ConditionalReweighting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "\n",
    "plt.rcParams.update({'font.size': 20})\n",
    "plt.rcParams[\"font.family\"] = \"serif\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras.layers import Dense, Input, Concatenate\n",
    "from keras.models import Model\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.callbacks import EarlyStopping\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow.keras.backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"\n",
    "os.environ['CUDA_VISIBLE_DEVICES']=\"1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    }
   ],
   "source": [
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "print(gpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=24220)]) #in MB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reweight(events,model,batch_size=10000):\n",
    "    f = model.predict(events, batch_size=batch_size)\n",
    "    f = np.clip(f,0.0001,1-0.0001)\n",
    "    weights = f / (1. - f)\n",
    "    return np.squeeze(np.nan_to_num(weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reset_weights(model):\n",
    "    for layer in model.layers:         \n",
    "        if hasattr(layer, 'kernel_initializer') and \\\n",
    "                hasattr(layer, 'bias_initializer'):\n",
    "            layer.set_weights([layer.kernel_initializer(shape=np.asarray(layer.kernel.shape)), \\\n",
    "                layer.bias_initializer(shape=np.asarray(layer.bias.shape))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "earlystopping = EarlyStopping(patience=10,\n",
    "                              verbose=1,\n",
    "                              restore_best_weights=True)\n",
    "\n",
    "def marginal_reweight(myT0, myT2, model):\n",
    "    #reweight T0 to T2.\n",
    "    #returns the weights applied to myT0.\n",
    "    #if myT0\\in\\mathbb{R}^n and myR0\\in\\mathbb{R}^m, then model takes as an n-dimensional input.\n",
    "\n",
    "    #Next, marginal reweighting\n",
    "    reset_weights(model)\n",
    "\n",
    "    xvals_1 = np.concatenate([myT2,myT0])\n",
    "    yvals_1 = np.concatenate([np.ones(len(myT2)),np.zeros(len(myT0))])\n",
    "    X_train_1, X_test_1, Y_train_1, Y_test_1 = train_test_split(xvals_1, yvals_1)\n",
    "\n",
    "    reset_weights(model)\n",
    "    model.compile(loss=\"binary_crossentropy\",optimizer='Adam',metrics=['accuracy'])\n",
    "    model.fit(X_train_1,\n",
    "              Y_train_1,\n",
    "              epochs=50,\n",
    "              batch_size=1000,\n",
    "              validation_data=(X_test_1, Y_test_1),verbose=True,callbacks=[earlystopping])\n",
    "\n",
    "    wT0 = reweight(myT0,model)\n",
    "    return wT0\n",
    "\n",
    "def conditional_reweight_2funcs(myT0, myT1, myT2, myR0, myR1, myR2, model, model2):\n",
    "    #reweight R1|T1 to R0|T0.\n",
    "    #returns the weights applied to (R2,T2).\n",
    "    #if myT0\\in\\mathbb{R}^n and myR0\\in\\mathbb{R}^m, then model takes as an n-dimensional input and model2 takes an n+m-dimensional input.\n",
    "    \n",
    "    xvals_1 = np.concatenate([myT1,myT0])\n",
    "    yvals_1 = np.concatenate([np.zeros(len(myT1)),np.ones(len(myT0))])\n",
    "    X_train_1, X_test_1, Y_train_1, Y_test_1 = train_test_split(xvals_1, yvals_1)\n",
    "\n",
    "    reset_weights(model)\n",
    "    model.compile(loss=\"binary_crossentropy\",optimizer='Adam',metrics=['accuracy'])\n",
    "    model.fit(X_train_1,\n",
    "              Y_train_1,\n",
    "              epochs=50,\n",
    "              batch_size=1000,\n",
    "              validation_data=(X_test_1, Y_test_1),verbose=True,callbacks=[earlystopping])\n",
    "\n",
    "    reset_weights(model2)\n",
    "\n",
    "    xvals_1 = np.concatenate([np.c_[myT1,myR1],np.c_[myT0,myR0]])\n",
    "    yvals_1 = np.concatenate([np.zeros(len(myT1)),np.ones(len(myT0))])\n",
    "    X_train_1, X_test_1, Y_train_1, Y_test_1 = train_test_split(xvals_1, yvals_1)\n",
    "\n",
    "    model2.compile(loss=\"binary_crossentropy\",optimizer='Adam',metrics=['accuracy'])\n",
    "    h = model2.fit(X_train_1,\n",
    "              Y_train_1,\n",
    "              epochs=50,\n",
    "              batch_size=1000,\n",
    "              validation_data=(X_test_1, Y_test_1),verbose=True,callbacks=[earlystopping])\n",
    "\n",
    "    wT = reweight(myT2,model)\n",
    "    wRT = reweight(np.c_[myT2,myR2],model2)\n",
    "    return wRT/wT\n",
    "\n",
    "def conditional_reweight_1func(myT0, myT1, myT2, myR0, myR1, myR2, model):\n",
    "    #reweight R1|T1 to R0|T0.\n",
    "    #returns the weights applied to (R2,T2).\n",
    "    #if myT0\\in\\mathbb{R}^n and myR0\\in\\mathbb{R}^m, then model takes as input a 2n+m-dimensional input.\n",
    "\n",
    "    #Make the 4d data for the special training\n",
    "    x = []\n",
    "    y = []\n",
    "    for k in range(1):\n",
    "        inds = np.random.randint(0,len(myT1),len(myT0))\n",
    "        #x = np.concatenate([x,np.c_[myR0,myT0,myT1[inds]]])\n",
    "        #x = np.concatenate([x,np.c_[myR1,myT0,myT1[inds]]])\n",
    "        #y = np.concatenate([y,np.ones(len(myR0))])\n",
    "        #y = np.concatenate([y,np.ones(len(myR0))])\n",
    "        for i in range(len(myT0)):\n",
    "            #if i%10000==0:\n",
    "            #    print(\"on i=\",i)\n",
    "            x+=[[myR0[i],myT0[i],myT1[inds[i]]]]\n",
    "            x+=[[myR1[inds[i]],myT0[i],myT1[inds[i]]]]\n",
    "            y+=[1]\n",
    "            y+=[0]\n",
    "       \n",
    "    xprime = np.c_[myR2,myT2,myT2]\n",
    "    #for i in range(len(myT2)):\n",
    "    #    xprime+=[[myR2[i],myT2[i],myT2[i]]]\n",
    "        \n",
    "    X_train_1, X_test_1, Y_train_1, Y_test_1 = train_test_split(x, y)\n",
    "    \n",
    "    reset_weights(model)\n",
    "    model.compile(loss=\"binary_crossentropy\",optimizer='Adam',metrics=['accuracy'])\n",
    "    model.fit(X_train_1,\n",
    "              Y_train_1,\n",
    "              epochs=50,\n",
    "              batch_size=1000,\n",
    "              validation_data=(X_test_1, Y_test_1),verbose=True,callbacks=[earlystopping])\n",
    "    \n",
    "    wT0 = reweight(xprime,model)\n",
    "    return wT0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeplots(myTt,myT0,myT1,myT2,mywT0,mywRT,mywRT2,xlabel=r\"$T$\",header=\"Gaussian Example\",plotname=\"Gaussian_T\",mymin=-12,mymax=4,w_withall=np.ones(1),nbins=50):\n",
    "\n",
    "    fig = plt.figure(figsize=(8, 8))\n",
    "    gs = gridspec.GridSpec(2, 1, height_ratios=[3,1]) \n",
    "\n",
    "    ax0 = plt.subplot(gs[0])\n",
    "    ax0.yaxis.set_ticks_position('both')\n",
    "    ax0.xaxis.set_ticks_position('both')\n",
    "    ax0.tick_params(direction=\"in\",which=\"both\")\n",
    "    plt.xticks(fontsize=0)\n",
    "    plt.yticks(fontsize=20)\n",
    "    ax0.minorticks_on()\n",
    "\n",
    "    if (\"Dijets\" not in plotname):\n",
    "        nTt,_,_=plt.hist(myTt,bins=np.linspace(mymin,mymax,nbins),label=\"Target\",histtype=\"step\",color=\"black\",lw=5,alpha=0.3,density=True)\n",
    "    else:\n",
    "        nTt,_,_=plt.hist(myTt,bins=np.linspace(mymin,mymax,nbins),weights=w_withall,label=\"Target\",histtype=\"step\",color=\"black\",lw=5,alpha=0.3,density=True)\n",
    "    nT0,bTt,_=plt.hist(myT0,bins=np.linspace(mymin,mymax,nbins),alpha=0.5,label=\"(I)\",density=True)\n",
    "    nT0wT0,_,_=plt.hist(myT0,weights=mywT0,bins=np.linspace(mymin,mymax,nbins),label=\"(I) + Marginal\",histtype=\"step\",lw=2,color=\"blue\",ls=\"--\",density=True)\n",
    "    nT1,_,_=plt.hist(myT1,bins=np.linspace(mymin,mymax,nbins),histtype=\"step\",color=\"black\",label=\"(II)\",density=True)\n",
    "    nT2,_,_=plt.hist(myT2,bins=np.linspace(mymin,mymax,nbins),alpha=0.5,label=\"(III)\",density=True)\n",
    "    nT2wRT,_,_=plt.hist(myT2,weights=mywRT,bins=np.linspace(mymin,mymax,nbins),label=\"(III) + Cond. (2f)\",histtype=\"step\",color=\"red\",lw=2,ls=\":\",density=True)\n",
    "    nT2wRT2,_,_=plt.hist(myT2,weights=mywRT2,bins=np.linspace(mymin,mymax,nbins),label=\"(III) + Cond. (1f)\",histtype=\"step\",color=\"red\",lw=2,ls=\"--\",density=True)\n",
    "    plt.ylabel(\"Normalized to Unity\")\n",
    "    plt.xlim([mymin,mymax])\n",
    "    plt.locator_params(axis='y', nbins=6)\n",
    "    plt.legend(frameon=False,fontsize=20)\n",
    "    _=plt.title(header,loc=\"right\",fontsize=20, fontstyle=\"italic\")\n",
    "    \n",
    "    ax1 = plt.subplot(gs[1])\n",
    "    ax1.yaxis.set_ticks_position('both')\n",
    "    ax1.xaxis.set_ticks_position('both')\n",
    "    ax1.tick_params(direction=\"in\",which=\"both\")\n",
    "    ax1.minorticks_on()\n",
    "    plt.xlim([mymin,mymax])\n",
    "    plt.locator_params(axis='x', nbins=6)\n",
    "    plt.xlabel(xlabel,fontsize=20)\n",
    "    plt.ylabel(\"1/target\",fontsize=20)\n",
    "    plt.xticks(fontsize=20)\n",
    "    plt.yticks(fontsize=15)\n",
    "    plt.axhline(y=1,linewidth=2, color='gray')   \n",
    "    plt.plot(0.5*(bTt[1:]+bTt[:-1]),nT0/(0.000001+nTt),color='tab:blue')\n",
    "    plt.plot(0.5*(bTt[1:]+bTt[:-1]),nT0wT0/(0.000001+nTt),color='blue',ls=\"--\")\n",
    "    plt.plot(0.5*(bTt[1:]+bTt[:-1]),nT1/(0.000001+nTt),color='black')\n",
    "    plt.plot(0.5*(bTt[1:]+bTt[:-1]),nT2/(0.000001+nTt),color='tab:orange')\n",
    "    plt.plot(0.5*(bTt[1:]+bTt[:-1]),nT2wRT/(0.000001+nTt),color='red',ls=\":\")\n",
    "    plt.plot(0.5*(bTt[1:]+bTt[:-1]),nT2wRT2/(0.000001+nTt),color='red',ls=\"--\")\n",
    "    plt.ylim(0.5,1.5)\n",
    "\n",
    "    plt.savefig(\"conditional/\"+plotname+\".pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian case\n",
    "\n",
    "First, no gap in phase space.\n",
    "\n",
    "$T \\sim N(\\mu,\\sigma)$\n",
    "\n",
    "(I): $\\mu = \\mu_0, \\sigma = \\sigma_0$\n",
    "\n",
    "(II): $\\mu = \\mu_0, \\sigma = \\sigma_0$\n",
    "\n",
    "(III): $\\mu = \\mu_1, \\sigma = \\sigma_1$\n",
    "\n",
    "$R \\sim N(T+b,\\epsilon)$\n",
    "\n",
    "(I): $b = b_0, \\epsilon = \\epsilon_0$\n",
    "\n",
    "(II): $b = b_1, \\epsilon = \\epsilon_1$\n",
    "\n",
    "(III): $b = b_1, \\epsilon = \\epsilon_1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = Input((1, ))\n",
    "hidden_layer_1 = Dense(50, activation='relu')(inputs)\n",
    "hidden_layer_2 = Dense(50, activation='relu')(hidden_layer_1)\n",
    "hidden_layer_3 = Dense(50, activation='relu')(hidden_layer_2)\n",
    "outputs = Dense(1, activation='sigmoid')(hidden_layer_3)\n",
    "model = Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "inputs2 = Input((2, ))\n",
    "hidden_layer_12 = Dense(50, activation='relu')(inputs2)\n",
    "hidden_layer_22 = Dense(50, activation='relu')(hidden_layer_12)\n",
    "hidden_layer_32 = Dense(50, activation='relu')(hidden_layer_22)\n",
    "outputs2 = Dense(1, activation='sigmoid')(hidden_layer_32)\n",
    "model2 = Model(inputs=inputs2, outputs=outputs2)\n",
    "\n",
    "inputsO = Input((3, ))\n",
    "hidden_layer_1O = Dense(50, activation='relu')(inputsO)\n",
    "hidden_layer_2O = Dense(50, activation='relu')(hidden_layer_1O)\n",
    "hidden_layer_3O = Dense(50, activation='relu')(hidden_layer_2O)\n",
    "outputsO = Dense(1, activation='sigmoid')(hidden_layer_3O)\n",
    "modelO = Model(inputs=inputsO, outputs=outputsO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 100000\n",
    "\n",
    "mu0 = 0.\n",
    "mu1 = 0.1\n",
    "sigma0 = 1\n",
    "sigma1 = 1.5\n",
    "\n",
    "b0 = 0.\n",
    "b1 = 0.5\n",
    "eps0 = 0.5\n",
    "eps1 = 0.3\n",
    "\n",
    "T0 = np.random.normal(mu0,sigma0,n)\n",
    "T1 = np.random.normal(mu0,sigma0,n)\n",
    "\n",
    "T2 = np.random.normal(mu1,sigma1,n)\n",
    "Tt = np.random.normal(mu1,sigma1,n)\n",
    "\n",
    "R0 = np.random.normal(T0+b0,eps0)\n",
    "R1 = np.random.normal(T1+b1,eps1)\n",
    "R2 = np.random.normal(T2+b1,eps1)\n",
    "Rt = np.random.normal(Tt+b0,eps0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "15/15 [==============================] - 0s 10ms/step - loss: 0.6936 - accuracy: 0.5013 - val_loss: 0.6933 - val_accuracy: 0.4989\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5011 - val_loss: 0.6932 - val_accuracy: 0.4999\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5011 - val_loss: 0.6932 - val_accuracy: 0.4974\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.4999 - val_loss: 0.6932 - val_accuracy: 0.4972\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5002 - val_loss: 0.6932 - val_accuracy: 0.4996\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.4989 - val_loss: 0.6932 - val_accuracy: 0.4998\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6932 - accuracy: 0.4997 - val_loss: 0.6932 - val_accuracy: 0.4988\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6932 - accuracy: 0.5002 - val_loss: 0.6932 - val_accuracy: 0.4996\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6932 - accuracy: 0.5001 - val_loss: 0.6932 - val_accuracy: 0.4997\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6932 - accuracy: 0.4999 - val_loss: 0.6932 - val_accuracy: 0.5012\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5023 - val_loss: 0.6933 - val_accuracy: 0.4990\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5012 - val_loss: 0.6932 - val_accuracy: 0.4998\n",
      "Epoch 13/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6932 - accuracy: 0.5007 - val_loss: 0.6932 - val_accuracy: 0.5000\n",
      "Epoch 14/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5008 - val_loss: 0.6932 - val_accuracy: 0.5001\n",
      "Epoch 15/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5013 - val_loss: 0.6932 - val_accuracy: 0.5006\n",
      "Epoch 16/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6932 - accuracy: 0.5007 - val_loss: 0.6932 - val_accuracy: 0.4993\n",
      "Epoch 17/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6932 - accuracy: 0.5009 - val_loss: 0.6932 - val_accuracy: 0.5016\n",
      "Epoch 18/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6932 - accuracy: 0.4995 - val_loss: 0.6932 - val_accuracy: 0.5028\n",
      "Epoch 19/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6932 - accuracy: 0.5027 - val_loss: 0.6932 - val_accuracy: 0.5007\n",
      "Epoch 20/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6932 - accuracy: 0.5016 - val_loss: 0.6932 - val_accuracy: 0.5006\n",
      "Epoch 21/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6932 - accuracy: 0.5003 - val_loss: 0.6932 - val_accuracy: 0.5026\n",
      "Epoch 22/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.4990 - val_loss: 0.6932 - val_accuracy: 0.5000\n",
      "Epoch 23/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6932 - accuracy: 0.4998 - val_loss: 0.6932 - val_accuracy: 0.4985\n",
      "Epoch 24/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5008 - val_loss: 0.6932 - val_accuracy: 0.5010\n",
      "Epoch 25/50\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.6932 - accuracy: 0.4988Restoring model weights from the end of the best epoch.\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6932 - accuracy: 0.5003 - val_loss: 0.6932 - val_accuracy: 0.5024\n",
      "Epoch 00025: early stopping\n",
      "Epoch 1/50\n",
      "15/15 [==============================] - 0s 7ms/step - loss: 0.6741 - accuracy: 0.6358 - val_loss: 0.6552 - val_accuracy: 0.6703\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6326 - accuracy: 0.7022 - val_loss: 0.6020 - val_accuracy: 0.7302\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5701 - accuracy: 0.7409 - val_loss: 0.5379 - val_accuracy: 0.7445\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5243 - accuracy: 0.7466 - val_loss: 0.5192 - val_accuracy: 0.7471\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5150 - accuracy: 0.7476 - val_loss: 0.5144 - val_accuracy: 0.7473\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5115 - accuracy: 0.7491 - val_loss: 0.5124 - val_accuracy: 0.7460\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5104 - accuracy: 0.7485 - val_loss: 0.5118 - val_accuracy: 0.7460\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5099 - accuracy: 0.7489 - val_loss: 0.5114 - val_accuracy: 0.7461\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5096 - accuracy: 0.7485 - val_loss: 0.5113 - val_accuracy: 0.7451\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5094 - accuracy: 0.7486 - val_loss: 0.5109 - val_accuracy: 0.7459\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5091 - accuracy: 0.7486 - val_loss: 0.5105 - val_accuracy: 0.7460\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.5088 - accuracy: 0.7487 - val_loss: 0.5102 - val_accuracy: 0.7462\n",
      "Epoch 13/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.5086 - accuracy: 0.7487 - val_loss: 0.5102 - val_accuracy: 0.7464\n",
      "Epoch 14/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5084 - accuracy: 0.7489 - val_loss: 0.5100 - val_accuracy: 0.7466\n",
      "Epoch 15/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5083 - accuracy: 0.7488 - val_loss: 0.5098 - val_accuracy: 0.7463\n",
      "Epoch 16/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5081 - accuracy: 0.7489 - val_loss: 0.5097 - val_accuracy: 0.7464\n",
      "Epoch 17/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5081 - accuracy: 0.7486 - val_loss: 0.5094 - val_accuracy: 0.7468\n",
      "Epoch 18/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5077 - accuracy: 0.7491 - val_loss: 0.5093 - val_accuracy: 0.7470\n",
      "Epoch 19/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5077 - accuracy: 0.7490 - val_loss: 0.5091 - val_accuracy: 0.7464\n",
      "Epoch 20/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5075 - accuracy: 0.7491 - val_loss: 0.5091 - val_accuracy: 0.7462\n",
      "Epoch 21/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5074 - accuracy: 0.7489 - val_loss: 0.5089 - val_accuracy: 0.7463\n",
      "Epoch 22/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5072 - accuracy: 0.7490 - val_loss: 0.5087 - val_accuracy: 0.7470\n",
      "Epoch 23/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5071 - accuracy: 0.7488 - val_loss: 0.5087 - val_accuracy: 0.7470\n",
      "Epoch 24/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5071 - accuracy: 0.7490 - val_loss: 0.5087 - val_accuracy: 0.7470\n",
      "Epoch 25/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5069 - accuracy: 0.7486 - val_loss: 0.5084 - val_accuracy: 0.7470\n",
      "Epoch 26/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5070 - accuracy: 0.7490 - val_loss: 0.5087 - val_accuracy: 0.7457\n",
      "Epoch 27/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5070 - accuracy: 0.7490 - val_loss: 0.5083 - val_accuracy: 0.7461\n",
      "Epoch 28/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5070 - accuracy: 0.7489 - val_loss: 0.5081 - val_accuracy: 0.7469\n",
      "Epoch 29/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5065 - accuracy: 0.7488 - val_loss: 0.5081 - val_accuracy: 0.7473\n",
      "Epoch 30/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5064 - accuracy: 0.7490 - val_loss: 0.5079 - val_accuracy: 0.7472\n",
      "Epoch 31/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5064 - accuracy: 0.7488 - val_loss: 0.5081 - val_accuracy: 0.7475\n",
      "Epoch 32/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5063 - accuracy: 0.7488 - val_loss: 0.5079 - val_accuracy: 0.7465\n",
      "Epoch 33/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5062 - accuracy: 0.7490 - val_loss: 0.5077 - val_accuracy: 0.7473\n",
      "Epoch 34/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5060 - accuracy: 0.7491 - val_loss: 0.5075 - val_accuracy: 0.7471\n",
      "Epoch 35/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5058 - accuracy: 0.7490 - val_loss: 0.5075 - val_accuracy: 0.7479\n",
      "Epoch 36/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.5058 - accuracy: 0.7488 - val_loss: 0.5074 - val_accuracy: 0.7471\n",
      "Epoch 37/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5057 - accuracy: 0.7489 - val_loss: 0.5073 - val_accuracy: 0.7469\n",
      "Epoch 38/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5057 - accuracy: 0.7488 - val_loss: 0.5073 - val_accuracy: 0.7468\n",
      "Epoch 39/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5055 - accuracy: 0.7490 - val_loss: 0.5071 - val_accuracy: 0.7473\n",
      "Epoch 40/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5054 - accuracy: 0.7490 - val_loss: 0.5071 - val_accuracy: 0.7468\n",
      "Epoch 41/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5054 - accuracy: 0.7487 - val_loss: 0.5071 - val_accuracy: 0.7477\n",
      "Epoch 42/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.5053 - accuracy: 0.7487 - val_loss: 0.5069 - val_accuracy: 0.7469\n",
      "Epoch 43/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5052 - accuracy: 0.7487 - val_loss: 0.5069 - val_accuracy: 0.7468\n",
      "Epoch 44/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5053 - accuracy: 0.7488 - val_loss: 0.5067 - val_accuracy: 0.7469\n",
      "Epoch 45/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.5051 - accuracy: 0.7489 - val_loss: 0.5067 - val_accuracy: 0.7473\n",
      "Epoch 46/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.5050 - accuracy: 0.7489 - val_loss: 0.5066 - val_accuracy: 0.7468\n",
      "Epoch 47/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5052 - accuracy: 0.7490 - val_loss: 0.5066 - val_accuracy: 0.7472\n",
      "Epoch 48/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.5050 - accuracy: 0.7492 - val_loss: 0.5069 - val_accuracy: 0.7463\n",
      "Epoch 49/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5049 - accuracy: 0.7490 - val_loss: 0.5066 - val_accuracy: 0.7469\n",
      "Epoch 50/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.5049 - accuracy: 0.7490 - val_loss: 0.5066 - val_accuracy: 0.7469\n",
      "Epoch 1/50\n",
      "15/15 [==============================] - 1s 46ms/step - loss: 0.6409 - accuracy: 0.6432 - val_loss: 0.5972 - val_accuracy: 0.7355\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.5592 - accuracy: 0.7556 - val_loss: 0.5135 - val_accuracy: 0.7818\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.4723 - accuracy: 0.7950 - val_loss: 0.4305 - val_accuracy: 0.8071\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.3959 - accuracy: 0.8192 - val_loss: 0.3642 - val_accuracy: 0.8306\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.3399 - accuracy: 0.8383 - val_loss: 0.3210 - val_accuracy: 0.8443\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 1s 45ms/step - loss: 0.3076 - accuracy: 0.8492 - val_loss: 0.3008 - val_accuracy: 0.8516\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2941 - accuracy: 0.8544 - val_loss: 0.2927 - val_accuracy: 0.8556\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2892 - accuracy: 0.8574 - val_loss: 0.2901 - val_accuracy: 0.8575\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2874 - accuracy: 0.8585 - val_loss: 0.2893 - val_accuracy: 0.8578\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.2864 - accuracy: 0.8585 - val_loss: 0.2887 - val_accuracy: 0.8582\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.2857 - accuracy: 0.8589 - val_loss: 0.2877 - val_accuracy: 0.8583\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2849 - accuracy: 0.8590 - val_loss: 0.2870 - val_accuracy: 0.8582\n",
      "Epoch 13/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.2843 - accuracy: 0.8592 - val_loss: 0.2863 - val_accuracy: 0.8586\n",
      "Epoch 14/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.2837 - accuracy: 0.8595 - val_loss: 0.2857 - val_accuracy: 0.8585\n",
      "Epoch 15/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2830 - accuracy: 0.8596 - val_loss: 0.2850 - val_accuracy: 0.8587\n",
      "Epoch 16/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.2826 - accuracy: 0.8599 - val_loss: 0.2848 - val_accuracy: 0.8589\n",
      "Epoch 17/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2823 - accuracy: 0.8594 - val_loss: 0.2842 - val_accuracy: 0.8589\n",
      "Epoch 18/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.2818 - accuracy: 0.8600 - val_loss: 0.2842 - val_accuracy: 0.8586\n",
      "Epoch 19/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2816 - accuracy: 0.8602 - val_loss: 0.2842 - val_accuracy: 0.8592\n",
      "Epoch 20/50\n",
      "15/15 [==============================] - 1s 41ms/step - loss: 0.2815 - accuracy: 0.8597 - val_loss: 0.2848 - val_accuracy: 0.8585\n",
      "Epoch 21/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.2816 - accuracy: 0.8599 - val_loss: 0.2834 - val_accuracy: 0.8587\n",
      "Epoch 22/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.2810 - accuracy: 0.8602 - val_loss: 0.2839 - val_accuracy: 0.8582\n",
      "Epoch 23/50\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 0.2810 - accuracy: 0.8602 - val_loss: 0.2833 - val_accuracy: 0.8585\n",
      "Epoch 24/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2811 - accuracy: 0.8599 - val_loss: 0.2834 - val_accuracy: 0.8588\n",
      "Epoch 25/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.2806 - accuracy: 0.8602 - val_loss: 0.2830 - val_accuracy: 0.8585\n",
      "Epoch 26/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2804 - accuracy: 0.8602 - val_loss: 0.2830 - val_accuracy: 0.8585\n",
      "Epoch 27/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2804 - accuracy: 0.8600 - val_loss: 0.2828 - val_accuracy: 0.8580\n",
      "Epoch 28/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.2806 - accuracy: 0.8596 - val_loss: 0.2830 - val_accuracy: 0.8587\n",
      "Epoch 29/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2803 - accuracy: 0.8603 - val_loss: 0.2826 - val_accuracy: 0.8585\n",
      "Epoch 30/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2802 - accuracy: 0.8601 - val_loss: 0.2829 - val_accuracy: 0.8589\n",
      "Epoch 31/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2801 - accuracy: 0.8604 - val_loss: 0.2827 - val_accuracy: 0.8584\n",
      "Epoch 32/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2803 - accuracy: 0.8601 - val_loss: 0.2825 - val_accuracy: 0.8588\n",
      "Epoch 33/50\n",
      "15/15 [==============================] - 1s 46ms/step - loss: 0.2802 - accuracy: 0.8602 - val_loss: 0.2829 - val_accuracy: 0.8582\n",
      "Epoch 34/50\n",
