1_part/4_10_Kg_kor_intro-to-recurrent-neural-networks-lstm-gru.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"colab":{"name":"intro-to-recurrent-neural-networks-lstm-gru_kr.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true}},"cells":[{"cell_type":"markdown","metadata":{"id":"IRIVCZnXN4ly","colab_type":"text"},"source":["※본 커널은 캐글을 통해 공유된 \"DJIA 30 Stock Time Series\"(https://www.kaggle.com/szrlee/stock-time-series-20050101-to-20171231/kernels) Dataset 의 Kernel 중 \"Intro to Recurrent Neural Networks LSTM | GRU\"(https://www.kaggle.com/thebrownviking20/intro-to-recurrent-neural-networks-lstm-gru) 의 내용을 기반으로 작성되었습니다."]},{"cell_type":"markdown","metadata":{"id":"MHtoKh6Wx3jd","colab_type":"text"},"source":["## Colab 사용 환경"]},{"cell_type":"code","metadata":{"colab_type":"code","id":"Xrtg3MR0x2TR","outputId":"3cd4cd45-3dec-4a18-9470-5d882ee0d72f","executionInfo":{"status":"ok","timestamp":1585819135210,"user_tz":-540,"elapsed":27203,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}},"colab":{"base_uri":"https://localhost:8080/","height":127}},"source":["from google.colab import drive\n","drive.mount('/content/gdrive',force_remount=True)"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"ba2ad25a-67ac-4d64-d4e7-45b2c6a91ce7","executionInfo":{"status":"ok","timestamp":1585819135801,"user_tz":-540,"elapsed":27781,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}},"id":"zSgqhuszx2TY","colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["cd /content/gdrive/My Drive/JK/TimeSeries/src"],"execution_count":2,"outputs":[{"output_type":"stream","text":["/content/gdrive/My Drive/JK/TimeSeries/src\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"NkSAaJDP9WlH","colab_type":"code","outputId":"40c83b2d-646a-4660-9481-368294795e26","executionInfo":{"status":"ok","timestamp":1585819135801,"user_tz":-540,"elapsed":27770,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["pwd"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content/gdrive/My Drive/JK/TimeSeries/src'"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"RWfQStDfzY7s","colab_type":"text"},"source":["# 1 Aim and motivation"]},{"cell_type":"markdown","metadata":{"_uuid":"340c411000d38f9d7e7cbfee7daf46d49571960a","id":"gDAGh6NSONOu","colab_type":"text"},"source":["이 커널을 만들기 위해 선택한 주된 이유는 다양한 작업과 응용 프로그램에 RNN을 실습하고 사용하기 때문입니다. 첫 번째는 시계열 데이터입니다. RNN은 순차 데이터 예측 방식을 완전히 바꿔 놓았습니다. 여기서 목표는 kaggle에서 RNN에 대한 최고의 참조를 만드는 것입니다."]},{"cell_type":"markdown","metadata":{"_uuid":"04a6c8887c7716dab64ea9bf77197c263e12d06f","id":"mb8YAjt5ONOy","colab_type":"text"},"source":["## Things to remember\n","* 이것이 마음에 들면, upvote 하고 이 커널을 공유하십시오. 이것은 가시성을 높이고 더 많은 사람들이 RNN의 위대함에 대해 배울 수 있게 됩니다.\n","* 이 커널에서는 keras를 사용합니다. keras 또는 신경망에 익숙하지 않은 경우이 커널 / 자습서 참조 : https://www.kaggle.com/thebrownviking20/intro-to-keras-with-breast-cancer-data-ann\n","* 시계열에 대한 의심과 호기심은 여기에서 처리 할 수 ​​있습니다. : https://www.kaggle.com/thebrownviking20/everything-you-can-do-with-a-time-series\n","* 설명이 당신을 위협하지 않도록하십시오. 생각보다 간단합니다.\n","* 결국 LSTM의 응용 프로그램을 더 추가 할 것입니다. 더 지켜봐 주시기 바랍니다!\n","* 이 코드는 Kirill Eremenko의 딥 러닝 과정에서 영감을 얻었습니다. : https://www.udemy.com/deeplearning/"]},{"cell_type":"markdown","metadata":{"id":"WVW64Z9AzjNL","colab_type":"text"},"source":["# 2 Recurrent Neural Networks"]},{"cell_type":"markdown","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"collapsed":true,"id":"JEe_Jq1BONOz","colab_type":"text"},"source":["## Recurrent Neural Networks\n","반복적인(recurrent) 신경망에서 네트워크의 하나 이상의 레이어로부터의 출력 활성화를 저장합니다. 종종 hidden layer activations 이 있습니다.(Often these are hidden later activations). 다음에 입력 예제를 네트워크에 공급할 때 이전에 저장된 출력을 추가 입력으로 포함시킵니다. 추가 입력은 이전 레이어에 대한 \"정상\"입력의 끝에 연결된 것으로 생각할 수 있습니다. 예를 들어, hidden layer 에 10 개의 정규 입력 노드와 128 개의 숨겨진 노드가 있는 경우 실제로는 다른 레이어가 아닌 138 개의 총 입력을 갖게됩니다 (레이어의 출력을 엘라 맨 자체에 공급한다고 가정). 물론, 처음으로 네트워크의 출력을 계산하려고 할 때 여분의 128 개의 입력을 0 또는 그 밖의 값으로 채워야합니다.\n","\n","Source: [Quora](https://www.quora.com/What-is-a-simple-explanation-of-a-recurrent-neural-network)\n","<img src=\"https://cdn-images-1.medium.com/max/1600/1*NKhwsOYNUT5xU7Pyf6Znhg.png\">\n","\n","Source: [Medium](https://medium.com/ai-journal/lstm-gru-recurrent-neural-networks-81fe2bcdf1f9)\n","\n","인터넷에서 찾은 반복 신경망에 대해 가장 좋은 설명을 제공하겠습니다.: \n","\n","https://www.youtube.com/watch?v=UNmqTiOnRfg&t=3s"]},{"cell_type":"markdown","metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","collapsed":true,"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":false,"id":"1lAoZRHsONO0","colab_type":"text"},"source":["이제 RNN은 매우 강력하지만 **Vanishing gradient 문제**로 인해 메모리가 과거 반복의 3-4 개 인스턴스를 저장하는 데는 좋지만 더 많은 수의 인스턴스는 좋지 않은 것처럼 장기 정보를 사용하지 못하게 됩니다. 일반 RNN 만 사용하지 않습니다. 대신, 우리는 더 나은 RNN 변형을 사용합니다 : **Long Short Term Networks (LSTM).**\n","\n","### What is Vanishing Gradient problem?\n","Vanishing gradient 문제는 그라디언트 기반 학습 방법 및 역전파를 사용하여 인공 신경망을 훈련시키는 데 있어서의 어려움 입니다. 이러한 방법에서, 각각의 신경망의 가중치는 각 트레이닝 반복에서 현재 가중치에 대한 에러 함수의 부분 미분에 비례하는 업데이트를 수신한다. 문제는 경우에 따라 gradient가 vanishingly small 이라서 weight가 값을 갱신하지 못하게 하는 것입니다. 최악의 경우, 이것은 신경망의 추가 훈련을 완전히 중단시킬 수 있습니다. 문제 원인의 한 예로서, 쌍곡선 탄젠트 함수와 같은 기존의 활성화 함수는 범위 (0, 1)의 그래디언트를 가지며 역전파는 연쇄 규칙에 따라 그래디언트를 계산합니다. 이것은 n-계층 네트워크에서 \"front\"층의 gradient를 계산하기 위해 이들 작은 수의 n을 곱하는 효과를 가지며, 이는 gradient (오류 신호)가 n과 함께 기하 급수적으로 감소하는 반면, front 층은 매우 느리게 훈련됨을 의미한다.\n","\n","Source: [Wikipedia](https://en.wikipedia.org/wiki/Vanishing_gradient_problem)\n","\n","<img src=\"https://cdn-images-1.medium.com/max/1460/1*FWy4STsp8k0M5Yd8LifG_Q.png\">\n","\n","Source: [Medium](https://medium.com/@anishsingh20/the-vanishing-gradient-problem-48ae7f501257)"]},{"cell_type":"markdown","metadata":{"_uuid":"79ac683d1b85322f6ed4a564637914a8d803221b","id":"B8gLH44pONO1","colab_type":"text"},"source":["## Long Short Term Memory(LSTM)\n","LSTM (Long Short-Term Memory) units (또는 blocks)는 RNN (Recurrent Neural Network) layer의 구성 단위입니다. LSTM unit로 구성된 RNN을 종종 LSTM 네트워크라고 합니다. 공통 LSTM unit은 cell, input gate, output gate 및 forget gate로 구성됩니다. 셀은 임의의 시간 간격에 걸쳐 값을 \"기억\"합니다. 따라서 LSTM에서 \"메모리\"라는 단어가 사용됩니다. 3개의 게이트 각각은 다층 (또는 피드 포워드) 신경망에서와 같이 \"conventional\" 인공 뉴런으로 생각할 수 있습니다. 즉, 가중 합의 활성화(활성화 함수를 사용하여)를 계산합니다. 직관적으로, LSTM의 연결을 통과하는 값의 흐름을 조절하는 것으로 생각할 수 있습니다. 따라서 \"gate\"라는 표현이 있습니다. 이 게이트와 셀 사이에는 연결이 있습니다.\n","\n","long short-term은 LSTM이 장기간 지속될 수 있는 단기 메모리의 모델이라는 사실을 나타냅니다. LSTM은 중요한 사건들 사이에 알려지지 않은 크기와 지속 시간의 시차가 주어지면 시계열을 분류, 처리 및 예측하는 데 적합합니다. LSTM은 전통적인 RNN을 훈련할 때 exploding 및 vanishing gradient 문제를 해결하기 위해 개발되었습니다.\n","\n","Source: [Wikipedia](https://en.wikipedia.org/wiki/Long_short-term_memory)\n","\n","<img src=\"https://cdn-images-1.medium.com/max/1600/0*LyfY3Mow9eCYlj7o.\">\n","\n","Source: [Medium](https://codeburst.io/generating-text-using-an-lstm-network-no-libraries-2dff88a3968)\n","\n","The best LSTM explanation on internet: https://medium.com/deep-math-machine-learning-ai/chapter-10-1-deepnlp-lstm-long-short-term-memory-networks-with-math-21477f8e4235\n","\n","더 깊은 통찰력에 대해서는 위의 링크를 참조하십시오."]},{"cell_type":"markdown","metadata":{"_uuid":"42c24cc294d93504880c5c76d383b491d89b29f1","id":"W_blZ8v3ONO3","colab_type":"text"},"source":["## Components of LSTMs\n","So the LSTM cell contains the following components\n","* Forget Gate “f” ( a neural network with sigmoid)\n","* Candidate layer “C\"(a NN with Tanh)\n","* Input Gate “I” ( a NN with sigmoid )\n","* Output Gate “O”( a NN with sigmoid)\n","* Hidden state “H” ( a vector )\n","* Memory state “C” ( a vector)\n","\n","* Inputs to the LSTM cell at any step are X<sub>t</sub> (current input) , H<sub>t-1</sub> (previous hidden state ) and C<sub>t-1</sub> (previous memory state).  \n","* Outputs from the LSTM cell are H<sub>t</sub> (current hidden state ) and C<sub>t</sub> (current memory state)"]},{"cell_type":"markdown","metadata":{"_uuid":"e7f3771ce38c69191eb23681561744863ee30ea8","id":"kuwQjgnOONO4","colab_type":"text"},"source":["## Working of gates in LSTMs\n","먼저, LSTM 셀은 이전 메모리 상태 C<sub>t-1</sub>를 취하고 현재 메모리 상태 C<sub>t</sub>인지를 결정하기 위해 forget gate (f) 게이트를 요소 단위로 곱한다. forget gate 값이 0 이면 이전 메모리 상태를 완전히 잊혀지고, f forget gate 값이 1 이면 이전 메모리 상태를 셀로 완전히 전달합니다. (f 게이트는 0과 1 사이의 값을 제공합니다).\n","\n","**C<sub>t</sub> = C<sub>t-1</sub> * f<sub>t</sub>**\n","\n","Calculating the new memory state: \n","\n","**C<sub>t</sub> = C<sub>t</sub> + (I<sub>t</sub> * C\\`<sub>t</sub>)**\n","\n","Now, we calculate the output:\n","\n","**H<sub>t</sub> = tanh(C<sub>t</sub>)**"]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"QAZeUx6GPMyT"},"source":["# 3 LSTM"]},{"cell_type":"markdown","metadata":{"id":"gFlmsR8Bz83M","colab_type":"text"},"source":["## 3.1 Import and Function"]},{"cell_type":"markdown","metadata":{"trusted":true,"collapsed":true,"_uuid":"2f82cc24efc6283e5a98cadb78fc1a465e44c143","id":"qpnZ9N-cONO6","colab_type":"text"},"source":["2017 년 IBM의 주식 가격을 예측하기 위해 LSTM을 사용합니다."]},{"cell_type":"code","metadata":{"id":"miX_6opEDxK6","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"1f5d368d-cc6a-4ad2-aab2-39db3290591f","executionInfo":{"status":"ok","timestamp":1585819135802,"user_tz":-540,"elapsed":27762,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}}},"source":["%tensorflow_version 1.x"],"execution_count":4,"outputs":[{"output_type":"stream","text":["TensorFlow 1.x selected.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"da0236e4b36ce514c1fec3fd72f236d1fa259131","id":"6bxXDfAXONO7","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"04e919bb-4470-4313-c287-28a2cf2df2ab","executionInfo":{"status":"ok","timestamp":1585819141552,"user_tz":-540,"elapsed":33503,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}}},"source":["# Importing the libraries\n","import numpy as np\n","import matplotlib.pyplot as plt\n","plt.style.use('fivethirtyeight')\n","import pandas as pd\n","from sklearn.preprocessing import MinMaxScaler\n","from keras.models import Sequential\n","from keras.layers import Dense, LSTM, Dropout, GRU, Bidirectional\n","from keras.optimizers import SGD\n","import math\n","from sklearn.metrics import mean_squared_error"],"execution_count":5,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"b288a8e2caf6196daec9cd2bc4ca78fe50345845","id":"oiUBFMQRONPA","colab_type":"code","colab":{}},"source":["# Some functions to help out with\n","\n","def plot_predictions(test,predicted):\n","    plt.plot(test, color='red',label='Real IBM Stock Price')\n","    plt.plot(predicted, color='blue',label='Predicted IBM Stock Price')\n","    plt.title('IBM Stock Price Prediction')\n","    plt.xlabel('Time')\n","    plt.ylabel('IBM Stock Price')\n","    plt.legend()\n","    plt.show()\n","\n","def return_rmse(test,predicted):\n","    rmse = math.sqrt(mean_squared_error(test, predicted))\n","    print(\"The root mean squared error is {}.\".format(rmse))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"531fb175886a2a3bff532e14a393c3e0f0afc249","id":"gG9TajWfl6S7","colab_type":"code","colab":{}},"source":["def plot_metrics(history):\n","    metrics_df = pd.DataFrame(data={\"loss\": history.history['loss']})\n","    metrics_df.plot()"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"lx63tLBQ01ap"},"source":["## 3.2 Data set"]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"4cf10cf27420eb383b93b15c0895139ea96c0ed3","id":"039nNY9UONPF","colab_type":"code","outputId":"4e2a44bb-7d28-49e5-8337-2cf7a7ec9bdd","executionInfo":{"status":"ok","timestamp":1585819143260,"user_tz":-540,"elapsed":35185,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}},"colab":{"base_uri":"https://localhost:8080/","height":235}},"source":["# First, we get the data\n","dataset = pd.read_csv('../input/stock-time-series-20050101-to-20171231/IBM_2006-01-01_to_2018-01-01.csv', index_col='Date', parse_dates=['Date'])\n","dataset.head()"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Close</th>\n","      <th>Volume</th>\n","      <th>Name</th>\n","    </tr>\n","    <tr>\n","      <th>Date</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2006-01-03</th>\n","      <td>82.45</td>\n","      <td>82.55</td>\n","      <td>80.81</td>\n","      <td>82.06</td>\n","      <td>11715200</td>\n","      <td>IBM</td>\n","    </tr>\n","    <tr>\n","      <th>2006-01-04</th>\n","      <td>82.20</td>\n","      <td>82.50</td>\n","      <td>81.33</td>\n","      <td>81.95</td>\n","      <td>9840600</td>\n","      <td>IBM</td>\n","    </tr>\n","    <tr>\n","      <th>2006-01-05</th>\n","      <td>81.40</td>\n","      <td>82.90</td>\n","      <td>81.00</td>\n","      <td>82.50</td>\n","      <td>7213500</td>\n","      <td>IBM</td>\n","    </tr>\n","    <tr>\n","      <th>2006-01-06</th>\n","      <td>83.95</td>\n","      <td>85.03</td>\n","      <td>83.41</td>\n","      <td>84.95</td>\n","      <td>8197400</td>\n","      <td>IBM</td>\n","    </tr>\n","    <tr>\n","      <th>2006-01-09</th>\n","      <td>84.10</td>\n","      <td>84.25</td>\n","      <td>83.38</td>\n","      <td>83.73</td>\n","      <td>6858200</td>\n","      <td>IBM</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             Open   High    Low  Close    Volume Name\n","Date                                                 \n","2006-01-03  82.45  82.55  80.81  82.06  11715200  IBM\n","2006-01-04  82.20  82.50  81.33  81.95   9840600  IBM\n","2006-01-05  81.40  82.90  81.00  82.50   7213500  IBM\n","2006-01-06  83.95  85.03  83.41  84.95   8197400  IBM\n","2006-01-09  84.10  84.25  83.38  83.73   6858200  IBM"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"fb4c9db6d8a5bcf20ffad41747cfa5b6215ba220","id":"DVufA2I3ONPJ","colab_type":"code","colab":{}},"source":["# Checking for missing values\n","training_set = dataset[:'2016'].iloc[:,1:2].values\n","test_set = dataset['2017':].iloc[:,1:2].values"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"bf5a9463d58e73852d2b70be9611e8cf1f4166fd","id":"tLrQ52FlONPO","colab_type":"code","outputId":"6c4c100f-c14e-4270-a9db-1f8501625efe","executionInfo":{"status":"ok","timestamp":1585819143262,"user_tz":-540,"elapsed":35170,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}},"colab":{"base_uri":"https://localhost:8080/","height":259}},"source":["# We have chosen 'High' attribute for prices. Let's see what it looks like\n","dataset[\"High\"][:'2016'].plot(figsize=(16,4),legend=True)\n","dataset[\"High\"]['2017':].plot(figsize=(16,4),legend=True)\n","plt.legend(['Training set (Before 2017)','Test set (2017 and beyond)'])\n","plt.title('IBM stock