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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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This commit is contained in:
Max Zwiessele 2015-09-18 16:21:59 +01:00
commit 111895f03a
25 changed files with 413 additions and 587 deletions
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2
GPy/core/gp.py
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@ -98,7 +98,7 @@ class GP(Model):
inference_method = exact_gaussian_inference.ExactGaussianInference()
else:
inference_method = expectation_propagation.EP()
print("defaulting to ", inference_method, "for latent function inference")
print("defaulting to " + str(inference_method) + " for latent function inference")
self.inference_method = inference_method
logger.info("adding kernel and likelihood as parameters")

2
GPy/core/model.py
View file

@ -255,7 +255,7 @@ class Model(Parameterized):
opt.model = self
else:
optimizer = optimization.get_optimizer(optimizer)
opt = optimizer(start, model=self, max_iters=max_iters, **kwargs)
opt = optimizer(x_init=start, model=self, max_iters=max_iters, **kwargs)
with VerboseOptimization(self, opt, maxiters=max_iters, verbose=messages, ipython_notebook=ipython_notebook, clear_after_finish=clear_after_finish) as vo:
opt.run(f_fp=self._objective_grads, f=self._objective, fp=self._grads)

5
GPy/core/sparse_gp.py
View file

@ -40,7 +40,7 @@ class SparseGP(GP):
"""
def __init__(self, X, Y, Z, kernel, likelihood, mean_function=None, inference_method=None,
def __init__(self, X, Y, Z, kernel, likelihood, mean_function=None, X_variance=None, inference_method=None,
name='sparse gp', Y_metadata=None, normalizer=False):
#pick a sensible inference method
if inference_method is None:
@ -73,11 +73,12 @@ class SparseGP(GP):
self.Z = Param('inducing inputs',Z)
self.link_parameter(self.Z, index=0)
if trigger_update: self.update_model(True)
if trigger_update: self._trigger_params_changed()
def parameters_changed(self):
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y, self.Y_metadata)
self._update_gradients()
def _update_gradients(self):
self.likelihood.update_gradients(self.grad_dict['dL_dthetaL'])
if isinstance(self.X, VariationalPosterior):