      "15/15 [==============================] - 1s 41ms/step - loss: 0.2802 - accuracy: 0.8602 - val_loss: 0.2824 - val_accuracy: 0.8584\n",
      "Epoch 35/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2801 - accuracy: 0.8600 - val_loss: 0.2823 - val_accuracy: 0.8583\n",
      "Epoch 36/50\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 0.2804 - accuracy: 0.8604 - val_loss: 0.2826 - val_accuracy: 0.8583\n",
      "Epoch 37/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2801 - accuracy: 0.8601 - val_loss: 0.2825 - val_accuracy: 0.8587\n",
      "Epoch 38/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.2799 - accuracy: 0.8600 - val_loss: 0.2826 - val_accuracy: 0.8581\n",
      "Epoch 39/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2801 - accuracy: 0.8604 - val_loss: 0.2823 - val_accuracy: 0.8584\n",
      "Epoch 40/50\n",
      "15/15 [==============================] - 1s 44ms/step - loss: 0.2799 - accuracy: 0.8601 - val_loss: 0.2826 - val_accuracy: 0.8583\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 41/50\n",
      "15/15 [==============================] - 1s 40ms/step - loss: 0.2801 - accuracy: 0.8604 - val_loss: 0.2825 - val_accuracy: 0.8579\n",
      "Epoch 42/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2799 - accuracy: 0.8605 - val_loss: 0.2828 - val_accuracy: 0.8586\n",
      "Epoch 43/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.2803 - accuracy: 0.8599 - val_loss: 0.2826 - val_accuracy: 0.8586\n",
      "Epoch 44/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.2798 - accuracy: 0.8605 - val_loss: 0.2824 - val_accuracy: 0.8587\n",
      "Epoch 45/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.2804 - accuracy: 0.8600 - val_loss: 0.2825 - val_accuracy: 0.8580\n",
      "Epoch 46/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2812 - accuracy: 0.8598 - val_loss: 0.2828 - val_accuracy: 0.8581\n",
      "Epoch 47/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2801 - accuracy: 0.8600 - val_loss: 0.2831 - val_accuracy: 0.8578\n",
      "Epoch 48/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.2798 - accuracy: 0.8602 - val_loss: 0.2822 - val_accuracy: 0.8587\n",
      "Epoch 49/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.2798 - accuracy: 0.8603 - val_loss: 0.2825 - val_accuracy: 0.8584\n",
      "Epoch 50/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.2799 - accuracy: 0.8603 - val_loss: 0.2826 - val_accuracy: 0.8588\n",
      "Epoch 1/50\n",
      "15/15 [==============================] - 0s 9ms/step - loss: 0.6932 - accuracy: 0.4976 - val_loss: 0.6818 - val_accuracy: 0.5253\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6760 - accuracy: 0.5487 - val_loss: 0.6711 - val_accuracy: 0.5703\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6660 - accuracy: 0.5829 - val_loss: 0.6624 - val_accuracy: 0.5865\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6594 - accuracy: 0.5934 - val_loss: 0.6591 - val_accuracy: 0.5935\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6579 - accuracy: 0.5955 - val_loss: 0.6586 - val_accuracy: 0.5941\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6573 - accuracy: 0.5959 - val_loss: 0.6581 - val_accuracy: 0.5944\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6569 - accuracy: 0.5967 - val_loss: 0.6578 - val_accuracy: 0.5947\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6567 - accuracy: 0.5967 - val_loss: 0.6577 - val_accuracy: 0.5943\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6565 - accuracy: 0.5968 - val_loss: 0.6574 - val_accuracy: 0.5948\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6564 - accuracy: 0.5970 - val_loss: 0.6573 - val_accuracy: 0.5949\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6563 - accuracy: 0.5972 - val_loss: 0.6572 - val_accuracy: 0.5941\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6562 - accuracy: 0.5973 - val_loss: 0.6573 - val_accuracy: 0.5942\n",
      "Epoch 13/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6562 - accuracy: 0.5973 - val_loss: 0.6571 - val_accuracy: 0.5945\n",
      "Epoch 14/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6561 - accuracy: 0.5973 - val_loss: 0.6570 - val_accuracy: 0.5945\n",
      "Epoch 15/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6561 - accuracy: 0.5970 - val_loss: 0.6569 - val_accuracy: 0.5944\n",
      "Epoch 16/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6560 - accuracy: 0.5971 - val_loss: 0.6570 - val_accuracy: 0.5946\n",
      "Epoch 17/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6560 - accuracy: 0.5972 - val_loss: 0.6569 - val_accuracy: 0.5942\n",
      "Epoch 18/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6560 - accuracy: 0.5968 - val_loss: 0.6569 - val_accuracy: 0.5944\n",
      "Epoch 19/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6560 - accuracy: 0.5972 - val_loss: 0.6568 - val_accuracy: 0.5943\n",
      "Epoch 20/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5971 - val_loss: 0.6569 - val_accuracy: 0.5945\n",
      "Epoch 21/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5973 - val_loss: 0.6569 - val_accuracy: 0.5943\n",
      "Epoch 22/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6560 - accuracy: 0.5971 - val_loss: 0.6569 - val_accuracy: 0.5944\n",
      "Epoch 23/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6560 - accuracy: 0.5969 - val_loss: 0.6569 - val_accuracy: 0.5945\n",
      "Epoch 24/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6560 - accuracy: 0.5968 - val_loss: 0.6570 - val_accuracy: 0.5945\n",
      "Epoch 25/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5971 - val_loss: 0.6568 - val_accuracy: 0.5945\n",
      "Epoch 26/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5974 - val_loss: 0.6568 - val_accuracy: 0.5940\n",
      "Epoch 27/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5971 - val_loss: 0.6568 - val_accuracy: 0.5946\n",
      "Epoch 28/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5971 - val_loss: 0.6568 - val_accuracy: 0.5946\n",
      "Epoch 29/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5972 - val_loss: 0.6568 - val_accuracy: 0.5942\n",
      "Epoch 30/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5967 - val_loss: 0.6569 - val_accuracy: 0.5946\n",
      "Epoch 31/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5975 - val_loss: 0.6568 - val_accuracy: 0.5943\n",
      "Epoch 32/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5969 - val_loss: 0.6569 - val_accuracy: 0.5944\n",
      "Epoch 33/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5970 - val_loss: 0.6568 - val_accuracy: 0.5944\n",
      "Epoch 34/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6559 - accuracy: 0.5970 - val_loss: 0.6568 - val_accuracy: 0.5943\n",
      "Epoch 35/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6559 - accuracy: 0.5970 - val_loss: 0.6568 - val_accuracy: 0.5942\n",
      "Epoch 36/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6560 - accuracy: 0.5972 - val_loss: 0.6568 - val_accuracy: 0.5945\n",
      "Epoch 37/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5973 - val_loss: 0.6567 - val_accuracy: 0.5942\n",
      "Epoch 38/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5968 - val_loss: 0.6568 - val_accuracy: 0.5941\n",
      "Epoch 39/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5971 - val_loss: 0.6567 - val_accuracy: 0.5944\n",
      "Epoch 40/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5971 - val_loss: 0.6567 - val_accuracy: 0.5941\n",
      "Epoch 41/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6558 - accuracy: 0.5972 - val_loss: 0.6568 - val_accuracy: 0.5941\n",
      "Epoch 42/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5966 - val_loss: 0.6568 - val_accuracy: 0.5943\n",
      "Epoch 43/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5969 - val_loss: 0.6567 - val_accuracy: 0.5943\n",
      "Epoch 44/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6560 - accuracy: 0.5970 - val_loss: 0.6568 - val_accuracy: 0.5946\n",
      "Epoch 45/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6560 - accuracy: 0.5970 - val_loss: 0.6568 - val_accuracy: 0.5944\n",
      "Epoch 46/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5968 - val_loss: 0.6569 - val_accuracy: 0.5944\n",
      "Epoch 47/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5971 - val_loss: 0.6569 - val_accuracy: 0.5945\n",
      "Epoch 48/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5971 - val_loss: 0.6567 - val_accuracy: 0.5941\n",
      "Epoch 49/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5975 - val_loss: 0.6567 - val_accuracy: 0.5946\n",
      "Epoch 50/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6559 - accuracy: 0.5975 - val_loss: 0.6567 - val_accuracy: 0.5945\n"
     ]
    }
   ],
   "source": [
    "wCondtional_2funcs = conditional_reweight_2funcs(T0, T1, T2, R0, R1, R2, model, model2)\n",
    "wCondtional_1func = conditional_reweight_1func(T0, T1, T2, R0, R1, R2, modelO)\n",
    "wMarginal = marginal_reweight(T0, T2, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tt_copy = Tt.copy()\n",
    "T0_copy = T0.copy()\n",
    "T1_copy = T1.copy()\n",
    "T2_copy = T2.copy()\n",
    "wT0_copy = wMarginal.copy()\n",
    "\n",
    "Rt_copy = Rt.copy()\n",
    "R0_copy = R0.copy()\n",
    "R1_copy = R1.copy()\n",
    "R2_copy = R2.copy()\n",
    "wRT_copy = wCondtional_2funcs.copy()\n",
    "wRT2_copy = wCondtional_1func.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeplots(Tt_copy,T0_copy,T1_copy,T2_copy,wT0_copy,wRT_copy,wRT2_copy,xlabel=r\"$T$\",header=\"Gaussian Example\",plotname=\"Gaussian_T\")\n",
    "makeplots(Rt_copy,R0_copy,R1_copy,R2_copy,wT0_copy,wRT_copy,wRT2_copy,xlabel=r\"$R$\",header=\"Gaussian Example\",plotname=\"Gaussian_R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 100000\n",
    "\n",
    "mu0 = 0.\n",
    "mu1 = 0.3\n",
    "sigma0 = 1.5\n",
    "sigma1 = 1.8\n",
    "\n",
    "b0 = 0.\n",
    "b1 = 0.2\n",
    "eps0 = 0.5\n",
    "eps1 = 0.3\n",
    "\n",
    "T0 = np.random.normal(mu0,sigma0,n*5)\n",
    "T1 = np.random.normal(mu0,sigma0,n*5)\n",
    "\n",
    "T1 = T1[(T1 < 1.5) + (T1 > 2)]\n",
    "T1 = T1[0:n]\n",
    "\n",
    "T0 = T0[(T0 < 1.5) + (T0 > 2)]\n",
    "T0 = T0[0:n]\n",
    "\n",
    "T2 = np.random.normal(mu1,sigma1,n)\n",
    "Tt = np.random.normal(mu1,sigma1,n)\n",
    "\n",
    "R0 = np.random.normal(T0+b0,eps0)\n",
    "R1 = np.random.normal(T1+b1,eps1)\n",
    "R2 = np.random.normal(T2+b1,eps1)\n",
    "Rt = np.random.normal(Tt+b0,eps0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "15/15 [==============================] - 0s 7ms/step - loss: 0.6936 - accuracy: 0.5018 - val_loss: 0.6932 - val_accuracy: 0.4991\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5003 - val_loss: 0.6932 - val_accuracy: 0.4989\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6931 - accuracy: 0.5027 - val_loss: 0.6932 - val_accuracy: 0.4989\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.4996 - val_loss: 0.6932 - val_accuracy: 0.4998\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5018 - val_loss: 0.6932 - val_accuracy: 0.4989\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5002 - val_loss: 0.6932 - val_accuracy: 0.4995\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5017 - val_loss: 0.6932 - val_accuracy: 0.4998\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5001 - val_loss: 0.6932 - val_accuracy: 0.5007\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5007 - val_loss: 0.6932 - val_accuracy: 0.4986\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5020 - val_loss: 0.6932 - val_accuracy: 0.4991\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5018 - val_loss: 0.6932 - val_accuracy: 0.5004\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5005 - val_loss: 0.6932 - val_accuracy: 0.5003\n",
      "Epoch 13/50\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.6931 - accuracy: 0.5072Restoring model weights from the end of the best epoch.\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5024 - val_loss: 0.6932 - val_accuracy: 0.4975\n",
      "Epoch 00013: early stopping\n",
      "Epoch 1/50\n",
      "15/15 [==============================] - 0s 7ms/step - loss: 0.6774 - accuracy: 0.5944 - val_loss: 0.6679 - val_accuracy: 0.6133\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6591 - accuracy: 0.6212 - val_loss: 0.6508 - val_accuracy: 0.6196\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6417 - accuracy: 0.6259 - val_loss: 0.6363 - val_accuracy: 0.6244\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6297 - accuracy: 0.6302 - val_loss: 0.6281 - val_accuracy: 0.6274\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6241 - accuracy: 0.6339 - val_loss: 0.6249 - val_accuracy: 0.6336\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6217 - accuracy: 0.6402 - val_loss: 0.6234 - val_accuracy: 0.6422\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6201 - accuracy: 0.6448 - val_loss: 0.6215 - val_accuracy: 0.6440\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6188 - accuracy: 0.6463 - val_loss: 0.6203 - val_accuracy: 0.6457\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6178 - accuracy: 0.6475 - val_loss: 0.6194 - val_accuracy: 0.6455\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6170 - accuracy: 0.6477 - val_loss: 0.6187 - val_accuracy: 0.6450\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6166 - accuracy: 0.6469 - val_loss: 0.6185 - val_accuracy: 0.6452\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6163 - accuracy: 0.6476 - val_loss: 0.6182 - val_accuracy: 0.6445\n",
      "Epoch 13/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6161 - accuracy: 0.6466 - val_loss: 0.6177 - val_accuracy: 0.6456\n",
      "Epoch 14/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6155 - accuracy: 0.6476 - val_loss: 0.6176 - val_accuracy: 0.6451\n",
      "Epoch 15/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6156 - accuracy: 0.6470 - val_loss: 0.6188 - val_accuracy: 0.6435\n",
      "Epoch 16/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6157 - accuracy: 0.6478 - val_loss: 0.6178 - val_accuracy: 0.6460\n",
      "Epoch 17/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6150 - accuracy: 0.6479 - val_loss: 0.6171 - val_accuracy: 0.6454\n",
      "Epoch 18/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6148 - accuracy: 0.6477 - val_loss: 0.6170 - val_accuracy: 0.6459\n",
      "Epoch 19/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6147 - accuracy: 0.6477 - val_loss: 0.6173 - val_accuracy: 0.6453\n",
      "Epoch 20/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6149 - accuracy: 0.6475 - val_loss: 0.6172 - val_accuracy: 0.6462\n",
      "Epoch 21/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6146 - accuracy: 0.6477 - val_loss: 0.6167 - val_accuracy: 0.6455\n",
      "Epoch 22/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6148 - accuracy: 0.6473 - val_loss: 0.6170 - val_accuracy: 0.6455\n",
      "Epoch 23/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6148 - accuracy: 0.6473 - val_loss: 0.6169 - val_accuracy: 0.6463\n",
      "Epoch 24/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6146 - accuracy: 0.6481 - val_loss: 0.6166 - val_accuracy: 0.6460\n",
      "Epoch 25/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6145 - accuracy: 0.6480 - val_loss: 0.6174 - val_accuracy: 0.6451\n",
      "Epoch 26/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6145 - accuracy: 0.6480 - val_loss: 0.6166 - val_accuracy: 0.6460\n",
      "Epoch 27/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6145 - accuracy: 0.6474 - val_loss: 0.6170 - val_accuracy: 0.6452\n",
      "Epoch 28/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6144 - accuracy: 0.6479 - val_loss: 0.6170 - val_accuracy: 0.6454\n",
      "Epoch 29/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6145 - accuracy: 0.6480 - val_loss: 0.6175 - val_accuracy: 0.6460\n",
      "Epoch 30/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6150 - accuracy: 0.6479 - val_loss: 0.6174 - val_accuracy: 0.6455\n",
      "Epoch 31/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6146 - accuracy: 0.6481 - val_loss: 0.6172 - val_accuracy: 0.6456\n",
      "Epoch 32/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6146 - accuracy: 0.6481 - val_loss: 0.6173 - val_accuracy: 0.6458\n",
      "Epoch 33/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6147 - accuracy: 0.6482 - val_loss: 0.6169 - val_accuracy: 0.6462\n",
      "Epoch 34/50\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.6116 - accuracy: 0.6500Restoring model weights from the end of the best epoch.\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6145 - accuracy: 0.6475 - val_loss: 0.6170 - val_accuracy: 0.6459\n",
      "Epoch 00034: early stopping\n",
      "Epoch 1/50\n",
      "15/15 [==============================] - 1s 41ms/step - loss: 0.6313 - accuracy: 0.6816 - val_loss: 0.5754 - val_accuracy: 0.7988\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.5216 - accuracy: 0.8304 - val_loss: 0.4581 - val_accuracy: 0.8556\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.4038 - accuracy: 0.8669 - val_loss: 0.3449 - val_accuracy: 0.8764\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.3073 - accuracy: 0.8806 - val_loss: 0.2712 - val_accuracy: 0.8848\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.2541 - accuracy: 0.8851 - val_loss: 0.2387 - val_accuracy: 0.8873\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2332 - accuracy: 0.8876 - val_loss: 0.2276 - val_accuracy: 0.8898\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2261 - accuracy: 0.8896 - val_loss: 0.2237 - val_accuracy: 0.8917\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.2233 - accuracy: 0.8908 - val_loss: 0.2222 - val_accuracy: 0.8924\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2222 - accuracy: 0.8914 - val_loss: 0.2215 - val_accuracy: 0.8928\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2215 - accuracy: 0.8917 - val_loss: 0.2212 - val_accuracy: 0.8926\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 1s 41ms/step - loss: 0.2209 - accuracy: 0.8917 - val_loss: 0.2204 - val_accuracy: 0.8929\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2203 - accuracy: 0.8921 - val_loss: 0.2199 - val_accuracy: 0.8932\n",
      "Epoch 13/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2199 - accuracy: 0.8922 - val_loss: 0.2198 - val_accuracy: 0.8932\n",
      "Epoch 14/50\n",
      "15/15 [==============================] - 1s 40ms/step - loss: 0.2196 - accuracy: 0.8920 - val_loss: 0.2193 - val_accuracy: 0.8926\n",
      "Epoch 15/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.2192 - accuracy: 0.8923 - val_loss: 0.2189 - val_accuracy: 0.8928\n",
      "Epoch 16/50\n",
      "15/15 [==============================] - 1s 39ms/step - loss: 0.2189 - accuracy: 0.8922 - val_loss: 0.2189 - val_accuracy: 0.8923\n",
      "Epoch 17/50\n",
      "15/15 [==============================] - 1s 44ms/step - loss: 0.2186 - accuracy: 0.8923 - val_loss: 0.2181 - val_accuracy: 0.8939\n",
      "Epoch 18/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2183 - accuracy: 0.8921 - val_loss: 0.2179 - val_accuracy: 0.8936\n",
      "Epoch 19/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2180 - accuracy: 0.8925 - val_loss: 0.2175 - val_accuracy: 0.8933\n",
      "Epoch 20/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2179 - accuracy: 0.8924 - val_loss: 0.2175 - val_accuracy: 0.8936\n",
      "Epoch 21/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2178 - accuracy: 0.8923 - val_loss: 0.2176 - val_accuracy: 0.8928\n",
      "Epoch 22/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2178 - accuracy: 0.8923 - val_loss: 0.2179 - val_accuracy: 0.8926\n",
      "Epoch 23/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2176 - accuracy: 0.8925 - val_loss: 0.2173 - val_accuracy: 0.8933\n",
      "Epoch 24/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2170 - accuracy: 0.8925 - val_loss: 0.2169 - val_accuracy: 0.8932\n",
      "Epoch 25/50\n",
      "15/15 [==============================] - 1s 46ms/step - loss: 0.2168 - accuracy: 0.8925 - val_loss: 0.2168 - val_accuracy: 0.8931\n",
      "Epoch 26/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2167 - accuracy: 0.8925 - val_loss: 0.2166 - val_accuracy: 0.8933\n",
      "Epoch 27/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2167 - accuracy: 0.8925 - val_loss: 0.2169 - val_accuracy: 0.8931\n",
      "Epoch 28/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2172 - accuracy: 0.8920 - val_loss: 0.2176 - val_accuracy: 0.8920\n",
      "Epoch 29/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2174 - accuracy: 0.8922 - val_loss: 0.2167 - val_accuracy: 0.8930\n",
      "Epoch 30/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2168 - accuracy: 0.8921 - val_loss: 0.2165 - val_accuracy: 0.8933\n",
      "Epoch 31/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2167 - accuracy: 0.8924 - val_loss: 0.2165 - val_accuracy: 0.8933\n",
      "Epoch 32/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2163 - accuracy: 0.8926 - val_loss: 0.2163 - val_accuracy: 0.8934\n",
      "Epoch 33/50\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 0.2163 - accuracy: 0.8926 - val_loss: 0.2162 - val_accuracy: 0.8936\n",
      "Epoch 34/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2162 - accuracy: 0.8925 - val_loss: 0.2164 - val_accuracy: 0.8933\n",
      "Epoch 35/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2163 - accuracy: 0.8926 - val_loss: 0.2164 - val_accuracy: 0.8936\n",
      "Epoch 36/50\n",
      "15/15 [==============================] - 1s 44ms/step - loss: 0.2163 - accuracy: 0.8927 - val_loss: 0.2168 - val_accuracy: 0.8927\n",
      "Epoch 37/50\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 0.2162 - accuracy: 0.8928 - val_loss: 0.2162 - val_accuracy: 0.8933\n",
      "Epoch 38/50\n",
      "15/15 [==============================] - 1s 39ms/step - loss: 0.2161 - accuracy: 0.8928 - val_loss: 0.2166 - val_accuracy: 0.8926\n",
      "Epoch 39/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2162 - accuracy: 0.8925 - val_loss: 0.2168 - val_accuracy: 0.8925\n",
      "Epoch 40/50\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 0.2161 - accuracy: 0.8925 - val_loss: 0.2164 - val_accuracy: 0.8930\n",
      "Epoch 41/50\n",
      "15/15 [==============================] - 1s 48ms/step - loss: 0.2160 - accuracy: 0.8929 - val_loss: 0.2168 - val_accuracy: 0.8924\n",
      "Epoch 42/50\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 0.2161 - accuracy: 0.8928 - val_loss: 0.2162 - val_accuracy: 0.8928\n",
      "Epoch 43/50\n",
      "15/15 [==============================] - 1s 39ms/step - loss: 0.2160 - accuracy: 0.8928 - val_loss: 0.2163 - val_accuracy: 0.8927\n",
      "Epoch 44/50\n",
      "15/15 [==============================] - 1s 39ms/step - loss: 0.2160 - accuracy: 0.8924 - val_loss: 0.2166 - val_accuracy: 0.8929\n",
      "Epoch 45/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2160 - accuracy: 0.8925 - val_loss: 0.2163 - val_accuracy: 0.8934\n",
      "Epoch 46/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2161 - accuracy: 0.8926 - val_loss: 0.2161 - val_accuracy: 0.8929\n",
      "Epoch 47/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2159 - accuracy: 0.8925 - val_loss: 0.2160 - val_accuracy: 0.8932\n",
      "Epoch 48/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2159 - accuracy: 0.8926 - val_loss: 0.2160 - val_accuracy: 0.8932\n",
      "Epoch 49/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.2158 - accuracy: 0.8924 - val_loss: 0.2161 - val_accuracy: 0.8927\n",
      "Epoch 50/50\n",
      "15/15 [==============================] - 1s 44ms/step - loss: 0.2160 - accuracy: 0.8924 - val_loss: 0.2160 - val_accuracy: 0.8930\n",
      "Epoch 1/50\n",
      "15/15 [==============================] - 0s 7ms/step - loss: 0.6824 - accuracy: 0.5435 - val_loss: 0.6800 - val_accuracy: 0.5622\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6786 - accuracy: 0.5685 - val_loss: 0.6771 - val_accuracy: 0.5720\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6763 - accuracy: 0.5744 - val_loss: 0.6756 - val_accuracy: 0.5791\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6753 - accuracy: 0.5789 - val_loss: 0.6752 - val_accuracy: 0.5794\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6751 - accuracy: 0.5792 - val_loss: 0.6751 - val_accuracy: 0.5790\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6749 - accuracy: 0.5799 - val_loss: 0.6749 - val_accuracy: 0.5800\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6747 - accuracy: 0.5790 - val_loss: 0.6747 - val_accuracy: 0.5794\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6746 - accuracy: 0.5794 - val_loss: 0.6747 - val_accuracy: 0.5802\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6745 - accuracy: 0.5796 - val_loss: 0.6745 - val_accuracy: 0.5798\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6743 - accuracy: 0.5792 - val_loss: 0.6742 - val_accuracy: 0.5802\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6742 - accuracy: 0.5793 - val_loss: 0.6741 - val_accuracy: 0.5804\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6740 - accuracy: 0.5796 - val_loss: 0.6739 - val_accuracy: 0.5801\n",
      "Epoch 13/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6738 - accuracy: 0.5797 - val_loss: 0.6738 - val_accuracy: 0.5803\n",
      "Epoch 14/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6736 - accuracy: 0.5796 - val_loss: 0.6735 - val_accuracy: 0.5803\n",
      "Epoch 15/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6734 - accuracy: 0.5792 - val_loss: 0.6733 - val_accuracy: 0.5805\n",
      "Epoch 16/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6731 - accuracy: 0.5800 - val_loss: 0.6730 - val_accuracy: 0.5803\n",
      "Epoch 17/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6729 - accuracy: 0.5797 - val_loss: 0.6729 - val_accuracy: 0.5794\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6727 - accuracy: 0.5800 - val_loss: 0.6724 - val_accuracy: 0.5801\n",
      "Epoch 19/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6723 - accuracy: 0.5800 - val_loss: 0.6721 - val_accuracy: 0.5805\n",
      "Epoch 20/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6720 - accuracy: 0.5801 - val_loss: 0.6718 - val_accuracy: 0.5801\n",
