{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bank Customers\n",
"*Jaime Avendaño* \n",
" \n",
"This is a machine learning notebook. The objective is to predict whether or not a bank customer will be able to retire. \n",
"The main model is based on Suport Vector Machines."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dataset:\n",
"* Customer ID\n",
"* Age (in years)\n",
"* 401K Savings (in dollars)\n",
"\n",
"### Target:\n",
"* Retire (1 = can retire; 0 = can't retire)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"from janitor import clean_names\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"from sklearn.model_selection import train_test_split, GridSearchCV\n",
"from sklearn.svm import SVC \n",
"from sklearn.metrics import classification_report, confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 52,
"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>customer_id</th>\n",
" <th>age</th>\n",
" <th>401k_savings</th>\n",
" <th>retire</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>39.180417</td>\n",
" <td>322349.8740</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>56.101686</td>\n",
" <td>768671.5740</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>57.023043</td>\n",
" <td>821505.4718</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>43.711358</td>\n",
" <td>494187.4850</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>54.728823</td>\n",
" <td>691435.7723</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" customer_id age 401k_savings retire\n",
"0 0 39.180417 322349.8740 0\n",
"1 1 56.101686 768671.5740 1\n",
"2 2 57.023043 821505.4718 1\n",
"3 3 43.711358 494187.4850 0\n",
"4 4 54.728823 691435.7723 1"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = pd.read_csv('Course_Data\\Bank_Customer_retirement.csv')\n",
"train = train.clean_names()\n",
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(500, 4)"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# EDA"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 250\n",
"0 250\n",
"Name: retire, dtype: int64"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.retire.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.PairGrid at 0x250a67fc4c0>"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 864x864 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 402.375x360 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12, 12))\n",
"sns.pairplot(train, hue='retire', vars=['age', '401k_savings'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Cleaning\n",
"\n",
"1. Drop columns: customer_id\n",
"2. Standard Scaler: age & 401k_savings"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"train = train.drop(columns=['customer_id'])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"scaler = StandardScaler()\n",
"num_features = ['age', '401k_savings']\n",
"\n",
"scaler.fit(train[num_features])\n",
"train_scaled = pd.DataFrame(scaler.transform(train[num_features]), columns=num_features)\n",
"train = pd.concat([train.drop(columns=num_features), train_scaled], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"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>retire</th>\n",
" <th>age</th>\n",
" <th>401k_savings</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>-0.973572</td>\n",
" <td>-1.134123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1.200747</td>\n",
" <td>1.246411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>1.319138</td>\n",
" <td>1.528209</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>-0.391363</td>\n",
" <td>-0.217598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>1.024339</td>\n",
" <td>0.834460</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" retire age 401k_savings\n",
"0 0 -0.973572 -1.134123\n",
"1 1 1.200747 1.246411\n",
"2 1 1.319138 1.528209\n",
"3 0 -0.391363 -0.217598\n",
"4 1 1.024339 0.834460"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Training"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"X = train.drop(columns=['retire']).values\n",
"y = train.retire.values"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SVC(random_state=42)"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svc = SVC(random_state = 42)\n",
"svc.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Testing"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"y_pred = svc.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"cm = confusion_matrix(y_test, y_pred)\n",
"sns.heatmap(cm, annot=True, fmt='d')"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.96 1.00 0.98 44\n",
" 1 1.00 0.96 0.98 56\n",
"\n",
" accuracy 0.98 100\n",
" macro avg 0.98 0.98 0.98 100\n",
"weighted avg 0.98 0.98 0.98 100\n",
"\n"
]
}
],
"source": [
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'C': 1.0,\n",
" 'break_ties': False,\n",
" 'cache_size': 200,\n",
" 'class_weight': None,\n",
" 'coef0': 0.0,\n",
" 'decision_function_shape': 'ovr',\n",
" 'degree': 3,\n",
" 'gamma': 'scale',\n",
" 'kernel': 'rbf',\n",
" 'max_iter': -1,\n",
" 'probability': False,\n",
" 'random_state': 42,\n",
" 'shrinking': True,\n",
" 'tol': 0.001,\n",
" 'verbose': False}"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svc.get_params()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Improvements"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"param_grid = {'C': [0.1, 1, 10, 100, 1000], \n",
" 'gamma': [0.001, 0.01, 0.1, 1, 10, 100], \n",
" 'kernel': ['linear', 'rbf', 'sigmoid']} "
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"grid = GridSearchCV(SVC(), param_grid, refit=True, verbose=2)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 90 candidates, totalling 450 fits\n",
"[CV] C=0.1, gamma=0.001, kernel=linear ...............................\n",
"[CV] ................ C=0.1, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.001, kernel=linear ...............................\n",
"[CV] ................ C=0.1, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.001, kernel=linear ...............................\n",
"[CV] ................ C=0.1, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.001, kernel=linear ...............................\n",
"[CV] ................ C=0.1, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.001, kernel=linear ...............................\n",
"[CV] ................ C=0.1, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
"[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
"[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
"[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
"[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
"[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.001, kernel=sigmoid ..............................\n",
"[CV] ............... C=0.1, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=0.001, kernel=sigmoid ..............................\n",
"[CV] ............... C=0.1, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=0.001, kernel=sigmoid ..............................\n",
"[CV] ............... C=0.1, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=0.001, kernel=sigmoid ..............................\n",
"[CV] ............... C=0.1, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=0.001, kernel=sigmoid ..............................\n",
"[CV] ............... C=0.1, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=linear ................................\n",
"[CV] ................. C=0.1, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=linear ................................\n",
"[CV] ................. C=0.1, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=linear ................................\n",
"[CV] ................. C=0.1, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=linear ................................\n",
"[CV] ................. C=0.1, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=linear ................................\n",
"[CV] ................. C=0.1, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
"[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
"[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
"[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
"[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
"[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=sigmoid ...............................\n",
"[CV] ................ C=0.1, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=sigmoid ...............................\n",
"[CV] ................ C=0.1, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=sigmoid ...............................\n",
"[CV] ................ C=0.1, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=sigmoid ...............................\n",
"[CV] ................ C=0.1, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=0.01, kernel=sigmoid ...............................\n",
"[CV] ................ C=0.1, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=linear .................................\n",
"[CV] .................. C=0.1, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=linear .................................\n",
"[CV] .................. C=0.1, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=linear .................................\n",
"[CV] .................. C=0.1, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=linear .................................\n",
"[CV] .................. C=0.1, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=linear .................................\n",
"[CV] .................. C=0.1, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
"[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
"[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
"[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
"[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
"[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=sigmoid ................................\n",
"[CV] ................. C=0.1, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=sigmoid ................................\n",
"[CV] ................. C=0.1, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=sigmoid ................................\n",
"[CV] ................. C=0.1, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=sigmoid ................................\n",
"[CV] ................. C=0.1, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=0.1, kernel=sigmoid ................................\n",
"[CV] ................. C=0.1, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=linear ...................................\n",
