diff --git a/stud/shukaylo/lab1.ipynb b/stud/shukaylo/lab1.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np # библиотека для работы с чиселками\n",
+ "import pandas as pd # data processing, работа с CSV файлами\n",
+ "import matplotlib.pyplot as plt # для графики\n",
+ "\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.linear_model import LinearRegression\n",
+ "from sklearn.metrics import mean_squared_error, mean_absolute_error"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Откроем датасет и посмотрим первые 5 его строчек"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
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+ " 3.222222 \n",
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+ "text/plain": [
+ " 0 1\n",
+ "0 1.404040 -12.034654\n",
+ "1 3.222222 -1.565204\n",
+ "2 4.515152 -0.025646\n",
+ "3 1.646465 -11.261848\n",
+ "4 5.646465 -0.593668"
+ ]
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataset = pd.read_csv('..\\\\..\\\\tasks\\\\lab1\\\\dataset\\\\lab1-07.csv', header=None)\n",
+ "dataset.head(5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Посмотрим датасет"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(dataset[0], dataset[1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Как видно из диаграммы рассеяния, данные представляют собой кривую насыщения (A - B*exp(-kx)). "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Подготовим данные"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Разделим датасет на обучающую и тестовую выборку в соотношении 4:1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_train, X_test, y_train, y_test = train_test_split(dataset[0].to_frame(), dataset[1], test_size=0.2, random_state=42)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Посмотрим на работу линейной регрессии"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Обучим модель Linear regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "LinearRegression() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org. "
+ ],
+ "text/plain": [
+ "LinearRegression()"
+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model_regression = LinearRegression()\n",
+ "\n",
+ "model_regression.fit(X_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Оценим работу"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_predict_regression = model_regression.predict(X_train)\n",
+ "test_predict_regression = model_regression.predict(X_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(dataset[0], dataset[1], color='blue', label='Начальные данные')\n",
+ "plt.scatter(X_test, test_predict_regression, color='red', label='Предсказанные данные')\n",
+ "plt.xlabel('X')\n",
+ "plt.ylabel('Y')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Метрика: MSE MAE\n",
+ "Train 8.130377816703561 2.353069764836676\n",
+ "Test 7.047462514179974 2.079107800557874\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Расчет метрик\n",
+ "train_mse_regression = mean_squared_error(y_train, train_predict_regression)\n",
+ "train_mae_regression = mean_absolute_error(y_train, train_predict_regression)\n",
+ "\n",
+ "test_mse_regression = mean_squared_error(y_test, test_predict_regression)\n",
+ "test_mae_regression = mean_absolute_error(y_test, test_predict_regression)\n",
+ "\n",
+ "\n",
+ "\n",
+ "print('Метрика: MSE MAE')\n",
+ "print('Train', train_mse_regression, train_mae_regression)\n",
+ "print('Test', test_mse_regression, test_mae_regression)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Вывод:\n",
+ "Как видно из диаграммы рассеяния, линейная регрессия плохо подходит для данного набора данных.\n",
+ "Значения MSE (8.13 / 7.04) и MAE (2.35 / 2.07) достаточно высоки, что свидетельствует о значительных ошибках в предсказаниях модели, что логично, ведь простая линейная регрессия пытается аппроксимировать кривую насыщения прямой линией."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Улучшение бейзлайна"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Т. к. данные имеют зависимость в виде кривой насыщения, попробуем создать линейные признаки, чтобы линейная регрессия могла их использовать"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.preprocessing import FunctionTransformer\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "from sklearn.model_selection import GridSearchCV"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Лучший параметр k подобран автоматически через GridSearchCV.\n",
+ "A = 0.5773568368488715\n",
