def test_plusmin_mstep():
# Set constants
n_seq = 1
T = 2000
# Generate data
plusmin = PlusminModel()
data, hidden = plusmin.generate_dataset(n_seq, T)
n_features = plusmin.x_dim
n_components = plusmin.K
# Fit reference model and initial MSLDS model
refmodel = GaussianHMM(n_components=n_components,
covariance_type='full').fit(data)
warnings.filterwarnings("ignore", category=DeprecationWarning)
# 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)
def test_met_enkephalin_mstep():
import pdb, traceback, sys
warnings.filterwarnings("ignore", category=DeprecationWarning)
try:
b = fetch_met_enkephalin()
trajs = b.trajectories
# While debugging, restrict to first trajectory only
trajs = [trajs[0]]
n_seq = len(trajs)
n_frames = trajs[0].n_frames
n_atoms = trajs[0].n_atoms
n_features = n_atoms * 3
print "n_features: ", n_features
data_home = get_data_home()
data_dir = join(data_home, TARGET_DIRECTORY_MET)
top = md.load(join(data_dir, '1plx.pdb'))
n_components = 2
# Superpose m
data = []
for traj in trajs:
traj.superpose(top)
Z = traj.xyz
Z = np.reshape(Z, (n_frames, n_features), order='F')
data.append(Z)
# Fit reference model and initial MSLDS model
print "Starting Gaussian Model Fit"
refmodel = GaussianHMM(n_components=n_components,
covariance_type='full').fit(data)
print "Done with Gaussian Model Fit"
# 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, N_iter=100,
verbose=True)
except:
type, value, tb = sys.exc_info()
traceback.print_exc()
pdb.post_mortem(tb)
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)
