Q-LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "96963d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import time\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "np.set_printoptions(suppress = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "efc40d1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "33a08936",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyqpanda import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "01c40c9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d48811",
   "metadata": {},
   "source": [
    "# 1. Prepare Dadaset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "757bbfef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.kaggle.com/datasets/sumanthvrao/daily-climate-time-series-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "91a53ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path = './data/DailyDelhiClimateTrain.csv'\n",
    "test_path = './data/DailyDelhiClimateTest.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ef5a3d26",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [1,2,3,4]\n",
    "\n",
    "train = pd.read_csv(train_path, usecols=cols, engine=\"python\")\n",
    "test = pd.read_csv(test_path, usecols=cols, engine=\"python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c112f41d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len(train)=1462\n",
      "len(test)=114\n"
     ]
    }
   ],
   "source": [
    "print(f'len(train)={len(train)}')\n",
    "print(f'len(test)={len(test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ca701d",
   "metadata": {},
   "source": [
    "## 1.1 Outlier Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5b3dc6f",
   "metadata": {},
   "source": [
    "### 1.1.1 outlier detection for train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9d076311",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b2bebd0fd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(train)), train[\"meanpressure\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba6f7435",
   "metadata": {},
   "source": [
    "### - remove outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bb83d8fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "remove outliers num: 9\n"
     ]
    }
   ],
   "source": [
    "unnormal_num = 0\n",
    "for i in range(len(train)):\n",
    "    mp = train.iloc[i][3]\n",
    "    if mp > 1200 or mp < 950:\n",
    "        unnormal_num += 1\n",
    "        train.iloc[i][3] = train.iloc[i + 1][3]\n",
    "print(f'remove outliers num: {unnormal_num}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c182752f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b2beb1c490>,\n",
       " <matplotlib.lines.Line2D at 0x1b2be9ce250>,\n",
       " <matplotlib.lines.Line2D at 0x1b2be9ce280>,\n",
       " <matplotlib.lines.Line2D at 0x1b2be9af2b0>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a2a352d5",
   "metadata": {
    "scrolled": true
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>meantemp</th>\n",
       "      <td>1462.0</td>\n",
       "      <td>25.495521</td>\n",
       "      <td>7.348103</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>18.857143</td>\n",
       "      <td>27.714286</td>\n",
       "      <td>31.305804</td>\n",
       "      <td>38.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>1462.0</td>\n",
       "      <td>60.771702</td>\n",
       "      <td>16.769652</td>\n",
       "      <td>13.428571</td>\n",
       "      <td>50.375000</td>\n",
       "      <td>62.625000</td>\n",
       "      <td>72.218750</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wind_speed</th>\n",
       "      <td>1462.0</td>\n",
       "      <td>6.802209</td>\n",
       "      <td>4.561602</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.475000</td>\n",
       "      <td>6.221667</td>\n",
       "      <td>9.238235</td>\n",
       "      <td>42.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meanpressure</th>\n",
       "      <td>1462.0</td>\n",
       "      <td>1008.247674</td>\n",
       "      <td>7.437992</td>\n",
       "      <td>991.375000</td>\n",
       "      <td>1001.625000</td>\n",
       "      <td>1008.563492</td>\n",
       "      <td>1014.875000</td>\n",
       "      <td>1023.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count         mean        std         min          25%  \\\n",
       "meantemp      1462.0    25.495521   7.348103    6.000000    18.857143   \n",
       "humidity      1462.0    60.771702  16.769652   13.428571    50.375000   \n",
       "wind_speed    1462.0     6.802209   4.561602    0.000000     3.475000   \n",
       "meanpressure  1462.0  1008.247674   7.437992  991.375000  1001.625000   \n",
       "\n",
       "                      50%          75%          max  \n",
       "meantemp        27.714286    31.305804    38.714286  \n",
       "humidity        62.625000    72.218750   100.000000  \n",
       "wind_speed       6.221667     9.238235    42.220000  \n",
       "meanpressure  1008.563492  1014.875000  1023.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "52776605",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meantemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>meanpressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>84.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   meantemp  humidity  wind_speed  meanpressure\n",
       "0      10.0      84.5         0.0   1015.666667"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6e536f",
   "metadata": {},
   "source": [
    "### 1.1.2 outlier detection for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6fa0149b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b2bea504f0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(test)), test[\"meanpressure\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a09b1874",
   "metadata": {},
   "source": [
    "### - remove outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d62ae65e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test.iloc[0][3] = test.iloc[1][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6cf7014f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b2bea02910>,\n",
       " <matplotlib.lines.Line2D at 0x1b2be8e1880>,\n",
       " <matplotlib.lines.Line2D at 0x1b2be8e18b0>,\n",
       " <matplotlib.lines.Line2D at 0x1b2be8e18e0>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pnh7TKhfuIdHWbl7DAXVd+b8/JeeCC/pdnv44LgFm27ZtDB8+nJUrVzJ58uT0dh/5yEeYPHkyP//5z/n973/PrbfeSnt7e3p9IpHA7/fz2GOP8bGPfYzPfOYzhEIhnnzyyfQ2L774ImeffTZtbW3k99H3HI1GiR5QqaFQiKqqKgUYkfdQvKGBVGeneYO0wVgGeX+w4nGiW7YQ2bjRtDaFOk3LU2dX72DidhEYP56MqVPxDh+e7nJKdXeb/TdsJLp5M9FNm4hu2kQyGCQwZQq5l32UnPPPH5AxLZZlmTEz0ShWInHQtBWpWIzY9u0m/GzYYMYrbdzY5/g6Z1bW3sHrZgoKZ1aWueK0vp54YyPJ1tY+y+Dw+/GNGoV30CAS7W0kGptINDUdfJXpITh8PrzDhuEbMYL8q64k49RT31llHEJ/A8yAzgPT0NAAQGlpaa/lpaWl6XUNDQ2UlJT0LoTbTUFBQa9tqqurDzrGvnV9BZi5c+fyne98Z2BeiIi8I56yMigrO97FkA8Yh8eDf+xY/GPHvqP9nZmZBCZNIjBpUnrZvqDR1wUB74bD4TDh/hDHdXq9+EePxj96NLmXXJJenmhvJ163B1dWJs7c3MPO17VPsqvbhLGNG0xL0EYTiKxwmMhbbxF5662D9nEVFeEdMgTv4MF4KipwZmWZAdYZAdO1NXy4Gbd2AozLfN9MZHf77bdzyy23pH/e1wIjIiJytNJB4wThzs8/6ivfXFmZZJxyMhmnnJxeZiWTxHbWEt24gXh9A+6iQtwlpWbqidKSAZnf6b0yoAGmbO8nr8bGRsrLy9PLGxsb011KZWVlNL2tKSyRSNDW1pbev6ysjMa3DYDa93PZIT7d+Xw+fJpBVERE5JAcLhe+YeYqL7sb0JuhVFdXU1ZWxvz589PLQqEQS5YsoaamBoCamhqCwSArVqxIb7NgwQJSqRTTpk1Lb7No0SLiB4xKnzdvHqNHj+6z+0hEREQ+WI46wHR1dbFq1SpWrVoFwPbt21m1ahW1tbU4HA5uvvlmvve97/H000+zevVqPvOZz1BRUZEe6Dt27FjOP/98rr/+epYuXcqrr77KTTfdxMyZM6moqADgk5/8JF6vl+uuu461a9fyl7/8hZ///Oe9uohERETkA8w6Si+++KIFHPSYPXu2ZVmWlUqlrDvuuMMqLS21fD6fdc4551gbN27sdYzW1lbr6quvtrKysqycnBzr2muvtTo7O3tt8+abb1rTp0+3fD6fVVlZad19991HVc6Ojg4LsDo6Oo72JYqIiMhx0t/zt24lICIiIieM/p6/B3QMjIiIiMh7QQFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsZ8ADTDKZ5I477qC6uppAIMDw4cP57ne/i2VZ6W0sy+LOO++kvLycQCDAjBkz2Lx5c6/jtLW1MWvWLHJycsjLy+O6666jq6troIsrIiIiNjTgAeYHP/gB9913H7/85S9Zv349P/jBD/jhD3/IL37xi/Q2P/zhD7nnnnu4//77WbJkCZmZmZx33nlEIpH0NrNmzWLt2rXMmzePZ599lkWLFnHDDTcMdHFFRETEhhzWgU0jA+Diiy+mtLSU3/3ud+llV1xxBYFAgD//+c9YlkVFRQW33norX/3qVwHo6OigtLSUBx54gJkzZ7J+/XrGjRvHsmXLmDp1KgDPP/88F154Ibt376aiouKI5QiFQuTm5tLR0UFOTs5AvkQRERE5Rvp7/h7wFpgzzjiD+fPns2nTJgDefPNNXnnlFS644AIAtm/fTkNDAzNmzEjvk5uby7Rp01i8eDEAixcvJi8vLx1eAGbMmIHT6WTJkiV9Pm80GiUUCvV6iIiIyPuTe6APeNtttxEKhRgzZgwul4tkMsldd93FrFmzAGhoaACgtLS0136lpaXpdQ0NDZSUlPQuqNtNQUFBepu3mzt3Lt/5zncG+uWIiIjICWjAW2D++te/8tBDD/Hwww/zxhtv8OCDD/LjH/+YBx98cKCfqpfbb7+djo6O9GPXrl3H9PlERETk+BnwFpivfe1r3HbbbcycOROAiRMnsnPnTubOncvs2bMpKysDoLGxkfLy8vR+jY2NTJ48GYCysjKampp6HTeRSNDW1pbe/+18Ph8+n2+gX46IiIicgAa8Baanpwens/dhXS4XqVQKgOrqasrKypg/f356fSgUYsmSJdTU1ABQU1NDMBhkxYoV6W0WLFhAKpVi2rRpA11kERERsZkBb4G55JJLuOuuuxg8eDDjx49n5cqV/PSnP+Wzn/0sAA6Hg5tvvpnvfe97jBw5kurqau644w4qKiq47LLLABg7diznn38+119/Pffffz/xeJybbrqJmTNn9usKJBEREXl/G/AA84tf/II77riDL3zhCzQ1NVFRUcF//dd/ceedd6a3+frXv053dzc33HADwWCQ6dOn8/zzz+P3+9PbPPTQQ9x0002cc845OJ1OrrjiCu65556BLq6IiIjY0IDPA3Oi0DwwIiIi9nPc5oEREREROdYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdo5JgKmrq+NTn/oUhYWFBAIBJk6cyPLly9PrLcvizjvvpLy8nEAgwIwZM9i8eXOvY7S1tTFr1ixycnLIy8vjuuuuo6ur61gUV0RERGxmwANMe3s7Z555Jh6Ph+eee45169bxk5/8hPz8/PQ2P/zhD7nnnnu4//77WbJkCZmZmZx33nlEIpH0NrNmzWLt2rXMmzePZ599lkWLFnHDDTcMdHFFRETEhhyWZVkDecDbbruNV199lZdffrnP9ZZlUVFRwa233spXv/pVADo6OigtLeWBBx5g5syZrF+/nnHjxrFs2TKmTp0KwPPPP8+FF17I7t27qaioOGI5QqEQubm5dHR0kJOTM3AvUERERI6Z/p6/B7wF5umnn2bq1KlceeWVlJSUcPLJJ/Pb3/42vX779u00NDQwY8aM9LLc3FymTZvG4sWLAVi8eDF5eXnp8AIwY8YMnE4nS5YsGegii4iIiM0MeIDZtm0b9913HyNHjuSFF17gxhtv5Etf+hIPPvggAA0NDQCUlpb22q+0tDS9rqGhgZKSkl7r3W43BQUF6W3eLhqNEgqFej1ERETk/ck90AdMpVJMnTqV73//+wCcfPLJrFmzhvvvv5/Zs2cP9NOlzZ07l+985zvH7PgiIiJy4hjwFpjy8nLGjRvXa9nYsWOpra0FoKysDIDGxsZe2zQ2NqbXlZWV0dTU1Gt9IpGgra0tvc3b3X777XR0dKQfu3btGpDXIyIiIieeAQ8wZ555Jhs3buy1bNOmTQwZMgSA6upqysrKmD9/fnp9KBRiyZIl1NTUAFBTU0MwGGTFihXpbRYsWEAqlWLatGl9Pq/P5yMnJ6fXQ0RERN6fBrwL6Stf+QpnnHEG3//+97nqqqtYunQpv/nNb/jNb34DgMPh4Oabb+Z73/seI0eOpLq6mjvuuIOKigouu+wywLTYnH/++Vx//fXcf//9xONxbrrpJmbOnNmvK5BERETk/W3AL6MGePbZZ7n99tvZvHkz1dXV3HLLLVx//fXp9ZZl8e1vf5vf/OY3BINBpk+fzr333suoUaPS27S1tXHTTTfxzDPP4HQ6ueKKK7jnnnvIysrqVxl0GbWIiIj99Pf8fUwCzIlAAUZERMR+jts8MCIiIiLHmgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYjgKMiIiI2I4CjIiIiNiOAoyIiIjYzjEPMHfffTcOh4Obb745vSwSiTBnzhwKCwvJysriiiuuoLGxsdd+tbW1XHTRRWRkZFBSUsLXvvY1EonEsS6uiIiI2MAxDTDLli3j17/+NSeddFKv5V/5yld45plneOyxx1i4cCF79uzh8ssvT69PJpNcdNFFxGIxXnvtNR588EEeeOAB7rzzzmNZXBEREbGJYxZgurq6mDVrFr/97W/Jz89PL+/o6OB3v/sdP/3pTzn77LOZMmUKf/jDH3jttdd4/fXXAfjXv/7FunXr+POf/8zkyZO54IIL+O53v8uvfvUrYrHYsSqyiIiI2MQxCzBz5szhoosuYsaMGb2Wr1ixgng83mv5mDFjGDx4MIsXLwZg8eLFTJw4kdLS0vQ25513HqFQiLVr1/b5fNFolFAo1OshIiIi70/uY3HQRx99lDfeeINly5YdtK6hoQGv10teXl6v5aWlpTQ0NKS3OTC87Fu/b11f5s6dy3e+850BKL2IiIic6Aa8BWbXrl18+ctf5qGHHsLv9w/04Q/p9ttvp6OjI/3YtWvXe/bcIiIi8t4a8ACzYsUKmpqaOOWUU3C73bjdbhYuXMg999yD2+2mtLSUWCxGMBjstV9jYyNlZWUAlJWVHXRV0r6f923zdj6fj5ycnF4PEREReX8a8ABzzjnnsHr1alatWpV+TJ06lVmzZqW/93g8zJ8/P73Pxo0bqa2tpaamBoCamhpWr15NU1NTept58+aRk5PDuHHjBrrIIiIiYjMDPgYmOzubCRMm9FqWmZlJYWFhevl1113HLbfcQkFBATk5OXzxi1+kpqaG008/HYBzzz2XcePG8elPf5of/vCHNDQ08K1vfYs5c+bg8/kGusgiIiJiM8dkEO+R/O///i9Op5MrrriCaDTKeeedx7333pte73K5ePbZZ7nxxhupqakhMzOT2bNn8z//8z/Ho7giIiJygnFYlmUd70IcC6FQiNzcXDo6OjQeRkRExCb6e/7WvZBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdhRgRERExHYUYERERMR2FGBERETEdgY8wMydO5dTTz2V7OxsSkpKuOyyy9i4cWOvbSKRCHPmzKGwsJCsrCyuuOIKGhsbe21TW1vLRRddREZGBiUlJXzta18jkUgMdHFFRETEhgY8wCxcuJA5c+bw+uuvM2/ePOLxOOeeey7d3d3pbb7yla/wzDPP8Nhjj7Fw4UL27NnD5Zdfnl6fTCa56KKLiMVivPbaazz44IM88MAD3HnnnQNdXBEREbEhh2VZ1rF8gubmZkpKSli4cCEf/vCH6ejooLi4mIcffpiPf/zjAGzYsIGxY8eyePFiTj/9dJ577jkuvvhi9uzZQ2lpKQD3338/3/jGN2hubsbr9R7xeUOhELm5uXR0dJCTk3MsX6KIiIgMkP6ev4/5GJiOjg4ACgoKAFixYgXxeJwZM2aktxkzZgyDBw9m8eLFACxevJiJEyemwwvAeeedRygUYu3atX0+TzQaJRQK9XqIiIjI+9MxDTCpVIqbb76ZM888kwkTJgDQ0NCA1+slLy+v17alpaU0NDSktzkwvOxbv29dX+bOnUtubm76UVVVNcCvRkRERE4UxzTAzJkzhzVr1vDoo48ey6cB4Pbbb6ejoyP92LVr1zF/ThERETk+3MfqwDfddBPPPvssixYtYtCgQenlZWVlxGIxgsFgr1aYxsZGysrK0tssXbq01/H2XaW0b5u38/l8+Hy+AX4VIiIiciIa8BYYy7K46aabeOKJJ1iwYAHV1dW91k+ZMgWPx8P8+fPTyzZu3EhtbS01NTUA1NTUsHr1apqamtLbzJs3j5ycHMaNGzfQRRYRERGbGfAWmDlz5vDwww/z1FNPkZ2dnR6zkpubSyAQIDc3l+uuu45bbrmFgoICcnJy+OIXv0hNTQ2nn346AOeeey7jxo3j05/+ND/84Q9paGjgW9/6FnPmzFEri4iIiAz8ZdQOh6PP5X/4wx+45pprADOR3a233sojjzxCNBrlvPPO49577+3VPbRz505uvPFGXnrpJTIzM5k9ezZ33303bnf/MpcuoxYREbGf/p6/j/k8MMeLAoyIiIj9nDDzwIiIiIgMNAUYERERsR0FGBEREbEdBRgRERGxHQUYERERsR0FGBEREbEdBRgRERGxHQUYERERsR0FGBEREbEdBRgRERGxHQUYERERsR0FGBEREbEdBRgRERGxHQUYERERsR0FGBEREbEdBRgRERGxHQUYERERsR0FGBEREbEdBRgRERGxHQUYERERsR0FGBEREbEdBRgRERGxHQUYERERsR0FGBEREbEdBRgRERGxHQUYERERsR0FmBNN/Zuw4HvQ3Xq8SyIiInLCch/vAsgBOurgj5dBuA02Pgezn4GMguNdqhNbuB0iIcgfcrxLIiIi7yG1wJwoUkn4+/UmvAA0roE/Xgo9bce3XCeyRBR+dy7cczJs/vfxLo2IiLyHFGCOVsdueOluSMYH9riLfgQ7XwVvFsx8BDKLoWE1/OljEA4O7HO9Xyz9LbRsAisJj18LzRuPd4lEROQ9ogBzNCwLnvg8vDQXfn8etG7te7tU6uiOu+MVWPgD8/3F/wtjLoTPPA0ZhVC/Cv74UVj9OLTvNGU4Xpo3mtf/hwth5UOQTBy/svS0waIfmu+zSiEagoc/cXCLVetW2PBPqH0dWraYLqfjWYciIjIgHJb1/nw3D4VC5Obm0tHRQU5OzsAdeO0T8MyXIdJhWksu+CFM/qQ5ga75G6z8MzSuM0Fk8tVHPl53K9w/HTr3wORZcNm9+9c1rIEHL9nfrQTmZD10Opx9BxRUD9zrOpzGdaaFaO0TwAF/LgXD4MNfh4lXgus9Hk713G2w5D4onQCf+jv8bgYEa6H6w+bnhrfg5Z/Chn/0LjOYYDj2Upj4cRh8BjiV40VEThT9PX8rwLwTHbtNS8SOl83PlVPMST4RPmAjB1zyc5gy++D9UymoXQxvPQprn4JoBxSOhBteAl9W721bt5qukt1LzRVKqb2tHr4cE5ImfnxgX9uB6t+ERT+G9U/vXzbmYqiYDK/fDz0tZlneYBh5Hgz7iAlXgfz+P0esG9q2g9MFTg+4PJBZBN7MQ+/TsgXunWbq4tNPwPCzoXGtGQ8T64K8IRDcuX/7sokQ7TRhMdbZ+1jZFTDqPBPG8oeYfZMx2L0c6pabr75smPnwwQOFEzH497dNC5o30wRabya4fXtbefb+axWNgpqbwJvR/3oREfmAUoA5lgEGzKDbV38OL961P1QUj4GTPw1t22D578yyi34Cp37OfN9RB2/8EVY9DB21+4+VNwSufgRKxx/+OeNhqHsD5v8P7HrdLDv5U6YV6O0n/M4G2LPSPOJhOO16EzTebscrsONVyK00J/GCYRDcBS//GDY9v3+7cR+FD3/NhAGAaBcs+z9TBwe2EDmcUDUNLvwxlE049Gvp2A1Lfg0rHjQB7kDuAPzHf8PpX+i7ZefRWbDhWRjxn/Cpx/cv3/gcPHI1YIHTDROvguk3Q/HoA+owYsLjmsdh3TMHP/eh5A6Ga56B/KH7j/PXz8DmF/q3f341XPoLqP5Q/7YXEfmAUoA51gFmnz2rzMl05HkwaCo4HObT9wvfhNd/ZbY540vQusUEAmvv+BhfDoy7FE6aCUPOPLpujGTCjJlZ9CPAMq0I2aUmVKWS0NMKXQ299/FkwFm37Q0FHhMgXvgmrHvy0M/jcMKEK2D6LVA6ru9tol2w7SXYvtB8bdlklru8MOM7MO3z+1+bZZmxKMt+C2ufNINvAfy54HCZIJiMQSJillecDJf+sncQ2v4yPHix2f7G16BkTO/yrH3SXMF1ymf6DmwHSkRh8zyoW2FabNp37m25cZhWtUFTTBfVv75lfn+5VebS9qwSePST5vW6/SZABvJMXcS6IRk1x3A4zOtZ+lsI1ZnnnPpZUy/+o/yb7GmDbS/CtoWmC+zML5vnfLtQPcR7TBB1OI7uOY7EsmD3MsCx/29dRGSAKcC8VwHmUCwL5n8HXvnf3suHfgimXANjLgJP4N09x/ZF8PcboLP+4HUOp2kRqjjZtAjVLjbLSyfAyHNhyf3mROdwmm6hWJfZLlhrlp00E6Z/BYpGHF2ZgrXwz6/tb70Zfo4JTpv/BW/9xazfZ+iHTNfKyHN7h5xVD8EL/23GGTndMO4y6G42IeLAIHDx2+r2WAnVm9DUugVyBpnWql1LwJMJn/zLkVtVIiGYdyes+IP5OavMBJAp1/TuVuqoM92KbdvNz/sCQtN6E7L2hd99x7jwRyYEg2k1W3i3ad2zUiZsDT8bRpxjWsSySnsHjlQKgjvMsYvHQOHww7+GHa/sbflbYn4uGG5a/yZdDTnlfe/TsMaMF8sdBJfd98HrQgvtMV3Nybj5m8mpgJxK02W5ryVPRA6iAHO8AwyYk/ErPzXdRqMvMies4lED+xyRDqhdguk2cZmWCW8WlIzdP54mlTKhYN4d5iqcfQbXmJPgvm4hMOM6Uol3d7KxLNOF9sI397em7OPNMoFk2n9B+UmHPkZnA/zjVtO69Xblk2HW45BV/M7LeLQ6G+CBi6F1s/nZlwuf+htUndr/Y2xfBE9/Edp3mJ8zi02Ayxtsfj9b5nPQgOMDlYyDYWeZMNi6xSwbc7EJK8t/Z1p7wIwlSr3tMn+332yXV2VaiRrXmtAKJiR+9F6Y9ImDn3PPShNcti7Yfxyne/++DheMvsB0L1ZM3r/fyofgH7fs//0P+w/TTfpuQ7tdJBNmAH7tawevc/lM1+b0r3xw6kPkKCjAnAgB5kTT3WoGne5eDh+6xVw9dCy7AZo2wN8/ZwY4jzgHTvoEjL7w6MLRphdM60P+UCgcYR7Ha3bizgZ46OOmHq9+pPcJu78SUdNK8spPe7dG7TNkuhkM7XCSDjNZZaY1JbfS/ByPmEvIX/35/vFXYFq0zvm2GUu181XY8m8TPFo202cwcvkgu2z/gOdzvwdnfNF8HwmZ4LLs/0iPKZpyDXzoq2ZQ87onzRV3+1r2AEadD2febMLYyj/tL1PdGxDvNq1xMx8Gj//o681uFtxlfkfebLjgbvPBIbTHBMJ9dZY/FC74EYw697gWVeREowCjAHNiSKVMV9Xbr66yK8sy44ze7WXjyTisfgxe+6VpzZj4cXMZ/ZG6cg7UsAae+4YZS/SRb5jWmb4CaSJmut46dpnQ5PKZcUWFI0wLyr++tX+8Vs1NMPh0+OfXzaX9YILuf3yz78v2mzaYMLb6sd5dXA6nGYg9/VZzwn7o4+bvYOS58Ik/myu1jlYyDm8+arpMvVkmSPmyYdCp+8PdiWDbS+aWIFhwxe96XyloWbDuKXj+9v31W3GK6VIec7EZcD5QHypatpixchMuN61kIjahAKMAI9I/lgWv3WPG6RyoYJgZZzTsrCMfo3UrvPwTEzAC+fDx3/Xeb/vL8NCVZqqB0ReaEON09b+MrVvNeK+65Qevc3lNl+SHbj26S/iPha4muO9M6G6CU2bDpff0vV20y4SL1+/t3YpWMAxOuwFOvf7gkNy6FZb9zgSc/KH7HwXDD74IYOdrZqB5uN20nl39Fxg548jlj4dNN6EGaMtxpACjACNydFY9Ak/NMSevM79sxrUc7RiNrmbTReTLPnjdtpfMbMmJCEy70XStHIllme6o524z3VC+XBh3iTnRRrtMK0bDarNtIN9MrHjq58Dt7X2cRMwc5/V7oXKqCRbvpBXocFJJ+PPl5nWWjIPPzT9yd2lnI2x6zky4uO2l/eOYSsaZqQiGnmm6817+MSy+9+CxTWDmkDrjJjPw3uOHtx6Dp75gjuXLNVMFeDLMFXSDpu7fL5kwz1m33Mz5tGeVqc/ySWZ8zthLjy5kigwQBRgFGJGj17LFXGZ/rO7uvfYJeOwa8/2FPzbzEx1KJGQC1b6JFIdMh4/dbwYi72NZZqzPv+6A5vVmmT/PdFWNudAMHt70vLn9x77B02DWX/WngRuPk4iaFqJ1T5p5jG546eBL/I8k2glv/RUWfG//3EpjLoZdS02LDpixUMVjzWtp32GuHNw3gWZmiRk/tfqx/ftedq+p760LIFAA1/3LzEm0+q9mksq2Q9wOBUxr0BlfgpOu6ntiyfYdZuD56AvMFVYiA0QBRgFG5MT08k/MAGGHEz75WN9dG23b4ZGZ0LzBXFV19rfMAONDtQgkE2bw8Etz+55WAMwJfvInzQSKibDp4pr5yLu/vDvaBX/5lJmnx+mBj/9+/+Xt70RPm6mfFQ+QHnxdMBzOn2uC14HdO9FOc5Xj4nshtHv/8pqb4D//x9RXtMtcEbXnDTMNgMu9P8wF8s0cVhWTTctL7iBzBdnSX++/YtGTASP/00xmOew/TKvNGw+ar2COec0zJvDYmWWZv7fiMepCO84UYBRgRE5MlmVaVlY9ZK7Smf20ma9o30lj+8tmluNwm7kCa+bDZlLB/kglTYvFxn+Ym3i2bTUTJZ55sxkn4800c9o8dJXpkhpyJlxyj+nWinWZk31moemW6WvgebTLjLnZ10XV0wYPX2Um+PNkmLE9I84ZkGqi7g1zpdmgU824mLd3ix0oGTetW2/9xXT9vP0WJt0t5lYb+1pcMopMIDz1ur67+6JdJhgt/XXvlqu3C+SboJNTCdc8a98Qc+Df5Edug/+4/XiX6ANNAUYBRuTElYjBnz4GO18xP2eVmqtxcgeZCf9SCRNqZj78zrsnLMtcfRXIP7gLpHaJuTIqGjr0/tkVZiLHVNK06nQ2mtADZibtjAIzFqer0XRbzXr86OYFeq+174QF3zXzKE299vD3G9vHsqB+lblyau2T0L4dssvNJIYnf8p0lz14CbRsNCFm9jO9r6SLR8xYo8O1aCQT5rhN68ykkSNmHP0Emu/Wwh+a28KAaUX7wmIoGvneluFE0NVsAmtwp7liMZUws7e/x1eRKsAowIic2Hra4PHPmgn+9t1WYp8JV8BHf3VsJ3qrewP+dp2Z38ebZd6kPZnmNhzdzf0/Tna5ualoydhjV9YTgWWZusos7n2FVFeTmeSxZaMJfdUfMmNz2raZ25pkFpvJMssmmu6ZcNCcHIO15mTZunn/4GXYOxP4J+AjX+/dohMJmS6enjYzgWekw1yaXzTShN9DzQh9JG89Zuargv03gh12Fnz6yePbldS23YzvGn+5aRU8nMZ1sOZv5nfwH986+vFXyTg8/SV48+GD1w0/21zFdrgWwAGmAKMAI2IPsR5zJdGeN8x9rCqnwJRrj+/JI9xuBjS37h3UnF1uJv3LKjFv9j1t5uQcDZkunr7uS/VB0tVkWmKaN7yz/T0ZZg4cbxbseNksc7hMkE3FzVVSbdsOf4zsctO6VFC9/7YNeUPMjN8uT9/77FwMf7zUBKgzvmRuUXLv6aZL8eO/N8//XrMs0333/O2mxc+fB+fcYf4nDhwD1rLFDBpf8zfTerVvd38eocsfpjZjAq3dUfweFwGPiwyvC6fTQXt3jNbuGG3dMTojcTwkOXvt7Qxp/DcWDmIZZVh5g/EUDMa18R8mJE68Ej72m6O7Z9+7oACjACMi8t7pbjEDj52u/Xe2z6k03UP1b5mQ2rrZ3Iw0b/DexxDTgpI7OH1yjO5cRmrB9wnsXHDwc+RUQlYplj+XmCebSNKJu2U9GcHNOEgdvD1AIJ/k6IvpHn4RbSWnE+tux9G0DnfLegat/hWeaDvJMRfjuupPpgwv/QBe+j6xjFIWnPMPThlZRUnOezR7dHeLaQnZ+A8Akt4cXDHTzdmRO5Z1wz9HQecGKhoWkN25/wqyhMPNW/5TyYi2MCa1mR7Lx43xm1mYmnTYp/MS55eeezjXtYKo5ebG+M0sSJ2SXn++bw2/cPwAD0n+xEX83HUNuRle8jK85Gd4yA14+eS0KqYMGdjZ0RVgFGBERAZUZyROe3ecUGTvI5wg2BOjrSdGsCdOZyTO4IJMJlbmMqEyh7wML52ROG/t7mDVriDr6kN0RRJEE0ki8RTRRIpoIkl07/eReJKuqJnY7xTHJi52vU6jlc8aayiNGaPJzC+hoydGfUeEaGJ/YPETZbxjB+OcO6l0tFDhaKXc0coIZz35dKa3i1kuvI7e3ZWrUsP4ZPwOCvLyKM720djawcPxmxnqbOS3iQv5ZfJj3FC2mUu9K6jsfAuny226Nj2ZZhzUGV9KX0kXiScJhffVT4KOnjh7OsLUByPsCYZp7opy4Bk3ZVkk41GqwhsYH1vFpdF/UkiQmOXix4mr+EPyAq52zedW92PkOnp6lTtmuVicGs+zqdN5ITmVEFkEiHC/52d8xPUWCVz8JPMrzHN9mHAsSTieJJ5IkZfpoTDTR2kAvtT6P4zvfp24w8MvS/6HZe5TaAhFqA9GCMdNPV3mfIWfee8F4Efxq3gyeSYxPET3Pn5y9WlcPGlgZ8JWgFGAEflAC8eStHRFcTgg2+8h2+fG6fzgXh6bTFmsrw8R2XtiMj10DoqyvJTm+PF79ndPdEUT7GztZkdLDxsaQqzbE2J9fYg9HZG+D34Ixdk+Wt520u4Pr9tJcZaPbL+bxlCE9p4+JvADCjK95GV4yPF7yAl48LmdNIYi7GztoSMcx0WS05wbuMC5lAtcSyl2dJDCQZ2jjJ2uIWxxDePX4XOoj/Uea/UR55s86P0BSZykLAeet4Wet3vOdz7fjX2SPeH+32KkxrmWz7ue4VTnRjIc0fTyTalKbo7PYZ01lGyfm5yAh0Hebq6P/5lJsVVs9Y5mma+G5d6phJ1ZVOT5GZSfQVVBgKr8DCpzXAxaeCuutX8zBywabeYHGnaWuX3IjlfMvEDbFkKs0wzEvvoRGP4f6TJYlkVHOE5rtxmblLvy1xS99j99vo6mGfdQMn12n+veKQUYBRiRdyWZsgj2xIglU7icDlwOB26nk1AkTlNnhMZQlMZQhJauKG3dMVq6YrR3x/B5nFTlZzAoP0BVQQYjSrIYXZqN23Vs+s8ty2JLUxcvbWzm5S0t7Grrobkzmv4kf6Bsn5uRpVlMH1HE9JHFTK7Kw+s++nI1d0ZZtSvI6roOXA4HRdleCjN9FGeb5nVzQnXjcx/7mWwj8SQvbmji2bfqWbS5mfJcPzXDCqkZXsQpg/NYXdfBv9Y28u/1jekTUl8KM70UZZnAcbjtAh4XOQE3OX4P2X43+Qd0KWT43Gxt6mJ1XQe1bftbDAblB5hclcdJg3IpyPThczvxe1y9vvo8TvxuFwVZXrJ9bhwHjIHq6Imzs62bPcEI+RkeynMDlOb6Dlu/HT1xGkIRMrwucvwesrwOXB07zVimA67AsiyL5q4ota09NIaiDMoPMLwki6wnr01PotiaMZznk1N5PDSOGB4CRAg4YpztXMm17hcA2JUq5qvxz7OMsSYw+93kBjyU5/opzw1QnuenJNuPLxVm/LqfMmz7/gGzMV8+obIawoM+RHLiVeRm55Dtd7/z/5lUCubdYWaetg7RtQZmmoIr/s8MvD6SRT+Gxb8yY2ISBwTZKx+E8Ze9s3IeggLMexRguqMJ6oJhirN85GV4ev3TReJJdreHae6M4nE58Lic6TfLYE+c9p4Y7T0xUhbMGFtCee4xvOLiBNUVTVAfDKfrxuNy4nI6SKRSJJIWiaRFfUeYlbuCrKxtZ2VtkGTK4tLJFVx92mBGlfYxh8VxYlkW21u6aeiIgAMcOHA4IMfvYUhhBpm+d3kDyGOkK5rgrV1B3qhtZ9WuILvawrR2m1CSGqB3h0yvi0lVeZwyOJ/BhRlkeM3AwoDXhWVBz94m7nAsQcoCpwOcDgdOhwP33v8dt9N87YwmaO6M0tIVpbEjwpLtbdQFw30+r9ftBAtiyb7fxDO8LkaVZqdPMhV5ZqxDdzRJdyxBVzRBNJ5K/z1GE0k2NHSyu73v53s7n9tJXoaHvICX3AwPeQEPJTk+ynMDVOYFKM/1U5brpzjbR4b3yH8fkXiS2rYetjV3sbW5m3V7Qry0sYnu2OFbCPbJ9rkpzPKm702eTFm0dEWJxA+un8JML4MLMxhVks24ihzGlucwpjybHP8hBsS+TUdPnK0tXVTlZ1CcPcC3bXgvxLrNLR4qTk5fUt0UilAXDLMnGKG+I0xbd4yT4m/xkfX/j0BPHQBWoABHwTC25ZWz0u9jQvFJjCqfhqNwmBmI