from lib_scikitlearn import plot_decision_regions
iris = datasets.load_iris()
X = iris.data[:,[2,3]]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
svm = SVC(kernel='rbf', random_state=0, gamma=0.2, C=1.0)
svm.fit(X_train_std, y_train)
y_pred = svm.predict(X_test_std)
print('Misclassified samples: %d'%(y_test != y_pred).sum(), '/%d' % len(y_test))
print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X=X_combined_std, y=y_combined, classifier=svm, plt=plt, test_idx=range(105,150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
#!/usr/bin/python3 import numpy as np import matplotlib.pyplot as plt from sklearn.svm import SVC from lib_scikitlearn import plot_decision_regions np.random.seed(0) X_xor = np.random.randn(200, 2) y_xor = np.logical_xor(X_xor[:, 0] > 0, X_xor[:, 1] > 0) y_xor = np.where(y_xor, 1, -1) svm = SVC(kernel='rbf', random_state=0, gamma=0.10, C=10.0) svm.fit(X_xor, y_xor) plot_decision_regions(X=X_xor, y=y_xor, classifier=svm, plt=plt) plt.legend(loc='upper left') plt.show() ''' plt.scatter(X_xor[y_xor==1, 0], X_xor[y_xor==1, 1], c='b', marker='x', label='1') plt.scatter(X_xor[y_xor==-1, 0], X_xor[y_xor==-1, 1], c='r', marker='s', label='-1') plt.ylim(-3.0) plt.legend() plt.show() '''