NN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "「「nientzu_NN.ipynb」的副本」的副本",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/s07362022/CNN_to_train/blob/master/%E3%80%8C%E3%80%8Cnientzu_NN_ipynb%E3%80%8D%E7%9A%84%E5%89%AF%E6%9C%AC%E3%80%8D%E7%9A%84%E5%89%AF%E6%9C%AC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDUpvV6Hv1Zs",
        "outputId": "f05f4ce4-5f99-4a60-a482-68966b7e622f"
      },
      "source": [
        "from google.colab import drive\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "drive.mount('/content/drive')\n",
        "project_path='/content/drive/MyDrive/'\n",
        "os.chdir(project_path)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OYYddWRrze2p"
      },
      "source": [
        "import numpy as np\n",
        "def shuffle(x,y):\n",
        "    x = x.T\n",
        "    y = y.T\n",
        "    combine = np.c_[x.reshape(len(x), -1), y.reshape(len(y), -1)]\n",
        "    np.random.shuffle(combine)\n",
        "    return combine[:, :x.size//len(x)].reshape(x.shape).T,combine[:, x.size//len(x):].reshape(y.shape).T\n",
        "class One_layer:    \n",
        "    def __init__(self, in_dim, out_dim, act,lr,apt_type,val):\n",
        "        self.in_dim = in_dim\n",
        "        self.out_dim = out_dim\n",
        "        self.weight = np.random.randn(out_dim,in_dim+1)/10\n",
        "        \n",
        "        #self.weight = np.random.rand(out_dim,in_dim+1)/10\n",
        "        \n",
        "        act_list = {\"relu\":self.sigmoid, \"tanh\":self.tanh,\"relu\":self.relu,\"sigmoid\":self.sigmoid,\n",
        "                    \"softmax\":self.softmax,\"linear\":self.linear}\n",
        "        dif_list = {\"relu\":self.dif_sigmoid, \"tanh\":self.dif_tanh,\"relu\":self.dif_relu,\"sigmoid\":self.dif_sigmoid,\n",
        "                    \"softmax\":self.dif_softmax,\"linear\":self.dif_linear}\n",
        "        self.act = act_list[act]\n",
        "        self.dif_act = dif_list[act]\n",
        "        self.lr = lr\n",
        "        self.apt_type=apt_type\n",
        "        if apt_type=='Momentum':\n",
        "          self.Momentum = val\n",
        "          self.weight_f = np.random.randn(out_dim,in_dim+1)/10\n",
        "        elif apt_type=='AdaGrad':\n",
        "          self.EPS = val\n",
        "          self.weight_f = np.zeros([out_dim,in_dim+1])\n",
        "        #self.batch_size = batch_size\n",
        "        self.input = np.array([]) #np.zeros((batch_size,in_dim))\n",
        "        self.local_grad = np.array([]) #np.zeros((batch_size,out_dim))    \n",
        "        self.value = np.array([])\n",
        "        \n",
        "    def forward(self,data):    \n",
        "        data = np.concatenate((np.ones(data.shape[1]).reshape(1,-1),data), axis=0)         \n",
        "        self.input = data\n",
        "        self.value = self.weight.dot(data)\n",
        "        return self.act(self.value) \n",
        "    \n",
        "    def output(self,data): \n",
        "        data = np.concatenate((np.ones(data.shape[1]).reshape(1,-1),data), axis=0) \n",
        "        return self.act(self.weight.dot(data))    \n",
        "    \n",
        "    def backward(self,err):\n",
        "        self.local_grad = self.dif_act(self.value,err)\n",
        "        return self.weight.T.dot(self.local_grad)[1:,:]    \n",
        "    \n",
        "    def weight_renew(self,batch_size):       \n",
        "        delta_w = self.lr*(self.local_grad).dot(self.input.T)#/batch_size#\n",
        "        if self.apt_type=='None':\n",
        "          self.weight = self.weight - delta_w\n",
        "        elif self.apt_type=='Momentum':\n",
        "          #self.weight = self.weight - delta_w + (self.Momentum * self.weight_f)\n",
        "          self.weight = self.weight + self.weight_f\n",
        "          #self.weight_f = (self.local_grad).dot(self.input.T)\n",
        "          self.weight_f = self.Momentum * self.weight_f - delta_w\n",
        "        elif self.apt_type=='AdaGrad':\n",
        "          self.weight = self.weight - delta_w * (1/np.sqrt(self.weight_f+self.EPS))\n",
        "          self.weight_f = self.weight_f + ((self.local_grad).dot(self.input.T))**2\n",
        "        return self.weight    \n",
        "    def sigmoid(self,z):\n",
        "        return 1/(1+np.exp(-z))\n",
        "    def tanh(self,z):\n",
        "        return (np.exp(z)-np.exp(-z))/(np.exp(z)+np.exp(-z))\n",
        "    def relu(self,z):\n",
        "        return np.maximum(0,z)  \n",
        "    \n",
        "    def linear(self,z):\n",
        "        return z \n",
        "    \n",
        "    def dif_linear(self,z,err):\n",
        "        return err\n",
        "    \n",
        "    def softmax(self,x):\n",
        "        shiftx = x - np.max(x)\n",
        "        exps = np.exp(shiftx)\n",
        "        return exps / np.sum(exps,axis=0)    \n",
        "    \n",
        "    def dif_sigmoid(self,z,err):\n",
        "        temp=self.sigmoid(z)        \n",
        "        return (temp*(1-temp))*err    \n",
        "    def dif_tanh(self,z,err):\n",
        "        return (1-self.tanh(self,z)**2)*err    \n",
        "    def dif_relu(self,z,err):\n",
        "        return np.maximum(np.sign(z),0)*err     \n",
        "    def dif_softmax(self,z,err):\n",
        "        local_grad = []\n",
        "        for i,ee in enumerate(z.T):\n",
        "            ee = ee.T.reshape(1,-1)\n",
        "            tt = self.softmax(ee.T)\n",
        "            local_grad.append((np.eye(ee.size)*tt-tt.dot(tt.T)).dot(err[:,i]))\n",
        "        return np.array(local_grad).T#(np.eye(self.out_dim)*S - S.dot(S.T)).dot(err)\n",
        "    def fetch_w(self):\n",
        "        return self.weight\n",
        "\n",
        "class RBF_layer:    \n",
        "    def __init__(self, in_dim, out_dim, cent_lr, wid_lr,data,act=\"gaussian\"):\n",
        "        self.in_dim = in_dim\n",
        "        self.out_dim = out_dim\n",
        "        self.centers = self.plus_plus(data, out_dim)\n",
        "        self.var_sqr_inv = np.ones((out_dim,in_dim))/10\n",
        "        \n",
        "        act_list = {\"gaussian\":self.gaussian, \"inv_quadratic\":self.quadratic}\n",
        "        act_diff_list = {\"gaussian\":self.diff_gaussian, \"inv_quadratic\":self.diff_quadratic}\n",
        "        \n",
        "        self.act = act_list[act]\n",
        "        self.act_diff = act_diff_list[act]\n",
        "        \n",
        "        self.cent_lr = cent_lr\n",
        "        self.wid_lr = wid_lr\n",
        "        self.output_norm1 = []\n",
        "        self.out = []\n",
        "        self.err_diff = []\n",
        "    def gaussian(self,data):\n",
        "        return np.exp(-data)\n",
        "    def quadratic(self,data):\n",
        "        return 1/(1+data)**0.5\n",
        "    \n",
        "    def forward(self,data): \n",
        "        o1 = []\n",
        "        for i,cc in enumerate(self.centers):\n",
        "            o1.append((data.T-cc))            \n",
        "        self.output_norm1 = np.array(o1) \n",
        "        \n",
        "        ot = []          \n",
        "        for i,dc in enumerate(np.array(o1)):\n",
        "            ot.append((dc**2).dot(self.var_sqr_inv[i].reshape(-1,1))[:,0])        \n",
        "        temp =  self.act(np.array(ot))\n",
        "        self.out = temp\n",
        "        return temp    \n",
        "    def output(self,data): \n",
        "        o1 = []\n",
        "        for i,cc in enumerate(self.centers):\n",
        "            o1.append((data.T-cc))             \n",
        "        ot = []           \n",
        "        for i,dc in enumerate(np.array(o1)):\n",
        "            ot.append((dc**2).dot(self.var_sqr_inv[i].reshape(-1,1))[:,0])  \n",
        "        temp =  self.act(np.array(ot))\n",
        "        #self.out = temp\n",
        "        return temp  \n",
        "    \n",
        "    def backward(self,err):\n",
        "        self.err_diff = err\n",
        "        return 0 \n",
        "    \n",
        "    def weight_renew(self,batch_size):   \n",
        "        self.act_diff()\n",
        "    \n",
        "    def diff_quadratic(self):\n",
        "        delta_c = []\n",
        "        for i,dc in enumerate(self.output_norm1):\n",
        "            delta_c.append(-(2*dc.T*self.var_sqr_inv[i].reshape(-1,1)).dot((self.err_diff[i]*-0.5*self.out[i]**3).reshape(-1,1))[:,0])\n",
        "        delta_c = np.array(delta_c)\n",
        "        \n",
        "        delta_wid = []\n",
        "        for i,dc in enumerate(self.output_norm1):\n",
        "            delta_wid.append((self.err_diff[i]*-0.5*self.out[i]**3).dot(dc**2))\n",
        "        delta_wid = np.array(delta_wid)\n",
        "        \n",
        "        self.centers -= self.cent_lr*delta_c\n",
        "        self.var_sqr_inv = np.maximum(0,self.var_sqr_inv- self.wid_lr*delta_wid) \n",
        "        \n",
        "    def diff_gaussian(self):\n",
        "        delta_c = []\n",
        "        for i,dc in enumerate(self.output_norm1):\n",
        "            delta_c.append((2*dc.T*self.var_sqr_inv[i].reshape(-1,1)).dot((self.err_diff[i]*self.out[i]).reshape(-1,1))[:,0])\n",
        "        delta_c = np.array(delta_c)\n",
        "        \n",
        "        delta_wid = []\n",
        "        for i,dc in enumerate(self.output_norm1):\n",
        "            delta_wid.append(-(self.err_diff[i]*self.out[i]).dot(dc**2))\n",
        "        delta_wid = np.array(delta_wid)\n",
        "        \n",
        "        self.centers -= self.cent_lr*delta_c\n",
        "        self.var_sqr_inv = np.maximum(0,self.var_sqr_inv- self.wid_lr*delta_wid) \n",
        "        \n",
        "    def plus_plus(self,ds, k):\n",
        "        centroids = [ds[0]]\n",
        "        for _ in range(1, k):\n",
        "            dist_sq = np.array([min([np.inner(c-x,c-x) for c in centroids]) for x in ds])\n",
        "            probs = dist_sq/dist_sq.sum()\n",
        "            cumulative_probs = probs.cumsum()\n",
        "            r = np.random.rand()\n",
        "            for j, p in enumerate(cumulative_probs):\n",
        "                if r < p:\n",
        "                    i = j\n",
        "                    break\n",
        "            centroids.append(ds[i])\n",
        "        return np.array(centroids)\n",
        "\n",
        "class NN_model:\n",
        "    def __init__(self,layer_list,batch_size,loss):\n",
        "        cost_list = {\"Binary\":self.CE_twoclass, \"Multi_class\":self.CE,\n",
        "                     \"Regression_mse\":self.MSE,\"Regression_mae\":self.MAE}     \n",
        "        cost_dif_list = {\"Binary\":self.dif_ce_twoclass, \"Multi_class\":self.dif_ce,\n",
        "                         \"Regression_mse\":self.dif_mse,\"Regression_mae\":self.dif_mae}\n",
        "        self.layer_list = layer_list\n",
        "        self.batch_size = batch_size\n",
        "        self.loss=cost_list[loss]\n",
        "        self.dif_loss=cost_dif_list[loss]\n",
        "        \n",
        "        acc_type_list = {\"Binary\":self.acc_binary, \"Multi_class\":self.acc_multiclass\n",
        "                         ,\"Regression_mae\":self.acc_regression,\"Regression_mse\":self.acc_regression}\n",
        "        self.loss_func = acc_type_list[loss]\n",
        "        \n",
        "    def output(self,x):\n",
        "        temp = x\n",
        "        for ll in self.layer_list:\n",
        "            temp = ll.output(temp)\n",
        "        return temp\n",
        "    \n",
        "    def forward(self,x):\n",
        "        temp = x\n",
        "        for ll in self.layer_list:\n",
        "            temp = ll.forward(temp)            \n",
        "        return temp\n",
        "    \n",
        "    def back_propagation(self,x):\n",
        "        temp = x\n",
        "        for ll in self.layer_list[::-1]:\n",
        "            temp = ll.backward(temp)\n",
        "        return temp\n",
        "    \n",
        "    def loss_dif(self,y,y_hat):  \n",
        "        return self.dif_loss(y,y_hat)    \n",
        "    \n",
        "    def MSE(self,y,y_hat,scale):\n",
        "        err_avg = np.sum(((y_hat-y)*scale[1])**2,axis=0)\n",
        "        return err_avg,sum(err_avg)/y_hat.shape[1]\n",
        "    \n",
        "    def MAE(self,y,y_hat,scale):\n",
        "        err_avg = np.sum(np.abs((y_hat-y)*scale[1]),axis=0)\n",
        "        return err_avg,sum(err_avg)/y_hat.shape[1]\n",
        "    \n",
        "    def CE_twoclass(self,p,y):\n",
        "        err = -(y*np.log(p)+(1-y)*np.log(1-p))\n",
        "        each_batch_avg_err = np.sum(err,axis=1)/err.shape[1]\n",
        "        total_avg_err = sum(each_batch_avg_err)\n",
        "        return err,total_avg_err\n",
        "    \n",
        "    def CE(self,p,y):\n",
        "        err = -np.log(np.sum(p*y,axis=0))\n",
        "        return err,sum(err)/p.shape[1]\n",
        "    \n",
        "    def dif_ce_twoclass(self,p,y):            \n",
        "        #return (np.sum(-(np.divide(y, p) - np.divide(1 - y, 1 - p)),axis=1)/y.shape[1]).reshape(-1,1) \n",
        "        return (-(np.divide(y, p) - np.divide(1 - y, 1 - p)))\n",
        "    \n",
        "    def dif_ce(self,p,y):\n",
        "        return -np.divide(1,p)*y \n",
        "    \n",
        "    def dif_mae(self,y,y_hat):\n",
        "        #return -(np.sum(y_hat-y,axis=1)/y.shape[1]).reshape(-1,1)\n",
        "        return -np.sign((y_hat-y))\n",
        "    \n",
        "    def dif_mse(self,y,y_hat):\n",
        "        #return -(np.sum(y_hat-y,axis=1)/y.shape[1]).reshape(-1,1)\n",
        "        return -(y_hat-y)\n",
        "    \n",
        "    def update_weight(self):\n",
        "        for ll in self.layer_list:\n",
        "            ll.weight_renew(self.batch_size)\n",
        "        return self.layer_list[len(self.layer_list)-1].weight\n",
        "    def shuffle(self,x,y):\n",
        "        x = x.T\n",
        "        y = y.T\n",
        "        combine = np.c_[x.reshape(len(x), -1), y.reshape(len(y), -1)]\n",
        "        np.random.shuffle(combine)\n",
        "        return combine[:, :x.size//len(x)].reshape(x.shape).T,combine[:, x.size//len(x):].reshape(y.shape).T\n",
        "    \n",
