Merge branch 'master' into devel

GPy/kern/constructors.py

@@ -44,7 +44,7 @@ def rbf(D,variance=1., lengthscale=None,ARD=False):
     part = rbfpart(D,variance,lengthscale,ARD)
     return kern(D, [part])
 
-def linear(D,variances=None,ARD=True):
+def linear(D,variances=None,ARD=False):
     """
      Construct a linear kernel.
 

GPy/likelihoods/EP.py

@@ -33,7 +33,9 @@ class EP(likelihood):
         self.Z = 0
         self.YYT = None
 
-    def predictive_values(self,mu,var):
+    def predictive_values(self,mu,var,full_cov):
+        if full_cov:
+            raise NotImplementedError, "Cannot make correlated predictions with an EP likelihood"
         return self.likelihood_function.predictive_values(mu,var)
 
     def _get_params(self):

GPy/likelihoods/Gaussian.py

@@ -43,15 +43,25 @@ class Gaussian(likelihood):
         self.covariance_matrix = np.eye(self.N)*self._variance
         self.precision = 1./self._variance
 
-    def predictive_values(self,mu,var):
+    def predictive_values(self,mu,var, full_cov):
         """
         Un-normalize the prediction and add the likelihood variance, then return the 5%, 95% interval
         """
         mean = mu*self._std + self._mean
+        if full_cov:
+            if self.D >1:
+                raise NotImplementedError, "TODO"
+                #Note. for D>1, we need to re-normalise all the outputs independently.
+                # This will mess up computations of diag(true_var), below.
+                #note that the upper, lower quantiles should be the same shape as mean
+            true_var = (var + np.eye(var.shape[0])*self._variance)*self._std**2
+            _5pc = mean + - 2.*np.sqrt(np.diag(true_var))
+            _95pc = mean + 2.*np.sqrt(np.diag(true_var))
+        else:
             true_var = (var + self._variance)*self._std**2
             _5pc = mean + - 2.*np.sqrt(true_var)
             _95pc = mean + 2.*np.sqrt(true_var)
-        return mean, _5pc, _95pc
+        return mean, true_var, _5pc, _95pc
 
     def fit_full(self):
         """

GPy/likelihoods/likelihood.py

@@ -25,11 +25,16 @@ class likelihood:
     def _get_param_names(self):
         raise NotImplementedError
 
-    def _set_params(self,x):
+    def _set_params(self, x):
         raise NotImplementedError
 
     def fit(self):
         raise NotImplementedError
 
-    def _gradients(self,partial):
+    def _gradients(self, partial):
         raise NotImplementedError
+
+    def predictive_values(self, mu, var):
+        raise NotImplementedError
+
+

GPy/likelihoods/likelihood_functions.py

@@ -48,14 +48,14 @@ class probit(likelihood_function):
 
     def predictive_values(self,mu,var):
         """
-        Compute  mean, and conficence interval (percentiles 5 and 95) of the  prediction
+        Compute  mean, variance and conficence interval (percentiles 5 and 95) of the  prediction
         """
         mu = mu.flatten()
         var = var.flatten()
         mean = stats.norm.cdf(mu/np.sqrt(1+var))
         p_025 = np.zeros(mu.shape)
         p_975 = np.ones(mu.shape)
-        return mean, p_025, p_975
+        return mean, np.nan*var, p_025, p_975 # TODO: better values here (mean is okay)
 
 class Poisson(likelihood_function):
     """
@@ -131,4 +131,4 @@ class Poisson(likelihood_function):
         tmp = stats.poisson.ppf(np.array([.025,.975]),mean)
         p_025 = tmp[:,0]
         p_975 = tmp[:,1]
-        return mean,p_025,p_975
+        return mean,np.nan*mean,p_025,p_975 # better variance here TODO

GPy/models/GP.py

@@ -140,7 +140,7 @@ class GP(model):
         KiKx = np.dot(self.Ki,Kx)
         if full_cov:
             Kxx = self.kern.K(_Xnew, slices1=slices,slices2=slices)
-            var = Kxx - np.dot(KiKx.T,Kx) #NOTE this won't work for plotting
+            var = Kxx - np.dot(KiKx.T,Kx)
         else:
             Kxx = self.kern.Kdiag(_Xnew, slices=slices)
             var = Kxx - np.sum(np.multiply(KiKx,Kx),0)
@@ -179,7 +179,7 @@ class GP(model):
         mu, var = self._raw_predict(Xnew, slices, full_cov)
 
         #now push through likelihood TODO
-        mean, _025pm, _975pm = self.likelihood.predictive_values(mu, var)
+        mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov)
 
         return mean, var, _025pm, _975pm