BayesianClassifier

BayesClassifier

def BayesClassifier(training_set,test_set):
    classAttribute = 'Volume'
    products = []
    max = -math.inf
    classWithMaxValue = "" 
    for x in training_set[classAttribute].unique():
        D = len(training_set[classAttribute].index)
        d = len(training_set[training_set[classAttribute] == x].index)
        pClassAttribute = d/D
        print("********")
        print(f'Step 1 calculate p({classAttribute}={x})={pClassAttribute}')
        p = 0
        probabilitiesProduct = 1
        print("********")
        print("Step 2 calculate product of probabilities")
        for A, values in training_set.iteritems():
            if not A == classAttribute:
                v = training_set[A].iloc[0]
                p = prob_continous_value(A, v, classAttribute, training_set, x)
                print(f'p({A}={v}|{classAttribute}={x})={p}')
                probabilitiesProduct *= p
        print(f"probabilitiesProduct={probabilitiesProduct}")
        print("********")
        # products.append(probabilitiesProduct)
        ptotal = pClassAttribute*probabilitiesProduct
        print(f'p({classAttribute}={x}|x)={ptotal}')
        if ptotal > max:
            max = ptotal
            classWithMaxValue = x
        print(f"winner is {classAttribute}={classWithMaxValue}")

    calculate_metrics(tp, tn, fn, fp, p, n)