Plots for multiple outputs

GPy/core/gp.py

@@ -184,3 +184,29 @@ class GP(GPBase):
         # now push through likelihood
         mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, noise_model = output)
         return mean, var, _025pm, _975pm
+
+    def _raw_predict_single_output(self, _Xnew, output=0, which_parts='all', full_cov=False,stop=False):
+        """
+        Internal helper function for making predictions, does not account
+        for normalization or likelihood
+        """
+        assert isinstance(self.likelihood,EP_Mixed_Noise)
+        index = np.ones_like(_Xnew)*output
+        _Xnew = np.hstack((_Xnew,index))
+
+        Kx = self.kern.K(_Xnew,self.X,which_parts=which_parts).T
+        #KiKx = np.dot(self.Ki, Kx)
+        KiKx, _ = dpotrs(self.L, np.asfortranarray(Kx), lower=1)
+        mu = np.dot(KiKx.T, self.likelihood.Y)
+        if full_cov:
+            Kxx = self.kern.K(_Xnew, which_parts=which_parts)
+            var = Kxx - np.dot(KiKx.T, Kx)
+        else:
+            Kxx = self.kern.Kdiag(_Xnew, which_parts=which_parts)
+            var = Kxx - np.sum(np.multiply(KiKx, Kx), 0)
+            var = var[:, None]
+        if stop:
+            debug_this # @UndefinedVariable
+        return mu, var
+
+

GPy/core/gp_base.py

@@ -34,7 +34,7 @@ class GPBase(Model):
         # All leaf nodes should call self._set_params(self._get_params()) at
         # the end
 
-    def plot_f(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=None):
+    def plot_f(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=None,output=None):
         """
         Plot the GP's view of the world, where the data is normalized and the
         likelihood is Gaussian.
@@ -62,7 +62,7 @@ class GPBase(Model):
             fig = pb.figure(num=fignum)
             ax = fig.add_subplot(111)
 
-        if self.X.shape[1] == 1:
+        if self.X.shape[1] == 1 and not isinstance(self.likelihood,EP_Mixed_Noise):
             Xnew, xmin, xmax = x_frame1D(self.X, plot_limits=plot_limits)
             if samples == 0:
                 m, v = self._raw_predict(Xnew, which_parts=which_parts)
@@ -80,7 +80,7 @@ class GPBase(Model):
             ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
             ax.set_ylim(ymin, ymax)
 
-        elif self.X.shape[1] == 2:
+        elif self.X.shape[1] == 2 and not isinstance(self.likelihood,EP_Mixed_Noise):
             resolution = resolution or 50
             Xnew, xmin, xmax, xx, yy = x_frame2D(self.X, plot_limits, resolution)
             m, v = self._raw_predict(Xnew, which_parts=which_parts)
@@ -89,6 +89,31 @@ class GPBase(Model):
             ax.scatter(self.X[:, 0], self.X[:, 1], 40, self.likelihood.Y, linewidth=0, cmap=pb.cm.jet, vmin=m.min(), vmax=m.max()) # @UndefinedVariable
             ax.set_xlim(xmin[0], xmax[0])
             ax.set_ylim(xmin[1], xmax[1])
+
+
+        elif self.X.shape[1] == 2 and isinstance(self.likelihood,EP_Mixed_Noise):
+            Xu = self.X[self.X[:,-1]==output ,0:1]
+            Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits)
+
+            if samples == 0:
+                m, v = self._raw_predict_single_output(Xnew, output=output, which_parts=which_parts)
+                gpplot(Xnew, m, m - 2 * np.sqrt(v), m + 2 * np.sqrt(v), axes=ax)
+                #ax.plot(self.X[which_data], self.likelihood.Y[which_data], 'kx', mew=1.5)
+                ax.plot(Xu[which_data], self.likelihood.Y[self.likelihood.index==output][:,None], 'kx', mew=1.5)
+            else:
+                m, v = self._raw_predict_single_output(Xnew, output=output, which_parts=which_parts, full_cov=True)
+                Ysim = np.random.multivariate_normal(m.flatten(), v, samples)
+                gpplot(Xnew, m, m - 2 * np.sqrt(np.diag(v)[:, None]), m + 2 * np.sqrt(np.diag(v))[:, None, ], axes=ax)
+                for i in range(samples):
+                    ax.plot(Xnew, Ysim[i, :], Tango.colorsHex['darkBlue'], linewidth=0.25)
+            #ax.plot(self.X[which_data], self.likelihood.Y[which_data], 'kx', mew=1.5)
+            #ax.plot(Xu[which_data], self.likelihood.Y[self.likelihood.index==output][:,None], 'kx', mew=1.5)
+            ax.set_xlim(xmin, xmax)
+            ymin, ymax = min(np.append(self.likelihood.Y, m - 2 * np.sqrt(np.diag(v)[:, None]))), max(np.append(self.likelihood.Y, m + 2 * np.sqrt(np.diag(v)[:, None])))
+            ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
+            ax.set_ylim(ymin, ymax)
+
+
         else:
             raise NotImplementedError, "Cannot define a frame with more than two input dimensions"