One vs all classification

GPy/models/one_vs_all_classification.py

@@ -0,0 +1,42 @@
+# Copyright (c) 2013, Ricardo Andrade
+# Copyright (c) 2013, the GPy Authors (see AUTHORS.txt)
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+from ..core import GP
+from .. import likelihoods
+from .. import kern
+from ..inference.latent_function_inference.expectation_propagation import EP
+
+class OneVsAllClassification(GP):
+    """
+    Gaussian Process classification: One vs all
+
+    This is a thin wrapper around the models.GPClassification class, with a set of sensible defaults
+
+    :param X: input observations
+    :param Y: observed values, can be None if likelihood is not None
+    :param kernel: a GPy kernel, defaults to rbf
+
+    .. Note:: Multiple independent outputs are not allowed
+
+    """
+
+    def __init__(self, X, Y, kernel=None,Y_metadata=None):
+        if kernel is None:
+            kernel = kern.RBF(X.shape[1])
+
+        likelihood = likelihoods.Bernoulli()
+
+        assert Y.shape[1] == 1, 'Y should be 1 column vector'
+
+        labels = np.unique(Y.flatten())
+
+        self.predictions = {}
+        for yj in labels:
+            Ynew = Y.copy()
+            Ynew[Y.flatten()!=yj] = 0
+            Ynew[Y.flatten()==yj] = 1
+
+            m = GPy.models.GPClassification(X,Ynew,kernel=kernel,Y_metadata=Y_metadata,inference_method=inference_method)
+            m.optimize()
+            self.predictions[yj] = m.predict(X)