如何为 gpytorch 中的所有功能学习一个公共高斯过程

如何解决如何为 gpytorch 中的所有功能学习一个公共高斯过程

我正在尝试重新实现本网站中的 example： 他们有代码的地方：

class DKLModel(gpytorch.Module):

    def __init__(self,feature_extractor,num_dim,grid_bounds=(-10.,10.)):
        super(DKLModel,self).__init__()
        self.feature_extractor = feature_extractor
        self.gp_layer = GaussianProcessLayer(num_dim=num_dim,grid_bounds=grid_bounds)
        self.grid_bounds = grid_bounds
        self.num_dim = num_dim

    def forward(self,x):
        features = self.feature_extractor(x)
        features = gpytorch.utils.grid.scale_to_bounds(features,self.grid_bounds[0],self.grid_bounds[1])
        # This next line makes it so that we learn a GP for each feature
        features = features.transpose(-1,-2).unsqueeze(-1)
        res = self.gp_layer(features)
        return res

model = DKLModel(feature_extractor,num_dim=num_features)
likelihood = gpytorch.likelihoods.softmaxLikelihood(num_features=model.num_dim,num_classes=num_classes)


if torch.cuda.is_available():
    model = model.cuda()
    likelihood = likelihood.cuda()

他们提到他们为每个特征学习一个 GP，但我希望所有特征都具有相同的高斯分布。有人知道怎么做吗？