"[CV] .................... C=0.1, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=linear ...................................\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CV] .................... C=0.1, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=linear ...................................\n",
"[CV] .................... C=0.1, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=linear ...................................\n",
"[CV] .................... C=0.1, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=linear ...................................\n",
"[CV] .................... C=0.1, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
"[CV] ....................... C=0.1, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
"[CV] ....................... C=0.1, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
"[CV] ....................... C=0.1, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
"[CV] ....................... C=0.1, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
"[CV] ....................... C=0.1, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=sigmoid ..................................\n",
"[CV] ................... C=0.1, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=sigmoid ..................................\n",
"[CV] ................... C=0.1, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=sigmoid ..................................\n",
"[CV] ................... C=0.1, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=sigmoid ..................................\n",
"[CV] ................... C=0.1, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=1, kernel=sigmoid ..................................\n",
"[CV] ................... C=0.1, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=linear ..................................\n",
"[CV] ................... C=0.1, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=linear ..................................\n",
"[CV] ................... C=0.1, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=linear ..................................\n",
"[CV] ................... C=0.1, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=linear ..................................\n",
"[CV] ................... C=0.1, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=linear ..................................\n",
"[CV] ................... C=0.1, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=rbf .....................................\n",
"[CV] ...................... C=0.1, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=rbf .....................................\n",
"[CV] ...................... C=0.1, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=rbf .....................................\n",
"[CV] ...................... C=0.1, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=rbf .....................................\n",
"[CV] ...................... C=0.1, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=rbf .....................................\n",
"[CV] ...................... C=0.1, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=sigmoid .................................\n",
"[CV] .................. C=0.1, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=sigmoid .................................\n",
"[CV] .................. C=0.1, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=sigmoid .................................\n",
"[CV] .................. C=0.1, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=sigmoid .................................\n",
"[CV] .................. C=0.1, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=10, kernel=sigmoid .................................\n",
"[CV] .................. C=0.1, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=linear .................................\n",
"[CV] .................. C=0.1, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=linear .................................\n",
"[CV] .................. C=0.1, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=linear .................................\n",
"[CV] .................. C=0.1, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=linear .................................\n",
"[CV] .................. C=0.1, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=linear .................................\n",
"[CV] .................. C=0.1, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=rbf ....................................\n",
"[CV] ..................... C=0.1, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=rbf ....................................\n",
"[CV] ..................... C=0.1, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=rbf ....................................\n",
"[CV] ..................... C=0.1, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=rbf ....................................\n",
"[CV] ..................... C=0.1, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=rbf ....................................\n",
"[CV] ..................... C=0.1, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=sigmoid ................................\n",
"[CV] ................. C=0.1, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=sigmoid ................................\n",
"[CV] ................. C=0.1, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=sigmoid ................................\n",
"[CV] ................. C=0.1, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=sigmoid ................................\n",
"[CV] ................. C=0.1, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=0.1, gamma=100, kernel=sigmoid ................................\n",
"[CV] ................. C=0.1, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=linear .................................\n",
"[CV] .................. C=1, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=linear .................................\n",
"[CV] .................. C=1, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=linear .................................\n",
"[CV] .................. C=1, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=linear .................................\n",
"[CV] .................. C=1, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=linear .................................\n",
"[CV] .................. C=1, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
"[CV] ..................... C=1, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
"[CV] ..................... C=1, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
"[CV] ..................... C=1, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
"[CV] ..................... C=1, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
"[CV] ..................... C=1, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=sigmoid ................................\n",
"[CV] ................. C=1, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=sigmoid ................................\n",
"[CV] ................. C=1, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=sigmoid ................................\n",
"[CV] ................. C=1, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=sigmoid ................................\n",
"[CV] ................. C=1, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.001, kernel=sigmoid ................................\n",
"[CV] ................. C=1, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=linear ..................................\n",
"[CV] ................... C=1, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=linear ..................................\n",
"[CV] ................... C=1, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=linear ..................................\n",
"[CV] ................... C=1, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=linear ..................................\n",
"[CV] ................... C=1, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=linear ..................................\n",
"[CV] ................... C=1, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
"[CV] ...................... C=1, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
"[CV] ...................... C=1, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
"[CV] ...................... C=1, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
"[CV] ...................... C=1, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
"[CV] ...................... C=1, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=sigmoid .................................\n",
"[CV] .................. C=1, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=sigmoid .................................\n",
"[CV] .................. C=1, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=sigmoid .................................\n",
"[CV] .................. C=1, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=sigmoid .................................\n",
"[CV] .................. C=1, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.01, kernel=sigmoid .................................\n",
"[CV] .................. C=1, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=linear ...................................\n",
"[CV] .................... C=1, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=linear ...................................\n",
"[CV] .................... C=1, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=linear ...................................\n",
"[CV] .................... C=1, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=linear ...................................\n",
"[CV] .................... C=1, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=linear ...................................\n",
"[CV] .................... C=1, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
"[CV] ....................... C=1, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
"[CV] ....................... C=1, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
"[CV] ....................... C=1, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
"[CV] ....................... C=1, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
"[CV] ....................... C=1, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=sigmoid ..................................\n",
"[CV] ................... C=1, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=sigmoid ..................................\n",
"[CV] ................... C=1, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=sigmoid ..................................\n",
"[CV] ................... C=1, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=sigmoid ..................................\n",
