def test_pca_using_expected_num_dimensions():
data = [[-1, -1],
[1, 1]]
expected = numpy.matrix([[-1], [1]])
assert numpy.array_equal(preprocess.pca(data, 1),
expected)
def test_pca_using_num_dimensions_func():
data = [[-1, -1], [1, 1]]
expected = numpy.matrix([[-1], [1]])
def selection_func(eigen_values):
return [i for i, v in enumerate(eigen_values) if v > 1]
assert numpy.array_equal(
preprocess.pca(data, select_dimensions_func=selection_func), expected)
def test_pca_both_expected_and_func():
with pytest.raises(ValueError):
preprocess.pca([], 1, lambda x: [0])
def test_pca_no_expected_or_func():
with pytest.raises(ValueError):
preprocess.pca([], None, None)