Sampling

Use this module for sampling.

class pybbn.sampling.sampling.LogicSampler(bbn)

Bases: object

Logic sampling with rejection.

__init__(bbn)

Ctor.

Parameters:

bbn – BBN.

get_samples(evidence={}, n_samples=100, seed=37)

Gets the samples.

Parameters:

• evidence – Evidence. Dictionary. Keys are ids and values are node values.

• n_samples – Number of samples.

• seed – Seed (default=37).

Returns:

Samples.

class pybbn.sampling.sampling.SortableNode(node_id, parent_ids)

Bases: object

Sortable node.

__init__(node_id, parent_ids)

Ctor.

Parameters:

• node_id – Node ID.

• parent_ids – List of parent IDs.

class pybbn.sampling.sampling.Table(node, parents=[])

Bases: object

Table association parent instantiations with cumulative distributions of node values.

__init__(node, parents=[])

Ctor.

Parameters:

• node – BBN node.

• parents – List of parent BBN nodes.

get_value(prob, sample=None)

Gets the value associated with the specified probability.

Parameters:

• prob – Probability.

• sample – Dictionary of variable-value sampled so far.

Returns:

Value.

has_parents()

Checks if the node associated with this table has parents.

Returns:

Boolean.