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debugging the coregionalisation kern

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This commit is contained in:
James Hensman 2013-03-07 13:49:54 +00:00
parent d00d10952c
commit 9a97ad7348
2 changed files with 16 additions and 15 deletions
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9
GPy/examples/regression.py
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@ -123,7 +123,7 @@ def coregionalisation_toy():
Y = np.vstack((Y1,Y2))
k1 = GPy.kern.rbf(1)
k2 = GPy.kern.coregionalise(2,1)
k2 = GPy.kern.coregionalise(2,2)
k = k1.prod_orthogonal(k2)
m = GPy.models.GP_regression(X,Y,kernel=k)
m.constrain_fixed('rbf_var',1.)
@ -147,8 +147,8 @@ def coregionalisation_sparse():
"""
A simple demonstration of coregionalisation on two sinusoidal functions
"""
X1 = np.random.rand(500,1)*8
X2 = np.random.rand(300,1)*5
X1 = np.random.rand(50,1)*8
X2 = np.random.rand(30,1)*5
index = np.vstack((np.zeros_like(X1),np.ones_like(X2)))
X = np.hstack((np.vstack((X1,X2)),index))
Y1 = np.sin(X1) + np.random.randn(*X1.shape)*0.05
@ -158,7 +158,7 @@ def coregionalisation_sparse():
Z = np.hstack((np.random.rand(25,1)*8,np.random.randint(0,2,25)[:,None]))
k1 = GPy.kern.rbf(1)
k2 = GPy.kern.coregionalise(2,2)
k2 = GPy.kern.coregionalise(2,1)
k = k1.prod_orthogonal(k2) + GPy.kern.white(2,0.001)
m = GPy.models.sparse_GP_regression(X,Y,kernel=k,Z=Z)
@ -180,7 +180,6 @@ def coregionalisation_sparse():
return m
def multiple_optima(gene_number=937,resolution=80, model_restarts=10, seed=10000):
"""Show an example of a multimodal error surface for Gaussian process regression. Gene 939 has bimodal behaviour where the noisey mode is higher."""