def fit_hmm(Xs, Xtest, N_samples=100):
model = MultinomialHMM(K, D)
for X in Xs:
model.add_data(X)
samples = []
lls = []
test_lls = []
pis = []
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())
# lls.append(model.log_likelihood_fixed_z())
test_lls.append(model.log_likelihood(Xtest))
# pis.append(testmodel.pis()[0])
zs.append(model.stateseqs[0])
lls = np.array(lls)
test_lls = np.array(test_lls)
pis = np.array(pis)
zs = np.array(zs)
timestamps = np.array(timestamps)
timestamps -= timestamps[0]
return model, lls, test_lls, pis, zs, timestamps
def fit_hmm(Xs, Xtest, N_samples=100):
model = MultinomialHMM(K, D)
for X in Xs:
model.add_data(X)
samples = []
lls = []
test_lls = []
pis = []
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())
# lls.append(model.log_likelihood_fixed_z())
test_lls.append(model.log_likelihood(Xtest))
# pis.append(testmodel.pis()[0])
zs.append(model.stateseqs[0])
lls = np.array(lls)
test_lls = np.array(test_lls)
pis = np.array(pis)
zs = np.array(zs)
timestamps = np.array(timestamps)
timestamps -= timestamps[0]
return model, lls, test_lls, pis, zs, timestamps
def fit_hmm(Xs, Xtest, D_hmm, N_samples=100):
print("Fitting HMM with %d states" % D_hmm)
model = MultinomialHMM(K, D_hmm)
for X in Xs:
model.add_data(X)
init_results = (0, None, model.log_likelihood(),
model.log_likelihood(Xtest),
(model.log_likelihood(np.vstack(
(Xs[0], Xtest))) - model.log_likelihood(Xs[0])))
def resample():
tic = time.time()
model.resample_model()
toc = time.time() - tic
return toc, None, model.log_likelihood(), \
model.log_likelihood(Xtest), \
(model.log_likelihood(np.vstack((Xs[0], Xtest))) - model.log_likelihood(Xs[0]))
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_hmm(Xs, Xtest, D_hmm, N_samples=100):
print("Fitting HMM with %d states" % D_hmm)
model = MultinomialHMM(K, D_hmm, alpha_0=10.0)
for X in Xs:
model.add_data(X)
compute_pred_ll = lambda: sum([
model.log_likelihood(np.vstack(
(Xs[i], Xtest[i]))) - model.log_likelihood(Xs[i])
for i, Xt in enumerate(Xtest)
])
init_results = (0, None, model.log_likelihood(),
model.log_likelihood(Xtest), 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_hmm(Xs, Xtest, D_hmm, N_samples=100):
Nx = len(Xs)
assert len(Xtest) == Nx
print("Fitting HMM with %d states" % D_hmm)
models = [MultinomialHMM(K, D_hmm, alpha_0=10.0) for _ in xrange(Nx)]
for X, model in zip(Xs, models):
model.add_data(X)
def compute_pred_ll():
pred_ll = 0
for Xtr, Xte, model in zip(Xs, Xtest, models):
pred_ll += model.log_likelihood(np.vstack(
(Xtr, Xte))) - model.log_likelihood(Xtr)
return pred_ll
init_results = (0, None, np.nan, np.nan, compute_pred_ll())
def resample():
tic = time.time()
[model.resample_model() for model in models]
toc = time.time() - tic
return toc, None, np.nan, 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)
def fit_hmm(Xs, Xtest, D_hmm, N_samples=100):
print("Fitting HMM with %d states" % D_hmm)
model = MultinomialHMM(K, D_hmm)
for X in Xs:
model.add_data(X)
init_results = (0, None, model.log_likelihood(),
model.log_likelihood(Xtest),
(model.log_likelihood(np.vstack((Xs[0], Xtest))) - model.log_likelihood(Xs[0])))
def resample():
tic = time.time()
model.resample_model()
toc = time.time() - tic
return toc, None, model.log_likelihood(), \
model.log_likelihood(Xtest), \
(model.log_likelihood(np.vstack((Xs[0], Xtest))) - model.log_likelihood(Xs[0]))
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_hmm(Xs, Xtest, D_hmm, N_samples=100):
print("Fitting HMM with %d states" % D_hmm)
model = MultinomialHMM(K, D_hmm, alpha_0=10.0)
for X in Xs:
model.add_data(X)
compute_pred_ll = lambda: sum([model.log_likelihood(np.vstack((Xs[i], Xtest[i])))
- model.log_likelihood(Xs[i])
for i,Xt in enumerate(Xtest)])
init_results = (0, None, model.log_likelihood(),
model.log_likelihood(Xtest),
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)
