def test_mnn_with_vector_gamma():
n_sample = len(np.unique(digits['target']))
# vector gamma
build_graph(data,
thresh=0,
n_pca=20,
decay=10,
knn=5,
random_state=42,
sample_idx=digits['target'],
kernel_symm='gamma',
gamma=np.linspace(0, 1, n_sample - 1))
def test_mnn_with_square_gamma_wrong_length():
n_sample = len(np.unique(digits['target']))
# square matrix gamma of the wrong size
build_graph(data,
thresh=0,
n_pca=20,
decay=10,
knn=5,
random_state=42,
sample_idx=digits['target'],
kernel_symm='gamma',
gamma=np.tile(np.linspace(0, 1, n_sample - 1),
n_sample).reshape(n_sample - 1, n_sample))
def test_mnn_with_vector_theta():
n_sample = len(np.unique(digits["target"]))
# vector theta
build_graph(
data,
thresh=0,
n_pca=20,
decay=10,
knn=5,
random_state=42,
sample_idx=digits["target"],
kernel_symm="mnn",
theta=np.linspace(0, 1, n_sample - 1),
)
def test_mnn_with_matrix_theta():
n_sample = len(np.unique(digits["target"]))
# square matrix theta of the wrong size
build_graph(
data,
thresh=0,
n_pca=20,
decay=10,
knn=5,
random_state=42,
sample_idx=digits["target"],
kernel_symm="mnn",
theta=np.tile(np.linspace(0, 1, n_sample), n_sample).reshape(
n_sample, n_sample
),
)
def test_mnn_with_vector_theta():
with assert_raises_message(
TypeError,
"Expected `theta` as a float. Got <class 'numpy.ndarray'>."):
n_sample = len(np.unique(digits["target"]))
# vector theta
build_graph(
data,
thresh=0,
n_pca=20,
decay=10,
knn=5,
random_state=42,
sample_idx=digits["target"],
kernel_symm="mnn",
theta=np.linspace(0, 1, n_sample - 1),
)
def test_mnn_with_matrix_theta():
with assert_raises_message(
TypeError,
"Expected `theta` as a float. Got <class 'numpy.ndarray'>."):
n_sample = len(np.unique(digits["target"]))
# square matrix theta of the wrong size
build_graph(
data,
thresh=0,
n_pca=20,
decay=10,
knn=5,
random_state=42,
sample_idx=digits["target"],
kernel_symm="mnn",
theta=np.tile(np.linspace(0, 1, n_sample),
n_sample).reshape(n_sample, n_sample),
)