# Do the transition model stuff first
X = np.loadtxt(open("dump/observations_simulated_trans_"+str(students)+".csv","rb"),delimiter=",")
#load problem IDs for these observations
P = np.loadtxt(open("dump/problems_simulated_trans_"+str(students)+".csv","rb"),delimiter=",")
#S = np.loadtxt(open("dump/skills_simulated_trans.csv", "rb"), delimiter=",")
states = np.loadtxt(open("dump/states_simulated_trans_"+str(students)+".csv","rb"),delimiter=",")
times = get_mastery_time(states)
#get params learned from model
pdictl = json.load(open("dump/PARAMS_simulated_trans_"+str(students)+"_L1_second_trans_2states_1000iter.json","r"))
# L1 transition model, transition AFTER emission
model = MLFKTTransitionModel(X, P, 0, 0.15, True, 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
print pdictl[-1]
print "Uh oh,", k, "not in learned params for transition model"
"""params['D_8'].set(0.8834557934379552)
params['D_9'].set(1.140053275980339)
params['D_2'].set(-2.500678825112307)
params['D_3'].set(-1.7398224556239437)
P = np.array(P)
print str(Xtest.shape[0]) + " test sequences"
print str(X.shape[0]) + " training sequences"
if "1" in sys.argv[8]:
L1 = True
else:
L1 = False
trans_first = False
if len(sys.argv) > 9:
trans_first = True
if "y" in sys.argv[4]:
model = MLFKTTransitionModel(X, P, intermediate_states, 0, L1, trans_first)
else:
model = MLFKTTransitionModel(X, P, intermediate_states, 0.1, L1, trans_first)
mcmc = MCMCSampler(model, 0.15)
burn = int(sys.argv[1])
for c in range(20):
mcmc.burnin(int(math.ceil((burn + 0.0) / 20)))
print ("finished burn-in #: " + str((c + 1) * burn / 20))
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()
