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import numpy as np
from numpy.testing import assert_array_almost_equal
from astroML.dimensionality import iterative_pca
def test_iterative_PCA(n_samples=50, n_features=40):
np.random.seed(0)
# construct some data that is well-approximated
# by two principal components
x = np.linspace(0, np.pi, n_features)
x0 = np.linspace(0, np.pi, n_samples)
X = np.sin(x) * np.cos(0.5 * (x - x0[:, None]))
# mask 10% of the pixels
M = (np.random.random(X.shape) > 0.9)
# reconstruct and check accuracy
for norm in (None, 'L1', 'L2'):
X_recons = iterative_pca(X, M, n_ev=2, n_iter=10, norm=norm)
assert_array_almost_equal(X, X_recons, decimal=2)
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