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Shape of heteroscedastic variance corrected

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Ricardo 2015-09-03 17:28:31 +01:00
parent 06f540584b
commit 2745904c01
1 changed files with 11 additions and 6 deletions
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17
GPy/likelihoods/gaussian.py
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@ -335,20 +335,25 @@ class HeteroscedasticGaussian(Gaussian):
print("Warning, Exact inference is not implemeted for non-identity link functions,\ print("Warning, Exact inference is not implemeted for non-identity link functions,\
if you are not already, ensure Laplace inference_method is used") if you are not already, ensure Laplace inference_method is used")
super(HeteroscedasticGaussian, self).__init__(gp_link, np.ones(Y_metadata['output_index'].shape[0])*variance, name) super(HeteroscedasticGaussian, self).__init__(gp_link, np.ones(Y_metadata['output_index'].shape)*variance, name)
def exact_inference_gradients(self, dL_dKdiag,Y_metadata=None): def exact_inference_gradients(self, dL_dKdiag,Y_metadata=None):
return dL_dKdiag[Y_metadata['output_index']][:,0] return dL_dKdiag[Y_metadata['output_index']]
def gaussian_variance(self, Y_metadata=None): def gaussian_variance(self, Y_metadata=None):
return self.variance[Y_metadata['output_index']] return self.variance[Y_metadata['output_index'].flatten()]
def predictive_values(self, mu, var, full_cov=False, Y_metadata=None): def predictive_values(self, mu, var, full_cov=False, Y_metadata=None):
_s = self.variance[Y_metadata['output_index'].flatten()]
if full_cov: if full_cov:
if var.ndim == 2: if var.ndim == 2:
var += np.eye(var.shape[0])*self.variance var += np.eye(var.shape[0])*_s
if var.ndim == 3: if var.ndim == 3:
var += np.atleast_3d(np.eye(var.shape[0])*self.variance) var += np.atleast_3d(np.eye(var.shape[0])*_s)
else: else:
var += self.variance var += _s
return mu, var return mu, var
def predictive_quantiles(self, mu, var, quantiles, Y_metadata=None):
_s = self.variance[Y_metadata['output_index'].flatten()]
return [stats.norm.ppf(q/100.)*np.sqrt(var + _s) + mu for q in quantiles]