def test_ala2():
# creates a 4-state HMM on the ALA2 data. Nothing fancy, just makes
# sure the code runs without erroring out
trajectories = AlanineDipeptide().get_cached().trajectories
topology = trajectories[0].topology
indices = topology.select('symbol C or symbol O or symbol N')
featurizer = SuperposeFeaturizer(indices, trajectories[0][0])
sequences = featurizer.transform(trajectories)
hmm = GaussianHMM(n_states=4, n_init=3)
hmm.fit(sequences)
assert len(hmm.timescales_ == 3)
assert np.any(hmm.timescales_ > 50)
def test_ala2():
# creates a 4-state HMM on the ALA2 data. Nothing fancy, just makes
# sure the code runs without erroring out
trajectories = AlanineDipeptide().get_cached().trajectories
topology = trajectories[0].topology
indices = topology.select('symbol C or symbol O or symbol N')
featurizer = SuperposeFeaturizer(indices, trajectories[0][0])
sequences = featurizer.transform(trajectories)
hmm = GaussianHMM(n_states=4, n_init=3, random_state=rs)
hmm.fit(sequences)
assert len(hmm.timescales_ == 3)
assert np.any(hmm.timescales_ > 50)
def test_pickle():
"""Test pickling an HMM"""
trajectories = AlanineDipeptide().get_cached().trajectories
topology = trajectories[0].topology
indices = topology.select('symbol C or symbol O or symbol N')
featurizer = SuperposeFeaturizer(indices, trajectories[0][0])
sequences = featurizer.transform(trajectories)
hmm = GaussianHMM(n_states=4, n_init=3, random_state=rs)
hmm.fit(sequences)
logprob, hidden = hmm.predict(sequences)
with tempfile.TemporaryFile() as savefile:
pickle.dump(hmm, savefile)
savefile.seek(0, 0)
hmm2 = pickle.load(savefile)
logprob2, hidden2 = hmm2.predict(sequences)
assert (logprob == logprob2)
def test_pickle():
"""Test pickling an HMM"""
trajectories = AlanineDipeptide().get_cached().trajectories
topology = trajectories[0].topology
indices = topology.select('symbol C or symbol O or symbol N')
featurizer = SuperposeFeaturizer(indices, trajectories[0][0])
sequences = featurizer.transform(trajectories)
hmm = GaussianHMM(n_states=4, n_init=3, random_state=rs)
hmm.fit(sequences)
logprob, hidden = hmm.predict(sequences)
with tempfile.TemporaryFile() as savefile:
pickle.dump(hmm, savefile)
savefile.seek(0, 0)
hmm2 = pickle.load(savefile)
logprob2, hidden2 = hmm2.predict(sequences)
assert(logprob == logprob2)
def test_1():
# creates a 4-state HMM on the ALA2 data. Nothing fancy, just makes
# sure the code runs without erroring out
dataset = AlanineDipeptide().get()
trajectories = dataset.trajectories
topology = trajectories[0].topology
indices = topology.select('symbol C or symbol O or symbol N')
featurizer = SuperposeFeaturizer(indices, trajectories[0][0])
sequences = featurizer.transform(trajectories)
hmm = GaussianFusionHMM(n_states=4,
n_features=sequences[0].shape[1],
n_init=1)
hmm.fit(sequences)
assert len(hmm.timescales_ == 3)
assert np.any(hmm.timescales_ > 50)
from msmbuilder.featurizer import SuperposeFeaturizer
from msmbuilder.example_datasets import AlanineDipeptide
from msmbuilder.hmm import GaussianHMM
from msmbuilder.cluster import KCenters
from msmbuilder.msm import MarkovStateModel
dataset = AlanineDipeptide().get()
trajectories = dataset.trajectories
topology = trajectories[0].topology
indices = [
atom.index for atom in topology.atoms
if atom.element.symbol in ['C', 'O', 'N']
]
featurizer = SuperposeFeaturizer(indices, trajectories[0][0])
sequences = featurizer.transform(trajectories)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~ HIDDEN MARKOV MODEL ~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
lag_times = [1, 10, 20, 30, 40]
hmm_ts0 = {}
hmm_ts1 = {}
n_states = [3, 5]
for n in n_states:
hmm_ts0[n] = []
hmm_ts1[n] = []
for lag_time in lag_times:
strided_data = [
s[i::lag_time] for s in sequences for i in range(lag_time)
import matplotlib.pyplot as plt
plt.style.use("ggplot")
from msmbuilder.featurizer import SuperposeFeaturizer
from msmbuilder.example_datasets import AlanineDipeptide
from msmbuilder.hmm import GaussianHMM
from msmbuilder.cluster import KCenters
from msmbuilder.msm import MarkovStateModel
dataset = AlanineDipeptide().get()
trajectories = dataset.trajectories
topology = trajectories[0].topology
indices = [atom.index for atom in topology.atoms if atom.element.symbol in ['C', 'O', 'N']]
featurizer = SuperposeFeaturizer(indices, trajectories[0][0])
sequences = featurizer.transform(trajectories)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~ HIDDEN MARKOV MODEL ~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
lag_times = [1, 10, 20, 30, 40]
hmm_ts0 = {}
hmm_ts1 = {}
n_states = [3, 5]
for n in n_states:
hmm_ts0[n] = []
hmm_ts1[n] = []
for lag_time in lag_times:
strided_data = [s[i::lag_time] for s in sequences for i in range(lag_time)]
hmm = GaussianHMM(n_states=n, n_init=1).fit(strided_data)
