ДЗ_7_Ultra_Предсказание_котировок_PLZL_.ipynb

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      "name": "ДЗ_7_Ultra_Предсказание котировок PLZL .ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ikonushok/My_studying_Data-Science-and-Neyro-Nets-on-Python/blob/master/%D0%94%D0%97_7_Ultra_%D0%9F%D1%80%D0%B5%D0%B4%D1%81%D0%BA%D0%B0%D0%B7%D0%B0%D0%BD%D0%B8%D0%B5_%D0%BA%D0%BE%D1%82%D0%B8%D1%80%D0%BE%D0%B2%D0%BE%D0%BA_PLZL_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "55GpVXn9pAuG"
      },
      "source": [
        "# Ultra PRO задание\n",
        "-------\n",
        "Найдите любую базу с временными рядами и напишите прогнозирование этого ряда на 10 шагов:\n",
        "1. Спарсите, разделите и нормализуйте данные\n",
        "2. Разделите данные на обучающую и проверочную выборки\n",
        "3. Напишите сеть для прогнозирования временного ряда\n",
        "4. Сделайте визуализацию результата\n",
        "5. Напишите ваши результаты и выводы"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2dLIvrL8iqn9"
      },
      "source": [
        "#!pip install yfinance\n",
        "#import yfinance as yf ## Библиотека для получения данных об акциях\n",
        "# valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo\n",
        "# (optional, default is '1d')\n",
        "import yfinance as yf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xW7fydp3xDDF"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from tensorflow.keras.models import Sequential, Model, load_model\n",
        "from tensorflow.keras.layers import concatenate, Input, Dense, Dropout, BatchNormalization\n",
        "from tensorflow.keras.layers import  Flatten, Conv1D, Conv2D, LSTM, Bidirectional, LeakyReLU\n",
        "from tensorflow.keras.optimizers import Adam, Nadam, RMSprop, Adamax\n",
        "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator # для генерации выборки временных рядов\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint, LambdaCallback\n",
        "from tensorflow.keras import utils\n",
        "from keras.utils import plot_model\n",
        "\n",
        "from google.colab import files\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i3FK7L1wzn5j",
        "outputId": "854907f3-c6cd-426e-87bb-197a32cdedf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9oijn6iYz8yw"
      },
      "source": [
        "import os\n",
        "os.chdir('/content/drive/My Drive/Занятие_7_Временные ряды/data')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dkHKYXJwpnRb"
      },
      "source": [
        "## Подготовка данных"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WclY-kXapq7n"
      },
      "source": [
        "### Загружаем данные"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wy0kOTUupmOC",
        "outputId": "b5a2a0b6-cfad-4cf3-a387-85937662942e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "source": [
        "## Загрузка и сохранение первичных данных\n",
        "tickers = ['PLZL.ME']\n",
        "stock = yf.download(tickers,start='2017-01-01', interval = '1d')\n",
        "del stock['Adj Close']\n",
        "stock[-3:]\n",
        "#stock['Adj Close'].head()\n",
        "#stock['Date'].head()\n",
        "\n",
        "# Polus Zoloto - 'PLZL.ME' - Россия\n",
        "# Polimetal - POLY.ME - Россия\n",
        "\n",
        "# df = stock.reset_index() # если надо перевести дату из индекса в значения"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-09-14</th>\n",
              "      <td>17077.5</td>\n",
              "      <td>17117.0</td>\n",
              "      <td>16778.0</td>\n",
              "      <td>16880.0</td>\n",
              "      <td>183042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-15</th>\n",
              "      <td>16980.0</td>\n",
              "      <td>17073.0</td>\n",
              "      <td>16776.0</td>\n",
              "      <td>16893.0</td>\n",
              "      <td>155579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-16</th>\n",
              "      <td>16938.5</td>\n",
              "      <td>17194.5</td>\n",
              "      <td>16886.0</td>\n",
              "      <td>17150.5</td>\n",
              "      <td>67267</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Open     High      Low    Close  Volume\n",
              "Date                                                  \n",
              "2020-09-14  17077.5  17117.0  16778.0  16880.0  183042\n",
              "2020-09-15  16980.0  17073.0  16776.0  16893.0  155579\n",
              "2020-09-16  16938.5  17194.5  16886.0  17150.5   67267"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4G7fYWHtVmg6",
        "outputId": "3a9e2ce7-d894-4c5d-c1e6-6be27ad4f7e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "data = stock.pct_change() # Рассчитали дневную доходность\n",
        "data.fillna(0, inplace=True) # Заменили NA значения на 0\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-01-03</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-04</th>\n",
              "      <td>0.009664</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.003383</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.036626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-05</th>\n",
              "      <td>0.000890</td>\n",
              "      <td>0.001102</td>\n",
              "      <td>0.017195</td>\n",
              "      <td>0.005557</td>\n",
              "      <td>0.324411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-06</th>\n",
              "      <td>0.007341</td>\n",
              "      <td>-0.001542</td>\n",
              "      <td>0.004893</td>\n",
              "      <td>-0.000442</td>\n",
              "      <td>-0.723525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-09</th>\n",
              "      <td>0.000220</td>\n",
              "      <td>0.000220</td>\n",
              "      <td>-0.003762</td>\n",
              "      <td>-0.000442</td>\n",
              "      <td>2.333333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Open      High       Low     Close    Volume\n",
              "Date                                                        \n",
              "2017-01-03  0.000000  0.000000  0.000000  0.000000  0.000000\n",
              "2017-01-04  0.009664  0.000000 -0.003383  0.000000  0.036626\n",
              "2017-01-05  0.000890  0.001102  0.017195  0.005557  0.324411\n",
              "2017-01-06  0.007341 -0.001542  0.004893 -0.000442 -0.723525\n",
              "2017-01-09  0.000220  0.000220 -0.003762 -0.000442  2.333333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mu8T7vFT7MBh",
        "outputId": "7cefb28a-22a7-4dd8-ca0a-b32388af7ec9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "data = stock.values\n",
        "print(data.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(930, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZwWKkJy0h_Ni"
      },
      "source": [
        "### Рисуем график"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TVEy1W0nyohV"
      },
      "source": [
        "#!pip install --upgrade mplfinance\n",
        "#import mplfinance as mpf  \n",
        "# https://pypi.org/project/mplfinance/\n",
        "# https://github.com/matplotlib/mplfinance/blob/master/examples/using_lines.ipynb\n",
        "# https://github.com/matplotlib/mplfinance/blob/master/examples/external_axes.ipynb\n",
        "# https://github.com/matplotlib/mplfinance/blob/master/examples/addplot.ipynb "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PeKl6gNB-IjR",
        "outputId": "1600ee3e-efe9-4883-9cc6-533e3baa1954",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "print('\\nНа графике показан диапазон провеки стратегии на данных, которые не видела Нейросеть')\n",
        "print('и на которых будет считаться прибыльность торговой стратегии на основе НС')\n",
        "\n",
        "# create a MACD plot (Moving Average Convergence Divergence)\n",
        "exp12 = stock['Close'][-150:].ewm(span=12, adjust=False).mean()\n",
        "exp26 = stock['Close'][-150:].ewm(span=26, adjust=False).mean()\n",
        "macd = exp12 - exp26\n",
        "signal = macd.ewm(span=9, adjust=False).mean()\n",
        "histogram = macd - signal\n",
        "apds = [mpf.make_addplot(histogram, type='bar', width=0.7,panel=1, secondary_y=False),\n",
        "        mpf.make_addplot(macd,panel=1,secondary_y=False),\n",
        "        mpf.make_addplot(signal,panel=1,secondary_y=False),\n",
        "       ]\n",
        "\n",
        "# А это обычный график:\n",
        "mpf.plot(stock[-150:], figsize=(11, 5), main_panel=0, volume_panel=2, panel_ratios = (4,1,1),\n",
        "         type = 'candle', volume = True, style = 'yahoo',\n",
        "         vlines=dict(vlines='2020-08-10',linewidths = 180, alpha = 0.2),\n",
        "         mav = (7, 20),\n",
        "         addplot = apds,\n",
        "         )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "На графике показан диапазон провеки стратегии на данных, которые не видела Нейросеть\n",
            "и на которых будет считаться прибыльность торговой стратегии на основе НС\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1100x500 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cREpFPWkgRIl"
      },
      "source": [
        "## Формирование выборок\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2iyuL1nvSgA"
      },
      "source": [
        "#Формируем параметры загрузки данных\n",
        "xLen = 10                       #Анализируем по 20 прошедшим точкам \n",
        "valLen = 50                    #Используем 100 записей для проверки\n",
        "stepsForward = 10\n",
        "trainLen = data.shape[0]-valLen #Размер тренировочной выборки\n",
        "batch_size = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oFtBv8qgVig"
      },
      "source": [
        "trainLen = data.shape[0]-valLen #Размер тренировочной выборки\n",
        "\n",
        "#Делим данные на тренировочную и тестовую выборки \n",
        "xTrain, xTest = data[:trainLen - stepsForward], data[trainLen+xLen+2: - stepsForward+1]\n",
        "\n",
        "#Масштабируем данные (отдельно для X и Y), чтобы их легче было скормить сетке\n",
        "xScaler = MinMaxScaler()\n",
        "xScaler.fit(xTrain)\n",
        "xTrain = xScaler.transform(xTrain)\n",
        "xTest = xScaler.transform(xTest)\n",
        "\n",
        "#Для подготовки yTrain на 10 шагов вперед необходимо создать дополнительный датасет\n",
        "yTrain = np.array([data[:trainLen,0][i:i + stepsForward] for i in range(trainLen - stepsForward)])\n",
        "yTest = np.array([data[trainLen+xLen+2:,0][i:i + stepsForward] for i in range(valLen-xLen - stepsForward-1)])\n",
        "yScaler = MinMaxScaler()\n",
        "yScaler.fit(yTrain)\n",
        "yTrain = yScaler.transform(yTrain)\n",
        "yTest = yScaler.transform(yTest)\n",
        "\n",
        "#Создаем генератор для обучения\n",
        "trainDataGen = TimeseriesGenerator(xTrain, yTrain,           #В качестве параметров наши выборки\n",
        "                               length=xLen, sampling_rate=1, #Для каждой точки\n",
        "                               batch_size=batch_size)                #Размер batch, который будем скармливать модели\n",
