{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8d8584aa",
   "metadata": {},
   "source": [
    "# Lesson 19: Regression demonstration\n",
    "\n",
    "Supervised machine learning is the task of learning a mapping from inputs to outputs using labeled training examples. Each example (or observation) pairs a set of input features with a known target value. We will use linear regression to explore the core concepts of model training, evaluation, and regularization, implemented with [scikit-learn](https://scikit-learn.org/stable/); the standard Python library for machine learning.\n",
    "\n",
    "1. **What is regression**\n",
    "    - Regression problems\n",
    "    - Model training\n",
    "\n",
    "2. **Regression metrics**\n",
    "    - Residuals: RSS, MSE, RMSE and MAE\n",
    "    - R-squared\n",
    "\n",
    "3. **Evaluating regression model performance**\n",
    "    - Train-test split\n",
    "    - Overfitting\n",
    "\n",
    "4. **Dealing with overfitting**\n",
    "    - Ridge (L2) penalty\n",
    "    - Lasso (L1) penalty\n",
    "\n",
    "5. **Optimizing regression models**\n",
    "    - Hyperparameter tuning\n",
    "    - Grid search/Randomized search\n",
    "\n",
    "6. **Scikit-learn pipelines example**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f87fdc",
   "metadata": {},
   "source": [
    "## Notebook set up\n",
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d839e96b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b87cd6",
   "metadata": {},
   "source": [
    "## 1. What is regression?\n",
    "\n",
    "A regression model predicts the value of a continuous output variable (label) from one or more input variables (features).\n",
    "\n",
    "### 1.1. Regression problem: circumference of a circle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9742c621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diameter</th>\n",
       "      <th>circumference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.2</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.6</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.8</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.1</td>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.7</td>\n",
       "      <td>27.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.3</td>\n",
       "      <td>35.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.9</td>\n",
       "      <td>37.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   diameter  circumference\n",
       "0       3.0            9.1\n",
       "1       3.2           10.2\n",
       "2       3.6           11.0\n",
       "3       4.8           14.8\n",
       "4       6.1           19.4\n",
       "5       7.2           22.1\n",
       "6       8.7           27.2\n",
       "7      10.0           31.2\n",
       "8      11.3           35.3\n",
       "9      11.9           37.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "url = 'https://media.githubusercontent.com/media/gperdrizet/fullstack-2605/refs/heads/main/data/diameter-circumference.csv'\n",
    "df = pd.read_csv(url)\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93abb9be",
   "metadata": {},
   "source": [
    "### 1.2. Knowledge based regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "28751100",
   "metadata": {},
   "outputs": [],
   "source": [
    "def regression_model(diameters: pd.Series) -> pd.Series:\n",
    "    '''Calculates the circumference of a circle given its diameter using the formula C = π * D.\n",
    "    takes a list like object of diameters and returns a list like object of circumferences'''\n",
    "\n",
    "    circumferences = np.pi * diameters\n",
    "\n",
    "    return circumferences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2b775b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "circumference_predictions = regression_model(df['diameter'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d2a70546",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Knowledge based model predictions')\n",
    "plt.scatter(df['diameter'], df['circumference'], color='black', label='Data')\n",
    "plt.plot(df['diameter'], circumference_predictions, color='red', label='Model predictions')\n",
    "plt.xlabel('Diameter (inches)')\n",
    "plt.ylabel('Circumference (inches)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba56180a",
   "metadata": {},
   "source": [
    "### 1.2. Regression model training\n",
    "\n",
    "A linear regression model predicts the output $y$ as a weighted sum of the input features:\n",
    "\n",
    "$$y = mx + b$$\n",
    "\n",
    "| Symbol | Name | Description |\n",
    "|--------|------|-------------|\n",
    "| $y$ | Predicted value | The model's estimate of the target |\n",
    "| $m$ | Slope | Change in $y$ per unit increase in $x$ |\n",
    "| $x$ | Feature | The input variable |\n",
    "| $b$ | Intercept | Value of $y$ when $x = 0$ |\n",
    "\n",
    "In this example $x$ is the diameter and $y$ is the predicted circumference, so the model learns $m \\approx \\pi$.\n",
    "\n",
    "In the ML literature the same model is written using different symbols that generalize naturally to multiple features:\n",
    "\n",
    "$$\\hat{y} = \\mathbf{w}^\\top \\mathbf{x} + b$$\n",
    "\n",
    "or, written out term by term:\n",
    "\n",
    "$$\\hat{y} = w_1 x_1 + w_2 x_2 + \\cdots + w_n x_n + b = \\sum_{j=1}^{n} w_j x_j + b$$\n",
    "\n",
    "| Symbol | Name | Description |\n",
    "|--------|------|-------------|\n",
    "| $\\hat{y}$ | Prediction | Estimated target value — the hat $\\hat{\\phantom{y}}$ signals \"predicted, not observed\" |\n",
    "| $\\mathbf{w}$ | Weight vector | Learned parameters, one per feature |\n",
    "| $w_j$ | Weight | Contribution of feature $j$ to the prediction |\n",
    "| $\\mathbf{x}$ | Feature vector | All input features for a single sample |\n",
    "| $x_j$ | Feature value | Value of the $j$-th input feature |\n",
    "| $b$ | Bias | Intercept term; sometimes written $w_0$ with a dummy feature $x_0 = 1$ |\n",
    "| $n$ | | Number of features |\n",
    "\n",
    "The correspondence with the slope-intercept form is: $m \\leftrightarrow w_1$, $x \\leftrightarrow x_1$, $b \\leftrightarrow b$, and $y \\leftrightarrow \\hat{y}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b405eb0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Create an instance of sklearn's LinearRegression() class\n",
    "model = LinearRegression()\n",
    "\n",
    "# Train the model\n",
    "result = model.fit(df['diameter'].to_frame(), df['circumference'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b655e91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model predictions:\n",
      "\n",
      "9.26\n",
      "9.89\n",
      "11.14\n",
      "14.91\n",
      "19.0\n",
      "22.45\n",
      "27.16\n",
      "31.24\n",
      "35.33\n",
      "37.21\n"
     ]
    }
   ],
   "source": [
    "# Get predicted circumferences from diameters using the fitted model\n",
    "predicted_circumference = model.predict(df['diameter'].to_frame())\n",
    "\n",
    "print('Model predictions:\\n')\n",
    "print('\\n'.join(str(round(c, 2)) for c in predicted_circumference))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78c3d7ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Regression model predictions')\n",
    "plt.scatter(df['diameter'], df['circumference'], color='black', label='Data')\n",
    "plt.plot(df['diameter'], predicted_circumference, color='teal', label='Model predictions')\n",
    "plt.xlabel('Diameter (inches)')\n",
    "plt.ylabel('Circumference (inches)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "99ba70e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: 3.141\n"
     ]
    }
   ],
   "source": [
    "print(f'Slope: {model.coef_[0]:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b8bdab",
   "metadata": {},
   "source": [
    "The model learns Pi from the data! In this case we knew the relationship between x and y ahead of time, but we don't need to. This is the core concept of supervised machine learning, the model 'learns' the relationship between the input and output from the data. Importantly - it can do this with much more complex relationships where the output depends on the values of many input features and their interactions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda636d3",
   "metadata": {},
   "source": [
    "## 2. Regression metrics\n",
    "\n",
    "### 2.1. Residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "041c197e",
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals = df['circumference'] - predicted_circumference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6ee95d61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Regression model predictions')\n",
    "plt.scatter(df['diameter'][:3], df['circumference'][:3], color='black', label='Data')\n",
    "plt.plot(df['diameter'][:3], predicted_circumference[:3], color='teal', label='Model predictions')\n",
    "\n",
    "# Add vertical dotted lines for residuals\n",
    "for i in range(3):\n",
    "    plt.plot(\n",
    "        [df['diameter'].iloc[i], df['diameter'].iloc[i]], \n",
    "        [predicted_circumference[i], df['circumference'].iloc[i]], \n",
    "        linestyle=':', color='purple', label='Fit residuals' if i == 0 else None\n",
    "    )\n",
    "\n",
    "plt.xlim(2.8, 3.8)\n",
    "plt.ylim(8.5, 11.5)\n",
    "plt.xlabel('Diameter (inches)')\n",
    "plt.ylabel('Circumference (inches)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "687208ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RSS:  0.4547 inches²\n",
      "MSE:  0.0455 inches²\n",
      "RMSE: 0.2132 inches\n",
      "MAE:  0.1683 inches\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import (\n",
    "    mean_squared_error,\n",
    "    root_mean_squared_error,\n",
    "    mean_absolute_error\n",
    ")\n",
    "\n",
    "# Calculate metrics using sklearn\n",
    "MSE = mean_squared_error(df['circumference'], predicted_circumference)\n",
    "RMSE = root_mean_squared_error(df['circumference'], predicted_circumference)\n",
    "MAE = mean_absolute_error(df['circumference'], predicted_circumference)\n",
    "\n",
    "# RSS is not directly available in sklearn, but can be calculated from MSE\n",
    "n = len(df)\n",
    "RSS = MSE * n\n",
    "\n",
    "print(f'RSS:  {RSS:.4f} inches²')\n",
    "print(f'MSE:  {MSE:.4f} inches²')\n",
    "print(f'RMSE: {RMSE:.4f} inches')\n",
    "print(f'MAE:  {MAE:.4f} inches')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4bec4a",
   "metadata": {},
   "source": [
    "Each metric summarizes the residuals differently:\n",
    "\n",
    "- **RSS**: square every residual, then add them up. Squaring means large errors are penalized much more than small ones, and the result grows with dataset size.\n",
    "- **MSE**: divide RSS by the number of data points ($n$), giving the average squared error per prediction. Comparable across datasets of different sizes, but in squared units.\n",
    "- **RMSE**: take the square root of MSE to bring the error back into the same units as the target (inches here). The most common summary metric for regression.\n",
    "- **MAE**: take the absolute value of every residual, then average them. Treats all errors equally regardless of size, making it more robust to outliers than RMSE."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb3d882f",
   "metadata": {},
   "source": [
    "### 2.2. R-squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5d3be23f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "circumference_variance = np.var(df['circumference'])\n",
    "residual_variance = np.var(residuals)\n",
    "\n",
    "fig, axs = plt.subplots(2,2, figsize=(8,8))\n",
    "axs = axs.flatten()\n",
    "\n",
    "axs[0].set_title('Linear regression')\n",
    "axs[0].scatter(df['diameter'], df['circumference'], color='black', label='Data')\n",
    "axs[0].plot(df['diameter'], circumference_predictions, color='teal', label='Model predictions')\n",
    "axs[0].set_ylim(5, 45)\n",
    "axs[0].set_xlabel('Diameter')\n",
    "axs[0].set_ylabel('Circumference')\n",
    "axs[0].legend(loc='upper left')\n",
    "\n",
    "axs[1].set_title('Fit residuals')\n",
    "axs[1].scatter(df['diameter'], residuals, color='black')\n",
    "axs[1].set_ylim(-20, 20)\n",
    "axs[1].set_xlabel('Diameter')\n",
    "axs[1].set_ylabel('True - predicted circumference')\n",
    "\n",
    "bins = np.linspace(5, 45, 10)\n",
    "axs[2].set_title(f'Original circumference distribution\\nvariance={circumference_variance:.3f}')\n",
    "axs[2].hist(df['circumference'], color='grey', edgecolor='black', bins = bins, orientation='horizontal')\n",
    "axs[2].set_xlabel('Count')\n",
    "axs[2].set_ylabel('Circumference')\n",
    "\n",
    "bins = np.linspace(-20, 20, 10)\n",
    "axs[2].set_title(f'Original circumference distribution\\nvariance={residual_variance:.3f}')\n",
    "axs[3].set_title('Fit residual distribution')\n",
    "axs[3].hist(residuals, color='grey', edgecolor='black', bins=bins, orientation='horizontal')\n",
    "axs[3].set_xlabel('Count')\n",
    "axs[3].set_ylabel('True - predicted circumference')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "370dc7b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of variance explained: 0.9995\n"
     ]
    }
   ],
   "source": [
    "variance_explained = 1 - (residual_variance / circumference_variance)\n",
    "print(f'Fraction of variance explained: {variance_explained:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "74e1e17f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scikit-learn R-squared: 0.9995\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "sk_r2 = r2_score(df['circumference'], predicted_circumference)\n",
    "print(f'Scikit-learn R-squared: {sk_r2:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a84d95e5",
   "metadata": {},
   "source": [
    "## 3. Evaluating regression model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a0446b1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Columns: 465 entries, area to distance_to_water\n",
      "dtypes: float64(465)\n",
      "memory usage: 17.7 MB\n"
     ]
    }
   ],
   "source": [
    "url = 'https://gperdrizet.github.io/FSA_devops/assets/data/unit3/distance_to_water_preprocessed.csv'\n",
    "df = pd.read_csv(url)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5ad3b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>quality</th>\n",
       "      <th>area_log</th>\n",
       "      <th>area_sqrt</th>\n",
       "      <th>area_power</th>\n",
       "      <th>area_quantile_uniform</th>\n",
       "      <th>...</th>\n",
       "      <th>hash_6 hash_7</th>\n",
       "      <th>hash_6 hash_8</th>\n",
       "      <th>hash_6 hash_9</th>\n",
       "      <th>hash_7^2</th>\n",
       "      <th>hash_7 hash_8</th>\n",
       "      <th>hash_7 hash_9</th>\n",
       "      <th>hash_8^2</th>\n",
       "      <th>hash_8 hash_9</th>\n",
       "      <th>hash_9^2</th>\n",
       "      <th>distance_to_water</th>\n",
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       "      <th>1</th>\n",
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       "      <td>-0.601307</td>\n",
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       "      <td>1.492859</td>\n",
       "      <td>1.836667</td>\n",
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       "      <td>1.589323</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.115644</td>\n",
       "      <td>-0.840557</td>\n",
       "      <td>-0.302526</td>\n",
       "      <td>-1.364800</td>\n",
       "      <td>0.32744</td>\n",
       "      <td>-0.174431</td>\n",
       "      <td>-0.349037</td>\n",
       "      <td>-0.276483</td>\n",
       "      <td>-0.382535</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.318311</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.327001</td>\n",
       "      <td>13.430377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.214393</td>\n",
       "      <td>-1.053487</td>\n",
       "      <td>1.214892</td>\n",
       "      <td>1.284614</td>\n",
       "      <td>-0.209732</td>\n",
       "      <td>0.32744</td>\n",
       "      <td>0.498002</td>\n",
       "      <td>0.371657</td>\n",
       "      <td>0.434467</td>\n",
       "      <td>0.560494</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-0.327001</td>\n",
       "      <td>37.928220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.348350</td>\n",
       "      <td>0.665536</td>\n",
       "      <td>1.499702</td>\n",
       "      <td>-1.150032</td>\n",
       "      <td>-1.313338</td>\n",
       "      <td>0.32744</td>\n",
       "      <td>0.597342</td>\n",
       "      <td>0.494455</td>\n",
       "      <td>0.548238</td>\n",
       "      <td>0.692516</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "       area  temperature  humidity  wind_speed  rainfall  quality  area_log  \\\n",
       "0  1.049357     2.109251  0.809476    0.744979 -1.446606 -1.90156  1.021565   \n",
       "1  2.145387    -1.237192  0.751604    0.854545 -0.601307  0.32744  1.492859   \n",
       "2 -0.468882     0.115644 -0.840557   -0.302526 -1.364800  0.32744 -0.174431   \n",
       "3  0.214393    -1.053487  1.214892    1.284614 -0.209732  0.32744  0.498002   \n",
       "4  0.348350     0.665536  1.499702   -1.150032 -1.313338  0.32744  0.597342   \n",
       "\n",
       "   area_sqrt  area_power  area_quantile_uniform  ...  hash_6 hash_7  \\\n",
       "0   1.073922    1.061607               1.226256  ...            0.0   \n",
       "1   1.836667    1.688007               1.589323  ...            0.0   \n",
       "2  -0.349037   -0.276483              -0.382535  ...           -0.0   \n",
       "3   0.371657    0.434467               0.560494  ...            0.0   \n",
       "4   0.494455    0.548238               0.692516  ...            0.0   \n",
       "\n",
       "   hash_6 hash_8  hash_6 hash_9  hash_7^2  hash_7 hash_8  hash_7 hash_9  \\\n",
       "0            0.0            0.0 -0.318691            0.0            0.0   \n",
       "1            0.0            0.0 -0.318691            0.0            0.0   \n",
       "2            0.0            0.0  3.000000           -0.0           -0.0   \n",
       "3            0.0            0.0 -0.318691            0.0            0.0   \n",
       "4            0.0            0.0 -0.318691            0.0            0.0   \n",
       "\n",
       "   hash_8^2  hash_8 hash_9  hash_9^2  distance_to_water  \n",
       "0  3.000000            0.0 -0.327001          95.888293  \n",
       "1 -0.318311            0.0 -0.327001          32.734564  \n",
       "2 -0.318311            0.0 -0.327001          13.430377  \n",
       "3 -0.318311            0.0 -0.327001          37.928220  \n",
       "4 -0.318311            0.0 -0.327001         577.643954  \n",
       "\n",
       "[5 rows x 465 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "579f25fa",
   "metadata": {},
   "source": [
    "### 3.1. Train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0cbb74b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataframe shape: (5000, 465)\n",
      "Training dataframe shape: (3750, 465)\n",
      "Testing dataframe shape: (1250, 465)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_df, test_df = train_test_split(df)\n",
    "\n",
    "print(f'Original dataframe shape: {df.shape}')\n",
    "print(f'Training dataframe shape: {train_df.shape}')\n",
    "print(f'Testing dataframe shape: {test_df.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3edfba29",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-1.755434</td>\n",
       "      <td>1.862404</td>\n",
       "      <td>0.015015</td>\n",
       "      <td>1.44194</td>\n",
       "      <td>0.394179</td>\n",
       "      <td>0.248139</td>\n",
       "      <td>0.318063</td>\n",
       "      <td>0.414276</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-0.327001</td>\n",
       "      <td>67.776354</td>\n",
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       "    <tr>\n",
       "      <th>195</th>\n",
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       "      <th>2372</th>\n",
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      "text/plain": [
       "          area  temperature  humidity  wind_speed  rainfall  quality  \\\n",
       "2855  2.254697    -0.597214  0.801857   -1.307860  0.094637  0.32744   \n",
       "1178 -0.068005     1.826612  0.727853   -1.247204  1.347683  0.32744   \n",
       "4433  0.084802    -1.436764 -1.755434    1.862404  0.015015  1.44194   \n",
       "195   1.208175     2.158445  0.056115   -0.215953  1.492728 -0.78706   \n",
       "2372 -0.628039     0.755211 -1.415713   -0.379636  0.205195 -0.78706   \n",
       "\n",
       "      area_log  area_sqrt  area_power  area_quantile_uniform  ...  \\\n",
       "2855  1.531775   1.905947    1.742546               1.603443  ...   \n",
       "1178  0.260186   0.095706    0.171522               0.223567  ...   \n",
       "4433  0.394179   0.248139    0.318063               0.414276  ...   \n",
       "195   1.101242   1.193507    1.163215               1.307456  ...   \n",
       "2372 -0.396722  -0.549977   -0.490346              -0.632159  ...   \n",
       "\n",
       "      hash_6 hash_7  hash_6 hash_8  hash_6 hash_9  hash_7^2  hash_7 hash_8  \\\n",
       "2855            0.0            0.0            0.0 -0.318691            0.0   \n",
       "1178            0.0            0.0            0.0 -0.318691            0.0   \n",
       "4433           -0.0           -0.0           -0.0 -0.318691            0.0   \n",
       "195             0.0            0.0            0.0 -0.318691            0.0   \n",
       "2372            0.0            0.0           -0.0 -0.318691            0.0   \n",
       "\n",
       "      hash_7 hash_9  hash_8^2  hash_8 hash_9  hash_9^2  distance_to_water  \n",
       "2855            0.0 -0.318311            0.0 -0.327001         181.512517  \n",
       "1178            0.0 -0.318311            0.0 -0.327001         130.971723  \n",
       "4433            0.0 -0.318311            0.0 -0.327001          67.776354  \n",
       "195             0.0 -0.318311            0.0 -0.327001         105.835494  \n",
       "2372           -0.0 -0.318311           -0.0  3.000000         164.903483  \n",
