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Maximum number of samples to use from each set.
cupy: bool
If True and a compatible CUDA environment is available,
cupy will be used for performance.
Note: this requires setting up your hyper_prior properly.
"""
if cupy and not CUPY_LOADED:
logger.warning("Cannot import cupy, falling back to numpy.")
self.samples_per_posterior = max_samples
self.data = self.resample_posteriors(posteriors, max_samples=max_samples)
if not isinstance(hyper_prior, Model):
hyper_prior = Model([hyper_prior])
self.hyper_prior = hyper_prior
Likelihood.__init__(self, hyper_prior.parameters)
if sampling_prior is not None:
raise ValueError(
"Passing a sampling_prior is deprecated and will be removed "
"in the next release. This should be passed as a 'prior' "
"column in the posteriors."
)
elif "prior" in self.data:
self.sampling_prior = self.data.pop("prior")
else:
logger.info("No prior values provided, defaulting to 1.")
self.sampling_prior = 1
if ln_evidences is not None:
self.total_noise_evidence = np.sum(ln_evidences)
else: