diff --git a/denysyefimov_code/task1_titanic_houseprice/denysyefimov_houseprices.ipynb b/denysyefimov_code/task1_titanic_houseprice/denysyefimov_houseprices.ipynb
new file mode 100644
index 0000000..3205953
--- /dev/null
+++ b/denysyefimov_code/task1_titanic_houseprice/denysyefimov_houseprices.ipynb
@@ -0,0 +1,1510 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.018319,
+ "end_time": "2021-01-28T15:32:29.942463",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:29.924144",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**First, I import all the libraries and data for work.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+ "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:29.978113Z",
+ "iopub.status.busy": "2021-01-28T15:32:29.977549Z",
+ "iopub.status.idle": "2021-01-28T15:32:31.063348Z",
+ "shell.execute_reply": "2021-01-28T15:32:31.062663Z"
+ },
+ "papermill": {
+ "duration": 1.104771,
+ "end_time": "2021-01-28T15:32:31.063528",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:29.958757",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/kaggle/input/house-prices-advanced-regression-techniques/sample_submission.csv\n",
+ "/kaggle/input/house-prices-advanced-regression-techniques/data_description.txt\n",
+ "/kaggle/input/house-prices-advanced-regression-techniques/train.csv\n",
+ "/kaggle/input/house-prices-advanced-regression-techniques/test.csv\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " Id \n",
+ " MSSubClass \n",
+ " MSZoning \n",
+ " LotFrontage \n",
+ " LotArea \n",
+ " Street \n",
+ " Alley \n",
+ " LotShape \n",
+ " LandContour \n",
+ " Utilities \n",
+ " ... \n",
+ " ScreenPorch \n",
+ " PoolArea \n",
+ " PoolQC \n",
+ " Fence \n",
+ " MiscFeature \n",
+ " MiscVal \n",
+ " MoSold \n",
+ " YrSold \n",
+ " SaleType \n",
+ " SaleCondition \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 1461 \n",
+ " 20 \n",
+ " RH \n",
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+ " NaN \n",
+ " Reg \n",
+ " Lvl \n",
+ " AllPub \n",
+ " ... \n",
+ " 120 \n",
+ " 0 \n",
+ " NaN \n",
+ " MnPrv \n",
+ " NaN \n",
+ " 0 \n",
+ " 6 \n",
+ " 2010 \n",
+ " WD \n",
+ " Normal \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 1462 \n",
+ " 20 \n",
+ " RL \n",
+ " 81.0 \n",
+ " 14267 \n",
+ " Pave \n",
+ " NaN \n",
+ " IR1 \n",
+ " Lvl \n",
+ " AllPub \n",
+ " ... \n",
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+ " IR1 \n",
+ " Lvl \n",
+ " AllPub \n",
+ " ... \n",
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+ " 78.0 \n",
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+ " IR1 \n",
+ " Lvl \n",
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+ " \n",
+ " \n",
+ "
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+ "
5 rows × 80 columns
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+ "
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+ ],
+ "text/plain": [
+ " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n",
+ "0 1461 20 RH 80.0 11622 Pave NaN Reg \n",
+ "1 1462 20 RL 81.0 14267 Pave NaN IR1 \n",
+ "2 1463 60 RL 74.0 13830 Pave NaN IR1 \n",
+ "3 1464 60 RL 78.0 9978 Pave NaN IR1 \n",
+ "4 1465 120 RL 43.0 5005 Pave NaN IR1 \n",
+ "\n",
+ " LandContour Utilities ... ScreenPorch PoolArea PoolQC Fence MiscFeature \\\n",
+ "0 Lvl AllPub ... 120 0 NaN MnPrv NaN \n",
+ "1 Lvl AllPub ... 0 0 NaN NaN Gar2 \n",
+ "2 Lvl AllPub ... 0 0 NaN MnPrv NaN \n",
+ "3 Lvl AllPub ... 0 0 NaN NaN NaN \n",
+ "4 HLS AllPub ... 144 0 NaN NaN NaN \n",
+ "\n",
+ " MiscVal MoSold YrSold SaleType SaleCondition \n",
+ "0 0 6 2010 WD Normal \n",
+ "1 12500 6 2010 WD Normal \n",
+ "2 0 3 2010 WD Normal \n",
+ "3 0 6 2010 WD Normal \n",
+ "4 0 1 2010 WD Normal \n",
+ "\n",
+ "[5 rows x 80 columns]"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn import linear_model\n",
+ "from sklearn.metrics import mean_squared_error\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "\n",
+ "import os\n",
+ "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+ " for filename in filenames:\n",
+ " print(os.path.join(dirname, filename))\n",
+ " \n",
+ " \n",
+ "def encode(x):\n",
+ " return 1 if x == 'Partial' else 0\n",
+ "\n",
+ "def encode1(x):\n",
+ " if (x == 4):\n",
+ " return 3\n",
+ " else:\n",
+ " return x\n",
+ " \n",
+ "train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')\n",
+ "train.head()\n",
+ "test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')\n",
+ "test.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.016814,
+ "end_time": "2021-01-28T15:32:31.097770",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:31.080956",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Now let's check the deviations. In order for the statistical approaches that I will use to work. I will also reduce these deviations.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:31.158571Z",
+ "iopub.status.busy": "2021-01-28T15:32:31.157935Z",
+ "iopub.status.idle": "2021-01-28T15:32:31.384590Z",
+ "shell.execute_reply": "2021-01-28T15:32:31.385122Z"
+ },
+ "papermill": {
+ "duration": 0.270032,
+ "end_time": "2021-01-28T15:32:31.385291",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:31.115259",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.hist(train.SalePrice, color='black')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.017644,
+ "end_time": "2021-01-28T15:32:31.421592",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:31.403948",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**It turns out that these deviations are too large. I will reduce them.**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.01955,
+ "end_time": "2021-01-28T15:32:31.459635",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:31.440085",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**I will take the logarithm of my data and get a graph that is more similar to what I need.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:31.498643Z",
+ "iopub.status.busy": "2021-01-28T15:32:31.498048Z",
+ "iopub.status.idle": "2021-01-28T15:32:31.633225Z",
+ "shell.execute_reply": "2021-01-28T15:32:31.632734Z"
+ },
+ "papermill": {
+ "duration": 0.155574,
+ "end_time": "2021-01-28T15:32:31.633369",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:31.477795",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Deviation: 0 12.247694\n",
+ "1 12.109011\n",
+ "2 12.317167\n",
+ "3 11.849398\n",
+ "4 12.429216\n",
+ " ... \n",
+ "1455 12.072541\n",
+ "1456 12.254863\n",
+ "1457 12.493130\n",
+ "1458 11.864462\n",
+ "1459 11.901583\n",
+ "Name: SalePrice, Length: 1460, dtype: float64\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "deviation = np.log(train.SalePrice)\n",
+ "print(\"Deviation: \", deviation)\n",
+ "plt.hist(deviation, color='black')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.020019,
+ "end_time": "2021-01-28T15:32:31.673254",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:31.653235",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Now let's look at what influences the price of the house itself and analyze the data.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:31.723225Z",
+ "iopub.status.busy": "2021-01-28T15:32:31.721222Z",
+ "iopub.status.idle": "2021-01-28T15:32:31.735762Z",
+ "shell.execute_reply": "2021-01-28T15:32:31.735273Z"
+ },
+ "papermill": {
+ "duration": 0.042427,
+ "end_time": "2021-01-28T15:32:31.735916",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:31.693489",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "SalePrice 1.000000\n",
+ "OverallQual 0.790982\n",
+ "GrLivArea 0.708624\n",
+ "GarageCars 0.640409\n",
+ "GarageArea 0.623431\n",
+ "Name: SalePrice, dtype: float64 \n",
+ "\n",
+ "YrSold -0.028923\n",
+ "OverallCond -0.077856\n",
+ "MSSubClass -0.084284\n",
+ "EnclosedPorch -0.128578\n",
+ "KitchenAbvGr -0.135907\n",
+ "Name: SalePrice, dtype: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "influences = train.select_dtypes(include=[np.number])\n",
+ "influences.dtypes\n",
+ "percent = influences.corr()\n",
+ "print (percent['SalePrice'].sort_values(ascending=False)[:5], '\\n')\n",
+ "print (percent['SalePrice'].sort_values(ascending=False)[-5:])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.019309,
+ "end_time": "2021-01-28T15:32:31.775203",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:31.755894",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Thus, we got that the price is most affected by: OverallQual, GrLivArea, GarageCars, GarageArea, YrSold, OverallCond, MSSubClass, EnclosedPorch, KitchenAbvGr.**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.01934,
+ "end_time": "2021-01-28T15:32:31.814930",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:31.795590",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Let's chek the more datails**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:31.874022Z",
+ "iopub.status.busy": "2021-01-28T15:32:31.873365Z",
+ "iopub.status.idle": "2021-01-28T15:32:32.085718Z",
+ "shell.execute_reply": "2021-01-28T15:32:32.086253Z"
+ },
+ "papermill": {
+ "duration": 0.251297,
+ "end_time": "2021-01-28T15:32:32.086433",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:31.835136",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " SalePrice\n",
+ "OverallQual \n",
+ "1 50150\n",
+ "2 60000\n",
+ "3 86250\n",
+ "4 108000\n",
+ "5 133000\n",
+ "6 160000\n",
+ "7 200141\n",
+ "8 269750\n",
+ "9 345000\n",
+ "10 432390\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "quality_pivot = train.pivot_table(index='OverallQual',\n",
+ " values='SalePrice', aggfunc=np.median)\n",
+ "print(quality_pivot)\n",
+ "quality_pivot.plot(kind='bar', color='black')\n",
+ "plt.xlabel('Overall Quality')\n",
+ "plt.ylabel('Median Sale Price')\n",
+ "plt.xticks(rotation=0)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.022143,
+ "end_time": "2021-01-28T15:32:32.130957",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:32.108814",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Now let's see the rest of the factors that affect the price.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:32.180010Z",
+ "iopub.status.busy": "2021-01-28T15:32:32.179030Z",
+ "iopub.status.idle": "2021-01-28T15:32:32.333141Z",
+ "shell.execute_reply": "2021-01-28T15:32:32.333622Z"
+ },
+ "papermill": {
+ "duration": 0.18013,
+ "end_time": "2021-01-28T15:32:32.333814",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:32.153684",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(x=train['GrLivArea'], y=deviation, color=\"black\")\n",
+ "plt.ylabel('Sale Price')\n",
+ "plt.xlabel('Above grade (ground) living area square feet')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:32.402518Z",
+ "iopub.status.busy": "2021-01-28T15:32:32.401954Z",
+ "iopub.status.idle": "2021-01-28T15:32:32.531501Z",
+ "shell.execute_reply": "2021-01-28T15:32:32.530996Z"
+ },
+ "papermill": {
+ "duration": 0.175873,
+ "end_time": "2021-01-28T15:32:32.531631",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:32.355758",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(x=train['GarageArea'], y=deviation, color=\"black\")\n",
+ "plt.ylabel('Sale Price')\n",
+ "plt.xlabel('Garage Area')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.021909,
+ "end_time": "2021-01-28T15:32:32.576544",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:32.554635",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Thus, we got the graphs and now it would be worth removing the points that could shift our line from the \"center of events\".**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.022023,
+ "end_time": "2021-01-28T15:32:32.621015",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:32.598992",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Let's set the maximum value of the garage area to 1200, so that the line doesn't run away from us.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:32.694297Z",
+ "iopub.status.busy": "2021-01-28T15:32:32.690842Z",
+ "iopub.status.idle": "2021-01-28T15:32:32.981289Z",
+ "shell.execute_reply": "2021-01-28T15:32:32.980736Z"
+ },
+ "papermill": {
+ "duration": 0.337574,
+ "end_time": "2021-01-28T15:32:32.981426",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:32.643852",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "plt.scatter(x=train['GarageArea'], y=np.log(train.SalePrice), color=\"black\")\n",
+ "plt.xlim(-1,1600)\n",
+ "plt.ylabel('Sale Price')\n",
+ "plt.xlabel('Garage Area')\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "plt.scatter(x=train['GrLivArea'], y=deviation, color=\"black\")\n",
+ "plt.ylabel('Sale Price')\n",
+ "plt.xlabel('GrLivArea')\n",
+ "plt.show()\n",
+ "\n",
+ "train = train[train['GrLivArea'] < 4000]\n",
+ "train = train[train['GarageArea'] < 1200]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.02521,
+ "end_time": "2021-01-28T15:32:33.031977",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:33.006767",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**After that we have**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:33.103945Z",
+ "iopub.status.busy": "2021-01-28T15:32:33.102226Z",
+ "iopub.status.idle": "2021-01-28T15:32:33.245725Z",
+ "shell.execute_reply": "2021-01-28T15:32:33.245212Z"
+ },
+ "papermill": {
+ "duration": 0.18919,
+ "end_time": "2021-01-28T15:32:33.245886",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:33.056696",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(x=train['GarageArea'], y=np.log(train.SalePrice), color=\"black\")\n",
+ "plt.xlim(-1,1600)\n",
+ "plt.ylabel('Sale Price')\n",
+ "plt.xlabel('Garage Area')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.026994,
+ "end_time": "2021-01-28T15:32:33.300457",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:33.273463",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Now let's check all NULL**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:33.366169Z",
+ "iopub.status.busy": "2021-01-28T15:32:33.365542Z",
+ "iopub.status.idle": "2021-01-28T15:32:33.373600Z",
+ "shell.execute_reply": "2021-01-28T15:32:33.374537Z"
+ },
+ "papermill": {
+ "duration": 0.047603,
+ "end_time": "2021-01-28T15:32:33.374845",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:33.327242",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 0\n",
+ "PoolQC 1447\n",
+ "MiscFeature 1399\n",
+ "Alley 1361\n",
+ "Fence 1172\n",
+ "FireplaceQu 689\n",
+ "LotFrontage 258\n",
+ "GarageYrBlt 81\n",
+ "GarageCond 81\n",
+ "GarageType 81\n",
+ "GarageFinish 81\n",
+ "GarageQual 81\n",
+ "BsmtFinType2 38\n",
+ "BsmtExposure 38\n",
+ "BsmtQual 37\n",
+ "BsmtCond 37\n",
+ "BsmtFinType1 37\n",
+ "MasVnrArea 8\n",
+ "MasVnrType 8\n",
+ "Electrical 1\n",
+ "Id 0\n",
+ "Functional 0\n",
+ "Fireplaces 0\n",
+ "KitchenQual 0\n",
+ "KitchenAbvGr 0\n",
+ "BedroomAbvGr 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "nulls = pd.DataFrame(train.isnull().sum().sort_values(ascending=False)[:25])\n",
+ "print(nulls)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.026107,
+ "end_time": "2021-01-28T15:32:33.428700",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:33.402593",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**We got a table with NULLs**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.027084,
+ "end_time": "2021-01-28T15:32:33.482173",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:33.455089",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Now let's chek data without numbers(non-numeric)**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:33.551707Z",
+ "iopub.status.busy": "2021-01-28T15:32:33.548343Z",
+ "iopub.status.idle": "2021-01-28T15:32:33.649679Z",
+ "shell.execute_reply": "2021-01-28T15:32:33.650172Z"
+ },
+ "papermill": {
+ "duration": 0.141244,
+ "end_time": "2021-01-28T15:32:33.650328",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:33.509084",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " MSZoning \n",
+ " Street \n",
+ " Alley \n",
+ " LotShape \n",
+ " LandContour \n",
+ " Utilities \n",
+ " LotConfig \n",
+ " LandSlope \n",
+ " Neighborhood \n",
+ " Condition1 \n",
+ " ... \n",
+ " GarageType \n",
+ " GarageFinish \n",
+ " GarageQual \n",
+ " GarageCond \n",
+ " PavedDrive \n",
+ " PoolQC \n",
+ " Fence \n",
+ " MiscFeature \n",
+ " SaleType \n",
+ " SaleCondition \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " count \n",
+ " 1452 \n",
+ " 1452 \n",
+ " 91 \n",
+ " 1452 \n",
+ " 1452 \n",
+ " 1452 \n",
+ " 1452 \n",
+ " 1452 \n",
+ " 1452 \n",
+ " 1452 \n",
+ " ... \n",
+ " 1371 \n",
+ " 1371 \n",
+ " 1371 \n",
+ " 1371 \n",
+ " 1452 \n",
+ " 5 \n",
+ " 280 \n",
+ " 53 \n",
+ " 1452 \n",
+ " 1452 \n",
+ " \n",
+ " \n",
+ " unique \n",
+ " 5 \n",
+ " 2 \n",
+ " 2 \n",
+ " 4 \n",
+ " 4 \n",
+ " 2 \n",
+ " 5 \n",
+ " 3 \n",
+ " 25 \n",
+ " 9 \n",
+ " ... \n",
+ " 6 \n",
+ " 3 \n",
+ " 5 \n",
+ " 5 \n",
+ " 3 \n",
+ " 3 \n",
+ " 4 \n",
+ " 4 \n",
+ " 9 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " top \n",
+ " RL \n",
+ " Pave \n",
+ " Grvl \n",
+ " Reg \n",
+ " Lvl \n",
+ " AllPub \n",
+ " Inside \n",
+ " Gtl \n",
+ " NAmes \n",
+ " Norm \n",
+ " ... \n",
+ " Attchd \n",
+ " Unf \n",
+ " TA \n",
+ " TA \n",
+ " Y \n",
+ " Fa \n",
+ " MnPrv \n",
+ " Shed \n",
+ " WD \n",
+ " Normal \n",
+ " \n",
+ " \n",
+ " freq \n",
+ " 1144 \n",
+ " 1447 \n",
+ " 50 \n",
+ " 921 \n",
+ " 1307 \n",
+ " 1451 \n",
+ " 1047 \n",
+ " 1375 \n",
+ " 225 \n",
+ " 1255 \n",
+ " ... \n",
+ " 865 \n",
+ " 605 \n",
+ " 1303 \n",
+ " 1318 \n",
+ " 1332 \n",
+ " 2 \n",
+ " 156 \n",
+ " 48 \n",
+ " 1264 \n",
+ " 1195 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
4 rows × 43 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MSZoning Street Alley LotShape LandContour Utilities LotConfig \\\n",
+ "count 1452 1452 91 1452 1452 1452 1452 \n",
+ "unique 5 2 2 4 4 2 5 \n",
+ "top RL Pave Grvl Reg Lvl AllPub Inside \n",
+ "freq 1144 1447 50 921 1307 1451 1047 \n",
+ "\n",
+ " LandSlope Neighborhood Condition1 ... GarageType GarageFinish \\\n",
+ "count 1452 1452 1452 ... 1371 1371 \n",
+ "unique 3 25 9 ... 6 3 \n",
+ "top Gtl NAmes Norm ... Attchd Unf \n",
+ "freq 1375 225 1255 ... 865 605 \n",
+ "\n",
+ " GarageQual GarageCond PavedDrive PoolQC Fence MiscFeature SaleType \\\n",
+ "count 1371 1371 1452 5 280 53 1452 \n",
+ "unique 5 5 3 3 4 4 9 \n",
+ "top TA TA Y Fa MnPrv Shed WD \n",
+ "freq 1303 1318 1332 2 156 48 1264 \n",
+ "\n",
+ " SaleCondition \n",
+ "count 1452 \n",
+ "unique 6 \n",
+ "top Normal \n",
+ "freq 1195 \n",
+ "\n",
+ "[4 rows x 43 columns]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "categoricals = train.select_dtypes(exclude=[np.number])\n",
+ "categoricals.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.027154,
+ "end_time": "2021-01-28T15:32:33.704198",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:33.677044",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Let's use one-hot encoding method for our non-numeric data**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:33.770243Z",
+ "iopub.status.busy": "2021-01-28T15:32:33.769323Z",
+ "iopub.status.idle": "2021-01-28T15:32:34.114997Z",
+ "shell.execute_reply": "2021-01-28T15:32:34.114467Z"
+ },
+ "papermill": {
+ "duration": 0.383456,
+ "end_time": "2021-01-28T15:32:34.115151",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:33.731695",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
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+ ""
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+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "train['enc_street'] = pd.get_dummies(train.Street, drop_first=True)\n",
+ "test['enc_street'] = pd.get_dummies(train.Street, drop_first=True)\n",
+ "condition_pivot = train.pivot_table(index='SaleCondition', values='SalePrice', aggfunc=np.median)\n",
+ "condition_pivot.plot(kind='bar', color='black')\n",
+ "plt.xlabel('Sale Condition')\n",
+ "plt.ylabel('Median Sale Price')\n",
+ "plt.xticks(rotation=0)\n",
+ "plt.show()\n",
+ "\n",
+ "train['enc_condition'] = train.SaleCondition.apply(encode)\n",
+ "test['enc_condition'] = test.SaleCondition.apply(encode)\n",
+ "train['enc_condition1'] = train.KitchenAbvGr.apply(encode1)\n",
+ "test['enc_condition1'] = test.KitchenAbvGr.apply(encode1)\n",
+ "condition_pivot = train.pivot_table(index='enc_condition', values='SalePrice', aggfunc=np.median)\n",
+ "condition_pivot.plot(kind='bar', color='black')\n",
+ "plt.xlabel('Encoded Sale Condition')\n",
+ "plt.ylabel('Median Sale Price')\n",
+ "plt.xticks(rotation=0)\n",
+ "plt.show()\n",
+ "data = train.select_dtypes(include=[np.number]).interpolate().dropna()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.028351,
+ "end_time": "2021-01-28T15:32:34.171553",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:34.143202",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Thus, we redid the reasons for the sale and got a simpler model, which is easier to work with both for us and the computer. \n",
+ "And also, those data that we do not use have been converted to zero, also in order to make it easier for us to work.**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.02752,
+ "end_time": "2021-01-28T15:32:34.227622",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:34.200102",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Now let's start building our linear model.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:34.289960Z",
+ "iopub.status.busy": "2021-01-28T15:32:34.288481Z",
+ "iopub.status.idle": "2021-01-28T15:32:34.326305Z",
+ "shell.execute_reply": "2021-01-28T15:32:34.325386Z"
+ },
+ "papermill": {
+ "duration": 0.070912,
+ "end_time": "2021-01-28T15:32:34.326495",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:34.255583",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "y = np.log(train.SalePrice)\n",
+ "X = data.drop(['SalePrice', 'Id'], axis=1)\n",
+ "X_train, X_test, y_train, y_test = train_test_split(\n",
+ " X, y, random_state=42, test_size=.33)\n",
+ "lr = linear_model.LinearRegression()\n",
+ "model = lr.fit(X_train, y_train)\n",
+ "predictions = model.predict(X_test)\n",
+ "actual_values = y_test\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.048531,
+ "end_time": "2021-01-28T15:32:34.424462",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:34.375931",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Now let's build our line with a predicted price.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:34.507615Z",
+ "iopub.status.busy": "2021-01-28T15:32:34.502778Z",
+ "iopub.status.idle": "2021-01-28T15:32:34.631707Z",
+ "shell.execute_reply": "2021-01-28T15:32:34.632242Z"
+ },
+ "papermill": {
+ "duration": 0.176278,
+ "end_time": "2021-01-28T15:32:34.632439",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:34.456161",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(predictions, actual_values, alpha=.7,\n",
+ " color='black')\n",
+ "plt.xlabel('Predicted Price')\n",
+ "plt.ylabel('Actual Price')\n",
+ "plt.title('Linear Regression Model')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.029628,
+ "end_time": "2021-01-28T15:32:34.692327",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:34.662699",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Now let's change our alpha a little and get some graphics. With alpha = 1 we get better prediction.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:34.760862Z",
+ "iopub.status.busy": "2021-01-28T15:32:34.760230Z",
+ "iopub.status.idle": "2021-01-28T15:32:34.985425Z",
+ "shell.execute_reply": "2021-01-28T15:32:34.986025Z"
+ },
+ "papermill": {
+ "duration": 0.264434,
+ "end_time": "2021-01-28T15:32:34.986202",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:34.721768",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:12: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
+ " if sys.path[0] == '':\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "alpha = 1\n",
+ "rm = linear_model.Ridge(alpha=alpha)\n",
+ "ridge_model = rm.fit(X_train, y_train)\n",
+ "preds_ridge = ridge_model.predict(X_test)\n",
+ "plt.scatter(preds_ridge, actual_values, alpha=.75, color='black')\n",
+ "plt.xlabel('Predicted Price')\n",
+ "plt.ylabel('Actual Price')\n",
+ "plt.title('Ridge Regularization with alpha = {}'.format(alpha))\n",
+ "overlay = 'R^2 is: {}\\nRMSE is: {}'.format(\n",
+ " ridge_model.score(X_test, y_test),\n",
+ " mean_squared_error(y_test, preds_ridge))\n",
+ "plt.annotate(s=overlay,xy=(12.1,10.6),size='x-large')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.032337,
+ "end_time": "2021-01-28T15:32:35.051240",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:35.018903",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Finally, we get our final answer.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T15:32:35.130010Z",
+ "iopub.status.busy": "2021-01-28T15:32:35.129032Z",
+ "iopub.status.idle": "2021-01-28T15:32:35.140715Z",
+ "shell.execute_reply": "2021-01-28T15:32:35.141324Z"
+ },
+ "papermill": {
+ "duration": 0.058258,
+ "end_time": "2021-01-28T15:32:35.141504",
+ "exception": false,
+ "start_time": "2021-01-28T15:32:35.083246",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Final predictions are: \n",
+ " [125312.14675073 123818.61581104 177152.39469018 197395.06994946\n",
+ " 180659.86804346]\n"
+ ]
+ }
+ ],
+ "source": [
+ "submission = pd.DataFrame()\n",
+ "submission['Id'] = test.Id\n",
+ "feats = test.select_dtypes(\n",
+ " include=[np.number]).drop(['Id'], axis=1).interpolate()\n",
+ "predictions = ridge_model.predict(feats)\n",
+ "final_predictions = np.exp(predictions)\n",
+ "print (\"Final predictions are: \\n\", final_predictions[:5])"
+ ]
+ }
+ ],
+ "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": "3.7.9"
+ },
+ "papermill": {
+ "default_parameters": {},
+ "duration": 11.207938,
+ "end_time": "2021-01-28T15:32:35.783310",
+ "environment_variables": {},
+ "exception": null,
+ "input_path": "__notebook__.ipynb",
+ "output_path": "__notebook__.ipynb",
+ "parameters": {},
+ "start_time": "2021-01-28T15:32:24.575372",
+ "version": "2.2.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/denysyefimov_code/task1_titanic_houseprice/denysyefimov_houseprices.py b/denysyefimov_code/task1_titanic_houseprice/denysyefimov_houseprices.py
new file mode 100644
index 0000000..05eedb6
--- /dev/null
+++ b/denysyefimov_code/task1_titanic_houseprice/denysyefimov_houseprices.py
@@ -0,0 +1,143 @@
+from sklearn.metrics import mean_squared_error
+from sklearn import linear_model
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+
+
+def one_hot_encode(x):
+ return 1 if x == 'Partial' else 0
+
+
+def encode1(x):
+ return 3 if x == 4 else x
+
+
+if __name__ == '__main__':
+
+ # add data in file
+
+ train = pd.read_csv('path')
+ test = pd.read_csv('path')
+
+ # making some changes
+
+ deviation = np.log(train.SalePrice)
+
+ # Now let's look at what influences
+ # the price of the house itself and analyze the data.
+
+ influences = train.select_dtypes(include=[np.number])
+ influences.dtypes
+ percent = influences.corr()
+ print(percent['SalePrice'].sort_values(ascending=False)[:5], '\n')
+ print(percent['SalePrice'].sort_values(ascending=False)[-5:])
+ quality_pivot = train.pivot_table(index='OverallQual',
+ values='SalePrice',
+ aggfunc=np.median
+ )
+ print(quality_pivot)
+
+ # Thus, we got that the price is most
+ # affected by: OverallQual, GrLivArea, GarageCars,
+ # GarageArea, YrSold, OverallCond,
+ # MSSubClass, EnclosedPorch, KitchenAbvGr.
+ # Now let's see the rest of the
+ # factors that affect the price.
+ # Thus, we got the graphs and now
+ # it would be worth removing the points that could
+ # shift our line from the "center
+ # of events".
+ # Let's set the maximum value of
+ # the garage area to 1200, so that the line
+ # doesn't run away from us.
+ # And makes some changes in GrLivArea
+
+ train = train[train['GarageArea'] < 1200]
+ train = train[train['GrLivArea'] < 4000]
+
+ # Now let's check all NULL
+
+ nulls = pd.DataFrame(train.isnull().
+ sum().sort_values(ascending=False)[:25])
+ print(nulls)
+
+ # Now let's chek data without numbers(non-numeric)
+
+ categoricals = train.select_dtypes(exclude=[np.number])
+ categoricals.describe()
+
+ # Let's use one-hot encoding method for our non-numeric data
+
+ train['enc_street'] = pd.get_dummies(train.Street,
+ drop_first=True
+ )
+ test['enc_street'] = pd.get_dummies(train.Street,
+ drop_first=True
+ )
+
+ train['enc_condition'] = train.SaleCondition.apply(one_hot_encode)
+ test['enc_condition'] = test.SaleCondition.apply(one_hot_encode)
+ train['enc_condition1'] = train.KitchenAbvGr.apply(encode1)
+ test['enc_condition1'] = test.KitchenAbvGr.apply(encode1)
+
+ data = train.select_dtypes(include=[np.number]).interpolate().dropna()
+
+ # Thus, we redid the reasons for the sale and
+ # got a simpler model, which is easier to work
+ # with both for us
+ # and the computer. And also, those data that
+ # we do not use have been converted to zero,
+ # also in order to
+ # make it easier for us to work.
+ # Now let's start building our linear model.
+
+ y = np.log(train.SalePrice)
+ X = data.drop(['SalePrice', 'Id'], axis=1)
+ X_train, X_test, y_train, y_test = train_test_split(X,
+ y,
+ random_state=100,
+ test_size=.33
+ )
+ lr = linear_model.LinearRegression()
+ model = lr.fit(X_train, y_train)
+ predictions = model.predict(X_test)
+ actual_values = y_test
+
+ # Now let's build our line with a predicted price.
+ # Now let's change our alpha a little and get some
+ # graphics. With alpha = 1 we get better prediction.
+
+ alpha = 1
+ rm = linear_model.Ridge(alpha=alpha)
+ ridge_model = rm.fit(X_train, y_train)
+ preds_ridge = ridge_model.predict(X_test)
+ plt.scatter(preds_ridge,
+ actual_values,
+ alpha=.75,
+ color='black'
+ )
+ plt.xlabel('Predicted Price')
+ plt.ylabel('Actual Price')
+ plt.title('Ridge Regularization with alpha = {}'.format(alpha))
+ overlay = 'R^2 is: {}\nRMSE is: {}'.\
+ format(ridge_model.score(X_test, y_test),
+ mean_squared_error(y_test, preds_ridge))
+ plt.annotate(s=overlay,
+ xy=(12.1, 10.6),
+ size='x-large'
+ )
+
+ # Finally, we get our final answer.
+
+ submission = pd.DataFrame()
+ submission['Id'] = test.Id
+ feats = test.select_dtypes(include=[np.number]).drop(['Id'],
+ axis=1
+ ).interpolate()
+ predictions = ridge_model.predict(feats)
+ final_predictions = np.exp(predictions)
+ print("Final predictions are: \n", final_predictions[:5])
+ submission['SalePrice'] = final_predictions
+ submission.head()
+ submission.to_csv('submission1.csv', index=False)
diff --git a/denysyefimov_code/task1_titanic_houseprice/denysyefimov_titanic.ipynb b/denysyefimov_code/task1_titanic_houseprice/denysyefimov_titanic.ipynb
new file mode 100644
index 0000000..a967837
--- /dev/null
+++ b/denysyefimov_code/task1_titanic_houseprice/denysyefimov_titanic.ipynb
@@ -0,0 +1,345 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.007449,
+ "end_time": "2021-01-28T14:19:37.222518",
+ "exception": false,
+ "start_time": "2021-01-28T14:19:37.215069",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Import libraries and all datas for work**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+ "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+ "execution": {
+ "iopub.execute_input": "2021-01-28T14:19:37.244834Z",
+ "iopub.status.busy": "2021-01-28T14:19:37.243842Z",
+ "iopub.status.idle": "2021-01-28T14:19:38.671901Z",
+ "shell.execute_reply": "2021-01-28T14:19:38.672745Z"
+ },
+ "papermill": {
+ "duration": 1.443522,
+ "end_time": "2021-01-28T14:19:38.672933",
+ "exception": false,
+ "start_time": "2021-01-28T14:19:37.229411",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/kaggle/input/titanic/train.csv\n",
+ "/kaggle/input/titanic/test.csv\n",
+ "/kaggle/input/titanic/gender_submission.csv\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " PassengerId \n",
+ " Pclass \n",
+ " Name \n",
+ " Sex \n",
+ " Age \n",
+ " SibSp \n",
+ " Parch \n",
+ " Ticket \n",
+ " Fare \n",
+ " Cabin \n",
+ " Embarked \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 892 \n",
+ " 3 \n",
+ " Kelly, Mr. James \n",
+ " male \n",
+ " 34.5 \n",
+ " 0 \n",
+ " 0 \n",
+ " 330911 \n",
+ " 7.8292 \n",
+ " NaN \n",
+ " Q \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 893 \n",
+ " 3 \n",
+ " Wilkes, Mrs. James (Ellen Needs) \n",
+ " female \n",
+ " 47.0 \n",
+ " 1 \n",
+ " 0 \n",
+ " 363272 \n",
+ " 7.0000 \n",
+ " NaN \n",
+ " S \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 894 \n",
+ " 2 \n",
+ " Myles, Mr. Thomas Francis \n",
+ " male \n",
+ " 62.0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 240276 \n",
+ " 9.6875 \n",
+ " NaN \n",
+ " Q \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 895 \n",
+ " 3 \n",
+ " Wirz, Mr. Albert \n",
+ " male \n",
+ " 27.0 \n",
+ " 0 \n",
+ " 0 \n",
+ " 315154 \n",
+ " 8.6625 \n",
+ " NaN \n",
+ " S \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 896 \n",
+ " 3 \n",
+ " Hirvonen, Mrs. Alexander (Helga E Lindqvist) \n",
+ " female \n",
+ " 22.0 \n",
+ " 1 \n",
+ " 1 \n",
+ " 3101298 \n",
+ " 12.2875 \n",
+ " NaN \n",
+ " S \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " PassengerId Pclass Name Sex \\\n",
+ "0 892 3 Kelly, Mr. James male \n",
+ "1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n",
+ "2 894 2 Myles, Mr. Thomas Francis male \n",
+ "3 895 3 Wirz, Mr. Albert male \n",
+ "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n",
+ "\n",
+ " Age SibSp Parch Ticket Fare Cabin Embarked \n",
+ "0 34.5 0 0 330911 7.8292 NaN Q \n",
+ "1 47.0 1 0 363272 7.0000 NaN S \n",
+ "2 62.0 0 0 240276 9.6875 NaN Q \n",
+ "3 27.0 0 0 315154 8.6625 NaN S \n",
+ "4 22.0 1 1 3101298 12.2875 NaN S "
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "\n",
+ "\n",
+ "import os\n",
+ "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+ " for filename in filenames:\n",
+ " print(os.path.join(dirname, filename))\n",
+ "\n",
+ "train_data = pd.read_csv(\"/kaggle/input/titanic/train.csv\")\n",
+ "train_data.head()\n",
+ "test_data = pd.read_csv(\"/kaggle/input/titanic/test.csv\")\n",
+ "test_data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.008734,
+ "end_time": "2021-01-28T14:19:38.694862",
+ "exception": false,
+ "start_time": "2021-01-28T14:19:38.686128",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**Let's calculate the percentage of male passengers who survived(men and women).**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T14:19:38.723831Z",
+ "iopub.status.busy": "2021-01-28T14:19:38.723205Z",
+ "iopub.status.idle": "2021-01-28T14:19:38.728593Z",
+ "shell.execute_reply": "2021-01-28T14:19:38.727874Z"
+ },
+ "papermill": {
+ "duration": 0.02556,
+ "end_time": "2021-01-28T14:19:38.728714",
+ "exception": false,
+ "start_time": "2021-01-28T14:19:38.703154",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "% of women who survived: 0.7420382165605095\n",
+ "% of men who survived: 0.18890814558058924\n"
+ ]
+ }
+ ],
+ "source": [
+ "women = train_data.loc[train_data.Sex == 'female'][\"Survived\"]\n",
+ "rate_women = sum(women)/len(women)\n",
+ "print(\"% of women who survived:\", rate_women)\n",
+ "\n",
+ "men = train_data.loc[train_data.Sex == 'male'][\"Survived\"]\n",
+ "rate_men = sum(men)/len(men)\n",
+ "print(\"% of men who survived:\", rate_men)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "papermill": {
+ "duration": 0.00581,
+ "end_time": "2021-01-28T14:19:38.741027",
+ "exception": false,
+ "start_time": "2021-01-28T14:19:38.735217",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "source": [
+ "**End reading the datas and let's get started the machine learning. Train our model.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2021-01-28T14:19:38.769694Z",
+ "iopub.status.busy": "2021-01-28T14:19:38.769014Z",
+ "iopub.status.idle": "2021-01-28T14:19:39.362758Z",
+ "shell.execute_reply": "2021-01-28T14:19:39.361887Z"
+ },
+ "papermill": {
+ "duration": 0.615597,
+ "end_time": "2021-01-28T14:19:39.362892",
+ "exception": false,
+ "start_time": "2021-01-28T14:19:38.747295",
+ "status": "completed"
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "All is Done!\n"
+ ]
+ }
+ ],
+ "source": [
+ "y = train_data[\"Survived\"]\n",
+ "\n",
+ "features = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\"]\n",
+ "X = pd.get_dummies(train_data[features])\n",
+ "X_test = pd.get_dummies(test_data[features])\n",
+ "\n",
+ "model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)\n",
+ "model.fit(X, y)\n",
+ "predictions = model.predict(X_test)\n",
+ "\n",
+ "output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})\n",
+ "output.to_csv('my_submission.csv', index=False)\n",
+ "print(\"All is Done!\")"
+ ]
+ }
+ ],
+ "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": "3.7.6"
+ },
+ "papermill": {
+ "duration": 7.265766,
+ "end_time": "2021-01-28T14:19:39.488918",
+ "environment_variables": {},
+ "exception": null,
+ "input_path": "__notebook__.ipynb",
+ "output_path": "__notebook__.ipynb",
+ "parameters": {},
+ "start_time": "2021-01-28T14:19:32.223152",
+ "version": "2.1.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/denysyefimov_code/task1_titanic_houseprice/denysyefimov_titanic.py b/denysyefimov_code/task1_titanic_houseprice/denysyefimov_titanic.py
new file mode 100644
index 0000000..0926ad6
--- /dev/null
+++ b/denysyefimov_code/task1_titanic_houseprice/denysyefimov_titanic.py
@@ -0,0 +1,35 @@
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+
+
+# Let's calculate the percentage of
+# male passengers who survived(men and women).
+
+train_data = pd.read_csv("Your path")
+train_data.head()
+test_data = pd.read_csv("Your path")
+test_data.head()
+
+# End reading the datas and let's get
+# started the machine learning. Train our model.
+
+y = train_data["Survived"]
+
+features = ["Pclass", "Sex", "SibSp", "Parch"]
+X = pd.get_dummies(train_data[features])
+X_test = pd.get_dummies(test_data[features])
+
+model = RandomForestClassifier(n_estimators=100,
+ max_depth=5,
+ random_state=1
+ )
+model.fit(X, y)
+predictions = model.predict(X_test)
+
+output = pd.DataFrame({'PassengerId': test_data.PassengerId,
+ 'Survived': predictions}
+ )
+output.to_csv('my_submission.csv',
+ index=False
+ )
+print("All is Done!")