       "\n",
       "[5 rows x 465 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e3f0f025",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training RMSE: 93.1\n"
     ]
    }
   ],
   "source": [
    "# Fit linear model on training data\n",
    "model = LinearRegression()\n",
    "\n",
    "fit_result = model.fit(train_df.drop(columns='distance_to_water'), train_df['distance_to_water'])\n",
    "\n",
    "train_predictions = model.predict(\n",
    "    train_df.drop(columns='distance_to_water')\n",
    ")\n",
    "\n",
    "train_rmse = root_mean_squared_error(train_df['distance_to_water'], train_predictions)\n",
    "print(f'Training RMSE: {train_rmse:.1f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a2315af2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing RMSE: 109.4\n"
     ]
    }
   ],
   "source": [
    "# Used pre-fitted linear model to make predictions for test set features\n",
    "test_predictions = model.predict(\n",
    "    test_df.drop(columns='distance_to_water')\n",
    ")\n",
    "\n",
    "test_rmse = root_mean_squared_error(test_df['distance_to_water'], test_predictions)\n",
    "print(f'Testing RMSE: {test_rmse:.1f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ead84f10",
   "metadata": {},
   "source": [
    "### 3.2. Cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "59dbb06f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean cross-validation RMSE: 105.2 ± 3.7\n",
      "95% CI: ± 101.5 to 108.9\n",
      "Test RMSE: 109.4\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Make a linear regression instance\n",
    "model = LinearRegression()\n",
    "\n",
    "# Run cross-validation scoring\n",
    "cv_scores = cross_val_score(\n",
    "    model,\n",
    "    df.drop(columns='distance_to_water'),\n",
    "    df['distance_to_water'],\n",
    "    scoring='neg_root_mean_squared_error',\n",
    "    n_jobs=5,\n",
    "    cv=5\n",
    ")\n",
    "\n",
    "# Need to flip the sign - cross-validation used negative RMSE \n",
    "cv_scores = -cv_scores\n",
    "\n",
    "# Get mean and standard deviation, construct 95% CI\n",
    "rmse_mean = np.mean(cv_scores)\n",
    "rmse_std = np.std(cv_scores)\n",
    "moe_95 = 1.96 * rmse_std / np.sqrt(len(cv_scores))\n",
    "\n",
    "print(f'Mean cross-validation RMSE: {rmse_mean:.1f} ± {moe_95:.1f}')\n",
    "print(f'95% CI: ± {rmse_mean - moe_95:.1f} to {rmse_mean + moe_95:.1f}')\n",
    "print(f'Test RMSE: {test_rmse:.1f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "03dd9fd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pXmpqKrZu3fpA7ZU9RuWXX37BiRMnyi0v+3l58cUXERMTg6ioqHI12dnZKCkpeaDt05OBZ5CIHkDDhg3xzTffYOjQofD19dW5k/Yff/yBzZs3Y+TIkfdtZ8GCBdizZw86d+6MN954AyYmJvj8889RWFiIxYsXS3XNmjVD9+7d4efnB3t7e5w4cQI//vijNPj377//Ro8ePfDiiy+iWbNmMDExwZYtW5CVlYVhw4bdtx+mpqYYPHgwvvvuO+Tm5mLp0qU6y21sbKRxTcXFxXjqqafw22+/ISUlRb8Dd9fYsWOxcuVKjBgxArGxsXBxccGGDRtgYWGhU+fj44OGDRtixowZuHLlCmxsbPDTTz9VOPanLPBNmTIFQUFBMDY2rnTfq3vca4K5uTl2796N0NBQdOjQAbt27cKvv/6Kt99+W7p01q1bN4wfPx4RERGIi4tDr169YGpqisTERGzevBmffPKJzoB2ffTr1w8BAQF45513kJqaitatW+O3337Dtm3bEBYWpjMY+mHp27cvPv74Y/Tu3RsvvfQSrl69ilWrVqFRo0Y6Y+n0sWjRIvz222/o1q0bxo0bB19fX2RkZGDz5s04cuQI7OzsMHPmTGzfvh3PP/88Ro4cCT8/P+Tm5uLMmTP48ccfkZqaCgcHhxreW3psGOS7c0SPib///luMHTtWeHp6CjMzM2FtbS2effZZ8emnn4qCggKpDoCYOHFihW2cPHlSBAUFCSsrK2FhYSECAgLEH3/8oVOzYMEC0b59e2FnZydUKpXw8fERCxcuFEVFRUIIIa5fvy4mTpwofHx8hKWlpbC1tRUdOnTQ+Rr9/ezZs0cAEAqFQly6dKnc8suXL4tBgwYJOzs7YWtrK1544QWRnp5e7iv01fmavxBCXLx4UfTv319YWFgIBwcHMXXqVOlr7fd+zf/8+fMiMDBQWFlZCQcHBzF27Fjpa99r166V6kpKSsTkyZOFo6OjUCgUOl/5l/dRiOod97J9kX+V/MCBA+X6WZHQ0FBhaWkpkpOTRa9evYSFhYVwcnISc+bMEaWlpeXqv/jiC+Hn5ydUKpWwtrYWLVu2FLNmzRLp6elSjYeHR6W3fKjoa/5CCHH79m0xbdo04erqKkxNTUXjxo3FkiVLdL6yL0TlP6eVHYc5c+YIANLtCuT7fa+vv/5aNG7cWCiVSuHj4yPWrl0rrV+dPnh4eIjQ0FCdeRcvXhQjRowQjo6OQqlUCm9vbzFx4kRRWFios+/h4eGiUaNGwszMTDg4OIhOnTqJpUuXSu8foooohKihUYZERKRj5MiR+PHHH5GTk2PorhCRnjgGiYiIiEiGAYmIiIhIhgGJiIiISIZjkIiIiIhkeAaJiIiISIYBiYiIiEiGN4p8QFqtFunp6bC2tq72Iw6IiIjIsIQQuH37NlxdXWFkVPl5IgakB5Senl6jz2siIiKiR+fSpUt4+umnK13OgPSAyh76eOnSpRp/bhMRERE9HBqNBm5ubjoPb64IA9IDKrusZmNjw4BERERUx9xveAwHaRMRERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyRg0IB06dAj9+vWDq6srFAoFtm7dqrP8559/Rq9evVC/fn0oFArExcWVa6OgoAATJ05E/fr1YWVlhZCQEGRlZVW5XSEEZs+eDRcXF6hUKgQGBiIxMbEG94yIiIjqMoMGpNzcXLRu3RqrVq2qdHnnzp3x4YcfVtrGtGnT8Msvv2Dz5s04ePAg0tPTMXjw4Cq3u3jxYqxYsQKfffYZjh07BktLSwQFBaGgoOBf7Q8RERE9HhRCCGHoTgB3bvm9ZcsWDBw4sNyy1NRUeHl54a+//kKbNm2k+Wq1Go6Ojvjmm28wZMgQAMCFCxfg6+uLmJgYdOzYsVxbQgi4urriv//9L2bMmCG14+TkhMjISAwbNqxa/dVoNLC1tYVareajRoiIiOqI6v7+rtNjkGJjY1FcXIzAwEBpno+PD9zd3RETE1PhOikpKcjMzNRZx9bWFh06dKh0HQAoLCyERqPReREREdHjqU4/rDYzMxNmZmaws7PTme/k5ITMzMxK1ymrqe46ABAREYF58+b9uw4T0UOTl5eHCxcu1Ehb+fn5SE1NhaenJ1QqVY206ePjAwsLixppi4gevjodkB6l8PBwTJ8+XZrWaDRwc3MzYI+I6F4XLlyAn5+fobtRqdjYWLRt29bQ3SCiaqrTAcnZ2RlFRUXIzs7WOYuUlZUFZ2fnStcpq3FxcdFZ597xTXJKpRJKpbJG+k1ENc/HxwexsbE10lZ8fDxeeeUVbNy4Eb6+vjXSpo+PT420Q0SPRp0OSH5+fjA1NcW+ffsQEhICAEhISEBaWhr8/f0rXMfLywvOzs7Yt2+fFIg0Gg2OHTuGCRMmPKquE1ENs7CwqPEzNL6+vjzrQ/SEMmhAysnJQVJSkjSdkpKCuLg42Nvbw93dHTdv3kRaWhrS09MB3Ak/wJ2zQM7OzrC1tcXo0aMxffp02Nvbw8bGBpMnT4a/v7/ON9h8fHwQERGBQYMGQaFQICwsDAsWLEDjxo3h5eWF9957D66urhV+g46IiIiePAYNSCdOnEBAQIA0XTbGJzQ0FJGRkdi+fTtGjRolLS/7Cv6cOXMwd+5cAMCyZctgZGSEkJAQFBYWIigoCKtXr9bZTkJCAtRqtTQ9a9Ys5ObmYty4ccjOzkbnzp2xe/dumJubP6xdJSIiojqk1twHqa7hfZCIHl8nT56En58fB1YTPYaeiPsgERERET0MDEhEREREMgxIRERERDIMSEREREQydfo+SLVBUW4RioyLDN0NojotOSkZt3NuG7obkoQLCTCFKc79dQ4l+SWG7o7E2soaDRs1NHQ3iOq0otzq/c7mt9geUNko+LfwFszB2wMQERHVBQUowAf4gN9iIyIiItIXzyA9oLIzSNfSr/E+SET/QlxcHDp37owjR45U+TzEJx2PE1HN0Gg0cHR1vO8ZJI5B+pfMLM1gZmlm6G4Q1VkmKhMUoxgmKhO+l6rA40RUM8xKq/f+4SU2IiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZfovtX8otKoJxUfm7chobGcHcxESnrjJGCgVUpqYPVJtXXIzK7tSgUChg8YC1+cXF0FZxBwhLM7MHqi0oKUGpVlsjtRamplAoFACAwpISlNRQrcrUFEZ3a4tKS1FcWlojteYmJjA2MtK7tri0FEVV1CpNTGDyALUlWi0KSyq/S7SZsTFMjY31ri3ValFQRa2psTHM7qnNLykBTE2RX1JS7mf/3lqtEMgvLq5Wu/erNTEygvLu+1MIgbwaqtXnfa/vZ8S9+BlRvVp+RtxR1z8jqlurz2dEdTAg/UuuH30EmJe/k3afxo3x60svSdMNli6t9IO1m4cHokeOlKY9P/kE1/PyKqxt5+qK42PHStPNVq3CRbW6wtpmjo4498Yb0vQzX36J89euVVjrYWuL1LAwabprZCROpKdXWOtgYYFrM2dK08GbNuHgxYsV1lqYmiL37bel6ZAffsDOxMQKawFAzJkj/fvVLVvw4/nzldbmhIdLH5bjd+zAulOnKq29OmMGHC0tAQDTo6Kw+sSJSmtTpk6Fp50dAOCdffuwNCam0tqzEyageYMGAIBFhw9j3sGDldb+OWYMnnnqKQDAJ0ePYtbevZXWHggNRXdPTwDAF7GxmLRrV6W1O4YPR98mTQAAm86cwaht2yqt/WHIELzQvDkAYEt8PF788cdKa9cOGICRd++3E5WUhOe//bbS2pXBwZjYvj0A4HBaGgLWrau0dnFgIGY++ywA4GRGBjrv2gW8886d/8r2c063bpjbvTsAIP7aNbRYs6bSdmf4+2NJr14AgDS1Gl6ffFJp7Rvt2mFV374AgOt5eWiwdGmltaGtWyNy4EAAdwKEVUREpbVDmjXD5hdekKarqtX3M+LjVq2kaX5G3MHPCE8Aj/9nRPuvvqq09kE/I6qDl9iIiIiIZHgn7QdUdift9GsV30mbl9gqruXpc54+l58+P3riRKV3iOYltjuMFArEnzkDPz8/xMbGommLFlXW8jPiDn5G3FHXPyNq+hJb2e9v3kn7IbM0M9N5w1ZVp0+b1XXvB1ZN1qoeUu29vxBqslZpYgLlQ6g10+Oa9cOqNb3ng6Uma02MjGBSzZ81fWqNjYyq/TNsbGQElYkJUFwMlYlJlesZKRTVblefWsVDqgUe3vuenxH61/IzQv/a2vIZ8TDe99Vqr8ZaIiIiInpMMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyRg0IB06dAj9+vWDq6srFAoFtm7dqrNcCIHZs2fDxcUFKpUKgYGBSExMlJZHR0dDoVBU+Dp+/Hil2+3evXu5+tdff/1h7SYRERHVMQYNSLm5uWjdujVWrVpV4fLFixdjxYoV+Oyzz3Ds2DFYWloiKCgIBQUFAIBOnTohIyND5zVmzBh4eXmhXbt2VW577NixOustXry4xvePiIiI6iYTQ248ODgYwcHBFS4TQmD58uV49913MWDAAADA+vXr4eTkhK1bt2LYsGEwMzODs7OztE5xcTG2bduGyZMnQ6FQVLltCwsLnXWJiIiIytTaMUgpKSnIzMxEYGCgNM/W1hYdOnRATExMhets374dN27cwKhRo+7b/qZNm+Dg4IAWLVogPDwceXl5VdYXFhZCo9HovIiIiOjxZNAzSFXJzMwEADg5OenMd3JykpbJff311wgKCsLTTz9dZdsvvfQSPDw84OrqitOnT+PNN99EQkICfv7550rXiYiIwLx58/TcCyIiIqqLam1A0tfly5cRFRWFH3744b6148aNk/7dsmVLuLi4oEePHkhOTkbDhg0rXCc8PBzTp0+XpjUaDdzc3P59x4mIiKjWqbWX2MrGB2VlZenMz8rKqnDs0Nq1a1G/fn30799f72116NABAJCUlFRpjVKphI2Njc6LiIiIHk+1NiB5eXnB2dkZ+/btk+ZpNBocO3YM/v7+OrVCCKxduxYjRoyAqamp3tuKi4sDALi4uPyrPhMREdHjwaABKScnB3FxcVJASUlJQVxcHNLS0qBQKBAWFoYFCxZg+/btOHPmDEaMGAFXV1cMHDhQp539+/cjJSUFY8aMKbeNK1euwMfHB3/++ScAIDk5GfPnz0dsbCxSU1Oxfft2jBgxAl27dkWrVq0e9i4TERFRHWDQMUgnTpxAQECANF02xic0NBSRkZGYNWsWcnNzMW7cOGRnZ6Nz587YvXs3zM3Nddr5+uuv0alTJ/j4+JTbRnFxMRISEqRvqZmZmWHv3r1Yvnw5cnNz4ebmhpCQELz77rsPcU+JiIioLlEIIYShO1EXaTQa2NraQq1WczwS0b9w8uRJ+Pn5ITY2Fm3btjV0d2otHieimlHd39+1dgwSERERkaEwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyRg0IB06dAj9+vWDq6srFAoFtm7dqrNcCIHZs2fDxcUFKpUKgYGBSExM1Knx9PSEQqHQeX3wwQdVbregoAATJ05E/fr1YWVlhZCQEGRlZdX07hEREVEdZdCAlJubi9atW2PVqlUVLl+8eDFWrFiBzz77DMeOHYOlpSWCgoJQUFCgU/f+++8jIyNDek2ePLnK7U6bNg2//PILNm/ejIMHDyI9PR2DBw+usf0iIiKius3EkBsPDg5GcHBwhcuEEFi+fDneffddDBgwAACwfv16ODk5YevWrRg2bJhUa21tDWdn52ptU61W4+uvv8Y333yD5557DgCwdu1a+Pr64ujRo+jYseO/3CsiIiKq62rtGKSUlBRkZmYiMDBQmmdra4sOHTogJiZGp/aDDz5A/fr18Z///AdLlixBSUlJpe3GxsaiuLhYp10fHx+4u7uXa/dehYWF0Gg0Oi8iIiJ6PBn0DFJVMjMzAQBOTk46852cnKRlADBlyhS0bdsW9vb2+OOPPxAeHo6MjAx8/PHHlbZrZmYGOzu7KtuVi4iIwLx58x5wb4iIiKguqbUBqbqmT58u/btVq1YwMzPD+PHjERERAaVSWWPbCQ8P19mWRqOBm5tbjbVPREREtUetvcRWNqZI/u2yrKysKscbdejQASUlJUhNTa203aKiImRnZ+vVrlKphI2Njc6LiIiIHk+1NiB5eXnB2dkZ+/btk+ZpNBocO3YM/v7+la4XFxcHIyMjNGjQoMLlfn5+MDU11Wk3ISEBaWlpVbZLRERETw6DXmLLyclBUlKSNJ2SkoK4uDjY29vD3d0dYWFhWLBgARo3bgwvLy+89957cHV1xcCBAwEAMTExOHbsGAICAmBtbY2YmBhMmzYNr7zyCurVqwcAuHLlCnr06IH169ejffv2sLW1xejRozF9+nTY29vDxsYGkydPhr+/P7/BRkRERAAMHJBOnDiBgIAAabpsjE9oaCgiIyMxa9Ys5ObmYty4ccjOzkbnzp2xe/dumJubA7hz2eu7777D3LlzUVhYCC8vL0ybNk1nrFBxcTESEhKQl5cnzVu2bBmMjIwQEhKCwsJCBAUFYfXq1Y9or4mIiKi2UwghhKE7URdpNBrY2tpCrVZzPBLRv3Dy5En4+fkhNjYWbdu2NXR3ai0eJ6KaUd3f37V2DBIRERGRoTAgEREREcnU+fsgEVHd52ylgCr7byCdf7NVRpX9N5ytFIbuBtETgwGJiAxuvJ8ZfA+NBw4Zuie1ly/uHCciejQYkIjI4D6PLcLQ2ZHw9fExdFdqrfgLF/D5Ry+hv6E7QvSEYEAiIoPLzBHIt2sCuLYxdFdqrfxMLTJz+KVjokeFF/yJiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZEwM3QEierLl5eUBAE6ePGngnvy//Px8pKamwtPTEyqVytDdAQDEx8cbugtETxQGJCIyqAsXLgAAxo4da+Ce1A3W1taG7gLRE4EBiYgMauDAgQAAHx8fWFhYGLYzd8XHx+OVV17Bxo0b4evra+juSKytrdG4cWNDd4PoicCAREQG5eDggDFjxhi6GxXy9fVF27ZtDd0NIjIADtImIiIikmFAIiIiIpJhQCIiIiKSYUAiIiIikmFAIiIiIpLht9gestLSUhQXFxu6G0R1jpmZGYyM+DccERkGA9JDIoRAZmYmsrOzDd0VojrJyMgIXl5eMDMzM3RXiOgJxID0kJSFowYNGsDCwgIKhcLQXSKqM7RaLdLT05GRkQF3d3e+f4jokWNAeghKS0ulcFS/fn1Dd4eoTnJ0dER6ejpKSkpgampq6O4Q0ROGF/gfgrIxR7XlsQlEdVHZpbXS0lID94SInkQMSA8RLwsQPTi+f4jIkBiQiIiIiGQYkIiIiIhkGJBIcvv2bYSFhcHDwwMqlQqdOnXC8ePHdWpGjhwJhUKh8+rdu7e0vLCwEK+++ipsbGzQpEkT7N27V2f9JUuWYPLkydXqj0ajwTvvvAMfHx+Ym5vD2dkZgYGB+PnnnyGEAAB0794dYWFh/27HiYiIZPgtNpKMGTMGZ8+exYYNG+Dq6oqNGzciMDAQ58+fx1NPPSXV9e7dG2vXrpWmlUql9O8vvvgCsbGxiImJwa5du/DSSy8hKysLCoUCKSkp+PLLL3HixIn79iU7OxudO3eGWq3GggUL8Mwzz8DExAQHDx7ErFmz8Nxzz8HOzq5G95+IiKgMAxIBAPLz8/HTTz9h27Zt6Nq1KwBg7ty5+OWXX7BmzRosWLBAqlUqlXB2dq6wnfj4ePTv3x/NmzeHt7c3Zs6cievXr8PR0RETJkzAhx9+CBsbm/v25+2330Zqair+/vtvuLq6SvObNGmC4cOHw9zc/F/uMRERUeUMeont0KFD6NevH1xdXaFQKLB161ad5UIIzJ49Gy4uLlCpVAgMDERiYqK0PDU1FaNHj4aXlxdUKhUaNmyIOXPmoKioqMrtdu/evdxlotdff/1h7KKO3KKiSl8FJSXVrs2XPbqksjp9lJSUoLS0tFzwUKlUOHLkiM686OhoNGjQAE2bNsWECRNw48YNaVnr1q1x5MgR5OfnIyoqCi4uLnBwcMCmTZtgbm6OQYMG3bcvWq0W3333HV5++WWdcFTGysoKJibM9kRE9PAY9LdMbm4uWrdujddeew2DBw8ut3zx4sVYsWIF1q1bBy8vL7z33nsICgrC+fPnYW5ujgsXLkCr1eLzzz9Ho0aNcPbsWYwdOxa5ublYunRpldseO3Ys3n//fWn6UdyzyCoiotJlfRo3xq8vvSRNN1i6FHmVPMOtm4cHokeOlKY9P/kE1/PyytWJOXOq3Tdra2v4+/tj/vz58PX1hZOTE7799lvExMSgUaNGUl3v3r0xePBgeHl5ITk5GW+//TaCg4MRExMDY2NjvPbaazh9+jSaNWsGBwcH/PDDD7h16xZmz56N6OhovPvuu/juu+/QsGFD/O9//9O5dFfm+vXruHXrFnx8fKrdfyIioppk0IAUHByM4ODgCpcJIbB8+XK8++67GDBgAABg/fr1cHJywtatWzFs2DD07t1bZ4Cwt7c3EhISsGbNmvsGJAsLi0ovEz2pNmzYgNdeew1PPfUUjI2N0bZtWwwfPhyxsbFSzbBhw6R/t2zZEq1atULDhg0RHR2NHj16wNTUFKtWrdJpd9SoUZgyZQr++usvbN26FadOncLixYsxZcoU/PTTT+X6UTYAm4iIyFBq7XWKlJQUZGZmIjAwUJpna2uLDh06ICYmRucX9b3UajXs7e3v2/6mTZuwceNGODs7o1+/fnjvvfeqPItUWFiIwsJCaVqj0eixN3fkhIdXusxY9tTyqzNmVFprJLuBXurUqXr3pSINGzbEwYMHkZubC41GAxcXFwwdOhTe3t6VruPt7Q0HBwckJSWhR48e5ZYfOHAA586dw1dffYWZM2eiT58+sLS0xIsvvoiVK1dW2KajoyPs7Oxw4cKFGtkvIiIifdXagJSZmQkAcHJy0pnv5OQkLZNLSkrCp59+et+zRy+99BI8PDzg6uqK06dP480330RCQgJ+/vnnSteJiIjAvHnz9NwLXZZ6PJX8YdVWqz1LS1haWuLWrVuIiorC4sWLK629fPkybty4ARcXl3LLCgoKMHHiRGzatAnGxsYoLS2Vzg4VFxdX+ggJIyMjDBs2DBs2bMCcOXPKjUPKycmBubk5xyEREdFD89jcB+nKlSvo3bs3XnjhBYwdO7bK2nHjxiEoKAgtW7bEyy+/jPXr12PLli1ITk6udJ3w8HCo1WrpdenSpZreBYOLiorC7t27kZKSgj179iAgIAA+Pj4YNWoUgDvBZObMmTh69ChSU1Oxb98+DBgwAI0aNUJQUFC59ubPn48+ffrgP//5DwDg2Wefxc8//4zTp09j5cqVePbZZyvty8KFC+Hm5oYOHTpg/fr1OH/+PBITE/G///0P//nPf5CTk/NwDgIRERFq8RmksvFBWVlZOmcnsrKy0KZNG53a9PR0BAQEoFOnTvjiiy/03laHDh0A3DkD1bBhwwprlEqlzv1+HkdqtRrh4eG4fPky7O3tERISgoULF0pPUjc2Nsbp06exbt06ZGdnw9XVFb169cL8+fPLHZuzZ8/ihx9+QFxcnDRvyJAhiI6ORpcuXdC0aVN88803lfbF3t4eR48exQcffIAFCxbg4sWLqFevHlq2bIklS5bA1tb2oRwDIiIioBYHJC8vLzg7O2Pfvn1SINJoNDh27BgmTJgg1V25cgUBAQHw8/PD2rVrYWSk/0mxsl/iFV0mepK8+OKLePHFFytdrlKpEBUVVa22WrRooXNLBuDOpbPVq1dj9erV1WrD1tYWERERiKji23/R0dHVaouIiEgfeqWJq1evVrm8pKQEf/75Z7Xby8nJQVxcnBRQUlJSEBcXh7S0NCgUCoSFhWHBggXYvn07zpw5gxEjRsDV1RUDBw4EcCccde/eHe7u7li6dCmuXbuGzMxMnTFKV65cgY+Pj9Sv5ORkzJ8/H7GxsUhNTcX27dsxYsQIdO3aFa1atdLncBAREdFjSq8zSC4uLsjIyECDBg0A3Pma986dO+Hm5gYAuHHjBvz9/SsdfCt34sQJBAQESNPTp08HAISGhiIyMhKzZs1Cbm4uxo0bJz16Yvfu3dLNDPfs2YOkpCQkJSXh6aef1mn73sHACQkJyLt7nyAzMzPs3bsXy5cvR25uLtzc3BASEoJ3331Xn0NBREREjzG9ApL8/jSpqakolt3MUJ972HTv3r3KeoVCgffff1/nho73GjlyJEbec8PEinh6eupsw83NDQcPHqx2H4mIiOjJU+PfYlPI7tFDREREVNc8Nl/zJyIiIqopel1iUygUuH37NszNzSGEgEKhQE5OjnRX6Qe5uzQRERFRbaP3GKQmTZroTJfdBLBsmpfYiIiIqK7TKyAdOHDgYfWDiIiIqNbQKyB169btYfWDiIiIqNbQa5B2SUmJzhPtgTuP/pg3bx5mzZqFI0eO1GjniOjxplAosHXrVkN3g4ioHL0C0tixYzFlyhRp+vbt23jmmWewatUqREVFISAgADt37qzxTtLDp1AoqnzNnTv3kfXF09MTy5cvr9E2u3fvjrCwsBptUy46OhoKhQLZ2dnVqit7OTo6ok+fPjhz5sxD7R8REVWfXgHp999/R0hIiDS9fv16lJaWIjExEadOncL06dOxZMmSGu8kPXwZGRnSa/ny5bCxsdGZN2PGDKlWCIGSkhID9vbxkJCQgIyMDERFRaGwsBB9+/ZFUVGRobulN/nNYomIHgd6BaQrV66gcePG0vS+ffsQEhIiPVk9NDQU586dq9ke0iPh7OwsvWxtbaFQKKTpCxcuwNraGrt27YKfnx+USiWOHDmCkSNHSs/FKxMWFobu3btL01qtFhEREfDy8oJKpULr1q3x448/VtqP7t274+LFi5g2bZp0hqXMkSNH0KVLF6hUKri5uWHKlCnIzc2Vlq9evRqNGzeGubk5nJycMGTIEAB37rh+8OBBfPLJJ1KbqampFW6/sjbuty+pqanSY3Pq1asHhUJx37u8N2jQAM7Ozmjbti3CwsJw6dIlXLhwodL6ixcvol+/fqhXrx4sLS3RvHlznTO2O3fuRJMmTaBSqRAQEIDIyEidM1pz586VHvxcZvny5fD09JSmjx8/jp49e8LBwQG2trbo1q0bTp48qbOOQqHAmjVr0L9/f1haWmLhwoUAgG3btqFt27YwNzeHt7c35s2bpxOkExMT0bVrV5ibm6NZs2bYs2dPlceHiMiQ9BqkbW5ujvz8fGn66NGjOmeMzM3NkZOTU3O9qwNyi4pgLPurv7CoSOfxJkIIFOQUyleVKHDnSfdlSrXaGqkFAHMrpRQy7ldrbKSbl8vqy/771ltv4cPFi+Ht7Q2H+vWlOq1Wi7K9FUJA3LPOokWL8M2mTfjss8/QuHFjRB88iFdeeQX29etXOOj/559/RuvWrTFu3DiMHjMGQgiUarVITk5G79698f78+fjyq69w7do1TJ0yBZMmTcLatWvx5/HjmDJlCtatWwf/Tp1w8+ZNHDlyBKVaLT5etgx///03WrRogffffx9aIeDg4FDueJw4cQJTpkzB+vXr8eyzz+LmzZs4dOhQuX1ZdTdEHT50CK+88gocHR3RuXNnbP7xR7wwZAjOx8fDxsYGKpVKZxtGssBXqtWiVKuFWq3Gt99+e+f/gYkJSrVanVqtEBBC4I033kBRUREOREfD0tIS58+fh8rCAkIIXL58GYMHD8Ybb7yBMWPHIvbECcycOVNnO1rZY32EENK8sn5mq9V49dVXsfyTTwAAyz7+GH369MHff/8NSysrad25c+di0aJF+Ojjj2FiYoKDhw5hxIgRWLFiBTp37ozEpCRMeP11aIXA7NmzodVqMXjwYDRwckLM0aO4rdFIlzy1d/snV6rV6ryPtEIgv4qzVabGxjAzNq5WrYmREZQmJtJxyJPV5peUAKamyC8pQWFJSZW19zI2MoK5yf9/rOZWcUZQn1ojhQIqU9MHqs0rLq70kU4KhQIWD1ibX1xc7mfqXpZmZg9UW1BSUuVnlT61Fqam0vuosKQEJTVUqzI1hdHd2qLSUhRX8fxRfWrNTUykz2F9aotLS1FURa3SxAQmD1BbotWisIqrBWbGxjC9+57Tp7ZUq0VBFbX3vpf1qdXnM6I69ApIbdq0wYYNGxAREYHDhw8jKysLzz33nLQ8OTkZrq6u+jRZ57l+9BFw9+G5ZTwsLbGue3d4350uzivGYpsPH33nAITnhMPM8s4HSvz165X+oJkZG6OVk5M0rRUCf2VmAgCSbt4EAIwIC4NDy5bIMzKCvb29VJt48yZu3/3Avpqbi5yiIvyVmYmiwkIsWrQI+/ftg7+/PwCg1NYWvffsweIVK2DTtGm5frRzdYWxsTGsra2Rr1TiVkEBMjIzsWDOHPQaOBBdhw5FDgCVtTWWLV+O5wICsGbNGsTGx8PcwgJPP/MMbiqVgIsLOr/wgrQPpqamsLCwgLOzMy5mZ+P0tWvltn3w9GmYW1igV3AwHOrVg4eHBxy8vXX2ZdV336FB69ZQA2jVuzeGvfQSPv/88zthT6UCAGQKgVwAyM+/87rL18FB58O97CHP+XcfpNy1Vy/k29nhr8xMNK1fH9ZKJQDgel4e0tRq/J2Sguf69EGJoyPUAJ5q1w4AoC4sxJo1a9CwYUO8vWABUrOz4dujB4KGDMH6VatwOisL1gUFyMzJ0fllcqugAJc1GhSVlkrHqV6zZqjXrBnyAXja2eGLL76AnZ0ddu7di0YdO0rr9ujfH62Dg6G+O/3enDl46623EBoaituFhbilUmHU9On4dOFC9Bs3DkcPHkT8hQtYvG4dXBo1wn+srLBo0SIEBwfjn+xsafs6SkpQfM+XQuKvXUOLNWvK1901w98fS3r1AgCkqdXwuhvyKvJGu3ZY1bevdHwbLF1avuidd9B51y6Epqcj8u6Z0rziYlhFRFTa7pBmzbD5hRek6apq+zRujF9fekmabrB0aaXhq5uHB6LvOSPp+cknuH7350aunasrjo8dK003W7UKF9XqCmubOTri3BtvSNPPfPklzlfw3gAAD1tbpN4zjq9rZCROpKdXWOtgYYFrdwM6AARv2oSDFy9WWGthaorct9+WpkN++AE7ExMrrAUAMWeO9O9Xt2zBj+fPV1qbEx4uvefG79iBdadOVVp7dcYMOFpaAgCmR0Vh9YkTldamTJ0KTzs7AMA7+/ZhaUxMpbVnJ0xA87sPeF90+DDmVfEs0D/HjMEzTz0FAPjk6FHM2ru30toDoaHofvfs7xexsZi0a1eltTuGD0ffu/cw3HTmDEZt21Zp7Q9DhuCF5s0BAFvi4/FiFWf81w4YgJF3z0pHJSXh+bt/6FVkZXAwJrZvDwA4nJaGgHXrKq1dHBiImc8+CwA4mZGB9l99VWntnG7dMPfuVQt9PiOqQ6+ANHv2bAQHB+OHH35ARkYGRo4cCRcXF2n5li1b8OzdnaLHj2+rVnrVX0pNRUF+Pnr27CnNEwCKiorQtEULvdr6+/x5JMXHY/eWLdI8Be6cfUhJSUHn7t3h8vTTGOjvD//u3eEfEICA4GCY3w0t1dGha1e4PP00fJs0Qe/evdG7d288c/cPgLJ9mTR8uM46JcXFOjdL1ceXW7ZAaW6OsydPYu2nnyL8gw+kZe3/8x9cSku7069OnfBhZCSGvvYaPggPx9GDB9G+Sxc816cPGjdrBgCIj49Hhw4ddNpv5eend59uXLuGNYsX4+QffyD75k1oS0uRl5eHy5cu6QQk39atddY7f+YMjh89Kl1u0woBrVaLwoICFOTnIyUxEU6urnB0dpbWKQvNRES1kUJUdj61EvHx8fjtt9/g7OyMF154QedyzxdffIH27duXG+fwONJoNLC1tUX6tWuwsbHRWVZYUIDMy5fh7e0tPZalLl1ii4yMRFhYGG7cPXMUHR2NwB49cP3GDdjd/avJ2MgIr732Gm7cuIEtW7ZIl9gmT5qEc+fPY//+/Th27Bie7dQJ0dHReOruX0Vll4uUSqV0BkXeB09PT4SFhWHK1KnS6f4WzZsjMDAQkyZPlmrLLkO5u7vDxNQUxcXFiI6Oxp49e7Dl559hZGSEo8eOwc7ODj2eew5t2rTB8uXLpT5UpKSkBIcOHsSePXvw008/SW0kJCTg2U6dsG//fmlfyvpgbm4ONzc37D9wAD2ee07nON2rrL/R0dEICAjQqVu6dCl27NiB6OhoAMCltDRp/I7S3Fw6M3vp0iXs/PVX7NmzB7/++iuWLF2KKZMnY/DgwahXrx6++vprad+2b9uGwYMHS9uZP38+tvz8M07d/StaCIHFS5ZgzerVSP7nHwBAn+Bg3Lx5E7PnzIGnpydU5ubw9/fH22+/jSlTpwIATIyN8dNPP2HAPePPrCwtMW/ePAwePFjn0h0AeHt7Y+XKlfh0xQokJiVBoVDASKGAWq2GnZ1dubbKFBQU4GJqqvQ+epSX2OLi4tC5c2ccOXIE7dq25SW2Cmp5iY2X2OrqJbay399qtbrc7+976XUGCQB8fX3h6+tb4bJx48bp21ydZ2lmpvOGBQBjrVZnrIlCoYDK2ly+6iMnH2NU3fp7/3tvG46Ojjh79qxOYDt16hRMTU1hbGSEli1aQKlUIi0tTa+bjJqZmaG0tPTOh8rd49i2bVvEx8ej6T2PupFTmpkhqFcvBPXqhXlz58LOzg4Ho6MxePBgqU0AOu2W22czM/Ts2RM9e/bEnDlzpDZ69uwJpVKJK5cv47m7g7HlzO9eEoMQ1TrW9x7PyZMm4cMPPsD2bdswaNAgnYHT9/L08MAbb7yBN954A+Hh4fj6q68wdcoU+Pr6Yvv27Tr79ueff+psx6lBA2RmZkqPBFIoFDh9NyyV9eOPP/7A6tWr0e/55wHcCWTXr1+HQqHQ2Scj2c9C27ZtkZCQgEaNGlXY7+bNmuHSpUu4mpUlnXU+evRohW3de3zufR8ZKRTl3muV0adWUUGtysQEKC6GysRECkeV1ValNtTeG2pqslb1kGrvDY01Was0MYHyIdSa6TGu5WHVmt4TPmqy1sTICCbV/FnTp9bYyKjaP8P61Orzvq8OvQLSoUOHqlXXtWvXB+oM1S3PPfcclixZgvXr18Pf3x8bN27E2bNnpUtO1tbWmDFjBqZNmwatVovOnTtDrVbj999/h42NDUJDQyts19PTE4cOHcKwYcOgVCrh4OCAN998Ex07dsSkSZMwZswYaZDynj17sHLlSuzYsQP//PMPunbtinr16mHnzp3QarVoeneck6enJ44dO4bU1FRYWVnB3t5eJ9gBqLKN6uyLh4cHFAoFduzYgT59+kClUsHqnoHNVbGwsMDYsWMxZ84cDBw4sMJnGoaFhSE4OBhNmjTBrVu3cODAAemPlddffx0fffQRZs6ciTFjxiA2NhaRkZE663fv3h3Xrl3D4sWLMWTIEOzevRu7du3S+QuqcePG2LBhA9q1aweNRoOZM2dCVY3LlLNnz8bzzz8Pd3d3DBkyBEZGRjh16hTOnj2LBQsWIDAwEE2aNEFoaCiWLFkCjUaDd955p1rHhojIIIQeFAqFMDIyEkZGRkKhUFT4MjIy0qfJOkutVgsAQq1Wl1uWn58vzp8/L/Lz8w3Qs39v7dq1wtbWVpo+cOCAACBu3bpVrnb27NnCyclJ2NraimnTpolJkyaJbt26Scu1Wq1Yvny5aNq0qTA1NRWOjo4iKChIHDx4sNLtx8TEiFatWgmlUinu/RH9888/Rc+ePYWVlZWwtLQUrVq1EgsXLhRCCHH48GHRrVs3Ua9ePaFSqUSrVq3E999/L62bkJAgOnbsKFQqlQAgUlJSym33fm1UZ1/ef/994ezsLBQKhQgNDa1w/yo7nmlpacLExERnm/eaNGmSaNiwoVAqlcLR0VG8+uqr4vr169LyX375RTRq1EgolUrRpUsX8b///a/cdtasWSPc3NyEpaWlGDFihFi4cKHw8PCQlp88eVK0a9dOmJubi8aNG4vNmzcLDw8PsWzZMqkGgNiyZUu5/u3evVt06tRJqFQqYWNjI9q3by+++OILaXlCQoLo3LmzMDMzE02aNBG7d++utC0hDPs+io2NFQBEbGzsI982ET1cVf3+vpdeY5Dq168Pa2trjBw5Eq+++iocHBwqrCu7L9LjrKprmAUFBUhJSYGXlxfMzQ1/aY2eTGVjnW7dulXhmKjazpDvo5MnT8LPzw+xsbFo27btI902ET1c1R2DpNeglIyMDHz44YeIiYlBy5YtMXr0aPzxxx+wsbGBra2t9CIiIiKqy/QKSGZmZhg6dCiioqJw4cIFtGrVCpMmTYKbmxveeecdPn6CiIiIHgv6fa3pHu7u7pg9ezb27t2LJk2a4IMPPoBGo6nJvhHRv9C9e3cIIerk5TUiIkN7oIBUWFiIb775BoGBgWjRogUcHBzw66+/6txdmYiIiKiu0utr/n/++SfWrl2L7777Dp6enhg1ahR++OEHBiMiIiJ6rOgVkDp27Ah3d3dMmTIFfncfY3DkyJFydf3796+Z3hEREREZgN530k5LS8P8+fMrXa5QKKQ7FhMRERHVRXoFJO19nuUFAHmVPGGaiIiIqK544G+xyRUWFuLjjz+Gt7d3TTVJREREZBB6BaTCwkKEh4ejXbt26NSpE7Zu3QoA+N///gcvLy8sW7YM06ZNexj9pDrK09MTy5cvr3Z9dHQ0FAoFsrOzH1qfSJdCoZDey0REdIdeAWn27NlYs2YNPD09kZqaihdeeAHjxo3D8uXL8fHHHyM1NRVvvvnmw+orPURlT3ev7DV37twHavf48eMYN25ctes7deqEjIyMh35H9rIgVvZydHREnz59cObMmYe6XSIiqhv0GoO0efNmrF+/Hv3798fZs2fRqlUrlJSU4NSpUxU+fZzqjoyMDOnf33//PWbPno2EhARp3r1PpRdCoLS0FCYm9//xcXR01KsfZmZmcHZ21mudfyMhIQE2NjZIT0/HzJkz0bdvXyQlJcHMzOyR9aEmFBcXw9TU1NDdICJ6bOh1Buny5cvS1/tbtGgBpVKJadOmMRw9BpydnaWXra0tFAqFNH3hwgVYW1tj165d8PPzg1KpxJEjR5CcnIwBAwbAyckJVlZWeOaZZ7B3716dduWX2BQKBb766isMGjQIFhYWaNy4MbZv3y4tl19ii4yMhJ2dHaKiouDr6wsrKyv07t1bJ9CVlJRgypQpsLOzQ/369fHmm28iNDQUAwcOvO9+N2jQAM7Ozmjbti3CwsJw6dIlXLhwodL6ixcvol+/fqhXrx4sLS3RvHlz7Ny5U1q+c+dONGnSBCqVCgEBAYiMjNTZn7lz56JNmzY6bS5fvhyenp7S9PHjx9GzZ084ODjA1tYW3bp1w8mTJ3XWUSgUWLNmDfr37w9LS0ssXLgQALBt2za0bdsW5ubm8Pb2xrx583QeAZSYmIiuXbvC3NwczZo1w549e+57jIiInkR6BaTS0lKdv6xNTEx0zixQJYQAcnMN8xKixnbjrbfewgcffID4+Hi0atUKOTk56NOnD/bt24e//voLvXv3Rr9+/ZCWllZlO/PmzcOLL76I06dPo0+fPnj55Zdx8+bNSuvz8vKwdOlSbNiwAYcOHUJaWhpmzJghLf/www+xadMmrF27Fr///js0Go3eY2rUajW+++47AKjy7NHEiRNRWFiIQ4cO4cyZM/jwww+l98ClS5cwePBg9OvXD3FxcRgzZgzeeustvfoBALdv30ZoaCiOHDmCo0ePonHjxujTpw9u376tUzd37lwMGjQIZ86cwWuvvYbDhw9jxIgRmDp1Ks6fP4/PP/8ckZGRUnjSarUYPHgwzMzMcOzYMXz22We8JE5EVBmhB4VCIfr06SMGDRokBg0aJExMTESvXr2k6bLXk0CtVgsAQq1Wl1uWn58vzp8/L/Lz8+/MyMkR4k5UefSvnBy9923t2rXC1tZWmj5w4IAAILZu3XrfdZs3by4+/fRTadrDw0MsW7ZMmgYg3n33XWk6JydHABC7du3S2datW7ekvgAQSUlJ0jqrVq0STk5O0rSTk5NYsmSJNF1SUiLc3d3FgAEDKu1n2XYsLS2FpaWlACAAiP79+1e5fy1bthRz586tcFl4eLho1qyZzrw333xTZ3/mzJkjWrdurVOzbNky4eHhUek2S0tLhbW1tfjll1+keQBEWFiYTl2PHj3EokWLdOZt2LBBuLi4CCGEiIqKEiYmJuLKlSvS8l27dgkAYsuWLZVu31DKvY8eodjYWAFAxMbGPvJtE9HDVdXv73vpNQYpNDRUZ/qVV175l/GM6pJ27drpTOfk5GDu3Ln49ddfkZGRgZKSEuTn59/3DFKrVq2kf1taWsLGxgZXr16ttN7CwgINGzaUpl1cXKR6tVqNrKwstG/fXlpubGwMPz+/at236/Dhw7CwsMDRo0exaNEifPbZZ9Ky5s2b4+LFiwCALl26YNeuXZgyZQomTJiA3377DYGBgQgJCZH2Jz4+Hh06dNBp39/f/759kMvKysK7776L6OhoXL16FaWlpcjLyyt3XOX/P06dOoXff/9dOmME3DnrW1BQgLy8PMTHx8PNzQ2urq7/qn9ERE8CvQLS2rVrH1Y/Hm8WFkBOjuG2XUMsLS11pmfMmIE9e/Zg6dKlaNSoEVQqFYYMGYKioqIq25EPJlYoFFWGmYrqRQ1dOvTy8oKdnR2aNm2Kq1evYujQoTh06BCAO+OJiouLAQAqlQoAMGbMGAQFBeHXX3/Fb7/9hoiICHz00UeYPHlytbZnZGRUru9l2ygTGhqKGzdu4JNPPoGHhweUSiX8/f3LHVf5/4+cnBzMmzcPgwcPLrddc3PzavWPiIju0PtRI/QAFApA9svscfD7779j5MiRGDRoEIA7v6BTU1MfaR9sbW3h5OSE48ePo2vXrgDunDU5efJkucHQ9zNx4kRERERgy5YtGDRoEDw8PCqsc3Nzw+uvv47XX38d4eHh+PLLLzF58mT4+vrqDDgHgKNHj+pMOzo6IjMzE0II6csNcXFxOjW///47Vq9ejT59+gC4M7bp+vXr9+1/27ZtkZCQgEaNGlW43NfXF5cuXUJGRgZcXFwq7B8REd1RY3fSpidP48aN8fPPPyMuLg6nTp3CSy+9VK3LWjVt8uTJiIiIwLZt25CQkICpU6fi1q1ben+70sLCAmPHjsWcOXMqPUMVFhaGqKgopKSk4OTJkzhw4AB8fX0BAK+//joSExMxc+ZMJCQk4JtvvkFkZKTO+t27d8e1a9ewePFiJCcnY9WqVdi1a5dOTePGjbFhwwbEx8fj2LFjePnll6UzWFWZPXs21q9fj3nz5uHcuXOIj4/Hd999h3fffRcAEBgYiCZNmiA0NBSnTp3C4cOH8c477+h1jIiInhQMSPTAPv74Y9SrVw+dOnVCv379EBQUhLZt2z7yfrz55psYPnw4RowYAX9/f1hZWSEoKOiBLitNmjQJ8fHx2Lx5c4XLS0tLMXHiRPj6+qJ3795o0qQJVq9eDQBwd3fHTz/9hK1bt6J169b47LPPsGjRIp31fX19sXr1aqxatQqtW7fGn3/+qfONPAD4+uuvcevWLbRt2xavvvoqpkyZggYNGty370FBQdixYwd+++03PPPMM+jYsSOWLVsmnQkzMjLCli1bkJ+fj/bt22PMmDE645WIiOj/KURNDeZ4wmg0Gtja2kKtVsPGxkZnWUFBAVJSUuDl5cWxHwag1Wrh6+uLF198EfPnzzdoX6KjoxEQEIBbt27Bzs7OoH2pawz5Pjp58iT8/