Multioutput is working

GPy/models/__init__.py

@@ -11,3 +11,4 @@ from gplvm import GPLVM
 from warped_gp import WarpedGP
 from bayesian_gplvm import BayesianGPLVM
 from mrd import MRD
+from gp_multioutput import GPMultioutput

GPy/models/gp_classification.py

@@ -31,9 +31,8 @@ class GPClassification(GP):
             kernel = kern.rbf(X.shape[1])
 
         if likelihood is None:
-            #distribution = GPy.likelihoods.binomial_likelihood.Binomial(link=link)
-            distribution = likelihoods.binomial()
-            likelihood = likelihoods.EP(Y, distribution)
+            noise_model = likelihoods.binomial()
+            likelihood = likelihoods.EP(Y, noise_model)
         elif Y is not None:
             if not all(Y.flatten() == likelihood.data.flatten()):
                 raise Warning, 'likelihood.data and Y are different.'

GPy/models/gp_multioutput.py

@@ -0,0 +1,56 @@
+# Copyright (c) 2013, Ricardo Andrade
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+import numpy as np
+from ..core import GP
+from .. import likelihoods
+from .. import kern
+
+
+import pylab as pb
+
+class GPMultioutput(GP):
+    """
+    Multiple output Gaussian process
+
+    This is a thin wrapper around the models.GP class, with a set of sensible defaults
+
+    :param X_list: input observations
+    :param Y_list: observed values
+    :param L_list: a GPy likelihood, defaults to Binomial with probit link_function
+    :param kernel: a GPy kernel, defaults to rbf
+    :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)
+    :type normalize_X: False|True
+    :param normalize_Y:  whether to normalize the input data before computing (predictions will be in original scales)
+    :type normalize_Y: False|True
+
+    .. Note:: Multiple independent outputs are allowed using columns of Y
+
+    """
+
+    def __init__(self,X_list,Y_list=None,likelihood=None,kernel=None,normalize_X=False,normalize_Y=False,W=1):
+
+        if likelihood is None:
+            noise_model_list = [likelihoods.gaussian(variance=1.) for Y in Y_list]
+            likelihood = likelihoods.EP_Mixed_Noise(Y_list, noise_model_list)
+
+        elif Y_list is not None:
+            if not all(np.vstack(Y_list).flatten() == likelihood.data.flatten()):
+                raise Warning, 'likelihood.data and Y_list values are different.'
+
+        X = np.hstack([np.vstack(X_list),likelihood.index])
+
+        if kernel is None:
+            original_dim = X.shape[1]-1
+            kernel = kern.rbf(original_dim) + kern.white(original_dim)
+
+        mkernel = kernel.prod(kern.coregionalise(len(X_list),W),tensor=True) #TODO W
+
+        #kern1 = kern.rbf(1) + kern.white(1)
+        #kern2 = kern.coregionalise(2,1)
+        #kern3 = kern1.prod(kern2,tensor=True)
+
+
+        GP.__init__(self, X, likelihood, mkernel, normalize_X=normalize_X)
+        self.ensure_default_constraints()