Changes to allow multiple output plotting

GPy/core/sparse_gp.py

@@ -5,7 +5,7 @@ import numpy as np
 import pylab as pb
 from ..util.linalg import mdot, jitchol, tdot, symmetrify, backsub_both_sides, chol_inv, dtrtrs, dpotrs, dpotri
 from scipy import linalg
-from ..likelihoods import Gaussian
+from ..likelihoods import Gaussian, EP,EP_Mixed_Noise
 from gp_base import GPBase
 
 class SparseGP(GPBase):
@@ -314,3 +314,37 @@ class SparseGP(GPBase):
         elif self.X.shape[1] == 2:
             Zu = self.Z * self._Xscale + self._Xoffset
             ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
+
+
+    def predict_single_output(self, Xnew, output=0, which_parts='all', full_cov=False):
+        """
+        Predict the function(s) at the new point(s) Xnew.
+        Arguments
+        ---------
+        :param Xnew: The points at which to make a prediction
+        :type Xnew: np.ndarray, Nnew x self.input_dim
+        :param which_parts:  specifies which outputs kernel(s) to use in prediction
+        :type which_parts: ('all', list of bools)
+        :param full_cov: whether to return the folll covariance matrix, or just the diagonal
+        :type full_cov: bool
+        :rtype: posterior mean,  a Numpy array, Nnew x self.input_dim
+        :rtype: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
+        :rtype: lower and upper boundaries of the 95% confidence intervals, Numpy arrays,  Nnew x self.input_dim
+
+
+           If full_cov and self.input_dim > 1, the return shape of var is Nnew x Nnew x self.input_dim. If self.input_dim == 1, the return shape is Nnew x Nnew.
+           This is to allow for different normalizations of the output dimensions.
+
+        """
+        assert isinstance(self.likelihood,EP_Mixed_Noise)
+        index = np.ones_like(Xnew)*output
+        Xnew = np.hstack((Xnew,index))
+
+        # normalize X values
+        Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
+        mu, var = self._raw_predict(Xnew, full_cov=full_cov, which_parts=which_parts)
+
+        # now push through likelihood
+        mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, noise_model = output)
+        return mean, var, _025pm, _975pm
+