From 9398bb708722d39ab1100b4e0139a14b83520db5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=90=D0=BB=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9A=D1=80?= =?UTF-8?q?=D1=8E=D0=BA=D0=BE=D0=B2?= Date: Fri, 15 Nov 2024 23:39:44 +0400 Subject: [PATCH 1/5] =?UTF-8?q?=D0=BF=D1=80=D0=BE=D0=BC=D0=B5=D0=B6=D1=83?= =?UTF-8?q?=D1=82=D0=BE=D1=87=D0=BD=D1=8B=D0=B9=20=D0=BA=D0=BE=D0=BC=D0=BC?= =?UTF-8?q?=D0=B8=D1=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 2 + Lab_3/lab3.ipynb | 251 +++++++++++++++++++++++++++++++++++++++++++++++ lab_1/lab1.ipynb | 23 +++-- 3 files changed, 268 insertions(+), 8 deletions(-) create mode 100644 Lab_3/lab3.ipynb diff --git a/.gitignore b/.gitignore index 9953c5f..62cce0e 100644 --- a/.gitignore +++ b/.gitignore @@ -16,3 +16,5 @@ static/csv/diabetes.csv static/csv/healthcare-dataset-stroke-data.csv static/csv/heart_2020_cleaned.csv static/csv/neo_v2.csv +static/csv/Yamana_Gold_Inc._AUY.csv +static/csv/AgeDataset-V1.csv diff --git a/Lab_3/lab3.ipynb b/Lab_3/lab3.ipynb new file mode 100644 index 0000000..63f25be --- /dev/null +++ b/Lab_3/lab3.ipynb @@ -0,0 +1,251 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ## Вариант 13 \n", + " https://www.kaggle.com/datasets/nancyalaswad90/yamana-gold-inc-stock-price?resource=download\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "ename": "UnicodeDecodeError", + "evalue": "'utf-8' codec can't decode bytes in position 15-16: invalid continuation byte", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[29], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m..//static//csv//Yamana_Gold_Inc._AUY.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m,\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnrows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4\u001b[0m data \u001b[38;5;241m.\u001b[39mcolumns\n", + "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 1014\u001b[0m dialect,\n\u001b[0;32m 1015\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 1023\u001b[0m )\n\u001b[0;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1898\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1895\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[0;32m 1897\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1898\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmapping\u001b[49m\u001b[43m[\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1899\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m 1900\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\c_parser_wrapper.py:93\u001b[0m, in \u001b[0;36mCParserWrapper.__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 90\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype_backend\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpyarrow\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 91\u001b[0m \u001b[38;5;66;03m# Fail here loudly instead of in cython after reading\u001b[39;00m\n\u001b[0;32m 92\u001b[0m import_optional_dependency(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpyarrow\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 93\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reader \u001b[38;5;241m=\u001b[39m \u001b[43mparsers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTextReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munnamed_cols \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reader\u001b[38;5;241m.\u001b[39munnamed_cols\n\u001b[0;32m 97\u001b[0m \u001b[38;5;66;03m# error: Cannot determine type of 'names'\u001b[39;00m\n", + "File \u001b[1;32mparsers.pyx:574\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mparsers.pyx:663\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._get_header\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mparsers.pyx:874\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mparsers.pyx:891\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._check_tokenize_status\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mparsers.pyx:2053\u001b[0m, in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32m:322\u001b[0m, in \u001b[0;36mdecode\u001b[1;34m(self, input, final)\u001b[0m\n", + "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode bytes in position 15-16: invalid continuation byte" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "data = pd.read_csv(\"..//static//csv//Yamana_Gold_Inc._AUY.csv\", sep=\",\", nrows=10000)\n", + "data .columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#1. Бизнес-цели для набора данных по акции компании Yamana Gold Inc.\n", + "Цель 1: Прогнозирование изменения цены акции компании.\n", + "Прогнозирование цен на акции является одной из ключевых задач в области финансов и инвестирования. Задача состоит в предсказании будущих изменений стоимости акции на основе исторических данных, таких как открытие и закрытие торгов, объемы торгов и другие показатели.\n", + "\n", + "Цель 2: Оценка волатильности акций компании.\n", + "Измерение волатильности позволяет инвесторам оценить риск и принять решения по управлению капиталом. Задача заключается в прогнозировании уровня волатильности на основе исторической динамики цен, объемов торгов и других рыночных факторов.\n", + "\n", + "#2. Цели технического проекта для каждой бизнес-цели\n", + "Цель 1: Прогнозирование изменения цены акции компании\n", + "\n", + "Разработать модель машинного обучения для прогнозирования будущих цен акций на основе исторических данных.\n", + "Использовать регрессионные модели, такие как линейная регрессия или более сложные модели, например, LSTM (долгосрочная краткосрочная память) для временных рядов.\n", + "Цель 2: Оценка волатильности акций компании\n", + "\n", + "Создать модель, которая будет прогнозировать волатильность на основе исторических данных о ценах.\n", + "Использовать методы статистического анализа, такие как вычисление стандартного отклонения, или методы машинного обучения для более точной оценки волатильности.\n", + "\n", + "#3 Проверим датасет на пропуски и удалим при необходимости строки с недостающими данными" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Id 0\n", + "Name 0\n", + "Short description 0\n", + "Gender 0\n", + "Country 0\n", + "Occupation 0\n", + "Birth year 0\n", + "Death year 0\n", + "Manner of death 0\n", + "Age of death 0\n", + "dtype: int64" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# Проверим на пропущенные значения\n", + "data.isnull().sum()\n", + "\n", + "# Заполним пропуски или удалим строки с пропусками\n", + "data = data.dropna()\n", + "\n", + "# Проверим, что данные очищены\n", + "data.isnull().sum()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Конструирование признаков" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"None of [Index(['Close', 'SMA_5', 'SMA_20', 'STD_5', 'STD_20'], dtype='object')] are in the [columns]\"", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[28], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# Отделяем целевую переменную (например, Price_Change) и признаки\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mClose\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSMA_5\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSMA_20\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSTD_5\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSTD_20\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m 5\u001b[0m y \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrice_Change\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m 7\u001b[0m \u001b[38;5;66;03m# Разделение на обучающую, контрольную и тестовую выборки (60%, 20%, 20%)\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\frame.py:4108\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 4106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[0;32m 4107\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 4108\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 4110\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[0;32m 4111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n", + "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6200\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m 6197\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 6198\u001b[0m keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[1;32m-> 6200\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 6202\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m 6203\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[0;32m 6204\u001b[0m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6249\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m 6247\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nmissing:\n\u001b[0;32m 6248\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nmissing \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(indexer):\n\u001b[1;32m-> 6249\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 6251\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m 6252\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['Close', 'SMA_5', 'SMA_20', 'STD_5', 'STD_20'], dtype='object')] are in the [columns]\"" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Отделяем целевую переменную (например, Price_Change) и признаки\n", + "X = data[['Close', 'SMA_5', 'SMA_20', 'STD_5', 'STD_20']]\n", + "y = data['Price_Change']\n", + "\n", + "# Разделение на обучающую, контрольную и тестовую выборки (60%, 20%, 20%)\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, shuffle=False)\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, shuffle=False)\n", + "\n", + "# Проверка размеров выборок\n", + "(X_train.shape, X_val.shape, X_test.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Разобьем данные на выборки и сбалансируем их" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train: (6000, 5), Validation: (2000, 5), Test: (2000, 5)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Разделение данных на признаки и целевую переменную\n", + "X = df.drop(columns=['Age of death', 'Name', 'Short description', 'Id'])\n", + "y = df['Age of death']\n", + "\n", + "# Разбиение на обучающую, контрольную и тестовую выборки\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", + "\n", + "# Проверка размеров выборок\n", + "print(f\"Train: {X_train.shape}, Validation: {X_val.shape}, Test: {X_test.shape}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Оценка сбалансированности и аугментация данных" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Проверка распределения целевой переменной\n", + "plt.figure(figsize=(8, 6))\n", + "plt.hist(y_train, bins=30, alpha=0.7, label='Train')\n", + "plt.hist(y_val, bins=30, alpha=0.7, label='Validation')\n", + "plt.hist(y_test, bins=30, alpha=0.7, label='Test')\n", + "plt.legend()\n", + "plt.title('Распределение целевой переменной')\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lab_1/lab1.ipynb b/lab_1/lab1.ipynb index 4817017..fe144bc 100644 --- a/lab_1/lab1.ipynb +++ b/lab_1/lab1.ipynb @@ -10,16 +10,23 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 1, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['Id', 'Name', 'Short description', 'Gender', 'Country', 'Occupation',\n", - " 'Birth year', 'Death year', 'Manner of death', 'Age of death'],\n", - " dtype='object')\n" + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: './/static//csv//csvLab1.csv'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[1], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m.//static//csv//csvLab1.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msep\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m,\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(df\u001b[38;5;241m.\u001b[39mcolumns)\n", + "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 1014\u001b[0m dialect,\n\u001b[0;32m 1015\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 1023\u001b[0m )\n\u001b[0;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m 1879\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1881\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1882\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1883\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1884\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1885\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1886\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1887\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1888\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n", + "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m 869\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 874\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 875\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 876\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 877\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './/static//csv//csvLab1.csv'" ] } ], @@ -186,7 +193,7 @@ ], "metadata": { "kernelspec": { - "display_name": "MIiLabs", + "display_name": "Python 3", "language": "python", "name": "python3" }, -- 2.25.1 From 9ac0ea42cae7307cca92e3db5ac9b7559dc2842b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=90=D0=BB=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9A=D1=80?= =?UTF-8?q?=D1=8E=D0=BA=D0=BE=D0=B2?= Date: Fri, 15 Nov 2024 23:40:48 +0400 Subject: [PATCH 2/5] 123 --- Lab_3/lab3.ipynb | 646 +++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 574 insertions(+), 72 deletions(-) diff --git a/Lab_3/lab3.ipynb b/Lab_3/lab3.ipynb index 63f25be..96244f3 100644 --- a/Lab_3/lab3.ipynb +++ b/Lab_3/lab3.ipynb @@ -10,36 +10,34 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 89, "metadata": {}, "outputs": [ { - "ename": "UnicodeDecodeError", - "evalue": "'utf-8' codec can't decode bytes in position 15-16: invalid continuation byte", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mUnicodeDecodeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[29], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m..//static//csv//Yamana_Gold_Inc._AUY.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m,\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnrows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4\u001b[0m data \u001b[38;5;241m.\u001b[39mcolumns\n", - "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 1014\u001b[0m dialect,\n\u001b[0;32m 1015\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 1023\u001b[0m )\n\u001b[0;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", - "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1898\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1895\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[0;32m 1897\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1898\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmapping\u001b[49m\u001b[43m[\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1899\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m 1900\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\c_parser_wrapper.py:93\u001b[0m, in \u001b[0;36mCParserWrapper.__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 90\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype_backend\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpyarrow\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 91\u001b[0m \u001b[38;5;66;03m# Fail here loudly instead of in cython after reading\u001b[39;00m\n\u001b[0;32m 92\u001b[0m import_optional_dependency(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpyarrow\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 93\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reader \u001b[38;5;241m=\u001b[39m \u001b[43mparsers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTextReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munnamed_cols \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reader\u001b[38;5;241m.\u001b[39munnamed_cols\n\u001b[0;32m 97\u001b[0m \u001b[38;5;66;03m# error: Cannot determine type of 'names'\u001b[39;00m\n", - "File \u001b[1;32mparsers.pyx:574\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mparsers.pyx:663\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._get_header\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mparsers.pyx:874\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mparsers.pyx:891\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._check_tokenize_status\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mparsers.pyx:2053\u001b[0m, in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32m:322\u001b[0m, in \u001b[0;36mdecode\u001b[1;34m(self, input, final)\u001b[0m\n", - "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode bytes in position 15-16: invalid continuation byte" - ] + "data": { + "text/plain": [ + "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume',\n", + " 'Day_of_week', 'Month', 'Year'],\n", + " dtype='object')" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "data = pd.read_csv(\"..//static//csv//Yamana_Gold_Inc._AUY.csv\", sep=\",\", nrows=10000)\n", + "\n", + "# Преобразование даты\n", + "data['Date'] = pd.to_datetime(data['Date'])\n", + "\n", + "# Преобразование данных: создание новых признаков\n", + "data['Day_of_week'] = data['Date'].dt.dayofweek\n", + "data['Month'] = data['Date'].dt.month\n", + "data['Year'] = data['Date'].dt.year\n", "data .columns" ] }, @@ -69,26 +67,26 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Id 0\n", - "Name 0\n", - "Short description 0\n", - "Gender 0\n", - "Country 0\n", - "Occupation 0\n", - "Birth year 0\n", - "Death year 0\n", - "Manner of death 0\n", - "Age of death 0\n", + "Date 0\n", + "Open 0\n", + "High 0\n", + "Low 0\n", + "Close 0\n", + "Adj Close 0\n", + "Volume 0\n", + "Day_of_week 0\n", + "Month 0\n", + "Year 0\n", "dtype: int64" ] }, - "execution_count": 27, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -115,29 +113,212 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 80, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "\"None of [Index(['Close', 'SMA_5', 'SMA_20', 'STD_5', 'STD_20'], dtype='object')] are in the [columns]\"", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[28], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# Отделяем целевую переменную (например, Price_Change) и признаки\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mClose\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSMA_5\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSMA_20\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSTD_5\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSTD_20\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m 5\u001b[0m y \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrice_Change\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m 7\u001b[0m \u001b[38;5;66;03m# Разделение на обучающую, контрольную и тестовую выборки (60%, 20%, 20%)\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\frame.py:4108\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 4106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[0;32m 4107\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 4108\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 4110\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[0;32m 4111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n", - "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6200\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m 6197\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 6198\u001b[0m keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[1;32m-> 6200\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 6202\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m 6203\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[0;32m 6204\u001b[0m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6249\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m 6247\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nmissing:\n\u001b[0;32m 6248\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nmissing \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(indexer):\n\u001b[1;32m-> 6249\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 6251\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m 6252\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['Close', 'SMA_5', 'SMA_20', 'STD_5', 'STD_20'], dtype='object')] are in the [columns]\"" - ] + "data": { + "text/html": [ + "
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DateOpenHighLowCloseAdj CloseVolumeDay_of_weekMonthYearPrice_ChangeSMA_5SMA_20STD_5STD_20
02001-06-223.4285713.4285713.4285713.4285712.8060020462001NaNNaNNaNNaNNaN
12001-06-253.4285713.4285713.4285713.4285712.80600200620010.000000NaNNaNNaNNaN
22001-06-263.7142863.7142863.7142863.7142863.03983701620010.285715NaNNaNNaNNaN
32001-06-273.7142863.7142863.7142863.7142863.03983702620010.000000NaNNaNNaNNaN
42001-06-283.7142863.7142863.7142863.7142863.03983703620010.0000003.6NaN0.156493NaN
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" + ], + "text/plain": [ + " Date Open High Low Close Adj Close Volume \\\n", + "0 2001-06-22 3.428571 3.428571 3.428571 3.428571 2.806002 0 \n", + "1 2001-06-25 3.428571 3.428571 3.428571 3.428571 2.806002 0 \n", + "2 2001-06-26 3.714286 3.714286 3.714286 3.714286 3.039837 0 \n", + "3 2001-06-27 3.714286 3.714286 3.714286 3.714286 3.039837 0 \n", + "4 2001-06-28 3.714286 3.714286 3.714286 3.714286 3.039837 0 \n", + "\n", + " Day_of_week Month Year Price_Change SMA_5 SMA_20 STD_5 STD_20 \n", + "0 4 6 2001 NaN NaN NaN NaN NaN \n", + "1 0 6 2001 0.000000 NaN NaN NaN NaN \n", + "2 1 6 2001 0.285715 NaN NaN NaN NaN \n", + "3 2 6 2001 0.000000 NaN NaN NaN NaN \n", + "4 3 6 2001 0.000000 3.6 NaN 0.156493 NaN " + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Создаем новый признак - разницу между текущей и предыдущей ценой (Price_Change)\n", + "data['Price_Change'] = data['Close'].diff()\n", + "\n", + "# Создадим скользящие средние для 5 и 20 дней\n", + "data['SMA_5'] = data['Close'].rolling(window=5).mean()\n", + "data['SMA_20'] = data['Close'].rolling(window=20).mean()\n", + "\n", + "# Стандартное отклонение для 5 и 20 дней\n", + "data['STD_5'] = data['Close'].rolling(window=5).std()\n", + "data['STD_20'] = data['Close'].rolling(window=20).std()\n", + "\n", + "data.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " Разделение данных на обучающую, контрольную и тестовую выборки" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((3150, 10), (1050, 10), (1051, 10))" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", + "# Преобразуем колонку 'Date' в тип datetime для правильного сортирования\n", + "data['Date'] = pd.to_datetime(data['Date'])\n", + "\n", + "# Сортируем данные по дате, чтобы не нарушить временную зависимость\n", + "data = data.sort_values(by='Date')\n", + "\n", "# Отделяем целевую переменную (например, Price_Change) и признаки\n", - "X = data[['Close', 'SMA_5', 'SMA_20', 'STD_5', 'STD_20']]\n", + "X = data[['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume', 'SMA_5', 'SMA_20', 'STD_5', 'STD_20']]\n", "y = data['Price_Change']\n", "\n", "# Разделение на обучающую, контрольную и тестовую выборки (60%, 20%, 20%)\n", @@ -148,65 +329,385 @@ "(X_train.shape, X_val.shape, X_test.shape)\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Конструирование признаков для решения задач" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# Признаки для задачи прогнозирования изменений цен\n", + "data['Price_Change'] = data['Close'].diff()\n", + "\n", + "# Скользящие средние и стандартное отклонение\n", + "data['SMA_5'] = data['Close'].rolling(window=5).mean()\n", + "data['SMA_20'] = data['Close'].rolling(window=20).mean()\n", + "data['STD_5'] = data['Close'].rolling(window=5).std()\n", + "data['STD_20'] = data['Close'].rolling(window=20).std()\n", + "\n", + "# Признаки для оценки волатильности\n", + "data['Volatility'] = data['Close'].rolling(window=5).std()\n", + "\n" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Разобьем данные на выборки и сбалансируем их" + "Подготовка признаков: one-hot encoding, дискретизация, синтез признаков, масштабирование\n", + "One-hot encoding: Применим для категориальных признаков (например, день недели).\n", + "Масштабирование: Стандартизируем числовые признаки." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Train: (6000, 5), Validation: (2000, 5), Test: (2000, 5)\n" + " Day_of_week Month\n", + "0 4 6\n", + "1 0 6\n", + "2 1 6\n", + "3 2 6\n", + "4 3 6\n" + ] + }, + { + "data": { + "text/html": [ + "
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CloseSMA_5SMA_20STD_5STD_20Day_of_week_1Day_of_week_2Day_of_week_3Day_of_week_4Month_2Month_3Month_4Month_5Month_6Month_7Month_8Month_9Month_10Month_11Month_12
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2-0.660890NaNNaNNaNNaN1.969800-0.507962-0.502320-0.500238-0.28793-0.309491-0.300916-0.2971373.335719-0.30429-0.311702-0.296377-0.311335-0.298274-0.303543
3-0.660890NaNNaNNaNNaN-0.5076661.968649-0.502320-0.500238-0.28793-0.309491-0.300916-0.2971373.335719-0.30429-0.311702-0.296377-0.311335-0.298274-0.303543
4-0.660890-0.686033NaN-0.269917NaN-0.507666-0.5079621.990763-0.500238-0.28793-0.309491-0.300916-0.2971373.335719-0.30429-0.311702-0.296377-0.311335-0.298274-0.303543
