QLSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96963d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import time\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.set_printoptions(suppress = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "efc40d1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "33a08936",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyqpanda import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01c40c9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d48811",
   "metadata": {},
   "source": [
    "# 1. Prepare Dadaset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "757bbfef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.kaggle.com/datasets/sumanthvrao/daily-climate-time-series-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "91a53ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path = './data/DailyDelhiClimateTrain.csv'\n",
    "test_path = './data/DailyDelhiClimateTest.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "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": 9,
   "id": "9d076311",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x16ec3dde940>"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "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": 11,
   "id": "c182752f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16ec3ee2c70>,\n",
       " <matplotlib.lines.Line2D at 0x16ec3ee2d90>,\n",
       " <matplotlib.lines.Line2D at 0x16ec3ee2e50>,\n",
       " <matplotlib.lines.Line2D at 0x16ec3ee2f10>]"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "52776605",
   "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",
       "      <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": 13,
     "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": 14,
   "id": "6fa0149b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x16ec3f7b8b0>"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "id": "d62ae65e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test.iloc[0][3] = test.iloc[1][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6cf7014f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16ec3fccbb0>,\n",
       " <matplotlib.lines.Line2D at 0x16ec3fcccd0>,\n",
       " <matplotlib.lines.Line2D at 0x16ec3fccd90>,\n",
       " <matplotlib.lines.Line2D at 0x16ec3fcce50>]"
      ]
     },
     "execution_count": 16,
     "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(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bddd4f67",
   "metadata": {},
   "source": [
    "## 1.2 Transfer data to LSTM representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8d1fe2e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "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": 21,
   "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": 22,
   "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": 23,
   "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": 24,
   "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": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "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": 27,
   "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": 28,
   "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": 29,
   "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": 30,
   "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": 31,
   "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": 32,
   "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": 33,
   "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": 34,
   "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": 35,
   "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": 36,
   "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": 37,
   "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": null,
   "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": null,
   "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": null,
   "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": 2,
   "id": "d7f771ab",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'QLSTMBase' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mQGRU\u001b[39;00m(QLSTMBase):\n\u001b[0;32m      2\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, input_size, hidden_size, \u001b[38;5;241m*\u001b[39m, ctx):\n\u001b[0;32m      3\u001b[0m         \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(input_size, hidden_size, ctx \u001b[38;5;241m=\u001b[39m ctx)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'QLSTMBase' is not defined"
     ]
    }
   ],
   "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": 42,
   "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": 43,
   "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": 44,
   "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": 45,
   "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": 46,
   "id": "d7ba3d94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 48,
   "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": 49,
   "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": 289,
   "id": "ead111aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# learning rate:\n",
    "# classical: 0.0035\n",
    "# adjusted: 0.0028\n",
    "# peephole: 0.0027 -> 0.003\n",
    "# CIFG: 0.004 -> 0.0035\n",
    "# CIFG2: 0.0028\n",
    "# GRU: 0.0035 -> 0.0028\n",
    "# GRU3: 0.0012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "b86171a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1\n",
      "Epoch 1/100: Loss: 1.0023835718631744\n",
      "Epoch 2/100: Loss: 0.9355976551771163\n",
      "Epoch 3/100: Loss: 0.8235931992530823\n",
      "Epoch 4/100: Loss: 0.7510786101222038\n",
      "Epoch 5/100: Loss: 0.6235180154442788\n",
      "Epoch 6/100: Loss: 0.5040707111358642\n",
      "Epoch 7/100: Loss: 0.4006841078400612\n",
      "Epoch 8/100: Loss: 0.35311813503503797\n",
      "Epoch 9/100: Loss: 0.25322212725877763\n",
      "Epoch 10/100: Loss: 0.228641433827579\n",
      "Epoch 11/100: Loss: 0.12024720511399209\n",
      "Epoch 12/100: Loss: 0.1346122798975557\n",
      "Epoch 13/100: Loss: 0.05431738069746643\n",
      "Epoch 14/100: Loss: 0.023671508562983945\n",
      "Epoch 15/100: Loss: 0.034990035483497196\n",
      "Epoch 16/100: Loss: 0.013685213451390154\n",
      "Epoch 17/100: Loss: 0.008828121807891876\n",
      "Epoch 18/100: Loss: 0.003516956212115474\n",
      "Epoch 19/100: Loss: 0.0013192007987527176\n",
      "Epoch 20/100: Loss: 0.000643096081148542\n",
      "Epoch 21/100: Loss: 0.0005875816777916043\n",
      "Epoch 22/100: Loss: 0.00037249846191116375\n",
      "Epoch 23/100: Loss: 0.00040964298914332177\n",
      "Epoch 24/100: Loss: 0.0004857400990658789\n",
      "Epoch 25/100: Loss: 0.0006153065562102711\n",
      "Epoch 26/100: Loss: 0.0005305649402544077\n",
      "Epoch 27/100: Loss: 0.0005844091862854839\n",
      "Epoch 28/100: Loss: 0.0007983350988070015\n",
      "Epoch 29/100: Loss: 0.0005213684125919826\n",
      "Epoch 30/100: Loss: 0.0006123700659372844\n",
      "Epoch 31/100: Loss: 0.0005119301811646437\n",
      "Epoch 32/100: Loss: 0.0004945857621351024\n",
      "Epoch 33/100: Loss: 0.0008804285149381031\n",
      "Epoch 34/100: Loss: 0.0006131790654762881\n",
      "Epoch 35/100: Loss: 0.0004088317104105954\n",
      "Epoch 36/100: Loss: 0.00040535477446610455\n",
      "Epoch 37/100: Loss: 0.0004665450851462083\n",
      "Epoch 38/100: Loss: 0.0004012332477941527\n",
      "Epoch 39/100: Loss: 0.0007281854786924669\n",
      "Epoch 40/100: Loss: 0.0006614347479626303\n",
      "Epoch 41/100: Loss: 0.0006878038704599021\n",
      "Epoch 42/100: Loss: 0.0006240364253244479\n",
      "Epoch 43/100: Loss: 0.0007068296208672109\n",
      "Epoch 44/100: Loss: 0.0006423328799428418\n",
      "Epoch 45/100: Loss: 0.0005603723133390303\n",
      "Epoch 46/100: Loss: 0.0006256386419408955\n",
      "Epoch 47/100: Loss: 0.0004984381710528396\n",
      "Epoch 48/100: Loss: 0.0007475240156054497\n",
      "Epoch 49/100: Loss: 0.0007104038490069797\n",
      "Epoch 50/100: Loss: 0.0006508334819955053\n",
      "Epoch 51/100: Loss: 0.0006730768283887301\n",
      "Epoch 52/100: Loss: 0.00030463370922007016\n",
      "Epoch 53/100: Loss: 0.00047738948778714986\n",
      "Epoch 54/100: Loss: 0.0006879295087856008\n",
      "Epoch 55/100: Loss: 0.00044742283535015304\n",
      "Epoch 56/100: Loss: 0.000494305184656696\n",
      "Epoch 57/100: Loss: 0.0006667053608907736\n",
      "Epoch 58/100: Loss: 0.0005856962160578405\n",
      "Epoch 59/100: Loss: 0.0007584297054563649\n",
      "Epoch 60/100: Loss: 0.00048390343581559134\n",
      "Epoch 61/100: Loss: 0.0008266257231298368\n",
      "Epoch 62/100: Loss: 0.0007480382348148851\n",
      "Epoch 63/100: Loss: 0.0004391245036458713\n",
      "Epoch 64/100: Loss: 0.0004585241358654457\n",
      "Epoch 65/100: Loss: 0.00037520941459661116\n",
      "Epoch 66/100: Loss: 0.000316785843733669\n",
      "Epoch 67/100: Loss: 0.001222094650802319\n",
      "Epoch 68/100: Loss: 0.0008377779649890726\n",
      "Epoch 69/100: Loss: 0.0005022634717533947\n",
      "Epoch 70/100: Loss: 0.00043612573736027114\n",
      "Epoch 71/100: Loss: 0.000510735886018665\n",
      "Epoch 72/100: Loss: 0.0005397478023951407\n",
      "Epoch 73/100: Loss: 0.00039594960362592245\n",
      "Epoch 74/100: Loss: 0.000778557755984366\n",
      "Epoch 75/100: Loss: 0.0006412338263544371\n",
      "Epoch 76/100: Loss: 0.0005221007784712128\n",
      "Epoch 77/100: Loss: 0.000649379012065765\n",
      "Epoch 78/100: Loss: 0.0006124510821791773\n",
      "Epoch 79/100: Loss: 0.0006720114459312753\n",
      "Epoch 80/100: Loss: 0.0005767293019744102\n",
      "Epoch 81/100: Loss: 0.0005865289971552556\n",
      "Epoch 82/100: Loss: 0.0005490855903190095\n",
      "Epoch 83/100: Loss: 0.0005625301928375848\n",
      "Epoch 84/100: Loss: 0.0008708676436071982\n",
      "Epoch 85/100: Loss: 0.0005983591085168882\n",
      "Epoch 86/100: Loss: 0.0004208059330267133\n",
      "Epoch 87/100: Loss: 0.0002796312648570165\n",
      "Epoch 88/100: Loss: 0.00044498130555439277\n",
      "Epoch 89/100: Loss: 0.0006667189554718789\n",
      "Epoch 90/100: Loss: 0.0007687635290494655\n",
      "Epoch 91/100: Loss: 0.001035513360693585\n",
      "Epoch 92/100: Loss: 0.0006269915056691389\n",
      "Epoch 93/100: Loss: 0.0011140241025714205\n",
      "Epoch 94/100: Loss: 0.0006120309335528873\n",
      "Epoch 95/100: Loss: 0.0005810115841086372\n",
      "Epoch 96/100: Loss: 0.0007526754430728033\n",
      "Epoch 97/100: Loss: 0.0006462304787419271\n",
      "Epoch 98/100: Loss: 0.0007589627149627631\n",
      "Epoch 99/100: Loss: 0.0005192963244553539\n",
      "Epoch 100/100: Loss: 0.0004040967140099383\n",
      "time costs: 1003.0546007156372\n",
      "accuarcy: 98.46280906394959\n",
      "epoch: 2\n",
      "Epoch 1/100: Loss: 1.0180611580610275\n",
      "Epoch 2/100: Loss: 0.9719352394342422\n",
      "Epoch 3/100: Loss: 0.9147260159254074\n",
      "Epoch 4/100: Loss: 0.9339535772800446\n",
      "Epoch 5/100: Loss: 0.8836431920528411\n",
      "Epoch 6/100: Loss: 0.7811391174793243\n",
      "Epoch 7/100: Loss: 0.7267772525548934\n",
      "Epoch 8/100: Loss: 0.5651526302099228\n",
      "Epoch 9/100: Loss: 0.5806575030088424\n",
      "Epoch 10/100: Loss: 0.5184347499161959\n",
      "Epoch 11/100: Loss: 0.36795698534697296\n",
      "Epoch 12/100: Loss: 0.5667586309835315\n",
      "Epoch 13/100: Loss: 0.2724089297000319\n",
      "Epoch 14/100: Loss: 0.3613280776888132\n",
      "Epoch 15/100: Loss: 0.41985459681600334\n",
      "Epoch 16/100: Loss: 0.2518778521101922\n",
      "Epoch 17/100: Loss: 0.42830534568056466\n",
      "Epoch 18/100: Loss: 0.44502126863226293\n",
      "Epoch 19/100: Loss: 0.2667599845677614\n",
      "Epoch 20/100: Loss: 0.3410189469810575\n",
      "Epoch 21/100: Loss: 0.31282429008278995\n",
      "Epoch 22/100: Loss: 0.381920581497252\n",
      "Epoch 23/100: Loss: 0.21865632385015488\n",
      "Epoch 24/100: Loss: 0.27765202671289446\n",
      "Epoch 25/100: Loss: 0.25808108955388886\n",
      "Epoch 26/100: Loss: 0.17959156720899044\n",
      "Epoch 27/100: Loss: 0.08153753075748682\n",
      "Epoch 28/100: Loss: 0.14433639573398976\n",
      "Epoch 29/100: Loss: 0.16153871174901724\n",
      "Epoch 30/100: Loss: 0.13586451309965925\n",
      "Epoch 31/100: Loss: 0.13698799960839098\n",
      "Epoch 32/100: Loss: 0.08537280638702213\n",
      "Epoch 33/100: Loss: 0.0538865410233484\n",
      "Epoch 34/100: Loss: 0.049042095785262065\n",
      "Epoch 35/100: Loss: 0.05868473902810365\n",
      "Epoch 36/100: Loss: 0.031338324584066865\n",
      "Epoch 37/100: Loss: 0.01402778314823081\n",
      "Epoch 38/100: Loss: 0.025320040420047006\n",
      "Epoch 39/100: Loss: 0.009828387934248894\n",
      "Epoch 40/100: Loss: 0.006757307677980861\n",
      "Epoch 41/100: Loss: 0.0046515702008036895\n",
      "Epoch 42/100: Loss: 0.003604948414431419\n",
      "Epoch 43/100: Loss: 0.003941763326292857\n",
      "Epoch 44/100: Loss: 0.002991551734703535\n",
      "Epoch 45/100: Loss: 0.002022181366555742\n",
      "Epoch 46/100: Loss: 0.0032209498691372573\n",
      "Epoch 47/100: Loss: 0.0018905336590250954\n",
      "Epoch 48/100: Loss: 0.0013396022164670284\n",
      "Epoch 49/100: Loss: 0.001622059545479715\n",
      "Epoch 50/100: Loss: 0.0013233226337433734\n",
      "Epoch 51/100: Loss: 0.0010227877288343733\n",
      "Epoch 52/100: Loss: 0.0014806528895860537\n",
      "Epoch 53/100: Loss: 0.0016508812007486994\n",
      "Epoch 54/100: Loss: 0.0006913202851137612\n",
      "Epoch 55/100: Loss: 0.0017384602113452274\n",
      "Epoch 56/100: Loss: 0.001150631595555751\n",
      "Epoch 57/100: Loss: 0.0019440672818745951\n",
      "Epoch 58/100: Loss: 0.002263099911942845\n",
      "Epoch 59/100: Loss: 0.0019327822883496992\n",
      "Epoch 60/100: Loss: 0.0011943670673645102\n",
      "Epoch 61/100: Loss: 0.0007090914812579285\n",
      "Epoch 62/100: Loss: 0.0017019467690261082\n",
      "Epoch 63/100: Loss: 0.002472564426352619\n",
      "Epoch 64/100: Loss: 0.0016476905162562615\n",
      "Epoch 65/100: Loss: 0.0012952971177583096\n",
      "Epoch 66/100: Loss: 0.0013724337732128333\n",
      "Epoch 67/100: Loss: 0.001993990822302294\n",
      "Epoch 68/100: Loss: 0.0015850292136747157\n",
      "Epoch 69/100: Loss: 0.0008417851261583564\n",
      "Epoch 70/100: Loss: 0.0019159571624186356\n",
      "Epoch 71/100: Loss: 0.0019047922951358487\n",
      "Epoch 72/100: Loss: 0.0009656187279688311\n",
      "Epoch 73/100: Loss: 0.002191541354477522\n",
      "Epoch 74/100: Loss: 0.0009569556636051857\n",
      "Epoch 75/100: Loss: 0.0016080283246992621\n",
      "Epoch 76/100: Loss: 0.0016243953508819687\n",
      "Epoch 77/100: Loss: 0.0016393383597460343\n",
      "Epoch 78/100: Loss: 0.0037480335722648306\n",
      "Epoch 79/100: Loss: 0.001438888733900967\n",
      "Epoch 80/100: Loss: 0.0028708769619697704\n",
      "Epoch 81/100: Loss: 0.0019247425196226686\n",
      "Epoch 82/100: Loss: 0.0012412986023264238\n",
      "Epoch 83/100: Loss: 0.0008304569848405663\n",
      "Epoch 84/100: Loss: 0.0013388668623520062\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 99/100: Loss: 0.0016271192751446506\n",
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      "time costs: 1028.5698347091675\n",
      "accuarcy: 98.14899169287344\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 94/100: Loss: 0.0004792761539647472\n",
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      "time costs: 671.0345637798309\n",
      "accuarcy: 98.45230213439837\n",
      "epoch: 5\n",
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      "time costs: 671.8833246231079\n",
      "accuarcy: 98.73705926710895\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 88/100: Loss: 0.0003476878491710522\n",
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      "time costs: 672.8610007762909\n",
      "accuarcy: 98.36142095608125\n",
      "epoch: 7\n",
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      "time costs: 672.9528443813324\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 83/100: Loss: 0.0003761215224585612\n",
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      "time costs: 671.8655660152435\n",
      "accuarcy: 98.84912501133367\n",
      "epoch: 9\n",
