New model

GPy/models/sparse_GP_classification.py

@@ -0,0 +1,50 @@
+# Copyright (c) 2013, Ricardo Andrade
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+import numpy as np
+from ..core import sparse_GP
+from .. import likelihoods
+from .. import kern
+from ..likelihoods import likelihood
+from GP_regression import GP_regression
+
+class sparse_GP_classification(sparse_GP):
+    """
+    sparse Gaussian Process model for classification
+
+    This is a thin wrapper around the sparse_GP class, with a set of sensible defalts
+
+    :param X: input observations
+    :param Y: observed values
+    :param likelihood: a GPy likelihood, defaults to binomial with probit link_function
+    :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
+    :rtype: model object
+
+    .. Note:: Multiple independent outputs are allowed using columns of Y
+
+    """
+
+    def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, M=10):
+        if kernel is None:
+            kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
+
+        if likelihood is None:
+            distribution = likelihoods.likelihood_functions.binomial()
+            likelihood = likelihoods.EP(Y, distribution)
+        elif Y is not None:
+            if not all(Y.flatten() == likelihood.data.flatten()):
+                raise Warning, 'likelihood.data and Y are different.'
+
+        if Z is None:
+            i = np.random.permutation(X.shape[0])[:M]
+            Z = X[i].copy()
+        else:
+            assert Z.shape[1]==X.shape[1]
+
+        sparse_GP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
+        self._set_params(self._get_params())