def __init__(self): super(GPR, self).__init__() self.meanfunc = mean.Zero() # default prior mean self.covfunc = cov.RBF() # default prior covariance self.likfunc = lik.Gauss() # likihood with default noise variance 0.1 self.inffunc = inf.Exact() # inference method self.optimizer = opt.Minimize(self) # default optimizer
def __init__(self): super(GPC, self).__init__() self.meanfunc = mean.Zero() # default prior mean self.covfunc = cov.RBF() # default prior covariance self.likfunc = lik.Erf() # erf likihood self.inffunc = inf.EP() # default inference method self.optimizer = opt.Minimize(self) # default optimizer
def __init__(self): super(GPC_FITC, self).__init__() self.meanfunc = mean.Zero() # default prior mean self.covfunc = cov.RBF() # default prior covariance self.likfunc = lik.Erf() # erf liklihood self.inffunc = inf.FITC_EP() # default inference method self.optimizer = opt.Minimize(self) # default optimizer self.u = None # no default inducing points
def __init__(self, n_class): self.meanfunc = mean.Zero() # default prior mean self.covfunc = cov.RBF() # default prior covariance self.n_class = n_class # number of different classes self.x_all = None self.y_all = None self.newInf = None # new inference? -> call useInference self.newLik = None # new likelihood? -> call useLikelihood self.newPrior = False