price')\n","plt.show()"],"execution_count":10,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABBYAAAEeCAYAAAA+SZZgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsryHIYQQz4UbSTm03hJDao4KgJknk/i5szN+TuZUtjTB0lRRziMUQgjx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p59+YtmyZTg6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iY0USzg8GLLuacLjSNo4Vb6aeY965lzcXTyXrXv+FtrbPQZWmzNlPQ1LV0r8/R0gRnSxMeFaqC\neOphNrXtzYg1MBXgZnIOCZlKclWqEnUBOR6TxXcF/n0Ly9bxkeVqYrbeoAJAC3dLHC1N+K27K48y\nlViYKEjPUXE/LZcOhdpAnnqYhYq8jg0JmUo6e1rxbVsn9t7T3Qmjv7c1tuYmOgML7apY0sbDgrZV\nLJnVzAELk7ysC12BBcibAmJRhkE5IYQoCSneWM4++ugjQkND+fzzz7lw4QKXL19my5Yt6roAx48f\nZ968eZw6dYo7d+6wY8cO7t27h6+vLwA1atTgzp07nDlzhkePHukspAcwefJk9u7dy82bNzl37hx7\n9+5VH+P111/Hzc2NgQMHcujQIW7evMnhw4eZNm2aujOEsedp2LAhrq6uhIeHq5clJyfTp08fEhIS\nWLp0KWlpaURHRxMdHa3OCPD19aVTp05MnDiRiIgIIiIimDhxIl26dNGYfnDp0iXOnTvHo0ePSE1N\n5dy5c5w7d05rHD/++CPe3t60adOmyH8Db29vlEolS5cu5ebNm4SGhvLNN98UuV9hY8eOZe3ataxY\nsYJr167x5ZdfcvLkSaP2/eGHH9iyZQtXrlxhypQp3LlzhzfffBPIa8OZlJTEiBEjOHHiBDdv3uTA\ngQNMmDCB5OTHH1Y7dOiAk5MTc+fOZeDAgRpTFN5++20OHz7M7NmzuXr1Khs2bGDJkiW8++67Rl9f\n+/btqVu3LmPHjuXcuXNERETw8ccfa2VypKWlcebMGYKCgow+thCiYvnsRFKZn6M0ijQa0tTVgiWt\nHelY1ZJPGtszrK5mAURbM+0bM7sy6Oqgq/vCgVddGV63EhMDbJnTXHdA/1lxoKer1rL3wxNosOGB\nVsChoGUXUvH6KQrvtQ9oHPpAKzsuR6ni3KMs4jJ0P9Totdtwh5EsHZVC9+u56c8X9HdxT4VCgYuV\nKfYWJrhXMuVFFws+bKgZhF92IZXRf8TzMENJjgp23M7gpytp3NOTUfBhIztc9RQFfbuBrTp70NJU\ngUKhwMxEQR0H3c///ixBfQYhhChrElgoZ0FBQWzYsIFDhw4RFBREUFAQCxYsUD8Rtre359ixY/Tv\n358mTZrwr3/9i0mTJqmL8vXs2ZOXX36Z4OBgvL29CQ0N1XkepVLJRx99RPPmzenduzdubm4sW7YM\ngEqVKrFjxw68vLwYPnw4gYGBhISEkJCQoM5gMPY8pqamDB48mI0bN6qXnTlzhuPHj3Pp0iWaNGmC\nr6+v+qvgU/Tvv/8ef39/+vbtS9++ffH392f58uUax3/99ddp27YtmzZt4syZM7Rt25a2bdtqbJOc\nnMymTZsYOnSoUf8G/v7+zJkzh6VLl9KiRQtWrlzJrFmzjNq3oD59+jBlyhRmzZpF27ZtuXDhgkZR\nSENmzJjBkiVLaN26Nfv27WP16tXqmgpVqlRh27ZtmJiY0LdvX1q0aMGHH36IhYUFlpaP548qFAoG\nDRpEdnY2gwYN0jj+Cy+8wI8//si2bdt46aWX+Oyzz3jvvffUhRqNYWJiwurVq9VTM8aOHcuHH36o\nMQaAHTt2UL16dclYEKIcnX6YxXcXU7iZbDgFXx9DN4SlxeIpPKUfVMeGTV1c+KCRnVaxQmsdgYXS\nmiJQkKet9s3hCy4WfNXKiRlNHZ6oUGRF4GlrxoeNtDPfkrONn0Z3PTmXQwUyCRIylbTbGkPbrbE0\nCo3meIxmlsGeOxka9TN0yS4QWLiamE2tn+7z0THD03gM1daoblt0IOxCfDYJOn536jiYUcfBXO/7\nK0hPtxJ97T5XXNafqSGEEOVFkZCQ8Ez1rUlMTFS3SaxIiipa+E8SGxtL8+bN2b9/v0b3CVFyxr6/\n3n//fa5fv84vv/zyFEalW8eOHQkJCVHX09Cnov4u/xNduXJFIzNIPNu67oglPPrxjdjhYDcaVDa+\n+n5ajpKqq4ouOPikDrzqWq7V7eusjdKqATDe35ZZzUr3/6WMHBUNQx+oOwQsbu3I4DqlV5ixIgi9\nnsaoMN3FMPP5OpiRq4KrSbqDXa94WrHu76KX31xIYUqBIEDn6pZsePlx3Z4xf8Sx/prhwqHtq1ry\nSxcXowtoftLYng90BEjyPczIxWftA4PHeMXTihOxWRrdMMbUt+GLQAdMTRQcj8ni5e2aUyquD/Cg\nsp6pILlKFYvPpzCjUAZRHQczjvcx3KVLlA75+yjK2vP0Hnu2w+SiQnJ1dWXx4sXcvXu3vIfyj5GY\nmEhERATr1q0jJCSk3MYRGxtLcHAwr732WrmNQYh/socZuRpBBYCJR4pXF+FyEYUGS0tZFm40RpyO\nJ8uvlEHdByszBQd7ujGrmT2hLzs/d0EFgO41rIvcxsXahG419L++7tZ/t3ZWqjSCCgB77maSVaA1\n5CUj3qP52zfdFF3ktgCji2h16mJlyuFgw8Wkd93J0GqxOa2xvbqwY1NXc97wznutTBUwr4WD3qAC\n5BWEnBBgx53BVTSWX0/KQVmCwspCCFGWJLAgykS3bt1o3bp1eQ/jH2PgwIEEBwczaNAgunTpUm7j\ncHV1ZcKECUb1RxdClL5j0dqF6SJis+i1+6FGarghRXUwKC1PYyqEIbriGi3dyyaDwr2SKeP97fS2\nbXzWWZspCNNRa6GgHX1SrAAAIABJREFU1h6WBq8/v2uIvo4K1/+e1qNUqbSCXxf7e/Cuv2ZR6CPR\nWRyPydK60ddHV+vMwhpUNmdH1+J1PLIq8EZTKBR807YyZ15z5/rAKrxV39bAnppjK1gTJFcFSVkS\nWBBCVCwSWBDiObB9+3aioqKYN29eeQ9FCFGOKumoGwBw4H4mv9wwnDqe71J8yQvDDfCpVPRGf7Mo\n29qNRZr/kmbRxGVtnCQo+gQaOVtwoo/+J/oTA+xoZSBwcz4+m8MPMvnuou76AVF/F0W8lZxLeoHs\nBQczFR7WJvSppZ01cTTacLHGfP9pbvz0l5Yeuush6KIgr4VpYV52ZjgUs1CoU6HCjzdKWD9FiPKk\nuH8Lq9nvYTO8A5Umvo71rLcx3/MzZBn3uyoqNgksCCGEEM8JQ89mTz3U32avoPtpxrUw1uUFZ+Nr\nOZR3xkLvWtZ0rm6JuQl0q2FFL6+i0/mFYT4O5ujK7N/6igtWZgpMTRS8F6D7Kf3G6+l036m/08O9\nv9+XPxcKkNWupEShUFDVRvvEx2N1v+d9C3RbaOpqztC6xZueMraIaRP5VFBqwao7KZq/lwv/TCmV\n4wrxNFmuWYzZpTMoVCpM4mIxvXoeyzWLsNj0Q3kPTZQC3X1shBBCCPHMMTTdQV8bvILupuSw8XrR\nmQ2/dHGm/95HZBY6ZKCbBa/WtGLbrYwij1HeNRbszE00CgKK0vFFoCPvhz+u6zGyng1tqzx+yv9i\nCQt2nojJYoB3Jb65oHlD3dIp702oq31pWJTmU9Cxfjbq9p5ZuSqi03OpbmNa7Jv/KS/Yk5aj4npS\njkY3i8LKoHup2i83jctAEqLCUKkw++u4zlUWO9ej9PQmp1XnpzwoUZqeyYyFwr2OhRDPFvkdFqJs\nZBtIWUgqov1fQqaSNltjijzH/9o70b6qFTV0tFGs52jOJ43tqedohqUpTHlBf5V9BwuZdvA8erOe\nDfNbOFDbzpQJ/rbMLTTNwNexZM+0frycxp67msURzRTQt8rjKQFv1dfMJEgsVIfg5QI1HixMFXja\nmpUoo8DR0oSvWznxa1dXvmnjpHe7ck7KEaJiSTHc7tXq2y8gqXjFhkXF8sxlLNjY2JCQkICjo6PM\nhRTiGaRSqUhISMDOTv8NhxCiZHINBBaSswwXsfv6r2TiMw0HH95pYEvvWnl1FMx1/Am2NlNQ19Gc\n8F55c+0VCgW5SY+Yd13zKXVdh5Ld0Ilnw6j6tozSU5jQ18GM3l7WbC7BE/eQg5otLf2czLEr8Em2\nXhFBi4JTIErLGz6VeNHFnKPRWUwo1IHlu3aVS+08wV5WbLn5OBOoThlcixClTREbheW6ZeT6NiLX\nq+iWimZnwslp2/UpjEyUhWfufyUzMzPs7OxISkoqeuOnKCkpCXt7+/IehnhOPW/vLzs7O8zMnrn/\nfoSo8AxNhUguImPhj6iii2cVLJDnaKk/6bFg0KB2Je2AxvZiVtYXzw+FQsG37Zx4kK7dGrUoCYUy\nEArXVfB11F/jw9IUqumow1AafB3N8XU053B0Jhuu5QVM5rdw4NWapVe3Y3hdG43AQpVK5Vz9VIgi\nKOJisflwAABmJ/4wah+r/84lw9KSnOYdy3Jooow8k5/szczMcHAwvoLv0xATE4Onp2d5D0M8p+T9\nJYQwJC1HyTcXUvnhku6K+gDxmfozFlQqFSdii+4G4Wr9OJjwboAtR6Lj1D9P8Nf9hNrbRompIq9F\nHkBrDwtcdcyHF/8c5iYK+tSyLnZgobDqhQIFdQ08xXe0MCnzLJnlbZx409eGypYm1DUQ5CgJm0Ip\nQmk5xrXRFKK8GBtMKMxi1dcSWHhGPZM1FoQQQgjx2ITDCcw8mcRdAwUaYzOUpOfozlooWGzPEM8C\ndRVermZFjxp5c9b9HM0Y46c7sOBkDp82scfaVEEtO1O+CKxYDwZE+TAvogDB8T5ueNkZDkBl5mq+\nn12tTLDR03LVviwrKf5NoVDQwt2y1IMKANZmmuNPKyIDSYjyZnL3hsH1KT/sJe3T5dr7JSeAsuTd\niUT5eSYzFoQQQgjxmDGdHABiM3K1ii4mZin5X2Sa3n3e8LbGo5Ip7zfUrItiaqJgVcfKpOaosDbN\nayWoz/gAO97xt0UFmEhtBQHoawpib67g9uCqAHzb1onO2/W3oKxuqxl4UCgUpOoJnj3rxUILB0zS\ncitmYOFGUg5fnE5C9f/s3Xd4FHX+B/D3bG9JNj0EQguh9yogRUBAFBFFBM/G2fWsp9556nmed3In\n1rtTrD8LKhbgTsQOgkovAlFACL2kl02yvc3vj0CS3Z0t2bRN8n49j8/Dzs5MBrLu7vcznwLgkWHx\n6BHPpUZHJWp1wZ9TKAG5At4efSAKMgiibwaOfM8WKLZvgGA1wzn3Bnh79G3uy6UmwP/biYiI2jBn\nAxYYVolFV7Et9J2hpRMSg6aQC4IAg1QXxyD7tu2lHTWlYONGl4w11v55dJoa/5uRjMu+LpPc99oc\nPSz5vtt2XZ6OEauKAvad0lkTsK0t0foHFoIEUFqTKIqYtLq4dgLNkSo31s9Oa+WrotYiOIKPHXZc\nd2/tnz0DRgSModS++Gjtn2UnD8P63EeAjCV0sY6lEERERG3YzpLI69Sl0qctYVKqOb2BmkOyJvAr\nqEIArsr2vcs5IUON89JUAfsCgc0bASA7QYFhKYGlCPcOki7VaSt0foGFYGVNremuTSafsba7S134\n03YTlu4zo8jK1PYOxxm8IbB7zAW1f3ZdODfkaWQVpZDln2yyy6Lmw8ACERFRDDG7vPixwIHCCL+I\nP5tbHfS5QUm+C6wpa0rwQm41TpvdeCG3GiuOWsNOiyBqDskSU0UmZ6oDtsllApZPS27QuaX6GmqD\n1V60Ef6BBYtbhCjGzv+7RVYP3ssLLKl6eZ8FD2+vxKj/FkUUXLC4vPjPL9V4dm81qsKMyKXYJjhC\nlOhp6gKIniFj4e4/PPTJPO4muipqTiyFICIiihH7K1yY900p8q01X6jvG2TALf0NQUfLub0i1udL\n3xXKW5CBOzdW4Ody3+1/2VWFv+yqG9mcInHn+Jzb++sb+DcgikwfoxJxSsEnsDWti3S5QqJahplZ\nGnx1qi61eopEEOKcAokFbFvPvFHKBChlgOvsWtsrAk5vzRjNWHCwMvTCr8op4p1DFjw0NPTo7Nt/\nrMDqEzW/5y1FDqyYztG0bVaQUgjLkg98NwgC7A89C+1T90B+KFf6XLbgE48odjBjgYiIKAaIoog7\nN1bUBhUA4PmfzRi6ohB7y6TLHcodXnj9bloun5qEM9d0QqpWjh5x4e8flNql7wre1l+PR4aHXgQQ\nRUurqGn+eW5k5HlpKtzQO3gg68Ehvs1DHxsR/LW5qI/veTK07ePrbiz3WTCFGGd7zlO7q3G0KngA\n4kCFqzaoAABrzzhgj6G/IzWMIFEKYbvrSYhpmRI7C7Dd+/eg51Ku/6wpL42aSft4pyUiImrjTlk8\n2F3qCtju8ACTVpeg3B54F7bY5vtlvneCAhd11UKvrPl4v6hr9A3r/jHGCIOSXxOo+UzO1ODnK9Nx\nZGEGvpyVAk2QUZEAMCJVhU9nJOOegQZ8OiMFw1Kk+y4AwPW9dUhU15xLrxDw+UWpTX7trSFgMkSM\nLLq9oojr1peH3xHA8JVFWHNCOkV+5bHA7Q7/yCm1HX6lENY/L4Vn5ITg++vjYH75M3h6Dw54Srl1\nHXQPXg0h/0RTXyU1IX5jICIiigHHq0PXH2cvLwyYAOHfhyHV787s2HQ1JzFQTBMEAckaeUSlCpMy\nNXhiVAImhSiDAIAuBgU2X5aOdy5Iwra5achOaB+Vv/59IqxSzSRawR6JgGgo13xXjuWHrQE9IqSy\nGVwMLLRNoghZgV/DRXUEgW59HGyP/AuW5z4KeEpWnA/V6mVNdIHUHBhYICIiigEnqkPXKIsAur1f\ngFf2m2u/kPv3V0jV+BZcq+UCEiWa5IXDYAS1dZ10cszprkUXQ/sIKgCAzi+DKNxEl5byXZA+L6Hc\n/mMFEt/Ox4zPS/DKfjMAYJVExoKTwyRikiiKePeQBXO+KsVdGyuQb/EANit0v78Khusnw3DDBQHj\nJkVd5JNZxOR0eHIGBmxXblnb6Gun5tN+3m2JiIjasONhAgsAYPOI+OO2SihlwI19Dajwq2s2qgJD\nAtE0wzcoGVogijU6v/+ZbZ7YCCx0kRj7eU6yWoahKUqsOyMdfNhW7MS2YmfQ9ylmLMSmvWUu3L3J\nVPv4UKUbP+z6M2SlRZL7eztlQUxqWEmSc9YCaF98tFHXSS2LGQtEREStrNDqwbO55oj3X3m05s6e\nf2BBqu7cEcXiw7/5HRG1Pp0yNnss+FexpGpkKL8hE9vmpmHXFelYGcFkhwe3VkpuZ2AhNm0t9m0o\nvK3YCfmRA0H3d4+a3OCf4Rl+PuzX3eezzSvV+JFiBgMLREREzeiMxYOZn5eg6/v5eGp3leQ+i4Ns\nD2ZzUc2XOpPfnPce8YGJiLOiaOB4aXdtg48houYV2GMhNhbd/sHL6VkayAQBfYxKGM+WYi2dkBjV\nuZ2x0UaC/FQ38Bcjxhmj+jmewaP9NrA2JpYxsEBERNRErG4vyuwen6ZkL+2rxtZiJ6qcIp7eU41f\nTYGNzlYc9a0tVgjAT1ekY9vcNMmfo1MI+KXchS1FvneNkiX6KdzWP/K6VgD4YGoSRqYG77hPRK1D\nH6MZC28csPg8lsqSmpAR3XuKf8Naig1fnfLtnyCIXvw3ZWTQ/QVTWXQ/SO4XLGdgIaYxsEBERNQE\n9pY5MXxFEbKXF+K2HyvgOZvC+/I+3y/d//nFt+Th2b3VsPgtED6cloye8Qr0MSpxa7/AsgSrW8T5\nnxYHbO9tDMxYGJqiwqrpyRH9He4cYMCsrsxWIIpF/hkLa07YUCoxhra5efzKE3LLfYOlUk0Yo22i\nGSOxE6qnzO7BLr9JIKIgw5UD78PcQb+XPMY9eEx0P0zhH1gI34uIWg8DC0RERE3ghVwzCm016aEf\nHbHhN9+Vw+QITBc918nd7RWxrciBJ38KLIMYmKSs/fOfhscjJ8JxeUqZdAe0KZ01ETVx1ATvwUZE\nrSzOrznrZyfsGPe/YvxS7sJr+8349y/VMLtq3nP8Rzk2VG6ZE3/eUYn38ixwnw0knDK7ccFnxej+\nQQH+cbZ8S6oHQlO2RYimR0xHV2Lz4N8/V+O/xwJHetZndnnxXG41Xsitjnh0qVcUseyQNejzV82b\nCse8m3y2eXoNhLd34ISHSIgy3w8lgYGFmMapEERERFFyekQ8vbcaO0uc2OA3cu2rU3YM+Lgw4Bib\nR8SBCheu+a4MR6qk7zYm1StpSFDJsHFOGvp+VIAKR/AvifcPDl3yEKcUYHKG/pKuimaEBBG1iCx9\n4Nf2YpvXJ3vpyV1VGJuuxuYiBwYnKfHm5CR0j4v8675XFHHz9xVYWS/r4EilG4+PTMBTu6ux++yd\n6n/sqcakTDX+uac64Bx/HhEvee7nxxpx3xaT5HPBxEq5R1vh8YqYtqYEJ8w1ny2nR3lw18A4yX0X\nrS/Ht2endewqdWLZlNCZbRUOL2Z+XoKDlYGL+9v76/HI8HgYlDK4ul8D1+xrIFSUQigrgrd7H0AW\nZdQ6oBSCgYVYxowFIiKiKL110IJn9lYHBBXO8S9xAIBKpxeTVhcHDSrcN8gQsMBXywW8dH7o5mfB\nvjyeY5Tov+BPHSTjgYhaXydd+MWZ0wt8X+CAywvsKnVh6IoieLwiTA4v9pQ68X+/WpBb5gx6/OYi\np09QAQDePVRz53v5Yd871Rd9USr53vfbIFNl5mdrMamTuvbxzKzwjWXPZWBQcKIoYvHuKkxfU4Lk\nd/JrgwoA8NgO6cbATo9YG1QAarJfwnl2b7VkUOGNSYlYPMYIg9L3M0ZMTIG314DAcoaGkPu95tlj\nIaYxY4GIiChKf9gmPSItFP+Gi/X9to8ej49MkHxuVlctrs3RYVleYBpq4bWZ0ChCBwUS1TIcrw79\npYwZC0Sxy6iO7v/P5HfyA7Z9NSsF56WrA7bnlgU2ly1zeLGvIvI7xcGCmHqlDP+bkQyXt+a9Zk+p\nM6AJoL9qFzMWwvnkqE0yc+ScMrsHyX51bg6JehWXVwxaTrfmhA3/2Sc9EjlO2Yz3qWM8sCA7vA/y\ng7kQU9LhHjUp+syMdoIZC0RERDHiquzQjRP/Nd6I7HjfLy79jYqwQQUA6BzB3U51x/5ORBTTjKqm\n+9r+113Sd7LLgjSDfP2A9KKyoQRBqA1gDklWoo9f/5gZflkMd28y4f7NJlRx7qSkL0/acMsPFSH3\nufjLUgDA9mIHXvy5GgdNLrgkelfYg/Sz+OaUHdd8Vx70/NoIPn+i5t9jQfRCufa/zffzGkC+80fo\nnrwT6o9fheblv0L1/n9a+5JaHQMLRERErUgA0CtegT8Ni8PotNAj2QRBwNa56eh7dvqDSlbT3DES\nkTSAbEgtNhG1rM76pov8bS5ySjb2k8pYAIB3QjTsi5YgCPh0ZgruH2zAX0fGo+i6TMzuFlge8X8H\nLbjjx9CL547ow8NWLFwXfMF/zq8mNyavLsb0z0vx+M4qTP+8BEclstekGmW+tt+M+WtDj4rUNWdg\nQRDgjTP6bFIvexFCSUHz/cwIaf5vic9j5cavWulKYgcDC0RERE3oj0Pj0E9i7GMwhddlYucV6Xho\naDwEIfwXNKVMwPrZafh4WjI2X5aGS7pFNh5yYifftGedQsBj9YISmToZJmQEpkYTUWxI1coxr2fT\njYP1H327udDhU3cfjQeGhO714i9DJ8efRyTg7kFxUMsFTO4k/R605mT4HgAdiccr4rYGBFv21AsY\nVTpFPJcbWDph8+sJdMrsxkMRlPtpmrmEzj1+esA23Z9uaN1Gjl4vBItv1o9gt3X45pK8NUFERBSl\nnnHygDs/i/roMSxFhavC3OUBaoIQ6ii+lGkVAqZH0Pisvomd1BiYpMQvZ2fO3zHAgHsHGZChk+Gk\n2YPreuvZY4Eoxv17fCLGpKlQbPNiyd7gdfWReGxnFbYWO5GmlWFUqgobC4P3f4lEpk6G2/pLN26M\nVBdD8KVJqB4AHU1FI0tDvpAI1PhnLPxqimyRbFA27+/EecWNUH31sc82wemAcv1ncE2b26w/OxjB\nVCr9hNUM+GVYdCQMLBAREUVJ5pdhsOWyNKTr5JimkWF+thYfH7EFObLG0BRlc16eD7lMwGczU/DB\nYStSNTLM66mFTBDwm5zGLQSIqOVoFQJu7lczWnZGlgav7jejW5wCAxIV+OSoDUOSlXhgcBwWrC2L\nKPvg87MLzLcORlfqYFQJ2DI3Hceq3BiYpER8E/SBuLmvHq//agnYvqfUhVFhysU6CnMzNLV8NteM\nJecl1E53CNZzwV83QzM351GpYXtgCbTPPOizWb3sxVYLLMArHdgRLGaIDCwQERFRQ3i8Ik5ZfO/o\npGtrvpDJZQJem5iE1yYCeZUuvHvIin//Etj8rH9iywUWgJrJEHcOMLTozySi5jEyVYWRk5JqH8/t\noav9c5cmWOytuyQVl35VKjk2FwAGJinx5Mh4dNLJIxqFGanfDTTgoyNWVPktnt86aGFg4SypZpY/\nX5mOTYVOlNo9WNBLhz/vqMIHhyMPGC0/bMWOYie2zk2DQiZI9lzw99MV6RGV8DWWZ9AoeLL7Q35k\nv892obIcYkJSkKOaUdDAQjUk/9VEEYpNX0N2PA/u8dPh7dGnWS+vtTCwQEREFIXj1R446lVBpGpk\nSNIEfrnOSVDWNlv0l6HlGAYianpZIUoKItEjTo7hKUrkXpmO7OWFAc//elUGMpowmFBftzgFts5N\nR/+PfX/uB4et+NuoeMn32Y7G5PRdvnbSyZBlUGBBr7rf+3/ON6LI5sG6BvTNOFzlxqZCByZlaiQD\nC78bYMC9gw0otHrRO0HRouVztkf/A8OiKT7bAgILlmqoP34NQtFpuKZfAc/w85vnYoIGFiSmrbhd\nUL/1bG1zR+X61bA++yFEY3LzXFsrYvNGIiKiKBww+XZPDxY8AKTHxOkUAnsaEFGz6NLICRKj01QQ\nBAHJGjk66QLfv/TNXFefqZdjbvfARpU9lxdi1dGmn1DR1pyo9s2WOy8tsOmlTBCw4sJk7Lg8Dfvn\nZ0R87oNneytIlUL8ZWQ8UjRyDExStvznl0wGT3Y/321233JD1eplUG74DIoDu6F98VEIpjLAYYc8\ndxuEwtNNdy1e6bGsgsUvM9HtgvaJ23wmRghuF5TfrGi6a4khDCwQERFFwX+ud19j8LIGozrw43b1\nzJQmvyYiIgDIkiiFOD8j8jKCWV3rFvVz/Bb4WQY5DM05YvCsy3pIT8B4cGtlRGn67c33+Xbcs6kC\nHx624miVb2AhO146sC0IAnISlMjUy/Hv8ZHV/j+0rRJ7Sp14bIfv3fff9tFD0crNM0WtbymfYPcN\nMvk3edTfcwUMt8yE9tk/QP+Ha6D86pMmuQ4hSMYCrL4NVRU7vof85JGA3eSH9wdsaw9YCkFERNQE\negT5YgdIZyw0ZU0yEVF9UhkL0zprkKCS1TZsDOb63jrM7lY3deaPQ+Oxp9SFrcVOKATgT8MiG43b\nWMEWy2UOL/ZXuDAspeP0W/g+34E5X9dMGnrnUGDGRs/48J8nwxvw73XzDxWw+vXWqGzkJIomofEN\nNgnWehkC7vBTLNTLX4JgroTzoqsAfcNGo/oIEljQvPsCLINGQ0zLBADIc7dJH+9s3FjXWMWMBSIi\noij4Z4FOCjJ/HZDOWDCqWAZBRM1DKnCpkAHvTUnCh9NCN7t7aGi8z8Qbo1qGL2elYN0lqdh1RToW\n9tKFOLrpDEhUoE+CdHDhpFk6FT2WFVg9KLFFdt35Fg/u3FgB41tnkLO8AHO+DjLe8KwxEqUQ/nKC\n/FtKyasMXKSPjoHGmd7EVJ/HmqVPwnD9ZOgeWAjl5x9EdA7VZ+/BcMdsyI79Gv2FiMGDLOq3nz27\njwj5vl2S+8iP/QrNs3+A7PSx6K8hBjGwQEREFAX/TNyeITMWAoMIuhZIJSaijkkqZb3KJUIQBMzM\n0uLeQcGnw0i9XwmCgBGpKnSLa7lkZ0EQsGVuGu6TuNZCa/AF+p5SJ276vhz3bzZFvJBvTtUuL4xv\nnUG/jwqR82EhbtxQjtlfluDxHZWS0x0KrB70/7gQ7+fVZCaU2ENnCgxOUiI7gqCBSi5I/ltG6sqe\n0qUpLcnTZ5DkdllJAdSr/q9B51KtiSwQISlIjwUAUOzbBThsEIrPQFZZHny/3G3QPbIISXs2Rn8d\nMYaBBSIiogbyeAPre0M1KtcrZZiZVZdavLCXrkVSiYmIzqmfvb2ojx5qifesRX100CtjZ3kgEwQ8\nPjIBDw/zTVs/JHFHHQCKbR5c+lUpVhy14f8OWnDJl6Wwuls3hf+eTSafxyuP2fBjoRMv/mLGE7sC\npwj8bmNFg85/XnrkmQSPj0zApjlpuCq7LkgQya/79v76mJjG4e0zpMnOpdj5QyMuJPRrSrlhDfQP\nXRPRqTI3/C/664gxEb1zbNq0CQsWLEC/fv1gNBrx/vvv+zx/++23w2g0+vw3bdo0n30cDgcefPBB\n9OzZE5mZmViwYAHOnDnTdH8TIiKiFuLfLVsjR9hAwduTk7DkvAS8MM6If0XYRIuIKFo39dX7PJ5f\nbzHZLU6B9bPT8Mehcbi9vx6/G2DA6pkpeG5sbL43DUrybY57OEhgYWuRE1Wuuvfng5VuLD/culMk\nNuQHr6f//ETNVANRFFFi82BPqbNB4yGBhpU4AMCAJCVenZiE5VOT8OCQOGy+LA3PnJcQ8pirslum\n/CUcMT4RnszuEe3r7ZQF2/3/gDe9s/S5VOHLR4KfPHRgQf3BSxGfqnD8rOivI8ZE9Eq0WCzo378/\nFi5ciNtuu01yn8mTJ+PVV1+tfaxS+UbPHn74YXzxxRd48803kZiYiEceeQRXXXUVvv/+e8jlrR8B\nIyIiilS1yzewoI2grEGjEHBzv+jTUImIGuKugQb8VOrEIZMbt/bXo7ff5Jr+iUr0Tww+zSaWZPr1\njAhW4lDuCFzw/X5LJRZkt04mhiiKktd0TqHNi7s3VeCU2YP1IQIQoYSaSBTKRV21uOjs9A+NXMAD\nWysl97troAFDY6hRpjdnAOT5x8PuZ7v7bxAzu8E6eAyUaz6AesXrPs+LCcmNuIiGZ8FYn3wTip0/\nQPXpO3XXoFCibOj5SIz+SmJKRIGF6dOnY/r06QCAO+64Q3IftVqN9PR0yecqKyuxbNkyvPTSS7jg\nggsAAK+++ioGDRqEDRs2YOrUqdFcOxERUatYd8a3q7rU1AciotbULU6B72antfZlNIl0v8BCcb2+\nA4dMLhyqdGNiJ3XQRfyyPCtu69/ygV17BC0e3pWY8nCOTAAkKu9q6RRCg0ohglEFGSP51OgE3DEg\ntgLi3oyssPvY7v8HxMxuNQ8EAa7Zv4H7/BnQ3zuvbienLeprEPx6LHh69IU8RDNI56wF8HbNhrNr\nNlzjLoT88D540zvD22sAxMOHo76OWNNkHVi2bNmCXr16ISEhAePHj8djjz2G1NSazp179uyBy+XC\nlClTavfv0qUL+vTpg23btjGwQEREbUaxzYM7N/rWzE7rogmyNxERNVaKxjd4W2r3Ym+ZEw9sMWFH\niQsAkB0vD8hsOGfZIUuLBxZMDi/mf1vWqHM8PiIeb/xqwakgUzC+uTgVyiBBgYZI1UoHx2fE4Geb\ne9g4qD5+DUK9yQzuIefBfv8/Qh7nX/og2KMMLIgiZId+9t2k0cI9dCwUe7YE7O6cOR/Oq+oy/sWM\nLnBndInuZ8e4JgksTJs2DbNnz0a3bt1w8uRJ/O1vf8Oll16KDRs2QK1Wo7i4GHK5HMnJviknqamp\nKC4uDnrevLy8pri8FtPWrpfaFr6+qDnx9RWZrRUy3LXP94uWABGzdKXIyytppauKfXx9UXPi66tj\nSFBoUemuW0QWtTvxAAAgAElEQVRPWu37nnukyoMjVdIL8F9NrqhfJ9Ee9+hBFbaXNG6pZa0oxcdD\n3Pg4X4Hnj/lmJsghQlV6HHmNi13UenmgDHf8Uvf59nRfB7zFx5AXfKnWahLm3Y6kn7dAXVaE0uET\nUDZ0AsRwvyfRiyFyBWSemv4cgsOOo7l74NHqQx/nJyl3M7qtfstnm9Vux7HZN2KIRGDhYN/RcIe5\ntrbyHpaTkxPy+SYJLFxxxRW1fx4wYACGDh2KQYMG4euvv8all14a9XnDXXwsycvLa1PXS20LX1/U\nnDry68vjFfHOISv2lDmxsJcOY9ODN3PaXuzAXRsDZ4nHq2S4YHCv5rzMNq0jv76o+fH11XHE7S5E\npTu68ZEeUUB2r16QNXAaT7Svr1K7B19vLGzwcf56dk5Hv2wd/pwj4vlj+T7PeSCgd++me+3nALh6\nVJOdrnnl5ACXXAkPgMSz/0UkvTOQf6L2YW9zCdyDh4Y/ThQh37cL8kO5UK1+N+Bpnd6A7AED4ema\nDfnJI7Xbveld0GPYyJCnbk/vYc1SFNqpUydkZmbi6NGjAIC0tDR4PB6UlfmG1EpKSpCW1j5qv4iI\nqG0xu7xIficf928x4d1DVlz8ZSn2ljmxId8Os8sLt1eE6Wy97vFqN6Z/HhhUAIBLurX+bG8iIgrN\n1YJTJ5fnSfdN0MobFtjoaqgp7RAEIaKxkBSaNy3T57Hm1b8DLmfwAxw2yPdsgfLbldAueQCqTwOD\nCgCg+GUHAMA55waf7e4RExp1vW1Nk/VYqK+srAwFBQW1zRyHDh0KpVKJ9evX48orrwQAnDlzBgcP\nHsSYMWOa4xKIiIhCWrKn2uexVwxMrY3EX0bEN9UlERFREJk6OU5bostYAACnV4S6gQv7aK0+EVi/\nf2NfPZ4ek4Dkd/IljpA2vN40BqNKhhJ7C0ZH2iFPdv+APgiKLWvhnhg48lEoKYD2idshqzYFPOfv\nXENJz8gJcFx7D+S7foS3U1c4L72maS68jYgo9mU2m5Gbm4vc3Fx4vV6cPn0aubm5OHXqFMxmMx59\n9FFs374dJ06cwI8//ogFCxYgNTUVl1xyCQAgISEB1157LR5//HFs2LABe/fuxa233ooBAwZg8uTJ\nzfn3IyIikvRTaYi7FBHKvTIdqVqOTCYiam5/HBbXqOM/Ox79FICGKrD6BgD+MiIez441Qi4TMDwl\nsvGQSyck+gRCEtVMWWgs19TLArZp3nwauoeuAWy+WSbK7z6NKKgAAK5xF9b9edpc2P/wHJzX3Qs0\nsH9DWxfRK3T37t2YOHEiJk6cCJvNhsWLF2PixIl46qmnIJfLsX//flx99dUYOXIkbr/9dvTq1Qvf\nfPMN4uLq3gAWL16Miy++GIsWLcLMmTOh1+vx4YcfQi7nFzIiImp5PxZGH1iQCUDp9ZnoamiWxD8i\nIvIzpbMGv2vE6MM7NppQYI0+46EhKp2+gYUb+tQtMJ8anYCeccHXPxo5cM9AAxb20vlsvyjLt3Hw\n0OTIAhRUjz4O7pETAzbLik5D/dYzkJ06Ct2DV8Nw/WSovvgw7Ok8vQfDOXM+XLMWNMfVtjkRfSOa\nMGECTKbgEZtVq1aFPYdarcaSJUuwZMmSyK+OiIioGSzZUxX1sWlaGX68NA2KJhjxRUREkXt8ZDz+\ns88c9fErjlhx16DGZT6E4/SIqHaJtY8FAPGqus+L89LV+GleBqxuLzKXFfgcu+WyNPQ1KiBINJm8\na5ABr/9qgdVdc+7GZnB0VK7Jl0Cx84eA7cpt30G57buIzuEeNAr2B7im9cecGiIi6hBEUcTTe6qQ\n9V4+/r67OvwBfhQCcGs/PT6bmYL0ILPSiYio+ShlAn6T43sn/9/jjREf/9jOKuxpgjK4UJYf9k2p\nT9PKJKdR6BQyzOpal4UwOEmJPkGCCgCQopFj45w0PD4iHp/OSMbMLDYOjoZnwAh4eg2M+njHNXfD\nfvffmvCK2g/mcBIRUYfw7WkHnooioPDuBUm4uKsGcmYoEBG1un+NM2JaZzWUMgFTOquhlQv4/RYT\nnBH2Nbzky1JsmZuGrGYoZbO5Rdyz2TfLu7M+eCD6tYmJePeQFWo5cHkPXdhxmD3jFbhvMDMVGkUm\nh+0Pz0KxfT00r/8j4sNsDzwNz4CRgIz35YPhvwwREXUI89eWhd/J/5ieWlzaXcugAhFRjJDLBMzt\nocMl3bTQKWQQBAFfX5wasN/gJOkeBGa3iPfPjoP83zEbjG+dgfGtM/jPLw0PPPt740BgmcbsECOJ\nDUoZ7hhgwI19DWzO2JJUarjPnwnRENlUJ/tNf4Bn0GgGFcLgvw4REbV7P5e7gj6XqZNhehd1wPY4\npYCb+nWsjs5ERG3R0GQlJmTUjWZ86XwjnhyVEHT/MrsXTo+IGzaU1257dEcV3j1kAQDkVbqwaH05\nHj2owvFqd8TX8defAvv33DMo+oaT1LzcQ8YGfc6bkQX34DFwzLsZ7vEzWvCq2i6WQhARUbv37kFL\n0Ocu6abFo8Pj8foBCwQBuLmfHkcq3ehikCNFw14KRESxThAErJqRgnVn7MjQyjE0pSbI8OWsFFz0\nRWnA/ha3iE2FjoDtd28y4YXcahytPjc9QgHHZhP+NyMl7DWIogiXXzmGRo6w5Q3UepyXXgP5/l2Q\nVQS+Rqx/exNQqiSOomAYWCAionbN6RHxyVFr0Of/MSYBMkHA74fU1a2e+1JKRERtg1ImBDQ0HJuu\nRqZOhnyr74r/g8NWfHBY+nOhLqhQY0O+A06PCJU8dIDgh4LAQMW/xydGcunUSsSMLFiXfADl159A\n/cnrtds9PfowqBAFlkIQEVG79s4hC0xOUfK5ffMzeDeJiKgd2zgnrdHnOGkOXw6xIT8wsDC3Byc3\nxDylCq6Lr4brgtkAAFGjhfOKm1r5otomZiwQEVG79vjOwJpXALgmRxeyWzcREbV9SRo58q/thJzl\nhbC4pYPM4ewqdaFXgnQzyHNOWXwzHZ45LwEKNv5tGwQBjuvvh3PO9RBVakDPyRvRYMYCERG1W26v\nCKvfF8nZ3TT4z/lGPHNe5LPPiYio7dIpZJjSObBJb6Ru/aECohg6KLGrxOnzOCdMIIJijCBATExh\nUKERmLFARETt1q0/VARse/eCJAgsfyAi6lCCjXNUyQCnV/IpHz+XuzA4OXjdfb7VN2NhWAoDC9Sx\nMGOBiIh8hLsr01aU2DxYeczms62LXs6gAhFRB1QZJHqwdEJkDRa3FTuDPre71AlHvbiCQqgZWUzU\nkTCwQEREAID9FS6MXFmE7h8U1M7ybsv+LjFPfHQauzwTEXVEw4NM+xmarEIkk4Uf3FqJHX7BBY9X\nxKL15bjgsxKf7TqlwCA2dTgMLBARdWDHqtxYUyTHp8dtuHpdGQ5XuVHpFPHwtkpU+w/kbqRSuwdP\n76nC6wfMcHmbNyvitNmNtw8FjhJ7anRCs/5cIiKKTTf00Qds0ysE9IiX4+pevs/1TlBgbHpgIOKu\nTb7ldR8dseK/x20B+1UFmURE1J6xxwIRUQf1r5+r8eedVQDUQF65z3MWt4iT1R4MSIou/ryzxIlv\nTtuRppHh/E5qZGjlmPpZCU6YPbXPvzoxKeA4q9sLtxeIVwX/udUuL+7aaEKh1YN5PbW4sa8+4M7Q\nP/ZU+zxO18qwZ14GtAreQSIi6ogSJD5XOuvlkAkCnhmbgKmd1RABzOqqgUwQsK/chfGfFvvs/6vJ\njXK7B0lnUxy+LwgcMQnUlEIQdTQMLBARdUB7Sp1ngwrBmaPMWHj3kAV3bzKF3OejIzbM6GLF5T11\ntdve/NWMh7ZWwiMCdw4w4O+jE/BTiRNvHrSgZ5wCdwwwoMTuweBPimqP2VrsxMhUFYbWS3EVRRFf\nnLT7/LyZWRoGFYiIOrj+RgX2m9y1j2/sW5OpIBMEXNxN67tvogJD4j3YW+VbJ3H1unJ8dXEqgOB9\nF3430NCUl03UJrAUgoiaxPZiBy77uhQL15Yhr9LV2pdDYbx6IHwPhWpXZKmce8uceHibCe/nWeAV\nxbBBhXP+vrsusPFjgQMPbKkJKgDAS/vM2FPqxPy1ZXg/z4onf6rCX3ZW+gQVztlS5PvF7pcKN8od\nvkGRyZnRjxkjIqL2YWFOXTB7SLJSsjziHEEQ8J8BgRkJW4udqDj7GdM/MXDyw9BkJe4YwMACdTzM\nWCAiSW6viJf2mfFTqROzumpxVbYu6L7fnbHj8m/Kah/vKnViTjctkjQy3NbfEHTEE7UsURTx4REb\nPj1uw1en7GH3P2PxhN2nyOrBzM9LYTsbEbhzY2RBBQA4UuWB2ytCIRMw+6vSgOfv2WxCqb0uQBAs\nGPLw9kr0NiowtbMGAHDdd2UB+1zqdyeKiIg6nt8NMCAnQYESmxcXd9VALQ+dyaaRA10Ncpw0+34e\nVji8SFTL4PbrF/TB1CTM6srPG+qYGFggIh9FVg+qXV6sOGqrrVP/9Lgd35yy441JiQG17EVWj09Q\nAQCKbV68/mvNInBDvgNfzUphd+RW4BVFHK1y43CVG6NSVbjky1IcqJcCGs6+isDMkwqHF8sOWZCg\nkuGqbB1mfFFSG1SIRpHNi33l0hkue8siz3y57YcK/DI/A9+dseNYte8XwBfHGSGX8fVHRNTRCYKA\nmVkNW/hP7KTGe3m+zYBdXhGiKMJ/gqU2TKCCqD1jYIGIaq0+bsNN35cHfFACwMpjNozLUOHGvr7p\nff5p6P62FTvx7WkHpmdpmvJSCcD6M3Y8sr0SWoWAZ8YaMaxen4H1Z+yY+03gnfuGKLbVvRCqXV58\ndtyGO+plJNyzOfLshGC+PGnDA1srG32eErsXW4scAV/+AGBKZ5ZBEBFRdO4YYAj4bHlwayVyy5ww\n+U1/UDKwQB0Y85OJqNYLP1dLBhXO+euuqoCGflURNPibv7YMliYeXdjRub0i7t5swn6TG7tKXbjt\nhwqIYs0XnIMmF65aG3lQYWSCB130cuj9mhv+77gNbm9N1kP2BwU+QYWm0hRBhXPmfF2Gz/2aNg5P\nUSLLwBg6ERFFp3+iEn0SfD9HfihwBAQVAEDJuAJ1YAwsEFGtE9Wha+ornSK6vFeAf+6pa7oXSR0+\nANywvjz8ThSxk2YPTtWr+TxY6cYpiwdeUcRvN0hnnUjpk6DAywMd+GV+BnbPSw94fu7XpXjzV0vE\n54s1ay9Jbe1LICKiNi5OFVnEQMWMBerAGFggIgA19fhljshWj4t3V2PpPjNEUcT/jtkiOubbMw7Y\n3NHX4pOvz08E/rubHF7sKHZiX0VkfRQ66+R4+4IknGt/kaaVB+zzY6ETL+0zR3WNL4wzouT6TMzv\nGXk9qyHCkZBPjoxHuD3TtDLI2NuDiIgaSRlhn55I9yNqj5gfStRBmRxefH3aDrtbxOg0lU/3/Ug8\nvL0SZQ4vjH4THzK0MpQ7vJJ3uDsty8fi0Qm4nWOYGkUURbyyP3BCgs0tYntJ6J4X57w5KRHTszSI\nU8qQV28gw6yuGnxxMvzEiPrkAvDjnDT0T1TijMWDr07ZMCZNjYFJNWO4ujagFCE7QRG2aePK6cmY\n2lmDKZ01GP9pcdD95nASBBERNYEIY95Q8pYtdWAMLBB1QG6viGlrSnC4KvIJAVKe2VsdsO29qckY\nkaLE+Z8WS945f3h7JS7roUUnnRxFVg90SgFx/CRukCKbF2esgSUoy/Ksks0L6zOqBPxvRgqG1mv0\nWN+/xhvxxcnCBl3PS+cn1s7y7qyXBzT4FBF5poouzLe3FI0MY9Nrrn1AkhKXdNVgTZBAyE39gs8n\nJyIiitTpCMs+/XsVEXUkDCwQdUDbi52NDioEk6mTQxAEfDQtGQM/KZLcZ8gnhfhNjg5vHbQiUS3g\n/SnJGJfBzv2RqgrS8EAqqPD1rBSUObzYWeLE0GQVZnfThBz9maKRY2iyEnuCZA0YVQL2zMvAA1tN\n+KXchZlZGswLU+pwyhzZFzIAiA/S+erqXjoYlAJu7KuHTlEXiBqZqpIMLKy5KAV9jMqIfy4REVEw\n/mOMgzHwRgl1YAwsEHVA9ccIhnN+hgpze2jx+y3hu/fHqwSkaGo+VLsYFJiSqcZ3+Y6A/Zxe4K2D\nNYvgCoeIP2yrxI9z0iK+po7GK4p4aZ8Zz+ytRrVLxB+GxkV0nFoODEtRQSUXMKtr5GUBc3togwYW\nFvbSwaiW4Y1JSRGf7+JuWnx8NLJeHPcPjsPXpwNfM8+ONUIrcSforoEG/GVXVcD28xmoIiKiFqbn\nWAjqwBhWI+qATGFa/GdoZXh4WBweGBKHNycl4ca+Bnw5KwV/DLOgvb2/wacj8mU9IlvM/lzuQpFE\naj/VeO2ABY/tqEKlU4RXrGmeGYnseEVUHarvHmjALUHKCB4cEllQo76pndXoa6yJY8crBWy5LA3v\nXJCEewYa8MI4IxLVNdc4q6sGo9NUGJbim2lwaTeNZFABAOQyAatnpvhs+x17eBARURN6YmR82H3U\ncjZvpI6NGQtEzeSQyYVbfqjAGYsHDw+Lx2/7xk69tynM9Id3pyRhdJrvHd+x6WqMTVfjk6NWHKmS\nDgI85LfonN9Th7s3mSK6psu/KcXGOWkh0/Q7igqHF8er3RicpIRcJmCNxASISCSqo4sdC4KAf45J\nQF6lG+vrZZz8ZUQ8kjSBkyPCMShlWHdJKn4udyEnQYFkjRz9EpWY070m8HRZdy3KHV70iKspo5nb\nXYvdpTUZE/FKAU+OSgh5/omd1Fg5PRkv/WJGj3gFHowwo4OIiCgSt/Yz4ECFCx8eCf553EXf8M9H\novaEgQWiZvKPPdW16eS/32LCnO4aJEexKGsOFUECC10NcvxzTEJAUKG+QUkqHKkK/GDdNjcNcr9I\nvUYh4PDCDPRaHr4Z4L4KN/KtXnTugB/Mbq8Ip1eEAAF7y5yYv7YMVc6ahofHr+6EQ5XR9cN4YHD0\nC2xBELBsShKW7KnGF6fseHR4fG0gIBp6pQznpUu/roxqmc90kVv7G6CQCThS5cZveunQLS78R9XU\nzhpM7ayJ+vqIiIiC0SgEvDIxCb2N1firRPkdAFyUxUlE1LExsEDUxERRxNsHrVh1rG7xLQI477/F\nyFvYqVWu528/VWHNCTsmZ6rx1OgEHKv2Xag+OzYhoJN/MCNTlfjfcd/AwuAkZdBGeSkaOSpuyMSF\nn5dgZ0noMYKHK10dLrDwyPZKvLTPHPT57h8UhD3H/2Yk477NpoDmUv0SG9e80KCU4YlRCXgiTMZA\nU1PLBdzBcgYiIoox9w+Ow2XdtTha5ca8b8t8nru0O4Pb1LGxxwJREHa3iCV7qvD7LSb8apJeEC/e\nXQXjW2cwfEUh7O6aO8xP763GfVsC0/9L7F4cbaZJDKG8vN+CZ3PNOFjpxqsHLJj1ZSlWn/Dtop+o\nivytQKopXrg72YIg1I4jDOWgqeX/fVrT33ZVhQwqRKp3ghIfX5gMdb2YzNW9dMjQdawgDRERUXPr\nGa9Az/jAe7NDk6XHOBN1FMxYIAriT9sr8X8HLQCAb0/bsX1uOjT1GsitPGrFP/fUNNE7Wu1BxrJ8\nDExS4pfy4HflPz1uw32NSE9vCJPDi1t/KA/osL+t2Bmwr7EBtfiDkgIDBFM7h+/A7xXDn/vXDhRY\n2JBvxzO5kTVhDGVcugqZejkAOYqu64yDJheqXSJGpHDUIhERUXNI1wZ+b4qmWTJRe8KMBSIJLq9Y\nG1QAgJNmD7aX1C3IRVHEjd9XBBwXKqgA1NTSt5Tbf6yQHNsnxdiAjAW5TMDN9SYG9IpXYHBy+EVs\nviX81IdgmSHtTZHVg2vWlTf6PL3iFXh5QqLPtj5GJUamqtgEk4iIqJnolTL8fnBNyZ5SBrw6MTHM\nEUTtHzMWiCR8kGcN2Lan1ImJnWruzJ9rythQW4udeOtXC2Z11SC9GdLUfzW5cPdGk08QJBINnR7w\n15EJ6B6nQIXdixv76SGLYBF7fic1vssPHejYXOSEVxQjOl9b9tTuKpjd0QWZ9s5Lh0wATpk9GJ6i\n8smiISIiopbx2IgEXNdbD5VcQCeWHhIxsEDkr9TuwT2bA3sk1A8m7I0ysLDujAPrzjjw4FZg75UZ\nTd6o8E/bKhscVOgRJ0e3uIZdh1Yh4M4GNtcbmx5Z7eHOEmfIqRRtncXlxcqj0Y2PfHBIXO2EhCwD\n376JiIhaUyRTi4g6CpZCEPn5Ichd9VXHbNhW5IAoijhe3bheAG4RGPBxIUSxaUsjwmUESPn4wuQW\nyRDQRXhnvcwuPQqzvfj8pF0yW+HViYl4fWIiOulkGJCowPyevg0xJ2eq8fsW6s9BRERERNQQDLMR\n+bF5gi/2Z3xRCgCYkhn6jvqiPjr8WODE4TBTINbnOzClc9OMJ7I1MLV+Xk8tXp2QCLmsZVLp5REG\nL5ztO66AXX4ZJfN6avHU6ASkaeW1jwVBgNsrIk4lw8YCB67M1uGBIQwqEBEREVFsYmCByE8kd+/9\nMwMMCqH2LrRRJeBvoxKgV8pQYvMg58PCoOf5saDpAgumECvyvfPS8XxuNd4+ZIVeIWD5tOTafhEt\npVe8AvFKAVWu0AEQZ4jATnvgn60wsZO6NqgAoLbpokIm4Nmxxha9NiIiIiKiaDCwQOSnoXf+AWDD\npal481cLTls8uGugAXplTZVRqlaO4SlK/FQq3ZNBavTjOR6viJ0lTnhEICWCS7IHue5b++nRLU6B\nF8Yn4oXxrde1WKMQ8O/zE/H4zkokqmW4tZ8BHlHEqmM2rDtTF6hxtuDkjNZg9QusRFoiQkREREQU\nqxhYIPITqhRCSoJKQM94BRaPkb67/NxYIyZ/ViL5XKFVegSj1e3FgrXl+KGgZsG9MFOJpb1DX4fU\nda+emYLxETZNbAlzumsxp7tv74DtfsEVVzsvhbC6ff+CDCwQERERUVvH5o1Efkpt0ov9YF6ZkBiy\nfGJoigqbL0vDA4Pj8MehvnXyUk38Kp1eLN5dXRtUAICP8xWoCtN8wD9jYUiyEhM7qVush0K0VH7X\nV+Fo+5EFj1fEmhM2rD1tD2jQafX7PekZWCAiIiKiNo6BBaJ6Smwe/Pd44CjAniHGMc7MCt8joX+i\nEo+OiMcdfiMaLfXS4o9Xu3HV2jJ0e78A//7F7LOfBwLWh5n44J+xoJW3jQVroV8g57nc6la6kqZz\n648VuOa7csz7tgxP7Kryec4/sKBlYIGIiIiI2jgGFojOeuOAGTkfFuJ4te9Cd/3sVPw0LwPb56ZJ\nHic0YFSjQem7r8UtwuMVsb3YgaErivD1KXvQYxfvrvK5+73skAUTPy3GTd+XY3epE9d9V+6zv6aN\nLFg/O+H7d64O09wx1uVbPFhxtC449cLPZjjqBX38Aws6Bd+GiYiIiKht4zdaavdEUUSB1YNT5uCj\nH72iiL/vrpJ8rntcTSuS3kYlbu2n93nu6l66Bl2LTBACMgnyqtyY/nlp2GN/Nbmxp6ymCeSJajfu\n3mRCbrkLK47acMFnJSjzKyHopAueZRFLpnVu2ekUzemTI1b0/zhwCsh16+uCPhaWQhARERFRO8Pm\njdTuPbC1Em/+aql9fEUPLWZkaTA/uy4oUGTzosIReKd8RpYGieq6+NuToxKwq9SJnSUudNHL8ejw\n+AZfj1oO1M/+nxaksaOUjQUOZMcrMGpVEcLd1x+fETtNG0O5uZ8Ba+tNhdC0jXhIAJPDi5t/qJB8\nbu1pO1xeEUqZEDB1hKUQRERERNTWMbBA7dpJs9snqAAAK4/ZsPKYDTKhJi196T4zDpiksxn+OSbB\n57FKLuDbi1NxrNqDrgY5FFE0RtTIBaBeWECqgWMwh6vc6Pp+QUT7TuscvvdDLBiarPR5bFDGfiKV\nzS3ijQNmFNg8uKG3Hr2NSmwuCt4DwyMC92024flxRolSCAYWiIiIiKhtY2CB2i1RFPHETunyBgC4\n6Xvpu8vnHF6YgRSJ2+eCUDNeMlrqRjRVfOeQNeJ9M9pIKUSCyjeQUOn0QhTFBvWuaEk2t4hOy/Jr\nH7+234J5PbX48Ehg08/63suz4r28wN8fAwtERERE1NbF/q1BoigtP2zFymOhF3vBvD4xUTKo0BQa\nE1iI1E9XpDf7z2gqGoUAdb1/apc3cMJFLHloq8nnsVtE2KBCMCoZosp6ISIiIiKKJQwsULv1qcTY\nyEj1aERGQjjNHViY31PbqIyK1hCYtRC7gYVlElkH0WK2AhERERG1BwwsULvk9IhYnx+85j2UZLUs\noO6/KYVbSj4/1lj750xdw/8X/ed5xvA7xRipcohY4/GK+OM2U/gdz/r+0tSwv2sGFoiIiIioPWhb\ntzWJIrCv3IVJq4vRgJ6ItTK0Mqybndas6elj0lTILXcFff6GPjqMTlOhxO5Bpk6O0f8tjvjcT46M\n95li0VYkqHz/vSsdsRFYKLV7sO6MA90NcpyyePDKfkv4gwD0jJNjSLIKr01MDDopAgB0irb3uyIi\nIiIi8sfAArU7C9eVBQQVzktT4YtZKSh3eNFreWHAMR9MTcLMLA1kLdAw8I/D4rDmpA0F1sDF85Lz\nEiAIAgYkKQEoUe1q2AJ7chuZBOHPP2Oh0Nb6gYVSuwejVhVJjiENZ+vcmh4Xl/fQ4rTFgyd2STcR\nNaqZsUBEREREbV9Et8s2bdqEBQsWoF+/fjAajXj//fd9nhdFEYsXL0bfvn2RkZGBiy++GAcOHPDZ\nx2Qy4ZZbbkHXrl3RtWtX3HLLLTCZIk8rpvbJ6RFxtMoNSwMX0ME8tbsKJ82egO3TzwYNktUy9IgL\nbMo4Jk3VIkEFAEjWyLHukjTcO8iAyZlqXJCpxowsDdZdkoqb+xl89o1TypBlCN5Esp9RgXPJFfN6\najEoqflKOJqT/wSLfRXBMzpaynuHrFEFFW7uq4fqbB8NuUzAfYPjsHJ6suS+qc3UIJSIiIiIqCVF\nlLFgsVjQv39/LFy4ELfddlvA8y+++CJeeuklvPTSS8jJycHTTz+NuXPnYseOHYiLiwMA3HTTTTh9\n+jRWrBIMwMkAACAASURBVFgBALj77rtx66234qOPPmrCvw61FQ6PiBd+rsbi3dUAgE46GdbMTEWR\nzYPVJ2zoopfjt331DUoVF0URT++pDtj+uwEG3DmgZsEuCAKWTkjERV+U4tyS8aNpyUhu4QVepl6O\nv4xMiGjf/kYFTkkES0alKvHB1GQ4vTV31we30aACAPRJ8H0rqoqBHgvRBDdu7qfHn0fEB2yf1Ekt\nuf+zY9tePwwiIiIiIn8RBRamT5+O6dOnAwDuuOMOn+dEUcTSpUtx7733Ys6cOQCApUuXIicnBytW\nrMCiRYtw8OBBrF27Fl999RVGjx4NAHj++edx0UUXIS8vDzk5OU35d6I2YP63Zfi+oK65YoHVixGr\ninz2eXRHFdK0MszrqcVjwxOgDdPozizRVOG+QQY87reAPy9djYpFnRtx9S2rX6ISX5/2bUS5IFuL\nVyYm1T7urG/bd74NSt8Akj2aBhlN6O2DFnxyNPxUkVcmJEKnEGByejGnuzagpOMchUzA/GwtPvYb\nS5nZxn9vRERERERAE0yFOHHiBIqKijBlypTabVqtFuPGjcO2bdsAANu3b4fBYMCYMWNq9znvvPOg\n1+tr96GO43ClyyeoEEqxzYuX91lw5belYfctkajLv7W/QWLPtqVfYmAmwilLYAZDW6b2W1+/fciK\nQ6bWKYfYWuTAvZvDl2k9MTIeV/bU4tLuWlzXWx80qHDOX0b4Brg+nZHSqOskIiIiIooVjW7eWFRU\nc5c5NTXVZ3tqaioKCgoAAMXFxUhOToZQr4ZdEASkpKSguDh4x/u8vLzGXl6LamvX2xoKHQJm79A2\n+LiNhU58tecwsvXB72TvrZIBqGtemKryovrMUQQWR7Qt6mrfvxcADNeYkZfXfnqUmErkAHzLBRZ9\nW4C3h0Y3MrQxXslTIdxb439H2NBFY8XRIw079+ejBOypkmFIvBfplpOIpbcMvn9Rc+Lri5oTX1/U\nnPj6oubWVl5j4aoMYnoqRFsqkWBJR3iVTi+uXlMCwB3V8V9bk/H80MSA7Ta3iG9O23FTbrnP9l6J\nGuTkZEX1s2JJV48I7M332Xb/uG5tcqxkMIdUNuCg7+9vn1mOHtm9mnT0p8srYs0JG5xeYEYXDYwS\n/4blR0oBBA9oJKgEjB+QXdugsSFyAIxv8FHNj+9f1Jz4+qLmxNcXNSe+vqi5tafXWKNXJunpNWPV\nSkpKfLaXlJQgLS0NAJCWloaysjKIYt3dZlEUUVpaWrsPtW8/lTjR7f0C5FVGF1QAgFXHbD6vIQB4\n/YAZvZYX4Pr15QH7a6NY+MUitVzA8/3ttY9fn5jYroIKAKAJ0j/D0sS9Fn67oRyLNlTg1h8qMGxl\nIfaVB5ZbFNuCl5noFAKeGp0QVVCBiIiIiKi9anTGQrdu3ZCeno7169dj+PDhAAC73Y4tW7bgr3/9\nKwBg9OjRMJvN2L59e22fhe3bt8Nisfj0XaD26d1DFjy0NfK0/Q+mJsGokmF3mQuPbK+s3V7pFFHl\nErGp0I6X95lxtMqNfGvw6QG6MM0e25Lzk7wwtaGGkw2lD/K7KrR6wvYuiFSV04s1J+oCNBUOEYt3\nV+G9qXWjIL2iiOPV0sGvihsyfcq5iIiIiIioRkSBBbPZjKNHjwIAvF4vTp8+jdzcXCQmJiIrKwu3\n3347nnvuOeTk5KBXr1545plnoNfrMW/ePABAnz59MG3aNNx333144YUXAAD33XcfZsyY0W5SPyjQ\nabMbV68rR67EXWEAuHeQAWvPOPBLveevzdFhVteaHgzjMtR444AZx6rr7iAvWFuGLUXOiH5+ewos\ntHfJGungwXO51bgmR489pU5c0k2LHvHRxUKLrB7cs9kE//yHNSftEEWxNmCQb/HAHiRhgUEFIiIi\nIiJpEX1L3717N2bPnl37ePHixVi8eDEWLlyIpUuX4p577oHNZsODDz4Ik8mEESNGYNWqVYiLi6s9\n5o033sBDDz2EK664AgBw0UUX4emnn27ivw61pkKrB28csKDI5sG4DDUe21GJUrt0RkGcUsDDw+Jx\nbY4Hf9pRCadHxIND4jAuw7eBXyed3CewEGlQAWj7Ixg7kqQgpR0fHbHho7MjGpfsrcbueelI1jT8\n93rfFhO+OmWXfG71CTvmdK8JZh2pir5Uh4iIiIioo4oosDBhwgSYTMFT2QVBwMMPP4yHH3446D5G\noxGvvfZaw6+QYt7WIgdmfuE7DnJZnjXo/goB+POIeKjlArITFPhoWnLQfTvpogsOyATgmhx9VMdS\ny4uPoNyhyiXi61N2XB3F7/Xb09JBBQC4/ccKbCt2YH5PHU6apdMVpnZWS24nIiIiIqIYnwpBscvp\nEeERgVt+KMdnJ4Iv2urLjpdj3SVpkMuAOGVkdfMZEQYW3piUiCd3VeGE2QOlDHhjUhKyE/jybiuU\nEU5+OBFk4R+KwyPCFbwVB6xuES/vs2D5YSsqHNLNIudn6xr8c4mIiIiIOgquvKhBRFHE3ZtMITMS\ngnn7gmTJ8X6hdNIF37+rQY7reutxQaYaI1JVuLyHFtuKneikk6N7HF/a7VGw0ppQXtlvjmg/qaDC\nsBQl/jYqAeMzmLFARERERBQMV1/UIHvLXFEFFU7+plNE6e7+BiYpA7ap5cCWy9LR06+Rn0wQMDad\nC8D2zNbA8ZOnzG48vrMq6p/39uQkdGOQioiIiIgoJH5jpgZZE2HZg79oggoAcH6GGn0SFDhYWddU\nb8PstICgArV9w1KU2F0qPUHkHLsnfGChyunFbzeUY32+AxHsHtTwFCWDCkREREREEWiaAfHUYRjV\nwWvhb+2nx/KpSfj3eKPP9pXTgzdnDEchE/DVxal4cEgcLuuuxbIpSeiXGJjFQG3fjX3DN2WMJGNh\n5VEb1p4JHVS4sqc27Hn+NCw+7D5ERERERMSMBWogtTwwsFB2fSbkfs33eiUocMrsweRMNdK0jRv7\nmKiW4ZHhXOS1d9fk6DEsWYUndlXim9MOyX0sEQQWHtwafIINABRem4kTZjc+OWoLuR/7KhARERER\nRYYZC9Qg/neMF2RrA4IKADA2XY352bpGBxWoYxmQpMT0Lpqgz/9Q4EC+JXAyhCiKqHLWNHYMFXu4\nrb8eGoWA3gkKdNEHf2120smgVUQ2qYKIiIiIqKNjYIEaxOT07cqfZWDSCzUtTZgF/duHLD6PT5rd\nGLayCN0/KMD168tCHms82+tDEATMzAoewBiarIrwaomIiIiIiIEFipgoigHNGzuHuOtLFA2dRLlN\nfcsP100lKbV7MGJlEY5Xe+AVgU+Ph24uWr+UJ8sQ/LUbSZNIIiIiIiKqwcBCEylzAluLHDC7vOF3\n9iOKIvZXuFBuD0zxjiW7S104VG86g0IALu4a/K4vUTRSw5TPnDJ7IIoijla5MfiTIjTkfzllvXe8\ncSFGk07sxP4KRERERESRYh57E/il3IVLd2jhFEtrt03JVONf443oEkGpwI3fV2DVMRtUMmDphETk\nWzxIUMtweQ8t9AoBB0xufHfGjqmdNa06EeHbM753gy/sogm7CCRqqE668PHOnSUurDlhgzWCZo71\nqer1AxmZqsTVvXT4oF4GxLmff02OrkHnJSIiIiLqyBhYaALvHrLAKfqmb3+X78BvvivH95emhTz2\nLzsrsepYTXd6p7cmyHDO3Zt8u9s/uqMKt/XXo9jmxYVdNFiQrYUgNE+DuXK7B4t3V+Pjo1YsyNbh\nzoEGLN5d7bPPoGSOfaSml64LH6xasrcKuWWuBp+7fimEIAh4eUIiXhxvhAAgr9KNn0qdmNqZATMi\nIiIiooZgYKEJ5NUrD6hvb5kLhVYPMoIslDYWOvDCz+YG/axX9tc0rlt1zIYnd1Xi5yszJKcyNIbb\nK2LuN2XYe3bh9uoBC149YAnYb3yIVHKiaMUpZdArhJCjJfdXuCGPIqg2Ki2wKaPy7P8//RKVrZoR\nRERERETUVrHHQhPQh+hi3/ejQvxuYwW2FzvwxUkbTA4vvKIIryjiki9Lgx4XiXyrF6tP2Bp1jvpE\nUYTF5cWnx221QYVgFAIwsRM751PzmNdTG/L50xYPzljD9yTJSaiLnS6dkIj+DBwQERERETU5Ziw0\ngXcuSMInPx3FbT9LNzJ8L8+K9/J867iT1U0T01m0oQKd9XKMTFVB1oiyiFK7B9d+V44tRc6I9t89\nL73ZyjCIlpxnxOBkJcwuETOzNBjz3+IGn0OvELB9bhpfp0REREREzYyBhSYglwnYV92wQEGZI7CV\nvUIAGtiLDgAw/fOazAe1HPjv9BSMy2h4icILueaIgwoTMlTIiqApJVG0VHIBN/Y1NOocGToZgwpE\nRERERC2ApRBNpNQp4MLOamyaE7pZYzBLJySi9IbOMC3qjPnZgWngShlweQ8t/m9SYtBzODzATd+X\nQxQjj07sKHZi+IpC/GdfZL0eesTJ8fqkpIjPT9QU3rmg4a+5MWnsAUJERERE1BJ427mJ3N/ThZyc\nFADAjsvTMGpV5Knbo1NVWNirbrzdvYPi8M0pO0xOEcNSlPh6VipUZ7vZi6KIp/dW41eTdMPIfKsX\n7x6yYnY3DZI0cljdXjg9gFGi9MLs8uKqtWUol8ieqO+VCYmYmaWByysiSS1r8maRROHM6ipdZnRO\nnwQFFDJgX0Xd/xdPjopv7ssiIiIiIiIwsNAschKUMC3qjM2FDsyKoEHjkrEJPo/7JyqxZ14GTprd\n6JeorO1aD9SMyHvngqSQNef3bDbhns01f5YLgADgyVEJuH2Ab2r516fsIYMK3QxybLosDQYlE1uo\ndSnDBLM+ujAZaVoZ1pywo5tBjjGcWEJERERE1GIYWGhG4zLUeGGcEX/dVYVUjQyvT0rEoCQlVp+w\n4+8/VWFYihKLRycgSRM4jtKolsGolp660MeoxPKpSVi4rjzsNXjOVkU8vL0SnfVylDu8+OKkDUU2\nLw6aAic/JKtleGR4zZ3ey3toGVSgmDG1sxrrzjgCtv9rvBHd42reyuZn6wKeJyIiIiKi5sXAQjO7\noY8eN/TR+2yb012LOd1Dj9ML56KuWpgWdQYA/Gm7CS/vs4Q95rr14QMRX12cgpwEjuSj2POHoXEB\ngYWCazOhDTHulYiIiIiImh9vR7cDV/fSw9AEi6t/jzcyqEAxK04ie4ZBBSIiIiKi1sfAQjswMEmJ\nLXPT8Kdhcbiud/Sp4BeHaZBH1Jr6GBXoZqgrG5rTna9XIiIiIqJYwFKIdiLLoMBDQ2t6I9w90IAL\nPy9BhSPysZMXZWkkez0QxQqZIOCjC5Px9J5qxCmF2l4gRERERETUuhhYaId6JSj/v717j6qqzP84\n/uYc5KIJSEqOKCClYGOKIaZQqCmjmE6WFCqlplM2mslUMjRdxpn5lTPZ6oLiyHhtHMfUGm+kpqmh\nJuNllU0luFTGy6jgCBwUgcMBzu8PF2ckNQPZHi6f11r9AWx236f16Tmb7372s/k6rj2nL1Wy44yV\n3+wruuqYp0JasuRwCQB3eJp4J9LnVpcpUmuhPi1YPMDX2WWIiIiIiMgV1FhoorzcTHi5mbhou/p1\nkk+HtmJ2Px/+L8Kbw5YKQtu40tJVT8WIiIiIiIhI7emvySaudzu3Gs+i+7qbeKFnawBatTBxbzs3\nNRVERERERESkzrRioYkzubiwZIAvG0+W8W2BjZGdPflJS+2lICIiIiIiIvVDjYVmwOTiwvBAT4YH\nejq7FBEREREREWlitAZeREREREREROpMjQURERERERERqTM1FkRERERERESkztRYEBEREREREZE6\nc7FYLHZnFyEiIiIiIiIijZNWLIiIiIiIiIhInamxICIiIiIiIiJ1psaCiIiIiIiIiNSZGgsiIiIi\nIiIiUmdqLIiIiIiIiIhInamxICIiIiIiIiJ1psaCiEgTZrfrjcJiHOVLREREQI2FGzp69ChvvfUW\n3333nbNLkSYoNzeXU6dOUVxcDEBVVZWTK5KmpLCwkPLycsfX+iNQ6tPFixdrfK18SX3Kycnh4MGD\ngLIl9e/o0aMkJyezf/9+Z5ciTdCZM2fIzs4mPz8faD5zmBoL11FZWclLL71EVFQUx48fdwRDpD7Y\nbDYSExMZPHgwY8eOZdiwYVgsFkwm/S8pN89mszFt2jSGDBnCyJEj+cMf/kB5eTkuLi7OLk2agPLy\ncmbMmMHYsWOJj49nwYIFVFVVKV9Sb/bv3094eDhjx46lsrJS2ZJ6U1VVRVJSElFRUeTn52OxWJxd\nkjQhNpuN6dOnM3DgQCZNmkR0dDSHDh1qNnOY/oq5jvnz5/Pdd9+Rnp7OvHnziI6OBppPx0mMc+bM\nGR566CGOHTvGkiVLePHFFykrK+O1114DlDG5OaWlpTzxxBMcPnyYN998kx49erB69WqeeuopioqK\nnF2eNHKffvopERERHDp0iPHjx+Pq6sqiRYuYM2cOoPlL6se5c+fo378/VquVt99+G1C2pH6sWrWK\nb775hrVr17JgwQJiYmIcP1PG5GYUFhYSFxdHTk4OK1eu5N1336VTp0787ne/A5pHvlydXUBDY7fb\nKS0tZf369YwePZqIiAj27dvH119/TdeuXenVqxdeXl7OLlMasczMTIqLi1m6dCkdOnQgIiKCXbt2\nOVYrNJeuphgjJyeHrKws3nvvPR588EEGDx7MmDFjiImJYfHixTz77LN4eno6u0xphPLy8vj44495\n+OGH+e1vf4vZbCY2NpaZM2fyr3/9C6vViru7u7PLlEbMbrfj4uJCdnY2HTp0YNSoUSQlJfHkk0/S\noUMHZ5cnjVxlZSUffvgh0dHR9OvXjz179pCZmcmdd95JZGQkfn5+zi5RGrEvv/yS06dPs3TpUrp3\n7w7Az372M7755hvH3NbUacXC97i4uJCbm8uJEycYPHgwr776KuPGjWPNmjVMmjSJxx57jFOnTjm7\nTGnELly4QE5OjuMiKS8vj3379vGTn/yE3bt3O7k6aYyu7IJbLBb+85//0KdPH+DysrywsDASExOZ\nN28eWVlZzipTGqnqfFVUVBAWFkZCQgJms5mqqipatWpFeXk5FosFd3f3ZnFHRurXlZmpvvAuKSmh\nd+/ejBgxgo4dO/L73/8euHpfD5EbuTJf+fn5nDlzhqFDh/LGG28wYcIEMjMz+fWvf83w4cPZs2eP\nEyuVxujKfJWVlZGTk4O3tzcA58+fZ82aNfj7+7N+/XpnlXhLNfvGwty5c0lNTWXnzp2O7/n7++Pt\n7c2sWbM4efIk69at4+9//zvbtm0jJyeHd999l5KSEidWLY3FtfLVu3dvvL29GThwIE888QTdu3fH\ny8uLjIwM4uPjefPNN7FarU6sWhqLHTt2ADU3/Wzfvj0dOnRg+fLlNY595ZVXMJlMpKenA81jSZ7c\nnOp8VVZWApc/G6dMmUJISAjwvwwVFxdz9913A1pxJT/eteavaidPnqS0tBQfHx9mz57NypUrGTdu\nHJMnT+bkyZO3ulRphK6VLz8/P1xcXEhJSeHkyZN89NFH/O1vf+PgwYN4enqycOFC3TyUH+Va+YqM\njCQ0NJS4uDji4+MJDQ3F19eXs2fP8stf/pLnn3+es2fPOqvkW6LZNhb27NlDaGgoq1ev5qOPPmL8\n+PEkJSVhtVqpqKggIiKCDRs2UFVVRZcuXbjtttsIDAxk1qxZrFq1itLSUmcPQRqwa+VrxowZWK1W\n7rnnHrZs2cJrr71GTk4Oc+bM4ZNPPmHdunW89957zJ07l7y8PGcPQRqwzZs3ExoayqOPPkpWVhZm\ns5mKigoAvL29iYyMZOPGjVgsFlq0aOFoVD3zzDOsXLkS0B+Acn3fz5erq6ujuQD/ayiYzWYAsrKy\n6N27d42fiVzPteavK/NVVVXF4cOHiY2NBeDQoUO4u7uzadMmpkyZQkBAgLNKl0bgh/Jls9l4+OGH\n2bhxIzk5Odx55524ubnh6enJ66+/zmeffeZ4S5fItfzQ9VebNm1IT08nLS2N3NxcZs+ezbp161i0\naBErV65kzZo1Tb4x2mwbCytWrCAyMpKMjAzWrFnDX/7yFxYtWkRKSgqenp4MGTLEsdTTZDI5LsJ/\n+tOfYjKZOHbsmJNHIA3ZtfK1ePFi5syZQ1FREYGBgZw/fx6A0aNHOzqe9913H+Xl5Xq9qVzXp59+\nypw5c3jooYfo27cvL7zwAgCurq7Y7Xbatm3LgAEDKCoqYsGCBQC4ubkBl+/WeHp6NvkPNqm76+Wr\nuokANZtS3377LWfPnuXee+91/Oz48eOAXp8rV7tRvqqvue666y5WrVpF//79eeedd3jqqadwdXUl\nNzfXcZzI990oXy1atCAqKorg4GBcXV1p1aqVYz4LDw/HZrNpxYJc1w9df1Xz9fWlqKiIS5cuMWbM\nGMdcFR4eTnl5OUePHnVK7bdKs2wsnDt3jl27dhEREQFcvsMXExNDYmIiq1atYseOHcTGxjJmzBg2\nbdrE559/7piUMjIy6NmzJ2FhYc4cgjRgP5Sv1atXs2vXLuDyBXhVVRV5eXmOjRu3bNlCjx49HHf/\nRKpV3wn29/cnOjqa559/npdeeon9+/ezdu1a4PJrAAFGjBhBv379+Otf/0pmZqbjwik7O5s777xT\nd/zkKj8mX9V3Za60adMmunXrRqdOnfj6668ZNGgQgwcPpqKiQq/PFYcfmy+TyYTFYiEzM5OUlBT6\n9OnDF198wZtvvskTTzzB008/jdVqVbakhtp8Pvbt25cnn3ySf/7znyxbtoxLly4BsHHjRnr27Ml9\n993nnEFIg1Xbz0ez2cz58+fJzc11zFXr168nJCSEfv363foB3ELN4q0QBw8epHPnzo7NNKqfsSos\nLATAarXi4eFBUlISmzdv5h//+AcPPPAAU6dOpaCggISEBAYOHIiHhwefffYZr776Km5ubs1mh0/5\nYbXN1+bNmxkyZAjt27enTZs2jBw5kmeeeYYDBw7wySefMGPGDNq1a+fMIUkD8v18de/endDQUFxd\nXWnTpg2jR4/m5ZdfZuTIkbi7u1NRUUHr1q15+umnyc/P55FHHiE2NhaTycSnn35a49Vtmr+kNvmq\nXhVzZW6OHTtGt27d+M1vfsP8+fNJSEhg48aNNe7gSPNV23zZbDZ8fHyYO3cubdu2pWfPno5zTZ48\nGT8/P1q0aOGs4UgDU5t8ubm5UVlZibu7O+PHjyc/P59XXnmF5cuX0759e7Zt20ZSUhLe3t76fBSg\n9vNX9Yqr22+/nd69exMTE8PEiRM5deoU6enpTJ8+neDgYCePyljm5OTkmc4uwijr1q1j1KhRpKen\ns2DBAgoLCwkJCeG2227jzJkzrF69mkmTJuHh4UF5eTnu7u6YzWbS0tIcrzYaMWIEt99+OyaTifLy\nclJTUx3vvNWk07zVNV/z589nwoQJdOvWjbvuuovs7Gyys7Ox2WwsXryYoUOHOnto0gB8P18Wi4WQ\nkJAaSzc9PDzo0KEDK1asoKSkhAceeIDKykrMZjNt2rRhxIgReHl5UV5ejtVqJS0tjf79+wOav5q7\nuuar+sIJLu/Q/6tf/Yq9e/fi6urK6tWrGTdunJoKclP5MpvNBAcH0759+xrn9PX1JSoqSnOX3PT8\n5ebmRv/+/QkJCcHHxwe73U5KSopjXw9lrHmra74qKiowm820a9eOvn37kpuby+nTp6mqqmLhwoUM\nHz7cySMzXpNtLHz11VckJSUxefJkpk+fjr+/PykpKRQUFBAVFYWXlxdbt26loKCA6Ohox8V4165d\nmTt3Lr1796ZLly4AhIWFMWDAAIYNG8btt9/u5JFJQ3Cz+QoLC6Nr16507NiRUaNGMXToUBISEpQv\nAa6dr/fff5+CggLuu+8+PD09qaysxGQy4ePjQ1VVFW+//TbPPPMMrVq1wmq1UllZiaurK+Hh4cTE\nxDiapCI3k6+WLVtitVoxm80UFhZy6dIlXnnlFV599VXatm3r7KFJA3Cz81f1knU97iDXUh/zV3UD\nq0uXLvTr14+YmBh9Pgpw8/NXWVkZcHkjx2HDhhEbG0tcXBy+vr5OHtmt0eQaC9XLl6r3RkhJSSEg\nIIDw8HA8PDzYunUrVquVUaNGcfbsWT744ANiYmIcnfGDBw+yadMmHn/8cTp16uTk0UhDU1/5io+P\nd+TLxcUFDw8PZw5LGogb5Wvbtm1cuHCBqKgox0W3q6sr/v7+fP7553z11VeEhITw3HPPERQUpDlM\naqivfE2dOpWAgAC6devG4MGD6dy5s5NHJg1BfeYrMDBQ85fUoM9HMVJ95qt6/nJxcWl2j241uXZw\n9RKVEydO0Llz5xrLmZ588kl69erF2rVrOX78OM8++yw9e/Zk7NixLFu2jKNHj7Js2TL8/f0JDQ11\n1hCkAVO+xEg3ylfPnj3ZsmULWVlZAI5XaAUFBZGQkMDHH39MZGQkAL169brF1UtDV1/5cnFxUb7k\nKsqXGEmfj2IkzV/1o9GvWNi+fTupqalkZWVRWVnp2O28tLSUP//5z8THx+Pr60tVVRXu7u60aNGC\nXbt24e7uzoABA4iNjWXv3r1s3ryZZcuWUVRURGpqqjqZAihfYqy65Gv37t24u7vTp08fTCYTpaWl\nLFq0iNdff53IyEg+/PBDpkyZoufcRfkSQylfYiTlS4ykfBmj0a5YyM3NZfTo0UyePJni4mI+/vhj\nHn/8cbZv347dbmfQoEEEBgby/vvv1/i9QYMG4erqypEjRwDw8vJi6dKlbN68mRUrVrBz5066devm\njCFJA6J8iZFuJl8mk4mcnBzH986dO8eePXuYO3cu6enpypcoX2Io5UuMpHyJkZQvY7lYLBa7s4uo\nrZKSEl588UWsViuvv/46QUFBAAwbNox27drxwQcfUFVVxapVq5gyZQobNmwgKirK8fu/+MUvyMvL\nY8OGDYBeuyY1KV9ipPrOl8iVlC8xkvIlRlK+xEjKl/Ea5YqFli1b4ubmxpgxYwgKCnLsIDx06FCO\na18drQAABw5JREFUHDnieJ3MI488wkMPPURiYiIZGRnY7Xby8vLIycnhsccec5xPf/TJlZQvMVJ9\n50vkSsqXGEn5EiMpX2Ik5ct4jXLFAoDNZnPstFl9R/i5557DZrORlpbm+F5ZWRlxcXFkZWXRo0cP\nsrOz6dixI0uWLKFjx45OHoU0VMqXGEn5EiMpX2Ik5UuMpHyJkZQvYzXaxsK1jBgxgkceeYSJEydi\nt9sd76k9d+4c3333HV9++SUBAQHqNkmdKF9iJOVLjKR8iZGULzGS8iVGUr7qT5PZtvLEiRNkZ2dz\nzz33AJeXn9tsNsxmM35+fvj5+TFw4EAnVymNlfIlRlK+xEjKlxhJ+RIjKV9iJOWrfjXKPRauZLdf\nXnCxd+9ePD09iYiIAOCtt95i0qRJNXbvFKkt5UuMpHyJkZQvMZLyJUZSvsRIypcxGv2KheqN8Q4c\nOMDPf/5ztm/fTmJiIlarlXnz5hEcHOzkCqUxU77ESMqXGEn5EiMpX2Ik5UuMpHwZo0nssVBWVkZk\nZCT//ve/cXNz4+WXXyYxMdHZZUkToXyJkZQvMZLyJUZSvsRIypcYSfmqf41+xQKAh4cHAQEBPPjg\ng7zxxhu4u7s7uyRpQpQvMZLyJUZSvsRIypcYSfkSIylf9a9JrFgAqKysxGw2O7sMaaKULzGS8iVG\nUr7ESMqXGEn5EiMpX/WryTQWREREREREROTWa/RvhRARERERERER51FjQURERERERETqTI0FERER\nEREREakzNRZEREREREREpM7UWBARERERERGROlNjQURERK6yfPlyfHx8HP/ccccdhIaG8uijjzJ/\n/nwuXrxYp/NmZ2cza9YsTpw4Uc8Vi4iIiLO4OrsAERERabiSk5Pp3LkzNpuNc+fOsXv3bl5++WVS\nU1NZsWIF3bt3r9X5Dh8+zJ/+9Cfuv/9+AgMDDapaREREbiU1FkREROS6Bg0aREREhOPrF154gYyM\nDEaPHs2YMWPYt28fnp6eTqxQREREnE2PQoiIiEit9O/fnxkzZnDq1ClWrVoFwLfffsuUKVMICwvj\njjvuIDg4mIkTJ3Lq1CnH7y1fvpzx48cDMGLECMdjFsuXL3cc8+WXX/LYY48REBBA+/btGTp0KDt3\n7ry1AxQREZFaUWNBREREai0+Ph6A7du3A7Bjxw6OHj3K6NGjeeuttxg3bhyfffYZw4cPp6SkBICo\nqCgmT54MwIsvvkhaWhppaWlERUUBsHv3bmJjYyksLGTGjBnMnDkTq9XKo48+yq5du5wwShEREfkx\nXCwWi93ZRYiIiEjDsnz5cqZOncrWrVtrPApxpYCAAIKCgti5cyclJSW0bNmyxs/37t3LkCFDSEtL\nczQi1q1bx/jx49mwYQMPPPCA41i73U6fPn3o0KEDa9euxcXFBYDy8nKio6Px8vJiy5YtBo1WRERE\nboZWLIiIiEid3HbbbRQXFwPUaCoUFxdTUFDAXXfdhbe3NwcPHrzhub755huOHDlCXFwcBQUF5Ofn\nk5+fz8WLFxkwYAAHDhxwrHwQERGRhkWbN4qIiEidFBcX07ZtWwAsFgszZ85k3bp1FBYW1jjuwoUL\nNzzXsWPHAJg2bRrTpk275jEFBQVXrYoQERER51NjQURERGrt9OnTXLhwgeDgYAAmTJjA3r17mTp1\nKj169KB169a4uLgwceJEqqqqbni+6mNmzpxJWFjYNY+pbmKIiIhIw6LGgoiIiNTaypUrAXjwwQex\nWCx8/vnnJCcnk5yc7DimrKwMi8Xyo87XuXNn4PLjFQMGDKj3ekVERMQ42mNBREREaiUjI4PZs2cT\nGBjI448/jsl0+XLCbq+5H/S8efOuWq3QqlUrgKsaDmFhYQQHB5OamsrFixev+neeP3++PocgIiIi\n9UgrFkREROS6tm3bRk5ODhUVFfz3v/9l586d7Nixg06dOrFixQo8PDzw8PDg/vvvJyUlBZvNRqdO\nncjMzGTPnj34+vrWOF+PHj0wm828++67FBUV4enpSXh4OEFBQcyZM4e4uDj69u1LQkIC/v7+nD17\nli+++AK73U56erqT/iuIiIjID1FjQURERK7rj3/8IwBubm60adOGu+++m1mzZpGQkEDr1q0dxy1c\nuJDk5GSWLFlCRUUFkZGRrF+/nocffrjG+fz8/Hj//fd55513mD59OpWVlaSmphIUFERUVBRbt25l\n9uzZLFq0iIsXL+Ln58e9997LuHHjbum4RURE5MdzsVgs9hsfJiIiIiIiIiJyNe2xICIiIiIiIiJ1\npsaCiIiIiIiIiNSZGgsiIiIiIiIiUmdqLIiIiIiIiIhInamxICIiIiIiIiJ1psaCiIiIiIiIiNSZ\nGgsiIiIiIiIiUmdqLIiIiIiIiIhInamxICIiIiIiIiJ1psaCiIiIiIiIiNTZ/wOBxpY2cUzzTwAA\nAABJRU5ErkJggg==\n","text/plain":["<Figure size 1152x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"bcc9c36165fc07d258bd5ea87874d2da17fa4a4d","id":"Q2De6mI5ONPS","colab_type":"code","colab":{}},"source":["# Scaling the training set\n","sc = MinMaxScaler(feature_range=(0,1))\n","training_set_scaled = sc.fit_transform(training_set)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"fccfb866a2b4c702e0b2742f7c0289512d713d1b","id":"SYvqZraPONPW","colab_type":"code","colab":{}},"source":["# Since LSTMs store long term memory state, we create a data structure with 60 timesteps and 1 output\n","# So for each element of training set, we have 60 previous training set elements \n","X_train = []\n","y_train = []\n","for i in range(60,2769):\n","    X_train.append(training_set_scaled[i-60:i,0])\n","    y_train.append(training_set_scaled[i,0])\n","X_train, y_train = np.array(X_train), np.array(y_train)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"637f699d3c4bde4b783de56ed4dd70a1bf59760d","id":"NK5gtxUrONPa","colab_type":"code","colab":{}},"source":["# Reshaping X_train for efficient modelling\n","X_train = np.reshape(X_train, (X_train.shape[0],X_train.shape[1],1))"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"E5CNWAod0HCj"},"source":["## 3.3 Model"]},{"cell_type":"code","metadata":{"id":"vunWEB4y0MoO","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":271},"outputId":"d9df682f-e8f0-42f2-a7c1-340c5b5b7190","executionInfo":{"status":"ok","timestamp":1585819144046,"user_tz":-540,"elapsed":35915,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}}},"source":["# The LSTM architecture\n","regressor = Sequential()\n","# First LSTM layer with Dropout regularisation\n","regressor.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1],1)))\n","regressor.add(Dropout(0.2))\n","# Second LSTM layer\n","regressor.add(LSTM(units=50, return_sequences=True))\n","regressor.add(Dropout(0.2))\n","# Third LSTM layer\n","regressor.add(LSTM(units=50, return_sequences=True))\n","regressor.add(Dropout(0.2))\n","# Fourth LSTM layer\n","regressor.add(LSTM(units=50))\n","regressor.add(Dropout(0.2))\n","# The output layer\n","regressor.add(Dense(units=1))\n","\n","# Compiling the RNN\n","regressor.compile(optimizer='rmsprop',loss='mean_squared_error')"],"execution_count":14,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"rX5g5WtR0JDH"},"source":["## 3.4 Training"]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"df20eb7e8062dae0a3aff2182aa440faddd0017d","id":"1ns3rINUONPe","colab_type":"code","outputId":"27c844aa-7c9a-4bb2-f494-8f11c43014fc","executionInfo":{"status":"ok","timestamp":1585820022584,"user_tz":-540,"elapsed":825282,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# Fitting to the training set\n","history = regressor.fit(X_train,y_train,epochs=50,batch_size=32)"],"execution_count":15,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use tf.where in 2.0, which has the same broadcast rule as np.where\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n","\n","Epoch 1/50\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n","\n","2709/2709 [==============================] - 19s 7ms/step - loss: 0.0260\n","Epoch 2/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0124\n","Epoch 3/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0085\n","Epoch 4/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0073\n","Epoch 5/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0065\n","Epoch 6/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0056\n","Epoch 7/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0052\n","Epoch 8/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0047\n","Epoch 9/50\n","2709/2709 [==============================] - 17s 6ms/step - loss: 0.0043\n","Epoch 10/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0041\n","Epoch 11/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0037\n","Epoch 12/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0037\n","Epoch 13/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0035\n","Epoch 14/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0033\n","Epoch 15/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0032\n","Epoch 16/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0028\n","Epoch 17/50\n","2709/2709 [==============================] - 17s 6ms/step - loss: 0.0029\n","Epoch 18/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0028\n","Epoch 19/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0027\n","Epoch 