num_iterations = int(sys.argv[2])
loop = 20
per_loop = int(math.ceil((num_iterations+0.0) / loop))
for c in range(loop):
a = time.time()
mcmc.MH(per_loop)
b = time.time()
print("finished iteration: " + str((c+1)*per_loop) + " in " + str(int(b-a)) + " seconds")
end = time.time()
print("Finished burnin and " + str(num_iterations) + " iterations in " + str(int(end-start)) + " seconds.")
folder = "plots_" + fname
#plotting samples will also load the MAP estimates
mcmc.plot_samples(folder + "/", str(num_iterations) + '_iterations')
#load up test data and run predictions
model.load_test_split(Xtest, Ptest)
pred = model.get_predictions()
num = model.get_num_predictions()
mast = model.get_mastery()
err = pred - Xtest
rmse = np.sqrt(np.sum(err**2)/num)
errl = np.zeros(num)
predl = np.zeros(num)
mastl = np.zeros(num)
xtestl = np.zeros(num)
i = 0
