{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import statsmodels.api as sm\n",
"import statsmodels.formula.api as smf\n",
"import matplotlib.pyplot as plt\n",
"import patsy as pt\n",
"from sklearn import preprocessing, linear_model, model_selection, metrics, datasets\n",
"import itertools as itertools"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"6. In this exercise, you will further analyze the Wage data set considered throughout this chapter."
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"text/plain": [
" year age maritl race education region \\\n",
"0 2006 18 1. Never Married 1. White 1. < HS Grad 2. Middle Atlantic \n",
"1 2004 24 1. Never Married 1. White 4. College Grad 2. Middle Atlantic \n",
"2 2003 45 2. Married 1. White 3. Some College 2. Middle Atlantic \n",
"3 2003 43 2. Married 3. Asian 4. College Grad 2. Middle Atlantic \n",
"4 2005 50 4. Divorced 1. White 2. HS Grad 2. Middle Atlantic \n",
"\n",
" jobclass health health_ins logwage wage \n",
"0 1. Industrial 1. <=Good 2. No 4.318063 75.043154 \n",
"1 2. Information 2. >=Very Good 2. No 4.255273 70.476020 \n",
"2 1. Industrial 1. <=Good 1. Yes 4.875061 130.982177 \n",
"3 2. Information 2. >=Very Good 1. Yes 5.041393 154.685293 \n",
"4 2. Information 1. <=Good 1. Yes 4.318063 75.043154 "
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# clean up wages csv exported from .rda file\n",
"wages_df = pd.read_csv('wage.csv')\n",
"wages_df = wages_df.drop(wages_df.columns[0], axis = 1)\n",
"wages_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(a) Perform polynomial regression to predict wage using age. Use cross-validation to select the optimal degree d for the polynomial. What degree was chosen, and how does this compare to the results of hypothesis testing using ANOVA? Make a plot of the resulting polynomial fit to the data."
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"num_cuts = 9\n",
"X = pd.get_dummies(pd.cut(wages_df['age'], num_cuts + 1))\n",
"model = linear_model.LinearRegression().fit(X, wages_df.wage)\n",
"sns.scatterplot(x=wages_df.age, y=wages_df.wage)\n",
"sns.lineplot(x=wages_df.age,y=model.predict(X), color='tab:red')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"7. The Wage data set contains a number of other features not explored in this chapter, such as marital status (maritl), job class (jobclass), and others. Explore the relationships between some of these other predictors and wage, and use non-linear fitting techniques in order to fit flexible models to the data. Create plots of the results obtained, and write a summary of your findings."
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [
{
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"
1. White
\n",
"
2. HS Grad
\n",
"
2. Middle Atlantic
\n",
"
2. Information
\n",
"
1. <=Good
\n",
"
1. Yes
\n",
"
4.318063
\n",
"
75.043154
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" year age maritl race education region \\\n",
"0 2006 18 1. Never Married 1. White 1. < HS Grad 2. Middle Atlantic \n",
"1 2004 24 1. Never Married 1. White 4. College Grad 2. Middle Atlantic \n",
"2 2003 45 2. Married 1. White 3. Some College 2. Middle Atlantic \n",
"3 2003 43 2. Married 3. Asian 4. College Grad 2. Middle Atlantic \n",
"4 2005 50 4. Divorced 1. White 2. HS Grad 2. Middle Atlantic \n",
"\n",
" jobclass health health_ins logwage wage \n",
"0 1. Industrial 1. <=Good 2. No 4.318063 75.043154 \n",
"1 2. Information 2. >=Very Good 2. No 4.255273 70.476020 \n",
"2 1. Industrial 1. <=Good 1. Yes 4.875061 130.982177 \n",
"3 2. Information 2. >=Very Good 1. Yes 5.041393 154.685293 \n",
"4 2. Information 1. <=Good 1. Yes 4.318063 75.043154 "
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wages_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_, _ = plt.subplots(figsize=(10,10))\n",
"sns.stripplot(x='education', y='wage',hue='health_ins', data=wages_df)"
]
},
{
"cell_type": "code",
"execution_count": 233,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best predictor from CV: \n",
" predictor: education \n",
" rmse: 36.493249491738155\n"
]
}
],
"source": [
"sorted_cv_result = cv_result.mean().sort_values()\n",
"order = sorted_cv_result.index[0]\n",
"rmse = sorted_cv_result[0]\n",
"print('Best predictor from CV: \\n predictor: {} \\n rmse: {}'.format(order, rmse))"
]
},
{
"cell_type": "code",
"execution_count": 243,
"metadata": {},
"outputs": [],
"source": [
"# hmm.. with all categorical predictors what benefit is fitting a non-linear model?\n",
"# - TODO: think about what it means to have non-linear functions of one-hot columns..\n",
"# - values in a column are binary: what benefit does a non-linear function of 1 or zero\n",
"# bring compared a linear function?\n",
"# - ?feature engineer to get new columns with non linear relationships with the\n",
"# dependent variable?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"8. Fit some of the non-linear models investigated in this chapter to the Auto data set. Is there evidence for non-linear relationships in this data set? Create some informative plots to justify your answer."
]
},
{
"cell_type": "code",
"execution_count": 255,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Explore the non-linear relationship between displacement and acceleration (3)\n",
"# Linear Spline\n",
"\n",
"def spline_basis_extract_x(df, i):\n",
" f = 'bs(acceleration, df={}, degree=3, include_intercept=True)'.format(i + 4)\n",
" return pt.dmatrix(f, df)\n",
" \n",
"\n",
"cv_result = run_cv(auto_df,\n",
" 10,\n",
" spline_basis_extract_x,\n",
" lambda df: df.displacement,\n",
" lambda x, y: linear_model.LinearRegression(fit_intercept=False).fit(x, y),\n",
" range(1,10))\n",
"\n",
"# bump the index so index loc = number of knots\n",
"cv_result.columns = cv_result.columns + 1\n",
"\n",
"# print the best number of knots and score\n",
"sorted_cv_result = cv_result.mean().sort_values()\n",
"sorted_cv_result\n",
"knots = sorted_cv_result.index[0]\n",
"rmse = sorted_cv_result.iloc[0]\n",
"print('Best from CV: \\n knots: {} \\n rmse: {}'.format(knots, rmse))\n",
"\n",
"# plots\n",
"_, (ax1, ax2, ax3) = plt.subplots(nrows=3, figsize=(10, 10))\n",
"\n",
"# plot some splines\n",
"for i in range(1, 10):\n",
" x = spline_basis_extract_x(auto_df, i)\n",
" y = auto_df.displacement\n",
" model = linear_model.LinearRegression(fit_intercept=False).fit(x, y)\n",
" preds = model.predict(x)\n",
" sns.scatterplot(x='acceleration', y='displacement', data=auto_df, ax=ax2)\n",
" sns.lineplot(x=auto_df.acceleration, y=preds, ax=ax2)\n",
"\n",
"# plot the best spline \n",
"x = spline_basis_extract_x(auto_df, 2)\n",
"y = auto_df.displacement\n",
"model = linear_model.LinearRegression(fit_intercept=False).fit(x, y)\n",
"preds = model.predict(x)\n",
"sns.scatterplot(x='acceleration', y='displacement', data=auto_df, ax=ax3)\n",
"sns.lineplot(x=auto_df.acceleration, y=preds, ax=ax3)\n",
"\n",
"# plot rmse bars\n",
"bar_df = pd.DataFrame({'rmse' : cv_result.mean(), 'knots' : cv_result.columns})\n",
"sns.barplot(y='rmse', x='knots', data=bar_df, ax=ax1)\n",
"\n",
"# plot best spline\n",
"sns.scatterplot(x='acceleration', y='displacement', data=auto_df, ax=ax2)\n",
"sns.lineplot(x=auto_df.acceleration, y=preds, ax=ax2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"9. This question uses the variables `dis` (the weighted mean of distances to five Boston employment centers) and `nox` (nitrogen oxides concentration in parts per 10 million) from the Boston data. We will treat `dis` as the predictor and `nox` as the response."
]
},
{
"cell_type": "code",
"execution_count": 451,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
CRIM
\n",
"
ZN
\n",
"
INDUS
\n",
"
CHAS
\n",
"
NOX
\n",
"
RM
\n",
"
AGE
\n",
"
DIS
\n",
"
RAD
\n",
"
TAX
\n",
"
PTRATIO
\n",
"
B
\n",
"
LSTAT
\n",
"
Price
\n",
"
\n",
" \n",
" \n",
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0
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0.00632
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18.0
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2.31
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0.0
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0.538
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6.575
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65.2
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4.0900
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1.0
