Practice4-1_DeepLearning_IntroKeras_KK.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **딥러닝 분석용 라이브러리**\n",
    "\n",
    "[![Open in Colab](http://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/thekimk/All-About-Deep-Learning/blob/main/Practice4-1_DeepLearning_IntroKeras_KK.ipynb)\n",
    "\n",
    "**1) 머신러닝 및 딥러닝 플랫폼:** `Scikit-learn, Theano, Tenserflow, PyTorch, MXNet, NLTK, Keras 등`\n",
    "\n",
    "<center><img src='Image/Expert/DL_Platforms.webp' width='1000'>(https://towardsdatascience.com/best-python-libraries-for-machine-learning-and-deep-learning-b0bd40c7e8c)</center>  \n",
    "\n",
    "<center><img src='Image/Expert/DL_Platforms_Comparing.webp' width='1000'>(https://towardsdatascience.com/deep-learning-framework-power-scores-2018-23607ddf297a)</center>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [`tensorflow`(텐서플로우)](https://www.tensorflow.org/?hl=ko)\n",
    "\n",
    "> - **`구글의 인공지능 개발부서`에서 개발하여 내부적으로 사용하다 `2015년 오픈소스로 공개`**\n",
    "> - **`파이썬의 문법과 다른 방식`으로 동작하기에 텐서플로우 이해가 쉽진 않음**\n",
    "> - **프로그래밍 언어들 중 파이썬은 `느린 언어`라 파이썬으로 딥러닝 구현 어려움**\n",
    "> - **`우수한 기능과 서비스`로 가장 많은 사용자 확보**\n",
    "> - **`병렬처리`를 지원하며 `고급 신경망 구현` 용이**\n",
    "> - **`웹, 모바일, 임베디드 시스템` 등에서 머신러닝 적용 라이브러리 제공**\n",
    ">\n",
    "> (1) `데이터 플로우` 그래프를 활용하여 풍부하게 표현 가능\n",
    ">\n",
    "> (2) 단순 `아이디어 테스트` 부터 실제 `현업 서비스` 단계까지 두루 사용\n",
    ">\n",
    "> (3) 목표와 계산/연산 방식만 설정하면 `자동으로 미분과 최적화`를 포함한 처리 완료\n",
    ">\n",
    "> (4) `파이썬` 뿐만 아니라 `C++, Go, Java, R` 등 다양한 언어 지원\n",
    ">\n",
    "> <center><img src='Image/Expert/Tensorflow_Structure.jpg' width='600'>(Tensorflow Structure)</center>\n",
    "<!-- https://www.itworld.co.kr/news/103541 -->\n",
    ">```python\n",
    "># 설치\n",
    "!pip install tensorflow\n",
    "!conda install tensorflow\n",
    "!pip install --upgrade --user tensorflow\n",
    "!pip install -U tensorflow-addons\n",
    ">\n",
    "># 불러오기\n",
    "import tensorflow as tf\n",
    ">```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [`keras`(케라스)](https://keras.io/)\n",
    "\n",
    "> **\"파이썬으로 작성된 `오픈소스(Open Source) 인공신경망 라이브러리`\"**\n",
    ">\n",
    "> - **`Tensorflow, MXNet, Deeplearning4j 등`을 뒤에서 작동시켜 `대부분의 신경망 프로그래밍 또는 모델링`을 `최소한의 방식으로 빠르고 간편하게` 만들고 학습시킬 수 있는 프레임워크**\n",
    "> - **일반적인 사례들을 간단하게 분석할 수 있는 `인터페이스 제공`하여 사용자의 `오류를 명확하게 피드백`하는 편**\n",
    "> - **백엔드로 `텐서플로우`를 사용하지만 저수준 연산기반이 아닌 `고수준 구성요소 연산` 제공**\n",
    "> - **`모듈 구조`로 구성되어 있지만 여러가지 새로운 아이디어를 반영하기 위해 쉽게 `Customize 가능`하여 `현존하지 않는 신규 알고리즘을 개발하거나 기존 기능이나 엔진들과 연동이 쉬움`**\n",
    ">\n",
    "> (1) `다양한 딥러닝 네트워크` 구조를 쉽게 생성 가능\n",
    ">\n",
    "> (2) 동일한 코드로 `CPU`와 `GPU` 모두에서 동일하게 실행 가능\n",
    ">\n",
    "> (3) 사용하기 쉬운 API로 딥러닝 모델의 프로토타입을 쉽게 구현 가능하고 자유롭게 조합도 가능\n",
    ">\n",
    "> <center><img src='Image/Expert/Keras_Structure.png' width='600'></center>\n",
    "<!-- https://cyc1am3n.github.io/2018/11/02/introduction-to-keras.html -->\n",
    ">```python\n",
    "># 설치\n",
    "!pip install tensorflow\n",
    "!conda install tensorflow\n",
    "!pip install --upgrade --user tensorflow\n",
    "!pip install -U tensorflow-addons\n",
    "!pip install keras\n",
    "!conda install keras\n",
    "!pip install keras-tqdm\n",
    ">\n",
    "># 불러오기\n",
    "import tensorflow as tf\n",
    "import keras\n",
    ">```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [`pytorch`(파이토치)](https://pytorch.org/tutorials/)\n",
    "\n",
    "> - **파이썬 언어로 사용하며, 진화된 `토치 C/CUDA 백엔드`를 사용**\n",
    "> - **`강력한 GPU 가속`이 적용되는 파이썬으로 된 `텐서와 동적 신경망`**\n",
    "> - **`동적 신경망(Dynamic Neural Network)`은 반복할 때마다 변경이 가능한 신경망**\n",
    "> - **각 반복 단계에서 `즉석으로 그래프를 재생성`하며, 텐서플로우는 `단일 데이터 흐름 그래프`**\n",
    "> - **속도를 극대화하기 위해 `인텔 MKL`, `엔비디아 cuDNN`, `NCCL`과 같은 가속 라이브러리를 통합**\n",
    "\n",
    ">```python\n",
    "># 설치\n",
    ">```\n",
    "> <center><img src='Image/Expert/PyTorch_Install.png' width='700'></center>\n",
    ">\n",
    ">```python\n",
    "># 불러오기\n",
    "import torch\n",
    "torch.__version__\n",
    "torch.cuda.is_available()\n",
    ">```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **다층퍼셉트론 데이터분석 프로세스**\n",
    "\n",
    "## Keras 불러오기\n",
    "\n",
    "**1) 목표:**\n",
    "\n",
    "> **(1)** `텐서플로우`를 사용하여 `머신러닝과 딥러닝 알고리즘을 구현`할 수 있음\n",
    ">\n",
    "> **(2)** 지도학습의 대표적 문제인 `회귀문제 및 분류문제`를 텐서플로우로 구현\n",
    ">\n",
    "> **(3)** `다층퍼셉트론`으로 `데이터를 학습시키는 방법`을 익힘"
   ]
  },
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    }
   ],
   "source": [
    "# Tensorflow 및 Keras 설치하기\n",
    "!pip install tensorflow==2.9.*\n",
    "# !pip install --upgrade --user tensorflow\n",
    "!pip install -U tensorflow-addons\n",
    "!pip install keras\n",
    "# !pip install --upgrade --user keras\n",
    "!pip install keras-tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_addons as tfa\n",
    "from tensorflow.keras.models import Sequential, Model, load_model\n",
    "from tensorflow.keras.layers import Input, Dense, Activation, Flatten, Dropout, Reshape\n",
    "from tensorflow.keras.utils import plot_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 예제 데이터 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 입력과 출력이 같은 데이터를 구성하여 최적의 가중치는 1\n",
    "import numpy as np\n",
    "\n",
    "X_train = np.array([1,2,3,4,5,6,7,8,9,10])\n",
    "X_test = np.array([3,7,9])\n",
    "Y_train = np.array([1,2,3,4,5,6,7,8,9,10])\n",
    "Y_test = np.array([3,7,9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 딥러닝 모델생성\n",
    "\n",
    "**0) 목적:** 입력과 출력 데이터를 사용하여 `최적의 가중치(Weight)를 추정`하는 것\n",
    "\n",
    "- 알고리즘이 이해할 수 있는 `전처리가 끝난 X, Y를 입력`\n",
    "- `Layer들을 구성하여 모델을 생성`\n",
    "- `적절한 평가함수 세팅`을 통해 Keras 라이브러리는 최적의 가중치 추정\n",
    "\n",
    "---\n",
    "\n",
    "**1) 모델링 방식 종류:**\n",
    "\n",
    "> **\"Keras는 `Sequential API, Functional API, Subclassing API`라는 3가지 방식으로 모델링 가능\"**\n",
    "\n",
    "<center><img src='Image/Expert/Keras_Sequencial_Functional.png' width='1000'></center>  \n",
    "\n",
    "| **생성 방식** | **장점** | **단점** |\n",
    "|:---:|:---|:---|\n",
    "| **Sequential API** | `단순하게 딥러닝의 층을 쌓는 방식`으로 쉽고 간단 | 다수의 입출력이나 층의 다양한 연결과 연산이 불가하기에 `복합한 모델링 한계` |\n",
    "| **Functional API** | 딥러닝의 층을 `함수형태로 정의`<br>Sequential API가 불가한 `대부분의 복잡한 딥러닝 모델링` 가능 | `직접적으로 연결되지 않는 딥러닝 층의 구현 불가` |\n",
    "| **Subclassing API** | `클래스의 구조`로 딥러닝 활용<br>Functional API가 불가한 `새로운 수준의 딥러닝 아키텍처` 모델링 가능 | `객체 지향 프로그래밍에 익숙`해야 해서 코드 사용이 까다로움 |\n",
    "\n",
    "---\n",
    "\n",
    "**2) Sequential vs Functional:**\n",
    "\n",
    "```python\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "# Sequential 방식\n",
    "model = Sequential()    # 모델 구성 선언\n",
    "model.add(Dense(5, input_dim=1, activation='relu'))    # 은닉층1: X를 입력하는 layer 추가\n",
    "model.add(Dense(3, activation='relu'))    # 은닉층2: layer 추가\n",
    "model.add(Dense(1, activation='linear'))    # 출력층: Y를 출력하는 layer 추가\n",
    "model.summary()    # 네트워크 구조 출력\n",
    "plot_model(model)    # 네트워크 구조 시각화\n",
    "```\n",
    "\n",
    "```python\n",
    "# Functional 방식\n",
    "inputs = Input(shape=(1,))    # Input 함수를 사용하여 입력층 생성\n",
    "hidden1 = Dense(5, activation='relu')(inputs)    # 은닉층1: 입력층을 입력받는 layer 추가\n",
    "hidden2 = Dense(3, activation='relu')(hidden1)    # 은닉층2: 은닉층1을 입력받는 layer 추가\n",
    "output = Dense(1, activation='linear')(hidden2)    # 출력층: 은닉층2를 입력받아 Y를 출력하는 layer 추가\n",
    "model = Model(inputs, output)    # 입력층과 출력층을 구성하며 최종 모델 완성\n",
    "model.summary()    # 네트워크 구조 출력\n",
    "plot_model(model)    # 네트워크 구조 시각화\n",
    "```\n",
    "\n",
    "| **문제 유형** | **출력층 Activation Function** |\n",
    "|:---|:---:|\n",
    "| 임의 값 Regression | 없음 / Linear |\n",
    "| 0과 1사이 값 Regression | Sigmoid |\n",
    "| Binary Classification | Sigmoid |\n",
    "| Multi Classification (단일 Label) | Softmax |\n",
    "| Multi Classification (다중 Label) | Sigmoid |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 5)                 10        \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 3)                 18        \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 4         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 32\n",
      "Trainable params: 32\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "# Sequential 방식\n",
    "model = Sequential()    # 모델 구성 선언\n",
    "model.add(Dense(5, input_dim=1, activation='relu'))    # 은닉층1: X를 입력하는 layer 추가\n",
    "model.add(Dense(3, activation='relu'))    # 은닉층2: layer 추가\n",
    "model.add(Dense(1, activation='linear'))    # 출력층: Y를 출력하는 layer 추가\n",
    "model.summary()    # 네트워크 구조 출력\n",
    "plot_model(model)    # 네트워크 구조 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 1)]               0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 5)                 10        \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 3)                 18        \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 4         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 32\n",
      "Trainable params: 32\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Functional 방식\n",
    "inputs = Input(shape=(1,))    # Input 함수를 사용하여 입력층 생성\n",
    "hidden1 = Dense(5, activation='relu')(inputs)    # 은닉층1: 입력층을 입력받는 layer 추가\n",
    "hidden2 = Dense(3, activation='relu')(hidden1)    # 은닉층2: 은닉층1을 입력받는 layer 추가\n",
    "output = Dense(1, activation='linear')(hidden2)    # 출력층: 은닉층2를 입력받아 Y를 출력하는 layer 추가\n",
    "model = Model(inputs, output)    # 입력층과 출력층을 구성하며 최종 모델 완성\n",
    "model.summary()    # 네트워크 구조 출력\n",
    "plot_model(model)    # 네트워크 구조 시각화"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3) Functional API:**\n",
    "\n",
    "```python\n",
    "# Functional 방식\n",
    "inputs = Input(shape=(1,))    # Input 함수를 사용하여 입력층 생성\n",
    "hidden1 = Dense(5, activation='relu')(inputs)    # 은닉층1: 입력층을 입력받는 layer 추가\n",
    "hidden2 = Dense(3, activation='relu')(hidden1)    # 은닉층2: 은닉층1을 입력받는 layer 추가\n",
    "output = Dense(1, activation='linear')(hidden2)    # 출력층: 은닉층2를 입력받아 Y를 출력하는 layer 추가\n",
    "model = Model(inputs, output)    # 입력층과 출력층을 구성하며 최종 모델 완성\n",
    "model.summary()    # 네트워크 구조 출력\n",
    "plot_model(model)    # 네트워크 구조 시각화\n",
    "```\n",
    "- **Input:** `신경망의 입력층`으로 입력되는 `샘플 크기를 제외한 나머지 크기 입력 필요`\n",
    "- **Dense Layer:** `신경망의 기본층`으로 시간흐름이 반영되는 경우 외 `대부분 사용 가능`\n",
    "\n",
    "> -  입력과 출력을 연결하는 `함수의 형태`로 구성되며 `추정해야 할 가중치를 내포`\n",
    "> -  **첫번째인자(5):**  `출력(뉴런) 갯수`로 1개의 변수가 `5개의 변수로 전처리(Feature Engineering)`\n",
    ">\n",
    "> - **두번째인자(activation='relu'):** 출력값을 목적에 맞게 변환하는 `활성화 함수`(linear / sigmoid / relu 등)\n",
    "\n",
    "```python\n",
    "_________________________________________________________________\n",
    " Layer (type)                Output Shape              Param #   \n",
    "=================================================================\n",
    " input_8 (InputLayer)        [(None, 1)]               0         \n",
    "                                                                 \n",
    " dense_45 (Dense)            (None, 5)                 10        \n",
    "                                                                 \n",
    " dense_46 (Dense)            (None, 3)                 18        \n",
    "                                                                 \n",
    " dense_47 (Dense)            (None, 1)                 4         \n",
    "                                                                 \n",
    "=================================================================\n",
    "Total params: 32\n",
    "Trainable params: 32\n",
    "Non-trainable params: 0\n",
    "_________________________________________________________________\n",
    "```\n",
    "\n",
    "- **파라미터 갯수:**\n",
    "\n",
    "> - **은닉층1:** 데이터가 `5개의 뉴런으로 입력`되고 `각 뉴런당 1개의 가중치와 1개의 편향 파라미터 추정`이 필요 $\\rightarrow$ `5 * 2 = 10`\n",
    "> - **은닉층2:** 5개의 뉴런이 `3개의 뉴런으로 입력`되고 `각 뉴런당 5개의 가중치와 1개의 편향 파라미터 추정`이 필요 $\\rightarrow$ `5 * 3 + 3 = 18`\n",
    "> - **출력층:** 3개의 뉴런이 `1개의 뉴런으로 입력`되고 `3개의 가중치와 1개의 편향 파라미터 추정`이 필요 $\\rightarrow$ `3 * 1 + 1 = 4`\n",
    "\n",
    "<center><img src='Image/Expert/MLP_Example_Parameters.PNG' width='600'></center>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 학습하기\n",
    "\n",
    "| **문제 유형** | **출력층 Activation Function** | **비용함수** |\n",
    "|:---|:---:|:---:|\n",
    "| **임의 값 Regression** | 없음 / Linear | mse |\n",
    "| **0과 1사이 값 Regression** | Sigmoid | mse / binary_crossentropy |\n",
    "| **Binary Classification** | Sigmoid | binary_crossentropy |\n",
    "| **Multi Classification (단일 Label)** | Softmax | categorical_crossentropy |\n",
    "| **Multi Classification (다중 Label)** | Sigmoid | binary_crossentropy |\n",
    "\n",
    "---\n",
    "\n",
    "**1) 비용함수, 최적화 알고리즘, 평가함수 선택:**\n",
    "\n",
    "```python\n",
    "model.compile(loss='mse', optimizer='adam', metrics='mae')\n",
    "```\n",
    "\n",
    "> - **loss:** 비용함수로 `출력값이 목표값과 얼마나 유사`한지 평가\n",
    "> - **optimizer:** `비용함수를 최소가 되는 방향으로 신경망 가중치를 업데이트`하는 알고리즘\n",
    "> - **metrics:** `문제 해결의 정도`를 평가하는 함수\n",
    "\n",
    "**2) 데이터 학습:**\n",
    "\n",
    "```python\n",
    "model.fit(X, Y, epochs=100, batch_size=3, verbose=1)\n",
    "```\n",
    "\n",
    "> - **epochs:** `전체데이터의 학습 횟수`로 100번을 학습한다는 예시\n",
    "> - **batch_size:** 1 epoch에서 `학습과 평가를 진행할 최소 row 샘플의 갯수`로 3개씩 학습 및 평가함\n",
    "> - **verbose:** `epoch마다 데이터를 학습과정 출력 여부`로 1은 출력 의미"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "4/4 [==============================] - 2s 10ms/step - loss: 11.9035 - mae: 3.0561\n",
      "Epoch 2/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 11.3402 - mae: 2.9780\n",
      "Epoch 3/100\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 10.7374 - mae: 2.8991\n",
      "Epoch 4/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 10.2557 - mae: 2.8276\n",
      "Epoch 5/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 9.6691 - mae: 2.7453\n",
      "Epoch 6/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 9.1603 - mae: 2.6689\n",
      "Epoch 7/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 8.6618 - mae: 2.5900\n",
      "Epoch 8/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 8.0963 - mae: 2.5029\n",
      "Epoch 9/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 7.6489 - mae: 2.4265\n",
      "Epoch 10/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 7.1100 - mae: 2.3411\n",
      "Epoch 11/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 6.6752 - mae: 2.2619\n",
      "Epoch 12/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 6.2365 - mae: 2.1809\n",
      "Epoch 13/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 5.8304 - mae: 2.1033\n",
      "Epoch 14/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 5.4657 - mae: 2.0272\n",
      "Epoch 15/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 5.0508 - mae: 1.9481\n",
      "Epoch 16/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 4.6821 - mae: 1.8730\n",
