def test_pca_increment_noncentred():
pca_samples = [np.random.randn(10) for _ in range(10)]
ipca_model = PCAVectorModel(pca_samples[:3], centre=False)
ipca_model.increment(pca_samples[3:6])
ipca_model.increment(pca_samples[6:])
bpca_model = PCAVectorModel(pca_samples, centre=False)
assert_almost_equal(np.abs(ipca_model.components),
np.abs(bpca_model.components))
assert_almost_equal(ipca_model.eigenvalues, bpca_model.eigenvalues)
assert_almost_equal(ipca_model.mean(), bpca_model.mean())
def test_pca_increment_noncentred():
pca_samples = [np.random.randn(10) for _ in range(10)]
ipca_model = PCAVectorModel(pca_samples[:3], centre=False)
ipca_model.increment(pca_samples[3:6])
ipca_model.increment(pca_samples[6:])
bpca_model = PCAVectorModel(pca_samples, centre=False)
assert_almost_equal(np.abs(ipca_model.components),
np.abs(bpca_model.components))
assert_almost_equal(ipca_model.eigenvalues, bpca_model.eigenvalues)
assert_almost_equal(ipca_model.mean(), bpca_model.mean())
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())
