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[heteroscedastic gauss] Implemented Heteroscedastic Guassian Lik with @ric70x7

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Max Zwiessele 2015-08-28 16:26:55 +01:00
parent 9ededd5a45
commit 0d32652c88
1 changed files with 28 additions and 0 deletions
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28
GPy/likelihoods/gaussian.py
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@ -48,6 +48,7 @@ class Gaussian(Likelihood):
def betaY(self,Y,Y_metadata=None):
#TODO: ~Ricardo this does not live here
print "Iam Here"
return Y/self.gaussian_variance(Y_metadata)
def gaussian_variance(self, Y_metadata=None):
@ -321,3 +322,30 @@ class Gaussian(Likelihood):
dF_dv = np.ones_like(v)*(-0.5/lik_var)
dF_dtheta = -0.5/lik_var + 0.5*(np.square(Y) + np.square(m) + v - 2*m*Y)/(lik_var**2)
return F, dF_dmu, dF_dv, dF_dtheta.reshape(1, Y.shape[0], Y.shape[1])
class Heteroscedastic_Gaussian(Gaussian):
def __init__(self, Y_metadata, gp_link=None, variance=1., name='het_Gauss'):
if gp_link is None:
gp_link = link_functions.Identity()
if not isinstance(gp_link, link_functions.Identity):
print("Warning, Exact inference is not implemeted for non-identity link functions,\
if you are not already, ensure Laplace inference_method is used")
super(Heteroscedastic_Gaussian, self).__init__(gp_link, np.ones(Y_metadata['output_index'].shape[0])*variance, name)
def exact_inference_gradients(self, dL_dKdiag,Y_metadata=None):
return dL_dKdiag[Y_metadata['output_index']][:,0]
def gaussian_variance(self, Y_metadata=None):
return self.variance[Y_metadata['output_index']]
def predictive_values(self, mu, var, full_cov=False, Y_metadata=None):
if full_cov:
if var.ndim == 2:
var += np.eye(var.shape[0])*self.variance
if var.ndim == 3:
var += np.atleast_3d(np.eye(var.shape[0])*self.variance)
else:
var += self.variance
return mu, var