      "Epoch 17/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 4.3645 - mae: 1.8032\n",
      "Epoch 18/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 4.0208 - mae: 1.7281\n",
      "Epoch 19/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 3.6710 - mae: 1.6504\n",
      "Epoch 20/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 3.4046 - mae: 1.5789\n",
      "Epoch 21/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 3.0743 - mae: 1.4975\n",
      "Epoch 22/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 2.8386 - mae: 1.4280\n",
      "Epoch 23/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 2.5535 - mae: 1.3512\n",
      "Epoch 24/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 2.3184 - mae: 1.2801\n",
      "Epoch 25/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 2.0867 - mae: 1.2095\n",
      "Epoch 26/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 1.8666 - mae: 1.1466\n",
      "Epoch 27/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.6966 - mae: 1.0888\n",
      "Epoch 28/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 1.5388 - mae: 1.0337\n",
      "Epoch 29/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 1.3688 - mae: 0.9712\n",
      "Epoch 30/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.2194 - mae: 0.9160\n",
      "Epoch 31/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 1.1010 - mae: 0.8648\n",
      "Epoch 32/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.9673 - mae: 0.8107\n",
      "Epoch 33/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.8767 - mae: 0.7674\n",
      "Epoch 34/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.7790 - mae: 0.7154\n",
      "Epoch 35/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.6869 - mae: 0.6705\n",
      "Epoch 36/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.6106 - mae: 0.6342\n",
      "Epoch 37/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.5471 - mae: 0.5971\n",
      "Epoch 38/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4772 - mae: 0.5605\n",
      "Epoch 39/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.4125 - mae: 0.5208\n",
      "Epoch 40/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.3661 - mae: 0.4921\n",
      "Epoch 41/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.3204 - mae: 0.4567\n",
      "Epoch 42/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2788 - mae: 0.4238\n",
      "Epoch 43/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2428 - mae: 0.3977\n",
      "Epoch 44/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.2131 - mae: 0.3757\n",
      "Epoch 45/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1808 - mae: 0.3475\n",
      "Epoch 46/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1596 - mae: 0.3262\n",
      "Epoch 47/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1398 - mae: 0.3063\n",
      "Epoch 48/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1259 - mae: 0.2904\n",
      "Epoch 49/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.1072 - mae: 0.2732\n",
      "Epoch 50/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0962 - mae: 0.2592\n",
      "Epoch 51/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0875 - mae: 0.2488\n",
      "Epoch 52/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0787 - mae: 0.2377\n",
      "Epoch 53/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0724 - mae: 0.2275\n",
      "Epoch 54/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0675 - mae: 0.2190\n",
      "Epoch 55/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0632 - mae: 0.2144\n",
      "Epoch 56/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0597 - mae: 0.2090\n",
      "Epoch 57/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0564 - mae: 0.2040\n",
      "Epoch 58/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0539 - mae: 0.1998\n",
      "Epoch 59/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0514 - mae: 0.1953\n",
      "Epoch 60/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0495 - mae: 0.1917\n",
      "Epoch 61/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0475 - mae: 0.1884\n",
      "Epoch 62/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0464 - mae: 0.1856\n",
      "Epoch 63/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0443 - mae: 0.1817\n",
      "Epoch 64/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0429 - mae: 0.1780\n",
      "Epoch 65/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0419 - mae: 0.1759\n",
      "Epoch 66/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0412 - mae: 0.1748\n",
      "Epoch 67/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0395 - mae: 0.1714\n",
      "Epoch 68/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0389 - mae: 0.1700\n",
      "Epoch 69/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0383 - mae: 0.1686\n",
      "Epoch 70/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0376 - mae: 0.1664\n",
      "Epoch 71/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0371 - mae: 0.1653\n",
      "Epoch 72/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0367 - mae: 0.1638\n",
      "Epoch 73/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0365 - mae: 0.1630\n",
      "Epoch 74/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0361 - mae: 0.1617\n",
      "Epoch 75/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0357 - mae: 0.1606\n",
      "Epoch 76/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0355 - mae: 0.1600\n",
      "Epoch 77/100\n",
      "4/4 [==============================] - 0s 8ms/step - loss: 0.0353 - mae: 0.1594\n",
      "Epoch 78/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0350 - mae: 0.1589\n",
      "Epoch 79/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0348 - mae: 0.1584\n",
      "Epoch 80/100\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0346 - mae: 0.1579\n",
      "Epoch 81/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0344 - mae: 0.1575\n",
      "Epoch 82/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0342 - mae: 0.1570\n",
      "Epoch 83/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0340 - mae: 0.1565\n",
      "Epoch 84/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0337 - mae: 0.1560\n",
      "Epoch 85/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0335 - mae: 0.1557\n",
      "Epoch 86/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0333 - mae: 0.1551\n",
      "Epoch 87/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0331 - mae: 0.1546\n",
      "Epoch 88/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0329 - mae: 0.1540\n",
      "Epoch 89/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0327 - mae: 0.1537\n",
      "Epoch 90/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0325 - mae: 0.1534\n",
      "Epoch 91/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0323 - mae: 0.1529\n",
      "Epoch 92/100\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0322 - mae: 0.1525\n",
      "Epoch 93/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0320 - mae: 0.1521\n",
      "Epoch 94/100\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0318 - mae: 0.1516\n",
      "Epoch 95/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0317 - mae: 0.1513\n",
      "Epoch 96/100\n",
      "4/4 [==============================] - 0s 7ms/step - loss: 0.0314 - mae: 0.1504\n",
      "Epoch 97/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0313 - mae: 0.1501\n",
      "Epoch 98/100\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0311 - mae: 0.1498\n",
      "Epoch 99/100\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.0309 - mae: 0.1494\n",
      "Epoch 100/100\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0308 - mae: 0.1491\n"
     ]
    }
   ],
   "source": [
    "# 데이터 학습하기\n",
    "model.compile(loss='mse', optimizer='adam', metrics=['mae'])\n",
    "model_fit = model.fit(X_train, Y_train, epochs=100, batch_size=3, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p0qVauXJlnYuIJF177bXat29fjdudTqfCw8OrLf7IZjP0yI09JEkLP81SwdmyyxwBAID/qVMZMU1TM2bM0JIlS/TJJ58oOTm5Xm+6ZcsWxcfH1+tYfzOuT6yu7BCh4nK3Xlz1ldVxAABodnUqI9OnT9eiRYv0xhtvKCwsTHl5ecrLy1NJyddzZcyZM0dTpkzx/jx37ly999572rdvn3bs2KE5c+YoLS1NM2bMaLzfwocZhqFfjK0aHXltfY6OuUotTgQAQPOqUxmZP3++CgsLNXLkSMXHx3uXxYsXe/fJzc3VwYMHvT+Xl5dr9uzZ6tevn4YPH661a9fqgw8+0K233tp4v4WPG9GjvQZ1aquySo+eX7nf6jgAADSrel/A2pxqewGML1v/VYHuevkzBdgNrZw9UoltQ6yOBABAgzTpBaxofEO6Ruu6btGqcJuat6Lmi3sBAPA3lJEWZNaNVbf2Ltl8hGfWAABaDcpIC5LSqa2u7RKlSo+phWt5Zg0AoHWgjLQwPxvRVZL05oaDzMoKAGgVKCMtzMge7dUrLkxF5W4t+jzH6jgAADQ5ykgLYxiGfjaiiyTplU+zVVrhtjgRAABNizLSAt3SL0EJEUHKP1umd7ccsToOAABNijLSAgXYbfrx8KrRkZdXH5Db0+KnggEAoN4oIy3UnYOTFBEcoAP5RUrfmWd1HAAAmgxlpIVq43Tonms7SZLmrzogH5goFwCAeqGMtGBTh3ZWoMOmrYdO6/Osk1bHAQCgSVBGWrD2YU7dnpIoSfrrJ0wRDwDwT5SRFu7nI7sqwG7o0/0F+iKb0REAgP+hjLRwiW1DdNu50ZHnljM6AgDwP5QRH/DAyG5y2Ayt3Z+vjYyOAAD8DGXEByRFfWN0ZAWjIwAA/0IZ8RHTR1WNjqzZl69NOYyOAAD8B2XERyRFhWjyVVWjI3O5dgQA4EcoIz5k+qhusp8bHdl88JTVcQAAaBSUER/SMTpEtw7sIInREQCA/6CM+JgZN1SNjqzee0KZh05bHQcAgAajjPiYTtFt9N0BCZKkv32y3+I0AAA0HGXEB00f1U2GIS3fdUw7j7qsjgMAQINQRnxQ1/ahmnBlvCTp+ZWMjgAAfBtlxEfNuKGbJGnZl7naf/yMxWkAAKg/yoiP6hUXrnF9YmWa0vMrv7I6DgAA9UYZ8WEzRnWXJP0r84iy84ssTgMAQP1QRnzYlYkRGtWzvTymND+D0REAgG+ijPi4GTdUjY6kbT6sw6eKLU4DAEDdUUZ8XEqntrquW7QqPaZeYHQEAOCDKCN+4KHRPSRJ72w8pEMnGR0BAPgWyogfuDo5SsO6tVOF22TeEQCAz6GM+IlHbqy6duSdTYeVU8CdNQAA30EZ8RMpnaI0okd7uT2m/sozawAAPoQy4kceubHq2pElmw8ri3lHAAA+gjLiRwYkRWp0rxh5TGnein1WxwEAoFYoI37m/OjIe5lHeGYNAMAnUEb8TN8OERp7RdUza+YuZ3QEANDyUUb80MNjqkZH3t+Wq125LovTAABwaZQRP3RFQrgmXBkvSfpL+l6L0wAAcGmUET/1yI3dZTOkj3ce09ZDp62OAwBAjSgjfqpbTJgmDewgSfoToyMAgBaMMuLHHh7dQw6bodV7T2hD1kmr4wAAcFGUET/WMTpE3x+cJEl69qM9Mk3T4kQAAFyIMuLnZt7QTYEOmzZkn9Ta/flWxwEA4AKUET8XHxGsu6/pJInREQBAy1SnMpKamqrBgwcrLCxMMTExmjRpkvbs2XPZ41atWqWUlBQFBQWpS5cuevHFF+sdGHX3wKiuCg6wa+vhQqXvPGZ1HAAAqqlTGVm1apWmT5+uzz77TOnp6aqsrNTYsWNVVFTzQ9mysrI0fvx4DR8+XFu2bNHjjz+uBx98UGlpaQ0Oj9ppF+rUj67rLKlqVlZGRwAALYlhNuAv04kTJxQTE6NVq1bp+uuvv+g+jz76qJYuXapdu3Z5102bNk1bt27V+vXra/U+LpdLERERKiwsVHh4eH3jtmqniso1/I8rdbasUv97T4rG9YmzOhIAwM/V9u93g64ZKSwslCRFRUXVuM/69es1duzYauvGjRunjRs3qqKioiFvjzpo2yZQU4dWXTvyHKMjAIAWpN5lxDRNzZo1S8OGDVPfvn1r3C8vL0+xsbHV1sXGxqqyslL5+Re/u6OsrEwul6vagoa7f1gXtQm0a2eui2tHAAAtRr3LyIwZM7Rt2za9+eabl93XMIxqP5//V/m315+XmpqqiIgI75KUlFTfmPiGqtGRzpKk51YwOgIAaBnqVUZmzpyppUuXauXKlUpMTLzkvnFxccrLy6u27vjx43I4HIqOjr7oMXPmzFFhYaF3OXToUH1i4iLuH141OrLjqEvLdx23Og4AAHUrI6ZpasaMGVqyZIk++eQTJScnX/aYIUOGKD09vdq6jz/+WIMGDVJAQMBFj3E6nQoPD6+2oHFEtQnUlHOjI3OX72V0BABguTqVkenTp2vRokV64403FBYWpry8POXl5amkpMS7z5w5czRlyhTvz9OmTVNOTo5mzZqlXbt2aeHChVqwYIFmz57deL8F6uQnw7so5NzoyApGRwAAFqtTGZk/f74KCws1cuRIxcfHe5fFixd798nNzdXBgwe9PycnJ2vZsmXKyMjQgAED9NRTT2nevHmaPHly4/0WqJOoNoGaMqSzJGnuCkZHAADWatA8I82FeUYaX8HZMg3/40oVl7uZdwQA0CSaZZ4R+K7ob8zK+qeP98jtafGdFADgpygjrdhPh3dVeJBDe4+d1dKtR6yOAwBopSgjrVhESICmjewqSfpL+j6VV3osTgQAaI0oI63cvUM7q12oUwdPFmvxRuZzAQA0P8pIKxcS6NDMG7pJkv66Yp9Kyt0WJwIAtDaUEejOq5PUITJYx8+U6R/rs62OAwBoZSgjkNNh1yM39pAkzc/4Sq5SnqYMAGg+lBFIkr43sIO6xYSqsKRCL68+YHUcAEArQhmBJMluMzR7bNXoyMtrDiivsNTiRACA1oIyAq9xfeI0qFNblVZ49MePdlsdBwDQSlBG4GUYhn59yxWSpCWbj2jb4dPWBgIAtAqUEVQzIClSkwYkSJJ+//4uHqIHAGhylBFc4Fc39ZLTYdOG7JP6aEee1XEAAH6OMoILJEQG66fXd5Ekpf5nt8oqmQgNANB0KCO4qGkjuqp9mFM5BcV6bV2O1XEAAH6MMoKLauN06Jdje0qS5n2yTyeLyi1OBADwV5QR1GhySqJ6x4frTGmlXli53+o4AAA/RRlBjew2Q4/eVDU68tpnOcotLLE4EQDAH1FGcEkjerTX1Z2jVF7p0bwVjI4AABofZQSXZBiGfnludOTtjYeUnV9kcSIAgL+hjOCyBneO0qie7eX2mPpz+l6r4wAA/AxlBLXyi3N31izdelQ7j7osTgMA8CeUEdRK3w4RuqVfvCTpz+l7LE4DAPAnlBHU2qwbe8huM7R813FtyjlldRwAgJ+gjKDWurQP1W1XJUqSnv2I0REAQOOgjKBOHhrTXYF2m9YfKNC6r/KtjgMA8AOUEdRJQmSw7rw6SZI0N32fTNO0OBEAwNdRRlBnD4zspkCHTRuyT+rT/QVWxwEA+DjKCOosLiJIP7ymo6SqO2sYHQEANARlBPXy85FdFRRg0+aDp7Vq7wmr4wAAfBhlBPUSExake67tJEn6S/peRkcAAPVGGUG9/WxEVwUH2LX1cKFW7jludRwAgI+ijKDe2oU6NXVoZ0nSnxkdAQDUE2UEDfLT67uoTaBdXx5x6aMdx6yOAwDwQZQRNEhUm0DdNyxZkvTHD3erwu2xOBEAwNdQRtBgP72+i6LbBOpAfpHe2nDQ6jgAAB9DGUGDhQUF6OEx3SVJc5fv05nSCosTAQB8CWUEjeLOqzuqS7s2Kigq14urvrI6DgDAh1BG0CgC7DY9enMvSdLf12Qpt7DE4kQAAF9BGUGjGXtFrAZ3bquySo/+9PFeq+MAAHwEZQSNxjAMPT6+tyQpbfNh7TzqsjgRAMAXUEbQqAZ2bKsJ/eJlmlLqf3ZZHQcA4AMoI2h0j47rJYfN0Jp9+dqUc9LqOACAFo4ygkbXMTpEk69KlCT97ZP9FqcBALR0lBE0iZ+P7CqbIa3cc0JfHim0Og4AoAWjjKBJdG7XRt/pnyCJ0REAwKVRRtBkpo/qJkn6cEee9h47Y3EaAEBLVecysnr1ak2cOFEJCQkyDEPvvffeJffPyMiQYRgXLLt3765vZviI7rFhurlvnCTp+ZWMjgAALq7OZaSoqEj9+/fX3/72tzodt2fPHuXm5nqX7t271/Wt4YPOj478e+tRZeUXWZwGANASOep6wM0336ybb765zm8UExOjyMjIOh8H39a3Q4Ru6BWjT3Yf1/yM/frjbf2tjgQAaGGa7ZqRgQMHKj4+XqNHj9bKlSsvuW9ZWZlcLle1Bb7r/OjIks1HdPhUscVpAAAtTZOXkfj4eL300ktKS0vTkiVL1LNnT40ePVqrV6+u8ZjU1FRFRER4l6SkpKaOiSaU0qmtrusWrUqPqeeW77M6DgCghTFM0zTrfbBh6N1339WkSZPqdNzEiRNlGIaWLl160e1lZWUqKyvz/uxyuZSUlKTCwkKFh4fXNy4stCnnlCbPXyfDkP49Y5j6doiwOhIAoIm5XC5FRERc9u+3Jbf2Xnvttdq3r+Z/ITudToWHh1db4NtSOrXVxP4JMk3pqfd3qgEdGADgZywpI1u2bFF8fLwVbw0LPXpTTzkdNn2edVIf7ThmdRwAQAtR57tpzp49q/37v54zIisrS5mZmYqKilLHjh01Z84cHTlyRK+99pokae7cuercubP69Omj8vJyLVq0SGlpaUpLS2u83wI+IbFtiO4fnqznV36l1P/s0qhe7eV02K2OBQCwWJ3LyMaNGzVq1Cjvz7NmzZIkTZ06Va+++qpyc3N18OBB7/by8nLNnj1bR44cUXBwsPr06aMPPvhA48ePb4T48DU/H9lNb288rJyCYr22Lkc/ub6L1ZEAABZr0AWszaW2F8DAN7z9xSH9Km2bwoIcypg9UtGhTqsjAQCaQIu+gBWt2+SURF0RH64zpZWay62+ANDqUUbQ7Ow2Q7+55QpJ0hsbDurAibMWJwIAWIkyAksM6Rqt0b1i5PaYem4FoyMA0JpRRmCZR27sIUlauvWo9h47Y3EaAIBVKCOwTN8OEbqpT5xMU5q7fK/VcQAAFqGMwFKP3NhDhiEt256nHUcLrY4DALAAZQSW6hkXpon9EiRJf0lndAQAWiPKCCz30JjushnS8l3HlXnotNVxAADNjDICy3VtH6rvDUyUJP3p4z0WpwEANDfKCFqEh0Z3l8NmaM2+fH2RfdLqOACAZkQZQYvQMTpEtw9KkiT99392yweeUgAAaCSUEbQYD43urqAAmzbmnNJHO/KsjgMAaCaUEbQYcRFB+snwqqf4PvOf3Sqv9FicCADQHCgjaFF+NqKr2oUGKrugWG98nmN1HABAM6CMoEUJdTq808Q/t2KfCksqLE4EAGhqlBG0OHcMSlK3mFCdKq7QCxn7rY4DAGhilBG0OA67TY+P7yVJeuXTbB0+VWxxIgBAU6KMoEUa1TNGQ7tGq7zSo2c/YiI0APBnlBG0SIZh6PHxvSVJ72Ue1VamiQcAv0UZQYvVt0OEbh3YQZL09Ae7mAgNAPwUZQQt2uxxPeV02LQh+6Q+2nHM6jgAgCZAGUGLlhAZ/I2J0HYxERoA+CHKCFq8aSO7ql2oU9kFxVr0GROhAYC/oYygxQt1OjTr3ERo8z7Zp8JiJkIDAH9CGYFP+P6gRPWIDdXp4gr99ZN9VscBADQiygh8QtVEaFW3+v5jfbZyCoosTgQAaCyUEfiMkT1jNLx7O1W4Tf1h2S6r4wAAGgllBD7l1xOukN1m6KMdx7R8J7f6AoA/oIzAp/SMC9P9w5MlSU/+60sVlVVanAgA0FCUEfich0f3UGLbYB0tLNVf0vdaHQcA0ECUEfic4EC7nprUV5K08NMsfXmk0OJEAICGoIzAJ43qGaNb+sXLY0qPv7tdbg/PrQEAX0UZgc96cuIVCgtyaNvhQr22PtvqOACAeqKMwGfFhAXpsZt7SZKe/WiPjp4usTgRAKA+KCPwaXcN7qiUTm1VVO7Wo2nbZJp8XAMAvoYyAp9msxn6n9v6KSjApjX78nmQHgD4IMoIfF6X9qF67Kaqj2v+sGy3svOZKh4AfAllBH5hypDOGtIlWiUVbs1+Zyt31wCAD6GMwC/YbIb+5/Z+CnU6tDHnlF5ec8DqSACAWqKMwG8ktg3RkxOvkCT9+eO92pN3xuJEAIDaoIzAr9yekqjRvWJU7vbokcWZKqt0Wx0JAHAZlBH4FcMwlDr5SkW1CdTOXJee/WiP1ZEAAJdBGYHfiQkL0n9P7idJenlNltbsO2FxIgDApVBG4JduvCJWd1/bUZI06+2tKjhbZnEiAEBNKCPwW0+Mv0LdY0J14kwZs7MCQAtGGYHfCg60a95dAxVot2n5ruNa9PlBqyMBAC6CMgK/1js+XI+ee5je79/fqf3Hud0XAFqaOpeR1atXa+LEiUpISJBhGHrvvfcue8yqVauUkpKioKAgdenSRS+++GJ9sgL18qOhnXV9j/Yqq/TosbTt8jA7KwC0KHUuI0VFRerfv7/+9re/1Wr/rKwsjR8/XsOHD9eWLVv0+OOP68EHH1RaWlqdwwL1YbMZSr31SoUE2rUx55Te+uKQ1ZEAAN9gmA24qs8wDL377ruaNGlSjfs8+uijWrp0qXbt2uVdN23aNG3dulXr16+v1fu4XC5FRESosLBQ4eHh9Y2LVm7h2iz97v2dCgtyaMWsEYoJD7I6EgD4tdr+/W7ya0bWr1+vsWPHVls3btw4bdy4URUVFRc9pqysTC6Xq9oCNNTUoZ3VLzFCZ0or9f/e32l1HADAOU1eRvLy8hQbG1ttXWxsrCorK5Wfn3/RY1JTUxUREeFdkpKSmjomWgG7zdAfvnel7DZDH2zL1Se7j1kdCQCgZrqbxjCMaj+f/2To2+vPmzNnjgoLC73LoUN8xo/G0bdDhH48LFmS9Jv3dqiorNLiRACAJi8jcXFxysvLq7bu+PHjcjgcio6OvugxTqdT4eHh1RagsTw8prs6RAbryOkS/Tl9r9VxAKDVa/IyMmTIEKWnp1db9/HHH2vQoEEKCAho6rcHLhAS6NDvv9dXkrTw0yx9dqDA4kQA0LrVuYycPXtWmZmZyszMlFR1625mZqYOHqya3XLOnDmaMmWKd/9p06YpJydHs2bN0q5du7Rw4UItWLBAs2fPbpzfAKiHUT1j9P1BiTJNadbiTBUWX/xiagBA06tzGdm4caMGDhyogQMHSpJmzZqlgQMH6sknn5Qk5ebmeouJJCUnJ2vZsmXKyMjQgAED9NRTT2nevHmaPHlyI/0KQP38dmIfdY4O0dHCUj3+3naeXQMAFmnQPCPNhXlG0FS2HjqtyfPXqdJj6tnb++u2lESrIwGA32gx84wALVn/pEg9cmMPSdJv//WlcgqKLE4EAK0PZQSt3rQRXXV1cpSKyt166K1MVbg9VkcCgFaFMoJWz24z9Jc7Big8yKHMQ6e53RcAmhllBJDUITJYqbf2kyTNz/hK6TuZnRUAmgtlBDhnQr943Tu0syRp1tuZOlhQbG0gAGglKCPANzw+vreu6hipM6WV+vnrm1Ra4bY6EgD4PcoI8A2BDpue/+FVimoTqB1HXfrtv3ZYHQkA/B5lBPiW+IhgzbtzoAxDWrzxkN7eyIMaAaApUUaAixjWvZ1mjamaf+Q3732pPXlnLE4EAP6LMgLUYPqobhrRo73KKj2a+eZmrh8BgCZCGQFqYLMZ+tP3+6tdqFN7j53VU+/vtDoSAPglyghwCe1CnfrLHf0lSa9/flAffplrcSIA8D+UEeAyhndvr5+N6CJJ+tU/t+nI6RKLEwGAf6GMALUwe2xP9U+KlKu0Uo+8lalKnl8DAI2GMgLUQoDdpnl3DlCo06EN2Sf1Px/tsToSAPgNyghQS52i2+gPt14pSfrf1Qe0YG2WxYkAwD9QRoA6+E7/BP1yXE9J0lPv79R7W45YnAgAfB9lBKijB0Z21X3XJUuSZr+zVSv3HLc4EQD4NsoIUEeGYejXE3pr0oAEVXpMPbBos7YcPGV1LADwWZQRoB5sNkN/vK2/RvRor5IKt3706hc6dLLY6lgA4JMoI0A9BTpsmn/3VeqfGKHTxRWa/sZmlVUyZTwA1BVlBGiAkECHXrg7RZEhAdp2uFB/+GCX1ZEAwOdQRoAG6hAZrL98f4Ak6R/rc/TvrUetDQQAPoYyAjSCUb1i9MDIrpKkx9K26cCJsxYnAgDfQRkBGsmsG3vo6uQoFZW79cDrm1VawfUjAFAblBGgkTjsNv31roFqFxqo3Xln9PiS7TJN0+pYANDiUUaARhQbHqTn7hwou83Qki1HNHf5PqsjAUCLRxkBGtl13drpqe/2lSQ9t2Kf3v7ikMWJAKBlo4wATeAH13TU9FFVF7TOeXe7Vu09YXEiAGi5KCNAE5k9tqe+N7CD3B5TDyzapB1HC62OBAAtEmUEaCKGYei/J/fTkC7RKip3675Xv1BWfpHVsQCgxaGMAE0o0GHTi/ekqEdsqI65ynT7i+v05RFGSADgmygjQBOLCA7Q6/dfqyviw5V/tlx3vfSZPjtQYHUsAGgxKCNAM2gf5tRbP7tWVydH6UxZpaYs3KCPd+RZHQsAWgTKCNBMwoMC9Np9V2tM71iVV3o0bdEmvb2R234BgDICNKOgALtevPsq3ZaSKI8p/eqf27RwbZbVsQDAUpQRoJk57Db9z2399ONhyZKk372/U/NW7GPqeACtFmUEsIBhGPr1hN56eEx3SdKf0/cq9T+7KSQAWiXKCGARwzD08Jge+s0tV0iSXlp9QI+/+6XcHgoJgNaFMgJY7MfDkvXfk6+UYUhvbjioh97aovJKj9WxAKDZUEaAFuCOwR3117sGKsBu6P1tufrJaxtVXF5pdSwAaBaUEaCFuKVfgv4+dbCCA+xatfeE7lmwQYXFFVbHAoAmRxkBWpARPdpr0f3XKDzIoU05p3THS+t13FVqdSwAaFKUEaCFSenUVm9PG6L2YU7tzjuj7//veuUWllgdCwCaDGUEaIF6xYUrbdpQJUUFK7ugWD94+XMdY4QEgJ+ijAAtVMfoEL35k2uV2DZYWflFuuulz/jIBoBfoowALVhi26pC0iEyWAfyi3Tny5/p+BkKCQD/Uq8y8sILLyg5OVlBQUFKSUnRmjVratw3IyNDhmFcsOzevbveoYHWJCkqRG/99FolRATpwImqEZK8QgoJAP9R5zKyePFiPfzww3riiSe0ZcsWDR8+XDfffLMOHjx4yeP27Nmj3Nxc79K9e/d6hwZam6pCMkTxEUH66kSRbn5utVbsOmZ1LABoFIZZx4dhXHPNNbrqqqs0f/5877revXtr0qRJSk1NvWD/jIwMjRo1SqdOnVJkZGS9QrpcLkVERKiwsFDh4eH1eg3AHxw6WaxpizZpx1GXJOneoZ312M29FBRgtzgZAFyotn+/6zQyUl5erk2bNmns2LHV1o8dO1br1q275LEDBw5UfHy8Ro8erZUrV15y37KyMrlcrmoLgKoRkiUPDNX95574++q6bH3vhXXaf/ysxckAoP7qVEby8/PldrsVGxtbbX1sbKzy8vIuekx8fLxeeuklpaWlacmSJerZs6dGjx6t1atX1/g+qampioiI8C5JSUl1iQn4NafDrl/fcoVeuXewotsEaleuS9/921qt2XfC6mgAUC91+pjm6NGj6tChg9atW6chQ4Z41z/99NP6v//7v1pflDpx4kQZhqGlS5dedHtZWZnKysq8P7tcLiUlJfExDfAtx12lmvnmFn2edVIOm6Fnb++vSQM7WB0LACQ10cc07dq1k91uv2AU5Pjx4xeMllzKtddeq3379tW43el0Kjw8vNoC4EIx4UF67cdXa2L/BFV6TD28OFMvrz5gdSwAqJM6lZHAwEClpKQoPT292vr09HQNHTq01q+zZcsWxcfH1+WtAdTA6bDruTsG6L7rqq4jeXrZLv3+/Z3yeOp0bToAWMZR1wNmzZqle+65R4MGDdKQIUP00ksv6eDBg5o2bZokac6cOTpy5Ihee+01SdLcuXPVuXNn9enTR+Xl5Vq0aJHS0tKUlpbWuL8J0IrZbIZ+c0tvxYY7lfqf3fr72izlFpbq2dv7KziQO20AtGx1LiN33HGHCgoK9Lvf/U65ubnq27evli1bpk6dOkmScnNzq805Ul5ertmzZ+vIkSMKDg5Wnz599MEHH2j8+PGN91sAkGEY+tmIrmof5tSjadv0wfZcHTpVrJenDFJseJDV8QCgRnWeZ8QKzDMC1M1nBwr080WbdKq4QrHhTv19ymBdmRhhdSwArUyTXMAKwDdc2yVa/5o+TN1jQnXMVabb/3edlm3PtToWAFwUZQTwUx2jQ5T2wFCN6NFepRUePfD6Zj36z21ylVZYHQ0AqqGMAH4sPChAC6YO0k+v7yLDkBZvPKSxf16tlbuPWx0NALwoI4Cfc9htenx8b739syFKbtdGea5S/ejVL/SLt7fqdHG51fEAgDICtBaDO0dp2YPDdf+wZBmGlLb5sEY+m6GFa7NUXumxOh6AVoy7aYBWaFPOSc1Zsl17j1U9YK9zdIgeu7m3xvWJlWEYFqcD4C9q+/ebMgK0UpVuj97eeFh/Tt+r/LNVz4K6OjlKf5zcT53btbE4HQB/wK29AC7JYbfpB9d0VMYvR2rmDd0UFGDThqyTmjBvjd7ZeEg+8O8UAH6CMgK0cqFOh34xtqdW/GKkrkmOUlG5W7/85zbNeHOLCou5DRhA06OMAJAkdYgM1hs/uVa/HNdTDpuhD7bl6ubnVmvNvhNWRwPg5ygjALzsNkPTR3XTP38+VJ2iQ3S0sFT3LNigqQs3aOdRl9XxAPgpygiACwxIitQHDw7XvUM7y2EztGrvCU346xo9sjhTh04WWx0PgJ/hbhoAl5SdX6RnP96j97dVPdsm0G7TnVcn6YGR3RQXwdOAAdSMW3sBNKqth07rmf/s1voDBZKkQIdNP7i6ox4Y2VUx4ZQSABeijABodKZpav1XBfrL8r36IvuUJMnpsOmOwUn64TWd1DMuzOKEAFoSygiAJmOaptbuz9df0vdq88HT3vUpndrqB1d31IR+8QoKsFsXEECLQBkB0ORM09Sn+wu06LMcpe86Jren6n8nEcEBunNwkqYM7awOkcEWpwRgFcoIgGZ13FWqtzce0psbDunI6RJJVbcK39QnTvcN66yrOrbluTdAK0MZAWAJt8fUyt3H9cq6LH26v8C7vk9CuL43sIMm9k9QLBe8Aq0CZQSA5XbnufTK2my9m3lE5ZUeSZJhSEO6RGvSgA4a1StG7cOcFqcE0FQoIwBajJNF5fpg21H9K/OoNuacqratV1yYhnZtp2Hdo3V1crRCnQ6LUgJobJQRAC3SoZPFWrr1qJZtz9WOb00x77AZGtS5rUb2jNGIHu3VKy6M60wAH0YZAdDiFZwt0/oDBfp0f4HW7j+hQydLqm2PDXdqWLf2Gto1WkO6RiuBO3MAn0IZAeBzsvOLtGrvCa3ae0LrvspXaYWn2vbO0SEa0jVaA5PaamDHSHVtHyqbjZEToKWijADwaaUVbm3MPqV1X+Vr3VcF2nb4tDzf+r9VmNOh/kmR6hYTqoTIICVEBis+IliJbYPVPtRJUQEsRhkB4FdcpRXacOCkvsg5qS0HT2v74UKVVLhr3N/psKlTdIg6RrU59zVESVHBSmwbosS2wQoJ5EJZoKlRRgD4tUq3R3uOndG2w4U6eLJYR0+XnFtKlecq9c4GW5OoNoGKjwhSfESQYsOrvsZFBCshIkjxkcGKjwhiSnuggWr795t/GgDwSQ67TX0SItQnIeKCbRVuj46eLlF2QbEOFhQpu6BYh04W6/CpEh0+VSxXaaVOFpXrZFH5BXf0fFPbkADFhAWpXVig2oc61T7MqehQp6LaBCq6TeC5r05FtglQmNPBnT9APVFGAPidALtNnaLbqFN0G0ntL9heWFKhw6eKdcxVqtzCUh0rrPpatZQot7BUxeVunSqu0KniCu05dvn3dNgMRYYEKDIkUFEhgWof7lRMmFMxYUGKCasqMueXtiGBsnM9C+BFGQHQ6kQEBygi+OKjKlLVAwBdJZU6Wlii/LNlOnGmTPlny5R/tlz5Z8pUcG5U5WRRuQqKylRa4VGlx6zafrb8su9vtxlqGxKosCCHQp1VSxunQ8GBdjkdNgU6bHI6bAoKsCsqJFDtwqpGYKJDAxUeFKCgALucATYFOewKsBuMyMDnUUYA4FsMw1BESIAiQgJqtX9phVunist1urhCp4qrSspxV5mOnynT8TOlOnHm60JTUFQut8c8V27KGpzVZkhBAXYFBdgVfK6khATaFeYMUGiQQ2FOh0KDHAoKsCvQ/nXROV92ggPt3uOrthty2GwKOPe901FVkJwBdu9xlB80NsoIADRQUIBd8RFVtxVfTqXbo5NFVSMoZ8sqVVRWqTNllTpbWqmSCrfKKz0qq6z6WvVRUbkKzpZ7R2bOllVUm3/FY0rF5W4Vl9d8Z1FjC7Ab3mITYK9a7DZDDrshh82Q3WaT3SbZDUN227cXm+yGvPs4bDbZbFXH2Qyj6rjz+xpVoz52myGbIdnO72MYsp3b7rB/87jzr/319vNf7TZDxvltRtXisBmy28+9zrljHLbqxzjs3/jeZpPhzSHZjKrXtBmGDMn7usa3f/dzOVAzyggANCOH3aaY8CDFNODJxaZpqtztUWmFR2UVbpVWeFRS4VbpuaWovFJny9w6W1qpM6UVOltWqbJKj7folFV6qpYKt0oq3Copd6ukwqMKt0eVbo8q3KYq3B6Vuz0qq/CotNKtb953WbXdraJmLED+4OtiUlVWbOfL0jfKjd1bys4VKMlbeGyGoQCHUVUAbTYFOKrKnc2Qt7idfx2b7etjLlbkql7z6yJlGIZuS0lU3w4X/+iyqVFGAMDHGMb5j0/sUnDtPkpqCNM0VekxVXpu5KbcXVVszn/v9piqcJtye0xVuquun3GbpjyeqnXucz9Xba/6/vzXi+3j8VS9n8esWtwenft6bp13X3m/P/+10vP1a54/xm1W/Q5fHy/ve7nPvZfb4zm3Xar0eOTxVH11e+TdVukxZZpVx5pm1b6Xu4X8m9weU26ZUgvtcFd1aksZAQC0TIZhKMBe9S9yXNz5cvJ1gTpfrM6VG1PV13vLUfX158tO1ffVX9M0q25bPz9yVTWS9fU2j3mumJ0rXx7PtzN9/V7n9zdNU6aqvu8eE2rZ+aOMAADQQIZhVF2vIq4NqQ9qLgAAsBRlBAAAWIoyAgAALEUZAQAAlqKMAAAAS1FGAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABL+cRTe03TlCS5XC6LkwAAgNo6/3f7/N/xmvhEGTlz5owkKSkpyeIkAACgrs6cOaOIiIgatxvm5epKC+DxeHT06FGFhYXJMIxGe12Xy6WkpCQdOnRI4eHhjfa6uBDnunlxvpsP57r5cK6bT2Oda9M0debMGSUkJMhmq/nKEJ8YGbHZbEpMTGyy1w8PD+c/7GbCuW5enO/mw7luPpzr5tMY5/pSIyLncQErAACwFGUEAABYqlWXEafTqd/+9rdyOp1WR/F7nOvmxfluPpzr5sO5bj7Nfa594gJWAADgv1r1yAgAALAeZQQAAFiKMgIAACxFGQEAAJZq1WXkhRdeUHJysoKCgpSSkqI1a9ZYHcnnpaamavDgwQoLC1NMTIwmTZqkPXv2VNvHNE3913/9lxISEhQcHKyRI0dqx44dFiX2D6mpqTIMQw8//LB3Hee5cR05ckR33323oqOjFRISogEDBmjTpk3e7ZzvxlFZWalf//rXSk5OVnBwsLp06aLf/e538ng83n041/WzevVqTZw4UQkJCTIMQ++991617bU5r2VlZZo5c6batWunNm3a6Dvf+Y4OHz7c8HBmK/XWW2+ZAQEB5ssvv2zu3LnTfOihh8w2bdqYOTk5VkfzaePGjTNfeeUV88svvzQzMzPNCRMmmB07djTPnj3r3eeZZ54xw8LCzLS0NHP79u3mHXfcYcbHx5sul8vC5L5rw4YNZufOnc1+/fqZDz30kHc957nxnDx50uzUqZN57733mp9//rmZlZVlLl++3Ny/f793H8534/j9739vRkdHm++//76ZlZVlvvPOO2ZoaKg5d+5c7z6c6/pZtmyZ+cQTT5hpaWmmJPPdd9+ttr0253XatGlmhw4dzPT0dHPz5s3mqFGjzP79+5uVlZUNytZqy8jVV19tTps2rdq6Xr16mY899phFifzT8ePHTUnmqlWrTNM0TY/HY8bFxZnPPPOMd5/S0lIzIiLCfPHFF62K6bPOnDljdu/e3UxPTzdHjBjhLSOc58b16KOPmsOGDatxO+e78UyYMMG87777qq279dZbzbvvvts0Tc51Y/l2GanNeT19+rQZEBBgvvXWW959jhw5YtpsNvPDDz9sUJ5W+TFNeXm5Nm3apLFjx1ZbP3bsWK1bt86iVP6psLBQkhQVFSVJysrKUl5eXrVz73Q6NWLECM59PUyfPl0TJkzQmDFjqq3nPDeupUuXatCgQbr99tsVExOjgQMH6uWXX/Zu53w3nmHDhmnFihXau3evJGnr1q1au3atxo8fL4lz3VRqc143bdqkioqKavskJCSob9++DT73PvGgvMaWn58vt9ut2NjYautjY2OVl5dnUSr/Y5qmZs2apWHDhqlv376S5D2/Fzv3OTk5zZ7Rl7311lvatGmTNm7ceME2znPjOnDggObPn69Zs2bp8ccf14YNG/Tggw/K6XRqypQpnO9G9Oijj6qwsFC9evWS3W6X2+3W008/rbvuuksS/203ldqc17y8PAUGBqpt27YX7NPQv52tsoycZxhGtZ9N07xgHepvxowZ2rZtm9auXXvBNs59wxw6dEgPPfSQPv74YwUFBdW4H+e5cXg8Hg0aNEh/+MMfJEkDBw7Ujh07NH/+fE2ZMsW7H+e74RYvXqxFixbpjTfeUJ8+fZSZmamHH35YCQkJmjp1qnc/znXTqM95bYxz3yo/pmnXrp3sdvsFTe748eMXtELUz8yZM7V06VKtXLlSiYmJ3vVxcXGSxLlvoE2bNun48eNKSUmRw+GQw+HQqlWrNG/ePDkcDu+55Dw3jvj4eF1xxRXV1vXu3VsHDx6UxH/XjemXv/ylHnvsMd1555268sordc899+iRRx5RamqqJM51U6nNeY2Li1N5eblOnTpV4z711SrLSGBgoFJSUpSenl5tfXp6uoYOHWpRKv9gmqZmzJihJUuW6JNPPlFycnK17cnJyYqLi6t27svLy7Vq1SrOfR2MHj1a27dvV2ZmpncZNGiQfvjDHyozM1NdunThPDei66677oJb1Pfu3atOnTpJ4r/rxlRcXCybrfqfJrvd7r21l3PdNGpzXlNSUhQQEFBtn9zcXH355ZcNP/cNuvzVh52/tXfBggXmzp07zYcffths06aNmZ2dbXU0n/bzn//cjIiIMDMyMszc3FzvUlxc7N3nmWeeMSMiIswlS5aY27dvN++66y5uy2sE37ybxjQ5z41pw4YNpsPhMJ9++mlz37595uuvv26GhISYixYt8u7D+W4cU6dONTt06OC9tXfJkiVmu3btzF/96lfefTjX9XPmzBlzy5Yt5pYtW0xJ5p///Gdzy5Yt3iktanNep02bZiYmJprLly83N2/ebN5www3c2ttQzz//vNmpUyczMDDQvOqqq7y3n6L+JF10eeWVV7z7eDwe87e//a0ZFxdnOp1O8/rrrze3b99uXWg/8e0ywnluXP/+97/Nvn37mk6n0+zVq5f50ksvVdvO+W4cLpfLfOihh8yOHTuaQUFBZpcuXcwnnnjCLCsr8+7Dua6flStXXvT/z1OnTjVNs3bntaSkxJwxY4YZFRVlBgcHm7fccot58ODBBmczTNM0Gza2AgAAUH+t8poRAADQclBGAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGCp/w+siz+lsntjggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 학습과정의 history 수치 출력 가능\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.DataFrame(model_fit.history['mae'], columns=['MAE']).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 예측 및 평가하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 90ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1.319353 ],\n",
       "       [2.2628102],\n",
       "       [3.2062676],\n",
       "       [4.1497254],\n",
       "       [5.093183 ],\n",
       "       [6.03664  ],\n",
       "       [6.9800973],\n",
       "       [7.923555 ],\n",
       "       [8.867012 ],\n",
       "       [9.809382 ]], dtype=float32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train 데이터 예측하기\n",
    "Y_trpred = model.predict(X_train)\n",
    "Y_trpred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 23ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[3.2062676],\n",
       "       [6.9800973],\n",
       "       [8.867012 ]], dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test 데이터 예측하기\n",
    "Y_tepred = model.predict(X_test)\n",
    "Y_tepred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 178ms/step - loss: 0.0306 - mae: 0.1488\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.030630597844719887, 0.1487932801246643]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train 예측 성능 평가하기\n",
    "# 학습과정의 결과와 유사\n",
    "model.evaluate(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 27ms/step - loss: 0.0202 - mae: 0.1197\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.02020941488444805, 0.11971942335367203]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test 예측 성능 평가하기\n",
    "model.evaluate(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **코드 정리**\n",
    "\n",
    "## 회귀문제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 1)]               0         \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 5)                 10        \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 3)                 18        \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 1)                 4         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 32\n",
      "Trainable params: 32\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 94ms/step\n",
      "1/1 [==============================] - 0s 38ms/step\n",
      "Prediction of Y_train:  [[1.001699 ]\n",
      " [2.0013828]\n",
      " [3.0010664]\n",
      " [4.0007505]\n",
      " [5.0004334]\n",
      " [6.000118 ]\n",
      " [6.9998016]\n",
      " [7.999485 ]\n",
      " [8.999169 ]\n",
      " [9.998853 ]]\n",
      "1/1 [==============================] - 0s 204ms/step - loss: 9.0118e-07 - mae: 8.1413e-04\n",
      "Evaluation of Y_train:  [9.01184648682829e-07, 0.0008141279104165733]\n",
      " \n",
      "Prediction of Y_test:  [[3.0010664]\n",
      " [6.9998016]\n",
      " [8.999169 ]]\n",
      "1/1 [==============================] - 0s 40ms/step - loss: 6.2221e-07 - mae: 6.9849e-04\n",
      "Evaluation of Y_test:  [6.222119850463059e-07, 0.0006984869833104312]\n"
     ]
    }
   ],
   "source": [
    "# 하이퍼파라미터\n",
    "tf.random.set_seed(1)\n",
    "HIDDEN_ACTIVATION = 'relu'\n",
    "OUTPUT_ACTIVATION = 'linear'\n",
    "LOSS = 'mse'\n",
    "OPTIMIZER = 'adam'\n",
    "METRICS = ['mae']\n",
    "BATCH_SIZE = 3\n",
    "EPOCHS = 500\n",
    "VERBOSE = 0\n",
    "\n",
    "# 입력과 출력이 같은 데이터를 구성하여 최적의 가중치는 1\n",
    "X_train = np.array([1,2,3,4,5,6,7,8,9,10])\n",
    "X_test = np.array([3,7,9])\n",
    "Y_train = np.array([1,2,3,4,5,6,7,8,9,10])\n",
    "Y_test = np.array([3,7,9])\n",
    "\n",
    "# 회귀문제 모델링\n",
    "inputs = Input(shape=(1,))    # Input 함수를 사용하여 입력층 생성\n",
    "hidden1 = Dense(5, activation=HIDDEN_ACTIVATION)(inputs)    # 은닉층1: 입력층을 입력받는 layer 추가\n",
    "hidden2 = Dense(3, activation=HIDDEN_ACTIVATION)(hidden1)    # 은닉층2: 은닉층1을 입력받는 layer 추가\n",
    "output = Dense(1, activation=OUTPUT_ACTIVATION)(hidden2)    # 출력층: 은닉층2를 입력받아 Y를 출력하는 layer 추가\n",
    "model = Model(inputs, output)    # 입력층과 출력층을 구성하며 최종 모델 완성\n",
    "model.summary()    # 네트워크 구조 출력\n",
    "plot_model(model)    # 네트워크 구조 시각화\n",
    "\n",
    "# 데이터 학습하기\n",
    "model.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=METRICS)\n",
    "model_fit = model.fit(X_train, Y_train, \n",
    "                      batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=VERBOSE)\n",
    "pd.DataFrame(model_fit.history[METRICS[0]], columns=[METRICS[0].upper()]).plot()\n",
    "plt.show()\n",
    "\n",
    "# 데이터 예측 및 평가하기\n",
    "Y_trpred = model.predict(X_train)\n",
    "Y_tepred = model.predict(X_test)\n",
    "print('Prediction of Y_train: ', Y_trpred)\n",
    "print('Evaluation of Y_train: ', model.evaluate(X_train, Y_train))\n",
    "print(' ')\n",
    "print('Prediction of Y_test: ', Y_tepred)\n",
    "print('Evaluation of Y_test: ', model.evaluate(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 분류문제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_3 (InputLayer)        [(None, 1)]               0         \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 5)                 10        \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 3)                 18        \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 1)                 4         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 32\n",
