reasoning under uncertainty from functools import reduce class tabfactor(factor) def __init__(selfvariablesvalues)factor __init__(selfvariablesself values values def get_value(selfassignment)return self get_val_rec(self valuesself variablesassignment def get_val_rec(selfvaluevariablesassignment)if variables =[]return value elsereturn self get_val_rec(value[assignment[variables[ ]]]variables[ :],assignmentprob is factor that represents conditional probability by enumerating all of the values probfactors py -(continued class prob(cpd,tabfactor)""" factor defined by conditional probability table""def __init__(self,var,pars,cpt)"""creates factor from conditional probability tablecpt the cpt values are assumed to be for the ordering par+[var""tabfactor __init__(self,pars+[var],cptself child var self parents pars graphical models graphical model consists of set of variables and set of factors belief network is graphical model where all of the factors represent conditional probabilities there are some operations (such as pruning variableswhich are applicable to belief networksbut are not applicable to more general models at the momentwe will treat them as the same probgraphicalmodels py -graphical models and belief networks from display import displayable from probfactors import cpd import matplotlib pyplot as plt class graphicalmodel(displayable)"""the class of graphical models graphical model consists of titlea set of variables and set of factors version june