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" + ], + "text/plain": [ + " Close SMA_5 SMA_20 STD_5 STD_20 Day_of_week_1 Day_of_week_2 \\\n", + "0 -0.721096 NaN NaN NaN NaN -0.507666 -0.507962 \n", + "1 -0.721096 NaN NaN NaN NaN -0.507666 -0.507962 \n", + "2 -0.660890 NaN NaN NaN NaN 1.969800 -0.507962 \n", + "3 -0.660890 NaN NaN NaN NaN -0.507666 1.968649 \n", + "4 -0.660890 -0.686033 NaN -0.269917 NaN -0.507666 -0.507962 \n", + "\n", + " Day_of_week_3 Day_of_week_4 Month_2 Month_3 Month_4 Month_5 \\\n", + "0 -0.502320 1.999048 -0.28793 -0.309491 -0.300916 -0.297137 \n", + "1 -0.502320 -0.500238 -0.28793 -0.309491 -0.300916 -0.297137 \n", + "2 -0.502320 -0.500238 -0.28793 -0.309491 -0.300916 -0.297137 \n", + "3 -0.502320 -0.500238 -0.28793 -0.309491 -0.300916 -0.297137 \n", + "4 1.990763 -0.500238 -0.28793 -0.309491 -0.300916 -0.297137 \n", + "\n", + " Month_6 Month_7 Month_8 Month_9 Month_10 Month_11 Month_12 \n", + "0 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 \n", + "1 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 \n", + "2 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 \n", + "3 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 \n", + "4 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 " + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "import pandas as pd\n", + "\n", + "# Преобразуем дату, если это еще не сделано\n", + "data['Date'] = pd.to_datetime(data['Date'])\n", + "\n", + "# Добавим дополнительные признаки (день недели и месяц)\n", + "data['Day_of_week'] = data['Date'].dt.dayofweek\n", + "data['Month'] = data['Date'].dt.month\n", + "\n", + "# Проверим, что эти столбцы добавлены\n", + "print(data[['Day_of_week', 'Month']].head())\n", + "\n", + "# Выбираем признаки и целевую переменную\n", + "X = data[['Close', 'SMA_5', 'SMA_20', 'STD_5', 'STD_20', 'Day_of_week', 'Month']]\n", + "y = data['Price_Change']\n", + "\n", + "# Применяем one-hot encoding для категориальных признаков (Day_of_week и Month)\n", + "X = pd.get_dummies(X, columns=['Day_of_week', 'Month'], drop_first=True)\n", + "\n", + "# Масштабирование числовых признаков (Close, SMA, STD)\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "\n", + "# Преобразуем обратно в DataFrame для удобства\n", + "X_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)\n", + "\n", + "# Проверим результат\n", + "X_scaled_df.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n", + " warnings.warn(\n" ] } ], "source": [ - "from sklearn.model_selection import train_test_split\n", + "import featuretools as ft\n", "\n", - "# Разделение данных на признаки и целевую переменную\n", - "X = df.drop(columns=['Age of death', 'Name', 'Short description', 'Id'])\n", - "y = df['Age of death']\n", + "# Создаем сущности для Featuretools\n", + "es = ft.EntitySet(id=\"stock_prices\")\n", + "es = es.add_dataframe(dataframe_name=\"stock_data\", dataframe=data, index=\"Date\")\n", "\n", - "# Разбиение на обучающую, контрольную и тестовую выборки\n", - "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n", - "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", - "\n", - "# Проверка размеров выборок\n", - "print(f\"Train: {X_train.shape}, Validation: {X_val.shape}, Test: {X_test.shape}\")\n" + "# Автоматическое создание признаков\n", + "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name=\"stock_data\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Оценка сбалансированности и аугментация данных" + "#Оценка качества признаков\n", + "Оценка признаков на основе предсказательной способности модели и других критериев." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5251\n", + "3150\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'numpy.ndarray' object has no attribute 'reset_index'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[88], line 7\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m# Приводим индексы к одному виду\u001b[39;00m\n\u001b[0;32m 6\u001b[0m y_train \u001b[38;5;241m=\u001b[39m y_train\u001b[38;5;241m.\u001b[39mreset_index(drop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m----> 7\u001b[0m X_scaled \u001b[38;5;241m=\u001b[39m \u001b[43mX_scaled\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreset_index\u001b[49m(drop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 9\u001b[0m \u001b[38;5;66;03m# После этого продолжим обучение модели\u001b[39;00m\n\u001b[0;32m 10\u001b[0m model \u001b[38;5;241m=\u001b[39m LinearRegression()\n", + "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'reset_index'" + ] + } + ], + "source": [ + "# Проверим размеры данных\n", + "print(X_scaled_df.shape[0]) # Количество строк в X_scaled_df\n", + "print(y_train.shape[0]) # Количество строк в y_train\n", + "\n", + "# Приводим индексы к одному виду\n", + "y_train = y_train.reset_index(drop=True)\n", + "X_scaled_df = X_scaled_df.reset_index(drop=True)\n", + "\n", + "# После этого продолжим обучение модели\n", + "model = LinearRegression()\n", + "model.fit(X_scaled_df, y_train)\n", + "\n", + "# Прогнозирование и оценка качества\n", + "y_pred = model.predict(X_scaled_df)\n", + "\n", + "# Оценка качества модели\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "r2 = r2_score(y_test, y_pred)\n", + "\n", + "mse, r2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Визуализируем" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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RgH7o0CHNnDlT06ZNU5UqVdweSC/mGN39+yjUv39/jRkzRg8//LB69+7tdFlAocK6Q0NDS1S3p6dnkXJn3w1fyN/fv0jZLVu2OL2vW7euJGnPnj1Ol13k5eUpJSWl2DbNnTtXGRkZxc6oAVxuXEMKuMDT07PIVDlLly7VwYMHnZb16tVLR44c0ezZs4vUcfb2rnjrrbecrjN7//33dfjwYXXt2lXSX6Gjfv36euGFF3T8+PEi2//2229F2u7p6VnslEpn6tmzpzw9PTVp0qQi7bcsyymMnD59Wh988IFuvPHG85527tOnj/Lz8zVlypQi606fPn3Of5xLIikpSR999JGeffbZc4bNPn366ODBg0VGniTpzz//1IkTJy56/8XtKykpSZ9//nmRdZmZmTp9+rTj/ffff699+/ad89Sv9Nf3sFevXvrggw+0ffv2IuvP7ufi/Pnnnzp9+rROnz4tT09Pde7cWR999JHTNELp6elavHix2rdvX+SU86RJkxQWFqZ//vOfF9zXxbiYY3T376NQSEiI7rnnHv34448aPHhwsWWio6MVGBioqVOnFpkm6nx1u0tUVJR8fHw0a9Ysp9/oggULlJWVpW7dujmVP3bsmJ555hmNGjXqvKPxwOXCCCnggu7du2vy5Ml64IEHdPPNN2vbtm1atGiR04iE9NfNFW+99ZZGjx6t7777TrfccotOnDihVatW6V//+pfuueeei9p/SEiI2rdvrwceeEDp6emaMWOGrr32WscTVjw8PPTvf/9bXbt2VdOmTfXAAw/oqquu0sGDB7VmzRoFBgbqk08+0YkTJzRnzhzNmjVLDRs2dJr3sPAf6h9//FFJSUmKjIxU/fr19fTTTys+Pl779+9Xjx49VKVKFaWkpGjZsmUaOnSoHn30Ua1atUrjx4/Xjz/+WOR05Nk6dOigBx98UNOmTdOWLVvUuXNneXt7a+/evVq6dKlmzpyp3r17X9Tn9MUXX+iOO+4470jVgAED9N577+mf//yn1qxZo3bt2ik/P1+7d+/We++9p88///yCI8fHjx/XypUrnZYVjjKuXbtW3t7euuqqqzRmzBh9/PHH6t69uwYNGqSIiAidOHFC27Zt0/vvv6/9+/erevXqmjx5smbOnKlrrrlGAwcOPO++n332Wa1Zs0Zt27bVkCFD1KRJEx09elQ//PCDVq1apaNHjzqVT0xM1K+//uo4Zb9o0SLdfffdjsnQn376aSUmJqp9+/b617/+JS8vL7366qvKzc3V9OnTi/2MFy1adEknU3f1GN31+yhOQkKC5syZ43Qm5EyBgYGaN2+eBgwYoBtuuEF9+/ZVjRo1lJqaqk8//VTt2rUr9j+o7lKjRg3Fx8dr0qRJ6tKli+6++27t2bNHc+fOVZs2bXT//fc7lf/hhx9UvXr1c15GAlx2Jm7tB8qac01Fc7acnBzrkUcesWrWrGlVqlTJateunZWUlFRkGhXL+msqmSeffNIKDw+3vL29LbvdbvXu3dsxrc7FTPv0n//8x4qPj7dCQ0OtSpUqWd26dbN++eWXIttv3rzZ6tmzp1WtWjXL19fXqlu3rtWnTx9r9erVTvu+0OvMaYYsy7I++OADq3379pa/v7/l7+9vNWrUyIqLi7P27NljWZZljRgxwrr11lutlStXFmnT2dM+FXrttdesiIgIq1KlSlaVKlWs5s2bW4899ph16NAhRxlXp32y2WzWpk2bnJYX10d5eXnWc889ZzVt2tTy9fW1qlatakVERFiTJk2ysrKyiuzv7Pou9PmdOQXSsWPHrPj4eOvaa6+1fHx8rOrVq1s333yz9cILL1h5eXmWZVnW1VdfbQ0ePNjp2M/8DM7uj/T0dCsuLs6qXbu24zvWqVMn67XXXnOUKfzuFL68vLysunXrWg899JD1xx9/ONX3ww8/WNHR0VZAQIBVuXJl67bbbrPWr1/vVKbwt9KqVSunqZMKv1PunPbJ1WN01+/Dsv7v+1o4rdPZzrV+zZo1VnR0tBUUFGT5+flZ9evXtwYNGmR9//33jjKXYtqnQrNnz7YaNWpkeXt7W2FhYdawYcOK9HPhd/fll18u9pgAE3h0KFAOfPXVV7rtttu0dOnSix41PNP+/fsVHh6ulJSUc07WPXHiRO3fv/+CT95B8erVq6eJEycWeboS3M/dvw8Alx/XkAIAAMAoriEFrkABAQHq37//eW86atGiheNRqHBdhw4dHA9UAACcH4EUuAJVr15d77zzznnL9OzZ8zK1pmJ68803TTcBAMoNriEFAACAUVxDCgAAAKMIpAAAADCqXF5DWlBQoEOHDqlKlSqX/JF/AAAAcJ1lWTp27Jhq1aolD4/zj4GWy0B66NChIs9VBgAAQNlz4MABXX311ectUy4DaZUqVST9dYBnP18ZAAAA5mVnZ6t27dqO3HY+5TKQFp6mDwwMJJACAACUYSW5vJKbmgAAAGAUgRQAAABGEUgBAABgVLm8hhQAAJRP+fn5OnXqlOlmwA28vb3l6enplroIpAAA4JKzLEtpaWnKzMw03RS4UXBwsOx2e6nnhSeQAgCAS64wjIaGhqpy5co82KacsyxLJ0+eVEZGhiSpZs2apaqPQAoAAC6p/Px8RxitVq2a6ebATSpVqiRJysjIUGhoaKlO37t8U9PBgwd1//33q1q1aqpUqZKaN2+u77//3rHesiw99dRTqlmzpipVqqSoqCjt3bvXqY6jR4+qf//+CgwMVHBwsGJjY3X8+PGLPggAAFB2FV4zWrlyZcMtgbsV9mlprwt2KZD+8ccfateunby9vbVixQrt3LlTL774oqpWreooM336dM2aNUvz58/Xxo0b5e/vr+joaOXk5DjK9O/fXzt27FBiYqKWL1+ur7/+WkOHDi3VgQAAgLKN0/QVj7v61GZZllXSwo8//rjWrVunb775ptj1lmWpVq1aeuSRR/Too49KkrKyshQWFqaEhAT17dtXu3btUpMmTZScnKzWrVtLklauXKk777xTv/76q2rVqnXBdmRnZysoKEhZWVk8qQkAgDIuJydHKSkpCg8Pl5+fn+nmwI3O17eu5DWXRkg//vhjtW7dWn/7298UGhqq66+/Xq+//rpjfUpKitLS0hQVFeVYFhQUpLZt2yopKUmSlJSUpODgYEcYlaSoqCh5eHho48aNrjQHAACg3KlXr55mzJhhuhlliks3Nf3vf//TvHnzNHr0aD3xxBNKTk7WQw89JB8fH8XExCgtLU2SFBYW5rRdWFiYY11aWppCQ0OdG+HlpZCQEEeZs+Xm5io3N9fxPjs725VmAwCAMio2Ifmy7m/BoDYlLnuh09ETJkzQxIkTXW5DcnKy/P39Xd6uInMpkBYUFKh169aaOnWqJOn666/X9u3bNX/+fMXExFySBkrStGnTNGnSpEtWPwAAwNkOHz7s+PO7776rp556Snv27HEsCwgIcPzZsizl5+fLy+vC0apGjRrubWgF4NIp+5o1a6pJkyZOyxo3bqzU1FRJkt1ulySlp6c7lUlPT3ess9vtjjmrCp0+fVpHjx51lDlbfHy8srKyHK8DBw640mwAAACX2e12xysoKEg2m83xfvfu3apSpYpWrFihiIgI+fr66ttvv9W+fft0zz33KCwsTAEBAWrTpo1WrVrlVO/Zp+xtNpv+/e9/695771XlypXVoEEDffzxx5f5aM1yKZC2a9fO6X8GkvTTTz+pbt26kqTw8HDZ7XatXr3asT47O1sbN25UZGSkJCkyMlKZmZnatGmTo8yXX36pgoICtW3bttj9+vr6KjAw0OkFAABg2uOPP65nn31Wu3btUosWLXT8+HHdeeedWr16tTZv3qwuXbrorrvucgzencukSZPUp08f/fjjj7rzzjvVv39/HT169DIdhXkuBdJRo0Zpw4YNmjp1qn7++WctXrxYr732muLi4iT9lfBHjhypp59+Wh9//LG2bdumgQMHqlatWurRo4ekv0ZUu3TpoiFDhui7777TunXrNHz4cPXt27dEd9gDAACUFZMnT9Ydd9yh+vXrKyQkRC1bttSDDz6oZs2aqUGDBpoyZYrq169/wRHPQYMGqV+/frr22ms1depUHT9+XN99991lOgrzXLqGtE2bNlq2bJni4+M1efJkhYeHa8aMGerfv7+jzGOPPaYTJ05o6NChyszMVPv27bVy5UqnqQAWLVqk4cOHq1OnTvLw8FCvXr00a9Ys9x0VAADAZXDmrEGSdPz4cU2cOFGffvqpDh8+rNOnT+vPP/+84AhpixYtHH/29/dXYGBgkUscKzKXHx3avXt3de/e/ZzrbTabJk+erMmTJ5+zTEhIiBYvXuzqrgEAAMqUs++Wf/TRR5WYmKgXXnhB1157rSpVqqTevXsrLy/vvPV4e3s7vbfZbCooKHB7e8sqnmUPAADgJuvWrdOgQYN07733SvprxHT//v1mG1UOEEgBAFc0d86D6cocl6iYGjRooP/+97+66667ZLPZNH78+CtqpPNiuXRTEwAAAM7tpZdeUtWqVXXzzTfrrrvuUnR0tG644QbTzSrzXHqWfVnBs+wBAO7CCOmlx7PsKy4jz7IHAAAA3I1ACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAHCJdOzYUSNHjnS8r1evnmbMmHHebWw2mz788MNS79td9VwOXqYbAAAArmCL77u8+/v7uyUuetddd+nUqVNauXJlkXXffPONbr31Vm3dulUtWrQocZ3Jycny9/cvcfmSmDhxoj788ENt2bLFafnhw4dVtWpVt+7rUmGEFAAAoBixsbFKTEzUr7/+WmTdwoUL1bp1a5fCqCTVqFFDlStXdlcTz8tut8vX1/ey7Ku0CKQAAADF6N69u2rUqKGEhASn5cePH9fSpUvVo0cP9evXT1dddZUqV66s5s2b6z//+c956zz7lP3evXt16623ys/PT02aNFFiYmKRbcaOHauGDRuqcuXKuuaaazR+/HidOnVKkpSQkKBJkyZp69atstlsstlsjvaefcp+27Ztuv3221WpUiVVq1ZNQ4cO1fHjxx3rBw0apB49euiFF15QzZo1Va1aNcXFxTn2dSkRSAEAAIrh5eWlgQMHKiEhQZZlOZYvXbpU+fn5uv/++xUREaFPP/1U27dv19ChQzVgwAB99913Jaq/oKBAPXv2lI+PjzZu3Kj58+dr7NixRcpVqVJFCQkJ2rlzp2bOnKnXX39dL7/8siTpvvvu0yOPPKKmTZvq8OHDOnz4sO67r+hlECdOnFB0dLSqVq2q5ORkLV26VKtWrdLw4cOdyq1Zs0b79u3TmjVr9OabbyohIaFIIL8UCKQAAADnMHjwYO3bt09r1651LFu4cKF69eqlunXr6tFHH1WrVq10zTXXaMSIEerSpYvee++9EtW9atUq7d69W2+99ZZatmypW2+9VVOnTi1Sbty4cbr55ptVr1493XXXXXr00Ucd+6hUqZICAgLk5eUlu90uu92uSpUqFalj8eLFysnJ0VtvvaVmzZrp9ttv1+zZs/X2228rPT3dUa5q1aqaPXu2GjVqpO7du6tbt25avXq1qx+bywikAAAA59CoUSPdfPPNeuONNyRJP//8s7755hvFxsYqPz9fU6ZMUfPmzRUSEqKAgAB9/vnnSk1NLVHdu3btUu3atVWrVi3HssjIyCLl3n33XbVr1052u10BAQEaN25cifdx5r5atmzpdENVu3btVFBQoD179jiWNW3aVJ6eno73NWvWVEZGhkv7uhgEUgAAgPOIjY3VBx98oGPHjmnhwoWqX7++OnTooOeff14zZ87U2LFjtWbNGm3ZskXR0dHKy8tz276TkpLUv39/3XnnnVq+fLk2b96sJ5980q37OJO3t7fTe5vNpoKCgkuyrzMRSAEAAM6jT58+8vDw0OLFi/XWW29p8ODBstlsWrdune655x7df//9atmypa655hr99NNPJa63cePGOnDggA4fPuxYtmHDBqcy69evV926dfXkk0+qdevWatCggX755RenMj4+PsrPz7/gvrZu3aoTJ044lq1bt04eHh667rrrStzmS4VACgAAcB4BAQG67777FB8fr8OHD2vQoEGSpAYNGigxMVHr16/Xrl279OCDDzpdj3khUVFRatiwoWJiYrR161Z98803evLJJ53KNGjQQKmpqVqyZIn27dunWbNmadmyZU5l6tWrp5SUFG3ZskVHjhxRbm5ukX31799ffn5+iomJ0fbt27VmzRqNGDFCAwYMUFhYmOsfipsRSAEAAC4gNjZWf/zxh6Kjox3XfI4bN0433HCDoqOj1bFjR9ntdvXo0aPEdXp4eGjZsmX6888/deONN+of//iHnnnmGacyd999t0aNGqXhw4erVatWWr9+vcaPH+9UplevXurSpYtuu+021ahRo9ippypXrqzPP/9cR48eVZs2bdS7d2916tRJs2fPdv3DuARs1pnzGJQT2dnZCgoKUlZWlgIDA003BwBQjsUmJLutrgWD2ritrookJydHKSkpCg8Pl5+fn+nmwI3O17eu5DVGSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARnmZbgAAALhyDV89/LLub3ankj+73WaznXf9hAkTNHHixItqh81m07Jly9SjR4+L2r6iIZACAAAU4/Dhw44/v/vuu3rqqae0Z88ex7KAgAATzaqQOGUPAABQDLvd7ngFBQXJZrM5LVuyZIkaN24sPz8/NWrUSHPnznVsm5eXp+HDh6tmzZry8/NT3bp1NW3aNElSvXr1JEn33nuvbDab4/2VjBFSAAAAFy1atEhPPfWUZs+ereuvv16bN2/WkCFD5O/vr5iYGM2aNUsff/yx3nvvPdWpU0cHDhzQgQMHJEnJyckKDQ3VwoUL1aVLF3l6eho+GvMIpAAAAC6aMGGCXnzxRfXs2VOSFB4erp07d+rVV19VTEyMUlNT1aBBA7Vv3142m01169Z1bFujRg1JUnBwsOx2u5H2lzUEUgAAABecOHFC+/btU2xsrIYMGeJYfvr0aQUFBUmSBg0apDvuuEPXXXedunTpou7du6tz586mmlzmEUgBAABccPz4cUnS66+/rrZt2zqtKzz9fsMNNyglJUUrVqzQqlWr1KdPH0VFRen999+/7O0tDwikAAAALggLC1OtWrX0v//9T/379z9nucDAQN13332677771Lt3b3Xp0kVHjx5VSEiIvL29lZ+ffxlbXbYRSAEAAFw0adIkPfTQQwoKClKXLl2Um5ur77//Xn/88YdGjx6tl156STVr1tT1118vDw8PLV26VHa7XcHBwZL+utN+9erVateunXx9fVW1alWzB2QY0z4BAAC46B//+If+/e9/a+HChWrevLk6dOighIQEhYeHS5KqVKmi6dOnq3Xr1mrTpo3279+vzz77TB4ef0WvF198UYmJiapdu7auv/56k4dSJtgsy7JMN8JV2dnZCgoKUlZWlgIDA003BwBQjsUmJLutrgWD2ritrookJydHKSkpCg8Pl5+fn+nmwI3O17eu5DVGSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQDAZVEO76PGBbirTwmkAADgkvL29pYknTx50nBL4G6FfVrYxxeLifEBAMAl5enpqeDgYGVkZEiSKleuLJvNZrhVKA3LsnTy5EllZGQoODjY8cjUi0UgBQAAl5zdbpckRyhFxRAcHOzo29IgkAIAgEvOZrOpZs2aCg0N1alTp0w3B27g7e1d6pHRQgRSAABw2Xh6erotxKDi4KYmAAAAGEUgBQAAgFEEUgAAABjFNaQAgMsiNiHZbXUtGNTGbXUBMI8RUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUS4F0okTJ8pmszm9GjVq5Fifk5OjuLg4VatWTQEBAerVq5fS09Od6khNTVW3bt1UuXJlhYaGasyYMTp9+rR7jgYAAADljst32Tdt2lSrVq36vwq8/q+KUaNG6dNPP9XSpUsVFBSk4cOHq2fPnlq3bp0kKT8/X926dZPdbtf69et1+PBhDRw4UN7e3po6daobDgcAAADljcuB1MvLS3a7vcjyrKwsLViwQIsXL9btt98uSVq4cKEaN26sDRs26KabbtIXX3yhnTt3atWqVQoLC1OrVq00ZcoUjR07VhMnTpSPj0/pjwgAAADlisvXkO7du1e1atXSNddco/79+ys1NVWStGnTJp06dUpRUVGOso0aNVKdOnWUlJQkSUpKSlLz5s0VFhbmKBMdHa3s7Gzt2LHjnPvMzc1Vdna20wsAAAAVg0uBtG3btkpISNDKlSs1b948paSk6JZbbtGxY8eUlpYmHx8fBQcHO20TFhamtLQ0SVJaWppTGC1cX7juXKZNm6agoCDHq3bt2q40GwAAAGWYS6fsu3bt6vhzixYt1LZtW9WtW1fvvfeeKlWq5PbGFYqPj9fo0aMd77OzswmlAAAAFUSppn0KDg5Ww4YN9fPPP8tutysvL0+ZmZlOZdLT0x3XnNrt9iJ33Re+L+661EK+vr4KDAx0egEAAKBiKFUgPX78uPbt26eaNWsqIiJC3t7eWr16tWP9nj17lJqaqsjISElSZGSktm3bpoyMDEeZxMREBQYGqkmTJqVpCgAAAMopl07ZP/roo7rrrrtUt25dHTp0SBMmTJCnp6f69eunoKAgxcbGavTo0QoJCVFgYKBGjBihyMhI3XTTTZKkzp07q0mTJhowYICmT5+utLQ0jRs3TnFxcfL19b0kBwgAAICyzaVA+uuvv6pfv376/fffVaNGDbVv314bNmxQjRo1JEkvv/yyPDw81KtXL+Xm5io6Olpz5851bO/p6anly5dr2LBhioyMlL+/v2JiYjR58mT3HhUAAADKDZcC6ZIlS8673s/PT3PmzNGcOXPOWaZu3br67LPPXNktAAAAKjCeZQ8AAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjXJqHFAAAlC+xCcluq2vBoDZuqws4EyOkAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwyst0AwAA7hebkOy2uhYMauO2ugCgOIyQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKJ5lDwCAm8QmJLutrgWD2ritLqCsY4QUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYFSpAumzzz4rm82mkSNHOpbl5OQoLi5O1apVU0BAgHr16qX09HSn7VJTU9WtWzdVrlxZoaGhGjNmjE6fPl2apgAAAKCcuugnNSUnJ+vVV19VixYtnJaPGjVKn376qZYuXaqgoCANHz5cPXv21Lp16yRJ+fn56tatm+x2u9avX6/Dhw9r4MCB8vb21tSpU0t3NACAK4I7n4gEwLyLGiE9fvy4+vfvr9dff11Vq1Z1LM/KytKCBQv00ksv6fbbb1dERIQWLlyo9evXa8OGDZKkL774Qjt37tQ777yjVq1aqWvXrpoyZYrmzJmjvLw89xwVAAAAyo2LCqRxcXHq1q2boqKinJZv2rRJp06dclreqFEj1alTR0lJSZKkpKQkNW/eXGFhYY4y0dHRys7O1o4dO4rdX25urrKzs51eAAAAqBhcPmW/ZMkS/fDDD0pOLnq6JC0tTT4+PgoODnZaHhYWprS0NEeZM8No4frCdcWZNm2aJk2a5GpTAQAAUA64NEJ64MABPfzww1q0aJH8/PwuVZuKiI+PV1ZWluN14MCBy7ZvAAAAXFouBdJNmzYpIyNDN9xwg7y8vOTl5aW1a9dq1qxZ8vLyUlhYmPLy8pSZmem0XXp6uux2uyTJbrcXueu+8H1hmbP5+voqMDDQ6QUAAICKwaVA2qlTJ23btk1btmxxvFq3bq3+/fs7/uzt7a3Vq1c7ttmzZ49SU1MVGRkpSYqMjNS2bduUkZHhKJOYmKjAwEA1adLETYcFAACA8sKla0irVKmiZs2aOS3z9/dXtWrVHMtjY2M1evRohYSEKDAwUCNGjFBkZKRuuukmSVLnzp3VpEkTDRgwQNOnT1daWprGjRunuLg4+fr6uumwAAAAUF5c9Dyk5/Lyyy/Lw8NDvXr1Um5urqKjozV37lzHek9PTy1fvlzDhg1TZGSk/P39FRMTo8mTJ7u7KQAAACgHSh1Iv/rqK6f3fn5+mjNnjubMmXPOberWravPPvustLsGAABABcCz7AEAAGAUgRQAAABGuf0aUgAAUHqxCUUfQANUVIyQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCjmIQUAnBfzYQK41BghBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAY5VIgnTdvnlq0aKHAwEAFBgYqMjJSK1ascKzPyclRXFycqlWrpoCAAPXq1Uvp6elOdaSmpqpbt26qXLmyQkNDNWbMGJ0+fdo9RwMAAIByx6VAevXVV+vZZ5/Vpk2b9P333+v222/XPffcox07dkiSRo0apU8++URLly7V2rVrdejQIfXs2dOxfX5+vrp166a8vDytX79eb775phISEvTUU0+596gAAABQbtgsy7JKU0FISIief/559e7dWzVq1NDixYvVu3dvSdLu3bvVuHFjJSUl6aabbtKKFSvUvXt3HTp0SGFhYZKk+fPna+zYsfrtt9/k4+NTon1mZ2crKChIWVlZCgwMLE3zAaBCik1INt0EVEALBrUx3QSUI67ktYu+hjQ/P19LlizRiRMnFBkZqU2bNunUqVOKiopylGnUqJHq1KmjpKQkSVJSUpKaN2/uCKOSFB0drezsbMcoKwAAAK4sXq5usG3bNkVGRionJ0cBAQFatmyZmjRpoi1btsjHx0fBwcFO5cPCwpSWliZJSktLcwqjhesL151Lbm6ucnNzHe+zs7NdbTYAAADKKJdHSK+77jpt2bJFGzdu1LBhwxQTE6OdO3deirY5TJs2TUFBQY5X7dq1L+n+AAAAcPm4HEh9fHx07bXXKiIiQtOmTVPLli01c+ZM2e125eXlKTMz06l8enq67Ha7JMlutxe5677wfWGZ4sTHxysrK8vxOnDggKvNBgAAQBlV6nlICwoKlJubq4iICHl7e2v16tWOdXv27FFqaqoiIyMlSZGRkdq2bZsyMjIcZRITExUYGKgmTZqccx++vr6OqaYKXwAAAKgYXLqGND4+Xl27dlWdOnV07NgxLV68WF999ZU+//xzBQUFKTY2VqNHj1ZISIgCAwM1YsQIRUZG6qabbpIkde7cWU2aNNGAAQM0ffp0paWlady4cYqLi5Ovr+8lOUAAAACUbS4F0oyMDA0cOFCHDx9WUFCQWrRooc8//1x33HGHJOnll1+Wh4eHevXqpdzcXEVHR2vu3LmO7T09PbV8+XINGzZMkZGR8vf3V0xMjCZPnuzeowIAAEC5Uep5SE1gHlIAOD/mIcWlwDykcMVlmYcUAAAAcAcCKQAAAIwikAIAAMAoAikAAACMIpACAADAKJefZQ8AAFBa7pwJgrv/yz9GSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBR32QNAGcHz5wFcqRghBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUV6mGwAAAMqH2IRk001ABcUIKQAAAIwikAIAAMAoAikAAACMIpACAADAKG5qAnBFctfNGQsGtXFLPQBwJWOEFAAAAEYRSAEAAGAUgRQAAABGcQ0pAJQCE4UDQOkxQgoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjXAqk06ZNU5s2bVSlShWFhoaqR48e2rNnj1OZnJwcxcXFqVq1agoICFCvXr2Unp7uVCY1NVXdunVT5cqVFRoaqjFjxuj06dOlPxoAAACUO16uFF67dq3i4uLUpk0bnT59Wk888YQ6d+6snTt3yt/fX5I0atQoffrpp1q6dKmCgoI0fPhw9ezZU+vWrZMk5efnq1u3brLb7Vq/fr0OHz6sgQMHytvbW1OnTnX/EQIwKjYh2W11LRjUxm11AQDKDpcC6cqVK53eJyQkKDQ0VJs2bdKtt96qrKwsLViwQIsXL9btt98uSVq4cKEaN26sDRs26KabbtIXX3yhnTt3atWqVQoLC1OrVq00ZcoUjR07VhMnTpSPj4/7jg4AAABlXqmuIc3KypIkhYSESJI2bdqkU6dOKSoqylGmUaNGqlOnjpKSkiRJSUlJat68ucLCwhxloqOjlZ2drR07dhS7n9zcXGVnZzu9AAAAUDFcdCAtKCjQyJEj1a5dOzVr1kySlJaWJh8fHwUHBzuVDQsLU1pamqPMmWG0cH3huuJMmzZNQUFBjlft2rUvttkAAAAoYy46kMbFxWn79u1asmSJO9tTrPj4eGVlZTleBw4cuOT7BAAAwOXh0jWkhYYPH67ly5fr66+/1tVXX+1YbrfblZeXp8zMTKdR0vT0dNntdkeZ7777zqm+wrvwC8uczdfXV76+vhfTVAAAAJRxLo2QWpal4cOHa9myZfryyy8VHh7utD4iIkLe3t5avXq1Y9mePXuUmpqqyMhISVJkZKS2bdumjIwMR5nExEQFBgaqSZMmpTkWAAAAlEMujZDGxcVp8eLF+uijj1SlShXHNZ9BQUGqVKmSgoKCFBsbq9GjRyskJESBgYEaMWKEIiMjddNNN0mSOnfurCZNmmjAgAGaPn260tLSNG7cOMXFxTEKCgAAcAVyKZDOmzdPktSxY0en5QsXLtSgQYMkSS+//LI8PDzUq1cv5ebmKjo6WnPnznWU9fT01PLlyzVs2DBFRkbK399fMTExmjx5cumOBAAAAOWSS4HUsqwLlvHz89OcOXM0Z86cc5apW7euPvvsM1d2DQAAgAqKZ9kDAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgv0w0AgJKKTUg23QQAwCXACCkAAACMIpACAADAKAIpAAAAjOIaUgDAFeMnzSzV9g31sJtaAuBMjJACAADAKEZIAQAVzoj0ccUufzH49xJtv9+3kTubA+ACGCEFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBR3GUPAMBlVNq5UCXmQ0XFwwgpAAAAjCKQAgAAwCgCKQAAAIziGlIAANzoXE+JKnShp0XxlChciRghBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYxU1NAACUUEkmtb/QTUsAimKEFAAAAEYRSAEAAGAUp+wBADhLvdzdppsAXFEIpACAS+JCE8RfyCthT7upJRVPSa5lPZ+GethNLQHcg1P2AAAAMIpACgAAAKM4ZQ9UELEJyW6ra8GgNm6rCwCAC2GEFAAAAEYxQgoAKBdcuZGHyemB8oVACgC4LFwNiftLeSc5gPKDU/YAAAAwihFSAAAqiJJO6D8is/g5Ypn7FaYwQgoAAACjCKQAAAAwikAKAAAAowikAAAAMIpACgAAAKO4yx4AcEGuTEpfiMnpAZQUI6QAAAAwikAKAAAAowikAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwinlIARQRm5BsugmA6uXuNt2EK86I9HEXve0rYU+7sSW40rgcSL/++ms9//zz2rRpkw4fPqxly5apR48ejvWWZWnChAl6/fXXlZmZqXbt2mnevHlq0KCBo8zRo0c1YsQIffLJJ/Lw8FCvXr00c+ZMBQQEuOWgAADAubnjoQWPZFZzQ0uAv7h8yv7EiRNq2bKl5syZU+z66dOna9asWZo/f742btwof39/RUdHKycnx1Gmf//+2rFjhxITE7V8+XJ9/fXXGjp06MUfBQAAAMotl0dIu3btqq5duxa7zrIszZgxQ+PGjdM999wjSXrrrbcUFhamDz/8UH379tWuXbu0cuVKJScnq3Xr1pKkV155RXfeeadeeOEF1apVqxSHAwAAgPLGrdeQpqSkKC0tTVFRUY5lQUFBatu2rZKSktS3b18lJSUpODjYEUYlKSoqSh4eHtq4caPuvfdedzYJAMq90lzXV1pcFwjgcnBrIE1LS5MkhYWFOS0PCwtzrEtLS1NoaKhzI7y8FBIS4ihzttzcXOXm5jreZ2dnu7PZAAAAMKhcTPs0bdo0BQUFOV61a9c23SQAAAC4iVsDqd1ulySlp6c7LU9PT3ess9vtysjIcFp/+vRpHT161FHmbPHx8crKynK8Dhw44M5mAwAAwCC3BtLw8HDZ7XatXr3asSw7O1sbN25UZGSkJCkyMlKZmZnatGmTo8yXX36pgoICtW3btth6fX19FRgY6PQCAABAxeDyNaTHjx/Xzz//7HifkpKiLVu2KCQkRHXq1NHIkSP19NNPq0GDBgoPD9f48eNVq1Ytx1yljRs3VpcuXTRkyBDNnz9fp06d0vDhw9W3b1/usAdQ4fykmaabUKo5J/eXgfYDqPhcDqTff/+9brvtNsf70aNHS5JiYmKUkJCgxx57TCdOnNDQoUOVmZmp9u3ba+XKlfLz83Nss2jRIg0fPlydOnVyTIw/a9YsNxwOAAAAyhuXA2nHjh1lWdY519tsNk2ePFmTJ08+Z5mQkBAtXrzY1V0DAACgAioXd9kDAACg4iKQAgAAwCgCKQAAAIxy65OaAADAleHs2RtcnZGhoR52Z3NQzhFIAQBAuRabkOy2uhYMauO2ulBynLIHAACAUYyQAoa583/2gLvVy91tugkArgAEUgAo40rzpCWUP/wnAFciAikAACg1V4P0iMxxTu9fCXvanc1BOUMgBYASGJE+7sKFivFi8O/a79vIza0BgIqFm5oAAABgFCOkuGIwLQgAAGUTI6QAAAAwihFSALjEuGsaAM6PEVIAAAAYRSAFAACAUZyyBy4CT1cCAMB9GCEFAACAUYyQAgAA4y724RMST3mqCAikAK4IpfnHDgBwaXHKHgAAAEYxQgqgwvpJMx1/fjH494uq45HMau5qDoAzXOxv8kz8PisORkgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUd9kDKJPOvEPeJHfcCQyg/HDXo6EXDGrjlnquFIyQAgAAwChGSFGmuet/qgAAoOxihBQAAABGEUgBAABgFIEUAAAARhFIAQAAYBQ3NQFwyYj0cZdlP8VNt7Tft9Fl2TcA4PJihBQAAABGEUgBAABgFKfsAQBAuVR4ac/+UjzZraEedldzUAqMkAIAAMAoRkiBcqg0Nxa9Eva0G1tybjwDHgBQUoyQAgAAwChGSIEz/FSK65CkinMt0vk+B0Y+AQDuRiCF28UmJJtuAgAAKEc4ZQ8AAACjGCEF3Ki0p/xL6kKnzR/JrHbOdSW5IYrT8gCAy4lAClRAFTVQ1svdbboJAIBLgEAKAADKtdL8Z3VE5rjLNh0ezo1rSAEAAGAUI6QAAOCK9WLw7zx6tAxghBQAAABGMUIKGMDNOQAA/B9GSAEAAGAUgRQAAABGccoe5VpJJnk/F6b5AACgbCCQ4opVXJh1ZUL5/b6N3NkcAACuWARSAABwRSvVjaa+7mvHlYxrSAEAAGAUI6QoM366iImJK+oz2wEAuJIwQgoAAACjGCGFQ2xCsukmlCtMbg8AgHsQSGHUmXe6c/odAIArE6fsAQAAYBSBFAAAAEYRSAEAAGCUsUA6Z84c1atXT35+fmrbtq2+++47U00BAACAQUZuanr33Xc1evRozZ8/X23bttWMGTMUHR2tPXv2KDQ01ESTyi133BlfeGPRiFLUcbE3JL0YXIqdAgCACsFIIH3ppZc0ZMgQPfDAA5Kk+fPn69NPP9Ubb7yhxx9/3ESTrmjc3Q4AAEy67IE0Ly9PmzZtUnx8vGOZh4eHoqKilJSUVOw2ubm5ys3NdbzPysqSJGVnZ1/axpYDeX8eL30dufluaAkAAFegguL/HSaj/N9nYFnWBcte9kB65MgR5efnKywszGl5WFiYdu8ufqLxadOmadKkSUWW165d+5K0EQAAoGRuL3bpO/+6zM0ow44dO6agoKDzlikXE+PHx8dr9OjRjvcFBQU6evSoqlWrJpvN5lienZ2t2rVr68CBAwoMDDTRVFwE+q38oc/KJ/qt/KHPyif67S+WZenYsWOqVavWBcte9kBavXp1eXp6Kj093Wl5enq67HZ7sdv4+vrK19fXaVlwcPA59xEYGHhFfwHKK/qt/KHPyif6rfyhz8on+k0XHBktdNmnffLx8VFERIRWr17tWFZQUKDVq1crMjLycjcHAAAAhhk5ZT969GjFxMSodevWuvHGGzVjxgydOHHCcdc9AAAArhxGAul9992n3377TU899ZTS0tLUqlUrrVy5ssiNTq7y9fXVhAkTipzeR9lGv5U/9Fn5RL+VP/RZ+US/uc5mleRefAAAAOAS4Vn2AAAAMIpACgAAAKMIpAAAADCKQAoAAACjKlQgnTNnjurVqyc/Pz+1bdtW3333nekm4f+bNm2a2rRpoypVqig0NFQ9evTQnj17nMrk5OQoLi5O1apVU0BAgHr16lXkAQow59lnn5XNZtPIkSMdy+izsungwYO6//77Va1aNVWqVEnNmzfX999/71hvWZaeeuop1axZU5UqVVJUVJT27t1rsMVXtvz8fI0fP17h4eGqVKmS6tevrylTpjg9/5s+M+/rr7/WXXfdpVq1aslms+nDDz90Wl+SPjp69Kj69++vwMBABQcHKzY2VsePH7+MR1F2VZhA+u6772r06NGaMGGCfvjhB7Vs2VLR0dHKyMgw3TRIWrt2reLi4rRhwwYlJibq1KlT6ty5s06cOOEoM2rUKH3yySdaunSp1q5dq0OHDqlnz54GW41CycnJevXVV9WiRQun5fRZ2fPHH3+oXbt28vb21ooVK7Rz5069+OKLqlq1qqPM9OnTNWvWLM2fP18bN26Uv7+/oqOjlZOTY7DlV67nnntO8+bN0+zZs7Vr1y4999xzmj59ul555RVHGfrMvBMnTqhly5aaM2dOsetL0kf9+/fXjh07lJiYqOXLl+vrr7/W0KFDL9chlG1WBXHjjTdacXFxjvf5+flWrVq1rGnTphlsFc4lIyPDkmStXbvWsizLyszMtLy9va2lS5c6yuzatcuSZCUlJZlqJizLOnbsmNWgQQMrMTHR6tChg/Xwww9blkWflVVjx4612rdvf871BQUFlt1ut55//nnHsszMTMvX19f6z3/+czmaiLN069bNGjx4sNOynj17Wv3797csiz4riyRZy5Ytc7wvSR/t3LnTkmQlJyc7yqxYscKy2WzWwYMHL1vby6oKMUKal5enTZs2KSoqyrHMw8NDUVFRSkpKMtgynEtWVpYkKSQkRJK0adMmnTp1yqkPGzVqpDp16tCHhsXFxalbt25OfSPRZ2XVxx9/rNatW+tvf/ubQkNDdf311+v11193rE9JSVFaWppTvwUFBalt27b0myE333yzVq9erZ9++kmStHXrVn377bfq2rWrJPqsPChJHyUlJSk4OFitW7d2lImKipKHh4c2btx42dtc1hh5UpO7HTlyRPn5+UWe9BQWFqbdu3cbahXOpaCgQCNHjlS7du3UrFkzSVJaWpp8fHwUHBzsVDYsLExpaWkGWglJWrJkiX744QclJycXWUeflU3/+9//NG/ePI0ePVpPPPGEkpOT9dBDD8nHx0cxMTGOvinu70v6zYzHH39c2dnZatSokTw9PZWfn69nnnlG/fv3lyT6rBwoSR+lpaUpNDTUab2Xl5dCQkLoR1WQQIryJS4uTtu3b9e3335ruik4jwMHDujhhx9WYmKi/Pz8TDcHJVRQUKDWrVtr6tSpkqTrr79e27dv1/z58xUTE2O4dSjOe++9p0WLFmnx4sVq2rSptmzZopEjR6pWrVr0Ga4YFeKUffXq1eXp6Vnk7t709HTZ7XZDrUJxhg8fruXLl2vNmjW6+uqrHcvtdrvy8vKUmZnpVJ4+NGfTpk3KyMjQDTfcIC8vL3l5eWnt2rWaNWuWvLy8FBYWRp+VQTVr1lSTJk2cljVu3FipqamS5Ogb/r4sO8aMGaPHH39cffv2VfPmzTVgwACNGjVK06ZNk0SflQcl6SO73V7kRuvTp0/r6NGj9KMqSCD18fFRRESEVq9e7VhWUFCg1atXKzIy0mDLUMiyLA0fPlzLli3Tl19+qfDwcKf1ERER8vb2durDPXv2KDU1lT40pFOnTtq2bZu2bNnieLVu3Vr9+/d3/Jk+K3vatWtXZEq1n376SXXr1pUkhYeHy263O/Vbdna2Nm7cSL8ZcvLkSXl4OP9z7OnpqYKCAkn0WXlQkj6KjIxUZmamNm3a5Cjz5ZdfqqCgQG3btr3sbS5zTN9V5S5LliyxfH19rYSEBGvnzp3W0KFDreDgYCstLc1002BZ1rBhw6ygoCDrq6++sg4fPux4nTx50lHmn//8p1WnTh3ryy+/tL7//nsrMjLSioyMNNhqnO3Mu+wtiz4ri7777jvLy8vLeuaZZ6y9e/daixYtsipXrmy98847jjLPPvusFRwcbH300UfWjz/+aN1zzz1WeHi49eeffxps+ZUrJibGuuqqq6zly5dbKSkp1n//+1+revXq1mOPPeYoQ5+Zd+zYMWvz5s3W5s2bLUnWSy+9ZG3evNn65ZdfLMsqWR916dLFuv76662NGzda3377rdWgQQOrX79+pg6pTKkwgdSyLOuVV16x6tSpY/n4+Fg33nijtWHDBtNNwv8nqdjXwoULHWX+/PNP61//+pdVtWpVq3Llyta9995rHT582FyjUcTZgZQ+K5s++eQTq1mzZpavr6/VqFEj67XXXnNaX1BQYI0fP94KCwuzfH19rU6dOll79uwx1FpkZ2dbDz/8sFWnTh3Lz8/Puuaaa6wnn3zSys3NdZShz8xbs2ZNsf+OxcTEWJZVsj76/fffrX79+lkBAQFWYGCg9cADD1jHjh0zcDRlj82yzngUBAAAAHCZVYhrSAEAAFB+EUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGDU/wMod4MlEPyCtQAAAABJRU5ErkJggg==", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -216,13 +717,14 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", - "# Проверка распределения целевой переменной\n", - "plt.figure(figsize=(8, 6))\n", - "plt.hist(y_train, bins=30, alpha=0.7, label='Train')\n", - "plt.hist(y_val, bins=30, alpha=0.7, label='Validation')\n", - "plt.hist(y_test, bins=30, alpha=0.7, label='Test')\n", + "# Визуализируем фактические и предсказанные значения\n", + "plt.figure(figsize=(10,6))\n", + "plt.plot(y_test.index, y_test, label='Actual Price Change', color='blue')\n", + "plt.plot(y_test.index, y_pred, label='Predicted Price Change', color='red')\n", "plt.legend()\n", - "plt.title('Распределение целевой переменной')\n", + "plt.title(\"Actual vs Predicted Price Change\")\n", + "plt.xlabel(\"Date\")\n", + "plt.ylabel(\"Price Change\")\n", "plt.show()\n" ] } -- 2.25.1 From 00742a3a0ed6c8fdc9dc0ca8e7b315725eb6b9cc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=90=D0=BB=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9A=D1=80?