+ "B = 37.66311497454749\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Функция для создания признака exp(-k*x)\n",
+ "def exp_feature(x, k=1.0):\n",
+ " return np.exp(-k * x)\n",
+ "\n",
+ "# Pipeline с трансформером и линейной регрессией\n",
+ "pipeline = Pipeline([\n",
+ " (\"exp\", FunctionTransformer(func=lambda X: exp_feature(X, k=1.0), validate=False)),\n",
+ " (\"linreg\", LinearRegression())\n",
+ "])\n",
+ "\n",
+ "# Сеточный поиск по k\n",
+ "param_grid = {\"exp__func\": [lambda X, k=k: exp_feature(X, k) for k in np.linspace(0.01, 2, 200)]}\n",
+ "grid = GridSearchCV(pipeline, param_grid, scoring='neg_mean_squared_error', cv=5)\n",
+ "grid.fit(X_train, y_train)\n",
+ "\n",
+ "best_model = grid.best_estimator_\n",
+ "print(\"Лучший параметр k подобран автоматически через GridSearchCV.\")\n",
+ "\n",
+ "# Извлечение коэффициентов\n",
+ "A = best_model.named_steps[\"linreg\"].intercept_\n",
+ "B = -best_model.named_steps[\"linreg\"].coef_[0]\n",
+ "print(\"A =\", A)\n",
+ "print(\"B =\", B)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Оценим работу"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_predict_regression = A - B * best_model.named_steps[\"exp\"].transform(X_train)\n",
+ "test_predict_regression = A - B * best_model.named_steps[\"exp\"].transform(X_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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Dy8lRdTWWWZSok7otk0CmMoHsSbx9ct3wy80b24smYvtO5QtKTINvBK1e3Ydmc8L9ONnU8f54UPzGYCmFGCxRpnPS4do4xGN3AE92GK1hQ03LzXV2/PH6edAPFqhRcvt5j2G9ZEolUEBs9XjjjdB4chxOYgw23nuoZ5yalT2z5mrXNzIPciqcBis5OSrLsk7ybQMl0zeSh0GH5RPvR4W7m/becU6o0TK7KJWZJXHwkmEYNfk3uzMMllKIwRJlumSOEwgqjMdUp7UpeoG22/v0e32pRJdHSaa2FM+FXc1RvADR8Qy23RYBUZJjsHbvobg1Lzk52o5DXAQ56QxkEj3pz7fT/XHKwyDPKljZ//rlJFSzVJZXoFXmo2Ypx2aosNB09p7p39dsn6ZrmmCwlEIMlijTJXucsKtjtaoDMpuG7eSEgAKBhZ+SOf4k8jmN58rp7WOIsirWWVCuhk70AzEOInbPudkwWEzvA8uj09yk3kOpykykJJBJ9KQHpl4f5KuGD3Mc1CwlHqzsH9rMqRboVlbNhou9TN9eve9QD2Zy/egZc/GeQgyJx7QNSEG/DgZLKcRgiTKdF8cJp1P19cLiZO4T9RROR4oSGVnyIsngNDngNouFIUr94GU2MwsHJrPnXB2wrAKieC+sgwO43euZqpoXPwMZT2qW/DrIOwhE9s+YSzxYMX3P7fuDXmrSZ2lDXmHkfae/11+ovg2CNCf3ve/tHfs2THS6pgsMllKIwRJlOrcrz7s5ploFK/oMtGRPjRtHbgu3bZxyjgxUIiNLXgSPTstOzO7L2DjQ+I0/3jRwHJRinnMMvSXaYCo6oEiwLUCqMkt6FuXXxvnO3swOA5no2XDVMjjR2ybT8CvBg7z+eiwaOlfbkGsdiGA3BjaaG+mrZLGNkxPei2fllmhLb6n+7aN8t00PKi12NqTVe9vh27D6E+BTv46sDpYee+wxrVWrVlqtWrW0U089VXvvvfdst589e7Z29NFHq+07dOigvfLKK67uj8ESBYFXjQ6dZFTcDDu5OTnpM+SmKWYyz4XTzJIxi2X27T26E3K8+h/TqNWj6C9uWwCL91C8mpf9Q0X5NttYTN2PulzPkGBGXNw3c9QbIfp4++nY6gHFz40K1fmqEaJVkJNow68kDvLGu7QKsmPe8+WRgCbRYMXt+9sM9sPNLFPD2zAVMVKVrA2WZs6cqdWsWVN75plntBUrVmgDBw7UDjnkEO27774z3f6dd97R8vLytD//+c/a559/ro0aNUo74IADtE8//dTxfTJYoqBIttEhTqNG2X+AJVo87eXJychSIsuj4MCDb82YVaRqiXAHcT7Vo+MYq4xR9FBJQj1mPBhXREdrJ2VNZu8hzBK0qnkxGyqy2uYBucOyd1J0hkQ99Hjt2vcFMmaboa8THq/pa2b3eia4hEkiB303709jzObFMHOys/LLyyOTQ/RhZacnqxm0Hq9yUiVrgyVkkgYNGlT1e0VFhdaiRQtt/Pjxpttfdtll2vnnnx9zXseOHbUbb7zR8j527dqlnlj9tH79egZLFBhm62ElmmEx+wDzu47W7QevnXjtA/Rv8n1lqvYPuVr7QRrEbtCokVZp+AptXF5CDx7z4mSM9CLcPjLV/dEsyYr1yoJCFUA4DT6N7yH0n7LKmhmHgeJtoz/neB6GyiPqudczJNWC4OgdQWU8TlFRiQfNtJMKhNwc9I0Pxa6jvB6gGpqbe/J2MJ1oUJJ4Zie6ITuG1OO9x6xm0Hpc153dwdLu3btVlmjevHkx5w8YMEC76KKLTK9TWFioPYJq0ij33HOPdtxxx1nez+jRo9WTazwxWKKgwgdQvA9nqw844weY02+1Tjr5+v3t2G5f43WItioK1otsMVSkH2Tw/CAj5WSnESC4jgQTHVfc9wJiCCqZA2j0wTlePZbTbZI9WHrYTNuU0yFLJ48j0TUNrb4MJDPMjOsg/jd+HhR4kNlJdgatH/3XsjJYKisrUw96yZIlMeffcccdKuNkBkNu0w2fqH/729+0pk2bWt4PM0sURnraPJEP1+gPMKffaqMPvG6X8kj2YKLD5WYHbqu1uJyeoqdq6weZ94Y5iyKRSbGr/7E8YsQ7EtmsBOw0wDUOqeiPzYsasHj35bY+2o+WR8an2uylcXvQT2Q42ElHeSc1imZvlXj3OXu2++fLSZCJzx4M8fv1mllhsORjsGTEmiXKNMkURyb7LdfJgRMHQgRLThdgdXty+g3UbKozipA3SyNPmici+NIPWKpGxsF1kIGyqu2Jm16JV4Rs8cZIdNgmene8mkDg1TCQH8209acwXiDkZAFn/TEmU98Xr6O83dvBKmiJV5id50EfNGMnfbfPgQernGR3sJSqYTgjBkuUSbwojoz+MHP6bS/6A8zpgdPJsEUyB/C4T9S+hnvRN1Dh01pYhzYu1yrzrSNCPRt1QG553J43jl88F0XIiR609cAUbR3MCnpxGbIRxnqcZPo2OnmIfmWWvKzJc/q3lcx73ey5iq4jwuFPr31yumaj7Ovsr9+msaWHH0Xsxvv2SlYGS4AM0mCsJBlV4J2fn29b4H3BBRfEnNepUyfbAm8jBkuUKfwoaE30oOMkQ+VkppXbk6PhmhRN2TMuL3FVvUhzQfM+P7GNA1VvKUMHb7/mTycz1Vs/GbMpCJrMlspJtm+j0y8DfjTTBi9XTUk2WErkcdg9f4k+tjwX60oaX59EH7NXhd5Z3ToA/ZKmTJmiWgHccMMNqnXApk2b1OVXXnmlNmLEiJjWATVq1NAmTJigffHFF6p4m60DKIj8KmhN5qCD8/Bt1W76sNVMKwQLxgOw8UPZmLVwHE/4PGXPbnkJLDj7SyP7GWN+FLIm+u0+0UL8ZEcM3eyrXW9Iuwyn3uzUzfPs9K2DmV/x/mbcZHLinZAhivc44j1/idQsSgKvvZvn0e758+LvI2uDJXj00Ue1li1bqn5LyDS9++67VZedeeaZ2lVXXVWtKeVRRx2ltj/mmGPYlJICKRUFrYlkApLZL+MQQqLpfr/SA1az4eyWl8DzhSE5J7PBjM+J1436nHy7r13bv4Oam8eT6JcBJ73F7DIhZu9BJ/V1+hcEu78ZJ19E3PQpivc44j1/uDyRWbGSwGufyiWH7GR1sJRqDJYoE/hV0JrsCg5+71dCHEZw8WbDbZO6qhg8meUlnD4nfjTqS1VPLC8Oal4E3VZL8NhlpozPOTJGqNzQr2cXLOCncXgT2dLojFa8LyJusj12X16cPn9eZZckzmufyiWHvDh+5woRhULz5t5uBxUVIqWlIjNmiDRsKLJ6tUhJicj06ZGfa9aI9OyZ+v1KWpcuIgUFIjk55pfn5Miuuo1kgxSYXvyDNJK7Zaw0lG1yqHwnRVIifWS6+nmYrJF5EudJEXfPSXGxSK9eIhs2xF5eVhY5H5cnYuPG5PcxVffj9DbMtsvLi7zkc+aYXweHXhg2LPKet3vOf/hB5OWXI//PtTmC4jbx9jrwQJEFCyK33bixyObNIhMninTtKtK6dWRb7Fd+fuz18fbE+XfdZf9Wjfc43D5/Rx4pMnt25DnzwsaNif0JZtxnRvJxGTGzRJnA64JWrzIZfhXaJi3OV3o0aozu4I1mkegqncx6W8YMg5PnxI9aND3T4kWBcTLZhVQOM7u5vtf1/8jWxKu1shuSTKQtg/F5cPv8oT2A36/93ARnw7FmKaAYLFGmSKa2yOx27D7c07FfnrMZW3QS5Dntp2O3rEO858TrWjQvZhym46DmpBeX3RIgboaDvR6edDPBwavXzThElciXlmTeKzkuHpfdMihmt8vZcAHFYIkySaK1RTon36pxudsDYLL75Rubr/ROl2dw+u3YSasE43NiVWcT7+BoxqteVm5OXr6+bjIsxiyom6DTy/YATk9Ogl28NZ12uze7vUS+tBgbSOY4eP7dBjT62oJOTl5/ZjBYSiEGS5Rpkpk15fSgMnq0+/vwejZXKsQLaNwcwM16D8UbfvHqYJui1lIxJwR6qXg9nBywnWYK9UaNXjx+N7PZnBYrJzusneyXlrkOZhf6tTSNk/YIbjFYSiEGSxQmiX6r9mKhzUyVyOry+lCbWVdrr5v2ORnucHpAqlfPu2DJyzW8jM+N3oHabijUGDh4vSRLvEDJzWw2N89VssPayX5pKfe4pUc66xoZLKUQgyUKEy/WCctGZgegZGq/3LwOTp5zp0EwskHxAgp9DbF0F+0nUs+VqpotPbviVyCQscPaCUpXXSNbBxCR6/YA+Nm5c2RKr1v4aLOatpwNMNW6qEikT5/ITxg6dP/z4va5cjrVG7cRr32Dm2nWPXqYT2Vv0iRyX2gZsXatyJNPRs43Tv3Wf8f0eK+mn3vZTgDPFfYfj2Pq1Mh0fi+NGhXbVgPPwaRJ3j9X0Y/DTSuPTNWzp30LhbQ/Ln9itezCzBIFlVV7gDvuMP8WrE+ld9t5Ohulcqq7E24zHE6GatKd3UjVc+zmZFV7lO7nKijKU1zX6PT4XSPNsRoRpYnedM+Y+UCjwwkTRC6/XGTWrP3nXyLFMkmGSqHs79K3XgpkqEyq1oQxVc0OM1kyTRSjm/bh9TDLTiErgcuxnRN6hgOvOa4bfZtmGQ49U2YH3/aRiVq8OPI4kL3C/vidUfLqOfLjfWqVwUv3cxUUeQ7ed+nAYTiiLIShn3hDRO+8sz8ljkBpjvSS/KhACfKlTJ2Py9PWWTdDJdu53I/hGz+GOozDj6k8+Cf7HHn5PsX9FRbaB69mQ7XRQ+DZOHwdGP4muLIDh+EoaNysE5Un5do6KdAqLDbCorFYDw1Dcmnrxp2BQwVeFfb6MXwTxBYOfjxHThpdOjklUoTsx1p/5B4LvInIkpt1ot4au1gNvVl9WOSKJi1lvZwhi1NW2Jvu4Uus6YW1vfr23b/Gl3F9Nq8yQ34U8qYzG+QHs+do1arIeoZ2WRu718gIl+N0xx3VJ0C4zcz5tdYf+SiBQIwMmFmioHFVGOtwzvmghtND/604kVYAqS7sDVvWKBFuszZuGy0m8xz7sdYf+X/8zsE/fgZj2WDHjh1y8MEHy/bt26V+/frp3h0KKHzz9br40+o2cT6yIfEKY5HByFtcGkmfxLuvBSWS1y0DKzM9oj9nxmyA6XOW5/9rawYZCdSiRe8j9gnZk7RPvU7zxAU9c2SVATK+RmihsWSJ969ZqbM/J5Udy8RC56w9ficRkNE+zCxRsvyoX4h3m46bwKWzvW4G8Xoqf6qzXqg/C3u2KQhZGzcL+pL/WLNEFBB+1C/Y3eZll1bI2/eWSs/dM6R0TKkUtqiwr7/wq6telrUCSOfsRpxGj7avsQpS41SrWWPIDFll/gDPw/r1ke2COkuS0oPBElGGT+F32xHb7jYv1opljbSWM0ZHqpPxc21Oa/l0bLF98XDGt9fN7oNcvCDBKEiFxE4L6jM9oDX2hrIqKHfSgoBSj8ESURr58U3Y6jateiXllJVJhzG9pE+tYvuZUWFbXyFEBzm3B/9EA/FMz7pmQkAbLwvGRG0wMVgiSgGrD1A/vgmbbZsrFar7tohW/Y/ezZEzbHPOXcjkg1wiB/9MGJLyOuua7oDWaRaMidrgYbBE5DO7D1A/vgmbbdtF7HslZfyRM0Nk6kEuXpBgJ1OXpkkk65rOgNZtFizLE7WBw2CJyEfxPkB/+MH7b8JmB87mEoBijoDIxIOcm+aKQSkkTjTrmo6ANtHawyxO1AYOgyUiD2fjuP0AvfVWkYcf9vabsNmBc6NkQDFHiGTiQc4qSLCS6YXEyWRdUx3QBmEWHiWHwRKRh7NxEvkAbdIksW/CdgGcfuDEcg9qX6SLrJcCqZQMrE4mzxiDhLFjM7PGyolk649SGdAGYRYeJYfBEpFPPZDcfIC6/SbsJIDr0UOkdu3I/yslT4ZKJN1kDJi0IBw5ybHoIOGee0Tmzs28GqugF9QbZcIsPPIXlzvxAJc7Cadklrfwc1mD4hcq5NHLFksz2aiG15A1QjBkXM7B7P7RPgCz4lDsrdvVpFBq/31iZh85KSmpWm7FD2ZLuCCjNDGD3rKulg8KyPOeLXY4PH4zWPIAg6VwSjbY8eMDtGJOsXx3xVBpUbH/yIHhNWSN5knPmNucPTuSdTJrI4DZcc33BVs3Te0iV/TjJzhlriAEe3oWGqL/3uOtSUfBOH5zGI7IpzoEz4cRioslt3cvaRYVKEG+lKlmk8gaRReSWqX8kYVaJEUyU/qon83yM+yoQxSAgvqgtJUgbzBYIvKxDsHuAxSZHxRg286w06u4p00Tuekm06aSuRL5GjtRhqmsEcyf76z3DorLkflyOsOPiDK7rUQiM3cpPg7DeYDDcOGUyDCa1XCB8fzNmyNtA6LrMHBbyERVfbCaFWvEUSQlKlsEKOwFs6EBM9Xun4gCxewjg3/X9jgMR5Qkt8NodjPUoocRtm4VufzyODPsrKbhxRHdfBJN8DAjzmnvnSAtrkpE3s3cpfiYWfIAM0vh5mQ2jv5BZfxrMhZ3Oplh1zK/QtZIa8lxGSgZM0vRxed6ZgsfnMOHRzJbVvfPWTtE2TVzN5vtyLbM0tq1a+W6666Tww47TA488EA5/PDDZfTo0bJnzx7b6xUVFUlOTk7M6SZVG0LkrA7BzVIHThpVtt6w2HWghN5J66RQtREwKz7XM1vIMFkFSsl2GmatBFF6sIO4/2pISHz55ZdSWVkpkydPliOOOEI+++wzGThwoPzyyy8yYcIE2+tiu3vvvbfq9zp16qRgjylI9GAj2Q8qJzPsHK/jto/eZHKYTFQz3eyKz/3qNMxaCaL0YQdx/4UmWDr33HPVSdemTRtZuXKlPPHEE3GDJQRHzZo1c3xfu3fvVqfoNB5lLzcfVE5m2Dlex22fDVKgAiX0WTKm3Y1LQfjRadhqCFKvleC0aSJ/sYO4/0IzDGcGY5AN9cWxbEybNk0aN24sHTp0kJEjR8rOnTtttx8/frwa49RPhShgoazl5oPKyXpXawu6iOZgzv/7t0yVrlIibWRNtUDJqodTsuttebXaOhF5x+u/azKhhdTXX3+t1a9fX3vyySdtt5s8ebL22muvaZ988ok2depULT8/X7vkkktsr7Nr1y5t+/btVaf169fjsKD+T9mnvFzTCgo0LScH4UH1E84vLIxsB3PnRs4zbq+fh8utNqqUHHUqfwEbRTbDfUffDu5L3YYFR/fvUEmJ+WM2nrAdEfnHy7/rbLJ9+3ZHx++MD5buvPNO9UDsTl988UXMdTZs2KAdfvjh2nXXXef6/hYuXKhuc9WqVZ4/2RRebj+oHAU5c+dqlYaNvpFC7RKZqzVpomnDhkWCkN27Iz+nT4/81IOyePvrNsgyg/t0EixhOyLyl1d/19lku8Pjd8a3Dti8ebNs2bLFdhvUJ9WsWVP9/9tvv1Uz3E477TSZMmWK5Oa6G2lEQXjdunXltddek3POOcfRddg6INi8WnfK7YKf8e4Xt3fTwAppv3X/Om76ornREi2k9uJx+7VYMBGFdx29TJKVC+mWlZVJ165d5aSTTpKpU6dKXgLvkHfeeUdOP/10Wb58uRx33HGOrsNgKbi8nsXlZeB16aXOt0dNQjoKqbnaOhEFWdYFSwiUkFFq1aqVPPfcczGBkj7TDdt069ZNnn/+eTn11FNl9erVMn36dDnvvPOkUaNG8sknn8jw4cOloKBAFi1