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Updated distributions.py and test_distributions.py according to the latest scipy.stats

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master
Per.Andreas.Brodtkorb 14 years ago
parent 8a0004ba59
commit 79fa7ab190
2 changed files with 410 additions and 99 deletions
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pywafo/src/wafo/stats/distributions.py
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@ -1,8 +1,8 @@
# Functions to implement several important functions for
# various Continous and Discrete Probability Distributions
#
# Author: Travis Oliphant 2002-2010 with contributions from
# SciPy Developers 2004-2010
# Author: Travis Oliphant 2002-2011 with contributions from
# SciPy Developers 2004-2011
#
from __future__ import division
@ -45,6 +45,38 @@ def _moment(data, n, mu=None):
mu = data.mean()
return ((data - mu)**n).mean()
def _moment_from_stats(n, mu, mu2, g1, g2, moment_func, args):
if (n==0):
return 1.0
elif (n==1):
if mu is None:
val = moment_func(1,*args)
else:
val = mu
elif (n==2):
if mu2 is None or mu is None:
val = moment_func(2,*args)
else:
val = mu2 + mu*mu
elif (n==3):
if g1 is None or mu2 is None or mu is None:
val = moment_func(3,*args)
else:
mu3 = g1*(mu2**1.5) # 3rd central moment
val = mu3+3*mu*mu2+mu**3 # 3rd non-central moment
elif (n==4):
if g1 is None or g2 is None or mu2 is None or mu is None:
val = moment_func(4,*args)
else:
mu4 = (g2+3.0)*(mu2**2.0) # 4th central moment
mu3 = g1*(mu2**1.5) # 3rd central moment
val = mu4+4*mu*mu3+6*mu*mu*mu2+mu**4
else:
val = moment_func(n, *args)
return val
def _skew(data):
data = np.ravel(data)
mu = data.mean()
@ -121,18 +153,34 @@ _doc_pdf = \
"""pdf(x, %(shapes)s, loc=0, scale=1)
Probability density function.
"""
_doc_logpdf = \
"""logpdf(x, %(shapes)s, loc=0, scale=1)
Log of the probability density function.
"""
_doc_pmf = \
"""pmf(x, %(shapes)s, loc=0, scale=1)
Probability mass function.
"""
_doc_logpmf = \
"""logpmf(x, %(shapes)s, loc=0, scale=1)
Log of the probability mass function.
"""
_doc_cdf = \
"""cdf(x, %(shapes)s, loc=0, scale=1)
Cumulative density function.
"""
_doc_logcdf = \
"""logcdf(x, %(shapes)s, loc=0, scale=1)
Log of the cumulative density function.
"""
_doc_sf = \
"""sf(x, %(shapes)s, loc=0, scale=1)
Survival function (1-cdf --- sometimes more accurate).
"""
_doc_logsf = \
"""logsf(x, %(shapes)s, loc=0, scale=1)
Log of the survival function.
"""
_doc_ppf = \
"""ppf(q, %(shapes)s, loc=0, scale=1)
Percent point function (inverse of cdf --- percentiles).
@ -141,6 +189,10 @@ _doc_isf = \
"""isf(q, %(shapes)s, loc=0, scale=1)
Inverse survival function (inverse of sf).
"""
_doc_moment = \
"""moment(n, %(shapes)s, loc=0, scale=1)
Non-central moment of order n
"""
_doc_stats = \
"""stats(%(shapes)s, loc=0, scale=1, moments='mv')
Mean('m'), variance('v'), skew('s'), and/or kurtosis('k').
@ -153,9 +205,40 @@ _doc_fit = \
"""fit(data, %(shapes)s, loc=0, scale=1)
Parameter estimates for generic data.
"""
_doc_expect = \
"""expect(func, %(shapes)s, loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds)
Expected value of a function (of one argument) with respect to the distribution.
"""
_doc_expect_discrete = \
"""expect(func, %(shapes)s, loc=0, lb=None, ub=None, conditional=False)
Expected value of a function (of one argument) with respect to the distribution.
"""
_doc_median = \
"""median(%(shapes)s, loc=0, scale=1)
Median of the distribution.
"""
_doc_mean = \
"""mean(%(shapes)s, loc=0, scale=1)
Mean of the distribution.
"""
_doc_var = \
"""var(%(shapes)s, loc=0, scale=1)
Variance of the distribution.
"""
_doc_std = \
"""std(%(shapes)s, loc=0, scale=1)
Standard deviation of the distribution.
