p.BarGraph(
output_experiment, path,
"Experimental output of (f.) question") # plotting graph of Experiment
#(H.) output
J = np.array([0, 1, 1, 0, 1])
B = np.array([1, 0, 0, 1, 1])
alpha = 0.7
output_alpha = m.MetropolisAlphaSampling(
J, B, alpha) # call function of alpha sampling
print "alpha mean value is" + str(output_alpha[0])
p.Histogram(output_alpha[1], path,
"output of (h.) question") # plot histogram of alpha sampling
#(H.) running Experiment of alpha_random to check if we get same output
output_e_alpha = e.MCAlphaExperiment(J, B, alpha)
print "alpha mean value through experiment is" + str(output_e_alpha[0])
p.Histogram(output_alpha[1], path, "output experiment of (h.) question")
#(J.) output for uniform distribution question
J = np.array([0, 1, 1, 0, 0, 0, 1, 0])
B = np.array([1, 1, 0, 1, 1, 0, 0, 0])
alpha = 0.5
output_Jalpha = m.MetropolisAlphaJ(J, B, alpha)
print "Jar mean value is" + str(output_Jalpha[1])
p.BarGraph(output_Jalpha[1][:], path,
"output of (J.) question") # plot Bar Graph of Jar sampling
print "alpha mean value is" + str(output_Jalpha[0])
p.Histogram(output_Jalpha[2], path,
"output of (J.) question") # plot histogram of alpha sampling
