#Project 1 for the University of Tulsa's CS-7313 Adv. AI Course
#Approximate Inference Methods for Bayesian Networks
#Professor: Dr. Sen, Fall 2021
#Noah Schrick - 1492657

import json

def main():
    bayes_net = import_bayes()
    print(len(bayes_net))
    #print(is_root("3", bayes_net))

#Import the BN from the json
def import_bayes():
    with open ("gen-bn/bn.json") as json_file:
        bayes_json = json.load(json_file)
    json_file.close()
    return bayes_json



#Checks if node has parents
def is_root(node, BN):
    return (BN[node]["parents"]) == []

#Return a list of the root nodes
def get_root(node, BN):
    roots = []
    for i in range(len(BN)):
        if ((BN[node]["parents"]) == []):
            roots.append(node)
    return roots


#print(bayes_json["x"]): prints the information about node x (an int)
#print(bayes_json["x"]["parents"] prints the information about node x's parents
#class BayesianNetwork:


#Compute the estimate of P(X|e), where X is the query variable, and e is the observed value for variables E
def likelihood_weighting(X, e, bayes_net, samples):
    #Vector of weighted counts for each value of X, initialized to zero
    W = {}
    #Init True and False probabilities
    T = 0
    F = 0

    for i in range(samples):
        x,w = weighted_sample(bayes_net, e)
        print("Hello")

#Returns an event and a weight
def weighted_sample(bayes_net, e):
    w = 1
    #x = get_variable_nodes


 #   for

def gibbs_sampling():
    print("Hello")

def metropolis_hastings():
    print("Hello")

if __name__ == '__main__':
    main()