def test_partial_fit(): X = Xdigits.copy() n = 7 rbm = BernoulliRBM(n_components=64, learning_rate=0.1, batch_size=10, random_state=9) n_samples = X.shape[0] n_batches = int(np.ceil(float(n_samples) / rbm.batch_size)) batch_slices = np.array_split(X, n_batches) for i in range(n): for batch in batch_slices: rbm.partial_fit(batch) assert_almost_equal(rbm.score_samples(X).mean(), -21., decimal=0) assert_array_equal(X, Xdigits)
def test_small_sparse_partial_fit(): for sparse in [csc_matrix, csr_matrix]: X_sparse = sparse(Xdigits[:100]) X = Xdigits[:100].copy() rbm1 = BernoulliRBM(n_components=64, learning_rate=0.1, batch_size=10, random_state=9) rbm2 = BernoulliRBM(n_components=64, learning_rate=0.1, batch_size=10, random_state=9) rbm1.partial_fit(X_sparse) rbm2.partial_fit(X) assert_almost_equal(rbm1.score_samples(X).mean(), rbm2.score_samples(X).mean(), decimal=0)