cGAN_hybrid_final.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EdWangLoDaSc/Dropout-as-a-Grid-Search_Representing-Model-Uncertainty-in-Deep-Learning/blob/main/cGAN_hybrid_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dv3f02PXKCKU",
        "outputId": "a18fb771-38b8-4d7d-8a89-611a077ebae4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at ./content\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('./content')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 138,
      "metadata": {
        "id": "pwNw8A_NGkjN"
      },
      "outputs": [],
      "source": [
        "from __future__ import print_function, division\n",
        "\n",
        "import numpy as np\n",
        "from keras.layers import Input\n",
        "from keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam, SGD\n",
        "from sklearn.metrics import mean_absolute_percentage_error\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "#from network import build_discriminator, build_generator\n",
        "\n",
        "\n",
        "import keras\n",
        "from keras import Input, Model\n",
        "from keras.layers import Dense, LeakyReLU, concatenate\n",
        "\n",
        "\n",
        "def build_generator(network):\n",
        "    seed = network.seed\n",
        "    random_normal = keras.initializers.RandomNormal(seed=seed)\n",
        "\n",
        "    if network.activation == \"linear\":\n",
        "        activation = \"linear\"\n",
        "        kerner_initializer = keras.initializers.RandomUniform(seed=seed)\n",
        "    elif network.activation == \"elu\":\n",
        "        activation = \"elu\"\n",
        "        kerner_initializer = keras.initializers.he_normal(seed=seed)\n",
        "    elif network.activation == \"selu\":\n",
        "        activation = \"selu\"\n",
        "        kerner_initializer = keras.initializers.he_normal(seed=seed)\n",
        "    elif network.activation == \"relu\":\n",
        "        activation = \"relu\"\n",
        "        kerner_initializer = keras.initializers.he_uniform(seed=seed)\n",
        "    elif network.activation == \"lrelu\":\n",
        "        activation = LeakyReLU()\n",
        "        kerner_initializer = keras.initializers.he_normal(seed=seed)\n",
        "    elif network.activation == \"tanh\":\n",
        "        activation = \"tanh\"\n",
        "        kerner_initializer = keras.initializers.RandomUniform(seed=seed)\n",
        "    elif network.activation == \"sigmoid\":\n",
        "        activation = \"sigmoid\"\n",
        "        kerner_initializer = keras.initializers.RandomUniform(seed=seed)\n",
        "    else:\n",
        "        raise NotImplementedError(\"Activation not recognized\")\n",
        "\n",
        "   \n",
        "\n",
        "    if network.architecture == 3:\n",
        "        x = Input(shape=(network.x_input_size,), dtype='float')\n",
        "        x_output = Dense(100, activation=activation, kernel_initializer=kerner_initializer)(x)\n",
        "\n",
        "        noise = Input(shape=(network.z_input_size,))\n",
        "        noise_output = Dense(100, activation=activation, kernel_initializer=kerner_initializer)(noise)\n",
        "\n",
        "        concat = concatenate([x_output, noise_output])\n",
        "\n",
        "        output = Dense(64, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        output = Dense(64, activation=activation, kernel_initializer=kerner_initializer)(output)\n",
        "        output = Dense(64, activation=activation, kernel_initializer=kerner_initializer)(output)\n",
        "        output = Dense(64, activation=activation, kernel_initializer=kerner_initializer)(output)\n",
        "        output = Dense(network.y_input_size, activation=\"linear\", kernel_initializer=random_normal)(output)\n",
        "\n",
        "        model = Model(inputs=[noise, x], outputs=output)\n",
        "\n",
        "    elif network.architecture == 4:\n",
        "        x = Input(shape=(network.x_input_size,), dtype='float')\n",
        "        x_output = Dense(100, activation=activation, kernel_initializer=kerner_initializer)(x)\n",
        "\n",
        "        noise = Input(shape=(network.z_input_size,))\n",
        "        noise_output = Dense(100, activation=activation, kernel_initializer=kerner_initializer)(noise)\n",
        "\n",
        "        concat = concatenate([x_output, noise_output])\n",
        "\n",
        "        output = Dense(100, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        output = Dense(75, activation=activation, kernel_initializer=kerner_initializer)(output)\n",
        "        output = Dense(75, activation=activation, kernel_initializer=kerner_initializer)(output)\n",
        "        output = Dense(75, activation=activation, kernel_initializer=kerner_initializer)(output)\n",
        "        output = Dense(75, activation=activation, kernel_initializer=kerner_initializer)(output)\n",
        "        output = Dense(50, activation=activation, kernel_initializer=kerner_initializer)(output)\n",
        "        output = Dense(network.y_input_size, activation=\"linear\", kernel_initializer=random_normal)(output)\n",
        "\n",
        "        model = Model(inputs=[noise, x], outputs=output)\n",
        "\n",
        "    elif network.architecture == 5:\n",
        "        x = Input(shape=(network.x_input_size,), dtype='float')\n",
        "        x_output = Dense(150, activation=activation, kernel_initializer=kerner_initializer)(x)\n",
        "\n",
        "        noise = Input(shape=(network.z_input_size,))\n",
        "        noise_output = Dense(150, activation=activation, kernel_initializer=kerner_initializer)(noise)\n",
        "\n",
        "        concat = concatenate([x_output, noise_output])\n",
        "\n",
        "        output = Dense(150, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        output = Dense(75, activation=activation, kernel_initializer=kerner_initializer)(output)\n",
        "        output = Dense(75, activation=activation, kernel_initializer=kerner_initializer)(output)\n",
        "        output = Dense(75, activation=activation, kernel_initializer=kerner_initializer)(output)\n",
        "        output = Dense(75, activation=activation, kernel_initializer=kerner_initializer)(output)\n",
        "        output = Dense(75, activation=activation, kernel_initializer=kerner_initializer)(output)\n",
        "        output = Dense(network.y_input_size, activation=\"linear\", kernel_initializer=random_normal)(output)\n",
        "\n",
        "        model = Model(inputs=[noise, x], outputs=output)\n",
        "\n",
        "    else:\n",
        "        raise NotImplementedError(\"Architecture does not exist\")\n",
        "\n",
        "    return model\n",
        "\n",
        "\n",
        "def build_discriminator(network):\n",
        "    seed = network.seed\n",
        "    random_uniform = keras.initializers.RandomUniform(seed=seed)\n",
        "\n",
        "    if network.activation == \"linear\":\n",
        "        activation = \"linear\"\n",
        "        kerner_initializer = keras.initializers.RandomUniform(seed=seed)\n",
        "    elif network.activation == \"elu\":\n",
        "        activation = \"elu\"\n",
        "        kerner_initializer = keras.initializers.he_normal(seed=seed)\n",
        "    elif network.activation == \"selu\":\n",
        "        activation = \"selu\"\n",
        "        kerner_initializer = keras.initializers.he_normal(seed=seed)\n",
        "    elif network.activation == \"relu\":\n",
        "        activation = \"relu\"\n",
        "        kerner_initializer = keras.initializers.he_uniform(seed=seed)\n",
        "    elif network.activation == \"lrelu\":\n",
        "        activation = LeakyReLU()\n",
        "        kerner_initializer = keras.initializers.he_normal(seed=seed)\n",
        "    elif network.activation == \"tanh\":\n",
        "        activation = \"tanh\"\n",
        "        kerner_initializer = keras.initializers.RandomUniform(seed=seed)\n",
        "    elif network.activation == \"sigmoid\":\n",
        "        activation = \"sigmoid\"\n",
        "        kerner_initializer = keras.initializers.RandomUniform(seed=seed)\n",
        "    else:\n",
        "        raise NotImplementedError(\"Activation not recognized\")\n",
        "\n",
        "\n",
        "    # CA-housing and ailerons\n",
        "    if network.architecture == 3:\n",
        "        x = Input(shape=(network.x_input_size,), dtype='float')\n",
        "        x_output = Dense(100, activation=activation, kernel_initializer=kerner_initializer)(x)\n",
        "\n",
        "        label = Input(shape=(network.y_input_size,))\n",
        "        label_output = Dense(100, activation=activation, kernel_initializer=kerner_initializer)(label)\n",
        "\n",
        "        concat = concatenate([x_output, label_output])\n",
        "        concat = Dense(50, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        concat = Dense(50, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        concat = Dense(50, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        concat = Dense(50, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "\n",
        "        concat = Dense(50, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "\n",
        "        validity = Dense(1, activation=\"sigmoid\", kernel_initializer=random_uniform)(concat)\n",
        "\n",
        "        model = Model(inputs=[x, label], outputs=validity)\n",
        "\n",
        "    elif network.architecture == 4:\n",
        "        x = Input(shape=(network.x_input_size,), dtype='float')\n",
        "        x_output = Dense(100, activation=activation, kernel_initializer=kerner_initializer)(x)\n",
        "\n",
        "        label = Input(shape=(network.y_input_size,))\n",
        "        label_output = Dense(100, activation=activation, kernel_initializer=kerner_initializer)(label)\n",
        "\n",
        "        concat = concatenate([x_output, label_output])\n",
