def test_pca_variance_after_trim():
samples = [np.random.randn(10) for _ in range(10)]
model = PCAVectorModel(samples)
# set number of active components
model.trim_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_trim():
samples = [np.random.randn(10) for _ in range(10)]
model = PCAVectorModel(samples)
# set number of active components
model.trim_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_vector_init_from_covariance():
n_samples = 30
n_features = 10
centre_values = [True, False]
for centre in centre_values:
# generate samples matrix and mean vector
samples = np.random.randn(n_samples, n_features)
mean = np.mean(samples, axis=0)
# compute covariance matrix
if centre:
X = samples - mean
C = np.dot(X.T, X) / (n_samples - 1)
else:
C = np.dot(samples.T, samples) / (n_samples - 1)
# create the 2 pca models
pca1 = PCAVectorModel.init_from_covariance_matrix(C,
mean,
centred=centre,
n_samples=n_samples)
pca2 = PCAVectorModel(samples, centre=centre, inplace=False)
# compare them
assert_array_almost_equal(pca1.mean(), pca2.mean())
assert_array_almost_equal(pca1.component(0, with_mean=False),
pca2.component(0, with_mean=False))
assert_array_almost_equal(pca1.component(7), pca2.component(7))
assert_array_almost_equal(pca1.components, pca2.components)
assert_array_almost_equal(pca1.eigenvalues, pca2.eigenvalues)
assert_array_almost_equal(pca1.eigenvalues_cumulative_ratio(),
pca2.eigenvalues_cumulative_ratio())
assert_array_almost_equal(pca1.eigenvalues_ratio(),
pca2.eigenvalues_ratio())
weights = np.random.randn(pca1.n_active_components - 4)
assert_array_almost_equal(pca1.instance(weights),
pca2.instance(weights))
assert pca1.n_active_components == pca2.n_active_components
assert pca1.n_components == pca2.n_components
assert pca1.n_features == pca2.n_features
assert pca1.n_samples == pca2.n_samples
assert pca1.noise_variance() == pca2.noise_variance()
assert pca1.noise_variance_ratio() == pca2.noise_variance_ratio()
assert_allclose(pca1.variance(), pca2.variance())
assert pca1.variance_ratio() == pca2.variance_ratio()
assert_array_almost_equal(pca1.whitened_components(),
pca2.whitened_components())
def test_pca_vector_init_from_covariance():
n_samples = 30
n_features = 10
centre_values = [True, False]
for centre in centre_values:
# generate samples matrix and mean vector
samples = np.random.randn(n_samples, n_features)
mean = np.mean(samples, axis=0)
# compute covariance matrix
if centre:
X = samples - mean
C = np.dot(X.T, X) / (n_samples - 1)
else:
C = np.dot(samples.T, samples) / (n_samples - 1)
# create the 2 pca models
pca1 = PCAVectorModel.init_from_covariance_matrix(C, mean, centred=centre,
n_samples=n_samples)
pca2 = PCAVectorModel(samples, centre=centre, inplace=False)
# compare them
assert_array_almost_equal(pca1.mean(), pca2.mean())
assert_array_almost_equal(pca1.component(0, with_mean=False),
pca2.component(0, with_mean=False))
assert_array_almost_equal(pca1.component(7), pca2.component(7))
assert_array_almost_equal(pca1.components, pca2.components)
assert_array_almost_equal(pca1.eigenvalues, pca2.eigenvalues)
assert_array_almost_equal(pca1.eigenvalues_cumulative_ratio(),
pca2.eigenvalues_cumulative_ratio())
assert_array_almost_equal(pca1.eigenvalues_ratio(),
pca2.eigenvalues_ratio())
weights = np.random.randn(pca1.n_active_components - 4)
assert_array_almost_equal(pca1.instance(weights),
pca2.instance(weights))
assert(pca1.n_active_components == pca2.n_active_components)
assert(pca1.n_components == pca2.n_components)
assert(pca1.n_features == pca2.n_features)
assert(pca1.n_samples == pca2.n_samples)
assert(pca1.noise_variance() == pca2.noise_variance())
assert(pca1.noise_variance_ratio() == pca2.noise_variance_ratio())
assert_allclose(pca1.variance(), pca2.variance())
assert(pca1.variance_ratio() == pca2.variance_ratio())
assert_array_almost_equal(pca1.whitened_components(),
pca2.whitened_components())