/MTnzbggMIOGT/2cmV35WAyQ7WkzLS7bXoLtC81VX4NOgxFnm7vDl09+Z89rWWYweyJi7p01wFcoKcAcowCzubGTFTvNG/2qXUE2NXam3+T9HicVeQFy/B7qO8I0hqKHP9gBnA6YPrKYq6YOYsbYUpIpi7a9I8XD8WT609HbQ9JAau6M8trWFna3h6kqyGBYUSZDizLJegcnXsuyCMeTdEUShONJUpZ5s7Qsi7pgmNe3tfH6tlZW13WQfBdnySlD8pkxthQLi1giRSyRwus2LQBDCjMYXJhBbsBDRzhOKBzf+zVhvkbidPTEiSdT6QDldTvJ8LrIDZhPlHkZXmKJFNtazAljW3MX0USKyjxzAqrICxCKxFm8tZXXt7XS1Hno33lJto+hRZkMLcyguiiL6qIMhhZlkhvw0B1N0hNL0B1NkkxZ6bJ4XaY82X432X4PXrcTy7LoiibSfx+hSIKuSILuqDnhxpIpc9Nsy9R3LJGiJ5akO2YCQlc0kd4nFImzJxg+bFDxuBwkU1Z6G6/bSWmOj9JsP6U5foqyvBRm+SjI9FKQ6SUcS7KrvYfd7WFq23pYvydEZx+tIfulcHpbcPr34PQ1YCVyiXdMglT/Z8j1up1Mqy7gP0aXML4ih+JsH8XZPrL2fpKPxJN0Rsx4jZW1QV7e0sJrW1oO29JwOA4HjCjOYlJVHh6Xg+bOGC1dJlR1hON0Rg73evuW5XNTlOXF43Jise/3Z0JLd9T8H8WTff+iKvMCXHxSOeeOL6MpFGHxtlYWb21lc1MXZTl+zh1fyrnjypg2rADP2z7V7+suqO+I0NwZpSDTy5DCDLL7GVT6I5FK8JeNf+HprU8zJHsIM4bMYHrldDI873IW5BNFtBNe+CbJlX/iZb+Ph3OzWBzY/4G0Mp7g7J4ezukOc0o0iiOnEj76S3OJ8nvJsmwxy7ACzDEKMJ/5/VIWbeo9R0S2z33IN+hMr4vSXD+plEU8ae09uVjkBjzkZ3jJz/QS7ImxbEd7eh+Hg0P2GXtcDkqy/YyvyOGkQblMHJTH2PJsijJ9h+zfjyaS6UFku4PmUwOAY+9zNYaivLqlhQ0NnX3uX5bjZ+rQfKZVF3BadSHDijNp6YpS3xGhocMcd1dbD7VtPexqD9MUitAVTfQ6KTpcXTj9u3EFdoPlINZekz5B5fjdpCxMANn7SdnhAI/TidvlIDfgYdKgPE4enMcpQ/LpiiZ4dGkt/17f1Cv8OL2NeAsX4XB3kghNJN55EqTe209+XreTIQXmdVlAKmXtbWnruw//aPncTlKWdcgT2TtVmRdg8mDTQjKyJIuiLB9F2V4KMrzpZuxUyiKRsvC4HP0O0d3xbizLwe62BG/sNK1ozV1RwrEkweRWWjxPEfdsw3L0DhIOy0M+U8hPTSeQHEkiCfGkRSKVItPrpijbR3GWCSljy7OpGVZEwHt03TWplMWmpk52tHSnP1HXd0RwOhxk+txk+91keF343C48LodpAXI7GVKQyUlVuYdtiUimTMjcF5o7wvF0q2tj+lO8eb7GUKTPFpBDyfa5GVacyfDiLIYVZ6a7ivr6nXRHE2R4XcfsQ09/LGtYxtylc9ncvrnXcr/Lz/TK6Vw8/GI+POjDeJy967M2VMvShqVUZFYwoXgCOd6Bex9PWSn+vvnv/H3z3wknwliWhbW3XSrLm0WON4ccbw7FgWI+MeYTVGVXHfZ4kUSEJ7Y8wR/XPMjubtMS4wAmuLLZlOgi6tj//zrOmcXnz/gWZw278Lj+Xt6tRCrBztBOyjPLBzyIKsAcowBz30tbWbSpmcmD89In1dIcP5F4Mv3GFArHKc81/f/5/WgxSaaSrNqzi7+uXM2CLZtp74mR6BqL1+WlMNNLwONKj/I/iCOK09eCy5UgJ2CRE3AQ8HpJhavo7PEQihzdp8HxFTmMKs1md3sP21u6aU9twOXfRTJWTCpSiZXIwfxr9sERx521Ead/Fw53Nw5nGKe7B6enHYcn2GtTL7lcVnUTnz3lo1Tm7f+kYlnmk76rH4Mtm0IRHluxmzfq17Az9QyNyWWk7x0DkPISD00k0TUahzOO35vA74uT6c6n3H06eX7T+uF1O4knzVUQsUSK7mgifcIJhuM4HTCsyJwshhVnEvC4qNsbCOuCYdxOB9OGFVIzrJDi/E4awrsZlDWIyuzK9JtyR0+cbS1dbG5uo7a1h91tCba3dLO9pZueWJJMn5tMr4sMnxuXw0EsacoSTaQIxxL7uwgcUbDcgOmCKcj0khvwkOVzk+lzkelz43U7ce3tfnE4wONykuFzkek1J+R9J+csn2nVGZQfoDTHTzAS5LkdzxGMBCnKKKLIX0RRoIghuUOO+uSxtmUtf1z3R/614194XB4urL6Qq0ZfxbjCcTR2N3LPynt4euvT6e0D7gCj8kcxMn8kbzW/xab2Tel1ToeTLE8Wub5ccrw5nFxyMteMv4bSzNKjKlN/WZZFa6SV3Z27qeuqI2kl8Tg9eJwevC4vI/NGUp518KRp9V31LNy9EJ/LR3VuNdW51eT6cmkNt7KscRlL65eypmUNo/JHcd3E66jOrU4/X3csSVMoQktXjEQytfc9wyKWipDnzyTT5yHT5yLD6ybH7z7ie0o4EebxTY/zz23/pCq7iouHX0xNRU2vkGBZFs3hZlrDrQSjQTqiHYRiIXwuHzneHHJ9uWR5s+iJ99AeaScYDdKT6OFDlR9icM7gg56zO97N/Nr5dEQ7iCajRJNRtrRv4d+1/wYg15fL9ROvpyXcwryd86jrqkvvWxwo5rIRl/GfQ/6TlU0r+ce2f/BWy1u9jj80ZygnFZ9ETUUNH6r8ELm+3IN+b6FYiGgySiKVIJky/zNlWWW9Xvf61vV87/XvHXT8Q8nyZPHtmm9zfvX5B63rinXxl41/4Y/r/khbxNyAM9ubzRUjr+AToz/BoOxB9MR7WLxnMQt2zmPergWE9958c2zBWP5r0n9x1qCzcL0Hd/1OppLEUjEC7v4PVeiOd9MabqU10kpruJX67no2tW9iY9tGtga3EkvFuG/GfUyvnD6gZX1fBJhf/epX/OhHP6KhoYFJkybxi1/8gtNOO61f+57og3gty2J543L+uO6PvFL3ColU75AxPHckd03/LuOLxqeXxRIpdra38MK2RSzZs4KtnWvoTNWC4+BPb5blIBWpJNE9glS4Co/TR1FWgOKsDPIzMvCTh8eRg9NhTmrTqgs5Y3ghhVmmxWJrcCs/XfFTFu1e1Pu4yUyS4QqIlZLlrKTYP4S8TAc9nmXsji0hmuru8/U6cFCdW824wnGsbV3L9g7TB3zO4HO4ZcotOHAQjAYJRoN4XB4mFU/q9Y8WT8Z5YecLPLTuITa0bcDj8uB2uvE4Pek3jn3HG1c4jme2PsOO0I5D1n+hv5BrJ1zLlaOuHJBPD7FkjPvfvJ/fr/k9yb2zyrocLsozy8n0ZNIebU+/sQPk+fIoyyyjLLOMEXkjmF45nUnFk3A73enXu7RhKS/uepEtwS0097TQHG4mnOghy5PNpcMu41PjrqYq5/CfDFvCLdSGaulJ9NAd76Yn3oPP5aM8q5yyjDKKMop4s+lNHtv0GPN2ziOeOjgkO3AwpmAMU8umcmrpqQzJHYLb4cbldOFyuIgkIgSjQUKxEM09zTyz7RlWNK7oszxjCsawM7Qz/SZ+6fBLuXb8tVTnVqffxC3LYl3rOv62+W/8c/s/6Y4f/DfldXq5YtQVfHbCZynLLOvzufZ9Qowmo/jdfgKuAH63n/ZoO/Vd9ezp3kN9Vz1tkTaC0SDtkXbao+00dDeky3coI/NHctagszij4gy2BLfw3PbneKPpjYO2y/Xl0hHt6LNOzx96Pp876XMU+AvYGtzKluAWtga3sqdrD/Xd9dR31xNOhKnKruLS4Zdy6fBLqcgyt1UIJ8Ksb13P+rb1ZHmyGJQ9iEFZg8j0ZPLXTX/lwbUP9vq/AMj35fOfQ/6ThJVgS3AL24Lb6Ip3HfZ19sXtcHPV6Kv4/KTPk+/PJ56M8/jmx7n/zfsPek4wAfTKUVdy0+SbyPPnAeZ3vKFtA89tf46ntj51yP0mF0+mOdzMrs5dvda5HC4ml0xmWvk02sJtbAluYUtwC8Fo8KDjeJ1eRuSPYGyBmSn5iS1PkLJSZLgz+PykzzO2cCxOnDgcDlJWiq5YF6FYiFAsxLyd83iz+U0Arhh5Bd847RsAvL7ndV7a/RLzds6jM2ZarisyK7hmwjV8dPhHD/me0h5p58G1D/LIhkfoSZhBzqUZpenf79DcoYet+2QqediwY1kWe7r3sLZlLeta17E1uJWmcBPNPc20RlqxLItxheP40KAPMb1yOhMKJxCKhajvrqehu4HdnbvZHtrOtuA2tndspz3afsjnAvPB41unf4tLh7+Lm5f2wfYB5i9/+Quf+cxnuP/++5k2bRo/+9nPeOyxx9i4cSMlJSVH3P9YBRjLskhaSVJWKv01mozSFm6jJdJCS7iFYMR8UumJ9xBOhElaSQoDhZQESijOKCYYDfLndX9mfdv69HFdDhfFGcWUZZSxM7ST9mg7LoeL6yZexw0n3cCKxhU8ueVJFtQuSJ8E9ynwF+B3ZeJyeHFYbrrjnbTG9vTr9eT58ijOKGZQ1iAGZQ+iKruKLe1b+Nvmv5G0krgdbqZXTqeuu45twW3pk/OhlGaUclbVWRQHisnz5ZHry6U4o5gxBWPI9JiR/7FkjN+89Rt+t/p3JKy+W4e8Ti+nlJ7CmRVnkrASPLL+EZrCTX1u63Q4OW/oeVw/8XpG5o9M/55WNq3kyS1PsiO0gwx3BhmeDALuAMsblrOne0+67s4beh5Oh5NYMkY8FSeajBJJRMwjGcHn8jEib4R55I9gaM5Qcrw56U/BbzW/xR2v3sG2DjNT6ODswXvDRv8Geu6T483hzIozAXi57uUjnlwcOJheOZ1Lhl/C+MLxDMoehNPhJJ6Ks2j3Iv626W+8uudVUoe5CsGBI910DuZT4bjCcbSGW2kJt9AUbqKpp+96Pxy3w8351efzqXGfIhwP89dNf2XeznnpoD65eDLfOO0bTCiacNjjxFNxgpFg+oTS2N3IwxseZmXTSgA8Tg+nlJ5CSaCEoowiCv2FNHQ3sLZ1LRvaNhz172Afp8NJWUYZldmVeF1eEskEsVSMnngPm4Ob+6xTBw5OLjkZr8vLto5tveptdP5oTi07lfFF43lhxwu8tOuld1SuU0pOIZKMsKlt0yH/d/apzKrk0+M+ze7O3fxz+z/7DAkuh4t8fz55vjzyfHnkeHOIJCOEoqa+u+JdZLgzzHp/HuFEOB1OszxZXDHyChbsWpAOGFXZVUwsmojP5cPr8pLhyeCCoRcwtvDQt1mIJ+O8uOtF/rb5byzes5jxheO5ePjFnDf0PIoCRQC0RdpY07KGFY0rWLR7EVuCWw55PJfDhdvpxu10k0wliSQPvvT7/KHn89WpXz1iK14ileDeVffyf6v/DwuL0oxSgtFgr/fgoTlD+dzEz3HhsAsP6gY7lPZIO39a9yf+svEvhGL778c1qXgSZw8+mw9XfpjhecNxOBy0R9r5x7Z/8OSWJ9nUvolJxZM4Z/A5nDP4HAZlD6K2s5bFexazeM9i3mh6o88QdyhOh/Ow7w8AGe4MCvwFFAYKKQ4UMzJ/JKPyRzE6fzSV2ZU4HQM/+Nj2AWbatGmceuqp/PKXvwQglUpRVVXFF7/4RW677bYj7n+sAszXFn6N53c8PyDH8rv8XDr8UmaOmcmw3GHpZN0WaeP7S77PCzvM5Xlep5dYav8YgWG5w5hWPo1TSk5hcsnkPj+BNnQ3sLRhKUvql7AluCXdpJq0kvQkemgLtx3xDfCcwedw8yk3pz8VRBIRtgS3mObDjq1sDZpHLBnj7MFnc9Gwi5hSOqXff9Cb2jfx3cXfZVXzKgLuQDrwtEfaaexpPGj7okARM0fP5MLqC3E69weOPF8eJRlHDrX7xFNxnt36LL956zfs7trd7/0O5HV6KQoUke/PZ33belJWikJ/Id86/VvMGDIDy7JoCbewM7STSDJCvj+ffJ85USSsBA3dDTR0N1DfVc/K5pW8UvfKQZ/UiwJFnFV1FlNLp1KSUUJxoJjCQCErm1byyIZHeKXulV7bZ3oyGZ0/mp2hnbRGWtPLq7KryPJkpQNcJBGhvruexu5GElaCgDvABdUXcOWoKxlfOP6g7onmnmaWNy5nWcMyljcupy3SRjKVJJFKkEgl8Lv95PpyzcOby4SiCXxi9CcOOjm0hluZXzuf4kAxZ1Wd9Y77/y3LYknDEu5bdV+frR4HynBnkOXJIpwME0lEiKfiZHoyKc8spyKrgvLMcooDxb1O4mWZZZRnluM5xPTzwUiQl+teZuHuhSxrWEZpRikXDbuI84ae1+t/sTveza7OXZRmlJLvz+91jA1tG/jtW79l3s55gPkdjcgbwfC84VRlV1GeVU55Zjm53lxernuZp7Y8xZKGJb2OURQoYkLhBCLJCLs7d1PfXU/SSjIkZwifm/g5Lhp2UfqEmkgleL3+dRbtXkSuL5fhecMZnjucoTlDD/k6D+X1+tf5yfKfsKFt/20DCv2F3DjpRi4fdXm/T+J9sSyrX38Xuzt3s3D3Qt5sfpOyjDJG5o9kRN4IqnOr8bv3z5qbslLUddaxvm09G9o20NjTyEXDLuKMijOOqlyv17/O7S/fTku4BTCtLWdVncVZVWdxWtlp77gLKJqM8tKul3h669O8UvdKrzBRkVnB0NyhLG1YelAL/T75vvyDWkncTjcj80Yyvmg8Y/LHUJZZRnFGMcWBYlJWitf2vMYrda+wuH5xuvWoKFBEWUYZ5VnlVOdWMyx3GNW51QzNGXpcBlrbOsDEYjEyMjJ4/PHHueyyy9LLZ8+eTTAY5Kmnnjpon2g0SjS6PxWHQiGqqqoGPMB8fdHXeW77cwctz/PlUegvTJ/UMj2ZBNwBAu4ADoeD1nArTT1NNIebiSfjXDTsIq4cdWW6SbUv/9rxL773+vdoj7aT7c3mwuoL+diIjzGucNy7HvyVslKEoiGaw82m6bBrN7s6d7G705zQPzPuM0wtm/qunqO/4qn4QX3z2zu28+qeV3l1z6vEkjEuG3EZ5w89H69r4C7XS6QSPL/jeTa2bTRjHFx7xzk4vQQ8AfwuPwF3gM5YZ7qJenP7ZprDB9/o75Jhl/CN075xUL98fyVTSVa3rOblupexLIuPVH2EiUUTDxsGd4Z28vimx1nesJxN7Zt6hdxCfyEfHfFRLh95OUNyhhzyOVsjrelwY0drW9aytWMrzT3NtIRNC2i+P58JRROYUDiBoblDe9VhMpXE6XCeMIMnO6IdeF3efo1L2NO1h4W7F5Lvz2dS0STKMst6vY5EKkFbpI1Cf+ExH1ORslL8Y9s/eHzT49RU1PCZcZ+x7d9Qf7VF2lhSv4RhucMYlT9qwP+GmnuambdzHovqFrGsflmv/+dxheO4bMRlnFZ2GksbljJ/53yWNy43reRON5OLJ1NTUcO08mmMLRjbr/fJRCpBS7iFAn/BgL6vDgRbB5g9e/ZQWVnJa6+9Rk1NTXr517/+dRYuXMiSJUsO2uf//b//x3e+852Dlg90gOmKdZG0zJugy+HC6XCmmyuPhWAkyLq2dUwpnYLPZcO5FN6HYskYLWEzJqWlp4XSzNIjdoUca/FUnO0d29nYtpFsbzZnVp75rj4Ji8jx0xPvYWnDUnZ07OCMyjMYlT/qoG2CkSA7QjsYlT/qfRceP3AB5r1qgREREZFjp78B5oScGrSoqAiXy0VjY+9xEI2NjZSV9X3Fgc/nw+dTC4WIiMgHwbG5Ocm75PV6mTJlCvPnz08vS6VSzJ8/v1eLjIiIiHwwnZAtMAC33HILs2fPZurUqZx22mn87Gc/o7u7m2uvvfZ4F01ERESOsxM2wHziE5+gubmZO++8k4aGBiZPnszzzz9PaemxmX1TRERE7OOEHMQ7EE70mXhFRETkYP09f5+QY2BEREREDkcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGxHAUZERERsRwFGREREbEcBRkRERGznhJ2J993aNz9fKBQ6ziURERGR/tp33j7SPLvv2wDT2dkJQFVV1XEuiYiIiBytzs5OcnNzD7n+fXsrgVQqxZ49e8jOzsbhcAzYcUOhEFVVVezatUu3KHgHVH/vjurv3VH9vTuqv3dOddd/lmXR2dlJRUUFTuehR7q8b1tgnE4ngwYNOmbHz8nJ0R/hu6D6e3dUf++O6u/dUf29c6q7/jlcy8s+GsQrIiIitqMAIyIiIrajAHOUfD4f3/72t/H5fMe7KLak+nt3VH/vjurv3VH9vXOqu4H3vh3EKyIiIu9faoERERER21GAEREREdtRgBERERHbUYARERER21GAOUq/+tWvGDp0KH6/n2nTprF06dLjXaQTzty5czn11FPJzs6mpKSEyy67jI0bN/baJhKJMGfOHAoLC8nKyuKKK66gsbHxOJX4xHb33XfjcDi4+eab08tUf4dXV1fHpz71KQoLCwkEAkycOJHly5en11uWxZ133kl5eTmBQIAZM2awefPm41jiE0cymeSOO+6gurqaQCDA8OHD+e53v9vrvjSqv/0WLVrEJZdcQkVFBQ6HgyeffLLX+v7UVVtbG7NmzSInJ4e8vDyuu+46urq63sNXYVOW9Nujjz5qeb1e6/e//721du1a6/rrr7fy8vKsxsbG4120E8p5551n/eEPf7DWrFljrVq1yrrwwgutwYMHW11dXeltPv/5z1tVVVXW/PnzreXLl1unn366dcYZZxzHUp+Yli5dag0dOtQ66aSTrC9/+cvp5aq/Q2tra7OGDBliXXPNNdaSJUusbdu2WS+88IK1ZcuW9DZ33323lZubaz355JPWm2++aV166aVWdXW1FQ6Hj2PJTwx33XWXVVhYaD377LPW9u3brccee8zKysqyfv7zn6e3Uf3t989//tP65je/af3973+3AOuJJ57otb4/dXX++edbkyZNsl5//XXr5ZdftkaMGGFdffXV7/ErsR8FmKNw2mmnWXPmzEn/nEwmrYqKCmvu3LnHsVQnvqamJguwFi5caFmWZQWDQcvj8ViPPfZYepv169dbgLV48eLjVcwTTmdnpzVy5Ehr3rx51kc+8pF0gFH9Hd43vvENa/r06Ydcn0qlrLKyMutHP/pRelkwGLR8Pp/1yCOPvBdFPKFddNFF1mc/+9leyy6//HJr1qxZlmWp/g7n7QGmP3W1bt06C7CWLVuW3ua5556zHA6HVVdX956V3Y7UhdRPsViMFStWMGPGjPQyp9PJjBkzWLx48XEs2Ymvo6MDgIKCAgBWrFhBPB7vVZdjxoxh8ODBqssDzJkzh4suuqhXPYHq70iefvpppk6dypVXXklJSQknn3wyv/3tb9Prt2/fTkNDQ6/6y83NZdq0aao/4IwzzmD+/Pls2rQJgDfffJNXXnmFCy64AFD9HY3+1NXixYvJy8tj6tSp6W1mzJiB0+lkyZIl73mZ7eR9ezPHgdbS0kIymaS0tLTX8tLSUjZs2HCcSnXiS6VS3HzzzZx55plMmDABgIaGBrxeL3l5eb22LS0tpaGh4TiU8sTz6KOP8sYbb7Bs2bKD1qn+Dm/btm3cd9993HLLLfz3f/83y5Yt40tf+hJer5fZs2en66iv/2XVH9x2222EQiHGjBmDy+UimUxy1113MWvWLADV31HoT101NDRQUlLSa73b7aagoED1eQQKMHJMzZkzhzVr1vDKK68c76LYxq5du/jyl7/MvHnz8Pv9x7s4tpNKpZg6dSrf//73ATj55JNZs2YN999/P7Nnzz7OpTvx/fWvf+Whhx7i4YcfZvz48axatYqbb76ZiooK1Z+cUNSF1E9FRUW4XK6DrvRobGykrKzsOJXqxHbTTTfx7LPP8uKLLzJo0KD08rKyMmKxGMFgsNf2qktjxYoVNDU1ccopp+B2u3G73SxcuJB77rkHt9tNaWmp6u8wysvLGTduXK9lY8eOpba2FiBdR/pf7tvXvvY1brvtNmbOnMnEiRP59Kc/zVe+8hXmzp0LqP6ORn/qqqysjKampl7rE4kEbW1tqs8jUIDpJ6/Xy5QpU5g/f356WSqVYv78+dTU1BzHkp14LMvipptu4oknnmDBggVUV1f3Wj9lyhQ8Hk+vuty4cSO1tbWqS+Ccc85h9erVrFq1Kv2YOnUqs2bNSn+v+ju0M88886DL9jdt2sSQIUMAqK6upqysrFf9hUIhlixZovoDenp6cDp7nxpcLhepVApQ/R2N/tRVTU0NwWCQFStWpLdZsGABqVSKadOmvedltpXjPYrYTh599FHL5/NZDzzwgLVu3TrrhhtusPLy8qyGhobjXbQTyo033mjl5uZaL730klVfX59+9PT0pLf5/Oc/bw0ePNhasGCBtXz5cqumpsaqqak5jqU+sR14FZJlqf4OZ+nSpZbb7bbuuusua/PmzdZDDz1kZWRkWH/+85/T29x9991WXl6e9dRTT1lvvfWW9dGPfvQDexnw282ePduqrKxMX0b997//3SoqKrK+/vWvp7dR/e3X2dlprVy50lq5cqUFWD/96U+tlStXWjt37rQsq391df7551snn3yytWTJEuuVV16xRo4cqcuo+0EB5ij94he/sAYPHmx5vV7rtNNOs15//fXjXaQTDtDn4w9/+EN6m3A4bH3hC1+w8vPzrYyMDOtjH/uYVV9ff/wKfYJ7e4BR/R3eM888Y02YMMHy+XzWmDFjrN/85je91qdSKeuOO+6wSktLLZ/PZ51zzjnWxo0bj1NpTyyhUMj68pe/bA0ePNjy+/3WsGHDrG9+85tWNBpNb6P62+/FF1/s8/1u9uzZlmX1r65aW1utq6++2srKyrJycnKsa6+91urs7DwOr8ZeHJZ1wPSKIiIiIjagMTAiIiJiOwowIiIiYjsKMCIiImI7CjAiIiJiOwowIiIiYjsKMCIiImI7CjAiIiJiOwowIiIiYjsKMCIiImI7CjAiIiJiOwowIiIiYjsKMCIiImI7/x+DJ4tjIsDQWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bddd4f67",