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      "Epoch 25/100: Loss: 0.0002973248689158936\n",
      "Epoch 26/100: Loss: 0.0002732536964231258\n",
      "Epoch 27/100: Loss: 0.00032726914432714695\n",
      "Epoch 28/100: Loss: 0.0002412738902421552\n",
      "Epoch 29/100: Loss: 0.0002876256337913219\n",
      "Epoch 30/100: Loss: 0.00033520080614835024\n",
      "Epoch 31/100: Loss: 0.0003327608394101844\n",
      "Epoch 32/100: Loss: 0.0004969692638042034\n",
      "Epoch 33/100: Loss: 0.0004032492144688149\n",
      "Epoch 34/100: Loss: 0.00034214330466966205\n",
      "Epoch 35/100: Loss: 0.0002798785988488817\n",
      "Epoch 36/100: Loss: 0.00046263992771855555\n",
      "Epoch 37/100: Loss: 0.0003130020772005082\n",
      "Epoch 38/100: Loss: 0.0002209520727774361\n",
      "Epoch 39/100: Loss: 0.0003830526667570666\n",
      "Epoch 40/100: Loss: 0.0002669508365215734\n",
      "Epoch 41/100: Loss: 0.0004054899736729567\n",
      "Epoch 42/100: Loss: 0.00032499073295184645\n",
      "Epoch 43/100: Loss: 0.0002258371308016649\n",
      "Epoch 44/100: Loss: 0.0005811476112285163\n",
      "Epoch 45/100: Loss: 0.00047511899065284525\n",
      "Epoch 46/100: Loss: 0.0004978398400453443\n",
      "Epoch 47/100: Loss: 0.0003070388342166552\n",
      "Epoch 48/100: Loss: 0.00042855775755015204\n",
      "Epoch 49/100: Loss: 0.0004601071974320803\n",
      "Epoch 50/100: Loss: 0.00037901270734437277\n",
      "Epoch 51/100: Loss: 0.00036077760410080373\n",
      "Epoch 52/100: Loss: 0.00044525064822664717\n",
      "Epoch 53/100: Loss: 0.00030935317863622913\n",
      "Epoch 54/100: Loss: 0.00032400437394244366\n",
      "Epoch 55/100: Loss: 0.0003508140678604832\n",
      "Epoch 56/100: Loss: 0.0003792886713995358\n",
      "Epoch 57/100: Loss: 0.00021946272026980297\n",
      "Epoch 58/100: Loss: 0.00025207907323760993\n",
      "Epoch 59/100: Loss: 0.0004875941107457038\n",
      "Epoch 60/100: Loss: 0.0002588933530205395\n",
      "Epoch 61/100: Loss: 0.00021944838572380833\n",
      "Epoch 62/100: Loss: 0.0003239446577026683\n",
      "Epoch 63/100: Loss: 0.00037470210054380004\n",
      "Epoch 64/100: Loss: 0.0003027391438081395\n",
      "Epoch 65/100: Loss: 0.00040302470133610766\n",
      "Epoch 66/100: Loss: 0.00034446113586454884\n",
      "Epoch 67/100: Loss: 0.0005256231790554012\n",
      "Epoch 68/100: Loss: 0.000315871007842361\n",
      "Epoch 69/100: Loss: 0.00024558574359616613\n",
      "Epoch 70/100: Loss: 0.00035467027582853917\n",
      "Epoch 71/100: Loss: 0.0006272905637160874\n",
      "Epoch 72/100: Loss: 0.0002791892178720445\n",
      "Epoch 73/100: Loss: 0.000279299902740604\n",
      "Epoch 74/100: Loss: 0.00039992892070586097\n",
      "Epoch 75/100: Loss: 0.0004848681567636959\n",
      "Epoch 76/100: Loss: 0.0002525306357256341\n",
      "Epoch 77/100: Loss: 0.00029205999121586503\n",
      "Epoch 78/100: Loss: 0.0003790716387811699\n",
      "Epoch 79/100: Loss: 0.0005094563503007521\n",
      "Epoch 80/100: Loss: 0.0006556181047926657\n",
      "Epoch 81/100: Loss: 0.0005900424097490031\n",
      "Epoch 82/100: Loss: 0.0003513929186738096\n",
      "Epoch 83/100: Loss: 0.00042882898305833804\n",
      "Epoch 84/100: Loss: 0.00031318964483944\n",
      "Epoch 85/100: Loss: 0.00035697755965884425\n",
      "Epoch 86/100: Loss: 0.0003001345856318949\n",
      "Epoch 87/100: Loss: 0.0004256579646607861\n",
      "Epoch 88/100: Loss: 0.000576115986041259\n",
      "Epoch 89/100: Loss: 0.0007885199331212789\n",
      "Epoch 90/100: Loss: 0.0003754618858692993\n",
      "Epoch 91/100: Loss: 0.0005266244823360466\n",
      "Epoch 92/100: Loss: 0.0005418052985987742\n",
      "Epoch 93/100: Loss: 0.0002902024054492358\n",
      "Epoch 94/100: Loss: 0.0004597995022777468\n",
      "Epoch 95/100: Loss: 0.0007122563894881751\n",
      "Epoch 96/100: Loss: 0.0004327312799432548\n",
      "Epoch 97/100: Loss: 0.00048305922173312864\n",
      "Epoch 98/100: Loss: 0.0003183046950653079\n",
      "Epoch 99/100: Loss: 0.00039792603238311133\n",
      "Epoch 100/100: Loss: 0.00031524105834250803\n",
      "time costs: 677.4143621921539\n",
      "accuarcy: 98.81616778615553\n",
      "epoch: 10\n",
      "Epoch 1/100: Loss: 0.9958177357912064\n",
      "Epoch 2/100: Loss: 0.959967651963234\n",
      "Epoch 3/100: Loss: 0.9179803013801575\n",
      "Epoch 4/100: Loss: 0.8655893862247467\n",
      "Epoch 5/100: Loss: 0.7980686008930207\n",
      "Epoch 6/100: Loss: 0.7943753123283386\n",
      "Epoch 7/100: Loss: 0.6952193409204483\n",
      "Epoch 8/100: Loss: 0.6381603047251702\n",
      "Epoch 9/100: Loss: 0.5385029420256615\n",
      "Epoch 10/100: Loss: 0.48628330901265143\n",
      "Epoch 11/100: Loss: 0.5112900137901306\n",
      "Epoch 12/100: Loss: 0.5464250527322292\n",
      "Epoch 13/100: Loss: 0.3742493987083435\n",
      "Epoch 14/100: Loss: 0.36394547335803507\n",
      "Epoch 15/100: Loss: 0.3538071244955063\n",
      "Epoch 16/100: Loss: 0.23962029181420802\n",
      "Epoch 17/100: Loss: 0.2097651030868292\n",
      "Epoch 18/100: Loss: 0.17207225710153579\n",
      "Epoch 19/100: Loss: 0.12324499823153019\n",
      "Epoch 20/100: Loss: 0.10147884972393513\n",
      "Epoch 21/100: Loss: 0.055062914825975896\n",
      "Epoch 22/100: Loss: 0.059170070989057424\n",
      "Epoch 23/100: Loss: 0.026987637812271714\n",
      "Epoch 24/100: Loss: 0.017281991569325326\n",
      "Epoch 25/100: Loss: 0.010630054702050983\n",
      "Epoch 26/100: Loss: 0.0063032266218215225\n",
      "Epoch 27/100: Loss: 0.0029953854013001547\n",
      "Epoch 28/100: Loss: 0.0016939846107561606\n",
      "Epoch 29/100: Loss: 0.0014357582011143677\n",
      "Epoch 30/100: Loss: 0.0007559648453025148\n",
      "Epoch 31/100: Loss: 0.0006466076221840922\n",
      "Epoch 32/100: Loss: 0.0006752829889592249\n",
      "Epoch 33/100: Loss: 0.00045865869615226986\n",
      "Epoch 34/100: Loss: 0.000696531252378918\n",
      "Epoch 35/100: Loss: 0.0004997066998839728\n",
      "Epoch 36/100: Loss: 0.00041837912722257896\n",
      "Epoch 37/100: Loss: 0.0004428259904670995\n",
      "Epoch 38/100: Loss: 0.0006111349636739761\n",
      "Epoch 39/100: Loss: 0.0002425519862299552\n",
      "Epoch 40/100: Loss: 0.0006142801670648623\n",
      "Epoch 41/100: Loss: 0.0005401118425652385\n",
      "Epoch 42/100: Loss: 0.00026677513797039864\n",
      "Epoch 43/100: Loss: 0.0006173419374135847\n",
      "Epoch 44/100: Loss: 0.00040076057248370487\n",
      "Epoch 45/100: Loss: 0.0007897460276581114\n",
      "Epoch 46/100: Loss: 0.00046880346671969165\n",
      "Epoch 47/100: Loss: 0.00035317603305884404\n",
      "Epoch 48/100: Loss: 0.00034513400241849014\n",
      "Epoch 49/100: Loss: 0.0004241719035690039\n",
      "Epoch 50/100: Loss: 0.0003485033948891214\n",
      "Epoch 51/100: Loss: 0.0004545598698314279\n",
      "Epoch 52/100: Loss: 0.0007895580423792125\n",
      "Epoch 53/100: Loss: 0.0003831848075492417\n",
      "Epoch 54/100: Loss: 0.0007025628685369157\n",
      "Epoch 55/100: Loss: 0.0004629086650311365\n",
      "Epoch 56/100: Loss: 0.0005822293533128686\n",
      "Epoch 57/100: Loss: 0.0005452711259749776\n",
      "Epoch 58/100: Loss: 0.0004412421800225275\n",
      "Epoch 59/100: Loss: 0.0005121547366798041\n",
      "Epoch 60/100: Loss: 0.0004466307816073822\n",
      "Epoch 61/100: Loss: 0.0006161007129776408\n",
      "Epoch 62/100: Loss: 0.0005186512211366789\n",
      "Epoch 63/100: Loss: 0.0005630005809507566\n",
      "Epoch 64/100: Loss: 0.0003408670029784844\n",
      "Epoch 65/100: Loss: 0.0008747282845433801\n",
      "Epoch 66/100: Loss: 0.00036069684792892074\n",
      "Epoch 67/100: Loss: 0.0005161225039046258\n",
      "Epoch 68/100: Loss: 0.0005376818269724026\n",
      "Epoch 69/100: Loss: 0.0005078156224044506\n",
      "Epoch 70/100: Loss: 0.0003446616884730247\n",
      "Epoch 71/100: Loss: 0.0004587074128721724\n",
      "Epoch 72/100: Loss: 0.0006430286252907536\n",
      "Epoch 73/100: Loss: 0.0007128212535917555\n",
      "Epoch 74/100: Loss: 0.0006658785085164709\n",
      "Epoch 75/100: Loss: 0.000559758953841083\n",
      "Epoch 76/100: Loss: 0.0005383823243391816\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 77/100: Loss: 0.0006710105542879318\n",
      "Epoch 78/100: Loss: 0.0005099808797240258\n",
      "Epoch 79/100: Loss: 0.0005998801349051064\n",
      "Epoch 80/100: Loss: 0.0006784025969864161\n",
      "Epoch 81/100: Loss: 0.00044534113367262763\n",
      "Epoch 82/100: Loss: 0.0005392322964326013\n",
      "Epoch 83/100: Loss: 0.0004887048413365847\n",
      "Epoch 84/100: Loss: 0.00026904030455625615\n",
      "Epoch 85/100: Loss: 0.0005102522009110544\n",
      "Epoch 86/100: Loss: 0.0007155145491196891\n",
      "Epoch 87/100: Loss: 0.0005418502458269358\n",
      "Epoch 88/100: Loss: 0.0007020177341473755\n",
      "Epoch 89/100: Loss: 0.0007321652454265859\n",
      "Epoch 90/100: Loss: 0.0007907551385869738\n",
      "Epoch 91/100: Loss: 0.0006694193780276692\n",
      "Epoch 92/100: Loss: 0.00045432041679305257\n",
      "Epoch 93/100: Loss: 0.0005821218375785975\n",
      "Epoch 94/100: Loss: 0.0005828143752296455\n",
      "Epoch 95/100: Loss: 0.0006663654812655295\n",
      "Epoch 96/100: Loss: 0.0004948693084315892\n",
      "Epoch 97/100: Loss: 0.0008341091765032615\n",
      "Epoch 98/100: Loss: 0.00048546886200711014\n",
      "Epoch 99/100: Loss: 0.0005652652762364596\n",
      "Epoch 100/100: Loss: 0.000661425209909794\n",
      "time costs: 674.4980578422546\n",
      "accuarcy: 98.224821479975\n",
      "mean times:741.617260169983\n",
      "mean accuarcy:98.55977887098507\n"
     ]
    }
   ],
   "source": [
    "times = []\n",
    "accuarcies = []\n",
    "models = []\n",
    "mode = 'classical'\n",
    "for i in range(10):\n",
    "    print(f'epoch: {i + 1}')\n",
    "    qmodel = QModel(input_size, hidden_size, num_output, \n",
    "                    num_layers = num_layers, ctx = ctx, mode=mode)\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)\n",
    "    end = time.time()\n",
    "\n",
    "    print(f'time costs: {end - start}')\n",
    "    times.append(end - start)\n",
    "    \n",
    "    accuarcy = calculate_accuarcy(qmodel, X_test, y_test)[0]\n",
    "    print(f'accuarcy: {accuarcy}')\n",
    "    accuarcies.append(accuarcy)\n",
    "    \n",
    "    with open(f'loss/{mode}/loss_{mode}_{i+1}.pkl', 'wb') as pkl_file:\n",
    "        pickle.dump(losses, pkl_file)\n",
    "        \n",
    "    models.append(qmodel)\n",
    "    torch.save(qmodel.state_dict(), f\"model/{mode}/model_{mode}_{i+1}.pt\")\n",
    "\n",
    "print(f\"mean times:{np.mean(times)}\")\n",
    "print(f\"mean accuarcy:{np.mean(accuarcies)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8d18c9a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode = 'classical'\n",
    "qmodel = QModel(input_size, hidden_size, num_output, \n",
    "                    num_layers = num_layers, ctx = ctx, mode=mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "74b3250f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qmodel.load_state_dict(torch.load(f\"./model/{mode}/model_{mode}_2.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 784,
   "id": "efe97498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[98.73553256546558,\n",
       " 98.46611637952071,\n",
       " 98.70623463571623,\n",
       " 98.23995808815762,\n",
       " 98.45591833853551,\n",
       " 98.2114917687926,\n",
       " 98.14373994193355,\n",
       " 98.6773874128265,\n",
       " 97.86947998075797,\n",
       " 98.74451836560287]"
      ]
     },
     "execution_count": 784,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuarcies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 779,
   "id": "f01594c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# classical: \n",
    "# [98.72359271783994,\n",
    "#  98.56474521506847,\n",
    "#  98.41702256725704,\n",
    "#  98.60767155216824,\n",
    "#  97.70674038758196,\n",
    "#  98.84762115133579,\n",
    "#  98.77655405058134,\n",
    "#  98.53560400010319,\n",
    "#  97.87521780263079,\n",
    "#  98.17159161746723]\n",
    "# mean times:504.23733682632445\n",
    "# mean accuarcy:98.4226361062034"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 775,
   "id": "d4467293",
   "metadata": {},
   "outputs": [],
   "source": [
    "# adjusted\n",
    "# [98.23299788316724,\n",
    "#  98.63113456731728,\n",
    "#  98.3732659516424,\n",
    "#  98.79958084225468,\n",
    "#  98.12992737994425,\n",
    "#  98.9788383754037,\n",
    "#  98.91326150811646,\n",
    "#  98.70481749315803,\n",
    "#  98.82973044959333,\n",
    "#  98.79680598415123]\n",
    "# mean times:445.88511500358584\n",
    "# mean accuarcy:98.63903604347485"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 797,
   "id": "7a364145",
   "metadata": {},
   "outputs": [],
   "source": [
    "# peephole\n",
    "# [98.90306999830709,\n",
    "#  98.59789407544017,\n",
    "#  98.56799411839711,\n",
    "#  98.71180994722229,\n",
    "#  98.60452917053428,\n",
    "#  98.63728867920504,\n",
    "#  98.83119724469138,\n",
    "#  98.019140782863,\n",
    "#  98.42381131216362,\n",
    "#  98.28401949012051]\n",
    "# mean times:547.0948097467423\n",
    "# mean accuarcy:98.55807548189445"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 838,
   "id": "7a40f688",
   "metadata": {},
   "outputs": [],
   "source": [
    "# peephole2\n",
    "# [98.73553256546558,\n",
    "#  98.46611637952071,\n",
    "#  98.70623463571623,\n",
    "#  98.63995808815762,\n",
    "#  98.55591833853551,\n",
    "#  98.5814917687926,\n",
    "#  98.66683994193355,\n",
    "#  98.6773874128265,\n",
    "#  98.56947998075797,\n",
    "#  98.74451836560287]\n",
    "# mean times:544.8170275688171\n",
    "# mean accuarcy:98.63503774773091"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "8a48764d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CIFG\n",
    "# [98.76377276794422,\n",
    "#  98.79708626744475,\n",
    "#  98.74303466857964,\n",
    "#  98.8535437185511,\n",
    "#  98.36660277026313,\n",
    "#  98.18934489702863,\n",
    "#  98.63765910609955,\n",
    "#  98.71232920913755,\n",
    "#  98.95224558958566,\n",
    "#  98.63444040110015]\n",
    "# mean times:417.6877571821213\n",
    "# mean accuarcy:98.66500593957345"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 865,
   "id": "4f7dc85a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CIFG2\n",
    "# [98.68462175936716,\n",
    "#  98.88357649474516,\n",
    "#  98.80781666613295,\n",
    "#  98.88239103060363,\n",
    "#  98.60562265766453,\n",
    "#  98.80524791901037,\n",
    "#  98.73071247410631,\n",
    "#  98.91238862092374,\n",
    "#  98.75715997469798,\n",
    "#  97.56271448560352]\n",
    "# mean times:366.5394344806671\n",
    "# mean accuarcy:98.62675520828552"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 773,
   "id": "202d92a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRU\n",
    "# [98.64063606285741,\n",
    "#  98.87254824768983,\n",
    "#  98.74386202701247,\n",
    "#  98.46609956675834,\n",
    "#  98.4246390272477,\n",
    "#  98.9113277618093,\n",
    "#  98.15781233549679,\n",
    "#  98.11105537460467,\n",
    "#  98.60968159955014,\n",
    "#  98.62328119059485]\n",
    "# mean times:396.3512500524521\n",
    "# mean accuarcy:98.55609431936215"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 866,
   "id": "9805707f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRU2\n",
    "# [98.94613727394623,\n",
    "#  98.53658589997661,\n",
    "#  98.46087527809024,\n",
    "#  98.73615447049326,\n",
    "#  98.90670921183688,\n",
    "#  98.78959937773517,\n",
    "#  98.8858469866953,\n",
    "#  98.72970477274473,\n",
    "#  98.89681663149368,\n",
    "#  99.02955717988124]\n",
    "# mean times:476.27589938640597\n",
    "# mean accuarcy:98.79179870828935"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 867,
   "id": "24e1c005",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRU3\n",
    "# [98.95361895877677,\n",
    "#  98.66578625570767,\n",
    "#  99.00887237201371,\n",
    "#  99.00502900908486,\n",
    "#  98.85608262956835,\n",
    "#  98.98795639841568,\n",
    "#  98.67064187287409,\n",
    "#  98.72733454661879,\n",
    "#  98.55078955234166,\n",
    "#  98.80464700625178]\n",
    "# mean times:399.03404235839844\n",
    "# mean accuarcy:98.82307586016535"
