how_to_bilstm_classification_malay_10fold.ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of lstm_classification_malay_10fold.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ny02hYzdvSpv",
        "outputId": "0c30e965-f889-4394-fb30-89eb68fb76fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# !pip uninstall tensorflow\n",
        "# !pip install tensorflow==2.1.0\n",
        "# !pip install --upgrade gensim --user\n",
        "# tf.compat.v1.disable_v2_behavior()\n",
        "# !pip install -q pyyaml h5py  # Required to save models in HDF5 format\n",
        "# !pip install np_utils\n",
        "# !pip install --upgrade --user keras\n",
        "# import nltk\n",
        "# nltk.download('punkt')\n",
        "# import tensorflow as tf\n",
        "# print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMVyz3b-QEwr",
        "outputId": "1e1cdd16-9356-4f50-a186-2062d7110029",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHd8fAfjr3P5"
      },
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "import os\n",
        "import glob\n",
        "import pickle\n",
        "#from os import path\n",
        "# import malaya"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xw6oEX0hRIOw",
        "outputId": "0266f008-e66f-4691-871f-4e4f268ebef3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "df_allnews_unpickled = pd.read_pickle(\"./malaya_fake_news_preprocessed_dataframe.pkl\")        #Load FakeNews Dataset from your path\n",
        "df_allnews_unpickled"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>news</th>\n",
              "      <th>tokens</th>\n",
              "      <th>rejoined</th>\n",
              "      <th>length</th>\n",
              "      <th>label</th>\n",
              "      <th>real</th>\n",
              "      <th>fake</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>demi lovato telah jujur kejam tentang pertempu...</td>\n",
              "      <td>[demi, lovato, telah, jujur, kejam, tentang, p...</td>\n",
              "      <td>demi lovato telah jujur kejam tentang pertempu...</td>\n",
              "      <td>448</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>selamat pagi inilah yang perlu anda ketahui se...</td>\n",
              "      <td>[selamat, pagi, inilah, yang, perlu, anda, ket...</td>\n",
              "      <td>selamat pagi inilah yang perlu anda ketahui se...</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tahniah untuk bintang glee lea michele pelakon...</td>\n",
              "      <td>[tahniah, untuk, bintang, glee, lea, michele, ...</td>\n",
              "      <td>tahniah untuk bintang glee lea michele pelakon...</td>\n",
              "      <td>247</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ia adalah satu kejutan yang mengejutkan untuk ...</td>\n",
              "      <td>[ia, adalah, satu, kejutan, yang, mengejutkan,...</td>\n",
              "      <td>ia adalah satu kejutan yang mengejutkan untuk ...</td>\n",
              "      <td>718</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>transkrip minggu ini dengan george stephanopou...</td>\n",
              "      <td>[transkrip, minggu, ini, dengan, george, steph...</td>\n",
              "      <td>transkrip minggu ini dengan george stephanopou...</td>\n",
              "      <td>668</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37587</th>\n",
              "      <td>katie holmes tidak mengandung anak jamie foxx ...</td>\n",
              "      <td>[katie, holmes, tidak, mengandung, anak, jamie...</td>\n",
              "      <td>katie holmes tidak mengandung anak jamie foxx ...</td>\n",
              "      <td>336</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37588</th>\n",
              "      <td>donald trump menegaskan bahawa serangan siber ...</td>\n",
              "      <td>[donald, trump, menegaskan, bahawa, serangan, ...</td>\n",
              "      <td>donald trump menegaskan bahawa serangan siber ...</td>\n",
              "      <td>248</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37589</th>\n",
              "      <td>ia bukan cerita pertama yang bermula dengan pe...</td>\n",
              "      <td>[ia, bukan, cerita, pertama, yang, bermula, de...</td>\n",
              "      <td>ia bukan cerita pertama yang bermula dengan pe...</td>\n",
              "      <td>663</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>37590</th>\n",
              "      <td>presiden donald trump mengumumkan bahawa dia t...</td>\n",
              "      <td>[presiden, donald, trump, mengumumkan, bahawa,...</td>\n",
              "      <td>presiden donald trump mengumumkan bahawa dia t...</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37591</th>\n",
              "      <td>jika hillary clinton dipilih sebagai presiden ...</td>\n",
              "      <td>[jika, hillary, clinton, dipilih, sebagai, pre...</td>\n",
              "      <td>jika hillary clinton dipilih sebagai presiden ...</td>\n",
              "      <td>688</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>37592 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    news  ... fake\n",
              "0      demi lovato telah jujur kejam tentang pertempu...  ...    0\n",
              "1      selamat pagi inilah yang perlu anda ketahui se...  ...    0\n",
              "2      tahniah untuk bintang glee lea michele pelakon...  ...    0\n",
              "3      ia adalah satu kejutan yang mengejutkan untuk ...  ...    0\n",
              "4      transkrip minggu ini dengan george stephanopou...  ...    0\n",
              "...                                                  ...  ...  ...\n",
              "37587  katie holmes tidak mengandung anak jamie foxx ...  ...    1\n",
              "37588  donald trump menegaskan bahawa serangan siber ...  ...    0\n",
              "37589  ia bukan cerita pertama yang bermula dengan pe...  ...    0\n",
              "37590  presiden donald trump mengumumkan bahawa dia t...  ...    0\n",
              "37591  jika hillary clinton dipilih sebagai presiden ...  ...    1\n",
              "\n",
              "[37592 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lx1TuO9GcMeH"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
        "\n",
        "import tensorflow as tf\n",
        "from __future__ import division, print_function\n",
        "import gensim\n",
        "from gensim import models\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.layers import Dense, Dropout, Reshape, Flatten, concatenate, Input, Conv1D, GlobalMaxPooling1D, Embedding, Bidirectional, LSTM\n",
        "# from keras.layers.recurrent import LSTM\n",
        "# from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.models import Model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import KFold\n",
        "import numpy as np\n",
        "\n",
        "# demonstration of calculating metrics for a neural network model using sklearn\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import average_precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vy2li9HNry5N"
      },
      "source": [
        "Build vocabulary for all the words inside the dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4aWDcBP3YJ_U",
        "outputId": "e1a8b55e-926b-4ef8-d206-57946aa4a7c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "all_words = [word for tokens in df_allnews_unpickled[\"tokens\"] for word in tokens]\n",
        "all_sentence_lengths = [len(tokens) for tokens in df_allnews_unpickled[\"tokens\"]]\n",
        "ave_sentence_lengths = sum(all_sentence_lengths)/len(all_sentence_lengths)\n",
        "ALL_VOCAB = sorted(list(set(all_words)))\n",
        "\n",
        "print(\"%s words total, with a vocabulary size of %s\" % (len(all_words), len(ALL_VOCAB)))\n",
        "print(\"Max sentence length is %s\" % max(all_sentence_lengths))\n",
        "print(\"Average sentence length is %s\" % ave_sentence_lengths)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "16195904 words total, with a vocabulary size of 186872\n",
            "Max sentence length is 991\n",
            "Average sentence length is 430.8337944243456\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AZ_2wHrDr6nV"
      },
      "source": [
        "Load Word2Vec Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7orZb4jw9NF"
      },
      "source": [
        "from gensim.models.callbacks import CallbackAny2Vec\n",
        "\n",
        "loss_list = list()\n",
        "\n",
        "# # This function is only redefined because the Malay Word2Vec is trained with this function\n",
        "# # For some reason, gensim could not Word2Vec without this function\n",
        "# # Not used in this Notebook\n",
        "class LossLogger(CallbackAny2Vec):\n",
        "    '''Callback to print loss after each epoch.'''\n",
        "\n",
        "    def __init__(self):\n",
        "        self.epoch = 0\n",
        "        self.loss_to_be_subbed = 0\n",
        "\n",
        "    def on_epoch_end(self, model):\n",
        "        loss = model.get_latest_training_loss()\n",
        "        loss_now = loss - self.loss_to_be_subbed\n",
        "        loss_list.append(loss_now)\n",
        "        self.loss_to_be_subbed = loss\n",
        "        print('Loss after epoch {}: {}'.format(self.epoch, loss_now))\n",
        "        self.epoch += 1"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZ32wAfYoZMd",
        "outputId": "d01d1ce7-49c8-4637-fa3a-d82107bae190",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "word2vec_path = './mswiki.model'\n",
        "# word2vec = models.KeyedVectors.load_word2vec_format(word2vec_path, binary=True)\n",
        "# word2vec = gensim.models.KeyedVectors.load(word2vec_path)\n",
        "word2vec = gensim.models.Word2Vec.load(word2vec_path)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:252: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nRf5GwszQPlE"
      },
      "source": [
        "Convert list (of words) into sequences (of int) based on the vocabulary list.\n",
        "<br> Add reserved indices at the beginning of the converted sequence."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3TFcinToxnRC",
        "outputId": "4deaa1ac-c905-42a4-e642-79a40979107b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "tokenizer = Tokenizer(num_words=len(ALL_VOCAB), lower=True, char_level=False, oov_token='<UNK>')\n",
        "tokenizer.fit_on_texts(df_allnews_unpickled[\"tokens\"].tolist())\n",
        "sequences_all = tokenizer.texts_to_sequences(df_allnews_unpickled[\"tokens\"].tolist())\n",
        "\n",
        "for i in range(len(sequences_all)):\n",
        "  for j in range(len(sequences_all[i])):\n",
        "    sequences_all[i][j] += 2         # # The first 4 [0,1,2,3] indices are reserved\n",
        "sequences_all = [[1] + sub for sub in sequences_all]    #at <START> tag at the beginning of all subitems\n",
        "\n",
        "all_word_index = tokenizer.word_index\n",
        "print('Found %s unique tokens.' % len(all_word_index))\n",
        "\n",
        "\n",
        "# # The first 4 [0,1,2,3] indices are reserved\n",
        "# all_word_index = {k:(v+2) for k,v in all_word_index.items()}\n",
        "for key, value in all_word_index.items():\n",
        "  all_word_index[key] += 2\n",
        "all_word_index[\"<PAD>\"] = 0\n",
        "all_word_index[\"<START>\"] = 1\n",
        "all_word_index[\"<UNUSED>\"] = 2\n",
        "all_word_index[\"<UNK>\"] = 3  # unknown"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 186873 unique tokens.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3l1eZccfjp6c",
        "outputId": "e7509607-ef1e-4b49-bfc6-6826055cd7b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "all_word_index = tokenizer.word_index\n",
        "print('Found %s unique tokens.' % len(all_word_index))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 186876 unique tokens.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WsP7P6iuQyHX"
      },
      "source": [
        "Define function(s) to decode sequences back to text"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DjIlIOaOQxGs"
      },
      "source": [
        "reverse_word_index = dict([(value, key) for (key, value) in all_word_index.items()])\n",
        "def decode_news(text):\n",
        "    return ' '.join([reverse_word_index.get(i, '?') for i in text])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3VzRBgKqUsAM",
        "outputId": "71e0ddcb-0651-4838-d7c2-f1909faf8dde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# MAX_SEQUENCE_LENGTH = 50\n",
        "MAX_SEQUENCE_LENGTH = max(all_sentence_lengths)\n",
