def learn_sparse_components3(shapes,
n_components,
lmbda,
batch_size,
transform_n_nonzero_coefs,
fit_algorithm,
n_iter=5000):
"""Learn sparse components from a dataset of shapes."""
n_shapes = len(shapes)
# Learn sparse components and predict coefficients for the dataset
dl = MiniBatchDictionaryLearning(
n_components=n_components,
alpha=lmbda,
batch_size=batch_size,
n_iter=n_iter,
transform_n_nonzero_coefs=transform_n_nonzero_coefs,
verbose=1,
fit_algorithm=fit_algorithm,
transform_algorithm='lasso_cd',
positive_code=True)
dl.coefficients = dl.fit_transform(shapes)
# Compute frequency of activations and argsort
# (but do not apply argsort as we would also need to sort coefficients and all inner
# stats of the sklearn object)
dl.frequencies = np.count_nonzero(dl.coefficients.T, axis=1) / n_shapes
dl.argsort_freqs = np.argsort(-dl.frequencies)
return dl
