def test_pca_n_active_components_too_many():
samples = [PointCloud(np.random.randn(10)) for _ in range(10)]
model = PCAModel(samples)
# too many components
model.n_active_components = 100
assert_equal(model.n_active_components, 9)
# reset too smaller number of components
model.n_active_components = 5
assert_equal(model.n_active_components, 5)
# reset to too many components
model.n_active_components = 100
assert_equal(model.n_active_components, 9)
def test_pca_n_active_components_too_many():
samples = [PointCloud(np.random.randn(10)) for _ in range(10)]
model = PCAModel(samples)
# too many components
model.n_active_components = 100
assert_equal(model.n_active_components, 9)
# reset too smaller number of components
model.n_active_components = 5
assert_equal(model.n_active_components, 5)
# reset to too many components
model.n_active_components = 100
assert_equal(model.n_active_components, 9)
def test_pca_orthogonalize_against_with_less_active_components():
pca_samples = [PointCloud(np.random.randn(10)) for _ in range(10)]
pca_model = PCAModel(pca_samples)
lm_samples = np.asarray([np.random.randn(10) for _ in range(4)])
lm_model = LinearModel(np.asarray(lm_samples))
# set number of active components
pca_model.n_active_components = 5
# orthogonalize
pca_model.orthonormalize_against_inplace(lm_model)
# number of active components must remain the same
assert_equal(pca_model.n_active_components, 5)
def test_pca_orthogonalize_against_with_less_active_components():
pca_samples = [PointCloud(np.random.randn(10)) for _ in range(10)]
pca_model = PCAModel(pca_samples)
lm_samples = np.asarray([np.random.randn(10) for _ in range(4)])
lm_model = LinearModel(np.asarray(lm_samples))
# set number of active components
pca_model.n_active_components = 5
# orthogonalize
pca_model.orthonormalize_against_inplace(lm_model)
# number of active components must remain the same
assert_equal(pca_model.n_active_components, 5)
def test_pca_variance_after_change_n_active_components():
samples = [PointCloud(np.random.randn(10)) for _ in range(10)]
model = PCAModel(samples)
# set number of active components
model.n_active_components = 5
# kept variance must be smaller than total variance
assert (model.variance < model.original_variance)
# kept variance ratio must be smaller than 1.0
assert (model.variance_ratio < 1.0)
# noise variance must be bigger than 0.0
assert (model.noise_variance > 0.0)
# noise variance ratio must also be bigger than 0.0
assert (model.noise_variance_ratio > 0.0)
# inverse noise variance is computable
assert (model.inverse_noise_variance == 1 / model.noise_variance)
def test_pca_variance_after_change_n_active_components():
samples = [PointCloud(np.random.randn(10)) for _ in range(10)]
model = PCAModel(samples)
# set number of active components
model.n_active_components = 5
# kept variance must be smaller than total variance
assert(model.variance() < model.original_variance())
# kept variance ratio must be smaller than 1.0
assert(model.variance_ratio() < 1.0)
# noise variance must be bigger than 0.0
assert(model.noise_variance() > 0.0)
# noise variance ratio must also be bigger than 0.0
assert(model.noise_variance_ratio() > 0.0)
# inverse noise variance is computable
assert(model.inverse_noise_variance() == 1/model.noise_variance())
def test_pca_n_active_components_negative():
samples = [PointCloud(np.random.randn(10)) for _ in range(10)]
model = PCAModel(samples)
# not sufficient components
model.n_active_components = -5
def test_pca_n_active_components():
samples = [PointCloud(np.random.randn(10)) for _ in range(10)]
model = PCAModel(samples)
# integer
model.n_active_components = 5
assert_equal(model.n_active_components, 5)
def test_pca_n_active_components_negative():
samples = [PointCloud(np.random.randn(10)) for _ in range(10)]
model = PCAModel(samples)
# not sufficient components
model.n_active_components = -5
def test_pca_n_active_components():
samples = [PointCloud(np.random.randn(10)) for _ in range(10)]
model = PCAModel(samples)
# integer
model.n_active_components = 5
assert_equal(model.n_active_components, 5)