        "EMBEDDING_DIM = 300\n",
        "print (MAX_SEQUENCE_LENGTH)\n",
        "print (EMBEDDING_DIM)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "991\n",
            "300\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "volPAhlliQp1"
      },
      "source": [
        "Pad the sequence by MAX_SEQUENCE_LENGTH"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UH6fn8FVb9h"
      },
      "source": [
        "# train_lstm_data = pad_sequences(training_sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
        "all_data = pad_sequences(sequences_all, value=all_word_index[\"<PAD>\"], padding='post', maxlen=MAX_SEQUENCE_LENGTH)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZnLU6PSJjDq1"
      },
      "source": [
        "Initialize Embedding Layer weights based on Word2Vec values.\n",
        "<br> The embedding will be initialized randomly for words that does not exist in Word2Vec model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_lK-4yF_awkF",
        "outputId": "ac16bf8f-abc6-4556-b962-ec90ad79e09a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "count_vpre = 0\n",
        "\n",
        "embedding_weights = np.zeros((len(all_word_index), EMBEDDING_DIM))\n",
        "for word,index in all_word_index.items():\n",
        "  # embedding_weights[index,:] = word2vec[word] if word in word2vec else np.random.rand(EMBEDDING_DIM)\n",
        "  if word in word2vec:\n",
        "    embedding_weights[index,:] = word2vec[word]\n",
        "    count_vpre += 1\n",
        "  else:\n",
        "    embedding_weights[index,:] = np.random.RandomState(3435).rand(EMBEDDING_DIM)\n",
        "\n",
        "\n",
        "print(embedding_weights.shape)\n",
        "print(\"Number of words present in the set of pre-trained word vectors %s\" % count_vpre)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(186876, 300)\n",
            "Number of words present in the set of pre-trained word vectors 60562\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NAUyOMwakcae"
      },
      "source": [
        "idx = 8\n",
        "\n",
        "for key, value in all_word_index.items():\n",
        "  if value == idx:\n",
        "    print(key, value)\n",
        "\n",
        "print(embedding_weights[idx])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsuDvKk6ooAx"
      },
      "source": [
        "label_names = ['real', 'fake']\n",
        "labels_list = df_allnews_unpickled[label_names].values\n",
        "# print (labels_list[42])\n",
        "# print (sequences_all[42])\n",
        "# # Zip back the news and labels into a temporary list\n",
        "all_newsnlabels = list(zip(all_data, labels_list))\n",
        "# print (all_newsnlabels[42][0])\n",
        "# print (all_newsnlabels[42][1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CBUPN9CJT9-H"
      },
      "source": [
        "Define LSTM Architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSmaNykpSG6G"
      },
      "source": [
        "def BiLSTM(embeddings, max_sequence_length, num_words, embedding_dim, labels_index):\n",
        "    \n",
        "    embedding_layer = Embedding(num_words,\n",
        "                            embedding_dim,\n",
        "                            weights=[embeddings],\n",
        "                            input_length=max_sequence_length,\n",
        "                            trainable=False)\n",
        "    \n",
        "    sequence_input = Input(shape=(max_sequence_length,), dtype='int32')\n",
        "    embedded_sequences = embedding_layer(sequence_input)\n",
        "\n",
        "    \n",
        "    # TODO start\n",
        "    l_bilstm = Bidirectional(LSTM(embedding_dim))(embedded_sequences)\n",
        "\n",
        "    x3 = Dropout(0.3)(l_bilstm)  \n",
        "    # x2 = Dense(embedding_dim, activation='relu')(x1)    \n",
        "    # x3 = Dropout(0.2)(x2)\n",
        "    preds = Dense(labels_index, activation='softmax')(x3)\n",
        "    # TODO end\n",
        "\n",
        "    model = Model(sequence_input, preds)\n",
        "    # model = Sequential(sequence_input, preds)\n",
        "    \n",
        "    \n",
        "    model.compile(loss='binary_crossentropy',\n",
        "                  optimizer='adam',\n",
        "                  metrics=['accuracy'])\n",
        "    model.summary()\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AokXV_O313UT",
        "outputId": "91094253-28c1-4720-e401-95cfbde4c862",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "model = BiLSTM(embedding_weights, MAX_SEQUENCE_LENGTH, len(all_word_index), \n",
        "                EMBEDDING_DIM, len(list(label_names)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 2546)]            0         \n",
            "_________________________________________________________________\n",
            "embedding (Embedding)        (None, 2546, 300)         58192500  \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (None, 600)               1442400   \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 600)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 2)                 1202      \n",
            "=================================================================\n",
            "Total params: 59,636,102\n",
            "Trainable params: 1,443,602\n",
            "Non-trainable params: 58,192,500\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wc35nr9dxk4J"
      },
      "source": [
        "# model.save_weights('./lstm/lstm_model_10fold.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyWNU5_Ou8ga"
      },
      "source": [
        "model.load_weights('./lstm/lstm_model_10fold.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kI0jPjMvkwz"
      },
      "source": [
        "weights = []\n",
        "for layer in model.layers:\n",
        "    weights.append(layer.get_weights()) # list of numpy arrays\n",
        "\n",
        "print ((weights[2][0]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jFEdktxoSD23"
      },
      "source": [
        "K-fold Cross Validation stars here"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LD3nbtcKh6qb"
      },
      "source": [
        "num_folds = 10\n",
        "num_epochs = 2    #Changed to 2 from 3 due to view of overfitting in 5fold\n",
        "batch_size = 50\n",
        "fold_no = 1\n",
        "kfold = KFold(n_splits=num_folds, shuffle=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcPpQlkgykjn"
      },
      "source": [
        "# data_train, data_test = train_test_split(all_newsnlabels, test_size=0.10, random_state=42)\n",
        "inputs = all_data\n",
        "targets = labels_list\n",
        "print (len(inputs))\n",
        "print (len(targets))\n",
        "\n",
        "# x_train = np.asarray([row[0] for row in data_train])\n",
        "# y_train = np.asarray([row[1] for row in data_train])\n",
        "# x_test = np.asarray([row[0] for row in data_test])\n",
        "# y_test = np.asarray([row[1] for row in data_test])\n",
        "# print (x_train[42])\n",
        "# print (y_train[42])\n",
        "# print (inputs[39505])\n",
        "# print (targets[39505])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qBCrlwUudN-S",
        "outputId": "a6deca91-a4ef-4ab0-d693-41a322faaf64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "fold_no = 1\n",
        "results_history = []\n",
        "\n",
        "acc_per_fold = []\n",
        "loss_per_fold = []\n",
        "\n",
        "# # Lists for metrics result\n",
        "all_accuracy = []\n",
        "all_precision = []\n",
        "all_recall = []\n",
        "all_f1 = []\n",
        "\n",
        "# # Lists for plotting Precision-Recall Curve\n",
        "all_arr_precision = []\n",
        "all_arr_recall = []\n",
        "all_thresholds = []\n",
        "all_ave_precision = []\n",
        "\n",
        "# # Lists for Confusion Matrices\n",
        "all_cf_matrices = []\n",
        "\n",
        "for train, test in kfold.split(inputs, targets):\n",
        "  model.load_weights('./lstm/lstm_model_10fold.h5')     #Load the same initial weights for all splits\n",
        "  x_train = np.asarray(inputs[train])\n",
        "  y_train = np.asarray(targets[train])\n",
        "  x_test = np.asarray(inputs[test])\n",
        "  y_test = np.asarray(targets[test])\n",
        "  \n",
        "  # # Generate a print\n",
        "  print('------------------------------------------------------------------------')\n",
        "  print(f'Training for fold {fold_no} ...')\n",
        "  \n",
        "  checkpoint_path = \"./lstm/lstm_training_10fold/cp_10fold_{}.ckpt\".format(fold_no)\n",
        "  # Create a callback that saves the model's weights\n",
        "  cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
        "                                                 save_weights_only=True,\n",
        "                                                 verbose=1)\n",
        "  \n",
        "  hist = model.fit(x_train, y_train, epochs=num_epochs, validation_split=0.1, \n",
        "                 shuffle=True, batch_size=batch_size, callbacks=None)    #callbacks=[cp_callback]\n",
        "  \n",
        "  # results_history.append(hist)\n",
        "  results_history.append(hist.history)\n",
        "\n",
        "  #EVALUATE MODEL ON TEST SET\n",
        "  results_scores = model.evaluate(x_test, y_test)\n",
        "  print(f'Score for fold {fold_no}: {model.metrics_names[0]} of {results_scores[0]}; {model.metrics_names[1]} of {results_scores[1]*100}%')\n",
        "  acc_per_fold.append(results_scores[1] * 100)\n",
        "  loss_per_fold.append(results_scores[0])\n",
        "\n",
        "\n",
        "  # METRICS CALCULATION STARTS HERE\n",
        "  # predict probabilities for test set\n",
        "  yhat_probs = model.predict(x_test, verbose=0)\n",
        "  # y_probs = np.max(yhat_probs, axis=1)  #Predicted Probabilities\n",
        "  y_probs = yhat_probs[:, 0] #Predicted Probabilities     #Consider only positive-predicted class for Precision Recall Curve\n",
        "  \n",
        "  # predict crisp classes for test set\n",
        "  ## supposed use argmax but index of labels are arranged as [real, fake] = [0, 1]\n",
        "  ## argmin is used to invert and get [1, 0] instead\n",
        "  yhat_classes = yhat_probs.argmin(axis=-1)   #Predicted Classes\n",
        "  y_test_conv = y_test.argmin(axis=-1)        #Actual Classes\n",
        "  \n",
        "  ## Metrics calculation\n",
        "  # accuracy: (tp + tn) / (p + n)\n",
        "  accuracy = accuracy_score(y_test_conv, yhat_classes)\n",
        "  all_accuracy.append(accuracy)\n",
        "  # precision tp / (tp + fp)\n",
        "  precision = precision_score(y_test_conv, yhat_classes)\n",
        "  all_precision.append(precision)\n",
        "  # recall: tp / (tp + fn)\n",
        "  recall = recall_score(y_test_conv, yhat_classes)\n",
        "  all_recall.append(recall)\n",
        "  # f1: 2 tp / (2 tp + fp + fn)\n",
        "  f1 = f1_score(y_test_conv, yhat_classes)\n",
        "  all_f1.append(f1)\n",
        "\n",
        "  ## Precision-Curve calculation\n",
        "  arr_precision, arr_recall, thresholds = precision_recall_curve(y_test_conv, y_probs)\n",
        "  thresholds = np.concatenate([thresholds,[thresholds[thresholds.size-1]]])\n",
        "  all_arr_precision.append(arr_precision)\n",
        "  all_arr_recall.append(arr_recall)\n",
        "  all_thresholds.append(thresholds)\n",
        "\n",
        "  ave_precision = average_precision_score(y_test_conv, y_probs)\n",
        "  all_ave_precision.append(ave_precision)\n",
        "\n",
        "  ## Confusion Matrix calculation\n",
        "  cf_matrix = confusion_matrix(y_test_conv, yhat_classes)\n",
        "  all_cf_matrices.append(cf_matrix)\n",
        "\n",
        "  # Increase fold number\n",
        "  fold_no = fold_no + 1\n",
        "####################################\n",
        "\n",
        "# == Provide average scores ==\n",
        "print('------------------------------------------------------------------------')\n",
        "print('Score per fold')\n",
        "for i in range(0, len(acc_per_fold)):\n",
        "  print('------------------------------------------------------------------------')\n",
        "  print(f'> Fold {i+1} - Loss: {loss_per_fold[i]} - Accuracy: {acc_per_fold[i]}%')\n",
        "print('------------------------------------------------------------------------')\n",
        "print('Average scores for all folds:')\n",
        "print(f'> Accuracy: {np.mean(acc_per_fold)} (+- {np.std(acc_per_fold)})')\n",
        "print(f'> Loss: {np.mean(loss_per_fold)}')\n",