"""
_doc_interval = \
"""interval(alpha, %(shapes)s, loc=0, scale=1)
Endpoints of the range that contains alpha percent of the distribution
"""
_doc_allmethods = ''.join([docheaders['methods'], _doc_rvs, _doc_pdf,
_doc_cdf, _doc_sf, _doc_ppf, _doc_isf,
_doc_stats, _doc_entropy, _doc_fit])
_doc_logpdf, _doc_cdf, _doc_logcdf, _doc_sf,
_doc_logsf, _doc_ppf, _doc_isf, _doc_moment,
_doc_stats, _doc_entropy, _doc_fit,
_doc_expect, _doc_median,
_doc_mean, _doc_var, _doc_std, _doc_interval])
# Note that the two lines for %(shapes) are searched for and replaced in
# rv_continuous and rv_discrete - update there if the exact string changes
@ -199,6 +282,7 @@ _doc_default_example = \
"""Examples
--------
>>> import matplotlib.pyplot as plt
>>> from scipy.stats import %(name)s
>>> numargs = %(name)s.numargs
>>> [ %(shapes)s ] = [0.9,] * numargs
>>> rv = %(name)s(%(shapes)s)
@ -247,13 +331,23 @@ _doc_default_before_notes = ''.join([_doc_default_longsummary,
docdict = {'rvs':_doc_rvs,
'pdf':_doc_pdf,
'logpdf':_doc_logpdf,
'cdf':_doc_cdf,
'logcdf':_doc_logcdf,
'sf':_doc_sf,
'logsf':_doc_logsf,
'ppf':_doc_ppf,
'isf':_doc_isf,
'stats':_doc_stats,
'entropy':_doc_entropy,
'fit':_doc_fit,
'moment':_doc_moment,
'expect':_doc_expect,
'interval':_doc_interval,
'mean':_doc_mean,
'std':_doc_std,
'var':_doc_var,
'median':_doc_median,
'allmethods':_doc_allmethods,
'callparams':_doc_default_callparams,
'longsummary':_doc_default_longsummary,
@ -267,11 +361,15 @@ docdict = {'rvs':_doc_rvs,
docdict_discrete = docdict.copy()
docdict_discrete['pmf'] = _doc_pmf
_doc_disc_methods = ['rvs', 'pmf', 'cdf', 'sf', 'ppf', 'isf', 'stats',
'entropy', 'fit']
docdict_discrete['logpmf'] = _doc_logpmf
docdict_discrete['expect'] = _doc_expect_discrete
_doc_disc_methods = ['rvs', 'pmf', 'logpmf', 'cdf', 'logcdf', 'sf', 'logsf',
'ppf', 'isf', 'stats', 'entropy', 'fit', 'expect', 'median',
'mean', 'var', 'std', 'interval']
for obj in _doc_disc_methods:
docdict_discrete[obj] = docdict_discrete[obj].replace(', scale=1', '')
docdict_discrete.pop('pdf')
docdict_discrete.pop('logpdf')
_doc_allmethods = ''.join([docdict_discrete[obj] for obj in
_doc_disc_methods])
@ -362,9 +460,15 @@ class rv_frozen_old(object):
def pdf(self, x): #raises AttributeError in frozen discrete distribution
return self.dist.pdf(x, *self.args, **self.kwds)
def logpdf(self, x):
return self.dist.logpdf(x, *self.args, **self.kwds)
def cdf(self, x):
return self.dist.cdf(x, *self.args, **self.kwds)
def logcdf(self, x):
return self.dist.logcdf(x, *self.args, **self.kwds)
def ppf(self, q):
return self.dist.ppf(q, *self.args, **self.kwds)
@ -379,6 +483,9 @@ class rv_frozen_old(object):
def sf(self, x):
return self.dist.sf(x, *self.args, **self.kwds)
def logsf(self, x):
return self.dist.logsf(x, *self.args, **self.kwds)
def stats(self, moments='mv'):
kwds = self.kwds.copy()
kwds.update({'moments':moments})
@ -402,6 +509,9 @@ class rv_frozen_old(object):
def pmf(self,k):
return self.dist.pmf(k, *self.args, **self.kwds)
def logpmf(self,k):
return self.dist.logpmf(k, *self.args, **self.kwds)
def interval(self, alpha):
return self.dist.interval(alpha, *self.args, **self.kwds)
@ -463,9 +573,13 @@ class rv_frozen(object):
def pdf(self, x):
''' Probability density function at x of the given RV.'''
