[merge] into new devel

GPy/models/__init__.py

@@ -22,9 +22,7 @@ from .gp_var_gauss import GPVariationalGaussianApproximation
 from .one_vs_all_classification import OneVsAllClassification
 from .one_vs_all_sparse_classification import OneVsAllSparseClassification
 from .dpgplvm import DPBayesianGPLVM
-
 from .state_space_model import StateSpace
-
 from .ibp_lfm import IBPLFM
-
 from .gp_offset_regression import GPOffsetRegression
+from .gp_grid_regression import GPRegressionGrid

GPy/models/gp_grid_regression.py

@@ -0,0 +1,36 @@
+# Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+# Kurt Cutajar
+
+from ..core import GpGrid
+from .. import likelihoods
+from .. import kern
+
+class GPRegressionGrid(GpGrid):
+    """
+    Gaussian Process model for grid inputs using Kronecker products
+
+    This is a thin wrapper around the models.GpGrid class, with a set of sensible defaults
+
+    :param X: input observations
+    :param Y: observed values
+    :param kernel: a GPy kernel, defaults to the kron variation of SqExp
+    :param Norm normalizer: [False]
+
+        Normalize Y with the norm given.
+        If normalizer is False, no normalization will be done
+        If it is None, we use GaussianNorm(alization)
+
+    .. Note:: Multiple independent outputs are allowed using columns of Y
+
+    """
+
+    def __init__(self, X, Y, kernel=None, Y_metadata=None, normalizer=None):
+
+        if kernel is None:
+            kernel = kern.RBF(1)   # no other kernels implemented so far
+
+        likelihood = likelihoods.Gaussian()
+        super(GPRegressionGrid, self).__init__(X, Y, kernel, likelihood, name='GP Grid regression', Y_metadata=Y_metadata, normalizer=normalizer)
+