Functions from pyAgrum

Useful functions in pyAgrum


about() for pyAgrum

pyAgrum.fastBN(arcs, domain_size=2)

rapid prototyping of BN.

  • arcs – dot-like simple list of arcs (“a->b->c;a->c->d” for instance). The first apparition of a node name can be enhanced with a “[domain_size]” extension. For instance “a[5]->b->c;a[2]->c->d” will create a BN with a variable “a” whos domain size is a.nbrDim()==5 (the second “a[2]” is not taken into account since the variable has already been created).
  • domain_size – the domain size of each created variable.

the created pyAgrum.BayesNet

pyAgrum.getPosterior(bn, evs, target)

Compute the posterior of a single target (variable) in a BN given evidence

getPosterior uses a VariableElimination inference. If more than one target is needed with the same set of evidence or if the same target is needed with more than one set of evidence, this function is not relevant since it creates a new inference engine every time it is called.

  • bn (pyAgrum.BayesNet) –
  • evs (dictionary) – events map {name/id:val, name/id : [ val1, val2 ], …}
  • target – variable name or id

posterior Potential

Input/Output for bayesian networks

Returns:a string which lists all suffixes for supported BN file formats.
pyAgrum.loadBN(filename, listeners=None, verbose=False, **opts)
  • filename – the name of the input file
  • listeners – list of functions to execute
  • verbose – whether to print or not warning messages
  • system – (for O3PRM) name of the system to flatten in a BN
  • classpath – (for O3PRM) list of folders containing classes

a BN from a file using one of the availableBNExts() suffixes.

pyAgrum.saveBN(bn, filename)

save a BN into a file using the format corresponding to one of the availableWriteBNExts() suffixes.

Input for influence diagram


read a gum.InfluenceDiagram from a bifxml file

Parameters:filename – the name of the input file
Returns:an InfluenceDiagram