PwQGxtrkNBPRA9PVb+/72XQM0iHDh1Cv3794OrqWuHzoIQQmD17NlxcXKBSqRAYGIjExESdmps3b+Lll1+GjY0N7OzsMHr0aOTcZ0B0QUEBJk6ciPr168PKygohISHIysqq6d2jR+TixYv48ssv8ffff+PMmTOYMGECUlJS8NJLLxm6a0REVEcZNCDl5uaidevWWLVqVYXLFy9ejBUrVuCzzz7DsWPHYGlpiaCgIBQUFEg1L7/8Ms6dO4c9e/Zgx44dOHTo0H2f/TVt2jT88ssv2Lx5Mw4ePIj09PQKv/lDdYORkREiIyPxzDPP4Nlnn8WZM2ewd+9eaWwQERGR3h72DZmqC7Kb1Wm1WuHs7KxzA8Ds7GyhVCrFt99+K4QQ4vz58wKAOH78uFSza9cuoVAodG6Gd6/s7GxhamoqNm/eLM2Lj48XAERMTEy1+6vXjSKJSG+8USQRPQzVvVFkrR2knZKSgszMTAQGBkrzbG1t0aFDB8TExAAAYmJiYGdnp3PDvMDAQBgZGeHYsWMVthsbG4vi4mKddn18fODu7i61W5HCwkJoNBqdFxERET2eam1AyszMBAA4OTnpzHdycpKWZWZmlvt2j4mJCezt7aWaito1MzMrN2D23nYrEhERAVtbW+nl5uZ2330QHP9O9MD4/iEiQ6q1Aam2CQ8Ph1qtll6XLl2qtLbszs95eXmPqntEj52yO4cbGxsbuCdE9CSqtXfSdnZ2BnDnuVRld/0tmy67Q7Kzs3O5Z3iVlJTg5s2b0voVtVtUVITs7Gyds0hZWVmVrgMASqUSSqWyWn03NjaGnZ2d1DcLCwu9b1pI9CTTarW4du0aLCwsYGJSaz+miOgxVms/eby8vODs7Ix9+/ZJgUij0eDYsWOYMGECgDsP2szOzkZsbCz8/PwAAPv374dWqy330NAyfn5+MDU1xb59+xASEgIASEhIQFpaWo0+uLMsbFX1EFYiqpyRkRHc3d35xwURGYRBA1JOTg6SkpKk6ZSUFMTFxcHe3h7u7u4ICwvDggUL0LhxY3h5eeG9996Dq6srBg4cCADS3YzHjh2Lzz77DMXFxZg0aRKGDRsmPbH8ypUr6NGjB9avX4/27dvD1tYWo0ePxvTp02Fvbw8bGxtMnjwZ/v7+6NixY43tm0KhgIuLCxo0aFDuYaREdH9mZmYwMuIoACIyDIMGpBMnTiAgIECanj59OoA7TzOPjIzErFmzkJubi3HjxiE7OxudO3fG7t27de6qu2nTJkyaNAk9evSAkZERQkJCsGLFCml5cXExEhISdMYDLVu2TKotLCxEUFCQ9LiImmZsbMwxFERERHUMHzXygKp7q3Iiqnv4qBGix1edeNQIERERUW3EgEREREQkw4BEREREJMOARERERCTDgEREREQkw4BEREREJMOARERERCTDgEREREQkw4BEREREJMOARERERCTDgEREREQkw4BEREREJMOARERERCTDgEREREQkw4BEREREJMOARERERCTDgEREREQkw4BEREREJMOARERERCTDgEREREQkw4BEREREJGNi6A4QEdWEvLw8XLhwoUbaio+P1/lvTfDx8YGFhUWNtUdEDxcDEhE9Fi5cuAA/P78abfOVV16psbZiY2PRtm3bGmuPiB4uBiQieiz4+PggNja2RtrKz89HamoqPD09oVKpaqRNHx+fGmmHiB4NhRBCGLoTdZFGo4GtrS3UajVsbGwM3R0iIiKqhur+/uYgbSIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIimVofkG7fvo2wsDB4eHhApVKhU6dOOH78uLRcoVBU+FqyZEmlbc6dO7dcvY+Pz6PYHSIiIqoDTAzdgfsZM2YMzp49iw0bNsDV1RUbN25EYGAgzp8/j6eeegoZGRk69bt27cLo0aMREhJSZbvNmzfH3r17pWkTk1p/KIiIiOgRqdWpID8/Hz/99BO2bduGrl27Arhz9ueXX37BmjVrsGDBAjg7O+uss23bNgQEBMDb27vKtk1MTMqtS0RERATU8ktsJSUlKC0thbm5uc58lUqFI0eOlKvPysrCr7/+itGjR9+37cTERLi6usLb2xsvv/wy0tLSqqwvLCyERqPReREREdHjqVYHJGtra/j7+2P+/PlIT09HaWkpNm7ciJiYmHKX1gBg3bp1sLa2xuDBg6tst0OHDoiMjMTu3buxZs0apKSkoEuXLrh9+3al60RERMDW1lZ6ubm5/ev9IyIiotpJIYQQhu5EVZKTk/Haa6/h0KFDMDY2Rtu2bdGkSRPExsYiPj5ep9bHxwc9e/bEp59+qtc2srOz4eHhgY8//rjSs0+FhYUoLCyUpjUaDdzc3KBWq2FjY6P/jhEREdEjp9FoYGtre9/f37V6DBIANGzYEAcPHkRubi40Gg1cXFwwdOjQcmOMDh8+jISEBHz//fd6b8POzg5NmjRBUlJSpTVKpRJKpVLvtomIiKjuqdWX2O5laWkJFxcX3Lp1C1FRURgwYIDO8q+//hp+fn5o3bq13m3n5OQgOTkZLi4uNdVdIiIiqsNqfUCKiorC7t27kZKSgj179iAgIAA+Pj4YNWqUVKPRaLB582aMGTOmwjZ69OiBlStXStMzZszAwYMHkZqaij/++AODBg2CsbExhg8f/tD3h4iIiGq/Wn+JTa1WIzw8HJcvX4a9vT1CQkKwcOFCmJqaSjXfffcdhBCVBpzk5GRcv35dmr58+TKGDx+OGzduwNHREZ07d8bRo0fh6Oj40PeHiIiIar9aP0i7tqruIC8iIiKqPar7+7vWX2IjIiIietQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZGp9QLp9+zbCwsLg4eEBlUqFTp064fjx49LykSNHQqFQ6Lx69+5933ZXrVoFT09PmJubo0OHDvjzzz8f5m4QERFRHVLrA9KYMWOwZ88ebNiwAWfOnEGvXr0QGBiIK1euSDW9e/dGRkaG9Pr222+rbPP777/H9OnTMWfOHJw8eRKtW7dGUFAQrl69+rB3h4iIiOoAhRBCGLoTlcnPz4e1tTW2bduGvn37SvP9/PwQHByMBQsWYOTIkcjOzsbWrVur3W6HDh3wzDPPYOXKlQAArVYLNzc3TJ48GW+99Va12tBoNLC1tYVarYaNjY1e+0VERESGUd3f37X6DFJJSQlKS0thbm6uM1+lUuHIkSPSdHR0NBo0aICmTZtiwoQJuHHjRqVtFhUVITY2FoGBgdI8IyMjBAYGIiYmptL1CgsLodFodF5ERET0eKrVAcna2hr+/v6YP38+0tPTUVpaio0bNyImJgYZGRkA7lxeW79+Pfbt24cPP/wQBw8eRHBwMEpLSyts8/r16ygtLYWTk5POfCcnJ2RmZlbal4iICNja2kovNze3mttRIiIiqlVqdUACgA0bNkAIgaeeegpKpRIrVqzA8OHDYWR0p+vDhg1D//790bJlSwwcOBA7duzA8ePHER0dXaP9CA8Ph1qtll6XLl2q0faJiIio9qj1Aalhw4Y4ePAgcnJycOnSJfz5558oLi6Gt7d3hfXe3t5wcHBAUlJShcsdHBxgbGyMrKwsnflZWVlwdnautB9KpRI2NjY6LyIiIno81fqAVMbS0hIuLi64desWoqKiMGDAgArrLl++jBs3bsDFxaXC5WZmZvDz88O+ffukeVqtFvv27YO/v/9D6TsRERHVLbU+IEVFRWH37t1ISUnBnj17EBAQAB8fH4waNQo5OTmYOXMmjh49itTUVOzbtw8DBgxAo0aNEBQUJLXRo0cP6RtrADB9+nR8+eWXWLduHeLj4zFhwgTk5uZi1KhRhthFIiIiqmVMDN2B+1Gr1QgPD8fly5dhb2+PkJAQLFy4EKampigpKcHp06exbt06ZGdnw9XVFb169cL8+fOhVCqlNpKTk3H9+nVpeujQobh27Rpmz56NzMxMtGnTBrt37y43cJuIiIieTLX6Pki1Ge+DREREVPc8FvdBIiIiIjIEBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZBiQiIiIiGQYkIiIiIhkGJCIiIiIZE0N3oM7LzQWMjQ3dCyIiIqqO3NxqlTEg/VuurobuAREREdUwXmIjIiIikuEZpH8rPR2wsTF0L4iIiKg6NJpqXf1hQPq3LC3vvIiIiKj2Ky2tVhkvsRERERHJMCARERERyTAgEREREckwIBERERHJMCARERERyTAgEREREcnU+oB0+/ZthIWFwcPDAyqVCp06dcLx48cBAMXFxXjzzTfRsmVLWFpawtXVFSNGjEB6enqVbc6dOxcKhULn5ePj8yh2h4iIiOqAWn8fpDFjxuDs2bPYsGEDXF1dsXHjRgQGBuL8+fOwsrLCyZMn8d5776F169a4desWpk6div79++PEiRNVttu8eXPs3btXmjYxqfWHgoiIiB4RhRBCGLoTlcnPz4e1tTW2bduGvn37SvP9/PwQHByMBQsWlFvn+PHjaN++PS5evAh3d/cK2507dy62bt2KuLi4B+6bRqOBra0t1Go1bHgnbSIiojqhur+/a/UltpKSEpSWlsLc3FxnvkqlwpEjRypcR61WQ6FQwM7Orsq2ExMT4erqCm9vb7z88stIS0ursr6wsBAajUbnRURERI+nWh2QrK2t4e/vj/nz5yM9PR2lpaXYuHEjYmJikJGRUa6+oKAAb775JoYPH15lKuzQoQMiIyOxe/durFmzBikpKejSpQtu375d6ToRERGwtbWVXm5ubjWyj0RERFT71OpLbACQnJyM1157DYcOHYKxsTHatm2LJk2aIDY2FvHx8VJdcXExQkJCcPnyZURHR+t12Ss7OxseHh74+OOPMXr06AprCgsLUVhYKE1rNBq4ubnxEhsREVEdUt1LbLV+ZHLDhg1x8OBB5ObmQqPRwMXFBUOHDoW3t7dUU1xcjBdffBEXL17E/v379Q4sdnZ2aNKkCZKSkiqtUSqVUCqVD7wfREREVHfU+oBUxtLSEpaWlrh16xaioqKwePFiAP8fjhITE3HgwAHUr19f77ZzcnKQnJyMV199tdrrlJ1441gkIiKiuqPs9/b9LqDV+ktsUVFREEKgadOmSEpKwsyZM2Fubo7Dhw8DAIYMGYKTJ09ix44dcHJyktazt7eHmZkZAKBHjx4YNGgQJk2aBACYMWMG+vXrBw8PD6Snp2POnDmIi4vD+fPn4ejoWK1+Xb58meOQiIiI6qhLly7h6aefrnR5rT+DpFarER4ejsuXL8Pe3h4hISFYuHAhTE1NkZqaiu3btwMA2rRpo7PegQMH0L17dwB3xjFdv35dWnb58mUMHz4cN27cgKOjIzp37oyjR49WOxwBgKurKy5dugRra2soFIp/vZ9EVHuUjTG8dOkSxxgSPWaEELh9+zZcXV2rrKv1Z5CIiB413ueMiGr11/yJiIiIDIEBiYiIiEiGAYmISEapVGLOnDm8tQfRE4xjkIiIiIhkeAaJiIiISIYBiYiIiEiGAYmIiIhIhgGJiIiISIYBiYiIiEiGAYmI6K5Dhw6hX79+cHV1hUKhwNatWw3dJSIyEAYkIqK7cnNz0bp1a6xatcrQXSEiA6v1D6slInpUgoODERwcbOhuEFEtwDNIRERERDIMSEREREQyDEhEREREMgxIRERERDIMSEREREQy/BYbEdFdOTk5SEpKkqZTUlIQFxcHe3t7uLu7G7BnRPSoKYQQwtCdICKqDaKjoxEQEFBufmhoKCIjIx99h4jIYBiQiIiIiGQ4BomIiIhIhgGJiIiISIYBiYiIiEiGAYmIiIhIhgGJiIiISIYBiYiIiEiGAYmIiIhIhgGJiIiISIYBiYiIiEiGAYmIiIhIhgGJiIiISOb/AHHLVHruNEp1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Cross-validation performance')\n",
    "plt.boxplot(cv_scores)\n",
    "plt.axhline(rmse_mean - moe_95, linestyle='--', color='teal', label='95% CI')\n",
    "plt.axhline(rmse_mean + moe_95, linestyle='--', color='teal')\n",
    "plt.axhline(test_rmse, color='purple', label='True test set R-squared')\n",
    "plt.axhline(train_rmse, color='red', label='Training R-squared')\n",
    "plt.ylabel('RMSE')\n",
    "plt.xlabel('')\n",
    "plt.legend(loc='center left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2e9b6301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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moqKiz7R8PZk1axa+vr48+OCDeHh4cPXVV7utf/LJJ8nJyWH27NnExcVRX1/P66+/TnR0tFstiP4YPXo0M2fOdEsNC/DEE08cc9+kpCSefvppHn74YSoqKrjqqqvw9fXl4MGDfPrpp9xxxx08+OCDeHl58fTTT3PnnXcybdo0Fi5cyMGDB1mxYsWA/FpfeeUVMjMzSU9P54477iAhIYGKigrWrFlDQUEBgBxE/sc//pHrrrsOLy8v5s6de8b66OLaa6/lwQcf5MEHHyQoKOiImajZs2fz17/+lSuuuIJf/OIX1NfX89prr5GcnMxPP/101LYvv/xyYmNjue2223jooYfw8PDgnXfeITQ01E1o+fn58fe//50bb7yR9PR0rrvuOnmbNWvWcPHFF/O3v/2NkpISpk+fzrXXXsvIkSPx9PTk008/pa6uzi2VrUAgOP9wfafec889zJw5Ew8PjxP6Xhjoe3Dx4sWYzWamTZtGdHQ0lZWVvPrqq4wbN44RI0b02fbxfG+PGjWKjIwMHn74YcxmM0FBQaxateqISb3hw4eTlJTEgw8+yKFDh/Dz8+Pjjz8eUJKKk30fC4YYg5JDSnDW0l9q2FGjRvW5vcPhkH73u99JISEhkre3tzRz5kyptLT0iFSeknQ4TefDDz8sJScnSyqVSgoJCZEuuugi6S9/+Ytkt9sH1L8bbrhBAqQZM2YcsW7jxo3SvHnzpMjISEmlUkmRkZHS9ddff0Qavr4ApCVLlkjvvfeeNGzYMEmtVkvjx493S5EnSf9Lf9dfuruPP/5YyszMlHx8fCQfHx9p+PDh0pIlS6Ti4mK37V5//XUpISFBUqvV0sSJE6WcnBxpypQpx0wNK0mStGfPHunnP/+5FBAQIGk0Gik1NVX605/+5LbNU089JUVFRUlKpfKIFICnso/H4uKLL5YAafHixX2uf/vtt+XrPXz4cGnFihV9pn3t63nauXOndOGFF0oqlUqKjY2V/vrXvx415eHMmTMlf39/SaPRSElJSdKiRYvkVMAmk0lasmSJNHz4cMnHx0fy9/eXLrzwQunDDz8c8LkKBIKzH9d3xPbt2/tc39f3bnd3t/TrX/9aCg0NlRQKxTHTxMbFxUmzZ8/uc91A3oOrV6+WLr/8ciksLEz+frvzzjul2tpauZ2+0rhK0sC/t8vKyqQZM2ZIarVaCg8Pl/7whz9I69evP6LNoqIiacaMGZJOp5NCQkKk22+/Xdq9e/cR16j39/bJvI8FQw+FJPXydxAIzkMUCgVLlizhb3/722B3RSAQCAQCgWDIIGImBAKBQCAQCAQCwQkhxIRAIBAIBAKBQCA4IYSYEAgEAoFAIBAIBCfEkBET8fHxKBSKI36WLFkCQEdHB0uWLCE4OBidTsfVV19NXV2dWxtVVVXMnj0bb29vwsLCeOihhwaUklRw7iNJkoiXEAgEAoFAIDhOhoyY2L59O7W1tfKPq2iKqwLyb37zG7788ks++ugjvv/+ewwGA/Pnz5f3dzgczJ49G7vdztatW3n33XdZuXIljz766KCcj0AgEAgEAoFAMNQZstmc7rvvPr766isOHDhAS0sLoaGhvP/++yxYsACA/fv3M2LECLZt20ZGRgZff/01c+bMwWAwEB4eDsAbb7zB7373OxoaGlCpVIN5OgKBQCAQCAQCwZBjSBats9vtvPfee9x///0oFAp27txJV1eXW1Gs4cOHExsbK4uJbdu2kZaWJgsJOFwd9+6772bv3r2MHz++z2N1dna6VWt0Op2YzWaCg4NRKBSn7yQFAoHgNCBJEq2trURGRqJUDhnj9FmN0+nEYDDg6+sr3gsCgeCc4HjeFUNSTHz22Wc0NTWxaNEiAIxGIyqVioCAALftwsPDMRqN8jY9hYRrvWtdfyxbtmxAFZAFAoFgKFFdXU10dPRgd+OcwGAwEBMTM9jdEAgEglPOQN4VQ1JMvP3221x55ZVERkae9mM9/PDD3H///fLn5uZmYmNjqa6uxs/P77QfXyAQCE4lLS0txMTE4OvrO9hdOWdwXUvxXhAIBOcKx/OuGHJiorKykg0bNvDJJ5/Iy/R6PXa7naamJjfrRF1dHXq9Xt4mLy/PrS1XtifXNn2hVqtRq9VHLPfz8xMvDYFAcPZjNEJQEPSKCxPuOKcO17UU7wWBQHCuMZB3xZBzmF2xYgVhYWHMnj1bXjZhwgS8vLzYuHGjvKy4uJiqqiomT54MwOTJkyksLKS+vl7eZv369fj5+TFy5MgzdwICgUBwpqiuhosvhuuuA7t9sHsjEAgEgnOQIWWZcDqdrFixgptvvhlPz/913d/fn9tuu43777+foKAg/Pz8+PWvf83kyZPJyMgA4PLLL2fkyJHceOONPP/88xiNRh555BGWLFnSp+VBIBAIhjTV1XDppVBefviz2QxHscIKBAKBQHAiDCkxsWHDBqqqqrj11luPWPfSSy+hVCq5+uqr6ezsZObMmbz++uvyeg8PD7766ivuvvtuJk+ejI+PDzfffDNPPvnkmTwFgUAgOP30FBKJifDdd0JICAQCgeC0MGTrTAwWLS0t+Pv709zcfFTfWIfDQVdX1xnsmUBw6lGpVCJ96FCjLyHRI9PQQL/DhgLLli3jk08+Yf/+/Wi1Wi666CKee+45UlNT5W06Ojp44IEHWLVqldtEU8/sflVVVdx9991kZ2ej0+m4+eabWbZsmZsF/GicS9dUIBAI4Pi+14aUZWIoIEkSRqORpqamwe6KQHDSKJVKEhISRFHHocIxhMS5xvfff8+SJUu44IIL6O7u5g9/+AOXX345RUVF+Pj4APCb3/yGNWvW8NFHH+Hv78/SpUuZP38+W7ZsAQ5P/MyePRu9Xs/WrVupra3lpptuwsvLi2effXYwT08gEAiGBMIycZwcS6nV1tbS1NREWFgY3t7eImOKYMjiKsTl5eVFbGyseJbPdgYoJM7lWfSGhgbCwsL4/vvvycrKorm5mdDQUN5//30WLFgAwP79+xkxYoRc0PTrr79mzpw5GAwG2Vrxxhtv8Lvf/Y6GhoYBCelz+ZoKBILzE2GZGCQcDocsJIKDgwe7OwLBSRMaGorBYKC7uxsvL6/B7o6gP84zi0R/NDc3AxAUFATAzp076erqYsaMGfI2w4cPJzY2VhYT27ZtIy0tzc3taebMmdx9993s3buX8ePHH3Gczs5OOjs75c8tLS2n65QEAoHgrEc4Q59CXDES3t7eg9wTgeDU4JqVdTgcg9wTQb8IIQEctqTdd999XHzxxYwePRoAo9GISqVyqz8EEB4ejtFolLfpKSRc613r+mLZsmX4+/vLP6L6tUAgOJ8RYuI0INxBBOcK4lk+yxFCQmbJkiXs2bOHVatWnfZjPfzwwzQ3N8s/1dXVp/2YAoFAcLYi3JwEAoFgKCKEhMzSpUv56quvyMnJITo6Wl6u1+ux2+00NTW5WSfq6urQ/zdVrl6vJy8vz629uro6eV1fqNVqUZ9IIBAI/ouwTAgEAsFQQwgJ4HD2vKVLl/Lpp5+yadMmEhIS3NZPmDABLy8vNm7cKC8rLi6mqqqKyZMnAzB58mQKCwupr6+Xt1m/fj1+fn6MHDnyzJyIQCAYVAwGAzk5ORgMhsHuypBEiAkBAIsWLUKhUKBQKPDy8iI8PJzLLruMd955B6fTOeB2Vq5ceYR/skAgOIUIISGzZMkS3nvvPd5//318fX0xGo0YjUba29sB8Pf357bbbuP+++8nOzubnTt3cssttzB58mQyMjIAuPzyyxk5ciQ33ngju3fvZt26dTzyyCMsWbJEWB8EgvOE0tJS8vPzKS0tHeyuDEmEmBDIXHHFFdTW1lJRUcHXX3/N1KlTuffee5kzZw7d3d2D3T2BQCCEhBt///vfaW5u5tJLLyUiIkL++eCDD+RtXnrpJebMmcPVV19NVlYWer2eTz75RF7v4eHBV199hYeHB5MnT+aXv/wlN910E08++eRgnJJAIBgEkpOTSU9PJzk5ebC7MiQRMRNnKQcOHOCdd96hoqKC+Ph4br31VoYNG3Zaj6lWq2Uf4aioKNLT08nIyGD69OmsXLmSxYsX89e//pUVK1ZQXl5OUFAQc+fO5fnnn0en0/Hdd99xyy23AP8L3H3sscd4/PHH+fe//83LL79McXExPj4+TJs2jeXLlxMWFnZaz0kgOGcQQuIIBlImSaPR8Nprr/Haa6/1u01cXBxr1649lV0TCARDiMjISCIjIwe7G0MWYZk4C1mxYgXDhw/nhRde4MMPP+SFF15g+PDhrFy58oz3Zdq0aYwdO1aeyVMqlbzyyivs3buXd999l02bNvHb3/4WgIsuuojly5fj5+dHbW0ttbW1PPjgg8DhtLlPPfUUu3fv5rPPPqOiooJFixad8fMRCIYkQkgIBAKB4CxFWCbOMg4cOMDixYv7jFO47bbbyMzMPONmuOHDh/PTTz8BcN9998nL4+Pjefrpp7nrrrt4/fXXUalU+Pv7o1AojsiCcuutt8p/JyYm8sorr3DBBRdgtVrR6XRn5DwEgiGJEBICgUBwwhgMBkpLS0lOThbWh9OEsEycZbzzzjv95vZXKBS8/fbbZ7hHh10JXH3asGED06dPJyoqCl9fX2688UYaGxtpa2s7ahs7d+5k7ty5xMbG4uvry5QpUwCoqqo67f0XCIYsQkgIBALBSSGCq08/QkycZVRUVPTrByxJEhUVFWe2Q8C+fftISEigoqKCOXPmMGbMGD7++GN27twp+yHb7fZ+97fZbMycORM/Pz/+85//sH37dj799NNj7icQnNcIISEQCAQnjQiuPv0IN6ezjPj4+KNaJuLj489ofzZt2kRhYSG/+c1v2LlzJ06nkxdffBGl8rAO/fDDD922V6lUOBwOt2X79++nsbGRP//5z8T8dzC0Y8eOM3MCAsFQRAgJgUAgAE7eTUkEV59+hGXiLOPWW289qmXitttuO23H7uzsxGg0cujQIfLz83n22WeZN28ec+bM4aabbiI5OZmuri5effVVysvL+fe//80bb7zh1kZ8fDxWq5WNGzdiMploa2sjNjYWlUol7/fFF1/w1FNPnbbzEAiGNEJICAQCgYxwUzr7EWLiLGPYsGG8/fbbKJVKPDw83H6//fbbp9VM98033xAREUF8fDxXXHEF2dnZvPLKK3z++ed4eHgwduxY/vrXv/Lcc88xevRo/vOf/7Bs2TK3Ni666CLuuusuFi5cSGhoKM8//zyhoaGsXLmSjz76iJEjR/LnP/+Zv/zlL6ftPASCIYsQEgKBQODGsdyURPXqwUchDSRRt0CmpaUFf39/mpub8fPzc1vX0dHBwYMHSUhIQKPRnNRxSktLefvtt+U6E7fddpvw9xOccU7lMy04BmdISBztO0xwYohrKhAMHjk5OeTn55Oenk5WVtZgd+ec4Xi+10TMxFlKcnLyEbP+AoHgHEVYJAQCwXlAz/gH4JSkbHW1JSZcBw8hJgQCgWAwEUJCIBCco/QOns7NzSU7O5upU6cSEhJCfn4+wEmJCRFgPfgIMSEQCASDhRASAoHgHMQlIkwmk1s9qaKiIqxWK3DYkmAymSgpKcFkMpGRkUFkZKQoMjcEEWJCIBAIBgMhJAQCwTmKKwOTTqejtbWVvLw8vv/+e2pqavD19SU+Pp7IyEhCQkLYtGkTCoWCkJAQIiMjKS0tJTs7m4KCAhYsWCAExRBAiAmBQCA40wghIRAIzmFc8Qsmk4ndu3dTU1NDQEAAvr6+BAcHu1knxo0bh9FoRKfTycsKCgqwWCyUlpYKMTEEEGJCIBAIziRCSAgEgnOM/gKrXZ/NZjNBQUFyLSrX8sjISFJSUrBarbLAiIyMZMGCBW7tCc5uhJgQCASCM4UQEgKB4BzE5dZkMpmoqanBYrEAkJWVxfz584+6r06nQ6lUypYJ+F9AtqtQnSuWIjc3F0COrxCcHQgxIRAIBGcCISQEAsE5Qu+BvcuCkJeXx8aNG0lLS3OzKhwtqNpqteJ0OmXLhAuXQAHkWIre8RWCswMhJgQCgeB0I4SEQCAYQqxbt441a9aQkZFBdHT0ESKgtLSUL7/8kubmZgDmz59PZGQkOTk5tLa2ykXOcnJySE5OPmpQdX91InovT05OZtq0aX1uKxhclIPdgePh0KFD/PKXvyQ4OBitVktaWho7duyQ10uSxKOPPkpERARarZYZM2Zw4MABtzbMZjM33HADfn5+BAQEcNtttx2hhgWnnscff5zw8HAUCgWfffbZYHfntHHppZdy3333yZ/j4+NZvnz5SbV5KtoQDCJCSAgEgrMcg8FATk4OBoMBg8HAm2++ybfffsvHH39Mfn6+7G7kIjk5maSkJPz9/d32v+CCC7jiiivQ6/Xk5ubK+yYnJxMYGCgHVQ+EyMhIsrKyZOERGRnJ/PnzZeEiOHsYMpYJi8XCxRdfzNSpU/n6668JDQ3lwIEDBAYGyts8//zzvPLKK7z77rskJCTwpz/9iZkzZ1JUVIRGowHghhtuoLa2lvXr19PV1cUtt9zCHXfcwfvvvz9Yp3bOs2/fPp544gk+/fRTMjIy3O7ZifL444/z2WefUVBQcPIdPI1s374dHx+fAW27cuVK7rvvPpqamk64DcFZhhASAoFgCNDTpQggJCSEUaNGcfXVV8uWCRcul6VZs2bJwdQuy0NgYCAjR46kqqqKoKAg0tPT5X2jo6OPaKv3sYVIGJoMGTHx3HPPERMTw4oVK+RlCQkJ8t+SJLF8+XIeeeQR5s2bB8C//vUvwsPD+eyzz7juuuvYt28f33zzDdu3b2fixIkAvPrqq8yaNYu//OUv4iE+xTgcDhQKBWVlZQDMmzcPhUIxyL06Nna7HZVKdUraCg0NPSvaEAwCQkgIBIKzGJco0Ol0mEwm+bePjw+jRo0iMzOT9PT0I7Zfv34969atY+LEiTzyyCPy2MmVzjU6Opr09HR0Op3s+VFaWkpVVRXp6elHjLX6c3MSDB2GjJvTF198wcSJE7nmmmsICwtj/Pjx/POf/5TXHzx4EKPRyIwZM+Rl/v7+XHjhhWzbtg2Abdu2ERAQIAsJgBkzZqBUKvnxxx/7PG5nZyctLS1uP+cil156KUuXLmXp0qX4+/sTEhLCn/70JyRJkrfp7OzkwQcfJCoqCh8fHy688EK+++47ef3KlSsJCAjgiy++YOTIkajVam699Vbmzp0LgFKpdBMTb731FiNGjECj0TB8+HBef/11tz7V1NRw/fXXExQUhI+PDxMnTuTHH39k5cqVPPHEE+zevRuFQoFCoWDlypV9nteiRYu46qqreOKJJwgNDcXPz4+77roLu91+xLnfd999hISEMHPmTAD27NnDlVdeiU6nIzw8nBtvvBGTySTvZ7PZuOmmm9DpdERERPDiiy8ecfzeLkpNTU3ceeedhIeHo9FoGD16NF999RXfffcdt9xyC83NzfI5Pf744322UVVVxbx589DpdPj5+XHttddSV1cnr3/88ccZN24c//73v4mPj8ff35/rrruO1tZWeZvVq1eTlpaGVqslODiYGTNmYLPZ+ryGghNACAmBQHCW4nJJ+vvf/85DDz3Ek08+SWFhIUajkezsbLZv305lZSUrV650s1bk5uby0UcfsWXLFqqqqti5cye5ubl88skn5ObmkpmZydSpU8nIyCArKwur1Up2djbvvPMOJSUlxMbG9ikYIiMjZeuGwWA4ped4qtoTHJ0hY5koLy/n73//O/fffz9/+MMf2L59O/fccw8qlYqbb74Zo9EIQHh4uNt+4eHh8jqj0UhYWJjbek9PT4KCguRterNs2TKeeOKJE++4JEFb24nvfzJ4e8NxWALeffddbrvtNvLy8tixYwd33HEHsbGx3H777QAsXbqUoqIiVq1aRWRkJJ9++ilXXHEFhYWFDBs2DIC2tjaee+453nrrLYKDg4mIiODSSy/llltuoba2Vj7Wf/7zHx599FH+9re/MX78eHbt2sXtt9+Oj48PN998M1arlSlTphAVFcUXX3yBXq8nPz8fp9PJwoUL2bNnD9988w0bNmwAkP02+2Ljxo1oNBq+++47KioquOWWWwgODuaZZ55xO/e7776bLVu2AIcH/dOmTWPx4sW89NJLtLe387vf/Y5rr72WTZs2AfDQQw/x/fff8/nnnxMWFsYf/vAH8vPzGTduXJ/9cDqdXHnllbS2tvLee++RlJREUVERHh4eXHTRRSxfvpxHH32U4uJiALc0eT3bcAmJ77//nu7ubpYsWcLChQvdhF1ZWRmfffYZX331FRaLhWuvvZY///nPPPPMM9TW1nL99dfz/PPP8/Of/5zW1lZ++OEHN+EoOAmEkBAIBGcB/WVQcrkkffXVV5SXl2Oz2ZgwYQIGg4H9+/fjcDjo7Ozk0KFDrF271q0uhCRJJCUlYbPZmDx5MoCcYemaa64hKytLPk5ycrI8WZaQkMCSJUuA/wVlu/pkMBhYvXo1FosFk8lESEhIn1mfjgfhOnVmGTJiwul0MnHiRJ599lkAxo8fz549e3jjjTe4+eabT9txH374Ye6//375c0tLCzHHMzBoa4M+BoVnBKsVjsPXPiYmhpdeegmFQkFqaiqFhYW89NJL3H777VRVVbFixQqqqqrkf8wHH3yQb775hhUrVsj3pauri9dff52xY8fK7QYEBACg1+vlZY899hgvvviinH86ISGBoqIi/vGPf3DzzTfz/vvv09DQwPbt2wkKCgLcTaA6nQ5PT0+3NvtDpVLxzjvv4O3tzahRo3jyySd56KGHeOqpp1AqDxvnhg0bxvPPPy/v8/TTTzN+/Hj5vADeeecdYmJiKCkpITIykrfffpv33nuP6dOnA4cFSXR0dL/92LBhA3l5eezbt4+UlBQAEhMT5fX+/v4oFIqjntPGjRspLCzk4MGD8nP4r3/9i1GjRrF9+3YuuOAC4PD/y8qVK/H19QXgxhtvZOPGjbKY6O7uZv78+cTFxQGQlpZ2zOsoGABCSAgEgrMEl2j4/vvvsdvtHDhwgGHDhhEXF4eHhwehoaGyUIiIiJCFRVFRESNHjuTiiy+WJ/JMJhPl5eW0t7fT3d2Nl5cXISEhZGRkyMfrbXWIjIyUszrpdLp+szqVlpZisVjkeMpTIQKE69SZZciIiYiICEaOHOm2bMSIEXz88cfA/waqdXV1REREyNvU1dXJM8V6vZ76+nq3Nrq7uzGbzf0O4NRqNWq1+lSdxllNRkaGmxvS5MmTefHFF3E4HBQWFuJwOORBsIvOzk6Cg4PlzyqVijFjxhz1ODabjbKyMm677TbZ6gGH74XLwlBQUMD48eNlIXEyjB07Fm9vb7fzslqtVFdXy4PpCRMmuO2ze/dusrOz+7QOlJWV0d7ejt1u58ILL5SXBwUFkZqa2m8/CgoKiI6OPuIaHg/79u0jJibGTdCOHDmSgIAA9u3bJ4uJ+Ph4WUjA4f8f17M/duxYpk+fTlpaGjNnzuTyyy9nwYIFpyQw/rxGCAmBQDCI9Kz94OPjQ15eHjabjYaGBnJzc6mtrWXr1q1MnjyZuLg4kpOT8fb2lsc4F198MR0dHdTV1dHe3s6UKVNkAWAymdi0aRPV1dUkJSURFhaGXq+XMyz1x6xZs/Dz8yMzM/OI2IrS0lLZxQn+N/B3WSZOhsjISGGROIMMGTFx8cUXy+4fLkpKSuTBYEJCAnq9no0bN8rioaWlhR9//JG7774bODyIbGpqYufOnfLgcdOmTTidTrdB4SnF2/uwhWAw6DGAPlmsViseHh7s3LkTDw8Pt3U9B9xarfaYQdaugKx//vOfR1x3V9tarfZUdHvA9M6WZLVamTt3Ls8999wR20ZERAw4tV1PzuQ5eXl5uX1WKBQ4nU7g8DVev349W7du5dtvv+XVV1/lj3/8Iz/++KNbUgPBcSCEhEAgGETy8/N54YUXKCwsRK1W4+npiVarZdKkSYSGhsrvH4fDQU1NDfX19Wg0GlpaWmhtbSUlJYXQ0FAiIyMZOXIko0aNkl2NelafdjqdpKWlERoa6hac3R/p6elu20VGRrJgwQI58Nvl8tTTPUqIgKHHkAnA/s1vfkNubi7PPvsspaWlvP/++7z55puyD55CoeC+++7j6aef5osvvqCwsJCbbrqJyMhIrrrqKuCwJeOKK67g9ttvJy8vjy1btrB06VKuu+660/fwKhSHXY0G4+c4Myf1DkLPzc1l2LBheHh4MH78eBwOB/X19SQnJ7v9DMTVqCfh4eFERkZSXl5+RFuuweyYMWMoKCjAbDb32YZKpcLhcAzoeLt376a9vd3tvHQ63VHd1dLT09m7dy/x8fFH9NHHx4ekpCS8vLzcrpnFYqGkpKTfNseMGUNNTU2/2wzknEaMGEF1dTXV1dXysqKiIpqamo6w3B0NhULBxRdfzBNPPMGuXbtQqVR8+umnA95f0AMhJAaNnJwc5s6dS2RkZJ81bETtIcG5isFg4C9/+Qu33XYbr732Go888gjr1q2jtLSUkpIS6uvr8fb2xmq1UlhYiNPpxM/PD5VKRX19PVarlYCAAEJDQ9FoNBw4cIBdu3ahVCoZNWoU8fHxcnC1wWAgMjKS++67jzvuuAO1Wo3FYqGiouKEgpxd9SOsVmufNSwEQ48hY5m44IIL+PTTT3n44Yd58sknSUhIYPny5dxwww3yNr/97W+x2WzccccdNDU1kZmZyTfffCPXmIDDgb9Lly5l+vTpKJVKrr76al555ZXBOKWzjqqqKu6//37uvPNO8vPzefXVV+UMRSkpKdxwww3cdNNNvPjii4wfP56GhgY2btzImDFjmD179nEd64knnuCee+7B39+fK664gs7OTnbs2IHFYuH+++/n+uuv59lnn+Wqq65i2bJlREREsGvXLiIjI5k8eTLx8fEcPHhQdh3y9fXt1x3Nbrdz22238cgjj1BRUcFjjz3G0qVL5XiJvliyZAn//Oc/uf766/ntb39LUFAQpaWlrFq1irfeegudTsdtt93GQw89RHBwMGFhYfzxj388aptTpkwhKyuLq6++mr/+9a8kJyezf/9+FAoFV1xxBfHx8VitVjZu3Ci7Znn3si7NmDGDtLQ0brjhBpYvX053dze/+tWvmDJliluWsqPx448/snHjRi6//HLCwsL48ccfaWhoYMSIEQPaX9ADISQGFZvNxtixY7n11lv7dLUQtYcE5xI93ZjMZjP/+Mc/qKurY9OmTXR1ddHZ2Qkczpzo6elJaGgoNTU1VFdXy1aKyspKGhoaUKvVaDQaJkyYgEqlwm63s3fvXmpqavjggw/o7OyktraW+Ph4QkJC3NyGCgsLUSqVWK1W2TOh94Rsfn4+mzdvPiK9bE+OFdfQXwC54OxjyIgJgDlz5jBnzpx+1ysUCp588kmefPLJfrcJCgoSL4l+uOmmm2hvb2fSpEl4eHhw7733cscdd8jrV6xYwdNPP80DDzzAoUOH5OCro92T/li8eDHe3t688MILPPTQQ/j4+JCWliZXj1apVHz77bc88MADzJo1i+7ubkaOHMlrr70GwNVXX80nn3zC1KlTaWpqYsWKFSxatKjPY02fPp1hw4aRlZVFZ2cn119/vZx2tT8iIyPZsmULv/vd77j88svp7OwkLi6OK664QhYML7zwguwO5evrywMPPEBzc/NR2/3444958MEHuf7667HZbCQnJ/PnP/8ZgIsuuoi77rqLhQsX0tjYyGOPPXZEPxUKBZ9//jm//vWvycrKQqlUcsUVV/Dqq68e/YL3wM/Pj5ycHJYvX05LSwtxcXG8+OKLXHnllQNuQ4AQEmcBV155Zb/Prag9JDgX6C0gPv74YxQKBT4+PtTX19PZ2UlzczNKpZKQkBA0Gg12u53u7m6qq6vx8/OjpaUFSZLw8vKivb0dT09PIiIiaG5uJiAggMWLF7Nu3Tq2bdtGQ0MDTU1NdHZ20t7eTmJi4hGDfbvdTkdHBxqNhpiYGDd3Jdf/zObNm1m7di2lpaVybEXPc3KJhJ7uTb050YxMQoSceRSSyAd5XLS0tODv709zc7OcpcBFR0cHBw8eJCEhwc0aMhS49NJLGTdunFs9g3OBRYsW0dTUdIT7g2BgDOVn+rQyhIXE0b7DhjIKhYJPP/1UdmstLy8nKSmJXbt2uaVrnjJlCuPGjePll1/mnXfe4YEHHsBiscjru7u70Wg0fPTRR/z85z/v81idnZ3yLDD8L8vfuXZNBYPLunXrePnll2loaECSJJqammhqaiIuLg5/f3927dqFw+FAo9HQ3d1NUlISI0eOZOfOncDhxCJFRUVUVFQQGRmJWq2mqqoKvV7PRRddRG1tLbNnz2bx4sW88sorrF27FoVCgZ+fHx0dHdjtdqZPn86kSZNkQZGbm0t5eTn19fWkpKQwa9YsedCfnp4ui4P8/HxWrlyJl5cX8+bNcxMNOTk5R2wPR4qAExUF/bUvOD6O510xpCwTAoFAMOgMYSFxPnG6ag/BKag/JBD0QX5+PmvXrqWzsxO1Ws3mzZvZunUrnp6eaDQaLBYLkiSh1WoZM2YM5eXltLa2yoHUcFjoOhwOxo4dy7Bhw8jJycHpdKJWq+WMfq7/iYCAADljYmZmJgCpqalotVp0Oh0VFRUUFRWRnZ0t97GqqgqVSkVkZCQpKSlug3yX4DAYDFitVmbPnk1xcTE6nc5NGPTn3tTbEnGiGZlEWtgzjxATAoFAMFCEkBBwCuoPCQT/xWAwsHbtWtatW0dBQQFtbW1YrVZUKhU+Pj4oFAp8fX0JDg7GbrejUCgwGo1s2LBBth64sje1tbVRWVmJ1Wpl7969lJWV4evri4eHB2FhYSgUCjo7O1EqlTQ2NhIeHk58fDzwv6xLrkG/Xq/HarXicDgIDAwkOTmZwsJCysvLSU1Npbm5mZKSkiMyPuXk5GAymaiqqkKpVGKxWNi8eTPR0dFUVVUdtSjdqRIBIi3smUeICQGAW/Xkc4mVK1cOdhcE5wpCSAwpTlftITi/6g8JTg+uWIgNGzbw5ZdfcujQIRQKBSqVCqfTKads9fHxwcfHh+rqaqxWK93d3VgsFtRqNVqtFi8vL8LCwqioqKC4uBi1Wo3D4aCurg6VSiVnMgsODmbkyJHU1dXJbep0Ojlzmas/ubm5NDQ0MHfuXOLj4wkMDJRrRKxevVrOJChJEuXl5bS1tZGZmYnVapVFRGxsLLGxsZjNZlpaWrBYLERHR5Oeno7JZOo3DkKIgKGLEBMCgUBwLKqqYOpUISSGEGdt7SHBeYtr1l+n0/HPf/6T7777DqfTiclkQpIkJEmSsyOZzWbZElFZWUlHR4dbrSA4XA9JkiQcDgceHh44nU46OzvRarVIkkR0dDSZmZkcOnQIu91OQ0MDDQ0NtLS0yAXqXNYFV2VqV5rWoqIiAJxOpyw4UlNTKS0tJSMjA41GQ25uLrt27aKlpQWdTkdsbCzp6elye1VVVYwcOdLNEmEwGE64KJ0IrD57EWJCIBAIjoYQEmctVqvVLUe9K110UFAQsbGxcu2hYcOGyalh+6s99MYbb9DV1XX6aw8Jzjt6zvrv37+f2tpaiouL6ejowMfHB0/P/w3FHA4Her0eDw8P7HY7drud9vZ2nE4nCoUCpVIpp0JvbW3F4XDQ3t7OJZdcQl5eHkqlEqVSiUajYfjw4QQFBREUFCQLDovFQlNTE/X19bS1tVFWVkZbWxupqamEh4cTFxdHR0eHXPMoNjZWtiYUFxcTHBxMdHQ0ycnJFBUV0dDQgF6vJyUlxU0wmEwmYmNjycjIcPtf6s/6MBChcKLZnfq7J0KYnDqEmDgNuGYPBIKhznmf7E0IibOaHTt2MHXqVPmzK47h5ptvZuXKlaL2kGBQcYmIzz//nE2bNtHS0kJ3d7ccJA2HrWWenp54eHjgcDhoa2vDaDSiVCrx8fFBpVLh4eFBZ2cnTqdTFg+9Xe0iIyP55S9/SXFxMe3t7XR3d1NZWcmhQ4eYOnUqAQEB1NbW0tzcjFqtJiUlhaCgIIqKirBYLGzfvh2j0ci4ceMICgqivLycvXv3EhERgdVqpaamBovFIsdPlJaW4nA4GD9+PLNmzXIbkOfm5rJp0yamTZs2YOEwEKFwKgOrT6UwEQgxcUpRqVQolUoMBgOhoaGoVCoUx1mFWiA4W5AkiYaGBhQKBV5eXoPdnTOPEBJnPZdeeulRBa+oPSQ4k/QcJH/33Xe8/PLLtLa2Ul9fT3NzM93d3UfsI0kSTqcTb29v2tvb8fLyQqFQYLPZ6OjoICkpCU9PT4qLi/H09JQDs51OJ11dXWg0GvR6PQaDgaamJry9vYmKikKr1bJv3z7ZarB7924qKipQKBRERUURExODVqtl1qxZVFRUsGHDBg4ePIhWqyUqKop9+/ZRXFzMmDFjGD58uJzlqfdMfnt7O6tXrz6iON3Rxj59DeQHIhROZUyFyPh0ahFi4hSiVCpJSEigtrb2uMvLCwRnIwqFgujoaNlH97xBCAmBQHCcrF27ljVr1hAXF8cnn3xCfX09Hh4e+Pv7ExoaSkNDwxGCwt/fXy4kp1Qq8fDwIDAwEJvNRmdnJzabDbVajd1ul4ujbtq0SR6MS5JESUkJTU1NeHh4EBQUhNPp5Ntvv6WtrU2OYwgKCiIuLo59+/YREBAgWyKmTZtGSEgI7e3twOHv/NjYWLq7uzEYDBgMBtrb24mOjmbmzJlyv10De1d9ClcGKBdTp04lIyND/tw7NazJZMJkMmEwGPpNA3s6XZFEsPepRYiJU4xKpZL/EV1mTIFgqOLl5SWEhBASAoGgH/Lz81mzZg1arZb169eze/ducnJy5HgGX19fdDodzc3NcpC0SqWSRYXVasXpdGK32+XA6ba2NnQ6HS0tLVRVVeHp6Sm7QO3fv5+Ojg5CQkKIjo6muLiY1tZWLBYLPj4+HDx4kK6uLqqqquju7iYnJ4d58+Yxf/58cnJysNls2Gw29u7dK7tk63Q6QkNDsVgs7N+/Hz8/P2bNmoVKpWLLli00Nzf3e/6ZmZmUlpbi5eVFbm4uRUVFlJWVMXfuXLfBek9rRFZWFiEhIWRnZ1NTU8OCBQv6HNifblckETdx6hBi4jTgcgs5L11DBIKhTG8hkZ0thIRAIDgCV4G5vXv3sm/fPpqbm+UMTE6nUy4up1KpOHToEG1tbcDhCRqXBcLpdOLh4UFHRwfd3d0oFAra29tpb29Hq9XicDhkdybXum+//ZaYmBiSk5NJSUmRg7SDg4NpbW2lrq4Ob29v2e0aDleD/+STTzCbzaSlpZGbm8u+ffuIjIwkPj4eq9VKW1sbnZ2dWK1WysrKqKioIDo6mssuu4ygoCA3K0NP0tPT0ev1lJaWYjKZKCsro6mpCbPZTE5OjjxQ7+1WlJycTEFBARaLhdLS0j4H86fbFUnETZw6hJgQCAQC6FtIxMYOdq8EAsFZQH5+Pps3byYzMxO9Xs9rr73Gli1b8PX1RalU0tLSQnt7O0qlEp1Oh9PpxNPTE0mSUKvVdHR0oFAo8PDwoKurC6fTiY+PD15eXrKFwGWt6OrqAg5XY7fb7QBotVo8PDzw9PSUg6vr6+vp6OgAkNOzqlQqvLy8iI6OprW1leDgYOrr6+V4iWuuuYaUlBRyc3Px9fXFarWSnJzMtGnT5BTKQUFBmM1mCgoKmDZtGvPnzx/QNYqPj2fu3Llu1wz6rmYdGRnJggULZMtAX5yIK9LxWBtE3MSpQ4gJgUAgEEJCIBD0oGdNiPz8fN58800OHjxIamoqixYtoqGhAZvNRlNTE5IkoVAo8PT0xNPTE51Oh9lspq2tDa1WS3x8PJIk0djYSFtbm7w8KCgIo9GI3W5Ho9EQFhYmx0q4isrZ7XZMJhOenp74+PgQEBBAV