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psi statistics for the linear kernel

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Nicolo Fusi 2013-02-06 17:51:54 +00:00
parent 71e461a780
commit 7d8e2183a2
3 changed files with 24 additions and 24 deletions
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34
GPy/kern/kern.py
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@ -325,11 +325,11 @@ class kern(parameterised):
# MASSIVE TODO: do something smart for white
# "crossterms"
psi1_matrices = [np.zeros((mu.shape[0], Z.shape[0])) for p in self.parts]
[p.psi1(Z[s2],mu[s1],S[s1],psi1_target[s1,s2]) for p,s1,s2,psi1_target in zip(self.parts,slices1,slices2, psi1_matrices)]
for a,b in itertools.combinations(psi1_matrices, 2):
tmp = np.multiply(a,b)
target += tmp[:,None,:] + tmp[:, :,None]
# psi1_matrices = [np.zeros((mu.shape[0], Z.shape[0])) for p in self.parts]
# [p.psi1(Z[s2],mu[s1],S[s1],psi1_target[s1,s2]) for p,s1,s2,psi1_target in zip(self.parts,slices1,slices2, psi1_matrices)]
# for a,b in itertools.combinations(psi1_matrices, 2):
# tmp = np.multiply(a,b)
# target += tmp[:,None,:] + tmp[:, :,None]
return target
@ -340,21 +340,21 @@ class kern(parameterised):
[p.dpsi2_dtheta(partial[s1,s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[ps]) for p,i_s,s1,s2,ps in zip(self.parts,self.input_slices,slices1,slices2,self.param_slices)]
# "crossterms"
# 1. get all the psi1 statistics
psi1_matrices = [np.zeros((mu.shape[0], Z.shape[0])) for p in self.parts]
[p.psi1(Z[s2],mu[s1],S[s1],psi1_target[s1,s2]) for p,s1,s2,psi1_target in zip(self.parts,slices1,slices2, psi1_matrices)]
partial1 = np.zeros_like(partial1)
# # "crossterms"
# # 1. get all the psi1 statistics
# psi1_matrices = [np.zeros((mu.shape[0], Z.shape[0])) for p in self.parts]
# [p.psi1(Z[s2],mu[s1],S[s1],psi1_target[s1,s2]) for p,s1,s2,psi1_target in zip(self.parts,slices1,slices2, psi1_matrices)]
# partial1 = np.zeros_like(partial1)
# 2. get all the dpsi1/dtheta gradients
psi1_gradients = [np.zeros(self.Nparam) for p in self.parts]
[p.dpsi1_dtheta(partial1[s2,s1],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],psi1g_target[ps]) for p,ps,s1,s2,i_s,psi1g_target in zip(self.parts, self.param_slices,slices1,slices2,self.input_slices,psi1_gradients)]
# # 2. get all the dpsi1/dtheta gradients
# psi1_gradients = [np.zeros(self.Nparam) for p in self.parts]
# [p.dpsi1_dtheta(partial1[s2,s1],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],psi1g_target[ps]) for p,ps,s1,s2,i_s,psi1g_target in zip(self.parts, self.param_slices,slices1,slices2,self.input_slices,psi1_gradients)]
# 3. multiply them somehow
for a,b in itertools.combinations(range(len(psi1_matrices)), 2):
gne = (psi1_gradients[a][None]*psi1_matrices[b].sum(0)[:,None]).sum(0)
# # 3. multiply them somehow
# for a,b in itertools.combinations(range(len(psi1_matrices)), 2):
# gne = (psi1_gradients[a][None]*psi1_matrices[b].sum(0)[:,None]).sum(0)
target += (gne[None] + gne[:, None]).sum(0)
# target += (gne[None] + gne[:, None]).sum(0)
return target
def dpsi2_dZ(self,partial,Z,mu,S,slices1=None,slices2=None):