a5Pi+GSwFk9NGkpnWYM5MOoMSrrZOREGVdU0p33zzTVm1apUsXLhQBTvNmzevOun27t2r2gnos90wdLdgwQLp3r27tG3bVm677Ta59NJL5aWXXkrjI6FUSNksrgQ6Ncbr25RpTee42joRhV1oMkvpxMxS8KSk1ibBMT7EVVhbLhHoMI7159KBtRJEFNbjd2iaUhJlVMfbJDo1JtM4Lp1N5+y6nBMRBVlohuGIMqbjbZJjfPEazJlh0zkiIv8wWKKs5FvHWwRAjz6a1KqWyNBgpE7fj3jsOnYTEVHyGCxRVrILSBIOPjD0hmlsw4cnPcbXo4fImDEiDRrEnl+3rohxWF0vpMZ1XNaSExGRAwyWKGslO4tLn+g2c1qFrLn2XtHQGMnNNDaLMT495ho9WmTr1sh5WOJw7FiRbdsi56HwHMXc+Il2AYDroGgdxeH4id9xW0RElBzOhvMAZ8MFWyKzuPSJbqdsKJZJcosUSpnj+6uUHNnVqEDqfFe9KVIivZ8ytV8UEVGmy7qmlOnEYCm76MHJxVqxzBF0Y9Qcp2gRKMF19efI2Y/3VFktPTiL14zSrPFkItchIqIsbUpJlAr6RLccrUImyVBXgRJskALpJXNkyo6e0r9/7HBZvGaUZnXhiVyHiIjcYZ8lIhf04ORMWSyF4q7N9jB5RB6VIdUWwkXrJZQ7nXuu+7pw3/tFERERgyUiN/VLetDRXJxHHxh6Q0bJLFACfSD8tdfc14X72i+KiIgUDsMRGWah2c0o04OOjeIs+qjc93OYTDQNlNww6/3kW78oIiKqwmCJKKpo21j/o69OUvxCpE/AGWUzpFfjUlkinWW9FFQVbMerUZonyU1Hs+r95Eu/KCIiisFgibJevNVJLtGK5bQ+kZRTbv++8sIPXWW1HC4zJLJirTFg0m/m88vHSpf8tUkHSvF6PyXbL4qIiOyxdYAH2Dog2NBYEkNuZi4R8/YAmuSooOghuV36yozYYm+MeyGd07NnVQ0UMlRYDu6HH9zt2+DBkeJvJ72fEukXRUSUzXY4PH6zwJuyntVMsVyxbg+gQqWcHBnaeKbM/8tqOXrLEjmuyUbJzY+NUvCjqChynQMPjAzpgdOvKAiU9OvHE31fRETkHQZLlBXssi5WM8W6xGkPkKNpUnvzerm8cInIlfGjFH24DEN+8VZF0ZtJsjCbiCj9WLNEku2z3KxmlDluD+CiiRECprVrI2u6YVjODAuziYgyC4Mlyu5ZbsXWM8qctgdw28RIHy575BGRuXMjgVo0FmYTEWUWFnh7gAXemcntumn64rj69qhZWp/XWppXlO0r545zA0nsJwuziYhSjwXelPXcrJuGTA8yOT16RAcueXLoD5Mk5zJUZefEVmV7OFbGwmwioszGYIlCK5F106oHLhZV2cgo7WsPQERE4cZgiULLs3XTqqecOFZGRJRFGCxRaOmz3FDMbVaZZzc9v3odUZ7kcayMiCgrcTYchVai66Y5WVCXiIiyB4MlCjW366Y5aTWArBOWSJkxI/ITvxMRUXixdYAH2Dog8zmZnu+k1UDDhiK1a0eCJx0CsRtuEDnySJYzERGF8fjNYMkDDJbCv6CuG8haYfiPE+WIiMJx/OYwHJH7VUtsRQ/ZERFR8DFYIkps1RJLeq4Wa7+xnomIKPgYLBGJ/YK6idC7g48ZwyJwIqKgY7BE5KDVQKLuv5+tB4iIgo7BEpHDVgONGiUeRLGOiYgouEIVLLVu3VpycnJiTg888IDtdXbt2iWDBg2SRo0aSd26deXSSy+V7777LmX7TKkXr08SAqa1a0VKSkSmT4/8xO9PPhm5PJGAiXVMRETBFarWAQiWrrvuOhk4cGDVefXq1ZODDjrI8jo333yzvPLKKzJlyhQ1fXDw4MGSm5sr77zzjuP7ZeuA4EBmx2xNXKdT/c2u7xaCL66cQkSUfk6P36FbGw7BUbNmzRxtiyfn6aeflunTp8tZZ52lznv22WelXbt28u6778ppp53m895SKunduY1fD/QhMrOO3vHW1P36a5GnnnIXPHnVooCIiFIjVMNwgGE3DKmdcMIJ8tBDD0l5ebnltsuWLZO9e/fK2WefXXVe27ZtpWXLlrJ06VLL6+3evVtFo9EnymwY+kJGyCyP6naIDIXgyAz16SNyzz37h+xGjUptiwIiIkqNUGWWbrnlFjnxxBOlYcOGsmTJEhk5cqRs3LhRHn74YdPtN23aJDVr1pRDDjkk5vxDDz1UXWZl/PjxMnbsWM/3n7xjXN4Ev9tlf/Sp/riO2yEyPXhC64EpUyKZKrOgDLVOGPLDdkREFBwZn1kaMWJEtaJt4+nLL79U2956661SVFQkxx13nNx0003yl7/8RR599FGVCfISgjAM4emn9TjKUkYNt2GqPqbs9+0b+XnZZc6uO3++P60H9N8nTuS6cUREQZPxmaXbbrtNrr76attt2rRpY3p+x44d1TDc2rVr5eijj652OWqb9uzZI9u2bYvJLmE2nF3dU61atdSJglOXtHWrs+sjmEHmJ9F13fTWA2ZF5LhtrhdHRBQ8GR8sNWnSRJ0S8fHHH6uZbU2bNjW9/KSTTpIDDjhAFi5cqFoGwMqVK2XdunXSqVOnpPabUg9DbcNvqZAztMXSXDbKRmkui6WLVIq7VA5ql1DEnWgGyFgEjmFABGDMKBERBVPGB0tOoSD7vffek65du6oZcfh9+PDh0r9/f2nQoIHapqysTLp16ybPP/+8nHrqqWq6IFoNYPgOdU6YNjhkyBAVKHEmXPB8Ma5Y/lM2VAplf0pnvRTIUJkk88R5SifR2iWzOiYiIgq+0ARLGBabOXOmjBkzRtUoHXbYYSpYQiCkw8w3ZI527txZdd4jjzyisk/ILOF655xzjjz++ONpehSUsOJiOWZ0L9EkdvwtX8pkjvSSXjLHVcDE6f1ERBTKppTpwqaUGTD+1rq1aBs2iFlz7UrJkQ1SIIfJGsdDcmwcSUQUfjscHr8zfjYcUVwYM7MIlCBXNGkp66WLLFaF1lj3zWrJEpxfWMjp/UREtB+DJQo+h2NmKPr+9ddIOwHg9H4iInKCwRIFn8OW2JgdhxYCEyaI3H57JMMUDVknJ0ueEBFRdglNgTdlMYyZIdKxaJ2t1yyhjQAuRgZp5kyR1atFlizh9H4iIrLHYImCT2+djW6UiISiAiYESjBMJlYVd+tLmyBQYhE3ERHFw2E4Cge9dbZhbA0ZJau2AWwPQERETjCzRMFfJVcfP9vXOvvjRxfLg8Pjd/B2WOpERERZjn2WPMA+Syle/M1s4TUMw+2rzN7XdsmqhEmN1OEqa9awRomIKJvtYJ8lCu0qudGBEiAqwvnFxVVJJ7PFdBNpD4DbKy0VmTEj8hO/ExFRdmFmyQPMLKWAni4yBkq6nBzZ2bBA2tVeI+vK9kdBCIiiAxw0nESg5KQ9gIMkFhERBRgzSxTKLt2WNE3qbFkvh5Utjjm7sjLyc9iwyBImGHpzGijFSWIREVGWYLBEoevSHU3vqzR3rvM+SshEIaNklnPVz0PwxSE5IqLswGCJQtel20jvq4TklEdJLFe3R0REwcbWAZTS2f2p6NJtxWlfJa+3IyKiYGNmiTyHeh7UYnftGlm0Fj/xe1J1PnqXbpMVcDWTLt3J9FXyejsiIgo2BkvkKV8Lo/d16dZMVsC9sdEc+VeOeeU2YivMgkNyyk0SyxCTJXx7RESUJcHSt99+6++eUOClojC6WHpKa22tFEmJ9JHp6mdrbY00uDYSKBkDHLd9leIksRK6PSIiypJg6ZhjjpHp06f7uzcUaH4XRutZK/RRWiRFMlP6qJ/rv82TCRNEbr+92tJwKkOEJePc9kWyWGou4dsjIqIsKPAeN26c3HjjjTJv3jyZPHmyNGzY0N89o8DxszA6XtYKGZ+ZM0VWrxZZssSbwvJ9S815W6hOREThzSz94Q9/kE8++US2bNki7du3l5deesnfPaPA8bMw2mnWCoFSUZFInz6Rn8kGNri+l7dHREQhbx1w2GGHyVtvvSWPPfaY9OzZU9q1ayc1asTexEcffeT1PlJAxJndX7WAbSKF0ZzOT0REgemz9M0330hxcbE0aNBAevToUS1YouylF0ajrgiBUXTAlGxhNKfzExFRuriKdJ566im57bbb5Oyzz5YVK1ZIkyZN/NszCiS9MNpsAVqnC9gmkrWCRo0itU04cbiMiIi8kqNpVoeeWOeee668//77MnHiRBkwYIBnO5BNqxZnE887eEfNhgO7dy2CKmS4OGONiIi8OH47zixVVFSoAu8CHImIHBZGpyJrZaQ3wOQUfyIiSmlmiawxs5T6rFVpqchll4ls3Wq+jV5MvmYNh+SIiCi54zeXO6HAQfCDk1Wg5EUDTCIiIh2DJQokthIgIqJUYbBEgcRWAkRElCoMliiQMLvOuG6bsWapsDCxBphERETR2FGSAtNeIHr7r78W2bXLfLtkG2ASERFFY7BEaYGeSWaNK636I5ltbwVrPD/5JNsGEBGRN0IzDFdaWio5OTmmpw8++MDyekVFRdW2v+mmm1K679lGby5pDHz0/ki43Mn2Vg48UKRHD+/2l4iIslto+izt2bNHthrmkt99992ycOFCWb16tQqCrIKlo446Su69996q8+rUqeOqXxL7LDmHobTWra0DH2N/pHjbWykp8b4pJhERhYvnHbwzXc2aNaVZs2ZVv+/du1fmz58vQ4YMsQyUooOj6OuSPxD4PPqofeAT3R8JwQ5+ug2UgC0DiIjIK6EZhjN68cUXZcuWLXLNNdfE3XbatGnSuHFj6dChg4wcOVJ27txpu/3u3btVNBp9InsYSkOGaPhwd8FOokEPWwYQEZFXQpNZMnr66aflnHPOibuWXd++faVVq1bSokULtfbdnXfeKStXrpRiY+FMlPHjx8vYsWN92Otw0muO3Az46sGO26BHH8ZjywAiIsqamqURI0bIgw8+aLvNF198IW3btq36fcOGDSoAmj17tlx66aWu7u+tt96Sbt26yapVq+Twww+3zCzhpENmqbCwkDVLJtzWHCHYadxY5JFHIn2UOncWwcuA4u9471R9tJUL6BIRUVbVLN12221y9dVX227Tpk2bmN+fffZZadSokVx00UWu769jx47qp12wVKtWLXWi+KJrjnKlQrrIYmkuG2WjNJfF0kUqJbYREgKizZtF+veP/I4sUZ8+IhMmRIIhu4AJ26K3EgMlIiLyUsYHS02aNFEnp5AoQ7A0YMAAOeCAA1zf38cff6x+NmfRiyf0mqNLpFgmyVAplP0ppvVSIENlkswT6+gGgdZDD4lccIHIu++K/PBDbHA0cKDIkUc6a2pJREQUymDJLQyjrVmzRq6//vpql5WVlakhtueff15OPfVU1VJg+vTpct5556lMFGqWhg8fLmeccYYcd9xxadn/sEEQg0BpjvRCKBtzWb6UqfN7yRx56+Cesn279e28/HLkJ+Lmfv0ifZQYHBERUSrkhrGwu3PnzjE1TNHtBFC8rc92Q7uBBQsWSPfu3dX2GPJDjdNLL72Uhj0Ppy6dK+SxvKEqUDK+2XL3BU9/zR0mP22vcHR7yCyhyzdaajFQIiKiVMj4Au8gYFNKG6WlIl27xt2sSEpkkTjrImlsXElEROTn8Tt0mSXKMA4bJaHo26noxpVERER+Y7BE/nJYKF/euHnV1H+n2KWbiIhSgcES+QtV2Bgzs4qEcH5hofR5PNJF0k3AxAmLRESUCgyWyF8oKkJFtlkkpP8+caL07J2nmkk2bBj/JvfFV+zSTUREKcFgifyHLpGIhNCSOxoyTlHtttEOoHZtZzeJ5pMs7iYiolQIXZ8lylAIiBANoSobxUYmXSRxEZY1iWfMGHbpJiKi1GGwREmt+2YT+1SHC4uKLG9n7lxn94uO3URERKnCYIkSUlwsMnRo7AK5GFVDeZKbrI/Z7cTDwm4iIkol1iyRawhwevWqHuBgCA3n4/JkbscKC7uJiCgdGCyRKxgyQybIrO+7ft6wYZHtEr0dM1ET51jYTUREKcVgiVxBbZFdJihHq5A260tl5ZgZkaVOLKKmeLdjZJg4R0RElDIMlsizrtmXSLGsldZSKl2l/f19I2vCtW5tOi7ntPv24MEiJSWRdeAYKBERUTowWCJXrIqrESjNkV6SL84KmZwWaV96aWQCHYfeiIgoXXI0zWnVCCW7anEYYFQNySLEQPo7J1cqVEYJgVKuVcERxtGQHtoX9ZjdTpyrEBERpeX4zcwSJb16SRdZLIVWgRIgGlq/PlKoZHM7OhZzExFRJmGwREmvXtJcHBYgGQqVHK6CQkRElFZsSkkJQSBTWSnSu7fIRnFYgGRSqORgFRQiIqK0Ys2SB7KpZkmn1xxh+v/+mqUyyRUWIBERUTCwZol8Fd0nqVLyZKhECpAqJbYASdN/ZwESEREFFIMlSoixT9I86Sm9ZI6USWwB0s5GLEAiIqJgY80SJcSsTxICpvnSQ82OQ9E3apnGzOoiRd2YUSIiouBisEQJQRE2ypCMfZIwJLdIiqrKlLoUmdc7saCbiIiCgsNwlJBE+yShkTcKw7ESSl/7FVGIiIgyAoMlSpjbPkkIiLDyyQZnK6IQERFlBLYO8EA2tg5wO6wW3WrADLsLEBFRph6/WbNESUNwg8VunbYaiLciSrzbIiIiSiUOw1FaWg0kux0REVGqMFiitLUaSGY7IiKiVOEwHLmWyNR/q1YDuqpWA118220iIqKEMLNEriQ69T/RVgNERETpxmCJHEt26r/bVgNERESZgK0DPJANrQO8nPrPDt5ERBSk43dgMkvjxo2Tzp07S506deSQQw4x3WbdunVy/vnnq22aNm0qd9xxh5SXl9ve7tatW6Vfv37qScLtXnfddfLzzz/79CiCy83Uf6etBvr0ifxkoERERJksMMHSnj17pHfv3nLzzTebXl5RUaECJWy3ZMkSee6552TKlClyzz332N4uAqUVK1bIm2++KS+//LK8/fbbcsMNN/j0KIKLU/+JiChbBW4YDgHQsGHDZNu2bTHnv/rqq3LBBRfIt99+K4ceeqg67+9//7vceeedsnnzZqlZs2a12/riiy+kffv28sEHH8jJJ5+sznvttdfkvPPOkw0bNkiLFi0c7VM2DMOVlkaKueMpKWFTSSIiCobQDcPFs3TpUjn22GOrAiU455xz1BOBzJHVdTD0pgdKcPbZZ0tubq689957lve1e/dudbvRp7DTp/4bZ7LpcH5hIaf+ExFR+IQmWNq0aVNMoAT677jM6jqobYpWo0YNadiwoeV1YPz48SoS1U+FiBJCBkXYyCbNmBH5CZz6T0RE2SitwdKIESMkJyfH9vTll19Kphk5cqRK2emn9ahsDmkvpf59K2RM11IZ2nSGNP6sVObMquDUfyIiyipp7eB92223ydVXX227TZs2bRzdVrNmzeT999+POe+7776ruszqOt9//33MeZg9hxlyVteBWrVqqVOYeymhku0SKZZJMlQKZYPIVhEZLbKzUYGs/fskWdy4J6f+ExFRVkhrsNSkSRN18kKnTp1UewEEP/rQGma4oWALRdxW10Gh+LJly+Skk05S57311ltSWVkpHTt2lGyDobehQ/cHSnOkF5oCxGxTe0uZ5FzWS4qQSurDVBIREYVfYGqW0EPp448/Vj/RJgD/x0nvidS9e3cVFF155ZWyfPlyef3112XUqFEyaNCgqiwQMk9t27aVMrScFpF27drJueeeKwMHDlSXvfPOOzJ48GC54oorHM+EC2MvpVypUBklBErGN0gugifET8OGRaIrIiKikAtMsIR+SSeccIKMHj1aBUj4P04ffvihujwvL0/1ScJPZIz69+8vAwYMkHvvvbfqNnbu3CkrV66UvXv3Vp03bdo0FUB169ZNtQw4/fTT5cknn5Rsg7hn4cLI/7vIYjX0ZvXmyBEXHSiJiIgCLnB9ljJR0PssoU4Jw296h+4rZIbMkL7xrzh9eqQNNxERUYiP32mtWaLMKujWbZTmzq6M6u44uA4cEREFXWCG4cjfgu5oi6WLrJcCqZTkOlBGtyDo2zfyE7/jfCIioqBgsJTFrBbHrZQ8GSqRDpTVAiaHHSj1jJXx9lFbj/MZMBERUVAwWMpidovezpOe0kvmSJm470BplbEC/TxOpiMioqBgzVIWi1dyhIBpvvSQZY8slt8c6rzoyCpjFR0w6ZPpuOguERFlOgZLWUxfHBdDY2ZZIIy45RfkybFDikTyvMlYJbIdERFROnEYLoshQeTH4rgOJsm52o6IiCidGCxlOZQeoQTJq8VxUYeEU8OG1ts4nExHRESUETgMRyog6tEj+X5IxuaWZpLJWBEREaUDgyVSELgkU2xt1tzSDDJWCJTcZqyIiIjShcESOWbVjduuVYCuUSORWbMiARkzSkREFCQMlijhITZkiVAgjvoku6E32LIlEiQxUCIioqBhsERxF3GzGmLTu3EjiHKCrQKIiCiIGCyRadrol4YFsmboJDlqRE/bbtwo2J42zdndsFUAEREFEVsHZDuLRdwO3Fom7Uf3khubFMftxr15s0iTJtV7NenYKoCIiIKMwVI2s6nMzpXIeWN3DJNcib+IW79+3je3JCIiygQMlrJZnEXcEDC1lPXSRRbHvSn0afKyuSUREVGmYM1SNnNYcd1crLdD5ggBkV4P7kVzSyIiokzCYCmbOay43ijm25kNsSXb3JKIiCjTcBgumyHtU1AgmphXZldKjqyTQlkskcrs+vVjL+cQGxERZQMGS9kMaSB0lcyJBEbR9N+HyUSplEjaaMeOyKy3YcNESkpE1qxhoEREROHHYCnb9ewpOXPmyK5GsZXZG6RAeskcmSex0dAPP0Tiq61bWYtERETZIUfT4i19SvHs2LFDDj74YNm+fbvUN45VBUVFhbw9brHMnrRRPtvaXA296Rklq6JuZJYYMBERUdiP38wsZWl7pdJSkRkzIj/xO6KeM+4pkknf95GLHymyDJQA4fX69ZFZb0RERGHH2XBZxm5BXNQfIVN06KHObotrvRERUTZgZimLWKxsUrUgLi53s4Yb13ojIqJswGApS9isbFJ1Hma5Ybt9HQW41hsRERGDpewRZ2WTmDokvaMAcK03IiLKdgyWsoTT+iJ9O9Qvca03IiIiFnhnjUTqkBAQca03IiLKdgyWsgSCHGSJUMxtRV8QNxrXeiMiomzHYbgsMX++yK5d9tv8+mtkOyIiIgpgsDRu3Djp3Lmz1KlTRw455JBqly9fvlz69OkjhYWFcuCBB0q7du1kkl6lbKN169aSk5MTc3rggQckjC0Dtmyx3w5LmES3ECAiIqIADcPt2bNHevfuLZ06dZKnn3662uXLli2Tpk2bytSpU1XAtGTJErnhhhskLy9PBg8ebHvb9957rwwcOLDq93r16kk2tAwwwjaY7YYWAqhVYm0SERFRgIKlsWPHqp9Tpkwxvfzaa6+N+b1NmzaydOlSKS4ujhssIThq1qyZZGPLALsWAqxVIiIiCtAwXCKwMF7Dhg3jbodht0aNGskJJ5wgDz30kJSXl9tuv3v3brX4XvQpUyW6JAmXMiEiIgpYZsktDMPNmjVLXnnlFdvtbrnlFjnxxBNVUIXrjBw5UjZu3CgPP/yw5XXGjx9flenKdIkuScKlTIiIiCJyNM1JNYs/RowYIQ8++KDtNl988YW0bdu26ncMww0bNky2bdtmeZ3PPvtMunbtKkOHDpVRo0a52qdnnnlGbrzxRvn555+lVq1alpklnHTILKFOCpms+vXrS6bVLLVuHWkZ4OSVRs0SWgisWcOaJSIiCjccvw8++OC4x++0ZpZuu+02ufrqq223Qe2RG59//rl069ZNFXe7DZSgY8eOahhu7dq1cvTRR5tugyDKKpDKNPrSJZjlhkDILmDiUiZEREQZFiw1adJEnbyyYsUKOeuss+Sqq65SrQYS8fHHH0tubq6aWRcW+tIlmBUXXeyNgAiZJx0ySgiUuJQJERFRAGuW1q1bJ1u3blU/KyoqVFADRxxxhNStW1cNvSFQOuecc+TWW2+VTZs2qcvROkAPyN5//30ZMGCALFy4UPLz89Vsuffee08N2WFGHH4fPny49O/fXxo0aCBhYrZ0SefOqO3iUiZEREShCJbuueceee6556p+x8w1KCkpkaKiIpkzZ45s3rxZ9VnCSdeqVSs1pAY7d+6UlStXyt69e9XvGEqbOXOmjBkzRtUgHXbYYSpYQrAVRmZLl7A9ABERUQYXeGdbgRgREREF7/gd6j5LRERERMlisERERERkg8ESERERkQ0GS0REREQ2GCwRERER2WCwRERERGSDwRIRERGRDQZLRERERGHo4J1tsGZb9NIkXIqEiIgoPRgsZaDi4uqL3mKR20mTnC9yy2CLiIjIGxyGy8BAqVev2EAJysoi5+NyJ7fRurVI164ifftGfuJ3J9clIiKiWAyWMgiyQcgoma3Wp583bFhkOytz5ohcemlywRYRERHtx2Apg2DYzBjkGAOm9etFxowRKS2tHjS98ILIFVdYXxcnBGN2wRYRERHFYrCUQVBf5MT991cfWsPPyy6LHwghGBs3Lvl9JSIiyhYMljIICrHd0IfWMPSGjJFTo0dzOI6IiMgpBksZBDPWMOstJ8fZ9nod0x/+YD98ZyZe7RMRERFFMFjKIJjaj/YA4CZg2rzZ/X2h9gk1UkRERGSPwVKGQR8lDKvl52dOjRQREVE2Y7CUoQHT2rUiJSUio0Y5u07jxs6zUYnWSBEREWUjBksZPCRXVBRpE2BXx4TzCwtFHn98/+/x6NdBjRQRERHZY7AU8Dom1CyhCWXDhiL33CPSoIH97em3MXEilz8hIiJyIkfTzPpFkxs7duyQgw8+WLZv3y7169dP2XpxCHasZrQhePrd70TeeSf2OsgoIVByusYcERFRth+/GSwFJFiKXhx3/vxIwOMkgzR7dqSeiQvqEqVPRUWF7N27N927QZR1DjjgAMmzOegxWAphsKQHTOjc7aSvEgIm1DutWcMAiSgd8PG6adMm2bZtW7p3hShrHXLIIdKsWTPJMallcXr8ruHzPlKK148zW0sO10GxOBGllh4oNW3aVOrUqWP6YU1E/n1Z2blzp3z//ffq9+ZJTAFnsBQwifRGYj8lovQMvemBUqNGjdK9O0RZ6cADD1Q/ETDhb9FuSM4OZ8MFTCKBMfspEaWeXqOEjBIRpY/+N5hM3SCDpRCvH8d+SkTpx6E3ouD/DTJYCun6ceynRERE5A0GSyFdPw7ZJ2zDfkpERETJYYF3QCEI6tEjMtMNBdxNm0bOR9E/+ykRERF5h8FSCNaPI6Jw0xvSpqq57NVXX61m8v3rX/+KOb+0tFS6du0qP/74o+pdQ5QtAjMMN27cOOncubOqarf6I0URl/E0c+ZM29vdunWr9OvXTzWjwu1ed9118vPPP/v0KIiI3C91hEa0XbuK9O0b+YnfcT4RpUZggqU9e/ZI79695eabb7bd7tlnn5WNGzdWnS6++GLb7REorVixQt588015+eWX5e2335YbbrjB470nInIPAVGvXtUb0ZaVRc5Pd8C0ZcsW6dOnj+Tn56svsscee6zMmDEjZht8aY3OUE2ZMiXmC+/q1aulR48ecuihh0rdunXllFNOkQULFlS7rzFjxlT7Mhz9+d66dWuZaLMOVFFRkQwbNqzabf7mN7+JyahZHTNw27iPaP/4xz+kXbt2Urt2bWnbtq08/vjjEg+yc8bHYUwA3HnnnXLUUUep57RNmzZy9913x0x7N+539O3q3eKNzzOsXbtWbfPxxx+bXicazsNl2Eb32Wefye9//3v1OuH1uvLKK+WHH36QbBCYYGns2LEyfPhw9cfopK25fsKb2MoXX3whr732mnrDd+zYUU4//XR59NFHVTbq22+/9eFREBE5H3rD4tlmC1Lp5+HYb7WYdirs2rVLTjrpJHnllVfUgRRfNHEAff/99x3fBjL55513nixcuFD++9//yrnnnisXXnihrFu3rlo35mOOOabqi/Bll10m6TRt2jS555571KgHjiV/+tOfVFDz3HPPObr+ypUr1eMwC/Dq1aungp3PP/9cJk2aJE899ZQ88sgjkk7btm2Ts846S0444QT58MMP1bHzu+++S/vrkCqhq1kaNGiQXH/99Soav+mmm+Saa66x7LGwdOlSFVydfPLJVeedffbZkpubK++9955ccsklptfbvXu3OkWvLUNElMqljfxezgiZdmQQjF3JoyGjdPvtt1f9PmTIEHn99ddl9uzZcuqpp6rz8IX1119/tbyf448/Xp109913n8ybN09efPFFGTx4cNX5yKygGzO+BAP+H/05nGqjR4+Wv/zlL9Jz35Tjww47TAU3kydPlquuusryevo+47k76KCD1LpkRqNGjar6P7JZeI7xJf6Pf/yjpMtjjz2mAiUEhbpnnnlGCgsL5auvvlKZsDALVbB07733qsgXqcs33nhD/vCHP6hvLbfccovluk1ofx6tRo0a0rBhQ3WZlfHjx6tMFxGRX5wuU+TXckYo5H7iiSdizsOXyP79+8cETzh4IjgqKytT5RIIBqK7lnfo0EHmzJkjvXr1UivAG+EzGsNKyE4h01JeXq6CK2NmCV9KEVzYwfAVAg3cf/v27VXW57e//W3V5Rgmw0iCDvuL7cyCROxry5YtZejQoXLttdfGbPPLL7+o4UPUuA4cOLDqfOy7WfBjHLrEccaus/usWbPkr3/9q7oPPD+4XeMir59++mlMMGsMZAGLw0Zvg+ycmYKCApVUaNy4sUoYTJgwodo2y5cvl5KSkmoBNGA/GSz5aMSIEfLggw/aboP0JsaCnUAKVIcIGG/ohx56yDJYStTIkSPl1ltvjfkjRnRNROQVp8sU+bWcEQKTI444Iua8DYZUFz5fMUyEoSSUSOA6qAtCEKLDZagDwmU1a9ZUB/7o8ghkTVAzigM07g8ZIwRW0bcBKI1o0aKF7T7fcccdqu5I/+zHcB6++OJ+9RrVu+66q2p7BCSoUzULEpHJ+ve//61GKozlH/okIAyPoYQjWry1x/73v/9Jq1atbEc8sJ/4Qn7OOeeo4AtZJWSxoh199NEq+2YVyOrDeR999FHV7whoUbtltHjxYrUtaprwePEc3X///dUe84UXXmh6zE5mgdqgSGuwdNttt6k3th0MpyUKb2KkdPFNp1atWtUuRzpXX41Yhz9kzJDTU71mcFtmt0dE5PXSRijmNksI4FiLy9O5nNE777yjirP1g3RlZaUakonO1iCzg4AFmSJkP4qLi2OGcnAbOA7oZQ84KOOgHQ23i4M+yizsIDOiB3j4Uou6Ityvfh4Cj+gAEKMIdkEiircfeOABlVWJhuJmBG4IfBDYuLFo0SLpYvOiLVmyRAVT0UHdN998U207BIDRj8UYyAJKSqK3QUbLDIYQUZKCbTGRCgGb0Yknnihz585Vw4JWtxNmaX3ETZo0USe/oOK/QYMGloFNp06dVNHasmXLVJEivPXWW+oP0/htgYgoHUsbYdYbAqPogClTljM68