stock price predict_LSTM_RNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "51eeeb41",
   "metadata": {},
   "source": [
    "## Topic 2: LSTM 으로 “현대자동차” 일별 주식가격 예측하기\n",
    "\n",
    "### 유튜브 영상 \" https://youtu.be/BfpOUU-dN7I \""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b59aba5",
   "metadata": {},
   "source": [
    "## [Task 2.1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66c4d5f6",
   "metadata": {},
   "source": [
    "### 데이터 분석을 하기 위해 패키지 설치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "762ec328",
   "metadata": {},
   "outputs": [],
   "source": [
    "# FinanceDataReader 패키지 설치 \n",
    "#!pip install finance-datareader\n",
    "import FinanceDataReader as fdr \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import argparse\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from copy import deepcopy # Add Deepcopy for args\n",
    "import datetime\n",
    "import seaborn as sns\n",
    "import os\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4d092ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fdf9d492",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2db6516f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "41d2ab7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>ISU_CD</th>\n",
       "      <th>Name</th>\n",
       "      <th>Market</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Close</th>\n",
       "      <th>ChangeCode</th>\n",
       "      <th>Changes</th>\n",
       "      <th>ChagesRatio</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Marcap</th>\n",
       "      <th>Stocks</th>\n",
       "      <th>MarketId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>005930</td>\n",
       "      <td>KR7005930003</td>\n",
       "      <td>삼성전자</td>\n",
       "      <td>KOSPI</td>\n",
       "      <td></td>\n",
       "      <td>59500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59500</td>\n",
       "      <td>59900</td>\n",
       "      <td>59100</td>\n",
       "      <td>7696187</td>\n",
       "      <td>457558919300</td>\n",
       "      <td>355202061725000</td>\n",
       "      <td>5969782550</td>\n",
       "      <td>STK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>373220</td>\n",
       "      <td>KR7373220003</td>\n",
       "      <td>LG에너지솔루션</td>\n",
       "      <td>KOSPI</td>\n",
       "      <td></td>\n",
       "      <td>482000</td>\n",
       "      <td>2</td>\n",
       "      <td>-3500</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>482500</td>\n",
       "      <td>483000</td>\n",
       "      <td>473000</td>\n",
       "      <td>333848</td>\n",
       "      <td>159178309141</td>\n",
       "      <td>112788000000000</td>\n",
       "      <td>234000000</td>\n",
       "      <td>STK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>207940</td>\n",
       "      <td>KR7207940008</td>\n",
       "      <td>삼성바이오로직스</td>\n",
       "      <td>KOSPI</td>\n",
       "      <td></td>\n",
       "      <td>821000</td>\n",
       "      <td>2</td>\n",
       "      <td>-14000</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>825000</td>\n",
       "      <td>831000</td>\n",
       "      <td>821000</td>\n",
       "      <td>21686</td>\n",
       "      <td>17848391000</td>\n",
       "      <td>58433854000000</td>\n",
       "      <td>71174000</td>\n",
       "      <td>STK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000660</td>\n",
       "      <td>KR7000660001</td>\n",
       "      <td>SK하이닉스</td>\n",
       "      <td>KOSPI</td>\n",
       "      <td></td>\n",
       "      <td>79000</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>0.77</td>\n",
       "      <td>78300</td>\n",
       "      <td>80100</td>\n",
       "      <td>78300</td>\n",
       "      <td>1813544</td>\n",
       "      <td>143667793100</td>\n",
       "      <td>57512186835000</td>\n",
       "      <td>728002365</td>\n",
       "      <td>STK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>051910</td>\n",
       "      <td>KR7051910008</td>\n",
       "      <td>LG화학</td>\n",
       "      <td>KOSPI</td>\n",
       "      <td></td>\n",
       "      <td>633000</td>\n",
       "      <td>2</td>\n",
       "      <td>-12000</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>640000</td>\n",
       "      <td>643000</td>\n",
       "      <td>627000</td>\n",
       "      <td>101519</td>\n",
       "      <td>64321516000</td>\n",
       "      <td>44684953119000</td>\n",
       "      <td>70592343</td>\n",
       "      <td>STK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Code        ISU_CD      Name Market Dept   Close ChangeCode  Changes  \\\n",
       "0  005930  KR7005930003      삼성전자  KOSPI        59500          3        0   \n",
       "1  373220  KR7373220003  LG에너지솔루션  KOSPI       482000          2    -3500   \n",
       "2  207940  KR7207940008  삼성바이오로직스  KOSPI       821000          2   -14000   \n",
       "3  000660  KR7000660001    SK하이닉스  KOSPI        79000          1      600   \n",
       "4  051910  KR7051910008      LG화학  KOSPI       633000          2   -12000   \n",
       "\n",
       "   ChagesRatio    Open    High     Low   Volume        Amount  \\\n",
       "0         0.00   59500   59900   59100  7696187  457558919300   \n",
       "1        -0.72  482500  483000  473000   333848  159178309141   \n",
       "2        -1.68  825000  831000  821000    21686   17848391000   \n",
       "3         0.77   78300   80100   78300  1813544  143667793100   \n",
       "4        -1.86  640000  643000  627000   101519   64321516000   \n",
       "\n",
       "            Marcap      Stocks MarketId  \n",
       "0  355202061725000  5969782550      STK  \n",
       "1  112788000000000   234000000      STK  \n",
       "2   58433854000000    71174000      STK  \n",
       "3   57512186835000   728002365      STK  \n",
       "4   44684953119000    70592343      STK  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 코스피 상장종목 전체 불러오기\n",
    "\n",
    "df_kospi = fdr.StockListing('KOSPI')\n",
    "df_kospi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e7d839c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 942 entries, 0 to 941\n",
      "Data columns (total 17 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Code         942 non-null    object \n",
      " 1   ISU_CD       942 non-null    object \n",
      " 2   Name         942 non-null    object \n",
      " 3   Market       942 non-null    object \n",
      " 4   Dept         942 non-null    object \n",
      " 5   Close        942 non-null    object \n",
      " 6   ChangeCode   942 non-null    object \n",
      " 7   Changes      942 non-null    int64  \n",
      " 8   ChagesRatio  942 non-null    float64\n",
      " 9   Open         942 non-null    int64  \n",
      " 10  High         942 non-null    int64  \n",
      " 11  Low          942 non-null    int64  \n",
      " 12  Volume       942 non-null    int64  \n",
      " 13  Amount       942 non-null    int64  \n",
      " 14  Marcap       942 non-null    int64  \n",
      " 15  Stocks       942 non-null    int64  \n",
      " 16  MarketId     942 non-null    object \n",
      "dtypes: float64(1), int64(8), object(8)\n",
      "memory usage: 125.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# 코스피에 대한 정보 \n",
    "df_kospi.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85cdeb2e",
   "metadata": {},
   "source": [
    "## 현대차 데이터 불러오기 (005380)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f5083742",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Open    High     Low   Close    Volume    Change\n",
      "Date                                                          \n",
      "2021-01-04  194000  211000  193500  207500   5651695  0.080729\n",
      "2021-01-05  207000  213000  205000  209500   3022595  0.009639\n",
      "2021-01-06  209500  211500  202500  203000   2848547 -0.031026\n",
      "2021-01-07  206000  210500  204500  206000   2478337  0.014778\n",
      "2021-01-08  240500  257000  225000  246000  19933702  0.194175\n",
      "...            ...     ...     ...     ...       ...       ...\n",
      "2021-12-24  210500  216500  210500  215500   1024355  0.031100\n",
      "2021-12-27  216500  217000  213500  213500    530039 -0.009281\n",
      "2021-12-28  215000  215000  211500  214000    880326  0.002342\n",
      "2021-12-29  213000  215000  211500  212000    659392 -0.009346\n",
      "2021-12-30  211500  212000  208500  209000    581448 -0.014151\n",
      "\n",
      "[248 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "# 현대차 데이터 불러오기 (005380)\n",
    "#HD_stock = fdr.DataReader('005380', '2021-01-01', '2021-12-31')\n",
    "\n",
    "start = (2021, 1, 1)\n",
    "start = datetime.datetime(*start)\n",
    "end = (2021, 12, 31)\n",
    "end = datetime.datetime(*end)\n",
    "HD_stock = fdr.DataReader('005380', start, end)\n",
    "print(HD_stock)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb004b89",
   "metadata": {},
   "source": [
    "### DataFrame형식의 HD_stock 데이터"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38b9dd8b",
   "metadata": {},
   "source": [
    "#### 위의 2021년 1월 1일부터 2021년 12월 31일까지의 데이터를 데이터 프레임의 형식으로 불러왔다. \n",
    "#### 248개의 row와 6개의 칼럼으로 구성되어 있는것을 확인할 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b093119d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(HD_stock))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "52b0448d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 248 entries, 2021-01-04 to 2021-12-30\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Open    248 non-null    int64  \n",
      " 1   High    248 non-null    int64  \n",
      " 2   Low     248 non-null    int64  \n",
      " 3   Close   248 non-null    int64  \n",
      " 4   Volume  248 non-null    int64  \n",
      " 5   Change  248 non-null    float64\n",
      "dtypes: float64(1), int64(5)\n",
      "memory usage: 13.6 KB\n"
     ]
    }
   ],
   "source": [
    "# HD_stock 데이터 셋에서 null값이 있는지 확인\n",
    "# null값이 있는날을 확인하고 없앨 수 있다.\n",
    "HD_stock = HD_stock.dropna() \n",
    "HD_stock.info()   #확인 가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "49beaf10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# min-max실행전에 우선 원래 데이터를 HD_stock1로 보관함\n",
    "HD_stock1 = HD_stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a2b00e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>194000</td>\n",
       "      <td>211000</td>\n",
       "      <td>193500</td>\n",
       "      <td>207500</td>\n",
       "      <td>5651695</td>\n",
       "      <td>0.080729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>207000</td>\n",
       "      <td>213000</td>\n",
       "      <td>205000</td>\n",
       "      <td>209500</td>\n",
       "      <td>3022595</td>\n",
       "      <td>0.009639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-06</th>\n",
       "      <td>209500</td>\n",
       "      <td>211500</td>\n",
       "      <td>202500</td>\n",
       "      <td>203000</td>\n",
       "      <td>2848547</td>\n",
       "      <td>-0.031026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-07</th>\n",
       "      <td>206000</td>\n",
       "      <td>210500</td>\n",
       "      <td>204500</td>\n",
       "      <td>206000</td>\n",
       "      <td>2478337</td>\n",
       "      <td>0.014778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-08</th>\n",
       "      <td>240500</td>\n",
       "      <td>257000</td>\n",
       "      <td>225000</td>\n",
       "      <td>246000</td>\n",
       "      <td>19933702</td>\n",
       "      <td>0.194175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume    Change\n",
       "Date                                                          \n",
       "2021-01-04  194000  211000  193500  207500   5651695  0.080729\n",
       "2021-01-05  207000  213000  205000  209500   3022595  0.009639\n",
       "2021-01-06  209500  211500  202500  203000   2848547 -0.031026\n",
       "2021-01-07  206000  210500  204500  206000   2478337  0.014778\n",
