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linear kernel psi statistics performance optimization

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This commit is contained in:
Zhenwen Dai 2014-08-18 16:44:15 +01:00
parent 6b8ac70210
commit 3d322301a2
2 changed files with 11 additions and 18 deletions
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11
GPy/kern/_src/psi_comp/linear_psi_comp.py
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@ -21,9 +21,7 @@ def psicomputations(variance, Z, variational_posterior):
psi0 = np.einsum('q,nq->n',variance,np.square(mu)+S)
psi1 = np.einsum('q,mq,nq->nm',variance,Z,mu)
tmp = np.einsum('q,mq,nq->nm',variance,Z,mu)
psi2 = np.einsum('q,mq,oq,nq->mo',np.square(variance),Z,Z,S) + np.einsum('nm,no->mo',tmp,tmp)
psi2 = np.einsum('q,mq,oq,nq->mo',np.square(variance),Z,Z,S) + np.einsum('nm,no->mo',psi1,psi1)
return psi0, psi1, psi2
@ -58,13 +56,12 @@ def _psi2computations(dL_dpsi2, variance, Z, mu, S):
variance2 = np.square(variance)
common_sum = np.einsum('q,mq,nq->nm',variance,Z,mu) # NxM
dL_dpsi2_2 = dL_dpsi2+dL_dpsi2.T
dL_dvar = np.einsum('mo,nq,q,mq,oq->q',dL_dpsi2,2.*S,variance,Z,Z)+\
np.einsum('mo,mq,nq,no->q',dL_dpsi2,Z,mu,common_sum)+\
np.einsum('mo,oq,nq,nm->q',dL_dpsi2,Z,mu,common_sum)
np.einsum('mo,mq,nq,no->q',dL_dpsi2_2,Z,mu,common_sum)
dL_dmu = np.einsum('mo,q,mq,no->nq',dL_dpsi2,variance,Z,common_sum)+\
np.einsum('mo,q,oq,nm->nq',dL_dpsi2,variance,Z,common_sum)
dL_dmu = np.einsum('mo,q,mq,no->nq',dL_dpsi2_2,variance,Z,common_sum)
dL_dS = np.empty(S.shape)
dL_dS[:] = np.einsum('mo,q,mq,oq->q',dL_dpsi2,variance2,Z,Z)

18
GPy/kern/_src/psi_comp/sslinear_psi_comp.py
View file

@ -22,11 +22,8 @@ def psicomputations(variance, Z, variational_posterior):
psi0 = np.einsum('q,nq,nq->n',variance,gamma,np.square(mu)+S)
psi1 = np.einsum('nq,q,mq,nq->nm',gamma,variance,Z,mu)
mu2 = np.square(mu)
variances2 = np.square(variance)
tmp = np.einsum('nq,q,mq,nq->nm',gamma,variance,Z,mu)
psi2 = np.einsum('nq,q,mq,oq,nq->mo',gamma,variances2,Z,Z,mu2+S)+\
np.einsum('nm,no->mo',tmp,tmp) - np.einsum('nq,q,mq,oq,nq->mo',np.square(gamma),variances2,Z,Z,mu2)
psi2 = np.einsum('nq,q,mq,oq,nq->mo',gamma,np.square(variance),Z,Z,(1-gamma)*np.square(mu)+S) +\
np.einsum('nm,no->mo',psi1,psi1)
return psi0, psi1, psi2
@ -67,18 +64,17 @@ def _psi2computations(dL_dpsi2, variance, Z, mu, S, gamma):
variance2 = np.square(variance)
mu2S = mu2+S # NxQ
common_sum = np.einsum('nq,q,mq,nq->nm',gamma,variance,Z,mu) # NxM
dL_dpsi2_2 = dL_dpsi2+dL_dpsi2.T
dL_dvar = np.einsum('mo,nq,q,mq,oq->q',dL_dpsi2,2.*(gamma*mu2S-gamma2*mu2),variance,Z,Z)+\
np.einsum('mo,nq,mq,nq,no->q',dL_dpsi2,gamma,Z,mu,common_sum)+\
np.einsum('mo,nq,oq,nq,nm->q',dL_dpsi2,gamma,Z,mu,common_sum)
np.einsum('mo,nq,mq,nq,no->q',dL_dpsi2_2,gamma,Z,mu,common_sum)
dL_dgamma = np.einsum('mo,q,mq,oq,nq->nq',dL_dpsi2,variance2,Z,Z,(mu2S-2.*gamma*mu2))+\
np.einsum('mo,q,mq,nq,no->nq',dL_dpsi2,variance,Z,mu,common_sum)+\
np.einsum('mo,q,oq,nq,nm->nq',dL_dpsi2,variance,Z,mu,common_sum)
np.einsum('mo,q,mq,nq,no->nq',dL_dpsi2_2,variance,Z,mu,common_sum)
dL_dmu = np.einsum('mo,q,mq,oq,nq,nq->nq',dL_dpsi2,variance2,Z,Z,mu,2.*(gamma-gamma2))+\
np.einsum('mo,nq,q,mq,no->nq',dL_dpsi2,gamma,variance,Z,common_sum)+\
np.einsum('mo,nq,q,oq,nm->nq',dL_dpsi2,gamma,variance,Z,common_sum)
np.einsum('mo,nq,q,mq,no->nq',dL_dpsi2_2,gamma,variance,Z,common_sum)
dL_dS = np.einsum('mo,nq,q,mq,oq->nq',dL_dpsi2,gamma,variance2,Z,Z)