[SciPy-User] rv_frozen when using gamma function

[Bruno Santos]

I am trying to write a script to do some maximum likelihood parameter
estimation of a function. But when I try to use the gamma function I get:
gamma(5)
Out[5]: <scipy.stats.distributions.rv_frozen at 0x7213710>

I thought it might have been a problem solved already on the new
distribution but even after installing the last scipy version I get the
same problem.
The test() after installation is also failing with the following
information:
Running unit tests for scipy
NumPy version 1.5.1
NumPy is installed in /usr/lib/pymodules/python2.7/numpy
SciPy version 0.10.1
SciPy is installed in /usr/local/lib/python2.7/dist-packages/scipy
Python version 2.7.2+ (default, Oct  4 2011, 20:06:09) [GCC 4.6.1]
nose version 1.1.2
...
...
...
AssertionError:
Arrays are not almost equal
 ACTUAL: 0.0
 DESIRED: 0.5

======================================================================
FAIL: Regression test for #651: better handling of badly conditioned
----------------------------------------------------------------------
Traceback (most recent call last):
  File
"/usr/local/lib/python2.7/dist-packages/scipy/signal/tests/test_filter_design.py",
line 34, in test_bad_filter
    assert_raises(BadCoefficients, tf2zpk, [1e-15], [1.0, 1.0])
  File "/usr/lib/pymodules/python2.7/numpy/testing/utils.py", line 982, in
assert_raises
    return nose.tools.assert_raises(*args,**kwargs)
AssertionError: BadCoefficients not raised

----------------------------------------------------------------------
Ran 5103 tests in 47.795s

FAILED (KNOWNFAIL=13, SKIP=28, failures=3)
Out[7]: <nose.result.TextTestResult run=5103 errors=0 failures=3>


My code is as follows:
from numpy import array,log,sum,nan
from scipy.stats import gamma
from scipy import factorial, optimize

#rinterface.initr()
#IntSexpVector = rinterface.IntSexpVector
#lgamma = rinterface.globalenv.get("lgamma")

#Implementation for the Zero-inflated Negative Binomial function
def alphabeta(params,x,dicerAcc):
    alpha = array(params[0])
    beta = array(params[1])
    if alpha<0 or beta<0:return nan
    return sum((alpha*log(beta)) + log(gamma(alpha+x)) + x * log(dicerAcc)
- log(gamma(alpha)) - (alpha+x) * log(beta+dicerAcc) - log(factorial(x)))

if __name__=='__main__':
    x =
array([123,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,104,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,24,1,0,0,0,0,0,0,0,2,0,0,4,0,0,0,0,0,0,0,0,12,0,0])
    dicerAcc = array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.048750000000000002,0.90085000000000004, 0.0504, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0023,
0.089149999999999993, 0.81464999999999999, 0.091550000000000006,
0.0023500000000000001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.00020000000000000001, 0.0061000000000000004,
0.12085, 0.7429, 0.12325, 0.0067000000000000002, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00020000000000000001,
0.012500000000000001, 0.14255000000000001, 0.68159999999999998,
0.14979999999999999, 0.012999999999999999])
    optimize.()


Am I doing something wrong or is this a known problem?

Best,
Bruno
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