diff --git a/denysyefimov_code/task2_face_mask_detect/denysyefimov_face_mask.ipynb b/denysyefimov_code/task2_face_mask_detect/denysyefimov_face_mask.ipynb
new file mode 100644
index 0000000..f0fe2a0
--- /dev/null
+++ b/denysyefimov_code/task2_face_mask_detect/denysyefimov_face_mask.ipynb
@@ -0,0 +1,533 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Копія записника \"Untitled2.ipynb\"",
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "38meempNFVDV",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "935faf38-4b76-44a6-e37e-800502b2fea0"
+ },
+ "source": [
+ "import matplotlib.pyplot as plt\r\n",
+ "import numpy as np\r\n",
+ "import os\r\n",
+ "import tensorflow as tf\r\n",
+ "from tensorflow.keras.models import load_model\r\n",
+ "from tensorflow.keras.preprocessing import image_dataset_from_directory\r\n",
+ "import pandas as pd\r\n",
+ "\r\n",
+ "from google.colab import drive\r\n",
+ "drive.mount('/content/drive')\r\n",
+ "\r\n",
+ "input_path = 'path'\r\n",
+ "output_path = 'path'\r\n",
+ "\r\n",
+ "\r\n",
+ "print(\"Done!\")"
+ ],
+ "execution_count": 41,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
+ "Done!\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gYzcH9QN-JKv"
+ },
+ "source": [
+ "**After we have imported all the necessary libraries and specified the path for the data, we will declare several variables for working with photos.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "hhc9IKEm-ejc",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "507a74a3-08ad-45e1-b4ba-125f1ffab3cb"
+ },
+ "source": [
+ "IMG_SIZE = (160, 160)\r\n",
+ "BATCH_SIZE = 32\r\n",
+ "epochs=10\r\n",
+ "tuning_epochs=8\r\n",
+ "learning_rate=0.0001\r\n",
+ "validation_koef = 5\r\n",
+ "\r\n",
+ "print(\"Done!\")"
+ ],
+ "execution_count": 42,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Done!\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6keEXHSbAIuo"
+ },
+ "source": [
+ "**Now let's compose our train and validation dataset.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Opm0F9jkAPO4",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "5f547ef4-3838-4717-d64d-bd19a59e2a18"
+ },
+ "source": [
+ "train_dataset = image_dataset_from_directory(input_path,\r\n",
+ " shuffle=True,\r\n",
+ " subset='training',\r\n",
+ " seed=1,\r\n",
+ " validation_split=0.2,\r\n",
+ " batch_size=BATCH_SIZE,\r\n",
+ " image_size=IMG_SIZE)\r\n",
+ "\r\n",
+ "validation_dataset = image_dataset_from_directory(input_path,\r\n",
+ " shuffle=True,\r\n",
+ " subset='validation',\r\n",
+ " seed=1,\r\n",
+ " validation_split=0.2,\r\n",
+ " batch_size=BATCH_SIZE,\r\n",
+ " image_size=IMG_SIZE)"
+ ],
+ "execution_count": 43,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Found 7553 files belonging to 3 classes.\n",
+ "Using 6043 files for training.\n",
+ "Found 7553 files belonging to 3 classes.\n",
+ "Using 1510 files for validation.\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Q-wuyMjXCC2_"
+ },
+ "source": [
+ "**There are a total of 32 batches of batch data in the verification set (one batch contains 32 images), and the 32 batches are divided into 26 and 6, 2 parts (verification and testing)**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UN9BgBcCC_wE"
+ },
+ "source": [
+ "**So now let's take a data, what we need and create test dataset.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lUMn3pIaDUBT",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "22f50832-0267-4142-899f-41f5adf3978b"
+ },
+ "source": [
+ "validation_batches = tf.data.experimental.cardinality(validation_dataset)\r\n",
+ "test_dataset = validation_dataset.take(validation_batches//validation_koef)\r\n",
+ "validation_dataset = validation_dataset.skip(validation_batches//validation_koef)\r\n",
+ "\r\n",
+ "print(\"Done!\")"
+ ],
+ "execution_count": 44,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Done!\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "M0DXcEIwElUK"
+ },
+ "source": [
+ "**Now let's configure the data for us.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7Ve5xzv3EnfJ",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "1fe8e246-0797-461d-876f-3e0d0a8b0a8d"
+ },
+ "source": [
+ "AUTOTUNE = tf.data.experimental.AUTOTUNE\r\n",
+ "\r\n",
+ "train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)\r\n",
+ "validation_dataset = validation_dataset.prefetch(buffer_size=AUTOTUNE)\r\n",
+ "test_dataset = test_dataset.prefetch(buffer_size=AUTOTUNE)\r\n",
+ "\r\n",
+ "print(\"Done!\")"
+ ],
+ "execution_count": 45,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Done!\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8LoIcYfMFVh3"
+ },
+ "source": [
+ "**Let's increase our data and change it a little. Very little))**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bTs6V3FvFmKn"
+ },
+ "source": [
+ "**First of make flip horizontally and vertically, spin, zoom, add contrast of each channel of each picture ,shifted up and down by 20%, left and right by 20%.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "N8-9G0dJFXm3",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "948ec56e-1ff9-4f67-db78-4e73b3acee22"
+ },
+ "source": [
+ "data_change=tf.keras.Sequential([\r\n",
+ " tf.keras.layers.experimental.preprocessing.RandomFlip(mode='horizontal'),\r\n",
+ " tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),\r\n",
+ " tf.keras.layers.experimental.preprocessing.RandomZoom(.5, .2),\r\n",
+ " tf.keras.layers.experimental.preprocessing.RandomContrast(factor=0.1),\r\n",
+ " tf.keras.layers.experimental.preprocessing.RandomTranslation(\r\n",
+ " height_factor=0.1, width_factor=0.1)\r\n",
+ "])\r\n",
+ "\r\n",
+ "print(\"Done!\")"
+ ],
+ "execution_count": 46,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Done!\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gJ9cQNiCL09k"
+ },
+ "source": [
+ "**Let's making the base model.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6fin32O-L7S0",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "2f67c8e3-4a94-433d-fe63-99b039dda8e2"
+ },
+ "source": [
+ "base_model=tf.keras.applications.EfficientNetB0(input_shape=IMG_SIZE+(3,),\r\n",
+ " include_top=False,\r\n",
+ " weights='imagenet',\r\n",
+ " drop_connect_rate=0.4\r\n",
+ " )\r\n",
+ "base_model.trainable=False\r\n",
+ "model=tf.keras.Sequential()\r\n",
+ "model.add(base_model)\r\n",
+ "model.add(tf.keras.layers.GlobalAveragePooling2D())\r\n",
+ "model.add(tf.keras.layers.Dense(1))\r\n",
+ "\r\n",
+ "print(model.summary())"
+ ],
+ "execution_count": 47,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_3\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "efficientnetb0 (Functional) (None, 5, 5, 1280) 4049571 \n",
+ "_________________________________________________________________\n",
+ "global_average_pooling2d_2 ( (None, 1280) 0 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (Dense) (None, 1) 1281 \n",
+ "=================================================================\n",
+ "Total params: 4,050,852\n",
+ "Trainable params: 1,281\n",
+ "Non-trainable params: 4,049,571\n",
+ "_________________________________________________________________\n",
+ "None\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3Sh7c_vwNMt8"
+ },
+ "source": [
+ "**Good, all works. Now let's make some modification with layer(normalize, pooling, dense and drop some data).**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bVONt_j8NuSJ",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "4a40e58b-427a-43c5-c1d2-3060977e157a"
+ },
+ "source": [
+ "input=tf.keras.Input(IMG_SIZE+(3,))\r\n",
+ "x=base_model(data_change(input),\r\n",
+ " training=False)\r\n",
+ "x=tf.keras.layers.GlobalAveragePooling2D()(x)\r\n",
+ "x=tf.keras.layers.Dropout(0.2)(x)\r\n",
+ "x=tf.keras.layers.BatchNormalization()(x)\r\n",
+ "output=tf.keras.layers.Dense(1)(x)\r\n",
+ "model=tf.keras.Model(input,output)\r\n",
+ "\r\n",
+ "print(model.summary())"
+ ],
+ "execution_count": 48,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Model: \"model_1\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "input_4 (InputLayer) [(None, 160, 160, 3)] 0 \n",
+ "_________________________________________________________________\n",
+ "sequential_2 (Sequential) (None, 160, 160, 3) 0 \n",
+ "_________________________________________________________________\n",
+ "efficientnetb0 (Functional) (None, 5, 5, 1280) 4049571 \n",
+ "_________________________________________________________________\n",
+ "global_average_pooling2d_3 ( (None, 1280) 0 \n",
+ "_________________________________________________________________\n",
+ "dropout_1 (Dropout) (None, 1280) 0 \n",
+ "_________________________________________________________________\n",
+ "batch_normalization_1 (Batch (None, 1280) 5120 \n",
+ "_________________________________________________________________\n",
+ "dense_3 (Dense) (None, 1) 1281 \n",
+ "=================================================================\n",
+ "Total params: 4,055,972\n",
+ "Trainable params: 3,841\n",
+ "Non-trainable params: 4,052,131\n",
+ "_________________________________________________________________\n",
+ "None\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HsVEkzziQAv1"
+ },
+ "source": [
+ "**Now let's get down to optimization. We will optimize by Adam's method.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "R10uHGltQRZ8",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "33c2f368-9f07-4286-f71f-d12a22b5838a"
+ },
+ "source": [
+ "model.compile(optimizer='adam',\r\n",
+ " loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\r\n",
+ " metrics=['accuracy'])\r\n",
+ "\r\n",
+ "print(\"Done!\")"
+ ],
+ "execution_count": 49,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Done!\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LLEt4nG-Qi_z"
+ },
+ "source": [
+ "**And fit our model.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Y65uHA1-QZ4k",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "b9db1007-b40c-46b6-9192-78cbe5c8e63f"
+ },
+ "source": [
+ "history=model.fit(train_dataset,\r\n",
+ " epochs=epochs,\r\n",
+ " validation_data=validation_dataset\r\n",
+ " )"
+ ],
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n",
+ "189/189 [==============================] - 1906s 10s/step - loss: 0.3614 - accuracy: 0.8288 - val_loss: 0.0685 - val_accuracy: 0.9746\n",
+ "Epoch 2/10\n",
+ "189/189 [==============================] - 266s 1s/step - loss: 0.1639 - accuracy: 0.9347 - val_loss: 0.0417 - val_accuracy: 0.9828\n",
+ "Epoch 3/10\n",
+ "189/189 [==============================] - 266s 1s/step - loss: 0.1543 - accuracy: 0.9384 - val_loss: 0.0342 - val_accuracy: 0.9861\n",
+ "Epoch 4/10\n",
+ "189/189 [==============================] - 263s 1s/step - loss: 0.1305 - accuracy: 0.9504 - val_loss: 0.0361 - val_accuracy: 0.9836\n",
+ "Epoch 5/10\n",
+ "189/189 [==============================] - 262s 1s/step - loss: 0.1145 - accuracy: 0.9580 - val_loss: 0.0332 - val_accuracy: 0.9820\n",
+ "Epoch 6/10\n",
+ "189/189 [==============================] - 266s 1s/step - loss: 0.1133 - accuracy: 0.9522 - val_loss: 0.0325 - val_accuracy: 0.9861\n",
+ "Epoch 7/10\n",
+ "189/189 [==============================] - 262s 1s/step - loss: 0.1184 - accuracy: 0.9533 - val_loss: 0.0310 - val_accuracy: 0.9828\n",
+ "Epoch 8/10\n",
+ "189/189 [==============================] - 267s 1s/step - loss: 0.1326 - accuracy: 0.9455 - val_loss: 0.0333 - val_accuracy: 0.9853\n",
+ "Epoch 9/10\n",
+ "189/189 [==============================] - 265s 1s/step - loss: 0.1271 - accuracy: 0.9503 - val_loss: 0.0318 - val_accuracy: 0.9845\n",
+ "Epoch 10/10\n",
+ "189/189 [==============================] - 266s 1s/step - loss: 0.1141 - accuracy: 0.9547 - val_loss: 0.0292 - val_accuracy: 0.9877\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qoHqgZqAA3CD"
+ },
+ "source": [
+ "**Let's save our model.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zsHTltLzAorv",
+ "outputId": "075eb504-a54f-408c-8e85-7a60be8038f7"
+ },
+ "source": [
+ "wqe=model.evaluate(test_dataset)\r\n",
+ "print(wqe)\r\n",
+ "model.save('/content/drive/MyDrive')\r\n",
+ "print(\"Done!\")"
+ ],
+ "execution_count": 53,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "9/9 [==============================] - 13s 1s/step - loss: 0.6880 - accuracy: 0.0056\n",
+ "[0.7190898656845093, 0.0069444444961845875]\n",
+ "INFO:tensorflow:Assets written to: /content/drive/MyDrive/assets\n",
+ "Done!\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YAxpk5bUMcUj"
+ },
+ "source": [
+ ""
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/denysyefimov_code/task2_face_mask_detect/denysyefimov_face_mask.py b/denysyefimov_code/task2_face_mask_detect/denysyefimov_face_mask.py
new file mode 100644
index 0000000..956ea67
--- /dev/null
+++ b/denysyefimov_code/task2_face_mask_detect/denysyefimov_face_mask.py
@@ -0,0 +1,87 @@
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image_dataset_from_directory
+
+from google.colab import drive
+drive.mount('path')
+
+input_path = 'path'
+output_path = 'path'
+
+
+IMG_SIZE = (160, 160)
+BATCH_SIZE = 32
+epochs = 10
+tuning_epochs = 8
+learning_rate = 0.0001
+validation_koef = 5
+
+train_dataset = image_dataset_from_directory(input_path,
+ shuffle=True,
+ subset='training',
+ seed=1,
+ validation_split=0.2,
+ batch_size=BATCH_SIZE,
+ image_size=IMG_SIZE)
+
+validation_dataset = image_dataset_from_directory(input_path,
+ shuffle=True,
+ subset='validation',
+ seed=1,
+ validation_split=0.2,
+ batch_size=BATCH_SIZE,
+ image_size=IMG_SIZE)
+
+validation_batches = tf.data.experimental.cardinality(validation_dataset)
+test_dataset = validation_dataset.take(validation_batches // validation_koef)
+validation_dataset = validation_dataset.skip(
+ validation_batches // validation_koef)
+
+AUTOTUNE = tf.data.experimental.AUTOTUNE
+
+train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)
+validation_dataset = validation_dataset.prefetch(buffer_size=AUTOTUNE)
+test_dataset = test_dataset.prefetch(buffer_size=AUTOTUNE)
+
+data_change = tf.keras.Sequential([
+ tf.keras.layers.experimental.preprocessing.RandomFlip(mode='horizontal'),
+ tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),
+ tf.keras.layers.experimental.preprocessing.RandomZoom(.5, .2),
+ tf.keras.layers.experimental.preprocessing.RandomContrast(factor=0.1),
+ tf.keras.layers.experimental.preprocessing.RandomTranslation(
+ height_factor=0.1, width_factor=0.1)
+])
+
+base_model = tf.keras.applications.EfficientNetB0(input_shape=IMG_SIZE + (3,),
+ include_top=False,
+ weights='imagenet',
+ drop_connect_rate=0.4)
+base_model.trainable = False
+model = tf.keras.Sequential()
+model.add(base_model)
+model.add(tf.keras.layers.GlobalAveragePooling2D())
+model.add(tf.keras.layers.Dense(1))
+
+print(model.summary())
+
+input = tf.keras.Input(IMG_SIZE + (3,))
+x = base_model(data_change(input), training=False)
+x = tf.keras.layers.GlobalAveragePooling2D()(x)
+x = tf.keras.layers.Dropout(0.2)(x)
+x = tf.keras.layers.BatchNormalization()(x)
+output = tf.keras.layers.Dense(1)(x)
+model = tf.keras.Model(input, output)
+
+print(model.summary())
+
+model.compile(optimizer='adam',
+ loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
+ metrics=['accuracy'])
+
+history = model.fit(train_dataset,
+ epochs=epochs,
+ validation_data=validation_dataset)
+
+wqe = model.evaluate(test_dataset)
+print(wqe)
+model.save('path')
+print("Done!")
diff --git a/denysyefimov_code/task2_face_mask_detect/metrics.txt b/denysyefimov_code/task2_face_mask_detect/metrics.txt
new file mode 100644
index 0000000..b5c0a95
--- /dev/null
+++ b/denysyefimov_code/task2_face_mask_detect/metrics.txt
@@ -0,0 +1,21 @@
+Metrics:
+Epoch 1/10
+189/189 [==============================] - 1906s 10s/step - loss: 0.3614 - accuracy: 0.8288 - val_loss: 0.0685 - val_accuracy: 0.9746
+Epoch 2/10
+189/189 [==============================] - 266s 1s/step - loss: 0.1639 - accuracy: 0.9347 - val_loss: 0.0417 - val_accuracy: 0.9828
+Epoch 3/10
+189/189 [==============================] - 266s 1s/step - loss: 0.1543 - accuracy: 0.9384 - val_loss: 0.0342 - val_accuracy: 0.9861
+Epoch 4/10
+189/189 [==============================] - 263s 1s/step - loss: 0.1305 - accuracy: 0.9504 - val_loss: 0.0361 - val_accuracy: 0.9836
+Epoch 5/10
+189/189 [==============================] - 262s 1s/step - loss: 0.1145 - accuracy: 0.9580 - val_loss: 0.0332 - val_accuracy: 0.9820
+Epoch 6/10
+189/189 [==============================] - 266s 1s/step - loss: 0.1133 - accuracy: 0.9522 - val_loss: 0.0325 - val_accuracy: 0.9861
+Epoch 7/10
+189/189 [==============================] - 262s 1s/step - loss: 0.1184 - accuracy: 0.9533 - val_loss: 0.0310 - val_accuracy: 0.9828
+Epoch 8/10
+189/189 [==============================] - 267s 1s/step - loss: 0.1326 - accuracy: 0.9455 - val_loss: 0.0333 - val_accuracy: 0.9853
+Epoch 9/10
+189/189 [==============================] - 265s 1s/step - loss: 0.1271 - accuracy: 0.9503 - val_loss: 0.0318 - val_accuracy: 0.9845
+Epoch 10/10
+189/189 [==============================] - 266s 1s/step - loss: 0.1141 - accuracy: 0.9547 - val_loss: 0.0292 - val_accuracy: 0.9877
diff --git a/denysyefimov_code/task4_face_recognition/denysyefimov_face_recogn.ipynb b/denysyefimov_code/task4_face_recognition/denysyefimov_face_recogn.ipynb
new file mode 100644
index 0000000..7ce221c
--- /dev/null
+++ b/denysyefimov_code/task4_face_recognition/denysyefimov_face_recogn.ipynb
@@ -0,0 +1,477 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Untitled6.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 483
+ },
+ "id": "gB6qWtj2P96g",
+ "outputId": "7fd50050-d941-4622-ec07-382cf3cb4589"
+ },
+ "source": [
+ "import numpy as np\r\n",
+ "import os\r\n",
+ "import cv2\r\n",
+ "from imageio import imread\r\n",
+ "from skimage.transform import resize\r\n",
+ "from scipy.spatial import distance\r\n",
+ "from keras.models import load_model\r\n",
+ "\r\n",
+ "\r\n",
+ "cascade_path = 'path'\r\n",
+ "model_path = 'path'\r\n",
+ "image_dir_basepath = 'path'\r\n",
+ "\r\n",
+ "names = ['keanu', 'denzel', 'benedict']\r\n",
+ "image_size = 160\r\n",
+ "\r\n",
+ "model = load_model(model_path)\r\n",
+ "\r\n",
+ "\r\n",
+ "def prewhiten(x):\r\n",
+ " if x.ndim == 4:\r\n",
+ " axis = (1, 2, 3)\r\n",
+ " size = x[0].size\r\n",
+ " elif x.ndim == 3:\r\n",
+ " axis = (0, 1, 2)\r\n",
+ " size = x.size\r\n",
+ " else:\r\n",
+ " raise ValueError('Dimension should be 3 or 4')\r\n",
+ "\r\n",
+ " mean = np.mean(x, axis=axis, keepdims=True)\r\n",
+ " std = np.std(x, axis=axis, keepdims=True)\r\n",
+ " std_adj = np.maximum(std, 1.0/np.sqrt(size))\r\n",
+ " y = (x - mean) / std_adj\r\n",
+ " return y\r\n",
+ "\r\n",
+ "\r\n",
+ "def l2_normalize(x, axis=-1, epsilon=1e-10):\r\n",
+ " output = x / np.sqrt(np.maximum(np.sum(np.square(x),\r\n",
+ " axis=axis,\r\n",
+ " keepdims=True),\r\n",
+ " epsilon))\r\n",
+ " return output\r\n",
+ "\r\n",
+ "\r\n",
+ "def load_and_align_images(filepaths, margin):\r\n",
+ " cascade = cv2.CascadeClassifier(cascade_path)\r\n",
+ " aligned_images = []\r\n",
+ " for filepath in filepaths:\r\n",
+ " img = imread(filepath)\r\n",
+ "\r\n",
+ " faces = cascade.detectMultiScale(img,\r\n",
+ " scaleFactor=1.1,\r\n",
+ " minNeighbors=3)\r\n",
+ " (x, y, w, h) = faces[0]\r\n",
+ " print(faces[0])\r\n",
+ " cropped = img[y-margin//2:y+h+margin//2,\r\n",
+ " x-margin//2:x+w+margin//2, :]\r\n",
+ " aligned = resize(cropped, (image_size, image_size), mode='reflect')\r\n",
+ " aligned_images.append(aligned)\r\n",
+ " return np.array(aligned_images)\r\n",
+ "\r\n",
+ "\r\n",
+ "def calc_embs(filepaths, margin=10, batch_size=1):\r\n",
+ " aligned_images = prewhiten(load_and_align_images(filepaths, margin))\r\n",
+ " pd = []\r\n",
+ " for start in range(0, len(aligned_images), batch_size):\r\n",
+ " pd.append(model.predict_on_batch(\r\n",
+ " aligned_images[start:start+batch_size]\r\n",
+ " ))\r\n",
+ " embs = l2_normalize(np.concatenate(pd))\r\n",
+ "\r\n",
+ " return embs\r\n",
+ "\r\n",
+ "\r\n",
+ "def calc_dist(img_name0, img_name1):\r\n",
+ " return distance.euclidean(data[img_name0]['emb'], data[img_name1]['emb'])\r\n",
+ "\r\n",
+ "\r\n",
+ "def calc_dist_plot(img_name0, img_name1):\r\n",
+ " if (calc_dist(img_name0, img_name1) < 0.8):\r\n",
+ " print(\"On photo two same human!\")\r\n",
+ " else:\r\n",
+ " print(\"On photo not two same human!\")\r\n",
+ " plt.subplot(1, 2, 1)\r\n",
+ " plt.imshow(imread(data[img_name0]['image_filepath']))\r\n",
+ " plt.subplot(1, 2, 2)\r\n",
+ " plt.imshow(imread(data[img_name1]['image_filepath']))\r\n",
+ "\r\n",
+ "\r\n",
+ "data = {}\r\n",
+ "name = names[1]\r\n",
+ "image_dirpath = image_dir_basepath + '/' + name\r\n",
+ "image_filepaths = [os.path.join(image_dirpath, f) for f in\r\n",
+ " os.listdir(image_dirpath)]\r\n",
+ "embs = calc_embs(image_filepaths)\r\n",
+ "for i in range(len(image_filepaths)):\r\n",
+ " data['{}{}'.format(name, i)] = {'image_filepath': image_filepaths[i],\r\n",
+ " 'emb': embs[i]}\r\n",
+ "\r\n",
+ "calc_dist_plot('denzel0', 'denzel1')\r\n"
+ ],
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n",
+ "keanu\n",
+ "denzel\n",
+ "benedict\n",
+ "[65 54 90 90]\n",
+ "[67 53 98 98]\n",
+ "On photo two same human!\n",
+ "[105 28 69 69]\n",
+ "[90 6 45 45]\n",
+ "On photo two same human!\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:84: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
+ "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:86: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
+ ],
