def start(self):
print(self.instance)
dataset = verboseload(self.inp)
if not isinstance(dataset, list):
self.error('--inp must contain a list of arrays. "%s" has type %s' % (self.inp, type(dataset)))
print('fitting...')
self.instance.fit(dataset)
verbosedump(self.instance, self.out)
print('All done')
def start(self):
print(self.instance)
if os.path.exists(self.top):
top = md.load(self.top)
else:
top = None
dataset = []
for item in self.trjs:
for trjfn in glob.glob(item):
trajectory = []
for i, chunk in enumerate(md.iterload(trjfn, stride=self.stride, chunk=self.chunk, top=top)):
print('\r{} chunk {}'.format(os.path.basename(trjfn), i), end='')
sys.stdout.flush()
trajectory.append(self.instance.partial_transform(chunk))
print()
dataset.append(np.concatenate(trajectory))
verbosedump(dataset, self.out)
print('All done')
def start(self):
print(self.instance)
if self.out is '' and self.transform is '':
self.error('One of --out or --model should be specified')
dataset = verboseload(self.inp)
if not isinstance(dataset, list):
self.error('--inp must contain a list of arrays. "%s" has type %s' % (self.inp, type(dataset)))
print('fit() on %d sequences of shape %s...' % (
len(dataset), ', '.join([str(dataset[e].shape) for e in range(min(3, len(dataset)))])))
self.instance.fit(dataset)
if self.transformed is not '':
transformed = self.instance.transform(dataset)
verbosedump(transformed, self.transformed)
if self.out is not '':
verbosedump(self.instance, self.out)
print('All done')
def load_quadwell(data_home=None, random_state=None):
"""Loader for quad-well dataset
Parameters
----------
data_home : optional, default: None
Specify another cache folder for the datasets. By default
all mixtape data is stored in '~/mixtape_data' subfolders.
random_state : {int, None}, default: None
Seed the psuedorandom number generator to generate trajectories. If
seed is None, the global numpy PRNG is used. If random_state is an
int, the simulations will be cached in ``data_home``, or loaded from
``data_home`` if simulations with that seed have been performed already.
With random_state=None, new simulations will be performed and the
trajectories will not be cached.
Notes
-----
"""
# V = 4*(x**8 + 0.8*np.exp(-80*x**2) + 0.2*(-80*(x-0.5)**2) + 0.5*np.exp(-40*(x+0.5)**2))
random = check_random_state(random_state)
data_home = join(get_data_home(data_home=data_home), 'quadwell')
if not exists(data_home):
makedirs(data_home)
if random_state is None:
trajectories = _simulate_quadwell(random)
else:
if not isinstance(random_state, numbers.Integral):
raise TypeError('random_state must be an int')
path = join(data_home, 'version-0_random-state-%d.pkl' % random_state)
if exists(path):
trajectories = verboseload(path)
else:
trajectories = _simulate_quadwell(random)
verbosedump(trajectories, path)
return Bunch(trajectories=trajectories, DESCR=QUADWELL_DESCRIPTION)
def load_doublewell(data_home=None, random_state=None):
"""Loader for double-well dataset
Parameters
----------
data_home : optional, default: None
Specify another cache folder for the datasets. By default
all mixtape data is stored in '~/mixtape_data' subfolders.
random_state : {int, None}, default: None
Seed the psuedorandom number generator to generate trajectories. If
seed is None, the global numpy PRNG is used. If random_state is an
int, the simulations will be cached in ``data_home``, or loaded from
``data_home`` if simulations with that seed have been performed already.
With random_state=None, new simulations will be performed and the
trajectories will not be cached.
Notes
-----
"""
random = check_random_state(random_state)
data_home = join(get_data_home(data_home=data_home), 'doublewell')
if not exists(data_home):
makedirs(data_home)
if random_state is None:
trajectories = _simulate_doublewell(random)
else:
assert isinstance(random_state, numbers.Integral), 'random_state but be an int'
path = join(data_home, 'version-1_random-state-%d.pkl' % random_state)
if exists(path):
trajectories = verboseload(path)
else:
trajectories = _simulate_doublewell(random)
verbosedump(trajectories, path)
return Bunch(trajectories=trajectories, DESCR=DOUBLEWELL_DESCRIPTION)
output = {}
for path in np.arange(prj.n_trajs):
featurized_path = feat.partial_transform(prj.load_traj(path))
try:
tica.partial_fit(featurized_path)
except:
print "skipping",path
for path in np.arange(prj.n_trajs):
featurized_path = feat.partial_transform(prj.load_traj(path))
output[path] = tica.partial_transform(featurized_path)
# save output
verbosedump(output, 'my-tics.pkl')
verbosedump(tica, 'tica-obj.pkl')
#Ignore below; the model fitting is very quick and can be done in iPython
#get the data
#X = [ output[i] for i in output.iterkeys()]
#model = GaussianFusionHMM(n_states=5, n_features=5)
#model.fit(X)
#save model
#verbosedump(model,"ghmm_s5_n5.pkl")
