New model SparseGPCoregionalizedRegression

GPy/models/__init__.py

@@ -15,4 +15,5 @@ from mrd import MRD
 from gradient_checker import GradientChecker
 from ss_gplvm import SSGPLVM
 from gp_coregionalized_regression import GPCoregionalizedRegression
+from sparse_gp_coregionalized_regression import SparseGPCoregionalizedRegression
 #.py file not included!!! #from sparse_gp_coregionalized_regression import SparseGPCoregionalizedRegression

GPy/models/sparse_gp_coregionalized_regression.py

@@ -0,0 +1,66 @@
+# 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 SparseGP
+from ..inference.latent_function_inference import VarDTC
+from .. import likelihoods
+from .. import kern
+from .. import util
+
+class SparseGPCoregionalizedRegression(SparseGP):
+    """
+    Sparse Gaussian Process model for heteroscedastic multioutput regression
+
+    This is a thin wrapper around the SparseGP 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 Z_list: list of inducing inputs (optional)
+    :type Z_list: empty 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 num_inducing: number of inducing inputs, defaults to 10 per output (ignored if Z_list is not empty)
+    :type num_inducing: integer | list of integers
+
+    :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, Z_list=[], kernel=None, likelihoods_list=None, num_inducing=10, X_variance=None, name='SGPCR',W_rank=1,kernel_name='X'):
+
+        #Input and Output
+        X,Y,self.output_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.output_index,likelihoods_list)
+
+        #Inducing inputs list
+        if len(Z_list):
+            assert len(Z_list) == self.output_dim, 'Number of outputs do not match length of inducing inputs list.'
+        else:
+            if isinstance(num_inducing,np.int):
+                num_inducing = [num_inducing] * Ny
+            num_inducing = np.asarray(num_inducing)
+            assert num_inducing.size == Ny, 'Number of outputs do not match length of inducing inputs list.'
+            for ni,Xi in zip(num_inducing,X_list):
+                i = np.random.permutation(Xi.shape[0])[:ni]
+                Z_list.append(Xi[i].copy())
+
+        Z, _, Iz = util.multioutput.build_XY(Z_list)
+
+        super(SparseGPCoregionalizedRegression, self).__init__(X, Y, Z, kernel, likelihood, inference_method=VarDTC(), Y_metadata={'output_index':self.output_index})
+        self['.*inducing'][:,-1].fix()