        "    # -------------------------------------- Acc --------------------------------------------------#\n",
        "    def acc_binary(self,ee,epochs,x_train,x_valid,y_train,y_valid,scale=[]):\n",
        "        y_va = self.output(x_valid)\n",
        "        vl = self.loss(y_va,y_valid)[1]\n",
        "        y_tr = self.output(x_train)\n",
        "        tl = self.loss(y_tr,y_train)[1]        \n",
        "        ta = self.bb_acc(y_tr,y_train)\n",
        "        va = self.bb_acc(y_va,y_valid)        \n",
        "        print(\"Epoch\",ee,\"/\",epochs,\"- loss: %.4f\" %tl,\n",
        "            \"- accuracy: %.4f\" %ta,\n",
        "            \"- val_loss: %.4f\" %vl,\n",
        "            \"- val_accuracy: %.4f\" %va)            \n",
        "        return [tl,vl,ta,va]\n",
        "    \n",
        "        out =(yhat> 0.5).astype(int)\n",
        "        return np.sum(np.equal(y.reshape(-1,1),out.reshape(-1,1)))/y.size\n",
        "    \n",
        "    def bb_acc(self,yhat,y):\n",
        "        out =(yhat> 0.5).astype(int)\n",
        "        return np.sum(np.equal(y.reshape(-1,1),out.reshape(-1,1)))/y.size\n",
        "    \n",
        "    def acc_multiclass(self,ee,epochs,x_train,x_valid,y_train,y_valid,scale=[]):\n",
        "        y_va = self.output(x_valid)\n",
        "        vl = self.loss(y_va,y_valid)[1]\n",
        "        y_tr = self.output(x_train)\n",
        "        tl = self.loss(y_tr,y_train)[1]         \n",
        "        ta = self.mc_acc(y_tr,y_train)\n",
        "        va = self.mc_acc(y_va,y_valid)        \n",
        "        print(\"Epoch\",ee,\"/\",epochs,\"- loss: %.4f\" %tl,\n",
        "            \"- accuracy: %.4f\" %ta,\n",
        "            \"- val_loss: %.4f\" %vl,\n",
        "            \"- val_accuracy: %.4f\" %va)            \n",
        "        return [tl,vl,ta,va]\n",
        "    \n",
        "    def mc_acc(self,yhat,y):\n",
        "        y_hat = np.argmax(yhat,axis=0)\n",
        "        yy = np.argmax(y,axis=0)\n",
        "        return np.sum(np.equal(y_hat,yy))/len(yy)\n",
        "    \n",
        "    def acc_regression(self,ee,epochs,x_train,x_valid,y_train,y_valid,scale):\n",
        "        y_va = self.output(x_valid)\n",
        "        vl = self.loss(y_va,y_valid,scale)[1]\n",
        "        y_tr = self.output(x_train)\n",
        "        tl = self.loss(y_tr,y_train,scale)[1] \n",
        "        print(\"Epoch\",ee,\"/\",epochs,\"- loss: %.4f\" %tl,\n",
        "              \"- val_loss: %.2f \" %vl) \n",
        "        return [tl,vl]\n",
        "    \n",
        "    def training(self,x_train,x_valid,y_train,y_valid,epochs,scale=[]):        \n",
        "        batches = int(x_train.shape[1]/self.batch_size)\n",
        "        max_index = x_train.shape[1]    \n",
        "        history = []\n",
        "        w_list = []\n",
        "        for ee in range(epochs):\n",
        "            x_train,y_train = self.shuffle(x_train,y_train) ## 每次training data 的順序打亂\n",
        "            for batch in range(batches):\n",
        "                st = batch*self.batch_size\n",
        "                end = min((st+self.batch_size),max_index)                  \n",
        "                out = self.forward(x_train[:,st:end])\n",
        "                err = self.loss_dif(out,y_train[:,st:end])\n",
        "                self.back_propagation(err)\n",
        "                w_list.append(self.update_weight())                \n",
        "            history.append(self.loss_func(ee,epochs,x_train,x_valid,y_train,y_valid,scale))\n",
        "        return np.array(history),w_list\n",
        "\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9yshZewVphPI"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np \n",
        "df = pd.read_csv(\"/content/drive/MyDrive/drug200.csv\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ae2mDtO4pvCz",
        "outputId": "b8b65772-ba5f-4b5c-ef31-4730101a1518"
      },
      "source": [
        "df"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>BP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
              "      <th>Drug</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>F</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>25.355</td>\n",
              "      <td>DrugY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>13.093</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>10.114</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>7.798</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>F</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>18.043</td>\n",
              "      <td>DrugY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>56</td>\n",
              "      <td>F</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>11.567</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>16</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>12.006</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>52</td>\n",
              "      <td>M</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>9.894</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>23</td>\n",
              "      <td>M</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>14.020</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>40</td>\n",
              "      <td>F</td>\n",
              "      <td>LOW</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>11.349</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Age Sex      BP Cholesterol  Na_to_K   Drug\n",
              "0     23   F    HIGH        HIGH   25.355  DrugY\n",
              "1     47   M     LOW        HIGH   13.093  drugC\n",
              "2     47   M     LOW        HIGH   10.114  drugC\n",
              "3     28   F  NORMAL        HIGH    7.798  drugX\n",
              "4     61   F     LOW        HIGH   18.043  DrugY\n",
              "..   ...  ..     ...         ...      ...    ...\n",
              "195   56   F     LOW        HIGH   11.567  drugC\n",
              "196   16   M     LOW        HIGH   12.006  drugC\n",
              "197   52   M  NORMAL        HIGH    9.894  drugX\n",
              "198   23   M  NORMAL      NORMAL   14.020  drugX\n",
              "199   40   F     LOW      NORMAL   11.349  drugX\n",
              "\n",
              "[200 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_-Q-Rj76XwH",
        "outputId": "d4f24706-647d-48e7-dc79-3aa057c92c77"
      },
      "source": [
        "df.columns.values"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K', 'Drug'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H3TXjIsZwT33",
        "outputId": "3c9a6d39-a73f-4eab-ae61-2483750f3ad7"
      },
      "source": [
        "df[\"Drug\"].value_counts()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DrugY    91\n",
              "drugX    54\n",
              "drugA    23\n",
              "drugC    16\n",
              "drugB    16\n",
              "Name: Drug, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svK-7CMyvkLW"
      },
      "source": [
        "df[\"Sex\"] = df[\"Sex\"].map({'F':0,'M':1}) \n",
        "df[\"BP\"] = df[\"BP\"].map({'LOW':0,'NORMAL':1,'HIGH':2})\n",
        "df[\"Cholesterol\"] = df[\"Cholesterol\"].map({'HIGH':1,'NORMAL':0})\n",
        "df[\"Drug\"] = df[\"Drug\"].map({'drugA':0,'drugB':1,'drugC':2,'drugX':3,'DrugY':4})"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "n8wE9i9zw2fr",
        "outputId": "cb0627a5-9984-4dd6-ce32-6cee51dabcf4"
      },
      "source": [
        "df"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>BP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
              "      <th>Drug</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>25.355</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>13.093</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10.114</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7.798</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>18.043</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>11.567</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>12.006</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9.894</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>23</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>14.020</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11.349</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Age  Sex  BP  Cholesterol  Na_to_K  Drug\n",
              "0     23    0   2            1   25.355     4\n",
              "1     47    1   0            1   13.093     2\n",
              "2     47    1   0            1   10.114     2\n",
              "3     28    0   1            1    7.798     3\n",
              "4     61    0   0            1   18.043     4\n",
              "..   ...  ...  ..          ...      ...   ...\n",
              "195   56    0   0            1   11.567     2\n",
              "196   16    1   0            1   12.006     2\n",
              "197   52    1   1            1    9.894     3\n",
              "198   23    1   1            0   14.020     3\n",
              "199   40    0   0            0   11.349     3\n",
              "\n",
              "[200 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NRDHwwR4w77K"
      },
      "source": [
        "label = df['Drug'].values\n",
        "dataset = df.drop(['Drug'],axis=1).values\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pfoUPQ_0xoHt",
        "outputId": "0f40e9ac-1ed0-4a80-f8ae-92f3352f8d9f"
      },
      "source": [
        "label"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 2, 2, 3, 4, 3, 4, 2, 4, 4, 2, 4, 4, 4, 3, 4, 3, 0, 2, 4, 4, 4,\n",
              "       4, 4, 4, 4, 4, 3, 4, 4, 3, 1, 3, 4, 3, 3, 0, 3, 3, 3, 4, 1, 4, 3,\n",
              "       3, 3, 0, 2, 4, 4, 4, 3, 4, 4, 1, 2, 1, 4, 3, 4, 4, 0, 4, 3, 1, 4,\n",
              "       0, 3, 4, 4, 1, 4, 3, 4, 4, 4, 0, 4, 0, 3, 1, 3, 2, 0, 2, 1, 3, 4,\n",
              "       4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 0, 0, 2, 3, 4, 3, 3, 4, 1, 4,\n",
              "       0, 3, 3, 3, 3, 4, 3, 3, 0, 4, 4, 4, 4, 4, 1, 4, 4, 3, 4, 3, 4, 4,\n",
              "       3, 4, 4, 3, 1, 0, 1, 3, 0, 4, 1, 4, 0, 3, 3, 0, 3, 2, 0, 1, 3, 3,\n",
              "       4, 2, 0, 4, 2, 3, 3, 1, 3, 4, 4, 4, 4, 3, 4, 0, 3, 3, 4, 4, 0, 4,\n",
              "       0, 4, 4, 4, 4, 3, 3, 4, 4, 4, 1, 0, 4, 4, 4, 0, 4, 2, 4, 2, 2, 3,\n",
              "       3, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pB6pK00Axpnr",
        "outputId": "ee7b05d5-69db-4f65-ca7f-040869b7cbab"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[23.   ,  0.   ,  2.   ,  1.   , 25.355],\n",
              "       [47.   ,  1.   ,  0.   ,  1.   , 13.093],\n",
              "       [47.   ,  1.   ,  0.   ,  1.   , 10.114],\n",
              "       [28.   ,  0.   ,  1.   ,  1.   ,  7.798],\n",
              "       [61.   ,  0.   ,  0.   ,  1.   , 18.043],\n",
              "       [22.   ,  0.   ,  1.   ,  1.   ,  8.607],\n",
              "       [49.   ,  0.   ,  1.   ,  1.   , 16.275],\n",
              "       [41.   ,  1.   ,  0.   ,  1.   , 11.037],\n",
              "       [60.   ,  1.   ,  1.   ,  1.   , 15.171],\n",
              "       [43.   ,  1.   ,  0.   ,  0.   , 19.368],\n",
              "       [47.   ,  0.   ,  0.   ,  1.   , 11.767],\n",
              "       [34.   ,  0.   ,  2.   ,  0.   , 19.199],\n",
              "       [43.   ,  1.   ,  0.   ,  1.   , 15.376],\n",
              "       [74.   ,  0.   ,  0.   ,  1.   , 20.942],\n",
              "       [50.   ,  0.   ,  1.   ,  1.   , 12.703],\n",
              "       [16.   ,  0.   ,  2.   ,  0.   , 15.516],\n",
              "       [69.   ,  1.   ,  0.   ,  0.   , 11.455],\n",
              "       [43.   ,  1.   ,  2.   ,  1.   , 13.972],\n",
              "       [23.   ,  1.   ,  0.   ,  1.   ,  7.298],\n",
              "       [32.   ,  0.   ,  2.   ,  0.   , 25.974],\n",
              "       [57.   ,  1.   ,  0.   ,  0.   , 19.128],\n",
              "       [63.   ,  1.   ,  1.   ,  1.   , 25.917],\n",
              "       [47.   ,  1.   ,  0.   ,  0.   , 30.568],\n",
              "       [48.   ,  0.   ,  0.   ,  1.   , 15.036],\n",
              "       [33.   ,  0.   ,  0.   ,  1.   , 33.486],\n",
              "       [28.   ,  0.   ,  2.   ,  0.   , 18.809],\n",
              "       [31.   ,  1.   ,  2.   ,  1.   , 30.366],\n",
              "       [49.   ,  0.   ,  1.   ,  0.   ,  9.381],\n",
              "       [39.   ,  0.   ,  0.   ,  0.   , 22.697],\n",
              "       [45.   ,  1.   ,  0.   ,  1.   , 17.951],\n",
              "       [18.   ,  0.   ,  1.   ,  0.   ,  8.75 ],\n",
              "       [74.   ,  1.   ,  2.   ,  1.   ,  9.567],\n",
              "       [49.   ,  1.   ,  0.   ,  0.   , 11.014],\n",
              "       [65.   ,  0.   ,  2.   ,  0.   , 31.876],\n",
              "       [53.   ,  1.   ,  1.   ,  1.   , 14.133],\n",
              "       [46.   ,  1.   ,  1.   ,  0.   ,  7.285],\n",
              "       [32.   ,  1.   ,  2.   ,  0.   ,  9.445],\n",
              "       [39.   ,  1.   ,  0.   ,  0.   , 13.938],\n",
              "       [39.   ,  0.   ,  1.   ,  0.   ,  9.709],\n",
              "       [15.   ,  1.   ,  1.   ,  1.   ,  9.084],\n",
              "       [73.   ,  0.   ,  1.   ,  1.   , 19.221],\n",
              "       [58.   ,  0.   ,  2.   ,  0.   , 14.239],\n",
              "       [50.   ,  1.   ,  1.   ,  0.   , 15.79 ],\n",
              "       [23.   ,  1.   ,  1.   ,  1.   , 12.26 ],\n",
              "       [50.   ,  0.   ,  1.   ,  0.   , 12.295],\n",
              "       [66.   ,  0.   ,  1.   ,  0.   ,  8.107],\n",
              "       [37.   ,  0.   ,  2.   ,  1.   , 13.091],\n",
              "       [68.   ,  1.   ,  0.   ,  1.   , 10.291],\n",
              "       [23.   ,  1.   ,  1.   ,  1.   , 31.686],\n",
              "       [28.   ,  0.   ,  0.   ,  1.   , 19.796],\n",
              "       [58.   ,  0.   ,  2.   ,  1.   , 19.416],\n",
              "       [67.   ,  1.   ,  1.   ,  0.   , 10.898],\n",
              "       [62.   ,  1.   ,  0.   ,  0.   , 27.183],\n",
              "       [24.   ,  0.   ,  2.   ,  0.   , 18.457],\n",
              "       [68.   ,  0.   ,  2.   ,  0.   , 10.189],\n",
              "       [26.   ,  0.   ,  0.   ,  1.   , 14.16 ],\n",
              "       [65.   ,  1.   ,  2.   ,  0.   , 11.34 ],\n",
              "       [40.   ,  1.   ,  2.   ,  1.   , 27.826],\n",
              "       [60.   ,  1.   ,  1.   ,  0.   , 10.091],\n",