        "\n",
        "#Создаем аналогичный генератор для валидации при обучении\n",
        "testDataGen = TimeseriesGenerator(xTest, yTest,\n",
        "                               length=xLen, sampling_rate=1,\n",
        "                               batch_size=batch_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXrWbdpjggU2",
        "outputId": "dea287e8-7604-4f16-ce73-a44fb06f9674",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(trainDataGen[10][0].shape,\n",
        "      trainDataGen[10][1].shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 10, 5) (1, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aBUjB9T-ghWq"
      },
      "source": [
        "#Создадим генератор проверочной выборки, из которой потом вытащим xVal, yVal для проверки\n",
        "DataGen = TimeseriesGenerator(xTest, yTest,\n",
        "                               length=xLen, sampling_rate=1,\n",
        "                               batch_size=len(xTest)) #Размер batch будет равен длине нашей выборки\n",
        "\n",
        "xVal = []\n",
        "yVal = []\n",
        "for i in DataGen:\n",
        "  xVal.append(i[0])\n",
        "  yVal.append(i[1])\n",
        "\n",
        "xVal = np.array(xVal)\n",
        "yVal = np.array(yVal)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IMxlPxvWflVZ"
      },
      "source": [
        "## Визуализация результатов"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbnDNN2Qfl66"
      },
      "source": [
        "# Функция рассчитываем результаты прогнозирования сети\n",
        "# В аргументы принимает сеть (currModel) и проверочную выборку\n",
        "# Выдаёт результаты предсказания predVal\n",
        "# И правильные ответы в исходной размерности yValUnscaled (какими они были до нормирования)\n",
        "def getPred(currModel, xVal, yVal, yScaler):\n",
        "  # Предсказываем ответ сети по проверочной выборке\n",
        "  # И возвращаем исходны масштаб данных, до нормализации\n",
        "  predVal = yScaler.inverse_transform(currModel.predict(xVal))\n",
        "  yValUnscaled = yScaler.inverse_transform(yVal)\n",
        "  \n",
        "  return (predVal, yValUnscaled)\n",
        "\n",
        "# Функция визуализирует графики, что предсказала сеть и какие были правильные ответы\n",
        "# start - точка с которой начинаем отрисовку графика\n",
        "# step - длина графика, которую отрисовываем\n",
        "# channel - какой канал отрисовываем\n",
        "def showPredict(start, step, channel, predVal, yValUnscaled):\n",
        "  plt.plot(predVal[start:start+step, channel], \n",
        "           label='Прогноз')\n",
        "  plt.plot(yValUnscaled[start:start+step, channel], \n",
        "           label='Базовый ряд')\n",
        "  plt.xlabel('Время')\n",
        "  plt.ylabel('Значение Close')\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "  \n",
        "# Функция расёта корреляции дух одномерных векторов\n",
        "def correlate(a, b):\n",
        "  # Рассчитываем основные показатели\n",
        "  ma = a.mean() # Среднее значение первого вектора\n",
        "  mb = b.mean() # Среднее значение второго вектора\n",
        "  mab = (a*b).mean() # Среднее значение произведения векторов\n",
        "  sa = a.std() # Среднеквадратичное отклонение первого вектора\n",
        "  sb = b.std() # Среднеквадратичное отклонение второго вектора\n",
        "  \n",
        "  #Рассчитываем корреляцию\n",
        "  val = 0\n",
        "  if ((sa>0) & (sb>0)):\n",
        "    val = (mab-ma*mb)/(sa*sb)\n",
        "  return val\n",
        "\n",
        "# Функция рисуем корреляцию прогнозированного сигнала с правильным\n",
        "# Смещая на различное количество шагов назад\n",
        "# Для проверки появления эффекта автокорреляции\n",
        "# channels - по каким каналам отображать корреляцию\n",
        "# corrSteps - на какое количество шагов смещать сигнал назад для рассчёта корреляции\n",
        "def showCorr(channels, corrSteps, predVal, yValUnscaled):\n",
        "  # Проходим по всем каналам\n",
        "  for ch in channels:\n",
        "    corr = [] # Создаём пустой лист, в нём будут корреляции при смезении на i рагов обратно\n",
        "    yLen = yValUnscaled.shape[0] # Запоминаем размер проверочной выборки\n",
        "\n",
        "      # Постепенно увеличикаем шаг, насколько смещаем сигнал для проверки автокорреляции\n",
        "    for i in range(corrSteps):\n",
        "      # Получаем сигнал, смещённый на i шагов назад\n",
        "      # predVal[i:, ch]\n",
        "      # Сравниваем его с верными ответами, без смещения назад\n",
        "      # yValUnscaled[:yLen-i,ch]\n",
        "      # Рассчитываем их корреляцию и добавляем в лист\n",
        "      corr.append(correlate(yValUnscaled[:yLen-i,ch], predVal[i:, ch]))\n",
        "\n",
        "    own_corr = [] # Создаём пустой лист, в нём будут корреляции при смезении на i рагов обратно\n",
        "\n",
        "      # Постепенно увеличикаем шаг, насколько смещаем сигнал для проверки автокорреляции\n",
        "    for i in range(corrSteps):\n",
        "      # Получаем сигнал, смещённый на i шагов назад\n",
        "      # predVal[i:, ch]\n",
        "      # Сравниваем его с верными ответами, без смещения назад\n",
        "      # yValUnscaled[:yLen-i,ch]\n",
        "      # Рассчитываем их корреляцию и добавляем в лист\n",
        "      own_corr.append(correlate(yValUnscaled[:yLen-i,ch], yValUnscaled[i:, ch]))\n",
        "\n",
        "    # Отображаем график коррелций для данного шага\n",
        "    plt.plot(corr, label='предсказание на ' + str(ch+1) + ' шаг')\n",
        "    plt.plot(own_corr, label='Эталон')\n",
        "\n",
        "  plt.xlabel('Время')\n",
        "  plt.ylabel('Значение')\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-07VCc1Qfo_C"
      },
      "source": [
        "# Создадим генератор проверочной выборки, из которой потом вытащим xVal, yVal для проверки\n",
        "DataGen = TimeseriesGenerator(xTest, yTest,\n",
        "                               length=xLen, sampling_rate=1,\n",
        "                               batch_size=len(xTest)) # Размер batch будет равен длине нашей выборки\n",
        "\n",
        "xVal = []\n",
        "yVal = []\n",
        "for i in DataGen:\n",
        "  xVal.append(i[0])\n",
        "  yVal.append(i[1])\n",
        "\n",
        "xVal = np.array(xVal)\n",
        "yVal = np.array(yVal)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gi0Nz6XPgkjr"
      },
      "source": [
        "## Нейросети"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lCbPL-fNhvYS"
      },
      "source": [
        "### CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uiSz3OlwhuOR",
        "outputId": "e1042818-b1b4-4e9f-ad72-a0ef0e7440fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "source": [
        "input = Input(shape = (xLen, xTrain.shape[1]), )\n",
        "x1 = Conv1D(5, 2, padding='same', activation='relu')(input)\n",
        "x1 = Dense(1000, activation='linear')(x1)\n",
        "#x1 = Flatten()(x1) # слой нужен, тк иначе не работают фукции проверки!!\n",
        "\n",
        "x2 = Dense(1000, activation=\"linear\")(input)\n",
        "x2 = Dense(1000, activation=\"linear\")(x2)\n",
        "x2 = Dense(1000, activation=\"linear\")(x2)\n",
        "#x2 = Flatten()(x2) # слой нужен, тк иначе не работают фукции проверки!!\n",
        "\n",
        "x = concatenate([x1, x2])\n",
        "x = Flatten()(x) # слой нужен, тк иначе не работают фукции проверки!!\n",
        "x = Dense(yTrain.shape[1], activation='linear')(x)\n",
        "model = Model(input, x)\n",
        "\n",
        "plot_model(model, dpi=50, show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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ZK1as6POG9I0bNyj/9fDhwzNnzpSXl2sfJ5vN3rNnz8qVKyUSifZ7A3oDuYZMbDb77Nmz6m5179695uZmYiPBMCwqKmr16tU8Hk8sFldUVCQmJsrdN+js7OTxeLdv38YX8/LyOjo6qqur+Xx+fn5+n7stLS3FMAzDsNGjRwcFBWVnZ1dXV2sf7bRp08LCwtatW6f9roDeQK4h0+bNm/GHT4aEhMybN2/ChAlsNru+vj4oKMjZ2dnS0pLL5fL5/FmzZm3durWiooLBYBQXF69Zs2b37t0xMTFPnjyZPn06IZGUlJTY29vj97OkUimfzzczM6NQKL3b0Gi0EydOjB49Gl+8du1acHCwjY3N0qVLf/zxR1V6CQ4OJur5c2+99Zazs7MhF1EGciDXkCkhIQF/ERsbO378+Lt373p5ed25c4fH482fP18gEHC53KysLLwKr6ura2RkJEJoy5Yt27dvT01NdXR0JOSsBCFUVlbm4uKCvz516pSvr29KSoryTUQiEYPBQAgxmcz+rp74+/ubm5tzudyHDx8ihFxcXMrKyggJGCG0bdu2mzdvfvfdd0TtEOgU5BoDYmpqyuFwpFIpQohOp1OpVB8fn0ePHumha4FAIJuJw2KxLl26FBISonwTa2vrtrY2hJBQKOyzVo6bm1ttbS1+iQef8kuj0QQCAYFhZ2RkpKSkVFZWErhPoCOQawyURCLp6uoqKCgYO3YshUKpqal58eJFe3s7/q5QKCT2yiiLxRKLxfhrX19fd3f3ATfx9PQ8ffp0S0tLTk7OjBkzFBtkZ2e3tbWZmJj09PTgv5wUi8VKJgRpgEqlHjt2bPXq1YN4LuigAbmGTOHh4bdu3UpOTk5JSTl58mRpaWl6ejr+LIGMjAw6nV5ZWRkZGTl58uSSkpJx48aVlZUlJia6uLjk5uZGRkY2NDTgl3u0x+Vy79+/j79OTk5OTk5GCOXl5fUuxNnc3DxlypTr16+7ublduHBh0aJFVCqVw+HY2NgsWLBArjFCyMHBwcPDg8lklpWVRUdHI4QePHhA+DQ8R0fHjz/++J133oEfZxo6zAAsXryY7BB0KCoqqqGhQa1NioqKNm3apGW/33zzzfHjx1VsLJVKw8LCnj171ntlU1NTYGCgintQpTGPx6uqqlJxh2lpaYWFhSo2/uyzz7Zt26ZiY0AKgz6uuX379ogRIzS4K9yf9PR0e3v7kSNH/vTTT3JvrVu3Dp8JYmFh4erqmpmZSVSnGti1a9ehQ4eKi4v11iOFQjlw4EBWVhbW6z53amqq6g/bHrBxfn5+WFjYmDFjtAq0H8uXLxeLxV999ZUudg4IYRC/UejPlClTYmNjidrb/fv3Dx069Msvvzx69IjP58u9e/Dgwfz8fHz6WWFh4dq1a5lM5oDXR3WElBo0TCaTx+P1XiO7TaaKARsvXLhQk7BUlpSU9NZbb7m4uEyZMkWnHQHNGOhxjUAgmDt3LpVKxZ/xzOfzZ86caWVltX//frmpKAihK1eu2NnZMRiMK1euyJop7vPixYurVq1is9lcLnfRokX9TU5hsVhLly6Nj48/evQoIf0C/Rg2bNjx48djY2MV/yEBhsBAc01mZqarq2tbW9vGjRsRQllZWQsWLHj06NGJEyc2btzYeyoKQujChQvJycnPnj0rLy+XNcMUamU8efJk7969lpaWo0ePLisrUz45xdXVtaWlhZB+gd5YW1vv378/MjISrhMbIAM9h6qqqlq2bJmZmRn+fNi6urr09HR8Shs+QaP3VJT169fHxsYeO3aMTqcXFBTImtna2vbep7W19bp162JiYr766qtPP/309ddfVxJAdXX1qFGjCOlXJBLFxsbqv1zuw4cPe3p6SktL9dwvUaqqqiZOnKjuVu7u7kuWLNm1a9eHH36oi6iAxgw01zg6OhYWFnK53Orq6ilTpjg5OSUkJMTFxXV2dv7+++9yjZ2dnb/55psjR45s3LhR1mzEiBFyzebMmfPxxx93d3d3d3fT6fQ++5VKpd3d3bdu3YqPj9+zZ8+DBw+079fKyorH49nb22vxfWgiPz+/vb09NDRUz/0S5ejRo5ptGB4evnbt2jNnzgQFBREbEtAKyffBMAzr6553XV2du7s7g8GYOHHijBkzGhsbuVyuhYVFdHR0cHAwnU6/evWqtbW1t7c3hmE7duwwNTV1cnI6f/68rFmfHW3dupXJZE6ePPmPP/6or6+fNm2a7K24uDgajWZiYjJ8+PCJEydmZGRgGEZIvxrc8yaEWve8DZBa97zldHV1LVq06M8//yQ2JKANg6gBGhAQcO7cObKj0JX33ntv586duqiVpdy3337b1ta2YsUKPfdLlE8++WTixIkaP3/myZMnK1as+Pbbby0tLYkNDGjGQK8Na4nP51P+yrjuTWCqVahRsZkcwuvUKG+vfBEhRFRdGzmOjo5bt25ds2YN4XsGmhmcucbW1lbu+E3ueq2BU7FCjQaFbDAd1KlR3l75IkKIwLo2cry9vd3c3KDuhIEYnLnGwAmFQh8fHzqdPnfuXPwBsn1WqKFQKL2r2PTXLDQ0VPUqNrqoU6O8vfJFfBMC69rIiYuL+/nnn69evaqLnQO1QK4hQWZmpoeHR2Njo+zHEH1WqCkqKupdxaa/Zrm5uaqfg+ioTo2S9soX8U2IrWvTG4VCOXz48IcffohPvwQkglxDgpqaGi8vLzqd7ufnV1tbq6Ql4VVsdFGnRnl75Yv4JoTXtemNTqcfOnTo3Xff7erq0lEXQBWQa0jA4XCKi4s7OzsvX77M4XAQQv1VqOldxUZJM9W71kWdGuXtlS/imxBe10bOK6+8snr1anhmJrkg15AgKiqqvLzc1ta2oqJi1apVCKH+KtT0rmLTX7P58+erXsVGF3VqlLdXvojvQRd1beS8+eab5ubmX375pU57Acroe0JPX6B+TZ+0rGLT51w+/dSpUVefdW20mcvXJ4lEsnjx4t9++43AfQLVwXGN4dJFFRs91KlRl07r2vQ2bNiwI0eObNiwoaOjQ9d9AUUG+nsogHRWxUbXdWrUpeu6Nr3Z29tv27YtJiaG3FpoQxMc14Chxdvb+6WXXsrOziY7kCEHcg0Ycj744IOzZ8/+9ttvZAcytBjEby9dXV1VuflqpO7fv8/hcMzMzNrb26lUKl6RRw/4fL5EInFwcNBPd4S7f/9+YmKixr+9VK6pqSk8PPw///mP/usKDVkGkWva29vx6lODmFgsDg0NzczM1H8hG+NlaWk5bNgwHe28uLj45MmTR44c0dH+gRyDyDVDwdq1axcuXKjP66BgQDt37uRwOBEREWQHMiTA9Rp9yM/Pp9PpkGgMDVy40Sc4rtG5+vr6lStX5uXlmZmZkR0LkPf06dOwsDC4cKMHcFyjW1KpdO3atZ988gkkGsNkZ2e3fft2uQlHQBcg1+hWYmJiUFDQq6++SnYgoF9eXl5sNvvEiRNkBzLIQa7RofLy8t9++814K/4OHR988MGZM2fwx34BHYHrNbrS3t7+5ptvnj592tramuxYwMAaGxvDw8Pz8vJk9X0AseC4RlfWr1//0UcfQaIxFg4ODps3b46LiyM7kEELco1OZGdnjx8/XpXKUsBwvPHGG3Q6PTc3l+xABic4hyJeVVUVj8f7z3/+o7s5r0BHuru7AwICMjIynJ2dyY5lsIFcQzB8sB4+fPjll18mOxagiaqqqg0bNpw7dw7+qSAWnEMRbPv27e+88w4kGuM1duzY0NDQ+Ph4sgMZbCDXEKm4uLijo2PAJxMAA/f222/X1NQQWxERwDkUYZqbm5ctW3bu3DmY7T4ItLe3BwYGnjp1SpXH1ABVwHENMTAMW7duXUpKCiSawYFOp3/88cfr1q0jO5DBA3INMT799NPZs2cP4opfQ9C0adNee+01KHBDFMg1mquuru7p6UEI3b17t6SkBP4NHHw2bdqUl5dXUVGBLzY1NZEbj1GD6zWamzdvnlAozM3NXbt27RdffAEF9wYlvOhEdnb222+/jRD67rvvyI7IWMEzWzTU3d1dWVlZV1f397//PTIyEhLNYGVnZ+ft7T116tTm5mYnJ6fu7u7hw4eTHZRRgnMoDV29erW1tRUh1NTUlJ6eHhQUBE84G3y6urpWrVqVmpra1NQklUpbW1t//PFHsoMyVpBrNHT8+HE81yCETExMTE1N4Wx08BGLxfX19d3d3fhia2vr559/Tm5IxgtyjYbKysoQQhQKxcnJKSMj4+uvv7a0tCQ7KEAwKyurgoKCtLS0UaNG4WvguEZjcG1YE/fv358zZ05nZ+ekSZO+/fZb430GE1BRXV3dP/7xjz/++MPKyurnn3+G36BoAI5rNJGbm9ve3r579+6ffvoJEs1Q8PLLL5eVla1fv14kEn377bdkh2OU/v+4ZvPmzbdv36ZSqWTHoxPt7e1isdjW1paoHf7xxx8vv/yyKlOEHz58OHr0aKL61bOurq4JEyakpqYqb/bDDz9s2LDByclJP1GRSygUNjQ0TJo0acCWZP3pHz9+7ODgoLfHqw7o7t271dXVCCGEYRiGYdHR0TU1NdggVVhYmJaWRkrXixcvJqVfQjQ0NERFRQ3YrKioKCUlRQ/xGIju7m5VmpH1p1+5cuXTp09J6bpPsu8BzqEAUI/hHDIYF8g1AAB9GOq5pr6+PjAwUIMNMQxLS0vTrFORSJSUlJSWlkalUm1tbfu8INLa2urv7y8SifBFiUQSERHBZDJXrFiB/whLSXu5xorbyq05c+ZMeXm5Zp8FaECzUafrIYeUjiK5ljdu3KD818OHDxFCA46ioZ5r2Gz22bNnNdjw3r17zc3NGmyIYVhUVNTq1at5PJ5YLK6oqEhMTMT+OvOgs7OTx+Pdvn1btiYvL6+jo6O6uprP5+fn58vtU669XGPFbeXWBAUFZWdn//8FPKB7mo06nQ45NNAoUtxtaWkpfiEGvwQ+4Cga6rlm8+bNHh4eCKGQkJB58+ZNmDCBzWbX19cHBQU5OztbWlpyuVw+nz9r1qytW7dWVFQwGAy8XNuaNWt2794dGho6ffp0tXosKSmxt7e3srJCCEmlUj6fb2ZmRqFQereh0WgnTpzofRfj2rVrwcHBNjY2S5cuVZxOJtderrHitoprgoOD9+/fr9YHARrDR53ckEMIKR91+JCLiYl58uSJWqNOlSGHBhpFqnSkfBQN9VyTkJCAv4iNjR0/fvzdu3e9vLzu3LnD4/Hmz58vEAi4XG5WVtaOHTsQQq6urpGRkXj7LVu2bN++PTc3V92zj7KyMhcXF/z1qVOnfH19U1JSBtxKJBIxGAyEEJPJlJ1YqdhYcVvFNS4uLvhMaKAH+KiTG3IIIeWjDh9yqampjo6Oao06HQ05f39/c3NzLpeLn0OhgUbRUM81ckxNTTkcjlQqRQjR6XQqlerj4/Po0SMCuxAIBLInK7JYrEuXLqlSn9ja2rqtrQ0hJBQKB6xKKddYcVvFNTQaTSAQaPXBgEZ6Dzmkm1GniyHn5uZWW1uLX99JT0/HVyofRZBr+iWRSLq6ugoKCsaOHUuhUGpqal68eNHe3i5rIBQKJRKJurtlsVhisRh/7evrq2IpP09Pz9OnT7e0tOTk5Az4iDu5xorbKq4Ri8UsFkvdzwIIp3zUGc6Qy87ObmtrMzEx6enpkU1qVT6KhnquCQ8Pv3XrVnJyckpKysmTJ0tLS9PT0/ft24cQysjIoNPplZWVkZGRkydPLikpGTduXFlZWWJiIkLIxcUlNzd3/vz5+OUe1XG53Pv37+Ovk5OTk5OTEUJ5eXn+/v6yNs3NzVOmTLl+/bqbm9uFCxcQQosWLaJSqRwOx8bGZsGCBcrbyzWWW1TcG0LowYMHnp6eWn6ZQEX4qPP09JQbckjpqMOHXGRkZENDg1qjrs8hhwYadcqHnIODg4eHB5PJLCsri46OxlcOMIrwK8kwb1hOUVHRpk2btO9acfKoVCoNCwt79uxZ75VNTU2BgYGq71bd9gPi8blHfrQAACAASURBVHhVVVVyK2HesDY0mDdMyKhTnDfc55DD1BxFqjTucxSpOm/49u3bI0aM0OyusKKcnBzZPfnQ0FC5d9etW4e/ZWFh4erqmpmZSUinmtm1a9ehQ4d08YQgCoVy4MCBrKwsrNdNx9TU1E2bNqm+E3XbK5efnx8WFjZmzBiidqglublFOCVDsc/2MsXFxTNmzGAwGHQ6fdq0adqHpLuBqqNR1+eQQ2qOogEbDzyK8JSj5Lhm586dZ86cUTH5KZeTk9PS0oJh2PXr1wsLCxUbODs7Nzc38/n8r776ytraOjc3l5B+4fdQmiHluKajoyMiIsLe3l4oFMq91edQVNIew7CHDx9aW1ufPn362bNnfD4/Li5OKpVqH5IqAxV+D4Ub+LhGIBDMnTuXSqUmJSXha/h8/syZM62srPbv3y83NeDKlSt2dnYMBuPXX3/t3Uxun6GhodbW1gih8+fP+/j49DdNgMViLV26ND4+/ujRo4T0C/Svra1twYIFNBpt5MiRfD7fx8eHTqfPnTu3paVF7o9oZ2c3cuTIxsbGDRs22NraKs4tUhyKvcm1lxtUp0+fXrJkyVtvvUWn01ks1t69eykUilAoVBIPQmjAkHByAxUo12+uyczMdHV1bWtr27hxI74mKytrwYIFjx49OnHixMaNG3tPDbhw4UJycvKzZ8/c3Nx6N8P6KsR19+7dcePGIYSUTxNwdXVtaWkhsF+gT4cPH+ZwOEKh8NKlS0ePHvXw8GhsbMTPOOTmlRw7dmz+/PkODg4rVqzo83xEcSgqITeoHj9+zOFwFHeoJB6E0IAh9SYbqEC5fn+xWlVVtWzZMjMzM9mvWuvq6tLT0/H5RfhddNnUgPXr18fGxh47diw7O1uumWLVmOzsbNkMOiWqq6vxwouE9Pv555+T8rSNioqKgIAA/fdLCLFYPGLECM22raqqCggIoFKpU6dOzczMDAwMpNPpfn5+eXl5eAPZH3HhwoX//ve/Hz58eO7cuQ8++KDPXckNRdWx2WzZLRiZmpoaJfEghAYMqTfZQJXz6NEjUv70Dx48WL58uZmZmf677lNVVRX+ot8/nqOjY2FhIZfLra6unjJlCkLIyckpISEhLi6us7Pz999/793Y2dn5m2++OXLkyMmTJ3s3UxypVVVV9vb2Sp56IZVKu7u7b926FR8fv2fPHqL6Xb58+YYNGwb+YogWEBBw7tw5/fdLiCdPnuzcuVOzbTkcztmzZ2fPno1hmJOTU3Fx8Zw5cy5fvqx4lEGhUOLi4mJiYhYvXmxi0seBtuJQVN2SJUtee+01Hx+fRYsWIYQOHz68ZMkSDoejJB5VQkJ9DVQ5o0aNIuVPHxkZuWfPnpEjR+q/6z79L+Hil20Urw3X1dW5u7szGIyJEyfOmDEDw7DGxkYul2thYREdHR0cHEyn069evWptbe3t7b1jxw5TU1MnJ6fy8vLezRQvFMXExOCXhzEMq6+vnzZtmuytuLg4Go1mYmIyfPjwiRMnZmRk4Ou17xeuDWtGm2vDAoHAz8+PRqN5enrW1dXNnTvXwsICvz4i90fEMEwikXC53M7OTgzDnj596u7uTqFQRo0aVVBQgPU1FHuTay83qDAMKy0tnTVrFj4f183NraWlpaWlRXk8ykPqb6DKgWvDONn3APNrdGvI5hoAuQanj7p8fD6f8ld8Pl933RELU61WiIrN5ED9Go0Z9aBSjvQhh4y3fo2tra1chiOwuriuqVgrRIOSIhjUr9GCUQ8q5cgdcgjq1+iO3CSL/mqFEF5SBOrXDFmGPOQQ1K/RHblJFv3VCiG8pAjUrxmyBtmQg/o1qqqpqfHy8sInWdTW1ippSWxJEahfM2QNpiEH9WvUgE+y6OzsxCdZKKkVQmxJEahfM2QNpiGnQf2aoXvPW26SxePHj+3t7Z2cnCZPnvzGG29UVlayWKy33367qKjI3Nx8+PDhvr6+ra2t/TVTnNaBU7zxWVxczOPx8NdJSUlJSUkYhp07d87Pz0/WRnGOSXd3d1hYmJWVVXh4uEQiUd5errHcouLeMAwrLS3dsGGDXKhwz1sbin96/Qw5xXvefQ45bKBRp3zInTt3zsbGhkaj+fr68vl8fGWfowjm16hKy5IiUL9myNJ4fo2WQ85Y69cAwkuKQP0aoJx+hhzSe/0aeFroAK5cuUL4PplMJo/H671GlR+jatNeuYULFxK4N6Al/Qw5pOYoGrDxgKMIjmsAAPoAuQYAoA8U/BTuyJEjskIeg8+LFy8kEomlpaUqjbu6uvh8/ksvvURI1y0tLQNOhzFk/v7+a9euVd7m7t2727Zt0088hk8sFotEIgcHB7L+9CKRyMrKqr9SGKTAa2tQMChh91c1NTVpaWn9/TgNAOV+/fXXr7/++qOPPiI7EINjQMnPQAwbNqzPH1IDoIqenp5hw4aRHYUhglwjD3IN0Abkmv5ArpFnamqqwWNMAcBBrukP5Bp5cFwDtAG5pj+Qa+RBrgHakEgkkGv6BLlGHuQaoA04rukP5Bp5kGuANnp6ejR4jtVQALlGHuQaoA04rukP5Bp5kGuANiDX9AdyjTzINUAbkGv6A7lGnomJCf5QZwA0ALmmP5BrACAS5Jr+QK4BgEiQa/oDuQYAIkGu6Q/kGgCIBLmmP5BrACAS5Jr+wATH/7l3715hYWFHR8ejR4+2bNkybNiw9957z9nZmey4gHHYv39/Y2Pjn3/+SafTa2trX3rppdDQULKDMiBQl+9/fvvtN09PT/y5ogihkSNHVldX0+l0cqMCxsLPz+/y5cuyxeXLl3/22WckxmNo4BzqfyZOnOjo6ChbnDBhAiQaoLro6GgGg4G/ZrFYsbGx5MZjaCDX/EV4eDh+sk2lUt977z2ywwHGxM/PT1bM3MbGZsqUKeTGY2gg1/xFVFSUvb09QmjkyJGBgYFkhwOMyfDhwydNmoQQolAoS5YsITscgwO55i/s7OzYbDZCaMyYMSo+4wUAmejoaCsrKwcHhzVr1pAdi8GBXCMvOjra3NwcTqCABubNm2dlZeXo6Ojk5ER2LAbnf/e8v/vuu46ODhJDMRBUKtXMzMzU1BR/gBbgcrl2dnbK21y+fFksFusnHgPHZrOnTZsGgwc3Y8YMW1tb/PX/7nn//e9/j4iIIC8q3Tp9+vTcuXNVfA7hvXv38BNv7d27d08oFM6aNYuQvenf1atXQ0NDB3ws/IwZM1auXKmfkPTvq6++WrhwoZWVlSqNKysrR40aZW5urn2/d+7cEYvFr7/+uva7IsX3338fGRnp4+ODL/7vuMba2vrdd98lKSqd+/XXX0NCQkaPHq3nfvPz8x8/fmy8X6yKpXxsbW2N9zMOqLy8fNmyZb3nQ+jHt99+29bWtmLFCj33SxS58yS4XgMA0AfINQAAfYBcg+rr6zWYSoNhWFpamsadikSipKSktLQ0KpVqa2ubmpqq2Ka1tdXf318kEiGEJBJJREQEk8lcsWJFf+c1StorX0QInTlzpry8XOOPM2SRMnj6Q/igunHjBuW/Hj58qOUggVyD2Gz22bNn1d3q3r17zc3NmvWIYVhUVNTq1at5PJ5YLK6oqEhMTJT7YVpnZyePx7t9+za+mJeX19HRUV1dzefz8/PzFfepvL3yRYRQUFBQdnZ2dXW1Zp9oyNL/4OmPLgYVQqi0tBTDMAzDRo8ereUggVyDNm/e7OHhgRAKCQmZN2/ehAkT2Gx2fX19UFCQs7OzpaUll8vl8/mzZs3aunVrRUUFg8EoLi5es2bN7t27Y2Jinjx5Mn36dLV6LCkpsbe3x+9rSKVSPp9vZmZGoVB6t6HRaCdOnJBdzL527VpwcLCNjc3SpUt//PFHxX0qb698Ed8kODh4//79an0QoP/B0x9dDCpF2gwSyDUoISEBfxEbGzt+/Pi7d+96eXnduXOHx+PNnz9fIBBwudysrKwdO3YghFxdXSMjIxFCW7Zs2b59e2pqqqOjo7oHlmVlZS4uLvjrU6dO+fr6pqSkKN9EJBLhv+tjMpn4AbBa7ZUv4pu4uLiUlZWp9UGA/gdPf3Q0qPz9/c3Nzblc7sOHD5F2gwRyjTxTU1MOh4M/SoFOp1OpVB8fn0ePHhHYhUAgoNFo+GsWi3Xp0qWQkBDlm1hbW+PFLoRCoSqzhOTaK1/EN6HRaAKBQIuPBfQxePqji0Hl5uZWW1uLX+JJT09H2g0SyDXKSCSSrq6ugoKCsWPHUiiUmpqaFy9etLe34+8KhUKJRKLBblkslmyWra+vr7u7+4CbeHp6nj59uqWlJScnZ8aMGeq2V76IbyIWi1kslgYfB/RJR4OnP7oYVNnZ2W1tbSYmJj09PRYWFki7QQK5BoWHh9+6dSs5OTklJeXkyZOlpaXp6en79u1DCGVkZNDp9MrKysjIyMmTJ5eUlIwbN66srCwxMdHFxSU3NzcyMrKhoQE/Y1cdl8u9f/8+/jo5OTk5ORkhlJeX5+/vL2vT3Nw8ZcqU69evu7m5XbhwYdGiRVQqlcPh2NjYLFiwQN32yhfxPTx48MDT01O773LI0f/g6Y/2g0quMULIwcHBw8ODyWSWlZVFR0cjLQcJ9l+LFy/GBq/o6Oiamhq1NikqKtq0aZOW/Z4/fz4jI0NupVQqDQsLe/bsWe+VTU1NgYGBqu9Z3fYD4vF4VVVVciszMjLOnz8/4LaDe/BERUU1NDSotQkhg+ebb745fvy4io21H1SqNO5zkPQnLS2tsLBQtjjwcc3t27dHjBihwY29/hw9enTt2rXHjx9XfGvdunX4zXwLCwtXV9fMzEyiOtXArl27Dh06VFxcTPieKRTKgQMHsrKysF63JFNTUzdt2qT6TtRtr1x+fn5YWNiYMWOI2iGO2MEjN91D7t0hMnj6o/2gGrCxtoNElnWU/NO0c+fOM2fOqJjMlDt37tz06dMPHTr02muv5efnKzZwdnZubm7m8/lfffWVtbV1bm4uIf1qcFxDiD6Pa4yI9sc1BA6e8vJy2XSPPulo8GhwXEMItY5rDJAaxzUCgWDu3LlUKjUpKQkhxOfzZ86caWVltX//frnZBAihK1eu2NnZMRiMK1euyJop7lMikbz++uuRkZGvv/76ixcv+ptfwGKxli5dGh8ff/To0d79IoWJDCr2C/RMF4NHjrqDh6h+gWaUPbMlMzPT1dX14sWLu3btQghlZWUtWLCgoKBg9uzZGRkZ2dnZly5dioiIuHPnDpvNvnDhQnJyckRExMcffyxrFhMTIzebaPHixYmJiWZmZlOmTElNTTU3N1cyv8DV1fXYsWO9+42JiYmNjT1x4oSs66KiIlX6FYvFBQUFI0eO1O7rUtsvv/wiFAq//vprPfdLlNu3b2tW9kkXgwch5O/v393d/dprr+Xk5IwePVqtwaNxv2KxOC8vz9raWoPvQRtlZWVisdh460PevXt34sSJskVluaaqqmrZsmV44SiEUF1dXXp6Oj4rCb/H3ns2wfr162NjY48dO0an0wsKCmTNZJVycLt37w4PDy8tLf3000/37t27fft2JQFUV1ePGjVKsd/eXavYL4ZhL1680H89p66uru7ubuOtI6XxfVldDB58uoelpeWePXvS09PxI6b+9Dd4NOiXrMHT3d09mAaPslzj6OhYWFjI5XKrq6unTJni5OSUkJAQFxfX2dn5+++/yzV2dnb+5ptvjhw5snHjRlmzESNGyDXr6OhoaGgoLS29d++eg4NDn/1KpdLu7u5bt27Fx8fv2bPnwYMHvXdYWVmpQb80Gi0oKEj/9WtsbGweP368fPlyPfdLFI1LNepi8GRnZ/v4+DAYDNl0D0VKBo/G/dJotODgYP3Xr7G0tGxrazPewSMUCv+yLLtyo3h5r66uzt3dncFgTJw4ccaMGY2NjVwu18LCIjo6Ojg4mE6nX7161dra2tvbG8OwHTt2mJqaOjk5nT9/XtZM8XJRc3Pz2rVr33jjjXXr1gkEgvr6+mnTpsnejYuLo9FoJiYmw4cPnzhxIn5VtXe/GIbJda1iv3BtWDMaXxvWxeA5d+6cjY0NjUbz9fXl8/nqDh6N+4Vrw5qRuzYM82t0a8jmmsEEco1m1J5fozE+n0/5Kz6fr7vuCIepVmRExWZyoH6NcjB4lDDSwaPDXGNrayuX5+QuuRk4FYuMaFCLBIP6NQOBwdMf4x08Q/f3UEKh0MfHh06nz507t6WlBSHUZ5ERCoXSuxBJf81CQ0NVL0QC9WuMHQweDQbP0M01mZmZHh4ejY2NsvnsfRYZKSoq6l2IpL9mubm5qp+DQP0aYweDB6k/eIZurqmpqfHy8qLT6X5+frW1tUpaEl6IBOrXGDsYPEj9wTN0cw2HwykuLu7s7Lx8+TKHw0EI9VdkpHchEiXNVO8a6tcYOxg8mgwe2bW3wX3bUvGed0tLy9y5cy0sLPBTbgzDHj9+bG9v7+TkNHny5DfeeKOyspLFYr388svm5ubDhw/39fVtbW3tr5mPj0/vuR4yfd7zLi4u5vF4+OukpKSkpCQMw86dO+fn5ydr8/TpU3d3dwqFMmrUqIKCgu7u7rCwMCsrq/DwcIlEom575Yv4HkpLSzds2CAXKtzzxvq6562fwdPnPW/tB49c4wHbazx4YH6NerQsRAL1awYBjefXaDl4+sw1+qlTo64+B4/+5tcMDrooRDJ06tcMcYY5eIgdOUjlwaPs91AAIXTlyhVd7JbJZPJ4vN5rZBX5VaRue+UWLlxI4N4AzjAHD7EjB6k8eOC4BgCgD/87rhGJRJ9//jmJoejUgwcPzpw5o/+5p7/++is+16u/3yUbuLKyMlXq1wgEAiMdPB0dHQP+aaqqqr755hvF33/r2s2bNzs7O4cNG6bnfolSXl7eu34NRXbWV1BQ8Pz5c5KiGuSysrKGDx++ZMkS2cwII/L3v/+9v+ofMufPn+/s7NRPPETp6em5ePFiRUVFXFzc8OHDyQ5ncJozZ46dnR3+moL99ZcUQEdKSkri4+OXL1++fPlyxXJzQM9KS0s/+OCDwMDA6OhoExO4kqAPkGv0p7u7Oz09/bvvvtuzZ8+kSZPIDmeIamxs3Lp1K4Zh+/btg7mL+gS5Rt/q6+s/+OADBoMRHx9Pp9PJDmcIkUgkBw8ezMvLS0xMVP23joAocPSob2w2OysrKyAgIDAw8LPPPiM7nKGivLx80aJFCKHLly9DoiEFHNeQpqura//+/aWlpXv37p0wYQLZ4QxaAoEgPj5eKBTu3bvXuIrgDDKQa0hWXV29efPmyZMnb926lUqlkh3OoCKVSr/44osvv/xy165df/vb38gOZ6iDcyiSjRkz5vTp09OmTVu0aFFBQQHZ4QweN27cWLhwoVAoLCgogERjCOC4xlB0dnZ+/PHH9+7d27t3r7OzM9nhGLGWlpaPPvpIJBIlJyfDSZPhgFxjWP78888tW7a4ubm9//775ubmZIdjZPCTppMnT+7atev1118nOxzwF3AOZVjGjx//7bffTps2beHChZcvXyY7HGNy8+ZN/KTpwoULkGgMEBzXGKjnz58nJydXVFSkpqaOGjWK7HAMmuykKSkpSf+PbAcqglxj0O7fv7958+bXXntt27ZtZmZmZIdjcGQnTR9++CGXyyU7HKAMnEMZtFdfffXs2bNjxoxZtGhRn0/bGMpkJ00FBQWQaAwfHNcYB5FI9OGHH7a2tu7Zs0f2w9khC06ajBHkGmNy69at7du3+/n5rV+/3njLmmgDw7DPP//85MmTO3fu9PT0JDscoAY4hzImr732Wn5+vrW19YIFC37++Weyw9G3X375RXbSBInG6MBxjVEaatPVhELhrl27RCLRxx9/DKeQRgpyjRG7efPm9u3b58+fP4gLPsFJ06AxOAfoEDFt2rSCggIajfbGG2/0fiA0/nwyEgPTmEQi+eSTT2SLt27dgpOmwYPYR1IBUjx58uSdd97h8Xitra3Hjh2j0+mKDyE0Cv/85z+trKy+++67lpaWmJiYFStWNDU1kR0UIAbkmsGjqKjIy8uLzWYjhKytrY8dO0Z2ROpJTU21trZGCDk7O/v6+v70009kRwSIBOdQgweeaJqamhBCQqFw27Zt169fJzsoVV25ciUhIUEoFCKEmpqaPD094aRpkIFrw4PHnTt35syZ09raKlvj7OxcVlZmb29PYlSqqKys9Pb2fvz4sWyNjY3N3bt3HR0dSYwKEAuesTt4WFhYvP/++1euXKmrq3v27FlbW1tdXd28efNu3rxpyOUpWltbfX19Hz9+TKPRRowYwWAw2Gy2j49PV1cX2aEBIpF/XBMREdHR0UFuDINPV1eXUChsbGzk8/l2dnaurq4IoefPn5uamuo/70gkkufPn/f53EgMw27evNnW1jZy5Eh7e3tra2t4LBzhZs+eHR0dTXYUBnBc09LScvr0abKjGBIyMjLGjh3r6+ur536rqqoOHz6clJSk534BQujZs2cxMTGQaxBCiEKhGPIR/mBiamo6fPhw/X/bZmZmw4YNg78yKV68eEF2CP8P7kMBAPQBcg1ACKH6+vrAwEB1t8IwLC0tTbMei4uLKRQK/pgavDpEWloalUq1tbVNTU1VbN/a2urv7y8SifBFiUQSERHBZDJXrFjR09OjvL1cY8Vt5dacOXOm9zzs3lQJVXnv6n405YuyUMPDwykUyurVqwf43skDuQYghBCbzT579qy6W927d6+5uVnjTtetW9fc3IxhWFRU1OrVq3k8nlgsrqioSExMlLtl0dnZyePxbt++LVuTl5fX0dFRXV3N5/Pz8/Pl9izXXq6x4rZya4KCgrKzs6urq+V2q0qoA/auvPGA0fYX6vHjxy9evKjW969nkGsAQght3rzZw8MDIRQSEjJv3rwJEyaw2ez6+vqgoCBnZ2dLS0sul8vn82fNmrV169aKigoGg1FcXLxmzZrdu3fHxMQ8efJEgwfXMplMBoNRUlJib29vZWWFEJJKpXw+38zMjEKh9G5Jo9FOnDgxevRo2Zpr164FBwfb2NgsXbpUsWKhXHu5xorbKq4JDg7ev3+/3G5VCXXA3pU3HjDa/kIdPny4gU+kglwDEEIoISEBfxEbGzt+/Pi7d+96eXnduXOHx+PNnz9fIBBwudysrKwdO3YghFxdXSMjIxFCW7Zs2b59e2pqqqOjY38nHQMqKytzcXHBX586dcrX1zclJWXArUQiEYPBQAgxmUzZ2YeKjRW3VVzj4uJSVlam/1AHjFbFUA0Q+fehgAEyNTXlcDhSqRQhRKfTqVSqj4/PxYsX3d3dCe9LIBDIStKwWKxLly6p0ou1tXVbWxtCSCgU2tjYqNVYKpXKbau4NxqNJhAI9B/qgNEqBt9nqAYIjmvAACQSSVdXV0FBwdixYykUSk1NzYsXL9rb2/F3hUKhRCLRZv8sFkssFuOvfX19VUxnnp6ep0+fbmlpycnJmTFjhlqNFbdVXCMWi1kslv5DHTBaFUM1QJBrAEIIhYeH37p1Kzk5OSUl5eTJk6Wlpenp6fv27UMIZWRk0On0ysrKyMjIyZMnl5SUjBs3rqysLDEx0cXFJTc3NzIysqGhAb/co5bW1tb29nYul3v//n18TXJycnJyMkIoLy/P399f1rK5uXnKlCnXr193c3O7cOECQmjRokVUKpXD4djY2CxYsEB5e7nGcouKe0MIPXjwwNPTU263qoQ6YO/qfrQBg8dD7e7uxn92a7jI+4n5/1u8eDHZIQwVBw4cuHjxolqbFBUVbdq0Sct+//zzz40bNyruGSFkbm4ulUrDwsKePXvW+92mpqbAwEDVu1C3/YB4PF5VVZXcbg051LCwMITQe++9J/dua2trWFgYgd1pbNDmGqlUmpqaSnh7kUjk5+cnFAr7fLeoqMjT05NOp1taWk6dOlX13jWOR632GuQab29vS0vLoqIitbaS02eu6U0oFKampkqlUtma999//4cfflC9C3XbK3f+/Pnr16/3uVuDDbU/hpNrBu21YXWnfqjSXnEqRG+1tbX/+Mc/jhw54ufn9+LFC3zmheINUQLj0aa9iq5cuUL4PhUxmUwej9d7jey+mIrUba/cwoUL+9utwYZqBMhOdn0c17S2ts6fPx+fl9nc3Dxv3jxLS0tvb2+BQLBkyZK5c+e++uqrL7300uPHj+Ua19bWjhgxgkaj7d27d9asWQihsLCwGTNmMBiMffv2YRgmt/njx4/l2vN4vObm5t6bKHr99dfx4xr8IoVs/d69e6OiouQat7S0aBO/XDxaxq/BcQ0hBjyuAbpjOMc1hnht+PDhwxwORygUXrp06ejRox4eHo2Nja6urpmZmXKzP+Qajxo1SiQS3bp16+LFi/jUD1dX1wULFjx69OjEiRMYhsltzmaz5dqnpqZmZWX13kRJnHKTSh4/fszhcOTaZGZmahO/XDwbN24kMH4A9MkQz6GqqqoCAgKoVOrUqVMzMzMDAwPpdLqfn19eXh7eoPfsj96NCwsLV65c2dDQ4O3tjbesq6tLT0/HZ6DJ5iDINldsr7iJ6k9fYrPZspsUMjU1NdrE3+dH0Dj+Fy9e7Ny58+DBgyp+IqI8f/68qakpICBAz/0ChJBEIjGQf3IMMddwOJyzZ8/Onj0bwzAnJ6fi4uI5c+ZcvnxZ8ahBrvHnn3+enJz8yiuvbN68GSEkFAodHBwSEhLi4uI6OztHjBhRWVnZe9ucnBy59hKJxMnJqfcmqoe9ZMmS1157zcfHZ9GiRQihw4cPL1myhMPhaBO/XDy///67NvGbm5vv3LnTz89P9Q9FiMrKStkddKBnbW1ta9euJTsKhJBBXq8RCAR+fn40Gs3T07Ourm7u3LkWFhZz585taWkJDg6m0+lXr161trb29vaWa3zw4EEajTZ79mxzc/OTJ0+yWCw/Pz8ul2thYREdHY1hmNzm2dnZcu3ffvvtxsbG3pv09vTpU3d3dwqFMmrUZU+fPwAAFxpJREFUqIKCgvr6+mnTpvVuUFpaOmvWLHyirZubW0tLS0tLizbxy8WjZfxwvWYIMpzrNeTXAA0ICDh37hy5MQwRBw8eHDduHBzXDCn4cc0XX3xBdiAwb7h/fD6f8ld8Pp/soPQNU61CjYrN9InwQjPff/+9g4MDnU4PCQnB665rVmhmyIJc0y9bW1u5g0DVrxMPGipO29HR7B6NYTooNMPn8wsLC5uamoRCYUlJiWJ7FQvN6OHjGybINUOaUCj08fGh0+n45SSEUH8VanRayIZwuig0Exoa6urqamlpSafT8V97a1ZoRtef3WBBrhnS5Kb/IIT6q1Cjh0I2BNJdoZna2lorKyv8F96DtdCMjkCuGdJqamq8vLzw6T+1tbXKG8sK2Tx69Eg/4WlMIBDQaDT8NV5oJiQkZMCtBiw009TUdPDgwcOHD/fZXvkiMp5CMzoCuWZIw6f/dHZ2yqb/KKlQo+tCNgTSRaGZmpqao0ePJiQk4PXYFdsPmkIzOgK5ZkiLiooqLy+3tbWtqKhYtWoVQqi/CjVIl4VsCNdnoRk0UFkc5YVmPvnkkx07dgwfPpxCoezduxdpWmhGf9+CodH3hB4FUL9Gb7SZy6dNIRv9z+Xrs9AMpmbtGB0VmiFwh6ownLl8cFwDVLJr165Dhw4VFxeTHYhKKBTKgQMHsrKysL/e505NTd20aZOKO1Gr8YDy8/PDwsLGjBlD1A6NjiH+HgoYIP0UsiGQYqEZpGbtmKFbaEY34LgGAKAPkGsAAPpA/m8vX3311VdffZXcGIYIPp9vbm6OTzDTWEdHh5mZmampGmffL1684PP5bDZbm36BZnp6euzs7I4fP052IAaQa4Bx2blzZ0BAwNSpU8kOBBgZOIcCAOgD5BoAgD5ArgEA6APkGgCAPkCuAQDoA+QaAIA+QK4BAOgD5BoAgD5ArgEA6APkGgCAPkCuAQDoA+QaAIA+QK4BAOgD5BoAgD5ArgEA6APkGgCAPkBtc6ASqVR669YthNCTJ09+//13DMMYDMYrr7xCdlzAaEBdPqCql19+uaurC8MwCoXS1dUVFhb26aefkh0UMBpwDgVUFRoa+vTp06dPnzY1NZmamkZFRZEdETAmkGuAqlavXm1vb4+/ZjKZrq6u5MYDjAvkGqCqsWPH2tjYIIQoFMqbb75JdjjAyECuAWpYtmzZsGHD7O3t33vvPbJjAUYGrg0DNTQ2Nk6dOpXFYlVUVJAdCzAycM97AN3d3VKplOwoDIW1tbW1tXVwcPCLFy/IjsWAmJqaDhs2jOwoDB0c1wzA09PTycmJ7Cj0gc/nI4RsbW2VN6utrbW3t6dSqUT1K5FIHj58OG7cOKJ2qGetra1/+9vf4uPjyQ7E0MFxzQBGjhz59ddfkx2FPnz11VcIoWXLlilvJpVKTUyIvMzX0tISGxt74sQJAvepT7/++uupU6fIjsIIwLVhoB5iEw0YOmDcAAD0AXINGFh9fX1gYKAGG2IYlpaWplmnIpEoKSkpLS2NSqXa2tqmpqYqtmltbfX39xeJRPiiRCKJiIhgMpkrVqzo6emRa/z99987ODjQ6fSQkJCuri65xorbyq05c+ZMeXm5Zp8FIMg1QBVsNvvs2bMabHjv3r3m5mYNNsQwLCoqavXq1TweTywWV1RUJCYmyt3H6Ozs5PF4t2/flq3Jy8vr6Oiorq7m8/n5+fly++Tz+YWFhU1NTUKhsKSkRK6x4rZya4KCgrKzs6urqzX4OABBrgGq2Lx5s4eHB0IoJCRk3rx5EyZMYLPZ9fX1QUFBzs7OlpaWXC6Xz+fPmjVr69atFRUVDAajuLgYIbRmzZrdu3eHhoZOnz5drR5LSkrs7e2trKwQQlKplM/nm5mZUSiU3m1oNNqJEydGjx4tW3Pt2rXg4GAbG5ulS5f++OOPcvsMDQ11dXW1tLSk0+l2dnZyjRW3VVwTHBy8f/9+9b8/gBDkGqCKhIQE/EVsbOz48ePv3r3r5eV1584dHo83f/58gUDA5XKzsrJ27NiBEHJ1dY2MjMTbb9myZfv27bm5ueqefZSVlbm4uOCvT5065evrm5KSMuBWIpGIwWAghJhMpuzESk5tba2VlZW7u7tcY8VtFde4uLiUlZWp9UGADOQaoDZTU1MOh4NPcaTT6VQq1cfH59GjRwR2IRAIaDQa/prFYl26dCkkJGTAraytrdva2hBCQqEQ/+mWnKampoMHDx4+fFixseK2imtoNJpAICDqMw41kGuAViQSSVdXV0FBwdixYykUSk1NzYsXL9rb22UNhEKhRCJRd7csFkssFuOvfX193d3dVdnK09Pz9OnTLS0tOTk5M2bMkHu3pqbm6NGjCQkJ+EREucaK2yquEYvFLBZL3c8CcJBrwMDCw8Nv3bqVnJyckpJy8uTJ0tLS9PT0ffv2IYQyMjLodHplZWVkZOTkyZNLSkrGjRtXVlaWmJiIEHJxccnNzZ0/fz5+uUd1XC73/v37+Ovk5OTk5GSEUF5enr+/v6xNc3PzlClTrl+/7ubmduHCBYTQokWLqFQqh8OxsbFZsGCBXPtPPvlkx44dw4cPp1Aoe/fulWsst6i4N4TQgwcPPD09tfwyhy4MKLV48WKyQ9CTL7/88ssvv1Rrk6Kiok2bNmnZr0AgiIiIkFsplUrDwsKePXvWe2VTU1NgYKDqe1a3/YB4PF5VVZXcyjt37mzfvp3AXgYrOK7RxN69ey0tLalUquI/19h/Z5RgGk0tkZsw0tu6desovdy4cSM0NLS/ADTrXV27du06dOgQfsuJWBQK5cCBA1lZWViv+9ypqambNm1SfSfqtlcuPz8/LCxszJgxRO1wqIHfQ2ni5MmTQqHQxMSEy+XKvSWbUaLB1BLFCSO9HTx4MD8//8aNGywW66effsLD6C8AjSe2qOXKlSu62zmTyeTxeL3XyG6HqUjd9sotXLiQwL0NQZBr1LZt27bbt2+bm5v/+eef+Jr6+vpJkyZ1dXV99NFHZ8+e/eGHH9rb22/evPnDDz88fPiwpqamoqLiww8/vHbtmkAgqK+vf/bs2fXr19lsttye8QkjsvTx5MmTgIAAxbvFFApl5syZCCHZNVfFAHbv3o0Qam9v37Fjx5tvvqliAADoDuQatSUkJBQUFNy+fVv2vzqbzRaJRPfv31+/fv2WLVvmzJkTHx9//vz5OXPmMBgMFxeXgoKC2bNnZ2RkZGdnX7p0KSIi4s6dOwP+r+7o6KiYaEaOHIkQSk5O7n12oBgAl8v9+eef4+PjP/744wULFqgYQGdnJ36XV5+ePXvW3d2t/36J8vz5cwwKs6gAcg0BCgsLV65c2dDQ4O3tLfdWXV1deno6PskNn5rRe3KKBpqbmxVLzBAVwLFjx3R6WtSnrq6ue/furV27Vs/9EkUkEo0dO5bsKIwA5BoC5OTkJCcnv/LKK5s3b0a9ZpQIhUIHB4eEhIS4uLjOzs7ff/9dnwHg/3VyclI9gHXr1g1Yv4ZwUL9mqCDzJpgxULznvX//fgqFsnjx4tDQUBMTk5SUlOzsbBqNNnv2bHNz85MnT7JYrLfffruyspLFYvn5+XG5XAsLi+jo6ODgYDqdfvXqVWtra29vb8W+nj596u7uTqFQRo0aVVBQUF9fP23aNNm7+LHJ2LFjHz9+jK9REsDMmTPxMBobG1UMQIN73oTo8563EYF73iqCGqADCAgIOHfuHNlR6IOKdfkINziOa6AG6IBgfg1p+Hw+5a/wir9GBFNtFo+KzZRTpZyNWhVqBqxfAwVriAW5hjS2trZyB5kD1hU3NCrO4tF+sg+mQjkbpGaFmgHr10DBGmJBrgHKCIVCHx8fOp0+d+7clpYWuQo1eHmamJgY5YVsZM2ePHmibiEbnCrlbJCaFWoGrF+DoGANoSDXAGUyMzM9PDwaGxtdXV0zMzPlKtTg5WlSU1OVF7KRNetzxpAq1Cpno2KFmgHr1yAoWEMoyDVAmZqaGi8vLzqd7ufnV1tbq6SljgrZ4FQvZ6N6hZoB69cgKFhDKMg1QBkOh1NcXNzZ2Xn58mUOh6NYoUY2mUh5IRvNqtjIqFjORq0KNQPWr0FQsIZQkGuAMlFRUeXl5ba2thUVFatWrZKrUIOXp8FPlJQUspE1a2hoULeQDU6VcjZIzQo1A9avQVCwhlj6ntBjbKB+jSq0KWSjylw+QsrZaKDPgjVyYC6fiuC4BhBAd4VscISUs1EXFKwhFvweChBAD7/Y1L6cjbqgYA2x4LgGAKAPcFwzAKlUqocCd4YAv+Or/w8rFArFYrHxfslCoZDsEIwD/PZyADt37tTFhBED1NnZiRCSTWPpT01Nja2tLT7tjRBSqbStrY3JZBK1Q/1bvHixZs87H1Ig1wD17Ny5MyAgYOrUqWQHAowMXK8BAOgD5BoAgD5ArgEA6APkGgCAPkCuAQDoA+QaAIA+QK4BAOgD5BoAgD5ArgEA6APkGgCAPkCuAQDoA+QaAIA+QK4BAOgD5BoAgD5ArgEA6APkGgCAPkCtLKASqVS6cePG1tbWqqqqkSNHWllZ/f3vf8efDAWAKqDeMFCJiYnJjz/+ePPmTXxx2LBhbm5u5IYEjAucQwFVrV271tzcHH/t4OAQERFBbjzAuECuAaoKDQ21s7PDX7/88svwoGugFsg1QFWWlpZjx45FCFGp1KioKLLDAUYGcg1QQ3R0NIPBsLW1DQ4OJjsWYGQg1wA1LFy40NLScvz48XQ6nexYgJGB+1CauHHjxu3bt8mOQlcwDJNKpcOGDevzXUdHRxcXl6NHj+qia4lEYmpq0GMyICBAdtEKqMWg/64G65tvvnFycho1ahTZgejEH3/8cffu3f7OksLCwjgcjo4yQnx8/I4dO3SxZ0KcPn160qRJkGs0A7lGQ15eXpMmTSI7Cp0YOXJkT09PQECA/rs+evQoKf2q6N69e2SHYMTgeg0AQB8g1wAA9AFyDVBDfX19YGCgBhtiGJaWlqZZpyKRKCkpKS0tjUql2trapqamyjX4/vvvHRwc6HR6SEhIV1cXQkgikURERDCZzBUrVvT09Ki1eObMmfLycs1CBUpArgFqYLPZZ8+e1WDDe/fuNTc3a7AhhmFRUVGrV6/m8XhisbiioiIxMVHuB8N8Pr+wsLCpqUkoFJaUlCCE8vLyOjo6qqur+Xx+fn6+WotBQUHZ2dnV1dUaRAuUgFwD1LB582YPDw+EUEhIyLx58yZMmMBms+vr64OCgpydnS0tLblcLp/PnzVr1tatWysqKhgMRnFxMUJozZo1u3fvDg0NnT59ulo9lpSU2NvbW1lZIYSkUimfzzczM6NQKL3bhIaGurq6Wlpa0ul0/CbRtWvXgoODbWxsli5d+uOPP6q1iBAKDg7ev38/Qd8Z+H+Qa4AaEhIS8BexsbHjx4+/e/eul5fXnTt3eDze/PnzBQIBl8vNysrC71u7urrKik5s2bJl+/btubm56p6elJWVubi44K9PnTrl6+ubkpLSZ8va2lorKyt3d3eEkEgkYjAYCCEmkykSidRaRAi5uLiUlZWp/e0ApSDXAM2ZmppyOBypVIoQotPpVCrVx8fn0aNHBHYhEAhoNBr+msViXbp0KSQkRLFZU1PTwYMHDx8+jC9aW1u3tbUhhIRCoY2NjVqLCCEajSYQCAj8FABBrgFEkUgkXV1dBQUFY8eOpVAoNTU1L168aG9vlzUQCoUSiUTd3bJYLLFYjL/29fXFD1vk1NTUHD16NCEhgUql4ms8PT1Pnz7d0tKSk5MzY8b/tXd3IU29cRzAn4mKk7PlahWCYAtvlM1GBi6FWBJkvtGFF4a7i7qQbL6CklchU5xvRJrOF2YkWBhBEYLSWilGKhx6gUjSlcs64VRMYb6Ap4uH/zid1f7PtrMdq9/nyj3n8fwOEx/Ozu+c7zL8eokQ2tzchKfYBQdrDfCDwWCgadpsNre0tAwODo6Pj3d2dra2tiKEurq6KIr68OHDxYsX1Wr1s2fPkpKSXr582dDQgBBKTk6+e/fuuXPn8OUecjqd7v379/hns9lsNpsRQo8ePcrOzvbMuXHjRl1dXVRUlEQiaW5uRgjl5eXFxMSoVKr9+/fn5OT49RIhNDs7e/LkSYHeM/AfFvivpqbm7du33uM0Tcvl8gcPHghSxeFwpKenUxRlMBh2dnZ4W0tKSvBfUCqVqtXq7u5uQYqyLDs5OWkymfz6ladPn1ZVVQVfOj8/nzeyu7tbXFy8vr7OHfz27dv58+eDL/c7RqNxbm7Oe9xkMk1OToau7t8NzmuEpNVqKyoqhNpbS0tLYWEhwzAsy46MjPC2dnR0JCYmLi0tOZ3Oa9eu1dTU3Lt3T6jS/rp+/fqtW7dwy0lYEonk5s2bfX19LKfP3d7eXlVVJXgt7PHjx8XFxUePHg3R/v9ZsNYIY3l5OSsrKyYmpqmpCY+4XK7MzEy5XN7W1sbrENtstkOHDslkstevX3On8fa5tbUlk8mio6PT0tJmZma+fv36y4bxgQMHioqK6uvr8bPXwdcNgM1m29jY0Ov1we/KW1xcnNFo5Pa5TSZTZmZmKGohhHJzc/1tzAMSsNYIw2KxaDSa79+/l5eX45G+vr6cnByn02m1WsvLy7kd4pGREbPZvL6+npqayp3G/nyL2tWrV1tbW2UyWXNz8+rqanx8vI+GsUajWVlZEaQuAKEAz3kLY25u7sKFC9HR0Z6whYWFhc7OTnynCW6gejrEV65cqaio6O/vHxgY4E1TKpWefarVanxZtK2tDTdlfZifn8cZF8HXRQjRNN3R0SHYu0PM6XSKUpcQTdMhOnf7F8BaI4z4+PixsTGdTjc/P6/VahFCCQkJJpOpsrLS7Xa/e/eOOzkxMfH+/fs9PT2Dg4Pcafv27eNO6+3tzc/P397etlgsVqv1l3V3d3d3dnZomq6vr29sbBSkLkIoLi4uKSkp6HfFb7GxsaLUJfTq1SuxD+FPJu6l6T+Udx9qYWHh2LFjMpksJSUlIyODZVmGYXQ6XWxsbGlpaWFhIUVRz58/VygUp0+frquri4yMTEhImJ6e5k7jVRkaGqIoSqFQNDY2siy7uLiYlpbm2VpZWSmVSiMiIqKiolJSUrq6uvB48HUD6EMJxbsPtadAHyoY8L2XgaitrTUYDH9rVtaLFy/sdnttbW34SxcUFDx8+DD8dQk1NDTo9Xq49SYwcG14r3C5XJKfuVwusQ+KFEsWGUE4jdD/Zk0ghNbW1rKzs/FTTsgrawLiI8IJ1pq9QqlU8s45eddr9zLCyIiAkyW8sQRZE26322g0clPoIT5CRLDWACKrq6tnzpyhKCorK2tlZYWXGoEjI8rKynyHS3im/e5eIXIkWRNSqdRqtR45csQzAvERIoK1BhCxWCwnTpxgGEaj0VgsFl5qBI6MaG9v9x0u4Znm+14hEuRZE1wQHyEiWGsAEYfDodfrKYo6e/bsp0+ffMwMUbgED2HWBA/ER4gI1hpARKVS2e12t9s9OjqqUqm8UyM8kRG+wyUCS5bwRpI14Q3iI0QEaw0gcvny5enpaaVS+ebNm0uXLvFSI3BkBP6g5CNcwjPty5cv/oZL8JBkTSwtLWm12qmpqdTUVPzwKsRHiAjuGwZEFArFkydPuC8ZhuFOwB16u91eWlqK//MRQnK5/JfTEEIzMzPBHM+pU6d6eno2NjYoiqqursaD6enp/f39njkHDx7kfRVyZGTknTt3uCPDw8NGozGYIwGE4LwGCCl04RI8gmRNQHxEOMF5DRCSzWYLWy2cNcEd8USvE8rNzRX0iIAvcF4DAAgHWGsAAOEAn6ECdPv27cOHD4t9FCHx+fPnjx8/4sTyMGMYRpS6hCYmJiC/JmDwnHcgHA6H7/vZwN/q+PHj+MEI4C9YawAA4QDXawAA4fADo/BBs13dtQwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7PtauLbbK5pS",
        "outputId": "d48e65d2-4f19-4803-c2aa-35f79c22c571",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "source": [
        "input = Input(shape = (xLen, xTrain.shape[1]), )\n",
        "x1 = Conv1D(5, 2, padding='same', activation='relu')(input)\n",
        "x1 = Dense(1000, activation='linear')(x1)\n",
        "x1 = Flatten()(x1)\n",
        "#x1 = Flatten()(x1) # слой нужен, тк иначе не работают фукции проверки!!\n",
        "\n",
        "x2 = Dense(1000, activation=\"linear\")(input)\n",
        "x2 = Dense(1000, activation=\"linear\")(x2)\n",
        "x2 = Dense(1000, activation=\"linear\")(x2)\n",
        "x2 = Flatten()(x2)\n",
        "#x2 = Flatten()(x2) # слой нужен, тк иначе не работают фукции проверки!!\n",
        "\n",
        "x3 = Bidirectional(LSTM(1000))(input) # хороший результат\n",
        "x3 = Dense(1000, activation=\"linear\")(x3)\n",
        "\n",
        "x = concatenate([x1, x2, x3])\n",
        "#x = Flatten()(x) # слой нужен, тк иначе не работают фукции проверки!!\n",
        "x = Dense(yTrain.shape[1], activation='linear')(x)\n",
