quimb.experimental.belief_propagation.hv1bp

Hyper, vectorized, 1-norm, belief propagation.

Module Contents

Classes

HV1BP

Object interface for hyper, vectorized, 1-norm, belief propagation. This

Functions

initialize_messages_batched(tn[, messages])

Initialize batched messages for belief propagation, as the uniform

_compute_all_hyperind_messages_tree_batched(bm)

_compute_all_hyperind_messages_prod_batched(bm[, ...])

_compute_all_tensor_messages_tree_batched(bx, bm)

Compute all output messages for a stacked tensor and messages.

_compute_all_tensor_messages_prod_batched(bx, bm[, ...])

_compute_output_single_t(bm, bx, _reshape, _sum[, ...])

_compute_output_single_m(bm, _reshape, _sum[, ...])

_compute_outputs_batched(batched_inputs[, ...])

Given stacked messsages and tensors, compute stacked output messages.

_update_output_to_input_single_batched(bi, bo, maskin, ...)

_update_outputs_to_inputs_batched(batched_inputs, ...)

Update the stacked input messages from the stacked output messages.

_extract_messages_from_inputs_batched(...)

Get all messages as a dict from the batch stacked input form.

iterate_belief_propagation_batched(batched_inputs_m, ...)

contract_hv1bp(tn[, messages, max_iterations, tol, ...])

Estimate the contraction of tn with hyper, vectorized, 1-norm

run_belief_propagation_hv1bp(tn[, messages, ...])

Run belief propagation on a tensor network until it converges.

sample_hv1bp(tn[, messages, output_inds, ...])

Sample all indices of a tensor network using repeated belief propagation
quimb.experimental.belief_propagation.hv1bp.initialize_messages_batched(tn, messages=None)

Initialize batched messages for belief propagation, as the uniform distribution.

quimb.experimental.belief_propagation.hv1bp._compute_all_hyperind_messages_tree_batched(bm)
quimb.experimental.belief_propagation.hv1bp._compute_all_hyperind_messages_prod_batched(bm, smudge_factor=1e-12)
quimb.experimental.belief_propagation.hv1bp._compute_all_tensor_messages_tree_batched(bx, bm)

Compute all output messages for a stacked tensor and messages.

quimb.experimental.belief_propagation.hv1bp._compute_all_tensor_messages_prod_batched(bx, bm, smudge_factor=1e-12)
quimb.experimental.belief_propagation.hv1bp._compute_output_single_t(bm, bx, _reshape, _sum, smudge_factor=1e-12)
quimb.experimental.belief_propagation.hv1bp._compute_output_single_m(bm, _reshape, _sum, smudge_factor=1e-12)
quimb.experimental.belief_propagation.hv1bp._compute_outputs_batched(batched_inputs, batched_tensors=None, smudge_factor=1e-12, _pool=None)

Given stacked messsages and tensors, compute stacked output messages.

quimb.experimental.belief_propagation.hv1bp._update_output_to_input_single_batched(bi, bo, maskin, maskout, _max, _sum, _abs, damping=0.0)
quimb.experimental.belief_propagation.hv1bp._update_outputs_to_inputs_batched(batched_inputs, batched_outputs, masks, damping=0.0, _pool=None)

Update the stacked input messages from the stacked output messages.

quimb.experimental.belief_propagation.hv1bp._extract_messages_from_inputs_batched(batched_inputs_m, batched_inputs_t, input_locs_m, input_locs_t)

Get all messages as a dict from the batch stacked input form.

quimb.experimental.belief_propagation.hv1bp.iterate_belief_propagation_batched(batched_inputs_m, batched_inputs_t, batched_tensors, masks_m, masks_t, smudge_factor=1e-12, damping=0.0, _pool=None)
class quimb.experimental.belief_propagation.hv1bp.HV1BP(tn, messages=None, smudge_factor=1e-12, damping=0.0, thread_pool=False)

Bases: quimb.experimental.belief_propagation.bp_common.BeliefPropagationCommon

Object interface for hyper, vectorized, 1-norm, belief propagation. This is the fast version of belief propagation possible when there are many, small, matching tensor sizes.

Parameters:

  • tn (TensorNetwork) – The tensor network to run BP on.

  • messages (dict, optional) – Initial messages to use, if not given then uniform messages are used.

  • smudge_factor (float, optional) – A small number to add to the denominator of messages to avoid division by zero. Note when this happens the numerator will also be zero.

