def test_MDM_predict(): """Test prediction of MDM""" covset = generate_cov(100, 3) labels = np.array([0, 1]).repeat(50) mdm = MDM(metric='riemann') mdm.fit(covset, labels) mdm.predict(covset) # test fit_predict mdm = MDM(metric='riemann') mdm.fit_predict(covset, labels) # test transform mdm.transform(covset) # predict proba mdm.predict_proba(covset) # test n_jobs mdm = MDM(metric='riemann', n_jobs=2) mdm.fit(covset, labels) mdm.predict(covset)
def test_MDM_predict(): """Test prediction of MDM""" covset = generate_cov(100, 3) labels = np.array([0, 1]).repeat(50) mdm = MDM(metric='riemann') mdm.fit(covset, labels) mdm.predict(covset) # test fit_predict mdm = MDM(metric='riemann') mdm.fit_predict(covset, labels) # test transform mdm.transform(covset) # predict proba mdm.predict_proba(covset) # test n_jobs mdm = MDM(metric='riemann', n_jobs=2) mdm.fit(covset, labels) mdm.predict(covset)
def test_MDM_fit_predict(): """Test Fit & predict of MDM""" covset = generate_cov(100,3) labels = np.array([0,1]).repeat(50) mdm = MDM(metric='riemann') mdm.fit_predict(covset,labels)
def test_MDM_fit_predict(): """Test Fit & predict of MDM""" covset = generate_cov(100, 3) labels = np.array([0, 1]).repeat(50) mdm = MDM(metric='riemann') mdm.fit_predict(covset, labels)