return self.dist.pdf(x, *self.par)
def logpdf(self, x):
return self.dist.logpdf(x, *self.par)
def cdf(self, x):
'''Cumulative distribution function at x of the given RV.'''
return self.dist.cdf(x, *self.par)
def logcdf(self, x):
return self.dist.logcdf(x, *self.par)
def ppf(self, q):
'''Percent point function (inverse of cdf) at q of the given RV.'''
return self.dist.ppf(q, *self.par)
@ -479,6 +593,8 @@ class rv_frozen(object):
def sf(self, x):
'''Survival function (1-cdf) at x of the given RV.'''
return self.dist.sf(x, *self.par)
def logsf(self, x):
return self.dist.logsf(x, *self.par)
def stats(self, moments='mv'):
''' Some statistics of the given RV'''
kwds = dict(moments=moments)
@ -492,13 +608,14 @@ class rv_frozen(object):
def std(self):
return self.dist.std(*self.par)
def moment(self, n):
par1 = self.par[:self.dist.numargs]
return self.dist.moment(n, *par1)
return self.dist.moment(n, *self.par)
def entropy(self):
return self.dist.entropy(*self.par)
def pmf(self, k):
'''Probability mass function at k of the given RV'''
return self.dist.pmf(k, *self.par)
def logpmf(self,k):
return self.dist.logpmf(k, *self.par)
def interval(self, alpha):
return self.dist.interval(alpha, *self.par)
@ -1072,7 +1189,7 @@ class rv_continuous(rv_generic):
Parameters
----------
momtype : int, optional
The type of generic moment calculation to use (check this).
The type of generic moment calculation to use: 0 for pdf, 1 (default) for ppf.
a : float, optional
Lower bound of the support of the distribution, default is minus
infinity.
@ -1099,7 +1216,7 @@ class rv_continuous(rv_generic):
The shape of the distribution. For example ``"m, n"`` for a
distribution that takes two integers as the two shape arguments for all
its methods.
extradoc : str, optional
extradoc : str, optional, deprecated
This string is used as the last part of the docstring returned when a
subclass has no docstring of its own. Note: `extradoc` exists for
backwards compatibility, do not use for new subclasses.
@ -1112,20 +1229,29 @@ class rv_continuous(rv_generic):
pdf(x, <shape(s)>, loc=0, scale=1)
probability density function
logpdf(x, <shape(s)>, loc=0, scale=1)
log of the probability density function
cdf(x, <shape(s)>, loc=0, scale=1)
cumulative density function
logcdf(x, <shape(s)>, loc=0, scale=1)
log of the cumulative density function
sf(x, <shape(s)>, loc=0, scale=1)
survival function (1-cdf --- sometimes more accurate)
logsf(x, <shape(s)>, loc=0, scale=1)
log of the survival function
ppf(q, <shape(s)>, loc=0, scale=1)
percent point function (inverse of cdf --- quantiles)
isf(q, <shape(s)>, loc=0, scale=1)
inverse survival function (inverse of sf)
moments(n, <shape(s)>)
non-central n-th moment of the standard distribution (oc=0, scale=1)
moment(n, <shape(s)>, loc=0, scale=1)
non-central n-th moment of the distribution. May not work for array arguments.
stats(<shape(s)>, loc=0, scale=1, moments='mv')
mean('m'), variance('v'), skew('s'), and/or kurtosis('k')
@ -1136,10 +1262,31 @@ class rv_continuous(rv_generic):
fit(data, <shape(s)>, loc=0, scale=1)
Parameter estimates for generic data
expect(func=None, args=(), loc=0, scale=1, lb=None, ub=None,
conditional=False, **kwds)
Expected value of a function with respect to the distribution.
Additional kwd arguments passed to integrate.quad
median(<shape(s)>, loc=0, scale=1)
Median of the distribution.
mean(<shape(s)>, loc=0, scale=1)
Mean of the distribution.
std(<shape(s)>, loc=0, scale=1)
Standard deviation of the distribution.
var(<shape(s)>, loc=0, scale=1)
Variance of the distribution.
interval(alpha, <shape(s)>, loc=0, scale=1)
Interval that with `alpha` percent probability contains a random
realization of this distribution.
__call__(<shape(s)>, loc=0, scale=1)
calling a distribution instance creates a frozen RV object with the
Calling a distribution instance creates a frozen RV object with the
same methods but holding the given shape, location, and scale fixed.
see Notes section
See Notes section.
