rename _models to models_modules to include in doc

GPy/models_modules/gp_classification.py

@@ -0,0 +1,41 @@
+# Copyright (c) 2013, Ricardo Andrade
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+import numpy as np
+from ..core import GP
+from .. import likelihoods
+from .. import kern
+
+class GPClassification(GP):
+    """
+    Gaussian Process classification
+
+    This is a thin wrapper around the models.GP class, with a set of sensible defaults
+
+    :param X: input observations
+    :param Y: observed values, can be None if likelihood is not None
+    :param likelihood: a GPy likelihood, defaults to Bernoulli with Probit link_function
+    :param kernel: a GPy kernel, defaults to rbf
+    :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
+
+    .. 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):
+        if kernel is None:
+            kernel = kern.rbf(X.shape[1])
+
+        if likelihood is None:
+            noise_model = likelihoods.bernoulli()
+            likelihood = likelihoods.EP(Y, noise_model)
+        elif Y is not None:
+            if not all(Y.flatten() == likelihood.data.flatten()):
+                raise Warning, 'likelihood.data and Y are different.'
+
+        GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
+        self.ensure_default_constraints()