def test_tensor_input():
X = np.random.normal(size=(100, 5, 5))
mds = ClassicalMDS(n_components=3, dissimilarity="euclidean")
mds.fit(X)
assert_equal(mds.dissimilarity_matrix_.shape, (100, 100))
X_transformed = mds.fit_transform(X)
assert_equal(X_transformed.shape, (100, 3))
def use_fit_transform():
A = np.ones((4, 4)) - np.identity(4)
mds = ClassicalMDS(n_components=3, dissimilarity="precomputed")
B = mds.fit_transform(A)
Ahat = _compute_dissimilarity(B)
# Checks up to 7 decimal points
assert_almost_equal(A, Ahat)
def use_euclidean():
A = np.array([
[-7.62291243e-17, 6.12372436e-01, 4.95031815e-16],
[-4.97243701e-01, -2.04124145e-01, -2.93397401e-01],
[5.02711453e-01, -2.04124145e-01, -2.83926977e-01],
[-5.46775198e-03, -2.04124145e-01, 5.77324378e-01],
])
mds = ClassicalMDS(dissimilarity="euclidean")
B = mds.fit_transform(A)
target = np.ones((4, 4)) - np.identity(4)
assert_almost_equal(mds.dissimilarity_matrix_, target)