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Mainly updated the documentation

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master
Per.Andreas.Brodtkorb 14 years ago
parent 51de2e3530
commit 9d10be02be
2 changed files with 196 additions and 75 deletions
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53
pywafo/src/wafo/stats/distributions.py
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@ -96,7 +96,14 @@ from scipy.misc import doccer
all = alltrue
sgf = vectorize
import new
try:
from new import instancemethod
except ImportError:
# Python 3
def instancemethod(func, obj, cls):
return types.MethodType(func, obj)
# These are the docstring parts used for substitution in specific
# distribution docstrings.
@ -280,7 +287,13 @@ docdict_discrete['default'] = _doc_default_disc
# clean up all the separate docstring elements, we do not need them anymore
for obj in [s for s in dir() if s.startswith('_doc_')]:
exec('del ' + obj)
del s, obj
del obj
try:
del s
except NameError:
# in Python 3, loop variables are not visible after the loop
pass
def _build_random_array(fun, args, size=None):
@ -854,7 +867,7 @@ class rv_generic(object):
# self._size is total size of all output values
self._size = product(size, axis=0)
if self._size > 1:
if self._size is not None and self._size > 1:
size = numpy.array(size, ndmin=1)
if np.all(scale == 0):
@ -4202,7 +4215,7 @@ class loggamma_gen(rv_continuous):
def _munp(self,n,*args):
# use generic moment calculation using ppf
return self._mom0_sc(n,*args)
loggamma = loggamma_gen(name='loggamma', longname="A log gamma",
loggamma = loggamma_gen(name='loggamma', longname="A log gamma", shapes='c',
extradoc="""
Log gamma distribution
@ -5489,7 +5502,7 @@ class rv_discrete(rv_generic):
shapes=None, extradoc=None):
super(rv_generic,self).__init__()
self.fix_loc = self._fix_loc
if badvalue is None:
badvalue = nan
self.badvalue = badvalue
@ -5518,17 +5531,17 @@ class rv_discrete(rv_generic):
self.qvals = numpy.cumsum(self.pk,axis=0)
self.F = make_dict(self.xk, self.qvals)
self.Finv = reverse_dict(self.F)
self._ppf = new.instancemethod(sgf(_drv_ppf,otypes='d'),
self, rv_discrete)
self._pmf = new.instancemethod(sgf(_drv_pmf,otypes='d'),
self, rv_discrete)
self._cdf = new.instancemethod(sgf(_drv_cdf,otypes='d'),
self, rv_discrete)
self._nonzero = new.instancemethod(_drv_nonzero, self, rv_discrete)
self.generic_moment = new.instancemethod(_drv_moment,
self, rv_discrete)
self.moment_gen = new.instancemethod(_drv_moment_gen,
self._ppf = instancemethod(sgf(_drv_ppf,otypes='d'),
self, rv_discrete)
self._pmf = instancemethod(sgf(_drv_pmf,otypes='d'),
self, rv_discrete)
self._cdf = instancemethod(sgf(_drv_cdf,otypes='d'),
self, rv_discrete)
self._nonzero = instancemethod(_drv_nonzero, self, rv_discrete)
self.generic_moment = instancemethod(_drv_moment,
self, rv_discrete)
self.moment_gen = instancemethod(_drv_moment_gen,
self, rv_discrete)
self.numargs=0
else:
cdf_signature = inspect.getargspec(self._cdf.im_func)
@ -5541,14 +5554,14 @@ class rv_discrete(rv_generic):
#correct nin for generic moment vectorization
self.vec_generic_moment = sgf(_drv2_moment, otypes='d')
self.vec_generic_moment.nin = self.numargs + 2
self.generic_moment = new.instancemethod(self.vec_generic_moment,
self, rv_discrete)
self.generic_moment = instancemethod(self.vec_generic_moment,
self, rv_discrete)
#correct nin for ppf vectorization
_vppf = sgf(_drv2_ppfsingle,otypes='d')
_vppf.nin = self.numargs + 2 # +1 is for self
self._vecppf = new.instancemethod(_vppf,
self, rv_discrete)