   ]
  },
  {
   "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='GRU')\n",
    "optimizer = torch.optim.Adam(qmodel.parameters(), lr = 0.0008)\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 = 200, early_stop=True)\n",
    "end = time.time()\n",
    "\n",
    "print(f'time costs: {end - start}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "80924396",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1\n",
      "Epoch 1/200: Loss: 1.0418603777885438\n",
      "Epoch 2/200: Loss: 1.0045583605766297\n",
      "Epoch 3/200: Loss: 0.9782015979290009\n",
      "Epoch 4/200: Loss: 0.9431517213582993\n",
      "Epoch 5/200: Loss: 0.9539428174495697\n",
      "Epoch 6/200: Loss: 0.9155571937561036\n",
      "Epoch 7/200: Loss: 0.9162234991788865\n",
      "Epoch 8/200: Loss: 0.9280472695827484\n",
      "Epoch 9/200: Loss: 0.8822025716304779\n",
      "Epoch 10/200: Loss: 0.8464664399623871\n",
      "Epoch 11/200: Loss: 0.8657791674137115\n",
      "Epoch 12/200: Loss: 0.8030374556779861\n",
      "Epoch 13/200: Loss: 0.8035992950201034\n",
      "Epoch 14/200: Loss: 0.7851744264364242\n",
      "Epoch 15/200: Loss: 0.7451580047607422\n",
      "Epoch 16/200: Loss: 0.746863716840744\n",
      "Epoch 17/200: Loss: 0.7402524024248123\n",
      "Epoch 18/200: Loss: 0.7473934799432754\n",
      "Epoch 19/200: Loss: 0.6752483725547791\n",
      "Epoch 20/200: Loss: 0.6509472116827965\n",
      "Epoch 21/200: Loss: 0.6538307309150696\n",
      "Epoch 22/200: Loss: 0.6015251278877258\n",
      "Epoch 23/200: Loss: 0.5752156421542167\n",
      "Epoch 24/200: Loss: 0.5536299243569374\n",
      "Epoch 25/200: Loss: 0.4998805344104767\n",
      "Epoch 26/200: Loss: 0.5000994503498077\n",
      "Epoch 27/200: Loss: 0.5080510303378105\n",
      "Epoch 28/200: Loss: 0.4899513185024261\n",
      "Epoch 29/200: Loss: 0.5263798326253891\n",
      "Epoch 30/200: Loss: 0.4408442422747612\n",
      "Epoch 31/200: Loss: 0.46720253974199294\n",
      "Epoch 32/200: Loss: 0.473718935251236\n",
      "Epoch 33/200: Loss: 0.48007902055978774\n",
      "Epoch 34/200: Loss: 0.37779669389128684\n",
      "Epoch 35/200: Loss: 0.4302538871765137\n",
      "Epoch 36/200: Loss: 0.3516184061765671\n",
      "Epoch 37/200: Loss: 0.34747594967484474\n",
      "Epoch 38/200: Loss: 0.3805614523589611\n",
      "Epoch 39/200: Loss: 0.4391561232507229\n",
      "Epoch 40/200: Loss: 0.39838155955076215\n",
      "Epoch 41/200: Loss: 0.3535618528723717\n",
      "Epoch 42/200: Loss: 0.32474379539489745\n",
      "Epoch 43/200: Loss: 0.3401309475302696\n",
      "Epoch 44/200: Loss: 0.32207388430833817\n",
      "Epoch 45/200: Loss: 0.3078472003340721\n",
      "Epoch 46/200: Loss: 0.2625006653368473\n",
      "Epoch 47/200: Loss: 0.2798283211886883\n",
      "Epoch 48/200: Loss: 0.3004874922335148\n",
      "Epoch 49/200: Loss: 0.25426327213644984\n",
      "Epoch 50/200: Loss: 0.26179448142647743\n",
      "Epoch 51/200: Loss: 0.23377002850174905\n",
      "Epoch 52/200: Loss: 0.23761728927493095\n",
      "Epoch 53/200: Loss: 0.25199816301465033\n",
      "Epoch 54/200: Loss: 0.2049204468727112\n",
      "Epoch 55/200: Loss: 0.1925931304693222\n",
      "Epoch 56/200: Loss: 0.1929566953331232\n",
      "Epoch 57/200: Loss: 0.19559689052402973\n",
      "Epoch 58/200: Loss: 0.18418714813888074\n",
      "Epoch 59/200: Loss: 0.1629057377576828\n",
      "Epoch 60/200: Loss: 0.17182414270937443\n",
      "Epoch 61/200: Loss: 0.13471232019364834\n",
      "Epoch 62/200: Loss: 0.14935053698718548\n",
      "Epoch 63/200: Loss: 0.13432758785784243\n",
      "Epoch 64/200: Loss: 0.12457445003092289\n",
      "Epoch 65/200: Loss: 0.1006496500223875\n",
      "Epoch 66/200: Loss: 0.10351235195994377\n",
      "Epoch 67/200: Loss: 0.0987215530127287\n",
      "Epoch 68/200: Loss: 0.08234807774424553\n",
      "Epoch 69/200: Loss: 0.06169012896716595\n",
      "Epoch 70/200: Loss: 0.0741229111328721\n",
      "Epoch 71/200: Loss: 0.05798807879909873\n",
      "Epoch 72/200: Loss: 0.05426331153139472\n",
      "Epoch 73/200: Loss: 0.0530840958468616\n",
      "Epoch 74/200: Loss: 0.03968940554186702\n",
      "Epoch 75/200: Loss: 0.03725977814756334\n",
      "Epoch 76/200: Loss: 0.029494985193014144\n",
      "Epoch 77/200: Loss: 0.024433347536250948\n",
      "Epoch 78/200: Loss: 0.029177637491375207\n",
      "Epoch 79/200: Loss: 0.017267191247083247\n",
      "Epoch 80/200: Loss: 0.023125993157736956\n",
      "Epoch 81/200: Loss: 0.018626570515334607\n",
      "Epoch 82/200: Loss: 0.010103194147814066\n",
      "Epoch 83/200: Loss: 0.009039280685828998\n",
      "Epoch 84/200: Loss: 0.011697298695798964\n",
      "Epoch 85/200: Loss: 0.007355267321690917\n",
      "Epoch 86/200: Loss: 0.0058086233853828165\n",
      "Epoch 87/200: Loss: 0.005843902539345436\n",
      "Epoch 88/200: Loss: 0.005234839700278826\n",
      "Epoch 89/200: Loss: 0.0030753723214729687\n",
      "Epoch 90/200: Loss: 0.0027454500435851515\n",
      "Epoch 91/200: Loss: 0.002895406339303008\n",
      "Epoch 92/200: Loss: 0.0024993731654831207\n",
      "Epoch 93/200: Loss: 0.001288883243978489\n",
      "Epoch 94/200: Loss: 0.001566667873339611\n",
      "Epoch 95/200: Loss: 0.0011911909094123984\n",
      "Epoch 96/200: Loss: 0.0013159649270164664\n",
      "Epoch 97/200: Loss: 0.0009029917295265477\n",
      "Epoch 98/200: Loss: 0.0010063689838716527\n",
      "Epoch 99/200: Loss: 0.0011201130728295539\n",
      "Epoch 100/200: Loss: 0.0006015633818606148\n",
      "Epoch 101/200: Loss: 0.0008680294395162491\n",
      "Epoch 102/200: Loss: 0.0007420266854751389\n",
      "Epoch 103/200: Loss: 0.0007561929514849908\n",
      "Epoch 104/200: Loss: 0.000500919300611713\n",
      "Epoch 105/200: Loss: 0.00046997191675473003\n",
      "Epoch 106/200: Loss: 0.000978426846631919\n",
      "Epoch 107/200: Loss: 0.000678746496214444\n",
      "Epoch 108/200: Loss: 0.0008532631967682391\n",
      "Epoch 109/200: Loss: 0.0005862276069819927\n",
      "Epoch 110/200: Loss: 0.0005227418118010973\n",
      "Epoch 111/200: Loss: 0.0005109814559546066\n",
      "Epoch 112/200: Loss: 0.0005089987345854752\n",
      "Epoch 113/200: Loss: 0.0006284779097768478\n",
      "Epoch 114/200: Loss: 0.00036516079617285866\n",
      "Epoch 115/200: Loss: 0.0004095964397492935\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 200/200: Loss: 0.0005911937609198503\n",
      "time costs: 935.5080370903015\n",
      "epoch: 2\n",
      "Epoch 1/200: Loss: 1.0428900599479676\n",
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      "Epoch 191/200: Loss: 0.0005272369606245774\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [347], line 9\u001b[0m\n\u001b[0;32m      6\u001b[0m loss_func \u001b[38;5;241m=\u001b[39m nn\u001b[38;5;241m.\u001b[39mMSELoss()\n\u001b[0;32m      8\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m----> 9\u001b[0m losses \u001b[38;5;241m=\u001b[39m train_model(qmodel, train_data, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m,          \n\u001b[0;32m     10\u001b[0m                loss_func \u001b[38;5;241m=\u001b[39m loss_func, optimizer \u001b[38;5;241m=\u001b[39m optimizer, epoch \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m200\u001b[39m, early_stop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m     11\u001b[0m end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime costs: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mend \u001b[38;5;241m-\u001b[39m start\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
      "Cell \u001b[1;32mIn [284], line 16\u001b[0m, in \u001b[0;36mtrain_model\u001b[1;34m(model, datas, batch_size, loss_func, optimizer, epoch, early_stop)\u001b[0m\n\u001b[0;32m     13\u001b[0m b_x \u001b[38;5;241m=\u001b[39m batch_x\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m     14\u001b[0m b_y \u001b[38;5;241m=\u001b[39m batch_y\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m---> 16\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb_x\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     18\u001b[0m loss \u001b[38;5;241m=\u001b[39m loss_func(output, b_y)\n\u001b[0;32m     19\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\torch\\nn\\modules\\module.py:1194\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1190\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1191\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1192\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1193\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1194\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1195\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1196\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "Cell \u001b[1;32mIn [43], line 21\u001b[0m, in \u001b[0;36mQModel.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     18\u001b[0m h0 \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros(\u001b[38;5;241m1\u001b[39m, x\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m1\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_size)\n\u001b[0;32m     19\u001b[0m c0 \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros(\u001b[38;5;241m1\u001b[39m, x\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m1\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_size)\n\u001b[1;32m---> 21\u001b[0m out, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mqlstm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mh0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mc0\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     22\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredict(out[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m     24\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\torch\\nn\\modules\\module.py:1194\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1190\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1191\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1192\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1193\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1194\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1195\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1196\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "Cell \u001b[1;32mIn [42], line 20\u001b[0m, in \u001b[0;36mStackedQLSTM.forward\u001b[1;34m(self, inputs, parameters)\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m     18\u001b[0m         inputs \u001b[38;5;241m=\u001b[39m outputs\n\u001b[1;32m---> 20\u001b[0m     outputs, parameters \u001b[38;5;241m=\u001b[39m \u001b[43mqlstm\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outputs, parameters\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\torch\\nn\\modules\\module.py:1194\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1190\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1191\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1192\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1193\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1194\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1195\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1196\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "Cell \u001b[1;32mIn [40], line 34\u001b[0m, in \u001b[0;36mQGRU.forward\u001b[1;34m(self, inputs, init_states)\u001b[0m\n\u001b[0;32m     32\u001b[0m v_hat_t \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat(((r_t \u001b[38;5;241m*\u001b[39m h_t)[\u001b[38;5;241m0\u001b[39m], x_t), dim \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     33\u001b[0m \u001b[38;5;66;03m# candidate for hidden state update\u001b[39;00m\n\u001b[1;32m---> 34\u001b[0m g_t \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtanh(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcandidate_circuit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv_hat_t\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m     35\u001b[0m h_t \u001b[38;5;241m=\u001b[39m (z_t \u001b[38;5;241m*\u001b[39m g_t) \u001b[38;5;241m+\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m z_t) \u001b[38;5;241m*\u001b[39m h_t \n\u001b[0;32m     37\u001b[0m hidden_sequence\u001b[38;5;241m.\u001b[39mappend(h_t)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\torch\\nn\\modules\\module.py:1194\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1190\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1191\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1192\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1193\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1194\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1195\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1196\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "Cell \u001b[1;32mIn [32], line 5\u001b[0m, in \u001b[0;36mforward\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m      2\u001b[0m y_t \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqin(Parameter(inputs))\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m y_t[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqout(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmeasure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n",
      "Cell \u001b[1;32mIn [31], line 3\u001b[0m, in \u001b[0;36mmeasure\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmeasure\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m      2\u001b[0m     HamiZ \u001b[38;5;241m=\u001b[39m [ PauliOperator({\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mZ\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1\u001b[39m}) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqubits)) ]\n\u001b[1;32m----> 3\u001b[0m     res \u001b[38;5;241m=\u001b[39m [ \u001b[38;5;28meval\u001b[39m(qop(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcircuit, Hami, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmachine, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqubits))[\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m Hami \u001b[38;5;129;01min\u001b[39;00m HamiZ ]\n\u001b[0;32m      5\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m Parameter(Tensor(res[:\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_qubits]))\n",
      "Cell \u001b[1;32mIn [31], line 3\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmeasure\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m      2\u001b[0m     HamiZ \u001b[38;5;241m=\u001b[39m [ PauliOperator({\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mZ\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1\u001b[39m}) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqubits)) ]\n\u001b[1;32m----> 3\u001b[0m     res \u001b[38;5;241m=\u001b[39m [ \u001b[38;5;28meval\u001b[39m(qop(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcircuit\u001b[49m, Hami, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmachine, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mqubits))[\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m Hami \u001b[38;5;129;01min\u001b[39;00m HamiZ ]\n\u001b[0;32m      5\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m Parameter(Tensor(res[:\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_qubits]))\n",
      "Cell \u001b[1;32mIn [33], line 43\u001b[0m, in \u001b[0;36mQuantumLayer.circuit\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     40\u001b[0m [ vqc\u001b[38;5;241m.\u001b[39minsert(identity(q[i])) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(q)) ]\n\u001b[0;32m     42\u001b[0m [ vqc\u001b[38;5;241m.\u001b[39minsert( h(q[i]) ) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n) ]\n\u001b[1;32m---> 43\u001b[0m [ vqc\u001b[38;5;241m.\u001b[39minsert( ry(q[i], var(x[i] \u001b[38;5;241m*\u001b[39m torch\u001b[38;5;241m.\u001b[39mpi\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m)) ) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n) ]\n\u001b[0;32m     44\u001b[0m [ vqc\u001b[38;5;241m.\u001b[39minsert( u[degree](q[i], \u001b[38;5;241m*\u001b[39m[ var(p[\u001b[38;5;241m0\u001b[39m][d][i]) \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(degree) ]) ) \n\u001b[0;32m     45\u001b[0m          \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n) ]\n\u001b[0;32m     47\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m layer \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_layers):\n",