= =?UTF-8?q?=D1=8E=D0=BA=D0=BE=D0=B2?= Date: Fri, 15 Nov 2024 23:59:43 +0400 Subject: [PATCH 3/5] =?UTF-8?q?=D0=93=D0=BE=D1=82=D0=BE=D0=B2=D0=B0=D1=8F?= =?UTF-8?q?=20=D0=BB=D0=B0=D0=B1=D0=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Lab_3/lab3.ipynb | 91 ++++++++++++++++++------------------------------ 1 file changed, 34 insertions(+), 57 deletions(-) diff --git a/Lab_3/lab3.ipynb b/Lab_3/lab3.ipynb index 96244f3..4824c01 100644 --- a/Lab_3/lab3.ipynb +++ b/Lab_3/lab3.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 105, "metadata": {}, "outputs": [ { @@ -21,7 +21,7 @@ " dtype='object')" ] }, - "execution_count": 89, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -29,7 +29,7 @@ "source": [ "import pandas as pd\n", "\n", - "data = pd.read_csv(\"..//static//csv//Yamana_Gold_Inc._AUY.csv\", sep=\",\", nrows=10000)\n", + "data = pd.read_csv(\"..//static//csv//Yamana_Gold_Inc._AUY.csv\", sep=\",\")\n", "\n", "# Преобразование даты\n", "data['Date'] = pd.to_datetime(data['Date'])\n", @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 106, "metadata": {}, "outputs": [ { @@ -86,7 +86,7 @@ "dtype: int64" ] }, - "execution_count": 79, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" } @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -265,7 +265,7 @@ "4 3 6 2001 0.000000 3.6 NaN 0.156493 NaN " ] }, - "execution_count": 80, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -294,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -303,7 +303,7 @@ "((3150, 10), (1050, 10), (1051, 10))" ] }, - "execution_count": 81, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -338,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 109, "metadata": {}, "outputs": [], "source": [ @@ -367,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -568,7 +568,7 @@ "4 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 " ] }, - "execution_count": 83, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } @@ -612,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -645,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -657,27 +657,31 @@ ] }, { - "ename": "AttributeError", - "evalue": "'numpy.ndarray' object has no attribute 'reset_index'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[88], line 7\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m# Приводим индексы к одному виду\u001b[39;00m\n\u001b[0;32m 6\u001b[0m y_train \u001b[38;5;241m=\u001b[39m y_train\u001b[38;5;241m.\u001b[39mreset_index(drop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m----> 7\u001b[0m X_scaled \u001b[38;5;241m=\u001b[39m \u001b[43mX_scaled\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreset_index\u001b[49m(drop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 9\u001b[0m \u001b[38;5;66;03m# После этого продолжим обучение модели\u001b[39;00m\n\u001b[0;32m 10\u001b[0m model \u001b[38;5;241m=\u001b[39m LinearRegression()\n", - "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'reset_index'" - ] + "data": { + "text/plain": [ + "(np.float64(0.05230198011754029), 0.5415652186272203)" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# Проверим размеры данных\n", + "# Проверим размерности данных после удаления NaN\n", "print(X_scaled_df.shape[0]) # Количество строк в X_scaled_df\n", "print(y_train.shape[0]) # Количество строк в y_train\n", "\n", + "# Если данные имеют разные размеры, синхронизируем их\n", + "df = pd.concat([X_scaled_df, y_train], axis=1).dropna()\n", + "X_scaled_df = df.drop(columns=y_train.name)\n", + "y_train = df[y_train.name]\n", + "\n", "# Приводим индексы к одному виду\n", "y_train = y_train.reset_index(drop=True)\n", "X_scaled_df = X_scaled_df.reset_index(drop=True)\n", "\n", - "# После этого продолжим обучение модели\n", + "# После этого продолжаем обучение модели\n", "model = LinearRegression()\n", "model.fit(X_scaled_df, y_train)\n", "\n", @@ -685,8 +689,8 @@ "y_pred = model.predict(X_scaled_df)\n", "\n", "# Оценка качества модели\n", - "mse = mean_squared_error(y_test, y_pred)\n", - "r2 = r2_score(y_test, y_pred)\n", + "mse = mean_squared_error(y_train, y_pred) # Используем y_train, потому что данные для теста не созданы\n", + "r2 = r2_score(y_train, y_pred)\n", "\n", "mse, r2\n" ] @@ -695,37 +699,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Визуализируем" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", + "MSE = 0.0523: Модель в среднем делает ошибку около 0.0523 при прогнозировании значений.\n", + "R² = 0.5416: Модель объясняет примерно 54.16% изменчивости целевой переменной\n", "\n", - "# Визуализируем фактические и предсказанные значения\n", - "plt.figure(figsize=(10,6))\n", - "plt.plot(y_test.index, y_test, label='Actual Price Change', color='blue')\n", - "plt.plot(y_test.index, y_pred, label='Predicted Price Change', color='red')\n", - "plt.legend()\n", - "plt.title(\"Actual vs Predicted Price Change\")\n", - "plt.xlabel(\"Date\")\n", - "plt.ylabel(\"Price Change\")\n", - "plt.show()\n" + "Визуализируем" ] } ], -- 2.25.1 From 39f65612d8dff2a28aacfb809bc6cacbfec84210 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=90=D0=BB=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9A=D1=80?= =?UTF-8?q?=D1=8E=D0=BA=D0=BE=D0=B2?= Date: Sat, 30 Nov 2024 13:16:02 +0400 Subject: [PATCH 4/5] =?UTF-8?q?=D0=BD=D0=B0=D0=BA=D0=BE=D0=BD=D0=B5=D1=86-?= =?UTF-8?q?=D1=82=D0=BE=20=D1=8F=20=D1=81=D0=BC=D0=BE=D0=B3,=20=D0=BC?= =?UTF-8?q?=D0=BD=D0=B5=20=D0=BD=D0=B5=20=D0=BF=D0=B5=D1=80=D0=B5=D0=B4?= =?UTF-8?q?=D0=B0=D1=82=D1=8C=20=D1=81=D0=BB=D0=BE=D0=B2=D0=B0=D0=BC=20?= =?UTF-8?q?=D0=BD=D0=B0=D1=81=D0=BA=D0=BE=D0=BB=D1=8C=D0=BA=D0=BE=20=D0=B3?= =?UTF-8?q?=D0=B5=D0=BD=D0=B8=D0=B8=20=D1=81=D0=B8=D0=B4=D1=8F=D1=82=20?= =?UTF-8?q?=D0=BD=D0=B0=20=D1=81=D1=82=D0=B5=D0=BA=D0=B5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Lab_3/lab3.ipynb | 1048 +++++++++++++++++++++------------------------- 1 file changed, 481 insertions(+), 567 deletions(-) diff --git a/Lab_3/lab3.ipynb b/Lab_3/lab3.ipynb index 4824c01..cb9478f 100644 --- a/Lab_3/lab3.ipynb +++ b/Lab_3/lab3.ipynb @@ -10,35 +10,46 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 46, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume',\n", - " 'Day_of_week', 'Month', 'Year'],\n", - " dtype='object')" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 5251 entries, 0 to 5250\n", + "Data columns (total 7 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Date 5251 non-null datetime64[ns]\n", + " 1 Open 5251 non-null float64 \n", + " 2 High 5251 non-null float64 \n", + " 3 Low 5251 non-null float64 \n", + " 4 Close 5251 non-null float64 \n", + " 5 Adj Close 5251 non-null float64 \n", + " 6 Volume 5251 non-null int64 \n", + "dtypes: datetime64[ns](1), float64(5), int64(1)\n", + "memory usage: 287.3 KB\n", + "None\n", + " Date Open High Low Close Adj Close Volume\n", + "0 2001-06-22 3.428571 3.428571 3.428571 3.428571 2.806002 0\n", + "1 2001-06-25 3.428571 3.428571 3.428571 3.428571 2.806002 0\n", + "2 2001-06-26 3.714286 3.714286 3.714286 3.714286 3.039837 0\n", + "3 2001-06-27 3.714286 3.714286 3.714286 3.714286 3.039837 0\n", + "4 2001-06-28 3.714286 3.714286 3.714286 3.714286 3.039837 0\n" + ] } ], "source": [ "import pandas as pd\n", "\n", - "data = pd.read_csv(\"..//static//csv//Yamana_Gold_Inc._AUY.csv\", sep=\",\")\n", - "\n", - "# Преобразование даты\n", + "ddata = pd.read_csv(\"../static/csv/Yamana_Gold_Inc._AUY.csv\")\n", "data['Date'] = pd.to_datetime(data['Date'])\n", "\n", - "# Преобразование данных: создание новых признаков\n", - "data['Day_of_week'] = data['Date'].dt.dayofweek\n", - "data['Month'] = data['Date'].dt.month\n", - "data['Year'] = data['Date'].dt.year\n", - "data .columns" + "# Первичный анализ данных\n", + "print(data.info())\n", + "print(data.head())" ] }, { @@ -65,42 +76,129 @@ "#3 Проверим датасет на пропуски и удалим при необходимости строки с недостающими данными" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Обработка пропусков \n", + "Пропуски в данных могут негативно влиять на обучение моделей. Сначала оцениваем количество пропусков по столбцам. Если пропуски присутствуют, удаляем строки с отсутствующими значениями.\n" + ] + }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 47, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "Date 0\n", - "Open 0\n", - "High 0\n", - "Low 0\n", - "Close 0\n", - "Adj Close 0\n", - "Volume 0\n", - "Day_of_week 0\n", - "Month 0\n", - "Year 0\n", - "dtype: int64" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Date 0\n", + "Open 0\n", + "High 0\n", + "Low 0\n", + "Close 0\n", + "Adj Close 0\n", + "Volume 0\n", + "dtype: int64\n", + "Данные после очистки: (5251, 7)\n" + ] + } + ], + "source": [ + "# 3. Проверим датасет на наличие пропусков и удалим строки с недостающими данными\n", + "print(data.isnull().sum()) # Суммируем пропуски по каждому столбцу\n", + "data.dropna(inplace=True) # Удаляем строки с пропусками\n", + "print(\"Данные после очистки:\", data.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Добавление признаков \n", + "Для выполнения задач добавляем два новых признака: \n", + "Daily_Change: разница между ценой закрытия и открытия торгов. \n", + "Volatility: относительная волатильность, рассчитываемая как отношение разницы между максимальной и минимальной ценой к цене открытия." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Date 0\n", + "Open 0\n", + "High 0\n", + "Low 0\n", + "Close 0\n", + "Adj Close 0\n", + "Volume 0\n", + "dtype: int64\n", + "Количество строк после удаления пропусков: 5251\n" + ] + } + ], + "source": [ + "print(data.isnull().sum()) # Вывод количества пропусков\n", + "data.dropna(inplace=True) # Удаление строк с пропущенными значениями\n", + "print(f\"Количество строк после удаления пропусков: {data.shape[0]}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Создание новых признаков " + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Daily_Change Volatility\n", + "0 0.0 0.0\n", + "1 0.0 0.0\n", + "2 0.0 0.0\n", + "3 0.0 0.0\n", + "4 0.0 0.0\n" + ] } ], "source": [ "\n", - "# Проверим на пропущенные значения\n", - "data.isnull().sum()\n", + "data['Daily_Change'] = data['Close'] - data['Open']\n", + "data['Volatility'] = (data['High'] - data['Low']) / data['Open']\n", + "print(data[['Daily_Change', 'Volatility']].head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проведем масштабированние данных" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", "\n", - "# Заполним пропуски или удалим строки с пропусками\n", - "data = data.dropna()\n", - "\n", - "# Проверим, что данные очищены\n", - "data.isnull().sum()\n", + "scaler = StandardScaler()\n", + "scaled_columns = ['Open', 'High', 'Low', 'Close', 'Volume']\n", + "data[scaled_columns] = scaler.fit_transform(data[scaled_columns])\n", "\n" ] }, @@ -108,251 +206,39 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Конструирование признаков" + "## Разбиение данных на выборки \n", + "Для предотвращения просачивания данных используем разбиение на три части: \n", + "Обучающая выборка (60%): для тренировки модели. \n", + "Валидационная выборка (20%): для подбора гиперпараметров модели и проверки ее производительности на новых данных. \n", + "Тестовая выборка (20%): для оценки финальной производительности модели. " ] }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 51, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Date Open High Low Close Adj Close Volume \\\n", - "0 2001-06-22 3.428571 3.428571 3.428571 3.428571 2.806002 0 \n", - "1 2001-06-25 3.428571 3.428571 3.428571 3.428571 2.806002 0 \n", - "2 2001-06-26 3.714286 3.714286 3.714286 3.714286 3.039837 0 \n", - "3 2001-06-27 3.714286 3.714286 3.714286 3.714286 3.039837 0 \n", - "4 2001-06-28 3.714286 3.714286 3.714286 3.714286 3.039837 0 \n", - "\n", - " Day_of_week Month Year Price_Change SMA_5 SMA_20 STD_5 STD_20 \n", - "0 4 6 2001 NaN NaN NaN NaN NaN \n", - "1 0 6 2001 0.000000 NaN NaN NaN NaN \n", - "2 1 6 2001 0.285715 NaN NaN NaN NaN \n", - "3 2 6 2001 0.000000 NaN NaN NaN NaN \n", - "4 3 6 2001 0.000000 3.6 NaN 0.156493 NaN " - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Создаем новый признак - разницу между текущей и предыдущей ценой (Price_Change)\n", - "data['Price_Change'] = data['Close'].diff()\n", - "\n", - "# Создадим скользящие средние для 5 и 20 дней\n", - "data['SMA_5'] = data['Close'].rolling(window=5).mean()\n", - "data['SMA_20'] = data['Close'].rolling(window=20).mean()\n", - "\n", - "# Стандартное отклонение для 5 и 20 дней\n", - "data['STD_5'] = data['Close'].rolling(window=5).std()\n", - "data['STD_20'] = data['Close'].rolling(window=20).std()\n", - "\n", - "data.head()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " Разделение данных на обучающую, контрольную и тестовую выборки" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((3150, 10), (1050, 10), (1051, 10))" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: (3150, 5)\n", + "Размер валидационной выборки: (1050, 5)\n", + "Размер тестовой выборки: (1051, 5)\n" + ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", - "# Преобразуем колонку 'Date' в тип datetime для правильного сортирования\n", - "data['Date'] = pd.to_datetime(data['Date'])\n", + "X = data[scaled_columns]\n", + "y = data['Close'] # Заменить на целевую переменную, если другая\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", "\n", - "# Сортируем данные по дате, чтобы не нарушить временную зависимость\n", - "data = data.sort_values(by='Date')\n", - "\n", - "# Отделяем целевую переменную (например, Price_Change) и признаки\n", - "X = data[['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume', 'SMA_5', 'SMA_20', 'STD_5', 'STD_20']]\n", - "y = data['Price_Change']\n", - "\n", - "# Разделение на обучающую, контрольную и тестовую выборки (60%, 20%, 20%)\n", - "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, shuffle=False)\n", - "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, shuffle=False)\n", - "\n", - "# Проверка размеров выборок\n", - "(X_train.shape, X_val.shape, X_test.shape)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Конструирование признаков для решения задач" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [], - "source": [ - "# Признаки для задачи прогнозирования изменений цен\n", - "data['Price_Change'] = data['Close'].diff()\n", - "\n", - "# Скользящие средние и стандартное отклонение\n", - "data['SMA_5'] = data['Close'].rolling(window=5).mean()\n", - "data['SMA_20'] = data['Close'].rolling(window=20).mean()\n", - "data['STD_5'] = data['Close'].rolling(window=5).std()\n", - "data['STD_20'] = data['Close'].rolling(window=20).std()\n", - "\n", - "# Признаки для оценки волатильности\n", - "data['Volatility'] = data['Close'].rolling(window=5).std()\n", + "print(f\"Размер обучающей выборки: {X_train.shape}\")\n", + "print(f\"Размер валидационной выборки: {X_val.shape}\")\n", + "print(f\"Размер тестовой выборки: {X_test.shape}\")\n", "\n" ] }, @@ -360,349 +246,377 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Подготовка признаков: one-hot encoding, дискретизация, синтез признаков, масштабирование\n", - "One-hot encoding: Применим для категориальных признаков (например, день недели).\n", - "Масштабирование: Стандартизируем числовые признаки." + "## Оценка и балансировка классов \n", + "Проверяем распределение классов в целевой переменной для каждого набора данных. Если данные несбалансированы (например, если цена часто растет, а иногда падает), применяем SMOTE (Synthetic Minority Over-sampling Technique) для генерации синтетических примеров из меньшинства.\n" ] }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " Day_of_week Month\n", - "0 4 6\n", - "1 0 6\n", - "2 1 6\n", - "3 2 6\n", - "4 3 6\n" + "Проверка на пропуски после масштабирования:\n", + " Date 0\n", + "Open 0\n", + "High 0\n", + "Low 0\n", + "Close 0\n", + "Adj Close 0\n", + "Volume 0\n", + "Daily_Change 0\n", + "Volatility 0\n", + "dtype: int64\n" ] }, { "data": { - "text/html": [ - "
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j165d9fjjj3vMe+655/Txxx9Xuf7OnTu1YMECvfXWW1U+I1FeXq6OHTtq1qxZVa7fpEmTE/a9usrLy9W7d29NmDChyuXHX+XIyso6Ze4k6bbbbjthYXDRRRd5TM+cOVMXX3yxnE6nNm7cqMcff1x16tQ5rStK06dPV0BAgMaPH69Dhw6ZjlMVM/vobUxvPx8V7rzzTj3//PPq0qWLsrKydMstt+hf//pXlbHffPPNKn93x3+xEhISok8++cRj3n//+19NmTKl0rrNmzfXyy+/7DFv0aJFJywyvVVaWqqJEydqxIgRlY7B6mjbtq0kaevWre5nJM041VV7m82mxYsXa/369frkk0+0fPly3XnnnfrXv/6l9evXKzw8vNrnFQDmUEwBMGXx4sXq2bOnXn31VY/5+fn5atSokXu6RYsW2rBhg5xOp08fdq749ruCYRj66aef3H9UVgxs4XA4lJiYeMp43333nZxO50kLyIq4hmEoPj7eqz+6duzYIZvNVuWVjj/GXLlypa688kr34AAn06hRo0r7dLJBIlJTU9WpUyfdeuutJ9z+d999p169ep3WrZfeaNGihY4cOeLV70T6PX+XXnrpCZdHRUWpXr16crlcXsfs3LmzewS3Pn366Ndff9X06dM1ceJERUVFKTQ0VLt27aq03g8//KCAgIBKxeX+/fv19NNPa9q0aapXr57Piykz++jrz0eFIUOGaPz48brvvvv0l7/8pcrb2SpiR0dHexU7MDCwUrvjR92rEBYWVqntli1bPKabNWsmSdq1a5fHrZqlpaXKyMiosk/PP/+8cnJyqhy5szpuuOEGTZs2TW+99ZapYqpZs2YqLy/X7t271a5dO/f87Oxs5efnu/etQrdu3dStWzc98cQTWrhwoYYMGaJ3331Xd911V7XPKwDM4ZkpAKYEBgZWGk560aJF+vXXXz3mDRgwQAcPHtRzzz1XKcbx61fHG2+84fFcxeLFi3XgwAH16dNH0u9/MLdo0UIzZ87UkSNHKq2fm5tbqe+BgYFVDjv+R/3791dgYKAmT55cqf+GYXj8IV1WVqb3339fXbp0OemtarfccotcLpemTp1aaVlZWdkJ/7D0Rnp6uj7++GP985//PGGhdMstt+jXX3+t9I2/JB07dkxHjx41vf2qtpWenq7ly5dXWpafn6+ysjL39DfffKOff/75hLeLSb8fhwMGDND777+vbdu2VVp+/O+5KseOHVNZWZnKysoUGBiopKQkffzxxx5DbWdnZ2vhwoW66qqrKt2mNnnyZMXExOivf/3rKbdlhpl99PXno0JkZKRuvPFGff/995WG4a6QnJwsh8OhJ598ssrnebz5nZyOxMREBQUF6ZlnnvH4jL766qsqKChQ3759PdofPnxYTzzxhO6///6TXgX1RkJCgq699lq98sorVX7BUVpaqr///e8nXL9iNNPjR7WsuGpc0ffffvut0vmn4gpjxRDqVp5XAPx/XJkCYMr111+vKVOmaPjw4briiiu0detWvf322x7fBEu/Pwj/xhtvaNy4cfr666/1pz/9SUePHtXKlSt1zz336MYbbzS1/cjISF111VUaPny4srOzNWfOHLVs2VIjR46U9PuzMK+88or69OmjDh06aPjw4TrvvPP066+/avXq1XI4HPrkk0909OhRzZ07V88884xat27t8V6bij8yv//+e6WnpyshIUEtWrTQ448/rtTUVO3Zs0f9+vVTvXr1lJGRoQ8//FCjRo3S3//+d61cuVITJ07U999/X+kWpuN1795dd999t6ZNm6YtW7YoKSlJdrtdu3fv1qJFi/T000/rL3/5i6k8rVixQr179z7pFYKhQ4fqvffe01//+letXr1aV155pVwul3744Qe99957Wr58+Smv2B05ckTLli3zmFdxdWfNmjWy2+0677zzNH78eP3nP//R9ddfrzvuuEOdO3fW0aNHtXXrVi1evFh79uxRo0aNNGXKFD399NO64IILdPvtt5902//85z+1evVqde3aVSNHjlT79u2Vl5enb7/9VitXrlReXp5H+7S0NP3yyy/u2/zefvtt/fnPf3a/qPXxxx9XWlqarrrqKt1zzz2qU6eOXnzxRZWUlGjGjBlV5vjtt98+rRe9nkp199FXn4+qLFiwQHPnzvW4Av1HDodDL7zwgoYOHapLL71UAwcOVFRUlPbu3atPP/1UV155ZZVfrvhKVFSUUlNTNXnyZF177bX685//rF27dun555/X5Zdfrttuu82j/bfffqtGjRqd8NbT6nrjjTeUlJSk/v3764YbblCvXr0UFham3bt3691339WBAwdO+K6piy++WMOGDdNLL72k/Px8de/eXV9//bVef/119evXTz179pT0++Afzz//vG666Sa1aNFChw8f1ssvvyyHw+EuyKw8rwD4Az+MIAigBjvRcM3HKy4uNh544AGjcePGRt26dY0rr7zSSE9PrzTUsGH8PtzyP/7xDyM+Pt6w2+1GbGys8Ze//MU99LSZodHfeecdIzU11YiOjjbq1q1r9O3b18jMzKy0/ubNm43+/fsbDRs2NIKDg41mzZoZt9xyi7Fq1SqPbZ/q549DcRuGYbz//vvGVVddZYSFhRlhYWFG27ZtjZSUFGPXrl2GYRjGvffea1x99dXGsmXLKvXp+OGQK7z00ktG586djbp16xr16tUzOnbsaEyYMMHYv3+/u011h0a32WzGpk2bPOZX9TsqLS01pk+fbnTo0MEIDg42GjRoYHTu3NmYPHmyUVBQUGl7x8c7Vf7+OEz44cOHjdTUVKNly5ZGUFCQ0ahRI+OKK64wZs6caZSWlhqGYRjnn3++ceedd3rs+x9zcPzvIzs720hJSTGaNGniPsZ69eplvPTSS+42FcdOxU+dOnWMZs2aGX/7298qDWX97bffGsnJyUZ4eLgRGhpq9OzZ01i3bp1Hm4rPSqdOnTyGF684pnw5NHp199FXnw/D+P/Ha8XQ58c70fLVq1cbycnJRkREhBESEmK0aNHCuOOOO4xvvvnG3caKodErPPfcc0bbtm0Nu91uxMTEGKNHj670e644dmfPnl3lPplVVFRkzJw507j88suN8PBwIygoyGjVqpVx7733Gj/99NNJt+N0Oo3Jkye7z5dNmjQxUlNTjeLiYnebb7/91hg0aJDRtGlTIzg42IiOjjauv/56j9xW8Oa8AsA8m2Gcxn02AHCGffHFF+rZs6cWLVrkk29V9+zZo/j4eGVkZJzwRaKTJk3Snj17tGDBgtPe3rmoefPmmjRpku644w5/d+Ws5+vPBwDg5HhmCgAAAABM4JkpAOe08PBwDRky5KQDRFx00UWKi4s7g706u3Tv3t39smcAAM4mFFMAzmmNGjXSW2+9ddI2/fv3P0O9OTu9/vrr/u4CAACW4JkpAAAAADCBZ6YAAAAAwASKKQAAAAAwgWemJJWXl2v//v2qV6+ebDabv7sDAAAAwE8Mw9Dhw4cVFxengICTX3uimJK0f/9+NWnSxN/dAAAAAFBD7Nu3T+eff/5J21BMSapXr56k3xPmcDj83Juaw+l0asWKFUpKSpLdbvd3d84q5NY65NY65NY65NYa5NU65NY65NY63ua2sLBQTZo0cdcIJ0MxJblv7XM4HBRTf+B0OhUaGiqHw8GH2cfIrXXIrXXIrXXIrTXIq3XIrXXIrXWqm1tvHv9hAAoAAAAAMIFiCgAAAABMoJgCAAAAABMopgAAAADABIopAAAAADCBYgoAAAAATKCYAgAAAAATKKYAAAAAwASKKQAAAAAwgWIKAAAAAEygmAIAAAAAEyimAAAAAMAEiikAAAAAMIFiCgAAAABMqOPvDuDskpubq8LCQktiOxwORUVFWRIbAAAAqC6KKfhMbm6ubht+l/IOF1kSP7JeqN6a/woFFQAAAGoEiin4TGFhofIOFykqYYDCImN8GvtoXrZy099XYWEhxRQAAABqBIop+FxYZIwc0ef7PG6uzyMCAAAA5jEABQAAAACYQDEFAAAAACZQTAEAAACACRRTAAAAAGACxRQAAAAAmEAxBQAAAAAm+LWYcrlcmjhxouLj41W3bl21aNFCU6dOlWEY7jaGYejRRx9V48aNVbduXSUmJmr37t0ecfLy8jRkyBA5HA7Vr19fI0aM0JEjR8707gAAAAA4h/i1mJo+fbpeeOEFPffcc9q5c6emT5+uGTNm6Nlnn3W3mTFjhp555hnNmzdPGzZsUFhYmJKTk1VcXOxuM2TIEG3fvl1paWlasmSJvvzyS40aNcofuwQAAADgHOHXl/auW7dON954o/r27StJat68ud555x19/fXXkn6/KjVnzhw98sgjuvHGGyVJb7zxhmJiYvTRRx9p4MCB2rlzp5YtW6aNGzfqsssukyQ9++yzuu666zRz5kzFxcX5Z+cAAAAAnNX8WkxdccUVeumll/Tjjz+qdevW+u677/TVV19p1qxZkqSMjAxlZWUpMTHRvU5ERIS6du2q9PR0DRw4UOnp6apfv767kJKkxMREBQQEaMOGDbrpppsqbbekpEQlJSXu6cLCQkmS0+mU0+m0andrnYpceJsTl8uloCC77AFSHZX7tC/2ACkoyC6Xy3VW/I6qm1t4j9xah9xah9xag7xah9xah9xax9vcVif3fi2mHnroIRUWFqpt27YKDAyUy+XSE088oSFDhkiSsrKyJEkxMTEe68XExLiXZWVlKTo62mN5nTp1FBkZ6W5zvGnTpmny5MmV5q9YsUKhoaGnvV9nm7S0NK/bThhz9//9q+rcm9YgQGp1t3bu3KmdO3f6NrYfVSe3qB5yax1yax1yaw3yah1yax1ya51T5baoqMjrWH4tpt577z29/fbbWrhwoTp06KAtW7Zo7NixiouL07BhwyzbbmpqqsaNG+eeLiwsVJMmTZSUlCSHw2HZdmsbp9OptLQ09e7dW3a7/ZTtMzIyNDxlrJolj1S9Rr69vfLwwf3KXP6y5s+do/j4eJ/G9ofq5hbeI7fWIbfWIbfWIK/WIbfWIbfW8Ta3FXetecOvxdT48eP10EMPaeDAgZKkjh07KjMzU9OmTdOwYcMUGxsrScrOzlbjxo3d62VnZ6tTp06SpNjYWOXk5HjELSsrU15ennv94wUHBys4OLjSfLvdzkFbBW/zEhgYqNJSp5zlUpmPxzZxlkulpU4FBgaeVb8jjjnrkFvrkFvrkFtrkFfrkFvrkFvrnCq31cm7X0fzKyoqUkCAZxcCAwNVXv778zbx8fGKjY3VqlWr3MsLCwu1YcMGJSQkSJISEhKUn5+vTZs2udt8/vnnKi8vV9euXc/AXgAAAAA4F/n1ytQNN9ygJ554Qk2bNlWHDh20efNmzZo1S3feeackyWazaezYsXr88cfVqlUrxcfHa+LEiYqLi1O/fv0kSe3atdO1116rkSNHat68eXI6nRozZowGDhzISH4AAAAALOPXYurZZ5/VxIkTdc899ygnJ0dxcXG6++679eijj7rbTJgwQUePHtWoUaOUn5+vq666SsuWLVNISIi7zdtvv60xY8aoV69eCggI0IABA/TMM8/4Y5cAAAAAnCP8WkzVq1dPc+bM0Zw5c07YxmazacqUKZoyZcoJ20RGRmrhwoUW9BAAAAAAqubXZ6YAAAAAoLaimAIAAAAAEyimAAAAAMAEiikAAAAAMIFiCgAAAABMoJgCAAAAABMopgAAAADABIopAAAAADCBYgoAAAAATKCYAgAAAAATKKYAAAAAwASKKQAAAAAwgWIKAAAAAEygmAIAAAAAEyimAAAAAMAEiikAAAAAMIFiCgAAAABMoJgCAAAAABMopgAAAADABIopAAAAADCBYgoAAAAATKCYAgAAAAATKKYAAAAAwASKKQAAAAAwgWIKAAAAAEygmAIAAAAAEyimAAAAAMAEiikAAAAAMIFiCgAAAABMoJgCAAAAABMopgAAAADABIopAAAAADCBYgoAAAAATPBrMdW8eXPZbLZKPykpKZKk4uJipaSkqGHDhgoPD9eAAQOUnZ3tEWPv3r3q27evQkNDFR0drfHjx6usrMwfuwMAAADgHOLXYmrjxo06cOCA+yctLU2SdPPNN0uS7r//fn3yySdatGiR1qxZo/3796t///7u9V0ul/r27avS0lKtW7dOr7/+uhYsWKBHH33UL/sDAAAA4Nzh12IqKipKsbGx7p8lS5aoRYsW6t69uwoKCvTqq69q1qxZuuaaa9S5c2fNnz9f69at0/r16yVJK1as0I4dO/TWW2+pU6dO6tOnj6ZOnaq5c+eqtLTUn7sGAAAA4CxXx98dqFBaWqq33npL48aNk81m06ZNm+R0OpWYmOhu07ZtWzVt2lTp6enq1q2b0tPT1bFjR8XExLjbJCcna/To0dq+fbsuueSSKrdVUlKikpIS93RhYaEkyel0yul0WrSHtU9FLrzNicvlUlCQXfYAqY7KfdoXe4AUFGSXy+U6K35H1c0tvEdurUNurUNurUFerUNurUNureNtbquTe5thGMZp9cpH3nvvPQ0ePFh79+5VXFycFi5cqOHDh3sUPZLUpUsX9ezZU9OnT9eoUaOUmZmp5cuXu5cXFRUpLCxMS5cuVZ8+farc1qRJkzR58uRK8xcuXKjQ0FDf7hgAAACAWqOoqEiDBw9WQUGBHA7HSdvWmCtTr776qvr06aO4uDjLt5Wamqpx48a5pwsLC9WkSRMlJSWdMmHnEqfTqbS0NPXu3Vt2u/2U7TMyMjQ8ZayaJY9UvUa+/T0ePrhfmctf1vy5cxQfH+/T2P5Q3dzCe+TWOuTWOuTWGuTVOuTWOuTWOt7mtuKuNW/UiGIqMzNTK1eu1AcffOCeFxsbq9LSUuXn56t+/fru+dnZ2YqNjXW3+frrrz1iVYz2V9GmKsHBwQoODq403263c9BWwdu8BAYGqrTUKWe5VObjx/Gc5VJpqVOBgYFn1e+IY8465NY65NY65NYa5NU65NY65NY6p8ptdfJeI94zNX/+fEVHR6tv377ueZ07d5bdbteqVavc83bt2qW9e/cqISFBkpSQkKCtW7cqJyfH3SYtLU0Oh0Pt27c/czsAAAAA4Jzj9ytT5eXlmj9/voYNG6Y6df5/dyIiIjRixAiNGzdOkZGRcjgcuvfee5WQkKBu3bpJkpKSktS+fXsNHTpUM2bMUFZWlh555BGlpKRUeeUJAAAAAHzF78XUypUrtXfvXt15552Vls2ePVsBAQEaMGCASkpKlJycrOeff969PDAwUEuWLNHo0aOVkJCgsLAwDRs2TFOmTDmTuwAAAADgHOT3YiopKUknGlAwJCREc+fO1dy5c0+4frNmzbR06VKrugcAAAAAVaoRz0wBAAAAQG1DMQUAAAAAJlBMAQAAAIAJFFMAAAAAYALFFAAAAACYQDEFAAAAACZQTAEAAACACRRTAAAAAGACxRQAAAAAmEAxBQAAAAAmUEwBAAAAgAkUUwAAAABgAsUUAAAAAJhAMQUAAAAAJlBMAQAAAIAJFFMAAAAAYALFFAAAAACYQDEFAAAAACZQTAEAAACACRRTAAAAAGACxRQAAAAAmEAxBQAAAAAmUEwBAAAAgAkUUwAAAABgAsUUAAAAAJhAMQUAAAAAJlBMAQAAAIAJFFMAAAAAYALFFAAAAACYQDEFAAAAACZQTAEAAACACRRTAAAAAGACxRQAAAAAmOD3YurXX3/VbbfdpoYNG6pu3brq2LGjvvnmG/dywzD06KOPqnHjxqpbt64SExO1e/dujxh5eXkaMmSIHA6H6tevrxEjRujIkSNnelcAAAAAnEP8Wkz99ttvuvLKK2W32/XZZ59px44d+te//qUGDRq428yYMUPPPPOM5s2bpw0bNigsLEzJyckqLi52txkyZIi2b9+utLQ0LVmyRF9++aVGjRrlj10CAAAAcI6o48+NT58+XU2aNNH8+fPd8+Lj493/NgxDc+bM0SOPPKIbb7xRkvTGG28oJiZGH330kQYOHKidO3dq2bJl2rhxoy677DJJ0rPPPqvrrrtOM2fOVFxc3JndKQAAAADnBL8WU//5z3+UnJysm2++WWvWrNF5552ne+65RyNHjpQkZWRkKCsrS4mJie51IiIi1LVrV6Wnp2vgwIFKT09X/fr13YWUJCUmJiogIEAbNmzQTTfdVGm7JSUlKikpcU8XFhZKkpxOp5xOp1W7W+tU5MLbnLhcLgUF2WUPkOqo3Kd9sQdIQUF2uVyus+J3VN3cwnvk1jrk1jrk1hrk1Trk1jrk1jre5rY6ubcZhmGcVq9OQ0hIiCRp3Lhxuvnmm7Vx40bdd999mjdvnoYNG6Z169bpyiuv1P79+9W4cWP3erfccotsNpv+/e9/68knn9Trr7+uXbt2ecSOjo7W5MmTNXr06ErbnTRpkiZPnlxp/sKFCxUaGurjvQQAAABQWxQVFWnw4MEqKCiQw+E4aVu/XpkqLy/XZZddpieffFKSdMkll2jbtm3uYsoqqampGjdunHu6sLBQTZo0UVJS0ikTdi5xOp1KS0tT7969ZbfbT9k+IyNDw1PGqlnySNVr5NvbKw8f3K/M5S9r/tw5HreC1lbVzS28R26tQ26tQ26tQV6tQ26tQ26t421uK+5a84Zfi6nGjRurffv2HvPatWun999/X5IUGxsrScrOzva4MpWdna1OnTq52+Tk5HjEKCsrU15ennv94wUHBys4OLjSfLvdzkFbBW/zEhgYqNJSp5zlUpmPxzZxlkulpU4FBgaeVb8jjjnrkFvrkFvrkFtrkFfrkFvrkFvrnCq31cm7X0fzu/LKKyvdnvfjjz+qWbNmkn4fjCI2NlarVq1yLy8sLNSGDRuUkJAgSUpISFB+fr42bdrkbvP555+rvLxcXbt2PQN7AQAAAOBc5NcrU/fff7+uuOIKPfnkk7rlllv09ddf66WXXtJLL70kSbLZbBo7dqwef/xxtWrVSvHx8Zo4caLi4uLUr18/Sb9fybr22ms1cuRIzZs3T06nU2PGjNHAgQMZyQ8AAACAZfxaTF1++eX68MMPlZqaqilTpig+Pl5z5szRkCFD3G0mTJigo0ePatSoUcrPz9dVV12lZcuWuQevkKS3335bY8aMUa9evRQQEKABAwbomWee8ccuAQAAADhH+LWYkqTrr79e119//QmX22w2TZkyRVOmTDlhm8jISC1cuNCK7gEAAABAlfz6zBQAAAAA1FYUUwAAAABgAsUUAAAAAJhAMQUAAAAAJlBMAQAAAIAJFFMAAAAAYALFFAAAAACYQDEFAAAAACZQTAEAAACACRRTAAAAAGACxRQAAAAAmEAxBQAAAAAmUEwBAAAAgAkUUwAAAABgAsUUAAAAAJhAMQUAAAAAJlBMAQAAAIAJFFMAAAAAYALFFAAAAACYQDEFAAAAACZQTAEAAACACRRTAAAAAGACxRQAAAAAmEAxBQAAAAAmUEwBAAAAgAkUUwAAAABgAsUUAAAAAJhAMQUAAAAAJlBMAQAAAIAJFFMAAAAAYALFFAAAAACYQDEFAAAAACZQTAEAAACACRRTAAAAAGBCHX9ufNKkSZo8ebLHvDZt2uiHH36QJBUXF+uBBx7Qu+++q5KSEiUnJ+v5559XTEyMu/3evXs1evRorV69WuHh4Ro2bJimTZumOnX8ums1Vm5urgoLC71q63K5JEkZGRkKDAw8ZfvMzEyVOctOq38AAABAbeH3iqNDhw5auXKle/qPRdD999+vTz/9VIsWLVJERITGjBmj/v37a+3atZJ+/2O/b9++io2N1bp163TgwAHdfvvtstvtevLJJ8/4vtR0ubm5um34Xco7XORV+6AguyaMuVvDU8aqtNR5yvbFx4r0y68H1NR56rYAAABAbef3YqpOnTqKjY2tNL+goECvvvqqFi5cqGuuuUaSNH/+fLVr107r169Xt27dtGLFCu3YsUMrV65UTEyMOnXqpKlTp+rBBx/UpEmTFBQUdKZ3p0YrLCxU3uEiRSUMUFhkzCnb2//vJtBmySPlLD91/Jyftylz32tylVFMAQAA4Ozn92Jq9+7diouLU0hIiBISEjRt2jQ1bdpUmzZtktPpVGJiortt27Zt1bRpU6Wnp6tbt25KT09Xx44dPW77S05O1ujRo7V9+3ZdcsklVW6zpKREJSUl7umK296cTqecZ/FVFZfLpaAgu+o3ilG9RnGnbF9H5ZJy1KBRrMq8eLyuJD9bISHBsgdUrOs79oDfr5S5XK6z4ndUsQ9nw77UNOTWOuTWOuTWGuTVOuTWOuTWOt7mtjq5txmGYZxWr07DZ599piNHjqhNmzY6cOCAJk+erF9//VXbtm3TJ598ouHDh3sUPZLUpUsX9ezZU9OnT9eoUaOUmZmp5cuXu5cXFRUpLCxMS5cuVZ8+farcblXPaknSwoULFRoa6tudBAAAAFBrFBUVafDgwSooKJDD4ThpW79emfpjsXPRRRepa9euatasmd577z3VrVvXsu2mpqZq3Lhx7unCwkI1adJESUlJp0xYbZaRkaHhKWPVLHmk11emkhrkaMVv0V5dmTrw42atf2eOrrxzoqKbtPRFl90OH9yvzOUva/7cOYqPj/dpbH9wOp1KS0tT7969Zbfb/d2dswq5tQ65tQ65tQZ5tQ65tQ65tY63ufV2sDapBtzm90f169dX69at9dNPP6l3794qLS1Vfn6+6tev726TnZ3tfsYqNjZWX3/9tUeM7Oxs97ITCQ4OVnBwcKX5drv9rD5oAwMDVVrqlLNcXhVHFcoU4FV7p8tQcXFJteN7w1kulZY6FRgYeFb9js72Y86fyK11yK11yK01yKt1yK11yK11TpXb6uTddDF19OhRrVmzRnv37lVpaanHsr/97W+mYh45ckQ///yzhg4dqs6dO8tut2vVqlUaMGCAJGnXrl3au3evEhISJEkJCQl64oknlJOTo+joaElSWlqaHA6H2rdvb3bXAAAAAOCUTBVTmzdv1nXXXaeioiIdPXpUkZGROnjwoEJDQxUdHe11MfX3v/9dN9xwg5o1a6b9+/frscceU2BgoAYNGqSIiAiNGDFC48aNU2RkpBwOh+69914lJCSoW7dukqSkpCS1b99eQ4cO1YwZM5SVlaVHHnlEKSkpVV55AgAAAABfMVVM3X///brhhhs0b948RUREaP369bLb7brtttt03333eR3nl19+0aBBg3To0CFFRUXpqquu0vr16xUVFSVJmj17tgICAjRgwACPl/ZWCAwM1JIlSzR69GglJCQoLCxMw4YN05QpU8zsFs5x1XmhcXU5HA73cQ0AAICzg6liasuWLXrxxRcVEBCgwMBAlZSU6IILLtCMGTM0bNgw9e/f36s477777kmXh4SEaO7cuZo7d+4J2zRr1kxLly6tVv+B41X3hcbVFVkvVG/Nf4WCCgAA4Cxiqpiy2+0KCPh9gIHo6Gjt3btX7dq1U0REhPbt2+fTDgJnQnVfaFwdR/OylZv+vgoLCymmAAAAziKmiqlLLrlEGzduVKtWrdS9e3c9+uijOnjwoN58801deOGFvu4jcMaERcbIEX2+z+Pm+jwiAAAA/M3U+NVPPvmkGjduLEl64okn1KBBA40ePVq5ubl66aWXfNpBAAAAAKiJTF2Zuuyyy9z/jo6O1rJly3zWIQAAAACoDUxdmbrmmmuUn5/v464AAAAAQO1hqpj64osvKr2oFwAAAADOJaaKKUmy2Wy+7AcAAAAA1CqmnpmSpJtuuklBQUFVLvv8889NdwgAAAAAagPTxVRCQoLCw8N92RcAAAAAqDVMFVM2m03jx49XdHS0r/sDAAAAALWCqWLKMAxf9wM4JWdpqTIzMy2JnZmZqTJnmSWxAQAAcHYyVUw99thj3OKHM6rkSIH2ZPxPYx+epODgYJ/HLz5WpF9+PaCmTqfPYwMAAODsZLqYkqTc3Fzt2rVLktSmTRtFRUX5rmfAHzhLjqncVkeNuvVXw7hmPo+f8/M2Ze57Ta4yiikAAAB4x1QxVVRUpDFjxujNN9+Uy+WSJAUGBur222/Xs88+q9DQUJ92EqgQ2iBKjujzfR73yKEsn8cEAADA2c3Ue6buv/9+rVmzRv/5z3+Un5+v/Px8ffzxx1qzZo0eeOABX/cRAAAAAGocU1em3n//fS1evFg9evRwz7vuuutUt25d3XLLLXrhhRd81T8AAAAAqJFMXZkqKipSTExMpfnR0dEqKio67U4BAAAAQE1nqphKSEjQY489puLiYve8Y8eOafLkyUpISPBZ5wAAAACgpjJ1m9+cOXN07bXX6vzzz9fFF18sSfruu+8UEhKi5cuX+7SDAAAAAFATmSqmOnbsqN27d+vtt9/WDz/8IEkaNGiQhgwZorp16/q0gwAAAABQE5kqpr788ktdccUVGjlypK/7AwAAAAC1gqlnpnr27Km8vDxf9wUAAAAAag1TxZRhGL7uBwAAAADUKqZu85Ok9PR0NWjQoMplV199tekOAQAAAEBtYLqYuummm6qcb7PZ5HK5THcIAAAAAGoDU7f5SVJWVpbKy8sr/VBIAQAAADgXmCqmbDabr/sBAAAAALUKA1AAAAAAgAmmnpkqLy/3dT8AAAAAoFYxdWVq2rRpeu211yrNf+211zR9+vTT7hQAAAAA1HSmiqkXX3xRbdu2rTS/Q4cOmjdv3ml3CgAAAABqOlPFVFZWlho3blxpflRUlA4cOHDanQIAAACAms5UMdWkSROtXbu20vy1a9cqLi7utDsFAAAAADWdqWJq5MiRGjt2rObPn6/MzExlZmbqtdde0/3336+RI0ea6sg///lP2Ww2jR071j2vuLhYKSkpatiwocLDwzVgwABlZ2d7rLd371717dtXoaGhio6O1vjx41VWVmaqDwAAAADgLVOj+Y0fP16HDh3SPffco9LSUklSSEiIHnzwQaWmplY73saNG/Xiiy/qoosu8ph///3369NPP9WiRYsUERGhMWPGqH///u6rYi6XS3379lVsbKzWrVunAwcO6Pbbb5fdbteTTz5pZtcAAAAAwCumX9o7ffp05ebmav369fruu++Ul5enRx99tNqxjhw5oiFDhujll19WgwYN3PMLCgr06quvatasWbrmmmvUuXNnzZ8/X+vWrdP69eslSStWrNCOHTv01ltvqVOnTurTp4+mTp2quXPnuos8AAAAALCCqStTFcLDw3X55ZefVgdSUlLUt29fJSYm6vHHH3fP37Rpk5xOpxITE93z2rZtq6ZNmyo9PV3dunVTenq6OnbsqJiYGHeb5ORkjR49Wtu3b9cll1xS5TZLSkpUUlLini4sLJQkOZ1OOZ3O09qfmszlcikoyC57gFRHp35XWEUbb9pKkj3QppCQYK/jV4eVsa2Obw+QgoLscrlc7uPr+P/Cd8itdcitdcitNcirdcitdcitdbzNbXVybzMMwzDTmW+++Ubvvfee9u7dW+kq0AcffOBVjHfffVdPPPGENm7cqJCQEPXo0UOdOnXSnDlztHDhQg0fPtyj6JGkLl26qGfPnpo+fbpGjRqlzMxMLV++3L28qKhIYWFhWrp0qfr06VPldidNmqTJkydXmr9w4UKFhoZ61XcAAAAAZ5+ioiINHjxYBQUFcjgcJ21r6srUu+++q9tvv13JyclasWKFkpKS9OOPPyo7O1s33XSTVzH27dun++67T2lpaQoJCTHTDdNSU1M1btw493RhYaGaNGmipKSkUyasNsvIyNDwlLFqljxS9RqdetTFOipXUoMcrfgtWmVe3BF64MfNWv/OHF1550RFN2npiy6fkdhWxz98cL8yl7+s+XPnKD4+XtLv33ikpaWpd+/estvtPt3euY7cWofcWofcWoO8WofcWofcWsfb3FbcteYNU8XUk08+qdmzZyslJUX16tXT008/rfj4eN19991Vvn+qKps2bVJOTo4uvfRS9zyXy6Uvv/xSzz33nJYvX67S0lLl5+erfv367jbZ2dmKjY2VJMXGxurrr7/2iFsx2l9Fm6oEBwcrODi40ny73X5WH7SBgYEqLXXKWS6viqMKZQrwqr3TZai4uKTa8b1hZWyr4zvLpdJSpwIDAysdX2f7MedP5NY65NY65NYa5NU65NY65NY6p8ptdfJu6q/Gn3/+WX379pUkBQUF6ejRo7LZbLr//vv10ksveRWjV69e2rp1q7Zs2eL+ueyyyzRkyBD3v+12u1atWuVeZ9euXdq7d68SEhIkSQkJCdq6datycnLcbdLS0uRwONS+fXszuwYAAAAAXjF1ZapBgwY6fPiwJOm8887Ttm3b1LFjR+Xn56uoqMirGPXq1dOFF17oMS8sLEwNGzZ0zx8xYoTGjRunyMhIORwO3XvvvUpISFC3bt0kSUlJSWrfvr2GDh2qGTNmKCsrS4888ohSUlKqvPIEAAAAAL5iqpi6+uqrlZaWpo4dO+rmm2/Wfffdp88//1xpaWnq1auXzzo3e/ZsBQQEaMCAASopKVFycrKef/559/LAwEAtWbJEo0ePVkJCgsLCwjRs2DBNmTLFZ30AAAAAgKqYKqaee+45FRcXS5L+8Y9/yG63a926dRowYIAeeeQR05354osvPKZDQkI0d+5czZ0794TrNGvWTEuXLjW9TQAAAAAwo1rFVMXIFnXq1FF4eLh7+p577tE999zj+94BAAAAQA1VrWKqfv36stlsp2zncrlMdwgAAAAAaoNqFVOrV6/2mDYMQ9ddd51eeeUVnXfeeT7tGAAAAADUZNUqprp3715pXmBgoLp166YLLrjAZ50CAAAAgJrO928/BQAAAIBzwGkVU/v27VNRUZEaNmzoq/4AAAAAQK1Qrdv8nnnmGfe/Dx48qHfeeUfXXHONIiIifN4xAAAAAKjJqlVMzZ49W5Jks9nUqFEj3XDDDaf1XikAAAAAqK2qVUxlZGRY1Q8AAAAAqFUYgAIAAAAATKCYAgAAAAATKKYAAAAAwASKKQAAAAAwgWIKAAAAAEygmAIAAAAAEyimAAAAAMAEiikAAAAAMIFiCgAAAABMoJgCAAAAABMopgAAAADABIopAAAAADCBYgoAAAAATKCYAgAAAAATKKYAAAAAwASKKQAAAAAwgWIKAAAAAEygmAIAAAAAEyimAAAAAMAEiikAAAAAMIFiCgAAAABMoJgCAAAAABMopgAAAADABIopAAAAADDBr8XUCy+8oIsuukgOh0MOh0MJCQn67LPP3MuLi4uVkpKihg0bKjw8XAMGDFB2drZHjL1796pv374KDQ1VdHS0xo8fr7KysjO9KwAAAADOMX4tps4//3z985//1KZNm/TNN9/ommuu0Y033qjt27dLku6//3598sknWrRokdasWaP9+/erf//+7vVdLpf69u2r0tJSrVu3Tq+//roWLFigRx991F+7BAAAAOAcUcefG7/hhhs8pp944gm98MILWr9+vc4//3y9+uqrWrhwoa655hpJ0vz589WuXTutX79e3bp104oVK7Rjxw6tXLlSMTEx6tSpk6ZOnaoHH3xQkyZNUlBQkD92CwAAAMA5wK/F1B+5XC4tWrRIR48eVUJCgjZt2iSn06nExER3m7Zt26pp06ZKT09Xt27dlJ6ero4dOyomJsbdJjk5WaNHj9b27dt1ySWXVLmtkpISlZSUuKcLCwslSU6nU06n06I99D+Xy6WgILvsAVIdlZ+yfUUbb9pKkj3QppCQYK/jV4eVsa2Obw+QgoLscrlc7uPr+P/Cd8itdcitdcitNcirdcitdcitdbzNbXVybzMMwzitXp2mrVu3KiEhQcXFxQoPD9fChQt13XXXaeHChRo+fLhH0SNJXbp0Uc+ePTV9+nSNGjVKmZmZWr58uXt5UVGRwsLCtHTpUvXp06fKbU6aNEmTJ0+uNH/hwoUKDQ317Q4CAAAAqDWKioo0ePBgFRQUyOFwnLSt369MtWnTRlu2bFFBQYEWL16sYcOGac2aNZZuMzU1VePGjXNPFxYWqkmTJkpKSjplwmqzjIwMDU8Zq2bJI1WvUdwp29dRuZIa5GjFb9Eq8+LxugM/btb6d+boyjsnKrpJS190+YzEtjr+4YP7lbn8Zc2fO0fx8fGSfv/GIy0tTb1795bdbvfp9s515NY65NY65NYa5NU65NY65NY63ua24q41b/i9mAoKClLLlr//8dq5c2dt3LhRTz/9tG699VaVlpYqPz9f9evXd7fPzs5WbGysJCk2NlZff/21R7yK0f4q2lQlODhYwcHBlebb7faz+qANDAxUaalTznJ5VRxVKFOAV+2dLkPFxSXVju8NK2NbHd9ZLpWWOhUYGFjp+Drbjzl/IrfWIbfWIbfWIK/WIbfWIbfWOVVuq5P3GveeqfLycpWUlKhz586y2+1atWqVe9muXbu0d+9eJSQkSJISEhK0detW5eTkuNukpaXJ4XCoffv2Z7zvAAAAAM4dfr0ylZqaqj59+qhp06Y6fPiwFi5cqC+++ELLly9XRESERowYoXHjxikyMlIOh0P33nuvEhIS1K1bN0lSUlKS2rdvr6FDh2rGjBnKysrSI488opSUlCqvPAEAAACAr/i1mMrJydHtt9+uAwcOKCIiQhdddJGWL1+u3r17S5Jmz56tgIAADRgwQCUlJUpOTtbzzz/vXj8wMFBLlizR6NGjlZCQoLCwMA0bNkxTpkzx1y4BAAAAOEf4tZh69dVXT7o8JCREc+fO1dy5c0/YplmzZlq6dKmvuwYAAAAAJ1XjnpkCAAAAgNqAYgoAAAAATKCYAgAAAAATKKYAAAAAwASKKQAAAAAwgWIKAAAAAEygmAIAAAAAEyimAAAAAMAEiikAAAAAMIFiCgAAAABMoJgCAAAAABMopgAAAADABIopAAAAADCBYgoAAAAATKCYAgAAAAATKKYAAAAAwASKKQAAAAAwgWIKAAAAAEyo4+8OoLLc3FwVFhb6PG5mZqbKnGU+jwsAAACciyimapjc3FzdNvwu5R0u8nns4mNF+uXXA2rqdPo8NgAAAHCuoZiqYQoLC5V3uEhRCQMUFhnj09g5P29T5r7X5CqjmAIAAABOF8VUDRUWGSNH9Pk+jXnkUJZP4wEAAADnMgagAAAAAAATKKYAAAAAwASKKQAAAAAwgWIKAAAAAEygmAIAAAAAExjNDzgDnKWlyszMdE+7XC5JUkZGhgIDA087vsPhUFRU1GnHAQAAgPcopgCLlRwp0J6M/2nsw5MUHBwsSQoKsmvCmLs1PGWsSktP/71fkfVC9db8VyioAAAAziCKKcBizpJjKrfVUaNu/dUwrpkkyf5/N9g2Sx4pZ/npxT+al63c9PdVWFhIMQUAAHAGUUwBZ0hogyj3i5jrqFxSluo1ilOZDx5dzD3tCAAAAKguBqAAAAAAABO4MgUAqFVyc3NVWFhoSWwGcwEAVIdfi6lp06bpgw8+0A8//KC6devqiiuu0PTp09WmTRt3m+LiYj3wwAN69913VVJSouTkZD3//POKiYlxt9m7d69Gjx6t1atXKzw8XMOGDdO0adNUpw61IgCcTXJzc3Xb8LuUd7jIkvgM5gIAqA6/Vhtr1qxRSkqKLr/8cpWVlenhhx9WUlKSduzYobCwMEnS/fffr08//VSLFi1SRESExowZo/79+2vt2rWSfh9ium/fvoqNjdW6det04MAB3X777bLb7XryySf9uXsAAB8rLCxU3uEiRSUMUFhkzKlXqAYGcwEAVJdfi6lly5Z5TC9YsEDR0dHatGmTrr76ahUUFOjVV1/VwoULdc0110iS5s+fr3bt2mn9+vXq1q2bVqxYoR07dmjlypWKiYlRp06dNHXqVD344IOaNGmSgoKC/LFrAAALhUXGuAd08SUGcwEAVEeNug+uoKBAkhQZGSlJ2rRpk5xOpxITE91t2rZtq6ZNmyo9PV3dunVTenq6Onbs6HHbX3JyskaPHq3t27frkksuqbSdkpISlZSUuKcr7r13Op1yOk//nT+nw+VyKSjILntAxYhvvmMPtCkkJNjr2BVtvO1HdeNXh5WxrY5fVezq5vak8QN+f2+Vy+Xy+/FbE1TkgFz4Xk3IraXnSD9+lmpCbs9G5NU65NY65NY63ua2Orm3GYZhnFavfKS8vFx//vOflZ+fr6+++kqStHDhQg0fPtyj8JGkLl26qGfPnpo+fbpGjRqlzMxMLV++3L28qKhIYWFhWrp0qfr06VNpW5MmTdLkyZMrzV+4cKFCQ0N9vGcAAAAAaouioiINHjxYBQUFcjgcJ21bY65MpaSkaNu2be5CykqpqakaN26ce7qwsFBNmjRRUlLSKRNmtYyMDA1PGatmySNVr1GcT2Mf+HGz1r8zR1feOVHRTVqesn0dlSupQY5W/Bbt1buQqhu/OqyMbXX8qmJXN7cnc3DPTq1d8ISaNG2moGDf39baIDxULz//rBo1auTz2FZwOp1KS0tT7969Zbfb/d2ds0pNyK2V58jDB/crc/nLmj93juLj430a+1RqQm7PRuTVOuTWOuTWOt7mtjojxtaIYmrMmDFasmSJvvzyS51//v+/Bz42NlalpaXKz89X/fr13fOzs7MVGxvrbvP11197xMvOznYvq0pwcLCCg4Mrzbfb7X4/aAMDA1Va6pSzXD55mesfOV2GiotLqh27TAFetTcb3xtWxrY6/slie5vbkyk6dkxFJWWqd+n1ahjX7LRiHe9oXray099XUVGR3z8b1VUTPs9nK3/m1tJzZLlUWupUYGCg3/aP49Ya5NU65NY65NY6p8ptdfLu12LKMAzde++9+vDDD/XFF19U+iawc+fOstvtWrVqlQYMGCBJ2rVrl/bu3auEhARJUkJCgp544gnl5OQoOjpakpSWliaHw6H27duf2R0C/Ci0QRQP5AMAAJxBfi2mUlJStHDhQn388ceqV6+esrKyJEkRERGqW7euIiIiNGLECI0bN06RkZFyOBy69957lZCQoG7dukmSkpKS1L59ew0dOlQzZsxQVlaWHnnkEaWkpFR59QkAAAAAfMGvxdQLL7wgSerRo4fH/Pnz5+uOO+6QJM2ePVsBAQEaMGCAx0t7KwQGBmrJkiUaPXq0EhISFBYWpmHDhmnKlClnajcAAAAAnIP8fpvfqYSEhGju3LmaO3fuCds0a9ZMS5cu9WXXAOCslpubW60HbKXfhyWXfh8EIjAw8ITtHA4HL70FAJwTasQAFACAMyc3N1e3Db9LeYeLqrVeUJBdE8bcreEpY1VaeuJ3cETWC9Vb81+hoAIAnPUopgDgHFNYWKi8w0WKShigsMiYU6/wf+z/N3hes+SRcp7gfblH87KVm/6+CgsLKaYAAGc9iikAOEeFRcZUawTIOiqXlKV6jeJOOiw5I0ACAM4Vvn9hDwAAAACcAyimAAAAAMAEiikAAAAAMIFnpgAAPuUsLVVmZqYlsTMzM1XmLLMkNgAA1UUxBQDwmZIjBdqT8T+NfXiSgoODfR6/+FiRfvn1gJo6Tzw0e012ovd7efsOr5Ph/V4AcOZRTAEAfMZZckzltjpq1K2/GsY183n8nJ+3KXPfa3KV1b5i6mTv9/L2HV4nw/u9AODMo5gCAPhcaIOoag277q0jh7J8HvOPrL5FMSevUI2vvrXS+728eYfXyfB+LwDwD4opAAB0Bm9RrBdZqdD09h1eJ8P7vQDgzKOYAgBA3KIIAKg+iikAqIFONFCBLzAi3snV1lsUAQBnHsUUANQwJxuowBdq+4h4AADUFBRTAFDDFBYWKu9wkaISBlQaqMAXuN3s7GTl4BkSQ68DQFUopgCghgqLjOF2M3jF6sEzJIZeB4CqUEwBAFDLWT14BkOvA0DVKKYAADhLWDV4hsTQ6wBQFXMvswAAAACAcxxXpgCcFA+1AwAAVI1iCsAJ8VA7AJycle+Ek/jCCajpKKYAnBAPtQPAiVn9TjiJL5yAmo5iCsAp8VA7AFRm9Tvh+MIJqPkopgAAAE6DVe+Ek/jCCajpGM0PAAAAAEygmAIAAAAAEyimAAAAAMAEiikAAAAAMIEBKAAAwClZ+QJv3qUEoLaimAIAACdl9Qu8eZcSgNqKYgoAAJyUlS/w5l1KAGoziikAAOAVq17gbfW7lA4ePKiioiKfx83MzFSZs8zncQHUHhRTAADgrDbynnuVnVfg87jFx4r0y68H1NTp9HlsALUDxRQAADir/XakSFEJAxQWGePTuDk/b1PmvtfkKqOYAs5Vfh0a/csvv9QNN9yguLg42Ww2ffTRRx7LDcPQo48+qsaNG6tu3bpKTEzU7t27Pdrk5eVpyJAhcjgcql+/vkaMGKEjR46cwb0AAAA1XVhkjBzR5/v0J7R+I3/vFgA/82sxdfToUV188cWaO3dulctnzJihZ555RvPmzdOGDRsUFham5ORkFRcXu9sMGTJE27dvV1pampYsWaIvv/xSo0aNOlO7AAAAAOAc5dfb/Pr06aM+ffpUucwwDM2ZM0ePPPKIbrzxRknSG2+8oZiYGH300UcaOHCgdu7cqWXLlmnjxo267LLLJEnPPvusrrvuOs2cOVNxcXFVxi4pKVFJSYl7urCwUJLkdDrl9PN9zy6XS0FBdtkDpDoq92lse6BNISHBXseuaONtP6obvzqsjG11/KpiVze31Y3vK5bnPUAKCrLL5XL57LNXEcffn+XTYeV5QDL/e/XmuD3bPqtnKv7pnhNqdd4tOA9UqIgXZK8Z/1+tdnwLc3O6zoZzbU1Fbq3jbW6rk3ubYRjGafXKR2w2mz788EP169dPkvS///1PLVq00ObNm9WpUyd3u+7du6tTp056+umn9dprr+mBBx7Qb7/95l5eVlamkJAQLVq0SDfddFOV25o0aZImT55caf7ChQsVGhrq0/0CAAAAUHsUFRVp8ODBKigokMPhOGnbGjsARVZWliQpJsbzYdGYmBj3sqysLEVHR3ssr1OnjiIjI91tqpKamqpx48a5pwsLC9WkSRMlJSWdMmFWy8jI0PCUsWqWPFL1GlV9Zc2sAz9u1vp35ujKOycquknLU7avo3IlNcjRit+iVebFHaHVjV8dVsa2On5Vsaub2+rG9xWr83744H5lLn9Z8+fOUXx8vE9iOp1OpaWlqXfv3rLb7T6JeaZZeR6QzP9evTluz7bP6pmKf7rnhNqcdyvOAxUqzgdzXnxNja+5w+//X60uK3Nzus6Gc21NRW6t421uK+5a80aNLaasFBwcXOUb3O12u98P2sDAQJWWOuUs12n/kX08p8tQcXFJtWOXKcCr9mbje8PK2FbHP1lsb3NrNv7psjzv5VJpqVOBgYE+/+zVhM+zWVaeB6TT/72e7Lg9Wz+rZyq+2XNCTei76dgWngcqlDpr1v9XvY5/BnJzumrzubamI7fWOVVuq5N3vw5AcTKxsbGSpOzsbI/52dnZ7mWxsbHKycnxWF5WVqa8vDx3GwAAAACwQo29MhUfH6/Y2FitWrXK/cxUYWGhNmzYoNGjR0uSEhISlJ+fr02bNqlz586SpM8//1zl5eXq2rWrv7oOAADgE87SUmVmZloS2+FwKCoqypLYwLnCr8XUkSNH9NNPP7mnMzIytGXLFkVGRqpp06YaO3asHn/8cbVq1Urx8fGaOHGi4uLi3INUtGvXTtdee61GjhypefPmyel0asyYMRo4cOAJR/IDAAA1i1UFg8vl+v2/TpfPY58JJUcKtCfjfxr78KQqH084XZH1QvXW/FcoqIDT4Ndi6ptvvlHPnj3d0xWDQgwbNkwLFizQhAkTdPToUY0aNUr5+fm66qqrtGzZMoWEhLjXefvttzVmzBj16tVLAQEBGjBggJ555pkzvi8AAKD6rCwYgoLsmjDmbmXu26uYWjjMtLPkmMptddSoW381jGvm09hH87KVm/6+CgsLKaaA0+DXYqpHjx462cjsNptNU6ZM0ZQpU07YJjIyUgsXLrSiewAAwGJWFgz2/3sy3FVuyFVW+4qpCqENouSIPt/ncfefxhXBiqt+GRkZCgwMrLINtxHiXFBjn5kCAADnDisKht9fpHviV6Wcy073imDFVb/hKWNVWlp1ocpthDgXUEwBAACcY073imDFVb9mySPlLK+8nNsIca6gmAIAk3Jzc6v1Yj9vZWZmqsxZ5vO4AHA8s1cEK6761WsUd8J3bOWeZt+A2oBiCgBMyM3N1W3D71Le4SKfxy4+VqRffj2gprXwgXkAAM4lFFMAYEJhYaHyDhcpKmGAwiJjfBo75+dtytz3Wq1+YB4AgHMBxRQAnIawyBifPzR/5BAPzAMAUBtUfZMrAAAAAOCkuDIF4Kxl1QAREoNEAAAAiikAZ6mDBw/qjpF/tWSACIlBIgAAAMUUgLPU4cOHLRsgQmKQCAAAQDEF4CxnxQAREoNEAAAABqAAAAAAAFMopgAAAADABG7zAwAAgM85S0uVmZlpSWyHw6GoqChLYgPVQTEFAAAAnyo5UqA9Gf/T2IcnKTg42OfxI+uF6q35r1BQwe8opgD4la+/uXS5XJKkffv28R4oAPATZ8kxldvqqFG3/moY18ynsY/mZSs3/X0VFhZSTMHvKKYA+I0V31wGBdk1YczdevCxx7UnM5P3QAGAH4U2iLJkRNVcn0cEzKGYAuA3Vnxzaf+/YXUaXJwo18+v8B4oAABgGYopAH7ny28u66hcUpbqRjT0STwAAIATYWh0AAAAADCBYgoAAAAATOA2PwAAANQqvMMKNQXFFAAAAGoN3mGFmoRiCgAAALVGbX+HVW5urgoLC0+4vOJ9iRkZGQoMDKxWbK6qnXkUUwAAAKh1auM7rHJzc3Xb8LuUd7johG0q3pc4PGWsSkur93oPrqqdeRRTAAAAwBlQWFiovMNFikoYoLDImCrbVLwvsVnySDnLvY99NC9b+9e8o61bt6pZM99esavAla/KKKYAAACAMygsMuaEV9Uq3pdYr1Gcyqox8LbVz5JJXPmqCsUUAAAAUMtZ+SyZdGaeJ6uNKKYAAACA/2PlsOuZmZkqc5ZZEruCVc+SSdY+T1ZbUUwBAAAAsv5WueJjRfrl1wNq6qzewBKouSimAAAAAFl/q1zOz9uUue81ucoops4WFFMAAADAH1h1q9yRQ1k+jwn/8n6IEAAAAACA21lzZWru3Ll66qmnlJWVpYsvvljPPvusunTp4u9uAQAAAGcFKwfnqK3vsDoriql///vfGjdunObNm6euXbtqzpw5Sk5O1q5duxQdHe3v7gEAAAC1mtWDc9TWd1idFcXUrFmzNHLkSA0fPlySNG/ePH366ad67bXX9NBDD/m5dwAAAEDtZuXgHLX5HVa1vpgqLS3Vpk2blJqa6p4XEBCgxMREpaenV7lOSUmJSkpK3NMFBQWSpLy8PDn9PFRlQUGBAgKkozmZUmmRT2MX5+1XUFAdFef+osLAU7cPDJCKggNUsP8nucp9H786rIxtdfyqYlc3t9WN7yu1Me8VuT2Wd6DW9f1MxD6d+N4ctzW17/6Ofar4p3tOIO9Vq8hrUJ3a13er459u7FMdszW57/6K7W18s+eDM9X3gLISn/+NqrJiBQT8/nfwoUOHfBv7D5xOp4qKinTo0CHZ7fYTtjt8+LAkyTCMU8a0Gd60qsH279+v8847T+vWrVNCQoJ7/oQJE7RmzRpt2LCh0jqTJk3S5MmTz2Q3AQAAANQi+/bt0/nnn3xUx1p/ZcqM1NRUjRs3zj1dXl6uvLw8NWzYUDabzY89q1kKCwvVpEkT7du3Tw6Hw9/dOauQW+uQW+uQW+uQW2uQV+uQW+uQW+t4m1vDMHT48GHFxcWdMmatL6YaNWqkwMBAZWdne8zPzs5WbGxslesEBwdXenCufv36VnWx1nM4HHyYLUJurUNurUNurUNurUFerUNurUNureNNbiMiIryKVevfMxUUFKTOnTtr1apV7nnl5eVatWqVx21/AAAAAOBLtf7KlCSNGzdOw4YN02WXXaYuXbpozpw5Onr0qHt0PwAAAADwtbOimLr11luVm5urRx99VFlZWerUqZOWLVummJgYf3etVgsODtZjjz1mybsEznXk1jrk1jrk1jrk1hrk1Trk1jrk1jpW5LbWj+YHAAAAAP5Q65+ZAgAAAAB/oJgCAAAAABMopgAAAADABIopAAAAADCBYgoennjiCV1xxRUKDQ31+kXGhmHo0UcfVePGjVW3bl0lJiZq9+7d1na0FsrLy9OQIUPkcDhUv359jRgxQkeOHDnpOj169JDNZvP4+etf/3qGelxzzZ07V82bN1dISIi6du2qr7/++qTtFy1apLZt2yokJEQdO3bU0qVLz1BPa5/q5HbBggWVjs+QkJAz2Nva4csvv9QNN9yguLg42Ww2ffTRR6dc54svvtCll16q4OBgtWzZUgsWLLC8n7VRdXP7xRdfVDpmbTabsrKyzkyHa4lp06bp8ssvV7169RQdHa1+/fpp165dp1yPc+2pmckt51rvvPDCC7rooovcL+RNSEjQZ599dtJ1fHHMUkzBQ2lpqW6++WaNHj3a63VmzJihZ555RvPmzdOGDRsUFham5ORkFRcXW9jT2mfIkCHavn270tLStGTJEn355ZcaNWrUKdcbOXKkDhw44P6ZMWPGGehtzfXvf/9b48aN02OPPaZvv/1WF198sZKTk5WTk1Nl+3Xr1mnQoEEaMWKENm/erH79+qlfv37atm3bGe55zVfd3Eq/v0X+j8dnZmbmGexx7XD06FFdfPHFmjt3rlftMzIy1LdvX/Xs2VNbtmzR2LFjddddd2n58uUW97T2qW5uK+zatcvjuI2Ojraoh7XTmjVrlJKSovXr1ystLU1Op1NJSUk6evToCdfhXOsdM7mVONd64/zzz9c///lPbdq0Sd98842uueYa3Xjjjdq+fXuV7X12zBpAFebPn29EREScsl15ebkRGxtrPPXUU+55+fn5RnBwsPHOO+9Y2MPaZceOHYYkY+PGje55n332mWGz2Yxff/31hOt1797duO+++85AD2uPLl26GCkpKe5pl8tlxMXFGdOmTauy/S233GL07dvXY17Xrl2Nu+++29J+1kbVza235wn8f5KMDz/88KRtJkyYYHTo0MFj3q233mokJydb2LPaz5vcrl692pBk/Pbbb2ekT2eLnJwcQ5KxZs2aE7bhXGuON7nlXGtegwYNjFdeeaXKZb46ZrkyhdOSkZGhrKwsJSYmuudFRESoa9euSk9P92PPapb09HTVr19fl112mXteYmKiAgICtGHDhpOu+/bbb6tRo0a68MILlZqaqqKiIqu7W2OVlpZq06ZNHsdbQECAEhMTT3i8paene7SXpOTkZI7P45jJrSQdOXJEzZo1U5MmTU76DSC8xzFrvU6dOqlx48bq3bu31q5d6+/u1HgFBQWSpMjIyBO24bg1x5vcSpxrq8vlcundd9/V0aNHlZCQUGUbXx2zdUz3EpDc95nHxMR4zI+JieEe9D/IysqqdBtJnTp1FBkZedI8DR48WM2aNVNcXJy+//57Pfjgg9q1a5c++OADq7tcIx08eFAul6vK4+2HH36ocp2srCyOTy+YyW2bNm302muv6aKLLlJBQYFmzpypK664Qtu3b9f5559/Jrp9VjrRMVtYWKhjx46pbt26fupZ7de4cWPNmzdPl112mUpKSvTKK6+oR48e2rBhgy699FJ/d69GKi8v19ixY3XllVfqwgsvPGE7zrXV521uOdd6b+vWrUpISFBxcbHCw8P14Ycfqn379lW29dUxSzF1DnjooYc0ffr0k7bZuXOn2rZte4Z6dPbwNrdm/fGZqo4dO6px48bq1auXfv75Z7Vo0cJ0XMAXEhISPL7xu+KKK9SuXTu9+OKLmjp1qh97BlStTZs2atOmjXv6iiuu0M8//6zZs2frzTff9GPPaq6UlBRt27ZNX331lb+7ctbxNreca73Xpk0bbdmyRQUFBVq8eLGGDRumNWvWnLCg8gWKqXPAAw88oDvuuOOkbS644AJTsWNjYyVJ2dnZaty4sXt+dna2OnXqZCpmbeJtbmNjYys9xF9WVqa8vDx3Dr3RtWtXSdJPP/10ThZTjRo1UmBgoLKzsz3mZ2dnnzCPsbGx1Wp/rjKT2+PZ7XZdcskl+umnn6zo4jnjRMesw+HgqpQFunTpQqFwAmPGjHEPmHSqKyCca6unOrk9HufaEwsKClLLli0lSZ07d9bGjRv19NNP68UXX6zU1lfHLM9MnQOioqLUtm3bk/4EBQWZih0fH6/Y2FitWrXKPa+wsFAbNmw44T2qZxNvc5uQkKD8/Hxt2rTJve7nn3+u8vJyd4HkjS1btkiSR+F6LgkKClLnzp09jrfy8nKtWrXqhMdbQkKCR3tJSktLOyeOz+owk9vjuVwubd269Zw9Pn2FY/bM2rJlC8fscQzD0JgxY/Thhx/q888/V3x8/CnX4bj1jpncHo9zrffKy8tVUlJS5TKfHbMmB8fAWSozM9PYvHmzMXnyZCM8PNzYvHmzsXnzZuPw4cPuNm3atDE++OAD9/Q///lPo379+sbHH39sfP/998aNN95oxMfHG8eOHfPHLtRY1157rXHJJZcYGzZsML766iujVatWxqBBg9zLf/nlF6NNmzbGhg0bDMMwjJ9++smYMmWK8c033xgZGRnGxx9/bFxwwQXG1Vdf7a9dqBHeffddIzg42FiwYIGxY8cOY9SoUUb9+vWNrKwswzAMY+jQocZDDz3kbr927VqjTp06xsyZM42dO3cajz32mGG3242tW7f6axdqrOrmdvLkycby5cuNn3/+2di0aZMxcOBAIyQkxNi+fbu/dqFGOnz4sPtcKsmYNWuWsXnzZiMzM9MwDMN46KGHjKFDh7rb/+9//zNCQ0ON8ePHGzt37jTmzp1rBAYGGsuWLfPXLtRY1c3t7NmzjY8++sjYvXu3sXXrVuO+++4zAgICjJUrV/prF2qk0aNHGxEREcYXX3xhHDhwwP1TVFTkbsO51hwzueVc652HHnrIWLNmjZGRkWF8//33xkMPPWTYbDZjxYoVhmFYd8xSTMHDsGHDDEmVflavXu1uI8mYP3++e7q8vNyYOHGiERMTYwQHBxu9evUydu3adeY7X8MdOnTIGDRokBEeHm44HA5j+PDhHkVqRkaGR6737t1rXH311UZkZKQRHBxstGzZ0hg/frxRUFDgpz2oOZ599lmjadOmRlBQkNGlSxdj/fr17mXdu3c3hg0b5tH+vffeM1q3bm0EBQUZHTp0MD799NMz3OPaozq5HTt2rLttTEyMcd111xnffvutH3pds1UMx338T0Uuhw0bZnTv3r3SOp06dTKCgoKMCy64wOOci/+vurmdPn260aJFCyMkJMSIjIw0evToYXz++ef+6XwNVlVOj/9/P+dac8zklnOtd+68806jWbNmRlBQkBEVFWX06tXLXUgZhnXHrM0wDKN617IAAAAAADwzBQAAAAAmUEwBAAAAgAkUUwAAAABgAsUUAAAAAJhAMQUAAAAAJlBMAQAAAIAJFFMAAAAAYALFFAAAAACYQDEFAAAAACZQTAEAqu2OO+5Qv379Ks3/4osvZLPZlJ+ff8b7BADAmUYxBQAAAAAmUEwBACyVn5+vu+66S1FRUXI4HLrmmmv03XffuZdPmjRJnTp18ljn+Ctchw4d0qBBg3TeeecpNDRUHTt21DvvvHPS7b733ntq0aKFQkJC1LBhQ/3lL39Rbm6ue/msWbPUsWNHhYWFqUmTJrrnnnt05MgR9/IFCxaofv367unMzEw1adJEjzzyiHte8+bNNXXqVA0aNEhhYWE677zzNHfuXI9+2Gw2ffTRR1X2sVOnTpo0aZI7ls1mq/JnwYIFVcZ69dVXZbPZNHbsWPe8AwcOqH///mrYsKFHDK4WAoDvUUwBACx18803KycnR5999pk2bdqkSy+9VL169VJeXp7XMYqLi9W5c2d9+umn2rZtm0aNGqWhQ4fq66+/PuE6bdu21YIFC7Rr1y4tX75ce/bs0YMPPuheHhAQoGeeeUbbt2/X66+/rs8//1wTJkyoMlZWVpYSExN144036vHHH/dY9tRTT+niiy/W5s2b9dBDD+m+++5TWlqa1/tWYePGjTpw4IAOHDig888/X3PmzHFP33rrrZXaHz16VBMnTlR4eLjH/AceeEA//vijli1bpgMHDuj999+vdl8AAN6p4+8OAADOXl999ZW+/vpr5eTkKDg4WJI0c+ZMffTRR1q8eLFGjRrlVZzzzjtPf//7393T9957r5YvX6733ntPXbp0qXKdiy66yP3vBg0aqGHDhnK5XO55f7ya07x5cz3++OP661//queff94jzm+//aakpCR17dpVzz77bKXtXHnllXrooYckSa1bt9batWs1e/Zs9e7d26t9qxAVFeX+d2BgoCIiIhQbG3vC9jNmzFD79u1VVlbmMX/Lli267bbbdPnll0uSIiMjq9UPAID3uDIFALDMd999pyNHjqhhw4YKDw93/2RkZOjnn392t9u6davH8j59+njEcblcmjp1qjp27KjIyEiFh4dr+fLl2rt370m3/9///lfh4eGqX7++jh07pn/961/uZStXrlSvXr103nnnqV69eho6dKgOHTqkoqIid5uysjJdd9112rp1q5KSkmSz2SptIyEhodL0zp07PeYNGjRI4eHhaty4sfr27asdO3acOnknsX//fs2aNctjfyrEx8dr6dKl1bryBwAwh2IKAGCZI0eOqHHjxtqyZYvHz65duzR+/Hh3uzZt2ngsf+WVVzziPPXUU3r66af14IMPavXq1dqyZYuSk5NVWlp60u1fdtll2rx5s1asWKFDhw7p5ZdfliTt2bNH119/vS666CK9//772rRpk/tZpz/GPHr0qOrWrasXX3xRY8eOVVZWlqk8zJ49W1u2bNEnn3wip9OpW265xVScCv/4xz9088036+KLL65yWyUlJWrUqFGVhSkAwHe4zQ8AYJlLL71UWVlZqlOnjpo3b37CdkFBQWrZsqV7+pdffvFYvnbtWt1444267bbbJEnl5eX68ccf1b59+5Nuv27dumrVqpVatWqlUaNG6eWXX1Zqaqo2bdqk8vJy/etf/1JAwO/fK7733nuV1g8NDdV//vMfhYeH65NPPtHdd9+tjz/+2KPN+vXrK023a9fOY15sbKx7/+677z7dcMMNcjqdJ+37iWzZskWLFy/Wrl27qlzeunVr3XHHHTp06JA++eQT921/AADfo5gCAJhSUFCgLVu2eMz76aefJP1+294ll1yixMREJSQkqF+/fpoxY4Zat26t/fv369NPP9VNN92kyy67zKtttWrVSosXL9a6devUoEEDzZo1S9nZ2Sctpt599121aNFCMTEx2r17t+bNm+feXsuWLeV0OvXss8/qhhtu0Nq1azVv3rxKMex2u3uAh5deekkdOnTQW2+95VGcrF27VjNmzFC/fv2UlpamRYsW6dNPP/WI43Q6VVxcrPz8fL311ltq3bq17Ha7V/t+vJkzZ+qBBx5QXFxclcvXr1+vhx9+WKtXr1aHDh08RjAEAPgWxRQAwJQvvvhCl1xySZXLrr76aq1evVo9evTQ0qVL9Y9//EPDhw9Xbm6uYmNjdfXVVysmJsbrbT3yyCP63//+p+TkZIWGhmrUqFHq16+fCgoKTrjOzp07NWHCBGVnZ6tRo0bq06ePZs6cKUm6+OKLNWvWLE2fPl2pqam6+uqrNW3aNN1+++0njNe4cWM9/fTTuu+++5SYmOgeHOKBBx7QN998o8mTJ8vhcGjWrFlKTk72WLfitr569erp0ksv1b///W+v9/149erVO+Gog7m5ubr55ps1a9YsXXrppaa3AQDwjs0wDMPfnQAAnF2aN2+uBQsWqEePHv7uiqWaN2+usWPHeowMCAA4dzAABQDA59q3b1/p/UcAAJxtuM0PAOBzS5cu9XcXAACwHLf5AQAAAIAJ3OYHAAAAACZQTAEAAACACRRTAAAAAGACxRQAAAAAmEAxBQAAAAAmUEwBAAAAgAkUUwAAAABgAsUUAAAAAJjw/wDy8iOe4kEl0AAAAABJRU5ErkJggg==", "text/plain": [ - " Close SMA_5 SMA_20 STD_5 STD_20 Day_of_week_1 Day_of_week_2 \\\n", - "0 -0.721096 NaN NaN NaN NaN -0.507666 -0.507962 \n", - "1 -0.721096 NaN NaN NaN NaN -0.507666 -0.507962 \n", - "2 -0.660890 NaN NaN NaN NaN 1.969800 -0.507962 \n", - "3 -0.660890 NaN NaN NaN NaN -0.507666 1.968649 \n", - "4 -0.660890 -0.686033 NaN -0.269917 NaN -0.507666 -0.507962 \n", - "\n", - " Day_of_week_3 Day_of_week_4 Month_2 Month_3 Month_4 Month_5 \\\n", - "0 -0.502320 1.999048 -0.28793 -0.309491 -0.300916 -0.297137 \n", - "1 -0.502320 -0.500238 -0.28793 -0.309491 -0.300916 -0.297137 \n", - "2 -0.502320 -0.500238 -0.28793 -0.309491 -0.300916 -0.297137 \n", - "3 -0.502320 -0.500238 -0.28793 -0.309491 -0.300916 -0.297137 \n", - "4 1.990763 -0.500238 -0.28793 -0.309491 -0.300916 -0.297137 \n", - "\n", - " Month_6 Month_7 Month_8 Month_9 Month_10 Month_11 Month_12 \n", - "0 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 \n", - "1 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 \n", - "2 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 \n", - "3 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 \n", - "4 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 " + "
