diff --git a/bilby/core/prior.py b/bilby/core/prior.py
index be34de2031e1f4ed0f9a37a257037b64dde89011..7e57c7f1ff27bd86ef8182d7ec7efb86476bf158 100644
--- a/bilby/core/prior.py
+++ b/bilby/core/prior.py
@@ -2434,50 +2434,20 @@ class FermiDirac(Prior):
return lnp
-class MultivariateGaussianDist(object):
+class JointPriorDist(object):
- def __init__(self, names, nmodes=1, mus=None, sigmas=None, corrcoefs=None,
- covs=None, weights=None, bounds=None):
+ def __init__(self, names, bounds=None):
"""
- A class defining a multi-variate Gaussian, allowing multiple modes for
- a Gaussian mixture model.
+ A class defining JointPriorDist that will be overwritten with child
+ classes defining the joint prior distribtuions between given parameters,
- Note: if using a multivariate Gaussian prior, with bounds, this can
- lead to biases in the marginal likelihood estimate and posterior
- estimate for nested samplers routines that rely on sampling from a unit
- hypercube and having a prior transform, e.g., nestle, dynesty and
- MultiNest.
Parameters
----------
names: list
- A list of the parameter names in the multivariate Gaussian. The
+ A list of the parameter names in the JointPriorDist. The
listed parameters must have the same order that they appear in
- the lists of means, standard deviations, and the correlation
- coefficient, or covariance, matrices.
- nmodes: int
- The number of modes for the mixture model. This defaults to 1,
- which will be checked against the shape of the other inputs.
- mus: array_like
- A list of lists of means of each mode in a multivariate Gaussian
- mixture model. A single list can be given for a single mode. If
- this is None then means at zero will be assumed.
- sigmas: array_like
- A list of lists of the standard deviations of each mode of the
- multivariate Gaussian. If supplying a correlation coefficient
- matrix rather than a covariance matrix these values must be given.
- If this is None unit variances will be assumed.
- corrcoefs: array
- A list of square matrices containing the correlation coefficients
- of the parameters for each mode. If this is None it will be assumed
- that the parameters are uncorrelated.
- covs: array
- A list of square matrices containing the covariance matrix of the
- multivariate Gaussian.
- weights: list
- A list of weights (relative probabilities) for each mode of the
- multivariate Gaussian. This will default to equal weights for each
- mode.
+ the lists of statistical parameters that may be passed in child class
bounds: list
A list of bounds on each parameter. The defaults are for bounds at
+/- infinity.
@@ -2488,7 +2458,7 @@ class MultivariateGaussianDist(object):
else:
self.names = names
- self.num_vars = len(self.names) # the number of parameters
+ self.num_vars = len(self.names)
# set the bounds for each parameter
if isinstance(bounds, list):
@@ -2513,83 +2483,12 @@ class MultivariateGaussianDist(object):
"a prior transform.")
