began to merege the SVIGP code into GPy.

Example is implemented, but the step length is a bit crazy!

GPy/models/__init__.py

@@ -4,6 +4,7 @@
 from gp_regression import GPRegression
 from gp_classification import GPClassification
 from sparse_gp_regression import SparseGPRegression
+from svigp_regression import SVIGPRegression
 from sparse_gp_classification import SparseGPClassification
 from fitc_classification import FITCClassification
 from gplvm import GPLVM

GPy/models/svigp_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 ..core import SVIGP
+from .. import likelihoods
+from .. import kern
+
+class SVIGPRegression(SVIGP):
+    """
+    Gaussian Process model for regression
+
+    This is a thin wrapper around the SVIGP 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
+    :rtype: model object
+
+    .. Note:: Multiple independent outputs are allowed using columns of Y
+
+    """
+
+    def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, q_u=None, batchsize=10):
+        # kern defaults to rbf (plus white for stability)
+        if kernel is None:
+            kernel = kern.rbf(X.shape[1], variance=1., lengthscale=4.) + kern.white(X.shape[1], 1e-3)
+
+        # Z defaults to a subset of the data
+        if Z is None:
+            i = np.random.permutation(X.shape[0])[:num_inducing]
+            Z = X[i].copy()
+        else:
+            assert Z.shape[1] == X.shape[1]
+
+        # likelihood defaults to Gaussian
+        likelihood = likelihoods.Gaussian(Y, normalize=False)
+
+        SVIGP.__init__(self, X, likelihood, kernel, Z, q_u=q_u, batchsize=batchsize)
+        self.load_batch()