      "Trainable params: 32\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 89ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "Prediction of Y_train:  [[0.28270864]\n",
      " [0.28270864]\n",
      " [0.28270864]\n",
      " [0.28270864]\n",
      " [0.82786214]\n",
      " [0.82786214]\n",
      " [0.82786214]\n",
      " [0.82786214]\n",
      " [0.82786214]\n",
      " [0.82786214]]\n",
      "1/1 [==============================] - 0s 229ms/step - loss: 0.4033 - accuracy: 0.9000\n",
      "Evaluation of Y_train:  [0.40330952405929565, 0.8999999761581421]\n",
      " \n",
      "Prediction of Y_test:  [[0.82786214]\n",
      " [0.28270864]\n",
      " [0.82786214]]\n",
      "1/1 [==============================] - 0s 37ms/step - loss: 0.2367 - accuracy: 1.0000\n",
      "Evaluation of Y_test:  [0.2366967797279358, 1.0]\n"
     ]
    }
   ],
   "source": [
    "# 하이퍼파라미터\n",
    "tf.random.set_seed(1)\n",
    "HIDDEN_ACTIVATION = 'relu'\n",
    "OUTPUT_ACTIVATION = 'sigmoid'\n",
    "LOSS = 'binary_crossentropy'\n",
    "OPTIMIZER = 'adam'\n",
    "METRICS = ['accuracy']\n",
    "BATCH_SIZE = 3\n",
    "EPOCHS = 500\n",
    "VERBOSE = 0\n",
    "\n",
    "# 입력과 출력이 같은 데이터를 구성하여 최적의 가중치는 1\n",
    "X_train = np.array([0,0,0,0,1,1,1,1,1,1])\n",
    "X_test = np.array([1,0,1])\n",
    "Y_train = np.array([0,0,0,0,0,1,1,1,1,1])\n",
    "Y_test = np.array([1,0,1])\n",
    "\n",
    "# 분류문제 모델링\n",
    "inputs = Input(shape=(1,))    # Input 함수를 사용하여 입력층 생성\n",
    "hidden1 = Dense(5, activation=HIDDEN_ACTIVATION)(inputs)    # 은닉층1: 입력층을 입력받는 layer 추가\n",
    "hidden2 = Dense(3, activation=HIDDEN_ACTIVATION)(hidden1)    # 은닉층2: 은닉층1을 입력받는 layer 추가\n",
    "output = Dense(1, activation=OUTPUT_ACTIVATION)(hidden2)    # 출력층: 은닉층2를 입력받아 Y를 출력하는 layer 추가\n",
    "model = Model(inputs, output)    # 입력층과 출력층을 구성하며 최종 모델 완성\n",
    "model.summary()    # 네트워크 구조 출력\n",
    "plot_model(model)    # 네트워크 구조 시각화\n",
    "\n",
    "# 데이터 학습하기\n",
    "model.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=METRICS)\n",
    "model_fit = model.fit(X_train, Y_train, \n",
    "                      batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=VERBOSE)\n",
    "pd.DataFrame(model_fit.history[METRICS[0]], columns=[METRICS[0].upper()]).plot()\n",
    "plt.show()\n",
    "\n",
    "# 데이터 예측 및 평가하기\n",
    "Y_trpred = model.predict(X_train)\n",
    "Y_tepred = model.predict(X_test)\n",
    "print('Prediction of Y_train: ', Y_trpred)\n",
    "print('Evaluation of Y_train: ', model.evaluate(X_train, Y_train))\n",
    "print(' ')\n",
    "print('Prediction of Y_test: ', Y_tepred)\n",
    "print('Evaluation of Y_test: ', model.evaluate(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **회귀문제 예제**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from sklearn import preprocessing\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# 예제 데이터 전처리\n",
    "rng = np.random.RandomState(123)\n",
    "X_train = np.sort(5 * rng.rand(500, 1), axis=0)\n",
    "Y_train = np.sin(X_train).ravel()                        # sin함수의 예측을 목표로한다\n",
    "X_test = np.arange(0.0, 5.0, 5/X_train.shape[0])[:, np.newaxis]\n",
    "Y_test = Y_train.copy()\n",
    "Y_test[::5] = Y_test[::5] + 3 * (0.5 - rng.rand(100))\n",
    "\n",
    "scaler = preprocessing.MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# 예제 데이터 시각화\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(X_test, Y_test, s=20, c='darkorange')\n",
    "plt.title('Example Data')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression\n",
    "\n",
    "**1) 알고리즘 함수세팅:** \n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "Y \\approx \\hat{Y} &= f(X_1, X_2, ..., X_k) = w_0 + w_1X_1 + w_2X_2 + \\cdots + w_kX_k \n",
    "= [w_0~w_1~w_2~\\cdots~w_k]\\begin{bmatrix} 1 \\\\ X_1 \\\\ X_2 \\\\ \\vdots \\\\ X_k \\end{bmatrix} \\\\\n",
    "&= [1~X_1~X_2~\\cdots~X_k]\\begin{bmatrix} w_0 \\\\ w_1 \\\\ w_2 \\\\ \\vdots \\\\ w_k \\end{bmatrix} \n",
    "= \\begin{bmatrix} 1~X_{11}~X_{21}~\\cdots~X_{k1} \\\\ 1~X_{12}~X_{22}~\\cdots~X_{k2} \\\\ \\vdots \\\\ 1~X_{1t}~X_{2t}~\\cdots~X_{kt} \\end{bmatrix}\n",
    "\\begin{bmatrix} w_0 \\\\ w_1 \\\\ w_2 \\\\ \\vdots \\\\ w_k \\end{bmatrix} = XW = WX\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "---\n",
    "\n",
    "<center><img src='Image/Expert/Example_LinearRegression.png' width='900'></center>\n",
    "\n",
    "**2) 함수 추정을 위한 비용함수:** 나의 주장 기반 알고리즘의 `예측값`($\\hat{Y}$)과 `실제 데이터`($Y$)의 차이를 평가하는 함수\n",
    "> - **손실함수(Loss Function):** `하나의 데이터(Single Row)`에서 예측값과 정답의 차이를 평가\n",
    "> - **비용함수(Cost Function):** `모든 데이터`에서 예측값과 정답의 차이를 평가\n",
    "> \n",
    "> $$\n",
    "\\begin{aligned}\n",
    "Y - \\hat{Y} &= Y - WX = \\text{residual} = \\text{cost} \\\\\n",
    "&= \\sum_{i=1}^{m} \\left[ \\sum_{j=1}^{k} (Y_{i} - w_{j}X_{j}) \\right]\n",
    "\\end{aligned}\n",
    "$$\n",
    ">\n",
    ">\n",
    "> - `회귀분석`은 여러가지의 비용함수 중 `최소제곱법/최소자승법`을 사용 \n",
    "> - `최소제곱법/최소자승법`를 최소로 하는 `직선`을 추정하여 `계수(coefficient)`를 결정\n",
    ">\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\hat{W} = \\underset{W}{\\arg\\min} \\sum_{i=1}^{m} \\left[\\sum_{j=1}^{k} (Y_{i} - w_{j}X_{j})^2 \\right]\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "\n",
    "<!-- - **비선형변수 효과:** 로그 또는 제곱근 등의 변환된 변수 사용시 회귀분석 성능 향상 가능\n",
    "    - 독립 변수나 종속 변수가 심하게 한쪽으로 치우친 분포를 보이는 경우\n",
    "    - 독립 변수와 종속 변수간의 관계가 곱셈 혹은 나눗셉으로 연결된 경우\n",
    "    - 종속 변수와 예측치가 비선형 관계를 보이는 경우 -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9LGWacRwAgB5CWXHAn9HfBp4szCrNf9Mo8FB+iOsUAOAWJEyAP6K/DbyJNY1ECQDgNizJA/wR/W0AAACcQsIE+Bv62wAAADiNhAnwN/S3AQAAcBoJE+Bv6G8DAADgNBImwN/Q3wYAAMBpJEyAP6K/DQAAgFMoKw74I/rbAAAAOIWECfBn9LcBAABoFwkTAPQUW55RpZAZPQAAvAYJEwC4Wo3NaBacv/7CsdSZxp6xMKv74gIAAB2i6AMAuFp2plSwsfWxgo3SusXuiQcAADiNhAkAXMmWZ8ws2RtbH7c3GsfLDronLgAA4BQSJgBwpYrD7b9efqhn4gAAAJ3CHiYA3Y/iBhdED2n/9ZihPRMHAADoFBImAN2H4gaXih1mfA8KNrZelmcJNJoF+3tCCQCAh2NJHoDuQ3GDts3JMpKjllKmGccBAIBHY4YJQPdoLm5wsZbFDfx1NiXMKs1/0/gelB9iqSIAAF6EhAlA93CmuIG/JwnWNL4HAAB4GZbkAegeFDcAAAA+yGsSpl/+8peaOHGiIiMjlZiYqFtvvVUHDhxwd1gAmjUXN7AEtj5uCTSOM7MCAAC8kNckTJs3b9aDDz6ojz76SBs2bFBDQ4NmzJih6upqd4cGoBnFDQAAgI/xmj1Mb775ZquPX3jhBSUmJmrHjh2aMmWKm6IC0ArFDQAAgI/xmoTpYhUVFZKk2NhYh+fU1dWprq7u/MeVlZWSpPr6etXX17s2wA40f353xwHv4VXXTO9U448keUO8Psqrrhl4BK4ZmMU1A7M86ZpxNgaL3W63uziWbme32zVv3jyVlZVpy5YtDs/72c9+pscff/yS48uWLVOvXr1cGSIAAAAAD3b27FllZmaqoqJCUVFRDs/zyoTpwQcf1Lp167R161b179/f4XltzTANGDBAp0+fbveb0hPq6+u1YcMGTZ8+XcHBwW6NBd6BawZmcc3ALK4ZmMU1A7M86ZqprKxUfHx8hwmT1y3Je+ihh7RmzRq999577SZLkhQaGqrQ0NBLjgcHB7v9L6iZJ8UC78A1A7O4ZmAW1wzM4pqBWZ5wzTj7+b0mYbLb7XrooYf02muv6d1339WgQYPcHRIAAAAAH+c1CdODDz6oZcuWafXq1YqMjNTJkyclSdHR0QoPD3dzdAAAAAB8kdf0YfrLX/6iiooKXX/99erbt+/5Py+99JK7QwMAAADgo7xmhskLa1MAAAAA8HJeM8MEAAAAAD2NhAkAAAAAHCBhAgAAAAAHSJgAAAAAwAESJgAAAABwgIQJAAAAABwgYQIAAAAAB0iYAAAAAMABEiYAAAAAcICECQAAAAAcIGECAAAAAAdImAAAAADAARImAAAAAHCAhAkAAAAAHCBhAgAAAAAHSJgAAAAAwAESJgAAAABwgIQJAAAAABwgYQIAAAAAB0iYAAAAAMABEiYAAAAAcICECQAAAAAcIGECAAAAAAdImAAAAADAgSB3BwDAQ9nypIrDUsxQyZrm7mgAAADcgoQJQGs1Nik7U8pff+FY6kxpTpYUZnVfXAAAAG7AkjwArWVnSgUbWx8r2CitW+yeeAAAANyIhAnABbY8Y2bJ3tj6uL3ROF520D1xAQAAuAkJE4ALKg63/3r5oZ6JAwAAwEOQMAG4IHpI+6/HDO2ZOAAAADwECROAC2KHGQUeLIGtj1sCjeNUywMAAH6GhAlAa3OypJRprY+lTDOOAwAA+BnKigNoLcwqzX/TKPBQfog+TAAAwK+RMAFomzWNRAkAAPg9luQBAAAAgAMkTAAAAADgAAkTAAAAADhAwgQAAAAADpAwAQAAAIADJEwA4KvKDhn/LT/s3jgAAPBiJEwA4GtqbNKKm6QXxxsf/2ec8XFtmXvjAgDAC5EwAYCvyc6UCja2PlawUVq32D3xAADgxUiYAMCX2PKk/PWSvbH1cXujcbzsoHviAgDAS5EwAYAvqehgv1L5oZ6JAwAAH0HCBAC+JHpI+6/HDO2ZOAAA8BEkTADgS2KHSakzJUtg6+OWQOO4Nc09cQEA4KVImADA18zJklKmtT6WMs04DgAATAlydwAA/Iwtz9hnEzOU2Q5XCbNK89+UTu2XPjgg3b1TShjh7qj8G9c9AHgtEiYAPaPGZpS7zl9/4VjqTGPWI8zqvrh8WcwQSQe++C/cguseALweS/IA9Ax6A8Efcd0DgNcjYQLgevQGgj/iugcAn0DCBMD16A0Eb2LLk4680fWEhuseAHwCe5gAuB69geANunu/Edc9APgEZpgAuB69geANunu/Edc9APgEEiYAPYPeQPBkrtpvxHUPAF6PJXkAekZzb6Cyg8beDfrRwJM4s9+oM9cr1z0AeD0SJgA9y5rGDSM8j6v3G3HdA4DXYkkeAADsNwIAOEDCBACAxH4jAECbWJIHAIDEfiMAQJtImAAAaIn9RgCAFkiYAACAc2x5RkVBZt8A+BESJgAA0L4am9HYN3/9hWOpM439XWFW98UFAD2Aog8AAKB92ZlSwcbWxwo2SusWuyceAOhBJEwAAMAxW54xs2RvbH3c3mgcLzvonrgAoIeQMAEAAMcqDrf/evmhnokDANyEhAkAADgWPaT912OG9kwcAOAmJEwAAMCx2GFGgQdLYOvjlkDjONXyAPg4EiYAANC+OVlSyrTWx1KmGccBwMdRVhyAefRiAfxLmFWa/6ZR4KH8EP/2AfgVEiYAzqMXC+DfrGkkSgD8DkvyADiPXiwAAMDPkDABcA69WAAAgB8iYQLgHHqxAAAAP0TCBMA59GIBAAB+iIQJgHPoxQIAAPwQCRMA59GLBQAA+BnKigNwHr1YAACAnyFhAmAevVgAAICfYEkeAAAAADhAwgQAAAAADpAwAQAAAIADJEwAAAAA4AAJEwAAAAA4QMIEAAAAAA6QMAEAAACAAyRMAAAAAOAACRMAAAAAOBDk7gAAAIADtjyp4rAUM1Syprk7GgDwSyRMAAB4mhqblJ0p5a+/cCx1pjQnSwqzui8uAPBDLMkDAMDTZGdKBRtbHyvYKK1b7J54AMCPkTABQHey5UlH3pDKDro7EngrW54xs2RvbH3c3mgc59oCgB7FkjwA6A4soUJ3qTjc/uvlh9jPBAA9iBkmAOgOLKFCd4ke0v7rMUN7Jg4AgCQSJgDoOpZQoTvFDjNmJy2BrY9bAo3jzC4BQI8iYQKArnJmCRVgxpwsKWVa62Mp04zjAIAexR4mAOgqllChu4VZpflvGrOT5YfowwQAbkTCBABd1byEqmBj62V5lkBjVoAbXXSWNY3rBwDcjCV5ANAdWEIFAIBPYoYJALoDS6gAAPBJJEwA0J1YQgUAgE9hSR4AAAAAOMAMEwAA8D62PKOkP8tfAbgYCRMAoPtwEwtXq7FJ2ZlGU+hmqTONAithVvfFBcBnkTABALqOm1j0lOxMo4R/SwUbpXWLjcIrANDN2MMEAOi69m5ige5iyzOS8pb9ziTj4/z1RpVKAOhmJEwAgK7hJhY9peJw+6+XH+qZOAD4FRImAEDXcBOLnhI9pP3XY4b2TBwA/AoJky+x5UlH3uBpLoCexU0sekrsMGNvnCWw9XFLoHGcQiMAXICEyRfU2KQVN0kvDJdWzpaeH2Z8XFvm7sgA+ANuYtGT5mRJKdNaH0uZZhwHABegSp4voGIQAHebk2X8zGlZJY+bWLhCmNX43VZ20FjuSQl7AC5GwuTtmjdbX6zlZmt+kQBwNW5i0dOsaVxjAHoECZO3c2azNb9QAPQUbmIBAD6GPUzejs3WAAAAgMuQMHk7NlsDAAAALkPC5AuoGAQAAAC4BHuYfAGbrQEAAACXIGHyJWy2dsyWZxTI8NVk0te/PgAAADchYYJvq7EZfapall5PnWksVwyzui+u7uLrXx8AAICbsYcJvq29pr6+wNe/PgAAADcjYephdrtdu04+rzPnjrs7FN/X3NTX3tj6eMumvt7M178+AAAAD0DC1MOKznyk7cf/rJUH7lBF3Ks6dTbX3SH5Lmea+nozX//6AAAAPAAJUw/LKVkqSbKrSXW99mrd4a9ozYH7dKRsk5ounilA1/h6U19f//oAAAA8AAlTD7LVHNKxyg8vOV5cvVsbj/w/vbznNuWWLFd941k3ROeDPLWpry1POvJG15fMeerXBwAA4ENImHpQTsmydl8/c65IHx77jZblzta2oj+q6lxxD0Xmw5xp6ttdCUxHamzSipukF4ZLK2dLzw8zPq4t6/yYNC0GAABwKcqK95Cz9aU6ZMt26txzjWe0u/hf+qz4RQ2JnamMxCWK7zXCxRH6qPaa+vZ0Se72KtrNf7NzY9K0GAAAwKVImHrIscoP1WSvN/Ueuxp1yJatQ7Zs9e09XhlJd2lg1CRZLEwMmtZWU19XJDCONFe0u1jLinZdSXRoWgwAAOASJEw9ZFjcXMX3GqnckmU6ZMtWo/2cqfefqNqhE1U7FB2aoozEJUqLm62ggHAXResHXJ3AXMyZinYkPAAAAB6HqYoeFBs+RFNSfqJF6Wt1WeL9sjT2Mj1GRV2BthY+pWU5c/TJ8b/obP1pF0TqB3q6JDcV7QAAALwSCZMb9AqO09ikBxR/4tu6JvkHiglNNT1GXWOFPj35nLJy52pzweOy1dBzx5SeTmCoaAcAAOCVSJjcyGIP1rDYW3XHqFc0c8gf1C/yCtNjNNnrlVe6Riv2LVT2wQd1rPJD2e12F0TrY9yRwFDRDgAAwOuwh8kDWCwBGhg9SQOjJ6n07AHllCzV4bL1arI3mBqn6MxHKjrzkaxhQ5SRdJeGWm9SYECIi6L2AXOyjAIPLfcyuTKBoaIdAACA1yFh8jBxvYbr+tSfa2K/h7T31Evad3qF6horTY1RVntY7xU8ru1FT2tUwgKNSpivsCAXlMn2du5KYKhoBwAA4DVImDxUREiCJib/ly7vc78O2l5XTskyVdYVmhqjpqFUO078RbtOPq+0uLnKSMxUTFiqawL2ZiQwAAAAcICEycMFB4ZrVMICjYifr6MVW5RT8qJOVn1qaoxGe532n16h/adXaGDUZGUkLVHf3hNksVhcFDUAAADgG0iYvESAJVCpMdcrNeZ6nareo5ySpfq8bKPsajQ1ztHKLTpauUVx4cOVkXiXhsROV4Al2EVRAwAAAN6NKnleKCFitKYOekqL0lcrI/EuBQdEmB6jtOaA3i34iZbn3qJdJ/+pugZz+6QAAAAAf0DC5MV6h/TVVf2/o8yMbF3V/7vqHdLX9BjV9SXafvxPWpY7Wx8U/tr0PikAAADAl7EkzweEBPZWRmKmRicsUH75O8opflElZ3NNjdHQVKM9p17SnlMvKzXmBmUkLlFSxGXscwIAAIBfI2HyIQGWIA22TtegmGkqqf5Mn5W8qPzydySZaWRrV375JuWXb1Jir3SlJy3RoJipCrBwqQAAAMD/cBfsgywWi5J6X6bpvS9TZV2hckuW60DpajU01Zgap+RsrjYd+aF6h/RVesJiDY+fp5DA3i6KGgAAAPA87GHycVGhA3TNgP+nzPRsXdHvW4oITjQ9RtW5E/qo6HdaljNbHx37P1WdO+GCSAEAAADPQ8LkJ0KDonRZn3u0KH2Nbkj9heLCh5seo76pWjklL2p57jxtOvKoTlXvcUGkAAAAgOdgSZ6fCbAEa2jsbA2xztKJqh3KKVmqoxXvmRrDrkYdLluvw2XrlRRxucYk3aWB0VMUYAl0UdQAgG5ly5MqDksxQyVrmrujAQCPRsLkpywWi/pFTlC/yAkqr81Xbsky5ZWuVaO9ztQ4xdW7tOHzXYoKHaD0xEwNi71ZwYHhLooaANAlNTYpO1PKX3/hWOpMaU6WFGZ1X1wA4MFYkgfFhKVq0sBHlZmxTuP7fkPhQXGmx6isK9QHhb9SVu5sbS96WtXnTrkgUgBAl2RnSgUbWx8r2CitW+yeeADAC5Aw4bywIKvG9f2KFqev1ZSUn8oaNsT0GHWNldpV/IKW75mrd/IfU+nZAy6IFABgmi3PmFmyN7Y+bm80jpcddE9cAODhvGpJ3nvvvaff/OY32rFjh06cOKHXXntNt956q7vD8jmBASEaHneLhsXerKIzHymnZKmOVX5oaowme4MO2dbpkG2d+kVOVEbiXRoQdY0sFnJ0mGDLk45tlmSRBlwn2e3GvgtLoFRZIFUXSxF9uvc1WaSogcZNJPs74EsqDrf/evkhqXdqj4QCAN7EqxKm6upqXXbZZfryl7+s+fPnuzscn2exWNQ/6mr1j7patppDyilZpkO2bDXZ602Nc/zMdh0/s10xoanKSFqiobGzFRQQ5qKo4TWaN523TFSaE5gj2dLuv0plHjBDmThOuvJRqdamVsmUJfDCf0m04A2iO1g1EDO0Z+IAAC/jVQnTrFmzNGvWLHeH4Zdiw4fqupTHNLHfg9p76mXtPfWK6horTI1RXpevLUef1Pbjz2hU/J0alXCnwoNjXRQx3K7l7FDUwAtJReJl0ruPSEVb3R2hc0p2Sq/fYf59yZOktPnSuaoLs1kkUXCn2GFGgYeCja2X5VkCpZRpxvVZb+6BGAD4A69KmMyqq6tTXd2Fqm+VlZWSpPr6etW7+ZdC8+d3dxxmBStKlyV8RaPj7tLh8je153SWKusKTI1R21CmnSf/rl3F/9SQmJkaHZ+pmLBBLorYd3j8NVN2SDr+vlR/Vjq4Qjq5vf3zLT5eTfH4DuNPS3EZ0o1/kkIije+VLFLytVKM+f2CzvD4awY9b+Z/pPX3SQVvXziWcqM083mpxe9Grhk4i2sGZnnSNeNsDBa73W53cSwuYbFYOtzD9LOf/UyPP/74JceXLVumXr16uTA6/2GXXefCDups5EeqD8vv9DghNUPU68xVCq4bLIss3RcgAAAA0IazZ88qMzNTFRUVioqKcnieTydMbc0wDRgwQKdPn273m9IT6uvrtWHDBk2fPl3BwcFujaXTyg5JlUek6MFSzBCV1hzQntNZOlK+QXY1dvz+NljDhmhU/GINjp6hwICQbg7Yu3ncNVNbZpQiPm6uIAhMisuQJn5XShhjeibK464ZeDyuGZjFNQOzPOmaqaysVHx8fIcJk08vyQsNDVVoaOglx4ODg93+F9TMk2JxmoPGh33mZKnP4CdVfe7b2nNqufadXqlzjWdMDV1We1jvH3tCO0/+RaMTFmpkwnyFBcV0b/xezu3XjC1POrVL2vA1qa7cfXH4i9PbpDcWGv/fvC8quLepAhNuv2bgdbhmYBbXDMzyhGvG2c/v0wkTXKS9xofz31RESKKuSP6Wxvb5ivJKX1dOyVKdOVdk6lPUNJTqkxPP6NOT/9CwuJuVnpipmLCUbvwiYFpbiTJ6VtHWtotlJE+Srv+dVHOaCn0AAHQzr0qYqqqqdOjQofMfHzlyRLt27VJsbKwGDhzoxsj8SHPjw4u1bHz4xc1acGAvjU5cqJEJd6igYrNyipequHqXqU/XaK/TvtOvat/pFUqJnqKMxCXq03ucLBb2OfW4thLlnmAdIV32dWnwbOPj8kOSJUg6UyBVFUsRSUYFuu56zS4pLFb6+EmjQp43KNoqLb3iwsfJk6Rb10iBvd0XEwAAPsKrEqZPPvlEN9xww/mPH3nkEUnSPffco3/+859uisrPONP48KKn2wGWQA2KmapBMVNVUp2rnOIXdaT8bdnVZOIT21VQsVkFFZsV32ukMhKXaLB1mgIsTP+7nC1P2vNP180sJY2XRt4tBUdcSGCObTYSl7ZKcbc3e9Kdrw273XgAcHEyFZUi2RuMY83/bflaXZm04/+kEx85/nyuVrRV+nuKdLsbElwAAHyMVyVM119/vby0RoXv6GLjw8SIdN04+H90pu649pxarv2nV6m+qdpUCKfP7tM7+T/WtqI/aXTiIo2Iu02hQZGmxkAbLm4kW39WOrC88zf+fa6SRiwyEqGolAtJRctZHUfLxzxlSZk1rXOxDF9gJFvHNkvl+dLhVVLpnu6Orn31Z6SXr5eGZBl7zYITevbzAwDgI7wqYYIHcKbxoRMiQ/vpqv6PaFzfB7T/9GrtOZWlqnMnTYVSXV+sbUV/0KcnntXwuHkanbhYUaHJpsbokuYEw5v2jFycFHV3I9mgCGnSk8byuY6+J2a/Z972/W6ZbE1+4sJsVcxQqa5CevPenkui1i6Srvye93zvAADwICRMMG9OllHgoeUSrZRpxnGTQgIjNSbpLqUnLtKRsk3KKXlRp86au4msbzqr3FNZ2nPqJaXG3KCMxLuU1HuM6Vic5qBKoOZkSWHWro3tqqSgxiatntc9SZEjzftmuvo9uJgrv9896eLZqntzWy/5sze4Lpk6/qG08os9YK76ewIAwEeRMMG8MKs0/83WT8y7eHMfYAnSkNgZGmydruLqXcopXqr8indlrOFyjl1NOlL+to6Uv63EiAxlJN6l1JjrFWDp5su8gyqBndLdSUHLmaS6cteWAL/+987NKHWWK77fnsLRkr+Lk6nu3BdVtFX62wDnZwIBAPBzJEzovM7u72iHxWJRn95j1af3WFXUFir3VJbySleroanW1Dgl1Tl6+8h/q3dIP2UkLtawuHkKCYzoeoAmqgSa0l1JwYlt0vovS6V7zcdgWoCUOl0a/23XfQpXfb+9wcX/vlruizpXLe17USr+pHNjN1RL7z5s/OlzlTRiodHbqa0iG4A38rYlvAA8GgkTPFZ02ABdO+D7Gt/3a9p/eqX2nHpJZ+tPmRqj6txxfXjst9px4m8aEXe7RicuVO+QPp0PqhNVAjvU1aSguZFsT1dmS53eqWWYprji++3NWiZR4799YRYqPOGLfWhbzI958iPjT7MBU6VbXmXJHryTryzhBeBRAtwdANCRsKBoXd7ny1o0+nVdn/JzxYUPNz3GucYqfVbyby3PvUWbjjyqU2f3dS6YLlYJbJMzSUFbTmyT/jNeemG4tHZhzyVL8ZdJS7YbM1+uvgFxxffbl1jTpEGzpD4TpEXvSTe/2vUxCzdJyycZyRjgbdqbrQeATmKGCV4jMCBYaXFzNDR2tk5UfaLPil9UYaW5IgZ2Nepw2XodLluvPr3HaUziXRoYPVkWi5PPDrqpSmArZpOC2jJpzZdc1xfpYi3Lg/f0ki1XfL992bD5bX+/zCrdKz0/jNkmeBd/XsILwKVImOB1LBaL+kVOVL/IiSqvPaKckmU6WLpOjfY6U+OcrNqpk1U7FRU6UBmJi5UWe7OCA8M7fmM3VgmUZD4pWLdYKnqnc5/LGRc3knX3DUZ3f799XVvfr84q3CRlTZLGf0eKGmhcn+wJgadiCS8AFyFhgleLCRukyQN/pAl9v6l9p1/R3lOvqKbBZmqMyrqjer/wV/rk+F81Mn6+RicuUK/2mny6oEqgU0lBbZnx3+Mfykz1wDZdnBRJ3fe1dDdXfL99WcvvV+lBaU+91O8aqejtzo1n2ytteKD1MUqTwxOxhBeAi5AwwSeEB1s1ru9XNSbpHh22vamckqUqq+3gaeNF6hortKv4eX1W8m8Nsd6kjMS7FNernRvz7qwS2FFSYMuT1t0jRf2g858jeZI09iEpcWzbcXt6EuKCqow+zZom9U6V9mRL89+QSndL6++XTn/W9bGLtkrPDpau+SnV9eA5WMILwEVImNyl7IuN/OWHpYQR7o3FhwQFhGp4/DwNi7tFx858qJziF1V05mNTYzTZG3TQtlYHbWuVHHmlMhKXqH/UNbJYLC6KuoWWSYEtTzqSLeU8ZzQxtYRLUSbHi79MuupRx0kS/EefCdI9u1uXJj+wvPPFQs6VS+9+58LH8ZdJM58zPg/gLizhBeACJEw9rbnkacF70pAs6T/jpJQplDztZhaLRQOirtGAqGtkqzmonOKlOlT2hprsDabGKTrzsYrOfKyYsMHKSMzU0NjZCgoIdU3QzX1DwuOlTQ9JJ8wlepKMWaS0Ozxn/xE8T1ulyVffZiTlXXF6t7R0onENXv87qeY0yyfR81jCC8AFSJh62vmSpyEXjnWmQSmcFhueputSf6aJyQ9q76lXtPfUq6prrDA1Rnnt59py9AltP/5njUpYoFHxdyo8uBsS3Itnkbri5hXSsNu7HhP8izVNWrRFWnOHUeShq4q2SkuvuPBx4njpiu8zy4mexRJeAN2IhKkntSx52nJ1FyVPe0Sv4ARN6PdNXd7ny8orXafckmWqqCswNUZtQ5l2nvibdp98QWmxc5SeuETW8EHmgzmxTXrrge7ZTyIZ6/ZJltBZYVZpwdsXluvt+H3XE/hmJTuMPmESs08AAK9EwtSTKHnqEYICwjUq4Q6NjL9dRyu2KqfkRZ2o2mFqjEb7Oe0vfU37S1/TgKhrlZF4l/pFTux4n9OJbdLGb0glO7vwFVxkwFTW58O85iWgLROX5qfyafO7b8appYtnn2JHSWO+Kg2ezc8+AIDHImHqSZQ89SgWS4BSYqYoJWaKTp3dp9zipTpc9pbsMtfws7DyfRVWvq/Y8GHKSFyiIdaZCgwIbn1S8961bmk2G2DcaI7/DvuUYF5b12LqzNb7KC+ecTpXLe17USr+pHtjse2V3n3Y+NPnKml+Nns5AQAeJ8DdAfiV5pKnlsDWxy2BxnFufN0moddI3TDoCS1KX6MxSfcoJLC36TFsNXnaXPBTLd8zV7tOPq/ahhb7pFbPk/Lf6p5gU26QFm+VxnyFawbmnd9H2ULzPsqLWdOkjK8YxSHu2i7dlyfNfUnqe1X3x3XyI+kfaRf6jQEA4CFImHranCyjxGlLlDz1GL1D+ujK5G9pcXq2ru7/PUWGJJse42z9aW0//mdl5c7W+0ceV8VL442lSF1tNhuXYfz3lpU8hUfnNO+jtF80i9pyH2V7rGnS8AVS5odG8nTD742KjN2ltlRaNa/7xgMAoBuQMPW05pKnd3+xh+XuncbH3AB7lJDACKUnLtaC0a9p2qBfKyniMtNjNDTVam/ZGr081K630gfrRHRE51KmuFHSku1S5tbOvBu4wJl9lM6ypknjvi19tVBKnty1uFoq2iLlvCAdeaPjBA4AgB7AHiZ3iRki6cAX/4WnCrAEapD1Rg2y3qji6hzlFL+o/PJNsqvJ+UEsFhUkxKggIUbxldUaU1iiQafKFNBW9tTc/DM0+tIeIvX13fI1wY+5Yh9lmFVa9N6FvjfhCdK7jxiJT2e9dd+F/0+eJN26hodKAAC3IWECnJQUkaGkwb/Smbrjyj2VpQOnV6m+6aypMU5HRWjT6EGKqE1W+rESjTh+WiHRI9quFMb+JHS35n2UBRtbL8uzBBpLg7tyzbXse9OcQJV8Km3/TdeKRRRtlZ4dLD3wOUkTAMAtSJgAkyJD++nq/t/V+L5f1f7Trym3ZLmq64tNjVEdFqKPh/bXzrQhGp4wX+kJdyoytJ+LIgZamJNlFHhoWSXPFfsomxOo4QsuzD7FDJXqKqT195vrQXauXPp7ijH7SgNcAEAPI2ECOikkMFJjkr6k9MTF+rzwReUc/aNO9zK3LbDeXqfckmXaU7JcqTFTNSbpLiVGZLgoYkAX9lG2TGJcnYC0nH2SpHt2Syc/kd681/kGufVnLjTAvbgMOgAALkTCBHRFjU0Br9+hoYXvaIikk9ERyhmQpIL4aKmjJrYt2NWkI+UbdaR8o5IiLlNG4l1KiblOAReXoIfvaquRrCtdnMT0tD4TpHtzjXL7K2aae29zGfT5b7omNgAAWiBhAroiO1MqfEeSZJHUt6JafSs+V0V4qHL7J+pA3zg1BpqbdSqu3q3iI7sVGZKs9MTFGh43T8GBvVwQPDyCM41k3aGnErjUGW3vq2pPcxn0/A1S6nTXxQYAgCgrDnRec0+bNkTX1Onag4XK/CBHEz4vUni9+YLiZ84V6cNj/6tlubP0cdEfVV1f0tWI4YnMNJLtCTU2acVN0gvDpZWzpeeHGR+7sqFsW/3pnLFihrT0aprdAgBcioQJ6Iwam1M3tGENTRrbkKbFEz/SdSmPKzbc/JP6c41V+qz4X3p1/22qiH1NpTX7OxMxPFFXG8m6gjsSuOZ9VfflSXNfkkKinH/vyY+kf6SRNAEAXIaECeiM7EypZFfH533RQyYwIETD4ubq9hFZmj30LxoQda3pT2lXo+oicvT6oXu1Nu+rKih/T3a7iX5Q8Dzd2Ui2O7g7gWuuqvdAvrlmuLWl0vLJNLoFALgEe5gAs9pZitdK8iSjH00LFotFyVFXKDnqCpXVfK7ckmU6aFunRvs5UyGcqNqhE1U7FB2aovTETA2Lm6OggHBTY8ADuKKRbFc4k8D1RKGIls1wSz6VPvx5x9X0SvcYywc9Yf8XAMCnMMMEmNXRTaUkDZgq3bqm3VOs4YM1OeXHWpy+TuP6fk1hQeZv8CrqCvR+4S+1LGeOPjn+jM7WnzY9BtyouZHsxdUQLYHG8Z6uYudpCVzzjNMtK51/jzv3fwEAfBIJE+AsW5702bPSyR3tnzf/LWnB204/4Q4PjtX4vl/V4vS1mjzwx4oJG2Q6tLrGCn168h/Kyp2rzQWPy1bD0iSv0VbBA1c0knWGpyVwF8flDHfu/wIA+CSW5AEdqbFJr99xvny4Q5ZA40a3k2WOgwLCNCL+Ng2Pm6djlR8qp2Spis58bGqMJnu98krXKK90jZIjr1JG0hL1j7xaFhM9odDD3NFItj1zsowZmpbLTt2VwLU0J0tac4dUuMm581fMkiY/JSWOde/3EwDg9UiYgI6snicVbe34vG66qbRYAjQg+loNiL5WpWfzlFOyVIfL3lSTvcHUOEVnPlLRmY9kDRuijMQlGhJ7k4ICQrscH1zE3Y1km3laAtcyrgVvSyc/kd68t+M9TRWHpbULjf9nXxMAoAtYkgc4UmMzKm85kyxJ0tQ/dfsNWVyvYbo+9XEtGr1WYxLukaUxzPQYZbWH9d7Rn2t57lztPPGsauopvwwnWNOkQbM8I1lqqc8E6d5cowR54jg59Wssf4O06haXhwYA8E0kTEBbbHnSq9Olog+cf48LS0BHhCRoXJ9vKP7Ew7qq3/cUFTrA9Bg1DTbtOPFXZeXO0ZajT6q89ogLIgV6iDVNunOjlHi5Eyc3GQ8+/pluzFABAGACCRPQUo1NWnGT9MJwqWSnJBN9jnqggpjFHqIRcXfozlErNH3wb9Wn9zjTYzTa67T/9Eq9svcOrT/0sI6f2S673e6CaAEXC7OaWwZbukdaOtH4N06jWwCAk0iYgJayM42yxGa4oYJYgCVQqTHX6+Zhz+rW4f/REOtMWRTY8RsvcrRyi9Yd/Lpe279EB0uz1dhU74JoAReKHWb0PDMjfwOlxwEATiNhApo1N6S1N5p7n5sriCVEjNLUQU9pUfoajUm8W8EBEabHKK05oHcLfqLle27WrpMvqK6h0gWRAi5y6xopLM7EG5qMf+sszwMAOIGECWhWssuJkyxS3Cij19Lt2cbG8/lvekT1rd4hfXRl/4eVmfGGrur/XfUO6Wt6jLP1p7T9+NNaljtL7xf+WpV1hS6IFOhmYVbp/oNS8mRz79vwNdfEAwDwKZQVByRj75IzN0+pMzy+PHFIYIQyEjM1OmGB8svfUU7xiyo5m2tqjIamWu099ZL2nnpZqdHXKyPpLiVFXEY/J3iuMKu06L0L5dDff0wq3iGpnf15JTuN8z2tEiAAwKOQMAGS9OoM6Vy549fjRkvzXvOqG6sAS5AGW6drsHW6iqs+U07Ji8ovf0d2M4UsZFd+xTvKr3hHCb1GKyPpLg2KmaoACz864KGa+1n1verSBrxt2Z8ljVjsVf+2AQA9iyV58G/NvZZKdrR/3tWPefUNVVLvMZo2+NdaMPo1pScsVlBAuOkxTp3do01HfqiX9szTZ8Uv6lxjlQsiBbpJcwPem19t/7wPfio9P4zKeQAAh0iY4N+yM53rtZQ41vWx9ICo0P66esD3lJn+hq5I/rYighNNj1F17qQ+Lvo/LcuZrY+O/U5n6k64IFKgmwybb1SxtHRQRTL/LWn5JGOJHgAALZAwwX81V8XraIla8mSvnl1qS2hQpC5L+pIWpa/RDalPKD58hOkx6puqlVOyVC/tmae3j/xQJdXm9kkBPWZOltTv6g5Oskule43Zpn9dTgU9AMB5JEzwXxWHOz4nLE66dbXrY3GTAEuwhsbO0q0jXtTctL9rYPQU02PY1ajPy97S6gP3aM2B+3WkfJOazJZmdxdbnnTkDWYVfF2YVbryUefPP73b+xvccm0DQLdh5zb8V/SQ9l9PmiDd8ZZHV8TrLhaLRX0jx6tv5HiV1xYot2SZ8kpfV6O9ztQ4xdW7VPz5LkWF9ld6QqaGxd2s4MBeLoq6C2psxnLMlgUBUmd6fAVEdEFH/97bUrDRKBwx/83uj8dVuLYBoNsxwwT/ljiujb0NAVLyJOmu7X55gxETlqJJA3+ozIx1mtD3mwoPMtMQ1FBZd0wfHPu1snLnaFvRn1R9rsQFkXZBdqZxM9xS880xfFPsMOf2MrVkbzQSD2+apeHaBoBuR8IE/1NjM5bavDDc6MNy8fKx1OnSrWvcE5sHCQuyamzf+7U4fa2uS/mpYsOGmh6jrrFSu4v/qazcuXon/zGVnj3ggkhNat67dvHfuzfeHMOcOVlSyjTz7ys/1P2xuALXNgC4BEvy4H/aegKrACnxcmnucp8r8NBVgQEhGhZ3i9Jib1bRmY+VU7JUxyqdqCzYgl2NOmRbp0O2deoXOVEZiUs0IOpaWSxueGbT0d618kNcA76qudR4q+a2ThR3iDH/sMAtuLYBwCVImOBfTmxz0MiyyZhtgkMWi0X9o65S/6irZKs5rNySpTpoy1aTvd7UOMfPbNfxM9sVE5qq9KQlSoudraCAMBdF3YaO9rJ4y80xDLY8I1GIGep8MtCyue2aO6TCTe2fv+kh79gDxLUNAC7Bkjz4D1uetC6z/XO8ZemNm8WGD9GUlMe0OH2dxvV5QGFBMabHKK/L19ajTyord452HP+rztaXdn+gbXG0l8USaBznCbx3aLm0duXszjWfDbNKC96W7suTbvi9FDe67fO8ZQ8Q1zYAuAQJE3xfyxurjpas8ATWlF7BcRrf7+tanL5Okwb+SNGhKabHqG0o186Tzyord442F/xcthonyr13VVt7WVKmGcfhHbqzuIE1TRr3bemWlW2/7k17gLi2AaDbsSQPvq/NPUttSBzHE9hOCgoI08j42zUi7lYVVr6vnJKlOn5mu6kxmuz1yitdrbzS1eofdbUyEu9ScuSVslgs3R/wxXtZzCzngvudbzp9kZaJTWf+Pn1hDxDXNgB0OxIm+LYj6x3sWWrD9L+5NhY/YLEEaGD0ZA2MnqzSswf0WclSHba9KbvMNbI9VvmhjlV+qNiwocpIWqIh1psUGBDS/QE372WBd3FVYtPRHqDN35fOnZESx3r+dcO1DQDdhiV58E3Ny/BW3uTc+YnjpD4TXBuTn4nrNVw3pP5ci9PX6rKkexUSGGl6DFvtIW0ueFxZuXO188Rzqm0o7/5A4X1cVdygo15NpbnS2oWd2y8FAPBaJEzwTc4uw2vG7JLLRIQk6orkh5SZnq1r+n9fUaH9TY9R01CqHSf+omU5s7X16FMqry1wQaTwGq4sbjAnS+p3dcfneUshCABAl5EwwbfY8qQdv2+7eWObAowbLGaXXC44sJdGJy7UnaNWatrg3ygp4nLTYzTa67Tv9Aq9svd2rT/8HZ04s0N2u737g4Xnc1VxgzCrdOWjHZ/nTYUgAABdwh4m+IYamzGr5Ox+pWap06ke1cMCLIEaFDNVg2KmqqQ6VzklS3Wk7G3T+5yOVrynoxXvKT58hDKS7tJg6zQFWIJdFDU8jiuLG3S05K8lbygEAQDoEhIm+AYzS/BmvCD1TqJ6lAdIjEjXjYN+qTP9TmjPqeXaf/o11TdVmxrjdM1+vZP/Y20r+qNGJyzSiPjbFRpkfr8UvJQrihvEDpOSJ0lFWzs+18KvUQDwdSzJg/drroTnzBK8xHFSxr3SoFkkSx4kMrSvrur/HWVmZOuq5EfUO6SP6TGq60u07fgftSx3lj4o/I0q6465IFL4jVvXSGFxHZ+3Yoa0fDIFIADAh5EwwXvV2IwbFWcr4UkUd/BwIYG9lZG0RAtHr9bUQb9UQq/RpsdoaKrRnlPL9fKe27Tx8++ruGq3CyKFzwuzSvcflJInd3xu0Vbp2cEkTQDgo0iY4J1qbEZpX2eWzDRLnkxxBy8RYAnSEOsMzRv+L9087B9KjZkqyVwDW7uadKT8ba3Ju0+rD9yrz8s2qMne4JqA4ZvCrNKi96T5TuyNPFcuPUfSBAC+iMXX8E6r50m1pc6fHxYn3bradfHAJSwWi/r0vlx9el+uyrpC5ZZk6UDpGjU01Zgap6Q6R28f+YF6h/RTeuJiDY+bp5DACBdFDZ/jVMVNSXXl0qp5RpIFAPAZzDDB+9jyTM4sTTKW1oRZXRdTTyg7ZPy3/LB743CTqNABumbA95WZnq2J/R5Sr+AE02NUnTuuj479VstyZunjY79X1bkTLogUPsdM1byiLZQaBwAfQ8IE71Jjk9bc1vF5M16Qbs+W7suTFm3x7mSpxiatuEl6cbzx8X/GGR/76dKf0KAoXd7nXi0a/bquT/mF4sKHmx6jvqlan5X8R8tz52nTkUd1qnqvCyKFz3DUKNeR8kOujQcA0KNYkgfv0bxvqaOleMmTjUp4vuJ8yfSQC8cKNkrrFht9aPxUYECw0uJma2jsLJ2o+kQ5xUt1tHKLqTHsatThsvU6XLZefXqPVUbiXRoYPVkBzt4Yw3/MyTL+zTnT623rT6RzZ6TEsVTjBAAfQMIE7+HMviVf26tky7twg9ay5oG90ThedtDvb8gsFov6RU5Uv8iJKq89opySLB0sXatGe52pcU5WfaqTVZ8qKnSAMhIzlRZ7s4IDw9s+2ZYnVRyml5c/adkot+RT6a0HpHOVbZ9bskNau9D4/+RJX5Qo9+JZbgDwcyzJg3c4sr7jfUuxo3xjr1JLFR3sV2LpTysxYYM0eeCjysxYp/F9v67woFjTY1TWFer9wl8pK3eOthf9WdXnTl14sXl55AvDpZWzjRlPP14e6ZesadLwBdID+VLi+I7Pp+Q4AHg9EiZ4tuYbVGd6Ld26yreSJanjzeYxQ3smDi8TFmTVuL4PaFH6Wk0Z+JisYSY27X+hrrFCu4qf1/I9c/Vu/mMqPZvXYnlkC83LI+FfwqzSpF84dy4lxwHAq5EwwXPZ8qRXp0sFGzo+N3myby6NcrTZ3BJoHPfFr7kbBQWEanj8PM0f+ZJuGvonJUdeZXqMJnuDDtrWaeX+xVoXfViF1gjZW57Qcnkk/IuZ6nnNJccBAF6HPUzwPDU240m+M5urJd/bt3Sx5s3mBS16u6RMM47DKRaLRQOirtGAqGtkqzmonJJlOmR7Q032elPjHI+N0vHYKMVU1yijsERDi20KavoifSo/RALrb5ofaBRsdK5XU3PJca4TAPAqzDChfbY86cgbPfv0vK1lT474So+l9jRvNr97p/Hx3TuNj335a3ah2PA0XZfyUy1OX6uxfe5XaGC06THKI8K1ZUSKsq5O147UvqoJDmJ5pL+ak2U8wHAW+w4BwOsww4S2tTXLkzrTuDlw5Y16y6pwHZn/lpQ63XWxeJqYIZIOfPFfdFWv4HhN6PdNXd7ny8orXafckmWqqCswNUZtSLB2Duqr3Sl9NbRyuTLClsgaPthFEaNbdVelw4ur563/ilR/xvH5JNYA4HVImNC29ja3u7L3T0dV4ZoljvOvZAkuExQQrlEJd2hk/O06WrFVOSUv6kTVDlNjNAZIB0pX6UDpKg2IulYZiUvUL/IKWSyWjt+MnuWqh0HWNONPynSjwENd+UUnBEjJ17AcDwC8EEvycKnmWZ6L1+S7enO7LU+yHXDu3Ol/c00M8FsWS4BSYqZo7rC/67YRL2qodZYsMt/AtrDyfWUf+qZW7l+svNLX1dhkbp8UXMzVlQ7DrNJXPjcK0bTSZJQYpww9AHgdEiZcqid7/9jypAMvSy9OMHrbvPudDt4QYDwN7jOh+2IALhLfa6RuGPSEFqW/rsuS7lFIYKTpMWw1B7W54GdavmeuPj35vGobKlwQKUzpqYdBYVZp0XtG0mS56Nds/gbplWlUVQTcwR37suETWJKHS/VE758am7R6XsfNaC+WOp3qcOgxvUOSdEXytzS2z1d0oHSNckuW6cy5IlNjnK0/rU+O/1m7Tv5Dw2JvVnpipqLDBrooYi/WXXuK2uPMw6Du+ty2PKMq3iWapJKdRtPjntgXCsB9+7LhM5hhwqVc2funeUbpH0PMJ0sznqU6HNwiOLCX0hMXacHo1zRt0G+UFHGZ6TEammq19/Qrennv7Xrr8CM6cWan7HZ7x2/siLc/MW1uTv3CcGnlbCORcNWytZ5sBO3MfkyaHgM9g6bjruPtv4OcxAwT2tbc+6fl05iu9P7p7IxSS/2v6/x7O6snnnrDawRYAjXIOlWDrFNVXJ2j3OKlOlL+tuxqMjGKXQUVm1VQsVnxvUYpI3GJBltvVIAl2FwwvvLEtCcLzDjqm2QJNH6+dee/cWea2rZcCsjPF8A1HFXf5d9f1zjzO8iH7qFImNC2lqVyyw917WI/sU16dbp0rrJz73fFzUxHfOVmFC6TFJGhpMH/ozN1x5V7arkOnF6l+qZqU2OcPrtX7+T/SNuK/qj0xEUaEX+b8/ul3FXJsju540amux8GOWKmqW3Jp15/MwF4rJ5ciutP2vsdNHuZz91DsSQP7bOmSYNmde6HSfNSm2VXdj5ZklxzM9MRpu/hpMjQfrq6/yPKzFinK5MfVkRwkukxquuL9XHRH7QsZ7Y+PPZbVdZ1sE/KXZUsu1tPFphp1vww6L486fZs47+uWurrbFPbDV+jch7gKj25FNdfdPQ7aPWtPncPRcIE18nONCpCdUZIjDT3JdfezDjiKzej6FEhgZEak3S3FqWv1tTUpxTfa5TpMeqbziq3ZJle3nOrNn7+3yquzmn7RHckGq7gzhuZrjwMclbL5Cy2neuhrlx6dYbf7AUAepQr92X7q45+BxVt8bl7KBImuMaJbV9MxZrZ2/GF5EnSA59Lwxe45weZr9yMwi0CLMEaEjtTtw7/t+YOe04p0ddLMtfA1q4mHSnfqDUH7tXqA1/WkbK31dTyl4+vPDH1lxsZa5p0zU/bP6f4k9aFL5ZPZtYJ6C5tzfa6Y/WKr3Bmj6YjXnoPxR4mdC9bnnRss/TRE+belzRemvh9KXGs+2+SfOVmFG5lsVjUt/dY9e09VhW1R5V7Kkt5pWvU0FRrapyS6s+08cj3FRmSrPTExRoWd4tCerJ4gav11J4id0u43Nz5RVulf6RJ9x/02jX/gMfozn3ZaL+ATr+r2y/w5aX3UCRM6B4ntklvPSCd/sz59wRFSJOelAbP9qwfXL50MwqPEB02UNcO+G+N7/t17T+9QntOvaSz9adNjXHmXJE+PPa/2nHirxoRP1+jZ/xRvd/6lvcnGv5yIxM7zJg9N1MptLbUWKp313bXxQX4E2uab/58cYf2HnatW9z+PVR9fc/H20UkTOiarpQLb6iW3n1Yyn/D8yqn+MtTb/SosKBoXd7nPmUk3q3DZeuVU/KibDXm1nOfa6zSZ8X/Uk7xixo8brrGXPuI4msavT/R8IcbmVvXGLNGtaXOv6f4E2n5FOnW1Z71MxKAf2vvYZcP3kORMKHzamzGWnszv/wlyRIg2VvsbfLEUsj+8tQbbhEYEKxhcXOVFjtHx89sV07JiyqsfN/UGHY16nDZmzpc9qb69h6vDMsSDbQPkcXC1lSPFWY1lti9Ml0q2eH8+46/73k/IwFAavthlw/eQ5EwofNWzzOZLFkk2VsnS5JnN4/zh6fecBuLxaLkqCuUHHWFymqOKLdkqQ7a1qnRfs7UOCeqduhE1Q5FhQ5URmKmhsXNVVBAuIuiRpeEWaW7v5g1Ov7+pT8P22Jv8tyfkQDgiA/dQ/EoEp1jyzO/DC9xbPuve2nlFKA7WMMHaXLKj7U4fZ3G9f2awoLML7+qrDuq9wv/R8ty5mj78Wd0tt7k7C96zq2rpZTp5t6z/X+lz57z2rK8AOCtmGFC53RUerulxHHS3OWS3W6UzXXESyunAN0pPDhW4/t+VZcl3aNDtjeUU7JU5bWfmxqjrrFCu07+Q58V/1shsaNUVjtMicEjXRQxOuXiJSvhCdKGr7e/VC/n78YfSYq/TJr5nNRnQs/ECwB+jBkmdI6zNfgHTJXu3GhMyfpLzxWgGwQFhGpE/K26Y+TLumnIn5QceaXpMZrs9aqN2K3VB+9S9sFvqrDiA9ntdhdEi05rbqDbZ4KxVC9xvHPvO71bWjqRfk0A0ANImDyRN3R7b05+2rqEgiKk639vdLdf8Hbryk40jwNMsVgsGhB9jWanPaPbRyxXWuxcBVjMLw4oOvOx3jz8kF7dt0D7T69SQ1OdC6JFl13xfXPnN/drImkCAJdhSZ4nqbFJ2ZmtyzCmzvS8ktvN2iobmTzJKJ3rKF4frJwC9JS4Xmm6PvVxXZH8X9pz6hXtO/Wq6horTI1RXvu5thz9hT45/meNSrhTI+PvVHiwB/588VdmG9xKRvGdVfOkRe91ezgAABImz5KdaZTYbskTS24360ry40OVU4Ce1is4QRP7fVOXJ31ZB21rlVOyTJV1R02NUdNg044Tf9Ouk/9UWtwcZSRmKiZskIsihtMcNc7uSNEWKX+DlGqykAQAoEMsyfMUtjxjpubiX5AtS257kpbLBpvX4JMAAT0qODBcoxLu1IJRKzRj8O/Up/c402M02uu0//RKvbL3Dr156NsqOrONfU7u1tbSZWesmCGtuInleQDQzZhh8hQdVZ0rP+QZCUmNzei/1LKkuCcvGwT8gMUSoJSY65QSc51OVe9VTslSfV62QXaZmKGQVFi5VYWVWxUXPlwZiUs02DpDgQHBLooaDrWcvS/5VNrxf9KJj5x7b/4Gz12VAABeihkmT9FR1TlPKLldY5OeH3Zp/6XmZYMA3C4hYpSmDnpSi9LXKD1+iSxNoabHKK05oHcLHtPyPTdr18kXVNtgbp8Uuok1TRq+QMr80Ciic8PvpeCIDt70RZPbk5/0RIQA4BdImDyFN5TcXj3P2Fx8MU9dNgj4sd4hfTSh70OKO/6wruj7HfUO6Wd6jLP1p7T9+NPKyp2t9wt/rYraQhdECqdY06Rx35a+WiglT+74/NW38zMZALoJCZMn8eSS27a8S2eWLlZ+qGdiAeC0AHuoRsUv1MLRr+nGQb9SYsQY02M0NNVq76mX9PLe27Th8Pd0supT9jm5S5jVqIY3f33751UVGisCXr6RPU0A0EXsYfIknlxyu6M9VpJnLBsE0KYAS5AGW6dpsHWaiqs+U07Ji8ovf0d2NZkYxa78ineUX/GOEnqNVkbiXRpkndqpvlDootQZUuI4qWRn++cVbpJemSbNXe45v08AwMsww+SJPLHqXEd7rJIne1a8ABxK6j1G0wb/WgtGv6b0hMUKDuhleoxTZ/doU/4P9dKeefqs+EWdazzjgkjRrul/de68kp3GbNPyycw2AUAnkDChYzU26Z1vOX49LE66dXXPxQOgW0SF9tfVA76nxenZuiL524oITjI9RtW5k/q46P+0LGeOPjr2O52pO+GCSNGmPhONPa7O/iov2ir9I42kCQBMImFCx9pqqNsseZJ0/0FKigOu1LLvmQuEBkXqsqQvaVH6at2Q+qTie400PUZ9U7VySpbqpT3z9PbnP1BJda4LIsUl5mRJydc4f35tqfTqDNfFAwA+iIXnaF9zQ11HZj5PsgS4So3NeGDR8t+gC/ueBViCNTT2Jg2xztTJqk+VU/KiCirek+R8gQe7GvV5+QZ9Xr5BSRGXKSPxLqXEXKeAiyuAonuEWaVFW6TlU6SiLc69p/gT6cWJ0h1v8fO7J9jyjH3AnrQvGYApzDChfc401AXgGm3N7vZA3zOLxaK+keM0Y8jvdOeoFRoZf4cCLeb7ORVX79bGI/9PL++5Tbkly1XfeNYF0UKSsSx6wFTnzy/+RHp2sHTgZcqPu0qNTVpxk/TCcGnlbGMf2YqbWBIJeCESJrTPGxrqAr6oeXbX3tj6eA/3PYsJS9GkgT9UZka2JvT9psKD4kyPceZckT489hsty52tbUV/VPW5EhdE6ufCrNKCt40GtzOede5n87lyae1CbuRdxU0PPAB0PxImtM8bGuoCvsjDZnfDgmI0tu/9Wpy+Vtel/EyxYeYflpxrPKPdxf9SVu5cvZP/E50+u98Fkfo5a5qU8RVpyTajII+zuJHvXh7ywANA9yBhQvtqbFJj/aU/9Ptf5xkNdQFf5aGzu4EBIRoWd7NuH7lcs4c+o/5RJgoOfMGuRh2yZeu1/Uu0Nu9rKqh4T3a7mX5QkNR+MZAwq1GQJ3G8c2M138iXO9FzDx3zsAceALqGog9omy1POrZZ2vkHyXbxU+AAKTCYzcKAKzXP7hZsbP3AwhIopUxz++yuxWJRctSVSo66Uraaw8otWapDtjfUaD9napwTVZ/oRNUnig5NUUbiEqXFzVZQQLiLovYRzhYDCbNKd38i/WeCVLLDubFPfSbJ/H41XMRDH3gA6BxmmNDaiW3Svy4zNqlu+KpUuufS2SU1saQA6AlzsozkqKWUaR43uxsbPkRTUh7TovS1GtfnqwoLijE9RkVdgbYWPqVlOXP0yfG/6Gx9afcH6ivM7o25c4Pzy/N2/qFrscHAcnbApzDDBGM26dQuafuvpWInn0JKxpICfugDrhNmlea/aTycKD/k8WWJewXHaXy/r+myPvfokC1bOcVLVV6Xb2qMusYKfXryOe0u/peGxs5WRmKmYsN5Gn+eo1YPLffGXHyNNC/PWzWv49LjJZ9KkZLqyqXghO6K2j/NyTKS2JZ/Xx74wANAx0iY/FlbyzrMYEkB0DOsaZ6VKHXQVyYoIEwj4m/X8LhbVVj5gXJKlur4mW2mPkWTvV55pauVV7pa/aOuVkbiEiVHXiWLxdJdX4V3cmZvTFvXSphVWvSekVDlPi9t+5/2x/n3WOm+HJZed4WXPfAA4BgJk7+y5RlPvk7tMv9eD9lDAaCHmWyka7EEaGD0JA2MnqTSsweUU7JUh8vWq8neYOrTHqv8UMcqP5Q1bIgyku7SUOtNCgwI6epX4526ujfGmiaN/nLHCVOtzZiRWvSeufj8VXsPETztgQcA09jD5G9aNtIr2Sl1pjIVSwoA/9SFvjJxvYbr+tSfa9Ho13V50pcVGhhl+tOX1R7WewWPKyt3rnaeeE61DX7YN6g79sY0j9HRLUDRFil/Q6dD9Qs0pwX8AgmTv1k9r/O/ABPHGU0R57/JMg3A33RTX5mIkERNTP4vLU7P1jUD/ltRof1Nh1LTUKodJ/6iZTlztPXoUyqvzTc9hlfrjmIgc7KkZCdKwq+YIS2fTALgCM1pAb/Akjx/UWMznn6d/Lhz7x8wVbrlVRIlwF91du+MA8GB4RqdsEAj4+fraMV7yilZqpNVn5oKqdFep32nV2jf6RUaGDVZGUl3qW/v8b6/z6k79saEWaVFW5wrOV60VfpHmlE4gt8BF3SmAAcAr0TC5C+yM80nS0ER0qQnpcGz+aEP+DsX9ZUJsAQqNeYGpcbcoFPVe5RTslSfl22UXRe3M2jf0cotOlq5RfHhI5SRtESDrdMVYAnuVExeozv2xty5wUiGajso415byp6mi3XzQwQAnoslef7A0VOw9iRPkr5WKI3/Nj/wAfRIX5mEiNGaOugpLRy9WhmJdyk4IML0GKdr9uud/J9oee4t2n3yX6prqOxyXD6tueR48uSOzy3aQv+9lmhOC/gNEiZ/cGyzkydaLuxTWrSFpRcAWuuhRrqRoX11Vf/vKDMjW1clP6LeIX1Nj1FdX6Jtx/+oZbmz9UHhb1RZd6xbY/QpzSXH5zvxYK38kOvj8RY0pwX8htNL8o4dO6b+/c1vzoUbme2zlDrDYXlgAOjpvjIhgb2VkbREoxMXKr/8HeUUv6iSs7mmxmhoqtGeU8u199TLSom5XmMS71JixBjf3+fUGakzpH5Xt38Osyat0ZwW8AtOJ0zp6en605/+pLvvvtuV8aA7tVW9py2J46S5y3kaBsA5PdxXJsASpMHW6RoUM00l1Z/ps5IXlV/+jiS702PY1aT88k3KL9+kxF7pSk9aokExUxVgYStvK3OXS29/4Pj19fdJt67hwVozmtMCfsHpJXlPPfWUHnzwQc2fP1+lpR1sDoX7OSoBfLEBU6U7N/IDHoDHs1gsSup9maYP/o0Wjn5NoxMWKigg3PQ4JWdztenID/XSnluVU7xU5xqrXBCtlwqNMf7bz0HJ8eaKeZQZb82aJg2axe9SwEc5nTB985vf1O7du1VWVqbRo0drzZo1rowLXVFj67gHxNhvGXuVFrzNk0J4DluedOQNNpajQ1GhA3TNgO8rMz1bE/s9pF7BCabHqDp3Qh8V/U7Lcmbro2P/p6pzJ1wQqZea+ifHr9WWGr2Z+HcKwE+YWoswaNAgbdq0SU8//bTmz5+vkSNHKiio9RA7d+7s1gDRCdmZUsmu9s8Z+188CYPnaGu/XepM9tShQ6FBUbq8z73KSFyiI+Ub9FnxiyqtOWBqjPqmauWUvKjckiwNtk5TRuISJUSMdlHEXqLySPuvl+6Rnh/Gv1MAfsH04u2CggKtWLFCsbGxmjdv3iUJE9zsxLb2izxYAo0NqSRL8CRt7bcr2GjMlM5/0z0xwasEBgRraOxsDbHO0omqHcopWaqjFeZ6BtnVqMNl63W4bL369B6rjMQlGhg9RQEXV0HzB1GDnDsvfwP/TgH4PFPZzrPPPqvvfve7mjZtmnJzc5WQYH4JBFxs4zfafz3hMqr3wLM46hNmbzSOlx0kwYfTLBaL+kVOUL/ICSqvzVdOyTIdLF2rRnudqXFOVn2qk1WfKip0gNITMzUs9mYFB5rfL+W1rEONfnxFWzs4scn4d3ryE6nPhB4JDfB7tjyjcTJFRnqM03uYbrrpJv33f/+3nn76aa1cuZJkydPY8qQdv5dKOlgSOXc5SyfgWSoOt/86fV/QSTFhqZo88FFlZqzT+L7fUHhQnOkxKusK9UHhr5SVO1vbi55W9blTLojUQ926Rgpz8nu2+nb2NAGuVmOTVtwkvTBcWjnbWBa74iaKsPQApxOmxsZGffbZZ/rSl77kynhg1olt0n/GG/943v1O++cmjuNJBDxP9JD2X6fvC7ooLMiqcX2/okXpr2tKyk9lDevgmmtDXWOldhW/oOV75urd/MdUejbPBZF6mDCrdP9BKXlyx+dWFRo3by/fyM0b4CrtLV+HSzmdMG3YsIHGtZ6k+SnDsis7nlVqNv1vro0J6IzYLzaOX7xPxBJoHCfJRzcJCgjV8LhbNH/kS5o19Gn1j+qgSWsbmuwNOmhbp5X7F2vdwW/oaMVW2e1NLojWQ4RZpUXvGVVVO3q4IUmF73DzBriCo3YxLZevw2WcTpjgYbIzjc22zkocx/pyeK45WUYxkpZSprHfDi5hsVjUP+pqzRr6tOaPfEnD4uYpwBJsepzjZ7Zp/eFv69V9C7T/9Eo1NNW6IFoPYU2T5jrz79HOzRvgCixfdytK3HkjR5vk28PsEjxZmNWoslV20Pihz0ZW9JDY8KG6LuUxTez3Te099Yr2nnpFdY0VpsYorz2iLUef1Pbjz2hU/J0alXCnwoNjXRSxG/WZaMz65m+Q1MGsWvkh/g0D3Ynl627FDJM36qjHUisBxi84ZpfgDaxp0qBZ3Gihx/UKjteEft9QZsY6TRrwqKJDU0yPUdtQpp0n/66s3Dl6r+AXKqv53AWRutmcLCn5mo7P4+YN6F4sX3crEiZvtKudDuwXS53OsiYAcFJQQLhGJszXnaNe1Ywh/6e+vc0/bGq0n9OB0lV6dd+deuPQQyqq/Fh2u90F0bpBmFVatKWdQhAWbt4AV2H5utuwJM+b2PKkY5s77ouRPEka+5CUOJZfWgDQCRZLgFKipygleopOn92vnJKlOmxbL7saO35zC8cqP9Cxyg8UG56mjMQlGmKdqcCAEBdF3YNuXS2tuUMq3NT6eJ8rpfT7pPy3pMoCSRZpwHX8LgK6A8vX3YaEyRvU2L4o8uDEvqXrfy+N/7bLQwIAfxHfa4RuSP2Fruj3kPacWq59p1fqXOMZU2PYag5qc8HPtK3oTxqdsFAjE+YrLCjGNQH3hDCrtOBt48bt2GbpXLV0cIVUtEVau/DS8/tcJc3Ppg8g0B2saSRKPYwled6grbr7jgye7dpYAMBPRYQk6orkbykzPVtX9/9/igxJNj1GTUOpPjnxjJblzNbWo79URe1RF0Tag6xpUsZXpPw3pOMfOD7v5EfSP9Lo0QTAK5EweTpHdffbkjyZJw4A4GLBgb2UnrhIC0a/pmmDf6OkiMtMj9For9O+06/q5b23663Dj+jEmZ3eu8/J2d9TtaXSqnk9ExMAdCOW5HmyGpu5BoBj/8t1sQAAWgmwBGpQzFQNipmqkuoc5RQv1ZHyt2XvqOR2K3YVVGxWQcVmxfcaqYzEJRpsndapvlBu01F/mJaKthjL+Hi4B8CLMMPkybIzzZUQTxzrslAAAI4lRmToxsH/o4WjVys9cYmCAyJMj3H67D69k/9jLc+dp93F/1Zdg7l9Um7TUX+Yix3b7Jo4AMBFSJg81fnmtE48qaQGPwB4hMjQfrq6/yPKzFinK5MfVkRwkukxquuLta3oD8rKna0PC/9XlXVFLoi0GznqD+PIWw9IK25iPxMAr0HC5KnMPIGjBj8AeJSQwEiNSbpbi9LXaGrqL5XQa7TpMeqbzir3VJZe3nOrNn7+fRVXfeaCSLtJW/1h2lOw0dyScwBwI/YweZoT26SN35BKdrZ/3vy3JHsDNfgBwIMFWII0JHaGBlunq7h6l3KKlyq/4l1Jzhd4sKtJR8rf1pHyt5UYMUYZiUuUGnODApyd0ekJF/eHqa+RPnhMKt3T9vn2RmMVBfuZAHgBEiZP4WyiJIuUOkNKnd4jYQEAus5isahP77Hq03usKmoLlXsqS3mlq9XQVGtqnJLqz/T2kc8UGZKs9MRFGhY3TyGB5vdLuUzL/jDDbpc+e07a8IDj88sPkTAB8HgsyXO32jJp+WRp2ZVOJEuSZJeufcLlYQEAXCM6bICuHfB9LU7P1sR+/6VewQmmxzhzrkgfHvutsnJn6+Njf1DVuZMuiLQb9J/S/usWntsC8HwkTO72rwypaKu599Scck0sAIAeExYUrcv7fFmLRr+u61N+rtjwYabHONdYpc9K/q3lubdo05Ef6dTZfS6ItAs6KgixYoa09GoKQADwaCRM7lBjk9bcbvz/uU6UjY0Z2r3xAADcJjAgWGlxc3T7iGWanfYXDYiaZHoMuxp1uOxNrdp/l9bmfVUF5Ztlt5vpB+VCHRWEOPmR9Oxg6cDLxp4mAPAwXpcwPfPMMxo0aJDCwsI0fvx4bdmyxd0hmZedKRW8Y/59lA8HAJ9lsViUHHmFbhr6B90x8lWNiL9dgZZQ0+OcqNqhtz5/RC/vna+9p15RQ1ONC6I1Icwq3fDH9s85Vy6tXSg9P4yS4wA8jlclTC+99JIefvhh/ehHP9Knn36qyZMna9asWTp69Ki7Q3Oemf5KF6N8OAD4BWv4IE0e+CMtTl+n8X2/pvCgWNNjVNYd1fuF/6NlOXO0/fgzOlvvxuXcZlplUHIcgIfxqoTpd7/7ne6//3595Stf0ciRI/X73/9eAwYM0F/+8hd3h+a8isPOn5s4Trp5hXR7tnRfnlGyNczqutgAAB4lPNiqcX2/qkXpazV54E8UEzbY9Bh1jRXadfIfysqdq3fzf6rSsz247K3GZswYbfiq8+9pWXIcADyA15SnOXfunHbs2KEf/OAHrY7PmDFDH3zwQZvvqaurU11d3fmPKysrJUn19fWqr693XbDtiUiVLOGqt4QbsXzx31aCe0u3rZWSxrY+7q6Y4RGar1m3XbvwOlwzviRAQ6LnaHDUbBVVfaS9p7N0vGqbqRGa7A06aFurg7a16tt7okbHZyq591WyWCznz+n2ayb7Huno+1Jbv+s6smqhdOd6KTSme2KBS/BzBmZ50jXjbAwWu93ufPc8Nzp+/LiSk5P1/vvv65prrjl//KmnntK//vUvHThw4JL3/OxnP9Pjjz9+yfFly5apV69eLo0XAABXaggu1tnIj1TbK0eydK7AQ2B9gnqduUph1RmyeM8zVADoFmfPnlVmZqYqKioUFRXl8Dyv++nY8kmYJNnt9kuONfvhD3+oRx555PzHlZWVGjBggGbMmNHuN8Xl6spVv/5r2hB0j6Z/fp+C7TVSwmXSDX+4dFYJ+EJ9fb02bNig6dOnKzg42N3hwAtwzfiDL+ts/WntL31VB2wrVddYaerdjcGndCb2ddUnbNWIuPkaEnWLtrzzSfdcMwUbpDV3dG0MSUq4XLr6x1L0YClmSNfHQ7fi5wzM8qRrpnn1WUe8JmGKj49XYGCgTp5s3ZyvpKRESUlJbb4nNDRUoaGXVhgKDg52719QcIJ0y0tSdraCb/6PguPSqHwHp7n9+oXX4ZrxbdHBfXVlr4c0Pvkryitdq5ySZaqsM1cMqbaxTLtKntNnp/6tEGu6qhtHKqGX+b5QrcQOleztVOib/5ZUVyZ9+HOpdI/j80o+lFbPMf4/cZw0/W9Snwldiw3djp8zMMsTrhlnP7/XFH0ICQnR+PHjtWHDhlbHN2zY0GqJntdJmU6yBMA/2PKkI2+wmd9FggLCNSrhTi0YtUIzBv+f+vYeb3qMJvs51fbeqVUHF+vNQ99S0Zlt6vTKfUdNa5tbZKROl4YvkG5Z6fyYJTulpRMpPQ6gR3nNDJMkPfLII7r77rs1YcIEXX311fr73/+uo0eP6utf/7q7QwMAOFJjM/rP5a+/cCx1ptEmgcqf3c5iCVBKzBSlxEzRqbP7lFP8oj4v2yC7Gk2NU1j5vgor31ds+DBlJC7REOtMBQaYfBo8J8soEd7y7/7iFhmxw6TkSVLRVufHbS49Pv9Nc/EAQCd4VcK0cOFClZaW6uc//7lOnDih9PR0ZWdnKyUlxd2hAQAcyc40bnBb4oa3RyT0Gqmpg57UFckPaU/JS9pfulLnGqtMjWGrydPmgp9q+/E/aXTCQo2In6+woGjn3hxmNf6Oyw5K5YekmKFtr6q4dY309xSp/oxz47YsPc4qDQAu5jVL8pp985vfVH5+vurq6rRjxw5NmTLF3SEBABxpbtZtv2h2g147Pap3SB9d2f/bWpyerav7f0+RIcmmxzhbf1rbj/9ZWbmz9X7hr1RRW+j8m61p0qBZjpObMKu04G3TMan8kPn3AIBJXpcwAQC8SEfNurnh7VEhgRFKT1ysBaNf07RBv1ZixBjTYzQ01WrvqZf18t7b9Nbh7+pE1aed3+fUUp+Jbe95ak/M0K5/XgDogFctyQMAeJnoDspAc8PrFgGWQA2y3qhB1htVXPWZckqWKr98k+wy08/JroKKd1VQ8a4Seo1WRuISDbLeqABLF24t2trz1BZLoLEXiuV4AHoACRMAwHWaK6UVbGy9LI8bXo+R1HuMknqPUWVdkXJOLtO+UytlDzhnaoxTZ/doU/6j6n28j0YnLNKI+FsVEhhpPpi29jzVVUgbvmZUyGt2ceEIAHAhEiYAgGs5UykNbhcVmqwr+j2skt0DlTqhRvtKX1Z1fbGpMarOndTHRb/XzhPPanj8rUpPWKTI0H7mg7Fe1J/w7h1GElX4riSLNOA6KiwC6DEkTAAA13K2Uho8QoA9TOkJt+uyvkv0ednbyil5UafP7jM1Rn1TtXJLlmpPSZZSY6ZqTNJdSozIMB+MLc/YBxceL73/E0rTA3ALEiYAQM+4eNYAHi3AEqyhsTdpiHWmTlZ9qpySF1VQ8Z4k5ws82NWkI+UbdaR8o5IiLlNG4l1KiblOAR0Vdmird5csrc/J3yCtukVatMXpeACgM0iYAACAQxaLRX0jx6lv5DiV1xZoT0mWDpSuUaO9ztQ4xdW7VXxktyJDkpWemKnhcbcoOLBX2ye31bvrkkStyWh2u3yKdOtq1880Nc92MUMK+B3KigMAAKfEhKXo2oE/UGZGtib0e1DhQXGmxzhzrkgfHvuNluXO1sdFf1TVuYv2STnq3eXI8feNPXKuUmOTVtwkvTBcWjlben6Y8XFtmes+JwCPQsIEAABMCQuK0dg+92lx+lpdl/IzxYabn3E513hGnxX/S8tzb9Y7R36s02f3Gy901LvrYvYm1zZBbmu2q2Cja5M0AB6FhAkAAHRKYECIhsXdrNtHZGn20Gc0IOpa02PY1ahDZW/otf1LtDbvqyoIrjCxS6qFFbOkk5905p2OOZrtsje6NkkD4FHYwwQAALrEYrEoOepKJUddqbKaz5VTslSHbNlqtJvr53SiaodOVO1Q9LUTlXEkX2knTyuoycn0qeKwtHRi91bP62i2q/wQ+5kAP8AMEwAA6DbW8MGakvITLU5fp3F9v6awIPOJS0VIo7YOH6BlV2fok0F9dTYkyEiE+l6lS6rlXSx/Q/ctl4se0v7rMUO75/MA8GgkTAAAoNuFB8dqfN+vanH6Wk0e+GPFhA0yPUZdSJA+Te2rrGvHafOEq2Wb/bSUOLaDd32xpylvZecCbyl2mJGoXVwG3RJoHGd2CfALJEwAAMBlggLCNCL+Nt0x8mXdNOSP6hd5hekxmtSgvNI1WvH5V5U9fowKYyM73uf0+vzuqWY3J0tKmdb6WMo04zgAv8AeJgAA4HIWS4AGRF+rAdHXqvRsnnJKXtThsvVqsjeYGqeo9jMVXZYma1WNMgpLNLTYpkC7g/SpuZrd/Dc7H3iY1Xh/2UFjzxJ9mAC/Q8IEAAB6VFyvYbo+9eea2O8h7T31svadflV1jZWmxijrHa73RqZo++B+GlV0SqOOn1JYvYNqdh8+IUX0kQZc1/lkx5pGogT4KRImAADgFhEhCZqY/KAu73OfDtpeV07JMlXWFZoaoyY0WDsG99OulD5KO1mqjGMlijlb1/qkD35y4f8HTJVuebV7qugB8AvsYQIAAG4VHBiuUQkLdOeoFZo++Lfq07ujwg6XagwM0P7kBL1y5Witzxii4zG9297nVPgOTWcBmMIMEwAA8AgBlkClxlyv1Jjrdap6j3JKlurzso2yq7HjN7dwND5aR+OjFXfmrDIKSzS4pKzFPif7haazLLED4ARmmAAAgMdJiBitqYOe0qL01RqTeLeCAyJMj1Ea2UvvjkrVS1eN1q6BSaoLalEefMUs6eQn3RgxAF9FwgQAADxW75C+urL/w8rMyNZV/b+r3iF9TY9RHRai7UOStezqdH2Q1l+VYSFSxWFp6UTpP+NJnNB5tjzpyBvGjCV8FkvyAACAxwsJ7K2MxEyNTlig/PJ3lFP8okrO5poaoyEoUHv6J2pPcoJST1coo7BYSSU7ZVk6UUqeJN26hmIQcE6NTcrONJZ3NkudafTn4hryOcwwAQAArxFgCdJg63TNG/Ev3TLseQ2KuVEWs7czFovyE2L0+rjhWj1+uA4nxKjp+Fbp2cHSgZeZLUDHsjONPl8tNff9gs9hhgkAALRWsEGK8/y+Q0m9L1NS78tUWXdMuSVZOlC6Wg1NNabGOBUVoU3pg9W7pk7px05p+BuLFdLYxGwBHLPltZ5Zatbc94uCIj6HGSYAAGAsMVpzu/H/a+6Qnh8mrbhJqi1zb1xOiArtr2sG/D9lpr+hK/p9SxHBiabHqAoP1Udp/bXsmgx9NCRZVSc3M1uAtlUcbv/18kM9Ewd6DAkTAAAwlhgdfbf1MS9bYhQaFKnL+tyjRelrdEPqLxQXPtz0GPVBgcoZmKTlV47U270O6NTJN10QKbxa9JD2X48Z2jNxoMeQMAEA4O+alxjZL+p31HKJkRcJsARraOxs3TZiqeak/U0Do6eYHsMeYNHnSbFadfxHWnPgfuWXv6Omi78/8E+xw4wlm5bA1sctgcZxluP5HPYwAQDg75xZYuSFN4EWi0X9IieoX+QEldfmK7dkmfJK16rRXmdqnOLqXdrw+S5FhQ5QemKmhsXerODAcBdFDa8wJ8uYfW25lyllmnEcPoeECQAAf+cHS4xiwlI1aeCjmtDvG9p3aqX2lGSpptHc/qzKukJ9UPgr7Tj+F42Iv12jExYpIiTBNQHb8oxENmaoVyarPi/MKs1/05h9LT/E35OPI2ECAKA7ePMNbvMSo6Pvtz5uCTSemnvb19OOsCCrxva9X2OS7tbhvN/os5KlKuttbraorrFSu4v/qZySFzXYOlNjEpcorpf5/VJtor+Pd7F6fjVJdB17mAAA6Ioam1FN7oXh0srZXlVdrpU5WdLA61sf8+ElRoEBIRqWeKfmb9+nWbsOqn9phekxmuwNOmRbp5X7M7Xu4Nd1tGKL7PamrgVGfx/A4zDDBABAV7R3gzvfiyqshVmlW1ZK2dnSLa96RR+mLosdJkvqTPUv2Kj+ZYdliwhTTv9EHeoTq6YAc8+Uj5/ZruNntismNFXpSUuUFjtbQQFh5uKhvw/gkZhhAgCgs3ysutx5KdP958Z8TpYxkyYptrpW1x04qsUf5GrckRMKO1dverjyunxtPfqksnLnaMfxv+psfanzb6a/D+CRSJgAAOgsbnC9X/Pm/fvypNhRkqRe9Q0an39Ciz/M1aT9BYqurjU9bG1DuXaefFbLc+fqvYKfy1bTwbUi+UXxDcAbkTABANBZ3OD6DmuatHirFBx5/lBQk10jT5Tqzm17NfOzQ+pbdsb0sI32czpQulor9i3QG4ce0rHKj2S329s+mf4+gEciYQIAoLO4wfUtYVZpwduXHLZIGlhaqbm7Duq27fs09GSpLAq89P0dOFb5gd449KBW7lukvNI1amw6d+lJLZYInufDxTcAb0DCBABAV3CD61v6TGw7Cf5CfFWdbiiN0+IBf9JlSfcqJDCyzfPaY6s9pM0Fjysrd64+PfEP1TaUX3ix5RLB27ON/85/k5LigBtRJQ8AgK6ggaXvmZNlVDlsq2KdmqSSnYr491W6InWmxs5apryqLco9tUyVdcdMfZqahlJ9cuIZfXryHxoWd7PSEzMVE5ZivEh/H8BjkDABANAduMH1HRcnwZYgacsPpFO7W1dEzH9LwWsWavSiLRqZcIcKKjYrp3ipiqt3mfp0jfY67Tv9qvadXqGB0ZM1JvEu9ek9ThaLpXu/LgCdQsIEAADQluYk2JYnlexs4wS7VLRVWna1Am7P1qCYqRoUM1Ul1bnKKVmqI2Vvy67GNt7niF1HK97T0Yr3FB8+QhlJd2mwdZoCLMHd9RUB6AT2MAEAALSno/LxJz6SVt1y/sPEiHTdOOiXWjh6tTISlyg4IML0pzxds1/v5P9Yy3PnaXfxv1XXYL5CH4DuQcIEAADQno7Kx0vGTNPyKVJt2flDkaF9dVX/R5SZka0rk7+j3iF9TH/q6vpibSv6g5blztIHhb9RZV2R6TEAdA0JEwAAQHtih0mJ4zo+r2irlDVJ+uw5Y//TF0ICe2tM0l1aOHq1pqb+Ugm9RpsOoaGpRntOLdfLe27Vxs+/r+Kqz0yPAaBz2MMEAADQkel/lZZe0cFJdsm2V9rwgPHhgKnSLa+eLwkeYAnSkNgZGmydruLqXcopXqr8ineN9znJriYdKX9bR8rfVmJEhjISlyg15gYFWLilA1yFGSYAAICONPdnMqNwk1Ge/CIWi0V9eo/V9CH/q4WjX9OohIUKCggzHVJJdY7ePvIDvbTnNuWULNO5xmrTYwDoGAkTAACAM+ZkGbNGZuSvl05+4vDlqNABunbA95WZ/oYm9ntIvYITTIdVde64Pjr2Wy3LmaWPj/1eVedOmh4DgGMkTAAAAM4Is0oL3pbuy5PiRsvp26gNX+vwlNCgKF3e514tGv26rk/5heLCh5sOr76pWp+V/EfLc2/RpiOP6lT1XtNjALgUCRMAwDfZ8qQjb7TafA90C2uatGiLlDrdufNLdjp9HQYGBCstbrZuG7FUc9L+qgFRk0yHZ1ejDpet16oDd+v1vAeUX/6u7PYm0+MAMLBDEADgW2psUnamsRSqWepMYznVF5vvgS4Ls0rz3zQSobWLHDS2baH8kJFoOclisahf5ET1i5yo8tojyinJ0sHStWq015kK82TVTp2s2qmo0AHKSMxUWuzNCg4MNzUG4O+YYQIA+JbsTKlgY+tjBRvb3HwPdJk1Tbpzo9TnqvbP+/iXrXo0mRETNkiTBz6qxenrNL7v1xUeFGt6jMq6Qr1f+Ctl5c7R9qI/62z9qU7FAvgjEiYAgO+w5RkzS/bG1sftjcZxlufBFcKs0pIPpeTJcnhrVfS+9Mq0Ll2D4cFWjev7gBalr9WUgY/JGuZEQ92L1DVWaFfx88rKnat383+q0rN5nY4H8BcsyQMA+I6Kw+2/bnJZFGDKraulVbcYDWwv0WQs23t+mJQ4Xhp1lxTcWxpwnelrMiggVMPj52lY3C06duZD5RS/qKIzH5sao8neoIO2tTpoW6vkyCuVkbhE/aOulsXCs3TgYiRMAADfEd3BE/eYoT0TB/xTmFW68lFp5ez2zyvZYfxp1ucqacJ3pMSxpvc5DYi6RgOirpGt5qBySpbpkO0NNdnrTYVddOZjFZ35WDFhg5SRuERDY2crKCDU1BiAL+MxAgDAd8QOMwo8WAJbH7cEGseZXYKrdZS0t+XkR9Lahcbs04qbOrXXKTY8Tdel/FSL09dqbJ/7FRoYbXqM8toj2nL0CWXlztGOE39XTb3N9BiALyJhAgD4ljlZUsq01sdSphnHAVdzlLQ7q4sFSnoFx2tCv28qM2Odrh3wQ0WHppgeo7ahTDtP/E1ZuXO0peAJldUc6XQ8gC9gSR4AwLe0LPdcfshYhsfMEnrSnCwj6WlZ2t5ZLQuUdOG6DQoI16iEOzQy/nYdrdiqnJIXdaJqR8dvbKHRfk77S1/T/tLXNCDqWmUk3qV+kRNlsVg6HRfgjUiYAAC+yZpGogT3aJm0r7pVsu01P0Y3FSixWAKUEjNFKTFTdOrsPuUWL9XhsrdkV2PHb26hsPJ9FVa+r9jwNGUkLtEQ600KDAjucnyAN2BJHgAAgCtY06TFW6WwOPPvra/p9nASeo3UDYOe0KL01zUm6R6FBPY2PYat5qA2F/xMy/fM1acnn1ddQ0W3xwl4GhImAAAAVwmzSvcf/KJHkwmvz5eWT+50s9v29A5J0pXJ31Jm+hu6uv/3FBmSbHqMs/Wn9cnxP+uV/fN0JiZblXVHuz1OwFOwJA8AAMCVwqzSovcu7KuzBElnCqTyI9K2pxy/r2ir9I80I+EKs3Z7WMGBvZSeuFijEhaooHyzckpeVHH1blNjNNhr1RD5iVbmLVRK9BRlJN2tPhGXs88JPoWECZ7Hlmc0n2SjNroD1xMAT3Hxvrojb0jbOnhPbam0ap6RcLlIgCVQg6xTNcg6VcXVOcopflH55ZtkV5OJUewqqNisgorNiu81ShmJSzTYeqMCLOxzgvcjYYLnqLFJ2ZmtqwqlzjSqDbngyRp8HNcTAE/nbM+moi3SR09Kwxe4/MFPUkSGkgb/Smfqjiv3VJYOnF6l+qazpsY4fXav3sn/kbYV/VHpiYs0Iv42hQRGuihiwPXYwwTPkZ1p9J9oqYv9KODHuJ4AeLrmnk3O3I69/2Ojse2/LpdOfuLqyBQZ2k9X9/+uMjOydWXyw4oITjI9RnV9sT4u+oOW5czWh8d+qzN1x10QKeB6JEzwDLY8YybAflGZ05b9KABncT0B8BZzsqTU6c6ff3q3tHSitOImlxSEuFhIYKTGJN2tRemrNTX1KcX3GmV6jPqms8otWaaX9szTxs//W8XVOS6IFHAdEiZ4horD7b9efqhn4oBv4HoC4C2aezYt2S6FRDn/vh6eMQ+wBGtI7EzdOvzfmjvsOaVEXyfJXGEHu5p0pHyj1hy4V2sO3KcjZW+r6eIHW4AHYg8TPENH67hjhvZMHPANXE8AvE2fCdID+UaBh6ItHZ/fcsa8BwvaWCwW9e09Vn17j1VF7VF9dnKp9p9eLQXUmxqnuHq3io/sVmRIstITF2t43DwFB/ZyUdRA1zDDBM/QvI7bEtj6uCXQOE51M5jB9QTAGzWXH5+/vuNzm5V86rp4OhAdNlBXJX9P8Sce1rikb6hXcLzpMc6cK9KHx/5Xy3Jn6eOiP6rqXLELIgW6hoQJnmNOlpQyrfWxlGnGcW9jyzPKxbJXxn186XoC4F9SZ3xRDMIJnz7t2licENAUrjGJ92jR6LW6LuVxxYabfyh1rrFKnxX/S8tzb9Y7R36s02f3uSBSoHNYkgfP0byOu7mxnzf2zaGUtefwhesJgP+akyWtuUMq3NT+eUVbpPwN5gpHuEhgQLCGxc1VWuwcHT+zXTklL6qw8n1TY9jVqENlb+hQ2Rvq23u8MhLv0sDoSbJYeMYP9yFhgue5uLGfN2mvlPX8N90Tk7/z5usJgP8Ks0oL3jYe+nzyv9Jnf3d87ooZHvVwzmKxKDnqCiVHXaGyms+VW7JMB23r1Gg/Z2qcE1U7dKJqh6JDU5SemKlhcXMUFBDuoqgBx0jXge5CKWsAQHezpknjv9vxeR7aZ84aPliTU36sxenrNK7v1xQWZD6hq6gr0PuFv9SynDn65PgzOlt/2gWRAo6RMAHdhVLWAABXcFTIpiUPfzgXHhyr8X2/qsXp6zR54I8VEzbI9Bh1jRX69OQ/lJU7V5sLHpetxjO/VvgeEiagu1DKGgDgKm0VsmmLG6vmOSMoIFQj4m/THSNf1k1D/qjkyCtNj9Fkr1de6Rqt2LdI2Qe/qcLKD2S3210QLWAgYQK6C6WsAQCu0lzIpqOS4x5QNc8ZFkuABkRfq9lpz+j2EcuVFjtXARbzW+uLznysNw89pBX7Fmr/6VVqaKpzQbTwdyRMQHeilDUAs2hDADNSZ0jJkxy/3lw1z4vE9UrT9amPa3H6Wl2edJ9CA6NMj1FWe1hbjv5Cy3PnaueJv6umvswFkcJfUSUP6E6UsgbgLNoQoLPGPiQVbXX8+ooZ0oCp0i2vetW11Cs4QROTH9Tlfe7TQdvryilZpsq6QlNj1DTYtOPE37Tr5D+VFjdHGYmZndovBbTEDBPgCtY0adAskiUAjrXXhgBoT8LlHZ9TuEl6ZZpXzlwGB4ZrVMICLRi1UjMG/059eo8zPUajvU77T6/UK3vv0JuHvq3jZ7azzwmdRsIE+DqW+wCehzYE6IrmPbMd3caV7JSeHyYtnyzVet8SNYslQCkx1+nmYc/q1uH/0RDrTFnUTqVABwort2rdwa/rtf1LdLB0nRqb6l0QLXwZCRPgq2ps0oqbpBeGSytnG780V9zklb80AZ9DGwJ01ZwsKfFy584t2ir9I82rf/4nRIzS1EFPaVH6Go1JvFvBARGmxyitOaB3Cx7T8j03a9fJF1TbUOGCSOGLSJjgXZgtcR7LfQDPRRsCdFWY1VxBodpSadU818XTQ3qH9NGV/R9WZsYburr/d9U7pJ/pMc7Wn9L2408rK3e23i/8tel9UvA/JEzwDsyWmMNyH8Cz0YYA3eH80jwneWEFPUdCAiOUnpiphaNf042DfqXEiAzTYzQ01WrvqZf00p7btOHw93Sy6lP2OaFNJEzwDsyWmMNyH8Dz0YYA3WFOllERz1krZkgv3+gzDxwDLEEabJ2mecP/qVuGvaBBMTfKYvr21q78inf0et5XtPrAPTpse0tN9gaXxAvvRFlxeL7m2ZKLtZwt4Wlsayz3ATwfbQjQHcKs0oK3jevo2GZp2686fijWXEFv7nKfuuaSeo9RUu9fq7LumPaULNf+0lVqaKoxNcaps3u0Kf+H6n28j0YnLNaI+HkKCYx0UcTwFswwwfMxW2Iey30A70EbAnQHa5qU8RVpyTYpLK7j85sr6Png8vao0P66esD3lJn+hq5I/rYigpNMj1F17qQ+Lvo/LcuZo4+O/U5n6k64IFJ4CxImeD5mSzqH5T4A4H/CrNL9B6W40c6d78PL20ODInVZ0pe0KH21bkh9QvHhI0yPUd9UrZySpXppzzy9feSHKqnOdUGk8HQsyYPna54tKdjYuoiBJdBIAHgq2zaW+wCAfwqzSresNAoldcQPlrcHWII1NHaWhlhv0smqncopWaqCivckOV/gwa5GfV72lj4ve0tJEZcrI2mJUqKvU8DFKzngk0iY4B3mZBlPwFruZWK2xDnWNJ/9JQgAcKD5YWNbe4DbUn7I539XWCwW9Y0cr76R41VeW6DckmXKK31djfY6U+MUV+9S8ee7FBXaX+kJmRoWd7OCA3u5KGp4AhImeAdmSwAAMGdOlrTmDqPIQ0fqzRVH8HYxYSmaNPCHmtDv69p3aqX2nHpJNQ2lpsaorDumD479Wp+c+ItGxs/X6ISFighJdFHEcCf2MMG7sDkaAADnNFfQuy9PmvFs+/uaXp8vLZ/scwUgOhIWZNXYvvdrcfpaXZfyU8WGmd8Xfa7xjHYX/1NZuXP1Tv5jKj17wAWRwp2YYQIAAPBlzUuzEy6Tll7h+LyirdI/0oyiEWHWnovPAwQGhGhY3C1Ki71Zx89s02clL+pY5QemxrCrUYds63TItk59e0/QmKS7NCDqWlkszE94OxImAAAAf1BzuuNzakulVfOkRe+5Ph4PZLFYlBx1pZKjrpSt5rByS5bqoC1bTfZ6U+OcqPpEJ6o+UXRoijISlygtbo6CAsJcFDVcjZQXAADAH3TUpqNZ0RYpf4NrY/ECseFDNCXlMS1OX6dxfR5QWFCM6TEq6gq0tfApZeXO0SfH/6Kz9eb2ScEzkDABAAD4g+bKec7c/q2Y4ZNNbTujV3Ccxvf7uhanr9OkgT9STGiq6TFqG8r16cnnlJU7R5sLfi5bzeHuDxQuQ8IEAADgL+ZkSanTnTs3f4PPNrXtjKCAMI2Mv113jHpFM4f8Qf0iJ5oeo8ler7zS1Vqxb4HeOPRfOlb5kex25/tBwT3YwwQAAOAvmtt0nPxEeuVG6VxlOyc3GX2c8lZKw27vsRA9ncUSoIHRkzQwepJKzx5QTslSHS5bryZ7g6lxjlV+qGOVH8oaNkQZSXdpqPUmBQaEuChqdAUzTAAAAP6mzwTpgXwpeXLH574+n+V5DsT1Gq7rU3+uRaNf12VJ9yokMNL0GGW1h/VewePKyp2rnSeeU20D32dPQ8IEAAC6zpYnHXnDaDAO7xBmNarhzV/f8bksz2tXREiirkh+SJnp2bpmwH8rKrS/6TFqGkq148RftCxnjrYefUrltfndHyg6hSV5AACg82psUnamsXSrWepMY6+Mn/Xy8VqpM6TEcVLJznZO+mJ53o4/SINn00DegeDAXhqdsEAj4+fraMV7yilZqpNVn5oao9Fep32nV2jf6RUaGD1FGYlL1Lf3eFksFhdFjY4wwwQAADovO1Mq2Nj6WMFGZiO8zfS/Onfeuw9Lzw+T/jPe2AeFNgVYApUac4NuHvac5g3/l4ZYZ8qiQNPjHK14T+sOfk2r9t+lQ53oB4XuQcIE38FyEADoWbY8Y9bB3tj6uL3ROM7PY+/RZ6LzJcclYzZq6URpDcUgOpIYka6pg57SwtGrlZF4l4IDIkyPcbpmv97J/4mW596i3Sf/pbqG9op1oLuRMMH71diMzagvDJdWzjaefLE5FQBcr6KDXjLlh3omDnQPMyXHmx191yWh+KLI0L66qv93lJmRrauSH1HvkL6mx6iuL9G243/UstzZ+qDwN6qsO+aCSHExEiZ4P5aDAIB7RA9p//WYoT0TB7pHc8nxm191/j3Ns4vlNGJ1Vkhgb2UkLdHC0as0ddAvldBrtOkxGppqtOfUcr285zZt/Pz7Kq7a7YJI0YyECd6N5SAA4D6xw4xlXJaL9mZYAo3jFAbwTsPmt/332p6Kz10Xj48KsARpiHWG5g3/l24e9g+lVodIJpvY2tWkI+Vva03efVp94F59XrbBdD8odIyECd6N5SAA4F5zsqSUaa2PpUwzjsN7tfX32p6weNfF4uMsFov6nOul6ds+0sKP9mj0sRIFNTR2/MaLlFTn6O0jP9BLe25TTskynWusckG0/omy4vBuLAcBAPdqXsZVdtB4SBUzlJklX9Dy77XkU+nTp6WiLY7P//gXUv/Xey4+X/PFA+Co2nO65uAxjT9yQvv6xWtP/wSdDQ0xNVTVueP66NhvteP4XzUi/jalJy7q1H4pXMAME7wby0EAwDNY06RBs/i562usadLwBUaD2/b2NhW8TZnxrrjoAXBoQ6MuP1qsRR/u0fV78xUXkmp6yPqmauWUvKjlufO06cijOlW9t5uC9T8kTPB+LAcBAMD1gnu1//qGr/VMHL7IwQPgQAUoLXyibhv9quak/U0DoyabHtquRh0uW69VB+7W63lfUX75u2q6eO832sWSPHg/loMAAOB6HS2DL9lp/C7md3DnzMkyKvzmr79w7IsHwBaLRf0iJ6hf5ASV1+Yrp2SZDpauVaO9ztSnOFn1qU5Wfaqo0AFKT8zUsNibFRwY3s1fiO8hYYLvsKbxQxoAAFeJHSYljjMSI0eObeZ3cWc5+QA4JixVkwc+qon9vqG9p17V3lMvq6bBZupTVdYV6oPCX2nH8b9oZPx8jUpYqIiQhO76SnwOS/IAAADgnOl/bf/1tx6geXxXObkfMCzIqnF9H9Ci9LWaMvAxWcM6mAFsQ11jpXYVv6Dle+bq3fzHVHo2r7NR+zQSJgAAADinz8SOezTRPL5HBQWEanj8PM0f+ZJmDX1ayZFXmR6jyd6gg7Z1Wrl/sdYd/IaOVmyV3d7kgmi9E0vyAAAA4Ly29tq01LJ5PMvzeozFYlH/qKvVP+pq2WoOKadkmQ7ZstVkrzc1zvEz23T8zDbFhA1SRmKmhsbOVlBAmIui9g7MMAEAAMB5zXttpj/b/nmfPm0kTehxseFDdV3KY1qcvlZj+3xFoYHRpscorz2iLUefVFbuXO04/jfV1JvbJ+VLSJgAAABgXv8p7b/+6R+l54dJL9/IniY36RUcrwn9vqHMjHWaNOBRRYemmB6jtqFMO0/+XVm5c/RewS9UVvO5CyL1bCzJAwAAgHmxw6SUGzs+r3CT9Mo0afIvpcoCSRZpwHUs1+tBQQHhGpkwXyPib9PRyq3KKV6qE1XmGg032s/pQOkqHShdpQFR1yojcYn6RV4hi8Xioqg9BwkTAAAAOmfm89LbH3R8XslOacXM1scGTJVuedVY4udvbHlSxeEe7x1psQQoJXqKUqKn6PTZfcopWabDtvWyy1wj28LK91VY+b5iw9OUkbhEQ6wzFRgQ4qKo3Y8leQAAAOic0JjOv7dwk/9V06uxGWXXXxgurZxtLFl0Uxn2+F4jdUPqL7Qo/XVdlnSPQgIjTY9hqzmozQU/0/I9N+vTk8+rtqG8+wP1ACRMAAAAcI/89dJJc0vDvFp2plF2vSU3l2HvHZKkK5K/pcz0bF3d//8pMiTZ9Bhn60/rk+N/1rKc2Xr/6P+oovaoCyJ1HxImAAAAdI0ze5kcWbvIP6rp2fKMBNF+0fK3lmXY3Sg4sJfSExdpwejXNG3Qb5QUcZnpMRrtddp7+hW9vPd2vXX4EZ04s1N2u90F0fYsEiYAAAB0zcznjT1JnVFx2Fia9sJoaccf3J44uEzF4fZfLz/UM3F0IMASqEHWqbpl+POaN/yfGhwzXRbTKYNdBRWbtfbgA1p14Es6ZHvTdD8oT0LCBAAAgK4JjZEWvC3dlyfNeFa65gkpbrRkCXR+DNte6d2HfbcUefSQ9l+PGdozcZiQGJGhGwf/jxaOXq30xCUKDogwPcbps3v1Tv6PtDx3nj4r/rfqGs+4IFLXImGCa9jypCNv+O5TIgAAcClrmpTxFenqH0mLtkgp0zo3TuEmadUt3Rubu8UOk1JnXppEWgKN466ulteFe7PI0H66uv8jysxYpyuTH1ZEcJLpMarri/Vx0R/0yv55OhOzXmf+f3v3HqVVed8L/DfMDAyo4CAqKjggF5E23iAkYgwaQVRqVk7wiidqo6mu1qQmq8mhSeplNT1dTaN/2CYmLm9tqsQTL62nIgJpRElyWjRgvUS5CBgkanBkIIKIwz5/vBmYF2YP8w7zvnu/M5/PWrNknvcyP2Yexv3dz7N/+4ONJb9HVrQVp2dtby5c0LjuyT1jo2ZGzJrXN9uGAkBf1dAYMXtB4QB98+qIn90Y8dYvI2JX117/xtKIH30y4jP/1nuOIWbNKzR4aH+c1DS9MF4uPXhs1r/2kDjxyM/FHx5xaax99z/iv9/+l9i07eWS3uPDXdviw0P+Mx559cIYdehZ8ZEjPxdHHvSRkt6j0qww0bNy2P0FAMhQ47iI0edFXLgwYtSM0l77xtLetdLUFiI/vzLis/ML/529oLyBsAzHZv1q6mPM0JnxmeP/OS4Yf1c0DTkzIkq7gW0Su2Lt5p/EwjVfjtZdH3S7lkoQmOg5Oe/+AgBkqH1YOOLU6NphaLJnpak3XdPUFiIrsQ2vjMdmNTU1MfzgU+KcMbfGxRMfjYmHXxx1/RpKeo+Jh1+c+5veCkz0nCrp/gIAZKhxXMRFi0tbbeptK02VUsFjsyENI+P0kf8rLvvD+fHRo/8sBtUP2+9ramv6x8RhF/VYDeUiMNFzqrD7CwCQgbbVpsuXRQw7sQsv+P1K0798tHetNJVbBsdmDXVD4uThn49L/+DfY1rTLTF04PjU544bOisG1uf/+jSBiZ6TdfcXAKC6DJ8cceXzhW16h/1B7Pc6mLeejbizKeLNZytS3gHJQ8fgDI/NavvVx/jD/ig+O+GBOH/cHTFy8On7POcPj7i8bF+/JwlM9KxZ8/ZtIVru7i8AQHVrHFdoQ37MvgfV+9i5NeL+j0b8cFI+g9P25oiHz4249/iIR84v3Ffq4XOzWxnL+NispqYmjjlkSpw79va48ISHYvzQz0TsqosRh0yNxoGjK1LDgdJWnJ61dwvRQ8daWQIA9q+hsRCafvTJwva7SDp//tu/LASnoRMjTvyTiMNOKDQzyPrYo7OudLMXVL6eHB2bNQ4cHVOPmRvNL4yJKRM+nkkN3SEwUR6N4wQlAKB0n/m3QoOHN5Z27fnNL0c8dUPxWFb3gGzrSre39l3psjo+ytGxWb9dB8XgASOzLqPLbMkDACA/2laajpjU/ffI6h6QOgb3SgITAAD5c9GiiIbDuvfathWd/76rsk0XdAzulQQmAADyp6Ex4upVEcec0f33WPSFQtOFH06OePX/lD886RjcKwlMAADkU0NjxKVPF+7XdMSp3X+ft5+L+PdLKtOxTsfgXkfTBwAA8m345IjPPVdoI77o2kKHvO5atyhi3iciJlwWcdDwiMHH9mx3vRx1paNnCEwAAFSH9sHpyasjNv13N95kV6Gz3s//at+HerK7Xo660nFgBCYAAKrL8MkRVz5fWMXZsCTid292HIBK1bb6NOnLESOnCTxEhMAEAEC1ar+Ks3FpoZ140noAb/j71adFXyh8OuykiI9/PeKIU4SnPkzTBwAAql9HzRYO1KbnK9csgtwSmAAAqH5tzRY+vzLijx6MOHJyz75/23a9St/bqbd59/c37928n5v85ogteQAA9B5t2/SOv3hPp7qauohn5h5Ydz3b9bqveWXE2vkRL9wV0fxaxJh5ET88NaLpkz3XZKOMBCYAAHqn9tc4DZ8c8diFEb/+j55577btehERx3wi4pQvCk/NKwtNOKKm0DTj/XcjFn6huJthzcA9f16/OOLxyworgzkmMFWb5pURLWv09AcAKEVDY8TFP2nXWe+tiIOOjBjcdOCrT28sLXxERBwxKWLK13pveNo7FCVJxG9XRPzn/4747fOlvVfSGrHuycLPJMffK4GpWmxvjpg/pzCp2vTkvQIAAPqCju6PNODQiIXXdPO+Tnt5+7nilaczb4vYvqm6T3Y3r+x+KOqKzatz/b0RmKrF/DmFZcv2SlnGtDIFAFCsoxPSdQdFfPhez7z/G0sj7p+y5/MjJkVM/J8R9QfvWZ1pv1qTh2O0thWk996K6H9IxKqHI954prxf89Cx5X3/AyQwVYPmlcX/kNt0ZRnTyhQA9C1OknZdRyekW98vrAx97Os91CyinbefK3ykaZwQcdJ1EcedXxymBh8bsWV9FG2Da/9Y0tq1n3f77XR7v2dH1xuVW01toRV8zudp1QSmv/mbv4nHH388VqxYEf3794/NmzdnXVLltOyn7WJny5gHujIFAFQHJ0lL09kJ6TeW7gkgPd0sojPvvhLx1A2Fj+5o2wL49oooCkU7t0W8+qOI3/y/nqu1JzRNL8zPnKuawPTBBx/ERRddFKeddlrcfffdWZdTWUPGdP542jLmgaxMAQDVxUnS0nT1hHRbs4g3n41YcFXEOy9VpLxu2XsLYJ5d/FTEiOqotWoC0y233BIREffdd1+2hWRh6PjCGaL1iwthp83+ljEPZGUKAKgeTpKWrtQT0sMnR1z1YvG9nXa8G7Ho2ogdm8tWZq8xdGLEiX8ScezMiJ+/GnHkKVlX1GVVE5i6Y8eOHbFjx47dn2/ZsiUiInbu3Bk7d+7MqqzdNbT/737N/GHEk5+PWP+TPWNNZ0fMvCci7T0OGlXc635vB49Ofy25U/Kcoc8zZyiVOVPFmld3/v/8d1ZFHDyqx79sVc+ZQ0ZHjPqjiNef2veE9LFnFr5fHf29Dh5V/L286qyIf78sYuPPy1puVRk6IWL69yMGDI5oeS1iyHERhxYCamGuvJqLOdPVGmqSJEnKXEuPuu++++KGG27o0jVMN9988+6VqfYeeOCBGDRoUBmqAwAAqsG2bdtizpw50dLSEoMHD059XqYrTGmBpr1ly5bF5MmTu/X+f/mXfxlf+cpXdn++ZcuWGDlyZJxzzjmdflMqYefOnbFo0aKYMWNG1NfXl+8L7dicvjI14NDyfV16XMXmDL2GOUOpzJkq99hn01dLPv1IWb5kr5kzm9fssxLSI+/XMCxi6derf/XpyI9GnPJnhWPH5MMD+j7lac607T7bn0wD0/XXXx+XXnppp88ZNWpUt99/wIABMWDAgH3G6+vrM/8BtSl7LfWHR8z+v3v222oxWvXyNH+pDuYMpTJnqtSsfy40eGh/LVPTzMJ4mX+eVT9nDp9Q+CjH+126uPi6p63rI5IotPKOiFg7P2LF9wsd8vJi6MSIsf8jYsiost0fKg9zpqtfP9PANGzYsBg2bFiWJfQdHd3VGgDoPRoaC93wnCTNn86Owxr/POLUPy/83DYsKQ5TbZ8Pbto3aG1YEvHBexG/+peIt57tWh3DPx4x4dKI+oM6fk/zpkNV0/Th9ddfj+bm5nj99dejtbU1VqxYERERY8eOjYMPPjjb4gAA8sJJ0urU0c+ts59j22OT/rw4JEekB639zQvzpkNVE5huvPHG+Kd/+qfdn59ySqEV4U9/+tM488wzM6oKAAAytnfYEnx6VL+sC+iq++67L5Ik2edDWAIAAMqlalaYAAAOWPPKwo3dXacBdJHABAD0ftubI+bPKe4gN2pmxKx5hWYJ0Bs4IVAWAhMA0PvNnxOxfnHx2PrFhTbcsxdkUxP0FCcEyqpqrmECAOiW5pWFA8n2N3SNKHy+7slChzGoZp2dEOCACUwAQO/WsqbzxzevrkwdUA5OCJSdwAQA9G5DxnT+eNu9a6AaOSFQdgITANC7DR1fuJ6jprZ4vKa2MO7ieKqZEwJlJzABANlpXhmx9onybxuaNS+iaXrxWNP0wjhUMycEyk6XPACg8ird1auhsdAN791VhS1K2i7Tm8yaV2jw0P7fkxMCPUZgAgAqL6s2343jBCV6n3KeEHBvJ4EJAKiwtq5ee2vf1auPHpjBAenJEwLu7bSba5gAgMrS1Qvyz72ddhOYAIDK0tUL8s29nYoITABAZenqBflmFbiIwAQAVJ4233SmUu3m6ZhV4CKaPgAAlafNNx3RaCAf2laB1y8u3pZXU1s4sdHH/q1aYQIAstM4LmL0eX3uAIwUGg3kh1Xg3awwAQCQPe3m88Uq8G4CEwAA2etKo4E+esCeKTd7tiUPAIAc0GiAnBKYAADInnbz5JTABABAPmg0QA65hgkAgHzQaIAcEpgAAMgXjQbIEVvyAAAAUghMAAAAKQQmAACAFAITAABACoEJAAAghcAEAACQQmACAABIITABAACkEJgAAABSCEwAAAApBCYAAIAUAhMAAEAKgQkAACCFwAQAAJBCYAIAAEghMAEAAKQQmAAAAFIITAAAACkEJgAAgBQCEwAAB2b9ooh3V2VdBZSFwAQAQOm2N0c89tnCnx+7MOKe8REPnxvx/rvZ1gU9TGACAKB08+dEvP5U8dj6xRGPX5ZJOVAuAhMAAKVpXhmx7smIpLV4PGktjNueRy8iMAEAUJqWNZ0/vnl1ZeqAChCYAAAozZAxnT9+6NjK1AEVIDABAFCaoeMjRs2MqKktHq+pLYw3jsumLigDgQkAgNLNmhdx7JnFY03TC+PQi9RlXQAAAFWooTHi049EzJ8f8emHIg4bZ2WJXklgAgDgwDTNiKivz7oKKAtb8gAAAFIITAAAACkEJgAAgBQCEwAAQAqBCQAAIIXABAAAkEJgAgAASCEwAQAApBCYAAAAUghMAAAAKQQmAACAFAITAABAirqsCwAAADLUvDKiZU3EoWMjGsdlXU3uCEwAANAXbW+OmD8nYt2Te8ZGzYyYNS+ioTG7unLGljwAAOiL5s+JWL+4eGz94ojHL8umnpwSmAAAqLzmlRFrn4h4d1XWlfRNzSsLK0tJa/F40loY93PZzZY8AAAqxzawfGhZ0/njm1e7nun3rDABAFA5toHlw5AxnT9+6NjK1FEFBCYAACrDNrD8GDq+sLJXU1s8XlNbGLe6tJvABABAZXRlG9j+uPap58yaF9E0vXisaXphnN1cwwQAQGUcyDYw1z71vIbGiNkLCuFz82r3YUphhQkAgMo4kG1gXbn2yepT9zSOixh9nrCUwgoTAACVM2teIeS0Xyna3zawtmuf9tZ27dObyyJ+9ldWnygLgQkAgMrpzjaw/V37tOi6iN8+XzzWtvo0e8GB1UufJzABAFB5jeO6vgVsf9c+vf3Lfcfad96z1YwD4BomAADyrbNrn444tfPXdqXzHnRCYAIAIP/SWmDP+H7nr3MDVg6QLXkAAORfZ9c+jZpZuGap/Q1xa2oLgcp2PA6QFSYAAKpHRy2w3YCVMrLCBABAdXMDVspIYAIAoHcopfMedJEteQAAACkEJgAAgBQCEwAAQAqBCQAAIIXABAAAkEJgAgAASKGtOABANWleGdGyxr2GoEIEJgCAarC9OWL+nIh1T+4ZGzUzYta8wo1bgbKwJQ8AoBrMnxOxfnHx2PrFEY9flk090EcITAAAede8srCylLQWjyethfF3V3X9fdY+0fXnA7bkAQDkXsuazh/fvLrz65ls54Nus8IEAJB3Q8Z0/vihYzt/3HY+6DaBCQAg74aOL6wI1dQWj9fUFsY7W13qqe180EcJTAAA1WDWvIim6cVjTdML453pyna+CNc3QQrXMAEAVIOGxojZCwqBZvPqrt+HaX/b+QYOi3j4XNc3QQorTAAA1aRxXMTo87p+09r9bef72V+V7/omq1b0AgITAEBvl7ad7/S/Ls/1TdubC6tW9x4f8cj5EfeML3z+/rvdez/IkC15AAC9Xdp2vrVPdP66/bUrT9NZV77ZC0p/P8iQwAQA0Fc0jisOQAfarrwjbV359tZ+1ao7IQwyYkseAEBfdSDtytN0tSsfVAmBCQCgL+tuu/I05Vi1ggzZkgcA0Jd1t115mrZVq/WLi5tJ1NQWgpjteFQZK0wAAJTerrwzPb1qBRmywgQAQM/q6VUryJDABABAeezdlQ+qkC15AAAAKQQmAACAFAITAABACoEJAAAghcAEAACQQpc8AIAsNK+MaFmj5TbknMAEAFBJ25sj5s+JWPfknrFRMws3dW1ozK4uoEO25AEAVNL8ORHrFxePrV8c8fhl2dQDdEpgAgColOaVhZWlpLV4PGktjL+7Kpu6gFQCEwBApbSs6fzxzasrUwfQZQITAEClDBnT+eOHjq1MHUCXCUwAAJUydHyhwUNNbfF4TW1hXLe8/GleGbH2Cdsl+zBd8gAAKmnWvEKDh/Zd8pqmF8bJD90M+T2BCQCgkhoaI2YvKKxYbF7tPkx51Vk3w9kLsqmJTAhMAABZaBwnKOVVWzfDvbXvZuhn12e4hgkAANrTzZB2BCYAAGhPN0PaEZgAAKA93QxpR2ACAIC9zZpX6F7Ynm6GfZKmDwAAsDfdDPk9gQkAANLoZtjn2ZIHAACQQmACAABIITABAACkEJgAAABSCEwAAAApBCYAAIAUAhMAAEAKgQkAACCFwAQAAJBCYAIAAEghMAEAAKQQmAAAAFIITAAAACkEJgAAgBQCEwAAQAqBCQAAIEVVBKZ169bF1VdfHaNHj46BAwfGmDFj4qabbooPPvgg69IAAIBerC7rArrilVdeiV27dsUPfvCDGDt2bLz44ovxhS98Id577734zne+k3V5AABAL1UVgencc8+Nc889d/fnxx13XLz66qtxxx13CEwAAEDZVEVg6khLS0sMHTq00+fs2LEjduzYsfvzLVu2RETEzp07Y+fOnWWtb3/avn7WdVA9zBlKZc5QKnOGUpkzlCpPc6arNdQkSZKUuZYet2bNmjj11FPj1ltvjWuuuSb1eTfffHPccsst+4w/8MADMWjQoHKWCAAA5Ni2bdtizpw50dLSEoMHD059XqaBKS3QtLds2bKYPHny7s83btwY06ZNi2nTpsVdd93V6Ws7WmEaOXJkbNq0qdNvSiXs3LkzFi1aFDNmzIj6+vpMa6E6mDOUypyhVOYMpTJnKFWe5syWLVti2LBh+w1MmW7Ju/766+PSSy/t9DmjRo3a/eeNGzfGWWedFaeddlrceeed+33/AQMGxIABA/YZr6+vz/wH1CZPtVAdzBlKZc5QKnOGUpkzlCoPc6arXz/TwDRs2LAYNmxYl577xhtvxFlnnRWTJk2Ke++9N/r1q4qO6AAAQBWriqYPGzdujDPPPDOOPfbY+M53vhO//e1vdz82fPjwDCsDAOgDmldGtKyJOHRsROO4rKuBiqqKwLRw4cJYvXp1rF69OkaMGFH0WBX2rAAAqA7bmyPmz4lY9+SesVEzI2bNi2hozK4uqKCq2Nd21VVXRZIkHX4AAFAm8+dErF9cPLZ+ccTjl2VTD2SgKgITAAAV1ryysLKUtBaPJ62F8XdXZVMXVJjABADAvlrWdP745tWVqQMyJjABALCvIWM6f/zQsZWpAzImMAEAsK+h4wsNHmpqi8dragvjuuXRRwhMAAB0bNa8iKbpxWNN0wvj0EdURVtxAAAy0NAYMXtBocHD5tXuw0SfJDABANC5xnGCEn2WLXkAAAApBCYAAIAUAhMAAEAKgQkAACCFwAQAAJBCYAIAAEghMAEAAKQQmAAAAFIITAAAACkEJgAAgBQCEwAAQAqBCQAAIIXABAAAkEJgAgAASCEwAQAApBCYAAAAUghMAAAAKQQmAACAFAITAABACoEJAAAghcAEAACQQmACAABIITABAACkEJgAAABSCEwAAAApBCYAAIAUAhMAAEAKgQkAACBFXdYFVFKSJBERsWXLlowridi5c2ds27YttmzZEvX19VmXQxUwZyiVOUOpzBlKZc5QqjzNmbZM0JYR0vSpwLR169aIiBg5cmTGlQAAAHmwdevWGDJkSOrjNcn+IlUvsmvXrti4cWMccsghUVNTk2ktW7ZsiZEjR8avf/3rGDx4cKa1UB3MGUplzlAqc4ZSmTOUKk9zJkmS2Lp1axx99NHRr1/6lUp9aoWpX79+MWLEiKzLKDJ48ODMJwvVxZyhVOYMpTJnKJU5Q6nyMmc6W1lqo+kDAABACoEJAAAghcCUkQEDBsRNN90UAwYMyLoUqoQ5Q6nMGUplzlAqc4ZSVeOc6VNNHwAAAEphhQkAACCFwAQAAJBCYAIAAEghMAEAAKQQmMroe9/7XowePToaGhpi0qRJ8cwzz3T6/CVLlsSkSZOioaEhjjvuuPj+979foUrJi1LmzCOPPBIzZsyIww8/PAYPHhynnXZaPPnkkxWsljwo9fdMm5/97GdRV1cXJ598cnkLJHdKnTM7duyIb3zjG9HU1BQDBgyIMWPGxD333FOhasmDUufM/fffHyeddFIMGjQojjrqqPjjP/7jeOeddypULVl7+umn44ILLoijjz46ampq4l//9V/3+5q8HwMLTGXy4IMPxg033BDf+MY3Yvny5XHGGWfEeeedF6+//nqHz1+7dm2cf/75ccYZZ8Ty5cvj61//enzpS1+Khx9+uMKVk5VS58zTTz8dM2bMiPnz58dzzz0XZ511VlxwwQWxfPnyCldOVkqdM21aWlriiiuuiLPPPrtClZIX3ZkzF198cfzkJz+Ju+++O1599dWYN29eTJgwoYJVk6VS58zSpUvjiiuuiKuvvjpeeuml+PGPfxzLli2La665psKVk5X33nsvTjrppPjHf/zHLj2/Ko6BE8piypQpyXXXXVc0NmHChGTu3LkdPv9rX/taMmHChKKxa6+9Nvn4xz9ethrJl1LnTEcmTpyY3HLLLT1dGjnV3TlzySWXJN/85jeTm266KTnppJPKWCF5U+qceeKJJ5IhQ4Yk77zzTiXKI4dKnTN///d/nxx33HFFY7fffnsyYsSIstVIfkVE8uijj3b6nGo4BrbCVAYffPBBPPfcc3HOOecUjZ9zzjnx85//vMPX/OIXv9jn+TNnzoxnn302du7cWbZayYfuzJm97dq1K7Zu3RpDhw4tR4nkTHfnzL333htr1qyJm266qdwlkjPdmTOPPfZYTJ48Ob797W/HMcccE+PHj4+/+Iu/iO3bt1eiZDLWnTkzderU2LBhQ8yfPz+SJIm33norHnrooZg1a1YlSqYKVcMxcF3WBfRGmzZtitbW1jjyyCOLxo888sh48803O3zNm2++2eHzP/zww9i0aVMcddRRZauX7HVnzuzt1ltvjffeey8uvvjicpRIznRnzqxatSrmzp0bzzzzTNTV+fXf13Rnzrz22muxdOnSaGhoiEcffTQ2bdoUf/qnfxrNzc2uY+oDujNnpk6dGvfff39ccskl8f7778eHH34Yn/70p+Mf/uEfKlEyVagajoGtMJVRTU1N0edJkuwztr/ndzRO71XqnGkzb968uPnmm+PBBx+MI444olzlkUNdnTOtra0xZ86cuOWWW2L8+PGVKo8cKuX3zK5du6Kmpibuv//+mDJlSpx//vlx2223xX333WeVqQ8pZc68/PLL8aUvfSluvPHGeO6552LBggWxdu3auO666ypRKlUq78fATjGWwbBhw6K2tnafsy9vv/32Pgm6zfDhwzt8fl1dXRx22GFlq5V86M6cafPggw/G1VdfHT/+8Y9j+vTp5SyTHCl1zmzdujWeffbZWL58eVx//fURUTgYTpIk6urqYuHChfGpT32qIrWTje78njnqqKPimGOOiSFDhuweO+GEEyJJktiwYUOMGzeurDWTre7Mmb/927+N008/Pb761a9GRMSJJ54YBx10UJxxxhnxrW99KxerBeRLNRwDW2Eqg/79+8ekSZNi0aJFReOLFi2KqVOndvia0047bZ/nL1y4MCZPnhz19fVlq5V86M6ciSisLF111VXxwAMP2B/ex5Q6ZwYPHhwvvPBCrFixYvfHddddF8cff3ysWLEiPvaxj1WqdDLSnd8zp59+emzcuDF+97vf7R5buXJl9OvXL0aMGFHWesled+bMtm3bol+/4sPL2traiNizagDtVcUxcEbNJnq9H/3oR0l9fX1y9913Jy+//HJyww03JAcddFCybt26JEmSZO7cucnnPve53c9/7bXXkkGDBiVf/vKXk5dffjm5++67k/r6+uShhx7K6q9AhZU6Zx544IGkrq4u+e53v5v85je/2f2xefPmrP4KVFipc2ZvuuT1PaXOma1btyYjRoxILrzwwuSll15KlixZkowbNy655pprsvorUGGlzpl77703qaurS773ve8la9asSZYuXZpMnjw5mTJlSlZ/BSps69atyfLly5Ply5cnEZHcdtttyfLly5P169cnSVKdx8ACUxl997vfTZqampL+/fsnp556arJkyZLdj1155ZXJtGnTip7/1FNPJaecckrSv3//ZNSoUckdd9xR4YrJWilzZtq0aUlE7PNx5ZVXVr5wMlPq75n2BKa+qdQ586tf/SqZPn16MnDgwGTEiBHJV77ylWTbtm0VrposlTpnbr/99mTixInJwIEDk6OOOiq5/PLLkw0bNlS4arLy05/+tNPjk2o8Bq5JEuujAAAAHXENEwAAQAqBCQAAIIXABAAAkEJgAgAASCEwAQAApBCYAAAAUghMAAAAKQQmAACAFAITAABACoEJgF6vtbU1pk6dGrNnzy4ab2lpiZEjR8Y3v/nNjCoDIO9qkiRJsi4CAMpt1apVcfLJJ8edd94Zl19+eUREXHHFFfH888/HsmXLon///hlXCEAeCUwA9Bm333573HzzzfHiiy/GsmXL4qKLLor/+q//ipNPPjnr0gDIKYEJgD4jSZL41Kc+FbW1tfHCCy/EF7/4RdvxAOiUwARAn/LKK6/ECSecEB/5yEfil7/8ZdTV1WVdEgA5pukDAH3KPffcE4MGDYq1a9fGhg0bsi4HgJyzwgRAn/GLX/wiPvnJT8YTTzwR3/72t6O1tTUWL14cNTU1WZcGQE5ZYQKgT9i+fXtceeWVce2118b06dPjrrvuimXLlsUPfvCDrEsDIMcEJgD6hLlz58auXbvi7/7u7yIi4thjj41bb701vvrVr8a6deuyLQ6A3LIlD4Beb8mSJXH22WfHU089FZ/4xCeKHps5c2Z8+OGHtuYB0CGBCQAAIIUteQAAACkEJgAAgBQCEwAAQAqBCQAAIIXABAAAkEJgAgAASCEwAQAApBCYAAAAUghMAAAAKQQmAACAFAITAABAiv8PmRxBmM4oVI4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear Regression\n",
    "X_train_fes = sm.add_constant(X_train)\n",
    "X_test_fes = sm.add_constant(X_test)\n",
    "model_lr = sm.OLS(Y_train, X_train_fes).fit()\n",
    "\n",
    "# 데이터 예측하기\n",
    "Y_trpred = model_lr.predict(X_train_fes)\n",
    "Y_tepred = model_lr.predict(X_test_fes)\n",
    "\n",
    "# 예측 시각화\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(X_test, Y_test, s=20, c='darkorange')\n",
    "plt.plot(X_test, Y_tepred, color='yellowgreen', linewidth=5, label='Predict')\n",
    "plt.title('Example Data')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest\n",
    "\n",
    "**\"데이터를 `복원샘플링`하여 나눈 샘플들로 `같은 알고리즘`으로 학습 후 `보팅` 방식으로 최종 예측\"**\n",
    "\n",
    "<center><img src='Image/Expert/Ensemble_Bagging.jpg' width='400'></center> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest\n",
    "model_rf = RandomForestRegressor(n_estimators=100, random_state=123)\n",
    "model_rf.fit(X_train, Y_train)\n",