       "2021-01-08  240500  257000  225000  246000  19933702  0.194175"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HD_stock1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a21ca21f",
   "metadata": {},
   "source": [
    "#### 데이터를 0 과 1 사이로 MinMax scaling 작업"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7005b15e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0375    , 0.17021277, 0.06382979, 0.18918919, 0.27024741],\n",
       "       [0.2       , 0.19148936, 0.22695035, 0.21621622, 0.13591107],\n",
       "       [0.23125   , 0.17553191, 0.19148936, 0.12837838, 0.12701792],\n",
       "       ...,\n",
       "       [0.3       , 0.21276596, 0.31914894, 0.27702703, 0.02644981],\n",
       "       [0.275     , 0.21276596, 0.31914894, 0.25      , 0.01516098],\n",
       "       [0.25625   , 0.18085106, 0.27659574, 0.20945946, 0.01117836]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "# 스케일을 적용할 column을 정의합니다.\n",
    "scale_cols = ['Open', 'High', 'Low', 'Close', 'Volume']\n",
    "# 스케일 후 columns\n",
    "scaled = scaler.fit_transform(HD_stock[scale_cols])\n",
    "scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "30381868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>2.480000e+02</td>\n",
       "      <td>248.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>223770.161290</td>\n",
       "      <td>226645.161290</td>\n",
       "      <td>220729.838710</td>\n",
       "      <td>223381.048387</td>\n",
       "      <td>1.220738e+06</td>\n",
       "      <td>0.000588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15400.378458</td>\n",
       "      <td>16273.444526</td>\n",
       "      <td>14480.952056</td>\n",
       "      <td>15145.881835</td>\n",
       "      <td>1.743217e+06</td>\n",
       "      <td>0.022717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>191000.000000</td>\n",
       "      <td>195000.000000</td>\n",
       "      <td>189000.000000</td>\n",
       "      <td>193500.000000</td>\n",
       "      <td>3.626760e+05</td>\n",
       "      <td>-0.062124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>210375.000000</td>\n",
       "      <td>212000.000000</td>\n",
       "      <td>208000.000000</td>\n",
       "      <td>209500.000000</td>\n",
       "      <td>6.071025e+05</td>\n",
       "      <td>-0.010923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>223750.000000</td>\n",
       "      <td>225000.000000</td>\n",
       "      <td>221250.000000</td>\n",
       "      <td>223500.000000</td>\n",
       "      <td>8.231965e+05</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>235125.000000</td>\n",
       "      <td>238000.000000</td>\n",
       "      <td>230875.000000</td>\n",
       "      <td>234250.000000</td>\n",
       "      <td>1.168822e+06</td>\n",
       "      <td>0.008457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>271000.000000</td>\n",
       "      <td>289000.000000</td>\n",
       "      <td>259500.000000</td>\n",
       "      <td>267500.000000</td>\n",
       "      <td>1.993370e+07</td>\n",
       "      <td>0.194175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open           High            Low          Close  \\\n",
       "count     248.000000     248.000000     248.000000     248.000000   \n",
       "mean   223770.161290  226645.161290  220729.838710  223381.048387   \n",
       "std     15400.378458   16273.444526   14480.952056   15145.881835   \n",
       "min    191000.000000  195000.000000  189000.000000  193500.000000   \n",
       "25%    210375.000000  212000.000000  208000.000000  209500.000000   \n",
       "50%    223750.000000  225000.000000  221250.000000  223500.000000   \n",
       "75%    235125.000000  238000.000000  230875.000000  234250.000000   \n",
       "max    271000.000000  289000.000000  259500.000000  267500.000000   \n",
       "\n",
       "             Volume      Change  \n",
       "count  2.480000e+02  248.000000  \n",
       "mean   1.220738e+06    0.000588  \n",
       "std    1.743217e+06    0.022717  \n",
       "min    3.626760e+05   -0.062124  \n",
       "25%    6.071025e+05   -0.010923  \n",
       "50%    8.231965e+05    0.000000  \n",
       "75%    1.168822e+06    0.008457  \n",
       "max    1.993370e+07    0.194175  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HD_stock.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7b071e06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0375    , 0.17021277, 0.06382979, 0.18918919, 0.27024741],\n",
       "       [0.2       , 0.19148936, 0.22695035, 0.21621622, 0.13591107],\n",
       "       [0.23125   , 0.17553191, 0.19148936, 0.12837838, 0.12701792],\n",
       "       ...,\n",
       "       [0.3       , 0.21276596, 0.31914894, 0.27702703, 0.02644981],\n",
       "       [0.275     , 0.21276596, 0.31914894, 0.25      , 0.01516098],\n",
       "       [0.25625   , 0.18085106, 0.27659574, 0.20945946, 0.01117836]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 스케일링한 HD_stock을 scaled의 어래이 형식 확인\n",
    "scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1286b1ad",
   "metadata": {},
   "source": [
    "####  array형식의 데이터를 DataFrame형식으로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4e57741a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(scaled, columns=scale_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0e84820c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.03750</td>\n",
       "      <td>0.170213</td>\n",
       "      <td>0.063830</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>0.270247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.20000</td>\n",
       "      <td>0.191489</td>\n",
       "      <td>0.226950</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.135911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23125</td>\n",
       "      <td>0.175532</td>\n",
       "      <td>0.191489</td>\n",
       "      <td>0.128378</td>\n",
       "      <td>0.127018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.18750</td>\n",
       "      <td>0.164894</td>\n",
       "      <td>0.219858</td>\n",
       "      <td>0.168919</td>\n",
       "      <td>0.108102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.61875</td>\n",
       "      <td>0.659574</td>\n",
       "      <td>0.510638</td>\n",
       "      <td>0.709459</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>0.24375</td>\n",
       "      <td>0.228723</td>\n",
       "      <td>0.304965</td>\n",
       "      <td>0.297297</td>\n",
       "      <td>0.033809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>0.31875</td>\n",
       "      <td>0.234043</td>\n",
       "      <td>0.347518</td>\n",
       "      <td>0.270270</td>\n",
       "      <td>0.008552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>0.30000</td>\n",
       "      <td>0.212766</td>\n",
       "      <td>0.319149</td>\n",
       "      <td>0.277027</td>\n",
       "      <td>0.026450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>0.27500</td>\n",
       "      <td>0.212766</td>\n",
       "      <td>0.319149</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.015161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0.25625</td>\n",
       "      <td>0.180851</td>\n",
       "      <td>0.276596</td>\n",
       "      <td>0.209459</td>\n",
       "      <td>0.011178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>248 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Open      High       Low     Close    Volume\n",
       "0    0.03750  0.170213  0.063830  0.189189  0.270247\n",
       "1    0.20000  0.191489  0.226950  0.216216  0.135911\n",
       "2    0.23125  0.175532  0.191489  0.128378  0.127018\n",
       "3    0.18750  0.164894  0.219858  0.168919  0.108102\n",
       "4    0.61875  0.659574  0.510638  0.709459  1.000000\n",
       "..       ...       ...       ...       ...       ...\n",
       "243  0.24375  0.228723  0.304965  0.297297  0.033809\n",
       "244  0.31875  0.234043  0.347518  0.270270  0.008552\n",
       "245  0.30000  0.212766  0.319149  0.277027  0.026450\n",
       "246  0.27500  0.212766  0.319149  0.250000  0.015161\n",
       "247  0.25625  0.180851  0.276596  0.209459  0.011178\n",
       "\n",
       "[248 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6cb0d380",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dec35886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.409627</td>\n",
       "      <td>0.336651</td>\n",
       "      <td>0.450069</td>\n",
       "      <td>0.403798</td>\n",
       "      <td>0.043843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.192505</td>\n",
       "      <td>0.173122</td>\n",
       "      <td>0.205404</td>\n",
       "      <td>0.204674</td>\n",
       "      <td>0.089071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.242187</td>\n",
       "      <td>0.180851</td>\n",
       "      <td>0.269504</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.012489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.409375</td>\n",
       "      <td>0.319149</td>\n",
       "      <td>0.457447</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>0.023531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.551562</td>\n",
       "      <td>0.457447</td>\n",
       "      <td>0.593972</td>\n",
       "      <td>0.550676</td>\n",
       "      <td>0.041191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open        High         Low       Close      Volume\n",
       "count  248.000000  248.000000  248.000000  248.000000  248.000000\n",
       "mean     0.409627    0.336651    0.450069    0.403798    0.043843\n",
       "std      0.192505    0.173122    0.205404    0.204674    0.089071\n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000\n",
       "25%      0.242187    0.180851    0.269504    0.216216    0.012489\n",
       "50%      0.409375    0.319149    0.457447    0.405405    0.023531\n",
       "75%      0.551562    0.457447    0.593972    0.550676    0.041191\n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "df.sort_index(ascending=False).reset_index(drop=True)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scale_cols = ['Open', 'High', 'Low', 'Close', 'Volume']\n",
    "df_scaled = scaler.fit_transform(df[scale_cols])\n",
    "df_scaled = pd.DataFrame(df_scaled)\n",
    "df_scaled.columns = scale_cols\n",
    "\n",
    "df_scaled.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff419b2",
   "metadata": {},
   "source": [
    "## TensroFlow Dataset을 활용한 시퀀스 데이터셋 구성"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25c69a87",
   "metadata": {},
   "source": [
    "### Hyperparameter를 정의"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "071728a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "TEST_SIZE = 75\n",
    "WINDOW_SIZE = 60\n",
    "\n",
    "train = df_scaled[:-TEST_SIZE]\n",
    "test = df_scaled[-TEST_SIZE:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f2925b",
   "metadata": {},
   "source": [
    "#### dataset 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ba81ab1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_dataset(data, label, window_size=60):\n",
    "    feature_list = []\n",
    "    label_list = []\n",
    "    for i in range(len(data) - window_size):\n",
    "        feature_list.append(np.array(data.iloc[i:i+window_size]))\n",
    "        label_list.append(np.array(label.iloc[i+window_size]))\n",
    "    return np.array(feature_list), np.array(label_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df49bc7",
   "metadata": {},
   "source": [
    "#### window_size는 얼마동안(기간)의 주가 데이터에 기반하여 \n",
    "#### 다음날 종가를 예측할 것인지 정하는 parameter입니다.\n",
    "#### 즉, 과거 60일을 기반으로 내일 데이터를 예측"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b719252d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((107, 20, 4), (46, 20, 4))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "feature_cols = ['Open', 'High', 'Low', 'Volume']\n",
    "label_cols = ['Close']\n",
    "\n",
    "train_feature = train[feature_cols]\n",
    "train_label = train[label_cols]\n",
    "\n",
    "train_feature, train_label = make_dataset(train_feature, train_label, 20)\n",
    "\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(train_feature, train_label, test_size=0.3)\n",
