Xtest = np.array(Xtest)
Ptest = np.array(Ptest)
X = np.array(X)
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
if "y" in sys.argv[4]:
model = MLFKTModel(X, P, intermediate_states, 0, L1)
else:
model = MLFKTModel(X, P, intermediate_states, 0.1, L1)
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()
newX = []
newP = []
for c in range(X.shape[0]):
if X[c,-1] >= maxl-1:
newX.append(X[c,:])
newP.append(P[c,:])
X = np.array(newX)
P = np.array(newP)
start = time.time()
if 'y' in sys.argv[4]:
model = MLFKTModel(X, P, intermediate_states, 0)
else:
model = MLFKTModel(X, P, intermediate_states, 0.1)
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()
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)