   "metadata": {},
   "source": [
    "## 1.2 Transfer data to LSTM representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8d1fe2e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d28fd514",
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_process(data, window_size, predict_size):\n",
    "    scaler = StandardScaler()\n",
    "    data = scaler.fit_transform(np.array(data).reshape(-1, 1))\n",
    "    \n",
    "    data_in = []\n",
    "    data_out = []\n",
    "    \n",
    "    for i in range(data.shape[0] - window_size - predict_size + 1):\n",
    "        data_in.append(data[i:i + window_size].reshape(1, window_size)[0])\n",
    "        data_out.append(data[i + window_size:i + window_size + predict_size].reshape(1, predict_size)[0])\n",
    "        \n",
    "    data_in = np.array(data_in).reshape(-1, window_size)\n",
    "    data_out = np.array(data_out).reshape(-1, predict_size)\n",
    "    \n",
    "    data_process = {'datain': data_in, 'dataout': data_out}\n",
    "    \n",
    "    return data_process, scaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b642d83",
   "metadata": {},
   "source": [
    "## 1.3 prepare train/test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b1b5ca0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_size = 4\n",
    "window_size = features_size * 3 # features num * time steps\n",
    "predict_size = features_size # features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "da56bdd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_processed, train_scaler = data_process(train, window_size, predict_size)\n",
    "X_train, y_train = train_processed['datain'], train_processed['dataout']\n",
    "\n",
    "test_processed, test_scaler = data_process(test, window_size, predict_size)\n",
    "X_test, y_test = test_processed['datain'], test_processed['dataout']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "89c5ab59",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = torch.from_numpy(X_train.astype(np.float32))\n",
    "X_test = torch.from_numpy(X_test.astype(np.float32))\n",
    "\n",
    "y_train = torch.from_numpy(y_train.astype(np.float32))\n",
    "y_test = torch.from_numpy(y_test.astype(np.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81da8b10",
   "metadata": {},
   "source": [
    "### - data loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "881c39bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.utils.data as Data\n",
    "\n",
    "train_data = Data.TensorDataset(X_train, y_train)\n",
    "test_data = Data.TensorDataset(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "000e5717",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of train_data: 5833\n",
      "size of test_data: 441\n"
     ]
    }
   ],
   "source": [
    "print(f'size of train_data: {len(train_data)}')\n",
    "print(f'size of test_data: {len(test_data)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fd5abd4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([-0.6262, -0.4504, -0.6498,  1.7473, -0.6323, -0.4327, -0.6428,  1.7523,\n",
       "         -0.6329, -0.4445, -0.6389,  1.7543]),\n",
       " tensor([-0.6293, -0.4814, -0.6469,  1.7508]))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3f77a830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([-0.6067, -0.4426, -0.6376,  1.7437, -0.6006, -0.4629, -0.6372,  1.7437,\n",
       "         -0.6039, -0.4520, -0.6346,  1.7438]),\n",
       " tensor([-0.6001, -0.4797, -0.6333,  1.7377]))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a99ad9fa",
   "metadata": {},
   "source": [
    "# 2. Quantum Enhanced LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ee1c6bb",
   "metadata": {},
   "source": [
    "## 2.1 initiate quantum environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7f83f918",
   "metadata": {},
   "outputs": [],
   "source": [
    "class InitQMachine:\n",
    "    def __init__(self, qubitsCount, cbitsCount = 0, machineType = QMachineType.CPU):\n",
    "        self.machine = init_quantum_machine(machineType)\n",
    "        \n",
    "        self.qubits = self.machine.qAlloc_many(qubitsCount)\n",
    "        self.cbits = self.machine.cAlloc_many(cbitsCount)\n",
    "        \n",
    "        print(f'Init Quantum Machine with qubits:[{qubitsCount}] / cbits:[{cbitsCount}] Successfully')\n",
    "    \n",
    "    def __del__(self):\n",
    "        destroy_quantum_machine(self.machine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1b73cfac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init Quantum Machine with qubits:[5] / cbits:[0] Successfully\n"
     ]
    }
   ],
   "source": [
    "# maximum qubits size\n",
    "ctx = InitQMachine(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64fd176a",
   "metadata": {},
   "source": [
    "## 2.2 Quantum Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "993c3c69",
   "metadata": {},
   "source": [
    "### - Tool Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "467e68dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw(prog, filename=''):\n",
    "    dir_path = './images/'\n",
    "    \n",
    "    if filename != '':\n",
    "        draw_qprog(prog, 'pic', filename=f'{dir_path}{filename}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d1db12c",
   "metadata": {},
   "source": [
    "### 2.2.1 Quantum Layer Base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a9372635",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "from torch import Tensor\n",
    "from torch.nn import Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0ef35c60",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QuantumLayerBase(nn.Module):\n",
    "    def __init__(self, input_size, output_size, *, n_qubits, n_layers = 1, ctx = None):\n",
    "        super(QuantumLayerBase, self).__init__()\n",
    "        \n",
    "        self.data = None # need to input during forward\n",
    "    \n",
    "        self.input_size = input_size\n",
    "        self.output_size = output_size # hidden size, not n_qubits\n",
    "        \n",
    "        # quantum infos\n",
    "        self.n_qubits = n_qubits\n",
    "        self.n_layers = n_layers\n",
    "        \n",
    "        self.ctx = ctx\n",
    "        self.qubits = ctx.qubits\n",
    "        self.machine = ctx.machine\n",
    "        \n",
    "        # convert quantum input/output to match classical computation\n",
    "        self.qin = nn.Linear(self.input_size, self.n_qubits)\n",
    "        self.qout = nn.Linear(self.n_qubits, self.output_size)\n",
    "        \n",
    "    @property\n",
    "    def circuit(self):\n",
    "        raise NotImplementedError('Should init circuit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "21d0b1ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure(self):\n",
    "    HamiZ = [ PauliOperator({f'Z{i}': 1}) for i in range(len(self.qubits)) ]\n",
    "    res = [ eval(qop(self.circuit, Hami, self.machine, self.qubits))[0,0] for Hami in HamiZ ]\n",
    "    \n",
    "    return Parameter(Tensor(res[:self.n_qubits]))\n",
    "\n",
    "QuantumLayerBase.measure = measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8dd09ad2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward(self, inputs):\n",
    "    y_t = self.qin(Parameter(inputs))\n",
    "    self.data = y_t[0]\n",
    "    \n",
    "    return self.qout(self.measure())\n",
    "\n",
    "QuantumLayerBase.forward = forward"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73293db3",
   "metadata": {},
   "source": [
    "### 2.2.2 Quantum Layer Design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3f45cef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QuantumLayer(QuantumLayerBase):\n",
    "    def __init__(self, input_size, output_size, *, n_qubits, degree = 1, n_layers = 1, ctx = None):\n",
    "        super(QuantumLayer, self).__init__(input_size, output_size, \n",
    "                                         n_qubits = n_qubits, n_layers = n_layers, ctx = ctx)\n",
    "        \n",
    "        self.degree = degree\n",
    "        self.angles = Parameter(torch.rand(n_layers + 1, degree, self.n_qubits))\n",
    "        \n",
    "    @property\n",
    "    def qparameters_size(self):\n",
    "        return self.angles.flatten().size()[0]\n",
    "        \n",
    "    @property\n",
    "    def circuit(self):\n",
    "        if self.data == None:\n",
    "            raise ValueError('Need to feed a input data!')\n",
    "        \n",
    "        n = self.n_qubits\n",
    "        q = self.qubits\n",
    "        x = self.data\n",
    "        p = self.angles\n",
    "        degree = self.degree\n",
    "        \n",
    "        # quantum gates - must use small case!\n",
    "        identity = VariationalQuantumGate_I\n",
    "        h = VariationalQuantumGate_H\n",
    "        ry = VariationalQuantumGate_RY\n",
    "        cz = VariationalQuantumGate_CZ\n",
    "        u = [\n",
    "            None,\n",
    "            VariationalQuantumGate_U1,\n",
    "            VariationalQuantumGate_U2,\n",
    "            VariationalQuantumGate_U3\n",
    "        ]\n",
    "        \n",
    "        # init variational quantum circuit\n",
