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[missing_data in sparse gp] can be extended towards missing_data handling in gp itself. Setting up gpy issue

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This commit is contained in:
Max Zwiessele 2014-10-09 10:34:01 +01:00
parent de801c9d29
commit 829e40b25c
5 changed files with 15 additions and 11 deletions
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6
GPy/core/sparse_gp.py
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@ -218,6 +218,7 @@ class SparseGP(GP):
print message,
for d in xrange(self.output_dim):
ninan = self.ninan[:, d]
print ' '*(len(message)) + '\r',
message = m_f(d)
print message,
@ -249,9 +250,8 @@ class SparseGP(GP):
if self.missing_data:
self._outer_loop_for_missing_data()
else:
self.posterior, self._log_marginal_likelihood, self.grad_dict, gradients, _ = self._inner_parameters_changed(self.kern, self.X, self.Z, self.likelihood, self.Y_normalized, self.Y_metadata)
self.kern.gradient = gradients['kerngrad']
self.Z.gradient = gradients['Zgrad']
self.posterior, self._log_marginal_likelihood, self.grad_dict, full_values, _ = self._inner_parameters_changed(self.kern, self.X, self.Z, self.likelihood, self.Y_normalized, self.Y_metadata)
self._outer_values_update(full_values)
def _raw_predict(self, Xnew, full_cov=False, kern=None):
"""