1cXtbW1qNVqFAoFOp0OPz8/WVh0dXVht9tlC0hYWBgdHR3o9Xo5ZsElGEpKSjCZTMTHx8uxE8XFxXKNi97Xo69BumuGPz09XRYeA6kncTriFo7H2iDiJk4dQkwIBILzGyEkBAJBDwwGA6tXryYvL4+qqioaGho4ePAgdrudgoIC3nvvPWw2G83NzdhsNrn2g1arBcDHx4fGxkacTidKpVKOeXA6nbS1tcnxEUFBQdTV1aFUKpEkiaamJrq7u4mNjWXu3LmEhYWxefNmdu7ciclkorOzk+7ubjw9Peno6ECr1eLt7U1cXByhoaFs2bIFh8MhZzgLCQlhypQpBAQEsHv3bkaOHCkPnl0xFB9++CE2mw1fX1+Cg4NpbGykpKSEqKgoJk+eLF+PvgbpLmtNamqqXKyup+jIysrq9/r2tEj0FCknG8cgrA2DgxATAoHg/EUICYFAgPsANzc3lw0bNlBQUCAnUnHFOqjVavbv34/dbqejo0OuByFJEp2dnXh7exMRESEHRXt5edHW1obVapVjIyRJoqWlhaKiIvz9/XE6nbS2ttLS0gKAyWTCarUSFhaGWq1Gr9djt9vp7u4mKSkJlUpFTU0NGo2GwMBAOQ2s1WolMDAQlUqF2WwmLCyM6667joqKCqxWK7m5uZjNZjkGQqfT4e3tTWlpKSUlJVx88cVkZGRQWlpKQ0MDRqORQ4cOUVNTI6d+7TlI37x5M9nZ2QDcc889AOTk5BwhOnoLhJ7CBNzdoXqLluMVF8LaMDgIMSEQCM5PhJAQCM578vPzWbVqFbt375bjB5qbm9m3bx9weHb/iiuuYMuWLVRXV8uB0AqFQq4s3dXVRWtrK3DYKhEaGkpNTQ1GoxFJkvDw8KCtrU2Oo1AqlTgcDjo7OwHk2ApXbaquri6+/vprMjIysNvtqFQqfH190Wg0+Pn50dXVhZ+fn1zF2mg00tLSgsPhQK1W43A4aGlp4dChQ7LlQKlUsmPHDgoLC9Hr9dTU1GC329m2bRsNDQ20tbVRXV1NRkYG4eHhcptWq5Uff/wRb29vFi9ejMFgkAOrMzMzAeTfcDgzlCtuxEVvgdCX9aCvAO2+9hWcnQgxIRAIzj+EkBAIzmtcM945OTl88cUXcgyCwWCgtbUVlUpFeHg4CQkJNDQ00NnZSXNzMxqNBrVaTUBAACqVisbGRlpbW/Hw8ECn0xEdHY3RaKSzsxOFQoFCoSAwMBBvb298fHzo6OigubkZi8WCJElyGnkvLy98fHwICgrCarXS1dVFaWkpw4YNo6WlhY6ODqxWKy0tLahUKqKjo+nq6pJdrVwZncaOHcvBgwdpaGjAarXywQcfcPHFF+N0OlGr1SQkJNDZ2UllZSUWi4XOzk5CQkKw2WxUVVWRm5vLxRdfTH5+Pjt27JBrXWzdupVZs2bJg3uTyQRAdHQ0DQ0NvPLKK2RmZsqpbq1Wq3ytewsEl/WgL6tDb8tCX8LjZF2hRErYU48QEwKB4PxC1JEQCM5LDAaD7OpjNBpxOBxERUUxfvx4OVjZx8eHzs5OOetSRUUFTqeTjo4O/Pz8GDZsGGFhYZjNZgoLC2lubsbLywtfX18CAgKoqamhu7tbXqbVagkJCcHHx0cepG/ZsgWlUomXlxcOhwMfHx+8vb2Bw5mUVCoVBoOBkpISiouL5XTzarUaPz8/wsPDkSSJsLAwtFot1dXVSJKEw+HAbrcTFBSEr68vra2tVFdXs3v3boKCgpg+fTqJiYkUFhaiVCrlqtvh4eHU1dVRW1tLQ0MDc+bMwWg08sMPP8jH9PLycotzMJlMZGdnI0kSSqWS6upq4LCVQqlU0t7ezltvvYXRaGTWrFl9xk8MxOrQl9vS8Vgr+hIOwtpx6hFiQiAQnD8IISEQnHe4BpR5eXl88cUXcgrXCRMm4OPjQ2BgIBERETgcDkaOHElCQoIcNwCH3ZDi4uJwOp14eXlRVVVFU1MTLS0tdHV1oVQq6erqwmQy0d3djU6nw9/fXx6o7tmzB6vVSnl5ObGxsYwbN476+noOHDiAzWaT24XDFbH9/PwICQnhp59+oq2tDW9vb6ZMmYK3tzd2u53k5GRqamrQarXs378fhUKBVqslOjoam82G0WgkICCAsLAwfH19qaurk92a0tLS8PHxYdOmTXh6euLh4UFVVRX+/v7MmDGDkSNHotPp0Ov1XHnllUiShNPpJC4uDp1O51bF2oWPjw/FxcWyZcJisbBmzRoOHDhAa2srfn5+pKenH3FfTjRY+nj260s4iCDtU48QEwKB4PyguloICYHgPKBnWler1YrJZKKqqop9+/bR0dFBR0cH7e3tbN++nezsbEwmE5IkMWHCBBITE9myZQt6vR6dTsfevXtpbW2lqakJnU6HWq3GYrHI2ZRCQ0Pp7u6WC8t5enqSkJBAd3c3TU1NKJVKTCYTDoeD+vp6tFotYWFhGAwGOjo68PT0JDg4mODgYFQqFe3t7TidTpKSkpg8eTKFhYVERkYyb9481qxZQ0lJCWq1mltuuQWTySRnX7Lb7XIchpeXF6GhoXJwtt1ux2KxYDAYWLNmjVwB29vbm5kzZ+Ll5UVqaiqzZs0iMjKSnJwcrFYrc+bMkQPSzWYza9eulQPSs7KymD9/vnytXVWsDQYDBQUFtLS0yC5VISEhbgXsXJxosPTR9uttiehLOJzKIG3hMnUYISYEAsG5T3U1XHqpEBICwTmMa2BXUlIiuxLFxsYSFRWF1WpFoVDg6+uLn58fFRUVGI1GuTCcKyYhMTGRvXv3EhISgkKhwGAw0NLSQktLC21tbURHR+Pr64uXlxcJCQlcf/315OXlUVZWhsPhoLS0lOLiYvR6vZy9yRVAfdlll9HZ2cnGjRsxm81otVouvPBC1Go1Go0GhUKBJEkcOnSIqqoqEhISuOmmm1i3bh0PP/wwHR0dREREcMEFF5CcnExJSQmxsbEcOnSIPXv2UFxcjEqlQqfTkZSUBIDFYiEuLo5Zs2axb98+KioqCAwMRK/Xo1QqcTqd6HQ6uUK3wWDAZDIRGxtLe3s7y5cvp6SkhObmZsLCwoiLiyMnJwedTkd6ejq5ubls2rRJLm7XsxhdSUkJBQUFlJeXU19fD5x+t6LelojTnd1JuEwdRogJgUBwbiOEhEBwXuAa2LlERWBgIPHx8RiNRsrKyigrK8NiseDh4YGXlxcBAQGMHTuWuro68vLy0Ol0xMfHc8stt7B+/XrWrVtHTEwMfn5+VFdXY7PZaG1tRalUAiBJEps3byYvLw+TyURQUBCdnZ1y3YiOjg46OzsJCAhg2rRpLFu2jMLCQrZs2YKnpyfh4eGEhoZy4MABOe2rK6DaZrPx9ddfs2PHDioqKjh48CCSJBEYGEheXh4bN25k+/btACQlJckuSCaTCQ8PDxQKBS0tLWi1WsaPH8+CBQt4//332bFjB+3t7UyZMgU/Pz8uuOACiouLsVgs8v4ucbB9+3Y++ugj7HY7UVFRpKWlyedcX1+PXq8HcCtuB/8bxJtMJhQKBXq9npSUlDPiVnSmXJh6Wr96p8w9HxFiQiAQnLsIISEQnDe4BnSuwm4+Pj5ERESwb98+lEol6enpVFRU0N7ejkqlYtKkSdx2220UFhby5ptvEhISIqeH/f3vf8/+/fvx9/cnIiKCmJgYDh06hLe3t5wK1mg0YjKZqKmpoauri87OTjQaDdHR0YwbN45vv/0Wq9WKTqejo6OD0tJSbDabnLFJrVZTXV2N2WyWg761Wq3sQlVVVUVRURHe3t6o1Wq0Wi0Oh4P3338fnU5Hd3c3HR0dhIWFMWvWLLZs2cKuXbtQq9WUlJTQ0NDABRdcQGZmJqWlpdTX16NSqXA6nVRWVjJ69GjS0tJIS0uTXXVKSkowGAyYzWb0ej3x8fGo1WrGjBnDrFmzAOTsVqtXryYzM5Nrrrmmz8F0RkaGXAX7TM3an6k6Ez2rfvdXnO98QogJgUBwbiKEhEBwTtLTT91oNLJ582YyMzPR6/WUlJSwc+dOAJxOpzw4ttlsNDY2UllZiYeHB2PGjGH27NlERkaSm5uLp6enHDztGvR3d3fjdDrl9K7t7e20t7fT1tYmp3N1OBxoNBrgsKXC09OT5ORkYmNjGTNmjOzOpFQqycvLo7y8HLPZjNPpxGaz0d7eTmtrKzabDYfDQUJCAhqNhu7ubrnitSvI29vbG4fDgclkIjg4mKlTp7J//36USiV2u53LLrsMnU7Hzp07KS8vR5IkAgICyM/P5+OPPyYsLIwLLriAsrIySkpKgMOD4qysLDnewWg04uXlJWdhSklJkWNOrFYrWVlZ6PV63n77bXbt2kV0dLQcO+Fyf3IJsnO5gJwI4nZHiAmBQHDuIYSEQHDO4RIRrsEtwPfff8+XX35JQUEBc+bMYc2aNRw6dIjExESampr49NNP8ff3R6vVYrFYqK+vx8/Pj4aGBlauXAmA2WymoqICb29vNm3aJM/gh4WFMW7cOFJSUqisrKS7u1t2SXIJDJ1Oh4eHB7W1tUiSRGhoKE6nk2+//RZvb2+mTp2Kr68vOTk5vPTSSygUCpxOJ3A4vaorVsHT0xOr1UpFRQUOh0MWKyEhIQQFBVFbW4tKpSI4OJiuri7GjBnD9OnT6ejooK6ujr1795KYmMiIESNobGykvb0db29vJkyYgNFo5ODBg5jNZhISEqiqqkKj0ZCWlkZycrLbdbVYLHR3d2OxWGTxYDAYZAsDHJ7912q1lJaWUl5eDiDHTrgC112cqeDkMx0IfS4LpRNBiAmBQHBuIYSEQDDk6Z2RKTk5mdLSUj7//HOampqYPHmy7Jbjchf66quvGD16NOnp6URERPDmm2/S3NxMTEwMv/jFL8jPz2f//v3ExMTQ0NDATz/9xObNm4mOjiYwMBCFQkFRUREHDhzA19eXMWPGMGLECLkGg9lsJjw8HLvdjkKhQKPREBsbS1VVFQ6HA51Ox89+9jM5y1JDQwNff/01wcHBlJSUyNmcwsPDcTqdWCwWt3P28fHBx8cHm80mZ4cKDg7G4XAQGBhIcHAw/v7+BAYG0tjYyN///ncaGhpwOBxYrVYKCwtJS0tj4sSJBAYGEhoaSlBQEN7e3iQmJtLV1UVTUxMajYbJkydz6623ArB69WosFgtpaWkEBgZSWFhIUVERl19+OXDkwNlgMLBjxw5MJhN1dXXycpvNRnNzMyNHjpTv15kKThaB0IOLEBMCgeDcQQgJgeCcwDU4VCqVWCwWCgoKyMzMpKmpiZ07d2IymTAajVxwwQX85je/kS0S48aN45577iE/P5+JEycSGRlJWloaAOnp6cyYMQOz2cz69evR6/VkZmYCyG5SMTExfP7551gsFiIjI9m4cSMtLS0cOHCApqYmtFotcLi4XGZmJp2dnQByNevCwkJiYmLQ6XRUVFTQ1NREVVWVXMguPDwcDw8PlEql7FrkcDjw9fUlOTmZzMxM6urq2LJlC1qtFg8PDzo7O9HpdGi1Wg4ePEhcXBx1dXUYjUbMZjN2u90toFuv16PX6zEajRQWFuLh4SEXsXOlrp0xY4acBtYVlG42m2lpaaG5uRlJkiguLmbmzJl93htJkggJCSE1NRU4HB9RU1ODxWIhJCSkzyrWpxPhdjS4DBkx8fjjj/PEE0+4LUtNTWX//v0AdHR08MADD7Bq1So6OzuZOXMmr7/+OuHh4fL2VVVV3H333WRnZ6PT6bj55ptZtmwZnp5D5jIIBIL+EEJCIDhncA0KdTodmzdvlt1uJk+eTElJCZWVlTQ2NuLn58eCBQtkdxvX4NZqtZKSkkJkZCT5+fns2LEDvV7P+PHj8fb2JjQ0lGnTpgHw2muvYbVaGTlyJDNnzmT79u389NNPtLe3YzQaaWpqoru7W64y7Qp87uzspLa2FqfTSUhICF5eXnh6eqJWq6mvr8dsNtPd3Y2Pjw8BAQFyfQsXKpWKCy+8EKfTiSRJjB07loyMDMxmMx0dHbS2tsouTocOHZIzOrW2tqLT6fD09JQtGJGRkfz85z9HrVZz6NAhPDw8KCsrIykpCb1ez86dO/H19SUuLg4/Pz/i4+Pl62wymSgqKmLLli0Acqan1NTUPuMgkpOTWbhwIWazmaCgIAwGg1tK2J7uUL2tBKfLHUm4HQ0uQ2oUPWrUKDZs2CB/7ikCXDMTH330Ef7+/ixdupT58+fL/xwOh4PZs2ej1+vZunUrtbW13HTTTXh5efHss8+e8XMRCASnECEkBIIhTe9BpmtgWFpaKldWdlVeDgoKory8nI6ODjlTUWVlJVqtVrYcuAa0JSUldHd309raSnt7OxUVFcyePZtp06axa9cunnrqKUwmExEREXI/Wlpa5HoPzc3NNDc34+XlxciRI1EoFFRVVaFQKLBarVRWVtLe3k5UVBSXX345jY2NcgpaQM7QFB0dTXFxMS0tLSiVSrq7uzGbzQCMGDECp9OJwWDgb3/7GzExMSiVSjw9PRk+fDh+fn789NNPdHR0IEkSNTU1+Pj4oNFo0Gq16HQ6srKy5IJ6cXFx2O12mpqa0Ov1pKenYzQa0Wg0dHR0yG5RrmsOh8dIoaGhAKSkpDBr1ixyc3NZtWoVHR0daDQa5s6dy/z58wEICQkBDk/SuiwRAxnQC3ek08NgF88bUmLC09NTzmvck+bmZt5++23ef/99eaZhxYoVjBgxgtzcXDIyMvj2228pKipiw4YNhIeHM27cOJ566il+97vf8fjjj6NSqc706QgEglOBEBICwZCn5yDT9TkvL48tW7Ywe/ZsFi9eLK/LyMiQ/3aNCaZOnUp5eTnff/897e3t2Gw2ebAeGhpKVVUVDQ0NSJKEXq8nICCA1atXU1NTg5+fH5deeikZGRnk5ubS0NCAWq2mrq5Orhvh6elJVFQUMTExhIeHY7VasdlshISE0NzcTFBQEDt27KC6uprq6mrsdjtKpVLOvgTg7++Pr68vZrOZtrY2lEolra2t7N+/n6ioKIxGIw0NDdTX1+Pl5QVAdHQ0NpuN8PBwvL29aWxsxGg0YrPZ0Gg0JCcnM2bMGNrb2ykrK5NrSuTm5lJZWQkgW3YsFgs2m43Ro0ej0+l45513KCwsJDY2lsmTJ8tVrhsaGkhJSQGgqamJuro62Q2q572KjY097hoLwh3p9DDYIm1IiYkDBw4QGRkpBw8tW7aM2NhYdu7cSVdXFzNmzJC3HT58OLGxsWzbto2MjAy2bdtGWlqam9vTzJkzufvuu9m7dy/jx4/v85idnZ2yTyRAS0vL6TtBgUBwfAghIRAMGY42e9pzkOkaGO3bt4+GhgaMRiMGg4Hc3FzgcPalNWvWEBAQQEhICFlZWWRkZPDdd99RUlJCRUWF7Cqk1+vRarVyQTlXitgNGzZgt9vx8fFh8uTJbgIFDrsgqdVqoqKi8PX1pbS0lIMHD6JSqdBoNFRWVmK329FqtaSlpWE2mykqKqKjowMvLy+6u7vdUrw2NTWRmJjIhAkT+PDDDwHw8vLCx8eH7u5uAgMD6ejowG63ExISQm1tLVarle3bt9Pd3Y23tzfXXnst5eXlfPLJJ7S3t+Pp6cmwYcNITExk3759+Pr60tzcjNFolN2ltm3bJrte2Ww27HY7o0aNoqKiQvbSaGlpYfLkyQC0tbXJFiDXPdmwYQMHDx7s8171Dsw+1uz4sawXp3qGfbBn7M8Ugy3ShoyYuPDCC1m5ciWpqanU1tbyxBNPcMkll7Bnzx6MRiMqlYqAgAC3fcLDwzEajQAYjUY3IeFa71rXH8uWLTsiVkMgEJwFCCEhEAwpjjZ72nOQaTQaUSqVTJ8+nbFjx5KZmUlubi7//ve/CQgIIDExEZVKhVKpRKfTYTAYWL16NZ2dnaSkpODv78/Bgwfx8/MjJCSEqqoquRBcTEwMMTExWK1WgoODSU1NZdy4cVRVVWE2m8nPz5fTwDqdTpRKpRy87LIGBAQE4O/vj5eXF42Njeh0OlJTUzlw4AAajYbg4GDq6upQq9V4enpiMBjkqtiu+g9KpRK1Wk1raytarRaTySS7U9lsNqxWq+wS5UoHW15ejk6nIyYmBpPJRFJSEtHR0Xz99dfU1NTgdDrx8fGhtbWVOXPmyJW/g4ODufrqqykoKAAOZ43Ky8vD19cXf39/uU5GUFAQERER+Pn5yffE5da0adMmgoKCjrhXA72/Ax3Un+oZ9sGesT9TDHbMyJARE1deeaX895gxY7jwwguJi4vjww8/lH0kTwcPP/ww999/v/y5paWFGDFgEQgGFyEkBIIhhyvY12QyyUG7LnoONq1WK06nU64m7bI0uCwRERERGAwG9u/fz6pVq0hJSWHXrl0kJSVx6623kpuby+bNm6mtrSUtLQ2FQkFDQwMqlYqYmBh5Fh4OD6B9fHwoLi6mpKSEH374QR7g63Q6NBqNnGbV19eX8PBwEhISmDBhAk1NTXz00UeyNSI5OZmOjg5iY2OJjY0lMDCQ0tJSLBYLXl5eKBQKtFot8fHxtLS00NbWRnd3NwEBAXLtDFeRPJVKhYeHBx4eHuh0OoxGIzk5OYwaNYrk5GSUSiUqlYqioiKMRiMtLS1yfIYkSeTn56PX60lKSsJqtVJeXk5DQwO1tbU0NjYSEBDAxRdf7BaPUlhYSEtLC8XFxaxevZoFCxbI1yk+Pp7c3FzKy8tJTEwkIyPjqNal3gx0UH+qZ9gHe8b+fGHIiIneBAQEkJKSQmlpKZdddpkcbNTTOlFXVyf7U+r1evLy8tzacOVH7isOw4VarUatVp/6ExAIBCeGEBJnJf/+97954403OHjwINu2bSMuLo7ly5eTkJDAvHnzBrt7x+S1117jhRdewGg0MnbsWF599VUmTZo02N06Z+gZ7NszaNdFz8GmTqdDqVRiNpvZvXs3kiQxbdo0brnlFnnQbbfbMZlM7NmzBz8/P3Q6HXq9ntzcXMxmM/7+/tTW1soDZLVaTXJyMmq1mvXr15OWlsasWbOoqKhg+/btOBwOOU4gOzubhoYGUlNTmTNnDk1NTUiSJLsEdXR0kJKSQnJyMu3t7ezZswer1UpaWhp1dXXk5ubS2dnJlClTUCgUdHZ2olKpkCQJm83GxIkT6ejoYNeuXTgcDi699FKKioowmUxyjYnY2FimTJmCTqejrq6O8vJyuru7CQ4ORqfTyVYIu91OXFwckydPxmQyERkZyfjx4zGZTMTExFBbW8uePXtoaWkhNDRUjsvw8fEhMzOT9PR0+f6sWbMGg8GAl5cXYWFhbvdr3759bNmyBYVCQUZGhhyA3Tto3nVPe1siBjqod7Xhqqh9su5Jgz1jf74wZMWE1WqlrKyMG2+8kQkTJuDl5cXGjRu5+uqrASguLqaqqkqegZg8eTLPPPMM9fX1hIWFAbB+/Xr8/PwYOXLkoJ2HQCA4DoSQOCv5+9//zqOPPsp9993HM888g8PhAA5P+ixfvvysFxMffPAB999/P2+88QYXXnghy5cvZ+bMmRQXF8vvC8HJcayg3d4xE64q0eHh4bIbjk6nw2QyERsby4gRI4iPjyciIoL09HQqKirYtm0be/bsQa/XM2zYMLcaEDExMaSkpPDTTz9hsVjw9vZm8+bNHDhwAKvVyvjx45k1axaLFy/mV7/6FV9//TWRkZEEBQWxYsUKDh48iK+vLyEhIbJ7lau4W0dHBzU1NXR1ddHa2kptbS3d3d3s37+fiIgI2cVJrVYzcuRIZsyYwa5du/jhhx+w2+0UFRWhUqmIjY2lqamJ+vp6IiIiGD58OJmZmXKxPa1WS2RkJA6HQ7YqrFmzhvLycoKCgjCbzbS3t2MymXA6neTl5bFnzx4aGhoAuOyyy0hJSWHbtm2YzWa5AjjAypUrMZvNcnXrrq4uOZA9NjaW7u5u9uzZg6enJ6GhobKFyVWJ3HWPXYP/3paI3kLDFf/Sl4Wj5/Pi2l9wdjNkxMSDDz7I3LlziYuLw2Aw8Nhjj+Hh4cH111+Pv78/t912G/fffz9BQUH4+fnx61//2i2o6vLLL2fkyJHceOONPP/88xiNRh555BGWLFkiLA8CwVBACImzlldffZV//vOfXHXVVfz5z3+Wl0+cOJEHH3xwEHs2MP76179y++23c8sttwDwxhtvsGbNGt555x1+//vfD7whmw08PE5TL4cutbW1WGpqSAgLY9KYMXIaVmw2amtr5XoIWRMmAKCIjETZ3k5jYyMdXl4oJQnDgQMYy8pwOp3ExMTg4+PD4uuvJyIigoKCArauX09lZSXq7m6Gx8Qwc+ZMtoaFsW/fPiL8/LjwwgsBaDYYSE9NlV2l4/47wz5p0iRoa2PzunWkRkdjHj2aSaNG4dHRgaWmhnaTCZ1CwZgLLyRGr6d6/35WrlxJXl6eHKjd0dFBd3c3YT4+h2M0nE5So6NRtLXR3t5OU1MTXU1NeHR00Go04mhpoaujg4N79hATE0OIVkuEnx8BXl7YLRbe+8c/qCsv55prrmHPmDFYLBaGRUYSFRWFTqejcv9+OhobcbS0UG+14tHRgUanI1ynO1yDw8MDRVsbGocDRVsbod7eAHh2dmKur+dQSQlP7d+Pv78/lZWVREREcPett1JSUkJ1dTX/99ZbNDU1cdVVV3HDVVcx7L+DeovFwpZvvz1cbXvECJIiIykrLOSnggKU7e1E+vsz7L/3MCky8vD/RQ+2f/cdH/7f/+Hv70+Yjw+R/v5HPDNH219whjie6y4NERYuXChFRERIKpVKioqKkhYuXCiVlpbK69vb26Vf/epXUmBgoOTt7S39/Oc/l2pra93aqKiokK688kpJq9VKISEh0gMPPCB1dXUdVz+am5slQGpubj4l5yUQCAZAVZUkJSZKEhz+XVU12D0aspyO7zCNRiNVVFRIkiRJOp1OKisrkyRJkkpKSiSNRnPKjnM66OzslDw8PKRPP/3UbflNN90k/exnP+tzn46ODqm5uVn+qa6uPnxN4fAzKn7Ej/gRP0P8pxmkgb4rhoxlYtWqVUddr9FoeO2113jttdf63SYuLo61a9ee6q4JBILTibBInPUkJCRQUFBAXFyc2/JvvvmGESNGDFKvBobJZMLhcPSZ7W///v197iOy/AkEAsH/GDJiQiAQnIcIITEkuP/++1myZIlcoTcvL4//+7//Y9myZbz11luD3b1TTr9Z/gwG+G9azXONnu5ILjelvpYda7/NmzdTUFDAuHHjyMzMPGKburo6PvroI2w2G+PGjWPmzJnyfjk5OQQEBPDzn/8cgLKyssPuPpWVWCwWAgMDiYuLw2q1kpSURFlZGQUFBSiVSpqamvDw8CA8PJzAwEA5uP7TTz+lqamJUaNGYbFYOHDgAMOGDSMwMJC1a9diNBoJCwujq6uLxMREYmNj2bJlC7m5uWg0GtLT05k1axYAFRUV1NfXs3//fvbt24dWqyUgIIC2tjbMZjMqlYr29nY6OjoICQkhJCQEh8OBWq2mpKQEh8PB6NGjiY6OJj8/n+rqaoKCgvjtb38r96ezsxO1Wk1ISAh79uyhsbERu92Or68vN954I8nJyXz66adIksSUKVMIDg4mKSmJvLw81q5dS3V1NVarlVmzZvH73//e7dr3vF5OpxObzUZdXR1Tpkxh3rx5vP766+Tk5JCVlcWvfvUrt/tcUFDA1q1bSUlJQaPR0NjYSHV1NePGjZPX97znfdHXszEQTnS/09XOOUNLCwwwXkWICYFAcHYihMSQYfHixWi1Wh555BHa2tr4xS9+QWRkJC+//DLXXXfdYHfvqISEhODh4SFn93PRMxtgb/rN8ufjc/jnHOSAwUD+vn04tVoi/hssHZGcLP/dH723SUxLo95mo95mw9DcTGRkpFvbAKHx8cz8b5D2gdJSJG9veT8AydubyMhIIpKTycnJ4WB9/WHBUF9PYHQ0WTNnyts5/5vi1Wq1ygHDidHR8r4NbW0Ehofj0Gho6uoiPDGRSpOJGosFDz8/Wg0GWv9by8qvvR3JZOKnsjLMnZ3ER0QQM3w4DW1tbNmyhdLSUiIjIyk5dIjK/2ZTuvkXvyAkJIQPP/yQiooKJIUChacnCp2OK66+GpVKxWeffUZTVxddXV3U22ykJyQQZbXSrlQSFhaGX0QEMfHxdG/ciLmlhUljxzJr1iy6Vq3i008/paWlhXidjm61mv3V1Sj/m8LWodGQmJaGBBSWl1NpMtHS1YWHtzee/v58sm4dcDjt6wGDAV1oKGMmT0an01FRUXG4WF1VFT779lFpMhESFcXoCy9EFRiIobkZ+F/QdYvDgd3LC1VgIJlZWRgMBgJLS0n87713arWH//bx6bfmRGJamtt2vTnR/QbKqWrnnOG/iTQGghATAoHg7EMIiSHHDTfcwA033EBbWxtWq3XIZEFSqVRMmDCBjRs3ctVVVwHgdDrZuHEjS5cuHdzOnUWcaL7+ngNAoM/0sH213bMStslkAqCoqAiHwyHvZzAYyMvLY9++fXKKU51OJ7fROy2owWAADru2udqNiooiKCiI+Ph4QkJC0Ol0rF27lrL/BnubTCYCAgIIDw+nubmZhoYGgoODUavVpKSksHPnTi6++GKSkpJoa2tDqVTK1akDAgLYt28fbW1ttLS00NXVhZ+fH4GBgfj4+FBXV4dGo8HpdBIYGIjdbqerq4vm5maSkpIICgpi5MiRmM1m9u7dS3V1NdXV1URFRaHX68nIyGDdunWYzWZ8fX3Ztm2b/L+nUqnYsmULa9asITg4GIfDQWZmJnq9Xi4+9+WXX9Lc3Ex6ejo6nY709HSysrKAwxkz29vbAdi6dSutra387Gc/Iysri/z8fPk+ZmdnU1BQIKeZdd3D3te+vzTAvbfpL7XsQPc7GUQa2RNHiAmBQHB2IYTEkMbb2xvv/2aNGSrcf//93HzzzUycOJFJkyaxfPlybDabnN1JcOIDrZ4DQKDP9LBHG3iaTCaKioooKysDICkpSS56l5ubyxdffEFLSwtNTU2MGTMGq9V6RB96DkxDQkLIz8+XK0YrlUqsVishISFk/XdGvaWlhZKSElpaWnA6nSQnJ5OYmMg333yDTqcjKiqKAwcO8PXXX+Pj48OECRO49dZbWbt2LV999RV2ux2dTkdAQABbtmyR6zro9XrUajUREREEBQVhMpkoLS2lpqYGDw8P4uLiiImJISMjA5PJhJ+fHx0dHXzyySc0NzfT2NhIfX09P/zwA2vXriUoKAhfX188PDxobGyku7sbb29vLrvsMmJiYvjmm28oKioiMDCQq6++mlmzZskF6gByc3MxGo1oNBpiY2PdigkmJyczd+5cioqK+P777+no6ECj0cg1QFx1PQoKCrBYLFitVlmIHIuBCNO+hIMoQHf2IsSEQCA4exBCYkiSkJCAQqHod315efkZ7M3xs3DhQhoaGnj00UcxGo2MGzeOb7755oigbEH/9OeC0p/V4WjCpPfg3+FwkJSUJNeEctU2KCoqIiAgAE9PT7q6urDZbH1W1y4tLT1iBt3l+uT63XOAXVhYiNlsxuFwyFWwExMTueKKK2TXt+zsbCwWCx4eHnIdjLa2NioqKujo6MDPz4/ExETS0tLk+lYKhYIffviBqqoqZs2axb59+9i7dy/h4eGy0AgODkaj0eDt7U1NTQ1Wq5WmpiYaGhpoaWmhvb0ds9nMxo0bGT58OElJSbS0tDBhwgScTqdc5dolNLRaLZ6enhw8eJBNmzZRWVnJ1KlTmT9/PhkZGbS1tZGYmCiLLDjs+pecnCxvI0mSXPm6oqKCkpISoqOjSU9PJzMzk82bN7tZhPp7Fnou7yk8+tq+r+dGWA7OXoSYEAgEZwdCSAxZ7rvvPrfPXV1d7Nq1i2+++YaHHnpocDp1nCxdulS4NZ0EA3VBOdZg0GAwsHr1aiwWC+A+qHS5NoWEhMhZuGbMmIG3tzcFBQX4+fkdUV3bYDBgMplQKpVYLBYqKioICQlBr9f32Rez2QwcrjvhGoQXFhYyfPhwHnnkEbnNyspKvv32W/z8/KisrCQ3N5eGhgYUCgXR0dGEh4czb948Zs6cKVtRysvLCQgIkAvLudymAgICCA4OZsSIESQmJmI2m1mzZg0dHR3o9XpGjBiBRqPhwIEDqFQq1Go1VquVxsZGAgMD8ff3Z/jw4SQmJvLvf/+bPXv20NraSlxcHBdccAE7d+6ksLCQtrY2goOD5XN1VbLuOWB3uYD1vHe33XYba9eupaioCI1G4zZxYLVasVgsbN68Wb6m/T0Lx7NcCIehhRATAoFg8BFCYkhz77339rn8tddeY8eOHWe4N4KTpb+Z5aNxNBeU42mvtLRUzszk2r4vceJyVeoZB2A2mwkKCnLrQ2lpKVVVVYwaNUoWIT3drnr3KygoiKSkJKZNm0ZGRgbvv/8+O3bswG63k5+fL1sxnnrqKe6++265kvOuXbt4//33USqVJCcno9frqa6uJicnB5PJRHZ2NvX19ajVagICAkhNTcXDw4PNmzfjcDgoKirCZrPJ7kQqlQqNRsPFF18sV/jesGED+/fvZ/jw4fj4+GAymeQMavX19QQEBDBs2DCsVitbtmyhq6uLO+64g+nTp9PU1ERgYKB8Xj2vpev+6HQ6SkpKZItNz2ve1tbGzp07GTZsGMOGDcPHx4dPPvkEs9mMh4cHFotFDkDv71no6SLV37PT+1k5kWfxeDkTxzjXEWJCIBAMLkJInLNceeWVPPzww6xYsWKwuyI4DvqbQT4afc0kuwZpJSUl7N69m6lTp5KRkXHUgVtvS0R/WK1WnE4nVquVyMhIQkJCqKqqIiUl5QirhGvwmpycLLtOJScnk5uby6ZNm5g2bRrz58+XA7RdA+7IyEgmTZrE3r172bp1K+3t7fj6+mIymeRgbdfv1157DZPJRFhYGBMnTqSmpoZt27Zx6NAhoqKiCAsLw2QycejQIaKjo9FqtTgcDry9vVGpVAQGBlJfX8/nn3/OmDFjGD16NE6nk6CgIKxWK/Hx8bJ7VkpKCnDYihAYGMioUaPQaDSsWbOGgIAAEhMT2blzJy0tLRiNRmbNmsVPP/1EZ2cnRUVFsvUlKCiIjIwMcnNz+fLLL+WUsICbpQGQU6V6e3tjtVopLi7mwIEDSJLEtGnT3Cwc/VkVet6z/p6dnJwct2fvRJ7F4+VMHONcR4gJgUAweAghcU6zevVqOXOM4OzjeOIcTgTXIM1qtSJJktsy6Hvg1jNzT+9tevbX1TedTifPkMfGxvZplegdZN2zTZvNxt69e4mPj2fz5s1YLBamTp2K0Whk9erVpKamyjUYtFotSqUSs9kst+saHDc3N8vB1Y2NjdTV1REUFISHhwf5+fns37+fmJgYLrjgAnQ6HTqdjszMTGpqapAkiRkzZlBYWMimTZvw9PSULSklJSVs2rQJvV5PWVkZZWVlNDQ0MHfuXEJDQ8nLy5PjOiwWCxqNhkmTJhEREUFtba0cdB0cHExjYyNlZWXk5+cjSRLR0dGEhIQA0NTUhEqlIi4ujsbGRiorK2VLA0B6ejrp6eluVozo6GgAWXgdi76eq97PYO9tTsWz2FdGsWPFZwiODyEmBALB4CCExDnD+PHj3fyoJUnCaDTS0NDA66+/Pog9ExyN4021ebzuIK7BWXt7O8XFxcTHx8sBzL1dWlz96ZkStne/cnNzyc7OlgOIIyMjycnJYdOmTSgUCq655poj3HZ6B1vn5OTI/Y+Pj0epVLJ9+3YqKioICAggLi6O5ORkVq9ezdq1ayktLWXRokVudSqCgoLc3KpycnKora1FqVRSXV3NoUOH6OzsJDAwEKvVSkNDA9XV1dhsNqZMmUJDQwNr167l1ltvJSMjg+zsbAoLC6mqqkKj0WAymaitraWmpga73Y5CoUCj0aBUKgkICCApKYmMjAyKiopobGxkz549xMfHYzab8fPzo7q6mra2NmbPni0LAECuH9HTMtFTmLlwuUj1dkfq/Wy40vEO9PlwLXO5hrmsVL1jNE71s9hXRrGe/RHxGSePEBMCgeDMI4TEOYWrPoMLpVJJaGgol156KcOHDx+cTgmOyfHOyB6vO4hrkJaTk+PmktSXSwv8b5B3tH65LBw9z2HatGmUl5eTk5MjC4f8/Hw5/axr8OoSAy43pZKSEmpra2lsbESr1TJ27FgWLFhAZGQkmZmZlJaW4uXlJac9dQ3KXVitVlJSUqirq8Nms+FwOGhra5NdlxobG2lvb0ev1xMbG4u3tze7d++mqqqKsrIy6uvriY2NxWq1smnTJtra2mhvb0eSJNasWYPT6SQqKorZs2djNptpaGigu7ubCy64AAC9Xk96ejqdnZ1UVVXR1NSEUqnEaDSyc+dO4PCA/2gCAJCD2l3Xu6amRk732nObYw3eB/J8uDJrSZLkdszjtQocz7PYO53tiRxPcHSEmBAIBGcWISTOOR577LHB7oLgBDjeGdkTHfj1t9/R0sa6LAw9LQl9ZR+KjIxk/vz5vPLKK2zYsIH6+noWLVokC4nS0lI2bdqEzWYjJCQEf39/2U2ptbWV8PBwRo0axeTJk+XAZNcxFy1adETaU9dAOy0tTbZ6BAQEkJCQgEajoaOjgzFjxhAbG0thYSGSJMmpXn19fdm5cyc2mw2NRkNJSQkajQZfX19aWlrQarXExMSwe/duuZBdQEAAISEhmM1muQDehg0b0Gq1HDp0SM7q1NLSgkqlQqvVcsEFF+Dn5yfHObjoTxD0HJhnZWWxYMECN4tR7236e2YG8nzodDrCwsKIiIjoM8B+oBzPs9g7vkZYIU49QkwIBIIzhxAS5wwtLS0D3tbPz+809kRwpjjRgVh/+x0rbWxfbk39ubx4e3sTFRVFZ2cnmzdvli0MRqMRvV5Pc3MzVVVV2O122f2nvb0df39/ufaEwWBg+fLlFBYWMnHiRJqbm/Hy8mLcuHFyytrKykri4uJk4bF69Wq8vb255ZZbyMzMlLM9lZaWEhwcjE6nkwvDBQQE0N3djZ+fH6mpqTidTsLCwrDZbAB4eXkRGBhIeHi4XNchLi4OnU5HUFAQOp2Ozs5OFAoFycnJ5OTk0NXVhVarJTo6mvb2diIjI9Fqtdxzzz1u16e0tFS2zPS+1j1n7nu6iLniVuCwVad3TMpA73NPrFYrvr6+boHyJ8LxPIvHEh4im9PJI8SEQCA4MwghcU4REBBw1EJ1cNglRaFQ4HA4zlCvBKcTV70E+F/Q7fEMxE5k0NbbramvdnJzc9myZQuhoaH4+vpSWVnJ6tWrWbBggRwrMXLkSPz8/CgvLycoKIisrCzeeustsrOz8fb2Jj09ndLSUgoLC6mpqUGhUOB0OklJSZHFgcViIS4ujgULFgC4iQuXeOkdr2EymbBarbS2tlJZWUl6ejrXXXcdJSUlbNmyhbq6OiZPnkx8fDxr166lrKyMoKAg6uvr2b9/P1qtlnHjxhEfH8+MGTOw2WxyNie9Xo+npyeJiYk4nU4yMjKIjo4+YtDsci2qq6ujs7OTsLAw2foCsGrVKgoLC/H29iYlJYX8/Hy3zE5wuFCgy2Wqr3sw0Ht7Itatkx3sH0t4iGxOJ48QEwKB4PQjhMQ5R3Z29mB3QXCGcbkMKRQKuTDc8QzEjrZtXwPG3m5NR5thb2pqkvdxuSL1dNVJTk52S0sLyEkCjEYjcHiGPi0tjbS0NMaNG4fJZCIzM1M+hslkcjuXnuKiZxaq/Px8YmNjCQkJIT4+npCQEPLy8qipqSEuLg6TyURJSQn79+/H6XSSmprK/PnzqaiooK6ujrFjx+Lp6cmWLVvYvn07iYmJFBcXYzabaWxspLGxEYCSkhKqq6spLCzEz88Pm83G7NmzZauC6zxdRfvKy8sxm800NTUxZswY+VxcAsqVRtZ1LSoqKjCZTHJa2qO5PQ30OTjWwL6voPz+LCqnCpHN6eQRYkIgEJxehJA4J5kyZcpgd0FwhnEFO7v+7uu3i77EwdEGbQOpgtxzoN5zcOtyOer5d89j9+dKNWvWLNnlyFVczsfHh8DAQIYPH47VapUDdl37uUS06zjt7e2888476PV6Zs2aRXJyMiaTib179+J0Opk6dSpZWVmYTCYqKiro6Ojgyy+/xGg04nA4OHToEF999RWzZs1yE08xMTEUFBTQ1dUlx3xYLBYaGxvx9fWVA83r6uqwWq14enpitVrJzs4mPDxcFiVtbW1YLBaioqIYM2YM1dXV+Pn5ERgYKAuGiRMnEhkZKQd2w2GrhyuQHZBTyPZ8Fnr+7q8gXV8czdLQV+al3vf7VCPiKE6ekxITrkqNAoFA0CdCSJxXtLW1yX7pPek5CyoYuriCnXsv62sgNhBx0JOeg9KeQdd9bdN7XX/9Ohbp6eno9XpWr14tB1UHBgZisVjYvHkzTqeTkpISSkpKAFCr1dTU1GA2mzEajRQUFFBTU8PWrVvx9fWlra2NBQsWEBISIgdQ96yDMW3aNOLj41mzZg0ATqeTqqoq6uvrKS0tdauBkZubK8dYTJs2DYfDQUNDAzt27CAgIICAgADi4+PRaDT89NNPjBkzhksuuUROWWs0GmXBEhgYSFBQEJGRkaSkpNDW1ibHd1RVVREaGkpwcDBardbtvvW0bBzrXvZXkA6OFA8ut6uCggI3q07ve9xzmRjsn90ct5hwOp0888wzvPHGG9TV1VFSUkJiYiJ/+tOfiI+P57bbbjsd/RQIBEMNISTOGxoaGrjlllv4+uuv+1wvYibODY7Hd/14XUd6ppHtOTPd28JwsjUH+hrYWiwWAgMD3awaP/zwA1988QVarRaj0YiXlxejRo2S44Q2b95MdnY2w4YN46KLLqKqqorKykrWrl1LbW0tUVFRzJo1i7Vr1/Luu+/K1o69e/cyadIkpkyZQl5eHna7Hb1eT01NDa+88oocDO5yR4qIiCAiIkKutH3ZZZfJdTsyMzPdxFBKSoqcwrZnHQzXeYWEhMgxHK5AcUC2chwtq1bvLFq9OR6rU3JyMgUFBbIrWu+Yi6ysLLfnQnD2c9xi4umnn+bdd9/l+eef5/bbb5eXjx49muXLlwsxIRAIhJA4z7jvvvtoamrixx9/5NJLL+XTTz+lrq6Op59+mhdffHGwuyc4RRxPfMSpSPnZ+3gDSW060LgNV60JnU7H1KlT3dqMjIzk5Zdf5sCBAwQFBXHBBRcQExNDWFgYFRUVAHh7ezNhwgS5wnR2djaBgYHk5+ezefNmMjMzSUlJoaSkhM7OTrq7uyksLMRqteLv78+CBQswmUyMGDGCgwcP8uWXX9LR0UFLSwtWq5WmpiY8PT3dKm674jAKCwvZsmULdrudSZMmkZmZKcc3GAwGIiMjCQkJoaqqSs6a5KqR4Yrh6BlXcbTB/EDvZc+Ykd73oec9dR2nZ+ar472HgrOP4xYT//rXv3jzzTeZPn06d911l7x87Nix7N+//5R2TiAQDEGEkDjv2LRpE59//jkTJ05EqVQSFxfHZZddhp+fH8uWLWP27NmD3UXBKWAgrkgnS18D194DTpcQ6OmGc6y0pb3bcrnupKeny7P5PYveTZs2DYvFQkxMDL/4xS/QarW0t7ejUqloamqioqKCadOmuVWYTk5OZvny5UiShMViITs7G6PRSEhICLNnz6a9vZ19+/aRmpoqZ446ePAgjY2NJCQkEBkZSUtLC9nZ2Xh4eNDa2kppaSnh4eHMnz+fkpISsrOzqa6upqysDI1Gg6enJzqdjp9++gkvLy85ML53DIPLtSgwMFCuI3EiAdRHo3cqX+CIVLOuYGrXde95X0wmk5sgEgwdjltMHDp0qM9/WKfTSVdX1ynplEAgGKIIIXFeYrPZCAsLAyAwMJCGhgZSUlJIS0tzc1kRDG1cA/1PPvnkiEHj6Tyei758+OHItKUDabO3645rMK1UKmW3p6VLl1JVVUVxcTEWi4WqqiqcTqfs+mQ2m49o/7rrriM6OprU1FS2b99OcXExkiThcDiYNGkSNpuN4uJiUlNTaWxsJDg4mGHDhrFo0SIqKir48ssv8fPzY9asWXz55Zc0NzdTUlIiB3FLkoS/vz+BgYEkJyeTnp5OTk4OP/30E97e3owYMYLk5GQqKiooKSnB29tbTo/rigdxDe57io3eQrGn65PreuXm5rqdc1BQEPHx8W4Whp6pfF21OSwWCx4eHhiNRsaNG9dnMLXLmpKfny8LIsHQ4bjFxMiRI/nhhx+Ii4tzW7569WrGjx9/yjomEAiGGEJInLekpqZSXFxMfHw8Y8eO5R//+Afx8fG88cYbREREDHb3zmkGq+BWX/UfTjdH8+E/mlWip+tRenq63Bb8zy2n52B67dq17Nq1S64/odPp2Lx5M0VFRbS0tBAQEIC/vz9wuFp2Xl4emzZtYvTo0Vx33XWMGzeO5ORkbDYbeXl5dHd3k5qa6hYrUFxcTHBwMN3d3VitVvbv3095eTltbW1IkoRer+fee+/l448/5uqrrwb+lyq3pKQEpVKJWq0mOTkZnU5HfX09FRUVrFmzhqCgIAAUCgW1tbUcOnSIwMBAWeCUlJQQFBQkB0z3fIZ6iipXnYmeQdM1NTXydY2OjmbYsGHydr1T+faMR/H29qaurk6u8dEXIkXr0OW4xcSjjz7KzTffzKFDh3A6nXzyyScUFxfzr3/9i6+++up09FEgEJztCCFxXnPvvfdSW1sLwGOPPcYVV1zBf/7zH1QqFStXrhzczp3jDIavee9B45mmpxDonYq0L3G1efNm1q5dS2lpKXq9/oiaEIBbJiVXvQcXer2eBQsWEB0dTW5uLpWVlSQlJQHw4Ycf0tjYSHV1NU6nk+joaLfBdVFREWVlZdhsNiIjI8nMzGTz5s2kpqZis9n48ccfKS8vp7m5mfj4eLq7u6mpqWH16tWkpqYyZcoUhg8f7nZeycnJGI1GysrKyM3NZf78+ej1et555x3KysqAw7ER4eHhaDQaysvL+f777+XPdXV1TJ06ldjYWNm1qLCwkIKCAllwuSwT7e3tvPLKK6SmpjJ16tSjWib6syS5frtETn9uTCJF6/FxNlXuPm4xMW/ePL788kuefPJJfHx8ePTRR0lPT+fLL7/ksssuOx19FAgEZzNCSJz3/PKXv5T/njBhApWVlezfv18OGBWcPgZjNnewBn0DnUHvnQ3KFaPg5eUlZw+C/mNAXGKppztVVlaW7NLV3t6OXq+ntrYWm80mt+Oa/ddqtXKMgEajobm5WR6Eu1KoarVabDabXMH66quvlovidXV1UVlZyffff4/NZgNg3LhxbulU9Xo9+fn5mM1m+brMmjWLiooKzGYzRUVFFBYW0tXVhUqloqGhAUmSmD59OiNHjiQjI0OOcRg7diweHh5s2bKFlpYWbr31VvkavfLKK3JtjXvuuee471NPK4RwYzq1nE1B6ydUZ+KSSy5h/fr1p7ovAoFgqCGEhABkFxIXLvcQwennfJnNzc/PZ+XKlXh5eTFv3rx+05r2XO7y1586dSq///3v3SorQ9/paHumoO3Lnaqn0Dh06BDjx48nOjoai8WCQqFAq9WSlZUlt1lfX+/mEtYz0NhsNhMXF8fYsWOJjo5m+PDhjBs3jsLCQv71r38BMGrUKDkFbEFBAZWVlaxevRpvb2/8/f0xGo3k5ubK2Z5qamrYtWuXfA1c1hIfHx9GjBjBL37xC7fnpWffWlpaKCsrcxNcrv/rnv/fx6KvQa7BYDiuQPmBcDbNzA8GZ5NbmKiALRAITgwhJAT/Zdq0aURFRXH99dfzy1/+kpEjRw52lwRDmL4GiWvXriU3N5e0tLQ+XWpc9BQILn/9/rZ3Hae9vb3P6s197eP6bDKZSEtLc6u+bTab5WxEPUVNQ0MDJSUlfPLJJ7IYcVV2vuaaa9wySyUnJ/Pyyy9TU1NDcHAwc+bMQa/Xy+lUASwWC9HR0SQlJVFWVoZeryc9PR2TyYTFYiEpKYmRI0fKLkglJSVYLBYSExPd0sDGx8dz7bXXysLooosuYtSoUW6D0/T09OOeGOhrkFtaWnpcgfIDoa/sXueTqDibJhKOW0wolUq5aEtfiOJEAsF5gBASgh4YDAZWrVrF//3f//HnP/+ZMWPGcMMNN3D99dcTHR092N0TDDH6mtnW6/VER0czefLkAQ2gelfL7ilQXMdwpSl1ZXDavHkzgJz9yFVTIiMjw62NnulNXX3JyMhg9erVHDp0CEAe3CYnJ9PW1sauXbvYtGmTm6Wjd2pblzVFq9UyZswYpk+fLrsjbdq0iWnTpslpXXU6HXv37pXP12XpcAmcnv0F3MZtPdubP38++fn51NTUuAWonwxHS+97KmfRj1ahe7A536wmxy0mPv30U7fPXV1d7Nq1i3fffZcnnnjilHXsWPz5z3/m4Ycf5t5772X58uUAdHR08MADD7Bq1So6OzuZOXMmr7/+OuHh4fJ+VVVV3H333WRnZ6PT6bj55ptZtmwZnp7CSCMQDAghJAS9CAkJYenSpSxdupSDBw/y/vvv8+677/Lwww+TlZXFpk2bBruLgiFEXwPPWbNmkZKScsKDUVc2ooKCAqKjo2W3oJ7ZmiorK+XYiq6uLlpbW/Hx8ZHjflavXk1lZSWBgYHo9Xq3mgg9MxcB8uBWp9PR3NzMqFGjSExMlGMp+qq7UFFRwa5duxg+fLhb3AL8Twz0TM9bXl5OYmIiQUFBbNq0CZvNRlJSklz52mw2U1BQwLhx47jmmmvcrl1PcWG1WmUx1TNAHehThPU3QD7aAPp0zKIfT4XuM83ZFM9wJjihAOzeLFiwgFGjRvHBBx+ckQrY27dv5x//+AdjxoxxW/6b3/yGNWvW8NFHH+Hv78/SpUuZP38+W7ZsAQ5bTWbPno1er2fr1q3U1tZy00034eXlxbPPPnva+y0QDHmEkBAcg4SEBH7/+98zduxY/vSnP/H9998PdpcEQ4z+3IuOZ1DWezDXMy1rdHS07FLU0/LhEgtdXV1yATh/f3954G+xWOjq6sLhcNDW1obVagUOi+n29nY5ANtms8mxAatXryY/P1+uyeGKpXAFjrsEjSuWwsfHh5EjR7qd69GyZykUCuLj45k2bRq5ubls3boVSZJITk4mLCwMhUJBUFAQycnJ5ObmAoczPbnERX5+Pjk5ORiNRioqKoiOjnarHdI7oN0lyFxVt139O1WF706Es8ndx8XZFM9wJjhl0/EZGRnccccdp6q5frFardxwww3885//5Omnn5aXNzc38/bbb/P+++8zbdo0AFasWMGIESPIzc0lIyODb7/9lqKiIjZs2EB4eDjjxo3jqaee4ne/+x2PP/44KpXqtPdfIBiyCCEhOAZbtmzhP//5D6tXr6ajo4N58+axbNmywe6W4Dyk92AuMjJSdhHqb2Y9Ojqa6Oho4uPjqaiooKioCIfD4Rbg3TNlanFxMeXl5WzatAm9Xo9Op5ML3LksFL0DmPsKHO/pKuUSDb1n+V0z8K6sUxkZGdTU1FBZWcnmzZtZsGABAJWVlSiVSsaOHUt6erp8DJdlRpIkrr32Wrnq98qVKykpKSEqKgofH58+r6MrYDw+Pl4ufLd27Vp27txJQECAnJ3pfBtAH41jCZxzzQ3qlIgJVx7iqKioU9HcUVmyZAmzZ89mxowZbmJi586ddHV1MWPGDHnZ8OHDiY2NZdu2bWRkZLBt2zbS0tLc3J5mzpzJ3Xffzd69e/ssutfZ2UlnZ6f8uaWl5TSdmUBwFiOEhOAoPPzww6xatQqDwcBll13Gyy+/zLx58/D29h7srgmGCCczuOpr3+MdzOXm5vLll1+SmJhIRkYGVqsVh8OBh4cHJpNJjn/omZq2Z1VsjUaDUqkkNTWVvLw8du3aJc/yHysOoa9Uqjk5OWRnZ5OTkyOncu1dF2PBggWyNcVVl8IlUoKCgo447tSpU4H/DfZdLl0pKSnMnj0brVbbp5DpmdLVVSfDbrcTEBBAYmKim2A7FwbGZ4JzzQ3quMVEYGCgm6+dJEm0trbi7e3Ne++9d0o715tVq1aRn5/P9u3bj1hnNBpRqVQEBAS4LQ8PD8doNMrb9BQSrvWudX2xbNmyMxoLIhCcdQghITgGOTk5PPTQQ1x77bVntK7EM888w5o1aygoKEClUtHU1HTENgOJk/vuu++4//772bt3LzExMTzyyCMsWrTotPf/XJudPBn6G1wN5BodbWDWV20KFz33MZvNlJaW0tbW5hYjUFJSItdZCAkJcXPzCQwMpKWlhbi4OKKjoyksLKS4uJiIiAi3onc9cQU/uywZJpOJmpoaLBaLW/9dblm7du2irq6OkJAQdDqdW9Ypl7XFJShKS0tpaGjAYrG4FZdzbdvTfcmVqvXiiy+W3ZRc9E6V29PiUFpaitPpJDExkUmTJrndF/E8D5xzzYpz3GLipZdechMTSqWS0NBQLrzwQtmsdzqorq7m3nvvZf369Wg0mtN2nN48/PDD3H///fLnlpYWYsRASnC+IISEYAC44tLONHa7nWuuuYbJkyfz9ttvH7F+IHFyBw8eZPbs2dx111385z//YePGjSxevJiIiAhmzpx5Wvt/rs1Ongz9Da4GIjKONjDruX9Plx0fHx+USiU1NTW88sor2O12kpOTCQ0NlS0RWVlZmEwmuRZDz7iLiooKvL29CQgIkOtAuERBdHQ01157bb8DRZvNhsFgAODHH3/Ez89PTslqMBjk2IbMzEw5G1rPgXxFRYWbi5RLUOzdu5euri4CAwMxGo3yMXoO8F3t5+bm0tDQwNy5cwHcivb15SLWV3am3iLCFUzeM1Vr7+MLDnOuWXGOW0ycidmavti5cyf19fVuZjuHw0FOTg5/+9vfWLduHXa7naamJjfrRF1dHXq9HjgcYJWXl+fWrmv2wLVNb9RqNWq1+hSfjUAwBBBCQnCW47Ia/z97Zx4W1Xn+73vYl2HfEdlEcMMFNUI0Ji6VRLNVY5o9JjZp8jOrZm1Ws5mtsUnbxKZtlrZJbTQm+SbaRCMkBIVoBNyFAALiMCAM27DDnN8f9pzODAMMODCA731dXDJnzpzznIPznufzvs/ywQcfWHzfmjy5TZs2ERMTw+9+9zsAJk6cSGZmJhs3bhx0MTHaZifPhZ6cK2tExvz583t0zMxLxMohO3ICdHZ2NqdOnWLmzJncdtttVFdXc/jwYcrLy7nmmmuIjo4mPj6e6Ohok7yL6upq8vLyUKlU6PV6q3Iy4Gzys5eXl5KT0dzczMUXX6zkPLz33ntkZmYSFhbGbbfdpqwmaDQaCgoK0Gg0nD592mR1As42iuzs7CQyMhJXV1e6urrIzs7utuoh5078/PPPuLu7d7uXxk37rP07yast06dPV/pd9LQCJBidWCUmDh06ZPUBzSss2YpFixZx+PBhk2233XYbEyZM4NFHH2Xs2LE4Ozuze/duVqxYAUB+fj5lZWWkpKQAkJKSwosvvkhVVRXBwcEA7Nq1C29vb9FkSSAwRggJwSjAmjy5rKwsk1w7eZ8HHnhg0O0bbbOTg0F/RUZfnzdPgJ44cSL5+flKj4WcnBy+++47GhoalB4NBoNBqdxknAyt0+moqKgwcex7+nsaz94HBARw5swZurq6UKlUjB07Vmm0V1RUhEqlYty4cd2avu3Zs4fCwkJ8fX2JiYmhpaWFjIwM9u3bx//93//h6+vL7NmzCQsLw9/fH8CkcZ8c2jRt2jSmTZuGv78/ycnJaLVa9Ho9+/bto7q6ulvYkzXIVaPkxG7z6lNCMI9urBIT06dPR6VSmbRdt4RKpRq0pnVeXl5MmTLFZJunpycBAQHK9tWrV7N27Vr8/f3x9vbm3nvvJSUlRelQuWTJEiZNmsTNN9/Mq6++ilar5cknn2TNmjVi9UEgkBFCQjBKsCZPrqd9GhoaaGlpUWZvjRGFOXrmXOPmrf18f4WYpSRnGeMVKL1eT0BAgOKAy5g7w+Hh4cTHx3P8+HFlZUyOnJDPZVyxSZ79j4yMJDExkaamJpqamggKCuK6665THP2IiAhUKhWzZ8/uJoJiY2Oprq6mq6sLZ2dn8vPzMRgMHD9+nJaWFsaPH8/kyZMpKytTenIYd4bOyMhQeloY34PCwkK0Wi2nT5+mtLRUqc5kLeala43/NnKolWB0Y5WYOHny5GDbYRM2btyIg4MDK1asMGlaJ+Po6MhXX33F3XffTUpKCp6entx6660899xzdrRaIBhGCCEhsDOPPfYYr7zySq/7HD9+nAkTJgyRRd0RhTl65lzzQAYrj8Ta45qHRRnvby504uLi6OjooKCggMzMTEVMyOeSQ6nMj1tYWIinpyeLFy/mmmuuMXH0T58+TU5ODhUVFQQFBSnHDA8PZ/Xq1TQ2NrJv3z4cHR2ZN2+esroSExNDaGgo0dHRACZ5H5auzfyaFy5cqDS7U6vVJjkUfdGbsBN5QecHVomJqKiowbZjQHz33Xcmr93c3PjTn/7En/70px4/ExUVxY4dOwbZMoFgBCKEhGAYsG7duj5z82JjY606ljV5cqGhod0q71RWVuLt7W1xVQJEYY7eMA4j6o9Dav55W4fF9HXc3lYuZGTH2DjBeNmyZQAkJCQox6muriYyMpLo6GiTRGn5Pmi1Wvz8/Jg3b55J9SaAzs5OfvrpJyoqKpSO1MYCxtvbm87OTry9vU1ySN3d3cnJyUGv15uUcjW+9z05/eaVnrZt20ZaWhoLFy5UStIOdKXJ1n9PUTFqeDLgPhPHjh2jrKyM9vZ2k+1XXnnlORslEAiGGCEkBP3EvEx4b5iXqeyNoKAggoKCBmqWCdbkyaWkpHSbYNq1a5eSa2cJUZijZ2SH1by8aH8/P1h29YQ1M+jm5WIXLFhAYGAgsbGxJsnMciiReZ8H2REuKCjg559/VjpxG9sXFxeHr68vWq2WefPmKf0vxo0bx+23387SpUvx9vZWmuCZ22ZtnoKlUCzj65a/29nZ2cq1GguOvrBGnA2EgdojGFz6LSaKi4v55S9/yeHDh03yKOT/eIOVMyEQCAYJISQEA+D3v/+98ntNTQ0vvPACqampihOelZXFN998w1NPPTVoNpSVlaHT6SgrK6Orq4u8vDzgrBOlVqutypO76667+OMf/8gjjzzC7bffTlpaGp988gnbt28fNLvPB0ZKpSpjpzopKambvZY6UZuXizX/Vy4/m5OTQ0lJCYBJ0zm9Xo8kSeh0um6rN+Hh4SxdupTs7GxKSkrQ6XRotVq0Wi2TJk1i+fLlhIaGkp2dTU5OjpJEbS6W+hJkxqFYtbW15OXlKSFXxjkQO3bsoLy8XJkQsHZlYDDDm/rK3xUMPf0WE/fffz8xMTHs3r2bmJgY9u3bR01NDevWreP1118fDBsFAsFgIYSEYIDceuutyu8rVqzgueee45577lG23Xffffzxj3/k22+/5cEHHxwUG55++mk+/PBD5fWMGTMASE9P55JLLrEqTy4mJobt27fz4IMP8uabbxIREcFf//rXQS8LO9oZKZWqZKfXPCnZ/H3j19HR0SxcuBDo3vDWuPxseXk5P//8s4nzq1ar6ezsxMPDg4KCAo4fP05UVJTiyMvnSEtLo6mpiXHjxjFlyhSqq6uVY2RnZ/OPf/xDKYWv0+mIj4/vtsrQk+Mvh2LJVajq6+upra1VqlfJ4kp+HRERoVSHGkjuiS0xT/YWDA9UUj8lXmBgIGlpaUydOhUfHx/27dtHQkICaWlprFu3jtzc3MGydVjQ0NCAj48P9fX1eHt729scgWDgCCFxXjIYY5harSYvL89iL4Dp06crZTVHK+K5MDIxbhDXUzlUSx205dCknJwcNBoNP//8M8uWLePXv/61yWdaWlrYt28fYWFhABw8eJDg4GCqqqooLy/H19cXLy8vnJ2d8fX1ZfLkyURHRysrEhUVFRgMBhITE5UO2Hq9noKCAvbs2YNOp6O9vZ3ExETCw8OVhG9ZGMmhZuZCSd4u7x8ZGak46HIfirKyMgwGA3PnzlWEhFwZcyA5CyLXYeTRn3Gt3ysTXV1deHl5AWeFhUajISEhgaioKPLz8wdmsUAgGFqEkBDYkICAAL744gvWrVtnsv2LL74gICDATlYJBL1jnN/QnyZtxqJZbiK3d+9eli5datL0LSMjAy8vL+Lj45XQqLCwMCZPnqxUToqOjiYzM5Pc3FwqKyuJj49XBMHSpUtNHHBZBERGRnLbbbdRUFBAXl4e8fHxxMfH09LSQn5+vrLi0Fv1JsDiSkZBQQFlZWWcOXOGjo4OtFot/v7+lJWV9bki0JtgEFWdRjf9FhNTpkzh4MGDxMTEMGfOHF599VVcXFx49913ra6wIRAI7IgQEgIbs379en7961/z3XffMWfOHAB+/PFHvv76a/7yl7/Y2TqBwJTe8iR6c4iNG9bJKxqLFi2itbUVZ2dnCgsLTT5jvJKQlJTEtddea/G4oaGhREREAPRYAUo+noODA9HR0SQlJaFWq9FqtZSWllJRUUFYWBi1tbVKFai+CA0NNTl+YWEhBw8exGAwcMEFFyBJkpIHK9+n3kRBb++NlBwawcDot5h48sknaWpqAuC5557j8ssv56KLLiIgIIB///vfNjdQIBDYECEkBIPAqlWrmDhxIm+99Rbbtm0DYOLEiWRmZiriQiCwlt5i/W0RKmMpT8K4Q3VZWRnQ8wy6nNOgUqlYuXIlq1atIjMzU1kRkNHr9VRWViqz+4GBgRaPJyc9y0nX0dHRSr6CcY+LzMxMamtrlbBB+fhy4YHU1FT8/PxM8h8sOfc9Of1xcXEsWLAA6DukyZIo6E0wjJQcGsHA6LeYME5Ki4uL48SJE+h0un6VCRQIBHZACAnBIDJnzhw++ugje5shGAX05OyeS6iMsRAxdnrNRURkZKTFqk7GGDd5kys3GQwGxck3XvmQnXOdTqf0brBU0tRYoIwfP15pdmeclF1bW4ufn5/Sw0M+flRUFFVVVYSGhpKUlKSsbBjba26/pe3mWBIAvYmC4S4YRN7G4NFvMfHPf/6TX/7yl3h6eirb5OQcgUAwTBFCQjDIFBUV8f7771NcXMzvf/97goOD+c9//kNkZCSTJ0+2t3mCEURfsf79DZXRaDRs3bqV2tpaAObPn684k8Z5CLKI6M3RNA5xknMJjD+r0Wh47733KCoqYu7cucTHxyslVjUaDcXFxRab+ckCBf4X6mTc+E+tVuPn50dCQoKyQrFgwQKlupEshvR6vUmytfm19JXXkJ6ejl6vp7y8XKkwZf6Z4eaUD4dytec7/RYTDz74IHfddRdXXnklN910E6mpqTg6Og6GbQKBwBYIISEYZL7//nsuu+wy5s6dS0ZGBi+88ALBwcEcPHiQv/3tb2zdutXeJgpGEL11ah6IE2g8q2+8GmG+SmFNSJXxCsKCBQu6CZCMjAyKioqoq6tDq9UqqxX+/v6Eh4dTVVXFJ598YtJdWs6tkEWEnMuwbds2k+Z4BoOB/Px8k2uRHWRzQSPbLtss2ygLBuO+EjJymNOxY8dMQqWMhZh8jMFyyvtqpmcJe5erFQxATFRUVPD111/zr3/9i2uvvRYPDw9WrlzJjTfeyIUXXjgYNgoEgoEihIRgCHjsscd44YUXWLt2rVLtD2DhwoX88Y9/tKNlAgHdBIOxk758+XJltt14xcDcQZVXJHQ6HdOnTzdpFmd+riuuuEJp8ubv76+IlsDAQPbt28fevXvR6XQmjePkMCmtVsv06dOJj4+nuLhYaRgn5zAYO9kA1dXVREZGmthi3H0cTPMm4uLiyMvLo7a2luzsbKVCkyzUli9frogcWawYixfz+2lrzO+JbHdvnEvYlsA29FtMODk5cfnll3P55ZfT3NzMZ599xscff8yCBQuIiIigqKhoMOwUCAT9RQgJwRBx+PBhPv74427bg4ODTZptCQT2wJITad5iy1w8mDuo5knXlhrcyZ9dvny54tDHx8ebdLeurq6mpKREERlgWvWpsrKSiooK9Ho9ra2tSsM4S9eQkZFhsbStJeda/j08PJxrrrlGyROxtErRUznc3t63FZbK1oLllSLjbT39PQRDQ7/FhDEeHh6kpqZSW1tLaWkpx48ft5VdAoHgXBBCQjCE+Pr6UlFRQUxMjMn23NxcxowZYyerBALLWOqibO6AG5eBlZOd5ZwGa3ot9FR2FlBCnIydcrkr9YIFC0xyJsyTqY1paWmhuLiYiRMnmmw3d/YtJVFrtVoyMjKU6pzmqxTmxzJfubE1fQkDS6FM8rbq6mqLtguGjgGJCXlF4qOPPmL37t2MHTuW66+/XsTFCgTDASEkBEPMddddx6OPPsqWLVtQqVQYDAb27NnDQw89xC233GJv8wSjlIEmAvenSpFxGVlLVZjMbbBUdtb4WD01yTN+T+6w3dc15+fnc+rUKfbv34+7u3u3BOme8g40Gg0ffPABBQUFTJ06FT8/P3Q6Xa8lcQc7ebmv4/e22lJdXS0Sq+1Mv8XEddddx1dffYWHhwfXXnstTz31FCkpKYNhm0Ag6C9CSAjswEsvvcSaNWsYO3YsXV1dTJo0ia6uLm644QaefPJJe5snGKX05ICea7Uh44pNcoM489WBnvpS9Ba/b16SVj5HcnKy1XH/xteckJBAYWEhbm5uJjP0sk0ODg7U1tZ2C2MqLCzE2dlZ6Zyt1+vx9/dXKk9ZYrCTl/s6fm8CUKPR9NmdWzC49FtMODo68sknn4gqTgLBcEMICYGdcHFx4S9/+QtPP/00hw8fRq/XM2PGDMaPH29v0wQjmL5EQU8O6LnOoluTH2FeRcm4jOv8+fP7DAvKzs7mH//4B76+vgAmzrDc+NE4qdp4tSEyMlLJRYqNjSUyMlIREenp6Tg6OjJp0iSio6PJzMyktLSUrVu3KoIiLi6Oq666SjmfLGp6E1+Dnbx8LscXidX2p99iQjQlEgiGIWVlsGCBEBICu/Dcc8/x0EMPMXbsWMYa/b9raWnhtdde4+mnn7ajdYKRSl+ioCcn0tpZ9J7EinHPh55WJIxzIsLDw7tVUDIvp2p+PXA212jcuHEAJttlIRMYGNjts3IIVHp6OtOmTSMyMlKxMy4ujvLycmprawkMDCQpKYnQ0FD+9re/kZubS0REhEmlJvnYgYGB5OTkmJxPIOgP55SALRAIhgFCSAjszPr167nrrrvw8PAw2d7c3Mz69euFmBAMCEudqq0JXepJZPSU3yB/xvjzlvIjgB5zIoxttVRO1XgftVpNdXU1V1xxhVLy1XhlYvr06eTn51NQUNCtBGxcXBzV1dVIkqQ0DE5PTwdg+fLlSqUm40TyyZMnU1lZaWK/8TWL/guCc0WICYFgJCOEhGAYIEkSKpWq2/aDBw8qDo9A0F+MRYHxzP9AZ8/7Kv9qDWq1GgcHB9RqtUVb5apMiYmJSpiSeZM8edViwYIFJuVWZeLj4zl48CB5eXnEx8cDmCRuG1ejys7ONilza0lIGe+v1Wq72d/fMCFrhJ214m+4ddMWDAwhJgSCkYoQEgI74+fnh0qlQqVSER8fbyIourq60Ov13HXXXXa0UDBaGIjjb+6o9lT+tT/o9XqlwZwlLFVs2rFjBzt27GDp0qXEx8crqxZqtdpifoTcidr8ei3ZLa9syNfbVzhYYWGhYn9fjnxvXcB76qItf85SmFdP90tUYhr5CDEhEIxEhJAQDAN+//vfI0kSt99+O+vXr8fHx0d5z8XFhejoaFHtT2ATBuL4mzuqtkjUNRck5g63pZULrVZLeXk5WVlZJCUlsWDBAiUcKj09HUmSTPIVLIVZ9ZYY3Z+cB/NwLPMu39aEgRl30S4sLLRY5tZSmJc191MwMrFKTDQ0NFh9QG9v7wEbIxAIrMBcSKSnCyEhsAu33norADExMcydOxcnJzE/JRg+9OWoDiRcx1yQmDvcllYuli5dSlVVFc7OzpSUlBAYGAicDZkKCQkhNDS0z7wQ81KyfXW87omeultbupaejhsefraLdnZ2NtXV1d1WRIw/N9D8FsHIwqqR39fX12I8rCW6urrOySCBQNALloTEf6t5CAT2oqmpid27d5Oammqy/ZtvvsFgMHDZZZfZyTLBSGEwYuf7clStCbGxtpmaXBrWuHSr7GTLVZXkvhTGlZvUajXx8fEmFaEsdXSWy9U2NTVRXl5uEl7U23X21sDO/HP9CQPrbUVECITzD6vEhFwpAKCkpITHHnuMVatWKcvXWVlZfPjhh2zYsGFwrBQIBEJICIYtjz32GC+//HK37ZIk8dhjjwkxIeiTwYyd760ErPG/lrC2mZosBJKSkggMDOSLL77gu+++Y9WqVUr+hHGDNbVaTUlJCZGRkYoQaWlpwcHBwWI3arlc7bFjx6itrSU7O7ub4LCEfF/1ej2VlZUsWLCgx0pV/RUBoyFESSSA2warxMTFF1+s/P7cc8/xxhtvcP311yvbrrzyShITE3n33XeVZW+BQGBDhJAQDGN+/vlnJk2a1G37hAkTKCwstINFgpHGYDqmvZWAtUUYjlzBSS7dCtDR0UFBQQGZmZlKbwjj8xtXdNLr9eTk5ODg4IDBYLDYjVrOo5D7RBivcPRmn3yMgoICtFptr9fRX/q6N0PtqA/kfCIB3Db0O8A1KyuLTZs2dds+a9Ysfv3rX9vEKIFAYIRIthYMc3x8fCguLiY6Otpke2FhIZ6envYxSjCiGMzQGFsKFUsOq6UKTqtWrSIzM5N58+Z1O0ZPCcryaoVOp1Nem59L/tdcvPRla1xcXDeBMthkZ2eTnp7e62qILRmIMBgNqyvDgX6LibFjx/KXv/yFV1991WT7X//6V5POpwKBwAacOiWEhGDYc9VVV/HAAw/w2WefKR19CwsLWbduHVdeeaWdrROc79hSqFjb9C0pKclkRcLcsZf3NxcJer1eqfAUHx+PwWBQ3pePUV1d3U289CRyZFvnz59vl1UE4x4Y50pfNg5EGIj8DtvQbzGxceNGVqxYwX/+8x/mzJkDwL59+/j555/59NNPbW6gzDvvvMM777xDSUkJAJMnT+bpp59WYnFbW1tZt24dmzdvpq2tjdTUVN5++21CQkKUY5SVlXH33XeTnp6OWq3m1ltvZcOGDaICiWB4cuoUXHKJEBKCYc+rr77KpZdeyoQJE4iIiACgvLyciy66iNdff93O1gkEfWOtM23JYbXGIbXWsY+Li2PatGlotVoSEhJwd3dXKj3JoVGJiYkkJSWZ2DDQztbmAsX48+eKcbM8W9DXyoMQBvaj31700qVLKSgo4J133uHEiRMAXHHFFdx1112DujIRERHByy+/zPjx45EkiQ8//JCrrrqK3NxcJk+ezIMPPsj27dvZsmULPj4+3HPPPSxfvpw9e/YAZ6tMLVu2jNDQUPbu3UtFRQW33HILzs7OvPTSS4Nmt0AwIISQEIwgfHx82Lt3L7t27eLgwYO4u7szdepU5s+fb2/TBAKrsDZEpieHdaCz5pZKz8bHx6PX63F3d1e+Q9u2bWPPnj2o1WquuOIKpTqUbNO5ipzIyMhuAuVcsbVzL0KShi8DmpIfO3bskDvgV1xxhcnrF198kXfeeYfs7GwiIiL429/+xscff8zChQsBeP/995k4cSLZ2dkkJyezc+dOjh07xrfffktISAjTp0/n+eef59FHH+XZZ5/FxcVlSK9HIOgRISQEIxCVSsWSJUtYsmTJkJyvpKSE559/nrS0NLRaLeHh4dx000088cQTJuP5oUOHWLNmDfv37ycoKIh7772XRx55xORYW7Zs4amnnqKkpITx48fzyiuvsHTp0iG5DsHw4Fwd1YEm8vZnRaGjo0PpYWGLhnzGiePR0dE9dvUeKvoSZGLlYfgyIDHxww8/8Oc//5ni4mK2bNnCmDFj+Mc//kFMTIzFZCNb09XVxZYtW2hqaiIlJYUDBw7Q0dHB4sWLlX0mTJhAZGQkWVlZJCcnk5WVRWJioknYU2pqKnfffTdHjx5lxowZg263QNAnQkgIRghvvfUWd955J25ubrz11lu97nvffffZ/PwnTpzAYDDw5z//mbi4OI4cOcIdd9xBU1OTElrV0NDAkiVLWLx4MZs2beLw4cPcfvvt+Pr6cueddwKwd+9err/+ejZs2MDll1/Oxx9/zNVXX01OTg5Tpkyxud2C4Ym1jmpPfRv6EiOy82/eQ8LaFYXk5GTld/Ok7YyMjAHlOhgnjssVpeTzW4Ot8yyMBZL8eqDHFiVfh5Z+i4lPP/2Um2++mRtvvJGcnBza2toAqK+v56WXXmLHjh02N1Lm8OHDpKSk0Nrailqt5rPPPmPSpEnk5eXh4uKCr6+vyf4hISFKKTStVmsiJOT35fd6oq2tTblG6F83cIGgXwghIRhBbNy4kRtvvBE3Nzc2btzY434qlWpQxMSll17KpZdeqryOjY0lPz+fd955RxETH330Ee3t7bz33nu4uLgwefJk8vLyeOONNxQx8eabb3LppZfy8MMPA/D888+za9cu/vjHP1qsXCg4v5EdXrmMK5iuDGg0GovOvSwAqqurSU9PJy8vT2k811sStYxcGtZ8m9zfQn7dHywJmf6szNi6rKqxPdYeu6d7Jkq+Di39FhMvvPACmzZt4pZbbmHz5s3K9rlz5/LCCy/Y1DhzEhISyMvLo76+nq1bt3Lrrbfy/fffD+o5N2zYwPr16wf1HAKBEBKCkcbJkyct/m5P6uvr8ff3V15nZWUxf/58k7Cn1NRUXnnlFaU0Z1ZWFmvXrjU5TmpqKp9//vlQmS2wA9bOXJvvZ9zxWl6ZMCY7O5u0tDQWLlxo4vwbi41jx46Rm5tLRESEyT5DXdrUfAXE/Jy2rp40kDCmvo7d0z0T+RVDS7/FRH5+vsWkOh8fH+rq6mxhU4+4uLgo/zFmzpzJ/v37efPNN/nVr35Fe3s7dXV1JqsTlZWVhIaGAhAaGsq+fftMjldZWam81xOPP/64yYOmoaFBlMAV2BYhJASCc6awsJA//OEPJtWjtFotMTExJvsZr0j7+fn1uGotVqxHN9Y67gPJTVCpVCavzZ3oSZMmUVlZiU6nM1nFsGVpU2vEkkajITs7GzgbRmW+n62rJ/VHLFl77J7umcivGFr6LSbkCgLmzYkyMzOJjY21lV1WYTAYaGtrY+bMmTg7O7N7925WrFgBnBU9ZWVlpKSkAJCSksKLL75IVVUVwcHBAOzatQtvb2+LnVtlXF1dcXV1HfyLEZyfCCEhGKGYz+b3xhtvvGH1vo899hivvPJKr/scP36cCRMmKK9Pnz7NpZdeysqVK7njjjusPtdAESvWI5+4uDiqq6uprq5Go9H0WqrV+N++sFQO1dyJlvcx72LdHwe4L7FgjeNeWFhIWloaKpWKwMBAZdu5iJveGIzVAiEahgf9FhN33HEH999/P++99x4qlQqNRkNWVhYPPfQQTz311GDYCJxdIbjsssuIjIyksbGRjz/+mO+++45vvvkGHx8fVq9ezdq1a/H398fb25t7772XlJQUJWlpyZIlTJo0iZtvvplXX30VrVbLk08+yZo1a4RYENgHISQEI5jc3FyT1zk5OXR2dpKQkABAQUEBjo6OzJw5s1/HXbduHatWrep1H+OJK41Gw4IFC7jwwgt59913TfYLDQ1VVqBlzFeke9pHrFiPbsLDwwkMDCQnJ4fAwECbOaS9herI/xqHPPXVh6GvnADzhO6ezmmJuLg4pQKmpTwFWzvqwvEfvfRbTDz22GMYDAYWLVpEc3Mz8+fPx9XVlYceeoh77713MGwEoKqqiltuuYWKigp8fHyYOnUq33zzDb/4xS+As8mADg4OrFixwqRpnYyjoyNfffUVd999NykpKXh6enLrrbfy3HPPDZrNAkGPCCEhGOGkp6crv7/xxht4eXnx4Ycf4ufnB0BtbS233XYbF110Ub+OGxQURFBQkFX7nj59mgULFjBz5kzef/99HBwcTN5PSUnhiSeeoKOjA2dnZ+DsinRCQoJiZ0pKCrt37+aBBx5QPrdr1y5lVdsSYsV6dGCNw22LRN6enOj+NruzNqHb2mNbSuo2PraoiDQ6GIq/o0oaYK/z9vZ2CgsL0ev1TJo0CbVabWvbhiUNDQ34+PhQX1+Pt7e3vc0RjESEkBDYkcEYw8aMGcPOnTuZPHmyyfYjR46wZMkSNBqNTc5jzOnTp7nkkkuIioriww8/xNHRUXlPXlWor68nISGBJUuW8Oijj3LkyBFuv/12Nm7caFIa9uKLL+bll19m2bJlbN68mZdeeqlfpWHFc2H0YitHbKDH6etzxp2xFyxYYJLTeq7nlLtiJyUlWdWAUoiP4Ylc8cvav6NMf8Y1h17ftcDtt99OY2MjLi4uTJo0iQsuuAC1Wk1TUxO33357fw8nEJxfCCEhGIU0NDRw5syZbtvPnDlDY2PjoJxz165dFBYWsnv3biIiIggLC1N+ZHx8fNi5cycnT55k5syZrFu3jqeffloREgAXXnghH3/8Me+++y7Tpk1j69atfP7556LHxAhCLsc6GKI1PDyc+fPnn7NzLK8wyF2rbUV4eDjXXHMNCxYssFhZ6pNPPlGSrM3p6b4Zr4b0pyv2YF2j4NyIi4uzeXdzc/od5vThhx/y8ssv4+XlZbK9paWFv//977z33ns2M04gGFUIISEYpfzyl7/ktttu43e/+x0XXHABAD/++CMPP/ywxTAKW7Bq1ao+cysApk6dyg8//NDrPitXrmTlypU2skww1IyEngLGJWXN+1D0NqPf27UZN9DrCfPKUtYc2zj8qz/3U5RjHZ4MRa6K1WKioaEBSZKQJInGxkbc3NyU97q6utixY4dSJUkgEJghhIRgFLNp0yYeeughbrjhBjo6OgBwcnJi9erVvPbaa3a2TjDaGQlOrOzQWWoy15tg6O3aemqgJ2OpspQ1xx6o8ykSrM9frBYTvr6+qFQqVCoV8fHx3d5XqVSiVJ5AYAkhJASjHA8PD95++21ee+01ioqKABg3bhyenp52tkxwPjCSnNjeuk5bcvp7u7a+Guj19tlzzW8YzfkRo/naBgurxUR6ejqSJLFw4UI+/fRTky6jLi4uREVFiZsuEJgjhITgPKKiooKKigrmz5+Pu7s7kiT1GmYhEIxWenJIjVcj5NeDtRIw0PApaygsLLRYRWo0MBLC5oYbVouJiy++GICTJ08SGRkpHhACQV8IISE4T6ipqeHaa68lPT0dlUrFzz//TGxsLKtXr8bPz4/f/e539jZRcJ4wlLPKvXWQ7q0PxLk4q/25voGGT1lznri4OPLy8qitraWwsFDpm2GtbcN59n8khM0NN/qdgJ2WloZare6WrLZlyxaam5u59dZbbWacQDBiEUJCcB7x4IMP4uzsTFlZGRMnTlS2/+pXv2Lt2rVCTAiGjKGcVTbvIN1THwhze8yd1f441tnZ2aSlpbFw4UKLxQ2MjzXQ8CnjcrPGdpt//pprrlHOJd+Pvu69ednZ3va1FyMpbG640G8xsWHDBv785z932x4cHMydd94pxIRAIISE4Dxj586dfPPNN0RERJhsHz9+PKWlpXaySnA+0pMDPRgz4eYdpI2Rz1FdXU1kZKTJ++bOan8FUE+RIeYiYKAlbQsLC6mtrcXPz6/X2Xnz6+hPE8DIyMhBL1cqGDr6LSbKysqIiYnptj0qKkpRmQLBeYsQEufMcF7+FlimqakJDw+Pbtt1Op3oFC0YUnqaVR6MFYueOkhD92ZyvZ2zP2E1vVVo6k0E9GdcHWhpWGtm9Ad6bMHwpt9iIjg4mEOHDhEdHW2y/eDBgwQEBNjKLoFg5CGEhE0QyW8jj4suuoi///3vPP/888DZmVODwcCrr77KggUL7GydQGD7OPi+nHNrZ/ehf2E11lR3OteE68EM8xEhRKOTfouJ66+/nvvuuw8vLy+lLff333/P/fffz3XXXWdzAwWCEYEQEjZjqB/6gnPn1VdfZdGiRfz000+0t7fzyCOPcPToUXQ6HXv27LG3eQKBzZ3Yvpxze8zAy9cod7Y2PvdoTyoW47x96beYeP755ykpKWHRokU4OZ39uMFg4JZbbuGll16yuYECwXCg14FKCAmbMtQPfcG5M2XKFAoKCvjjH/+Il5cXer2e5cuXs2bNGsLCwuxtnkBgc/pyznsax8yfJcZdrOVeEec6ThmPefLruLg4ZQLYGkaacy7GefvSbzHh4uLCv//9b55//nkOHjyIu7s7iYmJREVFDYZ9AsGwoMeBaoiExEgb2IcTo31Gzt50dHRw6aWXsmnTJp544gl7myMQDAnGYuFcyrX21cV6IBiPeT0Ji3MpKzscEeO8fem3mJCJj4+32AlbIBiNWByohnBFYqQN7MMJe8bong8i0NnZmUOHDtnbDIHAbvRnfDZ/lvTUxXqgY0dPnzMXFv2101YM1pgocjHsi1ViYu3atTz//PN4enqydu3aXvd94403bGKYQDCc6DZQDXFok5h1GZmcLyLwpptu4m9/+xsvv/yyvU0RCIac/ozP5s8SW1efMv+cpeMPxE5bcb6MiecbVomJ3NxcOjo6lN97QnTFFpwX2CFHQsy6jEzOFxHY2dnJe++9x7fffsvMmTPx9PQ0eV9MMglGM4MxPg907Bhoo7qh4nwZE883VJIkSfY2YiTR0NCAj48P9fX1eHt729scwVAjkq0FI5zBGMN6K/+qUqlIS0uzyXmGK+K5MLo4H8ITLXG+XrfAMv0Z1wacMyEQnHcIISEQWCQ9Pd3eJggENsPeoThD4dQbnwPOXnN1dbXSfFiICUF/sEpM9NTh0RLbtm0bsDECwbBFCAmBwCpOnToFwFjx/RCMUOwViiM7+JacelsLDPMqTzk5OURGRpKUlCRCkAT9xiox4ePjo/wuSRKfffYZPj4+zJo1C4ADBw5QV1fXL9EhEIwYhoGQEMvPguFMZ2cn69ev56233kKv1wNnq9Pce++9PPPMMzg7O9vZQoHAeuyVWyA7+JaceluvllgSTOL5MnKxt49glZh4//33ld8fffRRrr32WjZt2oSjoyMAXV1d/L//9/9ErKhg9DEMhATYf9ldIOiNe++9l23btvHqq6+SkpICQFZWFs8++yw1NTW88847drZQIBj+9NY1u7fVkoE4kpaqSglGLvb2EfqdgB0UFERmZiYJCQkm2/Pz87nwwgupqamxqYHDDZFodx4xTIQE2H/WQTB6GIwxzMfHh82bN3PZZZeZbN+xYwfXX3899fX1NjnPcEU8F0Y+I3mMzcjIICcnh6SkpH51uR6tjOS/5UAZjGse1ATszs5OTpw40U1MnDhxQuneKBCMeOwoJCwNCsOhpJ9A0BOurq5ER0d32x4TE4OLi8vQGyQQ9BN7z+yeCwPJ8RjNDvdI/lsOFHv7CP0WE7fddhurV6+mqKiICy64AIAff/yRl19+mdtuu83mBgoEQ46dVyTOx4FwODKaH7a25p577uH555/n/fffx9XVFYC2tjZefPFF7rnnHjtbJxD0zUjsf2A8RvV3RWI0P2dG4t9ypNNvMfH6668TGhrK7373OyoqKgAICwvj4YcfZt26dTY3UCCwFps4f8MgtEkMhMOD0fywtTW5ubns3r2biIgIpk2bBsDBgwdpb29n0aJFJsU5RMU/wXBksGd2B2Ny4lzGqNH8nDH+W1oqgSsmiGxPv8WEg4MDjzzyCI888ggNDQ0AIkZUMCzIzs4mLS2NhQsXDqyy2DAQEmD/5UrBWUbzw9bW+Pr6smLFCpNtojSsQPA/bDE5YS5IzmWMGoniaSBYKoELYoLI1gyoaV1nZyffffcdRUVF3HDDDcDZ/zje3t6o1WqbGigQ9AeVSjWwDw4TISEYPghRZz3GFf8EgqFmuDiuvWGLvAZzQTKcBcFwWdntqQSuwLY49PcDpaWlJCYmctVVV7FmzRrOnDkDwCuvvMJDDz1kcwNlNmzYwOzZs/Hy8iI4OJirr76a/Px8k31aW1tZs2YNAQEBqNVqVqxYQWVlpck+ZWVlLFu2DA8PD4KDg3n44Yfp7OwcNLsFQ0dycjIrV64kOTm5fx8UQkIgGJFceeWVREZG4ubmRlhYGDfffDMajcZkn0OHDnHRRRfh5ubG2LFjefXVV7sdZ8uWLUyYMAE3NzcSExPZsWPHUF2CwAbIjmthYaG9TemR8PBw5s+f3y/H2vy64uLihrSp3Lnc16G2tSeM7/tA/gYC6+i3mLj//vuZNWsWtbW1uLu7K9t/+ctfsnv3bpsaZ8z333/PmjVryM7OZteuXXR0dLBkyRKampqUfR588EG+/PJLtmzZwvfff49GozEJd+nq6mLZsmW0t7ezd+9ePvzwQz744AOefvrpQbNbMHQMaKAQQkIgGLEsWLCATz75hPz8fD799FOKioq45pprlPcbGhpYsmQJUVFRHDhwgNdee41nn32Wd999V9ln7969XH/99axevZrc3Fyuvvpqrr76ao4cOWKPSxIMgOHiuNoa8+saamfY/PwajUYpQ5uRkdFNuBsjHPfzDKmf+Pv7SydOnJAkSZLUarVUVFQkSZIknTx5UnJ3d+/v4QZMVVWVBEjff/+9JEmSVFdXJzk7O0tbtmxR9jl+/LgESFlZWZIkSdKOHTskBwcHSavVKvu88847kre3t9TW1mbVeevr6yVAqq+vt+HVCOxCWZkkxcZKEpz9t6zM3haNCk6fPi19//330unTp+1tisACo3kM++KLLySVSiW1t7dLkiRJb7/9tuTn52cyvj/66KNSQkKC8vraa6+Vli1bZnKcOXPmSL/5zW+sPu9ovqeCwWEwxsnBHnu///57aePGjdKbb74pbdy4UfG/BKOT/oxr/V6ZMBgMdHV1ddteXl6Ol5fXOUob65GbIPn7+wNw4MABOjo6WLx4sbLPhAkTiIyMJCsrCzjbkTUxMZGQkBBln9TUVBoaGjh69OiQ2T5UyLMIvc0enLcYrUi0hIej3bzZ6hUJcV97ZySEHAhGHzqdjo8++ogLL7wQZ2dn4OyYP3/+fJNeF6mpqeTn51NbW6vsY/zckPeRnxsCwWAwGOPkYI+98kpFQkICDg4OIkdWoNBvMbFkyRJ+//vfK69VKhV6vZ5nnnmGpUuX2tK2HjEYDDzwwAPMnTuXKVOmAKDVanFxccHX19dk35CQELRarbKPsZCQ35ffs0RbWxsNDQ0mPyOFkeDU2cUxNxMSH/361xS0tFht20i4r9YyGPd/JIYcCIFoO8rLy4e0gemjjz6Kp6cnAQEBlJWV8cUXXyjvWTPm97RPT88EGNnPBUF3zuX7P9DPDsY4Odhjrxy65O7ujsFgQK/XD8p5BCOPfouJ119/nT179jBp0iRaW1u54YYbiI6O5vTp07zyyiuDYWM31qxZw5EjR9i8efOgn2vDhg34+PgoPyOp3OFwdOrMB97eHPNBcfDMciTqP/+c+EWLLN6jnmwbjvd1oAyGMBqJsbKjSSDam0mTJlFSUjLgzz/22GOoVKpef06cOKHs//DDD5Obm8vOnTtxdHTklltuQZIkG1xJz4zk54KgO+fy/R/oZwdjnByqsXc0PQMFtqHfpWHHjh3LwYMH+fe//83BgwfR6/WsXr2aG2+80SQhe7C45557+Oqrr8jIyCAiIkLZHhoaSnt7O3V1dSarE5WVlYSGhir77Nu3z+R4crUneR9zHn/8cdauXau8bmhoGDEPjuFY2tK8XFxv5fJsXlrOQrJ16NixWPrLazQaqquriYyM7Gabve+rLcsgDudeCkNZ7nE434eRxrk68uvWrWPVqlW97hMbG6v8HhgYSGBgIPHx8UycOJGxY8eSnZ1NSkoKoaGh3Sr6mY/5Pe3T0zMBRvZzQdCdc/n+j/SxYyDjrLyfLKCGm58hGHr6JSY6OjqYMGECX331FTfeeCM33njjYNnVDUmSuPfee/nss8/47rvviImJMXl/5syZODs7s3v3bqV5Un5+PmVlZaSkpACQkpLCiy++SFVVFcHBwQDs2rULb29vJk2aZPG8rq6uuLq6DuKVDS7Drf62+cDbk2PemzPfGz1ebz+qNmk0GrZu3UppaSlRUVFWn3uosKXIsrcw6gn5byDHtQ+2jcP1PpyPBAUFERQUNKDPyuFVbW1twNkx/4knnqCjo0PJo9i1axcJCQn4+fkp++zevZsHHnhAOc6uXbuU54YlRvpzQWDKuXz/7Tl22OL5PtDnyXDpIyEYHvRLTDg7O9Pa2jpYtvTKmjVr+Pjjj/niiy/w8vJS4ll9fHxwd3fHx8eH1atXs3btWvz9/fH29ubee+8lJSVF6TuwZMkSJk2axM0338yrr76KVqvlySefZM2aNaP2wWDLL7wtBi5rB97CwkLKyspISko69wGun+VfCwsLqa2tpaOjg9raWgoLC4fVYDlYM2HDSXjKfwM/P78RO+N3vvLb3/5WKYwxmPz444/s37+fefPm4efnR1FREU899RTjxo1ThMANN9zA+vXrWb16NY8++ihHjhzhzTffZOPGjcpx7r//fi6++GJ+97vfsWzZMjZv3sxPP/1kUj5WIJAZbuOk8fNuILYN9Hky0ldkBLal32FOa9as4ZVXXuGvf/0rTk4DaqA9IN555x0ALrnkEpPt77//vrIkvnHjRhwcHFixYgVtbW2kpqby9ttvK/s6Ojry1Vdfcffdd5OSkoKnpye33norzz333FBdxpBjyy98b8Kkv4NYX/ur1eoBVYvodr0D6CMhf1atVqPX64fdYHkuM2G93Xd7zTRZssn472jvB7Y1DCcHw948/vjjQ3IeDw8Ptm3bxjPPPENTUxNhYWFceumlPPnkk8rkkI+PDzt37mTNmjXMnDmTwMBAnn76ae68807lOBdeeCEff/wxTz75JL/97W8ZP348n3/+uVLcQyAwpj/j5GCPC+bPu55s682OgT5PxGquwJh+q4H9+/eze/dudu7cSWJiIp6enibvb9u2zWbGGWNNHK6bmxt/+tOf+NOf/tTjPlFRUedVd1P5Cy8nM5/LoGbL/Ia+9tfr9SbVIqwdlE0GuFOn6LzoIpxKS+mMisLJyoZ0g+Wsnwvmxx3oeXq777YSnv21zZJNI+1BZQshJgRJ/0hMTCQtLa3P/aZOncoPP/zQ6z4rV65k5cqVtjJNMIrpzzg52BM05s93tVptMTF6JIQkifFvZNNvMeHr66vkJAhGDrYYTHpz8PrriPa1v7UzLj3y3xUJp9JS6gICyH/9deb0kiNxroOYfIzq6mrKysqst9NK5Ouvrq4mMDBwwOexdN+Nr3/+/Pk2s9Va20bDcrktrmEkPPAFgpHMUIbqwuCMbZauQR47kpKSLI7hQznGDsZEl2D4028x8f777w+GHYJBprfBZKgHWOP9e1oxMT9evwZDo9Cmzqgo8l9/nbEXXtjj7rYYxORjREZGWl0yrz/3XT5edXV1v89jjKW/k60H8f4+uEbaKoQlbHENo0FUCQTDmcFyWHsaywdjbLN0DX2NHb0VOrH1asBA73FcXBzV1dVUV1ej0WhG/DPhfMNqMWEwGHjttdf4v//7P9rb21m0aBHPPPPMkJSDFZw7vQ0mtqqaM5CBydqBx+pB2SxHonrzZtosNKQzxhZO3EBi/LOzs0lPT2fBggUsX77cqs9ER0cTGBjY78G/t7+NrZ3Y0SAO7IG4bwLB4DJYgn0oZ9UtXcNAx47BsHug9zg8PJzAwEBycnIIDAwUY+EIw2ox8eKLL/Lss8+yePFi3N3defPNN6mqquK9994bTPsE/cRah944LMdWVXOsHZiMbbTp4G4h2brg5Mk+q10YD8Q5OTlkZmYyb948kpKSrD71QAdza2vy97WMbe3nofvfRjixAlvwww8/8Oc//5mioiK2bt3KmDFj+Mc//kFMTAzz5s2zt3kCwaCNdUO5qmjLaxgMu621r6/CG4KRhdVi4u9//ztvv/02v/nNbwD49ttvWbZsGX/9619xcOh3I23BIGHJaZS/tMbViYzDchYsWGDVTHdfQsV4ILC2atC5duuUzxPv7k7oddeZCAmNo6PSq0KtVpORkdFnrkFmZibp6ekA/RITAyE5OVlZZejpuuT7d66DrHGFqnNNxD/fEImBffPpp59y8803c+ONN5Kbm6v0eaivr+ell146r4peCIYee39HR+qETG92D/Y9HQ2FNwT/w2oxUVZWxtKlS5XXixcvRqVSodFoTDpRC+yLJadT/tI6ODgoTZ3Mw3KMcxfkz5gPIoWFhaSnp5OXl8c111xjcaZf3j8jI0MZKMyPdy6Osfn5CgsLKdi9m6Q//xkqK02qNhVmZCi9KvR6fZ+5BhqNBg8PD5KSkkhISFAqkyUnJw9qJQ5LZGdnk5aWxvTp04mPj7dZcnRJScmgJIgPNsZ/d7D8/3OwEImBffPCCy+wadMmbrnlFjZv3qxsnzt3Li+88IIdLROcD4jv6MDpSTQM9j0VqxCjC6vFRGdnJ25ubibbnJ2d6ejosLlRgoFjKWQnMDAQBwcHEhISaGpqorq6WgkxKiwsBEyFQkREhEWHMy4ujry8PJNGbj0NOMYz4eY5Gecy+2B+vnh3d2b/9a+4V1Z2q9pk3KsiNDSU6upqxTZL5y8sLESv13PxxRcD8NVXXyFJkt3iN1UqFRUVFUp5XPOVpoHkpgw0cdveGP/dgSFtxCgeen2Tn59vUez6+PhQV1c39AYJzitGwnd0KFdP+nOuvp7hg3VPxSrE6MJqMSFJEqtWrTLpFN3a2spdd91l0mtisPpMCKxHHkgyMjI4cOAAY8eOJTY2Fnd3d9zd3ZUEJ/ifUxYXF0dGRga5ubl4eHgQGRnZrapCeHg411xzjckMseywt7S0mMzky5SUlCg5GbYIsZErPhQUFNB04gS/eOklnDQai1WbjHtV9JXcpdFolJAo+doWLFignHOokUOgLDXOG8iM0UASxO0dOmCMsf1arXZADQ0tYc29tPTQG073ZjgQGhpKYWEh0dHRJtszMzOJjY21j1GC84aR4JgOpODGQOnPM6In0TAS7ulw43x+LlgtJm699dZu22666SabGiM4dzQaDX/72984cuQIbm5u+Pn5kZycTERERDdHLDQ0FOg+iPj7+yuON2AS12/+RZEd9vz8fAoKClCpVCZCxTgnYyAJ2paShQGyt2zhpawsnBobTao2abVai8ndOTk5ZGRk4Obm1k0kGVe0SkxMVD6/fPlyxZa+bLY1vQ3kQzULJ4daLVy40OLDbygHTuP7UVhYaNLQ8FwY6L0UYRWm3HHHHdx///289957SvhrVlYWDz30EE899ZS9zRMIhgXWFtw4V/ozrgnRYDvO5+eC1WJC9JcYGRQWFlJcXMyxY8eor69n+vTpipAwrt4kVywy/lxXVxczZswwWVkoKChQHEpjgSF/UeSViYSEBCV3xli0REdHK4nM1s4om4e0mDus7tXVvJSVRXBjI/qQEPSbN1PQ0mKSFyI3d5M/t3XrVmWVxsnJyWR1Ijs7m9zcXGJjY9HpdHz55ZeMGzeO22+/3WKeyECxlfM9kMF/oIOcSqWy+THPFVuKqYE+SEdCWMVQ8thjj2EwGFi0aBHNzc3Mnz8fV1dXHnroIe699157mycQ2J3eCm7YCls3ID1fZtptdZ3n83Oh303rBMMXOVRn7ty5lJWVodPp6OjoIC4uziSh19HRkdzcXMrLyyktLUWtVnPttdeyYMEC1Go12dnZ6HQ64KyYaGpqori4mKNHjxIWFmbyRZFXJtzd3UlNTVW2y7PHJSUllJSUoNPpKCgo4MyZM0RERJCUlNTjF9j4C9nNme/qYuFzz+Ha2EhDUBCf/OY3xLe0mIRd6fV6pbkbnHUYZeGUkJCAu7t7ty+7p6cnYWFhFBQUcOzYMZqbmxXbzPNELN13a5KDB+p89zbQaTQasrOzgd4TxQcyyPX18LPmmIPxMBqoALClLWI2zxSVSsUTTzzBww8/rOQeTZo0ySahaALBaGAoxgxbT/CcLzPttrrO8/m5IMTEKMI4LGX16tX885//JDw8nPfee4+2tjY0Gg3Tp09n0qRJVFZWAmcdrLa2NqZNm8Z9991HRkYGaWlpnD59mtbWVgAuvPBCWltb2bNnD2PGjFHO5+/vj6enp7LaYFyCVs4/KC4uZsuWLbS3t+Pn54ePj49y3p6a5Zl/IWVn/uBXX+G3fj3uGg11AQFkvfAC8RMmoFaruzmJGo3GxBFOSkrqsdSr7DRXV1dz5swZ/P39iYqKUhLVzfNEjDG/DqDHVZVzCanpaXVEfq+vRPGBDHJ9fcaaYw6GgBoo58uD0Z64uLgwadIke5shEJyX2Hpm/HyZaT9frnMwEWJilKFSqdDpdDQ3NxMSEkJBQQGHDh0iMDAQLy8vCgoKiI+PZ8GCBURHRxMfH49Wq1Vm7uVciujoaIqLiykpKSEsLIykpCSqqqqorKzk008/VZKaQ0JCCAgIQK/XK86aXq+nqKiIcePG0dDQQH19Pe7u7syaNYvk5GSio6N577332LdvH0FBQd1mL81n+iMiIhjv5sbcJ5/E/cwZ6vz92XbvvUyfNYukpCSTMrTGyeKAku8g/268eiCvYsjnj46O5oorrlD2Lysr63FmPicnhx07dtDQ0EBXVxdRUVEm+xnniBiHXA1k6bm31ZG4uLh+JYr35KQP1nL2cMpJEA+MwWPBggW9hsSlpaUNoTUCwfmJrWfGz5eZ9vPlOgcTISZGEcYz7MePH6e1tZWYmBja29vx9vbG1dWV0tJSCgsLuemmm0hKSlKqsMjJ2LJzLa8q1NTUUFdXR2hoKFOnTqWgoAC9Xk9NTQ0ODg5UVlbS2dlJRkYGs2fPJikpiX379lFYWEhzczOzZ89mxowZ+Pr6ct111ynOf1FREXV1dbi7u5OZmUloaKiJADAuU6s7eJAb//pX3M+c4Yy3N3++5hqKSko4vX07oaGhlJeX88MPPxAcHGxyP3oqJyr/LudX6PV6tFqtSaKx8cqGpUTkzMxMvv76a7y8vLjssstMVgzMByXzkKv+YqmKlvF7xjb3VS3L3Ek37oQ+GP0nhlNOgnhgDB7Tp083ed3R0UFeXh5HjhyxWLxDIBAIBP1nuOaxCDExipCdpZycHL777jsMBgOtra3U1dVRVVXFwoULOXPmDJWVlXz77bcAFBcXs3fvXpYuXcrSpUspKChAo9Gg0Wg4ceIE9fX1HD9+nOzsbA4ePIgkSSxevJjAwEAKCgrIy8ujpaWFAwcOAGc7Wvv6+hIXF0dQUBCtra1K7sS//vUv0tLSKCoqwmAwMGXKFAByc3OJiIhQnGJ5Jv7o0aOU7dnD0+npuJ85Q2dUFMWvv05oXR17t22jtLQUgGPHjpGTk4Ovry833HCDcj/kMrLV1dVKIrixc1peXs7u3bvp7Ozs1sVdvpcajYZjx45x5swZjh49quQlzJs3j4aGBkJDQ1m6dKnFL7XxMeQyr9aUxjXvWG5cHra3z1kzm2+cW2LcEXyw+k9Y6r5uLGJ6uqbh4Pj3ZLugOxs3brS4/dlnn7VJ1S2BQDA0DFdnVXCW4RquK8TEKESv1xMQEEBQUBDHjh3jp59+oquri4aGBurq6ujs7MTV1ZWWlhZKS0spKyvD1dUVf39/du3axYkTJwgODiYkJITg4GB8fX2B//VdiI6ORq/Xk5SURHx8PC0tLezfv5+Ghga++OILfH19mTt3LgUFBRw+fBgHBwckSeLIkSNkZ2dTX1+Pj48P06ZNIyQkhKqqKgA+/vhj/vGPf5CQkMCUKVNQ19by4Jdf4t3aij4khJNvv02bWk1SbCxarZbdu3ezd+9e1Go13t7eFsvIGveWMA8x+uCDD8jKyiI0NJRf/vKXJlWs5MTmY8eOUVtbi1qtpri4mOzsbJYvX24xB6O3ROzw8PBeu4IbY96x3LhzeW+DhzWz+ea2GIsIWw9Mxvkkfn5+JtcwXAdEYyx1jh+utg5XbrrpJi644AJef/11e5siEAisYCSMzeczwzVcV4iJUYZc0SkxMZHk5GR27NhBbm4ujY2NdHR0UFdXh4+PDzNnzsTHx4fq6mqcnZ2prKxEp9OhVqvp7Oykuroaf39/Zs+eTVVVFdnZ2SZhSunp6Tg4OBAWFgbA6dOnyc/PJyQkBIADBw5w9OhRfH19ueSSS1i0aBGAEmbV0tLCnj17mDlzJsnJyeh0Ov7yl7+Qn59Pfn4+6tpanvnuO7xbW9F6evLc7Nkceukl9Ho9N910E7fffjsNDQ0cOnSIoKAgQkJCiIqKUmb+4X95EZYa8BUWFtLU1ISTkxMqlYqEhIRufSf27NlDQ0MDc+fOZeLEieTl5XW713IlpejoaDIzMyktLcXX1xeVSkVXV5eyr1whS26K19uAbV6ZylLjOkv0ZzZ/IE3s+kthYaEiJObNm2dyDcN1QDTG/O8wnG0drmRlZeHm5mZvMwQCgZWMhLH5fGY4rNpbQoiJUcQ333zDu+++S2BgIKmpqWRnZ1NXV8eMGTMwGAzEx8dTWFhIY2MjP//8MzExMUyYMAF/f3+qqqooLS0lICCAqVOncvr0aQ4dOkRbWxttbW3U1NRQU1PDsmXL8PT0pKysjBMnTtDZ2UlXVxclJSU4OjoyYcIEHB0dyc7OpqmpCTc3N8rKylCpVCQnJ+Pv709AQADb/hum1NXVpeRiuLq6EhYWxszgYNZs2YJ3Swt1/v48OWsWJ/6bfNzR0cHx48cJDw8nPj6ew4cPA7Bw4UIAk1yLw4cP4+fnR0REhJJMDf8TGb/61a/Yvn27IoTk0rayE6xWq/Hw8GDSpEkkJycTHx/frRO1XEkpNDSUw4cP09jYiJeXFx4eHsyYMQO1Ws3WrVvJzc1FrVazcuVKi2VwLWGcR9IX5kvTOTk5Si+R3lZQbJ2EbXyM3gRLTwOiJRustcvWy/PDddAejpg3NZQkiYqKCn766SfRtE4gGEEYj3si5ElgLUJMjCI++eQT9u/fz/jx4/Hx8WHPnj1UVVXh4eHBmDFjmDhxIq6urvz1r3/lzJkzhIWFcdlll+Hp6UlNTQ3p6ek4OTkxbtw4wsPDKSsr4+jRo0yfPh2VSsWpU6f4y1/+go+PD1VVVdTX1+Ps7ExnZycGgwEPDw9iY2M5cOAA7e3tODs74+npSUFBAUePHuXw4cMsWrSIqqoqXFxcCAoKIigoiAMHDtDc3IyjoyNXJSVx/2efEdbSQrmrK7+dOpVcjYaAgACmTZuGXq/Hy8uLbdu2UVdXpySDyzkJ3333HQ0NDURERODg4EBubi4eHh5KKI9xMrW8IlJRUWHSwE92gltaWsjPzyc6OtrkPssrEjqdjmnTpgFnE7oLCgpwcnLCx8eHiIgI6uvrycnJoba2lnHjxjFp0iTl2JYc1XNJhjZPEs/MzCQ9PR2gm5joqdRsT8nZ/XmQGB9j/vz5/X4AWVqxsbTNkm3WLM+Lh+PgIJd8lpEbWT733HMsWbLETlYJBIJzQYQ8CaxFiIlRRHh4OJ6enjg5OXHixAlKSkpoamoCoLKykra2NgIDA2lvbwcgKCiIBQsW8O2333LmzBm8vb1xc3PjyJEjODs74+PjQ319PTU1NYwfP57CwkJKS0tpbm7Gw8MDJycnGhsbmT17NlOmTMHFxYWqqiqampoICQnBycmJtrY2Ojo6lIpJxcXFHDlyhKqqKoKDg2lvb+f777+nsbGR8W5u3JudTVhrK2XOzvy/hAQiJ07kIs6GmgQHB7Nnzx7279/PgQMHSExMJDw8HLVaTUtLC99//z0nT57E09MTnU5HWFiY0k9DRqfTUVRURGNjI9u3b8fJyYnJkydz5swZnn/+ecLCwli6dCnz589n27Zt/Pzzz3h4eKDVaikqKlJKx/7jH//AxcWFuXPnEhERQUtLCy4uLkiShJubG/X19Zw6dYqZM2eSmJgInG2Mt3XrVpPGeeY9I8zzGKB7OJWlRGadTmdSmlMWR8YiSaanUrPmy9sD6f59LkvkcoieHArW2zEtPeSsObd4ONqerq4ubrvtNhITE/Hz87O3OQKBoAf6O5lyriFPYvLm/EGIiVFEVFQUDg4OHDt2DH9/fyUfQKVS0dDQQGFhIcHBwYwfP56ff/6ZkJAQkpOTyc3Nxc3NDV9fXxwdHcnPz6ejo4OoqCi8vb1pamri+PHjuLm50dTURE1NDR4eHjg6OtLe3o5OpyMhIYGSkhIqKyuZOnUqV155JTk5ORw/flxZyWhoaOCnn36ipaWFwMBALrnkEvR6PcXFxQQ0N/NxbS1jJIlaPz/+fPnllB86REBTE+PHj6erq4uWlhZ8fX2pq6ujqakJtVrNhAkTqK2tJT8/nyNHjigdvbdv387cuXOJj48H/hf+1N7eTk1NDYWFhXh4eBAXF0dtbS3bt28nIyMDg8FAXV0dsbGxZGdnKyJILmULZwVJa2urUmpXzquYO3cu8L9mfvv27SM0NBSdTsfBgwdpbm5Gp9NRWFhIbGwsYLoCoFarLSZDFxYWkpaWhkqlIiQkxKSMbXZ2Nunp6UybNo2VK1eaVIy67777LP4/6anUrPnydnV1NQ4ODorokG3p7cFgvuLSn5CqwsJCysrKSEpKMtnX0iqOpYecNWFJtowHFg/Kszg6OrJkyRKOHz8uxIRAMIzp72TKuYZ6itXi8wchJkYR/v7+tLW1UV1dTWNjIyqVitbWVhwcHHBwcKClpYWysjK8vLzw9fVFp9Px1FNPUV5eTkdHB11dXYSGhipVnpycnIiNjaWxsZHa2lqcnZ2VZMquri6cnZ1RqVRUV1dTWVmplIGVO2fHxsbS0tKCu7s7bm5u5Ofno9Pp6OrqorW1lbKyMpycnJioVvOXigpiJIkyJydOvvUWHYcO0ZCZyaFDh3BwcGDGjBkEBgZy8uRJpk+fjr+/P8uWLSM0NJTs7Gxyc3OpqqrC19cXJycnXFxcaGlpwcnp7H/xmpoaGhoa8PPzIyAgAIPBgI+PD5MmTWLMmDF4enri7++PTqfjwIEDpKWl0draysKFC1m6dCklJSXA2V4e2dnZhIaGEhQUhLe3N7W1tXh7eytJxnLC7uTJkykrK1OqWs2cOZPo6Gjc3d2VLuHyQJqTk0NSUpLFpnZxcXFKTohOp+u22iJJEv7+/syfP9/qilHGZWstlauVHfvJkycr/TYGMqvf02cGurJgbn9P9PSAsmUeRF/3w5rSsvZ4kFZUVNj8mFOmTKG4uJiYmBibH1sgEFhHX+PJUCdXi9Xi8wchJkYRycnJzJgxA41Go4Q3SZKEJEk4OTnR0dHByZMncXV1VbpW79y5k+bmZlQqFQEBAYSEhODm5oZWq6W9vR21Wk1YWBgFBQV0dnbi6OiIu7s7Xl5eSJJEe3s7lZWVNDc309HRQWdnJ+Xl5Rw5coR58+Yxa9Ys9u3bp6xYwNluuFVVVezatYtLJ0/mH6dOESZJFKtUXOPryw1aLfHx8fj7+9Pa2oqvry/t7e3885//RKPREBMTwy9/+UsA3nvvPTQaDVlZWTQ2NhIfH8/YsWPp7OxUnPaCggJOnDiBk5MTN910E6GhoWRlZaHX66mrq8PV1RWtVsvSpUtxcXGhvb2d3bt3K+VAQ0NDTfIOkpOTKS8vp7a2lkmTJim/Z2ZmmpRylcOVNBoNbW1tNDY2Mn/+fMrKymhubkav1yuOenV1NQUFBVRXVyu9LGTMG9MZJ4LLjQotVUmyJgeip4G8t+Tp/jyI1Go1Dg4O3bqcD3RlwRjz6zF+PRQPqL4elNaUlrXHg7S4uNjmx3zhhRd46KGHeP7555k5cyaenp4m73t7e9v8nAKBwJS+xpOhLiox1KvFAvshxMQo4vDhw5SVleHg4EBXVxcODg44OzvT0dFBe3u7Eirk4uKCg4ODIjAMBgMqlYrS0lJaW1vx8/PDxcWFtrY2zpw5w+nTp2lsbKSmpob6+npUKhUdHR2oVCplpaKtrY2uri5UKhVOTk5IkkRDQwNubm4UFxdTWVlJWFgY4eHhyspFlErFhqwsQlpa0Hp6cp2XFz/r9fzjH/8gNTUVNzc3qqqqyM/Pp6SkRCntumzZMuLi4ti6dStffPEFtbW1dHZ24ufnx8KFC2lpaWHv3r3o9XpCQkI4efIkR48eRZIkoqKiGDNmDGfOnMHT01NJoK6srMTX11dZnVixYgVarZauri6TvALZYZ03b56yWiGvSMgJ23JOhDwjPX36dIqKimhra+Po0aNMnjzZJPcBzjbQy83NVX63NkdB3kfOqUhOTmb+/PkW8w/kB011dbUiQHoayC09BAbyINLr9UqX8XM9ljnmD07j10PxgOrrGqwpLWuPB6kcYmcLnnvuOdatW8fSpUsBuPLKK01ydyRJ6lYmWSAQDA49jSfDOZRIVM0bHQgxMYrYvn07J0+epLOzExcXFwwGAwaDQXmQd3V10dXVpSRgOzg4EBgYSEBAAAUFBRgMBqqrq5WwJCcnJ1paWmhtbaWpqUkRI4AS8uTm5kZzczOenp74+Pjg4eGBn58fPj4+TJ06lePHj1NXV0draytnzpwBwGAwMN7VlX+fOUNIRwc6X192PPAAQfv2UbBnD+Xl5Wzbto2uri4aGxtxcXFhypQptLW1cdNNNyldrhMSEggKCqK9vR0HBwdSUlKIjY1Fp9Ph7e1NY2OjsqrR0tKCo6MjFRUVnDhxAq1Wy9SpU/H39wfONuSTe10A3HHHHSQlJZGZmWkyq26cJC2vSMiOudygTU6u3rp1K6WlpXR0dBAdHa0It8DAQJOVjoyMDKXiE9AtMdqYnqodyTkVgYGBhIeHm1R3Mg4jqq6u5ujRo8pM+UAqLvWH3pzlc33A9dSzwrhRYF8M5kPWGhvs8SCVe8PYgvXr13PXXXcplcMEAoH96Gk8EaFEgsFGiIlRRGdnp5KTAODu7o7BYMDR0RFJkhQHUpIk2traUKlUnDlzhvb2djo7O5XjyLPszs7O1NbW0tbWhrOzM87OzsqxVSoVnZ2d1NXV0dbWhqOjo5KP0NbWRnt7O62trfz888+0t7fj6empnGeylxcfa7VEdHRQ7urKsxdcQPF/KzqFhIQoIUD19fV0dXVx8OBBTp8+rYQSydWTtm/fztixYwkLC0Ov19PW1saePXuUHBEnJyfGjh1LZWUlDg4OhIaG4u3tTWFhIY6Ojri5ufH3v/9dSaDWaDQ0NDTg6urKsWPHAJRZddnpbGlpwcHBAZ1OpwgFtVpt0qBNDrMpLS2lpKREERvmjdtkZCcf6LZiYY4l59w4p8J4e1NTE0ePHlXCpsLDz3YEl0OxzBv59RdrekL05ixbWimx1hZL5x6IYy4esueGJEkAXHzxxXa1o62tjTlz5nDw4EFyc3OZPn268t6hQ4dYs2YN+/fvJygoiHvvvZdHHnnE5PNbtmzhqaeeoqSkhPHjx/PKK68oqy0CwUhHhBIJBhshJkYRGRkZJuEEcgKyWq2mra1NCS+Cs2VKm5ublURtc+RVDPk9BwcHk9/l3Ak3NzcqKiro7Ozk1KlTdHZ24uDgQHt7O7t27aK4uJiuri5cXFw4ffo0blVV/OP0aSK6upTyr5U6HUePHsXNzQ1/f3+Cg4Opr6+nrq4OR0dHGhsb0Wq1ODk5sX//fo4cOUJRURGurq7MmTOHRYsWodVqOXHiBPv378fHx4euri4lXyQxMZGamhpiY2M5deoUer0ePz8/pQ8EQHt7O+Xl5Xh5eXHRRRcp9zEpKUlpPCd379ZqtSxZsgRfX1+Ki4spKSkhOTlZEQSHDx9m//79tLS0KHkk8+bNIzQ0VKmKZIzs5Ofk5BAYGKgkYfeUEyCLF5nCwsJueRbGeR2Wyr/KvSzklQwZ43PKx+7JybeUkyGvzlgjEIxtsVVid38RD9lzx9L4MdQ88sgjhIeHc/DgQZPtDQ0NLFmyhMWLF7Np0yYOHz7M7bffjq+vL3feeScAe/fu5frrr2fDhg1cfvnlfPzxx1x99dXk5OQwZcoUe1yOQGBTzCv1DdeQJ8HIZUSJiYyMDF577TUOHDhARUUFn332GVdffbXyviRJPPPMM/zlL3+hrq6OuXPn8s477zB+/HhlH51Ox7333suXX36Jg4MDK1as4M033+yWIDrS0Gg0ysqDMZ2dnTQ2NnZ7r7GxUZlVlP+VcXJyUlYq5ORtFxcXmpubAXBxccHLywtvb2/FaYez4uX06dO4uLjQ0dGBTqejubkZtVpNYGAgwa2t/OP0aaK7uihWqfgFUFdejoeHhxKO1dbWRnNzM0FBQeh0OgICAhRR4OzsTGNjI8eOHVPKxLq4uPD+++8D4OXlRXV1NS0tLYwZM4awsDDKy8tpaGggJSUFV1dXTpw4gVqtJjQ0FB8fHyXpXJIkmpubcXJyIjw8nPnz5yu9KyoqKigvL6ezs5OysjJlRn/ZsmUUFxdz7NgxkpOTgbMlaKurqykqKsLLy0sJN9Pr9SYOMPyvE7dcASoyMtJktcB85l4WAHJCb3V1tSIYwNSp7qv8q0ajMUncNrbJ2MbeHHZLfSnk1Zm+PmuNLb1hKxEg4nXPnfj4+D4FhU6nG7Tz/+c//2Hnzp18+umn/Oc//zF576OPPqK9vZ333nsPFxcXJk+eTF5eHm+88YYiJt58800uvfRSHn74YQCef/55du3axR//+Ec2bdo0aHYLBPbA2ip09hQbw8EGQf8YUWKiqamJadOmcfvttyvVbYx59dVXeeutt/jwww+JiYnhqaeeIjU1lWPHjimJwjfeeCMVFRXs2rWLjo4ObrvtNu68804+/vjjob4cm5Kdnd2tgopMTyIDzoZCGedRACarG46OjkRGRjJnzhx2796tOK5tbW0ANDc3m+wvhzipVColNCogIIDUiRN5fOdOxvxXSCzz8KC8owPHlha6urpwdHQkKChISeB2dHRk7NixtLW1KU6AXq8nNzdX2X/s2LHU19dz9OhRHB0dmTZtGl5eXmg0GiorK7n88stxdXWlqKiI1tZWxo8fj7+/PxMmTGD8+PG4ubmh0+moqqrC39+fqKgoXF1diY2NJS4ujpdffpmMjAza2tpwc3PDz8+PmTNnUl9fj6urK56ensyYMUOZ/dfpdJSXlyvN/zw9PZk9ezaTJ0+mpaWFffv24e7uTnV1tSIMGhsbKS4uJjY2ViklK68WmM/cy9WhZAFSXV1tElpl3NzOOLTJEsb5FsavjZ10rVZrsRKT8TEsVYBSq9WUlJR0az7XEwNx6AdTBIgHWf9Yv359tw7YQ0VlZSV33HEHn3/+OR4eHt3ez8rKYv78+bi4uCjbUlNTeeWVV5TvTlZWFmvXrjX5XGpqKp9//vlgmy8YZpwP331rq9CB/UI/h4MNgv4xosTEZZddxmWXXWbxPUmS+P3vf8+TTz7JVVddBcDf//53QkJC+Pzzz7nuuus4fvw4X3/9Nfv372fWrFkA/OEPf2Dp0qW8/vrrI/4/rbEgsJbW1lZlZcHR0VHJhQAUB9pgMLBr1y4l/6KtrY22tjZcXV2V1QpLtgQEBODq6kpgczNPpqUR2tZGiYMDV3p6UvbfFY3W1lYlHEvurN3e3k5tbS0uLi7U1tYiSRLz5s3jhx9+oKGhAQ8PDyZOnMiSJUsoKChg7NixBAcHs3r1aoqLi9m8eTM6nQ6NRsO8efNwc3NTmtc1NzcTGhqKWq1GrVZTX1+PVqsFoKOjg9raWr788kul+lJ4eDiOjo44ODjQ0dHBRRddhFar5euvvyY8PJzbb79defhUV1fj6+uLu7s7c+bMISEhgaVLlxIeHs5bb71FTk4OY8eOxcnJSREG+/bt49ixY5SWlhIWFmbS+Vp2mHNycigvL1fEYmhoaLcZ/fDwcDIyMrolYveGpQHb2EkvLCy0WImpJ+TPZmRkWGw+N1IQD7L+cd111xEcHDzk55UkiVWrVnHXXXcxa9YspbqaMVqttlvvi5CQEOU9Pz8/tFqtss14H3lcsIQ8Bso0NDScw5UIhgvnw3ff2ip09gz9HA42CPrHiBITvXHy5Em0Wi2LFy9Wtvn4+DBnzhyysrK47rrryMrKwtfXVxESAIsXL8bBwYEff/xR6V0wEklOTjZ5uFmLeYiTcSJ2aGgoEydOZOfOnSaJ1/Jn9Hq9yeflxGf5ONXV1YS2t/NxWxuhQKmTE3eNH0+nwYDXf7tYu7q6AmdXSPz8/BTx4Ovry7hx42hoaKC+vp7Dhw9z+vRpVCoVLi4uODk5UVRURElJCYsXL2b27Nl8++23jBkzhlWrVnHs2DGampooLS1lwYIFwNn6+qGhocyePVtZIZg4cSJBQUFMmjSJtLQ0zpw5w5EjRzAYDMydO5ff/OY3lJSUkJ2dTWlpKRUVFbS3t9PR0YFGowFQchySk5M5evQoe/fuJTQ0lCuuuEIZtOfNmwdAYGAgRUVF6HQ6Jc9CriglN54zRy6vmp+fb9KvwNLKgHkidm8zbX0N2D31iOiLkf4gGOn2DyWDkS/x2GOP8corr/S6z/Hjx9m5cyeNjY08/vjjNrehLzZs2MD69euH/LyCwUV894dH6OdAbTgfVpaGK6NGTMizSL3NMGm12m4zaE5OTvj7+/c4CzVSZqDCw8NNhEB/cHV1xWAwWPy8XM5VRq4bL0kSnZ2duLq64u7uriQay6sXHh4ejJUk/q+tjXFAEbDUyYn2lhbCw8Nxd3dXju3n50dYWBhtbW3U1dVhMBiUBGy5KpMcciPPzv/888+UlJTQ2tpKe3s7e/fupbi4GBcXF+bPn8+qVauUxn1wtlFeUVERcPZvmJycTGtrq9JNeuLEibS1tdHU1ISzszOenp5K0nRmZiZdXV14enqyb98+mpqa8PT0pKWlRQkTksOLwsLC8Pb2VjqOZ2RkKP0n5GpOu3btUma//P39WbFiBf7+/krehTnW9CsA0+Z2Mn01ruttwO2pR0RfDIeH0bkw0u0fSswnI2zBunXrWLVqVa/7xMbGkpaWRlZWljIhITNr1ixuvPFGPvzwQ0JDQ7t1jJdfh4aGKv9a2kd+3xKPP/64SWhUQ0MDY8eO7fPaBMMb8d0f2ZwPK0vDlVEjJgaLkTIDpdFoKC8vH9BnjcOj3N3dcXFxQZIkampqOHXqVLeGU3KCbX19Pe3t7Xh7eyu5FHDWwYh2cOCLhgZigGJgIaDp7MRXr6e5uZnw8HCqqqpobW3F2dmZpqYmTp8+TUdHh1INqqCgAC8vL+Lj45kyZQoHDhwgNjZWceLlkrStra3U1tYqDfoyMjLw8PDg+eefB846+tOnT8fDw4N9+/axc+dODh06xJgxYzh48CAtLS1ERkZyySWXcOLECSorK3F2dqakpITMzExKS0vx9fXF0dGRo0ePotfriYqKYu7cuUqlp9zcXNRqNQsWLGDu3LnU1taSn59PbW0thw4dUoTNNddcQ2xsLMXFxVRUVKDX65XQJvP8BXPk8Cbjv7mlWRjj7ZaSpNPT08nLy+uzMZ6YpRP0haV8rHMlKCiIoKCgPvd76623eOGFF5TXGo2G1NRU/v3vfzNnzhwAUlJSeOKJJ+jo6MDZ2RmAXbt2kZCQoIxjKSkp7N69mwceeEA51q5du0hJSenx3K6urt1EjEAgsC/imWU/Ro2YkGeR5E7LMpWVlUrN8dDQUKqqqkw+J/dm6GkWaqTMQMnO9UAwnl3s6urC1dWVjo6ObsnVcDaUyd3dXclz6OjooKamxuQYYyWJz+rrzwoJlYpUZ2fK2tuhs1Pp2SD3snBzc8PX1xe9Xk9tbS0qlYqgoCAcHR1xdnbGwcGBmJgYqqqqKC8vx9nZmaeeeoqPP/6Yb775RqmC5ODgQEJCAj/99BPl5eWcOnWKrVu3EhERwTfffMOpU6dISEggLCxM6d49ceJEXF1dKS4upr6+nuLiYoKCgpReE3C2gZyvr6/Sxffiiy+muroaZ2dntFotO3bsUBrOhYaGotPp8PDwUJJB6+vrCQwMZMyYMcybN4/w8HBWr15NdnY2Op0Of39/RUj05OT3NNtiabtxeVbo3pQuLi6OvLy8XhvjyYhZOsFwJjIy0uS1HI43btw4IiIiALjhhhtYv349q1ev5tFHH+XIkSO8+eabbNy4Ufnc/fffz8UXX8zvfvc7li1bxubNm/npp5949913h+5iBALBOSOeWfZj1IiJmJgYQkND2b17tyIeGhoa+PHHH7n77ruBszNQdXV1HDhwgJkzZwJnw18MBoMyk2XOSJmBiouLw83NzaSXxECQk58BJf/BGIPBwOnTp3v8/FhgtyQpoU3XBgRQ09WFs1F4lNxgztHRkbCwMNrb26moqMBgMODl5UVHRwdNTU0YDAba29tpb2/Hzc2NxsZG8vPz+f3vf09jYyNNTU10dnbi7+/PypUr0el05OfnExsbS0BAAHv27CEgIIB9+/ZRXFzMvn37iI6OZvz48cTFxdHa2sqvf/1rtm/fzieffEJVVRUrVqxg2rRpSo7DsWPH0Gg01NTUkJiYyO233w7Ae++9x549e/D29mbKlClMmjQJOFsaVpIk4uPjqa2txdHRkdmzZ+Pv768IVnmw27NnD+PGjSM6Oprq6mocHBwsOvnGYU4ZGRnKSoTxLIy8GmFe4cmcnkrGGiPiTgWjBR8fH3bu3MmaNWuYOXMmgYGBPP3000pZWIALL7yQjz/+mCeffJLf/va3jB8/ns8//1z0mBAIBAIrGVFiQq7VL3Py5Eny8vLw9/cnMjKSBx54gBdeeIHx48crpWHDw8OVXhQTJ07k0ksv5Y477mDTpk10dHRwzz33cN11140Kp8nV1fWcxQScXa1RqVR0dHRY/Rk3NzfGShL/McqRWAC0OTjg9t/O2a6urkoXbQcHB1xdXWlvb+f06dM0Nzfj5eWlrAw4ODjg7e1Na2srBoOBiRMnEhkZSU1NDRUVFUrZVbmPA6A47LGxsYSFhbF9+3ZOnjyJh4eHEgJVX1+PwWBQErQbGhooKCgAzgrSpKQkSkpKyMnJQavVUlRUhFarxc3NTWk6FxcXpwiDqKgowsLCSE9PJyoqCnd3dwASEhKUMCetVkteXh5wNklbLiMrJ5ZXVVUREBBAYmIigYGBimgwzpGYP38+GRkZJisRxrMw8nuRkZEsWLCgVyFg3N/BWJzInEvcqaXytPbGluJICK3hS3R0tMUcjqlTp/LDDz/0+tmVK1eycuXKwTJNIBg12GoMFGPp6GJEiYmffvpJqcwDKOFHt956Kx988AGPPPIITU1N3HnnndTV1TFv3jy+/vprpccEnG1idM8997Bo0SKlad1bb7015Ndia+Qynv3B0dFRCWOSe0J0dnbi5uaGJEk0Njb2eQwnp7P/haIdHNje0kIs/xMSpwAnnY7w8HB8fHzw8/PDx8eHxsZGTp48qTTOU6vVSvfrgIAAAgICcHZ2RqfTUVtbS01NDc7OzjzwwAPU1dWRmZmJj48PiYmJVFVVceLECbZv3861117Lbbfdpsy4+/v7K82ycnJyOHHiBBMmTCA8PJwTJ04AZwXq6dOn8fDwICwsjJKSEtLS0tBoNPj4+DBu3Djmzp2rVFwyTpweN24c8fHxFBQUcPToUaqrq3Fzc0OtVuPu7q6sABQUFCgJnrKjHhkZyU033cSxY8eUlQTZ+ZaFgdycTqagoIDGxkaL1ZWMVynOtYt0T3Gn1gz+criWJElWlacdCmyZlCcS/AQCwfmMrcZAMZaOLkaUmLjkkkt6rR6iUql47rnneO6553rcx9/ff8Q3qLNEXFycxbAka+no6MDd3R1HR0elYlNfODo6AhDW2clXnZ2KkLjU1ZVaZ2ccW1owGAyo1WpmzpzJiRMnlL4ODQ0NqFQq3N3dGT9+PK6urgQHByu///TTT5SWltLy32O0tbVRUVFBcXExNTU1tLW1kZaWRmtrq5K8XVdXxxNPPKEMTHJlo5ycHCoqKkhKSiIpKYmcnByKi4vx9PRErVYze/ZsJWH65MmTjBkzBg8PD4KDg4mNjVWcfI1Gg06nIyMjg9mzZ3PttddSXV3NmTNnlNCslpYW5UdeAYiLiyM+Pt7EOZcdcnmlwthBN2/+Jje5y8vLo6mpiczMzG7J2AOJFe1JNPR0LGsG/7i4OEXwD5ckOFsm5Q3kWGIGTiAQDCWDOebIPZWqq6vRaDQDPr5Ilh5djCgxIegd2bkfKK2trUpeQ1dXl0lPCWNcXFxob2+nq6uLsUA6KKFNlwCajg6cjeyprq5m7969SrUmJycnRUhMnDiRyZMn09bWhpOTE/X19bS1tXHy5EkcHByYPn06AQEB+Pj44O7uTlBQEDU1NXh6euLn58fJkydRq9XU1dWh0Wj44IMPujnamZmZ7Nmzh/j4ePz9/cnLy0OSJKWLdUREBLW1tfz4449UV1ejUqlITEzEwcGBw4cPU15eriRFNzc3c+DAAeBscrOnpyexsbFERESg0WgUsZOfn09qamq3QV2j0SgDcU9dqo236fV6ZSVj4cKFHD161KrkaWvorwCxZvC3VJ7W3tgyKW8gxxIzcAKBYCgZzDEnPDycwMBAcnJyzmn1WSRLjy6EmBgl7Nixo88wJ3Nx4OTkZFKtqaOjQ1mVkLs+y52fZeQeEp2dnYQbDIqQOOngwKXOzlSrVHi7udHZ2UlHRweOjo60tLRQW1trchw3NzfGjRvHihUrOHPmDDt37iQsLIyQkBCOHz9OfX290u9BTtBubGxk6dKleHl5UVxcTGxsLHB2FiYpKYnGxkacnZ0pLCxEq9WSmZlJQkICHh4ejBkzBmdnZ4qLi2lpaSEiIoKoqCj8/f3x9PQkPz+fmTNncuTIESU/Q6fT0dDQQG1tLdnZ2QQGBpKQkACAh4eHEork5eWFg4OD0l177NixSgK3+aBeWFhodZdqWXhERkYqwsN4JUPkAowMxAycQCAYSgZ7zBFjmsAcISZGCRUVFSa5IZYwFxLm4kN+X5IkHB0dlVAntVqNJEk4OTnh5uaGs7Mz3vX1pHFWSJxydubZefOQysrwqK0lPDwcT09PtFotPj4+tLa2KtWaJEnC09OT4OBgpk+fTkREBGlpaZw8eRKDwYC7uzuurq5KRa6DBw9SV1dHYGAgEydORKvV4u7uTl1dHcXFxRw+fJiKigpqamq45JJLqK+vp7y8nOzsbAoKCjh48CDu7u5KzfhPP/2UlpYW4GzZ14ULF9LU1ERBQQHTp0/nF7/4hVKq9fDhwzg4OJCYmAicDZdKSkrivvvuIycnRxEr7u7uSh6Dt7c3CxYsICkpSbl3xl2kLXWptoRxiVc5bEhOlpa7ZJsnZJ8L1sxkiRn2gSFm4AQCwVAy2GOOGNME5ggxMUpYtmwZe/fuJS0tzaQJXU+4u7vT3Nxsss145UKuktTa2qokFTs5OTF16lTai4r4o1H518scHWn5+Wf0ej16vZ6uri6SkpJITk7G39+fpqYmJdHYycmJ6OhowsLCCAoK4ssvv6S2thY3Nzfa29uVxOjx48cDcOLECTw8PIiJiSEoKEhx8H19fVGr1YSHhyNJkrIiIVezamtrw9PTEy8vL8rKyoiOjqasrIy6ujplIDx69CjFxcVUVVVx9OhRPDw8WLp0qTJIyv0YZNRqtRInKneHdnd3Z/78+Wg0GiIiIoiIiFBEgkajITMzk9raWqWLtLVhQIWFhSYlXi058oOVC9DTKoWYjRIIBIKBIVZ/BaMZISZGEe3t7XR2dlq1b2tra7eGdLKQcHBwoKuri46ODgwGA52dnTQ0NNDU1ISfXs+Xej1jONuQ7gpPT8o6OnDU6VCr1fj5+REWFsbYsWOpqqqivr6e0NBQIiMj8fT0ZNy4ccoqQU5ODrm5uXR2djJ27Fjc3NxoaGhAr9dz4sQJXFxciImJYcqUKaSkpKDT6fj0009pa2tjwoQJAPziF78Azq5gREVF4eLiYlKW1dHRkc7OTqXT9uzZs1m1ahUlJSXU1tbS2tpKaWkpOp2O0tJSJRfBuB9DdXU1ZWVlODg4oNfrCQwMtNhZuqysjKSkJOVBYS4I+kNP1ZmMjzNYuQA9rXiI2SiBQCAYGGJlVzCaEWJilLBjxw6ysrKU0CUXFxe6urq6CQYZucqS7Ggbo1KpaGtro6urC0dHR0JCQtBqtYR1dbGlupoxQImDA1d5eXGqqwtPT0+l4dy4cePw9fXF09OT1tZWfHx8mDx5MvX19Wg0GiZOnKisYBw8eJDTp0/j7OyMr68vcXFxdHZ2cubMGTw9PZk5cyaxsbFER0ej1+vx9PTEx8eHqqoqdDodxcXFtLa2smzZMpqbm5k3b54SXpSYmKh0mZ40aZJyDNk5l/tEyHkRLS0tqNVq1Gq1yQySvOog93+QqysZhxuB5Vl7ueoFgFar7deslLnjPpSOvFiB6B0xwygQCPqLGFcFoxkhJkYJoaGheHl5KfkAXV1duLu709LSoggK4zAmuZpRdHQ0JSUlShgO/G9lQk4ujoyMZJKXF384fJiori5KnZy42tubwuZmWltblapInZ2d1NXVoVKpcHBwwM3NTSnzmp+fj6+vLxMnTlTeq6+vVxrUNTY20tnZycSJE4mOjsZgMHDBBRegVqv54IMPaGtrw9XVFUmS8PHxYfz48Rw7dozTp0/z7rvvEh0dreRZwP8qTpSVlREfH680nLP0fnJyslLRqaSkRAlNkvezVF3JPHm6p6pMctWL8vJyRegN92RpsQLRO2KGUSAQ9BcxrgpGM0JMjBKSkpKIiYnhzJkzSmlXOXfC2dkZLy8vfH19qa6upqmpCZVKRUtLC3q9noCAAFxcXJRjyT0T5GpOLpWVvFNczJiuLmp8fHhk0iRobsbvvysELi4uhIeHM27cOHx8fKivr0ev1yuJy7t27aKqqgpJkjh+/DiRkZEkJiaSmJjIqVOnlFUSg8FAaWmpknBcUFBAVlYWx44dw2Aw4OrqisFgYOrUqURFReHk5MShQ4eoqalhzJgxSudo2ck2ngnKzs4mLS2NhQsXsnz58m6VkgAlpKm30CTz2aW+HHvjnhHyysi5YsmZtVZgDNdZ9eFqlyXEDKNAIBAIBP9DiIlRgtyITa7A5OTkpJRidXFxISUlhebmZpqamnB0dMTV1ZXOzk7q6+tRq9XMmjWLzs5OYmNj2b9/PydPniQkJIRfTJjAM+npBLe2UuroyLOzZhE5YwZRKhWurq7U1NSg1+sJDQ0lPj6eTz75hLy8PKKiorjqqqvIycmhvb2dxMREYmNjKS8vB+CKK67g4YcfpqWlhdLSUhYuXEhUVBRarVYJSUpPT0ev1xMeHk57ezulpaU0NzfT0dGBWq0mOTmZ5ORk8vPzmTdvnvKZvLw85bXsnOp0OqXpHFjOcYCzPTESExOVUqzmmM8u9TVLPRizUZacWWtny4frrPpwtcsSYoZRIBDYg5E06WIPxP2xH0JMjBK0Wi1ubm5KBaOGhgZFTLi6uuLk5MSECRPQ6XS0trYyZcoUCgsLOXLkCHq9ntbWVsaNG6eEO8XGxvLI9ddz/Z//DHo9Fe7u3B4RQVRkJAkJCeTl5ZGcnMzy5cvZtm0b6enpAJw5c0YJWfL391eaz82bN4/Q0FCOHDkCnG0kd80113DLLbeQnp7OjBkzCAwMVMKItFotUVFRLFiwAE9PT/bt28f06dM5fPgwpaWllJaWcvnllzN//nxSU1OBswOJXIFJDlXKy8vjmmuuwd/fHx8fHwoKCti2bRvR0dEkJSV1c8jNBYa1Kw/mKxXGKxGD0TTI/JjWzpYP11n14WqXQCAQDBdG0qSLPRD3x34IMTFKWLp0KV988QU1NTW0tbWhUqmU3IWWlhb27dvHL37xC1xdXdFqtRw4cEDpJ9HR0YFer6euro6cnByampq4KimJ5W+9BadPow8JoeD111lQUoKbmxsFBQWcOXOGo0ePKiFCer0erVbLxRdfjF6vJzg4GE9PT1asWIFWq2Xp0qWUlJQwbtw4HBwclEZwAAsWLFCOU11dzZYtWygqKuLKK68kOTmZrVu3UldXR1RUFJdffjm7du3C09NT6d0gY1yBSa1Wk5mZSWlpKVu3bmXevHnMmDGD3Nxc0tLSuPbaa00SqKF7Twjo/8qDvL+Dg4PNciSswdrZcvP9hstMjpjtFwgEgt6x9aTLcBn/bYWYlLIfQkyMEkJDQ+no6KCrq4vOzk6cnJzw8fGho6OD5uZmamtrqaysxMnJiba2Nurr63FyOvvnd3FxwcXFhfr6epqbmxnv5saz33+Pa0MDFe7uvD5/PldFRjLJw4N//vOfdHZ24uzsTHl5ueKoBwUFsXfvXhISEggLC6OhoYH8/Hzuu+8+Exvlqkg5OTl89dVX+Pn5cdVVVykDWWBgoJIfERoaSnZ2Nrm5uTg4OODt7U1ERARz58416d1gjLFTGhoayt/+9jdyc3OJiIjgmmuuISIiArA82Mi9I4yPK1dkkvtL9DXg9jdHwt6DuZjJEQgEgpGBrSddRtv4Lyal7IcQE6OEwsJCJZTH3d2dhoYGPDw8qKurUyogubm5odVqlUTmrq4upbqTs7MzXV1dRDs68kVDA0EtLZS7urJ22jSmTp2qONU+Pj5K0ziNRsN//vMfPDw88PLyorGxkfz8fBwcHAgPD2fevHkmNhp/0TMzMzl+/DjOzs4sWbIEQEmK/sUvfoG/vz/JyclkZ2ejVquZNm0a8fHxJn0d+nLUw8PDmTx5MpWVlcpruWGcRqMxSdYGyysTxhWZzCs49XTO/gxm9h7MxUyOQCAQnJ+I8V9gK4SYGCXIPSMcHR1pb29HkiSampowGAxKtaWQkBCqq6sJCQmhtbWVxsZGamtrFUExPSCA906eJLy1lVo/P16aO5epc+Zw++23K+e54oorlATpr776ilOnTlFQUEBwcDDx8fHExsZSWVnJsmXLlJ4P5mg0Gjw8PPDz86O5uZn8/HxSU1NNchbkEKTk5GSlSZx5KVZrMP68MZaceEsrE2C7AdfSKoS9B3MxkyMQCATnJ2L8F9gKISZGCXq9nokTJ+Lk5ERAQACHDx+moqICf39//Pz8SEhIUMKTOjs7MRgMBAUFMWbMGE6fPo2fXs9fCgsZ09FBnb8/ry9bRm1HB/NiY8nOzubYsWPU1tYSFRVFdHQ0AFFRURQVFdHe3k5aWprS4yIyMpKmpqZuM/8yhYWF6PV6li9frjSbA8shRec62PX0+Z6azJlv6+0YPdFT6JIlAXOu12eLMCl7h1oJBAKBQCAYuQgxMUqIi4vjxhtvpKCggD179hAVFUVraysqlYrg4GDq6uqoq6sjMjKSuro6amtrcXJy4uabb+bErl088e23RBoMVHt78+eVK/nHjh20t7dTXFysiIfOzk70ej2FhYUEBARQU1ODwWCgsbFRWQnR6/X4+fkBZ8vVVldXm6wMyMnRciUl89WG/oQU9Qdzh7mnJnO2OGdPoUuDsQphizApe4daCQQCgUAgGLkIMTFKkJ3AjIwMCgoKqKmpQaVS4ezsjLe3N62trVRWVhIcHIy7uzsA/v7+jDEYuO3HHwkwGKj09KTqX/8iRKPBOzMTrVZLbW0tkydPJjY2ltraWkWUBAcHs2zZMvLz83F0dARg1qxZzJgxg+TkZLRaLeXl5eh0OsrKyoCzlZqMG8dZor8Jz9YylA6zrVY4zuVcQ30MgUAgEAgE5ydCTIwiCgsLqa2tpbOzk8bGRpycnEhMTCQlJYVPPvmExsZGTp06hZubGzExMaRERHDZK6/gW19PlZcXJ95+m5rWVgAuu+wyTpw4QVBQEPfccw9wNmk6LCyM0tJSJk2aRGpqKqmpqbz11lvodDqmTZumdJeW+zxEREQQHx+PWq3mb3/7G/v27cPPz69HMdHf1QlrQ3QGS6RYYijjUG1xLhE3KxAIBAKBYKAIMTGKUKvVdHR0MGbMGKXHwaRJk/D19cXX1xe9Xo+rqyu+vr74NzXx2Dff4Ftfj9bDg+cuvhjdV19x8uRJoqOj+dWvfsUVV1yhOOkZGRnKMVUqFZ6ensp5582bp1SPkp372tpa/Pz8lE7SGRkZnDhxgtraWjQajYnd5oKgPzPlfa04GB97sEKoBAKBQCAQCM5XhJgYRZSUlNDY2Iivry91dXWcPHmSPXv24OrqSkREBI2NjTQ1NRHW2cnjGRkEtrdTHxhI7osvElZZyYn0dGpqaggICFA6RMslVOU8h4yMDE6dOqVUYAJISkpSOlcbl2yVxYFc8nXJkiVoNBquvfZaE7sHEoJk3GnavJN1T8cW4TwCgUAgOF8RxTYEg4UQE6OQwMBA3NzcAPDy8qKoqIjJkyejVqvxqKlh7ZdfEtzeTomjIy/NmsWzl1/ONCAsLIzdu3djMBgoKSkhKSmJ7Oxs0tPTWbBgAcuXL1d6MMirEGA5qdq8ilFZWRm/+MUvlJKvxoOauZNvjbiQ9zEuI2sJc2Eji5ueKk0JBAKBQDAY2NuZLywsJD09nby8PK655hrx/BPYDCEmRhHJycmUl5dTW1tLcnIy1113HQUFBRw8eJDY2FhSJ00i8pZb8GlsROfry71RUfgEBFBYWMj8+fP59a9/jb+/P+np6SbHlftQQPdVCKBPp97SioCxYJg/f36/qx5Zu8pgKR9AVC8SCAQCwVBj72dPXFwceXl51NbWUlhYKJ5/ApshxMQoIjw8nGuuuaZb/kF8fDzx7u74XH017jU16ENC+Pzuu4lvaCAqKsrEIZebvKnVajIyMoiOjubaa6/tsx9Db069JYe+NzFgTULwuSQNi3AngUAgEAw19n72mPsIAoGtcLC3AQLbIIfuwNmBKjs7m23btgEwPyaG0Ouuw12joT4ggC/XruVoQwMdHR3MmzfPolNeUlJCTk4Oer2euLg4CgsLlbCm8PBwZTXB+Pf+MNDP2QJ7nlsgENiO6OhoVCqVyfUHy2kAACWHSURBVM/LL79sss+hQ4e46KKLcHNzY+zYsbz66qvdjrNlyxYmTJiAm5sbiYmJ7NixY6guQXAeMRyePcPBBsHoQ6xMjBKMl08B0tLSaGpqou7wYW55/32cSktpCQ9ny69/jX9cHFFubtTW1qLX69FoNGRnZwOg0+nIy8tj+vTpSg6EvZdmBQKBoCeee+457rjjDuW1l5eX8ntDQwNLlixh8eLFbNq0icOHD3P77bfj6+vLnXfeCcDevXu5/vrr2bBhA5dffjkff/wxV199NTk5OUyZMmXIr0cgEAhGGkJMjBLMl08XLlzIqb17uXLjRpzq6+mMiqJ+yxb8T50CzpZzlVcdsrOz+ec//4mPjw+xsbGoVCr8/f1NciAGq0eDvRPSBALByMbLy4vQ0FCL73300Ue0t7fz3nvv4eLiwuTJk8nLy+ONN95QxMSbb77JpZdeysMPPwzA888/z65du/jjH//Ipk2bhuw6BAKBYKQiwpxGCeahR8tnz2bN1q0E1tej8/PjwOuvEzp7NoGBgZSVlaHX602WOn18fBg3bhzLli1j5cqVJCcnmxxb/pycdH0uyCFZspAwTuYWCASC/vDyyy8TEBDAjBkzeO211+js7FTey8rKYv78+bi4uCjbUlNTyc/Pp7a2Vtln8eLFJsdMTU0lKytraC5AIBhCjJ+/AoGtOG9XJv70pz/x2muvodVqmTZtGn/4wx+44IIL7G2WbTh1Ci65BKfSUjqjovj59dcZe+GFgOVO0HLSdW+rA7ZMHBO9HwQCgS247777SEpKwt/fn7