def test_TSclassifier():
"""Test TS Classifier"""
covset = generate_cov(40, 3)
labels = np.array([0, 1]).repeat(20)
assert_raises(TypeError, TSclassifier, clf='666')
clf = TSclassifier()
clf.fit(covset, labels)
clf.predict(covset)
clf.predict_proba(covset)
def test_TSclassifier():
"""Test TS Classifier"""
covset = generate_cov(40, 3)
labels = np.array([0, 1]).repeat(20)
assert_raises(TypeError, TSclassifier, clf='666')
clf = TSclassifier()
clf.fit(covset, labels)
clf.predict(covset)
clf.predict_proba(covset)
def test_TSclassifier():
"""Test TS Classifier"""
covset = generate_cov(40, 3)
labels = np.array([0, 1]).repeat(20)
with pytest.raises(TypeError):
TSclassifier(clf='666')
clf = TSclassifier()
clf.fit(covset, labels)
assert_array_equal(clf.classes_, np.array([0, 1]))
clf.predict(covset)
clf.predict_proba(covset)
class wrapper_TSclassifier(machine_learning_method):
"""wrapper for pyriemann TSclassifier"""
def __init__(self, method_name, method_args):
super(wrapper_TSclassifier, self).__init__(method_name, method_args)
self.init_method()
def init_method(self):
self.classifier = TSclassifier(metric=self.method_args['metric'],
tsupdate=self.method_args['tsupdate'])
def fit(self, X, y):
return self.classifier.fit(X, y)
def predict(self, X):
return self.classifier.predict(X)
MDM_record.append(np.sum(pred == test_label) / box_length)
print('-----------------------------------------')
Fgmdm = FgMDM(metric=dict(mean='riemann', distance='riemann'))
Fgmdm.fit(train, train_label)
pred = Fgmdm.predict(test)
print('FGMDM: {:4f}'.format(
np.sum(pred == test_label) / box_length))
FGMDM_record.append(np.sum(pred == test_label) / box_length)
print('-----------------------------------------')
clf = TSclassifier()
clf.fit(train, train_label)
pred = clf.predict(test)
print('TSC: {:4f}'.format(np.sum(pred == test_label) / box_length))
TSC_record.append(np.sum(pred == test_label) / box_length)
print('-----------------------------------------')
lr = LogisticRegression()
csp = CSP(n_components=4, reg='ledoit_wolf', log=True)
clf = Pipeline([('CSP', csp), ('LogisticRegression', lr)])
clf.fit(train_CSP, train_label)
pred = clf.predict(test_CSP)
print('CSP_lr: {:4f}'.format(
np.sum(pred == test_label) / box_length))
CSP_lr_record.append(np.sum(pred == test_label) / box_length)
print('-----------------------------------------')