"[CV] ................... C=1, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=0.1, kernel=sigmoid ..................................\n",
"[CV] ................... C=1, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=linear .....................................\n",
"[CV] ...................... C=1, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=linear .....................................\n",
"[CV] ...................... C=1, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=linear .....................................\n",
"[CV] ...................... C=1, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=linear .....................................\n",
"[CV] ...................... C=1, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=linear .....................................\n",
"[CV] ...................... C=1, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=rbf ........................................\n",
"[CV] ......................... C=1, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=rbf ........................................\n",
"[CV] ......................... C=1, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=rbf ........................................\n",
"[CV] ......................... C=1, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=rbf ........................................\n",
"[CV] ......................... C=1, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=rbf ........................................\n",
"[CV] ......................... C=1, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=sigmoid ....................................\n",
"[CV] ..................... C=1, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=sigmoid ....................................\n",
"[CV] ..................... C=1, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=sigmoid ....................................\n",
"[CV] ..................... C=1, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=sigmoid ....................................\n",
"[CV] ..................... C=1, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=1, kernel=sigmoid ....................................\n",
"[CV] ..................... C=1, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=linear ....................................\n",
"[CV] ..................... C=1, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=linear ....................................\n",
"[CV] ..................... C=1, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=linear ....................................\n",
"[CV] ..................... C=1, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=linear ....................................\n",
"[CV] ..................... C=1, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=linear ....................................\n",
"[CV] ..................... C=1, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=rbf .......................................\n",
"[CV] ........................ C=1, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=rbf .......................................\n",
"[CV] ........................ C=1, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=rbf .......................................\n",
"[CV] ........................ C=1, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=rbf .......................................\n",
"[CV] ........................ C=1, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=rbf .......................................\n",
"[CV] ........................ C=1, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=sigmoid ...................................\n",
"[CV] .................... C=1, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=sigmoid ...................................\n",
"[CV] .................... C=1, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=sigmoid ...................................\n",
"[CV] .................... C=1, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=sigmoid ...................................\n",
"[CV] .................... C=1, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=10, kernel=sigmoid ...................................\n",
"[CV] .................... C=1, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=linear ...................................\n",
"[CV] .................... C=1, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=linear ...................................\n",
"[CV] .................... C=1, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=linear ...................................\n",
"[CV] .................... C=1, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=linear ...................................\n",
"[CV] .................... C=1, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=linear ...................................\n",
"[CV] .................... C=1, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=rbf ......................................\n",
"[CV] ....................... C=1, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=rbf ......................................\n",
"[CV] ....................... C=1, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=rbf ......................................\n",
"[CV] ....................... C=1, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=rbf ......................................\n",
"[CV] ....................... C=1, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=rbf ......................................\n",
"[CV] ....................... C=1, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=sigmoid ..................................\n",
"[CV] ................... C=1, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=sigmoid ..................................\n",
"[CV] ................... C=1, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=sigmoid ..................................\n",
"[CV] ................... C=1, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=sigmoid ..................................\n",
"[CV] ................... C=1, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1, gamma=100, kernel=sigmoid ..................................\n",
"[CV] ................... C=1, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=linear ................................\n",
"[CV] ................. C=10, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=linear ................................\n",
"[CV] ................. C=10, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=linear ................................\n",
"[CV] ................. C=10, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=linear ................................\n",
"[CV] ................. C=10, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=linear ................................\n",
"[CV] ................. C=10, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
"[CV] .................... C=10, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
"[CV] .................... C=10, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
"[CV] .................... C=10, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
"[CV] .................... C=10, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
"[CV] .................... C=10, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=sigmoid ...............................\n",
"[CV] ................ C=10, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=sigmoid ...............................\n",
"[CV] ................ C=10, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=sigmoid ...............................\n",
"[CV] ................ C=10, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=sigmoid ...............................\n",
"[CV] ................ C=10, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.001, kernel=sigmoid ...............................\n",
"[CV] ................ C=10, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=linear .................................\n",
"[CV] .................. C=10, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=linear .................................\n",
"[CV] .................. C=10, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=linear .................................\n",
"[CV] .................. C=10, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=linear .................................\n",
"[CV] .................. C=10, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=linear .................................\n",
"[CV] .................. C=10, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
"[CV] ..................... C=10, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
"[CV] ..................... C=10, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
"[CV] ..................... C=10, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
"[CV] ..................... C=10, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
"[CV] ..................... C=10, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=sigmoid ................................\n",
"[CV] ................. C=10, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=sigmoid ................................\n",
"[CV] ................. C=10, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=sigmoid ................................\n",
"[CV] ................. C=10, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=sigmoid ................................\n",
"[CV] ................. C=10, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.01, kernel=sigmoid ................................\n",
"[CV] ................. C=10, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=linear ..................................\n",
"[CV] ................... C=10, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=linear ..................................\n",
"[CV] ................... C=10, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=linear ..................................\n",
"[CV] ................... C=10, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=linear ..................................\n",
"[CV] ................... C=10, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=linear ..................................\n",
"[CV] ................... C=10, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
"[CV] ...................... C=10, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
"[CV] ...................... C=10, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
"[CV] ...................... C=10, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
