def test_class_weight(queue):
X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
y = np.array([1, 1, 1, 2, 2, 2])
clf = SVC(class_weight={1: 0.1})
clf.fit(X, y, queue=queue)
assert_array_almost_equal(clf.predict(X, queue=queue), [2] * 6)
def test_sample_weight(queue):
X = np.array([[-2, 0], [-1, -1], [0, -2], [0, 2], [1, 1], [2, 2]])
y = np.array([1, 1, 1, 2, 2, 2])
clf = SVC(kernel='linear')
clf.fit(X, y, sample_weight=[1] * 6, queue=queue)
assert_array_almost_equal(clf.intercept_, [0.0])
def test_decision_function(queue):
X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]], dtype=np.float32)
Y = np.array([1, 1, 1, 2, 2, 2], dtype=np.float32)
clf = SVC(kernel='rbf', gamma=1, decision_function_shape='ovo')
clf.fit(X, Y, queue=queue)
rbfs = rbf_kernel(X, clf.support_vectors_, gamma=clf.gamma)
dec = np.dot(rbfs, clf.dual_coef_.T) + clf.intercept_
assert_array_almost_equal(dec.ravel(), clf.decision_function(X, queue=queue))
def test_decision_function():
X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
Y = [1, 1, 1, 2, 2, 2]
clf = SVC(kernel='rbf', gamma=1, decision_function_shape='ovo')
clf.fit(X, Y)
rbfs = rbf_kernel(X, clf.support_vectors_, gamma=clf.gamma)
dec = np.dot(rbfs, clf.dual_coef_.T) + clf.intercept_
assert_array_almost_equal(dec.ravel(), clf.decision_function(X))