        "model3 = Model(input, x)\n",
        "\n",
        "plot_model(model3, dpi=50, show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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u7vjx45cuXTp06FCiIyIRmA7lBTdHBYSAS2mArH7++efp06cLbmlvb79y5UpaWpqxsXFISMiWLVvg33nQd73eHHXp0qWjRo0SavnmzZtBgwbBPfyAUkAaAel6enpWrFiRnp7+6NGj4cOHI4QePnx44sSJyspKf3////znP3DRPFAFwZujHjt2TPTmqGvXrq2pqcnOzraxsSE0UtAfwHQIpGhtbZ09e/aDBw/a2tp27do1bNiwO3fu/O1vf9u+ffvHH39MdHRgQOj15qjBwcFFRUVVVVVubm5nz57929/+RnSYQLPBd4dAkjdv3syYMePVq1dcLhchZGtre/bs2SlTpsBJUdnBd4dK19XVdfPmzSNHjty5c6elpQUhZGVltXLlyn//+99EhwY0mBbRAQDyunv37oQJE54/f47PhQihDx8+dHd3w1wIiKWnpzd79uyenh58LkQI1dfXHz582MfHp62tjdjYgOYiy8nSK1euXL16lego1KSjowMhZGBgoOZ+eTxeS0uLqampLI1LS0tzcnIMDAxoNBqPx9PW1qZQKFwuNyoqavLkyaoOVZxZs2b5+voS1TtJPH78+MSJE0RHQbCenp68vDxDQ0P+P2dcLvenn36yt7f39fU1NjYmNjyNwOFwzMzM1N9vU1OTsbGxlhaJDsaOHj2KyDMd/vrrr35+fqJXjvVLly5dQgjNmzdPzf02Njbu2rVr27ZtsjRubm7GMExPT49Kpao6MBm9ePEiLy8PpsPXr19/8skn8+fPJzoQInV3d//973/HHwtlKamSlsyWLVu2f/9+9fe7efPmlStXWlhYqL/rXoWFheEPyDIdIoRsbGwGDx5MdBTqgOeB+v9YIyMjIyMjzX2RORwO0SGQhbm5uea+j8oybNgwokPQbFQqlZAsMjIysrOzI0/NL/6JOhIdrgIAAABEgemQXGpra/38/BR4IoZh8fHxyg2msbHxwIED8fHxBgYGlpaWcXFxom2ampq8vb0bGxvxH7lcbmhoKJ1OX7p0aU9Pj1DjBw8eUP5QWVmZkZFRWFio3JgBAH2n2EDUl1FIltEG/XnAkWu0QQhJHXBgOiQXOzu7ixcvKvDEkpKS+vp6JUaCYdiKFSvCw8MjIiI6OjqKi4v37t0rtCynvb09IiKiqKiIvyUzM7Otra28vJzFYmVlZYnuNj8/H8MwDMOGDh3q7++fmppaXl6uxLABAH2n2ECk8Cgky2iDRAYcuUYbhJDUAQemQ3LZsmWLm5sbQigwMHD69OmjRo2ys7Orra319/cfMmSIkZERk8lksViTJk3atm1bcXGxsbFxbm4uQmjVqlV79uwJCgr69NNPlRJJXl6ejY0NXrucx+OxWCw9PT2hJRZUKjUlJUXwDqX37t0LCAgwNzdftGjRnTt3pPYSEBAQGxurlIABAMqCD0RCoxBCSPJAhI9CkZGRb9++lWsgkmW0QSIDjryjDZI24MB0SC7R0dH4g6ioqBEjRjx79szDw+PJkycRERGzZs1qaGhgMpnJyck7d+5ECDk7O/Ovidq6deuOHTvOnTunrNOPBQUFjo6O+OPz5897eXkdPHhQ6rMaGxvxa9zpdDr/DKogb29vfX19JpOJn75wdHQsKChQSsAAAGXBByKhUQghJHkgwkehuLg4W1tbuQYi9Yw2SNqAA9Mhqeno6DAYDB6PhxCi0WgGBgaenp41NTVq6LqhoYF/tbqFhcX169cDAwOlPsvMzKy5uRkhxOFwzM3NhX7r4uJSVVWFn/1PSEhACFGp1IaGBmXHDgBQGsFRCKlmIFLPaIOkDTgwHWoMLpfb1dWVnZ09fPhwCoVSUVHR2dnZ2trKb8DhcPi3j+k7CwsL/HYBCCEvLy9XV1dZnuXu7p6ens5ms9PS0iZMmCD029TU1ObmZi0trZ6eHrwUYkdHB3mWHwEApJI8ECk2CqlntEHSBhyYDsklJCTk8ePHMTExBw8ePHPmTH5+fkJCwjfffIMQSkxMpNFor1+/DgsLc3JyysvLc3BwKCgo2Lt3L0LI0dHx3Llzs2bNwr967Dsmk/ny5Uv8cUxMTExMDEIoMzPT29ub36a+vn7s2LH37993cXHBbyrk6+trYGDAYDDMzc19fHyE2g8aNMjNzY1OpxcUFKxbtw4hVFpa6u7urpSAAQDKgg9E7u7uQqMQkjgQ4aNQWFhYXV2dXANRr6MNkjbgyDvaIKkDDkYOO3bsePLkCdFRqMnZs2fPnj0r11Nu3bq1adOmPvbb0NAQGhoqY2MejxccHNzS0iK48f37935+frL3KLV9REREWVmZjHt78uTJjh07ZO+dJBoaGpYuXarEHZ4/f/4///mPEncIBqY5c+bI+xSlDETLli377bffBLf0Otpgcg44sjTudcDhvw5kPzosKioyNTVVbO1BrwSXraSlpfEXpgQFBYk2XrNmDf5bQ0NDZ2fnpKQkZYUhr127dh07dgy/iFQ9KBTKkSNHkpOTMYHLnePi4jZt2iT7TiS3z8rKCg4OhnuLKMXHH3+8e/du/o+C68YEP0SyryfDMCw+Pl6B9WdBQUESjgzevn3Lr1aILzXDMEz0KZj8K9gE9yxuhRn/E21gYPDXv/71wYMHiDSvFf5qrF69mkKhODg48O9OzuVy//GPf5iYmJiamuJXuIwdO5YigE6n43/XgQMH+Hv+6quvtLW19+3bp9zVvSoaiHodbZCcA47UxtIHHBknXlWTcHT41VdfZWRkKKWXtra20NBQGxsbDoeDYVhaWhqbzcYw7P79+zdv3uz1KUOGDKmvr2exWN9//72Zmdm5c+f6HoYCR4dKIdfRIQnB0SGu16PDCxcuREdHi3uKAh+i4uJixV7t7u7ucePG9fqrrq6urVu3Dh8+HMMwHo+3cOHCpqamXp8ib++Ce8atWbOm1xMP+Cf6w4cPUVFR//jHP0QbEPJaCb4aeIT8Njdv3ly6dGl7e3tNTQ3+38Phw4c7OjoOHz68Y8eOrq6uQ4cOYRjm5OQ0fPhwHo+HYVhnZ+eIESMmTpyI70HcS6HA0aFSiB4dEksDjg4bGhqmTZtmYGDA/5eHxWJNnDjRxMQkNjZWaEFMTk6OtbW1sbHx06dPBZsJ7VNo2UpQUBB+Q/crV654enpKWCtjYWGxaNGi3bt3nzhxou9hAKAK+/fv19fXHz9+/Nu3b/F1Y6IfInz7vHnzpk6dOm7cuB9++IGfpc3NzT4+PlQq1crKqqenB19DRqFQ8MMXDofj6elJo9GmTZvGZrOFMr+2tpZOpxsaGkq+Pj42NjYqKkpHRwf9eakZTvDjw+/d2NjY0NDQyMjo448/1tPTmzp1qtQ94/AVZhI+1J2dnZ999hn/NSH2tRJ9NfgGDx78yy+/vHr1yt7efvPmzQihtWvX6uvr47/V1dXFvxjz9vZubW39+eefEUJpaWmCd3iH1b0yIu90mJSU5Ozs3NzcvGHDBnxLcnKyj49PTU1NSkrKhg0bBBfEXL16NSYmpqWlxcXFRbAZJkNx42fPnjk4OCCEpK6VcXZ2ZrPZKgoDgD4KDw9vbm5mMplJSUn4WTXRDxG+ffv27ba2tunp6YJZmpiYyGAwOBzO9evXtbS08DVk3d3d+BOTkpLc3NzevXuHf2sgtCLNzs6usbHx8ePH165dExfeDz/84OXlZW1tjf8ouNQMJ/jxwXsvLCycM2cOm81euHDhnj17WCxWdXV1V1eX5D3j8BVmvX6oraysjIyMbty4MWbMGP5rQuxrJfpq8I0cOfLYsWORkZHTpk0rKSkR9/Lq6+uHh4d/++23CKErV64sXrxY6KUQ90TAR6KKFkLKysoWL16sp6fH/4+vuro6ISEBX/iJrx3hL4hZu3ZtVFTUyZMnU1NThZpZWlpK7ig1NZW/+F2y8vLywYMHKyWMpKSkn376Sd7XpI86OzufPHnCX7mvcdhs9scff0x0FORFo9H09fW9vLz4N6wS/RDxmZiYMBgMBoOxc+dOPEtHjBixbNky/Es10Z1XVFT4+fnRaLSZM2dmZmbiG/mZf/PmzWXLltXV1Yk7ekMI7du3D/+uDiE0atSouXPnCk1ggh8f/kZDQ0MDA4OhQ4caGxubmJhYWVnxF8CJ2/OLFy+QxBVm9fX1pqamxcXFPj4+JSUleAVQYl+rhoYGoVdDkKenp6en5+3bt0NDQ/l/qahVq1Y5ODikp6d7enpqa2vzt4t7Kerq6ggZDQoLCyMjI9Vf8FWcqqoq/AF5p0NbW9ubN28ymczy8vKxY8cihOzt7aOjozdu3Nje3v78+XPBxkOGDPnhhx+OHz9+5swZwWZSS92WlZXZ2Njo6upKaMPj8bq7ux8/frx79+59+/aVlpb2PYxFixapv14dh8P58ssv9+3bp+Z+leW///3vjRs3iI6CvLhcbmdnZ2ZmJr9uqOiHSIhgliYmJl68eHHy5MkYhuFDleAaMgaDkZubO2XKlBs3bjAYDKH9pKWlxcTEfPLJJ1u2bBEXHv8oDZ+x9u/fz19qhhP8+Li6usq+gk1oz/hjCSvM8HLW2tra7e3t/EM6Yl8rwYV3fK2tradOnfroo49MTU2nTJliZWUl+QWxsbFZsGDBjh07nj59+urVK/52cS+FjY0NIaNBZGTk//t//488C45DQ0N/f0Tc95d/InopTXV1taurq7Gx8ejRoydMmIBh2Lt375hMpqGh4bp16wICAmg02u3bt83MzKZOnbpz504dHR17e/vCwkLBZkK9/Pbbb66urhQKZfDgwdnZ2RiGRUZG4lfTYBhWW1sr9H3+xo0bqVSqlpaWrq7u6NGjExMTlRIGXEqjGLiUBtfrpTS5ubk2NjZ6enozZ85samoKCgrS0tJat26d0IcI344QolKpN27cEMzShoaGmTNnUqlUd3f3lpaW169f4wOWlpbWgQMH2Gz2tGnTDA0N8e/DhDI/NTWVSqVOnjxZX19/6NChWlpaBw8eFBf/yJEj8YAjIiLwLXhUhoaG/I8Pv3dDQ8MLFy6YmZkNHTr09OnT2traEnIA3zMuPz9//fr1Qh/qtWvX4uOelpaWvb19fHw8v3diXyv+q8GPEPfNN9/cvXuXwWDo6OgMHTo0MzMT/0PS0tJMTEwMDQ2PHDmCP0tfX//EiROPHz+OjY398OEDg8HQ0tLav38//6UQfbngUhoc/3Ug73TYj8F0qBiYDnH9Y92huKVmyiLXklbCqfTVEPdSwHSI04ArS/uOxWJR/ozFYhEdlIIw2ZZhydhMiNLrGiKJZclEnyu0Beog9gNSP33ilpr1fc9IA5e0KvxqSKXEl4LwUejnn38eNGgQjUYLDAzs6uqSPLCIjjMDut6hpaWl0H8BUi+rIS0ZC4kpUG8MU0FdQ8llyUSfK7QF6iD2A7J8+uh0ekREhGgdn77vefbs2cqqdKY2ir0aUinxpSB2FEIIsVismzdvvn//nsPh5OXlSR5YRMcZqHeoMYTWKokrJKb0emOqqGsouSyZ6HNFt8BKKQDUj8yjEEIoKCjI2dnZyMiIRqNZW1tLHlh6HaOg3qFmEFqrJK6QmNLrjamo0piExqLPFd0CK6UAUD+NGIWqqqpMTExcXV0lDyy9jlFQ71AzVFRUeHh44GuV+OtgeqXcemOqqDQmubHoc0W3QB1EANSP/KPQ+/fvjx49it9tQPLA0usYBfUONQO+Vqm9vR1fqyShkJhy642potKY5MaizxXdAnUQAVA/ko9CFRUVJ06ciI6Oxtd6Sh5Yeh2joN6hZlixYkVhYaGlpWVxcfHy5cvFFRJDyq43poq6hpLLkgn9KLo3BHUQASACqUYhJDIQHTp0aOfOnbq6uhQK5euvv5Y8sIiOKgjqHZJQX9Yd9qXeWK/rDtVT11Beva6UgnWHuP6x7hAQTuF1h32seqiieoey0Ox6h0CI0uuNqaGuobw0btEYAAOKekYhpPaBhbz3LAW9ysnJUfo+8QVPgltkvKe5wu0lmz17thL3BgBQLvWMQkjtAwscHQIAAAAwHQIAAACkOsj4/nAAACAASURBVFn6z3/+k06nEx2FOvz222+tra2i9zZTNS6X+/r165CQEDX3qyyNjY29Vt4ZgBITE69fv050FEAmeOEtIyMjogMRVlVVRcho8OLFi7Vr10ouq6dOjx8/xh9QMHIUau/s7Ozs7CQ6CjX55ZdfLly4oMBdboG+vr6+vj7RUciHzWZv3Ljx1KlTytohl8tta2tT1t6Aqr1//3737t2HDx8mOhAgFn5/OLIcHWriMKeY+vr6AwcOXLlyhUajER0L0Eg6Ojr4pxdoBBMTEzabDW8Z+cF3h2qFYdjq1avj4+NhLgRgQOHxeESHAKSA6VCtYmJipk+fLuON0AAA/cOwYcMqKiqIjgJIAdOh+hQWFhYVFYWHhxMdCABArZycnIqLi4mOAkgB06GaNDU1bd68+ciRI0QHAgBQN2dn52fPnhEdBZACpkM1Wb9+fXR0tNRaSACA/sfJyQmmQ/KD6VAdkpKSRo8eLbUQEgCgX6LRaC0tLURHAaQgy0KLfqykpOTy5cuXL18mOhAAAGH09fU7OzsHyHIyDQVHh6rV0dERGRl5/PhxLS14qQEYuBwdHV+8eEF0FEASGKNVKyIiYsuWLba2tkQHAgAgElxcSn4wHapQenq6hYWFl5cX0YEAAAgGF5eSH3x3qCrV1dUnTpzIzMwkOhAAAPFGjhz58uVLoqMAksDRoUpwudzw8PBjx46R567tAAAC6erqdnd3Ex0FkASmQ5X45z//uWTJEgaDQXQgAACyMDU1bWxsJDoKIBZMh8p37dq1tra2RYsWER0IAIBEnJycSkpKiI4CiAXToZK9f//+4MGDMTExRAcCACAXZ2dnuLiUzGA6VILOzs66ujqEEI/HW7ly5aFDh6hUKtFBAQDIBdZakBxMh0rw008/jRkzJiMjIzo6eu7cuY6OjkRHBEhh9+7dFhYWrq6uN27cGDx4sKmpaXNzM9FBAcIMGTKkurqa6CiAWLDQQgmOHz/e2NgYFhY2ePDgwsJCosMBZLFkyZKEhIQ3b97gP06ePBlKog9kFAoFwzCiowBiwdFhX2EY9vTpU4QQh8N58eKFq6trWVkZ0UEBUvj4448tLCzwx4aGhmvXriU2HkA4Ozu72tpaoqMAvYPpsK8ePnwoeAaMy+U2NDQQGA8glSVLlujo6CCELC0tfX19iQ4HEAy+PiQzmA776tSpU/j8Z2lpOXfu3KKios8++4zooABZLF261MbGBiHk5OQEF1gBuLiUzGA67KuffvpJW1t76NChZ8+evXDhgpGREdERARKxsrKytbWl0Wjr168nOhZAPBcXF7hzKWnBdNgnlZWVlZWVPj4+T548gVt1g16tXLlSS0vL09OT6EAA8czNzVksFtFRgN7978pSJpNpbW1NYCjqVFlZOXTo0L7vp66uzsXFBSEUEhIiS3sWi2VgYECj0freNSHKy8svXrzo4OAguVl6evrXX389cNJJMi6Xa2lp6e/vr6yskxdR/YpTW1v78OFDoqMgjJaWFpfLxb9RBqTyv7fE2tp64FRsnzt3LiF/7KFDh0aPHq25BwqbN2/u6emR2qy7uzsiImLx4sVqCEkjfPjwwcjIiKisI6pfcebOnUt0CET65JNPysrKRo4cSXQgQBicLAVA5eAbZcAHV9OQFkyHAACgPrDWgrRgOhRWW1vr5+cn77MwDIuPj1esx8bGxgMHDmAYdujQIQMDA0tLy7i4ONFmTU1N3t7eeIEYLpcbGhpKp9OXLl0q7uylhPaSf8zIyIB766gZUVkXHx8vIeWQPFn34MEDyh8qKysRQpBIvRozZszz58+JjgL0AqZDYXZ2dhcvXpT3WSUlJfX19Qp0h2HYihUrwsPDKRTK+vXrOzo6iouL9+7dK3Qzp/b29oiIiKKiIvzHzMzMtra28vJyFouVlZUlulvJ7SX/6O/vn5qaWl5ersCfAxRDVNZFRESISzkkf9bl5+djGIZhGH7lDiRSr6hUaltbG9FRgF7AdChsy5Ytbm5uCKHAwMDp06ePGjUKv6+Sv7//kCFDjIyMmEwmi8WaNGnStm3biouLjY2Nc3NzV61atWfPnsjIyLdv33766aeyd5eXl2djY8O/lSWPx2OxWHp6ehQKRbAZlUpNSUnhXx947969gIAAc3PzRYsW3blzR3S3kttL/hEhFBAQEBsbK+9LBxSGZ51QyiGE1JB14lIOyZ91oiCRekWlUj98+EB0FEAYTIfCoqOj8QdRUVEjRox49uyZh4fHkydPIiIiZs2a1dDQwGQyk5OTd+7ciRBydnYOCwtDCG3dunXHjh1xcXG2trZynSAqKCgQrIBx/vx5Ly+vgwcPSn5WY2OjsbExQohOp8tSX1uoveQfEUKOjo4FBQWy/xWgj/CsE0o5hJAask7GlEMyZJ23t7e+vj6TycRPliJIJDHgfCk5wXQohY6ODoPB4PF4CCEajWZgYODp6VlTU6Os/Tc0NAjeu8vCwuL69euBgYGSn2VmZobfKJXD4Zibm0vtRai95B8RQlQqFe68ShTBlEOqzzoZUw5JyzoXF5eqqir8u8aEhAR8IyRSr+BqGnKC6VAOXC63q6srOzt7+PDhFAqloqKis7OztbUV/y2Hw+FyufLu08LCoqOjg/+jl5eXq6ur1Ge5u7unp6ez2ey0tLQJEybI217yjwihjo4OfikGQCxVZ52MKYekZV1qampzc7OWllZPT4+hoSG+ERKpV87OznCrNhKC6VBYSEjI48ePY2JiDh48eObMmfz8/ISEhG+++QYhlJiYSKPRXr9+HRYW5uTklJeX5+DgUFBQsHfvXkdHx3PnzoWFhdXV1eFfPcqIyWS+fPmS/2NMTExMTAxCKDMz09vbm7+9vr5+7Nix9+/fd3FxuXr1qq+vr4GBAYPBMDc39/HxEWostb3kHxFCpaWl7u7ufXwlgezwrHN3dxdKOaT6rBOXckjOrBs0aJCbmxudTi8oKFi3bh2+ERKpVw4ODq9fvyY6CiAC+8OcOXOwAUOBP/bWrVubNm3qY7/x8fE3b94U3MLj8YKDg1taWoRavn//3s/PT8bdytVYFhEREWVlZaLbN23a9OLFC6lPP3v27NmzZ5UYT/9AVNaJ9ttr1smbRbK07zWRBtRQI46Pjw/RIQBh0o8Oi4qKTE1NFbgKXBzBlUwIoRMnTqxevfrUqVOiLdesWYMvYzI0NHR2dk5KSlJWDArYtWvXsWPHcnNzlbtbCoVy5MiR5ORk7M+XucfFxW3atEnGncjVWKqsrKzg4OBhw4Ypa4d9J5QzOAmZ2Wt7vtzc3AkTJhgbG9NotHHjxvU9JNUlqjqzTt4sktqehIlEHpaWlootkgEqxJ8YJfzL9tVXX2VkZChl+m1rawsNDbWxseFwOBiGXb58+dNPPz127Nhf/vKXrKws0fZDhgypr69nsVjff/+9mZnZuXPnlBIGUf+fih4dahZCjg6FckZQr5kpoT2GYZWVlWZmZunp6S0tLSwWa+PGjTwer+8hyZKoRGUd2Y7GyBYPIWJiYm7dukV0FOBPJB0dNjQ0TJs2zcDA4MCBAwghFos1ceJEExOT2NhY0QVSOTk51tbWxsbGOTk5/Gai+xRaycTlcsePHx8WFjZ+/PjOzk5xy6csLCwWLVq0e/fuEydOSI6EH8bTp08Fm/X53wbQV83NzT4+PlQq1crKisVieXp60mi0adOmsdlsoTfR2traysrq3bt369evt7S0FMoZJJKZQoTaCyVVenr6woULFyxYQKPRLCwsvv76awqFwuFwJMSDEJIaEk4oUQEQBy4uJSFJ02FSUpKzs3Nzc/OGDRsQQsnJyT4+PjU1NSkpKRs2bBBaIHX16tWYmJiWlpbCwkJ+M0zkPhdC5syZU1BQoKen9+uvv/r4+EhePuXs7MxmsyVHwg/DxcVFsJnUSICqffvttwwGg8PhXL9+/cSJE25ubu/evcNPLQqttzt58uSsWbMGDRq0dOnSXk88CmWmZEJJ9ebNGwaDIbpDCfEghKSGJIifqACIAxeXkpCkmltlZWWLFy/W09PDS3NVV1cnJCTgC4HxtUSCC6TWrl0bFRV18uRJGo2WnZ3Nb2ZpaSmhiz179oSEhOTn5x8+fPjrr7/esWOHhMbl5eWDBw+WHAk/jNTUVKFmgpE0NDQQUmXmzZs3+vr6hw4dUn/XSlFXV/fFF18o9tyysrK5c+caGBj89a9/TUpK8vPzo9FoM2fOzMzMxBvw38TZs2d/+eWXlZWVly9f/r//+79edyWYmXKxs7MTvJQXV1FRISEehJDUkATxE1V0OyFZV1xcTKqaStXV1USHQDw7O7u6ujqiowB/Imk0sbW1vXnzJpPJLC8vHzt2rL29fXR09MaNG9vb20VvqTBkyJAffvjh+PHjGzZs4DczNTWV3H1bW1tdXV1+fn5JScmgQYN6bcPj8bq7ux8/frx79+59+/YhhCREwg/jzJkzgs2EIrGwsIB6hwrYvHmzws9lMBgXL16cPHkyhmH29va5ublTpky5ceOG6LEahULZuHFjZGTknDlztLR6OYEhlJlyhbFw4cK//OUvnp6evr6+CKFvv/124cKFDAZDQjyyhIR6S1Qhw4YNg3qHaMDXO+TDv68SvTceIAz/W0TR77erq6tdXV2NjY1Hjx49YcKEd+/eMZlMQ0PDdevWBQQE0Gi027dvm5mZTZ06FcOwnTt36ujo2NvbX7lyhd9M9LvK3377zdXVlUKhDB48ODs7u76+fvXq1TNmzFizZk1DQ0Ntbe24ceP4jTdu3EilUrW0tHR1dUePHp2YmIhvlxAJP4zCwkLBZkJhwKU0iunLpTQNDQ0zZ86kUqnu7u7V1dXTpk0zNDTEv6sTTScul8tkMtvb2zGRnMFEMlOoI6H2QkmFYVh+fv6kSZPwu724uLiw2Ww2my05HskhiUtUIXApDY5s8RBlzZo1FRUVREcB/oeC/fGlGtn+hVQpov7YfnB0+MUXX0gt5P39998jhBYvXqyWoDQGUVlHto822eIhSmJiop2d3Zw5c4gOBPxOhXelYbFYlD9jsViq607VMNlqy8nYTBDUO+yLfpZmglSXcgjqHZIAXE1DNiqcDi0tLYUORSVfVkNyMtaWk7cEHQb1Dvumn6WZIBWlHIJ6h+Tg5OQE0yGpwD1Lfye08kyosBxCCK8tR6FQJNSf4zcLCgqSsf4c1DscyCRnHb+codKrHkK9QzIwNTWVpTobUBuYDn8ntPJMqLAc+qO23K1btyTUn+M3O3funIyniaDe4UAmOev45QyVXvUQ6h2ShJ6eXldXF9FRgN/BdPi7iooKDw8PfOVZVVWVhJbKrT8H9Q4HMjJkHdQ7JNDIkSNLS0uJjgL8DqbD3+Erz9rb2/GVZ6KF5dAfteUk159Dcpagg3qHA5nUrOPnknKrHkK9Q5KAW7WRCkyHv1uxYkVhYaGlpWVxcfHy5cuFCsshhPi15STUn+M3mzVrloz156De4UAmOev4KYeUXfUQ6h2SBFxcSi786/EG1NpYhf/YPtafg3qHAxlRWQf1Dkmro6Nj/vz5REcBfgdHh/JRev05qHcIpFJP1kG9Q/XT19fv7OwkOgrwO7nvgDzA5eTkKH2fdDo9IiJCaGN0dLTse5CrsVSzZ89W4t5A36kn6+TNIqntIZFkYWxs3NzczF9qBQgER4cAAECYMWPG/Pe//yU6CoAQTIcAAEAgZ2dnuLiUJP53C+/ly5e/f/+e2GjUhs1mS12u19DQoKenh687VpYPHz7o6Ojo6+srcZ9qduzYMTs7O8lt8vPz8YsVAUKop6enrq5u8ODBsmSdKhDVrzjm5uYpKSlER0EWZWVl8fHxmlsDtT+hYFAmXoyEhIThw4fPnDmT6ECAZmOz2Rs3bjx16hTRgQAy4vF48+bN4xedBgSCk6Vi9fT0aGtrEx0FAKA/09LS4vF4REcBEILpUAIulwvTIQBA1Wxtbd+9e0d0FACmQ/F6enp0dGAhCgBAteBWbSQB06FYcLIUAKAGcHEpScB0KBZMhwAANXBxcYE7l5IBTIdiwXQIAFADKyur3377jegoAEyH4sF0CABQDwqF0tPTQ3QUAx1Mh2LBdAgAUI/hw4dXVFQQHcVAB9OhWDAdAgDUA66mIQOYDsWC6RAAoB6w1oIMYDoUC6ZDAIB6ODk5QV0LwsF0KBZMhwAA9TAyMmptbSU6ioEOpkOxuFwu3JUGAKAe+vr67e3tREcxoMF0KBYcHQIA1Gb06NEvXrwgOooBDaZDsWA6BACoDVxNQziYDsWC6RAAoDbOzs5wqzZiQflfYS0tLQcOHOjq6srPz/fw8LC2tnZzc5s0aRLRcQHNc/369ZycnI6Ojl9//dXDw0NXV3fLli0mJiZExwXIqLa2NjAw0Nvb+8GDB/v27XN0dCQ6ogEHpsNeDB48+M2bN/hjfX397777buHChcSGBDTRjz/+GBISwr8+4qOPPnrz5g2FQiE2KkA29+/fnzt3rra2dnNzc2trq7W1dUlJiaWlJdFxDThwsrQXs2fP5j+2sbGZO3cugcEAzeXr62ttbc3/0c/PD+ZCIOqzzz6zsbGpq6vD11oYGRnBXEgImA57ER4ebmVlhT/+9NNP9fX1iY0HaCg9PT1nZ2f8sY2NzerVq4mNB5DWt99+yx9zjI2NiQ1mwILpsBdjx441NTVFCJmZmUVFRREdDtBgkZGRdDodIWRmZjZmzBiiwwEkxWQyx44diz92cHAgNpgBC6bD3s2YMQMhZG5u7u7uTnQsQINNnTqVTqdTKJT58+cTHQsgtaSkJFtbW4QQk8kkOpYBCqbD3oWHhxsZGfn6+sKXPaAvtLS0JkyYYGJisnLlSqJjAaQ2dOhQHx8fKpX66aefEh3LAPX7TciePXtWXl5ObChkY2hoOHr06MuXLxMdCImMGTNm+PDhktvcu3evvr5ePfFohL/+9a937twpKioqKioiOhYSYTKZgtcZ9aqkpKSsrEw98ZCBp6fn2bNna2pqBviwM3LkyJEjR6q/398XWqxfv97Ozs7CwkL9EajfL7/8IssXOSUlJcr9sofNZufk5AQEBChxn+r06tUrS0vLzZs3S27m4+MzcE4MpqenT5s2zdzcXHIzpedSSUkJh8PR3OWwt2/fDgoKEryEu1dRUVFWVlb8a0z6t7t37xobG3d0dKj56LCxsfHGjRuBgYHq7FSC6upqLS2tr776Sv1d/+8W1UFBQUOHDlV/BOrX09Njb28v9aOodJWVlW/evPniiy/U3K+y5Ofn//rrr1Kb6ejoaO7fKK+nT58GBgaq/4OTlZWl0bnU09MjY8uFCxcOnEtLrK2t1b+sq6ampqKigjy59OjRI6IOjuG7QwAAAACmQwAAAACmQyG1tbV+fn4KPBHDsPj4eMU6bWxsPHDgQHx8vIGBgaWlZVxcnGibpqYmb2/vxsZGhBCXyw0NDaXT6UuXLhV30klCe9GnC23JyMgoLCxU7G8BfIrlUl8SSQKl59iDBw8of6isrIScUSlyjks///zzoEGDaDRaYGBgV1cXkjbUkH+cgenwT+zs7C5evKjAE0tKShS7nBLDsBUrVoSHh0dERHR0dBQXF+/du1foRrLt7e0RERH86xIzMzPb2trKy8tZLFZWVpboPiW3F3260BZ/f//U1FS40riPFMslhRNJAlXkGEIoPz8fwzAMw4YOHQo5o1LkHJdYLNbNmzffv3/P4XDy8vKQtKGG/OMMTId/smXLFjc3N4RQYGDg9OnTR40aZWdnV1tb6+/vP2TIECMjIyaTyWKxJk2atG3btuLiYmNj49zcXITQqlWr9uzZExQUJO9VYXl5eTY2NniVAx6Px2Kx9PT0hBY7UqnUlJQU/vUa9+7dCwgIMDc3X7Ro0Z07d0T3Kbm96NNFtwQEBMTGxsr1hwAhiuUSnkiRkZFv375V1hWGqsgxUZAzqkPOcSkoKMjZ2dnIyIhGo+FrZiQPNeQfZ2A6/JPo6Gj8QVRU1IgRI549e+bh4fHkyZOIiIhZs2Y1NDQwmczk5OSdO3cihJydncPCwvD2W7du3bFjx7lz5+Q9/C8oKOBXcjl//ryXl9fBgwclP6WxsRG/qyGdTsdPbcnVXvTpolscHR0LCgrk+kOAEMVyCU+kuLg4W1tbZZ1KUlGOeXt76+vrM5nMyspKBDmjSmQel6qqqkxMTFxdXZG0oYb84wxMh5Lo6OgwGAwej4cQotFoBgYGnp6eNTU1SuyioaGBSqXijy0sLK5fvy51AZCZmVlzczNCiMPhSF3xJtpe9OmiW6hUakNDQ5/+MPBnasglcVSRYy4uLlVVVfjXjQkJCQhyRo3IMy69f//+6NGj3377Lf6j5KGG/OMMTIey4nK5XV1d2dnZw4cPp1AoFRUVnZ2deEEWHIfD4XK58u7WwsKio6MDf+zl5YX/kyWZu7t7eno6m81OS0ubMGGCvO1Fny66paOjY4Dck4EQknNJsUSSQBU5lpqa2tzcrKWl1dPTY2hoiCBnCELguFRRUXHixIno6GgDAwN8i+ShhvzjDEyHfxISEvL48eOYmJiDBw+eOXMmPz8/ISHhm2++QQglJibSaLTXr1+HhYU5OTnl5eU5ODgUFBTs3bsXIeTo6Hju3LlZs2bhp/hlx2QyX758iT+OiYmJiYlBCGVmZnp7e/Pb1NfXjx079v79+y4uLlevXvX19TUwMGAwGObm5j4+PvK2F3266JbS0lK4d3kfKZZLeCKFhYXV1dXJm0viqCLHBg0a5ObmRqfTCwoK1q1bhyBnVImc49KhQ4d27typq6tLoVC+/vprJDKSSB15SJcz+LVh69atq6iowAaGxMTEK1euyPWUW7dubdq0qY/9VlRUrFu3Tmgjj8cLDg5uaWkR3Pj+/Xs/Pz/Z9yxve6kiIiLKysqENt6+ffvAgQNSnztnzhwlRkJyCnxwlJJLV65cSUxMlLGxenKs15wRR8bP4IYNG169eiXjPjXd8ePHL126JNdTlJJL1dXVq1evFtqolJyRqtecefjw4ZdffqnEXmQn5eiwqKjI1NRUsWt8eyW4tqmyspLJZBobG//973/v9XB+zZo1+MImQ0NDZ2fnpKQkZYUhr127dh07dgy/WEu5KBTKkSNHkpOTMYGLmOPi4jZt2iT7TuRtL1lWVlZwcPCwYcOUtUOcSnMJIZSQkGBjY2NlZXX37l2hluRJJKTKXBJHDTmmopwRR7m5JLR+TuqaS/KkE8nHJcnUnDOy0JH867Fjxyqx/q3Q2qaDBw8GBAT8/PPPK1euvHr16pw5c4TaHz16NCsrC1/we/PmzdWrV9PpdEJuNZuTk6O6ndPp9IiICMEt/AvJZCRve8lUdDdXlebSy5cvjx079ujRo5qaGhaLJdSYPImEVJxL4qg6x9R8B2Dl5hK+fm7YsGF+fn55eXmtra348rglS5ZkZWWJ3kSUPOlE8nFJMvXfNVoqsUeHDQ0N06ZNMzAwOHDgAL6FxWJNnDjRxMQkNjZWaPlLTk6OtbW1sbHx06dPBZsJ7VNobVNnZ6exsbGent64ceMePHggYaGVhYXFokWLdu/efeLEib6HAdRMKJeE3hpxb2JOTo6Ed1Aol65du7Z8+XI7Ozsmk+nr6ysulwQTCSkjpYGaqWJcElo/J7Q8DsalgUPs0WFSUpKzs/O1a9d27dqFb0lOTvbx8cnOzp48eXJiYmJqaur169dDQ0OfPHly69atmJiY0NBQhND+/fv5zSIjIyWUz12/fv2CBQsiIiIsLCwWLFggdaGVs7PzyZMn+x4Gj8f75Zdf2tra5H2x+qi+vr68vPzChQtq7ldZnj9/rqMj5XRCr4RySfAdjIyMjIqKSklJEX0TZU8khNDbt2/Pnj27fft2Kyurc+fOjR8/XkIu4YmElJHSHR0d2dnZ6q9A9OjRIw6Ho7m5VFRUZG9vr8ATVTcu8dfPHT16VHB5nDrHpTt37nR2dirwsvRFQ0NDRUUFeXKpoqICv+Wb+okd3crKyhYvXqynp8cfAaurqxMSEvCVnvhiEf7yl7Vr10ZFRZ08eTI1NVWomaWlpbgunJyc8IuXYmNj8fUokpWXlw8ePLjvYWAY1tXVxb+MWG06Ozt7enrU36+ydHV1KTYdCuWS6DuIensTaTRadna2LImEEDIzM1uzZk1kZOT3339/+PDh8ePHS2iMJ1KvkSiQS52dnep/T7u6urq7uzU3lxReSaKicUlw/Zy863qVNS4hhGBcQgh1dnZif74bnNqIHd1sbW1v3rzJZDLLy8vHjh2LELK3t4+Ojt64cWN7e/vz588FGw8ZMuSHH344fvz4mTNnBJuZmppK6PvEiRNz5szp6upKSkpKSUkR14zH43V3dz9+/Hj37t379u0rLS3tYxja2trTpk0jpN5hRUXF3//+dzX3qywy1jsUJZRLQm/N69evBRvz38QNGzbImEgIoSlTpuzfv7+7u7u7u5tGo/XaRiiRkDJSmkql+vv7q7/eobm5+Zs3bzQ3lxQ+N6OKcamiouLs2bPR0dH4FOvu7n769OkZM2akpaWFh4eLi0Tp45KWltbUqVMJqXdYWlpKnlwisN6h2IUW1dXVrq6uxsbGo0ePnjBhAoZh7969YzKZhoaG69atCwgIoNFot2/fNjMzmzp16s6dO3V0dOzt7QsLCwWbCV3G+ttvv7m6ulIolMGDB2dnZ6elpdFoNDMzs3379mEYVltbO27cOMH2GzdupFKpWlpaurq6o0ePxq8s73sYCiy0UIpeF1poEIUXWgjlktBbI+5NvHLlirh3EBPJJQzDtm3bRqfTnZycXrx4IZRLvSYSpoxcImqFklwLLUhI4YUWqhiXIiMj+eNhTExMd3d3cHCwiYlJSEgIl8tV27ikwEILpeh1oQWBCFxoAesO1WfATof9GEyHioF1h6JgOsSRd91hX7BYLMqfiV4Brykw2cqGydhMCNQ7+ZnfIQAAIABJREFUlApySUbE5pLo3iCXVIpUuYSg3qEElpaWQnOv5KshyEzGsmEKVBfDoN6hDCCXZEF4LonuDXJJpUiVSwjqHfYPHA7H09OTRqNNmzaNzWYjhMRVoVN6sTqod9jPyJhLFApFMJHENZOrUh3hudTr3iCX+kIoncg8LiGod9g/JCUlubm5vXv3jn/LJXFV6JRerA7qHfYzMubSrVu3BBNJXDO5KtURnku97g1yqS+E0onM4xKCeof9Q0VFhYeHB41GmzlzZlVVleTGyi0wBvUO+xnZc0npleoIz6Ve9wa51BdEpZMCuYSg3mH/wGAwcnNz29vbb9y4wWAwEEISqtApt1gd1DvsZ2TPJcFEktBM9q4Jz6Ve9wa51BdC6UTmcQlBvcP+YcWKFYWFhZaWlsXFxcuXL0cIiatCh5RdrA7qHfYzsueSYCKJayZXpTrCc0l0bwhyqW+E0onM4xKCeoeaqC/rDvtSYAzqHfY/Cn9w+liprtd1hxqUS7DuUFRf1h32JZ2g3iEfHB3KR+kFxgZOvUMgSBWV6iCXBixyjkuSkTA3FLkj80CmigJjA6TeIRCkokp1kEsDEznHJclImBtwdAgAAAAIHB1mZGRo7t0Z5HL//v3Xr1/jS6TVicVilZaWfvfdd2ruV1lKS0vxZbmS9fT0aO7fKK/S0lJCPjhPnz5ls9mGhoZq7ldZCgoKZKx3eOnSJWtra1XHo2odHR0GBgaS2xQUFJiYmDQ1NaknJD42m/3q1SvyfGalLnVTHQp+arioqOjVq1dEBUFO8fHxQucKgKur6yeffCK5ze3bt9+/f6+eeDTChw8f0tLS8Mv/AN/f/va3QYMGSW7z5MmT0tJS9cSjOo8ePbp9+7Zg3Qwg2ZgxY0aPHq3+fikYQYUWyW/evHmXLl0iOgqg8dhs9saNG0+dOkV0IIAAp0+fvnLlSkpKitSjQ0A4uJRGLG1t7Z6eHm1tbaIDAQBopPj4+PLy8rNnz2ppwVUaGgCmQ7FgOgQAKAbDsH/9618dHR0KVFYCRIH/WcTCp0OiowAAaJienp7w8PCPPvpo3759RMcC5ADToVgwHQIA5NXW1hYYGOjl5bVixQqiYwHygZOlYsF0CACQC4vFWrJkybZt2yZPnkx0LEBuMB2KBdMhAEB2FRUVYWFhsbGxMtZ/AGQDJ0vFgukQACCjBw8efPHFF6mpqTAXai44OhQLpkMAgCxu3rx59OjRH3/80dTUlOhYgOJgOhQLpkMAgFQpKSk3btxIS0uDhfaaDqZDsWA6BABItn///rdv354+fRoW2vcD8BaKpaOjw+VyiY4CAEBGPT09q1evptFocXFxMBf2D/AuigVHhwCAXjU3Ny9cuNDLy2vNmjVExwKUBqZDsWA6BACIqq2tDQgIiIqK8vf3JzoWoEzw3aFYMB0CAIQUFRVt27YtKSlp6NChRMcClAymQ7FgOgQACMrIyEhNTU1LS6PT6UTHApQPpkOxYDoEAPDFx8eXlJRcuHBBV1eX6FiASkD5X2E8Hs/d3Z3D4fB4PD09PYTQlClTjh07RnRcQPMkJCQcOnRIV1e3tbXV2NiYx+Pdu3fP2NiY6LiAfLhcbmRkpKWl5VdffUV0LECF4FIaYVpaWoMGDXr16lVZWdnz588rKyunTZtGdFBAI02cOJHNZj979qyysrK4uNjAwADmQo3T0tKyePFiT09PmAv7PZgOexEVFWVmZoY/trKy8vX1JTYeoKFcXV35XzLp6ur+4x//IDYeIK+Kior58+dv3brVz8+P6FiAysF02IvJkyfzp8MRI0ZQqVRi4wGaa968eRQKBSFkbW29ePFiosMBcsjLy1u5cuXJkyfd3NyIjgWoA0yHvaBQKN7e3hQKxdDQcNWqVUSHAzTYqlWrbGxsEEJDhw41NzcnOhwgq+PHjyclJWVkZAwePJjoWICawHTYu8jISGtrawsLi9mzZxMdC9Bgw4YNMzc3NzAwWL16NdGxAJlwudzt27eXlZV99913RkZGRIcD1Aemw96NGDHCzMxs5MiRcJd60Eeff/65jo4OfPmkEdhsdkBAgJub2759++BOpAPN/xZanD17NiMjg9ho1APDsMbGRv63g+KUlpbSaLSPPvpIiV03NDRYWFgocYdqNmzYsP3790tuk5OTM3DWpcjyhnZ0dDx58mT8+PFK7JfD4dDpdPxbSU1kaGiYmppKdBTCSktLV69evX///nHjxhEdCyDA/5bhFxcX79ixw9HRkcBo1KO5uXn9+vUpKSmSm7HZbCMjI319fSV2vWDBgtOnTytxh2q2cOFCqW3Ky8sXL148QE4yy/iGvn371tbWVon9Ll269NChQyYmJkrcpzoFBAQQHYKwq1evHjly5MyZM/h3vWAA+tNdaXR1dZU7+pOTvr6+tra21L9UueMXTktLayC8wjo6OgPhz0Qyv6FKv78lnsCa+yKT7bg2Pj7+v//9b0ZGBn7nDTAwwclxAMDA1dbWFhoaSqFQvv32W5gLBziYDoXV1tYqcNUDhmHx8fGK9djY2HjgwIH4+HgDAwNLS8u4uLhemzU1NXl7ezc2NiKEuFxuaGgonU5funRprzdWldxYaIvQjxkZGYWFhYr9LYCv/yWSaHtNT6TXr1/PmTPn73//+/r164mOBRAPpkNhdnZ2Fy9elPdZJSUl9fX1CnSHYdiKFSvCw8MjIiI6OjqKi4v37t0reiPZ9vb2iIiIoqIi/MfMzMy2trby8nIWi5WVlSVvY6EtQj/6+/unpqaWl5cr8OcAvv6XSKLtNTqRsrKyVq1adfLkSU9PT6JjAaQA06GwLVu2uLm5BQYGTp8+fdSoUXZ2drW1tf7+/kOGDDEyMmIymSwWCyE0adKkbdu2FRcXGxsb5+bmrlq1as+ePUFBQZ9++qlc3eXl5dnY2ODXRPB4PBaLpaenJ/rNCpVKTUlJ4X8Fde/evYCAAHNz80WLFt25c0fexkJbRBsEBATExsbK+cqBP+k1kRBCQrnUayJFRka+fftWrlxSQyKJttfQRMIwbP/+/VeuXMnKyhoyZAjR4QCygOlQWHR0NEIoKipqxIgRz5498/DwePLkSURExKxZsxoaGphMZnJyMkJo586dCCFnZ+ewsDCE0NatW3fs2HHu3Dl5zw4VFBTwr+Y9f/68l5fXwYMHpT6rsbERvxk0nU7nn8iSvbHQFtEGjo6OBQUFcv0hQEiviYQQEsqlXhMpLi7O1tZWrlxSQyKJttfERGKz2f7+/paWlseOHYMvC4EgmA4l0dHRYTAYPB4PIUSj0QwMDDw9PWtqapTYRUNDA/+eqBYWFtevXw8MDJT6LDMzs+bmZoQQh8OReusv0cZCW0QbUKnUhoaGPv1hQIBgIiHV5JIaEkm0vcYlUlFR0fz583fs2IH/8wGAIJgOZcXlcru6urKzs4cPH44QolAoFRUVnZ2dra2teAMOh8PlcuXdrYWFRUdHB/7Yy8vL1dVVlme5u7unp6ez2ey0tLQJEybI21hoi2iDjo4Ojb5dAMkJ5pIGJZJoe81KpNOnT+/evfvHH3+U9xsNMEDAdCgsJCTk8ePH7u7uZ86cyc/PT0hI+OabbxBCiYmJNBrt9evX+P+VTk5OeXl5Dg4OBQUFe/fudXR0PHfu3KxZs+S9+T2TyXz58iX+OCYmJiYmBiGUmZnp7e0t2Ky+vn7s2LH37993cXG5evWqr6+vgYEBg8EwNzf38fERai+5MUJIaItog9LSUnd39z69jgOeuERCf86lXhMpLCysrq5OrlzqNZGQSC71JZGQtMwhbSJ1dnZu2LDh2bNn58+fhxupA7GwP2zbtu3Zs2fYANDU1BQcHCzXU27durVp06a+dz1nzhyhLTweLzg4uKWlRXDj+/fv/fz8ZN+tvO2lioiIKCsrE90uGr+o48ePX7p0SYnBkJksL4gQpeRScHBwU1OT4JZeEwmTMzdIlUjKUl5ePmPGjMzMTLX1CDSUlKPDr7/+2sjI6NKlS0FBQUL/q2J/LJDCFF0p9fbt2xEjRohuX7NmDUXAgwcPRHtXSgCy27Vr17Fjx3Jzc5W+ZwqFcuTIkeTkZEzgmvi4uLhNmzbJvhN520uWlZUVHBw8bNgwZe0Qh+eSgYGBEhMpNzfXysrKyMho4cKFXV1dog2Ecmn48OHEJhJSWS71mkhIztzQiESSy8WLF8PDw48dOwZFvIFUOpJ/febMGQ6Ho6enN3v2bCaTKfgr/gIpxVZKdXd3x8fHYyLrohBCR48ezcrKevDggYWFxd27d/EwhHrvewByycnJUd3O6XR6RESE4Bb8okTZydteMhXdbhTPJS0tLSUmEovFunHjhoODg6+v7y+//DJt2jShBkK5pK2tvXbtWqE26kwkpMpcEk0kJGduaEQiyaizs3Pbtm0YhmVmZsIVpEAWkqbDf/7zn0VFRfr6+hUVFfb29gih2traMWPGdHV1/fvf/7548eIvv/zS2tr68OHDX375pbKysqKiori4+F//+teGDRsCAwMbGhpqa2tbWlru379vZ2cntPPY2NioqCh8nfLbt2/nzp0relk5hUKZOHEiQoh/ZYGMAdy7d09y70DN+Ln06tUrJPF9bG1t3blz57x582R5K/E7QTc0NPB4vI8//lhcIqE/cgkSaYAoLS1ds2bNhg0b8K8wAZCFpJOl0dHRrq6uGIbx1+Ha2dk1NjY+fvz42rVr/AVS+ANnZ2cfH5+ampqUlBQMw0SXWwn64YcfvLy8rK2t8R97XWJlZWVFoVC+/vprwY0yBrBhwwYJvQP1E8olCe9jXFxccnKy7G9lSkqKlZWVq6srg8EQt1ZPNJcgkfqx9PT0zZs3p6SkwFwI5CLlZKmQmzdvLlu2rK6uburUqUK/qq6uTkhIwNcU81caCS234tu3b9+DBw/wx6NGjXrx4oVoX/X19ZaWln0JQFzvCKHi4uK5c+fK8BcrGVH9Ksu7d++Ush8J7yOS861cunTp/Pnz9+zZExsbK+57L34u8Y8OlZVIVVVVhLyhL168WLRokY6OfJ9f8qisrFTFbjs6OrZv345hWHp6uq6uriq6AP2YfB+ntLS0mJiYTz75ZMuWLUhggRSHwxk0aFB0dPTGjRvb29tNTU1fv34tYT/8f+HFzYV9DOD58+eS9+Ps7ExI3cG5c+devnxZ/f0qi7KGfgnvI5fLtbe3l/GtjI2N/fzzz01NTbW1teVa7q2sRBoyZAghb2hISEhCQoLm1jtUxf8QL168WLt27ebNm2fOnKn0nYMBgX+NqehCi9jYWAqFgl8SHRQUpKWl5erqSqVSJ0+erK+vf+bMGQsLiyVLlrx+/drCwmLmzJlMJtPQ0HDdunUYhgUEBNBotNu3b5uZmU2dOlXcha0jR47EMKy2tnbcuHH8jfg/5sOHD3/z5g2+Be/94MGDqampsgQguXcFFlooizqvL1cFhRda8HNJaiItWbLk3bt3Mr6VJ0+epNPpBgYGHh4edXV1QomEieSSchNJxhdEFUQXWmgWpb9ux48f9/Pze//+vXJ3CwYUWHeoVgN2OuyvYDpUjBJft99++y0oKGj//v09PT3K2icYmNRxVxoWi0X5M7wohAbBZFuRJmMzyfpetU7yj6LtyV+XDgeJJBc8kTAMO3TokIRckj2RpDZQfyJdu3YtODh469atW7Zs0dKCe2yBPlFHAllaWgpNwqLXyJCcjCvS+r5wDVNG1TrJP4q2J3ldOj5IJNnxE4lCoaxfv15cLsmVSFIbqDORWlpawsPDr127lpmZ+Ze//EUNPYJ+D/6f+h2Hw/H09KTRaNOmTWOz2eKq0KmoWB2fUqrWSa1Cpyl16TQRCRMJic8luRJJlgbqSaR79+7Nnz9/0aJFcXFx+vr6qu4ODBAwHf4uKSnJzc3t3bt3zs7OSUlJ4qrQqahYHZ9SqtZJrUKnEXXpNBQJEwnJnEvyZo76E6m7u/urr746dOjQhQsXPDw8VNcRGIBgOvxdRUWFh4cHjUabOXNmVVWVhJYqKnyIU0rVOqlV6Mhfl05zkTCRkMy5JG/mqDmRSkpKfH19x44d+/3339PpdBX1AgYsmA5/x2AwcnNz29vbb9y4wWAwJFShU0WxOj6lVK2TWoWO5HXpNBoJEwnJnEvyZo7aEgm/tmjnzp0pKSl+fn5K3z8ACKZDvhUrVhQWFlpaWhYXFy9fvlxcFTqkmmJ1fEqpWie1Ch1p69L1AyRMJCS+lKZciYQIqpRZXFzs4+NjYmKSkZFha2ur3J0D8D/8i/Rg3aEs+lisTupyK6VUrVOAuLp0QmDdoRCF18/1MZGkrjvsB4mEYVh7e/uXX365YMGCmpqaPocGgBRwdCgf1RU+xCmlap28yFCXbqD5/+3de1RTV9o/8B0ETSBBLkG0QC31UlG5VOxbFBaigtytOIDOklGXLh3t6MILVbvQWl0aLwiiFQWUCq1O1WpFQagXEOtoB+kMKl5GR4iAoEAg4Y4Qk/eP83vziwnGBEhOCN/PXznHfc5+BPThJPvZD36Q3uvmzZvBwcHOzs5nz56lOuoAaFV/3QKYLlptfEjpfdc6TdHbl25gwg+SCiKRaNu2bR0dHefPn++/+7JCv4N0CAB6JCsr6/Dhw1u3blXu+A2gVUiHAKAXqqurv/rqqzFjxly4cAH960H3GLKPFrZs2XL79m0zMzN6A9IBiURSWVk5cuRI3U9dVlbWtx/RPXv2jMVi2djY6Oa/j6ampvd+3HX8+PHU1FQbGxsdxEM7db6hEomktbWVKlfvK+Xl5Q4ODv13l84nT57IL3wVi8WHDh3Kz8+Pj48fM2YMjYHBQMaQKu2HCf1IfX19fn5+Tk5OZWWll5dXaGjopEmTlDd1Axo1NDSsX7/++PHjdAeip/Ly8nbv3h0VFbVw4UL86AKN8GZp/2ZtbR0REREREfHmzZu7d+9mZWXxeDwul+vr6xsYGMhms+kOEOCdXrx4sXXrVg6Hc+7cOSyZAdohHRqIQYMGubu7u7u7E0L4fP7Vq1eXLFnS1tbm5+c3d+5cBwcHugME+P9aW1t37979+PFjHo83duxYusMBIARvlhq2tra2vLy87Ozs58+ff/rpp76+vj4+PsbG+B1Ip/Bmqbw3b96kp6efOnVq7dq11EY2AHoC/zMaMlNT09DQ0NDQUELIw4cPs7OzDx06xGQyg4KCZs+ejU2QQceuXbu2e/fu0NDQ3Nxc/FoG+gY/kQPFhAkTJkyYQAipq6vLzc1duXJlU1OTt7e3r68v9RYrgPYUFxdv27bN1dU1MzMTH2mDfsKbpQOXWCz+5z//mZ2dXVJSYm9vHxISMmvWLDRT7XMD/M3SR48e8Xg8DofzzTffYANu0Gd4Ohy4jI2Nvby8vLy8CCFlZWVZWVmRkZHGxsb+/v7BwcF2dnZ0Bwj92/Pnz/fs2SMSibZv345qQtB/eDqEt7S2tubn52dnZ5eWlk6ePDkkJMTT0xPVYL0xAJ8OKyoqdu3a1djYuHXr1k8++YTucADUgnQI3ZNIJMXFxVlZWYWFhcOGDQsJCfH390dxWA8MqHRYWVmZkJDw4sWL2NhYNzc3usMB0ADeLIXuGRkZyQoZa2pqLl++vGLFCqFQ6OPj88UXX4wbN47uAEG/1NXVxcfHP3z48Ntvv8XiLOiP8HQIGmhvb79161ZWVtb9+/ddXFxCQ0O9vb2x27JqBv90WFtbu2fPHj6f//XXX3/22Wd0hwPQQ3g6BA2wWCxfX19fX1/yf6tvDh06ZGRkFBAQMHv27OHDh9MdIOgUn8+Pj4+vrq5ev369p6cn3eEA9AqeDqG3ZNuIv3jxwtPTE9uIKzDIp8OSkpKEhITGxkY8EYLBQDqEPiPbRryoqIjL5YaEhGAbcWJw6fAf//hHYmKimZlZbGwsthsFQ4I3S6HPqNhGPCws7MMPP6Q7QOg5qVSanZ2dlJT0ySefHDx48IMPPqA7IoA+hqdD0K62trbbt29nZWXdvXvXzc0tNDR0oG0j3t+fDru6un766af09HRvb+81a9Zgq1swVEiHoDvUNuK3b99msVgDYRvx8vJygUDQ1NS0f//+rVu3EkLGjx/PYrHojktdQqHw2LFjV65cmT9//l/+8hcsIQbDhnQINBAIBNevX8/Nza2pqTHgbcS/++67b775hslkSiQSIyOjzs7O6urqfrEr7H//+99Dhw49efJk6dKlc+fOHTRoEN0RAWgd0iHQybC3EW9oaHBxcamqqqIOQ0NDL168SG9I8rq6ukxMTOTPSCSS/Pz8lJQUMzOzmJiYiRMn0hUbgO4hHYK+KCsru3btWl5e3ps3b2bNmtXtNuLV1dV1dXWurq60RNgDn3/++Z07dwghQ4cOPXfu3MyZM+mO6P8pLS2dNWtWUVGRlZUVIaS5ufmnn346c+aMl5fX6tWrra2t6Q4QQNcGffvtt3THAEAIIZaWlu7u7hEREcHBwQKB4OjRowcPHnz06BGTybS3t6cKGVNTUxcvXtzV1eXt7d0vShtNTEyuXLkiFotHjBiRkJBgZGREd0SEEFJYWBgYGFhVVcVkMj/88MM9e/YkJia6u7vv2bNnxowZpqamdAcIQAM8HYL+kkgkhYWF2dnZ//73v+3t7YOCgnbu3Pmvf/2Lw+GMHz/+/Pnz+t8/r7W11cnJqbKycsGCBSdOnKA7HEIIycnJWbZsWXV1NSHE1tY2KCho1apVkyZNojsuAJohHUL/UFlZeeHChZ07d7569YoQwmAw7OzsDhw4MHfuXLpDe4/p06cXFxdfvXpVH3ZvSUpK2rZtW11dHXU4dOjQn3/+2c/Pj96oAPQB0qE+6uzsxPdFWXZ29sKFC9va2mRnrKysAgICjhw5os+rbzIzM1evXl1RUUH7u7sxMTEZGRnNzc2yMyYmJv/zP/+Tl5dHY1T9kbGxMVbbGh6kQ300duzYfrRaRGfu3LlTV1dnZGTEYDCMjIyoz+FMTEzs7e1HjRrVs3vW1NQMGTJEq+WPEomktLRUoR3869evq6urHR0dtTevgpcvX/L5fCMjI2Nj4yH/x8TExMTExMbGRmdhGAChUDht2rQtW7bQHQj0sQG0OUg/Mm7cuJ9//pnuKAaElJQUe3v74OBgrc5C1R3Kn3n+/HlCQsLBgwe1Oi9oQ1FR0a+//kp3FND39GKdG4Bh05MFpQCgAv6VAgAAIB0CvENVVdWcOXN6cKFUKj1w4EAPLiwoKGAwGEwmkxAiEon27t174MABJpPJ5XITExOVxzc2NgYEBIhEIupQLBYvWrTIwsJi8eLFb9680ejw/PnzRUVF7wpM02AUbq5pbPQGI5s9KiqKwWCsWLFC1fcMDAjSIUD37OzsMjMze3Dhw4cPZZUMmvrb3/5WV1cnlUqXL1++YsWK6Ojojo6OkpKSXbt2Kax6a29vj46Ovnv3ruxMVlZWW1tbWVmZQCC4dOmSRodhYWEZGRllZWXKIfUgGIWbaxobvcHIZj9+/Dg+IxxQkA4Burdhw4bJkycTQiIjI2fOnDlu3Dg7O7uqqqqwsLCRI0eamZl5eHgIBAIvL69NmzaVlJRwOJyCggJCyMqVK3fu3Dlv3rweFBpaWFhwOJwbN27Y2tqam5sTQiQSiUAgGDx4sEKdBovFSk9P/+ijj2Rnfv/99/DwcCsrq/nz59+6dUujQ0JIeHj4/v37lUPqQTDKN+9HwchmNzExsbW1Vf97B/0d0iFA93g8HvVi3bp1Y8aMefDggY+Pz71796KjowMDA+vr6z08PNLS0jZv3kwIcXZ2Xrp0KTV+48aNsbGxp0+fVvGOn2qFhYVOTk7U6zNnzvj5+cXHx7/3KpFIxOFwCCEWFhYikUijQ0KIk5NTYWFhnwSjfPN+FIyK2cGwodACQC3GxsaOjo4SiYQQwmazmUymr6/vr7/+qo0K0fr6+mHDhlGvra2tL1++rM4slpaWTU1NhBChUGhlZSWRSNQ/JISwWKz6+vo+CUYhEk1jozcYFbODYcPTIYDGxGJxZ2dnTk7OqFGjGAwGn89//fp1S0uLbIBQKBSLxT2+v7W1dUdHB/Xaz89PzYw7ZcqUs2fPNjQ0nDp1aurUqRodEkI6Ojq6bWTRg2CUb96PglExOxg2pEOA7kVFRRUXF8fFxcXHx588efLmzZuHDx9OSEgghCQnJ7PZ7GfPni1dunTixIk3btwYPXp0YWHhrl27CCFOTk6nT58ODAykPnrUSGNjY0tLi4eHx5MnT6gzcXFxcXFxhJCsrKyAgADZyLq6Ojc3tzt37ri4uOTm5hJCQkJCmEymo6OjlZVVUFCQRoeEkKdPn06ZMkV5oh4Eo3zzngWjMItugpHN3tXVVVNTo+l3EPoxKeif0NBQukMYKJKTk7OzszW65Pr16zExMb2cl8/nr169WvnOhJAhQ4ZIJJIFCxY0NzfL/2lNTc2cOXN6Oa8K0dHRpaWlyhPRGIzyLLoJhpp9wYIFhJC//vWvCn96586d7du39+F0oCfwdDiASDWsh1NzvEL1m4KCgoKpU6dyOBw2m+3u7q7+7D2Op8fj1bR9+/YjR45Qi0j7lo+Pj1Qq7ejoYDAYhw4dSktLk8qVECQmJsbExPT5pJRLly4tWLDg448/Vp6IxmCUZ9FBMLLZT5w4IZVKk5OT++rOoOewlGYA0bQeTp3xytVv8srLy+fOnXv06FF/f//Xr19TJWI9buygjfh7ID8/v8/vqczCwiI6Olr+jGylqzbI79qqPBFdwXQ7i7aD0fYGtqC38HTYPzQ1NQUFBbFYLBsbG4FA4Ovry2azZ8yY0dDQoFAVpzC4oqLCwsLC1NQ0Pj6eqoeLiory9PQ0Nzen6roULq+qqlIYv2bNGoFAIH+JPIUar5cvX8oX2509ezYiIuJPf/oTm822trbet28fg8EQCoW9iV8hnl7GDwBAwdNh/5CSkuLo6PjLL788evTo2LFjkydPzszMjI2NTU15BUh3AAAPTUlEQVRNXbduXXp6+uXLlxctWnTv3j07Ozv5wQ4ODiKR6MmTJ6tWrdq4ceO0adM4HI6Tk1NOTo63t/eaNWsULg8KClIYv2PHjj179gQFBckuUfF4N2LECPliuxcvXij3MEpNTe1N/ArxJCcnZ2Rk9Cb+trY2ap29LrW0tHR1del+Xui91tZWKfriGSKkw/6htLR09uzZTCZz0qRJqampc+bMYbPZ/v7+WVlZ1AD5qjj5wVevXl2yZEl1dfX06dOpkRUVFYcPH6aKx2XFVbLLlccrX8LlctUM287OTrYOUIbP5/cm/m7/Cj2Ov6ur6/DhwxcuXFDzb9RXWltb+Xz+l19+qeN5offq6+tdXFzojgL6HtJh/+Do6JiZment7S2VSu3t7QsKCqZNm3blypVu+8fKD/7xxx/j4uLGjh27YcMGQohQKBw+fDiPx1u/fn17e/vQoUOfPXsmf+2pU6cUxovFYnt7e/lL1A87IiLi008/9fX1DQkJIYSkpKREREQ4Ojr2Jn6FeB4/ftyb+E1MTGJiYnT/cRH6HfZf6HdosOhYzgrvoVxoUV9f7+/vz2KxpkyZUlFRMWPGDFNTU+qzt/DwcDab/dtvv1laWk6fPl1hcFJSEovF8vb2HjJkyMmTJ62trf39/T08PExNTamF/gqXZ2RkKIxfuHDhq1ev5C+RV1tb6+rqymAwHBwccnJyqqqq3N3d5QfcvHnTy8uL2sbFxcWloaGhoaGhN/ErxNPL+HtQaNEnui20gH4BhRaGiiHFm+D6Z/bs2RcvXqQ7igEhJSXF3t4eT4egPurpcMuWLXQHAn0MK0tBAwKBgPE2gUBAd1C6JlWvnFHNYfrsvZ0F8/Lyhg8fzmazIyMjOzs7qZMqeg2qPiSEqO50CKBVSIegAS6Xq/D2gvrLagyGmuWMWqp61BmpGp0FBQLB1atXa2pqhELhjRs3yPt6Dao+JISo6HQIoG1IhwBvUSiLJIQodDSUlTOqbnwoG6ZQi9lfqNNZcN68ec7OzmZmZmw2m2o0obrX4HsbDZJ3dzoE0DakQ4C3UGWRr169cnZ2Tk1NJYQodDSk2hkmJiaqbnwoG6ZQi9lfqN9ZsLy83NzcvNvmEpq2OSToNQj0QToEeAufz/fx8aHKIsvLy1UPljU+rKys1E14OlNfX89isajXVGfByMhI5WE1NTVJSUkpKSnd3kShs6DqQ+oS9BoEuiAdAryFKotsb2+XlUUqdzSUtTNU3fiwl10P6aVOZ0E+n3/s2DEej8dkMru9iaZtDgl6DQJ9kA4B3rJ8+fKioiIul1tSUrJs2TJCiEJHQ6qdIfWOqIrGh7Jh1dXVPWh8SDt1OgsePHhw8+bNJiYmDAZj37595H29Bt/baJDItV0E0DVdFzqCGtDvUGd6U4bfm8aH+l+GT0ubQ6lc20W9hTJ8Q4WnQ4Ae0l7jQ32g+zaH5O22iwA6hj1LAXpIN40PaaTjNocEvQaBVng6BAAAwNOhXhKLxf16Q5N+pLm5ubGxUfdf7YaGhvb2dnyX+yNZiSQYGGzhrY+io6Nli/VBq9ra2gYNGjRkyBDtTdHV1fXs2TNZSTvlzZs3LS0tGnXLAv0RFhZG9SwDQ4J0CKBdDQ0N69evP378ON2BAIAq+OwQAAAA6RAAAADpEAAAgCAdAgAAEKRDAAAAgnQIAABAkA4BAAAI0iEAAABBOgQAACBIhwAAAATpEAAAgCAdAgAAEKRDAAAAgnQIAABAkA4BAAAI0iEAAABB+18A7bl48eIvv/wiFov/85//TJw4kclk7t2719zcnO64AKAbSIcA2pKbmxsZGdnS0kIdOjo6lpWV0RsSALwL3iwF0BY/Pz8ul0u9ZjAYc+fOpTceAFAB6RBAW4yNjSdPnky9trW1XbFiBb3xAIAKSIcAWrRmzRorKytCCJfLHT16NN3hAMA7IR0CaNHUqVOHDh06aNCg+fPn0x0LAKiCdAigRQwGw9fX18zMbMmSJXTHAgCqGNMdABi4P/744+7du3RHoSMSiYQQYmT01m+ZH3zwgbW19aVLl7Q6tVgsNjbW63/OUVFRTCaT7igA3kmv//2AATh37py9vb2DgwPdgejC9evXBw8e7OnpKX9y2LBhLBZr2LBh2pu3sbHx9OnTq1ev1t4UvfTdd9998cUXSIegz5AOQet8fHwmTJhAdxS60NjYyGQyZ8+erXBe+UzfqqurKygo0PYsvXHhwgW6QwB4D3x2CAAAgHQIAACAdAigG1VVVXPmzOnBhVKp9MCBAz2bVCQS7d2798CBA0wmk8vlJiYmKgzIy8sbPnw4m82OjIzs7OykTjY2NgYEBIhEIkKIWCxetGiRhYXF4sWL37x5o/rw/PnzRUVFPQsVgHZIhwC6YGdnl5mZ2YMLHz58WFdX14MLpVLp8uXLV6xYER0d3dHRUVJSsmvXLoU9igUCwdWrV2tqaoRC4Y0bNwgh7e3t0dHRssXAWVlZbW1tZWVlAoHg0qVLqg/DwsIyMjKwLyv0U0iHALqwYcMGasO2yMjImTNnjhs3zs7OrqqqKiwsbOTIkWZmZh4eHgKBwMvLa9OmTSUlJRwOp6CggBCycuXKnTt3zps377PPPtNoxhs3btja2lINNCQSiUAgGDx4MIPBkB8zb948Z2dnMzMzNptNrX1lsVjp6ekfffQRNeD3338PDw+3srKaP3/+rVu3VB8SQsLDw/fv39/7LxeA7iEdAugCj8ejXqxbt27MmDEPHjzw8fG5d+9edHR0YGBgfX29h4dHWlra5s2bCSHOzs5Lly6lxm/cuDE2Nvb06dOavg9ZWFjo5OREvT5z5oyfn198fHy3I8vLy83NzV1dXZX/SCQScTgcQoiFhYVIJFJ9SAhxcnIqLCzUKE4APYF0CEADY2NjR0dHqmyfzWYzmUxfX9/Kyso+nKK+vp7FYlGvra2tL1++HBkZqTyspqYmKSkpJSWl25tYWlo2NTURQoRCoZWVlepDQgiLxaqvr+/DvwWAziAdAtBMLBZ3dnbm5OSMGjWKwWDw+fzXr1/LuiQSQoRCoVgs1vS21tbWHR0d1Gs/P79uH/74fP6xY8d4PN67CuSnTJly9uzZhoaGU6dOTZ06VfUhIaSjo8Pa2lrTUAH0AdIhgC5ERUUVFxfHxcXFx8efPHny5s2bhw8fTkhIIIQkJyez2exnz54tXbp04sSJN27cGD16dGFh4a5duwghTk5Op0+fDgwMlPWKUpOHh8eTJ0+o13FxcXFxcYSQrKysgIAA2ZiDBw9u3rzZxMSEwWDs27ePEFJXV+fm5nbnzh0XF5fc3NyQkBAmk+no6GhlZRUUFKT6kBDy9OnTKVOm9NHXDEC3pADatGnTpgcPHiifLy4uNjc3P3/+fJ/Mcu3aNVtbWzMzs4iIiNevX0ulUpFI5O/vLxQKux3/5ZdfUj//LBZr4sSJKSkpfRLGDz/8cObMGY0uuX79ekxMTC/nra2tXbJkicJJiUSyYMGC5uZm+ZM1NTVz5szp5XQqREdHl5aWKp9fsmRJbW2t9uYF6D08HQI93Nzc1q1b11d3UygYUKgWUJaUlDRy5Mi6urrKysrY2NhNmzadOXOmr4LRyPbt248cOUItIu1bDAbj0KFDaWlpUrniisTExJiYmD6fi3Lp0qUFCxZ8/PHHWro/gFYhHYKu1dfXz5gxg8lk7t27lzojEAg8PT3Nzc3379+vUIeQn58/bNgwDodz//59+WEK91QoGFCoFnj58uW7qhSsra3nz5+/Y8eOY8eO9T6MHsjPz29pafHx8en9rZRZWFhER0fLF1fweDyFHcb7UHBwsKbVIAD6A+kQdC01NdXZ2bmpqWnt2rXUmbS0tKCgoMrKyvT09LVr18rXIeTm5sbFxTU3N7u4uMgPk75dTk55V8HAiBEjVFcpODs7NzQ09FUYANAfoaMF6Fppaemf//znwYMHy/rzVVRUHD58mCq5o5bpy+oQVq1atW7duu+//z4jI0NhGJfLlb+t6oIB1crKyhwcHHofhlQqvXz5cm1tbc++Mj3W0tLy9OnTpKQkHc+rvoqKCrpDAHgPpEPQtREjRly9etXDw6OsrMzNzY0QYm9vz+Px1q9f397e/vjxY/nBI0eOPHfu3NGjR0+ePCk/bOjQofLD+Hz+3//+dx6Pp1ELXIlE0tXVVVxcvGPHjt27dz99+rSXYRBChg8fPnr0aM2+Ir3W2NjIZrN1P6/6TE1N6Q4B4H3oXckDBk95ZWlFRYWrqyuHwxk/fvzUqVOlUumrV688PDxMTU1Xr14dHh7OZrN/++03S0vL6dOnb9682djY2N7evqioSH6Ywixr1qyR/UjHxcXV1ta6uroyGAwHB4ecnJyqqip3d3f58evXr2exWEZGRiYmJuPHj09OTu6TMHqwsrRPdLuyVK9gZSnoP4YUH36ANn399ddRUVEDpP3vjz/+yGQyIyIidDxvXV3dpk2b0tLSdDyv+pYuXbp7924bGxu6AwF4Jyylgf5HIBAw3iYQCOgOqoek6vVvUnOYmt7b+Im83eaJKHV6Qi8nMDxIh9D/cLlchXc5FJbV9CNq9m/qcZsnZVI1Gj8pF26ilxMYPKRDAO0SCoW+vr5sNnvGjBkNDQ0KLZyo/k1r1qxR3elJNkxFDaWa1Gn8pFC4SZQ6PRH0cgKDg3QIoF2pqamTJ09+9eqVs7NzamqqQgsnqn9TYmKi6k5PsmHvraF8L/UbP8lDLycweEiHANrF5/N9fHzYbLa/v395ebmKkVrq9KRAzcZPCtDLCQwe0iGAdjk6OhYUFLS3t1+5csXR0VG5hZOsf5PqTk89a/OkTJ3GT8rQywkMHtIhgHYtX768qKiIy+WWlJQsW7ZMoYUT1b+JekdURacn2bDq6mpNOz0pUKfxk0KbJ0IIejmBwcOuNADaZWlpmZeXJ3/46tUr+QFUlUhBQcHq1aup5EQIMTc373YYIeSPP/7oTTze3t5Hjx5taWlhs9lfffUVdfLzzz///vvvZWNsbGwU+oEYGxufOHFC/szZs2ejo6N7EwmAXsHTIYBe0F6nJwV90vgJvZzA8ODpEEAv5Ofn62wuqvGT/Bkej6fRHYKDg/s0IgD64ekQAAAA6RAAAABvloIO/PDDD7a2tnRHoQv37983NjbWatVgt9ra2kpLSxMSEnQ8r/oePHhAdwgA74GOFqBdfD5fde05DBCenp4mJiZ0RwHwTkiHAAAA+OwQAACAkP8FP/GQVHlmXCgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WBRnd_iAvRaV"
      },
      "source": [
        "#LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8WYyXNl1EnPm",
        "outputId": "b58afa64-72e8-4966-a518-2339ce8951a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        }
      },
      "source": [
        "input = Input(shape = (xLen,xTrain.shape[1]), )\n",
        "x = Bidirectional(LSTM(1000))(input) # хороший результат\n",
        "x = Dense(1000, activation=\"linear\")(x)\n",