self._vecppf = instancemethod(_vppf,
self, rv_discrete)
@ -5908,6 +5921,8 @@ class rv_discrete(rv_generic):
instance object for more information)
loc : array-like, optional
location parameter (default=0)
scale: array-like, optional
scale parameter (default=1)
Returns
-------

218
pywafo/src/wafo/stats/estimation.py
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@ -57,32 +57,24 @@ def valarray(shape, value=nan, typecode=None):
class rv_frozen(object):
''' Frozen continous or discrete 1D Random Variable object (RV)
RV.rvs(size=1)
- random variates
RV.pdf(x)
- probability density function (continous case)
RV.pmf(x)
- probability mass function (discrete case)
RV.cdf(x)
- cumulative density function
RV.sf(x)
- survival function (1-cdf --- sometimes more accurate)
RV.ppf(q)
- percent point function (inverse of cdf --- percentiles)
RV.isf(q)
- inverse survival function (inverse of sf)
RV.stats(moments='mv')
- mean('m',axis=0), variance('v'), skew('s'), and/or kurtosis('k')
RV.entropy()
- (differential) entropy of the RV.
Methods
-------
rvs(size=1)
Random variates.
pdf(x)
Probability density function.
cdf(x)
Cumulative density function.
sf(x)
Survival function (1-cdf --- sometimes more accurate).
ppf(q)
Percent point function (inverse of cdf --- percentiles).
isf(q)
Inverse survival function (inverse of sf).
stats(moments='mv')
Mean('m'), variance('v'), skew('s'), and/or kurtosis('k').
entropy()
(Differential) entropy of the RV.
'''
def __init__(self, dist, *args, **kwds):
self.dist = dist
@ -200,8 +192,10 @@ class Profile(object):
#MLE and better CI for phat.par[0]
>>> import numpy as np
>>> R = weibull_min.rvs(1,size=100);
>>> phat = weibull_min.fit(R,1,1,par_fix=[np.nan,0.,np.nan])
>>> Lp = Profile(phat,i=0)
>>> phat = FitDistribution(ws.weibull_min, R,1,scale=1, floc=0.0)
>>> Lp = Profile(phat, i=0)
>>> Lp.plot()
>>> Lp.get_CI(alpha=0.1)
>>> SF = 1./990
@ -434,38 +428,150 @@ class Profile(object):
plotbackend.ylabel(self.ylabel)
plotbackend.xlabel(self.xlabel)
# internal class to fit given distribution to data
# class to fit given distribution to data
class FitDistribution(rv_frozen):
'''
Return estimators to shape, location, and scale from data
Starting points for the fit are given by input arguments. For any
arguments not given starting points, dist._fitstart(data) is called
to get the starting estimates.
You 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.
Parameters
----------
dist : scipy distribution object
distribution to fit to data
data : array-like
Data to use in calculating the ML or MPS estimators
args : optional
Starting values for any shape arguments (those not specified
will be determined by _fitstart(data))
kwds : loc, scale
Starting values for the location and scale parameters
Special keyword arguments are recognized as holding certain
parameters fixed:
f0..fn : hold respective shape paramters fixed
floc : hold location parameter fixed to specified value
fscale : hold scale parameter fixed to specified value
method : of estimation. Options are
'ml' : Maximum Likelihood method (default)
'mps': Maximum Product Spacing method
alpha : scalar, optional
Confidence coefficent (default=0.05)
search : bool
If true search for best estimator (default),
otherwise return object with initial distribution parameters
copydata : bool
If true copydata (default)
optimizer : The optimizer to use. The optimizer must take func,
and starting position as the first two arguments,
plus args (for extra arguments to pass to the
function to be optimized) and disp=0 to suppress
output as keyword arguments.
