params['S'].set(0.10018501374500711)
params['L'].set(np.array([0.92012063497620489, 0.079879365023795085]))
params['T'].set(0)"""
#run viterbi
state_estimates, masteries = model.viterbi()
#first val needs to be pulled off (I think)
state_estimates = [x[1:] for x in state_estimates]
time_estimates = get_mastery_time(state_estimates)
trans_errs = []
for c in range(len(times)):
trans_errs.append(time_estimates[c] - times[c])
model.load_test_split(X,P,False)
trans_pred = model.get_predictions()
trans_rmse = np.sqrt(np.mean( (trans_pred - X) ** 2))
#print trans_errs
# Setup KT-IDEAL model
pdictl = json.load(open("dump/PARAMS_simulated_trans_"+str(students)+"_second_ktideal_2states_1000iter.json","r"))
model = KTIDEAL(X,P,0,0.15,False,False)
params = model.get_parameters()
#Set the learned model parameters for transition model
for k, v in params.iteritems():
if k in pdictl[-1]:
v.set(pdictl[-1][k])
else:
print
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
for n in range(pred.shape[0]):
for t in range(pred.shape[1]):
if pred[n][t] == -1:
break
