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
- Return type:
str
- pyagrum.lib.dynamicBN.getTimeSlicesRange(dbn)
get the range and (name,radical) of each variables
- Parameters:
dbn (pyagrum.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)
Plot modifications of variables in a 2TBN knowing the size of the time window (T) and the evidence on the sequence.
- Parameters:
lovars (list) – List of variables to follow.
twoTdbn (pyagrum.BayesNet) – The two-timeslice dBN.
T (int) – The time range.
evs (dict) – Observations.
- Return type:
None
- 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) – List of variables to follow.
dbn (pyagrum.BayesNet) – The unrolled dBN.
T (int) – The time range.
evs (dict) – Observations.
vars_title (str or dict, optional) – String for default title or a dictionary with the variable name as key and its title as value.
- Return type:
None
- pyagrum.lib.dynamicBN.realNameFrom2TBNname(name, ts)
- Returns:
The dynamic name from static name and timeslice (no check).
- Return type:
str
- Parameters:
name (
str)ts (
int)
- 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
- Return type:
None
- 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: