def __init__(self, train_x, train_y, likelihood):
super(MultitaskModel, self).__init__(train_x, train_y, likelihood)
self.mean_module = gpytorch.means.MultitaskMean(
gpytorch.means.ConstantMean(), num_tasks=_num_tasks
)
self.covar_module = mk_kernel.MultiKernel(
[gpytorch.kernels.RBFKernel() for _ in range(_num_tasks)]
)
def __init__(self, train_x, train_y, likelihood):
super(MultitaskModel, self).__init__(train_x, train_y, likelihood)
self.mean_module = gpytorch.means.MultitaskMean(
gpytorch.means.ConstantMean(), num_tasks=2)
# self.mean_module = gpytorch.means.ConstantMean()
# self.covar_module = mk_kernel.MultitaskRBFKernel(num_tasks=2,log_task_lengthscales=torch.Tensor([math.log(2.5), math.log(0.3)]))
self.covar_module = mk_kernel.MultiKernel(
[gpytorch.kernels.RBFKernel(),
gpytorch.kernels.RBFKernel()])
def __init__(self, train_x, train_y, likelihood):
super(MKModel, self).__init__(train_x, train_y, likelihood)
self.mean_module = gpytorch.means.MultitaskMean(
gpytorch.means.ConstantMean(), num_tasks=n_tasks)
self.covar_module = mk_kernel.MultiKernel(kern_list)