"[CV] ...................... C=10, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
"[CV] ...................... C=10, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=sigmoid .................................\n",
"[CV] .................. C=10, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=sigmoid .................................\n",
"[CV] .................. C=10, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=sigmoid .................................\n",
"[CV] .................. C=10, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=sigmoid .................................\n",
"[CV] .................. C=10, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=0.1, kernel=sigmoid .................................\n",
"[CV] .................. C=10, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=linear ....................................\n",
"[CV] ..................... C=10, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=linear ....................................\n",
"[CV] ..................... C=10, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=linear ....................................\n",
"[CV] ..................... C=10, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=linear ....................................\n",
"[CV] ..................... C=10, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=linear ....................................\n",
"[CV] ..................... C=10, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=rbf .......................................\n",
"[CV] ........................ C=10, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=rbf .......................................\n",
"[CV] ........................ C=10, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=rbf .......................................\n",
"[CV] ........................ C=10, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=rbf .......................................\n",
"[CV] ........................ C=10, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=rbf .......................................\n",
"[CV] ........................ C=10, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=sigmoid ...................................\n",
"[CV] .................... C=10, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=sigmoid ...................................\n",
"[CV] .................... C=10, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=sigmoid ...................................\n",
"[CV] .................... C=10, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=sigmoid ...................................\n",
"[CV] .................... C=10, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=1, kernel=sigmoid ...................................\n",
"[CV] .................... C=10, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=linear ...................................\n",
"[CV] .................... C=10, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=linear ...................................\n",
"[CV] .................... C=10, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=linear ...................................\n",
"[CV] .................... C=10, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=linear ...................................\n",
"[CV] .................... C=10, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=linear ...................................\n",
"[CV] .................... C=10, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=rbf ......................................\n",
"[CV] ....................... C=10, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=rbf ......................................\n",
"[CV] ....................... C=10, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=rbf ......................................\n",
"[CV] ....................... C=10, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=rbf ......................................\n",
"[CV] ....................... C=10, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=rbf ......................................\n",
"[CV] ....................... C=10, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=sigmoid ..................................\n",
"[CV] ................... C=10, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=sigmoid ..................................\n",
"[CV] ................... C=10, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=sigmoid ..................................\n",
"[CV] ................... C=10, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=sigmoid ..................................\n",
"[CV] ................... C=10, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=10, kernel=sigmoid ..................................\n",
"[CV] ................... C=10, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=linear ..................................\n",
"[CV] ................... C=10, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=linear ..................................\n",
"[CV] ................... C=10, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=linear ..................................\n",
"[CV] ................... C=10, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=linear ..................................\n",
"[CV] ................... C=10, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=linear ..................................\n",
"[CV] ................... C=10, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=rbf .....................................\n",
"[CV] ...................... C=10, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=rbf .....................................\n",
"[CV] ...................... C=10, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=rbf .....................................\n",
"[CV] ...................... C=10, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=rbf .....................................\n",
"[CV] ...................... C=10, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=rbf .....................................\n",
"[CV] ...................... C=10, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=sigmoid .................................\n",
"[CV] .................. C=10, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=sigmoid .................................\n",
"[CV] .................. C=10, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=sigmoid .................................\n",
"[CV] .................. C=10, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=sigmoid .................................\n",
"[CV] .................. C=10, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=10, gamma=100, kernel=sigmoid .................................\n",
"[CV] .................. C=10, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=linear ...............................\n",
"[CV] ................ C=100, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=linear ...............................\n",
"[CV] ................ C=100, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=linear ...............................\n",
"[CV] ................ C=100, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=linear ...............................\n",
"[CV] ................ C=100, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=linear ...............................\n",
"[CV] ................ C=100, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
"[CV] ................... C=100, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
"[CV] ................... C=100, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
"[CV] ................... C=100, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
"[CV] ................... C=100, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
"[CV] ................... C=100, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=sigmoid ..............................\n",
"[CV] ............... C=100, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=sigmoid ..............................\n",
"[CV] ............... C=100, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=sigmoid ..............................\n",
"[CV] ............... C=100, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=sigmoid ..............................\n",
"[CV] ............... C=100, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.001, kernel=sigmoid ..............................\n",
"[CV] ............... C=100, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=linear ................................\n",
"[CV] ................. C=100, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=linear ................................\n",
"[CV] ................. C=100, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=linear ................................\n",
"[CV] ................. C=100, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=linear ................................\n",
"[CV] ................. C=100, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=linear ................................\n",
"[CV] ................. C=100, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
"[CV] .................... C=100, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
"[CV] .................... C=100, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
"[CV] .................... C=100, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
"[CV] .................... C=100, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
"[CV] .................... C=100, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=sigmoid ...............................\n",
"[CV] ................ C=100, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=sigmoid ...............................\n",
"[CV] ................ C=100, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=sigmoid ...............................\n",
"[CV] ................ C=100, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=sigmoid ...............................\n",
"[CV] ................ C=100, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.01, kernel=sigmoid ...............................\n",
"[CV] ................ C=100, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=linear .................................\n",
"[CV] .................. C=100, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=linear .................................\n",
"[CV] .................. C=100, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=linear .................................\n",