179/L4449TUVHBG2+8AYBWqyUmJsbkMyEhIcp7fn5+aLVaZZvxPlqttsfztrW10dbWprxuaGiw1SUJBIOKCFsWDAbnpZj497//zdq1a9m0aRNz5szh97//vTJbFRwcbG/zzo1Tp+i86CJFSDj98ANzxo5V3pZXGYw7QZtXRrIUenQu1ZPMsdT7QSAQCAAee+wxXnnllV73OX78OBMmTGDt2rXKtqlTp+Li4sJvfvMbNmzYgKur66DZuGHDBtavXz9oxxcIBgsxgScYDM5LMfHGG29wxx13cNtttwGwadMmtm/fznvvvcdjjz1mZ+vOAaMVibqAAPJff91ESMj0NJjIIqK6upqysjLgfzMXxknaycnJ5yQAhIAQCAQ9sW7dOlatWtXrPrGxsRa3z5kzh87OTkpKSkhISCA0NJTKykqTfeTXcp5FT/v0lIcB8Pjjj5sImYaGBsZaGGsFguGGeP4KBoPzTky0t7dz4MABHn/8cWWbg4MDixcvHtkxsv8VEhQX0zZmDNvuuIPpPTxw5YFEzlOQu0Jv3bqV2tpaEhMTSUpKUnpNyBWd0tPTkSRJWdEQCAQCWxMUFERQUNCAPpuXl4eDg4OywpySksITTzxBR0cHzs7OAOzatYuEhAT8/PyUfXbv3s0DDzygHGfXrl2kpKT0eB5XV9dBXfkQDA9EgRCBwDrOOzFRXV1NV1eXxRjZEydOdNt/RMTGGgkJYmPJe+UVGsrL0ev1PX7EPG6ysLCQ2tpa/Pz8lJWHjIwMk9yGBQsWKL8LBAKBPcnKyuLHH39kwYIFeHl5kZWVxYMPPshNN92kCIUbbriB9evXs3r1ah599FGOHDnCm2++ycaNG5Xj3H///Vx88cX87ne/Y9myZWzevJmffvqJd999116XJhgmiPwCgcA6zjsx0V+GfWysmZDgu+8Y6+hIm1GHS0uzK+ahTuZ5DJa2LV++fCivTCAQCHrE1dWVzZs38+yzz9LW1kZMTAwPPvigSfiRj48PO3fuZM2aNcycOZPAwECefvpppSwswIUXXsjHH3/Mk08+yW9/+1vGjx/P559/LnpMCER+gUBgJSpJkiR7GzGUtLe34+HhwdatW7n66quV7bfeeit1dXV88cUXJvtbWpkYO3Ys9fX1eHt7D5XZlrEgJLAQtyuvMCQlJZn0jhAIBOcfDQ0N+Pj4DI8xbJQg7qlAIBht9GdcO+/6TLi4uDBz5kx2796tbDMYDOzevdtijKyrqyve3t4mP8MCK4UEnJ1VkbtZCwQCgUAgEAgEtuK8DHNau3Ytt956K7NmzeKCCy7g97//PU1NTUp1p2FPP4SESCATCAQCgWDoEM9dwfnGeSkmfvWrX3HmzBmefvpptFot06dP5+uvv+6WlD0s6YeQAJFAJhAIBALBUCKeu4LzjfNSTADcc8893HPPPfY2o3/0U0iASCATCAQCgWAoEc9dwfnGeSsmRhwDEBIgGtQIBAKBQDCUiOeu4HzjvEvAHpEMUEgIBAKBQCAQCASDiRATwx0hJAQCgUAgEAgEwxQhJoYzQyAkNBoNGRkZaDQamx5XIBAIBAKBQDD6ETkTw5UhWpEQVScEAoFAIBAIBANFiInhyBCGNomqEwKBQCAQCASCgSLExHBjiHMkRNUJgUAgEAgEAsFAETkTwwmRbC0QCAQCgUAgGEEIMTFcEEJCIBAIBAKBQDDCEGJiOCCEhEAgEAgEAoFgBCLEhL0ZRkJClIkVCAQCgUAgEPQHkYBtT4aRkABRJlYgEAgEAoFA0D+EmLAXw0xIgCgTKxAIBAKBQCDoH0JM2INhKCRAlIkVCAQCgUAgEPQPkTMx1AxTISEQCAQCgUAgEPQXISaGEiEkBAKBQCAQCASjCCEmhgohJAQCgUAgEAgEowwhJoYCISQEAoFAIBDYmZFaAn6k2n2+IBKwBxshJAQCgUAgEAwDRmoJ+JFq9/mCEBODiRASAoFAIBAIhgkjtQT8SLX7fEGIicFCCAmBQCAQCATDiJFaAn6k2n2+IHImBgMhJAQCgUAgEAgE5wFCTNgaISQEAoFgyNi+fTtz5szB3d0dPz8/rr76apP3y8rKWLZsGR4eHgQHB/Pwww/T2dlpss93331HUlISrq6uxMXF8cEHHwzdBQgEAsEIR4Q52RIhJAQCgWDI+PTTT7njjjt46aWXWLhwIZ2dnRw5ckR5v6uri2XLlhEaGsrevXupqKjglltuwdnZmZdeegmAkydPsmzZMu666y4++ugjdu/eza9//WvCwsJITU2116UJBALBiEElSZJkbyNGEg0NDfj4+FBfX4+3t/f/3hBCQiAQjAB6HMNGGJ2dnURHR7N+/XpWr15tcZ///Oc/XH755Wg0GkJCQgDYtGkTjz76KGfOnMHFxYVHH32U7du3m4iQ6667jrq6Or7++murbBkt91QgEAhk+jOuiTAnWyCEhEAgEAwpOTk5nD59GgcHB2bMmEFYWBiXXXaZiSjIysoiMTFRERIAqampNDQ0cPToUWWfxYsXmxw7NTWVrKysHs/d1tZGQ0ODyY9AIBCcrwgxca4IISEQCARDTnFxMQDPPvssTz75JF999RV+fn5ccskl6HQ6ALRarYmQAJTXWq22130aGhpoaWmxeO4NGzbg4+Oj/IwVY75AIDiPGTFi4sUXX+TCCy/Ew8MDX19fi/sMeaKdEBICgUBgUx577DFUKlWvPydOnMBgMADwxBNPsGLFCmbOnMn777+PSqViy5Ytg2rj448/Tn19vfJz6tSpQT2fQCAQDGdGTAJ2e3s7K1euJCUlhb/97W/d3h/yRLvycrjiCiEkBAKBwIasW7eOVatW9bpPbGwsFRUVAEyaNEnZ7urqSmxsLGVlZQCEhoayb98+k89WVlYq78n/ytuM9/H29sbd3d3i+V1dXXF1dbX+ogQCgWAUM2LExPr16wF6XEnYuXMnx44d49tvvyUkJITp06fz/PPP8+ijj/Lss8/i4uLCpk2biImJ4Xe/+x0AEydOJDMzk40bN/ZfTCxbBiUlQkgIBAKBDQkKCiIoKKjP/WbOnImrqyv5+fnMmzcPgI6ODkpKSoiKigIgJSWFF198kaqqKoKDgwHYtWsX3t7eighJSUlhx44dJsfetWsXKSkptrwsgUAgGLWMmDCnvhisRLseEUJCIBAI7Ia3tzd33XUXzzzzDDt37iQ/P5+7774bgJUrVwKwZMkSJk2axM0338zBgwf55ptvePLJJ1mzZo2ysnDXXXdRXFzMI488wokTJ3j77bf55JNPePDBB+12bQKBQDCSGDErE31hi0Q7S0vabW1ttLW1Ka/r6+sBaIiMhC+/BB8fEJU8BALBCEGuPDQaqoK/9tprODk5cfPNN9PS0sKcOXNIS0vDz88PAEdHR7766ivuvvtuUlJS8PT05NZbb+W5555TjhETE8P27dt58MEHefPNN4mIiOCvf/1rv1ar5XspqjoJBILRQn+eFXYVE4899hivvPJKr/scP36cCRMmDJFF3dmwYYMSYmXM2LIymDzZDhYJBALBudPY2IiPj4+9zTgnnJ2def3113n99dd73CcqKqpbGJM5l1xyCbm5uQO2o7GxEUBUdRIIBKMOa54VdhUT1ibaWcNgJdo9/vjjrF27VnltMBjQ6XQEBASgUqmssm0oaGhoYOzYsZw6dUo0TbICcb/6j7hn/WO43i9JkmhsbCQ8PNzepowawsPDOXXqFF5eXr0+F4br/4m+EHYPLcLuoUXYbZn+PCvsKiasTbSzhsFKtLNUtaOn0rTDAW9v7xH1ZbA34n71H3HP+sdwvF8jfUViuOHg4EBERITV+w/H/xPWIOweWoTdQ4uwuzvWPitGTAJ2WVkZeXl5lJWV0dXVRV5eHnl5eej1ekAk2gkEAoFAIBAIBEPNiEnAfvrpp/nwww+V1zNmzAAgPT2dSy65ZMgS7QQCgUAgEAgEAsFZRoyY+OCDD/rsVj0UiXbDFVdXV5555hnRSMlKxP3qP+Ke9Q9xvwTmjNT/E8LuoUXYPbQIu88dlTQa6gMKBAKBQCAQCASCIWfE5EwIBAKBQCAQCASC4YUQEwKBQCAQCAQCgWBACDEhEAgEAoFAIBAIBoQQE6OEP/3pT0RHR+Pm5sacOXO6NfA7H9iwYQOzZ8/Gy8uL4OBgrr76avLz8032aW1tZc2aNQQEBKBWq1mxYkW3RoZlZWUsW7YMDw8PgoODefjhh+ns7BzKS7ELL7/8MiqVigceeEDZJu6XKadPn+amm24iICAAd3d3EhMT+emnn5T3JUni6aefJiwsDHd3dxYvXszPP/9scgydTseNN96It7c3vr6+rF69WilxLRi9REdHo1KpTH5efvllk30OHTrERRddhJubG2PHjuXVV1+1k7X/Y7g/W5599tlu93XChAnK+9aMYUNBRkYGV1xxBeHh4ahUKj7//HOT94fz2NGX7atWrer2N7j00kvtavtI9QessfuSSy7pdr/vuusuu9qNJBjxbN68WXJxcZHee+896ejRo9Idd9wh+fr6SpWVlfY2bUhJTU2V3n//fenIkSNSXl6etHTpUikyMlLS6/XKPnfddZc0duxYaffu3dJPP/0kJScnSxdeeKHyfmdnpzRlyhRp8eLFUm5urrRjxw4pMDBQevzxx+1xSUPGvn37pOjoaGnq1KnS/fffr2wX9+t/6HQ6KSoqSlq1apX0448/SsXFxdI333wjFRYWKvu8/PLLko+Pj/T5559LBw8elK688kopJiZGamlpUfa59NJLpWnTpknZ2dnSDz/8IMXFxUnXX3+9PS5JMIRERUVJzz33nFRRUaH8GI9N9fX1UkhIiHTjjTdKR44ckf71r39J7u7u0p///Ge72TwSni3PPPOMNHnyZJP7eubMGeX9vsawoWLHjh3SE088IW3btk0CpM8++8zk/eE8dvRl+6233ipdeumlJn8DnU5nss9Q2z5S/QFr7L744oulO+64w+R+19fX29VuISZGARdccIG0Zs0a5XVXV5cUHh4ubdiwwY5W2Z+qqioJkL7//ntJkiSprq5OcnZ2lrZs2aLsc/z4cQmQsrKyJEk6O2g6ODhIWq1W2eedd96RvL29pba2tqG9gCGisbFRGj9+vLRr1y7p4osvVsSEuF+mPProo9K8efN6fN9gMEihoaHSa6+9pmyrq6uTXF1dpX/961+SJEnSsWPHJEDav3+/ss9//vMfSaVSSadPnx484wV2JyoqStq4cWOP77/99tuSn5+fyffm0UcflRISEobAOsuMhGfLM888I02bNs3ie9aMYfbA3CEfSWNHT2Liqquu6vEzw8H2keoPmNstSZLJc9oS9rBbhDmNcNrb2zlw4ACLFy9Wtjk4OLB48WKysrLsaJn9qa+vB8Df3x+AAwcO0NHRYXKvJkyYQGRkpHKvsrKySExMJCQkRNknNTWVhoYGjh49OoTWDx1r1qxh2bJlJvcFxP0y5//+7/+YNWsWK1euJDg4mBkzZvCXv/xFef/kyZNotVqT++Xj48OcOXNM7pevry+zZs1S9lm8eDEODg78+OOPQ3cxArvw8ssvExAQwIwZM3jttddMwg6ysrKYP38+Li4uyrbU1FTy8/Opra0dcltH0rPl559/Jjw8nNjYWG688UbKysoA68aw4cBoGDu+++47goODSUhI4O6776ampkZ5bzjYPlL9AXO7ZT766CMCAwOZMmUKjz/+OM3Nzcp79rB7xDStE1imurqarq4uk/80ACEhIZw4ccJOVtkfg8HAAw88wNy5c5kyZQoAWq0WFxcXfH19TfYNCQlBq9Uq+1i6l/J7o43NmzeTk5PD/v37u70n7pcpxcXFvPPOO6xdu5bf/va37N+/n/vuuw8XFxduvfVW5Xot3Q/j+xUcHGzyvpOTE/7+/qPufglMue+++0hKSsLf35+9e/fy+OOPU1FRwRtvvAGc/b8RExNj8hnj75Kfn9+Q2jtSni1z5szhgw8+ICEhgYqKCtavX89FF13EkSNHrBrDhgMjfey49NJLWb58OTExMRQVFfHb3/6Wyy67jKysLBwdHe1u+0j1ByzZDXDDDTcQFRVFeHg4hw4d4tFHHyU/P59t27bZzW4hJgSjkjVr1nDkyBEyMzPtbcqw5dSpU9x///3s2rULNzc3e5sz7DEYDMyaNYuXXnoJgBkzZnDkyBE2bdrErbfeamfrBPbgscce45VXXul1n+PHjzNhwgTWrl2rbJs6dSouLi785je/YcOGDcOig+1I5bLLLlN+nzp1KnPmzCEqKopPPvkEd3d3O1p2/nDdddcpvycmJjJ16lTGjRvHd999x6JFi+xo2VlGqj/Qk9133nmn8ntiYiJhYWEsWrSIoqIixo0bN9RmAqKa04gnMDAQR0fHbhUIKisrCQ0NtZNV9uWee+7hq6++Ij09nYiICGV7aGgo7e3t1NXVmexvfK9CQ0Mt3kv5vdHEgQMHqKqqIikpCScnJ5ycnPj+++956623cHJyIiQkRNwvI8LCwpg0aZLJtokTJyohFfL19vZdDA0NpaqqyuT9zs5OdDrdqLtf5wPr1q3j+PHjvf7ExsZa/OycOXPo7OykpKQEGH7fpZH6bPH19SU+Pp7CwkKrxvzhwGgbO2JjYwkMDKSwsBCwr+0j1R/oyW5LzJkzB8Dkfg+13UJMjHBcXFyYOXMmu3fvVrYZDAZ2795NSkqKHS0beiRJ4p577uGzzz4jLS2tW8jAzJkzcXZ2NrlX+fn5lJWVKfcqJSWFw4cPmwx8u3btwtvbu5sjOdJZtGgRhw8fJi8vT/mZNWsWN954o/K7uF//Y+7cud1K9BUUFBAVFQVATEwMoaGhJveroaGBH3/80eR+1dXVceDAAWWftLQ0DAaD8kAQjByCgoKYMGFCrz/GORDG5OXl4eDgoIR/pKSkkJGRQUdHh7LPrl27SEhIGPIQJxi5zxa9Xk9RURFhYWFWjfnDgdE2dpSXl1NTU0NYWBhgH9tHqj/Ql92WyMvLAzC530P+XB6UtG7BkLJ582bJ1dVV+uCDD6Rjx45Jd955p+Tr62uSyX8+cPfdd0s+Pj7Sd999Z1Iyrbm5WdnnrrvukiIjI6W0tDTpp59+klJSUqSUlBTlfbmk2pIlS6S8vDzp66+/loKCgkZlqVNLmFeJEPfrf+zbt09ycnKSXnzxRennn3+WPvroI8nDw0P65z//qezz8ssvS76+vtIXX3whHTp0SLrqqqsslnecMWOG9OOPP0qZmZnS+PHjRWnYUc7evXuljRs3Snl5eVJRUZH0z3/+UwoKCpJuueUWZZ+6ujopJCREuvnmm6UjR45Imzdvljw8POxeGna4P1vWrVsnfffdd9LJkyelPXv2SIsXL5YCAwOlqqoqSZL6HsOGisbGRik3N1fKzc2VAOmNN96QcnNzpdLSUkmShvfY0ZvtjY2N0kMPPSRlZWVJJ0+elL799lspKSlJGj9+vNTa2mo320eqP9CX3YWFhdJzzz0n/fTTT9LJkyelL774QoqNjZXmz59vV7uFmBgl/OEPf5AiIyMlFxcX6YILLpCys7PtbdKQA1j8ef/995V9WlpapP/3//6f5OfnJ3l4eEi//OUvpYqKCpPjlJSUSJdddpnk7u4uBQYGSuvWrZM6OjqG+Grsg7mYEPfLlC+//FKaMmWK5OrqKk2YMEF69913Td43GAzSU089JYWEhEiurq7SokWLpPz8fJN9ampqpOuvv15Sq9WSt7e3dNttt0mNjY1DeRmCIebAgQPSnDlzJB8fH8nNzU2aOHGi9NJLL5k4W5IkSQcPHpTmzZsnubq6SmPGjJFefvllO1n8P4b7s+VXv/qVFBYW9v/bu/eYKK72D+DfpcCw3FFQQOm2ZinBqKRISjTFlVYqMRqrTSWwLWva2isBIVrEWCo0VtPEtLZJ6yVFxFiltgUq3kKsEMCotbjQKm6AQKAJqZdaw11kn/cPf87ryKXr/l6Vvu/3k5DsnHPmzDObMGefnXNmxd3dXaZMmSJJSUma335x5Br2MJw8eXLE8clisYjI+L52jBV7b2+vvPDCCxIUFCRubm5iMBhk1apVwxLOhx37P/XzwN/F3d7eLvPmzZMJEyaIoihiNBpl7dq1mt+ZeBRx6/4veCIiIiIiovvCNRNEREREROQUJhNEREREROQUJhNEREREROQUJhNEREREROQUJhNEREREROQUJhNEREREROQUJhNEREREROQUJhNEREREROQUJhNE/4V0Oh1KS0sfdRhERP8zVq5ciRdffFHdnj9/PlavXv3Q46isrIROp8Nff/31wI7R1tYGnU4Hq9X6wI5B/xxMJohw+8P3WH8bN2581CESEdF9WrlypXodd3d3h9FoRH5+Pm7duvXAj/3DDz/go48+cqjtw0gAiB4U10cdANF40NnZqb4uLi5Gbm4ubDabWubt7a2+FhEMDQ3B1ZX/PkRE411iYiJ2796NgYEBHDlyBO+99x7c3NyQk5MzrO3Nmzfh7u7+HznuhAkT/iP9EI13vDNBBCA4OFj98/Pzg06nU7cvXboEHx8fHD16FLNnz4aiKKipqRl2SxsAVq9ejfnz56vbdrsdmzdvxpNPPgm9Xo+oqCh89913o8axfv16xMbGDiuPiopCfn4+AODnn39GQkICAgMD4efnB5PJhLq6ulH7HOkbL6vVCp1Oh7a2NrWspqYGcXFx0Ov1CAsLQ3p6Onp6esZ+44iIxjlFURAcHAyDwYB33nkHCxYswI8//gjg31OTNm3ahNDQUERERAAAOjo6sGLFCvj7+2PChAlYunSp5no5NDSErKws+Pv7Y+LEiXj//fchIprj3jvNaWBgANnZ2QgLC4OiKDAajfj666/R1taG+Ph4AEBAQAB0Oh1WrlwJwLEx5MiRI3jqqaeg1+sRHx+viXMkKSkpSEpK0pQNDg4iMDAQRUVFAIBjx47h2WefVc9v8eLFaGlpGbXPwsJC+Pv7a8pKS0uh0+k0ZWVlZYiOjoaHhwemTZuGvLy8h3KXiB4sJhNEDlq3bh22bNmCxsZGzJo1y6F9Nm/ejKKiImzfvh0XLlxAZmYmXnnlFVRVVY3Y3mw24+zZs5qL9oULF9DQ0ICUlBQAQFdXFywWC2pqanD69GmEh4dj0aJF6OrqcvrcWlpakJiYiJdeegkNDQ0oLi5GTU0N0tLSnO6TiGg80uv1uHnzprp94sQJ2Gw2VFRUoLy8HIODg1i4cCF8fHxQXV2N2tpaeHt7IzExUd1v69atKCwsREFBAWpqavDnn3+ipKRkzOOmpqZi//79+Pzzz9HY2IgdO3bA29sbYWFh+P777wEANpsNnZ2d2LZtG4C/H0M6OjqwfPlyLFmyBFarFW+88QbWrVs3ZhxmsxmHDh1Cd3e3Wnb8+HH09vZi2bJlAICenh5kZWXh3LlzOHHiBFxcXLBs2TLY7fb7fLf/rbq6GqmpqcjIyMDFixexY8cOFBYWYtOmTU73SeOEEJHG7t27xc/PT90+efKkAJDS0lJNO4vFIkuXLtWUZWRkiMlkEhGR/v5+8fT0lFOnTmnavP7665KcnDzq8aOioiQ/P1/dzsnJkdjY2FHbDw0NiY+Pjxw6dEgtAyAlJSWa+K9fv67Wnz9/XgBIa2urGtObb76p6be6ulpcXFykr69v1GMTEY1nd1+n7Xa7VFRUiKIosmbNGrV+8uTJMjAwoO6zd+9eiYiIELvdrpYNDAyIXq+X48ePi4hISEiIfPLJJ2r94OCgTJ06VTMmmEwmycjIEBERm80mAKSiomLEOEe6TjsyhuTk5Mj06dM19dnZ2cP6utvg4KAEBgZKUVGRWpacnCxJSUkjthcRuXLligCQX3/9VUREWltbBYCcP39eRIaPmyIiJSUlcvfHzOeff14+/vhjTZu9e/dKSEjIqMelfwZO+iZyUExMzH21b25uRm9vLxISEjTlN2/exNNPPz3qfmazGQUFBfjggw8gIti/fz+ysrLU+j/++AMbNmxAZWUlLl++jKGhIfT29qK9vf3+Tugu9fX1aGhowL59+9QyEYHdbkdraysiIyOd7puI6FEqLy+Ht7c3BgcHYbfbkZKSonmoxsyZMzXrJOrr69Hc3AwfHx9NP/39/WhpacGNGzfQ2dmpmZLq6uqKmJiYYVOd7rBarXjsscdgMpkcjtuRMaSxsXHY1Ng5c+aM2a+rqytWrFiBffv24dVXX0VPTw/Kyspw4MABtU1TUxNyc3Nx5swZXL16Vb0j0d7ejhkzZjh8Dnerr69HbW2t5k7E0NAQ+vv70dvbC09PT6f6pUePyQSRg7y8vDTbLi4uwwaOwcFB9fWdW8iHDx/GlClTNO0URRn1OMnJycjOzkZdXR36+vrQ0dGhmd9qsVhw7do1bNu2DQaDAYqiYM6cOZrb9vfGCUAT691x3on1rbfeQnp6+rD9H3/88VFjJSIa7+Lj4/HVV1/B3d0doaGhwx6ece+1vbu7G7Nnz9Z8uXJHUFCQUzHo9fr73sfZMcQRZrMZJpMJly9fRkVFBfR6PRITE9X6JUuWwGAwYNeuXQgNDYXdbseMGTPGHGfGGg/vnE9eXh6WL18+bH8PD4//1/nQo8VkgshJQUFB+O233zRlVqsVbm5uAIDp06dDURS0t7ff17dRU6dOhclkwr59+9DX14eEhARMmjRJra+trcWXX36JRYsWAbg9Z/bq1atjxgncfmJVQECAGufdoqOjcfHiRRiNRofjJCL6J/Dy8rqva1t0dDSKi4sxadIk+Pr6jtgmJCQEZ86cwbx58wAAt27dwi+//ILo6OgR28+cORN2ux1VVVVYsGDBsPo7d0aGhobUMkfGkMjISHUx+R2nT5/+23OcO3cuwsLCUFxcjKNHj+Lll19Wx65r167BZrNh165diIuLA3D7AR1jCQoKQldXF3p6etTkbKRxxmazcZz5L8QF2EROeu6553Du3DkUFRWhqakJH374oSa58PHxwZo1a5CZmYk9e/agpaUFdXV1+OKLL7Bnz54x+zabzThw4AAOHjwIs9msqQsPD8fevXvR2NiIM2fOwGw2j/mtl9FoRFhYGDZu3IimpiYcPnwYW7du1bTJzs7GqVOnkJaWBqvViqamJpSVlXEBNhH9zzGbzQgMDMTSpUtRXV2N1tZWVFZWIj09Hb///jsAICMjA1u2bEFpaSkuXbqEd999d8zfiHjiiSdgsVjw2muvobS0VO3z22+/BQAYDAbodDqUl5fjypUr6O7udmgMefvtt9HU1IS1a9fCZrPhm2++QWFhoUPnmZKSgu3bt6OiokIzzgQEBGDixInYuXMnmpub8dNPP2mm2o4kNjYWnp6eWL9+PVpaWkaMIzc3F0VFRcjLy8OFCxfQ2NiIAwcOYMOGDQ7FS+PYo1ywQTQejbYAe6TFbLm5uTJ58mTx8/OTzMxMSUtLUxdgi9xe8PfZZ59JRESEuLm5SVBQkCxcuFCqqqrGjOH69euiKIp4enpKV1eXpq6urk5iYmLEw8NDwsPD5eDBg2IwGOTTTz9V2+CuBdgiIjU1NTJz5kzx8PCQuLg4OXjwoGYBtojI2bNnJSEhQby9vcXLy0tmzZolmzZtcuQtIyIal0Z6UIYj9Z2dnZKamiqBgYGiKIpMmzZNVq1aJTdu3BCR24uYMzIyxNfXV/z9/SUrK0tSU1NHXYAtItLX1yeZmZkSEhIi7u7uYjQapaCgQK3Pz8+X4OBg0el0YrFYRMSxMeTQoUNiNBpFURSJi4uTgoKCMRdg33Hx4kUBIAaDQbPYXESkoqJCIiMjRVEUmTVrllRWVmrGlXsXYIvcXnBtNBpFr9fL4sWLZefOnXLvx8xjx47J3LlzRa/Xi6+vrzzzzDOyc+fOMeOk8U8nMspqISIiIiIiojFwmhMRERERETmFyQQRERERETmFyQQRERERETmFyQQRERERETmFyQQRERERETmFyQQRERERETmFyQQRERERETmFyQQRERERETmFyQQRERERETmFyQQRERERETmFyQQRERERETmFyQQRERERETnlX5mgzKII3F5FAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2, figsize=(8,4))\n",
    "axs = axs.flatten()\n",
    "\n",
    "axs[0].set_title('True vs predicted values')\n",
    "axs[0].scatter(\n",
    "    train_df['distance_to_water'], train_predictions,\n",
    "    s=1, alpha=0.25, color='black', label='Data'\n",
    ")\n",
    "axs[0].plot(\n",
    "    list(range(-100, 700, 10)), list(range(-100, 700, 10)), \n",
    "    color='red', label='perfect predictions'\n",
    ")\n",
    "axs[0].set_xlabel('True value')\n",
    "axs[0].set_ylabel('Predicted value')\n",
    "axs[0].set_aspect('equal', adjustable='box')\n",
    "axs[0].set_ylim(-100, 700)\n",
    "axs[0].set_xlim(-100, 700)\n",
    "leg = axs[0].legend(loc='upper left', markerscale=5)\n",
    "leg.legend_handles[0].set_alpha(1)\n",
    "\n",
    "axs[1].set_title('Fit residuals')\n",
    "axs[1].scatter(\n",
    "    train_predictions, train_predictions - train_df['distance_to_water'], \n",
    "    s=1, alpha=0.25, color='black'\n",
    ")\n",
    "axs[1].axhline(0, color='red', label='perfect predictions' )\n",
    "axs[1].set_xlabel('Predicted value')\n",
    "axs[1].set_ylabel('True - predicted value')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f48e00a",
   "metadata": {},
   "source": [
    "### 3.3. Over fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7365b365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_training=np.array([1,2,3,4,5]) + np.random.normal(-0.5,0.5,5)\n",
    "y_training=np.array([1,2,3,4,5]) + np.random.normal(-0.5,0.5,5)\n",
    "\n",
    "x_test=np.array([2.5,3.5]) + np.random.normal(-0.5,0.5,2)\n",
    "y_test=np.array([2.5,3.5]) + np.random.normal(-0.5,0.5,2)\n",
    "\n",
    "poly_fit=np.polyfit(x_training,y_training,4)\n",
    "poly_model=np.poly1d(poly_fit)\n",
    "\n",
    "x_range=np.linspace(0,8)\n",
    "y_poly=poly_model(x_range)\n",
    "\n",
    "linear_model=LinearRegression()\n",
    "linear_model.fit(x_training.reshape(-1, 1), y_training)\n",
    "y_linear=linear_model.predict(x_range.reshape(-1, 1))\n",
    "\n",
    "plt.scatter(x_training,y_training, color='black', label='Training data')\n",
    "plt.scatter(x_test,y_test, color='red', label='Testing data')\n",
    "plt.plot(x_range, y_poly, linestyle='dashed', color='grey', label='Polynomial fit')\n",
    "plt.plot(x_range, y_linear, color='black', label='Linear fit')\n",
    "plt.xlim(0.5,6.5)\n",
    "plt.ylim(-5,8)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "836488e7",
   "metadata": {},
   "source": [
    "## 4. Dealing with overfitting\n",
    "\n",
    "Regularization adds penalty terms to prevent overfitting by shrinking coefficients.\n",
    "\n",
    "### 4.2. Ridge regression (L2)\n",
    "\n",
    "Penalizes model with an extra term: 'alpha' times the coefficients' sum-of-squares.\n",
    "- Large coefficient -> large penalty\n",
    "- Makes coefficients tend to be small but not zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "adde6f1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "# Train Ridge regression with different alpha values\n",
    "alphas = [1e-2, 1e-1, 1e0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6, 1e7, 1e8, 1e9, 1e10]\n",
    "training_scores = []\n",
    "testing_scores = []\n",
    "\n",
    "for alpha in alphas:\n",
    "\n",
    "    ridge_model = Ridge(alpha=alpha)\n",
    "\n",
    "    ridge_model.fit(\n",
    "        train_df.drop(columns='distance_to_water'),\n",
    "        train_df['distance_to_water']\n",
    "    )\n",
    "\n",
    "    ridge_training_predictions = ridge_model.predict(\n",
    "        train_df.drop(columns='distance_to_water')\n",
    "    )\n",
    "\n",
    "    ridge_testing_predictions = ridge_model.predict(\n",
    "        test_df.drop(columns='distance_to_water')\n",
    "    )\n",
    "\n",
    "    training_scores.append(\n",
    "        root_mean_squared_error(train_df['distance_to_water'], ridge_training_predictions)\n",
    "    )\n",
    "\n",
    "    testing_scores.append(\n",
    "        root_mean_squared_error(test_df['distance_to_water'], ridge_testing_predictions)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8115e39f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Ridge performance')\n",
    "plt.plot(alphas, training_scores, color='purple', label='Ridge training')\n",
    "plt.plot(alphas, testing_scores, color='teal', label='Ridge testing')\n",
    "plt.axhline(train_rmse, color='purple', linestyle='--', label='Linear training')\n",
    "plt.axhline(test_rmse, color='teal', linestyle='--', label='Linear testing')\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Alpha value')\n",
    "plt.ylabel('RMSE')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f4db7b",
   "metadata": {},
   "source": [
    "### 4.2. Lasso regression (L1)\n",
    "\n",
    "Penalizes model with an extra term: 'alpha' times the sum of the absolute value of the coefficients.\n",
    "- Larger coefficients -> larger penalty\n",
    "- Will drive coefficients to zero (feature selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "89c10514",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "# Train Lasso regression with different alpha values\n",
    "alphas = [0.025, 0.05, 0.1, 0.2, 0.4, 0.8, 1.6, 3.2, 6.4, 12.8, 25.6, 51.2, 102.4]\n",
    "\n",
    "training_scores = []\n",
    "testing_scores = []\n",
    "\n",
    "for alpha in alphas:\n",
    "\n",
    "    lasso_model = Lasso(alpha=alpha, max_iter=20000)\n",
    "\n",
    "    lasso_model.fit(\n",
    "        train_df.drop(columns='distance_to_water'),\n",
    "        train_df['distance_to_water']\n",
    "    )\n",
    "\n",
    "    lasso_training_predictions = lasso_model.predict(\n",
    "        train_df.drop(columns='distance_to_water')\n",
    "    )\n",
    "\n",
    "    lasso_testing_predictions = lasso_model.predict(\n",
    "        test_df.drop(columns='distance_to_water')\n",
    "    )\n",
    "\n",
    "    training_scores.append(\n",
    "        root_mean_squared_error(train_df['distance_to_water'], lasso_training_predictions)\n",
    "    )\n",
    "\n",
    "    testing_scores.append(\n",
    "        root_mean_squared_error(test_df['distance_to_water'], lasso_testing_predictions)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "54a68b6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Lasso performance')\n",
    "plt.plot(alphas, training_scores, color='purple', label='Lasso training')\n",
    "plt.plot(alphas, testing_scores, color='teal', label='Lasso testing')\n",
    "plt.axhline(train_rmse, color='purple', linestyle='--', label='Linear training')\n",
    "plt.axhline(test_rmse, color='teal', linestyle='--', label='Linear testing')\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Alpha value')\n",
    "plt.ylabel('RMSE')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "945bfb5a",
   "metadata": {},
   "source": [
    "## 5. Optimizing regression models\n",
    "\n",
    "Alpha is the first good example of a 'hyperparameter' we have encountered and there are many, many others. In general a hyperparameter is a value you can tune or a setting choice you can make that effects how the model learns from the data. The best values need to be determined empirically for the specific model and dataset.\n",
    "\n",
    "Scikit-learn provides several classes to automate this process. Rather than manually training and evaluating the model with a range of value, as we did for alpha above we can use an automated search. The 'search' comes in two main flavors:\n",
    "\n",
    "- **Grid search**: search space is discrete, you provide lists of values for each hyperparameter you want to optimize, sklearn evaluates every possible combination.\n",
    "- **Random search**: you provide lists or continuous distributions, sklearn samples randomly. In this case you need to set the number of trials you want to run.\n",
    "\n",
    "Both of those search strategies are avalible in `CV` and non-`CV` versions. The `CV` stands for cross-validation. I recommend using the cross-validation version to get better performance estimates for each combination of hyperparameter settings. The only downside is the additional compute. If you set `cv=5` on a random search with 100 runs, the model must be trained 500 times. Non-`CV` search can be useful if you have a very large search space and want to quickly run as many samples as possible to narrow it down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bf1b3427",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha: 4.243\n",
      "Best CV RMSE: 98.3\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import uniform\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Define parameter grid for Lasso regression\n",
    "# We will use a uniform distribution to sample alpha values between 1.0 and 10\n",
    "param_grid = {'alpha': uniform(loc=1.0, scale=9.0)}\n",
    "\n",
    "# Create GridSearchCV object\n",
    "grid_search = RandomizedSearchCV(\n",
    "    Lasso(max_iter=20000),\n",
    "    param_grid,\n",
    "    n_iter=100,\n",
    "    cv=3,\n",
    "    scoring='neg_root_mean_squared_error',\n",
    "    n_jobs=16\n",
    ")\n",
    "\n",
    "# Fit on training data\n",
    "grid_search.fit(\n",
    "    train_df.drop(columns='distance_to_water'),\n",
    "    train_df['distance_to_water']\n",
    ")\n",
    "\n",
    "print(f'Best alpha: {grid_search.best_params_[\"alpha\"]:.3f}')\n",
    "print(f'Best CV RMSE: {-grid_search.best_score_:.1f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f6569f6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE (linear):  93.1\n",
      "Test RMSE (linear):   109.4\n",
      "\n",
      "Train RMSE (lasso):   98.3\n",
      "Test RMSE (lasso):    100.8\n"
     ]
    }
   ],
   "source": [
    "# Evaluate best model on test set\n",
    "lasso = Lasso(alpha=grid_search.best_params_[\"alpha\"], max_iter=20000)\n",
    "\n",
    "result = lasso.fit(\n",
    "    train_df.drop(columns='distance_to_water'),\n",
    "    train_df['distance_to_water']\n",
    ")\n",
    "\n",
    "train_predictions_lasso = lasso.predict(train_df.drop(columns='distance_to_water'))\n",
    "train_rmse_lasso = root_mean_squared_error(train_df['distance_to_water'], train_predictions_lasso)\n",
    "test_predictions_lasso = lasso.predict(test_df.drop(columns='distance_to_water'))\n",
    "test_rmse_lasso = root_mean_squared_error(test_df['distance_to_water'], test_predictions_lasso)\n",
