B799Wain/xheiLsd7zXVbCaFz+2gDruUDT2sE2+nzReagNeLaVWz3j/rxd7/4eih0zxO3PY6aWIvOWjziR8W5LnTo2auEYxqTvfj4O4+jfKQbtSn5IWOconSix6JywoRgqHhvfw2ejXvPVdd17+IQnhn8fyGxm0+VPYJuyHgzPB8VgYHQfPVmxC7sXTqHZ879a4jS/KmYi/s0Xi3Ku/twuKsbp/fkraJz1xZnQ1MkILgz2lyWsc5ZthfKe/z47Hd/AJgmdEHAf+0vwgvv+C3Ueb1IG2SfX32p3hBtN3xs/dhsVwM61mvyCjfXCN18XM7OXBmA5wC81zTNC+T3OAALQV/GRAA3w6GQXjFu1LXZgQMHDhxsGrbd2rxVAkbbFi0x2Xiq9JGQauXEXhfuaK4J+ULWZG6MP2Bh8znc1NSLlvI0dB7YOB+686Wx+N6LFYg5tYjulbAWKrD77DAkVYdkAFA13PfSGUzfG46mfRm8sSBLqCm2DK763dmo350N0++5+EWvELdfqINiaHj3SB1Oxt+JNnfapQvZ0BqRtSFG6Aq40D2GhrLRbtQU5KF4hAjUCDKeKn1klUFKd4TXQn18HnRBhBSkW/72wFn80rXvqutfvNjPi91M9qIbu/BaxH4AwC0BP96M2IXXYkrwe5NVHKW5ZLJ3w43R8u4+/MvfPA9Fs9R27/vUo2g4wAzOpn0Z+Pgz96Lc14/a4myM77/6cERbiSMdPVZIGwAaTHgCgUuW2QysjPWluKt02L1CbJZi32XuzH0ZQCSA7woWvW1FJr4QwHOCIBgARFh+KU5IEgcOHDhwsCOwk9bm7W2MGiakRW3Vz+IC8fOKZ1LQQoDsUsi8H5fpYrdKud/LsQrqYvNRl2PRLReiJJz4XxYNrz4hH+3hOXBPmZg6QPjmE7w/Wlgec9ZQiQy7qlvpfS3n8KWnXoUS3Cn6zP+9Ex23rE1bzHaPc383BFg4jzfnmB8dlYzvLk5G4aEhpGMS6ZjEz6b2o7MyGXrQ4BQNoLSmH3/newUfe+Ze+Aoz8bFn7sWe0yM4XZiHupRsmOSeZPIAxLXY5eMJh54c8ozw4kpLyVb4k6KpQU7xeL9nCk2ZJQAAQeX9DTQP6yN5nPjdpIZz+Qpn+kNqto1ZTLSKSqIvpiTz9YljHP/5NAG/VgpwrFkOfXR4a98umBJQMUwEaqAhP6Ifr1fu5vwSlFnu1Jyfwny6jDPp+fjh1CHc3XYKIgDR1JEfN4JflR0I5aOy4+4pdnK7j7SWFIU6917cO3EqFB6kRUyHmWSF7Plp0q14bSx43qVl+MQUjtLcEMfaZ6mAVxSO7mbjdtbL2lgg9yfb3GTGo0Tc/dZZuFTL1xq6hqONvei5icX4HZ+JwBuZu/FG5m4AQOwMu45MGnJO48PYlIax0EezBgufM0P8sd5c2M2ViZeZ3H5UJPP16lrg7zXBzfy9wlysjecn2X2fLvFC/akM/P/svXl4HFl5Lv7W0ov2fd9aixftLVuyPcPABMIQIAmEkLDNDMwCMzaTHySTsDi595J7c/nNTUjgQpbZmGFPgCRwISEhFwiZzDBjW7LUkqzF1tZarX1Xq9W13T+q1ec7JbUlW5ItW/U+j56n1F2n+tQ5p+rUV+f93ldRIBnA6670oKF/APd/7DH05zA6tkhuSfJadB6NY5Bx0o1kPiCXp0kjO1k7iMusweWl6NYPu4m9NNbexsrcm6OUexVA9Wbf2dgHMAwImmF1AInOgCE76pYUa5pPalzD2kUjtwudbpOnGGsZw8HuN043u3BjY/gXTTFEW8FFLvAkkitHc0QBIFti95QVYtmig801Uyp/3U8Qq48ZhT27jCzzFiCry+zalxdJnugcax8Hn8IK5xK7kB0rJI8ywLM35GV2/xOobRq5EQiWm4JA5is6d6n81MzlkNI8UZr/a+2jEGkihaTO0n61zrkKyZ0UNHZAawqzFGTnTi3Q6DkYyjVugNLmegDXgrbAcm9liy6GEMf61bEchVVjqc6aSPuctqnld13kuZaMn3GJNapTis7koTmea27+4Akim1tFUkGHwI7nEjfqu6xDJRe9qlvzjEkOKrF9EVyb9134W7ZJ2ovLH7VY2dE+F4JkEleit4lBb3Bk27gZOaM4WHPzvg5GY7QQ3jf6EloTi9GVsDc+e5thnYYnb5IHeSPw+obhUDRIugFD0VDVMor+ezK3LrgDDNRk4AtPvxlvf7YdFRfGIBmAoWg41jaE9vJ8tJfn498zy7c+0A7Rkl7CB0dJ21c83gwV84P48+YXImq2v3fXY+hIvf6VP1++B488cAanenrRmF+GtlwPpCDQlFsW8ZdVJBmNN6iU+6OyevxaT1Mkb/Rizo2vsHclmnTnuineHmQzdLtycLboAxFRr91eFa0b8OO3zzdFBHY0QcS5Q1vnF98OaC4rwv2f+jA+/v2f4p6OXkiGAYem4lRPP/oL9+Ycj66OomZlEG3ugluiEG1gb421bdiwYcOGDRvXh4M2N+/rYDQ/OI2Hhn4GRZTwyYqHb2pAupvweQugOCQYigbNIeFSXR5iEf0t0m5hoCYDP/iIF4d9E4CqQ5UlNNfc3DbsSPXgbOmDqFn2m4HUDvuwdm6AW2n1zvTdUDAKmAFpR4qH+6wt24PHf/0M6sd6ca60DK15nk3LboX2LF7oZj3H8EbRlViIK+L26N1dsfnougFRr+3gVE8fpLDarAbg7081oKXYg0TMb1Hy9kBzWRG++K4348QVv0lDlmScO1SCKK5K20b5ygi7BsIvE46ujuKp0e8wy6Ls30b3NV407AkMXGM5y4YNGzZs2LBx03HA5uZ9HYwCiOS+lQsjaCowaaqJS4w6Iy4z+gDHGdL5VU26XwxZll8p5iXVXQuERpjHluKdUw54B/04OdCL8/fkoeUwC6qW5liOZUwi+x0pTDm4XJWNT/z5u/GrPZfQX58BsRZIlxnvhFIS3QIfpGqE8lPsYrQ/j5OXpaUU3n8fYFTG6vKr+OMvvQOVzWP4fo4XLVmFpmYiAMwSemAae9pOamLcG6syfewUaztKw1lL5SmXlDLbLWaiO9FcCZb7xtixV4NcGWcJy6lUMli+nBRglIr22ELeWzapJGLPspTPqCXKNtMU11LY9rocfmOqB40VHo7+EcMET7FiWcAiLFIoK6xNOpWiiPLtikXDJ36ISLSvsjG3msm3Y8wkoVHFsx+ilGaXxtNMDJl9J4bYsdWY6JQjSpWLnWDtPV/G03XOHSqFIrOV4+/dfRyGw8BSP6OTZx3lxyZ9u0dl3U+m+rn9Xlo5Gtn2LbDr672Z5yPbVe4RrkwluQ6+Pn8ish1QeYor/T8/gcnWz/YyerGomPVsTS7Dg4+fxj0tfWgqKEMPPABpBmqb4JriecyBXHYvUL3mQKmcHcRTrxCf3Pz3oTOtBNXLV7n83qPJc+hIYmwFxWLzY8PGvoHB22JFPqbPTpRJR249osVLQ6f7EfabbrFDoscgrijQXcQCws1T8ySOmssu3JyERW6/vFh2T8hwMv5rGqHiJov8tS6RCSJRYPfpoMjKLApuroxGTnBklU0+1MoFALDM7pOOxc2puc5Fvv1d5H/HEjtvOcCnOnFWFkFyMyOUUsPCv7bSdtkXFnuROELTJaeukduxxjcJCFsZWiyhAwe3R+2lPj9WCy2RpE0JpBk4Oq/l3DgGZhL5oavj2BYMMgYtz6ECefakdGlxjVjIWCiyukwo7uQrzWKrQ21f9AXWl6sye6aYccVxZWJlVodEmT2PWenl24kUJAu/OEZix3ZJ7PzcMj8ek92bW+ZopF/XAnwFKG2Xo+9TmyfLWOD6mVpDOfd9GHQgsM97QYAK02evJePWUgG9g358/YWnzdWSn0v44H99mAtIt0JnZS7i3rAx//Vm4EpVNq5UZaPFv/WqS13PEN7oG0ZTThnaszx7XzkA5eokarRxtK5gS5GfroRC/EHdo6idGzBtTm5vtdGbgvLFIXjnB3AxqzRC262YH8Txqz1oTdqcAl8xNwjvbD9eiTvMqUm3FHtw/+88jlM9fThXXoLmEs+GsncCWjweXHZem1JePeHHG0Y7wmkE0cdt3XQfF3TWrA6hEyW8ZZEooyW9FDeBMLEBe5mXYsOGDRs2bNi4fhykuXlfB6ND8el4obAeF7O37wNZvjJi5mA589Eds3uWEicHek2lTcO0QznZMXBdwejtgLqeIXzz/38RDsVcdTz99jNou0Ga6nZRrk7iqcCP4YAOpbcVny57cMuAtDOJrThuBzVjfjSMsNxQCu+QHyf8vbjguXnB981E+eIQPtf2Ihy6ivuHfo4nGz4MAPh845fDebcSPln5MM6DrUpWzA3i843Pw6GreHDgZ3j8nWc2BKQtxZ4NBvMHCdUTfjzzo6dNP2JRwqeOPhQ1IG1JL+V9cmPM+8a6jUu5fBUt6aXoSC1CzERw02PsKQ5uN9qwYcOGDRv7Ewdobt7XwWhIl/G92JNQRBfcs2zJwHCTdfn2K5HNI4fj8JT/W3DoGhRIOJv6DnQ7s3E0NI7ytBX4UkvQmVwEJZ4o3sbxVAequEWpJecOlUL5dxkI21m8eqgMatBsvqQ0xp2RRFaeUiAA4N7uLmSfX8T4yUS8Ws6otOlEKi5O5JPTxhRG5aEqZj1rvBpv+yILvA9lMuribJDRf3TVogCWRGgUPTF43cvDcChmwC1qCt7mb0RzmYcrspZEFOkIFTeud4XbT6L0aULHXatkAbxzegU1k344Ajq/apTq4WijVOkV4BXTKBTihKGGqUC1I348/4/hoEGW8cHHTqOx3AwajvUN4qtfYxYlDzzxOFqK2fnKy6y95o9QKhh/h+Bp/azMQin7gtJ8rdDcrIwjYDk5cmzNxdreMU/GiUXZzXCw/Y71dXI5tvWj5vXCPtPgnR9A55AnUsY7Z/qkSjDgUFW8rqsHA5rZZqqbHTuYTqpQyq6BeOcayjvGUOsbQas3H/83pzLy3dvzOiLbLQt88PaHBT+KbNfGDEW2lzTW/0s6PxbGNfa7pa6JyHbrAs8CCKiM3+QmlCHvsX4AwJFL48g4t4jzFR74jphjdMnFKLcJfayNRdVAw2hvpI0MXcX9ky/hq4n3oQ1sNVV3mGXas4rxqYqHULvoR2uimTctzZj1vowMXEo3WR+u2RBHoYv17zBRdVsQDpRIgo3dgaAZcCwpG6hwukwok4ReSJmLG972kw9EUl60UDMFwufl5gDK840yNwC8kmiig7+28t1zkW2aDpMs8fMaxTzJzxAJ79NJFEYTJf7lkkQqPrXKKJOBOf6+5lim9xv2ORUslS23B4mkZFAlUTHIp3EIa+QgEpk7iKo3rBRHMteLRAVfsNJQSV9SBV06N4eSLPOnTI9NqKaEfq1YqN302UylzFPLvUwMbU5xJgzSDXRzmoahErcGKYlXPNYWGLUbAukvN6lckoXuapWfXv9YjU7tpadOKbuG5XGOXhOUhkyf+1bW+DQgOgap0j2l7AKAKDNau0I403Rbs9LvSV/EkUaVXfx4XCP0cFqHEFFJDsXz1GUlwM5JXt6czm29N2kktelaKX1cX9DPaTrUTVHTPVhz874ORq8XtQsDcOhhOhw01IRGUaTM4ImllyHO6lBEB55s+DBa40uu+9gtHg8efPw0Tvb14bX6QjSXXR899Milq7jvyW6IIR3VThHDz6djuDZ164I3ERc8ZdBECZKmQgDwG50X8E8VDRtWE3cTbXGUqiihNXFnartWnBjqhUNbX9FWcbKvD421ZhB06kofWe1Wcaq3jwtG7wS0xRRs2r6KKMHQ1z/zcGV8aSVQRRkI5+W2pF/f9VLeMYY/+33TykhxSJj8TCJ8R3bPI3a3ceTSOP77x38ISdGgyBI+9N8eigSk0XAxu9RsI820uDk+3YuaWT8+fs9j6EjzbNi/K6Fw/wqwHaC3rzZs2LBhw8ZtgQM0N99RwWhrUjF7yIaEBcGN31l6GRJ0CAAMXYF3th+t+SWonPHj2FQ/GgvL0J7h2dbxWzwetHg80DJCW+9sQbVvFGJIh6gDUHQUN07vu2DUV+DB9+oa8J6m1yABkHQdDSO9Ow5Gj65dRc3aCNpc+WiFuaJbsTCI45OX0RZXhLMlD6BmZRC+1NJr5t7dCC4UlkFZt2qRZZwvZbnH5w6HxXjCK6Pnym5fi5IIPT2uCB0uFtB3u3NxNvc9qFkdhi/7SKR9P3X0IdQusZU6io6UIjx58iPwzvSjObsUHame66pLrW+EszI60TGwr4PRypZRyOH6QtVwstO/ZTDanunBR996GqfP/xjHp3sjK6R10/2bBqM2bNiwYcOGDRs2NuKOCka7Ekw/xtqFAVxaTkRNaBTCeiAKwBBE+FLNQPRLLz8HWVPxULeMJ95yGu0ZHlRP+XF8rBfNmaW4tM0Adbto9+aZhtCKDt0hYqAhfetCtwA/qG3Ab/iaTFqrJKMxn/lj1oz5cfflXlzM2n77HA2M4Kmp70WURP9gwQzA/7wl7BUqSDhb8gDa4opQuzgAAFsGpFVTfhyb6EPzNurRmu/BI+8/g+NTvThfWoqWIg9MUxKgubQIDz52Gif7+3C+pBQtxTsXQ/IOhRWXi8vQuocryhTlKyN4qv+bkTb+lPMhdMWzNux256LbnQstgVGGuhIK0JUYPeDqSClCR0oRZ2K+XbR68yNWRqpDwoXK3V3t3m101OVBDddXkSWcr/Bsq1x7pgdfLbsPNbP+iLrzdlaRy5eG4J29graYgl3Na78hGAfLy8yGDRs2bNjY9zhgc/O+DkYNQYDukDZIWCORcfJd+cxnQ5xaQg9S0ONKgZHkAFYSoKxcBAwNhiDgf5f/BjpTPPhA/88haypkGICmon64F9KagS+99Cxk3QzCHnvXGTQ6PezYJPfAyo1XCbc9PY5x62nO6LA3HU8/+0sobZpCX30GMk8tIgkBAEBLgAVBVmuXaSKx7RKjS226SRLEpQmWT5oUy7j/hiVnVJ5n3b8uid3s8eB33nwax8f7cDG7FD3OIsRMG6ia8uOvf/KMGaQKEs6WPghfCbOTEXSeZx/KNJNEKocmOCXR4yPdAEjOoqHhjaudeMt4S1hQR8YnKx9GR4wncqzEfib9Xb44hD/t+kokWH7k/jNozTf3lQPk90l6R2OSB68lrQdnOpwprE1G3xGL76EaAJAQ4r0y58eZ7U9sGjv46iKvTS9I5rnX9Qzhay+8EKnb/R9/LKI2K2h8AhTN71guYN/F884lcM+ysSaR3BbnFFudr14d4tVaA4PoXF/NJHkOWiyfd+EcYecrEjl5NZad92IhX+8kP6u4ksjGk97DkoJ+mFeHkU+m4lTXAM6VF6M5tRQIp2H9PPlQZL/8OJJzA6A1yALokVAq8n2z8DTNQErUETcfQl99BhLu4nNZKhxs3L8aYJ1ekXCV2+/cjCeyXZc2HNn+2+56dKRlo/+/pqLh3CjOlZWiJcEDjAHuOZJrU0TyoyfYdnuWB7/7usdQN92HlvRStOWywDv2KsuTWc8fNcfv1+DQFTOvPfZX0J59d2Q/YYa1iZ59k5gTB4gKZGOXIArQYiTOOgoABI3+T/JHya1HsIjKCyR/i8uPtO5HUrboMUSagmZ9dovyLCeLfN6agxyczsHUziVo8TlbITmjOvmhBJHdo0IWT5JJ4kmyGqL+NHxFJWJrQtNOabW3+5wqWKy/aI6cEUtyVYnWANUdMCuxeY6dNd+S9hG1z6CWJJCukRNJRPEMJ9FpiLWUUdjBaX6kIVnz/9jB1SWaZ8j2cS5acgtJWqUaR/IZ43lbFFEh1iwJJCk2hc1Dhsw/c+ludjyB5N5SKx0thh9naizrC64drf1PT10jeZQhVn5tzTKGSZ5w0MW+W9Z4a7QguYDXSCUUq+cOQTzJE02SN7dvAYDRNWYLR/UcEpysfCCWz3VddbE6aETHQnOR/nZb7Im0zetqvYeB6JVQyx0K3Xp97BUO0Ny8r4PRnaIrLh9ni+9HzcogGj0VERsQXyrJiZNktGSWom6yjw9QR3rRWOnZ1foM1qZjsNZcEc3E4hZ73zpcyvBsWHE8Pm62TyTgWfbDhyObH4DApE6bvqC6ICIjtIDemGzO0gIAJ7JTuzgQlepYuzjAckA1FScGeyPB6K3Gqa5+rm6nevpvivVJW4KHU2vd7bzbG0FzWRHLq44+D0VFvm8WH/rIq5DWdAiGKdagOiT89OvlmKrbponsNuG9PISTnX68Wnb0hnKGO1KL0JG6vVV178JAeKwDBnTUqONov+5f3G0cnLevNmzYsGHDxu2BgzM339HBKGAGpF1x+QimsNWejpQiPHniI/DO9qMpvwyXwoGPKpkBqiLJaCL01K1Q2z2Mhkt+NFZ5MHEiaesC+wx1fj/u6urD+eIy+Ao9m+5zMbvUbB9NNe0p4jffz4rOpEL8wbFH8ZarzXjraBPePtcCZU7C07lvQbxbQ2uySWt8y3hLhOp4rWCqNbGY5YBKMi4Ubb+fdgvHegdxqrsf546WcEJW58pLuLqdO3T9Qlk3gq74Anz68AdRs+RHW4InKv22YmEItfP9aE0uQWfSPhXTCcPTNAMppEM0zJeDog5Iqoas84u7Gox6Lw/ha//jq3CoGj4qvbRBUXm34SMvZ1SIaJOzty5kw4YNGzZs2LBxh2J/B6OiAC1ORuyVae7jUD6zO4FE5J9zWMApL/G65wKRJnfOr6EXWehNykIgKRaiaqAzqQgff91jqNAGIn6Ua6lEHp3QdAvzWH0qO8fwhc98F3I4P+6/fOGduFyVAwCYGjpPWwAAIABJREFUCBDqBoBsN6PgUYpPEuGX5jrmuDIFzpnI9pTKzu8fxo9z+yU6GZfH6WDUgvGrjAIhyPyavyEbqBvw4xvPPGvam0gyHnnwDKN4AogbM9ugK6EIH7v3cZzq7kRbfBG64gvg7mES+IGjmdyxqUVOX48McX4FjjCZyQkNZYsj+MKR90f2+YT3URwb7UZbXBGuIIOzdlksZlSi8ziChxvORLxBO1I8ERrTopf1uWCh6xirbKgnx7Olutx4tkLdMc4HBvEZTNZfD9OovJeH8ZXPfS2iFPv7n/stXKk2yy3lu/Eh54dwstOP8xUedFemIxYmH0ib4V9S6ITlQd18QpY4i1J0Ui4zWtBaDtvRsbCGzuQidCYXhY/H+t9IMO1JyldG8FTLt1he6dGH0FzJVrblFcavorQVxzLfjsEUImFPWONraay8IPJlnBlsfFPqumrRpm9fYXYsFa8fhfGsAD1kQNABXTSprmMnkqCRcv+0wnIuKWXoRHw/d2yNcMZemmQvMB4Z/gWcali8CCpOXb0C30nz+lXIkHZ2szG4nMfGZnIvf66r6ex3XIQG75o0x9IVKRufLnsAFcZV02oqpQiuabJ0nMCoYGoCT03aMxwgKpCN3YEuCVhLkiGvWWwxiI0IpSFKhFOqW6iLcNLvyG/wWQXcd3TB4Fp0VVFidZDJtm4ptKSx1IslnW0v6pt/DgAKoe2KJO+C3mski9fMvMLsopaD7BmA0kkBnu5K00/o/di5ZKEaL7H7Pn3+EZetHjCk/Sk1l1BFN1j2OCmFl5XXXHxfqjGEJslOletLKcCX0YiFix5DnrkSWCqK5ODbUSMpR3S+sdrXGYSuGpII5TYQhfoKcGOL2gC6kvnnOUrTNdLZc5aWxE78WpoLUoCVD2WyMkocTwGl9eOo6hZ2KWXC6sRWh3okqbLFIiWGtTG1P1tS+bG+rPH/ryNIKhcr8eMsycEqROfmOZWnO0eDQPjX9NoFAMNNrmUyNjmKtYWmK9IxQ55PDYvdjrRmobWzCrEy8k1asTxAc/P+DkZvMjrSPHjNc32Kql7fMGRFg6wbgKKh2jcaCUZvB5zqJfYmmooGfy86azwAgJqrftzd1YOWzFJcSvPgUpoHg3uUx9aZVIgeJW3T76qm/Dg+0RcRTvIVmH8A7x12M9Bwyc8pxXpbRyLBKAD4jhRGlFhlRLmp3QLUrAxyeaW1iwNo3gbNGgCqJ/04frUPF3NK0ZZzcyjA88dicf6bHqSdW0FffAZi5xUMNaRCrdvdXA1evEjG+Yq9P7+u+AL4Mo/u+e9sGwdowrNhw4YNGzZuCxygufmOD0bLl4dRszyIi9KRyMrRbsLnLYDqkIDwymi79xarY14nzpURexNJRqPHXDWquerHc997Gg7VzKv92L2PR+jMN4KfxR7FWwLdkGHa7vwsvnxb5SoWBvHnPlMUSBcEfO7Eb+LrpXdvXXCP0Fjl4ZRifbX5WxfaB+D8XKPklVbMD8I714/WpGJ0JpnXSvWkH0//6zOQwxTq0796Bu1ZnptS5/ljsZg/FovWVUYpzrqOXOuU5hWkn1/G9Ml44PDm+1yuysZnvvgOVLaM4ge5XrQc3t/05V2HKTN+q2thw4YNGzZs2FjHAZub93UwKmg6HPNrCBzmbVAcS4QfuEroqZNsGT6Yn4SK+UH8r95vwqGr+MD4y/jdex5DR6oHwXRGE1Bi+c6mNMnYEaKmWc7oDFOLjK7xH/mH8T+/9KuobLmKjroczB+LRxaWAAAl8YzO62mdxt0/6sXVE8mYqEvEUIitAlbGMAnVJZ2o24GnQRQ4GGXXm8zLrlL1M0VlAXFyBls6XFzgjy2EHPDlFePBx07j9b4+NOaXoSPZA3kFODHQa4rxwAB0FTWL/bhYUgJ3KjuGlsXaMWZ0iT92kPSR04FuZyE+Jf8WajCJtrgidMXlb1Ax0+IZFSSUbA7NqolBOMLCSaJh4BMXvofu/Fy0ZXvM8yYuMO4B1gahJP6VUpyHBTGHUhi9uCCG0aL1LH4sUNpKQVxYebYA+JPUX0NF8xg6j+VisioZy8vsd+NjGVVF1dj4CaTy50pp306q1Mx3EUiXYyWbXa4pXaxfDZlfLRTJNWGEaeydiUX4RO2j8M4PwJdcjK7EQsQNsDYpjp/FX1z8shl0SjI+9obH0ZHmwamOHk7Y68RgD7rj1wM2QkEiVTAsypAuQhun1FxV5+vtEFkbXVhhDIVPpPki2/8WiL4y/6a4rsj23//LcXz2934AWdFQ6phG5/O5GK41r7njaezaeW3Sg8mSMrxcUoac1xZw+v+8hPMVxWg5XAh9yoW6AT9O9fThfGlZJJfUPcPOL5BlURvuY+OepgaoCbw6YYyfjTs1lV1HoVRG53YsWOh1ewTjAL19tbFLEAHNLQIWGip0olhKqHAiob4JqqWMwO5r9NlLt97XqLAlGbPCNcYvZeA5JFaHBJm/tlJllpJB59wxlaUEWZVDaaoNBVXTdVsU8EN6FGaHJa1EJ3RDypAU50mbhqzKsaxdBYWwckQL5TaGnZ9OVNR1F6U7WlRgyf9qDNtW4izOAmT+opRJnSjjWtWUdUK5dGUyTnJOEpufFEu7SWSuUIhK6qrC01CXllnjaWQ+DmayejvmLS4DhBZNqZ5qSiy/n8jKhcgz5VoKG8+SRW1YDrB+UYgrRDCNlVFdFso2vVwo+9Zid0+Z1TLX9oQGH+LPdU1hv7tKaboKT8sNkAFJXR0kUrlkKcCVoQrV9Fqx9mVI3zwMcZLrVbM8U1BqNkfzJvRk3UKl5ZSayX7W+Y+jVhMqPp3PrSrJe4WDNDfv62B0p/DO9XMqrXXT/egg+ZC7hZ7qbPSEqZoJCG743tM6jSdO/xyyokFzivjRV2pwtSp5w363Ci0eD644+ZWyi7llTHFYlNGUs3OhoG53LroSD229I61begl0QYBoGBAACIaO+tHeSDB6K3ClKhtXqm6t8Ez50jBqFwfQmliMzhTPtsp0JRZGFTfyzvRB1lnQeWyqDx1pHrSkl3LjYN4Ziw92/AzNmaX4RenNEWi6EVT7RiETOnVx03QkGN0MlZ2j+OJnv2vmTssyHvyjR6DPO/Ctv3o28tkDHz29p+JGm4H2809u6i/bsGHDhg0bNmzsPe64YPRoYAQ1gSE0xVfAl1LClCujGNJXT/gjnpp7RT8suzgJSdEg6gAUHTkX5uGr2t90wLYsD07/2hnUj/aiKafsplEzrehI8+DzNe/Ck23fh2DoUEUHmvI2BsZ1g37cdcUUNVrPJ71TUT3px592fxUOXYMiSvhk5SMAEAlargiZWxxhI3xppZzdUXOGuTLZkVoU8dGcd8bhyeYfQtbMfR7OPwPfPrHVsaLdy3JBNYeEgfr0a+5/rHXYzJ3WDUBVcbJzAMaKxOVTn+rtu6nBaPnSMNfPXwG2p/xwvThAb19t2LBhw4aN2wIHaG7e18GoIQjQnRLcYyvc56uFVNUsI7JV3dOOpyb+EQ5Dw/tnXsWTdz2GJ+96DN6ZPrQmFOOyqwDyihahYVTMD5rURI3lRf4igwWsEmXYEJPl1WWenjMdZPSN8oSrke2WeZND+nLlIbzF0QmoGjRZxKs1ZRz18O+WWGCqWRRG747ti2y3rzH67aJF7SxRZiuyiTGMgkRVA6VBngPqIsK9sZNs1AeTzTK+fA+uuMy6uRZNOoZjlimkuZaIuXcev9JLqQ7yCuPlrGaytnPP8DyTgpSJyIuBS0meyOffbXgdugrzUH/VDIybDrPv1KwQjvUO4usvfBkOJbyqdfo0Wiv4gCwYYDSTwSVGvZpdY30XL/P1eWtmZ2T7WIw/sv2t6bu4/WKTWblLM2zF9FR/P461DqO5tgD/WcAnLWqXGR+c+ktbBZncs4wGs5RvtmlNbx9knYkR3TfZjPumfJGg5RM1j0asW+TzjLrqKMjljm042OXvn4zD2dz3oGZ1GI2Hq3A5psCkFBlAV3whuuILcX/fzxllV1dxd2cvut0eAIAaQ6g3ZTw7wCBjUCScOm/SMLcfpcG9Lu5KZPvpeSb2c3dsD1dmmIhevRxgbTzsTcefEPp8S1EhEGZaryhsLLjD6r4dx3Kg/i3L/R5/QyL8PdlmPvW6jZCnFGLIQuWfiG7gzm1bqHJruYyO6+qfjGzLbjYYvNNX4FjvZ11DIrC7BqvrOEB5KTZ2CYZJW7sWRZZTvCUcQkp3A3gKL30g0R38XGhNqYkcO9o1h43K3utY1Sx0TsKFpTTdoGGVWiX1I3N1vLSREQVspPZmuxn1VBTZ/diw1JM+BtBtSj2U1ni6M1VnFVbZnETv8+EfZscjbay6CU03hm97jppL+kGzMJU5NeQoKrlw8cJ+TqKaW5jKHkqOp/LzQzTMhtg7upkQ/75OTyaq/CFW2ZkA229unL+txgyzPg8lECpuCq9ursWwk11LlkkZ9puxk/y5aqSNKR13jfyObhFRp/R0SnG2XnsiVdolz65Ugdd6rzei3Ptlka93QNucpku3Q0b0cGKNXFPWa0IkERf9XTpP67qFFkuvD0IBp2rO1mcpndyD6DY2iNbTC458SijJVgXePcMBmpv3dTB6vagJjjDFUF2Fd6YP3yp7EzpSiiBabtwA4J3t5/Lh6ib78AvsPvXwclU2/uv/fide39GD3uOZ8NemA7iyZbmDhPLFIXzuFy9GhHI++tbTaM/0RL5vz/JEXZ091d0PhxJewVJVnOzrQ+vrr391cDdR3TWCL/3hdyKWPw995iH4jhRsXfAaqB3zo36kF/PuuIgnrirKAAQuaKldGLghH9HumDx0x+QhkLI5ndWXWsL9blPuzfd4vR5Q+jyUa+/bWZmLP/r8b6DaN4p2bx66q3LQpRbhgScex6nePlwoKkNLkSeyf+2IHyeGetGWUMqN091EW0whJzq1aKhLW5e6flwzoLBhw4YNGzZs3HQcpLn5jgpG29z57OFNlOFLu7ZNS+ThOrwy2pJ5fbYu14PLVdlYOxn9Lev1wtM6jbKLkyS4vb3hnR8wcxYNc9Xt+Hjfth/yzx0tgeKQgfDK6PnSvevH7aK+bYiz/DnRMbCjYLRq2o8v/uezcIRX6b5Q9w4khwJozixFzPga7ptqgaFrUEUJrUl7Y0/SkVKEJ+47HVm9vpV5u3uB7qocdFtsmVqKPWgp9kBcY28oa0f8ePHvnoZDM4PyM287vScBaXdMHs7mvw81q0NoiynE0vA3V7YudZ0wcKCoQDZs2LBhw8a+xwGbm++oYLTbnYuzWe9GTXAEjZVedKRc28qlM7kIH7v3cdRN9kW8NG8HpLcs4bdOX4QUzof762feiNnj8VsX3KcoXxxCxto8NMFUZ1RFGReztx9QNpcV4f5PfBh3tQzifGkpWjweiAhsXXAP0VRTyFn+XKjcWYBYN9VnqhuH8xeTQwF8veKXAQDxK6v4ZMXDTNDoBlZFt4tLGR5cyvBE/q8Z96N+rBf/aZTBV+iJWu5OwomhXtYXuorjV7f/4oSiYmEQtXMD6Awmo9udu+k+66vVNmzYsGHDhg0bdyL2dTAqGAbE4EZ+nWOBfeYYZt4XSmE62pGOdtRAUHU4Z1nupLS8uU3Ca/eV4DVCzaXS5Jx0tsJWRjLyea/DIJHEHguy3EmnhXdP8zpfCrI8hRInyxnrWeNVWrtDWZHtK0Hzu8xfLEFSNEg6AFVHadMUvl/gjey3SuS6Q2RbSbLkL8SSPAWSi+KeZfvETPF5lIaTHJvkiTpm+eCPSsuv5SRGtuP7+bYrXxnBU33fgMPQoAsiLifn45+LTqC5mAVvFnV8KInkn1Uz/6A5rwTNZesvHxQczpriygRVVu97M3sj25MkATBe4sdIzyqj+tKcUZfIa9PHEZuAk1mD5kYW8N+/9OuobB5Dx7FcDHrSkEIC5LFMlqPkmGHjR+NTgbGca/bRL3AID3f+NEKRveTywD0TrofAK+U6h0gHFmwe5ACAsMLqY8Sx3FlqnaQk8QkVgQw2Tqon/Hjmn8wVwg9flPHh95xBa54Hqxqf3xFcY+d35Spr04LYeW4/2o7/scR8aL1xg5Ht78ye5MoUkMFKLRUOx09w+11dYzmavQuMSaCRXJSOQUvQR1JbZHL9X8wug7LOqBBlNGeVRuT3qbR8bBe7rvUkPpepYqQDT03/AA5oUAQJZ/Pei+6YPITC10rFwhDqJnqwKMcgUV1FW4IHP8ZeQDhQeSk29hY0N5Tag4jXyBnlyrhZGc1icUFT0vhtcjxLNo6yynacd7LJPUbmJxWXxO7pcWQeoPltSTI/x3EWF+SH40RWPmTJj6NzR0Y8S2pbsdiuUSsUQ9ze9clZ5hBbHCPWkusYS+YbkvdoyLTvLJYtZF6ieaLWnFE1nm6z+khJ7DkiKZFvx4JENg9UJY5Ftkvd7P5pzTNc0lmFqH1OhpPPZNBI/p9KjjHgYqko7UG+fdYCpO0kMoZVS1/OkxxEct+XV6MvZynkmWstkdSNuMZYTpXLw7U8enCgVjrUDkbgckkt/bq2uS1O0JJTTdtfIRVyk+vG2kcU1BpmzWLlopNE7/kQuw6WgsQOJsSXMYhFDT0nLr+Wf9yFSO47lP4qWO4ZIrmO6Da9b0ma5eB7goM1N+/rYHS3UL40hLqp3oi/5e0Of30aNIcEqDo0WURP/a3Nj9wJapb9kTxf0dBwdG4YpQvj6CjNQ1uO51ZXb0fgLGCWr73vVmjLNdWNj1/txcWcMgwGogeZNwsNw73cam3DcC9a8zy3ulp7jrZcDz7y7jNoGOlFa9KN5YzWrI3CASZCVbM6HFkBrVgYwud8L8ChqxBhQAegCDJetNV0bdiwYcOGjYOBAzQ33/HBaPnSEP6s4yum0qgg4WzJA7d9QDriTcVfPvMmHGqaRE99Jvw1GcDc1uX2I9riPVAECYKhQoC5GGXoquklepsHo7uNtmxPJE8zqT907Z1vAhoL2AqhIsloLNjfgka7ibZcD9pyPXDN3dhs0ebKg7IkwYApTtQWw/KJa+eJPzLC14Sxl2q6e3JUGzZs2LBhw8aN4gDNzfs6GDUkEWqSG86ecf7zVCYyYiSY/IbylRFU97aiLcGDrvgCqAnmEn/N8iBTGjU0lEvjaCyrjJSXV7lDc5SIEHErEXS2XD63RDgVACpzWf0oTSTVyeuNpDvYd/MaW+Sg9IZcBx9VLumMtlDoZJTknxytQNNRT7iiQHECoyt2KYzau7zC6CxSKh/ASP2EGkTYAHHjjOugxvFDxDFFlvgMRqMQAjzFNVjCaDCuMUbNDeYxjq0aJ6ERFfh43uN469BFvH2wEZKumz6XWWURem4wlTs0HITpq7kYXSOzhLWPbOFeNKQNRbZnQoxL5CBcjsrYUa7MLOEcNa962G9a/AP6F9nKdE4sq1yGy+zv4rYpJL8SgM9bgI4Kc0XTmcj6IkQOJ63x9Bh6rkQdHYEs9o9rgafFCoTHrMWx/ZzdI9x+modRwkPJrC+d84SmFssfO/4qa9c+uQi/82ZT0Og/TrCcUUcXTzkL5rPxdOgQo2HNhfjrKNnB6FuLhC9/2MHoWr+Uxd8Lvjxfy36H2DDQaw0AlgmfLC2G/Q61PvIr/LkabjY2tFXqU0GtDfixQKmJq4eZ7ZQSz1OYOlGJT6ckomZ5EO3BRHSHEoDQIpyGgY61dCiCBBgaRBhQAaiijEVd2RM1XRs2rhuC+WelkFKmHv2OuxtLlmuGUHOVOGKlkWChFFoooeug4mKik7+GNUJDpOkCyyH+YGtuNs9Rmq5E5hG3wFN7Y0keD6WKpkpsjhxXk7gydL5JcrG0AsnJ0/50kg6jEisVJY5tB9OsgojsfioRuxyrRQ73v0Goi6QKomKx3yFUSINQcUOJ3G4IpRCKI6HmpiWzNqG0XADIInY3SeSBjFJxaZsCfL8kS+x+brUXoZYiE4qlsmG4HDz3NZREqKckBYu2PQCIKqXwss+dy6QNQvxziEjovFKIWJKQOUWwUHHp/9T2xeICyD0fcOWpPUnQck0FWKFADDt4II4/2Aq5+KzpTJHft1SIXi3UOmlV42nRSwr7bolcl6uEPq0u8/WRF9h17Zojdi5khhS16JEcV1Xdsh9HcSbf0WvFWsbGjrGvg9HtonxlBE8NfAsOw1z9/PThD6I9wVylaU0ugSLKMMK5dr7U3bduOUgoD4ygZmUIbXGF6IjbHdXajjQPOtI8+Nfi+oiY1M1Sar3rH3tR+7MRtP5yPtY+5EBGyxKyzi9i4mQiZqt3LgpV3DaF3zvzU4iKDtUh4fc+955IQHonYF3QaK6QTQjeYT9ODPbiQlEZfAWeW1e5MLJbFlD66iR66zMxuI+Up7viCtAVVwBxbJL7vDsmD2dz34MqxzTmHTFIVlbhSy7Gku/Z3VfTBQ7U21cbNmzYsGHjtsABmpvviGC0ZmWQ+YsaGmqW/GjPMYPRzqRCfKL2UVSv+OFLLdlSYXc7qLsyhJMdAzhfWYyWw3unXLrfUDk3iKcG/jYS9H/K/RC6EnbmnUlxKc1zUxWN7/peD9772SYAwNFz42ifzEXFN8YhhnToThGLL8bgqjd5i6NcG4cvTjCxKUWD1zd8RwWjVniH/PjKN56OWNA8/OAZnMu/dbT47JYF/OYjzRBDOjSHhL959pcwdCh164K3GN0xeWjPrdx6x53CwIESSbBhw4YNGzb2PQ7Y3Ly/g1HDMJfQ3TylRl5i9A8hqKDNkcf5i7Zk8LlrnUmFETsKUTMQckXvYKqg6yYUXqPCpIJ4Lw/ha3/yNThU09PygT98BEMJ7OHWE8eoorMhXm/kRFwf+05jq24rOju/DXQUwhlY0BgNpzyepytSqq+ews7v5auH2U5rPI3CTSgNMVPsFUwwlR3Lscw+r/b3c0G/d7QTVxLD5+jkaRTOWUZBEpbNhjwaHEPN2BjaYgpMsRaHOfyOBkZQExjCK/U1aF/vJ0ITci7x/bVcSJQGiZLieB+jBuslfBlZJLQep9mX1T81abkCzOv+6E8mICk6BB0QFB0Z55dwpdqksqbKrF/emNTFHTuQwPqvPcCC89SLS0i+GoAuiTBgQJFl/KSoAqNTKXC6GIlF9kdX0+UYweQtWfwoo8qoMTwFVFwlNBpCiTOy0rj9NEJNc02yRTc1kVXCucBzhlZyiOIeGWfrFCHO9kRTccLfi3MNpE1c7KKaXePpvD0GozvPr7E6/Iu7GgCQ55tDoGkSow3JGK8z6W9U2W+O8JTWKXTZFxYghXSIOmAoOg5/bxyF+bO4cjwb/TUZeGWEqDbH8zQ8YYzVQYshY05g5x03wdPrnHMhUoaoDk7zFHlxiSgZp7P7RyiXpYU6p26OPdFeGmsLgvBWAF+Emfr6ZcMw/pflexeArwM4DmAGwHsNw/CHvzsL4FEAGoCPGYbxb3tXUxvXBR2QgsaGsUPpawahJGpElZQqjwK8cqvqJmUstENKQ9SjKOtaQdNrdLKtaPw9M0QOaKUbRoOD8CfTJDaZJhJV70GDZ2LoUR4uRYmnc2oiaUdSVar2H0rkj6U7Ga2Rzp9W9WKOtkkVRvXN9zE/IHUj/UDviwCgx7L7oUyaUSPnrep8+1IKKE2BobRoyTLQEgTWxg4rr5VgTEmJbK+Sioe06MqvBqEkyytUBdqyH2kTxwprMNcMu9dTlwKAV46ml4EcjP5MSq8XynBVYi1lSJ/Ra4JTjrVer+RcNaKCT5V1AWCF/PAq4QoH9I1uF+sQySCiSsaK5fqaCLBn4ckFNv+Flti4kBf5/nIuCGSbfU6VjK0uDNIaUcMNUsXc6BMg7T/++rg5S5YHaW7elWB0Jye1G+iOycPZvPeiSp6EL6k4YnGxFzjZ4YdDVSHrBqCqONU5gKETO19pyW5ZQH7jHIy7Dcwd2xvRzJ2iJbMUiijB0DWoooQ21/ZXvI4Gx/DUxD9GVlXP5r4H3Y4iHA2M4Kmhv4PD0PD+//sqnnjL6UhAuo6aq37Uj/aiKa9sV0WNmt5UhKrzY5EYb/ltLiR/xQwADAcwWJ8WvfAWKGqdxuMffQmSokGXRHznjQ34/hvq9s1KevnKCCoXR9GaXLKrvqTnSyyiRp7dETXK883h/Y9egBTSoTlF/OBFbyQgvRaGG1KhOUXoIQO6JOLN/9oNSTP9eT//N2/BK6k783+9I7BHE54gCBKAvwZwH4ARAI2CIPzQMIxOstujAOYMwygTBOF9AP4UwHsFQagA8D4AlQByAfxUEITDhmHcDE39Owa3em62YcOGDRs3iAM0N+84GN3JSe30tym6Y/LQnl2+9Y47xPlKDxRZBsIro+cqdv4wu04llEI6jKcFvPaN4khAmtayjMzzS5g8mYCxqpQtjrS3uJTmwScrH0btwgBak4rRMx+7daEwaoIjPJV6dRjdiUWoCQyxz3UVb+9rwrHxPjTlmZYZ1ZN+/M2Pn4nQPh9/5xm8WrhzqjUA/Oe7jiBBXkP1T0fR/uY81D52FSu/EoOY10JYvcuJkdIbf8lQ2jTF6LmCgbH05H0ViK57uyqijE/UPoorwu7YA7UUefDIg2fQ4O9Fo6cMvnwPzJdnO0Nh42xkhROKjrzG+W0Fo2PeFHznhQZoLwnImFjCr/xTZ8Sf9/DFcfOuZWOvcAJAr2EY/QAgCMK3AbwTAJ0b3gngj8Pb/wDgrwRBEMKff9swjDUAA4Ig9IaP99pNqvttj/0yN9uwYcOGjX2FfTc378bK6A2flGEYN2etexfhO1KIB/7wEZzqHMC5imK0HCpE2g5NJPMb5yIP2rpiIP/7c0g7v4JQsoTK/3kVYsiA7hQw/WLCjnMYd4quhEJ0JZhBlTw/ve1ybe58RqUmVhZtsYWRzzVBwq/1XoCk63i0TcaZt53G8at9HO2zfrQXr2J3glEAuPDuElx4tylqVYurCB53Ing8TEOZv0bBLdBXn8F5wZ7fhZcWuwXq7WroKmrn+3ElZfe8an35nnAQunsWVz1cAAAgAElEQVQYCq9wQtGhOUSMNmz/OhjzpuDlgjIcuXQVv/zjy4CqQZNFXDmevXVhGztBHoBh8v8IgJPR9jEMQxUEYQFAWvjzc5ayeXtX1TsSB2putmHDhg0b28K+m5t3IxjdyUltGc0IBqAnxkT9Xk0n1nskl0teDHL7LRez/SiXnHLMASCYJsA74keDvxfnyphdRWiZ8eR7j2eg97hp25CAVWiEA39+0hPZrkq7yh2byrrTPNHVuxzQnxYAxYAhAYX/MAdBM3MSDA0QDcBQgMTXgmiqMFdHLy/ywQOVxB6bYytGgoP9prDAdzfNu6F5CTTfxLHKJ4+o8SyXQExjv2O4eE6/NDjBvlsLoQsJOBv/NtS4F9EWW4ju2HzMHk/Hq0jHE1PJOD7Rh7ypGfza6AVIMABdxYmhXjRnlnK0z3NlZXz+HsmtQQzJUbDkpXRNMLub+zyXI9vU8uPcKr9yeW98d2S7L8Ta2yrxP6UzyfhD7nAu70nguefegNKmKfTVZyDmiIq70c9+a8jDDkAWmA3JkoND+iV2gn23VMDqnTjA+xOt5bD6OGdp4rNZvi2uiHsxcMmZD3GCWQrJYCvwwRJeDt8RYHVYyd08n0JJ4M/Bmcnq0D3D7E7yExe4/fJjWPSfTPKfHIKGybpEfPfFehxrHsTMyTi4jikowjSSiKx/pYvZxvzN5Ju4YzekDAKvB7715ZNwvaKg81gurhzNRtBPfsfN5x6txbGTkpbZeKL2NtNV/LjPfZltu6bZeS8e4pWZHbPEFmmO5Zs5JQHlKyOoWRlEa3IJuuJ3TyAsGnaYl5IuCEIT+f85wzCe21mNbOwS9mxuFjUDzgUVosKzHqidi0EsRDibF4u1i0isXWiOJwSrJQnb1qjTEtlNly2D2cmuVVkmNi0Oi02LzPLsE4m9SJLEth2WpEFq55Issv1EckFZrUaoZVWA2IZYQ39Bi56rGA1cLifJw5X5RyHumYfLgyO5c6IlbZbWQSLp79Iq35faCrkfxm2eTzgZ4O+FbolYqZCHklgH65M4kbcToTmoNJ/ULfC5+bTPaC7wxAp7HlxZ4TVJhCA7By7f0pJHS+dC1yz7XWmJNbjotDxii5vPmVIwuhVPNOsj67igFkA0t1gnXaJbn/jJ84ZAnqUUnZ/XgmRwrWnsIEsC01WwXh9r5MfmFVahsQDPaBqdZS+WlXnWF+Iyq4O8bNEAIXIKG/KbtwEub1rhD0BzrAV184NT3Yi9xEGam/edgJEgCI8BeAwA3K6taXi7De+IHy+G1UDPvCzjQw+fiQSke4WZunj8x9ePIPP8ErLGF5D57WXzRiMCuihAA6DJIvrqM7Y61L5Gl5yF7vSqDZ+v24PUX+nFr1xtjtjwNGeW4lK6B4++7wwahnrRWFiG1jwPLK51+xZDtWkYqg3nnQavve+1UDvmR/1wL9rjSiNCXDtBV1w+zpY8gOrVoYgvr7g8u3XBW4yr3mT0nlzdesdNkO+bRVHTDAbr0/B/Pnhsl2u2e+BsqiZfwacPf3DvA9KdKfZNG4ZRH+W7UQC08vnhzzbbZ0QQBBlAEszcxe2UtXETQedml+vWMnRs2LBh447HAZqbdyMY3clJbUA4cn8OADyuJKN8cQhXxKzNdt0TNPh5NdCTA717HowCZkA6UxcPuV1D+j+uQIQB3SHg+584hrj5EPrqM3bkkVjXM4S7Lg7hXFkpWjye3av4LqIzuQi/X/9heGf7ccFzCJfSPQCA1jxPOAiNjmP9fpzy9+HckRI0l+0ejfdWo3bMj+f/3nw5oooynnjz6V0LSDuTb6ydqqb9eKu/CaEeAT+obkDrLlNydxslbZN48InXIuJHfV/MwJWq/UnR3cymak+DUQN76WXWCOCQIAjFMOeA9wH4gGWfHwL4EMx8k98C8O+GYRiCIPwQwN8KgvB5mCIJhwBc2LOa3pnYs7k5MTHfpvHasGHDxl7hgM3NuxGM3vBJbXXg9NAiPtf6Aj519CHugcxwsuV7aZnRN6hsdijN5D5WzA/CO9ePzqZMdMea6q8rhxgNUVR5OkJzJlEDlWW8drQUWowBaYbRFJZdPG2Y2nTkpC1Gtq1UByeRIKeWLVQCu7smG5NfT0D6+WVMn4yH7wijjnqcU5Ht+Ri+DoPz7JxcTvY72mAc6vx+fOPZF5gdzZnTAIBTPX04X2oGp2tEqyf5MqHABC00LEJP0GNZm1ilrqmNiEE03gVKYbFIzq9kO9CYXYZGlHFS8pSeBQCOBUJbSdRRN+DHt/7qOVPkSJbw0B8/hIEcfhW5NodROFvnmM9nVQqjUo8ovGBRa4AFay5CzXXH8vSjBY31RfdyTmS7d4G9PJgP8P0VWmT0UOc1roRTV8jLEV1F1WofzuUVI6WH9YuSwDeQvEL6P479jnSBt6RZu5etUrsVxkJQiVWNe4anPeVkjuMvX3oGTs38jXe1XcBD9z+BC7UksLUsXOuEclaTwdo7i3oLAch2MpruA4ntke0JIsP/H4EjXJlDLmZx1B1ibV8aOxnZvqu9j7N3KTg/g5dKDgEAUlKYpc3CkiUdwM3aWCX0v6UCdtuMu2qh+aez9k6YYceOH+KXxg0X6zOllL1sa/MX8LnVCR7crgjTPn8HwL/BVHN90TCMDkEQ/geAJsMwfgjgBQDfCIsgzMKcPxDe77sw8xtVAE/YSrrXjT2bm2GY93yrbYgYIl0UhQ1iWDigupvM50r0RxKdzCMaZVZSHT0LTVcg1FyHzOomWbh9KpmrqU0aZzVi8YpIJNTcBOIJFzT4eZ+C2lpEs3mxgh6Onp1msaij1aM2FlY6pxgi1OWARj4nc0qiky9DKLwSsSFx8LdwCOT8ghLrJCq/EB/HD4xFhe03FWL02QQpOp0oW2YpHtRKZ1HnvdE0sPpMrrFjr4aIndoqP+YklVBpSeaGlQ7qmmNfOqaYbgh93jEslHR6vcgB8jwYYJ2nO/nxY0jEsofUR+PZxRwFVyXXBE1r0pL4VBRHPBu38THsWTreydOiEx1srHM0drKtgT9XStOltOq5VV74Ugmy/cRV1nbyanSqOneupB3o86nVoUlU6XMtosJ6T2MHJPvc5in1+3Fu3nEwupOT2lYFDQ21iwM3tDpQMT+Iv7j4ZTh01bQTKXx/JCCNhtZcDz7y7jOoH+nFK3W3ZhVx9lgcZtftXVauve92cLKvz7SjMUw7mnc1NuHdTU2R4PTBx0+jucSz8x+6RTjVQ89Pw4mOAQzU3V6UZu+QHyf7e3H+UClaijyRzy/mlEIVZUAPW6UU7o5Vyo2iYcQMjtdv67Km4cRQLx+M7jOsix8Zig7dIaKldu/zMG8U3bH5OFv4ftQEhtCaWnpTckb38O0rDMP4FwD/Yvnsv5HtIIDfjlL2swA+u3e1u7Ox13OzDRs2bNjYQxyguXlXckZ3clLXPC4AAwIW5O1ZiJQvD6NmeRBt8UVoTTsC71w/HLrKKG+BoS2DUcAMSFtzPQjk3d5vP9ZxvrSUs6MRAC44PdnXd1sHo+cOhc8vvDJ6oXL/KNduB94hP77+wtPmy4Gfy/jgY6cjAWl7pieiKvxqeVlUunLl3CC8M/3wpZXgsnPvREcb803mwPrKqCpJuHCLA+StMOpNwTeevyuSM9qRvb9FWbtj89Edmw891rn1zruAvTTWtnFrsVdzsw0bNmzY2FscpLl53wkYWSHCwJnhH6M/JRddiSZl1TFMxFbC6mRHg2N4avS7cECDAgmfdD6MdnchFFGCoWtQRRkXDldhJTUF4hqhqazya/lSiKjKkm21gK3rU7oPABiEgqASbsDrk69w+zUFSiLbh92MrjimMIrt6BrvJZrnYiqnPauMzlccw4sdJpD6nSOKvktxOpoqC3H/7z6GuwZ7Ir6ov3mxCVDM4PR8WSnih9g5pFxmx1JjTMpI5dwgvLP98KWWoCPFXAWjyrruId4HhT5IU9qKmmFSZcoXh1De0YbmrFIAwLGJPrSklqAjzax7IItRVVwWi5UgYwDDsSTiUnoJPvTIGdTPXsa5o8Vozi+CK8DTS9uDLEcwJY5RSypiGX33J9MVXJkvl3w/sv33S4cj298YO8XtV5vM0rAo5Voh9NJgkKfSSjGMLtMw1sO9HLi7ow8dyWY/CYYREXhyLBtI6jePL6+wMVg9MYDPtb1oMgBEGZ86+iF0JZgramJQNdVZl/1orC1HZwpbwXT8W2Nke+0+ludO1S6PBIdxbKoPzRml6EjzoD3bgw+/+6P49a5GGAD+qbwBHakejs6kxln4TAuMRxPQogdYAcI7+vPp10e20wgXrMw1wZWZUZky4zdJv7wzu5Xb70clNUD48rs3vpd9PlwZ2Y6L5alJS2PsJZhjiahTUoFikacmxY6zYwTzmBIxVdYF+GvCMbe66efi0g5Ur64HB2jCs7FLMAxIQXWj2iShr9HvKH1OsFBpDZnOuSR9ReHnZk75lf7sNcYvVQgVRZoaY1EtJxRDStNdIzkimoX3R9VDHeR484RDqFgouyuEUxgkarq6yh+b/hQ9BMkI2fCgKgWjqORaaYfkX2mV0UNpfznnrCKBjP5K6yZYVOtpnQyRUJ9DrOKL6XwZF3meSnUxmdQ0JyvjslCkkw22H02HClpyeq6GmNDWfIidA6VIbxBGpacuRNkGoJNxy1FziYLuRsotoYouk7YPsQnUwi7lrgna3lZVajq1cjRdMh+7kvg5LjWetHcMo+Elu/j5KsPJaMiFLpZSnkzU7K2YIvTpq0GWBhTSLDR2jntO2pQqZlsiFT3KvYBmi4lW4igV6ibXhLjG78ilGnD1pDzdm6Ome5Dm5n0fjK57IXrnByLBKMXR4BhqVoeRoSzCgbDwB0xq77fz7sUnKx5G7eIAmgqOoCN1/1IJ9xrNJR74vCxX8oE/fASnLgxHckbjhqOXrZwbxOcvPA85rHL75ImPRALSG0H54hD+rOMrkHUVmihCgABR16BKMj72+sciAen1wFfowYXX5Wy9YxiVnaM41jqM9PuWMV5381WbKc4dLmEru6KMC0XmSmPtiB/3XOrFxexStGd6opavXRhgDABdNWnt4WC0fGUET/V9Aw5Dw/snX8GTDR/hAtJroXJuEJ9vfB6yppp984bHcT6vBG05HrTleKIKvR3r9+NUTx/OHSq97hX3ddXbzmO5OxLsuh7UdI2gvn0QjdVFaDu6NXPChg0bNmzYsGHDxu5gnwejAlQBUAUZvuSN1MujwTE8Nf73cBgaNEGEamrQQoWE1kRz/66EQnQlFGIt1bWhfDTUjPtRP9qLl7Wym6KkeyvQcqgQvoTDW+8IwDvbD1lXIYf9P72z/TsKRmsXWfAk6GbauwgAmopjU/03FIxGg/fyME50DKCpqgitR80ArbJzFF/61HcgKxqMvxPwgxe9exqQVneN4OiFcZyvKEbL4Y0vVJpLPLj/44/h1JV+tKQcQmu+B7Ujfrz4Laai+9G3nkZ37OZt3ppUDEWUI5Y462MfAGqW/UydNdx32w1GvbP9kLVwv2sqjk314TxKrlmmbtCPrz//bCQf+f7/73E0H914zpsh3zeLBz9iqt6+3tGDZ5+7d88D0pquETz7R9+EQ9WgyBIe++wDBy8gPUBvX23YsGHDho3bAgdobt7XwehwTDq+ml2HDiEHPVo65DmTEqBlmvS36lEfsUHQ8a9Z9Zh0JaE1sRg9q0mQV5niWigtkx2YLrFbVLGqpv149gdmEPCRizI++OhGn9HQCk81zMhkv5PsZNSEFT16ALxE+DZ3xzLaYKeUy+1Hj+FxM3pEQOfr4CIya9MLjLoYl8PoFStjvNl03ATlLbDNmSpGZ0nuCcGXUhwR0VFFGe2xRZADKpREkxJTOTuIOr2Po/C6Jxl9482BNtw73oaXsmvw70I5Lsl5EcVQTRAhCOGVUVFGW3IJ5DUDSf0mhaVibhDVKwNoySzFpXCQGkxlVJVQHqHjrjL6R0h0oK5nCF996mvhwEjChz7zEHxHClHfNgiHokHSDWgK4HpVRc/RLDgtvI7PjL8xsl3gZtTwxTVese/lidLItoMcI8m9ivKOMfzZ2e9BDmlmcPZ7j5mrhcSM3T0poTO+FJ3HSiOqcQ1DvIpu3XQf2io9pL/Ypds7kIyzee9FzeoQ2mIKcdmRAzFojoeW9DK8f/LlCFXdl1YSWdEUaxktmVPgDVOz2xI9UCXW7+eKD0GJI1XghfkAACf7eWukU8M9WHkH65epVXaAWImnUr+1aTaieiurGu5u7UXiXasc1U2ySPVmyky9ujqZUd/XLHStN6ReQXbLAvIb5/D9Q15crjJX0X+9ywenao4FqBqqXx3Hy0nhdklmNCoFRDmaUIlcFnvWtVSifEjoVWo8f706Ztn1QSntoWR2vTtneUrVXkAwDlZeio3dgaAbEAMhni93rf3pPxbqquAitEZCfdTc0WmI1xCs5WDoRP1dtxIgGWTC9YuXNr/udAtPc0Zj8+mSzubzcaJM7g/yL9NGVth3cyusjK7xdaPKn5yKJ7nnWqtJb6eOAFXM5fvIsUTooQpR012M7uHsXmI/5lhg9V7N4edC3UGUiMlXdJuqpwLAYoB9OezY3L/WlWCh6UqsDglE1XhW459xVslz0poa5XFX5ttHJ/L2lFarW4prMYQyG8fu2+IqX1cKzo2AqEiLQdJ5Vuo7V55sW2i69JFQc5FB4yLPGk6+bhmx7PkwL5blQyU7+LGQ72QTXYGTPYfmSQuIhqDB5kyXRJSDRSu1n9KdSduLm1N2ze82LW7ZKfr/dM6z0nLpNWFEoeMajuj3kt3CQZub93UwGpSc+LuiX4JjZnNJ2dbEYs4G4ScZXnQlmKswNBC9HtSPkIdpVcXJ/pvjM7qf0ZlchCcbTP/P9jgPOpPYSlfl7CC+8NpzUSm8b5u6iI8P/TMA4MRUD5x5b8eP047hbMkDKJfG4Us1V9q8s/3wZZRyZSvWaaJ6mCZ67+ORgHQ7ONXVb+ZihgONkx1++I4UwuctgOKQYCgadFlE7/HMTcsXt03hcNMErtRnQT2xzacfC2p9I2bgux6cXenfFnW10cMshlRJRlPetUWCumPy0B2zUZinK6EwQlW/mHv4ula0O5OLcPrtZ1B/tRdNOWVoz7p2vesG/ciZm4MmigB0KLKMc0e3Lyblr0+LqN4aDhETJxO3LrRNZLcs4DcfaYYU0lHvGMQffeGduFyVw40FRZJx7nDp1ge707AzY20bNmzYsGHDxm7jAM3N+zoY3QpdCQU4W/wB1KwMoS2uMBKI7gRN+bzP6PmS/a0UerPQmVyEzuSiDb6j3pm+a1J475nrBGC+GTcA3LPQjR+nHUNXXD6a88oj+3WkFAGWN33eWaaGDE1F3WTfdQWj58pLiIqwhPPhlcXOilw8+bnfhtc3jJXXu+HfhAp6+NI4Pv6xn0FSNGgOCd9+sQGj3pQN+22FVm++Jdi5Ns11Hb58Dx558Azu7uxFU14Z2rI9nEDF9WCdqh5Kvn511vYsz5ZBKLBOzzUVgTVJxLfvbcD37jmG5kNFOILxLcsDwIg3FV97/m54mmYQulvCVF3C1oUAJDUHkHougKLq6ai03vzGuciqq6RoqPaN4nJVDjcWfpxRi+bSvckrr1gYQu3CAFqTirmXOfsCB+jtqw0bNmzYsHFb4ADNzbd1MAoAXbH56NqGXct2QX1Gf1HNckbreoZwqqsf58pLbmsblN2GL62Uo/Cur3Su45WUChxf6o9cU68kHd3+sVNLWC6kJKMl8/pWrVoOFeL+s4/iVFc/mmoL4TvCgoDOilx0VuSiNnV007IVzWOQFA2SDkDVUdg4e0PBaFdlLj75F7+J/J8u4dzh6xs7vnwPumO2v/+txMn+XqYIrOsYS09B86HrD+xGvKkY8aai2DW1rf2TmgNoeGAIQsiAx/FS1DzTkYYUaE4RUHRosoR2L1tFXh8L/v6sDeWuF5Wzg/DO9MGXxjxCK+YHObXjT9Q8gh7cHHEmGzZs2LBhw4aN/Yz9HYwK5p+SFsd9TK0QhFXGtZcGmU2HUcwHqOur3ZWzg6gf7YEvuRhdiYVYLIrh9pPXgI40DzrSPFjJAQTN9IH82tdegENRoThkPPhfHuaEaDLjGO/eSXI3h9fSuGMHNMahj5dYbuk8yR8VLTlxvxx7ObL9s8ARRAO1zPDmsQCL5uitLfEiPXqUHJyUIbb6OV3Nr6alXmbnF9s3jwEk4Wzh+1HumIAvtQSdSYUQdCOSl/uvmfWApuOehW68knQUP6h4Q6R8zDBPpab9bDhEdCcU4A+OPYoj4hAu5pirgwDgZGmCEC+TfDtyelrI5PS3pJeh5fVlcOWuQAyHxGsaG/aX5pkCb7yDjaVWbwF+23ERUHVosoiLtUUYC5rBaGUKv9K3oLD+65ljQcZK0KzbUE4KQm8yz00I/4RBmlUkuT/03AAzR6hmwo/jY73okj2RVWeJ5CMqWfwKotzax7Yz2BgUVH4/geRLy+MsX2S5IZvsxNcnYZiVWSpgX06nxsAQBKgwwvTckshFtxxi+TSxpI07ZsjvALgnuSeyPRJKjWzTwHRK5am7ha/OQQgZkTzTY75B6CeEDRZJs1VxWHg+FsWN0/jhoWp0Hs0FVMA/yvqr+ugQV+bKVEZkW+5iHeYiw9ZNLBCqpv34wmvPRYLOJxs+jM7kIm6Ff13tuCeW9AuRlucsGZQoEvO7jIOUl2Jjl6DrEAJr17Y4IN8ZxOLCcPApDzRPVHeQMpa0LM7ihMgx6G6SZ+bg509JYt9pJH+UzgEAb8kmkYuw0Mks1Kw2Fjox4VgkSZFDZN4fW+VzIBeCbK4IrhL7M6uNDb30o9hVSCH+wpUJc0YK0pxRPrlfXGP/c/eYNZK3aMkFFjT2v0RsTASdzxnl7l/kZ2mevaDwYyZINDjmHax94shcMeaI/iI4SWLPg1YtjZkQ8zhZCbHvFJUNJsFq80NyLDUXyQvlZQigkpxmzc3GE21TQbXcXMm41WJYGVGhX1isjyR6TVwjj5JeVvQ7Ym/kkvmxECuzHNIYiW2nEzs1AMiQ2f8Z0jLZZucatOivUCskjVTWbamD5CZ6FUFyn1gjx9MsJ3sNy53Ix5bUVHpNCdQD5ho5uuDantr33Fja1vXiIM3N+zsY3WWs5zfSFYpfIHqAt46TA71wKCSPtHNgU1XUvUZWywLyLsxj9EQyhm6S7cV20BWbj5aCClTMD+ID/T+HL7UEfWCrTD9OO4Yfpx27oWN3Jhfh1dLtqf7uJi5XZeMvn3kTDjVNoqc+E/6ajK0L7QFqJvx45p+fNvNmBRlPntyZrc7/Y++94yO5ynThp1J3K6dRTq0wMwqjOJpkE+4uYMC7hLsLXIzB2Ngee9YssBhje9lvl+9usMG7fHcDCxiMsQkb7nIBcwF7MSw2DhM0Cq1RmFHO0ihndVf6/qhWn/eU1JJmRqORrHp+v/nNUXdV9alTp86pt87zPo8dKx6kPikdbZ6MdbctG+3B0T7LauZCsjf0ecVAD/7ifz8H0TAAQcDT7ziBusLtsVEaPhoLY2XFUxExcCT8Q0t/RSL6KxLRMr7+eV4tqsY6uaCzcrILLfG5/Aq/KKMxPh8IbHy8bcMemvAcOHDgwIGDXYE9NDfvqWB0Jb+RepduJhg9k1cIVVnJPZRxpmTzoizXgn31c0g8P4/FE9Ybvffe1QgpYEB3ifjhtw9juHJt5bkbgZLpXnzl3LdCgf4jB+4I0RR3K3rKk9cNQvN9YzhwfgR1FTloL0sLu9214PBQh5WTa5qAqaFy4tpsdSioB6kqSHg040NhA9Ky0R58/WdfC3mOPvCO+/F6tnUfHO3rgEvTIAEwTRP3Pv8KXqwu3ZaAdKwqFr/4TinSz86isyb5hnrG1icXcEHnCmW9JT4XD1XejYrpLjTGW+wBZWx+g6NtE/aYYp8DBw4cOHCw47HH5uYdH4yakgDJRjMR5onsdIBRCwJHWGCpzPK65+7JAJqVHCu/0QxaVew/sIrqMp+5es2/IceL2x+5O5Qz2hC9H2CMYFwUmBprQgyj8gTi+eY9ED0aKlPriZ/PVITKn973klXf8wGkfnwSCADxLhFt70tDdlCAxVQNCL81UZvPHvb3udjDLaXmzhGKpFTIUy+MZkbbFIni91w2oyBEjoWnjKjJ7HeqBs/zVMSJDlxEMJCbYrxGMZHRZoRJnpMqEXl01cXK0cP89Z8uYO1K2M4AiUPcEzztaSmC0YmWxxkVyL2P9SW/zbJnOJlRQhMi2HXtn4pHRVs/Pv0Xv4Si6Xi33IQ7/vwuNBzIsTsFhUAlywEgsp1wfkiXc8/y2zXsIzm5kNAsZcE9tszRRORJXm06UL0/VKYWItI0v12RMhyyRoKp45B0GRdSizl6ixSkyhzp72Ceo4aGsvlOnJ+1gtH6pEIYggDRNC1mvWHgxMVONAT9Oj2ECnQ0sTdU9ifwvKdXplm997lZf6ay8pKNe3PY0wucAHACGFuMCVm/TKuR3HaxitVRchvH8eGXz6GhIgstpRmIiWfX/0IfH4hLQ6wPCoSNRocMasFQF5OH+6OY+vDgeDKUWevc670FqAfLeXZ1EcpfPLsPBULRMqKuXHDKgYNtgWFatE7JxqUVyN/y2lQ2U+b3oTYJuouVNbfNukIJV2ZjgmCn6cqMmycRumLAZvMxG2A3+KKH3Xcuwu1z2byslkmuhZ/YWMxqbH6ZDvA0VkoVNjTSDgZ/rpRSqMyHKS/ycwVN3ZCXyAHM8HM4CGVacJPxRrXZk5DrTK2oRBsNVVkg4xe16aG0atv11z3kmhOLm8sLzKZFs9ny+O0+K0Es6PyYObLIxlZqzaOTtjc1G0XaTyjb5PnCZZub3VOkby2vTX0WbJR0QWXXiFq7cJRdm0hkWEqqncYejjG/DpM+jli4RIqMruMR+OsfQ6jQLrD66WTpbsJmZThHKNwSiaoUm4We281+a6qrcAcAACAASURBVJHQnc0lcoL0OQ82Ojgpixw1nL9eIvmb+84mnGnSW4daFblZ2dgmmu5ewo4PRrcSrbE5+HzpXSgW+lGXWoAL+7yb3rd+fw7q91vUXGFz2irXBM/rAQhq8EYLDmAh2wtJRNLIPAqbRtFRtrHoSlnrAGp8fRiJiEHC3CLOluah4eDWrlraqYi+yB2mGLrFONrcA0XTiW1MNxquA3W7KdmLB265H9Ujnbi0nLGlq80zShREmDABiDAxI0eG3bYurSDkOapKMmqzmcq0L92LL773D/HF534IwTCgKgpObxN74EqQ2ziO+06+BEk1oCoSHnziA3g5d2sp4Cvqw2WjPfjw4EtojM3bEpXv64o99PbVgQMHDhw42BXYQ3PzngpGASsgPZez/TmIm4X7fACe1wPQEwSYitUXdUXExfel4czv56PiuX5U/aQPv/OTi3jzz9vxN/9067oBaVnrAL7+6A+gqBpE08oDVxUZd37xTjSgZMvq3RKfiwdrLC/ShsR8dM1ay5RFS4MoX7gEn5KBNtf1obJuJarbe3G8rRuni/IwkBw+//BsqReqLAGaHrSNuX7BV1OyF03JXsS3bi21M05dgA5rENAAxGmLYbddCYrL5jpRm12IxgwvZ7r+78dO4FJaOo51d+D1E9mhFzc7CQW1YyGFZFPVUdk4sOXBKBCkNP/8a1B0Daoo4fMld6EufeeOOXtpwnPgwIEDBw52BfbQ3Lyzg1HThKAakOaWLaGVhV74onLRnMzsQ8RFRi1wXWY0VDsFdOZ3WLCgLLB1fWWep/WYAlt+X8ggtI5RRkHQo/l9DD9rxop8xt+lyroATze8sMDUftOC8pyZDVNIvmcaYsCA4RJx5gte7JtZwNTxSMRVL+JyIBZ6nQhRs9RDBU3HrS1NaHjzDH48Whk6XoKHUSpubumCoumQTKtfyyYATccRXw/OvIXRmiOG2XlTym4gmqcwaIRaIi2xsjKn4pKcgUspFtVRkpfwrsl6PDD0AkQYUEUFny+9C5cmGFVUy+WDaDWWUZ0CsYSuYeul7mmikOhi9dMIC1Uv5wO3iAuM8qOXsg2XpxmV5HBvN77/paegBHODP/qnnwgFVeNg+6fum0VPdTI+9aUPo9rXh9dL8tFenIoo+DHdx7jCrknWPtG8cDBHqUlqY/1EjeK5NzHdLEDcL4yEgv1LYNYkWqJNbZq0sT+N0ZTs1PWW+awgBViHJki4oGRBmvdDIMqQEZOsr3dI2Xj17ew+WiYaWoZioq4wF3WFudCTVSu6XdlOY9d1nvC17FRab+REqHywaRjZ5ybRfyQRs4cZ7c2wcZFeXWIrtMuEKue23XsTgUjUVWTjbUoLTNWArki4eDiVo4UJoo3W4yeU9Fj2nWuOfe6e4fdxzai4ufsiFH2Fsq6jaqwdlwxb7jFRrhSGGdXCPcFoZrPHtyeg30t5KQ62CIIAyDIg2jiAElHDJFQ2I4LMn26e4qYrYRRL16Eh0j4rEIqruUmTeNHW6enftLxAqIeKjaY7Z7BxaSjAXlxe9rMxd2qJH+MMQjdViIqoumyjcxL1UG4oW+f0RJo/QNvHRqWlNFJxiUz2Gvkh3aYwSuZZIUApqTZaNKG4KuTdJp2nTRstUo9g575E0nMWZ1j7LsTwFFCqhkwp10sq/7BA23t+js1r5iKhxS7wba/Msvq5mMg8XHO2sX6WtZ04z3iklAJK2woARKIwrUWyOtDnKpc/vIo6pUULdjYv/XsdgVgKOk/uIxzwOCn8i+kAWF0nyW/2q/zLe4lUIoI8VNoVfV0KO8iSRDm35J60qekSRjH3vEpfkEs2kUCOmkvVdG2K4DRtgNJxdY5W7ajpbjV2djAaRPHCAB7r/n5IaOVhz51ojdnd4jhrIffcBMRgXihUA54pDT2f5O1hBo/GM79ERcTw0fVFjBoqsqAqEqDqEA0TmoANV/LKR3pQM9iB2kxmp3KlKF4cwCeHXoAUzNQwDRUVs924FHtjVGk3g+OtXZZXpmGpJh9v6V53he9CSSYulGRiSVXCbrNVKJ7rw5danwkJRH2+9C60xl5bsNIak4OHi+9ExWwPmlxZaI3aOr/ea0F24yT+x73nQmJdzz1dgdEtECbqLE/BE199J7LPTqKpMhMXD6UD4xvvd6VoSCCUdUGCL2rnrRI7cODAgQMHDhzsBOyKYLR8oTcktGKaOipmu9+QwWjvkaSQTYWhiBg5FgvZ9oprtCoOzz1dgayzUxg+Go/RqtgwR7PQUpqBB5/4ACobB9AjJiJhbhFnSvMsa5qJ1dtXDvTgGz8JUgwlGfe979RVBaTlC30QVgJRAKYgojF25+URUpwuzocqM9XknZT3WDHbs8qr8kqD0eK5flTMdgfzGK37pzUmB60xORzDYCtQ3dEbEvwaTY/ZeAeCvHPjkIhYV+bZ6S0JRgErID1zIH/jDa8BLfG5ePDwPajpa4UvKgetkVmbflPtwIEDBw4cOHCwl7ArglFfVC5UQYJpWnTCnR7UXC0GKxPwy2eKkHZmFiPHYjFeFYM0zKzabrQqDuNVm3/AbynNQEtpBvrHw+dAruBITwcUPWglomuoGey4qmDUF5UDVZABU4cpCPjHvPegNTYH0uzSxjvfINTvz+FVk/fvnBcejbFe3qvyCu+BkulefKntO1AMHaoo4eGiO695ZTUcqjt68f0vfStEd/500ofQXJK58Y5BdB/ZFxLrMhQRgxus/t9olE70oHqsE3XJBWiXrdXllvhcdC5tfL/tCOwhKpADBw4cOHCwK7CH5uYdHYyaogDdI6HVk4lHcz+C8sU++CJzuFVRmhsaOJAeKutZ/EpKxBgjlktLjLNu2PJXDJk1iUFUwuUFkj8aw/eQlGRWhwsTrA4fyKnjtlNN9lvvTWDflbpYwBn3ZgV4s4gDmAcwj+/Nelm9yfJKyyJvQ1Ed3x8qdy2yZL6ZZfZAnBzP51GO9bJ8lkCcdU6vlRbg1G9lILgyOu2Kwt21L6I2w6LsRo2wtpvPYvTUqFE+wad1NguP5n0E5Qt9qE/ebymKGiZ3vUSb7LkpsgaP6WZ1NWzbTRexeovq2lLyJ34wjpqhjlC9ZwvIdiMsF0UmeYHykoJm+QCayyyxGWGY5KYms5XD6QW2PwAYTWx1WiGMXS2K7e+ZsOfesvJSEju/KJuNzYrk+4WIfDyifQzlcz3wxXhRV2jZoJRO9OB4SxN8ETloi7ACPj2BtY+ndwoAUDPZDMXQQ3mMh/z9uDTDKNPL2ex+oXkKi8l825OuCpWl0SJQwnJmTpzr5OjON7d0Yrja6oc9C4x2Hq3wOawr6K9IxHe/eQK5tRPorUnCXKUnNCjvd4+suQ/A3x9dSzwdXDPWzvGgOajGJJ+XpOaxc5IG2AVLuMjGkoPL/fiHl78R8l/9fAmhT5NclOUcnsEgEVskpbErVDazmE2UvBA+d2jLsMe8zBxsEUQBcCswRVtip0LtD8hcSvKt7HOuqdBkUFK2sQloDphB3mlSqxDNNleoEquDzNm88Ad3EbsJahuySB4CPCJvdzGts3G2bzkxVJ5ViZ2a7XckkhOnKCRn1M0/inH5aR7WJtTaxZ4zqBMrHFMk1l82axcxQNp4nuQG+sl47FrHVoqckmyz3aP5oCbJHxZj6DXmD0fHH3OBtQO1WFm25dT2zZAJdJnaCdnyY8l3ArHhkki15Xm+Qi4iN6IQHQq7DaAQIBYu+tqDqP1zkzyjcGXSbron/GM5Z2lity5RaZ4xyU0NsDZQbfPgkrF2itGizaaFakDoJjsetYBZWMfaRV8n2Vknug2mEb6fUNC+T3Oq6TWSl+39fu08UdOeM0rGE3pdDGI7Zdf2uC7YY3Pzjg5GKVojsyy62zWgZLoXlZNd8EXlXrdVod2Oeq8Xn7jtFI72dWAqIgp/+uKPQw/a973nFLqw+Wuwcs30mIiNN95CVPb34Bs//RpX71cKcjfe8Rp/82h3B07vL0RDjve6/EZrdDZn7VI60YO/f/VJi1ItSHg068OhgNQOX0QOv7KakAcsrLnpNeNMSR5Hd26q3Pyq6AoGKhMxUGk94MUhvJjCjUbV5U7mv6pfHX36hmMPTXgOHDhw4MDBrsAempt3TTB6rSiZ7sXf1X4rJADzUNm1C8C8UdGY5UVjlhf3vvai9aC9Qtkd6kBX2s4QuVkPR7s7VtX7FVy/YLSyvwff+Y6VZ/tHL8u44+5T1y0gpaga74K8otpq6ihf6gsbjLZFZOJz1XejYqobjQl5aInPhXthds1trxX1B3LwsS98AsdaunGmJA/+Q9df4OlGoT4l6L8afPGxK1MI9tCE58CBAwcOHOwK7KG5eUcHo6YswJ8oQ1rmrSuovHVgP6PFKrWXQmXtTcXcPjXdLZwAzCG1Hw1J+yEY/NUmzhOIGGXlZcL6UxJ4eqFG5MMjFEZbOD/DB0DvSroQKtcusYfWb42woPimhA5un0E/o9l+OOFMqPybqYPcdknRbJnr5nh2jPohFjzOBHiZeWpRIy0SOetgG5wpLMSpV2QguJp2Pq0Q/nhG80iqZ5zN5TT+Gi0TmnREO7OuMNIZTZPKZgOAvMy4F5QqQekRAJDQSpb0OAqSxRu9EFEQtCwJ1ju1EJ7LlIpBZOrJ7svJ4Wkv7jYi8T/P01He5DvL8mw1DTe1daA50Qt5ke0fMc4fW1re3Cjjame0VDOB5QnHXBjDxaVEaIIEmDo0SPAhGZhfgOhhwZ8psza+qGTiYooVrEoLGowIsh2xaJg6QCh1tjiS0mM00p30aUbrkhL8qD+YjfqD1iru70S0h77Li2DytTMav2J+wDMcKqfILFCe0MPnRy+TCvarrG9JtlFcI9Si9jF2M0d5GPcvYNjGmWF2nelYUGgOoHq0E3WpBbgYmYPPHj+JyolONCQVoCGD3ddxvZdDZSWG0MoAaNGs3sI+RvEDsQIIxG2PfLwDB1cDUxRh2iiFpkLppYSmS6i5ujs8xU3ys/tWsdH0KB2Pjs08XZW/Z6iryTK1bFF4jusyqeuSzu7NUZXNY7rNa2ZKY+OFSMabFA/j0kbLvDDcIrG5urzA8hy0SL7egSXSdpR6TFnMtgYyJVY/SkkUNds4QrN13DQXiVxLw+4bQiigfvaMI9gowEIYobZANKubZMvO4GjDflZXam8j2Fg8AqGbElb1qt/naM2Umkto3vb6UHon7Y/SMn9wapHDgdriGOso15HTpo5Eagx/vWgqEpnGYHMv42js9J6g9GRKiQX4vj5O8m7iZL4OlLZLyxJp8EktmttnxM/unQk/+27Wz8+F/gDtd+SaU8umdVix9JrTthJttkMc5ZW2vWw7OHkWMojNCx23NP4UHGwBdnQwakfJTB8qZrrhi/Ze8aqmLzKHiSCJMur3XV9FzTcCGrK9uP/3T+HwcAfOp1u5l3Ye/k5EU4oX9//eKdQMd6A2vRBNqd7r+nvnMwpDwa8qyTibU7jxTluAtohMPJr5P1A+0wmfOxNtrvSNd9rj2N80iiMvdeFsaR4aDl65QFXFUA+++p9fD1HAP3v8JJoTctGccH1p4NcLAvZWXooDBw4cOHCw07HX5uYdHYxGqH7c0fwrtIhW4PmE79tX7bPYFpmFR3NuQ/liH84UH0Jzovc61Zphf9MoSuqG0VKdjvayVO67zIYp5JybRN+RRDTk7Vy6sC/Ne9VeozcSTaneLQ9CKwZ6cLSvA3XJhfCls2P70ry4/z2ncHioA68VFaIxy/qucqAHR3o7cC63EF1bSBMuXhhA+Uw3fBHZaIvIRJuxS1RbbzD2N43iC3/8M0iqAVWWcNcX78SlitSNdySoGehgOaKGhsqJzl0biIawhyY8Bw4cOHDgYFdgD83NOzoYzZkbx0nf81BFGc9nVkMmNNuqHh/aY1woMidQvtwPnycbLfv3h/Z1TfP0GFMQ0BqVjdaobCzGxYeUyOyqWGawRcqHelAz2IHa7EI0ZnghEQ2VgBaeM1CWOAQAyPON4TN//CJk1YCmiPif//AefDP/TQCA0pZB/NPD/wopYEB3iQh8Uw6JtXhd49zxqJpfv8bofOWxA9x2ddPsgbjmYjcOnB/FpcOpiEpjHBQxYpnbB0RweKybHVsgtB4iEggAiB5i5clyQsWd4CkrRDgYM9VpoXLkCKuD0s8bnS4dZIGBIRMVw/5pbjstkdIpKZWDleMv8RYyS6mMWqJGrq3YRmm1AOCZYjSPfKkPT/0L8189+f5ToYDUPWPgYkQOLhbkYDZdhKADFYM9+Pa/fA1ykCr8uaq70RLPrpHoZ+1F6WzKLM8ZotRcQdVRtDSIx/r+BYqps9znNP5lBqcWHUOUIdN5bolngl0kSoNxT7FywMaQXcwglCGiXCjEMOqW282rTi5o7Fp2LzGl54LIy9x2sRLrG8kS41dRelxPgFfJ9ROarkj4On2LfIBumAIKai9DVg1IhgloOo63dqHuAGs79yR/X1PK/goz6Wx+IbTTjAJel1wAKlDomWDnbqSxm0da4McjSv8xJ1iDq+WM5hvdtw02SHtMsc/B1kEwDEDjqXAmRymlXDhWpFQ6gB97BDKmiDYWpKmvnV5BRW7tlEud0HmNAKvbkp/PP5hcZjkHLon9sChQ2jBfIapMGquwsUu2y9wSjAcYXZFSdueWbLw/kf2uFsnK/tgw3E7wtFZJIfRSmwqsQZV74/nUBPaj/HUVF0nDqoQf6uIfIQWNKBYTBVyZ0l2X+Hq7J6myKflNcl3tTUqvP00XEWzUVYU8t5HpBe5Zdn6irX0kldaVqB/b5mZKV8YSOTihLgsif64cyM9SJwDY1F0Nck8Yrs0djxN+Jp/rBr//PFF+HpPYZC/aJoU4mTXkpM76zDzhq/Yt8Q+L0wGWhkPTZExbvw2npmuSe2CV4i0ZZuj4QZ8BTVvbC4QyLZD+Ldio1FSJmlaVqhfTe+26YY/NzTs6GBVgQgJgGhoEASwPEBJ87kwUBYbx2MRPrAdzQcIjMR/j1EavFuVDPfjmD1ngce8HT6E+23tFxzj+sy4oAd2aU1QDJXVDeCnfCparGvshBQyIhvWdt3YiFIxeKw5cGMGffOpFSKoOXZHQ+jepaCpeX3SorHUARS814PT+fNTle7ekHm80HOlbw3+VrI4CwKHxHpQMduFcTiGO9HVANoLbGxoqprq5YHSzKF4YQPlCL3xRuWhzpaN8sQ+KqYdeylTObK96a1VPD453dOJ0YQHqCnfXimBTZSY0RYKp6lBlCbWHrrz+jZlsFfx8RiE6zZ0v6OXAgQMHDhw4cLBTsaODURMCNFhB6AuZh/FC5mFUTnShdTIKba50fGiulj2YmzrK53u3JBg9MmALPPo7rigYzfON4abnOiGY1kspXRLQUs18QesrsqG7REA1oCsiemqSwh/sClFSNwRJ1SEZADQDNb6+dYPRstYB/POj/wJZ1aHKMm7/1En4kgu2rD5vFJzLKbQ864IvKGoz+bzQQ+M9+MdffwNyMG/08be9nxNRaky4cpXV4rl+PNb9/dDLlkdzbluV+9wQt/Zxi+f7UT7Xg4bEgi0LViuGevCt//11KEHLlts/eR/q87xbcuztwMVD6fjC//c+ZL0+hdpDuWgsygbUjfezg1LX7b6wuxJ76O2rAwcOHDhwsCuwh+bmHR2M9kfuw3fSqlCXWhjKy2pOyEVkQz8AwOfOhDoffDAXJPiit2al5lyWLfDIvjJBmgPnRyHqBgRY4mD/detBXDqUhhW7xOaSTDz3dAUyz05j8Gg8Bg6tvyq6kl8q3mxgqjoMrSaIluoM6IoEaAZ0WURtefhApKx1APd+/xUoqgbJBKBrON7etWOC0ZLpXlROdaFFTQlrWbJdaMz04u7bTuHIGjmjAFA9yvtNxi8t4NS778fh4U6cTy9A77L1MqJkuhcVU93wRW/sdVsx2w3F1Cx2gKmhfLEP/550Ao/m3IZDwgga4vLWPEbxfD8ev/gMFFPHR4ZfvuL86nA40t8BRWOqwcfbO9cMRiva+nG0uQdnS71WwLeDcPFQOl7ILL3R1dhZ2EMTngMHDhw4cLArsIfm5h0djC4pLjxT9vZVvGk9JwUA0IwUfM6bHPJO7ByPgDg5BwAQpm0eijlMaZTy83UbBz+hXUM/svDgsZModHfjrLcQDdleiCTlS+zjLSnmC1lCQ/d8El4qOYB3KxcAVYemiHj5VsuG5ci+vtB233a9CXi/Va40WP7nq3Ms7xUAbus8jfK7ByEGTJhfF9D5g31YPOzGoMTnxH0gpdYq/C7wwsMlKH5xBCMHY/DeziYUxY+hqzwZ3fMs6C1uHsKXHv0/UFQNogloggBVkvF6aR6MbJKrNsefq07l5Gl+R4Dn3eseMcx2JPlD4fN2RMLJLx/oCAVUqijh4aI70RpjBTb+JJbnQPMF4ttZve2y3vISzRFgO/njWOWURb6jqZFEKl+1VHqbUryIHjIQOc6OF4gScSZ3P+5qftHymxRkdCzlois2G13pVp1lXUPJTB+eaHhqlQiXvECk8olc/KzggQhrPJIAzEjWtWiLyIQvtYg0HN+HKy5fCjEGYGi4o+dFfC/9rfBFHOC2k+bZ704dYLkfNAeH9u2XxTzcd5q9pDldnA8zmJvkclsrhJUX+/D0Xz4LWdWhKRI+/eUPYZbY/tA8UdXgh59BlfXplmXGJHh7dEuorCt8Xuer8+ycJgPsd3Iip7jtklwsB3VknuXGzPfGso0S+T4T28F+i1oBrOS8lV3uwfGudjQkFYRelglU1p/mDi3w+dquCWaLhDhWH3meDDTm9sxEeykvxcEWQbDyQ+22CDSviubF0/vCvo8p0QkifM4XZ+FBphGaWyramA7U1sIgeV5agB97llQ2F40tsXGE2rbZ7aLomBIpsPuW5pZSuwwA8Ovsd2WRtUlsJD8+0Dw6legnaETjYnmJb0fXNDu/yFFqT2HL0Ytc23IHxOZOWrT7hohrl+1jFLXZIHl5rllykUzeNkTnndLYZgLtC7bvyCHWyy2lOcQ0b5XmiSrz/E40p5nmiQrLfN4/lzurbo4hQ216uFxH0h112/WyX7/Qdi77duR4tInJ78gSP8d5pLXr7bfNzeNqzJrf0Tl30s9bB44tEusich+pdqshAs7OR1/7WROw5ZkbtEwtjey2Q2Q+ps1gd9+h25FDcHnq4dPCtxR7aW7e0cHoZtASnxvKxXOPX95g6+1Ba2k6HvnKH+CIrwct1RnWqihBbuM4Kl7tQ1NlJtoOrW/HEXd6CWLAtG4e1UT0aT8WD4cZvQEk1c/jrV++BMlvoOC1MZgCoLkkfOWfb0F3PgtGKxoGoGg6ZBPQBODVkkL8r/e+A3WFuZBwbdTDkuleVMx2o35fAZoTr261unyuh+RG6qiY7Q4Fo6WTvSFfxwv7bkze4qHxnpDXZF1UPppSvbj/1lO4ufUiGhLy18wPrZju4rxuK2bXz/eM1ZdgQIAEEzoExOmbE7TxRWRDDfqPijBRNdeFQ/N9+FzsPVeVt7qCunwvbv/USRxv7wqbX3yspQeyqkM2TEDVUdXYj5eOFq0+WBBZDZPIrZ1Ab00Slo8oYbfbaSi73IN/fiFo8SLK+OyJk7tXVfcGTXiCICQC+DcAXgA9AD5kmuaUbZtKAF8DEAtAB/DXpmn+W/C77wB4K4CVyP5O0zQbtqPuDhw4cODAwXXFHpqbd30wej1QOtWLr7z+JGTTWgH6m3e9H4lzCzibw2w7NkJraToG1xAlym0cxx/d9xvLXkKR8IWvvB94U/jjzByPgOESIKgmoAiYPx4+EAWAlDNzljhSMF9VNAFJM3Dg/Ah+lX8wtF1jZRZUWQI0S8xlJRC9VpRM9+Ir574VUpH9zM0n4Uu/8nxJX4yX5EZKaIy1jlE8148vtT3DHf9qA96rRSg/NOg1ef+tp0JWMr1kRc+Oxvh8qKIMcyWPNHb9dvFF57I2ECT4IjdHtW3zZODRjA/h9snXULXUE6T56lctokRRl+9dV+TqTIkXmiIFWQES6ivC03SzGibxsXtfD6lKP/d0BUar4sJuv1NQdrkH9za8AFlXrQH0jWLxsv14BMCvTNN8XBCER4J/P2zbZhHAHaZptguCkAHgvCAIL5imuSKx/ZBpmv+xjXV24MCBAwcO3sjY9rl5RwejpihAd4vQPPwaveZhdIDIEcLDcDE6C7L51Ugjgq26RPXOsd/Ij+W2W0qSUDrUbSmhwgQ0FX/+sx9CgAlVknHnnadQF+vl9gmMMyrrqIfRGYwoGwXY5UbO2cmQwJCp6th/fhQR72ArkUc8vdw+7THJmPuuB/vOzGM6LhLuVzSMqrGIr17ktpvWrTbpqknGQWUEclDJVxcAVZLwXEEZRmbZuY5kx+KTj9+Gal8f6spzUB+RhxWmkUEk55eTbVQHQnui9JgVWm7FLGk7Q0PFVBd6x9nvGlEsmNZtsvLuPmbh0hqZiUcKP4by+V7UZRxAS5z1oH9orD+0ughDw7FLzehJiQcAqEnseJTKCwDyEuNVeC6zFUZTZPusksCnbBKB/XGkpx2KHqyDruEtDS0YyrQsS6hlh7zAjrec4kFDXAE+E30SVeNduODJRUucFVwqE+Ra9o+Eim2Q8FDF3aiY6UZjXB7aA0zoShlbYFWzUYSMhGi04AC+FxOBQ5eeDQW/r1QcxFQaa5fIUXb7u+YpvYUdyzPMU2o0wtoWCxhNTVGs9m0+lImvfv13sL/2MtprUuAqB5aXWH+aIRzgd9U3carSiacX0VZmBfPUpqXLZudCkeWaDJUpfShO5leR/629OlQWCPfFjGOd2Azw51rT3hta/a7Ptl4clI9aK6KKrkEEQgJrTZ5cKLMqZvMY3TnpNUbFNSNc3LEXS9g5RZLxiMr6C0tXoa50pTBxI/NS3gfgvwXLzwD4DWwTnmmal0h5SBCEywCSAfB+Tw62FaYgwvC4OAoggFW2FKGPOVsEu08H4Hbx2gAAIABJREFUtfYg9iS2Y9OxVaAWELRetn04Ch+xZDNs1gyLy+z+pPRZuJfI53y9c1zMmixKZM8hYxp7BnCLvGVLhMTu6bQIdt8nuvnxKimSje/TcWzQpXRiWmcAWBpj24mECikF7BeJjZPSMjtXeTE899B0be5RkdqdCITC6yHX3zVjs9DysLrSfqKRNBnDlp4Rzp5NsFMuCdzT7PyUeTbJicv2/kjK1A5Etw2UtK8rpH1kQoOO5q+/HsnOlaaLUQsRG4uZo+NSKq5hIxLRuZnaAZnKOo1CECGtTTW3Y0FjzxCzKjs/ao8EAHNLxE5PZSel2+ZZaucCjdJ02cf266oskDJ5dqGpVvbrxdm+0NQAe6oBTa8hlF0u7WZzTXpt2GNz844ORm8U6lILoEmWEqoJAaJphgKPo90dqCvwXvWxLx1Og65IMFUjtHJ0E7rX3WdFtOimj3VBDBgwXCJ+/exBjFfFrNp2tCoW3//scdzxxGswNROGKOKJ+95hKeqSZ9uKtn7c+lLTlvf1+n0FTEVWklGfUgAMX92xVnxhVTIZNybkQRUlmIa1YuqL2v7VqIZE+wqnd9P7Nid60ZzohWtmc4FGS1wOC1pJALoZtEZn409uOhmiNK8owF5v9JQno6c8fAC5gpFjsSgLqkobiojeI1unKn2tuO3lM/jL538EwTSgSQru//1T8KV6cXioI+R3rENAfXwBnvW+bVvtdbYa15iXsk8QhFry95OmaT65yX1TTdNcGR1GAKSut7EgCEcBuAB0ko//WhCEPwfwKwCPmKbpX3NnBw4cOHDgYBdhL83N1xSMboZXHNxOB9AU/LPPNM33Xu1vlo31oGagA/XJBbiwz3u1h1kXTSlePHDL/ai+3IlpdyQePPMTmEHLjrN5TFm3srcHx7o78FpFXoi6WN46gJqmXtSW5WK4Jn7VsbvKk/GVf74F8a/Oo74iG80lmRsGowCw78w8RLKKlHJmbs1gFABippchGNZLa8M0kTDLv3WtaOvHU3/2LFxBsZz3P9+IP/vwH+Bf33x8cw20DpoTc/GZm0+iYqoL9SkFuJDkRdzwxMY7bhItcbn4fOldodXCS0jZsmNvug7xufjskXtwy2CdPa9+x6E5MfeqaMxloz04PNKJV0xLwOt6YLwqBr98pghpZ2YxciwWg6UJG++0Daju7MVf/uBHkE1LERu6isNDHfClenE+o5Cz7NntgSiAa337Om6aZk24LwVBeBFA2hpffYGrgmmaghB+6hUEIR3AdwF83DTNlffSj8KaKF0AnoT15vZ/Xln135i4EXOzAwcOHDjYQuyhuflaV0Y3wysGgCXTNCuv6hdMILbbCqaKZ/vwt76nQr6LDx6+F+0aC0b8OSz4Uyb5AEwP0nRLZvpQOdGBGTkScdoi6oXCEAUUAISg2t1FTw7OH7eoeRfTM1C+0IEzBQWoz82FoFuB6LNPfQ2KpuGBX8u4/dMnAQDf+IfvQQnmYT7+1Xejo4y9UBhctPLhBgvjkFs5CQnA+3yNyPq/U+itScJAZSK8yjgAILZuCXGnl5AXfxnuKR3+BAmmS4CpmjAVAQ1VORj0s4f3o5HshYT230SYTwswVBOGLGL2LRGoSh4Mff/uF5qgaHookJIME3/1bz/C8s0SWkvTcamJ5flRhT6Ap3BS1dXlREa9OJ+Yj8Zllg+5lMPyAE1RQOlULyonu9CsZKE1hj3Im27WHQVCj1Fea+bq0O7NQntkEqACwhSjaWIfo9zaFeiUQdYfFrOZylt0N1ttNDw8fUSaY7SVqD723UJuFLSAhHcN10HWNbxz8Lyl9hudDT129S1VMt2LipZuNCTmh/IK5SmmnhhIYfV2L/IBmdLYFSqbXmZvY0QxilYgVgm1aUNiPjrJhQlEMwpKfDuvBjh1gB3DT9Kbq7p68M/PW1TUuxtl3HHPKdTneq06yIR65Waru1Sl77Kff0kSq7BzXdIZt2hYTcDwoQQ0HbL+znONsbpK7LpcWGL9MV3hn6eHA+yeT5AZ3fmn/Ye47bxJVj8pujCM9N/M4nRRHur250KcZG3gnrD6zJvPdEPUTQgIMmVEEeczrJdQvlQvPnvk3lBb0/4LABETjFtkUuqWTXVSISvjC3mMxr5CmyuZ6kXVcicaE/LY+NSC64Lrqdhnmubbw/6uIIwKgpBumuZwcEJbU4FOEIRYAD8D8AXTNE+TY6+8ufULgvA0gM9tYdV3O67v3CwApiKupuXSfs7xZ/l9uV0IZY7SbA2J35DSGqnYJ1UiXUXT1demAJo6v6FBqIIBnY31y0T9VjP4+WFSZ+P2ssnGNZ1wlw2TP4d4hY1RiS6iXmuT9JwlnEs/Ub+dIxTJ/nn+ZfcQoT8upTH6pGm7RhHjbDvXPGkT6jJgnws5ZWSqSmujc3JqymR+mA7P6uFSGEj/cRG6qx7Lp924SP0MF2tvuxItHdsoG0mgavs21Waq/LyKmktBeeSEAm56WF8IJPA0XarQH45+q7ttaWmEfmu46HZ8dXTyU3okOwcpgj20uWU+pSdSZotVsTKbp2MkXt1ZJdxhmkJDVXLpfQMAAaJYrS+Tm9ROG6epX+Q7yc8+d9mIn+4Z0k/m11awXwU6NNEmlm0DEk0BoPdOmBSE64m9NDdfazC6Ia94K3HLaD1cphVEuUwd7xiuR3vyO9fdp3i+H+Xzvag3LQuIJxqfgmKo1qohBNw+8BI+V3U3F5Da4Uvz4rVc/vtj3cRzUddw/JIVNChaUElU01FSN8wFo3bk+cbw6ft/BVnVobtEfPebJ4A3WYFo+e2WncvKHGW4BPR+MQHKlIHZ4x4MHgy/ikRXnH5RWoaucn718OLhVOiyGBqQBVg8+fKGAbSWrq/ue60onerFV85+MyhAJOHh4jtXPdDvBlSPEV9RM6j2G71arKdkuhd/V8sEnf6x+D2IUxfRYqajNWprPDj5NpXxyIE71qzLZnGkvwOKzvxE3193Dse6OnAmvxB1BbtXpKfowjAe++yPIKs6VFnG7Q/fjYbE/au2O5dbiIAsw6VrMAQBf3XLH8KX6g1935yQG3qpcGi8B5VTXWEVlO0oXhxA+UIf6uSDIfq1HSVTvfjKuW+GLIA2Gp92MZ4D8HEAjwf//4l9A0EQXAB+BOBZuxgCmSwFWEZZF65/lXcNtnVuduDAgQMHbxhs+9x8rcHoZnnFniB3WQPwuGmaPw53QEEQTgI4CQCuSD7gsr8kEDZYwy6e78fj7d+FYur4yMhv8Z+pVUHxmxXvxqDFxlT3FT/snckrhCrLgBb0XDyQDwCcQm1L9fqB3YHaUUiqDtEATNVAbu0E8CbezsWEFSyKARNRzQH0/LUllAOmHYPMhil4GyYwdTwSM9XWa7TxqhiMV8Wga2ZtGusr7ylEXPsiypsHIQBQFQm+yqwraoOrQeVkFxE40lEx27Mrg9G65GBesa5BE6SwyriVk8zORTA0fKblxxBME5og4euZ70SsvoQ6+cA1qdzybaqhfK5nw2C0ZLoXB8/3ojazcFUu6bnsQqjBc9NFER84fw6SbgVwH7vvftR7vWsec6eirHUAb27rQPLoHBRVh2RYQfbxtm403LQ6GG3I8uITHz2Fo90dOJdTiMZML9yTq49bOtWLvzv/rVDQ+ODhe3AhNbxCcvHiAB7r/gEUU8dtY6/iofJPrBmQ0j5ztePTFeHGiSQ8DuDfBUG4G0AvgA8BgCAINQDuN03znuBnbwGQJAjCncH9VmTivy8IQjKsIbIBwP3bXP+djOs6N3tcO1/12oEDBw52NfbQ3LxhMLpFvOJc0zQHBUHIB/BrQRCaTNPsXGvDYILtkwAQlZRt6m4By8kWH+Fn4lG8e7QOctDu48eVx7BsMqqMZ4RRQbQYNw5NDRKvSg2CYQbFb6yAVIcATZDQ7MoKmRsH4lmTUPE89zih8cQDDTle3HH3KRzr6sBrx3JRV2g9/N/5xTtxtLkbZ0vzcCE503oMWKmfi9FEKuMHMHwsDvq3JEAzoMkiXi0rRLE+hMi4ZeSKExB55g4WTRdGdOshwB2Usk1vmMaH7qmFFBQ2+vl3DsFfRShDhI+QFzFh2Wk8YNlpaJKIhjfnYCbRg2duOgFfbpYV5MYzOmfAxgVxT7LjyaR+Cxk2OlI7UZJNYm36urIfd3T+ysq5EyRO/Icq7ZqEAiPttz2IzzGqk7+UPczLLzMboxi7/cgC2yd6jKmcFqnDKF8egM+ThVaVf8BSD7FjSMuM3hLVu4AeJOPhAx9HxWw36tL3h4JJMUDoMUsqLriyQ4JLEASIpgEJgGDqeGDwFxBNEx8ZfRmfL70LrTE5MCd4GqpaWcDOb5ZRZ6Q5VvbFeLk8xtqsg/AnWC8lPBOs3qM1FsenYqAHf/tf34IS9Mh84ub3I1ZfwLmsQvgyvHjtzdm4PfMkjl/qQsblKXz49TMhBsDRvg7UllhtrhOKzpsyGZ14ws8bvVNaD1WTnNN5ChPFnM64SRURTGHarvLnIZLOl5bZy59ot3U/lzQP4e/+9D+gqBoMSYQhWa+wVFnG6aI8GApVPGbl0yk5uOjyAgDck0BsL7sn1GiLjlTe180FjVXjnWgX2VApLDEKlHF5DGWBJihm8GWYoaF8tgfNiV5O0VJ3C6hLK8AdnUwgqz6lALrbroi5RbiBin2maU4AeNsan9cCuCdY/h6A74XZ/3evawV3OG7k3BwbnWnCXEMZl85ZpMuaIqEx2ui3nJKoSKmL/HY6oShSuiJVH111lpxTPSnb5laNqH0ui0T9W2bj0IDM02KpejdVIjXsXGGCKEKLTJTZ84pok+ektEi3tLbvd3IET32dJ1TWGUI1XDb4OVwKULozqytVqLWrhUp+Su1lX4p+G52XzH/iMkkLmWd1NVWbeN8EUX5dYvOaGM2e7cQEfm6WSQqEEcvmEd3N1ydcr6fUXtOm1CuopN9R2rGdAkqVz6kitIem0PD18ceS3yVP35R2blfJpY9gGjEg0CP4+ugR5KJFsvsyKor1ufgIPn0tXmF/pyrsuUi0NZyfVIrO4XQ7VePP1VBJuy4Tmu+yrb1J9xZJ28tkwUXhzSMg+QnFfdnWWVdwlfMaHZ+EMLRfG/v++mCPzc0bBqNbwSs2TXMw+H+XIAi/AVAFXnXpimBifWr4Chrj8ojqqYRfJlfil8mVqJzqwqwcgVhtCb4Y71VTGhtyvGjI8ULNZINuw8FsNBy0jifbZzwb+iuS8O1v3oyk1xfQejgdnWUpuLnuEqr+asBaFZWsOVM0rIm5/7+v9i3NPjvJ2WOkn53B5arYVdutILd2IrS9YhiofrkXqkvCMzeduKo2uFI0pXhx6t334/BwJ9qXM656VbRoeQjlywM4P1N21atGRf5hPDb2f0I5yI/G3IpWZV3RMA6t0dlojc6GFucKv01MNh4uuhMVM92YlSNwf9/zME0+MDUNHRUz3Ru2xQrl3BedizYPyx9ticvF56ruRsVUt5VjuIHf5dE+QsPVVTzyyg8hmJZ10b1/eAqvHMwO+YkebuvFH547D+g8A2C3oLJxILgaCkAw8fL79uOcmIfTxXmWr+7chocIi7pkm6py/Ppt45PSoKoSTBjW9glrr6KuiIBVj3ZyecbXAwJWpfA52CXYiXOzAwcOHDi4duy1uflaabqb4RUnAFg0TdMvCMI+ADcD+PLV/NhHLv0GcjBnVDF13NpRi38sCE8tbYnLwUPln0DFTDd8Ubmhh/2L5EH+RqO/IgmvH2QKvSln5iEGTCsAlYCLH0jHfIYbw0fjIVSvjsD7jyZCJ/YYw0fXp0/11iRZ2/sNCKb1sljWDNQ09cJXfP1puoAVkDaleJHYcmVWJSsoWh7CY6M/tOiO9WevOqeu3D/AVs5NHeXaMBeMlsz0hVR7L7ozrqqugBWQtkVabdsTkYryuR5MRcXggY6fhV6UNMaFp3eu1GWFcq4KEh7N/yhao9j1aonL3XQbnM1hNFwTAkSDWRcdGejAK2AvZ+rzvPjoA/fheEcnThcWoC5/d+UuNlRkQVUs6rwui3j99wrwpPyWLTl2c5LX8oGd7kJjfD5a4nIgreMN2iol4xHPLSjXR1BXfmzd69WcmIvW6N1HX3ewY7Ctc7MDBw4cOHBwtbjWYHQzvOJiAN8QBMGARdx53DTNK9aFLJ3owc3DVy4nueLTSOmTOxmXj0Wj2CUAqglDEdD+/lSMBlc60zCzavvhynj8+7drUPVc36aOP1CZiO9+8wTKfjqAyh/1QzRMaLKI2rLdE2SULw9w9OurzanzubOgChJMU4cmSPDJjOZZrI7iMd8vQvmADxd9HK0x1y44FFpNjXahJyoNFdNd8EXnbrgqWjHdxQfOC71cMHolaMzy4hO3ncJNFzsw7YnCQ6/+OGRddC6rcNX29Xle1Od5g3/duCSGq0FLaQYefOID+N22S7h4ONUS89pCVVrqA7sZtErJaJWSoe4kQaLddUkdbA7bNjc7cODAgYPrgD00N19TMLpJXvFrAMqu5viGC5jPFBA1IqK8pzu0bG3Cyvd8KaqC54sT3r4yzUtTU17vfCFbPYwcYpz54rk+FF0aDPljRoyzY9PcCtGWwlE23Y/jl7pw+kA+6rPZClfC/nFuOxeR1a6bZoHNW/a1h8q/PFiGi09mIL92DF01yZgv9wBB+n8GsbVIlVlgmijN48CPL0MMGDjw48v4wVNHMVhpiT+9K4GJWP3r6FEAQH1eNn76qQoUvW0YpXVDaK7OgKfawFFYAe2rGjsH1cOf7HQUy89wjbPuI/GpCFhIY+3lmmNtryzSHD0+Z1BeZHkOyjTLcwik8jmIvvEcqDNnQkFky1Ii3N3jAM0TnbYF7oks38ccGgUAtCIKj+7773jbYqvVqVKSgCgrBat8tAPKrBrK76uY6wn5SQoq3ybuHqJus0gaQtNQpI6gPDCExqxDq+jgK4GMPOeH6LeOaRzkAxt53qKAX3Bnc5TQ+n2F0IM5totpfJIJtXOJGmV1JcrtaN7nRX2wvZoL03G0pwNncy1PUZPkKBnxVrm6oxfHW7txriIH9QesOlKq/IzKcqs+kfYyV582P1tV9ghs5dCe/0nRrzJK+pjGrGLmDL7PSGS0Pj3J+u28nyXanC3Mw8/iKqw/WoCoONYQ88QOwTXCD4eSyo69lMLaOPEcs6BZzONF1mQy7gS8SezzuChuO/cQ659qCjs/eYH9ph5xre8KN4frKR/v4Mbges/NgmFCXFJX5VRxVgg0N5R2ZZutCs0TpfsYtu5vt3phB6C/b/uKprGRPEqofB3oq+oAHdcENt6oBr/PtJt9l+hh436SizF+qK0VwOe464SEN6Py4wPNGaW5qbQs2xI7EyJYYp1O2nguwOfyBebYWCaStE6DWI3YH4IlYjdCc+rkJdu1XCBzOMkTRgSbH+xX0SRzpuFn7WWSfGTJbr/iZuO7RHLzJY8tZYb0T6pJEUhk9dGi+PYR/cTaxVjP2oVYkpDf0SNZ+2qRfPuo5FGGphbTfroqZ5RMeWoceSaN4OdPdxxrB5HkR7sUNp/v8/BstGjJj7VA52kAUAVWQYlMGDIRNjFtN59B+p1E8kRF3mGOyxOljwTUXYbmiAKAvExyRslCE2/Fwv9O2PxPe98im1FrH9oXtmvO3Etz8/Y87WwB6lILGLVQEPD3+997RSsSG6F4rg9fav0OZEOHJsn41FvvQ3Oid8P9KoZ68M0fPglF0yy7iD++j6wiXR36KpLQV2E9yCZiYypr2plZiCRvNOfcZCgYXQ+XDqXh0iEr+EraxO/sFLR5MvBo2gdRvtwPnycbbZ6rp9ACwNsX26CYGt7Z0YKvZr4bz++rhi/aCxXB/D6InNDSZlGkjuCxqZ9CgQH1Ut26livF8/0on+tBY8Jq70rAWlH97JF7Qv6W7eK1nTNFQ7YXMIGjvR0AgNez+RXX6o5efP9LT1l9/DkJd/w/d4UC0rUQV7eIxNOLmDweiZnqyLDbOdgh2EMTngMHDhw4cLArsIfm5l0TjDalePHALfejerQTbbgyatxmUDHbA8XQQ7lzVZc7NxWM1gzY/EbbO685GL1SjByLRRnJG+07slroaD0cuDCCI409uHQ4DV3lydeplluLNk/GNQehwEreqKVwKsLAJwd/gd6IFLRGZeER19tRbozCJ6ZeldBSeWAICgxGrQ1juVI834/HLz1r5YOOvBzWe7UlPjek2qvMhs9NvFJU9vfg6e9+DUpQpOgjqSc5P9Hjrd1WHw/65x5r7g4bjBY2jeLIH/dB8JuABLT8v2lo+4OtC5w3g4MXhvGuM004X56Dpm3Kg97V2EMTngMHDhw4cLArsIfm5h0djEoBILbPwFKitcR/PjoP5/PzQFTUEdtDbEiSGPVCnucf1uV+RpkVdKY2K85bXIAmJYPlD4oyWpRciCpb/tc8jGZAldLPFBTivjMyUxutzgUSrDrpBr/8r+qMtmDI7LvaaW+onOqZ5fbJczNK4LTOVpk6/Uxop7MkFdKzJlLOzOHysRgkHF5AQnCls3U5vFhTrLKEAt9lPPSpFyCrBjRFxN/8063wZLIHeM1GqRIkIuUdxzgV7gme6qIRaomoCigf6UHNUAfO5BeiMcM6X8nPHztylP29SGiRko3WIacymrU0zahJWgKjOkljE3y9CbVWSGLBuk88CGP2DEQYQRq4iUOBflxIyUdryTG0BrfTPexWcU0QzXEAoFL1EezE65MqcVtzvUWtFSQ0SRkQg5YsLmLNUmIOhfJBBUPD/d2/wNe977ZyVDVCGSID03wu6+u6m+9nkePsuszks3rbHQdWJONrBoi6rqHhTf1taDli+dO6FQ2+oxlQn7NEgDRFQu/NSchN5o03xeCoWVo3DMFvQjQBUwOK/2IUL2cXhVb6D0d1h/bJlHkbm3NLTI22xDMYKi8T3tJPJqq5fdI9jO4a71pCYdMoHvmTX0BSDWiKhD9+/MO4UJIJLLJ2WB4j978rPPWG3qMKsTPwZ7D+p0fY7g+D2rSwe0JP5VeIPSOEZjTLqFJaHKWfrW3p4MDBjYYpCjA9Msx1ZO05OxeZ0jxtFFlis6G7KE3XzrOjx95cPSntT9TIsWE7AHV9Ib8TIPOdLPG02GWJjUuLGrtXY5XNVY6mGCgiT7lUTPb3AnngCBjSmmUAcEnEzsPNJs15hU9t0N2s3pQ2SufZVXMFtdVZh0ot+dl3eiTbSTLoPMa3I73Kon9t2qixwHt7cNUjx4ZmGzNpXyP1oX1L8/D9TCT7CCZ5DrH1W/p8yPV1ak/EXyJoZBqgli3hKLsAYJA5yowmFnPxfG5UbARrO+rkRK2BYmS+fWMIF1YnrWpPh5knXOFFnbWJi+SsUTowAAjyJiMp6r5EHqUkWrZTewk1V9DIXKqye2DV+EHpuJTOa081INtR2i9nQbWXZG63CTs6GN1OtEZm4dG8j6DUHAkpY24GjVle3P6ZkyxndP+NUeodr4rBeJWVexaDpQ22ZiiqG4G8Yn2hGSipG8b/zbyqNKKwKB/pwTd++jXIuoZ7zsu494OnQgHpdqNoeQjli30hem9rVBa+mvkufHLweQiwfGg3UrbdLFpjc/D50rtQMduNC0Yq2iLW7hsNifnQBBFiUCm6aHEQX259Gp8vvgttruu/qngmvxCqzF6onC3lz99XlIV7/+pjONLUg46jKWgpDV+n1uo0mJIAUzOt4N4wkV87hr6KJOQ0TqCocQSXj0Vjsjo67DGuBcV1I5BVA5JhAqqOal+fFYw6WBvm3spLceDAgQMHDnY89tjc7ASjW4AVT0YAELB19MntQFt1GjRFAjQDmiyipTp9452uEDVDHZB1RmWu6e+4IcFo0fIQHhv6d2aPkvZBtMYU4PmkavR6UnBIHUBjXN5Ve5+uhdbYHLTG5kAeC29o2ZKQi+cza/DegTOht8SyqaNithtt+65/MNqQ48XtnzqJ4+1dOL0/H80HU1B5sR9Hm7tRX5EDX1FW6F9y9Py6x+ooS8Wv/6wIv/tXbRAME7pLRFdNMnIaJ3DPvb+FrOoodgl46dn9wNGtP5fW6jRoiggzuDJaV7577FGKF/pxaGYQjXF5W56GsC720ITnwIEDBw4c7Arsobl5RwejhgQsJYmwCc0h5SX2QCyNs4d8M4JwSQTbOjpRZoscJJSPIL2ieGEAj3X/wApURBmfPXIvmuLZCpFniqp38YcWCP1Wi2TliXE3v2EyUYpLYvW7NZUp3tbN8pYPp2cZdTHLMx0qx8k8baVAYZ7mPeq+ULnQPRIqfyi3gdvnnybegumaaDz55FtR3dCLgSMJSKxaBBiTEjmJPJVymqjijfUxuuvyPm4zKCRmee1gIe45b628aaKMxvgCuKfNVe1IEYhllJF99XwgR9XqJNYkEAOMJmLm8UGcf18kSrqaeHuUwCBaxP0AgJaYHLQNuIFFQB4eAAAsHWbX33ATdeB2fuV5qYzRml1EBVgeYZUL5PB5vJSCKS9qeHFfJd49WBuiZ2mChMbYPPgJvVNeJCq3MmsDm6gip6ZL751AAj+y6RFsxwZvBhoOW21WUTuI7/ztM1A0Ddp/EKorgEQ3O/fKuP5QeZzwvX75/lJczEtDQe0YOmuSceytXUj/6gwUTbfqqppIPL2IV8v2c/WhatEU3x25KVQ+kdDFfTepsRNsm0xBW2YKev8mAUWvXcbponzUxecCQ4C0SKhXhK4XPUjoWrZxxj3N2sczRVQwCWU/5gKvmL1YwK5zZA+7/npcBLfdUiZrL1MSUDrZi8d934NsWqvTJ//gFHr8Vr8qnexF1XgnzgK2Gm4N9tLbVwdbB1MS16XCUQV6qsBqKPw+lJpL1Ufp59YxSJl+TtmT69HnqPC+rc+bCqHrEzquRMqGjZtHU1g0orQbINxVEfzgHE5BXLfxYqdVNu5PBVh5WWfHttN0af1oipAg2sb9SFYnlfyuQdRL7TTdcMLnlPqq9EyFAAAgAElEQVQMADqZJ7VoMkcRKqRoo0UKLvbcJipsH3OOPETo4ZXXQVV7bcqoHAWTUmlJH7SnudA+TdnTVFnV+pvQdBWSkuGm/Z6vDqU1a1FE6ddNqLgu24RO/vbEsueL9Dg+pYvStKnSMv3cbbOCoIrOlIpr749U3dl+H6wgxsVTgGc8bM7UZHbigmq7r8lPCfraZVG1tz3pT4QuveJKAPDzPMCnBxgucmHssQLHGycfm2G2uY7YS3Pzjg5GtxPlC72cd2XlZBea0reGrrnT0VuxD8bR63d3NWZ58YmPnMLRvg74ogvQlOq95mOWzPShYroLzXp4+qsdDYn5zB5FkOCLuHbf0K1Ca2wOHqy4B7cMnQdM4MXkyi3xNb0aHL/YxUS5rpLqShWhj6ELs8c9SFcECDBhKCJGjsVucISrR1NxFqYno3G8zQpc6wp3kKdnGFROdEI2NMhBAbWagQ70JGehdLIX/+vVJyEbGp4EDlyXH99DE54DBw4cOHCwK7CH5mYnGA3CF5XLCRg1JOZvvJODTaMxy4vGLC8iR6/97iqZ6cMTDU9BMTSLbptz26YC0pb4XDxYcw9qOpvhi8jedBB7vVE824fK6W40xOfhH/Pec6Org9MH80M5pFtFdV047MbFH6TC/4qMkWOxVn6zDqTVzyDz3DQGj8TD3CLablnrAL76xL9CUTWoiozbH7oHjYmFW3Pw64SGpAJoogwEV0ZrswoBP1A1ToLUbXsf68CBAwcOHDhwsD1wgtEgWqOy8Gj+R1EsjqAhMR8tCTt/NeVKkFi3gLjz81g+4YL/sGvjHbYRh8Z6cHikEw2J+biQ5N1w+4rpLiiGxui2i32bDixb4nPRlRhzjTW+eqys6DZF5KA1Jifob/uMFViLMh4u+vi2r4hWd/XgRH87Thfnof5ADuoKc/GRz92L4xe70PHmfVsmALRw2I22oK8tYAWi7/tEA6SAAd0l4uVnC7dE2Oiwrw+KGlzZ1TQcb+tC4007OxhtTszFn5w4iSKzG7VZhfClexHbo6F+XzBINTSskv3bIuwlKpADBw4cOHCwG7CX5uadHYyagBgAXLZ0gUA8sT+IIFx0wiOn5eLZPlS4utEQl4fW2BxoUSwvgaIexWjUDgIARM3AchQ7tkTo8LotlqPf+RNYWZ7nFzICCYynPjnH8kCe7jwRKke7ed798eTeULmG2GL8YopXvH1ndDM7j0UWSI/6Yyz7lgdegKLqiHaJ+Mm3KzFSFYeSCGaf4SGa2t44lruXFcHn8f3nVFGo7NrH8ge1GD45Qh1k+QeRw6wd5nJY2T0NlA/14J9e/DoUXYMmyPjMzSfRnJiLqGGWBzBewQePta378dGe/7LotqKMuqxiBOJiuVwN98Qyt4+nkbXjcgVrH8Eg0v0Kn2fqHmfnN+9lQZKan8ptR20LpLFZS7F3qR9nDpWHvGo9o8sonuvDl1uehmJYeckPld2F8oVeyKHAWkNh1BB+W1ESOp5I+rEUYP2W5p7othysJVK95RQqP2+TMFdMVHf24vt//yQUTYMqy/jIgydh/B5gHBDw2q0FmPJHQAHJRSFJNCLJS4mTWVvFSXxObVeAedfeHMGuw+x5GVLAgGgAUA0U1Y5i9Ji17+mlgtB2H0ytDZXPzPGMBT9piAiX1YcvHM6E+gNit3SwAAbJCRNJSo6L3KPRw3yuDs0lUqPZ77gnyD2q8jk4Eb3MamYxLz5U9ozarAlIDowezKdrTsxFbZJ1fq4ZE4FYCfWx+Xgg6n4cHulEe8PPL2GrYWJPUYEcbBFEAXqExNl8ALxNC80FMzhrF3ue4dr2CatA0//IrWrQPDObs4dI8tMMYjWxaiykf5L8T11j85po24emmtE8OtmexE9AbaqWwcqjfj5tYSLA0sMDui3xcKU+tidVmrdqEBKFadpz50iuokKPsXZbAYAUIMejFjC2qtFnIy2KfUktUmT7OzWSTi+6yfMczf9csDkEhMsTtef/Eas1+txHrfq0iPCdTtTJ3G6rAr3MhofkVJLnEHt+NJf3TNqeWrbIkbwIZnQUm28yY9j8khIRXhRRI/nEUcTOJc09s9bmAPh85vVzRsU1y4pksydS2N+qmzyHBPhOQ51+uP5Em87WZbiuT/qT4GdtZ8/xNV2kb5HnPi5/FLy1S1hsx5y5x+bmnR2MbgGKZ/vw5eanQytPD5Xdhaaogo13fANhxb5l5aH/4E9GkHluGuPHojBWdf1y9zaDI9Tj0tRQNd6J5sT1V6WbE3PxmZtPomq8Exc83u1VHt0AVLH3tldO4zNvOhkKSCtmu6EYJC95xnpBcrtkrXxpoozzGdu7gnf8YifLD9U1HL/Yidd+b3vuj8vHYqyHV9WAoYiYP+7eeKdNoLkkA7c/ZK3snj6Yj7rCXAjriwBvO0qme1E51YWGhHw0pa2fm34h2YsLyV7MNfx84bpUZg9NeA4cOHDgwMGuwB6am9/wwagVAGhcANCUvvphu2SqF5WTXfDFedESv3sounm+MRw4P4rIt/uxeHjth/kV+xZB02EIQOl/DAGm9Rb7F98pvaEB6bmsQqhSMD9RkFG/b3OBUHNiLpoTc+Ga0TbeeBtRvtTPCWFVjXeFgtHG2DyoogTTCOYlB1fqH7jlflSPdqIutQC+LRB3uhKcPljAeYyePrh9L2rGq2Lw62cPIuXMHC4fi8G+w1sXMdYV5u5Y4aLi+X483v7d0AuyPzlxcsMXMA4cOHDgwIEDB29E7OhgNMk/iwN6D9pcXu5zSotUFXYKyhwJTIJL9w3xeVD75RClsyE+H+7fMkqrdqTIEsRpJII4aR9EmycDrhEW3C3lxIXK9uX/sWTGTYlgTiqrrCLgJ9QHxtLF/gRmD3FhNA0U/kR2fr+eKQ6VcyMmkNkwhdv+6CykgAHjKRG/fvYgxqtikKowye8T0R3A7wBnvpeHip8MIuZfliDoQQZEwEDMaT8ulMUiWWaUjwwPo3KMLLPzBoDEaEY3nFpg/BrBw7eJP4XRJWYZSxOeXtZWhgQ0ZHtxz4dO4Uh/B149VIj6XK/VPKQdXXM2OlIEkdGPYdcotpPVLZDg4fYxkr2hsrzE6CPSAqHE2OhjwjLrT4EYYufRzlOAqWT4uYNluO3M6VB/a4rMhTKvYSErArU4iE+l3I+qy524NJOEjqUEKEtzaMnMRUvc2sGIGkWpU+zz5SRSTrZRqpYJpYrQY+Rpno6ix/nRcCgTt//p3XjfcAOaKjOhHRIQTWg9OZE8TdstsTZRSYVSSJ+LEXk+0/NTh0Ll/NSxUDlbngCOAHNH3IhAgKPmdi2nhMp+YmdQN8FsdADAI7Pr19fP/IUEG7ffNU1oYtQxgNDKaFsDPP0+grODYkUzhrdsCaSwm55StAwP+/2y8X7uBVlN/0V0GNZ9r8yyg8/n8H34ekDA3spLcbA1MEXLxsxOaaOMULs9SDhIfkqfZZ+LtvQcgwzVGrEuoQ4nhp0hS/6mrham3ZJGJfRishOlIQu2G0UiXGGZlBc1NqhMivxDQBQZVBRxbYsVgKf6GqSNKRXXDp18J5G6Kh7+hW2AnKthUIor2cjWjpTOy41rNpqu5glDcST8S1PgX5qLGjlXQneVadqVbHtUJTRLuJW1Pwfw/7P35uF1XfXZ6Luno3me56PBtiRrHiw5TiEFCoUPCFOhCZmDExva21sKlDwdv5Y+hH7fRwu0kBkIhOleCKE0KbkMoSGJZc2yJXnQcDRP1jwdnT3dP/bRWb+1pWPLtiTL0X6fx4/30dlrnbX3Xnuv9dvrfd+fEca+04mki1Jz7bIr+lnQCA11mT/3xhql45J0ajQ9kW19wKBNdRGJEKHmxkXxko7MaDYfywxj6cISXPzLW0qtpfIVgwxYdvotpeZS2Y1iSwGj6Wyco32VphdSbXRymaRFEmR6I9ppseQ6k/5Im2raLj99zggktYtA+hIWeSKREMIurEkvjMl3AINQxU16f5CuJRg7P2jut7F5TwejyYsL+Oa3v4GHbz+JjjT3NdXRHZ2Nz5bej4p5D9pirZUoCecBAEX6FEoGxpHsneMNcbxDOBeafoWabzyyG2c4zV1yw6LlUroJZqsioDdJEEyrk5uwuPGDtfGb7r+baE93oz3dvSFX6ZVQcsmDykt9aE7Jx1kSbN5IdMbl4NN1x1Ex3YeO6I2r7GcT3Dib4EZM+xT395JLHlRN9qIh98Cur462HsiG+vY9/Sh4U2FeCocBASbMQD7ZG4p9NOA5cODAgQMHNwX20di8p2egAgBF11Az0nPNwShgBaTdtpWnIn0Kj3pfhtJvQBNE6III0zSs/JOheyf/5OUwWBsP3SXCVA2YiojJusu7xK4edSHWBcBnvXX6xV8XY6Qi7rJl9ipKLnnwtd88DlnXoEkyPvWOE4hY9KJ8oR/t0bk4E8lW2YrnBlA+34+2+Dx07oJLcmdcDjrjciD5ghtZUNBjub/zlzjx3pO7HpA62B0ULQzik54XIcKACRGPpfwBuqNurOZZ2BmTXgcOHDhw4MDBNWI/jc17Ohg1AaiSjI7ofITOsovijWXL/JSaaYQQ5zsvz/HRothSvJKWgvKZfihew78aauA/M45gMiwWZ0OyApNDlZShzmkLbn5ZP3yStW0thlCI7bFhCGuTNsp4ur5U1u6y1DGuyCihybrbplDaNoIzFRkIf5sPs1UR+MkzVchsnEVvTRJGDscBPqAgZCJQhrr3/bL4EJK+vYiUhgVM1EXjdHFegI5ze2QfNkOPzDvHLqns2FMjGLV3ReMdikddTIc6N8lOhDeJXJdsPlgL72V1UIqG3e1u5jDwoV/2sfyLhoZ39zfhvT1NkP302M+EPIiumBwUzw/gf7c+HdDnfabqQTQdZCZBIbPs3K/F8fSR0Gl2rOETjLayeDCW24/qVimFeDmV3V7L6fwxKMtsRbp84gx3LOVzPThd7AbAU0VVwvhaiyfUFptsVosgdJtYUgHfbESGMGrQ+BK7RhEuRmHJCOPd9w6EMv70hMr6ZpLMaLorBs9NeiDp1cD2j+dqAtsHSV0A79gXTawLF4jdojt6hitzbobReeEl12+Rf7T54tg5iTvH/r7gZtcrbIZ/8EdfZDQocYlRs9fS2XErqs1BcIa1W14k1DQ/lbt8fj0tEaDBRHi0ydHjljMZNZc6T+8Y9pljn4NthAmIa7YXbtRhVtm8/wpq8A5Hab92x1tKfwyWcddOa6PjtkCcUUUfv59JvqPuvLQNmsaPD6sqG6/szrbrkG1c4zDiWh8isGdztMxLP4wtcJw13Ua5JG6mLpMNCmEh/MFS6qFG6JPaGnG/9fLHSqUflOG4ZiNWaVTqQH5HWaF0V7tTLzmGJRs3e30f2/OcSmooNdNw8fvp4eyzGsmOidKJNV5pAZ2oIzh6qMDXHUYdoslhX44CbISQYw9n1yginDjeRvIuufmRTMblDmGMqnBbJ1bIRICOwXScpvRdgKfj0t4UaptUyITOS516qdMzldMAtv55maGM3peUHk7v3Q0u28Q12ZQJxZ78fUMg52XnmFa34U6jdW/FWXensM/G5j0djE5GReP+P7oPnrXtX83qCMuCKkgwTctM5uWMKnTF5kBZ8F258A3AwbPj+LtP/wcUVYeqSHjhm1Z6lvV/K8bWcodOVUZhap3Ku3b5ffcyThXkQ5ckiJoGXbAeiFxAN9uPrpgclM/yBlbls/1owt7KOdmcxnJJqpKM09l7q30Otg/tMTYTq4S8Kxdy4MCBAwcOHDh4k2JPB6NTkdFoy3Ijtmf7Xw+cC8vAI+kfRXHYNNri8/a8g+7hllEoqg7JMGGqOjIbZzFeGXPlgm9mmKb/xZGJ8wkZgYBOE2W0x1k6vPa4XKgiM7Ba//tewplkN06++wSqx3rxWnEB2jPdO/I7FRcGUdfZj4bDuWg7uHfS4ewGKvs9qL/Yi1MH8jEK9w1rR3dUNj53+H6UrA2hLcGijbvmbuwLsP1kkuDAgQMHDhzcDNhPY/OeDkZNCVCjTSzk8EvllJ0Qd4Z9UKOpkyVPFQwZYTRC02XRa7pdbpxbTALGgNAxPz3WRemmjGpKl+tDFngKS+mEB5VTvZh3hSNCXkVTRgE6Ut0Im+SPx0uoEzTxdN9MAsrPDaHm7AB+d6AArSRQkP2Jg+WcUnxUaQI0A4YsorUiGyNrTO9ZH9kT2G5eZgHXffGnAtunVvkApDq8P7D9v6ZuYX+P8AS2oySePvTB1NbA9rND9YHttHCeWjKrMJpHZNZ0YHt6kV0j1cd3P28icYBzUb4GtxvkRQlHL/RCMgxIAEzTQFjYCh6+/SRqRnvQlF6A04fdAIAxHMT9pSdx7GwPmtIKcCbFDdcSq3sxi7isrvF3/koK+066DLVsKZMkMo9njY0YJy6RNtfB6WJStw9oicxFS14u1mxUWl8cq0MnjsVhY4xcslLIBzPiNE+ZBqxA9Nl/+CYUTYcqS3jon+7CzBFGzf3jtNOB7d/OFwa2M1y8m26sxGjxOiG4UCrQuyLPcWW6fIxKS52eQ0U+ufepBabznVqLDGxTB8HWsQyujHeVMQJE4m4YOsGuQ/mwB8/84HEomgZVlnH8QyfRke4GACSeITQlm8Z3OSsCh6ctk6xzSEd3pKUlV+YYFVeLsTnekm4irbLjkwcZveoiwtCdcysAwDWvYimH8cTWojenw+0o9tGA52B7IBh+V3IbS5e61ApBJPMbKXdkk3DmdBvNl3PXpGXoUME/UkAfu3RiJ6rB69aJgyoljao2Pt+aTJ1IiWyCbIdKNg2Fizl80jKRUnCa0qrInnGLWvCHAnXdpXXrNgdeiVCPvSQbgUacbH2KzWGU8FU56qrt2WHK5DwQ+qW6SCi7y8Hnc0oUaQ+hZcurNgde8qwWydhs2FySdc55n1BzSTYD3fYIp5/VSOr0bO8zhNZM+qCPZMpbs1lyaNGsz4RHsWueGMH6RVYEP+YWho0GtnMURtkNtXV2F6HSelTmBknlL4NrCQgGiVxM/TK8Wtq3NOqsa3PT1SlN19icigvwNG16G9B+Idne11I3W4FaaBPKLiLIRQYCGTas/UhbRf7+oNeZuiRT2YEp7RJ9dx+NzXs6GN0JFK0Mo2xlEB3h2egOz7xygSug5JIHX/3t45B1FRIAAwJUWcbD7z+Jllz3luooPzeEJ//mO1A0HQ/Lr+Luv76fC0gBoO1QFr7++G0oaJpET00yRstvTuOh7UJDXoGVH1PzU1tzCnA+1I2OVPeGfdsy3bggb/z7XkD5qAdH+nvQlFlwXSZdwbC+GlraMwyXqkEyAWg6as4M4OUjJVcs/2bAkcEeKJoG2TQBTUPtcE8gGLWjeM7KN9wWnwd9VcRXXnvCMskSJHz+0L2BgHQnUTbuwXvPN0INB16oqEVb9uZt3S7sp7evDhw4cODAwc2A/TQ276tgtGhlGF8c+B4UU7fyiebciXO4vqCuaqIXsq5BhvUSQ4IJ0+8AvNVgtObsABRNh2xYk+W6rv4NwSgADJQnYqDceuOlYHOh/35Ba44b9xw/iaPnenA6pwBtWW6ETV253F5C+agHT/3oG1B0K6B+6IMn0Rjr3rb6Kz0ePPvYN6GsB6Gw+qguimgq3TotPa1tDpmnZzF8JA6o3bbmAQASWxeR3LBoOUHvEIP6dDZ5cSHLaMzcXJNbPDeALzc+FTC7eim72n9vm4Cpo2zRs+PBaNm4B0/87Otw6daKyodbT+Oe+z+1swHpPhrwHDhw4MCBg5sC+2hs3lfBaNnKIBRTZ/lEVwZxLuT6gtGWlHxokgz4V0Z1CNAkGU0ZWzehaSrJgSpLgKZDlWU0FO89XeNeRGuOG2cS3Te6GZuifNSDW7p60HKZHKi1Qz1QdP+Kna6hZrgHjUWb77uh/mEPjgz24HR2Ad4o3Dwnbn1Pr/WSw7SeaQIstswLf1COjsJMZGJ+03IUaW1z+Mh9TZBUE7oi4JXvHsJ0ZeQVywFAaLMPYW/4EFcTgdmqiA3fJ7Yu4m33nIfoM2C4RLT8uxt9ZUlbqvtq0J7pxr0PnERdfw8acgtwLty96X4VM32c2RUA/71trYx2RG1ebjtRM2r1iXUSkKzrOOLp2fHVUQcOHDhw4MCBgxuBPR2MCiYg6Butt+M9RPNXwAj6VPMnL/B8esGnokNJZw66goS2hAIYYZcxAdJZfRJJ7RDVZ6BocSiQ0/LTR46jYroP865whId40Zyaj66YnA2pXWQmtwOl7nfkZeL4F+5G7RkP3ijKx9lDGYGVz9gIpk+bXGMVpoUy7R0AdHuZlu5Y1IXA9pPTx5DdPo38pimY9QKXV5RqDBIVlsZiSmO/E2LT9cUQzWBJHEtDs2zTshyKY4LZM5fSAtsJUUwbMTrBCyRNhbwGCiM6AC/P6ddiqQ6HdeGFaHL9bbqUZSI1XPURTRDpW/ISX8ZFZLCrYZvrTQBsWJEtH/Xgyf+HrXjef+9JtJlubh9RA5rSCqD6gx1VknHqUMEG/Yq0SjSEYdbxVfZ78MwPHwvoPz/u/gRaDrCVTjHDukanj2ZA/5UIwadDNAFdBHRFQv8fJeFomgcKEbqcWWErfvEKu0ZH/qMPks+EAEDymch7fgoLVVYjsxSmBf7JdHVgO12ZRXzLMm69pweiz8RR1zJeeMZyf46XWT9beiUUgs+AaACmauJg83ggGKUphBoW2bGtu66XnxtC7VkPXos7jFa3dW4FjVw/m77roujGxXxrP9LVuVQ8Z0OyidOthFfCSvDKwRKUL/RbtP7QDEAzIE6zIF7R+VeXgo/1TT2Gda7VEl7rGgzNaQVQRRmuQDAsoTGrYEP6nm2Dub+oQA62CSYgaCanqQJsujqi0RL1rXUyg2qxFF6DJmrk+c6GRQhEjyba7hOq1afSSbuGn2RAg0ifI0TEqiv8MfhC2NgjEh1muIsJ3OwpX1SisTOI8FWxpYCJFth8I4QcVJhdPEewSvKITPvYyz/NphkNldmYTiXvXpKebdaWVsdLjoOmwbFDUNiYQmvwhbC6DZctbQzRCdKkAFooa7f9uoo+otekWmCNbzdN4ULrprpOXwxfxgjfXOzslWx9nfZVUgWnOU3kGx6dzAaf7FimDS2NZbrQgtAJrgwdZ1Nlkk7P4L0hFgzb5MGPeZ1NWFZtuWYWNFYmPYSNa4ZNMzpD+tPMGqtvYY2VX7Lpen3LJDXiMkm7aNNry2y6AYXMkV3L7DrIXv6aUM2wSTSfpusyIY1A+wLxJwnl+yNNEUk1x3SuoEbsgmZ0n43NezoY3W6sO+iWYgLt0bnojsxC8bwHZUsD6IjM2TIFr2hxCF869y0ohg5VlPDpuofwXMHvAwBWUq7tlHYUZqKjMBM+dXsvSXb7NB566L8hqTqMJ0V8/+kjXEDqYPtRY1vxPOLpQVuGm9unbNSD2uEefOktH0C0uYzGnAK0bdFFt/5iL6F166g/188Fo+toPZiNf/jq+1DcMorFmFCkLC/gQnUK+q9i9dH+LKSf41qWkdCwjOm6CMD284kNSxB9JkQDgGogo3Fug/vzhepU6IoEaAZ0WcSF6tQttan83BCe/ttnoWg6Tkiv4u4TJwIB6fWgOyobnyu+P/CS6VyYpSnvjsyCoO4OLb4j1Y2H3/9J/I8LjdDCgBfKatGW5d7ZH91HA54DBw4cOHBwU2Afjc03VTBaMejBB9oaETJr4qXcmqD0x8vhXFgGOuMOAQCKlobw6MXvBDSknz9w95YC0vKFfiiGn+5r6KiY7kNn3N5MDZPfNAVJ1SH5g4KSF0aQ3TiDwdp44MjutaOsexjVZwZwpjIDncVbWyW6WdGUxa94nnbzlO2KIQ+e/AlbOX3grpNbDkQB4NSBfELrlnCqMDit+0JJKi6UWEFeQshS0P2CYeiD8cj58TRE1Vo9GPqgleU8vmUZR+/ph+AzYboEvPJvhegtY665l+oiYbgEQDWhKyJGamM31N1XloQvf/2dONg8jgvVqVum6Nae9bBg3NRQ19u7LcEoYAWk3VGWXpsmU99NdKRaRlwryVfe93ohYH+9fXXgwIEDBw72Ovbb2Ly3g1ETkLwCQqeAsjEPnv7Jv0MxrBWK9/Y34sEPfwo9oYSeqLPJoxrNUxhEH6OeapHWdyUzQ5yGtHymF+cltjpDMrFgLY3xOlpCD+DO0f8O5K5sTcoPUDbCptkKymoCf3opNZfSSFe62UqllsrTcLwhjFLTOMRMjYpSeSpHqMTqiCO8h7K3DAJPCjBUE6YkoPynwxa9yiXix89UBVar6sJ72fGtugPb6cpcwGRmLU6COAOMHYnBZGU0bo2+yMosszIAsKazY//YRBP+/K9+CUnVof5Axl99+QM4V5IGPYmnvSxEshMe6mL0lumxaG4/KZx9Z3g3tw9Xo/m7OGSGfbeWQO3a2T6h0zz1YpXERpTio0bzQUrEKCvnixbQGO3GffedxBFPD067C9CW7cZaCmtzVccFbuW06tJFNFRa11Zc49uwlsn6Q0S0Rd06n5WAr5a8HQebJ3ChOgUTmdHIAKP8JIax618RMxTYbpvnX7TUxnrYMRHeGt2eqorCb797EMkNS5isi0R03Roi4ENa0zxE1YTgp9ieuPgqZt9laUkbvVkYrXTh188WIrlhER1VmRgpj9tg6V4d5QGOAVPHohCHZeSQlAr9S/GB7ZkFRgvS1mS8mnsQJ+RXA07KDXn5gAkYIYTGt2I7jyQWTuwk6VzWWKO8yTzNKHyEcYa0GPada47QgmS+DwtL1v1a6B1F2cQQOsKycC4sAyFenq7lTWO0p0Mrg6ge60FzWgHOhrHnmeHaJfv4GwRBEOIB/BCAG4AHwEdN05zdZD8dwBn/x0HTNN/v/3sugB8ASADQDOBu0zRvbNLWfQJTBAyXuOHNPaXPCTSVAtmkaRk2QKbpPOwvg9h3XDoXsm3YUrvQVB+iTP/O70cUK9wEkJY3Qvh7XV8jaUgUVqMRvVgAACAASURBVMGKjw0WKwrfHWcJ3ZHSbyMRPLULBU0BE2Ljri6Rz5QOLNty7ITLrA4q1ZhXmayAjiEAMB7G5k9LXvYslCW+bp2MwYQViTVyflSXLW3MIjmPxFqASlQuk/nGliqIf2bSORy95jTFCsL58yiHkvkFGZOMMJ7OuRpB+gNl7JK0fWGxfGq8g4lM01NDxt8cF6PiZsnTtAhiSXo9FxlEvbZIxWuyOS+VWg152dxwao3XjlE5zISXSN5sfWZilflELJP+vUyouarXdlMRaq68RCjXtic07cauBULNXSHPEhv9msoBaDopk/R7U7CluCO0aiov0MP4dvN0XJK6hlBz7dLBNxtuxNi8t4NRgveea4Ri6JyxR+1wD3pyr31Fsj0ul9OQdoSxYK9wZRilcyMWfTeCn8R3xebgM1UPony2H+1xueiM35urogAwWxWBN76Ti4SGZWAYOPSjiQB1MrNxdgN10o6AycyaAcFkk5AXv1WCperNNQp2HGyeCKzOmqqO0rYRnCtJu3LBPYSKQU/AAKexZKPTsR1tWe6gpjOnDubzWtGC/E33W0fVxQHUn+tHW3UG2gutvthflsTotjNXdShXjZmqSMxUWYNRtH/StFAfigxFgAkTUASsHnVtKHepMgqXKqMw4tteWnjLgRx8/C8fRP25fpxOObhtq6LbhULvKL44+iPm2p3+UXRFbm5odnh2AF9ufBKy/8XWyXefwJlk9+422Lxhr18/D+BXpmk+KgjC5/2f/3KT/VZN06zY5O9fAvAvpmn+QBCExwA8COAbO9dcBw4cOHDgYJewj8bmmyYYpe+7TACmIARN0XAlFM8NBALJRzL/GGWrg+gIy8a5MIs+WrgyjC8Ofp/RdwvuRnvSIa6OrtgcdMXu3SCUYrbK72baCBz46RSgGjAUEcO1Vw4SkhsWIfoMiH5H1vVANu30PC5uMRi9UJ0S0AZqsoQzFTcXTbdiyINvfvcbUPypQT7+Jw+jNdd9zfW15OdwK6etucGD26qLA3juS09bv/2ChAf/4Z5AQHojsVwdAt+PUiC+4YVxNBTeyt1dxWs5kIOWAzmQJjcGwTcaZas846JsdQhd2PxZVTHTB9nwr5IbGqrHenc9GL2BVKDbAdzm3/42gFew+YC3AYIgCADeBuBOUv7v4QSjDhw4cODgTYD9NDbv6WDUVEx40zQAMn5cX4vbuxuh6BoMQcQ/vuPDaMtxI4QsHK8kMVpAbB9PvdDCrSX74vkB/O/WpwO5BD93+D58L+sPAvsJqoHShVGevjvbi/Mq4/lpGQlc3ZSatBbHqAF247twZpgGndBHVtNYjxPm+UsyZzB6RH4Oo+bOr/GBYGQkc0JrIe6js2GM95JWOYeXvnUYaacXMHYkGlXHRgCMAACeXygP7DdDuDJzdWEWXdDnp2SKgKkIMG8BQgV2gOXhA1x7fjLJ3FXlSh1ff/w2FDRN4pXig7hQkgoRJhJsVCDq8lcQcymw3SrwwetMD6NwmsRZ1yQuuSUHh7gynWeIIythoJgyO/cLBXxAZUZYVJ6qzgtQNEarre/tRUsBq2/qGKP8iISaYkaztgkyT3vpfn8MumGdowRCy5lf4fkfH3i5DS5Ng+Q3K7qluw/ny1I5uteil6eXUsxGsf1iXKvcd5dU1rc+HncqsP3iUmlgmzouA8BLC2WB7YiKZqDCotA+PvWWwN/t9DGKNYndH6NeXkc64SVUMB87pqRYpnUdP8/rSl3zxG2YnAbTFhuHzLHt1QR2jRJfY+deS+IpTAZx85QXGU/MjGbnVBzhrZRXy7PROFeCOxpPBWj8TfklMEWe4uWLtT435hTg7r5fBVbJX60swGK21Xi74+eOwMT1miQkCoLQRD4/YZrmE1ssm2Ka5rol9ziAlCD7hfp/QwPwqGmaP4VF/5kzTXO9sw0DuLnect3MEAA9RIS8yj8fBCKVoXRcjvVno+lSmh1P7bU5VZNy1PHWJM6vgu3Gp/VR91rbYw0CHXYJ+5JSCkUfX7dGXEE1lcgcZLa9rPIvyyj9cU5i8gO76y59hsYQ62Cd0BAV23M2nLyyT3AxiUFKyCK3X5qLPQwNUp/XZCdhfI1nTEUq7PkXzE0VAHTi3KuT8y2JZI5kGwup0y695DqhyApL/LyIUmEpBJvTLx3fTRf73ZBodjzxUStcmbgw9nl2lR2rV+PboGrsOisymQOQa5kQzs9xCiPHA9vpCrsOSRLLjkBpuQCQRM4dPXO6jZMeIW7OZdbIQLLg4+cKU8tsDkAdnVWDH3xWVlk/pi658BKXXJvEyEWoudQx15aggRt/qEMxN0+zjed0PwNkziXS8vbnDKFcU5puKE+/56i5kZSyy/bRtpbd7vqwz8bmPR2MUrRlu3HP/Z/EEU8PWpIL0G5zJ90qymf7uVyC5fP9AcOSdbRH50Id3Zy+e7NjqjIaU5WWNqAQl66wNzBTFYHfPVuAxIYl+OIkRM2tYbY+HPNVYRs0gJfDQHkiBsoT0bO4/XkkdxqnDuVBlQmt9kDerv322coMaIoEU9WhyRKayvZ+X1xPJdRbk4TB8gTu74Ut4/DUJGC4Iv4yNdz8+K/0Kggm8HJ6Fbpic6AsbD5ROJvoxsO3n0TNSA/mQiNwxNMDALuaV1S4ivt4E1wyTbMmaN2C8EsAm9kk/xX9YJqmKQhB3wPnmKY5IghCHoBfC4JwBthCklwHDhw4cODgJsV+GptvmmAUsCZobdluKItX3jcY2uNyoYpyYNWiPWajE2l3ZBb+svA+VExdYPRd3/71xZipisBMlfVaKEr0XmHvNx9aCnJw5188hPrzvTh1KB+t/lQbVX0e1F/swxuHc7mV0u3E+ZJU/M2/3o6S1hH8+tBBnCnK3JHf2S642y/hoRNWKiFdkfDEE2/BYHkCstun8fDx30LyU8S/9fQtGC3c6LB7I1G0MBhI69IdvTHoL1oaQtnSAM6EZaM7YvPrUDw3gC83PhVgXrycXnXF3+1IdQMAHn+BOSzfd99JtPjzot7MME3zHcG+EwRhQhCENNM0xwRBSAMwudl+pmmO+P/vEwThFQCVAH4MIFYQBNn/BjYT6zQPBw4cOHDgwEFQ7LWx+aYKRrcDXTE5+NxhlkuwO2pz/V13ZBYu6AmbfrfdqPR4UNfbi4b8fDRvki9yHcWdoyhvH0Z7eeauBSXxLctIbFjCpbrIQEC6H9GSb12X+vO9EPw0k+e++oSl5XxJxp2fOb6jAen5klQML17ebOp6Ed68hshTa8gon73mXLQFzZMslZCmI79pCoPlCaj+jwHIPiuVtuAzUP6zIZwu3L4V5ophD2qGLYOpa1lVLFwdwRd7fkjo+/dzaZ7saaAeybtr04C0YqaPY15UzPZt0JYXLQ6hsHMELSn5OJtotbVmZGNu2l0LRm+cLuVnAO4F8Kj//xfsOwiCEAdgxTTNNUEQEgEcA/DP/re1vwHwEViufZuWd+DAgQMHDm5K7KOx+bqCUUEQ/giWMLUIwBHTNJuC7PeHAL4CQALwlGmaj27tBwDBpcMXK6Ky34P6i704dSAfnbFsEisTuj+RVmA5mT80GkZ1aTno8ucFlVZsBHaaW5Dwz305iezPKr92Lqib95joQV6YsppA9BmJVt0VQx5869uPBcxx7nnwJDeZVv128lW9A/jnf/0xFH9uyXv+5n60HmSrNytEm/KezM7ANtUFLuq8viNJZjqFeJnp8srDLP1nTMsKau4ZhOgzYbgE/PbZA3ilsDiw3/+d0BjYbrFZhpfHDAe2J0lanXclsbbN2Ij3NKXIL0aKEAzRuUxrQa3lqc143zT/IsGVyjoKZRx4Z9k5EUJs+qdZdk6rBvvx3Ff8waci48fHqrjg4Za+HrSXZiAqmS3bhxAdyeQ4H0hmxbBj6LnE+tahZP4FVFIouy5eki6nKGossF0dw+t1C0KYLuXsKguoqiI83H4JEmvrG7/NwwPHX4Ok6vhjpRHPPHkMQ+UJ6PKm83WHMt3yr5eZqZfi17V4ahMBl5VKCIqAuLcuwoC44Zk6sRaFjmm+bpfMNFDjU+x8CZPsuoo2rddagonKfg+e+a7/HpJkPHD3SQws8SubCkkTsZTKzqPpsvpc2fwwH0TO9KKp6GBgv7LBQU5HXqIO42xkPoQQXkpxRtUZ80KQ0KWlIGRkAeO/b13jsnEPHn3h21A0DZok409//2E0JuShKYPPTduSXAA1Prj2djtxA00SHgXwI0EQHgQwAOCjACAIQg2AE6ZpfgLW2PK4IAgGLDXfo6ZpdvnL/yWAHwiC8AUArQCe3u0D2KvY6bHZlAT4okQu9QnAp2ORvDrZ/zJ1kXGW6rcMRQi6n8nLvIKCakPpU8henteakfL03rBR5gSiaTRtWsV1aLpNK060eF6daSU1m0aPakapTjSUCO4Um/DVENh+NL1bjMRrIsOJEJbWR8dfw3aC1CAXcFXj02do+hYvDEFkFGNbRYRszkDz2c7jmkrSwZDfpDpTADCJ2NBF0sVFhTHZRFbUHFcmJ4LZ08+FMw+HKS8/X6G+HQbR69J+lhrG0/iSXVQbukK22eTVZetoK0EcVReNENtn1laV6H9pmj1Z5OumutelNVafafDnUV9j+wkLrD5lke0n8ZYUUEhac5kS6myHQ7uWStKniGrwNE9BxyyRPD9E/p6kzyo9JPhzRg1n32lMMgydbO9Wapf9NDZf78roWQAfAvB4sB0EQZAA/DuAP4AlZG0UBOFnpNFXRGW/B8/92+OBgO2+e0+izU+VvNlxpL+HmeNoGur6ejZd2am/0AtF0yH7jWzqOvu5YHQnEH9qBaLP9DvomkhuWAIKd/Qnr4jS7mFUtg+hqTQHHYW7R1mtv9DHXScAlo7U3ydPFW2ke99MyG26xK1o5jZdwlD51TMDBsoT0fjdbMSfWsFMfTjmq8KBBaD5fW7UvuCB6HdU/tW7gr9suBIqPR7U9fWiIS8fLQfcqO/p5Qymaj09GEi8unujIyIbuiBCMHUYgoiOKDf3PacjF6VN6f0A0BWTjc+WPYDy+X50mqnoDuf7aGAFFJZ7btVkLxoL89CRZmlHqyZ70JhVgPZ0NyxPgB2GiRtmH2+a5jSAt2/y9yYAn/Bvvw6g1L6P/7s+AEd2so03MXZlbHbgwIEDBzuAfTY2X1cwappmNwAIwuZvBf04AqDH3zgIgvADWLbBWx7w6i/yk80j/T1vmmB0NjwCpiBAM02osoyGvM1TQJw6mG+59PlXRhsObz4ZLu0exq3/dXFbTGJm6sMtJ13VhKEImKzbDQux4CjtHsZjj3wPsv8cPPRPd6HrcPqVC24DTh0kJkayjJ/cWoWf3FqF+u4+NJS4d/zFwE6jvybRn35Hhy5L6K9JvHKhIJiv8gehBAPliXjsqdsQ+btVdFRkWnlmV4NUcBlUejz4zhOMSXDXJ0/gVEE+ezEgyWh0FwBLV67LjvXn/mbP/3UdeflCP9ri8zaYnlF0xWSjKyYbytTyhu/oCqgmymhJZjlmO9LcaMl1X33DrxM38O2rgx3Cbo3NDhw4cOBgZ7Cfxubd0IxmAKB5NoYB1AXbWRCEhwA8BACu5GgkJC1agdgviJtpYT70MOsqScROmi6dh87xV7Fkoh8VM31oi89D3zLRw9kGaz2K0DZ1RoORvCRNxxpPj9FiNk+tYacwUUvr6H4TpZMe/PUvfgrRMGGIIh59+wfQke4G59iuWMfRUpiNj3/qRICq3H4glaM7hCoqDneN4l8e+RFkVYemSPjz//VRtB1iNM23pvZw7fGajGJDqTxDqrUiNlSagN5nUpB1egarsQrCXlfxIakJC1XWiX5xma36nFvlg8L8UEY3PRLRG9j++SzLjzvhjebKVMUMBrapvXpxjEU7ffuLXVA0DZIBCLqGu4ZO45+r3xXYry6F0VW753j6ZHwoi3ySiNX9+djkwLbdzlxPZnSNgYIYnEy7AzUdgxi5JQ5CifX3hj/Mg2c+DnGwAo/saEb5GV5gVNPMTJZCBAByIhkVqCiGUV+HVnitZqzC2p2dwMrMEv7IxbVkrkwksYbPdM0gGKZ1Rp8+dlsvhn4Qh4g3fJisi0BZ9RDKMITXVw5wZXq87LyGS4zqJBJq0fMz1VyZX/f76a7hQNj7/TSsBWBpiee6iIRybc4xirSQwo7n2Au93Ar1Wxp78fSRd+D4B0/i6MUetKTmo1fPgWhLPyrOsbpDybYWZ53HkqUxyDAgApBgoHymF+e6UlC0Moyy5UF0ROSgOyIT5+NTIHhVyF6rD9nTwajR7J5aSWMvg1JOWQZzE4jDI/l3Iy9uHE0ZBehIc2ONXHKdtFuJ29yB14GDbcI1j81KVBxWkkWefgeeWietEXogodnZJ1h0nOQosvbUDEHiap5WG7yMaRCar411SlmpBtcGti2qNtowmXvoMjvWVVvqkmtBHG0QYcJSOmiILUeG/XMw0BQgsRJ7YUbppbqNpks/+wx2rCESz95YBnuABaPsUmkNwKdFoeN+VjhJQQP+3A8ssYcmpQpvoEWTz5SiSiUhsk37QWmtLjIZo+ltgI3peDb7TXquAGCWpM2jZpCKwH5nDnx7QgV2XSXSIcc1XvqzPm8D+PR8K2RQsaen4VLp+AhNe40/j+IKSeFCUhxRam4Iz3aGssQql9eCR1X0/tdC6HyepmKy9yUiByCXRfCRVE4uWyqmMELnpr8Tat8vyDal6UZe/z3ugMcVg9HL2f+aprnthhH+PDhPAEDEASsBZ2uuGx//04cDgdi6++Q6Sic8qBnrQYPbmtzZUTLl4RwuH3HfsYE+dyNQPd4LWffr1EwgdnXjSgpFa64brbluAICIja62FW1DkFU/lVfVUdE2xAWj14KxCsvx9KMPNEHyGTAfE9DxXAYWqsKQ2LqI1IYFjNdF7wp9t6cmedtW764FZ4os4yh3zOyVd74JsVIdgpXqECwZrivvfIPQmF0AXZQg6hp0UUJjpsUk6Eh340LotRtItcXkQhVIOqfwbBStDOOL/d9jpkW5Hw/qons16I7Mwis1Jdddz7ZhH719fTPhRo7N4SlZTq9x4MCBg53EPnrKXjEYvZz97xYxAoBGRNdkwU8DMfo2pnTCg8de/AZkQ8ODbTIe+sDJDQFp9XgvZ05Stjy4J4LR5tR8aJIMGH56YfbmFN2toq0iC5oiAf6V0baK6wtE15F1egaSz4BoAIZqIuaUdQGO3dsL0Weg1CVi4InEq9IY5nZM4ZZTPeisSseFks3mUxsxUJ6Irz9+G4paxgKB6B3fO4328sxrpusWd46ion0YbeWZaC9i5+tw1wgq2obRUpaNs8VXzNfrYFdh+p/R2/ek7o7OxiM5d6JsZRAd4dnoDs/Ex6Zf50yLypYHtiUY3UsQsL+oQG8m7JWx2YEDBw4cbC/229i8GzTdRgAHBEHIhTXQ/TGAO7dSUNMkTE9FQU7mxWUGWbKvmOuBbDA9adWlHjQfdCPGw6gOZyJyoUokt2hcPsxQa/XHF8c7zLpm2YqjeaE/sC3SNAuyjTIQxIkvbJKndQiJhC4RK6Ij1Y2T7z6BqsleNKcV4Fy4G7JN6xbeyCgo84WEmtDDa/KmXBpeyTyI+//+XtR19aOpJAftOVmIEdm5e32K15lGETrnhWVGv3xHHHO8zXNNwrhFgPGYpR3VFREdVVlI+90cRH+ACtXA7WfbMPlWRrud1Jm+lNJH/izp1wht9iHr5DTgA0yXgNbnMrFQFca5wV0IY+0pDWfOvDgKnKtLQ2rrPD58XzNE1YShiPjuM/U4Fc1clg/F8K60Nd0euJum4alJwG8OWMu4BWcm8MhnXwrQmv/+K+/H+ZJUHDo7jv/5uZ9BVjXoioSnnvw9/H95zEU4N4Kn3FJEyuyaFyew85sdxtNlV8nqI6UFfSy5gdtvmTjmSeTJROmytZH9XJlRleXvTCLufRUhY9x+i4RC9JUJNq99W2w3Ky/zboCizPIZU2r20CqjTS34+HsqNY614dIiow+FhPLOiatDjPJqhrP7N6yd9YvawR5IhgEJgGkYONrbA9eyierxXjSn5ONMshvABuNLuKaIm7LJ6pMWWRu64t3oincHPneEZrHVUlFGS9ohaNHhkJZZGdHmxu1zs7op/Wi6gqekEyNN7p735rKWK+IujESmecNMEhzccFzz2GzIgDdxY1J2SuGj7vaiSmh+NjYp/SwRZ3rZpikXdPadqJE+u8Xua5L7aaNTL/0dMp6TttnbTWm6psCOT5PZzW2XxVP3UtUI7jxrBOEkx5Lnht1Nl1oHS+Sk2PejDrrhAnuWRRP6rp0WS910x33sWWZ3Z6VjlMk9v9h2qMJTe8MV1oZwmbUtPZTxPpdsmQDmXcQ5ltJiza1NaRVCzbU7B1NQV+MoGyedOiBTqq9LZH8PlfhOM6uyeRt1NaaSqSiR7zVSkIiEUnEBwONlTLE5lZ2fBR+bQ8yu8vNGjfRHk8yrBZsrsbRK6PfkNHBdy9ZMSs2VV9n2Bvo9mT9LXup4u/k2AIjkHqX9zCRjrmmTyZnS5tRcNdLW18lp5ai5YeT5EW6793YC+2xsvt7ULh8E8DUASQD+UxCENtM03yUIQjosm/j3mKapCYLwJwB+Acs+/hnTNDsvU+1VoTGLT4lwepPVxc74HHzyD09YE9bUfIxM7E7+0K3gTLIb7Rnb58Tadihr21fyZqoi8LtnC5DYsISemhRMVFqDETU3WqrfXDe7GcLf8EFQrYmMoZqIO7Ua0KFuFUUvjEHymYG8laU/G8apIHkrczumcO8nX4fkM6C7RAx9LQ49pSkobhnjaM2HW0dwviQVh1tHIBNn2bymKWD7UmI6uA6czubv97mQcHz9vx6D7H/R9Ml3nQgEpNeLc+GZeCT7DpStDKIloxDd0Te3SZWD/YO9MDY7cODAgQMHW8H1uuk+D+D5Tf4+CuA95POLAF68nt8KhvZ0Nz7x0ZOoHbIS3rdnugEAh2cGUDHdi7aEfHTG5+BMsjswSY2fWAxeoYNNMVMVgZmqCExpViA6URmN17+Tj8SGJVyqi0RM9UYNazCsHHXBVPzO1YqA2fqdTdp0sHkiQDM2VQPFLWPoKU1BV1Uabie05s5KK4jvrMyw6M6aBl2W0FeTtKPtc8Cwnk/4jdJctORv1IC2Z7rxwB0ncWSwB6ezC3BrF2FGGBqqx3u3LRgFrID0XHgm1Ojdc5JePwdNRzPQdmjnA+D9RAXaL9gLY7MDBw4cOLh27KexeTdoujuO9nQ32tPdWGc+lg978C9vPBFYLfnzow+hKdFZ2tpuzFZFYLbK4jTEbGKoFAzeaheGvp8A7XUrEL3aVVEA6L49DcXPj/ppugLOvD+4lu9CdQp0lwhTNWAoIrqq0gAAPaUp+LN//igq24fQWp6FyRKL3nq+JBV/95X347au8+irScJgeQKwCBw6O47DrSOYuyUCA+W7a54EAImti0huWMRkXRR6ilKuXOAmA5dP+Bcy7vzM8aAB6fpLp7AZE5po6a41UUZzav6G/W8mcOfgZQn3/t19Ox+Q7qMBz4EDBw4cOLgpsI/G5j0djLpcGnIzp+AZ5Sf+JtGfrKayqxU5ZHG/63v8qyUwAVND6XI/LvaksfJE82m3gl/OYoTxMKUosC2qjCMuLlvaisKVYcvwZD4b58L81NhUXhtGoYaz340aYloCbyK9DDxXX1kmPPVQps/wxfC6C9HLNAc+wqF3xzGt4sQyn4aC2rXnh08Ftql2044sF9NLNi2TAJ+XIuD0EvsumegWO33+IKoUkMr87fQfVrzIxHO10UwHGSXxGooxNRY9ZaH47jNHkdU4g4GaBAyXxyNLYC63NFUNjgLPPVWPnMZpDNQmoMOdBfhPX/ytK+i8NQsuAB+IagUAJLUuIuXCAjrr0jFfEYYYrODWixfxp3/2a0iqDu1bEv7xq+/FxVLLeOnWOJYy58WJw4HtyjimdZVs4iqqEy2KGA1sD/j4Vdh53boWmW0zeMfxbggqYCqA9KyOmSprta55mad554awa/nzaZZKZ/xcLPKbptBbk4SB8kRkuNj5eiDpvwPb59aYFnRC4/tz2zwLjGpj2TWa8zEflN4p/n4NJ9pQauO/MM93mmOdfVzallubPOiMKODSR0SO8uexM96NT/2BRcFvScpDV2wOJJ+JKA/fZ3xJ7LeoLlycZw7WRox17xctD6FsaQAdkTnojrCOS1pieipvCnlGjPAsC9cSa99qPNGRhdl0KcSVX41g92vd6z3kHOio7+pHR+H2GJEFw356++pge2DKJnxJGqDatWVE50V0WTRdme1xDiKdg7K6uVYS4FM4cOnPyBi+oS8H6dt2rSvdj2sraZs9HQxNC0dlh/oSe7arOn88egiZRxC95bzAj7mqbv8xC5Ey0atvOIitIZToRCPIyY8hB75s8i+XY8l4Gk0eyFTjCQCLJGWKQnSvkki18LzeLjaE1ZceyjwJkhU2b6D6SgBYC2XneEmjqUsUbj867kaR34l2kbRktvNI09XQlGVrAj9dpjrRCNJRVDG4BpViXtt8nmXXC1M9KYVdRzu6yrwiJlYYk2dhje23vMLLqdRlUreX9Dn7fUNuLE5TbW66y8Y66D2q8jsKpKBI07RsuOHId+SSUS2oLtF70q4ZZdtcOhn+NEKn6VzI2KxHsestR/Dz753Cfhqb93Qweq1oyiwIuNRqoozmlHxg5crlrgaFK8P44uD3WdqH7DtYQLoFFM8NoGK2D21xeWhJvLlXcyjiWpaR2LCEsbKYq3LXvRaMVMRZq5Zb3Hekwm+yMxd8v6TWRbzz3i6IPgNlrmF8/+kjGKmIw4GmSUjrOlLVwOHWsUAwuhtwN01bOlv/8zC5YSkQjG4F+Wcm8fCnfgtJ1aErEh5/4q3QaoM/7G8EGnILoMoyoGlQZRkNuVtzlz6b5MbZJDdncHKtKFoewqM93wnc158vuDsQkG4VHuoSBwAAIABJREFU6xKBhtwDV00ZPnUo3zoHugZVlnD68PbpyTeFCcDYRyOeAwcOHDhwsNexz8bmN2UwCgD/mVcDAHgxrwZnk9yIGrD72l0fylYG+bQPK4NbDkZLpjz4P81W3lNdkPDzmRq8lFeDs4nuq2pDVc8A6s/14VRhHtqKb3zKibiWZdxydy9En4kCZQrPPHlsxwPS7UZKw0LAJdhUDWQ3zmCkIg4XAzlODWiyiM7KtCtXto3w1CRg/QWpqQCTdVenYSxqHmPBtGYgv2kK52t3L5jeCtqy3bj3gZOo67dyBrdlu3e9DWVLA/x9vTRwVcHo4ZkB/MvrlkTg3gu/umpDpZb8HNz5Fw+h/nwvWmszrztPsAMHDhw4cODAwV7Gng5GJcFAtGsNgsS/HZCHFVQMeFDX34NTB/LR6nYDABZzTVT2e/DE849B0axV0Z8fqoEeAkzUMppeQiejVNiZLpHnGXVRmGFLaHoO0+iZsoiOKDfUS69ZaR8ECR1RbpiKBHmBUUEEja88dsHiIByb6AzkPRVNDbf3nsJ7PM34099/GOeXeH2YHkKovecJtXPZg28/85SlLZNl3HP8JFpzrPOgxTA6QccSm8zaqQUviSxdybEURrnsX2UBZLTNzjxBsWiNmW0z+EBbCybrojBdGYl0eQ5pjfOQVBOCASiqjo882Yy+P0vEG4fZChelANeGDnB195IUMPEyo092rvKBdiTxFl8zGM3kksoCtEwXn0plzEfSnYQwOnCCi21PqDGQqq0V0XV96ciRWCiCDk9ZEr722NtwoGkSyltUHKocxyGMAwDeWGTH95G0lsD2zyfLAttvSzzPtSczkrVvTmd9szR0iNsvkNrlKPC9p+uQ1TiDodp43HJ0ALmwaEyjahxXprqzH3GnVjFbH4Y0dQ5pp+fhTZNhugQYqglTAdJSZhD2uA8DtQkYqYhDy6o7UJ6m/OldSebq/khyY2D72dFbAtsi4eTERvAvfibHGSc1/KJFqSof9qByptm6f3Ot35aXgLPxbpyNd8NQGH2PUuDmc3kKVEw/u8fkFUKBW+P7euggoy6vFbIXCQql6QsCOmJyoY7/tz+di4S2hHwYYQrkOYtaUbgyjOKFSbTFWZTgpQLCtwVQPNIC2bQkAoKu4aGWX+DpknfiVBmvWaesMz2NtVtYE9GamYvWzFy8o+4MbkVf4Du+B20j9s/LVwfbBcmEFK1CX+WnEDoY24KjmAWhtAIAzcCg0yEzSHoTqw6a5oXUbd+PUvMIhc+wMR9p6gj6s5SyaxsKue9EldS9QmiDLv5gtUjWoBWSSoOm2ACAtVD2WZHY8yHOxTiFcWSMBACFPFQkQi8NteekCYIVcmFoSjEA0Il8iI4P0Qr/rF9xsRO7SFKKxLhYGXs6mKQQJnU4FM7Sj4WSvDprAn/BwggtNpTQamWJpwDL5LS6yHfhMk/7paApdyJJahb7XEgK8uA07J2LQCQ3BT2ni4Ryq9roqfSzl9Q94+NTu1zysnnE2DyT1/hWWRnTRqvHMrt/xS1mK6H7cemXbKeD0mdpGdHOYCIfDTLfpSlg7I8CmtpJ9LH+pIWzc6W7+EIGl/aFtpOvm14+I4SkU4xgfSEqansXt4JiH43NezoYDYaKAQ+effobViD2axl3P3wiEJDW9/QyzdW6u2aKe9vb0B2RiUdyP46y5QF0ROSgO2LrK5MdkTlQRRmCoUIA/DkTNVRN9uJ8ztbMSur6ezh9XV1fTyAYvV7kd0yisGUc56pSMVW1UQOb2TaDu4+/AclnwHAJeOXZQ0AdsFAfijRFgGiagAHE/24ZcY0r6H8mCeOVMZv80t5AZtsM3E3TGD8SjdGKOPzw6VpkNc5g+EgcxipYEOspS4KnLAlHonpvSDtHK+Iwuk41xsCm+6S2zqPygWEIPhNuWUAFhiFqJgyXiDN/nY6QOR1rsRJKvzAK0WdCd4l47ql6LFVvPTVPMBSeHUNp2wheLSrAmaLg90P5sAfPfO8bUHTrRcrH/+ThQEB6o9EdmYXPH7oXZYsetMXloTuK3Y8cNV+U8RfVn0B7FB9kNqVbEgFB0yDCRM34BVRM9eMTiSfRnu7e5aPZGvaTLsWBAwcOHDi4GbCfxuatKa33GGgg5lJVfLCpKfDdqQJLc6UJwo67a3ZHZOKHyceuKhC1ymXh07XH8R+ZdfAJUqCtLclbb+u6vk4TBEtfl7c1fd2VkNN+CZ/91C/wwcda8NlP/QLu9ksb92maDqRKEVUTyQ3Wm83l6hCc/14KFm4NhSkAommJ1TMbZzfUsVeQ2TaDe4+/jrd9rRsfe7AR6W2zGK2IQ8PxfC4QXYe7Ywolj40gsXVvpgfKbJyF4DMD10ZU17cNhMzpuHgyBSFzOkQf+3tO4/SVK74CCs+O4Z8+/VPc/dQbeOyzz+EDL7UG3ffIYA8U3bp/FV1D/cUbE9wHQ3dkFn6Y9ntcIArw1HzZ0FAx27ehbEeqGw+/7yQaMg9Ah/W2T9ZV1Az3bNh3z2A9ufa1/HPgwIEDBw4cbD/20di8p1dGQ0QN7ohpLKTwqzbTmQogWOdbBPCRpkY8X1ODljw3WnPcuOvkCdza1ovGrAK0Z7gBADJx6VpOY+vwlJ4DAJGhLACRMtmqoLzMqCB6FG+/JS0y+oYRxpzdtHD+9FIXz961RHw98V34TVgxDpujaIvPQ4+YyVEJAJ6qQJ39LkpunHjfSdSM9KApowAtuTlYX9MPmWC8g7UEQh9a5fkI0zKjeSwlWO3OPD0DSTUg+TWTEa960Z7FtLD1SR6cr07F7ykXIfn1kz8tqoSy5OdhHAIyHp7FXacbYKomoAiYqQtHlGgde7rMAtNQG0d6iji39noZPTTSRo+hVNZxjV2vynBPYDtJWqJFMOyLD2wfi7kY2L6zqxuyalh0bdVAYfM4YqQVpJ1eQE9NciAgLY8cRHLrAt5z8iwEn4HDioSvPfY2eMqS8N64tkB9Qz5GNa6PZ9TnDY59hII0rzF6jdd0cfu5FRYoUlqPh5wrt4u9MNBvEaB/w0pjY0oiAAPQAUMR0FWVjnFfDNQqCQdck4CfijxZF4WuFeag2z3HKOmhMk93fWLpLYHt7NZpVHUMoqUsGyn/vQTFp0MyAVE38Nl/fxktCTloPZgNcY44PceaeP1wPk685jcqkmQ0Zh2AtCIijLz3kHys37sW2Lmzvylci2HnRCYmZVo0/8wQXcylWLlEKDbEWVuN5cu4JpdRtDyMsuUBNGccQnN2Ee6Yfh2moUETJHQhHXaYopVq6pcF5Tg6fAEmLObDVGIEvMRkWA9nBxKfyRwkZwat/lbV50HhayNoOJyLtoM7m9plP719dbA9EEUTIaE+rNrcYk0vu58MhVJfCa1O5cvo9LbjaLq2HyVcPZEMZYRJudHEjNCDEZz1C5E4fJq0DFe3rWpC5xPIY5LS/mTbDEvykeeNyipY02znhNB2aXMiFTYJiFV4V0aFNDCEUHPtlNs5nY37oaTh42TsWdTDbGXIGEV4jIaNc+0ifExKzaWuu8kh/IvcHPLgT1HYs5DWPaPzlFR6rJFkcqeF8O2hVGGN0G+9OjuGOFdwd0v73IOCut6uGexCh5FzT/9+uTIGucgLNnvXVZ1cF40dz8wa70a/SFxzNZJxwlwkHdV2v1L3a8lL+engQe5LMnUBZYArK3b67eY0W0Hj9xOpnI1S9l3sGAwb5ZbOSmTSWHr/U5owAGjE3ZtScTfSdEkdJIOFrLA+Fx269VSG14P9NDbv6WB0MxR3juJvv/NiIEgTAEi6jrreXrTkuQEArW43zoXusAvlNqE7IgvtyYeuqWxHqhsdqW7/p+3ptReqU6DKEqDpUGUJbRUbDVQGyxPw1JO/h6Q3FtFdnYbe0mQUgmk9RiricPF7SYg8tYal+hBMHd5I9Y1pWUVs4xJWj7qwVu3a8P1uwTgaCigCTJgwFBHeWAnvvq8Tos9AuWsIP36mOhCQpp2eD5gbQTNwoGkSnrKky//ALmO2KgLff/oIshtnMFgbjwxlBskNS5isi8R4qUWVHq+MwQvPVCCtcR7DtXEWhfoq3aaLOkfxz4/8BLKqQ1Mk/P1HbochWBRtAYBgGKjr6kfrJoFUa44b9zx0AnU9vWjIK9g2evl2o2h5GF/s+y4UU8cdk7/DZyofxGcqH0T1yHl0RLovy4iI9S7DgAAJJnQIiFtZDrqvHVV9Hjz3r0/4acwS7vnb+3c8IHXgwIEDBw4cOLgRuOmC0Yr2YSiaZuksAeiARVPNf/OkR7mR6C9LwvEv3I3aMx40lroxXBy/6X6D5QloOJS36XeARdldXtch2l4ixbSsovquQYiqiVgFGPtB/A0LSI2aUPh+lALxDS/+qzwLaaeZmy5UA5mnZwPB6NiRGBguEaZqQpdFXKxJvnzl14m4lmWkNC5gqZ6cyy2AprEJC/GxFDDkLeZ4ZQxGKuM2Kc2j8OwYytqG0VGRiXMlzPinvG0YsqpDNkxA1RG/vIy/ueOD+MfvPw/BNKAqChqKg78Qas1xozVrb78wKlsmzrqGhvLZfnzffRt6tMu/gCgb9yB1cRaaJME0dGjS1dHo6y/0cblG6zr7dy4YNbGvTBIcOHDgwIGDPY99Njbv6WB0UQ3Fb4YPIDqMRTNNZdm4Sz4FaDp0UcTzb6/AC7eVo6swHsYio7Cs+fjJu0BkcSupbLk+2sPTJym1SCA0kcUki7ZSMuVBXd8FtMVbbpoAIKikDkKrdc3wjluCj5ELFg8yemn4GKH52mi6tDNS2oJymZWsECLRNEkCZjWXjwpF4kj4X52HA9sZFbPor7Am3HYnPkot8RkSDpyZQHHLGFLfNoeZKkaleeoSo3PeHsccZl9bPohbX70IwWe57poALn0vDIuvhKHy9wawUGWdZ4XQh+Z1no5CaUfhJEuyx8eChEWJpxndGnEhsN24yoKg02uXgFIFKI2CqktYEl0WJUQ1YSgCjGNAXsgk5vRwzFZF4OVvF6O+xYPVoy58sPoMAKCT/G5hCFshziG/+YXxd3Ht+VhCQ2B7xcWOJ1606MVRLV6U3j0KwWeZD7387WJ0FLGA5NZI5q06YnPTHfWyz0kyo0T9ZqaQ2+9A5GRgmzoF+nTrGh/uGsEX//J5KKoOVZHw+S9/CK/kHAQA/PbQQdwpnw6soDdUWJTciwcSUXfGg1OFeWjJzgG8gCCzfhbRx/qjz+ZppRImFnWq1MLYPRE6zdOGY8fYjSCoxE13hXdLnKlh1yi+YSKwbRJavTzP33utiQW4Y/JVK6AUZXREuyHoBlazogL7LKfwHJ+SSx48/oJlzqSLIv7f8nq8UFqLpsPZoFwngVAV5y6wFz7R+XPoOJYK9SUJgq7DkEVI7wTeU9CJr2L7IQAQbkJ9iYMbC0EAXIoOr41HRul4pky/I5RdW38zuELBf5MyQqU1yvvb3FnXXh9t6uUNZjdvhGkzYOVcN+WtUQAp1ZfSgU2J31EnY/OKwp5RU6vsIbmi8ankYgktllJhQyT+mbniYvXRsXTR4OmhFJdU9sybWGNMp0tenj6rkYsUrbCxmV5j6oQL8GNUqkQkCwZzx6fOugCgEDpwGOFpiyH8taP0YM1u4+yHS+TPD62POubapTaUKhzMfWVO5echy4RyK5P66Pnx2ai9PoP1jTlCxZ1d5edFK15Wt+5jZUQfobev8fcHpenKWzSIlYJQ1+2ZKaQghsWCzu9Is05QCj+ltGthNldqMiegztoUdjddyn7WyGWx36P0s6AQV+oQ4qZL+vZOYb+NzXs6GN0MncUZeOif7kLNmQE0leaguSBn1367ZMqDf/vVYxZ9TpTxFzWfCASkewGVHg/q+nrRHlWAjmt07qzqGcA7X+lCS3kWOosvnzf1wJkJ/NWf/idk1YD5LeB3zxZwAWkweGoSLMt71YAhCTj801FLs/NvwMV/SML4HRuNg3YLM1UR+N2zBUhsWMJMfQRmbcczVRmFuVuuLsfntSDm1CpEf8AO1UBKwwJQtHO/l90+jbymKfTVJAF+06+q9iEoqg7JMGGqOsrahgPBaEdRJncftmZYgXLrwWw05+ztFc+tojsqG58rvh/lC/1oi89HV8yVVydrhpk5EwwDYzFxaM90gxfDXR4dhZk4/oW78bHBZvRUJ8NTnnjlQteDrTfNgQMHDhw4cLAb2Edj800XjALWZK2j0K/X0i6/73aiaqIXsq4FaHsVM317Jhit9HjwnSes/KqqJOP4h05edUBa1TOA5770tJWjVZHwf33pY+gtC05FLW4Zg6wakAwThgorgNtCMDpcEY9vP3kLCpvHETW2itIfjUA0AVMDDvztFFYOhQClV9X0bcVMVQRmqiIg7YJ6PKZlFRWvDWLsSCwmKtkb5/n6MBguAYJqaVkn6jbqbreC5NYFpJ2ex9iRGGCj/BeAFYh+4virkFQduiKh7UsZ6CzOQEt5FlRFgunXhXZU8BpJ7j7cA+bCRUtDKFsaQEdkDs6LKVcusAV0R2WjOyobhmtrxuNNmQVQJRnQrfvwdPa1uVx3FGYi4R27lMvMgQMHDhw4cODgBuGmDEZvFFpS8qH5J5qaKKMtPrhmcrdR10fyq+oaaod7rjoYre/ut+rw6wCr2ocuG4x2VaVBU6wVTlMBLtVtvmIY07KC+FMrmKkPB/xeTcMV8VisDkNa2xxKfjwKU7OMb2BYq4I3MhjdToQ0+xD6hg95ByfRZzuX69pZwZ/v8z+/WQbUW98tVoXizHPp0F+TMFEXjanKqA3a2yvB3TGF95w8C9FnwHCJeOMr+bhQkrphv7ymKUiqDskAoFnXvbPYCkg//+UPBTSj3YfTgKWNv7MXULQ4hEcvfsfKASpIeCT7DpwLu/zK/k6gPd2N4x8+iZrhHjTkFfhXRRkq+z2ov9iLhtyCQG7kG439RAVy4MCBAwcObgbsp7F5TwejaaHz+Kuil/CjyVru7z6NNXvNxwQaosTWtLV0ntMtTDFtROgM0WMk8SsekaOMBE855/KKgXMR2fizYw+herwXbQl56IyzVkV1ksJFJPpRe3oJeYFtR7/KciuuVjFao26zJpeI5XyMh7TNlpKmM6QAmigDhrUic+pAAbQIIIzJAiHYNCGrmbx9/KmD+daqDjSosoyXMw/zeh4AzdNseW02JRx9/5CAmrMDuFibjK6EdGDA+u5AtGXXntcxhcpPDkNWDeQoIrqeysRwhaWRG1yLx2BRPITPm/jIo80QDCso+13FASxdRr9CdS5bWb1MaF2C+bqAwdp4jFTEQSWajJ/MVge2qbbV/jtJ5OJRjSgA/I9wFiV+eZZdS+2sB2+95yJEn4nPuX6Bl79dbAWVAIbUBMS9PhzICWqqBmIbVrBYx457sTIUc2XWSrMCHYkKW35sWmEvQnSbFubdce0AgPzOKQh+MyZTNVHTPoCpKrbCuq5naa/IxG3KeWsFVJbQWJoDVbf6xmu5+Xgt128OtgIsLbNzYg4zzYpBLNBDp3gRhj0lwjpi+ngOirRG7stkadO/66H8sZqytR81GxJMDW/znsPZ9IOB/aL72SqjQVIz0VRM63rvosVBlC/0ozWpAF0x1j2uRvt1tDMelA/2oSU5H2cT3Viz+XvpIUBjnBuNh91QI5kDgRmlWcyDf3sKiqpBVSQ88D/vQXthFg4lTbHj1tjzrGN2Y+qYbcc+M0lwsLMwicYKArtXuWFE4McUmvYFtvEmGKg+TSBl7MVpiiia9sEmE+S+44aUzWWvAHidKE1PQyV/GzwgSLslWmaVf67pAvFmIAc1YRD9fAivo1wOZ8+yBZVVHm3Tt1EdYxTxCrjcWErLTKwy/ei8jx+nTdLWZZW1J8rF2mBPd0K1lxK5sOv+CQAQGhJEgAheU2vYxJt6kFQqsz42dkm2ByD1yVCpgNAmLhTJ+VLAjmGBCBJpChkAmCPnSxbJXJGknaHbAH9OZ1ZYu1e9vOmjukp+a5loRolOlGpEAUAmJu/Uh8Q+ZtNLxt17QcZ26ztyT2nB+xb1XBFIqjXQ8gZfnqZ60WnKFtJOmsrFKkO+I3JbNZqvW49gByWT55kis7/bU97tCPbZ2Lyng9HLobR7GFUdg3j14AG0HQrCP9wBdCa4cS5y76RZKLnkQdVELxryDuDE+06ierQHrxdtXJHZCloKcvDxzx5H/YVenCrKQ0tBDnIxedky7YVZaC/MQmLk5ktmB5vHA1ReaAZymqYDweg6Tn04H2MFMahoGwoEjDHYHopiQusSbrvnPET/6uP3nz6ya+lYkhuWIPqDzXXd53owCgBjR6L97rxWvs/B2ngkbiPfdbo+ArmKBGgGdFnE+erNqav9ZUn43P/5EMrbhtFekYkz+cFTllwJVX0e3NrWj9PuArRlua+5nqvFvBQeeHYLAN75/7P33mFyXHW68Fuhe3LOuScoTM6SbMx6vcYs4SM+Fy9OgC1sSyZdTFjr273f3btfsFnuNQtLMAbW5npN2uWyy7ewYDBgwPZImpxH6pnpSZqcY3el+0f19Pmdmm7NSJqRRp56n0eParpOnTpVdapO/eq8v/edasavUqrQE3Nl92rx8jD+rvs5OHQN942+gs9WnwwEpKVzHvz9H5+FrKtQJRmfuONRvFq4c3bEid4BOBSmknusy4O2o9fv2RUcN6dBtg0bNmzYsPHGxcEam2/KYLS8ZxRfO/N9yIqGh+RX8eDffOS6BqT7BWUzHvzDb74JWVPxYNevcepdp/FczVuwkXT1dTYX5aH5yO4F2xdq000qr6pDlUUM1QVv3FBlMtbrd9/eJfXsciAgNBQduefnrlswOnU8GsUBZd6teZ/T1bH4j+dLEdPgDQThuxmMAsCr7zRnNV9/Z+EWmjBFT2kmekr9M3FX6edcM+DBi195NpC3/OCHT19TQFox4UHdJTfaYgvQmRK6nuKlYTzm+TlEEO9hQ0fl0uAVB6OVS4Nw6Lydy2YwWj0zAFlXIcMAdBU1U/14FTsPRhuOFkBxyIBq+oeeKw19TNcTB8lY24YNGzZs2LgZcJDG5n0djK7pTrSs5SFa5mkmt/W4AyqfUFR86l9exrfuuw2vZDLrCqeTp7AslbDp/4ghRmewyk8rUaxc+AKxivCy6Xp5jZ+i30hmlJioIRZMSEsWaxcv2c7B2uBYVlC8ZFIDO8Jz0BPNAmsljtE6DJFRL3RJQM24Kai0+XJcN+JGZ2IewuYYPWHNbw1ZNezBLb9yozGnCG3+XFJnN2sDpTesFLF2Dk/zs5jxsYzLQWkLouWuaZwyj6ExPQeTT0cH8g47s7MAk8HL0VQAYCqMzRoeimIzsrM+Phc1L2ImsDyywdr3/oSmwPJvV0zp2eWqcBQ7xwPeoC2VuYiUWH/KdDIfHKt8fIGTUXN/vsr6VvuKOXOY3z6Nw02TOHeHD4s1Ju8j07EQKNdQcggj305G3vlZNFblAWvA4S9P4kJdGi5u5m4WAhGH/Z3QCyh66OBmhXDBKOWoZY6fyRyNS0BB+zQef+wlSIoO1SHhZ28px6yXF5caW2beKotrrJ9RcsvGooWGpbG1YRtkeVbCm5sGubzlExfc6ExwgbgC8PQYCzXNucL6XeUlD57592+Ys5CChM+XPoiemFz44vgPFvL0MqrneuHQVYhg3sOqIKFTzoK0YkbWC+XM7ia+g92XGzmsPtFnoMk4jPtGX4Hhn/1szC6CL06GN17E2fwifLjPpMKroozGzCLOxgEANh2JqkY8qJ10o6Go0MwNXZfQnFWA+z71CE5cGMDgnQkYK0lEMlYxusSuQ2w4+xJwPNnkvG8qHTcB26uD2bBxHSHKVms08jexdqHPDV3h73tBojYtdAVPs6NDDHWHoLRBWQn99kbtXCRLuVCUQpH8bohW2h+xgSPUSo0+oizNkcgxUSsNw/omRlIvDEKzVDaIVVu0xQJEZe8Hy8QubMlCcY12smc6tYORiV2KqvOU1A2N7WtyNQahsOplB08ndbxhbPvUcN6SRCLc5Xhi1RZD3g8WdP4L6YbB9kOt3xQLlTZaYtt5ycN6p5YtdN2swh+3Qq75EvENodRcmnYBACvEcpC+M9F0qHWF34aexw0vW6es8eWEJXaO5RXWNkpJlywfmglLG47V0DR2Cj1U1GDp61z2ELl1dKcljYdQcEPR5a10YErHpzRdeu9pFmt2audCy+kRfOXOONYHY6PZTZoaxdh/Cc7LeCvauCrs62AUMF/Eal/3oKc2A/3l5sxOe1W2qfLpUyEZwLGWQVR3juD+vzqJlr0yh99DFC8N4++6noNDV6EIEp44/CEuIA2FllReUKkpozBouaphD55/3vQ+VCQZD3/gdCAgvR7oLctAb5k/Kt6hVHV26xxcjbPw1CVhtuTqrVQ2lXvTzi4FbDJyMHfV9W0iv30anz79a0iKBnxHwPl/yg0EpBRjVQkYq0qA87yCT516OaBY+/TX34qBPZyhPdw0ERAlMhQN5a1j6K/YHYXZUDiXyyvJns+5OiVZAKgdd5uzkIYBGBoqF0PPcrZH5EIRJBiGBl0Q8VJ8BV5OqEBP1JXTjbsS8vD4sYdRNTeApvRCdCW5Aus6Ul147G2nUDfWj6b0wpCztVUjHjz3gv9+k2Xcf/oUmvwesc0FLjQXuJB7aDLotlZQpeNDwOHtt7gKHCAqkA0bNmzYsHFT4ACNzfs6GHWuqf4XMR2qQ8QXvvZ29JenorcsA2eefh/ue/4sqhuHIRmAoWo43j24b4PR4rVRVCx60B6Rs0Xl06QG+i1jDA0Vy54dBaOdSS588vZHUT3Vj3OuInSkuoKWO+Yh3oeaOYN6rcFoec8oatuH0VSeh/biq88xDIaijkl8+BOvQfLp0JwivvKNiGui1o5WJaKjdHfbeLhpMhDs6YqBxIa1oMFooHzjJFGs1XG4aWJPg9ELtenQHBIMRYfmkNBRtXNl2aq+ERzrGsS50nw0pB/i1tX0D+E5wwq7AAAgAElEQVREXz8ajhSiK4b/+NGW7cJD957G8UE3zpMZ+KtBUwYT5FIFCW1xob1LeyOycCb7g6jwjqI9Mhc9kdmAJIQsHwylc0OomexHa6IpTNaVkIejq8O4v+83aEkuQHO8OWPdkepCV+Llj+vYELnfVBUn3P2BYPRKwSkdb5FQ2QUYW83KbdiwYcOGDRs3EAdsbN7XwWjUuheyopkCMKqO0uZLGK5MwjtTO4A/AzwJySh/8BIM/2xTR10mYmLMaXWnxE+9z4yZDLfqQQ+OD7jRcKgQLfkuRA/wlKGNRKK+FsdOD53yT7jIbxM+w6b1DYlQaWNMGkXx8jCe9HwPDt1vO1FwP3odjDbYuZEIRZAAQwMgYMknQVwwZc4c8PsnLnvQEZEbmO1RkszApx+Z6E/JBFaBuEGzHd4EojCsSOiI4JV2Xz9aBF8sT4MQCZNH8LLj09d5mubMigM1/UP4xn//vpkbKMu4/7FTGHs7zyBcWCTUGUIfiiD06cL4GW6bCMlcV9EyBsmvAgtFh+v8LHrKgiuLHomcCCz/cO5YYPmOuB4AQGLzClLPrmCwPhlTfh/PwY3gQWDrBu8Z+8/rbCbxvfG80q5+mwD9OyIERYciS/jB0ePon07Fgo9xQd431IK887MYqk9Cwp+uwviOAF0xYDgA320yiqPGAQBzKjt3swp/Hj2rLMc2K5JRgKkaoFXxuHcxFb15qRj6YgKKG8fRWJ6H9txsbEzytB5Ks/b5KT/VF4fx3FPfDVzb+z79CJoLzfNS0z+EF59+NrDu5AdPoy3LBYDRf7oSXeiLMH9z+hnrkVOUb8MWE9oWufZMH4sPLE/8MRpncu5BxdowmjMOoyfWDOYiW4a4bZBgUlx7o3PQayQCKiAsrcCI5WfTw+fYsepR7GYWDDMQ/dJrTJjo43/2KADgy5tiRaKMT0qPBmZJ11LZ/eFN4kcLeUXE64eL8LFXJAiaCk2UcC63CIm5C1y5GKJw+e7MdgSDYkiYOh4D/VnTPgnaHmnrHaCvrzZ2F5KFpovw4Pw+g1AaDcsm2jp5DbHQLCkEQvUVaeZOKPVbAGIIyi2lAwKA6COKniFeAK3b0KwOjuppUPqupQ7yN6cIapFLIOKzMIgiLx1LdYV/7ntXictAFLsOK05+DHc4iPJrNKMbxoYxziZVwgUAr0LUfTXWBtoeAPASGqmuEoVYss1KHM+fjJEYFTKFVCeC7MfC0wwX2AsLpeKu6Xy7Y0S2jtJvkwhV1KpGT69lJHkxGlL5uic3mAYEpTFz58dCd/YSJwhVY/sVyMvYho8fp3VSTvUSB4RliyrxEitHhPc5iqtoobuSjCVOtd6xFnpMUCJpvyVK1pZbl1O+J/R0yWu9KYK7UfDq15b7lTwLuNQf8vpk6Qq84nUEaUM4356IcHbNkyOZ3HB+9GxgOSf82tl1O8IBGpv3dTCqREnQF7VAvp9VDXSiOg7/9cvvRmnLGLqqs9CeffnZr+pBD/7p68+wF+1PPIqLVyBAcrXgRFEMDRWrQ+gNZ8Fob3gmnkm4HR+b+y1E6Dg1+1sMhaWgNzwTxSsjeOrC/2T+iQX3BwLSksVhVC4MoC2+AN1xoWdeOtJcOPWO0yhfc+Nc/rWrnJ7oG+BzA939+DEqrqlOir7aNOhO8wVcd4RWgd0Oic0rAWuVYucEfv58WSAgvVZMVsfhp89VIuvcAl4qLQ1QyDdxpHMC9326ITC7+/oLBXjthUIkn13BzPFojJfGh6h599BVkonXC66sf5/oGWBes6qKExf6A8HoiQsWL9thdyAY3UTFJQ+Oe9xoyihCe7pr6w52gIpJD26faUd7ZC5+mHwr1NjQM867garZ/i3CRAD436b7Ocru9jAs/18dxqoS8P3vHEPu+Tlc/PsLF66psn0GQRASAfwQgAuAB8DdhmHMW8rcAeBL5KejAD5oGMa/CoLwPIDbAWx+1fiIYRite9xsGzZs2LBh4w2LGzE27+tgVI2U8KvvHsXaK+Hoq00LqgbaV5aOvk0xmIUtqzmccPMv0ycu9uNi0d4Ho22x+VBECYauQRUktEflAZYvVHH6BkQYkAAzYF0fQW94JiqWPQH/xM1AticqGyWLw/hi63fMPFNRxueqTm4bkL6+S1YbDUcKoMgsN7ChKHiu6tVioCIVv/ruUaSfXcLE8VgMFIRWgb0crNYqGecWdy0YBcyAdLI6Dv2LW9tXRmZ3DUVH8tkVXDydhvka/6c775ZN9gUaiv3X1v/BpuEwu7YNhwu5634+l88Jrbjkwbf+l5krqYoyTv0fp7cEpOVTphVRc1ohRpEAKyomTeEih2bmT5/JuxcdSXuTKrmJ1qRCkzlgmO1uTjWPOUATFmU0p+y8jx8fdEPSdfNe1nUcH3CjH9VX3b7NvOPlv7+wun3pq8CN+/j6BICXDcN4ShCEJ/x//yUtYBjGbwFUAYEB0g3gJVLkc4Zh/Mt1aq8NGzZs2LBxfXCAxuZ9HYwCwEx1DF4pOLp9wR2goYh/mW44VLhnF7t4ZQQVqx60xeajJyYXny95ENUzbrRH5aEnKhvCEv9e2R6eHRBhUQUJ7RE5OLpxCam+RagQYUBngSyAyoUBlmeqq6hcGLhsMLqbaC7Mw72ffRi3tntMtdB8F1Ixvav7mKmOwcymJ+fi5cuGgtVaZfwYUy3NaZtFfuMMZk5EY7xq92cpO6uzoBEP0ZnjVy/CdD3RcigX9/+fD+FEzyAaivPRnMmoy82Febjv04+YPrSHC9Enurht60dJrqSuonbczQWj5dMefO2lZyBrJh32iYL70RPF50bXXjKFiwIfX9aG0bFHuj2b6ErMw6dvfQTlywNoTmXCRP/5tkdQPTOAluSCK5oVPZtfxAX0ZwuuTsgpq3UeuefnArY/ewXhxlGB3gPgT/3L3wXwO1gGPAv+E4D/MAzDljK0YcOGDRtvaByksXlfB6ORgg8VYWNYjOZpev0bbCYqLZzZb4xJLNiYmuJnwIQkL5qTMnDfmZM43ulBw5ECNBfmIGqQ3yel+EddItvTXA8Hn2OgRDOOvxApo2RhCF/o+y5Txz30AHqic9CXmREop2Ulke1ltCANjy8kov5iJ9ojzBf0Jy/9CA5DgwYRv4gsxa+z6gMv721xBVBE2bSgEGW8fuQIllP8pHiSSuJcYQ0PW+DbTaXlvSQeC58i8vEx1twas8u0JxWi9c83Z5UNTPUlc+WENJKrQXITl4iFyISTl0pf8rJ1QyvEisPJ65FfQHpgedbHkgRWSJ5Ly8xdQBxQ9uQYatqHMXVrLNyuNGAeKO66hIcee9VUtv2mhGef/RMMVyZh3ZK4Q+XaPQp/fBS5ESx/YMFnCgX1laXja8/cgaLGKazGh0H+rY6+JTa7v6oG91S1WuRQafiRVXZOuBwVkodS3jOK6rYRNFbkoqM4GwsecmFlvm4fsVaRfKzTNGUUMmVmlc9L6hML0XfUXEfztkTVFB1SqLpzZhEgAI5Vsw/WD5vBqgQD0BTcO/tHfDf+LeiONwPeeLcXfXouVME/SylIaA/LhrTKcjh8R3ghJufF8cCylsVygUUvb9PDyccrrD9uxJs3fFN8ATpWXOZp2jDLvl5ZiNdhHivNP1lPI7ksG/z58aUqOJeahXsTHsabh/twrjQfHUeStni3roWz6/ryNPvYdkeyycTNbJ3HB0+eg+gzoDsF/O5/HsGe4cYNeGmGYWxewAkA2/HxPwjgactv/68gCP8XgJcBPGEYxj7lHLyxIAoGIhzKlt91B8khM4LrbVF7CgDQVTZGGU6SW6rx23OpfSFsI3Yq8bXFv4+zkaH2EqHzTAWaG0oaJxLbGMOS1ymQ/D9qCyVo1nJsmT576DNXd/LjuUZSQ7U1uh++3RskR258lVU+F8Wes6qPfzWkjwhDJ9dY49tg+GieINuI9hRV57dZ0KhOAscEDGDVkpBIrV2oNYxuyf+koDmjYcTnZ0Xlc2qnfOzdkeaMLioRXLnxFVZueSO4ZYsVGjl3PnKORYlto/r4Y9VJnijI+ZXX+GN1MOcREFc6ztLIakUm+Uj/5uySQh8DzTOlOaNWyxf6Lq0TQUFD4NtNnxkSOT4hRP7o1r+D561q/OWCFk6sdMg9IDos+cgOlm9N3z2THGwCqcC5u5MvIXGAxuZ9HYzuBVoO5aIpN7Qy526gam6AV8ddGeLUcYtXRlA+O2rOmsay2czu+DwMJpozaHfPNTB6LnRMyzHcLFJ3XC4+V3kykDPaEcJmYjtUTHjwzgvnoTmBn5bXb8kBvNnRWZKFzpIsFMUxsaQjTVTZVkNh4zSGK5NCV3KVGKo0A9jHHv1dwNLlv3/9rUHp5ruB8p5RfO3M9yErGlRZwqkn78XvwhK333CX0J7hwqPvOY36ETeaMrfmjDanFPjp6iokALVzblQsePCZ2o8GAtLuuDx8tvokaoe60R6Ru0V5+mZBc1EeeupCP79Lui6hqm0UrZXZGKjc2h9yzs8RirmB1LPLWyvZDRjYsd1SCCQLgtBI/n7WMIxnN/8QBOHXAPl6xPBXXDMMwxCE0G9xgiBkACgH8Evy8xmYA6UTwLMwv9z+7RUfgQ0bNmzYsLGfcMDG5gMXjF4PtCaSWUtBQns0ozoWr4zgqYsvmIJEoozPlz6I9uiteavtETk8bde59aW8Oy73mqi5FRMefPOnX4dTN78Eva/jHB6892NozndddZ03A/pq06A5JEDVocki+uv2zmKlqHGKs3Q50jS5Z8FoTfswZEXziw9pqGsfxu/qd09Yaidoz3CF9N/sSnLh8yUP4oGR36BmcSBAMa+aHwgEo4AZkPYnxgWt42ZAjXsIJ/oG0FKXjdYjWy2aqvpG8D/+27/AoWhQHBLOPP0+5sPrx0h9oqm2qRjQHQKmjoc2mr/BmDEMoy7USsMw3hJqnSAIk4IgZBiGMe4f0KYus5+7AfzEMIzAd37y5dYrCMJzAD57hW23YcOGDRs23oi4qcbmfR2MKoaECS0GeU7eAqR7jdl8RMuMRpETyxSMkiL5nMz5dUb1neplwYeVWkBhkHURc+wThTfWQh8SGDcgfF5Bb0wuPltzErWjfWiPykNveBYEVcdGZgxKlsfZjKeuonJ5EL1zJNBUzOvZK6fiTNr7US7MoD06Dz1RORB0Qi1Q+E8miY1MdlqPZbQTnUiYR0zx9Ig3D/TCoasBkoOsafjUL3+Bb1X9eSCgWE/kj5W6ojjH2bq1dP7DCaVYLS6xc69tsPaM6XyuZpiT0SOG1tiM3koMfy07V9nHnIp0RtMMRo8p6zZpuu661MAL//+fXYHBp5JQ0z6Mvrp09ORlAAvAh7Ne57b95VxZYPnVRea3WRE9ElhOb1lE1qvzuFCXhsGKFNyfwer4rx3vwkRWFO6Ue+BQzdnKnxeVomMmE5LIrl9mNOPUNPW5uDZQlkZEPKOMbKwwWlCpawwAMFCfAvX7EqBoUGQZv8kt5uho0Rn87JqvjZ1/NZIVTGhlHX+pkD+nEcSmJWKWWMPEEssBi8R72Dy7rheQgu8lvQnlS0OBDy1dcjac8xtYy2Z0LUcs49hoEaw9VvqQms/6gjzNjs8I429s5xRLZVCSWd2blFwAUMP5vk4cdzjrBYlQc5VCwnUHUNs4jhe/+i04VBXqv0t47Ml70FGcjVUvq+B49wAciur/QKHiROcgpmrN4HvTmsBdkYafP1+OjHOLGD8Wh6nK3RPeohBg3Mi8lJ8C+DCAp/z//9tlyt4D82trAGSwFAC8F0DnXjXUBg+HqCE9aplLFwB4Cia1nPKScqLIj10r1LqCUHZ1S4oApatSah+lHuq+0NTey431ItlMohYwSugxl9teJsdNlx18e6j9sWOVlQtb4Oumz20lkqRTeEPbwVBbC/qM0sIs55HQOw2ZPay9JHXAmtJhyJQySejJXv6dQqTXjFBPdUJPnVrj9RPa19gHuyyZ0XRFMi00ofEfJ4d8LG1mRmEf6sY3+HKLYexZHykRyq3Kfp/Y4J+tvhAdZcnHW9IsrLI6vGvs3FEasyDx55HScWn/VimlXbVQjUk6jUjOt8wPPdzfjlW2H+cyO49KlIUiSw6VUm4ly33EWSRxy6yMLyI0R56jB1uKURqxGkGPL7RFEgU9dfS+sV5GjbNzYe8uzjDejspBbCHDJda4GGIhFCnufTbIQRub93UwejOjOz4Pbt/WPENu1lSU0RoXmjLcG5aJ7oTdEW8Khtb4fKiCBIdh3nwigLqpi6h8eRAfv/PUlhmuinEPqnrcOOcqQmuuy1rdvkNZ9xj+4YkfmLTV70n4q6ffGwhIN+m71FvtSpHesoj3PNQK0adDc0j48jN3Am/my7QdzcFD/+1DuK2vH83+PM69Qm9ZBv7q6fci7bfLaCguQHNRHoSl7be73uiJzMaZ/HtR7h1De4yLo7Df7DhxkSh2qxpq24e3XPNzpS5zZl7RYEDAksV3bxNT1bG7qv4cEjduwHsKwI8EQTgJYAjmF1YIglAH4JRhGB/1/+0CkAPgFcv2LwqCkALz9aYVwKnr02wbNmzYsGFjj3GAxmY7GL3O6E7Iw+P1D6N28iJa48ycUXFD237DPUBPbC4+U/lR3DXZgsKNcRydHw3M2NZM9nPBaMW4B8/+q99yQ5LxkY+cRnecK1TV+wIcbVXRUN46toUOeS3IOr8QsG+BquNw4yR8b95q2t52NAf9VVfnlXql6C3LwI+jQzIz9g16IrPRnbj3tkrXGw2HmGK3KktoqthKo287moOffb4c736yDaJu4ENfasBoYSLc5denj2zBDRrwDMOYBXBnkN8bAXyU/O0BsCVPwTCMP9vL9tmwYcOGDRs3DAdobN7XwajXkOHxpeBQ2AT3e4LMKHfNC2xWhVJ2fRofFEQ52bR6RslkYHnsoiVfkNBT5FV2etZTGH0g6hLfQeQNNv2/lMu4M84EXrrMsWoGnV1JeejXzLxBaV2FQWg9RjyfGyZoLFDVewcCy1KxxfeQ8H/WMhl9JGKCzfwZYfw58UU50BZXiLacQhQvDuHLrz4LI6CEWghdAsLnzWO9xe02Z3xgerTe0uvGSCZ70dYlnv6xLjOqsEBoGDLhVPgsFBYfUfYTlti5n0jgu6kgsu1GV4iC8gI7d0Vp07hQlwb1e5u0VQm/LCjBwGwSRLJ9djTzjfm653ZuP7enugPL8yqjGv/4kukZ2ZOfhmqHGfCqkoSfFFSiuZvlPno3iOKfyB9rdDjrj80DrA/LRNEQADSirKco9Pqx+nov8TnoEW62X0qP0cZ5WnQ4Ud/zEeq5l7iIJHRzm5h5jH4s57DrEjPK+qm8xn9ckYkarprAzqO0ztNjIj2MZutNZ5yziB5GxVYzeaEpSuEtDptH5dIg2mLz0ZnGMw40ooC9nMdOCqW6iXxzED3KzvEsSb1V49jxiZO8EmPbsQw88NcP4Xj3IFqqs9FWmAP4gIeO8BRw5SUZ0AFRB2RVw5s73RBOCFgjDVq2ygHuBa5dJMHGAUS4pOBIzCTWNX6Mo4rkdN2Cj1AaFf55LkqEAkrpoVaqKOHSUmoeJ9proQByirWcWqiVUkj2RVdR/q5g2YYq7RJ6sUhfIEWLMippQ9gCe45Y1UINyuel1RGKrM9CuaTHR5V1Beu3bm4zQhWNpNRea3sIVZjQJyV+uOKuBR0r6Iq5FYs7wgp7B/ujxGy8qPrtHM2ZAE/HpX1r3ss/M6c2GCU4UmYvIpROPrvBt0cj66gitPWdcmOFDB4k/Ygq0FsVpTUHUY6m7z9UmdnyPOaouWsC+Z0vR1VuqUquvE72aWEAbySwdoukD1qp3SGEsTnKrlUROtTwRe9js61smfZVNYw11rHO90ddDt4genyG5Z3LCGPnQQ4jyspOXhU82hmcgiuSCxMubFUS33UcsLF5XwejNq4fupJc+NSbTF/F83lFW9R5m9IKoUoyoJszo43ZRfv+Rukty8CZp9+HitZRvFRQElRM5lrQXZqJz3zxP6G4aSJgpQJ1++1s7D5KFofwd13PmXZKoozPhp3kRJGuJ1oO56LlcC7CI9gom9G6gJxzcxg5lojxqnj01KRDdYiAqkN3CPDU7b6asw0bNmzYsGHDxn7HNQWjgiB8AMDfACgGcMw/hRusnAfAMgANgHo5haeDiOLlYVQuedAW60KfvHs00u1QOjeEqtl+tCYVoiPVha4k858vZqtXV2eKCx97yymUr/ajMbsIbZkuxIzu82gUZkDaW5YBz+zeWJx0l2aioWjndNPK3hHUdQ6hqyYTnSU3p23JfkTl/CAcugIJgKErqJwfvCHBaPWFYRzvHsTZknz0VJoz1pW9I7j7bxoh+XRoThE/+sc6/KK8HE999e0obp7A0q3hGK26fhY8VtxAkQQbewR7bLZhw4aNmxsHaWy+1pnRTgDvB/DNHZS9wzCMme2LMYSLCo6GjeOVFV7EJ8PJVHPrEoygv/esZnLbuFeYmNDwDHvxSy2Y48pNjTCO4koRm8p3zDE6w0YCTxGQidqZY40aZvPH41xkU/u+BJNHU7IwhC90P29avQgSzmR/kPNWpGp32q1M3VW6xOilAODLZhTMKEJ31KIYlSRsgv1evDqKJz3fC8wkPX7sYXQlmC/vEYR2vFjAeE5Ncfk47zDpj4IOLLoIfcSiAxQ1TA3B2e8reeyPMA8v3CIT0dzVfEKFnOXL6YS+NbrBZpSkCDYt2TfG599xxtEKa3ebwvpJStwK3QT/PlwaWKZ0m6VhXrFPTmEydjpRwtMthuCVvSP41n95wVTW/aHpA9pRnM2p7+kWJT3nIONbUdNmyoqWVy3KdxbqFABUjXpwrN+N87lFAS9ZahBNqecaUZWVLGyU+IuMIr9UwHg4EukzVkqPN4kdQ+Qgu0c5KhoAiMTwmqjmGjGMRmU1enfOmJ1m1StBhMlskQDMxUTDF8seb1QpN2qc9ZOFIlKGZ2thOZdt4yBCUFoG2768ZCiwfKRzAn975t9M2rZDwsefvAedJVm4rbc/4BlqKAYcf9CRUbKI1fpwNNa78IGE86iEqdB80XcD8kYP0IB3gLCnY7NTUJEbNosVjaepU5XSWSLvOucliupWriAHykm9TL+kdFDyFmO9h6marUaWqYo2ABjk2cOpipKxQvTyH18prdEgFF5OwdfyvBJ9rA7JF/pjriaFoE+Cjqv8MXAqt4TuaE0/EMjf1DGAsqJFJTRNk4iKckqoAD+m6HS/VFl5hR/PBxaJcr5K0pxEkqJkGVQmV1lKztIa64N8KgsgEQo4XaZQ1a06D0HL+SzlVtjJkzbI2EXPnZXZTcY83Rm8f1vfG6kyMslQ42i5AH9dRYUo3tI+Z1V3JuXUsOD9xwpKs6X0cloXwNO+6T2qW25/eo/S/kTPg9fSbm57Z/Blw3K5KMteIurQsqVfUFeGKJmdZP2yz609wgEam68pGDUMowcABGsuhY0do2p+gFm9GBoq1oe5YHQTxWujKB2eQFtc/jV5i26iYnUIDl1lXo9zA4Fg1MbuoubiEP7EcwEZ04twqLwP6F6q626iatSDf3yBiU+d/ODpQED6RkGctg4dAiQY0CAgTlnbfqNdRlnLGCeYVdM+jM6SLDRX5AYouaosoqcmmM/0jYJxoAa8gwJ7bLZhw4aNmxkHa2y+XjmjBoCXBEEwAHzTMIxnQxUUBOERAI8AQFrmGz+ltTWhAIogBTwXF8UI3D33OtojctEbkYXitVHcudCBuxbaIUOHIsr4XMVDcCNu+8ovg/aoPN5iZh8rm1YPenDC3Y+GokK05Luuqa4a9xBOdA+i4XAhmgv3PviuuTiEF7/wHThUFZooQpNEADpUWUJjEKXVvUC9xw2HZtqNiKqCd3eef8MFo+1Redx91JIcvD+XTXtQP+pGS3IhuhJ39/p3VmdB9Vu2qA4Jzf7r21mSFaDk9tSkw12ehgwsblObDRvXBVc1NidmBrcjsmHDhg0bNq4U20Z7giD8GkCwT/l/ZRjG5YxQKW4zDGNMEIRUAL8SBKHXMIzfByvoHwyfBYAjFeFvmM8CpbMeHB/tQ1usaeeyie74PDxx6AFUrAxhSYrAqdFfBii7z6TciVPTL8NpqBBgMj4MXUXl4iDcctU1tacnKhufL30Qd021bKGS7CfUDHjwT19/Fg5VhSLLuP+xU2g6dHVBRI17CC9+8dtwKGZd9z3+CDrr9pYWeaJ30FQh1k1ptB+/tQbjKXForcq+LrOiAHDeVQRNlCBpZj96X8c5/LSsfouP7M2MnqhsPHHkw6hY9qA9xsx9tqJs2oOv/uabkP2K0f/5TY/gVRRurewq0VeWjo8/eQ9q2ofRXJHL5QS7y9NunHXL5WDgQH19fSPhRo7NeWUxdqexYcOGjb3CARubtw1GDcN4y7XuxDCMMf//U4Ig/ATAMQBBBzwK1ZAwocYhP2yK+31ND/5Vdo0Qxj2rvCBIXcJwYDk5jCUnqhYeOLWAmVhkhvPqPFtezeU55jTfhKQ5IHrMLFc248FXfu9/CZZkfPJPHkVnvCtQrmu1AF3JBfjg6CscZfdNXjcchhbIhdMAqIKELiEdRrjFNmaO5S0KCyw3VFxnOTy+dN42Rl2fx13TrZB1FXdNteKz1SfRHZcHXzzrFqmNy9w2K3ksDyh8hhH8F4os14T8WTHpQd2o2xQ+Ajtua54pTfMNv2QS/m9r9AdzhgGoKm5r6YcRq+JEzwAaigvQqx5ixxNJLDsiLDdxrIYT3YNwKP66NBW3eC6iuYAFtrOd0dwmGwUs+dIgeSCSykfvopudEzWbbSNPOHE+5QgU6TcAVCiyhH95Uw1aDudC7okCzpnlnDRvh+TNAsBGCjsO5yLJexxjZSzp0ZykeuQE0Bfmwk8P1+P9Pa9DAiBpOt7cehFdd7hYQXJIcQPsunLy/ABEH+vgic0k35rcA9SqCAA2Ukm+mNsTWJZcFnXjDXbvhREPBIOcIC2cTwRRs82c4yYAACAASURBVFmfbhFj0YIj5uFYrBKUKAlVFwcg6357IkNFiTKI1xUWjPpiuU24nBVfHPvDEc6Sc0aXeYZC9K1evHqr2SePCOy59Y7Edq7cgI/ZGXRssI8SEpGPXw3xnNt17H8dMhtBcCPHZlPP4RLGlATud2p/sKQG93aQRb7DyTLJDSQ5lpol557mgNFUVS5P0ZIvT3PfaK6jYbGGoDnl1K6C5pbKlm1oOZpHx9lOWF4muVxMuspajljFUN0ILqXWkgtK0yrpccuWjAV6vqhDhU6WDcvYbAR3FdsCaw7pJqg8gLrEv3bOSWzcXV4llnAkZ1iy9Jm1ZVbO8JH3rzWLlQ5Z9jnI9SIJslZLEu74aN6yZdyX1ohmBrW7obmclnNFx1M1guQZE5sXyWvZz0bw5S25wERngb6HChqxHVIsOcw0z5O0h94PwFZLmAAuk8tJLWq4e9TJl6O5xSLpGvT4rPmf1FGKPguoVZthsYaiAzrtT07JYkVn9dbxI4x0bilEmV3HARqb95wHKwhCFADRMIxl//JbAfztXuyL2ifg+kw6bUHlJQ/qRtxozClCP8wZ0Jqpfsga8+isme5HZ7Jry7ZtcflQRAmGrkEVJfwhqRTlS0MwDA26IOKl+Aq8nFCOnsidHdxR7yVUrE2iPTIXvUG2qZojL+e6aiqQxu0udbFi3INn/5XlKz78/tNoz3TtePtzeUVQJBnwb78QEYUXv/DtwEzpQ39xGm3ZO6tvLioKhihA1QFFltFQnL/9RteIljwXPvTwaRyf68bZ0ny0HL4+1FwrfnakHu+60Gj6yEoyWlJ3b0bweqNkYQhVcwNoTSwIOgMaCo0ZRVBF055IEyWkL8+jYtyD9oyd13ElKOyYQnHTOHpqM4Dbty9/o3CQFPtsMFzPsdmGDRs2bFwZDtLYfK3WLu8D8A8AUgD8TBCEVsMw/lwQhEwA3zYM4x0A0gD8xC+kIAP4nmEYv7jGdm9BTtsc7n6Y2Se0PJ2DvrLrZ5MCAJVjHnzrn1ng9ck/fRSdyS40p/o9Ov2BQHNK8ECgJyYXny99EJWLg2iLy0dPTC6GHckB6mGvY3vhk+LVUVQs9mFRDMep+d8zld7ce9COYq5sa2JB4OVcFWW0Jex+cFY3xvIVoamoH3VfUTDamuPCyQ+eRv2wqQRbP+xmtFdVxbFh946C0Zr+IfzXH/4UgqbDkET87QPvMAPD5W03vWa05LnQcnvK9gX3EO3pLnzy9kdRPdWPltRCdF5BELefULIwhKfPf5upQB9/eMfCWx1pLpx652m8o/883tN7Du/vbsC7+hrxyPtOBwLSqhEPjg26cS6/CM2FrqtuZ1HHJP7y4/8BWdGhOkT84YVDmK2O3n7DIEhrWUTWuQXEAFHbl74KHKAB76BgP43NNmzYsGHjKnCAxuZrVdP9CYCfBPn9EoB3+JcHAFReTf3hgoKjzoktvzdtsJfPNIcpBFLePArZp5vUAEXHey+04fyfMj+GgXUWDFCZeesU/YqXUeOoTLiaSDgD4fzcubpgcgNqJ/nAq9gYwB+OFuAPKMBH8k7jll43zmcXoT3ThbgBVt9GKuMZ9Ah56Illx9eVbFJ4AZ5uocTyXIfw/hkc3biEJy/9CA5Dgw4BInTTd9HQUCZMomv9ELfN4IiEZ2Juw20b/fhjQinc64lwri9D2mD0KsHLn5+41ml23Mns5TpmhOdx+OLMc9cZxQe8LSlFkNcRFM55tkypFw21eWioNc+JN8nAqddkwDCDkfM5RQE6CKX9+pL5a3Tbz/10XwCqYSBhYQO6V4LgZe22ssrChtg59qaz6+Vc4KkpCkmfirjItuFkxi/wcQSlvWzSeqpGPTg24Ma53KJAgO1YCU7NpW2N72f7L5v2oGytH405phcsLXfBkY0LWeYMuXNJhYNYEq2mUSoYsd+Z5XlXehiRs7/EaLq+glT2+wbPH5LXWR8y6krYiuklrpyWzqj19IuguMIubJU+CJmoQFdP9qM3wqT7rqcy7s5GAt8fN6XlW/JcqJpzQ9L1wH1aNe/G2QoXKiYG8fx3n+Hyk1tcLvM85BG7JEIfpLQyAIhJNdta1ToCh6JB1AFB1bD6ShheKTwSKHckkj3XWlbY/f7p1N8Eln+3VoiUliW8/cEuiD4dh4DDsGFjB9jrsVmCjnhxHcsS/9Cc09iYQG1IKDXXKfPPB6eDjLPEZkOT+BcxPZI8R4i3FaW+Cta0Ako9pfYSVgY8GS4kH6HcUmsYixUHtb0KRWO0jnWUHqw7yDGo/HglEAowCP1S4OiXlp0R6mIoiwyzEXQd+0OnXFoL+1KntEg+Q4gD3YweO6Wu6mH8yVIMNlD6JFI5pWZa6IoCocyKZFle4xtOzxGlenJUZet5JOfHuMwbMk2poZRSetzWulVKL6eMbdIvJEufoecu1H4A/jpz/ZEoalMqr1l38L6lWe8Prg6yn5254nDtsW5D+5ZOLYloH7acR4VknGmRpA9TKrbVOoe0QSAny2GJAWhMIJHOIB0kzuwNwBtGrnasPh66U4CgGDAcAkaPJWy/0S7jnIunlJ5zFQXWtea40B3r2tP9V6yPsJxTGDAgQoVhquXGb531POodx6mlP8BhaCibvISh8BT0RGajeHk4MDvbJ13b7HJnsguPve0Uaif60ZReiPZ01zXV1+Jy4aF7TuPYsBm07XSWlbs2soyGo/tLPdhqv/LQPTunH2+ibNqDr778DGTdT4n+wOlrPt/7Da1J1z6b35jD36dn88379PhAP5effMLdHwhGrxQXatPM/DdFh+4QcWEboSxX+zQONU4h7C4fvLXs5Szj3BJEnw7/u/zuS40Z4F52bdiwYcOGDRs3GAdsbH7DBKMT1XFofzELcQ3rWDwRgfHi+OvehtZcFx5+/2nUj5ozoK05ruu6//aInIC9hS6IOBdZgLnYBPwqrRo9sbmc+AwAVPjGeI/TNVPk6cnh78Gha1BECU8UPoCeqJxgu9sxOlJd6Eh1XVMdFG3ZrkCgttM88tYcFx780Gkc87jxx1vy0Fy0vzxVqf0KtJ3TjylqJvnc5LoR9xsuGO1KyMPjxx9G1ewAWpMKArOiV4K2TBce/sBp1I248VpxEVpzXQCAswWFUGQZ8M+MNhRdfV7tYEUKfv58GTLOLWL8WBwGD4Wmabvap/GJU7+BpGgQvgOM/yAxEJCOH4sNBLXQLicdcrU4WF5mNmzYsGHDxv7HwRqb93Uw6jVkeNQktK3xoi+J8kpgWSE8irB6HRv1YQiDDs9MErfNIymvBJa/rDMRwtKoS1y5BpHNmPlimRfgeByT2pwY5pV61UOML9G26EKbPwh1WKwEKd1itpS1O2aEUmV4fkTYPONlyGss8rLSLYwIJ3oiXDgTdi/unO/AXfPtuGXVDWVNwm/DjkL2rUKP5SmF7bEFUJbPB7wZ2+LzUbEyBIfuD1B1DWXqGDri2AyvEMNmbahiafgUL9knEklXLZzxI7yxjKMRPsfTtdRIto7SLJO7+CB60cXOnWM1OM2kcmQkMHvalu1C5IQBN/LgduVBGgbS/OLKq+lssimM0IQBgDC7EdNH6KlevpxElPQotYQKulqpN2ELvPJdV3hhYMZPkWQ0pxXBsQZEj5HjI4F32CKhmayYB94Z4eJmDbvlfMQOsZOiRvH8GOcSO/+R00Dp3BCqZvvR60tHT5RJ59UjeTq4L471TzGT3QfyMjspgo+/ruIqWbfBTpBVEdogtDVKAV6pZLPzGwkSzqcW4rzfkkWJYuee9gWfxYY3YoqtW8wQcDbJhbPlLr/qsrmu6Wge7vvEozhxsR8NhwrRVp8OASbtNjqcHUNsOKMNF8bM8sdK6D+tpXloLTWXE3T+/vASbtKtbW7IfkqvrgCzf4hAT2kGLmyk40JxOkaeTUJB4zQufqXrAvYCB2jAs7E7kADEiCrCBV6+VtkBb8+qVikRxVKHgz07BAu/VCFSmzpRmKVqqoLCkwd0opSqUYqr5SMmpfpq5JAoXZHSdwFAJOUEjuYbfEwyy5E6qIqrYFEOphROQp/dQrml7SEDjhCibQCvAkyVf2WiYK5bVFJVQsqgbYOFnhyKRkxViXWLKrG0QZTYSX06eTu9HNWYwqpGz6n7hlKEDS26yrXBqgJLx3Tu+lNarWJtKKVpI+iyVZGYU9MlddNzCvDvY1TdWQsTg/4OWFR3yTulZrn+nFIz7U+0n1rOLz0mSnfWLOeRo0yTuun21j6sh5G2RpKVEYRiG2a9+aiaLnnmiHy5cMK/D5MsksWb2+/Fd+FgOEBj874ORvcravqHcOJCPxoOF6K5cH/NsPVEZqNidRgydG7GM5gCb29EFs5k/QUq1kfQllSInmhzlmlzdlUVJLTF7b3i7G6jfMqDr730DKO83nsaF+Ubf50qLzGLm95Ivj0dqS587K2nULHYj8bsoqtSeO1KyMPjxx5GxfIgmlNMpdnLvbxQlM4N4UuvPQtZV6EKEs4U3B8ISA8SWvJdaMl3AQBEeC9XdNcweiwBWoDSK2DqOG/BNFyZhOHKJCx/pWs1RBXXhgM04NmwYcOGDRs3BQ7Q2GwHo1eIsu4x/MPTP4BTVaGLAv7LPe/DP7+z5kY3i0N7VC4XULZHhrYT6Y3IQm9EFvRoc9a0JzoHZ3LvQcXaMNojc9Edd2OsSK4FtRP9WyivFwtubDBaecmDb/0Lywl97G2ntlCXO1JdaDx8bcF/V0Ie2rKuvI6q2X7iwamhYnVoSzBavDyM0rlRtCYV7FjB1sb2GK+Kx4//sRbZ5+axcat81aq7NmzYsGHDhg0bNxv2dTAqr+k49I1JzFZGY7QqcfsNrgNq2ofhVFVIhgFRM/B/f+8ncB9KvmH+kcHQE5mNM/n3omJ1GO0RuTv2Jd1Eb2R2UF/SmwVN6YW8kFRu0fYb7THqRvmc0Nrx/l3No71WtCYxmrAqSGiP4oPN4uVhfKHneci6BtVvqeLG5UV5bOwc41XxGK+KR6ZjfvvCu4kDJpJgw4YNGzZs7HscsLF5Xwej0YNeFH9pAkcck+h6MR0rtebsXb+P2UiEE4L9H9ZY0DHn4600FohXiEj43hsW7W6VEN9dUSwfbEUxie69dWnQXxAgagYEAIKh48TrI2gNN20bVnNJXqclxyR6mOS3sbRXbMST35088X49mSWtxAwTK44Mvt1xKptN8cU70IgENKIcjhXGeVei+W3EZHZOZFJOFtl+aC4gAIhjzNrFSGKKxVocL/Evr7DrUtXegYqNUbSHZ+OVd58I/B4xbZEZ97FzFznFlq35C1TSnOa2SP6ciY5UF069+zTqxtxozCpCb7QLsR7Wnk3bGQCId7P9rKXw+U40l9exysqtZPLl6HFQ6XWaT9EVzqvAtiYVQvZuHh8rFz3K5y/QfCF5XQ/6+1oaf12Tz86wcsQ2QY/gczTFNZZY1J2TGxAG6oh2BWbEHTMmM7Rqxh3IJYauot7dhcFklpi55mKCYZG9U9x+1Ay2TnSytqqWHGbRq5lKzkuDaMo6zGZfiay8Jb0DR9c8ODbkxrm8Ii64F/lUNiy7WB1KPKkkml2w8Ch+o41VltxyW8ZAYDnVySxpZhWeUiuTRCkvSTi6O/48V47aXngUlt/etMpmtbOdc9h7GIBhS9bbuDosaPw4u6gy27Rlhd3fPj10LmkYsXqRSHKYT7ZsQ56nKsn50iXyjLOMFXQbgeZoanw5ahVC806lMLJseaZIJA2cf+6TukKnrXE5qJocut2c5Qq1vLLUbW1foCrL7U1z+2j+KB1TDEtzQlnXhPrdXBm8DWEWLQ2dJh/Q477M22koqxCaXwlYzkkISxtDsPQFandDrovGD1cQyDXnc0ZJbuKWaxK8bsMbtIhZB8khdayxldacUXkj+HNcp++Ul7mul7NsUUmeZ0h7IUvdNDeUy4+15IxSOxZaB5c7q/OVc3WEsYZL4eyiREXy766RTlahRKymqO0UAESRixZBXiTCrcm8e46DNTbv62AUhtnZBRiIa9gIBKM7RVHHJIqbJ9BTk46YsY2A0i6uYRKzpzQTX/rknfj0V16GoOtQnTLOFtz4mbf9iuLlYTw5+b/gMDQogoRTk+loT3Pt+X7b0137Skm2M9mFT9zxKGqm+tGcWojOFNeNbtIWdCXkoSshj/MF3UR7VN6Oqd9Xi+LlYfxd93Nw6BruG30Fjx9/+LJ04LJpD776a5YbfPIvTqMty7Xr7XrD4wDlpdiwYcOGDRs3BQ7Q2Ly/g1HB/HJjOAQsnrjyQPSJj/8HZEWHJgmQBQOCakB3Cij42jQGKkJbLWyHf39nJQbzU1DVOoLWqhy0rLuuuq43OioXBzn7mNpL7usSjFpROj8UsANpieM9RktnPaieGUCDfmhP29aZ7EJn8t7Vv5foicrGmfz7ULHsQXtk7p7QuCuXBomSs4qq2YFAMFo6Z16jc7lFgUDemhtcP+J+wwWj6S2LyDq/gLH6629VZcOGDRs2bNiwsdfY18HodF40fvnuPLhrUzFcmAQsmL9T+4Q7YroDy6M6o7u9rbsTDr9dwibtQjAAKEBZyygma0yrlhiR53U4Cd9mnfAnVZ3xGWZXovD73EP4fe4hAIC+ynij+jyj1cqWjxrL+eyHqNHg9I+YUX5aftO2A+ApSEo0T1tQYtmljPQsB5a96YxGFTG+huLlEVQuDaItNh9NR4+QtrK2rWcyqpVzwUJNyGBBvKCyc1U23IOK9RG0R+SgNzwTeoJJG+6I5GfUOiPyETm9deYNAKKaRwPLRmJs0DIAEBHHTtgGoTFTaXsQx43SuSE8fe5bkDUVqiTjM9pH0R1vBjn5jjF8+VVTRfYjfb/Gx+88FQh2VCLrr0Yw3krMKC/3rREajO4ktFpixVOyMISa6X40pxSiK8kFxzyxOCH0KMcyf771MLbfsH5Gv10/wqjq1vb4UhllW15hNBNqsQIgIFoFABFn3ez3IhZoCmtsm14hBS21Jaw9M6zfR4ww7hVtGwBI68SuQWPHQ618AKAp5wjuG30Fhp/K3JJaCEMWUCCO4O9fexaypuJDF2Sc/KA5A/pHowgPdrLc4NfKC+FNNvvAFpl5IgUvxLJzrHuJRQS/CbIzGE02mfDqdVL5gsLT02+P6w0sr+msA/UryVy5qrCJwPIqKRcts+dRdJMX7/loKySfDs0pIgbgOZG7gQOWl2Jjd6BAwLQWjlmNF9taUtkzZYU8QKmdS5SD5y5Smi4dZxULtZfaMawKjKen0nudd0aDKJF0D1LQsNB0DbqOWMUYMnm2ey10TksdgXLkrcr6HKLWGhxN0/q8stJ2/aC2MeI6f9/SbThbjS1tIMtS8DZsod8Gb84WGjLXVmpxQimtmrXdZBsy/Fnt6yg0Ms6q4WTMtVBXQ9nscPRki90JZ6tDXuJ8uvVEkrZS+jQ9Bs3anuCpWqHOL8CfB0oBllf5ky95Q9B0HfRdkz8Geh65AdDSHoXc5tzxkV1a+wLNgKPXWIvg22lQmi6l31u54nQbiVxXmaR3RRBarYN/L0qLYu/FkTIr57Tw3SMITTdOZlz8SELZdQjBLV92FQdsbN7XwagvUsbvTh69bKcMhfFj8cwuQRJgGAIM3YAqi3DXpW5fwRsQxcsj+ELv83DoGhRRwqdSHkVXkuva610bxZOXfhSg4p7JvBvdOAxgqzrvbuzvSlE12w9Z8yvFaireeqkZVXMDaE0sQIk2ZKrIGmYuZM1k/65TaEtnPWbA6w+GP/knj+KC8+YViNordCXk4fFbHkHVbD9akgvRlWh+MKgbc5vXb3MGdNicAW3LcuGR97Lc4Ja8N5bCb975WUg+HaIOGIqOWCBm+62uAgeICmTDhg0bNmzcFDhAY/O+DkavBZPVsfjZcxXIOLeA8WPxaFnJQUnzOLprMrBUGbl9BVeA6ovDONE9iIaSfDQlH9rVuncTPA1SQ/XMwK4EhxWrwxwVt2J9JBCMAsw+5kahNakQql9dVxclvG2sEbKhQxFl/I8T7+GEhZrTCgPblU95UDvej6aMQrRnXL3lSvXMABcM10z340KWGYyWLAyhan7QtkvxYzNvlX6Zb8wqClw/RZJxnqgjt2e4Ap6s1UODON7fj7OFhWjOd13RfmvcQ7htuA/nSvPReiTn2g9kBwhr8iHidR+Sa5YxU83HmVmt84gdX4cuCzA0A7pDxJKmL4eo6tpwgAY8GzZs2LBh46bAARqb93Uw6tVlDK4mb/n99sS+wPLZVfZimhfGaIz14UPAmwC8CYjBFCJUL3AncCvc+PFsfaCcVSHrvcktgeWGFRaYxDoZ56TWxeikeW0zePj/+z1kVYMqS7j/rx8K2LxYu5GhsDfsFZKyGtnFqE0zlTw1KXya/R07wqgBcR6eJrCeyMqJRElYI2qALUmFuPfS7wM0yD5vOqL7TWVQYZFxRpwy6RYOvotQWo+SYnI3mp1Hcc/0q4F6m/NK4UsIJ1Ww4455tZ/Vlczb9WwUZwbdT4SHt7twTDA1UyWGUbNB6TaEHtMdm4u/PPphVC55kOJdwDummgJ5iUmLq/jUbY+gemYAzamF6InNg+Q1UDrrwVde+SYcugpFlPG58gfRE2teVyv1hlK5tHBCqx03Y4e+jVSoggT4qcp9C4nwlskom/Hg6cZvmwI8oozPVZ1Ea24hV3d0J1OmnbuNnZ/ICdZvw3oucdsgklFHtUTWF8RVCz2GUsYK2EytuM7q1lJ4unTYBJM+FDRGb/Gls2DKucDTgb1EtVmLYP3JF8v3dV8MO4/0HJ9Pc+HBe07j2LAb5wqK0JrtCqzb8B9e1bAHz3/3GThUFYos4/7HTqEln5WLdS0ElkuSJwPLY6txKO2+hC89/SM4FA2KQ8ITT78fPaUZyIxk1ONkmcWBg1528z6Q/Bp3DGMqU5imNN3XVg5z5Tp/l42//JiZ0/5njj489dW3w12ehg3NgcOdE/iLT56HrOgwRAGDR5Lw23cfwfJTr65i12EcqAHPxu5ANSRMqHGYsahJLyjsQy+l5oY7CDXewgGlzCeqZu/TpJDluLYQOp4g8H1ZJjRdRZWCLgOASv5WqQI5UfQVLBKjdGzllEgJC9mq7quT5xqll24pR4ZdK/U0FHh6cGhlXErBDEXNtZxGTjlWosd6GdIapchKROnVWjelF9PnvqQQ6quFgkoVYrUwQqu2tMdBqaxkv/IG+92qpsvVx1GpQ9N5EWLZSl2VyPHRYxV3yPqk58QqNCj6gnOmBUJ3NxwWqnGodlsVmEnfosq6PCXZul+yvUSXLR0gkh28KFPeL1uUZL5Bssx25iT3f0w4e/fIiOS/3cY5WVpRdjh7pwyzxADhpLMnyuy9OF5iw2+MVa5/T3CwxuZ9HYxeDmkti8g6t4Dumswb6kFa2DgNWdUg6wagajjePXhVnqOVYx7UD7txtqgIrTmu3W8ogJ7YXHyu/EFULQ6iNS4fF3fJJ7I7LhefqzyJyoUBtMUXBGxB9hN6YnLRE5OL4uVh3DXTxvISkwvQleRCV5KLG2xqpvvh0FUmprM4GAhGr3jfUdk4k/UXLKfWP0tcM2XShzf3UbkwsCUYtWGiLduFtmwXjBBPrGMeNxwqo/KecPdzwejlUNU6AlnRIOkGDEVDResoekozdq3twVDcNA5Z0SHpBqDqKG6egLvcvB9Lmi8F1hm6gYKeGeT0z+Mze5EzasOGDRs2bNiwcQNxUwajaS2LePeDbZB8OmqdQ3jhW7fcsIC0vy4FqiwB/pnRsyX5qL4wjOPdgzhbkr+jwLRyzIPvfP8bcGgqTr0q48EPn97TgHQzqJKXvNuU3jm643L3ZRBqRU9MLh6vfziQMxqKptycUghFlANBa2vc1dN0geBU5eZURh9WRRlt8QUhtj7YqBz1mLOiuUVodbmCljnnKoIiMypvQ9HOg/rWqhxzNkTRoDoktFftfT5vT20GVIcIqDpUWURPTXpgXXdNJlSHCMGnQTBMTQdZ3aOcUQOAfnC8zGzYsGHDho19jwM2Nt+UweiRf52A7NUhGKawR17jbCAYTWlZRtrZJUS82Yf1Wuc2NV07hiqTcerJe1HXPozGilxgFXjh/3kuQBd84K8f3DYgrR92cxYVxzzuPQtGbQDdCXno3iY/syvJhc/UfhRV8wNoTShAX/ju57xueo8eH7iwb2eUg6F4dQQVK0PoiMhBT9TeBm7lUx58/RfMS/ShB05zNN1NtOa6cN8nHsWJi/1oOFSI1tytZUKhqyQTn/7i3ajv8KC9KvuaZkWTW5aRenYZU8djMFkWF7Jcf3kqvvC1t6O4ycxj35wVBYALZen426+8C3f8ohd/8u8XIPmF15Y0zc4ZtWHDhg0bNg4CDtDYvK+DUREGImUvUpyMt53bNoujP5kw6dQwcwunj8cgRtpAassi7vpwL0SfDuPrAtpfzMJSjZk/17bOgo93JbK80EsKy/ECgALndGDZ7UhDVus88s7PYuRYAsaqzLKti3zQMFYRC3eF+TJ5z4vnTLqgbgCqijv6+zBUZ+Y16oQEPzvFcvH+cHsuTr0uA6qZO3g2vyiQv7FBcksrJkYDyqFnK11cG+Ivsk67lMt03cPn2ZeVpQLehkJeZ7l8MReCJ3+IXp5Pb82X3IRzkn9PpnkYwjLj2msFLO9RGuBzHcPnWF6fnkUOfH6RK6fnsBd3mlsa1THOyiTzgYA4y9rnBFvnZLvckhMyMCJgAIXACqCVs1slfHCWKweSV6QUsRl6JYmxKmmOCwDEdZq2ISOIxehKAbACOEeHIeemc+WMedbAxNeoljzJZUnmPSiVBHadOcufNT7PQb5ArHSymMK0IdIcWP4RUboygqf6X/ArMsv4TK1pk7PkYh9+4i9Yc1PZsjeJ1UeckwAAa+TQN69r+ZDFS3TYjZY8Fzk+khtcmoLuN5n9RlSZJDsALM4xbfpWhbUhN9HMHRmoSIFxq9nQfL8vUO8C62eH3/ouAAAAIABJREFUo1ieabIjeEwY27yOsg9dClixRL7gxXyN2QeS5SWu7OH4SSANwFsAlzADoAMA8B9LFWaBW4FXbjmKgfenIv/8DAbrk7H8wO/3IGcUB2rAs7E7UAwJE2o8VjTeS0UkiWLRDmZTJFkTBWldJKeN5ozKAv/soeOng9gxaGSbMCl08t26SqzaLPmo6+SZ4JVYuQ2d5HWGWWwxaE6jSi1bSJstzziV5seSnFONH5ohEcc5ieagSsHHACB0/ucWa5cQuYGXz1skViihciX5Ypb8RpIzarV2sYyNge1Jnqjo00Ouk9fJebTUJa+qZBvWZwSVbW/NGRVVdgFDWd8AVisdelJJXSHelwBLTi2xbLFa2oS6XtKG5YLRfZF2i/S4w6ydgVgX0W0sVdO0SiWa9AWJlrFYH9GcUZG2zVK3g+SWE2sWp4O122GxX0mMZON7NLGKipYZ2y8tjB9zM5zsPTLTwXJGUyxjc7jADtZBDiKe5ImGX+Z5tqs4QGPzvg5Gg6GwcRqiakAAoAtA13szMV5lvoynn12C6LdC0BUDcQ3rgWD0apDVOo/7PtoQeLn8/neOBQLSUGiuyGWUP1lCU8X2s10th3Jx3xMncaJnAE1xR4PO6lSNePDNf/tGYIboIwmnr2j2ZyfYnPFqj85DT9T1URS1cXOBV2RWUTU/EPBsLZ/0oG7cjT4lJzDLW7I4jLKpIbQmMauWK8E5VxE0UYSgadBEEedcRdtvdAMQ27yOvL+fC1ixQNGRdHY1EIxeLUYqEzFSuZcpCMaB8jKzYcOGDRs29j8O1th80wWj/XUp5pc0RYfuENHzXjbTNnE8FuX+dYZDwOIJFohmts4j5/wcRuoTgRM725fV5y/3/Ny2wWhHcTY+9uQ9qGkfRlN5LjqKd0ZjbDmUi5ZDuXAOhgddf8wThMp7jcFo2YwH1dP9aEkpROTqKp5yvxDwCn2i6AE7IL0BKF4ZwVtm22AYwMvJlehF8K/GNwptsflQRAmGrpm5tAlmnmv5pAfP/OwbkHUVqiDjcxUPAQC+2P6P5m+ijE/f+ghaYq4mL1bwf2y+cr/h64HY5nVU3DcG0WvSNXQR0BwiZo/bekOXgyAIHwDwNwCKARwzDKMxRLm3AfgyzG/q3zYM4yn/7/kAfgAgCUATgAcMw7geMoc2bNiwYcPGGxI3Ymze18GoV5fhWU7ERZ1RNl/LzMf6Nxw43DiJC3VpiK3ZQCTMqXm1TkLDCy4knV1F7G0K4mpVxGEZYU0+vOlkP0SfDt0p4rUXCgIzFjURQ9w+X1ouCyzXv2kA+jdZ4DtxLA7hgoo3J1zgtvmtcTSwrOoiZupj8VJ9GTwLCQBhF+g6Cyzikhj1eLmfBbi65YooySZf4o91+Tj9ez+VV5ZxPqeIk1tfSyUUC0KpWEtj+3Qus68s5VOmdYmsqVAlGb/IroVMvELLfaPoSi2EGs8Hx5RCQiksagLv3SqPM3qpms3seeRZdtzeCn6mLGyM0SUojUbPTuXKreawl/yYLmbno8czfRfdIaF4eRiVS4Noi83HRYlRWdcy2UeKmJYJVrFkoWG5GG803DPHfk/mdWSUGEZRjWwl1NdEYosyOEo3gRDH1m1Sl4uXh/F33c/DYZid5q2zLXgi/W70hpvrjUhCiSP0jbU8vj213T2oWPGgPdqFjkw2kyisbHDljAx2XRZLGHU5/vUxViiBv/69Udn4y6MfQeXSIF4rLkZnigsAcGvzBealChVHpREAgGz4fzNUlK0P4pU8U1ioctSDuhE3zuUz9WgqDb/J3KsfckMyNIgAJENDzdRFNFQTtgGh//i87OaxMK9wy2FmKUQtJygtsDhqnNuGWlA8Et8eWP7hMvMSbl53of4PgxB8BgQD0ERg+EQiXvt4EbwVDsB/j2Y5WP8BgH+avRWu9mkcapzCH8uK0Fdm9rWKOHbu3asp2HMYgGHcMJGETgDvB/DNUAUEQZAAfA3AXQBGAZwXBOGnhmF0A/gCgC8ZhvEDQRCeAXASwDf2vtk2dIhY07dqMsTKG0FKA+JlaG1eMuiphLIbIfEpIk5CwfXJwV9dImX+fcdH6tsgeQFW25g1B1s3a7DxRXGyctqGhV9ILVLIMqXmqpbnkOAMXs5Ki6R70qktRgSxv7KcUi2URIbVpkUPviyqlAJs3SaEVYy1HLUrITRUzobkMu2h4Gi1Cl9IIOMfpdlK6xaqqMrqEEmairBO+onIj/uil10YLZqNuY5Vvm41gtr+sN/p+bGm/ojkOKjtDKXShrJoAQDQ6+ULzas2HLRt1GLHQmMmfVqJDHEQ4GnjGnkl4OyELJYt/DqyQuTLCeTvcCc7poQIRsUNt9DvMyLZu2JuOBtb42S2TbzEp+q4HOxdMZGsy5H4Y40VQ7EpL6dBM3GZdVeJAzY27+tgNBQGK1IwWGG+qFVihFs3XxOF+ZoolJLcz4jXfQH6LhQdyWdXdkyfG3hfEgQATe9yBejANwLNhXn40MnTeG/z+V2pr2aynwUPugrAgCrKgH8Wqy322tRjbzTMwO45f26jhDMZd29Rs70SHF0fC1izdOHILraUoXLJA9nQAuO7bGio2BgJBKM7QencEJ7sZzPcn4s+edWWNKHQE5ODnpgcLKWwe6g1qZDrP03pZtAZ7LfKUQ/+8XuEcv4RXj26asSD+hE3zuYX4Wy+XyXX/xGm4dD+s74ZPZZg5iopOoz/3d6ZR8dxXWf+u71g3wESAAGSDYAgCYLERgikJMvykWRbI8eWrTiyvGmhLIbUaI6dOZElRckcz8THGkc5yYytTCTZWugcjSU5tizNRNFmSU7GNgiCxEYsJLGR2LgAxL52d735owr9XhW6saO7QdzfOTio7npV79brqnr16n33XjtheGvMgtu46q/gPx35EHa3F591NuKv/sedvgFp0AmRFEgI0QwAZH1zYKYCQKsQot0o+yqAO4moGcAtAL5mlDsG/U0uD0YZhmGY9c8G6pvX5WB0MUSenEH0H2YweX0EJq+PQIIi7e0/ELfg9lm1g7jlW2d8s6knP+9ae6MXwV011XB6PLjrVDUe+ObCKWCK+jp9QY9a4uRM5Kl0I62IMVB4d2s53t1ajpKBNjQ6tq36ACbYmH0bvSia7Fr2YLRg9AKe6nnNN8B7LPF+NMevfvvUJbjgIbtvZtRDdtRHLU0qXTLQBqcyw10ytPz8qEuhMXk7/vP1h1Ey0IbKvHw0bHYBAB6+/Qj2X2zDyYw833cV1ujRHTJ6dElXJ176JzlQve+Bo/j6Yw/iYEsHKnfnAOPAw+/+BpX5eTiV61rz41oMfSVJ+OWL+1Hw614U/roPRb/oRuGbvXjn5UJcKU3wu01+9WXY3V7YNUC4vdhb0xO6wejKgiSkEZEq4XleCPH8Ci1SyQJMbxy7ARyALv8ZEkJ4lO9XP+Q1wzAMw4SCDdQ3h/VgVNMII9NRSIqaNH3/Qbucmdq7T8raqsb1WZOtdVeR/lAjbDMaEiJs+PBnu/DvL+Yhu2oQ3RXJoBLhkzv8dtw8yzVhaF0yq4Z8s6nCreFATRu6r9fltL8aKjdt88kUKdv9RXeZbzkx2ixZyomXcoKOURmUJKbgsm+590KqaRtblJQnHOhs1SP1Gg/x+y+24kShPsAUSmQ3jyG3KO3sxPO/ftbns3fonqOoy3IBACrTc3Ak8ij297bi5JYdaErU93MyLxeJrbK9d2g9KO1vR02anpPTHSdlHvYpeaFEWuQoDkXy6o2Wp9nuuGEUD7WjLikX53rNsqfxfClXjm2SM9vkNsu14geU6GdjUm5hg5y5Pu3MhpvsEMKrD+qm4iEu6fuMPa/MXEUqWqkhc1Q1uyHfKbl61jzAu9KKM0JKh6P6ZXRVb5b8/XZN96J4uAN1iTlo2Wue0bMPyza2G9Lls0jBY9lfxa0jpwEAv0nYi6ZU6WOpym3UiLkxrVIS3TK52XTcpzbnwRult7O7wCx3ViMAOiYUWXRS4HSW/SVyXfSA3GYoz4HfIQ+/Qx4UNTpOJOXgZJ6cZY8YAWpSd8Bt5Fd1Oxz4w+48eGIEnONkHqhqHlw30oKTOdvgiPCg8EoP/uKFd3xpk77+Z4fRfFBKWd1ueT4d2K7L73c0XMKeU31oGcvwpU8ZmpFtV5Ag5TVqFD3ALNtVpbmaImG66tFnh6/ujUVq5TiKvD0gTZeVRf/BgwuF+vmQ6pDydACIvXka4gWC5hbQHDYM3xiD/Pgr6JyQ58/lyYVfmoUB/UKI8kAriegDAP5G2U8KId5cO7OYYBBjN8tiI+cPyQrAfP0A5ki7DkXma4dZouYWFpmsgSoBtm6jSoknFZnutMUfpn9KXmtjDinNnHIoEXgt8kLhkJ+9Uf6jrtrmUVyqbgk2863HtA9vpKr7VLa3zJqYpKI2tVxgG9SIrjYl+q01cqy54gCSXZgj6Non/ctQ58h0vUqkXVVC7FFluvM0pN3/eaHXKxuWJmSkVTE07K+4Xi5GPh/Y1cEABQ6G6fXKBlPdl6xuV6p02T4hrxXbhLTTmsEASjuY/E8sbkWmgYuSn1Io7eMYN+/bEyMNdEzblO+tdivVTinHFx94sGQ6H012BtwEDpsajVtWmhRhHgNkR8louDmR8vl5Z4SMeh9vM9+bUpTrd7N9XfStK2Vd9c1hPRhdLiVvXYDdyEMKt4bNx0fR+GC2T2a7BfJEzqwdwtaqq+iqSDHJcDvK0+CNsEEYs6lqMKTFUtjUi5LaLtSWbEXjnsVLLQNxPNcsWTyeN79k8UBbGxya8mDf1eobjAJAfYYL9Rn6Z8fk3DtEwYgudZ31K/32jYdRG7ecADQ6e4bP4+naF+DU9BQ2T2z7Kppj1iZPZXNsNp7I/QaKxs+jPnY7mi8vvE0g6qO3wW1zQBiD+vqYhWcaC0a78HTzS3AYg8I/Lzy0qNnUluisFcmJm2PMxz0b6TacqM904U/vPIp9E604npuHGpfLt67KZQxUNf0cvxoXjZe+9zKcHi8EEWya0B8QvB4cPNtmGoxa2dFwCX/xyNtwuDV4nDb892f+gymf53JIrxnGlqph9FyXhIul5vRBHdelQRjPrsIJdJan+tmDzkBpHD7+2S5sPj6K3xbtwvnitIBl1xQh1jSxthDithXuogeAKg/INr4bAJBERA7jDezs9wzDMAyzvtlgffM1NxjdWncVpW+eN+UhvXzA/0xPas0Ybj501pe65fUXyzFYqM90dBWn4p9+cj22Vw/gfHkq8suW5qBc2NSDv//u63C4vfA47fizp+/GxAH/kXIXS812F+596CgOtLeicof5Id4fx/PMfnwnti4tLUbxSIf0K/V6UNrfjtrtyx+MFg92wKl5fGlBiiYurNlgFNAHpM2xs/vvnrfsfLREZ+HRokMoGepAbVIOHEOT+Er/71Efsy2g/bf118Jp+H86hRefvlK7JtJef5iPOzypz3Thd9vnDpRrt7rwwH1HsX+sBZUFOTjY3AGnxwuHJuAhAY1sEAS47Q5U7pz/ZcyeU31wuDXYNQF4NBScuriiwWh6zTC+8EAd7NO6b+hv/zIfl+6SA9Ku4hT0vZricw/ozps/JctAaRwGSuNwfjxEA9FZwjuX2QkA+UZ0vh4A9wD4mhBCENFHAL4MPWrffQB4ppVhGIa5NthAfXNYD0Y9XhsGh2KRtsWc633fFimfe79/j2/5O1nvY2v9VTiUPKSnv7gFjXvND+ajmj4ozK28YsoLuKe6F5/85AVfua6booGbgN3oxVaHlAncEH/OtL/jY3KQ97WteoChsrfPw+n2wK7pcpPPn2vAx5+UkuCr01IHcfaKnN2JSjHLESKdUsoxPGNHVVoWqvZnGWoNuc67WZG6DOnTMyd3bse37j6K8q5WVG/dgVP5LtO+bf1KJNI4Kb24XK7b9nFWAb7e9bFvMFublouoQTXkqSKvGjXLPyZ3yAfsWTlRbVou3B1SQlpZUIixVCnNdY7KfU/lyFkla3S5iB4pS/XmyRlnd6KUV0V1WWQ46coMmhKp1xuvzHgnmme/tUh5ebS449ASna37j3a+Aic0uGHDEwmfQ0NxhbR1yrDV8kLL67TBnSglY/ZxKRnyJEu/QrIcq1CiOaqyJceo3N69yRyMS5VFe5ToizG95uhyM0myvdzxcpuxPPnyRt0eACKHFfmPsmpGfd8zj8+7U1GrqhEaASkvr961HU279UFedMwM3G9INcBThz6L5LEJVBW60LI7DZvi5Q73Jsv7wrA7Gk1lmfA4bYBHg9dhQ2v5JkTaPbguWUbQHlT0SFYZ4K6oXt9y10wqsqsGYZ/WYBOA8Ah86vtnkb5vBGP75UumYwMHsGv0Es4MpOPGIinff2Nwv2nfGRFSEt4wlOlb3p0gZUYzVo3XGiHW8O3rfBDRlwD8GMAmAP9CRLVCiM8S0RboYeLvEEJ4iOgRAO9CDzL6ohCi0djFYwBeJaLvA6gB8EIIDmNDYoOGeNsU7BbNnXcR6Zes11kayWs4xibva1EW7arNelM1UGW+1gi/o95oZVlep1dmzC+oR+zy3hoXKW2YmJH7m4kw169p/qW5qvsDWdSlqsRRUSTOkXOqkXZNkluluW1uc1trc6S1Rp0z5t/IJIVVot/apwPfB1TpqSqrhTVa7Ixcpz4TmOSyHks9JtmvUo96X7Leo2z+D1ZYJbvqdlOKTHdMPlNqM2Y5p21SSsVtyva2CPOPZJIRR8l1wilt0yyRWm1qe49Je2hccelS7AQAqG5Kqkw3KhKBILdiqxKFnywSYIeSCcCjRNp1TJrb1yQ9nvb/vVXabT7PlG2c5oKaW5EHKyexKtmNdZjbJFK5N2x2yL50iyLzz7IHdjdaL2ykvjmsB6OLZdfpiyis6UHCZyYxfDAaWgSB3AJeSx5SK30ViaacpRcPJCANVwOWXwo9FUkoc9p9D8Jt5YtP01BypgsVjR2oKsxB897MhTeYh7otLtRtcS1r2/pMF77zicPSZzTFZQ7/vkSakrbjibxv+tKONKYuz65QUjzSASc0fXYXGoo8fWjwU+6DTSX4TH8NHPDCAzvey5KDkT2D53Hd5WZdPhzmM5ihpn53Nr7++IM42NyOyoJctJSaZzZVKTxuNHf8rfvS8YNn7sCeU31oK9+E9iKzz+xS6atIgrAThPGyS2hAQuWUbzAad3IKf/7we7C7vfA67ah9JRvDZUuX9wcXEbK3r0KINwC84ef7XgB3KJ/fBvC2n3Lt0CP6MQzDMMw1xMbqm9f9YHTX6Yv4r99+Cw63FzgG1L+ShfpXsjD+71HorkieNx3L5dIEvH9sNzKOj+DigQT0l8YDqzQYvVSaiOeevxl51VfQVr4J54vTAr7ZVSk504WXDR85t8OOh77/TdTvDt2ApTHFhcYU16rtL9wlpAWjXb7cpI2Rc9Pb1CXkwA0bBDR4YEO9w//Lgua4rXg8+x5fOpimZF2SumfwPP7uxE90v1my44ncb+B0/NLk0xuNmvxtqMnXJc7RkG9Ii1u68Pffk1L4Hz97iy/l0yyt+9LRui99Ts7C5XCpNAH/76924Ka/PgehAVoEYeSgnG1JqJzyRciFR0Ny5cQ6GIwyDMMwDMOEjnU/GC2s6YHD7YVdE9DcQGLlJLoeTsGpAteitu8vjTcGoavP+eK0JQcmqWiUPnLweHFdQ2fQBqNFF/U0MCe27UB9pmvJ2xeMXvAN5OoT198Aq2C0Cz9sedmXm/TRork5Opvjt+GJhM+hyNOHekcmmp2BfRD9BSMqudou/WaFF0Xj53Ea66+twoGKxk443Ma14vZiZ/WlOYPR1ablK5mgPQJpx8fQfyAOkfulVH7kYBTSFTXE4MGF842GHIGQ5TJjGIZhGMYPG6xvDuvBKBHgiPCio98SlVL5fZJ27cSfOE9CuL3wOOz4ketWNJ/PRFqU2c/07k0nfMsDHhnWWYNZv/7z4RLf8t5omULnXwbk9zlRV0zbXBfb7luOtyspOywzoU5FOJ8XJcO7vuqW0ZftGQKCCB4S8DjtaNy/BfFR+mzQ9nwZBbihwxJxdUQ6mYhE+YA8mq/4HM5YfEyU0OBFlzvw3FvP6mkzqh04/KWjqM90YSjH7IOh7iH6ivwhdmjd+Juql32Rdx+NeBBNifpAbipV+iU4RuVyTK/ZP9adINepYesnMs2+EcKeoiyrYdQV/wWH+Xf1pshAM6MuOVuVXCV99EqGW025SUsvn8NZux752jYqfRFOfeJGnFL2HXlVztZ5lVDp05tS55RpjMiG22aH0Lzw2Byo2rEHk5vl8dmnzOfMvksdKB7sQF1yjmlgbB9TfHAs9ystQk3zI9uhv9j80iW+V54naoh+dfvJTVafIPl5TBmnRyvxvca3WWP3y8XJPMVuh+X6iJT2TM3I8zl9k/QJ2Z4gr4GBm+Lhed0OePRrv6E0y5e+oTxB+oUCQOO4lOtrIGTXXoWregBDB6J9UXG3OAdN27w/vNe3XBIrfckHy2IxWKb76Z6bUF5GFADnXr6MzKoh9FUk4eq+WMA43BOXzC81tiVKv+cYh2yTN+vkfQYBfMBWHREavxRm/eIkLzKcwxjxmoPyqf2pV3Ei05SbQLzNnPJM7TM32aX/aATMDpc2643OYErIe+6oZu4rRjV5r7/ikfc/q9+qmuplyiOX1ZgN7mizusKtpIrQbEo/q9w/bRPmekzVqhk7LIemllOXScmcY02rYtq3KQWMJZWOms5FtcGrfj/PQ7DqMmrx/7RNK+lKlLgGJv9PtyX9j1fxvXQs8pFU9Rl1KG1v6VPUY8K09A21+omqaJPyfBSKrXaLn6pIks+RpKSuEar/qMWn1pTGZlSpZ1D2B8KSyg5e//dnmjGXI6fSdk7Zf5pSAFnSwajPEY4o2Y6mdEIwP1t5Fbdsm1Kl1+LCqp6rpow0E2Yb3NGy3qkZJXaFW1aUbon9qd5DopSKUmxmn/F1zwbqm8N6MLoYGgqy8R+f+irK6i/g3HXpaC5cmY9lKNnT2Isjz/wb7JoGAcKbdxWjcU9w8rgfaGsz5TAt725d0uzo/ottMvKu5kHxULtvMLqa7O3vREXnOdQl5/hyo64W9THb4CY7ILwACCOO1Z/Zao7fhscK7scerRu1qXloTAl8DIWD5/G3NTIVzqMlD65Jm/pj75VOlF1uw++9+ahfps/xWnNuXwaOPPU1lNdfQHXRNlDR4kZv2bVXcd9Dv4d9RoP2j4TmL2XizJ0Zq+J9eKk0AZdK9YBUTsyTGy9MEADEBnr7yjAMwzDhzkbrm1f07p2IniaiFiKqJ6I3iMivgyYR3U5EZ4iolYgeX0mdKiVnLuC+134PADj2lRvWxUB0U80Iip7rRnrNyJx1JXXdcM54YBOATQjc9XoNCpuCkzrveF4e3A4HPERw2x2ozl6adPRkRh48dn17j82BuqTlp4AJxN7+Tvz4o+dwqP09/G3NC9gzfH7hjZZAc0w2nk3/NDQQCBqOdL2DgrGuhTdcaj3x2/BK/i3zDkQBoGRASnodxgA/GOy90olnPnwOh+vewU9++Y8o6u0MSr3LoaEgGy995QY0FCxeyu6qHvBF0bbPCBS+3os7D9Ui5dT4whtfawihv31d7h8TloS6b2YYhmFWwAbrm1c6M/o+gCeMEL8/BPAE9JC+PojIDuAfAHwaerLHE0T0lhCiaaGdC7cNnp4YaE6BsvZOHDzbjsqduWi6fjNKznTh2PdegsPrhcdux4M/uBcdJbq/WGFTD+5qr0V7+SZcKNalkv/70kHfftXp/7szq011xtnl9H8sSSlHRZwcCHw8stu0zf0pv/MtX/AoqUoscd21KsLtDzXCPqOhyNmN556/GeeL03Dz5la9rT4F4JiM1mkXGm5sakNfmb5PVc5X4Ooz7VuVMs+cl/IRb7xigxMm3InyhK2NdOHew0dwoK0Nlfl5qHFtByAQ02uR+CjZQWbi5brKLTk4lHoU13W14sTWHWiJd8lqxxTJ7Rapt5iVgszOwlVtzkdDur5dQqds+9ge/Tc52HUGTq+Sp3S0Ew2ZctBLSqRfb4I5cIw3UkpBHJPyuIdLZITVmEvTiJ/wwgYBOwAhvNg7042G+B2wK7Jfb6T5HY4nxul3nTtWqXPCfKk5JuTvMp6hSEuUF2EnM/Jwb6sDwkitczx3JybS9LIRsYqsLMksBZtM859ewW6JGD+aLfcxrrzH2fteOxyazC97oLPVF/l4RmahgeaUxroTFRlPvFmGRV65LiNDypEuXko0lduUKCV6mqJBS4hUQu2T+SZbukm+rOkYlfLth9M/MpVTt3PfQNCes4GmNUAANgHArSGzahgT5fK3+GKSFGM3TvtXKKTYzQPYqlEZ9EpTZIpb4s0vn0ZmpKbpwlV5zxCKpM4xsO6FK0zoWNO+2UEaUmxjiCJLWgz4f5Mfq6Rs2WQ3y3RTFPljsi2wGkULEABwUJNyx1HNfJMb1SasxQGYJcRWZqX+ADCj2QOWG1dkuqpk16vc69V7JGB2c1APx2qOmurFJHdUygjL7SHQIVnTxqjlTNuQae+mbdSULSY576Tl91fSlWBCccNRo4J6LenL3Krrhn+pqVVeClUWrUqAPZZySr1iHmluIIRH2ubpNk8MONzy2YGUNCsiRnn2sLgL0ZQirVXkwN5hSyq6xTBtvo5s0bJeilBcnlTZr0UGbYuQbexUUsJplmcczaFI0hXJrpqCyJrVSZWR29Smt9wivJPyt5yeljscU/pI9Zq04lQukEgKXI4Jb1b0tCOEeE/5WAk9yamVCgCtRqhfENGrAO4EsGCHN0tZeyde+Z/P6/6MDgceSLkPd35ciwiPntXM5vHi8x/W4Uclt6GwqQc/euw1ON0eeJ12/PQnN/kGpKFGnZFxuL3Iq75iCnDUXZKCD/9yN275fgtIE/BG2NBQsjiZrpoOpiqqYFn21Wx3oWa7CyJw/zsvdVku1GW5AACRcyd+/TI7C+fwenBH11lFAAAI9klEQVTo9Ac48rmjvgGpldrEHLhtcnBWm7L6s6+1SeY61mKGd7E0prrwaPGDKB5qR11SLk6nuYJSb3X2DrjtDsDw/63OurYCLPWXxuP9Y7uR+0Y/dvzqMsgLCCdh4GDswhtfg2wkKdBGIVh9M8MwDLM2bKS+eTVfvR8C8Jqf77MAqFrHbgAHlrLjg2fbTf6MFY0dfkrpr2HK6rqM6LoAPF7kVl8Jm8HoRKITpE/GgDRgPGmus/Xpu7MxsDMO2VWD6K5IxpnshaXHu0/34Qffe8OXDuYbR46ixuVa/QNYA8oum31Ny/taAw5GmxO24dF9D2Df5AXUpuSiMXl1fUZ9dRQdQslQB2qTc4PmoxmIpsRtQbehbosLD/3xUZR3t6ImfQfqM1xBrT8YzEbRHv5yNJIrJzB4MAZDZesg+u1asA4lPcySWLO+mWEYhlkjNlDfTGKBpKpE9AGADD+rnhRCvGmUeRJAOYC7hGWHRPRlALcLIb5lfP4mgANCiEcC1HcYwGHj414Ap+OB2HxgJ/QRpzgHnAWAfGAX6YHoxDngzCgw7q/sKLBWzmBpAPoXWzgLyMjQHwAAABeBnh7g4nzbLHW/AhCXgF5lv0uyMdjEAVk7gc0Izu+1XMK6DRH+9gFs42qwSwixqnmoiOgd6Me9XPqFELevlj3M4gmHvnlVDmRtCPdrGQh/G8PdPiD8bQx3+wC2cTXgvnmFLDgzKoS4bb71RHQ/gD8CcKu1szPoAbBV+ZxtfBeovucBPG/su1oIUR6obKgJd/uA8LeRiKpPChHWkafWQxuGs30A27gaEFH1wqWWxnrqrBgz3DcHJtztA8LfxnC3Dwh/G8PdPoBtXA24b145K42mezuA7wL4ghDCf6QA4ASAfCLKIaIIAPcAeGsl9TIMwzAM4x/umxmGYZj1wkrTqj8DIB7A+0RUS0TPAgARbSGitwFACOEB8AiAdwE0A3hdCNG4wnoZhmEYhvEP980MwzDMumCl0XT9htkUQvQCuEP5/DaAt5dRxfPLNC1YhLt9QPjbGO72AeFvY7jbB7CNq0G428eECdw3h719QPjbGO72AeFvY7jbB7CNq0G42xf2LBjAiGEYhmEYhmEYhmFWm5XKdBmGYRiGYRiGYRhmyYTVYJSIniaiFiKqJ6I3iCgpQLnbiegMEbUS0eNBtO9PiKiRiDQiChjZi4g6iajB8NVZ9Shbq2RjqNowhYjeJ6Jzxv/kAOW8RvvVElFQgmos1CZEFElErxnrjxORKxh2LcG++4noitJu3wqyfS8S0WUi8pvygXR+ZNhfT0RlwbRvkTZ+ioiGlTb8L0G2bysRfURETcZ1/G0/ZULejszGgvvmoNrIffPcOrlvXpl93Dev3D7um9cSIUTY/AH4DACHsfxDAD/0U8YOoA1ALoAIAHUA9gTJvgIAuwB8DKB8nnKdANJC1IYL2hjiNvwbAI8by4/7+42NdWNBbrcF2wTAwwCeNZbvAfBamNl3P4BnQnHeGfV/EkAZgNMB1t8B4F+h55Q9COB4GNr4KQD/N4RtmAmgzFiOh55T2fo7h7wd+W9j/XHfHBwbuW9eXptw37ygjdw3r9w+7pvX8C+sZkaFEO8JPcIfAFRCz3tmpQJAqxCiXQgxA+BVAHcGyb5mIcSZYNS1XBZpY8ja0KjnmLF8DMAXg1TvQiymTVTb/xnArUREYWRfSBFC/BuAq/MUuRPAz4ROJYAkIgpqjtlF2BhShBB9QohTxvIo9CinWZZiIW9HZmPBffPK4b552XDfvEK4b1453DevLWE1GLVwCPobBitZALqUz92Ye0KEGgHgPSI6SUSHQ22MH0LZhulCiD5j+SKA9ADlooiomogqiSgYneJi2sRXxngwGwaQGgTbTHUbBPrN/tiQh/wzEW31sz6UrIdrFwCuJ6I6IvpXIioMlRGG1KwUwHHLqvXSjsy1CffNawf3zXPhvnntWQ/XLsB98zXLilK7LAci+gBAhp9VTwoh3jTKPAnAA+CVYNpm1L2gfYvgE0KIHiLaDD3PW4vx1iecbFwz5rNP/SCEEEQUKJzzdqMNcwF8SEQNQoi21bb1GuP/APi5EGKaiP4U+pviW0Js03rjFPRzb4yI7gDwawD5wTaCiOIA/BLAd4QQI8Gun9l4cN8cNjauGdw3hwzum1cO983XMEEfjAohbptvPRHdD+CPANwqhPB3M+wBoL5Vyja+C4p9i9xHj/H/MhG9AV3GsWod3irYGLI2JKJLRJQphOgz5AuXA+xjtg3biehj6G+h1rLDW0ybzJbpJiIHgEQAA2tok7+6Z5ljnxBCteWn0H2Awok1Pe9WA7VzEUK8TUT/i4jShBD9wbKBiJzQO7tXhBC/8lMk7NuRWX9w37xyuG9eE7hvXnvCvk/hvvnaJqxkukR0O4DvAviCEGIiQLETAPKJKIeIIqA7qwclottiIKJYIoqfXYYe+MFvdLAQEso2fAvAfcbyfQDmvC0momQiijSW0wDcCKBpje1aTJuotn8ZwIcBHspCYp/FN+EL0H0awom3ANxrRJw7CGBYkYWFBUSUMetrREQV0O+RwXqogVH3CwCahRB/F6BY2Lcjc23BfXPQ4L55Ltw3rz1h36dw33yNI8IgitLsH4BW6HrrWuNvNjraFgBvK+XugB7Jqg26/CVY9n0JugZ8GsAlAO9a7YMeUa3O+GsMpn2LtTHEbZgK4DcAzgH4AECK8X05gJ8ayzcAaDDasAHAg0GybU6bAPhv0B/AACAKwC+M87QKQG6Qf9uF7HvKOOfqAHwEYHeQ7fs5gD4AbuMcfBDAEQBHjPUE4B8M+xswT9TLENr4iNKGlQBuCLJ9n4Du11av3AfvCLd25L+N9Qfum4NiY4jbkPvmtbOP++aV28h98zX8R0YDMgzDMAzDMAzDMEzQCCuZLsMwDMMwDMMwDLMx4MEowzAMwzAMwzAME3R4MMowDMMwDMMwDMMEHR6MMgzDMAzDMAzDMEGHB6MMwzAMwzAMwzBM0OHBKMMwDMMwDMMwDBN0eDDKMAzDMAzDMAzDBB0ejDIMwzAMwzAMwzBB5/8DIFHLdvpazpwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n1=100\n",
    "x_test1,x_test2=np.meshgrid(np.linspace(-2,2,n1),np.linspace(-2,2,n1))\n",
    "x_test_2d=np.hstack([x_test1.reshape((n1*n1,1)),x_test2.reshape(n1*n1,1)])\n",
    "y_test_net_2d=net.forward(x_test_2d)[0].reshape((n1,n1))\n",
    "y_test_2d=test_fun_2d(x_test_2d)\n",
    "y_test_2d=y_test_2d.reshape((n1,n1))\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.subplot(1,2,1)\n",
    "plt.pcolor(x_test1,x_test2,y_test_2d);\n",
    "cb1=plt.colorbar()\n",
    "plt.plot(x[:,0],x[:,1],'r.')\n",
    "plt.subplot(1,2,2)\n",
    "plt.pcolor(x_test1,x_test2,y_test_net_2d);\n",
    "cb2=plt.colorbar()\n",
    "plt.clim((cb1.vmin,cb1.vmax))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: Error1: 3.4421218475142927, Error2: 3.348819010757426e-05 \n",
      "Total fails (no activations): 0\n",
      "1: Error1: 0.00012333791877671752, Error2: 1.4626336580925918e-05 \n",
      "Total fails (no activations): 0\n",
      "2: Error1: 4.901029892289976e-06, Error2: 1.1168173471728394e-06 \n",
      "Total fails (no activations): 0\n",
      "3: Error1: 0.002557879535037038, Error2: 0.000952926937344474 \n",
      "Total fails (no activations): 0\n",
      "4: Error1: 2.1229774083702475, Error2: 0.00018138310319167672 \n",
      "Total fails (no activations): 0\n",
      "5: Error1: 0.0003957342373864747, Error2: 5.6255527057411125e-05 \n",
      "Total fails (no activations): 0\n",
      "6: Error1: 5.873351312693016e-05, Error2: 1.6985435170218807e-05 \n",
      "Total fails (no activations): 0\n",
      "7: Error1: 6.735964781093448e-05, Error2: 2.9905313821998084e-05 \n",
      "Total fails (no activations): 0\n",
      "8: Error1: 5.3802601466703256e-05, Error2: 2.408867622280767e-05 \n",
      "Total fails (no activations): 0\n",
      "9: Error1: 0.0003454412190647298, Error2: 0.00018164639044488317 \n",
      "Total fails (no activations): 0\n",
      "10: Error1: 4.736224069425623e-05, Error2: 2.391135538662925e-05 \n",
      "Total fails (no activations): 0\n",
      "11: Error1: 9.372948007692102e-05, Error2: 5.199653660203553e-05 \n",
      "Total fails (no activations): 0\n",
      "12: Error1: 0.00039174931140074345, Error2: 0.00025052489934835416 \n",
      "Total fails (no activations): 0\n",
      "13: Error1: 9.457498806491617e-05, Error2: 5.090137795166516e-05 \n",
      "Total fails (no activations): 0\n",
      "14: Error1: 0.0003975952690401519, Error2: 0.0002580445447143329 \n",
      "Total fails (no activations): 0\n",
      "15: Error1: 0.0003722351490077155, Error2: 0.00023456609480440168 \n",
      "Total fails (no activations): 0\n",
      "16: Error1: 3.35822581616591e-05, Error2: 2.2632205807168524e-05 \n",
      "Total fails (no activations): 0\n",
      "17: Error1: 7.419739325901787e-05, Error2: 5.0492717026868236e-05 \n",
      "Total fails (no activations): 0\n",
      "18: Error1: 3.648336862706231e-05, Error2: 2.554072949666005e-05 \n",
      "Total fails (no activations): 0\n",
      "19: Error1: 5.3674683011622617e-05, Error2: 3.997259706479596e-05 \n",
      "Total fails (no activations): 0\n",
      "20: Error1: 4.791537549630512e-06, Error2: 3.5803204464098374e-06 \n",
      "Total fails (no activations): 0\n",
      "21: Error1: 0.0004200991074063194, Error2: 0.000283868326550781 \n",
      "Total fails (no activations): 0\n",
      "22: Error1: 0.00014217468211863708, Error2: 0.00010422527425394219 \n",
      "Total fails (no activations): 0\n",
      "23: Error1: 6.332979979152054e-05, Error2: 4.7973722515949206e-05 \n",
      "Total fails (no activations): 0\n",
      "24: Error1: 0.000103328139042467, Error2: 7.640243760951246e-05 \n",
      "Total fails (no activations): 0\n",
      "25: Error1: 3.663247448565273e-05, Error2: 2.671728098071731e-05 \n",
      "Total fails (no activations): 0\n",
      "26: Error1: 1.0767328267362839e-05, Error2: 8.501944771970303e-06 \n",
      "Total fails (no activations): 0\n",
      "27: Error1: 3.458470550178399e-06, Error2: 2.759519185473031e-06 \n",
      "Total fails (no activations): 0\n",
      "28: Error1: 0.00031448549333772964, Error2: 0.0002513218475490037 \n",
      "Total fails (no activations): 0\n",
      "29: Error1: 0.00020626439142549938, Error2: 0.00016530569367476344 \n",
      "Total fails (no activations): 0\n",
      "30: Error1: 3.191614995593344e-05, Error2: 2.4076564971565338e-05 \n",
      "Total fails (no activations): 0\n",
      "0: Error1: 0.00013937259893535438, Error2: 0.00011465223645629831 \n",
      "Total fails (no activations): 0\n",
      "1: Error1: 0.0004920918498082159, Error2: 0.000405547506562012 \n",
      "Total fails (no activations): 0\n",
      "2: Error1: 0.0003236444406275478, Error2: 0.00027176709589547224 \n",
      "Total fails (no activations): 0\n",
      "3: Error1: 0.00017567581070246176, Error2: 0.00014778410542860062 \n",
      "Total fails (no activations): 0\n",
      "4: Error1: 5.601771150017902e-06, Error2: 4.314636348389855e-06 \n",
      "Total fails (no activations): 0\n",
      "5: Error1: 6.923067405156768e-05, Error2: 5.470261704993553e-05 \n",
      "Total fails (no activations): 0\n",
      "6: Error1: 5.4147346338518515e-05, Error2: 4.399609211570527e-05 \n",
      "Total fails (no activations): 0\n",
      "7: Error1: 6.932441778210603e-05, Error2: 5.757462499242176e-05 \n",
      "Total fails (no activations): 0\n",
      "8: Error1: 2.5611643180857907e-05, Error2: 2.1144084765902843e-05 \n",
      "Total fails (no activations): 0\n",
      "9: Error1: 0.0002984704423459122, Error2: 0.0002498294861124599 \n",
      "Total fails (no activations): 0\n",
      "10: Error1: 8.697260010122036e-05, Error2: 7.313137337410082e-05 \n",
      "Total fails (no activations): 0\n",
      "11: Error1: 4.687039541316309e-05, Error2: 3.8365152933392325e-05 \n",
      "Total fails (no activations): 0\n",
      "12: Error1: 0.00031408791553447224, Error2: 0.0002680449464214985 \n",
      "Total fails (no activations): 0\n",
      "13: Error1: 3.253970545003968e-05, Error2: 2.739830466554871e-05 \n",
      "Total fails (no activations): 0\n",
      "14: Error1: 0.0003541289858358924, Error2: 0.0002984067320246057 \n",
      "Total fails (no activations): 0\n",
      "15: Error1: 0.0002906468153315527, Error2: 0.0002495141076766266 \n",
      "Total fails (no activations): 0\n",
      "16: Error1: 1.7391646568330956e-05, Error2: 1.5168877569122149e-05 \n",
      "Total fails (no activations): 0\n",
      "17: Error1: 4.530876305603297e-05, Error2: 3.940459554123269e-05 \n",
      "Total fails (no activations): 0\n",
      "18: Error1: 2.6688468684720275e-05, Error2: 2.316522836986217e-05 \n",
      "Total fails (no activations): 0\n",
      "19: Error1: 5.2220594905246394e-05, Error2: 4.636143323565892e-05 \n",
      "Total fails (no activations): 0\n",
      "20: Error1: 6.339198692915911e-06, Error2: 5.588612092875116e-06 \n",
      "Total fails (no activations): 0\n",
      "21: Error1: 0.0003362640987884708, Error2: 0.0002845873819928618 \n",
      "Total fails (no activations): 0\n",
      "22: Error1: 0.00012334543591514054, Error2: 0.0001075105157070855 \n",
      "Total fails (no activations): 0\n",
      "23: Error1: 6.328407561164233e-05, Error2: 5.598844727684071e-05 \n",
      "Total fails (no activations): 0\n",
      "24: Error1: 9.232775444753488e-05, Error2: 8.007875430635512e-05 \n",
      "Total fails (no activations): 0\n",
      "25: Error1: 3.0285665712602643e-05, Error2: 2.5985271903153282e-05 \n",
      "Total fails (no activations): 0\n",
      "26: Error1: 1.6505948721711328e-05, Error2: 1.485826627216391e-05 \n",
      "Total fails (no activations): 0\n",
      "27: Error1: 8.800540627630146e-06, Error2: 7.904781651173572e-06 \n",
      "Total fails (no activations): 0\n",
      "28: Error1: 0.00032178218726511314, Error2: 0.00028569768376075414 \n",
      "Total fails (no activations): 0\n",
      "29: Error1: 0.00015025364879406409, Error2: 0.00013353092181442788 \n",
      "Total fails (no activations): 0\n",
      "30: Error1: 3.1923309367032865e-05, Error2: 2.7746750563079893e-05 \n",
      "Total fails (no activations): 0\n",
      "0: Error1: 9.242347728681247e-05, Error2: 8.316145589021996e-05 \n",
      "Total fails (no activations): 0\n",
      "1: Error1: 0.00041088509448290093, Error2: 0.0003677453893739112 \n",
      "Total fails (no activations): 0\n",