**Parameters for Methods**
@ -1199,8 +1346,9 @@ class rv_continuous(rv_generic):
New random variables can be defined by subclassing rv_continuous class
and re-defining at least the
_pdf or the cdf method which will be given clean arguments (in between a
and b) and passing the argument check method
_pdf or the _cdf method (normalized to location 0 and scale 1)
which will be given clean arguments (in between a and b) and
passing the argument check method
If postive argument checking is not correct for your RV
then you will also need to re-define ::
@ -1210,9 +1358,9 @@ class rv_continuous(rv_generic):
Correct, but potentially slow defaults exist for the remaining
methods but for speed and/or accuracy you can over-ride ::
_cdf, _ppf, _rvs, _isf, _sf
_logpdf, _cdf, _logcdf, _ppf, _rvs, _isf, _sf, _logsf
Rarely would you override _isf and _sf but you could.
Rarely would you override _isf, _sf, and _logsf but you could.
Statistics are computed using numerical integration by default.
For speed you can redefine this using
@ -1498,7 +1646,7 @@ class rv_continuous(rv_generic):
"""
Log of the probability density function at x of the given RV.
This uses more numerically accurate calculation if available.
This uses a more numerically accurate calculation if available.
Parameters
----------
@ -1943,7 +2091,7 @@ class rv_continuous(rv_generic):
else:
return tuple(output)
def moment(self, n, *args):
def moment(self, n, *args, **kwds):
"""
n'th order non-central moment of distribution
@ -1952,17 +2100,24 @@ class rv_continuous(rv_generic):
n: int, n>=1
order of moment
arg1, arg2, arg3,... : array-like
arg1, arg2, arg3,... : float
The shape parameter(s) for the distribution (see docstring of the
instance object for more information)
loc : float, optional
location parameter (default=0)
scale : float, optional
scale parameter (default=1)
"""
if not self._argcheck(*args):
loc, scale = map(kwds.get,['loc','scale'])
args, loc, scale = self.fix_loc_scale(args, loc, scale)
if not (self._argcheck(*args) and (scale > 0)):
return nan
if (floor(n) != n):
raise ValueError("Moment must be an integer.")
if (n < 0): raise ValueError("Moment must be positive.")
if (n == 0): return 1.0
mu, mu2, g1, g2 = None, None, None, None
if (n > 0) and (n < 5):
signature = inspect.getargspec(self._stats.im_func)
if (signature[2] is not None) or ('moments' in signature[0]):
@ -1970,28 +2125,20 @@ class rv_continuous(rv_generic):
else:
mdict = {}
mu, mu2, g1, g2 = self._stats(*args,**mdict)
if (n==1):
if mu is None: return self._munp(1,*args)
else: return mu
elif (n==2):
if mu2 is None or mu is None:
return self._munp(2,*args)
else: return mu2 + mu*mu
elif (n==3):
if g1 is None or mu2 is None:
return self._munp(3,*args)
else:
mu3 = g1*(mu2**1.5) # 3rd central moment
return mu3+3*mu*mu2+mu**3 # 3rd non-central moment
else: # (n==4)
if g2 is None or mu2 is None:
return self._munp(4,*args)
else:
mu4 = (g2+3.0)*(mu2**2.0) # 4th central moment
mu3 = g1*(mu2**1.5) # 3rd central moment
return mu4+4*mu*mu3+6*mu*mu*mu2+mu**4
val = _moment_from_stats(n, mu, mu2, g1, g2, self._munp, args)
# Convert to transformed X = L + S*Y
# so E[X^n] = E[(L+S*Y)^n] = L^n sum(comb(n,k)*(S/L)^k E[Y^k],k=0...n)
if loc == 0:
return scale**n * val
else:
return self._munp(n,*args)
result = 0
fac = float(scale) / float(loc)
for k in range(n):
valk = _moment_from_stats(k, mu, mu2, g1, g2, self._munp, args)
result += comb(n,k,exact=True)*(fac**k) * valk
result += fac**n * val
return result * loc**n
def nlogps(self, theta, x):
""" Moran's negative log Product Spacings statistic
@ -2106,7 +2253,7 @@ class rv_continuous(rv_generic):
return inf
else:
N = len(x)
return self._nnlf(x, *args) + N * log(scale)
return self._nnlf(x, *args) + N*log(scale)
def hessian_nlogps(self, theta, data, eps=None):
''' approximate hessian of nlogps where theta are the parameters (including loc and scale)
'''
@ -2230,6 +2377,8 @@ class rv_continuous(rv_generic):
args = (1.0,)*self.numargs
return args + self.fit_loc_scale(data, *args)
# Return the (possibly reduced) function to optimize in order to find MLE
# estimates for the .fit method
def _reduce_func(self, args, kwds):
args = list(args)
Nargs = len(args)
@ -2267,7 +2416,7 @@ class rv_continuous(rv_generic):
def func(theta, x):
newtheta = restore(args[:], theta)
return fitfun(newtheta, x)
return self.nnlf(newtheta, x)
return x0, func, restore, args
@ -2282,8 +2431,9 @@ class rv_continuous(rv_generic):
such.