    "x_train.shape, x_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8adb8193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((75, 4), (75, 1))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_feature = test[feature_cols]\n",
    "test_label = test[label_cols]\n",
    "\n",
    "test_feature.shape, test_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "29450f8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((55, 20, 4), (55, 1))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_feature, test_label = make_dataset(test_feature, test_label, 20)\n",
    "test_feature.shape, test_label.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6de54ec0",
   "metadata": {},
   "source": [
    "### 모형 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e594d498",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 모형학습\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras.layers import LSTM\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(16, \n",
    "               input_shape=(train_feature.shape[1], train_feature.shape[2]), \n",
    "               activation='relu', \n",
    "               return_sequences=False)\n",
    "          )\n",
    "\n",
    "model.add(Dense(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6de77809",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 16)                1344      \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,361\n",
      "Trainable params: 1,361\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a5c4211",
   "metadata": {},
   "source": [
    "#### 에폭 횟수(학습용 전체데이터를 몇 회 반복해서 학습할 것인가 입력)\n",
    "#### earlystopping은 5번, epoch통안 val_loss 개선이 없다면 학습을 멈춤.\n",
    "#### # val_loss 기준 체크포인터도 생성."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b3449185",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.2599 \n",
      "Epoch 1: val_loss improved from inf to 0.17659, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 8s 264ms/step - loss: 0.2408 - val_loss: 0.1766\n",
      "Epoch 2/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.1545\n",
      "Epoch 2: val_loss improved from 0.17659 to 0.08695, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 51ms/step - loss: 0.1404 - val_loss: 0.0870\n",
      "Epoch 3/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0633\n",
      "Epoch 3: val_loss improved from 0.08695 to 0.02225, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 51ms/step - loss: 0.0560 - val_loss: 0.0223\n",
      "Epoch 4/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0139\n",
      "Epoch 4: val_loss improved from 0.02225 to 0.01205, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 51ms/step - loss: 0.0136 - val_loss: 0.0120\n",
      "Epoch 5/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0132\n",
      "Epoch 5: val_loss improved from 0.01205 to 0.00834, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 52ms/step - loss: 0.0145 - val_loss: 0.0083\n",
      "Epoch 6/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0083\n",
      "Epoch 6: val_loss improved from 0.00834 to 0.00728, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 51ms/step - loss: 0.0075 - val_loss: 0.0073\n",
      "Epoch 7/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0078\n",
      "Epoch 7: val_loss did not improve from 0.00728\n",
      "7/7 [==============================] - 0s 47ms/step - loss: 0.0082 - val_loss: 0.0080\n",
      "Epoch 8/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0063\n",
      "Epoch 8: val_loss improved from 0.00728 to 0.00584, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 50ms/step - loss: 0.0069 - val_loss: 0.0058\n",
      "Epoch 9/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0059\n",
      "Epoch 9: val_loss improved from 0.00584 to 0.00553, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 50ms/step - loss: 0.0061 - val_loss: 0.0055\n",
      "Epoch 10/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0060\n",
      "Epoch 10: val_loss improved from 0.00553 to 0.00519, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 51ms/step - loss: 0.0064 - val_loss: 0.0052\n",
      "Epoch 11/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0057\n",
      "Epoch 11: val_loss did not improve from 0.00519\n",
      "7/7 [==============================] - 0s 47ms/step - loss: 0.0060 - val_loss: 0.0053\n",
      "Epoch 12/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0064\n",
      "Epoch 12: val_loss improved from 0.00519 to 0.00502, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 50ms/step - loss: 0.0058 - val_loss: 0.0050\n",
      "Epoch 13/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0057\n",
      "Epoch 13: val_loss improved from 0.00502 to 0.00475, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 50ms/step - loss: 0.0056 - val_loss: 0.0048\n",
      "Epoch 14/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0056\n",
      "Epoch 14: val_loss improved from 0.00475 to 0.00470, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 50ms/step - loss: 0.0055 - val_loss: 0.0047\n",
      "Epoch 15/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0051\n",
      "Epoch 15: val_loss improved from 0.00470 to 0.00466, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 52ms/step - loss: 0.0055 - val_loss: 0.0047\n",
      "Epoch 16/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0055\n",
      "Epoch 16: val_loss improved from 0.00466 to 0.00450, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 52ms/step - loss: 0.0055 - val_loss: 0.0045\n",
      "Epoch 17/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0059\n",
      "Epoch 17: val_loss did not improve from 0.00450\n",
      "7/7 [==============================] - 0s 47ms/step - loss: 0.0054 - val_loss: 0.0046\n",
      "Epoch 18/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0054\n",
      "Epoch 18: val_loss improved from 0.00450 to 0.00444, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 51ms/step - loss: 0.0053 - val_loss: 0.0044\n",
      "Epoch 19/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0054\n",
      "Epoch 19: val_loss improved from 0.00444 to 0.00434, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 53ms/step - loss: 0.0052 - val_loss: 0.0043\n",
      "Epoch 20/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0052\n",
      "Epoch 20: val_loss improved from 0.00434 to 0.00430, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 51ms/step - loss: 0.0052 - val_loss: 0.0043\n",
      "Epoch 21/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0053\n",
      "Epoch 21: val_loss did not improve from 0.00430\n",
      "7/7 [==============================] - 0s 48ms/step - loss: 0.0052 - val_loss: 0.0043\n",
      "Epoch 22/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0058\n",
      "Epoch 22: val_loss improved from 0.00430 to 0.00425, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 52ms/step - loss: 0.0052 - val_loss: 0.0043\n",
      "Epoch 23/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0056\n",
      "Epoch 23: val_loss did not improve from 0.00425\n",
      "7/7 [==============================] - 0s 47ms/step - loss: 0.0051 - val_loss: 0.0044\n",
      "Epoch 24/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0053\n",
      "Epoch 24: val_loss improved from 0.00425 to 0.00424, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 50ms/step - loss: 0.0051 - val_loss: 0.0042\n",
      "Epoch 25/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0054\n",
      "Epoch 25: val_loss improved from 0.00424 to 0.00417, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 50ms/step - loss: 0.0050 - val_loss: 0.0042\n",
      "Epoch 26/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0050\n",
      "Epoch 26: val_loss did not improve from 0.00417\n",
      "7/7 [==============================] - 0s 46ms/step - loss: 0.0052 - val_loss: 0.0042\n",
      "Epoch 27/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0052\n",
      "Epoch 27: val_loss did not improve from 0.00417\n",
      "7/7 [==============================] - 0s 46ms/step - loss: 0.0051 - val_loss: 0.0044\n",
      "Epoch 28/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0047\n",
      "Epoch 28: val_loss improved from 0.00417 to 0.00414, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 50ms/step - loss: 0.0050 - val_loss: 0.0041\n",
      "Epoch 29/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0057\n",
      "Epoch 29: val_loss did not improve from 0.00414\n",
      "7/7 [==============================] - 0s 46ms/step - loss: 0.0052 - val_loss: 0.0041\n",
      "Epoch 30/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0050\n",
      "Epoch 30: val_loss did not improve from 0.00414\n",
      "7/7 [==============================] - 0s 46ms/step - loss: 0.0050 - val_loss: 0.0043\n",
      "Epoch 31/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0047\n",
      "Epoch 31: val_loss improved from 0.00414 to 0.00410, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 51ms/step - loss: 0.0050 - val_loss: 0.0041\n",
      "Epoch 32/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0056\n",
      "Epoch 32: val_loss improved from 0.00410 to 0.00409, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 51ms/step - loss: 0.0050 - val_loss: 0.0041\n",
      "Epoch 33/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0056\n",
      "Epoch 33: val_loss improved from 0.00409 to 0.00407, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 50ms/step - loss: 0.0049 - val_loss: 0.0041\n",
      "Epoch 34/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0050\n",
      "Epoch 34: val_loss did not improve from 0.00407\n",
      "7/7 [==============================] - 0s 47ms/step - loss: 0.0049 - val_loss: 0.0041\n",
      "Epoch 35/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0050\n",
      "Epoch 35: val_loss improved from 0.00407 to 0.00407, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 50ms/step - loss: 0.0049 - val_loss: 0.0041\n",
      "Epoch 36/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0052\n",
      "Epoch 36: val_loss improved from 0.00407 to 0.00398, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 51ms/step - loss: 0.0048 - val_loss: 0.0040\n",
      "Epoch 37/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0041\n",
      "Epoch 37: val_loss did not improve from 0.00398\n",
      "7/7 [==============================] - 0s 48ms/step - loss: 0.0048 - val_loss: 0.0040\n",
      "Epoch 38/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0049\n",
      "Epoch 38: val_loss did not improve from 0.00398\n",
      "7/7 [==============================] - 0s 47ms/step - loss: 0.0048 - val_loss: 0.0040\n",
      "Epoch 39/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0049\n",
      "Epoch 39: val_loss did not improve from 0.00398\n",
      "7/7 [==============================] - 0s 47ms/step - loss: 0.0049 - val_loss: 0.0042\n",
      "Epoch 40/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0047\n",
      "Epoch 40: val_loss improved from 0.00398 to 0.00394, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 51ms/step - loss: 0.0047 - val_loss: 0.0039\n",
      "Epoch 41/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0039\n",
      "Epoch 41: val_loss improved from 0.00394 to 0.00393, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 50ms/step - loss: 0.0048 - val_loss: 0.0039\n",
      "Epoch 42/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0048\n",
      "Epoch 42: val_loss did not improve from 0.00393\n",
      "7/7 [==============================] - 0s 47ms/step - loss: 0.0048 - val_loss: 0.0040\n",
      "Epoch 43/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0044\n",
      "Epoch 43: val_loss improved from 0.00393 to 0.00390, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 51ms/step - loss: 0.0048 - val_loss: 0.0039\n",
      "Epoch 44/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0049\n",
      "Epoch 44: val_loss did not improve from 0.00390\n",
      "7/7 [==============================] - 0s 47ms/step - loss: 0.0049 - val_loss: 0.0041\n",
