def expect(xDistribution, function):

##################################################
#		Your code here
##################################################
    total = 0

    for n in xDistribution.keys():
        total += xDistribution[n] * function(n)

    return total
    # return sum([xDistribution[val] * function(val) for val in xDistribution.keys()])



##################################################
#		Your code below each comment
##################################################
def getVariance(xDistribution):

    #Step 1 - Calculate the expected value E(X)
    expected_val = expect(xDistribution, lambda x: x)

    def getSquaredDistanceToMu(x):
    #Step 2 - Calculate (X-E(X))^2
        return (x - expected_val) ** 2
    #Step 3 - Calculate Variance: Var(X)=E((X-E(X))^2)
    variance = expect(xDistribution, getSquaredDistanceToMu)

    return variance

def main():
    xDistributionExample1={1: 1/5, 2: 2/5, 3: 2/5}
    functionExample1=lambda x: x ** 2
    print("Expected value:", expect(xDistributionExample1, functionExample1))
    print("Variance:", getVariance(xDistributionExample1))

    xDistributionExample2={1: 1/6, -1/2: 1/3, 1/3: 1/4, -1/4: 1/12, 1/5: 1/6}
    functionExample2=lambda x: 1/x
    print("Expected value:", expect(xDistributionExample2, functionExample2))
    print("Variance:", getVariance(xDistributionExample2))


if __name__ == '__main__':
    main()