              "       [34.   ,  1.   ,  2.   ,  1.   , 18.703],\n",
              "       [38.   ,  0.   ,  0.   ,  0.   , 29.875],\n",
              "       [24.   ,  1.   ,  2.   ,  0.   ,  9.475],\n",
              "       [67.   ,  1.   ,  0.   ,  0.   , 20.693],\n",
              "       [45.   ,  1.   ,  0.   ,  0.   ,  8.37 ],\n",
              "       [60.   ,  0.   ,  2.   ,  1.   , 13.303],\n",
              "       [68.   ,  0.   ,  1.   ,  0.   , 27.05 ],\n",
              "       [29.   ,  1.   ,  2.   ,  1.   , 12.856],\n",
              "       [17.   ,  1.   ,  1.   ,  0.   , 10.832],\n",
              "       [54.   ,  1.   ,  1.   ,  1.   , 24.658],\n",
              "       [18.   ,  0.   ,  2.   ,  0.   , 24.276],\n",
              "       [70.   ,  1.   ,  2.   ,  1.   , 13.967],\n",
              "       [28.   ,  0.   ,  1.   ,  1.   , 19.675],\n",
              "       [24.   ,  0.   ,  1.   ,  1.   , 10.605],\n",
              "       [41.   ,  0.   ,  1.   ,  0.   , 22.905],\n",
              "       [31.   ,  1.   ,  2.   ,  0.   , 17.069],\n",
              "       [26.   ,  1.   ,  0.   ,  0.   , 20.909],\n",
              "       [36.   ,  0.   ,  2.   ,  1.   , 11.198],\n",
              "       [26.   ,  0.   ,  2.   ,  0.   , 19.161],\n",
              "       [19.   ,  0.   ,  2.   ,  1.   , 13.313],\n",
              "       [32.   ,  0.   ,  0.   ,  0.   , 10.84 ],\n",
              "       [60.   ,  1.   ,  2.   ,  1.   , 13.934],\n",
              "       [64.   ,  1.   ,  1.   ,  1.   ,  7.761],\n",
              "       [32.   ,  0.   ,  0.   ,  1.   ,  9.712],\n",
              "       [38.   ,  0.   ,  2.   ,  0.   , 11.326],\n",
              "       [47.   ,  0.   ,  0.   ,  1.   , 10.067],\n",
              "       [59.   ,  1.   ,  2.   ,  1.   , 13.935],\n",
              "       [51.   ,  0.   ,  1.   ,  1.   , 13.597],\n",
              "       [69.   ,  1.   ,  0.   ,  1.   , 15.478],\n",
              "       [37.   ,  0.   ,  2.   ,  0.   , 23.091],\n",
              "       [50.   ,  0.   ,  1.   ,  0.   , 17.211],\n",
              "       [62.   ,  1.   ,  1.   ,  1.   , 16.594],\n",
              "       [41.   ,  1.   ,  2.   ,  0.   , 15.156],\n",
              "       [29.   ,  0.   ,  2.   ,  1.   , 29.45 ],\n",
              "       [42.   ,  0.   ,  0.   ,  0.   , 29.271],\n",
              "       [56.   ,  1.   ,  0.   ,  1.   , 15.015],\n",
              "       [36.   ,  1.   ,  0.   ,  0.   , 11.424],\n",
              "       [58.   ,  0.   ,  0.   ,  1.   , 38.247],\n",
              "       [56.   ,  0.   ,  2.   ,  1.   , 25.395],\n",
              "       [20.   ,  1.   ,  2.   ,  0.   , 35.639],\n",
              "       [15.   ,  0.   ,  2.   ,  0.   , 16.725],\n",
              "       [31.   ,  1.   ,  2.   ,  0.   , 11.871],\n",
              "       [45.   ,  0.   ,  2.   ,  1.   , 12.854],\n",
              "       [28.   ,  0.   ,  0.   ,  1.   , 13.127],\n",
              "       [56.   ,  1.   ,  1.   ,  1.   ,  8.966],\n",
              "       [22.   ,  1.   ,  2.   ,  0.   , 28.294],\n",
              "       [37.   ,  1.   ,  0.   ,  0.   ,  8.968],\n",
              "       [22.   ,  1.   ,  1.   ,  1.   , 11.953],\n",
              "       [42.   ,  1.   ,  0.   ,  1.   , 20.013],\n",
              "       [72.   ,  1.   ,  2.   ,  0.   ,  9.677],\n",
              "       [23.   ,  1.   ,  1.   ,  1.   , 16.85 ],\n",
              "       [50.   ,  1.   ,  2.   ,  1.   ,  7.49 ],\n",
              "       [47.   ,  0.   ,  1.   ,  0.   ,  6.683],\n",
              "       [35.   ,  1.   ,  0.   ,  0.   ,  9.17 ],\n",
              "       [65.   ,  0.   ,  0.   ,  0.   , 13.769],\n",
              "       [20.   ,  0.   ,  1.   ,  0.   ,  9.281],\n",
              "       [51.   ,  1.   ,  2.   ,  1.   , 18.295],\n",
              "       [67.   ,  1.   ,  1.   ,  0.   ,  9.514],\n",
              "       [40.   ,  0.   ,  1.   ,  1.   , 10.103],\n",
              "       [32.   ,  0.   ,  2.   ,  0.   , 10.292],\n",
              "       [61.   ,  0.   ,  2.   ,  1.   , 25.475],\n",
              "       [28.   ,  1.   ,  1.   ,  1.   , 27.064],\n",
              "       [15.   ,  1.   ,  2.   ,  0.   , 17.206],\n",
              "       [34.   ,  1.   ,  1.   ,  1.   , 22.456],\n",
              "       [36.   ,  0.   ,  1.   ,  1.   , 16.753],\n",
              "       [53.   ,  0.   ,  2.   ,  0.   , 12.495],\n",
              "       [19.   ,  0.   ,  2.   ,  0.   , 25.969],\n",
              "       [66.   ,  1.   ,  2.   ,  1.   , 16.347],\n",
              "       [35.   ,  1.   ,  1.   ,  0.   ,  7.845],\n",
              "       [47.   ,  1.   ,  0.   ,  0.   , 33.542],\n",
              "       [32.   ,  0.   ,  1.   ,  1.   ,  7.477],\n",
              "       [70.   ,  0.   ,  1.   ,  1.   , 20.489],\n",
              "       [52.   ,  1.   ,  0.   ,  0.   , 32.922],\n",
              "       [49.   ,  1.   ,  0.   ,  0.   , 13.598],\n",
              "       [24.   ,  1.   ,  1.   ,  1.   , 25.786],\n",
              "       [42.   ,  0.   ,  2.   ,  1.   , 21.036],\n",
              "       [74.   ,  1.   ,  0.   ,  0.   , 11.939],\n",
              "       [55.   ,  0.   ,  2.   ,  1.   , 10.977],\n",
              "       [35.   ,  0.   ,  2.   ,  1.   , 12.894],\n",
              "       [51.   ,  1.   ,  2.   ,  0.   , 11.343],\n",
              "       [69.   ,  0.   ,  1.   ,  1.   , 10.065],\n",
              "       [49.   ,  1.   ,  2.   ,  0.   ,  6.269],\n",
              "       [64.   ,  0.   ,  0.   ,  0.   , 25.741],\n",
              "       [60.   ,  1.   ,  2.   ,  0.   ,  8.621],\n",
              "       [74.   ,  1.   ,  2.   ,  0.   , 15.436],\n",
              "       [39.   ,  1.   ,  2.   ,  1.   ,  9.664],\n",
              "       [61.   ,  1.   ,  1.   ,  1.   ,  9.443],\n",
              "       [37.   ,  0.   ,  0.   ,  0.   , 12.006],\n",
              "       [26.   ,  0.   ,  2.   ,  0.   , 12.307],\n",
              "       [61.   ,  0.   ,  0.   ,  0.   ,  7.34 ],\n",
              "       [22.   ,  1.   ,  0.   ,  1.   ,  8.151],\n",
              "       [49.   ,  1.   ,  2.   ,  0.   ,  8.7  ],\n",
              "       [68.   ,  1.   ,  2.   ,  1.   , 11.009],\n",
              "       [55.   ,  1.   ,  1.   ,  0.   ,  7.261],\n",
              "       [72.   ,  0.   ,  0.   ,  0.   , 14.642],\n",
              "       [37.   ,  1.   ,  0.   ,  0.   , 16.724],\n",
              "       [49.   ,  1.   ,  0.   ,  1.   , 10.537],\n",
              "       [31.   ,  1.   ,  2.   ,  0.   , 11.227],\n",
              "       [53.   ,  1.   ,  0.   ,  1.   , 22.963],\n",
              "       [59.   ,  0.   ,  0.   ,  1.   , 10.444],\n",
              "       [34.   ,  0.   ,  0.   ,  0.   , 12.923],\n",
              "       [30.   ,  0.   ,  1.   ,  1.   , 10.443],\n",
              "       [57.   ,  0.   ,  2.   ,  0.   ,  9.945],\n",
              "       [43.   ,  1.   ,  1.   ,  0.   , 12.859],\n",
              "       [21.   ,  0.   ,  2.   ,  0.   , 28.632],\n",
              "       [16.   ,  1.   ,  2.   ,  0.   , 19.007],\n",
              "       [38.   ,  1.   ,  0.   ,  1.   , 18.295],\n",
              "       [58.   ,  0.   ,  0.   ,  1.   , 26.645],\n",
              "       [57.   ,  0.   ,  1.   ,  1.   , 14.216],\n",
              "       [51.   ,  0.   ,  0.   ,  0.   , 23.003],\n",
              "       [20.   ,  0.   ,  2.   ,  1.   , 11.262],\n",
              "       [28.   ,  0.   ,  1.   ,  1.   , 12.879],\n",
              "       [45.   ,  1.   ,  0.   ,  0.   , 10.017],\n",
              "       [39.   ,  0.   ,  1.   ,  0.   , 17.225],\n",
              "       [41.   ,  0.   ,  0.   ,  0.   , 18.739],\n",
              "       [42.   ,  1.   ,  2.   ,  0.   , 12.766],\n",
              "       [73.   ,  0.   ,  2.   ,  1.   , 18.348],\n",
              "       [48.   ,  1.   ,  2.   ,  0.   , 10.446],\n",
              "       [25.   ,  1.   ,  1.   ,  1.   , 19.011],\n",
              "       [39.   ,  1.   ,  1.   ,  1.   , 15.969],\n",
              "       [67.   ,  0.   ,  1.   ,  1.   , 15.891],\n",
              "       [22.   ,  0.   ,  2.   ,  0.   , 22.818],\n",
              "       [59.   ,  0.   ,  1.   ,  1.   , 13.884],\n",
              "       [20.   ,  0.   ,  0.   ,  0.   , 11.686],\n",
              "       [36.   ,  0.   ,  2.   ,  0.   , 15.49 ],\n",
              "       [18.   ,  0.   ,  2.   ,  1.   , 37.188],\n",
              "       [57.   ,  0.   ,  1.   ,  0.   , 25.893],\n",
              "       [70.   ,  1.   ,  2.   ,  1.   ,  9.849],\n",
              "       [47.   ,  1.   ,  2.   ,  1.   , 10.403],\n",
              "       [65.   ,  1.   ,  2.   ,  0.   , 34.997],\n",
              "       [64.   ,  1.   ,  2.   ,  0.   , 20.932],\n",
              "       [58.   ,  1.   ,  2.   ,  1.   , 18.991],\n",
              "       [23.   ,  1.   ,  2.   ,  1.   ,  8.011],\n",
              "       [72.   ,  1.   ,  0.   ,  1.   , 16.31 ],\n",
              "       [72.   ,  1.   ,  0.   ,  1.   ,  6.769],\n",
              "       [46.   ,  0.   ,  2.   ,  1.   , 34.686],\n",
              "       [56.   ,  0.   ,  0.   ,  1.   , 11.567],\n",
              "       [16.   ,  1.   ,  0.   ,  1.   , 12.006],\n",
              "       [52.   ,  1.   ,  1.   ,  1.   ,  9.894],\n",
              "       [23.   ,  1.   ,  1.   ,  0.   , 14.02 ],\n",
              "       [40.   ,  0.   ,  0.   ,  0.   , 11.349]])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6b1hp4VOxrj1",
        "outputId": "79ae32e3-f109-4140-92c7-336b88243e7c"
      },
      "source": [
        "len(dataset)*0.8"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "160.0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eoDXQ7-3x22d"
      },
      "source": [
        "train_data = dataset[:160]\n",
        "train_label = label[:160]\n",
        "valid_data = dataset[161:]\n",
        "valid_label = label[161:]"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-MxTij7w27us"
      },
      "source": [
        "from keras.utils import np_utils\n",
        "train_label = np_utils.to_categorical(train_label)\n",
        "valid_label = np_utils.to_categorical(valid_label)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3MWzLZJZ0sza",
        "outputId": "e55d722e-d9ac-48ac-b461-43f3d678906e"
      },
      "source": [
        "train_data=train_data.T\n",
        "valid_data=valid_data.T\n",
        "train_label=train_label.T\n",
        "valid_label=valid_label.T\n",
        "print(train_data.shape)\n",
        "print(train_label.shape)\n",
        "print(valid_data.shape)\n",
        "print(valid_label.shape)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5, 160)\n",
            "(5, 160)\n",
            "(5, 39)\n",
            "(5, 39)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yu23xMJvyeeO",
        "outputId": "d1d318fa-78c2-4733-bbe7-42483f041eb9"
      },
      "source": [
        "train_data.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 160)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9NGEF7-U26Za",
        "outputId": "aaecbc5c-d21f-4094-8ba2-e9d273299ab4"
      },
      "source": [
        "out_history=[]\n",
        "all_w=[]\n",
        "eta=0.01\n",
        "model2 = NN_model([\n",
        "            One_layer(in_dim=train_data.shape[0],out_dim=32,act=\"sigmoid\",lr=eta,apt_type='AdaGrad',val=0.1),\n",
        "            One_layer(in_dim=32,out_dim=5,act=\"softmax\",lr=eta,apt_type='AdaGrad',val=0.1)],\n",
        "            batch_size = 4,loss='Multi_class'\n",
        "    ) \n",
        "history,all_w = model2.training(train_data,valid_data,train_label,valid_label,epochs=100)\n",
        "out_history.append(history)\n",
        "\n",
        "def draw(hh,title):\n",
        "    fig = plt.figure(figsize=(16,10))\n",
        "    fig.subplots_adjust(hspace=0.25, wspace=0.2)\n",
        "    plt.subplot(2,2,1)\n",
        "    for i,h in enumerate(hh):\n",
        "        plt.plot(h.T[2])\n",
        "    plt.title('Training accuracy')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "\n",
        "    plt.subplot(2,2,2)\n",
        "    for i,h in enumerate(hh):\n",
        "        plt.plot(h.T[3])\n",
        "    plt.title('Validation accuracy')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.subplot(2,2,3)\n",
        "    for i,h in enumerate(hh):\n",
        "        plt.plot(h.T[0])\n",
        "    plt.title('Training loss')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.xlabel('Epoch')\n",
        "\n",
        "    plt.subplot(2,2,4)\n",
        "    for i,h in enumerate(hh):\n",
        "        plt.plot(h.T[1])\n",
        "    plt.title('Validation loss')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.xlabel('Epoch')\n",
        "\n",
        "    fig.suptitle(title, fontsize=16)\n",
        "    plt.show()\n",
        "draw(out_history,\"AdaGrad\")"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0 / 100 - loss: 1.3175 - accuracy: 0.4437 - val_loss: 1.2701 - val_accuracy: 0.5128\n",
            "Epoch 1 / 100 - loss: 1.2782 - accuracy: 0.4437 - val_loss: 1.2355 - val_accuracy: 0.5128\n",
            "Epoch 2 / 100 - loss: 1.2383 - accuracy: 0.5437 - val_loss: 1.2045 - val_accuracy: 0.5128\n",
            "Epoch 3 / 100 - loss: 1.2102 - accuracy: 0.5500 - val_loss: 1.1776 - val_accuracy: 0.5385\n",
            "Epoch 4 / 100 - loss: 1.1870 - accuracy: 0.5687 - val_loss: 1.1594 - val_accuracy: 0.5385\n",
            "Epoch 5 / 100 - loss: 1.1539 - accuracy: 0.5938 - val_loss: 1.1393 - val_accuracy: 0.5641\n",
            "Epoch 6 / 100 - loss: 1.1387 - accuracy: 0.5938 - val_loss: 1.1348 - val_accuracy: 0.5641\n",
            "Epoch 7 / 100 - loss: 1.1224 - accuracy: 0.6000 - val_loss: 1.1092 - val_accuracy: 0.5385\n",
            "Epoch 8 / 100 - loss: 1.1061 - accuracy: 0.5938 - val_loss: 1.0987 - val_accuracy: 0.5641\n",
            "Epoch 9 / 100 - loss: 1.0937 - accuracy: 0.5938 - val_loss: 1.0877 - val_accuracy: 0.5641\n",
            "Epoch 10 / 100 - loss: 1.0833 - accuracy: 0.5938 - val_loss: 1.0797 - val_accuracy: 0.5641\n",
            "Epoch 11 / 100 - loss: 1.0736 - accuracy: 0.6000 - val_loss: 1.0733 - val_accuracy: 0.5641\n",
            "Epoch 12 / 100 - loss: 1.0653 - accuracy: 0.6000 - val_loss: 1.0659 - val_accuracy: 0.5641\n",
            "Epoch 13 / 100 - loss: 1.0577 - accuracy: 0.5938 - val_loss: 1.0583 - val_accuracy: 0.5641\n",
            "Epoch 14 / 100 - loss: 1.0493 - accuracy: 0.6062 - val_loss: 1.0565 - val_accuracy: 0.5641\n",
