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'''
This module contains tools for representing discrete nodes -- those with a finite number of outcomes and a finite number of possible parent values -- as class instances with their own *choose* method to choose an outcome for themselves based on parent outcomes.
'''
import random
[docs]class Discrete():
'''
This class represents a discrete node, as described above. It contains the *Vdataentry* attribute and the *choose* method.
'''
def __init__(self, Vdataentry):
'''
This class is constructed with the argument *Vdataentry* which must be a dict containing a dictionary entry for this particular node. The dict must contain an entry of the following form::
"cprob": {
"['<parent 1, value 1>',...,'<parent n, value 1>']": [<probability of vals[0]>, ... , <probability of vals[n-1]>],
...
"['<parent 1, value j>',...,'<parent n, value k>']": [<probability of vals[0]>, ... , <probability of vals[n-1]>],
}
Where the keys are each possible combination of parent values and the values are the probability of each of the *n* possible node outcomes, given those parent outcomes. The *Vdataentry* attribute is set equal to this *Vdataentry* input upon instantiation.
'''
self.Vdataentry = Vdataentry
'''A dict containing CPD data for the node.'''
[docs] def choose(self, pvalues):
'''
Randomly choose state of node from a probability distribution conditioned on parent values *pvalues*.
This method has two parts: (1) determining the proper probability
distribution, and (2) using that probability distribution to determine
an outcome.
Arguments:
1. *pvalues* -- An array containing the assigned states of the node's parents. This must be in the same order as the parents appear in ``self.Vdataentry["parents"]``.
The function goes to the proper entry in *Vdataentry*, as specified by *pvalues*, and samples the node based on the distribution found there.
'''
random.seed()
p = self.Vdataentry["parents"]
if (not p):
distribution = self.Vdataentry["cprob"]
else:
pvalues = [str(outcome[t]) for t in self.Vdataentry["parents"]] # ideally can we pull this from the skeleton so as not to store parent data at all?
for pvalue in pvalues:
assert pvalue != 'default', "Graph skeleton was not topologically ordered."
distribution = self.Vdataentry["cprob"][str(pvalues)]
# choose
rand = random.random()
lbound = 0
ubound = 0
for interval in range(int(self.Vdataentry["numoutcomes"])):
ubound += distribution[interval]
if (lbound <= rand and rand < ubound):
rindex = interval
break
else:
lbound = ubound
return str(self.Vdataentry["vals"][rindex])