        "print('------------------------------------------------------------------------')\n",
        "\n",
        "\n",
        "\n",
        "# # Save history object as a file\n",
        "with open('./lstm/LSTMTrainHistoryDict_10fold', 'wb') as file_pi:\n",
        "    pickle.dump(results_history, file_pi)\n",
        "    # pickle.dump(hist.history, file_pi)\n",
        "# hist = pickle.load(open('./lstm/LSTMTrainHistoryDict_10fold', \"rb\"))\n",
        "\n",
        "# # Create Dataframe to save all Metrics Result\n",
        "df_allmetrics_10fold = pd.DataFrame(acc_per_fold)\n",
        "df_allmetrics_10fold.columns = ['acc_per_fold']\n",
        "df_allmetrics_10fold['loss_per_fold'] = loss_per_fold\n",
        "\n",
        "df_allmetrics_10fold['all_accuracy'] = all_accuracy\n",
        "df_allmetrics_10fold['all_precision'] = all_precision\n",
        "df_allmetrics_10fold['all_recall'] = all_recall\n",
        "df_allmetrics_10fold['all_f1'] = all_f1\n",
        "\n",
        "df_allmetrics_10fold['all_arr_precision'] = all_arr_precision\n",
        "df_allmetrics_10fold['all_arr_recall'] = all_arr_recall\n",
        "df_allmetrics_10fold['all_thresholds'] = all_thresholds\n",
        "df_allmetrics_10fold['all_ave_precision'] = all_ave_precision\n",
        "\n",
        "df_allmetrics_10fold['all_cf_matrices'] = all_cf_matrices\n",
        "\n",
        "df_allmetrics_10fold.to_pickle(\"./lstm/lstm_all_metrics_10fold.pkl\")\n",
        "# df_allmetrics_10fold"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for fold 1 ...\n",
            "Train on 32003 samples, validate on 3556 samples\n",
            "Epoch 1/2\n",
            "32003/32003 [==============================] - 412s 13ms/sample - loss: 0.4294 - accuracy: 0.8080 - val_loss: 0.3446 - val_accuracy: 0.8569\n",
            "Epoch 2/2\n",
            "32003/32003 [==============================] - 408s 13ms/sample - loss: 0.3030 - accuracy: 0.8761 - val_loss: 0.3063 - val_accuracy: 0.8798\n",
            "3952/3952 [==============================] - 21s 5ms/sample - loss: 0.3063 - accuracy: 0.8793\n",
            "Score for fold 1: loss of 0.3063152463209291; accuracy of 87.93016076087952%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 2 ...\n",
            "Train on 32004 samples, validate on 3556 samples\n",
            "Epoch 1/2\n",
            "32004/32004 [==============================] - 409s 13ms/sample - loss: 0.4329 - accuracy: 0.8044 - val_loss: 0.3519 - val_accuracy: 0.8564\n",
            "Epoch 2/2\n",
            "32004/32004 [==============================] - 409s 13ms/sample - loss: 0.3064 - accuracy: 0.8766 - val_loss: 0.3256 - val_accuracy: 0.8683\n",
            "3951/3951 [==============================] - 21s 5ms/sample - loss: 0.3287 - accuracy: 0.8703\n",
            "Score for fold 2: loss of 0.32867315858489377; accuracy of 87.02859878540039%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 3 ...\n",
            "Train on 32004 samples, validate on 3556 samples\n",
            "Epoch 1/2\n",
            "32004/32004 [==============================] - 405s 13ms/sample - loss: 0.4283 - accuracy: 0.8076 - val_loss: 0.3409 - val_accuracy: 0.8597\n",
            "Epoch 2/2\n",
            "32004/32004 [==============================] - 405s 13ms/sample - loss: 0.2932 - accuracy: 0.8825 - val_loss: 0.2920 - val_accuracy: 0.8848\n",
            "3951/3951 [==============================] - 21s 5ms/sample - loss: 0.3172 - accuracy: 0.8694\n",
            "Score for fold 3: loss of 0.31722973227893153; accuracy of 86.9400143623352%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 4 ...\n",
            "Train on 32004 samples, validate on 3556 samples\n",
            "Epoch 1/2\n",
            "32004/32004 [==============================] - 408s 13ms/sample - loss: 0.4412 - accuracy: 0.7996 - val_loss: 0.3399 - val_accuracy: 0.8546\n",
            "Epoch 2/2\n",
            "32004/32004 [==============================] - 409s 13ms/sample - loss: 0.3062 - accuracy: 0.8749 - val_loss: 0.2980 - val_accuracy: 0.8792\n",
            "3951/3951 [==============================] - 21s 5ms/sample - loss: 0.2924 - accuracy: 0.8823\n",
            "Score for fold 4: loss of 0.2923507488428626; accuracy of 88.2308304309845%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 5 ...\n",
            "Train on 32004 samples, validate on 3556 samples\n",
            "Epoch 1/2\n",
            "32004/32004 [==============================] - 405s 13ms/sample - loss: 0.4307 - accuracy: 0.8072 - val_loss: 0.3452 - val_accuracy: 0.8542\n",
            "Epoch 2/2\n",
            "32004/32004 [==============================] - 406s 13ms/sample - loss: 0.3059 - accuracy: 0.8798 - val_loss: 0.3068 - val_accuracy: 0.8747\n",
            "3951/3951 [==============================] - 21s 5ms/sample - loss: 0.2945 - accuracy: 0.8817\n",
            "Score for fold 5: loss of 0.2944540378716106; accuracy of 88.16755414009094%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 6 ...\n",
            "Train on 32004 samples, validate on 3556 samples\n",
            "Epoch 1/2\n",
            "32004/32004 [==============================] - 409s 13ms/sample - loss: 0.4376 - accuracy: 0.8025 - val_loss: 0.3513 - val_accuracy: 0.8607\n",
            "Epoch 2/2\n",
            "32004/32004 [==============================] - 410s 13ms/sample - loss: 0.3082 - accuracy: 0.8761 - val_loss: 0.3250 - val_accuracy: 0.8639\n",
            "3951/3951 [==============================] - 21s 5ms/sample - loss: 0.3134 - accuracy: 0.8712\n",
            "Score for fold 6: loss of 0.31338433483467015; accuracy of 87.11718320846558%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 7 ...\n",
            "Train on 32004 samples, validate on 3556 samples\n",
            "Epoch 1/2\n",
            "32004/32004 [==============================] - 405s 13ms/sample - loss: 0.4365 - accuracy: 0.7999 - val_loss: 0.3419 - val_accuracy: 0.8566\n",
            "Epoch 2/2\n",
            "32004/32004 [==============================] - 406s 13ms/sample - loss: 0.3031 - accuracy: 0.8768 - val_loss: 0.3086 - val_accuracy: 0.8743\n",
            "3951/3951 [==============================] - 21s 5ms/sample - loss: 0.3122 - accuracy: 0.8822\n",
            "Score for fold 7: loss of 0.3122296767034461; accuracy of 88.2181704044342%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 8 ...\n",
            "Train on 32004 samples, validate on 3556 samples\n",
            "Epoch 1/2\n",
            "32004/32004 [==============================] - 408s 13ms/sample - loss: 0.4377 - accuracy: 0.8031 - val_loss: 0.3347 - val_accuracy: 0.8630\n",
            "Epoch 2/2\n",
            "32004/32004 [==============================] - 409s 13ms/sample - loss: 0.3074 - accuracy: 0.8769 - val_loss: 0.3116 - val_accuracy: 0.8722\n",
            "3951/3951 [==============================] - 21s 5ms/sample - loss: 0.2976 - accuracy: 0.8797\n",
            "Score for fold 8: loss of 0.29755846600507185; accuracy of 87.96507120132446%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 9 ...\n",
            "Train on 32004 samples, validate on 3556 samples\n",
            "Epoch 1/2\n",
            "32004/32004 [==============================] - 405s 13ms/sample - loss: 0.4435 - accuracy: 0.7960 - val_loss: 0.3461 - val_accuracy: 0.8584\n",
            "Epoch 2/2\n",
            "32004/32004 [==============================] - 407s 13ms/sample - loss: 0.3050 - accuracy: 0.8766 - val_loss: 0.3133 - val_accuracy: 0.8680\n",
            "3951/3951 [==============================] - 21s 5ms/sample - loss: 0.3038 - accuracy: 0.8753\n",
            "Score for fold 9: loss of 0.303813963374196; accuracy of 87.53480315208435%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 10 ...\n",
            "Train on 32004 samples, validate on 3556 samples\n",
            "Epoch 1/2\n",
            "32004/32004 [==============================] - 409s 13ms/sample - loss: 0.4383 - accuracy: 0.7997 - val_loss: 0.3683 - val_accuracy: 0.8339\n",
            "Epoch 2/2\n",
            "32004/32004 [==============================] - 409s 13ms/sample - loss: 0.3076 - accuracy: 0.8763 - val_loss: 0.3123 - val_accuracy: 0.8757\n",
            "3951/3951 [==============================] - 21s 5ms/sample - loss: 0.3142 - accuracy: 0.8748\n",
            "Score for fold 10: loss of 0.3142046250555673; accuracy of 87.48418092727661%\n",
            "------------------------------------------------------------------------\n",
            "Score per fold\n",
            "------------------------------------------------------------------------\n",
            "> Fold 1 - Loss: 0.3063152463209291 - Accuracy: 87.93016076087952%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 2 - Loss: 0.32867315858489377 - Accuracy: 87.02859878540039%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 3 - Loss: 0.31722973227893153 - Accuracy: 86.9400143623352%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 4 - Loss: 0.2923507488428626 - Accuracy: 88.2308304309845%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 5 - Loss: 0.2944540378716106 - Accuracy: 88.16755414009094%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 6 - Loss: 0.31338433483467015 - Accuracy: 87.11718320846558%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 7 - Loss: 0.3122296767034461 - Accuracy: 88.2181704044342%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 8 - Loss: 0.29755846600507185 - Accuracy: 87.96507120132446%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 9 - Loss: 0.303813963374196 - Accuracy: 87.53480315208435%\n",
            "------------------------------------------------------------------------\n",
            "> Fold 10 - Loss: 0.3142046250555673 - Accuracy: 87.48418092727661%\n",
            "------------------------------------------------------------------------\n",
            "Average scores for all folds:\n",
            "> Accuracy: 87.66165673732758 (+- 0.4815936141325969)\n",
            "> Loss: 0.3080213989872179\n",
            "------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0R2ySYs2DKqp",
        "outputId": "42921469-c097-4ddb-d401-62c5fc62000d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#TODO: Determine history for each fold\n",
        "\n",
        "# history_dict = hist.history\n",
        "# history_dict = results_history[0]\n",
        "# # history_dict = hist\n",
        "# history_dict.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DvbznpxfRIRl"
      },
      "source": [
        "Plot Loss and Accuracy for each Fold between Training and Validation for each epoch.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tyOe8kQrRHq9",
        "outputId": "6ec7035d-a681-402c-91f6-df106fe1fafc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = []\n",
        "val_acc = []\n",
        "loss = []\n",
        "val_loss = []\n",
        "\n",
        "for i in range(len(results_history)):\n",
        "  # history_dict = results_history[i].history\n",
        "  history_dict = results_history[i]\n",
        "  \n",
        "  # print(history_dict.keys())\n",
        "\n",
        "  acc.append(history_dict['accuracy'])\n",
        "  val_acc.append(history_dict['val_accuracy'])\n",
        "  loss.append(history_dict['loss'])\n",
        "  val_loss.append(history_dict['val_loss'])\n",
        "  \n",
        "\n",
        "\n",
        "# # Loss Plot\n",
        "epochs = range(1, len(acc[0]) + 1)\n",
        "fig_row = -(-(len(acc)) // 2)\n",
        "fig_col = 2\n",
        "plt.rcParams['figure.figsize'] = [12, 18]\n",
        "for i in range(len(loss)):\n",
        "  plt.subplot(fig_row, fig_col, i+1)\n",
        "  plt.plot(epochs, loss[i], 'r', label='Training loss')\n",
        "  plt.plot(epochs, val_loss[i], 'r--', label='Validation loss')\n",
        "  plt.title('BiLSTM Training and Validation Loss, Fold {}'.format(i+1))\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.legend()\n",
        "  plt.grid(True)\n",
        "  # plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
        "  # plt.minorticks_on()\n",
        "plt.tight_layout(pad=3.0)\n",
        "plt.savefig(\"./lstm/lstm_loss_10fold.png\", format='png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# # Accuracy Plot\n",
        "plt.clf()   # clear figure\n",
        "for i in range(len(acc)):\n",
        "  plt.subplot(fig_row, fig_col, i+1)\n",
        "  plt.plot(epochs, acc[i], 'b', label='Training acc')\n",
        "  plt.plot(epochs, val_acc[i], 'b--', label='Validation acc')\n",
        "  plt.title('BiLSTM Training and Validation Accuracy, Fold {}'.format(i+1))\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.legend()\n",