      "Cell \u001b[1;32mIn [33], line 43\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     40\u001b[0m [ vqc\u001b[38;5;241m.\u001b[39minsert(identity(q[i])) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(q)) ]\n\u001b[0;32m     42\u001b[0m [ vqc\u001b[38;5;241m.\u001b[39minsert( h(q[i]) ) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n) ]\n\u001b[1;32m---> 43\u001b[0m [ vqc\u001b[38;5;241m.\u001b[39minsert( ry(q[i], var(\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpi\u001b[49m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m)) ) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n) ]\n\u001b[0;32m     44\u001b[0m [ vqc\u001b[38;5;241m.\u001b[39minsert( u[degree](q[i], \u001b[38;5;241m*\u001b[39m[ var(p[\u001b[38;5;241m0\u001b[39m][d][i]) \u001b[38;5;28;01mfor\u001b[39;00m d \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(degree) ]) ) \n\u001b[0;32m     45\u001b[0m          \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n) ]\n\u001b[0;32m     47\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m layer \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_layers):\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(f'epoch: {i + 1}')\n",
    "    qmodel = QModel(input_size, hidden_size, num_output, \n",
    "                    num_layers = num_layers, ctx = ctx, mode='GRU')\n",
    "    optimizer = torch.optim.Adam(qmodel.parameters(), lr = 0.0008)\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 = 200, early_stop=True)\n",
    "    end = time.time()\n",
    "\n",
    "    print(f'time costs: {end - start}')\n",
    "    \n",
    "    acc = calculate_accuarcy(qmodel, X_test, y_test)[0]\n",
    "    if acc >= 99.0:\n",
    "        break"
   ]
  },
  {
   "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": "3432ace9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09d3f586",
   "metadata": {},
   "outputs": [],
   "source": [
    "calculate_accuarcies(qmodel, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 871,
   "id": "a05d18d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode = \"CIFG\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 872,
   "id": "80014c22",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 124,
   "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": 884,
   "id": "29c8cdfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QModel(\n",
       "  (qlstm): StackedQLSTM(\n",
       "    (qlstms): Sequential(\n",
       "      (QLSTM 1): QLSTM(\n",
       "        (input_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",
       "        (forget_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=5, bias=True)\n",
       "          (qout): Linear(in_features=5, out_features=32, bias=True)\n",
       "        )\n",
       "        (output_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",
       "      )\n",
       "      (QLSTM 2): QLSTM(\n",
       "        (input_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",
       "        (forget_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=5, bias=True)\n",
       "          (qout): Linear(in_features=5, out_features=32, bias=True)\n",
       "        )\n",
       "        (output_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",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (predict): Linear(in_features=32, out_features=4, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 884,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d4c19f4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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": null,
   "id": "7f034225",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.array([(1 - mase) * 100, rmse, mse, mae, mape])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9148dd94",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode = 'adjusted'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "4b289c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "def statistic(mode, epoch=10): \n",
    "    accuarcy = 0.0\n",
    "    rmse = 0.0\n",
    "    mse = 0.0\n",
    "    mae = 0.0\n",
    "    mape = 0.0\n",
    "    \n",
    "    for i in range(epoch):\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}_{i+1}.pt\"))\n",
    "        \n",
    "        results = calculate_accuarcy(qmodel, X_test, y_test)\n",
    "        accuarcy += results[0]\n",
    "        rmse += results[1]\n",
    "        mse += results[2]\n",
    "        mae += results[3]\n",
    "        mape += results[4]\n",
    "\n",
    "    accuarcy /= epoch\n",
    "    rmse /= epoch\n",
    "    mse /= epoch\n",
    "    mae /= epoch\n",
    "    mape /= epoch\n",
    "    \n",
    "    print(f'mean accuarcy: {accuarcy}%')\n",
    "    print(f'mean RMSE: {rmse}')\n",
    "    print(f'mean MSE: {mse}')\n",
    "    print(f'mean MAE: {mae}')\n",
    "    print(f'meab MAPE: {mape}')\n",
    "    \n",
    "    return [accuarcy, rmse, mse, mae, mape]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "a79fe2db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean accuarcy: 98.54994615686289%\n",
      "mean RMSE: 9.653941815807855\n",
      "mean MSE: 96.08898386543845\n",
      "mean MAE: 7.467789769363319\n",
      "meab MAPE: 0.2910629472268616\n"
     ]
    }
   ],
   "source": [
    "# classical\n",
    "statistic_results = statistic(mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "da9b9920",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean accuarcy: 98.55807548189445%\n",
      "mean RMSE: 9.632796836784154\n",
      "mean MSE: 96.15784603556865\n",
      "mean MAE: 7.425923675500803\n",
      "meab MAPE: 0.27546597701157033\n"
     ]
    }
   ],
   "source": [
    "# peephole\n",
    "statistic_results = statistic(mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "ace3641c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean accuarcy: 98.66323007886952%\n",
      "mean RMSE: 8.936394244765822\n",
      "mean MSE: 85.08853885502472\n",
      "mean MAE: 6.8843765962606795\n",
      "meab MAPE: 0.2903496047479904\n"
     ]
    }
   ],
   "source": [
    "# CIFG\n",
    "statistic_results = statistic(mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "48c74de7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean accuarcy: 98.82307586016532%\n",
      "mean RMSE: 7.882642366226747\n",
      "mean MSE: 63.0652681275086\n",
      "mean MAE: 6.061169447169975\n",
      "meab MAPE: 0.23865214187185257\n"
     ]
    }
   ],
   "source": [
    "# GRU\n",
    "statistic_results = statistic(mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "1cd2a679",
   "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": 250,
   "id": "55a4c520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qmodel.load_state_dict(torch.load(f\"./model/CIFG2/model_CIFG2_1.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "d6d579ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qmodel.load_state_dict(torch.load(f\"./model/GRU3/model_GRU3_4.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "37cda52f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([99.00231146,  6.99726309, 48.96169077,  5.13605894,  0.17694298])"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_accuarcy(qmodel, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "8dbabbe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4.73780477,  22.44679405,   3.86358781,   0.18066322,\n",
       "          2.9562802 ],\n",
       "       [ 11.65121763, 135.75087228,   9.69747171,   0.21705672,\n",
       "          1.65079302],\n",
       "       [  4.51153598,  20.35395689,   3.44812479,   0.49403132,\n",
       "          1.11807786],\n",
       "       [  4.30237501,  18.51043071,   3.62028622,   0.00358361,\n",
       "          2.42453491]])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_accuarcies(qmodel, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2df5acd3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": [
    {
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       "  <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",
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       "      <td>1013.619007</td>\n",
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       "      <th>1</th>\n",
       "      <td>19.213526</td>\n",
       "      <td>71.658590</td>\n",
       "      <td>-4.394649</td>\n",
       "      <td>1017.484953</td>\n",
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       "    <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",
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       "      <td>5.470593</td>\n",
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       "      <td>63.049363</td>\n",
       "      <td>14.097361</td>\n",
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       "      <th>...</th>\n",
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       "      <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": {
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       "  <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",
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       "      <th>0</th>\n",
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       "      <td>67.692835</td>\n",
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       "      <td>71.658590</td>\n",
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       "      <th>2</th>\n",
       "      <td>17.397308</td>\n",
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       "      <td>5.989264</td>\n",
       "    </tr>\n",
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       "      <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",
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       "    </tr>\n",
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       "      <th>106</th>\n",
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       "      <td>55.648947</td>\n",
       "      <td>8.164790</td>\n",
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       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>36.050094</td>\n",
       "      <td>50.632516</td>\n",
       "      <td>15.869078</td>\n",
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       "      <th>108</th>\n",
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       "      <td>48.289673</td>\n",
       "      <td>21.064595</td>\n",
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       "    <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": 273,
   "id": "f01d4a7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20fbe7d13d0>"
      ]
     },
     "execution_count": 273,
     "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": 217,
   "id": "d82f5d32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20fba214b80>"
      ]
     },
     "execution_count": 217,
     "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, 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, color=\"#FF6666\", linestyle='--')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "a0a91b13",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x192b7b5ce80>]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ockrfR6mTSwoIWTxPREQ0MoIgBH0uiuKg2wAphLrvvvswa9YsfP/73w+679lnn1V+PWfOHGRmZuLzn/+8Mt0VysaNG/GDH/wgDu+AiIiIKLIJt64oT3I12/vQ1++J63PLk1yDQy6uK8aii+uKREREcSP3Z8kTXbLW1tZB010OhwN333030tLSsGvXLmi1wT/TDLRo0SIAwOXLl8M+Zv369bDZbMpHfX39cN4GERER0ZAmXMiVnapDqk4NUQSuW+M7zSV3cmWEXVcceyHXh3VW3L3lXRy71pHsS1E4AtYV5ZCLxfNERETDU1JSgry8POzbt0+5zeVyYf/+/ViyZIlym91uR2VlJXQ6Hd5++22kpKQM+dwnTpwAAOTn54d9jF6vR3p6etAHERERUSJMuHVFQRBQlJ2K80121Lb3YFquKS7P6/Z4lXAmI0zxfEe3Cy63FzrN2MkWdxysxUfNDrx2qBYLp2Yl+3IAcJKLiIgoVl1dXUHTVDU1NaiurkZWVhaKioqwdu1abNiwAeXl5SgvL8eGDRtgNBqxYsUKANIEV2VlJXp6erBjxw7Y7XalOysnJwdqtRoHDx7EoUOHcNddd8FsNuPo0aN49tln8eCDD6KoqCgp75uIiIgo0IQLuQCgOMuohFzxIvdxAYNPV8w06qBRCXB7RbR3O5FvNsTtdUfqdIMNAHC2ceyUwMrF86aA4nlHn3vMBYRERERjxbFjx3DXXXcpnz/33HMAgCeffBKvvvoq1q1bh97eXqxZswZWqxW33nor9u7dC5NJ+su+48eP4/DhwwCAadOmBT13TU0Npk6dCr1ejzfeeAM/+MEP4HQ6UVxcjFWrVmHdunWj9C6JiIiIIpuYIZevlyue5fOdvpDLlKKBRh0cxKhUAial6dFs70OrfeyEXD0uN660dQEArrR1ocflhlGX/D8S/kkuLcwGLdQqAR6vCGuPC5b0oVcniIiIJpply5ZBFMWw9wuCgKqqKlRVVQ3r6wFgwYIFOHTo0Eguk4iIiCihJuRYTHG2dMJibXt33J7T38cVuqB1cqYUbH1YZ43ba47U+SY7vL6fZ0VR+nwsCOzkUqkEZPq+p+1dXFkkIiIiIiIiotAmaMglTXLVxnOSy3eyYuaA0nnZQ/MKAADbD1yD1xv5b0pHy+nrtqDPx8rKYmAnF+D/nsqnVxIRERERERERDTQhQ66iLCnkut7RC0+cAid5kmtgH5fskQVTYErR4Fp7D/5yoTUurzlSpxukUCtFK/0xONNgi/TwUSGKohJymVKkkCtc+fwv3ruKz/zoXRy62j66F0lEREREREREY86EDLkKMgzQqgW4PF402/vi8pxyJ1dGmEmuVL0Gj39SOnlo2wfX4vKaI3W2UQq17p9T4Ps8+ZNcff1eJXiUJ7my06TvaUeXM+ix2z64hgstDqz85WH857H60b1QIiIiIiIiIhpTJmTIpVYJmJLpW1mMUy+Xf10x9CQXAPz14mKoBOD9yzdwodkRl9cdrr5+Dy61SqXzj91SCAC42OKA0+1J5mXB4ZTCQkEAjDo1AP8kV0fAJNeNLicaOnsBAP0eEet+cwob//t83CbziIiIiIiIiGh8mZAhF+BfWaxrH9zL9a9/uYyf7b8S0/MpxfNh1hUBYEqmEZ+5OQ8AsO2DmpieP97ON9nh8YrITtXhE8WZyDBq0e8RcamlK6nX1dXn7+MSBAEAkJWqBwB0BHRynbreCQAozUnFM39VDgD42btXsfq14+hxuUfzkomIiIiIiIhoDJiwIVe48vlrN7rxT3suYOOfPoLNt4IYDbkUPdy6ouzLt5cAAHadaAiaTBptcv9WxWQzBEHAzQXpAPwrjMmi9HH5VhUBIMs3HRf4/TpZL13nvCkZeG75TfiXx+ZBp1HhnfMt2Po/l0fxiomIiIiIiIhoLJiwIZc8yTVwXfFgQIl5awx9XTalkyv8JBcALCzOxOzJZjjdXrx+pC7q54+3076Qa/ZkMwCgokD63zMNye3lUia5UgJCrjRpkqu9a/Ak15wp0nU/NG8yNn5uNgBg37mWUblWIiIiIiIiIho7JmzIVZydCgCoHbCuePBKQMjlCC46j0RZVxwi5BIEAX9z21QAwH8cvIZ+jzfq14gnOcyqmCxNcN3sC7vOJHmSy+H0ryvKsgd0comiqIR0cwozlMd9eqYFKgG41Nql9HURERERERER0cQQU8jldrvx3e9+FyUlJTAYDCgtLcUPf/hDeL3+oEYURVRVVaGgoAAGgwHLli3D2bNn437hIzU129/JJYpSWbkoikGTXC0xTHJFu64IAPfNyUeOSY8WuxN/OtMcy2XHRV+/BxdbpOL7Cl+4Ja8ryl1dyeKf5PKHhQOL5xttfbjR5YJGJWBWfrryOLNRi/lFmQCAdy+2jdYlExEREREREdEYEFPI9dJLL+Hf/u3fsHXrVpw/fx6bNm3CP/3TP+EnP/mJ8phNmzZh8+bN2Lp1K44ePYq8vDwsX74cDkdyTxMcqNC3ruhwumH1TWFdaetGW8D0ViyTXLYoiudleo0aD8+fDCB4cmy0XGxxwO0VkWnUYnKGAQBQkp2KVJ0aff1eXG1LXvl8qE4ueZLL2uOC1yviVL20qjg9z4QUrTro65felAOAIRcRERERERHRRBNTyHXw4EE89NBDuO+++zB16lR8/vOfR2VlJY4dOwZAmoTasmULnn/+eTz88MOoqKjA9u3b0dPTg507dybkDQxXilaNvPQUAP5ersApLiD6Sa5+j1dZs4tmkgsApuWmAQCuWwef7phopweUzgOASiVgpm8qKpkri10h1hXl76lXlLrPTl73rSpOyRj09Xf6Qq73L91I2iooEREREREREY2+mEKu22+/HX/+859x8eJFAMDJkyfx/vvv49577wUA1NTUoLm5GZWVlcrX6PV6LF26FAcOHAj5nE6nE3a7PehjtBTJK4u+ExYP+aaqJqVJoUqrPbpJrsBTGM1RTHIBgcX3ox9yBZ6sGEj+/GwSy+cdIYrndRoVTL7P27tdSun83CnmQV8/e7IZmUYtHE43qn0TX0RERERERET08RdTyPXtb38bjz/+OGbMmAGtVov58+dj7dq1ePzxxwEAzc1Sv5TFYgn6OovFotw30MaNG2E2m5WPwsLC4byPYSkOCJq8Xn8f1/1zCgAArY7oJrnk0vn0FA3UKiGqr5EDtobOXrhHeeJILp2fPSDkknu5kjvJJX0vAye5AP/KYnuXE6cjTHKpVQLuKJemufZf4MoiERERERER0UQRU8j1xhtvYMeOHdi5cyc+/PBDbN++HS+//DK2b98e9Dh5BU4miuKg22Tr16+HzWZTPurr62N8C8NXnO0PuS62OtDR7YJBq0blLCmka4lykqvTVzqfmRrdqiIAWEwp0GlU8HhFNNmiL7gfKZfbiwvNvtL5goEhl2+Sq9GulPGPNrl43pQSHHLJ5fPH66xwON3Qa1S4yZIW8jnkXq797OUiIiIiIiIimjA0Qz/E71vf+ha+853v4LHHHgMAzJ49G7W1tdi4cSOefPJJ5OXlAZAmuvLz85Wva21tHTTdJdPr9dDr9cO9/hEpyk4FANR1dCsF8AunZmJKphR+tdj7IgZ0sobOXgBAZpR9XIDUgVWYacCVtm7UdfQoRfiJdrHFAZfHC7NBi8IsQ9B95ZY06NQqOPrcqO/oVabNRlOoTi4AyEqV/oz870dScHVzQTo06tAZ7R03TQIgdY/d6HJiUlpy/nwRERERERER0eiJaZKrp6cHKlXwl6jVani90rpdSUkJ8vLysG/fPuV+l8uF/fv3Y8mSJXG43PgKXFeUQ65FpdnITZdCEafbC7tvsigSJSArzozp9ZPRy+Xv40ofFN5p1SrMyDdJj0vSyqLcyZU6KOSSus6O11kBhF5VlOWaUpTVy/cuRZ7mEkUxaVNrRERERERERBQ/MYVcDzzwAF588UX88Y9/xLVr17Br1y5s3rwZn/vc5wBIa4pr167Fhg0bsGvXLpw5cwZPPfUUjEYjVqxYkZA3MBLyumKrw6kEVYvLspGiVSPdty7XOsQJi6Io4r1LNwAAt5dPiun15ZBLLr4fDcrJigWDS9uBgF6uhuSEXMok16B1RSl49HilQGpuYejrlykri0P0cm3800dY8MK+pJxySURERERERETxE1PI9ZOf/ASf//znsWbNGsycORN///d/j9WrV+OFF15QHrNu3TqsXbsWa9aswcKFC9HQ0IC9e/fCZDLF/eJHKsOoU05DdDjdSNWplTJ2S3oKACkAi6TmRjcaOnuhU6twa0l2TK8vr0vWj2LIFe5kRZncy3U6ySGXKUzxvCzSJBfgD7nevXQDXm/oSa3r1h788v0aWHv6cehqR8jHiKKIt0824nitNarrJyIiIiIiIqLkiKmTy2QyYcuWLdiyZUvYxwiCgKqqKlRVVY302kZFcbYRp3yn9d1SkgWtr+cpN12PS61daBlikkue4lo4NRMGnTqm1x7tSa5+jxfnfaXzc6aEDrlumZoFADh8tQOdPS5kxNAzFg9y8fzgSS7/dZj0GpT4AsJwFhRnIk2vQUe3C2cabSFDsW0fXFMmw8L9Ph+vteKZ108AABaXZuOZvyrHotKsIXvaiIiIiIiIiGh0xRRyfRwVZflDriVl/kksi0ma5BrqhMXhrirKrw0Ate3dMX/tcFxsccDl9iI9RaO89kDT80yYmZ+O8012/P5kI1Yunjoq1yZzhCueT/OHXLOnmKFSRQ6ZtGoVlpRlY++5Fuy/0DYo5LL19uPXR+qUz9vCTOwF9qUdvNqOg1fbccvUTDw4twBeUTqt0uXxIitVhy8uLBzyuoiIiIiIiIgoMSZ8yFUccILg4lJ/UJWrrCuGn+Tq93hx6KrU5XXHtJyYX1s+3dDe54atpx9mozbm54jFaV+YN3uKOeIk0iMLJuP/+6Mdvzl+fVRDLqfbA5dbOsTApA/+XmQFTJQNtaooWzo9B3vPteCt6gasurMUKVr/pN3rR+rQ7fIon4f7fW72TXgtm56Doiwjfn2kHkevWXH02uD1RUu6Hp+aEfoUUSIiIiIiIiJKrJg6uT6OirOktbf0FA1m+UrXASDXJBWdt0aY5Kqu70SX041Mo1YpbI+FUadBju91RmNl8dQQfVyyh+ZNhlol4OR1Gy63OhJ+XbJupz90StUHr34GrivODbNqOdA9FfnIStXhSls3fvD7s8rtLrcXr35wDYAUXgHhf5/lgwdm5afjhw9V4N11d2H1naX49EwL7pudj8/Nn4zSHOnP0Pmm0fteEREREREREVGwCR9y3XHTJOSY9FhxazHUAatmcvF8pE4ueVXxtmmThr2mNpq9XHLp/JzJkSehckx63OULf35zvCHh1yWT+7gMWjU06uA/mpPS9JCHz+YURjfJlZWqw788Ng+CALx+pB6//fA6AOAPpxrRbO9DjkmPVXeUAgh/wIC8rir/ecgzp2D9vTPxiycX4l+/tAA/+uI8fHbeZADSIQRERERERERElBwTPuTKNxtw9PlP4zv3zAi63ZLum+SKcLrie5faAAB3lse+qihTerk6EhuQuNxefOSbNJo9xCQXADyyYAoAYNeJ60o5e6I5nP0ABpfOA4BBp8bz987Ed+6ZgckZhqif847yHDzzqXIAwPO7zuBCswM/f68GAPDUkqmYkik9V6ujD6I4+H22+NYY5T8PoZRMkia5GHIRERERERERJc+E7+QKJ9fkn+QSRXFQh5Wttx8n6zsBDK90XlboC7nqEzzJdbHFAZfHC7NBq3SBRfKpmbkwG7RosTvxweUbuPOm6IM8URTx5ocN8Ioi5kwxY1pO2qDJrFDkSS6TPvQfy6/6pq5i9cxfleN4rRXvX76BFT8/hPZuFwxaNb50axH0Gmktsq/fC4fTjfSU4C4weY1R7mgLhSEXERERERERUfIx5Aoj1ze543R7Ye9zw2wIDj8OXrkBrwiU5qSiIIbJooGKR2ldUT5BcvbkyKXzMr1GjQfnFuC1Q7V488PrMYVc7126gb//r5PK5ylaFW4uMOOBOfl46raSsF/XJZ+sGGKSayTUKgFbHpuH+378nrJ++MVbCpHhK7M36TVwON1otTuDQi6vV1QK6S1RhKLF17sAACAASURBVFwd3S509riU5yUiIiIiIiKi0TPh1xXDSdGqke4LW1pD9HLJfVwjWVUEgKLs0Qm5Tjf4T1aM1iOfkFYWd59phr2vP+qvO1wjnTg5KU2HNL0Gff1eHK+1our359DR7Qr7dUrIFWaSayQmpemxdcUCqFUCNCoBXw4I23KU1dTg32drjwv9HmmFUT6IIJRUvQZ5vhCM01xEREREREREycGQKwJ/+fzgXq73L0sh1+3Thr+qCPg7uRo7+9Dv8Y7ouSI53SCtVkbTxyWbO8WMspxUON1e/Peppqi/7kSd9Fp/Vzkdp75fiT//3VKl0+pqW1fYr3P0JS7kAoBbpmbhv762GL9+epESLgL+AKttQP+a/Ps+KU0H7RDrllxZJCIiIiIiIkouhlwRyCHXwAmfuvYe1Lb3QKMSsKgse0SvkWvSQ69RweMV0djZO6LnCsfp9uBCc/Sl8zJBEJRprjd9JxMOxeMVla6y+UUZUKkElOWk4SaLCQBwtS18CJSodcVAC4oysXBqVtBtgf1rgeTSefn+SEpyGHIRERERERERJRNDrgjkCZ+Bk1zvXZZOVVxQlDniqSNBEJRprkStLF5odqDfIyLTqFVOE4zWw/OlkOvoNStsvUOvLF5scaDb5UGaXoPyXJNye6lv0ulqhBBoqOL5RJF/n1sH/D7La6qRTlaURfP+iIiIiIiIiChxGHJFkJseesLn4BWpc+q2Ea4qyqINuS40O7DjUC1EUYzp+eXS+YooS+cD5ZlTMClNCnlq24cOcD6sswIA5haaoVb5X6s0Jw1A5HXF0ZjkCsU/sRd6XTFS6bxMWVeMMKlGRERERERERInDkCsCeYInsKtJFEUcqekAANxamhXy62JVKIdc7eFDLpfbi6e2HcF33zqD/RfbQj5GFEX8rroB1wZME53xlc7PiaF0PlCxr7+qNsL1yeQ+rgVFmUG3l0Qx6eTv5NKGfUwi5IYpnpfDzdxYQq4b3fB6YwshiYiIiIiIiGjkGHJFEKqrqba9B60OJ3RqFeYVZsTldYqjOGHxtx9eR5NNuo7zTY6Qjzl4pR3f/HU1vvCzg0GnGMqTXLH0cQVdXwzrlPIk1/yi4O9Nqa+zqra9G54wIVCXU1qHHO1Jrhx5XTHsJNfQ64qFWUZoVAJ6+z1KlxcRERERERERjR6GXBFY0geHH/IU19xCM1K06ri8zlDrim6PF6/sv6J8fiXMyt9p38RWm8OJ7751GqIooq/fg4stvtL5KcML5YqUSa7Iq3idPS6lWH5+YfAkV4HZAL1GhX6PiOvW0O9TXlcc/U4uKcxssw8MuXydXFEUz2vVKuX3kSuLRERERERERKOPIVcEgZNccg/WYV/I9cmS+KwqAgEhV3tPyL6tP55uCloVvNwaOuS6FHD7f59uxlvVDfio2QG3V0RWqg4F5qHDmlCiXVc84TtVsXRSKjJTdUH3qVTCkCuLXcq64iiHXL4w0+F0o9flUW5XQq4o1hWB6FYyiYiIiIiIiCgxGHJFIIcfTrcX9l4pgDlyTSqd/2RJdtxeR+7kcjjd6OwJPsHQ6xXxr3+5DAB4YG4BAGmSK1QYJodc8hrl9353FnvPNgOQVhVjLZ2XFWVJ4c1Q64pyH9e8otATY0oIFGbSKVnF8ya9Bila6R8FuZfL7fHiRlf064pAcC8XEREREREREY0uhlwRpGjVMBukEvRWRx8aO3tR39ELlQB8ojhziK+O7XXkIGVgkLTvfAsutnTBpNfge/fPgkqQCtrbuoJX60RRxGXfWuI/PjIb8woz4Ohz46f/K605DrePC/BPcjXb+9DX7wn7uBO+Pq6BpfMyuZcr3AmLSsg1ypNcgiAoU3vyamp7twteEVAJQHZalCFXDkMuIiIiIiIiomRhyDWEXF8peYvdiaPXpFXFisnmuAcxoXq5RNE/xbVycTFyTHrlcQNXFptsfeh2eaBRCSjLScPmR+cq00kAMHuYJysCQHaqDqk6NUQRYfu0vF4R1b5JroGl87KSSWkAwodA8rqiaZQnuYCA/jVfL5e8qphj0kOtim4CjpNcRERERERERMnDkGsIch9Ti73P38c1NX59XLLCECHX+5dv4NR1G1K0Knzl9hIAQFmOFBRdGbDyJ68qlkxKhVatQmlOGp6/d6Zy/0gmuQRBQFG2fDpi6JDrclsXHE43jDo1pltMIR/jn+QaHAJ5vCK6fX1Yoz3JBQw+SdN/smL0PWalvhCvrqMH/R5vnK+QiIiIiIiIiCIZ/TRhnMkNOGHxSAJK52XyhNaHtVa8daIBNTe68YdTjQCAxz9ZpKzMTctNw58/asWVAZNcl3yriuWWNOW2JxYVo8XuhE6jQkGGYUTXV5xlxPkme9iQ68NaaVVxzhQzNOrQ2Wmpb9Kp2d6HbqcbqQFhVrfLrfx6tDu5AGliC/CvK8phV24UJyvKLOl6GLRq9PZ7UN/Rg9KctKG/iIiIiIiIiIjigpNcQ5BDjvNNdmVF8JYETHLJvVd//qgVa9+oxr/8+RKutHVDp1Hh6TtLlcf5J7mCQy752qbl+qeoBEHA339mOp75q/K4XV+48nm5dD5cHxcAZBh1yPKdujhwpU9eVdSpVdBr1CO+3lj5w0wp3Gr1hVx55uj6uADp+x2vlcW+fg9eP1KHzh7XiJ6HiIgmhnfffRcPPPAACgoKIAgC3nrrraD7RVFEVVUVCgoKYDAYsGzZMpw9e1a5v6OjA9/4xjcwffp0GI1GFBUV4ZlnnoHNZgt6HqvVipUrV8JsNsNsNmPlypXo7OwclfdIRERENBSGXEOQu5r+fL4FADDdYkKmL6iJp9umTUJxthH55hQsKs3CY7cU4tt3z8BvvrYY+Wb/FFZZrhSiDJrk8n1enpuY6aEiX8hV2x46vPnQVzo/P0LIBfinuQaFXEk6WVEmh5ltyiSXb10xhkkuIPJKZiz+ee8FrP/taeXgACIioki6u7sxd+5cbN26NeT9mzZtwubNm7F161YcPXoUeXl5WL58ORwOaRK8sbERjY2NePnll3H69Gm8+uqr2L17N77yla8EPc+KFStQXV2N3bt3Y/fu3aiursbKlSsT/v6IiIiIosF1xSHI4YfcF5WIVUX5dfZ/664hHydPcjXa/Ct/oiiGXFeMp+IsXydXiEkuW2+/ErKFK52XlUxKxbFa66AQyNGXnJMVZfIBA0rxvG+iK5ZOLsAf4l0dwSSXxyvid9XSqurZRtsQjyYiIgLuuece3HPPPSHvE0URW7ZswfPPP4+HH34YALB9+3ZYLBbs3LkTq1evRkVFBd58803la8rKyvDiiy/iiSeegNvthkajwfnz57F7924cOnQIt956KwDg5z//ORYvXowLFy5g+vTpiX+jRERERBFwkmsI8iSXLFEhV7QyjDpMSpMmyeSgqM3hhL3PDZXgP+Ev3uR1xesdvfB4xaD7TtZLawpFWUZMSou83if3VF29ETyJpkxyJSvkGrCuKE9y5aZHv64IACU58qRa1xCPDO/w1XalG2ykE2FEREQ1NTVobm5GZWWlcpter8fSpUtx4MCBsF9ns9mQnp4OjUb6/+aDBw/CbDYrARcALFq0CGazOeLzOJ1O2O32oA8iIiKiRIgp5Jo6dSoEQRj08fWvfx3A0H0P49HASZ5kh1yAPyiSe7nkKaqp2akJ67MqyDBAqxbg8njR7Ourkh33lc4vGGKKC0DYziq5kyvZ64rWnn643F6lkyvWSa4S3wmLI+nkevtko/LrJlsfegJK+UdbzY1uPLXtCE5f50QZEdF41dzcDACwWCxBt1ssFuW+gdrb2/HCCy9g9erVQc+Tm5s76LG5ublhnwcANm7cqHR4mc1mFBYWDudtEBEREQ0pppDr6NGjaGpqUj727dsHAPjCF74AYOi+h/FIPnUPAKZmG2MOPRJhWu6AkMu3qjgtQX1cAKBWCZiSGbqX68CVGwCAT5ZkD/k8ZQGdVaLonwjrcvYDAExJmuTKNGqhVQsAgMbOXrR3S4XvMYdc2dL7a7E70e2MPZxyub3405ng/1AIN811os6Kw1fbY36NWGw/cA3/e6ENP/6fSwl9HSIiSjxBEII+F0Vx0G0AYLfbcd9992HWrFn4/ve/H/E5Ij2PbP369bDZbMpHfX39MN8BERERUWQxhVw5OTnIy8tTPv7whz+grKwMS5cuHdT3UFFRge3bt6Onpwc7d+5M1PUnXIpWDbNBC2BsTHEB/l4u+URFpXQ+QX1csqIs3wmL7f5eri6nWzlZ8Y7ySUM/R7YRKkH6urYup3K7I8mTXIIgKNNcZ3w9WFq1gEyjNqbnMRu1yA5zgmQ03r3YBltvP3JNemUyLlS/V1+/Byt/eQRf+sVhXLeGPvEyHs41Sislh6+2D1pTJSKi8SEvLw8ABk1btba2DprucjgcuPvuu5GWloZdu3ZBq9UGPU9LS8ug529raxv0PIH0ej3S09ODPoiIiIgSYdidXC6XCzt27MCXv/xlCIIw7L6H8dDTkOeb5rll6tgIuQZNciknK5oS+rpyL1dg+fyhK+1we0UUZxtR6AvBItFr1MpEWOCEkrVHmpxKVicX4J/aO90ghVy5ppSIfzMdTriVzGjIq4r3zylQfp+vtg3u97rU0oUupxtur4jfn2yK+XWiIYoizjdJ/zza+9zKr4mIaHwpKSlBXl6eMoEPSD/H7d+/H0uWLFFus9vtqKyshE6nw9tvv42UlOBp5sWLF8Nms+HIkSPKbYcPH4bNZgt6HiIiIqJkGXbI9dZbb6GzsxNPPfUUgOH1PQDjo6fh/3xqGu6fk497Z+cn+1IA+Ff+rt3ogdvjVSa6ErmuCISe5Hr/srSqePu0oae4ZHIIFFic/6vDdQCAGfnJ+9td+YRFuX9q4KED0Rr4/qLV43Jj3znpb8gfnFfgL+kP8TznmvwdWb+rbhjWdQ7lurUXjoCVy4NXol+NDFxFJSKixOvq6kJ1dTWqq6sBSGXz1dXVqKurgyAIWLt2LTZs2IBdu3bhzJkzeOqpp2A0GrFixQoA0gRXZWUluru78ctf/hJ2ux3Nzc1obm6GxyOdMD1z5kzcfffdWLVqFQ4dOoRDhw5h1apVuP/++3myIhEREY0Jww65fvnLX+Kee+5BQUFB0O3R9j3IxkNPwwNzC7B1xQKkJnHKKFCB2QCDVg2Xx4uT1zvR0e2CIPjXGBOl2Nc3VdvhD13eu9QGILpVRVnpgBMIq35/Fp09/bi5IB2P3ZK8kFM+SfFMgxxyDa9/TV4bvdAS2+TTvnMt6O33oDjbiLlTzCiVw7IQJzWeb/L33H3U7MCF5vj33p1tDL7+g1H2f/3LO5ew4IV9IyrfJyKi2Bw7dgzz58/H/PnzAQDPPfcc5s+fj+9973sAgHXr1mHt2rVYs2YNFi5ciIaGBuzduxcmkzQFfvz4cRw+fBinT5/GtGnTkJ+fr3wE/mz2q1/9CrNnz0ZlZSUqKysxZ84cvPbaa6P/homIiIhCGFZqU1tbi3feeQe//e1vldsC+x7y8/0TT6H6HgLp9Xro9cObmJmoVCoBpTmpONtox59OS1NyhZlGGHSJOVlRpqwrtvdAFEU02/twpa0bKgFYXBZLyOWfUNpzthl/PNUEtUrAS4/MgVY97Nx1xOROLruvH2y4IVdFgRmAf+0xWr/3rSo+OLcAgiAo36caX0l/YFgsd2UZtGr09nvw9skGfCtvxrCuN5xzvvXEWfnpONdkx5GaDrg9XmiG+D3605kmWHv6cfhquzLVNpG9c64F/3msHs/fN1MJiomI4m3ZsmURp2gFQUBVVRWqqqqG9fWyrKws7NixY7iXSURERJRQw0oUtm3bhtzcXNx3333KbdH2PVB8yFNbu89KIVd5glcVAf+6oqPPjc6efrx3SVpVnDMlQynnj4Y8oXSuyY5/eOsMAODpO0tRMdkc5yuOTa4pOGzNHea64s2+91Hf0Qur75TGoXT2uLD/ojQV9+BcaTqyKMsItUpAt8uDFru/pD+wK+srt5cAAH5X3Rj3FUE5SHvkE1NgNmjR5XTjTGPk6TRRFFHv62xrczgjPnai+MX7V7H3XAv++v8/ghtd/J4QERERERElSswhl9frxbZt2/Dkk09Co/EPgkXT90DxI4dc1629AIBpCT5ZEZBOmpR7qmo7evC+L+SKZVUR8K8rNtn60OpwonRSKr75V+XxvdhhGBhqWUzDm+QyG7SY6pt6k09qHOh8kx3/ebQev/3wOn5/shFb/+cy+j0iZuSZUG6RVkd0GpUSLAaWz8tdWTq1Ck8vLUWqTo3r1l586DvlMl7kIK2iIB23+k4WHaqXy9rTj26X1N3SypALANDhCzpr23vwN9uOojug54yIiIiIiIjiJ+aQ65133kFdXR2+/OUvD7pvqL4Hip+BJfOJPllRVpzl6+Vq78YHwyidB6TwyKD1r1b+4yNzkKJN7KplNHIHhFrDXVcEoEylhVpZ7Ov34NF/O4h1b57Cc/95Et94/QR+8X4NAKlwPpA89XYloN9K7soqt6QhPUWLypulVWF53TEebD39aOiUAtSZBelYXJYNADhw5UbEr7tu9R9K0Oroi9v1jGedPf0AAK1awOkGG/72Vx+i3+NN8lURERERERF9/MQcclVWVkIURdx0002D7pP7HpqamtDX14f9+/ejoqIiLhdKwcpyg7t9RmNdEQCKfBNKe842o73bBaNOjflFmTE9h0olKCHdykXF+KRvSijZBk1yDXNdEQBmyyHX9cEh1/FaKxxON9L0GtxRPgmLSrOwsDgTn55pwYpPFgU9Vp56C5zkkiesZvpOopSDsT+caoQ7TuGJ3Mc1JdOA9BStEnIdu2aFyx3+Neo7epVfc11RWt+UQ64ffXEeDFo13r3Yhm//5hRPoCQiIiIiIoqzsXFcIMVsanYqVALg9f13ctkohVzFvvW5vWdbAACLSrOh08Re7fYP98/C/outWLNsWlyvbySyU/VB31OLefiTXLOnhJ/kklf+KmdZsPmL8yI+T2BJvyywEB6QJumyUnW40eXCgSvtuPOmnJDP1Wrvw9/+6kN8eqYFf7usLOLrDnyNm3JNyErVoaPbhVPXO7Fwauhgsj5okoshV2+/By5f8Lhsei5++qUF+Op/HMNvTzRgbmEGnlwyNbkXSERERERE9DGSvKPsaERStGoU+gKnyRkGpOlHJ6+UJ7ncviQo1lVF2SdLsvCtz8xA6ihddzTUKgGT0qTpLYNWDdMIrk1eV7xuHVw+L6/8LfJNR0UiryteveGf5JIL4eVJLq1ahftmSyea/q46/Mri5n0XcbzWih2Haod8Xfk1ZhVIr6FSCVhUOnQv1/UBIddEn1ayBqwqpurUuGtGLtb6+ufkgwaIiIiIiIgoPhhyjWNy+fzAfq5EKs4OXpOMtXR+rJNXFi3pegiCMOznSU/xl88HTnN1O9045VthXFwaRcgVcMBAX78Htl5/V5Y8ZQUAD/lWFvecbUZfv2fQ81xu7cJ/HqsHALTY++D1Rg6fzg+Y5AKAxWXS7/XBq+FDrsB1RZfbC3vfxC5Z7+yRAs4Mo0758yRPwV1u7Qr7dURERERERBQ7hlzj2Mx8qWz+5oL0IR4ZP/K6IiAFQaMZsI0GuXw+dwSl87JQ5fNHr3XA7RUxJdOgTOJFMilNB1OKBqIonc4nh0+TMwwwG7XK4xYUZWJyhgFdTjd+/u7VQc/z8p4Lyhqm2yviRnf4VUKX24tLrQ4A/mkxwB/KHa+1wukeHKQBweuKANA2wcvn5T6uDIP/96rcdxJqvbUnZCBJREREREREw8OQaxxbdUcpXvhsBZ6+s3TUXjPDqIUpRVrju31azoimncaiXJM8yTXykGuOr5frTEDIJU9BLYliVRGQDnPw93J1DSqdl6lUAtZ+WlqD2/zORfzvhVblvhN1Vuw+2wyVAOVUy2Zb+PDpcmsX+j0iTCkaTMk0KLeX5aQix6SH0+3FibrOQV8niiIarNIkl/w6rfaJ3cslh1yZRp1yW3aqDhlGLUQRuNLGaS4iIiIiIqJ4Ycg1jmUYdVi5qBgZAf8BnWiC4D8Z8c6bPl6rigBwk0WajptuGfmEWqhJLrnPanGUIRcAlCm9XN2DurICfWFhIR7/ZBFEEfjmr6tR194DURTx0u6PAAAPL5iCm/Kk9xcp5AosnQ8MMQVBUKa5DoTo5WpzOOF0e6ESgIrJ0vVN9PJ5q29dMXDqThAETPMFl1xZJCIiIiIiih+GXBSzFx6qwHfvm4kH5hQk+1Li7olFxfj104uwKg7TcQPL5+19/cpU1+LS6APC0hwp5LrS1jXo1MOBqh6chXmFGbD19mP1juPYc7YZh652QKdR4dnlNyHfN6HWbI8QckUI0uRw7lCIkKveN8WVbzagIEOaAGub4CGX3MmVGRByAf6VRYZcRERERERE8cOQi2JWMdmMr95RCpXq47WqCAA6jQqLSrOh16hH/FwDy+ePXO2AV5ROTMwzR78OKa8rXmxx4FKLFIqEC7n0GjVeeWIBJqXpcL7Jjq/vPAEA+OtFxZicYVBetynCJFe4lUgAuGVqpvJ+PAPK6+WTFadkGpS1z1Z2cgHAoGnLMk5yERERERERxR1DLqIEmj0lA4AUCskrfotiWFUE/JNcZxrscHm8MOmDu7IGyjcbsHXFAqhVAjxeESa9Bl+/axoAKCFXS5iQSxTFiNNiJZPSYNSp0dvvwdUBfVL1HXLIZUSOEnJN7EkuqxJyBU9yySu/DLmIiIiIiIjihyEXUQLN9nVTnb5uU0rn5V6raE3NTkVgv/+MfNOQU3SLSrNR9eDNUAnA31XehMxUaZIof4hJrkZbH2y9/dCoBGWlLpBaJSineQZ2jQHSWiYAFGYZlFMqJ/q6oq1XXlcMnuSSQ65r7d3o93hH/bqIiIiIiIg+jhhyESWQ3Mt15FqHsga4KMaQK0WrxuQM/+RWuFXFgVYuKsb5F+7GU7eVKLdZhujkkvu4puWmhV3ZDFWoDwD1vnXFwkxjwLrixA65lEkuQ/AkV4HZAKNOjX6PiNr2nmRcGhERERER0ccOQy6iBJIDoY5uaaLnJkuassoXC7mXCwhdCB/OwKBKnuRqtvVBFMVBj5eDuEivMdv3ns4MDLk6pEmuKZkG/7pihIL7iUAunh/YyaVSCezlIiIiIiIiijOGXEQJlJ6iRcmkVOXzWFcVZaUBzxGqED5a8iRXb78H9l73oPuVkxUjvIYccp1ttCvl8x6viMZOeV3RqKwr2vvc6Ov3DPt6x7vOMJ1cgH9l8UobQy4iIiIiIqJ4YMhFlGDyNBcALI6xdF5W5iufV6sE3GQxDftaUrRqZPoClyZ776D7L7Y6ACDia5TmpMGgVaPH5UHNDSmgabb3we0VoVULsKSnIN2ggU4j/etlovZyiaKIzl4p5BrYyQWwfJ6IiIiIiCjeGHIRJZhcPi8IwK0lwwu55PXBGXkmpGhDd2VFK88s9Xs1Dyif7/d4Uefrh5IDmFDUKkG5HrmXSz5ZsSDDALVKgCAIyEmb2L1cDqdbmXSLNMl1yRcsEhERERER0cgw5CJKsMWlkyAIwC3FWcoph7FaUJSJHz8+H//y2PwRX09gL1eguo4euL0ijDo18nxrjeHIK4unr0vrjcrJiplG5TG56VLI1eaYmL1cnd3SFFeKVhUymFTWFVu74fUO7kcjIiIiIiKi2GiSfQFEH3ezp5jx1prbUBBwQmKsBEHAg3ML4nI9ci9X04CQ64pvba5kUipUKiHic1QMKJ+XJ7kKs/zvUT5hcaKuK3b2+krnDaGDzeIsI7RqAb39HjTaejElICAkIiIiIiKi2HGSi2gUzC3MGNapiokgT3K1DDj58OqNbgBQTv2LxF8+b4PXK6LeKoVcgUGNcsLiBA25rBFK5wFAo1ZharbUtXaJvVxEREREREQjxpCLaILJM0ee5Iom5CrLSUWKVoVulwc17d3KuuKUzMBJLul1Wu0TM+Tq7JEmuUKVzsvKLfLKIkMuIiIiIiKikWLIRTTByH1bAzu5rrRJQUup7yTHSDRqFWblS+XzZxpsuK6sKwZ0csnril0TNeSKPMkFANNyeMIiEREREREFc7m9+PrOD/GjfReTfSnjDkMuoglGKZ4PWFcURRFX2qJfVwT8K4sn6jrR5HuuwEku/7rixCyet/omuTIiTHKV5TLkIiIiIiKiYB9cuYE/nmrC1r9cRq/Lk+zLGVcYchFNMPK6oq23Hz0uNwCgo9sFW28/BEEqno+GXD6/92wzRFE6RTAnzd87xnXFKCa5fCHXpdYuiCJPWCQiIiIiIuDglXYAgMcr4rTvsC+KDkMuognGlKJFqk4NwL+yKE9xTc4wwOC7byhyyNVok6e4jBAE/6mMuelS4HWjywmPd+IFOP5OrvAhV1lOGgRBChxvdLlG69KIiIiIiGgM++DyDeXXJ+qsSbyS8YchF9EElDdgZdHfxxXdqiIAlOemQa/x/yskcFURALJTdRAEwCtKk2ITTWevb5LLEH5dMUWrRqHvREquLBIRERERkbXbhXNNduXzE3WdSbya8SfmkKuhoQFPPPEEsrOzYTQaMW/ePBw/fly5XxRFVFVVoaCgAAaDAcuWLcPZs2fjetFENDL5ZimQkie5rrbJJytGt6oISOXzM33l8wCUsCbw/uxUKeCZiL1c1ijWFQH/yuLlVkfCr4mIiIhoLGJtA5HfoavtEEVAp5bimg/rrPxnJAYxhVxWqxW33XYbtFot/vSnP+HcuXP453/+Z2RkZCiP2bRpEzZv3oytW7fi6NGjyMvLw/Lly+Fw8D/giMYKi++ExaYB64rRls7L5PJ5ACjMMgy6P0fu5XJMvF4um7yumBp+kgsIDLk4yUVEREQff70uD/73Qiv+9S+XseZXx7H0n/6CGf+wG7tOXE/2pRGNCQd8fVyfmz8ZGpWAVodT+e82Gpomlge/9NJLKCwsxLZt25TbDkpVNwAAIABJREFUpk6dqvxaFEVs2bIFzz//PB5++GEAwPbt22GxWLBz506sXr06PldNRCMin7DYMmhdMfpJLiA45JoyYJILkE5YPN8EtE3AkEuZ5DJEOcnVxpCLiIiIPv6e2nYEh2s6Bt2+50wLPjd/ShKuiGhs+eCK1Mf1qZm5ONdkx+kGG07UdaIgY/BQAQ0W0yTX22+/jYULF+ILX/gCcnNzMX/+fPz85z9X7q+pqUFzczMqKyuV2/R6PZYuXYoDBw7E76qJaETkTq4mWx+cbg/qO3oAANNinOSqCJzkChFy5Zqk8vmJFnJ5vCLsffK6YuRJrhl5JgDAqXobXG5vwq+NiIiIKFlEUUR1vdQvdPfNefi/987As5++CQDQ0NmbzEsjGhOabX242tYNlQAsKs3G/CJpa47l89GLKeS6evUqXnnlFZSXl2PPnj342te+hmeeeQb/8R//AQBobm4GAFgslqCvs1gsyn0DOZ1O2O32oA8iSqw837pis60Pte098IqASa9Bji+Uila5JQ2ZRi1StCpMnRQ+5Gq1T6zxWntvP+S1efMQk1w3F5gxKU0Hh9ONwzXtCbsmr1eccGEjERERSX/5tvV/LuH3JxuTfSmw9fbD6ftLvS2PzcPTd5bhMxXSfztet/Yk89KIxoQDvimuislmmA1af8hVz/L5aMUUcnm9XixYsAAbNmzA/PnzsXr1aqxatQqvvPJK0OMEQQj6XBTFQbfJNm7cCLPZrHwUFhbG+BaIKFaBpyteafWvKob75zQcrVqF3/ztErz5t0tgShkc5sihWVvXxApXrL4+rjS9BjpN5H/NqlUCls+Sfrjbczb0XwbEQ9Xvz+KWF9/B8Vr+LRAREdHHyetH6vCPf/oITrcn5P2b9nyEl/dexN/910n09Yd+zGiRe4WyUnVI0aoBAJN9K1jWnn50Od1JuzaisUDu41pSNgkAML8wEwBwuoFbH9GKKeTKz8/HrFmzgm6bOXMm6urqAAB5eXkAMGhqq7W1ddB0l2z9+vWw2WzKR319fSyXRETDIHdy3ehy4qNm6VCIWEvnZWU5abi5wBzyvly5eN4+sUKuzl5pVXGoKS5Z5Szp3537zrXA603MySlyuHX6Ov8WiIiI6OPC6fbge787g3/bfwXf2HkC/Z7g/wj+/clG/Gz/VQCAy+3Fqeu2ZFymotk33S8fggQAphStchp1g5UrizRxiaKIA5elSa4lZdkAgOJsIzKNWrjcXpxr4tZbNGIKuW677TZcuHAh6LaLFy+iuLgYAFBSUoK8vDzs27dPud/lcmH//v1YsmRJyOfU6/VIT08P+iCixMpK1UGnVkEUgYNXpb8tKMsdXsgVSW66b11xhGtyDZ29+M6bp7D7TNO4OD63UzlZMbqQa8m0bKTq1GixO3GqITE/fMp/c9re7UrI8xMR0fh3odnBlbFx5lJLF/o90s9Ge8+1YO0b1XD7gq5zjXas+80pAIBRJ01NHb02uPB9NLX4fh6R/8JVJk9z8c8fTWS17T1otPVBqxZwy9QsANKW3PwiaZqLvVzRiSnkevbZZ3Ho0CFs2LABly9fxs6dO/Hv//7v+PrXvw5A+g1Yu3YtNmzYgF27duHMmTN46qmnYDQasWLFioS8ASKKnSAIsJilAEr+l2XppNhOVoxGTpoccvWNKJx6/XAdfn20Hl/b8SE++68f4P1LN+J1iQnR6TtZMXOI0nmZXqPGshm5ABKzstjX70GHL9y6McFWR4mIKDptDice3Po+PvfTA0lfaaPonWuUJjsKzCnQqgX88VQT1v3mFNq7nHj6tWPo7ffgjvJJeG65VO5+JMSphqNJ/ku3wEkuAJiSKYdcnOSiiUs+VXF+USYMvmAaAOYXyuXz3MiIRkwh1y233IJdu3bh9ddfR0VFBV544QVs2bIFX/rSl5THrFu3DmvXrsWaNWuwcOFCNDQ0YO/evTCZTHG/eCIavvx06YcJ+W//EjnJ1dfvHVHHQk17t/Lrk9dteOKXh7Hi54dwpa1rxNeYCNae2NYVAeAzN0sri3sTEHLJP1ACQJuDk1xERDTYmQYbnG4v2hxO7D6TuI5Iii95fene2fn4yeMLoFYJ+O2JBiz/0bu4bu1FUZYRP3l8PhaVSqtPx2ut8CSoGiEaLb51xbxBIZd0gBEnuSLr6HZh+4FryveRPl7kPq7bfH1cMmWSq56TXNGIKeQCgPvvvx+nT59GX18fzp8/j1WrVgXdLwgCqqqq0NTUhL6+Puzfvx8VFRVxu2Aiig9LwJi4SpD2vePNqNMgTa8BMLKVxVpfyPWPD8/GU0umQqdW4cCVdqz9dXVcrjPelHXFKCe5AGDZ9Bxo1QKutHXjcmt8w7umgCO527s5yUVERINdaHEov379SF0Sr4RiIU9yzSpIx90VedjyxXlQCVIYYtCq8e9//QlkGHWYmZ8Ok16DLqcb55PY69MUZl1RnuRq6OQk1/9j78zD26ru9P9erbZkWd63xGucOHviLGSBkFBIIC20Jezp0NIZKB2gQ9oyfaD0x2S6QIdSCm3aYekGQ9la9tIGEsgG2UkcEsdO4sTxvsm7JVvr/f1x7zm6krVcSVe2bJ/P8/iBWLIsy/K9577nfd9vMFxuD+584Qj+691qbHx6H3bVdo73U2IoiMfD4wApnS/P9LltYaEZHAc09QyzaekyiFjkYjAYkwPp4qIwwwC9Rh3i3tFDJixGWz7P8zwauoVdvSXF6dj65Xn4+39cBgCobu1PyEgFiSuSElU5pCZpsUrctfnwtLI76K0SJxeLKzIYDAYjEGclIteh+h5cCOCWdnt4vHmsGRct1lG3McYej4enTi4yBOi6RQV46tZKzM1PxW9uq8TsPKHvWK3isKRYcIOMZ2SROJByR4lcxMnFRK5g/PqjczgmxtV6rA58889H8LP3T7OJe5OE2vZB9FgdMOjUWDQ9zee21CQtZoqpm6omb2SR53mc7RhMyOuh8YSJXAzGFEVqE492sqIciMjVFaW40mtzYnBEiDoWZQgLoJk5KUg3aOHhobjrSQl6RSdXWgROLgC4ep4whfbD6g5Fn097v3fBaGFxRQZjUrJ3715cd911KCgoAMdxePvtt31u53keW7duRUFBAZKTk7Fu3TpUV1f73Oe5557DunXrkJqaCo7j0Nc3uvujpKQEHMf5fDz44INx/dkYYwMRuUyiA/u1I6Mnnj+79zy+9/oJPPjm52P63BiBae4dxpDdBZ1GhbJsb7fqlxcV4B/3r8FVc32n219SKhRZj2f5PJmuGMzJxUSuwBy60I1tu+oAAE/ctAh3rC4BADy/rx43PbMfn9ZZcKqlHw3dVliG7KOmbDISG5fbg5cONQAAlpdkQKcZLdNUFvqWz9tdbjz05kls+NVePPgGOyZLYSIXgzFFyTNLRS7lS+cJOdTJFV13AIkq5qUmIUkruM04jkNFntDzV9s+GPRrx4v+YdHJFUEnFwCsn5MLjhN2aNr7letakDq5hp1u2BzR96MxGIzExGq1YtGiRdi2bVvA2x9//HE8+eST2LZtG44cOYK8vDysX78eg4PeY6jNZsM111yDH/7whyG/149//GO0tbXRjx/96EeK/iyMscft4XGuQ9g0+s6V5QCAv33W7OMQaekbxm8+Ei6yP2/uH9deJ4bA6TZhInNFrgladfjLOqnINR7Tqkecbup29y+enyaKXD1WB6wx9LhORvpsDmx5rQoeHrhx6XTcuHQ6tn55Hp67fSnMyVqcaO7H135/CNf+5hOs/cVuLPvpTiz9yY6AbkxG4lHXOYQb/nc/Xj4kxMSvW1QQ8H6VRd7y+fb+Edzy7EG8Km5GfFLXPSEm0I8VTORiMKYoviLXGDi5osyPN/YIUUX/zjBiv68dx16JYBAnV7oxMpErJzWJTk/ZUaOcm6vNr9+CubkYjMnHxo0b8dOf/hSbNm0adRvP83jqqafw8MMPY9OmTZg/fz5eeOEF2Gw2vPzyy/R+W7ZswYMPPoiVK1eG/F4mkwl5eXn0IyUlfucQxtjQ1GOD3eWBXqPCN1aXINukR7fVgZ2Sc9FP3juNYTESY3O46SYUY/ygfVz5qbLuv3C6GTqNCpYhB+rHIXJKNvCStWqkJml8bktN0tLPsV4uLzzP48E3TqKtfwSlWUb895fn0ds2zMvDP+5fg6vn5aI0y4hskx4GcSLfwIgLnzWwkvJExuPh8ft9F/ClX+/DieZ+mJI0+NUti3DDkmkB7y8tn79u2yeoauqDOVkLtYqDZcjuM2hqqsNELgZjiiK1iZclcFzxoiWYyCU4uaRFuYlCH52uGFlcERAWLICyUxb9T3rR/i4YDMbEpL6+Hu3t7diwYQP9nF6vx9q1a7F///6IH+9//ud/kJmZicWLF+NnP/sZHI7QwrndbsfAwIDPByOxIOfSmbkp0GvUuGnpdADeAvrdZzqxvbodahVH1w+ng2wy2V1uvH28JaapyvFmsjgeqiWl83LQa9RYLHb9jEdkUVo6z3HcqNvZhMXRvHqkCdur26FVc/j1rZUw6n3FwWlpyXj29mXY9cA6HHn4Kpz+8TX46mLBCUSSBYzE5P7XqvDT92tgd3lw+axsfPjdy3F95fSAfxsAUJ6TghS9BiNOYQru7DwT3r3vUszKFa6JPm/uH8unn9AwkYvBmKJkp+iRotdAq+ZokWE8yDQKQk+PNTr3UEOPsNNYnOkbqUzkuCIRudIjKJ4