"[CV] .................. C=100, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=linear .................................\n",
"[CV] .................. C=100, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=linear .................................\n",
"[CV] .................. C=100, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
"[CV] ..................... C=100, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
"[CV] ..................... C=100, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
"[CV] ..................... C=100, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
"[CV] ..................... C=100, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
"[CV] ..................... C=100, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=sigmoid ................................\n",
"[CV] ................. C=100, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=sigmoid ................................\n",
"[CV] ................. C=100, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=sigmoid ................................\n",
"[CV] ................. C=100, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=sigmoid ................................\n",
"[CV] ................. C=100, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=0.1, kernel=sigmoid ................................\n",
"[CV] ................. C=100, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=linear ...................................\n",
"[CV] .................... C=100, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=linear ...................................\n",
"[CV] .................... C=100, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=linear ...................................\n",
"[CV] .................... C=100, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=linear ...................................\n",
"[CV] .................... C=100, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=linear ...................................\n",
"[CV] .................... C=100, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=rbf ......................................\n",
"[CV] ....................... C=100, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=rbf ......................................\n",
"[CV] ....................... C=100, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=rbf ......................................\n",
"[CV] ....................... C=100, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=rbf ......................................\n",
"[CV] ....................... C=100, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=rbf ......................................\n",
"[CV] ....................... C=100, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=sigmoid ..................................\n",
"[CV] ................... C=100, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=sigmoid ..................................\n",
"[CV] ................... C=100, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=sigmoid ..................................\n",
"[CV] ................... C=100, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=sigmoid ..................................\n",
"[CV] ................... C=100, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=1, kernel=sigmoid ..................................\n",
"[CV] ................... C=100, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=linear ..................................\n",
"[CV] ................... C=100, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=linear ..................................\n",
"[CV] ................... C=100, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=linear ..................................\n",
"[CV] ................... C=100, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=linear ..................................\n",
"[CV] ................... C=100, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=linear ..................................\n",
"[CV] ................... C=100, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=rbf .....................................\n",
"[CV] ...................... C=100, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=rbf .....................................\n",
"[CV] ...................... C=100, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=rbf .....................................\n",
"[CV] ...................... C=100, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=rbf .....................................\n",
"[CV] ...................... C=100, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=rbf .....................................\n",
"[CV] ...................... C=100, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=sigmoid .................................\n",
"[CV] .................. C=100, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=sigmoid .................................\n",
"[CV] .................. C=100, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=sigmoid .................................\n",
"[CV] .................. C=100, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=sigmoid .................................\n",
"[CV] .................. C=100, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=10, kernel=sigmoid .................................\n",
"[CV] .................. C=100, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=linear .................................\n",
"[CV] .................. C=100, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=linear .................................\n",
"[CV] .................. C=100, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=linear .................................\n",
"[CV] .................. C=100, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=linear .................................\n",
"[CV] .................. C=100, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=linear .................................\n",
"[CV] .................. C=100, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=rbf ....................................\n",
"[CV] ..................... C=100, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=rbf ....................................\n",
"[CV] ..................... C=100, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=rbf ....................................\n",
"[CV] ..................... C=100, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=rbf ....................................\n",
"[CV] ..................... C=100, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=rbf ....................................\n",
"[CV] ..................... C=100, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=sigmoid ................................\n",
"[CV] ................. C=100, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=sigmoid ................................\n",
"[CV] ................. C=100, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=sigmoid ................................\n",
"[CV] ................. C=100, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=sigmoid ................................\n",
"[CV] ................. C=100, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=100, gamma=100, kernel=sigmoid ................................\n",
"[CV] ................. C=100, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=linear ..............................\n",
"[CV] ............... C=1000, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=linear ..............................\n",
"[CV] ............... C=1000, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=linear ..............................\n",
"[CV] ............... C=1000, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=linear ..............................\n",
"[CV] ............... C=1000, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=linear ..............................\n",
"[CV] ............... C=1000, gamma=0.001, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
"[CV] .................. C=1000, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
"[CV] .................. C=1000, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
"[CV] .................. C=1000, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
"[CV] .................. C=1000, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=rbf .................................\n",
"[CV] .................. C=1000, gamma=0.001, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=sigmoid .............................\n",
"[CV] .............. C=1000, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=sigmoid .............................\n",
"[CV] .............. C=1000, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=sigmoid .............................\n",
"[CV] .............. C=1000, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=sigmoid .............................\n",
"[CV] .............. C=1000, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.001, kernel=sigmoid .............................\n",
"[CV] .............. C=1000, gamma=0.001, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=linear ...............................\n",
"[CV] ................ C=1000, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=linear ...............................\n",
"[CV] ................ C=1000, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=linear ...............................\n",
"[CV] ................ C=1000, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=linear ...............................\n",
"[CV] ................ C=1000, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=linear ...............................\n",
"[CV] ................ C=1000, gamma=0.01, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
"[CV] ................... C=1000, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
"[CV] ................... C=1000, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
"[CV] ................... C=1000, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
"[CV] ................... C=1000, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=rbf ..................................\n",
"[CV] ................... C=1000, gamma=0.01, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=sigmoid ..............................\n",
"[CV] ............... C=1000, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=sigmoid ..............................\n",
"[CV] ............... C=1000, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=sigmoid ..............................\n",
