def fit_lds_model(Xs, Xtest, D, N_samples=100):
model = MultinomialLDS(K,
D,
init_dynamics_distn=GaussianFixed(mu=np.zeros(D),
sigma=1 *
np.eye(D)),
dynamics_distn=AutoRegression(nu_0=D + 1,
S_0=1 * np.eye(D),
M_0=np.zeros((D, D)),
K_0=1 * np.eye(D)),
sigma_C=0.01)
for X in Xs:
model.add_data(X)
model.resample_parameters()
init_results = (0, model, model.log_likelihood(),
model.heldout_log_likelihood(Xtest, M=1),
model.predictive_log_likelihood(Xtest, M=1000))
def resample():
tic = time.time()
model.resample_model()
toc = time.time() - tic
return toc, None, model.log_likelihood(), \
model.heldout_log_likelihood(Xtest, M=1), \
model.predictive_log_likelihood(Xtest, M=1000)
times, samples, lls, test_lls, pred_lls = \
map(np.array, zip(*([init_results] + [resample() for _ in progprint_xrange(N_samples)])))
timestamps = np.cumsum(times)
return Results(lls, test_lls, pred_lls, samples, timestamps)
def fit_lds_model(Xs, Xtest, D, N_samples=100):
model = MultinomialLDS(K, D,
init_dynamics_distn=GaussianFixed(mu=np.zeros(D), sigma=1*np.eye(D)),
dynamics_distn=AutoRegression(nu_0=D+1,S_0=1*np.eye(D),M_0=np.zeros((D,D)),K_0=1*np.eye(D)),
sigma_C=0.01
)
for X in Xs:
model.add_data(X)
model.resample_parameters()
init_results = (0, model, model.log_likelihood(),
model.heldout_log_likelihood(Xtest, M=1),
model.predictive_log_likelihood(Xtest, M=1000))
def resample():
tic = time.time()
model.resample_model()
toc = time.time() - tic
return toc, None, model.log_likelihood(), \
model.heldout_log_likelihood(Xtest, M=1), \
model.predictive_log_likelihood(Xtest, M=1000)
times, samples, lls, test_lls, pred_lls = \
map(np.array, zip(*([init_results] + [resample() for _ in progprint_xrange(N_samples)])))
timestamps = np.cumsum(times)
return Results(lls, test_lls, pred_lls, samples, timestamps)
def fit_lds_model(Xs, Xtest, D, N_samples=100):
Nx = len(Xs)
assert len(Xtest) == Nx
model = MultinomialLDS(K,
D,
init_dynamics_distn=GaussianFixed(mu=np.zeros(D),
sigma=1 *
np.eye(D)),
dynamics_distn=AutoRegression(nu_0=D + 1,
S_0=1 * np.eye(D),
M_0=np.zeros((D, D)),
K_0=1 * np.eye(D)),
sigma_C=1.)
for X in Xs:
model.add_data(X)
model.resample_parameters()
compute_pred_ll = lambda: sum([
model.predictive_log_likelihood(Xt, data_index=i, M=10)[0]
for i, Xt in enumerate(Xtest)
])
init_results = (
0,
None,
model.log_likelihood(),
# model.heldout_log_likelihood(Xtest, M=1),
np.nan,
compute_pred_ll())
def resample():
tic = time.time()
model.resample_model()
toc = time.time() - tic
return toc, None, model.log_likelihood(), \
np.nan,\
compute_pred_ll()
times, samples, lls, test_lls, pred_lls = \
list(map(np.array, list(zip(*([init_results] +
[resample() for _ in progprint_xrange(N_samples, perline=5)])))))
timestamps = np.cumsum(times)
return Results(lls, test_lls, pred_lls, samples, 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
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, D, N_samples=100):
Nx = len(Xs)
assert len(Xtest) == Nx
model = MultinomialLDS(K, D,
init_dynamics_distn=GaussianFixed(mu=np.zeros(D), sigma=1*np.eye(D)),
dynamics_distn=AutoRegression(nu_0=D+1,S_0=1*np.eye(D),M_0=np.zeros((D,D)),K_0=1*np.eye(D)),
sigma_C=1.
)
for X in Xs:
model.add_data(X)
model.resample_parameters()
compute_pred_ll = lambda: sum([model.predictive_log_likelihood(Xt, data_index=i, M=10)[0]
for i,Xt in enumerate(Xtest)])
init_results = (0, None, model.log_likelihood(),
# model.heldout_log_likelihood(Xtest, M=1),
np.nan,
compute_pred_ll())
def resample():
tic = time.time()
model.resample_model()
toc = time.time() - tic
return toc, None, model.log_likelihood(), \
np.nan,\
compute_pred_ll()
times, samples, lls, test_lls, pred_lls = \
map(np.array, zip(*([init_results] +
[resample() for _ in progprint_xrange(N_samples, perline=5)])))
timestamps = np.cumsum(times)
return Results(lls, test_lls, pred_lls, samples, timestamps)
testmodel.add_data(data["x"])
testdata = testmodel.data_list[0]
N_samples = 100
samples = []
lls = []
pis = []
psis = []
zs = []
for smpl in range(N_samples):
print("Iteration ", smpl)
testmodel.resample_model()
samples.append(testmodel.copy_sample())
lls.append(testmodel.log_likelihood())
pis.append(testmodel.pi(testdata))
psis.append(testmodel.psi(testdata))
zs.append(testdata["states"].stateseq)
lls = np.array(lls)
pis = np.array(pis)
psis = np.array(psis)
psi_mean = psis[N_samples // 2:, ...].mean(0)
psi_std = psis[N_samples // 2:, ...].std(0)
zs = np.array(zs)
z_mean = zs[N_samples // 2:, ...].mean(0)
z_std = zs[N_samples // 2:, ...].std(0)
testmodel.add_data(data["x"])
testdata = testmodel.data_list[0]
N_samples = 100
samples = []
lls = []
pis = []
psis = []
zs = []
for smpl in xrange(N_samples):
print("Iteration ", smpl)
testmodel.resample_model()
samples.append(testmodel.copy_sample())
lls.append(testmodel.log_likelihood())
pis.append(testmodel.pi(testdata))
psis.append(testmodel.psi(testdata))
zs.append(testdata["states"].stateseq)
lls = np.array(lls)
pis = np.array(pis)
psis = np.array(psis)
psi_mean = psis[N_samples//2:,...].mean(0)
psi_std = psis[N_samples//2:,...].std(0)
zs = np.array(zs)
z_mean = zs[N_samples//2:,...].mean(0)
z_std = zs[N_samples//2:,...].std(0)
