fiddling with plotting

GPy/core/gp.py

@@ -58,6 +58,7 @@ class GP(Model):
         self.parameters_changed()
 
     def parameters_changed(self):
+        print self.kern
         self.posterior, self._log_marginal_likelihood, grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y)
         self._dL_dK = grad_dict['dL_dK']
 
@@ -75,8 +76,8 @@ class GP(Model):
 
         """
         Kx = self.kern.K(_Xnew, self.X, which_parts=which_parts).T
-        LiKx, _ = dtrtrs(self.posterior._woodbury_chol, np.asfortranarray(Kx), lower=1)
-        mu = np.dot(Kx.T, self.posterior._woodbury_vector)
+        LiKx, _ = dtrtrs(self.posterior.woodbury_chol, np.asfortranarray(Kx), lower=1)
+        mu = np.dot(Kx.T, self.posterior.woodbury_vector)
         if full_cov:
             Kxx = self.kern.K(_Xnew, which_parts=which_parts)
             var = Kxx - tdot(LiKx.T)

GPy/core/parameterization/parameterized.py

@@ -214,6 +214,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
             return
         i = 0
         sizes = [0]
+        self._param_slices_ = []
         for p in self._parameters_:
             p._direct_parent_ = self
             p._highest_parent_ = self

GPy/inference/latent_function_inference/posterior.py

@@ -74,7 +74,7 @@ class Posterior(object):
     @property
     def mean(self):
         if self._mean is None:
-            self._mean = np.dot(self._K, self._woodbury_vector)
+            self._mean = np.dot(self._K, self.woodbury_vector)
         return self._mean
 
     @property
@@ -93,10 +93,14 @@ class Posterior(object):
     @property
     def woodbury_chol(self):
         if self._woodbury_chol is None:
-            B = self._K - self._covariance
-            tmp, _ = dpotrs(self._K_chol, B)
-            Wi, _ = dpotrs(self._K_chol, tmp.T)
-            _, _, self._woodbury_chol, _ = pdinv(Wi)
+            #try computing woodbury chol from cov
+            if self._woodbury_inv is not None:
+                _, _, self._woodbury_chol, _ = pdinv(self._woodbury_inv)
+            elif self._covariance is not None:
+                B = self._K - self._covariance
+                tmp, _ = dpotrs(self.K_chol, B)
+                self._woodbury_inv, _ = dpotrs(self.K_chol, tmp.T)
+                _, _, self._woodbury_chol, _ = pdinv(self._woodbury_inv)
         return self._woodbury_chol
 
     @property
@@ -109,7 +113,7 @@ class Posterior(object):
     @property
     def woodbury_vector(self):
         if self._woodbury_vector is None:
-            self._woodbury_vector, _ = dpotrs(self._K_chol, self.mean)
+            self._woodbury_vector, _ = dpotrs(self.K_chol, self.mean)
         return self._woodbury_vector
 
     @property

GPy/likelihoods/bernoulli.py

@@ -78,7 +78,7 @@ class Bernoulli(Likelihood):
 
         return Z_hat, mu_hat, sigma2_hat
 
-    def _predictive_mean_analytical(self, mu, variance):
+    def predictive_mean(self, mu, variance):
 
         if isinstance(self.gp_link, link_functions.Probit):
             return stats.norm.cdf(mu/np.sqrt(1+variance))
@@ -89,12 +89,13 @@ class Bernoulli(Likelihood):
         else:
             raise NotImplementedError
 
-    def _predictive_variance_analytical(self, mu, variance, pred_mean):
+    def predictive_variance(self, mu, variance, pred_mean):
 
         if isinstance(self.gp_link, link_functions.Heaviside):
             return 0.
         else:
-            raise NotImplementedError
+            return np.nan
+            #raise NotImplementedError
 
     def pdf_link(self, link_f, y, extra_data=None):
         """

GPy/plotting/matplot_dep/models_plots.py

@@ -156,11 +156,11 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
         raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
 
 
-def plot_f_fit(model, *args, **kwargs):
+def plot_fit_f(model, *args, **kwargs):
     """
     Plot the GP's view of the world, where the data is normalized and before applying a likelihood.
 
     All args and kwargs are passed on to models_plots.plot.
     """
     kwargs['plot_raw'] = True
-    plot(model,*args, **kwargs)
+    plot_fit(model,*args, **kwargs)