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changes tie_param to tie_params

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James Hensman 2013-03-11 16:46:47 +00:00
parent 4b9064bb0e
commit 5b86fce1b3
6 changed files with 27 additions and 8 deletions
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25
GPy/kern/kern.py
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@ -237,7 +237,7 @@ class kern(parameterised):
for i in range(K1.Nparam + K2.Nparam):
index = np.where(index_param==i)[0]
if index.size > 1:
self.tie_param(index)
self.tie_params(index)
for i in prev_constr_pos:
self.constrain_positive(np.where(index_param==i)[0])
for i in prev_constr_neg:
@ -391,9 +391,13 @@ class kern(parameterised):
target += p2.variance*(p1._psi1[:,:,None]+p1._psi1[:,None,:])
#linear X bias
elif p1.name=='bias' and p2.name=='linear':
raise NotImplementedError
tmp = np.zeros((mu.shape[0],Z.shape[0]))
p2.psi1(Z,mu,S,tmp)
target += p1.variance*(tmp[:,:,None] + tmp[:,None,:])
elif p2.name=='bias' and p1.name=='linear':
raise NotImplementedError
tmp = np.zeros((mu.shape[0],Z.shape[0]))
p1.psi1(Z,mu,S,tmp)
target += p2.variance*(tmp[:,:,None] + tmp[:,None,:])
#rbf X linear
elif p1.name=='linear' and p2.name=='rbf':
raise NotImplementedError #TODO
@ -426,6 +430,11 @@ class kern(parameterised):
elif p2.name=='bias' and p1.name=='rbf':
p1.dpsi1_dtheta(dL_dpsi2.sum(1)*p2.variance*2.,Z,mu,S,target[ps1])
p2.dpsi1_dtheta(dL_dpsi2.sum(1)*p1._psi1*2.,Z,mu,S,target[ps2])
#linear X bias
elif p1.name=='bias' and p2.name=='linear':
p2.dpsi1_dtheta(dL_dpsi2.sum(1)*p1.variance*2., Z, mu, S, target[ps1])
elif p2.name=='bias' and p1.name=='linear':
p1.dpsi1_dtheta(dL_dpsi2.sum(1)*p2.variance*2., Z, mu, S, target[ps1])
#rbf X linear
elif p1.name=='linear' and p2.name=='rbf':
raise NotImplementedError #TODO
@ -451,6 +460,11 @@ class kern(parameterised):
p2.dpsi1_dX(dL_dpsi2.sum(1).T*p1.variance,Z,mu,S,target)
elif p2.name=='bias' and p1.name=='rbf':
p1.dpsi1_dZ(dL_dpsi2.sum(1).T*p2.variance,Z,mu,S,target)
#linear X bias
elif p1.name=='bias' and p2.name=='linear':
p2.dpsi1_dZ(dL_dpsi2.sum(1).T*p1.variance, Z, mu, S, target)
elif p2.name=='bias' and p1.name=='linear':
p1.dpsi1_dZ(dL_dpsi2.sum(1).T*p2.variance, Z, mu, S, target)
#rbf X linear
elif p1.name=='linear' and p2.name=='rbf':
raise NotImplementedError #TODO
@ -478,6 +492,11 @@ class kern(parameterised):
p2.dpsi1_dmuS(dL_dpsi2.sum(1).T*p1.variance*2.,Z,mu,S,target_mu,target_S)
elif p2.name=='bias' and p1.name=='rbf':
p1.dpsi1_dmuS(dL_dpsi2.sum(1).T*p2.variance*2.,Z,mu,S,target_mu,target_S)
#linear X bias
elif p1.name=='bias' and p2.name=='linear':
p2.dpsi1_dmuS(dL_dpsi2.sum(1).T*p1.variance*2., Z, mu, S, target_mu, target_S)
elif p2.name=='bias' and p1.name=='linear':
p1.dpsi1_dmuS(dL_dpsi2.sum(1).T*p2.variance*2., Z, mu, S, target_mu, target_S)
#rbf X linear
elif p1.name=='linear' and p2.name=='rbf':
raise NotImplementedError #TODO