else:
bounds = [(-np.inf, np.inf) for _ in self.names]
-
- # set bounds as dictionary
self.bounds = {name: val for name, val in zip(self.names, bounds)}
- self.mus = []
- self.covs = []
- self.corrcoefs = []
- self.sigmas = []
- self.weights = []
- self.eigvalues = []
- self.eigvectors = []
- self.sqeigvalues = [] # square root of the eigenvalues
- self.mvn = [] # list of multivariate normal distributions
-
self._current_sample = {} # initialise empty sample
self._uncorrelated = None
self._current_lnprob = None
- # put values in lists if required
- if nmodes == 1:
- if mus is not None:
- if len(np.shape(mus)) == 1:
- mus = [mus]
- elif len(np.shape(mus)) == 0:
- raise ValueError("Must supply a list of means")
- if sigmas is not None:
- if len(np.shape(sigmas)) == 1:
- sigmas = [sigmas]
- elif len(np.shape(sigmas)) == 0:
- raise ValueError("Must supply a list of standard "
- "deviations")
- if covs is not None:
- if isinstance(covs, np.ndarray):
- covs = [covs]
- elif isinstance(covs, list):
- if len(np.shape(covs)) == 2:
- covs = [np.array(covs)]
- elif len(np.shape(covs)) != 3:
- raise TypeError("List of covariances the wrong shape")
- else:
- raise TypeError("Must pass a list of covariances")
- if corrcoefs is not None:
- if isinstance(corrcoefs, np.ndarray):
- corrcoefs = [corrcoefs]
- elif isinstance(corrcoefs, list):
- if len(np.shape(corrcoefs)) == 2:
- corrcoefs = [np.array(corrcoefs)]
- elif len(np.shape(corrcoefs)) != 3:
- raise TypeError("List of correlation coefficients the wrong shape")
- elif not isinstance(corrcoefs, list):
- raise TypeError("Must pass a list of correlation "
- "coefficients")
- if weights is not None:
- if isinstance(weights, (int, float)):
- weights = [weights]
- elif isinstance(weights, list):
- if len(weights) != 1:
- raise ValueError("Wrong number of weights given")
-
- for val in [mus, sigmas, covs, corrcoefs, weights]:
- if val is not None and not isinstance(val, list):
- raise TypeError("Value must be a list")
- else:
- if val is not None and len(val) != nmodes:
- raise ValueError("Wrong number of modes given")
-
- # add the modes
- self.nmodes = 0
- for i in range(nmodes):
- mu = mus[i] if mus is not None else None
- sigma = sigmas[i] if sigmas is not None else None
- corrcoef = corrcoefs[i] if corrcoefs is not None else None
- cov = covs[i] if covs is not None else None
- weight = weights[i] if weights is not None else 1.
-
- self.add_mode(mu, sigma, corrcoef, cov, weight)
-
# a dictionary of the parameters as requested by the prior
self.requested_parameters = OrderedDict()
self.reset_request()
@@ -2626,16 +2525,310 @@ class MultivariateGaussianDist(object):
Check is all the rescaled parameters have been filled.
"""
- return not np.any([val is None for val in
- self.rescale_parameters.values()])
+ return not np.any([val is None for val in
+ self.rescale_parameters.values()])
+
+ def reset_rescale(self):
+ """
+ Reset the rescaled parameters to None.
+ """
+
+ for name in self.names:
+ self.rescale_parameters[name] = None
+
+ def _get_instantiation_dict(self):
+ subclass_args = infer_args_from_method(self.__init__)
+ property_names = [p for p in dir(self.__class__)
+ if isinstance(getattr(self.__class__, p), property)]
+ dict_with_properties = self.__dict__.copy()
+ for key in property_names:
+ dict_with_properties[key] = getattr(self, key)
+ instantiation_dict = OrderedDict()
+ for key in subclass_args:
+ if isinstance(dict_with_properties[key], list):
+ value = np.asarray(dict_with_properties[key]).tolist()
+ else:
+ value = dict_with_properties[key]
+ instantiation_dict[key] = value
+ return instantiation_dict
+
+ def __len__(self):
+ return len(self.names)
+
+ def __repr__(self):
+ """Overrides the special method __repr__.
+
+ Returns a representation of this instance that resembles how it is instantiated.
+ Works correctly for all child classes
+
+ Returns
+ -------
+ str: A string representation of this instance
+
+ """
+ dist_name = self.__class__.__name__
+ instantiation_dict = self._get_instantiation_dict()
+ args = ', '.join(['{}={}'.format(key, repr(instantiation_dict[key]))
+ for key in instantiation_dict])
+ return "{}({})".format(dist_name, args)
+
+ def prob(self, samp):
+ """
+ Get the probability of a sample. For bounded priors the
+ probability will not be properly normalised.