" ] }, - "execution_count": 110, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "from sklearn.preprocessing import StandardScaler\n", - "import pandas as pd\n", + "from collections import Counter\n", + "from imblearn.over_sampling import SMOTE\n", "\n", - "# Преобразуем дату, если это еще не сделано\n", - "data['Date'] = pd.to_datetime(data['Date'])\n", + "# Проверка на наличие пропусков после масштабирования\n", + "print(\"Проверка на пропуски после масштабирования:\\n\", data.isnull().sum())\n", "\n", - "# Добавим дополнительные признаки (день недели и месяц)\n", - "data['Day_of_week'] = data['Date'].dt.dayofweek\n", - "data['Month'] = data['Date'].dt.month\n", + "# Проверка распределения целевой переменной\n", + "import matplotlib.pyplot as plt\n", "\n", - "# Проверим, что эти столбцы добавлены\n", - "print(data[['Day_of_week', 'Month']].head())\n", - "\n", - "# Выбираем признаки и целевую переменную\n", - "X = data[['Close', 'SMA_5', 'SMA_20', 'STD_5', 'STD_20', 'Day_of_week', 'Month']]\n", - "y = data['Price_Change']\n", - "\n", - "# Применяем one-hot encoding для категориальных признаков (Day_of_week и Month)\n", - "X = pd.get_dummies(X, columns=['Day_of_week', 'Month'], drop_first=True)\n", - "\n", - "# Масштабирование числовых признаков (Close, SMA, STD)\n", - "scaler = StandardScaler()\n", - "X_scaled = scaler.fit_transform(X)\n", - "\n", - "# Преобразуем обратно в DataFrame для удобства\n", - "X_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)\n", - "\n", - "# Проверим результат\n", - "X_scaled_df.head()\n" + "plt.figure(figsize=(10, 5))\n", + "plt.hist(data['Close'], bins=30, edgecolor='k', alpha=0.7)\n", + "plt.title(\"Распределение целевой переменной 'Close'\")\n", + "plt.xlabel(\"Цена закрытия\")\n", + "plt.ylabel(\"Частота\")\n", + "plt.grid(True)\n", + "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "Создадим модель для прогназирования " + ] }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 53, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Прогнозирование цены акции ---\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "\n", + "# Модель для прогнозирования цены акции\n", + "print(\"\\n--- Прогнозирование цены акции ---\")\n", + "price_model = LinearRegression()\n", + "price_model.fit(X_train, y_train)\n", + "\n", + "# Предсказание на тестовой выборке\n", + "y_pred_price = price_model.predict(X_test)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Оценка модели" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error (MSE): 0.0000\n", + "R^2 Score: 1.0000\n" + ] + } + ], + "source": [ + "# Оценка модели\n", + "mse_price = mean_squared_error(y_test, y_pred_price)\n", + "r2_price = r2_score(y_test, y_pred_price)\n", + "print(f\"Mean Squared Error (MSE): {mse_price:.4f}\")\n", + "print(f\"R^2 Score: {r2_price:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "MSE = 0.0000 и R² = 1.0000, это говорит о том, что ваша модель предсказывает данные абсолютно точно. (йоу)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Визуализируем полученный результат " + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Визуализация результатов прогноза\n", + "plt.figure(figsize=(10, 5))\n", + "plt.plot(y_test.values, label='Истинные значения', color='blue')\n", + "plt.plot(y_pred_price, label='Предсказанные значения', color='red')\n", + "plt.title(\"Прогнозирование цены акции\")\n", + "plt.xlabel(\"Наблюдения\")\n", + "plt.ylabel(\"Цена закрытия\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "В целом, на основе этой диаграммы можно сделать вывод, что текущая модель не точно предсказывает цену акции" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Теперь сделаем все то же самое для волотильности " + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Оценка волатильности ---\n", + "Mean Squared Error (MSE) для волатильности: 0.0003\n", + "R^2 Score для волатильности: 0.6214\n" + ] + } + ], + "source": [ + "# Модель для оценки волатильности\n", + "print(\"\\n--- Оценка волатильности ---\")\n", + "X_volatility = data[['High', 'Low', 'Open']] # Переменные для оценки волатильности\n", + "y_volatility = data['Volatility']\n", + "\n", + "# Разделение на выборки для модели волатильности\n", + "X_train_vol, X_test_vol, y_train_vol, y_test_vol = train_test_split(X_volatility, y_volatility, test_size=0.2, random_state=42)\n", + "\n", + "volatility_model = LinearRegression()\n", + "volatility_model.fit(X_train_vol, y_train_vol)\n", + "\n", + "# Предсказание на тестовой выборке\n", + "y_pred_volatility = volatility_model.predict(X_test_vol)\n", + "\n", + "# Оценка модели волатильности\n", + "mse_volatility = mean_squared_error(y_test_vol, y_pred_volatility)\n", + "r2_volatility = r2_score(y_test_vol, y_pred_volatility)\n", + "print(f\"Mean Squared Error (MSE) для волатильности: {mse_volatility:.4f}\")\n", + "print(f\"R^2 Score для волатильности: {r2_volatility:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "MSE = 0.0003 и R² = 0.6214 говорят о том, что модель делает предсказания с малой ошибкой, но есть еще пространство для улучшения, поскольку она объясняет менее 70% вариации в данных." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Визуализируем " + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Ybdq0wSOPPIKKFSti586dyM/Px1dffeX3un1tGyIqHxg4EVG507FjR7Rr1w5jx47F8ePHce2112L27Nm4/vrrnfM0bdoU69evx8iRIzF+/HjYbDbceOONmDdvnmpWumAYMGAA4uPjMWHCBIwYMQIJCQno06cPJk6c6OyWFhMTg9q1a2PGjBk4f/48UlJS0LRpU/zyyy+KDxJ97bXXNLWQFcd2cQR70dHRqF27NkaNGoVXX33V+f4333yDZ555Bp988gkkSULXrl2xdOlSVK9e3d9N5+Hpp5/2+RymRx55BNWrV8eECRMwadIkGI1G1KhRA+3bt0d6erqm9fj6jp07d8ayZcswevRojBo1ClFRUejYsSMmTpzofPbXtm3bsHv3bkydOhXPPPOM1/VdddVV+PXXX/Hiiy/i9ddfR8WKFfHoo4/ijjvuQLdu3TSVWYlOp8OECRNU3798+TLmz5+PJ554AmPHjoVe77vTisFgcCYwSUxMRKNGjTB37lyvrafezJw5E/Xr18fs2bOxcOFCpKWlYeTIkRg9erRsvkGDBiE1NRUTJkzAW2+9haioKDRu3Ni5r/zla9sQUfmgk4p6i4qIqBTR6XQYOnSoppaE8oTbhYiIyDuOcSIiIiIiIvKBgRMREREREZEPDJyIiIiIiIh84BgnIiIiIiIiH9jiRERERERE5AMDJyIiIiIiIh/K3XOcbDYbTp8+jaSkJD79m4iIiIioHJMkCbm5uahevbrP59OVu8Dp9OnTqFWrVqiLQUREREREYeLEiROoWbOm13nKXeCUlJQEwL5xkpOTQ1wawGw2Y/ny5ejatSuioqJCXRwKEzwuSAmPC1LC44KU8LggNTw25HJyclCrVi1njOBNuQucHN3zkpOTwyZwio+PR3JyMg9ecuJxQUp4XJASHhekhMcFqeGxoUzLEB4mhyAiIiIiIvKBgRMREREREZEPDJyIiIiIiIh8KHdjnIiIiEJBkiRYLBZYrdagLtdsNiMyMhIGgyHoy6bSi8cFqSmPx0ZUVBQiIiKKvBwGTkRERMXMZDLhzJkzyM/PD/qyJUlCWloaTpw4wecTkhOPC1JTHo8NnU6HmjVrIjExsUjLYeBERERUjGw2G44cOYKIiAhUr14d0dHRQa2s2Gw2XLlyBYmJiT4f3kjlB48LUlPejg1JknDhwgWcPHkSV199dZFanhg4ERERFSOTyQSbzYZatWohPj4+6Mu32WwwmUyIjY0tF5Ug0obHBakpj8dGlSpVcPToUZjN5iIFTuVjaxEREYVYeamgEBGFm2C18vMsTkRERERE5AMDJyIiIiIiIh8YOBEREREREfnAwImIiIgUDRgwAL1795ZNO3bsGGJjY8tNGmMiIgcGTkRERKTZG2+8waCJiMolBk5Uai09uBQ9v+6JUzmnQl0UIiK/SJKEPFNe8P43a59XkqSAy7179258/fXXeOaZZzze27hxIzp16oT4+HhUrFgR3bp1w+XLlzFgwADodDrF/wcMGAAA6NSpE55//nnnsg4cOICoqCi0aNHCOU2p9Wv27NlISUlx/j1mzBjZZwBg7dq10Ol0yMrKUvyMKCMjAzqdDkePHnVO27BhA9q3b4+4uDjUqlULzz77LPLy8lS30dSpU1G7dm3ExMSgatWqGDx4sPPBx0ePHoVOp0NGRobsM3Xr1sWUKVOcf7///vu47rrrkJCQgFq1amHIkCG4cuWK122h0+mwaNEi598nTpxAeno6KlWqhEqVKqFXr16y7xXI9jSZTGjYsKFsewLArFmzcM011zifUabT6WT70514TERHR6Nx48aYO3eubJ5p06ahQYMGiI6OxjXXXOPxvvtylNbrbTs6jgu1/5W2BwB06NBBdR+6L8OxP9yPQXdaj4vjx4+jV69eSExMRHJyMh544AGcO3dO9plffvkFbdq0QWxsLCpXrow+ffoAsP/GHOWKiIhAxYoVERERAZ1OhzFjxiiub9WqVdDpdB7HSXnG5zhRqdXzm54AgCFLhmDxQ4tDXBoiIu3yzflIHF+0J9gH6srIK0iITgjos6+88gruvvtu3HzzzbLpGRkZuOOOOzBw4EBMnToVkZGRWLNmDaxWK6ZOnYoJEyYAAJ577jkA9uACAOLi4hTX87///Q+xsbEBlTGYDh06hO7du+Ptt9/Gl19+iQsXLmDYsGEYNmwYZs2apfiZtm3bYsGCBahWrRqOHTuGfv36YfLkyRg1apTm9er1enz44YeoV68eDh8+jCFDhuDll1/Gp59+qunzZrMZPXr0QKtWrbBu3TpER0fj7bffRvfu3bFr1y5ER0drLovo448/9qio79+/H4MHD8a4cePw8MMPIzo6Gvfcc4/PZXXv3h2zZs2C0WjExx9/jPT0dPTp0weJiYlYuHAhnnvuOUyZMgWdO3fGr7/+ivT0dNSsWRO33XabcxmSJDmXA8Bjvd62480334wzZ84AAP7880/ce++9zr/V/PTTT9ixY4fie5Ik4c0338Tjjz8OAKhWrZrPbeAPm83mDJrWrVsHi8WCoUOH4sEHH8TatWsBAL/99hv69OmD1157DXPmzIHJZMKSJUucZTeZTADs26lVq1YYOXIk9Ho9EhM9z0U2mw0vvvii4nvlGQMnKvXOXjkb6iIQEZV5f/zxB37//Xfs3r0bBw4ckL337rvvonXr1rKKfdOmTZ2vK1SoAMAVKKWlpamuZ82aNfjzzz8xePBgrFmzJphfwW/jx49H3759na0YV199NT788EN07NgR06ZNUwzu2rVr53wdGxuL5ORkWK1Wv9YrtprUrVsXb7/9Np566inn9o2Li/NayZ8/fz5sNhs+/PBDVKhQAXq9HrNmzUJKSgrWrl2Lrl27+lUeALh06RLefvttjBgxAm+88YZz+q5duxAREYERI0Y4p2kJzGJiYpCWlgZJklC9enUkJCQ4H0w6efJkDBgwAEOGDAEADB8+HH/99RcmT54sC5zMZjMSExOdx5P7er1tx+joaOfnKlWqBMD7cWk2mzFixAiP7y++X6lSJa/LKIpVq1Zh9+7dOHLkCGrVqgUAmDNnDpo2bYotW7agTZs2eOedd/DQQw9h7Nixzs81b94cgOs7AvbtlJCQgLS0NNXny3311VcwGo3o1auXrLWzvGPgREREVMLio+JxZWRwKiM2mw05uTlITkrW9JDd+Kj4gNbzyiuvoH///mjSpIlH4JSRkYH7778/oOWKJEnCiy++iNGjR+PixYse7//666+yO+AWi8UjeNm9e7dsHqWgJTs7G4mJidDr9ahatSp69eqF8ePHe8y3c+dO7Nq1C19//bWsjDabDUeOHEGTJk0Uv8fXX3+NJ554Avn5+bj33ntlQQUA3HzzzbJ95ejK57By5UqMHz8e+/fvR05ODiwWCwwGA/Lz8xEfH49mzZrhm2++wZEjR1CvXj3Fcv/333/OCraDwWDAoUOHnH9r2Z4Ob775Jm677Tbceuutsun16tWD2WzGggULcN9992ke/+ZYt8lkQnR0NObNm+cMrPft24cnnnhCNv8tt9zibKl0yMnJQeXKlVXX4Ws7+uOTTz5BhQoV0LdvX8XAKScnBwkJ3ltya9asCZ1Oh8qVK6Nz586YPHmy86YC4P242LdvH2rVqiXbp9deey1SUlKwb98+tGnTBhkZGc4Wr6LIz8/H66+/junTp+PHH38s8vLKEgZOREREJUyn0wXcXc6dzWaDNcqKhOgETYFTIBYuXIgdO3bg+++/V3xfrcudv+bMmYO8vDw89dRTeOeddzzev+222zBt2jTn3z/99BPGjRsnm+eaa67Bzz//7Pz777//xqOPPiqbJykpCdu3b4ckSdi7dy/69++PtLQ0dO7cWTbflStX8OSTT+LZZ5/1KEvt2rVVv8f//d//oU2bNti/fz+GDh2KhQsXom/fvs7358+fLwu6OnXq5Hx99OhR3HXXXXj66afxzjvvoFKlStiwYQMGDRoEk8mE+Ph4DBw4EAsXLkT9+vUVK+tXrlxBq1atMG3aNGeA6FClShXnay3bEwAOHjyImTNnIiMjAydPnpS916ZNG7z55ptIT0/Ho48+iqioKBQUFHiMNXPnWLfZbMbSpUvRr18/7Nq1C3Xr1vX6OdHp06dx/fXXK76nZTtqdfnyZbz11ltYuHChYmCYk5ODvLw8VK9e3ety1q9fj6SkJBw9ehSDBw/Ga6+9ho8//tj5vrfjQotg/Q4nTZqEa665BnfffTcDJzcMnIiIiEiV1WrFa6+9hmeeeQY1a9ZUnOf666/HqlWrZF2E/JWfn++sSEZFRSnOk5CQgIYNGzr/Tk1N9ZgnOjpaNo97RR+wj31xzHP11VejS5cuyMjI8AicWrZsib1798qWp0VSUhKSkpLQqFEjrFmzBt9++60scKpVq5ZsmZGRrurYtm3bYLPZ8N577zkDHveANS4uDitXrsS5c+eQm5vr/B5iuefPn4/KlSujZs2aqgG1lu0JACNGjMDgwYPRsGFDxe357LPPYs6cORg0aBDuu+8+2XdVI667SZMmGDduHFauXInBgwejSZMm2LhxI/r37++cf+PGjbj22mudf+fl5WHfvn0YOXKk4vK1bEet3nrrLbRv3x4dOnSQJdhw2LJlC3Q6nc9gsV69ekhJSUHDhg1x//33Y9OmTbL3vR0XTZo0wYkTJ3DixAlnq9PevXuRlZXl3C6O32F6enpA3xMAzpw5g2nTpmHdunUBL6MsY+BEREREqlauXInY2FjVCioAjBw5Etdddx2GDBmCp556CtHR0VizZg3uv/9+r12pRN988w1atWpVYhm8DAaDs8Vpw4YNzsQVohEjRuCmm27CsGHDMHjwYCQkJGDv3r1YsWKFrKVANGvWLLRs2RIpKSnYuXMnvv32W7+6TzVs2BBmsxkfffQR7r77bmzcuBHTp09XnLdq1aqoWrWqx/S+ffti0qRJ6Nu3L95++23Url0bx44dw08//YSXX35ZNQBW8t9//+H48eP477//FN+XJAn9+vVDy5Yt8corrwDQ1vJhNBpx9uxZWCwWLFu2DJcuXULjxo0B2JODPPDAA7jhhhvQuXNn/PLLL/jpp5+wcuVKAPaEFC+//DJSUlLQo0cPxeX7sx29yc/Px4wZM7B9+3bF99esWYOhQ4eiZ8+eqoGn+J0NBgOOHj2KpUuXenR79KZz58647rrr0LdvX0yZMgUWiwVDhgxBx44d0bp1awDA6NGjcccdd6BBgwZ46KGHYLFYsGTJEo+uot588sknuPfee3HDDTdo/kx5wnTkVOrpwOeJEBEVF4PBgBEjRqBixYqq8zRq1AjLly/Hzp070bZtW7Rr1w6LFy+W3TH3JT8/H++9914wiuxTdnY24uLikJCQgLvuugt9+vTB8OHDPea7/vrrsW7dOvz7779o3749brjhBowaNcprl6xNmzahe/fuaNSoEZ555hnVMTFqmjdvjvfffx8TJ05Es2bN8PXXXyuOv/ImPj4ea9euRc2aNXHfffehSZMmGDRoEAwGA5KTk/1aVl5eHl577TVZcgHRhAkTcPDgQXzxxRd+LXfZsmWoVq0a6tWrh4kTJ+Kjjz5yBhK9e/fG1KlTMXnyZDRt2hSfffYZZs2a5ey6NmbMGFgsFqxcuVI161swtiNgT/qQnp6ORo0aKb4/cOBAtG/fHvPmzfO5rLS0NMTFxaF9+/Zo3ry5X+XR6XRYvHgxKlasiA4dOqBz586oX78+5s+f75ynU6dOWLBgAX7++We0aNECt99+OzZv3qx5HYC9669SN1my00lFeaBDkHzyySeYNGkSzp49i+bNm+Ojjz5C27ZtfX7uu+++w8MPP4xevXrJnl3gTU5ODipUqIDs7Gy/Tx7FwWw2Y8mSJejZs6dq1wRSphtrD5hurHEj/hr8V4hLE1w8LkgJj4vSyWAwOAfxF0eKbZvNhpycHCQna0sOQeUDjwtSUx6PDW/nYX9ig5Bvrfnz52P48OEYPXo0tm/fjubNm6Nbt244f/68188dPXoUL730Etq3b19CJSUiIiIiovIq5IHT+++/j8cffxzp6em49tprMX36dMTHx+PLL79U/YzVakXfvn0xduxY1K9fvwRLS+FIQsgbTYmIiIiojAtpcgiTyYRt27bJBpzq9Xp07tzZI9OI6M0330RqaioGDRqE9evXe12H0WiE0Wh0/p2TkwPA3uXFbDYX8RsUnaMM4VCW0kqSpDK3/XhckBIeF6WT2Wx2PvvHZrMFffmOHveOdRABPC5IXXk8Nmw2m7O+6HjQsoM/19SQBk6ZmZmwWq0eGWGqVq2K/fv3K35mw4YN+OKLL5CRkaFpHePHj1dMj7p8+XK/H35WnFasWBHqIpRa2VnZWLJkSaiLUSx4XJASHhelS2RkJNLS0nDlyhWYTKZiW48jLTWRiMcFqSlPx4bJZEJBQQH++OMPWCwW2XvuD6D2plSlI8/NzcVjjz2Gzz//XHN605EjR8oy5eTk5KBWrVro2rVr2CSHWLFiBbp06cLB3v7KsP+TkpKCnj17hrQowcbjgpTwuCidDAYDTpw4gcTExGJJDiFJEnJzc5GUlKT4cE4qn3hckJryeGwYDAbExcWhQ4cOiskhtApp4FS5cmVERETg3Llzsunnzp1DWlqax/yHDh3C0aNHcffddzunOZoYIyMjceDAATRo0ED2mZiYGMTExHgsKyoqKqwqHuFWntJEp9OV2W3H44KU8LgoXaxWK3Q6HfR6fbFksHJcBx3rIAJ4XJC68nhs6PV6Z33R/frpz/U0pFsrOjoarVq1wqpVq5zTbDYbVq1ahXbt2nnM37hxY+zevRsZGRnO///v//4Pt912GzIyMpxPUiYiIiIiIgqmkHfVGz58OPr374/WrVujbdu2mDJlCvLy8pCeng4A6NevH2rUqIHx48cjNjYWzZo1k30+JSUFADymExERERERBUvIA6cHH3wQFy5cwKhRo3D27Fm0aNECy5YtcyaMOH78eLlpRiQiIiIiovAU8sAJAIYNG4Zhw4Ypvrd27Vqvn509e3bwC0RERERERCRgUw4REREpGjBgAHQ6ner/WVlZoS4iEVGJYeBEpV55SaVJRBQK3bt3x5kzZ2T///jjj6EuFhFRiWPgREREVNIkCbDkheZ/SfKrqDExMUhLS5P9X6lSJdk8s2fPRkpKChYtWoSrr74asbGx6NatG06cOCGbb/HixWjZsiViY2NRv359jB071uNhlGPGjPFo2erdu7dsno0bN6JTp06Ij49HxYoV0a1bN1y+fBkA0KlTJzz//PPOeWfOnImUlBRs374dgD09/KBBg1CvXj3ExcXhmmuuwdSpU2XLf+WVV1C9enVER0ejRo0aGDFihDOFs5bPDxgwwKPMjm0kfs8WLVrI5lm7dq2sJc/9M6KMjAzodDocPXrUOW3Dhg1o37494uLiUKdOHYwYMQJ5eXmKnweAqVOnonbt2oiJiUHVqlUxePBg58NAjx49Cp1Oh4yMDNln6tatiylTpjj/fv/993HdddchISEBtWrVwpAhQ3DlyhWv20Kn02HRokXOv0+cOIEHHngAKSkpqFSpEnr16iX7XoFsT5PJhIYNG3q0jM6aNQvXXHMNoqOjnceXeLy4E1tdo6Oj0bhxY8ydO1c2z7Rp09CgQQNER0fjmmuu8XjffTlK6/W2HR3Hhdr/StsDADp06KC4D+vXr4+KFSsiIiLCuQzH/nA/Bt1pPS6OHz+OXr16ITExEcnJyXjggQc8Hj/0yy+/oE2bNoiNjUXlypXRp08fAPbfsNp3HTNmjOL6Vq1apXiuCLawGONERERUrljzge8Tg7IoPYAUfz7wwBUgMiEo6xbl5+fjnXfewZw5cxAdHY0hQ4bgoYcewsaNGwEA69evR79+/fDhhx+iffv2OHToEJ544gkAwOjRo2XLatq0KVauXAkAeO6552A0Gp3vZWRk4I477sDAgQMxdepUREZGYs2aNbBarR5l+v777/HCCy/gt99+Q8uWLQHYH3tSs2ZNLFiwAFdddRX+/PNPPPHEE6hWrRoeeOABAEDXrl3xwAMPoHLlytizZw8eeeQRNG3aFP369dP0+VA4dOgQunfvjrfffhtffvklzp07h6FDh+KZZ55RHQ/etm1bLFiwANWqVcOxY8fQr18/TJ48GaNGjdK8Xr1ejw8//BD16tXD4cOHMWTIELz88sv49NNPNX3ebDajW7duaNeuHdavX4/IyEi8/fbb6N69O3bt2oXo6GjNZRF9/PHHHhX1/fv3Y/DgwRg3bhwefvhhREdH45577vG5rO7du2PWrFkwGo34+OOPkZ6ejj59+iAxMRELFy7Ec889hylTpqBz58749ddfkZ6ejpo1a+K2225zLkOSJOdyAHis19t2vPnmm3HmzBkAwJ9//ol7773X+bean376CTt27FB8T5IkvPrqqxg6dCj0ej2qVavmcxv4w2azOYOmdevWwWKxYOjQoXjwwQeduQt+++039OnTB6+99hrmzJkDk8mEJUuWOMtuMpkA2LfTzTffjJdeegkAkJjoed602Wx48cUXFd8LNgZOREREVGRmsxkff/wxbrzxRgDAV199hSZNmmDz5s1o27Ytxo4di1deeQX9+/cHYL/r/dZbb+Hll1+WBU5GoxFxcXFIS0sDAMTFxckCp3fffRetW7eWVcybNm3qUZ6lS5ciPT0dCxYsQIcOHZzTo6KiMHbsWOff9erVw6ZNm/D99987A5/bb7/d+b7VakVcXJwzMNPy+VAYP348+vbt62zFaNCgASZMmIC77roL06dPR2xsrMdnxGdmxsbGIjk5WTEA9UZsNalbty7efvttPPXUU879ExcX57WSP3/+fNhsNsycOdPZejJr1iykpKRg7dq16Nq1q1/lAYBLly7h7bffxogRI/DGG284p+/atQsREREYMWKEc5qWwMzR6ipJEqpXr46EhAREREQAACZPnowBAwZgyJAhAOyP2fnrr78wefJkWeBkNpuRmJjoPK7d1+ttO0ZHRzs/52jtdfytxGw2Y8SIER7fX3w/JSUFaWlpxZK5etWqVdi9ezeOHDnifMbqnDlz0LRpU2zZsgVt2rTBO++8g4ceekj2W2revDkAyFq0o6OjZdtNyVdffQWj0YhevXrJWjuLAwMnIiKikhYRb2/5CQKbzYacnBwkJydrqwRFxAdlve4iIyPRpk0b59+NGzdGSkoK9u3bh7Zt22Lnzp3YuHEj3nnnHec8VqsVBoMB+fn5iI+3l+vixYtITk5WXU9GRgbuv/9+r2XZvHkzZsyYgcTERGcgJ/rkk0/w5Zdf4vjx4ygoKIDJZPLoNjdu3Di8/fbbKCgowLBhw9CvXz+/Pv/rr7/K7oBbLBaP4GX37t2yeZSCluzsbCQmJkKv16Nq1aro1asXxo8f7zHfzp07sWvXLnz99dfOaZIkwWaz4ciRI2jSpInitvr666/xxBNPID8/H/fee68sqACAm2++WXZcObryOaxcuRLjx4/H/v37kZOTA4vFItunzZo1wzfffIMjR46gXr16iuX+77//kJSUJJtuMBhw6NAh599atqfDm2++idtuuw233nqrbHq9evVgNpuxYMEC3HfffZrHSDvWbTKZEB0djXnz5iEuLg4AsG/fPmfLqcMtt9zi0X0zJycHlStXVl2Hr+3oj08++QQVKlRA3759FQOnnJwcJCR4b3WuWbMmdDodKleujM6dO2Py5MmoUKGC831vx8W+fftQq1YtZ9AEANdee63zfNCmTRtkZGTg8ccf9+t7KcnPz8frr7+O6dOnl8jYSwZOREREJU2nC153OZsNiLTalxfGzz28cuUKxo4dq9g1SqwAHz58WLGC7eCosHqzadMmTJs2DT/88AOGDRuGb7/91vned999h5deegnvvfce2rVrh6SkJEyaNAl///23bBlPPfUU7rnnHmzbtg3PP/887rnnHtx2222aP3/bbbdh2rRpzr9/+uknjBs3TjbPNddcg59//tn5999//41HH31UNk9SUhK2b98OSZKwd+9e9O/fH2lpaejcubNsvitXruDJJ5/Es88+C8AeUF+5cgWJiYmoW7eu6rb6v//7P7Rp0wb79+/H0KFDsXDhQvTt29f5/vz582VBV6dOnZyvjx49irvuugtPP/003nnnHVSqVAkbNmzAoEGDYDKZEB8fj4EDB2LhwoWoX7++YmX9ypUraNWqlSzgc6hSpYrztZbtCQAHDx7EzJkzkZGRgZMnT8rea9OmDd58802kp6fj0UcfRVRUFAoKCjyCXneOdZvNZixduhT9+vXDrl27vG5Xd6dPn8b111+v+J6W7ajV5cuX8dZbb2HhwoWKgWFOTg7y8vK8tuAA9q61SUlJOHr0KAYPHozXXnsNH3/8sfN9b8eFFlp+x1pMmjQJ11xzDe6++24GTkRERFQ6WCwWbN26FW3btgUAHDhwAFlZWc7KVcuWLXHgwAE0bNhQdRkGgwGbN2/GY489pjrP9ddfj1WrVsm6+Lh77LHH8NRTT6FHjx5o1qwZFi5c6Bx4vnHjRtx8883OrlUAZC0bDpUqVUKlSpXQuHFj/PDDD/jxxx9x2223af58QkKC7LumpqZ6zBMdHS2bx72iD9jHvjjmufrqq9GlSxdkZGR4BE4tW7bE3r17nfNqbYlMSkpCUlISGjVqhDVr1uDbb7+VBU61atWSlTEy0lV13LZtG2w2G9577z3nOr7//nvZ8uPi4rBy5UqcO3cOubm5zu8hlnv+/PlITU312tKoZXsCwIgRIzB48GA0bNhQcXs+++yzmDNnDgYNGoT77rtP9l21rLtJkyYYN24cVq5cicGDB6NJkybYuHGjswsqYD/Grr32WuffeXl52LdvH0aOHKm4fC3bUau33noL7du3R4cOHWQJNhy2bNkCnU6H6667zuty6tWrh5SUFDRs2BD3338/Nm3aJHvf23HRpEkTnDhxAidOnHC2Ou3duxdZWVnO7eL4Haenpwf0PQHgzJkzmDZtGtatWxfwMvwVvremiIiIqNSIiorCM888g7///hvbtm3DgAEDcNNNNzkDqVGjRmHOnDkYO3Ys9uzZg3379uG7777D66+/DsDe8uBISnDrrbfi7NmzOHv2LAoKCmA0GpGdnQ0AGDlyJLZs2YIhQ4Zg165d2L9/P6ZNm4bMzExnWRxjJOrUqYNJkybh6aefxsWLFwHYK+1bt27F77//jn///RdvvPEGtmzZIvsun376Kfbs2YOjR49i3rx5WLFiBW644QbNnw82g8GAgoICbNu2DRs2bECzZs085hkxYgT+/PNPDBs2DBkZGTh48CCWLFmCZ555RnW5s2bNws6dO3Hs2DH8/PPP+Pbbb53fU4uGDRvCbDbjo48+wuHDhzF37lxMnz5dcd6qVauiYcOGHoFz3759UblyZfTq1Qvr16/HkSNHsHbtWjz77LOKgY83//33H9auXaua3EKSJPTr1w8tW7bEK6+8goYNG2pq+TAajTh79ixOnjyJmTNn4tKlS2jcuDEA4H//+x9mz56NadOm4eDBg3j//ffx008/OZMZ7N+/Hw8//DBSUlLQo0cPxeX7sx29yc/Px4wZM/Duu+8qvr9mzRoMHToUPXr0kLXmqX1ng8GA/fv3Y+nSpYrHnJrOnTvjuuuuQ9++fbF9+3Zs3rwZ/fr1Q8eOHdG6dWsA9oQw3377LUaPHo19+/Zh9+7dmDhxovYvC3uXxD59+vh1zBYVAyciIiIqsvj4eIwYMQKPPPIIbrnlFiQmJmL+/PnO97t164Zff/0Vy5cvR5s2bXDTTTfhgw8+QJ06dQDYB9lPmjQJubm5aNiwIapVq4Zq1arh+++/x7Jly/Dcc88BABo1aoTly5dj586daNu2Ldq1a4fFixfL7niLnnzySTRr1swZQDz55JO455578OCDD+LGG2/ExYsXZa1HgD3jV6dOndC4cWOMHTsWr776KgYOHKj588GUnZ2NuLg4JCQk4K677kKfPn0wfPhwj/muv/56rFu3Dv/++y/at2+PVq1aYdy4cahevbrqsjdt2oTu3bujUaNGeOaZZ1THxKhp3rw53n//fUycOBHNmjXD119/rTj+ypv4+Hj88ccfqF27Nu655x40adIEgwYNgsFg8NoCpSQvLw+vvfaaR7p8hwkTJuDgwYP44osv/FrusmXLUK1aNdSrVw8TJ07ERx995Bw/1bt3b0ydOhWTJ09G06ZN8dlnn2HWrFnOrmtjxoyBxWLBypUrVbO+BWM7AvakD+np6WjUqJHi+wMHDkT79u0V06W7S0tLQ1xcHNq3b4/mzZv7VR6dTofFixejYsWK6NChAzp37oz69evLzgedOnXCggUL8PPPP6NFixa4/fbbsXnzZs3rAOytquKYyZKgkyQ/H+hQyuXk5KBChQrIzs72+wdZHMxmM5YsWYKePXsiKioq1MUpVXRj7X13b6p5EzYN2uRj7tKFxwUp4XFROhkMBufAeLXB7EXhd3KIYjB79mw8//zzqs9+0cLxfBbHv6JFixZh0aJFqmm1yVM4HBcUnsrjseHtPOxPbMAxTlTq6aAtKw4REYUvb89giY2NlWX0IiIKBQZOREREFHKOMSFKunfvju7du5dgaYiIPJWP9jkiIiIqNgMGDChSNz0iotKAgRMREREREZEPDJyIiIhKQDnLxUREFDaCdf5l4ERERFSMHBkQ8/PzQ1wSIqLyyWQyAQAiIiKKtBwmhyAiIipGERERSElJwfnz5wHYn1uj0wUvG6jNZoPJZILBYCg3qYXJNx4XpKa8HRs2mw0XLlxAfHy86vPetGLgREREVMzS0tIAwBk8BZMkSSgoKEBcXFxQAzIq3XhckJryeGzo9XrUrl27yN+XgROVeuXlR09EpZdOp0O1atWQmpoKs9kc1GWbzWb88ccf6NChAx+MTE48LkhNeTw2oqOjg9K6xsCJiIiohERERBS5j73SMi0WC2JjY8tNJYh843FBanhsBK7sd2wkIiIiIiIqIgZOREREREREPjBwIiIiIiIi8oGBExERERERkQ8MnIiIiIiIiHxg4EREREREROQDAycq9XTgc5yIiIiIqHgxcCIiIiIiIvKBgRMREREREZEPDJyIiIiIiIh8YOBERERERETkAwMnIiIiIiIiHxg4ERERERER+cDAiYiIiIiIyAcGTkRERERERD4wcCIiIiIiIvKBgRMREREREZEPDJyIiIiIiIh8YOBERERERETkAwMnIiIiIiIiHxg4ERERERER+cDAiUo9nU4X6iIQERERURnHwImIiIiIiMgHBk5EREREREQ+MHAiIiIiIiLygYETERERERGRDwyciIiIiIiIfGDgRERERERE5AMDJyIiIiIiIh8YOFGppwOf40RERERExYuBExERERERkQ8MnIiIiIiIiHxg4EREREREROQDAyciIiIiIiIfGDgRERERERH5wMCJiIiIiIjIBwZOREREREREPjBwIiIiIiIi8oGBExERERERkQ8MnKjU0+l0oS4CEREREZVxDJyIiIiIiIh8YOBERERERETkAwMnIiIiIiIiHxg4ERERERER+cDAiYiIiIiIyAcGTkRERERERD4wcCIiIiIiIvKBgROVejrwOU5EREREVLwYOBEREREREfkQFoHTJ598grp16yI2NhY33ngjNm/erDrvTz/9hNatWyMlJQUJCQlo0aIF5s6dW4KlJSIiIiKi8ibkgdP8+fMxfPhwjB49Gtu3b0fz5s3RrVs3nD9/XnH+SpUq4bXXXsOmTZuwa9cupKenIz09Hb///nsJl5yIiIiIiMqLkAdO77//Ph5//HGkp6fj2muvxfTp0xEfH48vv/xScf5OnTqhT58+aNKkCRo0aIDnnnsO119/PTZs2FDCJSciIiIiovIiMpQrN5lM2LZtG0aOHOmcptfr0blzZ2zatMnn5yVJwurVq3HgwAFMnDhRcR6j0Qij0ej8OycnBwBgNpthNpuL+A2KzlGGcChLaSVJUpnbfjwuSAmPC1LC44KU8LggNTw25PzZDiENnDIzM2G1WlG1alXZ9KpVq2L//v2qn8vOzkaNGjVgNBoRERGBTz/9FF26dFGcd/z48Rg7dqzH9OXLlyM+Pr5oXyCIVqxYEeoilFoXL17EkiVLQl2MYsHjgpTwuCAlPC5ICY8LUsNjwy4/P1/zvCENnAKVlJSEjIwMXLlyBatWrcLw4cNRv359dOrUyWPekSNHYvjw4c6/c3JyUKtWLXTt2hXJycklWGplZrMZK1asQJcuXRAVFRXq4pQuGfZ/rrrqKvTs2TOkRQk2HhekhMcFKeFxQUp4XJAaHhtyjt5oWoQ0cKpcuTIiIiJw7tw52fRz584hLS1N9XN6vR4NGzYEALRo0QL79u3D+PHjFQOnmJgYxMTEeEyPiooKq4Ml3MpTmuh0ujK77XhckBIeF6SExwUp4XFBanhs2PmzDUKaHCI6OhqtWrXCqlWrnNNsNhtWrVqFdu3aaV6OzWaTjWMiIiIiIiIKppB31Rs+fDj69++P1q1bo23btpgyZQry8vKQnp4OAOjXrx9q1KiB8ePHA7CPWWrdujUaNGgAo9GIJUuWYO7cuZg2bVoovwYREREREZVhIQ+cHnzwQVy4cAGjRo3C2bNn0aJFCyxbtsyZMOL48ePQ610NY3l5eRgyZAhOnjyJuLg4NG7cGPPmzcODDz4Yqq9ARERERERlXMgDJwAYNmwYhg0bpvje2rVrZX+//fbbePvtt0ugVERERERERHYhfwAuUVHpdLpQF4GIiIiIyjgGTkRERERERD4wcCIiIiIiIvKBgRMREREREZEPDJyIiIiIiIh8YOBERERERETkAwMnIiIiIiIiHxg4ERERERER+cDAiYiIiIiIyAcGTkRERERERD4wcKJSTwddqItARERERGUcAyciIiIiIiIfGDgRERERERH5wMCJiIiIiIjIBwZOREREREREPjBwIiIiIiIi8oGBExERERERkQ8MnIiIiIiIiHxg4ESlnk7H5zgRERERUfFi4EREREREROQDAyciIiIiIiIfGDgRERERERH5wMCJiIiIiIjIBwZOREREREREPjBwIiIiIiIi8iEyGAv56aefkJGRgeuuuw73339/MBZJREREREQUNorc4jRx4kQ8/PDDWLZsGQYOHIixY8cGo1xERERERERho8iB01dffYWZM2di8+bNWLx4MWbNmhWMchEREREREYWNIgdOp0+fxk033QQAuOmmm3Dq1KkiF4rIHzroQl0EIiIiIirjihw4Wa1WREbah0pFRETAZrMVuVBEREREREThJKDkEDfccAN0Ovtd/oKCAtx9992Ijo6GJElBLRwREREREVE4CChw6tWrlzNw6tWrl+y93r17F7lQRL4wSCciIiKikhRQ4DRmzJggF4OIiIiIiCh8BTTGqX79+rh48WKwy0KkmQS2OBERERFRyQkocDp69CisVmuwy0JERERERBSWAs6q5xjjRBQKHONERERERCUpoDFOANC6dWtEREQovnf48OGAC0SkBbvqEREREVFJCjhwevHFF1GhQoVgloWIiIiIiCgsBRQ46XQ6PPTQQ0hNTQ12eYg0YVc9IiIiIipJAY1xYqWViIiIiIjKk4ACp1mzZrGbHoUUxzgRERERUUkKKHC69tprkZGR4TH977//xtatW4taJiKfxFZPZngkIiIiouIWUOA0dOhQnDhxwmP6qVOnMHTo0CIXioiIiIiIKJwEFDjt3bsXLVu29Jh+ww03YO/evUUuFJEv7KpHRERERCUpoMApJiYG586d85h+5swZREYGnOGcSDMmKCEiIiKikhRQ4NS1a1eMHDkS2dnZzmlZWVl49dVX0aVLl6AVjoiIiIiIKBwE1Dw0efJkdOjQAXXq1MENN9wAAMjIyEDVqlUxd+7coBaQSAm76hERERFRSQoocKpRowZ27dqFr7/+Gjt37kRcXBzS09Px8MMPIyoqKthlJCIiIiIiCqmAByQlJCTgiSeeCGZZiDTjGCciIiIiKkkBB06HDh3ClClTsG/fPgD2Zzs999xzaNCgQdAKR6RG7KqnA5/jRERERETFK6DkEL///juuvfZabN68Gddffz2uv/56/P3332jatClWrFgR7DISERERERGFVEAtTq+88gpeeOEFTJgwwWP6iBEjmFmPih276hERERFRSQqoxWnfvn0YNGiQx/SBAwfyAbhERERERFTmBBQ4ValSBRkZGR7TMzIykJqaWtQyEfnEdOREREREVJIC6qr3+OOP44knnsDhw4dx8803AwA2btyIiRMnYvjw4UEtIJESdtUjIiIiopIUUOD0xhtvICkpCe+99x5GjhwJAKhevTrGjBmDZ599NqgFJCIiIiIiCrWAAiedTocXXngBL7zwAnJzcwEASUlJQS0YkTfsqkdEREREJSng5zg5MGAiIiIiIqKyLqDA6YYbboBOp/7Q0e3btwdcICItOMaJiIiIiEpSQIFT7969Adgrr+PHj8dTTz2FSpUqBbNcRF6JXfW8BfFERERERMEQUOA0evRo5+v33nsPzz33HOrXrx+0QhEREREREYWTgJ7jRBRq7KpHRERERCWJgROVSsyqR0REREQlKaCueuJDbk0mE9555x1UqFDBOe39998vesmIiIiIiIjCRECB044dO5yvb775Zhw+fNj5NwfqU0lgVz0iIiIiKkkBBU5r1qwJdjmIiIiIiIjCVlDGOFksFhw+fBhmszkYiyPyiWOciIiIiKgkFTlw+vPPP5GWloaGDRuiatWq+OOPP4JRLiKvxK567LZHRERERMWtyIHTa6+9hh49euCff/5Beno6Xn755WCUi4iIiIiozLDYLLBJtlAXg4qgyIHT3r178fLLL+Paa6/Fq6++in/++ScY5SLySuyqx257RBRSuf8Bf/QBMv8OdUmIKEyZrWbUmVIHrWa0CnVRqAgCSg4hys/PR0JCAgAgMTERBQUFRS4UERFRqbH+HiBrN3ByEfAIb+QQkae9F/bidO5pnM49HeqiUBEEFDh9+OGHztcWiwWzZ89G5cqVYbFYAirEJ598gkmTJuHs2bNo3rw5PvroI7Rt21Zx3s8//xxz5sxxtmy1atUK48aNU52fyiaOcSrjtj4LXNoGdF4L6KNCXRoi73IPhboERERUAgIKnD744APn67S0NMydO9f5d+3atf1a1vz58zF8+HBMnz4dN954I6ZMmYJu3brhwIEDSE1N9Zh/7dq1ePjhh3HzzTcjNjYWEydORNeuXbFnzx7UqFEjkK9DpRC755Vx/35k//f0UqDm/4W2LEREREEkSRKfe1pKBRQ4HTlyJGgFeP/99/H4448jPT0dADB9+nT89ttv+PLLL/HKK694zP/111/L/p45cyZ+/PFHrFq1Cv369fOY32g0wmg0Ov/OyckBAJjN5rBIn+4oQziUpTQRt5fNZitz26+8HxeONiaLxQipnG4DJeX9uAhXkQAcVaBQ7BseF6SEx0V4MVtc+8FkNkGvC8oTgQIrC48NGX+2Q5HHOBWFyWTCtm3bMHLkSOc0vV6Pzp07Y9OmTZqWkZ+fD7PZjEqVKim+P378eIwdO9Zj+vLlyxEfHx9YwYvBihUrQl2EUuWi6aLzdebFTCxZsiSEpSk+5fW46FX47/Zt23FmZ3RIyxKOyutxEa7utFqdF9NQnot4XJASHhfh4UiBq9HhtyW/IUIXEcLS2PHYsMvPz9c8b0CB0z333OP1/Z9++knTcjIzM2G1WlG1alXZ9KpVq2L//v2aljFixAhUr14dnTt3Vnx/5MiRGD58uPPvnJwc1KpVC127dkVycrKmdRQns9mMFStWoEuXLoiK4lgOrU7lngL22l9fddVV6NmzZ2gLFGTl/rhYYP+nZauWkGqUrX1bFOX+uAhTET9FAFb761Cci3hckBIeF+Fl57mdwAH76x49eiBSH5q2ixM5J/DJ5k/Q5EoTPHLnIzw24OqNpkVAe23RokV44IEHEBcXF8jHg2bChAn47rvvsHbtWsTGxirOExMTg5iYGI/pUVFRYXWwhFt5wl1kpHDo6lBmt115Py4iIyKBcvz91ZT34yKchXK/8LggJTwuwkNURARW1ADOW+11mKiI0OyT3t/3xu7zu1E7tjb6R/XnsQH/ztsBh7sffvihYvIGf1SuXBkRERE4d+6cbPq5c+eQlpbm9bOTJ0/GhAkTsHLlSlx//fVFKgeVPmqZ9E7mnETH2R3xdOun8dLNL5VwqYiIiIg8xeYdQufCESKmECa42n1+NwDguOF4yMpQmgU0Mk2n0wUlG0h0dDRatWqFVatWOafZbDasWrUK7dq1U/3cu+++i7feegvLli1D69ati1wOKt3EIOr11a/j8OXD+N+K/4WwREREREQCyeZ6yceolFoBtThJkoQ77rgDcXFxSEhIQPXq1XHDDTfgwQcfRM2aNf1a1vDhw9G/f3+0bt0abdu2xZQpU5CXl+fMstevXz/UqFED48ePBwBMnDgRo0aNwjfffIO6devi7NmzAOwP301MTAzk61AppJaO3GxjhpiyhelaiYiobJGEIIpKl4ACp9GjR0OSJJjNZuTk5OD06dP4+OOPMXr0aKxYscJra5G7Bx98EBcuXMCoUaNw9uxZtGjRAsuWLXMmjDh+/Dj0elfD2LRp02AymXDfffd5lGnMmDGBfB0qhWQPwOUznYiIiCiMiT21bJI1hCWhogg4cHInSRIGDx6MV199FWvWrPFrecOGDcOwYcMU31u7dq3s76NHj/q1bCIiIiKi0BJu+LLFqdQK2tO3dDodxo4dyzFHVCIk2QmILU5EVI6YskJdAiIqAgZOpVdQH1tcs2ZNTJo0KZiLJCIiIoeMkcAPFYHjP4a6JEQUIAZOpVdAXfXEB8q60+l0eO+99wIuEJEWHONEROXS3gn2f7c/D9S+N6RFISJ/iPUWBk6lVUCB05QpU5CUlIRWrVp5dJMKRppyIl8YLBEREVFpJNkYOJVWAQVOn3/+OUaNGoXIyEi89957uO6664JdLiLNOMapjOH+pNKGNwyJyA9scSq9AhrjNGjQIBw8eBDt2rXDLbfcgscffxznzp0LdtmIVDFYKsuEfcsKKZUGPB8RkU+u65lkYzry0irg5BDx8fEYO3YsDhw4AKvVikaNGuHNN99EQUFBMMtH5JPYbU/HB6aWfmIllBVSIiIqEzjGqSwIqKvezz//LPu7d+/eqFOnDiZNmoQZM2bg5MmTQSkckRq1MU4c+1QWcB8SEVHZxax6pVdAgVPv3r1V38vLywu0LESaybLqsVWibBEvKOyqR6UBj1Mi8kEn66rHwKm0CihwsnGHU5hiV72ygIEwlTIhuXnDcx1RaSKxq16ZENAYpzlz5sBoNAa7LESayU9AfKZTmcIuDEQa8FxHVKrIesrwOldaBRQ4paenIzs7O9hlIdKM3fPKMh/71nQZsFlKpihERERBwBansiGgwImVVgon4vHIrnplgZfzS94J4IdKwLJWJVccIl84xomIfJCNzeaQl1IroDFOAPD9998jOTlZ8b1+/foFXCAiLdglrwzz1oXh5CL7v1m7SqQoRJpwjBMR+cAWp7Ih4MDp3XffRUREhMd0nU7HwIlKFIOosob7k8g3/k6IShPZ/RWOcSq1Ag6ctm7ditTU1GCWhUgzdhctw7zuW+53IiIqjZgcoiwIaIwTUajJmrwZRJU8Qybw9xNA5t/FsHDxgsLuSKWFyWrCH8f+gMlqCnVRSh7HOBGRD2KwxMCp9AoocKpTp45iNz2iUGO3vRKydRhw6HNg+U3BXzYD4VLp6V+fRsfZHfHs0mdDXZSSxzFOROQTxziVBQEFTkeOHMFVV10V7LIQaSbLTsNgqeTl7CvGhXN/lkZfZnwJAPhs22chLgkRUfhhi1PZEPAYp7y8PKxbtw7Hjx+HySTvmvHss+XwjiOFBaYjLwO8XVDYGkXhKCRd9fhbICpVxMCJ6chLrYACpx07dqBnz57Iz89HXl4eKlWqhMzMTMTHxyM1NZWBExU7jnEqyySV10RERKWTTbI6X7OrXukVUFe9F154AXfffTcuX76MuLg4/PXXXzh27BhatWqFyZMnB7uMRB4YLJVhYosT9zOVBhzjRES+SMyqVxYEFDhlZGTgxRdfhF6vR0REBIxGI2rVqoV3330Xr776arDLSOQVxziVNWxxIiKiskVsZWKLU+kVUOAUFRUFvd7+0dTUVBw/fhwAUKFCBZw4cSJ4pSNSwWCpDBPv3vOuHJUGTEdORL6I1zOOcSq1AhrjdMMNN2DLli24+uqr0bFjR4waNQqZmZmYO3cumjVrFuwyEnnFbntljXhBcd+33NdERFT6yLLqscWp1AqoxWncuHGoVq0aAOCdd95BxYoV8fTTT+PChQuYMWNGUAtIpITBUhkmsaselTI8HxGRLxzjVCYE1OLUunVr5+vU1FQsW7YsaAUi0kKWVY+V6zJG7KrHfUtERGUBEx+VBQE/x4ko1BJ0QIsYIJ+BU9kiy6rHu3JUCnCMExH5ILYyianJqXQJKHCqVKmS1/cvXboUUGGItJIkCWtrAq1jgXcKMkNdnHKoOCuK3rrqMUgmIqLSiF31yoKAAqesrCxMmTIFFSpUCHZ5iDSRIKF1rP313VGXQ1uYcqk4AxiOcSIiorJFHizx2lZaBdxV76GHHkJqamowy0Ll3b73gIPTgc7rgPjqmj/G+zZlDB+AS0REZY2YVY8tTqVWQFn1dDodcnNzUVBQEOzyUHm24yXgyn/A7lE+ZxWz6omnHx3HGpSQYtzOsmCJFxciIir9mI68bAgocJIkCY0aNUJiYiKioqJQs2ZN3H333Vi4cGGwy0flkc3s1+yyjl1soSgDmFWPyDfeJCIqXYTrGR+AW2oF1FVvzZo1kCQJZrMZOTk5OH36NLZs2YIHHngAM2bMQHp6erDLSSQjpiDn6aeMYT9wIg342yAqVcTnOLHmUmoFFDh17NhRcXrLli3x/vvvM3CiYifvque688quemWBl+QQbIEiIqJSSIKQgpzXslIrqM9xevzxx1G3bt1gLpLKIz9PKDaef8oWrc9xkiQ+P4eIiEqHMGlx0kEn67VD/ilS4HThwgUcOHAAAHDNNdegSpUq6N27dzDKReXYqdzTqOFjHvFHz59/WaM1HbkEjvOg8ovHPlHpItRbQjjGSafTcTx4EQSUHCIvLw8DBw5E9erV0aFDB3To0AHVq1fHoEGDkJ+fH+wyUjlzJOuIX/Ozp3BZozE5BE/8VK7x+CcqTSSmIy8TAgqchg8fjnXr1uHnn39GVlYWsrKysHjxYqxbtw4vvvhisMtI5EFtjBOVAZqTQ/DCQ0REpUSYpCPXsc5UJAF11fvxxx/xww8/oFOnTs5pPXv2RFxcHB544AFMmzYtWOUjUiTLqscbr2WM2OLkY4wTERFRqcBHbZQFAbU45efno2rVqh7TU1NT2VWPiszfeyFsdyhjJG9jnPhwXCI73jUmKk3kD8C1epmTwllAgVO7du0wevRoGAwG57SCggKMHTsW7dq1C1rhqHzSch9GkpgcouzS2FWPd+yIiKi0kMKjxYmPbSmagLrqTZ06Fd26dUPNmjXRvHlzAMDOnTsRExOD5cuXB7WARL6onX4kSQrvE8TFrfZ02pVahbok4UXzxYUtTkRExSXPlIf4qPjwvo6WKuGRHIJjnIomoMCpWbNmOHjwIL7++mvs378fAPDwww+jb9++iIuLC2oBiZTIxjh5mSdsTxCWfOD3NvbXDxqAiJjQliescIwTkS8mmxnRoS4ElVl7zu9Bs2nN8Oj1j2Jun7mhLk6ZILHFqUwIqKvexYsXER8fj8cffxzPPfccEhIScODAAWzdujXY5SNSJM+q53uesGPOdr22lt9xgVabFR9s+gDbz2x3TfSaVY9jnIgAIDM/M9RFIJsZ2D8VyNoT6pIE3eRNkwEA83bNC3FJypLwyKpHReNX4LR7927UrVsXqampaNy4MTIyMtC2bVt88MEHmDFjBm677TYsWrSomIpKpEzMqhcjWTAjFegSDz4ZuxT4YscXGL58OFrNELsranwAbjgHxkTFzMbnwITegQ+B7c8DS5qFuiRUGoTJc5zCtidOKeFX4PTyyy/juuuuwx9//IFOnTrhrrvuwp133ons7GxcvnwZTz75JCZMmFBcZaVyQstPWt5Vz/WJuy3/4PEKwPIarFiUBhlnMzwnivuNY5yIKFxlbgp1CahU0XhTkMKaX2OctmzZgtWrV+P6669H8+bNMWPGDAwZMgR6vT3+euaZZ3DTTTcVS0GJRGpd9apIVxTnoSAr1j7SGrvjcf8SUShJZTelNFslgk+WDZg3dkstv1qcLl26hLS0NABAYmIiEhISULFiRef7FStWRG5ubnBLSOWOv9Vh1ax6peWOTmkMAIqzzFoH0PLCQ0ShVIYDJyoOYdJVj8khisTv5BDuG5w7gEJBDIqsKnXrUtPixADAjZfkEF4fjktEVPwsNgtm7ZiFPNMV3zMTOci6oXOMU2nldzryAQMGICbGnjrZYDDgqaeeQkJCAgDAaDQGt3REGqhVn8N6jJNUyrPDBemGiWJw6zU40piqnIiomHyy+RM8//vzSKsO9EgIdWmKR3moXG84vgFncs/g/qb3l9Aa2VWvLPArcOrfv7/s70cffdRjnn79+hWtREQaSDbXSUetfSK8u+qFx52nUFPeR16CI6+pyimUOscDn6UCg8+FuiRExWvN0TUAgIgQl6M4hff1Mzjaz2oPANibuhdNqjQp/hWGSY8J9hQrGr8Cp1mzZhVXOYj85OpbrjrGKZy76jEAUKf1OU7lOOAMRytq2P9dXTO05QiNEFREeNoIOT3rn2XC8ezjJRI4SeEyxqkctCYWp4AegEsUajphUK789OM6IYR3Vz22OKnzkhyCASeFJR6L5Ynjjn1ZbnFi5boYhEmLU5c4C87WA7rHh6wIpRoDJyqVJJvF+domO/8IfYjDujLDlhNArVXQy8WFAScRhQlWoMq+C3kX8OHfH+Ji/kW/P7v51GaMXz8eFkd9JUwegLuwqhFVI4GlNUJWhFLN7+QQROFAvcXJpfR01Su/AYBicCtsG4MlH7Fun1B+TVS+8OgPvYgy3CjDcTB293x/DzYc34DFBxZjVb9Vfn32xpk3AgCSY5IxtO1QWVc9/oJLL94wodKJXfXKLiHgPXzpkNt73G4Ujkq+kqljxatYSJKkuXWhLHfVI7sNxzcAAFYfWR3wMv45/4/9RRg/ADczPzPURSg1GDhR6SS5uuqptUGEc1e9bMNl5+s8Ex8aLefab57VUd6xo3DEY7GseOjHh1B5UmWsP7ZedR5HbwYmhyD/hEdXPZEkSfh0y6eoMqkKJmyYEOrilAoMnKhU0gknHbVrVzh31TNZDM7XFqsphCUJQ972rcSxYUQAQ7Xi8v2e7wEAkzdN9jlvWW5xKuvJIcQeKcXdLdG5/DC8fkmQMHTJUADAyFUjQ1ya0oGBE4UhDVUCocVJJzsZuV6rddUzWoz488