      "2: Error1: 0.00029944461187778255, Error2: 0.0002709862367325593 \n",
      "Total fails (no activations): 0\n",
      "3: Error1: 0.0001514222623627974, Error2: 0.000136984400932781 \n",
      "Total fails (no activations): 0\n",
      "4: Error1: 3.0223278939649053e-06, Error2: 2.6325260012463637e-06 \n",
      "Total fails (no activations): 0\n",
      "5: Error1: 5.996372374952984e-05, Error2: 5.272565066453734e-05 \n",
      "Total fails (no activations): 0\n",
      "6: Error1: 5.1332579949140316e-05, Error2: 4.574668965336926e-05 \n",
      "Total fails (no activations): 0\n",
      "7: Error1: 5.9601890054165835e-05, Error2: 5.338436699834918e-05 \n",
      "Total fails (no activations): 0\n",
      "8: Error1: 2.258769746172736e-05, Error2: 2.021244476780722e-05 \n",
      "Total fails (no activations): 0\n",
      "9: Error1: 0.0002794187619389934, Error2: 0.00025105282929046766 \n",
      "Total fails (no activations): 0\n",
      "10: Error1: 8.42769382738033e-05, Error2: 7.607087679148156e-05 \n",
      "Total fails (no activations): 0\n",
      "11: Error1: 3.895730149359529e-05, Error2: 3.436788040964844e-05 \n",
      "Total fails (no activations): 0\n",
      "12: Error1: 0.00028856673387862966, Error2: 0.00026131948146356 \n",
      "Total fails (no activations): 0\n",
      "13: Error1: 2.725200705706272e-05, Error2: 2.463126965249872e-05 \n",
      "Total fails (no activations): 0\n",
      "14: Error1: 0.0003343001582944981, Error2: 0.00029938335062166583 \n",
      "Total fails (no activations): 0\n",
      "15: Error1: 0.0002550707613831447, Error2: 0.00023126957737398736 \n",
      "Total fails (no activations): 0\n",
      "16: Error1: 1.3685162822088486e-05, Error2: 1.257472064826193e-05 \n",
      "Total fails (no activations): 0\n",
      "17: Error1: 3.618325850022279e-05, Error2: 3.314300543264156e-05 \n",
      "Total fails (no activations): 0\n",
      "18: Error1: 2.1246013708929014e-05, Error2: 1.937713664526036e-05 \n",
      "Total fails (no activations): 0\n",
      "19: Error1: 4.849513716093425e-05, Error2: 4.495877343050056e-05 \n",
      "Total fails (no activations): 0\n",
      "20: Error1: 5.763345665312454e-06, Error2: 5.29804047663192e-06 \n",
      "Total fails (no activations): 0\n",
      "21: Error1: 0.0003071046159324683, Error2: 0.00027507400751232295 \n",
      "Total fails (no activations): 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22: Error1: 0.0001145984454533882, Error2: 0.00010472054829866403 \n",
      "Total fails (no activations): 0\n",
      "23: Error1: 5.829235452027222e-05, Error2: 5.3790444573719254e-05 \n",
      "Total fails (no activations): 0\n",
      "24: Error1: 8.682646975805518e-05, Error2: 7.894613877656951e-05 \n",
      "Total fails (no activations): 0\n",
      "25: Error1: 2.7162422827165304e-05, Error2: 2.4503845518230438e-05 \n",
      "Total fails (no activations): 0\n",
      "26: Error1: 1.6606075019049366e-05, Error2: 1.5515063897670046e-05 \n",
      "Total fails (no activations): 0\n",
      "27: Error1: 1.0231985520219937e-05, Error2: 9.519224859858179e-06 \n",
      "Total fails (no activations): 0\n",
      "28: Error1: 0.0002998689615537803, Error2: 0.00027544140708440435 \n",
      "Total fails (no activations): 0\n",
      "29: Error1: 0.0001287424574105177, Error2: 0.00011851917886285441 \n",
      "Total fails (no activations): 0\n",
      "30: Error1: 3.071026730760571e-05, Error2: 2.795516829173024e-05 \n",
      "Total fails (no activations): 0\n",
      "0: Error1: 7.271963891148595e-05, Error2: 6.748983919787207e-05 \n",
      "Total fails (no activations): 0\n",
      "1: Error1: 0.0003617505081713562, Error2: 0.00033356339629039415 \n",
      "Total fails (no activations): 0\n",
      "2: Error1: 0.0002775206853507839, Error2: 0.0002581790507304707 \n",
      "Total fails (no activations): 0\n",
      "3: Error1: 0.00013999815369413008, Error2: 0.0001301841424998144 \n",
      "Total fails (no activations): 0\n",
      "4: Error1: 2.1987348920294418e-06, Error2: 2.001494458293277e-06 \n",
      "Total fails (no activations): 0\n",
      "5: Error1: 5.526609829681488e-05, Error2: 5.052931918893406e-05 \n",
      "Total fails (no activations): 0\n",
      "6: Error1: 4.9101004045389325e-05, Error2: 4.530317915775855e-05 \n",
      "Total fails (no activations): 0\n",
      "7: Error1: 5.276324969547795e-05, Error2: 4.868089000963119e-05 \n",
      "Total fails (no activations): 0\n",
      "8: Error1: 2.1122742543252846e-05, Error2: 1.9518153680621412e-05 \n",
      "Total fails (no activations): 0\n",
      "9: Error1: 0.00026473384771242826, Error2: 0.0002448284043039563 \n",
      "Total fails (no activations): 0\n",
      "10: Error1: 8.025444074411148e-05, Error2: 7.452442437544648e-05 \n",
      "Total fails (no activations): 0\n",
      "11: Error1: 3.4771178326191266e-05, Error2: 3.167369276831928e-05 \n",
      "Total fails (no activations): 0\n",
      "12: Error1: 0.0002710001566779796, Error2: 0.0002518153112662272 \n",
      "Total fails (no activations): 0\n",
      "13: Error1: 2.535014133370435e-05, Error2: 2.3584843299859048e-05 \n",
      "Total fails (no activations): 0\n",
      "14: Error1: 0.0003189552742887975, Error2: 0.00029354135808192406 \n",
      "Total fails (no activations): 0\n",
      "15: Error1: 0.00022634888325329577, Error2: 0.00021004036199843675 \n",
      "Total fails (no activations): 0\n",
      "16: Error1: 1.1643527373693073e-05, Error2: 1.094426792514424e-05 \n",
      "Total fails (no activations): 0\n",
      "17: Error1: 3.0935701656759225e-05, Error2: 2.8983960569661427e-05 \n",
      "Total fails (no activations): 0\n",
      "18: Error1: 1.7634425349930653e-05, Error2: 1.6443606010906677e-05 \n",
      "Total fails (no activations): 0\n",
      "19: Error1: 4.550506455226291e-05, Error2: 4.3022617375433e-05 \n",
      "Total fails (no activations): 0\n",
      "20: Error1: 5.160749253582239e-06, Error2: 4.836254454482433e-06 \n",
      "Total fails (no activations): 0\n",
      "21: Error1: 0.0002881056798763956, Error2: 0.00026485856034207773 \n",
      "Total fails (no activations): 0\n",
      "22: Error1: 0.0001079507509077759, Error2: 0.00010085686806347342 \n",
      "Total fails (no activations): 0\n",
      "23: Error1: 5.3410679458492256e-05, Error2: 5.026930930506083e-05 \n",
      "Total fails (no activations): 0\n",
      "24: Error1: 8.281306919144272e-05, Error2: 7.702558622650708e-05 \n",
      "Total fails (no activations): 0\n",
      "25: Error1: 2.4854174675165475e-05, Error2: 2.2976656964507045e-05 \n",
      "Total fails (no activations): 0\n",
      "26: Error1: 1.5822397551336996e-05, Error2: 1.5046232520655499e-05 \n",
      "Total fails (no activations): 0\n",
      "27: Error1: 1.0712555849641016e-05, Error2: 1.0137316296877002e-05 \n",
      "Total fails (no activations): 0\n",
      "28: Error1: 0.0002753799158440092, Error2: 0.00025729643456590405 \n",
      "Total fails (no activations): 0\n",
      "29: Error1: 0.00011499358138872906, Error2: 0.00010777582771240128 \n",
      "Total fails (no activations): 0\n",
      "30: Error1: 2.9392652218679857e-05, Error2: 2.737785704656219e-05 \n",
      "Total fails (no activations): 0\n",
      "0: Error1: 6.007236421987189e-05, Error2: 5.664112223820004e-05 \n",
      "Total fails (no activations): 0\n",
      "1: Error1: 0.00032387085667022345, Error2: 0.0003033327160768506 \n",
      "Total fails (no activations): 0\n",
      "2: Error1: 0.0002577953877884172, Error2: 0.00024332856675980552 \n",
      "Total fails (no activations): 0\n",
      "3: Error1: 0.00013240112404683192, Error2: 0.00012492372572537529 \n",
      "Total fails (no activations): 0\n",
      "4: Error1: 1.7761021664001297e-06, Error2: 1.6543656705640216e-06 \n",
      "Total fails (no activations): 0\n",
      "5: Error1: 5.192623403501415e-05, Error2: 4.844901583045548e-05 \n",
      "Total fails (no activations): 0\n",
      "6: Error1: 4.719953659451293e-05, Error2: 4.4349915582348116e-05 \n",
      "Total fails (no activations): 0\n",
      "7: Error1: 4.737287000777292e-05, Error2: 4.441033580458834e-05 \n",
      "Total fails (no activations): 0\n",
      "8: Error1: 2.013658251108062e-05, Error2: 1.893138462052811e-05 \n",
      "Total fails (no activations): 0\n",
      "9: Error1: 0.0002520917090852065, Error2: 0.00023684250392013567 \n",
      "Total fails (no activations): 0\n",
      "10: Error1: 7.63213660977677e-05, Error2: 7.196779772028806e-05 \n",
      "Total fails (no activations): 0\n",
      "11: Error1: 3.1836854248534294e-05, Error2: 2.9523661421113404e-05 \n",
      "Total fails (no activations): 0\n",
      "12: Error1: 0.0002567656115929583, Error2: 0.00024206611377706628 \n",
      "Total fails (no activations): 0\n",
      "13: Error1: 2.4371480267374655e-05, Error2: 2.303407509899089e-05 \n",
      "Total fails (no activations): 0\n",
      "14: Error1: 0.00030574549398045643, Error2: 0.0002857945810021171 \n",
      "Total fails (no activations): 0\n",
      "15: Error1: 0.0002019703514604315, Error2: 0.00018987276794423338 \n",
      "Total fails (no activations): 0\n",
      "16: Error1: 1.0221251405120042e-05, Error2: 9.730618905249819e-06 \n",
      "Total fails (no activations): 0\n",
      "17: Error1: 2.728554864390796e-05, Error2: 2.5890420968400297e-05 \n",
      "Total fails (no activations): 0\n",
      "18: Error1: 1.4962684862164001e-05, Error2: 1.4129726730969266e-05 \n",
      "Total fails (no activations): 0\n",
      "19: Error1: 4.2985988653305344e-05, Error2: 4.109909843337282e-05 \n",
      "Total fails (no activations): 0\n",
      "20: Error1: 4.665723438925355e-06, Error2: 4.4208933549829755e-06 \n",
      "Total fails (no activations): 0\n",
      "21: Error1: 0.00027326136703434797, Error2: 0.0002550170148170899 \n",
      "Total fails (no activations): 0\n",
      "22: Error1: 0.00010229495752797123, Error2: 9.681526534460855e-05 \n",
      "Total fails (no activations): 0\n",
      "23: Error1: 4.9030329681316484e-05, Error2: 4.6681156509591686e-05 \n",
      "Total fails (no activations): 0\n",
      "24: Error1: 7.950394950463703e-05, Error2: 7.49446090534616e-05 \n",
      "Total fails (no activations): 0\n",
      "25: Error1: 2.2956307819708564e-05, Error2: 2.1532619937333144e-05 \n",
      "Total fails (no activations): 0\n",
      "26: Error1: 1.4851208543875611e-05, Error2: 1.4269116793291035e-05 \n",
      "Total fails (no activations): 0\n",
      "27: Error1: 1.0856653265766752e-05, Error2: 1.0377613556086098e-05 \n",
      "Total fails (no activations): 0\n",
      "28: Error1: 0.0002521296061768278, Error2: 0.0002380327135904491 \n",
      "Total fails (no activations): 0\n",
      "29: Error1: 0.00010462361159875117, Error2: 9.913260747412829e-05 \n",
      "Total fails (no activations): 0\n",
      "30: Error1: 2.81297592922257e-05, Error2: 2.656500690367732e-05 \n",
      "Total fails (no activations): 0\n",
      "\r"
     ]
    }
   ],
   "source": [
    "# super basic test to see if classification works at all\n",
    "n_data=31\n",
    "# hidden_layers=[16,32,256,32,16]\n",
    "hidden_layers=[16,16,16,16]\n",
    "hidden_layers=[128,128,128]\n",
    "hidden_layers=[32,256,256,32]\n",
    "hidden_layers=[32,128,128,32]\n",
    "hidden_layers=[32,32]\n",
    "\n",
    "x,y_hot,y=test_fun_classification(n_data) # generate data\n",
    "\n",
    "for ii in range(1):\n",
    "    net=BNet7(hidden_layer_sizes=hidden_layers,input_dim=4,output_dim=2,lambda_initaliser=lambda_initaliser,initialiser_function=initialiser_function)\n",
    "    net.train(x,y_hot,print_error=False,passes=5,total_info=1,failed_attempts=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 1.]\n",
      "(array([[0.00117781, 0.9988288 ]]), 1)\n",
      "===\n",
      "[0. 1.]\n",
      "(array([[6.38600930e-04, 9.99367978e-01]]), 1)\n",
      "===\n",
      "[1. 0.]\n",
      "(array([[ 1.00417855, -0.0041773 ]]), 1)\n",
      "===\n",
      "[1. 0.]\n",
      "(array([[0.99729977, 0.00270164]]), 1)\n",
      "===\n",
      "[0. 1.]\n",
      "(array([[0.00145825, 0.99854857]]), 1)\n",
      "===\n",
      "[1. 0.]\n",
      "(array([[9.99958554e-01, 4.42586284e-05]]), 1)\n",
      "===\n",
      "[0. 1.]\n",
      "(array([[0.00203825, 0.99796972]]), 1)\n",
      "===\n",
      "[0. 1.]\n",
      "(array([[-0.00149771,  1.00150125]]), 1)\n",
      "===\n",
      "[1. 0.]\n",
      "(array([[ 1.0059914 , -0.00599068]]), 1)\n",
      "===\n",
      "[1. 0.]\n",
      "(array([[0.99348151, 0.00652134]]), 1)\n",
      "===\n"
     ]
    }
   ],
   "source": [
    "#testing whether classification worked\n",
    "\n",
    "n_test=10\n",
    "x_test,y_hot_test,y_test=test_fun_classification(n_test) #generating test data\n",
    "\n",
    "for ii in range(len(x_test)):\n",
    "    print(y_hot[ii,:])\n",
    "    print(net.forward(x[ii,:].reshape((1,4))))\n",
    "    print('===')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check some things with a standard Neural net\n",
    "\n",
    "\n",
    "n_data=1001\n",
    "hidden_layers=[16,16,16,16]\n",
    "# hidden_layers=[64,128,128,64]\n",
    "hidden_layers=[32,32,32,32]\n",
    "n1=int(np.sqrt(n_data))\n",
    "x1,x2=np.meshgrid(np.linspace(-2,2,n1),np.linspace(-2,2,n1))\n",
    "x=np.hstack([x1.reshape((n1*n1,1)),x2.reshape(n1*n1,1)])\n",
    "# x=np.random.rand(n_data,2)*4-2\n",
    "\n",
    "y=test_fun_2d(x)\n",
    "x_test=np.linspace(-2,2,1000).reshape(1000,1)\n",
    "nnet=NNet(hidden_layer_sizes=hidden_layers,input_dim=2,output_dim=1,initialiser_function=initialiser_function2)\n",
    "\n",
    "for ii in range(50):\n",
    "    nnet.train(x,y,print_error=False,alpha=0.01)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f2c3288f9b0>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the results from the standard neural net\n",
    "n1=100\n",
    "x_test1,x_test2=np.meshgrid(np.linspace(-2,2,n1),np.linspace(-2,2,n1))\n",
    "x_test_2d=np.hstack([x_test1.reshape((n1*n1,1)),x_test2.reshape(n1*n1,1)])\n",
    "y_test_net_2d=nnet.forward(x_test_2d).reshape((n1,n1))\n",
    "# print(y_test_net_2d.shape)\n",
    "# y_test_2d= 0.8*np.sin(x_test_2d[:,0]*3.4)+0.8*np.sin(x_test_2d[:,1]*3)#+np.random.randn(x.shape[0],1)*0.1\n",
    "y_test_2d=test_fun_2d(x_test_2d)\n",
    "y_test_2d=y_test_2d.reshape((n1,n1))\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.subplot(1,2,1)\n",
    "plt.pcolor(x_test1,x_test2,y_test_2d);\n",
    "plt.colorbar()\n",
    "plt.plot(x[:,0],x[:,1],'r.')\n",
    "plt.subplot(1,2,2)\n",
    "plt.pcolor(x_test1,x_test2,y_test_net_2d);\n",
    "plt.colorbar()\n",
    "# plt.plot(x[:,0],x[:,1],'r.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision\n",
    "import torch\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_data=torchvision.datasets.MNIST('.',transform=torchvision.transforms.ToTensor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot(x, class_count):\n",
    "    \n",
    "    vec1=np.zeros((1,class_count))\n",
    "    vec1[0,x]=1\n",
    "    return vec1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset MNIST\n",
       "    Number of datapoints: 60000\n",
       "    Root location: .\n",
       "    Split: Train\n",
       "    StandardTransform\n",
       "Transform: ToTensor()"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-414-5db8d848d12a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0mxx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mjj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_i\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0myy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mjj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mY_i\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m     \u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprint_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtotal_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfailed_attempts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m     \u001b[0msave_nets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-255-8cc445ea5dce>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, xx, YY, passes, print_error, return_error, failed_attempts, total_info)\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;31m#                             print('w')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[0;31m#                             print(w.shape)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m                             \u001b[0mw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mxl\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mL\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mYl\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mL\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m \u001b[0;31m#                                 w=np.nan_to_num(w)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36minv\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/home/peter/.local/lib/python3.6/site-packages/numpy/linalg/linalg.py\u001b[0m in \u001b[0;36minv\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m    545\u001b[0m     \u001b[0msignature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'D->D'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'd->d'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m     \u001b[0mextobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_linalg_error_extobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 547\u001b[0;31m     \u001b[0mainv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_umath_linalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    548\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mainv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "n_data=1001\n",
    "# hidden_layers=[16,32,256,32,16]\n",
    "hidden_layers=[16,16,16,16]\n",
    "hidden_layers=[128,128,128]\n",
    "hidden_layers=[32,256,256,32]\n",
    "hidden_layers=[256,256,256,256,32]\n",
    "hidden_layers=[512,256,128,62]\n",
    "\n",
    "\n",
    "n1=int(np.sqrt(n_data))\n",
    "x1,x2=np.meshgrid(np.linspace(-2,2,n1),np.linspace(-2,2,n1))\n",
    "x=np.hstack([x1.reshape((n1*n1,1)),x2.reshape(n1*n1,1)])\n",
    "# x=np.random.rand(n_data,2)*4-2\n",
    "# y= 0.8*np.sin(x[:,0]*3.4)+0.8*np.sin(x[:,1]*3)#+np.random.randn(x.shape[0],1)*0.1\n",
    "# y=y.reshape((y.shape[0],1))\n",
    "y=test_fun_2d(x)\n",
    "x_test=np.linspace(-2,2,1000).reshape(1000,1)\n",
    "# plt.plot(x,y,'.')\n",
    "net=BNet7(hidden_layer_sizes=hidden_layers,input_dim=784,output_dim=10,lambda_initaliser=lambda_initaliser,initialiser_function=initialiser_function)\n",
    "save_nets=[]\n",
    "n_data=1000\n",
    "xx=np.zeros((n_data,784))\n",
    "yy=np.zeros((n_data,10))\n",
    "for ii in range(50): #for observations in the dataset\n",
    "    print(ii)\n",
    "    for jj in range(n_data):\n",
    "        X_i=np.array(mnist_data[ii*n_data+jj][0]).reshape(1,784)#mnist_data[ii][0].flatten()\n",
    "        Y_i=one_hot(mnist_data[ii*n_data+jj][1],10)\n",
    "        xx[jj,:]=X_i\n",
    "        yy[jj,:]=Y_i\n",
    "    net.train(xx,yy,print_error=False,passes=1,total_info=1,failed_attempts=10)\n",
    "    save_nets.append(copy.deepcopy(net))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n"
     ]
    }
   ],
   "source": [
    "hidden_layer_options=[[512,256,128,64],[512,521,256,128,64],[512,512,512,256,128,64],\n",
    "                      [256],[256,256],[256,256,256],[256,256,256,256],[256,256,256,256,256],[256,256,256,256,256,256]]\n",
    "file_num=0\n",
    "for hidden_layers in hidden_layer_options:\n",
    "    net=BNet7(hidden_layer_sizes=hidden_layers,input_dim=784,output_dim=10,lambda_initaliser=lambda_initaliser,initialiser_function=initialiser_function)\n",
    "    save_nets=[]\n",
    "    n_data=1000\n",
    "    xx=np.zeros((n_data,784))\n",
    "    yy=np.zeros((n_data,10))\n",
    "    for ii in range(50): #for observations in the dataset\n",
    "        print(ii)\n",
    "        for jj in range(n_data):\n",
    "            X_i=np.array(mnist_data[ii*n_data+jj][0]).reshape(1,784)#mnist_data[ii][0].flatten()\n",
    "            Y_i=one_hot(mnist_data[ii*n_data+jj][1],10)\n",
    "            xx[jj,:]=X_i\n",
    "            yy[jj,:]=Y_i\n",
    "        net.train(xx,yy,print_error=False,passes=1,total_info=1,failed_attempts=10)\n",
    "        save_nets.append(copy.deepcopy(net))\n",
    "    \n",
    "    with open('net_save'+str(file_num)+'.pkl','wb') as output:\n",
    "        pickle.dump((hidden_layers,save_nets),output,pickle.HIGHEST_PROTOCOL)\n",
    "#         pickle.dump(hidden_layers,output,pickle.HIGHEST_PROTOCOL)\n",
    "    file_num+=1    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truth: 3, Predicted: 4\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 9, Predicted: 2\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 5, Predicted: 8\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 1, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 5\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 1, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 7, Predicted: 5\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 3, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 8\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 9, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 5, Predicted: 8\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 6, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 0, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 8, Predicted: 6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 1, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 0, Predicted: 3\n",