"[CV] ............... C=1000, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=sigmoid ..............................\n",
"[CV] ............... C=1000, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.01, kernel=sigmoid ..............................\n",
"[CV] ............... C=1000, gamma=0.01, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=linear ................................\n",
"[CV] ................. C=1000, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=linear ................................\n",
"[CV] ................. C=1000, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=linear ................................\n",
"[CV] ................. C=1000, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=linear ................................\n",
"[CV] ................. C=1000, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=linear ................................\n",
"[CV] ................. C=1000, gamma=0.1, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
"[CV] .................... C=1000, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
"[CV] .................... C=1000, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
"[CV] .................... C=1000, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
"[CV] .................... C=1000, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=rbf ...................................\n",
"[CV] .................... C=1000, gamma=0.1, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=sigmoid ...............................\n",
"[CV] ................ C=1000, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=sigmoid ...............................\n",
"[CV] ................ C=1000, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=sigmoid ...............................\n",
"[CV] ................ C=1000, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=sigmoid ...............................\n",
"[CV] ................ C=1000, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=0.1, kernel=sigmoid ...............................\n",
"[CV] ................ C=1000, gamma=0.1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=linear ..................................\n",
"[CV] ................... C=1000, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=linear ..................................\n",
"[CV] ................... C=1000, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=linear ..................................\n",
"[CV] ................... C=1000, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=linear ..................................\n",
"[CV] ................... C=1000, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=linear ..................................\n",
"[CV] ................... C=1000, gamma=1, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
"[CV] ...................... C=1000, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
"[CV] ...................... C=1000, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
"[CV] ...................... C=1000, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
"[CV] ...................... C=1000, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=rbf .....................................\n",
"[CV] ...................... C=1000, gamma=1, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=sigmoid .................................\n",
"[CV] .................. C=1000, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=sigmoid .................................\n",
"[CV] .................. C=1000, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=sigmoid .................................\n",
"[CV] .................. C=1000, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=sigmoid .................................\n",
"[CV] .................. C=1000, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=1, kernel=sigmoid .................................\n",
"[CV] .................. C=1000, gamma=1, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=linear .................................\n",
"[CV] .................. C=1000, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=linear .................................\n",
"[CV] .................. C=1000, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=linear .................................\n",
"[CV] .................. C=1000, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=linear .................................\n",
"[CV] .................. C=1000, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=linear .................................\n",
"[CV] .................. C=1000, gamma=10, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=rbf ....................................\n",
"[CV] ..................... C=1000, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=rbf ....................................\n",
"[CV] ..................... C=1000, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=rbf ....................................\n",
"[CV] ..................... C=1000, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=rbf ....................................\n",
"[CV] ..................... C=1000, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=rbf ....................................\n",
"[CV] ..................... C=1000, gamma=10, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=sigmoid ................................\n",
"[CV] ................. C=1000, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=sigmoid ................................\n",
"[CV] ................. C=1000, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=sigmoid ................................\n",
"[CV] ................. C=1000, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=sigmoid ................................\n",
"[CV] ................. C=1000, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=10, kernel=sigmoid ................................\n",
"[CV] ................. C=1000, gamma=10, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=linear ................................\n",
"[CV] ................. C=1000, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=linear ................................\n",
"[CV] ................. C=1000, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=linear ................................\n",
"[CV] ................. C=1000, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=linear ................................\n",
"[CV] ................. C=1000, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=linear ................................\n",
"[CV] ................. C=1000, gamma=100, kernel=linear, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=rbf ...................................\n",
"[CV] .................... C=1000, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=rbf ...................................\n",
"[CV] .................... C=1000, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=rbf ...................................\n",
"[CV] .................... C=1000, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=rbf ...................................\n",
"[CV] .................... C=1000, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=rbf ...................................\n",
"[CV] .................... C=1000, gamma=100, kernel=rbf, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=sigmoid ...............................\n",
"[CV] ................ C=1000, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=sigmoid ...............................\n",
"[CV] ................ C=1000, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=sigmoid ...............................\n",
"[CV] ................ C=1000, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=sigmoid ...............................\n",
"[CV] ................ C=1000, gamma=100, kernel=sigmoid, total= 0.0s\n",
"[CV] C=1000, gamma=100, kernel=sigmoid ...............................\n",
"[CV] ................ C=1000, gamma=100, kernel=sigmoid, total= 0.0s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 450 out of 450 | elapsed: 1.8s finished\n"
]
},
{
"data": {
"text/plain": [
"GridSearchCV(estimator=SVC(),\n",
" param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
" 'gamma': [0.001, 0.01, 0.1, 1, 10, 100],\n",
" 'kernel': ['linear', 'rbf', 'sigmoid']},\n",
" verbose=2)"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'C': 0.1, 'gamma': 100, 'kernel': 'sigmoid'}"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SVC(C=0.1, gamma=100, kernel='sigmoid')"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.best_estimator_"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"grid_pred = grid.predict(X_test)\n",
"cm = confusion_matrix(y_test, grid_pred)\n",
"sns.heatmap(cm, annot=True)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.96 1.00 0.98 44\n",
" 1 1.00 0.96 0.98 56\n",
"\n",
" accuracy 0.98 100\n",
" macro avg 0.98 0.98 0.98 100\n",
"weighted avg 0.98 0.98 0.98 100\n",
"\n"
]
}
],
"source": [
"print(classification_report(y_test,grid_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualizing the border"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(29.98287903, 62.57303128, 161499.2806, 936001.5611)"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x1_min, x2_min = scaler.inverse_transform([X_test[:,0].min(), X_test[:, 1].min()])\n",
"x1_max, x2_max = scaler.inverse_transform([X_test[:,0].max(), X_test[:, 1].max()])\n",
"x1_min, x1_max, x2_min, x2_max"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((34, 40), (34, 40))"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X1, X2 = np.meshgrid(np.arange(start = 25, stop = 65, step = 1),\n",
" np.arange(start = 150_000, stop = 1_000_000, step = 25_000))\n",
