reasoning under uncertainty elsereturn " ({self child}) __repr__ __str__ the simplest cpd is the constant that has probability when the child has the value specified probfactors py -(continued class constantcpd(cpd)def __init__(selfvariablevalue)cpd __init__(selfvariable[]self value value def get_value(selfassignment)return if self value==assignment[self childelse logistic regression logistic regression cpdfor boolean variable represents ( =true yk )using real-values weights so ( =true yk sigmoid( wi yi where for boolean yi true is represented as and false as probfactors py -(continued from learnlinear import sigmoidlogit class logisticregression(cpd)def __init__(selfchildparentsweights)""" logistic regression representation of conditional probability child is the boolean (or / variable whose cpd is being defined parents is the list of parents weights is list of parameterssuch that weights[ + is the weight for parents[ ""assert len(weights= +len(parentscpd __init__(selfchildparentsself weights weights def get_value(self,assignment)assert self can_evaluate(assignmentprob sigmoid(self weights[ sum(self weights[ + ]*assignment[self parents[ ]for in range(len(self parents)))if assignment[self child]#child is true return prob elsereturn ( -probversion june