    "        vqc = VariationalQuantumCircuit()\n",
    "\n",
    "        # in order to use each qubits => when n_qubits < len(ctx.qubits)\n",
    "        [ vqc.insert(identity(q[i])) for i in range(len(q)) ]\n",
    "        \n",
    "        [ vqc.insert( h(q[i]) ) for i in range(n) ]\n",
    "        [ vqc.insert( ry(q[i], var(x[i] * torch.pi/2)) ) for i in range(n) ]\n",
    "        [ vqc.insert( u[degree](q[i], *[ var(p[0][d][i]) for d in range(degree) ]) ) \n",
    "                 for i in range(n) ]\n",
    "        \n",
    "        for layer in range(self.n_layers):\n",
    "            for i in range(n - 1):\n",
    "                vqc.insert(cz(q[i], q[i + 1]))\n",
    "            vqc.insert(cz(q[n - 1], q[0]))\n",
    "            \n",
    "            [ vqc.insert( u[degree](q[i], *[ var(p[layer + 1][d][i]) for d in range(degree) ]) ) \n",
    "                 for i in range(n) ]\n",
    "        \n",
    "        return vqc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d956e7",
   "metadata": {},
   "source": [
    "## 2.3 Quantum-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0d437f9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QLSTMBase(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.input_size = input_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.ctx = ctx\n",
    "        \n",
    "    @property\n",
    "    def qparameters_size(self):\n",
    "        num = 0\n",
    "        for attr in dir(self):\n",
    "            if attr.endswith('_circuit'):\n",
    "                num += getattr(self, attr).qparameters_size\n",
    "        return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "be3c3cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward(self, inputs, init_states = None):\n",
    "    sequence_size, batch_size, _ = inputs.size()\n",
    "    hidden_sequence = []\n",
    "    \n",
    "    if init_states == None:\n",
    "        h_t, c_t = (\n",
    "            torch.zeros(1, batch_size, self.hidden_size).to(inputs.device),\n",
    "            torch.zeros(1, batch_size, self.hidden_size).to(inputs.device),\n",
    "        )\n",
    "    else:\n",
    "        h_t, c_t = init_states\n",
    "    \n",
    "    return hidden_sequence, (h_t, c_t)\n",
    "\n",
    "QLSTMBase.forward = forward"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce36bcc1",
   "metadata": {},
   "source": [
    "### 2.3.1 classical Quantum-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1539d83e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QLSTM(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # Parameters: angles\n",
    "        #  => Q * (n + 1) * degree\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # input gate:     5       2       3\n",
    "        # forget gate:    5       2       3\n",
    "        # candidate:      5       2       3\n",
    "        # output gate:    5       2       3\n",
    "        \n",
    "        # input gates\n",
    "        self.input_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                        n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # forget gates\n",
    "        self.forget_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # candidate\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # output gates\n",
    "        self.output_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(QLSTM, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((h_t[0], x_t), dim = 1)\n",
    "\n",
    "            # input gates\n",
    "            i_t = torch.sigmoid(self.input_circuit(v_t))\n",
    "            # forget gates\n",
    "            f_t = torch.sigmoid(self.forget_circuit(v_t))\n",
    "            # candidate for cell state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_t))\n",
    "            c_t = (f_t * c_t) + (i_t * g_t)\n",
    "\n",
    "            # output gates\n",
    "            o_t = torch.sigmoid(self.output_circuit(v_t))\n",
    "            # update output ht\n",
    "            h_t = o_t * (torch.tanh(c_t))\n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        # reshape hidden_seq p/ retornar\n",
    "        #\n",
    "        # [tensor([[[0.0444, ...]]] => tensor([[[0.0444, ...]]]\n",
    "        # \n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "702b338f",
   "metadata": {},
   "source": [
    "### 2.3.2 adjusted classical QLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c13c51b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AdjustedQLSTM(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # input gate:     4       2       3\n",
    "        # forget gate:    5       2       3\n",
    "        # candidate:      4       1       3\n",
    "        # output gate:    3       2       2\n",
    "        \n",
    "        # input gates\n",
    "        self.input_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                        n_qubits = 4, n_layers = 2, degree = 3, ctx = ctx) # 36\n",
    "        # forget gates\n",
    "        self.forget_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # candidate\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 4, n_layers = 1, degree = 3, ctx = ctx) # 24\n",
    "        # output gates\n",
    "        self.output_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 3, n_layers = 2, degree = 2, ctx = ctx) # 18\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(AdjustedQLSTM, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((h_t[0], x_t), dim = 1)\n",
    "\n",
    "            # input gates\n",
    "            i_t = torch.sigmoid(self.input_circuit(v_t))\n",
    "            # forget gates\n",
    "            f_t = torch.sigmoid(self.forget_circuit(v_t))\n",
    "            # candidate for cell state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_t))\n",
    "            c_t = (f_t * c_t) + (i_t * g_t)\n",
    "\n",
    "            # output gates\n",
    "            o_t = torch.sigmoid(self.output_circuit(v_t))\n",
    "            # update output ht\n",
    "            h_t = o_t * (torch.tanh(c_t))\n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cd5c586",
   "metadata": {},
   "source": [
    "### 2.3.3 peephole QLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1014f287",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PeepholeQLSTM(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # input gate:     4       2       3\n",
    "        # forget gate:    5       2       3\n",
    "        # candidate:      4       1       3\n",
    "        # output gate:    3       2       2\n",
    "        \n",
    "        # input gates\n",
    "        self.input_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                        n_qubits = 4, n_layers = 2, degree = 3, ctx = ctx) # 36\n",
    "        # forget gates\n",
    "        self.forget_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # candidate\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 4, n_layers = 1, degree = 2, ctx = ctx) # 24\n",
    "        # output gates\n",
    "        self.output_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 3, n_layers = 2, degree = 2, ctx = ctx) # 18\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(PeepholeQLSTM, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((c_t[0], x_t), dim = 1)\n",
    "            \n",
    "            # input gates\n",
    "            i_t = torch.sigmoid(self.input_circuit(v_t))\n",
    "            # forget gates\n",
    "            f_t = torch.sigmoid(self.forget_circuit(v_t))\n",
    "            # candidate for cell state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_t))\n",
    "            c_t = (f_t * c_t) + (i_t * g_t)\n",
    "            \n",
    "            # output gates\n",
    "            o_t = torch.sigmoid(self.output_circuit(v_t))\n",
    "            # update output ht\n",
    "            h_t = o_t * (torch.tanh(c_t))\n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15228ed7",
   "metadata": {},
   "source": [
    "### 2.3.4 Coupled Input and Forget gates QLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d4f58754",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CIFGQLSTM(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # coupled IF:     5       2       3\n",
    "        # candidate:      4       2       3\n",
    "        # output gate:    3       2       2\n",
    "        \n",
    "        # Coupled Input and Forget gate\n",
    "        self.coupled_IF_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 1, degree = 3, ctx = ctx) # 30\n",
    "        # candidate for cell state update\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 3, n_layers = 1, degree = 2, ctx = ctx) # 24\n",
    "        # output gates\n",
    "        self.output_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 4, n_layers = 1, degree = 3, ctx = ctx) # 12\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(CIFGQLSTM, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((h_t[0], x_t), dim = 1)\n",
    "            \n",
    "            # coupled input and forget gate\n",
    "            f_t = torch.sigmoid(self.coupled_IF_circuit(v_t))\n",
    "            # candidate for cell state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_t))\n",
    "            c_t = (f_t * c_t) + ((1 - f_t) * g_t)\n",
    "            \n",
    "            # output gates\n",
    "            o_t = torch.sigmoid(self.output_circuit(v_t))\n",
    "            # update output ht\n",
    "            h_t = o_t * (torch.tanh(c_t))\n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e262d9d5",
   "metadata": {},
   "source": [
    "### 2.3.5 Recurrent Gate Units - QGRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d7f771ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QGRU(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # update gate:     5       1       3\n",
    "        # candidate:       4       1       3\n",
    "        # reset gate:      3       1       2\n",
    "        \n",
    "        # update gates\n",
    "        self.update_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 1, degree = 1, ctx = ctx) # 45\n",
