mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-12 21:42:39 +02:00
48 lines
1.8 KiB
Python
48 lines
1.8 KiB
Python
# Copyright (c) 2014 The GPy authors (see AUTHORS.txt)
|
|
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
|
|
|
|
|
try:
|
|
import sympy as sym
|
|
sympy_available=True
|
|
from sympy.utilities.lambdify import lambdify
|
|
from GPy.util.symbolic import gammaln, logisticln
|
|
except ImportError:
|
|
sympy_available=False
|
|
|
|
import numpy as np
|
|
from ..util.univariate_Gaussian import std_norm_pdf, std_norm_cdf
|
|
import link_functions
|
|
from symbolic import Symbolic
|
|
from scipy import stats
|
|
|
|
class Negative_binomial(Symbolic):
|
|
"""
|
|
Negative binomial
|
|
|
|
.. math::
|
|
p(y_{i}|\pi(f_{i})) = \left(\frac{r}{r+f_i}\right)^r \frac{\Gamma(r+y_i)}{y!\Gamma(r)}\left(\frac{f_i}{r+f_i}\right)^{y_i}
|
|
|
|
.. Note::
|
|
Y takes non zero integer values..
|
|
link function should have a positive domain, e.g. log (default).
|
|
|
|
.. See also::
|
|
symbolic.py, for the parent class
|
|
"""
|
|
def __init__(self, gp_link=None, dispersion=1.0):
|
|
parameters = {'dispersion':dispersion}
|
|
if gp_link is None:
|
|
gp_link = link_functions.Identity()
|
|
|
|
dispersion = sym.Symbol('dispersion', positive=True, real=True)
|
|
y_0 = sym.Symbol('y_0', nonnegative=True, integer=True)
|
|
f_0 = sym.Symbol('f_0', positive=True, real=True)
|
|
gp_link = link_functions.Log()
|
|
log_pdf=dispersion*sym.log(dispersion) - (dispersion+y_0)*sym.log(dispersion+f_0) + gammaln(y_0+dispersion) - gammaln(y_0+1) - gammaln(dispersion) + y_0*sym.log(f_0)
|
|
#log_pdf= -(dispersion+y)*logisticln(f-sym.log(dispersion)) + gammaln(y+dispersion) - gammaln(y+1) - gammaln(dispersion) + y*(f-sym.log(dispersion))
|
|
super(Negative_binomial, self).__init__(log_pdf=log_pdf, parameters=parameters, gp_link=gp_link, name='Negative_binomial')
|
|
|
|
# TODO: Check this.
|
|
self.log_concave = False
|
|
|