20/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0025\n","Epoch 21/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0025\n","Epoch 22/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0025\n","Epoch 23/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0024\n","Epoch 24/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0023\n","Epoch 25/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0022\n","Epoch 26/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0021\n","Epoch 27/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0023\n","Epoch 28/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0019\n","Epoch 29/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0019\n","Epoch 30/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0021\n","Epoch 31/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0019\n","Epoch 32/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0020\n","Epoch 33/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0018\n","Epoch 34/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0018\n","Epoch 35/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0017\n","Epoch 36/50\n","2709/2709 [==============================] - 17s 6ms/step - loss: 0.0017\n","Epoch 37/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0018\n","Epoch 38/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0017\n","Epoch 39/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0017\n","Epoch 40/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0017\n","Epoch 41/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0016\n","Epoch 42/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0016\n","Epoch 43/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0016\n","Epoch 44/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0016\n","Epoch 45/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0016\n","Epoch 46/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0015\n","Epoch 47/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0015\n","Epoch 48/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0015\n","Epoch 49/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0014\n","Epoch 50/50\n","2709/2709 [==============================] - 16s 6ms/step - loss: 0.0015\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gpxH7_cNwyWe","colab_type":"code","colab":{}},"source":["regressor.save_weights('../models/lstm_weight.h5', overwrite=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"pG3534j8LK2n","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":282},"outputId":"9fa230e2-fea3-40ba-c003-00b3b8b65c02","executionInfo":{"status":"ok","timestamp":1585820121358,"user_tz":-540,"elapsed":1622,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}}},"source":["plot_metrics(history)"],"execution_count":17,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAbEAAAEJCAYAAAAaSRmpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwofMXwODwQCHwwGNRuOxXaPRoLGx0ec+jY2NmD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zkaUUioqHEEBskUzQKPJ7l+YiY9090\n4D1OuyciGjIMsUG0crIKWSM9146tOGhEfTtPKxIRDQWG2CBSSAW8eusIKC/NuofRKuLf91+AU+Qt\nqYiIBhtDbJBNHCHHUzmxHtv+52wnVhw0MsiIiAYZQ2wIPDwpBj/Rej6qZdvxdiw/wCAjIhpMDLEh\nIJW4ZitqIjzL+8a/2vHofiMcTgYZEdFgYIgNkRSVDB/eMQraSM8Sb/+uHQ/vv8AgIyIaBAyxIZSu\nluPDO0ZhdJRnmd/5vgP/9ukF2BlkREQDwhAbYmlxcnz4cw3G9AiynXUd+G0lg4yIaCAYYlfAuDgZ\n9tyh8bjjPQCU/9CBhz5pZpAREfUTQ+wKGRvrukaWFOMZZLtOWLD8gBEiZy0SEV02htgVlKqSYc8d\no5DSI8jerG3Hs1+2BqlXREThiyF2hSXHuIKs54jsxa9M2HLUFKReERGFJ4ZYEFwTI8P7s+IRr/Qs\n/5NVLXjne94wmIgoUAyxIBkfJ8e7s+IRIxM8tj/y6QX8/QwfqElEFAiGWBDljFLgzfyRkHf7W7CL\nwK/2NaOqsTN4HSMiChMMsSCbMSYCr906At3HYx0OEfP/bsC3RlvQ+kVEFA4YYiHg7rFRKLk5zmOb\n0SriZ3uaUHKkFS1WZ5B6RkQU2hhiIWLJxBiszvZ8MnSLVcT/rW5D1s5z2FDdCmMnw4yIqDuGWAgp\nylbhoQnRXttbrCI2HGlD1rvn8DzDjIjILeAQKy0tRVZWFrRaLXJzc3HgwIE+2+/fvx+5ubnQarWY\nPHkyysrKLvuYs2fPhlqt9vh68MEHA+1y2BEEAb+fGodnboxFnELwer3VKuL3R1wjs98faYXFzrt8\nENHwFlCIlZeXo6ioCCtWrEBlZSX0ej3mzZuH06dP+2x/4sQJzJ8/H3q9HpWVlXj88cexatUq7Nq1\n67KPuWjRIhw/ftz99eKLLw7g44Y+qUTAY5kq1MzTYW2OCmpfYWYT8Xx1G6Z90ID/4XR8IhrGAgqx\nLVu2YOHChVi8eDHS09NRUlICrVbrc3QFANu2bYNOp0NJSQnS09OxePFiLFiwAJs3b77sY0ZFRUGr\n1bq/4uLier7dVSlOIcGq7FjUzNPhqRtiMULpHWZ1bQ7c93cDfrXPgDMmexB6SUQUXH5DzGq14siR\nI8jLy/PYnpeXh8OHD/vcp6qqyqt9fn4+qqurYbPZLuuY7733Hq699lrcfPPNeOqpp9DW1hbQB7ta\nxCokeGKya2S2bkosRiq9/8p2n7Rg6vuN2PRVG6wOnmIkouFD5q+BwWCAw+GARqPx2K7RaNDY2Ohz\nn8bGRsyYMcOrvd1uh8FggCiKAR1z3rx5SEpKgk6nw7fffotnnnkGR48exfvvv99rf2tra/19JL8G\n4xhD4c5I4NYcYOtJOd6rl0HstrrMbBex7otW/OmoESvHWXGTenAnf4RqTYKNdfGNdfHGmvjmry5p\naWl9vu43xILp17/+tfv7SZMmITU1Ffn5+Thy5Aiys7N97uPvA/tTW1s74GMMtdKJwCPnrVhx0Ij/\nd95zQfQPHRI8/HUEfpEcgWdvjMX4OPmA3y8cahIMrItvrIs31sS3waiL39OJ8fHxkEqlaGpq8tje\n1NSEhIQEn/skJCT4bC+TyRAfH9+vYwJATk4OpFIp6urq/HX7qpczSoG/z9bgxZ+ofU7++OspC25+\nvxFrDhtxgVPyiegq5TfEFAoFsrOzUVFR4bG9oqICU6dO9bmPXq/32T4nJwdyubxfxwSAo0ePwuFw\nQKvV+uv2sCCVCCicEI0v7tViUVqU1+t2Edh6zIycd89h61ETr5cR0VVHWlRUtN5fI5VKheLiYuh0\nOkRERKCkpAQHDhzA5s2bEbWFsDUAABKRSURBVBcXh6VLl+LDDz/EnDlzAABjx47Fpk2b0NTUhKSk\nJPz1r3/FH/7wBzz33HOYMGFCQMf84Ycf8NprryE6OhpWqxVVVVVYvnw5EhMT8dRTT0EiGZp12s3N\nzYiPjx+SYw+VKJkEs5MjkZ8YgW+NNvzY7jnysjiAf5ztRPkP7bjQ6YTR6oRCKiBWIUAQvEdxPYVj\nTa4E1sU31sUba+LbYNQloGti99xzD5qbm1FSUoKGhgZMnDgRO3bsQHJyMgDgzJkzHu1TU1OxY8cO\nrF27FmVlZdDpdNi4cSMKCgoCPqZcLscnn3yCV155BWazGYmJiZg1axaKiooglXo+UJJcbkpQ4G+z\nNSj/oQPrvmjFGbPD4/XvWx3YcOTS7E6VXEDGCDmuHylH1kg57kyJQHwEa0tE4UMwGo08x9TN1XIB\ntsMu4pVjJrxQ04Y2W2B/xTEyAY9eH4NHro+BqtvzYa6Wmgw21sU31sUba+LbFZnYQeEpUibgf2ep\n8OW9WhSmR0Hi/6whTHbXPRqzdzZg61ETOnkNjYhCXEhPsaeBS4iU4sVpI/BYpgqV9Z34qtmGo802\nfH3Bhlar75AydDqxpqoFW46asDZHhRuYZUQUohhiw0SqSoZU1aW/blEUccbswNELNnzZZEPZt2YY\nekzFP2N24OH9RiRHRuDaH87D5hRhdwI2pwibE7A7RcgkAjJGyDA5XoHJ8XJkxcs9TkUSEQ0lhtgw\nJQgCkmJkSIqR4edJkfj362Ow5agJW742wdTj7vinOiQ41dHZ67G+arbhne87XMcFMC5WhsnxcmSP\nkiM/MQIT1bKAZkESEV0uhhgBcN2jcU1OLJZMiMYfatpQ9q0Z/XmgtAjgu1Y7vmu1470fOvB/Pm9F\nSowUP0+KwB3JEZimVUIhZaAR0eBgiJEHTaQUG6aqsSwjBhuOtOHt79ox0EtiJ00OvPqNGa9+Y0as\nXEB+YgRuv0aJjBFyXBsrQ6yCpx+JqH8YYuRTikqGrdNH4Okpsfjb1yeQlJgImUSAXALIJQJkgutP\nQ6cTNQYragw2HDHYUNti7zP0Wm0i3j/RgfdPdLi3aSIkGBcrw9hYGcbFypCuliE/UYkoGcONiPrG\nEKM+jY6SYtoIJ9ISI3ptc+topft7k82Jr5ttqD5vQ8WPFnxS34lOR6+7AgCaLE40Waw41Gh1b1Mr\nBPzqumg8NCEaKSr+MyUi3/jbgQZVjFyCm7VK3KxVYtmkGJhtTlT82ImPTluw97QFTZbALrQZrSJe\n+tqEzUdNuCMpAkszYjBdp+AEESLywBCjIRUtl+DOlEjcmRIJpyjiyyYbPjrdga+abahrdeBEmx32\nPs4/OkVgzykL9pyyIEMtwwPjo6CUCrA6RFgcIjqdcH8vkwCjI6XQRUkxOlra9b0E0ZzyT3TVYojR\nFSMRBNyUoMBNCQr3NrvTtV7t+1Y76lrt+NZo77pRsXeyHTPa8fQXrZf9vrEKAaMjpRgT3fUVJUVi\ntBSjo1w/J8dIEcfJJURhiSFGQSWTCO6F2PmJrm3P3RSHnXXtePWYCUcv2Af8Hq1WEa1WO4639H6s\nsSopckYpkB0vR/Yo18JtBhtR6GOIUciJlLkmdfyvtCgcaLDi1WMm7DllwVDeyvGHNgd+aOtA+Q+X\nZk2Oi5Xiujg5YuQComSur2iZBNFdP7capEiWtEMpEaCUAkqp4P4aoZDgmhgplFwTRzSkGGIUsgRB\nwC06JW7RKXHGZMc733fgpMkOpUSAQiogQoquPwUoJAIsDhH17Q6ca3egvt2B+nYnzrU7+rzm1pfv\nWx34vrWvqZVK4LsLvfcfwJgoKZJVUqTESJGikrn/TIpxndaUBXJnZiLqFUOMwsI1MTKsmKy67P2c\noojzFifq2x340ezAj+4/nfjR7MBZswN1bXY4h2CUJwI42+7A2XYHDjZ4vy4RXCGXFCNFUrTrz1ER\nUveoz+NLLsEIhQBdgMFnsYs4ZbKjvt2B0VFSXKeWD/4HJAoBDDG6qkkEAQmRUiRESjG5lwfIttud\n+KprsXb1eSv+abDheMvQBFt3TtF1k+UzZgcOBriPRAC0kRKM6ZqUMjrKNaIz2UWcarPjpMmBk212\nnOvwXMpw/Ug5Fo6PwvxxkRjVzwefiqKIH9oc+KrZBrVCgqkJCkTIOJKk4GKI0bAXJZNgqlaJqdpL\ni7bNXYu2Gy1OmG0i2u0izPZL37fbRTQ0t0AZo0KnQ+ya5g9YnSI67CIaO1ynMwc7B50iUN/uRH27\nE1+etwW839fNNqytasG6L1rws2sisCgtCrdfE+F3VNfQ7kBlfSc+qe/Exz92ejwtPEIK3KJTIi8x\nAvmJSqTH8UbPdOUxxIh8iJZLPELNl9raJqSljez19U6HiNOmi6Mj1wjphMmO0yYHTpscAS/8Hkw2\nJ/DhKQs+PGVBQqQE07RKyCWAVHDNFJUJgFQiwO4U8XmjFceMvc/otDiAf5ztxD/OduJJAIlRUuQl\nKiG3yCE3GGGyizDZnDDZRJi6wn90lASZI+XIHKlA5kg5xsZKIWHw0QAwxIiGiFIqYHycHOPjfF+P\n6rCLOGvuCjWzK9harE60212jObP94qjPiXabiEaLE+cDDD4BQGK0FKMiJKhptvk8NdrY4cQH3e5h\nOVBn2x34c207ADkAs882XzUDfztz6bE+0TIBk0bIcf1IOSJlAjrsIjocIix2Ee1df1qdIkYqJUiO\nkXY9Psi1ti85Rga1Qgho9Ndud+JCp4gLnU40dzrRZnVihFKC0VGuxfGRPC0athhiREESKes75Hzp\n7JqB+aPZ4TFZJVImICVGhhSV65f7NdFS9yNv6tsdeOe7drxZ247vWge27k4pBaaMUuCUyeFxarG/\nzHYRVU1WVDVZ/Tf2IUYmIEouQCoAUkGA7OKoUhAgCECL1YkLnU5Y/HR1hNK1IH50tBTaSClkEtep\nW6fomqDjFEWIIuAQ4fpPhuPSfzQ67E502EVYnUBCpARJMTIkdy2ivxi6NiswThQHbdRpdbhGufKu\n2bkyAcP2VC5DjCiMKKWC11O6/RkdJcXyLBUey4xBVaMV279rR/kPHWiz+b9iJxGAnHg5cscokTs6\nwj2ZQxRF/KvFjn+c7cS+sxZ8ds6KjqFcyNcLk130eohrf7hGafY+T58G4rzFiWM+F+hHQfb5j0iI\nlEAX5QpKXZTre03XRBu7U4RdBBxdf9qdIjodwHmLA+e7RuHnLU40WRxosXp+ZokAREhd6xUjpAJi\n5BJcFydDVrwcWSPlyBwpR2K09KoMOoYY0TAhCIJ7Akvx1DgcOGdFi9Xp/oXpEAGHE7CLru+ToqW4\nRaeEWul95xJBEJCuliNdLcfDk2JgsYs41NiJqkYrzhsMSNFpoJILiJEJiJZLECN3LQL/rsWOr5pt\nXV9Wn7cXu1rZRbiWdrQ7AQQ+KScQzq4RYrsd6Bo7orbFjj2nLO42I5Wu65FpcTJ0OES0dDrRYnWi\nxSqi1eb6vtOBrkX9AqLlAmK6Fve7f5ZLuv5Ou/5eZQJi5AKkgoAWqxNGq+s4xk7R/f3rt47AyH7O\niA0EQ4xoGIqSSTDzmt4fr3O5ImQCZoyJwIwxEaitbUBaWozPdjdqFHig63tRFPFjuxNfNVvxL6Md\nQtdxImUCIrtOk0XKBMgkAho7HDhlcuB0t4kxp0yOgEd/conrl/hIpQRqpQQquQBD1/rBhg7nkN4N\nJlQ0dzrxSddM076020WcH8T3PW9xMsSI6OojCAISo6VIjI7Ez5Muf39RFGG0upY32EXAIYoeI0mH\nE1ApBIxUShAt630CiMMpoqnbgviLs0YlgmuCjERwrTe8+H1k10jlYthGy11/SgQB9e0OnOoetGZX\n8J5qtcHkGLxTeQJcn83hBDoc4pCvaRwIo3VoZ+EyxIgoLAmCgBHKgQeDVOK6E4ouSoqcUQM71pho\nKaZoFF7ba2trkTh2HBo7ukZ/7U6c63Cgod2B5k4nJF2TUaQS15+ybn/GR0gwKkKCURGu2aaaSAlG\nKCSQdlvjZ3d2PZqoa71iY4drUXqNwXXq9utmG8yDcO2wP4xDfMqYIUZEdAVEySRIVUkua1JOoGQS\nATESATFdE10To11PZbjI4RRR12ZHjcGGcx1OqOQC4hQSxCkExMolru+VruuWHXYRZptrwozZ5oTZ\n7lrn12ZzLfUwd1v/d/E1u1NEnFICddcx1QrXads4hQQ3aIb2lmcMMSKiq5xUIiAtTo60AJZzqOQA\nIoe+T4OFD0wiIqKwxRAjIqKwxRAjIqKwxRAjIqKwxRAjIqKwxRAjIqKwJRiNxhBe601ERNQ7jsSI\niChsMcSIiChsMcSIiChsMcSIiChsMcSIiChsMcS6lJaWIisrC1qtFrm5uThw4ECwu3RFffbZZ3jg\ngQcwceJEqNVqvPXWWx6vi6KI4uJiTJgwATqdDrNnz8Y333wTpN5eGS+88AJuu+02JCUlYdy4cbj/\n/vtx7NgxjzbDsS6vv/46pk2bhqSkJCQlJeH222/H3r173a8Px5r09MILL0CtVmPlypXubcOxLsXF\nxVCr1R5f1113nfv1wagJQwxAeXk5ioqKsGLFClRWVkKv12PevHk4ffp0sLt2xZjNZmRkZGDDhg2I\njPS+hfWmTZuwZcsWbNy4Efv27YNGo8Hdd9+Ntra2IPT2yti/fz8eeugh7N27F7t374ZMJsNdd92F\nCxcuuNsMx7qMGTMGzzzzDD755BNUVFTg1ltvxaJFi/D1118DGJ416e7zzz/Hn/70J0yaNMlj+3Ct\nS1paGo4fP+7+6j5AGIyacJ0YgPz8fEyaNAkvvfSSe9sNN9yAgoICrFu3Log9C47ExET8/ve/x6JF\niwC4/rc0YcIE/OY3v8ETTzwBAOjo6EBaWhr+4z/+A4WFhcHs7hVjMpmQnJyMt956C3fccQfr0k1q\nairWrVuHX//618O6Ji0tLcjNzcVLL72EjRs3IiMjAyUlJcP230pxcTF2796NgwcPer02WDUZ9iMx\nq9WKI0eOIC8vz2N7Xl4eDh8+HKRehZaTJ0+ioaHBo0aRkZGYNm3asKqRyWSC0+mEWq0GwLoAgMPh\nwHvvvQez2Qy9Xj/sa7J8+XIUFBTg1ltv9dg+nOty4sQJTJgwAVlZWXjwwQdx4sQJAINXk2H/UEyD\nwQCHwwGNRuOxXaPRoLGxMUi9Ci0NDQ0A4LNG9fX1wehSUBQVFSEzMxN6vR7A8K7L0aNHMWvWLFgs\nFkRHR+PNN9/EpEmT3L98hmNN/vu//xt1dXV47bXXvF4brv9WbrzxRrz88stIS0vD+fPnUVJSglmz\nZuHQoUODVpNhH2JEgVi7di0OHTqEjz76CFKpNNjdCbq0tDR8+umnaG1txa5du7Bs2TJ8+OGHwe5W\n0NTW1uLZZ5/FRx99BLnc/9OTh4vbb7/d4+cbb7wR2dnZ2L59O2666aZBeY9hfzoxPj4eUqkUTU1N\nHtubmpqQkJAQpF6FFq1WCwDDtkZr1qzBe++9h927dyM1NdW9fTjXRaFQ4Nprr0V2djbWrVuHzMxM\nvPzyy8O2JlVVVTAYDLj55psRHx+P+Ph4fPbZZygtLUV8fDxGjhwJYPjVpaeYmBhMmDABdXV1g/Zv\nZdiHmEKhQHZ2NioqKjy2V1RUYOrUqUHqVWhJSUmBVqv1qJHFYsHBgwev+hqtXr3aHWDdpwYDw7su\nPTmdTlit1mFbk9mzZ+PAgQP49NNP3V85OTm499578emnn2L8+PHDsi49WSwW1NbWQqvVDtq/FWlR\nUdH6IehrWFGpVCguLoZOp0NERARKSkpw4MABbN68GXFxccHu3hVhMpnw7bffoqGhAX/+85+RkZGB\n2NhYWK1WxMXFweFw4I9//CPGjRsHh8OBJ598Eg0NDfjjH/8IpVIZ7O4PiSeeeAJvv/02/vSnP+Ga\na66B2WyG2WwG4PrPjyAIw7Iu69evh0KhgNPpxNmzZ7F161bs2LED69evd9dhuNUkIiICGo3G42vn\nzp1ITk7GokWLhu2/laeeesr9b+W7777DypUrUVdXhxdffBFqtXpQasJrYgDuueceNDc3o6SkBA0N\nDZg4cSJ27NiB5OTkYHftiqmursacOXPcPxcXF6O4uBgLFizA1q1b8dhjj6GjowMrV66E0WjElClT\nUF5eDpVKFcReD63S0lIAQEFBgcf21atXY82aNQAwLOvS0NCA3/72t2hsbERsbCwmTZqEd999F/n5\n+QCGZ00CMRzr8uOPP2LJkiUwGAwYNWoUbrzxRvz97393/24djJpwnRgREYWtYX9NjIiIwhdDjIiI\nwhZDjIiIwhZDjIiIwhZDjIiIwhZDjIiIwhZDjIiIwhZDjIiIwhZDjIiIwtb/B9Tr3QkMwhA1AAAA\nAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"DrySY-Vf0kVa"},"source":["## 3.5 Prediction"]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"326fa85615622feb484cc4c848edeec6f7133913","id":"JduakEA4ONPi","colab_type":"code","colab":{}},"source":["# Now to get the test set ready in a similar way as the training set.\n","# The following has been done so forst 60 entires of test set have 60 previous values which is impossible to get unless we take the whole \n","# 'High' attribute data for processing\n","dataset_total = pd.concat((dataset[\"High\"][:'2016'],dataset[\"High\"]['2017':]),axis=0)\n","inputs = dataset_total[len(dataset_total)-len(test_set) - 60:].values\n","inputs = inputs.reshape(-1,1)\n","inputs  = sc.transform(inputs)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"435b8024814939ac4fbd372baa0cd8cfc78f80bc","id":"WwJKTbxsONPm","colab_type":"code","colab":{}},"source":["# Preparing X_test and predicting the prices\n","X_test = []\n","for i in range(60,311):\n","    X_test.append(inputs[i-60:i,0])\n","X_test = np.array(X_test)\n","X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))\n","predicted_stock_price = regressor.predict(X_test)\n","predicted_stock_price = sc.inverse_transform(predicted_stock_price)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"trusted":true,"scrolled":false,"_uuid":"b774a8e79e53eac89694cafef6b11aa99226b95f","id":"hBm9jO7FONPo","colab_type":"code","outputId":"caaa3ea9-daff-4ffe-80e7-1ca441587ef8","executionInfo":{"status":"ok","timestamp":1585820135614,"user_tz":-540,"elapsed":1849,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}},"colab":{"base_uri":"https://localhost:8080/","height":325}},"source":["# Visualizing the results for LSTM\n","plot_predictions(test_set, predicted_stock_price)"],"execution_count":20,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAboAAAE0CAYAAABaTfYtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSaATQghRov0fzX9C8uTeSBQAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"f6f6db0b6e1f17ac63c06ce49856873d98ba5f00","id":"aAhHcWsXONPx","colab_type":"code","outputId":"1ea06d96-6deb-4c89-803f-04cf741c3a76","executionInfo":{"status":"ok","timestamp":1585820139660,"user_tz":-540,"elapsed":908,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["# Evaluating our model\n","return_rmse(test_set, predicted_stock_price)"],"execution_count":21,"outputs":[{"output_type":"stream","text":["The root mean squared error is 2.0859556263103034.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"LYJPtmAdMSih"},"source":["## 3.6 Just shift(1) prediction"]},{"cell_type":"markdown","metadata":{"id":"Q6ovj41aMbgz","colab_type":"text"},"source":["단순히 전일 가격을 익일의 예측치로 사용하는 경우의 rmse 를 구해본다."]