    "        # candidate for hidden state update\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 3, n_layers = 1, degree = 1, ctx = ctx) # 36\n",
    "        # reset gates\n",
    "        self.reset_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 4, n_layers = 1, degree = 1, ctx = ctx) # 18\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(QGRU, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((h_t[0], x_t), dim = 1)\n",
    "            \n",
    "            # update gates\n",
    "            z_t = torch.sigmoid(self.update_circuit(v_t))\n",
    "            # reset gates\n",
    "            r_t = torch.sigmoid(self.reset_circuit(v_t))\n",
    "        \n",
    "            v_hat_t = torch.cat(((r_t * h_t)[0], x_t), dim = 1)\n",
    "            # candidate for hidden state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_hat_t))\n",
    "            h_t = (z_t * g_t) + (1 - z_t) * h_t \n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63514807",
   "metadata": {},
   "source": [
    "### - QLSTMs Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5ab09833",
   "metadata": {},
   "outputs": [],
   "source": [
    "QLSTMMap = {\n",
    "    'classical': ('QLSTM', QLSTM),\n",
    "    'adjusted': ('QLSTM(adjusted)', AdjustedQLSTM),\n",
    "    'peephole': ('peephole QLSTM', PeepholeQLSTM),\n",
    "    'CIFG': ('CIFG-QLSTM', CIFGQLSTM),\n",
    "    'GRU': ('QGRU', QGRU)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6497332",
   "metadata": {},
   "source": [
    "## 2.4 Stacked QLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2c6cb0fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "class StackedQLSTM(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, *, num_layers = 1, ctx = None, mode = 'classical'):\n",
    "        super().__init__()\n",
    "        \n",
    "        label, qlstm = QLSTMMap.get(mode)\n",
    "        self.qlstms = nn.Sequential(OrderedDict([\n",
    "            (f'{label} {i + 1}', qlstm(input_size if i == 0 else hidden_size , hidden_size, ctx = ctx)) \n",
    "                for i in range(num_layers)\n",
    "        ]))\n",
    "\n",
    "    def forward(self, inputs, parameters = None):\n",
    "        outputs = None\n",
    "        \n",
    "        for i, qlstm in enumerate(self.qlstms):\n",
    "            if i != 0:\n",
    "                inputs = outputs\n",
    "            \n",
    "            outputs, parameters = qlstm(inputs, parameters)\n",
    "        \n",
    "        return outputs, parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55dbbb2f",
   "metadata": {},
   "source": [
    "# 3. Quantum Model and Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8313baa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QModel(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_output, *, num_layers = 1, ctx = None, mode = 'classical'):\n",
    "        super(QModel, self).__init__()\n",
    "        \n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_layers = num_layers\n",
    "        self.mode = mode\n",
    "        \n",
    "        self.qlstm = StackedQLSTM(input_size, hidden_size, \n",
    "                                  num_layers = num_layers, ctx = ctx, mode = mode)\n",
    "        self.predict = nn.Linear(hidden_size, num_output)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.unsqueeze(0)\n",
    "        \n",
    "        # sequence lenth , batch_size, features length\n",
    "        # \n",
    "        h0 = torch.zeros(1, x.size(1), self.hidden_size)\n",
    "        c0 = torch.zeros(1, x.size(1), self.hidden_size)\n",
    "        \n",
    "        out, _ = self.qlstm(x, (h0, c0))\n",
    "        out = self.predict(out[0])\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf519363",
   "metadata": {},
   "source": [
    "## 3.1 train QModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1abf2ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import RandomSampler\n",
    "\n",
    "def train_model(model, datas, batch_size, *, loss_func, optimizer, epoch = 50, early_stop = False):\n",
    "    losses = []\n",
    "    sampler = RandomSampler(datas, num_samples = batch_size)\n",
    "    \n",
    "    last_loss = 0.0\n",
    "    for step in range(epoch):\n",
    "        train_loss = 0.0\n",
    "        \n",
    "        for index in sampler:\n",
    "            batch_x, batch_y = datas[index][0], datas[index][1]\n",
    "            b_x = batch_x.unsqueeze(0)\n",
    "            b_y = batch_y.unsqueeze(0)\n",
    "            \n",
    "            output = model(b_x)\n",
    "\n",
    "            loss = loss_func(output, b_y)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            train_loss += loss.item()\n",
    "\n",
    "        loss = train_loss / batch_size\n",
    "        if early_stop and loss < 0.0002:\n",
    "            break\n",
    "            \n",
    "        last_loss = loss\n",
    "        \n",
    "        print(f'Epoch {step + 1}/{epoch}: Loss: {loss}')\n",
    "        losses.append(loss)\n",
    "    \n",
    "    return losses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5cdd42a",
   "metadata": {},
   "source": [
    "## 3.2 Evaluate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d7ba3d94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "84475602",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MAE_naive(actuals, predicteds):\n",
    "    n = len(actuals)\n",
    "    err = 0.0\n",
    "    \n",
    "    for i in range(1, n):\n",
    "        err += np.abs(actuals[i] - actuals[i - 1])\n",
    "    return err / (n - 1)\n",
    "\n",
    "def calculate_accuarcy(model, X_test, y_test, scaler=train_scaler):\n",
    "    n = len(X_test)\n",
    "    \n",
    "    actuals = []\n",
    "    predicteds = []\n",
    "    \n",
    "    for i in range(0, n, predict_size):\n",
    "        actual = scaler.inverse_transform(y_test[i:i+1].data)\n",
    "        actuals.append(np.array(actual[0]))\n",
    "        predicted = scaler.inverse_transform(model(X_test[i:i+1]).data)\n",
    "        predicteds.append(np.array(predicted[0]))\n",
    "    \n",
    "    actuals = np.array(actuals)\n",
    "    predicteds = np.array(predicteds)\n",
    "    \n",
    "    mae = mean_absolute_error(actuals, predicteds)\n",
    "    mase = mae / MAE_naive(actuals.flatten(), predicteds.flatten())\n",
    "    mape = mean_absolute_percentage_error(actuals, predicteds)\n",
    "    mse = mean_squared_error(actuals, predicteds)\n",
    "    rmse = mse ** 0.5\n",
    "    \n",
    "    return np.array([(1 - mase) * 100, rmse, mse, mae, mape])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cdcdb169",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_accuarcies(model, X_test, y_test, scaler=test_scaler):\n",
    "    n = len(X_test)\n",
    "    \n",
    "    actuals = []\n",
    "    predicteds = []\n",
    "    \n",
    "    for i in range(0, n, predict_size):\n",
    "        actual = scaler.inverse_transform(y_test[i:i+1].data)\n",
    "        actuals.append(np.array(actual[0]))\n",
    "        predicted = scaler.inverse_transform(model(X_test[i:i+1]).data)\n",
    "        predicteds.append(np.array(predicted[0]))\n",
    "    \n",
    "    accuarcies = []\n",
    "    for i in range(predict_size):\n",
    "        a = [row[i] for row in actuals]\n",
    "        p = [row[i] for row in predicteds]\n",
    "    \n",
    "        mae = mean_absolute_error(a, p)\n",
    "        mase = mae / MAE_naive(a, p)\n",
    "        mape = mean_absolute_percentage_error(a, p)\n",
    "        mse = mean_squared_error(a, p)\n",
    "        rmse = mse ** 0.5\n",
    "        \n",
    "        accuarcies.append([rmse, mse, mae, mape, mase])\n",
    "    \n",
    "    return np.array(accuarcies)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec9e103",
   "metadata": {},
   "source": [
    "## 3.3 Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "38531f1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_size = 4\n",
    "window_size = features_size * 3 # \n",
    "predict_size = features_size # features\n",
    "\n",
    "input_size = window_size\n",
    "num_output = predict_size\n",
    "\n",
    "hidden_size = 32\n",
    "num_layers = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "74b3250f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mode = 'classical'\n",
    "# qmodel = QModel(input_size, hidden_size, num_output, \n",
    "#                     num_layers = num_layers, ctx = ctx, mode=mode)\n",
    "# qmodel.load_state_dict(torch.load(f\"./model/{mode}/model_{mode}_2.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "057d34e2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200: Loss: 1.0047239392995835\n",
      "Epoch 2/200: Loss: 1.016366770863533\n",
      "Epoch 3/200: Loss: 0.9172667324542999\n",
      "Epoch 4/200: Loss: 0.9745915830135345\n",
      "Epoch 5/200: Loss: 0.9637506186962128\n",
      "Epoch 6/200: Loss: 0.9255561977624893\n",
      "Epoch 7/200: Loss: 0.8966167211532593\n",
      "Epoch 8/200: Loss: 0.8618280082941056\n",
      "Epoch 9/200: Loss: 0.8465825736522674\n",
      "Epoch 10/200: Loss: 0.8572704374790192\n",
      "Epoch 11/200: Loss: 0.7886811584234238\n",
      "Epoch 12/200: Loss: 0.761615177989006\n",
      "Epoch 13/200: Loss: 0.7448442697525024\n",
      "Epoch 14/200: Loss: 0.7661686301231384\n",
      "Epoch 15/200: Loss: 0.7275607407093048\n",
      "Epoch 16/200: Loss: 0.6426466703414917\n",
      "Epoch 17/200: Loss: 0.6621754571795464\n",
      "Epoch 18/200: Loss: 0.6676389157772065\n",
      "Epoch 19/200: Loss: 0.5918421611189842\n",
      "Epoch 20/200: Loss: 0.6010327577590943\n",
      "Epoch 21/200: Loss: 0.5484421625733376\n",
      "Epoch 22/200: Loss: 0.5824389085173607\n",
      "Epoch 23/200: Loss: 0.5228617407381535\n",
      "Epoch 24/200: Loss: 0.5260711550712586\n",
      "Epoch 25/200: Loss: 0.5047428265213967\n",
      "Epoch 26/200: Loss: 0.5057485796511173\n",
      "Epoch 27/200: Loss: 0.5385906666517257\n",
      "Epoch 28/200: Loss: 0.48116848766803744\n",
      "Epoch 29/200: Loss: 0.4318291716277599\n",
      "Epoch 30/200: Loss: 0.4081715114414692\n",