        "  plt.grid(True)\n",
        "plt.tight_layout(pad=3.0)\n",
        "plt.savefig(\"./lstm/lstm_accuracy_10fold.png\", format='png', dpi=150, bbox_inches='tight')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CQ0FjkC2IGQnq1tUu/nvugXHj4JFHYOpUGD6cqOTkQJfOhLlatWqxc+dOfv31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lli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huDzKy3mX1qZjPMbOharp9sZh+a2WQz+z8za5Nyrb2ZvWFmUxL3VCvr+pWbQw6Bm26KRQMW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\niEjW+X+lwL9/gEIdBwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x1296 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ePXvPsYcffph27drRvn17hg612718+XJOPvlk2rdvT6dOnVixYkUpfLJOaZGR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\n3vjwwy40b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Zx55gKaN1/NkiWwZEnFx1recvXnnqvthuxte4ZzkEilVlAAY8fCTTfBxx9D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UsvhRtugAMOiDYuERHJfMpBIukjqWfLmlkvM5tlZrPNbOB2Hj/QzCaZ2WdmNt3M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\nUSEjIiIiIiJpR4WMiIiIiIiknf8HYzLOWOYB0ToAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x1296 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OxY3Er8-ackQ"
      },
      "source": [
        "Calculate Model Metrics\n",
        "<br> Precision, Recall, Accuracy, F1-Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hy2iGSSZxyiK",
        "outputId": "34d40fa3-004d-455e-de08-6a8da6e262fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "idx_best = 0\n",
        "best_acc = 0\n",
        "\n",
        "# == Provide average scores ==\n",
        "print('------------------------------------------------------------------------')\n",
        "print('Metrics Score per fold')\n",
        "for i in range(0, len(all_accuracy)):\n",
        "  print('------------------------------------------------------------------------')\n",
        "  print(f'> Fold {i+1}')\n",
        "  print('Accuracy: %f' % all_accuracy[i])\n",
        "  print('Precision: %f' % all_precision[i])\n",
        "  print('Recall: %f' % all_recall[i])\n",
        "  print('F1 score: %f' % all_f1[i])\n",
        "  if (all_accuracy[i] >= best_acc):\n",
        "    idx_best = i\n",
        "    best_acc = all_accuracy[i]\n",
        "print('------------------------------------------------------------------------')\n",
        "print('------------------------------------------------------------------------')\n",
        "print('Average scores for all folds:')\n",
        "print(f'> Average Accuracy: {np.mean(np.asarray(all_accuracy))}')\n",
        "print(f'> Average Precision: {np.mean(np.asarray(all_precision))}')\n",
        "print(f'> Average Recall: {np.mean(np.asarray(all_recall))}')\n",
        "print(f'> Average F1 Score: {np.mean(np.asarray(all_f1))}')\n",
        "print('------------------------------------------------------------------------')\n",
        "print('------------------------------------------------------------------------')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------\n",
            "Metrics Score per fold\n",
            "------------------------------------------------------------------------\n",
            "> Fold 1\n",
            "Accuracy: 0.878796\n",
            "Precision: 0.858832\n",
            "Recall: 0.971294\n",
            "F1 score: 0.911607\n",
            "------------------------------------------------------------------------\n",
            "> Fold 2\n",
            "Accuracy: 0.870666\n",
            "Precision: 0.847582\n",
            "Recall: 0.969539\n",
            "F1 score: 0.904468\n",
            "------------------------------------------------------------------------\n",
            "> Fold 3\n",
            "Accuracy: 0.869906\n",
            "Precision: 0.858909\n",
            "Recall: 0.949357\n",
            "F1 score: 0.901871\n",
            "------------------------------------------------------------------------\n",
            "> Fold 4\n",
            "Accuracy: 0.882814\n",
            "Precision: 0.887570\n",
            "Recall: 0.937304\n",
            "F1 score: 0.911759\n",
            "------------------------------------------------------------------------\n",
            "> Fold 5\n",
            "Accuracy: 0.881549\n",
            "Precision: 0.885024\n",
            "Recall: 0.940101\n",
            "F1 score: 0.911731\n",
            "------------------------------------------------------------------------\n",
            "> Fold 6\n",
            "Accuracy: 0.871172\n",
            "Precision: 0.916632\n",
            "Recall: 0.879433\n",
            "F1 score: 0.897647\n",
            "------------------------------------------------------------------------\n",
            "> Fold 7\n",
            "Accuracy: 0.883068\n",
            "Precision: 0.871403\n",
            "Recall: 0.956417\n",
            "F1 score: 0.911933\n",
            "------------------------------------------------------------------------\n",
            "> Fold 8\n",
            "Accuracy: 0.879777\n",
            "Precision: 0.892097\n",
            "Recall: 0.924774\n",
            "F1 score: 0.908142\n",
            "------------------------------------------------------------------------\n",
            "> Fold 9\n",
            "Accuracy: 0.874462\n",
            "Precision: 0.898385\n",
            "Recall: 0.905518\n",
            "F1 score: 0.901938\n",
            "------------------------------------------------------------------------\n",
            "> Fold 10\n",
            "Accuracy: 0.874462\n",
            "Precision: 0.858952\n",
            "Recall: 0.962554\n",
            "F1 score: 0.907807\n",
            "------------------------------------------------------------------------\n",
            "------------------------------------------------------------------------\n",
            "Average scores for all folds:\n",
            "> Average Accuracy: 0.876667203352403\n",
            "> Average Precision: 0.8775385776829905\n",
            "> Average Recall: 0.9396290792704779\n",
            "> Average F1 Score: 0.9068902869664839\n",
            "------------------------------------------------------------------------\n",
            "------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "omMUUs8W9pPr"
      },
      "source": [
        "Plot Precision-Recall Curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEoLXKNQ0a1N",
        "outputId": "305e1efd-f73e-486a-fc6e-7b8af5948226",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        }
      },
      "source": [
        "import matplotlib.cm as cm\n",
        "\n",
        "colors = cm.tab10_r(np.linspace(0, 1, len(all_arr_recall)))\n",
        "\n",
        "\n",
        "plt.clf()   # clear figure\n",
        "plt.rcParams['figure.figsize'] = [20, 12]\n",
        "\n",
        "for i in range(len(all_arr_recall)):\n",
        "  plt.plot(all_arr_recall[i], all_arr_precision[i], color=colors[i], label='BiLSTM Fold {}, AP={:0.2f}'.format(i+1, all_ave_precision[i]))\n",
        "  # plt.plot(np.linspace(0, 1, thresholds[i].size), thresholds[i], linestyle='--', label='Threshold')\n",
        "plt.title('BiLSTM Precision-Recall')\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.legend(loc=4)\n",
        "plt.grid(True)\n",
        "# plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
        "# plt.minorticks_on()\n",
        "plt.savefig(\"./lstm/lstm_10fold_precision_recall.png\", format='png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WduWe89gQAh+8afns83dctfCk/ZV6k7+6fx/37x4hl0kyWqxx984h3nHVQtb0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\njzMaHmU4OMzzzz7Pxz56dwyIu9wc13WZnZ1lZWUFXdd57LHH3nEbuq4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ClQKOyhyW7BRl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\nrl1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jXeVBgwbRtGnTcr1Dyqy92DMLCL+jKSbLBc8syIYPu0GKc0nbwWbRxLYeU6bxf5u+\nmrwsB4YjhyFj7yjT/kaf7/ycCRucp8e93fNtekX3KtOzRURERERERCoKhUeXKduZM66yXzlm9ZRk\n/PxdAMRWDWTq3e3psW4PAK82ql1sH8MweP31113X3bp1K1dwZE/P5+Rrzv2DfGND8a17wd5DC5+D\n1YWbcB+u40+tvgvKtM/Rkhnb2bMmD3vBPga/dD3Vo4vehLsoKxJWuIKjF7u8qOBIREREREREKjWF\nR5ep/d0K99nxqlH0XkDltfNEOj/vOAXAb6N6cjAnj73ZeTwbG0WYd/E/Uh9//DH5+fkAPPDAA9Ss\nWbPMzy5IyCLxP5td1xF3nReMGQZsmOYWHC3tGkm7TvPw8yvds2wFdqaNXoE1zw7A7c+VPjjKseYw\nd99cJmyYQBXfKnzX7zsi/Et36pyIiIiIiIhIRaXw6G/gz9ycucDm4Kb3VgBwd5doAL48mYLFBINq\nhBfbLzk5mRMnTgDOPY7+jOCoxugOmH0t8N3DsGWmW9szN/2TXamfEd3gCYKDi5/d5LA7sFkd+Ph5\nYRgG37+7xRUc9RzqTVT90gVHS48v5bHfHnNdf9v3WwVHIiIiIiIicllQeHQZMqxWV7nh6lV/2rgL\ndpxk5IxNruuX+7Ug02bn04QkekWEEOnrXWzf1atXA+XfHNueVeAKjszBPtR4uj3m5a/C8rc82h67\n/k72Z34OXmai6z5Y7JjWAjtTHl8GQFiNAFJP5QBgOLJo2Cae5t0fLtW7PbzoYZYnLHddrxqyimCf\nkjcNFxEREREREakMFB5dhpI/mQZA+N134RUW9qeMeSo9zy04mjysLXbDoOHy7QAMjSp+lk1WVhZb\ntmyhbdu25QqODMPg5Pi1APi3rkb4oMaYclPcg6OgSGhxG/mxHdkf/xQAHTvMx2x2/xG32x3sXnmS\nmJYRTB9TGKydC44A8tM/ptcD35Xq3ebum+sKjmbfNJvYKrH4e/mX+RtFREREREREKiqFR5ehMxMn\nAhB01dV/yngfLD3Amwv2uq7DAryJqVeFF/cnuOquDg8pqisAb73lDHmuuOKKcj0/b3cKACZfCxFD\nmjgrF70IJgs07QM9/gWRzTEMg11b7sZiCaRTx5/x96/lNo7DYfDhI0sBWHa2LrJeCKcPZwBgGPn0\nf6IutZv9UKr3Wh6/nPFrxtO2elumXDcFX4tvub5PREREREREpCJTeHSZMQzDVQ7o2OFPGfP84KhJ\nl5psCYar1hfWLenQGC9z0fsqJSYmuspVq1Yt87MNq520eQfwqh5A5BNtnJWHl8PmGXDlP6DXy662\n23c8QkrqSho3HucRHGWl5jN9zEq3Oh9/L/o/2YpvXx/L8Z3b6HnXCGo3a37R98mx5jBx00Rm7ZkF\nQLhfOB9c+4GCIxEREREREblsKTy6jBiGge1sWONdpw4mc+mPpi/OgrOnqgEEtqvKlhD3kOimaqE0\nDSp+mdaqVauwWCw8+eST5Xp+wgvOpWVV72uEyWKGzFMwvY/zZo/RrnapqWs4c2YhALVqDgYgP8fK\n9+9uISezgKyUfFfbQc93JCMpl9i4aswY8ySnD+2nWnQ92t7Y76LvkmPNodOXndzqvr75awK9A8v1\nbSIiIiIiIiKVgcKjy8iepoX7CUWNH/+njDlx0T4Afni0K732H/a4f2+t4mcTfffdd669joKCgsr8\n7PxjzuVkmMGv4dm9m355wfln/8ng4wxt7PZcdu95FoCuV67BZDKzf8Npfpm60228djdE07lffQCq\n1g5i+2+/cPrQfgAGjX3joqfS7UzeyeD5g13XVf2r8kP/HwjyKft3iYiIiIiIiFQmCo8uE4bd7nbt\n26jhHx5zw5EU9pzKBMAeUniS2muNatMwwJccu4OuYUWfKrZjxw62bNkCQKdOnYpsczGGYZA+/5Dz\nZLVR7Z2VR1bA9q+g+9MQN9TV9tDhd8nNPUqbNjPw8alKwt5Uj+Do1tHtqBEb6ro+fegAi6dNJrpV\nG24ZMxaz2VLke9gddq6YdQU5NueG2jfWu5E3ur9R5u8RERERERERqawUHl0Gjt5xJz4NG7iuzQEB\nf8opa0OnOk84G927Mb03OmcgbbuiOdV9vS/WDYB58+YB0K5dOyIjI8v87NytZyg4lknYbQ0x+1og\nNw0+uwmq1IWu/3S127P3RRISZlKz5iDCw7qw/sfDrPvBOUOqz6OtqR4TTG6mlfCowqVluZkZfP/2\nqwSEVOHGx0YVGxzZHDY6zOyAzWED4IaYGxQciYiIiIiIyN+OwqNKzrDZyNmwgZwNG1x1jpyci/Qo\nnZPpuRTYHADc3Lku/7d6F0CJwZFhGKxZswar1UqLFi24+eaby/xsw2onfcERvGsGEtA2EgwD3oh2\n3rz2ZfAJACA9fTMJCTMBaFD/GTJT8lzBUZ1m4US3iADAP8jHNbbDbuenSf8mOzWFQS+/QUBIKEU5\nnH6Yvt/1BaBZRDM+v+FzbYotIiIiIiIif0sKjyo564kTl2TcLq/9BsCANrXocDY4+r5Ng4t1AWDS\npEkkJycD0Lt373I9O2HsarAbhN3eCJPZBL++6Lxh8YHmAwA4cHACR49+CED79t+SkWhm9rhVePla\nGPx8B0KrBXiMm3b6FJ88PgKAa0c8QlSDxkU+f8zyMcw/NB+AeqH1mH3T7IvuhyQiIiIiIiJyOVN4\nVMkVHDniURf54gt/aMykrMKTyd66vRWzlm0DoENo8aeKzZkzh927d7uu4+LiyrdJ9tEMsBuADb/Q\nM7D5R1j5rvPmM8fBZCI5eZkrOGra5DX8fZrz8bjfAbhiQP0igyOH3c6P7zqXnAVXrUara4sOtr7a\n+5UrOLqn+T081f6pMn+DiIiIiIiIyOVE4VEll/rlLI+6Kv0ufuR8SX7bnQjAu4Pj2JjhXAL3ftO6\nxc6+OXDggFtwBNCvHO9gOAySPtuJf+AWIuzPw6Tzbj60Grz9cDis7No9GoCOHb4nOLg5y2btBaBq\nnSBadK9V5Nirvv6SUwf3c/W9DxJ33U0e32IYBp2+7ESuLReA5YOWU8WvSpm/QURERERERORyY/5f\nv4D8MVlLl7rKobfdStM9uzEHFj9DqCRbjqcx+ptt1KriT9/WNfnhTBq+ZhPXVS16byCA7du3u12P\nGTOmXMu8kj7eiin3lDM4Ol/buyGyGQ6HjSVLm1BQkESrlpMJDm7Oif2p7FiWQOur6zDouY7OZW4X\nOLZjG2u/+4oWV/WiTe+bMZk9f+zvWXCPKzj6ccCPCo5EREREREREztLMo8tI9af++BKr/u+vBKBX\ns0gM4IfEdK4KDybYq+gTyfLz89m9ezdxcXF06dKFkJAQfH3LuLG03QrjqlINwO9sXa12kLARuj8N\nVzvDpP0HXnV1qVbtOmwFdn77Yg8hVf3o1C+2yKFPHdjH1+OeBeDqex4sss29C+5lU+Im/Cx+rB22\nFrNJmaqIiIiIiIjIOQqPKjFHdrarHHH/CLzCwv7QeFn5Nle5V7NIfkvJ5FSBlb7VaxbbZ/v27RQU\nFNCuXTsiIyPL9Vzjsz54zBcasdj559kZTMkpK4iPnw7A1VftA2DBxztIT8yl7z/i8Pb1DLesBfn8\nMPF1AIa98jbefn5u9x2Gg9aft3Zdrx66WsGRiIiIiIiIyAUUHlVi+Wc3y6418R1CynmyGcDqg8nk\n2ew8/fVWV11ioJmHth0C4LqIkGL7bty4kcjISGrXrl2+hx9bi+n4GgDs3pFYOgyCnmNcoRGAw2Hj\nwIHXALiiyxJMJguzx60lOSGb6jEh1GkSXuTQy7/8jIwzidw65mVqNGjkdq/AXkC7Ge0Kv+OOjXiZ\n9esgIiIiIiIiciH9a7kSOzpkKAA+deuWewzDMBjy8Rq3uq0vXkeXTXtc10FFLFlzOBwsXLiQkydP\ncuONN5Z9jyPDgC/6w6GlAJyyvk/kC0PB4jnzJyFhBllZe2jZ4gP8/etyaMsZkhOcs676PRFX5DfN\nfnE0J/btpu2N/YiJa+fR5uFFDxd+711bNeNIREREREREpBgKjyoxo6AAAO+60eUe41BStkddsL8X\nKVY7AN+3aVBkvylTpnDq1CkAWrVqVbaHph2DiS1dl3n2NgT27ompiOAoPz+Rg4feITy8G9WqXcfa\n7w+x4acjANz37274+Lv/CBuGwZRH7iUrOQmAbkPudrufa8ul48yOAHSO6syUXlPKtbm3iIiIiIiI\nyN+FwqNKzFKlCva0NCxB5T9d7fd9Z9yuN7/Qi00ZOQB80CyajlWC3O6npKSwdetWV3AE4HfBXkIl\nOi84SrMOxxo7nGrdPJe9GYadFSu7ANC40UskxWe5gqNr722GX6C3R58tv/zoCo7unzQNLx8f1718\ne74rOAKYfO1kBUciIiIiIiIiJVB4VEkZhoHhcFBlyOA/NMbLP+xyXT97YxPCAn348cAZvE0mrj1v\nryO73c64cePc+jdt2pSbb765bA/9YoCrmBwxk4LMatS4q1mRTU+c+AqAsLAriN/pz6+frAfguhHN\nadjefXNuwzCY/87r7FvrPC3uself4+PnX/is3GR6ftUTgNjQWL7r952CIxEREREREZFSUHhUSdnT\n0nBkZOATXf4la3tPZ7rKcx7oTKfYCOyGweTjZ+gZFkzIeXsdXRgcAdxyyy14e3vO/inWlllw8DcA\nUuovJHenlSoD6mLyLuKkNGs6e3d/gD1lEA3jXuDzZ1cD0KZXXY/gCGDqYyPIOHMagHvf+cgtODqT\nc4arv74agE5RnZh63dTSv7OIiIiIiIjI35zCo0rKevQoAD5/YL+j3hOXA/DLk91pFBkMwHP7EwAI\nOy/QyczM9Oh7//33lz44ysuA1+u4Lh13/EzOVCsAgUUEQQAHD77L7rkvA7D/N2dw1G1QQ1pdVcej\n7ZEtG13B0V0TJhFes5brns1hY/TvowG4KfYmXu/2euneWUREREREREQAhUeVVsGxYwD4xJQvPMrO\nt7nK54KjbLudzxKc+wW91bgwpNm5cycAAwYMICsri44dO5Y+ODq+Hj65tvC6dkeyjtQCjlH90bgi\nN8nOzNzL8k/qu9VVjwkpMjjKzcxgwYfvElG7Lne8NtFtjyOASZsnseH0BsZfOZ5+DfqV7p1FRERE\nRERExEXhUSVVcOQomM141/bcaLo03vplLwAjexSGNL8mZbjKgWeXrNntdhYsWEBwcDCtW7cu+4N+\nHl1YtvhiH/ITmW+ux695BD61gz2aG4bBml++oCDTucwspKofnfrG0qhjjSLbLvpkMrkZGQz410se\nwdGSY0v4ZMcn3NboNgVHIiIiIiIiIuWk8KiSKjh2DO+oKMwXBCalYRgGn648AsA/rm3oqh+5y7kU\nbkWnJq66c3sdBQW5n7pWKltnw4lNzvLALyC2J1lL4zEK7IT2KnrG1JH9v7DvV2dwdPdrVxIU5lvs\n8HtWLmPf6uV0HXwXkfXcZyotOLyAp39/mqbhTXmm4zNlf3cRERERERERARQeVVoFR4+We7Ps6auO\nuMp+Z/c2+u/pVFddgwA/AI6dXRoHMHz48LI95L8jYessZ/mJrRih0SRN20H+gTT8mobjXSPQo0t2\n9gGWzTwINOH6+5tdNDjKSDrD4mmTqdmoKR363up2b0/KHp7+/WkA3u75Nr6W4scRERERERERkYtT\neFRJFRw7RsiNN5Sr76QlBwH46sEurrrFyc4la1+1LpzBM23aNAD69etXtlPVTm4rDI6ufxXCYjj1\n5nrsKXkAhFzrGXod3HKc375cQ0FGE5pfZaFBO89laucYDgcLJ0/EYbNzwyP/xGwp3Nz7aMZRbv/h\ndgDe6fkOtYPLt6xPRERERERERJwUHlVCttRUHOnp5Zp5ZBgGSVn5NKkRTMd64a76HVm5dK0SRPdw\n5z5EiYmJrntt2rQp20O+f6yw3PZuHLk2V3AU3KM2PrXcl8CdPJjOgg/3AzUBuLJ/12KHdjjsvDPE\nuX9Rr/sfpUqNKNe9xxY/xtL4pQC82f1Nro2+tqghRERERERERKQMPI+6kgrPetS5N5FP3bKHR19v\njAcgPdfqqjuWm8+e7Dx6VQ1xjm+18sEHHwDw2GOPeQ5yod3z4cgKZ/m7h+HkFohsCWMSwDeIjCXH\nAaj+eBtCb6jn1nX/htN8O2Gj67rnsMZ4+1ooit1mdQVHAC2vud5Vfn/L+67g6P6W93NDvfLNyhIR\nERERERERd5p5VAkVnN2LyCem7OFRQmouADNHdHLV/Xp2yVqviFAADh065LoXERFx8QFz02DOMGf5\nhSTYMtNZvmMu+AZxYtwaHNlWsJjwqek+46gg18YvU3cCEFrvd2574gH8/GoW+6iJwwa4yv+c9T0m\nkwmA3+N/58OtHwLwTd9vaBTW6OLvLCIiIiIiIiKlpvCoEio4chTMZrxrl30/n3WHU2heM4TYaoVB\nzqLkDOr7+xIb4EtmZibbt28H4NZbby1uGCe7Fd44L8Caeo3zz5vehuAa5Gw/4wyOgGoPtvLovmb+\nBgBCY3/ntscvHhwdWL/GVX7887mYzM5JcxkFGTyy+BEAfrrlJ+oE17n4O4uIiIiIiIhImSg8qoQy\nly7BHBSE2cenTP3yrHY2Hkvlzs6Fgc/WzByWpGTSt3oV1q1bx08//QRAXFwcLVu2vPiAGz51vz65\nFQCj7d2kzNhF7o5kAKre1wLfuiFuTfdvOM32xTkE11lHpwE+Fw2Odi5bzIIP3sE/OIQHPvgMr/O+\ne/ya8VhMFr644QsFRyIiIiIiIiKXgPY8qmQMwyB/124cGRll7rvleBoFNgddYguXon18/AwA3cKC\nXMERQLNmzYoeJPM0JO5x/vnz0866Br0K79/xDZnLT7qCo9Ab6uHXMMxtCGuB3bVcreW1Dho2fLbY\nd85IOsOCD94B4JZnxroFR//6/V/8fPhnhrcYTstqJQRdIiIiIiIiIlIumnlUydhTU8vdd/AU59Kv\nDmdPWct3OJh7OhUz0Nvb4P3z2sbGxhY9yL8v2E+o44MQWhsO/AqAEXM1GVNXum4Hdq7hMcTGBbsB\nqNbqa1p3mFjs+xqGwaKpzrfq9cCj1GhQ+Ow9KXv46bAz7Hoo7qFixxARERERERGRP0bhUSVjTUgA\nIOqVV8rU755P17nKof7eAMStdM7+cQBLliwBoHv37jRv3hwvryJ+NH6f4Fl3/SvgsEFeGnR8gMTJ\nzqVrvg2qEHpTLGZf93Gy0nLZ9MsxgmrtpOONrTCbfYt9593LlwV6fIYAACAASURBVHB48wauuvt+\nWl3T21Wfnp/O7T/cTlX/qvy373/xNnuX6u9ARERERERERMpO4VElk7fTGfj4tWhe6j6GYbB0r3N5\nWuPIYFd9qs0OwNxIPxYt20VYWBhXX3110YNY8+C38e51w+aCxdv53zUv4iiwY03YD0DV4S0wmU0e\nwyyd8xuGw5vqrb4hJmaNx/1zstNSWfLZFGo2akpc7z6u+rc3vM2nO517LT3f+Xmq+FUp+S9ARERE\nRERERMpN4VElc2rsywB41yx+g+lzrHYHaw4lU2BzuOp+fLyr888zaQDcVTOCRbM+AaBFixbFD7b3\nR/frns9Cw15uVTkbTwNQbWQrj+Ao/UwmM15YD/gT1uhXevddVeyj0k6d5JMn7gfgupGPYzZbANh4\neqMrOOoV3Ytr6l5T/PuKiIiIiIiIyJ9C4VElYktKcpUtwcEXaek05tvtzN0Y77r++K72eFmce6Tf\nt+MIALdWDeHns/ebNGlS9EAOB8wd7iw/dxpObIa6nd2aGHaDzKXx+NQNxifa/WS1o0ensOgjM+Dc\nR6nrgC6YTEXv1Z50/CjTRz0CQKPOXYmo5TxBbfKWyXyw9QMAOtTowITuRSyhExEREREREZE/ncKj\nSuTAddeXqf28Lc79kaoG+ZKZZ6VXs0gA0q02V5sqyYkAREdHU7O42UyHnPshEVoHvP0guotHk4Tn\nVjib9KmHyVQ46ygxcQG7tkwjL3UcAN3utFO3Xt8iH5OeeMoVHFWvV5+bn3wGgN3Ju13B0cSrJmrG\nkYiIiIiIiMhfSOFRJWLk5AAQ1KNHqdpb7QYASVn53HtljKt+RVoWAOMb1uLA1nV4e3tzxx13uIU+\nbrbPdf75cNF7FOXuSXGV/ZtXLXxfw2Dr1ic49PNkAO4Y34rQqlU9+gM4HHamPjYCgBoNGjF03FsA\nFNgLeHbFswBMu34aHWp0uNgni4iIiIiIiMifrOi1Q1LhGIbhKofdeWeJ7dNzrG7XHWLCXeWlKZkE\nWcwMCg9i/fr1+Pv74+1dzIllO76FrV9Ckz7gG+T+Tg4Dw2GQufQ4ANUebu2219G6BUvY940zOPL2\nsxQbHAHMHfc8AI27dGPYK29jMpsxDIPX173OgbQDvH/N+wqORERERERERP4HNPOokrCnpbnK3jWj\nSmz//dYEt+v20WEAHMvN54sTybQO8uPfb74BQFRUMePZ8mHuvc5yy9s9bic8u8JVDupRG9+6hXsd\nJSdksWFeYdsHJhY/Wyo5/jjHd20H4KYnRrvqW33eCoA+sX3oXrt7sf1FRERERERE5NLRzKNKIntl\n4elk3sWFPWcZhsEL83a61VUP8QNg9F7nBtrBmYVh1MCBAz0HcThgfHVn2TcUmvd3u23PyHe7Drqi\ncL8kw2Ewe9w61/Wd4z33SDqnIC+Xz556CICHPp7pWjr3e/zvrjbPd36+2P4iIiIiIiIicmkpPKoE\nTk+YwIlRo1zXZn//i7ZffyTV7XpAm1qu8rltje4nF4DOnTtjsVg8Bzm6srD8yFqP21lrT7nKlnA/\nvEJ9XddL5ixxlR/6oBshVYt+X8Mw+M/dzhlNbXrfTEBIKAAns07yyOJHiA2NZeMdGwn0Diyyv4iI\niIiIiIhcelq2VgmkfDLNVW64auVFWjrd99l6t+tz+x3ZHAYbM7K5IyqCY8vXU6tWLXr37l30IFu+\ndP45ZA6EuM90Svv+IFmrTuBVzZ/IJ9u53Zs9fh3JzslN3PFGDczmovdSstts/PTeBABMZjNX3/sg\n4AyU+s3rB8BznZ7Dx+JT4veKiIiIiIiIyKWjmUeVjFd4eIlt2pzd32jts9cwoms9bm3nnHm0LTOH\nDJuDzn4mEhISaNSoUdED5GfCru+g7d3Q2D1ccuTbyVp1AoCgbrUwmU2uTbLTEnNIjnee5Nbi+pOE\nhjYr9h0nDuvPvrXOIOyJL7511Q+cP5BcWy6DGw+mY1THEr9VRERERERERC4thUeXob2nMhjQphaR\nIX4836cZvl7OZWmfnkgCIPKMc8lZseHRrnlgzYG4YW7VhsPgxEvOvZeCrqxJUMfCGUmpp7KZ+eIa\n5/jtvuCKPn2Kfb9lMwpnUt01YRIWL+cEuIcWPcSelD0APNPxmVJ/r4iIiIiIiIhcOlq2VsHlbNrk\nKvvHxZXY/lR6Hqcz8mldO9St3jAMvj7l3AvpxKGDBAcHU6NGjaIH2fQFRDSAOoUzfwybg4TnC5fM\nhd4Y69bly7HOfZHMPtlc0ecmvL3dn39OXlYWG35wzjS6772pVIl0vsPiY4tZkeA8vW3t0LVYzEXs\nwyQiIiIiIiIifznNPKrg4h9+xFWuM+WjEttvOe4MiFrXqeJWfzSvAIAbwoI4ePAgjRo1cp1s5mZi\nKzi+Bup2KdxdG8jeULhBdsRdzTBZCu+dPFB4clvTfh9QI7Jfke+WeuoE7983GIBhr7ztCo5OZZ/i\nH0v+AcCvt/1KgHdAid8pIiIiIiIiIn8NzTyq4OxpzmCm+tNPYwkJKbH9luPpeFtMNI1yb7si1bkX\n0b3eVn4rKCh6ydreBZB21Fnu8S9XteEwSPvuIABVh7fAr1GYW7eNC47iGwjR1z9Cg8bPFblJdurJ\nBKb940HXdY0Gjci2ZtP5y86uugk9JlAjsJjZUCIiIiIiIiLyP6GZR5WEV/VqpWq39XgazaJC8PN2\nX/a1MjWT6j5e/Pb1HADq1avn2XnWIOefda+AKnVc1fmH0gEIaFvdIzg6czyTozuSiWy2Eb+AEKKi\nbvMY1uGwu4KjkGqRPDVnPoZhuAVHtzS8hd4xxZz8JiIiIiIiIiL/M5p5VEl4VSs5PLI7DLYnpHNL\n21pu9YZhsCIti24hhcvBfHx83DvnOQMiIhrC8J/dbiVN3Q5A2IAGHs/c+PNRLD4F+NaaTt06j2Ox\n+Hm02bFkEQBVIqMY/u4UAHp+1dN1/z9X/4cetXuU+H0iIiIiIiIi8tdTeFRJ+LdtW2Kbg2eyyMq3\n0bq2+35H+3PyOVNgo01+JgnA4MGD3TsmH4T/nB2/w31utwpOOJe7YTFhumA2U+qpbA5uTiSi8SIs\nPrnUqjXU453yc3JYOecLajZuxuCX38BkMrH9zHZS8lIA2HznZrzM+jEUERERERERqai0bK2Cs4SG\nEjZ0COYLZwoVYctx5/5IF26WvSI1E4DwpFOYzWb3JWuGURgcAXQa6dY38b3NAET9q6NbveEw+OrV\n9WA4CGu0iObN3sbLK8itjcNhZ9K9A8lJT+Oqu+/HZDJxJucMQ38aitlkZvWQ1QqORERERERERCo4\n/cu9AnPk52NPT8ccWvSx9xfaejyNYD8vYqsGOvsbBjWXbgWgWUEW29esBsDX17ew0xf9C8utBrud\nsGbPyHeVLSHu4dXGBUexFTjwCkghsmYbatRwnrCWk57GqUP7iW3TgWVfTAPAbLFQo35DAB777TEA\nHmz1IEE+7mGTiIiIiIiIiFQ8Co8qsOSPPgIg86efqf7EEyW2X3s4hVa1QzGbnQHQ7uw8173uqxcV\n3enQUuefwVHQf7LbrexNiQBEjmrvVm8YBgc3O+/V7z2R5s2deyQ5HHYmP3CHxyOemPEtAKtOrGJn\n8k4GNx7Mw3EPl/g9IiIiIiIiIvK/p/CoArOdOQOAf1xciW23Hk/jQGIWDofhqluXnu3R7onzQ6ik\nA84/m94Mg2a4tTMMg5wNp/GpF4J3VX+3eycPpJN0PIvIdl9Qr/4teHs7Z0YteP8dj+fdNWESZrOF\nHUk7ePDXBwn2CWZUh1Elfo+IiIiIiIiIVAza86gC823cBIDqo54qse1nq44AuJ20tv5seNT54A4A\n6tWrR1hYWGGnSe2cf177ssd4uduSsCXlEti+hse9zb8ewuKTSWj0GurUGQ44l6vtW7vSrV3/0S9Q\nrW4Mu5N3M+THIQCM6TgGX4uvx5giIiIiIiIiUjFp5lEFZk9JBrMZS3j4RdslZubx380JADzQvb6r\nfm1aFjdXq0KtZc4ZRtddd11hJ6NwhhIRhX0ADJuDlFl7APBvWdXtXlpiDke2pRLRdBmRUVfh4xNO\nZkoSUx66B4CBL71GnWYtC9vnpTFw/kAA7mh6BzfXv7kUXy4iIiIiIiIiFYVmHlVgtqRkLGFhmCyW\ni7a7f/oGV9nHy/m/ND6vgIR8Kx0CvF33oqKinIWcFHg92lm++T2P8TKXxbvKZp/CZxuGwcwX1wAm\notul06rlBwCu4AhwC44AJm917qMUHRLNvzr+66LfISIiIiIiIiIVj8KjCsyWnIxXRESJ7bbGpwPQ\nP66mq25tWhYAJz7/GIBbb721sMOb9SDf2YfGN7iNZVjtZPx6FIBar3V1u3d0R7Kr3KDJUAB2LlsM\ngMls5pFps93aH0o7xJy9cxjYaCDzB8wv8TtEREREREREpOLRsrUKzJ6cjCXi4kvWjPOWnzWvGeoq\nr03PJsRU2C42NtZZyDjhPkBQdbfL3LMBkXdUICZT4QAOu4Mf398GQMP+j1M1YhsFebks+MC5SfZj\nn32Ft6+f21gTNkzA38ufR9o8ctFvEBEREREREZGKSzOPKijDMMjdsgV7UvJF2y3cedpVvqlVlKu8\nNj2bPvs3u64DAwOdhf2/FHZu1NtjvOwNp7CE+1H9sTZu9duWOJeyhcSs4spu8zCZzGxe4JxN1OW2\noR7B0YqEFaxIWMHI1iMJ97t4ACYiIiIiIiIiFZfCowrKnuwMjfL3779ou5EzNgLw9cgu1KziD0CK\n1cax1DRCEpzLz0aOHFnYYd8vEFoHhsyB26e7jWVLySP/YDqB7SIxmU1u9/ZvOAU4aHnDcQID65Of\nk8OGH74ltm0Hrrh9qFvbpNwkHlr0EHWD6zK0ifs9EREREREREalcFB5VUPa0NAC8qlcvoaVT4xrB\nrvL69GzqphTOSKpRo4azYMuHQ0uh4XXQuDd4u88Wyt54GkwQ0C7Srf704QwSj2QR2WY20dEjAPji\nmcfJy8qky23u4dDxzONc9dVVAIxsPRJvizciIiIiIiIiUnkpPKqg7OnODa2jXn212DZzNxaeihbi\nVxjSrEnL4qq9ziVrjRo1Kuyw5gOwZkOj6z3GMhwGORtP49ugCl5VfN3ubVtyDLN3HrVbZlEltD3p\niadIP30KgBr1G7q1HbVsFADBPsH0ie1Tmk8VERERERERkQpM4VEFZTu7bM3rIhtmj/p6KwAje9R3\nq1+fku4qDxo0yFnIz4JFY53lmG4eY+UfSMOelk9g+xpu9Unxmexbl0ho9EpiYp2zjH6ZMgmLtzcP\nfPCZW9s9KXvYnbyb7rW7s3LwSrcNt0VERERERESkctJpaxWUPSUVAEt4RIltuzeq6irn2B2cTkgA\nIC4uDovF4ryxdVZhB58AjzGSpu0AwL+Z+/PmjF8PQFjDJSRs6sDM6TcD0LRrT4Ijqrq1nbhxIsE+\nwbzW7TUFRyIiIiIiIiKXCYVHFUza3LlYT5wg/QfnSWZeYVWKbLfmkHNmUkxEAFfULwxxNmVkUy09\nBYDrrz+7PG3Ht/CTczkZ9/zkMZYtKdf5rMgATN6Fk9HSz5yt90+lYf1HmTduquvelYPudH+fk2tY\neWIlo9qPIsQnpNTfKyIiIiIi8ndjtVqJj48nLy/vf/0q8geFhoaye/fu//VrlImfnx+1a9fG27v0\nexQrPKpgTj7/gqtsDgnB5OPj0Wbj0VQGT1kDwB2do93urUvPJio9marVq+Pv7w+Zp2DuvYUNYq70\nGC97k3Oj7GrDW7jVb192BEx2oq95lS3zbnTVP/LJbPyCglzXDsPBOxvfISowisFNBpfpe0VERERE\nRP5u4uPjCQ4OJiYmRqs2KrnMzEyCg4NLblhBGIZBcnIy8fHx1KtXr9T9tOdRBWYyF/2/59bJq1zl\nZlGFs3wMw2D+qRSiMlKoFx0NhgH/blzY8bFNHmM5N8pOxLdhGJbQwo2yM1Py2LroJMG1NhMV8hBH\nt22mWkwsT82Z7xYcAfx46Ed2Je/i0TaP4mvxvfARIiIiIiIicp68vDwiIiIUHMlfzmQyERERUeZZ\nbwqPKhCjoMDt2p6W5tHm498PuV03PS88evFAAomJiXjZbdStUwdevmDJW4T7xtoA2etOYU/PJ7Bd\npFv95886A6oqDZZydH0iAANGv+jRf+nxpTy74lkAbqp3U3GfJiIiIiIiIudRcCT/K+X52VN4VIHY\nMzNLbPPKT+5rKcMCC5e1fRyfRFR6EgAxQe5BFM1v8RjLMAzSvjsAuG+UnXY6x1Vu0Wok+9evptOA\ngR4bZOdYc3jst8cAGH/leCxmS4nvLyIiIiIiIiKVi8KjCsSekeF2balS9GbZRfY1DACi0pOpFWQn\n+PNrCm8+fwZu/cSjjzU+CwCfeiFuG2Wv+u8eMNuof/NT/DrpWzAM2vS+2a2vYRh0+rITAM91eo5+\nDfqV+l1FRERERETkf8tisRAXF0fr1q1p27Ytq1Y5V5+cOHGC2267DYClS5fSp08fj77z58+nTZs2\ntG7dmmbNmvHRRx/xyiuvEBcX5zr1+1z5vffeY+zYsZhMJg4cOOAaY+LEiZhMJjZs2OAxfs+ePWnc\nuLFrjLlz5xb7HZ999hmPPvpokfeCLthy5Zzhw4dTvXp1WrRoUeT94vTv35/OnTu71b366qvUqlWL\nuLg4WrRowffff1/q8aZPn07Dhg1p2LAh06dPL7bdf/7zH5o0aULz5s0ZPXo0AMnJyVx11VUEBQUV\n+/1/pku6YbbJZOoNvAtYgKmGYbx+wf1/AiMAG3AGGG4YxtFL+U4VmeOCmUc+0dHFtIQRXetxa7va\nruvJxxLBMIhOP8P91vcKG46JBy/PTbcBcrYkgsVE1buau+oObz3D4S1pmL0LqOE3mp3JM/H28yew\nSphb39UnVwNgwqRNskVERERERCoZf39/tmzZAsDChQsZM2YMy5Yto2bNmhcNa6xWKw888ADr1q2j\ndu3a5Ofnc+TIERo3bsxzzz0HOEObc2MDjB07lpYtWzJ79myef/55AL7++muaN29e5DMAZs6cSfv2\n7f+MT/Vwzz338Oijj3LXXXeVuk9aWhobN24kKCiIQ4cOERsb67r35JNPMmrUKHbv3k23bt1ITEzE\nXMwexuekpKTw8ssvs2HDBkwmE+3ataNv376Ehbn/23vJkiXMmzePrVu34uvrS2Kic1sZPz8/xo0b\nx44dO9ixY0cZvr58Lll4ZDKZLMD7QC8gHlhvMpm+Nwxj13nNNgPtDcPIMZlMDwFvAoMu1TtVdPYM\n9/DIEhHhdp2eawVgzA1NeLCH+/5FeQ6D0NxsBlnnFVYGR4Fv0bu+Gw6DnG1n8GsSjtm/8Mdg92pn\ndlenx9vsX9UFgOHvfOje1zB4f/P71AiswY8DfizDF4qIiIiIiMj5Xtgfz46s3D91zBZB/oxrWLvk\nhmdlZGS4QosjR47Qp0+fYgOJzMxMbDYbEWf/verr60vjxo2LbHu+/v37M2/ePJ5//nkOHjxIaGho\nmY6KT0lJYfjw4Rw6dIiAgACmTJlCq1at3NocPnyYoUOHkpWVRb9+xa+O6d69O0eOHCn1swG+/fZb\nbr75ZiIjI5k9ezbPPvusR5umTZvi5eVFUlIS1atXv+h4CxcupFevXoSHhwPQq1cvFixYwJAhQ9za\nTZ48mWeeeQZfX+fhVOfGDQwMpGvXrm6zuS6lS7lsrSNwwDCMQ4ZhFACzAbf/e4ZhLDEM49wGO2uA\n0v90X4YcmRcsWwt3TxyPJmcDEB0R6NF3c0YObfLSacG+wsrerxX7rPyDaTgyrQTEFf5A52VZObo9\njSr1lxBT+y6O7dhKt6H3EBTuHmItT1jOtqRtPNDqAXwsRc9qEhERERERkYorNzeXuLg4mjRpwogR\nI3jhhRdK1S88PJy+ffsSHR3NkCFDmDlzJg6Ho8R+ISEh1KlThx07djB79mwGDbr4vJFhw4a5lq0l\nJyfz0ksv0aZNG7Zt28arr75a5KyhJ554goceeojt27cTFRVVqu8prVmzZjFkyBCGDBnCrFmzimyz\ndu1azGYz1apVY+bMma73P/+/c0sCExISqFOnjqtv7dq1SUhI8Bhz3759LF++nE6dOtGjRw/Wr1//\np35XaV3KZWu1gOPnXccDnS7S/j7g50v4PhXehTOPAjt0cLs+nOQMj+pVdQ+PDMNgU0Y2AzJSCisf\nWAo12xT7rJwtZzD5WvBvUhhQ7VoVj8NuJqZdFgvfcU5TbN3rBo9nvb/lfWoF1aJ//f6l/TQRERER\nEREpQllmCP2Zzl+2tnr1au66665SL3+aOnUq27dvZ9GiRbz11lv8+uuvfPbZZyX2Gzx4MLNnz2bh\nwoUsXryYTz/9tNi2Fy5bW7FiBd988w0AV199NcnJyWRcsG/wypUrXW3uvPNO/vWvf5Xqe0py+vRp\n9u/fT9euXTGZTHh7e7Njxw7XnknvvPMOM2bMIDg4mDlz5mAymRg2bBjDhg37w8+22WykpKSwZs0a\n1q9fz8CBAzl06NBfflrfJd3zqLRMJtMdQHugRzH3HwAeAIiMjGTp0qV/3ctdYud/S8CWzQQDKf98\nEkdgIKdDQ+G8+0sPOE9QO7pzAyf3FP6gnDTMZNv8qHl0HQDH6gzg0L502FfY93wmO8RsNZMdabB/\n5XLAGQrt/yUH37AE0pKc0+aCa0ezep17qrktZxu7kncxLGIYK5ev/INfL1I6WVlZl9Xvvcilot8V\nkdLR74pIyfR7cmmFhoaSWYrTti+1c+/QokULzpw5w+HDh8nJycHhcJCZmUlOTg42m63Id42JiWHE\niBEMGDCAli1b8p///KfIsQHy8/Px9vamR48ejBo1ijZt2mAymbDb7WRnZ3uMX1S9w+EgKyvLVWcY\nBpmZmeTl5VFQUEBmZiaGYZCVlYWXl5erXXF/z1lZWa7vLMnnn39OamoqMTExgHOZ3/Tp03nxxRcx\nDIOHH36Yxx9/3O3b58yZw3vvvecxVmxsLF988QXh4eGsWLHC9fzDhw/TtWtXj/epUaMGvXv3Jisr\ni6ZNmwLOpYVVqzpPQz//+8siLy+vTL/jlzI8SgDqnHdd+2ydG5PJdC3wHNDDMIz8ogYyDGMKMAWg\nffv2Rs+ePf/0l/3LLXDO7Dn/WxI3biLZ25sr7r+/yBTx+9NbqBmazHXXXOVWP/dUCq1+Xkj02b/e\nuv1foG5EfY/+5+RsTyLFtpvY3i1p1tA58+jQlhPsytpDQfY+0g/tAeDOsa/hHxzi6ucwHEz6YRJ1\ng+sy6sZReJkrRPYofwNLly7lsvi9F7nE9LsiUjr6XREpmX5PLq3du3cTHFz0/rR/pXPvsGfPHhwO\nB9HR0Rw/fhyz2UxwcDABAQF4eXm5vWtWVhYbNmxw/XysXbuW6Ohoj+85/9rX1xdfX18iIyN58803\nadSoEcHBwVgsFgIDAz36FlXfo0cP5s2bxwsvvMDSpUupVq0atWrVws/PDx8fH4KDg+natSs//vgj\nd9xxBzNmzPB4j/MFBQW5vvOcSZMmAXicXvbf//6XBQsW0KWLc1/gw4cPc+211zJhwgRMJhO+vr4e\nzxkxYgQjRowo5m/euQfUuHHjsNlsgHNj7LfeestjnNtuu421a9dy0003sW/fPmw2GzExMa7M4Pzv\nLws/Pz/atCl+tdKFLuW//tcDDU0mUz2codFgYOj5DUwmUxvgI6C3YRiJl/BdKgV7ZgaW4OAigyO7\nw+DbzQk0iwrxuLcxI4cgu5W6JOAIqIY5PNajzflSZu4GwLd+FQCs+XZ+/tAZGDkK9rvanR8cAfzf\n6