One can hold some parameters fixed to specific values by passing in
keyword arguments f0..fn for shape paramters and floc, fscale for
location and scale parameters, respectively.
keyword arguments ``f0``, ``f1``, ..., ``fn`` (for shape parameters)
and ``floc`` and ``fscale`` (for location and scale parameters,
respectively).
Parameters
----------
@ -2298,7 +2448,7 @@ class rv_continuous(rv_generic):
Special keyword arguments are recognized as holding certain
parameters fixed:
f0..fn : hold respective shape parameters fixed.
f0...fn : hold respective shape parameters fixed.
floc : hold location parameter fixed to specified value.
@ -2497,6 +2647,9 @@ class rv_continuous(rv_generic):
of the integration interval. The return value is the expectation
of the function, conditional on being in the given interval.
Additional keyword arguments are passed to the integration routine.
Returns
-------
expected value : float
@ -2506,19 +2659,21 @@ class rv_continuous(rv_generic):
This function has not been checked for it's behavior when the integral is
not finite. The integration behavior is inherited from integrate.quad.
"""
lockwds = {'loc': loc,
'scale':scale}
if func is None:
def fun(x, *args):
return x*self.pdf(x, *args, **{'loc':loc, 'scale':scale})
return x*self.pdf(x, *args, **lockwds)
else:
def fun(x, *args):
return func(x)*self.pdf(x, *args, **{'loc':loc, 'scale':scale})
return func(x)*self.pdf(x, *args, **lockwds)
if lb is None:
lb = loc + self.a * scale
if ub is None:
ub = loc + self.b * scale
if conditional:
invfac = (self.sf(lb, *args, **{'loc':loc, 'scale':scale})
- self.sf(ub, *args, **{'loc':loc, 'scale':scale}))
invfac = (self.sf(lb, *args, **lockwds)
- self.sf(ub, *args, **lockwds))
else:
invfac = 1.0
kwds['args'] = args
@ -3968,7 +4123,7 @@ class gumbel_l_gen(rv_continuous):
def _logpdf(self, x):
return x - exp(x)
def _cdf(self, x):
return expm1(-exp(x))
return -expm1(-exp(x))
def _ppf(self, q):
return log(-log1p(-q))
def _stats(self):
@ -4990,8 +5145,6 @@ for -1 <= x <= 1, c > 0.
# scale is the mode.
class rayleigh_gen(rv_continuous):
#rayleigh_gen.link.__doc__ = rv_continuous.link.__doc__
def link(self, x, logSF, phat, ix):
rv_continuous.link.__doc__
if ix == 1:
@ -5181,16 +5334,16 @@ class truncexpon_gen(rv_continuous):
#wrong answer with formula, same as in continuous.pdf
#return gam(n+1)-special.gammainc(1+n,b)
if n == 1:
return (1 - (b + 1) * exp(-b)) / (-expm1(-b))
return (1-(b+1)*exp(-b))/(-expm1(-b))
elif n == 2:
return 2 * (1 - 0.5 * (b * b + 2 * b + 2) * exp(-b)) / (-expm1(-b))
return 2*(1-0.5*(b*b+2*b+2)*exp(-b))/(-expm1(-b))
else:
#return generic for higher moments
#return rv_continuous._mom1_sc(self,n, b)
return self._mom1_sc(n, b)
def _entropy(self, b):
eB = exp(b)
return log(eB - 1) + (1 + eB * (b - 1.0)) / (1.0 - eB)
return log(eB-1)+(1+eB*(b-1.0))/(1.0-eB)
truncexpon = truncexpon_gen(a=0.0, name='truncexpon',
longname="A truncated exponential",
shapes="b", extradoc="""
@ -5222,13 +5375,13 @@ class truncnorm_gen(rv_continuous):
def _cdf(self, x, a, b):
return (_norm_cdf(x) - self._na) / self._delta
def _ppf(self, q, a, b):
return norm._ppf(q * self._nb + self._na * (1.0 - q))
return norm._ppf(q*self._nb + self._na*(1.0-q))
def _stats(self, a, b):
nA, nB = self._na, self._nb
d = nB - nA
pA, pB = _norm_pdf(a), _norm_pdf(b)
mu = (pA - pB) / d #correction sign
mu2 = 1 + (a * pA - b * pB) / d - mu * mu
mu2 = 1 + (a*pA - b*pB) / d - mu*mu
return mu, mu2, None, None
truncnorm = truncnorm_gen(name='truncnorm', longname="A truncated normal",
shapes="a, b", extradoc="""
@ -5251,11 +5404,14 @@ Truncated Normal distribution.
