Source code for lenstronomy.Sampling.Samplers.base_nested_sampler
__author__ = 'aymgal'
import lenstronomy.Util.sampling_util as utils
__all__ = ['NestedSampler']
[docs]class NestedSampler(object):
"""
Base class for nested samplers
"""
def __init__(self, likelihood_module, prior_type,
prior_means, prior_sigmas, width_scale, sigma_scale):
"""
:param likelihood_module: likelihood_module like in likelihood.py (should be callable)
:param prior_type: 'uniform' of 'gaussian', for converting the unit hypercube to param cube
:param prior_means: if prior_type is 'gaussian', mean for each param
:param prior_sigmas: if prior_type is 'gaussian', std dev for each param
:param width_scale: scale the widths of the parameters space by this factor
:param sigma_scale: if prior_type is 'gaussian', scale the gaussian sigma by this factor
"""
self._ll = likelihood_module
self.n_dims, self.param_names = self._ll.param.num_param()
lowers, uppers = self._ll.param_limits
if width_scale < 1:
self.lowers, self.uppers = utils.scale_limits(lowers, uppers, width_scale)
else:
self.lowers, self.uppers = lowers, uppers
if prior_type == 'gaussian':
if prior_means is None or prior_sigmas is None:
raise ValueError("For gaussian prior type, means and sigmas are required")
self.means, self.sigmas = prior_means, prior_sigmas * sigma_scale
self.lowers, self.uppers = lowers, uppers
elif prior_type != 'uniform':
raise ValueError("Sampling type {} not supported".format(prior_type))
self.prior_type = prior_type
self._has_warned = False
[docs] def prior(self, *args, **kwargs):
"""
compute the mapping between the unit cube and parameter cube
:return: hypercube in parameter space
"""
raise NotImplementedError("Method not be implemented in base class")
[docs] def log_likelihood(self, *args, **kwargs):
"""
compute the log-likelihood given list of parameters
:return: log-likelihood (from the likelihood module)
"""
raise NotImplementedError("Method not be implemented in base class")
[docs] def run(self, kwargs_run):
"""run the nested sampling algorithm"""
raise NotImplementedError("Method not be implemented in base class")