+ """
+
+ return np.exp(self.ln_prob(samp))
+
+ def _check_samp(self, value):
+ samp = np.asarray(value)
+ if len(samp.shape) == 1:
+ samp = samp.reshape(1, self.num_vars)
+
+ if len(samp.shape) != 2:
+ raise ValueError("Array is the wrong shape")
+ elif samp.shape[1] != self.num_vars:
+ raise ValueError("Array is the wrong shape")
+
+ # check sample(s) is within bounds
+ outbounds = np.ones(samp.shape[0], dtype=np.bool)
+ for s, bound in zip(samp.T, self.bounds.values()):
+ outbounds = (s < bound[0]) | (s > bound[1])
+ if np.any(outbounds):
+ break
+ return samp, outbounds
+
+ def ln_prob(self, value):
+ """
+ Get the log-probability of a sample. For bounded priors the
+ probability will not be properly normalised.
+
+ Parameters
+ ----------
+ value: array_like
+ A 1d vector of the sample, or 2d array of sample values with shape
+ NxM, where N is the number of samples and M is the number of
+ parameters.
+ """
+
+ samp, outbounds = self._check_samp(value)
+ lnprob = -np.inf * np.ones(samp.shape[0])
+ lnprob = self._ln_prob(samp, lnprob, outbounds)
+ if samp.shape[0] == 1:
+ return lnprob[0]
+ else:
+ return lnprob
+
+ def _ln_prob(self, samp, lnprob, outbounds):
+ '''
+ CHILD CLASS OVERWRITES THIS METHOD AND FILLS IN THIS PART OF ln_prob METHOD to samplethe lnprob for
+ the value of this sample
+ '''
+ return lnprob
+
+ def sample(self, size=1, **kwargs):
+ """
+ Draw, and set, a sample from the Dist
+
+ Parameters
+ ----------
+ size: int
+ number of samples to generate, defualts to 1
+ """
+
+ if size is None:
+ size = 1
+ # samples drawn from unit variance uncorrelated multivariate Gaussian
+ samps = self._draw_samp(size=size, **kwargs)
+ for i, name in enumerate(self.names):
+ if size == 1:
+ self.current_sample[name] = samps[:, i].flatten()[0]
+ else:
+ self.current_sample[name] = samps[:, i].flatten()
+
+ def _draw_samp(self, size, **kwargs):
+ """
+ Draw, and set, a sample from the joint dist (needs to be ovewritten by child class)
+
+ Parameters
+ ----------
+ size: int
+ number of samples to generate, defualts to 1
+ """
+ samps = np.zeros((size, len(self)))
+ """
+ Here is where the subclass where overwrite sampling method
+ """
+ return samps
+
+ def rescale(self, value, **kwargs):
+ """
+ Rescale from a unit hypercube to multivariate Gaussian. Note that no
+ bounds are applied in the rescale function.
+
+ Parameters
+ ----------
+ value: array
+ A 1d vector sample (one for each parameter) drawn from a uniform
+ distribution between 0 and 1, or a 2d NxM array of samples where
+ N is the number of samples and M is the number of parameters.
+ mode: int
+ Specify which mode to sample from. If not set then a mode is
+ chosen randomly based on its weight.
+
+ Returns
+ -------
+ array:
+ An vector sample drawn from the multivariate Gaussian
+ distribution.
+ """
+
+ # pick a mode (with a probability given by their weights)
+
+ samp = np.asarray(value)
+ if len(samp.shape) == 1:
+ samp = samp.reshape(1, self.num_vars)
+
+ if len(samp.shape) != 2:
+ raise ValueError("Array is the wrong shape")
+ elif samp.shape[1] != self.num_vars:
+ raise ValueError("Array is the wrong shape")
+
+ samp = self._rescale(samp, **kwargs)
+ return np.squeeze(samp)
+
+ def _rescale(self, samp, **kwargs):
+ '''
+ needs to be overwritten
+ :param samp:
+ :param kwargs:
+ :return:
+ '''
+ return samp
+
+
+class MultivariateGaussianDist(JointPriorDist):
+
+ def __init__(self, names, nmodes=1, mus=None, sigmas=None, corrcoefs=None,
+ covs=None, weights=None, bounds=None):
+ """
+ A class defining a multi-variate Gaussian, allowing multiple modes for
+ a Gaussian mixture model.