      "Epoch 45/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0045\n",
      "Epoch 45: val_loss improved from 0.00390 to 0.00387, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 50ms/step - loss: 0.0047 - val_loss: 0.0039\n",
      "Epoch 46/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0052\n",
      "Epoch 46: val_loss did not improve from 0.00387\n",
      "7/7 [==============================] - 0s 47ms/step - loss: 0.0048 - val_loss: 0.0039\n",
      "Epoch 47/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0049\n",
      "Epoch 47: val_loss did not improve from 0.00387\n",
      "7/7 [==============================] - 0s 47ms/step - loss: 0.0048 - val_loss: 0.0040\n",
      "Epoch 48/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0044\n",
      "Epoch 48: val_loss improved from 0.00387 to 0.00382, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 51ms/step - loss: 0.0048 - val_loss: 0.0038\n",
      "Epoch 49/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0043\n",
      "Epoch 49: val_loss did not improve from 0.00382\n",
      "7/7 [==============================] - 0s 47ms/step - loss: 0.0047 - val_loss: 0.0040\n",
      "Epoch 50/50\n",
      "5/7 [====================>.........] - ETA: 0s - loss: 0.0048\n",
      "Epoch 50: val_loss improved from 0.00382 to 0.00380, saving model to model\\tmp_checkpoint.h5\n",
      "7/7 [==============================] - 0s 51ms/step - loss: 0.0046 - val_loss: 0.0038\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "early_stop = EarlyStopping(monitor='val_loss', patience=5)\n",
    "\n",
    "model_path = 'model'\n",
    "filename = os.path.join(model_path, 'tmp_checkpoint.h5')\n",
    "checkpoint = ModelCheckpoint(filename, monitor='val_loss', verbose=1, save_best_only=True, mode='auto')\n",
    "\n",
    "history = model.fit(x_train, y_train, \n",
    "                                    epochs=50, \n",
    "                                    batch_size=16,\n",
    "                                    validation_data=(x_valid, y_valid), \n",
    "                                    callbacks=[early_stop, checkpoint])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ccc150",
   "metadata": {},
   "source": [
    "#### 저장한 ModelCheckpoint 를 로드."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d231639d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 6ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(55, 1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_weights(filename)\n",
    "pred = model.predict(test_feature)\n",
    "\n",
    "pred.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ca48681",
   "metadata": {},
   "source": [
    "#### test_feature를 활용하여 예측을 진행."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeb4c6b8",
   "metadata": {},
   "source": [
    "## 예측 데이터 시각화 \n",
    "#### 0과 1사이의 값으로 스케일링했던 주가를 환원하지 않고 그래프를 그려보았다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "590f4c12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G/PnzOXDgAMuWLePll1/m/vvvb/T2f/nLX9i8eTNz584lJSWFQ4cO8dVXX3HvvfcC0LdvXy6++GJuv/12tm7dSnJyMrfddluTWZTrr7+e2NhYZsyYwc8//8zRo0dZsWIFmzdvBtRupWPHjpGSkkJOTg7l5eX1jnHjjTcSGBjIzTffzO7du/npp5+49957mT17tr1+xVskYBFCeEXdGSya3vZOIWltFq3HTTfdRGlpKWPGjGHu3Lnce++93HHHHY3efsiQIaxbt45Dhw4xadIkhg8fzqOPPkpcXJz9Nu+++y6JiYlMmTKFq6++mjvuuIPOnTs3ekyz2czq1avp3Lkzl1xyCYMHD+aZZ56xZ3lmzpzJxRdfzLnnnkt0dDTLli2rd4zg4GBWrVrFmTNnGD16NNdccw3nn38+r7zyihvPjj4MitYo3soVFBQQERFBfn4+4eHh3j4dIUQz3lh/hKdX7ufq4V14YdYw+/Xp+aWMX/gjJqOBfU9cjNlPPle1B2VlZRw7dozu3bsTGBjo7dNxytSpUxk2bFitkfmitqb+fR19/5ZXAiGEV+SXVu/UXFNseCChAX5YrArHc4sbuqsQoh2SgEUI4RXVXUK1a1gMBgM9ZU8hIUQd0iUkhPCK6qJb/3rf6905lJ1peWrh7eCWPjMhnLN27Vpvn0K7IBkWIYRXFDSyJARSeCuEqE8CFiGEV+Q30iUE1bs2y5KQEEIjAYsQwiuqd2quvzKt7Sl0NLuYKotnp2cK39JGGldFHXr8u0rAIoTwisbmsAB0iQwi0N9IhcVK2tnSlj414QXaqPeSEi/t0Cw8Svt3rTvS3xlSdCuE8Aqt6LbuaH4Ao9FAz+hQ9pwu4FBmId2jWs+IduEak8lEZGSkfQO94OBgp/bREb5JURRKSkrIysoiMjKy0a0KHOFSwLJ48WKeffZZ0tPTGThwIIsWLWLSpEkN3nbjxo385S9/Yf/+/ZSUlJCUlMSdd97JAw88UOt2K1as4NFHH+XIkSP07NmTf/zjH1x11VWunJ4QwseVV1koq1SXehoqugW18HbP6QIOZRUxbWBLnp3wltjYWACP7/orWl5kZKT939dVTgcsy5cvZ968eSxevJiJEyfy+uuvM336dPbu3UvXrl3r3T4kJIR77rmHIUOGEBISwsaNG7nzzjsJCQmxjy3evHkzs2bN4sknn+Sqq67i888/57rrrmPjxo2MHTvWrR9QCOF7tBksBgOEBTT8MtQ7Rq1jkcLb9sNgMBAXF0fnzp2prKz09ukInfj7+7uVWdE4PZp/7NixjBgxgiVLltiv69+/PzNmzGDhwoUOHePqq68mJCSEDz74AIBZs2ZRUFDAt99+a7/NxRdfTIcOHRrc66AhMppfiNbjSHYR5z+/jrBAP3Y9dlGDt1m1J4M7P0hmUJdwvrm34QyuEKL188ho/oqKCpKTk5k2bVqt66dNm8amTZscOsaOHTvYtGkTU6ZMsV+3efPmese86KKLmjxmeXk5BQUFtb6EEK2DVnDbUP2KpleNabdWq3SOCNHeORWw5OTkYLFY6m0xHRMTQ0ZGRpP3TUhIICAggFGjRjF37lxuu+02+/cyMjKcPubChQuJiIiwfyUmJjrzowghvKipGSyapI7B+JsMlFVaOZUnnUJCtHcutTXXrdxWFKXZau4NGzawfft2XnvtNRYtWlRvqcfZYy5YsID8/Hz7V1pampM/hRDCW5qawaLxMxnpESUD5IQQKqeKbqOiojCZTPUyH1lZWfUyJHV1794dgMGDB5OZmcljjz3G9ddfD6iV4c4eMyAggICAAGdOXwjhI5qawVJTr5hQDmQWciirkHP7dW6JUxPCp1RZrDz1v31M7BXFhQOafp9t65zKsJjNZkaOHMmaNWtqXb9mzRomTJjg8HEURaG8vNz+9/Hjx9c75urVq506phCi9WhqBktN9j2FMiXDItqnn4/ksnTTcRZ89lu7r+Vyuq15/vz5zJ49m1GjRjF+/HjeeOMNUlNTmTNnDqAu1Zw6dYr3338fgFdffZWuXbvSr18/QJ3L8txzz3Hvvffaj3n//fczefJk/vnPf3LllVfy5Zdf8v3337Nx40Y9fkYhhI/Jb2Ljw5q0Ef2HZElItFOZBWUA5BRVsOd0AYMTIrx8Rt7jdMAya9YscnNzeeKJJ0hPT2fQoEGsXLmSpKQkANLT00lNTbXf3mq1smDBAo4dO4afnx89e/bkmWee4c4777TfZsKECXz88cc88sgjPProo/Ts2ZPly5fLDBYh2ihtDkuzS0I1OoUcqZUToq3JLaqwX157IKtdByxOz2HxVTKHRYjWY+5Hv/K/39L5++UD+MPE7o3errzKwoC/rcJiVdiy4HxiIwJb8CyF8L4nv9nL2xuPATCiaySf3T3Ry2ekP4/MYRFCCD04MocFIMDPRFKnYAAOZRV6/LyE8DW5RdX1nilpeeSVVDRx67ZNAhYhRItztEsIpPBWtG85NZaErAqsP5TjxbPxLtmtWYg6isur+DLlNGWVliZvN7FXFH1jw1rorNqW6jksjgQsYazakymFt6JdyrFlWPrHhbMvvYC1B7K4Ymi8l8/KOyRgEaKOx77aw6fJJ5u9XUx4AJsfOh+jUQpBneXokhBA7xgtwyJLQqL90TIs14xM4Mlv9rL+YDZWq9IuX3ckYBGihl0n8+3ByqWD4zA18qLw/b5MMgvK2XUqn6GJkS14hq2foij2OSxNTbrV9ItVi/D2pRe02xdq0T5ZrQpnitUMy0UDY3hh9YF23d4sAYsQNoqi8MQ3ewC4angX/j1rWKO3nfNBMt/tyWDtgWwJWJxUWmmh0qI2JzpSw9IzOgSzn5HiCgsnzpTQPSrE06cohE84W1KBNisuJjyQib2iWL03k5/aaXuzFN0KYfO/XelsO36WIH8Tf764b5O3ndo3GoCfDmS1xKm1KdoMFpPRQLDZ1Ozt/UxG+tlqhfaczvfouQnhS3KL1eWgDsH++JuMTO2rbk+xtp2+7kjAIgRQVmlh4cr9AMyZ0pO4iKAmb6+9cOw8mceZ4vbbZuiKmmP5HR0ENzBeXRbac7rAY+clhK/JKVSXgzqFqvvmaR+U2mt7swQsQgBvbzzGqbxS4iICuWNyj2ZvHxsRSL/YMBQFNhzKboEzbDvsY/kDHV+RHhCvpr/3SsAi2pEc24ehqFAzAPGRQfSNCWu37c0SsIh2L7OgjFd/OgzAQ9P7EeTAMgVQIz0rAYszChzcR6gmybCI9qhuhgWqsyztcVlIAhbR7j276gAlFRaGd410ar6B9sKhtRkKx9g7hBwouNX0jw3HaFBnUmTZNoMToq3LtXUIRdcIWKa049cdCVhEu7brZD7/tbUx/+2yAU5trjcyqQNhAX7kFlew65QUgzpKK7p1ZAaLJshssncHSZZFtBc5heqSUKcQs/26UUkdCTGbyCmqYHc7K0KXgEW0W4qi8PjX1W3Mw7t2cOr+/iYjE3tFAdIt5Ax7DYsDM1hqGqjVsaRLwCLaBy3DEhVWnWEx+1W/7rS35WgJWES79b9d6Ww/4Vgbc2Oq15Pb1wuHO5zZR6im6jqW9vWpUrRf2UX1MywA5/Zrn+3NErCIdsnZNubGSHuz86qn3DobsKgZFlkSEu2FtlNzzQwLtN/2ZglYRLv01oajTrUxN0bam52n1bA4G7AMsGVYTuSW2IMeIdoqRVHsGx9GhdQOWOIi2md7swQsot3JLChj8dojgHNtzI2R9mbnuDKHBaBjiJm4iEAA9qfLRoiibSupsFBWaQUgKsxc7/vtsb1ZAhbR7jy/2rU25sZoLxzr2mGboStcXRICqWMR7YeWXQnyNxFsrh/ca+3N6w40/7qzak8G17+xhd2tvJtRAhbR7vy4X/1E8n/T+jrVxtwYrb35THEFv7XyF4SW4MocFs0AqWMR7YR9OaiB7ApUtzfnFjfe3qwoCm9tOMqc/ySz+Wgub2046rHzbQkSsIh2paLKSo6t8r6vbUM9d9Vsb25P6VlXuTKHRSMTb0V7kWPvEApo8PtmPyPn9G68vdliVXj867089b99KLYEzPpDOa06CywBi2hXsm2fWvxNBjoEN/zJxRXS3uwYq1WpsSTkXA0LwIA4NWA5nFVIRZVV13MTwpfYMyyhDQcsQKO7N5dWWLjrP8ks3XQcgD9f3LdNZIElYBHtSka+Ota9c1ggRqP7y0EabT1Z2pubVlRRZf+058qSUEKHICKC/Km0KBzMlMJb0XblFtXe+LAhDbU35xSVc/2bW1i9NxOzn5FXbhjO3VN71cjGtN4ssAQsol3JtO1DExPe+KcWV8RFBEl7swO0oXFmPyOB/s53ZxkMBnuWRXZuFm2ZIxmWuu3NR7OLuHrxJlLS8ogM9ufD28Zy2RC1saAtZIElYBHtihawxNraY/WkpWd/2t96P8F4mjv1KxrpFBLtgZZh6dREhgWqA5GlPx9j5pJNpJ4pIbFjECvumsDobh3tt5vSp3rIpTaQrrVxfhFZtEkWq8L7m4/bo/rGDIqPYPrguBY6K/1l2DMsnghYonlt3RF7YZueS05thaszWGoa2MWWYZE9hUQblu1AhgXU5ejX1x/l19Q8AIYmRPDWzaOJrjMdVxtyuT+jkA2HcpgxvItHztuTJGARAHy3O4PHv97b7O0MBtj00Hkuj7L3tqwC9UXAEwFL3fbmYYmRuj9Ga+fODBbNgDjbJoinCyQwFG2WlgVpLsMyKqkjYYF+FJZVcUH/GF66fliDc1tA3YNof0Yhaw9kScAiWq+tx3IBGJYYyfCukQ3e5uud6eQUlbM/o7DVBixa0W2sBwIWrb35uz0ZrD2QJQFLA1zd+LCmntEhBPgZKa6wcOJMCd2jQvQ6PSF8htbWHN1MhsXsZ+S134/kaHYRN4xNwtREAD+1TzRL1rbeLLAELAKA5BNnAbhtUnd7kVZdWQXl/G9XOkeyijjXVq/R2mR6cEkI1GUhNWDJZt4FfTzyGK1ZQZn7NSx+JiP9YsPYeTKfPafzJWARbU5FldW+fNqpmYAFYGKvKPssqKaMaOVZYCm6FRSVV7HPVg8wMqlDo7fr2TkUgEOZRS1yXp7gqS4hjVPtzZb2t4GfvYbFhRksNWkTb6VTSLRF2muHyWgg0o3gvi5/k7FVtzdLwCLYmZaHVYEukUFNLvX01gKWrNY5/6KwrJLiCgvguQxLzfbm9QcbaR9UFNjwAixMgHX/8sh5+Co9loRAJt6Ktk1rfugYYtZ92UbrKvqpFbY3S8Ai2H5cXQ5qKrsC0DtGC1iKUJTWN94501ZwGxbgR0iA51ZDG5s+CajByvd/hx8eh6oy+OkfcOA7j52Lr9GKbt1ZEgIYIAGLaMMcmcHiKq29+bdW2N4sAYsgOdWxgKV7VAhGAxSWVZFV2Lp+0aHGcpAHZrDUpH2Cqbdvh9UC38yDn19U/95llPrn53dCXqpHz8lXaHNY3OkSAugfG47RoL6wZ9n+XYVoKxyZcuuq2IhA+seF24Zc5uh+fE+SgKWds1gVdpxwLGAJ8DPRrZNa4Hg4q/XVsXiyQ6immu3N9gyApRI+ux2SlwIGuOJl+MNKiB8BZXnw6R+gqu2P9NdrSSjIbKJHtJrxkyyLaGs8mWGBmlNvW1cdiwQs7dyhrEIKy6sINpvo58DuxdWFt62vjiWz0LaPkIcKbjX+JqN9yeJIdhFUlsLHN8LuFWD0h2vegRE3gV8AXLsUAiPg1Hb4/jGPnpcvcGfjw7q0OhYZICfamtxibadm/TMsoLY3Q+vbvVkClnZOq18Z3jUSP1Pzvw7VhbetL8OS2UIZFoDEjsHqY2ZnwX+ugUOrwC8Irl8Gg66uvmGHJJjxmnp5y6uw72uPn5s3aRkWd2tYQEb0i7Yrx7bkHhXmmQ9XddubWwsJWNq5X+3LQR2buaWqZuFta+PJsfx1JXQIogMFXLJjDpzYCOYwmP0Z9L6w/o37XQLj71EvfzEXzhzz+Pl5izaHxd0lIaieeCtLQqKtyfFwhqVme3Nr2vtMApZ2bruD9Sua3p3VZaPWWMOS6cGx/HX1DipiuflJEkv3Q3AnuOVrSJrQ+B0ueAwSxkB5Pnx6C1S1vqLm5lRZrBSV61N0C9UZlhO5JfalJiHaAk9nWKBGHUtj4xd8kAQs7VhWYRmpZ0owGGh0HH9dPaNDMRjUwUatrSXOkzs113Loey7ceB19jKfINnSEP3wL8cObvo/JH659F4I6QnoKrHrYs+foBYW27ApAmBubH2o6hJiJt/1b7k9vfTVVQjQmt9gWsIR4MmBpfe3NErC0Y9pyUN+YMIdT9EFmE10i1eFyrSnLYrUq9lZsT025pbIUvv0LfDgTc2k2B61duLb871g6OTiiPyIBrn5DvbztTdj9mWfO00u0LEiI2YS/A/VSjtAm3kodi2grrFaluq05zDNLQqBmmltbe7MELO1YspPLQZrWWHibU1yOxapgMDS/mZhLMnbDG+fCVrWA1jr6dq62PM1xa7S9dsYhvS+Ec+arl7+6D3KP6H+uXlI9ll+/UeMyQE60NQVllVTZOnc6eqiGRdPa2pslYGnHnK1f0fSOaX11LJn51XMNHOmGcpjVCptfhTfPhex9ENIZbvwvxkufo1Ok+uk/7UyJc8c892FImggVhfDfP6rTcdsA+9A4HQpuNTKiX7Q12gyW8EA/AvxMHn2smu3NllbQ3iwBSztVVmlht62dbZSDHUKaXq1wTyF7/YqeBbcF6fCfq2DVX8FSAX2mw12b7J1AiR3U1uaTZ0udO67JD2a+DeZQtZ7l6E/6nbMX6TmDRaMFLIezCqmosup2XCG8Jcc+5daz86KgTnvzyTyPP567JGBpp3adyqfSohAVGkBix8Y3PGyItiTUmjIsurc07/sGloyHo2vV+SqXvqDOWAmNtt8koYP6vDqdYQEIj4Phv1cvb1miwwl7n54zWDRdIoOICPKn0qJwsBUOMxSiLk9Pua2p9u7NzXQLZR/0+matLgUsixcvpnv37gQGBjJy5Eg2bNjQ6G0/++wzLrzwQqKjowkPD2f8+PGsWrWq1m2WLl2KwWCo91VWJnuEeIpWvzIqqQMGg3O7gWrTbjMLyu11Cb4uyx6w6PAikL4Tlv8eSs9C3FC4cz2MvhXqPI/a8DinMyyaMXcABji0GnIOuXnS3pev01j+mgwGQ/XEW1kWEm2AVnDbyQP7CDXkXG2z1sbam8sLYfWj6ge0n/4Bh9a0yHk1xOnc7PLly5k3bx6LFy9m4sSJvP7660yfPp29e/fStWvXerdfv349F154IU8//TSRkZG8++67XH755WzdupXhw6tbPcPDwzlw4ECt+wYGen5eRnvl6A7NDQkP9Cc2PJCMgjIOZxW5dIyWlqHnktD6ZwEF+l4C174Hfg2/sNgzLGddyLAAdOoJfafDgZVqMe+lz7t4wr6heklIv4AFYEBcOJuO5No6hRIbvlFRNuz+L5QVqLtkWyrUP6vK1D2cqsrU/Z4GX6N+CeElLZlhAZhiK7zV2ps7aY+rKOp2IqsfgcJ09bo+06FTrxY5r4Y4HbC88MIL3Hrrrdx2220ALFq0iFWrVrFkyRIWLlxY7/aLFi2q9fenn36aL7/8kq+//rpWwGIwGIiNjXX2dIQLFEXhV22H5m6uBRu9Y0JtAUthKwlYdBoal7Wvenz++X9vNFgBSLDVsJxyNcMCMHaOGrCkfATnPQJBvv9cN6a66Fa/GhaAgV2a2VMo9wi8PwPyHdgR+/Aa6NgduozU7wSFcEJOC2dYtPbmfekFbDiUw4zhXSBzL3z7ZzhuWz3p0A2m/wv6XNQi59QYp145KioqSE5O5qGHHqp1/bRp09i0aZNDx7BarRQWFtKxY+1Cz6KiIpKSkrBYLAwbNownn3yyVkBTV3l5OeXl1cNuCgokHeyoYznFnCmuwOxnZJBtjoWzenUOZcOhHA5lto46FvuSkLtD4za8oP7Z/3Lo3K/JmybaMizp+aVUWqyuzR7pPhk6D4SsPfDrBzDxPueP4SM8lWEZaPsd3nu6AKtVwWissTSXsQs+uBqKs9QX3R7ngl+guvGk/cv29wPfwuHv4b+3qst8geG6nqcQjmjpDAuo7c370gvYsvcoMzJfVTO6ikX9vzHpTzDhPvD3/oqHUwFLTk4OFouFmJiYWtfHxMSQkZHh0DGef/55iouLue666+zX9evXj6VLlzJ48GAKCgp48cUXmThxIjt37qR3794NHmfhwoU8/vjjzpy+sNHqV4YmRGD2c63u2j6iP7t1BCy6LAmdOaouKwBMerDZm0eHBRDgZ6S8ykp6XhldOwU7/5gGA4y7C766B355A8bdrXYRtUKemMMC0CMqhEB/I8UVFg5mFdIv1hZopG6BD69TtzuIHQy//7xWUXQ9g2bCa5Pg7DH435/UIX5O1ncJ4a5ce8DSMhkWgCl9ojm1/gP+79B/4FCeemW/y+Cip9UNWn2ES+9WdYs0FUVxqHBz2bJlPPbYYyxfvpzOnTvbrx83bhy///3vGTp0KJMmTeKTTz6hT58+vPzyy40ea8GCBeTn59u/0tLSXPlR2qVkJzc8bIi9tbkVZFjKKi3klahvlm4V3W78NyhW6D0N4oc1e3ODwUAXd+tYAAZfq+5HlJ8GB/7n+nG8rMADRbcAfiYj43p0Amp0Ohz+Xl0GKs+HruPh5m+aDlZAXW6b+TYYTLDrE9i5TNfzFMIRLdnWrBl2dhUvmV+hE3lUdegJv18Bv/vQp4IVcDJgiYqKwmQy1cumZGVl1cu61LV8+XJuvfVWPvnkEy644IKmT8poZPTo0Rw61HhnREBAAOHh4bW+hGNcnXBbk9bafCqvlOLyqmZu7V1ZtvqVAD+j6y21+SchxfYG5kB2RVM9i8WNgMU/EEb9Ub3ciluc7Ts16ziHRaMNwFp7IAv2fA4f/Q6qSqHXBfD7zyAo0rEDdR0L5y5QL//vQcg5rPu5CtEULcPSqaUCluwDBH73JwDerbqIny/8Uv1/44OcCljMZjMjR45kzZrabU1r1qxhwoTGd6JdtmwZt9xyCx999BGXXnpps4+jKAopKSnExcU5c3rCAXklFfaR+u4ELB1CzPaU5REfXxaqOYPF2RZuu59fAmsldJukvqk5qHoWixuFtwCjbgWjP6RuhlO/uncsL/HEHBaNtpFbj9QVKP/9o/pvNfBq+N0yMDu5FHfOfPXfubIY/vuHNrlztvBNpRUWiissQAstCVWUqLvDV5ZwIHgET1bNZnem7/6+O70kNH/+fN566y3eeecd9u3bxwMPPEBqaipz5swB1KWam266yX77ZcuWcdNNN/H8888zbtw4MjIyyMjIID+/erOyxx9/nFWrVnH06FFSUlK49dZbSUlJsR9T6GdHah6grvu7u09Fa1kWcnvKbVEW/Pqeenmy49kVqDmLxY0MC6iD5AZepV627VfU2nhiDoumW1QID4Wv4mm/NzEoVhh5C8x8q8kurkYZTWr9SlBHyPgNvn9M79MVokFawa3Zz0hoQAvUqn37Z8jaCyGd2TLsGawYfXqekdMBy6xZs1i0aBFPPPEEw4YNY/369axcuZKkJHWtKz09ndTU6vbB119/naqqKubOnUtcXJz96/7777ffJi8vjzvuuIP+/fszbdo0Tp06xfr16xkzZowOP6KoafuJM4B72RVNaym8zXS3Q2jzK+qcjoTR0H2KU3etnsXiZoYF1OJbUHdxLnSsyN1XlFVaKLeNzte76BaAdf9iToUaVK6NvhEuW6QGHq4Kj4cZtuW3LYvh4Kqmby+EDrSAJTo0wPVssKN2fgw7PgAMMPMtunfrATQxHsAHuBTC3X333dx9990Nfm/p0qW1/r527dpmj/fvf/+bf//7366cinCSOwPj6uod07oyLDFhLqwJl5yBbW+rlyc96HTXiC41LJouIyBxHKRtUc/pvIfdP2YLKbTVrxgMEKb3J8fiHFirzoB6pvJ3fJE/k82A2y/3fS+GsXfB1iXwxV0w52c10yWEh7TYlNvsA/DNA+rlqQ9BjykMtAVLx3KKKSqvapkMj5NkL6F2pNJiZadtg6tRLg6Mq6lXtLankG/v4aINjYt1JcOy9TWoKIKYwS4NTdIyLJkF5ZRVWpx//Lq0LMv2t6Gy9Wxdoc1gCQvwqz0nRQ/7vwHFijV2KO8aZpBRUMYBvfYVuvBxtSW6JBc+vwOsOvwbCtGIFpnBUqNuhe5TYPL/AWqRr7Zsvs9HsywSsLQje08XUFZpJSLInx5RoW4fr5ctw5J6pkSfN2MPycx3cePDsoLqepHJzmdXADqGmAk2q0sTp/N0WBbqdxlEJKpvoLs+df94LcRTM1gA2PsVAMYBVzC+Z532Znf5BcA174J/CBxbDz8v0ue4QjQgt9iWYXGzvrBJWt1KaIxa51Vj6VTbl2vPqfzG7u1VErC0IzXbmfX4lBsdGkBEkD9WBY5mF7t9PE/JLHQxYNn2FpTlQ1Qf6H+FS49tMBj0rWMx+cGY29XLW19T9/toBTw1g4XSs3BsnXp5wJW125v1EtUbLnlWvfzjP9QWdyE8ILvQlmFxZfnaEVrdisGoBiuhnWt9e0B8M9tceJkELO2IHvNXajIYDPZ5LA4V3h7+HpZeBv+ZCeufgxObPd4yqigKGfkudAlVlMDmV9XLk/4ERtf/q+haxwIw4ibwD4bM3dV7ffg4bQaL7i3NB74DaxV0HgBRve3tzduPn6WwTMedxIfdAAlj1HHlh1brd1whavBohqVm3cqUh9RtP+qwZ1h8tFNIApZ2QlEUXTuENFrh7eGmagbOHoePb1QDleMb1MDlxyfh3YthYSK8e6n6yfXIj1CubwFvQWmVvTulszNTbn99D0pyIDIJBrm3e69us1g0QR1g6PXq5VYySM6eYdF7aNw+dTlIy4B1iwqhe1QIVVaFnw/n6vc4BgP0maZePvyDfscVooYcW4YlWu8MS0UJfHJzjbqVhsczaPtyHcwspML2uulLJGBpJ07llZJZUI6f0cDQhEjdjtvTVnirDaOrpbIU1j4Dr45VCyMNJhg3V931c8CVEBINlnI4sRHW/ws+uAqe6QrvX6nu26MDbWhcZLA/gf4OtrlWlcPPL6qXz3nA7b17dJvFUtNY24yiA99CTuMToX2FR2awlBdWBw8DqpfspnhiWQiqp38eXQcWHbM3QtjkFtum3IboHLCs+ydk72uwbqWmhA5BhAf6UWlROOSDzRQSsLQT2nLQwPhwgsxuzKeoo3eMOoulVsCiKLD/f/DqGLXdtKpMnRx6189w8dMw9k647n148BDcsx0ufxGGzIKIrmrK/ehaeG2yOm/ETS5terhxERSmQ1i8uhTgJl1rWDTRfaDPxYACKx/0+VoWj+zUfHCVGvB27KkuCdlM7asFLNkoej4vsUMhOAoqCiHtF/2OK4SNfR+hMB2XhMoKYPs76uVLX6hXt1KTwWCw17H44rKQBCztxK86bHjYEK2G5XhOMZUWK+QegQ+vhY9vgLxU9U3/mnfh5q+hc//adzYY1ILGkbeok0Uf2AX3JKuzRioK1bHoX89TMzUu0mawdHY0YNn0Cqx9Wr089S9ql4ibEmw1LKf0zLCAupOqX6Aa4P22XN9j66yg1AM1LNpy0IAra3VwjevRiQA/o77tzaDWMfU8T718+Hv9jivqqbJYufGtLdy6dJu+QacPq7JYOVui1bDomGH59X0oL1CbB/pe0uzNB8Spy0K+OPFWApZ2Qqv6HpoYoetx4yICCTGbqLIq5G79GBaPg8Nr1H1vzpkP92yDQVc73hIc1Qtu+Z9a6IoBkt+FN8+H7IMunV+mveDWgReAzYthtW0Y25S/qIGUDrSi25yiCkoqdNwoslNPmPJn9fJ3C6BYx5oNndkzLIE61bBUlMAh255mA2p3cAX6m/Rvb9Zoy0ISsHhU8omz/Hw4lx/2Z/lsx4rezpRUoCjqS6W726bYWSqr69zG3+NQ84BWeCsBi/AKRVE4aJtGq43T14vBYKBXTBihlNBh3cNgqVA/hd69BS74OwS4MO/F5Afn/w1mf6bWuWTtgTemVO+W7ASHW5q3vg6rbLv0TnoQpi5w+rEaExHsT5jtjfqUnstCABPuU5dDSs/A6kf0PbaOCvSew3L4e7WAMLIrxA2r9+1zbd1CutexaBmWjN/UPaaER6w9WB1o6h50+qicQjW70jHYjEmv4Yp7voCCk+rr6JBZDt1lYJfq1mar1beyWxKwtAM5RRXkl1ZiNECP6BDdj98rOpQ7/b4hoPwMdOoFN3yiZkrc1fM8dRx698nqm9MXc+Dzu5zqJMrIV4vYmgxYfnlTHaYEapHteY+4NCSuKVqWJU3vZSGTP1z+EmCAnR+py0M+SPc5LDW7gxr4t9LqWHRvbw6Nhrih6uUjP+p3XFHLT/urg0Hdg04fpRXc6jblVlFg00vq5TF3gr9jy+I9o0Mx+xkpKq8i9YzOr1dukoClHThsK4jt2jHY8U4ZJwyLLOE200r1Lxc8rr6J6iUsBmZ/Aec+og472vkRvHkuHPreoULTZndq3v6OWrQKarbi/L/rHqxAdeHtSb0zLACJo6uHyblZ8+Mp9jkswTr8blSVV29GOODKBm+S1Klme3OO+49ZkywLeVRGfhn7M6prj35NzbN3mbVl2lh+3fYROrZezQT6BcHoWx2+m7/JSF9bM4WvLcdJwNIOaHv99Ors/jj+hpyf/iZBhgp2mwZAv0v1fwCjCab8H9z8DYTFQc5B+HAmvDYJdv0XLI3Xhdg3PmwoYEleWj1Iafw9cOETHglWoLq1Oc1Tn1jOe1QtcD57DNb9yzOP4QZdMyxH16pFhGFx0GVUozerbm/2UB3LkR/B6nuzKlq7dQfVjMrQxEh6dQ7FYlXYeEjnoNMHaRsf6pZh2fSy+ufw30Owc80W1QPkfGtEvwQs7YDWctxL5/oVADJ2E3dMbT9+vPx6LJ5c8uw2UV0iGjdX3dslcxesuBVeHqEu61TUDgaqLFb7p5aYiDovAr9+AF/fr14edzdMe8pjwQp4YHhcXYHhcOlz6uVNL0HGbs88jgsURamxl5AORbd7v1T/7H95k0WEHmtvThgN5jB1P6f0FP2OK4DqAHNqn2jPbLXgo7L1zLBk7lWbHzDA+LudvruvTryVgKUdOGQvuPVAhmXN3zCg8K11HNuqenoug6AJ6aTOcnlgt7pMFNwJ8k6oyzqLBqnZhRJ1om92UTkBShk9jJl0yklW57psXgz/exC+ulc93tg5anuwB4MVqDGeP8+Dz0+/S9U3cWuVGoz5yM7CpZUWqmzFe25nWCyV6owfaHZ/J4+1N5v8occU9bJMvdVVpcVqz6ZM7Rtt32ph3UGdg04fpGuGRdtWpP/l0LGH03cfYJt462sBi85zsoUv0jIs2hh93Rz5EY78AEZ/Pg2/FbLUx+oWpX9hbz3BHdVlovFzIeVDNauQlwo//UMd/BbRhc756ewLtL1RvdfAMUbfDhc/4/FgBSCho4czLJrp/1InsZ7aDtvehrF3ePbxHKDNYPEzGuw7V7vs+AYoy1MHuCVNaPKmWnvz2gPZrD2QTb/YcPceu6ZeF6jTmw9/r/4eCl38euIsheVVdAwxMyQhkiqrlWCziazCcvamF9hHx7dFWjY4yt0MS2FG9VymCfe5dIh+sWEYDOpmjFmFZXQOc3LjWA+RDEsbl1dSYf+PoI3R14XVCqv/pl4efRth8b0BWn6cszlYLTi9dwfMfBtiBkNlMeQcxFSpnksZAdChO3SdAAOvUpeArnpD3YG3BYIVqB4el19aaZ9J4hHh8Wo7OcAPj0P+Kc89loNqTrk1uPt879W6gy5rdLx4TVp7c82uE130Ol/98+Q2KM3T99jtmNbOPLl3FCajgQA/ExN6Rqnfa+PtzbplWLa+DtZKdQBn4miXDhES4Ed32wdPX5rHIgFLG6d1CHWJDCIkQMeE2m/L1RqSgAiY8ufqXZsz9d280GEmPxh8DczZALeugZu/5ouJXzCo7C3u7/E/uD8F/vgtXLsULl4IQ2e1WLACEBrgRwdbh8xJT2dZRv5R3Vm4oqi6XduLqvcRcvP3z2pRsxrQ7HKQRqtjST6hc3tzZFd1cqhigWPr9DtuO6cFltpSkHq5fdSxVHcJuRGwlBfB9rfVyxPudet8BvrgspAELG1cdcGtjtmVylL48Sn18qT5ENzRXtB7ONtLAYvGYIDEMdB9MgctcRQRTGxEkHfPycYjmyA2xGhU92cy+qlv8D88oe6Y7SW6DY1L3QzF2RAYqc7mcYC0N7ceWjuzwQCTbcW2UB2wtOX2ZkVRamRY3FgS2vEfKMtX99fqO92tc/LFibcSsLRxHim43bJEnZ4YnqBuZEh1fczhrCKfmY6Y4ew+Qh7mkU0QGxMzQB2CB7DheXhxKLwxVa3vaeHgRVsScnsfIW05qN+lTs368Vx7s21Z6PAPPr/5ZGugtTMPSYisNZo+oUNwm29vLiirosKitsi7vCRkqYIttmLb8XMdWjJtyoC46om3vkICljZOqynRreC2OAc2/lu9fP6j4K++CSd1DMbfZKCkwsLpfN8YXJZVoKZYndqp2YPsnUKezrBopv4VrnhZzUYYjHB6B3z/9+rg5ecXWyR4yS/RYQaL1Qr7vlYvO7gcpPFYe3PSRHXzyYJTkH1Av+O2UzXbmetq6+3NubbloNAAP9eHe+77Sm08CO4EQ693+5y0DMuxnGKKynXcA80NErC0cYf1nsGy7l/q0K7YITD4OvvVfiajvUhLW4byhLJKCz/uz6TK0vzALi3DEhvhGwGLx2ex1GU0woib1J2y/3RQ3Vq+ZvCy5m9q8PL+leoLnYdoU27dmsFyajsUnlbnn/Q816m7eqy92T9IDVpAloXcVLOd+dx+net9X6tpWdtG25tzbMtBLs9gUZTqQXGjb1ebEdzUKTTA/mFvn49kWSRgacMKyypJt+1WrEsNS+6R6oKuaU/WG9qlbazoyTXPf39/kD8u3c5LPx5u9rbVU2513KrdDQktVcPSkNBodTx3Q8HL0bWw5BzYvcIjD61LDYs2LK7PReDn3L9nzd2bdV9SkDoWXdRqZ+5Sv3V5dPcOBJtNZBeW+1QRqF5yi9zcR+jEJjj9q5rxG32bbudlHyB3yjcm3krA0oYdyS4GoHNYgPv1A6C2yVqroNeF0GNqvW+P7tYBgA2HPNd++N3uDACWb0vF0kStTElFFYW2T/bN7tTcQhJr7Cfk1U+JNYOXe5PV8fbl+fDfP8IXd0O5vq3p9rZmV5eEFKV6s8NG9g5qjrYefyJX52BRq2M5sanepGXhuJ8OVLczGxvYqbhme/O6g22vvdneIRTiYobl5xfVP4der/7/1om98FYyLMLTDmXqWL9yYrP6KddghAsfb/AmWtpW9x1ybY7lFNvfcDILylnfRGCUYcssBZtNhOrZzu0GbRZLUXmV73Q7dOwBf/wOJv+f+m+b8qG6R9PJZN0eIt/dDEvKh+qSlX9wdUbDSQmeqh+K6gMRiWAphxM/63vsdkSrTanZzlxXi7Y3K4par3cqGY7/3OR+ZXrQloSiwlzIsBxcBYdWqf9/x9+j63kN8LER/b7xSi48Qqtf6e1u/YrVCt89pF4ecRPEDGzwZt2i1BbSYznF/Hw4l4sHxbr3uHXUfaH6dHuafTBYXZk1Cm7dHlamk0B/E1GhAeQUlZN2ppTIYJ12ZXWXyR/OewR6ngcrblc3UHxnGpz7V5g4z+1uA23SrdNzWMoL1W0UfvtY/fuQWS6vzSd29FCHlsGgZlmSl6rLQr0v1Pf47UBj7cx11W1v1iVrnH8K0raqAXFeKuSnVV+urBHcduoFUxfAwKub3L/KVTmuLgmVF8I389XL4+dCVC9dz0ubxXIws5CKKitmP+/mOCTD0obpNoPlt4/VTd7MYer+PU3QWki1FkU9aV0EVw3vAsCavZmcKa5o8LZN7tLsRdobp1fqWJqTNAHu2qhOA7ZWqfNb3rsC8k+6dViX2ppPJquZnt8+Vj85TnkILnnO5XOo2aGl+3Kc1LG4xb47c5125rp0b2/e8R9149T//kHtntv+NhxaDdn7bcGKQd0BPTACcg+rG60umaC21+v8O+TyDJYfnlRHTEQmqV2BOkvoEER4oB+VFqXlp5g3QAKWNkz7BXMrYCkvgu9tS0BT/q/Z9VFPtZCWVljYfDQXgLun9mRQl3AqLQpfpjQ8et7XCm412tJEmi8GLABBHeCad+HKV9UdsU9shCUTIW2by4esOZq/WVaLOjfmnWlqpiciEW5ZCecuUKcZuyguMhCDAcoqrfb0u266TwaDSX1T8+KAvtbK3s7ct/naC13amytK1FqtL+dCVRl0Hqh2PE56EC5/CWZ/Aff+Co9kwp/2wQN71AxkYARk74NPZsPrk+HAd7oFLi5lWNK2wS9vqJcvX6RLZ1BdBoPBp5aFJGDRgSMtti2ttMLCSVv6262hcT8vgqIM6NBN3dm4GVoLaXp+GQd1HNO/5WguFVVWukQG0atzKNeOTATg0+0Nf/rXWppjfKSlWVOz8NZnGQww/PfqNgdxw9TNBj+cCRm7XDqcw3NYCk6rLdY/PKFmeAZeBXM2QtJ4lx63pgA/k71FU/fsVmAEJI5VL8vuzU6pvTtz4/UrGmfbm0srLOQUldu/zp7YTdUb50HKhygGI8UTH6LqjvUw8011rtTIm9W2+U49q7vRAsLUGq/7f4PJfwZzKGT8BstmwVsXqJvAuhm45NoyxQ4X3VZV2HacV2DoDepyrocMiFOXhXxh4q0ELG5aeyCLoY+vZv7yFMoqLd4+Hbsj2UUoCnQMMbu+N0VeanVv/7SnHGonrdlCqmdxnHasKX2jMRgMXDksHrPJyN70AnY30HKnZVh8ZWicxp5hOeOjGZaaOvWEP6xU34zL8uGDqyCn+XbymqxWhcJyB+aw7PtGTbcf36Bmdq58Vc30BEW68QPU5tFJwzWn3gqHNdfOXJcz7c3/2XKCYU+sZtRT3zPqqe954ukn8H/nfPxy9pGtRHBD+QIG/jCEi1/62bEPnUGRcN7DauAy8X7wC1LnA31wFXx6s7pliYtyCm0ZFkeLbn9+Uc32BEfBRf9w+XEd4Usj+iVgcUNZpYVHv9xNcYWFz3ac4qZ3fiGvROd0s4sO61G/8v1jasq02yTod5nDd9PStj/pGbAcrD0FMzLYzIUDYwC1+LYurejWd2tYfDjDUpM5BG74BGIHq/v4ODlkLruoHEUBowE6NFZkvHERLL8RSs+qGZ0716sZHp2LpT06aVgLWI6tUz/9Coc0185clyPtzVarwjPf7ueRL3ZTXmUlgAqe9HuHl8yvEGooY7NlAJeUL2SzVW0eOJxV5FwQG9IJLnwC5v2m7vxuMqsdlO9dDsW5jh/HpqzSYg/qo0IcCFiyD8L6f6mXp/8Tgjs6/ZjOGNilurXZ29uuSMDihnd/Pk7amVKiQs2EBfjxy7EzzFyyySc+PdtH8rsasKRutQ0SM8BFTzv15qF3e7PWzuxvMjChV5T9+utGqctCX6Scrpfd0tqafS1gqW6v9fIsFmcERarr+lF91AK/96+EwkyH7qoFB3ERQfibGni5OZWsLgGB2pJ56xrdOx00Hp00HDtU/bRbUQQnf9H/+G2UI+3MdTXV3lxeZeH+5Sm8tu4IAI+dE8z+7v9mtp+tIHry/zH+sQ1se+ZGjj9zKX1sIx9cCmJDO6s7v9/0pboh58lt8PYF6oBNJ2jLQf4mQ/PToK1W+Pp+sFSo87AGzXT+vJ3UMzoUs5+RovIqUr383iYBi4uyCst45cdDAPz1kv58etd44iICOZJdzFWLf+a3k3lePT9t00OXMiy12phnQ9wQp+6utTerO+Q6/4mjLm3L+dHdOtaaqXJOryjiIgLJL63k+33Vb6CKopBV6JtFt/G24s/SSov9hapVCIlSg5aIrnDmKHwwA0rONHs3LTjQMku1VJbC53NAsajtohf9A/w81+rt0UnDRmN1lmX/Sv2P3wY52s5cV2O7N+eVVDD77V/4eudp/IwG3ju3jFt23YwhfScEdYQbV6jFszWKt6uXaN0IYpMmwK2rq/9vvH0hnNzu8N1z7UPjApofwfDrUkjdpC6bXvaC7lnIhvibjPSNsU0x9/IAOQlYXPT8qoMUV1gYmhjJjGFd6Bcbzud3T6R/XDg5RRXMen0LP+xz7FOoJxzOdmMGy65P1DHP5jA471GXHl/P9mb7clCdLgKT0cDMEQlA7eLbM8UVVFrU7EXnMN/KsNQs/vSFTJxTIrrAzV9CaCxk7YUPr2l2Kq4WHGhvDLX88CTkHITQGLj0eU+ccS0Jni54HjBD/XPXpx4fNNYWONrOXFdD7c1pZ0qYuWQTvxw7Q1iAH19fWMCUX+5SJzgnjFELyHvXHzpYXQTv5v/F6L5w2/cQNxRKcmHpZbD/fw7d1d4hFNbMc1CQDmv+rl4+/1GI7OrOGTvFPqL/tHdH9EvA4oLdp/L5JFmtm/jbZQPsa6+xEYF8cuc4JveJprTSwu3vb+eDLSda/PzKqyz2ibBOT7mtKFZrVwAm/0lNe7pAr/bm0goLW2ztzA0NibtmpBqwrD+UTbptl2itQygq1Oz1QUcN8fgbpyd17AE3faG2P59KhmXXN1lsaM+w1A1Yjm2ALYvVy1e84vF1+JrncOpsqWfW4ntfqO6UW5wFR6T4tjnOtDPXVbO9+beTeVy1eBNHsouJiwhk1fkZ9F9/tzp9uO+l6hYUEQkNHqd6zIAO/xfDYtQW/N7ToKoUPr4Rfnmz2bvZNz5srn5l5YPqxrNdRsKYO9w/XycM9JHWZt97NfdxiqLwxDd7URS4Ymg8I5M61Pp+WKA/b988ilmjErEq8OgXu1n47b4WLVY6nlOCxaoQFuhHZ2dHPf/8IhSmq4OIxt7l8jno1d5ct525rm5RIYzp3hFFgc9+VWeyZNkKbn0tu6JJ9PVZLM3p3B9+v0LNwB3fAJ/c3Gih6ck8LcNSY0morECdg4GiTk7uM60FThriIgIxGQ1UWKxk2boydGXyh8HXqpd3LtP/+G2Is+3MdWn3+W5PBrNe30JOUTn948L5buJB4n+8X11mHPI7uO598G/8dUD3QY4BofC7ZTDiZkBRg4zVj6rL7I1waAbL3q9g/zdg9IMrXnZ7+rSzBtgm3no7YJHR/E76bncGvxw7Q6C/kYem92vwNv4mI8/MHExChyCeX3OQ19cd5Yd9WU3uadMh2J9/XTOUaFf2kigvhN+Wq392m8ThHPU/c+/Ooc6Npc9Lq95Ea9qTTf5Hb47W3rz2QDY/Hciib6xr2wPUbWduyHWjEvnl2Bk+2Z7G3VN72jMssT42g0XTUhmWtDMlPL1yHzeN72ZvNddNl5Fww3L4z9XqPibLfgdXv6l2UNQ6B62GpUaGZdVfIT9VTWlf9LS+59UEP5ORuIhATp4t5eTZEs/8fgy9Hra+ptaxlJ5VM1GiHmfbmevS2pu1DU4n9Y7irR7rCfjxKdsNbofp/2p2jL4uNSx1mfzg8hfV3+8fn4RNL6kzjPpcpC4ZxQ5WZ7vYNDjlVlHUuUSnd6hfv76nXj9xXqNbo3hS/7gwDAbILiwnq7DMax8GJWBxQlmlhae/3QfAHZN7Eh/ZQCGhjcFg4N7zexMfGcRfVvxmbzNuyrs/H+PPFzccBDWoKBt+eV2ddlhWvbZ4vimU1/37UWI6B7KjIap388VZilLdxpw0Efpf4fh5NOLcvp1ZeyCbtQeymDOlp9P3VxTF3vY4tYmivEsGx/L3L3dzIreEX46d8dkptxqt+NOTNSyKovDwF7tZfzCbsyUVjO/p/vC1erpNhFn/geW/V5dAXp+kzk7pqg5Rs1gVTuepbwT2DMvBVbDjA8AAM16r9cLdEhI6BHHybClpZ0sY1c0Dy1BxQ6HzALXGZ8/nMOqP+j9GG/DNb+mA+v/akXbmugL8TFzQP4avdp7mupFdWBjxGaZ1tg9bkx5Ui2sd+LCmZTtzisopq7QQ6K9T5sJggMkPqpOav5wLR39SvzSdeqm/K3FDCUoLoKvBzOiKbPjp8+ogpbhO23ZUH3WAnRcEm/3oHhXC0exi9p4uoHNfCVh83js/HyPtTCmx4YHMmdLDofvMHJnAmO4dOZDReHHi3vQCXlhzkBW/nuRP0/piau4/8JljsPkVdS+MKvXNmY491VT98Y0EluVxkWk7pG+HVxep+2H0mAoJI9UaleJsdSfSoqzqy8XZYK3ElTbmxmhr01p7c1hzk07rOJZTTOqZ+u3MdQWb/bhsSDzLt6fxafJJ/E3quftaS7NGe/M+5cEMy9oD2ay3FSvvTMun0mJtuK3YXb0vVIsNP7kZzhyBpZfABY/B+HvIKCijyqrgbzKo/xbFufClbTfZ8XPVgKeFJXYIZgtnOOmJ1mZQ/98MvR7WPAopyyRgaUBZpcW+pcbVIxquLXHEwqsHc9s5SQxOeRLDpnfUKy98Qh3q5qDwID/CAvwoLK/i5NkSerm7UWxdQ2dBzAA145a+U/0qOKlu45B7GHav4EHgwQAgpc59DSY1+I0fpn4NmulW1ttdD1/Sn0B/E0MTI712DhKwOCirsIxXf1SnfP5lel+CzY4/dYkdg2unxOuY3Cead38+RmZBOesPZTe6AzHpv6mj8vd8DoptTTR+BJwzTx3sZjSB1cI9zy8lMe8Xbk9IpWNOMhSehp0fqV/NGT9X/c+hg6RO7u3erBXl1W1nbsh1oxNYvj2N//2WzuAENcXsa1NuNYk1ZrFYrYpLnzCbUmmx8uT/9tr/XlppYX96of150V3sYLhjrTofYs9nsPoROLGJ9GHqBM74yCBMBuB/89WC1Ki+LnefuatF9nIacp26md7JX9TJwB6aK9Nard6bSUFZFV0ig5jgxlJliJ/CkF/+rHZlYVD30xl5i1PHMBgMJHQMZl96AWlnS/UPWED9/xE7uPrvxTlq4JLxG4XHksk59AsJhhz8OvfB0GUExA9Xv2IGgn/jWfyWdn7/GG+fggQsjqrZxnzl0C66HtvsZ2TG8C68+/NxPt2eVj9gKTgNX90Hh9dUX9fzPHU9s/vkWtmQKsXAqrw4Ki1XcOPvzqVjqAFSt8DRtZC1T11TD4lSu39Com1fUeqfwVG6R/BT+kRzLKeYdQeznA9YGmlnbsiIrh3oEa2mLH85ps4H8dUMS83iz+yict3P84PNJziaXUxUqJnuUSFsO36W7SfOeC5gAQgMh2veUWdSrPorHFhJ/9SdDDHMIbzDWHUI4d4v1E+NV73mtU+KLTJpOCwWep6v/n/duUxtQRV22mTqmSMTHA/WrVZ1xsnpHerO8ad3qG/6FUVqIerVb7g8RC2hQxD70gs