import sklearn
from sklearn import svm

#### SVC with rbf kernel
# default is rbf kernel
clf = svm.SVC()
x_data = [[0,0], [0,1], [1,0], [1,1]]

# linear
## And gate
y_data = [0, 0, 0, 1]
clf.fit(x_data, y_data)
# SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
#   decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
#   max_iter=-1, probability=False, random_state=None, shrinking=True,
#   tol=0.001, verbose=False)
clf.predict(x_data)
# array([0, 0, 0, 0]) # wrong

## Or gate
y_data = [0, 1, 1, 1]
clf.fit(x_data, y_data)
clf.predict(x_data)
# array([1, 1, 1, 1])

# non-linear
## Xor gate
y_data = [0, 1, 1, 0]
clf.fit(x_data, y_data)
clf.predict(x_data)
# array([0, 1, 1, 0]) # Correct answer


#### SVC with Linear kernel
clf = svm.SVC(kernel='linear')
clf.fit(x_data, y_data)
# SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
#   decision_function_shape=None, degree=3, gamma='auto', kernel='linear',
#   max_iter=-1, probability=False, random_state=None, shrinking=True,
#   tol=0.001, verbose=False)
clf.predict(x_data)
# array([0, 0, 0, 0])


#### LinearSVC
clf = svm.LinearSVC()
clf.fit(x_data, y_data)
# LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
#      intercept_scaling=1, loss='squared_hinge', max_iter=1000,
#      multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,
#      verbose=0)
clf.predict(x_data)
# array([0, 0, 0, 1]) # Correct answer