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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

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.'''

    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)

        # Check that we have at least one discrete parent.
        if not dispvals:
            print "Did not find any discrete parent. Consider using an Lg node."

        # 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))

@@ -4857,16 +4857,17 @@
 (pval)%0A%0A
+%0A
         
@@ -4872,80 +4872,113 @@
   # 
-Check that we have at least one discrete parent.%0A        if not dispvals
+error check%0A        try:%0A            a = dispvals%5B0%5D%0A            a = lgpvals%5B0%5D%0A        except IndexError
 :%0A  
@@ -5011,11 +5011,14 @@
 ind 
+LG 
 an
-y
+d
  dis
@@ -5027,41 +5027,20 @@
 ete 
+type 
 parent
-. Consider using an Lg node
+s
 .%22%0A%0A

Remove trailing whitespaces from lgandd.py.