+
+ Note: if using a multivariate Gaussian prior, with bounds, this can
+ lead to biases in the marginal likelihood estimate and posterior
+ estimate for nested samplers routines that rely on sampling from a unit
+ hypercube and having a prior transform, e.g., nestle, dynesty and
+ MultiNest.
+
+ Parameters
+ ----------
+ names: list
+ A list of the parameter names in the multivariate Gaussian. The
+ listed parameters must have the same order that they appear in
+ the lists of means, standard deviations, and the correlation
+ coefficient, or covariance, matrices.
+ nmodes: int
+ The number of modes for the mixture model. This defaults to 1,
+ which will be checked against the shape of the other inputs.
+ mus: array_like
+ A list of lists of means of each mode in a multivariate Gaussian
+ mixture model. A single list can be given for a single mode. If
+ this is None then means at zero will be assumed.
+ sigmas: array_like
+ A list of lists of the standard deviations of each mode of the
+ multivariate Gaussian. If supplying a correlation coefficient
+ matrix rather than a covariance matrix these values must be given.
+ If this is None unit variances will be assumed.
+ corrcoefs: array
+ A list of square matrices containing the correlation coefficients
+ of the parameters for each mode. If this is None it will be assumed
+ that the parameters are uncorrelated.
+ covs: array
+ A list of square matrices containing the covariance matrix of the
+ multivariate Gaussian.
+ weights: list
+ A list of weights (relative probabilities) for each mode of the
+ multivariate Gaussian. This will default to equal weights for each
+ mode.
+ bounds: list
+ A list of bounds on each parameter. The defaults are for bounds at
+ +/- infinity.
+ """
+ super(MultivariateGaussianDist, self).__init__(names=names, bounds=bounds)
+
+ self.mus = []
+ self.covs = []
+ self.corrcoefs = []
+ self.sigmas = []
+ self.weights = []
+ self.eigvalues = []
+ self.eigvectors = []
+ self.sqeigvalues = [] # square root of the eigenvalues
+ self.mvn = [] # list of multivariate normal distributions
+
+ self._current_sample = {} # initialise empty sample
+ self._uncorrelated = None
+ self._current_lnprob = None
+
+ # put values in lists if required
+ if nmodes == 1:
+ if mus is not None:
+ if len(np.shape(mus)) == 1:
+ mus = [mus]
+ elif len(np.shape(mus)) == 0:
+ raise ValueError("Must supply a list of means")
+ if sigmas is not None:
+ if len(np.shape(sigmas)) == 1:
+ sigmas = [sigmas]
+ elif len(np.shape(sigmas)) == 0:
+ raise ValueError("Must supply a list of standard "
+ "deviations")
+ if covs is not None:
+ if isinstance(covs, np.ndarray):
+ covs = [covs]
+ elif isinstance(covs, list):
+ if len(np.shape(covs)) == 2:
+ covs = [np.array(covs)]
+ elif len(np.shape(covs)) != 3:
+ raise TypeError("List of covariances the wrong shape")
+ else:
+ raise TypeError("Must pass a list of covariances")
+ if corrcoefs is not None:
+ if isinstance(corrcoefs, np.ndarray):
+ corrcoefs = [corrcoefs]
+ elif isinstance(corrcoefs, list):
+ if len(np.shape(corrcoefs)) == 2:
+ corrcoefs = [np.array(corrcoefs)]
+ elif len(np.shape(corrcoefs)) != 3:
+ raise TypeError("List of correlation coefficients the wrong shape")
+ elif not isinstance(corrcoefs, list):
+ raise TypeError("Must pass a list of correlation "
+ "coefficients")
+ if weights is not None:
+ if isinstance(weights, (int, float)):
+ weights = [weights]
+ elif isinstance(weights, list):
+ if len(weights) != 1:
+ raise ValueError("Wrong number of weights given")
+
+ for val in [mus, sigmas, covs, corrcoefs, weights]:
+ if val is not None and not isinstance(val, list):
+ raise TypeError("Value must be a list")
+ else:
+ if val is not None and len(val) != nmodes:
+ raise ValueError("Wrong number of modes given")
- def reset_rescale(self):
- """
- Reset the rescaled parameters to None.