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296.0
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15.3
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396.90
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4.98
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24.0
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\n",
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1
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0.02731
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0.0
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7.07
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0.0
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0.469
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6.421
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78.9
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4.9671
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2.0
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242.0
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17.8
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396.90
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9.14
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21.6
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2
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0.02729
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0.0
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7.07
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0.0
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0.469
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7.185
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61.1
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4.9671
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2.0
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242.0
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17.8
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392.83
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4.03
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34.7
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\n",
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3
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0.03237
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0.0
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2.18
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0.0
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0.458
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6.998
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45.8
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6.0622
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3.0
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222.0
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18.7
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394.63
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2.94
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33.4
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\n",
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4
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0.06905
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0.0
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2.18
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0.0
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0.458
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7.147
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54.2
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6.0622
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3.0
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222.0
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18.7
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396.90
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5.33
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36.2
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" \n",
"
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"
],
"text/plain": [
" CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\n",
"0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 \n",
"1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 \n",
"2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0 \n",
"3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0 \n",
"4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 \n",
"\n",
" PTRATIO B LSTAT Price \n",
"0 15.3 396.90 4.98 24.0 \n",
"1 17.8 396.90 9.14 21.6 \n",
"2 17.8 392.83 4.03 34.7 \n",
"3 18.7 394.63 2.94 33.4 \n",
"4 18.7 396.90 5.33 36.2 "
]
},
"execution_count": 451,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"boston_df = datasets.load_boston()\n",
"boston_df = pd.DataFrame(data=np.c_[boston_df['data'], boston_df['target']], columns= [c for c in boston_df['feature_names']] + ['Price'])\n",
"boston_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(a) Use the `poly()` function to fit a cubic polynomial regression to predict `nox` using `dis`. Report the regression output, and plot the resulting data and polynomial fits.\n",
"\n",
"(b) Plot the polynomial fits for a range of different polynomial degrees (say, from 1 to 10), and report the associated residual sum of squares.\n",
"\n",
"(c) Perform cross-validation or another approach to select the opti- mal degree for the polynomial, and explain your results."
]
},
{
"cell_type": "code",
"execution_count": 466,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best from CV: \n",
" order: 3 \n",
" rmse: 0.06115394767838496\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 466,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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PRMSTETE163q6SkQMiIiJEfFCRDwfEdtnXVOpRcTI4v/OLX8WRcT3s66rK0TED4r/pj0bETdHRO+sa+oKEfG94nd+rjv/b+0csk6IiB2BJcB1KaXPZF1PV4mI9YD1UkqPR0R/YBrw9ZTS9IxLK6niaRF9U0pLIqIc+BfwvZTSwxmXVnIRcRIwBlgjpbRn1vV0lYh4DRjTflPq1V1EXAv8M6X0h+Jq+D4ppQVZ19VVikf8zaF5Q/LXs66nlCJifZr/LRuVUqqNiAnAXSmlP2ZbWWlFxGdoPiloG6AeuBs4PqU0M9PCOmAPWSeklP5B8yrQHiWl9J+U0uPFnxcDzwPrZ1tV6aVmS4qX5cU/q/1/uUTEUOCrwB+yrkWlFxFrAjvSvNqdlFJ9TwpjRbsAL6/uYayNMqCyuO9nH+DNjOvpCp8CHkkp1aSUGoG/A9/IuKYOGcjUKRGxEbAl8Ei2lXSN4tDdk8Bc4J6UUk/43hcCPwIKWReSgQT8NSKmFY9q6wmGA/OAa4rD1H+IiL5ZF9XFDgRuzrqIrpBSmgOcD7wB/AdYmFL6a7ZVdYlngS9ExKCI6AP8N8tuWt9tGMi0UhHRD5gEfD+ltCjrerpCSqkppbQFzSdMbFPs9l5tRcSewNyU0rSsa8nIf6WUtgL2AL5dnKawuisDtgJ+n1LaEqgGTsu2pK5THKLdC7g161q6QkQMBPamOYh/AugbEYdmW1XppZSeB84F/krzcOWTQFOmRa2AgUzvqziHahJwY0rptqzr6WrFIZz7gd2zrqXEdgD2Ks6lugXYOSJuyLakrlPsPSClNBe4neb5Jqu72cDsNr2/E2kOaD3FHsDjKaW3sy6ki3wZeDWlNC+l1ADcBnw+45q6RErpqpTS6JTSjkAV8GLWNXXEQKYVKk5uvwp4PqX0m6zr6SoRMTgiBhR/rgR2BV7ItqrSSin9OKU0NKW0Ec3DOPellFb7/3oGiIi+xUUrFIfsvkLzMMdqLaX0FjArIkYWb+0CrNYLdto5iB4yXFn0BrBdRPQp/tu+C83zgld7ETGk+PcGNM8fuynbijpWsqOTVicRcTOwE7B2RMwGzkwpXZVtVV1iB+Aw4JnifCqAn6SU7sqwpq6wHnBtcQVWDpiQUupR20D0MOsAtzf/fxRlwE0ppbuzLanLfAe4sTh89wpwVMb1dIli8N4VOC7rWrpKSumRiJgIPA40Ak/wMThOaBWZFBGDgAbg29118YrbXkiSJGXMIUtJkqSMGcgkSZIyZiCTJEnKmIFMkiQpYwYySZKkjBnIJAmIiCMj4uKs65DUMxnIJPU40ewj/ftXPKBZklYJA5mk1VJEnBQRzxb/fD8iNoqIGRFxHc078Q+LiKMi4sWIeJTmjZBb3js4IiZFxGPFPzsU758VEddHxIPA9dl8M0mrI/8LT9JqJyJG07zr/LZAAI8AfwdGAEeklB6OiPWA/wFGAwtpPrP0ieIjfgtckFL6V/G4lSnAp4qvjaL5MPLarvo+klZ/BjJJq6P/Am5PKVUDRMRtwBeA11NKDxfbbAs8kFKaV2wzHtis+NqXgVHF45QA1oiIfsWfJxvGJK1qBjJJPUl1J9vlgO1SSkvb3iwGtM4+Q5I6zTlkklZH/wS+HhF9igdJ71O819YjwBcjYlBElAP7tXntrzQfvA1ARGxR6oIl9Wz2kEla7aSUHo+IPwKPFm/9Aahq1+Y/EXEW8BCwAHiyzcvfBS6JiKdp/nfyH8DxJS5bUg8WKaWsa5AkSerRHLKUJEnKmIFMkiQpYwYySZKkjBnIJEmSMmYgkyRJypiBTJIkKWMGMkmSpIwZyCRJkjJmIJMkScqYgUySJCljBjJJkqSMGcgkSZIyZiCTJEnKmIFMkiQpYwYySZKkjBnIJEmSMlbSQBYRu0fEjIiYGRGndfD6hhFxb0Q8HREPRMTQUtYjSZLUHUVKqTQPjsgDLwK7ArOBx4CDUkrT27S5FbgzpXRtROwMHJVSOqwkBUmSJHVTpewh2waYmVJ6JaVUD9wC7N2uzSjgvuLP93fwuiRJ0mqvrITPXh+Y1eZ6NrBtuzZPAd8AfgvsA/SPiEEppfltG0XEOGAcQN++fUd/8pOfLFnRkiRJq8q0adPeSSkNXlm7UgayzjgZuDgijgT+AcwBmto3SildAVwBMGbMmDR16tSurFGSJOlDiYjXO9OulIFsDjCszfXQ4r1WKaU3ae4hIyL6AWNTSgtKWJMkSVK3U8o5ZI8BIyJieERUAAcCk9s2iIi1I6Klhh8DV5ewHkmSpG6pZIEspdQInAhMAZ4HJqSUnouIsyNir2KznYAZEfEisA5wTqnqkSRJ6q5Ktu1FqTiHTJIkfVxExLSU0piVtXOnfkmSpIwZyCRJkjKW9bYXHzuFQmJBbT219U00pUTvsjwVZUF1XRO5HKQUFFKisjzH0oYCjYVEZXme8jzU1Ddfl+WCIf16kc/neGdJHY2FAilBUyHRuzzP2v16kctF1l9VkiR1EQPZB1AoJF6bX83bi5ZyysSnmV1Vy1dGDeHEnUdw8X0vccTnh3PqpKf5/MaDOHT7DTnhxscZ3K8X5+33WWrqC5xw4