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merge GPy upstream

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This commit is contained in:
Zhenwen Dai 2016-02-23 13:52:45 +00:00
commit 00e4ac152a
9 changed files with 53 additions and 312 deletions
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GPy/core/gp.py
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@ -181,7 +181,7 @@ class GP(Model):
def parameters_changed(self):
"""
Method that is called upon any changes to :class:`~GPy.core.parameterization.param.Param` variables within the model.
In particular in the GP class this method reperforms inference, recalculating the posterior and log marginal likelihood and gradients of the model
In particular in the GP class this method re-performs inference, recalculating the posterior and log marginal likelihood and gradients of the model
.. warning::
This method is not designed to be called manually, the framework is set up to automatically call this method upon changes to parameters, if you call
@ -371,13 +371,14 @@ class GP(Model):
mean_jac[:,:,i] = kern.gradients_X(self.posterior.woodbury_vector[:,i:i+1].T, Xnew, self._predictive_variable)
dK_dXnew_full = np.empty((self._predictive_variable.shape[0], Xnew.shape[0], Xnew.shape[1]))
one = np.ones((1,1))
for i in range(self._predictive_variable.shape[0]):
dK_dXnew_full[i] = kern.gradients_X([[1.]], Xnew, self._predictive_variable[[i]])
dK_dXnew_full[i] = kern.gradients_X(one, Xnew, self._predictive_variable[[i]])
if full_cov:
dK2_dXdX = kern.gradients_XX([[1.]], Xnew)
dK2_dXdX = kern.gradients_XX(one, Xnew)
else:
dK2_dXdX = kern.gradients_XX_diag([[1.]], Xnew)
dK2_dXdX = kern.gradients_XX_diag(one, Xnew)
def compute_cov_inner(wi):
if full_cov:
@ -464,7 +465,7 @@ class GP(Model):
m, v = self._raw_predict(X, full_cov=full_cov, **predict_kwargs)
if self.normalizer is not None:
m, v = self.normalizer.inverse_mean(m), self.normalizer.inverse_variance(v)
def sim_one_dim(m, v):
if not full_cov:
return np.random.multivariate_normal(m.flatten(), np.diag(v.flatten()), size).T