- """
+ # add the modes
+ self.nmodes = 0
+ for i in range(nmodes):
+ mu = mus[i] if mus is not None else None
+ sigma = sigmas[i] if sigmas is not None else None
+ corrcoef = corrcoefs[i] if corrcoefs is not None else None
+ cov = covs[i] if covs is not None else None
+ weight = weights[i] if weights is not None else 1.
- for name in self.names:
- self.rescale_parameters[name] = None
+ self.add_mode(mu, sigma, corrcoef, cov, weight)
def add_mode(self, mus=None, sigmas=None, corrcoef=None, cov=None,
weight=1.):
@@ -2744,69 +2937,37 @@ class MultivariateGaussianDist(object):
self.mvn.append(scipy.stats.multivariate_normal(mean=self.mus[-1],
cov=self.covs[-1]))
- def rescale(self, value, mode=None):
- """
- Rescale from a unit hypercube to multivariate Gaussian. Note that no
- bounds are applied in the rescale function.
-
- Parameters
- ----------
- value: array
- A 1d vector sample (one for each parameter) drawn from a uniform
- distribution between 0 and 1, or a 2d NxM array of samples where
- N is the number of samples and M is the number of parameters.
- mode: int
- Specify which mode to sample from. If not set then a mode is
- chosen randomly based on its weight.
-
- Returns
- -------
- array:
- An vector sample drawn from the multivariate Gaussian
- distribution.
- """
+ def _rescale(self, samp, **kwargs):
+ try:
+ mode = kwargs['mode']
+ except KeyError:
+ mode = None
- # pick a mode (with a probability given by their weights)
if mode is None:
if self.nmodes == 1:
mode = 0
else:
mode = np.argwhere(self.cumweights - np.random.rand() > 0)[0][0]
- samp = np.asarray(value)
- if len(samp.shape) == 1:
- samp = samp.reshape(1, self.num_vars)
-
- if len(samp.shape) != 2:
- raise ValueError("Array is the wrong shape")
- elif samp.shape[1] != self.num_vars:
- raise ValueError("Array is the wrong shape")
-
- # draw points from unit variance, uncorrelated Gaussian
samp = erfinv(2. * samp - 1) * 2. ** 0.5
# rotate and scale to the multivariate normal shape
samp = self.mus[mode] + self.sigmas[mode] * np.einsum('ij,kj->ik',
samp * self.sqeigvalues[mode],
self.eigvectors[mode])
+ return samp
- return np.squeeze(samp)
-
- def sample(self, size=1, mode=None):
- """
- Draw, and set, a sample from the multivariate Gaussian.
-
- Parameters
- ----------
- mode: int
- Specify which mode to sample from. If not set then a mode is
- chosen randomly based on its weight.
- """
-
- if size is None:
- size = 1
+ def _draw_samp(self, size, **kwargs):
+ try:
+ mode = kwargs['mode']
+ except KeyError:
+ mode = None
- # samples drawn from unit variance uncorrelated multivariate Gaussian
+ if mode is None:
+ if self.nmodes == 1:
+ mode = 0
+ else:
+ mode = np.argwhere(self.cumweights - np.random.rand() > 0)[0][0]
samps = np.zeros((size, len(self)))
for i in range(size):
inbound = False
@@ -2827,42 +2988,9 @@ class MultivariateGaussianDist(object):
if not outbound:
inbound = True
- for i, name in enumerate(self.names):
- if size == 1:
- self.current_sample[name] = samps[:, i].flatten()[0]
- else:
- self.current_sample[name] = samps[:, i].flatten()
-
- def ln_prob(self, value):
- """
- Get the log-probability of a sample. For bounded priors the
- probability will not be properly normalised.
