color='r',
marker='s',
label='-1')
plt.ylim(-3.0)
plt.legend()
plt.show()
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)
ml = SVC(kernel='rbf', C=10.0, gamma=1.0, random_state=0)
ml.fit(X_train_std, y_train)
y_pred = ml.predict(X_test_std)
print('총 테스트 개수: %d, 오류개수:%d' % (len(y_test), (y_test != y_pred).sum()))
print('정확도: %.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_region(X=X_combined_std,
y=y_combined,
classifier=ml,
test_idx=range(105, 150),
title='scikit-learn SVM kernel=rbf')
random_state=0)
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
# ml = Perceptron(eta0=0.01, max_iter=40, tol=0, random_state=0)
# ml = LogisticRegression(C=1000.0, random_state=0)
# ml = SVC(kernel='linear', C=1.0, random_state=0)
# ml = SGDClassifier(loss = 'perceptron')
# ml = SGDClassifier(loss='log')
# ml = SGDClassifier(loss='hinge')
# ml = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)
# ml = RandomForestClassifier(criterion='entropy', n_estimators=10, n_jobs=2, random_state=1)
ml = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')
ml.fit(X_train_std, y_train)
y_pred = ml.predict(X_test_std)
print('총 테스트 개수: %d, 오류개수:%d' % (len(y_test), (y_test != y_pred).sum()))
print('정확도: %.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_region(X=X_combined_std,
y=y_combined,
classifier=ml,
test_idx=range(105, 150),
title='Random Forest Classifier')
if __name__ == '__main__':
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)
ml = LogisticRegression(C=1000.0, random_state=0)
ml.fit(X_train_std, y_train)
y_pred = ml.predict(X_test_std)
print('총 테스트 개수: %d, 오류개수:%d' % (len(y_test), (y_test != y_pred).sum()))
print('정확도: %2f' % accuracy_score(y_test, y_pred))
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined_std = np.hstack((y_train, y_test))
plot_decision_region(X=X_combined_std,
y=y_combined_std,
classifier=ml,
test_idx=range(105, 150),
title='scikit-learn Logistic Regression')
import numpy as np
from mylib.plotdregion import plot_decision_region
if __name__ == '__main__':
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)
ml = Perceptron(eta0=0.01, max_iter=40, tol=0, random_state=0)
ml.fit(X_train_std, y_train)
y_pred = ml.predict(X_test_std)
print('총 테스트 개수: %d, 오류개수:%d' % (len(y_test), (y_test != y_pred).sum()))
print('정확도: %.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_region(X=X_combined_std,
y=y_combined,
classifier=ml,
test_idx=range(105, 150),
title='scikit-learn Perceptron')