From ccf5167b75832517a685a4187a04fb331f419440 Mon Sep 17 00:00:00 2001
From: James Hensman <james.hensman@gmail.com>
Date: Thu, 13 Mar 2014 12:13:00 +0000
Subject: [PATCH] chancges to where gradients are computed in laplace

---
 GPy/core/gp.py               | 27 ++++++++-------------------
 GPy/likelihoods/student_t.py |  6 +++---
 2 files changed, 11 insertions(+), 22 deletions(-)

diff --git a/GPy/core/gp.py b/GPy/core/gp.py
index 185cc149..3552c37e 100644
--- a/GPy/core/gp.py
+++ b/GPy/core/gp.py
@@ -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
 
diff --git a/GPy/likelihoods/student_t.py b/GPy/likelihoods/student_t.py
index ac93f204..50d91953 100644
--- a/GPy/likelihoods/student_t.py
+++ b/GPy/likelihoods/student_t.py
@@ -37,13 +37,13 @@ class StudentT(Likelihood):
     def parameters_changed(self):
         self.variance = (self.v / float(self.v - 2)) * self.sigma2
 
-    def update_gradients(self, partial):
+    def update_gradients(self, grads):
         """
         Pull out the gradients, be careful as the order must match the order
         in which the parameters are added
         """
-        self.sigma2.gradient = partial[0]
-        self.v.gradient = partial[1]
+        self.sigma2.gradient = grads[0]
+        self.v.gradient = grads[1]
 
     def pdf_link(self, link_f, y, extra_data=None):
         """