From 7ecf2337324ffaa5e8b45fed8653ac9d24c13600 Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Wed, 23 Oct 2013 12:08:59 +0100 Subject: [PATCH] Removed derivatives of variance wrt gp and derivatives of means with respect to gp from noise models --- GPy/likelihoods/noise_models/bernoulli_noise.py | 12 ------------ GPy/likelihoods/noise_models/exponential_noise.py | 12 ------------ GPy/likelihoods/noise_models/gamma_noise.py | 12 ------------ GPy/likelihoods/noise_models/gaussian_noise.py | 12 ------------ GPy/likelihoods/noise_models/noise_distributions.py | 4 ++-- GPy/likelihoods/noise_models/poisson_noise.py | 12 ------------ 6 files changed, 2 insertions(+), 62 deletions(-) diff --git a/GPy/likelihoods/noise_models/bernoulli_noise.py b/GPy/likelihoods/noise_models/bernoulli_noise.py index 5a11ba37..77242333 100644 --- a/GPy/likelihoods/noise_models/bernoulli_noise.py +++ b/GPy/likelihoods/noise_models/bernoulli_noise.py @@ -196,12 +196,6 @@ class Bernoulli(NoiseDistribution): """ return self.gp_link.transf(gp) - def _dmean_dgp(self,gp): - return self.gp_link.dtransf_df(gp) - - def _d2mean_dgp2(self,gp): - return self.gp_link.d2transf_df2(gp) - def _variance(self,gp): """ Mass (or density) function @@ -209,12 +203,6 @@ class Bernoulli(NoiseDistribution): p = self.gp_link.transf(gp) return p*(1.-p) - def _dvariance_dgp(self,gp): - return self.gp_link.dtransf_df(gp)*(1. - 2.*self.gp_link.transf(gp)) - - def _d2variance_dgp2(self,gp): - return self.gp_link.d2transf_df2(gp)*(1. - 2.*self.gp_link.transf(gp)) - 2*self.gp_link.dtransf_df(gp)**2 - def samples(self, gp): """ Returns a set of samples of observations based on a given value of the latent variable. diff --git a/GPy/likelihoods/noise_models/exponential_noise.py b/GPy/likelihoods/noise_models/exponential_noise.py index 56e63c75..450c11be 100644 --- a/GPy/likelihoods/noise_models/exponential_noise.py +++ b/GPy/likelihoods/noise_models/exponential_noise.py @@ -49,20 +49,8 @@ class Exponential(NoiseDistribution): """ return self.gp_link.transf(gp) - def _dmean_dgp(self,gp): - return self.gp_link.dtransf_df(gp) - - def _d2mean_dgp2(self,gp): - return self.gp_link.d2transf_df2(gp) - def _variance(self,gp): """ Mass (or density) function """ return self.gp_link.transf(gp)**2 - - def _dvariance_dgp(self,gp): - return 2*self.gp_link.transf(gp)*self.gp_link.dtransf_df(gp) - - def _d2variance_dgp2(self,gp): - return 2 * (self.gp_link.dtransf_df(gp)**2 + self.gp_link.transf(gp)*self.gp_link.d2transf_df2(gp)) diff --git a/GPy/likelihoods/noise_models/gamma_noise.py b/GPy/likelihoods/noise_models/gamma_noise.py index 6bf0dd7b..5229cb4f 100644 --- a/GPy/likelihoods/noise_models/gamma_noise.py +++ b/GPy/likelihoods/noise_models/gamma_noise.py @@ -52,20 +52,8 @@ class Gamma(NoiseDistribution): """ return self.gp_link.transf(gp) - def _dmean_dgp(self,gp): - return self.gp_link.dtransf_df(gp) - - def _d2mean_dgp2(self,gp): - return self.gp_link.d2transf_df2(gp) - def _variance(self,gp): """ Mass (or density) function """ return self.gp_link.transf(gp)/self.beta - - def _dvariance_dgp(self,gp): - return self.gp_link.dtransf_df(gp)/self.beta - - def _d2variance_dgp2(self,gp): - return self.gp_link.d2transf_df2(gp)/self.beta diff --git a/GPy/likelihoods/noise_models/gaussian_noise.py b/GPy/likelihoods/noise_models/gaussian_noise.py index 83cc2f47..0ce8ffd9 100644 --- a/GPy/likelihoods/noise_models/gaussian_noise.py +++ b/GPy/likelihoods/noise_models/gaussian_noise.py @@ -277,12 +277,6 @@ class Gaussian(NoiseDistribution): """ return self.gp_link.transf(gp) - def _dmean_dgp(self,gp): - return self.gp_link.dtransf_df(gp) - - def _d2mean_dgp2(self,gp): - return self.gp_link.d2transf_df2(gp) - def _variance(self,gp): """ Variance of y under the Mass (or density) function p(y|f) @@ -291,9 +285,3 @@ class Gaussian(NoiseDistribution): Var_{p(y|f)}[y] """ return self.variance - - def _dvariance_dgp(self,gp): - return 0 - - def _d2variance_dgp2(self,gp): - return 0 diff --git a/GPy/likelihoods/noise_models/noise_distributions.py b/GPy/likelihoods/noise_models/noise_distributions.py index c7ade68f..59465a5b 100644 --- a/GPy/likelihoods/noise_models/noise_distributions.py +++ b/GPy/likelihoods/noise_models/noise_distributions.py @@ -371,8 +371,8 @@ class NoiseDistribution(object): """ Compute mean, variance and conficence interval (percentiles 5 and 95) of the prediction. - :param mu: mean of the latent variable - :param var: variance of the latent variable + :param mu: mean of the latent variable, f + :param var: variance of the latent variable, f """ if isinstance(mu,float) or isinstance(mu,int): diff --git a/GPy/likelihoods/noise_models/poisson_noise.py b/GPy/likelihoods/noise_models/poisson_noise.py index 33de84cd..80d7951b 100644 --- a/GPy/likelihoods/noise_models/poisson_noise.py +++ b/GPy/likelihoods/noise_models/poisson_noise.py @@ -50,20 +50,8 @@ class Poisson(NoiseDistribution): """ return self.gp_link.transf(gp) - def _dmean_dgp(self,gp): - return self.gp_link.dtransf_df(gp) - - def _d2mean_dgp2(self,gp): - return self.gp_link.d2transf_df2(gp) - def _variance(self,gp): """ Mass (or density) function """ return self.gp_link.transf(gp) - - def _dvariance_dgp(self,gp): - return self.gp_link.dtransf_df(gp) - - def _d2variance_dgp2(self,gp): - return self.gp_link.d2transf_df2(gp)