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46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
# Copyright (c) 2014 The GPy authors (see AUTHORS.txt)
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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try:
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import sympy as sym
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sympy_available=True
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from sympy.utilities.lambdify import lambdify
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from GPy.util.symbolic import gammaln, ln_cum_gaussian, cum_gaussian
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except ImportError:
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sympy_available=False
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import numpy as np
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from ..util.univariate_Gaussian import std_norm_pdf, std_norm_cdf
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import link_functions
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from symbolic import Symbolic
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from scipy import stats
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if sympy_available:
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class Negative_binomial(Symbolic):
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"""
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Negative binomial
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.. math::
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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}
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.. Note::
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Y takes non zero integer values..
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link function should have a positive domain, e.g. log (default).
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.. See also::
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symbolic.py, for the parent class
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"""
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def __init__(self, gp_link=None):
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if gp_link is None:
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gp_link = link_functions.Log()
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dispersion = sym.Symbol('dispersion', positive=True, real=True)
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y = sym.Symbol('y', nonnegative=True, integer=True)
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f = sym.Symbol('f', positive=True, real=True)
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log_pdf=dispersion*sym.log(dispersion) - (dispersion+y)*sym.log(dispersion+f) + gammaln(y+dispersion) - gammaln(y+1) - gammaln(dispersion) + y*sym.log(f)
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super(Negative_binomial, self).__init__(log_pdf=log_pdf, gp_link=gp_link, name='Negative_binomial')
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# TODO: Check this.
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self.log_concave = False
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