def _predict(self, meanstar, kstar, kstarstar, prob):
self._updateConstants()
self._updateApproximation()
m = self._mean
tnu = self._tnu
K = self._K
Ln = self._Ln
Ssq = self._Ssq
LnSsq = self._LnSsq
LnSsqkstar = dot(LnSsq, kstar)
LnSsqK = self._LnSsqK
mustar = meanstar + dot(kstar.T, tnu) - dot(
LnSsqkstar.T,
dot(LnSsq, m) + dot(LnSsqK, tnu))
if prob is False:
if nom > 0.0:
return +1.0
return -1.0
sig2star = kstarstar - dotd(dot(LnSsqkstar.T, LnSsq),
kstar) + self._sign2
return LH.probit_sigmoid(mustar / NP.sqrt(1.0 + sig2star))
def _predict(self, meanstar, kstar, kstarstar, prob):
self._updateConstants()
self._updateApproximation()
m = self._mean
tnu = self._tnu
Lk = self._Lk
H = self._H
V = self._V
Ktnu = self._rdotK(tnu)
mKtnu = m + Ktnu
Vkstar = ddot(V, kstar, left=True)
Hkstar = dot(H, kstar)
mustar = meanstar + dot(kstar.T,tnu) - dot(Vkstar.T, mKtnu) + dot(Hkstar.T, dot(H, mKtnu))
if prob is False:
if nom > 0.0:
return +1.0
return -1.0
sig2star = kstarstar - dotd(kstar.T,Vkstar)\
+ dotd(dot(Hkstar.T, H), kstar) + self._sign2
return LH.probit_sigmoid(mustar/NP.sqrt(1.0 + sig2star))
def _predict(self, meanstar, kstar, kstarstar, prob):
self._updateConstants()
self._updateApproximation()
m = self._mean
tnu = self._tnu
Lk = self._Lk
H = self._H
V = self._V
Ktnu = self._rdotK(tnu)
mKtnu = m + Ktnu
Vkstar = ddot(V, kstar, left=True)
Hkstar = dot(H, kstar)
mustar = meanstar + dot(kstar.T, tnu) - dot(Vkstar.T, mKtnu) + dot(
Hkstar.T, dot(H, mKtnu))
if prob is False:
if nom > 0.0:
return +1.0
return -1.0
sig2star = kstarstar - dotd(kstar.T,Vkstar)\
+ dotd(dot(Hkstar.T, H), kstar) + self._sign2
return LH.probit_sigmoid(mustar / NP.sqrt(1.0 + sig2star))
def _predict(self, meanstar, kstar, kstarstar, prob):
self._updateConstants()
self._updateApproximation()
m = self._mean
tnu = self._tnu
K = self._K
Ln = self._Ln
Ssq = self._Ssq
LnSsq = self._LnSsq
LnSsqkstar = dot(LnSsq, kstar)
LnSsqK = self._LnSsqK
mustar = meanstar + dot(kstar.T,tnu) - dot(LnSsqkstar.T, dot(LnSsq,m) + dot(LnSsqK,tnu))
if prob is False:
if nom > 0.0:
return +1.0
return -1.0
sig2star = kstarstar - dotd(dot(LnSsqkstar.T, LnSsq), kstar) + self._sign2
return LH.probit_sigmoid(mustar/NP.sqrt(1.0 + sig2star))
