pyagrum.lib.dynamicBN

dynamic Bayesian Network are a special class of BNs where variables can be subscripted by a (discrete) time. See this notebook.

The purpose of this module is to provide basic tools for dealing with dynamic Bayesian Network (and inference) : modeling, visualisation, inference.

pyagrum.lib.dynamicBN.getTimeSlices(dbn, size=None)

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

• Parameters:
  • dbn (pyagrum.BayesNet) – a 2TBN or an unrolled BN
  • size (int or str) – size of the fig

pyagrum.lib.dynamicBN.getTimeSlicesRange(dbn)

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

• Parameters: dbn (gum.BayesNet) – a 2TBN or an unrolled BN
• Returns: all the timeslice of a dbn : [‘0’,’t’] for a classic 2TBN, range(T) for a classic unrolled BN
• Return type: Dict[str,List[T[str,str]]]

pyagrum.lib.dynamicBN.is2TBN(bn)

Check if bn is a 2 TimeSlice Bayesian network

• Parameters: bn (pyagrum.BayesNet) – the Bayesian network
• Returns: True if the BN is syntaxically correct to be a 2TBN
• Return type: bool

pyagrum.lib.dynamicBN.plotFollow(lovars, twoTdbn, 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
  • twoTdbn – the two-timeslice dbn
  • T – the time range
  • evs – observations

pyagrum.lib.dynamicBN.plotFollowUnrolled(lovars, dbn, T, evs, vars_title=None)

plot the dynamic evolution of a list of vars with a dBN

• Parameters:
  • lovars – list of variables to follow
  • dbn – the unrolled dbn
  • T – the time range
  • evs – observations
  • vars_title – string for default or a dictionary with the variable name as key and the respective title as value.

pyagrum.lib.dynamicBN.realNameFrom2TBNname(name, ts)

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

pyagrum.lib.dynamicBN.showTimeSlices(dbn, size=None)

Try to correctly display dBN and 2TBN

• Parameters:
  • dbn (pyagrum.BayesNet) – a 2TBN or an unrolled BN
  • size (int or str) – size of the fig

pyagrum.lib.dynamicBN.unroll2TBN(dbn, nbr)

unroll a 2TBN given the nbr of timeslices

• Parameters:
  • dbn (pyagrum.BayesNet) – a 2TBN or an unrolled BN
  • nbr (int) – the number of timeslice
• Returns: unrolled BN from a 2TBN and the nbr of timeslices
• Return type: pyagrum.BayesNet