apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-27 14:25:16 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

minor changes

Browse source
This commit is contained in:
Ricardo 2014-03-17 10:34:08 +00:00
parent 4692880cef
commit d4735e54c8
1 changed files with 37 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

37
GPy/examples/coreg_example.py
View file

@ -3,6 +3,42 @@ import pylab as pb
import GPy
pb.ion()
X1 = 100 * np.random.rand(3)[:,None]
X2 = 100 * np.random.rand(4)[:,None]
I1 = np.zeros_like(X1)
I2 = np.ones_like(X2)
_X = np.vstack([ X1, X2 ])
_I = np.vstack([ I1, I2 ])
X = np.hstack([ _X, _I ])
Bias = GPy.kern.Bias(1,active_dims=[0])
Coreg = GPy.kern.Coregionalize(1,2,active_dims=[1])
K = Bias.prod(Coreg,name='X')
K.coregion.W = 0
print K.coregion.W
print Bias.K(_X,_X)
print K.K(X,X)
pb.matshow(K.K(X,X))
stop
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()
"""
X1 = 100 * np.random.rand(100)[:,None]
X2 = 100 * np.random.rand(100)[:,None]
#X1.sort()
@ -28,3 +64,4 @@ 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)
"""