        "        concat = Dense(50, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        concat = Dense(50, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        concat = Dense(50, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        concat = Dense(50, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        concat = Dense(25, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        validity = Dense(1, activation=\"sigmoid\", kernel_initializer=random_uniform)(concat)\n",
        "\n",
        "        model = Model(inputs=[x, label], outputs=validity)\n",
        "\n",
        "    elif network.architecture == 5:\n",
        "        x = Input(shape=(network.x_input_size,), dtype='float')\n",
        "        x_output = Dense(100, activation=activation, kernel_initializer=kerner_initializer)(x)\n",
        "\n",
        "        label = Input(shape=(network.y_input_size,))\n",
        "        label_output = Dense(100, activation=activation, kernel_initializer=kerner_initializer)(label)\n",
        "\n",
        "        concat = concatenate([x_output, label_output])\n",
        "        concat = Dense(100, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        concat = Dense(75, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        concat = Dense(75, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        concat = Dense(50, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        concat = Dense(50, activation=activation, kernel_initializer=kerner_initializer)(concat)\n",
        "        validity = Dense(1, activation=\"sigmoid\", kernel_initializer=random_uniform)(concat)\n",
        "\n",
        "        model = Model(inputs=[x, label], outputs=validity)\n",
        "\n",
        "    else:\n",
        "        raise NotImplementedError(\"Architecture does not exist\")\n",
        "\n",
        "    return model\n",
        "\n",
        "\n",
        "class CGAN():\n",
        "    def __init__(self, exp_config):\n",
        "        if exp_config.model.optim_gen == \"Adam\":\n",
        "            self.optimizer_gen = Adam(exp_config.model.lr_gen, decay=exp_config.model.dec_gen)\n",
        "        else:\n",
        "            self.optimizer_gen = SGD(exp_config.model.lr_gen, decay=exp_config.model.dec_gen)\n",
        "        if exp_config.model.optim_disc == \"Adam\":\n",
        "            self.optimizer_disc = Adam(exp_config.model.lr_disc, decay=exp_config.model.dec_disc)\n",
        "        else:\n",
        "            self.optimizer_disc = SGD(exp_config.model.lr_disc, decay=exp_config.model.dec_disc)\n",
        "        self.activation = exp_config.model.activation\n",
        "        self.seed = exp_config.model.random_seed\n",
        "        #self.scenario = exp_config.dataset.scenario\n",
        "        self.x_input_size = 6\n",
        "        self.y_input_size = 1\n",
        "        self.z_input_size = exp_config.model.z_input_size\n",
        "        self.architecture = 3\n",
        "      \n",
        "       \n",
        "  \n",
        "\n",
        "        if exp_config.model.architecture is not None:\n",
        "            self.architecture = exp_config.model.architecture\n",
        "\n",
        "        # Build and compile the discriminator\n",
        "        self.discriminator = build_discriminator(self)\n",
        "        self.discriminator.compile(\n",
        "            loss=['binary_crossentropy'],\n",
        "            optimizer=self.optimizer_disc,\n",
        "            metrics=['accuracy'])\n",
        "\n",
        "        # Build the generator\n",
        "        self.generator = build_generator(self)\n",
        "\n",
        "        # The generator takes noise and the target label as input\n",
        "        # and generates the corresponding digit of that label\n",
        "        noise = Input(shape=(self.z_input_size,))\n",
        "\n",
        "        x = Input(shape=(self.x_input_size,))\n",
        "        label = self.generator([noise, x])\n",
        "\n",
        "        # For the combined model we will only train the generator\n",
        "        self.discriminator.trainable = False\n",
        "\n",
        "        # The discriminator takes generated image as input and determines validity\n",
        "        # and the label of that image\n",
        "        validity = self.discriminator([x, label])\n",
        "\n",
        "        # The combined model (stacked generator and discriminator)\n",
        "        # Trains generator to fool discriminator\n",
        "        self.combined = Model([noise, x], validity)\n",
        "        self.combined.compile(\n",
        "            loss=['binary_crossentropy'],\n",
        "            optimizer=self.optimizer_gen)\n",
        "\n",
        "    def train(self, xtrain, ytrain, epochs, batch_size=128, verbose=True):\n",
        "        # Adversarial ground truths\n",
        "        valid = np.ones((batch_size, 1))\n",
        "        fake = np.zeros((batch_size, 1))\n",
        "\n",
        "        dLossErr = np.zeros([epochs, 1])\n",
        "        dLossReal = np.zeros([epochs, 1])\n",
        "        dLossFake = np.zeros([epochs, 1])\n",
        "        gLossErr = np.zeros([epochs, 1])\n",
        "        genPred = np.zeros([epochs, 1])\n",
        "        genReal = np.zeros([epochs, 1])\n",
        "        mape = np.zeros([epochs, 1])\n",
        "\n",
        "        for epoch in range(epochs):\n",
        "            for batch_idx in range(int(xtrain.shape[0] // batch_size)):\n",
        "                # ---------------------\n",
        "                #  Train Discriminator\n",
        "                # ---------------------\n",
        "                # Select a random half batch of images\n",
        "                idx = np.random.randint(0, xtrain.shape[0], batch_size)\n",
        "                x, true_labels = xtrain[idx], ytrain[idx]\n",
        "                # Sample noise as generator input\n",
        "                noise = np.random.normal(0, 1, (batch_size, self.z_input_size))\n",
        "                # Generate a half batch of new images\n",
        "                fake_labels = self.generator.predict([noise, x])\n",
        "                # Train the discriminator\n",
        "                d_loss_real = self.discriminator.train_on_batch([x, true_labels], valid)\n",
        "                d_loss_fake = self.discriminator.train_on_batch([x, fake_labels], fake)\n",
        "                d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
        "\n",
        "                # ---------------------\n",
        "                #  Train Generator\n",
        "                # ---------------------\n",
        "                # Condition on x\n",
        "                idx = np.random.randint(0, xtrain.shape[0], batch_size)\n",
        "                sample = xtrain[idx]\n",
        "                # Train the generator\n",
        "                g_loss = self.combined.train_on_batch([noise, sample], valid)\n",
        "\n",
        "            dLossErr[epoch] = d_loss[0]\n",
        "            dLossReal[epoch] = d_loss_real[0]\n",
        "            dLossFake[epoch] = d_loss_fake[0]\n",
        "            gLossErr[epoch] = g_loss\n",
        "\n",
        "            ypred = self.predict(xtrain)\n",
        "            mape[epoch] = mean_absolute_percentage_error(ytrain, ypred)\n",
        "            #genPred[epoch] = np.average(ypred)\n",
        "            #genReal[epoch] = np.average(ytrain)\n",
        "\n",
        "            if verbose:\n",
        "                print(f\"Epoch: {epoch} / dLoss: {d_loss[0]} / gLoss: {g_loss} / {mean_squared_error(ytrain,ypred,)}\")\n",
        "\n",
        "            ypred = self.predict(xtrain)\n",
        "            #mape = mean_absolute_percentage_error(np.average(ytrain), np.average(ypred))\n",
        "            genPred[epoch] = np.average(ypred)\n",
        "            genReal[epoch] = np.average(ytrain)\n",
        "\n",
        "        return dLossErr, dLossReal, dLossFake, gLossErr, genPred, genReal\n",
        "\n",
        "    def predict(self, xtest):\n",
        "        noise = np.random.normal(0, 1, (xtest.shape[0], self.z_input_size))\n",
        "        ypred = self.generator.predict([noise, xtest])\n",
        "       # rmse = np.mean((ypred-y_test))\n",
        "        return ypred\n",
        "\n",
        "    def sample(self, xtest, n_samples):\n",
        "        y_samples_gan = self.predict(xtest)\n",
        "        for i in range(n_samples - 1):\n",
        "            ypred_gan = self.predict(xtest)\n",
        "            y_samples_gan = np.hstack([y_samples_gan, ypred_gan])\n",
        "        median = []\n",
        "        mean = []\n",
        "        for j in range(y_samples_gan.shape[0]):\n",
        "            median.append(np.median(y_samples_gan[j, :]))\n",
        "            mean.append(np.mean(y_samples_gan[j, :]))\n",
        "\n",
        "        return np.array(mean).reshape(-1, 1), np.array(median).reshape(-1, 1), y_samples_gan\n",
        "\n",
        "import typing\n",
        "\n",
        "\n",
        "class RunConfig(typing.NamedTuple):\n",
        "    save_fig: int = 0\n",
        "    save_pdf: int = 0\n",
        "\n",
        "\n",
        "class ModelConfig(typing.NamedTuple):\n",
        "    architecture: int = None\n",
        "    lr_gen: float = 0.001\n",
        "    lr_disc: float = 0.001\n",
        "    optim_gen: str = \"Adam\"\n",
        "    optim_disc: str = \"SGD\"\n",
        "    dec_gen: float = 0\n",
        "    dec_disc: float = 0\n",
        "    random_seed: int = 1985\n",
        "    activation: str = \"elu\"\n",
        "    z_input_size: int = 1\n",
        "\n",
        "\n",
        "class TrainingConfig(typing.NamedTuple):\n",
        "    n_epochs: int = 100\n",
        "    batch_size: int = 128\n",
        "    n_samples: int = 100\n",
        "\n",
        "\n",
        "class Config(typing.NamedTuple):\n",
        "    run: RunConfig\n",
        "    model: ModelConfig\n",
        "    training: TrainingConfig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "metadata": {