nXC2KXAfOd4e8QIikYLJV3BElky67mcjFYEwp2tsF0Tw317efJzc3l94ml/vvvx+vvvoqdu3ahfvuuw9PPfUU7rnnnpBf89hjj8FsNtOPwsLCyH4ARtw5K55LZ+UI59Zblgu/o0/qLKjrHMJ/vSv0t31zdQnWVeQA8LqI/Pn9vnpsea0Kv9pxNt5POyoeeecUVj720aSYUEaERrkiFwAsLyXl82Pv8qGl835RRUIkvVwjTjce+0cNdpxWtsdUDjzPo6nHBk+cI7s8z+MXH5wBAPzn1RVYMN0s6+tIJyxJFjASj4ERJ9470QoAePT6BXjhm8uRb04O+TVqFYdVM4Spi19amI8371mN4kwjFk4T3hcnW0b3aE5VmMjFYExRNGoV/vzN5fjTHZcg3Ri540gumUbByRW1yNUd2MlFdi26Bu1RP3Y8cLg8VJxKj7B4HgBKs4zIS02Cy8PjTBAB7+PaDsx9ZDt+8UFt2Mez2l0YEIv754mLYMtQ4rxeDAZj7PDfHeZ5PuiOcTC++93vYu3atVi4cCHuvPNOPPPMM/jDH/6A7u7uoF/z0EMPob+/n340NY0uNGeML2fFIS6zxA2k4kwjLi3PBM8D3/jjYTR025Bj0uP+q2bSc0kwJ9endRaf/yYaH1Z3oGPAjr1nu8b7qcREj9VBnVHE3S6H5SXjVz5PSufzzMFELmGt1yJD5HrpYAOe3XsB9796HJ2DYxPTcnt4/PNkG776u/1Y8/gu/Hx7+HVYLAzaXXSNe/vKEtlfZxY7YcmmKyPxIIOzclP12LyiSPa5+IkbF+H1u1dh222VMOiEzWsifjInlxcmcjEYU5hlJRm4bGZWXL9HRoog9HRHKaxQkSvD18ll1GvotMXa9sSJvhBrOMcBqREWzxNm5grOuvNBJkd+VNMJDw/8dtf5sLHGNnGyokmvQYnohrMwJxeDMaXIyxMcov6urc7OzlHurkgh/V11dXVB76PX65GamurzwUgsiJOrItcrlty6vAiAtx/pR9fOhSlJS11D1QGcXC63h463P9MxiIGRxLrI5nke3VbhHPh588R2PdSIImNxpgGmJPnrjaXF6VBxQuepkkNu5EC+X3CRS56Ty+5y4/l9FwAI/XBkIEK8GHG68ZdDDbjyl7vx7385hhPie/xknEUF4jY06TVIFru25ECSBH0srpiw1ImDPmbmyBeoAcBs0OKS0gwfUWyRGEE+2dI/aaLYscJELgaDEVdIXJEsKiPBandRQabIz8kFeHcua9sSJ7LYJ1rDU5OEIshoIIMAznUG/rnIBCwAeOCvJ0J2V7T2if0XaUnIMhHBkYlcDMZUorS0FHl5edixYwf9nMPhwJ49e7B69eqYHvv48eMAgPz8/JgehzF+ON0eXLCIF1y53vqCDfNykSGew1fPyMR1C4Xf8ew8EzhOuAD3d9DUtg/C5hDi9DwPHEuw4uuBERecbuEi8MQEdz1EWjpPMCVpMUf8msNj7OaiIlfYuGLoTq43j7WgY8BOaxheOdwY1yL9+189joffOoWL3TaYk7XYOF/YOIh3x2nngPD4pN9WLiSu2MfiigkLWeOXK1AZMysvBTq1Cn02J5p62NAGgIlcDAYjzmSmCCfmEacHNkdkJbTExZVu0FLrtRRaPp9AvVxk1ywtij4uArnIOBfAycXzPM6KJ8YCcxIGRly47+XjPmPepRAnV745GVni74LFFRmMycfQ0BCqqqpQVVUFQCibr6qqQmNjIziOw5YtW/Doo4/irbfewqlTp3DHHXfAYDBg8+bN9DHa29tRVVVFXVknT55EVVUVenqEC+EDBw7gV7/6FaqqqlBfX4/XX38dd999N7785S+jqKho7H9ohiJctFjhdPMw6tSYlubthNFr1Hhw42wsLU7Ho9cvoM4Bg06D0izBGVzjt8nkP80t0aa7STd5TrcNwOkOfO4cTwZHnLJ6N2kfV4QiFyCJLNaPscgVJq44TYaTy+X24Jk95wEA310/C+sqsuHy8HjiwzMKP1uBeosVH1R3QMUBP/rSHOx/8Av43vpZAKKfHC4XIqJFKnKZDSyumOiQNb50YyFa9Bo1ZueL5fOslwsAE7kYDEacMerU0GmEQ02kkcVGsXS+yK90nlCRJyzsahNowmKv2J2QFkUfF4FYl+sCiFxdg3b02ZxQccCL/7YCqUkaVDX1Be3nIn0dBWlJVHBk0xUZjMnH0aNHUVlZicrKSgDA9773PVRWVuKRRx4BAPzgBz/Ali1bcM8992DZsmVoaWnBhx9+CJPJG5V45plnUFlZibvuugsAcPnll6OyshLvvvsuACF2+Nprr2HdunWYO3cuHnnkEdx111145ZVXxvinZSgJmaw4K880qhfm5mWFeOPfV6Mky/c8TIQV//J5ImoRR87Riwkmckk6PB0uT0JtkgFCz9alP/8YX//D4bD3PR3hZEUpl5SOTy9XeCeX4NrvtjqCboy+f7INDd02pBu0uO2SQvzg6tngOOD9z9viEkF97YjQIbh2VjbuXFMGo15DRaf+YSfsLvmDgCKlUxQFIxW50qmTi4lcicq5KOOKwVhAyucnuENVKZjIxWAw4grHcZLIYmQi10XRyVUSIKoIeCcsnm0fjPuEG7lQJ1eUfVyA17rc3Ds8apF3VjwpFmcaUZ6TgiduWgQAeH5fPXYGmDDURuKK5mRkpUQWV7S73EEdYgwGI7FYt24deJ4f9fHnP/8ZgHAs3rp1K9ra2jAyMoI9e/Zg/vz5Po+xdevWgI9xxx13AACWLFmCgwcPoq+vD8PDw6itrcXWrVthMAQ+RjMmBv6TFeUwN0j5PBG57rysFABQ1dSXUG4p//NfohU1n2juw8CIC4cv9uB8V+BeTkDoiKoTb49G5CJOrjMdg+gfIyHE5fbQTbZgTi5zshamJCGCGKh83uPh8btdgovrXy8thUGnwdyCVFy/eBoA4Of/rFW0k8jp9uBvnzUDAG5Z7nWrmpO10KoFQTie7njyeuWYAr9ewUijxfPMuZ+IWO0u2nWo1IR70st1YoJ3DSoFE7kYDEbcyRTFlZ4Ie7m8pfOBL6BKMg3QaVQYdrrR2BO6v2GsIAuK9BjiihlGHRUGz3f6dkycJTvuor15w7w8/OulwsXEo/+oGfVYrTSumITsCOKKDpcH1/92P9Y8/jGs9shipgwGg8GYOJDNk1kRTOibVyC4BqpbvSJRW/8wWvqGoeKAG5ZOhzlZi2GnmxakJwJdfue/RCufvyjplfqoZvTGFeFcxxDcHh7pBm1QV1Qosk16lGYZwfNj18tlGXLA7eGhVnG0PiEQxM0VKLL4cW0nznQMIkWvwddXldDPf3f9LOjUKuw/341955Sb6vlRTScsQ3Zkpehx5Zwc+nmO4+iaKp6Rxa6oO7mENajVEXyzcu/ZLrxyuDGm5zfidOPFAxcVd0R2DdrxhSd248fvnVb0cRMFImBnpegUm3BPJiyeahlImI3/8YSJXAwGI+5kGIWTc6RxxYZuYbFXHCSuqFGrqNhTG+YE2zkwgh+/dxoXQuyMKkGvjXRyxXbSIm6uui7fn8srcnkvRu6/aiYA4ILFOmqX2htXTKZxxf5hZ1iH1pvHmnG6bQAdA3Y6KYvBYDAYiUHXoD3instgkPOKdLJiOEhcsd5ipc/jWINwrpiTnwpTkhZLi9MBAEcSKLJIzpHE2Zxo5fPS8vSdpzuD3u90m/C85xWYR0VM5XJZuTBd++Pa4GKakpA+rhyTPuRgHlo+3+crcvE8j227hL7Af1lZTHunAKAww4DbVxUDENxcSl3kv3pEEIFuWjYdWrXvZTMRnuIqclEnV2QiV2qSFuRt0R9kwuL3Xq/CQ2+ejHpd3Dk4glufO4hH3qnGf/7tRFSPEYyPajpwwWLFHz+tx6EL3Yo+diJAoopKlM4TZuakIEmrwpDdhfru+A1hmCgwkYvBYMSdaOOK1MkVJK4IABW5wkI73C7SCwcu4o+f1tOR0/GizxZ78TzgPfFJJykCgUUuc7IWZdmCECi1KfM8jzZxkZhnTkJasnfiY0+I34XT7aELSQBM5GIwGIwEoq5zEOt+sQv/8vtDMT/WiNONi+IF0awICpCzTXpkm/Tgee8mE4kqEnGL/PezhrHtfQoF2WxbO0tw5ZztGMSwI36dSpEiFbmONvTQnk9/YunjIqyfmwsA2HG6c0ycH7SPK0hUkRBswuKBC92oauqDXqPCv4lxWCn3XlEOk16D020DirjTWvuGsedsFwDglmWFo24fC5Er2umKKhVHBzYFiizaXW7q6o9mKuXp1gF8ddundH14qqUfQwq6/qXrzv9+7zTck8yZREvnFerjAoSNf+KwZb1cTORiMBhjABG5Qgkr/thdbjoZMJiTC5BMWOwIHYc41SLc3to3EvJ+seKNK8bm5CIZfemERZ7nqeg1y2/HfXGhkMWvavKe2AZGXLCKi/cCczJUKm8/miVEL9dbx1p8YgInmMjFYDAYCcPP/1kLq8ONE839MYsT57uG4OGFjZlIL6T9y+c/a/QVuZaJ/z16sVfRnqRY6BZrE+ZPS0VWih5uDz+qV2w8IYKjTqOChwd2nQns5oplsiJhZVkmTHoNLEN2HB+D83y7uKYLF68MFld8fq+wSXnL8sKA79UMow5XzBbEy4MKuH9eP9oEngdWlWWOGrwAjLGTKzWyv01A0ssVwMklXY83RVj38WF1O258Zj9a+0dQlmVEtkkPDw8cb1TOsXm80ft+PN02gL8ebVLssROBOnFKuhKTFaWQ8vlE6xocD5jIxWAw4k4GLTyXL3I19w7DwwMGnZrGCgJByudr20I7uciCsGMg3iKXUk4u4ec6LxG52vpHMGh3QaPi6Ph2glfk6pPcf5g+l2SdGgDCTliUurjILi87WTIYDEZicPBCN3bWCMKH28NH7JD2h7qDc0ZPVgyHtHx+2OFGdYtwriAi16LCNGjVHDoH7QH7lcYD4l7JStFj0XRyQZgYGzl2l5uWrW+qFIrUP6oZLXJ5PDxqxDVPLE4unUaFdaIotCPA4BqlaRddSblhRK5pacTJ5X3PNPXYsFt0VX3z0tEuLgKZGnm4PjYnl9vD43VxquKtl4x2cQHwdnINxWdd6XB5qBgVafE84K3NCDRhUboeb+yR/7f59vEW3P3SZ7A53LisPAtv3XMpVs/IBKDcJNUhuwtnRRHo7rVlAIAnPjyDgZHJMymSbGArGVcEgIUJdkwbT5jIxWAw4o43rih/t6tRjCoWZRhCLrxn5wti0MVuK0acgSMHnYMjdKctnjtuANArOrli7eQiuzsXu610PDW5GCnNMkKn8T18E5HrRFMf3TGXTlYkZIURHN8+3oLGHhsyjTr8fNMCqDihRyPe4iCDwWAwQuPx8HjMb8BIrMdmb+l85BdbUifX5819cHl45KbqqUiRpFXT+MzRBIksWmgnlx4LxWlkibKR09Rjg4cHUvQa3HqJMMlvz9muUR2ajT02DNld0GlUKAvgMIqEDeJm1oen22N6HDm0SwbhhILEFVskccVXDjeC54E1M7NGbfJJWSGKXMcae2OaDr3vXBda+0dgTtbi6nl5Ae8TbycXWTNrVFxUE7vJZmtvgLiidKMzksFNf9p/ETwvxDf/9M3lMBu01LFJ4sqx8nlTH3heEDu/v74CZVlGWIYc2PZxXfgvngCMSIZlKRlXBLwiV3XrAFwJNNV2PGAiF4PBiDukeD6SuCKx7JeEiCoCwk5ahlEHDz+6v4pQI3F5dVsdMS18wkEKPqNZkEjJMelhStLAwwMXLcLJMFhUEQBm56VCp1Ghf9iJi6JASCYrFkgWlN4Ji6MXZS6Ji+uuy8uQmaKn34tFFhkMBmN8ef9kG04098OoU1MhoHMwRpGrPfLSecI80UVU2z6AI2IH0tLidJ+NqWUJVj7fTZ1cOnpBeEIh10NDtxV//7w16ovLevFcX5JlwMJpZmSb9Biyu3Co3jd6t++c4GiqyDVBo47tUm5dRTa0ag4Xuqyo64zvYB5SPB+uk6tQjCtahhwYFqcDvi7G1b62oijk15bnpCDDqMOI04OTLdH/Xl89LHy/6yunIUmrDngfInLJmVgdDdI+LlWIov5gkHVofxgnl3/3WTBcbg9qxVTEv6+bQYv4lxYLwuLxxl5FhBUSnV1clAadRoX/d+1cAMCfPq2Pqj8s0TjfNQRejIiHSqpEQ1lWCow6NYadbpzvmvivVSwwkYvBYMSdzCjiinIOMw+YAAAgAElEQVRK5wFhjDNZnNe2B+7VIH0hhGBRvVjheZ7umMXaycVxnLd8XrRtn+kInuHXaVT0gqOqSbiYICWv+WneBSX5XVgC7Dy+U9WKhm4bMow63L5SmFK0SNzpVuoigMFgMMYbm8OFw/U9CdMTJYXneTzw1xO4+dkDOCopz7a73Hj8g1oAwN1rZ9DzXsdAbOcz73klcpGrONMIg06NEacHbxxrAQAsKUr3uc+yEtHlkQAil8PloRtRmSl6KnJd6LIqEoV64K8ncN/Lx3H7Hw5H5e6ptwgiU0mmESoVhyvFKOFOSZSwrX8Yj28/AwD4yuKCmJ+zKUmLVTOEKYvxjix2yIwrpiZrYNJrAAAtfcP4oLodliEHckx6XDknN+TXchyHS0oE0eVQkMji/vMW7A7SdQYAdZ1D2FkjvBa3XRJcVIu3k4s8bqRdeQQaVxwevfbu9nNyyTkWnu+ywu7yIEWvQVGGd21ekWeCSa+B1eGmx5NYIH1clWJC4YrZOVg7KxtON4+fvX865scfb+po6XxK1JNRg6FScZg/jUUWASZyMRiMMSCa4nli5Q1VOk8gvVzBJiz6l8rGK3o3aHdhxCnsYmWZYt+dmek3YfFcmDHvtJdLXCC0BowrCosl/x4XqYvrzjWlMIoLzIWFrMSSwWBMLp744CxufvYA3hSFmUTiWGMf/vZZMw7X9+DGZw7goTc/R5/NgZcONqKpZxjZJj3uXFOKHFEoiOV8ZrW7aO9RIIdwONQqjg5/IQ6LZaLAQCAuj7Odg1RgGi/IJpSKE1wumSneaOWpltjPcaRn58CFbnzp1/uou00uxMlFIohXiYLOzppO8DwPnufx4BsnMWh3YXFhGu5YXRLzcwakUxZHRxbrLdaIf45A8DxPe0LDxRU5jsM0yYTFvxxqAADcuryQuodCEaqXq3NgBN/442Hc8acj+M1H50aJO/UWKzY/fxAuD49VZZl0fRmI7BTh5+gatMdFMO8URa6cqEUuElcM4OSSrAFtDres9Xl1q/A3Mjc/1cdZplZxWFwkrD9jjSzyPE+7ZSvFxwSA/3ftHGhUHHbWdOJYBAX3LrcHNW0DCbWhQdb05QpHFQneXq6pvW5nIheDwYg7GaLINex0w+aQN2KYxBXDObkA74TF2mAil3hiJufkzjiJXB2ic8qUpIFBp4n58YiTq65rCB4P7x05HE7kEk9sZEFZIHFyZQWJKx5t6EW9xQpzshZfX1VCP0+dXJKuLwaDwZjIfCb2QwWbXDeevHK4EYA3Zv7K4SZc+cs9eHrnWQDA99bPgkGnQa44bS0WJxc5p2Sb9PQ8HSnS4nO9RjVq2l+2SY/iTAN4HhFdnMYDct7LMHrjX4sU2sgZHHHSgu+ybCM6B+249bmDeH7vBdnnTurkEkWuS8uzoNeo0NI3jNr2Qfz1aDP2nO2CTqPCEzctijmqSFgvimnHm/p84q9nOwZx7a/34ZZnD0Q8gc+fgWHvJmA4JxfgnbC452wXDl7ogYoD7SkLBxG5jl7shdtv+ug7Va1wuoXP/XLHWfz3e6fphNLGbhs2P38QnYN2VOSa8NuvLQn5fchm5rDTTSdZK0nMTq4QcUX/NaCcXi4ypXzetNHDDpYVe1/zWGjuHYZlyA6NiqN9foAgCH15keBcjGTS4iPvVmPj0/vw1vHE2dAg6YyZCpfOExaQrkEFhPuJDBO5GAxG3EnRa2hRupzIotvDo1mc9iK1RAdjdj7pBRktctkcLlwQd5grxRhFrPGOYNC+CRkLODmQQsq6jiG09A3D5nBDp1ahJIjwR0SumtYB2F1utPWPdnKRuKK/vf6kuMBfUZqBFL1XoKvIM0GvUWFgxEW7vhgMBmOiwvM87So5erE3ocT7/mEn/v55KwDgN5uX4PW7V2FmTgq6rQ4MjLgwMycFNy2dDsArFHTF0MlF+rhmxTDGfm6+90J00fS0UUNRAOkF8PiWz0v7uAje8vnYoj0tfd5pxu/ddxm+srgAbg+Pn/2jRnZh9kXaySWIXMk6NdbMFKKE/3ewAT/5uxDV+v76WYpOZcszJ2HRdDN43jvNsc/mwF0vHoXV4YaHj10EbBsQXp90gzZox5UU0jn3l4OC6PuF2bkoSEsO9SWUOfmpMCVpMGR3jaqreFMUOy4rF17XP++/iC2vVeGixYrbnj+Itv4RzMg24qU7V4QVfg06DYzi5Op4RBaJ4JgdxWRFIFxc0fdzTTKmnxInl1R8ItBYcoxOLuLimluQOup9cuMy4dj39xNtGJYhKjZ0W/GaOCHzjWPNMT0vJfFuWMdH5CJTY2vaBuLaQZzoMJGLwWDEHY7jIoostvUPw+H2QKvmZC1qZuWmgOOEnSniXiKcaR8EzwsOpvnijnOsRb3BIB1Y4UpV5UIWsRcsQzRyWZZtDLp7W5RhQIZRB4fbg9OtA2jtI8Xz4eOKJ8UdnwXTfBcvWrW364uVzzMYjIlOx4AdQ3bBUdw+MELFiUTg7eMtGHF6UJFrwpKiNFxSmoH3/2MN/vPqCiyabsbPb1hIj/8kwhTLps1eWmA+2pkhF6mTa0lxesD7kAvgWF0esSKdrEhYKJ7zTjTFJuK0iCLB9PRkGPUaPHXLYjywYRYAyHKR2BwuulEmnZhIOqhePtSIQbsLlUVpuHNNWUzPNRDeyGIHXG4P7nv5OO1GBYAzQTpP5ULWR3JcXIBX5HKIReZfWynPxQUI8bnltJfLW9pf0zaAmrYBaNUcfnNbJZ6+dTE0Kg7vnmjFVU/uQUvfMMqyjHjlrpWy3VPx7OWKvZNLjCtaA8UViatRWJuHc+p5PDwVDOcVjD5eLC5Mg4oTxF7/dXgkkD4usmkrZWVpJqanJ2PQ7sIH1eGngf52Vx118h280IPeCCpT4oXd5aZ/V0pPViQUZRiQZtDC4fLgVOvUdXMxkYvBYIwJ5ERKTqyhaBRPAIXpBqhlTJQx6DT0hLjTrziViENzC1IlHSbxcXKRbhS5i7hwTEtLRrJWDaebpz9XqN4UjuPoDs6uM12wizs4uWbvAokslnqsDmrRB0BPhPOnj96hW1TIyucZDMbk4HyX7wQ5pcbexwrP83j5kOBaue2SQlpIrNOocO8V5XjnvsuwVCIi5cbYyVXXOYj3T7YBADYtmRb1856dZ6JVAMuCiFzkeZ9oHt/YO3GvZEqcXOSc19I37FPGHSmk24x0fHEch9tXlUDFARcs1rC/J+LiSjNoqQMHAC2fB4T3wi9uXCRrXRQpG+blAQA+qbPg/71zCp/UWWDQqXHLskIAwesg5NIhc7IigYhc5P8vn5kd0fcL1MtFxMYvzM5BulGHryyeht9/YxmStWq4PDyKMw14+a6VdK0oh3iKXLF3cgnvo0BdeORvgaydw4lcTb02DNpd0GlUAV2ERr0Gc8RURSxiNhmcJO3jIqhUHG5YIri5/vZZaGdWU4+Ndi5mGHVwe3g6TGA8uWixwe3hYdJ7I+dKw3EcVpVlAgA+OWeJy/eYCEQkcm3duhUcx/l85OXl0dt5nsfWrVtRUFCA5ORkrFu3DtXV1Yo/aQaDMfHIJA4iGXHFizInK0q5WlygfVDtJ3KJO09z81NjvigIh9JxRZWKw4wcYUf3Q1HkClWCCngFqe2nhIuXrBQd9Bqv5ZuIjW6PdxLkkN1FS4P9nVyAby8Xg8FgTGTIZCuCEqXaSnCssQ9nOgah16hwvXghF4qcVG+/ossdeSTl6Y/qwPPAhrm5dBpXNCRp1bhleSEWTDNj1YzMgPcpFPuVRpwe6qIbDyziJlum0XtxmZqkRVm2cJ6NpcOGOAJJlxQAmJO1NNp14Hx3wK8jkB7SEr9hOzmpSVQ8fGCDsjFFKTNzUlCcaYDD5cErh4WI15M3L6ITHGOdmuetT5Arcnlfx80riiIW9lYQketiDzweHm4Pj3eqBNHj+krv39e6ihz89dur8O21M/Dqt1ZG7MT3ilzKryuV6uTqs/muu3mep2txMsEwXCdXtbiWnp1nClr+T96n0W4cCM4j4fssLgwsmN8oxrU/PW8J6cL93e7zcHl4rJmZha+vEqaFy3F/xRvSx1Weq/xkRSmXiTFnJnJFwLx589DW1kY/Tp48SW97/PHH8eSTT2Lbtm04cuQI8vLysH79egwOxj5OlMFgTGwiiSs29JDS+fCTFQlE5Dp4odunZNPHySUuFDrj1cnVL47HViiuCADl2cKCluzEhSuqJLtyZ8XpLdI+LkCIHxILO4ksVrf0g+eFxac0xkEgwll16wCcUVxMMRgMRqJAnFylYiRsvCN0BOLiunZhAczixWkoMo16qFUcPPzo+Hk4znUM0u6v+6+aGfmT9eOxTQvx3ncuo1N5/UnWqZGkFS45AkWnxopATi7Au5HzeQyRxeZeQSSY5lexQIS/cCIX2WiSRhUJT99WieduX4q74hBTJHAchw1iZBEA7r9yJq6Zn0831hq6bbDGIFBG6nQvyjQgSauCXqPCTUsLI/5+86eZkaxVo8/mxLnOIew/b0HHgB1pBi2umJ096r4Pbpw9ar0kh2xxzdQVgwswEDzPU5ErWidXuujksjrcPt1Mg3YXjYGSqYhNvaFFLjJ9NFBUkbBUjIgebYhu44B0SKUZtEG7ZwszDFhVlgmeB94M4uZq6RvG3z4ThFrhfSxcH+w9ZxlXkR3wTlaMV+k8YU258B4/1tg77j/zeBGxyKXRaJCXl0c/srOFF5HneTz11FN4+OGHsWnTJsyfPx8vvPACbDYbXn75ZcWfOIPBmFhkRCBykbiinNJ5QmmWEbNyU+Dy8PioVnA9uT08atsEkd3HyRWnTq4OhZ1cwOhJiuGcXP49BoF2TemERXEBRfq4gu3ml2QakJqkgd3lwZkYIwsMBoMxnhAn181iDOtMx2DAOM9Y0m/zFs5vXiGve0it4ugFdqTu5Kc/OgeeB66elxuwRDoeZIgX3D228evF6aadXL4iFzn3VcfQXyPt5JJCYkMHLsgTuUoCiFzT0pKxYV5eXJ0fAHDTskIka9X46uIC3H+lIH5mpujpmuFsDG6uSJ1cqUla/OXOFfjrt1dF5WTSqlU0Jnu4vptG165dmO/jbo+VeMUVB4a9QlSgzUc5mJI0IG8Zafk8EXuNOjWtwGjtGwnpCK2mfVzBjxfEyVXTNhiVIHpcnL66uDAt5HuduLn+dqw5YPz5f3fXwenmsXpGJpaVZKAi14TSLCMcLg92j/NEXXL+iVcfF6Eo04CiDANcHh4Hwwjsk5WIRa5z586hoKAApaWluPXWW3HhwgUAQH19Pdrb27FhwwZ6X71ej7Vr12L//v3KPWMGgzEhITunFhlxRbKjFInIBQDX0MiiYEm+2G3FsNONJK0KpVlGmn/vszlhdyk/7lnpuCIAn2hCklZFYx/BSDPofHbAAhX3E1edRRQcTwUpnSdwHMd6uRgMxqSAOLlWlGWgJNMAnvdeXI0Xbx1vht3lLZyXCzmnRdIzebbD28V1/5WzInuiMZAunneULn92e3jZPV8WOl3RVzQg0yXr/PraIoF2cvmJXMtLM6BWcWjssYWMV10URa7SACLXWDEr14STWzfgqVsroZLEA2eLm2uxbHJFWjwPAEuLM+j0y2ggvVwf13Zi+ylhXSiNKipBvEQuMiDJnCxvGmUgVCqOukKlCQci9mam6JGdoodOo4Lbw1Mh0h+e5yWTFYM7uQrSklFgToLbw9MpiZFAviZQ6byUjQvykKLXoKHbhiN+Tty2/mG8fkRweP2HKNRyHEfTHuR9MF4Qobg8TpMVpdDIYt3UjCxGJHKtWLECL774Ij744AM8//zzaG9vx+rVq9Hd3Y32duFNk5ub6/M1ubm59LZA2O12DAwM+HwwGIzJhzeuGH4h0NonnGj9F4vhIMWpe852Ydjhpn1cs/NSoRZP9mS8udKRRafbQyc3SYveY0UqcpXnpPgsPIMhXSAEdHKZfJ1cpAMhmMgFAAvFct5wcY7tp9pQ08aO4wwGI/EYHHFSQWhGdgqWkXjNOEYWeZ6nHUibVxRF5NbJiaJnkri4rpmX5zMZMd5E4uaWi9XuwjVP7cVXfvupzyCVYEgv7qXMEGsBGrttUUXyhx1uGhmd7rcRlaLX0HNrKEcF6eQaT5ELQMDpzUTkiqV8PtLieSUgvVy7znRh2OlGSaYhIhFZDlTkUjiuGGsfF4FEFvskblWLJLarUnEoFNfawXq5OgftsAw5oFZxtFw+GEtjOKYSkauyKHAfF8Gg0+BLC/IBAH892kQ/z/M8tn1cB4fbgxWlGVhZ5u0IJJHFXbWdGHEqv8ktB6fbQx2b8Y4rAsCackHk2idO0Z1qRCRybdy4ETfccAMWLFiAq666Cu+//z4A4IUXXqD38T858zwf8oT92GOPwWw204/Cwshz1wwGI/HJMHqn+oVi2OGm9wnkQgrFvIJUTEtLxojTg73nunz6uADh+ER2vjsVjix2DdrB84BGxSHLqJzIVZxhgFYtHENDTVaU4iNyBXgNScTFMmSH1e6izoZ504IvXmj5fAgn17HGXnz7pWPY8mqVrOfJYDAYY8n5LuECI9ukhzlZS+M1Y1k+73J7cORiD1462IBH3jmFW549iDMdg0jSqvDVysimHNLzmUyR60z7IP5BXFwKdHFFArnY7lUwrvjCgYs41zmEz5v7w7qweJ6n7mWy6UbINyfBqBMm7DWIYlMktPQJ4oBJrwnYp7YyTGRxYMRJhYdAccXxpiJGJ9eI041e0UmUnxp571W0LCpMg04i2l1fOV3xyGd2iiDaKe/kiq2Pi0Dej1IHZbffAIZCMTURbMIicXHNyDaGdZWRY2qoXi6n24Mf/O0E/uOV4zRJ0GN10KFTi2W4925cJjjy3j/ZhnqLFb/bXYcrf7kHfxG7DUnclrBwmhn55iRYHW58GgdnE8/zeOlgA/aeDS4oNXRb4fLwMOjUKIii/y1SVs/IgooTzntt/cFdpJOViOOKUoxGIxYsWIBz587RKYv+rq3Ozs5R7i4pDz30EPr7++lHU1NT0PsyGIyJi9y4Yqt4IE7Ra5CaFLjENhhSS/IH1e0+kxUJOSay863sgoREFXNMelluK7lo1CqUZQk7PrJFLskuWEGAXVOywO8ecuB02wB4XrhYIq9NwMekhfaDsDkCdy3sOyssHOq7reM6Jp7BYExt+mwO6qyVcl7sQ5khTtMjTq4TzX0+xczx5P5Xq3DTMwfwo7dP4cUDDTgsCmw3LyuUVTgvJVc8ZnfKvMD+9ceCi2vj/LywjgylSRcHniglcg2MOPHsngv038fCTHQbsrvo79i/eJ7jOMwQnRV1nZGLXE1BooqEcOXzJKqYbdIjJUh5/3gyO094r5zpGIzq3E5cXElaFVKTx+7nS9KqfTb9ro9QRJYDcVpZhhyy3IRyUcrJRQYN+Ti5Bh3iYwt/B6QaJJiT61RL+D4uAulBO97YB3eQ1+P5fRfw+tFmvHuiFdf+5hPc+cIRvHJYEKfKso0wG8IfB5cVp6M0ywibw40rntiNx7efwQWLFQadGvddUT5q0qtK5b0++GccIoun2wbwo7dP4et/PIwnPjgz6r3gcnvw0kHhZ5SbyogVs0GLBaJguG8KTlmMSeSy2+2oqalBfn4+SktLkZeXhx07dtDbHQ4H9uzZg9WrVwd9DL1ej9TUVJ8PBoMx+ZA7XbFV7KwoSEuKasft6nmCqP5RTSfdfZJGMrwdJso6uTpI30QcrPjXzM+DUafGuors8HcGMCffhGRxty1QrxmNKw7ZcbI5dB8XISc1CXmpSfDw3gWPP4fqhQW8w+XBwPDUnObCYDDGlyd3nMWSn+zAnz6tH3Ubca2SGPiMbCPSDVqMOD0xlY7Lxen20MEoa2Zm4e7Ly/DETYvw3n2XYet18yJ+vNwI4opOtwcfin2V915RHvH3ipV0ugZQpuT/j5/U+wwMOBamV42UbRt0ahh0o4UWElk8H0UvV7DSecKy4nRoVBxa+oYDumVIhKk0gonSY8nM3BSoOGH9Fk0sj3Q95aVGt66LhRVlgpC9vCQdRUEm9sUCEUzdHl5RlyJJG8Tq5CIOSp9OLj8nVziRS04fF2F2ngkpeg2G7C68cWz09MMLXUN4auc5AEJnmooDdtZ04hcfnAEQvo+LwHEcbl3uTX8tL0nH4zcsxOGHr8IDV1cEfJ8RkWtnTYfik8JJoTwAbNtVh7tf+oxONWzotuKmZw/gz/svAgC+slh5sTUYl5NeLgVErgtdQ7j5mQO48pe7FXcuxoOIRK4HHngAe/bsQX19PQ4dOoQbb7wRAwMD+MY3vgGO47BlyxY8+uijeOutt3Dq1CnccccdMBgM2Lx5c7yeP4PBmCCQPo5hpxvDjuB5eLJY9B/DLZdlJRnINOrQPyzY/znO2ycBxN/JJXdyUCR8d/0sfL71arqbGg69Ro3nvr4UT9+6mHa2SMmSxBVPhZmsKGVJsbD4CLQbbXe58ZlkJ13pOCiDwWDIoSTTAA8vdDP6U0edXIKgwXEclhYLF8GfhXECKUF16wBGnB6kGbR44ZuX4KEvzsGNS6djwXRzVDv7OREUzzd02+B0C1GZuWPs4gK8awAliuf7bA78YZ8gYm4S3TnHGkMXXZML+2CT6ojweb4zcpGrmYpcgUUUo15Dh7cEOn96JysqL8IoQZJWjRJRgIsmskiiUvljENHy518vLcXXVhThJ1+dH5fH16pV9L2tZC+XUk4uGle0jZ6uSAQ68r4ljkR/5ExWJGjUKty1pgwA8KO3TvmIzx4PjwffPAmHy4M1M7Pw2rdWYsf31mJT5TSQw9/qGVmyf7Y715ThmX9Zgo+/vxZ//fZq3Ly8MKQTcnlJOjKMOvTZnDhcr2xEvUGMWpbnpECnUWHH6Q5s+t2n+MMn9fji0/twvLEPpiQNnr51Mf7tslJFv3coLhN7uT6ts0TtNBR6IxvxpV9/gsMXe3C+y4ptH59T8mnGhYhErubmZtx2222oqKjApk2boNPpcPDgQRQXFwMAfvCDH2DLli245557sGzZMrS0tODDDz+EyRTfMZkMBiPxSdFraDdCd4jyea+TK7rFkFrF4ao53oh0aabRZ9eW7HwrLcIQkSuSyUGRoI7wAmjNzOygu0XS6OjJMJMVpayblQMA+Fh0Ikg50dQPuyTuIzc+w2AwGEqyZqbgeD3VMjBqt5m4dIjIBQDLSsaul+uo+D2WFacrElfJMck/n0kFvrGIyvhDHCU9Crhdnt93AYN2F2bnmfDDL80BIPx8UreKP12Dvhf2/pAIazQTFsnUxGBOLgBYJfZyHQzQy+WdrBj/MupoIb1ctW2Ri1zNPeFfn3iRbtThZ9cvkL1JGA2k51RJd4u3kyu2NWXAuKLfAIaiEJ1cfTYHFXHlDqr4zhfKsWFuLhxuD+7+v8/oZM1XjjTicH0PDDo1Hr1+gRATzk7Bk7csxsffX4dnb19KRWs5qFUcrpmfj7JseX83GrUK68XrA6WnLBKR66uLC/D63auQY9LjbMcQfvL307A63LikJAP/vH/NmLq4AKHE36BTo9vqQE175EOheqwO3P1/n+GhN09i2Ommbr6XDzcG7XBLFCISuV599VW0trbC4XCgpaUFb7zxBubOnUtv5zgOW7duRVtbG0ZGRrBnzx7Mnx8f5ZzBYEwsOI6ji8vuEL1cLeJkxWhFLgC4er5X5Jrjd1L2FvUq7OSS2PETHemCjFz0yRK5ZgsXjyea+0ddVPkv3JmTi8FgjAfZJj3mi0M0pFOlnG6Pz247Ybkocn3W0Bv3LkEipJEusFgh5zPLkCNs/KauUxAnxmKqVyCUcnJ1D9nxp08vAgC+t34WslL0KBFjaMeagrvx/CNa/kidXJG+D5p7hfdVKAc67eW60D3q8WlcMUGdXIBE5IrCyUVEwEgnZk8U6IRFBUUupacr+sYVhb/BLPFvsjBD+L30WB00Ykcg3bZFGQbZnYEqFYcnb1mMilwTugbt+Nb/HcVFixU//0ctAOCBDRW07J5QkmXE1fPy4i7AX7NAiCy+e6IVAyPKRKcB0IEVxZlGLC5Mw3vfuQyVRWnQqjn859UVeOVbK4M6PeOJTqOigy8ijSx2D9nxxaf34cPTHdCqOTz8xTl4777LcFl5FpxunsZOE5WYOrkYDAYjEuSMECdOrmjjioBgdzbqhE4q/1hGJB0mkUBFrjEcjx0tRGx0uD3w8ELnQ6BYoz85piQsmi6IYbtrfaNAROQikyCVjoMyGAyGXIjrdPcZ73GqodtGJ1tJY+Xzp5mh06hgGfJO94oHPM/j6EVBhCHCWqykG3T0mBvuAps6ucZJ5FJquuIze87D5nBj4XQz1s8VNrSWiMNWjoeInJLNtawgTq7iTCM0Kg5Wh5s6s+XSEiauSJ6jVs2hrX+Eiq2A8L7wxhUTs5MLkJbPR+4GCRfnnOjEQ+RSarpiWoCBD91+Ti5TkpYOhvB355yKoI9LSopeg+e/vgxpBi0+b+7Htb/5BIN2FxYXpuEbq0ui+lmU4PKZ2SjPSUH/sBO/3ze6tzFayLmDxHpzU5Pw5r+vRtUjG3DvFeURpzGUhEQWIy2f33G6A+0DI5iWloy3770Ud11eBpWKwwNXVwAA3jrejHMd0U1cHQuYyMVgMMYMInJ1hxK5+mOLKwJCf8TmFUXQqVU+0UXAu2BQvHg+znFFJTHoNDDovGOg5fRxEb4wWyz2l0QWpX1cl4tRIaWdcgwGgyGXteKQjn3nuuiEL2lUUVpKrNeoqXgfz8hivcWKbqsDOo0qomNuKFQqTtIzGfqcdk4UucbdyWVzRu2Yq2kbwIsHGgAIXZXk91gpTnQL1cvlvbAPLHJp1SpaTF4XQS/XiNNNBYlQTqVknRqVhcLzPCBxPvfanBgYEdwzJQlaPA94u03PdQwFnZoXjBYFNi8TGaVFLrvLTYcqxBpXJO6rPtHJ5XJ70Cv+v/RvoTBIZNHbxxV53LMo04DffflchJoAACAASURBVG0J1CoOQ3YXtGoOj9+4cFwFH7WKwwMbZgEA/rDvAj0uSBkcceK5vefRKHPTw2p30QiodLgBx3EwJsC01DVi+fzhiz0YcQbvRPanpk343X9xQZ5PH9viwjRcPS8XHh745YdnlX2yCsJELgaDMWaQwtdAJxVAKKVsE+OKsdraH9o4B6d/fDW12BOIY2lgxBXRwT4UPM/Tnd+JEFcEfMt3I7ngunKO4JDYd84Cu0t4/UgfV1aKjkYyWFyRwWCMF5WFaTAladBrc9LeQW8n1WghgcQHP7sYv/J54uJaPD0Neo06zL3lI6d83uPhqcg3M3d8enKJo8Tt4amoI4f+YSf+cqgB1//uU2x8eh/sLg+WFKVh3SzvtOElRUKpe1VTX1ABxiJurgWLKwJAeXbk5fNkcqBBp6ZumGCsJJFFSfl8vUX4XgXmJCRplXtfKE1RhgHJWjXsLg8uitEsOXg8fNjpkxMdWgGhUPE8Ect0GhVSk2MTSWhcURTNSCcex3lvA7wil/+ERSpyRSnMr56RhZ98ZT7UKiG2N2ucjj9Srp6XhwXTzLA63Pjf3ed9bnN7eNz78nE8+o9a/OidU7Iejzgz0w1a2ZHOsaQ8JwW5qXo4XB7srOmQvclQI0aTA/XZfX9DBTgO2F7djhNNoYd+jBdM5GIwGGNGuLiiZcgOh9sDFQfkxmjRVqk4aNSjD3GpSRokaYXPK+U2Ghh2YcQp9KFMhLgi4BvZkNPHRZhXkIockx42hxuHLgiuh0PirvSKskwqIrLieQaDMV5o1Coa0dgjRhYDlc4TllEnUPxELm8flzJRRUKujPL5lr5hjDg90KlVKBwnoSFJq6Y1AnJ7uX67qw6X/GwnHn7rFI439kGt4nDl7Bw8fWuljxuvItcEo06NIbsL5zoDx2cs4jkpK8TagvRyRVI+L+3jkj6nQJDy+X3nuvD83gvYeboDB8XzaGkA8TWRUKk4zMoVXp9IJixK13UTZX0UKUo7uWgfV4o+7HsqHP5xRRLbzTDofBxVhWKUtFkyYbHf5qTHzWicXITNK4pQ8+Nr8K3LZ0T9GErCcYLgBgAvHmyg0z8B4PHttdgrTubdX2cJOcyCIO3jSkQ4jsNl5cKmwH0vH8eyn+7EXS8exbN7grvVeJ5Hrejkmp0/WpiclWvC9eKQgCc+PBOnZx4bTORiMBhjRri4IrG056UmBRSolIDjOG8vl0JuI+LiMidrE3onVkqmxMkVicjFcRy+MJtMWewEABysF0SulWWZNA6qZDcFg8FgRMpa0emz56xwnCLunPIAcb2ZOcIivqHHFvWY9XAcbSB9XMqUzhPkDFMhwk9ZtjFu51Y5pEUwYdHl9uC3u+pgd3kwKzcFD39xDg4+dCX+cMfyUaXVGrUKiwoFN9exhsCuAv+y7UAQATSSuGJzBC6lyqI0pOgFh+HP/lGDO188il98IFwgJnJUkRBN+XyzZF2nHcf3XjxRWuTqVKh0HgDSkoX3u83hht3lpiKXf2y3KICT64kPz4DngVm5KTHHJnWaxPrdr5mZhRWlGXC4PPj1R3UAgHeqWvDs3gsAhPW8y8NjZ83oaeL+NIivWXFm4nbO3b22DCvLMqDTqNBtdWDH6Q489s9a3PTs/oDnvLb+EQyMuKBWcQHPmQDw3atmQavmsO+cBfvPR9b3NRYk1juOwWBMaoh7KJiTq1WByYpyyJXZYSKXiRZVBLxxxawUPb1IkgsRuT6q7fDp41pVlkEFxE6FO88YDAYjEkgvV1VTH3qtDpzvEnbbAxWvF6QlQaPi4HB5FNv8kNI1aEe9xQqO85akK0WOjGEq4106T4hkwmJN2yBsDjdSkzTYfv/luOvyspAX/eR1DebG8y/bDgSdsNglP45HonhyKhaStGq8dOcK/McXyvGlhfmYm5+KZK0aapV38yiRqRBjS8ThIYfJXjoPSEQuheKKSpXOA4ApSQNi2OofdgadMkomLJJOruONvXjpkNB/t/XL82J+HomG1M31+tEmvHeiFT/42+cAgHvWzaDl+Nur28M+VqI7uQDBefXqt1bh5NYNeOPfV+OHX5wNvUaFjgE7LlhGH+9q24W/8RnZxqDx+sIMA25cWggA+PvnbfF78lEy/m1oDAZjypBhDN3JRSYrxlvkypbRYRIJHWInR+4EsuJni4LjgmmpEdvhLy3Pgk6jQlPPMN74rAUjTqGPa0Z2CmwOoafL6nDDanclROmmUvA8Dw+PcS1NZTAY8sg3J6Mi14QzHYN441gzhuwuqLjAu+0atQrT0pPR0G1DQ7cN+WZlz0GfNQiRtIpcE8xhepsixetMDuHk6hjf0nlCuowJy4Sj4mu2tDgdKhnH3CXFxMk1WuQKVrbtT5kYGewatKN/2CmrX4fEFeWKOIsL07BYdJ0BQmeVw+2ZEC5wUj5/JoKJapGIgBMV0snVZ3PC7nLH3LnXpaCTS6XiYE7WotfmRJ/NCQuZMur32MTJ1dRrg9PtwQ/fOgWeBzYtmYbVM7Jifh6JyLKSDHxhdg4+ru3Ed145DgC4oiIb399QgbMdg/j1R+ew92xX2LVsA52smPhCrl6jxtLidCwtTsfO0504fLEHxxp7R7m1atqC93FJITHWRBw2xZxcDAZjzJAbVxwrJ5dSbiOvkyv2BclYsWFeHmbnmbB5RXHEX2vUa2i3yK92CpNVVpRl0kkypHdlsvVyffPPR3DFE7sVG1jAYDDiC3Fz/Xn/RQDCTnuwC1Aa15E5USsSjoil80r3cQHSuGIIJ1dX8KjmWJLh1w8UCuIQXlos7zUjkwsvWKyjnGIkHqnyK9v2x5SkpY7s8zJ7uci6JdpSdZWKmxACF+CNKzb22GBzyBse4BUBJ6/IZU7WQqsWhFgSB4yFLtFNGmtEkEBiwn02p9fR6BfbLUhLhooDRpwe/OKDM6hpG0CaQYuHvzhHkeeQqHxfnLQICCL307dVQq3iMDvPhOJMA+wuD3aLvY7BICJXIscVA1EpbgwcDzCVlkSSA/VxSaEDxayJt95nIheDwRgzwsUV6ZjpOC+GcqmTa+rGFedPM2P7lsuxfm5uVF9PpiySHceVougFyIvPTDTcHh57znahscc2avoQg8FITEgvF4lMBZqsSCAiV0OP/KiaXI6KpfNK93EB3gvhYMdbnudRR51c4zvZzOvkCl/m7BW55L1m6UYdyrKE3+/xJl83Fy3bNurCOnFp+bzMXi7y3poW5825RCArRY+sFD14HjjbEZkIOJlfH5WKoxf7SvRyKenkArzl8302B/1byPJzNGrVKupgfU7spfrhxjkh472TgXkFZtx5WSlmZBvx3O3LkJokvFYcx+Ga+XkAQkcW7S43WsXi+kSOKwaCbAwcDxDxJpHkOWGcXNkm4X1kUSiqqyRM5GIwGGMGcXLZHG4MO0a7YVrpYii+YlGuwhMAJ2JcMVauqPDtD1lV5r0QIQuzyeTk6rM5QKYuD46Ev0BjMBjjz7KSdCRLXDKBJisSyC58Y89w0PtEg83hwqnWAfH5KC9ykU2bXjEq5U/noB2DYlSzJGt8nQYZBnmdXC19w2jrH4FGxflE+8JRSXq5/MrnLUOBe4gCQYRQOU4uh8tDxcXJ3DklhUYW2+X1cjVPgbgioGz5vJKdXACQlkxELqf3byGAeEV6uQDgktIM3LRsuiLfP9H50bVz8dH3141yul4zTxC5Pq7pCOrgb+oZBs8DKXrNKHdcorOkSDi2nukYxJDd68wccbppT1c4J1cmraGJ3cGoNEzkYjAYY0aKXgOdOF0nkLV1rDq5cpiTK2YKMwx0nDjp4yKQhdlkKp+XRmwHRuTFNBgMxvii16ixeobXZRqqeL0oQxA3GruVdXJVNfbB7eFRYE6Ki5vFnKylk8sC9aKQPq6SEFHNsYI4ucLFFYnzbV5BKpJ18p8ziTb6l88HmygXCFo+L8PJ1d4/Ag8P6DWqUc6YycqsXOGi95wMJxfP87STa7KLgKSXS4nyeeWdXGJccdgBi7iWCSTIEDerVs3h0evnR9zXOtlYND0N+eYkWB1ufFoXeHogKZ0vyjBMuNcrJ1U4J/E8cKLJuzFQ1zkEt4eHOVkb9rqGdLvZHG7ZEeaxgolcDAZjzOA4ji4y/SOLNoeLFsPGvZOLTgBUyMklijm5U0jkAoAr5whRx5ViHxeBxGeUGqedCEh3qQaZyMVgTBhILxcgz8nVoHAcmfRxLS9V3sUFCOdV2ssVYDJkXafQrTLefVyAZLpiGJGLlMcvkdnHRSDl8yeaBGGREMq94s+MCOKKpG9qWnryhLvAjZYsE/kdhnc099qcGBYdMPmT3OmulJPL4+HpY+Qo1PPqjSs6Q04ZJQXzW66ahfJxjjYnAioVh6tFN9c/TwWOLF4kpfPj7JKNFnKMlUYWaR9Xninscc2oU0MvbrJYBhPLzcVELgaDMaYEK59v7RMW5ya9hmbi4wVxGg3aXbDaYxMsHC4PnVaTN8kXcf7ce0U5tlw1Ew/5FZPmpE6+uKLUecjiigzGxGHdLCFazXFAeQiRq1B0MfTZnOgfVu5vnEwJjEdUkZBLe7kCOLk6E6N0HvBebIebrnhUFLmWyezjIszMMSFFr4HV4caZdu8EwO4Q7hV/yHuksccWMP4ppXkK9E35Q9Zncs6DRATMMeknTLl+tCglcvUNO+ESBVo58Vo5pCV7hclgnVwA8NXKaah6ZD3uvaJcke87GSC9XDtrOuB0e0bdTpy/E62Pi0Aii8ck5fO0jys/dB8XIGyykD46S4KVzzORi8FgjClU5PLLb49V6TwgxCYNCk0AJDvnWjVH+0amCil6DbZcNWvUAj+Uq2CiIr0oGxhmTi4GY6JQlGnAo9cvwP/csBBmQ/ANlBS9hl74NSnk5nK5PdSVtDwOkxUJXndyICeXWDqfO/4il9fJFVwgGbK7UNNGOswie83Ukg6vDyRl0cS9IidSmG3Sw5SkgYcHLlpCvw+ap0gUT4opSQNAnqO5ZYr0cQHKiVxk3ZRh1NEYcqwQcbmtf5g664K5GtOm2Do2HMtLMpBp1KHP5sTh+p5RtxMnV3HGxDwGkB7D44294MXiWamTSw7kuGpJsI1tJnIxGIwxhSj+PX6K/1j1cQEk3hH8oiASSFQxx5QEVZipTVMFEldUKg6aCFh84orMycVgTCQ2ryjCzcsKw96PTljsVkbkqm0fhNXhhilJg1lxjP/QnskAFxlE5CrPHv/4EdkI6rM5fOKEUqoa++DhgenpyVFVANy8XPg9P7PnPBrF36OFulfCO2M4jqOx1nDl896+qckv4hCok8sux8k1dURApTq5aB+XglMNichF3s96jQrGCLrupjJqFYcN84Rqjn+eaht1e8MEd3LNzU+FTqNCr81JBbtacajEbBlOLsB7XPVP6Iw3TORiMBhjSvC4IhG5xibyRyKLgS4KIqG9X/j6qRZVDEXOJJyu2OMTV2ROLgZjMkIuVBp6lCmfJ5G5eQWpcd0EIWKQ/zCVHquDnmtn5Iz/RRhxiXh4YCBIJPQz0fm2NMI+LsJ1C/NxaXkm7C4P/uvdU+B5PmQPUSDKZfZykTjeVBK5iJNLjqO5ZQrFOZVycjWJ012V6uMCvH93RHTMStFPmQ45JSC9XB9Ud/iI8y63h76mE7WTS6dRYcE0MwDBzdU1aIdlyAGOAx0uFQ4aV0ywNT8TuRgMxpgSLq44Fk4uIHS8IxKm4mTFcBAnV/+wM+jY5YlGN3NyMRKcvXv34rrrrkNBQQE4jsPbb7/tczvP89i6dSsKCgqQnJyMdevWobq62uc+zz33HNatW4fU1FRwHIe+vj7409vbi9tvvx1msxlmsxm33357wPtNRIiTq1EhJ9fZDkHkqsiNr4uKRsT93LNEpJmWlgyDThPX5yAHnUYFk154Hj1Byudph1mUIhfHcfjxV+ZDp1Zh15kufFDdTp1ccqYrAvJFrqkk4hBMUXRyTbW4Iol9RcPes10AgCVFysWb05KF3xl5WnL/DhgCq2dkIc2gRdegHXvPddHPt/aNwOXhodOoaC/iRMTby9VLXVwlmUbZ5wzyfmJOLgaDMaUhxa/+xbOtY7xYJBcFZOe7rX8YW9+txk//fjpojCIQU3WyYihSkzW0S2KyTFiUnryZk4uRiFitVixatAjbtm0LePvjjz+OJ598Etu2bcORI0eQl5eH9evXY3DQW9Bts9lwzTXX4Ic//GHQ77N582ZUVVVh+/bt2L59O6qqqnD77bcr/vOMB1TkUqiT64wocs2S2W0SLd7ied9Nm3PiZMVE6OMipBu9kUV/3B4ex8UC5KURls5LmZGdgrvXlgEA/vu903S6YpbMIm85cUWX24O2fuH1ngpxPIK0kyucmNM8heKcROQadroxGOVAI4fLg0/qLACAL8zOUey5pfv1bMmJ7TK86DQqbKqcDgB4+VAj/fxFElXMMEzouhJvL1cfatsi6+MCvO+nWKO6SjP+2zoMBmNKQeIC3X4Hw7HeESVuo3OdQ/jp30/jxYMNcLiEySmFGQZ8Y3WJrMdpFxe5eWa2aCBwHIcckx7NvcPoHByhU8smMtL3KxO5GInIxo0bsXHjxoC38TyPp556Cg8//DA2bdoEAHjhhReQm5uLl19+GXfffTcAYMuWLQCA3bt3B3ycmpoabN++HQcPHsSKFSsAAM8//zxWrVqFM2fOoKKiQuGfamwpzlS2k+ts+9g4uXKCxBW9fVyJJXI19tjQYx3tBDrTPoghuwspeg0qYhQG772iHO9UtfoIlpE6uc53DcHt4aEOcAHbMWiH28NDq+ZoRH8qQEQul4fHiNOD5BDdTmRdN30KON0MOg1yU/XoGLDjdOsAVpZlRvwYRy72YMjuQlaKnkbIlMB/4IacKaMMXzavKMQfP63Hx7WdaO8fQZ45CQ3isWWi9nERiGuwtn0QxxqFuPjsPHl9XIDEyZVgIhdzcjEYjDGlVMytV7cOUCu728NTsWis4oqk72D3mS78/pN6OFwelGYJJ6rHt9dSZ5kUt4fHscZenwheO3NyBYT2ck2S8nmf6YosrsiYYNTX16O9vR0bNmygn9Pr9Vi7di32798v+3EOHDgAs9lMBS4AWLlyJcxmc0SPk6gUiSJXW/8w3fSIloERJ1rF89rMuItcevF7ujDs8J6fEmmyIiFDvODuDRBt+UyMKlYWpQUUliIhSavGf39lnuTfKjpVORyF6clITdJgxOnBPkk8ScrB890AhAvcieziiBSjTgPy44aKLPYPO+mG0FSIKwJesYAIBZHycW0nAGBdRbai7ymT3vs7A+R30zG8lOeYcElpBtweHq8daQIANFhI6fzE3sjNMych35wEt4fHzpoOAMDsfPnnLDIkwTLE4ooMBmMKU55jwqXlmXB5eDy/9wIAwDJkh9Mt7JaO1Y6oNF4wNz8Vf/rmcnz0vbVYWpwOq8ONR96p9rHiO90e3PfyMWz63X589bef0s6WDtbJFRA6YXESxBVdbo/PyHvm5GJMNNrb2wEAubm5Pp/Pzc2lt8l9nJyc0TGanJyckI9jt9sxMDDg85GIZKfokaxVw8N7+4Si5ZwYVcw3J8GcrA1z79gw6TVI1goCTueg181FnVw5iSNykehUoE6uWEvn/bmiIgcb5wul0ZlG+WXbGrUKNywV4kkvHWwMeJ8XDzYAAK6vnKbAM504qFQcUsRetYEQ50IyeTLDqEuIPrixgIpcDdF1FO4SRS4lo4qA8DtLk0QWs1gnV1R8bUURAODVI41wuT10GmHJBBe5AO971+kWrnvmROTkIiJXYq33mcjFYDDGnHvXlQMAXj3SBMuQnVra81KToFGPzWGpsjANj1w7F//7tSX4+3cuwxUVOVCpODy2aQG0ag47azrwQbVw0eZye7Dl1Sr885Tw79r2QVy37RPsOdsliSsykUsKLUIejK3YPxGQClwAc3IxJi7+F/k8z0c8ZSvQ/cM9zmOPPUaL6s1mMwoLCyP6nmMFx3GK9XKdaRcEpllxdnEBwvMmx9w3PmtGvcWKwREn7Ywqz47/c5AL6eQK5OQ6Kopcy2Lo4/Lnv66bhyVFadgsXqDK5WsrigEAH9d20DUK4fPmPpxo6oNOrcItyxPzvRxP5JTPT8XJk0uKhQLvqqbeiMvnL1qsuGCxQqPicNnMLMWfW5pEaGfF89Fx9bw8pBu0aOsfwe4zXWgUp/AWTfC4IiC4ZwlGnTqiv1simvbZnHC6Y3NAKwkTuRgMxpizakYmFhWmwe7y4I+f1NNoYEHa2AlFKhWHf72sFBsX5PvYwmflmvDttTMAAI+8U41eqwNbXqvC+yfboFOr8PiNC7G4MA39w07c8afDsIuRFhZX9CWHTq9MrJ2daOi2Cj8DeZsM2V3wRDCcgMEYb/LyBDeLv9uqs7NzlLsr3ON0dHSM+nxXV1fIx3nooYfQ399PP5qammR/z7GGRBZjFbnoZMU4l84TSsS4/a8/rsMVT+zG5Y/vAiBEx/07ecaTjCDDZzoGRtDcOwwVByyWXHDFSp45CW/ecynuvaI8oq8rz0nBqrJMeHjg1cO+bq4XDwguri8uyJuSJd6klyukk2sKTp6cV2CGVs3BMuRAU8/oyotQkKjiJaUZSE1S/u9VegzIlDmAgeFLklaNG5YIDs+/HGqg3Y2TwclVKZnmWZFniigum27Q0fWx/3F9PGEiF4PBGHM4jsM96wQh6f8ONOCMWM47Vn1c4bj3inKUZhnROWjHhqf24u+ft0Gr5vC//7IENy8rxGt3r8StywvpOOY0gxZJWnldH1MFMmmoYxLEFXvEngHSK8LzgNXBIouMiUNpaSny8vKwY8cO+jmHw4E9e/Zg9erVsh9n1apV6O/vx+HDh+nnDh06hP7+/pCPo9frkZqa6vORqBRnKFM+T85rY+HkAoCfb1qI+6+ciRWlGdCpVdSBOrcgsV5rElfs9YsrHhd7jCryUmkcbrz5l5WCm+vVI03UodBrdeC9E60AgNtXlYzXUxtXUmU5uaaeyJWkVWNegVAYH2kv18dxiioSpBMWmZMrem4THaG7znTB7vJAo+ImxXt8XkEqtGpBqZqdH9k5Q6XikGFMvMhiYpxFGAzGlGP9nFzMzEnBuc4h/PGTegCJsxhK0qrx6PULcNvzB9E1aBcErq8txZVzBKeCXqPGz29YiIXT07D13WpcUqJctGKy4C2en/hxRYu4M5VvTkZ7/wicbh6DIy4a2WAwEoGhoSHU1dXRf9fX16OqqgoZGRkoKirCli1b8Oijj2LmzJmYOXMmHn30URgMBmzevJl+TXt7O9rb2+njnDx5EiaTCUVFRcjIyMCcOXNwzTXX4K677sKzzz4LAPjWt76Fa6+9dsJPViQoNWHx/7d35/FR1ffewD9n1myTIfuQEEIiO2FHEaQSFIIby6W3teICr9taV0pERSk+bfRWYnmeS6nS4lKv+ogU2ype7WMpQSFCEYFAWJVFQgiQhYSQnZnMzHn+mDlnMpNMMpNktszn/XrlJZn5JTk5Jpnf+Z7vImdy+SnIZdBH4Kk5wwEA19ssOHShDicvN2D2KM8z9fwhPtr2d9P1jv/Ri/UAgPGD+m6qXG/ljklBkk6LK41GbD9RhbvHDcRfDpbDaLZiTGosJvVhxlkokTK5uupPKfXkCqdyRcDW26ik/BoOXajDQg/7tTUZzfim1DbIYJaPglztyxXDMfuwr9yQFIObs+Kx75xtSEZaXKTf2qz4UoRaiew0PQ5fuIZRXga5AFvJYk2TMaiaz/fq/0pBQQEEQZBHTgO2vgz5+flITU1FZGQkcnJycOLEiV4fKBH1LwqFgMfs2VzN9mlQwZLJBdhKKh/LuQEJ0Rr8YfEkzB7d8UJh8dTBKP5fs7HxgckBOMLgJjWev9IvMrls30NijEa+g82+XB3t/K4aj20q7hf/z0PRwYMHMXHiREycOBEAsGLFCkycOBG/+tWvAAArV65EXl4eHn/8cUyZMgWXLl3C9u3bodM5gjCvv/46Jk6ciIcffhgAcOutt2LixIn49NNP5TUffPABxo4di9zcXOTm5mLcuHF4//33/fid+pbUX6W8F+WKNU1G1DabIAiBafoeoVZi+g2J+NkPsuQyxmAhZZRcc+l1eOySLcg1NoiCXGqlAvdOsfXc2rSvDFariE3f2EoVH5qW4XU/u/7CEeTqIpPrmu33Jy0u9Eu5vCH15fImk2vPmRq0WURkJEQhy0e/r+3LFdtndZH3Ftv79QG26ar9xQt3j8aSaRn44STvh2lIgdPa/pDJdeDAAbz55psYN26c0+Nr167FunXr8O6772L48OH4zW9+gzlz5uDUqVNOGykionnjU7Gu8HTQprU/d8dIPJs7osvadGbzdE4aaV/bbEKbxQp1D+90iaKIp/9yBFeajPjvpTf2+PP0Rq094yAhWgtdhAq1zSZOWHRx8nIDHt1UDKPZipuzErBk+pBAH1LYycnJ6bLZsSAIyM/PR35+vts13T0PAPHx8di0aVMPjzL4tW8835PG/ABw2l6qmBEfhUgNS9nbk3tytStXFEURx6UgV1rwBLkAW3nSH3edxdfnavHf/ypF+dVWxEaoMH98eE1VbC82UipXZCaXK2lK3bcVjWgxmT2aLClNVZw1ItlngVMpsKWPVEOjCv3Mo0CaOyYF8dEaXG029Yt+XJLJGXE9nmwrNZ8PpnLFHv2UNzU14f7778dbb72FuDjHyRBFEevXr8fq1auxaNEiZGdn47333kNLSws2b97cZwdNRP2DWqnAI7dmye8HUyaXxJvmi+QQH6WByn7uevOi9z8ll/Hx4UvYfaZGvtPvb1KQKz5a49FUqXBT39qGxz4olocwMJOLQlnagEgoBKC1zdLjn+VTVf7txxVKpOmK9a1tMNv7XF2sa0VdSxvUSsFvjfo9lTYgUu6TtObzbwEAP5qSHtbBy+7KFZuNZrknXFqYBblSB0TCEBsBxlP6iQAAIABJREFUi1WUS3C7Iooidp7ybT8uwNY7FmA/rr6gVSnxc/u1yw+GJQX4aIJDgpzJFeLlik888QTuvvtuzJ492+nx0tJSVFZWIjc3V35Mq9Vi5syZ2Lt3b6efy2g0oqGhwemNiMLHj6akIysxGimxWrkXCoU+hUKQ05d7OmGx2WhGwT++ld8/EaggV7tyRU96kYQTq1XE038pcepfFEx38oi8pVEp5BsuZT0sWfT3ZMVQIvUGEkVboAuAnMU1PEUHrSr4gkf32xvQS0N1pYb04UrXTdm+NFkxNkLlk0mBwW7iYM9LFk9cbkB1oxFRGiWmZvmuv2uSfT9m4CTwPvHIrVk48qtczOmklUk4kvb7V4Jo/+d1kGvLli0oLi5GQUFBh+ek0dSuY6RTUlI6jK2WFBQUQK/Xy2/p6eneHhIRhbAItRKfLZuBnc/kcEJhPyOVLFb3MBti467vUdUuQHbicmBuglyVM7m0Ho1ODyd/3HUWO76thkalwH032aYOBVPjUaKe6G3zeX9PVgwlKqUCsfa/o9KERSlLd1wQ9eNqb+awJKTH2wKftw5PQmaQ9Tnzt+5u9kiliuHWj0silSweKrvW7VppquItQxN9GuCdNTIZT8y6ASvvGOmzrxFOBEFw6nMW7hzlisGz//MqyFVeXo7ly5fjgw8+QESE+0iwaz1xVz0NVq1ahfr6evmtvLzcm0Mion4gWqvyqG8BhRap+Xx1o2PCYovJLI+K78qF2ha8ufscAGDhhFQAgQtySenXCe0az7NcEdh95gr+q/A0AOA/F4zBzOG2tH1mclGoa9+Xy1uiKOJ0VRMAZnK5I/flarb9HZWCXNlB1o9LolAIWHXnKGQmRmOFfYJlOJMzuVo7fx28WGdvOh+ELSj8QWo+f/hCXZd9EgFb03nAt6WKgO2G8rNzR2JCenhOBCXfCsbG814FuYqLi1FdXY3JkydDpVJBpVKhqKgIr776KlQqlZzB5Zq1VV1d3SG7S6LVahEbG+v0RkREoU/K5JKyscwWKx56ez/+7Y978VHxxS4/ds3n38JktuKWoQl4yn5RcaqyEW32Hi7+5Gg8r2m3uWcm1//65DhEEbh3SjruvXEwknS2C9fa5uDZ5BD1xOB4W6bOhdpmrz/2cv11NBnNUCsFDOlHk7f6Upwc5DJBFEXHZMUgDXIBwF1jB2LnMzkMEqD7TK6L18Kz6bxkTKoeaqWA2mZTt4HyM9W2rM9gzWIk8oQU5Aqmm5xeBbluv/12HDt2DCUlJfLblClTcP/996OkpARZWVkwGAwoLCyUP8ZkMqGoqAjTp0/v84MnIqLglayz1+jbM7ne+OocDpbZsrjWFZ6G0Wzp9OP2nq3BthOVUCoE/HreGAyOj4IuQgWTxYoz9gwJf2mzWOW+MQkxWo9Gp4cDk9mK8/ZSrmfvGAGg3SanMXjS1Yl6Qi5X7OYCtbbJiOqG606PSZMVsxJjOMXMjXj7pLe6FhMu1rXiWpA2nafOSeWmjUZ3mVzhHeSKUCsxJtUWtOqqL1dds0lu0B/uJbAU2qSBBrVNpm6zF/3Fq1dfnU6H7Oxsp7fo6GgkJCQgOzsbgiAgLy8Pa9aswdatW3H8+HEsXboUUVFRWLx4sa++ByIiCkJyuWKDEccv1eN39tK2CLUCl6614s/fXOjwMW0WK1787CQA4MGbMzA8RQdBEDB6oC3L98Rl/zafr7NncSkEW8NkNp63kZqLqpUCEuxZGdJ0ndY2C5qN4X1+KLTJ5Ypd9OSyWEXc89oe3PZfRThf48j4kicrMmDjlpTJVddikpvOjzAEZ9N56shRtt/533lpKmlKGDc596Qv1zn7341UfQRbdlBIk4JcZqso3xgOtD6/xbRy5Urk5eXh8ccfx5QpU3Dp0iVs374dOh1f7ImIwomUyVVe14KnPiyB2SrijjEGvHD3aADAhp3fo8XkvEleu+07nKpqxIAoNfJmD5Mfl+6K+rsvV63cdF4DhULoNz25Vn18FPe9uQ8mc8/KP6XslWRdhNxzM1qjRITatq0IpjHSRN4abM/kqm02oclNwLauxYQKe2niM389Aot99J6UyTUiJcY/BxuCpJ5cdc0mHA2BUkVypmsX5Oosa0PqyyNd+IYjqS9XV5lc567YMtMzk5jFRaFNq1LKN4GDpfl8r4Ncu3btwvr16+X3BUFAfn4+KioqcP36dRQVFSE7O7u3X4aIiEKM1JPrdFUTzlQ3ITFGizWLxuLHU9IxOD4KNU1GvLv3vLz+/x2twFu7SwEArywaiwFRjg3ymFRbJtdJfwe5mhxBLqD7XiShoLL+Ov68vxxfn6uVp8B5S+qzlmQPZAK21/9gHCNN5K3YCDXi7JOz3GVzSVNXAeBgWR3e3mMblCFncnGyoltxUY7G88eDvOk8dSS9DlqsIlrbOrYdkG4OSa8H4UjK5PqusrHDzTyJlMmVlciAOIW+pCDry8VmAURE5BNSuaJk7b+PRXy0BhqVQs7SeqPoHOpb23C2uhEr/3YEAPDIrVm4I3ug08dKF0AnKxpgtfqv3l9qop4QbXvxjo3sukwjULzpgVB0ulr+d6VLPyFPSX3WUmKdL2ISgmyTQ9RTafZ+QpUNrZ0+LwXAFfbh4f9n+2l8V9mAM9WcrNid+Gjb39GrzUa56fy4NDZ0DxVRGiWU9h9819dCs8WKa/Y+U9LNoXCUOiAShtgIWKwijpR33mZByuTKYiYX9QOOCYv9JJOLiIioM4kxGtgr2bB46mDcNtIxZXfBhDQMS45BfWsbXv3iDB7ddAjNJgtuzorHs3NHdPhcNyRFQ6tSoMlo7rIZtCiK+FvxRUwr+AJPfVjS6+9BzuSKcc7kagiicsU/7T6H8S9ulzMiulN0+or8754GuaRMLtdAZlK75qNEoUwKbLv7WZYyuSZnxCFnRBJMZit+9t5BmMxWRKgVSI+L8tuxhhopS/fYpQa56fxwA7NZQoUgCIjR2l8LXfrvXG0x2dc4MvbClVSyeLi885LFUimTK4k/+xT6pPLkYLnJySAXERH5hEqpwE9vycTtI5Ox+q5RTs8pFQKezh0OAHh7TynOVjchJVaL1+6bBJWy40uTSqnASHtmhLvm8+VXW/DQf+/HM389gor669h6+BKKy9z3w/CEdCGbIJcrBl8m1z+OV6Lhuhlfflfd7VqzxYrdZ2rk9yvrO89S6U61m0yuYBwjTdQT8rSoZndBLtvPeHy0Br/94TjERqjkqXLDU3RQSCle1IGU4SP9nWDT+dDjuOHj/FoovWbGRWnkbK9wNX6QLch17GLHPYvFKsoTirM4WZH6gWDb/zHIRUREPvPCPaPx9tIbEa3tODlo7hiD3GxYpRDwx/snOfV4cjXaTfN5URTx7r9KMXf9V9h9pgYalUKexrhx19leHb9ruaK0sW8ymuVG04FWVmu7GyyVSXXlcPk1pwBdZX3PNiNyJpfL9Kxgu5NH1FOO0ovOf5YdQym0SImNwEsLHP1nR7AfV5dcM3zYdD70uBvCImU+JoRxqaJE+rk+1kmW9aW6VpjMVmhUCqQOiPT3oRH1Ocf+Lzgy+RnkIiKigBAEAfnzxyAzMRprFo3F5Iz4LtdLzeddg1zv/Os88j87iRaTBTdlxmPb8h9gw+KJEARgx7fV+K6y583q3ZUrAnA7dc2fmoxmeUNxpqr7JvJFp2ylitIUxKoelitWN0rlip1ncrFckUKddJHeXbmitG7BhFTcNdYAALgxs+u/ZeHOtVfTWPbjCjnuhrDUcLKibIw9yHWxrhV1Lhmh39fYJysmRId9xhv1D8zkIiIispucEYedz+Tgx1PSu10rNZ8/calebrTeYjLjDztt2Vp5s4dhy8M3IyspBllJMbhrrK15/cZd3/f4+OQpUfaLMq1KCY3K9tLpegc7EKQsLsA2qclssXa5XurHJZ2bip6WKzZI5YrOmVycrkj9hTxEwU25oiOTy/a3QRAE/P4nE/HRY9Pxw0mD/HOQIUofqZb7NQLM5ApF7kr3HcHf8J2sKNFHqjEkwdab77hLm4VzV6R+XCxVpP4hUe7JGhz7Pwa5iIgoJIw06KBUCKhtNsnlcu9/XYbaZhMGx0fhiVlDnfrgPDbzBgDAZ0cu40Kt+2b1XbnqciELtC/TCHwmV1m778tktuJCF035rzQ6Jpndaw8qSufRGyazVb7Ad83kYrki9RcJ3WzYr0plWe0yVtRKBSZnxDEzoxtKhYAB9km1bDofmmLlTC435YrM5ALguDl31KUvV2kNJytS/+LI5AqOTH4GuYiIKCREqJW4wb4hPHG5Hs1GM9746hwAYNltQ6F2aVifnaZHzogkWEXg9a96ls3lKL1wBHOkzb3rVKlAON8ukwvoui/X7jO2LK7stFh5491kNHudkSadE7VS6NBbJ4nlitRPJHo4XdG19I48E2c/byMNsWw6H4LclSu69rEMd1KWouv0YymTKzORAV7qH7rrY+lvDHIREVHIGNOu+fz7+8pwtdmEIQlR+LeJaZ2ufzxnKADgbwcvyiV2njKZrfIGvn0TXXeb+0BwzVA720WQSypVnDk8CdFaFXT2YQDe9uWS1ifFaDtMkJOCgfWtbTCZuy6dJApmjumKRrk8uj3XckXyTrw9QJ7NUsWQJJUrNrjJ5IpnJhcA983nWa5I/Y30mtlssqDVZAnw0TDIRUREIURqPr+/9CrelLO4hkGl7Pzl7KbMeEzJiIPJYsWf9pR69bWkTA2lQoDeXloDtOtFYgyeTK7hKba7we6az1usIr6Sg