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))