Class Bayesian

class Bayesian:
    # Baysian = { H: [pH, pD|H, pHpD|H, pH|D]}
    def __init__(self, bayDict, n=3):
        self.dic = bayDict
        self.n = n
    def calcPosterior(self):
        d = self.dic
        normalize = 0
        for k in d.keys():
            d[k][2] = d[k][0] * d[k][1]
            normalize += d[k][2]
        for k in d.keys():
            d[k][3] = d[k][2] / normalize
    def __str__(self):
        pr = self.n
        return '{'+''.join([str(k)+': %0.*f, %.*f, %.*f, %.*f\n ' %
                      (pr, v[0], pr, v[1], pr, v[2], pr, v[3])
                      for k,v in sorted(self.dic.items())])[:-2]+'}'

# Antibiotic on Bacteria
# Exercise: Bayes Theorum and Total Probability
# print('this is what it looks like before you run the class on it\n\n', positive, '\n\n -----\n')

positive = Bayesian({'a': [.999, .01, 0, 0], 'b': [.001, .99, 0, 0]}, 8)
positive.calcPosterior()
print("Bayesian = { H: [pH, pP|H, pH*pP|H, pH|P] }")
print(positive)
print("Bayesian = { S: [pS, pP|S, pS*pP|S, pS|P] }", '\n\n')

negative = Bayesian({'a': [.999, .99, 0, 0], 'b': [.001, .01, 0, 0]}, 8)
negative.calcPosterior()
print("Bayesian = { H: [pH, pN|H, pH*pN|H, pH|N] }")
print(negative)
print("Bayesian = { S: [pS, pN|S, pS*pN|S, pS|N] }", '\n\n')