Assorted work on combining the EP and sparse methods

GPy/models/sparse_GP_regression.py

@@ -0,0 +1,44 @@
+# Copyright (c) 2012, James Hensman
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+import numpy as np
+from sparse_GP import sparse_GP
+from .. import likelihoods
+from .. import kern
+
+class sparse_GP_regression(sparse_GP):
+    """
+    Gaussian Process model for regression
+
+    This is a thin wrapper around the GP class, with a set of sensible defalts
+
+    :param X: input observations
+    :param Y: observed values
+    :param kernel: a GPy kernel, defaults to rbf+white
+    :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
+    :param Xslices: how the X,Y data co-vary in the kernel (i.e. which "outputs" they correspond to). See (link:slicing)
+    :rtype: model object
+
+    .. Note:: Multiple independent outputs are allowed using columns of Y
+
+    """
+
+    def __init__(self,X,Y,kernel=None,normalize_X=False,normalize_Y=False, Xslices=None,Z=None, M=10):
+        #kern defaults to rbf
+        if kernel is None:
+            kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
+
+        #Z defaults to a subset of the data
+        if Z is None:
+            Z = np.random.permutation(X.copy())[:M]
+        else:
+            assert Z.shape[1]==X.shape[1]
+
+        #likelihood defaults to Gaussian
+        likelihood = likelihoods.Gaussian(Y,normalize=normalize_Y)
+
+        sparse_GP.__init__(self, X, likelihood, kernel, Z, normalize_X=normalize_X, Xslices=Xslices)