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8343615098
Conflicts: GPy/core/gp.py GPy/likelihoods/__init__.py GPy/likelihoods/likelihood_functions.py GPy/likelihoods/link_functions.py
115 lines
3.6 KiB
Python
115 lines
3.6 KiB
Python
# Copyright (c) 2013, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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import noise_models
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def binomial(gp_link=None):
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"""
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Construct a binomial likelihood
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:param gp_link: a GPy gp_link function
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"""
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if gp_link is None:
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gp_link = noise_models.gp_transformations.Probit()
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#else:
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# assert isinstance(gp_link,noise_models.gp_transformations.GPTransformation), 'gp_link function is not valid.'
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if isinstance(gp_link,noise_models.gp_transformations.Probit):
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analytical_mean = True
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analytical_variance = False
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elif isinstance(gp_link,noise_models.gp_transformations.Heaviside):
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analytical_mean = True
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analytical_variance = True
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else:
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analytical_mean = False
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analytical_variance = False
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return noise_models.binomial_noise.Binomial(gp_link,analytical_mean,analytical_variance)
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def exponential(gp_link=None):
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"""
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Construct a binomial likelihood
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:param gp_link: a GPy gp_link function
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"""
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if gp_link is None:
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gp_link = noise_models.gp_transformations.Identity()
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analytical_mean = False
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analytical_variance = False
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return noise_models.exponential_noise.Exponential(gp_link,analytical_mean,analytical_variance)
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def gaussian_ep(gp_link=None,variance=1.):
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"""
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Construct a gaussian likelihood
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:param gp_link: a GPy gp_link function
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:param variance: scalar
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"""
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if gp_link is None:
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gp_link = noise_models.gp_transformations.Identity()
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#else:
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# assert isinstance(gp_link,noise_models.gp_transformations.GPTransformation), 'gp_link function is not valid.'
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analytical_mean = False
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analytical_variance = False
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return noise_models.gaussian_noise.Gaussian(gp_link,analytical_mean,analytical_variance,variance)
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def poisson(gp_link=None):
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"""
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Construct a Poisson likelihood
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:param gp_link: a GPy gp_link function
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"""
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if gp_link is None:
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gp_link = noise_models.gp_transformations.Log_ex_1()
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#else:
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# assert isinstance(gp_link,noise_models.gp_transformations.GPTransformation), 'gp_link function is not valid.'
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analytical_mean = False
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analytical_variance = False
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return noise_models.poisson_noise.Poisson(gp_link,analytical_mean,analytical_variance)
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def gamma(gp_link=None,beta=1.):
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"""
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Construct a Gamma likelihood
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:param gp_link: a GPy gp_link function
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:param beta: scalar
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"""
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if gp_link is None:
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gp_link = noise_models.gp_transformations.Log_ex_1()
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analytical_mean = False
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analytical_variance = False
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return noise_models.gamma_noise.Gamma(gp_link,analytical_mean,analytical_variance,beta)
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def gaussian(gp_link=None, variance=2, D=None, N=None):
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"""
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Construct a Gaussian likelihood
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:param gp_link: a GPy gp_link function
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:param variance: scalar, variance
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"""
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if gp_link is None:
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gp_link = noise_models.gp_transformations.Identity()
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analytical_mean = True
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analytical_variance = True # ?
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return noise_models.gaussian_noise.Gaussian(gp_link, analytical_mean,
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analytical_variance, variance=variance, D=D, N=N)
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def student_t(gp_link=None, deg_free=5, sigma2=2):
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"""
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Construct a Student t likelihood
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:param gp_link: a GPy gp_link function
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:param deg_free: scalar, degrees of freedom
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:param sigma2: scalar, variance
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"""
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if gp_link is None:
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gp_link = noise_models.gp_transformations.Identity()
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analytical_mean = True
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analytical_variance = True
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return noise_models.student_t_noise.StudentT(gp_link, analytical_mean,
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analytical_variance,deg_free, sigma2)
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