    "\n",
    "# 데이터 예측하기\n",
    "Y_trpred = model_rf.predict(X_train)\n",
    "Y_tepred = model_rf.predict(X_test)\n",
    "\n",
    "# 예측 시각화\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(X_test, Y_test, s=20, c='darkorange')\n",
    "plt.plot(X_test, Y_tepred, color='yellowgreen', linewidth=5, label='Predict')\n",
    "plt.title('Example Data')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP\n",
    "\n",
    "**1) 방향:** 인공신경망의 `비선형성 접근 필요성`으로  `은닉층`을 반영항 `다층/심층신경망으로 딥러닝을 출발시킴`\n",
    "\n",
    "- `2개의 직선` 또는 `여러개의 직선이 결합된 1개의 곡선`을 이용하여 제대로 분류가 가능할 것이라 가정\n",
    "- `다층(여러개의 직선)신경망`으로 비선형 문제를 해결 (Hinton, 1986)  \n",
    "\n",
    "<center><img src='Image/Expert/DL_Compare_MLP.jpg' width='500'></center>\n",
    "\n",
    "- 여러문제가 해결되면서 `은닉층을 늘려 학습 결과를 향상`시키는 방법이 등장하며 `DNN(Deep Neural Network)`라고 함\n",
    "\n",
    "<center><img src='Image/Expert/DL_ANN_MLP.bmp' width='600'>(https://4ir.kisti.re.kr/)</center>\n",
    "\n",
    "- 은닉층의 수가 증가하면 더욱 어려운 문제를 풀수 있는데, 통상 `은닉층을 최소 2개이상 가진 알고리즘`을 `딥러닝(Deep Learning)` 알고리즘 이라고 함\n",
    "\n",
    "<center><img src='Image/Expert/DL_MLP_Custom.PNG' width='500'></center>\n",
    "\n",
    "**2) 한계:** 층(Layer)의 수가 많아지면서 `늘어나는 Weights를 수동으로 학습하기에 한계`가 있기에 여전히 데이터 학습 불가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_4 (InputLayer)        [(None, 1)]               0         \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 128)               256       \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_14 (Dense)            (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 8,577\n",
      "Trainable params: 8,577\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 0s 2ms/step\n",
      "16/16 [==============================] - 0s 2ms/step\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 6.3095e-05 - mae: 0.0046\n",
      "Evaluation of Y_train:  [6.309463060460985e-05, 0.004592777695506811]\n",
      " \n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1342 - mae: 0.1637\n",
      "Evaluation of Y_test:  [0.13418173789978027, 0.16368287801742554]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 하이퍼파라미터\n",
    "tf.random.set_seed(1)\n",
    "HIDDEN_ACTIVATION = 'relu'\n",
    "OUTPUT_ACTIVATION = 'linear'\n",
    "LOSS = 'mse',\n",
    "OPTIMIZER = 'adam'\n",
    "METRICS = ['mae']\n",
    "BATCH_SIZE = 3\n",
    "EPOCHS = 100\n",
    "VERBOSE = 0\n",
    "\n",
    "# MLP\n",
    "inputs = Input(shape=(1,))    # Input 함수를 사용하여 입력층 생성\n",
    "hiddens = Dense(128, activation=HIDDEN_ACTIVATION)(inputs)    # 은닉층1: 입력층을 입력받는 layer 추가\n",
    "hiddens = Dense(64, activation=HIDDEN_ACTIVATION)(hiddens)    # 은닉층2: 은닉층1을 입력받는 layer 추가\n",
    "output = Dense(1, activation=OUTPUT_ACTIVATION)(hiddens)    # 출력층: 은닉층2를 입력받아 Y를 출력하는 layer 추가\n",
    "model = Model(inputs, output)    # 입력층과 출력층을 구성하며 최종 모델 완성\n",
    "model.summary()    # 네트워크 구조 출력\n",
    "plot_model(model)    # 네트워크 구조 시각화\n",
    "\n",
    "# 데이터 학습하기\n",
    "model.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=METRICS)\n",
    "model_fit = model.fit(X_train, Y_train, \n",
    "                      batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=VERBOSE)\n",
    "pd.DataFrame(model_fit.history[METRICS[0]], columns=[METRICS[0].upper()]).plot()\n",
    "plt.show()\n",
    "\n",
    "# 데이터 예측 및 평가하기\n",
    "Y_trpred = model.predict(X_train)\n",
    "Y_tepred = model.predict(X_test)\n",
    "print('Evaluation of Y_train: ', model.evaluate(X_train, Y_train))\n",
    "print(' ')\n",
    "print('Evaluation of Y_test: ', model.evaluate(X_test, Y_test))\n",
    "\n",
    "# 예측 시각화\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(X_test, Y_test, s=20, c='darkorange')\n",
    "plt.plot(X_test, Y_tepred, color='yellowgreen', linewidth=5, label='Predict')\n",
    "plt.title('Example Data')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **분류문제 예제**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy as sp\n",
    "import matplotlib as mpl\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "\n",
    "# 예제 데이터 로딩\n",
    "np.random.seed(0)\n",
    "rv0 = sp.stats.multivariate_normal([-2, -2], [[2, 0.9], [0.9, 2]], seed=123)\n",
    "rv1 = sp.stats.multivariate_normal([1, 1], [[2, -0.8], [-0.8, 2]], seed=123)\n",
    "X0 = rv0.rvs(700)\n",
    "X1 = rv1.rvs(300)\n",
    "X = np.vstack([X0, X1])\n",
    "Y = np.hstack([np.zeros(700), np.ones(300)])\n",
    "\n",
    "# 예제 데이터 시각화\n",
    "plt.figure(figsize=(10,10))\n",
    "xx1 = np.linspace(-5, 5, 100)\n",
    "xx2 = np.linspace(-5, 5, 100)\n",
    "XX1, XX2 = np.meshgrid(xx1, xx2)\n",
    "plt.contour(XX1, XX2, rv0.pdf(np.dstack([XX1, XX2])), cmap=mpl.cm.cool)\n",
    "plt.contour(XX1, XX2, rv1.pdf(np.dstack([XX1, XX2])), cmap=mpl.cm.hot)\n",
    "plt.scatter(X0[:, 0], X0[:, 1], marker=\"o\", c='b', label=\"y=0\")\n",
    "plt.scatter(X1[:, 0], X1[:, 1], marker=\"s\", c='r', label=\"y=1\")\n",
    "plt.legend()\n",
    "plt.title('Example Data')\n",
    "plt.axis(\"equal\")\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# 데이터 분리 및 스케일링\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=123)\n",
    "scaler = preprocessing.StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression\n",
    "\n",
    "**1-1) 알고리즘 함수세팅:** 분류문제를 푸는 대표적인 알고리즘 `Logistic Regression`\n",
    "\n",
    "- `범주형 종속변수`의 적합/추정하기 위한 `변환과정` 필요\n",
    "- `Logistic/Sigmoid Function`를 사용하여 `곡선(S-curve) 형태로 변환`\n",
    "\n",
    "<center><img src='Image/Advanced/Linear_Logistic.png' width='600'></center>\n",
    "\n",
    "> **(1) 회귀분석 추정:**\n",
    ">\n",
    ">\\begin{align*}\n",
    "Y \\approx \\hat{Y} &= f(X_1, X_2, ..., X_k) \\\\\n",
    "&= w_0 + w_1X_1 + w_2X_2 + \\cdots + w_kX_k \\\\\n",
    "&= XW\n",
    "\\end{align*}\n",
    ">\n",
    "> **(2) 시그모이드 변환(Logistic/Sigmoid Transformation):** `Binary Classification` 반영하는 `곡선 형태`로 변경\n",
    ">\n",
    ">\\begin{align*}\n",
    "Pr(\\hat{Y}) &= \\dfrac{1}{1+exp(-\\hat{Y})} \\\\ \n",
    "&= \\dfrac{1}{1+exp(-XW)} \\\\\n",
    "&= \\dfrac{exp(XW)}{1+exp(XW)}\n",
    "\\end{align*}\n",
    ">\n",
    "> **(3) 로짓 변환(Logit Transformation):** `X`의 선형관계 형태로 변환하여 `변수들`로 `Y=1`인 확률 추정\n",
    ">\n",
    ">\\begin{align*}\n",
    "Pr(\\hat{Y}) \\left( 1 + exp(XW) \\right) &= exp(XW) \\\\\n",
    "Pr(\\hat{Y}) &= \\left( 1 - Pr(\\hat{Y}) \\right) exp(XW) \\\\\n",
    "\\text{Odds(ratio):} \\left( \\dfrac{Pr(\\hat{Y})}{1 - Pr(\\hat{Y})} \\right) &= exp(XW) \\\\\n",
    "\\text{Logit(log-odds): } log \\left( \\dfrac{Pr(\\hat{Y})}{1 - Pr(\\hat{Y})} \\right) &= XW = w_0 + w_1X_1 + w_2X_2 + \\cdots + w_kX_k \\\\\n",
    "\\end{align*}\n",
    "\n",
    "---\n",
    "\n",
    "**2) 함수 추정을 위한 비용함수:** 나의 주장 기반 알고리즘의 `분류값`($Pr(\\hat{Y})$)과 `실제 데이터`($Y$)의 차이를 평가하는 함수\n",
    "\n",
    "- **이슈: `잔차`를 사용하는 Linear Regression 비용함수 적용 어려움**    \n",
    "\n",
    "> (1) 분류문제에서는 $\\hat{Y}$를 사용한 `잔차(에러)계산이 무의미`   \n",
    ">\n",
    "> (2) 잔차($Y - \\hat{Y}$)를 `시그모이드 및 로짓 변환`을 하면 Non-convex 형태가 되서 `최소값(Global Minimum) 추정 어려움`    \n",
    ">\n",
    "> (3) 정확한 `수학적 방정식 기반` 계수추정 어렵기에 `확률론적 접근 필요`    \n",
    ">\n",
    "> <center><img src='Image/Advanced/Cost_Comparison.png' width='600'></center>\n",
    "\n",
    "- **방향:** 회귀문제와 달리 `새로운 비용함수`가 필요\n",
    "\n",
    "> - Y를 `잘` 분류하면 `cost=0`으로 그렇지 않으면 cost=$\\infty$가 되는 방향\n",
    ">> - (빨간선) 실제값이 `1`일때 예측값이 `1`이면 Cost는 `0`\n",
    ">> - (빨간선) 실제값이 `1`일때 예측값이 `0`이면 Cost는 `무한대`\n",
    ">\n",
    "> \\begin{align*}\n",
    "\\text{Cost} = \\begin{cases} -log(Pr(\\hat{Y})) ~~~~ & \\text{in the case of } ~~~ Y = 1 \\\\ -log(1-Pr(\\hat{Y})) ~~~~ & \\text{in the case of } ~~~ Y = 0 \\end{cases}\n",
    "\\end{align*}\n",
    "> <center><img src='Image/Advanced/Cost_Logistic.png' width='600'></center>\n",
    "\n",
    "- **Cross Entropy 등장:** Y가 0과 1인 경우의 `Cost를 결합`하여 하나의 식으로 표현\n",
    "\n",
    "> - 로지스틱 알고리즘은 `비용함수`로 `Cross Entropy`를 사용하고 `최소로 하는 계수/가중치 추정`\n",
    "> - `Y=0`인 경우 `파란부분`만 남고 `Y=1`인 경우 `빨간부분`만 남아 `Class별`로 독립적으로 작동\n",
    "> - 분류문제의 `Cost 함수는 다양`하고 많지만 통계학적으로 `Cross Entropy`는 계수 추정에 `효율적`인 편\n",
    "> - `Convex 형태`이기 때문에 `Global Minimum`을 찾기가 용이함\n",
    "> - 추정된 계수/가중치($\\hat{w}$)로 방정식을 만들어 Y=1인 `분류확률` 계산 가능\n",
    ">\n",
    ">\\begin{align*}\n",
    "\\text{Cost} &= \\sum_{i=1}^{m} \\left[ - \\color{red}{\\hat{Y}_{i} log (Pr(\\hat{Y}_{i}))} - \\color{blue}{(1-\\hat{Y}_{i}) log (1-Pr(\\hat{Y}_{i}))} \\right] \\\\\n",
    "\\hat{W} &= \\underset{W}{\\arg\\min} \\sum_{i=1}^{m} \\left[\\text{Cost} \\right] \\\\\n",
    "\\end{align*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9066666666666666\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Pred</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "      <th>0.0</th>\n",
       "      <td>184</td>\n",
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       "      <th>1.0</th>\n",
       "      <td>15</td>\n",
       "      <td>88</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pred  0.0  1.0\n",
       "True          \n",
       "0.0   184   13\n",
       "1.0    15   88"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "X_train_fes = sm.add_constant(X_train)\n",
    "X_test_fes = sm.add_constant(X_test)\n",
    "model_logitreg = LogisticRegression(fit_intercept=False)\n",
    "model_logitreg.fit(X_train_fes, Y_train)\n",
    "\n",
    "# 데이터 예측 및 평가하기\n",
    "Y_trpred = model_logitreg.predict(X_train_fes)\n",
    "Y_tepred = model_logitreg.predict(X_test_fes)\n",
    "print('Accuracy: ', accuracy_score(Y_test, Y_tepred))\n",
    "pd.crosstab(Y_test, Y_tepred, rownames=['True'], colnames=['Pred'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest\n",
    "\n",
    "**\"데이터를 `복원샘플링`하여 나눈 샘플들로 `같은 알고리즘`으로 학습 후 `보팅` 방식으로 최종 예측\"**\n",
    "\n",
    "<center><img src='Image/Expert/Ensemble_Bagging.jpg' width='400'></center> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.92\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "    .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>Pred</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>185</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>12</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pred  0.0  1.0\n",
       "True          \n",
       "0.0   185   12\n",
       "1.0    12   91"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "model_rf = RandomForestClassifier(n_estimators=100, random_state=123)\n",
    "model_rf.fit(X_train, Y_train)\n",
    "\n",
    "# 데이터 예측 및 평가하기\n",
    "Y_trpred = model_rf.predict(X_train)\n",
    "Y_tepred = model_rf.predict(X_test)\n",
    "print('Accuracy: ', accuracy_score(Y_test, Y_tepred))\n",
    "pd.crosstab(Y_test, Y_tepred, rownames=['True'], colnames=['Pred'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP\n",
    "\n",
    "**1) 방향:** 인공신경망의 `비선형성 접근 필요성`으로  `은닉층`을 반영항 `다층/심층신경망으로 딥러닝을 출발시킴`\n",
    "\n",
    "- `2개의 직선` 또는 `여러개의 직선이 결합된 1개의 곡선`을 이용하여 제대로 분류가 가능할 것이라 가정\n",
    "- `다층(여러개의 직선)신경망`으로 비선형 문제를 해결 (Hinton, 1986)  \n",
    "\n",
    "<center><img src='Image/Expert/DL_Compare_MLP.jpg' width='500'></center>\n",
    "\n",
    "- 여러문제가 해결되면서 `은닉층을 늘려 학습 결과를 향상`시키는 방법이 등장하며 `DNN(Deep Neural Network)`라고 함\n",
    "\n",
    "<center><img src='Image/Expert/DL_ANN_MLP.bmp' width='600'>(https://4ir.kisti.re.kr/)</center>\n",
    "\n",
    "- 은닉층의 수가 증가하면 더욱 어려운 문제를 풀수 있는데, 통상 `은닉층을 최소 2개이상 가진 알고리즘`을 `딥러닝(Deep Learning)` 알고리즘 이라고 함\n",
    "\n",
    "<center><img src='Image/Expert/DL_MLP_Custom.PNG' width='500'></center>\n",
    "\n",
    "**2) 한계:** 층(Layer)의 수가 많아지면서 `늘어나는 Weights를 수동으로 학습하기에 한계`가 있기에 여전히 데이터 학습 불가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_5 (InputLayer)        [(None, 2)]               0         \n",
      "                                                                 \n",
      " dense_15 (Dense)            (None, 128)               384       \n",
      "                                                                 \n",
      " dense_16 (Dense)            (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_17 (Dense)            (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 8,705\n",
      "Trainable params: 8,705\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
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    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 0s 2ms/step\n",
      "10/10 [==============================] - 0s 2ms/step\n",
      "22/22 [==============================] - 0s 4ms/step - loss: 0.1192 - accuracy: 0.9571\n",
      "Evaluation of Y_train:  [0.11919858306646347, 0.9571428298950195]\n",
      " \n",
      "10/10 [==============================] - 0s 8ms/step - loss: 0.2022 - accuracy: 0.9167\n",
      "Evaluation of Y_test:  [0.20221535861492157, 0.9166666865348816]\n",
      " \n",
      "Accuracy:  0.9166666666666666\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pred</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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      "text/plain": [
       "Pred    0   1\n",
       "True         \n",
       "0.0   187  10\n",
       "1.0    15  88"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 하이퍼파라미터\n",
    "tf.random.set_seed(1)\n",
    "HIDDEN_ACTIVATION = 'relu'\n",
    "OUTPUT_ACTIVATION = 'sigmoid'\n",
    "LOSS = 'binary_crossentropy'\n",
    "OPTIMIZER = 'adam'\n",
    "METRICS = ['accuracy']\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS = 100\n",
    "VERBOSE = 0\n",
    "\n",
    "# MLP\n",
    "inputs = Input(shape=(2,))    # Input 함수를 사용하여 입력층 생성\n",
    "hiddens = Dense(128, activation=HIDDEN_ACTIVATION)(inputs)    # 은닉층1: 입력층을 입력받는 layer 추가\n",
    "hiddens = Dense(64, activation=HIDDEN_ACTIVATION)(hiddens)    # 은닉층2: 은닉층1을 입력받는 layer 추가\n",
    "output = Dense(1, activation=OUTPUT_ACTIVATION)(hiddens)    # 출력층: 은닉층2를 입력받아 Y를 출력하는 layer 추가\n",
    "model = Model(inputs, output)    # 입력층과 출력층을 구성하며 최종 모델 완성\n",
    "model.summary()    # 네트워크 구조 출력\n",
    "plot_model(model)    # 네트워크 구조 시각화\n",
    "\n",
    "# 데이터 학습하기\n",
    "model.compile(loss=LOSS, optimizer=OPTIMIZER, metrics=METRICS)\n",
    "model_fit = model.fit(X_train, Y_train, \n",
    "                      batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=VERBOSE)\n",
    "pd.DataFrame(model_fit.history[METRICS[0]], columns=[METRICS[0].upper()]).plot()\n",
    "plt.show()\n",
    "\n",
    "# 데이터 예측 및 평가하기\n",
    "Y_trpred = model.predict(X_train)\n",
    "Y_tepred = model.predict(X_test)\n",
    "print('Evaluation of Y_train: ', model.evaluate(X_train, Y_train))\n",
    "print(' ')\n",
    "print('Evaluation of Y_test: ', model.evaluate(X_test, Y_test))\n",
    "print(' ')\n",
    "print('Accuracy: ', accuracy_score(Y_test, list(map(int, (Y_tepred>=0.5)))))\n",
    "pd.crosstab(Y_test, list(map(int, (Y_tepred>=0.5))),\n",
    "            rownames=['True'], colnames=['Pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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