},{"cell_type":"code","metadata":{"id":"KRihJyu5Q5n4","colab_type":"code","colab":{}},"source":["tmp_test_set = test_set.reshape(-1)\n","\n","# shift(1) 을 사용하여 prediction\n","predicted_test_shift = pd.Series(tmp_test_set).shift(1)\n","predicted_test_shift = predicted_test_shift.tolist()\n","predicted_test_shift[0] = tmp_test_set[0]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"mNjKAEalLZPU","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"ba4d27f9-79df-479f-f55f-127466f83dc9","executionInfo":{"status":"ok","timestamp":1585820777665,"user_tz":-540,"elapsed":586,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}}},"source":["return_rmse(tmp_test_set, predicted_test_shift)"],"execution_count":40,"outputs":[{"output_type":"stream","text":["The root mean squared error is 1.5372411517279048.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"BEzc83yu6YG2","colab_type":"text"},"source":["# 4 GRU (Gated Recurrent Units)"]},{"cell_type":"markdown","metadata":{"id":"6IZPKlAg1Soe","colab_type":"text"},"source":["LSTM은 폭풍처럼 딥 러닝의 세계를 점유한 유일한 종류의 장치가 아닙니다. **GRU (Gated Recurrent Units)**가 있습니다. GRU 또는 LSTM은 비슷한 성능을 가지고 있다고 알려져 있습니다. GRU는 LSTM보다 교육하기가 더 쉽습니다."]},{"cell_type":"markdown","metadata":{"trusted":true,"_uuid":"a4cf704ab3cd091f63b7b9a1b9224a49f0913171","id":"QFh5a-enONP1","colab_type":"text"},"source":["\n","간단히 말해, GRU 장치는 LSTM 장치와 같은 정보의 흐름을 제어하기 위해 메모리 장치를 사용할 필요가 없습니다. 제어없이 모든 숨겨진 상태를 직접 사용할 수 있습니다. GRU는 더 적은 매개 변수를 가지므로 일반화하기 위해 조금 더 빨리 훈련하거나 더 적은 데이터가 필요할 수 있습니다. 그러나 데이터가 크면 표현력이 높은 LSTM이 더 나은 결과를 가져올 수 있습니다.\n","\n","재설정 게이트와 업데이트 게이트라는 두 개의 게이트가 있다는 점을 제외하면 LSTM과 거의 유사합니다. 리셋 게이트는 새로운 입력을 이전 메모리에 결합하는 방법을 결정하고, 업데이트 게이트는 이전 상태를 얼마나 유지할지를 결정합니다. GRU의 업데이트 게이트는 LSTM의 입력 게이트 및 망각 게이트 입니다. 출력을 계산하기 전에 GRU에 2차 비선형성(second non linearity)이 없으며 출력 게이트도 없습니다.\n","\n","Source: [Quora](https://www.quora.com/Whats-the-difference-between-LSTM-and-GRU-Why-are-GRU-efficient-to-train)\n","\n","<img src=\"https://cdnpythonmachinelearning.azureedge.net/wp-content/uploads/2017/11/GRU.png?x31195\">"]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"e9b616c5112d707d16cc4b277007e286cffd58f6","id":"saqQljG4ONP3","colab_type":"code","outputId":"f673f167-9659-44ed-fcc7-64433400b83d","executionInfo":{"status":"ok","timestamp":1585821739115,"user_tz":-540,"elapsed":280434,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# The GRU architecture\n","regressorGRU = Sequential()\n","# First GRU layer with Dropout regularisation\n","regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(X_train.shape[1],1), activation='tanh'))\n","regressorGRU.add(Dropout(0.2))\n","# Second GRU layer\n","regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(X_train.shape[1],1), activation='tanh'))\n","regressorGRU.add(Dropout(0.2))\n","# Third GRU layer\n","regressorGRU.add(GRU(units=50, return_sequences=True, input_shape=(X_train.shape[1],1), activation='tanh'))\n","regressorGRU.add(Dropout(0.2))\n","# Fourth GRU layer\n","regressorGRU.add(GRU(units=50, activation='tanh'))\n","regressorGRU.add(Dropout(0.2))\n","# The output layer\n","regressorGRU.add(Dense(units=1))\n","# Compiling the RNN\n","regressorGRU.compile(optimizer=SGD(lr=0.01, decay=1e-7, momentum=0.9, nesterov=False),loss='mean_squared_error')\n","# Fitting to the training set\n","regressorGRU.fit(X_train,y_train,epochs=50,batch_size=150)"],"execution_count":41,"outputs":[{"output_type":"stream","text":["Epoch 1/50\n","2709/2709 [==============================] - 8s 3ms/step - loss: 0.1459\n","Epoch 2/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0344\n","Epoch 3/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0081\n","Epoch 4/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0046\n","Epoch 5/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0039\n","Epoch 6/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0036\n","Epoch 7/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0033\n","Epoch 8/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0031\n","Epoch 9/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0033\n","Epoch 10/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0030\n","Epoch 11/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0030\n","Epoch 12/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0029\n","Epoch 13/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0027\n","Epoch 14/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0029\n","Epoch 15/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0029\n","Epoch 16/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0027\n","Epoch 17/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0025\n","Epoch 18/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0027\n","Epoch 19/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0026\n","Epoch 20/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0025\n","Epoch 21/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0025\n","Epoch 22/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0025\n","Epoch 23/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0024\n","Epoch 24/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0024\n","Epoch 25/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0024\n","Epoch 26/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0023\n","Epoch 27/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0022\n","Epoch 28/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0022\n","Epoch 29/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0024\n","Epoch 30/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0022\n","Epoch 31/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0022\n","Epoch 32/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0021\n","Epoch 33/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0022\n","Epoch 34/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0021\n","Epoch 35/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0021\n","Epoch 36/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0021\n","Epoch 37/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0020\n","Epoch 38/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0021\n","Epoch 39/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0020\n","Epoch 40/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0020\n","Epoch 41/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0022\n","Epoch 42/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0021\n","Epoch 43/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0021\n","Epoch 44/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0020\n","Epoch 45/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0021\n","Epoch 46/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0021\n","Epoch 47/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0019\n","Epoch 48/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0020\n","Epoch 49/50\n","2709/2709 [==============================] - 5s 2ms/step - loss: 0.0020\n","Epoch 50/50\n","2709/2709 [==============================] - 6s 2ms/step - loss: 0.0020\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7f095ea31be0>"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"code","metadata":{"colab_type":"code","id":"9rvpHqPj6MNF","colab":{}},"source":["regressorGRU.save_weights('../models/gru_weight.h5', overwrite=True)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"_kg_hide-input":false,"_uuid":"98628386f141545aa77d70f48478ac82bd9c1608","id":"BQlJvhAHONP6","colab_type":"text"},"source":["현재 버전은 이전 버전에서 50 개의 unit가 있는 GRU 네트워크와 달리 100 개의 unit 가 있는 dense GRU 네트워크를 사용합니다."]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"f20ca021ea3ce05f6c6a98db93775f1b2c9c022c","id":"uxkYo76SONP7","colab_type":"code","colab":{}},"source":["# Preparing X_test and predicting the prices\n","X_test = []\n","for i in range(60,311):\n","    X_test.append(inputs[i-60:i,0])\n","X_test = np.array(X_test)\n","X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))\n","GRU_predicted_stock_price = regressorGRU.predict(X_test)\n","GRU_predicted_stock_price = sc.inverse_transform(GRU_predicted_stock_price)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"da8e9fa28510aa03e7dd06d5070d7b16e05ebb6e","id":"v7jw-8dBONP_","colab_type":"code","outputId":"ea0facfe-98dc-41c4-a4a5-80a9785a185d","executionInfo":{"status":"ok","timestamp":1585821743050,"user_tz":-540,"elapsed":1989,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}},"colab":{"base_uri":"https://localhost:8080/","height":325}},"source":["# Visualizing the results for GRU\n","plot_predictions(test_set,GRU_predicted_stock_price)"],"execution_count":44,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAboAAAE0CAYAAABaTfYtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhQjJa1MZyaenp8dvv/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RIpVItZ5QU+vTpQ8eOHRk8ePA7jWtaMx6AgYEBCoVCq4+/vz9v\n3rxRz+rOnDmDUqmkcuXKWFlZUbp0aY4dO0bz5s0zvJa7uzuenp4MGDAAiUSSLQcaBwcHSpYsmdvb\no1atWjrXCiH5vgwNDXnw4EGWs9HCSIG+odzd3XF3dwc01wNSOHv2LF5eXri5uQHJD4Kfnx/nz5/H\n29ub169f4+fnx9KlS9UP3cqVK6lZsyZHjx7V+iIqzOgdO6axrXRyQtGwITHbtqG3fz+Bdt35bmpN\njT4OhHOSxpQuK+FNh2S3c5W9PfHTpsHr1xqKTnrnDjYlEli3TkKzZsVITEz+un35Usqvv5amQYP8\nvT/Bu9GkSRMqV67MvHnz+Pnnn/nmm2+YMGECFhYWuLu7k5iYSEhICI8fP+brr7+mQoUKKJVKli1b\nhqenJ8HBwRoxaO9CfHw8T548AZJNl7/99htRUVG0bdtWZ383Nzfu3LlDsWLFsn2Nfv364eLigouL\nC9bW1ty7d48ZM2ZgbW2Ni4sLAPb29hw8eJBbt25RokQJzM3N6d69O76+vgwbNoxvv/2WiIgIvvrq\nKzw9PSlfvjwAY8eO5dtvv8XKyoo2bdoQExPDsWPHGDVqlIYMbdu2Zc2aNWpll9FHRV4xatQoWrdu\nzejRo/niiy8wMjLi9OnTNG/eHDs7O0aNGsWUKVNQqVQ0atSIqKgogoODkUqlDBgwIF9lK2g+6PCC\nhg0bsm/fPh4+fAgkf1lduXJFrcAuXbpEYmIiLVq0UB9TtmxZKleuzJkzZwpE5oLAcPZspOnigxRO\nTgAkNW/Oq6m+DF7lRlJSqunF3FzFjs0RlPh9Om8CAyGdxxsWFihLlVJvSpKSkN69S/XqSiZNitfo\numOHFWFhH/SjJAC15+D9+/fp168fS5YsISAggMaNG9OuXTvWrl2rdvSoUaMGP/30E8uWLaNhw4as\nW7eOH374IU/kOHr0qNpJpFWrVly4cIE1a9bQpEmTDI8pWbIkhoZZewan0LJlS/bv30/Pnj2pV68e\nw4YNw87Ojp07d1K8eHEA+vfvT6VKlWjevDkVKlQgKCgIExMTtm3bxps3b2jZsiW9evWifv36arMt\nwOeff87cuXNZt24dn376Kd26dePGjRs65Wjbti1//PEHX331FRvf5pXNL2rVqsX//vc/bt68SevW\nrWnZsiXbtm1Te9tOnjyZSZMmsWTJEho2bEjnzp35888/s+Xc87EjiYiIUGXdLf8pU6YMc+bMoXfv\n3up9CQkJjBkzhg0bNqhrEM2ZM4dBgwYBsGXLFoYNG8bz58817Oeenp5UqFCBBQsW5Lmct27dwumt\nEvkQkF6+TDE3NyRpnAiUlpa8CQuDtyan7783YuFCzZfE+vXReHhk7qVp2rEjemlMIdF+fiR5ehIf\nDy4uxQgPTzVhtmmTSEBATF7c0gfF69evc1SLDj5M02VBIsZDGzEmmmR3PHLz7xE+cK/LlStXcvbs\nWTZu3IidnR2BgYFMmTIFe6k1rQcAACAASURBVHt7WrXK2GswK26l81B838fnJaXXrcMsjZJTSSTc\nmTyZ129dj//914AlS2poHNOly1MqVbpPVrdhb2ND2ggk0759+eeHH3jZti0+PnImTkxN/vz33/r4\n+/+Hi8ubd76nD4kUF/SckuLSL0hGjIc2Ykw0yc54REZG8lRHPt+sJh8frKKLjY1lxowZrFmzhnbt\n2gHJ5pTLly+zePFiWrVqhbW1NQqFghcvXmBpaak+9tmzZ3z66acZnvtdZmR5OaOTPHmC4bJlqIoV\nI6FvX2RnzqCsVg1lDs5veuWKxnb8t99iPXCgWkEtX26EQpFqVixbVsmCBYaYm2d9DYMGDSDNOh1A\nuWnTsOzYkYpDrPjf/5I4fTr1EVq2rCJeXlHoFyJHzNevX+f4y1t8rWsixkMbMSaaZHc8zM3NsbOz\ny/H5P9iFlcTERBITE7U8J2UymdpVtk6dOujr63PkyBF1+6NHjwgLC1MvOH+wqFSYduuG4cKFGM2a\nhXmVKpj2708xV1dk6RLZZkh0NLLgYI1dCW+9UQEeP5bg76/pZfntt3GYm2fv9IpKlbT2SRQK9I4f\nRyKBmd9peh9euyZj0aKcz34EAoEgPylQRRcVFUVoaCihoaEolUoePnyoTrxqbm5Oo0aNmD59OidO\nnCA8PJz169ezadMmdSyNhYUFffv25fvvv+fo0aOEhIQwdOhQqlevTrNmzQry1rJE8vgxMh3ZySWJ\niRq14zJDLygISZoUQYry5VGl+dpZvNiQhITUtUt7eyXduyeSXZTVqqHSETskCw0FhYKmE5vTBz+N\nttmzDbl584P9fhIIBEWQAn0jXbx4ETc3N9zc3IiNjcXX1xc3Nzd+/PFHAFavXs0nn3zCkCFDaNiw\nIQsWLGDy5MnqFEWQnJzVw8ODgQMH0rZtW0xNTdm0adMHH0Mn0WFnTkFX+RwAVCr0/v4bg99/h4gI\nrZCCpLdhGADPn0tYs0ZzNjdmTHyOzIoqKysS0zgHpSC7cgW9Y8eQXbnCL3yNJc/UbQkJEkaNMiZd\neJJAIBAUGB+M1+XHQl6t0ekdPIhpt24625TW1rzRoewMVqzAeNIkABSVK4OenkZpnpg//iDxbTaU\nH34w5OefU23epUopuXTpTc4TMatU6G/bhskXX6TuKlaM+MGDMZo/H4CNeNMLTddpX99YfHzeLYPG\nh4Dwunx3xHhoI8ZEk/z2uhQ2pgJC8vx5hm3Sp09BR5aItNW/ZWFhWvXnkt7GIUVEwG+/aa6VjRoV\nn7tqAxIJiV27okyTsV8SFYX0bWwjgDeb8ORPjcOmTzcSJkyBQPBBIN5EBYTk2bNM29MHgEOycssI\nRfXqqN56nv7+uyGRkalrayVLKhkw4B1mVxIJypqaWVX0Tp5MbQaW44OFWaq9Mi5OwpAhxiRmf0lQ\nIBAI8gWh6AoISZpiibqQvs1YriYLjZH0Nn/dmzewbJnm2tzw4Qm8Qz5YABRvM6ur5XtbzDGFMvzL\n3CGaoQ6XLukxZ47wwhQIBAWLUHQFhDSrGV06RSf5779M+6c4osyfb8jLl6k/q7m5ii++iM/osGyj\nqF07yz69Kgbx2WeaCvmXXww5d+7DdgwS5J6dO3ciT2PWXr9+vbrW3PvGy8sLHx+fArn2h4qvr2+m\nMcV5iYeHR7bLKL1vhKIrINKv0WnNmNIpOmkmik4lk5Hk6sr9+xKWLtWcQQ0bFk8u1m61UDg7Z9lH\n9uA+8+fHYmOjTD1OIWHoUGPeJowXvAd8fHyQy+XI5XIsLS2pXbs23333nTprf37SpUsXLl26lO3+\nNWvWZPHixfkoUSonTpxALpfz4q015d69e+pxksvlWFtb4+zsrCWPr68vcrmcjh07ap1z1apVyOXy\nLJXJ5cuX6dmzJ5UqVcLGxgZnZ2f69u3L/fv3NWS5ePFiHt1t7vHw8NAak59//lmr0kN6/P39mTp1\n6nuSMmcIRVdApFd0SekUidaM7t9/MzxXoqcnymLmfPmlMfHxqWtzpUop+fLLd5/NASjLl0dZokSm\nfaQPHlCihIolSzQdaf75R8aUKcLD7H3SrFkzwsLCuHTpEt999x2rVq1iypQpOvumL7j6LhgbG2e7\n0OiHwrZt2wgLCyM4OJivvvqK6dOna9Vos7GxISgoSF0ENQU/Pz+tYrHpef78OZ999hnFihVj8+bN\nnD17lsWLF1OuXDnevPkwU+b17t2bsLAwzp07x9ChQ5k5c2aGHyQp9QmLFy+uUbD2Q0IougIivelS\nUbeuxrbk7Zeeun8GMzqFkxNxP//Mb78ZcPSoZpDc5Mlx5KCySeZIJCjq18+0i/StzK1bJ/H555oK\ndvVqQw4c+GAzzhU6DA0NsbGxoWzZsnTv3p3u3buze/duINWctX79eurUqYO1tTXR0dG8fv2a0aNH\nU7FiRcqWLUv79u21ZhgbN26kRo0a2Nra4uXlpZV3UJfp8uDBg7Rs2ZJSpUpRrlw5vLy8iIuLw8PD\ngwcPHjBlyhT1DCKFM2fO0L59e2xtbalatSpff/01kZGR6vaYmBh8fHwoU6YMTk5O/Pzzz7keqxIl\nSmBjY4O9vT19+vShRo0ahISEaPVxd3fXKA595coVbt26xWeffZbp+YOCgoiIiGDp0qXUqVMHBwcH\nXF1dmTFjBtWrVweg9tulgebNmyOXy/Hw8ACSC6bOmTOH6tWrY21tjaurq/p3TOHx48cMHjyYcuXK\nYWtrS+PGjTOsS/fgwQPq16/PsGHDSErKOKm7iYkJNjY2ODg4MGTIEJo2baq+ro+PD15eXixYsIBq\n1apRrVo1QNt0mZCQwIwZM6hRowbW1tbUrl1bo9TTjRs36NGjB2XLlqV69ep8/vnn6vJNeY1QdAVE\nemeU9KZBrRldOucPgKS6dYnevp1TYdZMnqw5Y2rcOInevfPW5VFRr16m7WmV84wZcVSooGnqGDnS\nmKdPs1+l+UNFLrfI9K9UKZss++TkLy8wMjIiMY1D071799i6dStr1qzh5MmTGBoa4uXlxePHjwkI\nCOD48eO4urrSsWNH/nv7kRUcHMzw4cMZMGAAJ06coG3bturkDhlx8OBB+vfvT/PmzTl69Ci7du2i\ncePGKJVK/P39KVOmDBMmTCAsLIywt17FV69epUuXLrRr146TJ0/i5+fH5cuXGTlypPq8U6ZM4ejR\no6xbt46dO3cSGhpKYHZT52WASqUiKCiImzdv4qzDVN+3b182btyoTkHo5+dHp06dsqyTZ2Njg1Kp\nZOfOnRnOnA8fPgykzi79/f0BWL58OYsXL2batGkEBgbi4eFB3759CQ0NBZKLyHp4eHD//n3Wr19P\nYGAgEyZM0HmNsLAw2rZtS+vWrVm+fLm6Ikx2SP/8nDp1iqtXr7J161Z27typ8xgfHx82bdrErFmz\n1LPYlBi4//77j/bt21O1alUOHTrEli1biIqKolevXurxzUvEJ3ZBEBODJM16iUpfH2XlyqgMDZHE\nJ8+EpK9eIXn2DNVbM1B6L8fYuXNJ+OILLl+R0aePiVatuWXLYpDm8WdMUhbVVaUPH4JSCVIppqaw\ncmUsbdqYolAky/bkiZQvvjBhx45oPvDENYWK8+fPs3XrVo3K0gkJCaxcuRJr6+T038eOHePy5cvc\nvn0b47e1Cb/77jv27dtHQEAAo0ePZsWKFTRt2pRx48YBULFiRS5cuICfn5/2Rd8yd+5cOnTowHff\nfafeV6NGcjUNExMTpFIpZmZm2NjYqNsXLVpE586dNQqZ/vzzz7i5ufHs2TOMjY3x8/NjyZIl6tqU\nS5cuVc8sckr79u2RSqUkJCSQmJiIj4+PzvW4Vq1akZSUxLFjx3B1dWXz5s1s2LCBo0ePZnr++vXr\nM3bsWHx8fBg3bhx169alYcOG9OzZE3t7ewB1ZfGU2WUKS5YsYeTIkXTv3h1IrikXGBjIkiVL+PXX\nX9m6dStPnz7lwIED6nOUK1dOS4bg4GB69OjB8OHD1b9fdlAqlRw+fJjDhw9rOPoYGhqyZMmSDCt7\n3Llzh23btrF161Z1pRlHR0d1+6pVq6hRowbTp08HkgPGV65ciaOjIxcvXtT5ofEuiBldAZB+fU5l\naQkyGcoqVTT2y1IqE6hUWnF1SgcHjp/Qo0OHYhpelgCLF8dgb5/3CW8Un3yiM/dlCpLERGRpCt7W\nq6dg7FhNE+bx43r4+oqQg/zm4MGDlClTBhsbG1q3bo2rqytz5sxRt5cuXVqt5ABCQkKIiYmhYsWK\nlClTRv13/fp17t69CyTPCOqnM1+n305PaGhopgVVdRESEsLmzZs15EipPn737l3u3r1LQkICDdJ8\neBUrVkxtBswpv/32GydOnODkyZOsWbOGbdu2MXPmTK1+MpmMnj174u/vz+7du7G0tMy2R+OUKVO4\nefMmCxcupFq1amzYsIGGDRtyLF0av7RERkby+PFjGjZsqLH/008/VRd6DQ0NpXr16molp4t///2X\nTp06MWbMmGwruTVr1qifn549e9KjRw8mTpyobq9atWqm5atCQ0ORSqUZ/vYhISEEBgaqf9/y5cur\nf7+U5y0vETO6AkCaXtG9fUgVNWogS7M2IL1yBerXx9TbG700ZplE9PhpfwN+XG2KUqmpeCZOjOOz\nzzIvqJprzM1JatEC/UOHMuxSrF07YlasILFHD5BKmTAhnsBAPU6eTH3U5s0zon59BW3apMqp7+eH\n8XffoSxenNjff8/STCrIHFdXVxYuXIienh62trbqKtMpmKYLrFQqlVhbW7N3716tc71vBwOlUkm/\nfv0YPny4VputrS23b9/O0+ulvGgBKleuzN27d5k1axbjxo3TSkvVu3dvGjVqxP379zWKRGeHEiVK\n0KlTJzp16sTEiRNxd3dnzpw5GjPt7CLJ5IMzPSVLlsTe3p5t27bRr18/jbXQjOjcuTOTJk3CwMAA\nW1tbrdzB6Z+fnKJUKnF3d1d/UMTHx6sVZ344MwlFVwCkn9Ep3/6wihqaBVJlly9jsHatOgtJPAZs\npwuzmMzV3x20zjtiRDyTJuWNl2VGxE+YkKmiAzAZNoy4W7eInzIFPT1YtSqGpk2L8d9/qTPPwYNN\n2LcvimrVlBARgfGECUhiY5G9fo3R1KlE79mTr/fxLkREvM60/UPIY2hiYqJ+eWeH2rVr8/TpU6RS\nqYaJKS2VK1cmOF1ZqPTb6alVqxYnTpzgizS5UtNiYGCg5bZeu3Ztrl+/nqH85cqVQ19fn3Pnzqll\njY6O5tq1axnKnhNkMhlJSUkkJCRo/Y4VKlSgbt26nD17Vr2OlhsMDAxwdHRUr38aGCQneUg7Fubm\n5tja2hIUFKShDE+fPk3lypWB5PENCAjgxYsXGc7qDA0N2bhxI97e3nTq1In//e9/WSo7CwuLHD0/\n6alZsyZKpZITJ07oLJJdu3ZtduzYgZ2dHfr6+vn+b0aYLguA9Om/UlJ3aSm6K1fQCwxEBSzDhzI8\nohcbuYpmP4lExXffxTFzZhw5+NDLFQoXFxJbtMiyn4GfH7xdeLexUbFqVQwyWao5NTJSQo8epty/\nL0H/8GEkaXJ76r2jU4Eg5zRr1oyGDRvSq1cvDhw4QHh4OGfPnuXHH39UO3kMHTqUo0eP8ssvv3Dn\nzh3Wrl3LX3/9lel5x44dy65du5g5cyY3btzg+vXrLF26lJiYGADs7e05ffo0//77rzq+bfTo0Vy4\ncIGvvvqKkJAQ/vnnH/bt28eYMWOAZDNl3759mTZtGkeOHOH69euMHDky104ML1++5MmTJzx69IgD\nBw6wYsUKmjRpgnkGhRu3bt3K7du3NdbSMmPfvn0MGTKEffv2cfv2bW7dusWyZcs4cOCAuuSYlZUV\nxsbGHDp0iKdPn/L6dfLH1KhRo1iyZIn6mrNmzeL06dPq9ctu3bphaWlJr169CAwMJDw8nD179mh5\nXRobG7Np0ybMzc3p3LkzERGatSTzmooVK9K5c2e+/PJLdu7cSXh4OIGBgWzatAmAL774gsjISAYO\nHEhwcDD37t3j6NGjjB49Ol9CLoSiKwAkL19qbKtNl+nyScquXUO1ez/ebGIEy3iBJemxtFQSEBDD\nuHHx+a7kUoj95ReUb2OHktzcuD17tlYf6dOnFGvaFIPFiyE2lkaNFMyYEafR5+FDKR07mvLggo50\naDqSWgvyD4lEwubNm2nSpAmjR4+mfv36DBw4kNu3b2Nrawskr8ctXryY1atX06hRI3bt2sWkt9U0\nMsLd3Z0//viDAwcO4ObmhoeHBydOnED61lPq22+/5eHDh3zyySdUqFABSHZW2bNnD/fv36dDhw40\nbtyYGTNmaJi0fvjhBxo3bkyfPn3w9PSkatWquLq65ureu3btSuXKlalVqxZjxoyhdevW/JEmgXp6\nTExMsmX+S6FKlSqYmpoyZcoU3NzcaNmyJdu2beOHH35g7NixAOjp6TF79mz8/PyoUqUKvXr1AmDY\nsGGMGjWK77//nk8//ZTdu3ezbt06ar59V5iamrJ7925Kly6Nt7c3n376Kb6+vjpNm8bGxgQEBGBm\nZvZelN2KFSvo1q0bkyZNokGDBgwfPlwdImJra8vff/+NVCqla9euaicnAwODTNf+coso05ND8qJM\nj+HMmRjNm6fejps0ifi3LwyzGjU0KgOMZR6/MFbrHBKJih49Epk1Kw5LywL4CRMTkTx+jMrOjlu3\nb1Pt8mVMBg3S2VXh6EiMnx+KGjUZN86IVas0H2Qrgwg2JXShBamV4iMvXUKVB2aod0WU6Xl3xHho\nI8ZEE1GmpxAiea25xqNKYyJRpPEcO0hLLSVnopfA4P5RnDkTxcqVsQWj5AD09VHZ25MyjUzs0oW4\nDOJ3ZOHhmIwYgUQCs2fH0bmzZiWFZwlyWnIYLzaxl7bcoTyJDzPPBSoQCATZRSi6AkCSzgadVtEl\nduqUvA8YzUKNfmVMXnLybDxzFyqoVCnvgyrfFUUmrtay0FD0N2zA4N4dfv01VkvZAWzGi/bspSJ3\nsPJsSatWpvj765NJAgeBQCDIEqHoCgBJmlRGAKo07tuJPXoQ+8svHMeNa6TO7iQoWf35IcqX//AU\nXApJWYQEmAwfTrGGDTE+foj11mOYwowM+6pUEoKD9Rg50oT27U15+PDjz6giEAgKBqHoCgAtRZfW\n5iyTkTBoEIva/E+jTxe282lXaz5ozMxI+uSTTLtIEhMx7doV45XLmcH3nKQRNQnN9JizZ/Vo2bIY\n9+4JZScQCHKOUHQFQHpFRzo35ufPJew6rOnVNcR2J8pcZn54n8RPmoQqBzn0GhHIJeqwl7b0wQ8X\ngijNI61+T55I6dnTlPRDJxAIBFkhFF1BkMkaHcDff+uRmJg6e3Ey+5eGW4dDuuwWHyJJbdrw5vJl\noo4cIerAgWwdI0VFW/7Gj34E8SmPKMuNJgNwd9dMSn3tmoyJE43zQ+xMyasSNgKBIPe8y79DoegK\ngMzW6AAOHtScEXX2KYGqeu4S1hYEKltbFJ98olVMVqufRELsTz/x5swZotKlnqrwJoRNm2Lo1EnT\naWXjRgOOHn1/GaGNjIzUwc0CgaBgUKlURERE5Dr1mEgB9r5RqbQVXZoZXVISHD6sOXNr3fojdTs0\nNERZogTSdAHyAEmffkrcpEko3qY2koSHa7RLnz5FKoVly2K5eVPGtWupym3MGGMCA6MwMclX6YHk\n9ElJSUnqTBXZITIyMsOsGkURMR7aiDHRJDvjYWZmlqPSQmnJ0VEJCQls2rSJEydO8OzZM6ZPn07t\n2rWJiIhg7969uLm5aRVdFKQjLg5JmrpOKn19SBMoGRws4/XrVLNliRJK6tbNvIT9h0zCiBEY/fAD\nACoDA6L37EFZoQKq4sU1+qnSpVOSPH0KSiUmJlIWLYqldWtTVKrkcQkPl/HTT0ZamVbyi5x+RT59\n+hQ7O7t8kubjQ4yHNmJMNMnv8ci2onv58iWenp5cu3YNa2trnj17pk4hY25uzqxZs7hx44a6vpBA\nNzpj6NKk60lvtmzRIumjrt0WP3w4kshIpFeukDBgQMZVCYyNUZmbq2e7kqQkJC9forK0pF49BYMH\nJ/Drr6kZVZYsMaBLlwTq1Plwwy0EAsGHQbbX6L7//nsePHjAvn37CAwM1FgYlEqldOzYkQPZdD4o\nymS1PhcUpK3oPmqMjYmbPp2YbdtI8vTMtKuyVCmNbUmaVGhTpsRRtmyqUlMqJYwebSKCyQUCQZZk\nW9Ht27ePoUOH4uLiojNhaIUKFXiY5sUk0E1moQVJSXDxoub0zdW16LzJlelMFwZr1qj/38wMfv5Z\nM9FzSIiMpUsN3odoAoHgIybbiu7NmzeUfZuxXhfx8fFadaUEOsgktOD6dSnR0akfEZaWShwcio5r\ne2LnzhrbBv7+Gk4qbdokaaUOmznTiEuXhPOwQCDImGy/IcqXL8/FixczbD98+DBVq1bNE6EKM1oJ\nndOYLs+d0zRb1q+veG+ldz4EEr28UJQrp96WJCVh8uWXkOYD6qef4rCwSFX+iYkSPv/chATt1JkC\ngUAA5EDR9e/fnw0bNrB582Z1gUOJREJMTAzTpk3j8OHDDBw4MN8ELSxkFlpw7pym2bJ+/SI2Q9bX\nJz5dBQS948eTa9q9xcZGxaJFmnFtd+7I2LHjww+mFwgEBUO2Fd3QoUPp3bs3Q4cOpW7dugAMGjQI\ne3t7Fi5cyOeff07v3r3zTdDCQmaKLjhYU9HVq1d01udSSOzRg6QmTTT2GS5cCGmqR3/2WRK9e2tO\n4RYvNkQkMBEIBLrIURzd/Pnz8fb2ZseOHfzzzz8olUrKlStH586dc13dt6ihFV7wNqHzq1cSbt1K\nVXRSqeqjjp/LNTIZMb/+ilmDBuqxkr56hfT2bZSVKqm7jRsXz4YN+urYuitXZBw7JqNZsyI4ZgKB\nIFNyHGbu4uKCi4tLfshSJMgovCD9bK5aNSXFir03sT4oVLa2JNWrh/6R1IrjsgsXNBRduXJKOnRI\nYteuVJPl1q0GNGum6ZkpEAgE2TZdhoWFERAQkGH75s2buXnzZp4IVZjJKLxAe32u6Jkt06J4ax5P\nQZbGEUp2+jQmPXrgEzdfo8+BA3ppLZwCgUAA5EDRTZ8+nW3btmXYvm3bNmbMyLiQpuAtGYQXpJ/R\nFTlHlHQo6tTR2JZdvAgREegdPIhpx47o799PywPfYaafOoN78kRKSMhHnEZGIBDkC9lWdMHBwTRJ\n5ySQliZNmhAcHJwnQhVmdDmjKJUQHKwdWlCUST+j0zt7FgtHR0y7dVPnCjUgkVZGJzX6/f23yFMu\nEAg0ybaie/36NSaZpIs3MjLi1atXeSJUYUbXGt2tW1IiI1MD5uRyJRUqFG0bnKp0aZTWWVdU7/Bm\no8b2/v1C0QkEAk2yregcHBwIDAzMsD0wMDDTzCkCkPz3H7KQEI19KktLzpzRNltKi3qyD4lEa1an\ni7a2lzS2L12SpbcOCwSCIk62X6fdu3dnx44dLFmyhKQ0mXSTkpJYvHgx//vf/+jWrVu+CFlYMPj9\nd40SPYpy5VCWL8+pU5qzkAYNirbZMoWEoUOz7FMq6SFOTqnjpVRKOH9erNMJBIJUsq3oxowZQ7Nm\nzZgyZQqVKlXC3d0dd3d3KlWqxNSpU3Fzc2Ps2LE5uvipU6fw9vamatWqyOVy1q9fr9Eul8t1/o0b\nN07dR6VS4evrS5UqVShVqhQeHh5cv349R3K8F+LjMVi1SmNXwvDhIJVqKbpGjYq2x2UKSc2b8+bY\nMeLGjSP+iy+IWbiQyKtXNfpI4uNxcdH8MEhfAUIgEBRtsv1G0NfXZ9u2bWzYsIE///yT8LfJduvX\nr89nn32Gt7c30hza26Kjo6lWrRo9e/Zk2LBhWu1hYWEa2xcvXsTb25tOnTqp9y1cuJClS5eydOlS\nnJycmDNnDp07d+bcuXOYpSuBU5BIr19HmmYNU2VhQUKvXty/L+Hhw9RxMzQsooHiGaCsXZv42rVT\nd8SlK7YaH4+LSxL+/qlVDNKbggUCQdEmR5++EomE3r1751mqr5RZIcDw4cO12m3SVZ3es2cPFStW\npHHjxkDybG758uWMGTOGzz77DIDly5fj5OTE1q1bP6jcm9InTzS2k5ydwdSUwF2aP4GzsyJtwXFB\negwNNTYl8fE0dNGcAQcH66FQ8FEXrBUIBHnHR+PyEBUVxfbt2+nfv796371793jy5AktWrRQ7zM2\nNsbV1ZUzZ87kixzGYWEYjxqF4U8/YTxyJGZVqmDi7Y3s+PFMj5M8e6axrbK0BODkSU1FV5Tqz+UK\niQSVgWYNuop2sZQsmeql+uaNhKtXP5pHWyAQ5DMZzuhGjBiBRCJh4cKFyGQyRowYkeXJJBIJS5Ys\nyVMBU9i6dSsJCQn07NlTve/J21mSlZWVRl8rKyseP36c90JER1NpxAj005Xake7bh/6+fcTOmUPC\nkCE6D5W8eKGxrbKyIikJ9u3T/AkaNxaKLkuMjEhbl0eSEE+DBgr27k1VbhcuyKhVq2iHaAgEgmQy\nVHTHjx9HKpWiVCqRyWQcP35cZ2XxtGTV/i6sXbuW9u3bY/l2JvQu3Lp1K1fHmZ8+TaV0Si4tkoUL\nudW8uc62sjdvYpxm+xmwa/N/PH9uod5XrFgS1tZh3Lr18aXhz+2Y5obaenqkLcpz9/p1ypWTAqnh\nLUeORNOo0b33JlN63ud4fAyI8dBGjIkm7zIeTk5OmbZnqOguX76c6fb7JDQ0lIsXLzJ16lSN/Slr\neM+ePcPOzk69/9mzZ1hnEmyc1aBkhMGxY5m2Gz16hJOtLbqyMRsnac7USlSpwrlzjhr7PD2VVKtW\nMVeyFSS3bt3K9ZjmBpmJCbx8qd4uX7o07u5yli1LK1MJnJwMdByd/7zv8fjQEeOhjRgTTfJ7PLK1\nkBEXF8fGjRs5f/58vgmSGWvXrsXBwYFmzZpp7HdwcMDGxoYjabLcx8XFcfr06XypsCC9ezfrPrdv\n69wvef5cYzuymC1/26nM5QAAIABJREFU/qlZLLRTp0QEWaPS4ZBSp46mp+qNG1Kiot6nVAKB4EMl\nW16XRkZGjB49mjlz5uDs7JxnF4+KiuKff/4BQKlU8vDhQ0JDQylevLh6hhYTE8OWLVv48ssvtUyj\nEokEHx8ffvnlF5ycnKhYsSLz5s3D1NQ0X4LXs1J0ScjYGxDHliXGPHggJTERbG1VVK2qoPGdqrgS\nghXJCu+brQ158SL1O8PcXEXz5mJ9LlukU3TExyOXQ8WKCm7fTna1VColhITIaNRIhGoIBEWdbIcX\nVKxYUe38kVdcvHgRT09P9bavry++vr707NmT5cuXA7B9+3aio6MzDGkYPXo0sbGxjB8/noiICJyd\nndm+fXu+xNBJ38YOpqCoWRNl2bLo791LGJXowWZCl9fWOm73bn3msRhYTAVuI0HF7d1lNPp88UU8\nBgVjafvoUKWLv5DExwNQt26qooNkhxSh6AQCQbYV3fjx4xk/fjwdOnSgevXqeXLxJk2aEBERkWmf\nPn360KdPnwzbJRIJ33zzDd98802eyJQhKpWWoovesQO9ffs4tFdJd7YQRdbK9Q7aa3COjgrGjYvP\nK0kLP+lndG+DyJ2dFWzenLpbpAITCASQA0V38uRJLC0tcXNzo0GDBpQrVw5jY2ONPhKJhHnz5uW5\nkB8CkidPkMTEqLdVZmaoSpbE/34zRjCEJPQzPjgTDAxULF4cSyaFIQTp0Fqjextq4OysOXs7f16k\nAhMIBDlQdKtXr1b/f1BQEEFBQVp9CrOiS5nNxWLEUFZSp9h/HOtvyp9/as9uu7KVL/gdM95whwqc\npQFBNCSE2hoKsUGDJH78MY569YR5LUdkMKOrUUOBvr6KxMTktdwHD6Q8fSrB2vrjC9cQCAR5R7YV\nXVGvNZfiiLKZHvjRD7/HwJ/a/RYwmi9ZRIrbTCMC6YcfADEYc4UaxFauiTxgPg4OKvIx9LDwkj5H\n2ts1OiOjZGV38WLqY33hgoy2bYWTj0BQlMlWeIFCoeDJkyfEpU+oW4RIUXQr0E4+DWBIHOvpxeg0\nSi49JsTSgHO4lvsXR0eh5HKLlukyzXOZ3nx54YJYpxMIijqZKjqVSsWMGTNwdHSkatWq2NnZ0bt3\n7yI5u4sfPZqz684TxKdabdUcIjmDC73YqNWW1KSJ1r70XoOCHJLedJkmHVj6yg9C0QkEgkxNlytW\nrGD+/PmUKVOGli1bcvfuXfbs2cPEiRP59ddf35eMHwamplTyrMCKFTcIDS3Hy5cSzMxUtG+fhJub\nCuOl3WBqqLp7krMzMZs3oypZEgu5XONU0gcP3rf0hQqt8IJMZnTnz8tQqRCzZ4GgCJOpolu/fj21\na9dm7969ag/LSZMm8fvvvzNv3jzMzc3fi5AfChIJODtH4e2tbcJNGDUKDAzQ37YNhbMzcd99p04F\nlujujv7+/eq+iWkSUwtygY6A8RScnJSYm6uIjEzWbK9eSbl9W4qTk0jwLBAUVTI1Xd65cwcvLy+N\nMILPP/8chUKhLrwqeItEQsKwYUQfOEDcTz9p5LuMmz5dva6ktLQksUOHgpKyUJDZGp1UCvXqaTqf\nBAUJ86VAUJTJVNHFxcVpVQsoWbIkQJaB3oJUlFWr8ub8eaLXriXq1ClUpUoVtEgfN5nM6ABcXDTN\nl2fOiHg6gaAok+UbID9L7xQlVGXLklS2bNYdBVmSUQqwFFzSVRw/e1bM6ASCokyWim7q1KnMnTtX\nva1QJH8tjxgxApN06TwkEonOQHKBIE/JIGA8BWdnBVKpCqUy+SPt5k0ZL19KKFFCBI4LBEWRTBWd\nq6urzhldKWF6ExQgGaUAS8HMDGrUUBIamjqTCwqS0b69CBwXCIoimSq63bt3vy85BILsk8WMDpLN\nl2kV3alTekLRCQRFlGxlRhEIPigySAGWlsb/Z+++w5sq3waOf09GJ12sskGgtFAKlQ3SsqeyrGwQ\nEFD4qYAKClZFkJeyRFFQEFAEBRRECorgKsioLEF22WDZFEp3mybn/aOQ9iRp2tKk8/lcV6+rZyR5\ncnqaO8+6n7bKoLZ7txiQIgillQh0QrEjmyzcJ1mo0bVtqxx5efy4ivv3xcAqQSiNRKATih/TGp1J\nHx1AuXIy/v6ZwU6WJfbuFaMvBaE0EoFOKHasTRjPKihI2Xz511+i+VIQSiMR6ITiJxd9dGAe6CIi\nRKAThNLIpoHu0Rw7QbCn3PTRQcaAFI0mc+7cuXNqLl0S3+0EobTJ9X/9woULrR5PTU1l6NCh+S6Q\nIOQoF310AB4e0Lq18svXr7+KWp0glDa5DnQffPABy5Yts3gsKSmJAQMGsGvXLpsVTBCyk9s+OoCu\nXXWKbRHoBKH0yXWgmzVrFtOmTePrr79W7I+Li6Nv374cOnSI9evX27yAgmAml310AN26Kfvp9uzR\nEB9vj0IJglBU5frr7csvv0xycjKvv/46zs7ODBgwgJiYGPr168eVK1fYtGkTLVu2tGdZBQGwUKOz\nEuh8fAzUqqXn8uWMqQWpqRLbt2vp31+X7WMEQShZ8tSOM3nyZJKTk3n55ZeJi4tjxYoV3L17ly1b\nttC4cWN7lVEQlHJYpicrSYK+fXV8/HHmHLoffhCBThBKkzx3WLz77rskJSXx5ptvUqlSJbZt20a9\nevXsUTZBsMxSH50sZ0Q1C559VsfHH2c2d/7xh4bYWPD0tGspBUEoIrINdG+88YbVB7q6utKgQQPF\nABVJkliwYIHtSicIlqhUyFotki5LrSwtzbym91BAgAEfHz3nzmXU6nQ6ifBwLSNGiFqdIJQG2Qa6\nL7/8MscH//HHH4ptEeiEAuPkBFkDXWpqtoFOkjJqdXPnZjZfrl7tIAKdIJQS2Qa6+/fvF2Q5BCFP\nZEdHpCzDJ6XUVKwtqzp4cBpz52Y2Xx4+rOHYMRWNGhnsWEpBEIoCkSZCKJ5MpxhYmUsHUKuWTMeO\nyhrc1187ZHO2IAglSa4D3d9//201O8pHH33EgQMHbFIoQciJaRowzcGDOT5mxAhlBpV16xzE0j1C\nodFu2IBLv36UadUKlyFDkK5dK+wilVi5DnRz587l+PHj2R4/ceIEc+fOtUmhBCFHZcooNp3HjEG9\nf7/Vh/TsmU7lyplNlUlJEl9+KWp1QsFzO3AAl7Fj0UZEoD5zBu22bThPm1bYxSqxch3ojh07RosW\nLbI93rx5c/7991+bFEoQcqLr3VuxLRkMuPbvj+ann3D47DPUkZFmj9Fq4aWXlHPuli1zyKnVUxBs\nzstkIB+A+tChQihJ6ZDrQJeUlISUzTylRxISEvJdIEHIjdTXXyf15ZcV+6S4OFyHDcP57bcp06MH\nml9/NXvcyJFplCmTOWzl9m0Vq1eLWp1QsFzOnTPbJ92+DQYxOMoech3o6taty59//pnt8d9//53a\ntWvbpFCCkCOVipT/+z9Spk7N9hSHzz4z2+fpCaNGKfvqPv7Y0VpyFUGwLYMB5wsXzHZL6elIMTGF\nUKCSL9eB7vnnn+e3337jzTffVEw9uHfvHlOmTOHPP/9k+PDhdimkIGQn9a230PXqZfGYdudOsNDK\n8OqrqTg7Z9bqrl9XsWaNqNUJBUO6ehV1UpLlYzdvFnBpSodcB7qxY8cyfPhwli9fTp06dfDz88PP\nz4+6deuyYsUKBg8ezPjx4+1ZVkEwJ0mkvPcessryrayxsHRUxYqyWa3uo49ErU4oALGxOL/zTraH\nVbduFWBhSo88zaP75JNP2LJlC6NHjyYgIICAgABGjx7N1q1bWbJkib3KKAhWGXx80A0ebPGY1kI/\nHcDEiak4OWXW6q5dU/HNN6JWJ9hRejplOndG+9NP2Z4ianT2keekzkFBQQQFBdmjLILw2JLnzUPW\nanFctUqxX7NjB6Sng0Z5q3t7y4wcmcbSpZlpwz75xJFRo9LIpnIoCPmiPnIE9fnzVs8RNTr7yPO/\ndGxsLJs3b+aTTz7hk08+ITw8nNjYWHuUTRByz9WVlI8/5sHNm8iursbdqps3cZw922In/8SJqTg6\nZtbqrlxRsW+f2uw8QbAFKS4u53NEjc4u8hToFi1aRP369XnhhReYPn0606dPZ+TIkdSvX59PPvkk\nzy++d+9eBg0aRP369fH09OTbb781O+f8+fMMGzaMGjVqULlyZYKDg4mKijIeT01NZcqUKdSuXZsq\nVaowaNAgrokMA6WXkxO6555T7lq4EDdfXxxnzoTkZOP+ypVlevVSpgVbu1Y0Xwp2oss5ibjq9u0C\nKEjpk+tAt3r1at5//31atmzJunXrOHLkCEeOHGH9+vW0atWK999/nzVr1uTpxRMTE2nQoAFz5szB\n2dnZ7Pjly5fp1q0bNWvWZMuWLURGRvLOO+/gmuUb+7Rp09i6dSsrV65k27ZtxMfHM3DgQPR6fZ7K\nIpQcqRYGRUnp6TgtXIjLsGEZa9c9NGSI8sMnPFxraaCmIOSfhUCXMnmyYlsSTZd2IcXGxlpL+m7U\npk0bKlasyI8//mg2cVyWZfr27cudO3fYt2/fYxWkatWqzJs3j6FDhxr3jRkzBkmSWL58ucXHPHjw\ngLp167JkyRIGDBgAQHR0NAEBAWzcuJFOnTo9VlmsOXfuHD4+PjZ/3uKsKF4Tl5AQtBayTwDER0Zi\nqF8fAL0eAgLcuH498zvfypVJhIQ8/hI+RfF6FCZxPTJof/wRl1GjjNu63r1Jeecd3LJknNLXqkXC\n0aOFUbxCZe97JNc1uosXL/L0009bzI4iSRLPPPMMFy9etFnBDAYD27dvx9fXl5CQEOrUqUOHDh3Y\ntGmT8ZyjR4+i0+no2LGjcV+1atXw9fVlfw55D4WSLWXmTGSTfJiPZM2YolbDc88pg9q2bXkeoyUI\nOTOp0claLYaKFRX7VLduKVocBNvIdaDz8PDg8uXL2R6/fPkyHh4etigTAHfu3CEhIYGFCxfSoUMH\nfvzxR0JCQhg7diw7duwA4Pbt26jVasqVK6d4bIUKFbgt2rpLNYO/P3EnTpDw669m2VO0D++fR55+\nWvkB9NtvWtKU0+wEIf9Mmy41GvDwQM6y5JSUnAy5GLQi5E2uv7p2796d5cuX06hRIwYMGGCs2cmy\nzIYNG4yTxm3F8DDnW8+ePXnllVcAaNSoEUePHmX58uV069btsZ/7nIU8cwX5+JKoyF4TLy8cWrem\nUZZdmn37iH/nHW4OHQoaDR4e4OXVmPv3tQDExUl8//0tWrZ8/A+cIns9Com4HlD+2jVqZdl+kJzM\nlfPnCShbFsfr1437/ztwgJRatUwfXuLl5x7Jqdkz14Fu+vTpHDx4kPHjx/Puu+8a81pevHiRu3fv\n4ufnx/Tp0x+7oKbKlSuHRqPB19dXsb9evXrG5suKFSui1+uJiYmhfPnyxnPu3LlD69ats33u/LQF\ni/4Gc0X+mvj4oK9fH/Xp08Zd1RYvpmJ0NMlffgkqFU8/LfPNN5kP+fffmgwb9njLGhT561HAxPXI\n4FC2rGLbvVw5fHx8UFevDlkCXS1nZ/Sl7HoVmT66smXLEhERwezZswkICODevXvcu3ePgIAA5syZ\nQ0REBF5eXjYrmIODA02aNDGL8ufPn6d69eoABAYGotVqiYiIMB6/du0aUVFRtGzZ0mZlEYo/Xffu\nZvscNm/OmHIA9OypbFaKiBD9dIKNWWq6BGSTrheR2Nn28vTf7OjoyLhx4xg3bpxNXjwhIcE4gMVg\nMBAdHc2xY8fw8vKievXqTJgwgVGjRtGmTRuCg4PZvXs3mzZtMs638/DwYPjw4UyfPp0KFSrg5eVF\naGgo/v7+tG/f3iZlFEqGtJdfxmHDBlTR0Yr9jkuWkPbyywQFVUCjkUlPz2iSP39ezX//SVSvLgYG\nCDaSnq7c1mY0lctZWqMAVHfvFlSJSo1c1+gaN27Mtm3bsj2+fft2GjdunKcXP3LkCMHBwQQHB5Oc\nnExYWBjBwcHMnj0bgGeeeYaPP/6YTz/9lDZt2rBs2TKWLl2q6J8LCwvj6aefZtSoUXTv3h1XV1fW\nr1+PWi0yXAiZ5PLlif/nH5JMlu6RdDpUp07h5gbNmyvnXu7cKWp1gu1I2QQ6g0mgk0Sgs7lc/ydf\nvXqVxMTEbI8nJiby33//5enFg4KCckwfNnToUMXcOlOOjo7Mnz+f+fPn5+m1hVLIwQHdkCHowsMV\nIy9VMTHogfbt04mMzPyX2LlTw/Dhjz+fThAULEwvAJBN+u5EoLO9PKUAs7bC+Pnz53Fzc8t3gQTB\n3gxVqii2H/WJdOig/Ma9c6dGLPgs2E52fXSmNTrRR2dzVmt0a9euZd26dcbtBQsW8PXXX5udFxsb\ny6lTp+huocNfEIoas87/h9+gmzTR4+4uExeX8YUuJkbFiRMqGjUS0U6wAdFHV2isBrrk5GRisny7\nSEhIQGVhDRNXV1deeOEF3nrrLduXUBBsLLtRbhoNtG2bzrZtWuOxnTs1NGokZo8L+Sdl03RZnPvo\npCtX0P74I4YGDUjv0gWstPoVJquBbvTo0YwePRrImKw9Z84cevbsWSAFEwR7MWsqyvLB0qGDMtBF\nRGiYMEEEOsEGStr0ggcPKNOuHaqH4yySli9H179/IRfKslwPRjl27Jg9yyEIBcasqSjLB0v79srm\npchIDSkpkCVLkyA8nlw2XUoxMSDLXLykZsMGLffuSdSta+DZZ3WUK1d0prtof/nFGOQAnKZNK/6B\nztTu3bv5/vvvuXnzJvXq1WPcuHHGidyCUJQZrHyDrlvXQLVqBqKjM5roU1Ik9u9X066dHZd90utR\nHT+O7O2NXLmy/V5HKFTZNV3i7Izs6or0cFS7lJ7OhaOJdOxbhQcPMpsCFyxwZOXKJNq2LRpLkGn2\n7FFsq+7ehdRUcHQspBJlz+qoyzlz5lC5cmXumrQZf/vtt/Tp04dvvvmG33//nc8++4yOHTty9epV\nuxZWEGwhu8EokNHF0K6d+ehL+xVGxuW553Br3x63Jk1Q79plv9cSClc2TZegvCdTcWD0eE9FkAO4\ndUtFv36u7NpVROYIm9ZQAfXhw4VQkJxZDXS7d++mY8eOijySqampTJs2DXd3d8LDw4mOjubLL780\nrjQgCEWdxaaiLPMITJsvd+2yX6BTHz2K9mEKOyk5GacPPrDbawmFLJumS1AOSPmI1zh6xhVLdDqJ\n4cNdOXMmTzPDHt+DB6gPHECKjjZbPkhlYTUbzWOuR2pvVq/WxYsXCQwMVOzbtWsX8fHxTJgwgeDg\nYFxdXenXrx8DBgxg586d9iyrINiGoyNyljmfkl6P9OCBcTs4WPmBdPSomhzyGjy2rGvjAWgOHbLP\nCwmFz7RGlyXQZf3y9TUjrD5NXJzEgAGu3L5t3xGO0vXruLVuTZmuXXFv2JAyrVujOnHCeFxlYf1R\n9d69di3T47Ia6O7fv0+lSpUU+3bv3o0kSWbL5AQGBnLz5k3bl1AQ7MBaP523t0yDBpn9IAaDxO7d\n9qnVyS4u5juzBF2h5DDro7PQdHkaP85Q37hfo5G5cCGO999PVjz26lUVQ4a4kJpqv/I6fPMNqiyr\nKqjPnMFl7FjQ6yEhAZWFNT81Bw5YbNIsbFYDnbe3Nzdu3FDsi4yMxMXFBT8/P+UTqVQ4ODjYvoSC\nYAfW+unAvFb311/2CXTS/ftm+9Tnz9vltYRCZqXp8lGN7kf6KU4JDk6nXDmZiRPTGDFCOc3l0CEN\n77xjv+HA6pMnzfedPo12wwZUly5ZfIyUmEjSqav8/bea3bvVxMfbrXh5YjXQNW3alHXr1hnzUZ44\ncYIjR47Qrl07s6TJUVFRVK1a1X4lFQQbsjaXDswHpPz5p30CnerWLfN9UVH5f96oKDR//IHd2lyF\nvLPSdPmoj24TzypO6dUr4z6UJFiwIJlOnZTPsXy5Iz//bKd7M5uFUJ3Cwix+GUvCmQksolbHRnTv\nXoZevcpQu7Y7o0c7Ex1duBPJrV6ht956i3bt2tG0aVN8fX05fvw4kiQxadIkxXmyLPPTTz/RsWNH\nuxZWEGwlp0m6bdumo9XK6HQZ/6AXLqi5cEFFnTq2TQcmWWj+UZ09m/n7yZNod+wgvVkz9IGBqG7e\nxODjk5mBQpaNvyclwZ07Evc37yNu+hLuUp5bDke58WQ3dE82pXwF6NgxncBAfVFNYFGiZTu9ADDU\nqkUMZTlMs8zzJVmxTqJWCytXJhEc7MbVq5l1lMmTnWnbNh4PDxsW1mCw2AcHoLpyBe3Dxa8fuUIN\nerKNU/hDlu+IOp3EDz84sH27lg8/TGbQoMJJkm410Pn6+rJlyxYWLFjA5cuXadmyJRMmTKB58+aK\n83bv3k2ZMmXo3bu3XQsrCLaSU35BNzdo00avGHH5228a6tSxbZYUSzU69cNApz50CNfevZGSkhTH\nY4Ke5rveqzi9aBfnrrtzzqURt/QVSEx+9OHX/eEPkAbsf/gDfPABPPlkOnPnptCiRdGYj1VqmDZd\nZumj0zdtymGaKg77N9Dj7a0c6ejpCatXJ9K5cxnj2ok3bqiYOdOJDz9MsVlRpf/+Q0rJ/vk027cb\nfz+NH135lWiyn0edmCgxbpwLJ06kMnNmChYySdpVjnXeFi1a8P3331s9Jzg4mH1FdFipIFhiNhjl\nzh2zc7p00ZkFunHjbBvosq3R6XQ4T5yoCHIJuPJ/hLJ49ysk7HYDQh4dyJMjRzR06+bKyy+n8d57\nKYiu9QJibR5d1aocdOsAWfq0mtS8A5gPVgoMNDBxYioffpjZP7dqlQMvvZRGvXq2aXFQZ9Ns+cij\n2ulvdKY/G3iAp+K4q6uMm5vMzZvKiLZ4sSPx8RIffZRcoMGugOOqIBQNssloYpXJoCuArl2V38D3\n7NHYtnPdYLAYYFWXLuH4ySfGwQAy8APPUp/TzGEaCeR/OSxZlli82JEePVy5fFm0YxYE06bLrH10\nSBKHXNspDjdxPEF2pkxJ5YknMmvker3EBx/YbmCKaf9c1hGijyzhf/TgF7Mg11/aSNSJGE6diufD\nD5NxclLWSr/+2oHx450LdHCmCHRCqWSoVk2xLVlYNNjHx0CtWpkfJqmpEuHhWrPzHpd07x6S3rz5\nUNLrjRPH4ynDINbzHD9YbRoC0JJGVaIJ5Ahd2cEw1vA6HxLGVMKcZ9Cli3n/yOHDGoKD3Vi50qEo\njgovWaxMLwD4J6WBYrt5fES2T+XkBNOnK5sWt27VcuCAbbKmqEwGm6SbJPOfy5u8whL0Jo2CL7KM\ndfJA3G9fRKWC0aPT2L49gQoVlDXN775zYORIF5KVsybsRgQ6oVQymORlVUVHm50jSRASovxwWrvW\ndu18koX+uazO4EtL9vM9A82OeXOTqYTxPf35l0bcw4tUHImmOkdowg66s4bn+ZDJTGUuU5PfZ8OK\nG4SHJ1CtmvJDJy5O4o03nPH3d2PKFCfCwzWilmcPVqYXxMRIXI3NrBlp0BF4Yh3qyEhcO3bEzd8f\n127d0K5ZYzynT590mjRRPuf06U6mCUwei2nTpa5HD+Pv3zKEqcxVHJcwMJ/JLGUcagyoTp82HgsM\nNPDzz4lUrqy87376SUu/fq7cv2//e00EOqFUkitXRs7SSaC6fRssdL4PHqwMdPv2abh0yTb/NpYm\n3D7yJx1owQFOo/yWr0HHFOZxnrqE8Tb92UgjjuMeUJ2cPi5UV67Qrp2e3bsT6NHDvHZ365aK5csd\nGTHClcBAd/r0KcBUU6WBlabLo0eVNbGGnMDl5mXK9OiB5p9/UF27hmb/flxefRXtjz8CGV/EZsxQ\n3rORkRq2b8/fdAPp/n3UJhl69E2bYvD25irVeZEvFMdcXQxs7LmMyXxovAfVJqvd1KtnYNu2RKpX\nVwa7v//W0L27K+fOOeerzDkRd7FQOmm1ZisFZM0C8UjdugZatVJ+a/7wQ9tkZ8+uRvcPT9KHcOJx\nV+xvWDeJI1IT5vEWZUg07k9cvZqE3btJffllq6/3KDehl5fM2rVJzJmTjKtD9oNrdu3S0L59Gf76\nq4gkES7mrGVGOXlS+VHclOyTIztNnmzs2w0K0ps1Sc+Y4YSFFvFcc/jqK8UgKEOVKhjq1sVQty6v\n8ilJZObhdJDSWLc+ie59lcFVYyG58xNPGNi+PYH69ZWFi4pS8+qrPnbN8iICnVBq5aafDmDYMGUw\n+OYbBw4fzseHvyyjOn4clclqH2nPP0+Uf2968IvZgJOBA9P49S8ddaY/p3wqlQp927YA6Bs3tvqy\nqitXjL+rT59i0rW3iEp7gpdYihtxFh+TkiIxdqwLd+6Ipsx8s9J0efaseY0uO6qYGBzDwozb06en\nIEmZ7ZVnzqhZu/Yx+5LT0nD4QlljS33xRVCp+EXuxhb6KI7NHRdFcLAefVPl1Aj1vn2oTp82SwZd\ntarML78k0KaN8lp8UH4BLkf+frwy54IIdEKpZdZPl02gG9z4OH5llMfGj3d+vJSU6em4duuGW1AQ\nTrNnKw7dKO9P9/gfuI23Yv/06SksXZqMiwukTZxI6iuvGI+ljR+PXLZsxlMHBSGrsw/Aj2p06kOH\nKNOpE46ffkpVrrOU8dygMuH0ZiIf05ijisfduqVi0iT7Ni2VClYC3blzyo9iP85YfSrtTz8ZA0jD\nhgYGDlTW6sLCnB5roIfmt99QZclZLLu6kjZyJLIMM/8brTi3tdNhRszK+B8y1K6NnGXGuqTT4da6\ndUYy6OBgpGvXjMc8PWHTpkQGV8hIaN6FXxkXFYrD8uV5L3AuWQ10ZcuWzdNPOZO5SYJQlJnW6CwN\nSNFs2YJX+6f4NGGUYv/Zs2pGj3ax1K1nleannzIS35qIw40+34/i8lVlE9CECam89lpqZiYTSSJl\n1izi9+0jfudOUv7v/4znypUrk/r668iSlPEBNWyY4rkc1q7F+cUXKdO5M5LJp6ArSfRmKx/zGkd4\nktccFyuO//yzlmPHxPfi/MhueoEsQ1SU8trWnD+GtCFD0HXqRMrkyTyIjlasuKG6fRvVqVPG7dDQ\nFBwdM2tO16+UMGErAAAgAElEQVSrWLYs7wOnTDOepA0aBJ6e/P67hkP/VVEcmzc/FZX64Y0pSaQ3\naWLxOdXHj+Pap09GU31sLA5Ll+IZ+gZr73TjR/qylHFIQOqrr+a5vLlltddSlmWcnZ3p3Lkz3t7e\n1k4VhGJHNg10Fmp0TgsWIOn1dOYPRrCKrxlpPPb771oGNr/D6l7fUrGcHu2GDej9/FBNnJjtazou\nXarYTsGRXbTjDT7kZHQFxbGBA9N4/33LkdTQoIHF/amhoaSNGYPs6orq0iUcvvnGeExKTsYhh+QP\nABIwL3USO58cx5EjmR8Rn37qyPLlBTQevCQyqdE96qOLiZGIjc0MdE5OMpVGdyFZ1UX58KeeQpsl\nI4kmIoI0f38AqleXGTs2jcWLM/uPFy50YsQIHV5euRyGmZiI9pdfFLt0ISHIMsyZo+yXfjoohoDh\n/op9+iZNjGsrmlKfP4/L6NHIrq5od+ww7u9LOABxzZohmywJZ0tWA93QoUPZunUr27Zto23btjz3\n3HP07t0bd3d3aw8ThGIhxxqdLCvyTn7OeE7RgIO0MO7b9V9dmn72P0ayiid5EsMZFVFn/+O/5m24\nelUiJSWzb0utBofjC9EQhwEVF6nNJZ5AttCw0qWLjsWLHy97hPzwS6mhZs28P/ghDXrefPkeg8dU\nNO7btEnLu++mUKOGDcavl0bZ1OhMmy3r1jVY/Lunt2+vDHQ7d5KWpRn7jTdSWb3agbi4jHsuLk7i\nww8dmTUrd80O2l9/NRuEom/Vit9/13D4sDJUvDnLAVCOoNQ/+aTV59fs2ZPtsXtdu+KVq1I+Hqv/\nRosXL+bcuXN8+eWXuLu7M3nyZHx9fRk5ciQ//fQTOtM/nCAUI2Z9dOfPK751S7dvK/L9OZNCOH1o\n4KxMdnuXCixgCkNZy3C+Ydap4Xz9tQMREVoiIzXGnz17NPyZ2Jpf6cbvdOEidSwGuWbN0lm1KkmR\nOOOxuLuj69TJ6imJmzYRd/48hipVzI51b3IDX19l9o1160S+sMeWy0BXr57lIZPpHTootjX79pF1\nqKKXl8zrryuD2hdfOHD1au4GEmnXrVMWt29fZEllVpvr2VNH48bmqcbSg4MxPOwvBtB17Up6UFCu\nXvtBmza5Ou9x5fh90cHBgd69e7N69WrOnj3LvHnziI2NZcSIEfj4+DBhwgROZ5kcKAjFhaF6deVc\numvXFKPZTEdFAlTmJn9WHEDLKpftUqaQkDTCwxNxdc353NxIXrhQ8eGTla5bN9I7dEAuX574gwfN\njqvjYnnpJeWI0w0btDaZkFzq6PVIWS6cLEkZVXzMR1zWrWs5X6WhXj3FFxIpKUkxMRvgpZfSqFIl\n8/FpaRJz5+acGky6ehXNb78p9un692fTJq1Zbe6tt7KpIbq7k/TDD6QNHEjKm2+StGYNyYsWITta\nn46jr1MHnZ27xvLUMOLu7s7w4cPZvHkzR44cISAggG+++YYtW7bYq3yCYD/u7uj6KIdLOy5caFwP\nLutw/Kwq3j3DrjJPM4tQXLLMZ3tcNbnMkMbHWL8+kRUrkm0W5ADkmjVJWr3aGOzSmzUjaflyElet\nIumrrzKX+3F1Nfv2rbp/n379dGi1mR/Q58+rzSY3C7lgZQ6deY0um8TMkoS+USPFLrXJ2oXOzjBt\nmjIQrV+vzTHTjcOaNYpArG/UiPh6gUyfrgySTz9tuTZnfNyTT5K8bBmpb78Njo4Yatcm9fXXrb62\naXoxe8jzFPo9e/awceNGwsPDiYuL46mnnqJdu3Y5P1AQiqCU+fPRREYah1RLsozDmjWkzJplsUYH\nGasoO549RSinmMAn/MzT/E0rrlIDR1KpyG1qc5EnuIQnGQufykjoUSt+qnKNepzFhWQSZoajb/f4\nfWrW6Nu2JeHwYaTr1zPWsstmuQLZU5mcV4qNxctLpnPndH75JbMd9bvvtDz5pFjiJ0+sBLqzZ5WB\nzscn+2ur9/NT9NOpzphPQxgyRMeiRXrOn8/4QqLXSyxY4MTixZYHEkn37+Pw5ZeKfWmjRjHzA2ei\nozPLptXKZplYciN10iQc1qyxOKpZVqlIG2ie4s7WchXojh8/zoYNG9i0aRPXrl2jcePGvPHGG4SE\nhFDZJLuEIBQncvnypMyYgctLLxn3ab//npT330fKJtBl5UYCg/iOQXyXr3IYAgLy9ficyF5eyF7W\nu/tNj0sPVycfODBNEei2bNEye3bBrylWnEnZjLhMSYErV8wHo2TH4Our2FZbCHRqdcbqBi+9lLnE\nz7p1WiZPTqFWLTlj9fldu5A9PEhv2xanefNQZVl4WC5Thm3lhrJ0qbLJcdy4NKtly5ajI0lLluDa\nt6+x1mjw9MTQsCFpzz+PoWFDyGFZoPyyGugWLFjADz/8QFRUFHXq1GHo0KH079+funXr2rVQglCQ\ndL16IU+ZghSXkR1Edfs2mj/+yLbp8nHJGo3ZBx6Arn1746TvwmRWo7t/H4Bu3dJxdZVJTMxo/rp+\nXcWhQ2qxcGteZFOju3hRhcGQ2axYrZrBatO1vn59xbbKpOnykZAQHfPm6blwIbNW9+GHTix58QBl\nevZEsrLeVNTz0xk7UTknumZNA1OmPP7Crvp27Uhaty5jCk6zZqSNG0dBLnNvNdD93//9H87OzvTq\n1YsmDycD/vzzz9meL0kSEyZMsG0JBcHeXFzQ9e2Lw+rVxl3aTZuybbrMSte9O7K7O+qjR0kbORJ9\nQABOgwejSUhA1mjQ9e1Leo8e6Pr2hdRUVOfPI+n1GGrWzMgEIcukjhtnz3eXa5aaLiGj36drVx0/\n/pjZ5BkerhWBLi+yCXS5HXH5iMHHR7GtunQJkpMz/khZaDQZtbpx45S1undOf0I9K0HuXu0nCYmY\nxP37WQZpqWSWLUsiv7PK0rt3J7179/w9yWPKsekyOTmZLVu25GrAiQh0QnGV9txzikCniYxEypIK\nyRJZpSJ5yRJkk4xAJzZuxCchAb2/vzJxtIsLhiyDCVLfess2hbeR7AIdQJ8+5oFu1qyUgvxSXrxl\nG+iUA3t8fHJoGnR1xVCjhvFLmCTLGSsNODqCLKMPDMz4HXjuOR3z52fW6tLTJeYd6soKNlh86mRN\nGfq4/s6p48qwEBqaSqtWxftLjdVA9++//xZUOQShUOmbN1c0LeamNmcICDALcgDp5cqR3qqVzcto\nb2Z9dA+bLgE6d07H2VkmOTkjskVHqzhyRE2TJsX7A7CgZNdHZzoQJdsRl1no/fwU92eZXr2MvxvK\nliVh507kGjXQaGDy5FTGj8+s1X3NCEL5P57gsuI5kyQXevtFsee4sgm9Tx8dr79ux2UFCojVQFej\nRo2CKocgFC5nZ/SNGqH5559cPyTdzpNcC5q1Gl2ZMhnBbuvWzEEpmzdrRaDLrVw2Xdatm/P1NPj5\nwa+/WjymuncPp9mzSX6Yaq5//4xa3cWLD2t1aHmPmazheRLCw9Hs3k3amSv0i17Mn0crKp6rRYt0\nPv88qUTU2vM1bio5OZm7d+/aqiyCUKj0zZtnf8xkEACUrkAH0Lev8sM6PFxMHs81C4FOls2bLnNT\no9P17Wv1uObXX3m0IN2jvrqsvmE4P2v7oA8K4tYr79Arbq1ZkKtfX8933yXh4kKJkGOgO3jwINOm\nTePdd9/l6NGM5Tv+/vtv2rdvT9WqValXrx4NGjRgdZb+DUEojvQtW1rcLzs4kPLuu+bnt25t7yIV\nqOxGXT7StatOkSH/yhUV//4r5hjkhlnTpVrNjRsSCQmZ1SU3N5lKlXL+5qBv0oSUyZOzPa66dw91\nlkw3/fvraFBLOQBlkP5b/veKKx07luGvv5QNew0a6NmyJTH3yaCLAat36d69e+nRowdLly7ls88+\no3v37uzYsYOQkBBu3rxJz5496dKlCykpKUyaNElkSBGKtfRsanRpQ4aQ3rYtslNmlgh9o0bI5csX\nVNEKhMG0j85kwT03N+jUSfmBvWVLfhNylhIWanSnTpnW5vS5biZMDQ0l9X//y3guC/MRNFmTP2tg\naf/tqMhsFk0wuLJ2rYOxSfMRf/+MIFehQskJcpBDoPvoo4+oWbMmx44dIzo6mmeeeYaxY8fSsGFD\njhw5wjfffMN3333HP//8Q506dfjss88KqtyCYHNytWro69VT7nN3z0hh5O5OysyZyFothkqVSM6S\nE7PEcHfPyMH4kBQfb7a0TJ8+yg/szZtF82WuWAh0J08qP379/fMwGVuSSJk9m7hz54g7dYqkFSsU\nh7MuhQPQXLePUP4Pa5o3T2fLlkTKly95f1Crge748eMMHz6c6tWr4+zszMSJE4mPj2fUqFE4Z5m3\n4enpybBhwzh58mSeXnzv3r0MGjSI+vXr4+npybfffqs4Pn78eDw9PRU/nTt3VpyTmprKlClTqF27\nNlWqVGHQoEFcy7KarSDkmiSRvGgRel9fDDVrkjp+PPF79iA/HJSV9uKLxN2+TfzJk+ifeqqQC2sH\nKhWmk6VMa3XduytzX168qDb7wBYssDDq8uRJZW2qYcO8D+yRK1QADw90nTsrEpSrT5+GLEvuqM+f\nZwbTmcF7Fp9nxIg0tm5NpFy5khfkIIdAd+fOHUWKr0e/W0r7ValSJRIT85bgNjExkQYNGjBnzhxF\n4Myqffv2REVFGX82bFDOAZk2bRpbt25l5cqVbNu2jfj4eAYOHIheL0aDCXmnb92ahP37if/3X1LC\nwoxBzihL1vmSKKd+Og8P6NhR+aEdHi6aL3Niurq4rNFw4oR5s+Fj8/RENl126tKlzN/Pn0cC3uMD\nImnFi33+45lndIwfn8q+ffEsWpSMU86LHBRbOa4wrsq6jImV5HbSY4xB7dq1K127dgXgfw/bm005\nOjpmu7r5gwcPWLNmDUuWLKHDw7Wali1bRkBAADt37qRTDmtxCYKgZPDyUqQ+Mx15CdC7t44dOzKD\nW3i4lrffTi0Rw9DtxiTQpaqczObQ5SvQkbHcTda/nerCBQz+/qDXo7qYuYZiK/ZTf5EBPJMsPU2J\nlGObQ3x8PHfu3DH+QEaAybrvzp07xD3ME2hrkZGR1K1bl6ZNmzJhwgRjGQCOHj2KTqejY8eOxn3V\nqlXD19eX/fv326U8glCSmebclK5fNzunZ890NJrMJq6zZ9WcOSOaL60yCXRn02qTnq7McWlSmc4z\nQ506iu1HwU39zz9IaZnrChrKlyffL1bM5JgCbPLkyUw2Gco6cuRIs/NkWX6sWp01nTt3plevXtSs\nWZOrV68ya9Ysevfuzc6dO3F0dOT27duo1WrKmWSnqFChArdv37ZpWQShNDD4+MCffxq31cePk967\nt+IcLy+Z9u3T+f13Za2ufv3in0HDXkynFxxPVg56ym9tDsBQu7ZiW33hAsgyTjNmKPbrGzfO92sV\nN1YD3VuFnIsvJCTE+Lu/vz+BgYEEBASwY8cOepv88+XFuXwuCZHfx5dE4pooFdfrUc7bmyeybKdE\nRnLewntp1ao8v/9ey7i9YYOBkJDs33NxvR62UjY6mqxh6J94P8XxypXvcu5c/gbReTg5kTXlc+qJ\nE9xas4Z6e/YozrvUrx9xRfDvkZ97xMck2bUpq4Fu6tSpj/3C9lC5cmWqVKnCxYdV8ooVK6LX64mJ\niaF8ljlNd+7cobWVybw5XRRrzp07l6/Hl0TimigV5+uh6tIFZs40brtfuGDxvYwaJREWJqPXZ7Ti\nXLjggsHgi6+v+RD54nw9bEVr0ur0170nFdvdurnj45O/NCQqkxY11xs3qGUS5HQdO+I9bBiWRz0U\nHnvfI8WqYT0mJoYbN24YB6cEBgai1WqJiIgwnnPt2jWioqJomU2WC0EQsmfw80POsgK56sYNpC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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"23aec5ab1a717e3458c8d5cae68db0e7add091ae","id":"TBvROHy1ONQC","colab_type":"code","outputId":"21d14148-1d9d-4b6a-c1a3-15bae603b1ae","executionInfo":{"status":"ok","timestamp":1585821973190,"user_tz":-540,"elapsed":877,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["# Evaluating GRU\n","return_rmse(test_set,GRU_predicted_stock_price)"],"execution_count":45,"outputs":[{"output_type":"stream","text":["The root mean squared error is 3.383726992535627.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"IR58lgQ-7i2z","colab_type":"text"},"source":["# 5 Sequence Generation"]},{"cell_type":"markdown","metadata":{"_uuid":"3aa099ecc057f972525df4c7ef4209744d27106a","id":"EqqpFIMlONQG","colab_type":"text"},"source":["여기서는 새로운 예측마다 마지막 60 개의 값을 사용하는 대신 초기 60 개의 값만 사용하여 시퀀스를 생성합니다. **테스트 세트 값을 사용하는 예측에 대한 다양한 의견의 의심으로 인해 시퀀스 생성을 포함하기로 결정했습니다.** 위의 모델은 테스트 세트를 사용하므로 새 값을 예측하기 위해 마지막 60 개의 실제 값을 사용하고 있습니다 (벤치 마크라고 함). 이것이 오류가 너무 낮은 이유입니다. 강력한 모델은 시퀀스에 대한 위의 모델과 유사한 결과를 가져올 수 있지만 이전 값을 가진 데이터 이상이 필요합니다. 주식의 경우 시장의 정서, 다른 주식의 움직임 등을 알아야 합니다. 따라서 원격으로 정확한 plot을 기대하지 마십시오. 오류는 크며 테스트 세트와 비슷한 추세를 생성하는 것이 가장 좋습니다.\n","\n","예측에 GRU 모델을 사용하겠습니다. LSTM을 사용하여 시도할 수도 있습니다. 최상의 시퀀스를 얻기 위해 위의 GRU 모델을 수정했습니다. 모델을 4 번과 2 번 실행했는데 약 8 ~ 9의 오류가 발생했습니다. 최악의 경우 약 11의 오류가 발생했습니다.이 반복이 무엇인지 봅시다.\n","\n","이전 버전의 GRU 모델도 좋습니다. 좋은 sequence를 얻으려면 약간의 조정이 필요했습니다. **이 커널의 주요 목표는 RNN 모델을 작성하는 방법을 보여주는 것입니다. 데이터를 예측하는 방법과 예측하는 데이터 종류는 귀하에게 달려 있습니다. 훈련과 테스트 세트의 목적지를 정하고 세계적 수준의 결과를 얻는 100 줄의 코드를 줄 수는 없습니다. 그것은 당신이해야 할 일입니다.**\n","\n"]},{"cell_type":"code","metadata":{"id":"rxzlQH5VZFeE","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"c0db6523-8804-4b12-e115-fb24147dd60c","executionInfo":{"status":"ok","timestamp":1585822592653,"user_tz":-540,"elapsed":901,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}}},"source":["X_train.shape"],"execution_count":60,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2709, 60, 1)"]},"metadata":{"tags":[]},"execution_count":60}]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"2949de3c668a788117221000567aaa7033cd1a17","id":"-4ym-6eLONQH","colab_type":"code","colab":{}},"source":["# Preparing sequence data\n","initial_sequence = X_train[2708,:]\n","sequence = []\n","for i in range(251):\n","    new_prediction = regressorGRU.predict(initial_sequence.reshape(initial_sequence.shape[1],initial_sequence.shape[0],1))\n","    initial_sequence = initial_sequence[1:]\n","    initial_sequence = np.append(initial_sequence, new_prediction,axis=0)\n","    sequence.append(new_prediction)\n","sequence = sc.inverse_transform(np.array(sequence).reshape(251,1))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"be390cade0cf4b31cb99e069570e1be2c5594c8b","id":"f-wQ_7DQONQL","colab_type":"code","outputId":"d5fb904a-f77b-4aaa-8b95-91e056af3a46","executionInfo":{"status":"ok","timestamp":1585822523242,"user_tz":-540,"elapsed":1121,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}},"colab":{"base_uri":"https://localhost:8080/","height":325}},"source":["# Visualizing the sequence\n","plot_predictions(test_set,sequence)"],"execution_count":59,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAboAAAE0CAYAAABaTfYtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNpWWfRYWFixdupQePXqwfft2evXqZb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KDsZ0wgYIffojcxCKzKculRvbkSZbaLBAI3l+E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\n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oqKirH05hBBCaiCb9ro8f/48IiMjERkZiaKiIsTHxyMyMhJLly41+Rjx8fF49tlnMXny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\n2DUKdIQQQuwaBTpCCCF27f8B3Zc4I6L30N8AAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"trusted":true,"_uuid":"59095a00a05a68b97d76fc13bb2d618c337e1c80","id":"eh86EP3kONQN","colab_type":"code","outputId":"eb15bf49-26de-4d92-93f8-03764a6d3ce5","executionInfo":{"status":"ok","timestamp":1585821996938,"user_tz":-540,"elapsed":1077,"user":{"displayName":"문성채","photoUrl":"","userId":"07687686498757557466"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["# Evaluating the sequence\n","return_rmse(test_set,sequence)"],"execution_count":48,"outputs":[{"output_type":"stream","text":["The root mean squared error is 19.06830825117668.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"_uuid":"c1003e9a17b23c1e3c7b7298250f8b071a93ae91","id":"BL_AExvvONQQ","colab_type":"text"},"source":["따라서이 경우 GRU가 LSTM보다 잘 작동합니다. 양방향 LSTM도 좋은 방법이므로 모델을 더 강력하게 만드십시오. 그러나 이것은 데이터 세트마다 다를 수 있습니다. **LSTM과 GRU를 함께 적용하면 더 나은 결과를 얻을 수있었습니다.**"]},{"cell_type":"markdown","metadata":{"_uuid":"50534b0ff4065793ebce8997bd6da6ddc0af070f","id":"3eqbuVuwONQR","colab_type":"text"},"source":["#### LSTM을 사용하여 텍스트 생성을 다루려고했지만 이미 언급 한 주제에 대해 [Shivam Bansal](https://www.kaggle.com/shivamb)의 훌륭한 커널이 있습니다. 해당 커널에 대한 링크: https://www.kaggle.com/shivamb/beginners-guide-to-text-generation-using-lstms"]},{"cell_type":"markdown","metadata":{"_uuid":"9164ee2b253583928226c76f6a5f5148717227d7","id":"jWuQX25vONQS","colab_type":"text"},"source":["#### This is certainly not the end. Stay tuned for more stuff!"]}]}