"X1.shape, X2.shape"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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heCTJ2VJkQPgy8DJJz5B0EPBTwLHAfETcD5Aen5WWPxq4t2n93ans6DTdWr5inRRCHgGe0WNbK0i6QNIOSTsef/zBNeyqmVn1dRsx0iNJVlNhASEi7iBv8t8CfAr4O+CJHquo02Z6lA+7TnMdr4iIxYhYPOCAI3tUzczMfO+D2VJoJ8WIuDIiXhwRLwP2AHcCS+m0AenxgbT4bvIWhrpjgPtS+TEdylesI2l/4LD0Ot22ZWZmQ5qfh/XrGy0Gc3P5vK9iqKair2J4Vnp8DvBzwIeA64H6VQWbgU+k6euBTenKhOeRd0a8JZ2GeFTSqal/watb1qlv6xzgptRP4dPA6ZIOT50TT09lZma2BvPzsGEDbNyYPzocVFfR4yB8VNIzgMeBCyPiYUmXAddKOh+4BzgXICJul3Qt8BXyUxEXRsSTaTuvA94PHAjckH4ArgSukXQXecvBprStPZLeCtyalntLROwpdlfNzMyqQ/kXbjvkkMVYXNwx6WqYmZmNzbZtui0iFjs956GWzczMrI0DgpmZmbVxQDAzM7M2DghmZmbWxgHBzMzM2jggmJmZWRsHBDMzM2vjgGBmZmZtih5JcWp85ztQq/W/fJYVVRMzM7PJc0BIDjxwsIN+reaQYGZm1eWAkAzaggAOCWZmVl0OCMkwLQj1R4cEs+m0tAS7dsHycn7r4oUF353QrM4BIRmmBaHOIcFs+iwtwc6dsHdvPr+8nM+DQ4IZOCDsM2wLQvO8Q4LZ9Ni1qxEO6vbuzcsdEMwcEBq+A9TWtgmHBLPpsbw8WLnZrHFASNYfCFuz/pc/rbbmPGFmEzQ31zkMzM2Nvy5mZeSAUDdEC0LWYRW3IphNh4WFlX0QANaty8vNzAGh4UDyI36fttbgNBwSzKZVvZ+Br2Iw68wBoW6IFoStOCSYTbP5eQcCs24cEOoGbEGoJwKHBDMzqyLfrGkEtqbHrMNzw46tYGZmNkkOCMPKVs7uCwkt5eCQYGZm08cBYS2ylbNboWs/BocEMzObJg4Ia5WtnN3aXmRmZjZ1HBBGIeuryK0IZmY2NRwQRiVrTLrTopmZTTsHhFHKGpMOCWZmNs0cEEYta0w6JJiZ2bRyQCiYQ4KZmU0jB4QiZCtnt3ZcKOeQYGZmZeShlouSsWJMhPqQzLVOy5pNuaUl3/TIrGrcglCkrK8ityLYVFtaym+bvLyczy8v5/NLS5Otl5mtjQNC0bLGpPsjWBXt2gV7964s27s3L7fhLS3B9u2wbVv+6MBl4+aAMA5Z49Ehwaqm3nLQb7mtzq0yq3OAKp4DwrhkrLhFdL2olUOCTZu5ucHKbXVulenNAWo83ElxnDJWhITTaOvLCAweEjrdQdJsXBYW8g/n5gPaunV5uQ3HrTK99QpQ7hw7Og4IE9QtJAxywK/V8h+HBJuU+geyr2IYnbm5zmHArTI5B6jxcEAYt4yOlz82G+Y0g0OCTdL8vAPBKLlVpjcHqPFwQJiEjJ5jJAzagtA87ZBgNv3cKtPd0hI88UR7uQPU6DkgTEpGW+eDetFaOio6JJhVg1tl2tU7J7b2P9h/fzjhBP++Rs0BYZIyOnZa7Hh5QxedwoRDgplVUafOiQD77edwUAQHhEnLaAsJg4zH3LS6mVmluXPieDkglEHGyjESsv5XPa3W/VJJtyKYWZW4c+J4FTpQkqT/KOl2SV+W9CFJT5V0hKQtku5Mj4c3LX+xpLsk7ZR0RlP5yZK+lJ57lySl8jlJH0nlN0s6rmmdzek17pS0ucj9HLla/z8edMnMZsXCQt4ZsZk7JxansIAg6WjgPwCLEfEiYD9gE3ARcGNEnADcmOaRdGJ6/iTgTOAPJO2XNvce4ALghPRzZio/H3g4Io4H3gFcnrZ1BHAJ8BLgFOCS5iBSSlnLdL8/OCSY2WyYn4f16xstBnNz+bz7HxSj6FMM+wMHSnocOAi4D7gY2JievwrYBrwJOAv4cEQsA3dLugs4RdLXgEMjYjuApKuBs4Eb0jqXpm1dB7w7tS6cAWyJiD1pnS3koeJDxe3qCGQ0WgcGtNrIjD7dYGZV4Ks7xqewgBAR/yTpd4B7gO8An4mIz0iaj4j70zL3S3pWWuVo4AtNm9idyh5P063l9XXuTdt6QtIjwDOayzusU27ZEOvU8geHBDMzG5UiTzEcTv4N/3nAs4GnSfqFXqt0KIse5cOu01zHCyTtkLTjwccf7FG16eHTDWZmNgpFnmL4ceDuiHgQQNKfAz8CLEk6KrUeHAU8kJbfDRzbtP4x5Kckdqfp1vLmdXZL2h84DNiTyje2rLOttYIRcQVwBcDiIYttAWJqZKw6fHOdbwRlZv1aWvJojrOsyIBwD3CqpIPITzG8HNgB/AuwGbgsPX4iLX898EFJbydvcTgBuCUinpT0qKRTgZuBVwO/37TOZmA7cA5wU0SEpE8Dv9XUMfF08r4P1ZXRc/jmfYtl/W/SN4Iym12toxbWb6kMDgmzosg+CDdLug74IvAE8Lfk39YPBq6VdD55iDg3LX+7pGuBr6TlL4yIJ9PmXge8HziQvHPiDan8SuCa1KFxD/lVEETEHklvBW5Ny72l3mGx0jK6Dt9c5xtBmVk/fEtlU8T0tqyP0uIhi7FjccekqzEatcbkaS1Fw94IatB1zWy6bdvW/bmNG8dVCyvatm26LSIWOz3nkRSrKKPrlQ3uqGhm/fCoheaAUFUZI78RlE81mM2OhYX2Oyd61MLZ4oAwI0Z1IyiHBBuGe8NPn/r74/dtdjkgVFlG25UNvhGUjZt7w08vj1o42xwQqi6j5TKG/lf1yIw2Cu4NbzadHBBmQUbTZQwDrFdzSLC169TRrVe5mZWDA8KsyPCNoGwi3BvebDo5IMySbMDla43J1YZvdkiwbtwb3mw6OSBYdxl9Dd9s1suoe8P7iojp4vdrejkgWG8Zqw7fDG5FsN5G1RveV0RMF79f062w2z1bhWSNSd9O2iap1xUR47a0BNu350MSb9+ez9tKZXq/bHAOCNafrDHpkGCTUpYrIurfjOuvW/9m7JCwUlneLxuOA4L1L2tMOiTYJHS78mHcV0T4m3F/yvJ+2XAcEGxoDgk2bgsL+RUQzSZxRUSVvxmP8tRJWd4vG44Dgg0mWzm7teNCOYcEG7X5eVi/vvENdG4unx93h7eqfjMe9amTsrxfNhxfxWCDy/DljzYxZbg/QFXHdihiWOwyvF82HLcg2HCylbNbM59qsNlR1W/GVT51YoNzC4INL6PRbFBrL6rzGAlWRVX8Zuxhsa2ZWxBsbbLGpDstmq3dJMdXcKdCa+aAYGuXNSYdEsxWGuSAP+nxFap66sSG41MMNhoZ+84t+O6PZrlBhxouopPgoKp46sSG4xYEK4RbEswGH1DJnQStTNyCYKOTMdAtogfadDZEfcwmbNADvjsJWpk4INhoZfQ1RsIgB/xazacmbDoNesAf5/gKvg2zrcYBwUYvY9VbRA9zmsEhwabNoAf8+gG66AO3b8Ns/XBAsGJk9Oy0OGgLQvO0Q4JNi2EO+OPoJFiGzpBWfg4IVpyMriHBHRVtVpTxqgB3hrR+OCBYsTI6hoSOlzd00Rom3Ipg1lm//QrcGdL64YBgY7XvyoZa/+tkHRZ3SDBbaZB+BVW92dRq3DFzMA4IVryMtisbBmlBOK3mkGC2mkH6FfTbN6JKB1R3zBycA4KNR0bLZQz9r+qRGc1WN2i/gtX6RlTtgOqOmYNzQLDxyWi6jGGA9WoOCWarGXW/gqodUN0xc3AOCDZeGekyhsFXdUgw627U/QqqdkB1x8zBOSDY+GUDLl9rTK42fLNDgs2qUQ+yVLUD6qx2zFwLBwQrv4y+hm82m3WjHHOhagfUcY1SWSUOCDYdMlYdvhncimA2KlU8oJZx0Koyc0Cw6ZHRc/hmyMPBMKM0OlRYVa3lUkUfUGfbuklXwGwgWWNya3uRbyNt1qR+qWK9L0H9UsWlpcnWy6aDWxBs+mR0bUkY9IBfDxQ+NWFVVLVLFW28HBBs6q0ICbXht+OQYFVTtUsVbbwcEGw6ZXS8siGrP9cn3wjKqqxqlyraeDkg2PTK6DxGQq3Dsv1tAnBIsOqo2qWKNl4OCDbdMnwjKLMuqnKpYpVuGjVNHBBs+mX4RlDW1awfXKb9UsWq3TRqmhR2maOk9ZJqTT/fkvRGSUdI2iLpzvR4eNM6F0u6S9JOSWc0lZ8s6UvpuXdJUiqfk/SRVH6zpOOa1tmcXuNOSZuL2k8riaxlut8fGpdLdrKWTo82eb7Mb/r1uhLDilVYC0JE7CR9BEvaD/gn4GPARcCNEXGZpIvS/JsknQhsAk4Cng18VtLzI+JJ4D3ABcAXgL8CzgRuAM4HHo6I4yVtAi4HXinpCOASYBEI4DZJ10fEw0Xtr5VAxppvBDXEqlZiVb/MbxZaR3wlxuSM6xTDy4F/jIivSzoL2JjKrwK2AW8CzgI+HBHLwN2S7gJOkfQ14NCI2A4g6WrgbPKAcBZwadrWdcC7U+vCGcCWiNiT1tlCHio+VOhe2uRlAy5fa1+91rpIzacaplWVDy5la3ovKqz4SozJGddIiptoHJznI+J+gPT4rFR+NHBv0zq7U9nRabq1fMU6EfEE8AjwjB7bMlspa0x2GpmxzqcaplO3g0gVDi5lanov8lTOwkJ+5UUzX4kxHoUHBElPAV4B/Nlqi3Yoix7lw67TXLcLJO2QtOPBxx9cpXpWWVlj0iGhWqp8cCmidWRpCbZvh23b8sd+D/BFhpX5eVi/vhHq5uby+aqdSimjcbQg/CTwxYio/6ktSToKID0+kMp3A8c2rXcMcF8qP6ZD+Yp1JO0PHAbs6bGtFSLiiohYjIjFIw84cugdtArIGpMOCdVR5YPLqFtH1tIKUPSpnPl52LABNm7MH6vw/k2DcQSEV7Hy3P/1QP2qgs3AJ5rKN6UrE54HnADckk5DPCrp1NS/4NUt69S3dQ5wU0QE8GngdEmHp6skTk9lZn1xSKiOqh5cRt06spZWgCqfypllhXZSlHQQ8BPArzQVXwZcK+l84B7gXICIuF3StcBXgCeAC9MVDACvA94PHEjeOfGGVH4lcE3q0LiHvK8DEbFH0luBW9Nyb6l3WDTrKqPr8M21lkXdcdHWaq2d+kY9CNJaWgE8YmM1Kf/CbYuHLMaOxR2TroaVQW3lbK/LHx0SbBitVyBAfkCd5OmP7du7Xy2wYcPq68/CJZdVtG2bbouIxU7PeSRFs1YZHVsSap2WNRtCGcdnWGsrwLSP2GjtHBDMOsnwGAlWmDKOz1Cm+za4NaIcHBDMusnYlwjcH8FGqayD/5ShFaBsA0DNsnENlGQ2nbLGpK9ssFGZ5PgMw451MC5lGgBq1jkgmK0ma0w6JFirYQ64kxqfYRpuXlXG0y+zyqcYzAbk0w1Wt5bm8Ek054+6c2QRfQXKevplFrkFwawf2cpZ3yLaYPqaw0f57byo1ogqD489bRwQzPqVrZzd2l5kM2bamsNHOeJhUeGoysNjTxufYjAbRIYvf7R9pq05fJQjHhYZjspwNYU5IJgNLmPVyx+zbLhTDQ4V02Xahhge5VgHZQhHHi+hWA4IZsPIyBNBBltr7SFhmBaEWs0tD9OmTIML9WtU384nHY48XkLxHBDMhpXRsyXBnRVnw6w2h086HJVxuOqqcUAwW4uMriFhmBaE+qNbEayX1ZrWx9X0PslwNG0dRKeRA4LZCK0ICbXht+OQkPM55narNa3PStN7GfpAVJ0DgtlaZXS8++OwLQjN87McEmblQDeo1ZrWZ6XpfdJ9IGaBA4LZKGS0h4RaxyX73QQw2yFhVg50g1qtaX1Wmt4n3QdiFjggmI1KxsqQkA22+mk1h4Rms3KgG9RqTeuz1PQ+qx1Ex8UBwWyUMpouYxhs1fqpiU5mMSTM0oFuEKs1rbvp3UbFAcFs1DL2jZEwkFojJNRGWZ8p5QNdZ6s1rbvp3UbFAcGsCBlrOsp3Wn3WWhF8oOtutaZ1N73bKDggmBUlG3D5Wv6w2vDNsxYSfKAzmwzfzdGsLLLG49aWIlg5kJKZWdEcEMzKJGNFS0K9qJVDgpkVzQHBrGyyxqRDgplNigOCWck5JJjZJDggmJVRtnJ2a8eFcg4JZlYEBwSzsspWzm5tLzIzK4wDglmZZX0VuRXBzEbOAcGs7LLGpPsjmNm4eKAks2mQsepASjB7AynZcJaWZmuEylnb31FxC4LZtMgak25JqK6lJdi+HbZtyx+Xlka//Z07V94eeufO0b9OWcza/o6SA4LZlHJIqJ5xHMx27Vp5AyzI53ftGt1rlMms7e8oOSCYTZNs5awvf6yWcRzMOt1Cu1f5tJu1/R0lBwSzaZOtnO11+aNDwnQZx8Fsbm6w8mk3a/s7Sg4IZtMoWzk7TWMkFH2OfZqN42C2sADrWj75163Ly6to1vZ3lFa9ikHS04DvRMReSc8HXgDcEBGPF147M+suo+0yhg5FQ7UiFHUlRP0ce70ZvX6OHdyrHPKDVvPvB0Z/MKv/nmelV/+s7e8o9XOZ418D/4+kw4EbgR3AK4HziqyYmfUhY9XLHwc92NdqxV0u2escuz+wx3cwm5+frd/3rO3vqPQTEBQR35Z0PvD7EfHbkv626IqZWZ8yeoaEMvVDcIex1flgZmXRV0CQtIG8xeD8AdYzs3HJ6BoShmlBqD+OuhVhbq5zGHCHMbPy6edA/0bgYuBjEXG7pAV6X11lZhO2IiTUht/OqEPCOM6xm9loKCImXYdSWDxkMXYs7ph0NczWprZy9jQY+PKGToFilCHBw96alce2bbotIhY7PdfPVQx/AbSmiEfIOyu+NyK+u/YqmtlIZKwICVuB02odl+x3E8BoWxJ8jt1sOvRzimEXcCTwoTT/SmAJeD7wR8AvFlM1MxtKxsqQkA22+mk13wjKzPoLCD8UES9rmv8LSX8dES+TdHtRFTOzNchouoxhsFXr/Rc6cUgwmx39jKR4pKTn1GfS9DPT7Pd6rSjp6ZKuk/RVSXdI2iDpCElbJN2ZHg9vWv5iSXdJ2inpjKbykyV9KT33LklK5XOSPpLKb5Z0XNM6m9Nr3Clpc3+/DrMKyZoeB/nBwzebWX8tCL8OfF7SPwICngf8Whph8apV1n0n8KmIOEfSU4CDgN8AboyIyyRdBFwEvEnSicAm4CTg2cBnJT0/Ip4E3gNcAHwB+CvgTOAG8ssuH46I4yVtAi4HXinpCOASYJG8/8Rtkq6PiIf7/L2YVUPGwC0IdfWWhCFXN7Mpt2pAiIi/knQC+RDLAr7a1DHx97qtJ+lQ4GXAa9J2vgd8T9JZwMa02FXANuBNwFnAhyNiGbhb0l3AKZK+BhwaEdvTdq8GziYPCGcBl6ZtXQe8O7UunAFsiYg9aZ0t5KGi3o/CbHZkAy5fa1+91rpIzacayshXiNgo9XuzppPJv9n/K+DnJb26j3UWgAeBP5H0t5Lel1od5iPifoD0+Ky0/NHAvU3r705lR6fp1vIV60TEE+RXVzyjx7bMbDVZY3Jre9E+PtVQLvX7XNQHoqrf58I3w7Jh9XOZ4zXA95N/iXgyFQdwdR/bfjHw+oi4WdI7yU8ndH2pDmXRo3zYdRovKF1AfuqC58w9p20Fs5mVseo9HmDwkOBWh+Gt1jrg+1zYqPXTB2ERODEGH1FpN7A7Im5O89eRB4QlSUdFxP2SjgIeaFr+2Kb1jwHuS+XHdChvXme3pP2Bw4A9qXxjyzrbWisYEVcAV0A+UNKA+2dWbRmF3AjKIWFw/dwF0/e5sFHrJyB8Gfg+4P5BNhwR35B0r6T1EbETeDnwlfSzGbgsPX4irXI98EFJbyfvpHgCcEtEPCnpUUmnAjcDrwZ+v2mdzcB24BzgpogISZ8GfqvpConTyYeLNrMhjepGUA4Jg+undcD3ubBR6ycgPBP4iqRbgH1/fhHxij7WfT3wgXQFwy7gteT9Hq5Nd4e8Bzg3be92SdeSB4gngAvTFQwArwPeDxxI3jnxhlR+JXBN6tC4h/wqCCJij6S3Arem5d5S77BoZgPIaB+ZsfnpbLDNFXkjqCrrp3XA97mwUVv1XgySfrRTeUR8rpAaTYjvxWDWQ23l7Kguf3RI6M/27d1bBzZsaMz7KgYb1JruxVC1IGBmQ8joePnjWm8E5ZaE/nRqHQB48sk8FNRDgO9zYaPUNSBI+nxE/GtJj7LyCgABERGHFl47MyuPjLZOi6NoRnBIWF39oP8P/5CHgronnmjvrGg2Kl0DQkT86/R4yPiqY2allrEiJAzagnBarXOmcEhY3fx8fvqgOSCAL2W04vQzDsI7yUc43D6G+phZ2WWs+UZQA65miS9ltHHq5yqGLwL/XdLzgY8BH4kI9+Yzm2UZ+VE+G3C92srVVzxVK6YVoUod93wp4+hV6e9j1PrppHgVcFW6AdK/BS6X9JyIOKHw2plZudUGX2W1kRlHGRL6GWBomvhSxtGq2t/HqPXTglB3PPkNm44jH6vAzGZZNsQ6tfxhXCGhasMP1+vsb7yjUbW/j1Hrpw/C5cDPAf8IXAu8NSK+WXC9zKyKMsYaEqp4zt6XMo5OFf8+RqmfFoS7gQ0R8VDRlTGzElgiH/d0GZgjvy9rQQekokOCz9lbL/776G3V2z1HxB8CT0o6RdLL6j9jqJuZjdsSsJPGoOrLaX6UtwzOVs5u7bhQbq23lF5YyM/RN/M5e6vz30dvqwYESb8M/DXwaeA30+OlxVbLzCZiF9ByTpa9qXyUspWzW9uLRmJ+Htavb3wjnJvL591Eb+C/j9X0c4rhDcAPA1+IiNMkvYA8KJhZ1XQ791rEOdmMjsM3txSt+VSDz9lbL/776G7VFgTguxHxXQBJcxHxVWB9sdUys4nodu61qHOyWWNya3vRPms91WBmg+snIOyW9HTg48AWSZ8A7iuyUmY2IQu0fyqsS+VFyRqTDglm5dFPJ8WfjYhvRsSlwH8HrgTOLrheZjYJ8+Ttg/UWg7k0X3QTbNaYbA0JzacXHBLMxqdrQJB0kKQDmubXAy8G9o+I742jcmY2AfPABmBjehzX+dmsMdkcEjrdItrMiterBeFT5KMmIul4YDt5Q+OFki4rvmpmE7ZE/le/LT2O8lI/W5VPN5hNVq+AcHhE3JmmNwMfiojXAz8J/HThNTObpHGMB2DtspWzRY6RYGa99QoI0TT9Y8AWgHR6ofVKabNqGdd4ANYuWzlb1BgJZtZbr4Dw95J+R9J/JL9R02cA0hUNZtU2zvEArF3WV5FbEcwK1Csg/DvgIfJ+CKdHxLdT+YnA7xRcL7PJGvd4ANYua0y6P4LZ+HUNCBHxnYi4LCLeEBF/11T+NxFxzXiqZzYhkxgPwNpljUmHBLPx6meoZbPZU7+0b0x3NbQeMvq+RfRAm83WUCezGeCAYNbNPA4EJdQtJAxywK/VRnM7abMq6xoQJP1g86mFludeFxHvKa5aZmZNMlakgXpIaDbMaQaHBLPuerUgfEzSuRFxW3OhpN8E/g3ggGBmhfrAErx5F9yzDM+Zg7etg/NSR9F6SKilZQc90NcDRT8hYWkJdu2C5eX8lsALC74DoLWr2t9Jr4BwLvBnks6LiO2SRB4Knk8+CKuZWWE+sAQX7IRvp/Eovr4MF6SOo+c1XU2SkYeEtXRU7BUSlpZg507Ym+qxvJzPw3R/+JfRNB9gq/h30jUgRMRtks4mb0m4kPyyR4AzfS8GMyvam3c1wkHdt/fCm/fCeWm+uT/CoKMpdbrHQ6eQsGtX40O/bu/evHySH/zTfDDtZNoPsGX9O1mLXn0QjgB2kw+z/HHgs8C/Bw6WRETsGUsNzWwm3dNlUKp7lslvIlXL5/f1R6it/TU7hYTlLvXoVj4O034w7aTbAfaOO/Lnyh6Ayvh3sla9TjHcRmO45UeBlwC3AErlviLczArznLn8tEKncqDt8sdBWxBOq/WXKebmOn/Iz01w0KwqflvtdSCdhgBUxr+Ttep1iuF546yImVmzty2s7IMAcNC6vHyfjMZRvsbAmleva21FWFhY+W0dYN26vHxSqvhttdsBtq7sAaiMfydrNdQ4CJJeEBFfHXVlzMzqzksHghVXMSw0yttkg21/a633oEv1kFA/IBVxvn/YfgRV/Lba6QDbqswBqMi/k0lRRKy+VOtK0j0R8ZwC6jMxi4csxo7FHZOuhpkNozb8qvXxFDptosgxElr7EUD+jXP9+tUPKmtZt8yaA1Mnc3OwYcN461R127bptohY7PRcr06K7+r2FPD0EdTLzGw0siHWqeUPqw3fXFRIWEs/gip+W4W8/vPz3QPQNDfXT6NepxheC/w6nW9w+6piqmNmI7OE7yXRp0mEhLX2I6gfTKuoqgFo2vQKCLcCX46Iv2l9QtKlhdXIzNZuCdgJ1L+BLad5cEioy1h1+Oa6Im4EVcV+BKNU5QA0LXoFhHOA73Z6wlc4mJXcLhrhoG5vKveHbkNGx5BQa10s63+T/d4Iqoq93q1ael3m6IGQzKZVt2bqEvcCn5iMtkTQWlTEjaDcjG5l16uT4mHAxcDZwJGp+AHgE8BlEfHNoitnZkOao3MYcPN1Zxk9Oy0O2oLQPL1aSHAgsLLqdYrhWuAmYGNEfANA0veRD738Z8BPFF89MxvKAiv7IACsw+Of9pKRJ4KsfYyEom4EZVZmvQLCcRFxeXNBCgqXS/qlYqtlZmtS/1bqqxgGk9GxJWGQyyjXEibMyqRXQPi6pP8KXBURSwCS5oHXAPeOoW5mthbzOBAMI2NNN4JqWn0ftyLYNOoVEF4JXAR8TtKzUtkScD3w80VXzMysDAa9EdRpNYcEq4ahhlquIg+1bBPhwYzKqzb8qpMavtlsUL2GWl43yIYkXT3g8l+T9CVJNUk7UtkRkrZIujM9Ht60/MWS7pK0U9IZTeUnp+3cJeldkpTK5yR9JJXfLOm4pnU2p9e4U9LmQeptNnJLwHZgW3pcojGYUf1qg/pgRksTqJ+1y1qm+/0htTrQueHBfRRsWvS6zPH61iLgNElPB4iIV/T5GqdFxENN8xcBN0bEZZIuSvNvknQisAk4CXg28FlJz4+IJ4H3ABcAXwD+CjgTuAE4H3g4Io6XtAm4HHilpCOAS4BFIIDbJF0fEQ/3WWez0ek2qqHwYEZll5EuYxh81Und48FsVHr1QTgG+ArwPvKDrMgPuL+7xtc8C9iYpq8i/071plT+4YhYBu6WdBdwiqSvAYdGxHbY14pxNnlAOAu4NG3rOuDdqXXhDGBLfbAnSVvIQ8WH1lh3s8F1G9WwGw9mVC7ZgMvXGpOrDd/skGBl1usUwyJwG/Bm4JGI2AZ8JyI+FxGf63P7AXxG0m2SLkhl8xFxP0B6rHeAPJqVV0fsTmVHp+nW8hXrRMQTwCPAM3psawVJF0jaIWnHg48/2OcumQ1o0AO+BzOabtnK2a3tRWZToWtAiIi9EfEO8rs6vlnSu+nd4tDJSyPixcBPAhdKelmPZdWpGj3Kh12nURBxRUQsRsTikQcc2WEVsxHodsDfn/b/QA9mVA1ZX0Xuj2CltmonxYjYHRHnkjfp/+kgG4+I+9LjA8DHgFOAJUlHAaTHB9Liu4Fjm1Y/BrgvlR/ToXzFOpL2Bw4D9vTYltn4LdA5CJwArKcRIObS/Az3P/jAEhy3HdZtyx8/MM0dNrPGpDst2jTq+yqGiPjLiPiNfpeX9DRJh9SngdOBL5OPo1C/qmAz+b0dSOWb0pUJzyP/+LwlnYZ4VNKpqX/Bq1vWqW/rHOCmyK/b/DRwuqTD01USp6cys/Gbp3sQmAc2kPfK2cDMh4MLdsLXl/Pmvq8v5/MdQ0Knq0LKKGtMOiTYtBn0lMEg5oGPpSsS9wc+GBGfknQrcK2k84F7gHMBIuJ2SdeSd4x8ArgwXcEA8Drg/cCB5C0ZN6TyK4FrUofGPeRXQRAReyS9Fbg1LfcW353SgMmNO+BRDVf15l3w7ZbOm9/em5ef1/y763ZVCJTzd5zR80ZQde60aGXjgZISD5Q0A1oPLJC3oc14s35ZrNvWoaMQ6WrQjU0F2+l+p8oNo6/XyNQakx5IycpiZAMlmU21bpcb7ppAXazNc7p05mwr73ZVyBRdHurTDTYNHBBsdlTgwFJlb1uAg1o+kQ5al5ev0O2qkLJfHpqtnN3acaGcQ4KVgQOCzY5pPbDMiPPm4Yr18Ny5/LTCc+fy+fNaT/90uypkGi4PzVbOeowEK7MiOymalcsCnfsgTMOBpapaOo2etwDnrdaPoB4YpvUmVxltnQ86FLnTok2cA4LNjmk/sFTNWq5GGPdVIaO++iXDVzZY6fkUg80WjztQHtPSabSou25mjUl3WrQyckAws8mYlk6jRQaZrDHpkGBl44BgZpMxLZ1GxxhkHBKsTNwHwcwmY1o6jc7RfWCmUcjoeIvoLGsPBYOGBPdfsLVwQDCzyZiWTqPjCDIZ7SGh1mGxrP9N1mru5Ghr44BgZpMzDfeoGFeQyejYktBUNNRpBocEG5YDgpnZasYVZDJ6jpEwaAtC87RDgg3KAcHMrEwyuo6RsJaOig4J5bC0BLt2wfIyzM3BwgLMl7QVzQHBzKxsMjqGhEHGZfZVD+WztAQ7d8Le1J9leRnuuCP/KWNYcEAwMyujjLaQ0PH+0Kuvvo9bESZr165GOGi1vJyHByhPSHBAMDObAlthoBaE02oOCWWzvMrYGXv35iHCAcFsWo16XH6zbjJaLmPof1Xf46F85uZWDwmrPT9ODghmg1jLDYasWFUNbhlNlzEMsF7NIaFsFhZW9kHoZK5EI4k6IJgNote4/FU4GE2rQYPbtIWJjHQZw+CrOiSUR/3UQf0qhlbr1uUhoiwcEMwGMS03GJo1gwS3aW0FygZcvtaYXG34ZoeE8ZmfbwSFsl/y6IBgNoiix+W34QwS3GalFSijr+GbwSFhUprDQhn5bo5mg1ig/b+mjDcYmjWD3BlyllqBspWzW9uLzLpyQDAbxDywnsaBZy7Nl/hbwEwYJLhNy22mRyXrq8gDK1kbBwSzQc0DG4CN6dHhYPIGCW6z2AqUNSa3thft45BgzRwQzKwa+g1us9oKlDUmHRKsHw4IZjZ7ZrUVKGtMOiTYahwQzMxmlEOC9eKAYGY2S7KVs1s7LpRzSJhtDghmZrMmWznryx+tEwcEM7NZlPVV5FaEGeaAYGY2q7LGpPsjWCsHBDOzWZY1Jh0SrJkDgpnZrMsak60hofkeDQ4Js8UBwczMuoaETnd/tNnggGBmZm18usF8u2czK48l8tsuL5MPgbzA7IxyWAYZ7beI7rLooCHBt5OePg4IZlYOS8BOYG+aX07z4JAwThkdQ0KtdbGs/03WavmPQ8J0cUAws3LYRSMc1O1N5Q4I45XRlghai4Y5zeCQMF0cEMysHJYHLLdiZexLBPVWhKaigVsQmqcdEqaDA4KZlcMcncPAXIcyG4+MriHBHRWrzwHBzMphgZV9ECC/zmphMtWxJKNjSBjk5g2dLpV0K0L5OSCYWTnU+xn4KoZS23dlQ63/dbIOizsklJ8DgpmVxzwOBGWU0XZlwyAtCKfVHBKmUeEDJUnaT9LfSvpkmj9C0hZJd6bHw5uWvVjSXZJ2SjqjqfxkSV9Kz71LklL5nKSPpPKbJR3XtM7m9Bp3Stpc9H6amVVa1jJf6//HwzdPp3GMpPgG4I6m+YuAGyPiBODGNI+kE4FNwEnAmcAfSNovrfMe4ALghPRzZio/H3g4Io4H3gFcnrZ1BHAJ8BLgFOCS5iBiZmZDyFqm+/3BwzdPo0IDgqRjgJ8G3tdUfBZwVZq+Cji7qfzDEbEcEXcDdwGnSDoKODQitkdEAFe3rFPf1nXAy1PrwhnAlojYExEPA1tohAqz8VoCtgPb0uPSRGtjtjZZeqwN8JN4+ObpUnQfhN8D/itwSFPZfETcDxAR90t6Vio/GvhC03K7U9njabq1vL7OvWlbT0h6BHhGc3mHdczGZxyjA3p4Yhu3bMDla43J1YZvdp+E8iisBUHSzwAPRMRt/a7SoSx6lA+7TnMdL5C0Q9KOBx9/sM9qmg2g1+iAo1APIPXxA+oBxK0UVibZytmt7UVWQkW2ILwUeIWknwKeChwq6U+BJUlHpdaDo4AH0vK7gWOb1j8GuC+VH9OhvHmd3ZL2Bw4D9qTyjS3rbGutYERcAVwBsHjIYluAMFuzokcH9PDENi0yVh2+GYY71eBWh2IUFhAi4mLgYgBJG4H/HBG/IOl/AZuBy9LjJ9Iq1wMflPR24NnknRFviYgnJT0q6VTgZuDVwO83rbOZ/MzuOcBNERGSPg38VlPHxNPrdTEbq6JHB/TwxDZNMnoO3wyDH+x9I6jiTGIchMuAayWdD9wDnAsQEbdLuhb4CvAEcGFEPJnWeR3wfuBA4Ib0A3AlcI2ku8hbDjalbe2R9Fbg1rTcWyJiT9E7Ztam6NEBPTyxTZuMniHBnRXLQ/mFAbZ4yGLsWNwx6WpYFRXZibC1EyTkAWT9CF/DrAi1xuRpTUXDtCDUuRVhcNu26baIWOz0nEdStOkwjT31W+v8QkZfZw9PbBWwoiWhNvx2fKphtBwQrPzGcangqI2zzh6e2KZRRsfLH9fSglCfd0gYDQcEK79p7Kk/jXU2G7eM9pBQ67jkqpvIskZYcEgYDQcEK79p7Kk/jXU2m4SMlSEhG2z1fTeCqq0sd0hYOwcEK79p7Kk/jXXuxzT2BemkKvtRFRlNlzEMtmq3yyXBIWGtHBCs/Iq+VLAI01jn1UxjX5BO1rIfDhbFyWgc4bMB1qt5+OaiOCDYeKzlg3Uae+pPY51XU5V+FcPuR1UCUplltN3gqV/1kDDEqtaFA4IVbxQfrNPYU38a69xLVfpVDLsfVQlIZZcNuHytffVa6yI1tyIMo9DbPZsBxd+wyMajW/+JaetXMex+VCUgVU3WmPTtpEfLAcGK5w/Walig/RNjGvtVDLsfVQlIVZQ1Jh0SRscBwYrnD9ZqmCcfwrn+vs0xnUM6D7sfVQlIVZU1Jh0SRsN9EKx4VezRP6uq0q9imP2oYsfTCvPlj2vngGDF8werjdIkLzWsSkCqqoyOwzd34pCwOgcEGw9/sNoo+FJDW01Gx5BQ67Ss9eQ+CGY2PXxFjPUj66vI/RFW4YBgZtPDV8RYv7LGpDstDscBwWzUloDtwLb0uDTR2lRLv1fE+D0wcEhYIwcEs1GqnyOvf6OtnyP3AWo0+rnU0O+BNcsakw4Jg3FAMBulMp8jr8K36n7GMCjze2CTkTUmHRL654BgNkplPUdepW/V88AGYGN6bL16oazvgZWGQ0J/HBDMRqmso0bO0rfqsr4HNlnZytmtHRfKOSTkHBDM+tVPE31Zh+OdpW/VZX0PbPKylbNb24usiQdKMutHvwP0lHXUyDk6h4FRf6ue5CiHdWV9D6wcMvq+RfTAm84GX6fMHBDM+tGrib71wFPGUSPHcT+MTiHqjvTjIZGtTDL2JYJu92wY9GBfq1Vv+GYHBLN+THsT/Ti+VXcKUXUeEtnKJqNnSHA/BAcEs/6Mq4m+SEV/q14tLHVrcTGblIyuIWGYFoT6Y1VaERwQzPoxS7esHrYfQbcQ1WxaWlxsJq0ICbXht1OVkOCAYNaPWen4tpa7JXYKUa2mqcXFZkNGx7s/DtuC0Dw/7SHBAWEWlKFneRXMQse3QTpjtmoNUa2q2uJi0y+jPSTUOi7Z7yaA6Q8JDghVt5ZvhDZ71toZszlEOZjaNMloCwmDDJJwWq16IcEBoerW8o3QZs8oO2POQouLVUvGyiN8reNSHXW7XBKmNyQ4IFTdtF+eZ+M1S50xzTrJaBzhswHWqzVCQsena9MXEhwQqq4Kl+fZ+MxKZ0yzXjLykFAbfNV6SBhi1dJxQKi6qn0jrPJ57bLsm08NmA1+k4Za++q11kVq09WK4Js1Vd08sJ5Gi8Fcmp/GA0CVblncqsr7ZjYLssZkVW4n7RaEWVCVb4RV7nBZ5X0zmxUZq97jAQYPCZNqdXBAsOlR5Q6Xk9q3spzWMKuKjEJuBDWJkOCAYNOjyh0uJ7FvHiPDrHCjuhHUJEKCA4JNj6p1uGw2iX3zaQ2zYmR0Hb55mAP9pG4E5YBg06PKl+BNYt+qfMrGbNIyug7fPC03gnJAsOlSZIfLSZ+PH3dn0iqfsjErg4yOLQmDXkI5qRtBOSCYwWyej6/yKRuzssjocBnDCDZRKz4kOCCYwWyej6/yKRuzMslYcWXDoC0I3e4sWXRIcEAwg9k9H1+VMTLMyi6j6TKGwVad1PDNhY2kKOmpkm6R9HeSbpf0m6n8CElbJN2ZHg9vWudiSXdJ2inpjKbykyV9KT33LklK5XOSPpLKb5Z0XNM6m9Nr3Clpc1H7aRXR7by7z8eb2ahkTY+D/LSs3qzIkRmLbEFYBn4sIh6TdADweUk3AD8H3BgRl0m6CLgIeJOkE4FNwEnAs4HPSnp+RDwJvAe4APgC8FfAmcANwPnAwxFxvKRNwOXAKyUdAVwCLAIB3Cbp+oh4uMD9tWlW1fPxk+54aWbtaoOvMonbSRcWECIigMfS7AHpJ4CzgI2p/CpgG/CmVP7hiFgG7pZ0F3CKpK8Bh0bEdgBJVwNnkweEs4BL07auA96dWhfOALZExJ60zhbyUPGhQnbWpl8Vz8fPYsdLs7LLhlinlj+MOyQUerMmSftJqgEPkB+wbwbmI+J+gPT4rLT40cC9TavvTmVHp+nW8hXrRMQTwCPAM3psy6y7eWADeXzdwPQfRHt1vDSz6ZE1Jsd5I6hCA0JEPBkRGXAMeWvAi3osrk6b6FE+7DqNF5QukLRD0o4HH3+wR9XMVrEEbCdvD9tOOe7COKsdL80qblwhYSy3e46Ib5J/dJ4JLEk6CiA9PpAW2w0c27TaMcB9qfyYDuUr1pG0P3AYsKfHtlrrdUVELEbE4pEHHDn8DtpsK+utmt3x0qw6spWz+0JCSzmMLiQUeRXDkZKenqYPBH4c+CpwPVC/qmAz8Ik0fT2wKV2Z8DzgBOCWdBriUUmnpv4Fr25Zp76tc4CbUt+HTwOnSzo8XSVxeiozG72yNuUv0P4fXoWOl2azKls5uxW6dngcRUgo8iqGo4CrJO1H/rF0bUR8UtJ24FpJ5wP3AOcCRMTtkq4FvgI8AVyYrmAAeB3wfuBA8s6JN6TyK4FrUofGPeRXQRAReyS9Fbg1LfeWeodFs5Era1N+FTtems26jI7DN9c6LbtGyr9w2+Ihi7Fjccekq2HTaDvd72mwYcx1MbPZUGtMntZetM9qVzZs26bbImKx03Nj6YNgVmluyjezccsak0V1WnRAMFureWA9jc5/c2neTflmVqSsMVlESHBAMBuFqo2hYGbTIWtMjjokOCCYmZlVxChDggOCmZnZNMtWzm7tuFBukJDggGBmZjbtspWzW9uLBuaAYGZmVgVZX0V9tyI4IJiZmVVF1phca38EBwQzM7MqyRqTawkJDghmZmZVkzUme4WEXhwQzMzMqihrTA4TEhwQzMzMZsCgIcEBwczMrKqylbO9xkho5YBgZmZWZdnK2X7HSHBAsHJYIr9t8rb0uDTR2piZVUvWV9EKDgg2eUvATmA5zS+neYcEM7PRyRqT/ZxqcECwydsF7G0p25vKzcxsdLLG5GohYf8i62EVtUR+8F4G5oAF1nZ74+UBy8ts1L8bM7NRy4Da6os5INhg6qcD6t/466cDYPgD4Rydw8DckNublCJ+N2ZmE+JTDDaYIk4HLND+l7gulU8Tnyoxs2mRrb6IA4INpojTAfPAehotBnNpftq+dVfpVImZVV/W+2mfYrDBFHU6YJ7pCwStqnKqxMwMtyDYoKpyOqAI/t2YWYW4BcEGU/+W75767fy7MbMKcUCwwVXhdEBR/Lsxs4rwKQYzMzNr44BgZmZmbXyKwcrJIxKamU2UA4KVj0ckNDObOJ9isPLxiIRmZhPngGDl4xEJzcwmzgHByqfbyIMekdDMbGzcB8HKZ4GVfRDAIxLabHOnXZsABwQrH49IaNbgTrs2IQ4IVk4ekdAs16vTrv9HrEDug2BmVmbutGsT4oBgZlZm7rRrE+KAYGZWZr6NuE2I+yCYmZWZO+3ahDggmJmVnTvt2gT4FIOZmZm1cUAwMzOzNg4IZmZm1sYBwczMzNoUFhAkHStpq6Q7JN0u6Q2p/AhJWyTdmR4Pb1rnYkl3Sdop6Yym8pMlfSk99y5JSuVzkj6Sym+WdFzTOpvTa9wpaXNR+2lmZlZFRbYgPAH8ekS8EDgVuFDSicBFwI0RcQJwY5onPbcJOAk4E/gDSfulbb0HuAA4If2cmcrPBx6OiOOBdwCXp20dAVwCvAQ4BbikOYiYmZlZb4UFhIi4PyK+mKYfBe4AjgbOAq5Ki10FnJ2mzwI+HBHLEXE3cBdwiqSjgEMjYntEBHB1yzr1bV0HvDy1LpwBbImIPRHxMLCFRqgwMzOzVYylD0Jq+v8h4GZgPiLuhzxEAM9Kix0N3Nu02u5UdnSabi1fsU5EPAE8Ajyjx7Za63WBpB2Sdjz4+INr2EMzM7NqKTwgSDoY+Cjwxoj4Vq9FO5RFj/Jh12kURFwREYsRsXjkAUf2qJqZmdlsKTQgSDqAPBx8ICL+PBUvpdMGpMcHUvlu4Nim1Y8B7kvlx3QoX7GOpP2Bw4A9PbZlZmZmfSjyKgYBVwJ3RMTbm566HqhfVbAZ+ERT+aZ0ZcLzyDsj3pJOQzwq6dS0zVe3rFPf1jnATamfwqeB0yUdnjonnp7KzMzMrA9F3ovhpcAvAl+SVEtlvwFcBlwr6XzgHuBcgIi4XdK1wFfIr4C4MCKeTOu9Dng/cCBwQ/qBPIBcI+ku8paDTWlbeyS9Fbg1LfeWiNhT0H6amZlVjvIv3LZ4yGLsWNwx6WqYmZmNjbbptohY7PScR1I0MzOzNg4IZmZm1sYBwczMzNo4IJiZmVmbIq9isFmzBOwCloE5YAGYn2iNzMxsSA4INhpLwE5gb5pfTvPgkGBmNoV8isFGYxeNcFC3N5WbmdnUcUCw0VgesNzMzErNpxhsNOboHAbmxl0RA9wfxMzWzC0INhoLtP81rUvlNl71/iD1wFbvD7I0sRqZ2RRyQLDRmAfW02gxmEvz/tY6fu4PYmYj4FMMNjrzOBCUgfuDmNkIuAXBrGq6xX73BzGzATggmFXJEvnN0lsJ9wcxs4E4IJhVSbd+Buvw6R8zG4gDglmVdOtn8ORYa2FmFeCAYFYl3foZuP+BmQ3IAcGsSjwehZmNiC9zNKuSej8Dj6JoZmvkgGBWNR6PwsxGwKcYzMzMrI0iYtJ1KAVJj5KPWF91zwQemnQlCuZ9rIZZ2EeYjf30PpbXcyPiyE5P+BRDw86IWJx0JYomaUfV99P7WA2zsI8wG/vpfZxOPsVgZmZmbRwQzMzMrI0DQsMVk67AmMzCfnofq2EW9hFmYz+9j1PInRTNzMysjVsQzMzMrM1MBgRJx0raKukOSbdLekMqv1TSP0mqpZ+fmnRdhyXpqZJukfR3aR9/M5UfIWmLpDvT4+GTruuweuxjZd7HOkn7SfpbSZ9M85V5H+s67GMV38evSfpS2p8dqaxS72WXfazUeynp6ZKuk/TVdBzZULX3EWb0FIOko4CjIuKLkg4BbgPOBn4eeCwifmeS9RsFSQKeFhGPSToA+DzwBuDngD0RcZmki4DDI+JNk6zrsHrs45lU5H2sk/SfgEXg0Ij4GUm/TUXex7oO+3gp1XsfvwYsRsRDTWWVei+77OOlVOi9lHQV8H8i4n2SngIcBPwGFXofYUZbECLi/oj4Ypp+FLgDOHqytRqtyD2WZg9IPwGcBVyVyq8iD0ZTqcc+VoqkY4CfBt7XVFyZ9xG67uOsqNR7WXWSDgVeBlwJEBHfi4hvUsH3cSYDQjNJxwE/BNyciv69pL+X9MfT3kSUmmxrwAPAloi4GZiPiPshD0rAsyZYxTXrso9QofcR+D3gvwJ7m8oq9T7SeR+hWu8j5AH2M5Juk3RBKqvae9lpH6E67+UC8CDwJ+mU2PskPY3qvY+zHRAkHQx8FHhjRHwLeA/w/UAG3A/87uRqt3YR8WREZMAxwCmSXjThKo1cl32szPso6WeAByLitknXpSg99rEy72OTl0bEi4GfBC6U9LJJV6gAnfaxSu/l/sCLgfdExA8B/wJcNNkqFWNmA0I6Z/1R4AMR8ecAEbGUDjh7gT8CTplkHUclNX9tIz83v5T6YNT7YjwwuZqNTvM+Vux9fCnwinRe98PAj0n6U6r1Pnbcx4q9jwBExH3p8QHgY+T7VKX3suM+Vuy93A3sbmqtvI48MFTqfYQZDQipc9uVwB0R8fam8qOaFvtZ4MvjrtuoSDpS0tPT9IHAjwNfBa4HNqfFNgOfmEgFR6DbPlbpfYyIiyPimIg4DtgE3BQRv0CF3sdu+1il9xFA0tNSp2hSk/Tp5PtUmfey2z5W6b2MiG8A90pan4peDnyFCr2PdbN6s6aXAr8IfCmdv4a8B+qrJGXk59C+BvzKJCo3IkcBV0najzwIXhsRn5S0HbhW0vnAPcC5k6zkGnXbx2sq9D52cxnVeR+7+e2KvY/zwMfy7yfsD3wwIj4l6Vaq815228eq/U++HvhAuoJhF/Ba0mdQRd5HYEYvczQzM7PeZvIUg5mZmfXmgGBmZmZtHBDMzMysjQOCmZmZtXFAMDMzszYOCGZWOEk/KykkvWDSdTGz/jggmNk4vIr8bpubJl0RM+uPA4KZFSrd8+SlwPmkgCBpnaQ/kHS7pE9K+itJ56TnTpb0uXSzn0+3jMJnZmPigGBmRTsb+FRE/AOwR9KLgZ8DjgN+APhlYAPsu0fK7wPnRMTJwB8Db5tAnc1m3qwOtWxm4/Mq8ts5Q34zplcBBwB/lm7e8w1JW9Pz64EXAVvScL37kd/9z8zGzAHBzAoj6RnAjwEvkhTkB/wgv8tfx1WA2yNiw5iqaGZd+BSDmRXpHODqiHhuRBwXEccCdwMPAf829UWYBzam5XcCR0rad8pB0kmTqLjZrHNAMLMivYr21oKPAs8GdpPf9ve9wM3AIxHxPfJQcbmkvwNqwI+MrbZmto/v5mhmEyHp4Ih4LJ2GuAV4aUR8Y9L1MrOc+yCY2aR8UtLTgacAb3U4MCsXtyCYmZlZG/dBMDMzszYOCGZmZtbGAcHMzMzaOCCYmZlZGwcEMzMza+OAYGZmZm3+f+EznsDBMB+AAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plot the boundary using the trained classifier\n",
"# If classified as 0 it will be magenta, and if it is classified as 1 it will be shown in blue \n",
"from matplotlib.colors import ListedColormap\n",
"plt.figure(figsize=(8, 8))\n",
"\n",
"plt.contourf(X1, X2, grid.predict(scaler.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('magenta', 'blue')))\n",
"plt.xlim(X1.min(), X1.max())\n",
"plt.ylim(X2.min(), X2.max())\n",
"X_test_unscaled = scaler.inverse_transform(X_test)\n",
"for i, j in enumerate(np.unique(y_test)):\n",
" plt.scatter(X_test_unscaled[y_test == j, 0], X_test_unscaled[y_test == j, 1],\n",
" c = 'magenta' if j == 0 else 'blue')\n",
"\n",
"plt.title('Bank customers: retirement predictions (Test set)')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('401K Savings')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}