+ "name": "stderr"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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vDtm6ts9jTz3Ht77zModHR2T5hOL1AsSy29tiOh3j4pid/V0Oi5xcS6ZZSac3oMxL8IvPpgsukHPtZWnlK1kuu1mAr6WJUvN6EmFNyN/6Wg1Lk55UFzzmZY0z5hldipbcW7T4EPEgGvr7mbi0TlN/vzgvHQGkdgmsJg+BGiiDJ41jru7vc3l7m3vFd/FFxnh8wrPP7fPkp2/yMyUU4jgaZUwLTwhDjg7fI02Umx9/kv3tHomFXkfY232M+M13GQwGfPann0c7fWynS/TJp5lMpkyGY+7duY/6QBoZpgrx1gDrBCkqa9zYynKvjG9TzTCVUE8zPWXGU2niy6K4NC6QM/F8wSJnrskv7puX5fwOzYxXrW+l9Zd13H4WWnJv0eJHCA/qnXK2B8fm5fcumodNvvcXhQnQzNcsCRgjTPOMJ598jqv9mxxby3fePebV77+G305xsXD5Ug+xCV4ibtiYvITj4ZitXszN69fopTGHd97F4TFSEIwlHWwTJTHX+n0Gl/YZ5TkOx/0QuNTf4qnH9rEYTg6OeeeddyliYZJNUF9gTQcnlqLI0VCtoWrCYlTHWi7RRZJfndClK991zf75+RsHZ9Hqryb1EJpzH3wgvSX3Fi1+xHDeLNKLulMuTY55CJ/193Mt1JZ7TexBFC8K1jApMl59/TXi633yXo84SVFjKREmkzGiJd20SzARhRqSJGZnZ5s4Ttju9ZgMjwnjDokTynzKwcmItLdFksYYMvb6lsc6Ozx5fZuT0ZThcMJ4lKF5wEyB7YStpz7Cq++8w/D+IcMiR6IO1jmCN5Wvu6nI2Eioh1Mfdp7oA6KWbeYSvJmHhHjADldL7i1a/IjhPHJf/H6WVHNq9uMDyDrvxx9/GZX1qoYqFIEzTKZjTBRx5949JEARAkmvz0mW8969e1x+p8ONJzps7+/jcahN6Ha3KIqSMs8JRYkVw+joGFt22d3vk25btne3OTl6l5N7P2Dn8esk3Q6mLNhKOoTtHuOTMeN7t5mSgS8osykWraQjEUw9mQmVOgQBGFGCqcn9A+T3jXVWTYyY9xIW5lTJuhmqZ1TJA6o4LVq0eNRYdJFcdZdc/X7WNYvnrDv3rHufd+3F3TjnMy+DKCpCXhZ0e33iJGWaF6gYPEIePCfDEaPhiOPDI7LxBGccFsN4NMYXHp8Hjo9OmI6mlLmnk3SJelt0egN2dnewEignx6SmoB97rl8ecHmnQyeCkI/JxydYCt584w3u378HCM5VUoxS5U/EzAY+K7dEvVDAtA8C55X1g5R9a7m3aPFIsd5n/IE9T844b5Ns0rg1rju2IWuLF5++LijNNPxqdyMhyNpokiKKjwLBWCDGBTDkZJHhq2/d4fOf+hjT40M6EhMXOSaxHBxOObqvDPpw5MZI6NDbimtpRLh7eMwPXn+b7cE21298hP3Hb9G7dIN0OuHK9ad597138Doh0KGcWoJXQhE4PhzxzsGItyclvZ0rjCbbFHGgNA5rYmwIICW+lmFKEYzUvR9xGFWchNkz1zJ49fyh1taX1BNBTf2puvDJyvcVaPOkTZk2E6EeWJVpLfcWLR41zrLA11lh51lpm9JY3Ncs8rzJ0q+Za/NfdcHyX+3e13waWZAvkFN/iBJswItFiLEKRj1lZPjWO3c4MB32HnuSGEvPWlDBRD2yzHB8lDM6yRgNJ+TjDAsQAvfv3UexXL/5NIPdqygJ06MTxsMxaW+L/s4VCmKOhiVFbtDCoqVjNPa8c/+E1+8e88bJlDExhe1Q4hDjMAiWgNGyGvqVEi+KF0ExKBZBFp5Z5jNOdZnYm/MQqdbrMLL0t27f7I8Znc/uIUv3XP53FlrLvUWLR4jFn9+iBv4wsbvP0sE3DZ5uJPcLmIKbGh1g1ng0WP88gsGiAoGApQogJiJ44N9+9UWu9VNeevllBr2ErJxiXcI0y7lz/x5Jt0PUSbFRjFrLvfsHxHGMKrz08sucDKdcuXKFrZ2rjKdjxpMj3n7nNkf3bjNII3b7A1wUMR6OuHv/mIPjCZ3+Djcef4o/fmOKr2WiIGCMBSNI0OqPalSzWnCjGtZc1/s5sxcGcy/K2gpX5kvvbSpzdPnc+SSmMyprDVpyb9HiEWOVgC+y2tJ5x1bJdZNEszTZZjEvcCa5S03+lSQgs5mb63oLG0MWKJg6TG5A6lC2AuIRI7z+5lu8Njym66BrLUpEHmCae7Alo2lGN8sx4wl5GSgKz+7eJb7+jW/x1Re/xr27Gfv7fa5ev8E0yxgOh4xGx1zZj3nuqScI4shKGE4Kjk4yTkYlJupgXYdJeYxaW1vSUnupGARfkXktuVilIvYN5XxWnZklf/a5zHLmUuN6+tyzKuqs9rkl9xYtHjFWLfbzCP68tM4bUFtn2a/uP6/ncIrEmsHFDVLQJsvdigUUU1vIIgIBjo6P2IsTpNPlE88/x7vv/IAs8wzznGmZYgKMJjnR8Yis9DgXsbu3B+K4+tgNPv3pwL17Q8bjMa+9+gpeq/g1iLK9uwcu5u337oO3HByOGefKcOo5PBlx8p1XyLRDkqQgQhkCapSggaDVjFSDzIjdILX2vr4BPdNyZ33Dfpblfu4CHhdES+4tWvwQsIngHzSNTeR+VkjhxesX0zkzvvyq5V5/Pgi5iwhG69meSBWbRQwiECUpgUCapqS9PrkP5EGJgjLNA0EyzPGQ4WRKmibs7e/R6W8RhiMuXb5Kf7DL8GTMycmIfr9L2u+QdiIm0xF7u9sUwXL77hFlpty7P+Tu8ZTMOySOyUtLcIKJ42q1qCwjhHoxlKZ8apK31Ja7Kr5p4Fj4pBp7WNu06cqCHYtTTjcUvcy6VDofVF0er70wWnJv0eJRYo1Rt0iGD2O9n3vLixC8zq3KpWtXzqv21dLKSl7PtUKpgv4GqfzFFQdaogqdXh8zGdLrDhiOxtWap0aIki5ZsGSTArUTvC9Ikoj+9i537x/h/QHBQxx16G9tM9jZQzWQ9jrY2HL3/nuAMvXCOIf790549+4xd08yJt6gcQqmmjQVjMGr4qnWVA0hYI2pB1cbq70aSgWZPf/acYh1jVtddrpA1I2CvqnW58dWrttwj7PQknuLFj8C2CSlLGJddMhN2u8mq351Sbfz8rLuutW8LAbNWs27hjDXmEWQeuFrn2XYUEsgAkkccXwypHQOiRLyfMrxOCNylvHJiLduv4uqcu3aY+zvXSGOU6yx7O3ts7u1TQglh+MTClVG4zF3DoecHJxwcDjiYJhzMMqY2oRQetJehLHCZDKtPFfqMrfG4EQwYjBa6e1W5y6FZl1wF6VyET0DM0JvypFqLGTWKOpcg18YPl2+vpVlWrT48cLDavHvW7s/iyvq0LV1aPGFiLPrLzprvw8lnkrSCL6yiFUDZZGTBA/eY0Kgn3aYDI+YTjImaSDLAknsGGwPODjM+fo3v0WSdCi88M1vfo+Tk2OcNWxv7fLc009zfHLMG++8SYHS39oiy0sO7x+TTQO5CsHFFCqIiygwVQQzqbWOU4HBmpgwC3KLKGaD53jl/b6xGE9Z6mdtr7Xqm/ptLfcWLX488DDEvs4Kf2iC35D+LK7J3Cuvsr4f2HpUVD3GGKwVVAJlUaJFhvUeYxWrAesD290Ok8gxyTxqIpKuoywzxlmJjTr0t3dJkpjvvfIqz3/sef7c53+O0cmY27ff4d/8m69S5AVjn3PneMxJFhCB7UGXS5euMCkDuRqCi4iThJyAYDD1whyBhQduvlPNqJ2LKXK26P0BhSZY51n1sGjJvUWLDwGrEstFfsQfJLE/TN6CrsYgn2OjlCSeoiwpQrXoRhpZOkkXGQ6JgVQg0kASxez2ekynJ5wMp3R7HVQcw/GUNI25+th1rDHcuvUkeGVra4ePf+wT+DLwxZ/983gR3j28zx9942v88Xe+TRBhZ2efEITp6JAsgE1ibBKTeU8SahJtjOK6AWus8IrYIVTRu5o4YuuffcNgZzOYWskqTYGcvT0rRpG5GV+3tmtfkTPy1ZJ7ixYfEh6WoB81sa9bPOSh7iPgIouxgnOCikIoKfOCrdgRe08aRUQoVpT9wYD7xxn3D4+Z5Blbgy5xZAkKeeGxJjC9d8CgP+BP/uRP+epXXySKYvY7lzgejbl9cJe37t2h9EJnMMBFKUfHQ6alx1tHnEQEU8sloc7gQrz5VU1Ea34NtVVvzyL4tYVZx/3SZdlFVwh98fjiILcuEPrDRKZsyb1FizUQkdeAE6p1eUpV/bMisgf8A+BJ4DXgK6p6cJH0VldFWmcdvx+f5gfC2uRrEm/u32gyld/fUp5XsZz/eUQUEamDLNbBtww4Y0mMkABRnhE7iwXEBwZph0G/z/18yDTLiSJLFHWxLqIoSiRyhKC89PJLjE4mFFlO8IEOfQqvkEZIJ8HYCFXDaJJx9/4BuQcbJxhjycoS4yziQWUmuCAqM+29seK11twbWt1oncOF9PAHqtWZJd/6ubdo8SjwRVW9u7D9t4B/oap/W0T+Vr39356ZwsJvctPSeas+45uwycccVZr1JGYWYE22m9IrqrlFlbeKUs8mrafbi6JSr+IpFa0FAc1LyrIgimLiJKYsPUEDpQ+VF4yLEGMI6smLkrJU4miblEBS5nQk5/r+Fj/1yef513/wz9jaHpB2CpIkkPgRzo/Z34l5ZVxQlIpIh1DA6NBzbWcPkylRyLk62GeYHjAulbGf0MtzukkPk3S5dzJB1eHSmONJycjEhDhma+8SpbEYMRR5SW4WykukXnDJICoYD4s29Gx4tR5PrRqD+bGZf/xCnQsgRlBT+cxXyoqutBJrvJZ8mBP7grVu6/kBs1s0Paoz5qi25N6ixcXxa8Av1t//V+D/5jxyP0PNWCXe86SPdTNO17k1CqcbjVNp1SapzCxEZg1EFfSqXu5NKjnFopQC29s7lN6TZRnWWVSVKIrwBCbTCdZF9HpdXBQzneQ48fRiy16nS08itjuO7PgeO4Mu21t9IqN4DUiUYiNDJEI/Tcl9tf9kNCIxCQEhKz39NCGNlEG3x+BSH3WB/G7GdJQxzEpStRAcWQgMpxNwlrTfI6hSFgVJ1MGqmfVIFgttqXE85c+ycFAr636mqa/4oM/nfDVRM1kjqp/5aqzUJnNf9wdAS+4tWqyHAr8vIgr8z6r6O8BVVb1dH38HuLruQhH5beC3ASIXNfuqRN+H5b7xGmSFUDaTegNbk1sVEKuKAWNqk1SbUUZpDM2K4Le3d8myDDGWpNMhz3NclBBQysITRQkYw9HJkCiKuHH9MT7zsY9ycu8d+ibn6O3XuHFpjxtXtjm43aXXSTCqqAZMGiES6BrPpb5y7+SEk+kUHyxq4HA8ZKfTQaOYJIro73a4dHUPl1jCtufe3QPyOwdEwTHNSk6GEyZ5jku7RLHDa6h19YA18wjtjXU8Ly+d/b/JPfEsd8Wl/Qt1Mrv2nHGSTdr6ujR0Uaxfg5bcW7RYj8+r6lsicgX4ZyLyncWDqqo18Z9C3RD8DkAn7ei6gcl1HjLnEfy6/SJN6Nl6NaGVQGGb0MgvwlyOMfXIYmWtV3xVWe/V38lwhIjBa8BYQ9LpUhRFTWzCyXhCt9vlYx9/nl/60pf4+T/3c2xHJX/nf/zvCcNDtjvQlZyblwa8ngrgcVGKsw6TJnjvSULGpW7GeDTmpJwQCEy9cn94grEGT2B3q48rhLduHyAOpITD4yHvHQ85GE44nhacZCUuTbFpQlYUuCQhdg6lxBhD0IXGsbG+Z+L6aYJf9IRcpF8RqhWSZjLJSt3IcoN+btiH1R5FjXWNwnl13JJ7ixZroKpv1Z/vicg/BF4A3hWRx1T1tog8Brx3kbROzdpcIPoHtdw3XbNu/+K9VmGkCWe78F2p2UpmpD4neEWNwYdQz7IU8rIkKwqOR0M+9tGP8su/8it89KMf5amnnuLq1as4J3TCCS/87Av8yb/55+wOLnOpm3Lr2i7f2+tzdJITJxFR1EERgoIJwsAaLvc6HI+GjCdjcnXYPGL6XkZiHeOiJB1FDEcnqFFcFJGVJYdHQ06mOSdZTjARW9s7mCghm0ywKC4SirLA2QgNc7u9IeGqNPzSvKYlb8SmTGsJa0bws6X56u1ZXS2pMLP6ONNyX5F4Vut9dRd4jisAACAASURBVOD9rLelJfcWLVYgIj3AqOpJ/f3LwH8H/GPgPwf+dv35f14gtXXpb7TCFz83HV97zcp9zvOyMITaeq8HVJseADIbYAxCHcmx2m8igy+Kinys5cq1azz/ied54YUXePzW43T7/Soda8nLkijtkmvKF375P6Ic3aN471U6oSBKHU8/cYPXXn8H4ywITCZTbFCslqQhkBBIJBAZyMqC4WSEk5hMLHrXYK1lmk/xqozLCVlZMJpkmChFbUSUJpgkRawjtYY8zyh8WT2erbsiq5q7UJG+6pKmvk6GaY6rLtfLsuVeJ7BSL2cHbON0i7BSp7DYSGym95bcW7Q4javAP6x/TA7431X1n4rIi8D/ISL/BfA68JXzkzr9Q1613i5quW9ymxSEoOFcT5ulbZTgy8pyNxZCFSLFWodKIATAWoL3ZKUnSVOm2ZS9vT2effZZfvqzP83zz3+CtJvS7fWq9I2h2+3inMUjTL2iJqHT2+HP/NzP871/W5LffZNhVvDUM8/wxhtvQznFiifyU0KeE4mllEBqYKfboYwcOi2rSJHBU6hyUJwQApQh4FFK4/GASbqodahxpP0tCjVYTOXxY2x1DlCUJRqiWk1q7PK5b8oimVfWd3UkoGvDNgQNCxEM5iZ9aNoQTru/rnsHmppZfGc2zTM4r5GAltxbtDgFVX0F+PSa/feAX3qI9E4F2GqwTrLZRNKbJhc13fNNg7WL92k+jYBEletfmecQIIqTyn0PMJGlCIHce2zkOJ6M+LW/+Jd4/hPP89STT2HjCFWl1++TpGm1eIgzOOuwzmKNxVuLjzuQW/af+Djj+3d5fTrhrTuHfOq553BJigkB4wvSVMnLjCLzRMbQSx3bdMgnFjUlEwfjUYkGofQleankqhRB8S6AUTr9HrlXbj7xJJ/49Gd4/c03uX37dvVMIgQ1WCeU3lfjDVo1ctL0VBYbYlmkfGZsv45OFQiNRDIjd63iiZnlHtQqyS/V4RnjMedtr0NL7i1afIh4P7NUG5y3luY65D4nn47ppB3EVtEajRUm+RTEUlLikoQk7XHrySf5yle+wvVbTwOVpR8nMXGcYJ0lThOiKEKswVhbhc01htIaCid4jSlCj6c+9QKT4ZhX/vg/4DvbbF29QXZ0gEyHuHxCf2A50pJcFadCN7Jsa4Q1lqkVuhooS2FawNB6itJXPuYWpnlO5JUsL9nbu8zzn/gUt556lj/99rf5xje/RpJ0MVY4OTkmSSLcbN4/gKJiKqlbG9u97hHR6NpVBMtF99FVPX62vSrSn1F/i3V/noa+eq3O8roeLbm3aPEh4GHjxKzrps/X2rw4kjTCe4OJqhHTfFpgIodLIkoVJEB/Z4s/9wu/yC988Yt0+31c1CGK4jmRG4NzjiiJsc5Vi0cbU3mPCGiAUAqdNKLMI0I04ObHPsP9oxHfe2/I1Wef541vf5MiG5HEEVpkePF4AZWANdAxBmOETmTY6kdkpTDMFS0KiqLEB09kKpfTJI6xUUrkIoYnQ3YvXeELP/+L3Hj8Fl//xtd57867RGmPoNWgcGVY11ZyCLVCsyiyLwg0Cmrmuxb1eJhr8IE5+V+kSh4mGuhFZ6u25N6ixYeIB4kMuSnI2INObgEYT8fVAGnwhKBEaUQQyPIMG8U8/+lP80u//GWefu6jxJ2UIJVsEyeVlY6Ac44kTSvZQ6vFpivum/vKR4AESJOU4WhIunOVZ3/qZ/n21/89z16/iXn7NlJMyEYFPs/wVgim8kBxBmJRjECsIM4xkSpO41SUsYE8CCHL6He7BBF6gy2eeeoZIhejAbYG23zqz/wUV67d4N/9+xd5+eXvELwn+Ly20k1NypVlXs/LxdQhGBZXM60Ke2U+0hqe1eY8NvP7qXGTC1ju66SZs2j+kZG7iPwK8D9QrVT1v6jq335U92rR4scRD0Lsq9cs7+RCVuIiothhDBR5QRwnFHnJeDTixs1b/PkvfZmf/flfqKz1tEvS7RCAJOrT63VwzpLnJd4HrJXK/1zng5ENB1mBCEULiKwl7fYpsynb127xqc/1KU7uEW1fJh8fcnz0Hp04JY4jIuPRvCSIIB6KMhBEyPMpxhsoIQRPwBPEU2ZTImMIYimyKf1ej0G/j216GS7i+s2UL+3tcePmTf7Vv/pXFMMMkWog2ojUg9LL5NrE2JE15bvWg6b5st6b8RQ2ae4XueZD09xFxAL/E/AXgDeBF0XkH6vqtx7F/Vq0+HHDRQdOL5QWtcV56nzFiKlIq7Yym/t6rXR2GyeIixA1/MxPv8Bf/PXf4MYTT+GSlKTXAxch1uJVSaKEoijJi2oiUJzGGCuIrVYsWiV3AWIDBKUoPN1BjxHCdDpl/8Yt/Hibu3duc3DnLbqXbyDjewzv3ydNo2oc0nvEWqyFUmA0mVAESxmUwgdKDQSB7W6KMWDjmEmecXDvLlcfv0WUJEyzgl7SIXERvX6ffn/A5cvX+L/+0T9gPB5X8eUFsDKTWowxEPyMvINq9XwLrjKLfu6qOn9onf23VJfN93WkvHQOiw3DbA2r5q6nzj8Lj8pyfwH4Xu11gIj8faq4HC25t2gBs5mksOInvcblrfnz3p/aJ/WEI1/7aQtaO2hUxJ6kCaPRiCRKkDB3wfO2SzCGAFx/4hl+6ctf5tM/9Vl6/S1snICJKF2CdRHGOWJjsFLNRp3fu1ppyWAwZkY983XpFLw3BDGoc2RB0H4PN+hyf3hM2uvwzOd+DrPV51tfe5F7t9+mUwjJ9F1iq+AUbKC20clNoBBL4QOal6QqdOKE1BxWE6AkEGyEMVXkx0IVUUOMJY5iirLAOsdzzz3L1l/+a3z9a1/jpZdeYnhyUoVPsA4nQmwjCp9Vs3brxghnUfWzelFYcotsGgZp3BypJ381Qs+Kt5SpZ7VWbcH8c36Hpv7nRdp8X/KWOuMde1TkfgP4wcL2m8DnFk+QhfgbzrnPbm/vNh78zP6v/5P5RaeObdxee/1yJlfdnNYcWd29FqfOltMbzXtgUGKUBCEWIQ/VnDhfj8pbmS/m1ex3SjU7UKGoXwCta1rrQX9ZshCqO4baomgsuwWDgMXRoHXHQlCKIifPC0IIs4Oqc/3xtE2xmERYeJnrvNZl1UyKOcswPdM20Y0bK3urZwzVrMoHF6Z/BLFxVms9oLc60SXPc4wxVRwYaxGp1gotfY4Ryxd+4ef50l/4Mo/fukWvv4WxEd4rUg9kWjMnliqgmKkbkSYPFQHOF6RbFqKrKC6KF6kWya7zt721xXR4CCI8+5GPcfPqZb7zx9/gB9/Imb5XMC7GePFo0iEEj9cSP1F8KRgn9PoRDqFEyDMwNqY32GF//xqf+DM/RbCOMihOKg8eEfDqMSiRi3j62WfZu7TPs889x8svvcSrr3yfe3fvcjyZsNXvEVmHilYNm8YE9csN78LAaqN9r5XgZb1Gs05eOcsib347q2d8KJr7eViMv3Hp0hX9T3/tK5Wv7MKLa2qXqplv7sLxdeeum4Z9ev+8y7vuR1J9N0v7G8zPAzi9SPFSOvU1QWxluQBWPbviueUMTxrDPoKZWN6zBW8xwTrHNU24VDjEGt6SCW/5EVtTMJ0u90vPO0XB0BrGRihQgigalK0sqV6yUJF65QtcUoaS0nu8D9XkFAJBqwkZIdTkHwJaelS1HmALTCYT3nrzTd56+23yLIPa6gsaqh8dy13MVVIp8wl5nlOW5dLAkbUW59xSvay8F0ufG96dhc+ZbbS2C6yqjIejjWn9OGH1fW32wbxxlYX9qsp0OiVNU/I8R6KIEAKjyYRkb8Bv/OWv8Iu/+Iv0twZ00i7WOnwAaw1GKq8Xa8EYRURrbXqB2Ot7VeQurM7wESo371APinqR2YLU3peoCmnSQYzQHezy0y/8HE9cv8YbL/8Jb3z/Ne6/c4fsZMLYjxhnI9wgQicTNMsxohgf8EXO0SRn78oend0r/PKv/gaPPfEMb987xtoYG7n6favya40hjiNQ6G1t8cxHnuPpZ5/h3t27/PE3v8H3v/s9Du7fZTyd0klSPIpagxVD6bP6+VnP5IuDqBvGQZp62UTqm977ZrB6fQuyHo+K3N8CHl/Yvlnv24h1xLx47McPzcSISqsTVSLv6Rphy8Yk1hC8J44ciVG2oy4q4Mclk6IkIsI5oW8jnAtVd7vMGQikznJSFuQiqDGUWlIYj1fFm8raL8VTiMeXAY8nEOgUgkfwUmmY3laeDBIEg0E0MJVAwFOGkqLIUV/WvYIqet+F36wFNFLARWditliPxaBgi3+V5ahzF72ZFV9NRvK+xLnKehWBW088zq/9tf+Mn37hBXZ2tiFo5QXjosp9MXgQwYpgxM6MIlP7k8zvDYjOiHPOPM2W1ta6VquWynzWZqmBKE4qF8jMUahg4g6PfeIFHvv4n+XZ2+9y7/Ztbr/6Bt/79rd57eXv8u6bb+FxSNeRRA5DQLIp2t/l0vWb/PKv/hrPffKnOJp40v4WJ5OMtNPFOYc1gklinBWSJMbEMZ3aiAnes3tpn6eeeZp7d+/wR3/4h7z4R19lOp6Qh6rHEllZS9bAQg94+bOpq7WXrBgxs88zbrHYXlzEl/5RkfuLwHMi8hQVqf8m8Fc2ni3rC2HTvs0W9enGYXXgSlfGndbP+mPttZvSOH28qTBBTWUlOwKxL+nh6GMwCuMi5y5CGYQ4JGRlwZ0i4131dI0hNhF9Iooko/Aeq4H9OEGNZVuUoIIvhWkReIMpLnYYgbLMyX1Wkbp4VD1CQEwXRFD8TPLBCMZU1hUopRM8MPEFU59TBF+dLeBd/exhg3Uxs0h0SR9erbvF3tgmrHP5W3+sIp4LjjH92GO9EVTJT3OKbX4PhqSTEkKgLEuyIufjH/84v/7rv87Tn/kMnd6AOOmAekCwThAs+Fp60cZir29Rx0E3prbfZd5LXarOBbXTEarZodJIMxUnBQWxjqACNibpRxhVRkVJjsFeirm+e4MnPv5ZPvel/5hiNObuO7c5OLjH3bvv1UHDwEUR0tmju73LY48/wSgrSbdi7r97h85gi06vj1jBWugkXZwRjAhH4zFpmpB0u+RZRl4WuDji+uOP859cvcrNW7f4Z7//e9y/e5e00+Hk+JButCyxzogWXdlm3rrK4pGaG2ZKTXPlikvjQkJLck9z3QWt90dC7qpaisjfAH6PyhXy76rqn170+lUCX7T8Vs/ZZAmuXl/tOzPPSw1Ck8a6e88U3fN8koMh1OQueDooWyqkXsmzjMPxCe+myo5J2SsjiiJw4AMHNjCI4IqH3dwwTA3T6RQrhoF1hKyk55U47iDWcXd4SJZUZlkpcJDnyHRavQNl5VYmCiNyfD3UY2tLPGjAh0quUVHykBN8yTQfkxdZ1RAYxYtSUi0gnITT+m7zzM1fo9MbY2bfV+Wy1evW1eN5dVX9MJb1+4t6E/y4YV2vFpq6qG27GaFUJZNlkypejHqe/+Tz/PW//tf55Cc/SZZ06fT7CEooIY4jXO3zJ84gqjMtXeq0Qp0HtNbaF0h8HZpokyqNNVSRWEDoRBGF96hC6SFgiCMLklSELxF5XlKUJSZ2GNthEHfZe/o5niYwzaYUZYEYoZQOhRfG04yoa7h/fMJgZ48oSUnThKLMsNbQ6aY4I/gypx9tk+UZKpB0O/jCETlHGsdERcELP/sz9LcG/Mt/8Qe89uorGGuXZKelZ14g27Wa+EoBqa6hdZlr93UnbCWRhbSa4+f8VB6Z5q6qvwv87qNK/8PG6oSSjSQPqAREK0lm21rSALlCqEOoigasekJWUJQFpptgYwcnHsk8mVOy3DOIHMYHKEuMcRhRsrJEjWGHFCsJh9MRdlQyMDFpnBC0BFMQu4h3LWTTDJ+VoJU2X2ggF6UQpbRKUgpF4bFZics9zlddfrWAkWqWXvGTSZ6PEpvGFjb1ZjaN56xLz1pDmZd0e1289zNdO01ThqMRxghx2uW3/8vf5nOfe4Gi9GSu6gGKgFhbTeUxVa9Mam8PIzRqOhjB5/XKojWz1+oNYprngLlFXzc69dTNRq+v+ghV1EVnHYriJEK08voRA7GAuAhNIkKhlEVJnheM8rIOcGYoTIfSJShCbFOCBGw3xhnL5f5WPfYUiCOLagcrgSqYQBWLPooSbBzNNHRJEgDKssRGEVEU8YlPfYo4jvl7/9vf4/7dDDGQ5zndbrcytoxdMmRWSVmkCumgKz1dZdlbahFh0Yqvx8So62HRQ2YWtfIMtDNUHwKLfqiLOPUDrB00qoV4AxGWjgopBpekZDE8WRpiH7BkWKbsWuWSc6Q+MDBKIh7JDU4dkU0ARdKIHGHqAndGY1557zYnRwV3jw458TnjUJD7kjRJSG3EIEq5un+J/qUuO50uzqYwzsjznIkqJyZwYkoCgX4B00nJZFSQTj15odj6x6ozbbXFg2CdEbBpMHodmZ81TqGqBK9EzjI8PsZFES5y1ViPhspy9Z6/+d/8TT7z2c+QdBLIcgqlXhNUayKuAmkZo1ixoJVnVyP0QOW9VWWhObexxmXWIDQkPyN37HJZUFnzYWa9yswLxAOiAROoYqQbwAmCQ8SQuhivQql1+Wnlgx6p4PMSCR5jbRXXpizwvqCZM2uUmaahWIyxiNTxBFSResxJXISzBlv3OJ957jm++MUv8k/+8T/Cl+NZHYQQsMZU5S/QdE50geBVGuOukaJqQ+mUab5Qn002Z6TOTJ5Z1R7PM7F+jMl9dTj6vO33eTdtXux5+pX2flr+gbnu2eQEFNNIISHgVTFiuYlhSkFplX7PEQVwZUCKMR0cpRTYMsW5tHbPKjmeTvjT11/lD196ie++9y6HWYELu5RWKJ1AJ8amMXFRQlZgcs/+vftc/X7OY/1tntm/xpP7V3E94WAy5D45d3XKUAtcFuioI447dAa7HGC4n0841IIsy/Hqcbi1Gtc6nfyDmCo/L8FZ4S692ReUIH+ksCj5Le57qLRQrBHiOMIYgy8Ldvf3ORkNycuC3/yt3+ILP/95+v0ePnhc7IhLJdR2tNSWIVK54tpGGG/cP1geT5k3UIun1MSpTbRFwVAtwNFIDlVF1Y0AimLqwXqZpYD31QQi6xBrqx6CFYIYQt1OBK1i0NtQkWqE4qzF+1B72AnOQlHMwxpXiVfNlaCIaRodBQ01+Rvi2FaNT/BELsJ1LZ///Bd4+603+fqL/3r2roUQ5oOcS3XI0su46t9+XmyYeWTKeeMl2khgD/Z+/MiS++mBo0qta+wBZr4ozXdqb47qeAiL2v38nDr1c+4+qzaWqWPRgjptTS3PNFPGrnqxOrlgQ8xdMWRG6BmlI47IxAQyIhMRqdIXYVBbQVNVvCkorWdvOsR3Ek4i+Pc/eJ3f+6N/yzuHRwyHYyKxpGKZppAXBT732FDAZMJUhF63R5Q6DoZHaGeP4UR4463bXDs+4qNXLnGrk/JZSbFHGcVJxptRTteVpPkYMxpyT0v+sFPw/5YnTKaBfhkzjk772y56bhRF0fiWnzrH1pZVCAFr7dqxjuVqquuiESObA7KwvyGndWn8CGPVEeBhYYypvJqAJEnwwXN8dATW8Ju/9Vv8lb/6V9ne2UGBwtfWLaHS1GcrZVPRsVQD7BWxzJUBDZWnC9RW/kyPbwaz64HXxoql0tbndnv1O5o9Zah6s1WDIPOfnFLr/VU62sg+dYaEer3XqmuCBDBWCHW+raHKv4AaCGKqzONmjVMVqt7WJ2ndawmzIYvIOibjAmcseVGyv3+JL3z+C3z3W19jPB7jK3KpnsoIDe1onefmISvGWgz3HGiahE1v6ez9rXsmizp8M8ax1Hic8V78yJL7g6Ep0cVHXf2xPIglv3jusidG451w2gtnZbvZ10gzwNR7fFkyzqlm/AGH6ukYwyCKGBhDaiOML8HEqCpRnDJOLG8fHvJP/+Uf8OL3v8uBL5A0RVyMBmE4nVJkJ3R7faKoWosyhCpmRuos6j2RE6aUoJajbMKoGDIaH3Gv3+OTl69yrdcjchFXiynCCBMZuk7YkogDSXhVc45NSRbFdTmw9OxLYxBneLmsGxjf6BWzUJbL1aunT7pAej9qWHUI2NjInYOyKOnGjtF4jHMOF0VMplP+xn/1X/MbX/lLdAf9aiJTWRBFcWW9m1AXXTO1dL5uKlqZTWUAH8D76jfQeFQ1xlYjv9CQPMykmUbuUZUZ4ZnZY/maUOvh2obcEYwY1EaI2Gq3goaAhEAaWQIQAtW8DdVqURG1dT8gzNaDVULlCy/S9JkJVVaxCM7GVR610cur7/l0StJxGARrhE7aochzLl26xJVrV3n1lVdBFescSuU3H0zdMCzWLQvEjs4jUZ5juZ+e56Gz+hCRWdiDizDZTwi5/3BwluSwjlQqHU5neltB5X6Yh4DUltZ7sRCL0i+hXwoDDF0VOtZiUZIo5Y/v3OGf/P7v8dpbP+CkyIk6HWIT4R1kecH2/i6hFCbjMVHUI4kMeV5iBFJbTXSKk4QsiTCJY4pw7Et8PmV0VPDWwSHPXb3OM9ce47FDy7gYkk2HRNNjLmvM08ZyvbB8Xw3HLoF6MsdiOcz8rXU1HsYy3q+V+pOCD0KOaWBt1SvtpJXbY5Zl/MznPsev/uqvEicJRV7M+FOsxxiDDU1rqbPVgqAikaBQakXuZal4X094s7WuTDPgKjThhhWdWewV6dcNlczJqHkrDMyMAKGyrFVNLZeYeoC2bjJCNckuhIIkqqfsV5MzCOpRUUqTghqUSlIyRqqoZSo1qVcEj1LNkBWwxtaUXxOmBowKNkkpioIkjinyHK194ZM45urVq7zy/VeAKiKmL6sYO7LwdLrUiC3LMBch97BoudMYiNQ9gwua7DVacr8gVr1jFrFpv6mtE6FZj7L2NrBSDeYIjCODCXDslbQMdDUQlyUdgV4Ucef2W/ydP/g97t29SzeO2Yli0sSgmqPWcciUNIqZjHO2EkdsFGP+f/beLFayJL3v+8VytlzuzbtWdXdVd3O6Z7p7ZnqG5JDD4czYFGlqRAqGSA0FQrItmJRhwoAtG/aLKcgL4Ce9+smwHgzbD9r8QNgQSEO0bNmADQokuMzW3dNLdW1ddevW3XI7a0T4Ic45eTJv3ltV3T3TNYQCSGTmyZNniRPxj+/7f5sjCpX3nMhnaKmgsOgwop/0UbpHnhcU1nJsHHkQ8PDggEMZ8MVMkJ9N0OMJozxHSYcCRGkxUlEoRfiI/lk3dv8VqF8gAFziTtpK9V02yv+pBVchBKGWpLMzkv4AHQSUxvKpV15lkuUkI4nUvoCGkIKyLHwyMeGlXbc4KIskEx5krPWeIMYaz/1WmoaRFDhQAq189LWzNSI3OCf8hTuxcINtyByQdVFp8G6VomXZRBNz0WgEwlMtAomtKr8QtNHSBmcdUjtC7bC1Dzt4A7MUYKytF5PGiNvYBbyrT0v5Wr9dBwGZqbAIKud8+g0EOgi5srtPFISYqvL3pFQNst2eq+vO4nl5a+oFtKZZRPMgL0BnsfRpeVm0dkFrPY5rw1ML7ue9C85TAU1bZ5xad5xHUSmX/W/dNXXV6PO+x2IR2YnnH71U5MkzIbwUYYznHHMElRQU9cSNlCObnfJ//sG/4Gh8n14i2U7g2vY2idakeUohJRtRROEyEIbR5pDZdEYv7iGQVJVlMIxRUmKsY1bMEFPQdUbAsD/AWTBW4ELJ+7M5rjL0qpyrTiKSPhPnuFXNuSMK5qKHsqqVVC7y+LiIb1+lHh6Lnqn59CdZVLseDYu8OJ9Ue/xF7bxKvvZoLbA3Yf9lniIVzNIpIX3iwZAXXn6Z/uYIdIiVfqwpIAhDBGBoPGqaxFbNHPPPxfPt1uuaNYhau/y8dCMJQy2yNvdBDfQWJ+3iyhuRXKhWGm2rGnVoCNfh8ZXwuWQcmqKs6vWgplmEP6eQAukcPhdXPS6lQKL9AlNz18LamsoWWGeQQqCkqiV8H/NRGovUmrL0AI9SiCCgyipGw036cY/pdIoxxrugVhXOdsHdtfx7A+jtc6sXUQQXjsvWMiho+6ABvkYnFs0i+oj21IJ70540AOlR+zyO5NRsv2iSOecujbIUNUDjFgPb1RPBSd1OCOcgMa7mBH32vLk1OCVJleHND97jnbMDrm0odvs9ntva5JmNAf0oBLFNZi0PZykPT8fcN4b97SHzWBFFEWEY4hxorUjTjLKsSGQAtqQwDhEnaCGwYUBROZLekCzPuRNYtk3AlZ19jh4e80Ex5Y9VxbuRI0USFuDkstzRNaY2wN7lBS8LXFrlnc/3ecPfPvr5dq+ney1/rlpXA2pIbyWJwh6JDhBByF/51V/hJ37qS/SHA+++6Gpnwxp/m6IUzoklYPd95ZGp+W7tAhyNMe1z8+PfIYT2aX9XFuoFH+/b6lNwNbBfxB833L1ztLSP1h6uvBTuOvSOP9k6g3ydfLF1sGgPDufGng/QWp/3xTl//l6v542qxvhKUKbCL1wrlInA5+dR6wXOH4bQ8dSD+yfVHidI6TKpsdVMWQxsL817tbT5PbAFDocRGofw7lzaMR6f8OYb32WjF/FiErGz0WN3IyIJLUrmxMmArWSLK1cjnIx4MCkZDAY4aqlOKR4eHgK+2vt4PMZYSWUsVmpOJnOqsmSwtUMVaZSyiFAwcXAnT9mIAqZBwOG04tvCcag1lZMEtsSsxF+sSpwXGkhXJtMPqnV5TSnl5ZLBj0Brx1ItsXkPkoX0poOQeBByfHrGX/+1X+PX/61/m8HmiLzy+dAbQqQRJG1NxzQScgPejaTpEDUl0wBjA/LeE8o5RxhqnPOSayA0UotHdnOXZLiYmGgoHP+PFuBFTQHV1ytdnR3V1RqFg+VlwtUOdf5M3idftvdpWQgbUvgC2qtaevflHRQ0g8GQo6OjdrHzErzqUJHNquUlbinOBys9inf/uNq/Avc17SK64aJ9Lmpdh0FRz0qJxGefDzJD+wAAIABJREFU9YPOiqKW3PHlyASIynDrnRuIh8d86vpVtqo5UWlxVYXoB6gkQcQ9VNwnDjYJwj69jQrnLEEQ+CLFSjHq+6g7KSVnZ2eM04y8KMnKCq2hcBIdOsJeTGYspsyI8oqzQHE7jDkajTgYTznIC3Lh1V4rVzNOL/pjiS9eEcl+GKC+ej2NtvCjBu1iBQVrZb9OuVtLqQLv/icEuSnJxikvv/IKf+Wbv0Z/Y4O0KIiSHk1wpGyAHO9S1wzfBbB7RbNJNtBI7A2wW+uldR+nYQCNUgprzZKhz1+eOyfkeDpljaTeuRbRHIBFQJTfXzRsRufPC9ZZLP2wOIOg1jKd8OAsFy7SztnF+HAd7ZP1wkpV+XGfxD2EkAghvZEZeY4ybhYaf6xPjhZ8OsD9nM629LQ/1CEvkrQvo2FW91tH0Vzkrra6vTvUmpsR9YaF1CIoVYVzCpxBItBOkJ6ccf+t7/N8f8hWZQmNQDuFlAEEETaKMGGMTDZJkl2ECwjlDGstZVkQBgprKkLpw6kDFTIa9hnubVFZS5oWRL0TRBDxwYMjrM2JwoCNzT2KwznvYDjRguO0wG7tYu4XqDxDRBanWdiP1rR1Ku06YP84QH7tAtuVQpvz/IhJ7s04aQdKw2U3MoEn3TtSfMDO3h6//V/+Vzz3/Ave06WsiJSkMj5HTONW2D0etgH3xhDepc289O4s7UuFi5GrlEJpiassXbfIRVe7dhJ0AX01q77nkGkXHliR3DsikjeiLva17VwTnU7r9GK7qoBE1gDv702KTipwlrXK5lzdZq2X0qMo8kJaDfD+eLaGLEF3clwkoT9Kcv+4BKCnA9zBh/5SG4mEl7aEw6f1RHj1smOUWwfSFxlLVzuyy/V2P3+cUqUDzNLC1Dz0juQrQMmAykqMEJTGYYXljXe+y9nkiJ29DTAppS6JtCQIFMpKhtGApDckSSJ0UPlBGwQo61Ba+VwWzhJIb1CqspwwDFE6QkUJYichGuxw594DRlu7lFVBvxdTFClshuy7mLQomcYa9eI+Oj9DnRRgVJ1Xulq6M1mHYTecrKglSmsX1EgQBHXgSH3rj9XXlynv3c52SzFNTfSNVr4vnjZov4y26t7uqoAghKhBvf5FCiyCwcYG//F/+p/x6VdfZZ4VGAe9fp/pvCAIQxyiDun3Iqa1rmPIpANMiwWgBXXn+WpY1FYQUqADWV9G915qObu+xKW7bKRwV+d379zhEqMiLn7q1i6Mv43B13eDav+xdlg1NiDR6dGVE3SxoiuxN9y4MaaW3htBz6dDlkpQVWXdh00Qpf/caD7nL+c8uF+2sKxr7TVess/TA+4sPd+l73BeClxHnazb57JF4FHeMqsGvkd55Vy0mNR7rN1UVl7Cqeqw66Ojh9y+eYNhLyLUjlBCEgcoLQm0agspaB0glEQohdYBOhDeRctZSmO86uw8sKowJAg0xvoqNEGcsLcbEcQDUIoP7t1lNhljraGoLFpKNgcDtNKoIGT/yh6n4wlIhemUGrvIjW9V+X6SxXN5n0cP4HOTdOU4n7QLZiMVwjJYrI6rhRTp86i3VISUbWhiEIWUVYWOIvI8J9KapNfjt/6Df5+v/OxXySuDCgJwgjSrsA6MdZ31wCd+k3j/9VZBrn9rvJJMZSmKEikVoCjylCTxFZEqU9LrJcSx5uDgIb1eQhj2akAXrY/6gpZpU4/RZpZ04ISgYzZgiUoRNanhaPxD/LU2Ir7vwAXAiVoAXNEQW1tBfebGvbMB+27CL1sH/jWVq6qqIk19wZk4jn3t2KJkPp8zn6etJ1YgdE1h1UX1OjSMEKKNwl736l5r07oZVLvHadrq4nPZ7HhqwP2RrcaLdSveOgBuPj/xaT7C8Z/YSCJ8qlNnAWMJlOSDW+9Tzs/Y3BkQK8cgUiSRIoxD+v0e/c0NdBh6NwCpsMKHUWu8yinqogxWCaqixOKIw4goisiRvnCBUgyTPsOtXUQQoAPFrZvv8+DwAWkpCKIYKSDQmjCK2N4aobWiMna12M7SvS9etJkCm376YQJsF0wfJ3f809Y6GQE6G70AYJ2jKHKQgpOzU37lm7/KL37jLxIlfR98ZH0iMVu/l7ZECl+gxQqBUgKJrwWwANSFMdRrYBbrrZX+sxU+5Qs+rcRg0CNNS05Ojtnbe7lNILZY1Lvfa4AWC468K8G3Lu3dtqIMLDj9S56jaHduNyxoooU/exNU6C9p4SHTtDAMyfOcPM+Zz+dkWYaUkul0WqdOPr9Y15kL2vM2oLu6mK9b2LtC5Lpx+lHG7o8MuLsVVeoiCf5xjKGPPNclUvtHOe7583hwl0KilSCdnHF47xbbw4QkgDgALQxSasIoQErQgfYGU63p9fsI1SMvDVJWC9VX+AAMoAVzYwxCK4qyQEaGMPAS/CzL0TogCkO0UtisQDhLnmWUxhAnCdvbW2xuDDh8eHzeIMYqsK8E4HwCkvOPOrjDitbaebfOomXAZDblZ77yFX7jN/8WQRBRlgYdBIShAOklQGNKqsL4vOpC1CJ7k4GxTjfQALyQ7VnLwlJV3o/bu/tZyrJC2oqqKglDxb1791DK1xnQWhPF4VrstbXsXoveNP6PTizucnVJ8NsaCbXD71z4GDsLywpYeyNq4y0jaDxmlsBdgnQS18l3VJYl0+mUoijY3NxkPp8jhcRUTcYc0y6EzXU2i9BCuXC18Xl5jvjrWlTVehyA/zDeNT8y4F4vmZ2vywCyuu3JDn0x/7XufOvAf/V/j/swaoWSQEoO73/A5PQh17cHxJEkjhVaGAajTYYbG75ogQ7o9Xv0hhskvQSLRkiJxktqRWWoTIUfvY6iyMAatNIU1idaSoYCrXwseVEWCLHgxaUsscbne+/3EuI4YjDcYGd7m/sHD7yqvtI9qwutXLMwPi4ls9xvHgwuDWKiyVGy3giualvNn4eWFzk6CHBSsH/lCn/3v/i7bO+OKEvv+VFVjqpwGB+KiZaa0pTe90Q6b0CtxdnKmjqJVR1c1GSIxBfT9vlkBHle1FxzSRCVaC2Zzwu+853v8qUv/STT6ZTR1iYLiF4gcQO5PpLD/+YcrZFUuPWJKkQH0FtPns5icL6dPzc0tjQ/Fhq7QSNRu05iQYHwabmFoCzLdswURUFZllRVRVmWlEXR+ribqhPT0aFHXHPHrvt+PrBvFdAvcj7ovj8pwD814H4Zlw0sAbv/ur4T1v13Xedd1rmXgcFlwL7OcHtpEwIpNNZUOGE5OzkmDiP6/T7DzRitDMNewmBrC6REhgqhNCoIUArydIrSEYO4T1UpDB7MXB0yLgRIVbuvOYs1hiCUSKlqw6NFK8V8Pm/vS+Aoy4I0zYkdaB0QhjFRqH2JMiVbQ1b3Prtqp5AS56q2Xx5Xel7t++VQqAv+05nUy1ykX7DCMPyRAvcFWbLcHIAQKO0X5b/9n/xtXnjhOlZ446azoOsxWFQV8zSnyH2AjQSfi1+I9gSVc61p3/dPHe8qHGXp+WchpA+/b0BMFERRyK1bN0mSmKoq2dnZJgiDzlV2rt65dtG11JkeOyDfzOlVUqf5Xyu5P1GreZ66mkWjmTTBTN0I3NZAvNQPtJlLgyDg5OSE27dvU1UV83nGeDyhKIoFxSKWUw90wb0RmhoNctWQepHE/nEAOzxF4A7nJyd0b2xZ3Vr+7dHf16k/FwH8Ze2y/S4yfFx8w35F11JSlTnj0zPCMOTo9AxhCjb7ETJWhEYRByFSSY7HU0pnffCEDFBCUc0mmCBECm+596pgJ3DDWCpToqLI8/HWgHPeaJRlKKUoqxKAMAiYzVPA5+dI53OCMCaJ47qazsX3tGQoemRPflxtebKsPn+t9Y8UuF/WtNYUZcmvfvObfP1rX2OeFQSeq0HUUnlpaqrAehQv8hKJQ9f0gxVQCUElpc+BImq0d01cAG1WUa27UakwH48ZjUacnY156aWXGI1G9AcJVWXWjPWONNvMLxZAfinD0v3qFh4yT/QYhdcdFsLcQorvnqj52ggWDYXpnCOKIk5PT7lz+w67oxFpmjKfzynLcvEfuTjOgp5ZCD9dUD9vDD0P8I8jtD5ue2rA/aIVDTjHoz0OEF8klT/uYnDR8brfL7r+x23OozsgKPOcNE3JZ3OmVcp4MqbM5uxsb3H96jMMBgm72wMi7UBYtABXVTjjCFRAGQ1QSiMcRGFIFEUoFWCNpSq8aukB3avfnpIpa6+ADJzPBT5LcwKtsNYSBQFWKkxVYYznbq27OIipeRfgIyjpqL4/IIB1/qRLk6c7MS6T3IUQ/wPwbwIPnHOfr7dtA/8YeBF4H/h159yJ8Af5b4G/DMyB33DO/fFjXaS1NNFE0jXX3F6D768avISgrtwlKG1FFCY4AWmWEcQRV68+yzd/9ZtsjXaorMNVFhVocgumspSVoax8+XMhLI6SCu81k1tovDmkDH3VJi2Zz+aEQUAvSZjNpsxmU44fPuT111/n9GiKDjTzyZygHzIeT0mSHqPRDkoF5FlJGGpaxxNLm1rX0aQoOA9UbjVyUyyepx9KYlWWqw8sahdpCERTD+AyYcJ7sSgBQjqk8GPEOAnWYpzXVpwCoSQICaUhSBT54YyDgzv04x7GzJnMzpjnM5J+zHw2877+UtS5ZRYkVFfzqNqMwue1ynWtpRc7fdL0z6qMuxozsNrWn+ExmxDifSHEt4UQfyqE+KN627YQ4veFEG/X71uPPtLKqsZKZzSvZu81Ev65I65ZKdd9Z/X7Bcddt+Jedo7Heflz+2oxZVGCc5TG0BuO2Nq7Sm+0x8HJlDfefZ+bd+/x1js3uP3BPSbjCWWRY8ocyhxbphw+eMC7777L0cOHZHlGlmU45wjCgF4vodfr1YaxEqm8Ch7UE1prhQ40QRAwHAzY398n0AEPHjygyHO01lRNAY5O7pj2tXpvLIN6Kz11Xh9nW6jwy81LYpfKL/8j8Esr234b+OfOuU8D/7z+DvDLwKfr128B/90TXGD9vtBqWvrFuc7vzb7C5yQRPpKyrEqU1qRpyl/7tb/G88+/gDEOJRVKaqoKfDqBOsVA+xxcs477iNZapjTOYaxtDYZZlnFycoJPsmURAiaTM46ODvFFSy2z2Zj5fM4777xDvz+gqiqkVFgrSVNDUTjK0jWFlOqUAGLBny9wD+hmD22uW6z0TP2qNYt6CewYgBcJ1Bo3yOUXLeg3o0+0L9oFlaa/akJFaUVe5J7+AuIoosgzijxjns7aiNwmzXJ3cWmPLZp4HVFXhlrECHQ/XzZHFlyRf7Xblm/q0vZxSO4/75x72PneTI6/J4T47fr7f37ZARy+PmNjdGlUf4Goc2gsuLluWzXkdbe1x16Z9E2FF4TwWRtFo5KyCPuWj04wdu4enkByX1oMhEVrydHhPagKIgTaOQIh+cLnPw/ScXLnNg8PD5gf58yOAoZhTF/HDJOIoiwYnz6gsBHTyYzp6ZSj8Zwg6THcGiElbGwMvCuk0egwxqoYFSbgYCA1gzAmRRAJQRb71KpVkZEISV8GjHp9pvMJpTa4QCGKReYwWU8L6wwCg5Ler7qt+KMkToo2DWqnQ1tp9qJ2OQm02Mui8QbBCtENHtEhIuzBmvwedf//P0KIF1c2/wrwF+rP/xPwL/Dj91eA/9n5h/cHQoiREOIZ59y9R17i+nP7q1+h8jwoScrK510vq4qwLnT9pZ/8Kb7xjW+g6qyFgejmQ7lQO1nSZjpXQJqmNAlkjo6PGI02EEJQlSVSSiaTMVmWMZ/NyNKMd7/zLT7zmc8wGo3aOqVxFDCbF21lrebVpPRZKN/dJGD1fbsORVPvuzxGOju45Wtf+v2xSMDuPss0kW2pH4fWamGWFZ4Ky2Zzqsowm80AT1sFQdBmhBSNPYMOHtV8eyOhrwqHxjxZWoLHwbrV9oOgZS6aHI9stfmlayJbUl8vnhCPT8G4dgFZ5f5qjwuWlISl862e60nbKvXkwR2KImMyPqXKc+aTM06PDkjzktt3brO3v8NACgb9AdNxxenpGTdv3GQYBiRXd8nTOQ/uH3A0rqiQ5E4wffcmqTGEScyVZ64wHPYY9vtcf+4Vru9dIUkSH9wkFZkxRGFIY4dSQjKbz8mKkjjqYYT3kZ5nGQ6BrSzSNQbS1ny0rGEJ0ZkgnX7qfq73u6wXH3vBPIdbrpXapdJrdri0XekA9n3gSv35OeB2Z7879bYPBe7+Ms8LEA4vZRtTEQUxpt6nPxjw7/zNv8nmaERelARh6INnhEMqjTHr+0osLQCLpoQkiSKyLGNjcwNnDQ8ODrhyZZ88S9Fac3BwgCkrzs7OePDgAeGwxyuvvIK1tvYHL5jNZrXbpEJrXXtcLdwqG+7AP+5OrhhYnuuNnNWK1PUPLcB3r3/ldxSXkxCr+qLwFZScwzTz0DqMs0gBvSThdD6jKivmsxm9JCHQ2rsT1yuWqr83fXweq5p8+zW4t4jWvK8H90bebObj4ooXn5bw8ZL2UcHdAf9M+HyX/71z7u9z8eRYakKI38Krt/T7g+WDrlmdVoFxzfHW7rME+PUluwv+0y4Aj8CUD2u97l5Xowpa6wikr7okqRCmoJzPEALG4wkvv/Q8m0mfWzduYJ33hsH5RSiQkiCJGW0OeOv9G5zNMyqhMCog2dhABxGzWUpVVVhj2ewdU1652pYJLvIcXU/KyhjSNKMyMDmdEsQxNtCkwjGpCtK8xBYWpYPzQ6qZa25xjxepnT+c5tXfQAco/eGHuHPOiaVcro959s7YDnTwiL3bc/l368AKZF0gOhCC6WzOL/3yL/Pln/kZqsqS9CIQisp646kU3bD3c9eyfjuCjY0NsixFSdgYDjk8PGR7a5M8z72Uai13795lMjljMBzw1Z/9WQBOTk4IgqAFN2/fWURi+hwuTQbHGs7FArhWt/nrbH7vclTr3nnE9sdrC5LHCyTO+nwLZeFjQJy1ZGlKnuVc2d1DYonj2AtF2tdjXSQdqy9jSQikpXyabc2761Jxa66rWeja7+1vYvFjq61dPK8+Krh/3Tl3VwixD/y+EOLN7o+XTY56Ifj7ADu7e+fxYkUSb9rjGEFXJfl2nzUS/rljdEdh51ouOteHaV2e3lqL0OCqAlnl7I0G/NizO0gtGW5t8crnXmNva59QSW6/f4OwFxFozXDQJw4DpJbsbG3z7HXD6dvvUpQWqST9/pAf+7GXGAx7dTV7Q57OePutNzk6OeX6iy+hghiltQ9U2tnh8OAAk5YEMiS3BYVwQEVxdkpWFAQyRFpFpVZcITs2k0Yl/eSAHa8SC4kOdO3P/0T/PmjoFiHEM8CDevtd4Hpnv2v1tnOtO7Z7ce+i8b/0vjA6ej5DSJ/J0Dh49rlr/NVvftMHK1mDDjTGQl7nDDKVQVxgW7io7621hJEmSSIm4wmbmxvcvXubk+NjpBCksxllWfDmm99ltLHJv/a1rzEYDhmPPVUzm80IgoDNzU36/X6Hjlm4vTacOrUtoKVmVkC92XcBksvuhRd/fpK52ABhnWtI+tM0vLtPJ2Aw1mDKiiAIyPOC4XBAEsdksynD4bB1r01nMw/0tevxOYG0BnZXLYKY2iu+DEOW+mFxiy0sPcEdfyRwd87drd8fCCF+B/gyF0+ORx3rQlplHQ3TfF79fXWfdedZPX63CfCBHheA/2UAv3qdl91r992aCo1hbzQgcjlFNkOrCO1K7t2+QT6eM+j1+NSLLzA7PeLalR2u7F+ln2hMkTEYDol7U8Iw5N7hAVl1yq0P7vHm999hZ2eHl37sGi8+f53R1ialtdy7c5u4N6A32MQJyebGgDCMsAhM4Rj2h8zGx75Mmas4PjqjKApCHVFmJULV0Xi1Pt2UPUOwMKB1AP6i+79IkX7yBXQhBbbPSXk3yMYw9gTtfwP+XeDv1e//a2f7fySE+EfAzwBnj8u3XzQ+12moNS7gjCUII6qy4ud+7uf4zCuvYaxFCEWeW1C16i8Fxsk6TcB5kNE1fdCcswFfW1ZgHYN+nzsnd1AS+knC4YMHbI1GWFNx74M7TM7GfPmnfort7S0mkwnj8ZiHD72JbW9vD601YRgSBEG7uDcSvZTeIOw/d/29/X0urtchhES142oB3uuHQtNXot3HXjrnF2Oj7R8rFj/QyenuIMtSD7LO0e/1MaUPCtzc3CSOY9I0bfvS2S41IzrnqDWCC6T3bhK9pfHuXF3zwS1J8OdpGtHpifXtQ4O7EKIPSOfcpP78DeC/4eLJcXGrO8AHTpwPelkF64tAfemQHWqg2XfB750H7u6CsW5AfRip/ZH719dnyxLpDNvDhP3BJrPJKZMsBZMjTUE2PqbIMuazCYMkZn9/n63tHeJAcXp8xDyfkiQxL33qRcKkR1paKusoK0Mo4c1vfZf7N9/ni5/7LJ959XPM04zDg3vsCkmcDChK7/lgjKE0FSYtEFVBmeUErsfJ/buYNAUZE4QhhSy9tOO890zjFy2FhNrVzNYT+dIIUbEYpOv6bH384oWduXQMJZXPhHmJt4wQ4h/i7UO7Qog7wH+NH7f/RAjx7wE3gV+vd/9dvBvkO3hXyN98gos7Z7fp9kl3jPo1UnrPFgfXnr/ON77xS95fXyqE8FyxqyAMI4ylBlTbzqHmWOvmUcONO6kxpvRpKJRkfHZGFIUc3LuHKUsm4zOOj47Z2dnm+evXmM9n3HxwiFKK7e1t9vb22N/fp9/vkWV5C3iNBL8A8sVC1gV2qD1Ea8FAyiYu4zHiQ5b6TGLqYh0tMyJobWcLqrC239f7uarurzr1pXMLTxeco8xzfyDnyIucQCkGgwGDwYDxeNzGTxSlpzbPtUbihiVVxdbnaV0hnWuFSUddSKXzDNcJqo+rDX8Uyf0K8Dv1STTwD5xz/7sQ4g9ZPzmeqK0C+EVS/GX7nJO2hVgKeb6o8x41uB4X6B+1jwOMrRBlTqhhN+7x3Haf8JkRRipyoSCI0KXg9PSUVDs2hxv04gSEIu5vMlIhmZFszgvCIEAHAfcPj+kPN9nc3CTUitHGgOn4jLfeeouz0zOeeeElpuldksEmQih0EBAEAXHS54PylLJIOTs5ZHt7k2EQYEcjPtB3MFJSmI6a6S6+x6VF9WOis56kNca9hsNc15xzf+OCv/8ba/Z1wH/4cV1f09YBvdAaDcznKX/h536e1z77KllhiAONFT66sjEENtWSpJStx0pznCZzYQP0sACVMFAUWYnF0u/3OTk+JpunGFMxn004OTnh5s0bfOH113n48JC333mHvedfII5jhBCcnZ0xmUzaxVtrzWAwoN/vE0VRez3rNTePd/66moWgoU0u5pGt9ck6EMvaz7l6GLXm2BXUmkXEn9/hjDeg2trTizqKW0IdDGgoi8L71DuB9asoURT5bJC1t0xbaalRudoHSS15L4C72dx2wurn7mrU4fGbhafrVlQH4F7aPjS4O+feA764ZvsRaybHEx77HEB3f2va4+zT3dfVnXcR5XPZ9Swd44JzXPS/S/exfqUOlSQJFb1Is7u1gU56nBUV9x6eUExmqDJnIw7oJSFboxE6iMmMQERDdp6JieOA+w8OSZKEQb/PydmEo/t3caai2tnmyt4eX/7pn+Tm7Xvc++AuV69/iunkjCCKCYoCaypOTk+Ym5Qqm7HVj/n8i9d5/tp1bj54SFla/uX33ia1lvACD4Cl/l7p2x8mwAsh0IGnC56Qb/+htnOg7hGvTvplefa5a/ylX/pLBGGAcb4ep3AL1d/UANU9VksX1OO8MW52fwfaJFl5ntcphqEoc3CW+/fvc+PGewRakyQJs9mMUCtOTk7o9XokSdLWE93Y2GB7e5soitpnXBQFaZoipaTX6y1A2PmSeP67PNcPi9zn60vTNQW614+rDkHNIgDqHKjXLymkL2KCB2+HQCF8ARAHee0CWlUVWkhwFcLZ9n6UUp08NL4/6WJJc0mPGIDrKJul/7dg3ywerkPAX37spyZCdbWtAsFFQN7Zo2OcuUCCZ0HLrJPa13Ghq8dZBvjuPo+3yJy/T0uWzqnKgqgfMIhDNvoxqTHk8xmmKkiPD5FS0Rtt8+yVK2xubCKDgNKCEIr+5pCIGUkcMZ5n7OcV01nmEx1NJyhriAOFjGM+/7nPIsIeKtkgHIwIghCtNaW15HkOSmBcyedeeZWvvP46zhj+0T/8x4xefJksn6OSPpgOz15Phs4NtR3SNaj+0CV3pb3q/DSjOyz1EVD7b0jm6Zxf+IVf4MUf+xSzeQFCUJbewNq48JlaMu+2df28bOR0dcWuEi0l1hiKovD/sz7/0K2b73NyfMRXv/pVXn3lM+zt7bGzvQ2DDZIkIYoiGm5dCEGSRIAkz/M66dgiirnI/bWrJninvhbVCeoRQvg4Umc5T9R17000ndbSHYK6RMZqXcdOf/hhatvPNTGzVquzNc04n8+ZjMcURYEWEmdKlIRer9cW6m76tinZ51wHK2i48+UmWLYreE/Pzhxy50Wly+fPxfPqqQH3y6T1xlhRhyBd8moGwEISEPXqWQtFNJ3R7OXqClB+n4YbW6TjbNTH1YFzHsjX39NloCYExNpxdPoAXYwZBrtoFTIvAo5nM7LcoqyjN9hlczQi6fUZbIwIezFOAcJiSTmdjIkR9Dc2CXtD0qJgsJGTFyVFteULPjjHdpLgZEBmBCrZIB6OqGr5JS0yhK3YUCdcf6nHleiYPgfsXX+ZF1+4xv20JDQCl1dYpRBK+dzipsBWFRhLU21KONpJ/Kg++KhNABof2WuNA+F926WOkTLwqW4/pLvcD7JdJkRUxnDt2jV+/ud/nqIofVFz6shSPCw5FpWI1hlSV6Xbhq5pgLcyFbZymCbjYVlgjSHPcw4PDxmNRmxsDHh4eMjB/fsURUmmfTWtsE5vEYZhHajjYwqiKKLX6zEYDOj1ekRRTBiE7fU0FE4D6k066qYvFn7j60ztoo0KhW5KA2hs+B1xFyEWxTpt2EdLAAAgAElEQVSg5tzbzw5TFK1vu8FQ2SaFr++P+WzGZDIBY1twDwNFFEXeDbQO9GrwpU0b0AqSjZtlA+jNklKXL2mZmGaR6WgXjx4+i3bJzk8FuHdd6WB54C9/XvnfOcl7sc96qXplonf4LLF0vO6D6gyiznEfB68eybk7h61KTo4fMioLiqJkNs/J7IxxmnJydsJ8PuXK1rPouI+ME4QOMc5LplEUcjaZkM5TJmengGBrZ4fhYOANn2WFUN4vtzKWRIDUEX0ZQdjD6QhhHEVZMh6fUlUlX//ylzh49w1CYYmjABUorr34Im//yVuoIMA0kmZ7E5znClmWSH/QUntzLf5peR73aUv120xuOu++HzsaDmCsJS1yvvKVr/Diiy9ycnLKYDgE6SVji/ApDj3vBUJ68LfV8rFW7r0VcqB9Vuk8a2uD5kVGZSrGkzHzdM5Xv/qzXL9+nSSKCYKA+Txl6gST6YTZbFZXJJrjrMVaRxh6t0itNVVVUZWlB2lXg7mqOXgpkTXX3lxKk3O9qkq/f5t3pZHQ/TcpwmZzPdnrzpTtLG3Bc1mZXGiajbaZZ3ntamoxzrtAWmdxOKrSV2FKZ3O0VJS6BFuhlc/KqbWmyLJagAFnLogzELTpqDubzjl1LC3MdBidBtBWKZjHnE9PBbjDeSn3vFH0vAT44YBjPfWyoFwW5+pa+ddd7+Pc06OuJZvPGJ+esNETzIuC09mcalZwcHzM4cMDjg4f8B11my9+8Qtcf/46QweiMPQCCYUjDvsoGXI2nnL//j1Madi9soeUmn4cg/AJwihLbKCRShJEIYVzPDx+iA4jpNL0+wk//uOvkx7f4eDWfd784DZlVvHln3+Gq8+/wL3/4/+DICDPc3QnpLprtOq2VZD5YdIyWmt0ECzcxT5BkHdA0ZT4rLVv0ajvxuKcj/ht6QlrGW5t8+Wvfo0KX1s3K0tfOakWU23VZGqsgUNZkIuScaJ++e+OIjcI51PZVlVFlmVUpadnJpMx6XzCZDZhnk85eHiPz77+Gi+/+mlGo20wjtksI4gHhGnK/uY2emd/qSZuQ/U0BlSbFuSVwwQVZeLztYRBgAg1gQxqj52Aqiopq6rOdQRFYSjzCuyiyIzSXjMQQoBeSFatpAxI26QrbjCwKdotcM4bbCtjMIWptZRFniRrLXmekaVzbOXLCArgg/duMjk9YzgYkAlBFAdkpqJwlgpHgUMLCMKAcl4n1HEsfN6dz9QpV71/1s2V7mchl5YmsbJfqwHU57hsVj014A6XA8CCK7u4XSTxd783UvyqcbTLwTfc2UUc/KOu9bH3cY6jgwOyNCXTmtPpnGlaMs0q7j14gBKO6Szj3YNT7p2c8dprr/Ds1WfY3tlhc2ODfq+PNYZBr8f1Z57j9OSMN773Ji/OM648c4XI+vwkEuhHIYQaGSiQMJ+lFFXOxtbIezr0I/Z2Nrj/3QcMgoh785w//ONvofde4qHr8eDsjFJICNTivi6hXFZpgcfts4+jNdLVUyG4X3ANSimKomxdB40xrUvdyy+/zPXr1zHWEEURaZrihCQMI6hdJFsuGaAuH9eesllEWJZaoSn0XDGZpIRhQFnkNbAWHB8fM5lO+emf+mmuXr3KxnCElIoiLxEI5lnWlp+bz+dtXnPvcqqWqBfb1BSwFdI6nFMI4ZASX+pPgrUVWS1Bg09BbUqLM6KOBJUEtWFcKYlznpRa8Oce5LXQLTQ0FNW6WqTGmPaltfaLXFWR5xnTyZgyz6lMxdHDh7z//vutTcFag5QRla3o9/s8d+0a3/32twkD3V774w6FBRfQHR4d9mHlWO23jvS+PI9+BDj31bbO2AmPBop1njCXdcZ6Y+l5qX7d9T1pa7SAhbdDxcnhAVVRMp7khBLK0nJ4MqEsS57d32Fra5s9kQKSb3/vLe7ee8DmcAMpBLPpnPdvH7A17PGX//WfZmu0ycnJKUXxfYSAvb1dhPRcZaj7VNYicOR1hKEUCq0g1D4HxuHRMc/t7RO/9lmef/YKf/rG9xnnJd+99R7zymCUIIhCXG7qCdQJ2PA6aNunq0a8tf3lzg/mpWezol5f1LxtZZExsHGDfDrQ/YImwDiLRrX0YF4U5FXF5z//eba2tiiLgrjX8xJy7VoI1OC+APguuNcFl5pT1M/J4OzCkFoVJcI50umMsiiYTM6oqpLvfe97jDY32b+y7/nzfo9+b4izni4CD5BlWXraoi4g3QX6pshFr9cj6fWI+31ALJJrGUNlLWGisFVFOp2QZTnGVFSV8TlyampGKc/La62QUpDnOc6Z9p6s80CfznNf/q553C2H2sw54UtTSl9gXinF5nCDhw8fMptOCUNPJT08fMDx0RH3PrjH3bt3GQ6HWOfq6zIgYXNzk5dfeok/+9M/QUrpA8Qa4+wS8eY6T2G5XY4dlwm466mci9pTBe7raJnV31Zv8KK8yN22KrWvbl8932rHNYP2ot8vuBtWH1Tjh9yAXZHnlPM5oVJM51OkhGlacDpOubK3jzEQ6pBnrmwilSLPC05PThifndVcaMiV/RGmqvizb32b1159hZ3dPW7dvslwY4AOFGHoQ8KzPKUKNP3hiLSy5GlB3BtgywwdKGxZMDk5RGYp6TzFOMGrX/gJdp69zrd//w9wSvtAp6pCCdlKZo1dolGDG5BtVPZVb462r2uVch38Pomk7/PzNBWgvNE9qiMmLzzBD7N1qOG21eNQaY0VYKxBhwHOOoZbW3z5y19u3RTTdI7WgQeZukC2A+rYG99HwrWuBk3+ME/J+/FtO9GrVV5Q5DlFXlCUeeu2+Nabb3H79m1e/PrXOXxwyOnJKb3egNFoh42NEVEUc+XqPmEQkvQjtna8gNH0r7VeMGmKSxdF4Smg+axD3y3m8GFZ+ojXyZiyKFtp39NPqg4+88XA0zQlzzMvsdeSe3Mc5yxxlCCEXKKHGopIiEW5PCEEQRAShgG9qIds7BimJM9z3nvvPe7cvkWWpkjhasrGp7zOipzBsM/u7i6jjQ0/vqAtjNM+7O6Dv2TwXcpSrAH4dfjkWtVtfXtqwH2VGmm2NW1h0LxYqn4UlbKwpD8qP80FuWkuOO8ld3XuP83Ac877A0trSOKIeSE5mc44m+bEgxEyinFYwiBib2NEnCQ+wZqAk9MTwiBkZ3cHpTWT8ZjQGqJegsCXUHvrnXdQWrC9vYVSgrIsEFFCOi+wUlM6gZYaM5+DVtgiQ5uK99+/Qb8Xs3PlGeTmDodZwd0HDzEWhKkIZMcDxpN/5+7zh99EraqD1l7ak1IuPBM+aQl+BeAdvviGbksm+sVSK80XvvgFPvf518AJpBTM53P6/T5ZDYCNp4ynW/yxpDkvK7bQ4sAagzVeem845+l0TKCDOhL1kG/92Z/xhc+/zk/8xE/w3LVrZFlOkRvGkykHBw+ZzzOkcq1kHscxcRwThiFJkpAkSZtYqygKplNf9GMyPsVa642sddGXBjSzLKeqylrTqr1qZIATiizLvAePtbXkLQnDoNYKG35dIoQm6fUIdNDSQg011ERI6zrR13w+ZzweM51OuXP3rj8GAqUFZ6en3Hj/Bvc/+IAoDBmNNiiNN6zGcUxQKZIkYTQaMZ9OGQw8JSraBa4WYoRtOOQPOVTW/3cdlfyo9lSBO1xmJF2/ml2kqlwO4Jd1zLKc9VE493Xg3rh7OefpEYWvnGSimLQsIISwP6SoHEZBEIVEUrIzHJD0ehhnCfU2Umt6/Zj+cMju/jbkhqPDB6TpjN7GkBvvvk1yO6IwBcN+QlkWhIXFVgJUgApjcgupkmzGIbPjY04O7jNL5+w/s89zL77IYW55781blHXJNuksgYOiI4k1+P5JwWeTVMIvmJ7LbtLONraTp6k1IyIIQwKtKcsShwejT7/2Cr/+N/46g8GA6XSKkLTGxEbrdM5iWXYxtU60PtVLo7cxjdiF9G6qClMZ8jSlEBlVkfOdb3+bO3du89prr3B0dEScJAyHm7zwwjWkCnHOl+u7f3CHLM+YTqccn54wm80oci8V23qlMdZgKkNZlThr0cK29Ejjz+1wnvKoUyboQFNI761jrEQGEXEcM9raZDgc0u/1iOKIwaBf0ypdukZijKcVlfbPPtBBna6hzg4a+FQB83nK2amPrH33nRvcuXOH05MTZtMxN2/e5N4HHyDwcQTNIijwAXFSSpIkpt/vMZtM6PV7jE9PUVJ00i13E55ZFq7bH9PYWcdeXLL/0wHuzSBcAcy1VEnHDW8dt34Z5+7dqtz5Sd8xVniDzeN54jwa4NdL7s2xq6pCK4UUjl5/gMZhwgIVhFhjUEpjKkMsJcMoZnNzRGFKNtUmKEnpLNYZwighCGK22KM8qFBZQFoW3L57B+tKrl7ZJ9CaPD0jz0vCpE9fBSi8NG6LgnQ6wZUFWzs79AYDhNZsb474/j/9v3z+8KCPqiowJa6+/saFdfWOP+pwPteva0wmXSm1pdsctRukn4yVOW9U+yRbU7yh1U6lJAhD8jznpZde4rf/zt/hlc++wuk0RSnvU51mOVmWehdI8K6EiJqKqqNRsbXrYV3pp2N/wPgi6MI55lnObDojTVOfO0XAjffe471330EpyXQ24Wx8hrsjGA5nnI7nhEFMFCXoIGR7d6cGVS8JNxTM6empP2bNxRdF4e8XsHnaeuhMp1Pm8zl5nrfePD75VoiMIpIoZnN7h+39K4w2R4xGI5JeXKcmgCgKa2ncG1t9FkpBWXnhA857aTV9AtAfSMIoZGtni9HmNtevX+fw8JA7d27xxltvURQFSS9BKsU8S1H1olBWFVtbWwyHQ9J0znCjz8ZwSDafY03t7w71ImZrG4q3Gwi7WIjXCaKrv1kplibQRXTxj4zkrpwPzpgHIUkFo8KgqMhDiZEhgoBSFXhud8GlSSGpmd26drvx6izCu5chUc07vtNOE1AOIguRcWjj+UorwEkwUlCsSTb1JOrQxfvIRcL/OsyhZ0vCJGRSGKTWUDikUswmKZPDjEgoemcZd8cpzz6Tcv3aNcp5icAw6PXp6QRlNK6nUVGPg5MJLhaEG89ycHSATgqEmjFIIpJAkSOJwwHSCQIq8uyEybTie+++wes//pPMH9xmsLtFqhLevnnEn71xEyECH6jkDBbr821Yi3C2zdVja5pAa4kKgo8PUJsoP9d8dkt+wtb5ACbnHEiJjkNUoL19Vymk8sm2PskmwSeHYgE+QRAglPQSula8/uNf5PqLL1AaS6+XMJ+ndRWtIUVRUFQVol64vHFTopTGOktVdQzKnVVPIpBaY42hNJaiKDwfnmU4axhPTnnv3e9z+OA+e/v7PPvss1x77hrXn3+BOBmggxiERsmgToE7bo2/Sik2NjZQSnH9+nWyPOf46IjpdEqapkynUybjM8azaW2QrJCBZnN7q124tA6I44jhYMjmyEvpYZwgdYjWiiDw0nkjrUdxhBT1IqkWLpI6CFhruKy3SgVSgHOaIFAY44jChDCOiJMEKeH69evc++AuRVEQaJ/hMs0y+r0eO7u77O7tIiVUVcnRw4ek6RwhmiRfrvFSrf3a/Tv2YlfgyxmKy+fO4zpzPBXgboWXAoPKoYygFI5cO0rtkA6U8XkfnPSdJuvSez7Jj0/0I4FKLyaQx3LvRSCwbefbGixKAVb6/4PDSr9qOunQ1rW+yKt00UcB98bN0n8GpXyRDGMsKogo8AA5m04Zn54yP53x01/4cTavDsjyjFsPDzk4OyOQkqqs6Ce+wtL+/j57164QBCH7+1cZT99ne3ef05MjrIUsK6jynMHVHdAKJwRZnhPICh0rprMp8yzn4cmYZ/auEg63OMos/+B/+R3GswxEwBKr2/DsK3aKZmA2YPpRAb4x1na2UFsjaewiC6OiQDaueErWLoJ4cJefvOQuWebcnQBnLXGS8It/8Rf5jb/1m8yzlETC5miToig4OjoiiEKSXkLoLGmaU5RF2wVlXTFJa4U1nZ5qbSFgsRS558Dns1lbiD1L57z3zjs8eHBAGARsbAzpJT2U8l5OYRjSG2wQBBFhmBCGkjTzQUQNh16WJXlWYGujqQo0URKDFPQGfZ577jniQKOkammS5vp6vd5S/6i6JKPSGlsbUo0xSAlBqInCkDDyVbVazr3xyGqTji23rp2uqix5XnpbQlEwPUs5PfUVpibjMc8++yxISWUq8iInSSJGW1u8/JnPcOXKFaazGXk+Qwea4+Njbw8LA6oip2NXbinKNiyhvZaLI5Lbx/YRuPp17akAdyNA2Iph5YFnHhmy0AcixMaihENaWavdsuUX2+K4snGZUzjppaQGqK10OOFw0qtLm0XthysFpXQY4et7Wkm9YAgGVV1IVywb4x4F7o9aAFZpI601k9IgYkFeVojGy8MWPHv1Cmaz5OozV3jpS6/x+uuvM5lM+ODOHfI04/TklLIouHvnDndOjin/9A+5du06n3r50+15ekmPjc1NekmILXMvMWrtOUgs1vqsAUUlefVzX+BPv/c24RdeZz61/JN/+nu8+e4trAgb3gNqc57tcu4rFNgnRYE0xjStPZgA4Hwfy09Ycl/XtNZMphN2dnf52te/zsZoxGQ6xeDI8pydnW2s3WU8nVIUGcONTZKkx9HxCWmW1d5InhtW1lMxjUbTUFTGea69KAqyNKWs3RWLouD09Jg7t2+RpxlRFPm+ktJrjWmKChOkTilLS5ZXKK1wNiWKPB+utcJa7zPvU/faTh73mjIRspOMa3leNBGqDQ3aGMSVlkjlCAKFc77wTKC1L7yiVSukNY4RULuGruljZxv+3JBlOdPplOl0SpZlZHPDbDolz3OiJGFvfx9nLRUQBAHPPPMM29vbWGsZj8dEkWa0uUGeZwjh6kCwAodrBUQfjetaIRTOU8aXNSGET/n7oUbU+fZUgDtY4qokriS5lhgM0lVoqxDWUgqLdnXFRdcU0vZFtRcSuiCoRLtqWulwCpy0OOHqEGVBP5dYAaUGtMDWocvaeGBXHd4M/ENZdbdcB96rwL4O5JvFotkehCGVDJhkBbMiJx56X1wdxWzEfcKh5PT0iN/93d/jj/74j/n0y59me3ub555/gShJ2Nwc8cprnyWdz/mj//f/5vd+75/x/AtvsLWzR1nk9Ho9dnd2CKWDKiSKQuIkIUwiyspQlQWi1yeM+xQq4Q/+5Nv8y++8S+kkt+4fUYkAU/e2cBbZesYs32cX2BcL4g8X5IXwvsytp0xNFzW87NPQusbOsqoQUnL48CFZkZP0e4RxRJ7nPLh/wI0bN/jMZ15hd2fEu+/d5PjkjOeuXeO5564ymc45OjpGKl+ko6xKdJ0615fps4vPzi28Zept89mMN994g+OjI1RNbQRa+myPSvukXtIfu6hyKpPhnCRQGfP5nCAI6hwruu5ftTQGFhWZJNb5xFoOL2R5DxfRalTNozG2dit0BiEgjsPakLkQsFST773uyNqcT2XV2lrrzto6NYP19QWUIghDHDDoJyS9+hVHZFlKfzBgb2+XXtJrF6swiupqUxFB6P384yT2VFdZtLmLGsN+K7mzLOhc5Ha9dL1/HiX32El2LUxdiUUyqBxx6RDOMteGaWgwzqCFREuFMaWv8iIkTgqccjghkJWsaRpJCFB5q7Xf5h+AtRVWCJyVCCPRtaeBtoLQ+mjONDDLNrzH8JZ5HFelVYOI0ho92ODw8AChBGQ+OVG/lyArw8ZwgHCWz+ztsr2zTWQEW1GfYjzjxpvvMp/PfKpVYLQ14qtf/RnipM8886lbR8M9tra2MPmcQEb0ewFREuEEVM4QKEkcx/SGI27cvM/tB6eUqsQKRSUinNQdiR0aaGqokNV83S2wrxm76/rjUfL0RbzkuuCorutb4ylDzc/yCInpB9467FJLzdS+2n/1m9/klVde5Z133uHa9WtEScy1a9cYj8e8++67bO9u86kfe4G3332Pt956i5dffonNrU2MsZycnKADjTWCqvAh9ZiFn7etfMZHnGMy8TlhxmdnvPP2O9y6eYs8z4jimMBWnrs3FdZaoiim3+8zGG4hZYitC6KbalyDWsl0Om192Rv3yG4uee8qmWCdRGuFVj5iuImPKE2F1oow9K6ggXA4p73NTNiabloYhj1YQsNJWTpRqu4CrxQh0FoBqk1JEUaR1zZKatpHsj3a5Fvf+habo03KsqS322N3yxt0d3d3wXk3yrPTKfv7+z6fe6hxLgRrsJVtH6yoNShB5/sFjh/nL1eAM+e2f9j2VID7UGiuJz2+k81QOK67mB2nOCky7kqHxRBQEssAicVUhU/yrwQGgRECJwVp5LMASifwjxSkcygvzCCASeRwUoCsw6GdQtXUjnEOU1msciBcG2X5uK6Qj+MLv+yeKdl77jo3P7gPlUUoQ6gUWviQZ62cNwanOW48I52m3Dg5Q0rF1eEmvavP1JGYkJoZ169d53Q84eHRCaYq2RgOfOh6mRMkoY9QLApM5QjCkDhSaB0idMi7tz7A6B65C3BOQB0U4qX2RmI/T1F1NZY2KvVxBRBBO3m7fXOZ+trs0xSD6FagV7Lr4w4I+VhBbj/o1nLNjaeX8MUgiqLkx7/4E1x77jp37t7h7t177O3u0ot77O9fYWtrk/du3OTg4A1eeP4agkO+/9bbPPPsM+zvX0FYyzxNmZcVVSuZG2xVUVUlpiiZTacUecb47IzpZMLtm7e4deNtlLJYW+KsQgqYjSdMTk/RQvoSfMaCNQjln70O/n/q3izWsizN7/p9a609nOmOMUdmVmRVZY3uchddXU3TtGRUtIQRqABLFrzYgIV5sMULDxheQLIs+cEYISFZMlPjBzAGC9my2jLdjaGh7erqqu6urinnIea4cacz72ENPKy999n3xo2IzKyiM1ipk+fcfXbss4e1vvWt//f//p/GkKJShYxUB+/UdYXSmgDYuu7iH9Z5FsslxqRUVXS4OthMR5aQNoIxglbx34RGwlp34w6Qc2MKCGwkBuLv9YKaHWsm9q0oiBqLmiAqBog1rOuKfJAyGQ/YGo9ZzafgLDvbO9iy4PDggGGesV6tcM4xHudkaUaWZrx+5x7Fco2SeHq6Kcjai0p17nsM9NPlJIQ26aENnLT7+ebqzo2dj8qQ6bcXwrhnHsYCVtdshZzP6hETUSzqEiEwdsJn05Rca6rlmtSYRtPaUzhP6T21wCytCQE8gg+C0wZtEqIUUHytTYEWjQkesQHvFJiEUqc4FajFMiBCNM+6mT8Za6b5nsBw9xLJaMz0+BgtinGW4a3DO8tquWA0yJmYDLeYs7YWkxiyPGeQaIbJhNFoQJImLEOkzaX5gMVyTZllKK2YzeeEao2vUqg1el0x3NkjMXmUJcgyxKS8f+8RFQlBVBeQU62E76ZbEv1tR99r7hvl83GKn3Y7v0Lq/36SJJ3WdggBpTeZsp9Ui4xHiQHUpl9FQxbI0pzHjw4ZpDk3rt1kOpvx/jt3qeqCK1cu8+qrr/ClL36Bg4NDHh8cMxoOubS3x+3b9ykWS27dugXOUazXBIGqrilWK2xV4qoKVxfYqmY+PWExnXLw4D5v/vD7rNdLSARrV2idYrShqipODh5DZVEugLXYsohVnwR0MkBZIsSpQ6wvkI+QwbgLYLfZwq6BQYJ3BG97HrZtROxAmzE4RQgKrxVKRbFLpQOi+mayMdadB3/Rf+1Ku6+wGS1vjBIFgni0CXGizRIUgrOG1BhW8yn3br/PJEsZZwnb2zsMxyNcXVOsl1y/cYPBMGd+eshiPqcqSwxxEkyMIVI24r3x4htterri26GBkFoCQNsvmlBElPFQzXjqYXcXQbz9MdbmPzytvRDGXYKnrFY4V7Ovx1yTlBO/5pEpWXnD5XrAzw0ytLU4Z9gejKOnoGDhKuauYh0sCyfYAGvvWQUoJFDqQG0MThtQsLeoGJmUgWioBes9lXEUac0y1fhcN9opZ8/xw3jl59tzA7CAUwkv3foMx0fH1LXF1g5vHME71uslKjiSxDHMByjtEeUwmTBdHlP6FVevXmM73ca2yVGNVILznuOjY8rVjFyD+AypA4MJjAkoAnmSIEqzKioeHp5So1Ch66qorpfRbGmM1LlgKpzvdB/iof8E7WmdPklj4ZF2gLSa4U88zE+o9W+LMYbpbMa3v/1tvvnNb7K1s8X2zjWc9dS24s6dO9y9e4/XXvssL710nStX9nj77dtMp0u+8IXP8+abb/Gtb32Lr33tn+GVnZu8+/5dHj54gFEaIbBcLSlXC4JzTE+n3Ltzm0f371FVJWmaUPmaNE0jrIInOBeFw2Yztnf3KNZr0AnKB0wWPdhhay7OB0gbb73D2xtPPgSPq8/2l4tXsxDxaukM3pN3rPV8o+MWg7iNd98wfM7+F/+BD6FnRImrUgLj8RBFYDZb8f77d7j/4FEkGDiYzeY4F9jZ32W8tU1R1hyfnCC+4MqlfT796qu89+ZblKsVdVk2q8PeWDjzV7PtqdTHi3rJT6e/vhDGHaDwJalR3MgnbIWEt+sjHg88zsENBtwqLPVsSSaaywMTjXuAtVIsdUItgVwy1s5ziuWEwLESTrUwFWHmPM7CV/yIS3rMVjJEEsWitjyyJXeKkso7fG66YM3TOKpte97DukhX5cy/J0oF7OxfYm//MsX0OIpFbU+ivKm3+GAJEihchU4MIRFsw/wJRljUK8JKqOpAbS3WxjqPWmtWRcF6tcJrcNUaPU6Z7O6TJgmESA8VEZbrNdPFEjEZUq9QhB4UA63fFET1BuCThn3T2uDSM+5PN288uex+8q+nxzf6BiMxm+pLIcSC3S8CLBObNLBMXEFqrRkOh3zjG99gPp9TWctka8L+/j6z+ZSvfvVnWa3WfOtb3+IHPxjxpS99kRs3blIUBfP5gq985Uvcu/eI73zn97j5yivcuHGD0WjEW6//mKPDQ4r1ivnpCavFnMPHB7zz5ttbMCMAACAASURBVOvMTk5JjKGqClQaoZF+oPXo8IiDgwOu3XwJW9v4nbMkklFZS2hXQT3jHojQp1KtOBjtIpkQFBLMhca9/dzJBPtWKTIa4LY7XRSU9F46w+59M7GoKM2wgXPafUPPsG/O4Xi5ploXnBwdc3B0TG09B4fxfW9vj8R5Hh+esCprtnZ3mIwGXL96g5PjQ/7gu7/HyckJidbU1kY5Y2nHRP91lud+/lrOX5fIT8+ww4cw7iLy3wH/CnAQQvhjzbY94H8GbgHvA386hHAi8ez+S2KV+BXwb4cQfu95v1Fr4UAVfKpO+HmbcpQJ38mEMii+tCj50niJqbf4fX3KahT4olvxOZeRjBMe65Ksqnm1ziDNWRrPrVBzmDkejjyX5oFQOb6nFqwFfsVfZscZUlNhdc0sDbxTlVTLktKmTAsdl55sjPNFUMPHwdzbjtwX1arrCnGOrWHG+riAJIGBkKsRIzSpB5VEWpkxBqMTjFekaUpKQlILxcmStVPozFAFx5oSmwZmdSzfNzBDJCiKdETIcwajlGEWA1lBJTw6mrJYVHiTIEE6MGZzIbHqTyu16prAW59JdH7Z6HnexBYT1/pNev9vW1NEKkJDEumvcdDaWNhYAqI0yiQok9IWiFBojDIkyvw0x8vHapsgc2uxiIG74ZBf+qVfYnd3h+V6RV3X5FnG/v4eJyfHLBYLfvmXf5mHDx/yxhtv8v77H/Dyyy+zt7fHfL7m6tWrTCYT7ty/z5tvvsH21hZf/vKXeXDvLj/8w++xmM+5d/c2Bw/uU1cVaRon9TzPceIiXOSBRtBuvVrx8OFDPl8UjLc3AWqRNgmoMcw+dMJaqvGaN+yy/vN7Ov2vNewbldT2ufafufQKQcuGJeNjPkhbHNwTYsIQ4CREinQT+glNhqgPQvCxuHiEShSHRyesliuUNugkI8lyrAtYD0makw1yRlsTxuNtjIHj4yMOHj7igw8+YDwas14syLOsmeji2rZdhfSffd+wP83h6dhGT7g7H799GM/9V4H/CvhbvW1/CfjNEMJfFZG/1Pz9HwF/Enitef0C8Dea92e24CFZOq7nO2SS8KBYMR1CWgg3XcYVk/GGLfihKZn6GuU9101KVpQs9ZpykBB0zWE4xdTw6VIx1BLT9lPDUBTrACpoBltDKqmppIBQkUrCnkkYSw3O4RIayt+z2/O8cnjyQbZBwBCi9ECr4/2Hv/9ddLVGK8E6S+1rBsmALMnZH4zxaQN39GR0nfc4CayKFUmakSYZXoSqqqNAUlNRJyWWUlNZzu5+ZM9MRiO0BCxCQPPw4LDjIvdoCZvrYJMs1OKGT2OxPI0t82HaBT/dHLf1g9r72vOShBg41TF7U2iYMgha6ReC497ek8aXAxHqquJLX/oSly5dwnsXS9flAxbzJXv7E27dusnJyZK6tly+fJk0TTk4OOCdd97hzTff5EqTUToYDHj11VusijX3793nnbfeINGaW6/eYms84u03X+fx4wOGacZwkLNer3F1jSQQnMc0EJ7RGuscBw8fMptN2bt8BcGjtYoSwY0RhsYAnYt9SBejit5y7E++2/f8q+8UbF7R5W+UFZ6Y7CPU0sTTfLuCVNR1ADYr0S4DvLeMiOOuOTawKtaMJhNCgGW25HOf+zzXr15HAXmeIyrGq6bTGaI0o2HG9b0tjAh5lrFaLLFVRaLyDTOmNeydgW+Du2e996d58R81YPq89lzjHkL4LRG5dW7zN4E/0Xz+H4D/k2jcvwn8rRDP8lsisiMi10MID575Iz6wt9Jc3d5i5gPvrOasXc1+LVxLxqQm43U5obaBV5Jtrqc7FD7h8ekxVeKZDEZUdcn3Zcp2GdiphmSSsnYVhyHj6nCP3Aqq9NzdtixXM6Rcsp9k7CUDMjRpqLCuolCegX1y5v24mPv5v9uq6SJR8e/HP/wDbr//Hp9/+QZOYLVcYOtdbJKxKgqu7OwxujRuGBexE8QVAFgbS4OpLMGVUWVyuZgzn81YLRbRI1MaYzSXLu9z5fIlxsMRIoq6rmMA2Xk+uH2n4x9/nHb+330kxsxPqemO474ZUK2Wy4vS5Nysd3h4xPe+9z22tra4duM6zjkuX9mmqjyrVc1gMKCu5+zs7HSFmVerFfP5nDfffJPf+Z3f4fLly1y7cYPx1pjtrS2uXf7j3Lt7hx9+73u89+67LOdzRoMhCs9yuSTRCp0YnHJ4bzFJhm+MOCFweholpZ2tY1GLEPtVkmeRp94a8XZMtBCKhE4z3jUvCBi1GQf9Wq8XebPnIPyG8RQ2i54OOxdCaB2lSGlsj9d/KYl8+g0EtJlIEpPibWA4GCNyxHyxpKos169cZXt3mzwfkKQp2XBAIFAVS0J5wg++/32WiyXKBwbDIZkxVFXZm+T6YObFhvuZ4+wcMvOTkDo+LuZ+tWewHwJXm883gTu9/e42254w7iLy54E/D3A5H7KfD9nRhsNqyrFaoJznkhowGWYc2TnVfMVn65Qvmh0MmrfdlJms+Ey6w80iY7DSnFxOcapApRk2gFp7LI7DxPKuKzj1JWpxSloUXEaj05xAxryumQdPpQUXF3k9T+TiG/lxjXvr8S8WC3784x/x/js/ZkDAVgXboxFHpwccH58gHpwyHJyeMklsp3iXpmlcdioVoQ+B5XrJcmop65KTxSnL2QxXliRKyNOEyWTM5StXuLyzS6KJdE8USicUleXuvfsorehK+HyMdmZgfVzX/WO2Ljs1aTTn3Qb+annHn2Rrk3YQwTuP84HhcEhVVxRFwdHREW++/Raj8ZiXbr7Mpcv7iAjD4bDRdClJkoSXXnqJ+XzOpUuX+OIXv0hd19y+fZs3336Lx4ePWcxn7O/tkjZB5KOjx5yenqAa7nTSlLOTKNASS8A5i1YKZy0BxcnJMd/9zu/y6ddeI5wodpIEpTW2qnBa48WjjG6SdwACzvkNBh96FMXgsX6jvd5vLT/+CTE1NvGuLkh6DtYKDQzSHlFJvLdtse6WeRMDpL4z9D4obF1jrWW5WmPrivVyxWqxBB8IznF0FHVjtnd32d3dJc0zsjxnMswpplG6IE1TpHaYRt5YkGaFGEAUEqJeVUOR6hyNvqPYX/mr3qpIqYYJ2Ksa1dJ9W5pvvybEs8zQTxxQDSEEEfnI4yeE8DeBvwlwa2c37GaGnbqkqJZcNg5X1nxaDxiYmgf1EZ9fD7g83MJq+MPqMQ/cnJdUxiQZ4MQw3zJ8bl6wJEMNM0QrrpUpXhLuYnndLbmfWV6Zz/mMHvNyuse+TJh7ze2w5o6yFFqhnQOvGm2Ij6af/LT9+ni0UorDw0N+8IMfMJtOIdgoYSqAs+RpyuHhY0LwXN29zMlyjk4DWZ5RUzBt9K+NjjICUTt7ybqCqq6YLqbUxRrlPXk+4PLuNld299jd2yERqIsSS0BMQpoa1qVlOl/ELMNnXNfZAObFBTj61Zcuuh8fZmXwxIQoPS+RzTK+27f5nCRJU6auXfZvjPuL0M4vZqbTKf/ir/wKX//617l79y537t1jsVzwzjvvcHJ63Hnq4/GY0WhEWZakaYpzriuwobXm6tWr3Lh5g6Jcc+/ePR7ev8fd27f50Q++z5uv/5hBljxxHhHe6CWgSQuhCHjP9OSERw8f8sqrn8HWNYpY2s8Rk36C21yLCgpRnrZYxvmX9EritUa/D820ry6rVSlEtyqLG0G0WABmk3kcGtwmBAjONfh6hHZ8UChPc6/qhqIZYs3U4Ak+4GxFVRQUqyUEx6ufeoVhlrM1mZAmKeg4IatG+kBrqKaCrS0qgDRxM6U2JPZIdY0ihM2NhXNOYoet9+CZsxNbdI1ag99febb3qotref/MMftxjfujFm4RkevAQbP9HvByb7+Xmm3PbiLsKsOl0jFJB6gs52hV8HJISNcrTCj59PA61TDl+zLlHaaY4HjZ7DKq4MfqhHcnlp8/EbYmIyogQZErxT1x3C+m+KpgNx1wa7LHp/2Yy/UQtwzcSQreoOSudtQCqY1R9357XhCkv5+1tvManXNngqchBB4+fMjrr7/OcrkkBE9ZFNy8eplBmpCrgKicdV3w6PFjCAr2FPrIsbezy2A4wFcVq2Uc2FVdsVwsWK3WkGcsV0uKYk2aaPIkYX9ni0t7e+zu7jIcDXFFia0qVGIIBBJJODw6pagqPBneO/S5a2s7XtvZWk+ivw3OdrxO1vgn9ZlDwIfYgftGoV/wJISo7ZFlWRSdar5L002Rhk+6dRhwx8OO1/DlL38Zay1VVXH16lW2y21u377L/fv3uXTpElmWEULUj1mtVly7dq2rV9oPaJtEMxoPGeY5O5Mx+zs73P3gfdLERAE82hIfDTTUwsE9vBzamIcwnU65/cEHvPzqrVg/FN09+xCaYzUGJgDSwIRnnk8z8fZx8L6hqqrqiZVx/LtdBZzFzCOGrpvfOcvYUrr1/JvJS2LPEyWkzeTWnr+1Fu98THIUYqm/5QJbljFfJk1JlIrOUwNhaaNRKlA3qpqIYJRCfCNTEiRKMj/Fx+3biec5O5EwcBaPb738/kQITdzvGc7LxzXufx/4s8Bfbd7/Xm/7XxSRv00MpE6fi7cTO5UnpXSAwDUZMMawLYZ5MWVna4xJUkyA13zGjtrHmMBnGJLUNTM742G14E1GvKaHOFsyqWp00Dy0J1S+5qtmm122uJQLg1IxXVvulCXfNxW3M8cqUeiGOYDaFIF+WrDjPG+17cD9FOx+seDZbMaDBw94//33O+O3WsUg6nCQYiQwyFLQgfF4zN1HB7x37y6zxYqr+ZiT6ZKt7S1CaMrz1ZZ1sY5ZgUB1cozzltFwSGI040HO/u4ueZaRpgkuONazGbW1jLd3cAJKp9x7eMC6qKiV4FXUeD/vDlzkNb9IrS3Q0RqpJ7D2T/iU+9z/1mBlWdSRcc6xs7PD2+++Q0D4xV/8RY6OHvPGG28wmUxwzvHgwYO4z9tv45xjMplw9epVQgi89dZb3L7zAcHHAL2EwNHhY956661Y5ShJoTPsNJ/bGxI6PnhssRpUWRbcufMBVRllLKIQ2wZe6U+20k66vQnXN6/gPS5s4JcWVjiDi5/z3B0e52000qrN6I0v0b1z9c0XAVKzWXX2cfu2rxojjVyvoSwjlKIlUK1X1MWaQZIwGmSkJiFPNKlRjdJsQAePCkKoLcdHR1SNFn3sXxvZAdXczbZEh++d+EW24yLDHico1U2I/Um8be19gg377mntw1Ah/ydi8PSSiNwF/lOiUf87IvLngA+AP93s/mtEGuTbRCrkv/O84wOsfeDbqyVve4ukCmMCRhnGRlEGwyQdcGwKtpaOlyrhJimFOLIUTG74jN1ix2Usx4ZCCwflgktB81IYoG3JzfGIr6odJt5QzVY8DI7XpeZ1U3IHz8ppUmJAyUvssOchmecZtdar6Rt0ay2np6ccHUX+8GKx6DwbpaLa3TDLyRKNBE9mDGKgVIHRZMTx6YL7jw84CSeMR6OmwlAMkPoQWTDt744GimGWNfwXyIxGtxEoifRFV0WMNx+OEZPjAixXReflJEkCtj5zXU+DQV6cJmgdC2K3zywW7NC9lcUnec6yeZcYLROJVMj79+51UMvnP/957t27z2//9m9z9eplvva1r3F8fMzjx4/J8xxrLXt7ezjnWCwW/OAHP6CqKvI85/q1a4QQJ4H59JRLl/b5Yz/zZf7gu98hWHvuTBoPF9+tUDcmKUo4OB94eP8BJycnTHZ2SfMYVOxWZ40x1o34Vwihq6oUQthkqAZPcLYz4m2/bw3XecOuGmEwaeiXG/ZVC1U03S9EQ++bySVpPJK2e8aC6ZtJ1VrPclmwXq+7YiGnjw7QosiNYm4rHt6/yys3bmIEJDg0EpO7fI2tLOvVgjt37+Jq26wsfIPjh17gdOMQtiul0H23aX245Tw0I7LRQmrtSfvZe99NkPBTMO4hhH/rKV9944J9A/AXnnfM861A+CehIk0tLg3oumbkcsYhJbMl12eeV1XNLMAozdGicLVlEhy7jNhOMsaSsHCKo9mcd9wRdrjFpyRn3ysMhlFw+ODI5xWnec2PBjXfV56y1uzZlKz0lFLjRgZVhy4Q8jTD3sfJYIORrVYrptMps9mMo6MjQghNvcjizPIKYlBpNMlIjGaQRr5skhhyFbWk6yAURcVibZkvZp2J0k0ld5FokLM0IXMW6zTBR1U/W9cUqzWDPGs8WwPGsLCWqqoZjg0BxXQ2JzQG0nr3VAyvM/Af9eH+EbSWKbMx7huICF6Mc24x99ZzHwwG/N7v/z7T6TSKdG1NGI3H3Lz5Eg8e3ueDDz5AKcVkMqEsy65ftTVLd3d3ew6IRylIk4R33y55/733uXv3LnVdk5x5oGcBx1bi6sy2xjAdnxxzdHTE5es3SPKcIAo9bHBxrc4Yeh/OYukbuEzIhsMzUFrd6M+0z6s17K1TpFOFSTfefXdO7eonxEnK+xYeojOj8XciTTg0FcRrazupX1vH1U2aGq5evky5LphPpzx+9JD/49d/nUt7e9y4fp2bN29y69VXuX7jBmmWsSoLFos5B48edVBIO1F579FdhmpUufQNch6aeMFFnvuZp9KzJe1kBhs70d/nozDAXpAM1cBKKdYhBRtVGlcEjl2NEcX92vFjb0mNYWRgaBS5SRmjmFjHKIm44yIEljawKhOWg4z7k5zgMtRqzWpoSJSQmporSvicjTP2/eAotaI2Gu0NyVpwjZcSvZKm8zQeiRCL9QbTVFt3Duscq+WSw6Mjjo6OYuq2QFWsNxOEImpwNBxgay1eAuNsyCAbYNI0JiqJsKMUjgLJctZKc0pFUdaU1lM1laOCsyRawAgmCN56zCCmfnvv0CYWNxhmCZkC5WqMEcbDlERqskzjdILLt7DpGBBwdccLVyKxkIj3keMM1MFRB/eE9660RrSOK+Xuuw/PJuqW+L0B3RmIMwQeoVFniZ6ij7rgWTpASSzkIKK7osoi4Jy94Jf/CJsEglhCHZCgIQg6KLI04+T+Q/6bv/7X+Pof/zJfee2z3NzbZffqda5++jPMV0vuHzwCEUaXL1MVJeWqjCX2EOrgqQh4rShszXI+Q2xgvL3LnW//HvduP8IkQ3yIVbsINdrVKO/QweHNGK90fOwNhh4/1zgJzJYrVqs51XqFzfMYz9AOkyjSLCpAxqLXVTTOKmYFx4xRH1cDEg1cIAYgldKo5mHWdYzb+BADpT40AXmvYu0GkajzpdoJB7yLeHvk0TdxzBCofBvQpYthVFXVFeO21mLMMMr2NlWb5nqFd6cQTvnU/ja3clDH96iO7nDn9YTjnR2uv/oZXvnCF9m/+RKJLVkeHWK8Qze2VUnApLHSVZwqW0yorb4VCxF18Y2wIaSFli0j54LtpjkAnIlZ+RBaJURodGg87qk4f3eoT741lY+8oNhkvPkAViksMNca7QPJqiANgRyJLyXkSmOUYmkM4jx4Q1KBsjEYlDnH2gcygRuZwWcpI3GMK0vqHCvxBBRaBPHRCEdPY3OGLXc7EHDWUbkY5DpqDPpqFTMMz2ShSszsjAeIb62X04Kdo6QxwMaQiqCatLtxljc1Vg2iM+Zqja5qjIOyrlECqVIMlGagDZM8YTIZsTUesbe9xf7OmK3xiJ2tEYlW1GWJ6EA+SKPWPTETr3LCq5/9Avfv32cxO+koe2Vdg9DwhNuK7o1HtLmcLrDWPclzK5qPw1bpVkthE/vrBwI2eGqkoCVJ2g1u6Xk9MfgW+dafXGvYRW0dxxAnKIXCVSX/12/+Bm9955/yO5f3uTYZsX3pCtd+7he4/spL7F/aJx9vMR6NSXUKVY2gCUpR4Vn6mqWrCNozyAwZhsXxnAdvvM3d19/i8OSI4daoOYdoQPFEEUWtITTsIvEdru3xODxIwNoagsdbS55m+OCoHYgVArGykkk2jCTvpYNDVFBx/PYKwsfH0FTpUo1Mb2fBNYjC+4C17iws07TzPPnN52gsY3UoGyFIF2mCWhuGwzFpmjTGv8Y6h2QD6nBKnmVsX7nE51++yfTOByTeYquSozvv8fZbb2L/79/ixmufY/fKFVxdNRDM5tkqJZ20QXsunbfexoJ7Dk9nsM8NC+l/aP44A4e22yTEF6HLpH9aezGMeziLPbWt+1sElyR4wPmADYEaKBGWIaCDA2dZSkw5V0nO3AYOFgWqdiReMVlbUhE+HRxeCYdGU6kEcbFQBxInGE+TKdcERFr8PFKwAqvVmvlizuPT467gb3/JBPFarLVxsug9xH7nbCeAUQKTVDFKBGUtWgvrypGlmtxlrOc1eZaACGpdMhJNVVl8XaGCZ4SwlSRsT4ZsT8bs7Wxz/co+u9tbpEaRGoUmoNIEj0OlhtJGNv/J6Sk/euNNvvxzv8xwcok7H7zL8fFxfCBGE7zFOhsLKgSPeI8KGwW+T6K1S/z+PYyxiA1X+vwK4Gm2XS6W1vjPgH8PeNzs9p+EEH6t+e4/Bv4c4ID/IITwjz7quTfLQJy1GKNI0oSj42NGwTJWVzk+eZ3f+N1/QlFVXH3pBj/39V/g1Vu3yHXC5Z19hvkAMxwheUbQAeUtZbnkcDnnwe17vP36u/zwW98lKxa8srvLvCqxIvhgoscvSaNcqM6ymbpxF/t7mqUUTcUn52IGbfAe29z/NE1Jm8ph/ZVXH0+Oyq0NDh4i88l7FzPS0zghK5GNPQshfu8uhnl6z+0s7BYEZ6POvGu86CyLHrpSiiyL5QGtdRijyQcZs6Mpzjom4zHbecKrn/0s33twl1B7SmcxacbApNw5mXHvd3+X7UuX2N2ZPP25PuX9oq73rPjdmbkgdpVuW7O42uzzHJ/lxTDuTbvQsDetslGuU4vgReEEKpqVisTZsk5Symads0A4ti4WyhYh9YIKgYfVCm0tdZKw8kDQ6MY9DI1cZ+1sTBZSCmstq/WK2WzG6ekp0+k0shxikdUzdMB+J4yca3dm4upzV0UE8RFS2so1QxOZAiF4skTwLjAaDAhKsyxXKNGIN4SguDQaYwS0d4yzlK3RgL3LEyZbY0aDAXmWkhiNiYsHTJJgdM5aeQgW0zhN08WMw8MTZouCfLTLS7e+wPblGY8fP2I2PWlWLm0hbCLlzXtCUJ+UbT8z2NsqQP1g6nlcsp2Un9J+lSelNQD+ixDCX+tvEJEvAf8m8GXgBvAbIvK5EJ5XXUGgoe516ekEqspSlTW58lDXnM4XzHe2GSWaL97c4+jkhDtvf59/9P7r3Hr5FV66cpVxmpNqg0kTSDTWCJUKYEuM88xO5pweLpisZygJzFdzUlEEbfBoHBovUX8lnpnvzqfzLZtyd2mSxozWJoBnjMH2AqAi0gXz+4U6nnhenKXQOhePH4toxFVM4+vGQKX3BFyzcm658U0JPh33jXr9vpHjUNi6wvuA1oLRsb6BIA2n3WItJCYhy6K58z7KJOd5Rq5A1o5LV69ig0SJ8CAsy4KVjyuPJM8ZjoabVfgT13jmDp4DVc4K3D2PmNGYleZZbN5bVpM0r7M6lBe3F8a4P8uwC5AS8cp2JeIlFugIsgmehbpsglYKlMaKgNGgFGWss0cpebw5dcTPReIxvXhQsbaqdZb1fM58Mefk+ITZbEZVVRtIhXhj9XkjEkKvkweCa3DHnvE/w/lNE67s73Hz2lUGBmy5olivsSiOFyXlsmJ7PMaYwGy+IOiYRJEGyyQfsD3YYpwaEi2xLGFwJAoSCWjxZElK2kvuUVmCrx0ajdKBuiwoq4KDx4fceu0y2gmJh5uvTNien3L46B6L0yNcqFC+hUia1UxzTX1q29kHypkJ4MNQKJ+2T3/7efaO1qqprdlCA/5McPVZvxsultZ4Wvsm8LdDCCXwnoi8DXwd+KfPuareBOMbzFXQWvAu1u9NkoRlVfPw8ITLkwGXxHNpaFjnCmsLhm6BXhmyMET7CPM4AWsgGEF7R4IwKRzDXDHeH3PvcBaVC7e2sUGwaJwoAgoUJKF+wkVsjVRMAFPMZvPonWcZQGfE+1mlzrknqjBtWC50cSvvfYOZNxIFrqVWtkXjQ4u3dfh0m4nd8r7b53r+ZSVgjDRMkggjWtv08ybYqXW856320mgwYJJsoVZzQrEkH02oEepmpaOyHKksaEWSxXvgbfm0J3y2j/aYMue9+fbzc+HKcMH7R3SoXkjj3kai+83UFkQ1hrsnDgSxViSBga9j1RMUIbimDJ9qPE1FEGGR5ojzJMGTIagmu8wD63XBYr3i4PCA1WpFURTx3IizfSv6FL1hc+a8+3BL9zoX1G5hmTad2FlHUTlGky1SqVGZkGWKdVGzrix5qahDYHuQMkp3ODmZs5ivUL4mJWOUacaZiXzcRCGuRvkaowKJUqTGkKURsvDBY0ixriYEh8aT6EBwFUdHB1z/1KdjEWSJnOat7T2Gg5z56TanRwdMjx/jnCA6xdXVmWVxP7GC7o59+Of9Yba33/Xvs/ce0xiW9p63pdOUUpskso8Ouf9FEfkzwHeA/zCEcEKU0fhWb59WWuOJJj1pjSRJWiQGOWMEBJMmBOtBGaz3PDo+xVdrTFEyGA9QtmJ2eMSjumBw/RqD8Ra50iSJIk01XgQL1EVFbR0mGAbJCJVrFpmwNdAsQhV1+SUloHESkBAD5N1KQvqeO03yFywWc5KGneOcY9CDYFrGS3Lu/rfp/2e8TyEWgem6iKYoLN412DFNgJSANAFIY1QTbNwkf6VZGr375rTbSXOgsx7nJ8KqkQ6rSBKDc7Filw8erWE0zhlu5fiiZnr8mCwIy7JmWhT4Yk1ZrJmu1hQimCwnHwzjKuVDSHhvzq+5cLnYwD+t9WGZNvjaf29f9N6f1l4I477pWGdnt34T7zrjLkFFwxkEaEn/oHz0jJCGYyuxVmoEjWO9VSsJhFhSr8Kj8axWKw4eH3FyOqWsamyoYofVqsukM02g8Vkz7nmPQquzS9UWv2yX/iIRHgAAIABJREFUs857Pnhwws///BBjLFJ5cu1xwTPIDRMHy7WN6GiiGV7Z51Arjg9PWGIZpZCpIWmiSLyJnkpwBFtDg5VLCD2cTiFEaMtI4MrehNwEitUpZTFnMJqQp0mzevCIaHb3r7C1tc3heMK9Ox9QrFexElbDRGi9qT+q1vfIRQTTS+rowzL9/T5ipuzfAP4ysVv+ZeA/B/7dj3KA0JPWGA6HgcbzDL5J+pEY7BNl8KLwDfRhy4qldSzKmlIUTnTU8G/gCyVC1iSopXlCLZZFuSJoUGaIbgK2IdQYA9tbGesiGnclHq0ShGi4n9Bbbm2RSJzkUSyXSwQY5DlFUXb9t58dm5jmXtPv+61xDzjfPpP22cXt0sRSo3srMdszxO19LrzWTUKRivVQQzMJ9PXZjVEED7VtsqcBbUzs884xSA2CwfqAc7EvOODhwZyT0xmXBxmnyyUnywLqEu8CdeMYpnmOSgxlVWGeU9Tr/EoxBvOfXabzIlpkv8XQnzT35slx9qyh90IYd9gMzPbiWtZJu/yz2GZ2Vihi0k+slBpo3HVKZTbR7OaiJUZzokfghLS26ERT2opH00MOjx6zXK1wFryF4BVBXPf7nbF4GvzQtCc5uU/ft4VvkiTl3fsHPDhZ8rlXLoGqoazJMsOgcixWNRpHqmJndSj2d7fIU0O5XrOqVqhVzXiYkyQpRhLyVJNnCSKxXqoAyrsmY5BYDFwJiXgGBia54nB9ynz2mMEoJU8zbFmgdUJVeYqyBhEuX3+Zye4+9+/dY/bgHVywT3TmP8rWTix9SKDtQ31NGe/9M72b8y2E8Kj9LCL/NfAPmj8/lrRGgzBscNMmqzGEqAUkxP6JD6TKsKxrjrxjqMCSYM2AwhtOlxVDU5MEYTQU0iwnMQErHqVTVDaC2mOLGu1hOMnZyTTHRyXKV4hYEhOtrvMB/LkVTdNVtW7564b5bM5iueSllz9FXdsnVmghBNZF1Uki0Bndbg9EYoZrq9bpXTSw0hSub51PpRVBRXdUNWX8YpHsSLOUZr9o56Qh3cRjaoGgwYfNucVzlYaiCS5EFo61Fus8RaU4PjoGhGw0Ym0dtQguxPtThgBKkSYmFpS3dcTzP0TrxsV5RkzPHjxhxHsEgJYdJrK5xg37ixiXCDS05adb9xfKuMOTeLv3HlFCqZpMN4mpxko2Hqk0/26lk+a75tmHWAdUiPRQQqA+OuF4dsLjxRFLt6TC4VFoSdEqQaEIEsW8+ufyPIPd7tNv7mxPj9sar90YgxoMsIuc7/7hj/n0p/4FhoMhQUX62aCGyTBQFVG/XpwlSTO2J2PG41FMBnEVgzxhMhywP8oZDgfkg2HknQMijcY10gREG+MughEoljNGA8Pj6Zr5/JDL1/bBKRIVMfUsy7DO4b1Q1B7MgFuf/QJcGfP+e+9ycnJyoVjY/9etP6noXsbemWC1tMUf/Efy3OWsRPW/Dvyg+fz3gf9RRP46MaD6GvDtD3fCzXBt+1Lv/05ijMiJwiqPt47HRWBswCQDXBpY2JL7x3NGyYBMJ4xsIK0i5DQeb1OmOaUY6lBSUWPxKKPIxZCYQGId2ju8E9A+xk9CzsbFjgHNQGS1KB9iBTAUBwcHfOUrP8tgMOjGWevw9A19v1A53X50Qf02YSp+F2Lt0RA6iL19fqHhakaSRE8e4byhpAd3EA2+BNCiEQW6XSn4mPBUW09ZVBRlESW39YTUGPRwSFVb5kVJ0Xj/dW1RxpCPRogxMX/XKPxT+lFHk24nq9A98o/MO9iAzfHCpH+c9uDwhF25qL0wxr1t5z14iMtZTXoGf6Nb/gWasulgK5x3iDGRx4uPs3SxZjqdMp/NWS/W+BC5vB4Q0RhRKBUQsdAjgpz3ws8EeZvztC5ycrWOYlytUFNXzbwNrvSWVa1mhDaGYrzLH7z9AV99/w7/7BeuI25JnhrILKtMkQwMq1mBDp5UO1Jj2BoN0DIkhFjp3lvLsqhwKLwYsjzim0FpvI4ZhUZr3PyEIBa0waE5PpmRi5CUJfXJjHq6JhmPUYnB1bHYeJqmFGVJVVYRT0Uw4+t8+meucnT4kIf37mCrNUYrVHB4V6EFqqBjfKR5fh1G2N4/mgSP5zRBgbgIs+EaxkIc7YkZkCYjtIo1O1uMXUSwTdp9CBdYhs3zvUha40+IyM82P/I+8O83x/mhiPwd4EeABf7C85kyF11P0yMkRFIAgmuWmyKxULSVDGshVYLROcoG7HrBorZM6sCyqEGtySRlOBmipEntJ2AEEgGDxwTHKDGsfaBwntp7QnCIj+Mn9Ix69BSjsfXBY130nu/cuRMpu6MRa++REM5h7OoMNTW+b44nqnk1v9QaK5O0fYPmOcVn5ZtKYBtojUYZVHqP8Sx8a13DRpKN3G87bwUfsBbqyuFciA5cahCdsLW1Rb1aUVYFp8s1x/MFShzWecbDnGw0IojCeocyTb7Hxf2o92A58/njrmz7DtMZWu8F709rL4hxPxsoO2/go+epNusy2pkyYBJFWZaxsyUxuOhtTV0WLBaLaNTn86i7LAJNVl5z1E6XOlKQQoM7ng0WtucF/Rsa1frquu7ggbaDiqgYgJXNQziPsXXH0RoH/NZvf5uvvvavsTfZYx0grQO72wbrPNWqaNQmAziLrwoGwyGj4YQQAmWxbjS1pVsWR9YAnUolAlmqqWuHRVivKuargrp2RFhLqCqH9h6lNQY26o/NixAHy9o5ssRw6epNtrd3mJ485vTwEbZconWCtTUOSLXpqG2IRMNyvj+2nolcgE2eGTDRxYvL3XiPtTYN9U26YNdZ/nPLib94EISLpTX+2wt3jvv/FeCvPO37j9NiXlG8v04EMSkm3aL2FuscqUCmDCZJKW1gWRSk2mN9Se4NdajwxmCDkAQhbWRtS1ujqpqRSVlUgYW31C4GzEPQkZSg2MSRYrCqwbNj9q8o4dGjR5RVwSAfsVqvoMeSae5Js8pt2TL03n3ntUfHLI5Z1Xj0LbzSPqIQpPHW29UXQJt30hYK2UwEbX+xdewobVZym3PnvaeqokRyhJWk4+ZPp2sSnRCMwTrNaLJFMAlV7VHGoNMcWt309h79lBDIZxnljsHX3MTWxG+Qrv+/ee7h7GzUX9qdx+D7dMJ28IpEjRVL4OTkpCkiPGc+n58RJtroQDzZLsLMzy/xN7Np7IDt90URje9wOOy8xiio7850wov4rqPMcDL13H18zG9+6/v8S7/8NbwZM9pKSIoViXiCg5OTU1RwGKXRElkuwRuyNCUzQ9brApNqEiNEooHH