# FIXME: RVS does not work.
class tukeylambda_gen(rv_continuous):
def _argcheck(self, lam):
# lam in RR.
return np.ones(np.shape(lam), dtype=bool)
def _pdf(self, x, lam):
Fx = arr(special.tklmbda(x,lam))
Px = Fx**(lam-1.0) + (arr(1-Fx))**(lam-1.0)
Px = 1.0/arr(Px)
return where((lam > 0) & (abs(x) < 1.0/lam), Px, 0.0)
return where((lam <= 0) | (abs(x) < 1.0/arr(lam)), Px, 0.0)
def _cdf(self, x, lam):
return special.tklmbda(x, lam)
def _ppf(self, q, lam):
@ -5638,24 +5794,59 @@ class rv_discrete(rv_generic):
generic.pmf(x, <shape(s)>, loc=0)
probability mass function
logpmf(x, <shape(s)>, loc=0)
log of the probability density function
generic.cdf(x, <shape(s)>, loc=0)
cumulative density function
generic.logcdf(x, <shape(s)>, loc=0)
log of the cumulative density function
generic.sf(x, <shape(s)>, loc=0)
survival function (1-cdf --- sometimes more accurate)
generic.logsf(x, <shape(s)>, loc=0, scale=1)
log of the survival function
generic.ppf(q, <shape(s)>, loc=0)
percent point function (inverse of cdf --- percentiles)
generic.isf(q, <shape(s)>, loc=0)
inverse survival function (inverse of sf)
generic.moment(n, <shape(s)>, loc=0)
non-central n-th moment of the distribution. May not work for array arguments.
generic.stats(<shape(s)>, loc=0, moments='mv')
mean('m', axis=0), variance('v'), skew('s'), and/or kurtosis('k')
generic.entropy(<shape(s)>, loc=0)
entropy of the RV
generic.fit(data, <shape(s)>, loc=0)
Parameter estimates for generic data
generic.expect(func=None, args=(), loc=0, lb=None, ub=None, conditional=False)
Expected value of a function with respect to the distribution.
Additional kwd arguments passed to integrate.quad
generic.median(<shape(s)>, loc=0)
Median of the distribution.
generic.mean(<shape(s)>, loc=0)
Mean of the distribution.
generic.std(<shape(s)>, loc=0)
Standard deviation of the distribution.
generic.var(<shape(s)>, loc=0)
Variance of the distribution.
generic.interval(alpha, <shape(s)>, loc=0)
Interval that with `alpha` percent probability contains a random
realization of this distribution.
generic(<shape(s)>, loc=0)
calling a distribution instance returns a frozen distribution
@ -6355,17 +6546,24 @@ class rv_discrete(rv_generic):
----------
n: int, n>=1
order of moment
arg1, arg2, arg3,...: array-like
arg1, arg2, arg3,...: float
The shape parameter(s) for the distribution (see docstring of the
instance object for more information)
loc : float, optional
location parameter (default=0)
scale : float, optional
scale parameter (default=1)
"""
if not self._argcheck(*args):
loc = kwds.get('loc', 0)
scale = kwds.get('scale', 1)
if not (self._argcheck(*args) and (scale > 0)):
return nan
if (floor(n) != n):
raise ValueError("Moment must be an integer.")
if (n < 0): raise ValueError("Moment must be positive.")