      "Epoch 21/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6717 - accuracy: 0.5796 - val_loss: 0.6714 - val_accuracy: 0.5804\n",
      "Epoch 22/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6712 - accuracy: 0.5803 - val_loss: 0.6709 - val_accuracy: 0.5800\n",
      "Epoch 23/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6707 - accuracy: 0.5801 - val_loss: 0.6705 - val_accuracy: 0.5802\n",
      "Epoch 24/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6702 - accuracy: 0.5802 - val_loss: 0.6697 - val_accuracy: 0.5800\n",
      "Epoch 25/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6696 - accuracy: 0.5806 - val_loss: 0.6691 - val_accuracy: 0.5800\n",
      "Epoch 26/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6690 - accuracy: 0.5806 - val_loss: 0.6687 - val_accuracy: 0.5800\n",
      "Epoch 27/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6684 - accuracy: 0.5801 - val_loss: 0.6680 - val_accuracy: 0.5803\n",
      "Epoch 28/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6677 - accuracy: 0.5805 - val_loss: 0.6674 - val_accuracy: 0.5805\n",
      "Epoch 29/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6669 - accuracy: 0.5806 - val_loss: 0.6664 - val_accuracy: 0.5810\n",
      "Epoch 30/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6664 - accuracy: 0.5804 - val_loss: 0.6659 - val_accuracy: 0.5804\n",
      "Epoch 31/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6657 - accuracy: 0.5801 - val_loss: 0.6652 - val_accuracy: 0.5791\n",
      "Epoch 32/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6649 - accuracy: 0.5803 - val_loss: 0.6644 - val_accuracy: 0.5807\n",
      "Epoch 33/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6642 - accuracy: 0.5809 - val_loss: 0.6639 - val_accuracy: 0.5808\n",
      "Epoch 34/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6636 - accuracy: 0.5807 - val_loss: 0.6633 - val_accuracy: 0.5809\n",
      "Epoch 35/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6632 - accuracy: 0.5795 - val_loss: 0.6629 - val_accuracy: 0.5802\n",
      "Epoch 36/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6627 - accuracy: 0.5798 - val_loss: 0.6620 - val_accuracy: 0.5806\n",
      "Epoch 37/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6621 - accuracy: 0.5804 - val_loss: 0.6619 - val_accuracy: 0.5796\n",
      "Epoch 38/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6617 - accuracy: 0.5809 - val_loss: 0.6614 - val_accuracy: 0.5795\n",
      "Epoch 39/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6615 - accuracy: 0.5805 - val_loss: 0.6613 - val_accuracy: 0.5787\n",
      "Epoch 40/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6610 - accuracy: 0.5805 - val_loss: 0.6606 - val_accuracy: 0.5801\n",
      "Epoch 41/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6605 - accuracy: 0.5806 - val_loss: 0.6602 - val_accuracy: 0.5800\n",
      "Epoch 42/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6604 - accuracy: 0.5806 - val_loss: 0.6604 - val_accuracy: 0.5809\n",
      "Epoch 43/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6601 - accuracy: 0.5809 - val_loss: 0.6601 - val_accuracy: 0.5789\n",
      "Epoch 44/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6597 - accuracy: 0.5801 - val_loss: 0.6594 - val_accuracy: 0.5798\n",
      "Epoch 45/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6594 - accuracy: 0.5802 - val_loss: 0.6590 - val_accuracy: 0.5808\n",
      "Epoch 46/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6591 - accuracy: 0.5804 - val_loss: 0.6587 - val_accuracy: 0.5806\n",
      "Epoch 47/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6589 - accuracy: 0.5807 - val_loss: 0.6587 - val_accuracy: 0.5812\n",
      "Epoch 48/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6588 - accuracy: 0.5805 - val_loss: 0.6586 - val_accuracy: 0.5802\n",
      "Epoch 49/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6588 - accuracy: 0.5807 - val_loss: 0.6583 - val_accuracy: 0.5803\n",
      "Epoch 50/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6584 - accuracy: 0.5807 - val_loss: 0.6581 - val_accuracy: 0.5808\n"
     ]
    }
   ],
   "source": [
    "wCondtional_2funcs = conditional_reweight_2funcs(T0, T1, T2, R0, R1, R2, model, model2)\n",
    "wCondtional_1func = conditional_reweight_1func(T0, T1, T2, R0, R1, R2, modelO)\n",
    "wMarginal = marginal_reweight(T0, T2, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tt_copy2 = Tt.copy()\n",
    "T0_copy2 = T0.copy()\n",
    "T1_copy2 = T1.copy()\n",
    "T2_copy2 = T2.copy()\n",
    "wT0_copy2 = wMarginal.copy()\n",
    "\n",
    "Rt_copy2 = Rt.copy()\n",
    "R0_copy2 = R0.copy()\n",
    "R1_copy2 = R1.copy()\n",
    "R2_copy2 = R2.copy()\n",
    "wRT_copy2 = wCondtional_2funcs.copy()\n",
    "wRT2_copy2 = wCondtional_1func.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeplots(Tt_copy2,T0_copy2,T1_copy2,T2_copy2,wT0_copy2,wRT_copy2,wRT2_copy2,xlabel=r\"$T$\",header=\"Gaussian Example\",plotname=\"Gaussian_T_withhole\")\n",
    "makeplots(Rt_copy2,R0_copy2,R1_copy2,R2_copy2,wT0_copy2,wRT_copy2,wRT2_copy2,xlabel=r\"$R$\",header=\"Gaussian Example\",plotname=\"Gaussian_R_withhole\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 100000\n",
    "\n",
    "mu0 = 0\n",
    "mu1 = 2 #scan this\n",
    "sigma0 = 0.5\n",
    "sigma1 = 0.5\n",
    "\n",
    "b0 = 0\n",
    "b1 = 0\n",
    "eps0 = 0.3 #scan this\n",
    "eps1 = 0.4\n",
    "\n",
    "T0 = np.random.normal(mu0,sigma0,n)\n",
    "T1 = np.random.normal(mu0,sigma0,n)\n",
    "\n",
    "T2 = np.random.normal(mu1,sigma1,n)\n",
    "Tt = np.random.normal(mu1,sigma1,n)\n",
    "\n",
    "R0 = np.random.normal(T0+b0,eps0)\n",
    "R1 = np.random.normal(T1+b1,eps1)\n",
    "R2 = np.random.normal(T2+b1,eps1)\n",
    "Rt = np.random.normal(Tt+b0,eps0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "15/15 [==============================] - 0s 12ms/step - loss: 0.6932 - accuracy: 0.4997 - val_loss: 0.6931 - val_accuracy: 0.5044\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.4997 - val_loss: 0.6932 - val_accuracy: 0.4985\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6932 - accuracy: 0.4982 - val_loss: 0.6932 - val_accuracy: 0.5000\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5010 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5011 - val_loss: 0.6932 - val_accuracy: 0.4985\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5009 - val_loss: 0.6932 - val_accuracy: 0.4966\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5002 - val_loss: 0.6932 - val_accuracy: 0.4971\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5010 - val_loss: 0.6932 - val_accuracy: 0.4949\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5001 - val_loss: 0.6932 - val_accuracy: 0.4984\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.4991 - val_loss: 0.6932 - val_accuracy: 0.4939\n",
      "Epoch 11/50\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.6931 - accuracy: 0.5139Restoring model weights from the end of the best epoch.\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6932 - accuracy: 0.5006 - val_loss: 0.6932 - val_accuracy: 0.5012\n",
      "Epoch 00011: early stopping\n",
      "Epoch 1/50\n",
      "15/15 [==============================] - 0s 7ms/step - loss: 0.6901 - accuracy: 0.5366 - val_loss: 0.6874 - val_accuracy: 0.5505\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6841 - accuracy: 0.5604 - val_loss: 0.6812 - val_accuracy: 0.5636\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6778 - accuracy: 0.5667 - val_loss: 0.6768 - val_accuracy: 0.5675\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6747 - accuracy: 0.5697 - val_loss: 0.6760 - val_accuracy: 0.5675\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6739 - accuracy: 0.5700 - val_loss: 0.6753 - val_accuracy: 0.5684\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6735 - accuracy: 0.5709 - val_loss: 0.6750 - val_accuracy: 0.5674\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6733 - accuracy: 0.5705 - val_loss: 0.6749 - val_accuracy: 0.5687\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6732 - accuracy: 0.5710 - val_loss: 0.6751 - val_accuracy: 0.5678\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6731 - accuracy: 0.5709 - val_loss: 0.6747 - val_accuracy: 0.5685\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6731 - accuracy: 0.5705 - val_loss: 0.6748 - val_accuracy: 0.5688\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6730 - accuracy: 0.5708 - val_loss: 0.6747 - val_accuracy: 0.5692\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6730 - accuracy: 0.5709 - val_loss: 0.6746 - val_accuracy: 0.5694\n",
      "Epoch 13/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6730 - accuracy: 0.5712 - val_loss: 0.6746 - val_accuracy: 0.5689\n",
      "Epoch 14/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6730 - accuracy: 0.5713 - val_loss: 0.6746 - val_accuracy: 0.5695\n",
      "Epoch 15/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6730 - accuracy: 0.5711 - val_loss: 0.6746 - val_accuracy: 0.5690\n",
      "Epoch 16/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6729 - accuracy: 0.5715 - val_loss: 0.6749 - val_accuracy: 0.5692\n",
      "Epoch 17/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6729 - accuracy: 0.5713 - val_loss: 0.6745 - val_accuracy: 0.5692\n",
      "Epoch 18/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6729 - accuracy: 0.5711 - val_loss: 0.6745 - val_accuracy: 0.5689\n",
      "Epoch 19/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6729 - accuracy: 0.5710 - val_loss: 0.6745 - val_accuracy: 0.5694\n",
      "Epoch 20/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6729 - accuracy: 0.5711 - val_loss: 0.6746 - val_accuracy: 0.5693\n",
      "Epoch 21/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6730 - accuracy: 0.5714 - val_loss: 0.6749 - val_accuracy: 0.5677\n",
      "Epoch 22/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6730 - accuracy: 0.5717 - val_loss: 0.6745 - val_accuracy: 0.5694\n",
      "Epoch 23/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.6728 - accuracy: 0.5711 - val_loss: 0.6745 - val_accuracy: 0.5694\n",
      "Epoch 24/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6728 - accuracy: 0.5714 - val_loss: 0.6746 - val_accuracy: 0.5683\n",
      "Epoch 25/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6728 - accuracy: 0.5711 - val_loss: 0.6747 - val_accuracy: 0.5701\n",
      "Epoch 26/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6729 - accuracy: 0.5712 - val_loss: 0.6749 - val_accuracy: 0.5683\n",
      "Epoch 27/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6728 - accuracy: 0.5712 - val_loss: 0.6745 - val_accuracy: 0.5687\n",
      "Epoch 28/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6728 - accuracy: 0.5715 - val_loss: 0.6745 - val_accuracy: 0.5692\n",
      "Epoch 29/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6729 - accuracy: 0.5711 - val_loss: 0.6744 - val_accuracy: 0.5691\n",
      "Epoch 30/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6728 - accuracy: 0.5715 - val_loss: 0.6745 - val_accuracy: 0.5699\n",
      "Epoch 31/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6728 - accuracy: 0.5714 - val_loss: 0.6745 - val_accuracy: 0.5690\n",
      "Epoch 32/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6728 - accuracy: 0.5715 - val_loss: 0.6745 - val_accuracy: 0.5690\n",
      "Epoch 33/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6728 - accuracy: 0.5712 - val_loss: 0.6746 - val_accuracy: 0.5695\n",
      "Epoch 34/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6728 - accuracy: 0.5710 - val_loss: 0.6745 - val_accuracy: 0.5685\n",
      "Epoch 35/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6728 - accuracy: 0.5707 - val_loss: 0.6745 - val_accuracy: 0.5687\n",
      "Epoch 36/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6728 - accuracy: 0.5717 - val_loss: 0.6745 - val_accuracy: 0.5696\n",
      "Epoch 37/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6728 - accuracy: 0.5709 - val_loss: 0.6745 - val_accuracy: 0.5689\n",
      "Epoch 38/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6728 - accuracy: 0.5719 - val_loss: 0.6745 - val_accuracy: 0.5690\n",
      "Epoch 39/50\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.6689 - accuracy: 0.5794Restoring model weights from the end of the best epoch.\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.6728 - accuracy: 0.5711 - val_loss: 0.6745 - val_accuracy: 0.5682\n",
      "Epoch 00039: early stopping\n",
      "Epoch 1/50\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 0.6714 - accuracy: 0.6423 - val_loss: 0.6467 - val_accuracy: 0.7204\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.6213 - accuracy: 0.7290 - val_loss: 0.5884 - val_accuracy: 0.7309\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.5559 - accuracy: 0.7368 - val_loss: 0.5204 - val_accuracy: 0.7374\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.4950 - accuracy: 0.7467 - val_loss: 0.4725 - val_accuracy: 0.7509\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.4620 - accuracy: 0.7577 - val_loss: 0.4572 - val_accuracy: 0.7575\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.4550 - accuracy: 0.7598 - val_loss: 0.4562 - val_accuracy: 0.7558\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.4535 - accuracy: 0.7597 - val_loss: 0.4535 - val_accuracy: 0.7584\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 1s 44ms/step - loss: 0.4522 - accuracy: 0.7602 - val_loss: 0.4527 - val_accuracy: 0.7585\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.4513 - accuracy: 0.7598 - val_loss: 0.4521 - val_accuracy: 0.7571\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 1s 40ms/step - loss: 0.4507 - accuracy: 0.7601 - val_loss: 0.4516 - val_accuracy: 0.7561\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.4498 - accuracy: 0.7604 - val_loss: 0.4505 - val_accuracy: 0.7582\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 1s 41ms/step - loss: 0.4493 - accuracy: 0.7601 - val_loss: 0.4499 - val_accuracy: 0.7585\n",
      "Epoch 13/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.4490 - accuracy: 0.7602 - val_loss: 0.4494 - val_accuracy: 0.7605\n",
      "Epoch 14/50\n",
      "15/15 [==============================] - 1s 39ms/step - loss: 0.4485 - accuracy: 0.7607 - val_loss: 0.4488 - val_accuracy: 0.7595\n",
      "Epoch 15/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4480 - accuracy: 0.7611 - val_loss: 0.4484 - val_accuracy: 0.7600\n",
      "Epoch 16/50\n",
      "15/15 [==============================] - 1s 39ms/step - loss: 0.4477 - accuracy: 0.7607 - val_loss: 0.4484 - val_accuracy: 0.7595\n",
      "Epoch 17/50\n",
      "15/15 [==============================] - 1s 40ms/step - loss: 0.4479 - accuracy: 0.7605 - val_loss: 0.4481 - val_accuracy: 0.7600\n",
      "Epoch 18/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.4474 - accuracy: 0.7610 - val_loss: 0.4480 - val_accuracy: 0.7599\n",
      "Epoch 19/50\n",
      "15/15 [==============================] - 1s 39ms/step - loss: 0.4472 - accuracy: 0.7605 - val_loss: 0.4476 - val_accuracy: 0.7603\n",
      "Epoch 20/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.4471 - accuracy: 0.7609 - val_loss: 0.4475 - val_accuracy: 0.7607\n",
      "Epoch 21/50\n",
      "15/15 [==============================] - 1s 39ms/step - loss: 0.4472 - accuracy: 0.7600 - val_loss: 0.4481 - val_accuracy: 0.7584\n",
      "Epoch 22/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.4469 - accuracy: 0.7604 - val_loss: 0.4476 - val_accuracy: 0.7596\n",
      "Epoch 23/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.4467 - accuracy: 0.7616 - val_loss: 0.4472 - val_accuracy: 0.7605\n",
      "Epoch 24/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.4469 - accuracy: 0.7607 - val_loss: 0.4482 - val_accuracy: 0.7581\n",
      "Epoch 25/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4467 - accuracy: 0.7607 - val_loss: 0.4471 - val_accuracy: 0.7604\n",
      "Epoch 26/50\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 0.4463 - accuracy: 0.7615 - val_loss: 0.4471 - val_accuracy: 0.7599\n",
      "Epoch 27/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4463 - accuracy: 0.7610 - val_loss: 0.4470 - val_accuracy: 0.7602\n",
      "Epoch 28/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4463 - accuracy: 0.7609 - val_loss: 0.4470 - val_accuracy: 0.7599\n",
      "Epoch 29/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4463 - accuracy: 0.7608 - val_loss: 0.4477 - val_accuracy: 0.7586\n",
      "Epoch 30/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4467 - accuracy: 0.7609 - val_loss: 0.4471 - val_accuracy: 0.7604\n",
      "Epoch 31/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.4465 - accuracy: 0.7605 - val_loss: 0.4466 - val_accuracy: 0.7606\n",
      "Epoch 32/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4462 - accuracy: 0.7609 - val_loss: 0.4468 - val_accuracy: 0.7600\n",
      "Epoch 33/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4462 - accuracy: 0.7606 - val_loss: 0.4467 - val_accuracy: 0.7603\n",
      "Epoch 34/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.4460 - accuracy: 0.7614 - val_loss: 0.4471 - val_accuracy: 0.7598\n",
      "Epoch 35/50\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 0.4461 - accuracy: 0.7611 - val_loss: 0.4465 - val_accuracy: 0.7607\n",
      "Epoch 36/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.4460 - accuracy: 0.7607 - val_loss: 0.4465 - val_accuracy: 0.7602\n",
      "Epoch 37/50\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.4459 - accuracy: 0.7610 - val_loss: 0.4466 - val_accuracy: 0.7602\n",
      "Epoch 38/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4460 - accuracy: 0.7609 - val_loss: 0.4474 - val_accuracy: 0.7591\n",
      "Epoch 39/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4464 - accuracy: 0.7608 - val_loss: 0.4473 - val_accuracy: 0.7596\n",
      "Epoch 40/50\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 0.4461 - accuracy: 0.7608 - val_loss: 0.4472 - val_accuracy: 0.7597\n",
      "Epoch 41/50\n",
      "15/15 [==============================] - 1s 40ms/step - loss: 0.4460 - accuracy: 0.7606 - val_loss: 0.4472 - val_accuracy: 0.7599\n",
      "Epoch 42/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.4461 - accuracy: 0.7609 - val_loss: 0.4463 - val_accuracy: 0.7606\n",
      "Epoch 43/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4460 - accuracy: 0.7610 - val_loss: 0.4472 - val_accuracy: 0.7607\n",
      "Epoch 44/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4460 - accuracy: 0.7608 - val_loss: 0.4465 - val_accuracy: 0.7607\n",
      "Epoch 45/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4457 - accuracy: 0.7608 - val_loss: 0.4468 - val_accuracy: 0.7600\n",
      "Epoch 46/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4458 - accuracy: 0.7607 - val_loss: 0.4470 - val_accuracy: 0.7595\n",
      "Epoch 47/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.4458 - accuracy: 0.7612 - val_loss: 0.4468 - val_accuracy: 0.7601\n",
      "Epoch 48/50\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 0.4459 - accuracy: 0.7608 - val_loss: 0.4464 - val_accuracy: 0.7605\n",
      "Epoch 49/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4457 - accuracy: 0.7611 - val_loss: 0.4474 - val_accuracy: 0.7589\n",
      "Epoch 50/50\n",
      "15/15 [==============================] - 1s 37ms/step - loss: 0.4459 - accuracy: 0.7605 - val_loss: 0.4464 - val_accuracy: 0.7605\n",
      "Epoch 1/50\n",
      "15/15 [==============================] - 0s 7ms/step - loss: 0.5869 - accuracy: 0.7684 - val_loss: 0.5124 - val_accuracy: 0.7814\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.4561 - accuracy: 0.8079 - val_loss: 0.3954 - val_accuracy: 0.8442\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.3440 - accuracy: 0.8772 - val_loss: 0.2882 - val_accuracy: 0.9104\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.2400 - accuracy: 0.9357 - val_loss: 0.1896 - val_accuracy: 0.9560\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.1536 - accuracy: 0.9663 - val_loss: 0.1192 - val_accuracy: 0.9719\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.1002 - accuracy: 0.9745 - val_loss: 0.0833 - val_accuracy: 0.9761\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0758 - accuracy: 0.9762 - val_loss: 0.0685 - val_accuracy: 0.9771\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0663 - accuracy: 0.9765 - val_loss: 0.0634 - val_accuracy: 0.9769\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0633 - accuracy: 0.9765 - val_loss: 0.0617 - val_accuracy: 0.9770\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0623 - accuracy: 0.9766 - val_loss: 0.0611 - val_accuracy: 0.9771\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0619 - accuracy: 0.9767 - val_loss: 0.0610 - val_accuracy: 0.9771\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0618 - accuracy: 0.9767 - val_loss: 0.0607 - val_accuracy: 0.9771\n",
      "Epoch 13/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0617 - accuracy: 0.9767 - val_loss: 0.0606 - val_accuracy: 0.9770\n",
      "Epoch 14/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0616 - accuracy: 0.9767 - val_loss: 0.0605 - val_accuracy: 0.9770\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0616 - accuracy: 0.9767 - val_loss: 0.0605 - val_accuracy: 0.9770\n",
      "Epoch 16/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0615 - accuracy: 0.9767 - val_loss: 0.0606 - val_accuracy: 0.9769\n",
      "Epoch 17/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0615 - accuracy: 0.9768 - val_loss: 0.0606 - val_accuracy: 0.9771\n",
      "Epoch 18/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0615 - accuracy: 0.9766 - val_loss: 0.0605 - val_accuracy: 0.9769\n",
      "Epoch 19/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0616 - accuracy: 0.9766 - val_loss: 0.0604 - val_accuracy: 0.9769\n",
      "Epoch 20/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9767 - val_loss: 0.0604 - val_accuracy: 0.9771\n",
      "Epoch 21/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0615 - accuracy: 0.9766 - val_loss: 0.0605 - val_accuracy: 0.9771\n",
      "Epoch 22/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0615 - accuracy: 0.9766 - val_loss: 0.0604 - val_accuracy: 0.9769\n",
      "Epoch 23/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0615 - accuracy: 0.9765 - val_loss: 0.0604 - val_accuracy: 0.9770\n",
      "Epoch 24/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9768 - val_loss: 0.0604 - val_accuracy: 0.9769\n",
      "Epoch 25/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9765 - val_loss: 0.0604 - val_accuracy: 0.9771\n",
      "Epoch 26/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9766 - val_loss: 0.0603 - val_accuracy: 0.9769\n",
      "Epoch 27/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9767 - val_loss: 0.0603 - val_accuracy: 0.9771\n",
      "Epoch 28/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0615 - accuracy: 0.9767 - val_loss: 0.0604 - val_accuracy: 0.9771\n",
      "Epoch 29/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0615 - accuracy: 0.9765 - val_loss: 0.0604 - val_accuracy: 0.9769\n",
      "Epoch 30/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0616 - accuracy: 0.9765 - val_loss: 0.0604 - val_accuracy: 0.9769\n",
      "Epoch 31/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0617 - accuracy: 0.9766 - val_loss: 0.0603 - val_accuracy: 0.9770\n",
      "Epoch 32/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0615 - accuracy: 0.9766 - val_loss: 0.0603 - val_accuracy: 0.9770\n",
      "Epoch 33/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.0614 - accuracy: 0.9766 - val_loss: 0.0603 - val_accuracy: 0.9771\n",
      "Epoch 34/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0614 - accuracy: 0.9767 - val_loss: 0.0603 - val_accuracy: 0.9771\n",
      "Epoch 35/50\n",
      "15/15 [==============================] - 0s 5ms/step - loss: 0.0614 - accuracy: 0.9766 - val_loss: 0.0603 - val_accuracy: 0.9770\n",
      "Epoch 36/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9765 - val_loss: 0.0603 - val_accuracy: 0.9769\n",
      "Epoch 37/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0615 - accuracy: 0.9764 - val_loss: 0.0603 - val_accuracy: 0.9769\n",
      "Epoch 38/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9765 - val_loss: 0.0604 - val_accuracy: 0.9771\n",
      "Epoch 39/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.0614 - accuracy: 0.9766 - val_loss: 0.0602 - val_accuracy: 0.9770\n",
      "Epoch 40/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0615 - accuracy: 0.9766 - val_loss: 0.0602 - val_accuracy: 0.9769\n",
      "Epoch 41/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9766 - val_loss: 0.0603 - val_accuracy: 0.9770\n",
      "Epoch 42/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9765 - val_loss: 0.0605 - val_accuracy: 0.9772\n",
      "Epoch 43/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0615 - accuracy: 0.9766 - val_loss: 0.0603 - val_accuracy: 0.9770\n",
      "Epoch 44/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9766 - val_loss: 0.0603 - val_accuracy: 0.9769\n",
      "Epoch 45/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0615 - accuracy: 0.9764 - val_loss: 0.0604 - val_accuracy: 0.9770\n",
      "Epoch 46/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9766 - val_loss: 0.0603 - val_accuracy: 0.9771\n",
      "Epoch 47/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9767 - val_loss: 0.0603 - val_accuracy: 0.9770\n",
      "Epoch 48/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9767 - val_loss: 0.0603 - val_accuracy: 0.9770\n",
      "Epoch 49/50\n",
      " 1/15 [=>............................] - ETA: 0s - loss: 0.0631 - accuracy: 0.9761Restoring model weights from the end of the best epoch.\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9767 - val_loss: 0.0603 - val_accuracy: 0.9770\n",
      "Epoch 00049: early stopping\n"
     ]
    }
   ],
   "source": [
    "wCondtional_2funcs = conditional_reweight_2funcs(T0, T1, T2, R0, R1, R2, model, model2)\n",
    "wCondtional_1func = conditional_reweight_1func(T0, T1, T2, R0, R1, R2, modelO)\n",
    "wMarginal = marginal_reweight(T0, T2, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tt_copy3 = Tt.copy()\n",
    "T0_copy3 = T0.copy()\n",
    "T1_copy3 = T1.copy()\n",
    "T2_copy3 = T2.copy()\n",