1gU6GPLMoBVYbxlkOZWDR8dTTmYLDk/niJlEHZvaYWyN1hHqcH5T4aa9f9ZaQrCs65JKQ56mXLl2k93dPQ4OHnD4+CAaqeCw3sUiEN43uGmLQ/ZdnI8D6UjDvY64bPvc2mfdF2/7qJj7T7+117hxCqIhVd33QQTfrUYVOhG8A28dwbkoKqc089WSpFohNiW4jEBGaQtQASMKxKAdSOUwlSNxgaFOSKWBYrwhSIIPjToj/YzJxvtsCkrHgteK2WzGcrGKdUSbfIL+mNj05dBhwq3HLQIN+bLNS6UlXLZZ+d63nO32GG3hi/MY9dPvcIQ/o1SuCF2Wa13XUaKXVinUAKqp06qo64qirJgdn3Ll+nVufupTvPf2W+SDASbLsM5HIUHVnPdH9MI/jtfeJXX17FF7LH/OsEvv80XthTDuAc4EMPutb+D7Hn07mOu6RinFcrXkg7t3mc/n3YA/A6PIs4s39G9m36hfKNLTePlKxULVdV0zm83w3rO9vQ3EAEw85vkI+llDnynI8pyiLvnH3/khV2+8zBc+dYWkPGWQpaShor60ixLPbDrF2pJhnpAYITWKPG15x5HClmYpSZpE3NEY8kGOVuBshRGNtY6T6Yr5quSt924zLywyMZQ+UDhLbquoxyL96yTysn2klImzBDzWCQVQO02ajnj5M19isn+Du/fusD69j22oisF7vKubAJpnUxvpOfSDp7SOjaRNp9nePitjTFepprvPn6h178gy3d/d9h626hvD7kMUuxKJRt96T2VraiyzxTLqmfiMQbJFkgveORQOEUVpQbtYgk5jyHRGGjQGF6sxOU1QhhCaYS/NeHoC7miUNVVkiy1XK4RAnmW9oObmiiJnvR2/0E7YSgWMOXfo5uvW+4aznrjIWcw5ft/cKdU7VufCA16aehmRUmmto65q6roCokhbnkUFS2cjrp4Yg62iUzhfLNjZ3WP/8iV+/KMfsrO3jUoMlY3Sw0kSqyOdsQStn3L+1v2U23lOfDj33bPaC2Hc2/ZRZjrnXDewnXPcvXOX09NTlN7wm9M07Y7ZDnz3DCzsPMZ+HvfqJhfo9KtFYucJIXRSBFtbWxGDD+6pE2s8nsfoeExvBhytLX/31/8f/uyf+pPcHGeoasUgy7iUj5hMxhwePmY2PSVNNUqgrkvSRJNmQ7Jm8krSBJ1EvFvp6HFooxmPRyymM05OptROeO/2A9658xDJBlQeMhULJUfpVov3veAkGy/BO4f2NnpXSnC1o6ostYs003yyx63XtqhOt3n08AHL+YzokanoBREr14TgzwyWZ+OIG++lDcK1nvt52YHzSnqftOcugLiAB7yC1kJ5H6sUqY5C2DBLjGJkYoWt0vlYCFqlTNEUxIxUrXOSWlEdrdkbDJDBkFMCKjh0U/acUOGdZ6AyBrYgryoKBHQKiSZpSNJRS6ZRMvQO4xUqCFqlFHVg++pNyuGY98uK68OkY72EEBrhsI2kb8+fbMaOELzBBWn0zRs2kzoL4YQGkgGh9pba280YVFFzRqTJpw0B1/wb30wKRbAoHU3ZulhTFAUiEYLJ8xyTJ0gKwQu2DlglsDxlNj2gXJe4UJENcr78Mz/L66+/RZanUTBPBELNIMuZLWZIEothiwcVQscz72eLbnQ3BbgYErzItrTvUe3y7L50k5tqqLS9G/cUZxVeMOPetvOeev9mtMGb1qiLSIert9rSLUzTcnI7OVGtm1JcT3rSfZnYi7z1/vKoxcNaD9EYw3A4RDWezunpKXmekyRPeqbteUDEr1WiUNpQhUAwGY9OFvxv//Af82e++Q2yfBttPDmGJM2ZbG1RrFfUVUmWJqSJ6TSlTVNWDKXQSdosoRWjrTHj8RjvPItHx1hJuXtwwOvv3WVZB5wkOCI7Al+jJTRd0mOrqqOAEhze1UjwmDaAaRvDIEJV17jVGp1kaKPYu3Sdra19jg4POHj8kGI1j8ZMoiFR2oCXBkd9cgCExivzeJSmiSd4nLN4BybLSNOsg4/67I3WqHcVmz5ZXGYzWPsDutskZ3aM/Truq00S/4mLYlcGoXKO+bpgqGAwyFjXNQ6P1QodPD64CIUEIah474ZZymigKGohqEAzLxO1G1sRgpjcpLRCiaKuKrxKyLMUrYSqWFOnUVajpSgmicb7rBtn56EEgc5Idi+AEAOf0DOKmxvSUfwijr+B7iKW3sCxYTMphLBhoYUQ5RCSJCHP0yh1LVBVUSG0qiJck3nfMICEPMvw1nH16jWuXLnCajWP1NQ0xTmwzkbYt8PdA+3SVmj6ag+Tb1kz9FchF/WLpxj4i75v8fful8959Be1F8S4hzOdA86e/HncGjiDbR8cHFxoIM4HQ+PE8CTs0773Dfvzbtz51hp4EWkCOHXsWDzJ/tlcNQTRaJNAXaDFgAjv37nH//IP/nf+1L/8DfTlfUZSk4hgjGJrZwdra7I0YTwakqWRRWSDZr6IgeM0z0izrCmiHZDEUCxXWMlR2ZCT5T0eHi9wksZclhBQwaIpoYVNGlaKd3ajXd10cpzrMNOOp41tOnXAe03pNCIJV69/it29KxwfPeLx4wesl3OMSQlP8WoubN2Aap/TRsO93daHZi6KbbzIre+8BKB2HlEanRqUjVLKwVsqG0gRFkVNFmBoEpx1+KqCRJFIlJ5IBJA2C9UxzBO2gmG1rKmcRUIdA6v0x1sjmmctggPi996W2HKFVoHpadkVaW/F2loIpz9unpiwIojcdJhopFx7sTTGsH1OEhUzkY35DF0gNWwMut/AOfGZ0xl2rTWDgSE1MdmxqgPLZdTEhzhWnfMoFVe4WZ6zWi65fv0aO3u7LJcz0iRBGwg4vPONs9gLDktAQgtD0cOpQtdfA72LvOCZf5T+8XHai2Hcz2BuZ4Op59kl/fckSTg+Pma1WsX99FmPu++Bd5St85oAbLz2n4Sz3f67wWDA1tYWItFT6NuWiwyNR5GYBPErTOLw3pHlOW+8f5///u/+Q/6Nb/6r/POfv4pRUQIhS1PqqsQ7y7p2iIHhICeQMNIpuW9UGxss1XuHRVMHjRru8PD2A/7w9XdZlB6nkuixB4fyFcqXMcIVgyDQFlioqliAWkVZ5BiujaZBhdBAVJbga7wr8aJYqUkns+qcYmf3Cju7exwePuLw4CHgEV889752A7p3/9rVT5Im3d/tIO8H+l6MgOrT29OW5w5BSxRFEwNRMthTrgryVFN5z8mqJNWaSZYiOqCDxYugdExE0iqm+3lvyU3C1kAzrwPrdUVlA9aMCKqZymUTBSEEdDNJKi2MspSBiQU0qqLsMqzbQKX3/gkJgra1UGj/Ws+SFTY28ezYiK6v717NmJeN1+4b/jrQZK4GvJdGckCRJiAhsFo7prM561WBNpo8yzGJYT2vKNbrrtC4iGY8mXDl8hWOHj9G4Viv5jhXYBJNluREmKXnP7e6AM35NyhNI8kb7+VFq8Zn2phz3v7T7OGHaS+Ece8HVLtt5y6kT73qD96Tk5Onemd9L05EcM4j55CSizrd+eN9WO8vTdPumP3389TOfnModJIgwZEQYYiyrMmGEx6cFvzq//pryK/8LL/8z32N4XDUrP6EqiyoXWBVVrgA2ShHZwk0jBYXfOTgK4Pz4ESzrgO/+wc//H/Ze9MYy7brvu+39z7THWrqqu7q8Q0U3yP1SEqkRA1GBlMUZZO0DVqI4dgG7Chx4CCwPyRAkAhBgHySIASGYTkJLCiW4hgJLFlSLFGD4VhyCEmmREpWOD8Oj69fv+7X1UNNdz7T3jsf9t7nnnvrVnf14xtacq9GdVWdOvecfc4+Z+21/uu/1uLW3QNk2qXOK++Oa2e529BCbH7doU52uL5QUtXv4TFI7cpDSIutASEpZBeLoqrtPH1cRVy8eIWNjQ32799ntP8aPOTeNi9Ne/EPyt0rDinm0ExYxNsNtN9uWGaVLCv1OWzhKIpGOHqnQDl6pFBUs5zKGqSPfezPHHTSkYK4MqjIopXyVEOJigQiUcSRwiYxBZIaw9Ekx5gEIyKEMM3iiXW0xiRKqKuaTtrhysVtdnc2qIWgjl1DGemNqNCoRtc1dVU1Vnn7uzpFuXc6nVMMtzn/3RiDtq6EAYCKYo+1B1jGWfSxcsdRUrh8C6CYGfLcQaTD4ZBer8f2zjZKKYbDEZPJhNkspyoqpId/4iRh5/wFyupzRBLSToa1CoH1rfvESst94Rl1o2/+X6WKH6xPTodnVumTB6n6x0K5g6sFA4Fi5awVG9wba9HaeHeqRmuHo49GQ4pihlKuNK8LTklcd3mNUm4StK49xncSloH5uYwJ41g1vjApPtHDB5MSD4u0a6w8EhQghGtjFiXkxgXQpEwwlSZJUqZ5yT/6ld/mCzeP+Cv/0V9k99waSVyi4imUU4QuSBDU5chhtLiU6bJ2XkFlnI19PK74td/9HP/miy+B6qCFQErt2vXh1HRVGXRdumCwsERKYI0ljiS6LqmFQakIbWtUpKhr4xo6CAuyXYVRI+yQuph7RCpWGCsoSotUXXYvP8uFjfPc3rvFaHSIoEYIA9a42kG4F05GYG1IXgpuuCJNE+RS7Z5gKQZWT2O5P6ZyAsIAl0QnlQtuG8fQiKUr85t2+xTjIVpDP8ooBIysQBiBUI4nb3HpmUpJ4kjR7XYoZUwkE4gSKqMp8ymV1T53xDXmCAqprjWFdk2gMZpYQCLdM2DjTlOQSwiakrwB7w7XBD6caC1FXZ9Q7G1Z9siNsU1WqTHG0Wm9cpcqbhS6MzyED7BqrNH++BFVbRkOh661Xl2ztr7G1tYmKlIMjgfcvXcPmxdY6xhlVeFa/6ko4ru/+4N87nN/xO3XXqXbSZrF1sUK5lj7HDc6BW5tfnrUZ2Juua9S8MuMwQfJWRpkvyUNDYJyN6aVNt6y0t0DNH9Zy7JmPB41SUSuWJidB2rsnM3SvORhMkQLKwvTYIPiP3ljV2H+gS0QEpjCg/tg1sdJMdYQyQQRxei6xAjVBDGNt0ZN3OUPvvQNDgb/Jz/88R/iu9/7LuLUuaDSpCAMUZIio9gxE5BoYVwLtrLgxqu3+JVf+3U++9X7ICNnmRWuEYOoax/IE2iDSwZSDdKJNZpISawRlEVBHIeaP/Ov+cIn5vCJzl03nkgBEdKAEBGu245AG8Nm/xwXL4LWFflsgDW6mQ/hI1JShrrh87kQSnoufsCpV7foexzYMg+S9jPSjN1YavQcUwbf0ciSdteoZjPKWlMICdZiKteoWSFIkwSVxMQ+3yGWgiR2TCnZ2yQeTpmWBdNOxGhaYXHVQqWFJHNlfatp7sr4eg/gpW98nb3XbnHh8mXizporB+yThVxJJ4Ulbl3H/PqMsShtTlwjOCsbf23GtOYKhfHkAK0dxDiv8ul0gqH9zDlueKU1FktVlYxGIwaDIUpK1jfW2dzcJE0TDg4OuX9/n7oqSWMXr7ACIhekQAjJ5atX+cQnfpif+Uc/xXQ2o9dNKMuCKI59FjAtGNn/bOaFCdplB2yzEDzKQ9FC61sL5ioSyNxTWC1nsdz/MW92QwN/s9pMmGUF33Cs/Us8nU6ZTqdNiVch5AJ2D3M8MBzztJVu2epobzttvOGGB+u9Pe5HkTm7J6IyNfM1f379AkmUxNy4dYef+tl/wgfe++38qQ++n+fecY1O1CVLEr8gRGhrsUIxMSU3X9vj05/5LL/zu59mNJmioq2mch4IHxPwSrkFYbSVY8C3HaylFyiogZUU8NcFpWo1Fomscd3chCAWrhSCNoa6rBmXJdYYzl+4wN29nOmkaF56byqFH1r3i8ZTkkq58Zp5U+wwjscdloEHvLzae7EuqoE1UFvnraadPoWF3JRY6VrnqcoxaeLYUhpBbV0BrAhI4og0jkhSRS9VdCLY6iccVYYplaNogqvKKKUrZe17JORlwd7eHnfv3kXGiqxrfSDbFb2LYteMXal5pnf7TisBabQ6aRB/XozrShW6KLly3QJrvJHVasZdlI633vCfrINQi+mUPJ8BbiGoq5puJ6PfX2NtbY0oUuSznHyWI4Wg1+1hyhIRK0xtqEpfBE8Ijo6OuXT5En/2ox/lN379k1S1IYoS6qqaxyYaz8HP4bKSbXkvZ53/5W3L+63a52G65qHK3b4lDQ04iZMuvaAwd2Pz3JUWCMq+uVhOZoMulINdQXE87QV7mIT9T4zhES33MHFRFDc9GkPfz+B5aGmZFRWdLGFSlfz+517k9/+/L5HGiu2tTa5dvcK1K5fo9rpUteFoMOTVW6/x9ZdeZjKduSTwuA9WNCyHKIoc1NFWKkuKPVxn7V++QP10Wapzxd+GPpq507UrtxxZmnCddRRIoSRRJKmKGbouqarS9UGVjl0URuO+ludsfl4llY//zpkyMK+b/zjDMqfGdfyiFtLwXeE7V9NHa03a6YKw5BNNgcOeRQ2xBaUMaVSTSIisIEI6r9Ia0BXdLCGLBNsbfcZWc280xdSuQFhZzrAy8pi6YyLNioI4TUnTjLLSHN5+zcMygQ4ZNYl84fdAQwwUxNqsfg9smCPrF2cLQki0de1mo8QtIgi3gFlriSLVGHm19rElbTDljKosnAERx6RJRJpljk2WxkxnM4bDAUrCua0NRzaoXdxiNssZDUfUlUYKRVGWHO4f8IMf+SG+8uUvcuPGy+S6Aphz+Rcsdzt/h4ICtnMu+lnDnw3ccspn2nDMqudnlXwrmPsb1tAgSdKFl3HZgg8X1lbuZVmezDhdUuztzzUrbDtET/ujp9+sE5BLi5YX8MaTN/7k8VYtIGGccRRRSemZAW6MzTVY1/VpMJoiVcCbU4racHx3xEt3voL8zOfcg28sKnbYpEst7bkAlDYo237+Wp5SUOpmkbEUzh8al2RZtnAN7QDm4rXZJq/cagHUTQKKMIZEOCpdlWtmsylaV1y8eJnRcMg8g7XNTFiUeas393s7u3mVcfC2il39bC1vEy3FIIRrDCMtXq0DNsBOgiRNMbpDXs4wxhIJRW4grQV5Lai0QCcSK5XLdo0jIqVYz1J6WUKK4LKKyMuCWTUlEglGQGlqaiOJVeLKCEeW87u7XLx8haSTcfHiehjt0rW0egMDdVWjfRBerGCngW9+I0JTjch105L4hKp5idtKW4qqpqprjHVwTV1rKh/z0togrSH19Mw0TX1/V5fnURYluqqJlaLT79HtZpRFTVUZhsMRRV4wKwo6SUaUJGxubfG1r77I5cu7fOf7P8Dde3scHozpdDt+3G4haiCR9jz6bY2OeUBhvGWjsvluHw7kvGGW+ynyBjc06Nu6rk8o9/DAB4sswAKz2ezkAQUn6HJB8cRx7NrkGeOKyy1Fn5fGNT/kaftZN6Y0TZnNZtR13TQKnh9nxRBPCbjWde3K61YFs9mUOWtCNkpKKUXW7aGNYVZotKkRvoeoFDGxb07gmMeuJnVjXQiJUBERpsngDewj6ReUcJ6qrMiyzoLnk6YuUSVk/gXlGj5zkmEkfL9LwBqMdoHBcF1a+3ozSpAXOVhDHKV0u2uMRwMXsJOu8NqyshZCNPXDtTHge3SGgHbbam8H+h53mT+3FomDzdx8+mcBVzirriviRJH1upS6QpuaGunKRxgotKU0YFBYIZjNClSc+OYzin43o7SW850Og0nGMB9h0SAjtBFXN+f7AAAgAElEQVTU1j0/BoGVim5/nSTrIjyNMMx/cIJdYLW5igXM3Vpvhq+8XuNjX56lJtzBpHA9Xo21lHVNXlTMclemd5a74n9CKYRURLEiSSIim9DJMu9BuE5VAEXhnvE0TcmyjCxzjUpms5K7d+8xGAyYTqbMZjkbaxskaYI1GUJKZrMZ165dQ0hFlnWAuaVurQsWC9MOtLrxN9a8EKtQxZXzvqDgH/KRN91yt290QwOWMgqXLhjm1llIRFhxkJXY6gkvgJMrZuu6TlX4bUUvlPBVJAWhnVsUrb6VqxT6spVv7byMqpQuUNbmutbaN0qIIrI0JU0FtbbU2lkwVV2hWaRFhTFLz00XPnV5vniecq1L0ExQnN1ul6IoyPOcyWQCuIWh2+265sneUvMnb57tlh8L1jgsVWistFRVQV1XWOOoZpsb55hOJkBQygZsgGrm8xY69FjrqZhLnsYyvPd2yyNZ7tDUfZe+56rP10TXGiHDYh/R6feYzsbkeQ3aoqylKy2zWFCmkipWVJ6uWFUVVV2TZSkREoxi99wGw3xGMRxT6Zo4yZA6xuLiIghJnHSorQVtFhb3EFR3cZeQxLRYtVHgqImrRDYNOET78UBIsMJS1SV5XlKUpYc+odNNkEIho9jh/crVEepEilS6TNvaFSB1CUeCBZ79YDBhf3+f4+MBw8HYwZOxs/ij2HVom80KLl++woULu9RVgQA63S6TyXBebM26OToxrdZ6I1M078DDnolly/0s4aEFBf+A5/t1KXfxRjc08O7Ww9zouq6ZzWan7rOslNqYuGv64evPneEGrmLLtAuKmXqxV+cyT9/9uDoRq33csPhINW8tZo1BGOstAYuyYHVNmVtM5LyEVEUkSmETl51n9KKhEHBu63H1UH++bbWvevrCi7lsMYcFbH19vXlh2o2Sl6la0nrmjAThMVNrXAs0KyTgLHcpBXlRMZ3mZFmHKHJcfdeM/KT1LoQgDsXBjEGqCMk8ealtJPxxSGJa/YcQJxIgfH1zLNrWRFKhraO3JmlKpStmheu0RVWRlIZeKekVgkQaelnq6I3TGRWQxAlZnFDnBdsbHcbFBvujEbMyJ816aBVTVK7sswLXACZOkJGik2RuXMwxcweT1CCEC65GCiUFSrkFqbHwly4x2AJC+C9fg10bg60NpjYIa4iVIoliVBTR73Wb8y/cLm9oWW2x2pX+lZ74lhelw9VHI+7cvcvB/j611myf32Wt13e0X2OJk5S8LJjmM65eu8b2zja3b98iTTNGwwIpIrf6+CXJwS9+AG2PfT4qt589OceLyLBt9m3g2FWPROvnsxIEzkKFfNMbGrQt9zaeu4zvBt7qqS/FklLyY1rE3JknHixj4Kus+WVpxiVMo4zbuPvS3Tvppq4ctnV8c+GKKmnrg79+/yiSGB044AarS2pd+uQgX+1O+QQqi6cwOgXpS3aH52Zu2YpT3L8VFmXbewIWariE6150LR3eKD2YYISfG2N9/1DjG0aYOW6uDTKK3fU7GgXGahQhx2A+Z6rpcj+3fsJ4VgXm31YRi5nSQZbv3fxLYnwRLNdOxs2TsAahFLWwvra+s6ajtEMaZ0zznGI6Rlcambvcg7IUZGkHpSqoB/TXesRC0EliRGLoS4U8v87gsMvNwylaG2ZCOww8i6GuuXx+h56AJFJo5bNFrUVG/tkDz8k3/t1w3rHWIKSYY+5LnrWI8GFjH1T1cEZVGepaI60gUxkynsOAMjBoVjy4dWmpClcJcpgXjMeuQc/BwQGDwYDZbEav1+P8+fNsb2+TrfWYzWbcvXsPbQz9zXXqWhN3e1y4fBFbV9x67Y4rvVxZ0ihDG9dP2QVDfLMfjG9G0y4Z5r0SXwzNjbltLLLwcwPRC2+MnSJyOX4xP/xKOQtb5s1vaGBZeCHbSjf8XFUldV167nc7GcKveAIU0hFntUFI69Ng3J2TjSs1x92tx87lwrHA1YY2LVgBpFLu5lqXmWesq7YotXTBHaNJmrZjcxtaEGpbO4vC+OSUMMHGhPfUImVEFHnc2DiWhFs8nLIP67uxrriSxX1OWJBRYNW6AxvCC+cCqwKoysoxZESwKvzCKVzwzvoO8asWquCOW+tojs5qFxjjm4gIi1QCow3auxHBKpMh0BmaN1h3Z6zWzn/2/HYhLHESkxeuGboUcfPAa20xRCBTZJSghURKRWlrXxJh0V0N3tTjKqdCNSKg7U4ahdEy9xbnx5JEEbLbcRUVpyOOx1NiHaO6CYNco21OL41Iex0i6bjtnTQhUTGVjbh2+RIzu89rgxlaWVBQFQVWCC5evsJwOsWMx/Q2t9Hei5NSkmUZSRL7Ugnz0h3tJ8fowAG3TbmA5YUNwgIokEoRS4WKRNMm1Rh8DobLVtW1pqor6srF6e7d2+f4eMjR0RGHh0dMJmPqWqOUpNvtce7cFt/23DvZ2dlxgfyq4njk9p9MJ/T7GxijWVtbY+fcFlVRcDwYcOvWTYbDIVhLUZaohZw5pyNES6W7eWomrGXatz61wqBsf38j5bHIUA2We/P7STCLsiz8AmCwNjxIc5fGuYDCZ7gZ/2VRUStN19jF+pzuZI2ym28/afmHlTlYKngamlSqCVLaeGnSbPgZwsMwd8EgLL0Bb5RS+fNI75LjLR/dsp49/izDNTt/dl4dZP7ihDRx61097ZMthAw4p7fCgpfgLZCT7Jd5bR5rg7VtfTDWNK3v3D40rmW47zJYpYQAVMgIdpmFEuu3uXpBxnoF1wSUrfeIXQVNISNMsHSMIZJzXNXdo8eAJXMGOQ13n7/oLTUf9m257cLXMIkkJFGM7vbIq4rJdIytC8pSYxiw3U24sLXGloiIak1Zjljf2WS938eQc35zg3FpOZrecXxzBIKEtNvDCMnhYMJoPOL4ay9RVRXGGLIsY3t7m62tLWcJZ9n8XWktPFovv9f+vTIGw7ypRumx9bK2lNrBtFXpep7muYv1HBzsU5YVs9mMyXjMdDqlrEo6Wd/x7qOIJEk4t3Oea9eusbOzQ5qmjWFijGE0mTKbzbj12k3iOGb73DbXnnqKrNtHKUlVlNy+fZtqOiHPc5RyxfymowndTjL3PubT4eZJzONkInjFjUW+OK/LCMEinLuakv16lP9jodyD5R5kGb91lnt1or7MSevSKcKAEQcLI8AIIUi0cOqVrvuicj9lyK5RQivlva7rxQJkZ1Qwbeiprqy3eAOkc3q9m+XA7PJ42+yRsyq7uQ5p4YgLcNccqmkHLxceVI8DmdCmb0WCEYD1dDbZaq6RJHFTxhjm4w/wRuSDaMEgaEMaq4KpfxyUPMzvt2hZe6JlCITra8wZrygFYOoKFTmKrOivk1vJZDajnNYYUTIpNYWFrNdlqxcTS+iXNbGQxNaSYji/3uXy9jpqlDPSipkuyadTfutTn+I7PvBdnNs5TxTFrK2ts7GxwdbWJhvr66RZiq41tQ/YuqB7QVkW3ntyeRFFUVAUbntda5+nUjeYfV27onmTvGIyq1wXMBNyQByHvtvrEscxm5tbXLp8mU7WIY5j1ta26HS6vgqru3+hPIX2LLzjwYCjoyNmsxlCWN7xjmfZ2togTTt0OjGzsubwcMTweECe56Sx67EKUFcVaZIQ8PO5gheeV+RiSGGe/OS5uVyh2NuyvL29GHyr8ngod04qk/YLCzRUxvD306CDIO0gYGPRae1xwAdj6w9T7OH47QArzIubrezedIrMrzGULm13pGor+jZmd3J8y9va9+6syTxuFCcTwZZ/d5DQXEm3GRTNl1fMQNPmcFHpGnRVexpo3GyP48QFqX2y7jxLF4Rsd1qaPydSzUvOPnaY+ymyPLZl683vRWO9N/vbBUUAgNEYq6llgkwy4n6EUR2K2ZT9sqYwNXnlkngub/c5v9lnMhr72jyCFMNaJLmytU5Z11STitIYhEipqprzFy9z5emn6EmXJBRFiqqqOToeMBwMGI0dDBKuKdx/R6NNED7jtdfrs7a2jhCCXq/jYE5vCIRrqoygtM4Dx9omocqxySxGu8Ui8NyN1tQ1TPMcWcqmSY+oNUpZ10+2LFFxxJVrF+n1emRZlyRyijkvKu7eGzCaTBkOxygh2Nk5TzWbcPXqVTY3N7h7e+KoynU5v+eNEST8Oyob73mu4EXb9zpljpcU/AooZ/7QnHGbl8dCubcVCpy03JuIfPszK/HHubW6HKAFFwySS8pvlQJo/7qMDbbP207gCT+3g42nTdOqY1mW6s8HGKOx4Jfu2YrF6TQ3P7xw7d9X7duolCVlfuLYLWRped6aL7v4+ZMWyhwXl2KRz9+Ug23TMnHwTsiKFK1zt7sDrZrTs7IL3hSxJ+fl1F2tXXgX2naiWNqvPfdSgDCasi6RUQfZ6RNHXbRKuH//DrrjEr72pwVJIknSmERAUVSsnztHJ44odclmptjupdw/PCZNevylv/ZX+L4f/BgTDQeHQ+4MD7h75w57e3sURcH29jaXLl3i6tUr9Hq9Vuaqms+TWAy+B4laZQmcRey8wtJCZXFsG+s7WLmKx45Y4Gmzznhz8SslLXVt/D6OmmmtgzbdeVwTeGsFIcwz8fBMWVYcD0YYBGma0kkz1vtd9gZHPPvss7z73e9meHREns/mULCHYtw7HsBGvxCLMHdi4ak7zZBcZbSd1eN/mDwWyh1OKtGgpAHyPF+p/Bet57nVG1Li29UBA76sa+26zTygfnu7dVgb1mkr8ljOa4mHWivT6bQpZap9n8a2LHsj4VxR5DJ0I6V8RUawnoHj7sFJD2XZml+2zBur1l9DKBmwvM/CSydoLO6FRbFlobtuVycXnDAXAaaytWloosueVtt6D1abMcb3w3W8+lrLJh4QFgIV43rCeiVSaw3WEnl2SXuu2+cUpy6zb42cptwXFsPw3Lc8Hofjno7Dzk8gkDjetxbCpfBHMVFvHTMcsD8ZohNLN5VOEWpDdmkbbUCMx/TX19nspKioIk62meY5+6XgQ3/q+5FxzDdv3ODu4YDJ/m2iKOI73vedXLp0ie3tDYyBTidBa6fYqsqQJIqqchVH58MWTTDWGsNkXLsWeFGElAZjXObptLRMK00cS/LcVSgFyLIYrSVaOxxfCLyVrlxnrigsEnOaJUBZGrSGsnSJUFXloKDZ+MhDvQapYjq9Hr3eGpFUFEXR1Iz683/+L3DpwgV+7uf+KYCj6gJVkVPVFd0sbT1di8vwsoI/k5zilQfP9VHksVHucBJSCC91w8tuyUlIBtomZRuDXaz/cvJ2P8yyWgV5LI+j7WVEUeSClivGuXQVzkK1LggsPB3yUR6I5fMvj7uNcZ9FlpOYVuwAnPI3Wh6AONkVKYwpHKcJkDNXdPN3QxDSt8PfHLNpsX7Msge2fJ7HFZZZJWfFWk/uJzz6LrE4yMwKi1WCc7u72FkXOz5kfzxDYihqw3o348LGGl0USRLTURIzqhiPx1zop9z+5m3+8T/8X/jwX/yrfOA7vgfZ6ZLFzkuIIkFd21bTdEsUCcoSkiQ0p3fvpDFhvK6shJIuW1rrijt39hiPJ0wmE46PjynLkijrUgvVGGidTsd3Vuo0PZGDAZGmqWt/V4WkN7H4HLXe/7KsqKrKQTlGEyuX6SykJI4zsm6XLOugq4qi1szynPPnNlEYvvd7v5c7d/b41Kc+1eiSrNMls4ayKHyVzIYAGSaJwBh7lLkOEFzbMw17Pppqf4yUe/tFbUuIop9FVim3AOk0Ab0zjONM21fMTbBUT9S8OXW8eI/DK2ARnfkFf9gYw7109TfOdv+Wj/Gge/Ew5Rk8qPYCG5KfrHXc5jldsc33XuR+azPPIXCZqe7eNpRNXz5BtMoOtGMz8DbDMqx+kV/PojM3YNq/g0WirUAHxW5N8/c4USjVJdcz8knJoIRawCt3D+n217jU6VBpTZlPqGZDepHg6rl19u4d8fnf+10Kk/CX1s5z6Zl3Em9nVJXG9eUQ5HlNlsV+ocWX306pa8eCkXLuRed5ztHRMffu3V/onmat6xzV7/e5ePE8nf4GUdaj2+2gXBEXEK6fbBIpVyfJzEseCCGoqto1DQmNQ/wzHxYBYQU2cuXXlFC4suKuLEmSpqRpBxWHvgyOKmm0dq31yhkbGxu88MILfOYzn21Kj4dgr5LKw4IBhnXB/2bKT4FUT51/y4JiX2VQnlUeD+VuT15E+D2wZM56bcuBxPYkrzz1I+Ch7fEtA6FBGS1AGGdS8KJRdsEFP8sYV2J1K467Knv2gWIf8vCx+OCt8hjC+ZerSC5Y19Y2DSnC/u44QbEvnqsNMQGNQllluS/DMn9yZO7oz2EcX0fGZ3W4e6IBjfAMLiGh2++jpWBWFhgtSYuKvcGY7v4RFzc7nOvFbPV75GXJuDL0pCazNe955zu4duE83SSiqjRRpPwzBVkWISUYI5rcjapytffrWnNwcMiNG6/wmc98lq9+9av0+33e97738fzzz7O+vsGlS5fY2tok8RUgpZRUnq5scfCfNaAiZ7WG0gIiFFX332rtasaEHg6h6Y4xrlGPY+xECw1c8rxyBcbSjChKXOmCukZYqKqS3d1dut0e4ypnMp3w1FNPsbu7y507d7BWUOau7n03y/CjnSt3XE0g127vpNfc1hPLIgO6YGlh717ZPKI98Hgod6wrSxpuj3UWrdGeBy1s4/A0r2p46YNShAWLxSmDYAlbjKlRSlJUJZIYJWNfeL+drBSSi5rQot82b+ThlM2igxTcsThyeJ2uK5I44WS2mT3xFZoHWxzvV0YKWwmQCqsdr93v4q9tDve0YY82Q6et2NtiWvsZv6BYaPXQdOni1mjPhQ/MB9NYK1g7T/cWuAYKWKyfr8CMsFYgrEYIQ6i1L6wrICatodYVVZWjlAUMdV0Sxy7jUUURNnf3vTYaD9w0wVZ3LIikQuHS3uFkqd+zLrBvqojVC/ZDcfRTj9dKmxFO2VsrMTYkcFik9dnMwoB0iWNJmlIjGJWa2UxDUbA2GLOzs8EF0XNUysggcZ7RO69eYlTcR8+mrG2sgYS8LplOQ9mBildeeZVvfvObKCW5fv0VBp5uWBQ5g8GQ69ev8+qrrzol2O1y4cIFPve5zy3Ue9ncdEr+ypWrPP3002zv7LC+teHpluskcewSpZRCyMjlTFiHr9faGW9p6tlYSKyRPl3DPavWJs7LM6bJpLXWkhe2MSa0seRF6fD4ygH2W+fOUc4mDIdDpqMRFy/usru7y82bN/27N/dKlZLzHI5mXuCEBejlYYZTgGZof28Wj7PLY6LcwZuMzG+Gb+FmzRxzOs1aDRPZooktBqpchF1KnxWpVFDVzc1sY8GtFYTl5dLt5i1hS3N8gSvLK4Xjd9toife6cLzWccUcOjD4sgPCFfrCWKzVCw9N+N4ut7sMxwTsv+1FWK/IwyphW8q9GVljLbuXvHHxm7rvbZdfLHhcLinFVRlu6l4LiZAukzSszsLPs9U1WldI5QKHxrfSc80f5glTYYwhgBZS74V1DSKCt9NmR7WV++Mgb9Q4lhf48LC6Ko7Sc64NWI0QGmld/R5rLHmZI6wg6vSYTXLG1YjbB4es9yI2U+jSJe0ndGOXs/H0pYvUyTYvff7z/Kuf/wVKmfHN2REbm5vUdc0XvvAFPvnJT7K1tcVzzz3Hb/3mb1LVrgfv+sYGnY6rLPrMM880gdPwTIY4mlKKV1+9yR989g8oi4KLly/z7m9/F8++41nSNGVjY4MkSdja2mJtbY2rV6+SZRn9fq/B4KVU8zRvb92GEhwBkBM4A0ZK6bs4WUrpvJq8KJlOc8bTKUVRIRFkScStW7dQuH36a2vEcQKWhjzh4KTEFXmzwrVH9A/5fHpaGPwjyBsVL3pslHsbcw9Kq83weL0vyIMYIquCb21v6GHwxCpoRClFXdcNo+OBqzTzk4UHpjnuAmh3Oq7dZuCcZg0213GWe7gC2nj4Rxb3N8Y0gVkHz0h/6kU4x9X0nj+Cji3jXtjQei2cv6kv8oA6Lc25l1+Ob+0deVNktRGC00zW1ZINHpKwIdAWOO/4v/tjWKfIBaFAXGAJSb8mC+8BCuI0ASkphwPujKbMvrnH8bTm+NmrvPDMFhfWOkSyoCxKtnuGr770Ij/zk59hbwDv/9jH+cgPfZhBMeEPfvv/4cpOwns/8DwXLl/l059NOd/fpZN2+K7vfD+JVBwdHtLb6oHFpf2XFXlRUBQhjibodTO2tzYZjsYI4O7eHpGE9fV1Ntb6PP3UNTqdLlmW8PLLL3Pnzl16/T55XrB7cZdrV6+ye/Ei6+trpFnsoRhDkszbMAocpKO1xeJKhs98zfmiKiiqnKKYUeQFZVEwxIWmO1lGknVZ39wkrypyC1oqrKlRkSDCgtYo4ZqWi/lKAh6WmfvO7WfRusJmhGdg/lxIVGNstp/j1xO3eayUOyxanlVVvaG46fINWw4eNsp9xf5tOe1Gu4BM5KAZb5msusbFbUBgn7TvAYuex2ljaCv3Vdd6VgW9PMZH+VxQqs3YfF/VYFULqRz8ZZf74hriWDSeUlVVPrNQNlBQuMaHKffl3IZv1ep5I+WsY5nPtfFeTvBM2s59K+bTKH1XYK3xJC2IBjp0+7cr7yZJDL0eg6pgMpwxqw8YTGr2D8c8f2WbZ3a3kNRU0yM20pKtbMytWxOeu3KR937b0/ybT7/CM5c2ORof0etIdJ2zc+EcQqZc2b3Cf/lf/OcoI/j5n/85SKGunCJPthLiOOH4eABAmmYURUEURQyOR9y+fZu8mDI4PuLo8ICXv/kydVVzbmebD37we/gP3/XtvPjii3zxS1/h/IUL/M7v/Btee+023/H+9/GBD3zA8+37XDh/Dmsto0lJksREkaQ2rrpGWWmGoykmcp5v1ukQJwndXhdTuY5jVVkCUBSFC/xLhZaG85cvI7/2VaqicoXUihlUhfMg/DwFa90iMQJC18i5nTGPra1i3UnBgnJvP8uPCuU9FsrdWcsnlVO7xslyoPXUYy1ZsKs++8A6Ni2o4mHnWCVtvPts4/autYdJ5hZcaxE5xep+qKXOorLmAQ9HM07OphhXej3hvK0qu04xzz3nxf2NV1zu3Iv0xsV7JoQ8lYW0/CKEr9eNa78Fsup5h+Cnheqa8xddMK+R1NxbG34OcMB8wX+YdHprDIcjNDWVsRwcDbhRz9iMLdfOb5BlMVcvX2Z9Z4dnxlOuPH2LT/3mr/PsUxf4/d/5HdayDmWlufPafW4fvARG+oYZMdrUVEVNrUvWu1tEUYwUkqIoGI3G3Llzh6IoHC9fa7bP7XD+/AW2t7dRCr7x0tf50pe+zM7ODjdv3uQ3f+tf8yu//El+8Ic+woc//AN89KN/lm+89DIf//if4+bNm3zhS5/nV3/1V7l48SJXrlxhfX2dd73r3Vy9sutq0smIOJIUpbs33W6Hys77wUohEdY/f8Y0VSSzNCPzjT7yPOdDf/pD3Nu7zYtf/LwrNy0lnV4PG4j1DaT78MyKVYr9jZbHQrkHCQ947elMgXERuKXL+wYI57TjNBmQS8lKoetTYHMALShIO+xYnSwpu+oc4e8hUcgYR7EKFMw4jhslc6J+jlfogUJZ1xXdTpd8NluAa1g6V/v62xL+Foo7Peg+t1PEw2fb19m2gk9jpIRrXq4bIxpTMywYCwzgVnKZJZSElVIhvALAhgqaIV5ifIed2BkCxiB8JcK2d9S+rgVr5+3EZexJRd786ZR5dY+GDR9vFH3z+4mfTyr2hxkVGkl/fYs07aK0dr1sjWA4njAajdjeOE/WTdnobHM1ydi9dIn0+ox/9av/nMODIwZ5yf3hhGm9R27g3IWLJCrm7t5r/N2/+z8xGQ/oZh22phcpy5Isy5rs0k6nw9raOnmeU1UOwhwMBmxsbBAnCZPJlPX1dTqdDmma8eyzzxLHCT/zM/87/+znf4m/8SM/wg9+5CMopfi2b/s23ved7+P69ZfZ27vDeDxlPJ5y794+a2trXLhwgStXrrC7e54sy+h0Yvq9mGnla75jscY6rrwx1FqTxDG9TtcFYbWL+dVFSRxFPPfOd/KNF7+M1Jo0U6RKUuiJw94byx0Wl+HT5cSC/AYaI4+Fcq+rcnxn79WvvZXnHA/eyrOxA+y/pWd8RBke3ftWPv66r28yONtElNMJR/df9xifXrVRCHEN+CfALu5N/Glr7U8KIc4BPw88g+tX8JettUfCvYE/CXwcmAI/Yq39o9czoGVIcD6mltfBksV+iuW+HGg9iyVYG+isb5FWNdPDQ/IqJy81w9GEWV6QpQmxEnQ6Cb1zm2SdhPuzI65//SsIrcnHOVvru/RETIlga3uHopzS31jnypVdDvYtm5tbxMka1lqqqibPC5RS/ufcGzjC17iBPC/Yu7OHUpHvuCZQKuLw8IjZLCfLurxy/RV+7Md+nF/75K/yH3zoB/jIRz7C009dY2vrHKPRiLIsGQ6H7O3tMR6PuX79Ont7e1y9epXLly83QVo88yYYBFVVUZcVSrreCbWpXF/juubO3XvcuHGD9fU11vprpEmC9u0hizxvFuAwN5YFHG2lrLLcT4NgX688Fsod+Jq19oNv9yDeLBFC/OGT63sspcY1d/8jIcQa8G+FEP8K+BHgt6y1PyGE+FHgR4H/DvgYrrvYc8D34XoJf9/DTnIadBZkITDPHIpxnA97BstdrFTwD4o9aCkRVqCilKS3hrUGGQNRzGQy5eDgkN3dTUyVMzy8j4oStnsRvRju3Bsg6YBNWF/bobAWYQTKwjPXrvIDH/r3+ewf/h7D4YiNjU2MsQyHQ7Q29Ho9+n3LwcEB53d2sdbS768xmUzY27vDjRvX0aZmPJrRyfrcu3efwWDksHkV8dzz7+b2nTv8/mf/Lb/3mT/kF3/hl/jP/uaP8Kc/9CEu7V5kPJly8cIuT197inv37rF/cMDB/j53bt+mKnIu7O5y/vx54umPk8gAACAASURBVKTT3CtjDMYHWPOidJh4FPHKqze5fv26g2iyjJ1zT/OVL36OOIoxRdF4r8KzyVzs2gdTF2bpdHmUBflR5XFR7k/kibzlYl2ryD3/80gI8SJwBfgErvsYwP8BfAqn3D8B/BPrNObvCyE2xWLLyW91PI7C94D3XJzy88pjPUBhKBUzK0siIpSM0VYyLUumM5iVNZPplHv3Cs6Zc0RZxnA2ZXxQoacjNvprdLNdRrrDvftHJN0uVkO3H7O51sXoksOjfbTBsVt6PXq9nocoJXGsuHTxCtZa9vcPePXVW9y9e5ejoyPG4zFlmZOmKd/93d/Dl198kVmeYw1UWjOZzNjY2ERKR1y4ceMm/+1//d/w3Avv5q/91b/Gn/mzH+bixcv0+102NjbY2dlhfOUSx8fHHB4e8tqtm8xmUzrddde719eNL8vSWe5KcXx4xP7+PlVZstbv845nnmF9fZ2D/X2+8PnPg3VMGlPlKGGozLwRuw1QD6JpPL9yHlfAzGFOHxQnepQ40hPl/kSeCCCEeAb4APAZYLelsO/gYBtwiv9m62O3/LYF5S6E+FvA3wKIfaGpFedbud3aeT5HmzHVNIJofTY0zgazUtE/iOtvrCWKYqx23Y1knGCNIa80VkjGkymSiPWNCpkoTF3xyjdeZHO9z2QkmGpLjcKqiDTL6PckaVKztdFHSsc0yYuazbVt1tbWKIqSNM3Y2NigKmtms5x79+4zmUzJ84K1tXWOjgbEccJ73vNeLl26xJe//BX27twhTlKSJPVejKTIS6IoRghFHKd0soxXX7nJj//Yj/NLv/CLfOzPfYyPf/zPcO3a0+xeOM/uhR3qumA4HBEnMVj40pe+znQ6ZTweU+QFwlrXXlApOlnGtStXWOv3OXdumySO2dvb4zd+7dcZHA2QWOqqQpcltdU0/YrxcR9wWStmkfG1HNdaJnhYO+8et0xYCMd42KLdlsdFuf/02z2AN1meXN9jLEKIPvBLwH9lrR0uvYBWhDquZxRr7U/j70k36z4yiLqMufutJ/7u2I+tpJlHEIlTZtoakIq006MaTSlrg0Zwb/+QXucC6+vrdNf7zIqCbpaSi4jB3gE3hjOy888SdzOiVKKUJVGWbhpzcPce+STn3PlLnNvcoSwrzp07h5SKu3fuMpvlWAtHR8eMRiNGowllWdLv93n++efpdDp84Qtf5M6du6RZSlGUbGyco9I1ujZUusZYUCoijjPXPtNnSr9y/RX+57//D/iX/+Jf8OEP/wCf+MRf4IUXnieOMpepmyRYCxd2zjfsGCw+ec5xzBPf1amTZQgLn/693+OXf/mfc/36dbY2+lgsxXRGEgvXd1W2npcwU+LBgMxKNtvS3x4kZ9nnsVDu/mX4EytPru/xFSFEjFPs/5e19v/2m+8GuEUIcQkIkdzXgGutj1/1295wOUldFQ2TosHT8Za7V/KPIsIaVx7CA/cSiYgS0q7EINk/POLi7iYqSVlb3yAbDtnc2uJrX36Fe0dHnLvyXtauXkWqhMP7N9FGUxYFuzvbDKcTLl96ChV3qWvHGjvYP8IYw/7+AUXhSvkeHx/z6qu3SNOU97znPfR6fV65/gqv3niVJE1J4sSXFO65Zj02tNEMDDmJQNDp9Oh2upRlwXg8QmvN17/+Db7x9a/xS7/4C/yFT/w5fviH/yLveeFdaFNjsWxtbDb3QjIPiKZpSlVW3Lm9x29//vN86lOf4ktf+hJ1VXHhwnlm0xlx5JqWSLQvfeHKiDjL3c/fWeaXJQoxNHN6mtJ/lEXgsVDuT+SJvB3i2S8/A7xorf17rT99EvhPgJ/w33+ltf3vCCF+DhdIHbxReHtbTtAg22yZdg5Hm5/xqNa7qajqEkGEQJCXJZtZxtb2OkLGTPKS/YNjjgZDNna26fT6PPPst/HpL99gfXub57/r/XztzhHHx/ehmrDVTbFFTi+L+fJXbpKPK4yccf9gj4P9A4SQpGnKcDii2+2RJAmbm+d4/vl3k6Ypr964yTf3vgkIyrJmMBiRph3SLKPT63FwcEiSZs5aTyJXDdQ37RA++1ZYQSfrsrW5wWw2ZZZPODg44B/8vb/P//ZT/5Af+MEP87GPfZT3vOc9jAe1Y8YYw2g04t7du+zf3+fll1/m3p273Lp1i6PDQ9b6fbIsoyoKjg4P6WQJWb+HFIayLIkjCb6HMDBX8kAzPafMb/i5Dcu4ktYnrfrl3J2zyBPl/kT+XZZ/D/jrwBeFEJ/z2/57nFL/Z0KIvwncAP6y/9tv4GiQL+GokP/pmzq6BnZpsWUWMNfAqOFUfvSpyXbGII0r6iaF8jWAJFJGRGlGWRte27vD3fsHbO/uYoWirCwiiumudclNza17txmOBig9xuSCF566QqQko8GI27fuY6OMsq7p9fpsbW2RJAmdzjHnz1/AGIf5Hx8fMxqOGQwGVFXVYPNp6pqyaAtVWZOlHaZ57pp7RKYp3GWtRZclkZJkWQewzPIxde2agSgliaJtEJb/91//Fq+88jIvvPDtfP7ffo2yrFxf17xoSp1IIdhY36DX6bC+tuZKiBjbWPZZlqF1TZS4evJ1VbAKtWv5W6undslyb35mtSJflZT32FvuQoiP4rjDCvhH1tqfeJuH9MgihPhZ4M8D96y17/Xb3nSu9Fslbycf/M0Ua+3vcvr794Mr9rfA335TB9U+35kx90fnRkthUZErFSysJO100PmE8XRKmu5Q1Zo792YcHQ8YT2cIJZnkOVGaUVWW+4cHnL+4yzuef4bUTpndv8m5c5u8cv1l+v01rl3rYVVGXrn2dKPRyJfejjk6OqIsXc31wWBIkReMx2PXXavWdLs9n+HtvJOqLhmPpyRZ2nDQpdS+pV8M2qB17SzpOPK4usGiESIBNCqSCGG5dOkSsa/ZLoSg1+m6hCVrfRVaS+yLnIXkxEgq0iRxyXQqcnCMUpiq8gl8i8X7Gqv9YfO7pNjb28/y2YfO8cOH8OaJcA0W/1ccf/gF4K8KIV54O8f0OuUfAx9d2vajOK70c8Bv+d9hkSv9t3Bc6cddAh/8BeD7gb/t5+lP0jW+qRJe5FX1b8L29rY2u2LOmFk6ZvODwBVBl2BDHXRXC93o1V+V7VLYLlWt0JWhzAsqbTieavZGBtavMbY9xmWf4UgxGMAdGROd20TUE+TBLf6Hv/4f857N81T3KibDNb5yveJffvolvnpzn0lZkJdThsc1k7FFij6D44rD/Sl39wbcvrXPvbsDygKUzFhf2ybL1sk66xgirEiojcRVrVZ0uz1fKE0gLU6hFyXldOpKW+A8i+m0pCoFadyn39kkVV1S2UPUCZHt0ok2uH3jPnGc0Ekzl/VsXbkQ6ctVC3wLvzhBgG/PV2JkRakLamuYzXK0lhitMJXC1BFWS4RRSKtQViFROPs5wlrXJERr4b+DMRJQCBH5gnkJKlLISCKkwPh/tanRVvui4LbZHn4/Td5uy/17gZestS8DeCzzE8BX3tZRPaJYa3/bU+na8rZwpd8Medz44H+SZBW1bf77EqtdnNzabAmBPLu0/bTzMgeHrfW1/gFjBcfDKfcPRxwej7l194B3vguefuoqH9zexkRdvnHjHqPRiF/8Z7/Anf0heWUBRV5pKltQDXO0uYcUEdOZYjab+RISEVEUubIfUlL7Eh0hGSiKIscP14sF5hbogCvvX1gk3ZWF/+tKo7Wh2+2SZa5PcbfTZf/+fayxrplGOIcxWDmvC9VAIGa+ICtcBzBjcOUGpHTHad90sTiOxbG2v4uFbU31Ab/R+HwH28pas40qP1v4/O1W7qt4ww/N+PtjIt8SV/pxlTeSD/5E3ijx2PujuPbWa3WvKAyu3sw4L/nG9VcZHR2wtb7FwTjn5dv3ufjMO9g4d54X3vd+Pv2HX+a1r77Mjd/+HTZ3rrB+7gIGzWA0Quua2WyCNiVp2sHaLlrXSKmI4gitDWVVEccuKBr5+vHGWMqqJCxUKxX7qVFjF7l0JX5DQ3WDErap7aS1q09UFAWT8RhtdFN+QGuNNobQnNx4T0r6/AJXwtogjGxwcdfsxu0rvDJ3RffCbKyGWpa/L19TbQz49jRzSqVoNdSxreXrwfJ2K/d/J+T1cKUfR3mj+eBP5I2R5qV3GqWNzp/6GdHqQBYofMZKbBwhVEpvZ5ed3W0OizFf/OarXHnu23n+uZRrzzzHu9/zHdy4M2Rzd4PKRlihiNKErhVIJcm6XYoyJ0szkB2KPGc0HjOZzRBS0u12m9oyUjjFVZuasqpQQpJE8Uo8+nTlHvruzpkruq6xwlUdNdqgtSSKukynU44HA6SK0Mxryxit0cGyDs1emCeSIVplpd2vDhZZWCSt62sj5nPSnoLlIOqqbFMXK2hvEyAWy3q36ZYPWsPfbuX+lvGG3wZ527nSb6Q8rnzwJ+LEveiPQJWzxrer84pKSIwwjIdjpuMhSsLxbAa2JtveZefZd3H52Xcjkozn3/dd/OGXXmZaSe7d3ifVitq6yqZRHGGsREYZRsYURU7WyehLp6DyPKfSNWknaxryGGuRcUQvS0FbbK0XWkg+LCMzUEYDdGW0b5Aduf4K49HAdVTq9+h2u+xsb3P/8AiBbiz3WmuwNBVkTfjdV6YNnpGDb1wJa4Pr7CbAQydewbeC4GKFR7Vq0QrXWdvlhvFzxW78xYYGTw8L3L6tAVXgD4DnhBDPChfW/is4LvGfBAlcaTjJlf4bwsn38yZxpd9IOQMfHP6YX+MfZ5mDKw2M/tAvgXP/weG7BoERCpF0SNfPIbvrqLUtyqTH2MbsPPM8QnUYTUr27hwQddawMibqdEBF5GXJrCgZjMYcDyZMpiV5XoEUlHVFUZUIJbECKl2TlwVlXaGtwQoQSoIUCOmUaehkFkVR01JyZZC5pfilFA0coo2hKFzNGCEEnU6H9fV1lFIUZbnQ3CVkqoZyDapVIjwwZlwJatPUi7Erms+4r5YCt2djwbS3a2OprUV7ZW6EcLEAKZsZa7Y/5Ll4Wy13a20thPg7wL/EUSF/1lr75bdzTK9HhBD/FBdY3BFC3AL+Rx4XrvQbI483H/yPmTwMZmh63yIwLaZMqDATLNXm0x6XdQE4u5DZunyORpGESJ2AUKXGCIFKM2ohmE4qOiLGpj1u3Dvg537l1/i+7/kgX3vpm/zeZ/8IKyJmxYS8rIhFAkKSdlJ0rSkpvZVeIaO5sivLsuk1EMeu6XX43ljMpeutG9pVtsccejyEa5r3ylUIabFWNnVeBE7ZSykpy9J1VTKGme+VUNc1KpbNQmJa/V1pKfZwfmuNa5unNZHvQWB0q9mMdxzaZQcWqaxzOT2A7voaWOYwk8tAdpFVV6ZmPibnTax8hNxxz+TCPZEn8kRel3Szrn3n088Bc8pjELGkRFyykgGcEpPgugQJMe+nGhS9/7vwNvgC3m5DaYJFxdHAAYA0ta8H7+h4BolFYq1gVhaUuubSlYt0uhnT3DXwULVmfXOLrNen09/g1t49jsdTVJKha0uUpOjKNb+Oo4iqKkG63x333FJV1UJv4bayFkJgK40uKhLPKw9URSkleZ43Vrz1CtopY+UXCOEx9hqjNQKNUpK6LimLgve//zuZzaa8euMVxrOcbtYhTVO01pRF2Sh4AU0wVTTzYiHSrPX7dNMMaS1SW5QCJaS/2baZC+tpSKuU+4N0rhamaVTfLBr+M9pDRbaF5X/xa19lMp2utBLebsz9iTyRJ9ISh996ZddWDGdJijnlOyxa7A0+b52da8ScZmc97iuiCBFH2EiR9vvM6ppqMGFzc5u402E8c9i5VJK6Kqlq18VMCun49Vh3YKyjE9a+VbRxRb6CaF2jvcVsrcXWrrZ6sNIDFh1F0QkoZgGP9tm2hHwCa7G6ZjLJObe1gZKCjY0Njo+PyPOcLMtI07RZaIQUKHz3NWMaPPtEEtnCfbRYu6iAT9AXzwDFLP5BOO9jaeHHgrT++tp1hh7wYDxR7k/kiTyGsqzAhD2Tfl8pK7Mgg3vv/uDpdhYRKUSkiOKYJE2YFTOMNfTWeqyt7TCZzkgRri+qdOwUZ+laBAYlI4SQKKl8GzuDtBCroEQtkYqaFpc2mid3WWvRVU0pygZOCS03lVKuEbWHbxoKo9YIGTUNqgXz4+G/53nuSg1XFYPBAGstaZI6OMa4GjH5LMdoF8hVPpGpsZJb9yncx0Z/t+maLcUeWDSnWe6nK3d/7KVUB+8YcGL1eIA8Ue5P5Ik8VtKyTBcs99en2k/jVzvFJeZ8as/RFhg6nQ5FndPtdYgqyfrmGnEc8+qLNymKgr4VGJkjgV63gzaWotRIEaGEAiuJZITRAisMWgin