# 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)
params['D_0'].set(-2.2268556721015127)
params['D_1'].set(-2.411186976135999)
if os.path.exists(rmsefname):
rmsel = json.load(open(rmsefname, "r"))
else:
rmsel = []
rmsel.append(rmse)
json.dump(rmsel, open(rmsefname, "w"))
paramfname = (
"dump/PARAMS_" + fname + "_trans_" + str(total_states) + "states_" + str(num_iterations) + "iter" + ".json"
)
if os.path.exists(paramfname):
paraml = json.load(open(paramfname, "r"))
else:
paraml = []
p = model.get_parameters()
pdict = {}
for id, param in p.iteritems():
# if "D_" in id:
if id == "L":
pdict[id] = [param.get()[0], param.get()[1]]
else:
pdict[id] = param.get()
# pdict['Trans'] = list( [list(x) for x in model.make_transitions()] )
pdict["Pi"] = list(model.make_initial())
model.emission_mask[0] = False
pdict["Emit"] = list([list(x) for x in model.make_emissions(0, 0)])
paraml.append(pdict)
json.dump(paraml, open(paramfname, "w"))