+PMrqpl6MBKrjlqaxoaC1z4txdb39fy2pWHjWHkuv0NZZIk9RAOWX4A86vreX1+TWsYAxg7ehgn3Pg4Y0cPaw1Vx+64cWv4On6nTYBc6zXA7KpaZr9by7jrpy3zvpbXjr1+KvOr67P6mpIkqYsZyD6A+sYm+lTkW8MTwIDKcmZX1bb+DZDPRevPAyrLyQXLvAdofU7b97WYXVVLfeNyBxZIkqTVlIHsA6goy1NT38TQgZWt9xbUNjB0YGXr39A8F6zl5wW1DRQSy7wHaH1O2/e1GDqwkoqyfIm/jSRJ6i4MZB/AoL4VbDioD+ftu3lriJo0bRaXHrIVk6bN4tyxzfev/McrXHrIVgwdWMllD7wMFFqvoTlwDV2rkisOG73M+1peu/KwMQzqW5HV15QkSV3MjWE/oGVXWULvslybVZZBSpBSovcHWGXZVChQSLSu2nSVpSRJq4fObgzrKssPKJcL1urbC/oue39An5W/d2Df5e8NWaP3qilMkiR9bDlkKUmSlDEDmSRJUsYMZJIkSRkzkEmSJGXMQCZJkpQxA5kkSVLGDGSSJEkZM5BJkiRlzEAmSZKUMQOZJElSxgxkkiRJGTOQSZIkZcxAJkmSlDEDmSRJUsYMZJIkSRkzkEmSJGXMQCZJkpQxA5kkSVLGDGSSJEkZK2kgi4jdI2JGRMyMiNM6eH2DiLg/Ip6IiKcj4r9LWY8kSVJ3VLJAFhF54BJgD2AUcFBEjGrX7GfAhJTSlsCBwKWlqkeSJKm7KmUP2TbAzJTSKymleuAWYO92bRKwRvHnNYE3S1iPJElSt1RWwmevD8xqcz0b2LZdm7OAv0bEd4C+wJdLWI8kSVK3lPWk/oOAP6aUhgL/DVwfEcvVFBHjImJqREydN29elxcpSZJUSqUMZHOAYW2uhxbvtXUMMAEgpfQQ0BtYu/2DUkpXpJTGpJTGDB48uETlSpIkZaOUgewxYEREDI+ICpon7U9u1+YNYBeAiPgUzYHMLjBJktSjlCyQpZQagROBKcDzNK+mfC4izo6IvYrNfggcGxFPATcDR6aUUqlqkiRJ6o5KOamflNJdwF3t7p3R5ufpwA6lrEGSJKm7y3pSvyRJUo9nIJMkScqYgUySJCljJZ1Dpo+mUEjMr66nvrGJirI8g/pWkMvFcm3eqa5jaUMT+QgqK/IMqFy2XWeeA9DYWODdmnrqmwo0FRKV5XnW7terw8/szPMkSVLnGMg6Mm8GDB6ZaQmFQmLG24s59rqpzK6qZejASq48fAwj1+nfGn46anPevpuzzhq92WhQX3K56NRzoDmMvfZuNfMW13HKxKffa3vYGEau+/6f2dHzJElS5zlk2d5zf4JLtoUX7lp52xKaX13fGnoAZlfVcux1U5lfXf++bU6Z+DSvz69pbdeZ5wDMXVLHrHdrW8NYa9vrV/6ZHT1PkiR1noGsvc12g/U+B7eNg3kvZlZGfWNTa+hpMbuqlvrGppW26VORb23XmecANDQV6FOR/9Cf2f55kiSp8wxk7ZVXwoE3QlkvuOUgWLowkzIqyvIMHVi5zL2hAyupKMuvtE1NfVNru848B6A8n6OmvulDf2b750mSpM4zkHVkzaGw/3VQ9VpzT1mh0OUlDOpbwZWHj2kNPy1ztQb1rXjfNuftuzkbDurT2q4zzwEY0q8Xw9Zqfv8ybQ9b+Wd29DxJktR58XE7qWjMmDFp6tSpXfNhj1wB/3cK7HgK7PyzrvnMNj7YKssC+WCVrbIsFBK9XWUpSdJHEhHTUkpjVtbOVZYdSCkREbDNsfDWU/CP82DwJ+Gz+3ZpHblcMLh/r5W2GdK/90d+DkBZWY4ha7z/sz7I8yRJUuc4ZNnOs3MWsudF/2LWuzUQAV+9ADbcAf50Aszuop45SZLUoxjI2qkoyzHr3RqOuPpR3q2uh7IK2P96WGM9uPkgWDg76xIlSdJqxkDWzmbr9OeqI7dmzoJajvrjY9TUN0LfQXDQeGhcCjcdCHVLsi5TkiStRgxkHdh6o7W46KAteWb2Ak648XEamgow5JOw7zUw9zm4/bhMVl5KkqTVk6ss38dNj7zBT25/hrFbDeX8/TZvnuj/8GVw96nwXyfBl89cpZ/X2Fhg7pI6IJES5HNBIUFToUAugn6981TXFWhoKlCWC/pU5FjakKgvXvcqy5EIBlaWU1Xb4CpISZIy5irLVeDgbTdg7uKlXPi3lxjcvxen7fFJ2PY4mPcC/Os3sPZmsMVBq+SzGhsLvPD2Yn5374sc8fnhXPvvVzni88M5dVLzUUZfGTWE7+yyGd+6YVqH10MHVnLpIVvx+GvzGTN8bY5vc9+zJiVJ6t4csmyvYSn860JoagDge7uM4OBtN+Cyv7/M1f96tXnl5X+fB8N3hD9/F954eJV87NwldRx/wzTGjh7GqZOebv275ZiisaOHtYavjq5nV9Vywo2Ps/Oo9VrDWMt9z5qUJKl7M5C199IU+NuZMPFoaGogIvj53p9ht0+vw9l3TmfyU29Cvhz2uxbWHAa3HAJVr3/kj21oKjC7qpYBleXL/N1iZdfQHL4KKXnWpCRJHzMGsvZG7Q27/T94fjLceiQ01pPPBb89cEu22WgtfjjhSf750jzosxYcPB4KDXDzgR/5zMvyfI6hAytZUNuwzN8tVnYNzccY5SI8a1KSpI8ZA1lHtv827H4uvHAn3HoENNbRuzzPlYePYZPB/Rh33TSmvf4urD2i+czLd16E8YdC44cfFhzSrxeXHTqaSdNmce7YzVv/bglXk6bN4veHjl7hdcscsvum/4fL2t33rElJkro3V1m+n0evhLtOhhG7wQHXQ1kv5i5eyv6XPcT86npuPnY7PrP+mvDULc1bYXx2P9jnCsh9uJzbssoySBQSlOWCpgRNhUQuWOEqy4amAnlXWUqS1O10dpWlgWxlHrsK/nISbLorHHADlPdmdlUN+1/2EHWNBcYftz2bDukH//xfuPds2OF7sOvZXVefJEnqtjobyByyXJmtj4Gv/RZm3gO3HAwNtQwd2IcbvrktEXDYVY80n3v5XyfBmGPgwd/CI1cs95hCITFvcR1zqmqYt7iOQiEtc//thbW8uaCWOVU1vFtdx/zqpcypquH1+dW8WbzX8h5JkrR6MZB1xugjYa+L4eX7mifw19ew8eB+XHf0tlTXNXLoVY8wd3Fd83YYI78K//cjmD659e2FQmLG24vZ59IH2eHc+9nn0geZ8fZiGhsLzHh7MT+9/Wlmzqtm/8sf4sSbnmDWuzW8+NYSDrjiYb543gPsf8XDvDx3Ca/NrzaUSZK0GnLI8oN48ib40wmw4Q5w8C3Qqz/TXq/isKseYdjAPow/bjsGlDXCdXvBW8/A4XfABtsxb3Ed+1z64DLbUQwdWMmE47Zn/8sf4vQ9R/HzO6czu6qWyw8bTUU+x+l3PLtc+5/v/Rk+s/6aDO7fa4UlFgqJ+dX11Dc2ERFU5IP6pkQhJSryOUiJAtBYaL7Xv3ee2vpEY3EeWlkuyOVyDOhdxjvV9dQX71dW5BhY2YtcLlq74ZmSAAAgAElEQVTnujU0FSjP56gsD6rrCzQVEpXledbu18s5a5Ik4U79pbHFwZCvgNvGwXV7wyETGb3hWlx5+BiOuuYxjrj6UW48djv6HTQertoVbjoAjrmH+rKhHe4N1n7vMWjeX6zl9fbt+1Tk33c/sZaeuGOvm7rM7v0X3/cS8xbXc9ZeowCoqW/ilIlP8/mNB3Ho9htywo2Pt7Y/d+zmvPTWwuV2+z9v381ZZ41Ghg2oZMbcJa2vdXRiwJWHjWHkup4MIElSZzlk+UF9dt/mFZdvPQPXfg2WzGWHTdfm4oO35Nk3F3H0NY9RXbYmHDqpeQPZG75B75q3OtwbrP3eY9C8v1hNfVOH7Wvqm953P7H51fWtYQze271/7OhhHL/TJrxb3cC71Q2cMrH5BIBjd9y4NYy1tD910tMd7vZ/ysSneX1+TeuJAu93YsCx13sygCRJH4SB7MP45FebN4V99xW4Zg9YOIevfHpdLjxgC6a+/i5H//ExavoNaw5lSxey1m37c/V+w5fbG2xIv15cefiYZfYcu+yBl1mrbznn7bv5Mu0v2P9zbDioz/vuJ1bf2NRhz9qAynIGVJbTpyJPn4p8a5t8Lj7Qbv99KvI0FpZ9bUUnBngygCRJneeQ5Ye1yc5w6G1w0/5wze5w+B187XMbU0iJH4x/km9eO5WrjtiayoPHE9fvw4h7juBPx0yiLt93mb3BRq7Tn3P22ZxCocCE47YnpURlRZ5EYvy47WgqJMpyQe+KPAMq338/sYqyPEMHVi4392xBbfO5nBX5XOu92VW1NBVSh+1bdvtvf7+mvomy3LKvtfTutW/ryQCSJHVeSXvIImL3iJgRETMj4rQOXr8gIp4s/nkxIhaUsp5VbsPt4YjJpLrFNF29B/NeeZIxGw7kp1/9FA+9PJ/Dr36EV/p8lkV7XQVvP0e/2w6lPNW1hrGWCfiFQoGmBCklKsqag9egvr1Zf2Afhg7sQ3lZntr6pmLbFS/CGFhZzuWHLb97/6Rps1p73tr2vl35j1e49JCtlml/7tjNO9zt/7x9N2fDQX1aTxR4vxMDrjzMkwEkSfogSrbKMiLywIvArsBs4DHgoJTS9BW0/w6wZUrp6Pd7bqarLDtQKCRee/4xBt12AH3K4Fdrn8POX/oKJ970OFU1DQzqW8EVh4/mjhsu5qz63/BQfivWOvpWRqw7kJfmLeGCe2ZwxOeHc+qkp9+bFH/4GEau0x9guUn6La+17ylrmdB/wT0zGDt6GOuu0ZtB/SroXZajvimRUqL8Q6yybGgqkFvBKsvGpgJlbVZZFgqJ3q6ylCSpVeY79UfE9sBZKaXditc/Bkgp/XIF7f8NnJlSuuf9ntvdAlnLlha//GIfNptyCGuV1XFi/IQpizZsbdO7PMfShgIH5e/ll+VXcU/+C3z627ew/5WPLrPlRYuhAyu5/YQdADrcLuP2E3ZYbuuLFW2t0VFbSZLUNbrDthfrA7PaXM8Gtu2oYURsCAwH7lvB6+OAcQAbbLDBqq3yI6pvbGJwv14M3eQzvLnP7ax17+FcWn028/b7A0uGfZG/PvcWv57yIjtsOojjvn42i55Yl10fPIfF9/+YW479FblccOM3t2VJXSP9epXRVEg0FRJBoq6x0OkJ8yua0O/kekmSur/ussryQGBiSqnD9JBSuiKlNCalNGbw4MFdXNr7q6zI86PdR3LY1Y+yz02zGFt3OvVrDmfwnw/njut/x9YbrcUn1+3HgzPns99lD/H4BkdyQ9k+9H/mOv59yThenVfNOX+ZTk1dIwtq6vnhhKc46o+P8Z9FdfTtle9w+4uOJsy3TOjvTFtJktS9lDKQzQGGtbkeWrzXkQOBm0tYS8k0FlLrvl4ATy/ozderf8LCwaP54eJf86+b/h8/2HUkAyrLmbeknuOun8bPluzL1Y27s3/Tnbwx/oeM3WooP5jwFO9WN3D8Tpswu6qWb90wjZr6AlcePma57TI6mjA/qG9Fp9tKkqTupZRDlo8BIyJiOM1B7EDg4PaNIuKTwEDgoRLWUjINHQwrzliQY+bYa9n0H9/jB7P+wOwZlSyo3Y5xO27CFf94BQjObjyMPE0cwR28+fy6zK7asXmfMJp7tGZX1dLYVGDkOv25/YQdqG9sWma7jPZattDoTFtJktS9lCyQpZQaI+JEYAqQB65OKT0XEWcDU1NKLadvHwjckj5uh2oWrWjvr6r6HFO3uYC6N7/H156+iAv7zqBygwsZ2KecqpoGIDiz8UjW6JVjn+cu53/6zaembmvqi9taDB1YSVk+Ry4XnZ6U/0HaSpKk7sPDxT+ilZ0f+ZM9RvLO5J+yx4JbeH7gl+h1wFXc+ex8Lrz3Jfr3LuO8sZ+lcspJfGHx/1G1zUks2OaHrRu4FlIin8tRkQ8KCRqaCjSl4tYSfd1aQpKk7i7zbS9KpbsFMqB1g9f6xibKy5oDVHVdE00J+vbK0dQEldMuo//fz2Rq7rMcUfN9evddk4W1DWw6pB8X7PdZNnnoNCqevYU/DTySwV89vXVfsq+MGsLJu41k/pL61rlq77cfmSRJ6j46G8i6yyrLj7WWocL1B/ZhSP/eDOjT/PMGa/VhUN/eDFmjN0vHfIufl3+PLZqe4+aKX0D1PNbsU84r86r5wa3P8OK2v+Tu/E58veqPPH/LT5ldVQM0H949p2rpMgsHZlfVcux1HuAtSdLqwrMsP6BCIbGgtp6GxuYd7OsbC0RASpCAXmV5BlaWU1XbsMzk+vrGJq5avC2v5Cq4tPy3TKw4iyOrT+WM/XfjtNue5YSbn2R29Td55NOD+ObLt1BfVsOvGw9gQGU5gHuMSZK0GjOQfQCFQuK1+dUsXtpA7/IcdY2Ji+97aZmjj74yagjf3WUzjr9h2jLDi4P6VTB0YCX3V23JIfU/4Q8V5/OnXmdB309x3THbcNQ1jxGR566Nf8Yary3hBCZTSR0LakavcOGAe4xJkrR6cMiyjUIh8W51HXOqapj1bjVvLqjl7YW1zFtc1zpP7PX5Nbxb3UA+l+eEGx9n7OhhrWEMmocYW8IYvDe8WJaL1n3CHk+b8e3ev6LvGgMYMOEbbL30YW765rb0ryzjF3e9wJJdzmV8/mscVTaFyik/ZP0BFa0HgoN7jEmStLqxh6yopffr7UVLl5k8f+7Yzbn236/yg11HskbvMvpUNPdK5aI5bA2oLF+m56r9NTS3q61vWm6fsDJ2I24+AMYfwuZ7/JqJxx3IYVc/yi/vnsGv9vkVi+YMZ8epv2PpP05mwFcuZPy47WhKzWdjuspSkqTVhz1kRS29X+0nz5866WnGjh7GsddNJSKoqW+ipr6JQmruqVpQ27DMkUXtr+G94cW2k/8H9+9Frv8QOPJOGLEb3HUymz59Prd/a3uGDqzk5EnP8NDwb8POP6P39FsZ8tdvs37/MjZYq3nhgGFMkqTVh4GsqL6xiT4V+dYwtuWwAVx+2Gj+d7/PsdmQfgzu14uKfLDpkH5stHYfKsqCq48cw6Rpszh37HvDiZOmzeKyQ0d3fnixoi8ccAOMORoevJB17/0uE47ZilHrrcG3bpjGrX0OhK+cA9P/BLccBPXVXfL7kCRJXcd9yIrmLa7j2TkLOf2OZxncrxcn7zaydW7Y0IGVXHLwlpTlcxx3/XuT9S8/bDRr962gKSVafo0VK1hludIerZTgXxfAvf8DG32B6n2u5fiJM/nnS+/w4z0+ybh+/yLu/D58Yis45Fbos9Yq/x1IkqRVy41hP6C2c8iWNhQ4/Y5nl5kLds2RWy93b+jASm4/YYdVe1zRU+Phjm/DoE2pO3A8J02Zz1+e/g9Hfn4jTt/0FfKTjoGBG8Kht8GAYSt/Xhdru0nuisIpsEwbz9yUJK2uOhvInNRflMsFGw3qy4A+5Sxe2rjcxPy2w5ktSrIX2OcOgP7rwvhD6XXNrlx0wM2st8Zw/vCvV3lr4Sf47cGT6DXhELh6Nzh0Egz51Kr9/I+go2OkLjt0NL+790X+On0uQwdWct3R21DXWFimjacOSJJ6OueQtZHLBWv17UWfirLlJubX1DetcLL+KrfxF+GYe6CsF7lrv8rPNn6J0/ccxZTpb3HwX/MsOvAOKDTC1bvDG4+s+s//kOZX17cGLWgOrMffMI2xo4e1Xr8+v2a5Np46IEnq6QxkHRjUt6J1zzBoDl4bDuqz3L1VuRdYoZCYt7h5D7R5i+sorD2SwjH30jDk0zDhcMZWj+eX+3yaZ2Yv5KsTqnjqKxNo6j2QdN3ezJ92O3MXLeXNqhpen9+8f1pjY2HFzy6UZpi6vrGpw17EltMGoAt7GiVJ+hhxyLIDuVwst2dYS/Bqf29VDLN1NNR33dHbAH2Yu9N1LB5/HLs+9Et23uRFftt7X2ZV1bLPzXO4aK9r+fQD32SDPx/F62N+ymHPjV5mqPCTxWHA9s8u1RDhik4UWFDb0Hrd0tPoqQOSJL3HHrIVWG7PsFx0eG9V6Gio7/X5Nbw+v4aTb5/BsdXHM3PUiQx5eRIXNJzNABZTSHDiHbO543NXMu8TuzB86i/45uLfk6epdahw7pK6Dp9dqiHCjnoWLzt0NJOmzWq9LnVPoyRJH0f2kHUDHQ31tZwI0Hw/mD/mJC56ssCvy6/gtoozOabhFF5N63HhP+fw7jZnsP4bvRhX9heGxTy+0/AdZldBY1OBlFKXDRF21LM4sLKcc/bZnDO/VvqeRkmSPq7sIesGWob62mo5EaDl/oLaBqatuSsH1/+UNaOa2yvO4Kv9Z/K5oQO49pHZ/DZ3OD9rOIqdck8yoeJsthhQQ1k+1+GzSzlE2L4Xsaws12U9jZIkfVwZyLqBFS0i2HBQn9ZDxS974GXO23dz3h6wBV+vP5uq3EAuavo5+6S/sU7/XlTXN3Fr7MYxDSezce4tbs3/jCFLZnT4bIcIJUnqXtwYtgNtNzeNCPIBuVyudWitUEgsqK2ntr6JppToXZ5f5rDv9pujdmZIrqP3AG0+B/r2ytHUBEsbC5TVL2Tw3cdT/toDLNniGG5d61ucc/dLrLdmby7dpYLP/P1YorYKvn4phVH7uBGrJEkZWGU79UfESR3cXghMSyk9+SHr+9BKHcg6WvF47tjNufbfr/KDXUcyYnA/3qiq4e1FS1sPIm+7chHoslWNNDXCPWfAw5fAxjvx+LYX8M0JL9NUSPxh7DC2fuR7MOsR+MIP4Us/g5wdopIkdaVVGchuAsYAfy7e2hN4GtgIuDWl9OuPVuoHU+pANm9xHftc+uBy2zKcvucofn7ndCYctz0z3lrc4TFKE47bnpQSB1zx8HKv3XbC5wmCusYmAogACIb0a55n1bbXLZeDlIKU0jK9Ze9U17G0oYl8BJUVeQZUNvd0NU27gdxdP6Cx3yd4YafL+f59S3l9fg2/2HMzDpj3W+KJ66gdvitLv/Z7Cr36s7S+sEzPHniUkSRJpbAqj04aCmyVUlpSfPCZwF+AHYFpQJcGslJ7v81NZ1fV0thUWOHmpm8uqKWp0PGqxpq6Jg696pHlet2+u8tmjBzSj1kLanl70VKuefBVjvj88GUONr/y8DH0Kstx+NWPtt47b9/NWWeN3gwbUMmMdb/GRflF/HzBuWxyx95c+qWLOGfmhpw2eQZ///QRbFLWi++/ejVx7Vd4fqcr+M5fF7/37MPG0Kt82Wd7lJEkSV2rM2NYQ4C6NtcNwDoppdp291cLK1qVuKC2gaEDKynL51Z4jNL86vrWdu1fe/Wd6mX2Ajt10tOMHT2sdb+w1+fXcMrE5nstYayl7bHXTeX1+TXL3Dtl4tO8Pr+GuUvqOP6GaUxZtCF71f2cmYX1GHH/OH439H6O2H5D/u+5t7l4yZf453ZXkltaxaZ37MWIhf9+79nXL/9sjzKSJKlrdSaQ3Qg8EhFnFnvHHgRuioi+wPSSVpeBjlYlnjt2cyZNm8WVh49hSL9ey6x+bGnzv/t9jsseeJnLHniZc8cu+9rlh47md/e+tMznLNPrVkitvW4t99q3bdmXrP29xjY9cm8xiP3qz2Ry0/YMeOiXnFp9Hr2LmfmkR/tz57Y38kZhMNdUnMfJZePJUVjhsz3KSJKkrrPSIcuU0s8j4v+AHYq3jk8ptUziOqRklWWk/eamLassz9ln89a5VRsN6suAPuWMH7cdTQnKc8GZk5/liVkLADh/ygx+vvdn2GRIPyrL8+RzMG/Jsp2Jy/S65aK1163lXttQdtwXNuITAyq574dfpKmQuPuZ//CZoQMY1K8Xvcty3DJuOwIopERTIdG7bEcWzryaNR48h8mVT3H00u9Rnx/Gj/62kOM+fy1rzr+YE1+dyJYxk1/1PZma+vfC15bDBvDdXUbQlJrPv3Q+mSRJpdepbS8i4r+AESmlayJiMNAvpfRqyavrQFdse/FBdbQys+08rPdbubmyOWTHfWEj9txiKN+6YVrre39/6GguuvdF5i2u50e7j+SUiU8zuF+v1p9b2t2002LWv+87LG0o8NIOv+GA+/tT11igb0WeSdu/wsaPnklUDmDubpdzwN10+Aznk0mS9OGtylWWZ9K8ynJkSmmziPgEzasrd3jfN5ZIdwxksPK9x9q+DpALSASD+1awYGkj9Y1NlJflaGws0FBI9C7L0ZSgoanAK/Oq+d29L7X2wLWs+gT4+Z3TGdyvF7/ed3MW1jYwv7qeyx54mSdmLWDowEr+dPAnWPOOo8m/M50LG8ZyUdPXSeSoyAeT912Tkf88Eapep3rH01n4uXEccOUjy60Qvf2EHRjcv1cX/jYlSVo9rMpVlvsAWwKPA6SU3oyI/h+xvtVOy3FAH+T1tj1nbXunOuqpOnfs5pw/ZQZPzFrQOtcMmnu1Tt5tJEf98bEO29b124B5+0/moYuO4KTyiWyee5mTGk5gUVNfDphczUXfmMiO08+k39/PIj/7ERZV7QP0a63R+WSSJJVeZyb116fmbrQEUJzMr1VgfnV96zDm8Ttt0hrA2v4M763KPH6nTYD35p8tqG3gu7uMWG5VZkvboQMrKS/LQUVfLuh7Eqc3HMkXc08zueJn7NDvPwzu14vDb3yBM3qdysIvnEnvV+/hnsqfMCZeaK2xlOdeSpKkZp0JZBMi4nJgQEQcC/wNuLIzD4+I3SNiRkTMjIjTVtBm/4iYHhHPFTeh7THa7nnWdnXlilZaDqgsb51DNmnaLC574GU2GNSnw7aD+lZw2aGjWVLbyFmTn+XcfT/H/WvszQH1p9MvV8/16afs36t5+4vrHn6Dg54dw4yv3srA/n0Z3+sXfDd/GxsMqPDcS0mSukBnVlmeHxG7AouAkcAZKaV7Vva+iMgDlwC7ArOBxyJickppeps2I4AfAzuklKoiYsiH/B4fSxHRuqKy7erKjlZaDh1YyScGVHLTsdtR39jEQdtsyLC1KskFHbZds7KcBTUNHF9cDDBvcT2n7zmKQX23oC6/DzNvPIxx7/yK8rLdOKfxEJ7/z2L2/3MZ539tIjvOPJeTnp/IiWvPoaz3leRya2Tx65Ekqcfo1OGGKaV7UkqnpJRO7kwYK9oGmJlSeiWlVA/cAuzdrs2xwCUppari58ztbOHdSaGQmLt4KbPerWZOVQ3/WVjD/OqlzK9e2nr95oJa5lTVMG9xHYVC80KKfNC6Z9llD7zcurdZ25/hvb3QvnvzE/xnQS3zl9Rz1B8fY/6Sek4a/9Ry+579/pCt+NHEpymk9/Yoe2LWAo67fhr7XvYQdb3X5r8XnMwfGvfgqLIp3FLxC9ZlPv16l3H8xJf4w+DTKHz9MirmPkPu8v+C6ZOz+cVKktRDrLSHLCK+AZxL8479UfyTUkor6zZZH5jV5no2sG27NpsVP+NBIA+clVK6u3Oldw8dbWlx8cFbUp4LFi1tXOFRSM1bSeS49t+vcs2RW7OwtoF8Dq47ehsAyvLBDcdsy9uLlrJW3wrOm/ICT8xawILaBiryudZ5ZPOW1HH+lBmcvucoBlSWU1PfxJK6Rp6YtaB1b7P2vWeFBOsO7M8vqg7jycKm/Kr8Su7u/WNe2up8Ln9rBOf/9UUe3Hg4vzvkbwye8i2YcBh87mDY41fQe82sftWSJK22OtND9mtgr5TSmimlNVJK/TsRxjqrDBgB7AQcBFwZEQPaN4qIcRExNSKmzps3bxV99KrRdmI+NM/fqqpuYO7i+vc9Cml+dT2D+lbwg11Hct6UF6hrLPCdm59k5//9O4df/SiLahv5f3dN54ArHua8KS/w3V02a+09W6tvOeft23x6wLljN2fekjqOu34aP7z1KXqX5/j13TMYOrCSDQf14crDlj114Lx9N6dXWXDlYaMZOrCSOwvbc2zv8ykbOIwx/z6ey4b8ifO/8Smemr2AXa+dxT3bXwc7/gieHg+Xfh5eeSCrX7UkSautzuxD9uCH2XMsIranucdrt+L1jwFSSr9s0+Yy4JGU0jXF63uB01JKj63oud1tH7I5VTXscO79y9wbP247AA644mHGj9uOA654eLn3PXjql1h/YJ/W/ckKhQJNCVJKVJTlGVhZTlVtQ+u+Zm2vKyvyJBJL6wvkckEqvq+8LEdZLqitf28vNIB3qutY2lAgH1BZkWdAZfH+kjqWNjSRywXlhTrWfvB/yD9+DQzdhtd3vphv/2Uuz85ZxKHbbcDpW9TS688nwPyXYJvj4MtnQUWfkv5uJUn6uFuV+5BNjYjxwJ9oc5h4Sum2lbzvMWBERAwH5gAHAge3a/MnmnvGromItWkewnylEzV1Gy2HkbcdFmw5imhFRyG13Uri/fYva39/uXYr2oCk3f0h/Xsvc93YWGDh0noamgokIBfBoDXXJPa8gEXrbU+/e05i2ITduO1rl3Dea8O58p+v8uir/bho378w8tkL4JHfw8v3wj6X07juVsxdUkdDU4HyfI4h/XqRy8X7bpIrSZKW1Zkesms6uJ1SSkev9OER/w1cSPP8sKtTSudExNnA1JTS5IgI+P/snXd8FGX+x98z23fTO5CQ0JEqBKSJAp7l1LOLDRU9KXqeV2xX9H6e97tiud/pnQV7A089ldNTzy4WikBAEJDQOyQhPbubbfP8/pjdzW52N9lAAkGe9+s1r919ZuaZZzaB+eRb+StwFhAA/iiEeKWtObubhezwYsg6LlJaW9QURaBpeh9Li8lAjsOiJxm0EklGo+6d9vs19jW4qXP5uGnBqvCanr9uLD6/YNZLKzHWbeNJ6yMMFNtxj7mR5X1u5taFG2lo9nHHmYO4vucu1LdvRjTspWrkT5i+cTI76vwx8yR7v+11Oejod5NMt4RkrtNZ65JIJBLJ8UuntU5K4kK/jnRDdjXdTZCB/uA+6PTg8Wmoim71Mhv1B3drt2LrB3syD/3QMQqCigYPD3+yiWsn9uGFJbFi75+zxlHv9ofLXYTqlhVlWkmzmjnQ0IzLG2Dmc8ujrHbPzRzL3W+tC49Z8PJnxytcFHgfd85wnOfM41dfuPn4u0rG9cnir+f1IevL32Hf8CobtSJ+5ZvFN6J/zDygWwTfvGkiOQ5LzL0CbfYB7cjPoKP9RNu6TkePl0gkEokkHskKsqTKXrTDpZ0wxzGNqirkpVopyrLTK9NOj3Qb2Q4r2Q5r+HPPDBu9Mu3kplqixFh5RSMXPraYSfd9xoWPLaa8ojFcFiPymN8uXEtjs58588vCiQLxEgY8fhEWY6GxG+eXUeP0UV7RiC+gi8bWxWTtZkPUmAczv3Rew6Ypj+M9uJ3sBafz1ND13H/RcNbva+CseWt4Ie8OrvPeTpri5E3z/3C38SVSFE/cQrVubyDuvR50emKSIkJJDx0hXnJF5Dzt7e/ofBKJRCKRdCadIcikueAQSeahHzrm4tIiqp3ecMX+yNdI4omtPbVuVFVh1osrMaoKmiCceRkiVCIjksJMG9tzp3F68314epSivPMzpm/7NR/MHsrQnmnc9345Sw2lnOG5nwWB0/ix8b8Me/ssLkgrj5lHVZS499rsC8Rdb0f7Z0Z2PYg3T3v7OzqfRCKRSCSdSTJB/e1xeD7P45hkHvqhYzJsJqqd3qhEgdYJA6OKMjAZVF6fO4Fqp5d5i7ayencdZwzJw6gq/PXSkSiKgsOi8uzMMeytbcZuNuDyBhhU4OCV2ePwBQQBTXCwyYvVpPL7tzdgzuxJ46X/wvrtU/DJvfTaW8Y/z5/HM/sGc/8H5XgVO3f7r2epfSoPmZ7hIefvOcMxlV87ryA1M5f7Lh4BiLj3aojoVhDiUPpnxkuuiJynvf0dnU8ikUgkks5EWsiOIqGHfiStH/qhY+rcvnDdsdavhZk2RhVlcMdZg7jiqWVcMm8pf3hnA7edOYg5k0v46WkDufzJZVz25DKmP7EUt0fDFxDc/dY6LntyGXe/tQ6nJ8D+umaufmY5P/i/L7jtX2vwBwS5qcF+lilWmHQL3PAxmFNQ51/ArObneXvuWAbkpwLg6nESy854i/cyruQs7UtWZvyaR0ds54XF2/D4Rdx7tZkNPHVNdK20Q+mfme0wtzlPe/s7Op9EIpFIJJ1Jm0H9wX6Utwgh/tbGMb8RQvypKxYXj+4Y1H+oJBM4Hjrmbx+V8+OT+/LMV9u4uLRI71dpNWFQIaDpZsornloWY9F5ZfZ4Ln8yejxe4H2iYPzX5kygIM0aHcjudcEHv4Gy56BgON4fPc7/rlR4celODKrC3FP7sq7sK37Z/Cgj1W3UF0zA9YO/UOfoG/deAZllKZFIJJLvJZ2WZakoynIhxEmdtrLD5PskyKBjWZaapqEJ8GkaBkXBZjaQZtELxrq8fk59YFHM/J/fPiVmPF6x2vYK2MZl47vwn5+Buw7t1F+xtMcMfv9uOZsqmhhVlMFtp/dj0N43yFp2H4rPBRNu5mDpLXgVqxQ4EolEIjku6Mwsy8WKojyiKMpkRVFGh7ZOWKOElsKwrTMw4x2Tn26jR4aN3lkOejPPtxUAACAASURBVGXayXLo9cVyUy3Yzca4LkFTsO9lJPEC+BMF9bcZMzX4HLhpGQw+B/WzPzBx0ZU8erqDdJuJ1bvrmPFsGc95TkO7aQXKiOkoi/9G7gun0OvAJ+SmSDEmkUgkEkmIZATZicBQ4F70Iq5/BR7sykVJOk6imKe8FEvMeHG2PWasMMvG36aP7HjMlCMHpr8AlzyLUruN/gvPZvEp63l99lhO6pPFo4u2csU/t7J10v1w3ftgSYNXZ8CCS6HmmGrKIJFIJBJJl3HYhWGPNN83l2Vnksj9GW8couO2Mm0mGjw+3N4AAQFWk0qOI77FLiGNFfDOL6D8XSgaj7jgMd7YYeEP72zA7Q0w99S+3HRqCdZVT8Nnf4aAFyb9DE7+OZgT9YGSSCQSieTYpTNjyNKB/wFOCQ59DtwrhKg/7FUeAlKQdXOEgLWvwnt36IJr2l1UDb2OP72/mYWr91KUZePe84YxtWcAPrwL1r0OqT31ZuXDLwW1MxJ/JRKJRCLpHnRmDNmzQCMwPbg1APH6W0okoCgw8nL4yTLoeyp8+FtyXzmbv52i8s9Z47EYDVz3/ArmvLWXvT94BK7/AFLyYOFseOZ02L3iaN+BRCKRSCRHnGQsZN8IIU5sb+xIIS1k3Z+wi9TnJ3Xbu6R+9lsUVzVMvBnvyXfw7PIKHv54MwA3TO7DxaN6kLV1Ialf/Qml6QAMnw6n/Q4yimLn9AcwGVWMqoLbK8tRSCQSiaR705kuy6XA7UKIr4KfJwEPCiEmdMpKO4gUZMmTTOxYaKzO7Q3GjwmsRgNGQ6zgiSy/oSgKQgh8mkATAqvJQI7DAsQ2C3/msv4MXHM/yuqXILMEfvQwuzNO4s431rJkazUAOSlm5l8zjEGbnkJZ+oh+A+PmwORfolkyYuZ84JIR3P9+OVVNHtn0WyKRSCTdls4UZCOBF4H04FAtcK0QYu1hr/IQkIIsORIVnbUYVa55dnl47MXr9RJzFQ3N3P76WnJTLNxy2gCKs+3sq3Pz4tId/OqHJ5BiMeLyBjjQ0EyG3YjHp+HyBrj9db25+RlD8rjrnCGgwNZKJ3//ZDOrd9cBesbmwpsmkXtwuV63rGYrnqGXsXzgrayrNfDMV9vDrZr+OWs8o9IaEZ/+Eda+irCk0zj2FvYOnMHuRhFuB1WYaePuc4cw56UyCjNtvH3zJAIaUUkKtW7fIRV1jRSyDosBt1fDG9AwqAo2s0qmrYPJDhKJRCI5bklWkLXZy1JRFBUYJIQYqShKGoAQoqGT1ijpQhI1Lv/D+cOixnZWuwC4+6115KZYuO3MQdz5xtqwYHvkylFUN3miRNxjV43G6xdhMTaqKINrJ/bhyqe/Dh9z38UjePCDclbvrmvpz9lnMty4GPH5A5gXP8yI9e/yru9yrClnMX1MIa+X7WH6E0u5blIfzhj9Zx4pP4mZrheY8tW9WFY/zTL7DG47/QYe/GgLq3fXkWEzAZCbYmF/XTNz5peFrz9vRil//2QTH26ojNsBIRGRQnZi32yunlDMjQtWRVnm8tP8lGQ7pCiTSCQSSafRZlC/EEID7gi+b5Bi7NghUeNyuzm60KvdbMBuNrCn1s3cKf3CYix0fK3Txy9eWxM1dtOCVeSkmMNj8c678421zJ3SD2hVYNZk4+C4O7nG/CDfaUX8xfQ0j7jvoGbzMh68dCRnDC3gyS+2Mf2JpSyqL2Cm706u8P6WHS4b11fdT/4/z+CuQXsozLBS5/YBcMtpA8JiLHT9ufPLuLi0KPx51osrqXZ62/3eIoXsrFP6hsVYaJ7bX1/LzmpXUnNJJBKJRJIsbVrIgnysKMptwKuAMzQohKjpslVJDptQU/LWvSld3kDUcaHPhZk2MmymGBEXEmuR7Kl1ownC88c7LzQer8Cs1x/gy/o8vuQuzlOXcJdpAU8234lz67dccN7vufDEntzwYln4+KXaUH7o/j0fnl6N5fP/pfSr2byXO5oHP7yEwozB9MlxJLx+5GevX79XTRMcbPLg9gUwqApGVcFkUMi0W6KErEFVEopal9fPvjqNXIeZuma/nmxg0P++cfsCGFUFh8VAmjW2FlyKVaWpWcOvCYyqgtWk4vEL8lL0zgshNE1w0Omh2RcIt8rKsMkEBolEIvk+kkzZi8uAnwBfAGXBTQZxdXMSVe4vzrbHVO0vzrbzwCUjOtRSyaAqPHDJCAozbdS5fXGPCcWOtXYVhsQiKLytTWKa50FeM55LyoaXUR8dw7jadyhMj+4QYDYa+dx8CtfYH6Vu2n04mg9wb/1v+DTnAdKrlse