3D0IFKmIJE0wxja9hq01vjeuWMDgYR6DKMtyYXuAZaLY3TNnvdPUUVdSkaYp48mEixcvcvv2bY6ODhFCMJ1OiXz/3aIoqOoKOTe3T7Xc516PD6CK9mdalnvwhU7V4Wez6IMaXyJHnoHl/kS5P5En8ljKCTz+WzjWaoqkV+w+pd8pKYeJJ52Utc4aO+fOMZwMGA6POTo+Yv+1I/pra6xLSVWVSCUoywqpIuLI1Y2xtcXUhkgISmNcrNYuLSrWUld1Q4UMXy4zVM5ruixBMaEBfZsmGb7cAiCRno9ujaW2NUWRU1cVQsDt27epa02v22E4mRIrp/5C4DaKojDEUy33dtwi+D4BjvG7zIOqD1Dspyn3JlPE+kqfNngki8HTs4RKnyj3J/JE3kJZlbhyYp8QyKMFzSzzpZvvQXH7389oEZoFtWXnnr61TCcTNrMtDo8OuLd/l1deeQUrLOf7l9g/OGA0m4IU9NbWsNJBNEhBLBUVdUMrtFZja43VrruTANAGEE2v0mAFR1KRqAikRPsAqgnQhodJhJBEkQueYqEWnj2DcXVnGgqi74Dkg6Mb6+voWnN4cIiQIKwlVhFKSIx1zTqsx/3bsE+49+FOO2hMNDTHuXqfT4oradC+76tneHG5bs2rbQVSrWg8gRDetYHofgbt/kS5P5En8hbJMo1v5T6A8liJEI61IcXqdBQfonzoYhHOvbA4hO5BVrTwXIuSinJcUOeaYirYvz8j61wgSROSbo81pciyjOFwzHhSEKkYhSXLMoz2lRXRGFGTdlNmM6foa2NIkqSpuBglCUmSUFUVeZ5jraXT7VLWGlPX1NpQW9C1xmqL0a4cQKQSOlmGkpK6rqmqCp0PsHU+vzbr4J+qquhEERe2thgfHZNKx52vy5rY32ChBImUEEUNhdS27gfWLQYCS6IiUiJ0XhOliS/wJal0tTKKLVC42ognRcpTLHhZtZbd+d8WaJlN+JsHKvknyv2JPJHHTJazMs+SpflGSpplREoxHrsyvRd2toniiLp0NMa1tTXABTyxDi6ZTqeAy1IN0ElYUAK8YlqYeqAygoNDSp9YpLVuyvFKKYnStMHCAx3U+sViuUfsKvgpjl153+Fw2EA9dV17rMNb0OEYwv9nrTea5x5U4J7Pz/WQwCiLQdhledT5XPUMPGxRf6Lcn8gTeROlCcCddf8W2+KtVOhB6rpG184uVFLR6XSI45iyKpmMx4zHYzY2NgB8UlJNpGKfjOSagbfHHUURcey2a1e/d8FiD4lMZVl6WEQQRS6gqpRECkkkneeiPTZflI7ZEs5ljHa9UFlU8GGBmE6nTKdT0tRVhlRKIazH8qWPN0jpsXK7oNRDxEMiFzyvcI7TPDC3w6L1HeRB8xqCsas+0762szxTT5T7E3kij5UsFpU6K+Ry6tEeYWFpjYAiL1jb3ELmktFoSF4UTbBPCNHQEiMVNYFRKaPmnKHZRijpC/MM0yDB8g5lgI1xCTxKeWWuNdrU1OE6cQtKmefUVd0ob2WMx9nb2PX/397ZhcqSXXX8t/auqu5z71wwEmeMMWow48P4MgYJgiLxRSd5GX2R+KBBhPEhgQR8ib6YRx/8AEGFCQ5GUENAg4MENQbBFz8ykZBkEgaHZMQMyQxinLl3Tn9U1V4+7I/aVae6zzn33nP6eO7+Q9/TXd1Vtbv69n+v/u//WsuTeN/3Pis1jLvrOj/ZtD1qgpItJhC8IE6JkreJWnd4HF080bbpYoemqYSeXcm5J/ZP3GdbM4nb9n26hdwLCi4UZyPoE3tNItDTIvm55+7mvD5q9gW/os98G+qbExY6Y71zEaFuatarzeicUV6xthq93lqb5JnpBFYFLb7tOggE6pzDdUNU7rNe24xcY/VGT+7T9x097H3fU9d1+uXgL5gZ/oogJv6d+tNjFK+TyH1w6hgr2evvDfs+sigPhXXmU9dUC7kXFFwxHFJzd84v1vV9T98FUo7OFSGRsIgn6kWzoO98mYBYMsBH7i5F8JHcm6ZJ0XOMgiNJx+P6+34h0hgvl0QS7YOl0VqbnDaq6nucclJzH47naW673bJcLum6jspUKRtYg/6errDkFWogUn3usc/Ps8uouk9z3wnd8zmLRHPTcNuDQu4FBQfASasd4bHP7px7Lo/E90Xq91Ivyrk+dEjycsa229KFycWKSTLHjRs3Q/bniuVySVVVKSnJR+4dzg1e9b7vOT4+TmQeibaua9brdZJsbF2F9+evhY+KAYU+En3vUh0YVYW2S4F7roOnSSH75TC0MwyELhIcLV5/r4LGIhrKqam/WePJveu69J4gNvUwmSU1+6UW2x/OXuexVj8/aXBi23k+40LuBQVXAENRsHGUflrEfr8j+pFHO0oAThErSeP2UfoiRNuOtm2D88UTWSzVGxOSYkXG6KKp6zqR27CvX2i1lR2IS8bRcRWeUxGcQN8Lqo6+m06Qg9wzrrhohm1hMRWTyTJhcdUEcoeB3PPLnHT2IY1p5jp6vf08n44k8X5uQoihukz+7kYh94KCA2CagZo9c/mDyc8uJBeJJ3NPdiY4VryLxaZF0L53iaiNmdZaH/ZJi5/BHQNDlBsrPdZ17cvqulDq1/m65pFoY30a1FegV/qQhDSOmqe/inKtPL6PpLFjQExaVDUxchdSPR+fQHW6DjI9/91d/7M4aXKC341C7gUFB8a40uGBB4NPmjJiRqQsIvRdnzT0eHMh4zSSZo64sGqMYblcAowi6njsxWKR9vEOmX6QV5wONscqSCpZ7XVVDW6ZMaHmBJ8Te7ovFmLkbnwyUprUosYeonajkdw75rBLRpFQa+Y88BPi3OTgJucpkXtBwZXF1DcdddxDIkbcRiURfEpKsl5Gadt25Etv2z5o0U3qnBQ1+O12SxOyUUUkNeyIRBurPYJf8BSFqjLBZuibUCOh2Ue7PqG5O6csTChhMxM5j39JxOsdJBkxgdxzt8yQsZui9zNy9LTeTbRUnvf6z2NK+jFy332GQu4FBQfGiHgOy+1+PEYQZ5I0E7Xz5dFR8q3HiLyuGkTakYc997lH+2Mk3LgYaYwZ2RpjspNt/CSSfhl0vSfyvme73YaoP5dc8Bmlk/eQ/zqYJXfxpE6Uj6LPXaL3fSLLsDtZ6V5kmNnrf79srfdjMAUFBefDLndE7MUEnnv2KawGYvkTUur83iKx2fbQqCM+zBOUvAvEk3fT1LTrNYvFkW+k7RzWWhaLhvW6CslMNpQP8O/FJzR5HbuuGpDYks6HstYObiARcK5DVbBWaOoF1aJKGaxOBAklA1JDDwbPvK8U2WMyt8qAYTFWxIS2ezEzNTK319L9HCEYE7JjBUw0k7s+1JcBVYdzcUE1+OlD85Gh3kv8JMGpOxNZ54u/g5M+PZlkKs0en2a1LOReUHChkNkvd0T+pY5FoYITciexy+TmvMdmzx4z2wdDSvbakK0J2KpCDDS2Zt1uaJqa4zvHmW4drYV9Sg7KF0hjxN91fdbg2gRziidHYxp8MpDzJQRUkWpYBPWLp/64uZTj5Y68CFsHTJ0xQ4emtIgqEs0xYFyK1o0RxFg0rDVYY6mMIhobZ/cozvvuUZwbfqVEm2f6bJLjJV7xsWtnl711zho7LTcwNOcelx7ehULuBQUPImQw6omSVnKNMfTOcfPGDd+H1MBisaDrWoDUuLptY7NrTYk3U81ZsigdQOmJ/ae9Nh8Jeqjb0qmv9BgdOY6erm1D/1ANx5NUaMxEtt6xqJkTYb7AayrB2CqM0WDqCjHVkHSlvrOUxro12qEyTBJ5hu0u5IQ9zTTe5VvPPfr537tBIfeCgoIUu0c/etTNt+0W29SsVmu0VzabTSgS5qUSv+hqgxY/IVTVwf0hLvHv/KIniFGM+kXNOI6YFVvXNdvN2vc8ZUhWqirrM0x3cODUGhnr2YgDax1GFRFLZR1G4tidT4rKHueTx5SoT8OuiWCOwKP0NH0+rlOcB4XcCwoeQEzlg/ivOk+am82GxhrW2zWV69m2Pa5zKUuz73uWyyXqSKUGIrnHiLp3PvKVkN3pb17X9sXBotzQ47pQpVxAQhnh1eoYawyVtdhKkC1EGUfEu2mmxDt9jzkhjogewakGX3vrSwxYl7R5MWAwOHFBXxk38DhL5B7POZ0I7ibb9G5wvqmgoOAaQUTeJiL/KCJfEZHnReRDYftHReRlEflCuL032+fXReRFEXlBRH7mcKO/V2SULlmJ25C4pEDvHNYYNttt2uvo6CgtujZNWCydpPvH+yJQN1VWz91bJrfbDV3X0rsexEffzaIJZQwG6cNam24mPM7rxVRVldYpZt9hdoxhHcC/b2vFlzaQKBf1wfHjrZhVZbGV8TdrMXZeC9+HORLfRey5/3+YCMe386JE7gUPMjrg11T130XkFvB5EflMeO73VPW38xeLyGPA+4AfBr4H+AcR+SFV7blLnOVn/ej153jtPvKJpA5jzV3wkbVUvpRv0zSs7txhsai5eXQTiDJJw+uvv07X9ici2LQAmBpqDJG7tzHC0Y2jEHlHcnOAIkZoqoqqOuLGzeDQ6XuOUdptS9e1wcnja6x3fZucPtPIOp4zj57j/bquEWNQCEzD7gAACddJREFUY73V04CK8xG79cXS/GTn3TF6xtZ2cwujc6+Z6u7x/hyJTyP+s6KQe8EDC1X9JvDNcP+2iHwVeOueXZ4EPqGqG+DrIvIi8C7gn8977ql7YpYMlFEHoOTVzp7P3suZzxkeZYSe/SuGrt2yWbVUywXtxi9kbjYbvvvht3D79m1Ule12k+q4x9K+c8RUVXmbuZzMfHGy3HlydLSk71yyUlprabdb1qsVm80mTRI9HZvNNiu+pYl4d5FmnmkLsN1uqJsF1oa+pChNUwP+/HVTB+nI93R1zqWmIfm1nCPefBLZR/Jzsk7uvJmOf+48+1BkmYICQER+APgR4F/Dpg+KyBdF5BkReVPY9lbgv7LdvsH+yWAnduuumeNkvMO5jps/nr3l59Fw/PDY2ipVbLTGcuPmTR5++BFgaLDRNAs2mw2bzSYjpJPn9ouzLjwXbqKhYFib9Pu+9y4ZwdsYN5s1d+68zur4GFVHXVdAOE5sgE28eeREevovIg0/WeLYGP1NY3d9KCOgs8R6L3r5CVvjZFLaR+RnOW8h94IHHiLyEPCXwIdV9XXgj4AfBB7HR/a/c87jPSUiz4nIc30/X48EduiujIl+2J60h+GLfwo55PdP3kYjGZUHj1bFo6Mjbt68ya2HHuLWrVus1+uUJRqbX0wnifE5HM75JKPoY0eirixYa1JpgqiLp6SiKOXY4BZX58+FS6NNEfLJ63+ejyu71kNnJU/ufSp/MMpFmLnO5z7bZN+hFPFuop8NCPacv8gyBQ80RKTGE/ufqepfAajqK9nzHwP+Jjx8GXhbtvv3hm0jqOrTwNMAR8sbe7/9+Rdcol9bIqF7YvM/3+eit7jjcP88EWUkRkUQVXxWpdL1Hcb4mi920bDpWo6P3+C1115jvV4n4ouWSEG8330mRT86ZzxRQ/S0O9cnIvUJR0EbN4a6Gpp4uK6nFy/drGekj/zanTtyDxbNvEGGLxAmPmvWC/7EJQl1J90yZ73WozPvmXzz1+T3dyU/7UOJ3AseWIj/xvwx8FVV/d1s+1uyl/0c8OVw/1ngfSKyEJG3A48C/3Y35971hR59Z0dEfVKq0exlOyO73QMYjqEpFg6JPi4kKrVsNhvu3LnDt7/9v6kuu9/2Bk3TUNnYN9WdICrVoROTjnqc+oJisY2fb6/XhsJgLiU+xV8ssRBZft1GyIhdJvfPhukkAU7V17QJXUB2ZY7eLXb/otp9y/c7y/lL5F7wIOPHgV8EviQiXwjbfgP4BRF5HP+tfwn4VQBVfV5EPgl8Be+0+YDeg1MmxxCdDYtxkXhj5H4Wq8yZtVmZP1ycSABW6xXr2y3HmzXWVMGfbjk+PubOndssl0f0bhuidsJ+YxKMGr2vmGMT+UbPe7Q0Ou0R8VUnNUtc6oD1ZsU2s2Puel/ni9z3QUAdTn3ZXuMPiGZ1YtJnNBNVnxW75J19Wvtp20fv4l5mn4KCgv04Wt7Qd3zfO2a/yHPRpkcWSY6ONnQAii+PssrcvknKyWqxxP3MiITz3X2SkgSL4BurYwRoe4exlrpuaNuW5Y0jFs2S27dvg/GlArquh1BBsus6xFqcamrsYaylCmUFjo6WbDZbqrpiuViyXq98lF4Z6rryi7XrFW27pdtuwTlfT75tadsN/bYLxDrozslNNJFL8u3x1qov8yvW3+qmoWpqLxMJLGxFJeJrx7e+vLHNhI68Xs20ZV76tHa4ZfZp6HPEHieQ3M4JXvL62rdeYrVZzc4uJXIvKLgkzP28jj5syKPNjHhPHGMopZKi+xFkdNfr6hJ048H+mA42t7uAOr9wacJ5jCNkbYIVr9FLXNDTmOk5EK3r+2AlDL86iKV0/baqalitNqxXG1BfE76qKlyvrNpVkm3U9RgMxhr6tvPHj9o3vpHG7mK8zFxbD2MqknVdc4lLk8buSx07nAa9fSJin1/+md9/17h3afujSWBPcF7IvaDggjEnleTRV07wp2EXIeyKEOcIKC3c7hmrqm+WHV9fVRVS2RSxpvcgEKWk6TnjvvlEFh+vVqtUIExEUoaoE4dqn+QoRKibCg314bOB+uPu4dW5XIJ4TGNNWmfw1yNOhDqeAM+AuyH3ucXYOd/7FGeRbCIKuRcUXALmforHL/OU4O9NLx4wjfxOS6rJ93OZXi4i1PVA7jkJSSo3HMlmTFCxk1Mk9/xxXGyN9WpEBNt4OciIoM7RthusGLZtl2yXMI60z7IWMSV6ayscvtOT5kR7fy79uXAWUofzL+QWci8ouFCcdHfMOT5yIt315d0Xtd+vyD0+H+u0x7owdV0jWbapOh2R4dyk5UsZmJTkFOu8pKYbMxNcXVUYK1TW0vcdXecXUruuTdG1Bilo0NzZGW3PuWdEfM0Z1IGCS+/h3EH7iWub47TP8TyLsafp87PnKQuqBQUXBxG5Dbxw6HHswZuB/z70IHbgKo8Nrsb4vl9Vv2vuiRK5FxRcLF5Q1R899CB2QUSeu6rju8pjg6s/vpLEVFBQUHANUci9oKCg4BqikHtBwcXi6UMP4BRc5fFd5bHBFR9fWVAtKCgouIYokXtBQUHBNUQh94KCC4KIPCG+1+qLIvKRKzCel0TkS+L7wj4Xtn2niHxGRP4j/H3Tace5j+N5RkReFZEvZ9tmxyMevx+u5RdF5J0HGt9H5f9Jf91C7gUFFwARscAfAO8BHsNXmnzssKMC4KdU9fHMwvcR4LOq+ijw2fD4svAnwBOTbbvG8x58ieVHgafwDVUOMT7w/XUfD7dPA8i4v+4TwB+G/wMHQyH3goKLwbuAF1X1a6q6BT6B78F61fAk8PFw/+PAz17WiVX1n4D/OeN4ngT+VD3+BfgOGdfdv6zx7ULqr6uqXwdif92DoZB7QcHF4L71W72PUODvReTzIvJU2PaI+kbhAN8CHjnM0BJ2jecqXc8L7a97v1DIvaDgwcFPqOo78RLHB0TkJ/MnVbNOHVcAV208AffUX/cyUci9oOBicKZ+q5cJVX05/H0V+BReNnglyhvh76uHGyHsGc+VuJ6q+oqq9qrqgI8xSC9XYnw5CrkXFFwMPgc8KiJvF5EGv9j27KEGIyI3ReRWvA/8NL437LPA+8PL3g/89WFGmLBrPM8CvxRcMz8GvJbJN5cGuYT+uvcLpXBYQcEFQFU7Efkg8HeABZ5R1ecPOKRHgE+FErMV8Oeq+rci8jngkyLyK8B/Aj9/WQMSkb8A3g28WUS+Afwm8Fs7xvNp4L34hcpj4JcPNL53yyX3171blAzVgoKCgmuIIssUFBQUXEMUci8oKCi4hijkXlBQUHANUci9oKCg4BqikHtBQUHBNUQh94KCgoJriELuBQUFBdcQhdwLCgoKriH+DwIwq3kiaGwYAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 298
+ },
+ "id": "Ov-v17gETT9z",
+ "outputId": "39312730-dca1-4f03-d010-e1dbeabddeca"
+ },
+ "source": [
+ "data = {}\r\n",
+ "name = names[0]\r\n",
+ "image_dirpath = image_dir_basepath + '/' + name\r\n",
+ "image_filepaths = [os.path.join(image_dirpath, f) for f in\r\n",
+ " os.listdir(image_dirpath)]\r\n",
+ "embs = calc_embs(image_filepaths)\r\n",
+ "for i in range(len(image_filepaths)):\r\n",
+ " data['{}{}'.format(name, i)] = {'image_filepath': image_filepaths[i],\r\n",
+ " 'emb': embs[i]}\r\n",
+ "\r\n",
+ "calc_dist_plot('keanu0', 'keanu1')"
+ ],
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[65 54 90 90]\n",
+ "[67 53 98 98]\n",
+ "On photo two same human!\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "xjrelvltTJNE",
+ "outputId": "6c2906c1-cddf-493d-95d8-848046c2abf2"
+ },
+ "source": [
+ "data = {}\r\n",
+ "ind = 0\r\n",
+ "for name in names:\r\n",
+ " ind += 1\r\n",
+ " image_dirpath = image_dir_basepath + '/' + name\r\n",
+ " image_filepaths = [os.path.join(image_dirpath, f) for f in\r\n",
+ " os.listdir(image_dirpath)]\r\n",
+ " embs = calc_embs(image_filepaths)\r\n",
+ " for i in range(len(image_filepaths)):\r\n",
+ " data['{}{}'.format(name, i)] = {'image_filepath': image_filepaths[i],\r\n",
+ " 'emb': embs[i]}\r\n",
+ " print(data)\r\n",
+ " if ind == 2:\r\n",
+ " break\r\n",
+ "\r\n",
+ "calc_dist_plot('keanu0', 'denzel1')"
+ ],
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[65 54 90 90]\n",
+ "[67 53 98 98]\n",
+ "{'keanu0': {'image_filepath': '/content/drive/MyDrive/data_k/images/keanu/keanu_0000.jpg', 'emb': array([ 0.03388546, 0.02739031, -0.08989979, -0.10612298, -0.04399209,\n",
+ " 0.00874972, 0.02651274, 0.07945874, -0.01793063, -0.15508766,\n",
+ " 0.1480676 , -0.04465884, 0.07449374, 0.08376494, -0.07402973,\n",
+ " 0.13262399, -0.08310092, -0.06312504, -0.06160539, -0.02911363,\n",
+ " 0.11056108, 0.17593656, -0.16531156, -0.13007529, 0.06026657,\n",
+ " -0.06182812, -0.00767491, -0.00363477, 0.02318595, 0.01369596,\n",
+ " -0.01952458, 0.24919014, 0.13181786, 0.04280305, -0.01838741,\n",
+ " -0.10742006, 0.03794361, -0.07652771, -0.10825144, 0.1193664 ,\n",
+ " 0.01766873, 0.07055256, -0.00862722, -0.04875271, -0.10148057,\n",
+ " -0.21356156, -0.06509517, 0.07767414, 0.1964838 , 0.02836855,\n",
+ " -0.00361238, 0.04422471, -0.05746792, -0.03491002, 0.13423242,\n",
+ " -0.04656419, 0.06191315, -0.01163986, -0.00095068, -0.0383492 ,\n",
+ " -0.11551193, -0.00423207, -0.03930436, 0.07830188, 0.0623434 ,\n",
+ " -0.03185433, 0.0553653 , 0.16526662, -0.15985209, -0.12472561,\n",
+ " -0.09671544, 0.07139865, -0.00445332, -0.03782793, -0.04027554,\n",
+ " -0.04594582, 0.00475838, 0.01616015, -0.11976563, -0.02675864,\n",
+ " -0.07728543, 0.07219528, -0.12018785, -0.01902488, 0.0635618 ,\n",
+ " -0.02554598, -0.22127461, -0.02059929, 0.00249035, -0.03043903,\n",
+ " 0.11370442, 0.04021911, 0.05046181, -0.12599568, 0.06207035,\n",
+ " 0.0789883 , -0.01540562, 0.05539006, 0.02195551, -0.14054795,\n",
+ " 0.09030771, -0.20965295, -0.1006393 , -0.06396834, -0.04715434,\n",
+ " -0.00492597, 0.06381867, 0.00230056, 0.01532707, 0.05711737,\n",
+ " 0.00100652, -0.09634561, 0.12122311, 0.02033096, 0.05709427,\n",
+ " -0.09657498, 0.16366793, -0.11147727, 0.06212679, 0.12376551,\n",
+ " 0.01612632, 0.06228566, 0.04349492, -0.01956217, 0.12800284,\n",
+ " 0.01019794, -0.1386094 , 0.09311582], dtype=float32)}, 'keanu1': {'image_filepath': '/content/drive/MyDrive/data_k/images/keanu/keanu_0001.jpg', 'emb': array([ 9.85553116e-02, 1.19494833e-01, -1.00038983e-01, -1.22491002e-01,\n",
+ " -4.39457223e-02, 1.51230232e-03, 5.93390465e-02, -1.22689623e-02,\n",
+ " -3.91260125e-02, -1.42128900e-01, 1.01193734e-01, -5.73836416e-02,\n",
+ " 3.02855093e-02, 5.72952814e-02, -1.95843706e-04, 1.23440996e-01,\n",
+ " -1.33225739e-01, -7.52649233e-02, -2.07473710e-02, -1.01758771e-01,\n",
+ " 6.89364672e-02, 1.80944145e-01, -1.42413527e-01, -1.15867831e-01,\n",
+ " -9.03921295e-03, -8.68890285e-02, -3.55300270e-02, -5.64861074e-02,\n",
+ " -4.87782173e-02, 9.30895433e-02, 7.45297084e-03, 1.92001849e-01,\n",
+ " 8.69025514e-02, 7.06886575e-02, -5.17634209e-03, -8.63878131e-02,\n",
+ " 6.92888498e-02, -5.89120239e-02, -9.54526663e-02, 1.36843756e-01,\n",
+ " 2.37595439e-02, 5.10650650e-02, 2.98331864e-02, -7.84232318e-02,\n",
+ " -9.26689804e-02, -1.62128419e-01, -7.75362179e-02, 9.74961296e-02,\n",
+ " 1.63102940e-01, 9.32544619e-02, -4.59120646e-02, -3.60282958e-02,\n",
+ " 1.65612977e-02, 3.63514796e-02, 8.29791054e-02, -5.79117499e-02,\n",
+ " 8.01061839e-03, -3.79971154e-02, -1.97389405e-02, -9.23326090e-02,\n",
+ " -2.01278150e-01, -2.09848564e-02, -5.88413514e-02, 5.88729233e-02,\n",
+ " 3.23207825e-02, -6.95228726e-02, 2.91605219e-02, 1.04332499e-01,\n",
+ " -1.93448439e-01, -5.77019751e-02, -1.36692123e-02, 7.37516806e-02,\n",
+ " 6.41113967e-02, -7.59886503e-02, -1.41102567e-01, -4.97810394e-02,\n",
+ " -1.22378962e-02, 1.83599349e-02, -1.32499367e-01, -7.18986467e-02,\n",
+ " -3.11195459e-02, 5.73000312e-02, -1.26197636e-01, -6.57242676e-03,\n",
+ " 9.80671048e-02, -8.58021453e-02, -1.74755752e-01, 1.49771944e-02,\n",
+ " 2.56530195e-02, -1.46573391e-02, 1.12151526e-01, 9.98762716e-03,\n",
+ " 6.99028596e-02, -1.17386408e-01, 4.93195914e-02, 5.19850329e-02,\n",
+ " 8.74353515e-04, 1.05211183e-01, 2.67520063e-02, -1.22171029e-01,\n",
+ " 1.18539758e-01, -2.34660804e-01, -5.23517765e-02, -4.75740358e-02,\n",
+ " -3.55590545e-02, -1.97637398e-02, 5.94371967e-02, 6.62233755e-02,\n",
+ " 2.75610411e-03, 4.92546335e-02, 1.75561439e-02, -1.31821752e-01,\n",
+ " 7.34224543e-02, 8.79062414e-02, 2.41947267e-02, -1.43661425e-01,\n",
+ " 1.65218472e-01, -1.32653818e-01, 6.01469092e-02, 1.44852012e-01,\n",
+ " 1.39645645e-02, 4.75664111e-03, 4.52221408e-02, -1.60046928e-02,\n",
+ " 9.90327150e-02, 1.08156325e-02, -1.23112947e-01, 1.13540344e-01],\n",
+ " dtype=float32)}}\n",
+ "[105 28 69 69]\n",
+ "[90 6 45 45]\n",
+ "{'keanu0': {'image_filepath': '/content/drive/MyDrive/data_k/images/keanu/keanu_0000.jpg', 'emb': array([ 0.03388546, 0.02739031, -0.08989979, -0.10612298, -0.04399209,\n",
+ " 0.00874972, 0.02651274, 0.07945874, -0.01793063, -0.15508766,\n",
+ " 0.1480676 , -0.04465884, 0.07449374, 0.08376494, -0.07402973,\n",
+ " 0.13262399, -0.08310092, -0.06312504, -0.06160539, -0.02911363,\n",
+ " 0.11056108, 0.17593656, -0.16531156, -0.13007529, 0.06026657,\n",
+ " -0.06182812, -0.00767491, -0.00363477, 0.02318595, 0.01369596,\n",
+ " -0.01952458, 0.24919014, 0.13181786, 0.04280305, -0.01838741,\n",
+ " -0.10742006, 0.03794361, -0.07652771, -0.10825144, 0.1193664 ,\n",
+ " 0.01766873, 0.07055256, -0.00862722, -0.04875271, -0.10148057,\n",
+ " -0.21356156, -0.06509517, 0.07767414, 0.1964838 , 0.02836855,\n",
+ " -0.00361238, 0.04422471, -0.05746792, -0.03491002, 0.13423242,\n",
+ " -0.04656419, 0.06191315, -0.01163986, -0.00095068, -0.0383492 ,\n",
+ " -0.11551193, -0.00423207, -0.03930436, 0.07830188, 0.0623434 ,\n",
+ " -0.03185433, 0.0553653 , 0.16526662, -0.15985209, -0.12472561,\n",
+ " -0.09671544, 0.07139865, -0.00445332, -0.03782793, -0.04027554,\n",
+ " -0.04594582, 0.00475838, 0.01616015, -0.11976563, -0.02675864,\n",
+ " -0.07728543, 0.07219528, -0.12018785, -0.01902488, 0.0635618 ,\n",
+ " -0.02554598, -0.22127461, -0.02059929, 0.00249035, -0.03043903,\n",
+ " 0.11370442, 0.04021911, 0.05046181, -0.12599568, 0.06207035,\n",
+ " 0.0789883 , -0.01540562, 0.05539006, 0.02195551, -0.14054795,\n",
+ " 0.09030771, -0.20965295, -0.1006393 , -0.06396834, -0.04715434,\n",
+ " -0.00492597, 0.06381867, 0.00230056, 0.01532707, 0.05711737,\n",
+ " 0.00100652, -0.09634561, 0.12122311, 0.02033096, 0.05709427,\n",
+ " -0.09657498, 0.16366793, -0.11147727, 0.06212679, 0.12376551,\n",
+ " 0.01612632, 0.06228566, 0.04349492, -0.01956217, 0.12800284,\n",
+ " 0.01019794, -0.1386094 , 0.09311582], dtype=float32)}, 'keanu1': {'image_filepath': '/content/drive/MyDrive/data_k/images/keanu/keanu_0001.jpg', 'emb': array([ 9.85553116e-02, 1.19494833e-01, -1.00038983e-01, -1.22491002e-01,\n",
+ " -4.39457223e-02, 1.51230232e-03, 5.93390465e-02, -1.22689623e-02,\n",
+ " -3.91260125e-02, -1.42128900e-01, 1.01193734e-01, -5.73836416e-02,\n",
+ " 3.02855093e-02, 5.72952814e-02, -1.95843706e-04, 1.23440996e-01,\n",
+ " -1.33225739e-01, -7.52649233e-02, -2.07473710e-02, -1.01758771e-01,\n",
+ " 6.89364672e-02, 1.80944145e-01, -1.42413527e-01, -1.15867831e-01,\n",
+ " -9.03921295e-03, -8.68890285e-02, -3.55300270e-02, -5.64861074e-02,\n",
+ " -4.87782173e-02, 9.30895433e-02, 7.45297084e-03, 1.92001849e-01,\n",
+ " 8.69025514e-02, 7.06886575e-02, -5.17634209e-03, -8.63878131e-02,\n",
+ " 6.92888498e-02, -5.89120239e-02, -9.54526663e-02, 1.36843756e-01,\n",
+ " 2.37595439e-02, 5.10650650e-02, 2.98331864e-02, -7.84232318e-02,\n",
+ " -9.26689804e-02, -1.62128419e-01, -7.75362179e-02, 9.74961296e-02,\n",
+ " 1.63102940e-01, 9.32544619e-02, -4.59120646e-02, -3.60282958e-02,\n",
+ " 1.65612977e-02, 3.63514796e-02, 8.29791054e-02, -5.79117499e-02,\n",
+ " 8.01061839e-03, -3.79971154e-02, -1.97389405e-02, -9.23326090e-02,\n",
+ " -2.01278150e-01, -2.09848564e-02, -5.88413514e-02, 5.88729233e-02,\n",
+ " 3.23207825e-02, -6.95228726e-02, 2.91605219e-02, 1.04332499e-01,\n",
+ " -1.93448439e-01, -5.77019751e-02, -1.36692123e-02, 7.37516806e-02,\n",
+ " 6.41113967e-02, -7.59886503e-02, -1.41102567e-01, -4.97810394e-02,\n",
+ " -1.22378962e-02, 1.83599349e-02, -1.32499367e-01, -7.18986467e-02,\n",
+ " -3.11195459e-02, 5.73000312e-02, -1.26197636e-01, -6.57242676e-03,\n",
+ " 9.80671048e-02, -8.58021453e-02, -1.74755752e-01, 1.49771944e-02,\n",
+ " 2.56530195e-02, -1.46573391e-02, 1.12151526e-01, 9.98762716e-03,\n",
+ " 6.99028596e-02, -1.17386408e-01, 4.93195914e-02, 5.19850329e-02,\n",
+ " 8.74353515e-04, 1.05211183e-01, 2.67520063e-02, -1.22171029e-01,\n",
+ " 1.18539758e-01, -2.34660804e-01, -5.23517765e-02, -4.75740358e-02,\n",
+ " -3.55590545e-02, -1.97637398e-02, 5.94371967e-02, 6.62233755e-02,\n",
+ " 2.75610411e-03, 4.92546335e-02, 1.75561439e-02, -1.31821752e-01,\n",
+ " 7.34224543e-02, 8.79062414e-02, 2.41947267e-02, -1.43661425e-01,\n",
+ " 1.65218472e-01, -1.32653818e-01, 6.01469092e-02, 1.44852012e-01,\n",
+ " 1.39645645e-02, 4.75664111e-03, 4.52221408e-02, -1.60046928e-02,\n",
+ " 9.90327150e-02, 1.08156325e-02, -1.23112947e-01, 1.13540344e-01],\n",
+ " dtype=float32)}, 'denzel0': {'image_filepath': '/content/drive/MyDrive/data_k/images/denzel/denzel_0000.jpg', 'emb': array([-0.03886576, 0.1359814 , 0.04339281, 0.01875704, 0.08691795,\n",
+ " -0.02580425, 0.08636746, -0.12902693, 0.01894239, -0.07208835,\n",
+ " 0.0124097 , 0.00107346, 0.11707677, -0.08650153, 0.11846551,\n",
+ " -0.13456228, -0.07649344, -0.05841228, -0.18117003, -0.02664453,\n",
+ " -0.05655304, -0.03457158, 0.01070869, 0.15768431, -0.1365508 ,\n",
+ " -0.00209188, -0.0190891 , 0.04535414, -0.05401638, -0.04130668,\n",
+ " 0.11466768, -0.11643124, -0.05992422, 0.03071943, -0.10900626,\n",
+ " -0.17165488, -0.01582484, -0.18270022, 0.14994714, -0.177149 ,\n",
+ " 0.0077288 , -0.12197547, 0.07237799, -0.03501713, 0.17682979,\n",
+ " 0.00548979, 0.1514566 , -0.16597334, 0.08442262, -0.10098895,\n",
+ " 0.01420983, -0.01972733, -0.07061633, -0.23053326, -0.0528637 ,\n",
+ " 0.07782535, 0.13265072, -0.06518418, 0.01335812, 0.14823592,\n",
+ " 0.03011798, 0.03191657, 0.07344961, -0.01658712, -0.07092518,\n",
+ " -0.01553668, -0.0047385 , -0.08979475, -0.03578591, -0.0116648 ,\n",
+ " 0.00959534, 0.04092744, -0.03697192, -0.05666998, -0.01183206,\n",
+ " 0.00814558, -0.13858296, -0.06345345, 0.03930849, -0.07670075,\n",
+ " 0.05730115, 0.08620992, 0.05226254, -0.01802053, -0.04127094,\n",
+ " 0.02692966, -0.0119398 , 0.06105222, -0.03385877, -0.18275137,\n",
+ " 0.00194577, -0.00436365, 0.00229555, 0.05932764, 0.17339918,\n",
+ " -0.08832864, -0.11933542, 0.05304851, -0.01110897, -0.04833056,\n",
+ " 0.06991106, -0.12145273, -0.0129342 , 0.03513632, -0.00711284,\n",
+ " 0.0155848 , -0.05224892, -0.07470934, -0.16701071, 0.03525981,\n",
+ " -0.15162 , 0.11405317, -0.00822911, -0.04675199, -0.03823649,\n",
+ " 0.07200316, -0.05297472, 0.00807633, -0.10074697, 0.18450645,\n",
+ " -0.03493045, 0.03820505, 0.19943762, 0.04730641, -0.07456595,\n",
+ " -0.08990929, 0.06073055, -0.09201896], dtype=float32)}, 'denzel1': {'image_filepath': '/content/drive/MyDrive/data_k/images/denzel/denzel_0001.jpg', 'emb': array([-0.11883365, 0.04315588, -0.07463256, -0.01691983, 0.02725481,\n",
+ " 0.00352354, -0.03434115, -0.09688209, -0.00348133, -0.00770755,\n",
+ " 0.09977498, -0.09967718, -0.01588117, -0.1355249 , 0.1255714 ,\n",
+ " -0.04343272, 0.03761308, 0.0190757 , -0.09248812, -0.10133453,\n",
+ " -0.01233626, -0.11904225, 0.04565611, 0.13285598, -0.13512139,\n",
+ " -0.0946117 , 0.01890879, 0.06431599, -0.00273007, 0.03642714,\n",
+ " 0.13277893, -0.03183872, 0.17283449, 0.08090508, -0.05138547,\n",
+ " -0.1872656 , 0.0196867 , -0.07462994, 0.16574576, -0.1829418 ,\n",
+ " -0.01269214, -0.11773232, -0.0024476 , -0.09877334, 0.09718255,\n",
+ " -0.05642628, 0.09577856, -0.10885289, 0.12409452, -0.10251385,\n",
+ " 0.11784764, -0.10050578, -0.04722283, -0.0415482 , 0.0324013 ,\n",
+ " 0.10910201, 0.0360182 , -0.12050545, -0.08201407, 0.06827743,\n",
+ " 0.02380029, 0.00556926, 0.11142044, 0.00295625, -0.02093039,\n",
+ " 0.01226128, 0.00575331, -0.01408527, 0.03279375, -0.04405515,\n",
+ " -0.05080106, -0.0084951 , -0.04696826, -0.03255923, -0.02238049,\n",
+ " 0.08129423, -0.09232999, -0.10677458, 0.07312531, -0.05347881,\n",
+ " -0.01219493, 0.14592826, 0.02777762, 0.01307143, -0.01641104,\n",
+ " 0.09134585, 0.00124506, 0.09022696, -0.07857133, -0.05259139,\n",
+ " 0.0129032 , 0.06952912, 0.03726411, 0.21270588, 0.11323366,\n",
+ " -0.10857657, -0.13352314, 0.06436776, -0.11072095, -0.01845616,\n",
+ " 0.02287539, -0.13769156, 0.04012917, 0.13220769, 0.05747055,\n",
+ " 0.02439999, -0.17113923, -0.03078534, -0.19449855, 0.03494337,\n",
+ " -0.15370159, 0.020098 , -0.16926368, -0.04208667, -0.15058085,\n",
+ " -0.02610345, -0.02495881, -0.11337139, -0.10149508, 0.10872771,\n",
+ " -0.12295903, 0.10031155, 0.17628822, 0.07135897, -0.06340873,\n",
+ " -0.00245846, 0.01423169, -0.06428787], dtype=float32)}}\n",
+ "On photo not two same human!\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 259
+ },
+ "id": "4aorc-fgTb3L",
+ "outputId": "66d8ccf3-e1f5-4144-e695-7fcd78450d1e"
+ },
+ "source": [
+ "calc_dist_plot('keanu1', 'denzel0')"
+ ],
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "On photo not two same human!\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/denysyefimov_code/task4_face_recognition/denysyefimov_face_recogn.py b/denysyefimov_code/task4_face_recognition/denysyefimov_face_recogn.py
new file mode 100644
index 0000000..5617374
--- /dev/null
+++ b/denysyefimov_code/task4_face_recognition/denysyefimov_face_recogn.py
@@ -0,0 +1,131 @@
+import numpy as np
+import os
+import cv2
+from imageio import imread
+from skimage.transform import resize
+from scipy.spatial import distance
+from keras.models import load_model
+
+
+cascade_path = 'path'
+model_path = 'path'
+image_dir_basepath = 'path'
+
+names = ['keanu', 'denzel', 'benedict']
+image_size = 160
+
+model = load_model(model_path)
+
+
+def prewhiten(x):
+ if x.ndim == 4:
+ axis = (1, 2, 3)
+ size = x[0].size
+ elif x.ndim == 3:
+ axis = (0, 1, 2)
+ size = x.size
+ else:
+ raise ValueError('Dimension should be 3 or 4')
+
+ mean = np.mean(x, axis=axis, keepdims=True)
+ std = np.std(x, axis=axis, keepdims=True)
+ std_adj = np.maximum(std, 1.0 / np.sqrt(size))
+ y = (x - mean) / std_adj
+ return y
+
+
+def l2_normalize(x, axis=-1, epsilon=1e-10):
+ output = x / np.sqrt(np.maximum(np.sum(np.square(x),
+ axis=axis,
+ keepdims=True),
+ epsilon))
+ return output
+
+
+def load_and_align_images(filepaths, margin):
+ cascade = cv2.CascadeClassifier(cascade_path)
+ aligned_images = []
+ for filepath in filepaths:
+ img = imread(filepath)
+
+ faces = cascade.detectMultiScale(img,
+ scaleFactor=1.1,
+ minNeighbors=3)
+ (x, y, w, h) = faces[0]
+ print(faces[0])
+ cropped = img[y - margin // 2: y + h + margin // 2,
+ x - margin // 2: x + w + margin // 2, :]
+ aligned = resize(cropped, (image_size, image_size), mode='reflect')
+ aligned_images.append(aligned)
+ return np.array(aligned_images)
+
+
+def calc_embs(filepaths, margin=10, batch_size=1):
+ aligned_images = prewhiten(load_and_align_images(filepaths, margin))
+ pd = []
+ for start in range(0, len(aligned_images), batch_size):
+ pd.append(model.predict_on_batch(
+ aligned_images[start:start + batch_size]
+ ))
+ embs = l2_normalize(np.concatenate(pd))
+
+ return embs
+
+
+def calc_dist(img_name0, img_name1):
+ return distance.euclidean(data[img_name0]['emb'], data[img_name1]['emb'])
+
+
+def calc_dist_plot(img_name0, img_name1):
+ if (calc_dist(img_name0, img_name1) < 0.8):
+ print("On photo two same human!")