-
- Parameters
- ----------
- value: array_like
- A 1d vector of the sample, or 2d array of sample values with shape
- NxM, where N is the number of samples and M is the number of
- parameters.
- """
-
- samp = np.asarray(value)
- if len(samp.shape) == 1:
- samp = samp.reshape(1, self.num_vars)
-
- if len(samp.shape) != 2:
- raise ValueError("Array is the wrong shape")
- elif samp.shape[1] != self.num_vars:
- raise ValueError("Array is the wrong shape")
-
- # check sample(s) is within bounds
- outbounds = np.ones(samp.shape[0], dtype=np.bool)
- for s, bound in zip(samp.T, self.bounds.values()):
- outbounds = (s < bound[0]) | (s > bound[1])
- if np.any(outbounds):
- break
+ return samps
- lnprob = -np.inf * np.ones(samp.shape[0])
+ def _ln_prob(self, samp, lnprob, outbounds):
for j in range(samp.shape[0]):
# loop over the modes and sum the probabilities
for i in range(self.nmodes):
@@ -2870,55 +2998,7 @@ class MultivariateGaussianDist(object):
# set out-of-bounds values to -inf
lnprob[outbounds] = -np.inf
-
- if samp.shape[0] == 1:
- return lnprob[0]
- else:
- return lnprob
-
- def prob(self, samp):
- """
- Get the probability of a sample. For bounded priors the
- probability will not be properly normalised.
- """
-
- return np.exp(self.ln_prob(samp))
-
- def _get_instantiation_dict(self):
- subclass_args = infer_args_from_method(self.__init__)
- property_names = [p for p in dir(self.__class__)
- if isinstance(getattr(self.__class__, p), property)]
- dict_with_properties = self.__dict__.copy()
- for key in property_names:
- dict_with_properties[key] = getattr(self, key)
- instantiation_dict = OrderedDict()
- for key in subclass_args:
- if isinstance(dict_with_properties[key], list):
- value = np.asarray(dict_with_properties[key]).tolist()
- else:
- value = dict_with_properties[key]
- instantiation_dict[key] = value
- return instantiation_dict
-
- def __len__(self):
- return len(self.names)
-
- def __repr__(self):
- """Overrides the special method __repr__.
-
- Returns a representation of this instance that resembles how it is instantiated.
- Works correctly for all child classes
-
- Returns
- -------
- str: A string representation of this instance
-
- """
- dist_name = self.__class__.__name__
- instantiation_dict = self._get_instantiation_dict()
- args = ', '.join(['{}={}'.format(key, repr(instantiation_dict[key]))
- for key in instantiation_dict])
- return "{}({})".format(dist_name, args)
+ return lnprob
def __eq__(self, other):
if self.__class__ != other.__class__:
@@ -2956,74 +3036,62 @@ class MultivariateNormalDist(MultivariateGaussianDist):
""" A synonym for the :class:`~bilby.core.prior.MultivariateGaussianDist` distribution."""
-class MultivariateGaussian(Prior):
+class JointPrior(Prior):
- def __init__(self, mvg, name=None, latex_label=None, unit=None):
- """
- A prior class for a multivariate Gaussian (mixture model) prior.
+ def __init__(self, dist, name=None, latex_label=None, unit=None):
+ if dist.__class__.__bases__ [0] != JointPriorDist:
+ raise TypeError("Must supply a JointPriorDist object instance to be shared by all joint params")
- Parameters
- ----------
- mvg: MultivariateGaussianDist
- A :class:`bilby.core.prior.MultivariateGaussianDist` object defining
- the multivariate Gaussian distribution. This object is not copied,
- as it needs to be shared across multiple priors, and as such its
- contents will be altered by the prior.
