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from numpy import vectorize
from numpy.random import random_sample
__all__ = ['randwppf', 'randwcdf']
# XXX: Are these needed anymore?
#####################################
# General purpose continuous
######################################
def randwppf(ppf, args=(), size=None):
"""
returns an array of randomly distributed integers of a distribution
whose percent point function (inverse of the CDF or quantile function)
is given.
args is a tuple of extra arguments to the ppf function (i.e. shape,
location, scale), and size is the size of the output. Note the ppf
function must accept an array of q values to compute over.
"""
U = random_sample(size=size)
return apply(ppf, (U,)+args)
def randwcdf(cdf, mean=1.0, args=(), size=None):
"""returns an array of randomly distributed integers of a distribution
whose cumulative distribution function (CDF) is given.
mean is the mean of the distribution (helps the solver).
args is a tuple of extra arguments to the cdf function (i.e. shape,
location, scale), and size is the size of the output. Note the
cdf function needs to accept a single value to compute over.
"""
import scipy.optimize as optimize
def _ppfopt(x, q, *nargs):
newargs = (x,)+nargs
return cdf(*newargs) - q
def _ppf(q, *nargs):
return optimize.fsolve(_ppfopt, mean, args=(q,)+nargs)
_vppf = vectorize(_ppf)
U = random_sample(size=size)
return apply(_vppf,(U,)+args)
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