+ else:
+ print("On photo not two same human!")
+ plt.subplot(1, 2, 1)
+ plt.imshow(imread(data[img_name0]['image_filepath']))
+ plt.subplot(1, 2, 2)
+ plt.imshow(imread(data[img_name1]['image_filepath']))
+
+
+data = {}
+name = names[1]
+image_dirpath = image_dir_basepath + '/' + name
+image_filepaths = [os.path.join(image_dirpath, f) for f in
+ os.listdir(image_dirpath)]
+embs = calc_embs(image_filepaths)
+for i in range(len(image_filepaths)):
+ data['{}{}'.format(name, i)] = {'image_filepath': image_filepaths[i],
+ 'emb': embs[i]}
+
+calc_dist_plot('denzel0', 'denzel1')
+
+data = {}
+name = names[0]
+image_dirpath = image_dir_basepath + '/' + name
+image_filepaths = [os.path.join(image_dirpath, f) for f in
+ os.listdir(image_dirpath)]
+embs = calc_embs(image_filepaths)
+for i in range(len(image_filepaths)):
+ data['{}{}'.format(name, i)] = {'image_filepath': image_filepaths[i],
+ 'emb': embs[i]}
+
+calc_dist_plot('keanu0', 'keanu1')
+
+data = {}
+ind = 0
+for name in names:
+ ind += 1
+ image_dirpath = image_dir_basepath + '/' + name
+ image_filepaths = [os.path.join(image_dirpath, f) for f in
+ os.listdir(image_dirpath)]
+ embs = calc_embs(image_filepaths)
+ for i in range(len(image_filepaths)):
+ data['{}{}'.format(name, i)] = {'image_filepath': image_filepaths[i],
+ 'emb': embs[i]}
+ print(data)
+ if ind == 2:
+ break
+
+calc_dist_plot('keanu0', 'denzel1')
+
+calc_dist_plot('keanu1', 'denzel0')
diff --git a/tasks/task_1/Classification_example_with_Iris_dataset.ipynb b/tasks/task_1/Classification_example_with_Iris_dataset.ipynb
deleted file mode 100644
index 7e8e1a2..0000000
--- a/tasks/task_1/Classification_example_with_Iris_dataset.ipynb
+++ /dev/null
@@ -1,492 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "entertaining-fishing",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import numpy as np\n",
- "\n",
- "from sklearn import datasets\n",
- "from matplotlib import pyplot as plt\n",
- "\n",
- "from sklearn.linear_model import LogisticRegression\n",
- "from sklearn.tree import DecisionTreeClassifier\n",
- "from sklearn.model_selection import train_test_split\n",
- "from sklearn.metrics import confusion_matrix"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "choice-location",
- "metadata": {},
- "outputs": [],
- "source": [
- "%load_ext pycodestyle_magic\n",
- "%flake8_on"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "accepting-threshold",
- "metadata": {},
- "source": [
- "# Classification example with Iris dataset"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "derived-yorkshire",
- "metadata": {},
- "source": [
- "This example dataset task is in classifying flower based on its features"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "increasing-johnston",
- "metadata": {},
- "outputs": [],
- "source": [
- "# This dataset boult in `sklearn` library so you can load it directly\n",
- "iris = datasets.load_iris()\n",
- "iris_features = iris['feature_names']"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "japanese-button",
- "metadata": {},
- "source": [
- "Print all flowers and features"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "conditional-jungle",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset features:\n",
- "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
- "Dataset classes:\n",
- "['setosa' 'versicolor' 'virginica']\n"
- ]
- }
- ],
- "source": [
- "print(f\"Dataset features:\\n{iris['feature_names']}\")\n",
- "print(f\"Dataset classes:\\n{iris.target_names}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "serial-savage",
- "metadata": {},
- "source": [
- "Now we should visually analyze the dataset\n",
- "\n",
- "As we are limited by 2D displays and cannot visualize 4d data in a single plot - let's print data 2-axis at a time"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "indirect-federal",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
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- "output_type": "display_data"
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "for j in [1, 2, 3]:\n",
- " for i, class_name in enumerate(iris.target_names):\n",
- " sepal_length = iris.data[:, 0][iris.target == i]\n",
- " sepal_width = iris.data[:, j][iris.target == i]\n",
- " plt.plot(sepal_length, sepal_width, '.', label=class_name)\n",
- "\n",
- " plt.title(\"Flowers\")\n",
- " plt.xlabel(iris_features[0])\n",
- " plt.ylabel(iris_features[j])\n",
- " plt.legend()\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "driven-animation",
- "metadata": {},
- "source": [
- "## Plotting decision boundaries\n",
- "\n",
- "Decision boundaries allows us to visualize how given classifier thinks data should be splitted into a different classes\n",
- "\n",
- "For this let's focus on first 2 features ('sepal length (cm)', 'sepal width (cm)') to have consistent 2D plot"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "accurate-central",
- "metadata": {},
- "outputs": [],
- "source": [
- "def plot_decision(clf, title):\n",
- " x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
- " y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
- " xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
- " np.arange(y_min, y_max, 0.1))\n",
- "\n",
- " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
- " Z = Z.reshape(xx.shape)\n",
- "\n",
- " plt.contourf(xx, yy, Z, alpha=0.4)\n",
- "\n",
- " for i, class_name in enumerate(iris.target_names):\n",
- " sepal_length = iris.data[:, 0][iris.target == i]\n",
- " sepal_width = iris.data[:, 1][iris.target == i]\n",
- " plt.plot(sepal_length, sepal_width, '.', label=class_name)\n",
- "\n",
- " plt.title(title)\n",
- " plt.xlabel(iris_features[0])\n",
- " plt.ylabel(iris_features[j])\n",
- " plt.legend()\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "intellectual-proxy",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Select first 2 features\n",
- "X = iris.data[:, [0, 1]]\n",
- "y = iris.target"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "meaning-conversion",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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ffeTkM2prawkEAhw3ZTJPPPkMAKtWrWHDho2MGHF4i9dOmTKZJ59w1kF4c/Y79O3X98AZhZfsjMAYk3pKJnT6clC4I44YSe2ePQwaVExx8UCKiweyfMUqpkw5A4CeeXk8OuN+srOzW7xu06Zqrr76RpqbnU7OP/vp/7Z4vkuXLvz1iT9x8023sLehge7duvHKq89w/fVXcsMN32P8+OPJycnh4YfvpWvXri1ee+ut3+frV3+L8vIT6NG9O488cp+n7znItzbUfrE21Ma0bfGKahoG9qChKIvN/TZwclmACXlVSXtGYG2ovRdrG2q7NGSMMRnOAoExaWpN0/ZET8GkCMsRGJNGDlQQSw2DBghl/bbSXz4B+iV6aiaJWSAwJk0EO4o2lATbSKRBBbGJC98vDYlItogsEJEXW3nuchHZKiIL3Z+r/Z6PMekotK10sI3EuMJSCwImKvE4I7gJWA4RV8D7m6p+Mw7zMCbtWFtp4wVfzwhEpAQ4C7D7PY3xmLWV9tfmzdV8+eIrYn7dOWdPP6jfULiOtqj2i99nBHcB3wfaKoW7QEROAFYB31bVDeE7iMg1wDUARX0H+DBNY1KLtZX236BBxfzN7fMTqr22zy+8+FS7Y8erRXW0fDsjEJGzgS2qOq+N3V4AhqrqWOA14NHWdlLVB1W1QlUreucXeD9ZY1JIaFvpTE0KL9q6lIeX/JVFW5d6Ml6kNtTjx00B4NFHn+S8c7/Cqaecy2mnnkd9fT2XTL+KsUcey4UXfJVjjzmNuXMXAHD4YeVs27adysoqjhxzDNdeezPjxh7HGadfyN69e4H2W1RXVlYx9cSzOfrokzj66JN4770PPXmfkfh5aeg4YJqIVAJPASeLyOOhO6jqdlXd5z58CDjKx/kYk/LC1xrOxKTwoq1Lufbf3+EPCx/m2n9/x5NgcNHF5zLz7/848HjmzOeZOLHl19GCBYv529N/5j9vvMAD9z9CQZ/eLP74PW67/Rbmz1/U6rirV6/j+uuvYtHidyko6M2zz77Q4vlgi+rf/u5nzJ//Jq+8+izdu3enf/9+vPzKTD766A2eeOIhvv1tf88gfLs0pKq3ALcAiMhU4LuqemnoPiJSrKrV7sNpOEllY3y1rj7AqroAw/NyKOuRGndQW1L4c3M/W0hjUyPNNBNobmTuZwsZV3REp8ZsrQ11yZDBLfb5wilTKSzsAzjtoW+88RoAxowZxZFjR7c67qGHljJ+/JEATJgwjvWVLa98R2pRXVdXz7e+9QMWLVpCdnY2q8PaU3st7p8CEbkDmKuqs4Bvicg0IADUAJfHez4ms6yrD3B3ZR0BhRzZx01D85I+GIQmhbsN2pV2baVjVTFgPLnZuQSaG8nJyqViwHhPxg1vQx2uI22fQ5vIZWdnsXdvIKrX3X3X/QzoX8T8+W/S3NxMz7zB7b+oE+LyCVDV2cBs9/dbQ7YfOGswJh5W1QUIKCjQpM7jZA8E4LSVrumzg1FDCxnaayslPWozMggAjCs6gj+e8lvmfraQigHjO302EHTRxedx3bXfZvv27bz+n1ns27c/4r7HHjuRv898nqknHc+yZStZ8nHHLmaEtqg++ugJ1NY6q5ft2rWbkpJBZGVlMWPGUzQ1NXX0bUUl+T8BxnhoeF4OObKPJoVscR6b1DOu6AjPAkBQeBvqysqqiPted/2VXHnFNxl75LGMGDGM0UeMpHfvSKVSkUVqUX3d9Vdy8cVX8NjjT/PF004mLy+vM2+tXdaG2mScVMsRBNtKLyncwahRXTmsYGtSt5WOVSq2oW5qaqKxsZFu3bqxdu0nnP7FC1i67AO6dOmS6KkBsbehTv5PgTEeK+uRGgHAJK/6+npOOeVcGhsDqCr33vvLpAkCHWGfBmOMiVF+fj5z5rye6Gl4xgKBMUksvK10dtb69GsrLaCqiEiiZ5IWVBVi/E9pgcCYJHUgCKR5W+ncLg3U1NRSWJhvwaCTVJWamlpyuzTE9DoLBMYkodAK4kOHLmNiYSXjCtOzeKyw30ZqtsG2bd2c+3pNx4kTWAv7bYzpZRYITMpLtbuA2pKJFcQ5OU30H7g+0dPIaKn9qTEZLxUrhSOxCmKTKLZ4vUlprVUKp7JBZf0p6JNH+dDBDO3Vx4KAiQsLBCalOZXCzv+RrVLYmI6xT41JaWU9crhpaF7a5AiMSQT71JiE8SrJa5XCxnSOfXpMQqRTkteYVGefPJMQqdoO2i8H7hiSGrp130t2Vl36VRCbpJW5nzyTUNYO+nO2EL1JNN8/fSKSDcwFNqnq2WHPdQVm4KxVvB34sqpW+j0nk3iW5HVkUgWxSV7x+PTdhLMWcWurNlwF7FDVw0VkOnAn8OU4zMkkgWRL8sazQjkTK4hN8vK1jkBESoCzgEgryXwJeNT9fSbwBbGuUyYBgsnrF7bs4+7KOtbV+1eYFlpBvLNsKxVldW4QsOIxkxh+F5TdBXwfaI7w/GBgA4CqBoBdQN/wnUTkGhGZKyJzd9Xu9GemJqPFq0K5ZRuJvW4biU8sCJiE8u38V0TOBrao6jwRmdqZsVT1QeBBcJaq7PzsjGkpHslrSwqbZOXnhdDjgGkicibQDeglIo+r6qUh+2wChgAbRSQH6I2TNDYmrvxOXltS2CQz3wKBqt4C3ALgnhF8NywIAMwCvga8D1wI/EdV7S9+06p3avaxYHcj5b1ymVLY1fPx/UheW1LYpIKo/l8vIn2AQcBeoFJVI13zj2asO4C5qjoLeBh4TETWADXA9I6Oa9LbOzX7eKLaWXVpeV0TgC/BwEvWVtqkioiBQER6AzcAlwBdgK04l3gGiMgHwB9U9Y1oDqKqs4HZ7u+3hmxvAC7q4NxNBlmwu/Ggx8keCADqB3ehoE8uo4YWMrH3KxYETFJq64xgJk6x1/GqujP0CRE5CrhMRMpU9WEf52cMAOW9cg+cCQQfp4rSAQU4J9NYEDBJKWIgUNVT23huHjDPlxkZ04rgX/9+5giMyVTR5gjGAkND91fVZ32ak0kzz326l4W7GxnfK5fzBnbv8DhTCrt6EgDSaY1jE9mS7ZtYsLWK8qJSxvQd7Pn+6aTdT4GIPAKMBZbyeWGYAhYITLue+3Qvr23fD3Dg384Eg86y9teZYcn2Tdz89lM0NjWRm53NXcdPb/PLPdb90000n4DJqjra95mYtLQwLMm7cHdjQgNBvNpfB+8Y+rR4P1nU0Strq+fHMJEt2FpFY1MTzSiNTU0s2FrV5hd7rPunm2haTLwvIhYITIeMD0vqhj+Ot3iscRysIF532H63gng2E3t/YhXEcVReVEpudjZZCLnZ2ZQXlXq6f7qR9uq3ROREnMKvT4F9gACqqmP9n97Bhh06Uu++I1IPO5OMvMoReMXPHEFoBXGxVRAnlOUIWuqTf9k8Va1o7bloPgUPA5cBHxO5eZwxEY3rlUv3bIn6r+9IX9TJvMaxVRAnnzF9B8f0hR7r/ukkmk/DVrcK2JiYxZqcjbR/Mid5Q4PAzrKtHDpgi7WVNiklmk/SAhF5AngB59IQYLePmujEmpyNtH+yrnFsbSRMOojmk9QdJwCcFrLNbh81UYm1vXOk/ZNxjWNrK23SRbufJlW9Ih4TMekp1vbOkfZPtjWOra20SSfRFJQ9CtwU7DfkdiL9jape6fPcjIe8vFMm1mRurMnZSPsnwxrHlhSOXbrfjZMOovlUjQ1tOqeqO0Sk3L8pGa95mWhNxWSuV8LzAZYUbl+mV+ymimgKyrLcswAARKQQf1c2Mx7zcj3eSGPFa83fRAqeCRT0yaN86GALAlForWLXJJ9ovtB/g1Nd/Hf38UXAz/ybkvGal4nWVErm+qGkpJD9/ZqwttLRCVbsBs8IMq1iN1VEkyyeISJzgZPdTeer6rL2Xici3YC3gK7ucWaq6k/C9rkc+BXO2sUA96mqlQ17zMtEa6okc01yGNN3MHcdP91yBEmurRXKeqrqHgD3i/+gL//QfVqxDzhZVfeISC7wjoj8S1U/CNvvb6r6zQ7O30TJy0RrpLE2NzSxui5Az2xp8XyktYa9SmBbW+nklskVu6mirU/N8yKyEHgemKeqdQAiUgacBFwM/AlnJbODuIvQB4NErvtjC9OnqUhrCkfa7lVyOROS1Mb4LWKyWFW/ALwOXAssFZHdIrIdeBwYCHxNVVsNAkEiku0Gky3Aa6o6p5XdLhCRxSIyU0SGRBjnGhGZKyJzd9XujOqNmfhqbU3htrZ7lVyOV5J68YrqFo+zs1b5chxjEqHNu4ZU9SVV/YqqDlXVXqraV1WPVdWfqeqn7Q2uqk2qOh4oASaKSHhm7QVgqNvJ9DXg0QjjPKiqFapa0Tu/IJr3ZeIsfA3h4ONI271qBx2PttJB9YO7sKZpO9vYzNBefSjp0c+3YxkTT3E5h1bVnSLyBnA6sCRk+/aQ3R4CfhmP+RjvRVpTONJ2r5LLfiepg2cCDQN7UFLUk/39msgr7ArYQjMmffgWCESkCGh0g0B34FTgzrB9ilU1eM49DVju13wyXUcSqpGSvLEa1C2bPU3KoG7ZHR6jrfn4VXEcGgTW7NtJQ5MyqrCre1nIKa3J3ryOnA2rCQwZRtOgsnbHjFRla9W3JpH8PCMoBh4VkWycM/enVfVFEbkDmOu2tv6WiEwDAkANcLmP88lYHUmoRkryxrq/V5XIsc6nsw4KAsVK6YACYC9De/VhbEEZPTZ8SM8XXoSmAGTnsOfib7UZDCJV2Vr1rUm0aCqLg0nfQSJSGvxp7zWqulhVy1V1rKqOUdU73O23Btc3UNVbVPUIVR2nqiep6orOvR3Tmo4kVCMleWPd36tK5Fjn0xnBVhINA3vQUJRFSUmhGwRC9tm5jrzNm6EpgKhCUxM5G1a3OW6kKlurvjWJFk3TuRuBnwCf8fkKZQokZKlKE7uOVP2W98o98Jd38HFH9veqEjnW+bQn/C6gUOvq9zCorD8NRc7fSeXDnb/O59RUsb5mB1DEYQVb+aQwjwHZ2dDUDNnZBIYMa/s9RKiytepbk2jRrFm8BpgUlthNGFuzuGPikSOItXAs1jl5kbMIveQTyZp9OykpKTwQAELNqaliG5s5pLAPhxVspWjreibULKBu0CD6HXZKu8e3HIFJlM6uWbwB2OXtlEy8dSShOqWwa0xfuLEmhWOdU6zzCRceBMaOPbhs5cPqTZTQehAAmFRYypwaqKuBtRRR2XUHW0ecy4S8KqrqlrTbeyhSlW3Wts3kbFhNluSAD4Fg1rqFzN60kqmDRzCtbLzn45vU1laLie+4v64DZovIP2m5VOVvfZ6bSSHJ3p46eN0/9JLPh9WbWt03UhAIcoJBFXU13ckrHE7l7lVAadTBINyylR9x0+J/s1+ELos3cTcwesTRMY3RllnrFvKrBa8A8NGWSgALBqaFtj6R+e6/Ve5PF/cHrFWECZPMaw23FgTa+7Jvz6RC5zr+nJoq8gqHs3ans70jwWBR1XL2i9AsQqP72MtAMHvTyoMeWyAwoSJ+IlX1dgARuUhV/x76nIhc5PfETGpJ1vbUfgSBcKN6DmP5HueOoUDuVHIaZ8f0+nGlo+iyeBONQK4q40pHeTq/qYNHHDgTCD42JlQ0n8pbgL9Hsc1ksGRrTx2aD6jft5+GoizPA4BXRo84mrtxzgTGlY7y9GwAPr8MZDkCE0lbOYIzgDOBwSJyT8hTvXAKwIxpIVnWGg4vBot0B1AyGT3iaM8DQKhpZeMtAJiI2vp0bgbm4bR+mBeyvRb4tp+TMu2L9dbLSPt71UaiI3PyQ/iloLbuAIpW35ol9N++gC19y9leGN8VyZat/KjVMwWvbjeN9XbWWFtqeDlX45+2cgSLgEUi8ldV9a+M08Qs1jtxIu3vZduGZLg7yI98QN+aJUx972aymhtpzspl9rF3tRkMFu9cx4S8Th3ygEh3E3nVkiLWlhfZm9fR8+l7om6p0dYxTHKJ2GJCRD4WkcXAPHe9gBY/cZyjCRNre4ZI+3vZtiGRi9cvXlHN4hXVNAzs4XlSuP/2BWQ1N5JFM1nNjfTfvqDF85MKS5lbtZH1NTtYu7MIgI3126iqW9LacDFpcTeRCIuqnJ6MXrWkiLXlRc6G1TG11PByrsZfbf3Jdrb77w3uv4+5/16K3T6aULHeiRNpfy/bNiTq7iC/8wFb+pbTnJUL7hnBlr7lB+0TLDJbX7MZKIKCCVA/H4i9piBUpLuJvGpJEWvLi8CQYZCdgzY1RdVSw8u5Gn9F02JigaqWh22br6oTfJ1ZBNZiwmE5gvglhaPNEcypcf7azSvcS3bWKob26sOEPGdbRwOC5QiMVzrbYkJE5DhVfdd9cCxRdi01/vHqTpy22jbEGjzieXeQH0nhSLYXjokqSex1xTFAc79BBDRAc79BLbbHuiD8ig9eYtHGFYwrGcnIyWe2O84nVctZtGkVBXvrWjzfNKgs6gDQ0bma+IvmU3sV8IiI9AYE2AFc6eusjKc6ksiNR4K5o+JRJNZRXlYce5VoXfHBS9y4YZGTdN6wiHuhRTAI98KCf/PLtXOd9+H+e055+w31TOpq9y97VZ2nquOAccBYVR2vqvP9n5rxSkcSufFIMMfKz6SwH0b1/PwaeiB3asyv9yrRumjjipZJ541tL/vx5qZVzi8iLR+btNVWQdmlqvp4SPO54HbAms6lko4kcuORYI5FKhaJdZZXidZxJSPpsmHR50nnISPb3P/EwcOdMwE3f3ji4OEdOq5JHW19IwTvhs5vY5+IRKQb8BbQ1T3OTFX9Sdg+XYEZwFHAduDLqlrZkeOZyDrS5iHSayItRu+nTAwC4Fxbv+v46Z1OtI6cfCb34pwZjBsyss3LQvD5ZaA3N63ixMHD7bJQBmiroOyP7q93qmpDB8beB5ysqntEJBd4R0T+paofhOxzFbBDVQ8Xkek4i9t/uQPHSlteLerSkUTu5oYmVtcF6JktLV4bad0BP+4aCs0H9O+5hslZy8jpdyLbaf9LsaxyFiXVs9lYPJV1Q6cd2B5rpXBt9ctU7/yA4oLJ5Bef3uK5jXWrWV+3jEPyRlOS9/mloG473mXNtpXk5B8KBYfF8I5bipRovf/j2by5aSUnDh7B9UdOPbA90l09zX2KaK7dRnOfohbjRLqj55zyU2IOAIladMcW++m8aD6tS0TkM+Bt9+cdVW13oRp17kvd4z7MdX/C71X9EnCb+/tM4D4REW3vntYMkcge/34vRh+N8CBwddVPydEAzdufa7fCt6xyFhWLfwXAwK0fAbBu6LSYK4Vrq1/mgS0zaBTI3bKK6+BAMNhYt5rHP/k5TRogW3K49NAfUZI3jNrql3l1zwvOa/au5Mq846Agtjtt2nL/x7N5YtUcgAP/Xn/k1IiVv35XKEPsVcpeSdRx0000yeLDgUuAj4GzcNpOLIxmcHfR+4XAFuA1VZ0TtstgnBXQUNUAzkpofVsZ5xoRmSsic3fV7ozm0GnBq4XfO8LvxejbEpoUrh/chYaiLMrql5GjgYgVvuFKqmcDzm1uoY/bqxQOV73zAxoFmkUIiPM4aH3dMpo0gNJMkwZYX7esldcI6zdVsXjnOs8qjt8MW18g+DhS5a/fFcptjeV3ZXGijptu2g0EIlICHAccD5QDS4G/RTO4qjap6nigBJgoIh2qqlHVB1W1QlUreucXdGSIlOQkbJ3/kQ7u8X/wdi+FJ4FbLkbv35wi5QNyRpxIc1YuzWRFrPANtbF4KvD5KWjwcbBSONpxigsmk6uQrUqOOo+DDskbTbbkIGSRLTkckje61dc0dBnK2p1FbNEJngSDE8PWEwg+PlD5K1ktKn/HlY6iiyrZqq1WKGchna76jTSWl8dIpuOmm2gqi5uBj4Cfq+rzHT6QyK1Avar+OmTbK8Btqvq+iOQAnwJFbV0ayrTKYq9yBB3h92L04dpLCsd6bT+ROYLQ1yzr6gQILyuOY80R+F2h3NZYliNIDm1VFkcTCMYBU4ATgFJgNfCmqj7czuuKgEZV3Ski3YFXcRLPL4bscwNwpKpe5yaLz1fVi9saN9MCQSTJ0PLZS351Dm3tC9/LttLRHiPYfmIkSyireZd9h4xmyFDnwlVp3hjPWjp0pAVEotgXdXx1qsWEqi4SkbXAWpzLQ5cCJwJtBgKgGHhURLJxrhg8raovisgdwFxVneWO8ZiIrAFqgOnRvqlMlgwtn70Uz/bRsSaLvTrGpMIxfFr5Opcv+Q3ZzY00rXif1754FUOGCq9vfJtfzP2w022fO9ImOlEsmZtcoskRzAXeB84DlgMnqOoh7b1OVRerarmqjlXVMap6h7v9VjcIoKoNqnqRqh6uqhNVdV3n3k5mSGTLZy+1lhQuHz7Y1/bRsSaLvTzGSY2byW4OkI2S3dzEwE8/IZA7lWU12z1p+9yRNtGJYsnc5BLNn5FnqOpW32diopboBeG9kKj20dG0lfbrGFv6ljMyKwfRAE2SzacDD6UYGF3Y15O2zx1pE50o1p46ubSbI0g2liNwpHKOINHtoxORIwj6tPJ1TpRNfJjfjy5lvRhbUEZO42x2N/SxHIHxVWfbUJskFO8F4b0Sz/bRi7p2ZX3vXhzStSslIdt7715H0bYF7Mvt3eJLOtIdQG2J1KI60jEWde3C6h690JwuRLNU/XMNu5m9fydTG3YzLWR7pC/8WNtEJzJwRKqa9nutBXOw1PsmMSkrnu2jI1X9Rqo4jrR/R7R1jHnbH0a3NSGSxeD8cxhbUMbqHTv4xdxXDkqczlq3kF8teAWAj7ZUAjCtbLxnSeFkTC77vR6zaV1baxaf39ZPPCdpUpufSeFIIlX9Rqo4jrR/R7R1DNUmlGaatYlFn+5i8c51fPBZFY1NgYMSp7PDKohnt1NBHKtkTC77vR6zaV1bZwTntPGcAs96PBeTwoLX/SOJd+fQYNVv8C/8YNXvxuKpDNz60UEVx5H274hojiGSTaCpkLU7i+jT+2iyszZDc3OLxOnUwSMOnAkEH4N3SeFkTC77vR6zaZ0li02nhV7yiSQR7aMjXfOPVHHckRxBJNEcY9M+p1o7r3AvO/e+RyBQyykDshnWp8+BiuNZ6xYye9NKpg4ewbSy8QfG8erafjImly1H4I9OVRYDiMhZwBFAt+C2YF1AvFkgSC6tXfePJDwIeHn3TmtiHb9q1e9YWreYI/LGUjr8274cI1yw4nhf11Vsr5/H2AGHcXbxfqDtimNjYtWpu4ZE5AGgB3AS8BBwIfChpzM0KakzyV8vK3y9GL9q1e+Y0fARZMO8ho/46qrftRsMvHgPkwpLeaX6beZtfpxmDbDis6XANM4u3h+x4tgYr7VbWQwcq6pfxVlA5nbgGMDWrstgXiR/vazw9WL8pXWLnV/cpVgPPPbwGJH0zK6hWQOA0qTNbNi1uc2KY2O8Fk0g2Ov+Wy8ig4BGnD5CJgN5VQwWaztov8c/Im+s84t7qfTAYw+PEckheaPJcttZZ0k2Q3oPAj6vOLZWysZv0XQf/TFwL/AF4Pc4dww9pKo/9n96B7McQeJ4XQdgOYLPvVL9Nj16bEdzenL04G7tVhwbE6vOVhb/UlX3Ac+IyIs4CeOOrGFsUpgfxWCRqnJjFenLONL4kfYv73cqX5R+zvYojx3pGLHegVTQ9RAqBh/H8j2rgc9be0WqvjXts0R79KIJBO8DEwDcgLBPROYHt5n0FrwUFK+K4FjFmrCNR3tqL6uUTcdYZXFsIgYCERmIs6ZwdxEp5/NCyV44dxGZNBeaD6jft/9AUjiZhCZscRO2bX2BR9o/1nHa0lqVcnuBYFJhKXOqqtjGDqAIWEd/2QYs6fAqZpmstcpiCwSRtXVG8EXgcpz1hn8bsn038CMf52SSQLw6hHZWrG2l49GeuqNVypMKS5lTA+trNgNFUDAB6udjwSB2Vlkcm2iSxReo6jMxDywyBJgBDMBJMD+oqneH7TMVeB74xN30bHuFapYs9l88m8N5IdaEbTzaU3emSnlOTRXb2MwhhX04rMDJF3R2jeNMZDmCljqbLH5XRB4GBqnqGSIyGjimvTWLgQDw36o6X0TygXki8pqqhnfzeltVz45iHiYOogkCsX7J+X13UKzHjdSe2qvkNUBJ3rCYAkDoYveTik9nTg3U1cBaisjOWgWUMiGviqq6jp0dZOKXoiXaoxdNIPiz+/M/7uNVwN9oZ81iVa0Gqt3fa0VkOU7OoeNtHY1vQi8FDaJHm0EglkRooiqII21PxkRubfXLPLBlBo0CuVtWcR24waCKupru5BUOp3J3x4OBJU5Ne6IpKOunqk8DzQCqGgCaYjmIiAwFyoE5rTx9jIgsEpF/icgREV5/jYjMFZG5u2p3xnJoE4XwfEBblcKxtmtOVAVxpO1etpv2SvXOD2gUaBYhIM5jcHIGAKN6DqOp2SnmD+ROjXl8a8ls2hNNIKgTkb441/kRkcnArmgPICI9gWeAm1V1d9jT84FDVHUcTtHaP1obQ1UfVNUKVa3onV8Q7aFNFGJNCgcToUJWVInQRFUQR9oe6/zjobhgMrkK2arkqPPYS8HEqVUom0iiSRZPwPmSHgMswbm37UJVbbcZi4jkAi8Cr6jqb6PYvxKoUNVtkfaxZLF3OpoUTrYcQazJXy/bTXslNEeQX3z6ge1zaqqoKC1h+Z7VHFaw9UDFcax5gkzMEZiWOpUsdpO9JwIjcGoJVqpqY3uvExHBySMsjxQE3FqFz1RVRWQizhlKtEWdphM6c2fQuH37OHXXbrbk7GN7Xvv7e5mEbU2k9YEjHTfWRG485Bef3iIAeM0Sp6Yt0bSh7gZ8A5iCc3nobRF5QFXbazNxHHAZ8LGILHS3/QgoBVDVB3BaWl8vIgGc5nbTNdVWykkxrRWJQfK0j45VpPWBjTHRi+auoRlALc7lIYD/Ah4DLmrrRar6Dp9XI0fa5z7gvijmYDzgRZGYlxW4XghdH1jdx+kWCOZWbbSKY+OraALBGFUNzai9ISKJv9XCxMTr9tFeVOB6IdL6wOkieOeQVRwbP0UTCOaLyGRV/QBARCYBc/2dlvFSeD6ghI63i9heOIbZx96VkAKx1gT/+m9tfeB0Yu0njJ+iCQRHAe+JSPDm41JgpYh8DKiqtr+Ch4mbdfUBVtUFGJ6XQ1mPnLi2j05UBfGuXmV0bdzFrl7Jsfh6R0Tz3y4YDIIVx5VZh7JF++DczGftJ0zHRRMI/LuVwXhqXX2AuyvrCChk0cA5WfsoKI5P59BEJZGTLXndEbG8BycYeFNxbExQNLePro/HREznraoLEFAnadoMVPXsxrY4dQ5NVBI52ZLXHRHre/g8b1DFNopoau7jjCN2qch0TDSVxSZFDM/LIQtFULJECEht3NpH+11BnGzH9VJH38OkwlL6MYj1NTtYu7OILTqBjfXbqKpb4vOMTbppt7I42VhlcWSLV1Qzf28DgX6FFA4Q+uVJXNtHJ1uX0VTSmfcQ3ra6v8ynpEc/OzMwLXS2DbVJAcGk8OgEriHgdwVxsh0XIreGiCTW9ZWjYUlk01kWCFJcKiwnma5aax/dVjDwM7FtSWTTGZYjSGGpspxkuorUPjoSv1tyTyosZVJhKXU13WlqHk7frhOYX2edRk377IwgyQW/7COxIJA4xQWTyd2yigDRtY9OtqpsY4IsECSx0GKwSDpbKWw6Lr/4dK6DqHMEyVaVbUyQBYIk1VpFcCQWBBIn1vbRiUxsGxOJBYIkY8lfY0y8WbI4iVjy13jpw882AFiRmWmXBYIkEbwU1DCwh3Pd34KA6aDgnUNWcWyi5VsgEJEhIvKGiCwTkaUiclMr+4iI3CMia0Rksbs+csbxo0OoyWzWfsLEws8cQQD4b3fN43xgnoi8pqqhi9qcAQxzfyYB97v/Zox0CgLp0OohnVjFsYmWb4FAVauBavf3WhFZDgwGQgPBl4AZ7jrFH4hIgYgUu69Na+mWFE6HdtDpyCqOTTTikiMQkaFAOTAn7KnBwIaQxxvdbeGvv0ZE5orI3F21O/2aZtykY1LY76pZ03HBttVWcWwi8T0QiEhP4BngZlXd3ZExVPVBVa1Q1Yre+QWezi/e0jUpnA7toNNZMBgY0xpf6whEJBcnCPxVVZ9tZZdNwJCQxyXutrSUTvmAcFY1a0zq8i0QiIgADwPLVfW3EXabBXxTRJ7CSRLvSsf8QOiloEH0SLsgEGRVs8akJj/PCI4DLgM+FpGF7rYfAaUAqvoA8BJwJrAGqAeu8HE+CZGO+QBjTHrx866hdwBpZx8FbvBrDolmQcAkow8/28BhBc7vdueQAes15JvwfIB1CDWJFryVdBubgSLAFrw3Dmsx4YN0Tgqb1GYVx6Y1dkbgoUxJCpvUZhXHJpydEXgkPB8QrBS2IGCSUXiRWeXuHQeKzOzsIPNYIPCAJYVNKgoPBoBVHGcoCwSdlK6VwiYzhFYc9+2akc1/DRYIOsWSwsaYdGDJ4g5It86hxpjMZmcEMbJ8gDEm3VggiIEFAZPOgmscg905lGns0lCUrFLYpCurODZ2RhAFSwqbdGcVx5nNzgjaYElhk0ms4jhz2RlBBJYPMJnIKo4zkwWCVlgQMJnMKo4zT8ZeGgp+2bfGksIm0wUTyOBUHG/fNz/BMzJ+8nOpykeAs4EtqnrQxUURmQo8D3zibnpWVe/waz6hQpO/rbHOocaYTOLnGcFfgPuAGW3s87aqnu3jHFoIbxM9duyQg/b5sHoTYAHAGJM5/Fyq8i0RGerX+LFq7bp/8Es/nAUBY0wmSXSO4BgRWQRsBr6rqkv9OIgVgxnTcbbGcfpLZCCYDxyiqntE5EzgH8Cw1nYUkWuAawCK+g6I6SBWDGZMx1jFceZI2O2jqrpbVfe4v78E5IpIvwj7PqiqFapa0Tu/IOpjWBAwpnOs4jgzJOyMQEQGAp+pqorIRJygtN2Lsa0i2BjvBCuO19e4ZwYFE6DeOTMAqzhOB37ePvokMBXoJyIbgZ8AuQCq+gBwIXC9iASAvcB0VdXOHteKwYzxXqT2ExPyqixvkAb8vGvoknaevw/n9lLPWFLYGP8EcwZ1Nd3JKxxO5e5VQKkFgzSQ6LuGPGP5AGP8F2w/4SSRi2hq7gNYEjnVpXwgCC8SsyBgjP/ayhtYMEg9KR0ILB9gTOK0Fgy21IElkVNPynYftSBgTOIFby+tq+nO2p1F1rY6RaVkIAjmAxoGOpeCLAgYkzi2hkHqS7lAsLeh0ZLCxiSZYDAY1XPYgTUMArlTEzgjE4uUCwTNuVkWBIwxxkMplyzep01WKWyMMR5KuTOCLrnZFgSMMcZDKRcIenTrkugpGGNMWkm5S0PGmOQ1t2ojeYWwdmcRsI7+sg0rMkt+FgiMMZ5o2X7CKo5TScpdGjLGJDdbwyD12BmBMcZzkdpWW/uJ5GRnBMYYX1jFceqwQGCM8Y1VHKcGCwTGGJPhfAsEIvKIiGwRkVbPAcVxj4isEZHFIjLBr7kYY4yJzM8zgr8Ap7fx/BnAMPfnGuB+H+dijDEmAt8Cgaq+BdS0scuXgBnq+AAoEJFiv+ZjjDGmdYm8fXQwsCHk8UZ3W3X4jiJyDc5ZA8Ce6UeNW+n/9DzRD9iW6EnEmb3n9Jdp7xfS4z0fEumJlKgjUNUHgQcTPY9YichcVa1I9Dziyd5z+su09wvp/54TedfQJmBIyOMSd5sxxpg4SmQgmAV81b17aDKwS1UPuixkjDHGX75dGhKRJ4GpQD8R2Qj8BMgFUNUHgJeAM4E1QD1whV9zSaCUu5zlAXvP6S/T3i+k+XsWVU30HIwxxiSQVRYbY0yGs0BgjDEZzgKBT0QkW0QWiMiLiZ5LPIhIpYh8LCILRWRuoucTDyJSICIzRWSFiCwXkWMSPSc/icgI93/f4M9uEbk50fPym4h8W0SWisgSEXlSRLolek5esxyBT0TkO0AF0EtVz070fPwmIpVAhaqmetFN1ETkUeBtVX1IRLoAPVR1Z4KnFRciko1zu/ckVV2f6Pn4RUQGA+8Ao1V1r4g8Dbykqn9J7My8ZWcEPhCREuAs4KFEz8X4Q0R6AycADwOo6v5MCQKuLwBr0zkIhMgBuotIDtAD2Jzg+XjOAoE/7gK+DzQneB7xpMCrIjLPbQmS7g4FtgJ/di8BPiQieYmeVBxNB55M9CT8pqqbgF8DVTjtb3ap6quJnZX3LBB4TETOBrao6rxEzyXOpqjqBJyusjeIyAmJnpDPcoAJwP2qWg7UAT9M7JTiw70MNg34e6Ln4jcR6YPTIPNQYBCQJyKXJnZW3rNA4L3jgGnuNfOngJNF5PHETsl/7l9OqOoW4DlgYmJn5LuNwEZVneM+nokTGDLBGcB8Vf0s0ROJg1OAT1R1q6o2As8CxyZ4Tp6zQOAxVb1FVUtUdSjO6fN/VDXt/oIIJSJ5IpIf/B04jeAq5WlKVT8FNojICHfTF4BlCZxSPF1CBlwWclUBk0Wkh4gIzv/OyxM8J8+lRPdRk/QGAM85nxNygCdU9eXETikubgT+6l4qWUd6tklpwQ30pwLXJnou8aCqc0RkJjAfCAALSMN2E3b7qDHGZDi7NGSMMRnOAoExxmQ4CwTGGJPhLBAYY0yGs0BgjDEZzgKByWgiMrW1DrGRtntwvHNFZHTI49ki0u6i6CJS7MV8RKRIRDLh1l4TAwsExsTXucDo9nZqxXeAP3X24Kq6FagWkeM6O5ZJHxYITFJzq5b/KSKL3H7wX3a3HyUib7pN7l4RkWJ3+2wRudvtl79ERCa62yeKyPtug7j3QiqCo53DIyLyofv6L7nbLxeRZ0XkZRFZLSK/DHnNVSKyyn3Nn0TkPhE5FqdHz6/c+R3m7n6Ru98qETk+wjQuAF52x84WkV+772+xiNzobq8UkV8E14QQkQnuf5u1InJdyFj/AL4S7fs36c8qi02yOx3YrKpngdP+WURygXuBL6nqVjc4/Ay40n1ND1Ud7za+ewQYA6wAjlfVgIicAvwc58s1Gv+D0yrkShEpAD4UkX+7z40HyoF9wEoRuRdoAn6M03uoFvgPsEhV3xORWcCLqjrTfT8AOao6UUTOBH6C09/mABE5FNihqvvcTdcAQ4Hx7vspDNm9yn3vvwP+gtP7qhtOy48H3H3mAj+N8r2bDGCBwCS7j4HfiMidOF+gb4vIGJwv99fcL9JsnBbBQU8CqOpbItLL/fLOBx4VkWE4LbNzY5jDaTiNBL/rPu4GlLq/v66quwBEZBlwCNAPeFNVa9ztfweGtzH+s+6/83C+4MMV47S8DjoFeEBVA+77rAl5bpb778dAT1WtBWpFZJ+IFLhrJmzB6aRpDGCBwCQ5VV0lIhOAM4GfisjrON1Nl6pqpKUhw/umKPB/wBuqep6IDAVmxzANAS5Q1ZUtNopMwjkTCGqiY5+p4BiRXr8XJ/jEMlZz2NyaQ8bu5o5pDGA5ApPkRGQQUK+qjwO/wrncshIoEneNYBHJFZEjQl4WzCNMwVlIZBfQG2dpRYDLY5zGK8CNbvdJRKS8nf0/Ak4UkT7irGoVegmqFufsJBaraHmm8BpwrTs2YZeGojGcNO8Oa2JjgcAkuyNxrskvxLl+/lNV3Q9cCNwpIouAhbTsEd8gIgtwrolf5W77JfALd3usf7X/H86lpMUistR9HJG7NsPPgQ+Bd4FKYJf79FPA99yk82Gtj3DQeHXAWhE53N30EE575MXu+/+v2N4OJwH/jPE1Jo1Z91GTVkRkNvBdVZ2b4Hn0VNU97l/tzwGPqOpznRjvPOAoVf1fD+b2Fk6ifUdnxzLpwc4IjPHHbe5ZzBLgE5xbNjvMDSKVnZ2UiBQBv7UgYELZGYExxmQ4OyMwxpgMZ4HAGGMynAUCY4zJcBYIjDEmw1kgMMaYDPf/AZ3v3Opp95HKAAAAAElFTkSuQmCC\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "l_regression = LogisticRegression()\n",
- "l_regression = l_regression.fit(X, y)\n",
- "plot_decision(l_regression, title=\"Log regression\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "suitable-greene",
- "metadata": {},
- "source": [
- "We can see that `LogisticRegression`model cannot properly divide 'versicolor' and 'virginica' classes based on that 2 features\n",
- "\n",
