def __fitGP1d(theta, g, hyp1, hyp2, hypvarconst, gvar=None, dampalpha=None, hypstarts=None, hypinds=None): return semPCGPwM.__fitGP1d(theta, g, hyp1, hyp2, hypvarconst, gvar, dampalpha, hypstarts, hypinds)
def __PCs(fitinfo): """Apply PCA to reduce the dimension of f.""" return semPCGPwM.__PCs(fitinfo)
def __standardizef(fitinfo, offset=None, scale=None): """Standardizes f by creating offset, scale and fs.""" return semPCGPwM.__standardizef(fitinfo, offset, scale)
def __negloglikgrad(hyp, info): return semPCGPwM.__negloglikgrad(hyp, info)
def __fitGPs(fitinfo, theta, numpcs, hyp1, hyp2, varconstant): return semPCGPwM.__fitGPs(fitinfo, theta, numpcs, hyp1, hyp2, varconstant)
def predictlpdf(predinfo, f, return_grad=False, addvar=0): return semPCGPwM.predictlpdf(predinfo, f, return_grad, addvar)
def predict(predinfo, fitinfo, x, theta, **kwargs): return semPCGPwM.predict(predinfo, fitinfo, x, theta, **kwargs)