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
def __init__(self, *data, **kwargs):
super(GPR, self).__init__(*data, **kwargs)
self.mean = kwargs.get('mean', mean.Const(self.samples.y.mean()))
self.cov = kwargs.get('cov', cov.RBF())
self.lik = kwargs.get('lik', lik.Gauss())
self.inf = kwargs.get('inf', inf.Exact())
self.optimizer = kwargs.get('optimizer', opt.Minimize)(self)