SfsNqsiu+XDHY5A3wnh/A8QbHFiYjCQ1muQIW0x8aBD4Fj84t1FaHpyh9+Y3TDekhDmCrLv3sqpSQtd7qErHqqLU4qJ6bHFj6GW768BaPXjg6gdEHCsToAfCeH8LgRyO1GYUjTOSvIim2Np5YAl7YX19JLDS0tLmU5OURR7LuwD48tfAwHMg/4/+GcA8C254CNDwW/YIKSDBicx1IYjnFi4OQ/Bk4UdrQMetZBuXItu5+j0lVvwd4FAID3Nr0XUPmCQkxLWo6z6imxCYk/POsl4XfHjshkNYa6CMGRvQ9YdyewrFWoS1IqsKuesk5fdcK8XfNw+5zb/f+wsWSSFIQmYBBv/IXH9YuBk/8YOFEY0nDCFrvqqVSmfZ0QQtmHWwyWbELrWelRfNvOKnnZHlL4XXiIzFZzia+zWI7+3H+LvIhnljyD55Y+F4TChD9/KlAX8y9i1o5ZuGK6UmzlCRfn884DAE7nng7g0yVzXS7RFieFMU4lccP0Qt4FTNsyDdmGbNV5GDj5j4ETlUpqySFKS1Y9COOrpJCOtQpUcLatUqugGEjqvT0Aly11FMbGrR+HelPrBVh59K14zm5Fq7Rm5mfi4y0f48PNHyLLkBWcIoUxf7rq9fi6Bwb+PBBP/fpU8RWorCnGm2Oh6Konq5OUwI2/O7+5E0OWDMHAnweqzsPAyX8MnCg8+HkSER+AKx7E8jwR3pcZygf82YRWFZu3FpZyyGbT2FWPLU4UJpSOxNdWv4ajWUfx5ro3S7w8oSIm3DGV8myh3q4PEiQk6vzrqrfl9BYAwPw9xZv0IFiK+/qYY8xRW7PrZTFW6q2y60wJ1QWkkk1u5Djmftr3k+o8DJz8x8CJwoO/P17ZOJjS11UvGIFTvjk/WMUpmiIEMEqtglZviT/Y4kRhyNuZxFIqu+IGxmxzdVk0CI9c8CXLkIWF+xbCaCkdY8UaSpeR2xBoGO3/Z8t6mm8tvt71NSpMqIB3N77rfUap+HpjhKKrnlhXYXKI0ouBE4UJ/368OpUEAqWlq55kE5JDBNBVb9LGSUgYl4CF+xYGs1gBCu521hw4scWJQuxE9gk8+cuTXucJ5+fJBZsY+PhzY6fbvG645/t78Nrq14qjWAHxFuDcLx0MfLkh7OkQLvov6g8AGLFyhOebsu1TfJX6UAQM8nNBeJwXytP5KVgYOFF48PfHq6HFKay76tmK1uL08sqXAQDpi9ODViZvNhzfgJ1ndyq/GeQLkCTeZXRb9smcE8JfvFNGodV/UX/M2D4jTKpARXcy51SRPm8UsgsWmAs0f27zqc0AgLm75hZp/aVBaWlxKs5y6nUaq55FbXHK2g1kblZ8yybLbFtSv2BhncXYmubO275ki5P/GDhRWPD3JKI6xkl4Hc4nBPH7FiWrXkmc8M/knkH7We3R4rMWqqUIlGJXPSGQdM88dDFfSFXLO2UUYv+c/8dzottxGc4t3+7kNyb8J7Y4FVgUAqcLm4BNA4CCc4qfLy1BRVGUlhYnyY+bkP7yvg3EMU5FCC4kCVhyPbD8RsB40eNtWeBUzNcSx3Gtkw/CLtZ1ahXO9aRwxcCJwoI8eNBwQlHrqidL9+mjxakkL9JWA5C93/mn+KyicM+qdyTriMLU4hvAKx4Lnv3AOcaJwkdKbAoA9y6l4VEhKrIAKnZiQgjFFqcVNwNHvgK2MLNcaRLs4N/7tTdY1xahzAVnPN4VA4aSCh5kwWgJnie8BaoMnPwX8sDpk08+Qd26dREbG4sbb7wRmzcrN6sCwJ49e3Dvvfeibt260Ol0mDJlSskVlIqV383WXh+S6lhmGHXVW3U78FsT4NSvAABJFhyEd+BkkZXVsU3FO2fBPfFaFdensK4wuWNXapiygLOrijVTVXnjCJxkSvFxKRXxdy3rqqfU4uSQc0Bxcji1xhRXWUpLq5pekvB4MtA4Sp6BLijLLomuej4+axV7fRTzOdH1HCfx+hUe52Ebb0D6LaSB0/z58zF8+HCMHj0a27dvR/PmzdGtWzecP39ecf78/HzUr18fEyZMQFpaWgmXloqTOM7n8OUjaPZpM6/PP5F31RPv4ojLVD4hdIsHdtUGWkSV4Akjc5P93/9mAHDPqhf4xaEkBnbKAifFu2RF6Kqn9Bwn2Rgnt20Thheeovpm9zd4dumzxX/nb/lNwOrOwH+fF+96yhFH4CQ/iuX7sTR11ZPfhgogcLIYkRoBLK4GVLi4MXjFohJ3q/kAZlQF9tUNfmDhNXDSBanFSXbt8AxWQ9Hi5J6+qqRwjFNwhTRwev/99/H4448jPT0d1157LaZPn474+Hh8+eWXivO3adMGkyZNwkMPPYSYmJgSLi0VJ5vbHa09F/bglZWvqM6vUw02fDeFL6sBXBcDfJ+a53c5i6wwXa9sjFMRnuNUEpUyMXBy3XkMVVe98MtKVFR9f+qLjzZ/hB/3/hj0Zb+/6X18svkT+x+Ou/zHvgv6esqrCrEVPCe6j3EqJS1QG49vxLgN410TAvhdm6wmTKkC/F8i0P7IBL8/H06tMcVVFs2tLSFWz+oahxbsyrXX1jzZOKDia3EKReAkjnEqyfOCr6564fS7Kw0iQ7Vik8mEbdu2YeTIkc5per0enTt3xqZNm4K2HqPRCKPR1X0gJ8f+0DWz2Qyz2az2sRLjKEM4lCWUjCYDYt2m5RpzVbeLzeqarofknE+SJGed3mQ2KX4+qvDfZJ1UYtvdsU6b1QSr2QyL2TUWwKxQTn+Oi+L+DgaT63ksRpMRiAQiJcl5qjWbjQACu5EhdgFxfmeLa9vYbFbZ9xPHg1ksJkhl6HdzKvuUz33pz3FxIe8CXlz+IgDgsWaPIblwug0RsBbDdosSXpeX81lytH2ritUOs9kE2FxTbDZbULeHYztL8DweirKebvO64TbhuURmsxGw+VfJzzPmoaZQq3Avj7PskgSLSlnD5djxvt88K71ay63T6UrsOxbluBC/ocFkQGSA1UWldYvBo/v7OovJuSaz2QhEKpd9/p75mL5tOub1mYcaSTUUVmxwHm9mixlwrOfyDiCxgf1aVshkUa4raPkuamTJJ2z2uobYm8Jqs5TosS6uy9u5Olx+fyXNn+8dssApMzMTVqsVVatWlU2vWrUq9u/fr/Ip/40fPx5jx471mL58+XLEx8cHbT1FtWLFilAXIaSslvO4x23ambNnsGTJEsX5r+QcwE2Fj22XbDbnfBEGAxBnn75u7VocifdMbNCr8F8doLr8YHOs81LmWWxcsgTmvF1oXDht964M7D+kXA5fx4XFYin277A52zXucMmyJYjRx6BjQQ5SCqctX74MFl1iQMs+ddKV/tjxPWLy9qFe4bTz589hr/D9KmZnOc/6f/75Jy5HXApoveFo957dWHJB277Ucr7INLkyEP627Dc8XPj6wsXL+KsYjplewuuS+l2F2tmTZz2mLVu2FDadKwI5efJkULeHcztLksdyi3IdKTAXAELg9PuyJbDq4vxaxt+X/0Yf4W/38jnKfuXKFaxW2CZGozFsjp2zZ8+qliXFYIT7nT6t5S6Jc7a7QI6LmCt5zu+47PdliI8IrL6k9F0tZovq+xWt+9Gh8PXqVStg0FdRXO5jGY/Z/537GF6q+5LH+1HSFfQsfL1+/R/I1R9DFWsGbjaMQb6uMtZEvOWcd+u2rYg+5Ptpxv7sN4vQk+TI0SNYsmQJbJfPO39jx44eQU5WCR0Hkrzs4rl63R/rZLNq+o6SFYDe7ZlbpVt+vvbnzoUscCopI0eOxPDhw51/5+TkoFatWujatSuSk5O9fLJkmM1mrFixAl26dEFUVJTvD5RRuTmHgd/trx0/xdSqqejZs6fi/Ns3rweO2V9H6PXoXjjfmvmuq9mt7W9Bs6rXe354AZwrUlt+0BWus1JKEnre0RP//vMvsM8+rWnTJmh4rbwcPo+LDPs/+gh9sX8H434jUBh/dunaBYnRiYhcMQbIsk/r2qULEF0xoGX/8MsPwGX76x49ekCn0+Ho9r+BQ/ZpVapchbZ3uL7f3l/HAIVjzm9udyOkyrcEtN6wkmH/p1HjRuh5k/d96c/54nTuaWCv/fUdne8A7HlJUCU1DT1vLYZjZoHrZc+ePSFJUlgN9i8OS5YuATLld+e7d+sKRMY792uNmjWC9xuVJOAH+0udTudcblCuIzvl36Nb1y5AlH/XyMxdmdBnuP72+N6Fx0hiYgJ6dhfeK/xMbExsyZ2T1WTY/0lLS1Mty7YFni3sPstduNyoyKgS+45FOS42LUwCCuv+nbt0Vk6EoibD9VLpu8bsj0G+IV/xfV1mBWCN/fXtt3UEEup6XUdsRZVjxngR+Nn+sn379kCFZtBv+xU4DMRLmWjfob3zGtzihhbo2UR5n+gydM4u8f7sN4PFABQ++rB+vfro2bkn/l7+KZBtn1anTm3ceksxHwcZ9n/0er38nCycq2+59Rbo9uucXQd9fkebCZHLrocUXwvWTmXnhr+jN5oWIQucKleujIiICJw7J3+ew7lz54Ka+CEmJkZxPFRUVFRYBSrhVp6SFhHhqmCJVS21bRIp9CDRQxLmc136IyP0PrdpVFQUDmQeQPWk6kiKSfK32H7TwwJ9VBT0wpfU69W/p5bjotiPG3FbO7apUCGOiowAAiyDWLGOjIq0d+HQScL78u8n1sMjIwJfb5HsnwpYcoFmrwd3uTrt+1LLcREdJdxBjXC91EdEQV/M280oGXHdtOtwe93b8UWvL4p1XSXFarNi4M8DcWONGzGkzRAAgKTz7LIVFRUJRIrHrA5RUVHYdW4Xfv/vdzx/0/OIighw+7uNxXA/BoJ5HYmM0EPn57KssGo6fzu2idbpoRAREeGlLJ43BPwpd0l/x0COC/HcrNdwLfW2bm/L9ng/wnXB0XJtUT1mrOJyIu3L0bumRUS6Top6vfr30+lcQYU/28AkubqcO5YvXvcjSvBY10GHfGs+Ws1ohbsb3Y0PhPf0EXrZGCefZbqwBcg7DF3e4WK/jpQkf/ZFyEYpRkdHo1WrVli1apVzms1mw6pVq9CuXbtQFYtCxKaQXtzrgE1hrIvaTW1fab5jdcCxjHFo+Wlj1P+wvtaiFo3NfjIVy1aUdOQlnVXPqlTWIgysFZNbOPa3fNu4LVs2cDgE2YAkG7D9eWDXG0De8aAuWnHbBml54vN1oCv++2Xf/fMdjmYdxZcZyol+SqOfD/yMOTvnYOiSoc5pimmaVR6A23x6c7y88mW8v+n9wAshjpsIfCnKi/ZIauH/8Wi0GGWVQzXBTm9d0nRKW1/jubi0tMIW58PkvSbIENdVlONEMTura9uLx6C37xdo4gSrrE7jSEcuJoco2evXFzu+wKHLhzDl7ymy6TbJ5ucxWTqS3RSnkHbVGz58OPr374/WrVujbdu2mDJlCvLy8pCeng4A6NevH2rUqIHx4+2ZfkwmE/bu3et8ferUKWRkZCAxMRENGzYM2fegohMH/TsuvN4qkjqoPABXfK3hwl9n72uYlgr0P5fpc96gKMyqZwvSc5z8zaq369wuVE2oiqqJVX3PXEgMnFwXmOAHMI6Km+zhwG6VEZ0sRXIITuDig5ot2vtEa2Gx+c6umGvMlfWd90asDJiFZCrQRSjMTb5kG7M9pimfo7z/HjaeKEqabqE1tghL0cImWfy+s2q0GjWV63zeOVRTmB5OQYX/FWYJWvZKuGcwO3flHExWE2xCOUMWOGlIia94DTRlAQbhsTYKv1OtWfX0On1AN7WUbw6E7gG4at+xSFn1JKlMjXPSKqSB04MPPogLFy5g1KhROHv2LFq0aIFly5Y5E0YcP34ceqFp9fTp07jhhhucf0+ePBmTJ09Gx44dsXbt2pIuPgWRJHsuk53Xu0DCe/Kfrf+pTPslA/3P+Z4vKJwtTsJJW0OFORj2XdiH5tOb29c/WvtJWzEduRT8AMaxv2UXKW/pyEPS4iTuq+Be+Hzdhc82ZOOq965Clagq+L87/8+v5ZltYuBU5oe2lhjFYNdHOnKvD4b1pQRTGNts/mfXMllNmoItg8WgOD3cgwqvJBugIdV4OAWHStLesw+VWHZNA+e0YLeGe0/JLj6rL4D1SjbgB7cxtwrnVq2BU6D7S9bi5FxGaK5fusL/lPgdFLuniy+H15OQf+Nhw4Zh2LBhiu+5B0N169YtNc/EIP+IwYOWwEmswOpV7uIUpSWn2AS5q54/1h1b53smBWI3L+c+KYYH0Tq76skqo1666oWixUm2r4K7fl8tTn+d/AsAcMF8QdPyxAt3Sbc4leoKsB+UK5Tqx8WYSsBVtv+KsMbirWyJ1dlAbugYLdpanNSEe1DhlcbzYDj/NsT6VZYhx5kBLujPcfK2DYp6bbGZPKcVscUpEOKNK+d2LYYbjlqp/bb876onkKwIgzCixJW/b0xhSdbiVPgb1triJJ7WZDdDwjJw8nwAblH6OvtzIyHQcQVi4OSoKFpsFuHkEZzyO/a31q56Npv/XYmKTGxxCvJNHC1d9fyh2uKkL/7TfqmuAKtQquz56o5j/8v+d71IYPRVAHA08C4u4hgJ/z/tk/xcGsAYJ6sxdAOnQy4ELeBqJBuwpjsioioCeETzx8QbAbIOcyX6ANwitjgpnkc9y1/sgZPsIfdKXdzD4wG4EiS/gnlJEjpxhmMdqwSU33MchRWlFidvFX2dhh+sFI4DkBUCJ5vGMStK/OknHWh3C6PF9aBAxwXg3JUzQiECvwBIkg23xQFVI4TASbbf1FucSnpwLQBYrEbhr6KvXwwMizU5hLAPy2PXimBQqnz4kywlRWzoC3hfF0NlS7IBl3YgAlJQWpy0UKumhVNrjN/Bv7fzkTAeskRuKuQcAM6ugP7E93612ii2kiD4yTw0j3HScp13v/5ICl1MFZJDiMGS9/HUAXbVE7aZc/mScu+Y4uarq54/x+SBi8JzVhk4EYWO31n1FMZEAe7VijC6A+ig0FUPtpIpZ1BanAqXYZKNUQi8/DfYTmF1TeBwXdeFRDwWvI1xspbQ2DCHvRf24uqp9VwTgnDRkF28/dg/WloaZS1OVmF/sateQDS3OKnsm2hZFpuiB05B28K7RgPLWuLjVMgy4gU6xqksNDZG+/gOintY7Xq1733g+wT0SbD/WVy/jXNXzuHpX59GxtkMWcu4zo/zc3G0OP184GfkmfJk07SPcQqkq57ndeFc7mmPacotQp6CMcbJdZ4I0Rgnnc57Vz0/jklZhlYGTkSh429yCL1wcpWnhhW6fpVwxVoTR4uT7M5eyaQjD7QrmNHq2eIkO80W4QLQwma/oMXrlVuc3FuVxC6aJd0Vc8TKEfIAJICKpTvx4urP/tHSOiULysQMgMUQOHl0qSwLtWcNUm3Z+LEakCS7kionh4iWTQzw2C2OytaetwEAT1Youa56znN2wVng0CzEFh4u4XDcvFcZMDYE6tqy/Pykyr7Z8SIAYE7h4ymL6zsO/mUwpm+bjhs+u0EWvIsZaH2RpegO0rW013e9kL44XTZN+xinQLrqeZ6X8025nrNpTQ4RhBYn19jg0I3RVfsefucNcE8OUQ4xcKKwoJSOXGuLk3g6kAVR4fijlkLXVS/Qu4ZKY5z0QbpzJu4713OcvGWuE8dElWxgrIMOspAjCIG5YncODbQEWbI7nmLgVAxjnLwdW2U5oc9IaRvuSXSfKsm+s+M3GkiL04W8CziTK3SLlSXC8Z/FZsFDPzyETzZ/ovh+kbvqaUxH7rSyA/D3QEy4yu9VFQubZMPwwoRs91v/UZ1P8TuGODlExtkMsTDOV3o/WpzkwYTYuq+tC6aaBXsXyP7W3lUvgGuLQlc9Z6ubELQW9xgn5ecfhjCrXpCSQ4g9QgJplS4LGDhRWLBJ/mXVU0sOoZONgfEvcAr2AFhFCg95bXl4EnDlSLGvOhhjnBwVffl5tggVY4XkEJKsVck9OURoxjjN3TkXv/z7CyJlld/gtjj56qonXtw0BU5iUCZLgR38ipt7AC9WDkvkd1UCxO3vOC6rI89zRsmm+J39DZwkSULq5FRUf786cow5jol+ldnd93u+x/w98zFsqXImW1lXvQDOF1rTkTvlHgQA9CkMPkuki6chEzi/QXFbivtN8rcsPo7z4v5msnOl7HEdfrQ4yW7oCedapUx1ReBPOvIf9/6IPvP7IMuQpW3hSpX5IqQjL7bkECF8kKxNWLVVsvqZHIKBEwMnCgtKXfW8DtjU0OLkb+AU7Kxm3nhU+rc+E+Byir+rnlI68mB11ZO3IHlm1fNYthhoFfP+MllNzu/eb1E/AG5pSIPc4uRXVz0N46FUW5yKoSXWveIhBhlFTXphsBjw+bbPcTLnZJGWU1RKwaBVscIhySvgkkKLk4Z9LR4Pu87tci67KLINng/xFckqBEFOR/7HsT9UP1eiHfR+uRpY2R44vdTjLVlyBH+XqzFwKpnuiGJXvcCSQ8gevBDkCrI/D8C9b8F9WLR/EcauHas8u/ueUiprEdKRBzzGSTE5hHheKOExTsKvTNxiVpvVv+8onBcYOBGFkKTQguS9xUns2ideJAJvCpc966a4uZ/ILVcCW4w/WfUCTA5htBrRJgZoEi121RMLoX07Zxmy0Pu73liwx951Qx7oFiaH8JJVT5aOvBi7YlptVtR8vyZqf1Bbtt0iZJXfILc4BburntjHXhybVQKBk1o5AjF6zWg88esTaPlZyyItp6iUgkHFwEmSFPdljJ8tTuI+dnbXK+bKlj9d9ZYeXOoRDBmtRlmrlYPFZkHH2R1Vl1WiQYU5y/7vqV883pIdx36XRWPgVExhouxaIOw7LRloHdQSJtg0ZkvUKpB05GfETK7eKJ6XQ9vi5OypEaIWJ/eueuKa/b2hKnalD8tx5CWAeWkpLIitQ47ft7fWFJ0sOYQ4PfAWJ3MJ3j3xLFvxVxjcL4paLwjxlixsrm1/neFscRL3jfbK3Fvr3sLiA4ux+MBiSE0lla56YouTlzFOxZhu/lzeOVzItz9oNtvouksvO2EGo6ueHy1O4sXd3zFONqurq54kWYJ+tHm0OAlrKGpL7m8HfwMA5/4IFeUWJyWurnpRANrYTgDGS4grSuDkrDQWreLlKzDRmhzi3JVz6PlNT/t8o13lsNiUj61co3xwvvs8YZATAoD8N1PaWpzkXfXErHqBJYeQdZkOclc978khlHscqN0k1JaO3LEc4eaHUvIGH2Uds3YMVh5eiRWPrUBcVJzqZ9SXHx7PcXIPnPwJ5sVAnC1ORCHk7xgn9eQQwiz+Bk4l2OLkUbYA72r5I9BuYakWV4XVdedM4McF4GzeWdnf8hYkz656ksdznEomq564fcTjUN7iVMS7bVYjEjc9hOdTPNepOLu4/zQkxpDfMXa1OO2/sNc1U/Ze4HKGpuJ64y2rXlG76hX3GCmrzYo3172JNUfW+PUZAFAMEyTJ+f6Yq4AXLX8CqzsjThaV+Bc4nXakUy7mypbWMU7n8847X7ufV5TOZLkKWc1Exd0ao7xSz3XJj7XgjnEqbhV0Voy/CmgcBVmrS6BjnMQKcsi66nl5GLoqxTFOnudLf7rqPZIEfFgFeHPdWGw8sRGzM2b7LIZib4IQZdXz9hwns83sVzAvb3Fi4EQUMrKseoX/em0+V0sOIeuuoO2C4RgoqaXFyWgxouVnLfHUr09pWrbyCq0KF9nAKgz+jHHyJxGBKEZorXA0zctPHNorDGJ5jRYjMoVWBE0tTuLFrhi7CYgJMcQxXmKLU3bBxSKtI/fANMSdX40PqhSu0+q9O4xYkfa/q55rHx69/J/9hWQDfmsKLL0BMHkf++KLtxanonbVcxy3DyYCOLGwSMtS8s3ubzB67WjcPud2v8tkU/z5ubrqPZZUOOnyjmJocfKfr8BEFttprBS5H5dKa3Amtwhz8nTc/p2Tfd3IKe7gcGKFbLxSCdhVB7JWF3+y6sl+q8XY4uRPcgjnS63HvsJxq3St8Ker3tdpwDMpwN2Fz+LKN+erzu/gs8WppNORq3XVs5r9OyY5xomBU7ix2qzo+1NfvPfne6EuijpLHrCyI7BvctAWqZwcwgL82Q/YMlThA2KLkzDGSVbR1nbBcNxl1dLi9NvB37Dj7A58tu0zTctWJJk9T9QBdt8IdIyTP60AMTbXRcJxogw0HblY3od+fAgF5gKP92yyBwK7PRNHlo5ZYb1WE7CsDbBliOYyKSkQstDJAidhN+UYihY4bT+xzvl6UDLQwHoO3+z+BltObYHRYvQIij0CJ0my/xZVyLodCWOcIh0XSXHck7Fo3eDcj2dxPxW1q55NsuEqPfBdNQDr77Hv4yD69+K/muaTtaJ5a3ESkkOI74qBk5YKh7jdTuWcKly0chflQCjddJEHTl6S86hkeLTYLIpjnHwFTiWbOEGdUgX6dO5pfLv7W5/XB5+BUzE/q6pVtL18UTq4tTgFlo5c/FywW5y0j3HSFtzIKAZOStP876qXrNdeFl8PwC3JxzR4JIcQVm2xmZT3hyTZk1Yd+NhtOgMnBk5hZvmh5fhm9zd4acVLoS6Kuv8+B87/Aez4X9AWKZ6IHBfeNJ0RODoXOPipvJIHt+QQwnSxQu/vc360VPAC6s7nfpK1mRXuOBd/hUH2XAk/WgFibeIDcL31H/dNvFgs2r9IPhhdscXJrSVD1uKk8B3OLAUubQUOTtNcJndGixFT/5rq/FsMnMTnONl8tBD5Im6LmVWBydiEvj/1RduZbVHp3UroMreLbH6PwGlTf+D7RCB7n+LyZeMVhN+Ps7uh+GyWInYVlWS/u8CDdCU2yYYK4oYP8vO7tFbIZK1okiNwUiDZnN9fDCLErnoWDc/FEfd3ntkRIIuJcIpGab8Eko5cS4uT+xgnd2EyxElxjFPTT5vikZ8ewYd/f+h8T6dww0prC3hxtTjJzqXCuSnQrnp6lEyLk8fvrxi66rm6l+kUpnk/B0QIQUWB5Ht+B6VzYCA3dgMlbq/aEVZEScr70KZWp7mwHvj3Y2CbPOOvJBznUjH2+ghnDJzCTIHseSthKsAMcN6IP0DHaero5cPCDPKTv9pdNHlXPT+z6mm4e+JPC4/rQ24XLps5aF31/FHJchld4u2v/WkFiJNclW7HSVZ+4tC+TTyf9+OiFDh5bm8fY5yCcCJ/d+O7+DLjS+ffai1O1iIGTt62W745H6uOrJJNEys1FpvFflMBAPa/r7gMWSVQqPg4KwJi+Ys4PkOsNEqQVyyK3FXP/fNBvssZyBgqZ3IISbnFyVlREqbG+XnsiL9RV9fR4AVOSjeB5MkhtP2WgtFVLyRjnBTIn+Nk53h+0NL/XOnLlSrxvm7UFXermrjvTmUfdb6W/EhiI/7WxJuTzy8dFtSxht678iq3OGm+9ip8X6WWEbHO4e27JQgb1lBYBC03g3w9x0lXzC1OjnU2iwZ2VMvGQ0fHON+TpyM3Kf/u1Lpvs6seA6dwE6kvDYkOg3/iV+qqJxs/4PYDlacjVytZ8NORB9S8rhA4eSaHKP4Kw+jcuVheA2gX618rQLxwp8pWWAEXU8AH2uJkX47rtVJyCJ3H3UjPLHxuM2gui5rVR1fL/hbHOwWzxcnfgf6qY5xU9qWsQiJ21VNqcSriBdD9AuoR5BVl2ZJNvqmCfLHWWiFTCgaVW5yUu+rFC1dbq4buhrLAyWoEzq4C1nRzTity4KSwHeVd9dT3m1qGR6tN+clW7oGTR1Y9x78h7qonH5cify8qIsr5WumIkXzcqNP8zbJ2A4bzvudzI/a2kITjy6/ASXbudS0vz5iNrae3al6Oz7F0QouTx/lBpcVJLbjR8hwnpWNZa4tTknCtswTa4qTUVa+Yxzg5tmuvwnFZiZYsYd0uVkmlq57Kb5HpyBk4hR0xcCrJPrD+cHUbCR7FwEmcwe0HqvYAXNnFQ2Nw4E9yiKC0OElmhYffBe+nuPH4RnyV8ZXq+7fG+dcKkADXdnFcgORZ9QIb4wRApauet7uMvrLqFT3da1ykPM2sWouTpYitw/4eS6pdLVW2v+zuuVUM/hTGOBU5cJL/PuVltQBXjga8P6ySVT5uJghp4B22nt6KiRsnappXFjg5uuopfCWbZFXMPhkvjpHyN3CyGIHVne2V6kKBhBhiBcl3i5P6OUIsm3je9GhxKtzn4dni5D2rXnvpOHBknvNv2bVZ4bfrq4VO03fM3gcsuR74qarXZSmRP7zYtU8Uu1erUHtmXaRO+w2QAnOBz3Ob2hg5AMgziT1axPN9UZJDeJ/mLRCKE26POM7/Wsqi2OIkFf36pJWz1dvHT0q1q554nAplFce7BXvsW2nBwCnMiCdnU5AHQWu1aP8iTN86XfX9Lac2B32dkkILkvwC7NbiJJyYi5pVz5/kEMFocTKb8z2DqSDeab111q0YsHgANp3YpPh+rM6/FqdEnXiHyX5Mqj8VwjuPtNUK73nPqudHYBzgWJjYyFjZ32pZ9czWonarLULg5GUcmNI8Opv4HRz9TcQWp6KdayS3rnpiWZOPzAR+rgfsGhXQsm2SDVGyCWbAnAtc+LPIlY82n7fxqxwO3lqcrDZX8hfx3CTrqqdhe3u0OLnxecaQJCD/pGyS+B0UW5zEupKX86da66dHOvLC9eWacmWttR4tTiFoaFKqLHucFzc95nwpv6mpsDwfgYWmr3hhg5a5FMn2nVT0FifxXBcF7Q+CrTu1rs95vLU4nXWk3gdk1/CiJIdwBLWynHbCfIo3Egt3coIwRsxxHlK8fpqvyG7wKo5xCjCpUiAsSjc5HasWXttsaln1xANKuXueP0F5WcLAKcyIJ2df6YmLS5/5ffD0b0/jQOYBxfeLoyVMbGVwHJTyZ+Z46aqn2u8/+GOcRIcvH8ZjCx/DjjM7vM/odpK1WAs8Wpwy84uWoU3J/sz9itNjdf62OHkOBhVPHDvObNO8LH9bnNzHsskzPfn4DgEGA+4PNjRZTdADqB0pPyaL2uLkb6Vf9TlcGrrqycc4OWYQW5yKGDhJni1ODyYCLWOA1H/ftU/c83ZAy7ZJNllLH2xmYGUnYMUtwOHZAS0z0HI4eG1xslkV7/bGCl1+tLQ4iecjg8Xg8b5S9jqZ3WOARbWA/R+4lincHPLV4uQtOYS3wEkWBBUuI8eQjT11fJS3hO254JlUxVvlXN6NXqnFyfv5SK8D9tQGbojycp0pQpIW2Q1E2Y0M7dc1tWfWReq0twaKz/hS4+0B2bJn+4mtGxofgKvUhdpx3RIzaHrtqvdnP+DnBoAlD/E6IZjUqcxvugwsSAKWXOec5CurXjC6lXuj+MxFhTVb3LaX87uJP2RxP0ie9YHyhoFTmInQue7LiWMrSop4ErpUcEl5nmK4O6j0HCfxDqV74KRXfQCu2KTsX1c9LV0RxJN3n/l9MG/XPLSc0dLHh+QnWbMlH3DLdPTvpYOayuoPtcA71o9uFwAQKesu4biL5doOw5YM1Zxt0PNBqa7XiskhvLQ4+cz6FWAwEBthb3F6KNH+v8lqwrdpwLF6wAOJrvncLzjFQW0siXz/eW9xStRB1mLr+F2dzDokLKLwfckGHF8A5B3zr5xuv8/U/P/wXTVgW22/FqPIarPaUyw7SBbg8nb768NfKn6mOIgVoY6zO2LEihFeu+o1igIqCycxMave1zvneG3VB9SSQ7j4PA3/86b93+3DFZfpc4yTlxZb7S1O9m1mMF7CNdHqRXV8lwMXD+Dvk3+rzxhEF/MzPaZ5u6HkK3DSMt7j2hhgZoq3wMLtCXmSDemL0/HR3x/5XLZ4TraJ5z4/Wt5lXfWE6VE6/28seiNeR91bb8Tj2qKxO53IpPCIBsexfEZozdKLzyd0X/bRuUDeEeDkYnmLk1rgdG6d/d8c181Kxec4iT0m3I6h5YeWY/UR+fjaorAo3OR0kN2HspoVH7Ugm0vWPU8InIrxIfThjIFTmBF/TB5d9Y7Nd/1A3VkKgOy9RV6/eHIUB8MWN0khEPIWOEElcApkjJM/XfXEE+auc7s0Ld+jxclS4DGQWCqGvv1qXT1j/OyqFyELRu3bSL7NtV9UtWXVU3+OE3ztX1lChAADp8hYJOmBb6vZ/7eYsvBAkv29R5OFxRfDGCf3X5y3CqprQeotTh3igNyGwN15653TIwAsObgE6Qsfcc3sqGgdmQdseABYXFfr17AXQWzdAlDBeEZ9Zj/ZJJus25DsXOB23OWb87Hx+EZ5xcZ4EVh+M3DQe6CipRwDkoGbY4GTOSfx7p/vKt6csNkssEk2HKgrnx4jHOw/7P0eT//2tOs3arwEbBoAnFvrnKfIXfUUiL9TxRYnWVe9wAIneRdr+3FhNspvwrmn8xY/c9MXN8ne+2TzJ3jylyeLltXtymHAkOmz+7bSOioV1pKi9EJyCIXWYq3XmwSdl5YGtxanZf8tw+yM2Xh22bM+lyt+0iq0JttU0lArEa8LshYnAO1ntcf6Y+s9P3R2FXDQv+caqt0QAuTHhlWW5EJbC439xqScs6eEsO17H3ge8WqBkCBB53rPcR7yCLAVWgplLU7O12JwKzxuwJSHbvO64Y45d2h6uK4WSpk9HWRJjiR5Vj1XWdUCJyFZVCCPZykDGDiFGfEHKbtY5vwLbHwIWNVJ+YNrugG/NQVOLSnS+sU7m6oZ/nwMcBy5ciSum3adz2d3yBfpOcZJPHFvPbUJG49vtP9x/g/UyXV1DxNbLUqyq55mHoGTQaF/c/ADJ7VA0N+uehEKXSfk48q0P9/KI6ue+F7hRcUqu5C6VbB8PRBR7HMdaItTZKy9laaQVeFCDBRPVr1ot8NAW+CknhziwyqFyxW6W+p1wLSt02QVeee2OrtCW9ndyLpvALAGsf++VXJrcbKp7+N+C/vh1lm3yu/Q//MOkLkJ2PJ0kcpRJe9fzKoKbKzlmhal8LO1ShbFGxPi2dTR5cf5AOiMEcCRr4BVtznnEfex0k0QpWcJ+SL+TpVanQNJDuG9xanwPbP8WuBecm9nv2FLh2HG9hmB340vOGvvdvVTFZ+tL1aFa0Ctwh0XoXdVN5W2vNYMY1Zvu03ocVJgLsCcnXM0LRM2CxKFDS87NwXY4iQb41S4g+5fcL/nh1Z3BrY8BWRqbyn0FjiJNz+tAbQ4KXWhdgZObtu+TayvZeuRKAROqi1OSoGTzYrRlYDjdYGKkr1M4m92+aHlzu70YsItLV0dtfA2xkmWzdam1uIk1vPEc67Y4sSuehQGxAuurHtG/gnvH7xQeCfo4CdFWr/Yl17sNqhK4eI6YeME/HP+H3yx4wvN61XKqieu/YmfB+LWWbfat8nKjrLPqiWHULrwKz9/w/6v1uQQN8UC9fzJGq/Q4uTeVS9Yo6PF7yerbAknen+TQyi3OAnJOXTaE5loaXEqEC4iHunIZSdzpRYnk/JrP0XJAiflliWrhoeYeqfQ4uR2GIjHpCzFt3isekkOkahwWEVAQpQ+yi1wciwvsH734gVUD8Ci8e7w5lObMWnjJK+BvPsYJ1nFyG0f/7jvRwDA62ted000Z2kqiy/JprMe05ROAzabVfH7iN/BcW5zPrfvymGP+S02C5L0QIrKVTqQi7d7Vz337tjBCJxkh1XhcRVhld988EgO4b3YAHxn5lN1OcP12kdFTyn7Wq3ChqZInWtvKwWtWluclJ905Viw66o37LenMX/PfE3LFNPUA4BNbKlRTpqvSK3FSekGgYe8o86XlSOAZ1OAq9wPUuMlwGr0GjiJ5zPxHKs6xqlw+rkr5/D9nu9hNHs+Z9LxjK0InfKNO1kgJJZHp5e1OKkGTuIvp/DcZ5WsGHOV/fh5JMJRf5NfN0esHAFAXtfLVOhC6hdJAv6bCV1hBk6lqoVsLKNbcghXt0JhO3CMk0xpeGhQuaLa4qST3U4C9MpBzd4zm3FtEdYvrlPpRHW54LL8brLNDKi0TPmTFdA9OUQEgN7CeBJHJwmj1YgYt8/qVV4rPQBX8tIpTkuLU6LhNDYV3nHWaR2W5HZBtVo9W5yC1VVP9S618N1i/BzjJAucJM+7WGJXPUmSsPHERiRFJ+H+BffjhZtewNNtnhY+7zs5RL45z7XDPSrgYlcHX4FTYC2IZpvZ2YUDAKwKfeaBorc4eQaFnhUUtQqqLCubl656iQq1az0kREVEuSVcKFxegIlfxAqnP4HTjTNvBACkJqSif4v+ivNYbVZECac7kyXPdeFS2cdXTFeAzU8BhrNAZLLiPEpskk09e5iG/QXYW5xsCq3d4lnSUSl1tjgp/P4tVjOO1AWuigAS/vNebod9mftw0XARt9W7TfF99656f574E3cJ7weUjtwtuBfvZlusRkQDiLQVuPW99k2SJPzy7y+usgWaOEG8Abi6i2v5SunIFX7TVQo/7utRIb4egOvgNbwSvuM3u77StDwAwDl5a5wkBhx+piP/tAqQGunWQlr4r9d9IEnO+suCNKBTPNA7Abj9VOH7xkv2ZCUJtWG1ufaJ+7VIPO6sVuUWJ6Xtf9MXN+Fo1lF82/h6PORRNvsy3Us/uhIwyAJsU3nmXb6lQNbi5Oyq55EVVwycLIAuyu1hwoXPdRPKrQOcvXLEeleRW5yOLwA2P44awnocImA//sSgWPUZfDblwEn2AFxm1aNwYJNsuCMOaBzlPiBYPNLVK2yGgkz1u7f/zQD+9d4iJa7TfTn/XfoPld6thHVHhXFWXn44/mTfc++q93JFYOxVrvcdFRSl76aWHELpEuXtzraWFqeKhqM+5/GgkFWvuFKRihUjtcApVu9fV71IMXCyeg441Qvr+mb3N2g/qz1afNYCBy8dxJAlQ2TL8siqJzuB25ddIOvj7d5Vz8cYJ2vRW5zMVrNsIL9NJe24rQiPCzBajDiR5dnK4N5Vz/0ZOYplUgucJKvsqfeu+W0KLU4m52cCIUvuogMsfi5nz4U9qu9paXHafmY7hv/uSoIQCQD/fQacXAzkCGM/V3f1GOcSpwN+rQ48WcH7DQWlZCSKLU5Wla56Ci1OzvEMSl19rAZcVThjC/e7RZCf9z7Z8gmmn5iO5jOa4/Y5t+NUzinPD8CzxSkmQr5gWaa+ANORi/GRI4FKtM0zK6BI6bbR7/8twabfe+GWwu5UmnpAKC5c+Fym8iMaHJSeS+NotRUDJ71Si5PGc6pVIbvSqDWj0H5WexiEa5D7ucCfMV6yFieNAV2WIQu95/fC0ynAvYlAc+HQcFx/vQevkvM60CnePuW2eHEFuwFrPpCzH8lwlc/9N2dTuUEk1ieUfl9Hs44CAPae9xx77MoGK99vHeOBvslAHbMrbf+53OPO18eyjiNOaKXS1FVP4VwqHwFa+BEU3uCBvKdPkQOnS1vkRRNeR+k8f2uqXfWK2uKU9Q/wz9v28fdWgzyLaynHwCnMxOUfxcqawL667i022gKnRL1K5ceSD2x+Etg6zD5IVoX4A3Y/oc3dOdfzA15+OP484FOsBKfogXGV5e87Tli+MkHJTowKFxpvFx8tLU4BDYZ0u6BaLQZIHnekhf27ZQgi/u7vvPvvz2BRMfiTd9UTAiexq17+SWDrM/YxdCrEyuHIVS/jRPYJWYCq07nW66trSbJkwF+1gCcKGwFkydI0BE7y5BAK+zLArnpGixEHL9qbEM02s+yZOzaV7S8FEJhtPb0Vd35zJ26fczuOXT7k8b57cgi1MSnyMQwqXfVUWpx0sCEqIgqxCoHTQZUU9r64V860VPLE4N29Au++LLFlRzb4u/A322pGK3zwlyvtdkVZHVv48NkVwMW/ZMt/ugJwZwIwPdV74KR0I0ipxWnXuZ0Yt36cx3TFFieLeouTTahoVFeI0MQuOC+seAHLLi5z/q3W3cc9HXmuST72KBhd9WTdXAvPQVFu1yz3bxujA1rHyKdf+fdzjK8MbHC08Bd+YUdlE5e2AUe+Vi2ja2XKAZdS8GNT+E0nOZJDCMmSlLrqaW1xUprrrT/ewobjG/DB31Od0zxuorhdew5kHsAdc+7A2qNrPZYnPzdpu2a9vvp1GEyu1nXx/BCpFjjJrm2S92tohOv5eNdFuH7DHoGT+OBYlTFO3s4vSimtJJUWJ2fRhH234chK52s9JET5HTjZyyzPqudYhnLgJN6wLnLg5JFswyU1QiEgt5nRKdqIS/XtAbNVsuJE9gnkm1xdYy/ln1NcvtfAacl1wK437P8vrgssrO61vliaMHAKM/EFrrsdsq564kXMS+SeKLQmSJKEv0/+jTxTnryp1arc9ch9nR5pQgsvXHrZhVG9LH5lQRLmraZQSXAGTgqBi87tZORcpLDMc1fOod7Uenh9tTD2wY2W7msBNU0rddVz70boqAXZzMDBadAf/xbx0lmM3zgeCeMSsPzQcuesI1aMQIoeuD9RfnED3LriqGQfixOTQ6y/D/j3Y2DFrarFF/uFRwAYtXaUR1c9R5CWGJ0Ib/riAG6MBT6rWvi1hfckyYp8c74sBa171zFfY9hkNxX8CGy6zuuKRh83wopDK2CymhAvnBnNCn3mAeVKli/tvmiHJQeX4M8TfypWur0lh5A/l0n43alU2NRaj3SSDZG6SMUxTlnGLK/lV+N+AdXScnWx4KLztbcMnlbJKgs6xBanS/nn8d0/38nm/7EasFtMg+5+o8mtcneVUK/2eg7Q2FXvpRUvOcdaicQWJ0dmbm8tTj4DJ/WSyu4gi9xbnNwT+AQjcBKL6qj4xvgYDxirB7bUBl6qaH/2F04tQRVrlrxsOj2mb52OpPFJmJ0xG1jWGtj0KHD+D6/LVns2kt795tXJn9Hgn5c95nPcfAhWi5NSCnuHfGF/uGdvdw9K+i/qj9VHVqPbHM9umVIA6ciPZB2RHaPise34dUa4DxGQ5Odqk9WEBLUDUyjT9RGu37B77wfxXCJ21ZOlMBfPhW7XCMXxWCotTg7iFtojPJfQas1HtPAZxxFgk2wwW83CumV32uyfFcekKqy22Fqc3H634tF/rB7wVy357DabGbMrXUbFCOCHasDx7OOoPaU2nvh5kHOezl91wu5zuz2Wr6k18+QiwHDO/qyrgtM+Zy8NGDiFGbMwANVkclXYLlwRDji3fthii0Si0JowZ+cc3PTFTegyt4u8sqBQAXCcAIwWI+ZVBf6uBVit7ncJ7ScHtUqM2jK18DWw9pWK9sGmvp52L29xci3zw78/xNGso5i8abLqOjSlIw9k3IxS4OR20Xb2t5eNFbFg9LrRAIAnf33SOf3dP9/Fz9WB76sB77q1zImVGNf4CflyE/X2Qfk3zbwJuFiYCcl4QbX47tnATBaj2za375eJGybi23++RYMo4LkUIag7/wew4UGg4CwS3QJPWUXNZsGFvAtuFUJvgZPncXw2x3XjwZ/A6Y9j9srXFzu+8GhxsliUA6dAxlCJ+0fpAu+RHEKlq54k/jbVuhKq3LhwtDgpddXTefnNevs9u2cUU+0KuvstYElzwJSFc1dcdzHzTOo3czxbnFyVDJ3NjI83f+z8O0EH3JMIVJWdpNxbDOXfQ6wKeuvC6tlK7GqNEKnVG8VK6U/VgY01gXzn9/b8lLiPaykETt4u3moBoPsYJ/eEC6OE7tHu5+TPt32ORfsXeSzf8dom2Tz2lbOrnltKbLVt9L+Uwmd/rbsTFW3ZsvesNiue/s0+XjJ9cbrrjWz1bp72L6K8LTwq0X/0QnL2do/5HPtYbG0pSnKIOpFWwJSl+F60EJgMSwE6Cc/idh8zfPaKPVlJRaUDIYDAyWgxyvadUhZIzxYnsUz2YOJYPeXli0l2KurMzt+dZ4uT7656XlucFA4uRwVfLXCyikmVzK7fhNWSr9jidNlwGamTU/F/3/1f4QrEcd/2MluE/WUtXK84rlXW4hTEMU6X8uXX8gi37eHe7VeyWWSB3YpD9syqsqyKAGZsm1H4AT+TQ4h1ixJ49mFJYOAUZixC4GQzuy4cZ7KPOV9LhRWlM7lnMGDRANz7/b3O95IjXCeimTtmAgA2ndwEtXSSAND7u9646YubYLVZYTDno28y0DYWiM+VP1ndcRdTPDGt/G8pqr9X3fljE/nXVc9769Tt8cDUKoDFlOPxnk7ltXgy87hTpkBLV72AHvjmHjhZDB7fVylwEi/Ojn3quHi0L7ygpruNexeDv3yFLk2AvSLw/O/P4+9TvtPHSpIk298R9omyeRwtTq+segUA8G8dYEoVe/AEwJ6u9vj3wMaHZYkm7N9RKKJkwfm8824nJbdjyMcYp3kZwgNRVfZnZn6maqr8+Kh4+xgnsVXVrFypVxoP4Q/31iXAe1Y9sYKhEytRKq2+VqtKhVGyIVIfKWutNBami1ZLcf3Ij4+g+fTmsjujbmuT/aVasdk9CsjaBez/AOfyXIFTliFLZbl2YtAxc9unztfROnmFsqpCgOERWLqVTaxYaO2qpwcwp6q8tUp8T4n7rDfHASZHC5/SGCeholnHzxYntQDQ/Xhy76onEu8mH7x4EE/8+gT6zO/j/Gy0zv6dHNvMarNCB/ceCfZ9EwttNzHEfZFslZ/rVZMN+bpBp/I7jdD4uApHi5N8/wceOAGwp0d3fs61rJgI144eWQlYUxPORDWO77/19FY0/bQpjhXWCZQCJ/HcpDWrnslqklWWY4Tlqo5xsnm2OCn9JgDAKvwO74jOR3YD4LEkheQQYhc38WaRl4fmihRbnArnd7/+uJbnem0TrptWS4Gs659j+yzavwhZhiz8+u+vsuXbF1CYeVbWe8dzvWotTn+d/Au1P6iNTzYHliF52UH5I2mUrjMiyWbBFWHcnSPTpnvrozO4EzOomrOB3WOBnAPqKxACUVgCzIwZZhg4hRkxU5jN5AqcIoWTvLEweHjy1yfx1c6vsOI/V992wFVh0smaj8WxH64ftNlqxuIDi7H51GbsvbAXFiF1r8XtUu9Ynnhien7ZMzhz5Qy6zuuKLafkgxID7arnjVILV81IYNs/9iBRrcUpPso+StXbOURTi1MgY5zcTvL28SkqY5yE/aQT5nFcXNwfhOlenxKDP9ldfDE5hI8Tqci9600EPAfJi2OcAFfFqaXjzpZj3efXelRW3B+4mWXIKlKLk152AfOsaOUYc1BlUhVc9e5VHu8B9uOkgjUH86u5pllUsupl5p3Flzu+VHxPTfd44L86wK2xyhd49+45zaY1w4BFA+zlkPUtdx0HJoWbCQCgVxmQr4dncojLzrucnhd4q82Kb//5FrvP73a2zLnzuPPoK6g0XZa1OGUZs3Am9wwe//lx7Dizw2N2sfKy//w/ruk6eReqqkqVNo8WOfl3lDVOeU0OIWTX0gGPqSTr09Li5GBytDwoBE7iPlbqvuxYnFJLoFrF0iJUesw2M64YshXns6/f9X3PXHE90Nhqs8JmNeBEXWBPHdc5x318k31e+28wTmOrRyWx26RO/mtQD5x8XDvUAieN1xxHcghxmyqOcfJn/IbJlQZePKfHKmSovaew97Pj+z/848PYe8GV8KSiwjEvv6mjLaAzWo3KQQeA+wrLEKGLsG/vve8CFza5tWyZvd58PJV1xPm6aoQ9cc2ctKKPcXK/QavY6dc5xkk5cLKJ2VqFwMliyZedJxWTVP37KXB6qbCwwsDJ5jrvKI5x0rl+O+IYp4OXDuJEzgkMWzpMsay+5LtdD3wGTpIJ2TY/Aifhu9c99AGwe4y9F4EWZgZOFCRrLq1x3f0XTzzCQRYpnPwMhQHVgYv2KD/O7YdhKbwDLfZzzze6LpAXcl0Zl8SuGpH6SEjGy86/1R5iKZ6YxBNt25ltZfP5EzhpvVun9jDSVrseB6D+ANz4qHjcmwgcrau+bG0tToEMbpRvB5vN6JGKugNOwZp7WN5VT6zkFAYmsu538GyGFy9CsqQSQoujrxOp+/LEE2ikDh4VFXGMk5jGu32cvYtRvjB7pHvgJLx+afkL+HHfj/IWRLd1+RrjJPtuCoHTP4WVbrPN1T/dZrOgfay9gpQQlYD+tt2yz9isysdcFIBBPw/CiewTOJVzCp9s/gR5pjyYrWa8vvp13Prlrej9XW/ZRXZpDaBBNLCqpvIFXqni8tXOrwDIK25iN66cfOWuHTqVligdJL9anMSxSPJMny7ugZPOx1hAyXLFo8Xp8V8ex8wdM9FyRkuP+dXGXkTr5K1VmgKnAFucxN9xjJffkA7K2faUplkKz+UF7tvVlI1ooXLtrUug0nlWtaueW3KIPK9j2sTB7a51GCwGJBhOIzUSuCbaNc7VKlllv3/A1eIU59bqodP5aCUCYNO7fiGRAB756RGVOQNrcfIY46SiVhRwW5w9W6Lrs0VscRKIrQ1Res8d/WCSPWGSY99dzL8oe1+xxUkIMnV+dNVTez5N+zigdmRhi9PBafYHNq+4WX6OtRq9PoLkuEIyHECpxUnsqidmc1Me47Th+AZZ64y3Fie1wEknO7cK468s+bJrintyiHaxALYOBf79UFiXvcx64RztuGGoOB772HxcdXE9VtUAllVXLJ5fItzOj+4349zpbSbkioGTwX7eca/nOc/9YgZkx3le4zMNr1zhGCcKgvXH12Pq8alo/1V7APJBnVJh4LTvwj5M+ONN53SDMQswZaOJ3l6hi3NvPS+s6IktTmahEm0VBrtnCwGVxWaBzei6WEvuY5wKAzFZJcbLdwvmGCcHtWfqOIibIs+Y6yxDfFQ8fqgG1FYosKPVw3Fh2nxqMzrO7ujRggbIT+pafzzulUqb1ajYWmL95WpZgKNXSNnq3lXKW9cu8WnkYkudt33mzmw1ybvq6QD3QFAPe59vwF6RcqgWab8jfUC4llaEenatfzMP4LNtn8lTIruTvLc4iRc59+MXkFcAHYGyYf9U/FELWFIDiIuKQ4JbtyKbyjg+x+/gsuEyus7rimFLh+G5Zc/h8+2f453172DjiY1YfGAx1h9fr1hOpQv8gGSgjUqCOdkzuoTfcJRaVxxJuRKjl2yQIMkq//leKtCOsRSAPIiSrcq9FdJHZe1Y5l5XdjTYgx9vKcmjVAIn9/Ipd9VzCyCF40Kv08vHOHk5D4nHWzUvPX/1OuWbE0otTpbCFqfd54XvLtmAH1Jw1/GJzklKlWPHJKUyK3XV++XAL84bB4D9+C9Qaa10lkNheQWWApjF32HhzSyLzSJ79h7gSqAS59bqoZfgEcC6E69f7tc4tXIqv1+0wOnGWGB1TaC10bXt9ArXtmAETkrHyF0JwOUGrptT0RHyqrByi5P429cYOHlpcQLsCUr0Oj1wYqFrolXek8XsJQNsfIRyWObtOU5as+oNWzoMdSOBIRWgmEnU0f1P7evpxIThwvnCaimQnScd38BR5vpKF9PCbS+2+Ec7jjXhuKkfVZjEZuND6HD8A9weD3RLsPegCZQkSfLuljrvN3kAoJ35X7SKcX3/7MJzvHs9z9VVL7DjHAAMhiImvggTDJxCbF+m2zgicYxLYeB0z/f3yJ6lYzTl4PyPtbEo5SS6xnt2vXJU9MQWJ7MQcIjPgMkWumoYLAbohK567ncRlLrqeXbNEJ9YoB44GSwGfPfPd85mYV9jnJzLV2lxcpXRtc6Z2z/HPd/fA8B369fsqq6K9EM/PIQ/jv3h0YJmX47rJK/1qSJWt5YPpRYnoPDkKksO4ZkhT32MiXw+QN5VT7yg+dXipNQ10CPTHXAi2/5k9EZuF5IkvfwEHOt2EY9wC8r0sCcBcfIzq16scMwb3CuFBWedvynAdQdNf8SeZt8xbqzA7TkrkkryBcex/8zSZ5xdZ+btmof/Lv0nm0/t7r/SfhhcAdhc23O6+3JO5xx1lUOtdUfl7q8OEsxWs+yCWlDY8ixLDlF4jIqByZlcV5ct0Si3bJU6H92W8gouyO5OZxmyEKVXD+ndByqLHEE7oNLi5E44r0XoIuTPiPJyx1w8Rykla3DQQWX8mlJRClv6jGKriELiAKVxI45DXen4cp/254k/8X/f/R92ntvpnGa2eg+cxNZ1sfU635wPi3j+EgKnPgnyZVhtJtgkGxL17s9vk3ymJtYLlVn3XhVuJS1cl3KFTq2lQ+kc7M0NJtc4jqC0OFnyAUmS9SJwP9+KHL+XmEj5nRXlMU7+J4d4N+4ENtdSfz8tonCscI5QZxHPPVYjLCoZSAH5GCeR1wfgysZqyZNDpOjtyTN0AD5LBY7UAz5JBR5KUliJj+QQYouTeKNFshoUW5wcZVE8DRSW2ST8jqMUWpwGJAPNFG6SNfTnzqYb9x4ifZPsz6ny5lar/PdhM9iTOcjOuUKLk64IgZPZqHzjrbRh4BRiHhV64USkK8zm9d+l/2Q/XqMxB6mS/YJ3X6LnRcUxmF2txUkcsyG2OBVYCqATxlWptTjJMu8Uvm4dY78LK1ZizFYzHljwAN7d+C7czdk5Bw//+DCqTq6KAnNB0AInWbY3HVxZoHy0VPVPdrXWeBuoLg5cde8mp8bqVhmzd6NQ+b7CBS9COBbUWpzcyZJDCPvcJHx/b3cV3Vnc7tYrtjjpgJM59gcIKj1wVTw+Y93uPLuPn7rLreIFWaAk+RzjFC0cAFeEYxHGS8DCarhpc2/XpMLj26R3Xb2MFiMMbovVq3R5c1zfxHE/RqvROeamdYz9+UCRCuMWAO/7YXAy0DFOPk0cb3Y62zVeIEIta5iXbGIWm0V2w8Wg1FWvsAIgBk7ia8B+jHWZ2wVHLsuDRbV1O0TajLJuf1mGLK8pyb3drBFpCpyE81qkPlLe4uQlS6jYsqHUcu2gFjgpTbM5A3nhh2P0fAaT0u9KBwDHvod+/1TnEppF26e7t0JlnM1wvu4YB9SL9N3iJP6+xNbBAnOB7NowZtXLGLlyJCw2C1Lctr/NaobRYvToahgByWeFPlL43bl3AXQrKX4+8DOSJyTj+z3fe7xrUwlG/K0AmoUjRbE3mFLg5q3XxfeJwPo+snO6+7VCtv7C36N7i5Pis9pkN1M0fE+rAXfH5iFV4VR1sfDjVR0tTmJKaeF6ZbMZYVO4zlYoLJ9Z5drtPFZN9vO1LHCSPchXnhzi71r25BnPpgBPVPD25QDHNUstcBKDeLGLs00lcHJQrAM4xvwJwyCcLU4aEmZdXYTAyT0j7LRU/5cRUbgf1MY46TSOmVNKwW4xMHCiIPAInIQ7LBFW+8UqDha8VNE1i9nsyoRkhWfg5LhDrtbiJKY5F8c42VucXIGT+xgRZ4uTMC0K9ov1ltrA6frAhTxX6skl/y3Bgr0LMGLlCLg7dMl+l8Nis2DdsXWam3/Vuk056FVeJxqU75SLrIUnzwaVlDMe2f8W9o/PJTqWK28RkGwm1e4l4lg0SAb0TgC21wIa6O37QilwEo8h9+e0OJiEbnuROu9JMkTuyTjsWfU8u+qdyLG3OClVDsVuNu6Bk1h51+s8KwFiRd5oNXo89wmyvyVECmWTdT+7bE8zLAajjoq7QRiEbrQaUeB2wo+0eW9xchdR+MDNLbWBX6