      "Epoch 31/200: Loss: 0.4327189393341541\n",
      "Epoch 32/200: Loss: 0.42755063250660896\n",
      "Epoch 33/200: Loss: 0.3677582949399948\n",
      "Epoch 34/200: Loss: 0.343965283036232\n",
      "Epoch 35/200: Loss: 0.22552804090082645\n",
      "Epoch 36/200: Loss: 0.34051920622587206\n",
      "Epoch 37/200: Loss: 0.35846567526459694\n",
      "Epoch 38/200: Loss: 0.3080854248255491\n",
      "Epoch 39/200: Loss: 0.360355231910944\n",
      "Epoch 40/200: Loss: 0.1789547735825181\n",
      "Epoch 41/200: Loss: 0.26155474297702314\n",
      "Epoch 42/200: Loss: 0.2654644062742591\n",
      "Epoch 43/200: Loss: 0.20254969578236343\n",
      "Epoch 44/200: Loss: 0.2065309775993228\n",
      "Epoch 45/200: Loss: 0.24068264821544288\n",
      "Epoch 46/200: Loss: 0.24474499570205807\n",
      "Epoch 47/200: Loss: 0.28622750248759987\n",
      "Epoch 48/200: Loss: 0.25631910460069773\n",
      "Epoch 49/200: Loss: 0.2920559856109321\n",
      "Epoch 50/200: Loss: 0.2862741488032043\n",
      "Epoch 51/200: Loss: 0.18904198687523605\n",
      "Epoch 52/200: Loss: 0.3007645738776773\n",
      "Epoch 53/200: Loss: 0.25097756162285806\n",
      "Epoch 54/200: Loss: 0.22028651917353273\n",
      "Epoch 55/200: Loss: 0.09229082963429391\n",
      "Epoch 56/200: Loss: 0.20323092979378998\n",
      "Epoch 57/200: Loss: 0.24678653590381144\n",
      "Epoch 58/200: Loss: 0.18089628396555782\n",
      "Epoch 59/200: Loss: 0.18417503428645432\n",
      "Epoch 60/200: Loss: 0.20474351714365185\n",
      "Epoch 61/200: Loss: 0.2628174079116434\n",
      "Epoch 62/200: Loss: 0.2457959255669266\n",
      "Epoch 63/200: Loss: 0.16207122639752924\n",
      "Epoch 64/200: Loss: 0.20000533710699528\n",
      "Epoch 65/200: Loss: 0.15231850672280417\n",
      "Epoch 66/200: Loss: 0.2489438131218776\n",
      "Epoch 67/200: Loss: 0.15113286717096344\n",
      "Epoch 68/200: Loss: 0.13220996554009617\n",
      "Epoch 69/200: Loss: 0.0967791895265691\n",
      "Epoch 70/200: Loss: 0.18544814675115048\n",
      "Epoch 71/200: Loss: 0.11609166719717905\n",
      "Epoch 72/200: Loss: 0.12883621535729617\n",
      "Epoch 73/200: Loss: 0.16677592117339374\n",
      "Epoch 74/200: Loss: 0.13801193445688115\n",
      "Epoch 75/200: Loss: 0.0603559588547796\n",
      "Epoch 76/200: Loss: 0.12448218571953476\n",
      "Epoch 77/200: Loss: 0.09850087324157357\n",
      "Epoch 78/200: Loss: 0.09786023221677169\n",
      "Epoch 79/200: Loss: 0.1452377017121762\n",
      "Epoch 80/200: Loss: 0.14205815442837774\n",
      "Epoch 81/200: Loss: 0.08401957177557051\n",
      "Epoch 82/200: Loss: 0.08488747898954899\n",
      "Epoch 83/200: Loss: 0.0736946202814579\n",
      "Epoch 84/200: Loss: 0.06991213564760983\n",
      "Epoch 85/200: Loss: 0.08642387751024216\n",
      "Epoch 86/200: Loss: 0.0648025888367556\n",
      "Epoch 87/200: Loss: 0.04194600245100446\n",
      "Epoch 88/200: Loss: 0.046333406725898384\n",
      "Epoch 89/200: Loss: 0.0475168855919037\n",
      "Epoch 90/200: Loss: 0.04200880310963839\n",
      "Epoch 91/200: Loss: 0.03818818257423118\n",
      "Epoch 92/200: Loss: 0.027941273878968786\n",
      "Epoch 93/200: Loss: 0.023514387974864805\n",
      "Epoch 94/200: Loss: 0.024180916690966114\n",
      "Epoch 95/200: Loss: 0.015066511351324152\n",
      "Epoch 96/200: Loss: 0.020210082111771045\n",
      "Epoch 97/200: Loss: 0.019077925802776008\n",
      "Epoch 98/200: Loss: 0.013349868565273936\n",
      "Epoch 99/200: Loss: 0.008865323312784312\n",
      "Epoch 100/200: Loss: 0.009605675343482289\n",
      "Epoch 101/200: Loss: 0.006548585603013635\n",
      "Epoch 102/200: Loss: 0.008117003745792318\n",
      "Epoch 103/200: Loss: 0.0037024587672931375\n",
      "Epoch 104/200: Loss: 0.00566197888001625\n",
      "Epoch 105/200: Loss: 0.004778507658556919\n",
      "Epoch 106/200: Loss: 0.004434507396945264\n",
      "Epoch 107/200: Loss: 0.0038713858481060014\n",
      "Epoch 108/200: Loss: 0.0032032599487138214\n",
      "Epoch 109/200: Loss: 0.0022541079079019255\n",
      "Epoch 110/200: Loss: 0.0020079566108506695\n",
      "Epoch 111/200: Loss: 0.002017931752925506\n",
      "Epoch 112/200: Loss: 0.0012279480914003215\n",
      "Epoch 113/200: Loss: 0.002772462244320195\n",
      "Epoch 114/200: Loss: 0.0012177129276096821\n",
      "Epoch 115/200: Loss: 0.0009631552475184435\n",
      "Epoch 116/200: Loss: 0.0010219165164016887\n",
      "Epoch 117/200: Loss: 0.000922118822199991\n",
      "Epoch 118/200: Loss: 0.0005879618434846634\n",
      "Epoch 119/200: Loss: 0.0006949980645003962\n",
      "Epoch 120/200: Loss: 0.0008156698786478955\n",
      "Epoch 121/200: Loss: 0.0012407971134962281\n",
      "Epoch 122/200: Loss: 0.0011534334447787843\n",
      "Epoch 123/200: Loss: 0.0011818475039035547\n",
      "Epoch 124/200: Loss: 0.00074583120567695\n",
      "Epoch 125/200: Loss: 0.0010404865784948924\n",
      "Epoch 126/200: Loss: 0.0008411885605710268\n",
      "Epoch 127/200: Loss: 0.0006358467710015248\n",
      "Epoch 128/200: Loss: 0.0005934303777394234\n",
      "Epoch 129/200: Loss: 0.0006559516022491607\n",
      "Epoch 130/200: Loss: 0.000551417568931356\n",
      "Epoch 131/200: Loss: 0.0008569556281145197\n",
      "Epoch 132/200: Loss: 0.0008108143882054719\n",
      "Epoch 133/200: Loss: 0.0005326981645339401\n",
      "Epoch 134/200: Loss: 0.0009585161473296466\n",
      "Epoch 135/200: Loss: 0.0008902615537408565\n",
      "Epoch 136/200: Loss: 0.0008604120980862717\n",
      "Epoch 137/200: Loss: 0.0008692069101016387\n",
      "Epoch 138/200: Loss: 0.0006238133726583328\n",
      "Epoch 139/200: Loss: 0.0009326230509032029\n",
      "Epoch 140/200: Loss: 0.0006234345124539687\n",
      "Epoch 141/200: Loss: 0.0007459454394847853\n",
      "Epoch 142/200: Loss: 0.0005956878718279768\n",
      "Epoch 143/200: Loss: 0.0007859085714244429\n",
      "Epoch 144/200: Loss: 0.000533815530980064\n",
      "Epoch 145/200: Loss: 0.0011564759550310554\n",
      "Epoch 146/200: Loss: 0.0007015907660388621\n",
      "Epoch 147/200: Loss: 0.0006661191950115608\n",
      "Epoch 148/200: Loss: 0.0005045090074418113\n",
      "Epoch 149/200: Loss: 0.0006549909127897991\n",
      "Epoch 150/200: Loss: 0.0007253552839756594\n",
      "Epoch 151/200: Loss: 0.00047585992970198274\n",
      "Epoch 152/200: Loss: 0.00042036239683511666\n",
      "Epoch 153/200: Loss: 0.0004479541508771945\n",
      "Epoch 154/200: Loss: 0.000513144463457138\n",
      "Epoch 155/200: Loss: 0.0005100036929434282\n",
      "Epoch 156/200: Loss: 0.0006426669140182639\n",
      "Epoch 157/200: Loss: 0.0007528690915023617\n",
      "Epoch 158/200: Loss: 0.0007396753375360276\n",
      "Epoch 159/200: Loss: 0.0008732494250580202\n",
      "Epoch 160/200: Loss: 0.0005953050774223812\n",
      "Epoch 161/200: Loss: 0.00039491746547355434\n",
      "Epoch 162/200: Loss: 0.0008532144600394531\n",
      "Epoch 163/200: Loss: 0.0005248683366517071\n",
      "Epoch 164/200: Loss: 0.0009667032571087475\n",
      "Epoch 165/200: Loss: 0.0005672800056345295\n",
      "Epoch 166/200: Loss: 0.0007049458685287391\n",
      "Epoch 167/200: Loss: 0.000444536943177809\n",
      "Epoch 168/200: Loss: 0.0008005356605281122\n",
      "Epoch 169/200: Loss: 0.0005528121539100539\n",
      "Epoch 170/200: Loss: 0.0007449908624948875\n",
      "Epoch 171/200: Loss: 0.0005899725825656788\n",
      "Epoch 172/200: Loss: 0.0005863273832801497\n",
      "Epoch 173/200: Loss: 0.0013432454497888102\n",
      "Epoch 174/200: Loss: 0.0007233883945900743\n",
      "Epoch 175/200: Loss: 0.0006266263477300527\n",
      "Epoch 176/200: Loss: 0.0006138009870483074\n",
      "Epoch 177/200: Loss: 0.0007652476519069751\n",
      "Epoch 178/200: Loss: 0.0005817777955257952\n",
      "Epoch 179/200: Loss: 0.0007266839213116328\n",
      "Epoch 180/200: Loss: 0.0004970774229150265\n",
      "Epoch 181/200: Loss: 0.000581066280756204\n",
      "Epoch 182/200: Loss: 0.0011541664847754874\n",
      "Epoch 183/200: Loss: 0.0006745186596162966\n",
      "Epoch 184/200: Loss: 0.0005864899581865757\n",
      "Epoch 185/200: Loss: 0.0008271010726275563\n",
      "Epoch 186/200: Loss: 0.0005122367500007385\n",
      "Epoch 187/200: Loss: 0.00040273732902278424\n",
      "Epoch 188/200: Loss: 0.0006575434716069139\n",
      "Epoch 189/200: Loss: 0.000619016129303418\n",
      "Epoch 190/200: Loss: 0.001049337181939336\n",
      "Epoch 191/200: Loss: 0.0005985923191474285\n",
      "Epoch 192/200: Loss: 0.0007038016912702006\n",
      "Epoch 193/200: Loss: 0.0006649473842117004\n",
      "Epoch 194/200: Loss: 0.00044999799592915224\n",
      "Epoch 195/200: Loss: 0.0007117512173863361\n",
      "Epoch 196/200: Loss: 0.000994377084316511\n",
      "Epoch 197/200: Loss: 0.000734769288192183\n",
      "Epoch 198/200: Loss: 0.000806112019927241\n",
      "Epoch 199/200: Loss: 0.0007739132312963193\n",
      "Epoch 200/200: Loss: 0.0008140114322486624\n",
      "time costs: 1037.2577514648438\n"
     ]
    }
   ],
   "source": [
    "qmodel = QModel(input_size, hidden_size, num_output, \n",
    "                num_layers = num_layers, ctx = ctx, mode='classical')\n",
    "optimizer = torch.optim.Adam(qmodel.parameters(), lr = 0.0035)\n",
    "loss_func = nn.MSELoss()\n",
    "\n",
    "start = time.time()\n",
    "losses = train_model(qmodel, train_data, batch_size=20,          \n",
    "               loss_func = loss_func, optimizer = optimizer, epoch = 100, early_stop=True)\n",
    "end = time.time()\n",
    "\n",
    "print(f'time costs: {end - start}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "79869185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 97.89852066,  16.23396277, 263.54154707,  10.81832791,\n",
       "         0.29530446])"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_accuarcy(qmodel, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09d3f586",
   "metadata": {},
   "outputs": [],
   "source": [
    "calculate_accuarcies(qmodel, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 872,
   "id": "80014c22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mode = \"CIFG\"\n",
    "# with open(f'loss/{mode}2/loss_{mode}2_1.pkl', 'wb') as pkl_file:\n",