- name: str
- See superclass
- latex_label: str
- See superclass
- unit: str
- See superclass
+ if name not in dist.names:
+ raise ValueError("'{}' is not a parameter in the JointPriorDist")
- """
+ self.dist = dist
+ super(JointPrior, self).__init__(name=name, latex_label=latex_label, unit=unit,
+ minimum=dist.bounds[name][0],
+ maximum=dist.bounds[name][1])
+
+ @property
+ def minimum(self):
+ return self._minimum
- if not isinstance(mvg, MultivariateGaussianDist):
- raise TypeError("Must supply a multivariate Gaussian object")
+ @minimum.setter
+ def minimum(self, minimum):
+ self._minimum = minimum
+ self.dist.bounds[self.name] = (minimum, self.dist.bounds[self.name][1])
- # check name is in the MultivariateGaussianDist class
- if name not in mvg.names:
- raise ValueError("'{}' is not a parameter in the multivariate "
- "Gaussian")
- self.mvg = mvg
+ @property
+ def maximum(self):
+ return self._maximum
- super(MultivariateGaussian, self).__init__(name=name, latex_label=latex_label, unit=unit,
- minimum=mvg.bounds[name][0],
- maximum=mvg.bounds[name][1])
+ @maximum.setter
+ def maximum(self, maximum):
+ self._maximum = maximum
+ self.dist.bounds[self.name] = (self.dist.bounds[self.name][0], maximum)
- def rescale(self, val, mode=None):
+ def rescale(self, val, **kwargs):
"""
Scale a unit hypercube sample to the prior.
-
- Parameters
- ----------
- mode: int
- Specify which mode to sample from. If not set then a mode is
- chosen randomly based on its weight.
"""
- Prior.test_valid_for_rescaling(val)
+ self.test_valid_for_rescaling(val)
# add parameter value to multivariate Gaussian
- self.mvg.rescale_parameters[self.name] = val
+ self.dist.rescale_parameters[self.name] = val
- if self.mvg.filled_rescale():
- values = np.array(list(self.mvg.rescale_parameters.values())).T
- samples = self.mvg.rescale(values, mode=mode)
- self.mvg.reset_rescale()
+ if self.dist.filled_rescale():
+ values = np.array(list(self.dist.rescale_parameters.values())).T
+ samples = self.dist.rescale(values, **kwargs)
+ self.dist.reset_rescale()
return samples
else:
return [] # return empty list
- def sample(self, size=1, mode=None):
+ def sample(self, size=1, **kwargs):
"""
Draw a sample from the prior.
Parameters
----------
- mode: int
- Specify which mode to sample from. If not set then a mode is
- chosen randomly based on its weight.
Returns
-------
@@ -3031,41 +3099,25 @@ class MultivariateGaussian(Prior):
A sample from the prior paramter.