      "Truth: 9, Predicted: 5\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 8\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 0, Predicted: 3\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 4\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 6, Predicted: 3\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 1, Predicted: 7\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 1, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 7\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 7\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 5\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 0, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truth: 3, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 9, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 5\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 5\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 8\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 7\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 7, Predicted: 0\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 2\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 5, Predicted: 8\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 0, Predicted: 4\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 0, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 9, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 8\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 6, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 9, Predicted: 8\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 5\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 4, Predicted: 5\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 7, Predicted: 0\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 8\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 8\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 8\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 9, Predicted: 8\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truth: 5, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 5, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 0, Predicted: 3\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 8\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 1\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 8\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 6, Predicted: 7\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 5\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 2\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 9, Predicted: 2\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 0, Predicted: 3\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 9, Predicted: 2\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 7, Predicted: 8\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 9, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 4, Predicted: 8\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 4\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 4, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 7, Predicted: 8\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 3, Predicted: 6\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 1, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 5\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 6\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 8\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 4\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 1, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 1, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 7, Predicted: 5\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 0, Predicted: 1\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 1, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 4, Predicted: 5\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 0, Predicted: 1\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 1, Predicted: 4\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 4\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 0, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 6\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 3\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 1, Predicted: 4\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 4\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 0, Predicted: 5\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 6\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 9, Predicted: 8\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 3, Predicted: 7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truth: 6, Predicted: 8\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 0, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 7, Predicted: 0\n",
      "Truth: 9, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 3\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 8\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 8\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 5\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 8\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 5\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 0, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 9, Predicted: 8\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 8\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 7\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 9, Predicted: 2\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 5, Predicted: 8\n",
      "Truth: 1, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 3, Predicted: 6\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 2\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 2\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 4\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 4, Predicted: 0\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 6\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 5\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 2, Predicted: 9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truth: 3, Predicted: 9\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 9, Predicted: 2\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 4, Predicted: 5\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 1, Predicted: 2\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 0\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 0, Predicted: 3\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 0, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 4, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 4, Predicted: 8\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 5\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 0, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 8\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 1, Predicted: 5\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 3\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 5\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 2\n",
      "Truth: 6, Predicted: 7\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 7, Predicted: 8\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 4, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 5, Predicted: 7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truth: 1, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 7, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 5\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 4\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 4, Predicted: 0\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 4\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 1, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 4, Predicted: 0\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 9, Predicted: 8\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 4, Predicted: 0\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 2, Predicted: 4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 8\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 7\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 7, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 1, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 5\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 0\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 7, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 5\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 8\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 7, Predicted: 8\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 0, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 8\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 8\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 7, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 6, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 8\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 7, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truth: 5, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 7\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 1\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 6, Predicted: 7\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 0, Predicted: 5\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 1, Predicted: 5\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 8\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 2, Predicted: 5\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 8\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 4\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 7, Predicted: 8\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 1\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 1\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 0\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 1, Predicted: 2\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 8\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 5\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 9, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 7\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 9, Predicted: 0\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 5\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 6, Predicted: 7\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 5\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 7, Predicted: 2\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 9, Predicted: 8\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 1, Predicted: 9\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 3, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 9, Predicted: 1\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 6\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 0, Predicted: 5\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 9, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 4, Predicted: 7\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 2, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 4, Predicted: 5\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 5\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 2, Predicted: 4\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 4, Predicted: 0\n",
      "Truth: 2, Predicted: 8\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 5, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 9\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 2, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 3\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 1, Predicted: 4\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 6\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 2\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 8, Predicted: 1\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 3, Predicted: 6\n",
      "Truth: 5, Predicted: 6\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 6, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 7, Predicted: 0\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 8, Predicted: 0\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 3, Predicted: 1\n",
      "Truth: 4, Predicted: 3\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 4, Predicted: 6\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 2, Predicted: 9\n",
      "Truth: 8, Predicted: 6\n",
      "Truth: 3, Predicted: 0\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 7, Predicted: 3\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 4, Predicted: 4\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 6, Predicted: 4\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 5, Predicted: 7\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 6, Predicted: 9\n",
      "Truth: 5, Predicted: 5\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 4, Predicted: 9\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 5, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 0, Predicted: 7\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 1, Predicted: 4\n",
      "Truth: 1, Predicted: 3\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 5, Predicted: 4\n",
      "Truth: 8, Predicted: 2\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 8, Predicted: 9\n",
      "Truth: 6, Predicted: 6\n",
      "Truth: 4, Predicted: 0\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 7, Predicted: 6\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 6, Predicted: 7\n",
      "Truth: 5, Predicted: 3\n",
      "Truth: 7, Predicted: 9\n",
      "Truth: 3, Predicted: 5\n",
      "Truth: 2, Predicted: 3\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 5, Predicted: 1\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 3, Predicted: 9\n",
      "Truth: 2, Predicted: 0\n",
      "Truth: 1, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 2, Predicted: 1\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 3, Predicted: 3\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 8, Predicted: 7\n",
      "Truth: 2, Predicted: 2\n",
      "Truth: 5, Predicted: 2\n",
      "Truth: 9, Predicted: 9\n",
      "Truth: 8, Predicted: 8\n",
      "Truth: 4, Predicted: 1\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 9, Predicted: 7\n",
      "Truth: 6, Predicted: 0\n",
      "Truth: 7, Predicted: 7\n",
      "Truth: 3, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "Truth: 4, Predicted: 2\n",
      "Truth: 0, Predicted: 0\n",
      "2433\n"
     ]
    }
   ],
   "source": [
    "correct=[]\n",
    "totals=np.zeros(10)\n",
    "totals_correct=np.zeros(10)\n",
    "for ii in range(5000):\n",
    "    ind=50000+ii\n",
    "    X_i=np.array(mnist_data[ind][0]).reshape(1,784)#mnist_data[ii][0].flatten()\n",
    "    Y_i=one_hot(mnist_data[ind][1],10)\n",
    "    Y_out=net.forward(X_i)[0]\n",
    "    print('Truth: ' + str(np.argmax(Y_i))+ ', Predicted: '+str(np.argmax(Y_out)))\n",
    "    totals[np.argmax(Y_i)]+=1\n",
    "\n",
    "    if np.argmax(Y_i)==np.argmax(Y_out):\n",
    "        correct.append(1)\n",
    "        totals_correct[np.argmax(Y_i)]+=1\n",
    "    else:\n",
    "        correct.append(0)\n",
    "print(sum(correct))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<__main__.BNet7 object at 0x7f03cb037080>\n",
      "0.6254\n",
      "[502. 534. 497. 521. 484. 457. 485. 527. 515. 478.]\n",
      "[413. 488. 259. 302. 363. 180. 353. 370. 227. 172.]\n",
      "[0.82270916 0.91385768 0.52112676 0.57965451 0.75       0.39387309\n",
      " 0.72783505 0.70208729 0.4407767  0.35983264]\n",
      "=====\n",
      "<__main__.BNet7 object at 0x7f03cb0375f8>\n",
      "0.6642\n",
      "[502. 534. 497. 521. 484. 457. 485. 527. 515. 478.]\n",
      "[423. 498. 276. 317. 365. 185. 398. 387. 267. 205.]\n",
      "[0.84262948 0.93258427 0.55533199 0.6084453  0.75413223 0.404814\n",
      " 0.82061856 0.73434535 0.5184466  0.42887029]\n",
      "=====\n",
      "<__main__.BNet7 object at 0x7f03cb0374a8>\n",
      "0.6848\n",
      "[502. 534. 497. 521. 484. 457. 485. 527. 515. 478.]\n",
      "[441. 508. 292. 331. 375. 202. 411. 387. 286. 191.]\n",
      "[0.87848606 0.95131086 0.58752515 0.6353167  0.77479339 0.44201313\n",
      " 0.84742268 0.73434535 0.55533981 0.39958159]\n",
      "=====\n",
      "<__main__.BNet7 object at 0x7f03cafc0f28>\n",
      "0.6954\n",
      "[502. 534. 497. 521. 484. 457. 485. 527. 515. 478.]\n",
      "[442. 506. 287. 332. 388. 189. 422. 393. 318. 200.]\n",
      "[0.88047809 0.94756554 0.57746479 0.63723608 0.80165289 0.41356674\n",
      " 0.87010309 0.74573055 0.61747573 0.41841004]\n",
      "=====\n"
     ]
    }
   ],
   "source": [
    "\n",
    "correct_fracton=[]\n",
    "\n",
    "for net1 in save_nets:\n",
    "    print(net1)\n",
    "    correct=[]\n",
    "    totals=np.zeros(10)\n",
    "    totals_correct=np.zeros(10)\n",
    "    n_test=5000\n",
    "#     correct_fracton=[]\n",
    "    for ii in range(n_test):\n",
    "        ind=50000+ii\n",
    "        X_i=np.array(mnist_data[ind][0]).reshape(1,784)#mnist_data[ii][0].flatten()\n",
    "        Y_i=one_hot(mnist_data[ind][1],10)\n",
    "        Y_out=net1.forward(X_i)[0]\n",
    "#         print('Truth: ' + str(np.argmax(Y_i))+ ', Predicted: '+str(np.argmax(Y_out)))\n",
    "        totals[np.argmax(Y_i)]+=1\n",
    "\n",
    "        if np.argmax(Y_i)==np.argmax(Y_out):\n",
    "            correct.append(1)\n",
    "            totals_correct[np.argmax(Y_i)]+=1\n",
    "        else:\n",
    "            correct.append(0)\n",
    "    print(sum(correct)/n_test)\n",
    "    print(totals)\n",
    "    print(totals_correct)\n",
    "    print(totals_correct/totals)\n",
    "    correct_fracton.append(sum(correct)/n_test)\n",
    "    print('=====')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<__main__.BNet7 at 0x7f03d20b23c8>,\n",
       " <__main__.BNet7 at 0x7f03d20f4128>,\n",
       " <__main__.BNet7 at 0x7f03d20f40b8>,\n",
       " <__main__.BNet7 at 0x7f03d20f4048>,\n",
       " <__main__.BNet7 at 0x7f03d20f4160>,\n",
       " <__main__.BNet7 at 0x7f03d20f40f0>,\n",
       " <__main__.BNet7 at 0x7f03d20f4080>,\n",
       " <__main__.BNet7 at 0x7f03d20ca278>,\n",
       " <__main__.BNet7 at 0x7f03d20ca5c0>,\n",
       " <__main__.BNet7 at 0x7f03d20ca400>,\n",
       " <__main__.BNet7 at 0x7f03d20ca588>,\n",
       " <__main__.BNet7 at 0x7f03d20ca470>,\n",
       " <__main__.BNet7 at 0x7f03d20ca048>,\n",
       " <__main__.BNet7 at 0x7f03d20ca0b8>,\n",
       " <__main__.BNet7 at 0x7f03d20ca518>,\n",
       " <__main__.BNet7 at 0x7f03d20ca3c8>,\n",
       " <__main__.BNet7 at 0x7f03d20ca4a8>,\n",
       " <__main__.BNet7 at 0x7f03d20ca080>,\n",
       " <__main__.BNet7 at 0x7f03d20ca208>,\n",
       " <__main__.BNet7 at 0x7f03d20ca1d0>,\n",
       " <__main__.BNet7 at 0x7f03d20ca438>,\n",
       " <__main__.BNet7 at 0x7f03d20ca5f8>,\n",
       " <__main__.BNet7 at 0x7f03d20ca668>,\n",
       " <__main__.BNet7 at 0x7f03d20ca630>,\n",
       " <__main__.BNet7 at 0x7f03d20ca6a0>,\n",
       " <__main__.BNet7 at 0x7f03d20ca550>,\n",
       " <__main__.BNet7 at 0x7f03d20ca2e8>,\n",
       " <__main__.BNet7 at 0x7f03d20ca6d8>,\n",
       " <__main__.BNet7 at 0x7f03d20ca780>,\n",
       " <__main__.BNet7 at 0x7f03d20ca7f0>,\n",
       " <__main__.BNet7 at 0x7f03d20ca7b8>,\n",
       " <__main__.BNet7 at 0x7f03d20ca828>,\n",
       " <__main__.BNet7 at 0x7f03d20ca710>,\n",
       " <__main__.BNet7 at 0x7f03d20ca748>,\n",
       " <__main__.BNet7 at 0x7f03d20ca160>,\n",
       " <__main__.BNet7 at 0x7f03d20ca198>,\n",
       " <__main__.BNet7 at 0x7f03d20ca0f0>,\n",
       " <__main__.BNet7 at 0x7f03d20ca860>,\n",
       " <__main__.BNet7 at 0x7f03d20ca8d0>,\n",
       " <__main__.BNet7 at 0x7f03d20ca940>,\n",
       " <__main__.BNet7 at 0x7f03d20ca908>,\n",
       " <__main__.BNet7 at 0x7f03d20ca898>,\n",
       " <__main__.BNet7 at 0x7f03d20ca978>,\n",
       " <__main__.BNet7 at 0x7f03d20ca9e8>,\n",
       " <__main__.BNet7 at 0x7f03d20cabe0>,\n",
       " <__main__.BNet7 at 0x7f03d20cac18>,\n",
       " <__main__.BNet7 at 0x7f03d20caa20>,\n",
       " <__main__.BNet7 at 0x7f03d20caa58>,\n",
       " <__main__.BNet7 at 0x7f03d20cab00>,\n",
       " <__main__.BNet7 at 0x7f03d20cab38>]"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_nets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f03ca9754a8>]"
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(correct_fracton)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[502. 534. 497. 521. 484. 457. 485. 527. 515. 478.]\n",
      "[341. 510. 196. 269. 324. 106. 367. 300. 331. 101.]\n",
      "[0.67928287 0.95505618 0.3943662  0.51631478 0.66942149 0.23194748\n",
      " 0.75670103 0.56925996 0.64271845 0.21129707]\n"
     ]
    }
   ],
   "source": [
    "print(totals)\n",
    "print(totals_correct)\n",
    "print(totals_correct/totals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 0.63155847,  0.17934295, -0.21901222,  0.36938726, -0.0520595 ,\n",
       "          0.28347194, -0.14613476, -0.03787791, -0.10506128,  0.09631389]]),\n",
       " array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]))"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_out,Y_i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f03de75f748>]"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx=np.linspace(-10,10,1001)\n",
    "y1=1/(1+np.exp(-xx+3))\n",
    "y2=1/(1+np.exp(xx))\n",
    "plt.plot(xx,y1)\n",
    "plt.plot(xx,y2)\n",
    "plt.plot(xx,y1*y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}