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, True)
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
def run_learned_model(skill, diff_params = None):
intermediate_states = 0
fname = skill.replace(" ","_")
fname = fname.replace("\"","")
X = np.loadtxt(open("dump/observations_" + fname + ".csv", "rb"), delimiter=",")
P = np.loadtxt(open("dump/problems_" + fname + ".csv","rb"),delimiter=",")
k = 5
#split 1/kth into test set
N = X.shape[0]
Xtest = []
Xnew = []
Ptest = []
Pnew = []
for c in range(N):
if c % k == 0:#random.random() < 1 / (k+0.0):
Xtest.append(X[c,:])
Ptest.append(P[c,:])
else:
Xnew.append(X[c,:])
Pnew.append(P[c,:])
X = Xnew
Xtest = np.array(Xtest)
P = Pnew
Ptest = np.array(Ptest)
model = MLFKTModel(X, P, 0, 0.1)
predl = []
errl = []
for c in range(3):
param_dict = json.load(open("feb20_exps/PARAMS_"+skill+"_2states_500iter.json","r"))
param_dict = param_dict[c]
params = model.get_parameters()
for k, v in param_dict.iteritems():
#print k, v
if k == "Pi":
val = np.array(v)
params["L"].set(val)
params["L"].save()
elif k == "Trans":
val = np.array(v)
params["T"].set(val)
params["T"].save()
elif k == "Emit":
G = scipy.special.logit(v[0][1])
S = scipy.special.logit(v[1][0])
params["G_0"].set(G)
params["S"].set(S)
params["G_0"].save()
params["S"].save()
else:
if diff_params is None:
params[k].set(v)
params[k].save()
else:
params[k].set(diff_params[k])
params[k].save()
params['Dsigma'].save()
model.load_test_split(Xtest, Ptest)
preds = model.get_predictions()
err = preds - Xtest
predl.append(preds)
errl.append(err)
return Xtest, Ptest, np.mean(predl,0), np.mean(errl,0)