"""
- if self.name in self.mvg.sampled_parameters:
+ if self.name in self.dist.sampled_parameters:
logger.warning("You have already drawn a sample from parameter "
"'{}'. The same sample will be "
"returned".format(self.name))
- if len(self.mvg.current_sample) == 0:
+ if len(self.dist.current_sample) == 0:
# generate a sample
- self.mvg.sample(size=size, mode=mode)
+ self.dist.sample(size=size, **kwargs)
- sample = self.mvg.current_sample[self.name]
+ sample = self.dist.current_sample[self.name]
- if self.name not in self.mvg.sampled_parameters:
- self.mvg.sampled_parameters.append(self.name)
+ if self.name not in self.dist.sampled_parameters:
+ self.dist.sampled_parameters.append(self.name)
- if len(self.mvg.sampled_parameters) == len(self.mvg):
+ if len(self.dist.sampled_parameters) == len(self.dist):
# reset samples
- self.mvg.reset_sampled()
-
+ self.dist.reset_sampled()
return sample
- def prob(self, val):
- """Return the prior probability of val
-
- Parameters
- ----------
- val: float
-
- Returns
- -------
- float:
-
- """
-
- return np.exp(self.ln_prob(val))
-
def ln_prob(self, val):
"""
Return the natural logarithm of the prior probability. Note that this
@@ -3074,14 +3126,14 @@ class MultivariateGaussian(Prior):
"""
# add parameter value to multivariate Gaussian
- self.mvg.requested_parameters[self.name] = val
+ self.dist.requested_parameters[self.name] = val
- if self.mvg.filled_request():
+ if self.dist.filled_request():
# all required parameters have been set
- values = list(self.mvg.requested_parameters.values())
+ values = list(self.dist.requested_parameters.values())
# check for the same number of values for each parameter
- for i in range(len(self.mvg) - 1):
+ for i in range(len(self.dist) - 1):
if (isinstance(values[i], (list, np.ndarray)) or
isinstance(values[i + 1], (list, np.ndarray))):
if (isinstance(values[i], (list, np.ndarray)) and
@@ -3093,11 +3145,10 @@ class MultivariateGaussian(Prior):
raise ValueError("Each parameter must have the same "
"number of requested values.")
- lnp = self.mvg.ln_prob(np.asarray(values).T)
+ lnp = self.dist.ln_prob(np.asarray(values).T)
# reset the requested parameters
- self.mvg.reset_request()
-
+ self.dist.reset_request()
return lnp
else:
# if not all parameters have been requested yet, just return 0
@@ -3115,27 +3166,27 @@ class MultivariateGaussian(Prior):
else:
return np.zeros_like(val)
- @property
- def minimum(self):
- return self._minimum
+ def prob(self, val):
+ """Return the prior probability of val
- @minimum.setter
- def minimum(self, minimum):
- self._minimum = minimum
+ Parameters
+ ----------
+ val: float
- # update the bounds in the MultivariateGaussianDist
- self.mvg.bounds[self.name] = (minimum, self.mvg.bounds[self.name][1])
+ Returns
+ -------
+ float:
- @property
- def maximum(self):
- return self._maximum
+ """
+
+ return np.exp(self.ln_prob(val))
- @maximum.setter
- def maximum(self, maximum):
- self._maximum = maximum
- # update the bounds in the MultivariateGaussianDist
- self.mvg.bounds[self.name] = (self.mvg.bounds[self.name][0], maximum)
+class MultivariateGaussian(JointPrior):
+ """
+ A synonmy class for MultiVariateNormal / deprecated, now use JointPrior with the dist object controlling
+ the type of joint prior
+ """
class MultivariateNormal(MultivariateGaussian):
diff --git a/examples/core_examples/multivariate_gaussian_prior.py b/examples/core_examples/multivariate_gaussian_prior.py
index 53d8f94e47ac41833bd536d2f596f3a78cc76e8d..e362d88c8e31162ebd6a3c08120f9e51a71c36db 100644
--- a/examples/core_examples/multivariate_gaussian_prior.py
+++ b/examples/core_examples/multivariate_gaussian_prior.py
@@ -50,8 +50,8 @@ mvg = bilby.core.prior.MultivariateGaussianDist(names, nmodes=2, mus=mus,
corrcoefs=corrcoefs,
sigmas=sigmas, weights=weights)
priors = dict()
-priors['m'] = bilby.core.prior.MultivariateGaussian(mvg, 'm')
-priors['c'] = bilby.core.prior.MultivariateGaussian(mvg, 'c')
+priors['m'] = bilby.core.prior.JointPrior(mvg, 'm')
+priors['c'] = bilby.core.prior.JointPrior(mvg, 'c')
result = bilby.run_sampler(
likelihood=likelihood, priors=priors, sampler='dynesty', nlive=4000,