[SciPy-User] scipy.stats.truncnorm behaviour
Joe Mellor
jcm71@cantab....
Tue Apr 2 11:36:16 CDT 2013
Hi all,
I have searched the mailing list, so hopefully I'm not repeating something
already on here.
I have been using both scipy.stats.norm and scipy.stats.truncnorm and have
found some (to me) unexpected differences in their behaviour.
The differences are in how each handles the size parameter given to the rvs
method.
for example
when I execute
from scipy.stats.norm
a = array([100.0,1000.0,10000.0])
b = norm.rvs(a,size=(10,3))
b is a (10,3) array where b[:,i] contains 10 samples whose mean is a[i]
However, when I do
from scipy.stats.truncnorm
a = array([100.0,1000.0,10000.0])
b = truncnorm.rvs(-a,inf,loc=a,size=(10,3))
I get a ValueError
----> 1 truncnorm.rvs(-a,inf,loc=a,size=(10,3))
/usr/local/lib/python2.7/dist-packages/scipy/stats/distributions.pyc in
rvs(self, *args, **kwds)
702 return loc*ones(size, 'd')
703
--> 704 vals = self._rvs(*args)
705 if self._size is not None:
706 vals = reshape(vals, size)
/usr/local/lib/python2.7/dist-packages/scipy/stats/distributions.pyc in
_rvs(self, *args)
1226 ## Use basic inverse cdf algorithm for RV generation as
default.
1227 U = mtrand.sample(self._size)
-> 1228 Y = self._ppf(U,*args)
1229 return Y
1230
/usr/local/lib/python2.7/dist-packages/scipy/stats/distributions.pyc in
_ppf(self, q, a, b)
5118 return (_norm_cdf(x) - self._na) / self._delta
5119 def _ppf(self, q, a, b):
-> 5120 return norm._ppf(q*self._nb + self._na*(1.0-q))
5121 def _stats(self, a, b):
5122 nA, nB = self._na, self._nb
ValueError: operands could not be broadcast together with shapes (3) (30)
Presumably the problem being that self._nb and q are of different sizes.
Whereas scipy.stats.norm is ok as its implementation of _rvs just returns
self._size standard normal samples which get reshaped in rv_generic.rvs
before being scaled and shifted.
It would be useful if they acted consistently.
I had a look at the code and the size parameter to rvs (which is really
more the shape parameter) is not passed down to the relevant methods
_rvs (and therefore not to _ppf).
I thought that perhaps either giving _rvs access to the size parameter by
storing it in a field like self._shape so that instead of the code
def _rvs(self, *args):
## Use basic inverse cdf algorithm for RV generation as default.
U = mtrand.sample(self._size)
Y = self._ppf(U,*args)
return Y
it would be
def _rvs(self, *args):
## Use basic inverse cdf algorithm for RV generation as default.
U = mtrand.sample(self._shape)
Y = self._ppf(U,*args)
return Y
Alternatively truncnorm._ppf could work out how to expand self._nb by
looking at the size of _nb and q.
I'm not that familiar with the code, so there are probably problems with
both.
I'm using version 11.0 of scipy.
Thanks
Joe
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