    "wT0_copy3 = wMarginal.copy()\n",
    "\n",
    "Rt_copy3 = Rt.copy()\n",
    "R0_copy3 = R0.copy()\n",
    "R1_copy3 = R1.copy()\n",
    "R2_copy3 = R2.copy()\n",
    "wRT_copy3 = wCondtional_2funcs.copy()\n",
    "wRT2_copy3 = wCondtional_1func.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeplots(Tt_copy3,T0_copy3,T1_copy3,T2_copy3,wT0_copy3,wRT_copy3,wRT2_copy3,xlabel=r\"$T$\",header=\"Gaussian Example\",plotname=\"Gaussian_T_onedge\")\n",
    "makeplots(Rt_copy3,R0_copy3,R1_copy3,R2_copy3,wT0_copy3,wRT_copy3,wRT2_copy3,xlabel=r\"$R$\",header=\"Gaussian Example\",plotname=\"Gaussian_R_onedge\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now try a physics example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/home/users/bpnachman/anaconda3/envs/tf2/lib/python3.8/site-packages/energyflow/emd.py:915: UserWarning: emd module requires 'wasserstein' or 'pot', both of which are unavailable\n",
      "  warnings.warn(message)\n"
     ]
    }
   ],
   "source": [
    "import energyflow as ef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = ef.mod.load(dataset='sim',subdatasets=[\"SIM1000_Jet300_pT375-infGeV\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen = ef.mod.load(dataset='gen',subdatasets=[\"GEN1000_pT375-infGeV\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim14 = ef.mod.load(dataset='sim',subdatasets=[\"SIM1400_Jet300_pT375-infGeV\"])\n",
    "sim18 = ef.mod.load(dataset='sim',subdatasets=[\"SIM1800_Jet300_pT375-infGeV\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen14 = ef.mod.load(dataset='gen',subdatasets=[\"GEN1400_pT375-infGeV\"])\n",
    "gen18 = ef.mod.load(dataset='gen',subdatasets=[\"GEN1800_pT375-infGeV\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "simjets_f = sim.jets_f[0:20000]\n",
    "sim14jets_f = sim14.jets_f[0:20000]\n",
    "sim18jets_f = sim18.jets_f[0:20000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "myfile = open(\"output.text\")\n",
    "delphes = []\n",
    "for line in myfile:\n",
    "    delphes+=[[float(line.split()[0]),float(line.split()[1]),\n",
    "               float(line.split()[2]),float(line.split()[3]),\n",
    "               float(line.split()[4]),float(line.split()[5]),\n",
    "               float(line.split()[6]),float(line.split()[7]),\n",
    "               float(line.split()[8]),float(line.split()[9])]]\n",
    "delphes = np.array(delphes)\n",
    "\n",
    "myfile = open(\"output14.text\")\n",
    "delphes14 = []\n",
    "for line in myfile:\n",
    "    delphes14+=[[float(line.split()[0]),float(line.split()[1]),\n",
    "               float(line.split()[2]),float(line.split()[3]),\n",
    "               float(line.split()[4]),float(line.split()[5]),\n",
    "               float(line.split()[6]),float(line.split()[7]),\n",
    "               float(line.split()[8]),float(line.split()[9])]]\n",
    "delphes14 = np.array(delphes14)\n",
    "\n",
    "myfile = open(\"output18.text\")\n",
    "delphes18 = []\n",
    "for line in myfile:\n",
    "    delphes18+=[[float(line.split()[0]),float(line.split()[1]),\n",
    "               float(line.split()[2]),float(line.split()[3]),\n",
    "               float(line.split()[4]),float(line.split()[5]),\n",
    "               float(line.split()[6]),float(line.split()[7]),\n",
    "               float(line.split()[8]),float(line.split()[9])]]\n",
    "delphes18 = np.array(delphes18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "gs = gridspec.GridSpec(1, 1) \n",
    "ax0 = plt.subplot(gs[0])\n",
    "ax0.yaxis.set_ticks_position('both')\n",
    "ax0.xaxis.set_ticks_position('both')\n",
    "ax0.tick_params(direction=\"in\",which=\"both\")\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "ax0.minorticks_on()\n",
    "\n",
    "_,_,_=plt.hist(delphes[:,6],density=True,alpha=0.5,bins=np.linspace(0,2500,50),label=\"Pythia 8 (P8)\")\n",
    "_,_,_=plt.hist(delphes14[:,6],density=True,alpha=0.5,bins=np.linspace(0,2500,50),color=\"tab:blue\")\n",
    "_,_,_=plt.hist(delphes18[:,6],density=True,alpha=0.5,bins=np.linspace(0,2500,50),color=\"tab:blue\")\n",
    "_,_,_=plt.hist(simjets_f[:,8],density=True,bins=np.linspace(0,2500,50),histtype=\"step\",color=\"black\",lw=2,label=\"Pythia 6 (P6)\")\n",
    "_,_,_=plt.hist(sim14jets_f[:,8],density=True,bins=np.linspace(0,2500,50),histtype=\"step\",color=\"black\",lw=2)\n",
    "_,_,_=plt.hist(sim18jets_f[:,8],density=True,bins=np.linspace(0,2500,50),histtype=\"step\",color=\"black\",lw=2)\n",
    "\n",
    "_,_,_=plt.hist(delphes[:,1],density=True,alpha=0.5,bins=np.linspace(0,2500,50),label=\"P8 + Delphes\")\n",
    "_,_,_=plt.hist(delphes14[:,1],density=True,alpha=0.5,bins=np.linspace(0,2500,50),color=\"tab:orange\")\n",
    "_,_,_=plt.hist(delphes18[:,1],density=True,alpha=0.5,bins=np.linspace(0,2500,50),color=\"tab:orange\")\n",
    "_,_,_=plt.hist(simjets_f[:,0],density=True,bins=np.linspace(0,2500,50),histtype=\"step\",color=\"black\",ls=\":\",lw=2,label=\"P6 + Geant4\")\n",
    "_,_,_=plt.hist(sim14jets_f[:,0],density=True,bins=np.linspace(0,2500,50),histtype=\"step\",color=\"black\",ls=\":\",lw=2)\n",
    "_,_,_=plt.hist(sim18jets_f[:,0],density=True,bins=np.linspace(0,2500,50),histtype=\"step\",color=\"black\",ls=\":\",lw=2)\n",
    "\n",
    "plt.ylim([0,0.008])\n",
    "#plt.yscale(\"log\")\n",
    "plt.xlim([0,2500])\n",
    "plt.xlabel(\"Truth $p_T$ [GeV]\")\n",
    "plt.ylabel(\"Normalized Counts\")\n",
    "plt.legend(frameon=False,fontsize=20,loc=\"upper left\")\n",
    "_=plt.title(r\"QCD dijets\",loc=\"right\",fontsize=20, fontstyle=\"italic\")\n",
    "plt.savefig(\"conditional/Dijets_pT_more.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-5f93906c2c09>:18: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  plt.hist(Delphes_R_tot/Delphes_T_tot,alpha=0.5,bins=np.linspace(0,1.5,50),density=True,label=\"P8 + Delphes\",color=\"tab:orange\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "gs = gridspec.GridSpec(1, 1) \n",
    "ax0 = plt.subplot(gs[0])\n",
    "ax0.yaxis.set_ticks_position('both')\n",
    "ax0.xaxis.set_ticks_position('both')\n",
    "ax0.tick_params(direction=\"in\",which=\"both\")\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "ax0.minorticks_on()\n",
    "\n",
    "G4_T_tot = np.concatenate([simjets_f[:,8],sim18jets_f[:,8]])\n",
    "Delphes_T_tot = np.concatenate([delphes[:,6],delphes14[:,6],delphes18[:,6]])\n",
    "\n",
    "G4_R_tot = np.concatenate([simjets_f[:,0],sim18jets_f[:,0]])\n",
    "Delphes_R_tot = np.concatenate([delphes[:,1],delphes14[:,1],delphes18[:,1]])\n",
    "\n",
    "plt.hist(G4_R_tot/G4_T_tot,bins=np.linspace(0,1.5,50),density=True,label=\"P6 + Geant4\",color=\"tab:blue\",alpha=0.5)\n",
    "plt.hist(Delphes_R_tot/Delphes_T_tot,alpha=0.5,bins=np.linspace(0,1.5,50),density=True,label=\"P8 + Delphes\",color=\"tab:orange\")\n",
    "plt.xlabel(\"$p_T^{reco}/p_T^{true}$\")\n",
    "plt.ylabel(\"Normalized to Unity\")\n",
    "plt.yscale(\"log\")\n",
    "plt.legend(frameon=False,loc=\"upper left\")\n",
    "plt.ylim([1e-3,200])\n",
    "_=plt.title(r\"QCD dijets\",loc=\"right\",fontsize=20, fontstyle=\"italic\")\n",
    "plt.savefig(\"conditional/Dijets_response_pT.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputsP = Input((1, ))\n",
    "hiddenP_layer_1 = Dense(50, activation='relu')(inputsP)\n",
    "hiddenP_layer_2 = Dense(50, activation='relu')(hiddenP_layer_1)\n",
    "hiddenP_layer_3 = Dense(50, activation='relu')(hiddenP_layer_2)\n",
    "outputsP = Dense(1, activation='sigmoid')(hiddenP_layer_3)\n",
    "modelP = Model(inputs=inputsP, outputs=outputsP)\n",
    "\n",
    "inputsP2 = Input((2, ))\n",
    "hiddenP_layer_12 = Dense(50, activation='relu')(inputsP2)\n",
    "hiddenP_layer_22 = Dense(50, activation='relu')(hiddenP_layer_12)\n",
    "hiddenP_layer_32 = Dense(50, activation='relu')(hiddenP_layer_22)\n",
    "outputsP2 = Dense(1, activation='sigmoid')(hiddenP_layer_32)\n",
    "modelP2 = Model(inputs=inputsP2, outputs=outputsP2)\n",
    "\n",
    "inputsM = Input((1, ))\n",
    "hiddenM_layer_1 = Dense(50, activation='relu')(inputsM)\n",
    "hiddenM_layer_2 = Dense(50, activation='relu')(hiddenM_layer_1)\n",
    "hiddenM_layer_3 = Dense(50, activation='relu')(hiddenM_layer_2)\n",
    "outputsM = Dense(1, activation='sigmoid')(hiddenM_layer_3)\n",
    "modelM = Model(inputs=inputsM, outputs=outputsM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn = 10000\n",
    "\n",
    "T_withall = np.concatenate([simjets_f[:,8],sim14jets_f[:,8],sim18jets_f[:,8]])\n",
    "R_withall = np.concatenate([simjets_f[:,0],sim14jets_f[:,0],sim18jets_f[:,0]])\n",
    "T0 = np.concatenate([simjets_f[0:nn][:,8],sim18jets_f[0:nn][:,8]])\n",
    "T1 = np.concatenate([delphes[0:nn][:,6],delphes18[0:nn][:,6]])\n",
    "T2 = np.concatenate([delphes[0:nn][:,6],delphes14[0:nn][:,6],delphes18[0:nn][:,6]])\n",
    "T2_all = np.concatenate([delphes[:,6],delphes14[:,6],delphes18[:,6]])\n",
    "R0 = np.concatenate([simjets_f[0:nn][:,0],sim18jets_f[0:nn][:,0]])\n",
    "R1 = np.concatenate([delphes[0:nn][:,1],delphes18[0:nn][:,1]])\n",
    "R2 = np.concatenate([delphes[0:nn][:,1],delphes14[0:nn][:,1],delphes18[0:nn][:,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "68/68 [==============================] - 0s 4ms/step - loss: 0.6446 - accuracy: 0.6573 - val_loss: 0.6375 - val_accuracy: 0.6668\n",
      "Epoch 2/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6369 - accuracy: 0.6666 - val_loss: 0.6372 - val_accuracy: 0.6668\n",
      "Epoch 3/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6368 - accuracy: 0.6666 - val_loss: 0.6370 - val_accuracy: 0.6670\n",
      "Epoch 4/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6366 - accuracy: 0.6670 - val_loss: 0.6368 - val_accuracy: 0.6671\n",
      "Epoch 5/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6365 - accuracy: 0.6671 - val_loss: 0.6370 - val_accuracy: 0.6671\n",
      "Epoch 6/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6364 - accuracy: 0.6672 - val_loss: 0.6365 - val_accuracy: 0.6671\n",
      "Epoch 7/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6362 - accuracy: 0.6671 - val_loss: 0.6363 - val_accuracy: 0.6674\n",
      "Epoch 8/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6361 - accuracy: 0.6672 - val_loss: 0.6361 - val_accuracy: 0.6672\n",
      "Epoch 9/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6362 - accuracy: 0.6672 - val_loss: 0.6362 - val_accuracy: 0.6672\n",
      "Epoch 10/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6360 - accuracy: 0.6672 - val_loss: 0.6360 - val_accuracy: 0.6675\n",
      "Epoch 11/50\n",
      "68/68 [==============================] - 0s 2ms/step - loss: 0.6359 - accuracy: 0.6672 - val_loss: 0.6360 - val_accuracy: 0.6673\n",
      "Epoch 12/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6359 - accuracy: 0.6672 - val_loss: 0.6361 - val_accuracy: 0.6673\n",
      "Epoch 13/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6359 - accuracy: 0.6672 - val_loss: 0.6359 - val_accuracy: 0.6674\n",
      "Epoch 14/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6358 - accuracy: 0.6672 - val_loss: 0.6360 - val_accuracy: 0.6674\n",
      "Epoch 15/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6360 - accuracy: 0.6672 - val_loss: 0.6358 - val_accuracy: 0.6675\n",
      "Epoch 16/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6359 - accuracy: 0.6672 - val_loss: 0.6360 - val_accuracy: 0.6673\n",
      "Epoch 17/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6359 - accuracy: 0.6672 - val_loss: 0.6358 - val_accuracy: 0.6675\n",
      "Epoch 18/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6360 - accuracy: 0.6672 - val_loss: 0.6357 - val_accuracy: 0.6674\n",
      "Epoch 19/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6358 - accuracy: 0.6671 - val_loss: 0.6357 - val_accuracy: 0.6674\n",
      "Epoch 20/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6357 - val_accuracy: 0.6674\n",
      "Epoch 21/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6358 - accuracy: 0.6672 - val_loss: 0.6359 - val_accuracy: 0.6673\n",
      "Epoch 22/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6357 - val_accuracy: 0.6674\n",
      "Epoch 23/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6358 - accuracy: 0.6672 - val_loss: 0.6357 - val_accuracy: 0.6674\n",
      "Epoch 24/50\n",
      "68/68 [==============================] - 0s 4ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6358 - val_accuracy: 0.6674\n",
      "Epoch 25/50\n",
      "68/68 [==============================] - 0s 4ms/step - loss: 0.6356 - accuracy: 0.6672 - val_loss: 0.6357 - val_accuracy: 0.6673\n",
      "Epoch 26/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6358 - val_accuracy: 0.6674\n",
      "Epoch 27/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6357 - val_accuracy: 0.6674\n",
      "Epoch 28/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6358 - val_accuracy: 0.6673\n",
      "Epoch 29/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6358 - val_accuracy: 0.6674\n",
      "Epoch 30/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6359 - val_accuracy: 0.6672\n",
      "Epoch 31/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6357 - val_accuracy: 0.6675\n",
      "Epoch 32/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6357 - val_accuracy: 0.6673\n",
      "Epoch 33/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6358 - accuracy: 0.6672 - val_loss: 0.6357 - val_accuracy: 0.6674\n",
      "Epoch 34/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6358 - val_accuracy: 0.6675\n",
      "Epoch 35/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6358 - val_accuracy: 0.6673\n",
      "Epoch 36/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6356 - accuracy: 0.6672 - val_loss: 0.6356 - val_accuracy: 0.6676\n",
      "Epoch 37/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6358 - val_accuracy: 0.6673\n",
      "Epoch 38/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6356 - accuracy: 0.6672 - val_loss: 0.6357 - val_accuracy: 0.6675\n",
      "Epoch 39/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6356 - accuracy: 0.6672 - val_loss: 0.6360 - val_accuracy: 0.6673\n",
      "Epoch 40/50\n",
      "68/68 [==============================] - 0s 4ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6358 - val_accuracy: 0.6673\n",
      "Epoch 41/50\n",
      "68/68 [==============================] - 0s 4ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6357 - val_accuracy: 0.6673\n",
      "Epoch 42/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6358 - val_accuracy: 0.6674\n",
      "Epoch 43/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6358 - accuracy: 0.6672 - val_loss: 0.6358 - val_accuracy: 0.6673\n",
      "Epoch 44/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6357 - val_accuracy: 0.6673\n",
      "Epoch 45/50\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6356 - accuracy: 0.6672 - val_loss: 0.6359 - val_accuracy: 0.6674\n",
      "Epoch 46/50\n",
      "51/68 [=====================>........] - ETA: 0s - loss: 0.6357 - accuracy: 0.6672Restoring model weights from the end of the best epoch.\n",
      "68/68 [==============================] - 0s 3ms/step - loss: 0.6357 - accuracy: 0.6672 - val_loss: 0.6356 - val_accuracy: 0.6674\n",
      "Epoch 00046: early stopping\n"
     ]
    }
   ],
   "source": [
    "w_withall = marginal_reweight(T_withall/1000., T2/1000., model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "30/30 [==============================] - 0s 4ms/step - loss: 0.6936 - accuracy: 0.5039 - val_loss: 0.6935 - val_accuracy: 0.4992\n",
      "Epoch 2/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6931 - accuracy: 0.5013 - val_loss: 0.6934 - val_accuracy: 0.5020\n",
      "Epoch 3/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6931 - accuracy: 0.5041 - val_loss: 0.6935 - val_accuracy: 0.5049\n",
      "Epoch 4/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6931 - accuracy: 0.5069 - val_loss: 0.6932 - val_accuracy: 0.5056\n",
      "Epoch 5/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6931 - accuracy: 0.5026 - val_loss: 0.6929 - val_accuracy: 0.5024\n",
      "Epoch 6/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6929 - accuracy: 0.5075 - val_loss: 0.6935 - val_accuracy: 0.5021\n",
      "Epoch 7/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6936 - accuracy: 0.4986 - val_loss: 0.6930 - val_accuracy: 0.5016\n",
      "Epoch 8/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6930 - accuracy: 0.5062 - val_loss: 0.6928 - val_accuracy: 0.5073\n",
      "Epoch 9/50\n",
      "30/30 [==============================] - 0s 8ms/step - loss: 0.6929 - accuracy: 0.5045 - val_loss: 0.6928 - val_accuracy: 0.5005\n",
      "Epoch 10/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6929 - accuracy: 0.5026 - val_loss: 0.6927 - val_accuracy: 0.5038\n",
      "Epoch 11/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6929 - accuracy: 0.5052 - val_loss: 0.6928 - val_accuracy: 0.5023\n",
      "Epoch 12/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6930 - accuracy: 0.5070 - val_loss: 0.6928 - val_accuracy: 0.5022\n",
      "Epoch 13/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6928 - accuracy: 0.5019 - val_loss: 0.6927 - val_accuracy: 0.5023\n",
      "Epoch 14/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5051 - val_loss: 0.6927 - val_accuracy: 0.5024\n",
      "Epoch 15/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5036 - val_loss: 0.6925 - val_accuracy: 0.5016\n",
      "Epoch 16/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6928 - accuracy: 0.5075 - val_loss: 0.6926 - val_accuracy: 0.4994\n",
      "Epoch 17/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5059 - val_loss: 0.6927 - val_accuracy: 0.5040\n",
      "Epoch 18/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6929 - accuracy: 0.5028 - val_loss: 0.6925 - val_accuracy: 0.5051\n",
      "Epoch 19/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6928 - accuracy: 0.5052 - val_loss: 0.6927 - val_accuracy: 0.5047\n",
      "Epoch 20/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6928 - accuracy: 0.5067 - val_loss: 0.6925 - val_accuracy: 0.5034\n",
      "Epoch 21/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6926 - accuracy: 0.5055 - val_loss: 0.6930 - val_accuracy: 0.5047\n",
      "Epoch 22/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6929 - accuracy: 0.5079 - val_loss: 0.6925 - val_accuracy: 0.4965\n",
      "Epoch 23/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5040 - val_loss: 0.6925 - val_accuracy: 0.5044\n",
      "Epoch 24/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5032 - val_loss: 0.6924 - val_accuracy: 0.5041\n",
      "Epoch 25/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6926 - accuracy: 0.5049 - val_loss: 0.6924 - val_accuracy: 0.5044\n",
      "Epoch 26/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5031 - val_loss: 0.6924 - val_accuracy: 0.5032\n",
      "Epoch 27/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5072 - val_loss: 0.6924 - val_accuracy: 0.4978\n",
      "Epoch 28/50\n",
      "30/30 [==============================] - 0s 3ms/step - loss: 0.6927 - accuracy: 0.5035 - val_loss: 0.6924 - val_accuracy: 0.4989\n",
      "Epoch 29/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6926 - accuracy: 0.5052 - val_loss: 0.6924 - val_accuracy: 0.5008\n",
      "Epoch 30/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5068 - val_loss: 0.6924 - val_accuracy: 0.5020\n",
      "Epoch 31/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5037 - val_loss: 0.6923 - val_accuracy: 0.5021\n",
      "Epoch 32/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5073 - val_loss: 0.6927 - val_accuracy: 0.5055\n",
      "Epoch 33/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6928 - accuracy: 0.5062 - val_loss: 0.6925 - val_accuracy: 0.5022\n",
      "Epoch 34/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5042 - val_loss: 0.6924 - val_accuracy: 0.5012\n",
      "Epoch 35/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6926 - accuracy: 0.5048 - val_loss: 0.6924 - val_accuracy: 0.5013\n",
      "Epoch 36/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6926 - accuracy: 0.5031 - val_loss: 0.6923 - val_accuracy: 0.5023\n",
      "Epoch 37/50\n",
      "30/30 [==============================] - 0s 3ms/step - loss: 0.6926 - accuracy: 0.5058 - val_loss: 0.6923 - val_accuracy: 0.5041\n",
      "Epoch 38/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6926 - accuracy: 0.5025 - val_loss: 0.6923 - val_accuracy: 0.5019\n",
      "Epoch 39/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5075 - val_loss: 0.6925 - val_accuracy: 0.5028\n",
      "Epoch 40/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5048 - val_loss: 0.6923 - val_accuracy: 0.5036\n",
      "Epoch 41/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6926 - accuracy: 0.5096 - val_loss: 0.6927 - val_accuracy: 0.5055\n",
      "Epoch 42/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5050 - val_loss: 0.6924 - val_accuracy: 0.5020\n",
      "Epoch 43/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6926 - accuracy: 0.5033 - val_loss: 0.6923 - val_accuracy: 0.5015\n",
      "Epoch 44/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6927 - accuracy: 0.5027 - val_loss: 0.6923 - val_accuracy: 0.5036\n",
      "Epoch 45/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6926 - accuracy: 0.5041 - val_loss: 0.6922 - val_accuracy: 0.5022\n",
      "Epoch 46/50\n",
      "30/30 [==============================] - 0s 3ms/step - loss: 0.6926 - accuracy: 0.5033 - val_loss: 0.6924 - val_accuracy: 0.5046\n",
      "Epoch 47/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6926 - accuracy: 0.5031 - val_loss: 0.6925 - val_accuracy: 0.5053\n",
      "Epoch 48/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6926 - accuracy: 0.5035 - val_loss: 0.6922 - val_accuracy: 0.5016\n",
      "Epoch 49/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6926 - accuracy: 0.5051 - val_loss: 0.6922 - val_accuracy: 0.5031\n",
      "Epoch 50/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6925 - accuracy: 0.5068 - val_loss: 0.6923 - val_accuracy: 0.5055\n",
      "Epoch 1/50\n",
      "30/30 [==============================] - 0s 4ms/step - loss: 0.6938 - accuracy: 0.5117 - val_loss: 0.6911 - val_accuracy: 0.6063\n",
      "Epoch 2/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6895 - accuracy: 0.5967 - val_loss: 0.6870 - val_accuracy: 0.6691\n",
      "Epoch 3/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6837 - accuracy: 0.6415 - val_loss: 0.6773 - val_accuracy: 0.6546\n",
      "Epoch 4/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6707 - accuracy: 0.6515 - val_loss: 0.6586 - val_accuracy: 0.6647\n",
      "Epoch 5/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6509 - accuracy: 0.6588 - val_loss: 0.6386 - val_accuracy: 0.6555\n",
      "Epoch 6/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6338 - accuracy: 0.6658 - val_loss: 0.6228 - val_accuracy: 0.6702\n",
      "Epoch 7/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6256 - accuracy: 0.6702 - val_loss: 0.6181 - val_accuracy: 0.6765\n",
      "Epoch 8/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6233 - accuracy: 0.6711 - val_loss: 0.6156 - val_accuracy: 0.6735\n",
      "Epoch 9/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6225 - accuracy: 0.6703 - val_loss: 0.6162 - val_accuracy: 0.6732\n",
      "Epoch 10/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6207 - accuracy: 0.6707 - val_loss: 0.6165 - val_accuracy: 0.6700\n",
      "Epoch 11/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6202 - accuracy: 0.6707 - val_loss: 0.6142 - val_accuracy: 0.6702\n",
      "Epoch 12/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6193 - accuracy: 0.6694 - val_loss: 0.6109 - val_accuracy: 0.6756\n",
      "Epoch 13/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6184 - accuracy: 0.6735 - val_loss: 0.6147 - val_accuracy: 0.6703\n",
      "Epoch 14/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6190 - accuracy: 0.6700 - val_loss: 0.6118 - val_accuracy: 0.6724\n",
      "Epoch 15/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6228 - accuracy: 0.6645 - val_loss: 0.6117 - val_accuracy: 0.6743\n",
      "Epoch 16/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6170 - accuracy: 0.6713 - val_loss: 0.6098 - val_accuracy: 0.6765\n",
      "Epoch 17/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6173 - accuracy: 0.6723 - val_loss: 0.6097 - val_accuracy: 0.6730\n",
      "Epoch 18/50\n",
      "30/30 [==============================] - 0s 4ms/step - loss: 0.6172 - accuracy: 0.6724 - val_loss: 0.6114 - val_accuracy: 0.6695\n",
      "Epoch 19/50\n",
      "30/30 [==============================] - 0s 4ms/step - loss: 0.6161 - accuracy: 0.6724 - val_loss: 0.6087 - val_accuracy: 0.6764\n",
      "Epoch 20/50\n",
      "30/30 [==============================] - 0s 4ms/step - loss: 0.6153 - accuracy: 0.6727 - val_loss: 0.6095 - val_accuracy: 0.6756\n",
      "Epoch 21/50\n",
      "30/30 [==============================] - 0s 4ms/step - loss: 0.6154 - accuracy: 0.6723 - val_loss: 0.6114 - val_accuracy: 0.6761\n",
      "Epoch 22/50\n",
      "30/30 [==============================] - 0s 4ms/step - loss: 0.6151 - accuracy: 0.6741 - val_loss: 0.6083 - val_accuracy: 0.6758\n",
      "Epoch 23/50\n",
      "30/30 [==============================] - 0s 4ms/step - loss: 0.6151 - accuracy: 0.6716 - val_loss: 0.6089 - val_accuracy: 0.6773\n",
      "Epoch 24/50\n",
      "30/30 [==============================] - 0s 3ms/step - loss: 0.6143 - accuracy: 0.6725 - val_loss: 0.6082 - val_accuracy: 0.6741\n",
      "Epoch 25/50\n",
      "30/30 [==============================] - 0s 3ms/step - loss: 0.6149 - accuracy: 0.6737 - val_loss: 0.6087 - val_accuracy: 0.6724\n",
      "Epoch 26/50\n",
      "30/30 [==============================] - 0s 3ms/step - loss: 0.6146 - accuracy: 0.6722 - val_loss: 0.6083 - val_accuracy: 0.6756\n",
      "Epoch 27/50\n",
      "30/30 [==============================] - 0s 3ms/step - loss: 0.6140 - accuracy: 0.6728 - val_loss: 0.6094 - val_accuracy: 0.6725\n",
      "Epoch 28/50\n",
      "30/30 [==============================] - 0s 3ms/step - loss: 0.6163 - accuracy: 0.6697 - val_loss: 0.6112 - val_accuracy: 0.6680\n",
      "Epoch 29/50\n",