        "id": "YZPmtvcJHo_Z"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "random_seed = 42\n",
        "exp_config = Config(\n",
        "        model=ModelConfig(activation=\"relu\", lr_gen=0.0001, lr_disc=0.001, dec_gen=0, dec_disc=0, \n",
        "                                 optim_gen=\"Adam\", optim_disc=\"Adam\", z_input_size=1, random_seed=42),\n",
        "        training=TrainingConfig(n_epochs=200, batch_size=256, n_samples=50),\n",
        "        \n",
        "        run=RunConfig(save_fig=1)\n",
        "    )\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "OQCcM7YyJUsu"
      },
      "outputs": [],
      "source": [
        "from numpy import random\n",
        "np.random.seed(exp_config.model.random_seed)\n",
        "random.seed(exp_config.model.random_seed)\n",
        "\n",
        "import tensorflow\n",
        "tensorflow.random.set_seed(exp_config.model.random_seed)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {
        "id": "CYeY9o-vJ88K"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "dataset = pd.read_csv('/content/drive/MyDrive/Hybrid_datasets.csv')\n",
        "dataset = dataset.drop(['diff'], axis = 1)\n",
        "dataset = dataset.astype('float64')\n",
        "dataset['NB2_S_x_NYZ_x_x_Fcw_x'] *= 0.28\n",
        "dataset['NB2_S_1_NYZ_sys_x_PcwOut_x'] *= 100000\n",
        "dataset['NB2_S_1_NYZ_sys_x_PcwIn_x'] *= 100000\n",
        "X = dataset.drop(['NB2_S_x_NYZ_x_x_Fcw_x'], axis = 1).values\n",
        "y = dataset['NB2_S_x_NYZ_x_x_Fcw_x'].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "metadata": {
        "id": "wb6-yrf8LMaD"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_val,y_train, y_val = train_test_split(X,y,test_size=0.2,random_state=10)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "NFr_ouldKat9"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "def train_test_val_split(X, y, train_ratio = 0.2,validation_ratio = 0.1,test_ratio = 0.1,random_state=42):\n",
        "    # random_state for reproduction\n",
        "    \n",
        "    [x_train, x_test,y_train, y_test] = train_test_split(\n",
        "        X, y,test_size=validation_ratio+test_ratio, random_state=random_state, shuffle=True)\n",
        "    [x_val, x_test,y_test,y_val ] = train_test_split(\n",
        "        x_test, y_test,test_size=test_ratio/(test_ratio + validation_ratio), random_state=random_state)\n",
        "    return x_train, x_test, x_val, y_train, y_test, y_val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "4S39njN6KhBE"
      },
      "outputs": [],
      "source": [
        "a = train_test_val_split(X, y, train_ratio = 0.2,validation_ratio = 0.1,test_ratio = 0.1,random_state=42)\n",
        "x_train = a[0]\n",
        "x_test = a[1]\n",
        "x_valid = a[2]\n",
        "\n",
        "y_train = a[3]\n",
        "y_test = a[4]\n",
        "y_valid = a[5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "id": "yLZtEvVsLxux"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "y = y.reshape(-1,1)\n",
        "standardscaler_x = StandardScaler()\n",
        "X = standardscaler_x.fit_transform(X)\n",
        "standardscaler_y = StandardScaler()\n",
        "y = standardscaler_y.fit_transform(y)\n",
        "\n",
        "y = y.reshape(len(y),)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qzhUQjtmVKeX",
        "outputId": "a27155ea-367d-4d23-8271-1c67a021e167"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(78187,)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "metadata": {
        "id": "eqYkGx8TLkci"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "y_train = y_train.reshape(-1,1)\n",
        "y_val = y_val.reshape(-1,1)\n",
        "scaler_x = StandardScaler()\n",
        "scaler_y = StandardScaler()\n",
        "x_train_scaled = scaler_x.fit_transform(X_train)\n",
        "X_val_scaled = scaler_x.transform(X_val)\n",
        "y_train_scaled = scaler_y.fit_transform(y_train)\n",
        "y_val_scaled = scaler_y.transform(y_val)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_val = pd.DataFrame(y_val)\n",
        "y_val.to_csv('/content/drive/MyDrive/y_val.csv')\n",
        "\n",
        "y_train = pd.DataFrame(y_train)\n",
        "y_train.to_csv('/content/drive/MyDrive/y_train.csv')\n",
        "\n",
        "y_test = pd.DataFrame(y_test)\n",
        "y_test.to_csv('/content/drive/MyDrive/y_test.csv')"
      ],
      "metadata": {
        "id": "vHMDrqul1b_8"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_val_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wRcckYz_x17A",
        "outputId": "d8a4c5d7-c736-429f-8a8f-0f1e23ec0834"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.45685302, -0.82554907, -1.52656355,  1.31699774,  0.97378519,\n",
              "         0.92080204],\n",
              "       [-0.20924968, -0.7605986 ,  0.75223879, -0.90160628,  0.84296223,\n",
              "         0.79065787],\n",
              "       [-0.29928726,  0.23050434, -1.52656355, -0.90160628,  0.5225757 ,\n",
              "         0.47193369],\n",
              "       ...,\n",
              "       [-1.19966307, -1.08553811,  0.57776975, -0.90160628,  0.67112832,\n",
              "         0.61940584],\n",
              "       [-0.72696577, -0.12681682, -1.52656355,  1.26938414,  0.92272347,\n",
              "        -1.44612596],\n",
              "       [-0.29928726, -0.41937468, -1.52656355,  1.15803234,  0.80330768,\n",
              "         0.75120907]])"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred_gan_train = cgan.predict(X_val_scaled)\n",
        "ypred_gan_train\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MMRX4VvjdPUh",
        "outputId": "a86d33ab-feca-47ec-bbcb-b220508d6f6e"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.443497  ],\n",
              "       [0.35957208],\n",
              "       [0.        ],\n",
              "       ...,\n",
              "       [0.        ],\n",
              "       [0.01779258],\n",
              "       [0.        ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_val_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jDgYoNsQf3N_",
        "outputId": "bebc79fa-2484-420d-f147-fb51df5252b0"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.09534673],\n",
              "       [ 0.57843418],\n",
              "       [-0.60397118],\n",
              "       ...,\n",
              "       [ 0.46957944],\n",
              "       [-0.1427421 ],\n",
              "       [ 0.67861636]])"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "id": "VUDRctffZGQD"
      },
      "outputs": [],
      "source": [
        "test_dataset = pd.read_csv('/content/drive/MyDrive/test_dataset.csv')\n",
        "test_dataset = test_dataset.drop(['Unnamed: 7'], axis = 1)\n",
        "test_dataset = test_dataset.drop(['MAPE'], axis = 1)\n",
        "scaler_testx = StandardScaler()\n",
        "scaler_testy = StandardScaler()\n",
        "x_test = test_dataset.drop(['NB2_S_x_NYZ_x_x_Fcw_x'], axis = 1).values\n",
        "X_test_scaled = scaler_testx.fit_transform(x_test)\n",
        "y_test = test_dataset['NB2_S_x_NYZ_x_x_Fcw_x'].values\n",
        "y_test = y_test.reshape(-1,1)\n",
        "y_test_scaled = scaler_testy.fit_transform(y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2HIBQD18apf4",
        "outputId": "71eb3960-be7e-489e-fbdf-d94bdb5e6468"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.11265656],\n",
              "       [-1.12478569],\n",
              "       [-1.1219225 ],\n",
              "       ...,\n",
              "       [ 0.59356997],\n",
              "       [ 0.59414488],\n",
              "       [ 0.58732321]])"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ],
      "source": [
        "y_test_scaled"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 141,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXT-bThOL-ZO",
        "outputId": "3c1263e2-dc27-43b9-f6cd-d2734a749ddb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 / dLoss: 0.6666291654109955 / gLoss: 0.8912947177886963 / 0.04070459903286716\n",
            "Epoch: 1 / dLoss: 0.36495761573314667 / gLoss: 2.403465747833252 / 0.14049793947545341\n",
            "Epoch: 2 / dLoss: 0.6476219296455383 / gLoss: 0.9583949446678162 / 0.0273329318726534\n",
            "Epoch: 3 / dLoss: 0.593117356300354 / gLoss: 0.8724207878112793 / 0.022914417887774943\n",
            "Epoch: 4 / dLoss: 0.6536688208580017 / gLoss: 0.9070591330528259 / 0.017637225143470586\n",
            "Epoch: 5 / dLoss: 0.6327432990074158 / gLoss: 0.8651013374328613 / 0.03951904390843228\n",
            "Epoch: 6 / dLoss: 0.6598810255527496 / gLoss: 0.8282672166824341 / 0.019912899802566365\n",
            "Epoch: 7 / dLoss: 0.7565890550613403 / gLoss: 1.12198007106781 / 0.020240810151626935\n",
            "Epoch: 8 / dLoss: 0.7135116159915924 / gLoss: 0.8139702677726746 / 0.017391958148901907\n",
            "Epoch: 9 / dLoss: 0.711276039481163 / gLoss: 0.5961063504219055 / 0.02816277561412207\n",
            "Epoch: 10 / dLoss: 0.6743270754814148 / gLoss: 0.6818193793296814 / 0.017025610623133937\n",
            "Epoch: 11 / dLoss: 0.5881549119949341 / gLoss: 1.2745310068130493 / 0.01878268294845716\n",
            "Epoch: 12 / dLoss: 0.6561563909053802 / gLoss: 0.6604401469230652 / 0.024506336148672026\n",
            "Epoch: 13 / dLoss: 0.7128502428531647 / gLoss: 0.5972054600715637 / 0.019405826965262947\n",
            "Epoch: 14 / dLoss: 0.6702710390090942 / gLoss: 1.1309758424758911 / 0.01610249784057391\n",
            "Epoch: 15 / dLoss: 0.7396867573261261 / gLoss: 0.6955363750457764 / 0.01684663013184902\n",
            "Epoch: 16 / dLoss: 0.6178892701864243 / gLoss: 0.8059554100036621 / 0.02926891239032432\n",
            "Epoch: 17 / dLoss: 0.523833155632019 / gLoss: 1.6252570152282715 / 0.025608815975296507\n",
            "Epoch: 18 / dLoss: 0.550739973783493 / gLoss: 1.5821471214294434 / 0.01884109667530746\n",
            "Epoch: 19 / dLoss: 0.6872167140245438 / gLoss: 0.5969299077987671 / 0.026565133365582964\n",
            "Epoch: 20 / dLoss: 0.7182535231113434 / gLoss: 0.6226113438606262 / 0.01662728628363172\n",