v9jb+peXu36qoIjERERERGRSuzcnkfgnMUzffp0LBaLR7vFixdTu3bh0rpZs2bx5ptv8uCDD+Lv\n709gYGCplqydM3hw+U7rHjt2LMOHD6dVq1YEBAQUebT9u+++y9ChQ3njjTcuumH2kCFDWLp0KUlJ\nSdSuXZuXX36Z++67jz179nDllVe6tT1y5AhHjx6lc+fOrrp69eoRGhrK2rVry/Ut4Nw76oUXXqDD\n2e1qXnzxRdfm2SNGjGDkyJG0b9+e4cOHM3z4cFq0aIGPjw/Tp093ZQYxMTFkZGRQUFDAd999xy+/\n/EKzZs3K/U4Xc8kSAMMwbCaT6VFgIWABphmGsdNkMv0fsMEwjO+BCUAQ8PXZjz9mGEbfS/VOFZ0j\nIxNLMWlh/Wd/AmDXyQyPe5tT0ukcf4hoTmCOuQousvYxY2nhNlQms7Pd/g2nATAcWYAdgCdnzXPr\nl5aXxjf7nWtHb6x3Yym/SERERERERCoiu91eZH1MTIxr76OePXuSm+t5Ely3bt0uOnZWVpbb9dix\nY4tsV9yyqaLqw8PD+e677zzq77nnHu655x7AGeqsXr3adW/8+PFFjl/chtdHjhzh7bffdquLiYkp\nciPrTZs2AdCsWbNyzyI7FwxdaOrUqa6yj4+PaxZVUe/7V7mk00cMw/gJ+OmCuhfPK197KZ9f2dgz\nMzGHeM4syrcV/lL7eLkfkJdrd9Dk95+JJp4qZEDdKy76jOzVJwGo/njh9LRtSw/gsKdRkDGNBh26\ncNU992M2FybOhmEwcdNEAD689kMsZs80WkRERERERKQymz9//v/6FSosrT2qQBwZGUXOPDqWnOMq\nf3RnO7d7X5xIokpOFvfytbMiuvjwyJFvx5FjJbBzFD41gwA4eSiZ5OM27PmbCa1enX6jnvPoN2rZ\nKH45+gudanTiylpXetwXERERERERkcuXueQm8lcwbDZyt27Fevq0x70NR1Nd5asaV3eV8+wOXjxw\nAm+v8/YZj2xe7DPydiVjWB0ExFVz1X375lYA7AW7GfbqRI8+ubZcfjn6CwAPtn6w9B8kIiIiIiIi\nIpcFhUcVRN7evQAUHDzocW/Mt9sB+PSeDm71u7PzCMjP40rbxsLKiywpS5njfIZPXefSuL0b1xXe\nNPI8NsgG+Gafc5+jV7q+QocaHTzui4iIiIiIiMjlTeFRq6xoxwAAIABJREFUBeHIzCqxzVVNqrtd\nb8/MISrjDD05u8P76MPF9rWecS59Mwd6uTbKXvSx85l56VP45+wfPPrk2/P5dMentI9sT9/6f9t9\nzEVERERERET+1hQeVRD2lOQi6/OsRe+AD7A9K5d3D78JgBFWDwLCi22bu/UMmCDy8bYAxO91Bk2G\nI4u73n3AddTf+b7b/x2JuYlariYiIiIiIiLyN6bwqIIJ6dPH7To+1TljaNR1jTzabsvMoX3uNgBM\nHe8vdkxHjpWMRccw+XphCfUF4JtXv8QwDFpff5Jq1a/26BOfGc/4teOp6l+VTjU6lft7RERERERE\npOKxWCzExcXRuvX/s3fnYVXXeeP/n5/DJoctETcWEZeQ/YCUOoqZM+ZUZFpOgYxOcZvL3F7TzUxl\nphR1jzaUM6lj3xm9LWWKH8dyiXQmASnMbSRsSEjMjWMKLqwCiiznnN8fJz6KrJp0zF6P6+K6znl/\nXu/X5/05cLwuX9d7CSMiIoJ9+/YBUFpayvTp0wHIyckh+rr/o4LlVLLw8HDCwsIIDAxkzZo1LF26\nFJ1Oh06nU3PrdDpWrVpFUlISiqJw/PhxNceKFStQFIW8vLw2+SdMmIC/v7+aY9OmTR0+x4YNG1iw\nYEG715ydndu0nT59mvvvv5/AwECCgoJYuXJl5x/UNaZOncro0aNbtS1btgwvLy90Oh3BwcF8/PHH\n3c6XkpLC8OHDGT58OCkpKe3GJCUlqfl1Oh3/+pflQPvU1FS1TafTodFoyM/P7/a9b5SctnabMJvM\nAHj8dn6r9m8rLcWjMUM9WrU3mcx8W1V+tWHozzvMXbunBAAHP8ueRvV1ddg4hGJqPsW4x9ovOk1L\nnwbAb3W/bXdWkhBCCCGEEEKIHy9HR0e12JCRkcGiRYvYtWsXnp6enRZrmpqamDNnDrm5uXh7e9PQ\n0IDBYMDf35/Fiy2ndzs7O7cqZCQlJRESEoJer2fJkiUAfPjhhwQFdXzgU2pqKpGRkbfiUVuxtbXl\nz3/+MxEREdTW1jJy5EgmTZpEYGBgp/2qq6s5ePAgzs7OnDx5kiFDhqjXEhISeO655ygqKiIqKooL\nFy6g0XQ+V6eyspJXX32VvLw8FEVh5MiRTJkyhd69e7eJbcl/rbi4OOLi4gAoKChg6tSp6HS67n4M\nN0yKR7cBs9lMwzdHANBcVxk9XVkPgI+7Y6v2pOMl/OycpUJ7fsRv6N9vRPu5TWZqPz0NQJ9Zli/D\ne4vfR9Hcjf/YShSl7R/01+Vfc8V4BYBf3f2rm30sIYQQQgghhBBdOHr0f6mtK7qlOV2cA7j77sRu\nx9fU1KhFC4PBQHR0NIWFhe3G1tbW0tzcTJ8+fQBwcHDA39+/y3tMnTqV9PR0lixZwokTJ3Bzc8PO\nzq7bY6ysrCQ+Pp6TJ0+i1WpZu3YtoaGhrWKKi4uZMWMGdXV1PProo+3mGThwIAMHDgTAxcWFgIAA\nSkpKuiwebdmyhUceeYT+/fuj1+t56aWX2sQEBARga2tLeXk5/fr1ayfLVRkZGUyaNAl3d8v2M5Mm\nTWLHjh3ExsZ22q89aWlpxMTE3HC/GyHL1m4DFzdvpuL/1gFgc13x6NvKy/Sy09DX2aFV+zsl5Tx2\n8lOg8yVrjYaLANgPdkVRFJoaGmhqsCyBi5r+UJt4k9lEzD8tf3RZ07Nu8omEEEIIIYQQQtzO6uvr\n0el0jBgxgtmzZ5OY2L1ik7u7O1OmTMHX15fY2FhSU1MxmUxd9nN1dcXHx4fCwkL0ej1PPvlkp/Fx\ncXHqkqyKigpeeeUVwsPDOXToEMuWLWPWrFlt+jz77LPMnz+fgoICtUDUGYPBwH/+8x9Gjep6q5a0\ntDRiY2OJjY0lLS2t3ZgDBw6g0Wjo27dvm2VlLT8tSwJLSkrw8fFR+3p7e1NSUtJu3tWrVxMaGkp8\nfDxVVVVtrm/cuPGmik43QmYe3QYu/fuA+lpxbD3D6HTlZXx6a1stHTObLUvc+tlWcLHJmX5DQjrM\nXf91BdgqeDwdDMCujRmAM469j6J1arvX0YavNwAwasAoBjgNuNlHEkIIIYQQQgjRDTcyQ+hWunbZ\n2v79+5k1a1aHs42ut27dOgoKCti5cyfLly8nKyuLDRs2dNkvJiYGvV5PRkYG2dnZrF+/vsPY65et\n7dmzh82bNwMwceJEKioqqKmpadVn7969aszMmTNZuHBhh/nr6up4/PHHWbFiBa6urp2O+/z58xw7\ndoxx48ahKAp2dnYUFhYSHGz5f/Zbb73F+++/j4uLCxs3bkRRlFbLyr6P+fPnk5iYiKIoJCYm8oc/\n/IF3331XvX7gwAG0Wq06lp4iM49uA6b6evX19fsLZR4+z7ELdVB7HhpqAShtaELbfIl7mgpwo67D\nvGazmfqvK+g1vDcaBxsAjucZAPjVi22n8JnNZj46/hEAq3+++ns9kxBCCCGEEEKIH4cxY8ZQXl5O\nWVlZt/uEhISQkJBAVlaWWrDpSnR0NO+99x6DBg3qsmBzs7qzZ29TUxOPP/44cXFxPPbYY13Gf/DB\nB1RVVeHn58fgwYMxGAytZh8lJCSQn5/P7t27iYqKAtpuaH39zCMvLy9Onz6t5jhz5gxeXl5t7t2/\nf39sbGzQaDQ888wz5Obmtrqu1+t7fNYRSPHotmCuv9xu+5Um49U3f74b3nkAgMK6epYf/rOlbzt7\nFrVoKr2EsboBxyDLWtSKc6dobvDHzunfuLi1ncK3cPdCii8W8/uRv6eXba+bfRwhhBBCCCGEED8i\nR44cwWg0qvsYdaauro6cnBz1fX5+Pr6+vt26j1arJTk5Wd1Y+0ZERUWRmpoKWE6B8/DwaFOAGjt2\nLHq9HkCNvZ7ZbOa//uu/CAgI4Pe//32ra6tXr2b16rYTKdLS0tixYwcGgwGDwcDBgwfV+3QkLi6O\n/Pz8Nj8tm5FPnjyZzMxMqqqqqKqqIjMzk8mTJ7fJc/bsWfX11q1bW80wMplMfPDBBz2+3xHIsrXb\ngulS+8WjkmrLjKSkh4bBp8CFwwDUFO3gV1XZAFRM/wiPdntDfWE5KNArwPIPQPqft6MoAegmtd1c\n+3TNaT4p/gSA2BE9X7UUQgghhBBCCGE9LXsegaWgkpKSgo2NTZu47OxsvL291fdpaWm88cYbzJ07\nF0dHR5ycnLq1ZK3FzRY6kpKSiI+PJzQ0FK1W2+7R9itXrmTGjBkkJyd3uGH23r17ee+99wgJCVGf\nf9myZTz00EMcOXKEsWPHtoo3GAycOnWK0aNHq21+fn64ublx4MABbpa7uzuJiYncc889ALz88svq\n5tmzZ89m3rx5REZG8sILL5Cfn4+iKAwePJg1a9aoOT7//HN8fHxanfzWU5SW/XN+LCIjI815eXnW\nHsb3FvjiJsYp/2bt68s58dDDNJ48CUDAkau77O8+VsbMd3LZ+EAzoz6fBbaOsOQcJLmpMcbEyna/\n4ADn/nIQG2c7+s4J5cyRI6SvKAVg3tv3tepjMpsI+0cYAEvHLWXK0Cm3/HmF+D5ycnKYMGGCtYch\nxG1PvitCdI98V4TomnxPelZRUREBAQHWHoa4TnR0NFu2bMHe3r7bfWpra3FxcenBUfWM9v4GFUU5\naDabI9uLl2Vrt4Hmiop220uqLDOPvHZ9N5XOY3ir65dwbLdwZGo0cuH/5dN84TL2fpZCU07abgA0\ntufa9NlTskd9LYUjIYQQQgghhBA/Rdu3b7+hwtFPiRSPbgOmixfbbS+prsdGozCASkvDuUOUXb6k\nXnf8bU67/cr+9hWN31o219bq+mI2m7l43rJC8Zm32k7de//w+/Rz7MeXM7/8Hk8hhBBCCCGEEEKI\nO5EUj24jvUJCWr0vqapngJOCrWJS28x/ux+Af3I/mn5t9y4ym800nb1aYLLrq+Vk/leYzT709jmM\nrZ1Tq/jPz3zO/rP7iRkRg53G7lY+jhBCCCGEEEIIIe4AUjy6jTiPH9/q/Znqejxtalq19av9BgCX\n4aNpT/OFq5tvD3jOslTxwLY9KIqGyIeC2sT/d/Z/A/D43Y/f/MCFEEIIIYQQQghxx5Li0W1Eo9W2\nel9aXY9XTX67seOnL2i3vWrLcQBcJ/li6+HIxQvnqPjWDZOxjOG6+1rFvn/4fQB+OfiXuPdy/77D\nF0IIIYQQQgghxB1Iike3kV5Bgepro8nMuYtX8LKptjSMmqde2+f4M3BwbtP/ytEqGk9ZZiq5/nwQ\nAP/J2onG1ou+fhdQlNa/7nUF6wB49Wev3tLnEEIIIYQQQgghxJ1Dike3AfuhQ3EICMBp9NWlaOdr\nrtBsMuNlPgfDJ4PLQPXa101D281T/m4hALYejmrb4X3FANz36wmtYpd/sZyKKxW8eO+LaO1az3gS\nQgghhBBCCHFns7GxQafTERYWRkREBPv27QOgtLSU6dOnA5CTk0N0dHSbvtu3byc8PJywsDACAwNZ\ns2YNS5cuRafTodPp1Nw6nY5Vq1aRlJSEoigcP35czbFixQoURSEvL69N/gkTJuDv76/m2LRpU4fP\nsWHDBhYsaH9ljrNzO5Murlzh3nvvJSwsjKCgIF555ZXOP6hr6HQ6YmJiWrXNmzcPPz8/dDodERER\n7N+/v9v5Xn/9dYYNG4a/vz8ZGRntxjz11FNqfp1OR36+ZXXSxYsXeeSRR9TnWL9+fbfvezNsezS7\n6BZT/WWcQkNbtZVU1wPgqZRD2UXw/dnV+IlxbXI0lV3d66j//0QAUH7mBGbjWAAG+ARfjTU2kXI4\nBYBpw6bdoqcQQgghhBBCCPFj4ejoqBYiMjIyWLRoEbt27cLT07PTYk1TUxNz5swhNzcXb29vGhoa\nMBgM+Pv7s3jxYsBStGnJDZCUlERISAh6vZ4lS5YA8OGHHxIU1HZf3hapqalERkbeikdtxcHBgU8/\n/RRnZ2eampoYN24cDz74IKNHt7+vcIuioiKMRiO7d+/m0qVLODldPYzqzTffZPr06WRmZjJ37lwO\nHTrU5TgOHz6MXq/n66+/prS0lF/84hccPXoUGxubNrEt+a/19ttvExgYyLZt2ygrK8Pf35+4uDjs\n7e27+UncGCkeWZnpyhWaS8/SePJkq3ZDueXENG+lHHoPg+Yr6rXJwa1PZQOoTD0CgPM4LxRby4Sy\njX88BcCQkZdRFEWNTchJAGDJqCUy60gIIYQQQgghrOiTTz7h3LlztzTngAEDePDBB7sdX1NTQ+/e\nvQEwGAxER0dTWFjYbmxtbS3Nzc306dMHsBRj/P39u7zH1KlTSU9PZ8mSJZw4cQI3Nzfs7Lp/4ndl\nZSXx8fGcPHkSrVbL2rVrCb1uEkZxcTEzZsygrq6ORx99tN08iqKoM5Kamppoampq9f/ljqSlpTFz\n5kyKiopIT09nxowZbWLGjx/fanZVZ9LT04mJicHBwQE/Pz+GDRtGbm4uY8aM6VZ/RVGora3FbDZT\nV1eHu7s7trY9V+KRZWtWVv9dRbL+q69atT+/ydLuqVSAZziUHwUg0/5+Bru2nXqn2Ft+lW4P+QFQ\ntO+Uem1y/EPq6w+PfsiuM7sAeML/iVv1GEIIIYQQQgghfkTq6+vR6XSMGDGC2bNnk5iY2K1+7u7u\nTJkyBV9fX2JjY0lNTcVkMnXZz9XVFR8fHwoLC9Hr9Tz55JOdxsfFxalLtSoqKnjllVcIDw/n0KFD\nLFu2jFmzZrXp8+yzzzJ//nwKCgoYOHBgO1ktjEYjOp2Ofv36MWnSJEaNGtXl+Ddu3EhMTAyxsbGk\npaW1G7Nt2zZCQiyTPd588011/Nf+/O53vwOgpKQEHx8fta+3tzclJSXt5l28eDGhoaEkJCTQ0NAA\nwIIFCygqKsLT05OQkBBWrlyJRtNzJR6ZeWRlpkuXOr2uVRog4jcY9b/GBjjtM75NTPPFBhq/rcVl\ngg+KxlIxLfy8ANBibMhEYzMRsCxXe23/awAsundRt6qrQgghhBBCCCF6zo3MELqVrl22tn//fmbN\nmtXhbKPrrVu3joKCAnbu3Mny5cvJyspiw4YNXfaLiYlBr9eTkZFBdnZ2p/v0XL9sbc+ePWzevBmA\niRMnUlFRQU1NTas+e/fuVWNmzpzJwoUL281tY2NDfn4+1dXVTJs2jcLCQoKDg9uNBcjLy8PDw4NB\ngwbh5eVFfHw8lZWVuLtbTi1//vnn+eMf/0jfvn1555131Lbnn3++y8+kK6+//joDBgygsbGROXPm\nkJyczMsvv0xGRgY6nY5PP/2UEydOMGnSJKKionB1df3e92yPzDyyMmP1xXbbw3o3EaX5bp1kn6Hs\nuethanHC6DKkVZzZZObc67kAOAy7CwCT0cT5k0aMjSeY9Zdfq7G/2vYrAMZ5jWNGQNspdkIIIYQQ\nQgghfnrGjBlDeXk5ZWVl3e4TEhJCQkICWVlZasGmK9HR0bz33nsMGjSox4ocNzJJ4q677uL+++9n\nx44dncalpaVx5MgRBg8ezNChQ6mpqWn1zG+++Sb5+flkZWWpRaiuZh55eXlx+vRpNceZM2fw8vJq\nc++BAweiKAoODg48/fTT5OZa/v+/fv16HnvsMRRFYdiwYfj5+XHkyJFuP/uNkuKRtZmMANj269eq\nueRiA15KOfQLBCCnsjd/Zg4Bw+5uHffSHvW1w2DLl+/gJ4UoGhfMpiO43WXZgKzZ1MyJiycASB6f\n3DPPIoQQQgghhBDiR+fIkSMYjUZ1H6PO1NXVkZOTo77Pz8/H19e3W/fRarUkJyerG2vfiKioKFJT\nUwHLKXAeHh5tClBjx45Fr9cDqLHXKysro7q6GrAs3cvKymLEiBEALFq0iK1bt7aKN5lMfPDBBxQU\nFGAwGDAYDKSnp3e4dK3F888/T35+fpufVatWATBlyhT0ej0NDQ0UFxdz7Ngx7r333jZ5zp49C4DZ\nbOajjz5Si1ODBg0iOzsbgPPnz/PNN98wZMiQNv1vFVm2ZmUaZxcAPN+4WtC5UlJAuckZT9sKePoT\nAI5p7PGxsWVsUEC7ebTh/dSNsnO3lwNw/4L+atX18zOfA7Biwgpc7XumwiuEEEIIIYQQ4sehZc8j\nsBQmUlJS2j3pKzs7G29vb/V9Wloab7zxBnPnzsXR0REnJ6duLVlrcf1R992VlJREfHw8oaGhaLVa\nUlJS2sSsXLmSGTNmkJyc3OGG2WfPnuU3v/kNRqMRk8nEE088QXR0NAAFBQVMmTKlVfzu3bvx8vLC\n09NTbRs/fjyHDx9WCzs3IygoiCeeeILAwEBsbW15++231c//oYceYt26dXh6ehIXF0dZWRlmsxmd\nTsff//53ABITE3nqqacICQnBbDaTnJyMh4fHTY+nK4rZbO6x5D0hMjLSnJeXZ+1hfG+BL25inPJv\nErb+EwC/rVvoFRAA5ccpXvUw9zf+hT/b/Y3H/3c7AL9/8y/UOTqxdsFcNUf1thPU7S3Ftp+WAb8f\nCcB5QzWb/vQlAL9Ovgs3twgAQlIsm3b9Z+Z/sNVIzVD8uOTk5DBhwgRrD0OI2558V4ToHvmuCNE1\n+Z70rKKiIgIC2p8YIKxn8uTJZGRk3FCf2tpaXFxcemhEPae9v0FFUQ6azebI9uJl2dptQnHoZXlR\n/g2vNlt2jbelGYDGpiacL9cysH//Vn3q9pYC0HvqULWtpXDkfe/f1MLRN5XfABDcJ1gKR0IIIYQQ\nQgghRDtutHD0UyKVhNuExvG74lHZN+SYLFMHhw207NxeWHIWjdnMgAFXi0fXzhiz93WztJmutvmN\ntEwrbDY1E/evOAD+9ou/9dwDCCGEEEIIIYQQ4o4kM49uE0qv74pH2a+qbYExywA4WGA5LtHf6+oa\ny+aKKwDcNW0Yio1lX6PslMMA2Dn9k5DQPwPwf4f+jwZjgyW21109+ARCCCGEEEIIIYS4E0nx6Dah\ncXAA4NotqBRXS7Go5KDlKL4wzwHqtUbDReDqCWsA3xw4D0DfwC9RFMuv9rPTnwHw9s/f7qGRCyGE\nEEIIIYQQ4k4mxaPbRMvMoxrtNUcc2tq3inHv5aC+biiuQaO1xbafFoC6KstMJJOxkomPWQpFZ2rP\nUFRZxH/r/pvx3uN7cvhCCCGEEEIIIYS4Q0nx6DahaCy/igtmy9KyP9q+A0BdXV278Y2Gi9gPdkNR\nLEvWCj8/BoDrgHTcegcC8OCWBwGYOmxqzw1cCCGEEEIIIYQQdzQpHlmZdtQoHCNHqu+31Q0HYJim\nBIB31m8AwME/UI1pKrtMc8WVVkvWvtp5GJOxksjHQgEwXDSo1wY4XV3uJoQQQgghhBBC2NjYoNPp\nCAsLIyIign379gFQWlrK9OnTAcjJySE6OrpN3+3btxMeHk5YWBiBgYGsWbOGpUuXotPp0Ol0am6d\nTseqVatISkpCURSOHz+u5lixYgWKopCXl9cm/4QJE/D391dzbNq0qcPn2LBhAwsWLGj3mrOzc4f9\njEYj4eHh7T5fR3Q6HTExMa3a5s2bh5+fHzqdjoiICPbv39/tfK+//jrDhg3D39+/w5PennrqKTW/\nTqcjPz8fgDfffFNtCw4OxsbGhsrKym7f+0bJaWtWZm5oQKPVfvfGzKrmxwDob1sPQFltLbZAeMTV\nAtP5Px8EwM7T8kWoqbhIU6M7pqYvGXz34wA88tEjAHzy2Cc/xGMIIYQQQgghhPgRcXR0VAsRGRkZ\nLFq0iF27duHp6dlpsaapqYk5c+aQm5uLt7c3DQ0NGAwG/P39Wbx4MWAp2rTkBkhKSiIkJAS9Xs+S\nJUsA+PDDDwkKCurwPqmpqURGRt6KR23XypUrCQgIoKamplvxRUVFGI1Gdu/ezaVLl3ByclKvvfnm\nm0yfPp3MzEzmzp3LoUOHusx3+PBh9Ho9X3/9NaWlpfziF7/g6NGj2NjYtIltyX+t559/nueffx6A\nbdu28dZbb+Hu7t6tZ7kZUjyyMlNjAza9e1ve1Fep7f369AGg1Pku3C/VcI/ftXshKdBsVmcefbrh\nXyhKfxzdD+PktJBLTZcAcLBxwNvF+4d5ECGEEEIIIYQQN6x62wkaSy/d0pz2nk7c9cjQbsfX1NTQ\n+7v/lxoMBqKjoyksLGw3tra2lubmZvp8939WBwcH/P39u7zH1KlTSU9PZ8mSJZw4cQI3Nzfs7Oy6\nPcbKykri4+M5efIkWq2WtWvXEhoa2iqmuLiYGTNmUFdXx6OPPtphrjNnzvDPf/6TxYsX85e//KVb\n909LS2PmzJkUFRWRnp7OjBkz2sSMHz++1eyqzqSnpxMTE4ODgwN+fn4MGzaM3NxcxowZ063+148t\nNjb2hvvdCFm2ZmXmhkaU705a41KZ2u706/cBuOtyLWdd3eljb6nzmZuMYAbn8V4otpZf35kjvTCb\nLhP4+BkAMgyW6W7vTH7nh3oMIYQQQgghhBA/IvX19eh0OkaMGMHs2bNJTEzsVj93d3emTJmCr68v\nsbGxpKamYjKZuuzn6uqKj48PhYWF6PV6nnzyyU7j4+Li1GVZFRUVvPLKK4SHh3Po0CGWLVvGrFmz\n2vR59tlnmT9/PgUFBQwcOLDD3P/zP//DG2+8gUbT/ZLIxo0biYmJITY2lrS0tHZjtm3bRkhICNB6\nWdm1P7/73e8AKCkpwcfHR+3r7e1NSUlJu3kXL15MaGgoCQkJNDQ0tLp2+fJlduzYweOPP97tZ7kZ\nMvPIyswNDWhaikd157mLWsYO6Q1uXlTXX8HlymX6jri631HDt7VgNOPg5wbAVzu/QbFxw2w2ERH+\nLiazia3HtjLEbQihHqHt3VIIIYQQQgghxG3iRmYI3UrXLlvbv38/s2bN6nC20fXWrVtHQUEBO3fu\nZPny5WRlZbFhw4Yu+8XExKDX68nIyCA7O5v169d3GHv9srU9e/awefNmACZOnEhFRUWbJWd79+5V\nY2bOnMnChQvb5N2+fTv9+vVj5MiR5OTkdONpIS8vDw8PDwYNGoSXlxfx8fFUVlaqy8Sef/55/vjH\nP9K3b1/eeecdta1lWdn38frrrzNgwAAaGxuZM2cOycnJvPzyy+r1bdu2MXbs2B5dsgYy88jqTA1X\n1JlHzTUXqMaFYf1cAPhPSSkK4N2vnxrfWHwRFHAYbCkeHf2iGACfe9KxdRhI2D/CyC/LZ9qwaepJ\nbEIIIYQQQgghREfGjBlDeXk5ZWVlXQd/JyQkhISEBLKystSCTVeio6N57733GDRoEK6url13uAld\n/T947969fPzxxwwePJiYmBg+/fRTfv3rX3faJy0tjSNHjjB48GCGDh1KTU1Nq2d+8803yc/PJysr\ni+DgYLWts5lHXl5enD59Ws1x5swZvLy82tx74MCBKIqCg4MDTz/9NLm5ua2u6/X6Hl+yBlI8srpr\nl6395WAjAFc0jgAcPXsegEAvy3Q7s9lMzc5vwUaDxtGWk/kXuHDKHrP5BMH3D2aHYYeaN3po93eM\nF0IIIYQQQgjx03XkyBGMRqO6j1Fn6urqWs3Yyc/Px9fXt+MO19BqtSQnJ6sba9+IqKgoUlNTAcsp\ncB4eHm0KUGPHjkWv1wOosdd7/fXXOXPmDAaDAb1ez8SJE3n/fcu2MYsWLWLr1q2t4k0mEx988AEF\nBQUYDAYMBgPp6ekdLl1r8fzzz5Ofn9/mZ9WqVQBMmTIFvV5PQ0MDxcXFHDt2jHvvvbdNnrNnzwKW\nesBHH32kFqcALl68yK5duzrd3+lWkWVrVmZZtmYPtef5f0cts4ls7S3Fo2/PX8AGCPYcAEDT2e82\nUWs2ce7kRT75u2VKocl4ir59n2Dx1jhLf40tHo4eP+yDCCGEEEIIIYT40WjZ8wgshYmUlJR2T/rK\nzs7G2/vqQUxpaWm88cYbzJ07F0dHR5ycnLq1ZK3F9Ufdd1dSUhLx8fGEhoai1WpJSUlpE7Ny5Upm\nzJhBcnLyTRVUCgoKmDJlSqu23bt34+Xlhaenp9o2fvx4Dh8+rBZ2bkZQUBBPPPEEgYGB2Nra8vbb\nb6uf/0MPPcS6devw9PQkLi6OsrIyzGYzOp2Ov//nYExGAAAgAElEQVT972qOrVu38sADD7Q6+a2n\nKGazucdvcitFRkaa8/LyrD2M7y3wxU2MU/5NwtZ/4vHb39J38jAGr7dsMrYyRsejOi9mvr0Wj9qL\nvPWiZZ3kpdxzVG05Rp/Zwby7/D9qLp+xb+H/8HKmb5vOYNfBpE9NR6PIpDJx58jJyWHChAnWHoYQ\ntz35rgjRPfJdEaJr8j3pWUVFRQQEBFh7GOI6kydPJiMj44b61NbW4uLi0kMj6jnt/Q0qinLQbDZH\nthcvFYbbgOLgAFWniNIcAuDB4IGYzWaGlpXiduXqkY0NhotonOyotb9aDTY1l3H3vTr+WfxPbBQb\nUh5MkcKREEIIIYQQQghxg260cPRTIlWG20Cvpq9gx0IUzIQpJ7C31XAg/6s2cQ2narD3deX04UoA\njE3FOA38K336TGTbiW2M9RqLe6+e3WFdCCGEEEIIIYQQPy1SPLIiBcuSQYd6y/GIZWY3+irVAOxI\n/wgAn1FjATDWNGKsuIK9rwv//ugkJmMlTXVbGXx/DWuP76G8vpz7vO+zwlMIIYQQQgghhBDiTibF\nI2v6brspxVwPQJn5Lvra1rcKCfIfDkDd/lIAiqstJ7Ipmt4A9O07ntxzlj2gpgxtvbGXEEIIIYQQ\nQgghxPclxSOrslSPNE1VXDHbUc5dmO2caGpqUiPCBw8CoPaz0wAcKagAoLE2lRFPnuCKbT8MNQae\nCXmGXra9fuDxCyGEEEIIIYQQ4k5na+0BCNA0VbLR+AAA+kvh9Fq6VL3moNFgNlmKTE0ahfIzdQD0\ncqnEwa2Rv58qxmQ28fCQh3/4gQshhBBCCCGEEOKOJzOPbhMeykUAFv5yhNrW5KgFoLncspTtSkR/\nAMzGszh716Dt8wC55y1L1obeNfSHHK4QQgghhBBCiB8xGxsbdDodYWFhREREsG/fPgBKS0uZPn06\nADk5OURHR7fpu337dsLDwwkLCyMwMJA1a9awdOlSdDodOp1Oza3T6Vi1ahVJSUkoisLx48fVHCtW\nrEBRFPLy8trknzBhAv7+/mqOTZs2dfgcGzZsYMGCBe1ec3Z2brd98ODBhISEoNPpiIxs92T6dk2d\nOpXRo0e3alu2bBleXl7odDqCg4P5+OOPu50vJSWF4cOHM3z4cFJSUtqNSUpKUvPrdDr+9a9/AWAw\nGHB0dFTb582b1+373gyZeXSbaPruVzE5qD//yPmu8e4gABq/rQHgfG0TGhu4XPUBzl4V/PaQ5cs9\nK3DWDz1cIYQQQgghhBA/Yo6OjuTnWw5vysjIYNGiRezatQtPT89OizVNTU3MmTOH3NxcvL29aWho\nwGAw4O/vz+LFiwFL0aYlN1gKICEhIej1epYsWQLAhx9+SFBQUIf3SU1NvaHCzo367LPP8PDw6HZ8\ndXU1Bw8exNnZmZMnTzJkyBD1WkJCAs899xxFRUVERUVx4cIFNJrO5+pUVlby6quvkpeXh6IojBw5\nkilTptC7d+82sS35rzd06NBWn3NPkuKRFbWctgZQYXYBwHylVm3zHXkPAI3f1qL0sqXEUEPTFQNg\nxGlgPcYLDgD8Jug3P9iYhRBCCCGEEELcOp9tWMuFUydvac5+vkO4/6k53Y6vqalRixYGg4Ho6GgK\nCwvbja2traW5uZk+ffoA4ODggL+/f5f3mDp1Kunp6SxZsoQTJ07g5uaGnZ1dt8dYWVlJfHw8J0+e\nRKvVsnbtWkJDQ1vFFBcXM2PGDOrq6nj00Ue7nbs7tmzZwiOPPEL//v3R6/W89NJLbWICAgKwtbWl\nvLycfv36dZovIyODSZMm4e7uDsCkSZPYsWMHsbGxt3Tct4osW7MSBfC2aQDA5DaUSrMrthqFf6xb\nA8DeocEMceqFqdHIpdxz1DrbUX3+MpibAah2tixve+1nr9FP2/kfpRBCCCGEEEIIca36+np0Oh0j\nRoxg9uzZJCYmdqufu7s7U6ZMwdfXl9jYWFJTUzGZTF32c3V1xcfHh8LCQvR6PU8++WSn8XFxceqS\nrIqKCl555RXCw8M5dOgQy5YtY9astitwnn32WebPn09BQQEDBw7sMLeiKDzwwAOMHDmStWvXdv3Q\nQFpaGrGxscTGxpKWltZuzIEDB9BoNPTt25fU1FR1/Nf+tCwJLCkpwcfHR+3r7e1NSUlJu3lXr15N\naGgo8fHxVFVVqe3FxcWEh4dz3333sXv37m49x82SmUe3AaXpEm8bpwJmFMXSVqV1YYijA+XrCzGb\nzWQftyxda76Sx7BHDLxSbKn7TRw00UqjFkIIIYQQQgjxfd3IDKFb6dpla/v372fWrFkdzja63rp1\n6ygoKGDnzp0sX76crKwsNmzY0GW/mJgY9Ho9GRkZZGdns379+g5jr1+2tmfPHjZv3gzAxIkTqaio\noKamplWfvXv3qjEzZ85k4cKF7ebes2cPXl5eXLhwgUmTJjFixAjGjx/f4VjOnz/PsWPHGDduHIqi\nYGdnR2FhIcHBwQC89dZbvP/++7i4uLBx40YURSEuLo64uLguP5OuzJ8/n8TERBRFITExkT/84Q+8\n++67DBw4kG+//ZY+ffpw8OBBpk6dytdff42rq+v3vmd7ZOaRlbn5XcZ86Xyb9grX3vj0sqexuIbL\n1xRxzcZSHPvXYzSbce/ljpuD2w84WiGEEEIIIYQQd5oxY8ZQXl5OWVlZt/uEhISQkJBAVlaWWrDp\nSnR0NO+99x6DBg3qsSKH0jIjoxNeXl4A9OvXj2nTppGbm9tp/AcffEBVVRV+fn4MHjwYg8HQavZR\nQkIC+fn57N69m6ioKIAuZx55eXlx+vRpNceZM2fUcV2rf//+2NjYoNFoeOaZZ9SxOjg4qEsHR44c\nydChQzl69GiXz36zpHhkZZ6jqrmIU5v2AS7O2H73N1/ebNkbydRcanmvvReARaMW/TCDFEIIIYQQ\nQghxxzpy5AhGo1EtRnSmrq6OnJwc9X1+fj6+vr7duo9WqyU5OVndWPtGREVFkZqaClhOgfPw8GhT\ngBo7dix6vR5Ajb3epUuXqK2tVV9nZmaqM4hWr17N6tWr2/RJS0tjx44dGAwGDAYDBw8eVO/Tkbi4\nOPLz89v8tGxGPnnyZDIzM6mqqqKqqorMzEwmT57cJs/Zs2fV11u3blXHWlZWhtFoBODkyZMcO3as\n1Sbet5osW7sNVH63WXaL6j79GOLYi4bj1QDk11v+IBprN+I56jzLjp8CYLxXx9PqhBBCCCGEEEKI\njrTseQRgNptJSUnBxsamTVx2djbe3t7q+7S0NN544w3mzp2Lo6MjTk5O3Vqy1iImJuamxpuUlER8\nfDyhoaFotdp2j7ZfuXIlM2bMIDk5ucMNs8+fP8+0adMAaG5uZsaMGfzyl78ELEW0sWPHtoo3GAyc\nOnWK0aNHq21+fn64ublx4MCBm3oWsOwdlZiYyD33WA7Kevnll9XNs2fPns28efOIjIzkhRdeID8/\nH0VRGDx4MGvWWPZJ/vzzz3n55Zexs7NDo9Hw97//Xe3fExSz2dx11G0kMjLSnJeXZ+1hfG9BL24i\n1uZTltil8oXJn181vgLAU72+YE9AJGMidMx9pxiT2cy2i5ZNsq9U/QXfXx/l1UrLKWsFvymw2viF\n+CHl5OQwYcIEaw9DiNuefFeE6B75rgjRNfme9KyioiICAgKsPQxxnejoaLZs2YK9vX23+9TW1uLi\n4tJ14G2mvb9BRVEOms3myPbiZdnabaDiu5lHE+2OAVDYzxs/Rwds3By4aLQU9xrrtmPnZOTId7W+\nDx/50CpjFUIIIYQQQggh7kTbt2+/ocLRT4kUj24DVd8Vj/poLmOvtex/NMTRHnOzkdqBzgCYmkto\numTDCeVuvJy98O/tb7XxCiGEEEIIIYQQ4qdDike3gUosm3w50ETj5UsA+DUpmC41U9FgxL5XM5gv\nMfCB03xVXcL9Pvd3awd5IYQQQgghhBBCiO9Like3gUqzC452CraKGSVyDPaKgkd5I2azmbKKK9Rf\ntBy3t7+PhiZTMz8f9HMrj1gIIYQQQgghhBA/FXLa2m2g0uyCg7kJgFLXPvg62NNcWsdloP5SM6bm\nUmy1zeR8t3F2eL9wK45WCCGEEEIIIYQQPyUy8+g2UIErtsYrABjMGoZoHWgqqeOi1g4AU3Mp9r84\nA8A9A+7BRtP2+EQhhBBCCCGEEEKIniDFo9tAldmFuxwtk8C+wQY/RwcaS+v44my9JUC5wLFezWgU\nDX+57y9WHKkQQgghhBBCiDuBjY0NOp2OsLAwIiIi2LdvHwClpaVMnz4dgJycHKKjo9v03b59O+Hh\n4YSFhREYGMiaNWtYunQpOp0OnU6n5tbpdKxatYqkpCQUReH48eNqjhUrVqAoCnl5eW3yT5gwAX9/\nfzXHpk2bOnyODRs2sGDBgnavOTs7t9teXV3N9OnTGTFiBAEBAezfv7/jD+oaU6dOZfTo0a3ali1b\nhpeXFzqdjuDgYD7++ONu5QJISUlh+PDhDB8+nJSUlHZjkpKS1Pw6nY5//etfAGRlZTFy5EhCQkIY\nOXIkn376abfvezNk2ZqVNZptKDAPwY8GBvgM4orJTNSxS5hqm9QYe7cGsursABN39brLeoMVQggh\nhBBCCHFHcHR0JD8/H4CMjAwWLVrErl278PT07LRY09TUxJw5c8jNzcXb25uGhgYMBgP+/v4sXrwY\nsBRtWnKDpQASEhKCXq9nyZIlAHz44YcEBQV1eJ/U1FQiIyNvxaO28eyzz/LLX/6STZs20djYyOXL\nl7vsU11dzcGDB3F2dubkyZMMGTJEvZaQkMBzzz1HUVERUVFRXLhwAY2m87k6lZWVvPrqq+Tl5aEo\nCiNHjmTKlCn07t27TWxL/mt5eHiwbds2PD09KSwsZPLkyZSUlHTzE7hxUjyysp2mkQAUX3bArNUC\n4L/rPPUmMwBNlz+jzN6yTO0Xg35hnUEKIYQQQgghhOgRuz84Svnpulua08PHmagn7u52fE1NjVq0\nMBgMREdHU1hY2G5sbW0tzc3N9OnTBwAHBwf8/f27vMfUqVNJT09nyZIlnDhxAjc3N+zs7Lo9xsrK\nSuLj4zl58iRarZa1a9cSGhraKqa4uJgZM2ZQV1fHo48+2m6eixcv8vnnn7NhwwYA7O3tsbe37/L+\nW7Zs4ZFHHqF///7o9XpeeumlNjEBAQHY2tpSXl5Ov379Os2XkZHBpEmTcHd3B2DSpEns2LGD2NjY\nLscCEB5+dS/koKAg6uvraWhowMHBoVv9b5QsW7Oyc2Z39fUVB0fLC0dbvrxsBMDUfI6G+0oBWDRq\n0Q8+PiGEEEIIIYQQd576+np0Oh0jRoxg9uzZJCYmdqufu7s7U6ZMwdfXl9jYWFJTUzGZTF32c3V1\nxcfHh8LCQvR6PU8++WSn8XFxcepSrYqKCl555RXCw8M5dOgQy5YtY9asWW36PPvss8yfP5+CggIG\nDhzYbt7i4mL69u3L008/TXh4OLNnz+bSpUtdjj8tLY3Y2FhiY2NJS0trN+bAgQNoNBr69u1Lamqq\nOv5rf1qWBJaUlODj46P29fb27nDm0OrVqwkNDSU+Pp6qqqo21zdv3kxERESPFY5AZh5ZXanZUq11\npJFqey19jEB9M+XNlplHZmMZxfa2DO81hH7aziuXQgghhBBCCCF+XG5khtCtdO2ytf379zNr1qwO\nZxtdb926dRQUFLBz506WL19OVlaWOpOnMzExMej1ejIyMsjOzmb9+vUdxl6/bG3Pnj1s3rwZgIkT\nJ1JRUUFNTU2rPnv37lVjZs6cycKFC9vkbW5u5ssvv+Svf/0ro0aN4tlnn+VPf/oT//u//9vhWM6f\nP8+xY8cYN24ciqJgZ2dHYWEhwcHBALz11lu8//77uLi4sHHjRhRFIS4ujri4uC4/k67Mnz+fxMRE\nFEUhMTGRP/zhD7z77rvq9a+//pqFCxeSmZn5ve/VGZl5ZGV3K5ZT1CbaH6emooKMnbWYzd8Vjky1\nlLtd4ugVI7YaqfMJIYQQQgghhLj1xowZQ3l5OWVlZd3uExISQkJCAllZWWrBpivR0dG89957DBo0\nCFdX15sdbqcURen0ure3N97e3owaNQqA6dOn8+WXX3ba54MPPqCqqgo/Pz8GDx6MwWBoNfsoISGB\n/Px8du/eTVRUFECXM4+8vLw4ffq0muPMmTN4eXm1uXf//v2xsbFBo9HwzDPPkJub26rPtGnT+Mc/\n/sHQoUO7+GS+HykeWVkdlqVqrsoVSpwsX56672b8Ndfvp+Fn5wB4Ovhpq4xPCCGEEEIIIcSd7ciR\nIxiNRnUfo87U1dWRk5Ojvs/Pz8fX17db99FqtSQnJ6sba9+IqKgoUlNTAcspcB4eHm0KUGPHjkWv\n1wOosdcbMGAAPj4+fPPNNwBkZ2cTGBgIWJaHrV69uk2ftLQ0duzYgcFgwGAwcPDgQfU+HYmLiyM/\nP7/NT8tm5JMnTyYzM5OqqiqqqqrIzMxk8uTJbfKcPXtWfb1161Z1tlN1dTUPP/wwf/rTnxg7dmyn\nY7kVZDqLldVg2STbDiO5vT2BS1QZLTOPTM1nKellxMvOhwf9HrTiKIUQQgghhBBC3Ela9jwCMJvN\npKSkYGNj0yYuOzsbb29v9X1aWhpvvPEGc+fOxdHREScnp24tWWsRExNzU+NNSkoiPj6e0NBQtFpt\nu0fbr1y5khkzZpCcnNzhhtkAf/3rX4mLi6OxsZEhQ4aoy+eOHDnSphBjMBg4deoUo0ePVtv8/Pxw\nc3PjwIEDN/UsYNk7KjExkXvuuQeAl19+Wd08e/bs2cybN4/IyEheeOEF8vPzURSFwYMHs2bNGsBS\n6Dp+/DivvfYar732GgCZmZldbtR9s5SWJVI/FpGRkea8vDxrD+N7C3pxE7E2n2JC4f/jIWY5HyIl\n4pdkf1ZH4V32HC+u5PLFt9n4kIFpd8ewZPQSaw9ZCKvJyclhwoQJ1h6GELc9+a4I0T3yXRGia/I9\n6VlFRUUEBARYexjiOtHR0WzZsqVbp6+1qK2txcXFpQdH1TPa+xtUFOWg2WyObC9eZh5ZWQ1O9NKY\nsHHUMviS5YS1C1WXMDWfo7zvJRrNCmM9e34KmhBCCCGEEEII8VO2fft2aw/htiV7HllZjVmLvWLC\n2MsRvzoTzWYztTVmzM3nuDLuPDaKDfcOvNfawxRCCCGEEEIIIcRPlBSPrKwGLXY0ccWhF08bmrho\nNINZwWQ8y1GjQni/cJzsnKw9TCGEEEIIIYQQQvxESfHIymrMTtiamqixdcDrsomypkYA6jSnKWnS\nMNZLlqwJIYQQQgghhBDCeqR4ZGWW4lEj1Ro7AMpowmS8SOPE4wD8zPNn1hyeEEIIIYQQQgghfuKk\neGRlNWixV4w4X3YEoNZkg9FYwkeK5foI9xFWHJ0QQgghhBBCCCF+6qR4ZEUmFGpxxJ5mXjzhTL3J\nTFOTHVW9v1ZjNIr8ioQQQgghhBBC3Fo2NjbodDrCwsKIiIhg3759AJSWljJ9+nQAcnJyiI6ObtN3\n+/bthIeHExYWRmBgIGvWrGHp0qXodDp0Op2aW6fTsWrVKpKSklAUhePHj6s5VqxYgaIo5OXltck/\nYcIE/P391RybNm3q8Dk2bNjAggUL2r3m7Ozcpu2bb75R8+p0OlxdXVmxYkXnH9Z3pk6dyujRo1u1\nLVu2DC8vL3Q6HcHBwXz88cfdygWQkpLC8OHDGT58OCkpKR3G/fWvf2XEiBEEBQXxwgsvAJCVlcXI\nkSMJCQlh5MiRfPrpp92+782w7dHsolOXcMSMBnvFCEBVswmA8wOPAqB/WG+1sQkhhBBCCCGEuHM5\nOjqSn58PQEZGBosWLWLXrl14enp2Wqxpampizpw55Obm4u3tTUNDAwaDAX9/fxYvXgxYijYtuQGS\nkpIICQlBr9ezZMkSAD788EOCgoI6vE9qaiqRkZG34lFb8ff3V8dmNBrx8vJi2rRpXfarrq7m4MGD\nODs7c/LkSYYMGaJeS0hI4LnnnqOoqIioqCguXLiARtP5RJDKykpeffVV8vLyUBSFkSNHMmXKFHr3\n7t0q7rPPPiM9PZ2vvvoKBwcHLly4AICHhwfbtm3D09OTwsJCJk+eTElJyY1+HN0mxSMrqjFrAbDH\nUjw633gZs9mO0t7lDHXzI8ij4y+SEEIIIYQQQogfv3PLltFQdOSW5nQIGMGAl17qdnxNTY1atDAY\nDERHR1NYWNhubG1tLc3NzfTp08dyLwcH/P39u7zH1KlTSU9PZ8mSJZw4cQI3Nzfs7Oy6PcbKykri\n4+M5efIkWq2WtWvXEhoa2iqmuLiYGTNmUFdXx6OPPtplzuzsbIYOHYqvr2+XsVu2bOGRRx6hf//+\n6PV6Xmrn8w0ICMDW1pby8nL69evXab6MjAwmTZqEu7s7AJMmTWLHjh3Exsa2ivvb3/7Giy++iIOD\nA4CaNzw8XI0JCgqivr6ehoYGNe5WkzVRVlSDEwC9sMw4KjVdotlUyhlbI6Nlo2whhBBCCCGEED2k\nvr4enU7HiBEjmD17NomJid3q5+7uzpQpU/D19SU2NpbU1FRMJlOX/VxdXfHx8aGwsBC9Xs+TTz7Z\naXxcXJy6tKyiooJXXnmF8PBwDh06xLJly5g1a1abPs8++yzz58+noKCAgQMHdjkmvV7fpljTkbS0\nNGJjY4mNjSUtLa3dmAMHDqDRaOjbty+pqamtlse1/LQsCSwpKcHHx0ft6+3t3e7MoaNHj7J7925G\njRrFfffdxxdffNEmZvPmzURERPRY4Qhk5pFVtcw86o0tJrOZZpMb55xyaUJh1IBRVh6dEEIIIYQQ\nQoiediMzhG6la5et7d+/n1mzZnU42+h669ato6CggJ07d7J8+XKysrLYsGFDl/1iYmLQ6/VkZGSQ\nnZ3N+vXrO4y9ftnanj172Lx5MwATJ06koqKCmpqaVn327t2rxsycOZOFCxd2mL+xsZGPP/6Y119/\nvctxnz9/nmPHjjFu3DgURcHOzo7CwkKCg4MBeOutt3j//fdxcXFh48aNKIpCXFwccXFxXebuSnNz\nM5WVlfz73//miy++4IknnuDkyZMoiuWUra+//pqFCxeSmZn5ve/VGZl5ZEU1WIpHrvYu1JoAbDk/\n8DAaFEYOGGnVsQkhhBBCCCGE+GkYM2YM5eXllJWVdbtPSEgICQkJZGVlqQWbrkRHR/Pee+8xaNAg\nXF1db3a4nWopqnTlk08+ISIigv79+3cZ+8EHH1BVVYWfnx+DBw/GYDC0mn2UkJBAfn4+u3fvJioq\nCqDLmUdeXl6cPn1azXHmzBm8vLza3Nvb25vHHnsMRVG499570Wg0lJeXq32mTZvGP/7xD4YOHdqt\n575ZUjyyohqzZdmaq0ZLWWMjAGd7nyOgjz+u9j3zRRJCCCGEEEIIIa515MgRjEajuo9RZ+rq6sjJ\nyVHf5+fnd2vPIACtVktycrK6sfaNiIqKIjU1FbCcAufh4dGmADV27Fj0esvBUy2xHWlZhnat1atX\ns3r16nZjd+zYgcFgwGAwcPDgQfU+HYmLiyM/P7/NT8tm5JMnTyYzM5OqqiqqqqrIzMxk8uTJbfJM\nnTqVzz77DLAsYWtsbMTDw4Pq6moefvhh/vSnPzF27NhOx3IryLI1K2qZedTH7MCZ5ks00sxZh0Z+\nObDnf/FCCCGEEEIIIX66WvY8AjCbzaSkpGBjY9MmLjs7G29vb/V9Wloab7zxBnPnzsXR0REnJ6du\nLVlrERMTc1PjTUpKIj4+ntDQULRabbtH269cuZIZM2aQnJzc6YbZly5dIisrizVr1rRqP3LkSJtC\njMFg4NSpU4wePVpt8/Pzw83NjQMHDtzUs4Bl76jExETuueceAF5++WV18+zZs2czb948IiMjiY+P\nJz4+nuDgYOzt7UlJSUFRFFavXs3x48d57bXXeO211wDIzMzscqPum6WYzeYeSdxTIiMjzXl5edYe\nxvcW9OImLuEIwP852HH0vC0HPf/FF74ZrJ20ljGeY6w8QiFuHzk5OUyYMMHawxDitiffFSG6R74r\nQnRNvic9q6ioiICAAGsPQ1wnOjqaLVu2YG9v3+0+tbW1uLi49OCoekZ7f4OKohw0m82R7cXLzCMr\ns8NIVZWliPSFbwYAun46aw5JCCGEEEIIIYT4ydm+fbu1h3Dbkj2PrMweI2WN0GBzWW1ztHW04oiE\nEEIIIYQQQgghrpLikZU5aizLBs+7nALgr/evsOZwhBBCCCGEEEIIIVqR4pGV9TX3wo5Gzmrz0AD3\nDpS9joQQQgghhBBCCHH7kD2PrMzdZEMTdpxz+RZf7V1o7bTWHpIQQgghhBBCCCGESmYeWZmDGUwY\nKXOtILSPv7WHI4QQQgghhBBCCNGKFI+szNZkplJ7lmZbIyM9x1l7OEIIIYQQQgghfgJsbGzQ6XSE\nhYURERHBvn37ACgtLWX69OkA5OTkEB0d3abv9u3bCQ8PJywsjMDAQNasWcPSpUvR6XTodDo1t06n\nY9WqVSQlJaEoCsePH1dzrFixAkVRyMvLa5N/woQJ+Pv7qzk2bdrU4XNs2LCBBQsWtHvN2dm53fa3\n3nqLoKAggoODiY2N5cqVKx1/UNfQ6XTExMS0aps3bx5+fn7odDoiIiLYv39/t3IBvP766wwbNgx/\nf38yMjLajXnqqafU/Dqdjvz8fADS09MJDQ1Fp9MRGRnJnj17un3fmyHL1qzEhAKAYmrm27sOAzDK\n6wFrDkkIIYQQQgghxE+Eo6OjWojIyMhg0aJF7Nq1C09Pz06LNU1NTcyZM4fc3Fy8vb1paGjAYDDg\n7+/P4sX/f3v3HlZ1te97/D1E5KrkPQQvlHcBJ0ql2wfj5LJ0LUIrK8TLKjNb7tWqXDtLU5P2Tovq\n7KM+2SmzkowF5aVQ19qZsjellRoWCYqmIpWXUwqWkIqh4/wBziXChOkFJubn9Tzzcc7f/I7x+/6m\njAf4Msb4zQAqijZn+wZISkoiIiKC9PR0Zg7c9NEAAB6JSURBVM6cCcCyZcvo06ePy/OkpqYSHR19\nOS61igMHDrBgwQJ27NiBn58f99xzD+np6dx33321tsvPz+f06dNs2LCBX375hYCAAOd7L774IqNG\njeKjjz7ioYceYtu2bXXmsWPHDtLT09m+fTsHDx7kd7/7Hd988w1eXl7VYs/2f64hQ4YQHx+PMYZt\n27Zxzz33sHPnTvc+hIug4pGHnMAXAHP6DFs6/x2A4MBgT6YkIiIiIiIiDSx5SzI7iy/vL/09W/Xk\nyRufdDv+2LFjtGzZEoDCwkLi4uLIy8urMbakpITy8nJat24NgI+PDz161L0Fy8iRI8nIyGDmzJns\n3buXoKAgvL293c6xuLiYCRMmUFBQgL+/P4sWLSIyMrJKzL59+0hMTKS0tJQRI0a47Ku8vJwTJ07g\n7e3N8ePH6dChQ53nT0tLY9y4ceTn55ORkUFiYmK1mMGDB1eZXVWbjIwMEhIS8PHxISwsjK5du7Jl\nyxYGDnTvJlrnzqr65ZdfMMa41e5iadmah52xzQDwMqbe/7NFREREREREAE6cOIHD4aBnz55MnDiR\nWbNmudWuVatWxMfH07lzZ0aPHk1qaipnzpyps12LFi3o2LEjeXl5pKenc++999YaP2bMGOdSraKi\nImbPnk1UVBTbtm1j7ty5jB8/vlqbRx99lMmTJ5Obm0twcM2TM0JCQnj88cfp1KkTwcHBBAUFceut\nda8Cevfdd0lISGD06NGkpaXVGLN69WoiIiKAitlCZ/M/9/HII48AFTOgOnbs6GwbGhrKgQMHaux3\nxowZREZGMmXKFMrKypzH33//fXr27Mkf/vAH3nzzzTqv4VJo5pGHnWpWDMDMqPs9nImIiIiIiIg0\ntAuZIXQ5nbts7fPPP2f8+PEuZxudb/HixeTm5rJ+/Xpeeukl1q1bx5IlS+psl5CQQHp6OmvXriUz\nM5O33nrLZez5y9Y2btzIihUrALjlllsoKiri2LFjVdp8+umnzphx48bx5JPVP9ujR4+SkZHBvn37\nuOaaa7j77rt55513GDt2rMtcsrOzadOmDZ06dSIkJIQJEyZQXFxMq1atAJg6dSrPPvssbdu25Y03\n3nAemzp1ap2fSV2ee+45rr32Wk6dOsWkSZNITk7m6aefBuCOO+7gjjvu4JNPPmHWrFmsX7/+ks/n\nimYeedhxv0MA3NRxuIczERERERERkavRwIEDOXLkCIcPH3a7TUREBFOmTGHdunXOgk1d4uLiWLp0\nKZ06daJFixYXm26t6lrRs379esLCwmjbti3e3t7ceeedzs3CXUlLS2Pnzp106dKF66+/nmPHjlW5\n5hdffJGcnBzWrVtHeHi481htM49CQkL4/vvvnX3s37+fkJCQaucODg7GGIOPjw/3338/W7ZsqRYz\nePBgCgoKOHLkSK3XcSlUPPKwvNCPAAgNqnuNqIiIiIiIiMjltnPnTk6fPu3cx6g2paWlZGVlOV/n\n5OTQuXNnt87j7+9PcnKyc2PtCxETE0NqaipQcRe4Nm3aVCtADRo0iPT0dABn7Pk6derEpk2bOH78\nONZaMjMz6dWrFwDTp0/n/fffrxJ/5swZ3nvvPXJzcyksLKSwsJCMjAyXS9fOmjp1Kjk5OdUeCxYs\nACA+Pp709HTKysrYt28fu3fv5sYbb6zWz6FDFRNOrLV88MEHzuLUnj17sNYC8OWXX1JWVubW/9/F\n0rI1DzNNTlT