/usr/lib/python3/dist-packages/emcee/utils.py is in python3-emcee 2.2.1-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import (division, print_function, absolute_import,
unicode_literals)
__all__ = ["sample_ball", "MH_proposal_axisaligned", "MPIPool"]
import numpy as np
from .mpi_pool import MPIPool
def sample_ball(p0, std, size=1):
"""
Produce a ball of walkers around an initial parameter value.
:param p0: The initial parameter value.
:param std: The axis-aligned standard deviation.
:param size: The number of samples to produce.
"""
assert(len(p0) == len(std))
return np.vstack([p0 + std * np.random.normal(size=len(p0))
for i in range(size)])
def sample_ellipsoid(p0, covmat, size=1):
"""
Produce an ellipsoid of walkers around an initial parameter value,
according to a covariance matrix.
:param p0: The initial parameter value.
:param covmat:
The covariance matrix. Must be symmetric-positive definite or
it will raise the exception numpy.linalg.LinAlgError
:param size: The number of samples to produce.
"""
return np.random.multivariate_normal(np.atleast_1d(p0),
np.atleast_2d(covmat),
size=size)
class MH_proposal_axisaligned(object):
"""
A Metropolis-Hastings proposal, with axis-aligned Gaussian steps,
for convenient use as the ``mh_proposal`` option to
:func:`EnsembleSampler.sample` .
"""
def __init__(self, stdev):
self.stdev = stdev
def __call__(self, X):
(nw, npar) = X.shape
assert(len(self.stdev) == npar)
return X + self.stdev * np.random.normal(size=X.shape)
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