            "Epoch: 21 / dLoss: 0.6762721836566925 / gLoss: 0.9281524419784546 / 0.014232450409595466\n",
            "Epoch: 22 / dLoss: 0.48273342847824097 / gLoss: 1.7544223070144653 / 0.02230999010219447\n",
            "Epoch: 23 / dLoss: 0.5473627150058746 / gLoss: 1.2514004707336426 / 0.023264281282842116\n",
            "Epoch: 24 / dLoss: 0.6654369235038757 / gLoss: 0.7423709630966187 / 0.02157002875717011\n",
            "Epoch: 25 / dLoss: 0.7433710992336273 / gLoss: 0.5564196705818176 / 0.017303906678232252\n",
            "Epoch: 26 / dLoss: 0.6574309170246124 / gLoss: 0.802149772644043 / 0.02566632813206465\n",
            "Epoch: 27 / dLoss: 0.6165707111358643 / gLoss: 0.9044206142425537 / 0.027829951663217316\n",
            "Epoch: 28 / dLoss: 0.5340981483459473 / gLoss: 1.7415425777435303 / 0.01790076990517111\n",
            "Epoch: 29 / dLoss: 0.6954410672187805 / gLoss: 0.7037251591682434 / 0.015083897858333998\n",
            "Epoch: 30 / dLoss: 0.540937602519989 / gLoss: 1.3810296058654785 / 0.027173282199857117\n",
            "Epoch: 31 / dLoss: 0.5401335954666138 / gLoss: 1.3514230251312256 / 0.024256532492307657\n",
            "Epoch: 32 / dLoss: 0.6673534512519836 / gLoss: 0.8133026361465454 / 0.016174934652332523\n",
            "Epoch: 33 / dLoss: 0.6516662240028381 / gLoss: 0.8371037244796753 / 0.017896191960622664\n",
            "Epoch: 34 / dLoss: 0.8047622740268707 / gLoss: 0.5745159387588501 / 0.014743186138520582\n",
            "Epoch: 35 / dLoss: 0.7484934031963348 / gLoss: 0.6875588893890381 / 0.015117813290674683\n",
            "Epoch: 36 / dLoss: 0.5693679749965668 / gLoss: 1.3090869188308716 / 0.02268693696399832\n",
            "Epoch: 37 / dLoss: 0.45969144999980927 / gLoss: 1.8253579139709473 / 0.024798971300387104\n",
            "Epoch: 38 / dLoss: 0.7338864505290985 / gLoss: 0.5547937154769897 / 0.01719214716033721\n",
            "Epoch: 39 / dLoss: 0.5473736822605133 / gLoss: 1.6099835634231567 / 0.017855241246588945\n",
            "Epoch: 40 / dLoss: 0.7305389046669006 / gLoss: 0.7071020603179932 / 0.014814020940981543\n",
            "Epoch: 41 / dLoss: 0.5753479301929474 / gLoss: 1.2659854888916016 / 0.015293822242167913\n",
            "Epoch: 42 / dLoss: 0.6845844388008118 / gLoss: 0.6397548913955688 / 0.01995851455592602\n",
            "Epoch: 43 / dLoss: 0.7516467273235321 / gLoss: 0.5808229446411133 / 0.01680789247467257\n",
            "Epoch: 44 / dLoss: 0.6340328454971313 / gLoss: 0.825677216053009 / 0.02345177441096551\n",
            "Epoch: 45 / dLoss: 0.6257540583610535 / gLoss: 0.9330623149871826 / 0.018271691138510234\n",
            "Epoch: 46 / dLoss: 0.5315315425395966 / gLoss: 1.557492733001709 / 0.01670760548255129\n",
            "Epoch: 47 / dLoss: 0.638338178396225 / gLoss: 0.7599880695343018 / 0.02614483089426378\n",
            "Epoch: 48 / dLoss: 0.5593039691448212 / gLoss: 1.6399519443511963 / 0.023175380727777833\n",
            "Epoch: 49 / dLoss: 0.7633897364139557 / gLoss: 0.5366496443748474 / 0.015710178348281315\n",
            "Epoch: 50 / dLoss: 0.753829151391983 / gLoss: 0.6283090114593506 / 0.014294805478110306\n",
            "Epoch: 51 / dLoss: 0.7042073011398315 / gLoss: 0.7356460094451904 / 0.013748223390187844\n",
            "Epoch: 52 / dLoss: 0.7005904912948608 / gLoss: 0.7942657470703125 / 0.013229680379433674\n",
            "Epoch: 53 / dLoss: 0.6491019129753113 / gLoss: 0.8106054067611694 / 0.024281425077882526\n",
            "Epoch: 54 / dLoss: 0.6626987159252167 / gLoss: 0.9333180785179138 / 0.015700863500189875\n",
            "Epoch: 55 / dLoss: 0.5729849934577942 / gLoss: 1.505966305732727 / 0.015848760140247338\n",
            "Epoch: 56 / dLoss: 0.6139613389968872 / gLoss: 0.9613465666770935 / 0.01559151712172219\n",
            "Epoch: 57 / dLoss: 0.6786658465862274 / gLoss: 0.694137692451477 / 0.016147003550445578\n",
            "Epoch: 58 / dLoss: 0.5697060227394104 / gLoss: 1.1596654653549194 / 0.020119360457965866\n",
            "Epoch: 59 / dLoss: 0.5347102284431458 / gLoss: 1.2721421718597412 / 0.017767651970584924\n",
            "Epoch: 60 / dLoss: 0.5621418356895447 / gLoss: 1.1546493768692017 / 0.02209376690410696\n",
            "Epoch: 61 / dLoss: 0.7017055749893188 / gLoss: 0.804973840713501 / 0.01331775355942199\n",
            "Epoch: 62 / dLoss: 0.618890255689621 / gLoss: 1.1403875350952148 / 0.014689474384865642\n",
            "Epoch: 63 / dLoss: 0.693787544965744 / gLoss: 0.7134565114974976 / 0.013343942926840343\n",
            "Epoch: 64 / dLoss: 0.6369323134422302 / gLoss: 0.9646239280700684 / 0.014575541479758319\n",
            "Epoch: 65 / dLoss: 0.7049230337142944 / gLoss: 0.6726739406585693 / 0.014900674149831584\n",
            "Epoch: 66 / dLoss: 0.7660494744777679 / gLoss: 0.5293536186218262 / 0.015639246160909417\n",
            "Epoch: 67 / dLoss: 0.7218909859657288 / gLoss: 0.5972681045532227 / 0.01817047291378081\n",
            "Epoch: 68 / dLoss: 0.6447311043739319 / gLoss: 0.9093921184539795 / 0.015211416896328777\n",
            "Epoch: 69 / dLoss: 0.6409283876419067 / gLoss: 0.8941224813461304 / 0.016457051807005452\n",
            "Epoch: 70 / dLoss: 0.7071852087974548 / gLoss: 0.8282727599143982 / 0.012702000345214206\n",
            "Epoch: 71 / dLoss: 0.6589785516262054 / gLoss: 1.0469176769256592 / 0.015440537793744944\n",
            "Epoch: 72 / dLoss: 0.5149682760238647 / gLoss: 1.4355707168579102 / 0.017516352706852114\n",
            "Epoch: 73 / dLoss: 0.6295862793922424 / gLoss: 0.9160952568054199 / 0.02269398020360103\n",
            "Epoch: 74 / dLoss: 0.6791152358055115 / gLoss: 0.7508389949798584 / 0.013547544913257777\n",
            "Epoch: 75 / dLoss: 0.6805771887302399 / gLoss: 0.751330554485321 / 0.014933541262105924\n",
            "Epoch: 76 / dLoss: 0.6573695540428162 / gLoss: 0.8216632604598999 / 0.016948190103734235\n",
            "Epoch: 77 / dLoss: 0.6554626226425171 / gLoss: 0.8271912932395935 / 0.016479363845406887\n",
            "Epoch: 78 / dLoss: 0.7367656230926514 / gLoss: 0.6153968572616577 / 0.013979718818158404\n",
            "Epoch: 79 / dLoss: 0.7055069804191589 / gLoss: 0.8103184700012207 / 0.013654694562081108\n",
            "Epoch: 80 / dLoss: 0.6119318008422852 / gLoss: 0.9007290601730347 / 0.01714246758137141\n",
            "Epoch: 81 / dLoss: 0.6652606427669525 / gLoss: 0.8550447225570679 / 0.015382680202493216\n",
            "Epoch: 82 / dLoss: 0.6803100109100342 / gLoss: 0.6818189024925232 / 0.015963081144901775\n",
            "Epoch: 83 / dLoss: 0.660546213388443 / gLoss: 0.9453331232070923 / 0.017232917747237037\n",
            "Epoch: 84 / dLoss: 0.5815287232398987 / gLoss: 1.2244492769241333 / 0.014699248313241662\n",
            "Epoch: 85 / dLoss: 0.6440566182136536 / gLoss: 0.7908821105957031 / 0.01706099966130292\n",
            "Epoch: 86 / dLoss: 0.7683757245540619 / gLoss: 0.6015301942825317 / 0.013239310472044777\n",
            "Epoch: 87 / dLoss: 0.6334447860717773 / gLoss: 0.9613766074180603 / 0.014636923997382971\n",
            "Epoch: 88 / dLoss: 0.6352722644805908 / gLoss: 0.9587008357048035 / 0.01718310220696148\n",
            "Epoch: 89 / dLoss: 0.6181924045085907 / gLoss: 1.0905475616455078 / 0.014148281178563173\n",
            "Epoch: 90 / dLoss: 0.6170722544193268 / gLoss: 1.0681631565093994 / 0.01604769740847473\n",
            "Epoch: 91 / dLoss: 0.6741792261600494 / gLoss: 1.013515830039978 / 0.013224678855000092\n",
            "Epoch: 92 / dLoss: 0.631628155708313 / gLoss: 1.1035112142562866 / 0.016131090564508784\n",
            "Epoch: 93 / dLoss: 0.7743811011314392 / gLoss: 0.5655841827392578 / 0.01426020301256458\n",
            "Epoch: 94 / dLoss: 0.6054907739162445 / gLoss: 1.0277549028396606 / 0.014893235657643462\n",
            "Epoch: 95 / dLoss: 0.6719362437725067 / gLoss: 0.9720638990402222 / 0.011985577052551521\n",
            "Epoch: 96 / dLoss: 0.6556772291660309 / gLoss: 0.7573625445365906 / 0.018978648547928845\n",
            "Epoch: 97 / dLoss: 0.6393887996673584 / gLoss: 0.8087537288665771 / 0.01718803031212894\n",
            "Epoch: 98 / dLoss: 0.5807759165763855 / gLoss: 1.0884268283843994 / 0.018517892587279382\n",
            "Epoch: 99 / dLoss: 0.5842494666576385 / gLoss: 1.194310188293457 / 0.01870786155069093\n",
            "Epoch: 100 / dLoss: 0.6147164106369019 / gLoss: 0.8343204259872437 / 0.01873248074926303\n",
            "Epoch: 101 / dLoss: 0.6460963785648346 / gLoss: 0.8454290628433228 / 0.014066370714423532\n",
            "Epoch: 102 / dLoss: 0.6766121983528137 / gLoss: 1.0566942691802979 / 0.013603356043887144\n",
            "Epoch: 103 / dLoss: 0.6597892642021179 / gLoss: 1.0941828489303589 / 0.015220244091787087\n",
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            "Epoch: 194 / dLoss: 0.6828583776950836 / gLoss: 0.6601656675338745 / 0.013767790948861618\n",
            "Epoch: 195 / dLoss: 0.6898902058601379 / gLoss: 0.6640335917472839 / 0.014999690120120044\n",
            "Epoch: 196 / dLoss: 0.6347975432872772 / gLoss: 0.9593574404716492 / 0.016219150281115306\n",
            "Epoch: 197 / dLoss: 0.6920709609985352 / gLoss: 0.6884241700172424 / 0.0158415608969944\n",
            "Epoch: 198 / dLoss: 0.7444020807743073 / gLoss: 0.7125792503356934 / 0.012270633436566472\n",
            "Epoch: 199 / dLoss: 0.617468923330307 / gLoss: 0.8745455741882324 / 0.013234005760070567\n"
          ]
        }
      ],
      "source": [
        "cgan = CGAN(exp_config)\n",
        "d_loss_err, d_loss_true, d_loss_fake, g_loss_err, g_pred, g_true = cgan.train(X, y, \n",
        "                                                                              epochs=exp_config.training.n_epochs,\n",
        "                                                                              batch_size=exp_config.training.batch_size)\n",
        "\n",
        "ypred_gan_test = cgan.predict(X_test_scaled)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred_gan_train = cgan.predict(x_train_scaled)\n",
        "ypred_gan_train = pd.DataFrame(ypred_gan_train)\n",
        "ypred_gan_train.to_csv('/content/drive/MyDrive/train.csv')\n",