            "Epoch 15 / 100 - loss: 1.0414 - accuracy: 0.6062 - val_loss: 1.0505 - val_accuracy: 0.5641\n",
            "Epoch 16 / 100 - loss: 1.0340 - accuracy: 0.6000 - val_loss: 1.0471 - val_accuracy: 0.5641\n",
            "Epoch 17 / 100 - loss: 1.0261 - accuracy: 0.6062 - val_loss: 1.0375 - val_accuracy: 0.5897\n",
            "Epoch 18 / 100 - loss: 1.0197 - accuracy: 0.6125 - val_loss: 1.0354 - val_accuracy: 0.5897\n",
            "Epoch 19 / 100 - loss: 1.0130 - accuracy: 0.6062 - val_loss: 1.0236 - val_accuracy: 0.5897\n",
            "Epoch 20 / 100 - loss: 1.0081 - accuracy: 0.5938 - val_loss: 1.0155 - val_accuracy: 0.5897\n",
            "Epoch 21 / 100 - loss: 1.0017 - accuracy: 0.6125 - val_loss: 1.0153 - val_accuracy: 0.5897\n",
            "Epoch 22 / 100 - loss: 0.9969 - accuracy: 0.6125 - val_loss: 1.0081 - val_accuracy: 0.5897\n",
            "Epoch 23 / 100 - loss: 0.9914 - accuracy: 0.6125 - val_loss: 1.0045 - val_accuracy: 0.5897\n",
            "Epoch 24 / 100 - loss: 0.9865 - accuracy: 0.6125 - val_loss: 1.0006 - val_accuracy: 0.5897\n",
            "Epoch 25 / 100 - loss: 0.9821 - accuracy: 0.6125 - val_loss: 0.9950 - val_accuracy: 0.5897\n",
            "Epoch 26 / 100 - loss: 0.9772 - accuracy: 0.6125 - val_loss: 0.9916 - val_accuracy: 0.5897\n",
            "Epoch 27 / 100 - loss: 0.9729 - accuracy: 0.6188 - val_loss: 0.9908 - val_accuracy: 0.5897\n",
            "Epoch 28 / 100 - loss: 0.9689 - accuracy: 0.6188 - val_loss: 0.9866 - val_accuracy: 0.5897\n",
            "Epoch 29 / 100 - loss: 0.9653 - accuracy: 0.6125 - val_loss: 0.9807 - val_accuracy: 0.5897\n",
            "Epoch 30 / 100 - loss: 0.9616 - accuracy: 0.6125 - val_loss: 0.9771 - val_accuracy: 0.5897\n",
            "Epoch 31 / 100 - loss: 0.9586 - accuracy: 0.6125 - val_loss: 0.9727 - val_accuracy: 0.5897\n",
            "Epoch 32 / 100 - loss: 0.9549 - accuracy: 0.6125 - val_loss: 0.9711 - val_accuracy: 0.5897\n",
            "Epoch 33 / 100 - loss: 0.9512 - accuracy: 0.6250 - val_loss: 0.9714 - val_accuracy: 0.5897\n",
            "Epoch 34 / 100 - loss: 0.9480 - accuracy: 0.6250 - val_loss: 0.9679 - val_accuracy: 0.5897\n",
            "Epoch 35 / 100 - loss: 0.9449 - accuracy: 0.6188 - val_loss: 0.9669 - val_accuracy: 0.5897\n",
            "Epoch 36 / 100 - loss: 0.9419 - accuracy: 0.6250 - val_loss: 0.9627 - val_accuracy: 0.5897\n",
            "Epoch 37 / 100 - loss: 0.9390 - accuracy: 0.6188 - val_loss: 0.9611 - val_accuracy: 0.5897\n",
            "Epoch 38 / 100 - loss: 0.9363 - accuracy: 0.6125 - val_loss: 0.9574 - val_accuracy: 0.5897\n",
            "Epoch 39 / 100 - loss: 0.9337 - accuracy: 0.6188 - val_loss: 0.9601 - val_accuracy: 0.5897\n",
            "Epoch 40 / 100 - loss: 0.9307 - accuracy: 0.6188 - val_loss: 0.9509 - val_accuracy: 0.5897\n",
            "Epoch 41 / 100 - loss: 0.9280 - accuracy: 0.6188 - val_loss: 0.9484 - val_accuracy: 0.5897\n",
            "Epoch 42 / 100 - loss: 0.9254 - accuracy: 0.6188 - val_loss: 0.9461 - val_accuracy: 0.5897\n",
            "Epoch 43 / 100 - loss: 0.9227 - accuracy: 0.6188 - val_loss: 0.9465 - val_accuracy: 0.6154\n",
            "Epoch 44 / 100 - loss: 0.9210 - accuracy: 0.6312 - val_loss: 0.9500 - val_accuracy: 0.5897\n",
            "Epoch 45 / 100 - loss: 0.9178 - accuracy: 0.6250 - val_loss: 0.9439 - val_accuracy: 0.6154\n",
            "Epoch 46 / 100 - loss: 0.9152 - accuracy: 0.6250 - val_loss: 0.9405 - val_accuracy: 0.6154\n",
            "Epoch 47 / 100 - loss: 0.9130 - accuracy: 0.6250 - val_loss: 0.9408 - val_accuracy: 0.6154\n",
            "Epoch 48 / 100 - loss: 0.9104 - accuracy: 0.6312 - val_loss: 0.9354 - val_accuracy: 0.5897\n",
            "Epoch 49 / 100 - loss: 0.9081 - accuracy: 0.6250 - val_loss: 0.9340 - val_accuracy: 0.6154\n",
            "Epoch 50 / 100 - loss: 0.9058 - accuracy: 0.6312 - val_loss: 0.9310 - val_accuracy: 0.6154\n",
            "Epoch 51 / 100 - loss: 0.9037 - accuracy: 0.6312 - val_loss: 0.9316 - val_accuracy: 0.6154\n",
            "Epoch 52 / 100 - loss: 0.9014 - accuracy: 0.6312 - val_loss: 0.9290 - val_accuracy: 0.6154\n",
            "Epoch 53 / 100 - loss: 0.8990 - accuracy: 0.6375 - val_loss: 0.9250 - val_accuracy: 0.6154\n",
            "Epoch 54 / 100 - loss: 0.8969 - accuracy: 0.6312 - val_loss: 0.9234 - val_accuracy: 0.6154\n",
            "Epoch 55 / 100 - loss: 0.8948 - accuracy: 0.6375 - val_loss: 0.9222 - val_accuracy: 0.6154\n",
            "Epoch 56 / 100 - loss: 0.8925 - accuracy: 0.6375 - val_loss: 0.9182 - val_accuracy: 0.6154\n",
            "Epoch 57 / 100 - loss: 0.8904 - accuracy: 0.6375 - val_loss: 0.9153 - val_accuracy: 0.6154\n",
            "Epoch 58 / 100 - loss: 0.8884 - accuracy: 0.6375 - val_loss: 0.9161 - val_accuracy: 0.6154\n",
            "Epoch 59 / 100 - loss: 0.8863 - accuracy: 0.6375 - val_loss: 0.9125 - val_accuracy: 0.6154\n",
            "Epoch 60 / 100 - loss: 0.8842 - accuracy: 0.6438 - val_loss: 0.9097 - val_accuracy: 0.6154\n",
            "Epoch 61 / 100 - loss: 0.8823 - accuracy: 0.6312 - val_loss: 0.9079 - val_accuracy: 0.6154\n",
            "Epoch 62 / 100 - loss: 0.8802 - accuracy: 0.6438 - val_loss: 0.9062 - val_accuracy: 0.6154\n",
            "Epoch 63 / 100 - loss: 0.8783 - accuracy: 0.6438 - val_loss: 0.9043 - val_accuracy: 0.6154\n",
            "Epoch 64 / 100 - loss: 0.8766 - accuracy: 0.6312 - val_loss: 0.8998 - val_accuracy: 0.5897\n",
            "Epoch 65 / 100 - loss: 0.8745 - accuracy: 0.6375 - val_loss: 0.8990 - val_accuracy: 0.5897\n",
            "Epoch 66 / 100 - loss: 0.8727 - accuracy: 0.6375 - val_loss: 0.8967 - val_accuracy: 0.5897\n",
            "Epoch 67 / 100 - loss: 0.8706 - accuracy: 0.6375 - val_loss: 0.8962 - val_accuracy: 0.6154\n",
            "Epoch 68 / 100 - loss: 0.8688 - accuracy: 0.6375 - val_loss: 0.8942 - val_accuracy: 0.6154\n",
            "Epoch 69 / 100 - loss: 0.8669 - accuracy: 0.6500 - val_loss: 0.8949 - val_accuracy: 0.6154\n",
            "Epoch 70 / 100 - loss: 0.8651 - accuracy: 0.6500 - val_loss: 0.8937 - val_accuracy: 0.6154\n",
            "Epoch 71 / 100 - loss: 0.8632 - accuracy: 0.6438 - val_loss: 0.8908 - val_accuracy: 0.6154\n",
            "Epoch 72 / 100 - loss: 0.8614 - accuracy: 0.6375 - val_loss: 0.8886 - val_accuracy: 0.6154\n",
            "Epoch 73 / 100 - loss: 0.8597 - accuracy: 0.6375 - val_loss: 0.8860 - val_accuracy: 0.6154\n",
            "Epoch 74 / 100 - loss: 0.8579 - accuracy: 0.6375 - val_loss: 0.8843 - val_accuracy: 0.6154\n",
            "Epoch 75 / 100 - loss: 0.8562 - accuracy: 0.6375 - val_loss: 0.8820 - val_accuracy: 0.6154\n",
            "Epoch 76 / 100 - loss: 0.8544 - accuracy: 0.6500 - val_loss: 0.8812 - val_accuracy: 0.6154\n",
            "Epoch 77 / 100 - loss: 0.8526 - accuracy: 0.6438 - val_loss: 0.8797 - val_accuracy: 0.6154\n",
            "Epoch 78 / 100 - loss: 0.8510 - accuracy: 0.6500 - val_loss: 0.8794 - val_accuracy: 0.6154\n",
            "Epoch 79 / 100 - loss: 0.8493 - accuracy: 0.6500 - val_loss: 0.8778 - val_accuracy: 0.6154\n",
            "Epoch 80 / 100 - loss: 0.8477 - accuracy: 0.6375 - val_loss: 0.8733 - val_accuracy: 0.5897\n",
            "Epoch 81 / 100 - loss: 0.8459 - accuracy: 0.6500 - val_loss: 0.8743 - val_accuracy: 0.6154\n",
            "Epoch 82 / 100 - loss: 0.8442 - accuracy: 0.6500 - val_loss: 0.8727 - val_accuracy: 0.6154\n",
            "Epoch 83 / 100 - loss: 0.8427 - accuracy: 0.6375 - val_loss: 0.8682 - val_accuracy: 0.6154\n",
            "Epoch 84 / 100 - loss: 0.8412 - accuracy: 0.6375 - val_loss: 0.8664 - val_accuracy: 0.5897\n",
            "Epoch 85 / 100 - loss: 0.8394 - accuracy: 0.6438 - val_loss: 0.8657 - val_accuracy: 0.6154\n",
            "Epoch 86 / 100 - loss: 0.8376 - accuracy: 0.6500 - val_loss: 0.8664 - val_accuracy: 0.6154\n",
            "Epoch 87 / 100 - loss: 0.8361 - accuracy: 0.6438 - val_loss: 0.8639 - val_accuracy: 0.6154\n",
            "Epoch 88 / 100 - loss: 0.8344 - accuracy: 0.6438 - val_loss: 0.8620 - val_accuracy: 0.6154\n",
            "Epoch 89 / 100 - loss: 0.8328 - accuracy: 0.6438 - val_loss: 0.8604 - val_accuracy: 0.6154\n",
            "Epoch 90 / 100 - loss: 0.8313 - accuracy: 0.6438 - val_loss: 0.8582 - val_accuracy: 0.6154\n",
            "Epoch 91 / 100 - loss: 0.8296 - accuracy: 0.6438 - val_loss: 0.8573 - val_accuracy: 0.6154\n",
            "Epoch 92 / 100 - loss: 0.8281 - accuracy: 0.6438 - val_loss: 0.8556 - val_accuracy: 0.6154\n",
            "Epoch 93 / 100 - loss: 0.8266 - accuracy: 0.6500 - val_loss: 0.8565 - val_accuracy: 0.6154\n",
            "Epoch 94 / 100 - loss: 0.8249 - accuracy: 0.6500 - val_loss: 0.8534 - val_accuracy: 0.6154\n",
            "Epoch 95 / 100 - loss: 0.8234 - accuracy: 0.6438 - val_loss: 0.8496 - val_accuracy: 0.6154\n",
            "Epoch 96 / 100 - loss: 0.8221 - accuracy: 0.6438 - val_loss: 0.8472 - val_accuracy: 0.5897\n",
            "Epoch 97 / 100 - loss: 0.8203 - accuracy: 0.6438 - val_loss: 0.8473 - val_accuracy: 0.6154\n",
            "Epoch 98 / 100 - loss: 0.8188 - accuracy: 0.6500 - val_loss: 0.8477 - val_accuracy: 0.6154\n",
            "Epoch 99 / 100 - loss: 0.8172 - accuracy: 0.6500 - val_loss: 0.8449 - val_accuracy: 0.6154\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "mbGv2V4JNd8b",
        "outputId": "c710217b-6d6c-4338-ed36-4a8493d5351f"
      },
      "source": [
        "plt.title('Weights change')\n",
        "plt.ylabel('Weights')\n",
        "plt.xlabel('Iterations')\n",
        "plt.plot(np.array(all_w).reshape((len(all_w),all_w[0].shape[0]*all_w[0].shape[1])))\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uth2pIDkPYLp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b9593d45-8497-4969-e114-483c7abe4bad"
      },
      "source": [
        "out_history=[]\n",
        "all_w=[]\n",
        "eta=0.001\n",
        "model3 = NN_model([\n",
        "            RBF_layer(in_dim=train_data.shape[0], out_dim=32 , cent_lr = eta, wid_lr=eta,data =train_data.T,act= \"gaussian\"),\n",
        "            One_layer(in_dim=32,out_dim=5,act=\"softmax\",lr=eta,apt_type='AdaGrad',val=0.1)],\n",
        "            batch_size = 4,loss='Multi_class'\n",
        "    ) \n",
        "history,all_w = model3.training(train_data,valid_data,train_label,valid_label,epochs=32)\n",
        "out_history.append(history)\n",
        "\n",
        "def draw(hh,title):\n",
        "    fig = plt.figure(figsize=(16,10))\n",
        "    fig.subplots_adjust(hspace=0.25, wspace=0.2)\n",
        "    plt.subplot(2,2,1)\n",
        "    for i,h in enumerate(hh):\n",
        "        plt.plot(h.T[2])\n",
        "    plt.title('Training accuracy')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "\n",
        "    plt.subplot(2,2,2)\n",
        "    for i,h in enumerate(hh):\n",
        "        plt.plot(h.T[3])\n",
        "    plt.title('Validation accuracy')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.subplot(2,2,3)\n",
        "    for i,h in enumerate(hh):\n",
        "        plt.plot(h.T[0])\n",
        "    plt.title('Training loss')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.xlabel('Epoch')\n",
        "\n",
        "    plt.subplot(2,2,4)\n",
        "    for i,h in enumerate(hh):\n",
        "        plt.plot(h.T[1])\n",
        "    plt.title('Validation loss')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.xlabel('Epoch')\n",
        "\n",
        "    fig.suptitle(title, fontsize=16)\n",
        "    plt.show()\n",
        "draw(out_history,\"AdaGrad\")"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0 / 32 - loss: 1.5688 - accuracy: 0.2500 - val_loss: 1.5663 - val_accuracy: 0.2308\n",
            "Epoch 1 / 32 - loss: 1.5660 - accuracy: 0.2500 - val_loss: 1.5636 - val_accuracy: 0.2308\n",
            "Epoch 2 / 32 - loss: 1.5637 - accuracy: 0.2562 - val_loss: 1.5616 - val_accuracy: 0.2308\n",
            "Epoch 3 / 32 - loss: 1.5619 - accuracy: 0.2625 - val_loss: 1.5599 - val_accuracy: 0.2308\n",
            "Epoch 4 / 32 - loss: 1.5602 - accuracy: 0.2625 - val_loss: 1.5584 - val_accuracy: 0.2308\n",
            "Epoch 5 / 32 - loss: 1.5586 - accuracy: 0.2625 - val_loss: 1.5570 - val_accuracy: 0.2308\n",
            "Epoch 6 / 32 - loss: 1.5572 - accuracy: 0.2625 - val_loss: 1.5558 - val_accuracy: 0.2308\n",
            "Epoch 7 / 32 - loss: 1.5559 - accuracy: 0.2625 - val_loss: 1.5546 - val_accuracy: 0.2308\n",
            "Epoch 8 / 32 - loss: 1.5546 - accuracy: 0.2687 - val_loss: 1.5534 - val_accuracy: 0.2308\n",
            "Epoch 9 / 32 - loss: 1.5534 - accuracy: 0.2687 - val_loss: 1.5523 - val_accuracy: 0.2308\n",
            "Epoch 10 / 32 - loss: 1.5523 - accuracy: 0.2750 - val_loss: 1.5513 - val_accuracy: 0.2308\n",
            "Epoch 11 / 32 - loss: 1.5512 - accuracy: 0.2750 - val_loss: 1.5503 - val_accuracy: 0.2308\n",
            "Epoch 12 / 32 - loss: 1.5501 - accuracy: 0.2750 - val_loss: 1.5494 - val_accuracy: 0.2308\n",
            "Epoch 13 / 32 - loss: 1.5490 - accuracy: 0.2750 - val_loss: 1.5484 - val_accuracy: 0.2564\n",
            "Epoch 14 / 32 - loss: 1.5480 - accuracy: 0.2750 - val_loss: 1.5475 - val_accuracy: 0.2564\n",
            "Epoch 15 / 32 - loss: 1.5470 - accuracy: 0.2750 - val_loss: 1.5467 - val_accuracy: 0.2564\n",
            "Epoch 16 / 32 - loss: 1.5461 - accuracy: 0.2750 - val_loss: 1.5458 - val_accuracy: 0.2564\n",
            "Epoch 17 / 32 - loss: 1.5451 - accuracy: 0.2750 - val_loss: 1.5450 - val_accuracy: 0.2564\n",
            "Epoch 18 / 32 - loss: 1.5441 - accuracy: 0.2750 - val_loss: 1.5441 - val_accuracy: 0.2564\n",
            "Epoch 19 / 32 - loss: 1.5432 - accuracy: 0.2750 - val_loss: 1.5433 - val_accuracy: 0.2564\n",
            "Epoch 20 / 32 - loss: 1.5422 - accuracy: 0.2750 - val_loss: 1.5426 - val_accuracy: 0.2564\n",
            "Epoch 21 / 32 - loss: 1.5413 - accuracy: 0.2750 - val_loss: 1.5418 - val_accuracy: 0.2564\n",
            "Epoch 22 / 32 - loss: 1.5403 - accuracy: 0.2750 - val_loss: 1.5410 - val_accuracy: 0.2564\n",
            "Epoch 23 / 32 - loss: 1.5392 - accuracy: 0.2750 - val_loss: 1.5402 - val_accuracy: 0.2821\n",
            "Epoch 24 / 32 - loss: 1.5380 - accuracy: 0.2750 - val_loss: 1.5395 - val_accuracy: 0.2821\n",
            "Epoch 25 / 32 - loss: 1.5364 - accuracy: 0.2750 - val_loss: 1.5387 - val_accuracy: 0.2821\n",
            "Epoch 26 / 32 - loss: 1.5339 - accuracy: 0.2750 - val_loss: 1.5379 - val_accuracy: 0.3077\n",
            "Epoch 27 / 32 - loss: 1.5329 - accuracy: 0.2750 - val_loss: 1.5371 - val_accuracy: 0.3077\n",
            "Epoch 28 / 32 - loss: 1.5322 - accuracy: 0.2812 - val_loss: 1.5363 - val_accuracy: 0.2821\n",