    "#     pickle.dump(losses, pkl_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 873,
   "id": "a1cc2abe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch.save(qmodel.state_dict(), f\"model/{mode}2/model_{mode}2_1.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24f6c1ec",
   "metadata": {},
   "source": [
    "### - describe trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9c3cde5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "qmodel = QModel(input_size, hidden_size, num_output, \n",
    "                num_layers = num_layers, ctx = ctx, mode='GRU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "29c8cdfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QModel(\n",
       "  (qlstm): StackedQLSTM(\n",
       "    (qlstms): Sequential(\n",
       "      (QGRU 1): QGRU(\n",
       "        (update_circuit): QuantumLayer(\n",
       "          (qin): Linear(in_features=44, out_features=5, bias=True)\n",
       "          (qout): Linear(in_features=5, out_features=32, bias=True)\n",
       "        )\n",
       "        (candidate_circuit): QuantumLayer(\n",
       "          (qin): Linear(in_features=44, out_features=3, bias=True)\n",
       "          (qout): Linear(in_features=3, out_features=32, bias=True)\n",
       "        )\n",
       "        (reset_circuit): QuantumLayer(\n",
       "          (qin): Linear(in_features=44, out_features=4, bias=True)\n",
       "          (qout): Linear(in_features=4, out_features=32, bias=True)\n",
       "        )\n",
       "      )\n",
       "      (QGRU 2): QGRU(\n",
       "        (update_circuit): QuantumLayer(\n",
       "          (qin): Linear(in_features=64, out_features=5, bias=True)\n",
       "          (qout): Linear(in_features=5, out_features=32, bias=True)\n",
       "        )\n",
       "        (candidate_circuit): QuantumLayer(\n",
       "          (qin): Linear(in_features=64, out_features=3, bias=True)\n",
       "          (qout): Linear(in_features=3, out_features=32, bias=True)\n",
       "        )\n",
       "        (reset_circuit): QuantumLayer(\n",
       "          (qin): Linear(in_features=64, out_features=4, bias=True)\n",
       "          (qout): Linear(in_features=4, out_features=32, bias=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (predict): Linear(in_features=32, out_features=4, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3497e649",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total parameters: 2460\n",
      "quantum paramers: 24\n"
     ]
    }
   ],
   "source": [
    "trainable = 0\n",
    "for p in qmodel.parameters():\n",
    "    if p.requires_grad:\n",
    "        trainable += p.numel()\n",
    "print(f'total parameters: {trainable}')\n",
    "\n",
    "qlstm = QLSTMMap.get(qmodel.mode)[1]\n",
    "print(f'quantum paramers: {qlstm(1, 1, ctx = ctx).qparameters_size}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0d33ae3",
   "metadata": {},
   "source": [
    "### - Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "22f1defd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model\n",
    "torch.save(qmodel.state_dict(), \"model/qmodel_GRU.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbfe49c2",
   "metadata": {},
   "source": [
    "## 3.4 Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "90abbd43",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "\n",
    "for i in range(0, len(X_test), features_size):\n",
    "    input_data = X_test[i:i+1]\n",
    "    result = qmodel(input_data)\n",
    "    result = train_scaler.inverse_transform(result.data)\n",
    "    \n",
    "    results.append(result[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "83cc34bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 = pd.DataFrame(test, columns=('meantemp',))\n",
    "test2 = pd.DataFrame(test, columns=('humidity',))\n",
    "test3 = pd.DataFrame(test, columns=('wind_speed',))\n",
    "test4 = pd.DataFrame(test, columns=('meanpressure',))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "990487ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>meantemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>meanpressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.062270</td>\n",
       "      <td>67.692835</td>\n",
       "      <td>4.195018</td>\n",
       "      <td>1013.619007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.213526</td>\n",
       "      <td>71.658590</td>\n",
       "      <td>-4.394649</td>\n",
       "      <td>1017.484953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.397308</td>\n",
       "      <td>65.875632</td>\n",
       "      <td>5.989264</td>\n",
       "      <td>1013.243156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.023481</td>\n",
       "      <td>66.304116</td>\n",
       "      <td>5.470593</td>\n",
       "      <td>1008.499735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.019680</td>\n",
       "      <td>63.049363</td>\n",
       "      <td>14.097361</td>\n",
       "      <td>1001.231936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>33.187116</td>\n",
       "      <td>55.648947</td>\n",
       "      <td>8.164790</td>\n",
       "      <td>1007.525079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>36.050094</td>\n",
       "      <td>50.632516</td>\n",
       "      <td>15.869078</td>\n",
       "      <td>1001.005951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>39.653435</td>\n",
       "      <td>48.289673</td>\n",
       "      <td>21.064595</td>\n",
       "      <td>994.058895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>41.032986</td>\n",
       "      <td>53.145303</td>\n",
       "      <td>13.516966</td>\n",
       "      <td>996.546199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>34.660491</td>\n",
       "      <td>59.192256</td>\n",
       "      <td>3.712413</td>\n",
       "      <td>1007.401731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      meantemp   humidity  wind_speed  meanpressure\n",
       "0    17.062270  67.692835    4.195018   1013.619007\n",
       "1    19.213526  71.658590   -4.394649   1017.484953\n",
       "2    17.397308  65.875632    5.989264   1013.243156\n",
       "3    23.023481  66.304116    5.470593   1008.499735\n",
       "4    24.019680  63.049363   14.097361   1001.231936\n",
       "..         ...        ...         ...           ...\n",
       "106  33.187116  55.648947    8.164790   1007.525079\n",
       "107  36.050094  50.632516   15.869078   1001.005951\n",
       "108  39.653435  48.289673   21.064595    994.058895\n",
       "109  41.032986  53.145303   13.516966    996.546199\n",
       "110  34.660491  59.192256    3.712413   1007.401731\n",
       "\n",
       "[111 rows x 4 columns]"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(results)\n",
    "results.columns = ('meantemp', 'humidity', 'wind_speed', 'meanpressure')\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "82c4df0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "result1 = pd.DataFrame(results, columns=('meantemp', 'humidity', 'wind_speed'))\n",
    "result2 = pd.DataFrame(results, columns=('meanpressure',))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "ca0ab922",
   "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>meantemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.062270</td>\n",
       "      <td>67.692835</td>\n",
       "      <td>4.195018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.213526</td>\n",
       "      <td>71.658590</td>\n",
       "      <td>-4.394649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.397308</td>\n",
       "      <td>65.875632</td>\n",
       "      <td>5.989264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.023481</td>\n",
       "      <td>66.304116</td>\n",
       "      <td>5.470593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.019680</td>\n",
       "      <td>63.049363</td>\n",
       "      <td>14.097361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>33.187116</td>\n",
       "      <td>55.648947</td>\n",
       "      <td>8.164790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>36.050094</td>\n",
       "      <td>50.632516</td>\n",
       "      <td>15.869078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>39.653435</td>\n",
       "      <td>48.289673</td>\n",
       "      <td>21.064595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>41.032986</td>\n",
       "      <td>53.145303</td>\n",
       "      <td>13.516966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>34.660491</td>\n",
       "      <td>59.192256</td>\n",
       "      <td>3.712413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      meantemp   humidity  wind_speed\n",
       "0    17.062270  67.692835    4.195018\n",
       "1    19.213526  71.658590   -4.394649\n",
       "2    17.397308  65.875632    5.989264\n",
       "3    23.023481  66.304116    5.470593\n",
       "4    24.019680  63.049363   14.097361\n",
       "..         ...        ...         ...\n",
       "106  33.187116  55.648947    8.164790\n",
       "107  36.050094  50.632516   15.869078\n",
       "108  39.653435  48.289673   21.064595\n",
       "109  41.032986  53.145303   13.516966\n",
       "110  34.660491  59.192256    3.712413\n",
       "\n",
       "[111 rows x 3 columns]"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "b1aed828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20fffe0c9d0>"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(test1, label=\"meantemp\")\n",
    "plt.plot(test2, label=\"humidity\")\n",
    "plt.plot(test3, label=\"wind_speed\")\n",
    "\n",
    "# plt.subplot(1,2,2)\n",
    "plt.ylim(0, 100)\n",
    "plt.plot(result1.index + 3, result1.values, color=\"#FF6666\", linestyle=\"--\")\n",
    "plt.legend()\n",
    "\n",
    "# plt.subplot(1,2,1)\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(test4, label=\"meanpressure\", color=\"#9966CC\")\n",
    "plt.ylim(980, 1050)\n",
    "plt.plot(result2.index + 3, result2.values, color=\"#FF6666\", linestyle='--')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65b63d93",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}