def test_mdla_dict_update():
n_kernels = 10
# n_samples, n_features, n_dims = 100, 5, 3
n_samples, n_features, n_dims = 80, 5, 3
X = [rng_global.randn(n_features, n_dims) for i in range(n_samples)]
dico = MultivariateDictLearning(
n_kernels=n_kernels, random_state=0, max_iter=10, n_jobs=-1
).fit(X)
first_epoch = list(dico.kernels_)
dico = dico.fit(X)
second_epoch = list(dico.kernels_)
for k, c in zip(first_epoch, second_epoch):
assert (k - c).sum() != 0.0
dico = MiniBatchMultivariateDictLearning(
n_kernels=n_kernels, random_state=0, n_iter=10, n_jobs=-1
).fit(X)
first_epoch = list(dico.kernels_)
dico = dico.fit(X)
second_epoch = list(dico.kernels_)
for k, c in zip(first_epoch, second_epoch):
assert (k - c).sum() != 0.0
dico = MiniBatchMultivariateDictLearning(
n_kernels=n_kernels, random_state=0, n_iter=10, n_jobs=-1
).partial_fit(X)
first_epoch = list(dico.kernels_)
dico = dico.partial_fit(X)
second_epoch = list(dico.kernels_)
for k, c in zip(first_epoch, second_epoch):
assert (k - c).sum() != 0.0
def test_mdla_dict_update():
n_kernels = 10
# n_samples, n_features, n_dims = 100, 5, 3
n_samples, n_features, n_dims = 80, 5, 3
X = [rng_global.randn(n_features, n_dims) for i in range(n_samples)]
dico = MultivariateDictLearning(n_kernels=n_kernels, random_state=0,
max_iter=10, n_jobs=-1).fit(X)
first_epoch = list(dico.kernels_)
dico = dico.fit(X)
second_epoch = list(dico.kernels_)
for k, c in zip(first_epoch, second_epoch):
assert_true((k-c).sum() != 0.)
dico = MiniBatchMultivariateDictLearning(n_kernels=n_kernels,
random_state=0, n_iter=10, n_jobs=-1).fit(X)
first_epoch = list(dico.kernels_)
dico = dico.fit(X)
second_epoch = list(dico.kernels_)
for k, c in zip(first_epoch, second_epoch):
assert_true((k-c).sum() != 0.)
dico = MiniBatchMultivariateDictLearning(n_kernels=n_kernels,
random_state=0, n_iter=10, n_jobs=-1).partial_fit(X)
first_epoch = list(dico.kernels_)
dico = dico.partial_fit(X)
second_epoch = list(dico.kernels_)
for k, c in zip(first_epoch, second_epoch):
assert_true((k-c).sum() != 0.)