            "Epoch 29 / 32 - loss: 1.5317 - accuracy: 0.2812 - val_loss: 1.5356 - val_accuracy: 0.2821\n",
            "Epoch 30 / 32 - loss: 1.5306 - accuracy: 0.2875 - val_loss: 1.5348 - val_accuracy: 0.2821\n",
            "Epoch 31 / 32 - loss: 1.5295 - accuracy: 0.2875 - val_loss: 1.5340 - val_accuracy: 0.2821\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7wAAAKUCAYAAAAnyzTYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde3xdZ3Xg/d+SbPkmJ44vshM7V1u5OE5IwAlNuARCAjaUENpOCZQCHdpAC33bUjrQMmUYBuadkhnotMPbEqZAmSmESwtN28hJGhIoJZCEJsSKQ5Ccu7Es2XESyXdJ6/3jbDknQraPZR0d6Zzf9/M5H52997P3Xke5PFrnefZ6IjORJEmSJKneNNU6AEmSJEmSqsGEV5IkSZJUl0x4JUmSJEl1yYRXkiRJklSXTHglSZIkSXXJhFeSJEmSVJdMeCVJOoyI+GxEZER86ijOyYj4yDHcc31E3BgRPRFxICJ2RsR3I+IDEbFgvNc9yhgejYgvTMa9JEmqFhNeSZIOISLmAL9cbL4lImZMwj3/J3ATcAD4PeBVwNuBe4A/Av7fascgSVK9qHrHLUnSNHY1cBylBPS1wDrgH6t1s4h4B/D/AL+XmX866vCNEfFx4JVHuMaszNxXpRAlSZpWHOGVJOnQ3g7sBN4B7Cm2D4qI5oj4WERsjYjdEXFHRJw7+iIRsSoi/k9EPBIReyLi4Yj4i4g4YVTTDwI/GiPZBSAz+zLzq2XXfUUxffoXiqnXfcC2o7wnEfE7xRTmvRFxT0S87Oh+TZIkTU2O8EqSNIaIOAm4AvhsZvZFxDeBX4iIEzJzZ9HsI5SmGX8SuAVYC9w4xuVOAp4AfpdSAn1Gcd5NwCXF/ZYDZwH/dRzh/jnQAfwqMLvSexb3fSfwp8AXgK8Aq4AvA/PHEYckSVOKCa8kSWN7K9AMfLHY/mvgzcCbgL8sRkp/D7g+M99ftLklIoaA/1Z+ocz8DvCdke2I+B7QDfxLRFyYmfcCK4rDj48OZNSzw5mZQ6Oa3JWZv36094yIJkpJ+82Z+WtlbfuAG8b+tUiSNH04pVmSpLG9HejKzDuL7X8Gfspz05rPA+YBXx113s8kihHREhF/FBE/jog9lApS/Utx+KzDBRERP1e0H3k9Nkazb4zzniuK1+jP8LfA4OHikiRpOjDhlSRplIhYC6wG/i4iFhRLAc0H/g74uYg4EzixaL5t1Omjt6FUWfkjwP8FXgdcDPxCcWxkCvKTxc9TRp27EbioeB2qYNbWcd5zzM+QmYPAjkPcS5KkacMpzZIk/ayRUdwPFK/R3kbpmV2ApcADZceWjtH+GuCLmfmxkR0R0VreIDO3RMRPKCWnHyrbv4vSkkRExKGS0BzPPXkuUX5ezMUU6kWHuJckSdOGI7ySJJWJiBZKz+r+gNISQKNf91EqDrUR2MVz6/SOuGaMy86lNKW43K+N0e5PgBdExO+MN/6jvOeTlApbjf4Mv4hfikuS6oCdmSRJz/c6SqObv5+Zd4w+GBGfAf4CuAD4FPChiOinNOJ7EfDOMa65AXh7RGykVDjqF4BLRzfKzM9FxIuAP42IlwNfA7YArZSeGb4C6K/wcxzxnpk5HBH/GfjfEfF5Ss8fr6K0PNKzFd5HkqQpy4RXkqTnezulpPJrhzj+ZUrLEL2dUnIbwK8D76U0Kvx6nj/FGeC3i3YfL7ZvojSKfNfoi2fmeyKiA3g38GfACcAA0An8T+D6Cj9HRffMzL8qpjq/rzjeWfz8vxXeR5KkKSsyx3rsR5IkSZKk6c1neCVJkiRJdcmEV5IkSZJUl0x4JUmSJEl1yYRXkiRJklSXTHglSZIkSXXJhFeSJEmSVJdMeCVJkiRJdcmEV5IkSZJUl0x4JUmSJEl1yYRXkiRJklSXTHglSZIkSXXJhFeSJEmSVJdMeCVJkiRJdcmEV5IkSZJUl0x4JUmSJEl1yYRXkiRJklSXTHglSZIkSXXJhFeSJEmSVJdMeCVJkiRJdcmEV5IkSZJUl0x4JUmSJEl1yYRXkiRJklSXTHglSZIkSXXJhFeSJEmSVJdMeCVJkiRJdcmEV5IkSZJUl0x4JUmSJEl1yYRXkiRJklSXTHglSZIkSXXJhFeSJEmSVJdMeCVJkiRJdcmEV5IkSZJUl0x4JUmSJEl1yYRXkiRJklSXTHglSZIkSXXJhFeSJEmSVJdMeCVJkiRJdcmEV5IkSZJUl0x4JUmSJEl1yYRXkiRJklSXTHglSZIkSXXJhFeSJEmSVJdMeCVJkiRJdcmEV5oCIqIjIt4+0W0lSdLPioiMiFXF+7+MiD+upO047vMrEXHLeOOUdOwiM2sdgzQtRcRA2eZcYB8wVGy/KzP/ZvKjkiSp/kXEBuCuzPzwqP1vAD4DrMjMwcOcn0B7ZnZXcK+K2kbEacAjwMzD3VvS5HKEVxqnzGwdeQGPA68v23cw2Y2IGbWLcvrw9yRJOgp/Dbw1ImLU/l8F/saEs7rsszWdmPBKEywiXhERT0bEByKiB/h8RJwQEf8YEX0RsbN4v6LsnDsi4teL9++IiO9GxH8v2j4SEevH2fb0iPhORPRHxD9HxKcj4v8eIu4jxbgwIj4fET8tjn+z7NgbIuK+iHg2IjZHxLpi/6MRcUVZu4+M3D8iTiumib0zIh4HvlXs/1pE9ETEM0Xs55adPyci/kdEPFYc/26x758i4rdHfZ77I+KNR/vPT5I0LXwTWAS8bGRHRJwA/DzwxYi4OCLujIinI2JrRPyviGgZ60IR8YWI+FjZ9h8U5/w0Iv79qLavi4h7i/7uiYj4SNnh7xQ/n46IgYi4ZKSfLjv/0oi4u+jD7o6IS8uO3RER/yUi/rXot2+JiMWHiNk+W6qQCa9UHcuAhcCpwLWU/lv7fLF9CrAH+F+HOf/FwEPAYuATwF9F/My32JW0/RJwF6U/Cj5C6ZvvQzlSjP+H0tTtc4E24FMAEXEx8EXgD4AFwMuBRw9zn9EuA84BXlNsdwDtxT3+DSifGv7fgRcBl1L6/f4HYJjim/6RRhHxAmA58E9HEYckaZrIzD3AV4G3le3+ZeDHmfkjSo8Y/R6lvvES4FXAbx3pukXy937gSkp90RWjmuwq7rkAeB3wmxFxdXHs5cXPBcVsrztHXXshpX7pzyj1y58E/ikiFpU1ewvwa5T6wJYilrHYZ0sVMuGVqmMY+E+ZuS8z92Tmjsz828zcnZn9wMcpdRqH8lhmfjYzhyh1DCcCS4+mbUScAlwEfDgz92fmd4EbD3XDw8UYEScC64F3Z+bOzDyQmd8uTn0n8LnMvDUzhzNzS2b+uLJfEwAfycxdxR8vZObnMrM/M/dRStJfEBHHR0QT8O+B3ynuMZSZ3yva3QicGRHtxTV/FfhKZu4/ijgkSdPLXwO/FBGzi+23FfvIzB9m5vczczAzH6X0XO/h+t0Rvwx8PjM7M3MXpX7ooMy8IzM3Fv3d/cCXK7wulBLkrsz8P0VcXwZ+DLy+rM3nM/MnZQn9BWNdyD5bqpwJr1QdfZm5d2QjIuZGxGeKaT3PUpr2tCAimg9xfs/Im8zcXbxtPcq2JwFPle0DeOJQAR8hxpOLa+0c49STgc2Hum4FDsYUEc0R8d+KKVbP8ty3zouL1+yx7lX8rr9C6XmuJuDNlL7dliTVqeKL3O3A1RGxEriY0swmIuLMYppvT9Gf/FdK/ciRnMTz+8rHyg9GxIsj4vZiKvEzwLsrvO7ItR8bte8xSqObI3rK3u/mEH2/fbZUORNeqTpGlz//feAs4MWZeRzPTXs61DTlibAVWBgRc8v2nXyY9oeL8YniWgvGOO8JYOUhrrmL0pSqEcvGaFP+u3oL8AZKU8iOB04ri2E7sPcw9/pr4FcoTVvbPXoqmSSpLn2R0sjuW4GbM3Nbsf8vKI2ethd92h9RWZ+7lef3laeMOv4lSiOUJ2fm8cBfll33SEuf/JTSFORypwBbKohrNPtsqUImvNLkmE/p+Zqni2d4/lO1b5iZjwH3AB+JiJaIuITnT5uqOMbM3ErpOZ3/ryiUMTMiRjrXvwJ+LSJeFRFNEbE8Is4ujt0HXFO0Xwv80hHCnk9peacdlDrd/1oWwzDwOeCTEXFS8c3yJRExqzh+J6Wp5P8DvymWpEbxRUoJ129QTGcuzAeeBQaKPuk3K7zeV4F3RMTq4gvj0f31fEqjp3uL52HfUnasj1I/dMYhrn0Tpam8b4mIGRHxJmA18I8VxjY6DvtsqQImvNLk+FNgDqVvPL8PbJik+/4KpWIdO4CPUZpCtO8QbY8U468CByh9Y94L/C5AZt5FqcDGp4BngG/z3DfYf0zp292dwH+mmGp2GF+kNL1rC7CpiKPc+4GNwN3AU8Cf8Pz/j30ROA8YsxK1JKm+FM/nfg+Yx/PrVLyfUjLaD3yWUv9XyfU6KPWH3wK6i5/lfgv4aET0Ax+mlCCPnLub0rO0/xql6tA/N+raOyhVkf59Sv3yfwB+PjO3VxLbKPbZUoUi80izLyTVi4j4CqUKllUfYa6FiHgbcG1mvrTWsUiSpEOzz9ZkcYRXqmMRcVFErCymLa2j9KzNN4903nRUTD37LeD6WsciSZIOzT5bk8mEV6pvy4A7gAFK6/79ZmbeW9OIqiAiXkPp2altHHkKliRJqhH7bE02pzRLkiRJkuqSI7ySJEmSpLo0o9YBTIbFixfnaaedVuswJEl14oc//OH2zFxS6zimM/tmSdJEOlTf3BAJ72mnncY999xT6zAkSXUiIh6rdQzTnX2zJGkiHapvdkqzJEkiItZFxEMR0R0RHxzj+LsjYmNE3BcR342I1cX+RRFxe0QMRMT/mvzIJUk6NBNeSZIaXEQ0A58G1gOrgTePJLRlvpSZ52XmBcAngE8W+/cCfwy8f7LilSSpUia8kiTpYqA7Mx/OzP3ADZTW7T4oM58t25wHZLF/V2Z+l1LiK0nSlNIQz/BKkqTDWg48Ubb9JPDi0Y0i4j3A+4AW4PKjvUlEXAtcC3DKKaeMK1BJko6GI7ySJKkimfnpzFwJfAD4j+M4//rMXJuZa5cssci1JKn6THglSdIW4OSy7RXFvkO5Abi6qhFJkjQBTHglSdLdQHtEnB4RLcA1wI3lDSKivWzzdUDXJMYnSdK4+AyvJEkNLjMHI+K9wM1AM/C5zHwgIj4K3JOZNwLvjYgrgAPATuDtI+dHxKPAcUBLRFwNvDozN03255AkaTQTXknStLNz137+8tub2Tc4fFTn/Yd1ZzG3xa5vLJl5E3DTqH0fLnv/O4c597TqRSZJjeOGux7nxz39Fbef0RS8/dLTOHnh3CpGNb3Z60uSpp0v3fU4n/nOwxw3++i6sd+74sxSfWFJkqaYvQeG+KNvbGRmcxOzZlT25OmzeweZ09LM77/6rCpHN32Z8EqSpp2Ozq1ccPICvvmel9Q6FEmSJsQj23cxnHDdv3sBV73gpIrOufy/30HXtoEqRza9WbRKkjStPPHUbjq3PMv6NctqHYokSROmu7eUuK5a0lrxOSvbWunuM+E9HBNeSdK0sqGzB4D1a06scSSSJE2crt4BmgLOWDKv4nPa21p5dPsuDgwdXU2LRmLCK0maVm7q3Mq5Jx3HKYss0CFJqh+bewc4ZeFcZs9srvic9qWtDA4nj+3YVcXIpjcTXknStLH1mT3c+/jTTmeWJNWdrt5+VrVVPp0ZYNWS+aVzfY73kEx4JUnTxs3FdOZ1TmeWJNWRwaFhHtm+i1Vt84/qvJVtpenPXb0mvIdiwitJmjY6Ons4c2nrUX8DLknSVPbYU7s5MJS0H2X/NrdlBitOmHOw4JV+lgmvJGla6Ovfx92PPuXoriSp7oxMSR7PF7qr2lod4T0ME15J0rRwy6YehhOf35Uk1Z3NxdJCK8eR8La3tfJw3wBDwznRYdUFE15J0rSwobOH0xbN5exlR/d8kyRJU13Xtn5OOn42rbNmHPW5q9pa2Tc4zJM7d1chsunPhFeSNOU9vXs/d27ewbo1JxIRtQ5HkqQJ1dU7wKql4/tCd6TQlZWax1bVhDci1kXEQxHRHREfHOP4+yJiU0TcHxG3RcSpZcc+EREPRMSDEfFnUfyFExF3FNe8r3i1VfMzSJJq79ZN2xgcTl57ntOZJUn1ZXg42dw3cNQFq0aMPPfb3WfCO5aqJbwR0Qx8GlgPrAbeHBGrRzW7F1ibmecDXwc+UZx7KfAS4HxgDXARcFnZeb+SmRcUr95qfQZJ0tSwobOH5QvmcN7y42sdiiRJE2rL03vYe2B43CsQHD9nJm3zZznCewjVHOG9GOjOzIczcz9wA/CG8gaZeXtmjkw2/z6wYuQQMBtoAWYBM4FtVYxVkjRF9e89wL90bWfdmmVOZ5Yk1Z2RJYXGO8IL0L601RHeQ6hmwrsceKJs+8li36G8E+gAyMw7gduBrcXr5sx8sKzt54vpzH8ch/jrJyKujYh7IuKevr6+Y/kckqQa+taPe9k/NGx1ZklSXerq7QfGtyTRiFVLWune1k+mlZpHmxJFqyLircBa4LpiexVwDqUR3+XA5RHxsqL5r2TmecDLitevjnXNzLw+M9dm5tolS5ZU+yNIkqqkY2MPbfNn8cJTTqh1KJIkTbiubQMsbp3Fgrkt477GqqXz2bV/iK3P7J3AyOpDNRPeLcDJZdsrin3PExFXAB8CrsrMfcXuNwLfz8yBzBygNPJ7CUBmbil+9gNfojR1WpJUh3bvH+SOn/Sybs0ympqczixJqj/dx1CwasTI+SPTo/Wcaia8dwPtEXF6RLQA1wA3ljeIiAuBz1BKdsuLTz0OXBYRMyJiJqWCVQ8W24uLc2cCPw90VvEzSJJq6NsP9bH3wDDrnM4sSapDmUn3toFjms4Mz02H7jLh/RlVS3gzcxB4L3Az8CDw1cx8ICI+GhFXFc2uA1qBrxXP5I4kxF8HNgMbgR8BP8rMf6BUwOrmiLgfuI/SiPFnq/UZJEm1dVNnDwvntXDxaQtrHUpDqGA5wXdHxMaiz/5u+eoLEfGHxXkPRcRrJjdySZqeevv30b9vkPalx5bwLprXwglzZzrCO4YZ1bx4Zt4E3DRq34fL3l9xiPOGgHeNsX8X8KIJDlOSNAXtPTDEtx7cxutfcBIzmqdEyYm6Vrac4JWUCk3eHRE3ZuamsmZfysy/LNpfBXwSWFckvtcA5wInAf8cEWcW/bkk6RBGlhJateTYEt6IYFVbK91FASw9x78gJElT0ne7trNr/xDrzzux1qE0ikqWE3y2bHMepWUEKdrdkJn7MvMRoBtrbEjSER2s0HyMI7wAq9rm09U7YKXmUUx4JUlTUkdnD8fNnsElZyyqdSiNoqLlBCPiPRGxGfgE8P8c5bkuGShJZbp7Bzh+zkyWtM465mu1t7Xy9O4D7Ni1fwIiqx8mvJKkKWf/4DC3burhitVLaZlhVzWVZOanM3Ml8AHgPx7luS4ZKEllunpLBasijn0lgoOFq7b5HG85/4qQJE05dz68g2f3DrJ+jdOZJ1FFywmWuQG4epznSpKAzb3HviTRiJHCV919JrzlTHglSVPOhs6tzGtp5mXti2sdSiOpZDnB9rLN1wFdxfsbgWsiYlZEnA60A3dNQsySNG3tGNjHjl37j3lJohHLjptN66wZdG+zcFW5qlZpliTpaA0NJ7c8sI3Lz1nK7JnNtQ6nYWTmYESMLCfYDHxuZDlB4J7MvBF4b0RcARwAdgJvL859ICK+CmwCBoH3WKFZkg5vZAmhiUp4I4KVba2uxTuKCa8kaUq565Gn2LFrP+vXLKt1KA2nguUEf+cw534c+Hj1opOk+jIy9bh96fwJu2Z7Wyvf+YlFAcs5pVmSNKV0dG5l9swmXnGWRY0kSfWra9sAc1uaOen42RN2zVVtrfT27+OZ3Qcm7JrTnQmvJGnKGB5ONnT2cNmZS5jb4iQkSVL96p7ACs0jRgpgdff5HO8IE15J0pRx7xM76e3fx2vPszqzJKm+jSS8E6m9bf7Ba6vEhFeSNGV0bOyhpbmJy89uq3UokiRVzbN7D9Dz7N4JT3iXnzCHWTOaXIu3jAmvJGlKyEw6Ont4afti5s+eWetwJEmqms3FCOzIiOxEaW4KVi5pdS3eMia8kqQpYeOWZ9jy9B7WWZ1ZklTnuiZ4SaJyq9paHeEtY8IrSZoSOjp7aG4Krjxnaa1DkSSpqrp7B2iZ0cTJJ8yZ8Gu3t7Wy5ek97No3OOHXno5MeCVJNZdZqs586cpFnDCvpdbhSJJUVd29A5yxeB4zmic+HWtfWho1frhv14RfezpyzQdJ0oTbMbCP1//5d3lq9/6K2mfCvsFhfv1lp1c5MkmSaq+rt58XrFhQlWuPTJPu6u3nvBXHV+Ue04kJryRpwnV09vDTZ/byqz93KnNbmis6Z9bMZt544fIqRyZJUm3t2T/Ekzv38EsvPLkq1z910TxmNIVLExVMeCVJE25DZw+nL57HR99wLhFR63AkSZoyNvcNkFmdglUAM5ubOG3xvIOFsRqdz/BKkibUzl37ufPhHaxbs8xkV5KkUUZGXkeeta2G9rZWR3gLJrySpAl164PbGBpOXrvmxFqHIknSlNPdO0BzU3DaonlVu0d7WyuP7djFvsGhqt1juqhqwhsR6yLioYjojogPjnH8fRGxKSLuj4jbIuLUsmOfiIgHIuLBiPizKIYJIuJFEbGxuObB/ZKkqaFj41ZWnDCHNcuPq3UokiRNOV29/Zy6aC4tM6qXiq1sa2U44ZHtVmqu2m85IpqBTwPrgdXAmyNi9ahm9wJrM/N84OvAJ4pzLwVeApwPrAEuAi4rzvkL4DeA9uK1rlqfQZJ0dJ7de4Dvdm9n3blOZ5YkaSzdvQO0V+n53RHtbfMP3qvRVXOE92KgOzMfzsz9wA3AG8obZObtmbm72Pw+sGLkEDAbaAFmATOBbRFxInBcZn4/MxP4InB1FT+DJOkofOvBXg4MJevPW1brUCRJmnL2Dw7z6I7dVStYNeKMJfOIgK5tJrzVTHiXA0+UbT9Z7DuUdwIdAJl5J3A7sLV43ZyZDxbnP1nJNSPi2oi4JyLu6evrG/eHkCRVrqNzK0uPm8WFJ59Q61AkSZpyHt2xi6HhPDgCWy2zZzZzysK5jvAyRYpWRcRbgbXAdcX2KuAcSiO+y4HLI+JlR3PNzLw+M9dm5tolS5ZMdMiSpFF27Rvkjof6WHfuMpqanM4sSdJoIwlotUd4wUrNI6qZ8G4ByldTXlHse56IuAL4EHBVZu4rdr8R+H5mDmTmAKWR30uK81eUnT7mNSVJk++Oh/rYNzjMOqszS5I0pq5tA0TAyiXVT3hXtrXy8PYBBoeGq36vqayaCe/dQHtEnB4RLcA1wI3lDSLiQuAzlJLd3rJDjwOXRcSMiJhJqWDVg5m5FXg2In6uqM78NuDvq/gZJEkV6ujcyqJ5LVx8+sJahyJJ0pTU3TfAihPmMKeluer3am+bz4Gh5PGndh+5cR2rWsKbmYPAe4GbgQeBr2bmAxHx0Yi4qmh2HdAKfC0i7ouIkYT468BmYCPwI+BHmfkPxbHfAv430F206ajWZ5AkVWbvgSFu/3Evrz53Kc1OZ56WjnEpwT+JiM7i9abJjVySpo+ubf2smoTRXXhu2nRXg09rnlHNi2fmTcBNo/Z9uOz9FYc4bwh41yGO3UNpqSJJ0hTxL13b2bV/yOnM01TZUoJXUioIeXdE3JiZm8qajSwluDsifpPSUoJviojXAS8ELqC0ssIdEdGRmc9O7qeQpKltaDh5ePsuXn7m5NQXGkl4u3sHeM25k3LLKWlKFK2SJE1vHRu3cvycmVy6clGtQ9H4HMtSgquB72TmYGbuAu4H1k1S3JI0bTzx1G72Dw5PSsEqgNZZMzjp+NkNX7jKhFeSdEz2Dw5z64PbuOKcpcxstluZpsa9lCClR4/WRcTciFgMvJLnF608yCUDJTWyrkms0DxiZVsrXb39k3a/qci/TCRJx+R7m7fTv3eQ9WuW1ToUTYLRSwlm5i2UHl/6HvBl4E5gaKxzXTJQUiMbSTwnM+Ftb5tPd+8Aw8M5afecakx4JUnHZENnD/Namnlp++Jah6LxO5alBMnMj2fmBZl5JRDAT6ocryRNO929Ayw9bhbHzZ45afdc1dbK3gPDbHl6z6Tdc6ox4ZUkjdvg0DA3P9DDq85ZyuyZ1V9iQVUz7qUEI6I5IhYV788HzgdumbTIJWma6O4doL1t/qTes33pc4WrGpUJryRp3O565Cl27j7gdOZp7hiXEpwJ/EtEbAKuB95aXE+SVMhMunsHJnU6M3BwCaRGTniruiyRJKm+dXT2MHtmE5ed5fOY090xLCW4l1KlZknSIfz0mb3s3j806QnvCfNaWNza0tCFqxzhlSSNy/BwsuGBHl5xZhtzW/z+VJKkQ+naVko42yc54YXSc7xdDTzCa8IrSRqXHz6+k77+faw/z+nMkiQdzsiU4valk/sMLzxXqTmzMSs1m/BKksalY2MPLc1NXH52W61DkSRpSuvuHWDhvBYWzmuZ9Huvamulf+8gvf37jty4DpnwSpKOWmZy8wM9vKx9MfMncXkFSZKmo1oUrBoxMo26UQtXmfBKko7a/U8+w5an97DO6sySJB1WZtJVw4R35L4jzxE3GhNeSdJR6+jsYUZTcOXqpbUORZKkKa1vYB/P7DlQk4JVAEvmz+K42TMatnCVCa8k6ahkJh2dW7lk5SIWzJ38Z5EkSZpODhasapv8glUAEUH70vlOaZYkqRIPbu3nsR27Wb/mxFqHIknSlDeSaNZqSjPAqiWtJrySJFViQ+dWmgJefa7TmSVJOpLu3gHmz5rB0uNm1SyG9qWt7Ni1n6d27a9ZDLViwitJOiodnT1cdNpCFrfWruOWJGm66No2wMq2ViKiZjGsbOBKzSa8kqSKdff209U7wHqrM0uSVJGu3oGaFawaMXL/rt7Gq9RswitJqljHxh4A1vn8riRJR/T07v1sH9hH+9LaJrwnHT+HuS3NjvBKknQ4HZ09vPCUBSw7fnatQ5EkacqbCgWrAJqagpUNWriqqglvRKyLiIciojsiPjjG8fdFxKaIuD8ibouIU4v9r4yI+8peeyPi6uLYFyLikbJjF1TzM0iSShm6wZ8AACAASURBVB7bsYtNW5+1OrMkSRWq9ZJE5drbTHgnVEQ0A58G1gOrgTdHxOpRze4F1mbm+cDXgU8AZObtmXlBZl4AXA7sBm4pO+8PRo5n5n3V+gySpOd0dI5MZ/b5XUmSKtHVO8DsmU0sXzCn1qGwsq2Vrc/spX/vgVqHMqlmVPHaFwPdmfkwQETcALwB2DTSIDNvL2v/feCtY1znl4COzNxdxVjVwPYNDvHPm3o5MDRc61CkKe0b/7aFNcuP4+SFc2sdiiRJE2LrM3v4wcNPVe36P3hkByuXtNLUVLsKzSNGCld98c7HpkQCPuJ155/IzObqTTyuZsK7HHiibPtJ4MWHaf9OoGOM/dcAnxy17+MR8WHgNuCDmblv9EkRcS1wLcApp5xyFGGr0Xz17if4479/oNZhSNPCh157Tq1DkCRpwvyXf9zETUVBxmp5y4unRi6yZvnxNDcF1938UK1DeZ5XndM2bRPeikXEW4G1wGWj9p8InAfcXLb7D4EeoAW4HvgA8NHR18zM64vjrF27NqsSuOrCP23cysol8/jfb7+o1qFIU1pzBCtOmDrfCEuSdKx+vLWfy85cwkeuOrdq9zh5ivSdJy2Yww/+6FX07x2sdSjPM7eluilpNa++BTi5bHtFse95IuIK4EPAZWOM1P4y8I3MPDjRPDO3Fm/3RcTngfdPaNRqKNsH9nHXI0/x3leu4vTF82odjiRJkibJvsEhHntqN687/8SG+TtwcessFrfOqnUYk6qaVZrvBtoj4vSIaKE0NfnG8gYRcSHwGeCqzOwd4xpvBr486pwTi58BXA10ViF2NYhbHtjGcLqmqCSNd2WF4tgnIuKBiHgwIv6s6KMlaUp7dPtuhoaz5ksGqbqqlvBm5iDwXkrTkR8EvpqZD0TERyPiqqLZdUAr8LViiaGDCXFEnEZphPjboy79NxGxEdgILAY+Vq3PoPrX0bmVUxfN5ZwTa18qXpJq5VhWVoiIS4GXAOcDa4CLGPWIkiRNRV29/UDt18hVdVV1wnRm3gTcNGrfh8veX3GYcx+lVPhq9P7LJzBENbCnd+/nzs07+PWXnYGDEZIa3LGsrJDAbEq1NQKYCWybhJgl6Zh09w4QASuXmPDWs2pOaZamtFs3bWNwOFnvmqKSNNbKCj/zpXOZgysrZOadwO3A1uJ1c2Y+ONZJEXFtRNwTEff09fVNSOCSNF5dvQOcfMJcZs9srnUoqiITXjWsDZ09LF8wh/NXHF/rUCRp2ihbWeG6YnsVcA6l4pTLgcsj4mVjnZuZ12fm2sxcu2TJkskKWZLGtLl34ODatKpfJrxqSP17D/AvXdt5zbnLnM4sSUe/ssJVZSsrvBH4fmYOZOYApZHfS6ocryQdk8GhYR7u2+Xzuw3AhFcN6Vs/7mX/0DDrz3M6syRxbCsrPA5cFhEzImImpYJVY05plqSp4vGndrN/aNiEtwGY8KohdWzsoW3+LF50ygm1DkWSau4YV1b4OrCZ0uoJPwJ+lJn/MLmfQJKOTnfvAADtS12po95VtUqzNBXt3j/IHT/p5d+96GSampzOLEkw/pUVMnMIeFd1o5OkidVVJLwrl8yrcSSqNkd41XC+/VAfew8MW51ZkiSpQW3uHeDE42czf/bMWoeiKjPhVcPp6OzhhLkzufj0hbUORZIkSTXQ1Tvg87sNwoRXDWXvgSG+9eNeXnPuMmY0+6+/JElSoxkeTrpNeBuGf/GroXy3azsD+wZZ53RmSZKkhvTTZ/aw58AQ7W0WrGoEJrxqKB2dPcyfPYNLVy6udSiSJEmqgZGCVY7wNoYjJrwR8fqIMDHWtHdgaJh/fnAbV56zlJYZ/istqT7Zb0vS4W0eWZLIhLchVNIhvgnoiohPRMTZ1Q5IqpY7N+/gmT0HnM4sqd7Zb0vSYXRtG2DRvBZOmNdS61A0CY6Y8GbmW4ELKS0q/4WIuDMiro0IJ71rWuno3Mq8lmZefuaSWociSVVjvy1Jh9fV2+905gZS0ZSnzHwW+DpwA3Ai8Ebg3yLit6sYmzRhhoaTWx7YxivPbmP2zOZahyNJVWW/LUljyyxVaG5fasLbKCp5hveqiPgGcAcwE7g4M9cDLwB+v7rhSRPjrkeeYseu/axfc2KtQ5GkqrLflqRD6+vfx7N7B1m1xIS3UcyooM0vAp/KzO+U78zM3RHxzuqEJU2sDZ1bmTWjiVec5XRmSXXPfluSDmGkQnP7Up/yaBSVJLwfAbaObETEHGBpZj6ambdVKzBpogwPJx2dPVx25hLmzarkX3lJmtY+gv22JI2p2yWJGk4lz/B+DRgu2x4q9knTwr1P7KS3fx+vPc/pzJIagv22JB1CV28/82fPoG3+rFqHoklSScI7IzP3j2wU763hrWmjY2MPM5uDy89pq3UokjQZ7Lcl6RC6ewdob2slImodiiZJJQlvX0RcNbIREW8AtlcvJGniZJamM7901WKOmz2z1uFI0mSw35akQ+juHXA6c4OpJOF9N/BHEfF4RDwBfAB4VyUXj4h1EfFQRHRHxAfHOP6+iNgUEfdHxG0RcWqx/5URcV/Za29EXF0cOz0iflBc8ysR4bfWOqSNW55hy9N7rM4sqZGMu9+WpHq2c9d+tg/sp73NglWN5IgVfDJzM/BzEdFabA9UcuGIaAY+DVwJPAncHRE3Zuamsmb3AmuLypG/CXwCeFNm3g5cUFxnIdAN3FKc8yeUqk/eEBF/CbwT+ItKYlLj6ejsobkpuHL10lqHIkmTYrz9tiTVu+6+omCVa/A2lIpK1kbE64Bzgdkj890z86NHOO1ioDszHy6ucQPwBuBgwlsktiO+D7x1jOv8EtBRJMUBXA68pTj215SqUZrw6mdkJhs6e7jkjEWcMM+JAJIaxzj7bUmqa13bioTXNXgbyhGnNBejqG8CfhsI4N8Bp1Zw7eXAE2XbTxb7DuWdQMcY+68Bvly8XwQ8nZmDR7pmRFwbEfdExD19fX0VhKt689C2fh7Zvot1a5bVOhRJmjTH0G9LUl3r7h1gzsxmli+YU+tQNIkqeYb30sx8G7AzM/8zcAlw5kQGERFvBdYC143afyJwHnDz0V4zM6/PzLWZuXbJkiUTE6imlZs29hABrz7X6cySGkrV+21Jmo66evtZ2TaPpiYrNDeSShLevcXP3RFxEnAAqKQC0Bbg5LLtFcW+54mIK4APAVdl5r5Rh38Z+EZmHii2dwALImJkKvaY15QANnRu5aJTF9I2f3atQ5GkyTSufrsahSYlaSopLUlkwapGU0nC+w8RsYDS6Ou/AY8CX6rgvLuB9qKqcgulqck3ljeIiAuBz1BKdnvHuMabeW46M5mZwO2UnusFeDvw9xXEogazuW+An2wbYP15TmeW1HCOut8uKzS5HlgNvDkiVo9qNlJo8nzg65QKTZKZt2fmBZl5AaU6G7t5rtCkJE0J/XsPsPWZvS5J1IAOW7QqIpqA2zLzaeBvI+IfgdmZ+cyRLpyZgxHxXkrTkZuBz2XmAxHxUeCezLyRUmfcCnytKKrxeGZeVdz7NEojxN8edekPADdExMcodb5/VemHVePY0NkD4PO7khrKMfTbE15o8hg+hiRNuM19uwBMeBvQYRPezByOiE8DFxbb+4DR044Pd/5NwE2j9n247P0Vhzn3UcYoSFV0xhdXGoMaU0fnVi44eQEnHm9RAkmN4xj67bEKTb74MO0PV2jyk4c6KSKuBa4FOOWUUyoIS5ImRndvqUJzuwlvw6lkWaLbIuIXgb8rphRLk2r3/kF+7yv38fTuA0duDCTQueVZ/nD92dUNTJKmpqr222WFJi8btf+IhSYz83rgeoC1a9f6N4WkSdPV209LcxOnLJxb61A0ySp5hvddwNeAfRHxbET0R8SzVY5LOujWTdu4+YFt7B8arqh9AK84awlvfOHhVsGSpLo1nn67GoUmJWnK6N42wOmL5zGjuZL0R/XkiCO8mWkpM9VUx8Ye2ubP4m/ffall5CXpCMbZbx8sNEkp0b0GeEt5g7JCk+sOU2jyD8dxb0mquu6+AdYsP77WYagGjpjwRsTLx9qfmd+Z+HCk59u9f5A7ftLLL6892WRXkiownn67ioUmJanm9h4Y4vGndnP1Bc7+a0SVPMP7B2XvZ1MqGPVDSksPSFX17Yf62Htg2GrLklS5cfXb1Sg0KUlTwcN9u8iE9qUWrGpElUxpfn35dkScDPxp1SKSytzU2cPCeS1cfNrCWociSdOC/bYkPV9Xbz/gkkSNajxPbT8JnDPRgUij7T0wxLce3MarVy+1wIAkjZ/9tqSG1t07QFPA6Yvn1ToU1UAlz/D+OaWVXqCUIF8A/Fs1g5IAvtu1nV37h1h/3om1DkWSpg37bUl6vu7eAU5bNI9ZM5prHYpqoJJneO8pez8IfDkz/7VK8UgHdXT2cNzsGVxyxqJahyJJ04n9tiSV6eodYKXTmRtWJQnv14G9mTkEEBHNETE3M3dXNzQ1sv2Dw9y6qYcrVi+lZYbTmSXpKNhvS1LhwNAwj27fxatXL611KKqRSjKJ24A5ZdtzgH+uTjhSyZ0P7+DZvYOsX+N0Zkk6SvbbklR4bMcuBofTglUNrJKEd3ZmDoxsFO/nVi8kCTZ0bmVeSzMva19c61Akabqx35akQte20v8O29vm1zgS1UolCe+uiHjhyEZEvAjYU72Q1OgGh4a55YFtXH7OUmbPtLiAJB0l+21JKnT3lhLelW1WaG5UlTzD+7vA1yLip0AAy4A3VTUqNbS7Hn2KHbv2s37NslqHIknTkf22JBW6egdYvmAOc1sqSXtUj474Tz4z746Is4Gzil0PZeaB6oalRrahs4fZM5t4xVlLah2KJE079tuS9Jyu3gHal/r8biM74pTmiHgPMC8zOzOzE2iNiN+qfmhqRMPDyYbOHl5xZpvfxEnSONhvS1LJ0HDycN8Aq5aY8DaySp7h/Y3MfHpkIzN3Ar9RvZDUyO59Yie9/ftYf57TmSVpnOy3JQl4cudu9g0OO8Lb4CpJeJsjIkY2IqIZaKleSGpkN23soaW5icvPbqt1KJI0XdlvSxLPFaxaZYXmhlbJnNENwFci4jPF9ruAjuqFpEaVWZrO/NL2xcyfPbPW4UjSdGW/LUmUnt8FXIO3wVWS8H4AuBZ4d7F9P6WKj9KE2rjlGbY8vYffvaK91qFI0nRmvy1JlNbgbZs/i+PnOJDSyI44pTkzh4EfAI8CFwOXAw9WcvGIWBcRD0VEd0R8cIzj74uITRFxf0TcFhGnlh07JSJuiYgHizanFfu/EBGPRMR9xeuCSmLR1NfR2cOMpuDK1UtrHYokTVvH0m9LUj3p7rNCsw4zwhsRZwJvLl7bga8AZOYrK7lw8czQp4ErgSeBuyPixszcVNbsXmBtZu6OiN8EPsFzawV+Efh4Zt4aEa3AcNl5f5CZX68kDk0PmUnHxq1csnIRC+b6qJkkHa1j7bclqZ5kJpt7B/jFFy6vdSiqscON8P6Y0rfCP5+ZL83MPweGjuLaFwPdmflwZu4HbgDeUN4gM2/PzN3F5veBFQARsRqYkZm3Fu0GytqpDv24p59Hd+xm3Rpn3UnSOB1rvy1JdaPn2b0M7Btk1VILVjW6wyW8vwBsBW6PiM9GxKuAOEz70ZYDT5RtP1nsO5R38lxRjTOBpyPi7yLi3oi4rhgxHvHxYhr0pyJi1lgXi4hrI+KeiLinr6/vKMJWLXR09tAU8OrVJrySNE7H2m9LUt3o2lYUrHIN3oZ3yIQ3M7+ZmdcAZwO3A78LtEXEX0TEqycyiIh4K7AWuK7YNQN4GfB+4CLgDOAdxbE/LGK6CFhIqTjHWPFfn5lrM3PtkiVLJjJcVcGGzq1cdNpClswf8/sLSdIRHGu/XY26G5JUKyMVmn2GV5UUrdqVmV/KzNdTmnJ8L4dIMkfZApxctr2i2Pc8EXEF8CHgqszcV+x+ErivmA49CHwTeGERz9Ys2Qd8ntLUaU1j3b0D/GTbAOudzixJx2w8/XZZ3Y31wGrgzcXjReVG6m6cD3ydUt2NEV8ErsvMcyj1y70T8mEkaZy6ewc4Ye5MFs2zNkyjO2LCWy4zdxYjp6+qoPndQHtEnB4RLcA1wI3lDSLiQuAzlJLd3lHnLoiIkaHZy4FNxTknFj8DuBroPJrPoKlnQ+dWANatObHGkUhSfTmKftu6G5LqSndvP6vaWimlDGpkR5XwHo1iZPa9wM2UlkP4amY+EBEfjYirimbXAa3A14olhm4szh2iNJ35tojYSOkZpM8W5/xNsW8jsBj4WLU+gyZHR2cPF56ygGXHz651KJLUqKpZd+Mg62tImgyZSVfvAKvaLFilwyxLNBEy8ybgplH7Plz2/orDnHsrcP4Y+y+fyBhVW4/v2M0DP32WD732nFqHIkmqQFndjcuKXSN1Ny4EHqe0HNI7gL8afW5mXg9cD7B27dqchHAlNaAdu/bz9O4DrGrz+V1VcYRXqkTHwenMPr8rSTVUlbobklQLIxWa2014hQmvaqyjs4c1y4/j5IVzax2KJDWyqtTdkKRa6O6zQrOeY8Krmtn6zB7ue+Jp1lusSpJqqop1NyRp0nVv66d11gyWHWd9GFX5GV7pcDZ09gC4HJEkTQHVqLshSbXQ3TfASis0q+AIr2qmY2MPZy2dzxlLnG4iSZKkidG1bYBV/n2pggmvaqK3fy93P/aUxaokSZI0YZ7Zc4De/n0+v6uDTHhVE7c8sI1MWH+eCa8kSZImRnevFZr1fD7DO00dGBrmyZ17ah3GuP3Dj37KGYvncdZSFwSXJElT23T/u6uR3PXIUwCuwauDTHinqQ/87f383b/9zBKJ08p7XrnSYgKSJGnK+4/f6OQr9zxR6zBUobktzaw4wSUvVWLCOw3tPTDEhs4eLj+7jatecFKtwxmXpqbg8rPbah2GJEnSEf3oyac5f8Xx/PuXnF7rUFSB0xbPo7nJQRWVmPBOQ9/5SR+79w/xjktP4+VnLql1OJIkSXVrcGiYh/t28WsvOY2rL1xe63AkHSWLVk1DGzp7OH7OTC5ZuajWoUiSJNW1J3buYf/QsM+EStOUCe80s39wmFsf3MaVq5cys9l/fJIkSdXUta0fsAiSNF2ZMU0z39u8nf69g6x3/VpJkqSq6+4rLXNjwitNTya800zHxh5aZ83gpe2Lax2KJElS3eveNsCy42Yzf/bMWociaRxMeKeRwaFhbtnUw6vOaWPWjOZahyNJklT3unoHaF/q6K40XZnwTiN3PfIUO3cfcDqzJEnSJBgeTjb3DTidWZrGTHinkY7OHubMbOayM12/VpIkqdp++swedu8fMuGVpjET3mlieDjZ8EAPrzhrCXNanM4sSZJUbd29pYJV7W3zaxyJpPEy4Z0mfvj4Tvr697H+vBNrHYokSVJDGEl4HeGVpq+qJrwRsS4iHoqI7oj44BjH3xcRmyLi/oi4LSJOLTt2SkTcEhEPFm1OK/afHhE/KK75lYhoqeZnmCo6NvbQMqOJy892OrMkSdJk6No2wKJ5LSyc1xB/bkp1qWoJb0Q0A58G1gOrgTdHxOpRze4F1mbm+cDXgU+UHfsicF1mngNcDPQW+/8E+FRmrgJ2Au+s1meYKjKTDZ1beXn7Ylpnzah1OJIkSQ2h24JV0rRXzRHei4HuzHw4M/cDNwBvKG+Qmbdn5u5i8/vACoAiMZ6RmbcW7QYyc3dEBHA5peQY4K+Bq6v4GaaEHz35DD99Zi/r1jidWZIkaTJkJl3b+k14pWmumgnvcuCJsu0ni32H8k6go3h/JvB0RPxdRNwbEdcVI8aLgKczc/BI14yIayPinoi4p6+v75g+SK11dG5lRlNw5TlLax2KJElSQ+gb2MezewdpN+GVprUpUbQqIt4KrAWuK3bNAF4GvB+4CDgDeMfRXDMzr8/MtZm5dsmSJRMY7eQqTWfu4dJVizl+7sxahyNJqlPHWHdjKCLuK143Tm7kUnV0bxspWGWFZmk6q2bCuwU4uWx7RbHveSLiCuBDwFWZua/Y/SRwXzEdehD4JvBCYAewICJmHO6a9eTBrf08tmM369csq3UokqQ6NQF1N/Zk5gXF66pJCVqqsq6RJYmWOsIrTWfVTHjvBtqLqsotwDXA8771jYgLgc9QSnZ7R527ICJGhmYvBzZlZgK3A79U7H878PdV/Aw119G5laaAV692OrMkqWrGXXdDqlfdvQPMnz2Dtvmzah2KpGNQtYS3GJl9L3Az8CDw1cx8ICI+GhEj3/5eB7QCXyufBpWZQ5SmM98WERuBAD5bnPMB4H0R0U3pmd6/qtZnmAo6Onu4+PSFLGr1f7aSpKo5lrobALOLuhnfj4hDFpOsp/oaqn9dvaWCVaWaqZKmq6qucZOZNwE3jdr34bL3Vxzm3FuB88fY/zClb6LrXndvP929A7ztknNrHYokScDz6m5cVrb71MzcEhFnAN+KiI2ZuXn0uZl5PXA9wNq1a3NSApbGqbt3gMvPbqt1GJKO0ZQoWqWxdWzsAeA15/r8riSpqo6l7gaZuaX4+TBwB3BhNYOVqm3nrv1sH9jvkkRSHTDhncJu6uzhRaeewNLjZtc6FElSfRt33Y2IOCEiZhXvFwMvATZNWuRSFXT3FQWrrNAsTXsmvFPUYzt28eDWZ63OLEmqumOpuwGcA9wTET+iVFjyv2WmCa+mte7ekSWJHOGVpruqPsOr8evoLE1nXmfCK0maBOOtu5GZ3wPOq2500uTq2jbAnJnNLF8wp9ahSDpGjvBOUR2dPZy/4nhWnDC31qFIkiQ1lK7efla2zaOpyQrN0nRnwjsFbXl6Dz964mlHdyVJkmpgc++Az+9KdcKEdwraUExnXr/mxBpHIkmS1FgG9g3y02f2+vyuVCdMeKegDZ1bOXvZfE5fPK/WoUiSJDWUzRaskuqKCe8U09u/l3se2+noriRJUg10mfBKdcWEd4q5+YFtZML683x+V5IkabJ19fYzszk4daGFQ6V6YMI7xXRs3MoZS+bR7reKkiRJk25z7wBnLG5lRrN/Jkv1wP+Sp5Cndu3nB488xfo1y4iwDL4kSdJk6+odcDqzVEdMeKeQWzf1MDScPr8rSZJUA3sPDPHEU7tNeKU6YsI7hXR09nDywjmce9JxtQ5FkiSp4Tzct4vhtGCVVE9MeKeIZ/Yc4F+7t7N+zYlOZ5YkSaqBrt5+ANqXmvBK9cKEd4q47cFtHBhK1q2xOrMkSVItbO4doCng9MXzah2KpAliwjtFdHT2sOy42VywYkGtQ5EkSWpIXb0DnLpoHrNmNNc6FEkTxIR3ChjYN8i3f9LHujXLaGpyOrMkSVItdFuhWao7JrxTwO0/7mX/4DDrnc4sSZJUEweGhnlk+y4TXqnOmPBOARs6e1jc2sLa0xbWOhRJkqSG9NiOXQwOJ+0mvFJdMeGtsb0Hhrj9oV5ec+4ymp3OLEmSVBPdvQMAtLfNr3EkkiZSVRPeiFgXEQ9FRHdEfHCM4++LiE0RcX9E3BYRp5YdG4qI+4rXjWX7vxARj5Qdu6Can6Havv2TPnbvH2L9mhNrHYokSVLD6tpWSnhXtlmhWaonM6p14YhoBj4NXAk8CdwdETdm5qayZv8/e/cdJkWV9XH8eyaQcxCQLEkFJA0gQcA1LJgQIxjWjJjXuLrBdd3VdV2zqAgq4Kqoi4qogBiICsggUUCSIGGAITNkmPP+0YXb8jIwwPTUTPfv8zz9TPWt0OfaOGdO1a1b04E0d99uZrcATwKXB+t2uHtOxez97j40VrHnp1FzVlOuRCptT9BwZhEREZGwLMrMonq54pQoErM/j0UkBLG8wtsGWOTuS9x9N/Au0D16A3cf4+7bg7eTgRoxjKfA2bV3H1/OXcNZJ1UhNVmjy0VEJDzHMiorWF/GzFaYWd/8i1ok7yxcoxmaReJRLKus6sDyqPcrgrac3ACMjHpfzMzSzWyymV14wLaPBQn3WTMrerCDmVnvYP/0zMzMo+pArH27aD1bd+2lW1PNziwiIuGJGpXVDTgZ6GVmJx+w2f5RWacAQ4mMyor2d2B8rGMViYV92c7izCxNWCUShwrEZUUzuwpIA/4d1Vzb3dOAK4DnzKxe0P4QcCLQGqgA/OFgx3T3/u6e5u5plStXjl3wx2DknAxKF02hQ/1KYYciIiKJ7ZhGZZlZK6AKMDqf4hXJUys37mDX3mwaVFHBKxJvYlnwrgRqRr2vEbT9ipmdCfwJuMDdd+1vd/eVwc8lwFigRfA+wyN2AQOJJOlCZ8++bEbPXcMZJx1H0ZTksMMREZHEdtSjsswsCXgauO9wH1IYRl9JYlq4diuAhjSLxKFYFrxTgQZmVtfMigA9geHRG5hZC+BVIsXu2qj28vuHKptZJaADMDd4Xy34acCFwJwY9iFmpizZwKbte+iq2ZlFRKQQOciorFuBEe6+4nD7FobRV5KYFgaPJKpfWY8kEok3MZuGzt33mtntwOdAMvCGu/9gZo8C6e4+nEiyLAX8N1K/8rO7XwCcBLxqZtlEivInomZ3ftvMKgMGzAD6xKoPsTRyTgbFU5Pp3FAJX0REQneko7I6R43KagecZma3EsnpRcwsy93/38RXIgXVorVZHFe6KGVLpIYdiojksZjOu+7uI4ARB7Q9HLV8Zg77fQs0zWHdb/IyxjDsy3Y+/2ENp59YmeJFNJxZRERC98uoLCKFbk8ic2j8ImpUVtfoUVnufmXUNtcSmdhKxa4UKgvXaoZmkXhVICatSjTTlm1kXdYuumk4s4iIFADuvhfYPyprHvD+/lFZZnZBsFn0qKwZZjY8h8OJFCruzuK1mqFZJF7pydohGDkngyIpSZx+4nFhhyIiIgIc/aisA7YfBAzK69hEYmn1lp1k7dqrK7wicUpXePNZdrYzas5qOjWoTKmiOt8gIiIiEqaFa4IJq47ThFUi8UgFbz6buWITGZt30q1JFZegTAAAIABJREFU1bBDEREREUl4i4IZmvUMXpH4pII3n42as5qUJOPMk6qEHYqIiIhIwlu4NotyJVKpWLJI2KGISAyo4M1H7s7IOavpUL+Spr0XERERKQD2T1gVPCJTROKMCt58NDdjCz9v2K7hzCIiIiIFgLuzYO1WTVglEsdU8OajkbNXk2Rw1skaziwiIiIStvXbdrNp+x5NWCUSx1Tw5qORczJoW7ciFUsVDTsUERERkYT3y4RVusIrErdU8OaThWu2sjhzG+c01XBmERERkYJg4dr9jyRSwSsSr1Tw5pORc1ZjBr9trIJXREREpCBYvDaLkkWSqVa2WNihiEiMqODNJyNmZ9CqVnmOK6NfqCIiIiIFwcJgwirN0CwSv1Tw5oOl67Yxf/VWump2ZhEREZECY+GaLE1YJRLnVPDmg5FzVgOo4BUREREpIDbv2MParbtoUEX374rEMxW8+WDUnAya1ShLjfIlwg5FRERERPjfDM31K6vgFYlnKnhjbMXG7cxcsZmuTaqFHYqIiIiIBBbvfySRrvCKxDUVvDE2KhjO3E3DmUVEREQKjIVrt1IkJUkj8ETinAreGBs1ZzUnVi1NnUolww5FRERERAIL12ZRr3IpkpM0Q7NIPFPBG0Nrt+xk2s8b6abhzCIiIiIFyqK1WTQ4TsOZReKdCt4Y+vyH1bjDOU01nFlERESkoNi+ey8rNu6gvgpekbgX04LXzLqa2Y9mtsjMHjzI+nvMbK6ZzTKzr8ysdtS6fWY2I3gNj2qva2ZTgmO+Z2ZFYtmHYzFi9mrqVS5Jgyp6vpuIiIhIQbF47TYAXeEVSQAxK3jNLBl4CegGnAz0MrOTD9hsOpDm7qcAQ4Eno9btcPfmweuCqPZ/Ac+6e31gI3BDrPpwLNZn7WLKT+s1nFlERESkgFmUuRVAV3hFEkBKDI/dBljk7ksAzOxdoDswd/8G7j4mavvJwFWHOqCZGfAb4IqgaTDwCPBKnkWdg89mZfDQh7Nyvf3ebCfboatmZxYRkULAzLoCzwPJwGvu/sQB6+8BbgT2ApnA9e6+LBid9RGRk+ipwIvu3i8/Yv54xkr+MmxOfnyUxJlde7NJSTJqV9SkoiLxLpYFb3VgedT7FUDbQ2x/AzAy6n0xM0snklifcPdhQEVgk7vvjTpm9YMdzMx6A70BatWqdVQdiFazQnEualnjiPY5vlwxGh9f5pg/W0REJJaiRmWdRSS3TjWz4e4+N2qz/aOytpvZLURGZV0OZADt3H2XmZUC5gT7rop13LUrljzi3Cyy38nVylAkRdPZiMS7WBa8uWZmVwFpQOeo5truvtLMTgC+NrPZwObcHtPd+wP9AdLS0vxYYzylRjlOqVHuWA8jIiJSEB31qCx33x3VXpR8nBCzec1yNK+p3CwiIjmLZVJaCdSMel8jaPsVMzsT+BNwgbvv2t/u7iuDn0uAsUALYD1Qzsz2F+oHPaaIiIgckYONyjroCKrAr0ZlmVlNM5sVHONfOV3dNbPeZpZuZumZmZl5ELaIiMihxbLgnQo0CGZVLgL0BIZHb2BmLYBXiRS7a6Pay5tZ0WC5EtABmOvuDowBLgk2vQb4OIZ9EBERkShRo7L+vb/N3ZcHE1DWB64xsyoH29fd+7t7mrunVa5cOX8CFhGRhBazgje4z/Z24HNgHvC+u/9gZo+a2f5Zl/8NlAL+e8Djh04C0s1sJpEC94mo+4j+ANxjZouI3NP7eqz6ICIikiCOaVTWfsGV3TnAaTGKU0RE5IjE9B5edx8BjDig7eGo5TNz2O9boGkO65YQuddIRERE8sYvo7KIFLo9+d8TEYBfjcrqesCorBrAenffYWblgY7As/kWuYiIyCEUiEmrREREJDzuvtfM9o/KSgbe2D8qC0h39+H8elQWwM/ufgGRUVlPm5kDBjzl7rND6YiIiMgBVPCKiIjIsYzK+gI4JbbRiYiIHB09fExERERERETikgpeERERERERiUsqeEVERERERCQuWeTRtvHNzDKBZXlwqErAujw4TmGRSP1NpL5CYvU3kfoKidXfMPta2931INljoNx81BKpv4nUV0is/iZSXyGx+lvgcnNCFLx5xczS3T0t7DjySyL1N5H6ConV30TqKyRWfxOpr5KzRPt3kEj9TaS+QmL1N5H6ConV34LYVw1pFhERERERkbikgldERERERETikgreI9M/7ADyWSL1N5H6ConV30TqKyRWfxOpr5KzRPt3kEj9TaS+QmL1N5H6ConV3wLXV93DKyIiIiIiInFJV3hFREREREQkLqngFRERERERkbikgjeXzKyrmf1oZovM7MGw44k1M1tqZrPNbIaZpYcdT14yszfMbK2ZzYlqq2BmX5jZwuBn+TBjzEs59PcRM1sZfL8zzOycMGPMK2ZW08zGmNlcM/vBzO4K2uPu+z1EX+P1uy1mZt+Z2cygv38L2uua2ZTgd/N7ZlYk7Fgl/yg3xw/l5rj+/a3cHL/fbaHIzbqHNxfMLBlYAJwFrACmAr3cfW6ogcWQmS0F0tw97h6SbWadgCzgTXdvErQ9CWxw9yeCP5rKu/sfwowzr+TQ30eALHd/KszY8pqZVQOqufv3ZlYamAZcCFxLnH2/h+jrZcTnd2tASXfPMrNUYCJwF3AP8KG7v2tm/YCZ7v5KmLFK/lBuji/KzcrNxMH3q9xcMHOzrvDmThtgkbsvcffdwLtA95BjkqPk7uOBDQc0dwcGB8uDifxyigs59DcuuXuGu38fLG8F5gHVicPv9xB9jUsekRW8TQ1eDvwGGBq0x8V3K7mm3BxHlJvjl3KzcjMhf7cqeHOnOrA86v0K4vgfb8CB0WY2zcx6hx1MPqji7hnB8mqgSpjB5JPbzWxWMKyq0A8jOpCZ1QFaAFOI8+/3gL5CnH63ZpZsZjOAtcAXwGJgk7vvDTZJhN/N8j/KzfEvrn935yAuf3/vp9wcf99tYcjNKnglJx3dvSXQDbgtGHqTEDwyzj/ex/q/AtQDmgMZwNPhhpO3zKwU8AHwe3ffEr0u3r7fg/Q1br9bd9/n7s2BGkSu7p0Yckgi+U25Ob7F7e9vUG4mTr/bwpCbVfDmzkqgZtT7GkFb3HL3lcHPtcBHRP4Bx7M1wX0X+++/WBtyPDHl7muCX1DZwADi6PsN7iH5AHjb3T8MmuPy+z1YX+P5u93P3TcBY4B2QDkzSwlWxf3vZvkV5eY4/P/7AHH5uzsn8fz7W7k5fr/b/QpyblbBmztTgQbBjGNFgJ7A8JBjihkzKxncaI+ZlQTOBuYceq9CbzhwTbB8DfBxiLHE3P4EE+hBnHy/weQJrwPz3P2ZqFVx9/3m1Nc4/m4rm1m5YLk4kYmK5hFJrpcEm8XFdyu5ptwcJ/9/H0Lc/e4+lDj+/a3cHL/fbaHIzZqlOZeC6cOfA5KBN9z9sZBDihkzO4HImWOAFOCdeOqvmQ0BugCVgDXAX4FhwPtALWAZcJm7x8VkEjn0twuRYTUOLAVujrqPptAys47ABGA2kB00/5HI/TNx9f0eoq+9iM/v9hQiE18kEzlZ+767Pxr8vnoXqABMB65y913hRSr5Sbk5fvqr3KzcTBx8v8rNBTM3q+AVERERERGRuKQhzSIiIiIiIhKXVPCKiIiIiIhIXFLBKyIiIiIiInFJBa+IiIiIiIjEJRW8IiIiIiIiEpdU8IrEITPbZ2Yzol4P5uGx65hZXDw/TkREJL8oN4uEIyXsAEQkJna4e/OwgxAREZFfKDeLhEBXeEUSiJktNbMnzWy2mX1nZvWD9jpm9rWZzTKzr8ysVtBexcw+MrOZwat9cKhkMxtgZj+Y2WgzKx5ap0RERAox5WaR2FLBKxKfih8wbOryqHWb3b0p0Bd4Lmh7ERjs7qcAbwMvBO0vAOPcvRnQEvghaG8AvOTujYFNwMUx7o+IiEhhp9wsEgJz97BjEJE8ZmZZ7l7qIO1Lgd+4+xIzSwVWu3tFM1sHVHP3PUF7hrtXMrNMoIa774o6Rh3gC3dvELz/A5Dq7v+Ifc9EREQKJ+VmkXDoCq9I4vEclo/ErqjlfWg+ABERkWOh3CwSIyp4RRLP5VE/JwXL3wI9g+UrgQnB8lfALQBmlmxmZfMrSBERkQSi3CwSIzrzIxKfipvZjKj3o9x9/+MPypvZLCJngnsFbXcAA83sfiATuC5ovwvob2Y3EDlbfAuQEfPoRURE4o9ys0gIdA+vSAIJ7hNKc/d1YcciIiIiys0isaYhzSIiIiIiIhKXdIVXRERERERE4pKu8IqIiIiIiEhcUsErIiIiIiIicUkFr4iIiIiIiMQlFbwiIiIiIiISl1TwioiIiIiISFxSwSsiIiIiIiJxSQWviIiIiIiIxCUVvCIiIiIiIhKXVPCKiIiIiIhIXFLBKyIiIiIiInFJBa+IiIiIiIjEJRW8IiIiIiIiEpdU8IoUcmY20syuyettjzCGLma2Iq+PKyIiUtCYmZtZ/WC5n5n9JTfbHsXnXGlmo482zkMcVzlbEkpK2AGIJCIzy4p6WwLYBewL3t/s7m/n9lju3i0W24qIiMQjMxsFfOfuDx/Q3h14Fajh7ntzcyx375NHMdUBfgJS93928LdArv8eEJGD0xVekRC4e6n9L+Bn4Pyotl+Sm5nppJSIiEjeGgxcZWZ2QPvVwNu5LXZFpHBQwStSgOwfZmRmfzCz1cBAMytvZp+aWaaZbQyWa0TtM9bMbgyWrzWziWb2VLDtT2bW7Si3rWtm481sq5l9aWYvmdlbuezHScFnbTKzH8zsgqh155jZ3OC4K83svqC9UtC3TWa2wcwmmJl+R4mISF4bBlQETtvfYGblgfOAN82sjZlNCvJRhpn1NbMiBzuQmQ0ys39Evb8/2GeVmV1/wLbnmtl0M9tiZsvN7JGo1eODn5vMLMvM2u3P01H7tzezqWa2OfjZPmrdWDP7u5l9E+TX0WZWKTf/MZSzJd7pH6ZIwVMVqADUBnoT+f90YPC+FrAD6HuI/dsCPwKVgCeB1w9yFjs3274DfEfkj4JHiJz5PiwzSwU+AUYDxwF3AG+bWaNgk9eJDNsuDTQBvg7a7wVWAJWBKsAfAc/NZ4qIiOSWu+8A3gd+F9V8GTDf3WcSucXobiK5sR1wBnDr4Y5rZl2B+4CzgAbAmQdssi34zHLAucAtZnZhsK5T8LNcMNpr0gHHrgB8BrxAJC8/A3xmZhWjNrsCuI5I7i0SxHK4mJWzJe6p4BUpeLKBv7r7Lnff4e7r3f0Dd9/u7luBx4DOh9h/mbsPcPd9RIZtVSOSjHK9rZnVAloDD7v7bnefCAzPZfynAqWAJ4J9vwY+BXoF6/cAJ5tZGXff6O7fR7VXA2q7+x53n+DuSp4iIhILg4FLzKxY8P53QRvuPs3dJ7v7XndfSuS+3kPl3f0uAwa6+xx330bkZPEv3H2su89292x3nwUMyeVxIVIgL3T3/wRxDQHmA+dHbTPQ3RdEFfTNc3Fc5WyJeyp4RQqeTHffuf+NmZUws1fNbJmZbSEy7KmcmSXnsP/q/Qvuvj1YLHWE2x4PbIhqA1iey/iPB5a7e3ZU2zKgerB8MXAOsMzMxplZu6D938AiYLSZLTGzB3P5eSIiIkckOJG7DrjQzOoBbYiMbMLMGgbDdVcHefdxIld7D+d4fp0rl0WvNLO2ZjYmuEVpM9Anl8fdf+xlB7RF51aIyunAdnLO/f8vZuVsiWcqeEUKngPPkN4LNALaunsZ/jfsKadhynkhA6hgZiWi2mrmct9VQM0D7uWpBawEcPep7t6dyNCpYUTOQuPuW939Xnc/AbgAuMfMzjjGfoiIiOTkTSJXdq8CPnf3NUH7K0SunjYI8u4fyV3OzeDXubLWAevfITJaqqa7lwX6RR33cFdHVxG5tSnaL7n1GChnS9xTwStS8JUmct/upuAenr/G+gPdfRmQDjxiZkWCM7rnH2a3/aYQObP8gJmlmlmXYN93g2NdaWZl3X0PsIXIEG7M7Dwzqx/cQ7yZyD1U2Qf/CBERkWP2JpH7bG8iGM4cKE0kP2WZ2YnALbk83vvAtWZ2cnDC+MB8XZrI6KmdZtaGyD23+2USyXkn5HDsEUBDM7vCzFLM7HLgZCLDj4+FcrbEPRW8IgXfc0BxIkOvJgOj8ulzryQyWcd64B/Ae0SeF3xI7r6bSLLsRiTml4Hfufv8YJOrgaXBMLE+wedAZIKPL4EsYBLwsruPybPeiIiIRAnuz/0WKMmv56m4j0gxuhUYQCT/5eZ4I4nk7K+JDPf9+oBNbgUeNbOtwMMEV0uDfbcTmaPjm2Dm41MPOPZ6IrNI30skLz8AnOfu63IT2yFiVs6WuGe6v1xEcsPM3iMyg2XMrzCLiIiIiOQFXeEVkYMys9ZmVs/MkoJHLXQncv+OiIiIiEihkBJ2ACJSYFUFPiTyvL8VwC3uPj3ckEREREREck9DmkVERERERCQuaUiziIiIiIiIxKWEGNJcqVIlr1OnTthhiIhInJg2bdo6d68cdhyFmXKziIjkpZxyc0IUvHXq1CE9PT3sMEREJE6Y2bKwYyjslJtFRCQv5ZSbYzak2czeMLO1ZjYnh/VdzGyzmc0IXg8H7Y2i2maY2RYz+32wroKZfWFmC4Of5WMVv4iIiIiIiBRusbyHdxDQ9TDbTHD35sHrUQB3/3F/G9AK2A58FGz/IPCVuzcAvgrei4iIiIiIiPw/MSt43X08sOEYD3MGsNjd91+e7g4MDpYHAxce4/FFREREREQkToU9S3M7M5tpZiPNrPFB1vcEhkS9r+LuGcHyaqBKTgc2s95mlm5m6ZmZmXkYsoiIiIiIiBQGYRa83wO13b0Z8CIwLHqlmRUBLgD+e7CdPfIA4RwfIuzu/d09zd3TKlfWRJoiIiIiIiKJJrSC1923uHtWsDwCSDWzSlGbdAO+d/c1UW1rzKwaQPBzbb4FLCIiIiIiIoVKaAWvmVU1MwuW2wSxrI/apBe/Hs4MMBy4Jli+Bvg41nGKiIiIiIhI4RSz5/Ca2RCgC1DJzFYAfwVSAdy9H3AJcIuZ7QV2AD2DYcqYWUngLODmAw77BPC+md0ALAMui1X8IiIiIiIiUrjFrOB1916HWd8X6JvDum1AxYO0rycyc3O++2ndNl4as4jHejShaEpyGCGIiIhIlAVrtjJg/BIev6gpqclhz8MpIiIFkbJDLi1am8XQaSv454j5YYciIiIiRE5G/3faCvqNXRx2KCIiUkCp4M2ls06uwnUd6jDo26WM/mF12OGIiIgkvN82rsp5p1Tjha8X8uPqrWGHIyIiBZAK3iPwYLcTaVK9DPcPncXKTTvCDkdERCTh/e2CxpQulsoDQ2eyd1922OGIiEgBo4L3CBRNSaZvr5bsy3buHDKdPUqsIiIioapYqih/u6AxM1ds5rWJP4UdjoiIFDAqeI9QnUoleaxHE6Yt28izXywIOxwREZGEd94p1fht4yo888UCFmdmhR2OiIgUICp4j0L35tXp2bomr4xbzISFmWGHIyIiktDMjL9f2ITiqck8MHQW+7I97JBERKSAUMF7lP56fmMaHFeKu9+bwdqtO8MOR0RE5LDM7A0zW2tmc3JY38XMNpvZjOD1cNS6cmY21Mzmm9k8M2sXtD9iZiuj9jknv/oT7bjSxfjr+SczbdlGBn+7NIwQRESkAFLBe5SKF0mm7xUtydq1l7vfm6GzySIiUhgMAroeZpsJ7t48eD0a1f48MMrdTwSaAfOi1j0btc+IvA0593q0qM7pjSrz5OfzWbZ+W1hhiIhIAaKC9xg0rFKaR85vzDeL1vPK2EVhhyMiInJI7j4e2HCk+5lZWaAT8HpwnN3uvimPwztmZsbjFzUlNSmJP3wwi2ydjBYRSXgqeI/R5a1rckGz43nmiwVMXXrEf0OIiIgUNO3MbKaZjTSzxkFbXSATGGhm083sNTMrGbXP7WY2KxgyXT6nA5tZbzNLN7P0zMzYzIFRrWxx/nTuSUxesoG3v/s5Jp8hIiKFhwreY2RmPNajCTUrlODOIdPZuG132CGJiIgcre+B2u7eDHgRGBa0pwAtgVfcvQWwDXgwWPcKUA9oDmQAT+d0cHfv7+5p7p5WuXLlGHUhcjK6Y/1KPDFiHis2bo/Z54iISMGngjcPlC6WSt9eLVmXtYv7h87EXUOoRESk8HH3Le6eFSyPAFLNrBKwAljh7lOCTYcSKYBx9zXuvs/ds4EBQJsQQv8VM+OfFzXFgYc+nK28LCKSwFTw5pGmNcryULeT+HLeWt74ZmnY4YiIiBwxM6tqZhYstyHyd8J6d18NLDezRsGmZwBzg+2qRR2iB3DQGaDzW80KJXio24lMWLiO/6avCDscEREJSUrYAcST6zrU4dvF63li5Dxa1ynPKTXKhR2SiIjIL8xsCNAFqGRmK4C/AqkA7t4PuAS4xcz2AjuAnv6/y6N3AG+bWRFgCXBd0P6kmTUHHFgK3Jw/vTm8K9vW5tNZGfz9s7l0aliZqmWLhR2SiIjkM0uEYT5paWmenp6eL5+1aftuznl+AqkpSXx6R0dKF0vNl88VEZH8Y2bT3D0t7DgKs/zKzUvXbaPr8+PpUK8Sr12TRnABW0RE4kxOuVlDmvNYuRJFeKFXC1Zs3KH7hkREREJWp1JJ7ju7EV/NX8uwGSvDDkdERPKZCt4YSKtTgXvPbsinszJ47LN5KnpFRERCdF2HurSsVY5Hhs9l7dadYYcjIiL5SAVvjNzSuR7Xtq/DaxN/4tkvFoQdjoiISMJKTjKevKQZO/bs4y/D5uhEtIhIAlHBGyNmxsPnnUzP1jV54etFvDRmUdghiYiIJKz6x5Xi3rMa8vkPa5STRUQSSMwKXjN7w8zWmtlBH09gZl3MbLOZzQheD0etK2dmQ81svpnNM7N2QfsjZrYyap9zYhV/XkhKMh7r0ZQLmx/Pvz//kdcn/hR2SCIiIgmrd6cTuKhFdZ4avYCPputRRSIiiSCWjyUaBPQF3jzENhPc/byDtD8PjHL3S4LHH5SIWvesuz+Vd2HGVnKS8dSlzdi1N5u/fzqXYqlJXNm2dthhiYiIJBwz44mLTyFj804eGDqLKmWK0b5epbDDEhGRGIrZFV53Hw9sONL9zKws0Al4PTjObnfflMfh5auU5CSe79mC0xtV5s/D5vDBNJ1VFhERCUORlCT6Xd2KOhVLcvN/prFwzdawQxIRkRgK+x7edmY208xGmlnjoK0ukAkMNLPpZvaamZWM2ud2M5sVDJkun9OBzay3maWbWXpmZmYs+5ArRVKSeOWqVrSvV5H7h87ks1kZYYckIiKSkMoWT2Xgda0plprMtQOnauZmEZE4FmbB+z1Q292bAS8Cw4L2FKAl8Iq7twC2AQ8G614B6gHNgQzg6ZwO7u793T3N3dMqV64coy4cmWKpyQz4XRqtapfnrnen8+XcNWGHJCIikpBqlC/BG9e0ZsO23Vw/aCrbdu0NOyQREYmB0Aped9/i7lnB8ggg1cwqASuAFe4+Jdh0KJECGHdf4+773D0bGAC0CSH0Y1KiSApvXNuaxseX4da3v2fCwvCvPouIiCSipjXK0veKFsxdtYU7hkxn777ssEMSEZE8FlrBa2ZVzcyC5TZBLOvdfTWw3MwaBZueAcwNtqsWdYgewEFngC7oShdLZfD1bTihckluejOdKUvWhx2SiIhIQjrjpCo82r0JX89fyyOf/KBn9IqIxJlYPpZoCDAJaGRmK8zsBjPrY2Z9gk0uAeaY2UzgBaCn/y/L3AG8bWaziAxffjxof9LMZgftpwN3xyr+WCtXoghv3diW6uWKc/2gqUz/eWPYIYmIiCSkq06tzc2dT+CtyT/Tf/ySsMMREZE8ZIlwJjMtLc3T09PDDuOg1mzZyWWvTmLjtt28c9OpNKleNuyQRETkMMxsmrunhR1HYVbQcnN2tnPnu9P5dFYGfa9owXmnHB92SCIicgRyys1hz9Kc8KqUKcbbN7alVNEUrnp9CtOWHfGTnEREROQYJSUZT13ajNZ1ynPPezOZulT5WEQkHqjgLQBqlC/Bu73bUa54KlcMmMKoOavDDklERCThFEtNpv/VadQoX5yb3kxncWZW2CGJiMgxUsFbQNSqWIIPbmnPSdXKcMvb03hz0tKwQxIREUk45UsWYeB1rUk247qBU1mXtSvskERE5Bio4C1AKpYqypCbTuWME4/j4Y9/4ImR88nOjv97rEVERAqS2hVLMuCaNNZs2ckNg6ayeceesEMSEZGjpIK3gCleJJl+V7Xiira16DduMfe8P4Pde/VcQBERkfzUslZ5+l7RkrkZW7j69Sls2r477JBEROQoqOAtgFKSk3jswibc/9tGDJuximsHfseWnTq7LCIikp/OOrkK/a5qxfyMrVwxYAobtqnoFREpbFTwFlBmxm2n1+eZy5rx3U8buKzfJFZv3hl2WCIiIgnljJOqMOCaNBZnZtGz/yQyt+qeXhGRwkQFbwF3UcsaDLyuNSs27qDHy9+wYM3WsEMSERFJKJ0bVmbgta1ZvmEHPftPYs0WnYAWESksVPAWAqc1qMx7N5/Kvmzn4le+ZfKS9WGHJCIiklDa16/E4OvbsHrzTi5/dRKrNu0IOyQREckFFbyFROPjy/Lhre2pUqYYv3v9Oz6ZuSrskERERBJKm7oVePOGtqzP2s3l/SexfMP2sEMSEZHDUMFbiNQoX4IP+rSnec1y3DFkOv3HL8Zdjy0SERHJL61ql+etG9uyefseLn91EkvXbQs7JBEROQQVvIVM2RKpvHlDG849pRqPj5jPXe/OYPvuvWGHJSIikjCa1SzHkN6nsmPPPi7vP4lFa7PCDklERHKggrcQKpaaTN9eLXigayM+nbWKi17+VmeYRUTksMzsDTNba2Zzcljfxcw2m9mM4PVw1LpyZjbUzOab2Twzaxe0VzCzL8xsYfCzfH71J0yNjy/Lu73cTyuTAAAgAElEQVTbsS/b6dl/siaVFBEpoFTwFlJmxq1d6jPoujas3rKT8/tO5Ov5a8IOS0RECrZBQNfDbDPB3ZsHr0ej2p8HRrn7iUAzYF7Q/iDwlbs3AL4K3ieERlVL827vdiQZ9Ow/mbmrtoQdkoiIHEAFbyHXqWFlPrm9I7UqlOCGwek89+UCsrN1X6+IiPx/7j4e2HCk+5lZWaAT8HpwnN3uvilY3R0YHCwPBi7Mg1ALjfrHleK9m9tRNCWJXgMmM3vF5rBDEhGRKCp440DNCiX44Jb29GhRnee+XMhNb6azeceesMMSEZHCqZ2ZzTSzkWbWOGirC2QCA81supm9ZmYlg3VV3D0jWF4NVMnpwGbW28zSzSw9MzMzdj3IZ3UrleT9m9tRqmgKvQZMZtyC+OmbiEhhp4I3ThRLTebpS5vx9+6NGbcgkwv6TmT+ag2tEhGRI/I9UNvdmwEvAsOC9hSgJfCKu7cAtnGQocseeXRAjsOM3L2/u6e5e1rlypXzPPgw7T/5XLNCCa4fNJUh3/0cdkgiIoIK3rhiZlzdrg7v3XwqO3bvo8dL3zJcz+sVEZFccvct7p4VLI8AUs2sErACWOHuU4JNhxIpgAHWmFk1gODn2nwOu8CoWrYY/+3Tjo71K/HQh7N5ctR83WYkIhIyFbxxqFXtCnx6R0eaVC/DnUOm8/dP57JnX3bYYYmISAFnZlXNzILlNkT+Tljv7quB5WbWKNj0DGBusDwcuCZYvgb4OB9DLnBKFU3h9WvS6NWmFi+PXcyd705n5559YYclIpKwYlbw6tEH4TquTDHevvFUrm1fh9cn/sRVr00hc+uusMMSEZEQmdkQYBLQyMxWmNkNZtbHzPoEm1wCzDGzmcALQM9gmDLAHcDbZjYLaA48HrQ/AZxlZguBM4P3CS0lOYnHezThwW4n8umsDK56bQobt+0OOywRkYRk/8tjeXxgs05AFvCmuzc5yPouwH3uft5B1g0m8liE18ysCFDC3TeZ2ZPABnd/wsweBMq7+x8OF0taWpqnp6cfa5cKrY+mr+ChD2dTplgqz1zWnI4NKoUdkohIoWZm09w9Lew4CrNEyc2fzlrFPe/PpHq54gy8tjV1KpU8/E4iInLEcsrNMbvCq0cfFBw9WtTgo1s7UKZ4Kle/MYV/jpzH7r0a4iwiIhJr551yPO/c2JZN23fT4+VvmLbsiP80EhGRYxD2Pbx69EE+OalaGT65vSO92tTi1XFLuKTftyxdty3ssEREROJeWp0KfHhrB8oWT6XXgCl8Nivj8DuJiEieCLPg1aMP8lnxIsk83qMp/a5qybL12zn3hQkMnbaCWA1rFxERkYi6lUry4a0dOKV6WW5753v6jVus/Csikg9CK3j16IPwdG1SjZF3nUaT6mW5778zuevdGWzZuSfssEREROJahZJFeOvGtpx7SjWeGDmfPw+bw149RUFEJKZCK3j16INwHV+uOO/cdCr3ntWQz2ZncO4LE/j+541hhyUiIhLXiqUm82LPFvTpXI+3p/zMdYOmsmm7ZnAWEYmVWD6WSI8+KOCSk4w7zmjA+ze3wx0u7TeJvl8vZF+2hliJiIjESlKS8WC3E/nXxU2ZsmQD5/edyNxVW8IOS0QkLsXssUQFSaI8+uBYbNm5hz9/NIfhM1fRtm4FnuvZnGpli4cdlohIgaTHEh075eaI73/eyK1vfc+mHbv518Wn0L159bBDEhEplPL9sURSuJQplsrzPZvz1KXNmL1yM12fm8CI2ZpFUkREJJZa1irPJ3d05JTq5bjr3Rn8/dO5uq9XRCQPqeCVX5gZl7SqwWd3nkadiiW49e3vuf2d79mwTfcWiYiIxErl0kV5+6a2XNu+Dq9P/ImrXp/CuqxdYYclIhIXVPDK/1O3UkmG3tKe+85uyOc/rOasZ8YxUld7RUREYiY1OYlHLmjMM5c1Y/rPmzj/xYnMXL4p7LBERAo9FbxyUKnJSdz+mwZ8ckdHqpUrxi262isiIhJzF7WswQe3tCfJjEtfncT7U5eHHZKISKGmglcO6cSqZfjo1g6/XO09+9lxjJqjq70iIiKx0qR6WT69oyNt6lTggQ9m8cePZrNr776wwxIRKZRU8MphRV/trVq2GH3e+p47hkzX1V4REZEYKV+yCIOvb0OfzvV4Z8rP9Oo/mTVbdoYdlohIoaOCV3Jt/9Xee89qyKg5GbraKyIiEkPJwfN6X76yJfNXb+W8Fyfy3U8bwg5LRKRQUcErRyQ1OYk7zmjA8Ns7UqWMrvaKiIjE2jlNqzHstg6UKppCz/6TeOGrhezL9rDDEhEpFFTwylE5qVoZht3WgXuirvZ+NisDdyVgERGRvNawSmmG396BC5odzzNfLODK1yazerOGOIuIHI4KXjlqqclJ3Blc7a1athi3vfM9NwxOZ8XG7WGHJiIiEndKF0vl2cub89SlzZi1YjPdnh/PV/PWhB2WiEiBpoJXjtlJ1cow7NYO/Pnck5i8ZD1nPTOeAeOXsHdfdtihiYiIxBUz45JWNSKPDSxbnBsGp/O3T37QLM4iIjlQwSt5IiU5iRtPO4HRd3eifb2KPDZiHhf0/YYZyzeFHZqIiEjcqVe5FB/d1p5r29dh4DdLuejlb1mSmRV2WCIiBY4KXslTNcqX4LVr0uh3VUvWb9tFj5e/4ZHhP7B1556wQxMREYkrRVOSeeSCxgz4XRorN+3gvBcn8sG0FWGHJSJSoKjglTxnZnRtUo0v7+nM706tzeBJSznzmcgjjDSplYiISN466+QqjLzrNJpUL8u9/53JPe/NIGvX3rDDEhEpEFTwSsyULpbK37o34aNbO1ChZFH6vPU9N705jZWbdoQdmoiISFypVrY4Q246lbvPbMiwGSs5/8WJzFm5OeywRERCp4JXYq55zXIMv70DfzznRL5ZtI6znhnHaxM0qZWIiEheSk4y7jqzAUNuOpWde/bR4+Vv6D9+sZ7ZKyIJTQWv5IvU5CR6d6rH6Ls70bZuBf7x2TzOe3Ei3/20IezQRERE4krbEyoy4s7TOL3RcTw+Yj69+k/m5/V6ZKCIJCYVvJKvalYowRvXtuaVK1uyZcceLnt1Ene9O501W3aGHZqIiEjcKF+yCK9e3YqnL23GvIwtdH1+PEO++1lzaYhIwolZwWtmb5jZWjObk8P6Lma22cxmBK+Ho9YtNbPZQXt6VPsjZrYyap9zYhW/xI6Z0a1pNb66twt3/qY+I+es5jdPjeXVcYvZvVfDnEVEYkW5ObGYGRe3qsGouzvRolY5HvpwNtcPmspanWQWkQQSyyu8g4Cuh9lmgrs3D16PHrDu9KA97YD2Z6P2GZFn0Uq+K14kmXvObsSXd3emXb1K/HPkfLo+P55xCzLDDk1EJF4NQrk54VQvV5z/XN+WR84/mUlL1nP2c+P5dNaqsMMSEckXMSt43X08oBs05bBqVYw8u3fgda1xh2ve+I7eb6azfIPuNxIRyUvKzYkrKcm4tkNdPrvzNGpXLMnt70znjiHT2bR9d9ihiYjEVNj38LYzs5lmNtLMGke1OzDazKaZWe8D9rndzGYFw7LK53RgM+ttZulmlp6ZqSuGhcHpjY5j1O9P44GujZiwcB1nPjOOZ79YwM49+8IOTUQkkSg3x7F6lUvxQZ923Hd2Q0bOzuDsZ8cz5se1YYclIhIzFsvJC8ysDvCpuzc5yLoyQLa7ZwX3+zzv7g2CddXdfaWZHQd8Adzh7uPNrAqwjkjS/TtQzd2vP1wcaWlpnp6efrjNpADJ2LyDx0fM55OZq6hRvjh/Pvdkftu4CmYWdmgiIpjZtIMM6y0UlJtlvzkrN3Pv+zP5cc1Wrmhbiz+dcxIli6aEHZaIyFHJKTeHdoXX3be4e1awPAJINbNKwfuVwc+1wEdAm+D9Gnff5+7ZwID97RJ/qpUtzou9WjDkplMpWSSFPm9N48rXpvDDqs1hhyYiEreUmxNLk+pl+fj2Dtzc6QSGfPcz3Z6fwOQl68MOS0QkT4VW8JpZVQsu15lZmyCW9WZW0sxKB+0lgbOBOcH7alGH6LG/XeJXu3oV+ezOjjzavTHzMrZw3osTeWDoTM0wKSISA8rNiadYajIPnXMS79/cDoCe/Sfzx49ms2XnnpAjExHJGzEbt2JmQ4AuQCUzWwH8FUgFcPd+wCXALWa2F9gB9HR3D4ZGfRTk2xTgHXcfFRz2STNrTmTY1FLg5ljFLwVHSnISv2tXh+7NqtN3zEIGfbuUT2dl0KdzPW467QSKF0kOO0QRkUJBuVly0rpOBT7/fSee+eJHXp/4E1/PW8tjPZpwxklVwg5NROSYxPQe3oJC9wnFl2Xrt/HEyPmMnLOaamWL8UDXRnRvVp2kJN3fKyL5ozDfw1tQKDcXXDOWb+IPQ2fx45qtXNDseP56/slULFU07LBERA6pwN3DK3K0alcsyStXteL9m9tRuXRR7n5vJhe+/A3f/aQnbYiIiByr5jXL8ckdHbn7zIaMnJPBmc+M4+MZK0mEiyQiEn9U8Eqh1aZuBYbd2oFnLmvG2i27uOzVSdzy1jSWrd8WdmgiIiKFWpGUJO46s8Evz+29690Z3DA4nVWbdoQdmojIEVHBK4VaUpJxUcsajLmvC/ec1ZCxP2Zy5jPj+Menc9m0fXfY4YmIiBRqDauU5oNb2vOX805m0uL1nP3seN6avIzsbF3tFZHCQQWvxIXiRZK584wGjL2/Cz1aVOf1b36i05Nj6DduMTv37As7PBERkUIrOcm4oWNdRt/dieY1y/HnYXPoOWAySzKzwg5NROSwVPBKXKlSphhPXtKMEXeeRsva5Xli5HxOf2os76cvZ5/ORouIiBy1mhVK8J8b2vDkJacwP2ML3Z6fQN+vF7Jrr04si0jBpYJX4tJJ1cow6Lo2vHNTW44rXZQHhs6i2/Pj+WreGk26ISIicpTMjMvSavLlPZ0546TjeGr0As55fgKTFq8POzQRkYNSwStxrX29Sgy7rQMvX9mSPfucGwanc/mrk5m2bGPYoYmIiBRax5UpxstXtmLgda3ZvS+bXgMmc897M1iXtSvs0EREfkUFr8Q9M+OcptUYfXcn/nFhE5as28bFr3xLn/9MY7HuPxIRETlqpzc6jtG/78ztp9fnk1mrOOPpcbwz5WdNaiUiBYYKXkkYqclJXHVqbcbdH5nRecLCTM5+djwPfTibNVt2hh2eiIhIoVS8SDL3/bYRI+86jROrluaPH83mkn7fMnfVlrBDExFRwSuJp2TRFO48owHjHjidq0+tzdBpy+n87zH8c+Q8Nm7To4xERESORv3jSvNu71N5+tJmLFu/nfP7TuSxz+aybdfesEMTkQSmglcSVqVSRXnkgsZ8dU8XujWpRv/xS+j05Bie+3IBW3fuCTs8ERGRQsfMuLhVDb66tzOXpdVkwISfOPOZcYyas1qTRopIKFTwSsKrVbEEz17enFF3daJ9/Yo89+VCOj05hv7j9QxfERGRo1GuRBH+eVFTPrilHWWLp9LnrWncODidn9dvDzs0EUkwKnhFAo2qlubVq9MYfnsHmtYox+Mj5tPpyTH8Z9JSdu/NDjs8ERGRQqdV7Qp8ckdH/nTOSUxasp4znx3H06N/ZMdunVAWkfyhglfkAKfUKMeb17fh/ZvbUadiSf7y8Q+c/tRY3k9fzt59KnxFRESORGpyEjd1OoGv7+1CtyZVefHrRZzx9Fg+m5WhYc4iEnMqeEVy0KZuBd67+VQGX9+GCiWL8MDQWZz93Hg+mblKj1sQERE5QlXLFuP5ni14/+Z2lC1RhNve+Z4rX5vCgjVbww5NROKYCl6RQzAzOjeszPDbO9DvqlakJBl3DJnOuS9O5PMfNAGHiIjIkWpTtwKf3N6Bv3dvzA+rttDt+Qk8+slctmjCSBGJARW8IrlgZnRtUpWRd3Xiucubs2P3Xm7+zzTOe3EiX8xdo8JXRETkCKQkJ3F1uzqMua8Ll6XVZOC3P/Gb4PYhjaISkbykglfkCCQnGRe2qM6X93TmqUubkbVrLze9mc4Ffb/hq3kqfEVERI5EhZKR2ZyH39aRWhVK8MDQWVz0yrfMXL4p7NBEJE6o4BU5CinJSVzSqgZf3tOZJy85hU07dnPD4HQufOkbxsxfq8JXRETkCDStUZahfdrz9KXNWLFxBxe+/A1/GDqLzK27wg5NRAq5XBW8ZlbSzJKC5YZmdoGZpR5mnzfMbK2ZzclhfRcz22xmM4LXw1HrlprZ7KA9Paq9gpl9YWYLg5/lc9dNkdhITU7isrSafH1vF/51cVPWb9vNdYOmcuHL3zLmRxW+IhI7R5ObRQqypCTj4lY1GHNfZ27sWJcPvl/B6U+N5eWxi9i5R48xEpGjk9srvOOBYmZWHRgNXA0MOsw+g4Cuh9lmgrs3D16PHrDu9KA9LartQeArd28AfBW8FwldanISl7euxdf3duGfFzVl3dZdXDdwKhe98i3jFmSq8BWRWDia3CxS4JUulsqfzj2Z0Xd34tQTKvLkqB8585lxfDprlfKpiByx3Ba85u7bgYuAl939UqDxoXZw9/HAhmOM70DdgcHB8mDgwjw+vsgxKZKSRK82tRhzXxce79GUtVt2cc0b33HxK98yVld8RSRvHXFuFilMTqhciteuSePtG9tSqmgKt78znUv7TdL9vSJyRHJd8JpZO+BK4LOgLTkPPr+dmc00s5FmFp2kHRhtZtPMrHdUexV3zwiWVwNVDhFwbzNLN7P0zMzMPAhVJPeKpCRxRdtafH1fZ/5xYRNWb97JtQOn0v2lbzSrs4jklVjlZpECpUP9Snx252k8cVFTlq7fRveXvuHu92aQsXlH2KGJSCGQ24L398BDwEfu/oOZnQCMOcbP/h6o7e7NgBeBYVHrOrp7S6AbcJuZdTpwZ49UDDlWDe7e393T3D2tcuXKxxiqyNEpmpLMVafWZuz9p/PERU3ZtH0PN72ZzjkvTGTE7Aw9ekFEjkUscrNIgZScZPQMRlDd0qUen83O4PSnxvLsFwvYvntv2OGJSAFmR3qlKZggo5S7b8nFtnWAT929SS62XQqkufu6A9ofAbLc/Skz+xHo4u4ZZlYNGOvujQ537LS0NE9PTz/cZiIxt3dfNh/PWMVLYxaxZN02GlYpxW2n1+e8U44nOcnCDk9EcsnMph0wx0SojiQ3FxTKzXIslm/YzhOj5vPZrAyqlCnKA789kR4tqpOkXCqSsHLKzbmdpfkdMytjZiWBOcBcM7v/GAOqamYWLLcJYlkfzDpZOmgvCZwdfCbAcOCaYPka4ONjiUEkv6UkJ3Fxqxp8cU9nnu/ZHHe4690ZnPXMOIZOW8HefdlhhygihcTR5GY9QUHiRc0KJXjpipYM7dOOqmWKce9/Z3Lhy98wafH6sEMTkQImt0OaTw7OGl8IjATqEpkNMkdmNgSYBDQysxVmdoOZ9TGzPsEmlwBzzGwm8ALQMximXAWYGLR/B3zm7qOCfZ4AzjKzhcCZwXuRQic5yejevDqf/74Tr1zZkqKpydz335mc/vRYhnz3M7v3qvAVkcM64tyMnqAgcSatTgU+urUDz17ejHVbd9FrwGRuGDSVBWu2hh2aiBQQKbncLjV4tt+FQF9332NmhxwL7e69DrO+L9D3IO1LgGY57LMeOCOXMYsUeElJRrem1ejapCpfzlvLi18v5KEPZ/PiVwvp3ekELm9di+JFNAeNiBzU0eTm8cHtRnmpO9AlWB4MjAX+kMefIZKjpCSjR4sadGtSjUHfLuWlMYvo+tx4Lkuryd1nNaRKmWJhhygiIcrtFd5XgaVASWC8mdUGCs19QiIFnZlx1slV+Pi2Dgy6rjXVyxfnkU/m0vFfX/PSmEVs2bkn7BBFpOCJVW7WExSkUCqWmkyfzvUYf//pXNu+Lh98v4Iu/x7LM6N/JGuXJrYSSVRHPGnVLzuapbh7ofjtoYkxpDD67qcNvDRmEeMWZFK6WArXtKvDdR3qULFU0bBDE0l4BW3Sqv1yk5sPNaGkmZUBst09y8zOAZ4PhipjZtXdfaWZHQd8AdwRXDHe5O7loo6x0d0Pex+vcrPE2s/rt/Pv0T/yycxVVCpVhLvObEjP1jVJTc7t9R4RKUyOddKqsmb2zP6zsmb2NJEzyiISI23qVmDw9W349I6OdKxfiZfGLqLjv8bw6Cdz9exBEYlJbnb3Le6eFSyPIDJsulLwfmXwcy3wEdAm2G1N8OQEgp9rjyUGkbxSq2IJXuzVgmG3deCEyqX4y7A5/PbZ8Yyas5qjveAjIoVPbk9xvQFsBS4LXluAgbEKSkT+p0n1srxyVSu+uLsT5zStxuBJS+n05Bge/GAWS9dtCzs8EQlPnudmPUFB4lHzmuV4r/epvH5NGklJRp+3pnFJv0lMW7Yh7NBEJB/kakizmc1w9+aHayuoNGxK4snyDdsZMGEJ705dzt7/a+/O46uq7/yPvz5ZIOwQAgQI2dhcUEBZBMIiLrVIq1br0qqoIFWrtbW12pn5jZ12nLZ22tpOrTuLVURrXVrUqRtrQHZERUXIAoEQ1gTCkpDk8/sjl05KCSBZzl3ez8cjD5Jz7z35fB8H7pvPPef7PdU1XHp2D+4Y15vTu7cPujSRmBEOlzSfSjaH7qAwDkgBSoAHgEQAd3/MzO4EbgeqgIPAPe6+2MyyqT2rC7ULXs5y9wdD++wMvAikA4XA1e5+wk5C2SxBqKqu4U8ri/j12+vZsa+Ci87oxr1f6k+/bu2CLk1EGqi+bD7ZhncJcK+7Lwr9PAr4b3cf0eiVNgGFqkSj7fsO8fSifJ5dUsj+ymrO79+F28b2ZlhWMqETNCLSRMKk4VU2i5yiA5VVTM8t4LF5GymvrOKKwT353oX96JXcOujSROQUNbThHQg8A3QIbdoDTHL3tY1aZRNRqEo0KztwmD++X8D03AJ27a9kcHpHbh/bmwtP70ZcnBpfkaYQJg2vslmkgfbsr+Sx+RuZsbiAGne+OTyDO8f3IUULRIpEnAY1vHV20h5qF7Uws++6+8ONWGOTUahKLDh0uJo/rdjM4wvyKNpzkD5d2/KtMdlcNqgnLRK0IqVIYwqHhvcIZbNIwxWXHeR3737OiyuKaJkQx5ScLG4dk027pMSgSxORk9QoDe9RO9zk7ukNrqwZKFQlllRV1/D6h8U8Om8jn27bR/cOSUzOyeK6Yem0aZkQdHkiUSGcGt66lM0iDZO3o5xfvb2e19cW06l1It8+vw/Xn5dBUmJ80KWJyAk0RcO72d17NbiyZqBQlVjk7sxfv4NH521kaf5uOrRKZNKIDCaN1L18RRoqjBteZbNII/iwqIyH/vYpCz/fSfcOSXz3wr5ceU4aCbqHr0jY0hleharEsFWb9vDYvI28ta6EpMQ4rh7Siyk52aR31uIcIqcijBteZbNII1q8cScP/e9nrNlcSnaXNnzvwn5celZ3rZEhEoZOqeE1s33AsZ5gQCt3j4jrIxWqIrU2bN/H4/PzeHXNFqprnC8P6M7UMdkM7NUx6NJEIkqQDa+yWaR5uTtvrSvhV299xvqSck5Lbcf3L+7Phad31V0RRMJIo5/hjSQKVZF/VLL3ENNzC3ju/UL2VVQxPCuZb43NZly/rvrUWuQkhOsZ3kiibJZIU13jzFm7lYff+Zz8nfsZmNaB71/cn9F9U9T4ioQBNbwKVZF/su/QYWYv28y03HyKyw7Rr1tbbh2tlZ1FTkQNb8MpmyVSVVXX8PLqLfz2nc/ZUnqQYZnJfP/ifgzP7hx0aSIxTQ2vQlWkXpVVNcxZu5UnFuTx6bZ9dGvfkptHZfGN4em01y0ZRP6JGt6GUzZLpKusquGFFZv5/XufU7K3gtF9U7jnon4MTu8UdGkiMUkNr0JV5ITcnQWf7+Tx+RtZvHEXbVsmcN2wXtySk0X3Dq2CLk8kbKjhbThls0SLQ4erefb9Qv4wbyO791dy4eldueei/pzRo33QpYnEFDW8ClWRL+SjLWU8viCP19duJc6MiWd3Z8robAb07BB0aSKBU8PbcMpmiTb7K6qYsbiAx+dvZO+hKiaclcrdF/Sjf2q7oEsTiQlqeBWqIqdk8+4DTM8tYPbyTRyorGZk787cOiabcf26aJEOiVlqeBtO2SzRquzgYZ5amMf03AL2V1Yx4azu3H1BX/p1U+Mr0pTU8CpURRqk7OBhnl+2iem5+ZTsraBv19ACV4N70DIhPujyRJqVGt6GUzZLtCs9UMlTC/OZnpvPgcPVanxFmlizN7xmNg2YCGx39wHHeHwc8BqQH9r0srv/pM7j8cAKYIu7TwxtmwGMBcpCT7vJ3decqBaFqkjjObLA1ZML8/mkeC8pbVty08gMvjk8g05tWgRdnkizUMPbcMpmiRV79lfy9KL/a3wvDTW+fdX4ijSqIBreMUA58MxxGt4fHGlmj/H4PcAQoP1RDe8cd3/pi9SiUBVpfO5O7oZdPLkwj/nrd5CUGMfXz+3F5JwsMlPaBF2eSJNSw9twymaJNXv2V/LUojxm5BZw4HA1E8/uwXfG91HjK9JI6svmhKb6he6+wMwyT+W1ZpYGXAo8CNzTiGWJSCMxM3L6ppDTN4XPtu3jqYV5zF6+iWeXFnLxGd2YMjqbIRmdNM9XREQE6NSmBfd+6TQm52Tz1MI8Zi4uYM7arXzl7B5854I+9OmqxlekKcQF/PtHmNkHZvammZ1ZZ/vDwA+BmmO85kEzW2tmvzGzlvXt2MymmtkKM1uxY8eOxq5bROron9qOX359ILn3jeeOcb15P283X39sCZc/kstfP9hKVfWx/imLiIjEnuQ2LfjhJaex8L7x3Da2N+98UsJFv1nAXc+v5rNt+4IuTyTqNOmiVaEzvHPquaS5PVDj7uVmNgH4rbv3NbOJwAR3v+Poy57NrDuwDWgBPAFsrDvvtz66bEqkeR2orOLPq7YwbVE++Tv307NjK24amXJMQmgAACAASURBVMk1w3rRPikx6PJEGkyXNDecslmk1u79lTyxII8/Lilgf2U1l5yZyp3j++g2gCJfUCCrNB+v4T3GcwuonbP7feAGoApIAtpTu6DV9Uc9fxzHmQNcl0JVJBg1Nc67n27nqYV5LM3fTduWCVwztBc3jcykV3LroMsTOWVqeBtO2Szyj/bsr2R6bj7TFxew71AVF5zWlbsu6MugXh2DLk0kIoRdw2tmqUCJu7uZDQNeAjK8TkHHOsPr7sVWOynwN8Ahd7//RHUoVEWC92FRGU8vymPO2mJq3PnygO5MGZ3F4PROQZcm8oWp4W04ZbPIsZUdPMzMxQVMy82n9MBhxvTrwnfG92FIZnLQpYmEtSBWaX4eGAekACXAA0AigLs/ZmZ3ArdTeyb3IHCPuy8+ah/j+MeG9z2gC2DAGuA2dy8/US0KVZHwUVx2kBmLC5i1dBP7DlVxbkYnpuRkcfGZqcTHaYEriQxqeBtO2SxyfOUVVfxxSSFPLcxj1/5KRmR35q4L+jAiu7MWhBQ5hkDO8IYLhapI+NlfUcWfVmxmWm4Bm3YfoFdyK24amcXVQ9Jop3m+EubU8Dacslnk5ByorGLW0k08viCPHfsqGJLRibsu6MuYvilqfEXqUMOrUBUJS9U1ztvrSnh6UR7LC/bQrmUC1w7rxaSRmaR10jxfCU9qeBtO2SzyxRw6XM2LKzbz6LyNFJcdYmBaB+44vw8Xnd6NOF0hJaKGV6EqEv4+2FzK04vyef3DYgAuGZDKlBzN85Xwo4a34ZTNIqemsqqGP68q4tF5G9m0+wB9u7bljvN785Wze5AQH/QdR0WCo4ZXoSoSMbaWHmTm4gJmLfu/eb6Tc7K4+IxuCnMJC2p4G07ZLNIwVdU1vP5hMY/M3cD6knJ6JbfitrG9ufKcNJIS44MuT6TZqeFVqIpEnKPn+aZ1Ct3Pd2gvzfOVQEVqw2tm04CJwPZ67qAwDngNyA9ternu/e7NLB5YAWyps6DkDGAsUBZ62k3uvuZEtSibRRrHkVsA/n7uBj7YXErXdi2ZOiab64al06ZlQtDliTQbNbwKVZGIdWSe77RF+Swr2P33eb43jcqiZ8dWQZcnMSiCG94xQDnwzHEa3nrvcW9m9wBDgPZHNbxz3P2lL1KLslmkcbk7izfu4pG5G1i8cRcdWydy88gsbhqZSYfW+pBYol992ayPfUQk7MXHGZcMSOWSAal/n+c7LbeAabkFfHlAKreOzmZgr45BlykS9tx9gZllnsprzSwNuBR4ELinEcsSkUZgZozqk8KoPims2rSHP8zdwG/eWc8TCzZy/YgMJudk0bVdUtBlijQ7neEVkYi0JTTP9/mlm9hXUcXQzE5MzsnmojO66X6+0uQi9QwvQKjhnXOcM7x/BoqArdSe7f049NhLwM+AdtQ5Cxw6wzsCqADeBe5394p6fvdUYCpAenr6uYWFhY04MhE52ifFe3l03kbmrN1KQnwcV52bxtTR2WSmtAm6NJFGp0ua1fCKRKXyiipeWL6Z6bn5FO05SHpya24ZlcnXh/TS3CVpMlHc8LYHaty93MwmAL91975mNhGY4O53HH3Zs5l1B7YBLYAngI115/3WR9ks0nwKdu7niYV5vLSyiKrqGr58VnduH9ubAT07BF2aSKNRw6tQFYlqVdU1vLWuhKcW5rFqUyntkxL4xvAMJo3MoHsHzfOVxhWtDe8xnltA7Zzd7wM3AFVAEtCe2gWtrj/q+eM4zhzgupTNIs1v+75DTM8t4NklheyrqGJ03xRuH9ubEb07Y6aroySyqeFVqIrEjJWFe5i2KJ83PyomzoyvDuzBlNHZnNGjfdClSZSI1obXzFKBEnd3MxsGvARkeJ3/LBzrDK+7F1vt/5Z/Axxy9/tPVIeyWSQ4ew8d5rn3NzEtN58d+yoYmNaB28f15uIzUonTtCCJUFq0SkRixrkZnTg3oxObdx9gWm4+LyzfzMurtzC6bwpTx2ST0ydFn2RLTDKz54FxQIqZFQEPAIkA7v4YcBVwu5lVAQeBa/3En4w/Z2ZdAAPWALc1Ufki0kjaJyVy+7je3Dwqk5dXbeHxBRu57dlVZHdpw7fGZHP54J60TNC9fCU66AyviES9sgOHeW5ZIdNzC9ixr4LTUtsxdUw2E8/uQYuEuKDLkwgUyWd4w4WyWSR8VNc4b35UzGPzN/LRlr10a9+SW0Zlcd3wdNrrvvcSIXRJs0JVJOZVVFXz2pqtPLkgj8+3l5PaPolbcjK5dpgCXb4YNbwNp2wWCT/uzqINO3ls/kZyN+yiXcsEvjE8nZtHZZHaQbc0kvCmhlehKiIh7s689Tt4Yn4eS/J20bZlAtcN68XNo7Lo0VELXMmJqeFtOGWzSHj7aEsZjy/I4/W1W4mPM746sCdTx2TTP7Vd0KWJHJMaXoWqiBzDh0VlPLkwj9c/LMaAiWd357ZxvTktVQtcSf3U8DacslkkMmzefYCnF9Wuh3HwcDXn9+/C1DG9OS87WethSFhRw6tQFZHjKNpzgGmLCpi9fBMHKqu54LSu3HF+b87NSA66NAlDangbTtksEln27K/k2fcLmbG4gF37KxmY1oGpY3pzyYBU4rWys4QBNbwKVRE5CaUHKpm5uJAZi/PZc+AwwzKTuf383ozr10WfZMvfqeFtOGWzSGQ6dLiaP68q4qmF+eTv3E96cmumjM7i6+f2olULrewswVHDq1AVkS/gQGUVs5dt5smFeRSXHeL07u25fVxvJgxIJSFeKzvHOjW8DadsFols1TXO2+tKeHzBRlZvKqVT60SuPy+DG0dk0qVdy6DLkxikhlehKiKnoLKqhtfWbOGx+RvZuGM/GZ1bM3VMNleek0ZSoj7JjlVqeBtO2SwSHdydFYV7eHJBHm9/UkJifBxXDOrJlNFZ9O2mBa6k+QTS8JrZNGAisN3dBxzj8XHAa0B+aNPL7v6TOo/HAyuALe4+MbQtC5gNdAZWAje4e+Xx6lCoikhD1dQ4b60r4dF5G/igqIwu7VoyJSeLbwxPp51uaRRz1PA2nLJZJPrk79zP04vy+NOKIiqqaji/fxduHZ3NiN6dNS1ImlxQDe8YoBx45jgN7w+ONLPHePweYAjQvk7D+yK1jfFsM3sM+MDdHz1eHQpVEWks7s6Sjbv4w7yNLNqwk/ZJCUwZnc3NozLV+MYQNbwNp2wWiV67QwtcPbOkgJ3llZzZoz23js7m0rO7k6hpQdJE6svmJv0b5+4LgN2n8lozSwMuBZ6qs82A8cBLoU0zgcsbWKaIyEkzM0b2SeHZKcP5y52jGJbVmV+/vZ6cX8zl9+99zr5Dh4MuUUREJFDJbVrwnQv6sui+8fz8a2dx6HA1331hDWMemssTCzayV1kpzSgcPmIZYWYfmNmbZnZmne0PAz8Eaups6wyUuntV6OcioOexdmpmU81shZmt2LFjR5MULiKx7ey0jjw1aQh/vTOHoZmd+O+31jP6obk8MncD5RVVJ96BiIhIFEtKjOfaYem8/b2xTLtpCJmd2/Bfb3zKyJ+9x0/nrGPz7gNBlygxIOiGdxWQ4e4Dgf8BXgUwsyPzflee6o7d/Ql3H+LuQ7p06dI41YqIHMNZaR14atJQ/nLnKM5J78Qv//YZo3/xHn+Yt4H9anxFRCTGxcUZ40/rxvNTz2POXTlccHpXZi4uYOwv53LHcytZWbgn6BIlijX5Ks1mlgnMOdYc3mM8t4DaObvfB24AqoAkoD3wcmjbDiDV3avMbATwY3f/0vH2q3lCItKc1mwu5bfvrGfuZzvo1DqRqWN6c+OIDNq0TAi6NGkkmsPbcMpmkdhWXHaQmYsLmbW0kL2Hqhic3pEpOdl86cxuuv2fnJLAbkt0vIbXzFKBEnd3MxtG7dzcDK9T1NELW5nZn4A/11m0aq27/+F4NShURSQIqzft4eF3Pmf++h0kt2nBt8Zkc8OIDFq3UOMb6dTwNpyyWUQA9ldU8dLKIqbl5lO46wA9O7bi5lGZXDO0lxaDlC8kqFWanwfGASlACfAAkAjg7o+Z2Z3A7dSeyT0I3OPui4/axzj+seHNpva2RMnAauB6d684Xh0KVREJ0srCPfz23c9ZsH4HKW1b8sMv9efKc9OIj9MtGiKVGt6GUzaLSF3VNc47n5Tw9KJ8luXvpm3LBK4Z2oubRmbSK7l10OVJBAjsDG84UKiKSDhYWbibB1//hFWbSjmzR3v+38QzOC+7c9BlySlQw9twymYRqc/aolKeXpTPnLXFuDuXDEhlck4W56R30v18pV5qeBWqIhIG3J2/ri3m5298wtayQ1xyZir/MuF00jvr0+tIooa34ZTNInIiW0sPMnNJAc8v3cTeQ1UM7NWRW0ZlMuEs3c9X/pkaXoWqiISRQ4ereXJBHo/O30hVtXNzTiZ3nt9H85UihBrehlM2i8jJ2l9RxcuripiWW0D+zv2ktk/ixpEZfGNYOh1btwi6PAkTangVqiIShkr2HuKXf/uMl1YW0blNC75/cX+uGdpL83vDnBrehlM2i8gXVVPjzFu/nacX5ZO7YRdJiXFceU4aN4/Kok/XtkGXJwFTw6tQFZEwtraolJ/OWcfygj2cltqOf594BiP7pARdltRDDW/DKZtFpCE+3baX6YsKeGXNFiqrahjXvwuTc7LI6ZOieb4xSg2vQlVEwpy78+ZH2/ivNz6haM9BLjy9G/926elkprQJujQ5ihrehlM2i0hj2Flewaylm3hmSSE7yyvo160tt4zK4vLBPUlKjA+6PGlGangVqiISIQ4drmZabj6PvLeBGoefXj6Aq85NC7osqUMNb8Mpm0WkMVVUVTPng2KeXpTPuuK9dGqdyDeGp3PDeZmkdkgKujxpBmp4FaoiEmG2lR3i7tmrWZq/m6+d05OfXjaANi0Tgi5LUMPbGJTNItIU3J1l+buZlpvPW+tKiDfj0rO7c/OoLAb16hh0edKE6stm/c9JRCRMpXZIYtat5/G7dz/nd+99zprNpfz+unM4o0f7oEsTEREJS2bG8OzODM/uzKZdB5i5pIAXlm/mtTVbOSe9I7fkZHHJmakk6LZGMUNHWkQkjMXHGd+7qB/PTRlO+aEqLv9DLs++X0gsXJ0jIiLSEOmdW/P/Jp7Bkh+N54GvnMGu/ZXcOWs1Yx6ay2PzN1J6oDLoEqUZqOEVEYkAI3un8MbdozkvuzP/9upHfHvWKsoOHg66LIlAZjbNzLab2Uf1PD7OzMrMbE3o69+PejzezFab2Zw627LMbKmZbTCzF8xMN8YUkbDRLimRm0dl8d73x/HUjUPITGnDz9/8lPN+9i7/+sqHbNi+L+gSpQmp4RURiRApbVsy46ah3P/l0/jbxyVc+ruFrNlcGnRZEnlmAJec4DkL3X1Q6OsnRz12N/DJUdt+AfzG3fsAe4DJjVKpiEgjio8zLjyjG7NuPY837x7NVwf24E8ri7jw1wuYNG0Z89fv0BVUUUgNr4hIBImLM24b25sXvzUCd7jq0cU8uSCPmhoFtJwcd18A7D6V15pZGnAp8FSdbQaMB14KbZoJXN7AMkVEmtTp3dvz0FUDWXz/eL5/UT/WFe9l0rRlXPjr+Tz7fiEHKquCLlEaiRpeEZEIdG5GJ974zmguOL0rD77xCVOeWcHu/ZqLJI1mhJl9YGZvmtmZdbY/DPwQqKmzrTNQ6u5H/ndYBPQ81k7NbKqZrTCzFTt27GiSwkVEvoiUti2564K+5N43nt9cM5BWLeL5t1c/YsTP3uPnb37K1tKDQZcoDaSGV0QkQnVonchj15/Lf3z1TBZ9vpMJv13IysI9QZclkW8VkOHuA4H/AV4FMLOJwHZ3X3mqO3b3J9x9iLsP6dKlS+NUKyLSCFokxHHF4DT+emcOf7ptBCN7d+aJBRsZ/dBc7py1ilWblK+RSg2viEgEMzMmjczk5TtG0jIxjpunL2PD9vKgy5II5u573b089P0bQKKZpQCjgK+aWQEwGxhvZs8Cu4COZnbkVodpwJbmr1xEpOHMjKGZyTx6/bnMv/d8JudkMX/9Dr72h8Vc9kgur63ZwuHqmhPvSMKGGl4RkSgwoGcHnpsynBYJcUyeuVyXN8spM7PU0LxczGwYtf9X2OXuP3L3NHfPBK4F3nP36712hZe5wFWhXUwCXgugdBGRRtUruTX/MuF03v/RBfzksjPZe/Awd89ew+hfzOXReRspO6C7JUQCNbwiIlEirVNrnrhxCMVlh7jtjyupqKoOuiQJQ2b2PLAE6G9mRWY22cxuM7PbQk+5CvjIzD4Afgdc6ydetvQ+4B4z20DtnN6nm6p+EZHm1qZlAjeOyOTde8by9KQh9O7ahl/8b+1tjR547SMKdu4PukQ5DouFpbeHDBniK1asCLoMEZFm8dcPtnLX86v52jk9+dXXBxI6WSeNyMxWuvuQoOuIZMpmEYlk67bu5elF+fzlgy1U1TgXnd6NKaOzGZrZSbkbkPqyOeFYTxYRkcj1lYE9yN+5n1+/vZ7eXdry7fP7BF2SiIhIVDmjR3t+dfVA7rukP88sKeS5pYW8ta6Es9M6MDkniwlndScxXhfThoMmOwpmNs3MtpvZR/U8Ps7MysxsTejr30Pbk8xsWeh2CB+b2X/Uec0MM8uv85pBTVW/iEgku2t8Hy4f1INf/u0zXl9bHHQ5IiIiUalr+yR+8KX+LL7/Ah68YgDlFVXcPXsNYx6ay2PzNc83HDTlGd4ZwO+BZ47znIXuPvGobRXAeHcvN7NEYJGZvenu74cev9fdX0JEROplZvz8yrPZvOcg97y4hp6dWjGoV8egyxIREYlKrVrE883hGVw3NJ1567fz1MJ8fv7mp/zu3c+5dmg6t47JonuHVkGXGZOa7Ayvuy8Adp/C6/zI7RCAxNBX9E80FhFpZEmJ8Txxw7l0bd+SKTNXsKX0YNAliYiIRLW4OGP8ad2Ydet5vPGd0VwyIJVnlhQw5qG53P/ntVrgKgBBX1g+InTp8ptmduaRjWYWb2ZrgO3A2+6+tM5rHjSztWb2GzNrWd+OzWyqma0wsxU7duxowiGIiISvzm1bMm3SUCoOVzN5xnLKK6qCLklERCQmnNGjPb++ehBzfzCOa4em8/LqLYz/1Ty+8/xqPt22N+jyYkaQDe8qIMPdBwL/A7x65AF3r3b3QdTevH6YmQ0IPfQj4DRgKJBM7W0Qjsndn3D3Ie4+pEuXLk01BhGRsNe3Wzv+cP05fL69nO88v5rqGl00IyIi0lx6Jbfmp5cPYNF953Pr6Gze/aSESx5eyJSZK1izuTTo8qJeYA2vu+89cumyu78BJJpZylHPKaX2ZvaXhH4uDl3yXAFMB4Y1c9kiIhFpdN8u/MdXz+S9T7fzn6+vC7ocERGRmNO1XRI/mnA6ufeP57sX9mV5wW4ufySXbz71Pos37iQWbhcbhMAaXjNLtdBNqsxsWKiWXWbWxcw6hra3Ai4CPg393D30pwGXA8dcAVpERP7Z9edlcMuoLKbnFvDH9wuDLkdERCQmdWzdgu9e2I/c+8fzLxNOY31JOd94cilfe3Qx76wrUePbyJpslWYzex4YB6SYWRHwALULUOHujwFXAbebWRVwELjW3T3U1M40s3hqm+AX3X1OaLfPmVkXwIA1wG1NVb+ISDT610tPp3DXfn78l49JT27N2H6a8iEiIhKEti0TmDqmNzeOyORPK4t4fP5Gpjyzgr5d23LzqCy+dk5PkhLjgy4z4lksfIIwZMgQX7FiRdBliIiEhfKKKq56dDFb9hxkxi1DOTcjOeiSIo6ZrXT3IUHXEcmUzSIi/+hwdQ1z1m7lqYX5fLx1L51aJ/LN4RncMCKDbu2Tgi4v7NWXzUGv0iwiIs2sbcsEpt00lDYtE7jy0SV848n3mfvZdl1CJSIiEqDE+DiuGJzGnLtyeGHqeQzNTOaReRvI+cV7fO+FNXxYVBZ0iRGpyS5pFhGR8NWjYyveumcMs5dtYtqiAm6evpz+3doxZXQWlw3qSYsEfR4qIiISBDNjeHZnhmd3ZtOuA0xfnM+LyzfzyuotDMtM5pacTC46I5X4OAu61IigS5pFRGJcZVXtJVRPLMjj02376Na+JTeNzOIbw9Pp0Cox6PLCki5pbjhls4jIydt76DAvLt/MjMUFFO05SFqnVtw0MpNrhvaiXZKyGurPZjW8IiICgLuz8POdPLEgj0UbdtKmRTzXDkvnlpwsenZsFXR5YUUNb8Mpm0VEvriq6hre+aSEpxfls7xgD21bJvD1IWlMGpFJZkqboMsLlBpehaqIyEn7eGsZTy7I469riwH4ytnduXVMNmf26BBwZeFBDW/DKZtFRBpmbVEp0xbl8/qHxVTVOOP7d+XmUVmM6tOZ0N1fY4oaXoWqiMgXtqX0INMX5fP8sk3sr6xmUK+OXDG4JxPP7k7nti2DLi8wangbTtksItI4tu89xLNLNzFraSE7yyvp160tN43M4orBPWnVInZua6SGV6EqInLKyg7Wzh16efUWPineS0KcMaZfFy4f3JOLTu8WU4EKangbg7JZRKRxHTpczZy1xUzPrb2tUYdWiVw7rBc3jsiMialJangVqiIijeLTbXt5dfVWXluzheKyQ7RpEc8lA7pzxeCejOjdOSZWjVTD23DKZhGRpuHurCjcw/TcfP73o20AfOnMVG4elcXQzE5Re7mzGl6FqohIo6qpcZbm7+bV1Vt448Ni9lVU0bVdSy4b1IPLB/fkjO7tYy5U5eQpm0VEmt6W0oM8s6SA2cs2U3bwMGf2aM+kkZl8dWAPkhKj6+osNbwKVRGRJnPocDXvfbqdV1ZvYd5n2zlc7fTr1pYxfbswNCuZoZnJJLdpEXSZjUYNb8Mpm0VEms/BympeWb2FGYvzWV9STsfWiVwztBfXD8+gV3LroMtrFGp4FaoiIs1iz/5KXv+wmDlrt7JqUymVVTUA9OnalqGZyQzPSmZoVnJEzydSw9twymYRkebn7ryft5tnlhTw1roSaty54LRuTBqZQU6flIi+MksNr0JVRKTZVVRVs7aojGX5u1lesJuVBXvYV1EFQM+OrRia2YmhWckMy0ymT9e2ERO0angbTtksIhKsraUHmbV0E88v28Su/ZVkd2nDjedlcOW5abRLSgy6vC9MDa9CVUQkcNU1zqfb9v69AV6Wv4ed5RUAdG7Tgm8OT+fmUVl0CvPLn9XwNpyyWUQkPFRUVfPGh8XMXFzIms2ltGkRz9fOSePGERn07dYu6PJOmhpehaqISNhxdwp2HWB5/m7eWlfCO5+U0KZFPNefl8Hk0Vl0bZcUdInHpIa34ZTNIiLh54PNpTyzpJC/rt1KZVUNI3t35pZRWYw/rStxYX4XBjW8ClURkbD32bZ9PDJ3A3PWbiUxPo7rhqUzdUw2PcJsvq8a3oZTNouIhK9d5RW8sGIzzy4pZGvZIbJT2nBLThZXnpNGqxbhubqzGl6FqohIxMjbUc6j8zbyyuotmMFV56Zx+9g+pHcOj5UkI7XhNbNpwERgu7sPOMbj44DXgPzQppfd/SdmlgQsAFoCCcBL7v5A6DUzgLFAWeg1N7n7mhPVomwWEQl/h6trePOjbTy1MI+1RWV0bJ3I9cMzuHFEBl3bh9dVWGp4FaoiIhFn8+4DPL5gIy8uL6LancsG9uCO83vTp2uwc4oiuOEdA5QDzxyn4f2Bu088arsBbdy93MwSgUXA3e7+fqjhnePuL32RWpTNIiKRw91ZUbiHpxbm8da6EhLj4vjqoB5Mzsni9O7tgy4PqD+bE4IoRkRE5GT0Sm7Nf15+FneN78uTC/J4bukmXlmzhQkDuvPt8/twRo/wCNlI4e4LzCzzFF7n1DbKAImhr+j/xFxERAAwM4ZmJjM0M5mCnfuZnpvPiyuKeGllETl9Upg8OouxfbuE5TxfneEVEZGIsau8gmm5+cxcXEh5RRUTzkrlnov6NfsZ30g9wwsQanjnHOcM75+BImArtWd7Pw49Fg+sBPoAj7j7faHtM4ARQAXwLnC/u1fU87unAlMB0tPTzy0sLGzEkYmISHMqPVDJrGWbmLm4gJK9FfTp2pbJOVlcMbgnSYnNP883kEuam2iuUBYwG+hMbfDe4O6Vx6tDDa+ISHQpO3CYpxfl8fSifA4erubywT353oX96JXcPHN8o7jhbQ/UhC5dngD81t37HvWcjsArwF3u/pGZdQe2AS2AJ4CN7v6TE9WhbBYRiQ6VVTW8/uFWnlyQz7rivaS0bcGkEZncMCKDjq2b7zaD9WVzXBP/3hnAJSd4zkJ3HxT6OhKQFcB4dx8IDAIuMbPzQo/9AviNu/cB9gCTm6BuEREJYx1aJ3LPxf1Z8MPzmZyTxetrixn/q3n826sfUrL3UNDlRSx33+vu5aHv3wASzSzlqOeUAnMJ5bu7F3utCmA6MKyZyxYRkQC1SIjjisFpvP6dHGZNGc6Anh341dvrGfGz9/jxXz5m8+4DgdbXpA2vuy8Adp/C6/xI4FJnrlBo0YzxwJGFMWYClzdGrSIiEnk6t23Jv156BvPvPZ+rh/Ri9rLNjHloLv/1xifs3n/ci3/kGMwsNZS1mNkwav+fsMvMuoTO7GJmrYCLgE9DP3cP/WnUZvJHQdQuIiLBMjNG9klhxs3D+Nt3xzDhrO48t7SQsb+cy52zVvFhUdmJd9IEwmHRqhFm9gEnniu0NPQpc6m7V4VeWwT0PNZOj5on1MRDEBGRIKV2SOLBK87iW2N68/C763lyYR6zlm5ick4WU0Zn0S4pMegSw4KZPQ+MA1LMrAh4gNoPlXH3x4CrgNvNrAo4CFzr7h5qameGsjkOeNHd54R2+5yZdQEMWAPc1pxjEhGR8NM/tR2/unog936pP9Nz85m1dBNz1hYzIrszU8dmM65fF0Kfrza5Jl+0qjHnClE7R+j90OXMmFkv4M1j7bsuzRMSEYktn5fs49dv72iXMgAACIFJREFUr+fNj7bRsXUit4/tzY0jMmnVonEW0YjkObzhQtksIhI79h06zOxlm5mWm09x2SH6dWvLraOzuWxQT1okNM5Fx0HN4T2uU5grtAvoaGZHzkynAVuasWQREYkAfbu149Hrz+Wvd+YwMK0jP3vzU8b+ci6bdgU7j0hERCQWtUtK5NYx2cy/93x+ffVA4sy496W1jH7oPbaUHmzS3x3oJc1mlgqUhC6X+oe5QsBhdy+tM1foF6HnzaX2kqvZwCRqV3kWERH5J2eldWDmLcNYlr+bV1ZvIa1Tq6BLEhERiVktEuL42jlpXDG4Jws+38nfPt5Gjw5JTfo7m7ThbaK5QvcBs83sP4HVwNNNOQYREYl8w7KSGZaVHHQZIiIiQu0CV2P7dWFsvy5N/ruatOF19+tO8Pjvgd8fY/taYHA9r8lDtzwQERERERGREwh0Dq+IiIiIiIhIU1HDKyIiIiIiIlFJDa+IiIiIiIhEJTW8IiIiIiIiEpXU8IqIiIiIiEhUUsMrIiIiIiIiUUkNr4iIiIiIiEQlNbwiIiIiIiISlczdg66hyZnZDqCwEXaVAuxshP1EilgabyyNFWJrvLE0Voit8QY51gx37xLQ744KyuZTFkvjjaWxQmyNN5bGCrE13rDL5phoeBuLma1w9yFB19FcYmm8sTRWiK3xxtJYIbbGG0tjlfrF2t+DWBpvLI0VYmu8sTRWiK3xhuNYdUmziIiIiIiIRCU1vCIiIiIiIhKV1PB+MU8EXUAzi6XxxtJYIbbGG0tjhdgabyyNVeoXa38PYmm8sTRWiK3xxtJYIbbGG3Zj1RxeERERERERiUo6wysiIiIiIiJRSQ2viIiIiIiIRCU1vCfJzC4xs8/MbIOZ3R90PU3NzArM7EMzW2NmK4KupzGZ2TQz225mH9XZlmxmb5vZ56E/OwVZY2OqZ7w/NrMtoeO7xswmBFljYzGzXmY218zWmdnHZnZ3aHvUHd/jjDVaj22SmS0zsw9C4/2P0PYsM1saem9+wcxaBF2rNB9lc/RQNkf1+7eyOXqPbURks+bwngQziwfWAxcBRcBy4Dp3XxdoYU3IzAqAIe4edTfJNrMxQDnwjLsPCG17CNjt7j8P/aepk7vfF2SdjaWe8f4YKHf3/w6ytsZmZt2B7u6+yszaASuBy4GbiLLje5yxXk10HlsD2rh7uZklAouAu4F7gJfdfbaZPQZ84O6PBlmrNA9lc3RRNiubiYLjq2wOz2zWGd6TMwzY4O557l4JzAYuC7gmOUXuvgDYfdTmy4CZoe9nUvvmFBXqGW9Ucvdid18V+n4f8AnQkyg8vscZa1TyWuWhHxNDXw6MB14KbY+KYysnTdkcRZTN0UvZrGwm4GOrhvfk9AQ21/m5iCj+yxviwFtmttLMpgZdTDPo5u7Foe+3Ad2CLKaZ3Glma0OXVUX8ZURHM7NMYDCwlCg/vkeNFaL02JpZvJmtAbYDbwMbgVJ3rwo9JRbem+X/KJujX1S/d9cjKt+/j1A2R9+xjYRsVsMr9clx93OALwPfDl16ExO89jr/aL/W/1GgNzAIKAZ+FWw5jcvM2gJ/Br7r7nvrPhZtx/cYY43aY+vu1e4+CEij9uzeaQGXJNLclM3RLWrfv0HZTJQe20jIZjW8J2cL0KvOz2mhbVHL3beE/twOvELtX+BoVhKad3Fk/sX2gOtpUu5eEnqDqgGeJIqOb2gOyZ+B59z95dDmqDy+xxprNB/bI9y9FJgLjAA6mllC6KGof2+Wf6BsjsJ/30eJyvfu+kTz+7eyOXqP7RHhnM1qeE/OcqBvaMWxFsC1wF8CrqnJmFmb0ER7zKwNcDHw0fFfFfH+AkwKfT8JeC3AWprckYAJuYIoOb6hxROeBj5x91/XeSjqjm99Y43iY9vFzDqGvm9F7UJFn1AbrleFnhYVx1ZOmrI5Sv59H0fUvXcfTxS/fyubo/fYRkQ2a5XmkxRaPvxhIB6Y5u4PBlxSkzGzbGo/OQZIAGZF03jN7HlgHJAClAAPAK8CLwLpQCFwtbtHxWIS9Yx3HLWX1ThQAHyrzjyaiGVmOcBC4EOgJrT5X6idPxNVx/c4Y72O6Dy2Z1O78EU8tR/WvujuPwm9X80GkoHVwPXuXhFcpdKclM3RM15ls7KZKDi+yubwzGY1vCIiIiIiIhKVdEmziIiIiIiIRCU1vCIiIiIiIhKV1PCKiIiIiIhIVFLDKyIiIiIiIlFJDa+IiIiIiIhEJTW8IlHIzKrNbE2dr/sbcd+ZZhYV948TERFpLspmkWAkBF2AiDSJg+4+KOgiRERE5O+UzSIB0BlekRhiZgVm9pCZfWhmy8ysT2h7ppm9Z2ZrzexdM0sPbe9mZq+Y2Qehr5GhXcWb2ZNm9rGZvWVmrQIblIiISARTNos0LTW8ItGp1VGXTV1T57Eydz8L+D3wcGjb/wAz3f1s4Dngd6HtvwPmu/tA4Bzg49D2vsAj7n4mUApc2cTjERERiXTKZpEAmLsHXYOINDIzK3f3tsfYXgCMd/c8M0sEtrl7ZzPbCXR398Oh7cXunmJmO4A0d6+os49M4G137xv6+T4g0d3/s+lHJiIiEpmUzSLB0Blekdjj9Xz/RVTU+b4arQcgIiLSEMpmkSaihlck9lxT588loe8XA9eGvv8msDD0/bvA7QBmFm9mHZqrSBERkRiibBZpIvrkRyQ6tTKzNXV+/l93P3L7g05mtpbaT4KvC227C5huZvcCO4CbQ9vvBp4ws8nUflp8O1Dc5NWLiIhEH2WzSAA0h1ckhoTmCQ1x951B1yIiIiLKZpGmpkuaRUREREREJCrpDK+IiIiIiIhEJZ3hFRERERERkaikhldERERERESikhpeERERERERiUpqeEVERERERCQqqeEVERERERGRqPT/AUsHnHRrkb0LAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "LeMEfwATIG8R",
        "outputId": "cc3d2311-dbca-425a-bee8-160dc2c20273"
      },
      "source": [
        "plt.title('Weights change')\n",
        "plt.ylabel('Weights')\n",
        "plt.xlabel('Iterations')\n",
        "plt.plot(np.array(all_w).reshape((len(all_w),all_w[0].shape[0]*all_w[0].shape[1])))\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0doFXubIHtJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}