Hi, I've got some problem with some of the continuous distributions defined in scipy.stats :
from scipy import stats, __version__ __version__ '0.7.0.dev3440' beta = stats.betaprime(5., 5.) beta.pdf( 0.5 ) array(0.68282274043590918) beta.cdf( 0.5>>> from scipy import stats, __version__ __version__ '0.7.0.dev3440' beta = stats.betaprime(5., 5.) beta.pdf( 0.5 ) array(0.68282274043590918) beta.cdf( 0.5 ) array(0.14484580602550423)
So, PDF and CDF methods work, but not PPF :
beta.ppf( 0.5 ) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/loic/tmp/pycvs/lib/python2.6/site-packages/scipy/stats/ distributions.py", line 112, in ppf return self.dist.ppf(q,*self.args,**self.kwds) File "/home/loic/tmp/pycvs/lib/python2.6/site-packages/scipy/stats/ distributions.py", line 563, in ppf place(output,cond,self._ppf(*goodargs)*scale + loc) File "/home/loic/tmp/pycvs/lib/python2.6/site-packages/scipy/stats/ distributions.py", line 382, in _ppf return self.vecfunc(q,*args) File "/home/loic/tmp/pycvs/lib/python2.6/site-packages/numpy/lib/ function_base.py", line 949, in __call__ raise ValueError, "mismatch between python function inputs"\ ValueError: mismatch between python function inputs and received arguments
It seems the same problem appears with at least foldcauchy, foldnorm and genexpon Is there any way to avoid this ? -- LB
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LB