コード例 #1
0
def test_covariances():
    """Test fit Covariances"""
    x = np.random.randn(2, 3, 100)
    cov = Covariances()
    cov.fit(x)
    cov.fit_transform(x)
    assert_equal(cov.get_params(), dict(estimator='scm'))
コード例 #2
0
def test_covariances():
    """Test fit Covariances"""
    x = np.random.randn(2, 3, 100)
    cov = Covariances()
    cov.fit(x)
    cov.fit_transform(x)
    assert_equal(cov.get_params(), dict(estimator='scm'))
コード例 #3
0
def test_covariances(estimator, rndstate):
    """Test Covariances"""
    n_matrices, n_channels, n_times = 2, 3, 100
    x = rndstate.randn(n_matrices, n_channels, n_times)
    cov = Covariances(estimator=estimator)
    cov.fit(x)
    covmats = cov.fit_transform(x)
    assert cov.get_params() == dict(estimator=estimator)
    assert covmats.shape == (n_matrices, n_channels, n_channels)
    assert is_spd(covmats)
コード例 #4
0
 f_name = dire + '/' + name
 subject = name.split('_')
 data = loadmat(f_name)
 data_IS = data[list(data.keys())[-1]]
 data_tensor = [data_IS[0][0]]
 for j in range(len(data_IS)):
     if j == 0:
         k = 1
     else:
         k = 0
     for i in range(k, len(data_IS[j])):
         temp = [data_IS[j][i]]
         data_tensor = np.concatenate((data_tensor, temp), axis=0)
 cov = Covariances(estimator='lwf')
 ts = TangentSpace()
 cov.fit(data_tensor, label)
 cov_train = cov.transform(data_tensor)
 ts.fit(cov_train, label)
 ts_train = ts.transform(cov_train)
 ts_shape = (np.shape(ts_train))
 pca = PCA()
 ann = MLPClassifier(max_iter=5000)
 clf = BaggingClassifier(base_estimator=ann, bootstrap=True)
 pipe = Pipeline(steps=[('pca', pca), ('clf', clf)])
 param_grid = {
     'pca__n_components': [20, 30, 40, 50, 60, 70, 80, 90, 100],
     'clf__base_estimator__hidden_layer_sizes':
     [(10), (20), (30), (40), (50), (60), (70), (80), (90), (100), (110),
      (120), (130), (140), (150), (160), (170), (180)],
     'clf__n_estimators': [
         10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150,
コード例 #5
0
def test_covariances():
    """Test fit Covariances"""
    x = np.random.randn(2, 3, 100)
    cov = Covariances()
    cov.fit(x)
    cov.fit_transform(x)
コード例 #6
0
def test_covariances():
    """Test fit Covariances"""
    x = np.random.randn(2,3,100)
    cov = Covariances()
    cov.fit(x)
    cov.fit_transform(x)