Module dynamic bayesian network

_images/dBN.png

Basic implementation for dynamic Bayesian Networks in pyAgrum

pyAgrum.lib.dynamicBN.getTimeSlices(dbn, size='6', format='png')

Try to correctly represent dBN and 2TBN as an HTML string

Parameters:
  • dbn – the dynamic BN
  • size – size of the figue
  • format – png/svg
pyAgrum.lib.dynamicBN.getTimeSlicesRange(dbn)

get the range and (name,radical) of each variables

Parameters:dbn – a 2TBN or an unrolled BN
Returns:all the timeslice of a dbn

e.g. [‘0’,’t’] for a classic 2TBN range(T) for a classic unrolled BN

pyAgrum.lib.dynamicBN.is2TBN(bn)
pyAgrum.lib.dynamicBN.plotFollow(lovars, dbn, T, evs)

plots modifications of variables in a 2TDN knowing the size of the time window (T) and the evidence on the sequence.

Parameters:
  • lovars – list of variables to follow
  • dbn – the dbn
  • T – the time range
  • evs – observations
pyAgrum.lib.dynamicBN.realNameFrom2TBNname(name, ts)

@return dynamic name from static name and timeslice (no check)

pyAgrum.lib.dynamicBN.showTimeSlices(dbn, size='6', format='png')

Try to correctly display dBN and 2TBN

Parameters:
  • dbn – the dynamic BN
  • size – size of the figue
  • format – png/svg
pyAgrum.lib.dynamicBN.unroll2TBN(dbn, nbr)

unroll a 2TBN given the nbr of timeslices

Parameters:
  • dbn – the dBN
  • nbr – the number of timeslice
Returns:

unrolled BN from a 2TBN and the nbr of timeslices