def fit_lds_model(Xs, Xtest, N_samples=100):
model = MultinomialLDS(K,
D,
init_dynamics_distn=Gaussian(mu_0=np.zeros(D),
sigma_0=np.eye(D),
kappa_0=1.0,
nu_0=D + 1.0),
dynamics_distn=AutoRegression(nu_0=D + 1,
S_0=np.eye(D),
M_0=np.zeros((D, D)),
K_0=np.eye(D)),
sigma_C=1)
for X in Xs:
model.add_data(X)
data = model.data_list[0]
samples = []
lls = []
test_lls = []
mc_test_lls = []
pis = []
psis = []
zs = []
timestamps = [time.time()]
for smpl in progprint_xrange(N_samples):
model.resample_model()
timestamps.append(time.time())
samples.append(model.copy_sample())
# TODO: Use log_likelihood() to marginalize over z
lls.append(model.log_likelihood())
# test_lls.append(model.heldout_log_likelihood(Xtest, M=50)[0])
mc_test_lls.append(model._mc_heldout_log_likelihood(Xtest, M=1)[0])
pis.append(model.pi(data))
psis.append(model.psi(data))
zs.append(data["states"].stateseq)
lls = np.array(lls)
test_lls = np.array(test_lls)
pis = np.array(pis)
psis = np.array(psis)
zs = np.array(zs)
timestamps = np.array(timestamps)
timestamps -= timestamps[0]
return model, lls, test_lls, mc_test_lls, pis, psis, zs, timestamps
def fit_lds_model(Xs, Xtest, N_samples=100):
model = MultinomialLDS(K, D,
init_dynamics_distn=Gaussian(mu_0=np.zeros(D), sigma_0=np.eye(D), kappa_0=1.0, nu_0=D+1.0),
dynamics_distn=AutoRegression(nu_0=D+1,S_0=np.eye(D),M_0=np.zeros((D,D)),K_0=np.eye(D)),
sigma_C=1
)
for X in Xs:
model.add_data(X)
data = model.data_list[0]
samples = []
lls = []
test_lls = []
mc_test_lls = []
pis = []
psis = []
zs = []
timestamps = [time.time()]
for smpl in progprint_xrange(N_samples):
model.resample_model()
timestamps.append(time.time())
samples.append(model.copy_sample())
# TODO: Use log_likelihood() to marginalize over z
lls.append(model.log_likelihood())
# test_lls.append(model.heldout_log_likelihood(Xtest, M=50)[0])
mc_test_lls.append(model._mc_heldout_log_likelihood(Xtest, M=1)[0])
pis.append(model.pi(data))
psis.append(model.psi(data))
zs.append(data["states"].stateseq)
lls = np.array(lls)
test_lls = np.array(test_lls)
pis = np.array(pis)
psis = np.array(psis)
zs = np.array(zs)
timestamps = np.array(timestamps)
timestamps -= timestamps[0]
return model, lls, test_lls, mc_test_lls, pis, psis, zs, timestamps
sigma_states = 0.1 * np.eye(D)
K = 3
# C = np.hstack((np.ones((K-1, 1)), np.zeros((K-1, D-1))))
C = np.random.randn(K - 1, D)
###################
# generate data #
###################
model = MultinomialLDS(K,
D,
init_dynamics_distn=Gaussian(mu=mu_init,
sigma=sigma_init),
dynamics_distn=AutoRegression(A=A, sigma=sigma_states),
C=C)
data = model.generate(T=T, N=N, keep=False)
# data["x"] = np.hstack([np.zeros((T,K-1)), np.ones((T,1))])
# Estimate the held out likelihood using Monte Carlo
M = 10000
hll_mc, std_mc = model._mc_heldout_log_likelihood(data["x"], M=M)
# Estimate the held out log likelihood
# hll_info, std_info = model._info_form_heldout_log_likelihood(data["x"], M=M)
hll_dist, std_dist = model._distn_form_heldout_log_likelihood(data["x"], M=M)
print("MC. Model: ", hll_mc, " +- ", std_mc)
# print "AN. Model (info): ", hll_info, " +- ", std_info
print("AN. Model (dist): ", hll_dist, " +- ", std_dist)
[np.sin(np.pi/24), np.cos(np.pi/24)]])
sigma_states = 0.1*np.eye(D)
K = 3
# C = np.hstack((np.ones((K-1, 1)), np.zeros((K-1, D-1))))
C = np.random.randn(K-1, D)
###################
# generate data #
###################
model = MultinomialLDS(K, D,
init_dynamics_distn=Gaussian(mu=mu_init,sigma=sigma_init),
dynamics_distn=AutoRegression(A=A,sigma=sigma_states),
C=C
)
data = model.generate(T=T, N=N, keep=False)
# data["x"] = np.hstack([np.zeros((T,K-1)), np.ones((T,1))])
# Estimate the held out likelihood using Monte Carlo
M = 10000
hll_mc, std_mc = model._mc_heldout_log_likelihood(data["x"], M=M)
# Estimate the held out log likelihood
# hll_info, std_info = model._info_form_heldout_log_likelihood(data["x"], M=M)
hll_dist, std_dist = model._distn_form_heldout_log_likelihood(data["x"], M=M)
print("MC. Model: ", hll_mc, " +- ", std_mc)
# print "AN. Model (info): ", hll_info, " +- ", std_info
print("AN. Model (dist): ", hll_dist, " +- ", std_dist)