1zJAACD3tZPy9sJi3LTeZd+XAAwIFItl2pdddPLiCgUSMGrNouIBpeAttUqoq6FvYd6Y1CcbaLclIy4AB8J9YT7TC7nPpbhrrPm881GMyrtN4tuYCYX9RMxWhW09p61wdCygkEuIiIKGVIm156zNbjabEJmYjQWTEjt8mOemGXL5vpgXxnO1zR3ubY9qewiLkrjlLEUTJlcUk+u2aNSAACnqzrP5Dp2qR51LW3QRagwabBtkpkc5PIyk8vRdL7jxb1CIchZb8GwySHqqQi1Us52dC2/aLjeBovVlt0VF63u8LHUvV/eNQq/u3d8t3+/KTjFRkq9KZ1v9rCM11lnzedL7fuQhGgN9FH8+0H9gyAIQTVhkUEuIiIKGaPtmVySZbcNdZvFJckZkYTJGXFoNlnwH+8e6DDK2x2p7CLRpewiWBrPX2+zoKLeFnCaPdoW5Pr+SpN88d1e0SlbFteMoYny+ZImI1Z6OWFRzuTSdczkAhwli5ywSKHOUbLo/DdDel+nVbGfVA8lx0bg3yYO6vbvNwUn6WaPa5ZjZ30sw122S8niuRqpHxdLFal/SZSHDwU+k5+vLEREFDL0kWqkx9vKXLISozF/fPdZAIIgYOMDk5A2IBLnaprxyPvFMJq77xfg7o60Y3Mf2HLFcvskRZ1WhfGDBkCjUsBotuJiXccJi7tOVwOw9eOSDOxhJldVF5lcQPsx0oHf5BD1RoKbRrry3wb2HaIwpevkZo/RbJHfd705FM7kvlz2CYvnrnCyIvVP7l4zA4FBLiIiCilSoOaZuSM8zgJI1kXgv5feCJ1Whf3nr2Ll34522ky6PXd3pOWGu62BzeSSShUzEqOgVAi4IUnqy+VcsnitxYQj5dcAADNHOIJcjkyuHvbkcpPJlRRE6epEveEovXXJ5GpiSRaFN0fZvuNmT12z7d9KhSBnPFPH5vOOpvPsx0X9iyOTK/D7Pwa5iIgopLxw92gUPZuDu8YO9OrjRhh0+OMDk6BSCPifksv43Y4zXa6XsjUS3GRyufYi8Tep6XxGgu1usNR8/nS1c/P5naeqYRWBkQYdBuoj5ccN9iCX99MVpcbznWdySSVeNY2B3+QQ9YbjrrRzkMvd3waicBHbSW9K6cI2PlrTYfJuOHNtPn+uxp7JxXJF6mccPbkCn8nPIBcREYWUCLVSDux46wfDkvCbhdkAgFe/OIO939e4XStd2LoPcgVJJle8rantsGRbkOusSybX9hNVAIBce98uidR4Xurr5Smp8by7TC5pk+Pax4go1Ei/+9IQCsnVZsfFPFE4cpQrtslZ0bUM/naqffP5Y5fqUSpncjHIRf1LQhBl8jPIRUREYeUnNw3GDycNAgB8dqTC7bpaN3132m/uA6nM3pNriD3gNzRZBwA4U+0Icl1vs6DotK3p/JzRBqePl4Jctc1GtFmsHn3NNotVPi/ue3IFzyaHqDcS3PSXk/82RLO5NoUn6WaPVQSaTbYel1LwN4H9uDqQms9/+V01mk0WKBUCBsczyEX9SzD1ZGWQi4iIws58+9j6L7+rctubS8reSHC5kI2NDJZMLqlc0XaHWCpXPFvdBKt9wuK/ztagxWTBQH0EstOcJ1PGR2mgVgoQRUefre5csa9TKwXERXV+ISNd4FxhuSKFOHd3pVmuSOEuUq2Eyl6SKN3wcWQ/M/jrSurL9fejlwEA6XGR0Kh4GU79SzDd5ORvFxERhZ2pmfGI0ihR1WDEicsNna6RL2Rd7krHdjJVyt/aLFZcrGsF4OjJNTg+ChqlAq1tFly6Znuu8KSjVFEQnHukKBRCu+bzrR59XSkYlhSjddtzheWK1F8kyuWKnffkYrkihStBEDqU7te6ec0kR5BL6lXEpvPUHzHIRUREFEARaiVmDE0EYCsf6Ex3PbkaAliuePlaKyxWERFqBZJ1tk2FSqmQe3ycqW6ExSpix7e2IJdrqaLE4OWERalJfXJs5/24ACDJfjxXm01yRhlRKJIyua42u5muyIt5CmOupfu10kRiBn87kJrPS9h0nvojKcBd19IGs4dtMHyFQS4iIgpLt41MBgB80UmQ63qbBU1G291p19ILaWPfYrIE7EX8vL3p/OD4KKeMqqH25vNnqppw+EIdappM0EWoMDUrvtPPk2Lvy1Xp4YRFKZNLCqx1RspusVhFXGsNbN8yot5wbNhNTr/rLFckan/Dx57JJd0YimG5oqv2zecBIJNN56kfiovS4Lk7RuJ///s4BPoepyqwX56IiCgwZtmDXEfKr+FKo1HOQAIcF7EqhSD34JJIG3sAaDKaMcBNbypfcvTjct4oD0/RAajA6aomuXTk9pHJUCs7v6dl8LZc0R4MS+kik0utVGBAlBrXWtpQ02RkSReFrLgoDQQBEEXbnekknRaiKLJckQgdJw3X8veiS9lpevkGVVYiyxWp/1EqBDyWc0OgDwMAM7mIiChMpcRGyH0ydp5yzub6tsLWpysxRtuhl5VaqUCE2vbyGai+XGW10mTFKKfHh0mZXNWN2H6iEgCQO6bzUkUAGChncnlWrljd0H0mF9CuLwObz1MIUyoExEdJfblsP8tNRjNM9qwuNtimcNahXNH+O5LIMt5OjW1XsngDM7mIfIpBLiIiCltSyeKX3zqCXBariP/9z1MAgLvGDuz046Tm84HqyyVlcg12yeQaZp+wePxSPc7XtkCjVODW4UluP4+UkVVV71m5YlVj95lcgKOMq4bN5ynEJbiMRJeyuCLVSkRqlAE7LqJAk8sVW13KFRn87dTYQbYgl06rcsocJ6K+xyAXERGFrdtH2YJcu89cgclsy87468FyfFfZCH2kGr+4fWinH+dapuFv7jK5MhKioVYKci+EW4YmIEbrvjOBwZ7JVdHgWblilT2TKym2m0wuHTO5qH+QLtilaVEsySKyiW2XydVqsqDFZAHAgQzuTM1MwJJpGXjhnlEdMsSJqG95FeTauHEjxo0bh9jYWMTGxmLatGn4xz/+IT8viiLy8/ORmpqKyMhI5OTk4MSJE31+0ERERH0hO1WPJJ0WzSYL9pdeRbPRjP8qPA0AWHbbULf9tqQyjYYANFa3WkWUXZWCXM6ZXGqlApntpjZ1VaoIOHpyVTUYIYrddwm9ImVy6brO5EoKojHSRL3RIZNLbq7NC3kKb7HtbvZIpYoapQK6Lm6shDOlQsCLC7Jx742DA30oRP2eV0GuQYMG4ZVXXsHBgwdx8OBB3HbbbViwYIEcyFq7di3WrVuHDRs24MCBAzAYDJgzZw4aGxt9cvBERES9oVAIuG2ENGWxCm8UfY8rjUZkJEThoWlD3H5cIDO5Khuuw2S2QqUQ5J5a7Q1L1gEABMGRqeaOVHZoMltR19J1wK7NYkWN/QI/pZtMLqlcUQoMEIUqqb+cdBHPpvNENu17cskTR2M0zFIiooDzKsg1b9483HXXXRg+fDiGDx+Ol19+GTExMdi3bx9EUcT69euxevVqLFq0CNnZ2XjvvffQ0tKCzZs3++r4iYiIeuU2eyDo82MVeHP3OQDAqjtHQqNy/xIZ69Jw15+kUsX0+CioOpmaONTefH5i+gAkd5NxpVEp5IBURTcTFqWsLJVCQFw3EyXlckVmclGIcw3YslyRyKb9zR7p94O/F0QUDHrck8tisWDLli1obm7GtGnTUFpaisrKSuTm5sprtFotZs6cib1797r9PEajEQ0NDU5vRERE/jJjaCI0SgWqGoy43mbFTUPiMbebMr/YyM4zuc7XNON6m8Vnxwq0azofH9Xp84unDsac0SlYddcojz6f1JerqqHr5vNV7SYrKhRd36lPZLki9RMJ8s+y1Hje9jOdwIt5CnOOTC6z/Lde+n0hIgokr4Ncx44dQ0xMDLRaLR599FFs3boVo0ePRmWlbVR5SkqK0/qUlBT5uc4UFBRAr9fLb+np6d4eEhERUY9Fa1WYmhUvv7/67u6bwsqbe6MjyPXZkcvI+T+7MK3gC/x223e4WNfik+M976bpvCQlNgJvPTQFNw6J7/R5V1Jfrsr6rgNS1fYgWFI3kxUBR7+iGpYrUoiTe3I1uzae58U8hTd5umL7ckUGf4koCHgd5BoxYgRKSkqwb98+PPbYY1iyZAlOnjwpP+96YSCKYpcXC6tWrUJ9fb38Vl5e7u0hERER9cqCCWkAgH+fPAjj0wd0u15qrNu+XPFvxRcBAHUtbdi463vcunYnfv5/D+L4pfo+PdYLV22ZXBkuTed7KkUvBbm6Llessk9KTPFg9Hn7xvOeNLQnClaJro3neTFPBMClXJG/F0QURLwef6HRaDB0qG2k+pQpU3DgwAH8/ve/x3PPPQcAqKysxMCBA+X11dXVHbK72tNqtdBqeTeMiIgC54eT0jAiRYdRA3UerZfvYLfaMrmajGZ8/X0tAODX80Zjx7dV+NfZWmw/WYXTVY3Y9eysPjvW8zW2TK4MN5lc3hooZXJ1U654xf58cjdN5wFH9ovRbEWT0SxnvhGFmgR7xlZtExvPE7UXG2mfMny9jeWKRBRUetyTSyKKIoxGIzIzM2EwGFBYWCg/ZzKZUFRUhOnTp/f2yxAREfmMIAgYO0jfaSP3zkhBmwZ7Jtfu01dgslgxJCEKS6cPwQc/uxl/XzYDgK28sK/6dDUZzfj+ShMAYEhiH2dyNXRdrij15Erpppk9AERpVIjSKAFwwqK/fPXVV5g3bx5SU1MhCAI++eQTp+dFUUR+fj5SU1MRGRmJnJwceTq2xGg0YtmyZUhMTER0dDTmz5+PixcvOq0ZMmQIBEFwenv++ed9/v0FSrw9YNtssqDVZHE02I5hkIvCm3Szp8lolkvTmclFRMHAqyDXL3/5S+zevRvnz5/HsWPHsHr1auzatQv3338/BEFAXl4e1qxZg61bt+L48eNYunQpoqKisHjxYl8dPxERkd+1L9MAgMKTVQCA2aNS5BL9Mamx8jppImJvfX60AkazFVmJ0cjqoyCXoydXd+WKtkyuFA96cgFsPu9vzc3NGD9+PDZs2NDp82vXrsW6deuwYcMGHDhwAAaDAXPmzEFjY6O8Ji8vD1u3bsWWLVuwZ88eNDU14Z577oHF4hykfemll1BRUSG/vfDCCz793gJJp1VBYw9+1zYbWa5IZCdNGRZF4OJV22tcAoO/RBQEvCpXrKqqwoMPPoiKigro9XqMGzcO27Ztw5w5cwAAK1euRGtrKx5//HHU1dVh6tSp2L59O3Q6z8o/iIiIQoFUptF4vQ1mixVfnqoGAMwe7SjPFwQBmYnROHqxHudrmzHC0PvXwr8ctPWt/Pcpg7ptju+pgXJPrq7LFavtmVxJHpQrArZeRheutrD5vJ/ceeeduPPOOzt9ThRFrF+/HqtXr8aiRYsAAO+99x5SUlKwefNmPPLII6ivr8fbb7+N999/H7NnzwYAbNq0Cenp6dixYwfmzp0rfz6dTgeDoesJpP2FIAhIiNGgov46Lta1otWelclyRQp3WpUCaqWANouIC3KQi+WKRBR4XmVyvf322zh//jyMRiOqq6uxY8cOOcAF2DYC+fn5qKiowPXr11FUVITs7Ow+P2giIqJAap/JVVxWh2stbRgQpcaUjDindUPszeHP1zT3+mueu9KEg2V1UAjADycN6vXnk0jlig3XzWgxmd2uq5YyuTwoVwQcFzvM5Aq80tJSVFZWIjc3V35Mq9Vi5syZ2Lt3LwCguLgYbW1tTmtSU1ORnZ0tr5H89re/RUJCAiZMmICXX34ZJlPXgUyj0YiGhgant1AiZaecqbaVCmuUCsRovW5rS9SvCIIgl+6brbYBI8xwJKJgwFdoIiIiL0llGo3XzXKp4m0jkjv09JL6Zp2v7X2Q66/26Y05I5I9Lhn0hE5r65/VYrKgsv46spJiOqxps1jl6VmeNJ4HWK4YTCorKwGgwyCglJQUlJWVyWs0Gg3i4uI6rJE+HgCWL1+OSZMmIS4uDvv378eqVatQWlqKP/3pT26/fkFBAV588cW++nb8Tmo+f6bKVtoZH63ps0xKolCmi1DJJbwAyxWJKDj0uvE8ERFRuJEyuVrbLNh2whYAaF+qKMlMtE1ALO1lJpfZYsXHh2xBrh9N7rssLsB2N96g73rCYk2TEaIIqBQC4qM8u4hJsl/sfP19LY6UX4PVfqefAsc1MCOKYrfBGtc1Tz31FGaxttPXAAAY4UlEQVTOnIlx48bhZz/7GV5//XW8/fbbqK2tdfs5Vq1ahfr6evmtvLy8d9+In0kX7qfbBbmIyPFaCACRaiWiNMyfIKLAY5CLiIjIS+1LlS7WtUKjVODW4Ukd1mXI5Yq9azy/+0wNqhqMiI/W4PZRHYNpvSU1n79U13nz+aMX6wEAaXGRUCg8y2CRsti+Kb2KBX/4F6YWfIHnPzqKwpNVaLNY++CoyVNS/6z2GVkAUF1dLWd3GQwGmEwm1NXVuV3TmZtvvhkAcPbsWbdrtFotYmNjnd5CiZSVeKbKVq7IbBUiGymrGWDwl4iCB4NcREREXlIpFYjWKOX3p92Q0GmPnkx7kKuy4TpaTZYOz3tKaji/YEIqNKq+f+keO0gPwDEl0tWnJZcBAHPHeN5sfMGENPz+JxNwZ7YB0RolrjQaseVAOZZvOQwLs7r8KjMzEwaDAYWFhfJjJpMJRUVFmD59OgBg8uTJUKvVTmsqKipw/PhxeU1nDh8+DAAYOHCgj44+8KQ+Q1LJLi/miWzaZ3IlMvhLREGCOaVEREQ9oItQo9keuOqsVBEA4qI10EeqUd/ahrKrzRhp8D6D5WqzCTu+tQWffjQ5vecH3IVFEwfhjaJz2HmqGlebTU4X8Y3X2+SvP398qsefU6kQsGBCGhZMSIPRbME3567iC/vniVAru/lo8lZTU5NTNlVpaSlKSkoQHx+PwYMHIy8vD2vWrMGwYcMwbNgwrFmzBlFRUVi8eDEAQK/X46c//SmefvppJCQkID4+Hs888wzGjh0rT1v8+uuvsW/fPsyaNQt6vR4HDhzAU089hfnz52Pw4MEB+b79wXViHINcRDY6ZnIRURBikIuIiKgHdBEqVNqHxM0elex23ZDEaBwpv4bzNT0Lcn1y+BLaLCLGpukxOtU3ZV4jDDpkp8Xi+KUGfHbkMpZMHyI/t/1EFYxmK25IisaYHn59rUqJW4cndVrSSX3j4MGDmDVrlvz+ihUrAABLlizBu+++i5UrV6K1tRWPP/446urqMHXqVGzfvh06nU7+mN/97ndQqVT48Y9/jNbWVtx+++149913oVTagpJarRYffvghXnzxRRiNRmRkZODhhx/GypUr/fvN+plreSInyBHZtM/kcg0GExEFCoNcREREPSBt7sem6TFQH+l2XWZCFI6UX0NpD/pyiaIolyr+aErfNpx3tWjiIBy/dBIfHbroFOT6nyO2UsUFE9I4US6I5eTkQBTdl4EKgoD8/Hzk5+e7XRMREYHXXnsNr732WqfPT5o0Cfv27evtoYacxGjXTC5ezBMBzplc7FVHRMGCPbmIiIh6IM4+ZXB2N43gHc3nvZ+weK6mGd9VNkKjVHhVKtgTCyakQqUQcPRiPc7Yp8hdaTTiX2drAHhXqkjUn7hevLMsi8gmtn0mF38viChIMMhFRETUA4/PugH33TQYS28Z0uW6TPuUwdJa74NcUrBpVGosBkT59gIiIUaLnBG2ssuPDl0CAHx+rAIWq4jxg/TytESicOMa1GLGCpGNU7kiMxyJKEgwyEVERNQDkzPiUbBoLPSR6i7XScGhsh4EuaQSx8yEKO8PsAd+OCkNgK0PmMUq4n9KbMGu+RPS/PL1iYJRhFoJXbvpqczkIrKJZbkiEQUhBrmIiIh8KNNerljVYESLyezVx0oljv7KorptVDL0kWpUNlzHhwfKcejCNQgCMG/cQL98faJg1f4CnmVZRDZOPbmYyUVEQYJBLiIiIh/SR6kxIMp2IXDey+bz5+3ZX0MS/BPk0qqUmDfeFtB66e8nAADTb0hAcmyEX74+UbCSJscpFYJT9gpROHOersjgLxEFBwa5iIiIfEwKUp33smRRDnL5sR/WDyfZpjheb7MCABaMZ6kikVSiGBelgULBKaNEgHOQi2W8RBQsGOQiIiLyMbn5vBcTFltMZlQ1GG0f76dMLgCYkD4AWfbj1SgVmJtt8NvXJgpWifYsFZYqEjmkDohESqwWY9P0iFArA304REQAAFX3S4iIiKg3pEwub5rPS6WNcVFq6KP8Vx4lCAJ+fGM6XvnHd5g9OrnbxvpE4UDqN8RsFSKHCLUSRc/OgpLZjUQURBjkIiIi8rEhibbpiN705CoLQKmi5GczMmGIjcDM4Ul+/9pEwWhQXCQAIM3+XyKyYQYXEQUbBrmIiIh8TMrkKvUik6vUz03n21MpFVg4kb24iCQLJ6bBbBVx+6jkQB8KERERdYFBLiIiIh+TsrGuNBrRZDQjRtv9y+/5msAFuYjIWYRaiQduzgj0YRAREVE32HieiIjIx/SRarmXz3kPm89LpY1SqSMREREREXWNQS4iIiI/GJJgC1aV1XrWl0sqV8wMQE8uIiIiIqJQxCAXERGRH0gli+c96MvVbDTjSqMRAJDBckUiIiIiIo8wyEVEROQHcvN5D8oVpUBYfLQG+ki1T4+LiIiIiKi/YJCLiIjID+RMLk+CXFI/rgT24yIiIiIi8hSDXERERH6QmeB5uaK0Zgj7cREREREReYxBLiIiIj+QpiTWNJlwrcXU5VqppDGT/biIiIiIiDzmVZCroKAAN954I3Q6HZKTk7Fw4UKcOnXKaY0oisjPz0dqaioiIyORk5ODEydO9OlBExERhRpdhBpZ9systf881eXaMnsmVwYzuYiIiIiIPOZVkKuoqAhPPPEE9u3bh8LCQpjNZuTm5qK52VF6sXbtWqxbtw4bNmzAgQMHYDAYMGfOHDQ2Nvb5wRMREYWS/1yYDQDY/M0FfPFtldt1pfaeXMzkIiIiIiLynFdBrm3btmHp0qUYM2YMxo8fj3feeQcXLlxAcXExAFsW1/r167F69WosWrQI2dnZeO+999DS0oLNmzf75BsgIiIKFbcMTcRPZ2QCAJ776Chqmowd1jReb5Mfl0ociYiIiIioe73qyVVfXw8AiI+PBwCUlpaisrISubm58hqtVouZM2di7969nX4Oo9GIhoYGpzciIqL+6tm5IzAiRYeaJhOe/+gYRFF0er6s1pbFlRijgS5CHYhDJCIiIiIKST0OcomiiBUrVmDGjBnIzraVX1RWVgIAUlJSnNampKTIz7kqKCiAXq+X39LT03t6SEREREEvQq3E7+6dAI1SgR3fVuEvB8udnpeazg9hqSIRERERkVd6HOR68skncfToUfz5z3/u8JwgCE7vi6LY4THJqlWrUF9fL7+Vl5d3uo6IiKi/GJ0ai6dzhwMAXvzspNxoHmjXdJ5BLiIiIiIir/QoyLVs2TJ8+umn2LlzJwYNGiQ/bjAYAKBD1lZ1dXWH7C6JVqtFbGys0xsREVF/97MfZOHmrHi0mCzI+7AEZosVQLum8+zHRURERETkFa+CXKIo4sknn8THH3+ML7/8EpmZmU7PZ2ZmwmAwoLCwUH7MZDKhqKgI06dP75sjJiIi6geUCgH/9eMJ0EWocPjCNbz25VkAwHl7JteQRGZyERERERF5w6sg1xNPPIFNmzZh8+bN0Ol0qKysRGVlJVpbWwHYyhTz8vKwZs0abN26FcePH8fSpUsRFRWFxYsX++QbICIiClVpAyLxm4W2vpavfXkGxWV1OM+eXEREREREPaLyZvHGjRsBADk5OU6Pv/POO1i6dCkAYOXKlWhtbcXjjz+Ouro6TJ06Fdu3b4dOp+uTAyYiIupPFkxIw87vqvFJyWX84s+HUdtsAsBMLiIiIiIib3kV5HIdc94ZQRCQn5+P/Pz8nh4TERFRWHlxQTYOnK/DpWu2zOjEGC1itF69RBMRERERhb0eT1ckIiKivqGPVGPdj8dDGkTMpvNERERERN5jkIuIiCgITM1KwBM5QwEAEwfHBfhoiIiIiIhCD2shiIiIgsTTucMxe3QKRhrYx5KIiIiIyFsMchEREQUJQRAwIX1AoA+DiIiIiCgksVyRiIiIiIiIiIhCHoNcREREREREREQU8hjkIiIiIiIiIiKikMcgFxERERERERERhTwGuYiIiIiIiIiIKOQxyEVERERERERERCGPQS4iIiIiIiIiIgp5DHIRERERhbivvvoK8+bNQ2pqKgRBwCeffOL0vCiKyM/PR2pqKiIjI5GTk4MTJ044rTEajVi2bBkSExMRHR2N+fPn4+LFi05r6urq8OCDD0Kv10Ov1+PBBx/EtWvXfP79EREREXmCQS4iIiKiENfc3Izx48djw4YNnT6/du1arFu3Dhs2bMCBAwdgMBgwZ84cNDY2ymvy8vKwdetWbNmyBXv27EFTUxPuueceWCwWec3ixYtRUlKCbdu2Ydu2bSgpKcGDDz7o8++PiIiIyBOCKIpioA+ivYaGBuj1etTX1yM2NjbQh0NEREQhgnsIG0EQsHXrVixcuBCALYsrNTUVeXl5eO655wDYsrZSUlLw29/+Fo888gjq6+uRlJSE999/H/feey8A4PLly0hPT8fnn3+OuXPn4ttvv8Xo0aOxb98+TJ06FQCwb98+TJs2Dd999x1GjBjh0fHx/xMRERF5y9P9g8qPx+QRKebW0NAQ4CMhIiKiUCLtHYLs/l3AlZaWorKyErm5ufJjWq0WM2fOxN69e/HII4+guLgYbW1tTmtSU1ORnZ2NvXv3Yu7cufj666+h1+vlABcA3HzzzdDr9di7d6/bIJfRaITRaJTfr6+vB8C9HhEREXnO031e0AW5pLT59PT0AB8JERERhaLGxkbo9fpAH0bQqKysBACkpKQ4PZ6SkoKysjJ5jUajQVxcXIc10sdXVlYiOTm5w+dPTk6W13SmoKAAL774YofHudcjIiIib3W3zwu6IFdqairKy8uh0+kgCEKff/6Ghgakp6ejvLycKfJ9iOe17/Gc9j2e077Hc9r3eE57ThRFNDY2IjU1NdCHEpRc91WiKHa713Jd09n67j7PqlWrsGLFCvl9q9WKq1evIiEhgXu9EMTz61s8v77F8+tbPL++Fe7n19N9XtAFuRQKBQYNGuTzrxMbGxuWPxi+xvPa93hO+x7Pad/jOe17PKc9wwyujgwGAwBbJtbAgQPlx6urq+XsLoPBAJPJhLq6OqdsrurqakyfPl1eU1VV1eHzX7lypUOWWHtarRZardbpsQEDBvT8G/IQf4d8i+fXt3h+fYvn17d4fn0rnM+vJ/s8TlckIiIi6scyMzNhMBhQWFgoP2YymVBUVCQHsCZPngy1Wu20pqKiAsePH5fXTJs2DfX19di/f7+85ptvvkF9fb28hoiIiCiQgi6Ti4iIiIi809TUhLNnz8rvl5aWoqSkBPHx8Rg8eDDy8vKwZs0aDBs2DMOGDcOaNWsQFRWFxYsXA7DdGf3pT3+Kp59+GgkJCYiPj8czzzyDsWPHYvbs2QCAUaNG4Y477sDDDz+MN954AwDw85//HPfcc4/HkxWJiIiIfEmZn5+fH+iD8DelUomcnByoVIzx9SWe177Hc9r3eE77Hs9p3+M5JW/t3bsX06ZNk4NP//znP/HGG2+grq4OCxcuxC233AKj0YiXXnoJr776KmJjY7Flyxan5u+zZ89GeXk5fvWrX+GNN97AiBEj8MEHHziVFt511104dOgQXnjhBbz//vu49dZb8ac//QkRERF+/567wt8h3+L59S2eX9/i+fUtnl/f4vntniByzjYREREREREREYU49uQiIiIiIiIiIqKQxyAXERERERERERGFPAa5iIiIiIiIiIgo5DHIRUREREREREREIS/sglx//OMfkZmZiYiICEyePBm7d+8O9CGFjIKCAtx4443Q6XRITk7GwoULcerUKac1oigiPz8fqampiIyMRE5ODk6cOBGgIw4tBQUFEAQBeXl58mM8nz1z6dIlPPDAA0hISEBUVBQmTJiA4uJi+XmeV++YzWa88MILyMzMRGRkJLKysvDSSy/BarXKa3hOu/bVV19h3rx5SE1NhSAI+OSTT5ye9+T8GY1GLFu2DImJiYiOjsb8+fNx8eJFf34bREGP+7y+wT2ff3EP2Pe4F/Qd7gv7HveJfUwMI1u2bBHVarX41ltviSdPnhSXL18uRkdHi2VlZYE+tJAwd+5c8Z133hGPHz8ulpSUiHfffbc4ePBgsampSV7zyiuviDqdTvzoo4/EY8eOiffee684cOBAsaGhIYBHHvz2798vDhkyRBw3bpy4fPly+XGeT+9dvXpVzMjIEJcuXSp+8803Ymlpqbhjxw7x7Nmz8hqeV+/85je/ERMSEsS///3vYmlpqfjXv/5VjImJEdevXy+v4Tnt2ueffy6uXr1a/Oijj0QA4tatW52e9+T8Pfroo2JaWppYWFgoHjp0SJw1a5Y4fvx40Ww2+/vbIQpK3Of1He75/Id7wL7HvaBvcV/Y97hP7FthFeS66aabxEcffdTpsZEjR4rPP/98gI4otFVXV4sAxKKiIlEURdFqtYoGg0F85ZVX5DXXr18X9Xq9+PrrrwfqMINeY2OjOGzYMLGwsFCcOXOmvMHh+eyZ5557TpwxY4bb53levXf33XeL//Ef/+H02KJFi8QHHnhAFEWeU2+5bl48OX/Xrl0T1Wq1uGXLFnnNpUuXRIVCIW7bts1/B08UxLjP8x3u+XyDe0Df4F7Qt7gv9C3uE3svbMoVTSYTiouLkZub6/R4bm4u9u7dG6CjCm319fUAgPj4eABAaWkpKisrnc6xVqvFzJkzeY678MQTT+Duu+/G7NmznR7n+eyZTz/9FFOmTMGPfvQjJCcnY+LEiXjrrbfk53levTdjxgx88cUXOH36NADgyJEj2LNnD+666y4APKe95cn5Ky4uRltbm9Oa1NRUZGdn8xwTgfs8X+Oezze4B/QN7gV9i/tC/+I+0XuqQB+Av9TU1MBisSAlJcXp8ZSUFFRWVgboqEKXKIpYsWIFZsyYgezsbACQz2Nn57isrMzvxxgKtmzZguLiYhw8eLDDczyfPXPu3Dls3LgRK1aswC9/+Uvs378fv/jFL6DVavHQQw/xvPbAc889h/r6eowcORJKpRIWiwUvv/wy7rvvPgD8We0tT85fZWUlNBoN4uLiOqzhaxgR93m+xD2fb3AP6DvcC/oW94X+xX2i98ImyCURBMHpfVEUOzxG3XvyySdx9OhR7Nmzp8NzPMeeKS8vx/Lly7F9+3ZERES4Xcfz6R2r1YopU6ZgzZo1AICJEyfixIkT2LhxIx566CF5Hc+r5z788ENs2rQJmzdvxpgxY1BSUoK8vDykpqZiyZIl8jqe097pyfnjOSZyxr9DfY97vr7HPaBvcS/oW9wXBgb3iZ4Lm3LFxMREKJXKDpHM6urqDlFR6tqyZcvw6aefYufOnRg0aJD8uMFgAACeYw8VFxejuroakydPhkqlgkqlQlFREV599VWoVCr5nPF8emfgwIEYPXq002OjRo3ChQsXAPDntCeeffZZPP/88/jJT36CsWPH4sEHH8RTTz2FgoICADynveXJ+TMYDDCZTKirq3O7hiiccZ/nG9zz+Qb3gL7FvaBvcV/oX9wnei9sglwajQaTJ09GYWGh0+OFhYWYPn16gI4qtIiiiCeffBIff/wxvvzyS2RmZjo9n5mZCYPB4HSOTSYTioqKeI47cfvtt+PYsWMoKSmR36ZMmYL7778fJSUlyMrK4vnsgVtuuaXDmPPTp08jIyMDAH9Oe6KlpQUKhfPLhVKplEdF85z2jifnb/LkyVCr1U5rKioqcPz4cZ5jInCf19e45/Mt7gF9i3tB3+K+0L+4T+wBf3e6DyRptPTbb78tnjx5UszLyxOjo6PF8+fPB/rQQsJjjz0m6vV6cdeuXWJFRYX81tLSIq955ZVXRL1eL3788cfisWPHxPvuu4/jYr3QfrKOKPJ89sT+/ftFlUolvvzyy+KZM2fEDz74QIyKihI3bdokr+F59c6SJUvEtLQ0eVT0xx9/LCYmJoorV66U1/Ccdq2xsVE8fPiwePjwYRGAuG7dOvHw4cNiWVmZKIqenb9HH31UHDRokLhjxw7x0KFD4m233Ra2o6GJOsN9Xt/hns//uAfsO9wL+hb3hX2P+8S+FVZBLlEUxT/84Q9iRkaGqNFoxEmTJsmjkKl7ADp9e+edd+Q1VqtV/PWvfy0aDAZRq9WKt956q3js2LHAHXSIcd3g8Hz2zGeffSZmZ2eLWq1WHDlypPjmm286Pc/z6p2GhgZx+fLl4uDBg8WIiAgxKytLXL16tWg0GuU1PKdd27lzZ6d/P5csWSKKomfnr7W1VXzyySfF+Ph4MTIyUrznnnvECxcuBOC7IQpe3Of1De75/I97wL7FvaDvcF/Y97hP7FuCKIqi//LGiIiIiIiIiIiI+l7Y9OQiIiIiIiIiIqL+i0EuIiIiIiIiIiIKeQxyERERERERERFRyGOQi4iIiIiIiIiIQh6DXEREREREREREFPIY5CIiIiIiIiIiopDHIBcREREREREREYU8BrmIiIiIiIiIiCjkMchFREREREREREQhj0EuIiIiIiIiIiIKeQxyERERERERERFRyGOQi4iIiIiIiIiIQt7/B6ike3b4nGrVAAAAAElFTkSuQmCC\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(test2)\n",
    "plt.subplot(1,2,2)\n",
    "plt.ylim(1000, 1030)\n",
    "plt.plot(result2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e86b4125",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "\n",
    "for i in range(0, len(X_train), features_size):\n",
    "    input_data = X_train[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": 53,
   "id": "c3f49d08",
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = pd.DataFrame(train, columns=('meantemp',))\n",
    "t2 = pd.DataFrame(train, columns=('humidity',))\n",
    "t3 = pd.DataFrame(train, columns=('wind_speed',))\n",
    "t4 = pd.DataFrame(train, columns=('meanpressure',))\n",
    "results = pd.DataFrame(results)\n",
    "results.columns = ('meantemp', 'humidity', 'wind_speed', 'meanpressure')\n",
    "result1 = pd.DataFrame(results, columns=('meantemp', 'humidity', 'wind_speed'))\n",
    "result2 = pd.DataFrame(results, columns=('meanpressure',))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4b7097e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20faf4a6910>"
      ]
     },
     "execution_count": 54,
     "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(t1, label=\"meantemp\")\n",
    "plt.plot(t2, label=\"humidity\")\n",
    "plt.plot(t3, label=\"wind_speed\")\n",
    "\n",
    "# plt.subplot(1,2,2)\n",
    "plt.ylim(0, 100)\n",
    "plt.plot(result1, color=\"#FF6666\", linestyle=\"--\")\n",
    "plt.legend()\n",
    "\n",
    "# plt.subplot(1,2,1)\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(t4, label=\"meanpressure\", color=\"#9966CC\")\n",
    "plt.ylim(980, 1050)\n",
    "plt.plot(result2, 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
}