8q/2OREREREREpIGc3fMIKgoTKSkpNd7pKzMzk9DQUOfrtLQ0XnjhBR566CH8/PwI\nCAhwa8naWeff6t5dSUlJTJgwgcjISPz9/UlJSakWM3/+fBITE0lOTna5YfZNN93EqFGj6NevH02b\nNiUqKopJkyYBkJubS3x8fJX4DRs2EBISUmVT7cGDB7Njxw5nYedi9OnTh3vuuYfevXvTtGlTFi5c\n6Pz8f//737N48WI6dOjAmDFjOHz4MNZaHA4Hr776KgArVqzg7bffxtvbGz8/P9599916rSuYs5Wq\nK0V0dLTNzs72dBqXrMu0ijusBXZ/hru7hDP7lvrd3ErkSpaVlUVsbKyn0xBp9DRWRNyjsSJSN42T\n+pWfn++c7SKNx2233cbatWsvqE1JSQnNmzevp4zqT01fg8aYrdba6JritWzN05qcZGyf6rvEi4iI\niIiIiEjDudDC0dVExSMP8ytvRpc2AzydhoiIiIiIiIhIjVQ88rA2Zf54efl6Og0RERERERERkRqp\neORhrU9ceWsjRUREREREROTqoeKRhzm6d/d0CiIiIiIiIiIiLql45GE3dtd+RyIiIiIiIiLSeNVr\n8cgYM8wYs8sYs8cYM62G932MMe9Wvr/ZGNOlPvNpjPqFDfV0CiIiIiIiInKV8fLywuFw0LdvX/r1\n68dnn30GwMGDBxk1ahQAWVlZxMXFVWu7Zs0aoqKi6Nu3L7179+a1115jzpw5OBwOHA6Hs2+Hw8GC\nBQtISkrCGMOePXucfcybNw9jDNnZ2dX6j42NpUePHs4+li9f7vI6lixZwsMPP1zje4GBgTUenz9/\nPuHh4fTp04d58+a5/pDOM3LkSAYMqDoBZO7cuYSEhOBwOAgPD2fVqlVu95eSkkK3bt3o1q0bKSkp\nNcYkJSU5+3c4HPzjH/+o8v53331HYGAgL730ktvnvRhN66tjY4wXsBAYCuwHvjDGrLLW7jgn7AHg\nqLW2qzEmAUgG7q2vnBqjQJ9Wnk5BRERERERErjJ+fn7k5OQAFbeonz59Oh9//DEdOnSotVjz66+/\nMmnSJLZs2UJoaChlZWUUFhbSo0cPZsyYAVQUbc72DRUFkIiICNLT05k5cyYAy5Yto0+fPi7Pk5qa\nSnR09OW41Cry8vJ4/fXX2bJlC82aNWPYsGHExcXRtWvXWtv99NNPbN26lcDAQAoKCrjuuuuc702Z\nMoXHH3+c/Px8YmJi+PHHH2nSpPa5OsXFxTzzzDNkZ2djjKF///7Ex8fTsmXLarFn+6/JX//6V4YP\nH+7GlV+aeiseATcCe6y1BQDGmHRgBHBu8WgEkFT5fDnwsjHGWGttPeYlIiIiIiIi0jj81zT4f7mX\nt89rI2D4826HHzt2zFm0KCwsJC4ujry8vBpjS0pKKC8vp3Xr1gD4+PjQo0ePOs8xcuRIMjIymDlz\nJnv37iUoKAhvb2+3cywuLmbChAkUFBTg7+/PokWLiIyMrBKzb98+EhMTKS0tZcSIETX2k5+fz003\n3YS/vz8AN998MytXruSJJ56o9fwrV67k9ttvp3379qSnp/PUU09Vi+nVqxdNmzblyJEjtGvXrtb+\n1q5dy9ChQ2nVqmJCydChQ/nwww8ZPXp0re3O9cEHHxAWFkZAQIDbbS5WfRaPQoDvz3m9H7jJVYy1\nttwY8zPQGjhybpAxZhIwCaB9+/ZkZWXVU8oNJ9LsIvz0brKy6v8/WeRKV1pa+psY9yL1TWNFxD0a\nKyJ10zipX0FBQZSUlADg8+spmpwuv6z9n/n1FGWV/bty4sQJIiMjOXnyJD/88AOrV6+mpKSE0tJS\nzpw5Q0lJCcePH6e8vNyZK4C3tzfDhw+nU6dO3HzzzQwbNoy777672kybc9uUlZURGBhIcHAwmzdv\n5u9//zvx8fGkpqbyyy+/VIkFOH36NKNHj8bPzw+AVatW8dxzz9G7d2+WLl3Kxx9/zNixY/n00085\nefIkp06doqSkhD//+c/cd999JCYmsmjRomp5AISFhfHxxx9TWFiIn58fq1evJioqqlrc+d555x2e\nfPJJ2rZty7hx4/jLX/4CgLWWsrIySkpK+OKLLzDG4Ovry+LFi1mwYEG1fq677jqWLl1KQUEB7dq1\nc563bdu2FBQUVMujrKyMt956iyVLlhAVFcWcOXNo2bIlpaWlzJ07l4yMDBYsWIC3t3ed13CukydP\nXtAYr8/i0WVjrV0ELAKIjo62sbGxnk3oMoiNjSUrK4vfwrWI1DeNFRH3aKyIuEdjRaRuGif1Kz8/\nn+bNm1e8iP/PejlHszre9/PzY9u2bQB8/vnnTJw4kby8PAIDA2nSpAnNmzfH39+fpk2b/jPXSikp\nKeTm5rJ+/XoWLlzIxo0bWbJkSZWYc9v4+Pjg4+PD2LFjWb16NWvXriUzM5P09HQCAgKq9e/l5UVa\nWlqVZWtbtmxhxYoVNG/enLi4OCZPnoy1Fl9fX5o1a0bz5s3ZvHkzGRkZeHt78+CDDzJ79uxqfUdH\nRzN9+nTuuusuAgIC6N+/Pz4+PtXizvXDDz9QUFDArbfeijEGHx8fvv32W8LDwzHG8Morr7Bs2TKa\nN2/Oe++9R4sWLZg4cSITJ0502aePjw/WWud5z35G5+fx2GOP8eyzz2KMYdasWSQlJfHmm2/yzDPP\n8PjjjxMcHOyybW18fX2JiopyO74+N8w+AHQ853Vo5bEaY4wxTYEgoKgecxIRERERERGRcwwcOJAj\nR45w+PBht9tEREQwZcoU1q1bx4oVK9xqExcXx9KlS+nUqRMtWrS42HRrZYypM+aBBx5g69atfPLJ\nJ7Rs2ZLu3bvXGv/ee+9x9OhRwsLC6NKlC4WFhaSlpTnfnzJlCjk5OWzYsIGYmBigYs+ms5tcn/s4\nuxl5SEgI33//z8Va+/fvJyQkpNq527dvj5eXF02aNOHBBx9ky5YtAGzevJknnniCLl26MG/ePObO\nncvLL79c9wd0keqzePQF0M0YE2aMaQYkAOdvO74K+GPl81HAf2u/IxEREREREZGGs3PnTk6fPu3c\nx6g25y9pzMnJoXPnzm6dx9/fn+TkZOfG2hciJiaG1NRUoGJmXJs2baoVoAYNGkR6ejqAM7YmP/74\nI1Bxp7KVK1eSmJgIwMsvv1xjASYtLY0PP/yQwsJCCgsL2bp1q/M8rowZM4acnJxqj7Obkd922218\n9NFHHD16lKNHj/LRRx9x2223Vevn0KFDzufvv/8+4eHhAGzYsMGZz2OPPcZTTz3l8q5zl0O9LVur\n3MPoYWAt4AW8aa3dboz5dyDbWrsKeANYaozZAxRTUWASERERERERkXp04sQJHA4HULFvT0pKCl5e\nXtXiMjMzCQ0Ndb5OS0vjhRde4KGHHsLPz4+AgIBqS9Zqk5Bwcb/2JyUlMWHCBCIjI/H396/x1vbz\n588nMTGR5ORklxtmA9x1110UFRXh7e3NwoULueaaa4CKItqgQYOqxBYWFvLtt98yYMAA57GwsDCC\ngoLYvHnzRV0LQKtWrZg1axY33HADAE8//bRz8+yJEyfypz/9iejoaJ544glycnIwxtClSxdee+21\niz7npTBX2kSf6Ohom52d7ek0LgutIxZxj8aKiHs0VkTco7EiUjeNk/qVn59Pr169PJ2GnCcuLo6V\nK1fSrFldO0b9U0lJyQXtNdRY1PQ1aIzZaq2Nrin+itgwW0RERERERESkPq1Zs8bTKTRa9bnnkYiI\niIiIiIiIXOFUPBIREREREREREZdUPBIREREREREREZdUPBIREREREREREZdUPBIREREREREREZdU\nPBIRERERERG5ynh5eeFwOOjbty/9+vXjs88+A+DgwYOMGjUKgKysLOLi4qq1XbNmDVFRUfTt25fe\nvXvz2muvMWfOHBwOBw6Hw9m3w+FgwYIFJCUlYYxhz549zj7mzZuHMYbs7Oxq/cfGxtKjRw9nH8uX\nL3d5HUuWLOHhhx+u8b3AwMAaj0+YMIF27doRHh5e5XhxcTFDhw6lW7duDB06lKNHj7o877nmzZuH\nr68vP//8s/NYVlYWQUFBOBwOevXqxTPPPONWXwBbt24lIiKCrl278sgjj2CtrTEuKysLh8NBnz59\nuPnmm+u8vkuh4pGIiIiIiIjIVcbPz4+cnBy+/vprnnvuOaZPnw5Ahw4dai3W/Prrr0yaNInVq1fz\n9ddf89VXXxEbG8uMGTPIyckhJyfH2XdOTg6PPPIIABEREaSnpzv7WbZsGX369HF5ntTUVGcfZ4tZ\nl8t9993Hhx9+WO34888/z5AhQ9i9ezdDhgzh+eefd6u/tLQ0brjhBlauXFnleExMDDk5OWRnZ/PO\nO+/w5ZdfutXf5MmTef3119m9eze7d++uMdeffvqJf/3Xf2XVqlVs376dZcuW1Xl9l6LpZe1NRERE\nRERERNz2zOrt7Dh47LL22btDC2bf7rowc75jx47RsmVLAAoLC4mLiyMvL6/G2JKSEsrLy2ndujUA\nPj4+9OjRo85zjBw5koyMDGbOnMnevXsJCgrC29vb7RyLi4uZMGECBQUF+Pv7s2jRIiIjI6vE7Nu3\nj8TEREpLSxkxYoTLvgYPHkxhYWG14xkZGWRlZQHwxz/+kdjYWJKTk2vNq6CggNLSUl555RXmzJnD\n/fffXy0mICCA/v37s2fPHvr161drf4cOHeLYsWMMGDAAgPHjx/PBBx8wfPjwKnF/+9vfuPPOO+nU\nqRMA7dq1q/P6LoVmHomIiIiIiIhcZU6cOIHD4aBnz55MnDiRWbNmudWuVatWxMfH07lzZ0aPHk1q\naipnzpyps12LFi3o2LEjeXl5pKenc++999YaP2bMGOeytaKiImbPnk1UVBTbtm1j7ty5jB8/vlqb\nRx99lMmTJ5Obm0twcLBb13OuH374wdnu2muv5YcffqizzYoVK0hISCAmJoZdu3bV2KaoqIhNmzbR\np08fdu3a5byu8x8//fQTBw4cIDQ01Nk2NDSUAwcOVOvzm2++4ejRo8TGxtK/f3/efvvtC77eC6GZ\nRyIiIiIiIiIeciEzhC6ns0vLAD7//HPGjx/vcrbR+RYvXkxubi7r16/npZdeYt26dSxZsqTOdgkJ\nCaSnp7N27VoyMzN56623XMampqYSHR3tfL1x40ZWrFgBwC233EJRURHHjlWdsfXpp586Y8aNG8eT\nTz7p1vXUxBiDMabOuOXLl5ORkUGTJk246667WLZsmXMPpg0bNhAVFUWTJk2YNm2ac5ne2c/9UpSX\nl7N161YyMzM5ceIEAwcOZMCAAXTv3v2S+66JikciIiIiIiIiV7GBAwdy5MgRDh8+7HabiIgIIiIi\nGDduHGFhYW4Vj+Li4pg6dSrR0dG0aNHiEjJ2zZ2Cjyvt27fn0KFDBAcHc+jQoSpLwWqSm5vL3r17\nGTp0KACnTp0iLCzMWTyKiYlhzZo1Vdrs2rXL5ayrrKwsQkJC2L9/v/PY/v37CQkJqRYbGhpK69at\nCQgIICAggMGDB/P111/XW/FIy9ZERERERERErmI7d+7k9OnTzn2MalNaWurcFwgqZtF07tzZrfP4\n+/uTnJzMjBkzLjjHmJgYUlNTgYoiS5s2baoVoAYNGuTclPts7IWIj48nJSUFgJSUFOe+SVu2bKlx\nmVxaWhrTp0+nsLCQwsJCDh48yMGDB/n2229dnqNHjx7OjcDPf1xzzTUEBwfTokULNm3ahLWWt99+\nu8b9m0aMGMHGjRspLy/n+PHjbN68mV69el3wNbtLxSMRERERERGRq8zZPY8cDgf33nsvKSkpeHl5\nVYvLzMwkNDTU+fjqq6944YUX6NGjBw6Hg9mzZ7s16+ishISEOjeNrklSUhJbt24lMjKSadOmOYs8\n55o/fz4LFy4kIiKixn2Czho9ejQDBw5k165dhIaG8sYbbwAwbdo01q1bR7du3Vi/fj3Tpk0D4Lvv\nvsPPz69aP+np6dx+++1Vjt1xxx1V7ip3MV555RUmTpxI165duf76652bZb/66qu8+uqrAPTq1Yth\nw4YRGRnJjTfeyMSJEwkPD6/1+i6FsdZecicNKTo62mZnZ3s6jcsiKyuL2NhYT6ch0uhprIi4R2NF\nxD0aKyJ10zipX/n5+fU6S0Qur6lTpzJu3Lhqd3eDirvPNW/e3ANZXZqavgaNMVuttdE1xWvPIxER\nERERERERF1588UVPp+BxWrYmIiIiIiIiIiIuqXgkIiIiIiIi0sCutC1k5LfjYr72VDwSERERERER\naUC+vr4UFRWpgCQNzlpLUVERvr6+F9ROex6JiIiIiIiINKDQ0FD279/P4cOHPZ2KXKKTJ09ecCHG\n03x9fQkNDb2gNioeiYiIiIiIiDQgb29vwsLCPJ2GXAZZWVlERUV5Oo16p2VrIiIiIiIiIiLikopH\nIiIiIiIiIiLikopHIiIiIiIiIiLikrnSdnc3xhwGvvV0HpdJG+CIp5MQuQJorIi4R2NFxD0aKyJ1\n0zgRcc9vaax0tta2remNK6549FtijMm21kZ7Og+Rxk5jRcQ9Gisi7tFYEambxomIe66WsaJlayIi\nIiIiIiIi4pKKRyIiIiIiIiIi4pKKR561yNMJiFwhNFZE3KOxIuIejRWRummciLjnqhgr2vNIRERE\nRERERERc0swjERERERERERFxScUjERERERERERFxScWjBmCMGWaM2WWM2WOMmVbD+z7GmHcr399s\njOnS8FmKeJYb4+SvxpgdxphtxphMY0xnT+Qp4ml1jZVz4u4yxlhjzG/+1rEiNXFnrBhj7qn83rLd\nGPO3hs5RpDFw42ewTsaY/zHGfFX5c9jvPZGniKcZY940xvxojMlz8b4xxiyoHEvbjDH9GjrH+qTi\nUT0zxngBC4HhQG9gtDGm93lhDwBHrbVdgf8DJDdsliKe5eY4+QqIttZGAsuBFxo2SxHPc3OsYIxp\nDjwKbG7YDEUaB3fGijGmGzAdGGSt7QM81uCJiniYm99XZgLvWWujgATglYbNUqTRWAIMq+X94UC3\nysck4P82QE4NRsWj+ncjsMdaW2CtPQWkAyPOixkBpFQ+Xw4MMcaYBsxRxNPqHCfW2v+x1h6vfLkJ\nCG3gHEUaA3e+pwD8BxV/iDjZkMmJNCLujJUHgYXW2qMA1tofGzhHkcbAnbFigRaVz4OAgw2Yn0ij\nYa39BCiuJWQE8LatsAm4xhgT3DDZ1T8Vj+pfCPD9Oa/3Vx6rMcZaWw78DLRukOxEGgd3xsm5HgD+\nq14zEmmc6hwrlVOkO1pr/96QiYk0Mu58X+kOdDfGfGqM2WSMqe2vySK/Ve6MlSRgrDFmP/AP4C8N\nk5rIFedCf6e5ojT1dAIiIhfCGDMWiAZu9nQuIo2NMaYJ8J/AfR5OReRK0JSKpQWxVMxm/cQYE2Gt\n/cmjWYk0PqOBJdba/22MGQgsNcaEW2vPeDoxEWk4mnlU/w4AHc95HVp5rMYYY0xTKqaDFjVIdiKN\ngzvjBGPM74AZQLy1tqyBchNpTOoaK82BcCDLGFMIDABWadNsuQq5831lP7DKWvurtXYf8A0VxSSR\nq4k7Y+UB4D0Aa+3ngC/QpkGyE7myuPU7zZVKxaP69wXQzRgTZoxpRsUmc6vOi1kF/LHy+Sjgv621\ntgFzFPG0OseJMSYKeI2KwpH2pZCrVa1jxVr7s7W2jbW2i7W2CxX7g8Vba7M9k66Ix7jz89cHVMw6\nwhjThoplbAUNmaRII+DOWPkOGAJgjOlFRfHocINmKXJlWAWMr7zr2gDgZ2vtIU8ndblo2Vo9s9aW\nG2MeBtYCXsCb1trtxph/B7KttauAN6iY/rmHig24EjyXsUjDc3OcvAgEAssq95P/zlob77GkRTzA\nzbEictVzc6ysBW41xuwATgNTrbWa+S1XFTfHyr8BrxtjplCxefZ9+kO3XI2MMWlU/NGhTeUeYLMB\nbwBr7atU7An2e2APcBy43zOZ1g+jcS8iIiIiIiIiIq5o2ZqIiIiIiIiIiLik4pGIiIiIiIiIiLik\n4pGIiIiIiIiIiLik4pGIiIiIiIiIiLik4pGIiIiIiIiIiLik4pGIiIhIHYwxp40xOcaYPGPMamPM\nNZe5/0JjTJvK56WXs28RERGRS6XikYiIiEjdTlhrHdbacKAY+LOnExIRERFpKCoeiYiIiFyYz4EQ\nAGPM9caYD40xW40xG4wxPSuPtzfGvG+M+bry8S+Vxz+ojN1ujJnkwWsQERERcVtTTycgIiIicqUw\nxngBQ4A3Kg8tAv5krd1tjLkJeAW4BVgAfGytvaOyTWBl/ARrbbExxg/4whizwlpb1MCXISIiInJB\nVDwSERERqZufMSaHihlH+cA6Y0wg8C/AMmPM2Tifyn9vAcYDWGtPAz9XHn/EGHNH5fOOQDdAxSMR\nERFp1FQ8EhEREanbCWutwxjjD6ylYs+jJcBP1lqHOx0YY2KB3wEDrbXHjTFZgG/9pCsiIiJy+WjP\nIxERERE3WWuPA48A/wYcB/YZY+4GMBX6VoZmApMrj3sZY4KAIOBoZeGoJzCgwS9ARERE5CKoeCQi\nIiJyAay1XwHbgNHAGOABY8zXwHZgRGXYo8D/MsbkAluB3sCHQFNjTD7wPLCpoXMXERERuRjGWuvp\nHEREREREREREpJHSzCMREREREREREXFJxSMREREREREREXFJxSMREREREREREXFJxSMRERERERER\nEXFJxSMREREREREREXFJxSMREREREREREXFJxSMREREREREREXHp/wPHwy+r7tT72wAAAABJRU5E\nrkJggg==\n",
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cH7lzjNr97zb"
      },
      "source": [
        "Draw Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UGRKcCCh9hma"
      },
      "source": [
        "# unique, counts = np.unique(y_test_conv, return_counts=True)\n",
        "# print(dict(zip(unique, counts)))\n",
        "# cf_matrix = confusion_matrix(y_test_conv, yhat_classes)\n",
        "# cf_matrix_best = all_cf_matrices[idx_best]\n",
        "# print(cf_matrix_best)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3zGbGIn-Q3L",
        "outputId": "9724b29a-f17b-45b8-c715-f88021d84c0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 794
        }
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [12, 9]\n",
        "for j in range(len(all_cf_matrices)):\n",
        "  cf_matrix_best = all_cf_matrices[j]\n",
        "\n",
        "  stats_text = \"\\n\\nAccuracy={:0.3f}\\nPrecision={:0.3f}\\nRecall={:0.3f}\\nF1 Score={:0.3f}\".format(\n",
        "                  all_accuracy[j],all_precision[j],all_recall[j],all_f1[j])\n",
        "\n",
        "  group_names = ['True Neg','False Pos','False Neg','True Pos']\n",
        "\n",
        "  group_counts = [\"{0:0.0f}\".format(value) for value in\n",
        "                  cf_matrix_best.flatten()]\n",
        "\n",
        "  group_percentages = [\"{0:.2%}\".format(value) for value in\n",
        "                      cf_matrix_best.flatten()/np.sum(cf_matrix_best)]\n",
        "\n",
        "  labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in\n",
        "            zip(group_names,group_counts,group_percentages)]\n",
        "            \n",
        "  labels = np.asarray(labels).reshape(2,2)\n",
        "  categories = ['Fake', 'Real']\n",
        "  # cmap = sns.dark_palette(\"red\")\n",
        "  # cmap = sns.cubehelix_palette(8, start=.5, rot=-.75, as_cmap=True)\n",
        "  # cmap = sns.color_palette(\"GnBu_d\")\n",
        "\n",
        "  plt.clf() \n",
        "  plt.figure()\n",
        "  svm = sns.heatmap(cf_matrix_best, annot=labels, fmt='', cmap='PuBu',xticklabels=categories,yticklabels=categories)\n",
        "\n",
        "  plt.ylabel('True Label')\n",
        "  plt.xlabel('Predicted Label' + stats_text)\n",
        "\n",
        "  # figure = svm.get_figure()    \n",
        "  # figure.savefig('./lstm/lstm_conf_matrix.png', dpi=150)\n",
        "\n",
        "  plt.savefig(\"./lstm/cfpr/lstm_best_10fold_conf_matrix_fold{}.png\".format(j+1), format='png', dpi=150, bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I8RzPOsA3IYSPMu+3Aj5K0z1JkqTKJdfnfK7osvtNyXohSZKknFBq8RljfDdl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\nYII=\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4fEIvKdX26j"
      },
      "source": [
        "for m in range(len(all_arr_precision)):\n",
        "  df_prerec = pd.DataFrame(all_arr_precision[m])\n",
        "  df_prerec.columns = ['precision']\n",
        "  df_prerec['recall'] = all_arr_recall[m]\n",
        "  df_prerec['threshold'] = all_thresholds[m]\n",
        "  df_prerec\n",
        "  df_prerec.to_pickle(\"./lstm/cfpr/lstm_best_10fold_precison_recall_curve_fold{}.pkl\".format(m+1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZtuwyjDF9Kz",
        "outputId": "47debc49-6f55-4af6-d2ff-d3d7f25d9bff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "!jupyter nbconvert --to html ./lstm/lstm_classification_malay_10fold.ipynb"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[NbConvertApp] Converting notebook ./lstm/lstm_classification_malay_10fold.ipynb to html\n",
            "[NbConvertApp] Writing 939027 bytes to ./lstm/lstm_classification_malay_10fold.html\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}