[SciPy-User] rv_frozen when using gamma function
Skipper Seabold
jsseabold@gmail....
Mon Mar 19 11:23:20 CDT 2012
On Mon, Mar 19, 2012 at 12:14 PM, Bruno Santos <bacmsantos@gmail.com> wrote:
> I believe the formula I have is accurate I checked it personally and also
> have it checked by two mathematicians in the lab and they come up with the
> same results. I left my notebook where I performed the transformations home
> so don't completely remember but I believe you can simply things to get rid
> of some of the parameters.
>
dicerAcc is a scalar as you mentioned.
> I managed to implement the function in python now and it is giving the
> same results as in R my question how to maximize it still remains though.
> Is it possibly to maximize a function rather than minimize it in Python?
>
Ok, then I guess my math is faulty. I only looked quickly and don't see the
other close parens in the formula.
To maximize put a negative in front of the function.
>
>
> On 15 March 2012 15:21, Skipper Seabold <jsseabold@gmail.com> wrote:
>
>> On Thu, Mar 15, 2012 at 11:07 AM, Bruno Santos <bacmsantos@gmail.com>wrote:
>>
>>> Thank you all very much for the replies that was exactly what I wanted.
>>> I am basically trying to get the parameters for a
>>> gamma-poisson distribution. I have the R code from a
>>> previous collaborator just trying to write a native function in python
>>> rather than using the R code or port it using rpy2.
>>
>>
>> Oh, fun.
>>
>>
>>> The function is the following:
>>> [image: Inline images 1]
>>> where f(b,d) is a function that gives me a probability of a certain
>>> position in the vector to be occupied and it depends on b (the position)
>>> and d (the likelihood of making an error).
>>> So the likelihood after a few transformations become:
>>>
>>> [image: Inline images 2]
>>> Which I then use the loglikelihood and try to maximise it using an
>>> optimization algorithm.
>>> [image: Inline images 3]
>>> The R code is as following:
>>> alphabeta<-function(alphabeta,x,dicerAcc)
>>> {
>>> alpha <-alphabeta[1]
>>> beta <-alphabeta[2]
>>> if (any(alphabeta<0))
>>> return(NA)
>>> sum((alpha*log(beta) + lgamma(alpha + x) + x * log(dicerAcc) -
>>> lgamma(alpha) - (alpha + x) * log(beta+dicerAcc) - lfactorial(x))[dicerAcc
>>> > noiseT])
>>>
>>
>> From a quick (distracted) look (so I could be wrong)
>>
>> Should this be alpha^2*log(beta) ? +lgamma(alpha) ? And lfactorial(x)
>> should still be +lgamma(alpha)*lfactorial(x) ? And dicerAcc a scalar
>> integer I take it?
>>
>>
>>>
>>> #sum((alpha*log(beta)+(lgamma(alpha+x)+log(dicerError^x))-(lgamma(alpha)+log((beta+dicerError)^(alpha+x))+lfactorial(x)))[dicerError
>>> != 0])
>>> }
>>> x and dicerAcc are known so the I use the optim function in R
>>> ab <- optim(c(1,100), alphabeta, control=list(fnscale=-1), x = x,
>>> dicerAcc = dicerAcc)$par
>>>
>>> Is there any equivalent function in Scipy to the optim one?
>>>
>>> On 14 March 2012 17:05, Bruno Santos <bacmsantos@gmail.com> wrote:
>>>
>>>> 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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>>>
>>
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