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 = MLFKTAdaptTransitionDifficultyModel(X, P, intermediate_states, 0, L1)
else:
model = MLFKTAdaptTransitionDifficultyModel(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()
mcmc.MH(per_loop)
b = time.time()
print("finished iteration: " + str((c+1)*per_loop) + " in " + str(int(b-a)) + " seconds")
P = Pnew
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 'y' in sys.argv[4]:
model = MLFKTConstrainedModel(X, P, intermediate_states, 0)
else:
model = MLFKTConstrainedModel(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()
mcmc.MH(per_loop)
b = time.time()
print("finished iteration: " + str((c+1)*per_loop) + " in " + str(int(b-a)) + " seconds")