Return
------
phat : FitDistribution object
Fitted distribution object with following member variables:
LLmax : loglikelihood function evaluated using par
LPSmax : log product spacing function evaluated using par
pvalue : p-value for the fit
par : distribution parameters (fixed and fitted)
par_cov : covariance of distribution parameters
par_fix : fixed distribution parameters
par_lower : lower (1-alpha)% confidence bound for the parameters
par_upper : upper (1-alpha)% confidence bound for the parameters
Note
----
`data` is sorted using this function, so if `copydata`==False the data
in your namespace will be sorted as well.
Examples
--------
Estimate distribution parameters for weibull_min distribution.
>>> import wafo.stats as ws
>>> import numpy as np
>>> R = ws.weibull_min.rvs(1,size=100);
>>> phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0)
#Plot various diagnostic plots to asses quality of fit.
>>> phat.plotfitsumry()
#phat.par holds the estimated parameters
#phat.par_upper upper CI for parameters
#phat.par_lower lower CI for parameters
#Better CI for phat.par[0]
>>> Lp = Profile(phat,i=0)
>>> Lp.plot()
>>> Lp.get_CI(alpha=0.1)
>>> SF = 1./990
>>> x = phat.isf(SF)
# CI for x
>>> Lx = phat.profile(i=0,x=x,link=phat.dist.link)
>>> Lx.plot()
>>> Lx.get_CI(alpha=0.2)
# CI for logSF=log(SF)
>>> Lpr = phat.profile(i=0,logSF=log(SF),link = phat.dist.link)
'''
def __init__(self, dist, data, *args, **kwds):
extradoc = '''
plotfitsumry()
Plot various diagnostic plots to asses quality of fit.
plotecdf()
Plot Empirical and fitted Cumulative Distribution Function
plotesf()
Plot Empirical and fitted Survival Function
plotepdf()
Plot Empirical and fitted Probability Distribution Function
plotresq()
Displays a residual quantile plot.
plotresprb()
Displays a residual probability plot.
profile()
Return Profile Log- likelihood or Product Spacing-function.
RV.plotfitsumry() - Plot various diagnostic plots to asses quality of fit.
RV.plotecdf() - Plot Empirical and fitted Cumulative Distribution Function
RV.plotesf() - Plot Empirical and fitted Survival Function
RV.plotepdf() - Plot Empirical and fitted Probability Distribution Function
RV.plotresq() - Displays a residual quantile plot.
RV.plotresprb() - Displays a residual probability plot.
RV.profile() - Return Profile Log- likelihood or Product Spacing-function.
Member variables
----------------
data - data used in fitting
alpha - confidence coefficient
method - method used
LLmax - loglikelihood function evaluated using par
LPSmax - log product spacing function evaluated using par
pvalue - p-value for the fit
search - True if search for distribution parameters (default)
copydata - True if copy input data (default)
par - parameters (fixed and fitted)
par_cov - covariance of parameters
par_fix - fixed parameters
par_lower - lower (1-alpha)% confidence bound for the parameters
par_upper - upper (1-alpha)% confidence bound for the parameters
'''
Parameters
----------
x : array-like
quantiles
q : array-like
lower or upper tail probability
size : int or tuple of ints, optional
shape of random variates (default computed from input arguments )
moments : str, optional
composed of letters ['mvsk'] specifying which moments to compute where
'm' = mean, 'v' = variance, 's' = (Fisher's) skew and
'k' = (Fisher's) kurtosis. (default='mv')
'''
# Member variables
# ----------------
# data - data used in fitting
# alpha - confidence coefficient
# method - method used
# LLmax - loglikelihood function evaluated using par
# LPSmax - log product spacing function evaluated using par
# pvalue - p-value for the fit
# search - True if search for distribution parameters (default)
# copydata - True if copy input data (default)
#
# par - parameters (fixed and fitted)
# par_cov - covariance of parameters
# par_fix - fixed parameters
# par_lower - lower (1-alpha)% confidence bound for the parameters
# par_upper - upper (1-alpha)% confidence bound for the parameters
#
# '''
self.__doc__ = rv_frozen.__doc__ + extradoc
self.dist = dist
numargs = dist.numargs

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