        "\n",
        "ypred_gan_val = cgan.predict(X_val_scaled)\n",
        "ypred_gan_val = pd.DataFrame(ypred_gan_val)\n",
        "ypred_gan_val.to_csv('/content/drive/MyDrive/val.csv')\n",
        "\n",
        "ypred_gan_test = cgan.predict(X_test_scaled)\n",
        "ypred_gan_test = pd.DataFrame(ypred_gan_test)\n",
        "ypred_gan_test.to_csv('/content/drive/MyDrive/test.csv')"
      ],
      "metadata": {
        "id": "4pIn5rnV9AAg"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#ypred_gan_train_real = scaler_y.inverse_transform(ypred_gan_train)\n",
        "#ypred_gan_valid_real = scaler_y.inverse_transform(ypred_gan_val)\n",
        "ypred_gan_test_real = scaler_testy.inverse_transform(ypred_gan_test)"
      ],
      "metadata": {
        "id": "O7mY41rlzYRn"
      },
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ypred_gan_train_real = pd.DataFrame(ypred_gan_train_real)\n",
        "ypred_gan_train_real.to_csv('/content/drive/MyDrive/train_real.csv')\n",
        "\n",
        "ypred_gan_valid_real = pd.DataFrame(ypred_gan_valid_real)\n",
        "ypred_gan_valid_real.to_csv('/content/drive/MyDrive/val_real.csv')\n",
        "\n",
        "ypred_gan_test_real = pd.DataFrame(ypred_gan_test_real)\n",
        "ypred_gan_test_real.to_csv('/content/drive/MyDrive/test_real.csv')"
      ],
      "metadata": {
        "id": "MPKloG9z0ZvH"
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = pd.read_csv('/content/drive/MyDrive/train_real.csv')\n",
        "x"
      ],
      "metadata": {
        "id": "36SU8WnO0-v0",
        "outputId": "c99fbac4-c6df-4ef4-b9d1-40836d5c22c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": 168,
      "outputs": [
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3d29a3d2-216b-4067-92d5-0903b17a11ea button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3d29a3d2-216b-4067-92d5-0903b17a11ea');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import explained_variance_score\n",
        "from sklearn.metrics import mean_absolute_percentage_error\n",
        "#plt.plot(ypred_gan_train)\n",
        "plt.plot(ypred_gan_train_real)\n",
        "plt.plot(y_train)\n",
        "plt.legend(['True','cGAN'])\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(18.5, 10.5)\n",
        "plt.title(\"cGAN\")  # 标题\n",
        "plt.show()\n",
        "plt.savefig('cGAN_train')\n",
        "\n",
        "print(\"EVS:\",explained_variance_score(y_train,ypred_gan_train_real))\n",
        "print(\"R2:\",r2_score(y_train,ypred_gan_train_real))\n",
        "print(\"mape:\",mean_absolute_percentage_error(y_train,ypred_gan_train_real))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "JKc6-O9Qcltz",
        "outputId": "e45bc325-d699-4546-ae85-79f8a4b37c3a"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1332x756 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EVS: 0.9878746928114924\n",
            "R2: 0.9867670345481968\n",
            "mape: 0.024895187969525986\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(ypred_gan_valid_real)\n",
        "plt.plot(y_val)\n",
        "plt.legend(['True','cGAN'])\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(18.5, 10.5)\n",
        "plt.title(\"cGAN\")  # 标题\n",
        "plt.show()\n",
        "plt.savefig('cGAN_valid.png')\n",
        "\n",
        "print(\"EVS:\",explained_variance_score(y_val,ypred_gan_valid_real))\n",
        "print(\"R2:\",r2_score(y_val,ypred_gan_valid_real))\n",
        "print(\"mape:\",mean_absolute_percentage_error(y_val,ypred_gan_valid_real))"
      ],
      "metadata": {
        "id": "JtcjA2c64DM1",
        "outputId": "717a656f-5b68-4bc7-879e-d3f988264b0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        }
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1332x756 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EVS: 0.9881589768343496\n",
            "R2: 0.9870996503209369\n",
            "mape: 0.024823273294055464\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(ypred_gan_test_real)\n",
        "plt.plot(y_test)\n",
        "plt.legend(['True','cGAN'])\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(18.5, 10.5)\n",
        "plt.title(\"cGAN\")  # 标题\n",
        "plt.show()\n",
        "plt.savefig('cGAN_test.png')\n",
        "\n",
        "print(\"EVS:\",explained_variance_score(y_test,ypred_gan_test_real))\n",
        "print(\"R2:\",r2_score(y_test,ypred_gan_test_real))\n",
        "print(\"mape:\",mean_absolute_percentage_error(y_test,ypred_gan_test_real))"
      ],
      "metadata": {
        "id": "OFB3J3Ed4grw",
        "outputId": "0231f451-fbb4-4207-de99-6eece709b161",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        }
      },
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1332x756 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EVS: 0.9432721530930214\n",
            "R2: 0.9431656189298676\n",
            "mape: 2963963642638610.5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gH4oYEPHcMpK",
        "outputId": "4c420a84-adc7-4f43-90a9-8d880fc2a4b7"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.85243553, -1.05296929, -1.52656355, -0.90160628, -1.40548782,\n",
              "         0.54474806],\n",
              "       [-0.99707851, -0.89049953,  0.70903657,  1.15526081, -1.40548782,\n",
              "         0.74825225],\n",
              "       [-0.23175907,  0.11688781, -1.52656355, -0.90160628,  0.44752854,\n",
              "        -1.44612596],\n",
              "       ...,\n",
              "       [-0.45685302, -0.41937468,  0.51673375, -0.90160628,  0.61059398,\n",
              "         0.55949528],\n",
              "       [-0.88453153, -0.84183348,  0.35146706, -0.90160628, -1.40548782,\n",
              "        -1.44612596],\n",
              "       [-0.25426847, -0.15938564, -1.52656355, -0.90160628, -1.40548782,\n",
              "         0.61848414]])"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "standardscaler_y = StandardScaler()\n",
        "ypred_gan_train = ypred_gan_train.reshape(-1,1)\n",
        "\n",
        "ypred_gan_train = scaler_y.inverse_transform(ypred_gan_train)\n",
        "\n",
        "#ypred_gan_test = ypred_gan_test.reshape(len(ypred_gan_test),)"
      ],
      "metadata": {
        "id": "3U9LCbsZ8cRB"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ypred_gan_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ixi8m-s29JQ8",
        "outputId": "0e2e7c27-4a6b-4615-b810-c0a074e826ca"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[780.40497],\n",
              "       [734.5197 ],\n",
              "       [700.8246 ],\n",
              "       ...,\n",
              "       [700.8246 ],\n",
              "       [700.8246 ],\n",
              "       [798.3305 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test = test_dataset['NB2_S_x_NYZ_x_x_Fcw_x'].values\n",
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lBkGKGj0bRPO",
        "outputId": "7be38f4f-6b5d-4748-d46a-62a944191a17"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([271.7519732, 269.0741259, 269.7062556, ..., 648.4493889,\n",
              "       648.5763148, 647.0702408])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.plot(ypred_gan_test)\n",
        "plt.plot(y_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "O-WdA-GO8yIw",
        "outputId": "2ea4760e-ed06-4685-dcfd-395f197722ca"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7efc530a1790>]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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/OmBnU5v1geXKBLQNw/K+93UL1tdFF3Eqwss7yHgmt/yXqW54815Si2g8zilJo4HxizWM2wacgVUEaG9FXXM7rnpmNrY3ekweFr8NTP4rYwQBkaVUSO/AH5LFBLTiBNIpF2Zh/c8iggcaAl2WCXA93aOfLFeed3vO+/xWMDTxD4eL1BvmsN8jh5kVcGZz3YtztROFFeod60gC78zfhG4iE2iJ1sVPhEhcPlR4tpgZMD0kgSTsTP3W1xegobUde972LiYoCMr0lTU47I8fYdHmZqOR6CWBf05cgZXbGm3HgriIzltXi7MfnYJHP1aP1ZzgFSxmSEfb61lKElPd7dFXcUjxsZ/LeJavPfQud99/BxMo7QakW/HSrLUYv3gLHld4L5ngY+qLF5TOD15SUCYLZHiiN15OIQnMWq1wOkhahnkVsxAlgZqm4kRDd1km4MedtWbjfEbKVQkiPr3PcejpqavVQeCU4ucvzMG0FTVs3609H3/TxyYux/Bb3/WccRx0xwf4/r+nAdDLZ//EpJV2ddDm/PnWi37od0tujwCQoIxAz1i2EcNvfVdZRwmcH/Bv/7cQWcqiUAHYmDGXzFbUGMcCej/NXr3D4dstoqUtg/veX4IL/jXNdtzp/eT+LnhKky83RS+FZQ11UKZdwViNcc0Zq5nTyqM+1diLck5T26yh4z/oe3j3yKfQSsqA9haUJhkZ83pPlr4dwEe/Z2qhj+4wx4PXp2KTBBQ2AT7h/MPbzjHNmfCMFZtN+54bE6Ahs+vmiq7LBHweqJbkberHFXVNvNdxqr613UwhLNLz5rYMxi3YjB8/PYu17da4D2N6eDzTn3oN9oZUGjNW7cCrs9fZQu7dqs6K6qCeA4HmGtS1tOP3by6MPG+/2AfVI0gaTODLde4GURUTmLCYxUPw/op5akwRnBv2AjCBJZvrccG/puHP7y0RjkrE3djfIbmyOiQBqe5Zq3eYxD+fkuGaWtavGSucUlJDO2u4hGTw5hcbsHK7f5LCrCABW+qg8DdAKcWjHy/DL1/5wresKQkMOADXTilHXYoC2XaUlRhMwDM6XujjtEeBfx0PfPYQsGme2Q+vPjrUQe/eDNw3AoDPIkmGJPD0pKXmIdEeRmxMwMDO1R73ET26LBOY72OscpcEhOOmflxvkD8/fW1+9LoGuBfCW/M2YPlWwYDbuBVY+oGt7K/Hzsf4xVawmDjItzWksMWYLWepsKBMRRWQacfD45fh2Wlr8IqboTwkxIhh1UfHJYFyuHsoqZhASZI9l1bD/lMjEGTTh5sP9QDqoO0NrJ4lm+uRSmeUmVzdXrdXsFgmS3Hhv6aZKRH4mblro1fFtWbYfVOFU8MX65kKK4ksbnj5C7w4w56vSgUxIIrTviUuCxY9+vEyre/hgQ+X4o25G3zLWQTT8LJBAqBZlOtIAhzn/pNt+VKOhjTEuklxY8lYYP6rtksoFIbh1lqgZQew9ANTElHCkARKYY078RkmbDYBo55pj/rfR4TIiQkQQn5JCFlECFlICHmJEFJBCBlBCJlBCFlOCHmFEFJmlC039pcb54dHcQNu4O513coUizbAnQnY9I4p5uq5obbZUW5H5TBfV1AOuSVXBuSjDuIzjlteW4BT/zbJPN729LnAi9/D5hp/jyQA+No943H0nyaYfTHVQRW9gUy72T/VB9yUSuMTjUhkjhP/8onpeivWpnoGSSOqtS9pdJzjUDEBXlUq7Txn+nAjN8Pwr16djxP+MhEtbVYblFJXIicfFW93Yy2TADbV2m1IW/OwbkQ7GJGrIM5nw59JCezPxGtWLKaweGmm9yThgQ+XOpZdzGSp/Rl61mCH+XVwV0sQgFKUlrB9zzUb+D3tMdp+3LAVkDljsD9ZixtLXgdevwrYZKlFs5RaBFrGi9/DV14J9wwmUEasZ+xmGG5XLTBTAIRmAoSQwQB+AeBISukoAEkAFwG4D8CDlNK9AewEcKVxyZUAdhrHHzTK5QXiIE66EFa3CYqttCEJ3DtuiaPcx43DcM4/PvNUOlFKMXnZNmQy1gyYHVeX99Phu/EIuoN9mG99vkpdAO6Mh1JY6qDKKt/0Ar8eOw8/enoWVmuoDgBgdU2z6Xo7br61iL3qVqs3fQIAGEbc3fhUTIDfW0tbBlOX22MHEg5JQF8dJD7vT42U3uJs84nJK10JJu/Tdck3cFfJU1i9vQlH/PEjrN/ZbKqtKo0JSj7VQW0GYSlTMIG08UxKJGO7V3+4N49uuhR5dn7di59j/9+/b+4H8ZSTJQHKmYAxE9dbuIcAJ94G9Nid7aZbgFQjSt/7Fd4rv80qtvA1oY8iqXbeuJfkytVBoiQgqoNESaAZFcCAUUBlX437iA65qoNKAFQSQkoAdAOwCcDJAHji92cAnGv8P8fYh3H+FBIkH20AiATGLdjKbfDZgjcMJqCa7RFkPcVPQoA35m7AZWNm4qVZdjdGt2HvN4iTilBOSilasuxDJ2n3wehlEzC9g8p72bxIVJes3MaIf3NbcHvBtJU1tnbdMJxscWkdKCUZx73w95OlwMVPzrCdMyUBYqmDFm6oMwOdcsHkZdvdbUvG8V+VvorLSz7CS7PWoaapDW9+sdEkAlaSw/xxgXZztu+8Xy4JJCXG+uGX4RZN18F7C+11l5foz35NJmBmCCYAzeoZhkWceCtw0Qvsfzql5mifPQS0MQ0AY/RGme79zSLpEScBAL6ZsCc83FzXaiXcU6iD3AzDBBQYfITl0VUghGYClNINAB4AsBaM+NcBmAOgllLK73g9gMHG/8EA1hnXpo3y/eR6CSFXE0JmE0Jmb9sWLmJSDMZwMxaJs+6sy0tJNTKXL9XCD7ycFx9bbywEs6bGrk5yI4Dehi2158Rny2uQ4mKkR24jtzaZOqgVrbSUBcVl0qGXf9QFAfGcbVaSNgyAlT1S7E8SGce9cFfYkViDC5JS+g3JMEyzaXz771Nw5TOzQxo07e/IVcJyudpLhSRidcXFeKD0X0E750DWUAdRxQTBTRLYqeOhEwLiOgX77d4TAHDSvtW+121tYGozaxjYbQJWskivWtjJMVNWYcnmeisANN0K1ewegLkqmy3wvqwH21bvj30WX4lttBdOT86yXTb63gn4wRPGZMTHJuCgLSTReVxECSF9wGb3IwAMAtAdwOm5dohS+jil9EhK6ZHV1f4DRAWNbAm2GZybtX7rJ/90HVkJn9mbzRNG+ujdBmsYmtSYSqONMpGTZN0lAXcDJtANKTSjnImugjoonwujuBHPxdk9AHBpgEH0vihBBos22m0xnKi+lfwNHij9t+2cbBOgRuTszFU7MOK2cZi6YjsyWYo731pk6uq9+y3tu9kEPNRv4qm6lnasXTpPqVK4IDnJcSwoWhOVAIA5S1Zh8rJtNn28JQkEJzqbPIIhRYjxE9zwDFiqMPHxjVtgqQtt4AKALAmASQI6rq3cfvfUtDU47x9TgRL2XJi0L1158u/Y1jQaUyGQy3h+l45FFglMzh6MIxPOrLdmSm1DHVQmSFviZE+mI7WtmYIzAYUDvDZOBbCKUroNAAghrwM4FkAVIaTEmO0PAcDN/hsADAWw3lAf9QZQ46w2d+gkpXLLLioygaHptcDm+Q5uTUnSrjbS7I9FytT9c+RO10Sb8RqJR5CVO1GiGERqkEYJm7Vk2rRyx+SkwiBqFVuWEqymA7A/1qKa1JqPSdSClSpsAl4za9MmQA0vGckw/OnSbSAgeHrqaizb2oAXrhrtqMPWx6wsCbiUk5mF8fy3NqTMjJsAcOPTn+CpLRehb8mJuDV9tXE0OuabTrAZb0XDGlw2ZiaG9q20zrmog3SwTdOI3SQwnaenrjb/c+YpLnrkuniQ+f5VNoGsll1hyrJt2N/439KeAXoaNoE3r3XGAQ06jG3bmOqT2QR4HhrjfrqzCWozLfd+fgpJ4Nx/fCbcmn08vTVvEy7v2UkkATA10GhCSDdDt38KgC8BfALgAqPMFQDeNP6/ZezDOP8xzdNUU0fcFgdO1kUSAKCOoE2WmuXcyOXWhhR2GstZyrNFt+7xHCyH71Hl0XMnuAdIIuvFBJjNQfZnz1LglORc7EZq2ccgqIMoBVC/ka2qNvmvgfrkB7c3v5MycbtK8BASmVJQgpWQCYgqWtQ4J48bVR+d71JPHTRuPpsLPTd9Db5vuIb2a1mNizf+GQBwdMJK06xidGHBb39YgklW63ZY0g5njCUKo3G+wRPH6US1vzRjHT5bvl3hHZQwmAA7rEdOjFrKewBDjmL/F9hdQtFrENu2NZr1mjWbwWVWHzy1ApwJELVXGrH95xJHHj0FFMjFJjADzMD7OYAFRl2PA7gFwE2EkOVgOv8xxiVjAPQzjt8E4NYc+u0J+UM9/7DBjjIPfrQM+xopItwMNQCAZqewQhOlvuqg215fgKc+W83qNw2BemH2z191NEYN7uVdCNYsSkcSyFKKm/87D4dLqZltRCxZakgCAha/w7YT7gIVcvqEWVvZtV0BdTCYAAQ3UR9JwAmr7vnGymomocgxbcSpD9pVNLoR6QcTZ0qDCeW/xjeTzKi4mu5uHi9TGHHDgq9cNYw4g8XSptE4f0zAbax/aahndCZsD45fikuenGFOvLiDQVaSBLxqUi7ofuWHwCEXA6sm249zKeGdG4EZjyNLqSUJGOqgdqPNLIg3LTCkjFKox52sUeDSTSGRk3cQpfQOSul+lNJRlNLLKKUpSulKSulRlNK9KaUXUkpTRtlWY39v43zANR71IauDupU7PRBemrkWqTTTJ9pTu7Jrn0ufyg7sXO0cQBITKEM7biwZiwqoibA80P3E125lJfjtWQd4lgGsWVQbmN4x4ZEGOkuBt+Y50/smjJnNwuxwZmDOtptfLgUF3vu1Wfa6O/7kGhgUFKonkCAUbShBI61AHzFWQCgs+7SrIOq4XzADoNixjTty679DNehq37GfeLP89571bqOW9BcpEzC21ahz1MvdZsPYBKKCjiTAwZ/9+4uYu67FBPTbs70WQoChR9kcKsZmvsG85Dje+zVAMw51ENdyURDb83OTRsqQxomJL/B86T02FVBCWF6SgEs3nUQS6MiQJYHKUnc3tBG3jcNjE63EXXygraID0UgrgE/vd6yWRJOlJgcnhBnwbix5HdeWvAkVlLnzfeAZim6Au5S2USMHjIc6yI3xlDIejTczX8ffJ65h9bjMDEcnrNwoKpvA+C+3YIOGcZV49Acg6EFacVnSSiSWpRQZQ/wuIf4ESzWznWLEDzQ06xk0deG2Whr/y9VbAFwnCQDQg1jPrUyD0emCj6IEoVhacQWGChJBISQBPwSJsOf3Qs0tI5g6ObJcUdbdtjsruy+QSIKKuYZo1hrtXBIwHlkWCdvEwLk+AtvvS+rxdNn9OC65CLeWvCTckzRB7GySQEeFLAl4MQEA+LeQfZC/FAqgB2kF0i3oC7s3Siprf/Hc1U50a7T1RxqkOgNfx01z8rLt2FjbYtoEqMdiLPeOU6+rXEqZ9NCKMpMo8PQNsmvr7sTKkriplqXwFXHVs7Px7Uck0dqAaEh8Z/4mX0YoBjeJi3roECxVGf44c531iu99Z3ObLUut+F45c1hKh5jHhhB3l+cewsI+ZYQ917YoIkiNG2+mzEB8KFlh9TcHw3BUCCMJ8Fm5Qx0UhhcQOwmso90x/NZ3MfaMubi//fus3kwWkCSBdh6bAmJT6aRl10SDoIvqwHOTomE4ZgJ5gUxke1WWal/LX4oYJt6f2NMxbG3KWnECYAMHsBsz7f2BWRZgC077QWdA//yFz3HXO1+a6qC6Bvco3tU1ztQXAFBmMIEUSk1m8vxURihemLEW2VJrptSLWHVc9exsHH//x4763HzMP9JZFB2M+a7MMp0sV/1QWLNWHVWJF1GTGcS/P12Jiw2f7ukrd2BHUxsopXh++hrfgLKFG+oxds56c79FSLinen+/KXnFta5vJBeYK7xxSSCtcN4LmpuKAKinlTgs9W9kKcE+Cau/bnEChURaK8qXwZIEZCbAj/vDkalTmm3VgY33xVuazedDHeogYgamZSTDsEMSUHTq5czJ5n/ZntDpbAIdFTIzDsIEEoIkcG3bLwAAvytl0YWt/UehtbwfmiXM0ncAACAASURBVFBhcv+3529CyiDCFS7h47LqQ8cVNEg4fcogFu0pzVWWBJQZKooULTWjS23Rje2twMEX4cvsMPtaxADqW93yoQTHaz87xvz/QobZYwYTpsKhFNgJFlzUB+55hThURI0zd79Z7+OTVuLztbX47f8W4vY3Fuh13sC9QrZR/jQSyGJ+lmWblMfHiuxA2/4Issm4xpplymhqC6YqYjSOIIUyrKCDcF5yitm7fEsClWjFnnXTHG6QIsJIAhxOScC9LqVhGLCWHTXAJ3Sra5rMiSARvYNoBiAJ09efSoZhR9S/QdB7Cuq+FLXokdgvAmqmwigkuiQTkNVBVSElgVWCxwYAbDr2Hrx16idog2UYzmSpqWIYlViFI4kzz1AQAnnNiXsBsMZB/x7lOPuQQZ7X8Bn8kPQa7XY4Sg3VT5sgCfDZchIZ5o3Tby8soUPRE2ppAvBPeeGn3uKunJQSDCLM++NfpQ+Z52soM9bJUpkK3pKA/yyL5/YRpRr+Dr1uQ0woyN8fm4l3w4rsQHOWybGAjkALtVIEmPp7bm8yxtjxI61UBe0B15wW7I54OXMihpDt+F3J8wDCSwL7krU4PuG/7sR/Sh/ApctvwkOlj7mWCTZ5sKuDqOEiGsTTnF/739nr8PMX5qCd2t/oGjoAAPDxkq1WOzZJIA0QYo73LIiNyaVdbALdYalqRfWRPJ5idVBEkAfW14brJ2RKCAOtHt1s57KUoj3LX7zVBv+I+pJGjC13Ll8n6gndxF8eRl9rxBbwgb33bt19mRiPGL4g9YZnORW4/j+NpKVyMXyazZlrSQUaaCV6EncmoJrRBTHYiUyCJ7TbP2FFmLaCEcvuxF/aUbmRmpKAj0+8G7Nq1SC+4qX8/THf7wSaUGFfwQ1srG2iffHLtmsAAN2JnB6B18W2n6/dGdg7iwom/PV0NwDAlSWGa7Rhc7is5EPP2bqMD8pvxXNlf8bPk2/itMQs13LHJJkjwTnJqcrzn5dfjcu3PaDdrps6KIBGycRvxs7HuAWb8e9PV5nH7mq/DE2wgum4JEazgotolkkC7WlrsugtCbBz4sJNjnxBAnjsQyHRJZmArEqp6hZcEqAgaKAyE2CcXjYG+YnTfJ3jhlQa5z1m/yCG9euG6bedgh8dOxyApVPk9DNBCM48yK42kNGECu+bcsGdby0CNQxdaSTRTrkkwJiAqRZKlqEB3QzjpZqwq5ibTuT2KfsxwsSN9xQEz2ZOcy3vF58BsOCnwVAbYcMYhgkRJQH39j8X1gOw1EHsXwvKTSJv1muI/ysok/T4ug58bFkzXlbH+Y9NxSVSgjwVtrgsczohyyJhZ2T3A2DZWapJPc5OTPetV8ZvSl/B42UPYgB2IEyAU1/SiBOb3vcvaEB02gD8DcOX/2cmjrvvY9u1Mr7Y4J4O3lIHZSTvIGKqg7IgNo81N3WQiKSHh1ssCUQEWRIIkqzUUgcRNAqzAsCQBDJZB/f3E6drBRfTBdJiNwlCsHvvCpQkEra+iwt6H7OXI8+eDaKxK8iM7umpq03VTwYJUx1UaqqDjLoSSTTQbighWcdslsPpGmd/D25G1ocuOhTP/PgoDK6yGNkSugceSZ+LDCUO1YgOE5hU/kt8VnEDniq9Dz0MFVZU3kG6yEqSQDMtt1J2GzDPgXnu8GdrRaMbYzFgl+951/IEE1UlaZRgUuYgk7lnhM/fS8rzw4yK63BTyauO42uyu4WuUwWndxCbNf9z4grjuH1sTFq6zUziyCGPHtHu4naO0qzdoEwSJrG3jrOrnRKxc7za1UGyVNCJIoY7MnIxUorqIHMhEgNZsORPFMQW5OGXR2j5VndjJteF89Wx+CCydMr+DEwcSKLuUQecKIrqIE4kzBkpSZiqsV5QeyA5XONgn5n9SbEmwx1nH4CeFaU4YZ9qc90Hfsk2WoUkoegLpv6wmIA+RTwpOQ8vlt1jO+bnXUTgnFEu3dJoO68Dcz1lgzSlkXQwoASYVMnHmczwrLqCj+cvN9bjnxNXsIR1Qq/bUYLDE8tRhnanp4wmaml3x7Erks4F4kX32Cggq4MoCNrSGTM2JYw9VWV856CuTEDwDjJSb3C64aYOAqzn5jWGeTqKQhqHuyQTED+a7i4ri7mB64zThr70G6kHrXqzQHuaIkvt6qBccq/woDC+VkAmy2cYDLIQ07PC6TYoEg0xB40feqIZfyx9irVLk2b6CYsJWHrPndTwznFxg3UaxPzVQT0rLDVdImEvyyNouSHYMpq61ak+fnBilXE9O9/Lx6ZA4ey3brI0EVlBfURBIEeWAhYT4ISIn3e4DQakBxTAuY99hvveX2KOJ47jEszj6XQPXb4fthrvpp5WYj1lRmuVZ5yO1BYEDnUQJVi9zbKR6DwnmfFRgQTK50w3cQ9JgJfh9MDpImo9/4nZQ5CipUh62gSI47p8o0syATHaduqtpwS6lqtHuOfEWjrADLTJUJbHRM4XEoWKocRkAtwmwNVB9oG5W89yvH3dca71jCn7q7ZK6JqSt7Bfgq36lUYCtUZ0a1/CPix+X1kQ1MKZ2E2EyjvITyLrUW4xNK4O4x/bdsMbqJrYE/i5zaLE93FG6l4c3Po41mZZpkfPlZ8UCOqBo4JdHUSQkSJLAX4vxErfQKKRBOatqzVnqm3pjI2AXZlmKbv6knqFKkIP3IbxbOY0/Ct9NgDLQ01E1Ko3VbBYVnOlOLf785YE+B8KavMiIg51EB9/TtuYKKWnjHGgVgex/khrGRcAXZIJjBrc2/zfO4BRGLAGrqwKAoDlxqpazCZgvaSKRPgXxlfoSko2AT425CHau7LUYeh2Ehe9D1oMnMogiS20DwArMjhhEKU7315ipj/g6hkZKu8gP9e9XoJUI/eZewOVG+oby89f/az5+QfaL8RiOgz16IGH0t8FAAwkNbBLCt798nJ31SWWnHAnDElAdiZgdTEixAmMm92DIlhQ1dodln5fXm1rW4/9lG0EASUJrMzujgfS38fzmW9iSuZANEiedKyNaAjZL5Kv4/2yWxwG1SwSaEr5Bw/a0nk4ZvvE4xwnyBKjIQm0cQcO2N+dlyQwlGxFViER2ooXQRLIZT2BDo19BvSw6XJ1YUkCFhPgr5VlBd0TcoBIWTJ3sbe34QbavweTOrobs+Tde9k9f/aq7uFgbPIHLRKq/j3KkGrPoiHlDDISo6LTSJgfMjdQHkmWAgCa08AOUx1kZwJ73vYuADtZVR1TQVQHmRlWRaMfLKIvG0tliLYcDs5IypC2fd7laEcK6iX8/jlxRSQrq01fyVelg6EOSjg+foKsKSUAznvlmLNmJ0YaGW85BldVYkNti7l1Q0NrO8TsExvqWoEKCEqo4EgSYrqXAsCU7EE4LrkIPdCMRoEZJG0z3qxN9RIEN5Wy1WrN4EHBJiCO/Wkra8yxJ2LP28fhokQDoJgPit+Am2H489U7cIlwbV1rO37x0lxbGX6vFz9peVntedu7uCW5Aj81qOyTmTPx+5LnpOeibnP/343DqQcPx99/cJiz0xGjyzKBV3/2dWwWVj965erReHPeRhw5rA9mrNyB3XqVI2ssUj24qhLjF2/BIUOrcHLvXsCbQN+eleiXLsPZhwxCci4BKHDuYYPRp7EaIxp7ItHShiMG9sExe/bDmU0LASF25g/fORCj9+yHHhUlaGnL4C8fLMGhQ/vgyOF98NKMtehWnsT6nS04dGiVGcPwteF98NcLD8EZB+2u3H/+yqPx7LTVuOucUagsS+L+7x6MD7/cgpXbGzEgXQYxmPf6E/fE0N36oDSZQP8e5ThkaG/saGrDSzPXYuGGelT3LMe2hhS6bSwD9wK95Jg9sf+BBwPPAUcOq8LgAw7Aj8dfDADYd1AV6ipHAOuA4wYnsLaiGj3LS9CrshT9e1jEdNHGegzv1x3dhaytK7c1oXe3UizaUIelWxrxf2ftj54VJdhan8IBg4RsjcZsbUCvctx/8sE4pLw/8DpwyOCe2HvvvdBvbhnQCgzvW4lRpb3QaEQrt7Zn8f2vDUVNXT2wELhk9DD0qdofCzfU4cC6fsAm4A9n74fZ8xcCxvK25x3QCxVVu2P2mh2gFBjSpxLz1tVhWL9uOGoEex+zVu9ASSKBbmVJNLdl8NWWBvTvUY7e6TZwYei6k/bGup3NqG9px6FD+2BzfQsOHNQb2xtTyGQpNuxsQfWaUnTr1gt16SS6NyZwzJB+2H9gLzSl0hiyphy9s5X41sDBwHLgmBF98EVjTwxJVQAtQGkygWtP2stUb67c3oSG1jSG9qnE1d/YEx99uQXfPGAAHvtkBSgotje2YU1NE/bdvSfKkgnsbG7HIW29UbE9iZ8evSeSCYIK2gLMAEb0q0RTSwrcTn7xUUMxfUUPXHDEECzZVI/u5SX4xj7VOGhwb2QpxQsz1mLO6p04fmR/HLa6CnTrJlx+xDD06VaG8s97Ay3AeQdVY1O6O3qUl+C4kdXo/1ES3E/h+pP2RAZJlCQSmL6yhgXkMZqOHxw1FFNX1KAplcFxe/dDYyqN+pY0Lhm9B9bWNANGOqrrek0GWoE9q3vg0uF7YO/1vdDekMI3hlejJEFw4CB7+vWt9Sk0t2cwvF83HLR5PrAS+M4hA7Gd9EVNUxvqW9pxbGV/Y9FbYL/de+GwZBV6VZTikKFVqFg4G6gDfvT1PVC9pAw8VrKstATXH7s32jJZ7LWsF7ADuHz0HljfnMSI/t2xeFMDhvSpRM+KEhy+ugrtmytwevdXcOOp+6D8nZcxsmd3HJKsQl1zG7o1WUyoopRgt+7s3f/sGyOw1+BovatcQSntsL8jjjiCFhzrZ1N6Ry9Kv3rfOnb3QHZsw+ds//kLKf3X8db5KQ+z82/dwLaZTGH7/PaNlN7Vn7V9Ry9K25rZ8dp1lM5/1f268XdZ12z5ktJUI/s/5SF2np9bMJbt37cnpa//LD/30J5ibX36F7a/dQnb5/1//CTrGavQ1szOT/qrdWzxO8Z7m0vpkves+6lZEb6f9ZusenTwj9GUvnQxpWOvovShg+3nnjuf0n+fSGnDFlbfjMfZ8dWfsf27B4bvJ8c7N1F63whrP9XE6p78IKWTHrDuhbetg5cvpfTRo6z9mU+wOuo328uNOd2qv73VWY/uc7xvBCv3xKlsO/dFdvyZ77D9VJN/HbPGGH3cZD++aor7M5j7ojFeVlL6nzOtcn8ebpWZ+g92rHmnut33b7e/x/tGUPr2L639aY9Z9T58mEVLWhv878kDAGZTTTrbJW0COcFYgxYJlVeRIbzJi0HXrgFKu1krEnnk9c8LKAUqegOn/sHaB4Ax3wJeu9LD0VwQgBMlVkZFWR/Jj+8+Cti+NLJuu/YFsHK68L7we3LTlZo+tcKQdrufVDRrImiBUtaPRNLZD37OvFdq3+YDbs8keEXCX95/SXcu7msacNVNGfVvM9by5fq6lRPZ9tM/+9fh9kyJBwm0PSvqPO4o49KurbxiHFiFI3w/+oiZgAy+8pS47qisJCbSwg9bFwMDDwW6GekpWsKtFRweRpCJPIDqNwjnVZcJAy2R9GACxkdYUZV/AsqfNWfC5kpgfkyAp2qVPjjznPAMUsFtRVY7Qf01s+ye5DFjnktY98yJpnmPETADuU3xHYdmNh51ihAJv8wgwrSX4oGW0vdY71wsyR2Kb9m1KL8van9WIj0Qy6jA379YXnwWXu+nQIiZgAyTCSisSCKB2rIQWGYs1Vi3Dug9BOg91Nhf77w2n6DUIDSSZ4HOLIWDeDABTpDLewLbvwL+fiTQHu3iLI6PwWQCGft5XyYgfHCGx5VjFtqWAxMIDIFBy/3gTCAhST2RR4y6Ea2Q7fDxxiG/K7OcSOxyIGqUmmv1AlBMynJYd8GTCYjfk/CsSipdyqhgvH+ORBL4/Fngrn6GhB4zgY4HlSQgg7+oFy5g65OmGtkKRSYTWOt+bV4gSQKmf6nPABWPl/d0n9WYkoDheluzLI/SDme0xvN3qBh8VFtuorp4TzlJM0ElAa7yUSQGMyUBiVlFSgA0iUxgqUClDvKQBHJRBwHAXmK8j9H2JcxrSLUOuBMa6iCVxA84x0/vIYoyLvcnM0xOX7Jpplp1lQTyqBKU0GW9g0KDD1YlEzBeZruQZ+WZb1v/+eCoLTATMCWBgB84zbJZzTWfMVWW22ybz/R6ConsclywXdEZ+y4JqQ7S0VW3uS++Ezm4OiCRVDA0/t5c7B9RwaaOECYGNKI5oM64y4mxUSCpUM+O/Caw9zeBJvcV2xxwEHoPR1m3+6oa6l+GQ1YHNQqLK636FLZxLzKMWBIoIrIehmHzZSoGDiFARS+g12Bgy5fO83kFFzmlvCM66qBECdBvL6O8ywDkx/c7S7g2T0sSEkkSyEqEMYhh2KamED62t38BbPFf3S0aeEgC4jlAYROIonmZuRrjJFf1TFB1UC6SAKVq9SwA9NgN2LkKaN6hPi/WoYJNHeQhCYjjp7fABByqPEfD7iqnJVJMQ2Ufa4KyzZlrK1+ImYAMHXXQaXcD3/i1egD1GFB4wzCFJAnwAauhDpIHqIpY8Zlq3xHA+U+w/7mK946+yDYBriKRJAG3dk0moKGmAICJ94bqZijDMIzZvtImQAQimidJQEXcZMIW2D7gIl1wLH4H2CpMhl69Ahj/h4BtCEgKTEB8x937A611wP0jgH+foPHswhiGJXWQKAmUGMGcKumSUmDWk2p11ZE/BtbNAOo3WceGfA1oMZjZM98umEooZgIyvCQBPoCq9wFO/i3wQ4GT84EpW/8LAm4TCGoYlkRVfo2bJAAovHaiBn+OkiqHfw+hbAIZ5we1+O1cO6oHUVXn6h2UT8Owoi6lVJJDnSq125QH7WXWTgOm/C18e7ZJmcgEqq3/m74Adq4OVrWOTWD1ZNjuuWoP63//kWz75nXOuls9VsE79gb2Dc16wjo28jTg+JuBkd9i++/d4tv9KBAzARkqV0O3APthXweGH28/Jn9gc58H3ro+0i464OodFJEkkBayaBIX0T9nyJKA8dE3brWfD+QdJM2wI0FQAu1jGLYx73wYhuHO6HOZaYpVqtRB4mzZDzqqHHFSJt5PD/sSsMj45BJyI/RsR2rXeA8f3C4ZhkUmsC/brp8JzHvFuy0AuGYacONCoM9w9uMxRT+fAYw8lTlfXPQiMOq7LC6nAIiZgBtUYqLqpZozFMF9VPyI37yWuYTlFS5xAlGpg0RR19TV50kSkOMEpj4CpNvsNoF/HA3c2RvY+IV1ndIwLOraFQQvckamADXeTUIhIZqeQ8T+3POtBlAypABt6vi29xigvralVnFMQ31qswkI7/jAc4HL3wLO/RfvnH9dIrzUQaIrsTirF72DyrpZ0sjbN9ivVz3TAQdYDFJ0tBCli2QJcMF/gMMv9+57RIiZgAylJOABThRFdVCkM08NUGrYhd1cRL0CWTSYgOgNlXDxuMkVDpuAIP5vXQSbJMCNZm9e67w+SDRne/jVtLSh4yLK//PJQj4Nw4CLTSBQpYCfF5ab774q4vylH/i35xa8mSwF9jwBKFEnBLSqCOEiKk5+apYxqf/amUCptJzrzUYk814nOfvtBa+A1AIiZgIyVGoFEypJIGk/l7O+NQxk7yB5VhyUCUjl++5p/TclgXzNooXnyNGw2fr/1Tjrv/iReqqDFDYBAGj3X7TegVARw3AahltqgY2fw/ZuuPtg5OPHJ+I9VJWK52yrk7JUKt8dY79ONevf/pV31C93fLAa9ygboWFYHh8l5UD1vs5yiSSwz+lA7Tp1X053SWuhe095RswEZCjVCh4vSPYicmMCURLN9haJALrECeRqExh6NGMAw75uPw/k3yYgPvOGzeqP22Z4U0kC4gxVcX2+4gXSKWD5BKFfxDku3r2ZbdfNVFRQIMOwTLS1q3R5V7Y0EdSej4pDpQ4CgL/tDyx83a1B+H+PPhMet+M81YutDgOypCj6+MvosZtHvIIb/Sge4RcRMwEZKrUCh9ImkLSfc/MOijKp3F/3Bf40SDgg+5vrRgwrfJiJ4ENOqV1XCRTOJgAAv1rGtq11UH7ELYJB0dMm4KYOCiEJ6BDL928Dnj+fxSKoEshls8BCI9o1Va9oIt+GYf6OpXuZ+QSwfLxGhRrqIHbCeaksCQw71vo/9kfuTfrNmv1Un8p6wJhAeW912T1PZNvjfsm2mxd4VQzH81S5Lbv1pbOqgwghVYSQsYSQJYSQxYSQYwghfQkhHxFClhnbPkZZQgh5hBCynBAynxByeDS3EDHC2gTcDMMc6eDr1LpCdj0zvUwiMgybMzrpYwfy5yKq+ni7V7Pn21rn/nGn2+zXK4PFXDxh8mUTWG+s35tuhS2BHCjrx44V3tfzvkYhbXnaBMRyWWDcr4Dnv6tXrzKJmlin0W6FkeOfB1iJTKC0OzBIY9EUxz2oxoIPEfViDt37GVVIdQw9CrizDjjpt349NBirWz91JIFOygQAPAzgfUrpfgAOAbAYwK0AJlBKRwKYYOwDwBkARhq/qwH8M8e28wNPJqCagUgGMFWiMMDfdS0XhE4gpxEnIJ9X5fRp2g6smRau7w5Is6OK3kBrLZhxUBE12mS4kKrem19el3wxAa5myrTDktKMcZJpsz/fr13FttVs2UfmCcUlhqjGjIaLaM1y/er8kv3xMgTAbgey/WOuYzPuVoU6qLS7X4Ow3YNnFtgQqrSqYWzrJq0ndbLrKGhDV5cECCG9AXwDwBgAoJS2UUprAZwD4Bmj2DMAzjX+nwPgWWPNg+kAqgghA9HR4GUY9nIRtamDFER3yt+s/OeRQ3IR5eB9SqeAGf+21kowL1NJAoIko5xFKj74578LPHV6jtKOy8dbUWVJAqJtYq+T2bbJWJ7KyzDs5gnTFoIJ6BhUS8qN+pssKa16H3ZszWdWHef+Ezjrr+z/cTex7c5VVl8jUQtpSgLc3VbMkOlZp0odJPeXAL0GArdtAI7+KVBZJamDjL7Z9PIuUCVhU50PY/DuOyL4NUroSCwiOr8kMALANgBPEULmEkKeJIR0BzCAUspjoTcD4A7Dg2Eu5AYAWG8cs4EQcjUhZDYhZPa2bQESQ0UGD5uACg7voKR6IE5/DPhvnvx+/RLITX0EeO83wFwpXsHXRdRLHSQwgU0GAdmxKuwdCO1L7VX0tmwC3fqxhGFDjgJOvJ2dr5M8MlSSgJsUli9JoNQgpG1N7EeINdN/7jxg0v3sf4ngatjb+BQaNoUjZF5wlfaEdnasZNsKF/24V51KiUuou7wHKy8zAT5uT7ub7cv2J7GcCKWEFNIw7NVOEKjUQUEkgSIiFyZQAuBwAP+klB4GoAmW6gcAYCxzFujJUkofp5QeSSk9srq62v+CqBFUHeSQBIi7+sErjDwnSMRavgf+4clGOZ04AYc6iDMB4UPkapogKgUZbh8gZwKcYFz8X+DKD4GBBwNlPSxDpsow3GMA69uOlS42gTwZhkuNxda3fcXsArVr7QRu4WtGV4W+cuLbWh+9YViGShLgahrZB14FV3VQ1l5GHjuVfRTeQYQFfB10ocfEy2hvxAlsq2LqugRV1/U7MLzq0LAJdEZ1ENhMfj2ldIaxPxaMKWzhah5jy+P+NwAQY8mHGMc6FpTeQV4uoknnvviByS6k+fCvd0gCkkHKdOv0CFbiEH3IVYSTEziRgPJ7lGfloaCQBFp2wmR0CSPCtqScPes5TzMiq5p1lZQDu+3PUvbOfAIOiJJAOgV8fHc4FVEmbX9W/BktfZ9tD72YSQc3yt4lQl/LDQNqqj5aSUBpQzXesaqdug2aaj3pOQMKyUrFBMQUEUL7FVVsMaa0i16eEGCAYV/wsq+5BkZGLF2pG/HZl9DZ4wQopZsBrCOE8OiJUwB8CeAtAFcYx64A8Kbx/y0AlxteQqMB1Alqo44D1YySQ2kn8DEMi2lngYBL4elCsgnI+nGTCUgfj4oJ1K8HvnjeXq+Ish5sKy7MkjZWGctl2Ua3D6bXIJZpUTWzrKhi24cOcrflDD0K2DgXWDuV7YvGZZFozXwCmPQXYNo/gnV71STgj/3s+aG4OmjDbKbC2v8ctl+1h8Ug5L5ySWDdTOCNq63jQROiKeFj/BeRbWer5nlCele9DFXWhjnuZQCgW3/LhgPY3+lu+zNd/93V9jK8HCC4J4dRB8nlxEMREOAw6qAuIAkAwPUAXiCEzAdwKIA/AfgzgG8SQpYBONXYB4BxAFYCWA7gCQA/z7Ht/MD3xUkQ1UCA8wNLSh4tfLHsKOHnHWQyAcmgtvgtYNti77rl51BuMAGeV0XM1d8WwfrDcnu9hwLtTepI059+Kuy42HJ6Sr4Hl70BnP8k+y/O+jlDyPjMgsUPvXEb8LpBsOc+ZwU7iYT+hFuttNgAcOqdQmUSE6ioAj5/BjY8fIh3f3yhMgwLcQIkCQw4iB3vZ2TE/OIlnyolhlzZh22nPgK8cinwwD5MUpTfZfdqpnayzeSNMtzQDwB/2QuY/6p0D8T6lmQHB35PvG9h4XctnwApoYgT8HMR7ezeQQBAKf3C0N8fTCk9l1K6k1JaQyk9hVI6klJ6KqV0h1GWUkqvpZTuRSk9iFI6O5pbiBhBbQLmMdEwLOlGB4wCTriFucJ99a6jhmggege5qYMCqKLcUi1wdz4+6xc9nvKxgDtP1tVaC6V6gaPVZRHyI35oBf0ALLLz4AuZUVaUBPizCbJe7QN7M0Mux9gfMQIljp0DvmO/5rDLrP8yEVC5TwJMkskFqmAxnn+JEJYEDQB2N5jBrCeAJePgDanvlYaHz+K3WWTtuplwvAvuj2/m1xfeed8RwCl3WPuvXwU08DQaBtPhUlzkhmENAvzrlcBNHotFqYh40AllkRBHDMtQ+pt7lFd6XkjEtv8+wEm3A8OOcUkTkCP80kZw/X2QAC8zSlc2DCfYjKitkUVQPn++da5mWZjeS5Da6zNc8uIblAAAIABJREFUOKV4Eec8xrZchSAz7+79gcvfFA4YdZRW2u0a/J0p15EQoSAmP3jZ+v/HfsDS99j/yj72fPeAtx74az9h26Gj7cdnPO7TJw+omGtbE7BpHrBzDdsvMxh7ZR/reb/sldRNUef1c+z7tWucZfiz4OkVZIni+JtYcNYRP2T7n9wjXEwsf/3IDcMa6N7P33MqaLBYB2EOMROQETROwJxti+ogcTAI/6v3Yx40kWcZ5cRaUgfxfe7COe1R/Sq5+kV1z2U9mE1A9gbaNE+/fl3sdoAghiv6wj9MrkbRde3ljIyDM0jd6zkOOAfY9wzgqo+tY801TCV0zTQFU/FQAZz1ACOCl/8PuPhVa62KFR8DW33Udp6Q2hn1XQCUpa7Ipi31VWUVcNUEx9UOqOwz3foCP/sMuNRQiWXa1OogQND5KyYZAJOaAUE1ZnxD+xtS1YHnefctyPHIoFAHadsEissMYibgQMA4ASK9yIRkExA/mP4jmRE1Ei8aAXIqaZmRBVn4nudJaal1/3DKezICKurUq/dj0kMqR7uA/MEkS4CBh6rP8b4AwLIP3cuo6i/vxdwxObK6koCE7xiMdcgRwEn/Zx3v1o8FSrm1z3bUdZZWAvucBlwyFui+G9C4GXhstLqsLxTvMOmSdrmiiklOHJ6TFUXfdx9lqZRUZboZdYuGX9X7ku04/Buq3pcxyYEHe/QnhGE4ChCiaFpTEiiyRBAzARlBcwdxiJJAVg6aMc5V78+2m+fn0kMFZO8gqU9BsM8ZbCu6Zcoo78EM3CLBH2SkggqbPsJrptZnmPu5Ed+wEzXd97Z1EbPPcNUCf+8ttcDYK92lGrGfB55n5cYBgBN+A5z8O/bfVfUWwBhYWmExuVwgN3PoJUC/va19zvgqDW8rfk6V3A5AaP06ZzD1641qXOohBBh9rWSI1WTurl3TkQRykRbC2ARidVDHhBcT0FERqQzDvMygw4CynsyLpK2J+aVrZW3067OLd5A8yNxWexLBja1e6qBEKVtEXDRk7nsGMw6+eGGOrqKK9kZ8g21VKhFCgNGio5nfhyWd54yMux9+9hBTk8hLBaow6gJ1fzyb14w/4fjeM/5lgmK3/ew6fG7s52ohLg26GapV6iAOL48X7tI7/k4Wj+A2yeDXmkxChzjnQFAjm4mHtQnEkkDHglecgBKyd5BsGBYGekkZcNAFwKLXWSroSX/Rz9roCS4JGO1sNmax8uBWzbyO/LF9X2QCbt/eKKPPYmrd3kOs429dr3bj84THh37w91kmx1PvUJ/nRAvQVwftfzbbcuOw7MrLZ8VuOOMvwH5nqRrQa1/+7waebsIt3bEfdHTho68BDjwf2PdMts/tLO/+yuMindmtwqmAe/g8eACUCQzNS4n1LXoxHQeCBGjJlxY4bYRsTywSYiYgw2TeullEFYZhWRUgvuTDLs21h07wnPUVBgHnC5aI/S3tDscHkih1ejxw4uelDtrD0E8vecc61r3aMugtep15yWzxcKlzg5u0dcKv7b7kcp8PN+ITde0t+xlMIN0KrJ3OGLIIP4JQWRXAecBWwOW/C5KlzI9/+LH+ZbXaVGDgwcCFT1muojyb5vKPQjTl09ZNugZu2dCqqw4KYxiOgggrDMN+9ceSQAeFUh3k9ZKkF1m1BzOQNgpucCIGHW6PIhZdIL1QuxZ455dq9RGfUXGjHCfs4gdZ3tPFhU02xJYylZWXOqj/Pvb90//MJIEe1fblBBe5rRSlQK7eG6feyWazPDrXDzxHTnsLMPXvqg6prwu6fKHjdEBJgFcZ+vmEuG7wkf5ldPquKtOjWmIEUauDcnk/UXsQafYllgQ6GIK6iMrHBhzAtqb7pERoEwng59OBW9extL08KZYfxv0GmP0fF/WR0UZJGcvfzl0LRYNpeU8oXdhU91RZxQh4vUtqp7Ju9v3R11j1HHQBcOof2P/PHvG5qQjRrS+bzfbQTDrI9d/pVrtEwxGW2PvNSEPli/GaZepcHpDI9BkGHHyRflZPW1sak6deg4CvX+/TN+GeddRBvrfo1ecIiHAYdVAcJ1BkUApMesC+iDngzQSUkLg5X6CCu2VSRV3lPZhXSUVvaH/cMuEVIX4k4mAUI2p1JQGAqVQatzBpYOWn6jZ/Otm9P8fdyHTZummJzb4UAPw58TTOT57iUjBkf4J4lWkPMQWB0UXY6yp6eWS99THoakFDYrJJApr1Bl1eMsi1/o27NepzPJYEioOdq4GP/wi8IHt4UI8P2UsSMLY8zYHpm6/rAeEDT4IqtiHMoEQmUNELjqRhbjMsvhoU4J4PSOmrLWDQ4fZ8+brI2+xIqnewy+qmnLm5vpco/dB11UEJjXYjaEdE76GMCfB1BhxVahiGvd6lrz5ckn60bS1hnlNEkoAMXUkgVgcVCdwlUF48WpleWcMmwMuUVjJXTDFsXkfk9YMXE7BJArAGn7hiU3kvRVsuDOrH7wM/zDHHkVemyqLCuN+y7pbnjYgBo6D1XnxFfI33GmTWXOhnyWMFVNKApzoooLrL0zsoAEHPyTAckRQaeo3hmAkUCW6DRcEEOHTtBFV7COogH/2p7vgLIwkky60i3avVban6X9GLRbzmAhKQcOU7rF91nyXleuVE5GoYDlM2F3VQaLWWF1HV9LTRmTy5SsmG9COvjaHTbiTlgkIxcYglgQ4O1xmDsSasNhSDWWQCvuogTULpFTnqZhMQB2WyDC5cwKXSHAdmaEkgz+og8YPj6ioxwRwPugtLdIN80IH05wU0DJttwr3dXI2dOuodrxXuXBFGjZcnw3CxUlgExK7LBLxcAIPYBMxTEhOoW2+lY3a9LMDH7WtwVEgCHJe+5hykuRjQdBCYCRRREnCk2M6F6PqoJVRlfYsVwTAspyDRrlNXHeQzC/ZT74Qt7zmuI7a7aHsHxZJAcaBy5/r4HrbyVxB1kApVw1jO84bN8JYEFP3Q7a94zE0SoJSpgfY+FQ7C5jtAcxwaYSWBvIvGQv19RrCtnFDNk+j6EZkg3kEFkgTCEBmTqKreoaZ3kFazfpJoUHVQCJtAsdJGdJA4gRL/Il0U8uDevACYdD/7XxYgaZfSJmAEg/G1b6MwDKvKZTNGjnUXm4C5DwVh0zVahURQJlCQVL8STr8XGPI1YPhxirK5GoZD9smtzkLbBMKqg4Lck855XZdtbcnBo55Cp40gjj9Fwa4rCcgQ3RlzHeA8IRdfezcKF1EvA51NEhCJr+RiJye248fd+pYLOpxNgFcv1F/WHTj8Mue9er2XKJmVtv48RxfRMO+SX7J6CvDB/9nXxo7EO0hzZq/9vHMZN1GMOdXEIZYEOjbkwSUmEHO8FI2XJNZny+vvpz+NQB3kIPYK5uBoS1fMDgnH4joGMu3GQiaV0oki2ATcCyO0US8fkgCKYBPgfZvwB7ZdOw34CV84x0MdpO0d5Ne8rI7Kp2E4SJkQiCOGOyo8LPmBbAKKYyYToN7qIDdCqYSmJKCrDjIlAZfm8iUJPHEycM/uHtcV0Cbg1YdciadW0UKog4CcbAIczTu8z/sddy3ndl+yOkizvjCG4SjGnJc6yP0iaVsc7LpMQBU9yxGFUdRsIyIXUaUkIKp9VMReZgaFtAm43Jvbgjp5twkY0PrgvcroEpkIvYNyNgyHgdQ3cV3fXHMHiedccyzxtnQlgRwMw5EghDpIDPAsInZhJuDFtSMyetFs9Ibh8/4N7GvksfdT+7jGDwjX5s0mEIDB2S/Mrd3I6g1rGM6Dd1BRgsWk+8jKi7vnWx0kLZXqWz4IQ80DVO3rridQZC6w69oEHNF9YmBKkLQRivocAzhCw/A+32Kup1+9K7TpJgmIbedBErhhPlyJTGBjZqFmujmqg4oRMcwaDlBWbiYCdZAoCUSRkTOwOkiz3kJJlFpt60oCMRMoDhySgAcTMI9r6hPzYRgWB5Roc+DtmH0mimvgtD9EIQl4rf3b0eIEIjfYRiBBFUISiMq2IS6U5GnnEo/nog4Kahj2YyoazyFnu0tAF9Fi64EM7MLqIJlA5ckm4PfBBJUECHF+IF5qH9tx8Z7zbRPoaHECBnQIb6EMw9rqxVyT8UUtCWjWGVSCDlq/qq18R8IHqldTEogNw8WClySQq01A0zAcRhIgCaE+hTrIZhPwaKujRgx3CJuA13vRJDI6TKQgEcMRMTO+5KRWnQEYhK4k4PusfM5rPYaIPbB0v7Eiq4N2XSYQlTpIVZ+2JBDARVQUi+UPyDOBXEi9Y8GDxfIsCQSZdelIAr7MM0LvoFxdRKOwCciBhl51aj1rTaIeWZyAR7tREuFQUfkxEygSwkgCmgjkIhpCHeQpCYjH4fwgHe6jeVQH2drTva6DSwJRGoa1i+YgCUSm1pLr0SDwuaiDgkhUtvIB27EViYDR2uxu0jm3azq7JEAISRJC5hJC3jH2RxBCZhBClhNCXiGElBnHy4395cb54bm2nRO8XERzzSIqE8AoXUSVhmEXSUBlG5DP5c1F1MfFL3BgTUTQsgloVRS+/mAN5S4JhLIJeJGGCPTuuuqdwOqgMH2Lggir2s/zRCsiRCEJ3ABgsbB/H4AHKaV7A9gJ4Erj+JUAdhrHHzTKFQ+OYDFxX345YY1UWe8xSZCjYdjHJuA4DumceFzVuRzg8O6Q4EjfHFG7bgjK1DpSxHAxgsX8VJ+efQ+g5nC1CfBJBB8nPnUFlRyihpZ6zO14J5YECCFDAJwF4EljnwA4GcBYo8gzAM41/p9j7MM4f4pRvkgIIwl4DDDP3EERSwIyQadwsQkIRVXSA6AxQEPCTxIQXQ5Zwdza04bOfXm9lyi9T3QlgSDpRWTki5lpED0tRlEow7DOc4hgDKrcsF3RNdRBDwH4DQD+pfcDUEsp5V/4egCDjf+DAawDAON8nVHeBkLI1YSQ2YSQ2du2bcuxex6Q34+OYVgJlaGpUC6ift5BCmbgyDCab5uALhPg13UAm0AuhuF8SAIO996AiMIwbEMU3kGaBQIzv2IZhkOogzq7JEAI+TaArZTSORH2B5TSxymlR1JKj6yuro6yarkl9/1C5Q4KbROQJQE37yDehrjtoJJAR7IJ5GIYDqSWKIA6KF9qLU/1hypw0QVRBYuZwzukBJcrVN3zS9LYQZBLxPCxAL5DCDkTQAWAXgAeBlBFCCkxZvtDAGwwym8AMBTAekJICYDeAGpyaD83eNkE5AEe2L0yH5KAWLf8gchEX9b7w4M4FUsS6MA2Aa33kifm6VZnTjQsjCTgcc730USgDiqoYThCBHER7ezeQZTS2yilQyilwwFcBOBjSuklAD4BcIFR7AoAfBXvt4x9GOc/prSIiT6CxAkE1SfqJr8Kkl9H/Bh04wQcieWEfhZbEvCK2M4rcrUJRFE/L9qRDcN+3kE6BN6rSA6Shlf5xi3ASxcDUx4E2luC1RGJB5bCJuCrOiwuE8hH7qBbALxMCLkbwFwAY4zjYwA8RwhZDmAHGOMoIgIwAS+oXnCQ3EHaul4Pw7CrdxCs40o7gnDe0bV8MwEXSSDfsyJd98WwaoWO6CIa6pnmog7SIW6aarOgwWJv38C2X70L7FwNnP2wRjsRjDmlpN05JIFImACldCKAicb/lQCOUpRpBXBhFO1FAs84gY6oDhINw5pxAipmoCsJ5Pxh+HzkXus55AURE+dCGoYBFNwm4Oci6n2xfx2yXcvtfC7eQV+8BJz1IJAQJnU5GbwDtt9JJIE4Ytjc9YoT4GU0B4mNSEdsGLapgxSLysjeQSZzkGfmedad8vbcdP+FtgkErj9Hw3CUfSlGArlcjAKReL1oSgqONgHs8XXg7EeATAqoW2sc9Kgn0pl4CElAXNq2CNh1mYCnYTjEY1FG5+ZBEmAX8oPWOT/vIDfDcL4kAb6GcHuz+nxORC0EAtHmXAzDAXIHBXIRLbRNIBedfYCHHVWwmHh+j6OBngPZ/6bt+n3JBarvS1cSqOiVt27pYBdmAlG5iPrZBFzKmMeDSAIe+n2bW14AdZAbcp0ddTNCQJpdHMAKbhMIon+NwDDcUVxE82Hg9h07/lU4xqPjdFB1kICy7s7xp/M+ojYM60oC5cVlArvuojKBDMM52AT8xNAgkoCs2jEJehY2IkdV1wQ0DOcqCfCPsGWH+nyhcwdF5iIaIfMslCQQShvkpw7SkQR0bAI+dWjHCQjnE6VAj93Y/xe/x7bdd/NvKxeo7kdbEuide/s5IJYEVPuuL83rQ1RIEh/+n486KMjjl/X+sD4QhyuoSgIIaBiOTBJwYQLFsgloz9DDEvs82ASKESzmNza1jL4a8A0W0+2/yARKgN5DgMo+1rGmrTqd0WzLqwpVHS7PY9M8tk3V595uDtiFmUBENgHV+7Vd72MY1tWNU0Hlo5zVq2wCKjtFoSSBvmzrqg4qdJxAUEnA5VyUarSC2QQiNgxHkhMnoCQQxEaRKGH7N38FHHsjsNcpPm1FAS91kAuWf8S2mxfko0Pa2HWZQKgEcpoQr4/MMJyVZvviObkNBfF3C4DLlyRQUQWQZAeyCZgNa5TRmXl3Ekkg7HVhXDLNUxppI/xm+oElAQGJJNuWlAPf/ANwyavB6wiKMOqgS15j24qq/PRJE7suEwgSMRw2epFVDO+PPSLDsEoSUHkJ6SaQy5URJhJMGtCVBDqNTcDv2nx4B+XoIhp5sFgU0pAukwmpDgrSVpQTDz5u2pqB53gCZZf6dx/FttX7Rdd+CMSGYXPX4yPjg8qLMIjnEoLfr20GLyEywzBcbAIejCPvwWJgdgE3JuBqE8gzcvbaybNXlVudYZlSvoLFdFQ9Ws8iIu8gWR3kdi5EV/QgMa3Fb1mnbOszC+i5O3Dp68CQI3NpOGfEkoC578EELnuD6RZ77q44qRhgiQRwxTvWfjrlUnEQMV8hUaiCxWSC4fgANG0CURCzyj7uhuGObBMAUBDDsPYzzkUdxK/P8ZrKvmrJUnmpi9pSVcY3TkA3bUQuTCCCsS7fz5aF1jkv75+9T4m9g4oGrzgBGdX7Mt1iEMI44njg+JvZ/3SrukxoSUAxq1dJAsoAtgJKAsky91mQG9PtCDYBAvf3EtZUkEthL0N1viC+h90PZq6+tWuMA1E8hICGYd/qvNRBhYB0P807gZ6DgF8tAwYdWoT+6GPXZQLyQLapJyIiRL2HsG0kkgAvL251vIO8rhGPy01FwQRK3ReP6cg2gUIZhrX1/EU2DHN1Bfdi8VMHyZMVFSoNY2jShWDnEizGDcOBEcUY5JOsDLu3Hl7xCR0Duy4TkImOOFsPRQAVAyhZzrZus+EgywYq8wCJkoBZqb0vXtKDeNzZOb1+eSFRCmTa1eeKljtIAzkFi0nvRoUjjWW3ddUAOQeL5agO4oFWNtWehk3Aq8xBFwIn3AocfY13HWHUQUFz8UQx4ZG/r2waIGGZUWGx6zIB+WNONVr/KwO4bHkNoBKDCbhJAoQEmA2qjLw+NgGRcHCi++H/AWumoSCSQCKpLwkUyiaQ72AxnsPeKwDo238DflcDdO+v0RejLTeXWj9EYRg2o7938kp92tQY08lS4KTbgN6D1eejNAzrIspYjGy6SGqp4Nh1mYA4UCkF2hqs/ZKKaNrwYwJBxHyahdP/2scmIJbdsYptF74GPHW6lVgrX3ECgI86qMBxAjx4Taf+XGbebvYfGW5qEBWqhgMNm4CGLaG65Mrov/4LYNDh/teUdWNSLWcCfuqgxs1sq/ssvDDvZc2CuTCBCCUBjmwmZgIdHnK2v7Yma3/AqGja8FUHBTUMC9fxY+wPXL2DOI69AfjaVUDfvdj+xrl67eYCL3VQoW0Cl4wFzviLpo7Wgzn79fOAc4L2zB/7fIttp/wN2LnGu6wDHv097Y/A1Z+oz4lEjSSYp9eOFWIB/6ZzYQK9DAlh3kv67XEU0yZgqoMyOfSjsOjaTGD5BODVH7rMxKn9v7gU3VE/Cd6WijiYkoDbxxDE4CcSejlOwMM7iB/vOQA466/WR79pvlA+T0iUuEsChbYJVA0Fjr7a/fxeJzNvDkBTGnEpk0gCJ94OnPOPwF10xe4Hse2MfwEPHww8dJB90uKHnIPFCJvdL34bePQowUvIBWf8hW1zYezDjgGGHSd0IYA6qLR7+HZDQ5LOs+mYCRQd62YBz58PLHoDWPmp87wsCWQzLHz7zjqWilYbHoOTqyDccpqHdRFVRv+6SQJS/yp6A/1GAmunWeXzhWSJuyQw9RGpn4X2gZRw2RvAzYut/bCGYQA48RbgsEsj6RYA9o6+eZe1X7sWWDlR79ooEsiJY2T7V/AMgLSVz/GdVg1FqMh1NztDPuFwvIjVQcXFqknAmFOt/SVvO8vY1BE0vCGHL17B3UFV51x94gN4B/lF/7pGDCsw8GBBtC+SJLByIvDVOOfxvMcJ6EDHMFyQjlg49gZgwEHWfqBF1EN0lkiSwG9WBbhWw0NKB4mkvneQ2F/PtNEKHGykmx51QbDrvNCJvIM6B6sKiqGj2RJzVUOZPn7pB4pCkiRAQ+rwRn2Xza653lZE9/7A6X8GRp6mvpYk9L0+VJKAuJ6AmGGUitco6uozQuhDPplAKZCVJAExD07tWut4oVfO8kKuLpn5wjVTgNVTgKfPAl67EqhZDpx4a54aE20ChEm1F/8X+N81LBWI24pxvDyAnCUB8fIg32ZJWbB2+o9kGoBIINoEOgd57ZqSQEkZ8OP3gPMfZ4bQpm2KuADRTmCog8Jw7pIyYL8z3Qfp6GuAfnupzyVKAuTQUUkCinNu3kEi9jxRs80ckSwFMpIk0GcEsO9Z7L9yNtvBJYFiMwdxjE68V+OCCFxE+TvZ51tMbVa1B3DsLz2ujUgS2Pql9d+XoBZ53LQZLuarJ7NtbBjuQCitYDPPTDsj/HxgijMZSlmZQr80Lz96GcpgMcWiMo6IYQWq97X+51USKHF6RtGstf6wzWDegWbeWpJAkYiOOEar99e7JlfDsHj9wEOAGxcAQ47QuDbHdypO1PxsA9wJo1ioWc62b17PtrFhuAOhtBvb1q0D7t4N+Owhtt8uEKAPbmcDLtf0yUFBkvrqIJvNQv7IPCQBFQHoXm39l2fqUaKiF1MH2Wb8lD3nkgq1JNDhbQJFZlYiYdHxEIoiWCxwKnU+SQnXtAlxguQnCfQyPLuKlZuf3zOf9HQiw3Dn6GUu4LPO7UvZdvydwKDD7JLA7DHAbgcU/qUlkkBWM2I4nbJmOyYPUBiGiXRcBfGjzufSdnx5v5Za6z1kDYmrpMIuCRRbzSJCRxIoFrMS1UF1axkT9w08C9FXcWnGoNcHWVPBC+IESWdW/TsXL7xCgE+meD/jiOEOBJ4O4qWLrGPPngNs+sJerrW+8OJbEEkgkxIimQVJIJ1ihJyH9oMADRsNSUeUECTcsho4437gsMu82z3qp3r9U8FkAjutY5k2ZisorezgNoEOCnmMTrrf54KQhDhZyhwsgBCSQETPT7SX6RDUZGnwvEFRgTtApFuB6f9i6wfXritOXwKic7CqXODm879YchutX5//vshIJPUNw+kUS80MWDOt/3yL5XkHgB4D2JYnJZtg+JW7fZCVfYCjfQh8rh4TnGmJM/5MG7sPWRLoLCi2xCITw3Uz/K8JS5RNghpWHRSlJNDBSZUYD/P+LWzbScZ315cEjvghUG4QRpuI2wHgZRjOpIEnTwXe+gUw8c/A0vetGbW4HF2LkdlxsJED5pAfsG3DpuITLBUxyKYZEyitjA3DYcDVQXwsUwpsmONePpcxYE46gt6rHMwYEuIEqdD2uqCQgyL7jQR+pIiD6YDo4E82AiSSwCm/Y//FxHB31gH99ylOnzi81EHbvwLWzwI+f8ZyBawzxEtVltNuRkbK3UcxAtG9PzzVQYUAJ1jiPXJ1UEm53ThvXtMRVDGdwDBMEkwKXPUp8MTJ9iy4DoSVBLi/fVibQI4Yfrz1v6NLAmI8zNWfAtfPdlmJsOOhgz/ZiMAHpZwddMQJlsG4GJANw9ks8N6vmZHaK4md6iMTdaGUAjMfZ//7jYymr2GQkJcIhKAOquzAhmHY+9PWzJiWqI8vFrMy+0CAFmH95pWfAPufzfTR79/C8g01bGYxMmHBx1TQGX1UwWJnPwzMN7KIdnR3S1ESSAYMVisyQjMBQshQAM8CGAD2th+nlD5MCOkL4BUAwwGsBvA9SulOQggB8DCAMwE0A/ghpfTz3Lqv21kXJnD6vcxnftyvCtINB0gSSNUBs54EFr4BrJnCjpf3srxpfvwhsMfRwJ29LRWAigmIMyXR4FqzLD991wHvJxfrsxlGUJJlLH6jTmWH6WCSQDYDPLAPMPw44GLdtMZ5BBEkAREf/pYxgUlG8ja+ChhgT8QWBJyYueV/cu2jlNYkLEorrNQjHV4SENS6xTJOh0QuclsawM2U0gMAjAZwLSHkAAC3AphAKR0JYIKxDwBnABhp/K4G8M8c2g4GPovg4eRcDZQsZdGPxcKaz9j23ZstBgAwb59GI3c8H1C/Wg78gns0KQilbfYhfHyXvxlVb4NDVgdxH+pkKbBlEZPCvuT960CSwJZFzAaTamAMta0BWPoeO1dsicVUB0ljYOdqtt3/bPvxX68EfvRuuLa4s0GQtQ8AewqTXGEuKtPBJYFDL7b+d3SGJSE0E6CUbuIzeUppA4DFAAYDOAfAM0axZwCca/w/B8CzlGE6gCpCyMDQPQ8CcfZ02f+AHwofRTENTq219v3yXs4yfY08Pz2qLVuAjjoIYCl99zwx116GhxzZzJlAotTy2lrxsXRNB5AEONNa8q77WhBFk1jk/FFg+av+v73zj7GiuuL45yy/fwi7y89VWH5pa9BWIZsKxVoVRbQoNrEpBgHFxtZqotW2iib98YdJtUZbU1PEamNa/FXASoiNba0xsWlo0YqiiGxVFAKCJtpGjQV7+8fceTs7zHvs2/dm7p2Z80le3rw7s/O+7+wNql3dAAANgklEQVSde+ace+fesMGOptiGjIIRY+g3c74Fp6+C4xf1U2MTCOuy7+mgqBPIWTqoKS2giEwFZgGbgQnGmL121z6CdBEEDiI6cHa3LYuf6woR2SIiWw4caCCf2eukkZ8544zYwiIOG534HcMNu2DKvN5lSSOakhrKXucy1Y/LkvDCDafSDtMKAwb1RCiHwicsPYoEzr8reD/0ScJaFI51Ji23OHBoT8ot+hRxoyvktXYGE9TVvWZvfAW8RsiJE4hSonQQACIyElgPXGuM6fX4qTHGUOdVY4xZY4zpMsZ0jRs37sh/0BfCCpTU0LhsKA/F7jJbWvpW2ZM0R8t8uXsKI7D1dmH1cJKtwSOCBmbi52Drg0F/x71nhH+UuczD+MzC4H3Pc7A6kk+PLrTu7Ilhe8lGF6k/+DF89C7cOx+2b+wpb3SIZn9pZjooJE8pljxppcHRQSIyiMABrDXGbLDF74hIhzFmr0337Lfle4DJkT+fZMvSp1bKJ6yobdPgm89kIqfCpwkrnjWlAoWRgGsnELH7j0ZD18pgO0x7DRl9+N9Um3E1S8L/wfMP9C6/LTIFt6sGdlQHLLgFZl4QrDAGPTNX7tnS+1hnTiAhZdXwOfMUCZQkHWRH+9wHbDfG3BHZtRFYYbdXAI9HypdLwBzgg0jaKF1q5v1tRR3W2vvuKguSKnYz+ijCi991JNAS+y1b7g/ew0n9BsXSFZ1zg7ndXdMXu7U7dFZfvLr3gIZoPbpkA5x3u/3gKHVV9kggZ+mgRiw7D1gGvCQi4bCVm4CfAI+KyOXALsAu28MTBMNDuwmGiF7WwHfXR62L2mXH8LLH4Jlb4eUNRz42j1SzbUtsyG7nXDjte3Ds/Gx0HYlqDc4pV8KkrmAEjuupi6NE6/ex8+FVG2G6igSSOq8bJX5D4TMtJXECxphnqf5fPuxqtv0DV/X3+xqiL+kgF4w/PniaOS0n4LqzNSnSaRkI023+P2xI26b54wCguhOYMhdmLs5WS1+I3+RU+sCylwKkc2OVp0ggTw6LMkwbAbXzia7nJEnz+53dCVqSIrDZy3scbxgJ+HbRVGtwfM1Lx3VVnIDrjuFmntNT2xcAz66+lKiZ43U9GiXN73cdCSRUr+isrpUJyjy7wKvVF9d9LNWI6xLXTuCwjcbJUySQM8rhBHxNB0GxI4Gkxn3gsMh2GAl41rhWqxO+NUQj7PMu1SKBQnUMe1ZHCkTJnEDScwJFTge5jgQSGoGBkeFzYVTg+n/QV3zTedVmuGZrQp+AdVbO/v9pdAyrE0gLz25tUsLndFCjd0tfewCGtyfvcx0JRAmnzY6OoQ5XQ/vkP2405Z3h7cErHgk4TwelEAn4ljJMYuUfc7OQTJRyOIGa6SDPIoF6795OuLD6PteRQPT7WwbAp1WcQHT5SaV+4h3rvqSDytYn0HmKawX9wrP4NiVqjg5yHQkUuE8g6fujTmDIyOA9ca1hpc94NzpIer83A00HpUY5nECf0kGO54JJBdeTskW+P2yQok4gZw/VuLdnH/ElHVS2SCCnlMMJ9Gnha9edaCFN1OE6EoheuKHto0/aur5jLQyxOlNrwsRMSOGGynXatsCUw7LhXOvt093qSCLNdJTrxjVpMrjovCqV5Qtzcoftq8x51wbv4ZDRivMt0BBR12nbAlOOGKttCixdF0xd7BtF7hOAoPP3o/eoNEjRdJDrtEVROOFCGPNXGHV08Nn1SJo00kGNMmM+vPU31yq8pBxOAOC4s10rSCZ+h9PMu+LPL2neufpNuN5s2CeQ53SQr6EAMPHEnm3X03Ck0THcKEvX9awYp/SiPE7giBSwY/ioCUc+Jm3iDUE0HRRfftJ38pK2ct2JGj4J3oxRX9LSnPrR0kJZst/1ok7ANYXv8KrhBCr7ctC4zloWLE2aB1yng0aMDd4/fr/2cX3h6i2wf3vj51Gqok7ANbWcwPWvZacjK6INVJ4igcW/cK2g77geUx8+BDiqo/FzjZn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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "-zm1gjbsZ2Gk"
      },
      "outputs": [],
      "source": [
        "ypred_gan_test= pd.DataFrame(ypred_gan_test)\n",
        "ypred_gan_test.to_csv('res_cGAN_median.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred_gan_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "E1O4YiyhVEBG",
        "outputId": "34f4f24e-73a0-4ba1-e460-b8bb89065a2a"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             0\n",
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              "1368  0.028408\n",
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              "1370  0.027452\n",
              "1371  0.035987\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5f47eed5-b3e7-4c23-97cd-e7a7da800ae6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5f47eed5-b3e7-4c23-97cd-e7a7da800ae6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "ypred_mean_gan_test, ypred_median_gan_test, ypred_gan_sample_test = cgan.sample(X_test_scaled, \n",
        "                                                                                exp_config.training.n_samples)"
      ],
      "metadata": {
        "id": "O7iAa2MkTDcf"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ypred_median_gan_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4VldIJMNU9JN",
        "outputId": "5ed22c8b-7438-4171-ac8b-4a89c83be1e7"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.21134269],\n",
              "       [0.20453255],\n",
              "       [0.18932298],\n",
              "       ...,\n",
              "       [0.03362945],\n",
              "       [0.02874486],\n",
              "       [0.02560688]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred_mean_gan_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nKuuyh4GU6Fn",
        "outputId": "1802456c-bf51-4520-d16e-325176e8e7db"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[304.85995],\n",
              "       [294.06866],\n",
              "       [285.99622],\n",
              "       ...,\n",
              "       [684.03876],\n",
              "       [681.7071 ],\n",
              "       [674.5199 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred_mean_gan_train, ypred_median_gan_train, ypred_gan_sample_train = cgan.sample(X_train_scaled, \n",
        "                                                                                   exp_config.training.n_samples)"
      ],
      "metadata": {
        "id": "3uAGpMuBTlIU"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ypred_mean_gan_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EtpXO4JRU4HE",
        "outputId": "fb348f91-5af8-4207-f4f4-9f27086a3be8"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[506.04437],\n",
              "       [509.3043 ],\n",
              "       [502.9573 ],\n",
              "       ...,\n",
              "       [837.35443],\n",
              "       [831.73334],\n",
              "       [832.21405]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Untitled",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1P6WDC06MppUNJ4DRJP6rfITpFxHuF-y7",
      "authorship_tag": "ABX9TyMDCBXmKi3X+b7DS0Mq8SDK",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}