  • thread_pool (bool or int, optional) – Whether to use a thread pool for parallelization, if True use the default number of threads, if an integer use that many threads.

iterate(**kwargs)
get_messages()

Get messages in individual form from the batched stacks.

contract(strip_exponent=False)
quimb.experimental.belief_propagation.hv1bp.contract_hv1bp(tn, messages=None, max_iterations=1000, tol=5e-06, smudge_factor=1e-12, damping=0.0, strip_exponent=False, info=None, progbar=False)

Estimate the contraction of tn with hyper, vectorized, 1-norm belief propagation, via the exponential of the Bethe free entropy.

Parameters:

  • tn (TensorNetwork) – The tensor network to run BP on, can have hyper indices.

  • messages (dict, optional) – Initial messages to use, if not given then uniform messages are used.

  • max_iterations (int, optional) – The maximum number of iterations to perform.

  • tol (float, optional) – The convergence tolerance for messages.

  • smudge_factor (float, optional) – A small number to add to the denominator of messages to avoid division by zero. Note when this happens the numerator will also be zero.

  • damping (float, optional) – The damping factor to use, 0.0 means no damping.

  • strip_exponent (bool, optional) – Whether to strip the exponent from the final result. If True then the returned result is (mantissa, exponent).

  • info (dict, optional) – If specified, update this dictionary with information about the belief propagation run.

  • progbar (bool, optional) – Whether to show a progress bar.

Return type:

scalar or (scalar, float)

quimb.experimental.belief_propagation.hv1bp.run_belief_propagation_hv1bp(tn, messages=None, max_iterations=1000, tol=5e-06, damping=0.0, smudge_factor=1e-12, info=None, progbar=False)

Run belief propagation on a tensor network until it converges.

Parameters:

  • tn (TensorNetwork) – The tensor network to run BP on.

  • messages (dict, optional) – The current messages. For every index and tensor id pair, there should be a message to and from with keys (ix, tid) and (tid, ix). If not given, then messages are initialized as uniform.

  • max_iterations (int, optional) – The maximum number of iterations to run for.

  • tol (float, optional) – The convergence tolerance.

  • damping (float, optional) – The damping factor to use, 0.0 means no damping.

  • smudge_factor (float, optional) – A small number to add to the denominator of messages to avoid division by zero. Note when this happens the numerator will also be zero.

  • info (dict, optional) – If specified, update this dictionary with information about the belief propagation run.

  • progbar (bool, optional) – Whether to show a progress bar.

Returns:

  • messages (dict) – The final messages.

  • converged (bool) – Whether the algorithm converged.

quimb.experimental.belief_propagation.hv1bp.sample_hv1bp(tn, messages=None, output_inds=None, max_iterations=1000, tol=0.01, damping=0.0, smudge_factor=1e-12, bias=False, seed=None, progbar=False)

Sample all indices of a tensor network using repeated belief propagation runs and decimation.

Parameters:

  • tn (TensorNetwork) – The tensor network to sample.

  • messages (dict, optional) – The current messages. For every index and tensor id pair, there should be a message to and from with keys (ix, tid) and (tid, ix). If not given, then messages are initialized as uniform.

  • output_inds (sequence of str, optional) – The indices to sample. If not given, then all indices are sampled.

  • max_iterations (int, optional) – The maximum number of iterations for each message passing run.

  • tol (float, optional) – The convergence tolerance for each message passing run.

  • smudge_factor (float, optional) – A small number to add to each message to avoid zeros. Making this large is similar to adding a temperature, which can aid convergence but likely produces less accurate marginals.

  • bias (bool or float, optional) – Whether to bias the sampling towards the largest marginal. If False (the default), then indices are sampled proportional to their marginals. If True, then each index is ‘sampled’ to be its largest weight value always. If a float, then the local probability distribution is raised to this power before sampling.

  • thread_pool (bool, int or ThreadPoolExecutor, optional) – Whether to use a thread pool for parallelization. If an integer, then this is the number of threads to use. If True, then the number of threads is set to the number of cores. If a ThreadPoolExecutor, then this is used directly.

  • seed (int, optional) – A random seed to use for the sampling.

  • progbar (bool, optional) – Whether to show a progress bar.

Returns:

  • config (dict[str, int]) – The sample configuration, mapping indices to values.

  • tn_config (TensorNetwork) – The tensor network with all index values (or just those in output_inds if supllied) selected. Contracting this tensor network (which will just be a sequence of scalars if all index values have been sampled) gives the weight of the sample, e.g. should be 1 for a SAT problem and valid assignment.

  • omega (float) – The probability of choosing this sample (i.e. product of marginal values). Useful possibly for importance sampling.