コード例 #1
0
    def test_computes_exponential_decay_function(self):
        a = torch.tensor([1.0, 2.0]).view(2, 1)
        b = torch.tensor([2.0, 4.0]).view(2, 1)
        lengthscale = 1
        power = 1
        offset = 1

        kernel = ExponentialDecayKernel()
        kernel.initialize(lengthscale=lengthscale, power=power, offset=offset)
        kernel.eval()

        diff = torch.tensor([[4.0, 6.0], [5.0, 7.0]])
        actual = offset + torch.tensor([1.0]).div(diff.pow(power))
        res = kernel(a, b).evaluate()

        self.assertLess(torch.norm(res - actual), 1e-5)
コード例 #2
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    def test_subset_active_compute_exponential_decay_function(self):
        a = torch.tensor([1.0, 2.0]).view(2, 1)
        a_p = torch.tensor([3.0, 4.0]).view(2, 1)
        a = torch.cat((a, a_p), 1)
        b = torch.tensor([2.0, 4.0]).view(2, 1)
        lengthscale = 1
        power = 1
        offset = 1

        kernel = ExponentialDecayKernel(active_dims=[0])
        kernel.initialize(lengthscale=lengthscale, power=power, offset=offset)
        kernel.eval()

        diff = torch.tensor([[4.0, 6.0], [5.0, 7.0]])
        actual = offset + diff.pow(-power)
        res = kernel(a, b).evaluate()

        self.assertLess(torch.norm(res - actual), 1e-5)
コード例 #3
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    def test_computes_exponential_decay_function_batch(self):
        a = torch.tensor([[1.0, 2.0], [3.0, 4.0]]).view(2, 2, 1)
        b = torch.tensor([[5.0, 6.0], [7.0, 8.0]]).view(2, 2, 1)
        lengthscale = 1
        power = 1
        offset = 1

        kernel = ExponentialDecayKernel(batch_shape=torch.Size([2]))
        kernel.initialize(lengthscale=lengthscale, power=power, offset=offset)
        kernel.eval()

        actual = torch.zeros(2, 2, 2)

        diff = torch.tensor([[7.0, 8.0], [8.0, 9.0]])
        actual[0, :, :] = offset + diff.pow(-power)

        diff = torch.tensor([[11.0, 12.0], [12.0, 13.0]])
        actual[1, :, :] = offset + diff.pow(-power)

        res = kernel(a, b).evaluate()
        self.assertLess(torch.norm(res - actual), 1e-5)