[SciPy-User] defining new distributions with rv_continuous

[josef.pktd at gmail.com]

On Mon, Apr 14, 2014 at 6:13 AM, Evgeni Burovski
<evgeny.burovskiy at gmail.com> wrote:
> In this specific case, have a look at truncnorm:
>
> http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.truncnorm.html
>
>>>> import scipy.stats as stats
>>>> import numpy as np
>>>> stats.truncnorm.pdf(0.1, 0, np.inf)
> 0.79390509495402362
>>>> stats.truncnorm.pdf(-0.1, 0, np.inf)
> 0.0
>
>
>
> On Mon, Apr 14, 2014 at 11:00 AM, Francesco De Gasperin <fdg at voo.it> wrote:
>> Hi,
>>
>> I have some problems in defining a new distribution using rv_continuous.
>> As an example I tried to create a gaussian defined only for positive
>> values of the random variable.
>>
>> This is the code:
>>
>> ###########################################
>> from scipy.stats import distributions
>> class gauss_pos(distributions.rv_continuous):
>>     def _pdf(self, x):
>>          if x <= 0: return 0
>>          sigma = 1.
>>          c = 0.
>>          return 1/(np.sqrt(2*np.pi)*sigma) * np.exp( -(x-c)**2 /
>> (2*sigma**2) )
>>
>> t_obj = gauss_pos(name="gauss_pos")
>> print (t_obj.rvs(size=10))
>> #############################################
>>
>> but when I run it I always end up with:
>> OverflowError: (34, 'Numerical result out of range')
>>
>> Then, I removed the "if x <= 0: return 0" line and I tried to define the
>> "a" and "b" parameters when creating the t_obj. Defining only the "a=0"
>> produces the same error, while using something as:
>>
>> t_obj = gauss_pos(a=0,b=2)
>>
>> instead produces a result of this type:
>> [ 2.          2.          1.5460072   1.09621782  1.62184086  0.42107306
>>    2.          1.09281742  0.82957403  2.        ]
>>
>> which I still do not understand... it seems that "b" is returned every
>> time the constraint that the random var must be between a and b is
>> violated...

The specific problem is that your pdf is not normalized correctly to
integrate to 1.
If you truncate the normal distribution, then you need to normalize
for the truncated probability.

The ppf will not be correct, and it is used in to create the rvs in a
generic way.

As Evgeni pointed out truncnorm, the best way is to look at the source
code for some distributions and use them as pattern.

There is also halfnorm.

Josef


>>
>> any help is appreciated!
>> cheers,
>> Francesco
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