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'''
This module contains tools for representing "LG + D" (linear Gaussian and discrete) nodes -- those with a Gaussian distribution, one or more Gaussian parents, and one or more discrete parents -- as class instances with their own *choose* method to choose an outcome for themselves based on parent outcomes.
'''
import random
import math
[docs]class Lgandd():
'''
This class represents a LG + D 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 particualr node. The dict must contain an entry of the following form::
"cprob": {
"['<parent 1, value 1>',...,'<parent n, value 1>']": {
"mean_base": <float used for mean starting point
(\mu_0)>,
"mean_scal": <array of scalars by which to
multiply respectively ordered
continuous parent outcomes>,
"variance": <float for variance>
}
...
"['<parent 1, value j>',...,'<parent n, value k>']": {
"mean_base": <float used for mean starting point
(\mu_0)>,
"mean_scal": <array of scalars by which to
multiply respectively ordered
continuous parent outcomes>,
"variance": <float for variance>
}
}
This ``"cprob"`` entry contains a linear Gaussian distribution (conditioned on the Gaussian parents) for each combination of discrete parents. 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 probability distribution conditioned on *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 entry of ``"cprob"`` that matches the outcomes of its discrete parents. Then, it constructs a Gaussian distribution based on its Gaussian parents and the parameters found at that entry. Last, it samples from that distribution and returns its outcome.
'''
random.seed()
# split parents by type
dispvals = []
lgpvals = []
for pval in pvalues:
if (isinstance(pval, str)):
dispvals.append(pval)
else:
lgpvals.append(pval)
# error check
try:
a = dispvals[0]
a = lgpvals[0]
except IndexError:
print "Did not find LG and discrete type parents."
# find correct Gaussian
lgdistribution = self.Vdataentry["hybcprob"][str(dispvals)]
# calculate Bayesian parameters (mean and variance)
mean = lgdistribution["mean_base"]
if (self.Vdataentry["parents"] != None):
for x in range(len(lgpvals)):
if (lgpvals[x] != "default"):
mean += lgpvals[x] * lgdistribution["mean_scal"][x]
else:
# temporary error check
print "Attempted to sample node with unassigned parents."
variance = lgdistribution["variance"]
# draw random outcome from Gaussian (I love python)
return random.gauss(mean, math.sqrt(variance))