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[plotting&kern] bugfixes in plotting and kernel size

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Max Zwiessele 2016-01-22 11:26:29 +00:00
parent 1bda209469
commit 7b5422b694
5 changed files with 21 additions and 17 deletions
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9
GPy/core/gp.py
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@ -365,13 +365,14 @@ 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:
@ -458,7 +459,7 @@ 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