class SVCImpl():
def __init__(self,
C=1.0,
kernel='rbf',
degree=3,
gamma='auto_deprecated',
coef0=0.0,
shrinking=True,
probability=False,
tol=0.001,
cache_size=200,
class_weight='balanced',
verbose=False,
max_iter=(-1),
decision_function_shape='ovr',
random_state=None):
self._hyperparams = {
'C': C,
'kernel': kernel,
'degree': degree,
'gamma': gamma,
'coef0': coef0,
'shrinking': shrinking,
'probability': probability,
'tol': tol,
'cache_size': cache_size,
'class_weight': class_weight,
'verbose': verbose,
'max_iter': max_iter,
'decision_function_shape': decision_function_shape,
'random_state': random_state
}
self._wrapped_model = Op(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def predict(self, X):
return self._wrapped_model.predict(X)
def predict_proba(self, X):
return self._wrapped_model.predict_proba(X)
def decision_function(self, X):
return self._wrapped_model.decision_function(X)
i += 1
clf = SVC(kernel=laplacian_kernel)
features = ['.cnn.', '.mfcc.', '.asr.', '.cnn+mfcc.', '.cnn+mfcc+asr.']
X_score = np.zeros((len(X_all), len(features)))
j = 0
for j, feature in enumerate(features):
if j <= 2:
X = X_all[:, dimension_i[j]:dimension_i[j] + dimension_i[j + 1]]
elif j == 3:
X = X_all[:, 0:150]
elif j == 4:
X = X_all
clf.fit(X, Y_all)
print('saving scores...')
X_score[:, j] = clf.decision_function(X)
# print (X_score[:, j])
np.save(event_name + feature + 'score', X_score[:, j])
cPickle.dump(clf, open(output_file + feature + 'score', "wb"))
clf = SVC(kernel='linear')
clf.fit(X_score[:, [0, 1, 2, 4]], Y_all)
fread.close()
cPickle.dump(clf, open(output_file, "wb"))
print 'SVM trained successfully for event %s!' % (event_name)