GPCoregionalizedRegresssion added

GPy/models/gp_coregionalized_regression.py

@@ -0,0 +1,44 @@
+# Copyright (c) 2012 - 2014 the GPy Austhors (see AUTHORS.txt)
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+import numpy as np
+from ..core import GP
+from .. import likelihoods
+from .. import kern
+from .. import util
+
+class GPCoregionalizedRegression(GP):
+    """
+    Gaussian Process model for heteroscedastic multioutput regression
+
+    This is a thin wrapper around the models.GP class, with a set of sensible defaults
+
+    :param X_list: list of input observations corresponding to each output
+    :type X_list: list of numpy arrays
+    :param Y_list: list of observed values related to the different noise models
+    :type Y_list: list of numpy arrays
+    :param kernel: a GPy kernel, defaults to RBF ** Coregionalized
+    :type kernel: None | GPy.kernel defaults
+    :likelihoods_list: a list of likelihoods, defaults to list of Gaussian likelihoods
+    :type likelihoods_list: None | a list GPy.likelihoods
+    :param name: model name
+    :type name: string
+    :param W_rank: number tuples of the corregionalization parameters 'W' (see coregionalize kernel documentation)
+    :type W_rank: integer
+    :param kernel_name: name of the kernel
+    :type kernel_name: string
+    """
+    def __init__(self, X_list, Y_list, kernel=None, likelihoods_list=None, name='GPCR',W_rank=1,kernel_name='X'):
+
+        #Input and Output
+        X,Y,self.noise_index = util.multioutput.build_XY(X_list,Y_list)
+        Ny = len(Y_list)
+
+        #Kernel
+        if kernel is None:
+            kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=GPy.kern.rbf(X.shape[1]-1), W_rank=1,name=kernel_name)
+
+        #Likelihood
+        likelihood = util.multioutput.build_likelihood(Y_list,self.noise_index,likelihoods_list)
+
+        super(GPCoregionalizedRegression, self).__init__(X,Y,kernel,likelihood, noise_index=self.noise_index)