Merge branch 'params' of github.com:SheffieldML/GPy into params

GPy/examples/coreg_example.py

@@ -0,0 +1,30 @@
+import numpy as np
+import pylab as pb
+import GPy
+pb.ion()
+
+X1 = 100 * np.random.rand(100)[:,None]
+X2 = 100 * np.random.rand(100)[:,None]
+#X1.sort()
+#X2.sort()
+
+Y1 = np.sin(X1/10.) + np.random.rand(100)[:,None]
+Y2 = np.cos(X2/10.) + np.random.rand(100)[:,None]
+
+
+
+
+Mlist = [GPy.kern.Matern32(1,lengthscale=20.,name="Mat")]
+kern = GPy.util.multioutput.LCM(input_dim=1,num_outputs=12,kernels_list=Mlist,name='H')
+
+
+m = GPy.models.GPCoregionalizedRegression(X_list=[X1,X2], Y_list=[Y1,Y2], kernel=kern)
+m.optimize()
+
+fig = pb.figure()
+ax0 = fig.add_subplot(211)
+ax1 = fig.add_subplot(212)
+slices = GPy.util.multioutput.get_slices([Y1,Y2])
+m.plot(fixed_inputs=[(1,0)],which_data_rows=slices[0],ax=ax0)
+m.plot(fixed_inputs=[(1,1)],which_data_rows=slices[1],ax=ax1)
+

GPy/util/multioutput.py

@@ -54,8 +54,8 @@ def ICM(input_dim, num_outputs, kernel, W_rank=1,W=None,kappa=None,name='X'):
         kernel.input_dim = input_dim
         warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.")
 
-    #K = kernel.prod(GPy.kern.Coregionalize(input_dim, num_outputs,W_rank,W,kappa,name='B'),tensor=True,name=name)
+    K = kernel.prod(GPy.kern.Coregionalize([input_dim], num_outputs,W_rank,W,kappa,name='B'),name=name)
-    K = kernel.prod(GPy.kern.Coregionalize(input_dim, num_outputs,W_rank,W,kappa,name='B'),name=name)
+    #K = kernel ** GPy.kern.Coregionalize(input_dim, num_outputs,W_rank,W,kappa, name= 'B')
     K['.*variance'] = 1.
     K['.*variance'].fix()
     return K