if (n == 0): return 1.0
mu, mu2, g1, g2 = None, None, None, None
if (n > 0) and (n < 5):
signature = inspect.getargspec(self._stats.im_func)
if (signature[2] is not None) or ('moments' in signature[0]):
@ -6373,27 +6571,21 @@ class rv_discrete(rv_generic):
else:
dict = {}
mu, mu2, g1, g2 = self._stats(*args,**dict)
if (n==1):
if mu is None: return self._munp(1,*args)
else: return mu
elif (n==2):
if mu2 is None or mu is None: return self._munp(2,*args)
else: return mu2 + mu*mu
elif (n==3):
if (g1 is None) or (mu2 is None) or (mu is None):
return self._munp(3,*args)
else:
mu3 = g1*(mu2**1.5) # 3rd central moment
return mu3+3*mu*mu2+mu**3 # 3rd non-central moment
else: # (n==4)
if (g2 is None) or (g1 is None) or (mu is None) or (mu2 is None):
return self._munp(4,*args)
else:
mu4 = (g2+3.0)*(mu2**2.0) # 4th central moment
mu3 = g1*(mu2**1.5) # 3rd central moment
return mu4+4*mu*mu3+6*mu*mu*mu2+mu**4
val = _moment_from_stats(n, mu, mu2, g1, g2, self._munp, args)
# Convert to transformed X = L + S*Y
# so E[X^n] = E[(L+S*Y)^n] = L^n sum(comb(n,k)*(S/L)^k E[Y^k],k=0...n)
if loc == 0:
return scale**n * val
else:
return self._munp(n,*args)
result = 0
fac = float(scale) / float(loc)
for k in range(n):
valk = _moment_from_stats(k, mu, mu2, g1, g2, self._munp, args)
result += comb(n,k,exact=True)*(fac**k) * valk
result += fac**n * val
return result * loc**n
def freeze(self, *args, **kwds):
return rv_frozen(self, *args, **kwds)
@ -6440,7 +6632,7 @@ class rv_discrete(rv_generic):
Parameters
----------
fn : function (default: identity mapping)
Function for which integral is calculated. Takes only one argument.
Function for which sum is calculated. Takes only one argument.
args : tuple
argument (parameters) of the distribution
optional keyword parameters

125
pywafo/src/wafo/stats/tests/test_distributions.py
Unescape Escape View File

@ -14,7 +14,7 @@ import wafo.stats as stats
from wafo.stats.distributions import argsreduce
def kolmogorov_check(diststr, args=(), N=20, significance=0.01):
qtest = stats.ksoneisf(significance, N)
qtest = stats.ksone.isf(significance, N)
cdf = eval('stats.'+diststr+'.cdf')
dist = eval('stats.'+diststr)
# Get random numbers
@ -242,6 +242,10 @@ class TestDLaplace(TestCase):
assert_(isinstance(val, numpy.ndarray))
assert_(val.dtype.char in typecodes['AllInteger'])
def test_rvgeneric_std():
"""Regression test for #1191"""
assert_array_almost_equal(stats.t.std([5, 6]), [1.29099445, 1.22474487])
class TestRvDiscrete(TestCase):
def test_rvs(self):
states = [-1,0,1,2,3,4]
@ -328,7 +332,7 @@ class TestSkellam(TestCase):
assert_almost_equal(stats.skellam.cdf(k, mu1, mu2), skcdfR, decimal=5)
class TestHypergeom(TestCase):
class TestHypergeom2(TestCase):
def test_precision(self):
# comparison number from mpmath
M = 2500
@ -487,7 +491,7 @@ class TestFrozen(TestCase):
assert_equal(result_f, result)
result_f = frozen.moment(2)
result = dist.moment(2)
result = dist.moment(2,loc=10.0, scale=3.0)
assert_equal(result_f, result)
def test_gamma(self):
@ -555,6 +559,100 @@ class TestFrozen(TestCase):
# the focus of this test.
assert_equal(m1, m2)
class TestExpect(TestCase):
"""Test for expect method.
Uses normal distribution and beta distribution for finite bounds, and
hypergeom for discrete distribution with finite support
"""
def test_norm(self):
v = stats.norm.expect(lambda x: (x-5)*(x-5), loc=5, scale=2)
assert_almost_equal(v, 4, decimal=14)
m = stats.norm.expect(lambda x: (x), loc=5, scale=2)
assert_almost_equal(m, 5, decimal=14)
lb = stats.norm.ppf(0.05, loc=5, scale=2)
ub = stats.norm.ppf(0.95, loc=5, scale=2)
prob90 = stats.norm.expect(lambda x: 1, loc=5, scale=2, lb=lb, ub=ub)
assert_almost_equal(prob90, 0.9, decimal=14)
prob90c = stats.norm.expect(lambda x: 1, loc=5, scale=2, lb=lb, ub=ub,
conditional=True)
assert_almost_equal(prob90c, 1., decimal=14)
def test_beta(self):
#case with finite support interval
## >>> mtrue, vtrue = stats.beta.stats(10,5, loc=5., scale=2.)
## >>> mtrue, vtrue
## (array(6.333333333333333), array(0.055555555555555552))
v = stats.beta.expect(lambda x: (x-19/3.)*(x-19/3.), args=(10,5),
loc=5, scale=2)
assert_almost_equal(v, 1./18., decimal=14)
m = stats.beta.expect(lambda x: x, args=(10,5), loc=5., scale=2.)