42VJjBkKi1Jk9vc5nZUAqf9mzizHdO/LJnR5Ytm1jJGBxQGNtzHq6dmQi7/58nDV7MzlTXFE9l+DsCXj/CnUXWINR3RBu4v3q+mcDTpwpodKiEGw2ER8RpM5E73mu+uUFU/tGs3TTcXt7s6NFwM21M9dlMBi4dmQi//xuv/06Xw1YahZ/pp0p0fU8zxZXsOj7gwD8aVpfcovKbQHLWf4wsbtuj9MggwHG3A4Jo+DTWwg5e5z/mh9jbeUf4H//VW8z+f+gywjPnkcTWmy37GHXqwHLb8vh3IebLf5sL07llbLxsNoddO3IZpaDTiXDni+qg5PyBjpUgjrAVa+rBa0uqpnxbGnJJ84CMKpOt6lomPwvakZzbcx6GRAfzqAu4VRaFPv6LrlH4N1L1GClQzeY+4v6KbaRYAWqJ9z2jA71SGDlrHE9OhHor7Y3H8hseshYTc21Mzfk6hFdqPkj+2rRLdTciE/fN85F3x+koKyK/nHhXDcqkRG239dfbS+MLSJ+ONy5nt/CJmM2WJiW+Za643PcMLUQ0Yu0DMvpvDLP7rLe9xIIiID8NDix0XOP08qsSD6JosD4Hp2aXCYnYze8daHaYXN8gxqs+AVCwmh1BsmVi+GuzfDgYbeCFfDc/0VHbLf9v/TU+0pb41LAsnjxYrp3705gYCAjR45kw4YNjd72s88+48ILLyQ6Oprw8HDGjx/PqlWr6t1uxYoVDBgwgICAAAYMGMDnn3/uyqnpzpE2Zr1cO1LdG+fT7Scha78arBScVKvD//Ct2u3TjMPu7iGks0B/E+N7aLs3N7xZWUN+cqCdua6Y8ED7fAazyejU9MyWZv9Up2Px58HMQv6zVd2Y8NHL+mMyGhiWGInJaCA9v4xTeS34CTIwgn+ELOCxypuwGvzUnW2vel2dVeJFncMC8TcZsFgVe/u7R/gHwcAZ6uUUmckC6qaEn9oy1deNbiK7otjmlygWdafwK16BOT/DglNqgfclz8LwG9ViVpP7iwSJHb2TYTlTXMHR7GJAXdIWzXM6YFm+fDnz5s3j4YcfZseOHUyaNInp06eTmtrwDq7r16/nwgsvZOXKlSQnJ3Puuedy+eWXs2PHDvttNm/ezKxZs5g9ezY7d+5k9uzZXHfddWzdutX1n0wH5VWOtzHr4cph8ZhNRpSMXVS9Mx2KMqDzQLjlfxAe79Ax7Ls0Ozvh1oO0IKKhzcoaoihK9RRMJ/YYAbhulPpCmNAhyLkZNC1MW5o4odO6uaIoPPnNXixWhYsGxth3tA02+zEgTp1SmdySWRbgZF4ZSy0Xs/uadTB3C3T2bMDvCJPRQJdID26CWNOwG9Q/934J5a4PT2wrth47Q9qZUsIC/Lh4YFzjN/xtOaRuBv9gNaM8YjbEDtIlOGlI9aaYLZth0bKePaND6ODDH658idMBywsvvMCtt97KbbfdRv/+/Vm0aBGJiYksWbKkwdsvWrSIP//5z4wePZrevXvz9NNP07t3b77++utat7nwwgtZsGAB/fr1Y8GCBZx//vksWrTI5R9MD2aTkb9O78/obh0cbmN2R2SwmVt7nmWZ+Sn8ys6oKfRbvnFqPP6hLDf2EPKQuu3NzXG0nbkhFw2M5fErBvLMTOc2bGxpPTuHAHAkW583srUHstlwKAd/k4G/XtK/1ve0dHPy8eY3K9RLpcVq3yohNrGXuqTpI6p3zPbwG1TiWHUrg8pi2Pd187dv47Ri28uGxhNkbmTeSVm+OhkWbHNMXG97dpQWsJwtqaSovOX2gEpO1epXPL8tRVvhVMBSUVFBcnIy06bVHqU9bdo0Nm3a5NAxrFYrhYWFdOxY/Y+0efPmese86KKLmjxmeXk5BQUFtb70ZjAYmD44jk/nTHCqjdllqVv4U/r/EWkoJoU+lN3wuVN7rFisij3D4itLQlDd3uzo7s3OtDPXZTAYuHlCN8Z09+0XAS2gPJxZ5NZeS1C7jfmPE7uT1Cmk1vftAUtqy2VY0vPKsCoQ4Gd0bXqzB2l1LLrsH9MUbSYLODZSoA0rLKtk5W51WJyWBW3Q2mfU1vdOvWD8PS1ybmGB/kQGq0uVHg9ia0g+bqtf6SbLQY5yKmDJycnBYrEQE1O7HzsmJoaMjAyHjvH8889TXFzMddddZ78uIyPD6WMuXLiQiIgI+1diYqITP4kPOrYePrgav8oifjUM5Mayv/D9MefW2E+dLaW8yorZz9h0QZsXOLN7szPtzK1Vt6hgTEYDheVVZBa4t69NzTbme86rP/NjlO0FcV96IcUt9AlSK2Ds4oNLcy2WYQEYMkv989gGdeuLduqb39Ipq7TSq3MowxobPJa5B7a+rl6e/i/wa7lAN9ETI/qbUFFlZefJPEAKbp3hUtFt3RcgR9tVly1bxmOPPcby5cvp3Ln2Moezx1ywYAH5+fn2r7S0VvxicOQn+PBaNXXc8zzWj15CMUFq8a0TDtkKbntEhTQ/LbeFndtP/ff+aX/Tuzc7287cWgX4mUjqpL5IOrJtQ2PqtjE3NE04LiKI+IhALFaFnWl5Lj+WM7RgoN4uzT7AvpdTS7w5dUiCbpMABX772POP56O05aDrRiU0/LquKPA/W6Ft/8vVOSUtqHp/r5bJsOw5nU95lZUOwf70iApp/g4CcDJgiYqKwmQy1ct8ZGVl1cuQ1LV8+XJuvfVWPvnkEy644IJa34uNjXX6mAEBAYSHh9f6apUKM+G/f1RH7PeZDr9bxowx6qfk9Yey7XUAjrDXr8T4Tv2KZmz3jgT6G8koaLq9efPRHKfbmVsrbdlOCzRd8e86bcyNGWnbN2d7CxXeap9Ua+3S7CNaNMMC1ctCKcvUN+Z25nBWIb+m5mEyGpgxvJGhm7s+hdRNajfZRQtb9gSp7hRqqQxLco12Zl/LQPoypwIWs9nMyJEjWbNmTa3r16xZw4QJExq937Jly7jlllv46KOPuPTSS+t9f/z48fWOuXr16iaP2SYoiroxVukZiB1i3wq9W1QIY7p3RFHgs19POXw4X6xf0dRsb/58xykOZBQ2+KVtiuZMO3Nr1csesLiWYTmYWciHddqYGzOyayTQcp1C9gyLjy1NQnUNS3pBGRVVHpzFohlwhdrxcuYInNzu+cfzMVqm+Ny+nRve5besQN3OAWDynyCy5Zf3WzrDUh2w+Hatna9xupJ0/vz5zJ49m1GjRjF+/HjeeOMNUlNTmTNnDqAu1Zw6dYr3338fUIOVm266iRdffJFx48bZMylBQUFERKijwu+//34mT57MP//5T6688kq+/PJLvv/+ezZubOMDl7a/o07DNAXA1W+CX3Vr23WjEvnl2Bk+3Z7G3VN7OvTmfciHAxZQ25t/OpDN6+uO8vq6o03f1sl25taoZuGtKxZ9f7BeG3NjtJ2Jf00965H9i+rSClp9McMSHRpAgJ+R8iq1k6lukbLuAsLUZY7flqvFt4mjPft4PqTSYmWF7UNXo8W26/4JRZlqR9WE+1rw7KrZa1haYBaLoigyMM5FTtewzJo1i0WLFvHEE08wbNgw1q9fz8qVK0lKSgIgPT291kyW119/naqqKubOnUtcXJz96/77q3fUnDBhAh9//DHvvvsuQ4YMYenSpSxfvpyxY8fq8CP6qJzD1Z8qLny83oyKSwbHEmI2cTy3hG3Hm/9UrCgKh21LLb66lHLZkDgGdQknKtTc5Ne4Hh2Z3A4Cll5uLAkpisLWo2qb8h2TezZ7+36xYQSbTRSWVbmc0XGGL9ewGAyGGrM3WmhYmLYstHsFVHpwYJ2PWXcgm5yicqJCzfY6tloy98IW20iM6c+2aKFtTS2ZYTl5tpTswnL8TQaGeHJ/rzbIpV7du+++m7vvvrvB7y1durTW39euXevQMa+55hquueYaV06n9bFUwed3QGUJdJ8CY+6sd5Ngsx+XDYln+fY0Ptme1mybbnp+GcUVFvyMBs9/YnRRp9AAvrl3krdPw2f0jA7FYFDnP+QWldMp1PEX68yCcnKLKzAZDQyMb75+y89kZFhiJJuO5LL9xBn6xnquzqms0mLvfPLFDAuoS1VHsotbro6l+2QI7wIFp+Dgt+qeYO2ANtn2quFd8DfV+XysKLDy/9RC236XQe8LGjhCy9DqmgrLqsgvqSQi2HMTmbefUD9oDIyPINC/kXk0okGyl5A3bHhe3dgrMAJmLGl0Y7RrbSnUlbvSmx1opH1q7hYVgtlP/llbgyCzyZ6BcDbrsed0PqBOyXT0RU/bYM3TdSynbVsABJtNPrs9QovvH2M0Vbc4t5NR/TlF5fywTx1jcG1DBeG7V6j7LPkFwkVPt/DZ1RZkNhEVqv6uevp3QjY8dJ28s7W0k8nqmi3ApS9ARCNV86jrmz2iQiipsLDSVozaGF8uuBWNc7Xwdu9pdVCiNnbfESNaKGCpWb/iq4XTXtmhVxvVf3gN5DteTN9afbHjFFVWhaGJkfTROhctVeqmrge+q14Sn/Qntf3by1qqe2z7calfcZUELC2pohg+u11NgQ6aCYObXgIzGAxcY8uyaKnVxvjapofCMdq/12EndrIG2GMLWAbGO74GPrxrBwwGOJFbQnahe8PqmuLL9SuaBPugsBbcPyaqNyRNBMUKO/7Tco/rBYqlih1b13KlcSNPhX8Bn9wEi8fD03Hw8ghYNgsK06FDd68V2tZVXcfiuSC2sKzSPtZBAhbnScDSktb8TW1tDIuHS55z6C4zRyRgNMC242c52sS+M4dsnSY9JWBpVVzNsOxJV5eEHKlf0UQE+dPH1pnkySyLL89g0WitzS29Qy8jb1H//PV9sFpa9rFbysntlL0ygVeLHuBF82IGH3lD3QAyay9YKtRZK7GDYfC1cMMn4N9Aq7MXVM9i8VwQuyM1D0VRf/86h/vGz92atMAGOQKAQ2tg21vq5RmvOrxHUEx4IFP7dubH/Vlc89pmQgIarlc4nad2HvjSpoeiedqQP2cClvzSSntQMMCJgAXUfUsOZBbya+pZLh4U69R9HeXLM1g0WoYlq7CcskpLyxU/9r8Cgv4MBSfh8A/QZ1rz99HZ4rWH+fiXNBQaH2IX4Gfi9kndmTW6q+MHriiGH59C2bKEIBQKlSAyg/vQa8BwiOoDUX3VLFNEYqN1e97UEsuE1fUrMn/FFRKwtISSM+qAOICxc6DneU7d/abxSfy4P4szxRWcKW78dlGhAfZdgEXroGVYsgvLHe5O2JeuLgd1iQwiMti5otaRXTvw0dZUtntw52ZfnsGi6RDsT4jZRHGFhVN5pfSMbqHMpH8gDL0BtrwKye+2eMCyL72A51YdwOrAwN2/rNjF8dwS/m9a3+bn9hz+Ab6ZB3mpGIAVlnN4snI2b/5hGnRrHW/OLVGInSzzV9wiAYunlZyBz+eog5Gi+sIFjzl9iKl9O/PTg1PJK6lo8nbdo0II8JM2udYkNMCP+IhATueXcTi70KHJl1r9irPZFajeCHH3qQKPZRZO2V7wE3y4hkWdxRLMgcxC0s6UtFzAAjDyZjVgOfgdFJyG8PgWeVhFUXjym71YFbigfwxzz218fs9P+7N46cfDLFl7hFNnS3n22iENv7aUnIFVD9t3o842dubBsj+wyTCMf143hNGtJFiB6ozgybOlDu+P5wyLVWFHqgQs7pCAxVPK8tWBSJtfhfICMPrB1W+Av2ufOrtHhQCSPWmLenYO5XR+GYcyixwMWJyvX9F07RhMVKiZnKIKdp/Kt0/A1UtJRRU5RWpg7ctFt6DWERzILGz5Opbovmrx7Ymf1eLbKX9ukYddszeTTUdyMfsZ+fvlA5pcshvetQNdO4Xw0Irf+GrnaTILynhj9qjqDKCiwJ7P4Nu/QHE2CgY+87+URwuvxhQQynuzRzKhV9PTl31NfGQgBgOUVFg4U1zh1FwkR+zPKKC4wkJYgF9115Rwiu8tJLZ2FcWw4QVYNATWLlSDlZhBcON/IX6Yt89O+CCt7sjROpa9LnQIaQwGg/3TnScKb0/Z3vzDAv08OnxLD/ZOoZaaxVJTCxfflldZ+MfKfQDcPqm7Q/VF14xMYOkfxhAa4MfWY2eY+domtSDVUgWf3qJu2lqcTVlkb/5oeoo/Fd5AZEQk/71rQqsLVkCt24mx7XXkiSD2V9v/t2FdI5vc90s0TgIWvVSWwebF8OJQ+OFxKMtTC82ueRfu3AA9z/X2GQof1TvG8U6hskqLfeaOKxkWqC7488TOzWmtYDlI0xJtrI3qfwUERkJ+mlr/4WHvbTrOidwSosMCuGtqL4fvd07vKD6dM57Y8EAOZxVx1eJNnPphMez9Aoz+HB1wD2Ny/85Pxd0ZEBfO53MnenSKsqd5so5luxTcuk0CFndZrbDtbXhpGKxaAMXZ6myBq16Hu7fAoKt9siJe+A5tFssRBwKWQ5lFVFkVIoP9iYtwrS1SGyD364mzKIoD1ZdO0N78E3244FZjHxTWkrNYNP6B1YPkkpd69KFyisp5+YfDAPzfRX0JDXCuEqB/XDifz51Av9gwqopyCPn5GQC29n2QC3ZMoKDSyJQ+0XwyZzwxrbxVt2Ydi96k4NZ98k7qrp8Xwf/mq0OQwhPg8pfgnm0w9HfqOG4hmqF1Cp3KK212C4aa9SuuFgUO6hKO2c9IbnEFx3ObfrPefSqfxWsPU17l2LKFNsOiNWRYvDaLRaMtC2nFtx7ywpqDFJZXMahLONeMaGTH5GbERQTx6ZzxPNfpayINxey3JnLDjgFYFfjd6ETeunmU04GQL6reFFPfIDazoIyTZ0sxGtQlIeEaCVjckX8S1j+rXp66AO77Ve0AMPn22r3wLZHBZqJsBX7NZVlcmXBbV4CfiSFd1Ps31d787a50Zi7ZxL++O8Cn2086dGx7hqVj68mw5BZXUNxMoOgR0X2h6wR18vWODz3yEPvSC/j4l1QA/nbZwObbk5sQlref80tWAvBY1c1YMPHgtD4svHpw/Y0NWylPzWLRsiv9YsPbRGDnLW3jt8xbVj+q7ricOA6m/MVrW6OL1q+3gxNv3ekQqmmkrb3519SG61je3niMuz/6lfIqK6C2uTqiNdWwRAT5Ex6ovnmcyvNylsUDxbeKovDE12ob86WD45rd8b2Zg8G3D2FQrCgDZnDDrBv54NYx3HNeb5/dL8oVnqph0fYP0sYKCNdIwOKqYxvUtj4McMm/oA39pxUtr7rwtvE9hSxWhf0Z6ved2fSwISO7qi+c2gtpzcd47Ks9PPnNXhTbvA6ATUdyKats/g21NWVYwEt7CtU0QCu+TYUjP+p66DV7M9l8VG1jfmh6P/cOtudz+87KhmlPcsXQeCb1jtbnRH2IVsNyyjaLRS/JMn9FFxKwuMJSpc4fABj1R4gb6t3zEa2eI4W3x3OLKamwEOhvpIebg860F85DWUXkl1QCUFph4a7/JLN003EAHr6kP2/eNJLOYQGUVlrY1sx03MKySvJsx2oNGRaoDqy8FrD4B3mk+NaVNuZGVZSo2WSAifMg0olx/a1MbEQgRgOUV1l12yC0tMLCnlNqZlQCFvdIwOKK7W9D1h4I6gDnPeLtsxFtQC8HZrFo9Sv9YsPdnuPQKTTANoxQXRbKLSrn+je3sHpvJmY/I6/cMJzbJ/fAYDAwpY/6SXrtgewmj6llVzoE+7eadfqEFtg/plkjblb/PPAtFKTrckhX25gb9POL6t5HEYkw8X5dzs9X+ZuMxEVoy0L6/E78djKPKqtCTHgAXSJbR+bRV0nA4qziHPjpH+rl8x51eBNDIZqiLQmlnilpdOlFr/oVjfZp7/Mdp7h6ySZS0vKIDPbnw9vGctmQ6nHxU/t2BmDtgabrWFpTh5AmsQX2j2lW537QdbxafJvyH7cPV7ON+c8utDHXkpeqdkICTHsSzK3n39ZV1fN59PmdqDl/pS3V+3iDBCzO+uFxdex+7JDqgjkh3NQpxExksD+KAkeyG86y7HVjD6GGaAHLVztPcyK3hMSOQay4a0K9/V/O6R2FyWjgSHZxk0snaa2sfgV8JMMC1a8lye+rs53c8Pzq6jbmmS62MdutfgSqyqDbJBgww71jtRJ6z2L5Veav6EYCFmecSoZfP1AvX/KszFkRujEYDPY6lsMNLAspiuLWSP6GjKrxAjo0IYLP7prY4CaAEUH+9iLdprIsJ1tRh5BGe3PyWg2LZsCVuhTf/mfLCT7epk8bM8fWw94vwWCEi59pN40Fes5iqbRY7RkWCVjcJwGLo6xWWPl/gAJDZkHXcd4+I9HGaHUsDQUsmQXl5BZXYDIa6KfT6PNenUO5cWxXrh+TyLI7xhEd1nhb/pS+zdexpJ1pPVNuNdqbU0FZFfmlld47Ef8gGHq9ejn5XafvbrUqPPPtfh75YjeKAjePT3KvjbluY0HsINeP1croOYvlx/1Z5JdWEh0WoNtSbnsmAYujdn6kZljMoXDhE94+G9EG2WexZNYPWLT6lZ7RIQT665PZMxgM/OOqwSy8egjB5qbrHKbaApam2ptbY4YlJMCPjiFmQL+aBZeNrFF8m5fm8N3KqyzcvzyF19YdAWD+hX147IqB7p1L8ruQtVdtLDj3YfeO1croOYvl0+3qv+PVI7rg10aG63mTPIOOKMuH7x9TL0/5M4TFevV0RNvU1CwWe/2Km/NXXDUgLrzJ9mZFUVrdDBaNVzdBrKlzf+g+WS2+/f7vDt0lv6SSm97+ha93nsbPaOC5a4dy3/luDnMryoIfn1Ivn/twu2ss0JYJT+eVYrG6Poslq7CMn2wZyWtHJupybu2dBCyOWPuMuqlhp94w9i5vn41oo7Q9hY7nllBRVbvwUo+R/O5orr05v7TSvg9Sl8jWk2GB6iUAr9exAEz7h1ozsnsFHN/Y5E3TzpQw87VNbD12hrAAP9774xiuGelGka3VqtboLR6n7jYfMwhG/sH147VSMeGB+JsMVFoUMgvKXD7O57+ewmJVGNE10v5/W7hHApbmZO2Dra+rl6c/A35m756PaLNiwwMJDfDDYlU4nltc63t70vVtaXbFuf0ab2/W6leiQgMIMreuYvQEb2+CWFPckOogYeWf1VqSBvx2Mo+rFm/icFYRcRGBfHrXeCb2inL9cdN3wjsXwVf3QEkuRPeDq98EU+uYp6Mnk9FAfKR7hbeKovCJbTno2lGSXdGLBCxNURT49s9qirbvpdDrAm+fkWjDDAaD/ZNYzTqW/NJKe0CgV0uzKyb2ary9ubp+pXUtB0HN1mYfyLCAOowyqIM6nHL7O/W+/cO+TGa9voWconL6xYbx+d0T6Rfr4u9FaZ7aTPDGVDj5i1qjN+0pmLMRYga49WO0Zu4uE+5Iy+NIdjGB/kYuGxKn56m1axKwNKWqHCKTwC8ILn7a22cj2oGGWpu1+pUukUFEBnsvw9dUe7NWoOjWCHgvSfSVGhZNcMfqCdo/PQXFufZv/WfLCW5/fzullRYm9Y7i0znjiY0IrL5vRQl8PQ/++0d1KXv3Z5CxGyrr/GyKAikfwSuj4Jc3QLHCwKvhnm0w4d52v+O8fZnQxSBWK7a9ZHAcYYHt+7nUU/vL9znDPxCufAXO/xuEdvb22Yh2oKHC273p+g6Mc8eUvtH8cvwMaw9kM3t8N/v19oLbVpxhSTtTgqIovjGNdOQf1L2FMnbBj09gvXQR/1p1wN4JdN2oBP5x1WD8a3aeVFXAJ7Ph8PcNHNCg7gEU3Rei+qgdj6mb1W9F9VHnSvWY6umfqtVwJ8NSWmHh653qFgtSbKsvCVgcIcGKaCG9G5jFovdIfndM7RvNs6sO2NubtRbr1jiWX6O9ORVXWMgrqaRDiA/UqRlNMP1ZePdilOT3eC5nPK8dUH835l/Yh3vP61U7sLJa4LPb1WDFPxgm3Af5JyHnIOQcUDsd806oX4dWq/fxD4Ypf4Fxd0ttXh3uDBT8dnc6ReVVdO0YzFh3ZuGIeiRgEcKHaDUsR7OLqbJY8TMZdZ9w6w6tvTmrsJxtx88wqbfaOdRaW5oBAv1NRIcFkF1YTtrZEt8IWACSxlPRfybmfSs4/9hzvGF8nGdmDq3fCaQo8PX9sPcLMPrDrP9Ar/Nrf784Rw1ccg5CziG1E2ncXRDh5uj+NsqdDMun208CcM3IBPcmDYt6pIZFCB/SJTKIQH8jFRarfSNEbQdnX8iwGAwG+xA5rb255gyW1phhAR+sY0H9dD879VKKlQBGGg+xcurphoOV1Y/Ajg/UIOSat2sHK6CO1A+Nhm7nqFNrL14IF/1DgpUmaDUs6fmlVFoc39spNbeEzUdzMRhgpjst5qJBErAI4UOMxupOocNZRRzKLMJiVYgM9ieuZnGlF9XdvTm3uILSSgsGA8RH+sY5OivBl2axoLUt/8zW3ECW+l0LQJ/fnoWygto3XP8cbH5FvXzFy+qeRMJtUaEBmP2MWBXIyHd8Fst/k9Vi23N6RdElsvVlG32dBCxC+BitjuVQVlGt+hWfKAalfnuz9iYfExZIgF/rmsGiSfShWSzVbcsV9I8LZ+bdT0PHnlCUCev/VX3Dra+rXUSgbk44/PfeOeE2yGg0OL0JosWq8N9kdTlIZq94hgQsQviYmhkWb0+4bUjd9ubWXL+iSXCzjVUvn+84aW9bntwnmk/uHEdspwg1IAHYsgSyD0LKMnVGFMDUBWo9itBVgpObIG46ksPp/DLCA/2YNiDGk6fWbknAIoSPsQ+Pyyr0qQ6hmqb2q65jSWuFmx7WpecOve54btVBrApcOzKBt28eVT3Do8806HMxWKvU1uUv56rXj7tb7fQRukt0chNErdj2ymFddNugVNQmAYsQPqbm8Lj9Geo8Fm9tetiYqX3UOpZNR3I5kqVuI9AaZ7Bo4my1N+l5pSiK6xveuaO8ysKpPDVgemh6v9ozVgAuehpMZsjer07fHnajbe8h31gqbGucybDkl1Ty3Z4MAK6T5SCPkYBFCB/TtWMwZpORskorJRUWAv2N9Ij2rc3T+seFEROu7t68yvZC3ZozLPER1bNYCsoa3r/H007Z3hiDzSY6NtRa3aknTHpQvdz/Crj8JTDKS7inaEucjtSwfLXzFBVVVvrFhjGoi299uGhL5LddCB/jZzLSIzrE/vd+seGYfGyeQ83dm7VdmhNacQ1LkNlEh2B1+SU93zvLQtWt4UGNF1hP/QvctwOue79dbkzYkpxZJvy0RrGtrxTHt0USsAjhg2puR+9r9Ssarb1Zk9iKMywAcbYsy+k87wQs9v2YmnseO/aQZaAWoHUJZRaWUV5lafR2+zMK+O1kPn5GAzOGxbfU6bVLErAI4YO01mbwrQ6hms7pHWXP/JiMBp+ZE+MqbYbM6TzH527oqWaGRXhfxxAzwWYTitL074RWbHtB/xg6hQa01Om1S5JTFMIH1cyw+MKmhw0JD/RnZFIHfjl2htjwQPzqFom2MlqGxVtLQlqtRGvc8botMhjUWSwHM4v47NeTtf5P1vT5jlMAXDtKJtt6mgQsQvigPrZdm01GA/1iw5q5tfdM7RvNL8fO0LUNvMnGR2pLQpJhEaquHYM5mFnEyz8ebvJ20WEB9pou4TkSsAjhg3p1DmX+hX3oHBbg0zMdZo9LIu1MKVcN7+LtU3Fb9ZKQt4puW/88m7ZmzpSeVFqUJvcTMhkN/H5cUqvPMLYGLgUsixcv5tlnnyU9PZ2BAweyaNEiJk2a1OBt09PT+dOf/kRycjKHDh3ivvvuY9GiRbVus3TpUv7whz/Uu29paSmBga17XVwIVxgMBu47v7e3T6NZYYH+LLx6sLdPQxfVS0Itn2EprbCQU1QBtP7i5bZkVLeOvPfHMd4+DWHjdEi4fPly5s2bx8MPP8yOHTuYNGkS06dPJzU1tcHbl5eXEx0dzcMPP8zQoUMbPW54eDjp6em1viRYEUK0FK1oOCO/DKu1ZYfHadmVsEA/Imzt1UKI2pwOWF544QVuvfVWbrvtNvr378+iRYtITExkyZIlDd6+W7duvPjii9x0001ERDTe7WAwGIiNja31JYQQLSU2IhCDASosVnKKy1v0savrVyS7IkRjnApYKioqSE5OZtq0abWunzZtGps2bXLrRIqKikhKSiIhIYHLLruMHTt2uHU8IYRwhr/JSOcwtS01vYULb6tnsEjBrRCNcSpgycnJwWKxEBNTeyfKmJgYMjIyXD6Jfv36sXTpUr766iuWLVtGYGAgEydO5NChQ43ep7y8nIKCglpfQgjhDm+1NkuGRYjmuVTWXHf0sKIobo0jHjduHL///e8ZOnQokyZN4pNPPqFPnz68/PLLjd5n4cKFRERE2L8SE2XDKSGEe7p4qbW5egaLZFiEaIxTAUtUVBQmk6leNiUrK6te1sWtkzIaGT16dJMZlgULFpCfn2//SktL0+3xhRDtk1Z429KtzZJhEaJ5TgUsZrOZkSNHsmbNmlrXr1mzhgkTJuh2UoqikJKSQlxcXKO3CQgIIDw8vNaXEEK4Iy7SO63N9hoWybAI0Sin57DMnz+f2bNnM2rUKMaPH88bb7xBamoqc+bMAdTMx6lTp3j//fft90lJSQHUwtrs7GxSUlIwm80MGDAAgMcff5xx48bRu3dvCgoKeOmll0hJSeHVV1/V4UcUQgjHxGsZlhasYSksqySvpBKQDIsQTXE6YJk1axa5ubk88cQTpKenM2jQIFauXElSUhKgDoqrO5Nl+PDh9svJycl89NFHJCUlcfz4cQDy8vK44447yMjIICIiguHDh7N+/XrGjJGBPUKIlqON52/JLiFtOahDsD+hATJ8XIjGGBRFadkJSR5SUFBAREQE+fn5sjwkhHBJVmEZY/7xAwYDHHxqOv4tMG59zd5Mbn9/O4O7RPD1ved4/PGE8DWOvn/L5gdCCGETFRKAv8mAokBmQctkWaRDSAjHSMAihBA2RqOBWFsdS0sV3kqHkBCOkYBFCCFqiI/QZrG0TOGtTLkVwjESsAghRA3xLTw8TjIsQjhGAhYhhKghzr4k5PkMi6IonJQaFiEcIgGLEELU0JIZlvzSSgrLqwDJsAjRHAlYhBCihvjIlsuwaMtBUaEBBPqbPP54QrRmErAIIUQNcS1YdCstzUI4TgIWIYSoQesSOltSSWmFxaOPJQW3QjhOAhYhhKghPMiPELO6POPpZSFpaRbCcRKwCCFEDQaDocV2bZYMixCOk4BFCCHq0FqbT3m4jkVqWIRwnAQsQghRh1bH4sldmxVFkQyLEE6QgEUIIeqIty8JeS7DkltcQWmlBYOhupVaCNE4CViEEKKOOFsAcdqDNSxadiUmLJAAP5nBIkRzJGARQog6WmIDRKlfEcI5ErAIIUQd9mm3eaUoiuKRx5D6FSGcIwGLEELUoU27La6wUFBW5ZHHkBksQjhHAhYhhKgjyGyiQ7A/4LnCW8mwCOEcCViEEKIBnt5T6KSthiVBaliEcIgELEII0QCttfm0B2axWK0KJ22BUKJkWIRwiAQsQgjRAHvhrQeWhLKLyqmosmIyGuxTdYUQTZOARQghGhDnwWm3J20Ft7HhgfiZ5GVYCEfI/xQhhGiAlmHxxH5CaWdsy0FSvyKEwyRgEUKIBsR7cMdmLcMiHUJCOE4CFiGEaIBWW5KRX4bVqu/wOHuGRQIWIRwmAYsQQjQgJjwQgwEqLFZyiyt0PfbJPC3DIktCQjhKAhYhhGiAv8lITJhtE0Sd61iqa1gkwyKEoyRgEUKIRsR5oLXZYlXsAZBkWIRwnAQsQgjRiOpdm/UrvM0oKKPKquBvMhATLjNYhHCUBCxCCNEIrfBWzwyLNpI/PjIIk9Gg23GFaOskYBFCiEZ4Yjx/2lnpEBLCFRKwCCFEI7Thcaf1zLCclQ4hIVwhAYsQQjTCE+P5pUNICNdIwCKEEI3QuoSyCsuotFh1OaZkWIRwjQQsQgjRiKiQAMwmI1YFMgv0ybKcPKu1NEuGRQhnSMAihBCNMBoNxNo7hdwPWCotVnvHUaJkWIRwigQsQgjRBK21WY9pt+l5ZVgVCPAzEh0W4PbxhGhPJGARQogmdNFx1+Y0W/1Klw5BGAwyg0UIZ0jAIoQQTdAKb/XIsGgFtzKDRQjnScAihBBNiNNxPL/W0iwdQkI4TwIWIYRoQryOGyDaMywyg0UIp0nAIoQQTYjXtYZFMixCuMqlgGXx4sV0796dwMBARo4cyYYNGxq9bXp6OjfccAN9+/bFaDQyb968Bm+3YsUKBgwYQEBAAAMGDODzzz935dSEEEJX2pLQmeIKSissbh1LaliEcJ3TAcvy5cuZN28eDz/8MDt27GDSpElMnz6d1NTUBm9fXl5OdHQ0Dz/8MEOHDm3wNps3b2bWrFnMnj2bnTt3Mnv2bK677jq2bt3q7OkJIYSuwgP9CDGbAPeWhcoqLWQWlAOSYRHCFQZFURRn7jB27FhGjBjBkiVL7Nf179+fGTNmsHDhwibvO3XqVIYNG8aiRYtqXT9r1iwKCgr49ttv7dddfPHFdOjQgWXLljl0XgUFBURERJCfn094eLjjP5AQQjTjghfWcTiriA9vG8vEXlEuHeNodhHnPb+OYLOJPY9fJG3NQtg4+v7tVIaloqKC5ORkpk2bVuv6adOmsWnTJtfOFDXDUveYF110kVvHFEIIvWh1LO60NmtdRvGRMoNFCFf4OXPjnJwcLBYLMTExta6PiYkhIyPD5ZPIyMhw+pjl5eWUl5fb/15QUODy4wshRFPi7dNuXS+81fYiig0P1OWchGhvXCq6rfvpQFEUtz8xOHvMhQsXEhERYf9KTEx06/GFEKIxWuGtOzUsGbaAJUYCFiFc4lTAEhUVhclkqpf5yMrKqpchcUZsbKzTx1ywYAH5+fn2r7S0NJcfXwghmqLNYjntRmuzPcMSIXsICeEKpwIWs9nMyJEjWbNmTa3r16xZw4QJE1w+ifHjx9c75urVq5s8ZkBAAOHh4bW+hBDCE+yzWNyoYcmUDIsQbnGqhgVg/vz5zJ49m1GjRjF+/HjeeOMNUlNTmTNnDqBmPk6dOsX7779vv09KSgoARUVFZGdnk5KSgtlsZsCAAQDcf//9TJ48mX/+859ceeWVfPnll3z//fds3LhRhx9RCCHcU3PHZleXwDNsLc0SsAjhGqcDllmzZpGbm8sTTzxBeno6gwYNYuXKlSQlJQHqoLi6M1mGDx9uv5ycnMxHH31EUlISx48fB2DChAl8/PHHPPLIIzz66KP07NmT5cuXM3bsWDd+NCGE0IdWw1JcYaGgrIqIIH+nj5ElRbdCuMXpOSy+SuawCCE8acSTazhTXMG390+if5xzrzEWq0KfR77FYlXYsuB8YiMkaBFC45E5LEII0V5py0KudArlFpVjsSoYDRAVatb71IRoFyRgEUIIB2jLQq7MYtFG8keFBuBnkpddIVwh/3OEEMIBWmuzKxmWDHtLsywFCeEqCViEEMIB1a3NzmdYZGicEO6TgEUIIRyg1bCccmEWS5Y9YJGhcUK4SgIWIYRwgD3D4sK024x8aWkWwl0SsAghhAO0DEtGfhlWq3PTIDIL1aLbzhKwCOEyCViEEMIBMeGBGA1QYbGSW1zh1H0zJcMihNskYBFCCAf4m4x0Dqse0e8M6RISwn0SsAghhIPiXGhtLqu0kF9aCUBMmAQsQrhKAhYhhHBQvAvD47RdmgP9jYQHOb19mxDCRgIWIYRwkCvD4zJr7NLsyi7PQgiVBCxCCOEgV8bzy9A4IfQhAYsQQjhIy7CcdibDIh1CQuhCAhYhhHCQlmFxZjx/pky5FUIXErAIIYSDtGm3WYVlVFqsDt1HloSE0IcELEII4aBOIWbMJiNWpTpz0pxMmcEihC4kYBFCCAcZjQZ74OHonkI1u4SEEK6TgEUIIZyg7SnkyLRbRVGqp9xKwCKEWyRgEUIIJ3SJdLy1Ob+0kooqtdYlOkyKboVwhwQsQgjhBGfG82vZlQ7B/gT6mzx6XkK0dRKwCCGEE5wZHpeRLx1CQuhFAhYhhHCCtiTkSIYlSwpuhdCNBCxCCOEEbUnIkaJbKbgVQj8SsAghhBO0JaGzJZWUVliavK1MuRVCPxKwCCGEE8ID/QgxqwW0zS0L2QMWGRonhNskYBFCCCcYDAb7iP7mhsfJkpAQ+pGARQghnBRnC1hONVPHIlNuhdCPBCxCCOGkeG08fxOtzZUWKzlFErAIoRcJWIQQwkla4W1TNSzZheUoCvgZDXQKMbfUqQnRZknAIoQQTorXWpubqGHRCm47hwVgNBpa5LyEaMskYBFCCCfF2/cTajzDIh1CQuhLAhYhhHBSnL2GpRRFURq8jb3gNkwCFiH0IAGLEEI4SathKa6wUFBW1eBt7C3NkmERQhcSsAghhJOCzCY62gppGyu8zZSND4XQlQQsQgjhAm1ZqLE6lsxCGcsvhJ4kYBFCCBdoy0KnG5nFkpEvU26F0JMELEII4QKttbmxJaEsW9FtZwlYhNCFBCxCCOGC6tbm+hmW4vIqCsvVYlwpuhVCHxKwCCGEC5qqYdE6hEID/AgN8GvR8xKirZKARQghXNDUjs32KbdScCuEbiRgEUIIF2gZloz8MqzW2sPjtIBFCm6F0I8ELEII4YKY8ECMBqiwWMkpLq/1vYx89e8SsAihH5cClsWLF9O9e3cCAwMZOXIkGzZsaPL269atY+TIkQQGBtKjRw9ee+21Wt9funQpBoOh3ldZWeMbiwkhhDf5m4x0DtNG9Nd+rapeEpKARQi9OB2wLF++nHnz5vHwww+zY8cOJk2axPTp00lNTW3w9seOHeOSSy5h0qRJ7Nixg7/+9a/cd999rFixotbtwsPDSU9Pr/UVGCj/2YUQviuukdbm6iUhqWERQi9OBywvvPACt956K7fddhv9+/dn0aJFJCYmsmTJkgZv/9prr9G1a1cWLVpE//79ue222/jjH//Ic889V+t2BoOB2NjYWl9CCOHL4hsZHmffqVkyLELoxqmApaKiguTkZKZNm1br+mnTprFp06YG77N58+Z6t7/ooovYvn07lZWV9uuKiopISkoiISGByy67jB07djhzakII0eK04XF1W5vtOzXLDBYhdONUwJKTk4PFYiEmJqbW9TExMWRkZDR4n4yMjAZvX1VVRU5ODgD9+vVj6dKlfPXVVyxbtozAwEAmTpzIoUOHGj2X8vJyCgoKan0JIURL0sbz12xttloV6RISwgNcKro1GAy1/q4oSr3rmrt9zevHjRvH73//e4YOHcqkSZP45JNP6NOnDy+//HKjx1y4cCERERH2r8TERFd+FCGEcJk9w1KjhuVMSQVVVgWDAaLDpIZFCL04FbBERUVhMpnqZVOysrLqZVE0sbGxDd7ez8+PTp06NXxSRiOjR49uMsOyYMEC8vPz7V9paWnO/ChCCOE2e4alRg2Ltulhp5AA/E0yOUIIvTj1v8lsNjNy5EjWrFlT6/o1a9YwYcKEBu8zfvz4erdfvXo1o0aNwt/fv8H7KIpCSkoKcXFxjZ5LQEAA4eHhtb6EEKIladNuMwvLqLRYAcgq1ApuJbsihJ6cDv/nz5/PW2+9xTvvvMO+fft44IEHSE1NZc6cOYCa+bjpppvst58zZw4nTpxg/vz57Nu3j3feeYe3336bBx980H6bxx9/nFWrVnH06FFSUlK49dZbSUlJsR9TCCF8UacQM2aTEUWp7gySoXFCeIbTu3LNmjWL3NxcnnjiCdLT0xk0aBArV64kKSkJgPT09FozWbp3787KlSt54IEHePXVV4mPj+ell15i5syZ9tvk5eVxxx13kJGRQUREBMOHD2f9+vWMGTNGhx9RCCE8w2g0EBsRSOqZEtLzy0joEGzf+FA6hITQl0HRKmBbuYKCAiIiIsjPz5flISFEi5n1+ma2HjvDi78bxpXDuvDQit/4eFsaD1zQh/sv6O3t0xPC5zn6/i0VYUII4YYukbWHx2kZltgIqWERQk8SsAghhBvqjufXuoRkHyEh9CUBixBCuCGuznj+rEIpuhXCEyRgEUIIN9Qcz19eZeFMcQUgAYsQepOARQgh3KDNYknPLyXLtoeQ2c9IZHDDc6aEEK6RgEUIIdygLQmdLankRG4JoA6Na2q7EiGE8yRgEUIIN4QH+hFiNgGwI/UsIMtBQniCBCxCCOEGg8FAnG1ZaEdaHiAdQkJ4ggQsQgjhJq2OJcUWsEiGRQj9ScAihBBuireN4dc6hGTjQyH0JwGLEEK4SSu81cRIhkUI3UnAIoQQbtKm3WpkSUgI/UnAIoQQbtL2E9JIhkUI/UnAIoQQboqLqB2gSMAihP4kYBFCCDfVrGEJD/QjyDaXRQihHwlYhBDCTUFmEx1DzADERkh2RQhPkIBFCCF0oC0LyXKQEJ4hAYsQQuhAWxaSgEUIz5CARQghdNAjOgSApI7BXj4TIdomP2+fgBBCtAV3TO5BfEQgVw1P8PapCNEmScAihBA6iAoN4JaJ3b19GkK0WbIkJIQQQgifJwGLEEIIIXyeBCxCCCGE8HkSsAghhBDC50nAIoQQQgifJwGLEEIIIXyeBCxCCCGE8HkSsAghhBDC50nAIoQQQgifJwGLEEIIIXyeBCxCCCGE8HkSsAghhBDC50nAIoQQQgif12Z2a1YUBYCCggIvn4kQQgghHKW9b2vv441pMwFLYWEhAImJiV4+EyGEEEI4q7CwkIiIiEa/b1CaC2laCavVyunTpwkLC8NgMOh23IKCAhITE0lLSyM8PFy347ZX8nzqR55LfcnzqR95LvXV1p9PRVEoLCwkPj4eo7HxSpU2k2ExGo0kJCR47Pjh4eFt8hfFW+T51I88l/qS51M/8lzqqy0/n01lVjRSdCuEEEIInycBixBCCCF8ngQszQgICODvf/87AQEB3j6VNkGeT/3Ic6kveT71I8+lvuT5VLWZolshhBBCtF2SYRFCCCGEz5OARQghhBA+TwIWIYQQQvg8CViEEEII4fMkYGnG4sWL6d69O4GBgYwcOZINGzZ4+5R83vr167n88suJj4/HYDDwxRdf1Pq+oig89thjxMfHExQUxNSpU9mzZ493TtbHLVy4kNGjRxMWFkbnzp2ZMWMGBw4cqHUbeT4dt2TJEoYMGWIfwDV+/Hi+/fZb+/fluXTdwoULMRgMzJs3z36dPJ+Oe+yxxzAYDLW+YmNj7d+X51ICliYtX76cefPm8fDDD7Njxw4mTZrE9OnTSU1N9fap+bTi4mKGDh3KK6+80uD3//Wvf/HCCy/wyiuvsG3bNmJjY7nwwgvt+0GJauvWrWPu3Lls2bKFNWvWUFVVxbRp0yguLrbfRp5PxyUkJPDMM8+wfft2tm/fznnnnceVV15pf+GX59I127Zt44033mDIkCG1rpfn0zkDBw4kPT3d/rVr1y779+S5BBTRqDFjxihz5sypdV2/fv2Uhx56yEtn1PoAyueff27/u9VqVWJjY5VnnnnGfl1ZWZkSERGhvPbaa144w9YlKytLAZR169YpiiLPpx46dOigvPXWW/JcuqiwsFDp3bu3smbNGmXKlCnK/fffryiK/G466+9//7sydOjQBr8nz6VKMiyNqKioIDk5mWnTptW6ftq0aWzatMlLZ9X6HTt2jIyMjFrPa0BAAFOmTJHn1QH5+fkAdOzYEZDn0x0Wi4WPP/6Y4uJixo8fL8+li+bOncull17KBRdcUOt6eT6dd+jQIeLj4+nevTu/+93vOHr0KCDPpabNbH6ot5ycHCwWCzExMbWuj4mJISMjw0tn1fppz11Dz+uJEye8cUqthqIozJ8/n3POOYdBgwYB8ny6YteuXYwfP56ysjJCQ0P5/PPPGTBggP2FX55Lx3388cckJyezffv2et+T303njB07lvfff58+ffqQmZnJU089xYQJE9izZ488lzYSsDTDYDDU+ruiKPWuE86T59V599xzD7/99hsbN26s9z15Ph3Xt29fUlJSyMvLY8WKFdx8882sW7fO/n15Lh2TlpbG/fffz+rVqwkMDGz0dvJ8Omb69On2y4MHD2b8+PH07NmT9957j3HjxgHyXMqSUCOioqIwmUz1silZWVn1olzhOK3qXZ5X59x777189dVX/PTTTyQkJNivl+fTeWazmV69ejFq1CgWLlzI0KFDefHFF+W5dFJycjJZWVmMHDkSPz8//Pz8WLduHS+99BJ+fn7250yeT9eEhIQwePBgDh06JL+bNhKwNMJsNjNy5EjWrFlT6/o1a9YwYcIEL51V69e9e3diY2NrPa8VFRWsW7dOntcGKIrCPffcw2effcaPP/5I9+7da31fnk/3KYpCeXm5PJdOOv/889m1axcpKSn2r1GjRnHjjTeSkpJCjx495Pl0Q3l5Ofv27SMuLk5+NzVeK/dtBT7++GPF399fefvtt5W9e/cq8+bNU0JCQpTjx497+9R8WmFhobJjxw5lx44dCqC88MILyo4dO5QTJ04oiqIozzzzjBIREaF89tlnyq5du5Trr79eiYuLUwoKCrx85r7nrrvuUiIiIpS1a9cq6enp9q+SkhL7beT5dNyCBQuU9evXK8eOHVN+++035a9//atiNBqV1atXK4oiz6W7anYJKYo8n87405/+pKxdu1Y5evSosmXLFuWyyy5TwsLC7O838lwqigQszXj11VeVpKQkxWw2KyNGjLC3k4rG/fTTTwpQ7+vmm29WFEVt0fv73/+uxMbGKgEBAcrkyZOVXbt2efekfVRDzyOgvPvuu/bbyPPpuD/+8Y/2/8/R0dHK+eefbw9WFEWeS3fVDVjk+XTcrFmzlLi4OMXf31+Jj49Xrr76amXPnj3278tzqSgGRVEU7+R2hBBCCCEcIzUsQgghhPB5ErAIIYQQwudJwCKEEEIInycBixBCCCF8ngQsQgghhPB5ErAIIYQQwudJwCKEEEIInycBixBCCCF8ngQsQgghhPB5ErAIIYQQwudJwCKEEEIInycBixBCCCF83v8DE4nOUBTSIzwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(test_label, label = 'actual')\n",
    "plt.plot(pred, label = 'prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7b8d6cba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.1700626 ],\n",
       "       [0.18314928],\n",
       "       [0.18712005],\n",
       "       [0.19821958],\n",
       "       [0.2056724 ],\n",
       "       [0.2092658 ],\n",
       "       [0.20721656],\n",
       "       [0.20787987],\n",
       "       [0.22142303],\n",
       "       [0.23546535],\n",
       "       [0.24005762],\n",
       "       [0.23819464],\n",
       "       [0.23246488],\n",
       "       [0.22887015],\n",
       "       [0.22963601],\n",
       "       [0.23745082],\n",
       "       [0.25345552],\n",
       "       [0.25892156],\n",
       "       [0.26617095],\n",
       "       [0.25594702],\n",
       "       [0.23824385],\n",
       "       [0.22603095],\n",
       "       [0.22345342],\n",
       "       [0.21791734],\n",
       "       [0.20973018],\n",
       "       [0.2038947 ],\n",
       "       [0.21518414],\n",
       "       [0.22074702],\n",
       "       [0.24450669],\n",
       "       [0.2517103 ],\n",
       "       [0.25194442],\n",
       "       [0.24135026],\n",
       "       [0.21377751],\n",
       "       [0.19292964],\n",
       "       [0.17359275],\n",
       "       [0.16047154],\n",
       "       [0.16871378],\n",
       "       [0.16750018],\n",
       "       [0.17215581],\n",
       "       [0.185394  ],\n",
       "       [0.19037184],\n",
       "       [0.19158626],\n",
       "       [0.2044926 ],\n",
       "       [0.20605284],\n",
       "       [0.21329959],\n",
       "       [0.21915686],\n",
       "       [0.22012082],\n",
       "       [0.21615669],\n",
       "       [0.20937711],\n",
       "       [0.2078703 ],\n",
       "       [0.20943049],\n",
       "       [0.22152351],\n",
       "       [0.23857373],\n",
       "       [0.24886766],\n",
       "       [0.2534749 ]], dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b1236321",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>248.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.409627</td>\n",
       "      <td>0.336651</td>\n",
       "      <td>0.450069</td>\n",
       "      <td>0.403798</td>\n",
       "      <td>0.043843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.192505</td>\n",
       "      <td>0.173122</td>\n",
       "      <td>0.205404</td>\n",
       "      <td>0.204674</td>\n",
       "      <td>0.089071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.242187</td>\n",
       "      <td>0.180851</td>\n",
       "      <td>0.269504</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.012489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.409375</td>\n",
       "      <td>0.319149</td>\n",
       "      <td>0.457447</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>0.023531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.551562</td>\n",
       "      <td>0.457447</td>\n",
       "      <td>0.593972</td>\n",
       "      <td>0.550676</td>\n",
       "      <td>0.041191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open        High         Low       Close      Volume\n",
       "count  248.000000  248.000000  248.000000  248.000000  248.000000\n",
       "mean     0.409627    0.336651    0.450069    0.403798    0.043843\n",
