def _do_lumping(self):
"""Do the MVCA lumping.
"""
model = LandmarkAgglomerative(linkage='ward',
n_clusters=self.n_macrostates,
metric=self.metric,
n_landmarks=self.n_landmarks,
landmark_strategy=self.landmark_strategy,
random_state=self.random_state)
microstate_mapping_ = model.fit_transform([self.transmat_])[0]
self.microstate_mapping_ = microstate_mapping_
def traj_load(irow):
i, row = irow
traj = md.load(row['traj_fn'], top=tops[row['top_fn']])
return i, traj
traj_dict = dict(map(traj_load, meta.iterrows()))
trajs = [traj for traj in traj_dict.values() if traj.n_frames > 1000]
print(len(trajs))
num_clust = 20
cluster = LandmarkAgglomerative(n_clusters=num_clust,
n_landmarks=int(totframes / 100),
linkage='ward',
metric='rmsd')
ctrajs = cluster.fit_transform(trajs)
# print('Fitting cluster labels for MSM')
# ctraj = {}
# count = 0
# for k, v in traj_dict.items():
# print(k, count)
# count +=1
# ctraj[k] = cluster.partial_predict(v)
#
# ctrajs = [traj for traj in ctraj.values() if traj.shape[0] > 1000]
print('Fitting MSM')
lag = 4000
msm = MarkovStateModel(lag_time=lag, n_timescales=50)
msm.fit(ctrajs)
