예제 #1
0
    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
예제 #2
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    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
예제 #3
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    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
예제 #4
0
    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