        "x = Dense(yTrain.shape[1], activation='linear')(x)\n",
        "modelBI = Model(input, x)\n",
        "\n",
        "plot_model(modelBI, dpi=60, show_shapes=True, show_layer_names=True)\n",
        "# ошибка при прогнозе 1.9749 0.8497 0.0719 -0.1305 -0.3831 0.0254 0.0182 0.0 0.0019 0.0027\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FsWwm3-Ygj0k",
        "outputId": "59dfc965-c061-4e0c-d734-11db7646e98c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        }
      },
      "source": [
        "# Коллбэки\n",
        "early_stopping = EarlyStopping(monitor = 'val_loss', patience = 6, verbose = 0, restore_best_weights = True)\n",
        "#Checkpoint = ModelCheckpoint('PLZL_Concatenate.h5', monitor = 'loss', verbose=1, save_best_only=True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=1e-06, verbose = 1)\n",
        "\n",
        "model.compile(loss=\"mse\", optimizer=RMSprop(lr=1e-4))\n",
        "\n",
        "history = model.fit_generator(trainDataGen, epochs=30, verbose=1, \n",
        "    validation_data = testDataGen,\n",
        "    callbacks = [early_stopping, reduce_lr]\n",
        "    )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "860/860 [==============================] - 13s 15ms/step - loss: 0.0042 - val_loss: 0.0227\n",
            "Epoch 2/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 0.0023 - val_loss: 0.0067\n",
            "Epoch 3/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 0.0020 - val_loss: 0.1704\n",
            "Epoch 4/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 0.0018 - val_loss: 0.0070\n",
            "Epoch 5/30\n",
            "857/860 [============================>.] - ETA: 0s - loss: 0.0017\n",
            "Epoch 00005: ReduceLROnPlateau reducing learning rate to 1.9999999494757503e-05.\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 0.0017 - val_loss: 0.0081\n",
            "Epoch 6/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 0.0011 - val_loss: 0.0061\n",
            "Epoch 7/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 0.0010 - val_loss: 0.0293\n",
            "Epoch 8/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 0.0010 - val_loss: 0.0124\n",
            "Epoch 9/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 0.0011 - val_loss: 0.0049\n",
            "Epoch 10/30\n",
            "860/860 [==============================] - 13s 15ms/step - loss: 0.0010 - val_loss: 0.0049\n",
            "Epoch 11/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 9.9354e-04 - val_loss: 0.0051\n",
            "Epoch 12/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 0.0010 - val_loss: 0.0048\n",
            "Epoch 13/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 0.0010 - val_loss: 0.0050\n",
            "Epoch 14/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 0.0010 - val_loss: 0.0047\n",
            "Epoch 15/30\n",
            "860/860 [==============================] - ETA: 0s - loss: 0.0010\n",
            "Epoch 00015: ReduceLROnPlateau reducing learning rate to 3.999999898951501e-06.\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 0.0010 - val_loss: 0.0061\n",
            "Epoch 16/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 9.4260e-04 - val_loss: 0.0041\n",
            "Epoch 17/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 9.3047e-04 - val_loss: 0.0043\n",
            "Epoch 18/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 9.2435e-04 - val_loss: 0.0044\n",
            "Epoch 19/30\n",
            "860/860 [==============================] - ETA: 0s - loss: 9.2005e-04\n",
            "Epoch 00019: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 9.2005e-04 - val_loss: 0.0043\n",
            "Epoch 20/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 9.0831e-04 - val_loss: 0.0043\n",
            "Epoch 21/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 9.0586e-04 - val_loss: 0.0043\n",
            "Epoch 22/30\n",
            "860/860 [==============================] - 12s 14ms/step - loss: 9.0667e-04 - val_loss: 0.0042\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDgRuAWMXQM1",
        "outputId": "a9bb62c3-bd54-43eb-aa58-d41203a2e04a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.plot(history.history['loss'], \n",
        "         label='loss на xTrain')\n",
        "plt.plot(history.history['val_loss'], \n",
        "         label='val_loss на xTest')\n",
        "plt.ylabel('Средняя ошибка')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zTTEeR9gqJs"
      },
      "source": [
        "**Отображаем результаты**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXl5ePPK3ib0"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGJkxWKOgs6p",
        "outputId": "d3709b74-c46d-4c7b-d36c-b71bea72d9c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "source": [
        "#Прогнозируем данные текущей сетью\n",
        "currModel = model\n",
        "(predVal2, yValUnscaled2) = getPred(currModel, xVal[0], yVal[0], yScaler)\n",
        "delta = predVal2 - yValUnscaled2\n",
        "\n",
        "n = stepsForward-1 # число шагов предсказания - можно менять для глубины прогноза\n",
        "#Отображаем графики\n",
        "showPredict(0, valLen, n, predVal2, yValUnscaled2) #\n",
        "showPredict(0, valLen, 2, predVal2, yValUnscaled2) # "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upuCxjCsXfTG",
        "outputId": "bbb9ada8-27d8-4001-9c33-99f5ab142e87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "#Отображаем корреляцию\n",
        "plt.figure(figsize=(10,5))\n",
        "showCorr([0, 4, 9], xLen, predVal2, yValUnscaled2) #учитывать число шагод на которые предсказываем!!\n",
        "# увеличивать или уменьшать [0, 1, 2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FQ-rLbGSg7Zi"
      },
      "source": [
        "## Функция ошибки\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BA0J-ZNxg8tI"
      },
      "source": [
        "### Функция ошибки\n",
        "def getError(x1, x2):\n",
        "  dx = (x1-x2) ** 2        #Вычислем среднеквадратичную ошибку по точкам\n",
        "  dxs = np.sort(dx)[::-1]  #Сортируем ошибку в порядке убывания\n",
        "  return np.mean(dxs[:10]) #Итоговая ошибка равны среднему 10 самых больших ошибок на всём отрезке"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXQwcPxOg_MM",
        "outputId": "6d118cfe-b4dd-4c4d-d04f-22570fe876c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "#Отображаем ошибку при прогнозе на несколько шагов вперёд\n",
        "for i in range(stepsForward):\n",
        "  x1 = predVal2[:,i]\n",
        "  x2 = yValUnscaled2[:,i]\n",
        "\n",
        "  print(\"Шаг\", i+1, \"ошибка\", round(getError(x1, x2),3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Шаг 1 ошибка 84838.278\n",
            "Шаг 2 ошибка 373914.06\n",
            "Шаг 3 ошибка 497154.573\n",
            "Шаг 4 ошибка 509131.357\n",
            "Шаг 5 ошибка 625861.682\n",
            "Шаг 6 ошибка 577610.36\n",
            "Шаг 7 ошибка 621971.796\n",
            "Шаг 8 ошибка 613681.689\n",
            "Шаг 9 ошибка 684901.061\n",
            "Шаг 10 ошибка 789044.631\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w98q3OWmhHmq"
      },
      "source": [
        "### Функция ошибки\n",
        "\n",
        "def getError2(x1, x2):\n",
        "  corr = []\n",
        "  yLen = x1.shape[0]\n",
        "\n",
        "  #Вычисляем корреляцию выхода сети в верным ответом\n",
        "  #Смещая выход сети назад от 0 до 9 шагов\n",
        "  for i in range(10):\n",
        "    corr.append(correlate(x1[:yLen-i], x2[i:]))\n",
        "  \n",
        "  corr = np.array(corr)\n",
        "  \n",
        "  #Возвращаем функцию ошибки, как произведение\n",
        "  #1 - корреляции без смещения (со смещением на 0)\n",
        "  #2 - того, во сколько раз корреляция без смещения больше, чем средняя корреляция при смещении на 1-5 шагов\n",
        "  return corr[0] * (corr[0] / np.max(corr[1:10]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IoWLmQAKhKD0",
        "outputId": "52f8f979-3acb-4132-9a6d-eebc612fd728",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "#Отображаем ошибку при прогнозе на несколько шагов вперёд\n",
        "for i in range(stepsForward):\n",
        "  x1 = predVal2[:,i]\n",
        "  x2 = yValUnscaled2[:,i]\n",
        "\n",
        "  print(round(getError2(x1, x2),4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.3205\n",
            "1.1875\n",
            "0.1546\n",
            "-0.1626\n",
            "-0.4861\n",
            "-3.5572\n",
            "0.0083\n",
            "0.045\n",
            "0.0658\n",
            "0.0986\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x84Vlb_lhLuw",
        "outputId": "3b39781e-0dba-4768-812d-737b8681908f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        }
      },
      "source": [
        "#Сравнимаем две модели по новой функции ошибки\n",
        "#При предсказании на несколько шагов вперёд\n",
        "model1 = model3\n",
        "model2 = modelBI\n",
        "\n",
        "#Получаем предсказания от обеих сетей\n",
        "predValM1 = yScaler.inverse_transform(model1.predict(xVal[0]))\n",
        "predValM2 = yScaler.inverse_transform(model2.predict(xVal[0]))\n",
        "yValUnscaled3 = yScaler.inverse_transform(yVal)\n",
        "\n",
        "#Здаём собственную функцию ошибки\n",
        "errorFunc = getError2\n",
        "\n",
        "for i in range(stepsForward):\n",
        "  x1 = predValM1[:,i]\n",
        "  x2 = predValM2[:,i]\n",
        "  x = yValUnscaled2[:,i]\n",
        "\n",
        "  print(\"Предсказание на\", i+1, \"шагов: M1 =\", round(errorFunc(x1, x),3), \"M2 =\", round(errorFunc(x2, x),3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-69-1f9effba6b5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mpredValM1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0myScaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxVal\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mpredValM2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0myScaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxVal\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0myValUnscaled3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0myScaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myVal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m#Здаём собственную функцию ошибки\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36minverse_transform\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m         X = check_array(X, copy=self.copy, dtype=FLOAT_DTYPES,\n\u001b[0;32m--> 434\u001b[0;31m                         force_all_finite=\"allow-nan\")\n\u001b[0m\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    572\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_nd\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m             raise ValueError(\"Found array with dim %d. %s expected <= 2.\"\n\u001b[0;32m--> 574\u001b[0;31m                              % (array.ndim, estimator_name))\n\u001b[0m\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Found array with dim 3. Estimator expected <= 2."
          ]
        }
      ]
    }
  ]
}