       "std      0.192505    0.173122    0.205404    0.204674    0.089071\n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000\n",
       "25%      0.242187    0.180851    0.269504    0.216216    0.012489\n",
       "50%      0.409375    0.319149    0.457447    0.405405    0.023531\n",
       "75%      0.551562    0.457447    0.593972    0.550676    0.041191\n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0f729bc",
   "metadata": {},
   "source": [
    "#### array형식을 DataFrame형식으로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55a4a041",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pred = pd.DataFrame(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "35894fb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_actual = pd.DataFrame(test_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ab416ec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.170063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.183149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.187120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.198220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.205672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.170063\n",
       "1  0.183149\n",
       "2  0.187120\n",
       "3  0.198220\n",
       "4  0.205672"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pred.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6e87704",
   "metadata": {},
   "source": [
    "#### 0 과 1 사이의 값을 이제는 원래 주가로 환원"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "96972cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0 과 1 사이의 값을 원래 주가로 환원 \n",
    "pred_close = (max(HD_stock1[\"Close\"])-min(HD_stock1[\"Close\"]))*data_pred + min(HD_stock1[\"Close\"]) \n",
    "actual_close = (max(HD_stock1[\"Close\"])-min(HD_stock1[\"Close\"]))*data_actual + min(HD_stock1[\"Close\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb14528e",
   "metadata": {},
   "source": [
    "#### 환원이 잘 되었는지 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ef6be969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>206084.632516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>207053.046584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>207346.883714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>208168.249100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>208719.757468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0\n",
       "0  206084.632516\n",
       "1  207053.046584\n",
       "2  207346.883714\n",
       "3  208168.249100\n",
       "4  208719.757468"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_close.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bd320d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>208500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>209500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>209000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>209000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>207500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  208500.0\n",
       "1  209500.0\n",
       "2  209000.0\n",
       "3  209000.0\n",
       "4  207500.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_close.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8968679c",
   "metadata": {},
   "source": [
    "### 환원된 주가의 실제값과 예측값 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "368a9dd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(actual_close, label = 'actual')\n",
    "plt.plot(pred_close, label = 'prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "225ad893",
   "metadata": {},
   "source": [
    "# RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c958db6",
   "metadata": {},
   "source": [
    "#### keras.layers.SimpleRNN으로 신경망을 설계\n",
    "#### input_shape은 LSTM과 마찬가지로 \n",
    "#### train_feature.shape[1]과 train_feature.shape[2]을 입력\n",
    "#### 활성화 함수는 relu를 사용하고 \n",
    "#### Dense 레이어는 1개로 구성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0e49e0ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# RNN 을 이용하여 신경망 설계\n",
    "RNN_model = keras.Sequential([\n",
    "keras.layers.SimpleRNN(16, return_sequences = True, #return_sequence=True로 설정하면 레이어의 출력을 다음 레이어로 그대로 넘겨줌\n",
    "input_shape=(train_feature.shape[1], train_feature.shape[2]), activation='relu'),\n",
    "\n",
    "keras.layers.SimpleRNN(16, input_shape=(train_feature.shape[1], train_feature.shape[2]), activation='relu'),\n",
    "keras.layers.Dense(1, activation=None),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04cc1d9c",
   "metadata": {},
   "source": [
    "#### 모델의 서머리를 통해서 좀 더 자세히 확인할 수 있었다. \n",
    "#### 총 Total 파라미터는 881"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5930585f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " simple_rnn (SimpleRNN)      (None, 20, 16)            336       \n",
      "                                                                 \n",
      " simple_rnn_1 (SimpleRNN)    (None, 16)                528       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 881\n",
      "Trainable params: 881\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(RNN_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5a27eb84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pydot in c:\\users\\otfoo\\anaconda3\\lib\\site-packages (1.4.2)\n",
      "Requirement already satisfied: pyparsing>=2.1.4 in c:\\users\\otfoo\\anaconda3\\lib\\site-packages (from pydot) (3.0.9)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_21684\\369751258.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'pip install pydot'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvis_utils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mplot_model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"RNN.png\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow_shapes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "# 코렙에서만 생성이 가능한 시각화 \n",
    "\n",
    "# import tensorflow as tf\n",
    "# import pydot\n",
    "# !pip install pydot\n",
    "# from keras.utils.vis_utils import plot_model\n",
    "# tf.keras.utils.plot_model(model, \"RNN.png\", show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dadb8804",
   "metadata": {},
   "source": [
    "#### loss 평션은 민스퀘어에러로 하고 옵티마이져는 아담을 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4d13dc52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "4/4 [==============================] - 8s 16ms/step - loss: 0.1837\n",
      "Epoch 2/30\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.1327\n",
      "Epoch 3/30\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.0664\n",
      "Epoch 4/30\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.0155\n",
      "Epoch 5/30\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.0334\n",
      "Epoch 6/30\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.0158\n",
      "Epoch 7/30\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.0141\n",
      "Epoch 8/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0174\n",
      "Epoch 9/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0129\n",
      "Epoch 10/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0109\n",
      "Epoch 11/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0124\n",
      "Epoch 12/30\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.0108\n",
      "Epoch 13/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0106\n",
      "Epoch 14/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0107\n",
      "Epoch 15/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0100\n",
      "Epoch 16/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0101\n",
      "Epoch 17/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0100\n",
      "Epoch 18/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0097\n",
      "Epoch 19/30\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.0097\n",
      "Epoch 20/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0096\n",
      "Epoch 21/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0094\n",
      "Epoch 22/30\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.0094\n",
      "Epoch 23/30\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.0093\n",
      "Epoch 24/30\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.0093\n",
      "Epoch 25/30\n",
      "4/4 [==============================] - 0s 18ms/step - loss: 0.0092\n",
      "Epoch 26/30\n",
      "4/4 [==============================] - 0s 19ms/step - loss: 0.0092\n",
      "Epoch 27/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0091\n",
      "Epoch 28/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0090\n",
      "Epoch 29/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0089\n",
      "Epoch 30/30\n",
      "4/4 [==============================] - 0s 17ms/step - loss: 0.0088\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x249c4704a00>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RNN 모델 실행\n",
    "RNN_model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "RNN_model.fit(x_train, y_train, epochs=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4dc4f965",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 5ms/step\n",
      "***예측값 보기***\n",
      "[[0.20392346]\n",
      " [0.18464933]\n",
      " [0.22605065]\n",
      " [0.18441755]\n",
      " [0.24361378]\n",
      " [0.21113679]\n",
      " [0.2527227 ]\n",
      " [0.22943255]\n",
      " [0.24153647]\n",
      " [0.23484865]\n",
      " [0.2714496 ]\n",
      " [0.25471598]\n",
      " [0.27447608]\n",
      " [0.24799308]\n",
      " [0.25824368]\n",
      " [0.25987223]\n",
      " [0.2570299 ]\n",
      " [0.28563008]\n",
      " [0.26326957]\n",
      " [0.30734938]\n",
      " [0.27302545]\n",
      " [0.28691426]\n",
      " [0.26293766]\n",
      " [0.28188515]\n",
      " [0.26649976]\n",
      " [0.2675178 ]\n",
      " [0.24240091]\n",
      " [0.27884534]\n",
      " [0.2259184 ]\n",
      " [0.32648638]\n",
      " [0.24624574]\n",
      " [0.3304168 ]\n",
      " [0.2579985 ]\n",
      " [0.296922  ]\n",
      " [0.25751004]\n",
      " [0.26812655]\n",
      " [0.2286992 ]\n",
      " [0.26262856]\n",
      " [0.20347291]\n",
      " [0.22818615]\n",
      " [0.2248503 ]\n",
      " [0.21549897]\n",
      " [0.22412297]\n",
      " [0.22091529]\n",
      " [0.22123948]\n",
      " [0.23860627]\n",
      " [0.23486526]\n",
      " [0.24894099]\n",
      " [0.24730426]\n",
      " [0.23993927]\n",
      " [0.24365953]\n",
      " [0.23469433]\n",
      " [0.23710461]\n",
      " [0.26474324]\n",
      " [0.2547556 ]]\n"
     ]
    }
   ],
   "source": [
    "# RNN 예측 수행\n",
    "RNN_pred = RNN_model.predict(test_feature)\n",
    "print('***예측값 보기***')\n",
    "print(RNN_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfb0076c",
   "metadata": {},
   "source": [
    "#### 스케일링 값을 환원하지 않고 그래프를 그려보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4f02c11f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(test_label, label = 'actual')\n",
    "plt.plot(RNN_pred, label = 'prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9795db6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0 과 1 사이의 값을 원래 주가로 환원\n",
    "data_RNN_pred = pd.DataFrame(RNN_pred)\n",
    "data_RNN_real = pd.DataFrame(test_label)\n",
    "pred_close_RNN = (max(HD_stock1[\"Close\"])-min(HD_stock1[\"Close\"]))*data_RNN_pred + min(HD_stock1[\"Close\"]) \n",
    "actual_close_RNN = (max(HD_stock1[\"Close\"])-min(HD_stock1[\"Close\"]))*data_RNN_real + min(HD_stock1[\"Close\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e969e35",
   "metadata": {},
   "source": [
    "#### 0과 1사이의 값을 원래주가로 환원한 값으로 \n",
    "#### 다시 그래프 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1f7632c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(actual_close_RNN, label = 'actual')\n",
    "plt.plot(pred_close_RNN, label = 'prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f47de148",
   "metadata": {},
   "source": [
    "## 그래프의 형태는 환원하기 전과 환원한 후가 동일함"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "vscode": {
   "interpreter": {
    "hash": "943b30e36007a11e13680dc03ef51b3271bb8ce8e97963534c0e28647a7e36da"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}