I try to define a custom distribution with pdf given via scipy.stats
import numpy as np
from scipy.stats import rv_continuous
def __init__(self, pdf=None):
self.custom_pdf = pdf
def _pdf(self, x, *args):
if self.custom_pdf is None:
# print 'PDF is not overridden'
return super(CustomDistribution, self)._pdf(x, *args)
# print 'PDF is overridden'
def g(x, mu):
if x < 0:
return mu * np.exp(- mu * x)
my_exp_dist = CustomDistribution(pdf=lambda x: g(x, .5))
IntegrationWarning: The algorithm does not converge. Roundoff error
is detected in the extrapolation table. It is assumed that the
requested tolerance cannot be achieved, and that the returned result
(if full_output = 1) is the best which can be obtained.
IntegrationWarning: The maximum number of subdivisions (50) has been
If increasing the limit yields no improvement it is advised to
analyze the integrand in order to determine the difficulties. If
the position of a local difficulty can be determined (singularity,
discontinuity) one will probably gain from splitting up the
interval and calling the integrator on the subranges. Perhaps a
special-purpose integrator should be used. warnings.warn(msg,
This seems to do what you want. An instance of the class must be given a value for the lambda parameter each time the instance is created. rv_continuous is clever enough to infer items that you do not supply but you can, of course, offer more definitions that I have here.
from scipy.stats import rv_continuous import numpy class Neg_exp(rv_continuous): "negative exponential" def _pdf(self, x, lamda): self.lamda=lamda return lamda*numpy.exp(-lamda*x) def _cdf(self, x, lamda): return 1-numpy.exp(-lamda*x) def _stats(self,lamda): return [1/self.lamda,0,0,0] neg_exp=Neg_exp(name="negative exponential",a=0) print (neg_exp.pdf(0,.5)) print (neg_exp.pdf(5,.5)) print (neg_exp.stats(0.5)) print (neg_exp.rvs(0.5))