9fsiCFvpsNhp0l+SBRi56fAmnPrCIy59cxvaDTvbUutlx0InJ2NJqKqCJuPO6vAE2HmjknrfXsbGyiQsfW8yk+z7joseXsLWqiZ+/8g2XPbmMXTVu9ta58Ps1yisaufCxxbxZtoddNR4ue3IZpz6wiMueXMa+eg8frtvHxopG/H4NaHGdXvTYEk65Xz+u/EAjO6qdaNqxVcxZIpFIJO3TZlB/MIZsghBi8ZFbUtvIoP7k6WiWpc+vUdXkZW5ELNbz143F5xfMeqklOeCJq0spSLPgD+hZlgYVapw+5rzUcl5bMVuaJthR7WRntStobQpQnG2nxL8D9b+3w64luHOGc3vDdN5p6MfA/BT21zfT1Ozn9CH5/OrMQVgNfqxr55Ne9g8MzgqcvU7m1qpzeL+huM0YsmqnlwsfWxxjOfzD+cMAGFmUTkWDJ2EM2aNXjiLDbqbG6SXFYuSBDzby4YbKqLkikw2ev+4k7GYD059Yyp5aN1/cMZUrn1oWc/2XZ43nyqeW8dqcCfTMsFHV6Em4zmG90slNtXTuL4tEIpFIuoROCeoXQmiKojwCjOq0lUmOGKGm5K2JN5YVLFmRm2pl4U2TYgRb67HWQis/VbR7TCQev8bdb62LEnDkD4Xr3oNvX8f68T084r2bB4edze7Rv+Latw7S2Ozn4+8q+HRjJVaTgSZPX/pmPMyC8Rso+PZx5nm/onnQVNyT7iQ1P5U/XjiC//lR9Hrai61zewMMyk/lldnj2VvrRhOC+T8+CUVRMBtVapq8XNUqeaGq0RvOKI10le6pdaMq4Ato4WtqQsS9vgiO+wO6haytdYZcrxKJRCL5/pCMy/ITRVEuVhRFBq4cB4REXK9MO7mpenmHeGPJnJeIRBmg1U6vXul/xKUoP10J0+7CsnMRJa9N43rnU6TRxB8vHI7ZqNLk8QOwrU7j4tUncmDmMjj9XqyVa8h8+SyML19E7sHl9MqwRa2nxV3aQsgN6QrWXVNVBVVRuPVfazj/0SVMefBzTn1gERv3N8YkD0QmL4TmCrlKCzNtaAJMhhY3qKooca+vBMeNwTi0ttYZTpCQSCQSyfeGZATZHOA1wKMoSoOiKI2KoshsS8khk8j6E2X5MdnglNs5cO1SXvdP5nrD+3xu+SUTql7H6/VEnbuvvpmnllXQUHoT/HwtnH4vVG6AF87V2zGV/xc03fKU7TDz1NXRsXUPXDKCLIeJ4ix72CKYl2Jh3ozSqONKcuxx1x06JzTXvEVbw+VBLEaFvBRLOJ7vrVV7eLzVvI/PKOXTDfuZN6OUvBRLyzqviV1ncbY9KkHiSKJpgqpGD3trXVQ1etA0EXess+Zua7wz1tNZa5dIJJLOIJnCsCpwFdBHCHGvoii9gR5CiK+PxAJbI2PIYvH7NSqbPPgCGiaDGpX515b7UNNEdIX+iGr7rePMIs8PXQ8EQkBACIxq8tdNFB+18KZJMe7U0LGpdRu5yzifSYb17FJ6cp/nEt7TTkKgYjWpNPs00m0mZk3uw8xJfUhR/fDNAlj8ENTtgrwhcPIvYeiFaIqBg0169qLaKssy3n36AhrGoKXw0nlLY9b92pwJCCG6MMtSw6BwVLMsky00fChdExLNPSA3hc1VTTHjg/JTgdiOEB1ZT6Jryo4PEomks+nMSv2PAxowTQhxgqIomcCHQoixnbPUjiEFWTR+v8bGisaoQPzHZ5Tyj3aKooYC60MV+iMr93v8WsIHVeh6f/9kE9dO7BNVRDaZ64aunezDMPpYF7cVb+MG70tYazexTivheesMZl4zC39A8I/PtvDJxkoy7CZmn9KXayeU4DAC69+ELx+EqnK9ddPEW2DkFWC2d+i7Pp4f4m0lGVz3/IqosXjC+lDmfm3OhHAyROv5AS58bDG5KRbmTulHhs2EyxsgxWLk0ieWtruejvxRIJFIJIdDZwqyVUKI0YqirBZCjAqOrRFCjOyktXYIKcii2VfnjvvQCmX6hT63ftBUNXpYt7c+HFgf4rmZY2PGIs8PXe/uc4fwh3c2dPi6IeJlgCYSNa2PzTArNK16hdSlD2Bs2IUomoBy+j3Qezzf7K7joY83sai8iiyHmVmT+zJjfG9SzQbY9F/48q+wtwxsWTD2xzB2FqTmJ/19d2TdXYXPF6CyyRO2sOWlWDCZDj2uLJl72lvrYtJ9n8Wc++rs8Vz25DJGFWWEhVFhpo0e6bakv5fQ3JFz1Ll9DO+VxsS/xF5z8Z1TAbj55dUxnSUev2o0v3trfTjJAmDlb0/DGxBhC3JeioWKxua497P4zqn0yuyYUJdIJJK26JQsyyA+RVEMgAhOnItuMZN0AyIz+EK0VRQ1hNcfiFv0NVEh2ND5oeu1VQy2reuGSJQBmuyxmROugbGXw6oXUL54AJ49EwacwYnT7ub5605i1a5aHvp4M/e9v5HHFm1hxrjeXHfyaeTdcDbsWgpLH4UvHoTFD8PwS2H8TVAw7JDWciTx+QJsrGzixlYW0cF5KYckypK1+rVVaHhUUUaMMOqI5dBsNHDGkLwYi+sTM0o5Y0heTFmRUFLDLacNiOkQceOCVVF/FMyZXML+Bk/U96XHBlrj3o9MmJBIJEeLZIL6/w4sBPIURfkj8BXwpy5dlSRpIjP4QiQqihqJ2WhotxDsqKIMnri6lHdvORnQLRlWo8ors8eTnWLmuZljGVWUEfe6o4oyeG7mWAJCtBkw3V5gdZv7jWY4aRbcshp+cA9i93J4YjKuBTMo8e/g+Zlj+c/NJ3PKgFzmfb6Nk+/7jN/8ex07HCPh8gXw0zIonQnrF8K8SfDCebDpA9C6b1mJyqYWcQFBETK/LBjT13HazHiNoK1Cw/GEUbKtqkJz33XOkJg55swv465zhsRcM9thJtthTtihITLJ4uqJfWK+r7nzy3B5tbj3c7QSJiQSiaRdC5kQYoGiKGXAaYACXCCE+K7LVyZJilA2YLwYMkj8oMl2mMMV+iNjyIqz7Tx1zRj+9lE5107swwtLtnPtxD7MeamM3BQLd5w1KOr4By4Zwf3vl1PV5OG568ZS3eRl4U0TSbeZ+Mt/v4uJJ4OWhAGTUaWp2R83CFvTRDig3hcQPPn5VpZsq45veTE70Cb+nM2Fl7B0/u+5eNM7ZG3+Dw19fsjgaXfym7MHc+W4Il5bsYd/rdzNK8t3cfbwHsw9tR9DzrqfmrG3YfrmBVLWPIPh5emQUay7M0ddDfashN9962SK1kH5XYFfi1/HzH+IGYJJZbyiWwYH5afGrVFnNMRvMZVsvTRVVRK2qTKoSsL6dnZLfKtdzwwbi++cGvyjwx//+wpoce/n+x4LKJFIui/JuCwRQmwENnbxWiSHgNGoMjg/ldfmTMAf0DAGsyzjFUWNRFUVSrIdZNhNvDp7PAEBVpMazrK857xh4VixkOXi7nOHhMUYtDTbfmX2eMwGleomL7f9a03coqmzXlzJ2zdPClfBjxR0uSkW9tS6w5aVd346iT21zVEi87GrRgMw68WVcePSqp1efvzqZvY0XcjfOJ3rje9z4+4PMT3zX7apY7nffT61GUN5+tqxfLWpin+u2M07a/czqncGu2tcHGwaSknG35n/g4P02jQf5aPfwad/hGEXw9gboLA06nrxkinmzShlcH5ql4oyo6rEFSHGQxQSiVyR8Vx3idy1NpPxsN1/ba0jkYs4x6GXE2ntbi1Is4Z/3/fVafG/L4N61N3PEolEEknX/jkvOSIYjSo9M2z0znbQM8OGyWRIqkirqipkOfTjemfZyUu1hgvBhirHR8aKJYobUwBFUcLtlaaXFvLczLH0SLfy8BWjmF5ayJ5aN25vIMY9dvvr0YVVJ/bNxunR49uemzk2fO5NC1bx89MH8NzMscHG3u5w30eItvTUk8Lf/JdQ8eOVPGW8guGBDfzHchd/avodC1+fz8yJxSz+1TR+MrU/a3bXcbBJd63tqPMz/asCDl76b7hxKYy+Gr57G56eBk9OgdXzwadfo7LJExZjoXuZexiuw2TJS7HErWMWql/WURK5Ijviujtac0Ra7RbfOTVu39R49eTmHcb3JZFIJF1FUhayQ0VRlLOAhwED8LQQ4i+t9s8EHgD2BoceEUI83ZVrkiRHyGIRahy+p9Yd9T5EyIoREkTTSwuZMaGY655fEWXdyrQbCSRoGxRKBAide9mTy2IsYwDVTl9McHbIIhXPwhIwp/HHph/xMKdxteFDrjd+wEPee/D883WMJ/+cK8ecyaOfbYlaz766Zv7vo3J+MrU/hef8FU77H1j7Kqx4Gt76CXzwGxg+HaXvxQldYV2JyWRgcF4Kr84e3ylZlolckR1x3R3NOdqzcsWzIB8J17JEIpF0lHbLXhzyxHpm5ibgdGAPsAK4QgixIeKYmcAYIcTNyc4ry14cGULZd6FYsjvfWBs3hqx10+7nZo4Ni7EQhZk23a1pVLnosSUJa1l9/MtTmfnc8pj9z80ci6IocfeFmnHHyxYMlWQInWPBy3Wpy7nV8QGmuq0E0nrzd/cZPNk4ETdWAGwmA16/hkAweUAu08cUceaQfIwGBXYuhrLnYcPbEPCwSenDAu8pvBWYSB2pUeuRSCQSiQQ6sQ7ZYSxgAnCPEOLM4OdfAwgh/hxxzEykIOu2hOpTaZpGQIAQApvZgF8T+PxalBUjJIgsRpVpf/08Zq4vbp9CYaY9bomF/DQLbm8AvyY49YFFMed+euupGFQl7r4vbp9C72xH1HpDFpZ0i4HyKmdMiYhFG/ZzecZ6ctbMQ9mznHpSeN5/Oh+nnMdvLj0FgeA3b65jR7ULgF4ZNq6ZUMylY4rIcpjBXUtgzb/wrnwR28Fv8QgjXxnH0e/0uRSO/iFGsylmnRKJRCI5PukOguwS4CwhxA3Bz1cD4yLFV1CQ/RmoQrem/UIIsTvOXLOB2QC9e/cu3blzZ5esWXJ4aJpgf707yioFsZasREVIExW5fXX2eARweZx5X509vs1CnrXOZpo8GpoQKIrCW6v28GrZnvB6Gsq/wPPFQ+Tu/QRNNfGF6WQWcBaXnHdBuJaV2aji9WuYDSpnDy/gqvHFjCnOJBAQ1GxfhW3dyzjK30RtroW0QhhxqV7bLH9oUt9be9manV0ItqMc7etLJBLJscyxIsiygSYhhEdRlDnAZUKIaW3NKy1k3Ztksw/jiRAg7rkFaRZqXV7cPo2bFqyKsnb1SLOQYTPH9IAMiT67WWVPXWxh0NB6QlXifzIswCzrJ6SV/wvV58SbfyL1I67HPeA83lpbxYm9M/lkYyVvlO2h0eNnYH4K08cUcf6JvfQYJr8HbeN7+Fe+iGnn5ygigMgbgjbsUg6W/IhmR8+4Yqu976uzC8F2lKN9fYlEIjnW6Q6CrF2XZavjDUCNECK9rXmlIDtyxLNmQUsdMUVRMCigqiqZNhMNHh9ubwCTUcHnFwQ0ES7DEdl0PMNqpLyyKUaEDMpLwenz4/To7kuTQcVuVmlo9uP1C77aVMGpg/NRFSWqNlmkgIkXS/bMtaUYDQYUdGtXnsOM2azns1Q1enj6iy2cM7IXNy1YxdRiK3Mzl+Nf9iTFYi+1pOE78VqsE35MWn4fXF4/76zZz8vLd/HN7joMqsLUQblcPLqQXpk2blqwCnftAa5KXcXcrFXYK3Qr29faYL6wTOHcy29kQHHvsChLZBUMWfD21rriWhzbswx2Fkf7+hKJRHKs05mtkw6VFcAARVH6oGdRXg5cGXmAoig9hBD7gx/PA2TB2W5CPGETr/H4fReP4IUl27njrMHUOr384rU1UfFhA3JtbK5qijrniRml/P2TTVElI/7+ySZ+dtpA5rQSafe83dKs/LGrRmNQFa586usogTB3fllYwMSrPP/jF8rC7XTCYicoyLIdZq6e2CfsDr1m5lgue76ZPe77OVldx7WGDzntm0dQ1jwGg36IffS1TC89jelji9hS2cjrZXt5c9UePv6uElUBvT5rOn9vnMqbxrN54Nw0vlr4BBcYFnO7bx7+l57C33syxpEXwuBz8QWiM0NDaw5la3Z2IdiOcrjX7w69P7vTOiQSiSQRXZb7LYTwAzcDH6ALrdeEEOsVRblXUZTzgofdoijKekVR1gC3ADO7aj2SjhFP2OysdsWM3fnGWi4uLWJ3jTssxkL7Zr24ksomT8w5c+aXcXFpUdT1Li4tCoux0HFzI44L1SJTlfgV3UMCJlHl+VBpjdalKVRVIRAhOloqxit8pQ1nlu9WTvH8jYZRs2HXMnj5UnhoOHz6R/qbavjVDwez5FfTePCSEbTWKHtq3VQZe/Bo4AJO997P2Z4/8ZT/bAz1O+E/P4MHB1DwxoXcmvIBedSGzwsVLoWWQrCRHE4h2I5yONcPifoLH1vMpPs+48LHFlNe0ZiwjVZX0V3WIZFIJG3RpcV4hBDvCSEGCiH6CSH+GBz7nRDi7eD7XwshhgohRgohpgY7Aki6AfGETaLG4xk2U8J9iZqfx2vl1JaQCn3WNBFfIAQFTKgeWev9oR6bkceGiOwHGogzPxnFNE3+HfzyO5j+IuSdAF88AA+PhBcvwPjdvzm1X3rsecAtr3wTfKewQZSwIPV6qmYugbmL4ZTbMbur+Kn/BZZZb+Y986/4Vcq7PHdOGnnB76ezC8F2lMO5frJ9Mrua7rIOiUQiaQtZHVESl3jCJl4z8pDYSbQvUfPz3FRL1EM+8nPruSM/W02GNiuvx6v4/sAlI5i3aGvCKu2R1dyf+mIbj101Or4AMZphyPkw4w34+bcw5VdQvQVev46cJ4bzn5I3ODttOwp6u57nZ45l1uQ+mAwt1qQMm4mPv6ukOmUATP0Nys9W45+zDOeYnzAgy8Rc/wIGvH4axsdGw3t3YNr5GYOzjLw6ezyf3z6FV2ePP6IB9ZGFaDt6/WT7ZHY13WUdEolE0hZdFtTfVcig/iND58WQpcTEkD11zRj65ziocnqj+m9uOeiMOm5eMNasdYPyUOPxRJXXI+OFTEFrWLMv0GaV9lDWpz+g4bAYaPZpyZV50AKw7TNY8ypi4zsoPhf+1EI8Qy7BNvoKtOyBVDZ52F7VxKJNVXy6sZKtVU4MqsLEftmcObSAM4bkk5emF6albjds/lDftn0OfjeY7NB3Cgw4Q9/Se3XyT7trqGr0cOFji2MSAuL1Ij0e1iGRSI5PjnqWZVchBdmR41CzLCMblYeKxrY1j9loINNmosbtpdkXwKAo2MwG0iwmat2+qGMiPx+pwOykA8I9TbDxXb3V0rbPQGjQYySMuExvUp5agBC60H37m338d90Bth90ogCDC1I5a1gB55/Yi5IcvdAtPjfs+Ao2fQCbP4C6Xfp4/nAYcDr0PRWKxoGpe3YGiCfqQ6L6SAbUd5d1SCSS4xMpyCTdltYPyDOG5HHLaQOjymC0fmAerYfqIV+3sQLWvaGLs/3fgKJCyclwwnlwwo/C4mxTRSNvrtrLR99VsK3KCUDfHAdTB+cxbXAeY0uyMBtVEAKqymHT+7r1bPfXoPnBYIHe46DPqdB3Cv68EVS6AgmLzB5pukt2Y3dZh0QiOf6QgkzSbWntQnri6lL+8M6GNl1KR8vt1CnXrSqHta/BhregejOgQO8JejzaCT8KuyB317j45LsKPi2vYtm2arx+DYfZwMkDcpg2OI+pg/JaXJueRti5FLZ/rrs2K74FIGBKZWlgMJ94BrMj5URunXERg3tkyGbaEolEcpToDnXIJJK4tA6yzrCZ2g267mhgdsgi4vEHUABFAVDatRi17iCgifh1uDoSEK5lD6T6pDvwjvol1rrNWDa/g3nTfzC/fye8fyfkDYEhF1A04lJmTurLzEl6AdrFW6r5dGMli8or+WB9BQDDeqUxdVAeUwblMbLfDzAOPEO/iPMgNes/5ssP3mCkfw3/Y1oBnpdwPnMX/t7jMPY7GXqPh16lYJYFXSUSiaS7IQWZ5IgTyuAMCZ06ty/qM+hWKLPRkPCceMeEiOdmDCUf3HLawJg2TiHitTFacMO4pK8bj3hreeCSudy/ZSpWyzYeHrKJ3F3/RVn0J1j0J8jqC4PPxT7wLE4fNI7Th+QjhGDjgUY+3VjJZxsrefSzLfzj0y2kWIyM65PFxP45TOqfjbXvufzMmQpAD6oZq5YzRi3ncude+OyPgADFALmDoM8p0HO0LtCy+oIqLWgSiURyNJEuS8kRp6tjyBK5Ge8+dwh/eGdDuKp/a+K1MTpjSF5MB4GOxK61tZaozgHaAVj/JmxbpLsiNR9YM/Tg/YFnQf/TwJYJQK3Ty+KtB1mytZolWw6yo9oFQIbdhMen4fYFoq712pwJ9LR4YPdy2Pqp/lq1EXx6zJpmSceTNxJ/j9HYS8ZiKBoDKfkhs2KXkExMl2xqLpFIvg9Il6Wk26KqCoPyU1l406SoDMrIz60f0PHOSRSY3Va1/taV+iOJV8T2ww2V/P68oUldNx5Jdw7I7gOTb9W35gY9S3PTB/r27b90y1bROOg/jcx+0zh32ImcO6JncA4XS7ZW89Xmg3y5uSosyAyKniDw4foDjOubzaD+p6OGXJxaAP+B7ziwcQkrF39E/52bGLz7KwzLg9+NNQOy+0H+MN2K1nMU5A7Wa7EdJsmIa9nUXCKRHG9IQSY5KqiqEhMU316QfLxz4pHIvRlyjbau1u2G3AAAIABJREFU1B8iVMS29XmC5K7b0bWE3sesx5qmB/wPOV+vc7Z3FWz6L2z5GD79X32zZeq1yfpNo7DfNKaPKWL6mCJ8vgArdtayfHsN3+6rZ92eer7YfBCANKuR4YXplBZnMaFvNrmpxcxcUckep55UYMXDlLQD3De2ifTa9VD1Haz5J6x6QV+XaoCcwZA/tGXLGQDpRfq+JElUOT8yUaKyyRMWY6FjbpxfdtSamidTuiVZod46TrErM2HbskTKzFOJpHshXZaS7x2dGUM2b0ZpwuMPdS0PXDKC+98vp6rJ0/H5nQd1t+aWT3T3Y9MBfTyrLxRPgpLJUDIJ0gsBEMGkhOXba1ixo4avthwMi5yWZujRfHH7FHpnB2uhaQGo3gp7VujZolUboWIdNOxtOcFg0a1p2f11gZY9IPjaH2wZMfPvrXUx6b7PYsYX3zk1LLZ2Vjs59YFFMcd8fvsUikNrO0IksuhZjCrXPLu8Q67srvgd6+i6B+XrcYayNpuk0xACAj491CLghYBff9V8+nggOK4Fx8PHJtiXcK4k5u3ouWf+CcbP7dKvR5a9kBzXRP71D7r4ECjkOszUNfsTWgUiK/a3Vdn/UNdiNOg1xZr9GkZVX4/ZnLyhOsqqYVDJdm1F3fYZ7Fysb831+oEZxS3irHgSZBaH762qsZnNlU1s2NfAgq934fG3uHAVYFBBKiOLMjixKINRvTPol5sS7ngQxlWji7ODm/VSHge36K8120G0xLAFbDn4M/qgpRdjzi7BkNOHOnMPbni7itX1dgLolrXWpUT21rq47Mll5KZYmDulHxk2Ey5vgCE9UslPP7KFcBPFAf7h/GFc9/yKqLH2yqHEi1MMx/nFiWvsinUvvGkSgOxecKwjBPib9QLS4c0VHHOBL/jqdeqbz9Wyed36v1MtECtSYsRO5OcEYkfzd/HNKmAwBzej/qqawBDazKAaW703t+xXTYnPHXCmXsuxK1cvY8gkxwsej5+DLm84+DvHbsZiMcY8WJKJXTIa1YQPxkgxZDMb8GsCn1+LEnaJ3EC5qZbDto5ommBHtZOd1S7sZgMub4D+eSVYht+A74TrMauQ7dyMumuJXuG//F34Zj4AIr2IxryTeGF7Ph85++JL78OC2RO4aHQhc15ayd66ZjLtJsb3zWZrZRMLV+/l1RW7ATAZFIqzHAwvTGdU7wyG9kxjcEEajuKJUDwxepEBH1r1dvZuWcPbn35BduMuBnkqGdKwBLX8TRAaGcDrgN+qsl/LptqYS0nP/qQt/gTSe0JqD/IdBbx0SQ8qAunctnBDy8/r6jHkplqPqBUnURyg3WyIGWuvHEq8OMW24hoPh/ZKxcj+nl1AyFLkjxBIvqBoihmLFE/u6M3vbn/M725/PfFQDWC0gdEKRktiwWK0gCUlgfBJUuy0ta/NuUzR+zoQEnEsIwWZ5JjG4/Gz6aAzJvh7YI4DiyX61zuZ2KVERIq5iX2zmTulHzVOL9VOL2+U7eYXpw9K2LczJPgqmzxhMRa6/tz5ZW1aRyIFnqooNDb7uPutdeHs1JunDeCmBV9HX++kEajjbwRN0+PAdnxF8+bPYfNH/JoGfm2BOreD7x4ZxIkTfsBbZ43ClTcagy2dFKuBVIsZAWw/2ETZzlpW76oLl91YuFp3VSoK9M6yMyAvlQH5KQzIS6F/Xgr9clNw2Yq54ss97Gk6S78JPxRabPzvJQMZbG/A6tzL24uWMCnXRXrzPno37se7cyXKzg/1BxT6f0x9gttCkUaVOZPqplT2PpNB8YjB2NPzdHdoSj44csCeA/ZssGd1+n/eieIAXd5o8VKYacNmNlDV6ElogU0Up5gorrEr1h0q2RLaN720kFmn9MUQXKffr33/CglrgTbEjivC0tSOWIo71mo+cSiiVtF71pqswdegYDLZ9bqBjpzgmE1/jTouNGaL/7n1mMHU6V+vpHOQLkvJMU3ItdX6oRMv+DuZ2KVEhNw/uSkW7jlvKD95eVVMfNo95w2L644KCb5EcVFRMVsRtBV/tnp3XVIdDkLo1/6M/speStVNnKhsZaJlG70Du1AQaEJhp1qIo+841J4jyRo0GbVgiP5XchAhBAcamlm/t4G1e+rYVNHE1qomdlQ78QVa/h8pSLNyoKE55n5emTWOHhk2jKoS/+dwxxR6WT3QuB8a9lN7YAfPvb+UAqWGXKWOLKWRLBootrpRvQ0JflKKnvBgy9STIyypYAm+mlP0h5EjN7iv1X5Liv6QM1qCFgQLKErSMWQvXn8SHr/WpgW2u8WQ/XvVbs4Z2YubFqw69PUIoQuekGtL8wdjenwtsTshN5ffCwFP9Pu4Yx7dzRZ6HxqHOOe2nsennxM5Jg7RAmm0tgijKIETb6wtIRQhtozWVsfZdUtQF5aZkRxdpMtSclzg1+JX0vfHiVaPtBiMKspg7pR+ZDvMKMGHbltusJD75+5zh4TFWOhad76xlrvPHYI/gTsq5AZKZB0xqArVTc14/CIq867W7Yux6N3++tpwDbP2OhxExa6pCmcMyefDDQpbAoW8yjSemzGW2f/+mpyG9YxStjBa3cyYre+TtvV1+BK91EZWSbD0xRiUonH0KBhG7sBchvRMC68102Zib30zWyob2VLZxLd7G6jeWBEl0gBmPr+Cokw7RVl2UixGmjwtcSezTi4GRWGn24LJ0pe8PifgL/Dx5pK+8QWnTQF3jZ7k4DoIrmq0poO46yoQzoMYvfVYAi4UbyPU7kA0NyCaG1C8DSgdeTgbTKgGC4MNZj5XTQR6pILRivEdO4rJyicFZrSeJhSDCT55kUWba/iZR+A3Ggg0qqx/2kLxyCLsVjMoBoyqkSEYeO9EPaFCVVUcW1Zh2OTVH9SKqj+YhaYLC6HpgkdoCTahW2QUFVCi9qlCY7AW4KN+PoSnEZUAlkVPo6DPN1gL8POAwopn6/m9z4Pd5MHYFCDwjAEtzwZKIEJM+RKIrODnLkGJdmcZzC1iyGDSk0mMFjA7wJjVEmNktES/ht7HE0JhYRXH4mS0yoLJkiOKFGSSYxqjqsR3AcURV9kOM09dM4a/fVTOtRP7cOcba5PKMNM0QUATFGbaEoqgkGuqLRdRXoqFeTNKo6wjj101msWbqxjcMz3GSpFhj3+tUA2ztjocxLOOPD6jFNBrqxVm2ijJsVNep1LOcBYzHAKAT/DvK3pS5FpPds03sH+t3itzw1uAnhjhz+jH+oYClnt6U+EYxNzLL2Rg7yL656WEv6/yAw3c8KIem5btMHP28AIamv3UOb3sOOikuVWs0rNLdvHskl0ENIHDbODi0YUM75XGzVP789DHmzjQ4An/nLIdZj1LI7VA34iwBn0eaw0KBLSWmmZuF30zDDx68QD6pwYwBZwEXA3sPbCPf36xBo+riXyb4MJhmeSYNdSAm4DXTaOzibU7KmlsgAyTxogeZhx+L5ZAQzCwOYDf72OIrxGDQcOAhoEARr+GdZ0SDKD2gxZAFQHS2vidbh+lRbiFXw3BLXJcRVFUbKqqW7BC+9BfFUXFKFRy/Rp+xYAC+DDg9BkJmFPBYo2I5QnF9xhb4oBUI0I14QooBDCgGs3YrVZUY+tzIuKBosRShKiKJ6BUo7QaSY4rpMtSckzTkRgy0B/cBxqa23Qttqaq0cNvF67lJ1MHUOP0hmO4Is9dcMM4Xl62g1MG5UcJvSeuLuWEgrQol1WoBpUvIHjy863MOqUv1z2/Iq7bNZ47NpTd11aHg2qnN24W3auzxxPQBEaDilFVuOjxJXHnH9YrveW7EEJ3I+5fQ8P2ldRtWUHP5k0Ym/aHz/On9sKYPwTyToC8IWi5g6myluBBF6omVSHTZgpnlPr8GhsPNLL9YBP76tw8umgrjc1tZ2ql20z0yrDRI91Kj3Qr2SkWijJt5KVZURWF219fw/76Fldp6Gfq9QfadGu3l/mYrFs86Ub0QujiTAhAJH5V1AiRpUaIrc4TKYeT9dmR7hkSyfGMdFlKjgssFiMDcxy8Ont8TJZlPFRVCdfmiqStDDOvP8CHGyr56bQBWE0qj181mhsjrFlPzCjFYTbwxJc7WL6jjrvPHUKGzUSd20dOq6DuUBbnzmonP/i/zwGYO6Vf3PWoCjx1zZiYB15+moXFd05ts8NBogw7IByvpmkiZv4HLhlBfpo1XPgU0AVAWk9I64mv11Tqh3u4cn4ZruYKTk3bz50jm8ls2oyxZjNs/xwCXlQgHwWy+ujN01vVJzPZsxhemM7wwnR2Vjv5y/vlMd/7gh+fhKIo7Ktv5kC9m/31zeyvb2ZffTNlu2qpc7XtKttT6+bXb65NaGk82OQhP83abuZjsm7xkAW29c8r6rsMfZ9tBFZHF44V5KUYDznoP1ER2tC4UYXHZ5TG/EFjMSrtBvcnmyTT0QK08dYMHLFiuhLJ0UIKMskxj8VipFcCARaPjjQqjzx+X30zf3hnA7kplrDocnkD9MiwEtD0OVbvrmPOS2XhOUM1n1oTGU8WcofG6xCQsF1URA5APKteMvcYakf15k0TafZpGBSwmQ1k2MxxLXomg4oQIqKCfhoLG9JYsc7Gq7N/rluLAn6o2QaVG/Q6ZZUboPI72PR+dK0iW1a4kGymo4Qr03ysaMxhl8jDg5nCTBt9clPiWmlCa2ps9lHn8iE0wc5aF3/+78YokWY2qJQfaKTa6Y37Mzj/0SUAOMyGmCK5VpPKX97fSEGaFUWBdJuRenfL+vPTLBxs8pBhN5NiMWJQFVRVoX+rPw7yUiwdshZ1ZtB/orkG5jrYVOUMj8+ZXBJec8hqu2RbdbvXba+sBnTcipZozWk2I1c+9fVhfycSSXdGuiwlxx0dfUiEjm8r9gw6Vvk88sEzsW82MyYUH16m22HeYzz8fo0atwevX4+hM6gKAU0cWgX9gB/qdgYLyW6JKCa7paXbABAQChVqLmk9+mPP64eaWQyZxXqh28xi/NYcNlY6Yx7Yg/JS2FLlZNZL0da+3FQLRek2vj3QwM0LVnGgwUNOiplrJ5bgMBlo9ASobvJQXtHI6l11eAMaBlUhzWrE49dwewMk8z+k2aBiNakYDSoNbh9+TWA2qJzQI5XsFAt2swGbyYAt+Go1GTAbVSzBTVEUHBYDTk+Av320KUpE5qaa+fNFIyhIs2I0KBhVBaOqYlAVjAZFfw19Do4ZVZUD9e6ErtbW4x//8lRmPre8Q67LZFy0SbtxgyRyoT5/3Ulhi3Iya5NIuhOyUr9E0gYddaOEjtc0jYDQS0Acbm/AyK4ADosBj190WYeAQ+lVWO9qZletJ8qdlSiuLRRPdUjXbG7AX7mZxn0bUas3Y6rfjs25F6VuJzgrow4VBiu7tGy2+7OpEFlsFwU4HYXcctFpKOmFfFtnxm7R3cXzFm2lqsnDa3MmkOsw6991hOUqskl5og4NmiZw+QLUNDazq9ZNQ7NPtyYCzQGNxmY/TR4/zT6Ng00e3vt2f1R9MrNBpSTHjl8TNHsDuH361uzr/EKwyZJuM1Hvjnb3lmTb2VHtijl2RGE6DnOLBdCgoL9X9M3l9bNiRy1uXwC72cDkATlk2c0YDAoGRcHlDfCvsj0x895wch8yHWZURcGgEnxVqHf5eOiTzTHH33nWIO5r5da+/+LhFKTrWcqhTVUUzAYVs1HFZFAwG/X3ZoNKiuXQXb8SyeEgBZlEIjks4gWz33PuYEr75MTEHA3OS8FgUDs/yNvrgvrdULsT6nZSv38Li1euordSSZFSSboSLSJ8wkAFmVSITPaLLCpEFhedOpbMgmJI6wVpPSC1R1R9tc6iI3XmNE3gDWj65tfw+PXXPbUubn1tDZWNnvCx2Q4Td551AqlWI35N6FtAwx/M/vUFNL36BRDQtOB+QZ3Ly7/K9kQlS9hMKhP6ZbNka3WUKDy5fw4rdtREtdGyGFWG9kzDqKoEhH7dUMaxJvTX0PX1Mf28gAgeJ/R9Lk9yVsYjQarVSIbdRIbNrL/azWTYTPHf201k2Eyk20xSyEkOCynIJBJJFIkCvBORSGCU3XUazT4txtrUUffUodDapZWKi9L0Bv7vjCzUpv28/eVKHJ5KCqihQKmhh1qLndgitVjS9ernKXl6oVizHRz5kJoPqT114ebI1TdLalKZjZ3Rp/JoxJAVZtp4edY4Gtz+Ti9YG891/uTVpQzIS0VAi7ALijiPL0B5RRN3vLGWA/XNFKRZ+P35w7CbVW7711oqGjzkpVr49Q8HU5RpR1EhoIFf09A0XQz6/LrQ9QV0oevxBah1+ah3e3F6AjT7AtS7fdS5fdS7fNS6vNS7fVExhK1JJOQy7SbSpZCTtIMUZBKJJMyhPOg70gUhdPyhdkI43PvokW4hoAkqGr1R1rt5V41mcIbA6DoADfugbpdeSLapCpzBrakCGg+AJ0H1f4O5pfp/Wi+9PZMtq2XMrr/3m9PZ5jTzs7d3Ul6n0jPTcUiipjMb3CeaK9440GnXjeRwXPmHu7ZkYyk1TdDo8VPn8lLn0sVa+H2EaKtzeYP79PfJCrlMu5l0W3whl+kwkR4Uepl2M2lW6Vr9viEFmUQiCbOvzs2H6/YxbUgPNCFQFYVPN+znjGE9w9Ybr9dPlbOlSXu2zczm6tgab4NyHeF6YpEcCQsZxD6w/ZoWzsCbM7mEqyf2QQvWWuuQqAj49Mr/zqpg+6Z9ulBrOqALONdBvQisu1bfmuvbnE4zOVCsaSih1kxRrZzSoj+bU8CW3rLPkqpb7Uz2btt78HBjFI8EXf07qWmCxmY/dW5vK+Gmv69z+cJCrjbi/aEIucyQ5S303t6+kJMlRLoHUpBJJJIw1U3N7Kv3xIirnukWslOseL1+yqtixdeAbAfV7haRluswxxVjcHQKhXbETejzBdoM7O8wAb8uyty1ehunkFBz1ejjnobg1gjNwdfIzz5nctdRjdFtf4y2Vn0U7dG9OEP7zBHvTY7EYyZbhxuyHwtFYTVNsKfOxSn3L4rZ15lW20MhUsjVRljbap0tFrh6d7SoC4m9th7ZqVYjmUG3aZrVhKpA2c5anN4AWXYT95w3lAH5Kcx6set7qXZ3juQfFFKQSSSSMO25HzvqnkzEkbaaJBtI7/MFWtontUpGOCxRdjgE/GjNjWzdu5//fWMZzoZa+qQG+PnkAnqYm1G9TeBz6U22fS49wcHn1t/73PrmD34OeMHX3PK+oxgsYDTrYs/saOnvGG6ObY3q/egSJhasqqLSreDGggcTNnsqt549kvS01AR9IoNC8Ai0QwoJxgP1zXE7a3S21fZIERJytWHXqTfsPo10pda5fVQ1eig/0Bi3r28k2Q4zL1x/EsN6pR+huzj6HOk/KGSlfolEEqa9avMdadLeFqqqHNEHXaKG7a1dN5VNnoiCtvq93Ti/rMOCs1MxGGlU7WT16suf5pbgD9Z6+2j9fs4ZUpjU9xg3UUPRdKHmdUWINxd4msDbpFvovE79fahhuM8Nnnr9VWgtgs/n1l21IbHnbwafG5vPzSzNB5HeVB/wVjsLVlQwB6155hSEOQWf0U7A5ECxpGFxpKOELH2qQU++sKWDNSO4pbXE7rXhyg11EchNsXDfxSNiagfGdE84RlBVhXS7iXR7+27sRH+sXDephOcW7wh/rnZ6OfcfX9Enx0FpcSZjSzIZU5JF3xwHyve0l2iyXSaONF0qyBRFOQt4GDAATwsh/pLguIuB14GxQghp/pJIOpn2mrB3pEl7Z3E41rS2Wv/Mm1EajpUJH99JgrMz8fkCOL0Bqpt8MZY7g9r+utpM1LCk6m7MLuJgo4dLHv2Cg3X12PBiVbyUpCn849ITyDT5Iyx4zRHizqmLQk8jeJsQnkacjXVs31OBye8i3dBMrtmHwedE0dpuiwXosXbhpIqsqFeTcDC6fj919SksfGcTf5w6HGvWUAqzU+mRbus2btWuJNEfK1eNK44SZLmpZqaPKWJTRROffFfB68G6cVkOM6XFmYwp1gXasF5pWBJ0MznWSKbLxNGgywSZoigG4FHgdGAPsEJRlLeFEBtaHZcK/Az4uqvWIpEc7+SlWOL2LAwJl1yHOe7+3C6yJByOy6C1EJkzuYRXZo9vM5C/LcG5r859VAKcK5s8aIKEljvaaHwQOn9uq3Pnzi+Lip/r9Li5INkOM/OuHdfy88uw8dtrxpCenwpJip2DoYB7V8TPxGpj4S8mkWsjaNFrDMbeNelxed6mljg9d030a802/X1zPRnA30O/unXAe+DBhJp3AmrP4ZA/NLgN08uffA/JS7Ewb0ZpjGA3G1v+LbSOIRNCsLXKSdnOGlbsqKVsZy0fbagA9Lp0IwszGFOSyZiSTEp7ZyVlqeuOdLR93pGiy2LIFEWZANwjhDgz+PnXAEKIP7c67iHgI+B24Lb2LGQyhkwiOTTaezi3zrJsK4D/cDmc7LdDqfeVKIYsJ8XEpfOWHZUA5311LnyBQ2xFRfvxc10dN3e48YJdViYl4Edz1bJ9924e/PdSfE3V9HM0M3tIgKymzSgV6/RSJyFSCqIFWv5QyBmox9Qd43RGCZGqRg9lO2tYuaOWFTtrWb+3PmxZHpSfSmlJ0M1ZnEVhpu2YcHMejzFkvYDdEZ/3AOMiD1AUZTRQJIR4V1GU27twLRLJcY/JZGjzQWc2G+nVRQKsNYfjMvAFtPjux4CWUCSYTAYG56VENf62m1V+9MiSNi1MXYkQesugQ3UVtxc/19Vxc4cbL9hlVgqDETU1lz6Dc7j3p0PDvwuZDjNK6HttqoLK9VAR3A58C1/Pa0mIUE2QOyhCqAXFWkr+EUlK6CyMRjXu73JHfr9zUy2cNawHZw3rAYDbG+Cb3XWs3FHDyp21/Oebfbz89S4A8tMsjCnJYkxxJmNLshhckNota6qpqsKg/FQW3jSpW5VtOWpB/YqiqMD/ATOTOHY2MBugd+/eXbswiUTS5RzOwziRELGaDG3+1dtakO6sdiYUdkcCAfx71Z42XcltkcglFTq3O8bNRZLtMPPUNWNifl6dFXAfEowhK9HuWleLWzolF1KmQN8pLScE/Hqz+4p1QaG2DnZ8BWtfbTnGnh20og1rEWq5g/VM0uMEm9nAhH7ZTOiXDUBAE5QfaIxyc767dj8ADrOBUb11F+eY4ixG9c7AYekeuYRHOgEpGY6ay1JRlHRgK9AUPKUAqAHOa8ttKV2WEsmxT2fGkIWFSKqFix5fkrQbtDNaHR0OVY0efrtwLbeeMRCHxURA0wv2ptkMpNuSe1C0VdW/s0qZdCVdXSalU1pRuWqgckOLSKtYDxUb9ExWAMUAOQNi3Z5pvY4pa1pnsrfOzcodNZTtrGXFjlo2HmhACL0x/Qk9UhlTnMXYkizGlGSSn/b9F7NHvQ6ZoihGYBNwGrAXWAFcKYRYn+D4RcgYMonkuKEzsiwjhUhFY3OHYpI6s2/kodDV1++Wtde6kHi/TwcamrtGdGsBqNkeIdDWQ8W3emuuENb0Vta0YZA3WK/zdpzR0Oxj9a6gm3NHLat314ab2xdl2RhbnBWMRcuif27KUXcddjZHPYZMCOFXFOVm4AP0shfPCiHWK4pyL7BSCPF2V11bIpF0fw7HZRAvNqajblCjUWVwfiqvzZnQ6f0bk6HW7YubJdlZtZDixc11VpZldyORxTXdZuwat7RqgJz++jb0gpbx5nqo/K5FqB1YB98s0LNDAVAgqy8UtHJ7pvcGtfvFWnUWaVYTpw7M5dSBuYAeB7phXwMrggLti80HeXP1XgDSbSa93EbQzTmiMB3r9/B3Nh6yUr9EIvlecCy084nkSDRjP15IlLX76uzxcd22R8otDYCmQd3OCEvaOn2r2Y4eSYheLDd/SITLcxjknaAXwj0OEEKws9rFyp21rNxRw4odNWyt0luLmQ0qw3qlBV2cWZQWZ5J1jBX2Peouy65CCjKJRJKIY6HhdYgj1Yz9eKAtcVvr8h01t3SbeJqgamO0Na1ivd4xIURGsS7OCiLcnpklHe49eixS4/RStrOWlcGSG9/uqccbtGz2y3UwpliPQRtbkkVxtr1bl9uQgkwikUi6MceaRa8705a4zbSZOlR366giBNTvaZVAsE7P/hRBN6vJrlvPIq1p+UP0rgXfY5p9Ab7dWx92c5btrKXerXd0yEkxhwXamJIshvZMw9SNym1IQSaRSCTdnGPJoted+d6LW587aE2LqJtWsU7vWhAirTC6blrBcMjqB4buUWais9E0wZaqJlbuCLo5d9awu0YX5FaTyolFGWE356jeGaRZj15XASnIJBKJRHLccNyJWyGg8UCsNe3gJr1pPIDBohe4LZ4EQy+EwrHf6+SBioZmXaAF3Zwb9jcQ0ASKAoML0oJ9OXUrWq8jFUOIFGQSiUQikRx/+D26KAsJtD0rYc9yvVSHLQv6/wCGXAD9p4HpyImSo4HT4+eb3XWsCNZEW7WzFqdX7wbSM93KmJIsLj+piIn9uraf6VEveyGRSCQSieQIY7To7sqC4S1j7jrY9D589x8ofw++fQ3MKXDSLJhw8/e2wbrDYmRS/xwm9dfvzx/Q2HigMejirOXr7dVMHtB97l1ayCQSiUQiOV7we2H7F/DNfFj/b91KNuZ6mPhTSC042qs7oggh0IIdBLqSZC1k319nskQikUgkkmiMZhjwA7j0ebh5BQw5H5Y9Dg+NgHdv07M8jxMURelyMdYRpCCTSCQSieR4JGcAXDgPfloGIy+Hsufh4RPh7Z8GC9dKjiRSkEkkEolE8v/t3XuwVWUdxvHvDw6mEqaOSCaU1y7mNU9eMk3FC3lBK5vMMiyLMgxQZ1KnRmcaaxq1EkVFE9SSxEuaxZCF2gzdNK5yTbEyxUtgpnYxE/n1x9oaHSFM2fs9e6/vZ4Y5a6+9ZvPM+8c+z3nXWu+qs823heEXw+i5sOdJcO8NcMmecOvnYMX9pdPVhoVMkiTBpkPgyAth7HzY5xS0S2CPAAAJpUlEQVRYfBtcuhfc9Mnqrk01lYVMkiT9x4A3wuFfhbEL4L2nwdLpcPl7YMrH4NG5pdN1LAuZJEl6uf5bwCHnVjNm7zsLHvw5XHkgTP4wPPyb0uk6joVMkiSt3cabw0Fnw9iFMPQceGQ2TDwUrh0OD/6idLqOYSGTJEnrtuEmsP8Z1anMw86D5UvgmiNh0vvhgTurxznpVbOQSZKkV26D/tVCsmPnw/svgKf+CNd9EK4aCvf92GL2KlnIJEnS/6/fRrD3yGq5jKPHwd+fgOuPhyv2r+7QXLWqdMK2YiGTJEmvXtfrqvXLvjAbjr0cnn8WbvwEXL4vzL+perC51slCJkmSXru+/WD3E2DUb+BDE4GAWz4N498Nc6+DF54vnbBXs5BJkqT1p09f2OU4OOVX8JHrqmvObhsFl7wLZk2Clc+VTtgrWcgkSdL616cPvONo+OwMOOFG6L8lTD2tel7m3ROqU5t6iYVMkiQ1TwS89XD49B1w4g+qZ2fefiZctCv8chw897fSCXsFC5kkSWq+CNj+IPjkNDhpGgx6J0w/By7aBWZcAP98unTCoixkkiSptbbZDz7xAzj5DhiyF9x1HnxrF7jrq/CPJ0unK8JCJkmSyhjybjjhhuo6s+0OgBnnVzNm08+Bv60ona6lLGSSJKmsrXar7sg85dfw1mHwq0uqYnb72fDMY6XTtYSFTJIk9Q6DdoLjJsKombDzB+GeK2DcrjD1dHjqodLpmspCJkmSepctdoBjL4PRc6rFZud8By7eo1rP7M+/K52uKSxkkiSpd9psm+o5mWPmQffJsOBmGN8Nt4yEFfeVTrdeWcgkSVLv9obBcMT5MGY+7DsKlvwILt0bbhwBjy8snW69aGohi4hhEXFfRDwQEWet4f3PRcSCiJgXEb+IiJ2amUeSJLWxAYPgsPNg7ELY/3R44E6YsB9cfwI8Mqd0utckMrM5HxzRF7gfOBRYBswEPpqZi1c7ZpPMfKaxPRz4fGYO+1+f293dnbNmzWpKZkmS1Eae/QvccyXcfRn88ynY4RA44Ivw5r1LJ3tJRMzOzO51HdfMGbK9gAcy8/eZ+S9gCnDM6ge8WMYa+gPNaYeSJKnzbLQZHHgmjF0AQ8+FR+fCpMPgmqPgDzOgSZNOzdDMQrY18PBqr5c19v2XiBgVEb8DzgdGr+mDImJkRMyKiFkrVtRroThJkrQOG25SncIcuwAO/xo8sRSuPRomDYOld7RFMSt+UX9mXpqZ2wNnAl9eyzFXZmZ3ZnYPHDiwtQElSVJ72KB/ddH/mHvhiAvh6WUw+UPw7YPht9N6dTFrZiF7BBiy2uvBjX1rMwU4tol5JElSHfTbEPb6DIyeC0dfDM8+CVM+ChPeC4tuhVWrSid8mWYWspnAjhGxbURsABwP/HD1AyJix9VeHgksbWIeSZJUJ10bwJ4j4NTZ8IErYOVzcNNJcNk+cO8N8MLK0glf0rRClpkrgVOBnwBLgBszc1FEfKVxRyXAqRGxKCLmAacDI5qVR5Ik1VTfLtjteBh1Dxx3NfTpgltHws+/UTrZS5q27EWzuOyFJEl6TVatgvumweBuGPDGpv5Xr3TZi66mppAkSept+vSBdxxVOsV/KX6XpSRJUt1ZyCRJkgqzkEmSJBVmIZMkSSrMQiZJklSYhUySJKkwC5kkSVJhFjJJkqTCLGSSJEmFtd2jkyJiBfDH0jl6gS2AJ0qHqBHHu7Uc79ZyvFvPMW+tkuP9lswcuK6D2q6QqRIRs17Js7G0fjjereV4t5bj3XqOeWu1w3h7ylKSJKkwC5kkSVJhFrL2dWXpADXjeLeW491ajnfrOeat1evH22vIJEmSCnOGTJIkqTALWRuJiCER8bOIWBwRiyJiTOlMdRARfSNibkRMLZ2lDiJi04i4OSJ+GxFLImLf0pk6WUSc1vg+WRgR10fEhqUzdZKImBQRyyNi4Wr7No+I6RGxtPFzs5IZO81axvyCxnfK/Ii4NSI2LZlxTSxk7WUlcEZm7gTsA4yKiJ0KZ6qDMcCS0iFqZBxwe2a+HdgNx75pImJrYDTQnZk7A32B48um6jjXAMN67DsLuDMzdwTubLzW+nMNLx/z6cDOmbkrcD9wdqtDrYuFrI1k5mOZOaex/VeqX1Rbl03V2SJiMHAkcFXpLHUQEW8ADgAmAmTmvzLzqbKpOl4XsFFEdAEbA48WztNRMnMG8GSP3ccA1za2rwWObWmoDremMc/Mn2bmysbLu4HBLQ+2DhayNhUR2wB7APeUTdLxLgK+CKwqHaQmtgVWAFc3ThNfFRH9S4fqVJn5CHAh8BDwGPB0Zv60bKpaGJSZjzW2HwcGlQxTQ58Cflw6RE8WsjYUEa8Hvg+MzcxnSufpVBFxFLA8M2eXzlIjXcC7gMszcw/g73g6p2ka1y4dQ1WE3wT0j4iPl01VL1ktdeByBy0SEV+iuvxncuksPVnI2kxE9KMqY5Mz85bSeTrcfsDwiHgQmAIcHBHXlY3U8ZYByzLzxZnfm6kKmprjEOAPmbkiM58HbgHeUzhTHfwpIrYCaPxcXjhPLUTEScBRwMeyF675ZSFrIxERVNfWLMnMb5bO0+ky8+zMHJyZ21Bd6HxXZjp70ESZ+TjwcES8rbFrKLC4YKRO9xCwT0Rs3Ph+GYo3UbTCD4ERje0RwG0Fs9RCRAyjuvxkeGb+o3SeNbGQtZf9gBOpZmrmNf4dUTqUtJ59AZgcEfOB3YGvFc7TsRozkTcDc4AFVL8Tev2K5u0kIq4Hfg28LSKWRcTJwNeBQyNiKdUs5ddLZuw0axnz8cAAYHrjd+eEoiHXwJX6JUmSCnOGTJIkqTALmSRJUmEWMkmSpMIsZJIkSYVZyCRJkgqzkEmqjYh4oXHL+6KIuDcizoiIPo33DoyIqY3tQRExtXHM4oiYVja5pE7XVTqAJLXQs5m5O0BEbAl8D9gEOLfHcV8BpmfmuMaxu7Y0paTacYZMUi1l5nJgJHBqY5X61W1F9RinF4+d38pskurHQiaptjLz90BfYMseb10KTIyIn0XElyLiTa1PJ6lOLGSS1ENm/gTYDvg28HZgbkQMLJtKUiezkEmqrYjYDngBWN7zvcx8MjO/l5knAjOBA1qdT1J9WMgk1VJjxmsCMD57PNQ3Ig6OiI0b2wOA7YGHWp9SUl14l6WkOtkoIuYB/YCVwHeBb67huD2B8RGxkuoP16syc2brYkqqm+jxh6EkSZJazFOWkiRJhVnIJEmSCrOQSZIkFWYhkyRJKsxCJkmSVJiFTJIkqTALmSRJUmEWMkmSpML+DdoAwlvXwJWvAAAAAElFTkSuQmCC\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cv_result = run_cv(boston_df,\n",
" 10, \n",
" lambda df, i: preprocessing.PolynomialFeatures(i).fit_transform(pd.DataFrame(df.DIS)),\n",
" lambda df: df.NOX,\n",
" lambda x, y: linear_model.LinearRegression(fit_intercept=False).fit(x, y),\n",
" range(1,10))\n",
"\n",
"# bump the index so loc corresponds to order\n",
"cv_result.columns = cv_result.columns + 1\n",
"\n",
"# print the best order and score\n",
"sorted_cv_result = cv_result.mean().sort_values()\n",
"order = sorted_cv_result.index[0]\n",
"rmse = sorted_cv_result.iloc[0]\n",
"print('Best from CV: \\n order: {} \\n rmse: {}'.format(order, rmse))\n",
"\n",
"# fit best polynomial from CV to all the data\n",
"x = preprocessing.PolynomialFeatures(order).fit_transform(pd.DataFrame(boston_df.DIS))\n",
"model = linear_model.LinearRegression(fit_intercept=False).fit(x, boston_df.NOX)\n",
"preds = model.predict(x)\n",
"\n",
"# plots\n",
"_, (ax1, ax2) = plt.subplots(nrows=2, figsize=(10, 10))\n",
"\n",
"# plot polynomial rmses\n",
"bar_df = pd.DataFrame({'rmse' : cv_result.mean(), 'order' : cv_result.columns})\n",
"sns.barplot(y='rmse', x='order', data=bar_df, ax=ax1)\n",
"\n",
"# plot best polynomial\n",
"sns.scatterplot(x='DIS', y='NOX', data=boston_df, ax=ax2)\n",
"sns.lineplot(x=boston_df.DIS, y=preds, ax=ax2)\n",
"\n",
"# plot standard error curves\n",
"cov = gen_coeff_covar_matrix(boston_df.NOX, preds, x)\n",
"err_curves = gen_pointwise_se_curves(cov, preds, x)\n",
"\n",
"sns.lineplot(x=boston_df.DIS, y=err_curves.error_pos, ax=ax2, color='tab:orange')\n",
"sns.lineplot(x=boston_df.DIS, y=err_curves.error_neg, ax=ax2, color='tab:orange')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(d) Use the bs() function to fit a regression spline to predict nox using dis. Report the output for the fit using four degrees of freedom. How did you choose the knots? Plot the resulting fit.\n",
"\n",
"(e) Now fit a regression spline for a range of degrees of freedom, and plot the resulting fits and report the resulting RSS. Describe the results obtained.\n",
"\n",
"(f) Perform cross-validation or another approach in order to select the best degrees of freedom for a regression spline on this data. Describe your results."
