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chancges to where gradients are computed in laplace

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James Hensman 2014-03-13 12:13:00 +00:00
parent 4d00b9db03
commit ccf5167b75
2 changed files with 11 additions and 22 deletions
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27
GPy/core/gp.py
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@ -27,7 +27,7 @@ class GP(Model):
"""
def __init__(self, X, Y, kernel, likelihood, inference_method=None, name='gp', **Y_metadata):
def __init__(self, X, Y, kernel, likelihood, inference_method=None, name='gp', Y_metadata=None):
super(GP, self).__init__(name)
assert X.ndim == 2
@ -42,10 +42,7 @@ class GP(Model):
assert Y.shape[0] == self.num_data
_, self.output_dim = self.Y.shape
if Y_metadata is not None:
self.Y_metadata = Y_metadata
else:
self.Y_metadata = None
self.Y_metadata = Y_metadata or {}
assert isinstance(kernel, kern.Kern)
#assert self.input_dim == kernel.input_dim
@ -67,8 +64,8 @@ class GP(Model):
self.add_parameter(self.likelihood)
def parameters_changed(self):
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y, **self.Y_metadata)
self.likelihood.update_gradients(np.diag(self.grad_dict['dL_dK']), **self.Y_metadata)
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y, self.Y_metadata)
self.likelihood.update_gradients(self.grad_dict['dL_dthetaL'])
self.kern.update_gradients_full(self.grad_dict['dL_dK'], self.X)
def log_likelihood(self):
@ -99,7 +96,7 @@ class GP(Model):
var = var.reshape(-1, 1)
return mu, var
def predict(self, Xnew, full_cov=False, **likelihood_args):
def predict(self, Xnew, full_cov=False, Y_metadata=None):
"""
Predict the function(s) at the new point(s) Xnew.
@ -123,7 +120,7 @@ class GP(Model):
mu, var = self._raw_predict(Xnew, full_cov=full_cov)
# now push through likelihood
mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, **likelihood_args)
mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, Y_metadata)
return mean, var, _025pm, _975pm
def posterior_samples_f(self,X,size=10, full_cov=True):
@ -147,7 +144,7 @@ class GP(Model):
return Ysim
def posterior_samples(self,X,size=10, full_cov=True,noise_model=None):
def posterior_samples(self,X,size=10, full_cov=True, Y_metadata=None):
"""
Samples the posterior GP at the points X.
@ -162,15 +159,7 @@ class GP(Model):
:returns: Ysim: set of simulations, a Numpy array (N x samples).
"""
Ysim = self.posterior_samples_f(X, size, full_cov=full_cov)
if isinstance(self.likelihood, Gaussian):
noise_std = np.sqrt(self.likelihood._get_params())
Ysim += np.random.normal(0,noise_std,Ysim.shape)
elif isinstance(self.likelihood, Gaussian_Mixed_Noise):
assert noise_model is not None, "A noise model must be specified."
noise_std = np.sqrt(self.likelihood._get_params()[noise_model])
Ysim += np.random.normal(0,noise_std,Ysim.shape)
else:
Ysim = self.likelihood.noise_model.samples(Ysim)
Ysim = self.likelihood.noise_model.samples(Ysim, Y_metadata)
return Ysim