def test_muller_potential_stats():
# Set constants
n_seq = 1
num_trajs = 1
T = 2500
num_hotstart = 0
# Generate data
warnings.filterwarnings("ignore", category=DeprecationWarning)
muller = MullerModel()
data, trajectory, start = \
muller.generate_dataset(n_seq, num_trajs, T)
n_features = muller.x_dim
n_components = muller.K
# Fit reference model and initial MSLDS model
refmodel = GaussianHMM(n_components=n_components,
covariance_type='full').fit(data)
model = MetastableSwitchingLDS(n_components, n_features,
n_hotstart=num_hotstart)
model.inferrer._sequences = data
model.means_ = refmodel.means_
model.covars_ = refmodel.covars_
model.transmat_ = refmodel.transmat_
model.populations_ = refmodel.startprob_
As = []
for i in range(n_components):
As.append(np.zeros((n_features, n_features)))
model.As_ = As
model.Qs_ = refmodel.covars_
model.bs_ = refmodel.means_
iteration = 0 # Remove this step once hot_start is factored out
logprob, stats = model.inferrer.do_estep()
rlogprob, rstats = reference_estep(refmodel, data)
yield lambda: np.testing.assert_array_almost_equal(stats['post'],
rstats['post'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[1:]'],
rstats['post[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['post[:-1]'],
rstats['post[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs'],
rstats['obs'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[1:]'],
rstats['obs[1:]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs[:-1]'],
rstats['obs[:-1]'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(stats['obs*obs.T'],
rstats['obs*obs.T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs*obs[t-1].T'], rstats['obs*obs[t-1].T'], decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[1:]*obs[1:].T'], rstats['obs[1:]*obs[1:].T'],
decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['obs[:-1]*obs[:-1].T'], rstats['obs[:-1]*obs[:-1].T'],
decimal=3)
yield lambda: np.testing.assert_array_almost_equal(
stats['trans'], rstats['trans'], decimal=1)
def test_muller_potential_mstep():
import pdb, traceback, sys
try:
# Set constants
n_seq = 1
num_trajs = 1
T = 2500
# Generate data
warnings.filterwarnings("ignore", category=DeprecationWarning)
muller = MullerModel()
data, trajectory, start = \
muller.generate_dataset(n_seq, num_trajs, T)
n_features = muller.x_dim
n_components = muller.K
# Fit reference model and initial MSLDS model
refmodel = GaussianHMM(n_components=n_components,
covariance_type='full').fit(data)
# Obtain sufficient statistics from refmodel
rlogprob, rstats = reference_estep(refmodel, data)
means = refmodel.means_
covars = refmodel.covars_
transmat = refmodel.transmat_
populations = refmodel.startprob_
As = []
for i in range(n_components):
As.append(np.zeros((n_features, n_features)))
Qs = refmodel.covars_
bs = refmodel.means_
means = refmodel.means_
covars = refmodel.covars_
# Test AQB solver for MSLDS
solver = MetastableSwitchingLDSSolver(n_components, n_features)
solver.do_mstep(As, Qs, bs, means, covars, rstats)
except:
type, value, tb = sys.exc_info()
traceback.print_exc()
pdb.post_mortem(tb)
def test_muller_potential():
import pdb, traceback, sys
try:
# Set constants
n_hotstart = 3
n_em_iter = 3
n_experiments = 1
n_seq = 1
num_trajs = 1
T = 2500
sim_T = 2500
gamma = 200.
# Generate data
warnings.filterwarnings("ignore", category=DeprecationWarning)
muller = MullerModel()
data, trajectory, start = \
muller.generate_dataset(n_seq, num_trajs, T)
n_features = muller.x_dim
n_components = muller.K
# Train MSLDS
model = MetastableSwitchingLDS(n_components, n_features,
n_hotstart=n_hotstart, n_em_iter=n_em_iter,
n_experiments=n_experiments)
model.fit(data, gamma=gamma)
mslds_score = model.score(data)
print("MSLDS Log-Likelihood = %f" % mslds_score)
# Fit Gaussian HMM for comparison
g = GaussianFusionHMM(n_components, n_features)
g.fit(data)
hmm_score = g.score(data)
print("HMM Log-Likelihood = %f" % hmm_score)
# Clear Display
plt.cla()
plt.plot(trajectory[start:, 0], trajectory[start:, 1], color='k')
plt.scatter(model.means_[:, 0], model.means_[:, 1],
color='r', zorder=10)
plt.scatter(data[0][:, 0], data[0][:, 1],
edgecolor='none', facecolor='k', zorder=1)
Delta = 0.5
minx = min(data[0][:, 0])
maxx = max(data[0][:, 0])
miny = min(data[0][:, 1])
maxy = max(data[0][:, 1])
sim_xs, sim_Ss = model.sample(sim_T, init_state=0,
init_obs=model.means_[0])
minx = min(min(sim_xs[:, 0]), minx) - Delta
maxx = max(max(sim_xs[:, 0]), maxx) + Delta
miny = min(min(sim_xs[:, 1]), miny) - Delta
maxy = max(max(sim_xs[:, 1]), maxy) + Delta
plt.scatter(sim_xs[:, 0], sim_xs[:, 1], edgecolor='none',
zorder=5, facecolor='g')
plt.plot(sim_xs[:, 0], sim_xs[:, 1], zorder=5, color='g')
MullerForce.plot(ax=plt.gca(), minx=minx, maxx=maxx,
miny=miny, maxy=maxy)
plt.show()
except:
type, value, tb = sys.exc_info()
traceback.print_exc()
pdb.post_mortem(tb)