# for i, ((tr_block, block_idx), conditional_blocks) in enumerate(datasets):
# cspn = cspns[i]
if i == 0:
# first time, we only care about the structure to put nans
mpe_query_blocks = np.zeros_like(tr_block[0:num_mpes, :].reshape(num_mpes, -1))
sample_query_blocks = np.zeros_like(tr_block[0:num_samples, :].reshape(num_samples, -1))
else:
# i+1 time: we set the previous mpe values as evidence
mpe_query_blocks = np.zeros_like(np.array(tr_block[0:num_mpes, :].reshape(num_mpes, -1)))
mpe_query_blocks[:, -(mpe_result.shape[1]) :] = mpe_result
sample_query_blocks = np.zeros_like(np.array(tr_block[0:num_samples, :].reshape(num_samples, -1)))
sample_query_blocks[:, -(sample_result.shape[1]) :] = sample_result
cspn_mpe_query = set_sub_block_nans(mpe_query_blocks, inp=block_idx, nans=block_idx[0:conditional_blocks])
mpe_result = mpe(cspn, cspn_mpe_query)
mpe_img_blocks = stitch_imgs(
mpe_result.shape[0], img_size=img_size, num_blocks=num_blocks, blocks={tuple(block_idx): mpe_result}
)
cspn_sample_query = set_sub_block_nans(sample_query_blocks, inp=block_idx, nans=block_idx[0:conditional_blocks])
sample_result = sample_instances(cspn, cspn_sample_query, RandomState(123))
sample_img_blocks = stitch_imgs(
sample_result.shape[0], img_size=img_size, num_blocks=num_blocks, blocks={tuple(block_idx): sample_result}
)
for j in range(num_mpes):
mpe_fname = output_path + "mpe_%s_%s.png" % ("-".join(map(str, block_idx)), j)
if mpe_query_blocks is None:
# first time, we only care about the structure to put nans
mpe_query_blocks = np.zeros_like(tr_block[0:10, :].reshape(10, -1))
sample_query_blocks = mpe_query_blocks
else:
# i+1 time: we set the previous mpe values as evidence
mpe_query_blocks = np.zeros_like(np.array(tr_block[0:10, :].reshape(10, -1)))
mpe_query_blocks[:, -(mpe_result.shape[1] - 10):] = mpe_result[:, 0:-10]
sample_query_blocks = np.zeros_like(np.array(tr_block[0:10, :].reshape(10, -1)))
sample_query_blocks[:, -(sample_result.shape[1] - 10):] = sample_result[:, 0:-10]
cspn_mpe_query = np.concatenate((set_sub_block_nans(mpe_query_blocks, inp=block_idx, nans=[block_idx[0]]),
np.eye(10, 10)), axis=1)
mpe_result = mpe(cspn, cspn_mpe_query)
mpe_img_blocks = stitch_imgs(mpe_result.shape[0], img_size=(20, 20), num_blocks=(2, 2),
blocks={tuple(block_idx): mpe_result[:, 0:-10]})
cspn_sample_query = np.concatenate((set_sub_block_nans(sample_query_blocks, inp=block_idx, nans=[block_idx[0]]),
np.eye(10, 10)),
axis=1)
sample_result = sample_instances(cspn, cspn_sample_query, RandomState(123))
sample_img_blocks = stitch_imgs(mpe_result.shape[0], img_size=(20, 20), num_blocks=(2, 2),
blocks={tuple(block_idx): sample_result[:, 0:-10]})
for c in range(10):
mpe_query_blocks = np.zeros_like(tr_block[0:10, :].reshape(10, -1))
sample_query_blocks = np.zeros_like(tr_block[0:10, :].reshape(
10, -1))
else:
# i+1 time: we set the previous mpe values as evidence
mpe_query_blocks = np.zeros_like(
np.array(tr_block[0:10, :].reshape(10, -1)))
mpe_query_blocks[:, -(mpe_result.shape[1]):] = mpe_result
sample_query_blocks = np.zeros_like(
np.array(tr_block[0:10, :].reshape(10, -1)))
sample_query_blocks[:, -(
sample_query_blocks.shape[1]):] = sample_result
cspn_mpe_query = set_sub_block_nans(mpe_query_blocks,
inp=block_idx,
nans=[block_idx[0]])
mpe_result = mpe(cspn, cspn_mpe_query)
mpe_img_blocks = stitch_imgs(mpe_result.shape[0],
img_size=(64, 64),
num_blocks=(2, 2),
blocks={tuple(block_idx): mpe_result})
cspn_sample_query = set_sub_block_nans(sample_query_blocks,
inp=block_idx,
nans=[block_idx[0]])
sample_result = sample_instances(cspn, cspn_sample_query,
RandomState(123))
sample_img_blocks = stitch_imgs(
else:
# i+1 time: we set the previous mpe values as evidence
mpe_query_blocks = np.zeros_like(
np.array(tr_block[0:10, :].reshape(10, -1)))
mpe_query_blocks[:,
-(mpe_result.shape[1] - 10):] = mpe_result[:,
0:-10]
sample_query_blocks = np.zeros_like(
np.array(tr_block[0:10, :].reshape(10, -1)))
sample_query_blocks[:, -(sample_result.shape[1] -
10):] = sample_result[:, 0:-10]
cspn_mpe_query = np.concatenate(
(set_sub_block_nans(mpe_query_blocks,
inp=block_idx,
nans=[block_idx[0]]), np.eye(10, 10)),
axis=1)
mpe_result = mpe(cspn, cspn_mpe_query)
mpe_img_blocks = stitch_imgs(
mpe_result.shape[0],
img_size=(20, 20),
num_blocks=(2, 2),
blocks={tuple(block_idx): mpe_result[:, 0:-10]})
cspn_sample_query = np.concatenate(
(set_sub_block_nans(sample_query_blocks,
inp=block_idx,
nans=[block_idx[0]]), np.eye(10, 10)),
axis=1)