      "30/30 [==============================] - 0s 3ms/step - loss: 0.6149 - accuracy: 0.6721 - val_loss: 0.6135 - val_accuracy: 0.6707\n",
      "Epoch 30/50\n",
      "30/30 [==============================] - 0s 3ms/step - loss: 0.6134 - accuracy: 0.6725 - val_loss: 0.6068 - val_accuracy: 0.6771\n",
      "Epoch 31/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6141 - accuracy: 0.6722 - val_loss: 0.6068 - val_accuracy: 0.6754\n",
      "Epoch 32/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6128 - accuracy: 0.6740 - val_loss: 0.6089 - val_accuracy: 0.6768\n",
      "Epoch 33/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6126 - accuracy: 0.6743 - val_loss: 0.6060 - val_accuracy: 0.6773\n",
      "Epoch 34/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6128 - accuracy: 0.6727 - val_loss: 0.6065 - val_accuracy: 0.6764\n",
      "Epoch 35/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6121 - accuracy: 0.6745 - val_loss: 0.6074 - val_accuracy: 0.6756\n",
      "Epoch 36/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6121 - accuracy: 0.6736 - val_loss: 0.6071 - val_accuracy: 0.6768\n",
      "Epoch 37/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6123 - accuracy: 0.6732 - val_loss: 0.6075 - val_accuracy: 0.6743\n",
      "Epoch 38/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6128 - accuracy: 0.6737 - val_loss: 0.6074 - val_accuracy: 0.6735\n",
      "Epoch 39/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6116 - accuracy: 0.6732 - val_loss: 0.6062 - val_accuracy: 0.6751\n",
      "Epoch 40/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6127 - accuracy: 0.6725 - val_loss: 0.6100 - val_accuracy: 0.6732\n",
      "Epoch 41/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6117 - accuracy: 0.6729 - val_loss: 0.6066 - val_accuracy: 0.6771\n",
      "Epoch 42/50\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6114 - accuracy: 0.6739 - val_loss: 0.6075 - val_accuracy: 0.6738\n",
      "Epoch 43/50\n",
      " 1/30 [>.............................] - ETA: 0s - loss: 0.6217 - accuracy: 0.6830Restoring model weights from the end of the best epoch.\n",
      "30/30 [==============================] - 0s 2ms/step - loss: 0.6114 - accuracy: 0.6739 - val_loss: 0.6066 - val_accuracy: 0.6769\n",
      "Epoch 00043: early stopping\n",
      "Epoch 1/50\n",
      "30/30 [==============================] - 0s 8ms/step - loss: 0.6838 - accuracy: 0.5907 - val_loss: 0.6707 - val_accuracy: 0.7920\n",
      "Epoch 2/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.6469 - accuracy: 0.8022 - val_loss: 0.6097 - val_accuracy: 0.8090\n",
      "Epoch 3/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.5515 - accuracy: 0.8135 - val_loss: 0.4795 - val_accuracy: 0.8427\n",
      "Epoch 4/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.4145 - accuracy: 0.8458 - val_loss: 0.3619 - val_accuracy: 0.8488\n",
      "Epoch 5/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.3310 - accuracy: 0.8619 - val_loss: 0.3090 - val_accuracy: 0.8695\n",
      "Epoch 6/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2945 - accuracy: 0.8733 - val_loss: 0.2893 - val_accuracy: 0.8695\n",
      "Epoch 7/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2785 - accuracy: 0.8766 - val_loss: 0.2777 - val_accuracy: 0.8729\n",
      "Epoch 8/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2698 - accuracy: 0.8801 - val_loss: 0.2723 - val_accuracy: 0.8767\n",
      "Epoch 9/50\n",
      "30/30 [==============================] - 0s 9ms/step - loss: 0.2652 - accuracy: 0.8810 - val_loss: 0.2653 - val_accuracy: 0.8791\n",
      "Epoch 10/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2627 - accuracy: 0.8806 - val_loss: 0.2656 - val_accuracy: 0.8763\n",
      "Epoch 11/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2618 - accuracy: 0.8789 - val_loss: 0.2626 - val_accuracy: 0.8783\n",
      "Epoch 12/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2583 - accuracy: 0.8811 - val_loss: 0.2588 - val_accuracy: 0.8799\n",
      "Epoch 13/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2553 - accuracy: 0.8827 - val_loss: 0.2593 - val_accuracy: 0.8789\n",
      "Epoch 14/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2557 - accuracy: 0.8809 - val_loss: 0.2573 - val_accuracy: 0.8792\n",
      "Epoch 15/50\n",
      "30/30 [==============================] - 0s 9ms/step - loss: 0.2536 - accuracy: 0.8835 - val_loss: 0.2574 - val_accuracy: 0.8797\n",
      "Epoch 16/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2542 - accuracy: 0.8818 - val_loss: 0.2589 - val_accuracy: 0.8776\n",
      "Epoch 17/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2555 - accuracy: 0.8814 - val_loss: 0.2552 - val_accuracy: 0.8822\n",
      "Epoch 18/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2519 - accuracy: 0.8834 - val_loss: 0.2551 - val_accuracy: 0.8810\n",
      "Epoch 19/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2520 - accuracy: 0.8830 - val_loss: 0.2545 - val_accuracy: 0.8802\n",
      "Epoch 20/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2505 - accuracy: 0.8825 - val_loss: 0.2533 - val_accuracy: 0.8802\n",
      "Epoch 21/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2504 - accuracy: 0.8836 - val_loss: 0.2546 - val_accuracy: 0.8795\n",
      "Epoch 22/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2503 - accuracy: 0.8833 - val_loss: 0.2545 - val_accuracy: 0.8789\n",
      "Epoch 23/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2498 - accuracy: 0.8827 - val_loss: 0.2524 - val_accuracy: 0.8823\n",
      "Epoch 24/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2491 - accuracy: 0.8838 - val_loss: 0.2522 - val_accuracy: 0.8812\n",
      "Epoch 25/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2487 - accuracy: 0.8824 - val_loss: 0.2518 - val_accuracy: 0.8824\n",
      "Epoch 26/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2489 - accuracy: 0.8833 - val_loss: 0.2545 - val_accuracy: 0.8750\n",
      "Epoch 27/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2501 - accuracy: 0.8832 - val_loss: 0.2517 - val_accuracy: 0.8801\n",
      "Epoch 28/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2470 - accuracy: 0.8842 - val_loss: 0.2533 - val_accuracy: 0.8793\n",
      "Epoch 29/50\n",
      "30/30 [==============================] - 0s 8ms/step - loss: 0.2472 - accuracy: 0.8833 - val_loss: 0.2523 - val_accuracy: 0.8807\n",
      "Epoch 30/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2476 - accuracy: 0.8806 - val_loss: 0.2499 - val_accuracy: 0.8805\n",
      "Epoch 31/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2449 - accuracy: 0.8861 - val_loss: 0.2492 - val_accuracy: 0.8819\n",
      "Epoch 32/50\n",
      "30/30 [==============================] - 0s 7ms/step - loss: 0.2477 - accuracy: 0.8815 - val_loss: 0.2527 - val_accuracy: 0.8787\n",
      "Epoch 33/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2450 - accuracy: 0.8853 - val_loss: 0.2505 - val_accuracy: 0.8811\n",
      "Epoch 34/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2448 - accuracy: 0.8842 - val_loss: 0.2485 - val_accuracy: 0.8814\n",
      "Epoch 35/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2437 - accuracy: 0.8834 - val_loss: 0.2525 - val_accuracy: 0.8793\n",
      "Epoch 36/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2457 - accuracy: 0.8828 - val_loss: 0.2505 - val_accuracy: 0.8798\n",
      "Epoch 37/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2445 - accuracy: 0.8845 - val_loss: 0.2522 - val_accuracy: 0.8805\n",
      "Epoch 38/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2437 - accuracy: 0.8840 - val_loss: 0.2497 - val_accuracy: 0.8794\n",
      "Epoch 39/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2433 - accuracy: 0.8845 - val_loss: 0.2530 - val_accuracy: 0.8770\n",
      "Epoch 40/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2430 - accuracy: 0.8837 - val_loss: 0.2480 - val_accuracy: 0.8818\n",
      "Epoch 41/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2433 - accuracy: 0.8834 - val_loss: 0.2473 - val_accuracy: 0.8808\n",
      "Epoch 42/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2418 - accuracy: 0.8849 - val_loss: 0.2463 - val_accuracy: 0.8813\n",
      "Epoch 43/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2419 - accuracy: 0.8847 - val_loss: 0.2484 - val_accuracy: 0.8832\n",
      "Epoch 44/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2424 - accuracy: 0.8835 - val_loss: 0.2458 - val_accuracy: 0.8825\n",
      "Epoch 45/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2413 - accuracy: 0.8852 - val_loss: 0.2471 - val_accuracy: 0.8815\n",
      "Epoch 46/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2435 - accuracy: 0.8808 - val_loss: 0.2566 - val_accuracy: 0.8731\n",
      "Epoch 47/50\n",
      "30/30 [==============================] - 0s 9ms/step - loss: 0.2451 - accuracy: 0.8796 - val_loss: 0.2507 - val_accuracy: 0.8768\n",
      "Epoch 48/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2458 - accuracy: 0.8821 - val_loss: 0.2506 - val_accuracy: 0.8748\n",
      "Epoch 49/50\n",
      "30/30 [==============================] - 0s 5ms/step - loss: 0.2424 - accuracy: 0.8834 - val_loss: 0.2464 - val_accuracy: 0.8803\n",
      "Epoch 50/50\n",
      "30/30 [==============================] - 0s 6ms/step - loss: 0.2412 - accuracy: 0.8850 - val_loss: 0.2459 - val_accuracy: 0.8815\n",
      "Epoch 1/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.6801 - accuracy: 0.5817 - val_loss: 0.6728 - val_accuracy: 0.6049\n",
      "Epoch 2/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.6748 - accuracy: 0.5984 - val_loss: 0.6722 - val_accuracy: 0.6049\n",
      "Epoch 3/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.6742 - accuracy: 0.5984 - val_loss: 0.6716 - val_accuracy: 0.6049\n",
      "Epoch 4/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.6737 - accuracy: 0.5984 - val_loss: 0.6711 - val_accuracy: 0.6049\n",
      "Epoch 5/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.6729 - accuracy: 0.5984 - val_loss: 0.6700 - val_accuracy: 0.6049\n",
      "Epoch 6/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.6716 - accuracy: 0.5984 - val_loss: 0.6680 - val_accuracy: 0.6049\n",
      "Epoch 7/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.6693 - accuracy: 0.5984 - val_loss: 0.6654 - val_accuracy: 0.6049\n",
      "Epoch 8/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.6659 - accuracy: 0.5987 - val_loss: 0.6609 - val_accuracy: 0.6043\n",
      "Epoch 9/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.6607 - accuracy: 0.5995 - val_loss: 0.6556 - val_accuracy: 0.6053\n",
      "Epoch 10/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.6538 - accuracy: 0.5978 - val_loss: 0.6465 - val_accuracy: 0.6054\n",
      "Epoch 11/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.6414 - accuracy: 0.5994 - val_loss: 0.6324 - val_accuracy: 0.6054\n",
      "Epoch 12/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.6276 - accuracy: 0.5969 - val_loss: 0.6208 - val_accuracy: 0.6034\n",
      "Epoch 13/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.6172 - accuracy: 0.6007 - val_loss: 0.6121 - val_accuracy: 0.6027\n",
      "Epoch 14/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.6111 - accuracy: 0.5991 - val_loss: 0.6072 - val_accuracy: 0.5997\n",
      "Epoch 15/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.6070 - accuracy: 0.5999 - val_loss: 0.6070 - val_accuracy: 0.5953\n",
      "Epoch 16/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.6049 - accuracy: 0.5999 - val_loss: 0.6017 - val_accuracy: 0.6043\n",
      "Epoch 17/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.6026 - accuracy: 0.5984 - val_loss: 0.5998 - val_accuracy: 0.6060\n",
      "Epoch 18/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.6024 - accuracy: 0.6023 - val_loss: 0.5998 - val_accuracy: 0.6038\n",
      "Epoch 19/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.6002 - accuracy: 0.6001 - val_loss: 0.5977 - val_accuracy: 0.5958\n",
      "Epoch 20/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.5981 - accuracy: 0.6017 - val_loss: 0.5957 - val_accuracy: 0.6043\n",
      "Epoch 21/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.5976 - accuracy: 0.5970 - val_loss: 0.5964 - val_accuracy: 0.6038\n",
      "Epoch 22/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.5969 - accuracy: 0.6027 - val_loss: 0.5953 - val_accuracy: 0.6048\n",
      "Epoch 23/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.5966 - accuracy: 0.6009 - val_loss: 0.5940 - val_accuracy: 0.6040\n",
      "Epoch 24/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5961 - accuracy: 0.5986 - val_loss: 0.5932 - val_accuracy: 0.6038\n",
      "Epoch 25/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.5959 - accuracy: 0.5999 - val_loss: 0.5933 - val_accuracy: 0.6036\n",
      "Epoch 26/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.5952 - accuracy: 0.6012 - val_loss: 0.5942 - val_accuracy: 0.5993\n",
      "Epoch 27/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.5953 - accuracy: 0.6012 - val_loss: 0.5931 - val_accuracy: 0.6047\n",
      "Epoch 28/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.5944 - accuracy: 0.5999 - val_loss: 0.5925 - val_accuracy: 0.5994\n",
      "Epoch 29/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.5945 - accuracy: 0.6013 - val_loss: 0.5967 - val_accuracy: 0.6018\n",
      "Epoch 30/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.5955 - accuracy: 0.5976 - val_loss: 0.5924 - val_accuracy: 0.5972\n",
      "Epoch 31/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38/38 [==============================] - 0s 4ms/step - loss: 0.5940 - accuracy: 0.6040 - val_loss: 0.5915 - val_accuracy: 0.6006\n",
      "Epoch 32/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5937 - accuracy: 0.6007 - val_loss: 0.5916 - val_accuracy: 0.5987\n",
      "Epoch 33/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5939 - accuracy: 0.6010 - val_loss: 0.5921 - val_accuracy: 0.5986\n",
      "Epoch 34/50\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.5943 - accuracy: 0.6058 - val_loss: 0.5918 - val_accuracy: 0.6042\n",
      "Epoch 35/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5934 - accuracy: 0.6038 - val_loss: 0.5923 - val_accuracy: 0.6037\n",
      "Epoch 36/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5934 - accuracy: 0.6011 - val_loss: 0.5910 - val_accuracy: 0.6105\n",
      "Epoch 37/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5932 - accuracy: 0.6033 - val_loss: 0.5910 - val_accuracy: 0.6012\n",
      "Epoch 38/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5931 - accuracy: 0.6021 - val_loss: 0.5904 - val_accuracy: 0.6061\n",
      "Epoch 39/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5929 - accuracy: 0.6000 - val_loss: 0.5914 - val_accuracy: 0.6017\n",
      "Epoch 40/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5936 - accuracy: 0.5987 - val_loss: 0.5930 - val_accuracy: 0.5994\n",
      "Epoch 41/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5936 - accuracy: 0.6029 - val_loss: 0.5914 - val_accuracy: 0.6040\n",
      "Epoch 42/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5923 - accuracy: 0.6056 - val_loss: 0.5908 - val_accuracy: 0.5987\n",
      "Epoch 43/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5925 - accuracy: 0.6051 - val_loss: 0.5897 - val_accuracy: 0.6034\n",
      "Epoch 44/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5921 - accuracy: 0.6071 - val_loss: 0.5909 - val_accuracy: 0.6004\n",
      "Epoch 45/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5925 - accuracy: 0.6034 - val_loss: 0.5914 - val_accuracy: 0.6010\n",
      "Epoch 46/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5924 - accuracy: 0.6044 - val_loss: 0.5900 - val_accuracy: 0.5996\n",
      "Epoch 47/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5928 - accuracy: 0.6010 - val_loss: 0.5919 - val_accuracy: 0.5993\n",
      "Epoch 48/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5925 - accuracy: 0.6017 - val_loss: 0.5926 - val_accuracy: 0.6023\n",
      "Epoch 49/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5921 - accuracy: 0.6058 - val_loss: 0.5897 - val_accuracy: 0.6023\n",
      "Epoch 50/50\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5923 - accuracy: 0.6050 - val_loss: 0.5946 - val_accuracy: 0.6039\n"
     ]
    }
   ],
   "source": [
    "wCondtional_2funcs = conditional_reweight_2funcs(T0/1000., T1/1000., T2/1000., R0/1000., R1/1000., R2/1000., model, model2)\n",
    "wCondtional_1func = conditional_reweight_1func(T0/1000., T1/1000., T2/1000., R0/1000., R1/1000., R2/1000., modelO)\n",
    "wMarginal = marginal_reweight(T0/1000., T2/1000., model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tt_copy4 = T_withall.copy()\n",
    "T0_copy4 = T0.copy()\n",
    "T1_copy4 = T1.copy()\n",
    "T2_copy4 = T2.copy()\n",
    "wT0_copy4 = wMarginal.copy()\n",
    "\n",
    "Rt_copy4 = R_withall.copy()\n",
    "R0_copy4 = R0.copy()\n",
    "R1_copy4 = R1.copy()\n",
    "R2_copy4 = R2.copy()\n",
    "wRT_copy4 = wCondtional_2funcs.copy()\n",
    "wRT2_copy4 = wCondtional_1func.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0evRojDFkZmbSqlWrsNSZmGj9cXDkyBGvxw8dOpRvX1JSEmDdYpo6dWpY2qGUUsWV9rSoEskxe6dJkyZu+48dO1boOi+++GIA1q9f77WnZvv27fn2OQbwus4mcvX777/zySefcPToUec+b7eelFKqJNCgRZVIjvEla9ascdu/ZMmSQtdZt25drrjiCvbt28fnn3/udmzDhg1kZ2fnOyc1NZW2bduydOlSNmzYkO/46NGjGT58OOXKlXPuq1y5MgDHjx8HYPPmzVx44YWsW7eu0G1XSql4oLeHSiAd1Av3338/w4cPZ9CgQUycOJELL7yQ5cuXM3jw4JDqffnll+nQoQN33XUX06ZNo23btqxbt4577rmHtm3bsmzZsnznTJ48mc6dO9O7d29effVV2rVrx6FDh8jMzGTChAnMnj2bs8468/dF69atKVWqFIsXL6Z///5MmTKF7du3U6dOnZDarpRSsU57WlSxs3XrVkSErl27AvDll18iIgwYMMBZZtiwYUyfPp2yZctyxRVXULNmTUaPHs24cePcznnzzTed9Y0ePRqArl27Oo95Ov/88/nmm2+46KKL6NWrF+eeey73338/zz33HI0bNwagTJkybuc0a9aM77//niuuuII777yTqlWr0q5dO3744QeWLFlC9+7d3crXr1+fcePG8e2333LuuecyY8YMpk6d6nWgr1JKFScS7yveFnepqalmxYoVhT4/HntViutMqa5du7J06VIOHjyYL3BRShVfYxe43/q9/8qmUWpJfBCRbGNMqrdj2tOiVBjNmjWLQYMG5dufk5PD8uXL6d69uwYsSilVSBq0KBVGeXl5TJo0ieeee46cnByOHj3K8uXLue666yhTpgzPPvtstJuolFJxSwfixjhHRty0tLRC3TYprrdaYtVf/vIXHn74Yd5++22eeuopcnNzqV69On/5y1+YPHky5513XrSbqJRSMSkrK8sxpMFnRlwd0xLjQh3TopRSKrp0TEtw4mJMi4gkiMhYEdkuIsdFZIOIPCoipYOsp4yIjBSRjXY920TkvyJSsYBzaonIJBH5TUSOicgaERkqXrJ4iUhlEXlARBaLyAER+UNEfhWRqSJyoY/6u4iIKeDxaDCvUSmllCqJYuL2kIgkAF8DlYFbgGygJzAZ6CAiacaYUwHUUxqYB7QBbgM+A9oC7wDdRKSjMeaIxzl1gWXAAaAHsAnoC7wCtAYyPC6zHGgIPAr0AQ4BnYBxwI0i0tsYs8BL844B+VOiWn7399qUUkqpki4mghbgCeBC4GpjjCMl6SwRGQX8F7gbeDWAeu4FrgCGGWMcc32/FJHhwExgJPB3j3NeA2oBPYwxP9j7MkWkJTBcRD40xsxzKV8OGGeMGeOyb46IDAAWABNFpIHJf9/tO2NMlwBeg1JKKaW8iHrQIiKVgIHAbuBjj8NvAs8C9+MnaLFv5dwHnASmeBz+CNgPDBGRfxljjtvnNAF6A8tcAhaHScBw+9quQctqYLqXJnyO1ZtSH2gC5M/JrpRSMcJznAXoWAsV+2JhTEs3rN6LZZ69E8aYfVhf/o1FxN//plZAXeBHY4zbcrrGmD+xbutUxLqV49DL3i71Ut8a4CjQRUQquNTl2hvkeg0DOG496Yp2SimlVJjFQtDS0t5u9XHcsb+lj+Oh1OPzHHsMzQ6s3qjmfq6NiFQBqgK7sMbFeKoqIhNEZKuInLAH/c4UkTb+6lZKKaVUDNweAmra2wM+jufa2xoRqCdc1wZrUC7AMz4GDTcH3gcuB/YCFwMvAd+IyO3GmLcDuIZSSqk48+9+3TiwZ6fz+YvJyWzdujV6DYpjsRC0lLe3J30c/8PeVvBxPJR6wnJtu5flX8BXWIGIp7XApcaY5S77lonINcAvWIN3vzDG7C7oOkoppeLPgT07eX7+eufzEd2bRbE18S3g20Mi8q8IteGYvfWVj8WxUMvRCNQT8rVF5GxgKpAH/J8x5rRnGWPMPo+AxbF/N5CFFTzd4K3+nJwcUlNTnY/MzExfTVEeNm7cyNdffx1Q2VmzZpGbmxvZBimllPIqMzPT+T2HNdTCq2DGtIwUkUjkIP/N3lb2cTzJ3u6JQD0hXduesTQeaAF0N8bk+GmjN5vt7fneDlarVo0VK1Y4HxkZnmljlDfTp0+nTZs2nD5txZAiku/RpUsXZ/lNmzbRqlUrVq1aFZ0GK6VUCZaRkeH8nqOA3GXB3B4SYLWILMP6op5lz8oJ1Vp729DH8QYe5cJZj89zRKQUUA84Bfzs5bgjYLkC6GKM2eqnfb4UyUwjb9MbY0k4p1rOmzePfv368eGHH9KxY0cAjDFs3bqVhg0bkuzlfvKDDz7Inj176NGjB8uXL6d+/fpha49SSqnwCHb2UF1gNvAYsFNEnhGRxiG2YSFwAmjrmTZfRM4FmgKbjTH+vnXXADuBFnbuF9d6zsbKknsYWOxyyJF/5VIv9bXCGsuyyBjjdnvIbuc4oDseAYuI3CwiF3iUXy4ivfCukb1d7+O4CsKhQ4cYOHAgvXr1onfv3kGd+/jjjwMwdOjQSDRNqZjy737dGNG9mdujQYMG0W6WUgUKJmi5wxiTZ4z5nzGmFXAdUA1YJSIL7S/roNYJArBzqryOlZX2Ko/DA7B6Il5w7LDXKJojIm/ZvSGOegzwItb4lH4e9VwDVAHGOxLL2edsxApc2noGGsCd9vYF1512wPIa1jIDXb30sAzBCpBcVQOu9tiHiNQB0oDjWBl7VYgmTpzI7t27eeCBB4I+t0KFCgwdOpS5c+eyevXqCLROqdjhGBzq+ti2bVu0m6VUgQIOWowxb3k8/8YYcwdQB5iLlUF2p4g8G0AiOE+PAD9hpc+/XETKi8h1wChgPlavhkN3rACgP9a0YVcvAIuAMSKSZtfTGXgZK5PtKC/XHoI1tuUdEWklIhVEJAMYDEwyxszxKP8q1rICO+3rvOP6wBrf4skAg+0FIOvZizpeipWp92xgiDFml783Sfk3depUypUrx2WXXVao87t16+asRymlVGwJacqzfWsoA6tHpLz96A7cLSLZwNPGmE/81WOMyRORDsBorBT51bEWF3zGrsN17Mw3WNOE9wE/etRzUkR6YgVBL2DdztoDvA2MMsYc9nLt7SKSirX+0QIgESs53P1YiyZ6Gmxv29uPQHQBbsXq8XkQ67bT71iLRA43xnwbYD2qALm5uaxcuZKWLVty9tmF+2hfcIHV4bZw4cJwNk0ppVQYBPybXUROGWNK2eNDrsfqbeiCdfvmIFZvSKYxZpWInIP1Jf2aiEwwxjzpr35jTB7W2kH3+Sm3izPjQLwdP4G1MOLIAF6Wa513BFg26IGzxphtwBj7oSJk3bp1GGOoXbt2oeuoUqUKZcuWddblMcxKKaVUFAUzpkVE5GngV6zekK5Y6/kMBGobY4YaY1YBGGOOGGMmYK3z89fwNlkp73btsu6wJSUlhVRPYmIiR48eJS8vLwytUkopFS7B9qE/iJVE7TWsXpU1fson2A+lIu7oUWuSV5kyZfyULFjZsmUBOHLkSMgBkFJKqfAJdsrzHVi9KsP9BSwichmwEu+LB6oA5eXlkZGRQVZWVrSbEvMc41hOnfK29FPgHOcXdlyMUkqp4GVlZTkSqCb6KhPMb+UvPWcQ+bEUK9NsOBLQlViJiYmauj9AiYnW5/z48eN+Shbs2DFrdYeEBO0kVEqpopKWlkZaWhoTJkzweW8+mJ6WLwo6KCLfiMjnItIMwBhz2h7bciKIayhVaOedZ60ykZNTmNUULKdOnSI3N5datWpRvnx5/ycopZQqMkGtPeTn+P+wVkUe56ecUhHRuHFjEhMT2bJlS6Hr2LJlC8YYx6JdSimlYkhQs4cKOmiMeQe4CUgJqUVKFVKpUqXo0aMHO3bsYO/evYWqIzs7G4BevXytuqCUUipafAYtdrr8+vYjGTB2Ntf6Ph5Ngduw1vdRKioc6wa9++67bvtFhIYNrXUxt23blm+VZ4cZM2aQkJDArbfeGvG2KqWUCk5BA3HvB/7l8lyArQHU6S2LrIqycK6iHMs6d+7Mtddey9NPP82gQYMoV64cYK3y7M/q1auZNWsWY8eO1UG4SikVgwoKWlYBk+1/C9YihJN9lDXAAWAFMCNcjVOqMCZNmkTHjh255ZZbeO+99yhd2v86njt37uSGG26gT58+3HvvvUXQSqWUUsHyGbQYYz7CWtAPABHpby+QqFRMq1y5MkuWLOGuu+5i4sSJDBkyxO85Dz30EH369GHUqFGaul8ppWJUMHlaukasFUqFWVJSEu+//74zS64/48ePp0KFChFulVJKqVAEPHvIGPNlIOVEZFLhm6M8aUbc0AQaiGjAopRS0RVIRtxg0/gH4vYI1FliOTLipqWlRbspSimlVMSkpaU5MsD7zIjr8/aQiLwCJAPXGGNOicgv4W+iUkoppVRgChrTciOQhLV+0O9AA2Bb5JuklFJKKZVfQUFLZyDJGPO7Y4cxpqG/CkXkdDgappRSSinlqqApzz977Ap0hedgVoJWSimllApIMLOHAs3R4m9hRaWUUkqpoEVi9lDhl9hVSimllPIhmORyiEh54EqgCVABPys/K6WUUkqFS8BBi4hcDMwFalBwsOJ/ZTqllFJKqSAFc3voReA08BCQDnTDSu3v+ugW7gaWdJoRt/A2btzI119/HXD5WbNmkZubG7kGKaWU8incGXFTgf8zxjxrjJljjFlkjPnS47EI2B5as5WrcGbEzc4GEd+P7OwzZTMyfJdLSXGvt6A6reSGlszMgsuG0/Tp02nTpg2nT5+ZgS8i+R5dunRxHt+0aROtWrVi1apV4W1MCLp06eLW3quvvjqg8x544AGfrzOWjBkzhqSkJL744osiva7r+xqr741SJU0gGXGDCVoOAz/6KxRILhelImnevHn069ePqVOn0rFjR+d+YwxbtljjxJOTkzHGsGjRIufxBx98kJtuuokePXqwfXtsxN6LFi3CGENycjJgvbbVq1cXeM6+ffsYP348AJ07d873OmPJ9u3bycvLY+/evUV6Xcf7qpSKL8EELe8DV/grpAsmxp6MDOuRkgLG+H649qBkZvou59ojAwXXafX0nWlHQWXD4dChQwwcOJBevXrRu3fvoM9//PHHARg6dGjIbVm0aBEiwptvvhlyXYAzcHnyyScLLPfiiy9StWrVsFwz0l555RV27tzJzTffHO2mKKXiQDBBy4NAXxH5q4g0FJGyPsrpgokxZsIE61ESTJw4kd27d/PAAw8U6vwKFSowdOhQ5s6d67dHo6gNHTqU8uXLM3PmTDZu3Oi1zKFDh3jllVf429/+VsStK5yzzjqL2rVrR7sZSqk4EUzQkgf8H/A8sAk4KiKnPB8RaaVSAZo6dSrlypXjsssuK3Qd3bp1c9YVS6pXr85dd93F6dOneeqpp7yWee2117jsssu48MILfdbz66+/8vjjj3PppZdStWpVypUrR4sWLXjyySc5efKkW9mnnnrKbWzM4cOHuffee6lTpw6lSpXK15OUlZVFu3btKF++PFWqVOG6665jw4YNNGjQwFnHgAEDAPcxRqNGjfJ5zUOHDjF48GCqVatG+fLl6dixI99//32+17V+/XoeeughLrnkEpKSkqhQoQKXXHIJ48aN01tBShUTwQQtAiz28/gq3A1UxV9KSv7BvYWRm5vLypUradq0KWefHVQKIjcXXHABAAsXLgy9UWH2t7/9jbPPPpspU6awY8cOt2PHjx/n+eef5+GHHy6wjnfeeYcnnniCjIwMNm/ezO7duxk9ejT//e9/ufXWW93KPvTQQxhj6Ny5MwB9+/YlJSWFH3/8ka+//ppy5co5y06bNo1rrrmGihUrsmbNGnbv3s2gQYO47rrrOHjwoHMckSPIMcZ4HYDrec077riDHj16sHnzZhYuXMgvv/xCr169OHLkiNt5L730EuPHj2fkyJH8+uuvbNu2jcGDB/PXv/6Vv//974G9wUqpmBZURlxjTFc/jy5owjkVpO+/tx6hWrduHcaYkG83VKlShbJlyzrriyXJycnceuutnDx5kmeffdbt2Ouvv875559P+/btC6yjevXq/OMf/+DOO+8kMTGRypUrc+ONNzJy5EhmzpxJtuegJRdt2rTh9ttvJykpiUsvvZQRI0bQsGFDDh48yLBhw6hYsSIzZ86kSZMmlC1bll69enHfffdx4MCBQr/mDh06cN1115GQkED79u3561//yp49e1iwYIFbubp16zJmzBhn4FStWjUyMjIYOnQoL7zwAnv27Cl0G5RSsSGYoKXgP9/OCHSNIqXCateuXQAkJSWFXFdiYiJHjx4lL8/nzLuoeeihhxARJk6cSE5ODgB//vknzz77rN9eFoD+/fs7Bxy7atmyJUCBuW1uuukmt+dPPPEEnTt35qOPPiIvL4/u3btTuXJltzJ9+vTx26aCXHPNNW7PmzdvDpBvXM9DDz3E4MGD853fsmVL/vzzT5YtWxZSO5RS0RfMgolPB1hOV3lWUXH06FEAypQpE3JdZcta48w9b0H44plPRUTo2rUrYN3eKCg/TLCaN2/Otddey7Fjxxg7dixg3Zo599xz6dGjh9/zT58+zdSpU7n88supUaOGs01XXGFNDiyoV6RevXpe969cuRKAZs2a5TtWsWJFqlSp4rddvnj2nFWsWBE48/N2OHHiBC+//DJt2rTh3HPPdb6uu+66Cyj4dSml4oPPoEVEfinKhijvwpER95JLrEdx5xjHcupU6OPBHXUEOjamfv36NGvWzO3h+IKvWbNmvmP169cPqX2OHpVXX32VAwcO8NRTTwXUywIwePBg+vXrR5MmTfj22285deqU2/iSgm6JlS9f3uv+gwcPAnDOOed4PV6pUqWA2hbINcXOROjaTmMM6enp3HPPPfzlL39hzZo1nD59GmMMb7zxRr7ySqnYE0hG3IJ+IzcId4NU8BwZcUNRwBCFYiUx0fqcHz9+POS6jh07BkBCQkJA5SdPnpxv36JFi+jatStjxoxxzpgJlzZt2nDFFVfw+eefk56ezunTp7n++uv9nrdz504mTJhA9erVyczMpHTp0mFpj+O999UzdejQoZACF3+WLl3K/PnzufjiixkzZkzErqOUipy0tDTS0tKYMGGCz/vyBf4ZGcAU5mPAPmANMAOYavTPGRUl5513HoBznEdhnTp1itzcXGrVquWzZyEWPPzww3z++ecsWbKE119/nbPO8n+3d9u2bQA0aNAgX8DiCNQK4xK7K2/dunX5jh0+fJj9+/dHNGjZunUrAE2aNMl3LJTXVRKNXbDB+e/7r2waxZYUb67vM+h7HSh/fd/+FkAsB1QFLgaeBm4TkauNMX8G2xARSQBGY+WCqY61htFk4GljzMmCzvWopwzWoOHbgHrAHuA9YJQx5rCPc2oBTwC9sLqlNgLjgNc8gzARqQzcCVwDtATOAfYCi4CnjDE/FNC2vsB9wAXAEeBz4J/GmM2Bvr7iaNCg8NTTuHFjEhMTnan6C2vLli0YY0hNTQ1PwyLkiiuuIC0tjZycHG677baAznHcstq0aRPHjh1zC8qWLFlS6Lakp6eTlJTEggULOHDggNtg3OnTpxe63kA5brf98MMPGGOct5AgtNellIotBf5p5mVBRM/Hp8aYacaYvwHNgCTg7mAbYQcsXwM3ArcClYF/2I+PRKRUgPWUBuYBI+xHZaA/0BdYLCL5briLSF1gBdAW6IEVhL2Mtar1eC+XWQ48A3wMXGiXHwx0AbJF5EofbXscmAp8ANQALgPq2ue0COT1FVYkFiQMp8xM94UVC6tUqVL06NGDHTt2hLSWjWPKb69evUJvVITNnj2bpUuXBjz4uF69etxwww3s37+f/v37s2XLFg4ePMhbb73FCy+8UOh2VKpUiVdffZXDhw9z4403smnTJk6cOMG8efOYOnUqNWrUKHTdgejQoQNt27blp59+4t5772X37t3s37+f5557jnfeeSei11ZKFSFjTNgewJXA4kKc9xJggF4e+x+w9w8NsB6v5bF6bwzwjJdzsoDTwIUBtulX4BUv9fzFLr8NEI9jqfY1ZnjsrwMcB5b5ek0pKSkmVAWv+GPM+PFnyo4fX3BZV5dc4rvcoEFnyq1YUXCdK1aE/BKdFi1aZADzv//9z8v7QL5H586d85W7/vrrTUJCgsnLywupLV988YUBzBtvvFHoOm6//fZ8bf7iiy98lt+yZYvX13n77bc7yxw7dsw8/vjjpmnTpqZs2bKmWrVq5uabbzaZmZlu52zZssW88cYbXuvbsmWL1+vPnTvXtG3b1pQtW9ZUrVrV3Hbbbea3334zderUMU2aNHEr663eL774wus1He1PTk722Zbc3Fxz3333mfPOO8+UKVPG1K5d2wwcONCMGTPGrbwxxnTu3DlfPSNHjizkTyk+Aeb5+evdHpVr1HF7T5KTk6PdzGLB87329t6rM4AVxsd3opgwDkERkUrAFmNMwKu12efsBQ4AdYxLg0TkXCAH2GyMyX+z2r0ewbqlVAM41xhzyOXY2Vi3icoA1Ywxx+39TYANWEHDpR71XQx8D3xmjLnSZf9cYIwxZolHecG65VMeaGaM2eBybCpWb89VxphPPM77CEgHLjfG5EuQkZqaalasWFHQSw9IQT0t48efWdgwMxPuLqCvzPXjkpLiOyncoEFnek+ys6GgOy0rVoQnI67Dddddx/Lly9m0aZNbxtZArF69mosvvpixY8fy17/+NXyNKsFOnTpFuXLl6NixY0xmGS6pRITn568vsMyI7s101lUYeL7XI7o3y/fe65iWM0Qk2xjj9VsjqIy4ATgGBDbd4oxuWGNjlhmP/x3GmH1YQUVjEfH3E22FdbvlR9eAxa7nT6zbOhWBTi6HHP3/S73UtwY4CnQRkQoudV3tGbDY+w1W0AL5swI7rvOtl+s4rn21l2NhU1BfR2FXYs7O9l3O9XZPMKtLh8OkSZNISkrilltuybeWTkF27tzJDTfcQJ8+fbj33nvD26gS4OGHH2aCl5U5P/30U/78889CrbqtlFKuwh201MP6og9GS3u71cdxx/6WPo6HUo/Pc4wxp4AdWIOVm/u5NiJSBWt8yy6sBSUd++tija3JNcbkBtguFYLKlSuzZMkSSpUqxcSJEwM+76GHHqJPnz5MmTLFbSCnCszu3bt56KGHyMrK4tChQxw4cIDZs2dz9913k5KSwpAhQ6LdRKVUnPM5e0hEJhlj7gyyvj64fGEHqKa9PeDjeK699TeSrzD1hOvaYL12sMbNuE4VD+c1VICSkpJ4//3382VNLcj48eOpUKGC/4LKq2HDhlG2bFn+/ve/s2vXLo4fP06DBg3o168fjzzySExPH1dKxYeCpjzfjjW11y97XMqdwL+wZtYEw/GbzFc//h/21t+3SWHqCcu17V6Wf2Gtcv1SGNrllJOT4zb1NiMjw5ExUAUgmCBEA5bQtGnThjZt2kS7GUqpOJSZmemaSNXnuNiCghYREX+j5soA1YCGQCmsgbBjg2gnWONgAHyl5nTM5fT3J3Nh6gn52vYg36lAHvB/xpjTYWiXU7Vq1QjHQFyllFJFz3GneUT3ptz/8jbqNT0R3QbFKNc/yEXkd1/l/CWX6xLg9Q4BM4FHjDG+boP48pu9rezjeJK99beufGHqCena9oyh8UALoIsxxlsq1nC9PqWUUqpE8xe0dC3gmMHKMbIP+MVz5k8Q1trbhj6ON/AoF856fJ5jJ7SrB5wCfvZy3BGwXIEVsGz1dlFjzK8ikgskiUiSl8G43tqllFKqGHl+vpUFY8bY6lFuSXwrMGgxxnxZBG1YCJwA2oqIeMnT0hQrT8sGXxXY1gA7gRYiUslLnpY2wGFgscs584AXALccLbZWWONMPjfGuN26sQOWcUB3PAIWEbkZ+MEY86PHdW61r+OWpwVo71JGKaVUMfbtx0nRbkJcK2jKs68ei7Cyg4vXgVrAVR6HB2DlPHnBsUNEEkRkjoi85Zre3w52XsQaO9LPo55rgCrAeEdiOfucjVjBQlsRucDjHMcg5Bdcd9oBy2tAT6Crlx6WIVgBkitHHW4Dm0WkNtbSASu85X5RSilVHCUzonszt0eDBg2i3ai44LOnxRizrQjb8QjW+JlMEbkFyMYKCkYB87F6NRy6cyYR20tY6wY5vICVyG2MiOwAPsNaU+hlYLVdn6chWEnf3rEXNNyEtdjiYGCSMWaOR/lXsdZXWmpfx7O+fOsIGWOWi8iTwCMi8hDwCtZU6Dewepnu8NIupZRSxdJW5+0ihxHdm0WpLfHF35iWImGMyRORDlirPE/nzCrPz2Ct8uy6avQ3wC9YY2l+9KjnpIj0xAqCXsDKkLsHeBsfqzwbY7aLSCrWKs8LsFZ53gTcjxVceBpsb9tz5tZOIK/xnyKyDmuV55FY2XMXAgOMMcHmtlFKKRUHVqwoeBkTFZyYCFrAClywvtDv81NuF9CogOMnsIKCkUFcexcB9nYYYwqdKtUYMwWYUtjzlVJKxZdwL1NS0oU7jb9SMWPjxo18/XW+NSgLZdasWeTm5oalLqWUUoWjQUuMy8vLIyMjg6ysrGg3Ja5Mnz6dNm3acPq0letPRPI9unTp4izv7/imTZto1aoVq1atKtoXopSKa54JzOs2Pk7dxse9Fy7hsrKyHAnmEn2VEV12PLalpqaacGTEbdCgAdu2FeXY6uAkJyezdevWsNQ1b9480tPT+fDDD91WFt66dSsNGzb0eS1/x//2t78xZcoUli9fTv369cPSVqWiRUR4fv76AsuM6N4M/Y4IjTVXQ9/rYIhItjHG60gg7WkpIbZt24YxJmYf4QqoDh06xMCBA+nVq5dbwBIOjz/+OABDhw4Na71KKaUCE3TQIiKlReQGEXlaRMaLyFMicr2I+FpbR6kiM3HiRHbv3s0DDzwQ9rorVKjA0KFDmTt3LqtXrw57/UoppQoWVNAiIpcDm4F3gQeBQcDfgfeATSJyWdhbqFQQpk6dSrly5bjsssh8FLt16+a8jlJKBWtE96aM6N402s2IWwFPeRaR5sDHWGsOfQisw8o1cg7QHLgS+FhE2hpj1oW/qUoVLDc3l5UrV9KyZUvOPjsys/kvuMBKnLxwob8F0JVSSoVbML/Z/wV8D1xnjNnvedBeJ+gD4DGgb3iap1Tg1q1bhzGG2rVrR+waVapUoWzZss5recmIrJRSKkKCCVq6YC0OmC9gATDG7BORu4FFYWiXUkHbtWsXAElJSRG9TmJiInv37iUvLy/i11Iq0maMre62iJ9nenkVmksuge+/j3Yrio9ggpbKWOnzC7IZSCp0a5QKwdGj1mLcZcqUieh1ypYtC8CRI0c0aFFxJysri02brDQYPy5dyIG9nXD9tf3jUuvW5wXtu0WhdcVPdrZj2rMKh2AG4u4COvgpcznwa+Gbo1ThOcaxnDp1KqLXcdQfqXEzSkXaiBGdnP++PH0xd40eV0BppWJHMEHLh8AUEbnC20ERuRJ4C5gVhnYpm2bEDVxiovXX4/Hjkc02eezYMQASEhIieh2llCpJAsmIG8yfiv8BrgHmi8g+YD1nZg81A87Fuj30RGEbrPJLTEwkMzMz2s2IC+eddx4AOTk5EbvGqVOnyM3NpVatWpQvXz5i11FKxb+srCzS09OAM7fdLkvbbT//GdDbcK7S0tJIS0tjwoQJeb7KBBy0GGP223lYXgPSAddEGKexZg4NM8bkFrK9SoWkcePGJCYmsmXLlohdY8uWLRhjSNW15lUxc26tyAX76ozzU3+OdhPiWlA35Y0xvwHXiUhNIAWrCycXWGGM2Rv+5ikVuFKlStGjRw9mzJjB3r17qV69etivkZ2dDUCvXr3CXrdS0XTt4Pej3YRiITMT7r7b8Swtmk0plgq19pAx5jdjzFxjzNvGmHnGmL0i0lVEGoa7gUoFw7Eu0Lvvvuu2X0Ro2ND6eG7bts3rKs8FHXeYMWMGCQkJ3HrrrZF5AUqpYm3diuasW9E82s2IW8FkxP3FGHNeAUX+BzQTkbuNMW+E3jQVTsnJyTGdCC05OTks9XTu3Jlrr72Wp59+mkGDBlGuXDkAv6unBrK66urVq5k1axZjx47VQbhKKa8yMqwH4JxAkZ5+5vjXWZ0BvU1UWMH0tDTwczwVGAz8s9CtURGzdevWqK/kXNBj69atYXutkyZNIikpiVtuuYWTJ0+Gpc6dO3dyww030KdPH+69996w1KlUtDz//OJ8+14fOZjXRw6OQmuKj8xM66EiJ5gxLQX+KWqMOSEi7wIvhdYkpUJTuXJllixZwl133cXEiRMZMmRIyHU+9NBD9OnTh1GjRsV0j5VSgWjc2OfkjJgUaMqHtLTojiFxjGVx9LSo8PMZtIhIJ6zU/a77HgN8/cYuh5V8LnJTN5QKUFJSEu+//74zS26oxo8fT4UKFcJSl1JKqcIpqKelKzDS5bkBRvupLxfoH2KblAqbcAUaGrCo4iQtPd3qOh85hC9TLmdKuq5xq+JDQUHLm5xZ/FCAz7ECGW8McADYZIw5Fq7GqTMZcR1Jd5RSKhIuITvaTSjWmuV8av9Lxw35kpWV5bgVGHxGXGPMNmCb47mIbDfGfBnWFiq/NCOuUipSnh1+LWB9oU7EGvs1kNei2KLix9mr9fKHADxoj7DQ9zm/QDLiBjx7yBijOViUUkpFTf1PPiEtPZ1WL78c7aaoKNFlapVSSsWlsvv2AXDi3HOj3BJ3YxdsAODHlTudOXE/uLodmxvWotUPW9lTqUX0GhfnNGhRSqkSJptLgO+j3Yygbe/Zk+09ezqfd7/jDgCyZs+OVpPcGHMmYHGYnHYrS7LepknDWgD0WLQKWMXi1I5F38BioFBp/JVSSsWvVLKJtyU/09LTSXNNLQucLlUqSq0J3OLUjkxweT6t3M1MLt8vau2Jd9rTopRSJUyjRrls3gwdlv3MZcvX83WbZtFuUqGcdepUtJsQtPOPbwRgMR2i3JL4pD0tSilVwowd+1W0m1AspaTA80Pru+2bOHKIWzr5FL4nJQ5vzcUK7WlRxdbGjRvZu3cvl112WcSvNWvWLLp27UpSUlLEr6VUqBzTcJ9t15xv2lkrDr+6/FEAynM4ii2Lb99/D1ZyeBUpYe9pEZFXw12nUsGaPn06bdq04fTp0wCISL5Hly5dnOVDPb5p0yZatWrFqlWriuYFKhVm0+jHNHSshYptkehpuRsYGoF6S6SwZcT9Ykz4GhUJXR8OW1Xz5s2jX79+fPjhh3TsaI3Qd6wk3bBhQ5KTk/OtKh3q8QcffJA9e/bQo0cPli9fTv367l3ESsU6a0aRUtETUkZcEfklEo1SwdGMuME5dOgQAwcOpFevXvTu3btIr/34448zZcoUhg4dypw5c4r02kqFKpuUaDdBlXChZsRtgLXmkOujvL2/AZBg70tw2ZeIS+p/pYraxIkT2b17Nw888ECRX7tChQoMHTqUuXPnsnr16iK/vlKhyGACGW6Tc2Pftu7d2da9e7SboYpQgWNajDENHQ+gHbAByAAqGWOq2seqApWwbgvtBK4sTENEJEFExorIdhE5LiIbRORRESkdZD1lRGSkiGy069kmIv8VkYoFnFNLRCaJyG8ickxE1ojIUBERP9fqIiK/iIgJoJwp4PFoMK9R+TZ16lTKlStXJINvvenWrZuzHUqp8MmaPZus//SDNe85H2s61WDN8OHRblpQVrdI5suUy6PdjLhV0JiW8R7PnwHGGGM+8SxojDkCTBCRnXa5/wumESKSAHwNVAZuAbKBnsBkoIOIpBlj/E7ItwOceUAb4DbgM6At8A7QTUQ62m11PacusAxrleoewCagL/AK0BorSPO8zjnAU8CtQJUAX+YxYLuPY78HWIcqQG5uLitXrqRly5acfXZ0JsZdcMEFACxcuDAq11eqJGn6+Wr44SQbbr012k0BYNAgeGtqfUZ03+HcN9GjTI2cXJLOPl20DStGClrleYjHrquAQX7q+wwr0AjWE8CFwNXGmCX2vlkiMgr4L1YvTiCzku4FrgCGGWOy7H1fishwYCYwEvi7xzmvAbWAHsaYH+x9mSLSEhguIh8aY+Z5nLMQ2Ae0An4N8DV+Z4zpEmBZVQjr1q3DGEPt2rWj1oYqVapQtmxZZ1v8dNYppQJU/5NPYOcGtrdp6tzX7Is1wJqYCVoyM2HChB08P389AD8uXchArFW0HWrm5AE+h2woP4KZ8lwJqOenTD2gQjANEJFKwEBgN/Cxx+E3AQPcH0A9AtwHnASmeBz+CNgPDBGRci7nNAF6YwUUP3icM8neerv2f4wxvYwxO/21SxWdXbt2AUQ9V0piYiJHjx4lL09/ManYVJudRC+0L5yLXn2Viz5aFu1mhKwvU+ib7ytKBSqYoGUNMM6+lZOPiCRi9VoEOwKxG1Y2nmXGGLexIcaYfVjjaBqLSFNvJ7toBdQFfjTGHPKo509gOVAR6ORyqJe9XeqlvjXAUaCLiLgFYi69OCqGHD16FIAyZcpEtR1ly5YF4MiRI35KKhUd1w3NYXeAZccu2OD2UL5lZ+ff12nFV7T6YavzuebDCU0wN/6fAD4EtovIfGA9cAQ4BzgfawBuRayei2C0tLdbfRzfCjSzyxX0PyaQehzl5vs7xxhzSkR22NdujjXOJhRVRWQC1vtUC2sMzRLgaWPM8hDrVuAcx3IqyuuROK4frXE1SvmTQSYnPfZpnpbQpXpZhbJ/1tsArLmwQdE2ppgK+LeqMSZLRPpjDVC9AdyWUxAgF7jN20BdP2ra2wM+jufa2xoRqCdc1w5Ec+B94HJgL3Ax8BLwjYjcbox5OwzXKNESE618RMePH49qO44dOwZAQoLXTkmlou6iV18lE3jWZZ8jT8vrIwdHpU1KBSKoPwWNMdNEZDZwDdaXbgLWiKLvgdnGmMIsWlHe3noG/g5/2Ft/Y2UKU0+4ru3PWuBSjx6VZSJyDfALMFFEvjDG5OuxzcnJIdUlfM/IyCAjI9+EJgWcd955gPWeRcupU6fIzc2lVq1alC9f3v8JSsUIR46Wu9FklqroZWZmuiZSreqrXND91/Z4kan2IxyO2Vtf+VgcAxSORqCecF27QPbYnH1e9u8WkSzgRqzeq5c8y1SrVo0VK1aEcvkSo3HjxiQmJrJly5aotWHLli0YY9wCTaXiwaddWrOnUgvuSh0HwAXtu/HvfnUY0b2ZW7kXvSxjocLDdczQ/Vf6G8ZZvLj+QS4iPtOAFOqmuz0wtSVQwxgzW0TKGWMK2yf/m72t7ON4kr3dE4F6wnXtUGy2t+dH8BolQqlSpejRowczZsxg7969VK9evcjbkG2PxOvVq5efkkrFlh6LVgGrWJza0bnvsSn58w15BjFKFaWgVnkWkZoiMgWr1+Ab4AP7UGc7g+0VhWjDWnvb0MfxBh7lwlmPz3NEpBTWFO5TwM9+rh0KTeQRRkOHWmt1vvvuu277RYSGDa0f87Zt27yu4hzKcYcZM2aQkJDArTGSN0KpwtqxoSw7NpSNdjOctnXvzrbUxm77Fg/pxeLnn49Si1Q0BNzTIiI1sDLH1gNygB1Y41oAVgCfAlkicpkxZmUQbVgInADaioi4TnsWkXOBpsBmY4y/uXZrsJYRaCEilVynPYvI2VhZcg8Di13OmQe8AFzqpb5WWGNZPjfGhHR7SESWAyO9JKkDaGRv14dyDb/CuIpyLOvcuTPXXnstTz/9NIMGDaJcOSstj8ds+nxCPQ6wevVqZs2axdixY3UQrop7Y4cnA/D8/NiY5rxm+HArfb+LvDrnQuPGPs6ITXP+ksLuhJb+CyqvgulpGYkVXFxhjKlhjHHetDfG7DPG3IOVwj+ob0c7uHgdaxrwVR6HB2D1RLzg2GGvUTRHRN6ye0Mc9RjgRazxKZ6T4K/BSrc/3vU2ljFmI1bg0lZELvA45057+wKhqwZc7blTROoAacBxrIy9KgwmTZpEUlISt9xyCydP+hpjHV47d+7khhtuoE+fPtx7771Fck2lisKPSxfy49LYXJbiyqdncuWAAdFuhpO34YdfplzO6hbJzue9P8tm0AdvFl2jiplggpZeQD9jzBcFlBmPtbBisB4BfsJKn3+5iJQXkeuAUVg5Vca5lO2OFQD050xPj8MLwCJgjIik2fV0Bl7GSno3ysu1h2CNbXlHRFqJSAURyQAGA5OMMXMK8Xo8GWCwvQBkPXtRx0uxMvWeDQwxxuwKw3UUULlyZZYsWUKpUqWYONFz5Y/IeOihh+jTpw9TpkzR1P0q5mXRm3jLkNn5nnvo/JJ7q8sdOka5/fuj1KL8UlLy75uS3pf53c58Va0r15gfK7QowlYVL8EMxK2BNbW5IIeAoEc/GmPyRKQDMBqYbtexHavn5mk7o63DN1jThPcBP3rUc1JEemIFQS9gZcjdA7wNjPI2JdsYs11EUrGS5y0AErEWTbwfKydNPvaaSCM99jnuH4w2xozyOKUL1uKK1wAPYt12+h1rkcjhxphvvV1HFV5SUhLvv/++M0tupI0fP54KFUKdGa9U0UgnCxCe8XKs04qv3AbjxoqEbdui3YSwWHy8a7SbENeCCVr2ARdhjV/xpR2FnGljjMnDWjvoPj/ldnFmHIi34yewAoqRvsr4qPOOIMqPwnuvja/y24Ax9kMVoaIKJDRgUfFk9uws0tMLLjPIztcSD+sRt3r5ZZLnz4fx4yGKeay8XfrfL42i9J9HeOO2v1hl7Hw43/FaUTat2AgmaPkEeFtEBhljvvQ8KCKNsXom5oarcUoppcKv4/33swJrFoTDs8OvZX21Hs7nmdwNwED9cg3YhAn599X6PZIZM0qeYIKWUVi3hxaKyC/YCyOKyGSs6cTtsWYV/Tu8TSzZ8vLyyMjIIC0tjbS0tGg3RylVDCRt3kwKZ4KWy1s3ov3A54APWTrxAZas2lzA2bEnef58cmtX4aucJZBVy7lff2fGl6ysLLKyssAapuFVwANxjTG/Ap2BlVi3Z67HmtlzG9Z6OtlAF2PMbz4rUUFLTEwkMzNT//MppZQfNdbtoMZ330W7GaqQ0tLSHKn883yVCXbtoZ+BVBFpA7TFioZygWXGmFBXQVZKKRUlSyc+4Pz3dXOWRrElgcv6j3t2i7RHpwCLyJo9OzoNUhFXqDT+9sJ/y/0WVEoppZQKk4BvD4nI8yLSwMexyiLyhYgsFJHPw9Y6pZRSRW5W7/bRbkI+i59/nsVD4n9Nr9+qJbKtVj3nc9fkfbGcyC9WBNPTch9wh4gMN8ZM8zh2GGugbiWshGlKKaVU2OQ1bgxHz6wQk9ahudvxrG8iuURcYC65BL73k81sT7UkcsvXK7iQ8qkwt4cmi0gvrCyuB8FK6gZ8KSI+R/wqpZSKHxmMt/8VG5laEjdtgp37rPWGYlR2NngmxP733Q+RfOBM/tCLftoGbGNKel+3chWTqvD6yCHO5y8mJ7N169YItjY+BRO0GKxFBKcBfYAOItLPGLMkIi1TKkQbN25k7969XHbZZUVyvVmzZtG1a1eSkpKK5Hqq8FasWEHZsmVp2TIyC9d9+umntGzZktq1a0ek/lClMRtIp1MBZWItT0unESOA/INv/fG2qkYA65+GzbbayZQrvc753FcwePP9Tzj/fUH7bozo3qwomhd3gll7CGPMDqxpz48CtYEvROQJ14ULVQzbtcv6H+z5izQlxdqf7TIBbNQoa9+oUWf2Of6M8Fxgo3Zta/8ul+WTMjKsfdb0NUtWlrXPc/q2iPffLCGYPn06bdq04fTpM78YRCTfo0uXLmE7vmnTJlq1asWqVavC+lrC5eTJk4wfP54rr7ySGjVqUKZMGapUqUK7du247777+OSTT/jzzz/9V1QEBg8e7HyPGzRoENa6n3vuOXr27EnZsmWd+44ePcr48ePp3r071apVo3Tp0tSoUYPrrruOr7/+2mddBw8e5L777qNhw4aUK1eOWrVqcf3115Obm0urVq34/PPYHOL3R/dW+FtULZNBZDKoSNpTnCXv2kaNvbnO55nc7QwIVfCCClrAWk3ZGPMkcBmwBXgI+MbOiKtU1M2bN49+/foxdepUOnY8s4aKMYYtW7YAkJycjDGGRYsWhe34gw8+yE033USPHj3Yvn175F5gITgCqieffJLbb7+dtWvXcvDgQZYtW0b//v2ZMWMGV111FU2bNo12UwEYN24cxhiSk5P9Fw7C+PHj+ec//8mnn37q9lrT09MZPHgwLVq04LvvviM3N5cZM2awfv16OnbsyJtvvum1vuHDh/PKK6+QmZlJbm4u7733HnPmzKFdu3aMGjWK9PR0vvc3yCEK/tnzAy7xUyaFbFIoPpksjDnziBRvf3s9Nv4p+s9YlG//XXU2cVedTTTL+ZRmOZ9GrlHFTNBBi4MxZgXQGpgEtMHKlqtheZg5MuLaWQILJyPDetSubf2P3eWxoHR2trXftQdl1Chrn2tPS0qKtS/b4xfZrl3WftcenMxMa5/rYhxpadY+z9cSxt8khw4dYuDAgfTq1YvevXuHpc5gPP744wAMHTo05LoWLVqEiPj8wgzUb7/9RqdOnThy5AjLli3jtttuo3r16pQrV44mTZowbNgwFi1aRFJSEvtjaMXccNu+fTsjRowgIyODFI/ewuPHj9OrVy9eeOEFGjZsyDnnnEPnzp354IMPOOuss7jnnns4ePBgvjrnzp3LRRddxJVXXkm5cuW4/PLL+fnnn6lTpw7Dhg2jUaNG3HnnnZw6daqoXmZAOo0Y4TccSeF7UvyukRtbVl/TjtUB/N9LSfG+InNRaj/wOdoPfI4HX/6QVj9sjW5jYkRWVhYZ1ndG6BlxARGReiJS17HDGHPUGDMIKzvuCeDpwjZWeReWjLgTJnhfFKMYmjhxIrt37+aBBx7wXzgCKlSowNChQ5k7dy6rV6+OShs83XPPPezevZunnnqKmjVrei3TtGlThg0bVsQtK1rPPfccR48e9frZOP/887n99tu97m/cuDGHDx/m22/zL8a+f/9+Klas6LavUaNGlC5dGhHhgQceYPXq1cyZ4+9mjAqH7W2asr1nT7d948dbD1fff+9/lk9R6rFoFRNdBuGWVIFkxA0maNkOLIb8t0KNMR8CLYHYvIFb0nn7X1tMTZ06lXLlyhXZ4FtvunXr5mxLtG3ZsoX333+fChUqcP311xdYdsCAAQwZkv8X5/bt2xk4cCB169alTJky1K1bl0GDBrFjxw63cj179nQb67NhwwZ69epFpUqVSExM5Oabb+b333/3eu2srCzatWtH+fLlqVatGv3792fPnvAtNGeM4e2336Zx48ZebzlNnDiRm266yeu5lSpVctbh0KVLF8S+F/Dll186X/eAAQPczo2lz0JJ0GbKQtr82335O0dHcyzJojf/6DeDpRMf4Nnh10a7OXEl4NlDxpgGfo7/BnQXkcgMx1eFF2v/Yz05epFCuQUG5ObmsnLlSlq2bMnZZxcq2XNYXHDBBQAsXBj9JFELFizAGEPLli0pV65cgWUbN27MmDFj3Pb98MMPdO3alRo1avDhhx/SsmVL1q5dy+23305KSgpffvklzZtb+TI++eQTwBqwvG/fPoYOHcpTTz1Fs2bNmD17NgMGDODIkSP5eh2mTZtGv3796Nq1K2vWrKF+/fp8/vnn3HjjjRw/ftxvuwOxdu1afv/9dzp06BDUeadOnWLz5s2UL1+eNm3aOPc7xjKJCJ07d3Yb2+SqXr16JCQkxMRnoSSouX4nsDPazSiQYAW/f+c95757yzzP8T/OIVaml8eySPxmvw5YG4F6VXEVpq7zdevWYYyJ+jTTKlWqULZsWWd7xNvovCKyfv16AOrWreunpHf9+/dn3759fPXVV5x//vkApKamMnPmTC644AL69+/P8uX5V/T44YcfeOutt7jkEmu4Z9++fZk2bRrz5s1j//79VKlSBbBm3wwbNoyKFSsyc+ZMKleuDECvXr3YuXMnGRkZYRmM+9NPPwEE/dn45JNPOHDgAPfdd5+zzcGqVasW69evZ+/evVSvXr1QdSg4VLcuHM8/rsgfxwTGWPnb7ZuJf8u3739/WNO5Y2V6eSwr9EDcAoyMQJ0qFFlZIfdiRNTs2dYjRLvsAcaxkCclMTGRo0ePkpfn89ZskXAMHi1fvnzQ5y5btoyVK1dy8cUXOwMWh+bNm9O6dWtWrFjBd15W1a1bt64zYHE9xxjD5s2bnfs++ugj8vLy6N69uzNgcejTp0/QbfalMJ+NP/74g7///e80bdqUJ554wv8JPiQmJrq1QRXOoldfZdF91wR93t13Ww9VPPjsaRGRfwD1jDHD7efavxmv0tOtbVFmVApGKIOMXRw9ehSAMmXKhKW+UDhygBw5ciSgL8ouXbrw5Zdfej12xx13cMcdd7jtK+iWhCvHF+axY8f8lvXk6EHxDFgczj//fFauXMny5ctp27at2zFvPRqOAauOnxPAypVWWvZmzfIn0qpYsWKhezc8FeazMXz4cHJycvj666+pUKFCoa/t+lmIprELNgDw48qdBPI/TnO0BG/8+PwB0o7qtSl76nB0GlQMFXR76CEgQUT+bYzZA3QJsM4Y/WYswaIw9TcaHONYYmF6qaMNgY6tqV+/fr4v7qNHj7Jjxw5q1qzpDD5cywfCkYvk119/Dai8K0cv0TnnnOP1uGN/bm5uvmPeenYct8lcB7Q6eoJ8XcMxCDZUwX42Ro8ezcyZM/nss89o0qRJSNcO9rNQFHKoCngfFO3gyNHynd+MLkWj1csvw/5fWHNt6Is5DopQPJaRkT9oGT3sMc3DEkYF/S+6DqhuBywAGGP83k4SER1JFGti+dYQhO2ms+OL/fjx46G2KGSOno2EhISAyk+ePDnfvkWLFtG1a1fGjBmTb1ZKoHr06MFZZ53F2rVr/Q5qPXbsGEeOHCEhIYEyZco4e4h89RA49nve1gmG42fm6xqHDh0KS+ASzGfjiSee4MUXX+Szzz7Ld4urMByfBc/AM5qqkwMIzxRQJtZytCTPnw8QlqDFNVG3ii8+gxBjzCJjzAyXXd77rvMLtJxSljDddD7vvPMAyMnJCbmuUJw6dYrc3Fxq1apVqLEk4ZScnMzNN9/M0aNHef/99wsse91111G/fn1ngjnHLZ+ff/a+eq5jv+etoWA4goJ169blO3b48OGwJbsL9LPx5JNP8txzz+ULWD755BO++eabQl37999/j8hyBMH6dz9rPRtrUT6hUoWCA+q+TKEvU4qmccWEt2Co3+xpdF+4Mv8BVSgBD8Q1xnQNZzkVmLBkxI11gwaFpb+2cePGJCYmOlPtR8uWLVswxpCamhrVdji8+OKLJCcn88gjj/Dbb795LfPRRx/x6aefcs899zgT0LVp04bU1FRWrVqVL6j4+eefWb16NampqSG9zvT0dJKSkliwYAEHDhxwOzZ9+nSf5/33v/+lQ4cOrF0b2ETFSy65BBEp8LMxZswYnn32WRYsWJCvh+Wdd95hvv2XfjBOnDjBrl27uPDCC8MydTsUB/bs5Pn567lr9GssSrmcrxoU3EM2jX5MI7jFCeNFdnb+xN7h4O1vr87ZS+yVnZU/4c6IGxAR+SXcdZZkYcmIG4EFCcMqMzMs/bWlSpWiR48e7Nixg71794ahYYWTbf827NWrV9Ta4KpatWosXLiQxMRELr30UqZOnUpOTg7Hjx9n/fr1PProo9x8883069cvX56WyZMnU7VqVW644QZWrFjBH3/8wYoVK7jxxhupWrWq19tawahUqRKvvvoqhw8f5sYbb2TTpk2cOHGCefPm8b///c/nradRo0axdOlSxgeYNLFatWq0adOG1atXe10U8umnn+aRRx6hfv36PPvss9xyyy1uD1+DpP1ZtWoVp06dipnPgkNx/SLN+k8/sgKYiZiaaj1ixaddWjM57dZoNyPqQsqIKyL1C/FIBsK7wpkKH8/AJS3N2ufai5OZae1zHV9SlKtDh8ix5s+7776b75iI0LBhQwC2bdvmdRXnUI47zJgxg4SEBG69NXZ+CZ133nlkZ2fzyCOPMGnSJM4//3wqVapEx44dWblyJVOmTGHy5MmcdZb7r4TmzZuTnZ1N+/btufbaa6lYsSLXXHMNl156KdnZ2c7EcmBl1PXMEvvmm2+ydetWRITRo0cD0LVrV7dbJX369GHOnDkcOnSICy+8kFq1ajFhwgQ++OADEhISnO/14MGDnefceuutVK5c2W+WX1dDhw7l4MGDfPzxx/mOvfaalR9jzZo1vPvuu/keW7dudSvvKyOuZ7kZM2ZQqlQpBkVq5KdyU/bgUcru2+e2L9KLJIZDj0Wr6J/1drSbERcKGoi7FZ0JVDwYE9s9LWHUuXNnrr32Wp5++mkGDRrk1iVv/PzmCvU4wOrVq5k1axZjx44NeBCuL126dAnomoEqXbo0GRkZju7XgNWrV48JAaxd9eabb/pc3NHf6+jVq5fX3gjPIMAhMzPT8RdZwG677TZeeuklRo8eTe/evd2S/vm6ji+BTDffs2cP48aN45577qFRo0ZB1R/LkpOT3d675OTkoN+/SOn+zPvA+wH1tsSSaeVu5pREP1VDPPA3By/Yvl+BYnoTNN55+9LwNk7G20IdjtWhPXm7KTxqlHsvC5xZHdpThJJtTZo0iY4dO3LLLbfw3nvvUbp06Yhcx9POnTu54YYb6NOnD/fee2+RXFMFrlSpUsycOZPLLruM4cOH88orr0TsWgcPHuSaa66hbdu2PPXUUxG7TjR4BijRzPjs6fRZAhKJnKmRdf7xjQAsJrhlJkqiAoMWY8wdBR33RkT6F745SoWucuXKLFmyhLvuuouJEyd6XQQwEh566CH69OnDqFGjYuoXuTqjQYMGLFu2jNtuu42PP/6Yq666KiLXGTNmDK1bt+bFF190JpeLV6+PHOz2fATw/PwNRd6O1UOHws6CR8+eddoA7rl4HHelIzHw1pU1WcIae/jj0uByscba9PJYVlDQEnTAEuJ5SoVNUlIS77//vlv21UgbP358SJlTVdGoW7cuixYtiuhn47HHHtPPQpht79kT1hwK+rzvNR4oVnwGLcaYtwpZp46DUTGjKL849EsqvkTy51UcPgtz/pLC7oSW3HXROAAuaN+NEd2bRrlVsW327CznqikOk9Nupcahn6LToGIoEnml3yD4sTBKKaWKyCh7XdtzWO2zTO/PsoFsll3UzrkvGreFHNLsaCDrP6EPm1yxIuQqArY4tSPNcoqux7e4CypoEZGKwCDgMqAy1sBbpZRSceTlWsMo9+dB/lpA0PJbtUTOOfwnE0fmHxM2dv76SDYv4jyzL4RLWnq6davB4z17dvi1kblgCRRw0CIi1YCvgcb2LoP3oEVvD4WRIyNuWlpaaAnmlFLKdu3g9/0u4jfl5q60Xfo7nbOXFFGriq/LWxefKe+RlJWV5cj+7jMjbjA9LY8DR4CLgB+BPx0LKIpIOaAj8BoQ+iIyysmREVcppcLF0XvirwdgSnpfLtqwlqRDeTzwwBiee+5h4MzsmAvad4toO4O1LbUxVDnPbzlHVodI/Wp1vK+BBiurWySTW75eZBoTRxx/nE+YMMFnRtxggpargD7GmLUAIuLsUTHGHAcWiMhdwGPA54Vss1JKqRiVTeirXkfSmmvbQ6sb3fZ5S0bsyJUYK38P1sjJJens0277PJP4OfbFSiK/aAkmC08twHWpytMi4pm16zugUCs6iEiCiIwVke0iclxENojIo16u4a+eMiIyUkQ22vVsE5H/2uNxfJ1TS0QmichvInJMRNaIyFDxk2xDRLqIyC+uAZyf8n1FZLmIHBWRHBF5R0S031ApFbP+9renGDj6NfISksgmhWwiNCAkDJp+vpqmb7unww/T0mYBEUyhBnrWzMkjefcOt31bt27FGOP22Lat+K0XFaxggpa9QJLL853ABR5lLqQQg3NFJAFrvMyNwK1Yg3z/YT8+EpFSAdZTGpiHlf9ohF1Pf6AvsFhEzvFyTl1gBdAW6AFUBV4GXgS8rsYmIueIyEvA+0DDANv2ODAV+ACogTWYuS6QLSItAqlDKaWiKYMJZOB/SYdoafbFGpq98060mxG0vkyhL1Oi3Yy4EEzQ8hPwN5fnq4FXReQCESkrIm2ACcA6r2cX7AmsgCfDGLPEGHPMGDMLGIV1WyrQcTL3AlcADxtjsux6vgSGAxeDPc/P3WtYvUi3GGNWG2OOGGMygXHAIBHxtjzrQqAR0CqQRolIKvAo8J4xZowx5pAxZgNwM1AOa5q4UkrFtAzGk+H9b7mYlZ0d+Wy4DoPIpDBLY06jH9N0BZyABBO0zAJGiMgc+/mzQDtgDXAU+BZoCTwXTANEpBIwENgNeC6/+ibWbKT7A6hHgPuAk5AvZP0I2A8MsQcNO85pAvQGvjPG/OBxziR76+3a/zHG9DLG7PTXLtt9WD1Qk1x32ud/CrQVkcsCrEsFaOPGjXz99ddFdr1Zs2aRm5tbZNdThbdixQrWrl0bsfo//fRTdkVoba2icHnrRlzeuhF31dnkfDTL+ZRM7iYzSnMtsmbPDjpHS1ZWFqmpkJrqNjMlYqz3R0VSMEHLW0Ab4EEAY8wSrFsvm4E/gV+Ae4wxM4JsQzes3oZlxmMpWGPMPmAD0FhE/KVibIV1u+VHY4xbrmdjzJ/AcqAi0MnlkKMXZamX+hzBWBcRcUtvaYwJ9pPvuM63Xo45rn11kHUGLzvbWu3Z18P1z5GMDN/lPJMcFFSn683kzMyCy4bR9OnTadOmDadPnxncJiL5Hl26dAnb8U2bNtGqVStWrVoV1tcSLidPnmT8+PFceeWV1KhRgzJlylClShXatWvHfffdxyeffMKff/4Z7WYCMHjwYOd73KBBg7DW/dxzz9GzZ0+fawKtXr2aiy++GBHxO+jx4MGD3HfffTRs2JBy5cpRq1Ytrr/+enJzc2nVqhWff65zElThjF2wId9DBRG02Ldaso0xP7vsm2aMaWqMKWuMaWKMebUQbWhpb7f6OO7Y39LH8VDq8XmOMeYUsANrhlVzP9f2yR4zUxnINcbkBtguFYJ58+bRr18/pk6dSseOHZ37jTFs2bIFsEbhG2NYtGhR2I4/+OCD3HTTTfTo0YPt27dH7gUWgiOgevLJJ7n99ttZu3YtBw8eZNmyZfTv358ZM2Zw1VVX0bRpbKRpHzduHMYYkpOTw1rv+PHj+ec//8mnn36a77X++eefPP7443Tq1Il16wK7y+1YLTozM5Pc3Fzee+895syZQ7t27Rg1ahTp6el8r4vfhEX9Tz6h/vLAvrg73l9w5/wll1gPFX/Cvoa3iPwryFNq2tsDPo7n2tsaEagnXNcuSFFco2AZGdYjJQWM8f1w7UHJzPRdzvMGcUF1OhIiONpRUNkwOHToEAMHDqRXr1707t07LHUG4/HHHwdg6NChIde1aNEiRIQ333wzpHp+++03OnXqxJEjR5wrHFevXp1y5crRpEkThg0bxqJFi0hKSmL//v0htztWbd++nREjRpCRkUGKl5Soffv2Zd68eSxbtowaNQL77zh37lwuuugirrzySsqVK8fll1/Ozz//TJ06dRg2bBiNGjXizjvv5NSpU/4rK0K12UltAr27HRsuevVVLvpoWdDn7bRfrauiHOeiwivsQQveB7sWpLy9Penj+B/21t8KZIWpJ1zXDne7nHJyckhNTXU+CpVobsKEM4kJirmJEyeye/duHnjggahcv0KFCgwdOpS5c+eyerXvFOlF6Z577mH37t089dRT1KxZ02uZpk2bMmzYsCJuWdF67rnnOHr0qM/Pxu23387XX3/N+eefH3Cd+/fvp2JF92wKjRo1onTp0ogIDzzwAKtXr2bOnDk+aoiO89I20r7Lh9Fuhk9ZWVkgwuphwwo1DmWNy2e5Nrupze5wNk9FQGZmpvN7DmsWr1dBBS0icp2IvC8ia0Vks52jxO1RiLYes7e+8rGUsbf+VpwqTD3huna42+VUrVo1VqxY4XxkuPZcqHymTp1KuXLluOyy6I1r7tatm7Mt0bZlyxbef/99KlSowPXXX19g2QEDBjBkSP51ZrZv387AgQOpW7cuZcqUoW7dugwaNIgdO9zzSvTs2dNtrM+GDRvo1asXlSpVIjExkZtvvpnff//d67WzsrJo164d5cuXp1q1avTv3589e/YU/oV7MMbw9ttv07hxY5+3nHr16kWpUgFlV6BLly7OxF9ffvml83UPGDDArVwsfRZcZZAZ01OXHS569VXS0tOdiyUWJK3Dmbv4nSpsJq3iD7DmvUg2L2J+XLrQ7VESZGRkOL/nAO+/KAhu7aEHsGYMGaxbGgdDbKPDb/a2so/jSfbW32+wwtQTrmsXpCiuEd8cXfUh9tfm5uaycuVKWrZsydlnR2IB88BccIGVvmjhwuj/slmwYAHGGFq2bEm5cuUKLNu4cWPGjBnjtu+HH36ga9eu1KhRgw8//JCWLVuydu1abr/9dlJSUvjyyy9p3tz6svjkk08Aa8Dyvn37GDp0KE899RTNmjVj9uzZDBgwgCNHjuTrdZg2bRr9+vWja9eurFmzhvr16/P5559z4403cvz4cb/tDsTatWv5/fff6dChQ8h1Ac6xTCJC586d3cY2uapXrx4JCQkx8Vlw1T/LSsC25sIG0W1IAbZ1707y/PnBnbTwyYCKOcb9h+mudMjm/CWF3Qk6rDEQwfxm/yvWFOR/GGNyfBUSkdO+jvngmHfoK0lbA49y4azH5zl2Qrt6wCngZ8/jgTLG/CoiuUCSiCR5GYzrrV0lS5gGKq5btw5jDLVr1/ZfOIKqVKlC2bJlne3xk1g5otavt1bjrVu3bqHO79+/P/v27eOrr75y3jZJTU1l5syZXHDBBfTv35/ly5fnO++HH37grbfe4hJ7tGPfvn2ZNm0a8+bNY//+/VSpUgWwZt8MGzaMihUrMnPmTCpXtmL7Xr16sXPnTjIyMsIyGPenn34CiMpno1atWqxfv569e/dSvXr1Ir9+OJ3J0RLsr/ngrRk+nDXDh3vsjO2eEytLS/A9WL0/ywayWXZRu7C3qbgJ5vbQucCDBQUsttFBtmEhcAIrV4nbb3cRORdoCmy2k7EVZA1Wlt4Wdu4X13rOxpqufRhY7HJonr291Et9rbDGmSwyxoRye8jfddp7lFGF5MiLkZSUFN2GYC10efToUfLyfK77VSQOHrQ6RMuXL++nZH7Lli1j5cqVXHzxxfnGeTRv3pzWrVuzYsUKvvvuu3zn1q1b1xmwuJ5jjGHz5s3OfR999BF5eXl0797dGbA49OnTJ+g2+xLNz0ZiYqJbG+JZkedpWfOe+yPGFfbdWVeuMT9W0MTogQgmaPkOqOS3FHwRTAPsnCqvY2Wlvcrj8ACspGwvOHbYaxTNEZG3XNP72zleXsQaO+KZgegaoAow3l7c0XHORqxgoa2IeC5JcKe9fYHQOeq403WniNTGWjpghZ33RoXg6FErtixTpoyfkpHnyAFy5MiRgMo7xki4Prp27QrAHXfcUWB+mII4vjCPHTvmp2R+jh4UXwNTHfu99bR469FwDFh1/JwAVq60ljNr1qyZ1/KOHplQRfOzEexnQVk633MPnV86Mwg3rUNzt0dxsvh4V74+qvlFAxHM7aF7gX+LyL3GGF/Td8EKWgIbzXbGI0AXIFNEbgGygZ5YafznY6XUd+jOmURsL2GtG+TwAlYitzEisgP4DGtNoZexlh0Y5eXaQ7CSvr0jIn2BTcBtwGBgkjEm5GH/xpjlIvIk8IiIPAS8gjUV+g2sXqY7Qr1GgUpIQgLHOJZYmF7qaEOgY2vq16+f74v76NGj7Nixg5o1azqDD9fygXDkIvn1118DKu/K0Ut0zjn5luxy2+8tC7C3nh1HR6prDklHT5Cva1SqFMjfSf5F87MR7Gch3Fxn34Q6qLMobw8lxOHigD/ZKb2C+dK4vHUj2tuJ5I+0X19iBt4WVsD/i4wxa0Xkf8APIrIBK+3+cT+nBVp3noh0wLq1NB2oDmwHngGetjPaOnyDlX13H/CjRz0nRaQnVhD0AlaG3D3A28AoY8xhL9febq8N9ASwAEjEClzuxwou8hGRUXhM7XZZ6Xm0MWaUl+v8U0TWYaX0Hwkcwbo1NsAYs8nbdcKmhCQkcHyxHz8elo9lSBw9GwkJCQGVnzx5cr59ixYtomvXrowZMybfrJRA9ejRg7POOou1a9f6HdR67Ngxjhw5QkJCAmXKlHHeSvHVQ+DY73lbJxiOn5mvaxw6dCgsgUs0PxuOz4Jn4BkNr48cDMBE8s8SC4Tj5sdAXgtbmyItm6L7o625vfRebE1wL16CmT3UCZgLnIN1K8eXQo3HNsbkYX2h3+en3C6sxQp9HT+BFRQEnC/GrjPg3g47KBkVaHmX86aQf10kFSbnnXceYOW2iaZTp06Rm5tLrVq1CjWWJJySk5O5+eabmT59Ou+//z59+/b1Wfa6665j8eLF/PLLL9SsWZO2bdsC8PPP3sehO/Y7yhWGY9yLtwy0hw8fZv/+/WEJWqL52fj9998jshyBCkwq1h9ts4nsukOqaAQzpuVZYD3wf1iDVBt6eZyHNQZFqcANGmQ9QtS4cWMSExOdqfajZcuWLRhjHEmSou7FF18kOTmZRx55hN9++81rmY8++ohPP/2Ue+65x5mArk2bNqSmprJq1ap8QcXPP//M6tWrXZNBFUp6ejpJSUksWLCAAwfc7zpPnz7d53n//e9/6dChQ8CLHl5yySWISJF/Nk6cOMGuXbu48MILwzJ1O1R3jR7HXaPHsbppSzY1iFwS7ljSmyx6ewQs48dbj1jz/NDAbvuWZMEELRcCNxljZhljfjDGbPPy2Iq1sKIKk7y8PDIyMkJbnTQCCxKGVWam+8KKhVSqVCl69OjBjh072Lt3bxgaVjjZ9u24Xr16+SlZNKpVq8bChQtJTEzk0ksvZerUqeTk5HD8+HHWr1/Po48+ys0330y/fv3y5WmZPHkyVatW5YYbbmDFihX88ccfrFixghtvvJGqVat6va0VjEqVKvHqq69y+PBhbrzxRjZt2sSJEyeYN28e//vf/3zeeho1ahRLly5lfIDfPNWqVaNNmzasXr26SBeFXLVqFadOnYr6Z+H++zu6PX+p71Bm9W7vo7R/S2Z34vWRgxnRPTbWqipIFulk4Z6czrGySaw554/faZbzKc1yPo12U6IiKyvLkUDV573UYIKWHQSQUM4YE9lBpSVMYmIimZmZpKWlhV5ZuFdiTknxXc71N0Iwq0uHyLHmz7vvvuvl5QsNG1opebZt2+Z1FedQjjvMmDGDhIQEbr311jC9qtCdd955ZGdn88gjjzBp0iTOP/98KlWqRMeOHVm5ciVTpkxh8uTJnHWW+6+E5s2bk52dTfv27bn22mupWLEi11xzDZdeeinZ2dnOxHJgZdT1zBL75ptvsnXrVkSE0aOtbAhdu3Z1u1XSp08f5syZw6FDh7jwwgupVasWEyZM4IMPPiAhIcH5Xg8ePNh5zq233krlypX9Zvl1NXToUA4ePMjHH3/s9fibb77pnJ21zR4E2rBhQ6+Zbn1lxPVcFXrGjBmUKlWKQWHoSQzF5s1Jbs8vaN+Ny1s3cj6KC+n2sNvz+18NNgNHdL3xrxcA+PvLs5zjj0qStLQ0x1I1PnNFBDOc/Umswan/LKiQiJwyxgQ7e0hFkjGx3dMSRp07d+baa6/l6aefZtCgQW5d8sZP+stQjwOsXr2aWbNmMXbs2IAH4frSpUuXgK4ZqNKlS5ORkRH0UhD16tVjQgBrV7355ps+F3f09zp69erltTfCMwhwyMzMDHodrttuu42XXnqJ0aNH07t373xJ/wYMGBDwgGdfGXBd7dmzh3HjxnHPPffQqFFsBQa3Dr2ec/7IYe2/PLNDBOby9MVcnr44Jr9YR715OaMGFJxBwvHRiZXelsP14zvpYFEKdg5eioh8hzUgN2yzh1QRCPTLL5h+00B7SByrSxeRSZMm0bFjR2655Rbee+89Spf2texTeO3cuZMbbriBPn36cO+99xbJNVXgSpUqxcyZM7nssssYPnw4r7zidXJgWBw8eJBrrrmGtm3b8tRTT0XsOoVVY9OP/gsFYewC99yf918Z+m2jxc8/Dxs/C/q80ZM7OoOWsUNHwqP5y9xtZ4CLlaDlSAkZXxQOwdweehMrR0oq1syccVh5Rlwfb4a3eUoFr3LlyixZsoRSpUoxceLEIrvuQw89RJ8+fZgyZUpUU/cr3xo0aMCyZcv48ccffd4mCocxY8bQunVrPvnkE2dyueLg0y6tmZxm3fZ8bNyTGKRw00UDkNe4MXl1zo1Q7ZGRwgpSCnFejcVrqbG45K7kEoxge1r8jVcRrOy2SkVVUlIS77//vlv21UgbP348FSpUKLLrqcKpW7cuixYtiuhn47HHHiuWn4Uei1YBq1ic2tFf0ZAlbtoEO/fFVeDyvR2y3BLkeZv7XRn+xhRTQQUtxhi/M4NEZFLhm6NUeBXlF0dx/JIqziL584rVz0K/2dPonB3aiiGrWyTTLOdTZvxfCg++vCNMLcuv04gRAGT9p3DjbqLhEgo3qaDRlAUA7O3cKpzNKZaCuT3UXkTq24sY+mSMCaZOpZRSEda9uzUbKulYaEHGs8OvZX63i8PRpGIp25nKrnDaD3yOB1/+0L7tpreYvQmmp2UpVrbbd4HYmcuplFKqQP/s+QG/z4f53S5mfreLuWvxmmg3qcisvqYd1CnMSBMVi4Id03Iv4H/uo1JKqZjRacQIsrHSmgOUyT1Mxe3RS8AYKeNH5B9cvb1NU2jV0/ncStSZ5vJvSE93z4N1ySXRWbJtf6vzeL1TK1r9sJU9lVoUfQPiQDBBywHgDWPMH5FqjMrPkRE3LS0tPAnmlFIl3q/pHTj/5Q9DrieTQcB0RnR3X6H8xeRknzl2Iimj96p8+9pMWUjN9VP47rYu7Dm/HgCrrnmCiz5aRhazi7iF3i2d+MCZJ6s2F+mA51iSlZXlCCR9ZsQNJmj5EkgBviqokIj8Yow5L4h6VQEcGXGVUipcDrRu5P5FWUgpZANNuWu0lfH3gvbdAPIFMdFUc/1OANpOXeS3bBGmk1JeOP44nzBhQlgy4v4NeEtE/m6MWVpAuQZB1KmUUipOpfB9tJvgJnNOa8B7j0uwHKmWohHIZOBYU+t00V88xgUTtEwEEoAlIrIbay2iY17KaayqlFIlQF+mAFCew2Gv+1DdunDc73J3bu5+/irAPWjxNmU67dEpbs+jvDRUPpnYKXtHuu+/v+ibEnOCCVq6uPy7tv3wRoMWpZQqAaZhBQQDeS3sdS969VVY815IdaR1aJ5vX9Y3Pzvz+M7GGogbrjvwa2gJaGbbSAo2uZzfHCwiov1ZSilVzHiuBr1k1eYotSR0tdgVkXovYg0gPBNiPVn0Jo05vHTrEFY3a8UF7bvRcu67/OXFf4WjmXEtmERwnwZYzm/WXKWUUkUnh6rkRLsRQWr18su0+rCg4ZOFt4s67KKO83l2dnSmOPuSxpx8+y6d8lIUWhJ7Ag5ajDFXBVjO3/pESimlilB1cqge7UYEKXn+fJJXbIpI3X9wNn+43GhITbUeoRrvMoQ2FGLnxF3d7Exa/y3tuvJW+QqIiPPRoEGDMFwtvgSbXA4AEUkF2gBJWPlbvjPGxNYwcqWUUgA8//xi7KV8IuL1kYMjV3kElOHPiNSbYedefdZPuUA1y7FucFy6fRMNf1xE9ToNef7VD5zHY2lqeVEJKmgRkWbAZCBfTCoiy4H+xpgNYWqbUkqpMLj+kyc4B/CZ/ELlMz4cXSaF9Mzw6/Ltq7h9LxUpflmMgxVw0CIidYHFQFVgFbAOOAKcAzTH6nn5UkRSjTE7w9/Ukkkz4iqlQpU8fz4ZhK8HwNNdo8cBVnK5Ed2bRugq/pmFY8JWV0ZG2KpSAQp3Rtx/ATlAZ2PMOs+DItIcmGGXuzu4pipfNCOuUipWzflLCrsTWka7GYW2aVMiI0Z0inYzlC3cGXF7Atd6C1gAjDE/i8gdwKwg26mUUioO9f4sG8im6bZNdM5eAhSfBGiOvxWj0ePy4rv/BeCvN/8NsKaXt7eP/bh0IXBmyYSSJpgpz9XxnzVnNVCt8M1RSikVT7bVqhftJjil3D2AlLsHBFy+ceM81t3Sx56rI27H7r7bekTDzpxG7Mxp5L9gCRRMT0sOcCGwsoAyrUBHCimlVEnw7PBrWV+tBwBT0vtyQftu3B+mGS2rhw6FncElT/l+Y62Aym1LbQxVrHV9y+7fH3TbVPQEE7R8irVg4v8ZYzZ6HhSRpsAbwMfhapxSSqmSaXvPnrDmUETqXnNte2h1o/M6DebP91lW3DtgClxA0crScjdNwtBGT6tbJJNbPnZ6taIlmNtDo4FawM8islxEpojIOHu7AvgJ6xbS45FoqFJKqdiXzSXEUHJZv/IaNw6o3MiR/ld8nuDM1BJ+NXJyabBrW4Rqjx8B97QYY3aISFesPC0p9sPVCqw8LTrdWSmlYsgoRgKjOacIrpVNCvB9vi+IYKWlpwPeV2mOFHu6LWlpaX4DlEhxXePJ8e8lqzZTMycPzbQT/IKJPwCXiEhbrLwsiUAuVkbcFeFvnlJKqVBNbXQfmzePDnkhP39+XLqQiXZfg2sAEJNCXEHaG8dg3nDnw/l173n0ZQoA5Tkc5trjS6HS+BtjvgO+C3NblFJKRcDYsV9hd1xEnGNcR4yGKoWWYncduS6s6G1fuKR3+tbt+TSsHqeBvBb+i8URn0GLiEwyxtxZlI1R+WlGXKVUqNLS0zFEJiOuY30chwf5EICsCFzLn0FXFzS5NTTfe1ldz9u+cPhm4t/cntet/ktkLhRjQs2IezugQUuUaUZcpZQKTOYDnxTqvMVDekGTv4S5NSpYoWbEFRFZWIjrGmPMFYU4TymllIqotA7N3Z5nffMzeXXOhQBnEano8jem5csA67kMcISp/rLmeiUiCVjTqv8Pa+r0dqyZSk8bY04GUU8Z4GHgNqAesAd4DxhljPE6gklEagFPAL2wuqU2AuOA14zxPobcHow8GmiPNXU8G3jCGPOZl7JdgC8KaPZjxpj/BPDylFIqJrjOcnFYsmqz89/pLoNokpOT2bp1a8TblL2hJgApTX8L6rwrn54Jpeey4M03I9AqFU4FBi3GmNEFHRcRwQoQutq73gSGBtsIO2D5GqgM3IIVAPTEClo6iEiaMeZUAPWUBuZhzWy6DfgMaAu8A3QTkY7GmCMe59QFlgEHgB7AJqAv8ArQGsi38oSIdAfmYq2z1AL4A/g3MF9E+htjpnpp3jGsQMyb3/29NqWUiiezZ88GrC5/8czQFiGpg+8Agl/tudyhY1i/olWsKyho8RewVAOmYvWwHAeGGWPeLGQ7nsBaIuBqY8wSe98sERkF/Bdr1ehXA6jnXuAKuy2OcWBfishwYCYwEvi7xzmvYSXN62FP6QbIFJGWwHAR+dAYM89RWETOAd4CdmPlpTlu7x8GdABeE5H5xhjP5Qy+M8Z0CeA1KKVU3PtopZWya1O5DYU6X7A7uR913x9sQFLcjV3g/v7ef2XTKLWkaPjMiFtQL4uIXIG1OOKVwAagXWEDFhGpBAzECgI8lwB4EzAEsHCo3etzH3AS7AntZ3wE7AeGiEg5l3OaAL2xAoofPM6ZZG89r90HqAm87QhYAIwxp+32VgQG+WuvUkop37p3j63sr4MGWY+C9mXNnk2k+pTm/CWFCdcPcNtXuUYdRnRv5vZo0KBBhFoQG4LK02IHBv8GHsIKeKYDGZ63XILUDSgHLPMcP2KM2SciG4BmItLUGFNQyN4KqAusMsa4LVhhjPlTRJZj3f7pBDgWmuhlb5d6qW8NcBToIiIVjDFH7f1XF3DOUpcyTxTQVqWUKpasPC0Ap0Oq55nGz/BM+Wy2t7F6DtIfney1XPaGms7bQpHkbRJnUU7s7P1ZNpDNsovaOfc9NiX/XJkRYVqwMlYFvPaQiNTBGpj7MFZvxhBjTN8QAxaAlvZ2q4/jjv0tfRwPpR6f59hjaHZgBXbNAznHZd+FXo5VFZEJIrJVRE6IyG8iMlNE2vhor1JKFUqDBg0QEUSE9PR0agO1qVMk187kbjK5O+R6Lnr1VS76aJnz+SVNdnNJk90h1xtJ9T/5JGLd7OvKNebHCi0iVHv8CKinRUR6Y63gfC6wGbjRGLMqTG2oaW8P+Diea29rRKCecJ/jKF/Jo3cGrMDnfeByYC9wMfAS8I2I3G6MedtHG5RSKijbtm3D0XGdlZXFJ5/U57efvgYWFFkbJo4c4vy33/v7Acge/6bX/SlNfyuScS6OrLcpKb73XfTqq2QSmSR+i493JYMJbu8rwNj56yNwtdhVYNAiImcDzwB/BQTrS/dOz9svHufUN8b4miXjTXl762ta8x/2tkIE6gn3OX+4/LsC1u0lsKaBX2qMWe5yfJmIXAP8AkwUkS+MMfn+jMjJySE1NdX5PCMjg4yMfBOalFLKpwwy+Y1vWEODiF9LMAwiMyy9Lf5kPNcTKHxSuWC4/BrOpygWV4zc+tGxITMz0zWRalVf5QpK438e1lThFOBP4EFjzP8CuPYWoFTgTXXOMyvt43gZe3vUx/FQ6insOef4OKeMy7+d5xhj9gH7PAsbY3aLSBZwI3ADVs+Lm2rVqrFiha5FqZQqvItefZWLgDUXNiiS600gg9OjrTEtnj0D3uSfEZ1GIHHAhLkXA6EHLbm1q0D5yiHVEWmCYSe1qc1uHnhgDM899zBgLVIJcEH7btFsXshc/yAXEZ9pQArqaVmJNRMmD2vK8TIRqe/numI/guHIAuTrE5Nkb/dEoJ7CntPIPsezR8lR/pDHraGCOLIxnR9geaWUimn1mm4NqvyKFTBqFMyZ47uMdLO+pCNxK+iroVdDqxsLLFMUvSmBarRvEb9VS+TE2QnRbkqRKyhoqWRvk7B6XAIhEFCA7MqRQbehj+MNPMqFsx6f54hIKayMuqeAnz3OaWSfs9rHNTynTxekaLIuKaVUEbmu9UvUzQl8kb+UFMhyWWExKysLimhVaoAa63bA8e/Y07Zt0V20EOqwi2eGXwfAnmpJ5JavF+UWFb2CghaD9cUczJeqcKbnIFALgRNAWxER12nPInIu0BTY7Ge6M1hTlHcCLUSkkuu4G3tsThvgMLDY5Zx5wAvApV7qa4U1LuVzj16TecC19jkfepzT3qWMkz3deqRrkjoXjlzYJWs0lVKqWHJ8qTpYU6DvJs2OSryuVu+4P7RzJ9SuHeEW5td26iJgkfeDsdTF4uKin7YB25iS3jfaTSlSBU15FmPMdmPMtiAeWwmy58AOLl7Hykp7lcfhAXZ9LzgbJZIgInNE5C27N8RRjwFexBpr0s+jnmuAKsB4j4RwG7ECjLYicoHHOY4Vrl/w2D8da/bPrR6J6s7CWhn7COQbMVWNM/ldcDmnDpCGlVF4pudxpZSKd9YU6ADVqeOcirOte/eItSke1am2mTrVzvQJfNqlNZPTbo1ii6LDZ0+LMSbgHC5hOO8RoAtW+nzXtYdGYSWCG+dStjtnAoCXANdRqi9gJYwbIyI7OLP20MtYt3JGebn2EOBb4B0R6Yu19tBtwGBgkjHG7S6rMeawiAwAZgOTReQ+zqw91AoYYIzxHH9jgMEishtrCYA9wCV2u84GBhpjdvl+e5RSKjiO9O4/rtyJl76NIpPJIGACtXwcz8rKcmtfbl4eAGuGD2dNJ3+ZLsIj6z+ef+dC2qNWYvWsgnqIPGzr3p1P58/3Wy5Yl7duxOWtX7OfNWLJqs30WLQKWMXi1I5hv14sCyojbqQYY/JEpAPWekfTObPK8zNYqzz/6VL8G6xpwvuAHz3qOSkiPbGCoBewMuTuAd7GxyrPxpjtIpKKlcF2AdYqz5uwUgu84qO9H4tIR6wg6GesHqvvgZ7GGG+f2C7ArVg9Pg9i3Xb6HWuRyOHGmG99vztKKRW/UrCSmezbt4/ud9wBtWrBLu9/ozmDhzXvBVR3JJPNrb6mHdRJKbiQy7SnLnXrsujVV7l7/nyeiViroMPA/wLwYAkdDhkTQQtYgQvW2kH3+Sm3izPjQLwdP4G1MOLIIK69CwgqD7QdaPQMsOw2YIz9UEqpIpVF76hdO4XvrX/cYf+K3b37zJd9iONFfCWcC4ftbZpCq4B+xQPw66+/kp6eTuVK5f0XVoUWM0GLUkqpyEjHusXxDNf5KRmay1u7/z25ZJXveRlvV6xIMCMyxo/wXE83stpMWQgJ61j+2GP+Cy98kk3f/MxsYN1vPnOvhsU3E/9mva8vR/QyMUuDlhiXl5dHRkYGaWlpAd1TVUopT3eNHkeznE+jcu1nh1/r/PffX54FwApSaHb4+6Dqyei9Koyt8q/m+p1YE1JVUcnKynKMIUr0VUaDlhiXmJjomtpYKaWC9ti4Jyn750Gm3Nw12k0BXG4ZOZ9b8ylG8SIAaR2a5zvHm3AknPN2raxvfvZSUkWa44/zCRMm5Pkqo0GLUkoVQ5dut/7Y+TNnM8m7d0S5NZZnhl/H5a0b0XfgFKAf0+z9eY0awTFf69ZaMue0ZtRbl7N7X6UCy5V0ycnJiMsA4eTkZLZu3Rq9BoWZBi1KKaWKzJJVm5nGcwCs79OHtHfeoWX37mxvdDZ5nOvzvLuf90zjFV1ZWVkkPv88948Ywago9Mx82qU1eyq1AKDTiq/ob6/xdD+4DXCW/As7xTUNWpRSqpjIcsmFX+H5mVz00zZnmu5Y1Owda4WYBvPn0wDv+VIcIrHmUFA8vvzTgMVDehHcyJzwceRp2ZN8lBqHtrodCya3TLwpVAI5pZRSsW33T82i3QRVRNZc2MBtwHNxpkGLUkoVI4mbNnEJ8ArDAStHi2CQoNeyjZxnh1+LYPWsZP2nX8y1z82a9yi75C3nU0ebs/7Tj7w6vm9nRVpMv2cRpLeHlFKqGOk0YgTZwLPDJ/Es1wKRz89SXK3v2iraTSiUj1ZaU7U3lfO3znD80aBFKaVURHgmmwNrIO63P1wJfEj6o5OLvlFB2HDFRQCcSKhAbu0qJO3a77WcI6Hc5a0bcXnN6kXWPjiT+wZKRmp/DVqUUqoY8xY4RNsHi4ZiLS3n+iWbDHSKToO88BwU/NXQq32ULHq+fqauM4qKKw1aYpxmxFVKFTfPDL+O82u6Byjp9qrKscAz4VwsJpv7ZuLf3J4vWbWZHi+vIp5XftaMuMWAZsRVSikViGnlbuaUlIl2MwpNM+IqpVQJ0KBBA7Zt2wZQAueTKIfzj28EYDEdotySyNGgRSml4ty2bdswxlhd6+np0W5OgRzjMdqfl3+6cKBrDinvPNd0Ko40aFFKqWIkh6rRbkJAlv6yL9pNKLYm2in9wU7rX4xo0KKUUsXA2AUb+HHlTtLJATQ3SyQM/O8H7M094ravcqXyUWpNYDKBjGg3Iow0aFFKqWLkmrtnknxgabSbUSievS/ebiFFg+O21d7cI5iFT7odi7Ueo13UYtrwdgAMmf0NKdv3MnaBe5K5+69sGo2mhYUGLUopFadcF0j8celCAO5f8SJJx3Ywv9vF0WqWihJHWn9HL1vF7XtJAV6zPxsXtO8WraaFjQYtSikVx9LTrfxNr48czCVkMxFrPIMGLSVP2xbz3Z7P+UsKs75exySXMS4ALyYns3Xr1iJsWfho0KKUUnHEdXozuE5xLv4p3FXBbuj2mtvz3p9l0xuQ0dZ+R0/LiO7xuwK4Bi0xTjPiKqVcOaY3A3ExxTlQNc9NoMPA59z2JddIYuv0v0epRaqoaUbcYkAz4iqlCjKKkQCcM3x1lFsSmg+eHpRvn2cQowr2697zWPDdLfy8tQ0QfwsoakZcpZQq5qY2uo/jB/fxV6ygJRYXSFSRd3nrRnQY+N9oNyPiNGhRSql4lJJC2vffk/j88yz7ZXG0W6NigOsiiktWbYaXrX9PHDmEf9/9kHOGGZyZeRZvww40aFFKqRjmua6QAZAz3f6dRoygE/Ds8Guj0DoVLx4b/xTbatVja+1k2ka7MSHQoEUppWJYPK0rpGLLX28aAVhrEvWfsYiyfx6kc/YSOgPi8llKjqMp0Bq0KKVUDMrKyiItPZ3xwMDHx7FuRXMSuZxOLNFelSiKtQy4Balb/RcAXln4MLfzBeSAsQfn3uUyDTqepkBr0KKUUjEsA8jL+ZQDh05zC++yizrRblKRqXluAtLtEefzop4C3aDPM2zbk+vWHhVdGrQopVSUeCaKW1C+PH85dgxmz3Yr9+DLHwIf8mmXCTx74bVF2sZo8pwGXdRToLftyXVbayieellcZ5Fd3no+MN9tcK6neFmfSIMWpZSKErfxKsDpG24AIC09nTm4Zrv1Tqc3q8J6bNyT/HvwI84ZRT/GyfpEGrTEOM2Iq1Tx47rQISKkAVmzZ3PiXzeThXVLKOvRKc4iOoYl8jxvBYF1OyieeleCUe3AHiaOHMKnXVrzerQbYwskI6440kGr2JSammpWrFgR7WYopcIoKyvLXuhQnL0pubWrAPDV0KtJf3Syc8AknAlaSnrPSoeBz7ndrgkn6fZIvrqLW8CyZNVm57+7L1zJRT+duTU5cLT7ukXXXHxm7FRR/8EsItnGmFRvx7SnRSmlIsBzvEr16tVp3fo75s9PBtLcghKApF37AVj32yHANZyBZ7gu8g0uxrz1onhKrpFUJG2JFa1/2gqcmU30usdK0O8mVeHm+58AYFO5DTEzxiVmghYRSQBGA/8HVAe2A5OBp40xJ4OopwzwMHAbUA/YA7wHjDLGHPZxTi3gCaAXVrfURmAc8Jrx0RUlIm3t9rYHzgKygSeMMZ8V0La+wH3ABcAR4HPgn8aYzb7OUUrFLsdtnoEDB7J3717nfgNsBea/8QYnzj3XWWb+/AYB1fvMcA1SvEmukeQ2m8ixz9+MIs8Btd4s/WVfsetZCY71VXdZ2pecn/qzWxDz49KFZB1f73wezaEKMRG02AHL10Bl4BasAKAnVtDSQUTSjDGnAqinNDAPaIMVtHwGtAXeAbqJSEdjzBGPc+oCy4ADQA9gE9AXeAVojXV72fM63YG5wCygBfAH8G9gvoj0N8ZM9XLO48BjwCNAN6AWMAnIFpEOxpif/L0+pVR0uY1FsaWlp7MHyG3UiDeGTWLEiE7soja12c2AO7qzm9p8kHg9e/L28mmX1qy5sAEARyfOpcLxkz7Hq5T0W0HeeAtOPIMYb70qJa0XxRfXz9Q3E//mNpvI0ePyzLYH2JDamIpJVdwCl6zq1Zk4cWKRttebmBjTIiIvAcOBq40x81z2PwD8FxhmjHk1gHq8lheR/wNmAs8aY/7ucU4WcDXQyhjzQwBtOgcrsDkJNDXGHLf3nwWsBM4DGhlj9rqckwp8B8w0xtzksr8OsBlYbYxp5+016ZgWpaLDW4By//0d2bS5MgCjsLpaLwFGUZ421cow5eaugGOKsm+Tb+pCxSPHuH7uMh2vEoT2552bb59nkFLYXC4lsZdlyarNPj+rw8v+j5dP3Ou2L8tjKr6rcPa+FDSmJepBi4hUAvZi9XTUcb0dIyLnAjnAZmNMEz/1CNYtpRrAucaYQy7Hzsa6TVQGqOYSaDQBNgDLjDGXetR3MfA98Jkx5kqX/QOBCVi3rR7yOOd+4HngUWPMEy77p2L13lxljPnE45yPgHTgcmPM156vS4MWpYrGmcGxnhoAZ8amjMQKWEYxktGMchubsrpFMvO7XUyNvbl0+O5nGm/d4/Vak2/qwp7qSW77NGgpHG+BjCdvAYnneSUxaPGmvZ0LJ4PxZHK31zKOT3z1AnpfQgliYn0gbjegHFbg4BZBGWP2icgGoJmINDXGbPBag6UVUBdY5Rqw2PX8KSLLsW7/dALm24d62dulXupbAxwFuohIBWPMUXv/1QWcs9SlzBMu+x3X+dbHOen2OfmCFqVUcLz1kHiTnp7G0KGr6dlzOwCffFLfR8lt7K6WSM2cPGew8du7XzAqZzSjGO31jD3Vk5jVu73Pa1/euhEF/hWmIk6DFO8cPX9N+Jhnsf7t2RtjgMnl+3H73sWku62Hlczs2S8BPm6lhqE3JhaClpb2dquP41uBZna5goKWQOpxlJvv8m+v5xhjTonIDvvazbHG2fi7jmPfhY4d9piZykCuMSbXT7tKnMzMTDIy8g0bUnFg8W230WnaNBg0CDIzo9aOYIIUgNmkkcYcejObd9650hm0APlm9Djl5PmtP95u83z45Rqu7dwq2s0IWUkMPiL5s/P8/C5Ztdk5k+2Z4dfR6oet9Fi0iquPz8FwwOPsbZCeTiaQYj/gzEw41/9f399/P5eMHUtuo0Z8NXZswO2LhaClpr31fPUOufa2RgTqCfc5jvKVXHpnQnp9x48fD/iX8nfffUfbtuFfdDxS9QI888wz1KpVK+z1xuN7EW9tnvf223QCmDDBeniRNXs2He+/n6TNmzlUty6Vfv3Va7lt3buzrWdPOo0YUWA5gMXPP0/yJ5+QPH8++1q0IO0n72PYfyhzEVtm/ps0+y9BzxvhWaTDfqx+TmA31qj7/whU93HX3HFLxzF2Baxf8ktWbY5YsBKpumcvjswXX6TaG4/vcbz97MB7m11ns625sAFrLmxQ4Litdi2SqbL7GIsPNAWWe/1j4LmxY5kGnNy82fl/NBCxELSUt7e+pjX/YW8rRKCecJ/zh8u/K2DdXgrp9ZX78UefP9BFF97MoSf7AlD9gXH8e+M8r+XAfQDVX665mfLmmNdyGytdyLpp1tTAs99ZxlVvP4GvDr0PBj1D6bTzAbjolvupf9T7zO2DksCXH52ZUOX6etIAXJ57vqZ2hXxNrm329pp88feaHPUW9Jo8BfqaHFlRfb0mV8G8ps/P7w120FLYn5O31zTOGAZf046LPlrms1x6ejorsP7i+vXXX2nuo9yn8+czfv58sv2UA7h/xAjuxgowfvzpJytw8uLEH6tJT0/3mwrfoV2LZPK6XcxbBZTx9QW0ZE0Ev1AjWHckRKq98fgex9vPDvK32Vv7l6zanG/W299fnkXbFvO5oZuVpO7Xvefx4oznvV7j2eHX0hpot3AIy37qEVT7YmEg7svAMGCkMeZxL8ffAW7GzwwiEfkb8CzwhjHmTi/HnwL+gcsMIhGZgzWW5HZjzGQv53wLtMNlBpGIHAbOAZKNMds9ypcDHN8y5xhjjtozh5YDW4wx53m5Rk/gY2C5MSbfn8MicggrD4xDDvC7j7chEfDfjx28SNULUBXfrycU8fhexFubI/Wzg/h7L+Lxc6H/9yJfd7z97CB670VVoJr979PGmEreCsVCT8tv9rayj+NJ9tb7MPzQ6insOY3sc7b7KH/IZeBuSK/P1w9OKaWUKmnO8l8k4tba24Y+jjfwKBfOenyeIyKlsDLqngJ+DvA6jms4870YY37FGreSJCJJAbZLKaWUUh5iIWhZCJwA2tq5VpzsPC1NsfK0FDRzCKwpyjuBFnbuF9d6zsbKknsYWOxyyDG4wC1Hi60V1jiTRS69Jv7Oae9RJpRzijUR6SciKSLSOtptUYWjP7v4JCIdRSTFf0kVi0r6zy/qQYudU+V1rLT2V3kcHoCVx+YFxw4RSRCROSLylt0b4qjHAC8CpYF+HvVcA1QBxjsSy9nnbMQKFtqKyAUe5zjGxbzgsX86VjK8W+0xLI52nQXcjrWmkOdUCkcdbmNtRKQ2Vu6YFcaYJZQgxpgpxphsY8yqaLdFFY7+7OKTMeYrY0y2/5IqFpX0n1/UB+ICiEgi8A3WQB3PtYe+wRoI+6dd9gasBRAB2hhjVrjUUxorB8sl5F97aA9W1lm3RRNFpD5W0rd9WFlrN9nnvgJMNsbc5aW9VwGzsdYeuo8zaw/dDQzwMaj3Cax1hx62664JvIGVn+Uy1yUElFJKKZVfTAQt4AxcfK3y/IdLudrAV1hBRmdj3OeEikhZrODgNqwMua6rPLtlyvWo03WV501Yqzy/UsAqz5diZfN2rPL8PdYqz/O9lbfP6YcV5LTA6pFZCDxijNnk6xyllFJKWWImaFFKKaWUKkjUx7QopZRSSgVCgxallFJKxQUNWpRSSikVFzRoUUoppVRc0KBFKaWUUnFBgxallFJKxQUNWpRSSikVFzRoUUoppVRc0KBFKaWUUnFBgxallFJKxQUNWpRSSikVFzRoUUoppVRc0KBFKaWUUnFBgxallFJKxQUNWpRSSikVFzRoUUoppVRc0KBFKaWUUnFBgxallFJKxQUNWpRSSikVFzRoUUoppVRc0KBFKaWUUnFBgxallFJKxQUNWpRSSikVFzRoUUoppVRc0KBFKaWUUnFBgxallFJKxQUNWkIkIneIyD4RGRVg+VwRWeTxeCnCzVRKKaXi3tnRbkC8EpHKwDvAeqBKEKeuMsZ0iUijlFJKqWJMe1oK7xxglDHm3mg3RCmllCoJtKelkIwxvwK/RrsdSimlVEmhQUvRqyki04B6gAGWAU8ZY/ZHt1lKKaVUbNOgpehtBp4wxvwkIonA28AqEbnYGLPPs3DFihWNMcb5vGrVqlSrVs1rxXl5eSQmJoa9wZGqFyAnJ8fn6wlFPL4X8dDmP39eyVl/GM46/wJyDh4M7Gd34ACULQunT8KezeyiLDkHj3NRjbMgsR7sPwb79kHr1rBtG5QtS1758iQaA7m5kJzMocOH2bBhA3WAmgDVq0O9elb9J0/CH39w9OBOypw6gtRqRalSpSL+XhRFvaHUvXnzZnJzc6lQoQLNmzfPdzyU/3t//PEHJ06coFKlSmFrrz/hqPfPP2HjRqhRA6q4jET88cc/Ofvss2nWLMRG2gywcvVpSp19iosuKB30+Rs3buTkyZO0aNHC6/FgfnanT59m5cqVVKxYkcOHD9OkSRMSEhIA2LRpE3/88Yfbdfbuz2PPMUiuUoGE8r7b/v2qNZQrX54WzZoA8FvecX4/fIIL6yRy4s/TbNhziCpnn+S3X7fRvHlztm7dSnljaHj8OFSuDLWrwf5NHMorR6WjRzlcpQo7jh8H4OjRo4eNMfk/XADGGH2E+MD6jI4q5Lnn2ef/w9vxlJQUE6hBgwYFXDYYkarXGGOCeX3BiMf3Ih7avH5EDbNkWF1jTBA/u4QEY/76V2P+do8xgnnqpuamTiUxpktZY8YMMWbgQGNq1SqwzV999ZUBzANgTp91ljEPP5zvMmv/0dkcHFDB/P777z6bEg/vcTjq/uOPP0xCQoI566yzTOnSpc2xY8fylQnl/17nzp1N2bJlzZEjR/Idi8X3+NAh63HsmDFgzH/+4368SpUd5pprQmufq4/X7jLlGuw1SdV3FOr8GjVqmNtvv93n8WB/dnXq1DGAOeuss8zBgwed+wcMGGDq1q3rVvauQXebJv+cZ56Y+5PP+nYdOGw4q5Tp3X+oc9+bX28xyf+YY/YePG6+37bfJP9jjhk75UMDmAULFpjq1aub4W3aWD8AMGb6AHN6TD3zU7Vk6/mzzzrrAlYYH9+ZOhA3+rYAp7CCl5CkpaWF3poirDeS4vG9iIc2i+NXTjDOPhtOnYKhg2HoOZQrJZQuBaw5CSs3QdOm0LVrgW0uXdr6i+9FQE6fhgoVzhzctg3eeIMai7ZQcdpRTp065bMp8fAeB1L3r7/+Ss2aNVnw/+2ddXxT1/vH3ydJm7q3tMWKu/twhmxjbMx9Yxtz5tuPubt858aEKWPGhmxQYMDYcLfiUNpSd03TJuf3x0matE3atBS/79crr7T3nntzcpOb+9xHPs+SJS63WbduHYWFhVxzzTVUVFSwdevWJpvPmjVr+OeffygvL2flypUezbcpOJb9vvACREaCjw8EBkJmZvX1en0YTekc2ptejD7QhK4yvMHbpqenk5GRQe/evZtsPu3btwegT58+1bxjkZGRZGVl2W+gAbh40kR6tQhmQ6L7jIXtew6B1UK7to7LVnSwj5p/gYkiU6VaFhkBQG5uLr6+vgT7qDHM+xX2LUD0vIrJt32klhUXe/ReNKPlBCKEuFYIcVGNxdGAHjh6rPs/FX8sThan47E4HebcYlYuQz8+Cg25CBoMyi8f0xwi9Ph46zHoBNwbAE9OhUcfhR9+UGPffhsiI5k0cSLMnQvNm8OhQ1VGiwB4+eXqRs6WLXDLLRR3iMB8ie9ZYbT88MMPZGRk8MEHriWeFi1ahF6v58knnwSUEdNUvPbaa/gGBKP3MvLrHws8mm9TcCz7LSwEW0SEqCjIyqq+PjDQj4iIY5hcDfJKzfiFmMnP96Wy0vWY3Nxcli9fXmv5li1bAGVgNBV2o2XEiBHVlkdGRlJeXk6xk8EwadIk+seFsSOlgDKz63Np++59AHSzhYYAYmxGS2pBGcXl6k3HNlMHNS8vDyEEgd7eanDRYbCaoftleHsZyI1uqULIHqAZLccJIYSPEGKHEOJLp8UdgceEEIG2MXrgJSAfmHniZ3nyuf3220/2FDQagDnSlgan03n+2en1ymg5lAxbzfhhxcv+y6OrkXtiMEB2tsqDiYmBCy4AHx8MBvW6HwoBmzbB0KGObWw/dhWhAdDBUKfRcqbwg83I++uvv0hPT6+1ftGiRZxzzjl06dKFFi1auDRaXH1+Vqtk1rokXv4zgeTc0lrrV23cxrx58/DueQHGlt347rd5/LQhqdqd+qlIQQFVnpTIyNqeloMHlb3cVOSUmInpWsgzz6iUq5qsWbOG3r17M2bMGBYuXFhtnd0r1qtXL7f7b+jvZocOyrgYPnx4teX2vJisGlbcgLhQKq2Srcn5Lve3d/9BNccuHauWxQT7AnZPi3rTsc3U/nNzcykqKsIaFgZDhsCiZWqjoFgm71rB3t5D4fHHPXovmtFyDAghfhFCrLD9O8Wmbmv3pAjAD/B12uQnYDuw3LbdWiAAGCSlTD4xsz610IyW04uyONudUkOMFnt46N9VMNdEUKVFhYcWmWDFZrjySrj2WjU2Olo9p6fDwIHw+ecQG1vlaUkWQmVRlpU59m+7e/MuMKFPtZzxRsuOHTvYsWMH06ZNw2KxVBkwdjIzM9m0aRMTJkwAYNCgQaxdu7bWfmp+fkdySrjm87U88fsOvvjvMGPeXsEzc3eSWaiSIzcm5jL5jv9DGIy8/fx0HrnlSszZyTw8cxm3fbuRrKLy4/SOjx1nT8uIEdCt2/F9vbwSM217lvLss+DrdAWwWq289dZbjBgxAoPBQJs2bXjooYeocLJstmzZQps2bQgJCXG7/4b+bo4fP55BgwYxukYY1p3R0q9VGEKoz9wViYcPgdDRs7MjPBTu742XXpDmFB6KDAnEx8eHvLw8iouLyW/TRh38v9erjXzDaJubQtyujR6/F81oOQaklFdIKUdJKYWUMs729zzbujIpZTsp5bVO43dLKe+UUva3jR0gpbxKSrnv5L0LDY2GYLuj1jXgp2PBAnjmGbj6anggBBnojUEH7KqA/UkqJyXHVjjnbLQ4YTda/vL2hk8+gfnzHSttnpagXekYZpZiceePP0OYNWsWer2ep59+msGDBzNz5sxqno7FixcDcN555wHKaDl8+HCtC5Mdq1Xy1X+HOe/df0lIK+SJ0TF8MiGYK/q3ZNa6JIa/sZxpszZz+Vvzydm2jGtuvIlbx/biyskXAnBuYBor92dz3rsr2Z1WeJzffeMoKHAYLa+/Du+951iXkQGXXgou0nMaTU6JmTBfb9LT1WsD5OTkcPHFF/Poo49y8cUXs3nzZt5//3327NnDxx9/XLXt1q1bmzSfBVSoae3atbUMIXdGS7CfF52aBbLhSJ7L/R1NPoIxtBk+Ru+qZTqdoFmQD+kFZRTajJYAo4GwsDAyMjIc1Waffw4z7wadF3j78/0Ft7K76wCwhTLrQzNaNDQ0PCZ0rS1k0BCjpWdPiItT/vlIP7y8wEsn4OFAmHaT8prYE2vtRktGhspViYyExYurwkP+9td1vn21GS0l7aLgOj8slS788acwzz//PNddd51HY61WKz/++CPjx48nKiqKm2++mV27drFp06aqMYsWLSIyMrIqJ2Lw4MGA67wWU4WFaz5fywsLEhjcNoyfb+7BW/dezfmjh+O19VeWPDiciT1i+HNHGoEHFqND8sozTwDQtWtXmjdvTumhzSy4dxjF5ZX8vPHUdBjfdhtMnep6XWYm/P577ZDRsZBbUo53hR8xMSpda8OGDfTt25f4+Hg++OADfvnlF0JCQpg4cSITJkzg2WefJSsri6KiIvbv39+k+Sx14c5oAegfF8rmI3lYrLVDf9lpyQRGNq+1PDbYl9QCE8WmSgKMBvQ6QWhoKElJSQAMSEiAkBBISQG/MBACHy89LQ4nwJo1Hs1ZM1o0NDQ8prStN3ktfKqLXNTHzz/DkiXqh2pjOYGmChUeApXTUlbmMEJiY+G661QCbkQEXHEFREdXeVpeKLeFIJyNlm7dYPt28vu3hfYGrNbTKzw0b9485syZQ6UHHqLVq1dz5MgRrrWF06688kp8fHz4+uuvAWXUxMfHM2HCBHQ2A69fv37o9XqXRsvXqxNZdziXly/pzozr+3D/bVNITEzkoosu4oUXXuDem6/hmQlxLLunH4f/m8u1115L69atARBCMGHCBJYsWULbcF96twyps+LkZHLTTY4I5Hffqa9Zns2JkJ+vnuuIxjQIKSV5JRW0iNWh00nmz/+MYcOGIYRg9erVTJs2DSEEoI7hO++8Q3FxMc888wzbt28HaHJPizvsRkt2dnatdQPiwigur3TpPSvIPEp4dItay6ODfapyWgJ91I1GWFhYldESqNMp19PSLeCrfkM6pR0kKj3F4W2tB81o0dDQ8Jj84f7suDhalWB4ygsvwGefwe7d8HsOQYVm/JAwrwxWb61utAQGwvffw8iRSjzu44+hZ88qoyXVLhrnXPLs5wc9emAot0BiJdZyU9O82ROA1Wpl9+7dmEwm9u7dW+/4H374AV9fXyZPngxASEgIl1xyCbNmzcJkMrF582ays7OrQkMAfn5+9OjRo1ZeS36pmY+XH2B0p0iuG9SaRx55hCVLlvDpp5/yxx9/8NFHHxEfH0///v154enHKSkp4f/+7/+q7WPChAkUFBSwfv16BrYJIyG1sCoJ81Ti0CGV1wLKSZiW5vCs2MM3TVXyXFxeidliJcjLgo/PzSxadCdjxoxh06ZN9O/fv9b4Ll26MG3aNGbMmME333wDnDijxd/fH6PR6NLTMiBOGRU181qKioqoKM4ntmXrWtvE2IyWQlMFAUZltISGhpKcrDxwfnZP6Q6bpwUIsFbgXV7m+IDqQTNaTnEKCgq4/fbbme8cw9fQOEkIi0RvtoLV6vlG3t5gMsGwYfBsZ8qb++CrAw5UQkoGTJgAAwZU36ZGyYU9PBRvN26cPS1FRfDBB0Ss2APflCKLixrxzo4P//zzD/PmzXO7/siRI5TZkortpa7uMJvN/Pzzz1x88cUEBARULb/55pvJy8tj/vz5LFq0CCEE48ePr7btoEGDWL9+PVanz+3jFQcpKq9k+vmdmTFjBu+//z4PPvggt9xyC0II7r77blasWEFJSQlff/01F154Id27d6+237Fjx6LT6YiPj2dAXBhWCZuT8j09PCcEKaFDB3jzTfW/XUjWfp1uaqMlr6SCyuJc3r73KkpLv6Vt22dZsGAB4eHuNVueffZZQkND+fzzzwkPD6dFi9pejOOBEKJKq6UmsSG+NA/xZdXB6h6QPfsOANDGSaPFTkywD2aLlSM5pdU8LfZEY1+70TK1LfiGApDUuRer+4yCykrmz59vTzJ2+2loRsspTnBwMDNmzDgttVI0zjyaf53H0M9tybOeEhAAJSXK0IgIwMtbR4WXDh4KhBuugC+/hDvvdIwfPRomTlR+++Bg+PDDKk/LPm9vePddh4Q/qDu0++6jMtAPbvSjUn/q/Kzdf//93HPPPW7X7969u+rv+gTgFi9eTG5ubq38lzFjxtCiRQtmzpzJokWL6NevXy2J98GDB1NYWFjlzTmaX8bXqxO5tE8LMvZu4Z577uG8887jjTfeqLbd0KFD2bx5M3feeWetdaAuSAMGDCA+Pp6+rUPR1VFxcrIoKVE2trNOCzg8LTodtGjRdOGhnJJyijbNJ/nQXoYMWYCv73Nu20rYCQ0N5aWXXgKUl8UePjoRuDNaACZ0i2bF3kzyS81Vy7YkqLqRju3b1RofbSt7PpRVQqCPOmdDQ0Or1vvY31d5XpWnxcegJyU0Btq0YdKkScyYMQOgwN18T52zW0ND45SnPNah0+IxgYFK7TI3F/7LxT/P5NBp0btofxYcrKqH/Pzg5puhe/cqo+XloiKl0+KsBGZLxJU+3tDGgEWcGpohaWlpbNu2jZSUFHLcxOsTEhIAaNeuXb2ellmzZhEeHl5VymxHr9dz4403Eh8fz9q1a6uFhuwMGjQIcCTj/m+xuvBc2yOAyy67jPbt2zN79uwqj5YzMTExfPLJJy77F4EKEW3YsAFzSSHdYoNZf/jUMlrsUQdnnRZweFquuQaSkxsW8ayL3BIzpiPb6d6rL888cwFPPOHZdrfddhsXXnghV111VdNMxEPqMlou7ducCotk/va0qmUJe/cD0L1zh1rj7QJzZou1mqfFjqVHD5WYvzG9KqfFaBC0SdnvPlO6BmeV0SKEuLGe9b8KIb4UQkSfqDlpaJxOlLewNVBriNESEKCMlpwc+OkQgemlhFkl/F4G/21UzdOclV2jo5XRYveqjBpVZbTs8fZWSbrOWiw2o8VQVA4HK5GlJcf4LpsGe+kxwLZt21yOSUhIIDo6mtGjR7N161a3Im3FxcXMnTuXK664oupYODNlyhSsVisWi6WWUQPQqVMngoODWbt2LbvTCpmzJYUpQ1rz7KP3U1payh9//NHoZoQTJkzAarWydOlSBsSFsTU5n/LKUycZ2h7+sXtaIiPhssuqO+uakqOZuZjT9zN85CjOO8+RAFwfer2e+fPnc9tttx2fibmhLqOlW2wQnZoF8vvmlKpl+w8cRHj70aFl7ctkTIhP1d+ujBbrRRdBuQlSyqs8LUYvPeH5mY7M6Ho4q4wW6ledXQ30Az47AXPR0Dj9kI3QaXn7bYiPhzZt4N0xmLoF4C+ApErIyFZhIOccmehopYrrlNdir4QpNBjgtddcisv5pObC96Xo8j378TvexMfHV3XTdWe07N69my5dutCnTx9yc3NJSUlxOe6PP/6gtLTUbWl0hw4dGDp0KMHBwVUlzs7odDoGDBjAunXreH3RHgKNBkJSVvPnn3/yyiuv0OkY2hsPHDiQ4OBg4uPjGdgmlPJKKzuPuvXun3Dsnha70eLtDb/+qsSWQeW6eFhx7hHr164GaWX82DEUF8OGDSrt6lSlLqNFCMElfZuzOSmfw9nqZiDpSCKGkGiiQ3xrjY/wN9Il5wj/fXIzzUzqwDuHhwL8/WHtEhjvU+Vp8fHS89XQK1VteG79XrqzzWipM1AopfwfMBYYUdc4DY2zlaB1NmOhIUZLixbQqpVSxg0NxMsgKfDRwf2BMMomK+6cWBsdrYyjrCy1ra1E1MvLCz97TNx5vM1oqYgKhZv9MPs67vY8wWSCl14CW7Vpk2C1WlmyZAkXX3wx0dHRLvNVpJQkJCTQtWvXqmoRdyGiX375hZYtW3LOOee4fc2ZM2eyYMEClyEeUHktO3bsYNnOZK7t5s8T0x9h+PDh3HfffQ1+f84YDAbGjh1LfHw8/VqrC9T6w6eG4Qjqq/fhh9CjR/Xldvt70yZlWDQV2zesAp2Bc0cOZ906Jey8eXPT7b+piYyMpLi4GJPJddXd5N7NEYIqb0t6ShI+YTEEGmt/z3Q6wQMb59CiMItu21YB1T0t4dOnQw+bUW1LxDUadPiU2qy7gvqN3bPNaKkz2C2E8AYGAqdezZ6GxilAaRdvMtv6q5CPp6xZo7wjZjMsTsT3aKlDp8XekM3ZCBk4EB57DLy8VILu2LGAUsW9sqBAGUzOiY1CwOHDZF86DFoZsBoa9rO2aBE8/TT06gU33giJiQ3a3CX20uMJEybQu3dvl56W1NRUCgsL6dq1Kz179kQI4dK4MZvNLFu2jAsvvLDK4+SKDh06MGzYMLfre/Tph8ViIag4meVfvkRFRQVfffVVnfv0lAkTJnD06FEykw7SNtL/lNJriYmBe+6pHg664AKwF1gVFDRdEi7Avi3rCGzVBX9/f2Jj1bKjx9wO9/hRl1YLKO2VYe0jmLPlKJWVFvIyjhLSrIXbZOGUjso6NNh+I+yeFiEEBrMZ8grgYKUjPGTQM3ifzWr0oNPzGW20CCGeFUJY7A+1yPF/zQdQBswH4k/uzDU0Tk0Kh/uTMDFGJdd6yj//qGZoJhP8sA3fwyVEV0j4tRR22KpnnHVX+vaFV19VyQdPPQU2TRKDwUCGl5frbrBxcegrrLCvAhpY8tyrFzz7rLqw/fILdOoE337boF3UIj5e/YSMGzeOXr16kZCQgNlsrjbGXjnUpUsXAgIC6NChg0tPy+rVqykuLnaZq+IpUkriM9VnZln9NYvj43nttdequv8eK+eddx5CCJ555hn6twxiY2IuVhdKqieDjAzlRXPW7jMa1XJwFKk1Bfn5+WQe3kNUp36ASr+C08NocRciArikT3NS8sqI37gHS0U5kbHuS7LTBijvqZ9Off52T0tAQACirAxM5ZBvdQoP6ZD2IMjZbrQAicBKp4es8b/z4x/gD+Ap4K4TP1UNjVMfXakV74LKhum02L0y5eXw7Q2YhwYSIIF0KwidqhBy1nyQUt3+Fhaqv21Jt15eXhz09XV9W/zuu4Qt2gA/lmHIdP/j64o2beC551QIYf9+5W2xFds0mvj4ePr160dUVBS9e/emoqKiWnkzOCqHunbtCqhSV1eelkWLFmEwGBgzZkyj5zNrfRJ/J5oIj2nBnp3bGDlyZJ2l2A2lZcuWvP3228yZM4c1X71AQWk5ezNOjUSOWbOUYVrilJ/t3OnZuQO0R5Tmwj7X97UrV65ESittegwEVB5NQACkpjZy8ieACFslXl1Gy4Ru0fh56/lxqfKItGwV535/wb6sat0TXXQzwGG0BAYGqt+N9rHQz7uap+W7fhPVxh4k/5zRRouU8hsp5Wj7AxDO/9d4jJFSXialfFVKWb+5p6FxFtLs2wLO+TbR40x/wGG0lJRAYCAGg5XMQB1MC4ArLoevvgLnXismkzJMPvwQhg+H888HlNGyIDIS3nmn9mu88QaGwlKY6k9ZWFCD3tOiRUohFVQKzeefK2+L2ayMmIZSUFDA6tWrqzwjvXr1AmrrsCQkJBAWFkaUrda2T58+JCYmkm/XlbcRHx/P0KFD1Y9+I9iVWsDz8xMY0TGS888djb+/f5OFhZx58MEHee211/gvfi45C99j3SHX4YYTjT1NwjmiGRWlcr2tVmUvd+7cgB1u+BJmXQkptTsTL1++HJ3Bmw49+lYti409/T0t/kYD53WPZvla5Qls37F2ubOdiV+/SYipmIpRqqO0vSotICBA3bjo7XlptpwWLx25vsFUDBpc3ePqhjPaaHHBzSd7Ag1FU8TVOJUwt2iETov9alFUBH/twmtfCVVpJzoXSaO+vuoWNT1deWGuvx5Q4aGnjxxR4aaaGI2qj1FzPdYGzK2gQNlEttY91bj0UqVx19Cm0cuWLatWetyxY0d8fX1r5bXYK4fsuQH2ZFxn4yY9PZ2tW7c2OjRUZKpg2qwthPp58c6VvXjnnf+xefNm2rpQM20Kpk+fznPPPUfJzmW8+dTD1RR4TxaFheor6JwGFRmpHHh5eaoJuU3XzTOy9qjn/2obz8uXL8e3ZVeiQhwW0nvvwSOPNHLyJwBPjBaAS/u0oCBpNzrfILp0cG+0hOWm0CUgiVipXFl6vZ6QkBBldI8YARVmyPEBvSrdNxp0tMpPR1qtxB86pCniOiOl/OZkz6GhaIq4GqcSFdHHYLQUF8OvG/HaXUbrUiv8VAqffqESbnfsqL6NXavl1ltVvAbladkUHKziOTXx9kZfWg4JFeiKPOthAo6KIVetXqZOVZ6W777zeHeA8owEBgYyZMgQQP1o9+jRw6WnxR4aAqo6+zqPs2u9uBKMqw8pJU/8vpMjOSW8f3UfwgOMRERE0LFjxwbvqyE888wz9J98Kwkr/uCBBx44rq/lCa7CPwMGQKOLplZvgO9KYNsCyHa44rKzs9m2bRuGFt0J9fOuWn7eeSq3/FQlJCQEvV5fr9EypF041sz9eMd0IMZFubMdv9xMdAcriXvlmaploaGhytMyaRIUloDVv2qdj5eeCr2BSr8AJowerSniukIIca4Q4jshxG4hRI5t2UAhxFNCiMb5YDU0zgKEpRE6LaNGqazHAQPgu/vgQl/8JJBrhdIy5cqomVxrN1oslipNFi8vL9qVlalb45oYjegLiuGXMoyp6R5PzW4f2CI41bj4YujfH55/XoWKanLfffcxevToauEcKSXx8fGMGTOmmghcr169qonHZWVlkZ2dXc1oadasGdHR0dWScePj44mKiqoKMTWERTvTmb8tlYfGdWRQW/d9b5oaIQT3PvokAX0m8sEHH7Bnz54T9tquKCx0aLTYOecc5QHR6VRk8rffPNyZlJCTAocskAqseq9q1T82D6BPq56EBTiMlqQk+P13R4n1qYZOpyMiIqJeo6W0pBhT5hGMMZ1oFuReVkDk2/JS0h2JPJ06daJdu3bQrh28NhZ6tapaZzTo2NCiG7r0NPjrr/rnW++IMwihmAEsBq4DOgEhttUm4B5guRCiiXLJNTTOLPw3las/GmK0+PioJAKDAXz90QsLqWF65F1BDk31mrHs6Ghl6Nx8M9gu7F5eXhilrF4ebcfbG6ufL9zpT1G05xforVvVFGJiaq8TQoUNjhxR7ZGcKSoq4vPPP2fFihWcf/75FNkSCPfv309iYmKtcE7v3r3Jy8urEo+zJ+HWlMZ3Tsa1Wq0sXryYCRMmNDj/pLi8kufnJ9A1Jog7R9buEXO8Gdg2nJCh1+DlbeS9996rf4PjyP33w+uv115uMqlcpq1bPSpaURSmQoJtsOgJ23+CQpUQtXz5cnx9/TDGdCDc32G0/PGHCjW6qSg+JahLYM7Oxo0bkVIyaewIujd3kzeWlwlmW0jQSeTx999/55NPPlF3AR+tqqocApWIa9YbMO7f61Hyz1lltAB3ArcA3wMTgarbFynldqA9UAw8eFJmp6FxilPay5vUrsFVgm4ekZ+vhFA2bICFW2GbGT8vkDq9Q9m2piFy443w6KNw5ZXqGZXT0rG4GA4cqP0aS5eS/ewd0EyP1VB3czpntm1TXhZ3/enGj1fNqRcurL587ty5mEwmpk+fzoYNG5g4cSIlJSVVpc41jZaaybj2SiJnTwuoEFFCQgLl5eXVtF4ayvt/7ye90MRLl3THcBIaSHaMCiQsIpLOQ8/nm2++IdcDpdPjxbBhKirhTHq6+sq9+6763+PqoZz9UGpzmXy/BtaWwMr3AWW09Og3EKH3qhYespc9n8oVRJGRkW51WuzY+1a9e+/lGN2dY0c3wzgjeAEFjjCtj48P3t7eyu11MA9KHcfH6KUj2GQzBDfWTm6uydlmtEwFHpBS3iSlXCilrBZIl1KWAI8Al5+U2WlonOKUDvNlz7jmKg/FU+ySsxs2wMKNsLOSjoVWxA9FDrGMmkbLxIkqqeTCC+HuuwHlaSnV6x1XAWdCQtCbLbCzAkON6pu6mD1bdRlwhxDqTnnu3JrbzaZVq1a88sorzJo1i1WrVnHRRRcxd+5cOnToUCvRtWfPnoBDzj8hIYGAgABatKiud9G7d28qKyvZtWsXixYtQgjBeLsKmofsSS/ky/8Oc83AlvRtFVr/BscBnU7Qv3Uouh4XUFZWxueff35S5gGwahXs21d9WbjNGWevDvPYaDmaAAmV8MDd6nv4Vxm89xGZSftJSEigc1+VxxTuFB46XQTm6vO0rFu3jvbt2xMeXocnM3srtDYoKYMcF7ll5eVQVglGh6fGaNBT5uWDNBiqte5wx9lmtHSi/v5DO4HWJ2AuGhqnHfrcSvwzyhoWoLeX6hYXw+fT4To/OjaPhWIJ3bvDtGkqhORMaSns3KmyKG3l1ffddx9eQUHgqnLhm28I/mEh/FaGX0pa7fVuaN9eNZ2ti/BwZbzk5Kiq7ZycHOLj47nqqqvQ6XRceeWVfPPNNyxfvpy///7bpWckMDCQdu3aVXlaEhISqlUO2XFOxo2Pj6dv375V1R2eYLVKnvp9J0E+Bv5vQkPqeJue6wa3osi/OeEd+/L+Bx9Q4eqClL0fdvx6XOdx5ZXwxhvVl3l5qT6dDTZaChPh/kh4+HFlyV49GQZI0ue/DEDr7gMAXHpaTmejRUrJunXrqrqFuyVhBfi0guhAaOdfe72U0MMArR3J9EaDDrPBi7LYltW7t7vhbDNaLEB9heCt0WT8NTRcEjqrhEGzDzdMXM7XV131i4vBWxknHS8djLi/JVx9terwXDNn47//VLOYBx+EsDCoqOCGG27A97vv4C4X2o9z5mDcsQ/u8SevVTOPprVuHXz8cfXei+44elQVLf30E8yZM4fKykquueaaqvXXX389n3/+Od7e3lx+uWtHrbOc/+7du2uFhgDatWtHQEAAK1asYM2aNQ2uGvptcwobj+Tx+PldCPVvQAjvODCmczO+uKk/Pr0nkXr0KJ99Pav2oPn3w5zboPz4CdG5E4+LilJOwBEjHKlV9bJ3G/xaAnv3qpKzH3+HLmNpfWgBkcEBBLZUjSdD/RyeSHu+1KkcHoqIiCA3N5dKN/X9KSkppKWl1W20WCpg6Rr4cDfovOASF3kv5eVgEFXCcqAUcQEyBwxVNzH1cLYZLZuAV4S7pgmKx4AmbJ+loXHmUGHXaanzFKqBTgf+/spoWbwRVpdDRYnSaTCZXJfm2KtlgoOri8ldeqlq0VsToxFhsUKEHouHORy//aZsIjf9BasRG6vuzP/4Q4WGOnbsWKWrYufWW2+lsLCQkSNHutxHr169OHDgACkpKaSmpro0WnQ6Hb169WL27NnVtF48Ib/UzKsL99CvdSiX93Mvs34iGd0pinlv3IcxLJbHXnid7Sn5jpXJG+DIKpBWSHPdBftYsViUd6xm9RAoQ6VHDyX749yXqE6Wb4OjpdVzusyDCX4nhz9b+FFoshLi51Utj8jLC5YvV9HOUxW7Ny8nJ8flens+S51GS9p2KLQl6vdqD0UZUFnj3B47ElIqwTukapE9Pya7c0/XXtQanG1Gy1vArcA+IcSTQogLAYQQo4UQtwgh/gFuBN48mZPU0DhVsUbafjIaYrSA0mopKoJV22F7Baw5DDNSlNfEle5Ks2bqFjU3Fx54QP3yW61w+eVQo+IGAG9vRHkFbDVjzPEs6XPrVnVj50l6jhCqBVJ8fBrLly/n6quvdtkwzuiqL5INu5Hz008/AbUrh5zHVVRUEBgYyODBg+ufnI0XF+ymoKyClyZ3R6dr4OdzHOnbOozHHn2IkpTdXPzMTHYetUlw/PWaI7fh6Kbj8tp2VXhXnpapU6skgDyjogw22UIoTp9d0YTrsOqgW2EJuSVmwvxqe7hGjXKdinWqUJ/A3Pr16/H29q679P7IKiiR4OcL/+yCFwohI7H6mEE9VBwj0JEXY7R5WvZccLmK5dXDWWW0SCn/Av4PaAO8ANjT65YCnwPnAA9JKZeenBnWRlPE1TiVEBVStUpvqNFy8CB89hm8+X9wZwCUm8AsVWzGVQkzKAGNTZtUsm5lpRr7ww+q9KMmRiOivBzmmghMrj+nRUpltDRE/uSSS8Bs/gUpJVdffbXnG9qw/+D/+OOPQO3KITt242bs2LHVtF7q4rdNKfy2OYV7RrWjS0zD2hicCB6ZdgdBwcFkr/2Dv3akwSdvw51zoNklEBp33IwWu4S/K0/LTTdBcjL06+fhznIOQjM9BPlXy73YnZpKWqQOH5NZGS0uwnKrV6seSCeMo5shP8nj4fUZLevWraNPnz51GuUkrYFKf4hqBm1trqvUg471VisM7Q63+NcqeQYor7Awf/58TRG3JlLKt4B+wGfAZuAgKmz0MdBHSnlyRQVqoCniapxKGHe4COV4gp+fChMZbD96PX3h4a6qWsCd0dK3L+zerTRb9u1zdLxzNd5oVO1Q7wsgu139t7Tp6ZCV5VoJ1x3DhoHBMJvQ0F5uvSR10bJlS0JDQ9m0aRM+Pj7ExcW5HDdggErmPN/Wc6k+DmQW8dQfOxncNoz7xx5ftdu6qCs3OyAggKcnT+bq3SsZ+fht8MB08BEweAr8kA9fLXS/8TEQEQF//gnjxtVeV1amwjZpnuZt5+yHPCvU6Luza9cudhpAl1WBd9Yhl7lE33yjQpEnhEozfDcZ4p/weJO6jJbKyko2btxYd2jIalVGS0WAsgRbx6nl6YccY/LzYdSNsMnsMqelvNLKpEmTNEVcV0gpt0kp75ZSDpBSdpRSDpRS3iul3Hmy56ahcSpTNsBIUt/6M/xr8dFHqrZ4xUZYYgJzseo7VFrq3mi57jrVvfC991S4yK4A9vfftce+9x4Fy2dDqA6Lof6fNbtIa0OMlpSURCor13DDDQ33soBSirV7Wzp16oRe71rrolevXixfvpybb3a0SjNVWCgorV0fUGa2cPcPm/E36nn/6j7oT0JYSEqYMUMZCG69CRYLDy5YwOdAx4QN0MsLnrkcolrBhiTYlQtFnisZe4q/P1xwgeuclS++gBUrGtBb6tB2sEgYMrTa4l27dnGoTH3nLlr1YzVhOTuxsaqrtKv0rSbnyH9gKlA5Qx5W+dmNFldaLbt27aK0tLRuoyV7L5TlwZQrlFxBtx5qeWayY4zJZNthRTVPi7feYbR4wllltAghnql/lIaGhjvKz/Fh/8hGBOf//FOJouzYB9sqYFU2vL+77vBQ585wyy2qSUx4uKPMx1VMx8sLXVk5bDLjm+k6mdCZ0aNV6KABKSNVuSgPPtg4owUcoR93oSE7o0aNwuCUIfzCggQGvLyUJ3/fwdF8R7nTs/N2sj+zmHeu6k1UHdLqx5NXX4U77lC6Ya+84uY6uXYt+pwc5gWF8OX4YOREH7jjNWXpDBsAZUBK04eIUlJUZXKhC8kQW3Ntzw2J7RshxQoXXlRtcUJCAsuj2yCb69EFl7sMD9nzWTz26hwL2+bCRjPkpikFXw+wa6+48rR4lIR7ZLV6vvEedcDX21pRZKY4xpTbknStAoyObjlCCIwGHeUVFo/melYZLcCzJ3sCGhqnM/rUSkKPeKp57kRAgPKUTL8XHgkEWQl6Hdxwg0oucMfSpfDtt8ojY7+62LRMqvHXX/i9+D4sMBGc5Nkde1BQw4R9Z8+ezeDBg9m3L67R+Ql2T0t9RktNdh0twM+o5+eNyYx6czmPz9nBjJUH+XljCveObs/wDp5ruTQFUjoMgalTVbrSp5/Crl01mnAXFcEvv8DQobBmDfNumspt3crIjR0NYTYBvquvhxwrrPyzyef5778qgdqVRoq9zNljoyW8ED45H2pUh+3atQtDz/5Yp/clqL3rnBa7wFyjyp7tHgpPkBJ+/Bn+NEGqBY7WrzALSm06LCzMrdESHh5ed2fwpDXg3wy2HFaW2fDh0NoXfJ28g3ajZWR4rZw4Hy+9x54WD4r9ziiEEGKZB+PMQAowX0o5t77BGhpnC4E/ldCn4mD9A2ttGKiMFntOSz9vmDxAdXGuiyeeUEq6wcHKPTJvnuvMyc2b8Vq4HO7zJy28/nLfu+5Sv6vXXlv/1KWUzJw5k61bt/Lee+/xySdqSldf3bAWTABDhgxBp9MxsIFtfw27E3jcO5fhD93CJ5sy+WlDMmaL9aTlsTz9NCxbpoyCqCi4/XblCLM5oxSpqcqVdfSoUvAbPJh7980h+JDg9UPNmG4fd9mVMO1++L+v4I6Pm3SedsPKnU4LuC5Gq4WUsHQ7xJvgggxopRr+FRUVkZSUxO23306pUUe/fYsomFw7ebrRAnM//wxXXaWEFrt1q3/80c2wIhO6tIacbHjsJZh3sUcv5a5p4rp16xg4cKDLarkqjqyByP4wdqwqxzt8GJotB2O5Y4zdaPGr3ZPYaNBRXql5WtwxEhhVz2M8qkfRHCHEXCGE581MNDTOYCwtDUh9I/Im7J6WddtgQZmqHNIZVGVQXd3qBg1SQipdu6oLx0UX1bgy2rBXNfjrsNQzv9JSlYNRU9rdFYWFhVx//fXceuutjBw5kilTpnDJJeris6kR0YxOnTpx9OjRBknzF5RV0OHgDq5693FifQQvTu7OmkFWfitZyIeXdzvheSw7d8Jrr0HNG29fX1i8GEYNt6g8pK5dlYzwq69Cp05gLqVbxlyWpXjxxfx1jg2jo6FFKIhKSvPLuewyZQw1BfbqIVdGi93TcsstHuyoOAM2F0GpuVp3TXvjy27dumH4dAMhP+fQMntvrc07d1Y55RMnNvAN2Duae2odf/ceZFvh0cfBEg4LNiohPA9wpYpbWFhIQkJC3aGh/CQoTAF/m/U3Zow6uD5RkONkpUVHQ9sQOFBeaxdGLx2mCi2nxRWxwDbgFaA7ECSl1AFBtv9fBf5EdX6OA+4FhqM1UNTQAMAapkM25iIZGKgqDI4chT2VsKwc3t+kKoTuv9/9dkOGOMqdMzNVcq6r7Fl7nGe9mYD0uhu/7dypplJfufP69evp06cPP/30Ey+++CJ///03QUFBTJwIej38/nvd27sjOjras4G5ubBxI8m5pczuNYE1s+ZD3hb44x7CX7uGfj99QsR3tzespcIxIqXquhAcrJoN1sol3rQJS6s4pa3Tty9s2QL/939q3b9vYShJ5+2swRzYtaWq4zUAM56Fe/346NUk5syBmfU1W/GQwkI1x5pNxEGlSb3zjvK41Uv2PkizgJ9PNWEfZ6MlY9gYAFruqp0objQqw8Vd+pZb8vKUAp6n1WrfzINQH7juZpgwEvTAGy5aXAOYCqHCEXpyZbRs2rQJKWU9+Sxr1LPR1v3mwQdVW/QbZ8PvTgZTs2YQ4w1Brjwtes3T4oZXgbeklE9JKROklMUAUspi2/9PAj8CL0opk6SUHwP3A9efxDlraJwyiLJGXiBffFHd9t58k8ppCRHg6113Ii44DJR//1UXwB9+cPj1nbF7Wv4uJ+xIZp1TsSnp12m0fP/99wwdOpTKykr++ecfnnrqqapqn/BwJf3+xx91vsyxc+ONMHkyyWl5XG79j/4H74JZV8DuefDwVfDpXZC3EFa85n4fpblNatT8+KPKWXn11RptYqRUluDatZRmlbLMazymn+ZCR1voKmsv1v/e5/sd17Al7REAfne2+vqOAx0U/fErV14h+fLLpplvQYHKXXIV2TAYlG1VX+8pQPVICtDB8NqVQ0ajkbZt27JjrIo1Bu/d7HIXixap06BB7NmjvFR79qiDXtdnmZIAJcVwzQSYPh2e+wN6esF336mMZGekhC/GViuLdmW02JNw6wxnHlkFxmCoDLDvyKbaqIdSE5hL1fLiYhjqDzfUthJ9vHSUa54Wl5wHzKlnzBzgMqf/FwH1awtraJwFeO2tQGdpxEXQftXQ2zwig40w7RxltLi6DbZjz2CcPduh0+LKVe7jg/Txgdv9SOpRtyb76tXpBASk4UYmBYBPP/2UDh06sHXrVoYOHVpr/SWXqAuiG9XzxlNR4Yj9v/UW/P47R/MLedLwA5sTO1B51S/w6AG47HO45APodg0seAU2f1t9P+XF8Ocj8EYbSGi6tLwZM2CAcyrSunVw2WUq3+K99+DOO9n4yyHOrYjn54W2O2opyZ/1MPml/jy06EUGnN8M/2ZxzJnj+Cm2hnei4vdKXtj3BJ/evb3B2oXuePhhVbh2zGTshVwr9OxbbfGuXbvo3Lkzer2e5MBoKv0MGDdsd7mLlSvh+eddVzK5xGxWuSEHD8KECSq/Kz7e/fjk5XCrP9z+iEq4evYxGOKtjMn//a/62LStqkw5M6FqUWRkJNnZ2VhtfcUyMjKIj4+nQ4cOhIWF4Zbk9dByINjLpR97TLmvwkOhEiiylUytWAFvHIaMSpKT1dc72VYRbTToMWmeFpeE2h51EQY4994uRjVaPCloirgapxLlw40cHBLb8A3XrIHrr4eVq2FuGeRbQadXlRF1eVoCA1UCxYcfOpRw7XF+Z6ZMQaTshBgD1noy0JYuvRk/v5vqTBPIzMykZ8+ehIa6/rm44w71gxse7nJ1wziwVBkdJpOqsnnqKbW8c2cYMICYg7MJNRbyzt43MXQZ70hmBnj+X1geAPMfgP02Ie/E/+CTc2DDF6px3UFPag88Iz5e9WzSb9mo5jp4sNLN8fFReUt6PaMuCqZzZ/WRASTO+5mQvH95d8ez7DgUyaBBYOwwhJUrV1bd2efm68n0UQnU8UsEr7zSNPONi1MRxjrZuVN9pyx1/Mz/uwaa+0MNj8OuXbvoZkuQzSutoBIduu3pLj0i48erl1i+3MPJe3lB//7Kw3jjjardxfTprpuV5ubC+t/B1Br6jlRJV/c/Di0j4fpBUFOc9I9PYF4Z/FvdaLFYLDz22GMMGDCA6OhoVqxYwaWXXup+juYSZfw07wfnnadODHs1YHAgmKRDf6dEWWtlyw+TlASPPqryfMCWiFth1RRxXbAXeMNdYq1t+ZuAc4reAOBEVNe7RFPE1TiVsAw2kji4EUbL0aMqtHMkBQ5Vqh/Md/5T6+oyWvR69UPtHMsZNsz12PIKWFNOyNG63R+BgUcIDk6sc0xWVhZRrsJQNry9lcNn+3Zljx0Ty1+FefdB7m5VfeGcP1BpZnDGTyxLHE6rIapqqkoMTQh48il4eQY06wo/3wh/3A1fT1TrpvwJ7UarO+FjJDlZefeNRmhptt35b90KL72kVm7eDLfdVjWtadNUhdXG//IIWvskmzP7c9unU2jWDOLC/TEXX4nVauXrr5UXKCICYu5WzSHN8xfxww/HPGUA5s9XVfNuKSpSwoeXXaY+0Dlz4P33HRm8VQcgEXIrqknrFhYWkpycXGW05JSYWTZuNEzwgdxD1GTIEOVUXLLEw8kLAZf3g6t94cIWSgRn+3bXCn6vvwyP/g1dxiiPzLnnqi/KkUiIKVaVd1arOiCjRsE9n6uS6MhCsKgvVEubAt/bb7+Nt7c3L730Eps3b+bVV191P8f0narhZUwvVVH16acq7ywhAfYlKu+UzdNSmafOy5WbHPZIXp56tpc8a4q4tXkXuA7YI4R4QwhxqxDiatuz3Vi5GtVYESHE+cBXwNqTNWENjVMJ3f4KIvfnN3zDAFu8u2dPeDgU2hgg1F/5iMeMqXvbJUtUPOLwYfW/q5LnrVvh7kdgcTkRR1z3T7GTnZ1NZqb7vBez2Ux+fn6VSmhd2D3hr7xS9426WypM8MMayKlU4ZyXXlJNIe3s+Jlwazav//cAnTrBxRerkEcV11wD50+C635V0uhbf4CBd8BdqyFuqHLbZ+1WaqWNQErYsUNV3Q4fbnMgJCeDNIEsg5bNlDesBjfeqHJJdMtfJNQnh4gp/6NlK3W5iYvwI6BPCEK04eWXf+Pjj1VLBd3AsdBMx4VJHxKSvqdR863Jc89VbxJejR9/VDk369bB2rXKSHjqKZUY/txzjnEVJmheAD9Nr/Zed9vcBFWelhIzC0deAwO8IXVLrZczGpW9sHixh5OfNw9W/w0dDbDw/6B0D7Rrp+rN7SFEKVUF3mczoJ0Bxt6oEmeCg1V46bsdsGwvJO6H9u1V9d3+vTDOCI8NAj8JxcoTctFFF7FkyRIyMzNZtWoVTz75JH369Km71Dltq3qO7a2SnRYuVHOKjobxY6GrAYrSKCuD2V+oRqb6ayZW9UjNz7cdG63k2TVSypnAU0Br4BFgBvCD7flhoAXwuJTyO9smVwPJwDcnfrYaGqcePr+X0ePPRui02I2W4mLQG2G4Ee4cpa7A9WmWXH01fPWVSkicP9+17npGBsz9E67z40CfVm53ZbVaycrKoaCggHL7D38N7FLmdXla7Pz4I1xxBTz5pHL/N1g8LG0r7CyHrVFKCGyrk4vBakX+9x47ytqx+OC5dO2qcpA/+cRhvwHqiv/5LLjxL7hjJVzwBnj7q3UtbV6bFM9Exuzs3q28JW3aKDtz7Vp46CFbalKvNjDNGx4KgFDXnU8CA+HI6k30tXyFGHgHrQY6PGVtIvwxRpXQZ9j5FBT8zT335PPxx6gQQwcDYQXJXJH7mcsoSEMpLHRd7symTUqkJyNDWZ52wcJfZ4PRu3pTztxDsNQEN71fbRe7du0CHEZLbomZYr84WFmpBBFdMG6cOoY1HTku+fYbWLwP+t8GrYfBsy+BNKsvmskEf/2lLKHoaCgohohAOOp0bvj7w68fwXlG2LtafZCzZsHMu2CoL4SMgxXlUKCSdL28vBg7dmyVOq5HpG4F/ygIjFHxngsuUOdoWBgsjIdxYVCUzm23Qc6RfADGXhpJSIja3Nlo0Uqe3SClfAXoDDwD/AEsA35HGTOdpJRvOI29SUo5WkrpNigshLhZCJEjhHjOk9cXQoQIIWYKITYJITYIIWYLIZodw1vS0DhhWFvqqfRuxM+G3Wg5dAh+K4SjFrAIpSFRl04LqOxPg0GNnTTJda2xveTZAFLnPlE4NzcPUD+O7jra2r0wnnhagoPVdeDLL9WFvVcv5RmvorjY8cvsiuR1cKc/zJwLLQfD0mcdXpF9CxE5+/hMTuSlr7Lo1Us5APR6eNZZ23v9emX8bTug3PTOxPYFoVev0wAyM1XZce/eqv1TSgrcsPVhdWF6fRoY9ND7ctj0NaRtq70DUyEhf9+mLmajqzfuaxbog4+Xjr4XDQUq6Np1gaqKDmoOQ2Ip7tCCj7mzzsPmKQUFboyWFi3UBf/yy+HKK9Wy5A2w9CbojVLxtVfcZO+DvRUQVv1ivmvXLnx8fGhjcxvklJgJDgyAFaXwrYv+WKiOFHv3uplTTdb+ByUSwofANT/CnX3hunJ48ma1gw4d1Of+5utwQ6gyfmuGjoZPVlaSX44qd7vmGtj/J7Q6BxLL4J9ySPFMx8UladvUd04IFWrr3t1R8VdcDKZQKExl5UqIHhoFgQI27MXXV52y9vCQVvLsBiHECCHECMBbSvmSlPIyKeU4KeXlUspXpJSJDdhXqBAiHuiDSt71lN8Af6C/lHIAUAQs1ATsNE4HZKAOqW/Ez0ZQkCqFNJvhiBl+KIW34lWyaV0VEaDyDXr0UK1yb73VtWfGXvK8rYLwZPc5LUePOgwVdyEiuzHjiacF1O/1LbfAxo0q17jaZjfdVHeda/J6iGoH7XvAxLeUwbLsZeVi/+9dyvxbsFA3gPGjDfj5KWXV++6D779X6Q2ASoDcvdt1mM0YANHdPTJatmxR0SlQaUPZ2eo6N3UqxC6YoSpQ/pwPH/4F7S+Bif8Dv3BYOL164qmUMP9+yEuEy74An6Bqr6PTCeLC/bGEtSM2NpZOneaotCYhoM8QrDdFkRncicL96fDII45QSCMoLFRfvVq88Yaq1HrxRZXTsfxV+GoCWCrgihFgtcCwfmpM0k4wAVHV9XUSEhKqKodAhYfCAn2hYwxYy9U+amBP/q63Cl1KyM2Hdt4wYJI6hvf/CZEt4PsrYPGLsPcT6HkE/P6AthZYPhueqdFezy8MUsLgllfUcczaC1l7oOvF0MGmsHsgoeare0ZFmdpXbG/1f16eStyxqQUTGwsv74bCNG67DbqO9IUYPTRrjhDq/sVufPt46bSGiW5YASwHHmiCffkDz0kp7/N0AyHEucAY4CUpq762L6IMn8vcbqihcYogyiQ0RvajbVt1+z5tGjzdCfp7QbAthFGf4taBAyqWbzarrMoOLhQI7EbL1gqiktyLyyUm1m+02Jd7arTY6dJF5U9U6ZdYLLB/f1W5t9mscoqPHFGpPEWFEn5aAjttxyG6BwyYChu/hHWfQcp6tre6gfxtcRzZHlD1OtOnQ3CQpCo/Uq9Xxp8rjhyBFoNUM0KL+3bGf/yhDJUZM1QZt17v9LF8/TXcfbcyjv53FUzxg3GPgG8InPuM6juz8zfHzjZ8AbvmwJinVF6NC+LC/UnMK2PSpEkscc5Mbd6XoIoD5CccJu7aoaqMerNr3ZP6KC9Xj2peDSlVCGPfXrj1Jgg2K2Pln9egx+Vw1yp44C8Y01F9X797CNL3qe/8ZZdX279z5ZCpwkKJ2UKYvxcMHwQ5Fkhy4YECPv5YpdLUmQOVkQFlZhjWA7xs3+2ASLj8R/gsB/7vRdj1uzIM15RAt5uhx3kqLFOT2M5gLVUhr4R5almXSeBry885tL/eY+l6jrtAWiCmt0ryzc6ubiGOHKmOW24qTz8NPVpkwUgjXHwFoAxw+3fM6KXHpDVMdIkELpFS3nnMO5IyRUrZ0LqBC4ASYIfTfpKAo7Z1GhqnNLpDlRjMx6gAoDfCGB8YY2tfX5/RcvPNDkW44mKlw18TX1/lybnSl9393eu0pKQ4DJr6jJZ6w0P/vAELH6u12N6XyGzRK3fISy9Bbi5vvaVu8GfOVFGWj19JhF15kFDk2Hj0k+AbBoumg18Ey33GU7CyM4sXOFRYw0KsJIy4k6+6vQ2orgb//SupmPag8kyAuji/8Yaq+V2TDxUlkLmr1lwrKlRF+aWXKs/++vU1yrhfeUUd/4ED4dMPYevXMOwiiOyk1ve5XoUHFj+tyl9TtyjBsg7jYegDbg9dXIQ/ybmltG4dR3FxMSV2DZ7mtiTrgn3Ku1ZZqYTVwHWprx0poaxQxbJsOU9eXsoBVaUps/oDeDoW9iwG/d8Q+xt8Ogxy9sPlX8GlM8AnGPResGgnfHc3JH0L/9l0bpwMw5qVQ3mlqutimL8R+g1VV5rfv3c51ZAQZYfXaYtt/0+p2XbsX315TEd49HWY9iFMPwx3/gc/7YYZu+xRz9pMnAw3eEF5Ljz3DlR2hqAYpf8CkJRUx0TqwJ5sHNNLeVksFnX8q15X9SyQ2alkZkjkr8vhqxIwqFDu118rIxnsibhWpAdCiGdbw8RswHWw8cTQHkiXtT+Zo7gRsEtNTeX5558/7hPT0PCEJ8cZWZsXwN+N+E5e8dNPHGjfnnGH9uDbw8ou3U66AV/88ANHV650u12rDh2wdujA2PJyWiclMfuWW9jrQtbc++6pPM5HpOxKdnvOrFixu+rv77//nsPVMloVS5cuReh0THhjETohAIkAvLEw0CuZAJ0ZL1nBI3yKFxV8sM5EnnDouezf356ffrqO/PxFDBmyjqkzZpDtE84zRzbTtetehPiV3r0vYufCvXCtHzMqzyHNab69ZV8uZjHLSjsyd/lBLKV9SUqK5/nn1yIsFi6eO5de27fzb94wllYU89cr57OxciDfkE2SsTX/+yqXO2I/4dVdSu8l8b0lxD0k+fOz59goeld7r19+eTPJya3o1m0n48bN5bPPqntj+m3cSMsePWixZw+WcecQda2JL/YEcNRpvi1lZ25hG+teuYAOHEaPkc/2t6OsjrDYvspwKixt+GetShB+8sknCQ0NxSjLeUgayPv6bhZEjWVUu7a0ufVWVn31FW0PHeKnq66iyMl1EpadzQV//UX++Eh67d6O4Z8yfly2jH01PE+95C4mE09iSQyFQ5uT2SaSCrypwIsDpjiKftsJv1VPKg7N0XPZxmCal6dTEu3Px8uWUbpelY8n21TRtm/fzvPPP0+O1RfoxpI/f8ectIMpQNbXP/BxYe3kleJiP+BRHntsGSNGuG6yNLxkNWMs8M/y3ayocHOu2T4D/f/9HwaLhXI34jaxMo3bgOVfPcbo1WnsCG7GnOefZ+K8efQTUHLgEG834ny+SMbTEV/eevdLdBYLvSdORG+xsMG2rx7btnEpIEpMdIrLZ3/bg4Rb4aupU0lp2ZLvvrsOk8mHtLQv2VYRjZQtePb5F9GLug2Xs81oWQiMBlyoUzkQQliklMcjxyQAcBWgLUf1P9LQOKWR/b05kNi4r2q7gwcpCgzEkFEBu8xEt1eJjhVOvVxckdS6ddXfOWFhpDk1rKs2N3SwspzWllKHK7MGOl2W7VnnuLuvQUlJCXq/YAqlLyHChEQggaPWIFZXtGac9366sB9vKgAYxFYWMbpq+w4dDtCl3S5eX/x/ZHs342j/Vny99nr0egvDJiznV1N3OozawtAD2ymu8CPNq3oYaivdKMGPQ7QiO1u5PSIistFXVnLZr7/SZc8e/h4zhv9GjKD9gQNsqBzEV/1vZqbxZiryvOhj3cwze16iICiIpb3O5cNV0/iu8G7K0gP4++hojEYzgwevwWCwMnjwWoYN+4+OHfdXqdB6mc00y8ggpWVLNvXvz6Z+/ei4fy+T/JZymBYcFdV1epJFc7bLzgxiKxZ0fM2VlIm6vWdBQv0MSt+QqmMeGhpKuTDyVeXV9Mrey60tZ5N5VSjZ30cydNUqcsLCMNXwyvmWlRGRk03r7HT0/5ZhjTNgbKU8gRWZOjYeGMAlPedzUdlCCld7UVjpTdeErSx+8EFKAgKoi/CcHJqvUcZJapfWlDqNt+c92b1xJqm+wz5UcqRVa9Kvb0VlnBFXBASUEh2dxsGDbd0aLa2NKeTeEsPmYBfl/TWweHlhqeMcyiCSSvQM9tkC0wNZqlO5T39edBG6cUa6GBsXHoomkzSiQAisBgObBwyotr73FpsnxiRpHpiGF2bQQbktad7Xt4y8PGXs620xZws69ErLNQJ3SCnPmgcQifK0XINKxnU3ztrA/UpUfkt94+YCB1wsXwescrVNv379pIbGqULFJKPcfmHHxm0cEyPlbbdJ+eV5Up7jLeVN50r5ySdS5ubWvd3Bg1Ju2CAlSKnXux6TlyflpAul1CF394txu6sHHnhA+vv7y1atWskbb7zR5ZiLLrpIGpu1lc/N21lt+cz/DsnW0xfIP7akSPnNRVK+00PKX2+V8uXmUpYVVBu7d0WqXKibIEuviZLm52JkoHeB/PBDKR/7bbtsPX2BbD19gcwZ3VIe6dRGzp3req5Wq1VGX7hDgpSHDlikvOACdQzefdcxqLJSygsvlFKnk/KvvxzLP/tMypQUKa1WWbhmpzz80nUybXp3qdOpXcTHuzlAeXlSDh4sZWCAlAcPSLlqlVq++Xspnw2Sct8S19sVHJXy3Z5SrpvhZsfVySgsk62nL5BPz/hdAnL+/PnV1vv6WuU3//e7lO/2kvLxQClHtpHy0AG10myW8oknpKyokHLPHil/vUfKZ8Ok/L97pNQLKYOElG/cLyt8A+Qzwc/JiudjpHyxn5QtWqjjdN99Hs1RWixqG5By//5qqx566CHp4+MjKysrpZRS/rElRbaevkDuzyhSA+KflPKFSCkPLne560cfldLLS8qiIhcrTUVSDvOV8qaRns3TE2aMUZ/fZ6OqL//3f2q5qbBh+zOXSfl8mJRLnlP/79kj5Ysvqu+cnZkzpbxgjJQPBshLey2VclislKE+VavvvFPKyEj197erD8vW0xfIzEKTlFJKYKN0cx0923Ja1qHKnb8HSoUQaUKIQzUfNC7V0BMOANGitlpPLNDIbCgNjROH/s9yOi+trfbpEQEBKifF4A3jfODW8+HOO8GNVH4VL78Mkycr6dl581zntFitMH8BjDOyY4j7nJbNm7MxmyMJC2vmNqflaFo6wieINhH+1ZbfMCSOXi2CmTF/JfLQP9B6EsRdDuYi2Fq91LTjyBj0jwzEt6MJL1nCL/d+wZU91/Hb5hSu6NeCSZ2DCCnLwFJupEUL13PNKi6nNMsXL28rreJ0cOGFKmfAuSu2Xq/EYnr2VAmmdinZ229XmY4rVhA4pDtx8TuI1h8hfV8qhw4pqY/aL5gFI4bChnVwvgWu7AZjhsMnF8Kyl6BZD2h/ruvJBsXCfVth4G2u19cgMsCIv7eeAnxtL129/DwyUrAsYzLcsx4ueAFG5cDB71TexNSpKhHn6adV3svXM2HgLfD6h/DvP+DrB//3Hqa4aB4Y9xnWI97wXqIqYQ4MhMcf92iO6HRVIRhqdDnetWsXXbp0qaocyim257TYSu/3BsD7BXDnefDtpUo51k55EXcM/IGE6Rfi+3EnVdHjzOGVsNUE+4poMuy5Ql0vdiz791+44hX4oxQKjnq2n+3bVV3/zn/AWumoHIqPV5/Hu+86xk6ZAt99AUE6urVKV33GvB0eoZAQpQYgpUrEBTwqez7bjJY4wAwkoUTjTIBw8The/IWqOupuXyCEaIkStVt4HF9XQ6NpaKmnwqeRUWW70fLpFthkhtxSlY1Yn5TsAw+oi3FFhUruW7269hh79ZAFJSvuhszMLCoqIomMjHJrtKRnZKHzDyYuvLrRotcJXrm0ByPLV6iA0YsL4Iu50GIgrP/MkSgqJSTtYmzAp+zyuhZrdG8mzH8eedNNVFis3DO6Pe8Ms6Cb4MNz19zAv7n7XSYgJueWEjJyDz8uyUGvB+66S12waxIQoHrnBASommvnjnxDh6rj9+922F1BpGl9lRppNY4ehSEDYM9uuCEc7n0FnrgPHh4DuiJVJTLmSdftku00oMuhEIK4CH+yKn2A2knRERG2/nsGbxj2APSbAv+9C799rOq9o6OV4dIhHHr6w3BbAvKQ4bA7EUa1JaBDCtY23mRd+Dmcd77K/NyzR23rKdddp0p3a+SLOFcOgUrE1QkI9rVdlOO6QZ4FlpTDEwvgicHw+50w53Z4qyPtVt5J+z1b0S/JhO+vVN247SQshGIJR9JpMtqfCwZf6DbZsSw4WPWM2lEJuUc828+rryrD5duv1P8xvdWz3ei87jrHWJMJth2EIgsdo1Mhwgpma5X+TWioOqVLS1UiLuBR2fPZZrQgpWxT34MmMFyEED5CiB1CiKom61LKv1El1086eVueArYAvx7ra2poHG+En8BqaOTPRvv2qjQlvxwWmOCF75Ukf306HD16qPLJTp2U8dK9e+0xdnG5g5XEHs6tvd5GYWE2EEFsrHujJTc7C71fSC1PC0C3mCBuCVjDektHstp1V9U5g+5QqqkHbKW7mzZBu56IgxV0m/YUuoFTYVgln5x7Aed1iyYuwh/D0Q0AtOgxgjd+OsoFN+XWst2O5JQikJz30g3w2Wd1H6PmzZWq3caN1ctOvb3h7bdVyflWi/s+RM/cBSlJcEd76PUgnHMfXPoGvLwE7vgHHtkHnc6vew4NJC7Cn6PFVnx9fWt5Wjp2dOgRAjDhFYjoAMkfwS8/KON38vlwSSmMugsCnfQ5QyNg6R5WD32Fsd/NxWvoOPjuO6WZ0xCDBZQxfPSoagRoo7CwkJSUFLp27Vq1LKfETIifN3qd7Wd90iRVMv399yAjYGYJPDlTGSSdLoVPTfDrUViaD1sS4ZcpSiNGSlizSO3joYcaNte66DhBVRuFxjmW9ewJ0+5SVUeJHmq1FNm8P+s3g08IhNg0WXJz1bltF5YDZSSOGUflej3Dex2F6Ero004p9aIchhUV6l+jQXlaPCl7PtuMlnrO/Co8SqUWQvwihFhh+3eKEGKFEOIi+2rAD6iZkXYZysOzUQixAQgBzpdSnrRO0hoaHlMiEdZGRk9//lnV+75wIQz0hkCbUeDjU/d2ycmwbJkqab72WtcXHoMBhECmWIhKLqy93kZxcRZ6fSTR0cpoqenhMJlMlJUW4+0fQmyIi2TS1M1EmI6wzHgu1wy5nfKHH4G0QDBFYFnzMekFJig/Cv0NcNEdENwcul9GeZtQ+kQncPuItmo/782EH+HFK4bQ1bsVi74LZ8V/1St3DqSVMXR2Kv5L4j3zYrRs6bovU2Wl6sZ8uAI2La++zlKpxOGar4AXzoUhT8CDT8KiRfW/3jHSJtyflLwyoqJqG5A//gizZzst8PZXQnWlOVA+TxkEV4WCfxAMvZ9a6L1YXvEgW9N7eaY+2wASbJLHzp6W3GKzIzRkJyxMeR5Wr1H11xUtYfp+EGOgopK/bvmVLCLYs7wTHP5HlYpn74NEW6imvp5cDcXLxfe5g+0G4ICHvZ6GD4euXaG3twoN2b+XSUnKWK6ocIwdNQoAg9Gf1r57wRu48YKqcLDRqE5bAKOX5mlxiZTyLg/HeWS0SCmvkFKOklIKKWWc7e95tnVlUsp2Uspra2yTJ6WcIqXsJ6UcIKW8SkqZ0fB3o6FxgpESki14mdyLlHmEwQjn+0CrZspg0dXzM/TDD6prbXa26t7nKqdFCOjcGetII9uHuq4uAigtzcZojCAqKgqz2UxBjSYwVVUhUZGOu2Zntv4IeiPDxt/Agcxi7vhwOSWTL2f3Yi/0h1dw/WvfcHj1/5CXtoSJTwJQoffhDzmCCzLX0GfZzyqMZEmHuBboDHqeuCMUdFY++qb6+9q9o4znkt6koHlXJbnbWMxm+PVXdRu1eJtSMgWVu9IpGhZ/DOdMg4cXwrVTYOVKlR9znImL8MdilQSFhrttqVCNmF4w9jnY+ycs/j/Ys0B5hPxcC5I/+CAkJjoih02FS6Ol1IXRYqd1a1ixAlatBi8fpRr73nuMf+4cijsPoF3KFj749zZYP4OC76ZBkRXCw5xUCo8j772nnhM9zFObPh22bYFmGY7QEKiw2+HDDl1+gM6dkYGBlFv8kBm7YLEJPphbtfrAAaVZuGcP+Ng8LeUe9B86q4wWTxFCaF4PDY2aSAkX+5BwfqfGbf/22yqh9vNVsMIEZkv9wnKg+qX8849SPuvZ0yE0V5OEBCoH++BOZau0tJTKylI6doysUruteYdv/79FjIt2YJVm2PkrdLqAYVdcwLe7f2F3Kbx037v8ev/7VApvZuk/oM3hTXxvvIZSW9nv/G2pzCgdhWF9Gdz/EGTuhT4WeFUJ043qEUJwXAHLF1fPFeo+72c6cIDs6W86bkkbQ0CAOnbnDVe5Kalb4J+/YdQwyMqFcx6BjE6QbOu1M3x441+rAdjDb35BobWMll9+gREjXEQOB90F7ceqnkd+ETDYvU6on5+yF5qapKQkhBC0dtp5bomZMD83RgvAOec4+jvExsJ992FoGUObvqHou3dFTHqF5cnnEly4HhkeCzm5vPhsJS+8oOxNZ1ugSenfH/QCcj24b87JUTcMmQmQZoIMJ2tw1iz48MPqhpbVSrlXAMn7dYjyAsizwi6HcZSXp5p/7t/v8LSYPEjEPdt0WgAQQkQCg4BQjm/irYbGGYSEXt5kVTSyv+eRI8r46Nsc/jFDt0OeGS2tW6tHerq6mnXs6Hao7j8TLfWuf+Ht3ZvvuSeimtHS0Wl/GRnKaGnTMrb2DvbHq95A3a6A29ozolcv1k0a61g/53KiPpoJy8r54q7W/Pr5Or64sT+f/XMIQ1Rn5A3DEaWpcPg/ZQDaOjALIRg+1syCGaFs2llOv+5GKCtjysaPWcq5DLm5CXJJevWCX36HN9vCTx/BM7NVScAPH8DAa1RrhCuvVFeRE4TdaNH5BZN5eF+1dTk5qrglJ0dd46vQ6WDyJ/DdJTD4bjAGut3/jz+qhol3HrP+eXUyMzMJCwvD4GRI5pWYCWtTh9Hijs8/R+fryzQhMOV/Rfn3l2IcezGWFyfz1eiWJNrEag0Gldb14IMqF72wENatUzmx27crW+LNN1WKVYN47TXonwWpm+of+8orSsJ26bswrww2/wbX25phDhigHs5kZ+OTm0aAv+33QgeEOyoF7UWD+fnQ1p6I64Gn5awzWoQQr6N6D511711D45iQElaVE+vtYXlkTQIDVQLlIxdD4T4YORSGXVL/dhkZqpvfsGGqK687rr0W/Xoz/hGuReOcBcGaNVM/pDU9LfuT1HvrFOeiDnnbbAhoBp0nwFMTq68rLYVnV4CPHt55gqfGT+LeHzcz/p1/yCut4O0reiF8pqmEyxeehlWlMN3xGvfd7M+fX1fy/cI8+nWPplTvxZXNZ1JW2JrVAU10X/XZN7AtEP6aBcF6JTM/9Gq1bs0aR6O7E0SonxdBPgakTxBZWVlIKbHXJ9g7KGRl1TBaAAKiVI+gevj+e2XnNrXRkpWVVa0vlbnSSm6pmciARsSh/PxUDOuuu/B55hm452/o1Qv93UYOH7mTsjL11Z83D+bOVacCKIPFXrYeE6NOq5UrYedOx7HzCCEgpCUkzFVhy7pCtevXq6T4rJ3QKQBWblMWR0CA6ik2frzqB2EnLIx1fe5kb56JG5kDBgH9+1atDglRz/n54KOVPLtGCHEHcD/wEXAbystyi+1xDzAbpdHiYSH/8aegoIDbb7+d+fPnn+ypaJz1SFhaTruVx6DTUlkJwgsm+sItl6pwUX2sWgXnn6+qcjZtUnoPrli3joo4A393dd3o0G60fPNNhNvw0H5bEmSPdjW0XkpyYF889LgCdu+p6m9ThZ8fnHchPP4dPPAy47o248fbBqMTgthgHyb1ioXOF0JKAGzOgtEd1DY2xg0OYNwraznsdxAKC0nKLWWt90AqurpphNgY5s+HvUBrH4hfAAfNjl4xHTvWnxDdxAghaBPhT7khAJPJRHFxcdU6e5Qh233vy3opLKTJk3BBfWec+1JlFJqQEmJDGnn8Dh5UOievvw6pqSpva5/yPPn6qsjSa6+pPkpTpqhNBg2Cv/9W+cipqcqIeeSRBhosoF530qvwcyGUuK6mA1SC7aZNypvywiyoDFZSBX//zaIfclR12wsvVN/GYODDbp/wbwvbjUalBD9HSZjdaMnLc5Q8r9u4mdtvvx3A7Sd3VhktwO3AvVLKh6SUXwFSSvmN7fGplPI6YDrQ7uRO00FwcDAzZsxg0qRJJ3sqGmc7UkJzHebG3FGCo4Z15jLlXt6dCFu31r/dyJHKE3DkiIrB73ejw2g0ggV0blLSsquugJFE2K6KNY2WI0fTQO9F17gaIbD98WCtgLYXQN++8MwztV/gzTer3Wn2aRXK4gdHMOfuoXgbdKoRn+wKpRLuuq7W5pf2jyF5TyIVPXoi3nyLyEs288Usk+v32hgWLoQdh1WTxt4TVDXXL7/U3YjwOBMX4U8RynhzzmuxX3yPxWgpKKhe/d1U1PS0pOYrIzom2INQpyv69IEWLWDxYmWgQ3WtHSfsjpCgIFVcZD9OXbqoJpygqt7ff1+drpmZsHatcpK4JC4OWsbAYQsUpLif465d6mahRXMoK4A2bSAoCLkonken2M6h62p/pwN3rKZXqa0yKVwP+w5WrTMYVIjIbHaUPHfp1oMZqotiQa2d2Y+B+1mekXQC5tQz5iu0jssaGi6QYBRYDY1sy9WypeoWXGiCLRXw8JtK+Kw+wsNVye7YscpH7i5w7+2NPttK9xTXlSjO4SEvLy/CwsLIyKiegHg0PQODXzCxNS9A++IhIBpa9FVxBxc/0C6nHmAkOtjpDvzVGfC/0dC3dpirX0A4H376PtbUDHZ3Vm701uF+tcY1Gh8fZTgJVR7OL7+ouEN91VvHkbhwfwptCcvOBmRUlJL8OJbKn8LC42O0ZGZmVjNa0gqUYdloT0tYmGrVXVamvC3guWqvC774Qmmg+PtDs2aqWOkp1TsTKVWuj9lsG9ypE1x2EZRJyDjgWF4Tu9Xz0kvgZYVH7oVzz6VyyTIiUeeVHDCw1mbvHZ7E3TtseS89g2t5VnNy1C4bUvJ8tuV1VADOvuU8IUSMlDLNaZmRupo1aWicrUgJxRKdrpF35pMnq8ey9yBvG2TrPUvELSxUCbz9+sFFF7kfZzSiK7YSm1eKxWKpkli3k5WVDeiJjFSeZ1f6INlZ2fgGh6FzLne2VMDBZUoC3ddXJaw2lqg2cJuLRvNS0vete9BVrmdq28+pzO5Jzi9BZE/1JqR941+uFg88oMpcy8o8O/bHmTYR/uhsTROdPS0RESqX41g4HuGhyspKcnJyqoWHUguO0dMCyijv1EkpRAcEKDHARvLRRyral5ysdtOmjdo1qNPo2mvV8pdeUssrcrsxCJAHErj8eVXk16FDjZ2OHAnvvANlCXDwB9UW4K5g/kofRNyNvwAgnCT67XiNG60MY6GD8JDqOS84ZF6qSp41nZZaHALG1Pj/nhpjngDq8JNpaJytSMi04lXq7nbMQ4KC4Dwf1TfHkwvnkSPKWJk7V0n4m1yHTKxdulLayY9/+3qRmppaa31aWhYQQUiI+tlzZbQU5GYTHBpefcOktVBeCB3Gq9LOGt6ZJuGll9DNnsVnHZ5jZtoNrFhVSfGhCLt4aNNxwQXw7LPHVkLdhLQO90PvpywLdwrFjSUtzeG4aCpycnIAqnla0gtMBPkY8DcewzEVAuxdxzt1alBLhJro9UpM95134N57VcsquxEycqSKEgYEKOOlXz/w//wDAMp372PjRkeaUzU6dVIGb3QK9O2p2kKMH4/f3i2MCN6uxtQ45woLIcHYR+XD+ETCzEPKS+nE66+rlkVeeoEQmiKuK5YA3wgh7F29vgWeEEJsEEL8KITYgTJi5p20GWponKJIaYWrfNl5aa/G7WD7duXz/99s+K4ETOWeGS3t26tAfUWF6qXjwiABKHpvJjmjm6EDtm1LrLU+Ozub4OAIBrY7CNkHahktFquktDCv2l00oPJZdF5gaaHCQr//7vl79pTWrWHqVLyefAJrmZHUDc3w9rU0WHW+XsaPh+eeO2WMljYR/uj8VAynplbLlVd6Fj10h9FYLde5SbB/X6p5WvJNx+ZlsfPFFypfykUfqqZCCDjvPOXF+u03pdvY4sGrIMgLH1MGgwbBN99QPUxUUqK+83/8BFv+hS6T1Pfo3XcZF7WNKTfBuxP+4s0t1ZtpJiTAd7Nsxpd/R7BI+PPPamNWr1b54UIIjAad5mlxwVfALJQ+C8AMlCHTD7gK6AZsAJ47GZPT0DiVkVJCZy/yWzcyemq1KmG4ozlwyAIFRZ5dVXx91S3hZZcpeXk3V/KiIgjcUES3w5WsXZtYa31+fhZ9+kQyIv8+mDeNZs2qd3pOzS/DUppPbHSNJNx9iyFuKHTvo+Z/xRUNedeeceON8PnnXDbZC6G3UpEZTLNW5mO54T4tCPHzJjQoEC8f31qelqNHVSFNY0hKUlW4CR621PEUu2FVPaeljJjG5rM4M2GCqtBxmzXbdOh0KlJz7bUQ8r9n4KNJ0MbK1KkqeXfBAqfBGzeqwfdMg7/KoMtF6py8/35V5fTFFywoGcNvf1fv1p6UBMP4T/1zjk1kvobGUmioQzjPx0tPueZpqY6Ucp+U8l4p5Ru2/81SygnAUOA6YDgwRErZhD3BNTTODKTFAotNtNiQ2Lgd2KuHRgyAc7zhyQeV/7o+KiuVLGhhofphd2Po+Dz/GEFb84jKlRw6VHuO2dnZqmoo7zDkJxEVFUVubi4Vtn4pu5MzkRXltGru1AYgLxGy96rQkBBKkTc8vNa+G01+vkpCSFcdfYOD4ZrbVZggrt3ZIcwdF+GPMaC2Km5Vp+dGsG+fOqzHUn3kCleelrSCJvK02NE3MtH9WPCOgtxkJkxQvTe/+MJp3bp16nlKF5jcFpqp9gV79sD/vo2A0lJmHB7L1q3VWw8lJcE7PEj5I09CuO1GZ+jQai8bEqJOAVBlzyZNxt8zpJRrpJQ/olo6xZ3k6WhonJJIqwXWmGm95nDjdhBoUy9t1hU+/BIeeU4ZAfVhsSjvxscfq0xCNyUOIjERq7eetYODSE5OrLU+NTWLBfPCkYVpUJROs0j1Q2q/WO48qFLZOrZu7thov61zc/MR8NhjVfoZTcbffyuXwIEDVYtmvO1Px76lXDTuxOqmnCziwv3AJ6iWpyUyUonLNQabDdjk4bWanhZThYXcEjOxwafxZzVnDlz1KXyTjEGWMXWqih5WSRGtXw9t4sC4C8ZfVZVvs2EDtCjaBUDbo/9RXq4qo+0kJcGGoHEY33zJ0YW9hhZQaKi6F7FYVNmzJi5XAyFEfapY7wN7hRA3n4j5aGicTkgpoZkOU2PvKu2ellWr4Za3VRtfd5orznh7q3yYqCjVOdZNIxbvAG90UqIX/qSlJVZbZ7FYKCrKJdLHB4EVpIWWYaqe1n6x3H1YGS3tWjl5WvbFQ1hbSC2Bd99VMYumZOlSZcwNGlS1yN9Xx95NfjzywKmRd3K8iQv3x2IMJDPTtaelMSkex8toyczMRKfTERammjTay51jXHUEP13o3Rvat4RMCxQc5dlnVcFPVdrT+vUQ4Q97y1QFnY0NG+BRv0+wXnc9uberPlqbnLoBJCVBn5g0VY6UmalCSnuqd5Nu1kwpIRQXg4+XltPiirh61vcH7gSePP5T8QxNEVfjVEECGGi8ToufnzI6CguVqNy116q7vPoQQsmH33ILLFvmaFpSg8AII/oKC0PW5ZObXd0blJubi5SSVsGOucfabCi70XI4WSX42iX+MZdC4r/QYYIqST18WM2/KVmyRO3Tq3a56NlCXIQfOr9g0mpUZfXsCePGuWia6AFpaerrZnfuNRWZmZlERESgs2nbpNmE5U5rT0vbtjByCBRLyE+qyqNKSgJLSpqqnU5Nhr8tEOuQ4V+/HuL6R6D7/jtCPnmVYcOq6+r873/w1TVLlFTvTz+pxknnnVftpe+8U71OcLDytBw5mqYp4tagTptdSlkO/AS4721/gtEUcTVOFWRlJZRI9ObK+ge7QghYvlwlGzz0kFrmqVbI77+r0ufRox2u5hpYDEaolEQUV6AvTsJicbia7W79VsGOn4AoHxWAtxstKWnqolmVZHl4JVSaIOYc9X9MzDGVotbi8GEl4T52bP1jz2Diwv3R+4WQm52tvHk2rr1WFZs0prtAaanKzWjqROaaarhnhKcFoGVbpWKWpDwhK1YoDcelO6Nhxxa40gtevLFKiNBsVvcd9h6JOp1qcHn99Y5dtm0Lba+znTuVlSoJt1s3t1Po0yqE4T3b1auIe0b7H4UQI4BRNZY9jfvOzj7AOUAjg/YaGmcussIM+RJv/THqtMTFqVrW//3P85rU++9Xpc+PPab0y12U7K7O6UTPZsFk3daR7GfWkZqaSsuWqoeQXcK/VbBD4yVEr+6SMzMzqbRYqynmAjbpfl8473Z4IEG9dlOyebO6qo4b17T7Pc1QRksQFRVmCgsLCW4CRbhPPjk+3Qlq9h1KqxKWO409LQBffKeeD+6BMUpFNywMvvhSMOH5IxBUAWNvrBpeUgK33lrLcVJ1zMvL4csvYfyYODqCapz05puqvrmlo6/Xnj3KAfPCC/DCxd09muoZbbQAo4Fnnf6XwPP1bJMP3FjPGA2Nsw5p0MNNvmwLHMQ5jd3J+PHKh2zP2PPU07JiBXz6qaoeKi52abSs6X8vPsG/EWJzsCQmJlYZLXaDZHTfEvAJhooyfM15eHt7k5mZSUpeGZXF+Rh9fPH391eJFPsWQ9xwuLWj8vA0NZddppI23IS7zhZC/b0JCAknD/U52Y2WnTtVn8wvvlAfe0M5Ht0JsrKy6NOnT9X/qQUmQv28qroUn7bc/wC88xQUq2Qgo1FV4fu89zoV8me8TAZ41PG+Q0OVw9SZ5cttgtfLVAuBe++F77/V0dHX15FovmKFUruzYTYrFYOpUx1em/o4042Wr4EVtr8F8DfKkHGFBPKAA1JKN21kNTTOXqSUEOdFqdcx3AlnZ6vwzmGbM9NTo6VtW+VtmTzZbbygqAgwWwn/YR8Xo4yW4cOH215WeVpG9sqDilZgKkQUpVYJzB3OKcFSmk+YvTQzczcUpsDIR+GWKY1+u/ViS+g822ke3YxklCejfXvVt8DPD1JSHEm1DeHaa5WI8tVXN+08a3la8suattz5ZPHIIxC8EIyOBKL7O8fT2voYlvXekAv4OryiqakqL9753qF1a5WutnGjah0A0KoVqmWEPYG9S5dqL2u3193k1rvkjDZapJRHgCP2/4UQSVLKf07ilDQ0TltkhRnmltIy9kD9g90REKB+6YqKVFC8c2fPtlu4UNVFOt2l1aTbms/psWEjGHSEoYwWO3ZPi7cpE0LjwLsACtOIiooiIyODxOwSLKWFNGtmy1fYHw9bzTDiGNuQrVypDLUaPVfYuhWmT1cVSTV+yM9G4lrGsJba/Yeg4WXPJpNqClhH+kSjMJvN5Ofn18ppaRF6Bhgt5eVQHAhFtstlQQGtX5xKRmArmt2Yh+Xib9A7ua7OO09Feec5ace3aaOMkE2bHMZMqzidajmdlQUvvlhL4iAkRD3btVo84YxOxBVCXCaEqOreIaVsczLno6FxOiNNZthaSeymxMbvJCBAhXcCAmDYMMeVqT7eektVDy1Y4LYGVldUiE9hGTlTezKTGA4fdswzOzsbvT6QktRUCG4BQbFQeLTK05KYXQJlBTSPsdXI7lsO/0mY8b3L1/IIKeGqq2DVqtrr4uNh8eKzPjRkp30rpY1zNM3hVgkMVE65hgrEHa9yZ7u3rrqE/xniaZk/Hx6aCzMS1Pf2kYchNZXAezpg9QtG32di1dCSEhXd7d27+i6EgP79laclKUn9HxuLihP16KEG1UiiDwhQWnoN8bSc0UYL8AuQLYRYKIS4UwgRe7InpKFx2mL0gjBBWVhA4/cREKC8LElJ8O23kJvr2XY//KAE5uqoouvUQ/0gRspDQOtqqrhZWVmE+oUT6FVgM1pioDCVqKhIW3ioFEyFjgtSwSF492rVEbmxbN+urqB26XJnY2vJEvVD3uTNhU5PurdTRsvBJEdfKSGUTdtQT4vdaIlp4hrQmsJyJeWVFJoqm0bC/2QzZAjERUNRJTzaC774EgYb8Mtag+6/CGRmTpU3ZMsWlXDrKgelf3/VemHfPmWweHmhkpJmz1YDanyYQqgOHQ3JvT6jw0MoWf6LgEnAx8BHQoitwFxgvpTyGJufa2icPUgBCKESchvL0KFK6nT9erjpJnVh9ySvIzoannwSpkxxW8faa6ARvgTvtXn8bvDi/sOOu7rs7GxaBgcDufD9f2AEQsuJiwpWRktWMRUlNtd/hQkKUqD3dUr9qrHMnq1uIydPhttvV1UTTz+tYvz//Qd33934fZ9hdIgNR3j7kpiSVm35lVeqorGGcDyF5cBhtNjLnWPPBE9L8+YwcAAsXgTd4+DBVioxaGMaPPEy193mR4ZJaSFu2KA2cWW0TJyovvJ33ukkXP3UU8pd9vLLLsPB9i4BnnJGGy1SylXAKmC6EKIdMBllwDwNPCuEOArMR3V1XialrHC3Lw2Nsx1pMoFJoi8/htPk/vvV83e2EktPE3H/+QcOHYKb3YtVF5R5K0WqSujeSpCapLRa9Ho9WVlZtAi0JRIuWgcdW8I50CbciMlk4khyCpYKs/K0ZO6H74ohLM992n59SKkEtcaOVYZPcbGK6U+erK6q5eVnfamzM21sWi12rRw777zT8H1VVqprcFN7Wmr2HTpjyp3tRLaC/Aq47jeYYhM77A/c8RBDPxZMm6Y6Q2/YAC1auDYKhw6t1V5IhYF/+00lsDRB364zPTxUhZTyoJTybSnlKCAKmAKsRTVKXAjkCCF+FULcIIQ4ZVL6NUVcjVMFaTJBicS7uBESpTUpsxXoeWq0/PijymmZO9ftkIc/iGNL9HgY7E3GtNFUVlaSmqrCDdnZ2bQIsP0Qf/clPDINgJYh6iewPEslIEZFRcGhbVAuwfsYfmA3blQVUvbylfffV/krN9+sfOujR4OtskkDQvy88AoIJisrs9a6hsr4X365qjo63uGhtHybp+V0F5az89tv6nnZMvUd/fZb9SwEd9yhcmgfflg5SN96y/1uCgvVZ7B6tW1BXJx6vucex3nvxCOPqH0CzJ8/X1PEdYWUMk9K+Z2U8kogEjgP+BZlV34DZAghVgohHj6Z8wRNEVfj1MHq6wN3+bF9yqjG7+SDD1QA255556nR8vrr6oJ/7bVuhyyXo3j73EXIiBDKj6ofx8TERKSUZGVlMbATWNFDTBv4ag6kW2jmq9SwKmxGS2RkJOhyYWoA3Hhb499n377qx/+SS9T/ERFK2GLTJiUqt2yZoxeTBkIIgkMjyM/Nqbb8scdUae2pQGZmJgaDgRBbyUuqzdMSFWSsY6vTiI8+UnGdESNUwvtNN1XdJBgM6tRNSlJ55Vdd5X43o0Yp+2fzZtuCqs6LQEFtodukJBUtBpg0aVK9irhnpdHijJSyQkq5WEo5TUoZB/QBXgT8gNdP6uQ0NE4hJECUAXOwf31D66aw0JGQ56kibnAwvPJKnQHwoiIIDBRUlsbQ75t4BqOMltLSUkwmEwM6VqKzRMLCRbBoKRRCuJe6WzZnO3lacg+Bbyj4HYPDVa9X3hTnDMPLL1fJxMuXu7zjPNsJC4+gJL96Yravr0qHcL7u1cfDDzuikE1JVlYWkZGRCFtOVVq+iYgAI8ZjyfE6lbj0UiUl7OurEt7//luFM22MGKG+vkuWqLJyd9iNzKrCQLsmk/NKJ0JCtJLnY0JKuU1K+YKUsj/Qst4NNDTOFkwmmF1Ci393N34fdu/CVVepMgSjh3epGzfC99/XqevSs+BfXv8uBp05FKNfJQJltNjd+sGiDHlYD/fdp2o2+7UgiGIARF4yYDNa3psLf3mQt7NokSM3x5n161VvJVdlLx9+qJIBSkvr3/9ZRkx0M8wl+ZRXOHpG2S98OTluNnLBypWqeqWpqSUsV2gi9kyoHLKTna1EHPfsUcnuY8bUSnr/6CMl2qevw0670aYnXyU/9PLLjkaj/rVveDSjpQmRUqbVP0pD4yyhpAT2Wmi2PbHx+7AbLb6+SujB0452K1fCo4+q0mcXVFZChdlKUEk6+tg2+Nxs4HBodDWjxZxVSEq3/spbExMDQbH4mtWdvSkrCbCFh6xFEOyBlyU5Wf1Cf/tt9eXffqvuWF0p90ZFqfVNkJB4ptEythlYLexKdJQ9242Whmi1pKcfn0ryWs0S88vOnCRcUP2BsrJg/363QyIj4fHH625Kfu216vPq1cu2oHt39XBhsIBK9TKZ6vbeOKMZLS4QQhw62XPQ0DjVsAYFQICgNCqk8TsJDFTP8+bBzJmeb3fXXXDddSprzwVSwl3327w2vuqK1SNWGS3Z2dnoBLQIyqMysi0MHKjKkT/cgyhKR2f0p7K8jMDAQHz0wIhKeOi6+uc0ZYq6G506VVU3gVLt/fVXpdxrf68aHtHOJjC340By1TK7Y8NTo8VqPX5GS2ZmZi013DNCWM7O8OEqdNsE+ZPVbPLdu9W5XlLicmz79jBypGa0HCtxJ3sCGhqnGtLmFZF1+Ybro3VrlVD7559Ks8RTfH3h7bdhzRqXq7284OqbbEaLTyTMKeWZ3PQqT0szf4G3vpKArUkq1GQ2g1WPNTsFnX8IYPOy5B0GJIS1q3s+v/yiOvr9+iu0a6cSbvftU8ZLRkbdmYoaLunSRhktuw+nVC1r1w6mTXMYL/WRm6u8bsfLaLGHhwpNFRSXV55Z4SHw3PPZELZvVwZLleulOldcofoo2iX96+OM1mkRQjS2W3MDi+w0NM58ZEkJmCWG0mMoee7SBb76Cq65pmFSp/v2KQPh1ltdri4rg9Rkb9oBGEORwQZiQn1JWp1ERkYGrYJ1ICURM74H33B4801on49+6bMEB8eSk2vLZ/nhG/iwGC6oI0G4vFyJxV14ocpp+fNPGDxYlWR37apCYBdc0KDDogFtWyrB8oNJjqh869aqasVTSkth0KCGC9LVh8lkoqioqFa58xnlaTle2CX8PU26r4cz2mhBdXnWDBANjSZAFBeDGbyLPfTjukNKdeflabkzwN69ShG3qAhefbXW6u3bYfLFYWwacTWxreIQV3ZDV2KkcuVhtm3bRlyoCsLnLP2biM6d1EZB6s6+ZZg/OYdtRosogigddOrvfi4LF6rMwetsIaS2bZXhEhmpGiBef32T/UCfTdgNgqTU6qmEZjNUVLhNiahGq1awdm3Tz82eF2X3tKSeacJyxxO7m8yNl3TnTrjsMvj4Yzj33Pp3d6YbLb8C56IUbz1FADccn+loaJy+WMNC4eEAtoeNY0Rjd5KRoeRKLRZ1S+wp55+v/Mg//eTSaCkqgnRiOPjSj8SeAxkbOmIo2AbAxo0buaplAAgzgb16OsqQn/kc0k10aObLVmwXpHZecFNzCK5Dvn/WLPVDPHasY5ld0/zddz1/TxrVsBsE6RnVBeaiopRkyLG0gTpW3AnLxZwpwnLHk3oaC3l5KUeqvf1CfZzpRsvdwE7gZynlQk83EkKcMkaLXRF30qRJmsCcxklFSgkBOqx+x3B36e+vDBZomKfFYFC3Ym70TYqK1LM99zVnVSVdFu2mBbB371469m8OGQLj99+rxFmjEWJaQIGgSzOVCxMVFQXZ2+vOZyksVB1xp05Vc9JoMry9vfHxDyQ/JxtzpRVvg0q5DA/3PBH300/hs8/UTb2r4q3GUlPCP72gDJ2AZoFniLDc8aRGZ+ea2HNZ8vOVIq5N/f3sVMSVUmYD9wCfCyEa0EeS45CN1Dg0RVyNU4biIphZQovlOxq/D3vY5I473JYvuyQzU4lEFBa6XF1UBEEU0HNoALz/PkWR3RDtDLQOUadyyyA9lv0GpTxmq9c0v/MhjDTSIVwZHzHBwXDHX7ChjpydbduUsXKdB9VFGg0mNDyCytICkvMcOjaRkZ4bLfv2qYrdpjRYoLanJbXARFSgDwb9GX0JbTqaN1d9AFzgbLRoiriAlPI34CWgWwM2a3OcpqOhcfpSVAxJFsL2I2nZVAAAER5JREFUpNQ/1h06nUpU9fdXves9JTcXnnsOnn3W9dSKwIw3utISKCsjt/cFcLEvbToooY9QnZWVrYcobRWd+tlLLLCQIwNpF6Sk/GOD/WGAF/To7n4ew4erEFdDQlsaHhMZGYm1NJ8jOY7y2IgIz42W41nuDM4dnsuIOdMqh44n7josopyevr6Ozh71ccYbLQBSyk+llKvrH1k1/sjxnI+GxumINTIcvKEk9hj7iQYEwP/+p5JXPaVTJyXF6UbqdPRoeP8Tmxu6vBx9M1U+0rtlCACxAeVkV7ZUt+0JCbBzJ8mbdhH4XiYdD6rEzzZxoTDOB0a6yQa0h7X8/I5PaagGLWKisZQWcji7uqfF00KztLTjZ7R4e3sTaIs/puWbiNUqhzxn8WKVj+aGyZOhQwfPdnVWGC0aGhrHjpRS1eLpjvFn48471XMdHZtrIQR8/TUsXepyddeucNudeqUvbjYTHBWC5Z1SLj6ch68Bwo0mOmzcplrP3n8/TJjATqsfec1DCA0TbN68mT7hXqqyKbyt6zm8/74q33QTotI4dmKjo7CWFpCW78hduvRSz3sJpac3fXdncKjhCiGQUpJacIap4R5vgoLqFGKZNUtFjD1ByyRrJEKIrsD7gC+queJC4BkpZZ2tvYQQe4CaedJZUsorjstENTSaioJCsIDXsZY8P/usqrJpSCJuRQW89Zaq2HHRdO3AAZt+ldEI5eX0HyDQDW5JcJCJlkk6KJV027sKtm5VWjHr1rE718y6a0Zxkfcm+vTpA0Ovh6RSeMyN0TJrlkoqDApq1NvWqJ9mzZphKSsgLd/haWlIOt/gwW5TJ44JZ2G5/NIKTBVWrXLoJKF5WhqBECISWA78IaUcCgwDJgJve7B5upRyVI2HZrBonPLoiorACl7Fx9ih2GxWWXcNMVp0OtXl+eGHXa5+5RWl9cbUqTBwIHo9iGkXEdjdSqtgAf46Xrvxb6WhYjRCURE5BxKRgTFQlAWV5XBOKxgSqjo812T9eqWkqyXgHldU7ycrSWmOeJDFAomJnjXVmzkTHnyw6efl3HcorcAuLKd5WpqKm29WUv6eoBktjeM+VIXRJwBSyhKUwXKPEKIB2YUatkxxjdOAypgoeDaIXXddBBzDZ2e/dW6I0aLXw7hxbrtCFxXZyp3fe0/puQC//tMRH0spfWNU24HRl7ZSKnRvvQWA9/59dNiUBK8UQfpB6G6A8S6kxnNy4MoroWVL1W/oDOFUPPfshkFKWkbVsoMHoU0b1a7qZOHsaUk7BYTlTsXP7liorFQ58p6gGS2N4wJgo5TS4rRsNaAHxp+cKWGvbz9t9gvH7+Q7HY/FqT5nKVWVjT2npdGfnV3fpA7VWJdz/vVXpUbrgiqjRcqqhFnx/Wp4o4jHrxiI3FHBsDWfsdUpGbBNTgqW/v1hqDcc2QOJ+yDUReHgxx+rDM/ffoMw10nIp+P34lQ89+yGQVZWJlarEjOPi1NfuYUL99a57bp1Kml35cqGv259c3b2tKTaPC2xHoSHTrfPDk7O71BIiGeeNNCMlsbSHkitseyo7bm+HGh/IcQMIcRKIcS/QohPhBAtm2JSp/pF70RyOh6LU33OIq8QPi2m+d9bjm1HUVHKwrjnHrdDXM75/ffdGi3FxTajpVMnuEFpQ64PGAeDvAnOS8CaZqRyzjzSV64ELy+SJ15Gckg0/uPOgzE+sPgveG4fmFyEhp58UmnD21VvPZ1vE3G6nX/HMl+7YVBenE9OiRlQaUStWsHGjXXXxKalqdLoxjTXrmvOpaWllJSUVBlUu44WEORjIDKgfmG50+2zg5NrtFit9e9HSKm15mkoQggL8KmU8h6nZTrAArwnpXygjm2/Bz6WUq4WQvgAHwGXAQOklPtdjC+iunGZBbhTLQimDlGeY+B47RcgAvfv51g4HY/F6Tbn4/XZwel3LE7H74V27h3/fZ9unx2cvGMRAdj7eVullC7NT616qHEUAzXNbPv/JdSBlPJ6p79NQoh7gWuBh4C7XIxvxH2DhoaGhobGmYcWHmocB4CaCbf2/2t5S+pCSlmKCjW5qbPU0NDQ0NDQAM1oaSx/Af2FEHqnZeegwkOL3W0khDhXCDG1xjID0AxHToyGhoaGhoaGCzSjpXG8j9IGvQNACOGHCu98JKWsStAVQiwTQjhLeLYEHhdCOKtjPQV4AR8f91kfR4QQvYQQW4QQUggR58H484QQ/wghioQQeUKIBUKIfnWM1wkh7hNC7BRClAkhUoUQn9c4ljW3CRJCvCOESBJCmIQQ+4QQTwkhvBr5Ns96tGN6chBCRAghfradX1PqGdtRCPGLECJbCFEihFgnhLiqnm2O+/l4NiEUk4QQs23nilkIkW8rwLihju20z64+pJTaoxEPVAPGv4FVwFbgVcCrxpg1wCqn/1ui9Fw2AiuAdcCfQP+T/X6O4TgYgGdQCVZlKGMurp5tbrGNex8IA1oAc4ByYJSbbb4BzMDNKAXiPsBu4AgQ7WJ8ELADSEGJ//kClwBFKE+Z/mQfu9PtoR3Tk3bcLwMygDzbeTOljrG9gELb70t722f2jG27J9xsc9zPx7PtgboZlcAS27HxA7oAc23Lv9I+u0Ye25M9Ae1xej+An4C1QGcgsT6jxXZSlaEMNuG03B/V3iAJMNbY5jLbft+osbyfbfnPLl7nA9u6C2osf9i2/O6TfexOt4d2TE/KMb8LlfM2Efi6LqMF5TnfhioUiKqxbj4qfN29xvITcj6ebQ/gJdvxC6ix3Bs4aDtOY7TPruEPLTykcax8AwyVUu7xcPydgA8wU9rOFqhSFf4J5Y26vMY2dmHur5wXSik3AduBy4UQLezLhRCBwFQgDdUTypmvUSfncRD7PnPRjulJYwfQTUrpSUvsMUBPYIGUMrPGuq9QF8aarQeP+/l4lnIU+EZKWey8UEppRnlfAMY6rdI+Ow/RjBaNY0JK+ZesrgxcHxNtz2tcrFtTYwxCiFBUknO+G8NoDaqlwgVOy8agTuZ1ziezbb45wD6gvRCiYwPmfbajHdOTgJTyPyll3apqDhp0bjVmm0aej2cdUspPpJTT3awusj0Lp2XaZ+chmtGiccKwVVt1tf2b6GKIfVkPp2XdUCeSq/HutulRY50n22jUjXZMT33cfkZSynTABMQIIcLhhJ6PGtWxG/bODQe0z85DNKNF40QSiorpSimlK2XEfNtzM6dl0bZnd3ebTbWNRt1ox/TUp77PyH7O2T+jE3U+atgQQoQBE4AtwCKnVdpn5yGa0aJxIrF3GKtws95se3bupHeittGoG+2Ynvo09DPSzq0Tzxuo/K8ba4RZtc/OQzSj5SxHCJFo037w9PH9Mbxcme3ZnaaHt+259CRso1E32jE99WnoZ6SdWycQIcR1wBTgOinlzhqrtc/OQ7TeQxrfAOENGL/+GF4rD2XNewshgl24NUNszxlOy9Jtzy7a7zbpNhp1ox3TU590VN6Cu88o2PZs/4xO1Pl41iOEGAd8AdwupZzjYoj22XmIZrSc5Ugpnz2Br2URQiQAvYE2KFE+Z+Jszzuclu3Cpv3iZreutrH/3aYB22jUjXZMT312AOfi4jMSQkSjqr/SbNVeJ/J8PKsRQowFfgfukVJ+5WaY9tl5iBYe0jjR/GV7Huxi3ZAaY7CVe64BQoQQnd1sI6muHbIMpQg5UAjhXFaILfu+I3BQSrmvUe/g7EQ7pqc+DTq3GrNNI8/HsxYhxLnAH8D9zgaLEKJbDXl+7bPzEM1o0TjRfIoq37vZ+eInhPAHrkRJxP9aY5t3bc+3OC+09dfoCcyRUibbl0spi4AvgRjg/Br7moIq+3sXDY/Rjulpwd+ou+QLXfSRuQWwouTenTnu5+PZihBiDEq2/wEp5Zc1Vg9AqR3b0T47TznZkrza48x54IGMv23cbVTvl9Ec1S/DDJzrZpvvcd0vIxmIdTE+GOUOddUnJx4wnOzjdbo9tGN60o//19Tfe6iP7fNYAbRD9a952rbd0262Oe7n49n2AEajElrTgNkuHuuAFdpn14hje7InoD1O7wfqLlu6eXxdx3bnA/+iem3ko9yYbhtH4pCx3oW6u0hDJbY1q2ObYNSdRTIqtLHf9iPgfbKP2+n60I7pCT/ecXWcX4lutumMusPOsV04NwDX1PM6x/18PJseOAzMuh4rtM+u4Q9hexMaGhoaGhoaGqc0Wk6LhoaGhoaGxmmBZrRoaGhoaGhonBZoRouGhoaGhobGaYFmtGhoaGhoaGicFmhGi4aGhoaGhsZpgWa0aGhoaGhoaJwWaEaLhoaGhoaGxmmBZrRoaGhoaGhonBZoRouGhsZZjRDiTSFE/Mmeh4aGRv1oRouGhsbZzkBgfX2DhBCGEzAXDQ2NOtCMFg0NjbMSIYS3EMIMjACeEkJIIUSCbV0L2/9XCyGWCSFMwC1CCLMQwttpH/ZxnZ2WNRdCfCuEyBFC5AshfhNCNDvhb1BD4wxEM1o0NDTOViqBIba/BwExwFDb/71sz9OBt4BuQCiQIKU0O+2jD6qx3T4AIUQbYDNwFNUJexQQAXx6vN6EhsbZhObu1NDQOCuRUlqFEDFAEbBBVu8e2xvVAfcKKeUBACFET2Brjd30AXZKKa22/z8FvpRSPmEfIIR4EZhzXN6EhsZZhuZp0dDQOJvpA2yTtdvd9wL+shsszmNrjOuNzZARQrQGxgMPCCGK7Q9gPlBxHOauoXHWoRktGhoaZwxCiCG2HJMPhBDXCCH+FUIUCCHKhRDrhRCja2zSG9jiYle9gX+c9usLdKS2p2UADkOmF1AI9LRtb3/0QBk8Ghoax4gWHtLQ0DiT6Gd7HgvcBfyJCtn0tS37SwjRSUqZZBvXC1jovAMhhD/QDpWbYqcdoAf2O40bAbTAYchUAP5AupSyuOnekoaGhh3N06KhoXEm0df2HAuMklJeLKWcLqUcB3wA+AD3OY03AJ2FELFCiBDbsp62561O47IBCfQHEEL0Bz63LdtuG7MWyAO+E0L0EUK0E0KME0J8JITQfms1NJoA7UTS0NA4k7AbLfdKKf+rse4L23NXp2VPAlcDKcCrtmW9gP3O3hIpZTrwBPClECIZeBCYDRy0j5NS5gHnA8HAcpTR8xaQ4pSoq6GhcQyI2vlnGhoaGqcfQggjqhIoHYiraSgIIZqjjJOlNs+LhobGaYbmadHQ0DhT6AF4AfFuPBtxtuckF+s0NDROAzSjRUND40zBHhpKdLN+ou158fGfioaGxvFAM1o0NDTOFOxGS0jNFUKIMOB2IBmYK4QYbyuNrutx4wmcu4aGhgdoRouGhsaZgr3c+XJb2TIAQogAYBYQDjwgpTQB/6Jk++2PZODtGstmn7ipa2hoeIKm06KhoXHaY+vA3AMl9BYMbBdCzAOMwMWoEujHpZRzAKSUZUCZbdtglN7KKluVkIaGximK5mnR0NA4E+iGMlDWoKT09wG3AjcAu4EJUsrX3GzbFxDAphMwTw0NjWNA87RoaGicCdjzWbZIKfej9FI8pR+Q7aSSq6GhcYqieVo0NDTOBKqMlkZu25jtNDQ0TjCa0aKhoXEm0BeoBHY0clstNKShcRqgGS0aGhqnNba+Pr2AvbbKoIZsG4Dq3ry5vrEaGhonHy2nRUND47TGpn4b0MjN+6CScDWjRUPjNEDztGhoaJzN9AUKgEMneyIaGhr1ozVM1NDQ0NDQ0Dgt0DwtGhoaGhoaGqcFmtGioaGhoaGhcVqgGS0aGhoaGhoapwWa0aKhoaGhoaFxWqAZLRoaGhoaGhqnBZrRoqGhoaGhoXFaoBktGhoaGhoaGqcFmtGioaGhoaGhcVqgGS0aGhoaGhoapwX/Dw30qYMI+WuWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeplots(Tt_copy4,T0_copy4,T1_copy4,T2_copy4,wT0_copy4,wRT_copy4,wRT2_copy4,xlabel=r\"$p_T^{true}$\",header=\"QCD dijets\",plotname=\"Dijets_T\",mymin=-1500,mymax=2500,w_withall=w_withall,nbins=100)\n",
    "makeplots(Rt_copy4,R0_copy4,R1_copy4,R2_copy4,wT0_copy4,wRT_copy4,wRT2_copy4,xlabel=r\"$p_T^{reco}$\",header=\"QCD dijets\",plotname=\"Dijets_R\",mymin=-1500,mymax=2500,w_withall=w_withall,nbins=100)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}