9c526695805c9639896b31364958d0e77bdeba62,gpytorch/kernels/matern_kernel.py,MaternKernel,__init__,#MaternKernel#Any#Any#Any#Any#Any#Any#,80

Before Change

    

    def __init__(self, nu=2.5, ard_num_dims=None, batch_size=1, active_dims=None, log_lengthscale_prior=None, eps=1e-6):
        if nu not in {0.5, 1.5, 2.5}:
            raise RuntimeError("nu expected to be 0.5, 1.5, or 2.5")
        super(MaternKernel, self).__init__(
            has_lengthscale=True,
            ard_num_dims=ard_num_dims,
            batch_size=batch_size,
            active_dims=active_dims,
            log_lengthscale_prior=log_lengthscale_prior,
            eps=eps,
        )
        self.nu = nu

    def forward(self, x1, x2, **params):
        mean = x1.contiguous().view(-1, 1, x1.size(-1)).mean(0, keepdim=True)

After Change

        >>> covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.MaternKernel(nu=0.5))
        >>> // Batch: different lengthscale for each batch
        >>> covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.MaternKernel(nu=0.5, batch_size=2))
        >>> covar = covar_module(x)  // Output: LazyVariable of size (2 x 10 x 10)
    

    def __init__(

Instances