def test_svm_classification():
y_signed_train = (y_train * 2) - 1
y_signed_test = (y_test * 2) - 1
for kernel in [RBF(gamma=0.1), Linear()]:
model = SVM(max_iter=250, kernel=kernel)
model.fit(X_train, y_signed_train)
predictions = model.predict(X_test)
assert accuracy(y_signed_test, predictions) >= 0.8
def __init__(self, C=1.0, kernel=None, tol=1e-3, max_iter=100):
"""Support vector machines simplified SMO optimization implementation.
Parameters
----------
C : float, default 1.0
kernel : Kernel object
tol : float , default 1e-3
max_iter : int, default 100
"""
self.C = C
self.tol = tol
self.max_iter = max_iter
if kernel is None:
self.kernel = Linear()
else:
self.kernel = kernel
self.b = 0
self.alpha = None
self.K = None
def classification():
# Generate a random binary classification problem.
X, y = make_classification(n_samples=1200,
n_features=10,
n_informative=5,
random_state=1111,
n_classes=2,
class_sep=1.75)
# Convert y to {-1, 1}
y = (y * 2) - 1
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1111)
for kernel in [RBF(gamma=0.1), Linear()]:
model = SVM(max_iter=500, kernel=kernel, C=0.6)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print("Classification accuracy (%s): %s" %
(kernel, accuracy(y_test, predictions)))