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from __future__ import division, print_function, absolute_import
from numpy import vectorize, deprecate
from numpy.random import random_sample
__all__ = ['randwppf', 'randwcdf']
# XXX: Are these needed anymore?
#####################################
# General purpose continuous
######################################
@deprecate(message="Deprecated in scipy 0.14.0, use "
"distribution-specific rvs() method instead")
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 ppf(*(U,)+args)
@deprecate(message="Deprecated in scipy 0.14.0, use "
"distribution-specific rvs() method instead")
def randwcdf(cdf, mean=1.0, args=(), size=None):
"""
Returns an array of randomly distributed integers given a CDF.
Given a cumulative distribution function (CDF) returns an array of
randomly distributed integers that would satisfy the CDF.
Parameters
----------
cdf : function
CDF function that accepts a single value and `args`, and returns
an single value.
mean : float, optional
The mean of the distribution which helps the solver. Defaults
to 1.0.
args : tuple, optional
Extra arguments to the cdf function (i.e. shape, location, scale)
size : {int, None}, optional
Is the size of the output. If None, only 1 value will be returned.
Returns
-------
randwcdf : ndarray
Array of random numbers.
Notes
-----
Can use the ``scipy.stats.distributions.*.cdf`` functions for the
`cdf` parameter.
"""
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 _vppf(*(U,)+args)
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