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coregionalisation seems to be a go-go

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This commit is contained in:
James Hensman 2013-03-08 18:21:29 +00:00
parent af510d166a
commit f881e65761
3 changed files with 20 additions and 12 deletions
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22
GPy/examples/regression.py
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@ -94,7 +94,8 @@ def coregionalisation_toy2():
m.constrain_fixed('rbf_var',1.) m.constrain_fixed('rbf_var',1.)
m.constrain_positive('kappa') m.constrain_positive('kappa')
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize() m.optimize('sim',max_f_eval=5000,messages=1)
#m.optimize()
pb.figure() pb.figure()
Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1)))) Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1))))
@ -129,7 +130,7 @@ def coregionalisation_toy():
m.constrain_fixed('rbf_var',1.) m.constrain_fixed('rbf_var',1.)
m.constrain_positive('kappa') m.constrain_positive('kappa')
m.ensure_default_constraints() m.ensure_default_constraints()
m.optimize() #m.optimize()
pb.figure() pb.figure()
Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1)))) Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1))))
@ -147,26 +148,29 @@ def coregionalisation_sparse():
""" """
A simple demonstration of coregionalisation on two sinusoidal functions A simple demonstration of coregionalisation on two sinusoidal functions
""" """
X1 = np.random.rand(50,1)*8 X1 = np.random.rand(500,1)*8
X2 = np.random.rand(30,1)*5 X2 = np.random.rand(300,1)*5
index = np.vstack((np.zeros_like(X1),np.ones_like(X2))) index = np.vstack((np.zeros_like(X1),np.ones_like(X2)))
X = np.hstack((np.vstack((X1,X2)),index)) X = np.hstack((np.vstack((X1,X2)),index))
Y1 = np.sin(X1) + np.random.randn(*X1.shape)*0.05 Y1 = np.sin(X1) + np.random.randn(*X1.shape)*0.05
Y2 = -np.sin(X2) + np.random.randn(*X2.shape)*0.05 Y2 = -np.sin(X2) + np.random.randn(*X2.shape)*0.05
Y = np.vstack((Y1,Y2)) Y = np.vstack((Y1,Y2))
Z = np.hstack((np.random.rand(25,1)*8,np.random.randint(0,2,25)[:,None])) M = 40
Z = np.hstack((np.random.rand(M,1)*8,np.random.randint(0,2,M)[:,None]))
#Z = X.copy()
k1 = GPy.kern.rbf(1) k1 = GPy.kern.rbf(1)
k2 = GPy.kern.coregionalise(2,1) k2 = GPy.kern.coregionalise(2,2)
k = k1.prod_orthogonal(k2) + GPy.kern.white(2,0.001) k = k1.prod_orthogonal(k2) + GPy.kern.white(2,0.001)
m = GPy.models.sparse_GP_regression(X,Y,kernel=k,Z=Z) m = GPy.models.sparse_GP_regression(X,Y,kernel=k,Z=Z)
m.scale_factor = 10000.
m.constrain_fixed('rbf_var',1.) m.constrain_fixed('rbf_var',1.)
m.constrain_positive('kappa') m.constrain_positive('kappa')
m.constrain_fixed('iip') m.constrain_fixed('iip')
m.ensure_default_constraints() m.ensure_default_constraints()
#m.optimize() m.optimize_restarts(5,robust=True,messages=1)
pb.figure() pb.figure()
Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1)))) Xtest1 = np.hstack((np.linspace(0,9,100)[:,None],np.zeros((100,1))))
@ -177,6 +181,10 @@ def coregionalisation_sparse():
GPy.util.plot.gpplot(Xtest2[:,0],mean,low,up) GPy.util.plot.gpplot(Xtest2[:,0],mean,low,up)
pb.plot(X1[:,0],Y1[:,0],'rx',mew=2) pb.plot(X1[:,0],Y1[:,0],'rx',mew=2)
pb.plot(X2[:,0],Y2[:,0],'gx',mew=2) pb.plot(X2[:,0],Y2[:,0],'gx',mew=2)
y = pb.ylim()[0]
pb.plot(Z[:,0][Z[:,1]==0],np.zeros(np.sum(Z[:,1]==0))+y,'r|',mew=2)
pb.plot(Z[:,0][Z[:,1]==1],np.zeros(np.sum(Z[:,1]==1))+y,'g|',mew=2)
print Z
return m return m

6
GPy/kern/coregionalise.py
View file

@ -64,13 +64,13 @@ class coregionalise(kernpart):
partial_small = np.zeros_like(self.B) partial_small = np.zeros_like(self.B)
for i in range(self.Nout): for i in range(self.Nout):
for j in range(i,self.Nout): for j in range(self.Nout):
tmp = np.sum(partial[(ii==i)*(jj==j)]) tmp = np.sum(partial[(ii==i)*(jj==j)])
partial_small[i,j] = tmp partial_small[i,j] = tmp
partial_small[j,i] = tmp
dkappa = np.diag(partial_small) dkappa = np.diag(partial_small)
dW = 2.*(self.W[:,None,:]*partial_small[:,:,None]).sum(0) partial_small += partial_small.T
dW = (self.W[:,None,:]*partial_small[:,:,None]).sum(0)
target += np.hstack([dW.flatten(),dkappa]) target += np.hstack([dW.flatten(),dkappa])

4
GPy/kern/product_orthogonal.py
View file

@ -40,8 +40,8 @@ class product_orthogonal(kernpart):
def K(self,X,X2,target): def K(self,X,X2,target):
"""Compute the covariance matrix between X and X2.""" """Compute the covariance matrix between X and X2."""
if X2 is None: X2 = X if X2 is None: X2 = X
target1 = np.zeros((X.shape[0],X2.shape[0])) target1 = np.zeros_like(target)
target2 = np.zeros((X.shape[0],X2.shape[0])) target2 = np.zeros_like(target)
self.k1.K(X[:,:self.k1.D],X2[:,:self.k1.D],target1) self.k1.K(X[:,:self.k1.D],X2[:,:self.k1.D],target1)
self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],target2) self.k2.K(X[:,self.k1.D:],X2[:,self.k1.D:],target2)
target += target1 * target2 target += target1 * target2