- "To divide classes properly we need to introduce non-linear models such and Neural Networks or Decision Trees"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "retained-crossing",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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ISJKIrBORNpN/isg1IlIuIuvdny/Fuj/GGGNa6o7HR28GtgAZId7/m6p+vRv6YYwxJoiYXhGISD5wIWDPexpjepV9+8r49JXXRrzexRdd1abeUGudLVEdK7G+IrgD+D7Q3lC4y0TkDGAb8G1V3d26gYjcANwAkDN0eAy6aYwxLeXl5fK3J/7SZnlHZZ+f++fjHW67u0pUhytmVwQichFwUFXXtNPsOaBAVacBLwMPBmukqveq6ixVnTU4PTP6nTXG9Cobyj/g/qJH2FD+QVS2F6oM9fST5wPw4IOP8alPfo5zzv4k557zKerr6/nMVdczbeo8Lr/sC8w77VxWr14HwLgTZlBRUUlJSSlTp5zGjTd+i5Onnc4F51/O0aNHgY5LVJeUlLLwzIs49dRFnHrqIt55Z1VUjjOUWN4aOh1YIiIlwOPAWSLycGADVa1U1Qb35X3AKTHsjzGmD9hQ/gE3vvId/rT+fm585TtRCQZXXPlJnvz7P5pfP/nks8ye3fJ0tG7dRv72xF947fXnuOfuB8gcMpiNm97hJz+9hbVrNwTd7vbtxXz1q9ezYePbZGYO5umnn2vxvr9E9e9+/3PWrn2DF196mtTUVIYNy+aFF5/k/fdf59FH7+Pb347tFUTMbg2p6i3ALQAishD4rqp+PrCNiOSqapn7cglOUtmYmCqu97KtzsuEtGQKB1q5rd5m9YH1NDY14sOH19fI6gPrOTlncpe2GViGury8kszMTPJHjWzR5hNnLyQrawjglIf+xjduAGDKlJOYOm1S0O2OHTua6dOnAjBz5snsKml55ztYiWqAurp6vvnNH7BhQxFJSUlsb1WeOtq6/VMgIrcBq1V1GfBNEVkCeIEq4Jru7o9JLMX1Xu4sqcOrkCwN3FyQZsGgl5k1fDopSSl4fY0ke1KYNXx6VLbbugx1a50p+xxYRC4pycPRo96w1rvzjrsZPiyHtWvfwOfzMShtZMcrdUG3fAJUdTmw3P371oDlzVcNxnSHbXVevAoKNKnz2gJB73JyzmT+7+zfsfrAemYNn97lqwG/K678FF+58dtUVlby6mvLaGg4HrLtvHmz+fuTz7Jw0QI2b95K0abO3cwILFF96qkzqa11Zi+rqTlMfn4eHo+HpUsfp6mpqbOHFRb7BJiEMiEtmWRpoEkhSZzXpvc5OWdy1AKAX+sy1CUlpSHbfuWr13HdtV9n2tR5TJw4nkmTT2Tw4FBDpUILVaL6K1+9jiuvvJaHHn6C8849i7S0tK4cWoesDLVJON2VI7Ay1OHpjWWom5qaaGxsZMCAAezc+RHnn3cZH2x+j379+sW7a0DkZajt65BJOIUDLUlsuqa+vp6zz/4kjY1eVJW77vp1jwkCnWGfBmOMiVB6ejorV74a725EjVUfNcbEl0Bvu0Xdk6kqSGTrWCAwxsRVSr9jVFXVWjCIAlWlqqqWlH7HIlrPbg0ZY+IqK3sPVRVQUTHAea7XdJ44gTUre09Eq1kgML2ejRTu3ZKTmxg2Yle8u5HQ7FNjejUbKWxM11mOwPRqwUYKG2MiY4HA9GrOSGHnf2QbKWxM59inxvRqhQOTubkgzXIExnSBfWpM3EQryWsjhY3pGvv0mLiwJK8xPYflCExcWJLXmJ7DAoGJC0vyGtNzxPzTJyJJwGpgr6pe1Oq9/sBSnLmKK4FPq2pJrPtk4s+SvMb0HN3x6bsZZy7iYLM2XA8cUtVxInIV8Cvg093QJ9MD9LQkr41QNokqpreGRCQfuBAINZPMJcCD7t9PAp8QkQjr5hnTdf7k9XMHG7izpI7iestZmMQR6xzBHcD3AV+I90cCuwFU1QvUAENbNxKRG0RktYisrqmtjk1PTUKz5LVJZDELBCJyEXBQVdd0dVuqeq+qzlLVWYPTM7veOWNaseS1SWSx/L/9dGCJiCwGBgAZIvKwqn4+oM1eYBSwR0SSgcE4SWNjupUlr00ii9kVgareoqr5qloAXAW81ioIACwDvuj+fbnbxiqSm6BWVDVwV8kRVlQ1xGT7hQOTOT9ngAUBk3DC+j9eRIYAecBRoERVQ93zD2dbtwGrVXUZcD/wkIjsAKpwAoYxbayoauDRMmfWpS11TQDMz+ofzy4Z02eEDAQiMhi4CfgM0A8ox7nFM1xE3gP+pKqvh7MTVV0OLHf/vjVg+THgik723SSQdYcb27y2QGBMdLR3RfAkzmCvBapaHfiGiJwCXC0ihap6fwz7ZwwAMzJSmq8E/K+NMdERMhCo6jntvLcG6PLTQMaEy//tf93hRmZkpNjVgDFRFG6OYBpQENheVZ+OUZ9MH/PM/qOsP9zI9IwUPjUitdPbmZ/VPyoBwEYQJ4aiyr2sKy9lRs5opgwdGfX2fUmHnwIReQCYBnzAxwPDFLBAYDr0zP6jvFx5HKD5d1eCQVdZ+evEUFS5l2+99TiNTU2kJCVxx4Kr2j25R9q+rwnnEzBXVSfFvCemT1rfKsm7/nBjXANBsBHEFgj6nnXlpTQ2NeFDaWxqYl15absn9kjb9zXhjCN4V0QsEJhOmd4qqdv6dXezEcSJYUbOaFKSkvAgpCQlMSNndFTb9zXhfAqW4gSD/UADIICq6rSY9sz0Cf5v/9HIEUSDjSBODFOGjuSOBVeFfc8/0vZ9TTifgvuBq4FNhC4eZ0xIJ2ekkJokYX/7DpXMtTmOTSSmDB0Z0Qk90vZ9STifhnJ3FLAxEYs0ORuqvSV5jYmdcD5J60TkUeA5nFtDgD0+asITaXI2VHtL8hoTO+F8klJxAsC5Acvs8VETFic520CThpecDdU+0u0YY8LX4adJVa/tjo6YvinS5Gyo9pbkNSZ2whlQ9iBws7/ekFuJ9HZVvS7GfTNRFM3RtJEmcyNNzoZqb0ne3imRR+z2FuF8qqYFFp1T1UMiMiN2XTLRFs1EqyVzTSQSfcRubxHOgDKPexUAgIhkEduZzUyURXM+3lDbsjl/TTDBRuyaniecE/rtOAPK/u6+vgL4eey6ZKItmolWS+aaSPhH7PqvCBJtxG5vEU6yeKmIrAbOchddqqqbO1pPRAYAbwL93f08qao/btXmGuA3OHMXA/xRVe8Lv/smHNFMtFoy10Qi0Ufs9hbtzVA2SFWPALgn/jYn/8A2QTQAZ6nqERFJAVaIyL9V9b1W7f6mql/vZP9NmKKZaA21rX3Hmthe52VQkrR4f0VVQ9B5BKKVwLay0j1bIo/Y7S3a+9Q8KyLrgWeBNapaByAihcAi4ErgzzgzmbXhTkLvDxIp7o9NTN9HhZpTONTyaCWXLUltTNeFTBar6ieAV4EbgQ9E5LCIVAIPAyOAL6pq0CDgJyJJbjA5CLysqiuDNLtMRDaKyJMiMirEdm4QkdUisrqmtjqsAzPdK9icwu0tj1ZyuScnqYvrQ10sG9OztPvVSVWfB57v7MZVtQmYLiKZwDMiMkVViwKaPAc8pqoNInIj8CAf5yICt3MvcC/A+LEn2lVFDxRqTuFQy6OVXJ6QloyHY/hwyuJ6KmrYWHmo08cRTXmFwyjKOkQF+8jwlJM/MDveXTImqG65hlbVahF5HTgfKApYXhnQ7D7g193RHxN9oeYUDrU8WsnlI6XlXOzxUDpoACP6QWb/ARyLwvFEw46GakYPzyQtqz8FGV6gNt5dMiaomAUCEckBGt0gkAqcA/yqVZtcVS1zXy4BtsSqP4muMwnVUEneSOUNSOJIk5I3IKnT2wjWn40fllFcf4S8wmGcMS3oXcWYy6opIrt6HRWZM6gaPKV5+aqyvRxrUirYRxrNw3BCjrK10bcmnmJ5RZALPCgiSTi5iCdU9Z8ichuw2i1t/U0RWQJ4gSrgmhj2J2F1JqEaKskbaftojURuvf09+2sYcMwJAsdyPKwq2xty3VgZfXQbF5X+jCT10iTJ3Df6vyhNnQDAjqbK5quBEzLLAdh+6BC/XP1im1G2NvrWxFtYgcA9mQ8PbK+q7Q4RVNWNQJtSFKp6a8DftwC3hNtZ0zmdKeEcLMnbXiAI1T5aZaVbbl85ODiduXn9OZbjPO8wY0L3nzhP2v4ayerFgw9RL/MH7mHL+EUAHK9qatN+c1Vl0HlxE32+XBN/HZaYEJFvAAeAl4F/uT//jHG/TBR1Zp7eGa3mFm79Otz2ofYdaZ9ab3961sdTXsYjCAAcHDoDnycFHx58nhQODv34e8+cLGcE7a6qQ+yszgEgOy2JZI/gAZI9Qt4gL6V1ReQN8gZdHvhjTCyFc0VwMzCxVWLX9CKdScyGSvJG2j5aI5EDt59TX8/83DRWl9R0eByxVJk1heXz7mBY5ToODp1BZdaUFu/PyRrNyiqoq4Kd5JDkmcZlUyeyu2YfowbnUdtvBGvrgH6juWzq8LbLXTPTSimtK2J0WsvtGxMt4QSC3UB8P3Gmyzozsnh+Vv+IksSRJoUj7ZO/Pxs/7DlP31RmTWkTAAI5waCUuqpU0rImcBRYPG5xm3ae2lfgYAX5ngKG5Mxs8d7aOhgma4HOB4NlxetZvncrC0dOZEnh9E5tw/Rd7ZWY+I77ZzGwXET+RcupKn8X476ZXsTKU4fmv020sqqUCva1eT+57D2eO/gCjSKk7NjOxYcP4c2dG9AiBzJnQn3ngsGy4vX8Zt2LALx/sATAgoFpob0cQbr7U4qTH+gXsGxQ7LtmepPuLk+9uqTtCbWnm5M1mmzyqKtKbfFTe3ALjSL4RPCKUHtwS4v3AXZW53BQnSuFSHMGy/dubfe1MSG/mqnqTwFE5ApV/XvgeyJyRaw7ZnqX7ihPvfFDZ8jJsRED2dFQTX5+VtwSxZ3lvzoIVNuwkOUHl+JFSVY4cdhC0gOuIk4aNJ4tR7YD4E1ZSHLj8oj2uXDkxOYrAf9rYwKF86m8Bfh7GMtMAot1eeq+EARCSc89n68AZdXvkZs5l/Tc86O6ff9tIMsRmFDayxFcACwGRorIHwLeysAZAGZMC7GaazhwBPGxHA/59J0g4Jeee37UA0CgJYXTLQCYkNr7dO4D1uCUflgTsLwW+HYsO2U6FmnJiFDto1VGojN9CkfrIADdP25gaFVRyEdEY23AobfZUbGV5PSxkHlC8/JolaToqORF3iAvY4d93D67vIizS96gn+dMGtLGdWkfpudoL0ewAdggIo+oamOodqb7RfokTqj2kZaRiGafwtFTgsDCd76Fx9eIz5PC8nl3tAgGK6tKg973D2VlVfhz9g449DYvHXmORoGUo1u5Lu10yCwMWaoiUqFKWwQuT/YIPzhtBnhgVNVOznr3d0hTI1ryLO9d+D9QkNepfZiepb1bQ5twJ5IRkTbvq+q02HXLtCfS8gyh2kdaRiKafQpXXuEwpk0bxaqyvXG5HTSsch0eXyMefOBrZFjlOiqzpjSf0NOyjjb/3VFA8D8+OiZrSLvt/I4fWEej4DxNBNQcWEtyYxb7jiSHLEnR0RNFgY+ehiptEbjc61O2VBYzJqeAsZVb8TQ5/xY+XyNDyzZytINAYOUzeof2PqkXub9vcn8/5P7+PDbTWFxF+iROqPah5grojj71Fv4yErhXBAeHzmgRBPy/66pSQ14dBLZPYwizh4dXKfVQ3Xxe3fG4+zSRMnVUAaPTpjAjZ2/QCeH9QcCbsjDo9pIbl7cYoRxqYvnA5cke4aShhdTj46OhE/ElpaBNjagnhcrcaQzs4Bhs8vreQZwZJdtpILJOVWe0WrZWVWeGWieWxo89Ue+8zea3T4QcwcYPyzg2YmBcrwigZY7geTIA56Se5NlGQYbz7X5ndQ67qg6RTV6LYBAYBALbh6u+dD3HKoqYkJfH/GmfbF7e+r67PwisrWv/RDtM1pI/MLs5GISXI/gUG6uL2Vmdw4KjDRwvfoN+hWfSUDCOkanVABT2qwi5T8sR9AxD0q9eo6qzgr0XzqdVROR0VX3bfTGPMIrVmdiK1mT07ZWRiDR4RKtPPY2/jETgrZ0TMssZJh+RP7CWPfUVzshfcthVtY+VVR+XlvC39weBmWnh5wgAto8QNvfLI3PkqS2WB04IX1pXxJ76Cg7qzOYCd63l7FxG/aHNJI0ogKngH6EcamL5j0q3sGHvNjw5Q1okiytyprDKN5jZOaNID3MKIJu8vucL51N7PfCAiAzGmQ3wEHBdTHtloqozidzuSDD3Jv5v9v4gAAR8sy5yyj+4wQCO8vju9YxNywpo7wSBSMpDFFXubU4KP1u8M2ii1X8lEBgE/KOR/Ubue5qHmtZyvJ/Qr/JDvrnpRA5OmUmochXPrXuFX+9c7Rz37sNo0rMMGj017H6b3qfDb/aqukZVTwZOBqap6nRVXRv7rplo6UyZh1DrhJqMPhGkZR1tvr8/LbOwefnotCnN8xH73x+TNYRZo/Ob2wS2D1ewRGswgTmBkwaNB5wrEv+PHNvCcbeERaMIleXtT+Lzxt5tzh/uQyLvl22KuO+md2nvqaHPq+rDAcXn/MsBKzrXm3QmkdsdCWbTvmglWnOHzKHfkZdpBFJUyR9xCkfaaX/myAms3Lka3Pzhqbl2NdDXtXdGSHN/p3dmwyIyAHgT6O/u50lV/XGrNv2BpcApQCXwaVUt6cz+TGidKfMQap1I5ykwnTdl6EjuWHBVlxOtnhOu4zs7oaRyBWlDT6L/9Ks50hD6ov7iGWcDzpXBjJwhnDbtEjZWF3dq36Z3aG9A2f+5f/5KVcPLCrXUAJylqkdEJAVYISL/VtX3AtpcDxxS1XEichXO5Paf7sS++qxQCdtIn9DpTCJ337Emttd5GZQkLdYNNe9ALEYWB4p0hG9hyTLyy5azJ3chxQVLOr2d2rIX8BxcTnLDSdDq0c/Ap2vo9/E39tSKt9mxd1ubEcGRCpVovXvTct7Yu5UZOVlcMXVh8/Ls8iI+sfsN+rOoxbHVDcyj8sgwjg8cxtgg/W8daC6ecTYXzzib0rqisOvJdPQEUqyeGorXfvuScD6tRSJyAHjL/Vmhqh1OVKPOc6n+K9AU96f1s6qXAD9x/34S+KOIiHb0TGuCiGeN/1hPRh+p0Ue3sfCdn4cc4dtaYckyZm38DQAjyt8HoLhgSYcjhVurLXuBew4udUb3HtwF62HMSc6J/eMRvl6SPR4umzqcnb7dJJe9x7+r3fkFAkYER8vdm5bz6LaVAOytq+ZA4yOcUXgao6p2svCd20nyedHdzzYfW+tjGFs8BEZmRW2EMoQ3SjkWI4vjtd++Jpxk8TjgM8Am4EKcshPrw9m4iCS5bQ8CL6vqylZNRuLMgIaqenFmQhsaZDs3iMhqEVldU1sdzq77hO6u8R8oVFI4Xn0qrN/cPMLX447wbU9+2XLAecwt8HXgSOFwtlNW/d7Ho3tFqK/+gJ3VOWysLua9A6U0NnnxAY0+ZeOBnQBt5hfYtbeUjdXF7KmviHgugdZzF5fWFfHqnpbJ2+2VzjP+mbvXk+TzktTq2Fofw579zhNBm6sqw0pGhyNUYjvchHdv229fE87k9fnA6cACYAbwAfC3cDauqk2qOh3IB2aLSKcqdqnqvao6S1VnDU7P7MwmeqVoTfzeGbGejD5SxQMnhZwoPpg9uQuBjy9B/a/bm3A+mNzMuaQoJKkzV0BSvxnNE9IPGXwqHk8ygiCSRKN3AnVVqZw4bGGLdY71K2ieWCaSYBA4UjjwZ9aI2S3a5QyaQl1VKmWD56BBjq3lMSj5I5wxRZOyhpKSlIQH6fKoX39iu/W2Qi2Plnjtt68JZ2SxD3gf+IWqPtvpHYncCtSr6m8Dlr0I/ERV3xWRZGA/kNPeraFEG1kcrRxBZ4QaONZdfWo9svjs7ENxyxEEzhUQOFAs1bOJDftr8DZlMab/Kc2jigPX2dx/EtBydLF/YFmocQUdjRR+s/hdNpfvIC9jGgX9LwacR0ZDHVtt2Qt8eHA56cNOYub0K6hsWMvMtFIOHxvS7n300roivCkLm0cWnzRoPKsO7Gb28FGkpx1rMbLYcgQ9W1dHFs8A5gOfFZEfAtuBN1T1/vZWEpEcoFFVq0UkFTgHJxkcaBnwReBd4HLgNcsPtBTP0bqhRh3Hat6BjnQ0UXxrNRmF9G+soSaja/fnW88V4IwahroqGMVALivbRtngAkbkfnzSLuifz+zkEzjYP5/0gAnsTySJ/G3/ZveYSYwqkObaP4EnrYwBh5pHCh8vPszYyq18NHQiu7M+TjqPzFqCl7YlLdo7Bl//SXjd+kh+0Rz1G2pboZZH60Qd6X5NWx1+alV1g4jsBHbi3B76PHAm0G4gAHKBB0UkCeeOwROq+k8RuQ1YrarL3G08JCI7gCrgqs4fSuKwCeE7NrSqiDPeuZkkn5dJAUnhcMpKhxJ4wp2TNZr9Ja9yTZGbnPW8xvKM4SH3MSdrSkD7Rpo+fJeXz7ueUQXCq3ve4perVzUXefvStBNJTT+T48WHuead35Lka6LJk8zdU37ErozxzX3IJrVFnyJNhHfWrqpDYRfOC8WSuT1Lh2cPEVmNMxbgHZynhs5Q1V0draeqG3GuJlovvzXg72OAzX8coViVfA7knwsglMKBg6K6v2CK64+Qx0BWlbU/EjaYht2vNydOmwLKR4dTVjqYXVWHmmsI+S1q3Eeyz9tclrmjfYyr2UySr5EkFFEv4w6soKFgQYuy0l6fUlHXxKh0KKx62z0GBZ+XRY372JL1iZDHHGq/geqqUlnFbpI8h4DRQPv5CufKpLi5qF5d1R7GpmWF9x+hHVaeumcJ5+xxgaqWx7wnJmyxLvnsDwL1I/uRn9/2Q79nTxVFHGdc/8wWy2d1UJs+EqtL9lHfcJxjOZ6Iq46urCplzOBJNHmSwAdeTxKvp+QxgvDKSof+ttty7oFg26KdfWQOzuEcTzKiXtTjIWnM+KBlpSdlDWV8Wim7x0xCt75LU1NTi2MIJVR//OYE3KJKy5pAyeFtwOiQ5S+cQWSj21RWjWRynVCsPHXP0mGyuKdJtGRxKLFKFm/8sIwVTZUcyxfyTmw7IZFf/QGnEv24JOdp3wHlvqj1Aej0BPWBidy5jasZsf8j1qVnsytzUXMxtsUc7rCsdDAlhw/R5JvQvJ32krP+5a+n5LErY3zz9k9tOMzMqnXU5eWRfcLZze2D3S8vrSviSMlW+u9PYU3aTFaQ1WFOINxEeOtKqqEEK6+9sqqUWaPz2ySLI2XJ3O7V1WSx6YFikZj9x9rt7OvfwICpg8gaXk9hdnnIE2NJ9i6KK3LYRz3Z5AFJQdt1VmcmqG99ciutLWFtjo+kgdkksY20LOckXlm2msmHN7LleBMVuSc2tz9au5Hd+5ucb+RDgh33aEoOO9v5+FZR8AT24MPF9Ct7lxFDZ1NRkN8cZI4dL+W+pgwWZZ5EdgfHMzptCn/1lPImuyhIzWFM6gnNZa4Dg1ng/sNNqPsT3ikl7zCysaxNMtqvriq1TS4iWmKdRLZAEz4LBAZoGQQGDq/nnJHLOTlrNFAbtP0wqWD24I9YVTOW4gogm7CfYImFlVWlzTOAnZBZTvrxDdy18UO8Ph/Jnu1cNvUSjnu2ccbhMi7Z6Uy2d2X5+/RPu5rqwkkcrX2D+zZ+iNen7SQvi3Duq5cDOdRVBZ+zOHBU88TqTeTnVLJ9whzSj2/gF++/h9enLcpKh0qcLitez/2bncFj26vf5JxxMCZrIaOqijjjndtJ9nm7lBRezGHOKPozST5v0GS0X3f+N41WEtmS0ZFpr/rope2tqKpPR787Jp6mTB/D8ewmMrJ3cXLW6HZr549Oc25dHNQhnJIzjVUHdlNX1Y2dDcK5932UksOHOFDThNfnc0f9+thds4/s9PMoLP0H4Iw4VmBy2VoeKczlQE36x+2DJC8DB4GVHD6EM+9AcIGjmhUo2PUBqbM/w78+fA2vT9udIzhw+fK9W1tsd1tFMTfOupoB258nyU1Sh0oKh2NY5brmZLeEkYzuDtFKIlsyOjLtXRFc3M57ClggMD1G60RocnIFHk8y6msiyZOExzOJXVWH2DJ0AVOrtzWPOF6bMZcm38ft8XlJ9gh5g7wtTv6BM4Dtqkpu93bJntyFjCh/v3kfJWMmk8rHI3nbmyM4cPnCkRN5/2BJ83YnZDtJ3Y+GTqTJk4y4VwQdjY4OpaPkcjxEK4lsyejItFd99Nru7IgxXRUYDDKz5jG/EMqPHGTowFPo3zCB/gCT57I6Lat5xDEFS6irKqWGk5hXMAqfbzOjBudR228Ea+sCt9726ZlQiguWUFxXxYK6jawadiLVEyYxDRg/ZEjQstKhyk0vKZwOwPK9W5mWnU7m0MkA7M46gbun/IhFjfvCHh0dTGXWFJbPuyOiUdaxFq3S29HaTqIIK0cgIhcCk4EB/mWqelusOmUSQ6SlHsIR+GRLzuFUljT2Y3OdsCvj4/eKC5a0KDnhT5z6iu+hwruHurRCdk5oOxlLsCAQ6hhW5p5F0+gvsOXIdk7g46dyIh3tWjg4h5rjR8kb5G2RrdmVMT4qt3EiHa3dHaI1IthGFocvnAFl9wADgUXAfTilIFbFuF+mj4v1KNjAROiZnn/y5rw7qWynfW7FUyzVEkiCDce2sXjT32kc+YUWbWI1kjecUsrJHuGyqcMZGjo10aHOPP8fr+S/6V7hXBHMU9VpIrJRVX8qIrcD/451x0zfFs4o2K5uPzAR2tH2P6jb6DwBKwKqHPDu5IIOToLtjVKuYB9bjjhzHFc2lLOxupiZboK9dRI+VGKzxXKfsrtmH0d9u90rk9QQvWrLHwAC51AOx+rSPUGfijJ9TziBwD/mvl5E8nCmlMyNXZdMIoh1ojLS7U9Om8aaY+83z9M7OW1ap/YROJbBP0J5aP+ZAKytg2GyltZlHfIGOQlqr09bJKr9yxt94BEPHs+kiJ/rDxw1veXI9rDW8UvLCl5aw/Q94QSCf4pIJvAbYC3OE0M2tNd0SawTlZFuf/SEb/OFbb/ng7qNTE6bxugJ345oH6+n5LGLDNKyjpLhKacgw0tlQ3lzEAAnIBxsgIN1rTbUbzSXTR3O7pp9LRPV7vKNB3Y2J7wh/JNyuKOH25PkOUSTb0iL0hqm7wknEPxaVRuAp0TknzgJ487MYWxMC9FKVIZK2Ibafqj2M7LP4TzJdpaHue/KrClBy1SkH9/AKweaGDV4P1NzFje331kd6iZ/DtnpUznqg53VLZePz5naoqxFOFoHgWGylvyBHY1lDubj0dR1ValRqTPUXWxkcfjCCQTvAjMB3IDQICJr/cuMiadIE7b+9uJrjKg8dSiBt14Cg4AzgthHkscDU2FqzmJWHdjd/ORRpCL5Jh4qCLQ3QDCY0roidwKdlsHgo7oqZhFZvqG72cjiyLQ3sngEzpzCqSIyg4+nf83AeYrImLiLNOk8rHId4msMuzx1OGaNzmfLke0UZAxhWmZhwAhiwB3VPNW9EOiuMhz+HEVlQ3mnggA4tY5K64rc6qTF7Kx2jtV3oOcXqrSRxZFp74rgPOAanPmGfxew/DDwoxj2yZiwRZIUXllVyv6UPE4Mszx1ZwWOIE72CKMGR688twmPjSyOTHsjix/EmWHsMlV9KtINi8goYCkwHCfBfK+q3tmqzULgWeAjd9HTNlDNRCLcpLD/Fk5FQT5LM69kRm0Fa9JmsoY0sqtKmRPF5HXgCOK8QV5q+7U3i4CJBRtZHJlwcgRvi8j9QJ6qXiAik4DTOpqzGPAC/6Gqa0UkHVgjIi+r6uZW7d5S1Ys60XcTJ/4kXN4gL/Tr+JtWLEYQhyNwToCKjLTme+b7PNk8P+AgSQPrKaQp4KmYDOaMvzoq+/aPai2tK2pVqiK4wMnuA+dHjpZETJzayOLwhRMI/uL+/Kf7ehvwNzqYs1hVy4Ay9+9aEdmCk3NoHQhMLxLpaNdYjyAOtf3ApPBETxIPzvsu/TK9HK19g6c2tS1PHfhUTHc/Illb9gL3HFxKo0DKwW18BaIaDLYfOsQvV79oiVMTkieMNtmq+gQ4uS9V9QJNkexERApw5i9eGeTt00Rkg4j8W0Qmh1j/BhFZLSKra2qrI9m1CVPhwEHs2VNF6YFqiity2FNf0aL6pp+ThPM2j3bdeGCnW4I6+EjXwCSsx03CRlOo7fuXJ+Ej2dfEgob9AFTUNTUncr0+H40Nx5on34l05G20lFW/R6OATwSvOK+jaXNVZZvEqTGBwrkiqBORoTj3+RGRuUBNuDsQkUHAU8C3VPVwq7fXAmNU9YiILAb+AbSZGUNV7wXuBWeqynD3bSIzrn8mx5I87KOeVTVjcVI3wUbBemj0KR7xMHTgKe0+4x6vEcT+5eprpMmTRGXuNOBYm0TuSUMLqSe602xGKjdzLikHt+FFSVbndTSFKn9tjF84geA7wDLgBBF5G2dGjsvD2biIpOAEgUeCTWQTGBhU9XkR+ZOIZKtq5BOgmqipPzCQYnIoyBgSchRsuKNd4zWC2L+8Yffr7B11ArnDJ0HD2jaJ3LFZBe4k7fGTnns+X4GY5QhClb82xq/DQOAme88EJuKMJdiqqo0drScigpNH2KKqvwvRZgRwQFVVRGbj3KoKd1CniYEB5T7G5QzlOJnsrD4atM2oqsNcuq+JssHCiIKOv13GutTx4MPF5FSsoyFlcJuRxSvdsg+BxbECE7nemPUqMum558ckSexniVPTnnDKUA8AvgbMx7k99JaI3KOqHZWZOB24GtgkIuvdZT/CmfQVVb0H58riqyLixSlud5Wq2q2fOJlVkMfqkn3Nr+uqUtvcN88uL2LhO7eT5POintdYnjE8rvXsA+cHHlH+PkCLuQaMMR0L59bQUpwZzO9yX38WeAi4or2VVHUFH49GDtXmj8Afw+iD6WbNE7yUtkwsfmL3G83lnX0xKB8dqdbzA+eXLbdAgFM1FOCETGeaTWhb/roj/ocFNlYXN8/TvLp0D7vqDjVXVjV9QziBYIqqTgp4/bqI2COgCaL1vf/+LMK3+9keM89t6/mB9+QujGd3egT/jGu7qvYBOZA5E+rblr8Ox9q60ZQcPkSTb0LzQwFj07Ki22ETd+EEgrUiMldV3wMQkTnA6th2y/RUPW2eW/+3f/8cxHY14PAHg7oq2EkOJZ6xHNQhEW/Hmac5ucUcCL2pAqkJTziB4BTgHRHx/9cfDWwVkU2AqmrHM3iYblNc72VbnZcJackUDgxrSuqIRVreOdZqMgrp31hDTUZht+0z2mI1f/PKqlLqqlJJy5rQqrx1eILN02z6nnDOFLF7lMFEVXG9lztL6vAqJEsDNxekxSwYtBbrEcQ9bb/RFMtjCAwGnZmqMpLZ0EzvFc7jo7u6oyOm67bVefGqkzRtUud1dwWCWM9B3NP2G02xPoZQif9I1jV9W/ecJUy3mJCWTLI00KSQJM7r7hLrEcQ9bb/R1F3HYCd1E4oFgj6kcGAyNxekxTxHEEy8ksg9LXndGX3hGEzvZoGgjykc2L0BIFCsRxD3tP0CDDj0NjsqtpKcPhYyT+iw/ZjD2zmp8o2w51c2pjtYIDCmk2rLXuClI8855aOPbuW6tNMhM/STS6OqdnJN0e0k+7y9NrFt+qZwylAbY4JoWT5aKD+wo932Yyu3kuSOyo5FSW5jOssCgTGdlJs5lxSFJFWSVckZPq7d9h8NnUiTJxkfnl6b2DZ9k90aMqaT0nPP59z6Qwxo2oqmj2Xg6PZzBLuzTuDuKT9iUeM+SwqbHsUCgTFdcGzI6UwZ/Wm2HNkOlHfYflfGeLZkfSL2HTMmAnZryBhjEpwFAmOMSXAWCIwxJsHFLBCIyCgReV1ENovIByJyc5A2IiJ/EJEdIrJRRGbGqj/GGGOCi+UVgRf4D3dSm7nATSIyqVWbC4Dx7s8NwN0x7I+JsaFVRZy0/SGGVkU+AYoxJn5i9tSQqpYBZe7ftSKyBRgJBM5udgmw1J2n+D0RyRSRXHdd04v0hXLQxiSqbskRiEgBMANY2eqtkcDugNd73GWt179BRFaLyOqa2upYddN0QWApZRs1a0zvEvNAICKDgKeAb6nq4c5sQ1XvVdVZqjprcHpmVPtnosNfStlGzRrT+8R0QJmIpOAEgUdU9ekgTfYCowJe57vLTC9jpZSN6b1iFghERID7gS2q+rsQzZYBXxeRx4E5QI3lB3ovK6VsTO8UyyuC04GrgU0ist5d9iNgNICq3gM8DywGdgD1wLUx7I8xxpggYvnU0ApAOmijwE2x6oMxxpiO2chiY4xJcBYIjDEmwVkgMMaYBGeBwBhjEpwFAmOMSXAWCIwxJsFZIDDGmARngcAYYxKcBQJjjElwFgiMMSbBWSAwxpgEZ4HAGGMSnAUCY4xJcBYIjDEmwVkgMMaYBGeBwBhjElzMAoGIPCAiB0WkKMT7C0WkRkTWuz+3xqovxhhjQovlVJV/Bf4ILG2nzVuqelEM+2CMMaYDMbsiUNU3gapYbd8YY0x0xDtHcJqIbBCRf4vI5Dj3xRhjElIsbw11ZC0wRlWPiMhi4B/A+GANReQG4AaAnKHDu62DxhiTCOJ2RaCqh1X1iPv380CKiGSHaHuvqs5S1VmD0zO7s5vGGNPnxS0QiMgIERH379luXyrj1R9jjElUMbs1JCKPAQuBbBHZA/wYSAFQ1XuAy4GviogXOApcpaoaq/4YY4wJLmaBQFU/08H7f8R5vNQYY0wcxfupIWOMMXFmgcAYYxKcBQJjjElwFgiMMSbBWSAwxpgEZ4HAGGMSnAUCY4xJcBYIjDEmwVkgMMaYBGeBwBhjEpwFAmOMSXAWCIwxJsFZIDDGmARngcAYYxKcBQJjjElwFgiMMSbBWSAwxpgEF7NAICIPiMhBESkK8b6IyB9EZIeIbBSRmbHqizHGmNBieUXwV+D8dt6/ABjv/twA3B3DvhhjjAkhZoFAVd8EqtppcgmwVB3vAZkikhur/hhjjAkuZpPXh2EksDvg9R53WVnrhiJyA85VA8CRC7+wYGvsuxcV2UBFvDvRzXrZMf8qGhvpZcfcZYl2vNA3jnlMqDfiGQjCpqr3AvfGux+REpHVqjor3v3oTnbMfV+iHS/0/WOO51NDe4FRAa/z3WXGGGO6UTwDwTLgC+7TQ3OBGlVtc1vIGGNMbMXs1pCIPAYsBLJFZA/wYyAFQFXvAZ4HFgM7gHrg2lj1JY563e2sKLBj7vsS7Xihjx+zqGq8+2CMMSaObGSxMcYkOAsExhiT4CwQxIiIJInIOhH5Z7z70h1EpERENonIehFZHe/+dAcRyRSRJ0XkQxHZIiKnxbtPsSQiE93/vv6fwyLyrXj3K9ZE5Nsi8oGIFInIYyIyIN59ijbLEcSIiHwHmAVkqOpF8e5PrIlICTBLVXv7oJuwiciDwFuqep+I9AMGqmp1nLvVLUQkCedx7zmquive/YkVERkJrAAmqepREXkCeF5V/xrfnkWXXRHEgIjkAxcC98W7LyY2RGQwcAZwP4CqHk+UIOD6BLCzLweBAMlAqogkAwOBfXHuT9RZIIiNO4DvA74496M7KfCSiKxxS4L0dWOBcuAv7i3A+0QkLd6d6kZXAY/FuxOxpqp7gd8CpTjlb2pU9aX49ir6LBBEmYhcBBxU1TXx7ks3m6+qM3Gqyt4kImfEu0MxlgzMBO5W1RlAHfDD+Hape7i3wZYAf493X2JNRIbgFMgcC+QBaSLy+fj2KvosEETf6cAS957548BZIvJwfLsUe+43J1T1IPAMMDu+PYq5PcAeVV3pvn4SJzAkgguAtap6IN4d6QZnAx+parmqNgJPA/Pi3Keos0AQZap6i6rmq2oBzuXza6ra575BBBKRNBFJ9/8NnAsEnZCor1DV/cBuEZnoLvoEsDmOXepOnyEBbgu5SoG5IjJQRATnv/OWOPcp6npF9VHT4w0HnnE+JyQDj6rqC/HtUrf4BvCIe6ukmL5ZJqUFN9CfA9wY7750B1VdKSJPAmsBL7COPlhuwh4fNcaYBGe3howxJsFZIDDGmARngcAYYxKcBQJjjElwFgiMMSbBWSAwCU1EFgarEBtqeRT290kRmRTwermIdDgpuojkRqM/IpIjIonwaK+JgAUCY7rXJ4FJHTUK4jvAn7u6c1UtB8pE5PSubsv0HRYITI/mjlr+l4hscOvBf9pdfoqIvOEWuXtRRHLd5ctF5E63Xn6RiMx2l88WkXfdAnHvBIwIDrcPD4jIKnf9S9zl14jI0yLygohsF5FfB6xzvYhsc9f5s4j8UUTm4dTo+Y3bvxPc5le47baJyIIQ3bgMeMHddpKI/NY9vo0i8g13eYmI/NI/J4SIzHT/bXaKyFcCtvUP4HPhHr/p+2xksenpzgf2qeqF4JR/FpEU4C7gElUtd4PDz4Hr3HUGqup0t/DdA8AU4ENggap6ReRs4Bc4J9dw/CdOqZDrRCQTWCUir7jvTQdmAA3AVhG5C2gC/h9O7aFa4DVgg6q+IyLLgH+q6pPu8QAkq+psEVkM/Binvk0zERkLHFLVBnfRDUABMN09nqyA5qXusf8e+CtO7asBOCU/7nHbrAZ+FuaxmwRggcD0dJuA20XkVzgn0LdEZArOyf1l90SahFMi2O8xAFV9U0Qy3JN3OvCgiIzHKZmdEkEfzsUpJPhd9/UAYLT796uqWgMgIpuBMUA28IaqVrnL/w5MaGf7T7u/1+Cc4FvLxSl57Xc2cI+qet3jrAp4b5n7exMwSFVrgVoRaRCRTHfOhIM4lTSNASwQmB5OVbeJyExgMfAzEXkVp7rpB6oaamrI1nVTFPhv4HVV/ZSIFADLI+iGAJep6tYWC0Xm4FwJ+DXRuc+Ufxuh1j+KE3wi2ZavVd98Adse4G7TGMByBKaHE5E8oF5VHwZ+g3O7ZSuQI+4cwSKSIiKTA1bz5xHm40wkUgMMxplaEeCaCLvxIvANt/okIjKjg/bvA2eKyBBxZrUKvAVVi3N1EolttLxSeBm40d02rW4NhWMCfbw6rImMBQLT003FuSe/Huf++c9U9ThwOfArEdkArKdljfhjIrIO55749e6yXwO/dJdH+q39v3FuJW0UkQ/c1yG5czP8AlgFvA2UADXu248D33OTzicE30Kb7dUBO0VknLvoPpzyyBvd4/9sZIfDIuBfEa5j+jCrPmr6FBFZDnxXVVfHuR+DVPWI+639GeABVX2mC9v7FHCKqv5XFPr2Jk6i/VBXt2X6BrsiMCY2fuJexRQBH+E8stlpbhAp6WqnRCQH+J0FARPIrgiMMSbB2RWBMcYkOAsExhiT4CwQGGNMgrNAYIwxCc4CgTHGJLj/DyA7Q4sDcmAKAAAAAElFTkSuQmCC\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "tree = DecisionTreeClassifier()\n",
- "tree = tree.fit(X, y)\n",
- "plot_decision(tree, title=\"Decision Tree\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "olympic-peeing",
- "metadata": {},
- "source": [
- "# Train-Test split"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "great-ferry",
- "metadata": {},
- "source": [
- "Let's now evaluate model using [test-train split](https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/) approach\n",
- "\n",
- "This is a common technique for checking model generalization. \n",
- "You train model on some part of the dataset (lets say 67%, or 75%) and that you check if you model generalizes well by prediction on data that left \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "noble-compression",
- "metadata": {},
- "outputs": [],
- "source": [
- "X = iris.data # Use all iris features as predictors\n",
- "y = iris.target"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "studied-professor",
- "metadata": {},
- "source": [
- "Split data randomly using [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)\n",
- "\n",
- "Set `test_size=0.25` to use 25% data for test and 75% for train"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "intelligent-quest",
- "metadata": {},
- "outputs": [],
- "source": [
- "X_train, X_test, y_train, y_test = train_test_split(\n",
- " X, y, test_size=0.25, random_state=117\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "weird-hamburg",
- "metadata": {},
- "outputs": [],
- "source": [
- "# function for printing results\n",
- "def eval_model(clf, X_test, y_test):\n",
- " pred = clf.predict(X_test)\n",
- " accuracy = np.mean(pred == y_test)\n",
- " print(f\"Model accuracy: {accuracy*100:0.2f}%\")\n",
- " df = pd.DataFrame(confusion_matrix(y_test, pred))\n",
- " df.columns = [\"Classified as \" + x for x in iris.target_names]\n",
- " df.index = iris.target_names\n",
- " return df"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "velvet-bench",
- "metadata": {},
- "source": [
- "Train model on `train` data "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "wanted-devil",
- "metadata": {},
- "outputs": [],
- "source": [
- "l_regression = LogisticRegression()\n",
- "l_regression = l_regression.fit(X_train, y_train)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "outdoor-firewall",
- "metadata": {},
- "source": [
- "And evaluate on `test` data "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "several-ballet",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model accuracy: 94.74%\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " Classified as setosa \n",
- " Classified as versicolor \n",
- " Classified as virginica \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " setosa \n",
- " 14 \n",
- " 0 \n",
- " 0 \n",
- " \n",
- " \n",
- " versicolor \n",
- " 0 \n",
- " 8 \n",
- " 0 \n",
- " \n",
- " \n",
- " virginica \n",
- " 0 \n",
- " 2 \n",
- " 14 \n",
- " \n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " Classified as setosa Classified as versicolor \\\n",
- "setosa 14 0 \n",
- "versicolor 0 8 \n",
- "virginica 0 2 \n",
- "\n",
- " Classified as virginica \n",
- "setosa 0 \n",
- "versicolor 0 \n",
- "virginica 14 "
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "eval_model(l_regression, X_test, y_test)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "wound-aircraft",
- "metadata": {},
- "source": [
- "You should see accuracy close to 100% as Iris is quite easy dataset where data can be almost perfectly classified if all 4 features used\n",
- "\n",
- "Using confusion matrics printed above you may also see that all examples of 'setosa' classified as 'setosa',, while there are some errors between 'versicolor'-'virginica' classes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "confident-admission",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.7.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tasks/task_1/Classification_example_with_Iris_dataset.py b/tasks/task_1/Classification_example_with_Iris_dataset.py
deleted file mode 100644
index a642673..0000000
--- a/tasks/task_1/Classification_example_with_Iris_dataset.py
+++ /dev/null
@@ -1,193 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-import pandas as pd
-import numpy as np
-
-from sklearn import datasets
-from matplotlib import pyplot as plt
-
-from sklearn.linear_model import LogisticRegression
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import confusion_matrix
-
-
-# In[2]:
-
-
-get_ipython().run_line_magic('load_ext', 'pycodestyle_magic')
-get_ipython().run_line_magic('flake8_on', '')
-
-
-# # Classification example with Iris dataset
-
-# This example dataset task is in classifying flower based on its features
-
-# In[3]:
-
-
-# This dataset boult in `sklearn` library so you can load it directly
-iris = datasets.load_iris()
-iris_features = iris['feature_names']
-
-
-# Print all flowers and features
-
-# In[4]:
-
-
-print(f"Dataset features:\n{iris['feature_names']}")
-print(f"Dataset classes:\n{iris.target_names}")
-
-
-# Now we should visually analyze the dataset
-#
-# As we are limited by 2D displays and cannot visualize 4d data in a single plot - let's print data 2-axis at a time
-
-# In[5]:
-
-
-for j in [1, 2, 3]:
- for i, class_name in enumerate(iris.target_names):
- sepal_length = iris.data[:, 0][iris.target == i]
- sepal_width = iris.data[:, j][iris.target == i]
- plt.plot(sepal_length, sepal_width, '.', label=class_name)
-
- plt.title("Flowers")
- plt.xlabel(iris_features[0])
- plt.ylabel(iris_features[j])
- plt.legend()
- plt.show()
-
-
-# ## Plotting decision boundaries
-#
-# Decision boundaries allows us to visualize how given classifier thinks data should be splitted into a different classes
-#
-# For this let's focus on first 2 features ('sepal length (cm)', 'sepal width (cm)') to have consistent 2D plot
-
-# In[6]:
-
-
-def plot_decision(clf, title):
- x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
- y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
- xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
- np.arange(y_min, y_max, 0.1))
-
- Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
- Z = Z.reshape(xx.shape)
-
- plt.contourf(xx, yy, Z, alpha=0.4)
-
- for i, class_name in enumerate(iris.target_names):
- sepal_length = iris.data[:, 0][iris.target == i]
- sepal_width = iris.data[:, 1][iris.target == i]
- plt.plot(sepal_length, sepal_width, '.', label=class_name)
-
- plt.title(title)
- plt.xlabel(iris_features[0])
- plt.ylabel(iris_features[j])
- plt.legend()
- plt.show()
-
-
-# In[7]:
-
-
-# Select first 2 features
-X = iris.data[:, [0, 1]]
-y = iris.target
-
-
-# In[8]:
-
-
-l_regression = LogisticRegression()
-l_regression = l_regression.fit(X, y)
-plot_decision(l_regression, title="Log regression")
-
-
-# We can see that `LogisticRegression`model cannot properly divide 'versicolor' and 'virginica' classes based on that 2 features
-#
-# To divide classes properly we need to introduce non-linear models such and Neural Networks or Decision Trees
-
-# In[9]:
-
-
-tree = DecisionTreeClassifier()
-tree = tree.fit(X, y)
-plot_decision(tree, title="Decision Tree")
-
-
-# # Train-Test split
-
-# Let's now evaluate model using [test-train split](https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/) approach
-#
-# This is a common technique for checking model generalization.
-# You train model on some part of the dataset (lets say 67%, or 75%) and that you check if you model generalizes well by prediction on data that left
-#
-
-# In[10]:
-
-
-X = iris.data # Use all iris features as predictors
-y = iris.target
-
-
-# Split data randomly using [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)
-#
-# Set `test_size=0.25` to use 25% data for test and 75% for train
-
-# In[11]:
-
-
-X_train, X_test, y_train, y_test = train_test_split(
- X, y, test_size=0.25, random_state=117
-)
-
-
-# In[12]:
-
-
-# function for printing results
-def eval_model(clf, X_test, y_test):
- pred = clf.predict(X_test)
- accuracy = np.mean(pred == y_test)
- print(f"Model accuracy: {accuracy*100:0.2f}%")
- df = pd.DataFrame(confusion_matrix(y_test, pred))
- df.columns = ["Classified as " + x for x in iris.target_names]
- df.index = iris.target_names
- return df
-
-
-# Train model on `train` data
-
-# In[13]:
-
-
-l_regression = LogisticRegression()
-l_regression = l_regression.fit(X_train, y_train)
-
-
-# And evaluate on `test` data
-
-# In[14]:
-
-
-eval_model(l_regression, X_test, y_test)
-
-
-# You should see accuracy close to 100% as Iris is quite easy dataset where data can be almost perfectly classified if all 4 features used
-#
-# Using confusion matrics printed above you may also see that all examples of 'setosa' classified as 'setosa',, while there are some errors between 'versicolor'-'virginica' classes
-
-# In[ ]:
-
-
-
-
diff --git a/tasks/task_1/README.md b/tasks/task_1/README.md
deleted file mode 100644
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@@ -1,3 +0,0 @@
-# Classification example with Iris dataset
-
-Basic example for visualizing data and classifiers training