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[SSGPLVM] linear kernel cpu ready

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This commit is contained in:
Zhenwen Dai 2014-05-15 12:11:55 +01:00
parent dad476faf6
commit ab501ddcba
1 changed files with 4 additions and 22 deletions
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26
GPy/kern/_src/psi_comp/linear_psi_comp.py
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@ -68,24 +68,6 @@ class PSICOMP_SSLinear(object):
variance2 = np.square(variance)
mu2S = mu2+S # NxQ
common_sum = np.einsum('nq,q,mq,nq->nm',gamma,variance,Z,mu) # NxM
# _dpsi2_dvariance = np.einsum('nq,q,mq,oq->nmoq',2.*(gamma*mu2S-gamma2*mu2),variance,Z,Z)+\
# np.einsum('nq,mq,nq,no->nmoq',gamma,Z,mu,common_sum)+\
# np.einsum('nq,oq,nq,nm->nmoq',gamma,Z,mu,common_sum)
#
# _dpsi2_dgamma = np.einsum('q,mq,oq,nq->nmoq',variance2,Z,Z,(mu2S-2.*gamma*mu2))+\
# np.einsum('q,mq,nq,no->nmoq',variance,Z,mu,common_sum)+\
# np.einsum('q,oq,nq,nm->nmoq',variance,Z,mu,common_sum)
#
_dpsi2_dmu = np.einsum('q,mq,oq,nq,nq->nmoq',variance2,Z,Z,mu,2.*(gamma-gamma2))+\
np.einsum('nq,q,mq,no->nmoq',gamma,variance,Z,common_sum)+\
np.einsum('nq,q,oq,nm->nmoq',gamma,variance,Z,common_sum)
#
# _dpsi2_dS = np.einsum('nq,q,mq,oq->nmoq',gamma,variance2,Z,Z)
#
# _dpsi2_dZ = 2.*(np.einsum('nq,q,mq,nq->nmq',gamma,variance2,Z,mu2S)+np.einsum('nq,q,nq,nm->nmq',gamma,variance,mu,common_sum)
# -np.einsum('nq,q,mq,nq->nmq',gamma2,variance2,Z,mu2))
dL_dmu = np.einsum('mo,nmoq->nq', dL_dpsi2, _dpsi2_dmu)
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)+\
@ -95,10 +77,10 @@ class PSICOMP_SSLinear(object):
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)
# 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)
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)
dL_dS = np.einsum('mo,nq,q,mq,oq->nq',dL_dpsi2,gamma,variance2,Z,Z)
dL_dZ = 2.*(np.einsum('om,nq,q,mq,nq->oq',dL_dpsi2,gamma,variance2,Z,mu2S)+np.einsum('om,nq,q,nq,nm->oq',dL_dpsi2,gamma,variance,mu,common_sum)