]
},
{
"cell_type": "code",
"execution_count": 475,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best from CV: \n",
" knots: 4 \n",
" rmse: 0.1253672577242327\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 475,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def spline_basis_extract_x(df, i):\n",
" f = 'bs(DIS, df={}, degree=3, include_intercept=True)'.format(i+4)\n",
" return pt.dmatrix(f, df)\n",
" \n",
"\n",
"cv_result = run_cv(boston_df,\n",
" 10,\n",
" spline_basis_extract_x,\n",
" lambda df: df.NOX,\n",
" lambda x, y: linear_model.LinearRegression(fit_intercept=False).fit(x, y),\n",
" range(1,21))\n",
"\n",
"# bump the index so index loc = number of knots\n",
"cv_result.columns = cv_result.columns + 1\n",
"\n",
"# print the best number of knots and score\n",
"sorted_cv_result = cv_result.mean().sort_values()\n",
"sorted_cv_result\n",
"knots = sorted_cv_result.index[0]\n",
"rmse = sorted_cv_result.iloc[0]\n",
"print('Best from CV: \\n knots: {} \\n rmse: {}'.format(knots, rmse))\n",
"\n",
"# plots\n",
"_, (ax1, ax2, ax3) = plt.subplots(nrows=3, figsize=(10, 10))\n",
"\n",
"# plot some splines\n",
"for i in range(1, 10):\n",
" x = spline_basis_extract_x(boston_df, i)\n",
" y = boston_df.NOX\n",
" model = linear_model.LinearRegression(fit_intercept=False).fit(x, y)\n",
" preds = model.predict(x)\n",
" sns.scatterplot(x='DIS', y='NOX', data=boston_df, ax=ax2)\n",
" sns.lineplot(x=boston_df.DIS, y=preds, ax=ax2)\n",
"\n",
"# plot the best spline \n",
"x = spline_basis_extract_x(boston_df, 2)\n",
"y = boston_df.NOX\n",
"model = linear_model.LinearRegression(fit_intercept=False).fit(x, y)\n",
"preds = model.predict(x)\n",
"sns.scatterplot(x='DIS', y='NOX', data=boston_df, ax=ax3)\n",
"sns.lineplot(x=boston_df.DIS, y=preds, ax=ax3)\n",
"\n",
"# plot rmse bars\n",
"bar_df = pd.DataFrame({'rmse' : cv_result.mean(), 'knots' : cv_result.columns})\n",
"sns.barplot(y='rmse', x='knots', data=bar_df, ax=ax1)\n",
"\n",
"# plot best spline\n",
"sns.scatterplot(x='DIS', y='NOX', data=boston_df, ax=ax2)\n",
"sns.lineplot(x=boston_df.DIS, y=preds, ax=ax2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"10. This question relates to the College data set."
]
},
{
"cell_type": "code",
"execution_count": 659,
"metadata": {},
"outputs": [
{
"data": {
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Private
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"
Books
\n",
"
Personal
\n",
"
PhD
\n",
"
Terminal
\n",
"
SFRatio
\n",
"
percalumni
\n",
"
Expend
\n",
"
GradRate
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
1
\n",
"
1660
\n",
"
1232
\n",
"
721
\n",
"
23
\n",
"
52
\n",
"
2885
\n",
"
537
\n",
"
7440
\n",
"
3300
\n",
"
450
\n",
"
2200
\n",
"
70
\n",
"
78
\n",
"
18.1
\n",
"
12
\n",
"
7041
\n",
"
60
\n",
"
\n",
"
\n",
"
1
\n",
"
1
\n",
"
2186
\n",
"
1924
\n",
"
512
\n",
"
16
\n",
"
29
\n",
"
2683
\n",
"
1227
\n",
"
12280
\n",
"
6450
\n",
"
750
\n",
"
1500
\n",
"
29
\n",
"
30
\n",
"
12.2
\n",
"
16
\n",
"
10527
\n",
"
56
\n",
"
\n",
"
\n",
"
2
\n",
"
1
\n",
"
1428
\n",
"
1097
\n",
"
336
\n",
"
22
\n",
"
50
\n",
"
1036
\n",
"
99
\n",
"
11250
\n",
"
3750
\n",
"
400
\n",
"
1165
\n",
"
53
\n",
"
66
\n",
"
12.9
\n",
"
30
\n",
"
8735
\n",
"
54
\n",
"
\n",
"
\n",
"
3
\n",
"
1
\n",
"
417
\n",
"
349
\n",
"
137
\n",
"
60
\n",
"
89
\n",
"
510
\n",
"
63
\n",
"
12960
\n",
"
5450
\n",
"
450
\n",
"
875
\n",
"
92
\n",
"
97
\n",
"
7.7
\n",
"
37
\n",
"
19016
\n",
"
59
\n",
"
\n",
"
\n",
"
4
\n",
"
1
\n",
"
193
\n",
"
146
\n",
"
55
\n",
"
16
\n",
"
44
\n",
"
249
\n",
"
869
\n",
"
7560
\n",
"
4120
\n",
"
800
\n",
"
1500
\n",
"
76
\n",
"
72
\n",
"
11.9
\n",
"
2
\n",
"
10922
\n",
"
15
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Private Apps Accept Enroll Top10perc Top25perc FUndergrad \\\n",
"0 1 1660 1232 721 23 52 2885 \n",
"1 1 2186 1924 512 16 29 2683 \n",
"2 1 1428 1097 336 22 50 1036 \n",
"3 1 417 349 137 60 89 510 \n",
"4 1 193 146 55 16 44 249 \n",
"\n",
" PUndergrad Outstate RoomBoard Books Personal PhD Terminal SFRatio \\\n",
"0 537 7440 3300 450 2200 70 78 18.1 \n",
"1 1227 12280 6450 750 1500 29 30 12.2 \n",
"2 99 11250 3750 400 1165 53 66 12.9 \n",
"3 63 12960 5450 450 875 92 97 7.7 \n",
"4 869 7560 4120 800 1500 76 72 11.9 \n",
"\n",
" percalumni Expend GradRate \n",
"0 12 7041 60 \n",
"1 16 10527 56 \n",
"2 30 8735 54 \n",
"3 37 19016 59 \n",
"4 2 10922 15 "
]
},
"execution_count": 659,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# clean up the college csv which was exported from a .rda file\n",
"coll_df = pd.read_csv('college.csv')\n",
"coll_df = coll_df.drop(coll_df.columns[0], axis=1)\n",
"coll_df.Private = coll_df.Private.map({'Yes' : 1, 'No' : 0})\n",
"coll_df.columns = [x.translate({ord(c): None for c in '.'}) for x in coll_df.columns]\n",
"coll_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(a) Split the data into a training set and a test set. Using out-of-state tuition as the response and the other variables as the predictors, perform forward stepwise selection on the training set in order to identify a satisfactory model that uses just a subset of the predictors.\n",
"\n",
"(b) Fit a GAM on the training data, using out-of-state tuition as the response and the features selected in the previous step as the predictors. Plot the results, and explain your findings.\n",
"\n",
"(c) Evaluate the model obtained on the test set, and explain the results obtained.\n",
"\n",
"(d) For which variables, if any, is there evidence of a non-linear relationship with the response?\n",
"\n",
"...hmmm, whilst we can select the best model for each number `p` of predictors, we cannot select between models with different numbers `p` of predictors without using the test set, cross validation or a woeful metric like adjusted R squared. I assume from wording they want the latter as the question specifically says 'on the training set'.\n",
"\n",
"- I could do CV on the training set, even though it seems a bit odd to split into a train and test set then do cross validation with the training set - especially given already small dataset.\n",
"\n",
"- I could do everything inside the KV fold: For every model in the forward subset selection, fit the GAM and evaluate result with test error.. but that would be quite alot of models?"
]
},
{
"cell_type": "code",
"execution_count": 725,
"metadata": {},
"outputs": [],
"source": [
"target = 'Outstate'\n",
"predictors = coll_df.columns.drop(target)\n",
"cv_result = pd.DataFrame()\n",
"for train_idx, test_idx in model_selection.KFold(n_splits=10).split(coll_df):\n",
" train, test = coll_df.iloc[train_idx], coll_df.iloc[test_idx]\n",
" stepwise_predictors = []\n",
" stepwise_rmses = []\n",
" while len(stepwise_predictors) < len(predictors):\n",
" remaining_predictors = predictors.drop(stepwise_predictors)\n",
" lowest_rmse = np.finfo('d').max\n",
" best_p = None\n",
" for p in remaining_predictors:\n",
" ps = stepwise_predictors + [p]\n",
" train_x = train[ps]\n",
" train_y = train[target]\n",
" model = linear_model.LinearRegression().fit(train_x, train_y)\n",
" test_x = test[ps]\n",
" test_y = test[target]\n",
" preds = model.predict(test_x)\n",
" rmse = np.sqrt(metrics.mean_squared_error(test[target], preds))\n",
" if rmse < lowest_rmse:\n",
" lowest_rmse = rmse\n",
" best_p = p\n",
" stepwise_predictors.append(best_p)\n",
" stepwise_rmses.append(lowest_rmse)\n",
" cv_result = cv_result.append(pd.Series(stepwise_rmses), ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 726,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best from CV: \n",
" predictors: 10 \n",
" rmse: 1864.77390764911\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 726,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"means = cv_result.mean()\n",
"means.index = means.index + 1\n",
"\n",
"# print the best number of predictors and score\n",
"sorted_cv_result = means.sort_values()\n",
"predictors = sorted_cv_result.index[0]\n",
"rmse = sorted_cv_result.iloc[0]\n",
"print('Best from CV: \\n predictors: {} \\n rmse: {}'.format(predictors, rmse))\n",
"\n",
"bar_df = pd.DataFrame({'predictors' : means.index, 'rmse': means})\n",
"sns.barplot(x='predictors', y='rmse', data=bar_df)"
]
},
{
"cell_type": "code",
"execution_count": 730,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9700.0"
]
},
"execution_count": 730,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# How big is the search space for forward stepwise with a few basis functions?\n",
"\n",
"def n_choose_k(n, k):\n",
" return np.math.factorial(n) / (np.math.factorial(n - k) * np.math.factorial(k))\n",
"\n",
"def search_space_size(k, p, b):\n",
" fwd_selection_space_wo_basis = p * ((1 + p) / 2)\n",
" fwd_selection_space_wi_basis = sum([n_choose_k(p, min(b, p)) for p in range(1, p + 1)])\n",
" return k * (fwd_selection_space_wo_basis + fwd_selection_space_wi_basis)\n",
" \n",
"# number of folds in CV\n",
"k = 10 \n",
"# number of predictors\n",
"p = len(coll_df.columns) - 1\n",
"# number of basis functions of predictors\n",
"b = 2\n",
"\n",
"search_space_size(k, p, b)"
]
},
{
"cell_type": "code",
"execution_count": 728,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Return to this\n",
"# - What's the best way to contrain the search for non-linear models?\n",
"# - .. try adding a search over splines / polys in the forward stepwise?\n",
"# - .. looking at graphs and guessing..?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"11. In Section 7.7, it was mentioned that GAMs are generally fit using a backfitting approach. The idea behind backfitting is actually quite simple. We will now explore backfitting in the context of multiple linear regression.\n",
"Suppose that we would like to perform multiple linear regression, but we do not have software to do so. Instead, we only have software to perform simple linear regression. Therefore, we take the following iterative approach: we repeatedly hold all but one coefficient esti- mate fixed at its current value, and update only that coefficient estimate using a simple linear regression. The process is continued un- til convergence—that is, until the coefficient estimates stop changing.\n",
"We now try this out on a toy example."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(a) Generate a response `Y` and two predictors `X1` and `X2`, with `n = 100`.\n",
"\n",
"(b) Initialize βˆ1 to take on a value of your choice. It does not matter what value you choose.\n",
"\n",
"(c) Keeping βˆ1 fixed, fit the model:\n",
"\n",
" `Y − βˆ 1 X 1 = β 0 + β 2 X 2 + ε .`\n",
" \n",
"You can do this as follows:\n",
"\n",
"```\n",
" a=y-beta1*x1\n",
" beta2=lm(a∼x2)$coef[2]\n",
"```\n",
"\n",
"(d) Keeping βˆ2 fixed, fit the model\n",
"\n",
"`Y − βˆ 2 X 2 = β 0 + β 1 X 1 + ε .`\n",
"\n",
"You can do this as follows:\n",
"\n",
"```\n",
" a=y-beta2*x2\n",
" beta1=lm(a∼x1)$coef[2]\n",
"```\n",
"\n",
"(e) Write a for loop to repeat (c) and (d) 1,000 times. Report the estimates of βˆ0, βˆ1, and βˆ2 at each iteration of the for loop. Create a plot in which each of these values is displayed, with βˆ0, βˆ1, and βˆ2 each shown in a different color.\n",
"\n",
"(f) Compare your answer in (e) to the results of simply performing multiple linear regression to predict Y using X1 and X2. Use the abline() function to overlay those multiple linear regression coefficient estimates on the plot obtained in (e).\n",
"\n",
"(g) On this data set, how many backfitting iterations were required in order to obtain a “good” approximation to the multiple re- gression coefficient estimates?\n",
"\n",
"\n",
".. All answered in question 12"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"12. This problem is a continuation of the previous exercise. In a toy example with p = 100, show that one can approximate the multiple linear regression coefficient estimates by repeatedly performing simple linear regression in a backfitting procedure. How many backfitting iterations are required in order to obtain a “good” approximation to the multiple regression coefficient estimates? Create a plot to justify your answer."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"predictor_df = pd.DataFrame(np.random.normal(1,10, size=(100, 10)))\n",
"predictor_df[0] = np.repeat(1, 100)\n",
"\n",
"# make y a linear combination of the predictors, so we can see the coefficients end up @ the \n",
"# correct solution. Spread out the solution for a pretty plot.\n",
"\n",
"solution = pd.DataFrame(np.arange(1, predictor_df.shape[1] + 1))\n",
"\n",
"y_df = predictor_df @ solution #pd.DataFrame((predictor_df[1] * 5) + (predictor_df[2] * -2) + 11)\n",
"\n",
"# doesn't matter where the coefficients start, kick them off at 0\n",
"coeff_df = pd.DataFrame(np.repeat(0, predictor_df.shape[1])).T\n",
"\n",
"\n",
"history = pd.DataFrame()\n",
"i = 0\n",
"while i < 10:\n",
" for p in predictor_df:\n",
" # fit p, keeping the rest constant\n",
" constant_preds = predictor_df.drop([p], axis=1)\n",
" constant_coeffs = coeff_df.T.drop([p], axis=0)\n",
" a = y_df - (constant_preds @ constant_coeffs)\n",
" m = linear_model.LinearRegression(fit_intercept=False).fit(pd.DataFrame(predictor_df[p]), a)\n",
" coeff_df[p] = m.coef_[0][0]\n",
" intercept = m.intercept_\n",
" history = history.append(coeff_df, ignore_index=True)\n",
" \n",
" i += 1"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplots(figsize=(20,20))\n",
"for p in predictor_df.columns:\n",
" sns.lineplot(x = history.index + 1, y = history[p])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}