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Removed derivatives of variance wrt gp and derivatives of means

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with respect to gp from noise models
This commit is contained in:
Alan Saul 2013-10-23 12:08:59 +01:00
parent 9b99061b09
commit 7ecf233732
6 changed files with 2 additions and 62 deletions
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12
GPy/likelihoods/noise_models/bernoulli_noise.py
View file

@ -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.

12
GPy/likelihoods/noise_models/exponential_noise.py
View file

@ -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))

12
GPy/likelihoods/noise_models/gamma_noise.py
View file

@ -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

12
GPy/likelihoods/noise_models/gaussian_noise.py
View file

@ -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

4
GPy/likelihoods/noise_models/noise_distributions.py
View file

@ -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):

12
GPy/likelihoods/noise_models/poisson_noise.py
View file

@ -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)