general bugfixing

GPy/core/gp.py

@@ -55,7 +55,8 @@ class GP(Model):
 
         self.add_parameter(self.kern)
         self.add_parameter(self.likelihood)
-        self.parameters_changed()
+        if self.__class__ is GP:
+            self.parameters_changed()
 
     def parameters_changed(self):
         self.posterior, self._log_marginal_likelihood, grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y)
@@ -111,7 +112,7 @@ class GP(Model):
            This is to allow for different normalizations of the output dimensions.
 
         """
-        # normalize X values
+        #predict the latent function values
         mu, var = self._raw_predict(Xnew, full_cov=full_cov, which_parts=which_parts)
 
         # now push through likelihood

GPy/core/sparse_gp.py

@@ -39,7 +39,7 @@ class SparseGP(GP):
             if isinstance(likelihood, likelihoods.Gaussian):
                 inference_method = varDTC.VarDTC()
             else:
-            #inference_method = ??
+                #inference_method = ??
                 raise NotImplementedError, "what to do what to do?"
             print "defaulting to ", inference_method, "for latent function inference"
 
@@ -51,19 +51,21 @@ class SparseGP(GP):
         self.X_variance = X_variance
 
         GP.__init__(self, X, Y, kernel, likelihood, inference_method=inference_method, name=name)
-
         self.add_parameter(self.Z, index=0)
+        self.parameters_changed()
+
 
     def parameters_changed(self):
         self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.X_variance, self.Z, self.likelihood, self.Y)
 
-        #The derivative of the bound wrt the inducing inputs Z
-        self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
-        if self.X_variance is None:
-            self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X)
-        else:
-            self.Z.gradient += self.kern.dpsi1_dZ(self.grad_dict['dL_dpsi1'], self.Z, self.X, self.X_variance)
-            self.Z.gradient += self.kern.dpsi2_dZ(self.grad_dict['dL_dpsi2'], self.Z, self.X, self.X_variance)
+        #The derivative of the bound wrt the inducing inputs Z ( unless they're all fixed)
+        if not self.Z.is_fixed:
+            self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
+            if self.X_variance is None:
+                self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X)
+            else:
+                self.Z.gradient += self.kern.dpsi1_dZ(self.grad_dict['dL_dpsi1'], self.Z, self.X, self.X_variance)
+                self.Z.gradient += self.kern.dpsi2_dZ(self.grad_dict['dL_dpsi2'], self.Z, self.X, self.X_variance)
 
     def _raw_predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
         """

GPy/inference/latent_function_inference/__init__.py

@@ -26,5 +26,6 @@ etc.
 from exact_gaussian_inference import ExactGaussianInference
 from laplace import LaplaceInference
 expectation_propagation = 'foo' # TODO
+from varDTC import VarDTC
 from dtc import DTC
 from fitc import FITC

GPy/kern/parts/rbf.py

@@ -53,7 +53,7 @@ class RBF(Kernpart):
 
         self.variance = Param('variance', variance, Logexp())
         
-        self.lengthscale = Param('lengthscale', lengthscale)
+        self.lengthscale = Param('lengthscale', lengthscale, Logexp())
         self.lengthscale.add_observer(self, self.update_lengthscale)
         self.update_lengthscale(self.lengthscale)
         

GPy/likelihoods/gaussian.py

@@ -88,12 +88,9 @@ class Gaussian(Likelihood):
         return mu, var, low, up
 
     def predictive_mean(self, mu, sigma):
-        #new_sigma2 = self.predictive_variance(mu, sigma)
-        #return new_sigma2*(mu/sigma**2 + self.gp_link.transf(mu)/self.variance)
         return mu
 
     def predictive_variance(self, mu, sigma, predictive_mean=None):
-        #what on earth was the sum of precisions doing here? JH
         return self.variance + sigma**2
 
     def pdf_link(self, link_f, y, extra_data=None):
@@ -305,3 +302,11 @@ class Gaussian(Likelihood):
         gp = gp.flatten()
         Ysim = np.array([np.random.normal(self.gp_link.transf(gpj), scale=np.sqrt(self.variance), size=1) for gpj in gp])
         return Ysim.reshape(orig_shape)
+
+    def log_predictive_density(self, y_test, mu_star, var_star):
+        """
+        assumes independence
+        """
+        v = var_star + self.variance
+        return -0.5*np.log(2*np.pi) -0.5*np.log(v) - 0.5*np.square(y_test - mu_star)/v
+

GPy/models/sparse_gp_regression.py

@@ -6,6 +6,7 @@ import numpy as np
 from ..core import SparseGP
 from .. import likelihoods
 from .. import kern
+from ..inference.latent_function_inference import VarDTC
 
 class SparseGPRegression(SparseGP):
     """
@@ -43,8 +44,7 @@ class SparseGPRegression(SparseGP):
 
         likelihood = likelihoods.Gaussian()
 
-        SparseGP.__init__(self, X, Y, Z, kernel, likelihood, X_variance=X_variance)
-        #self.ensure_default_constraints()
+        SparseGP.__init__(self, X, Y, Z, kernel, likelihood, X_variance=X_variance, inference_method=VarDTC())
 
     def _getstate(self):
         return SparseGP._getstate(self)