began to merege the SVIGP code into GPy.

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

GPy/examples/__init__.py

@@ -5,3 +5,4 @@ import classification
 import regression
 import dimensionality_reduction
 import tutorials
+import stochastic

GPy/examples/stochastic.py

@@ -0,0 +1,41 @@
+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+import pylab as pb
+import numpy as np
+import GPy
+
+def toy_1d():
+    N = 2000
+    M = 20
+
+    #create data
+    X = np.linspace(0,32,N)[:,None]
+    Z = np.linspace(0,32,M)[:,None]
+    Y = np.sin(X) + np.cos(0.3*X) + np.random.randn(*X.shape)/np.sqrt(50.)
+
+    m = GPy.models.SVIGPRegression(X,Y, batchsize=10, Z=Z)
+    m.ensure_default_constraints()
+    m.constrain_bounded('noise_variance',1e-3,1e-1)
+
+    m.param_steplength = 1e-4
+
+    fig = pb.figure()
+    ax = fig.add_subplot(111)
+    def cb():
+        ax.cla()
+        m.plot(ax=ax,Z_height=-3)
+        ax.set_ylim(-3,3)
+        fig.canvas.draw()
+
+    m.optimize(500, callback=cb, callback_interval=1)
+
+    m.plot_traces()
+    return m
+
+
+
+
+
+
+