/usr/lib/python3/dist-packages/emcee/sampler.py is in python3-emcee 2.1.0-5.
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# -*- coding: utf-8 -*-
"""
The base sampler class implementing various helpful functions.
"""
from __future__ import (division, print_function, absolute_import,
unicode_literals)
__all__ = ["Sampler"]
import numpy as np
class Sampler(object):
"""
An abstract sampler object that implements various helper functions
:param dim:
The number of dimensions in the parameter space.
:param lnpostfn:
A function that takes a vector in the parameter space as input and
returns the natural logarithm of the posterior probability for that
position.
:param args: (optional)
A list of extra positional arguments for ``lnpostfn``. ``lnpostfn``
will be called with the sequence ``lnpostfn(p, *args, **kwargs)``.
:param kwargs: (optional)
A list of extra keyword arguments for ``lnpostfn``. ``lnpostfn``
will be called with the sequence ``lnpostfn(p, *args, **kwargs)``.
"""
def __init__(self, dim, lnprobfn, args=[], kwargs={}):
self.dim = dim
self.lnprobfn = lnprobfn
self.args = args
self.kwargs = kwargs
# This is a random number generator that we can easily set the state
# of without affecting the numpy-wide generator
self._random = np.random.mtrand.RandomState()
self.reset()
@property
def random_state(self):
"""
The state of the internal random number generator. In practice, it's
the result of calling ``get_state()`` on a
``numpy.random.mtrand.RandomState`` object. You can try to set this
property but be warned that if you do this and it fails, it will do
so silently.
"""
return self._random.get_state()
@random_state.setter # NOQA
def random_state(self, state):
"""
Try to set the state of the random number generator but fail silently
if it doesn't work. Don't say I didn't warn you...
"""
try:
self._random.set_state(state)
except:
pass
@property
def acceptance_fraction(self):
"""
The fraction of proposed steps that were accepted.
"""
return self.naccepted / self.iterations
@property
def chain(self):
"""
A pointer to the Markov chain.
"""
return self._chain
@property
def flatchain(self):
"""
Alias of ``chain`` provided for compatibility.
"""
return self._chain
@property
def lnprobability(self):
"""
A list of the log-probability values associated with each step in
the chain.
"""
return self._lnprob
@property
def acor(self):
return self.get_autocorr_time()
def get_autocorr_time(self, window=50):
raise NotImplementedError("The acor method must be implemented "
"by subclasses")
def get_lnprob(self, p):
"""Return the log-probability at the given position."""
return self.lnprobfn(p, *self.args, **self.kwargs)
def reset(self):
"""
Clear ``chain``, ``lnprobability`` and the bookkeeping parameters.
"""
self.iterations = 0
self.naccepted = 0
def clear_chain(self):
"""An alias for :func:`reset` kept for backwards compatibility."""
return self.reset()
def sample(self, *args, **kwargs):
raise NotImplementedError("The sampling routine must be implemented "
"by subclasses")
def run_mcmc(self, pos0, N, rstate0=None, lnprob0=None, **kwargs):
"""
Iterate :func:`sample` for ``N`` iterations and return the result.
:param pos0:
The initial position vector.
:param N:
The number of steps to run.
:param lnprob0: (optional)
The log posterior probability at position ``p0``. If ``lnprob``
is not provided, the initial value is calculated.
:param rstate0: (optional)
The state of the random number generator. See the
:func:`random_state` property for details.
:param kwargs: (optional)
Other parameters that are directly passed to :func:`sample`.
"""
for results in self.sample(pos0, lnprob0, rstate0, iterations=N,
**kwargs):
pass
return results
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