def test_histogram(self):
images = xp.array([[[0, 1, 1, 3], [3, 1, 2, 2], [2, 0, 1, 2],
[0, 1, 1, 1]],
[[2, 0, 1, 2], [1, 3, 0, 1], [2, 2, 2, 3],
[1, 3, 3, 1]]])
expected = xp.array([[1, 2, 0, 1, 0, 1, 2, 1, 2, 1, 1, 0, 0, 3, 1, 0],
[1, 1, 1, 1, 1, 2, 1, 0, 0, 1, 2, 1, 0, 1, 1, 2]])
pcanet = PCANet(None,
None,
None,
None,
None,
None,
n_l2_output=2,
filter_shape_pooling=2,
step_shape_pooling=2)
assert_array_equal(pcanet.histogram(images), expected)
images = xp.array([[[1, 0, 1], [2, 0, 0], [1, 3, 3]],
[[2, 0, 0], [1, 1, 1], [3, 0, 1]]])
expected = xp.array([[2, 1, 1, 0, 3, 1, 0, 0, 1, 1, 1, 1, 2, 0, 0, 2],
[1, 2, 1, 0, 2, 2, 0, 0, 1, 2, 0, 1, 1, 3, 0, 0]])
pcanet = PCANet(None,
None,
None,
None,
None,
None,
n_l2_output=2,
filter_shape_pooling=2,
step_shape_pooling=1)
assert_array_equal(pcanet.histogram(images), expected)
def test_histogram(self):
image = np.array([[0, 1, 1, 3], [3, 1, 2, 2], [2, 0, 1, 2],
[2, 0, 1, 2], [1, 3, 0, 1], [2, 2, 2, 3]])
expected = np.array([2, 2, 1, 1, 0, 2, 3, 1, 1, 1, 3, 1, 1, 2, 2, 1])
pcanet = PCANet(None,
None,
None,
None,
None,
None,
n_l2_output=2,
block_shape=(3, 2))
# assume that n_l1_output = 2
assert_array_equal(pcanet.histogram(image), expected)