p78_support_vector_machine_gamma.py

#!/usr/bin/python
''' support_vector_machine_gamma.py illustrates changing the gamma parameter
    for a SVM.  This is a cut-off parameters for the Gaussian sphere.  A 
    higher value tightens the decision boundary around the samples
    
    Run the SVM with two values of gamma and plot the decision regions
    
Created on Jun 23, 2016

from Python Machine Learning by Sebastian Raschka under the following license

The MIT License (MIT)

Copyright (c) 2015, 2016 SEBASTIAN RASCHKA (mail@sebastianraschka.com)

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in the Software without restriction, including without limitation the rights
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SOFTWARE.

@author: richard lyman
'''
import numpy as np
import ocr_utils
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

y_train, X_train, y_test,  X_test, labels  = ocr_utils.load_E13B(chars_to_train = (48,49,50) , columns=(9,17), test_size=0.3, nChars=300, random_state=0) 

sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))


svm = SVC(kernel='rbf', random_state=0, gamma=0.2, C=1.0)
svm.fit(X_train_std, y_train)

ocr_utils.plot_decision_regions(X=X_combined_std, 
                      y=y_combined, 
                      classifier=svm, 
                      labels = labels,
                      test_idx=range(len(X_test_std),len(X_combined_std)),
                      title='SVM with gamma 0.2')

svm = SVC(kernel='rbf', random_state=0, gamma=100.0, C=1.0)
svm.fit(X_train_std, y_train)

ocr_utils.plot_decision_regions(X=X_combined_std, 
                      y=y_combined, 
                      classifier=svm, 
                      labels = labels,
                      test_idx=range(len(X_test_std),len(X_combined_std)),
                      title='SVM with gamma 100')


print ('\n########################### No Errors ####################################')