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45 lines
1.3 KiB
Python
45 lines
1.3 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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import sympy as sym
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from GPy.util.symbolic import normcdfln
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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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#from GPy.util.functions import clip_exp
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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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class Skew_exponential(Symbolic):
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"""
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Negative binomial
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.. math::
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.. Note::
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Y takes real values.
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link function is identity
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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, shape=1.0, scale=1.0):
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parameters={'scale':scale, 'shape':shape}
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if gp_link is None:
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gp_link = link_functions.Identity()
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#func_modules = [{'exp':clip_exp}]
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scale = sym.Symbol('scale', positive=True, real=True)
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shape = sym.Symbol('shape', real=True)
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y_0 = sym.Symbol('y_0', real=True)
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f_0 = sym.Symbol('f_0', real=True)
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log_pdf=sym.log(shape)-sym.log(scale)-((y_0-f_0)/scale) + normcdfln(shape*(y_0-f_0)/scale)
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super(Skew_exponential, self).__init__(log_pdf=log_pdf, gp_link=gp_link, name='Skew_exponential', parameters=parameters)
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# TODO: Check this.
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self.log_concave = True
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