    "\n",
    "print(f'Train RMSE (linear):  {train_rmse:.1f}')\n",
    "print(f'Test RMSE (linear):   {test_rmse:.1f}')\n",
    "print()\n",
    "print(f'Train RMSE (lasso):   {train_rmse_lasso:.1f}')\n",
    "print(f'Test RMSE (lasso):    {test_rmse_lasso:.1f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e526070b",
   "metadata": {},
   "source": [
    "## 6. Scikit-learn pipelines\n",
    "\n",
    "Pipelines combine preprocessing and modeling steps to prevent data leakage and simplify workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e9c03118",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load raw data again for pipeline demo\n",
    "url = 'https://gperdrizet.github.io/FSA_devops/assets/data/unit3/distance_to_water.csv'\n",
    "raw_df = pd.read_csv(url)\n",
    "\n",
    "# Split before any preprocessing\n",
    "train_df, test_df = train_test_split(raw_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "570d55a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-2\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;Preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;Numerical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;area&#x27;, &#x27;temperature&#x27;,\n",
       "                                                   &#x27;humidity&#x27;, &#x27;wind_speed&#x27;,\n",
       "                                                   &#x27;rainfall&#x27;]),\n",
       "                                                 (&#x27;Categorical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;encoder&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  [&#x27;quality&#x27;, &#x27;region&#x27;])])),\n",
       "                (&#x27;Model&#x27;,\n",
       "                 Lasso(alpha=np.float64(4.2427939582759056), max_iter=20000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('steps',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-steps;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples\">\n",
       "            steps\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-steps;\">\n",
       "            steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators &lt;combining_estimators&gt;` for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">[(&#x27;Preprocessor&#x27;, ...), (&#x27;Model&#x27;, ...)]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transform_input',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-transform_input;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone\">\n",
       "            transform_input\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-transform_input;\">\n",
       "            transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing &lt;metadata_routing&gt;`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('memory',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-memory;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone\">\n",
       "            memory\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-memory;\">\n",
       "            memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Preprocessor: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for Preprocessor: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"Preprocessor__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transformers',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-transformers;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples\">\n",
       "            transformers\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-transformers;\">\n",
       "            transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {&#x27;drop&#x27;, &#x27;passthrough&#x27;} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings &#x27;drop&#x27; and &#x27;passthrough&#x27; are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">[(&#x27;Numerical&#x27;, ...), (&#x27;Categorical&#x27;, ...)]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('remainder',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-remainder;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27\">\n",
       "            remainder\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-remainder;\">\n",
       "            remainder: {&#x27;drop&#x27;, &#x27;passthrough&#x27;} or estimator, default=&#x27;drop&#x27;<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``&#x27;drop&#x27;``).<br>By specifying ``remainder=&#x27;passthrough&#x27;``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;drop&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sparse_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-sparse_threshold;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3\">\n",
       "            sparse_threshold\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-sparse_threshold;\">\n",
       "            sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary &lt;n_jobs&gt;`<br>for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transformer_weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-transformer_weights;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone\">\n",
       "            transformer_weights\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-transformer_weights;\">\n",
       "            transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose_feature_names_out',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose_feature_names_out;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue\">\n",
       "            verbose_feature_names_out\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose_feature_names_out;\">\n",
       "            verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out=&quot;{transformer_name}__{feature_name}&quot;`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``&quot;{feature_name}__{transformer_name}&quot;``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Numerical</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"Preprocessor__Numerical__\"><pre>[&#x27;area&#x27;, &#x27;temperature&#x27;, &#x27;humidity&#x27;, &#x27;wind_speed&#x27;, &#x27;rainfall&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"Preprocessor__Numerical__scaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-copy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy;\">\n",
       "            copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-with_mean;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
       "            with_mean\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-with_mean;\">\n",
       "            with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-with_std;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
       "            with_std\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-with_std;\">\n",
       "            with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Categorical</div></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"Preprocessor__Categorical__\"><pre>[&#x27;quality&#x27;, &#x27;region&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"Preprocessor__Categorical__encoder__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('drop',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-drop;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=drop,-%7B%27first%27%2C%20%27if_binary%27%7D%20or%20an%20array-like%20of%20shape%20%28n_features%2C%29%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
       "            drop\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-drop;\">\n",
       "            drop: {&#x27;first&#x27;, &#x27;if_binary&#x27;} or an array-like of shape (n_features,),             default=None<br><br>Specifies a methodology to use to drop one of the categories per<br>feature. This is useful in situations where perfectly collinear<br>features cause problems, such as when feeding the resulting data<br>into an unregularized linear regression model.<br><br>However, dropping one category breaks the symmetry of the original<br>representation and can therefore induce a bias in downstream models,<br>for instance for penalized linear classification or regression models.<br><br>- None : retain all features (the default).<br>- &#x27;first&#x27; : drop the first category in each feature. If only one<br>  category is present, the feature will be dropped entirely.<br>- &#x27;if_binary&#x27; : drop the first category in each feature with two<br>  categories. Features with 1 or more than 2 categories are<br>  left intact.<br>- array : ``drop[i]`` is the category in feature ``X[:, i]`` that<br>  should be dropped.<br><br>When `max_categories` or `min_frequency` is configured to group<br>infrequent categories, the dropping behavior is handled after the<br>grouping.<br><br>.. versionadded:: 0.21<br>   The parameter `drop` was added in 0.21.<br><br>.. versionchanged:: 0.23<br>   The option `drop=&#x27;if_binary&#x27;` was added in 0.23.<br><br>.. versionchanged:: 1.1<br>    Support for dropping infrequent categories.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;first&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sparse_output',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-sparse_output;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=sparse_output,-bool%2C%20default%3DTrue\">\n",
       "            sparse_output\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-sparse_output;\">\n",
       "            sparse_output: bool, default=True<br><br>When ``True``, it returns a SciPy sparse matrix/array<br>in &quot;Compressed Sparse Row&quot; (CSR) format.<br><br>.. versionadded:: 1.2<br>   `sparse` was renamed to `sparse_output`</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('categories',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-categories;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27\">\n",
       "            categories\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-categories;\">\n",
       "            categories: &#x27;auto&#x27; or a list of array-like, default=&#x27;auto&#x27;<br><br>Categories (unique values) per feature:<br><br>- &#x27;auto&#x27; : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values within a single feature, and should be sorted in case of<br>  numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.<br><br>.. versionadded:: 0.20</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dtype',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dtype;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64\">\n",
       "            dtype\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dtype;\">\n",
       "            dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&lt;class &#x27;numpy.float64&#x27;&gt;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('handle_unknown',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-handle_unknown;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27ignore%27%2C%20%27infrequent_if_exist%27%2C%20%27warn%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27error%27\">\n",
       "            handle_unknown\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-handle_unknown;\">\n",
       "            handle_unknown: {&#x27;error&#x27;, &#x27;ignore&#x27;, &#x27;infrequent_if_exist&#x27;, &#x27;warn&#x27;},                      default=&#x27;error&#x27;<br><br>Specifies the way unknown categories are handled during :meth:`transform`.<br><br>- &#x27;error&#x27; : Raise an error if an unknown category is present during transform.<br>- &#x27;ignore&#x27; : When an unknown category is encountered during<br>  transform, the resulting one-hot encoded columns for this feature<br>  will be all zeros. In the inverse transform, an unknown category<br>  will be denoted as None.<br>- &#x27;infrequent_if_exist&#x27; : When an unknown category is encountered<br>  during transform, the resulting one-hot encoded columns for this<br>  feature will map to the infrequent category if it exists. The<br>  infrequent category will be mapped to the last position in the<br>  encoding. During inverse transform, an unknown category will be<br>  mapped to the category denoted `&#x27;infrequent&#x27;` if it exists. If the<br>  `&#x27;infrequent&#x27;` category does not exist, then :meth:`transform` and<br>  :meth:`inverse_transform` will handle an unknown category as with<br>  `handle_unknown=&#x27;ignore&#x27;`. Infrequent categories exist based on<br>  `min_frequency` and `max_categories`. Read more in the<br>  :ref:`User Guide &lt;encoder_infrequent_categories&gt;`.<br>- &#x27;warn&#x27; : When an unknown category is encountered during transform<br>  a warning is issued, and the encoding then proceeds as described for<br>  `handle_unknown=&quot;infrequent_if_exist&quot;`.<br><br>.. versionchanged:: 1.1<br>    `&#x27;infrequent_if_exist&#x27;` was added to automatically handle unknown<br>    categories and infrequent categories.<br><br>.. versionadded:: 1.6<br>   The option `&quot;warn&quot;` was added in 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;error&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_frequency',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_frequency;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone\">\n",
       "            min_frequency\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_frequency;\">\n",
       "            min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide &lt;encoder_infrequent_categories&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_categories',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_categories;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone\">\n",
       "            max_categories\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_categories;\">\n",
       "            max_categories: int, default=None<br><br>Specifies an upper limit to the number of output features for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide &lt;encoder_infrequent_categories&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('feature_name_combiner',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_name_combiner;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=feature_name_combiner,-%22concat%22%20or%20callable%2C%20default%3D%22concat%22\">\n",
       "            feature_name_combiner\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_name_combiner;\">\n",
       "            feature_name_combiner: &quot;concat&quot; or callable, default=&quot;concat&quot;<br><br>Callable with signature `def callable(input_feature, category)` that returns a<br>string. This is used to create feature names to be returned by<br>:meth:`get_feature_names_out`.<br><br>`&quot;concat&quot;` concatenates encoded feature name and category with<br>`feature + &quot;_&quot; + str(category)`.E.g. feature X with values 1, 6, 7 create<br>feature names `X_1, X_6, X_7`.<br><br>.. versionadded:: 1.3</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;concat&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Lasso</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.Lasso.html\">?<span>Documentation for Lasso</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"Model__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-alpha;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.Lasso.html#:~:text=alpha,-float%2C%20default%3D1.0\">\n",
       "            alpha\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-alpha;\">\n",
       "            alpha: float, default=1.0<br><br>Constant that multiplies the L1 term, controlling regularization<br>strength. `alpha` must be a non-negative float i.e. in `[0, inf)`.<br><br>When `alpha = 0`, the objective is equivalent to ordinary least<br>squares, solved by the :class:`LinearRegression` object. For numerical<br>reasons, using `alpha = 0` with the `Lasso` object is not advised.<br>Instead, you should use the :class:`LinearRegression` object.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">np.float64(4.2427939582759056)</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.Lasso.html#:~:text=max_iter,-int%2C%20default%3D1000\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
       "            max_iter: int, default=1000<br><br>The maximum number of iterations.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">20000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.Lasso.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('precompute',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-precompute;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.Lasso.html#:~:text=precompute,-bool%20or%20array-like%20of%20shape%20%28n_features%2C%20n_features%29%2C%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DFalse\">\n",
       "            precompute\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-precompute;\">\n",
       "            precompute: bool or array-like of shape (n_features, n_features),                 default=False<br><br>Whether to use a precomputed Gram matrix to speed up<br>calculations. The Gram matrix can also be passed as argument.<br>For sparse input this option is always ``False`` to preserve sparsity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_X',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-copy_X;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.Lasso.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
       "            copy_X\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy_X;\">\n",
       "            copy_X: bool, default=True<br><br>If ``True``, X will be copied; else, it may be overwritten.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.Lasso.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>The tolerance for the optimization: if the updates are smaller or equal to<br>``tol``, the optimization code checks the dual gap for optimality and continues<br>until it is smaller or equal to ``tol``, see Notes below.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.Lasso.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
       "            warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>See :term:`the Glossary &lt;warm_start&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('positive',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-positive;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.Lasso.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
       "            positive\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-positive;\">\n",
       "            positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.Lasso.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance, default=None<br><br>The seed of the pseudo random number generator that selects a random<br>feature to update. Used when ``selection`` == &#x27;random&#x27;.<br>Pass an int for reproducible output across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('selection',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-selection;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.Lasso.html#:~:text=selection,-%7B%27cyclic%27%2C%20%27random%27%7D%2C%20default%3D%27cyclic%27\">\n",
       "            selection\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-selection;\">\n",
       "            selection: {&#x27;cyclic&#x27;, &#x27;random&#x27;}, default=&#x27;cyclic&#x27;<br><br>If set to &#x27;random&#x27;, a random coefficient is updated every iteration<br>rather than looping over features sequentially by default. This<br>(setting to &#x27;random&#x27;) often leads to significantly faster convergence<br>especially when tol is higher than 1e-4.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;cyclic&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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      "text/plain": [
       "Pipeline(steps=[('Preprocessor',\n",
       "                 ColumnTransformer(transformers=[('Numerical',\n",
       "                                                  Pipeline(steps=[('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['area', 'temperature',\n",
       "                                                   'humidity', 'wind_speed',\n",
       "                                                   'rainfall']),\n",
       "                                                 ('Categorical',\n",
       "                                                  Pipeline(steps=[('encoder',\n",
       "                                                                   OneHotEncoder(drop='first',\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  ['quality', 'region'])])),\n",
       "                ('Model',\n",
       "                 Lasso(alpha=np.float64(4.2427939582759056), max_iter=20000))])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "# Define numeric and categorical columns\n",
    "numeric_features = ['area','temperature', 'humidity', 'wind_speed','rainfall']\n",
    "categorical_features = ['quality', 'region']\n",
    "\n",
    "# Create preprocessing steps\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('encoder', OneHotEncoder(drop='first', sparse_output=False))\n",
    "])\n",
    "\n",
    "# Combine preprocessing steps\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('Numerical', numeric_transformer, numeric_features),\n",
    "        ('Categorical', categorical_transformer, categorical_features)\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Create full pipeline with preprocessing and model\n",
    "pipeline = Pipeline(steps=[\n",
    "    ('Preprocessor', preprocessor),\n",
    "    ('Model', Lasso(alpha=grid_search.best_params_[\"alpha\"], max_iter=20000))\n",
    "])\n",
    "\n",
    "display(pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48a04eec",
   "metadata": {},
   "source": [
    "### 6.2. Train and evaluate pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "acc2cb5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline RMSE: 102.2\n"
     ]
    }
   ],
   "source": [
    "# Fit pipeline on training data\n",
    "pipeline.fit(train_df.drop(columns='distance_to_water'), train_df['distance_to_water'])\n",
    "\n",
    "# Make predictions on test set\n",
    "predictions = pipeline.predict(test_df.drop(columns='distance_to_water'))\n",
    "\n",
    "# Evaluate performance\n",
    "pipeline_rmse = root_mean_squared_error(test_df['distance_to_water'], predictions)\n",
    "\n",
    "print(f'Pipeline RMSE: {pipeline_rmse:.1f}')"
   ]
  }
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