sgj1RAbDvD4rH344YdV0a9xaAvZFhyQwVj6gNtAAIWsBbItGCHAZ7Bu/fr1apgOX2wvv/xy2/3Bl10UnSOzhHoaBEdus/64b9yGnlnCsBmGEt99992Y7ZD5wagF7gNF6fhSjsJn9KCKHnnQIVP20ksvqWMMgg691ANDZRge27x5szoe4vlAgIdsEmYGYngSNVx+wr7jMSNIffXVV9VkJ6NBgwapTBpmP6J2CoHmqlWr1H5iaDNeRi3oAjMbDm8eBD/6txP8Hyc9HYo3IV4wzMjAC4g0Kr69oOBQhxkaGNJDKlL/1oA3OcaccRm+4aCg8Iorroib7iUiStfSRpmynBHqg5BxwHARhneQkY/XrsUIARYCLfTRQ8CE28Jt6jDMh8901KGihijeMByG8RAgYWgJQYbbUQAcS3AbCAbw+DBD+rTTTqu2HYarcMxBuxoM05155plqBpseFBohoMTwIoIxZNEwdIUThi1RyoHACC666CI18xvHIrQHwPWiS0z8gNcNjxkZLxTboy7XqEWLFuoYieNv9+7d1WPGviMjhQxW6GkBcdVVV+F7VbVTSUmJuvzVV1/VfvOb32h169bVDjroIO3444/X/v73v2sVFRVVt4FtcZ01a9ZUnbdlyxatT58+6nr169fXrrnmGu2nn35ytW/bt29Xt4ufXisvx35r2vTpkZ/4nYgy36+//qp9/vnn6mey+DkQfDj+nHnmmaaX/fjjj1qrVq1Svk/Z4lebv0Wnx+/ADDwiYsfJCjJEONnBNx/jbAB8e5g+fbpkIjScQ5+V6KFofKNEaj7d3yiJKHW4tFHwocbIrEYKkJnxsySFkpcFubNgyvTOvURE5ByGGVGbZQZtAT744IOU7xM5x2ApAwWhcy8REVG2YLAU8M69RERE5K/A1CxlE6cdeefOjfxEy46Qz9okIiJKG2aWMpDTZqiPPYZus1g7iDVMREREfmGwlMGdey264VfDom8iIiL/MFjK4M694CRgYtE3ERGRfxgsBaxzrxUWfRMREfmDwVKGB0xYT7KkRGTwYG+Lw4mIrGBhW6zTZnXCmppE2YTBUkA69156qbfF4UQUIBhfLy0VmTEj8jMF4+1YEWHjxo0xJywAS5SNGCyFpOgb52NxbWxHRCGCmRuY8oqpr337pmwKLBagxQKr0Sfjch1YggoLqf7rX/+SI488UmrXrq0Wwl2PmoAo8+fPV4vj4vI2bdrI2LFjpby8PGabMWPGVMtgGRflxUKuWLaqTp06avFd3NePP/6oLsP5WNhVh0VusW8fffSR+h0LwF533XVqoVssGnv00UerRXqjjRgxQi0Yi6VJ8vPz5c4771QL3zq9PjJyxn3Wn6Pox4kFcqOVlpbGZOyM14mGBeSx7VoMO+zzn//8Ry2Cqy8ifMstt8gvv/wi8ZhlDXH7un/+859y8sknS7169dTr37dvX/n+++8t9zv6dvGeAOyn8XahdevWMnHiRNPrGOH5wvOmw/1hIWMsEYPu52eddZYsX75c/MRgKSDfKPNmz5BpA0slV6uoFjDpv+N9x35LRCESgHWPdu7cKePGjZPnn39eBTM4kF1xxRVVly9evFgGDBggQ4cOlc8//1wmT56sggFcx+iYY46pymJddtllMZfhYNutWzdp3769LF26VAUIF154oQpijGbPni3Dhw+XF198UQVpgKCnoKBAXnjhBbUf99xzj/zf//2f2lbXvXt3efnll2XVqlUq2HryySdl6tSpjq+fDqtXr1ZZwEsvvVQ++eQTmTVrlnpuBjus3Xj22WfV8/3+++9Xu2zv3r1y3333qUAEgQwCHwSE6da7d28VtL366quybNky9RrjvbF161bf7pNNKTOZYSXdM0RkR6MCGSqT5B9b9q+ki4wTAiUurkuURese4VsSMik9eqT1WxIOqI899ph07NhR/f7cc89Ju3bt1MH31FNPVVkkZGyuuuoqdTkySzgA//GPf5TRo0dX3c7u3btVZgQZDMD/cZ7uz3/+s8pyPP744zHBlREOoNdcc40Kas44A5+aEQcccIDaFx0yRAi6EOzogRkyFDoEYdgHPRhzcv10GD9+vPTr168qq4YM31//+lc588wz5YknnlDZPDP6c4vsDJ7zXbt2Vdvm2muvrfp/mzZt1O2ecsop8vPPP0vdunUlHRAI4r2FYAnZT5gwYYIK5ubMmSM33HCDL/fLYCnTv1EaPijrbC2TJ6WXDB07Rz49sqeqUWIHb6IsX/cIhY1pUqNGDXUA1bVt21YNIX3xxRcqWEJWAhmn6EwSAhAcnJGVwpAabNmyRQ2pWEFmCRkFOziIIhuEA7kevEX729/+Js8884ysW7dOfv31V9mzZ0+1IbE//elPcv/996vLkZ1BVszN9ZGZig4kMNxoDFg+/fTTmG3MsmPbt29X2+Tm5sqhhx4qPXr0UIGREZ5fZJSmTZtWdZ6maSoTtmbNGhW4msHzDXbPObI2GP5avny5Gu7UhyTx+JHh0yHj5mQhYTwWHV57oz59+kheXp4a9kO26KGHHoq5H/3xIlhr1KhRzPl4PZBl8wuDpQB+o8TYbod/DJMOa9L7jZKIfOR0amuGT4HFgQ0ZmZ4mqe/oIOJ///ufytZYQZYnHmR6kE1BhgGBzgwUxO8zc+ZMuf322+Uvf/mLdOrUSR2QcTB+7733Ym7jpptuUvuKQAHZGvy/a9eujq+PbbEPuuLiYhWARUO9E4YIdbiN/v37x2yD20e9FQIfDPshM4cM0Nlnn13t+b3xxhtVnZJRy5YtLZ8rPN9g9Zyj5gk1YThNmzZNZaAQJOF3BInRMNSK/dUhu2WE4cHowA01ZkaPPPKIenwYysUQJzJ2n332WbXH27x5c1UvZWRV5+UFBkuZKCDfKInIR06ntqZ5CiwyJx9++KHKIsHKlSvVwU4/MCJDgPOOOOIIy9tAlglZoSuvvNJym+OOO04WLlwYMxRmhOsj2Pn9738vHTp0kHnz5skll1yiLkN2C9mNP/zhD1Xbm2UiUMSOEzJkCLowAxABkNPrH3TQQTGPtWnTptW2QQF59DYbTD7vkYXRt0Hw8bvf/U5l14zBEp5fBFN2z6+ZRYsWqSJrq6zQl19+qbJPDzzwgCoaB7zOZhBwxQtUcBvR+4iMpBGCQX0b1LihJg3DvMbHu2nTJnV97H+qsMA7E4XkGyURhX8KLGp5hgwZorIjyMagAPi0006rCp5QCI3ibwQ5K1asUMNzyNKMGjWqKlOAbeD0009XB0KcMKyCuhoMR8HIkSPlgw8+UMEKhp1wMEcG54cffqjaF322XqtWrVTW5+abb64abkLAgYP966+/Ll999ZXcfffd6vaioR4K+4hCZhR2v/nmm3LCCSc4vr7XEETiecDzilodBIBGmLG3ZMkSlUlDMPX111+r2Yd2Bd7YDkOKGNbUn+/Nmzery/B8YVgQWSkEdY8++qjKQiEThlozPyEwwmPG/uD5P+qoo9T7KxqCRWT2MOvwjTfeUK8VHv9dd91lGcx5gcFSJgrIN0oiStO6Rxk0BRY1RzhgY1r5b3/7W1VngyEXHYZtUMeDAxtqmxBIYbgFAY1enIvA5qefflJZBQyx4ITC6ddee01lGAAHTtwGalYQiOGAiaDALEMBGJpCcIFATv8dQ2qXX365qmdCUBCdJYJXXnlFDQ8hq4TgDkNBepGzk+t7CUEihh6RqbrgggtUhuzWW281zbghS4QADu0DENwh+EQLBCvYBjPg8Lzrz7ce3CIYQesHDLth1iIK5du3b68yTHit/IRhNzxmvNbYv+j3kQ5lKP/+979V8T4K+bEtZl9+8803qrbLLzkaBkQpKTt27JCDDz5YvbntiuVc1SwhvYjpwWYvDz4o8Y1zzZq0f1ASkTl8Q0aBLYYorGYkJTIrVkFGKQOmwOJgirqeZDp66/1zovvo6DDDCSfcD3kHAYfVoR8F63jOUznElc6/RafHb9YsZfI3SsyGQ2AU/abOoG+URJQCCIjQHgA1ihh6D9kUWLsp6Diw4UBG3rLLwDRu3FjNSKNYDJYyfSVd4zdKNlUiyt51j0IIM8ysoNkiTuQt1ARZWbBgQUr3JSg4DJeJw3DGIbmQfqMkCjPPhuGIKCkchssGIf5GSUREFAScDUdE5CMm74mC/zfIYImIyAd6fxizZR2IKHX0v0FjzyY3OAxHROQDzChCV2Ms+Kn3I8KUbSJKXUYJgRL+BvG3mMwsPwZLREQ+wfINoAdMRJR6CJT0v8VEMVgiIvIJMknojoz1wYxrXBGR/zD05kXfKAZLREQ+w4c1G/0RBRcLvImIiIhsMFgiIiIissFgiYiIiMgGa5Y8bHiFtulEREQUDPpxO17jSgZLHvjpp5/Uz8LCwnTvChERESVwHMcacVa4kK4HKisr5dtvv5V69ep52nQOES8CsPXr13u/QG+GCPtj5OMLvrA/Rj6+4Av7Y9zh4+NDCIRAqUWLFpKba12ZxMySB/AEFxQU+Hb7eHOE8Q8gmx4jH1/whf0x8vEFX9gfY32fHp9dRknHAm8iIiIiGwyWiIiIiGwwWMpgtWrVktGjR6ufYRX2x8jHF3xhf4x8fMEX9sdYKwMeHwu8iYiIiGwws0RERERkg8ESERERkQ0GS0REREQ2GCwRERER2WCwlIHefvttufDCC1VHUXQE/9e//iVhMn78eDnllFNUx/OmTZvKxRdfLCtXrpQweeKJJ+S4446raqLWqVMnefXVVyWsHnjgAfVeHTZsWLp3xRNjxoxRjyf61LZtWwmbsrIy6d+/vzRq1EgOPPBAOfbYY+XDDz+UMGjdunW11xCnQYMGSRhUVFTI3XffLYcddph67Q4//HC577774q5xFjQ//fST+lxp1aqVepydO3eWDz74IOX7wQ7eGeiXX36R448/Xq699lrp2bOnhM2iRYvUBxYCpvLycvm///s/6d69u3z++edy0EEHSRigozsCiCOPPFJ9eD333HPSo0cP+e9//yvHHHOMhAk+uCZPnqyCwzDB67RgwYKq32vUCNfH5Y8//ii//e1vpWvXriqQb9KkiXz99dfSoEEDCcv7EgGF7rPPPpPf/e530rt3bwmDBx98UH0pw2cL3qsIcq+55hrVjfqWW26RsLj++uvVa/fPf/5TJRCmTp0qZ599tjpe5Ofnp25H0DqAMhdeonnz5mlh9v3336vHuWjRIi3MGjRooP3jH//QwuSnn37SjjzySO3NN9/UzjzzTG3o0KFaGIwePVo7/vjjtTC78847tdNPP13LFnhvHn744VplZaUWBueff7527bXXxpzXs2dPrV+/flpY7Ny5U8vLy9NefvnlmPNPPPFE7a677krpvnAYjtJu+/bt6mfDhg0ljPDtdubMmSpjiOG4MEGG8Pzzz1ff9MIGWRZ8k23Tpo3069dP1q1bJ2Hy4osvysknn6wyLRgOP+GEE+Spp56SMNqzZ4/KSCBb7+Vi5+mE4aiFCxfKV199pX5fvny5/Oc//5Hf//73Ehbl5eXq87N27dox52M4Do81lcKVV6bAqaysVOPRGA7o0KGDhMmnn36qgqNdu3ZJ3bp1Zd68edK+fXsJCwSAH330UVrqB/zWsWNHmTJlihx99NGyceNGGTt2rHTp0kUNB6DWLgz+97//qWGcW2+9VQ2F43XE8E3NmjXlqquukjBB3ee2bdvk6quvlrAYMWKE7NixQ9XS5eXlqaBi3LhxKrAPi3r16qnPUNRitWvXTg499FCZMWOGLF26VI444ojU7kxK81jkWtiH4W666SatVatW2vr167Ww2b17t/b1119rH374oTZixAitcePG2ooVK7QwWLdunda0aVNt+fLlVeeFaRjO6Mcff9Tq168fqmHUAw44QOvUqVPMeUOGDNFOO+00LWy6d++uXXDBBVqYzJgxQysoKFA/P/nkE+3555/XGjZsqE2ZMkULk1WrVmlnnHGGOhZiSO6UU05RQ41t27ZN6X4ws0RpM3jwYHn55ZfV7D8URIcNvqHr335OOukk9c190qRJqhg66JYtWybff/+9nHjiiVXn4ZstXsvHHntMdu/erb7thsUhhxwiRx11lKxatUrConnz5tUynfj2PnfuXAmTb775RhXqFxcXS5jccccdKrt0xRVXqN8xkxGPFbONw5QZPPzww9WkIJQxIJOG9+3ll1+uhsdTiTVLlHJImCFQwrDUW2+9paa+ZsuQI4KIMOjWrZsaZvz444+rTqh/wRAA/h+mQAl+/vlnWb16tfqgDgsMfRtbdqD+BVO0w+TZZ59VNVmorQuTnTt3Sm5u7CEcf3f4nAmjgw46SP39YRbn66+/rmYXpxIzSxn6wRz9DXbNmjXqAIQC6JYtW0oYioKnT58u8+fPV2PSmzZtUudjyisK98Jg5MiRqtASrxf6hODxlpaWqj/yMMDrZqwxw4cZ+vWEofbs9ttvV73OEDh8++23asVzHIj69OkjYTF8+HBVJPynP/1JLrvsMnn//fflySefVKewQOCAYAmZlrC1fsD7EzVK+IxB6wC0JXn44YdVEXuYvP766+oLNuoHcVxERg11WmiTkFIpHfQjR0pKStT4rPF01VVXaWFg9thwevbZZ7WwwJRe1GLVrFlTa9KkidatWzftjTfe0MIsTDVLl19+uda8eXP1+uXn56vfUTsRNi+99JLWoUMHrVatWqoG5Mknn9TC5PXXX1efLStXrtTCZseOHervrWXLllrt2rW1Nm3aqOn0qJUMk1mzZqnHhr/FZs2aaYMGDdK2bduW8v3IwT+pDc+IiIiIgoM1S0REREQ2GCwRERER2WCwRERERGSDwRIRERGRDQZLRERERDYYLBERERHZYLBEREREZIPBEhEREZENBktERERENhgsERFFqaioUGum9ezZM+b87du3S2Fhodx1111p2zciSg8ud0JEZPDVV1/Jb37zG3nqqaekX79+6rwBAwbI8uXL5YMPPpCaNWumexeJKIUYLBERmfjrX/8qY8aMkRUrVsj7778vvXv3VoHS8ccfn+5dI6IUY7BERGQCH41nnXWW5OXlyaeffipDhgyRUaNGpXu3iCgNGCwREVn48ssvpV27dnLsscfKRx99JDVq1Ej3LhFRGrDAm4jIwjPPPCN16tSRNWvWyIYNG9K9O0SUJswsERGZWLJkiZx55pnyxhtvyP3336/OW7BggeTk5KR714goxZhZIiIy2Llzp1x99dVy8803S9euXeXpp59WRd5///vf071rRJQGzCwRERkMHTpU/v3vf6tWARiGg8mTJ8vtt9+uir1bt26d7l0kohRisEREFGXRokXSrVs3KS0tldNPPz3msnPOOUfKy8s5HEeUZRgsEREREdlgzRIRERGRDQZLRERERDYYLBERERHZYLBEREREZIPBEhEREZENBktERERENhgsEREREdlgsERERERkg8ESERERkQ0GS0REREQ2GCwRERERibX/B3Lrjc8k3PQWAAAAAElFTkSuQmCC",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(dataset[0], dataset[1], color='blue', label='Начальные данные')\n",
+ "plt.scatter(X_test, test_predict_regression, color='red', label='Предсказанные данные')\n",
+ "plt.xlabel('X')\n",
+ "plt.ylabel('Y')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Метрика: MSE MAE\n",
+ "Train 1.3371825755537106 0.9272723524863202\n",
+ "Test 1.251725088637151 0.9121469428966027\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Расчет метрик\n",
+ "train_mse_regression = mean_squared_error(y_train, train_predict_regression)\n",
+ "train_mae_regression = mean_absolute_error(y_train, train_predict_regression)\n",
+ "\n",
+ "test_mse_regression = mean_squared_error(y_test, test_predict_regression)\n",
+ "test_mae_regression = mean_absolute_error(y_test, test_predict_regression)\n",
+ "\n",
+ "\n",
+ "\n",
+ "print('Метрика: MSE MAE')\n",
+ "print('Train', train_mse_regression, train_mae_regression)\n",
+ "print('Test', test_mse_regression, test_mae_regression)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Вывод:\n",
+ "Как видно из диаграммы рассеяния, этот подход лучше работает с предоставленными данными.\n",
+ "Ошибки уменьшились в несколько раз, что показывает значительное улучшение качества модели. Учет экспоненциального признака и подбор оптимального параметра k позволяют построить насыщающую кривую, которая гораздо точнее описывает данные."
+ ]
+ }
+ ],
+ "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": "3.10.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}