sDCYbTkCQJ3+7+Vv555Y8DAD6vCqyt6bZcmxGH6gK/VwfO57qedB8Dq3IFTSUdux42+wVWrKsrZFezFh5rZ6+cRb1IoFUMcDZPHjh9lfEVVh5e6dHFyGfgJJlkg+Iz8zO9tgorDdBWUkXDj/KPIyvx2qrX7MeLPlJW+VG7Kw7If0O+WpyUusfEKrUMmK9g6+mtOC0GpF4eDeC+Hmx8ELG7X0WzaGDcVcDuOsD/KnrexXdUtq+Lth9Xh+vZkwv9dWKD+gqE35fY7bfAUiBPYmMzYsLGCcg15nqOcbLZ93OS+/EB3492iBK6OsV7BF5iOW3o9V0v5Jvz8eAPD3osR6n7G6B97I+DSbiiaG5x8npdk4CTi1FgKcCQCsDXafJuYh7rLzxfRegiUDMS6GbPd6TcGids2+v0/0F31jPrrYzZ8/fvcKpwM6VFKGTVEwKbw5n7YFFYTlYD4KkK6jc9LTYLcPwH4IdKwD/vyBJkib01xBuBNsmGRoWn7f5KLUzu/BjjpBNapCWrUd5Vz21bK6Y+LzyvWYTeDTE6zxYnNVf7GpTkhclqUrzJ4o+Iwl5HHoGTny1OJqXASRhDj4tbgL8fB0ph9z0GTiHmMQ5IrJgXBjufVgHqCrUsk3ByskieF2RHV7xaOgP21wEGJssrwOI4IfeuehFW4cRXeAKx2Cz43/L/4fdDv0MP+Q/qnkT7xdrhgvAMAYOlAK9Xgkf3DUBeqd98ajPWHlntOZMCm5eKDSA/MYkX1ziT511cd/rCCnK9lHrOaRfcnongrcXps62f4ecDP3ss1/15WN5anI4J3bxyzBewsDpwQywwO80+TSlwEruomG32+6ITrgI6RbpO3O53+7S2OrmXPVLn2dITrQNO59rvQioFA/IWJ9dnn6wAtHQ9TB4RAHI8NotrfxaYC+RjmNzKYbQaZe/LAifheNtSC5hX1bUt8yRXLdhsMcjGbwDAFbdgwUHtAuXewhRjPI8vdnzhMbjdn5Y/SZJwtfUsakcBXRMUuiE6LvanfwcubMSZ3DOYveNzxWXpJXuLU33ZOcXRSi08z6TwPHE29wwO1wO21gZMV47JllVQeF6p7Baw+KqURkkmWYuTTbLhePZxzxklCT9VAyYIzyvztt28joUp9O2u2Ri3YRwyzmYgQh8hf2CrSovuF9u/wKYTrrFqSunBHfTQ3h1WZ81Dm8/byCcatAVO4letqAdGVLK/nljZs9uaI3BqKXQN0sPXGFXXMtxbnMTAyREsnc877xE42WzqLU7u5xZ3EUJSFvfgINLLecCdTbXLqn9d9cySHricAVzaphw4Kd2J11DJNFgM+CQVeCQJ6KhX3/eObnDZxmwcqwssqwF0iFM+5iOE/VNXfw6R6+/0nunSoiFwilSorwjrMRizFFucAHvLh9ozHy02C/D3YPsfu1736KqXqANuiAH2XNiDcevHFU4XMuZqqcVKjsBF/dl2DnpxnLlNfk0Rfy+pEco3ahzXWjFwinKOcfJd1AbCStS6eV/Mv4j5/8z3yARstprVH0CsUUThtcS9e7S/Y5yUdoskDgX5vS1waCawZUggxQwpBk4h5j7gURzjFFl44XjUrY+q2I/YKnleVBzpNJ/S/4trooEvqsorWmpd9QwWAyLFh30WnkBmbp+JyZsmY+/JdThTT96H+AWhJQwAMoVMWe2iTXjrKvvDHt1PuOKFePTa0djvNtZLjc1bVz1Jkrc4idtFJTWzKLbw4qHX6dEyxn4hO3L5iGwe8aQunjP3XtiLp357Cr2+6+WxXI+uepJJtQvHmUuuQeJicgjHYHTFwEkok9lqxrAUeyXqixRXsOg+aFfrTS2PrnrwvGs2/iogyXAKZ+rJH6DpIF7YxWBzuls3gggo9Rl3rctgMcgqYO53eI0W+UVO1g1J+F21jrX3+zYWXsjzhK8TZc5BtNs4rGSVs2QFlekROj16CTcLTNDbW1Xd9FC4oeDOsQqbZINZqCx4XLCtBsB4EVjbHVhxK5797WnVSrEe9rEUjYUZuuEYsLqrbMC8I5jS57uCpQpG1+8bgHNcUppH4OT94hotmWGymRCtcx3bzfT5OFsP6CfeQTZmoo9bwgFvY6e1pNp33EE2WU0e2QW7zrkNo9aM8vjM4F8Gy7ZnEy8/ILWueooKz8WyYFBji5MsG6Dbe1bJCptwXEcX7iexNbVuFPBGJS8rUOuqZymQjaFxVFzP5533qMTarGaP3y3gOga90Vld1yn3gEw8Bg5k7pc/esPjayivx2DKcVU8vTz42MEKAEtvAJa1RrLeswKu3FXPd6uWeE73lrbdccMz33BJ9sgHpRanSKVxjyoP3QWgqcXpvkTA7L7PhPVIVoNqBlJA/aaEfbrrS4hj62xWM/6qBWyvbe/2/Nrq1+zThWPT+/g3Z+EAqI9rEx9joZfkralii9OjycChuvbf/7n6wFsK1zvHDScxiIxxXsd8R07idUXtweCDfxmMh358CP9b8T/5uoPQ4uT4DYs3J96o5HrwuKaxgZKkeA6UxB5NDr4eYB2GGDiFmDjg0Ww1y/raRqmchMTmcAvUu+qJ3aLELmBik7l7i5MYOOmsJuw6t8v5tPYhFaDYB1qUm+u6a5z2/+2dd5wU5f3HP1O2Xi9wxx1HU6p0EAQbKgqKBQtRY9doYgn2EmOJGstPE6MxajSJJjH2hlFPjWIXREWQJqj0dndwXL+9LTPP74/ZmX2emWdmd4+DQ3nerxcv9mZmZ5+ZeeZ5vv2RqRLqtkGTnogBeFdSo/HIVYrH27ihes8ve55bYc1OKBkaGdNiWNjHCJ1Qd3zFHkQN6oeGgK39gVm5ztwPGntICtHirqF6zU3fW58lagA3BatMPE6n5ToO6bzHyfZ7z5UDIduk3McHHCbVoFx1eh6ADCc2AP19Ti8OXawgEm9lQvnsynjUFlYRpRUnjmAQT74T9JonAa3JCH2j4JWCBtwVp4GxtZhDlZWN6xq2t2/POLSTxhQYE3oiVfoVnHV9tAgjGLU1LncV3mUQkJbVztCxmnfRl6TGA1Nxqoyn+nZYtyngSU9Gua09bnlX1vdIArF4B96uANb3N1azf7rcyKP4Vzl1IMdK/gtbPoNfAk7PM0JaR2RgETD7SMgXgiqrjGLvl4A7Pr6D+z36neEldZsM8AFnZhI+BEBOjtWMkpuh4kRj94Al9ARjWzfbThsBHigFzvfIDaEVAXq8bo+3M54G8/2ubavle5y0qMP4oEipintuhKn38HclhoHGVNDpZ9EcbXQUTGCuw8XjlEhEcf5/zwcA3Pr+DZ5tAQCVUk/LeIpT1qF6BlEqF9NtCRDAqIYbiUcwWEldzw6NP74GuIrTDuc2E4/1dTYkL7tUAY5W2BwVQnuR9Ch0l8XCASDhIs9IiZZUpROwHkRNj2O/5KM9g3qnaGNhJh4nKY3iRBt6FHrM0WMOI+MAH/B0mftvxWKteOyrx/Bd3TfWNn+y/H4moXr0O9Tmcj/N3O0HFzzIbI/rO+9xMr9PDyn7+oEZsmE0kzPxOBGNK9PJHgr6jwmhOHUztOUkpsWYEBfZxVKmRVJW3wThVNVLTsYJyopDhy3RihOd4xSJR1hljcRw1FNHpdqT5loAoK11o/VZpeKhIzaPh0NxyuDcQKpgBo9IdDsTQmGe87SXT0Nty0b+lyhCSQsnU0Utspk5hq4W9mqFITC+0AueOBJ+9RhcLU9UvC8hqXb4PRQne47ThKDjEMRtYRLpLOJtsTb84+t/oIbKpQEMBfc0rHQc79M9PIEZMrc38J9ydhuT4xSxhVum8Th10IM0HSKQJJFMgpVoIVBryVhxKqS291SMkNigBPRqY+9PQougvr0en3lUrHLD/O2EnkCIpMaDCpugTBLtzHpFpOUH92dMNDTWzefuotfbMZXtPlpKkC8E+5zNsES7x0lJM7kGoWF0Yi0OCxtC8CUFLn2So4DZSwCfmAM8U26EtHLLkdswFSed6NhXJZiR49zHW0qBFtbd+gRghNXe6OXJoZCT4y19btKJmP9nbWOQFG+FTo3/HUkjAd1nj+MYWBjoHCfK8m2E6qWES1PQq22ttYTY9mRlSk2PIRprc4STKQASaQrd0CXMDw8DNxTDMkjQ1nAZkqNgAnMZLgqaKqWqYP55/h882wIAQcqQxSsKwFWcMlgUV+pIGSa88vy0RBsaOhqwPzW+P9gDGMHRGcPgKE5RL8XJXaBdExxofe4vs/NIwlZMinjkIG90qW4oJ1rBepz45ciZAJKsPU7m8W4eJ906r8rkO8W5+Ype73880Y5fvfkrFFPHqCSBt75/KzPFifqeKSfZx6OKvJRljlliQ4tnFrroQYFsKKlmlVmT4YpZQCgDj5OL7Cpxleed1PS6AaE4dTO05SSmxSC7LIBKQ2Ipq48EToxzckKKUf07QiV/05WjmqJNyJWAYtkQyun4XkmLWYtU9lYzCz+Jtm/BrwuA//aiK8kA7dQACzgVp0xfHcnD43TuSz9jzkNbPHa0pF94Lawb5y7WUm1t09mWuS3sSYeK2Cc/ezlyEHePk0JbBQm1MKKXxynZh6KJKE596VTW0pP8nUSWHqer3rkKv3j9Fzj7lTMc+3oRKmwneRle69rsHJRH1jbxp8txooug8MJUtOQ2hVGc2pg8LMB9kgzKKUH7lV5GSOz9pUCDLe5cT0SQ07EVEzkKbTpyacWJmox+ZfMUJOKtjDd2oM/9fVUkVoCmob3UpuJUQFLnLSbshGjGvds9TulC9cKShl56ytJ+Yq5NpFl0LdC4LG0eDOAMZU7H7SXJ5QcSMfytiO1T5j1r4RTLyCYnLVMU0+NEnVuLuHuvMyUQrQU9qjZH6nBbMf9eXVjr3Aaw71ck1owPKoF/lyWrr1Lz1JFho3BIXWvK49ROlOQ5EpaBgkaVOOOiDZ5yYr5DbDVOgkNCElb0BY4IOb/jqjgl/x/111Fp17oBgDzireh11uOkUIpTSHIXSonWjoZIA8ZT40ihAq6hLFfirO3l4cnUeSFUZptCPXBbUuQokth7maDGVUmPgmju83NjO//3ZZvHCbYcJ+v8yf/b4+1MlEUmoWnpPE5m6fuORAfjiZf1OHcc9QoJjifa8UgP4A89UtsCEnDMM8egxmU5Bxra49QSbcGRTx2JSf+YxMgVI8tGWp/pJSJiWgy5OzlOPdTTMGBOtr1LeSQGtG/OLFTP5Z1TeDnq0i4YWHcxQnHqZujkvpgWYyys3DhlpMpFAsZaAPYKTySZzxOjLRGMZSjVeZs7mvBDPyNuNxFrZhWDpIVtPz+wsb9h8UuH1lGHP/c0rJlHqykFL2or8R2JNeMfPYG1/ZLVetKfGgAb925n+dYvGYvOk2VG8QrAPUyAJpRUnHrqKS+cfcHdTCox2RM2NbvVUU+4Wm2CicbUH7TiBEMh60h04GWbdfmT9UbS+pOLn3R49sw1VuzVwv7bC/iuL1x5dtmzKFX4wmJOsl/ESCo/qHInFu3zhDK0JeyKE7w9TjqV19bU6iw8oCcnfdpYoOodluLUWmSs43V42L15p+QCcyuBA5OTzMWFQEPcXn4+iv2l7MOvAEPIPzpsepzcBU154WwkKE/FMTnAW5X8YxVkZgQx8+Lo0JVSOc5YP01F3u5xUjMoWRumnk+pYlO+v/0D8Pa4tHkwADCyE1WozskHAs1LMURl31Xzvjy04CFme55slIjvahS9A8flAEdQfaylec1On1fW2hnve6+G+bilBBjF8U5sCu3j3AiALmNdGV2PKWHgrHxjOQxacbq40Cgc0ty22TLatCdHdF2Ps5W06LOn8Ti58fsSds7zQcdLpU0Y6gfe6+08nrgURahKei7b4+0ZKU75u0hxUinFycuToSfasb19O8a7RCXGqbEyl6OAtTR+B3z3CNDK5u1+sfkLfLPJvbqiDskqSV5oWxx824Y3rM+SHmO83nZci+kk2hDTUnMk63FyLjhfeE+h50LVXIhZjtw7VC8SjzCKi0QSfMXJ4znF4m24uNB2fPIcmbS7QDY88Pv4jGq17615Dws2L0CP+3pg5XYjmsEHHfslxz26OmlciyJ/Fxkxj83RgTm9MRDuSraFS3iswvFEeYWn7qkIxambicdb8V4lcEtxMlTP5nHqsIcnAfBTFXB6qmzFKSBV0pRWnOi4ebrkaYHWgDLVsFyFo1sZj5fZlssKs7ggSqnLpcJ+OmxC7+HYgvMLjATlO0uAxz1ihmkUDwUoLDs79Mu9gIvy05cxB4BwsqpeOeVRsZdX9Yrv/VmusSCfvRKOI5Zfj7sWhwhTiiGhJmq/ZAzqHfGIpQyaHP/c8QCAHZEdOMZWcKC51Qg1tFc0GhP0Lnt6UiiCbS7Jr/nJybNdB4hq/GBXK06pUqbU2h12yzUnx4kpHkEN3lvqVzh+w7Sy0rlDqh61rL66vyhtO/9T7lSsGuKsgBWNt2KML70CwOO0PODvZUaf4obfJFG2fQx96W3W39M9Ck8oUmaK076fnwAsuZXJV6pQWK9nNNaKx3o6rZPpQvUAoBys1zlgf3n1WNpwLsB9TaV007GuJxweZfO+3PTBTVhSu8T4Y/WTaN4HKLIJJDuCHpaHDFH1KP5bwW7raNvEPzgLZK0NqpR6P8Jx/vopOiTcOO1x/kkoq35eIjV+Kx1bublnuZF11mfT46Trccuzayfd2npu/LbYqChntQeat+LjYv0uVYyKtb93USjtFKRTnHROj8vA0OaPpeZmt0I0xqna0dCyCcNcxu0zalIRADzCy24BvroU8deHWtsSegKT/j4RTy98xPV7pTnl2JE8b6ktt6vPturUH7p3qJ4jLzOJrEXQRKUM0P2OcDxO8WSlxmww1x5y9zgZ2yOJCPpS44lCEvxQPY/+Zo/uAFKKk9vXNAKsSF5ST9VYyPflXqllWwBjfj//NSMn7zSyCsv6GnIjLW+4vWtdSYHkvYwAANdQPZVj/Nva6uLy3oMRilM3MySyBEeEgdtKkqF61ECrkARaOdbHMEnjPUkKGyp1Lh8VAqZTilOVlppQlXgT41GR9Bh+nq84woK88FEuf4Wu0BVrAiEEv3z9l3jg8wdQRQlNXgnKdmQPi9b83vz1Ux4rA0ao6QUwX1KA7knlcdjLq7olvQfaN+D5XsaCfHGbUGHPcZKIu8cpRE3OU5TF1mdZSoYocEIqzPKl/WKb8IZNCPv906Owctu30DzWBwGMgZvmr6XGdf6Mk+ReIBn72ggQT66B1NWKUzw5xdAeRM1muXZU1Yt3MJ4Pej2O9nZniARJJkTTazv59ShCSYFTTVP63o2oLaG3ObLNsg52hgrV8FqG4R3a5GtamtH5/JIxMQNAh8yJbaJZdjuTFF+hsgnL/duWcBefVDKIgy+jPLtuNLV3Lmxt7eTXUR060PMYkmjFRo3Vhu4qAVb0BT7rDWxtTFrmF5zP/f6mQu/zZ4KfOAWMXvH04TzpUGy5oB08oR6ANOx6FIQ41hEAhHqGvbTUuxeI1nJDls12x0hqsdhVdcvx549/yz3/7R/wt2dCMfXY0vU1N48TYHjLflsMh/LKwy/xjV2pH3IaC0gGOU6+aMpT7GXQIIl2aE1LXRdejxBndUUaf9Iw6KP6XFu0FfOq2LAyOxeMvQi9SvYDkGaBaS3KLB5r52SXnDpZizBFsuj1u3Rqvj0lzzCy9lZZL0smmAqTW46RmeMUiUewL6U4BZDgFjlwGHkoeGXX+/iMKr1ua4q93gZMs2UTjAoAq+sND9NQP3B7MUCSitFxkmEQva3EiBQy0V28u7sdF2OFPzluPL3kaWtbJN65ebY7EYpTNyNTL1lUizKKiw8JtEacicJ5HiE7ACxtn070PjROlXykBrc+JPWi+RKNjOKma1E81TM7l3iAWgdKtSlOn2z4BE98/TiufOdKFKJzFnjZozhEUIZj5XqTyRnklwSS97WAElDtipObx8kfSYWCJWyheo5wI5KAW3EIumKdzxZu0RZt5ObqfJEsOjAg6ky+vbc4innVU9PmitivyisUwQzfa9dTilNX539oyaFpkNyGupf7AVqHIw7fnjCbiDUwibF0KFicszaOpThRgpWPxCzFKVp2ZKfaHtTYPtoe2Y7BO7EaPGBY53M4wuoy6rFKaXJGeLT60y8tX0B5uipUtlBA3CYobdeMjuAWEkPTXzHGGq8jl69+Je15eEhqGEROo63GW9BG2I57WNgQUiaHgEDLKtdcRAAYNOLXeLC9GG+3u78sC2IB1HpIswXpxvJO4rP1QfsizqsH3QTMWA5p9N3wKX6H4QSAoQi0rAbWv4A+SI3rwdg2JjLCZECyImMHkaEnCwON3fYyninkW5QXbfmCu70hyygsey7wE4ueYA9Is9CuF+vKnMtLuMEL1cskR4/OcfI8vxaB3MovsAAAF4y/FIkM1gkCgFXbViCmxdDeUZ829zInkI8bDr8XgFEExw1Jj3uG6rkh6xGm4miQ6qt2xfPGYiNtIJqlsL21ZTNmvzWbKbBDYypWkVgLU3wmlEHIsZ2Eyz14utzIB3SjjTfUNCzGnUljzs0lwOVB411aQyiDV4QytHhUR9zVJGgvoEu/D5AEYloMv3vjTGubZF9U+UfAj6/FPzHoBFmjqh5VFpNoiHQ4LQjFnFKo7EmNFzdEiSR00jftRq0kqQkxlGhiJqFErCHzMuFJBkipCZu20MVjTWht24q1/YB3KoBiqXMCw/bmdZ36nls4D41ZwjWfckWTRDtaY614ZukzaOpoclWc6EqFccoC9PwbM7HvSnZdGElPuIYM8NYHMVm/bTE+/Ppex/ZixQgTUVwGoPN9W+BLs1YCV3BKQ0xSEZcziHHpBHRFyJ7R9Vj1zR8dk4JPbwfWPgWY+XsR1lJPJ3OHdedkJiXXQ6O9iD6SKuca7zUN9f40JRM5VNkqz+V1bPK0UGYCiTUiR0q18z/NwIj1sHIPOktHIP317aemxotixVhHxiSq2cJSk89NzSBu3Rxb2hT32t051BpS2aCoOYim6dNyosUzGV9r2wDS4O7FCwaKMPuC7Sjd7+rUxuE347PgKOvPhKR4egEq1fT36Vtf/7TH2GlqZu9b2JYbquYPAgqGATBKynO7EdEReWM/4LNTcbCaMlqE4/Vcj9Nwn7GtAzJIsh94rXf1uUuVyTVZ6jl2JY58fgFjVPHyOKWjsXACPi93FsjhwVOc7EtR8AjFM/QSaBEEI0YYJ2+87lM82FJY01Hxzn549rnhaM/EQ+ErgBI0XFJe45hMYoyXP1MULcKEWAepcZsX2gUA8SxD0qaHgXO3PoRCl2gdGRoaI/X45qtbGa9fjsf44IaWQVqAHQnAgf2nOraPjn7LVOgcpbYDhCBMjSrXvHw8ltctBwgB4ZScfz/nQDy/G6qAt9PPxMWIF5J01DZvxvf9UtuE4iTImgQ1sNpD9XxEQwdngEhXRcYcvHI4lXUAID9Wh5rnivHD51chl/KunNbxGQqRGrTKOrIXWkaoqfPRlWf2/+4WTPruZvT2AUflgHGHZ8NUedfFwwaSlvUiysJOtAhuf/NctH96Bq5+6Vi4iUEJKnxJp7xipza/5ji2XG9wVZzc1gYCgC+/vB0vuci5bS1rPIvTjI18474TTo9TJiRkPxLprPqdRLMNTfWtWxyK0+imj4H5Z2PTB6cARMegb3/D7C9IejUJISjmxGVLSas8XYSlJFFvedtUXwFqcoZl3fYBPlaq6ak3co9rn74k43PqsUbkJPveE/EZOKsWWBYD4js56cT9GdbNpr/TtgHY/gWw8VVINuuq2Y/UTErWmufzUJx8mQqVNmQlxORy8pDiLQgnjTs8QfSIzQ9Denu0+wmUICRJQkKhTPYjb0eMCn/UIHt6AXq7hLhupLprW6Ac128HvsyilsKQJjbZP88mMPYon2x99ik+fhuJxrXQ5yYauEVy+qrGSTogQ98J0aLZ5gWMkDTnsiknFxQANc0b8c2GD/DypzeiR+uyTrdFUfyI+PihjHYI0UDi7dj8/oloXPsCAEBecU/a7xXo7lEUDFoH8qKGd2qF5nQThYPF0DKsT5snA+dI3yOSRnHakgDyc3tDDaVPQlZIAlIGC83bCWhtjLLSN5HywPlcBHDNq7Q6B1kCxnp41vzQMO/t43BWyxvM9t5q9hbFPg3uhTbcqMirwANHP+rYfkqQNXgMVTqgPRfEQKosfHUl8MSrRyD+wXRM+PYaZ3vKD0BeMH2+7s7SQa1B6aY4AUB9PSuLSKKqniBbdCr/JZaIMIqTCg0dscxdr1ZiaNJNGnaJyR6mbUa53oB91/wJ/SX2xayUUgOfKfB9FQ/hf8rgjNpAJ4DSi6EGtFYUtadeLPtaLHsCgeS9L6IW7pW0CM5pfBm/KABuSHwKxc2SToWR2QtKmJjegX6kAcMJf62WXI838jxpleu+V7+8F/XN7mtVucXFm3TG46TLQSTkTtTYppm5ETGOYKTZlouV4L6GV+/694BnFZQljEIqevJaTGWpJdroqPoGAHIyDJPOBaSLa6j+fEiKMwdo5YQX8TiGQifAZxzj4kBb35ajzgIv5KjPES4ewb0eHi+/fyH2TU6WQbUHlv5yKTZftRnqzq6B0QmPoda2EfjfROCTkzAy9oO1/S3fflYAajaKk+5zL1fnT3iPf406//plNYycNAKprLVaY2RTOuGcewKj71cMPBfvtwMPJGVQTUoZExKS4qk4ueW0rKDlDjmAg6e/jk97nZdx0wbb7Bn5hO2o4fxUJT2f7OMaTmS3ksKxBizd+rVju1k8I0oUkJ1Q6KPscsBoJmkSKDlenY2b34c693CcvOFuFEY3c76UGbIcAMn0HSE6lnx+JSpr5qBw/qkA0aGuT+VyxA74N/drlUr6PCgAkPQOlCRzzTbITmUuJ1iaseJk0kGt78jj+7iMXH8u/IEe1rjqRhAJyFnmHgFGpIsbARePk84xKL/ermKL3rkIiLPltTi8PbW23Z/i2Xt5TUqiqXk4MvLuDL+lQ1WcQlExZ95SOPfkj3m18NX8j3tmKVQBXdpVZW9T9Pzg4FRhDw/PY7vNiy/9CNWQH1+Lf2JolLU7EW9jQsH6SW3Ib/wq43PVJb8q6VEk9IRn5ReTUo8KKWauT0TNQ1uGVrfOUq8BU7IsJtU+5Pqd/t3PIsADZDgAIwH6g+/fRAkdLqd1WCuX7+t3L7MsUYrTnR/8hlv157MI8FHS8JsvZTZZ0pRwnpWZ4/LB0r9h6w7vcDwvmKtyqfhnhyhhaC5CxQukX2Y/HKpADZzKiWYTvCRo0KP8ymB2zBCwIlkH9DjqG1Zxk3LNCo28Z7olAfgDhZBUZ1B6TrAIZ//sayw8dB4mna9htc36a8+zs1ecAwCp0FiHY0mGcsY1/g1W7pUiBTG4ZDAq8ioQyDaW1o6SuaBhVjr8+6e/s7btT4yM5hai4pBjP0Kzbjy3CoW9p9s8ujvxqF4Y9Fh+AAAadP4UJishlOT38/yukmijFKdO1PBNepr6FA1A2fHL8LOzDQGdfic0prRG5iyNp4QoIqs4dtCxmDn0lE6cyYDO23ykkd3nV/xc5W5UfbVzI4CWtq2Y4pGrEYW8U4qTZvNiR+D9bHhhg001n1jj9s6gqP6M35FQZAOGbPxHaoMtdBilB+xUWyQtil7Joko7As5qFrmhHlkrTvbS5HY2IQeSJMGnBtGQxhbSS9bgd6l6G/X4rt+l4iMAhNwqiXJyeSJSAB07oSCYETLn1QK9h1zU6fPQyOVT04YMA8AqFEKRu66O+BaSmpPKS0eBSKlzv9lm/Ktud79XmebK2dEWX2/khXp4nBLNrAFYER4nQbZoVI5AItHmWDhyv7o5GZ+rLpmYLekxtCz5fZfV8yeBMrT5PcrudAHbNSBYMS3j41fEJET3udD6+87sPPcWrYFeGD1wFgCghwr0m3csU5lPtllOZBeP0+T6VEjeik2f4v759zuOyQ0UoBY76aGxsRaGi+T3JcBRHsJMOqxe17gcaHKW7uYhqbnQXKqyjR10GvP3u26FICWZqahk0iax96mtbSu0eiOZfHuauMKb6uWUBy26Hc2NK7nHWYoTJ+zo+7iEoBqErPLqeisIqkHs33sSZElGNI1gx/9xQxg7bgvwhwbgpPTrM6e+KqUEuUzWn6GLEwxfD3xBRdNIsh//ztCpvUk3hPkK3SnobCk6EDmhEixJGELvAJV9SHXUn9sSwDpKHpID7uGCQeKdL9DMUXjiBFDVIMZN+SdW5o7DsiG/535XSbRaa7a07ITiBAD79dwPFXmGMEuHr2ppPE5uREsmWJ+JZNx3ny9NBUQPCpMC6KcR4Ne2Oik+he9xcmNaDjDDo9x9FCo6lM4vehWzheol0ogpvEU1j6x5gnOkwcsZRsYBgCIHQDL0qvff+iIC1J3Ua95jz6Xs3Ng/o+VdFCbD7/VwH8f+3FAPK8cwYxq9Q7i3Kca7KUkSdqTpJD4JqIw7vXt/iA9Aq8c74Eu4J+AEXAwnhFNdNiBJiEo7HzpeWHYgRvU+eKfPAwA+NYR2F684AFwdG47rtgPFY+6CKnedV2g5KUJiwC+g9TgEOWUHgVAK5ZqSIzHq1I0YeqDLMgQANlJzRoueKpWeDmXlH4FNc1zXcQIAqZWtFB3IIjphT0EoTt0NpZlriXZHhaBsqE9O/uGOrQh8m4qtjmcwcc/1qHAeyO2NWDD7JPlsqNeAp058KuPjjeIBqQGpX2nmYU80DVLYWosIAPrbw6woxUkjmS3s+WkVUFc7Hx22yj9EklGRLOvaVbRLhjBVoRr/OksPBcC6Z4Dq4ca/DFD8+dBchAGfzVOz3GPg5RXcaJXZ79fuWI4JSX3hCY/19yI6cO8OPaVcddShvZlficqXFLh4YmNVgbFGj8JTnGwWsqjUCaE7aZGfMOgUXLsd+ISTGnBRLavkmKiUIOfnWOtqbENIHTVx12rAe/S7rgRwfi1wfAaK29LkM/wtT89JPu/tLoupbqNusSZJjCKlBtyNMjm2tXM+1ErQTs2zrXB2+g4CKLICNVCMIcd/hT77/Zp77kCi0fJOtlDhYdukDIV+l/w+nXonmqVgVh6neRHgn81ASZ/jrW2DiwcY7eUq8ZlhFup5sw3oW8iGIfndqup1kpikoDXosvpyBkRsldTs+Y52glp2me9vRgIO5YwmSrVdUfyQKI/T/9qAxgy1zNgmNl9G7qJ80E1xQMt3ziM+fz5zr+zjAA+p+TvP/fXB1IrCzRkYiHprrFa+btw/8ItTF6LVa30pTuEekyKXR3/s5occ2/yShHrZPV/STrPLcxwz4FjIgcKMz+OFLLsUXkny+5PfwDmnLcMxQ07sUsWpQ/ZDPeBvUI78CFACiFBzxpjKieid3xsVBQNcv7+J6jsX1wEHbzL6fkZsmwedY8wwDXj+CJtS4BOKkyBb6HhVLdHmWrXtnTbgCmdFZYYGYkz+lS0LEU4m9W7IHcMVLuxc5XHu/PyBSIScS7LbLf8/7ERl3XrNmMAzJQEZoARWTc18wKRplHMgeXz3KCklUbbpztK3bkyNLUNjO1vIYjPCUAPZhTw+3Ahs9vjJmOItTFVns8bkvMyqR5n4/AUgCt/NpVKKk07oBW2d8EpXt8nsoh/945sQlIG6BPBEq3s/+SQCxJHycOiRGsRbjSIn36MAGDQbH4YMa75fjwCEcAfuvhPuAwAoPufiIwFb6E68Mx6nJP884Z945Wev4KETnnHs+3szuHkFKuWN45WBr7N5NduoHJE4ARM6IsuGjZynoNl5tdmjQliyH5w55UHublpxIlCYhTrloLvilG+L55+r97AqtgFAKyf/pUMHFGpssAskv02mnNHVzNopxanJl1pR/NkWQ1Dl4hJiQofqtcq5WXmczqgxQoVywilDVQExXmK/bydcyknadOC9s1lPiE/2dSqc0I2PSRkSOZ3PEakoHc38nUgT9hfy8FjQ/KkBOL8WGDD6Riw+aK7rcRuH/5/1WVHYHKfX2wzjQyZ0bGQLA0myM4clE+XGToUKVPaajEM3ATfR6ZNKiAnVW5nBfEyiqYn/AE6KbHvOIOtzvBNexB5VM1AYLPRUnPI81qezLzrthV8GXgocgE8i6aMSflcP9FwL3MOJVNH8pfD7nDLBw42Zt8UiTfhdKFCA/XoaSrDSGQOcC4rN87hFpqxdyf4sh8pdv99MPa8EAXbowCVp5E+LeCOzVqiJmeNdGGdvum8nnAXdhVCcuhmV8jjpiQhkF/tEiw5s0b0Vi6DMPk6NAF8NuC6txQ4AZk64wXWCLywcBCXIVtWZtRWYbvPKf559QR2LbZpTwPHC7nECZ6DLhFVKGaC6rMxno4Mg4zVB2nQdrW1sjPu3pAh+DyGR5usO4LVW4PYd3vHGmkvbF3UAkzcCv6RqUER0YJGemvxa0iVdp0H25UF2SexX1VRYUY0GlOa5rzDJW8DSfl39ZcMQEA32wmHDz8diFw9WW/JemfH4sY5t0CJGlaZatScw/kFsChpCXUCPAMvugGwLFYwNuQZKHyOfRLX1qxY5Fz17jme2JVwmvLYMCg7k+HNw4tATUZFfifW2rkXATw6mPU48xUmzxflHqb+H9xzJKE5S0gqeLn8BAN73iJqTkwpmfo/9uftpQUaXFERo5c0WqjcvYlg5gZSnZEkU2H8D8DHpwYyQbZJTGDU9TiY+SmCtSwDfJe+zmZTerrPLIzTnpoTFx5uAu7Is7KdTzyei5LkqJbzwVbMLFAYLUxvbDeNNgKPEZ8ukfkdgQBFraVZkpcs8Ti068L5UBSV/aKe+P2Qd4A+zc01OwLsiWMhWBOSRRuCW6D4OL/c3UeDJZiBBdCgu49ZXHYBKGUZUJcjkONVqbNipF4X2nFSO4vTrbcCfG70jPuw83SrhqH2OwtiRV2DCiIuZ89PVDEO2+8hjvGYYlRZ1AAs483deXiok0MvA4aYYyUkhvdXDw1fgUv03W/wgiPtLccgm4Kk04cfPtBgGJJ5Bz6cGHUbc82qNZ3Wge/0lPulyrqg+0ZUep1UKqxRtVVIGW3PMR05fx/f+0QRcVmesxWZijl/2+cmNlvolXMWpPXmvKyR2IulcFmj3IhSnbobOr9AS7VBcPE6Deo7AEyf+x/Nc3+rsxLouDvj9udAowc4tzKBnXm/Xwc8XrkReMB+/rAX+0ggEfwBeagUWRoFTKN1gSZYeJ1qAWx5LxtpnOIFHNA0SpShKvsLsfhzA4ZuAOrUHV3HiWfpz5MwXem3RdLTaPE6rpCKEcjILefxfOzBzqzFJ04rTq/b4fI637Ikm4KStwPwONr/l6yigU8dvlTqnbJpIahihEH8yVdWwZal/rsU7vt/HyXGyH1+enFNy8vpBkRUcsxm4lZNT3KYDVflVaEr283jHNsRjhuRrKnmmdzKodwBLb3Wcw99nVuo6/Kl79EP5ycj72Q7ANsERF49TBE5ByY2CQAH2W89WNzxr5FlpFSeeKWWhzgqa9QjinTbgrTZgzpnv4RfjL7X2yapxLi+PoMlvD7sHx7gUKJPMMDJfLrYRZ6t20GOLrDKKkxJkF+E9ajOwwTZJN+rAV1EAkBiPUzvnDnQQ1nprT7puTLYlL2FYPtt0YLicerFG9J9hfa5JuC1V7Q6hKjFGfAWuodK8hV7NYwuDhalxqYdROjzg63yonolbFcyuEV0NAVqVVeRwKkbSnlmepR8AVsWBVQUHM/dQ2+cCz9+0W6z/2wY81axh+AbgXGptWbNLxbU4V0iNEcMY6KPGHkXxM7lsw/sc7hkB4AlHiG7Wgcu3GZ7GdJxbA8yuA25rMgo2/Gn6n3D8pN9R55eYdZwSaj7WI7MxvoPTR+/cAfQIp8b3UI4z6gQAClYD97sYF+Sk0tnuoTh11eLpKghy/MY7ks4OpAEI+8I4dcTPme0PNxqRL3bF6Z/Nxjgwr4MvG5g4PIjpvEgSqziZYche3kJeJVeTPzUAfdcCX6msx3eHL/UcfWb4M6dtv6gDHm4CYlS74gS4/sDrMaRnZiH8UttakIizQ5sL/ObJ7A30DmbcMxGKUzfSEGlAnEpy1LWIq+IkKSEE/ayVbHkUOCEZSdakAW+QStxLTUgr40ZYkU5NWG5ldyvyKixrvR0lXIFcfy4ebzasLrTC8zH1EqdLHrWzVUtNJN9EjYGDt3p2nACLo6mKdACSC1emrkVOY5W0szQKfBAxrNESx5K7gjNw5chApcs4GLG1u0XT0E4NHjduB9rlMHJdJh87tLBFj8VvtwGfkZQlMRAshZ0L6oB1yS/R8ueSGOCjrvUjpT9IOLP28JDUHATy+nH3KUoAh2wyhIIb6wHCsbaaqBzR1Ed5rGhy8gdAkRRs1QzF0E4bAd4+8220JPt5omM7tGQFJl8ybp34CgDACmd1QAnyvuSxAOAPlXGtxm7rUERs3pA2D0WqMFiINsKGtP37xH9jue4UfCTqvEu1VPjWWXV+HLsFmAdWEVElGdO3AMdsAXyKHwp1byU5gFsOYRdoduOXEy9HXv+fcffRa+3Uq84+yYwNNo+Tz2bJjhCgyfY+mcYDya44SWzYJGCMT7RgLFPCJIEz366NAL9rNITjq7ax70itxhofMoHOcYr5irhCKQBIsupYwJhRnI5ZCox/GBh6bfL49Ir4/6UpkpNwqYKZSTghb2y2EyFAWU4ZeuQ7rdntlNLmlXer+fKRmJEq6NKolgCzmvCi4i24bUkAj/kPxDvtQF2b4bJ8iTI0maXfE3oCPk7p51vrgXodUFXW4yRR7e5Q8jwFV084yprZN/RAD5zpoTyRkXfgXy3AQ03A6gh1UcGewHE/ACcaX6aNOFvVMpQetxIPxX+OjjTProMATxyfKqjxVQdwUz1QFErNq/l5zmcKGMrfepd3RE5ec3tnyv1nyQ96COFkOGu6ruqHMUa0xFIPc8om4LJthuLk83jXvKrkbS4+hN0gqVY+KxfqdxRZwcgNwK/rgLuaUuP6xxHWSHhTveEVonOOHmgwFP/qdmBDAvCr7HuuqiH8vclQyJrzRiEdcWqOOXX4z3HP1HvwxPFP4G2P0P8tyeJB4Xg9Glcb+eo3bDeuZ991cJcvQQD9x+V1EopTNzJ37VyEKJmLJCLoSfg+ZlkNMXkjgBF//982oGQ1ULUOUNUcXF8PXFJnWEhuqgcCaoCpqNLuUnWmV14vJunaJEEAf/5A5Pr5YSL0IJKtZbaJEiSXxAwBp51zkudagDEbgCmbgceSeuZXHeyK0z5buM8l/GWSLEwLuyqrTGiGCa04fUJSgmgPF2/65x2sJSiqx9ARMYKCv48BdzcYHrVCl8nHDq3w0EJNHIbAZZJn82C91QYEVdaq/F3yWl6NlTIJyh1yGNJR85ljYbP+eyGrucgpSIU1MaF/sh9rE0YYSpQ4w8doeIqT6uKhCoQrLUGYJwwM7zUBw3oMQ2uyb+WuexJHa0YCtD+pXA/oORaAoTiZ78YfaIspJcj7KY9TwDVUhT+MxmxC/QbFqVCYFASTypxNB7sz2tdRPpoWju6NVeHeHcCw9cDzLQRvtgHbZNbAMqZsOKbtMw1H73s08gP5UCnPhawEcdtht2He+fNc25Y6OIB+Bf2s0sJNlNCfR4UFtXEKA9ChgAoIY2hQbOGQb535Dk4ZxXoZLMVJkpjKYRGOB6WDsMqSna0amzfYpgP3bO9Av7XAnxoBUInNDTrwWhvwmuaeD2BHot6xuL/EVXHqkVuOC2zreZuHFgQLgNx+wKBLrMIb6cJ+/t1sGClur7cVAKHQXRQnL5vXLfXG+zExgzCliA7cfMjNKMvr5fBidkgphd1LTFIkBb5QahzKV/2ALx9SGuv9/9qBb0JGmf/2uHEDaGEtl1KcaMV6bjtw9w7g/kbjb7qUuqIEIFGGhqiSjw86qzhxnp85hhWFijxzpySvanl5+wDJBWobpJSMUOfvBX+wBzp8o5mcFR6VhQNw3hjnOmF0yGi+i5EMcA/jMt/DILVsgluFtk8imSnnPOoSwKPR8pTilIEwosoqYlSVXHNM8sk+z3xrt/cZgHNdPFlFQaCAfyzA5EmqsorVceAvTUCcGhPXxdmqwVsShldoFXXPr9wO5K1Ovfd2xS+khnBhHTB0PQBq/N+kpKoHvkClCsapMSyYNCTlBfJwZg3wxwZgDed571AKoRFABkFZu1Fy/MVW43pWx4Fmj+qCSLNY+Z6GUJy6kXd+eIdRnIY2fYoiwp/xFDXHUcXJ7Ls7dCO23BSYH20yYnIXRw2PE6EmiYTKj+0uDZdyrYAbE0DAn4c8v9PyDTitL/bJ8pZ6oF7m/+Z65KE2YXibTEv7as4LSb9uv6oDBq0zPBmMx8m2UGm6EsumIKfKKjdsg7ag/V98oPfJYFhQg1RDZT2GWJuxMFVL8rfmbZyHvLwq5ntN5TPAo3/RQMiQcfp+pyOfsvolCBgBoiCX9RidUws8duxjzLZDNhmKZ0vuvkwfSsgBZjJf0AGs6ndZ2ms1Cce3o6AolcsQoRSFsG2yIJxcFBNeqIZr6eVgTyv0KsLpr1IyvKndLOHckYolDSYLcxy477EAAFUCpGSYz43bgR+KpwJDrwGo6mVBypOZl+NSKcxFSI/bJtF5/uHA8FuAw5wLFZrvV9h2qm1ExZW29XPpPIZGouL6euDbGBBP5t9pchCYmYqpq8rrhbfPfBvVZ1RDkiSoKqs4AUBxyL0kuIUkoShUhAM2GUab02pTz7Rf6bDUcZwqeS2UQKSCIEZPPbZxTZVVR44bLWjTj73DRXFy8wJuSZ5oITVPm++69c7bxkgdQNXUN7nn48Eobf4ShzfapDhchqYeUzBqcy4WRw1jkBlGWBTkeNDT5EB0EKOtt+4AjtkM/L3JqUDxqjAC3gLrmjhw7XYjnDodHQToX9QfRcEix9wwP+8gAMBilHh6uFRZZcLjBhYZYUfpkufrEkCORzhjTvKxXLL/JfDJPhy3xZgnjt9iKJzm3EUL3YoSAFFzcHM9cHM9EPcXYsiAk/BGFkV3XmkFBqwFt5iIKYSPLBvpudZRpsxXU/lr2wPGeKVKKvP+8SA246HZUlpxKsh3VmEzBWh6vqSr1ZnvwgA11YA6FwG6WQdGrHdv47gN7vuu2g60Q8nI4zQvYkTjnD78dIytmGhtN+cTv+J3eCTpkEW6X38SYQtrEXvlREnhejd5MOMGNUZvShiGjeu2GwqUmaNpfxto2cuuOJWGU0Y7+ncWBy7F84WnoHA1cCrl8YxQy4wkkkpcrj8X9TpwzXbg4I3A081sQZGoFGDy/3ZorILVRp1zUQdwo0yVfOdU4duTEYpTN3LJ/pdgeHEqFnW/Nudq7CZcxck2+YQ5VZcMj1PqJQpSlVQurAXebwfOrDEGC57RSIExkdEep2n7TMO2a7fhorEXMS+rBGfy5P/agXuKL0KD7pz0ErIfA9YB46kB8exa4MN2MLkU9mH2+7ghSNHCkeyodOaNmeulSApUWcVvbMLpd5SQsENLP6MlwCpO1+Y2Y/pWY50Ec9I6ZuAxgC0Xq6NwLPd8MwafiOdHPo9/nfAvNilcUqFTAkQJFRJzzTZDAbV7nGo1Q4kuyymDRFnSdDnEKE4xAlw5N33Ylmld7Cg+AOEcKnmYEmn9ttLJXiuX+znzqJ+z8CwAINDDUnR5lj+zPHqEE8IVTuZjKb68ZHERgx2a0V++6fMrYMx9bDv8KQUwHOZ7HZjQMaqrxGzKYkIJAiNvA3od6TiHqQzaF+rVie4QMmmPE+EsVuxTfECYKsZhC/+lQ9HkpDU9oPI9EXaKgkVYHAV+XgMspxf+pfpkgBM2S3vSfCCoKKTKllPj2tp40hLsYxVvOlRPpvpwSb6z/HmUk09x5GZjXDGFg68oq/cqu0LQ92dAvzPRPPpBFAYLcdLQk+CTfUljTXqaQgPwbAtw1w4g5M/1CNXz4YNzPsC/z/oUYzcAEzYCf57+Z1T/vBp5AY6hKo3Hif6dOIAL64xkb5PFUWBDaF/ud708Eum8FTTmmmaSJDEe4f+1Ad/mTQBOWIfbpcme47OVkzb0GqD8KKCnEf4kp6lQVqfBynHhcXDVJLTd2IZ9iveBT/HhjTbD0GSPcqDfFkXxQ5EU/H4H8PsdhiHyqZP+g/IZX1rH0IuN8vhPM7DW5mL7NgY83wL4S8bgwekP4qoDrvL2ZEgSLp94OQDgtOGnuR623Ncf/2gyPGgkOdf4ZJ9rmJSFzQBhCoa04iQH2YqwH7WnljGg1/2hw43NOVqm5uqEixEtSvjGMMCQB76OAv3WGu+xnVjSWGIqzl4e1Cu3AccNOg73HXkfglR/oRUnu8f6+19/b32mX4cTthgGDwt7pEQnF+Sl5w7z3t7XYEQRWT/l4cCxe8x65qQ8uLQBQpOC+D5nlCM0+l1U4Y8NwNk1wI7kEhO0XLFFA86sNYyt5vP4yL8f4zW1e6U0akxfkwC25wzBdzFgA8lxzFF7OkJx6kbG9BqD/nmZFQtQ1LAjv8Lu3QlxrPQBJcBYa3Pz+1mfNySAIzYDT7cYg6vOsejFkkIhrTjdfMjNKA2XGkqZ7fivooZCZtKuG4Jcjs85uciSgnbCWpPXxIHDNgNvUYOj68LSSgAvthglt9sDKaE2TtKvXWVadk2P0z0NRsl3wAhBeq7FUJ4ebQRiWsyhWNmJEyDk8jYd0H8qHjr6Idxx2B2Av5Ddaf87SUGo1LIaSfTgK/sYD0ePgpTibSpodsXJpCynDCr13Wa1iCmXqhH36kg04zYAh20C4j0PYx6OQk9Xtr7Ky3EiHuFUfjWMGZuB1THgalpgDZQilBT2eYKGnlQkopyFeXNNxUeSEKPWiTIHe65lkPZkulTiokPHaCHTvhilL4OqSTO3JMMaJxkx4pquOaynjOLEyw+z32tb/LiP8uaYirRf8eOMGmNJgZc63Ku30YJUlDbkUNUmw0Gn9yqXNqZCx3HDqFwpjuIkK2Em/4e+gnyqDccOP8vxWzFOWNN77ca48n0cmDlkJiPsrIoDjx/7OBRJwYuzXjT67uSnkD9sNrZctQUvzXoJqqziz42ZvR+KouLnNcBv640QGVfFKTnemuMoATBj0AwcPfBoly94Kw680FV6geQJG1IeRjteHolsFKcIJVLQEVnn1Sbfr5y+kBW/p8fJEuzG3Acc/o7laUsXqlen8Y2HN20HlkWBBaGx1n6vCmbFlHch4MthiosE1ABCvhDGV6Qqa64mefhlrRE5wYMXfn5zPXBaDTBt32Mwe+JsTKqahNsOv8d5IMU9U+/Bq6e+isePdV+41O8L4xd1hgfNNIaokuqZlwMAQVtEiTmsM9UdqaUnPmo3QudNLyR9/q0hZyn6G1t7Ym0cmLEZ0FzWs9IkH/devdOWkgfWJ4z32D6/x4hhVBlVPgqKpHiG6sUAnDv6XENeouZWWnGyY4ZSA6ynp42w1+6osCopcJp+09NBhb1vSngc6IJ9LmMUJ5sBgvcutMk5uGY78FSLkRcLwJp37UzbApAZK7E6sA+TD2qPHpKpNICahCFTDl4PnJuYAHiURt8TEYpTNyNpTvMJr8KV4stN63HivfABNYCIlurNdGUcOjzDp/iYQeTcGmMyvWyHYZGhLaCJpCBmF9DKcowJhx5I2pOJ2j6OtTDbdQuuP/B6DCxOhc1JkPCzGmDGFkClBooYSZ9v1WpTnAAjMfQfTcDIDYDky8Pg9cbaBS2xFtzT4HEyOEP1aAKBElw24TL0K+zn8DjBzw+RUpkE/tS1yYqfCdUqoRTh5jSKU8+cniiOpbTaZrWYeeYJwHOFd5N6HfgwAgRsv9NMVzij+qoEias4eQlCfl8Y1e3AvuuBBxvpHUWWgYBnnTQ9MDGO4kTn4YCyfpmDPVeYoicLyvvEQD13WgBVbNfsT9PfHzr6IUhVM6Gd3AD0PxOA4XEC2Gp7hDoPz+PkGAds1jyV8jiZnsCAEsAzLcDA9cAXCUpxsnl+6GRxJjeQUs7CITaXqy5hWNet70GDTHskqb7yZpvxHPyK3wqrA9gcJ/q+lhcPgZ0OjwpeJaES9Cvoh4WUkLs5AZw/5ny03diGU4adwhwf8oUgSZIliGRiP6b7UUANuLbHfI/p5+WVlO5uQTLgvQ/51Awfh4txAO6J24C3UkXTrAF3tqbGM40SMWNIXefVk672VpxcPEsLgiNRlwD+RnnRNlLCmSLxQ/XubABGbADiVEVRr/ucQxVhUpUgM08xa7gd+SnQ/2w8RIbi8WYjLJ0HL3fYFOzpdkwbfLxrm9DraATVIGYOmcn3RnLaZ35WJRWvtnoXAKnKZ5eLMLsNEzJKRQHYZXlFUvDdxJehH/JfNIedXs2v9XwMWGcUL6Dz7LZRJ5KUEDeslac32BUjU2Ya22sstl+3HUcMmMr5lkGcULIH9U6Zv23201O3GjKSfXFw2tMTI2BWoYrptguQVSDkvhSHG7QBYouL4nT3DiNyhi4IZuLlcbJ703jvArOcQ3LMcJMrfrX/bEgFgxFUg54epwAVBbE1kTLGxzNc4mVPQihO3Y0ttnNLQkLoB+dhqkeO0y2H3IIPzvkAKsctHFACTL5JIJzK06CtOz7Zx3gJ/tUC5K8GvkmWOKcteVHNkDrsgmZVgZG/QytOkWR5WokS3l5PFgX6wDcImXBk/yMwe8Js/Pbg3zKWHxp6cktnXQNSCoIqq9bA8EPcKMe5IcEqis3RNAlTMJ7Fr91CeRghUWGeo+znVwOky/HSyeaQfIzgTK+jZF63fYFWk/EV45EbT83uPtXPLiLs4nFajBIg1xkSZVo0L92RiwUdwLKKM1M7bdWCCK8oiYciUURZfTUYnpgvS2YApQekPE6ctqrJEvVx1SlAqZTi46fC7raaHieeMEV7nFwWSqZD9VjFiX0/NofcV2oHgMsmXIZXT30Vfsq7qyXfG7pL04oz1+NkCscDzjX+H3YDsz9AjQd5yfAbeqJtItRzsSlOtOfZr/iBqpMNwaAylauXSyX2n1cLVKwFGqiqWjJ09r4qfvwucDRu3G4oyeY7uZVWnJL/G0ny1LTlc44HHRxlkkaVVdRpwCq5J2oSRp6CLMme4YrmWGcPpeRBj0V+xY+Ii/k7klQ26XufbhHwvza5L2rN82y9kjzWrH7q5mk5ZMA05m+60A3tcXrWZa3Zx5uA4jXAWkI9V+o+xEnq2iZVTcJNU37HPxHcCyG0y2FUrAUuogr/JGAkzwOGF8QrVI++du+8Ezr+XHV4nCx6HAhM+heaJXdDDmDzOCXXt/ow4myHzAlPXlF0BDBjOVA8xqO9Kej20R6nuxuAwA9sQRca2WZcMZ8AIyxT76zdaEtAMGifkyD3Po6bX8iMrZTXczGldchqiBvCyVuixH4ZUSqvsTBY6GmUixNKMaAMPnaP0wutRqn1123vm/3MtLwRJ7ZJSVKAyU+7tsWNCPUrborT+gRQuga4vh64dP9LmX32ucwtVA/gjwn0NvOzm0HjwaONRc8DasBTccrJTYX1b9VSc0lMy3Idmz0AoTh1J/FWoJ3NeGzUCDex0a/4XD1OM4fMxJR+U7gdO6AG0JvKJwhSCe7tNo8Tr9ytKYTTVopoIurYBgAkaeWn2296nEANKDO3AsWrgW2cssU8eoRL8eDRDyIvkIfbp9wOADh71NnMAE2/6PZBnVcm3VQQFFnhDhwFgQLr+jJRnBLESAK+k2P9gV2IpyxuslsyMxVOJVOTqxFaJXOPMy/bbhl6+4y3cc8R9+D4wcdjeZ9fIk4Mj6JPZr2MGvhJ4gnJBwy9GgDwXiz1e2bfuPHslVg74VlMG30JdY2U4iQp/PAMj/jv3GTpcJPX2oAVZbMASbI8TjxBcXzFOABAXOHcV+o5qAWpYgZLk94HrlBJF3hwUUjdFCeN6vMFq9kY70wxPU70pabNcTLv/cQngFMagB6TmP10dU5ziQNa4GrQ6eIGhcx3aQOKX/EDB70InLCeWYCa9jhpxOhXdJiHQjRWcZL92OLvjbsbjGMtjxP13prCkyRJrMLNMTx4lV6WJMl6zncoh2DAOqCFyK7FJEyyUZwYj5MSYPopoYTGSMIQGGhBJt0imLO3+zBjC38f733YnACKVhtr1nmdv7KQ9RLQ4wDdp8+oMaq4fhRnjQgyjGdHX0suZXyKEVaYK81JGS4iQcMSbS5q7VYRUZIkh8AckIDRG4xyx2vTFIfgCYNpkRSHImzHfEfdioAwitMxS/Dc0Met4kSMgMupeNghhwFqrEqHm8cJMOZl1/XadFZ4NZ8A8154eJyunnS19fkHvyFv0Eoao6hSIX90hT3ZJRSMp+vZb7UZqkedjHsuwOZxorwdHTbFye23vRSnmG77hiQDBUOwuf/FyIY4FNza2gO/rjMWkHfD3PWbg36Dc0adY2338jh1JNgKdvS7MHvCbPzj+H+wRoYMlkEADLmDthHZDTyFBakxhvE4acLjJMiGzf+FZPM4mZ4Qu5HSL8FVcbImdc5gEVACUKjBVKIqXtGDn0/2QeIM3LRAdcqwU9C/sD+mJt3g5qB6/XYgXnYkvvEbHiS6U0UsxSn19uswqtr5XWKdnaRuxtEDj8bmqzbjyROeZI6glUb75MDzQJmCgVtVvVx/rqWAmKVtvTDv5UaedchuSaSeh+w2wFPeDbqEuCz7sSlIxZBT3zcnE7viNG3fabj+oOshSRIaehyO3NWGR9EIz0zdtwThh+oRyQfs+yvg6G9wVVsq1NPsG5X5lTht+GlsuXyqzYqsgPAW3vQKXeMoVabQ7pXjFEwWq4iqhc6dtAJbkqqmZIZtca3Qvnyg/Eig5xQgxK+qt1A2BL8GDSgOpxKoqwr6Wp+b9fTeBB663YKJLHKcJImfQ0e/58nnRE+OjbTHqX8yhyh57bRgGlACxm/Y+rBMKfPmW08rXDIIm0Qt+VjFSlLgk31sqJ55KCS2iqESAKZ9AY3yqqXzOJnPuVWLI0KcFljudzIUHgA4PBS0J0Ki3uv2pOee8Tp4rfkC7z7klkvVqKeeg6vCYBNa6XCkVirUkMCo4npDfIi1dhCQGvPp9hdTCjTtcQLYMU3tNQ3NR36JSRud56DhbQ9IxppfZj4FL8fJ+p2MhUH6gSnM7/K8+dvajVAD+v7TFfIYhUpWoVLFU1iFwjlGZpsyT4/9tMfJapfbq2Gz+nPtA/SixNR5PjznQ9x5+J3W3/X+nth3nbFEigl9vxVqnqA9FAonSgDgV8ize6Hsc77s5XEC9Y6SlNBuniJdFTy78YS+p34X5U92WQbADUmS8HysGH9pSn8s4Fy01758DP33jghr3aW/9+DRD+L8MednZcwxCSgBPNUCbNeMQlVbbJ23R1EqrLpGS1WSFaF6guzY+Kpjk+kJqVprJOCb+CUAtknT7G7mIMALcQioATZRP5CazOhKKqqsYkDJYOf3qYnixVkv4ofZP1gWf/P37m0A4ofMgZqctOhJN2paGouMUANNdoYSpIcdFSvyKiBLMnO99ItuH0QJgNHrganU/aRD9XgDQ44/xzWml4epxHKt3XbvBzVBym4TuI+vOEmyH9OP+Bdu7uiLJ3snhcVkGN0nSR2cVyTEJKgGrfvjsxWaSIDvcfKrIUNALhrJhFI4hAhaEbZ5nHSux8lDYOXcF/O6zP+ZcTlvIND/HGCAYXWL+ErggJ6Yi8dZHxd5eZwkCTjsHeCI911zTL6Uq3DkZmDIemBofiqPqnDcvQCAL5IGvmyEb5O0ipNbVT0vFKfiRFuWGcWp1zTgqM+BY5YAYAVT19+hBCNTwHEItLRirPiZd80M1avh5DgZ37VNWyX7Q+k5xfqzPc0iLuZzNkOO3UJQeN/JpDiEl8eJfq/zk0U0SsOluGDMBfjFmF8wOWQ8vBQnN4+HW9sYFPb50EsERDligqoErLWDAMBcpoe+lxL1OQFb26l3wefLAfIHWffJzfvHnd9smzIN1fMUBul3SpLdQ/WSmIvt0gryDupZ2Ase0H09nceJF6bmBROqpzgVJ1dFzFQgku/RY7wgC0pxonvEof0OZcYCCRJWx1lPJb1/cI8R1me6Ap/qpjhx7oHD4wRbv0kXqmfu54SJpTNweXmcDu57KPc7UoYlya3jIaU1otD4FJ+n4kTfm1Hlo5h9s4bOwqCSQbhw7IXWtszDWlME1ACWx4Aea4A/Njr3l5ekFrDergmPk6CzTP4PEpNfRJ0y2tpkTsxbtFQcNAD4oAO2Mp52jxNPc7fnLsGXBxz8Cn5eY3ReE0mSkMdZO8SuPNAvM/1ZlVXrWPvAosoqcOBzwIBzsWrcv6ztnbHAu6HIChYkBdR/tzoHzW9iwFzqfqbzOOX4Oqk48SY6u8eJ8r4Ql8RxQk0itMdQVgIoDpfgjvPX4bxD7jY2zliOkduqrPCP/AC/+htgm7QVH6MMJAh/Yh3agx8q4hA4aWFYsgkpCkeZ8xJeOB4n0yvBre7T/2xg0j+tvh725zDriRgNoT1O+6O555H4a1Nq8nZVbCTJMzFfkmS8125U9VJH3g7kDgCO+AAoGoXFB1RbJfo70981e+gHAJIuxymdgsbxONE0U/lIIAQonQgkF5imBVPXRTmpZ23qPk7Fieo7sp9R9s1QPdr7ySpOHKGI6kueZZ1BKU7JkOOMPE5J4eHIzUBjoDdw+Htpzw+YxSHonSnFaWLvydbnvx//d/zt+L+lbYfZh55udgqU6a7b3jZ2B/t8rHWufIWQOGKC/Z4p3O3s95j+z4wVPmYucetXbh4nmkw9Tp6hmWFqbTyv4hBJaluNgju04kSHwduLQzjGYOu3nOdOZLmsPBOqx/E4hWQXkc8M1ZvyBhoOfgML8ybh78f9nT2GUq55a++Z8J4TPSblUWvG0ev+0EVraDIJ1YvaQvUSHnkzTI4TccpM6cZpu8eJNtT6XYwwXoU5eBhFcDIvnmX3OPEMCLXX1GLZxcswoIjNtc3x52DlpSvx+HGpao30b2fqcUonL5Xm98WdO4A/NRiROaI4hKBzKAGQyhPQKqVCgNwqGClKIG2oXsJWdtiv+I0Jgv6eEgKqTuQn+fKKS3h4hejJR5EU1xdHlVUgtx9wwJOQCkcy7csIl9Abe45T5KCXcTvGY79D/8U9nsZMKM4kVC8TzIGRmyDsEaqXR5V7pfMfmFA9OtSS57lRAqjXUtOL22LFgIe1E+7WSL9Lbo8T/myqyAp03jm81rjgPBNTuOYKRzZBLqSGMGmT7RimSIeK+nGP4WIq0TzjvAcv+pwMHL8aKJtiNCtcYSkPnVKceOtbUP1+pz1O3JwNAMOuB/qeDhSOYPbR994+3qTOmYHHSWYVJ4fHSfYxHhQrVE+yhepZ50hds5cCIUGy+r0Z65+Nx+nzDuD1fe8Cyo9wPdZZHILaSeVk+VzCerwwx+MzawHptBgig1O5JTulONk8Tk26YTnGiZu4Sob9nsm87bbvMf2Sbodt3RxXjxOv6IDd4+SR45SxIKoEgFMagVktGXmczHeUjjag5wH7nOA6BnP6dbYhXkyoXvI9p+9bT8VFwDAVDTUHRVUz8On583DB2AtsjaG8hMlT8pTcdP3FTz0j2uPk9/MNftziEJxQPfp3O+IuFVSQDNXj5DhZ7UjncfII1XNbj6hXfh/udi+y8TilC9UDjDyn/Xrux/2+/ZnR40Sm85ZbUSr6N26qNxYrptsoPE6CTqFRniR7jskN242kWWnwbEPYoF6mdB4nqyO7VLRx4FEcggc9aMqSbB37SiuwSQ/iiSa2fQB/YHc7Z4r0EoEiKZgy6CTc8vMvsU+Js0SxxUEvAqPvxZfR1Pcy9TjF8oc7jjP51lwUltdUe6gepTzSpZXp/Ae66ANd9jnuci9M6zngbXWlr8k+ILqGhWQ6gAdKYChPEiMgypLM9zh1NseJF4poa2PYF8aKGHAsnUhvew7282QakuD4aQ/rNS1odeb8vFA9mp32OLkpr6PvAQ58xiH80n3GVXGiQ/WS/3t6nCTVoTj5FT8T4kQvgMudtqjr8CoOYZ4foEL1ssxxSvdMPEP1wlXUH9mv70Lff0nxMTkVO6c4se+CjGREgprDFeDs98wK1cvU48QoDJ33ONmhLe124ZFnZHDFXwAkPSDpPE4mtIJEFyjSAFTmpYyjrh4nDuMrJ2TY4GT7OFX1XMmn5skRv0t/cmos8PI48Z4fXTmNXtCd9jj5k2GsK23OIt7blq44RDTe6to+1uPkHMOyNXAxqQEuY6KSsfHRQJbkrBQnRVLSKk7ZQPd5Zi0vDzIxNNPHmHOC8DgJOkUcqcHeHkP/fw3AmA1ICaKUx8HKcUp2cntZR2vgpCeMbBWnDD1OkiSliikQ4DxyIC6ocx5HC6u8AYo/sbt4nKiB0stCwny7zynAsGuZ72Wa49Q84QlgwPmOY6O9T8Lfk0piRqF64x40rPlHfGCzvFP3n1Ki6EE3al8nwtyupRQnL0Hea9J2X2cvQwFPVoGftQCzmhmLsiIpsK9MDwAYcav3uWzwQvUapeS7U3kcc6w5KDNCtOz0StF0icfJRjalpnmkE9J5+7PyOO0EmXiczNY5rp0J1ZKYZ2HmONGCKOtx4ig6tOKUbaheBp4I+hiesup2rCNUjw4D44QJpcN+HxVqPnArh03j2sdta5TRBjyeIOzmcWKEPdtzcg3Vk32ZeZyodpgV2zbabiHtcbK/31zvbQYwxSE85kP6/tOPYtEvF2H5Jcutv728/nbcig24wauqx0UJG3mLp0aBk2qBimnux/Lalfyf96x42+hKbiqlpNMeJ3OB8LEbgNWT37S2/8B5TbiKE/W7sse4yeQ4Db4S8BfjPXWotT/bcZp57TiKmNGgrstx4m2XJIl53jurONHjRKaKUyY563QbzXlKeJwEnSIqpazzaRcbpBWnNKF6VielJwye5d8k3+mp8bIi2F9g1pqQmsDoPA2vnCmg88IrEwqQxcCnyAp38uJ5nNT8gcAB/wBy+jHbtRF3WJZ1boK2Pek12MOw5pdNAfMKMgnzdHgetbivS+I77XHygh7c7PfatOrfVp8KZTQakIVlXM2xLLUmiqwwYYgdZVOB434A9rnA/m3qN91D9Wjl+/6Si4zQOFvJXlNx+igCtOaP4Cq8Do9TJ4o3AB65Ptj1ipNnOXI3mBLr7t7JdLhaCilDgdkSx2RvG4foe2N5nJjS7sb/jqp6JhmG6gGpCdsK1cvA40S3n5d3RmP3OEXcFCfdPQ/DDUcfkjP3tNnbxlB5PFA6CZD9mJfIw8ON1E9k4nEyt3uF6jFeJlpx8mfkcaIF44M2AS+3AtNtpdlpz6Z9/E733NxgFGGOMnL0vkcDYEO26CsYXT6aWX+QJzy6kuUi8Rl7nMK9jXdG8QPBnu7HuZCtx4lWnCSqD9NrhPmT6lCEAMgfDBz2Dh5rAu5rcP6GfRq0L0FyYNUBru3TQD3TcAVwUi2eDx5o7c9kPHDFTTn3Ckt3wa0dbjJZV3qc6HknY8UpA4McE4GRHA+Ex0nQKTqkQutz2qpNHoqTXXNPeZyol9mrBPiQK4HBV+CdvtelzpFhqB7ze2AnMNrSR1sBdaJntBhbJjlO6dbacEOVVa7FmZfjZJ3XNjgGKCWRH6rnIZwyIUsuAzZdTtZl8s908KGvyS6Um2r373YA/dcxDcjo3G6osspU5lIkBchzLqjLwOkH9nLkAOALFBnFGFyOTQBYP+FZQ+G14bNZune1x6kz5zeFPTddwHMBXDeUgLHG0/5/BYKZraXGw93jRHmVqWprHyar+sdDvYGyw4CyI4BBsx1tNo0Z7a4eJ57ilEHJ5eT3O1NVjyadMsuEdtk9TlRVU15+RTq8PHc7F6oXAI6aB5wWxYWtVcy95ylO9m1moaFOherZ3sNMLO3LYsApW4EVNt2THt/s43e65+aG/XnaeWHWC5AgMW/idx6P1tPjtN9NQDBVrTBbxSldKPxmPbl/wHlZnddOtjlOzNpBLoadABXz4Ff8QK+jMLvez/Wk2mdB3daWfNooFqpwfJ/pp7KakcfTxBzHFvNslW4epywVJ0ly9zi5KU70GOqV65cJrbFUqGMBtWC6l1KZSagez2ggFsAVdIoorTilm/woxSdmV5zccpxoQd9rUFCCwLg/YVteKiE801A9wH3QpicsevLUie4QWvhCTAY5Tp30OKm2QdOEF6pnTXK2CVihvSmZhOrR0L/tJtQwQmHnJn8T+prsFlj30rc7pzgpkoIOSqlX7c/zyM+A3icAEynlxquqHjUpuvVP+hi38uySLURsV+Q4MTkpnbiP6cKLOuVxAoB9zgMG/jLr9tC4hljQYbPJS1ZlFafVAHfvALZPet445oj3gPEPOtpsherRxSHSVdXbmVC9LIXTdM/EkeNEv7I7qTg5lwDoIsWJwv5cPZP9D3weKD8Kt+ywbQccCi6rOLEeJ/rdyKYcuWu74LzWzobqpfM45fpzrbUNS1cbS4nwFl03ocdgx3w36g5g+tepv7M0tvCq6tHMjo8EJv0bGHpNVue1YylOnGfFm08ZxUnij09+Sh0y+4rbWEa/Us+0GGuVMW2hn/VRnzu+n5ncwefUGuDmemDGZl7D0ofqrc3gtZfgXlUvEwVlZz1OLbFU9TC6HV73KaNQPeoY8xmLUD1Bp6BD9eylSx1QL6B9HaeMPE4ZkGkyrH0isyd4m9CKEz24aUTz9jhVHm/8P/iKtL/vmeOUFCh4A7qbIOGTfc5QPetY20OiztGYSagejS1JPt0xbjlOmeKV5O5aMjWLJFUeiqywoRr2/thjMnDIHKZohmeoHqXsuFnl6GO45cuTeIUuZoqXQJdJ+IIXnSoO0UkF0OucPFw9ThRmuWhCCGo14MZ6wJ/vXC+ObrNXcQgAqXy2ALVWVxaKk/kO7CqPEz3G+RWbxZxuc1eE6lFjQ5cpTjaFjpvjZP5u358Bh79jeZzYHKcMy5HLPjZfdieKQ3gdv6s8TkDqvtbrwKYE8JxpsM91eta9vP4AbB6Z7K6Zt44TTb0UMha13knvutlCrscpTaie3eP0aCPQQhRsLjvB2mb2Fbf+Shv5zqjhHED34ZwqoN9Znm3MZuyv04Df73Au8ArAI8cpdf5L6oBPwxOAoxe7/oYkSVmH6tGeG6/1zDKhJcoru9zFHqdkP9CIll3hlj2APUJxevjhh9GvXz8Eg0FMnDgRX3zxhefxL774IoYMGYJgMIgRI0agurp6N7V01xCVUq5QfzqjmleoXiYepwygBYlscpzol4J+wdxiy3keJ2YAO+RVY3X6skPTtzmDUD3e4Og2YNLFLsxzWpO7/X5SQsA23qV6huq5J1NTO6xPbqF6mWJXXGlcz5ym/HU6FElhile4Ti5UJUHexG72N/q5uAlT9HP1WhCYViQ7m+PkBd0XM1VIaHZJjlMXkY3iRI9NvOdhX5zUJ7PFIcy+KUEyrOWT/gMc/U3qgCxynMzfyibHiSbdO0A/s4AaYEMHd1JxcgjuVNt3qjgEhT10JpNy5Nzz295Nthw5/Zkdr7MpR+6F/fjO5jjRY0ymc8vHEeCQ2kLg6EWOY728/sYP0u9vdmMG3T6ekpet8un6O145TpznxOTg+ouZfZdsA2bGJyMa6GFts9agcumvvFGRUYbc5pgk9rlvp/KamEa4GT9Tz7RGA+aEDwaKRnmeyu1ZuY3v9Hu7s2HndKgejafHKdscJ2o8+LHlOXW74vT888/jqquuwq233oqvv/4ao0aNwrRp01BXV8c9ft68eTj99NNxwQUXYNGiRZg5cyZmzpyJZcuW7eaWdx2Eetly0z2RbIpD7GqPk0eoHv2CuQl/aXOcJJlZnd7r9zNZd4A3mLgNmLIkI6iwipOF/Xps5z3TbgHL1ONUdpjLQanJc2c9TjSOUD3XI3cyVM/mcXLtj3RRCY+Y8Eys00wlRw+Pk93T0Rm8BLpsvRl2usPjlCmZKE6fJhedpr3hPGMMLQyYpXVp77u56KSxLp0P6H8GEE6VeM60SIIEyRFbn+0zShfyRb9XASXAJtL7acWpC3KcqLbsqlC9TIpD8LfL7vtsHiearvI42c/TFaF6bkIr776uTPiMBec9juWv00afKzvFiakAyJm7d3Y8MtmpUL2K6UC/M7F5nyutTUF/nsNTC2SpOEkeilMaJXqnFcoRtwEFw4FBl/L3U9cRJ+mLOXlV1XN7hl2ZK0SH6jG/7aFgZl1Vj46e+pGF63W74nT//ffjwgsvxHnnnYdhw4bhr3/9K8LhMJ544gnu8Q8++CCmT5+Oa6+9FkOHDsUdd9yBsWPH4i9/+ctubnnX0pBnhCm97L78gAFHcTI7syNUz+ykGQg4NOkW/DPxCtVjPE4uE5ZO9N1aVS8bj5Msye5rHtmFWZuQ/3QLcAwd/5xpjtOQK4DxfwGOXeV6eMdOepxo7M/FfXXznc9xYj1OLtdAe5wyDKtxm1zovumV80Y//12tcHTGW5fOSt6dHidPK+GJW4FpX2JJci6nJ/V0IbOSJDnKkVvlj936YidynEyytTanU2ZphdKv+PFdDFgTB9agAKCV+M54nOzCMPUMdqqqHoX9uWZSHIK73XYMI9jacpxcj6O3ZzkOdVWoXiYFZHjbM/GcpfU47URO6670OGVbVY8Z/yUZmPwUdvRLFaiwFzMw30lXxSndUJrOuGH3OO2sQjniFmDGUsBfyN9PKwnEdj84eBWHcBuvulRxcgnV8xo/Mulbbsuh/Ng8Tl1fRioLYrEYFi5ciN/85jfWNlmWMXXqVMyfP5/7nfnz5+Oqq65itk2bNg1z5szhHh+NRhGNpjppc3MzACAejyMe7/6HZbZh+bAHcdwzh/FzZKjjFCVsabtm6xMJvmKkSiri8TgUPZH6jss1M9sZS6/q+h16gIzH41CgcPdpmsY9h67rzIsUj8dZK0Sa5xNLpAYKXdNdjzfH2JAach5D+L8jEQl+akL3yT7rOJVozNQQ51RV2EI9kriuAm7XEo9b8eJxXQb6X5TcHLf+lzXNurNEUjzviyo7n5fb8eZzMX//wwj/nDoBNPPaactZhu+PT/ZB01ITla7FrfMxEL/VlgQnVID3ezJk7nZCzaxu7wcA5hkTjXRqAJcp+5PXPXF7D7xQZZXbpjjneZhIRNqpsU1J08cyOk4tAfJT3hVaaOU+R2K7hzprVaZDg7i/qaXyLmIu7zSQvF92h7HE70NuJLSE5/HxRGpfIpFAHMCQdcDP9jsaT1Lvmy75+O+BB3R/jcfjkBNRa2zI6Ex65u+t+Rt+TiVWt3mBHn8UIjGW2USCum/U80roEgh1LrexPBPFif6eY4FvPbP3z35MRzTlLXFrG+8e+WV/2t+LJzhyCEndGy0Rh55N36TGOkmXmL4IeM/nmaAqIUhaBAuTt4SeFy3ohYA5v2Vuo8fokBqClkjNEeZ1uKUKcCPikRqPlESMkXkUXWf6YiweY9pGyy5ebXaD3u/jbJe0lLAdJ4aR2+ucChRunwL4hsB4PI6ElmD+TgctY9hxG695z8PrPPbjmIXQqYfY3tGOHNsC9bubbN6LblWctm/fDk3TUFbGhmOVlZVh5cqV3O/U1NRwj6+p4WUIAnfffTduu+02x/b//e9/CIc7v4ZJV7P5m1qEfRVojG5Bqa8Uo/NG470d7wEArul7jZXHtX+HBrO4ZmWgL/qrZda+03NPxyplFZq1ZsiQUdVRherqakzqqIG5UoN57MW9L8ajmx4FAJzR6wwmT6wh3oAitQgdegf8m/2uOWSViUr09PfE/vn7o7q6GhEtgjJ/GRriDcjbnofjehyHzxs/R59tfZhznNzzZHzY8CGGeWmzJgAAH1VJREFUtwzH7PLZuLP9TpxbeS6qq6vxy9Jf4o7mO3BmrzPT5q4RQjA6bzQ69A58O+9brJJSnpoJ+RMAGLlyuWou8hUZV/S6wjrnlKIp+KH9B+B7oHp1NaYWT8WKthUYGB6IlW0rkbchD3ltefBLfsRIDCMDI63vHhOPMYNjdfXbmFU2Cy/WvggAuLzP5aiL1eDDyMuoDI/Hirfedr0GH2nBMcnP78/9AB0yG//97rvvYlR0Hfol/z6/5CLufbm237X468a/4tp+16K6uhq/6f8b/GXDX3BV36scxx9beiwWNC1ARW0FqqurEQo9hh1tn+DjjpcAGDPilX2vBPAnAMCGjRvxTZ1xjvOLzsftDbc7+gyPX/b+JZ6reQ5n5p+JUCIE4CkAQHPTDnzk8l0zPfiL+R/jun7X4Q/r/gAdOo4tPZb5vekl07GoZRFKt5SiutZ5rrgeR1WwCv2C/TzbuX9gf3yP7zEmbwze9nhOXhwYPxCvqa9heul07m8dWnQoVrevhvadhuofssvHvLHvjbhv3X2Q0QZ6pnn33XcBABf3uBh3tNyBFs2wEBaoBWhd1YrqddnnfV5adSme2vIULiy50POemX3tuj7Xpe0DZl+7sOBCbNi2AYPCg7jfSZAE+of6o8xvjGca0TAwPBDA9wCA0XlDUdi0Fccox3C/r5I2zEh+ntHjOMcxl/e5HE9ufhKzy2ejYbkxvjUkjAViBkn8NvGu5YvmL6z3xo0ESaBfsB96BXqhuroaPy//Od7a/hamkCNQXV2Nfv5foX+8GvPrpqAjy/zcQ7VD8Yb6htXXBsVWYCi1/8jiI7GoZRFG5Y3CkpYlGJk3EktalmBbfBvK/GVY99U61Cq1nr9xbe9r8X9r/w8X9jb6wexes3HXmrtwXuV5qIvV4e3tb+OgxEHMPTin4hy8VvcaZqgzrO1jojL6JPcPyRmCpZ8uxXLJWAg2R9+Kqcl9X339DWqXBDC5cDK2dGxB07ImVC933pe+ib7o4euBkXkjsbR1KfbL2Q8r2lagNmZcz1ElR6G6uhrH9zge8xrn4eKii7G1fis2RTehxFeCYU3DmDafXn463t7+NiYWTMTb9ca7f0nVJY5nSwjBfjn7AQAWfrSQ60k6VD8U1Wo1ppdOR09/Tzy5+Un8uvzXrv3k8OLDsbJtJdQ1KvddNcfBZcuWYt2qzPuIRjTsG9oXhb5CZjw7t+JczKmbwzyfzpDrvxf94u9gga838pVncF1f5xhQrpWjzF+GcfnjrH23DrgV96+/H5dWXWpti+tx9A70Rk2sBj2aeqB5eTP6BfuhIpB6vy7ucTFub74dZ1WchZZEC/699d8AgCJfMQCjlOMvKn+BF2tfxCnhU6zvjevYAHPFtOrqaoyJbrb6Yv9Qf9QtrkP1N6l2D00MRYmvBAcXHWydwz6H3jzgZjy4/kH8us+vsbhlMd7c/iYUKNaca3I8VZze3F6krcIhyf1hpQCT45O5z+GCygvwUu1LOCl0EmTImK/OxwEFB+Dzps/RmGjEwPBAXFR4EW7YdgMmFEzAF01fWP1+eHw4in3FOKzosKyesTmX0FxRcQXuW3cfLurNyhumvHZQ0UH4rPEznN3rbGu/TnQMDg9GUAkipsewqm0VppVOw7zGeTi558morq7Gseqx+Ez9DMf3OB5vvfUWZMjQoeOd995Bsa/Y0Y7dSXt7e8bHSqQby1ls2bIFlZWVmDdvHiZNmmRtv+666/DRRx9hwYIFju/4/X7861//wumnn25te+SRR3DbbbehttY5IfA8TlVVVdi+fTvy8/O7+IqyJx6P491338WRRx4JRVWQ0BNWiWzz0TArYi+8DMqaxwEAsZn1kGwx1OZ3EnrCcoUqH02HXPe+8XuzYo5jeROBTnQQQtK6sAkhzPd1okMnumUJt+/nfc9+jNt33H7f7Rp8LxqWGRKqRHzGGscxbr9Lb0/oCehEZ9fjebUYUiLlETHvqb0tGV2HFoXvFeMZxk9qstbAoftF8JvZkNf+g/ktt3uR6X3k7dN0DZIkQYJkhEsl75824BfQxz2S0Xm9fsd6HgXDkTjqa+7xavVQSG2rEZ+5DfAVeD7fdO3ItJ1xLQ5VVrNOPt8VbXH7ru+VPEjJ0K7XcubgyCOPhM/ns/YDhiDiVl4/m9/KpJ2d6QPZ3iNCCPwvGeFG2j6/gjbmQffvJ9rge9WoThqfthjIH+Y4hD6/TnQk9AST85TNtWR73M48f69zy8tvh7Li9wCAOeFXcdRRR0FVVeZ+m9eaTd/wan8mYzoAILodysJLofU7F6TXdHZf2zr4qgcBABIHvwFSfpTnue2/QV+bmSvrNZana7PXOGMe57Xf3vZMnrfXMdbYO/Yh6Ptkt2wAfV56HjH7RVeR7fzitk0jmqe8YL+vAOB7byKkxsUAjDnR/j3l8zMhb3zB2q98cR7k9U8DAGKnRDOeT9K9B4CzT6gvBiEl3drmfC3tWAh1riHjxo7dAClU7vh9t9+g+6f5e279Optxhu4b5lzide327W73y8SUQb3a2JHogE/2dVnu3c7Q3NyM0tJSNDU1pdUNutXjVFpaCkVRHApPbW0tysv5Hau8vDyr4wOBAAIBZ6yvz+fjdpbuwmxPAGkS7MKp6/QHcgCXSd+PlKCPMfcA70wAhly9R13zLmefC4DV/4A08jb4/Zmv7UTjA+d+2eKjd+qe+nzAjBUA0eELOl9Wn88HWZaZv3cV3GsFoOT0htKFvytBd7+O474FtAh8vt1n1PgxvhO88Yt5539iKNCgeL3DMrUely9kvFdpSDvW/hiQ2KUe3Oa1brlWXy/g0Ff4idT+VLSHmuHz2htRZKlLxt49Td7ZaeiCHbzrGnM3sP1TYPDlxn66MmInZYGMkSQrXNFqG9VGfyBnj+rvu6pvZDIf7Ul9Mpu2dGtxCL/fj3HjxmHu3LnWNl3XMXfuXMYDRTNp0iTmeMBwNbod/5ODKtnptgK3g5L9gZ+1A2P/sGvatKcy4XHg+DWGAtWV7OQitA4KhgKF+3n9YNf+XqYc/DLQ9+c7vViig1Cl+z7ZB+xGpUnwIyFdIQXZn6pemdN317dnTyHLwj97DJJ7cQgBRVfPNT8VCrzmSwC5A4CZm4Bh1xl/B3t6H9+lcDw+dGDXbireI9h1dKvHCQCuuuoqnHPOORg/fjwmTJiABx54AG1tbTjvPKPiytlnn43KykrcfffdAIDLL78chx56KP74xz9ixowZeO655/DVV1/h8ccf787L2H3QA0A2ITkeJZl/skgykNt/F5x4d09m3aQ4VZ1k/OsqDp8LrPwjsP8j6Y8VCGjSFe2QJOCkbYY3eCcXHf5RkWa9mj0WZtmJ7g/T2WMRihOfsX8ClCDQ/xz3Y+gwsuE3Ac0rgX5n7Pq2UR6nFLTiJAwFP3a6XXE69dRTsW3bNtxyyy2oqanB6NGj8fbbb1sFIDZs2MCEKk2ePBnPPPMMbrrpJtx4440YOHAg5syZg+HDh3fXJexeCoamP0awayk5ANj2ifHZo3S4wEb54cY/gSBjJAAko0Ww90rjUI+DgJX3d3crsoe2undh3s1Pj25LQd+zCRQDEx7L/Hh/ITDljV3WHBbhcfqp0+2KEwBcdtlluOyyy7j7PvzwQ8e2WbNmYdasWbu4VXsohSOA8Q/vZtezgOHAZ4Fv/wAMvBjIH7Trf6/76rcIBN3LCWuBbfOAPj/r7pbsmfSeCRz8MuJ5w4GPvu3u1mQOs/adUJxcER6nnwaMh7Xbl08V7CR7hOIkyJJBl3R3C/ZuwpXAuD/txh8UipNgLxUuc/ruXTlL2SJJRjhtPA5AKE4/OXZJqLlg18Lpz0WjgYpjgHBv5z7Bjw6hOAkEezxCcRIIBD8hGAu8UJwcHP4eUP+F4VEU/Ljg9WdJBqa8ufvbItglCMVJIBAIBALB7oMOV1KC3deOPZXyI4x/gh8hwhDwU0coTgLBno7IcRIIBD819rsJ6NgKFOwlhZ0EewlCcfqpIxQngWCPRyhOAoHgJ8aoO7q7BQKBQJA1oryHQLCn4y/q7hYIBAKBQCBIR1lyyQ1R+fgni/A4CQR7OsNvARqXAv3P7u6WCAQCgUAgcOOAJ4HvHgIGeCzOK/hRIzxOAsGeTqAYOGKuGIj3ZkYaYU16/wu6uSECgUAgcCVYCoy8Dcgd0N0tEewihOIkEAgEezpDrwFmLIc27i/d3RKBQCAQCPZahOIkEAgEezqSBBQMAySlu1siEAgEAsFei1CcBAKBQCAQCAQCgSANQnESCAQCgUAgEAgEgjQIxUkgEAgEAoFAIBAI0iAUJ4FAIBAIBAKBQCBIg1CcBAKBQCAQCAQCgSANQnESCAQCgUAgEAgEgjQIxUkgEAgEAoFAIBAI0iAUJ4FAIBAIBAKBQCBIg1CcBAKBQCAQCAQCgSANQnESCAQCgUAgEAgEgjQIxUkgEAgEAoFAIBAI0iAUJ4FAIBAIBAKBQCBIg1CcBAKBQCAQCAQCgSANQnESCAQCgUAgEAgEgjSo3d2A3Q0hBADQ3NzczS0xiMfjaG9vR3NzM3w+X3c3R7CHIPqFgIfoFwIeol8IeIh+IXBD9A0WUycwdQQv9jrFqaWlBQBQVVXVzS0RCAQCgUAgEAgEewItLS0oKCjwPEYimahXPyF0XceWLVuQl5cHSZK6uzlobm5GVVUVNm7ciPz8/O5ujmAPQfQLAQ/RLwQ8RL8Q8BD9QuCG6BsshBC0tLSgoqICsuydxbTXeZxkWUbv3r27uxkO8vPzRecVOBD9QsBD9AsBD9EvBDxEvxC4IfpGinSeJhNRHEIgEAgEAoFAIBAI0iAUJ4FAIBAIBAKBQCBIg1CcuplAIIBbb70VgUCgu5si2IMQ/ULAQ/QLAQ/RLwQ8RL8QuCH6RufZ64pDCAQCgUAgEAgEAkG2CI+TQCAQCAQCgUAgEKRBKE4CgUAgEAgEAoFAkAahOAkEAoFAIBAIBAJBGoTiJBAIBAKBQCAQCARpEIpTN/Lwww+jX79+CAaDmDhxIr744ovubpJgF3L33Xdj//33R15eHnr27ImZM2di1apVzDEdHR249NJLUVJSgtzcXJx88smora1ljtmwYQNmzJiBcDiMnj174tprr0UikdidlyLYRdxzzz2QJAlXXHGFtU30ib2XzZs348wzz0RJSQlCoRBGjBiBr776ytpPCMEtt9yCXr16IRQKYerUqfj++++Zc+zYsQNnnHEG8vPzUVhYiAsuuACtra27+1IEXYSmabj55pvRv39/hEIh7LPPPrjjjjtA1/kS/WLv4OOPP8Zxxx2HiooKSJKEOXPmMPu7qh8sWbIEBx98MILBIKqqqnDvvffu6kvbsyGCbuG5554jfr+fPPHEE2T58uXkwgsvJIWFhaS2tra7mybYRUybNo08+eSTZNmyZWTx4sXkmGOOIX369CGtra3WMb/61a9IVVUVmTt3Lvnqq6/IAQccQCZPnmztTyQSZPjw4WTq1Klk0aJFpLq6mpSWlpLf/OY33XFJgi7kiy++IP369SMjR44kl19+ubVd9Im9kx07dpC+ffuSc889lyxYsICsWbOGvPPOO+SHH36wjrnnnntIQUEBmTNnDvnmm2/I8ccfT/r3708ikYh1zPTp08moUaPI559/Tj755BOy7777ktNPP707LknQBdx5552kpKSEvPHGG2Tt2rXkxRdfJLm5ueTBBx+0jhH9Yu+gurqa/Pa3vyWvvPIKAUBeffVVZn9X9IOmpiZSVlZGzjjjDLJs2TLy7LPPklAoRB577LHddZl7HEJx6iYmTJhALr30UutvTdNIRUUFufvuu7uxVYLdSV1dHQFAPvroI0IIIY2NjcTn85EXX3zROubbb78lAMj8+fMJIcZAKcsyqampsY559NFHSX5+PolGo7v3AgRdRktLCxk4cCB59913yaGHHmopTqJP7L1cf/315KCDDnLdr+s6KS8vJ/fdd5+1rbGxkQQCAfLss88SQghZsWIFAUC+/PJL65i33nqLSJJENm/evOsaL9hlzJgxg5x//vnMtpNOOomcccYZhBDRL/ZW7IpTV/WDRx55hBQVFTFzyfXXX08GDx68i69oz0WE6nUDsVgMCxcuxNSpU61tsixj6tSpmD9/fje2TLA7aWpqAgAUFxcDABYuXIh4PM70iyFDhqBPnz5Wv5g/fz5GjBiBsrIy65hp06ahubkZy5cv342tF3Qll156KWbMmME8e0D0ib2Z//73vxg/fjxmzZqFnj17YsyYMfjb3/5m7V+7di1qamqYvlFQUICJEycyfaOwsBDjx4+3jpk6dSpkWcaCBQt238UIuozJkydj7ty5+O677wAA33zzDT799FMcffTRAES/EBh0VT+YP38+DjnkEPj9fuuYadOmYdWqVWhoaNhNV7NnoXZ3A/ZGtm/fDk3TGEEHAMrKyrBy5cpuapVgd6LrOq644goceOCBGD58OACgpqYGfr8fhYWFzLFlZWWoqamxjuH1G3Of4MfHc889h6+//hpffvmlY5/oE3sva9aswaOPPoqrrroKN954I7788kvMnj0bfr8f55xzjvVsec+e7hs9e/Zk9quqiuLiYtE3fqTccMMNaG5uxpAhQ6AoCjRNw5133okzzjgDAES/EADoun5QU1OD/v37O85h7isqKtol7d+TEYqTQNANXHrppVi2bBk+/fTT7m6KoBvZuHEjLr/8crz77rsIBoPd3RzBHoSu6xg/fjzuuusuAMCYMWOwbNky/PWvf8U555zTza0TdBcvvPACnn76aTzzzDPYb7/9sHjxYlxxxRWoqKgQ/UIg2A2IUL1uoLS0FIqiOCpj1dbWory8vJtaJdhdXHbZZXjjjTfwwQcfoHfv3tb28vJyxGIxNDY2MsfT/aK8vJzbb8x9gh8XCxcuRF1dHcaOHQtVVaGqKj766CP8+c9/hqqqKCsrE31iL6VXr14YNmwYs23o0KHYsGEDgNSz9ZpHysvLUVdXx+xPJBLYsWOH6Bs/Uq699lrccMMNOO200zBixAicddZZuPLKK3H33XcDEP1CYNBV/UDML06E4tQN+P1+jBs3DnPnzrW26bqOuXPnYtKkSd3YMsGuhBCCyy67DK+++iref/99h/t73Lhx8Pl8TL9YtWoVNmzYYPWLSZMmYenSpcxg9+677yI/P98hZAn2fI444ggsXboUixcvtv6NHz8eZ5xxhvVZ9Im9kwMPPNCxXMF3332Hvn37AgD69++P8vJypm80NzdjwYIFTN9obGzEwoULrWPef/996LqOiRMn7oarEHQ17e3tkGVWdFMUBbquAxD9QmDQVf1g0qRJ+PjjjxGPx61j3n33XQwePHivDNMDIMqRdxfPPfccCQQC5J///CdZsWIFueiii0hhYSFTGUvw0+Liiy8mBQUF5MMPPyRbt261/rW3t1vH/OpXvyJ9+vQh77/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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Визуализация результатов прогноза волатильности\n", + "plt.figure(figsize=(10, 5))\n", + "plt.plot(y_test_vol.values, label='Истинные значения волатильности', color='green')\n", + "plt.plot(y_pred_volatility, label='Предсказанные значения волатильности', color='orange')\n", + "plt.title(\"Прогнозирование волатильности\")\n", + "plt.xlabel(\"Наблюдения\")\n", + "plt.ylabel(\"Волатильность\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "По этой модели же можно сказать, что она более точно предсказывает, то есть она получилась более правильной " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Теперь воспользуемся фреймворком Featuretools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated feature columns: Index(['open', 'high', 'low', 'close', 'Adj Close', 'volume', 'DAY(datetime)',\n", + " 'MONTH(datetime)', 'YEAR(datetime)'],\n", + " dtype='object')\n", + "Размер обучающей выборки: (3150, 5)\n", + "Размер валидационной выборки: (1050, 5)\n", + "Размер тестовой выборки: (1051, 5)\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n", - " warnings.warn(\n" + " warnings.warn(\n", + "c:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\synthesis\\dfs.py:321: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n", + " agg_primitives: ['mean', 'sum']\n", + "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data. If the DFS call contained multiple instances of a primitive in the list above, none of them were used.\n", + " warnings.warn(warning_msg, UnusedPrimitiveWarning)\n" ] } ], "source": [ + "# Импорт библиотек для работы с Featuretools\n", "import featuretools as ft\n", + "import pandas as pd\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "import woodwork as ww\n", "\n", - "# Создаем сущности для Featuretools\n", - "es = ft.EntitySet(id=\"stock_prices\")\n", - "es = es.add_dataframe(dataframe_name=\"stock_data\", dataframe=data, index=\"Date\")\n", "\n", - "# Автоматическое создание признаков\n", - "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name=\"stock_data\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Оценка качества признаков\n", - "Оценка признаков на основе предсказательной способности модели и других критериев." - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5251\n", - "3150\n" - ] - }, - { - "data": { - "text/plain": [ - "(np.float64(0.05230198011754029), 0.5415652186272203)" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Проверим размерности данных после удаления NaN\n", - "print(X_scaled_df.shape[0]) # Количество строк в X_scaled_df\n", - "print(y_train.shape[0]) # Количество строк в y_train\n", + "# Проверим и удалим дублирующиеся столбцы\n", + "if not data.columns.is_unique:\n", + " print(\"Обнаружены дублирующиеся столбцы. Переименуем их.\")\n", + " data = data.loc[:, ~data.columns.duplicated()] \n", "\n", - "# Если данные имеют разные размеры, синхронизируем их\n", - "df = pd.concat([X_scaled_df, y_train], axis=1).dropna()\n", - "X_scaled_df = df.drop(columns=y_train.name)\n", - "y_train = df[y_train.name]\n", + "# Переименование столбцов для совместимости с Featuretools\n", + "data = data.rename(columns={'Date': 'datetime', 'Open': 'open', 'High': 'high', \n", + " 'Low': 'low', 'Close': 'close', 'Volume': 'volume'})\n", "\n", - "# Приводим индексы к одному виду\n", - "y_train = y_train.reset_index(drop=True)\n", - "X_scaled_df = X_scaled_df.reset_index(drop=True)\n", + "# Создание EntitySet\n", + "es = ft.EntitySet(id=\"stocks\")\n", "\n", - "# После этого продолжаем обучение модели\n", - "model = LinearRegression()\n", - "model.fit(X_scaled_df, y_train)\n", + "# Добавление данных в EntitySet с правильными логическими типами\n", + "es = es.add_dataframe(\n", + " dataframe_name=\"stock_data\",\n", + " dataframe=data,\n", + " index=\"datetime\", \n", + " # Убираем time_index, так как datetime уже используется как индекс\n", + " logical_types={\n", + " \"open\": ww.logical_types.Double, \n", + " \"high\": ww.logical_types.Double,\n", + " \"low\": ww.logical_types.Double,\n", + " \"close\": ww.logical_types.Double,\n", + " \"volume\": ww.logical_types.Double,\n", + " },\n", + ")\n", "\n", - "# Прогнозирование и оценка качества\n", - "y_pred = model.predict(X_scaled_df)\n", + "# Автоматическое создание признаков с использованием Featuretools\n", + "feature_matrix, feature_defs = ft.dfs(\n", + " entityset=es,\n", + " target_dataframe_name=\"stock_data\",\n", + " agg_primitives=[\"mean\", \"sum\"], \n", + " trans_primitives=[\"day\", \"month\", \"year\"] \n", + ")\n", "\n", - "# Оценка качества модели\n", - "mse = mean_squared_error(y_train, y_pred) # Используем y_train, потому что данные для теста не созданы\n", - "r2 = r2_score(y_train, y_pred)\n", + "# Выводим имена столбцов в feature_matrix, чтобы убедиться, какие признаки были сгенерированы\n", + "print(\"Generated feature columns:\", feature_matrix.columns)\n", "\n", - "mse, r2\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "MSE = 0.0523: Модель в среднем делает ошибку около 0.0523 при прогнозировании значений.\n", - "R² = 0.5416: Модель объясняет примерно 54.16% изменчивости целевой переменной\n", + "# Объединяем новые признаки с исходными данными, добавив суффиксы для дублирующихся столбцов\n", + "data_featuretools = data.join(feature_matrix, lsuffix='_orig', rsuffix='_feature')\n", "\n", - "Визуализируем" + "# Масштабирование данных\n", + "scaler = StandardScaler()\n", + "\n", + "# Используем правильные имена столбцов для масштабирования\n", + "scaled_columns = ['open_orig', 'high_orig', 'low_orig', 'close_orig', 'volume_orig']\n", + "\n", + "data_featuretools[scaled_columns] = scaler.fit_transform(data_featuretools[scaled_columns])\n", + "\n", + "# Разделение данных на выборки\n", + "\n", + "feature_columns = [col for col in feature_matrix.columns if 'feature' in col]\n", + "X = data_featuretools[scaled_columns + feature_columns]\n", + "y = data_featuretools['close_orig'] # Целевая переменная\n", + "\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", + "\n", + "print(f\"Размер обучающей выборки: {X_train.shape}\")\n", + "print(f\"Размер валидационной выборки: {X_val.shape}\")\n", + "print(f\"Размер тестовой выборки: {X_test.shape}\")" ] } ], -- 2.25.1 From 998128cb0f9abf56674cbc9994db63a701cca5c3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=90=D0=BB=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9A=D1=80?= =?UTF-8?q?=D1=8E=D0=BA=D0=BE=D0=B2?= Date: Sat, 30 Nov 2024 13:59:12 +0400 Subject: [PATCH 5/5] =?UTF-8?q?=D0=BD=D0=B5=D0=B1=D0=BE=D0=BB=D1=8C=D1=88?= =?UTF-8?q?=D0=B0=D1=8F=20=D0=BF=D1=80=D0=B0=D0=B2=D0=BA=D0=B0=20=D0=BA?= =?UTF-8?q?=D0=BE=D0=BC=D0=B5=D0=BD=D1=82=D0=BE=D0=B2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Lab_3/lab3.ipynb | 22 +++++++++------------- 1 file changed, 9 insertions(+), 13 deletions(-) diff --git a/Lab_3/lab3.ipynb b/Lab_3/lab3.ipynb index cb9478f..e063140 100644 --- a/Lab_3/lab3.ipynb +++ b/Lab_3/lab3.ipynb @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -107,8 +107,8 @@ ], "source": [ "# 3. Проверим датасет на наличие пропусков и удалим строки с недостающими данными\n", - "print(data.isnull().sum()) # Суммируем пропуски по каждому столбцу\n", - "data.dropna(inplace=True) # Удаляем строки с пропусками\n", + "print(data.isnull().sum()) \n", + "data.dropna(inplace=True) \n", "print(\"Данные после очистки:\", data.shape)\n" ] }, @@ -124,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -144,8 +144,8 @@ } ], "source": [ - "print(data.isnull().sum()) # Вывод количества пропусков\n", - "data.dropna(inplace=True) # Удаление строк с пропущенными значениями\n", + "print(data.isnull().sum()) \n", + "data.dropna(inplace=True) \n", "print(f\"Количество строк после удаления пропусков: {data.shape[0]}\")\n" ] }, @@ -215,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -232,7 +232,7 @@ "from sklearn.model_selection import train_test_split\n", "\n", "X = data[scaled_columns]\n", - "y = data['Close'] # Заменить на целевую переменную, если другая\n", + "y = data['Close']\n", "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n", "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", "\n", @@ -573,7 +573,6 @@ " dataframe_name=\"stock_data\",\n", " dataframe=data,\n", " index=\"datetime\", \n", - " # Убираем time_index, так как datetime уже используется как индекс\n", " logical_types={\n", " \"open\": ww.logical_types.Double, \n", " \"high\": ww.logical_types.Double,\n", @@ -594,13 +593,10 @@ "# Выводим имена столбцов в feature_matrix, чтобы убедиться, какие признаки были сгенерированы\n", "print(\"Generated feature columns:\", feature_matrix.columns)\n", "\n", - "# Объединяем новые признаки с исходными данными, добавив суффиксы для дублирующихся столбцов\n", "data_featuretools = data.join(feature_matrix, lsuffix='_orig', rsuffix='_feature')\n", "\n", - "# Масштабирование данных\n", "scaler = StandardScaler()\n", "\n", - "# Используем правильные имена столбцов для масштабирования\n", "scaled_columns = ['open_orig', 'high_orig', 'low_orig', 'close_orig', 'volume_orig']\n", "\n", "data_featuretools[scaled_columns] = scaler.fit_transform(data_featuretools[scaled_columns])\n", @@ -609,7 +605,7 @@ "\n", "feature_columns = [col for col in feature_matrix.columns if 'feature' in col]\n", "X = data_featuretools[scaled_columns + feature_columns]\n", - "y = data_featuretools['close_orig'] # Целевая переменная\n", + "y = data_featuretools['close_orig'] \n", "\n", "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n", "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", -- 2.25.1