# Write a program that will iteratively update and # predict based on the location measurements # and inferred motions shown below. def update(mean1, var1, mean2, var2): new_mean = float(var2 * mean1 + var1 * mean2) / (var1 + var2) new_var = 1./(1./var1 + 1./var2) return [new_mean, new_var] def predict(mean1, var1, mean2, var2): new_mean = mean1 + mean2 new_var = var1 + var2 return [new_mean, new_var] measurements = [5., 6., 7., 9., 10.] motion = [1., 1., 2., 1., 1.] measurement_sig = 4. motion_sig = 2. mu = 0. sig = 10000. #Please print out ONLY the final values of the mean #and the variance in a list [mu, sig]. # Insert code here for i in range(len(measurements)): [mu, sig] = update(mu, sig, measurements[i], measurement_sig) [mu, sig] = predict(mu, sig, motion[i], motion_sig) print [mu, sig]
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