assert_almost_equal(m, 19/3., decimal=14)
ub = stats.beta.ppf(0.95, 10, 10, loc=5, scale=2)
lb = stats.beta.ppf(0.05, 10, 10, loc=5, scale=2)
prob90 = stats.beta.expect(lambda x: 1., args=(10,10), loc=5.,
scale=2.,lb=lb, ub=ub, conditional=False)
assert_almost_equal(prob90, 0.9, decimal=14)
prob90c = stats.beta.expect(lambda x: 1, args=(10,10), loc=5,
scale=2, lb=lb, ub=ub, conditional=True)
assert_almost_equal(prob90c, 1., decimal=14)
def test_hypergeom(self):
#test case with finite bounds
#without specifying bounds
m_true, v_true = stats.hypergeom.stats(20, 10, 8, loc=5.)
m = stats.hypergeom.expect(lambda x: x, args=(20, 10, 8), loc=5.)
assert_almost_equal(m, m_true, decimal=13)
v = stats.hypergeom.expect(lambda x: (x-9.)**2, args=(20, 10, 8),
loc=5.)
assert_almost_equal(v, v_true, decimal=14)
#with bounds, bounds equal to shifted support
v_bounds = stats.hypergeom.expect(lambda x: (x-9.)**2, args=(20, 10, 8),
loc=5., lb=5, ub=13)
assert_almost_equal(v_bounds, v_true, decimal=14)
#drop boundary points
prob_true = 1-stats.hypergeom.pmf([5, 13], 20, 10, 8, loc=5).sum()
prob_bounds = stats.hypergeom.expect(lambda x: 1, args=(20, 10, 8),
loc=5., lb=6, ub=12)
assert_almost_equal(prob_bounds, prob_true, decimal=13)
#conditional
prob_bc = stats.hypergeom.expect(lambda x: 1, args=(20, 10, 8), loc=5.,
lb=6, ub=12, conditional=True)
assert_almost_equal(prob_bc, 1, decimal=14)
#check simple integral
prob_b = stats.hypergeom.expect(lambda x: 1, args=(20, 10, 8),
lb=0, ub=8)
assert_almost_equal(prob_b, 1, decimal=13)
def test_poisson(self):
#poisson, use lower bound only
prob_bounds = stats.poisson.expect(lambda x: 1, args=(2,), lb=3,
conditional=False)
prob_b_true = 1-stats.poisson.cdf(2,2)
assert_almost_equal(prob_bounds, prob_b_true, decimal=14)
prob_lb = stats.poisson.expect(lambda x: 1, args=(2,), lb=2,
conditional=True)
assert_almost_equal(prob_lb, 1, decimal=14)
def test_regression_ticket_1316():
"""Regression test for ticket #1316."""
@ -563,5 +661,26 @@ def test_regression_ticket_1316():
g = stats.distributions.gamma_gen(name='gamma')
def test_regression_ticket_1326():
"""Regression test for ticket #1326."""
#adjust to avoid nan with 0*log(0)
assert_almost_equal(stats.chi2.pdf(0.0, 2), 0.5, 14)
def test_regression_tukey_lambda():
""" Make sure that Tukey-Lambda distribution correctly handles non-positive lambdas.
"""
x = np.linspace(-5.0, 5.0, 101)
for lam in [0.0, -1.0, -2.0, np.array([[-1.0], [0.0], [-2.0]])]:
p = stats.tukeylambda.pdf(x, lam)
assert_((p != 0.0).all())
assert_(~np.isnan(p).all())
lam = np.array([[-1.0], [0.0], [2.0]])
p = stats.tukeylambda.pdf(x, lam)
assert_(~np.isnan(p).all())
assert_((p[0] != 0.0).all())
assert_((p[1] != 0.0).all())
assert_((p[2] != 0.0).any())
assert_((p[2] == 0.0).any())
if __name__ == "__main__":
run_module_suite()

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