logging.captureWarnings(True)
faces = fetch_olivetti_faces()
images = faces["images"]
print("faces loaded", images.shape)
# Split Images Vertically in 16 blocks
img_size = (64, 64)
num_v_blocks = 16
num_blocks = (num_v_blocks, 1)
datasets = []
for i in range(num_v_blocks):
block_ids = np.arange(i, -1, -1)
datasets.append((get_blocks(images, num_blocks=num_blocks, blocks=block_ids.tolist()), 1))
num_mpes = 1
num_samples = 10
cspns = []
mpe_query_blocks = None
sample_query_blocks = None
for i, ((tr_block, block_idx), conditional_blocks) in enumerate(datasets):
print("learning", i)
conditional_features_count = (tr_block.shape[1] // len(block_idx)) * conditional_blocks
if i == 0:
# spn
ds_context = Context(meta_types=[MetaType.REAL] * tr_block.shape[1])
ds_context.add_domains(tr_block)
ds_context.parametric_types = [Gaussian] * tr_block.shape[1]
images = faces['images']
# images = images * 256
# images = add_poisson_noise(images)
# images = standardize(images)
# Learn cspns for image blocks like this:
# |0|1|
# |2|3|
# P0(0|labels)
# P1(1|0,labels)
# P2(2|1,0,labels)
# P3(3|2,1,0,labels)
datasets = [
# block of 0
get_blocks(images, num_blocks=(2, 2), blocks=[0]),
# block of 1|0
get_blocks(images, num_blocks=(2, 2), blocks=[1, 0]),
# block of 2|1,0
get_blocks(images, num_blocks=(2, 2), blocks=[2, 1, 0]),
# block of 3|2,1,0
get_blocks(images, num_blocks=(2, 2), blocks=[3, 2, 1, 0])
]
cspns = []
mpe_query_blocks = None
sample_query_blocks = None
for i, (tr_block, block_idx) in enumerate(datasets):
if i == 0:
# spn
ds_context = Context(meta_types=[MetaType.REAL] *
tr_block.shape[1])