def __init__(self, predictions, measurements, uncertainties, prior_pops=None, weights_alpha=None):
"""Bayesian Energy Landscape Tilting with Jeffrey's prior.
Parameters
----------
predictions : ndarray, shape = (num_frames, num_measurements)
predictions[j, i] gives the ith observabled predicted at frame j
measurements : ndarray, shape = (num_measurements)
measurements[i] gives the ith experimental measurement
uncertainties : ndarray, shape = (num_measurements)
uncertainties[i] gives the uncertainty of the ith experiment
prior_pops : ndarray, optional, shape = (num_frames)
Prior populations of each conformation. If None, use uniform populations.
Notes:
------
This feature is UNTESTED.
"""
BELT.__init__(self, predictions, measurements, uncertainties, prior_pops=prior_pops)
self.alpha = pymc.Uninformative("alpha",value=np.zeros(self.num_measurements))
self.initialize_variables()
@pymc.potential
def logp_prior(populations=self.populations,mu=self.mu):
return log_jeffreys(populations,predictions,mu=mu)
self.logp_prior = logp_prior
def __init__(self, predictions, measurements, uncertainties, regularization_strength=1.0, precision=None, prior_pops=None):
"""Bayesian Energy Landscape Tilting with maximum entropy prior and correlation-corrected likelihood.
Parameters
----------
predictions : ndarray, shape = (num_frames, num_measurements)
predictions[j, i] gives the ith observabled predicted at frame j
measurements : ndarray, shape = (num_measurements)
measurements[i] gives the ith experimental measurement
uncertainties : ndarray, shape = (num_measurements)
uncertainties[i] gives the uncertainty of the ith experiment
regularization_strength : float
How strongly to weight the MVN prior (e.g. lambda)
precision : ndarray, optional, shape = (num_measurements, num_measurements)
The precision matrix of the predicted observables.
prior_pops : ndarray, optional, shape = (num_frames)
Prior populations of each conformation. If None, use uniform populations.
"""
BELT.__init__(self, predictions, measurements, uncertainties, prior_pops=prior_pops)
if precision == None:
precision = np.cov(predictions.T)
if precision.ndim == 0:
precision = precision.reshape((1,1))
self.alpha = pymc.Uninformative("alpha",value=np.zeros(self.num_measurements)) # The prior on alpha is defined as a potential, so we use Uninformative variables here.
self.initialize_variables()
@pymc.potential
def logp_prior(populations=self.populations, mu=self.mu, prior_pops=self.prior_pops):
if populations.min() <= 0:
return -1 * np.inf
else:
return -1 * regularization_strength * (populations * (np.log(populations / prior_pops))).sum()
self.logp_prior = logp_prior
rho = np.corrcoef(predictions.T)
rho_inverse = np.linalg.inv(rho)
@pymc.potential
def logp(populations=self.populations,mu=self.mu):
z = (mu - measurements) / uncertainties
chi2 = rho_inverse.dot(z)
chi2 = z.dot(chi2)
return -0.5 * chi2
self.logp = logp
def __init__(self,
predictions,
measurements,
uncertainties,
prior_pops=None,
weights_alpha=None):
"""Bayesian Energy Landscape Tilting with Jeffrey's prior.
Parameters
----------
predictions : ndarray, shape = (num_frames, num_measurements)
predictions[j, i] gives the ith observabled predicted at frame j
measurements : ndarray, shape = (num_measurements)
measurements[i] gives the ith experimental measurement
uncertainties : ndarray, shape = (num_measurements)
uncertainties[i] gives the uncertainty of the ith experiment
prior_pops : ndarray, optional, shape = (num_frames)
Prior populations of each conformation. If None, use uniform populations.
Notes:
------
This feature is UNTESTED.
"""
BELT.__init__(self,
predictions,
measurements,
uncertainties,
prior_pops=prior_pops)
self.alpha = pymc.Uninformative("alpha",
value=np.zeros(self.num_measurements))
self.initialize_variables()
@pymc.potential
def logp_prior(populations=self.populations, mu=self.mu):
return log_jeffreys(populations, predictions, mu=mu)
self.logp_prior = logp_prior
def __init__(self,
predictions,
measurements,
uncertainties,
regularization_strength=1.0,
precision=None,
prior_pops=None):
"""Bayesian Energy Landscape Tilting with maximum entropy prior and correlation-corrected likelihood.
Parameters
----------
predictions : ndarray, shape = (num_frames, num_measurements)
predictions[j, i] gives the ith observabled predicted at frame j
measurements : ndarray, shape = (num_measurements)
measurements[i] gives the ith experimental measurement
uncertainties : ndarray, shape = (num_measurements)
uncertainties[i] gives the uncertainty of the ith experiment
regularization_strength : float
How strongly to weight the MVN prior (e.g. lambda)
precision : ndarray, optional, shape = (num_measurements, num_measurements)
The precision matrix of the predicted observables.
prior_pops : ndarray, optional, shape = (num_frames)
Prior populations of each conformation. If None, use uniform populations.
"""
BELT.__init__(self,
predictions,
measurements,
uncertainties,
prior_pops=prior_pops)
if precision == None:
precision = np.cov(predictions.T)
if precision.ndim == 0:
precision = precision.reshape((1, 1))
self.alpha = pymc.Uninformative(
"alpha", value=np.zeros(self.num_measurements)
) # The prior on alpha is defined as a potential, so we use Uninformative variables here.
self.initialize_variables()
@pymc.potential
def logp_prior(populations=self.populations,
mu=self.mu,
prior_pops=self.prior_pops):
if populations.min() <= 0:
return -1 * np.inf
else:
return -1 * regularization_strength * (
populations * (np.log(populations / prior_pops))).sum()
self.logp_prior = logp_prior
rho = np.corrcoef(predictions.T)
rho_inverse = np.linalg.inv(rho)
@pymc.potential
def logp(populations=self.populations, mu=self.mu):
z = (mu - measurements) / uncertainties
chi2 = rho_inverse.dot(z)
chi2 = z.dot(chi2)
return -0.5 * chi2
self.logp = logp