def __init__(self, samples, ref=None, conf=0.95):
r""" Constructs a sampled HMSM
Parameters
----------
samples : list of HMSM
Sampled HMSM objects
ref : HMSM
Single-point estimator, e.g. containing a maximum likelihood HMSM.
If not given, the sample mean will be used.
conf : float, optional, default=0.95
Confidence interval. By default two-sigma (95.4%) is used.
Use 95.4% for two sigma or 99.7% for three sigma.
"""
# validate input
assert is_iterable(
samples), 'samples must be a list of MSM objects, but is not.'
assert isinstance(
samples[0],
_HMSM), 'samples must be a list of MSM objects, but is not.'
# construct superclass 1
_SampledModel.__init__(self, samples, conf=conf)
# construct superclass 2
if ref is None:
Pref = self.sample_mean('P')
pobsref = self.sample_mean('pobs')
_HMSM.__init__(self, Pref, pobsref, dt_model=samples[0].dt_model)
else:
_HMSM.__init__(self,
ref.transition_matrix,
ref.observation_probabilities,
dt_model=ref.dt_model)
def _estimate_param_scan_worker(estimator, params, X, evaluate, evaluate_args,
failfast):
""" Method that runs estimation for several parameter settings.
Defined as a worker for Parallelization
"""
# run estimation
model = None
try: # catch any exception
estimator.estimate(X, **params)
model = estimator.model
except:
e = sys.exc_info()[1]
if isinstance(estimator, Loggable):
estimator.logger.warning("Ignored error during estimation: %s" % e)
if failfast:
raise # re-raise
else:
pass # just return model=None
# deal with results
res = []
# deal with result
if evaluate is None: # we want full models
res.append(model)
# we want to evaluate function(s) of the model
elif _types.is_iterable(evaluate):
values = [] # the function values the model
for ieval, name in enumerate(evaluate):
# get method/attribute name and arguments to be evaluated
name = evaluate[ieval]
args = ()
if evaluate_args is not None:
args = evaluate_args[ieval]
# wrap single arguments in an iterable again to pass them.
if _types.is_string(args):
args = (args, )
# evaluate
try:
# try calling method/property/attribute
value = _call_member(estimator.model, name, failfast, *args)
# couldn't find method/property/attribute
except AttributeError as e:
if failfast:
raise e # raise an AttributeError
else:
value = None # we just ignore it and return None
values.append(value)
# if we only have one value, unpack it
if len(values) == 1:
values = values[0]
res.append(values)
else:
raise ValueError('Invalid setting for evaluate: ' + str(evaluate))
if len(res) == 1:
res = res[0]
return res
def _estimate_param_scan_worker(estimator, params, X, evaluate, evaluate_args,
failfast):
# run estimation
model = estimator.estimate(X, **params)
# deal with results
res = []
# deal with result
if evaluate is None: # we want full models
res.append(model)
# we want to evaluate function(s) of the model
elif _types.is_iterable(evaluate):
values = [] # the function values the model
for ieval in range(len(evaluate)):
# get method/attribute name and arguments to be evaluated
name = evaluate[ieval]
args = None
if evaluate_args is not None:
args = evaluate_args[ieval]
# evaluate
try:
# try calling method/property/attribute
value = _call_member(model, name, args=args)
# couldn't find method/property/attribute
except AttributeError as e:
if failfast:
raise e # raise an AttributeError
else:
value = None # we just ignore it and return None
values.append(value)
# if we only have one value, unpack it
if len(values) == 1:
values = values[0]
else:
raise ValueError('Invalid setting for evaluate: ' + str(evaluate))
if len(res) == 1:
res = res[0]
return res
def __init__(self, samples, ref=None, conf=0.95):
r""" Constructs a sampled MSM
Parameters
----------
samples : list of MSM
Sampled MSM objects
ref : obj of type :class:`pyemma.msm.MaximumLikelihoodMSM` or :class:`pyemma.msm.BayesianMSM`
Single-point estimator, e.g. containing a maximum likelihood or mean MSM
conf : float, optional, default=0.95
Confidence interval. By default two-sigma (95.4%) is used. Use 95.4% for two sigma or 99.7% for three sigma.
"""
# validate input
assert is_iterable(
samples), 'samples must be a list of MSM objects, but is not.'
assert isinstance(
samples[0],
MSM), 'samples must be a list of MSM objects, but is not.'
# construct superclass 1
SampledModel.__init__(self, samples, conf=conf)
# construct superclass 2
if ref is None:
Pref = self.sample_mean('P')
MSM.__init__(self,
Pref,
dt_model=samples[0].dt_model,
neig=samples[0].neig,
ncv=samples[0].ncv)
else:
MSM.__init__(self,
ref.Pref,
pi=ref.pi,
reversible=ref.reversible,
dt_model=ref.dt_model,
neig=ref.neig,
ncv=ref.ncv)