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kernel_dis
Author | SHA1 | Date | |
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09c80e2d54 | ||
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65e0637b17 | ||
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209f9e641b | ||
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ba537fe1d5 | ||
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b0cd2de18e | ||
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7d353f5b5a |
@ -3,8 +3,11 @@
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import numpy as np
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import torch
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from prototorch.functions.helper import (_check_shapes, _int_and_mixed_shape,
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equal_int_shape)
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from prototorch.functions.helper import (
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_check_shapes,
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_int_and_mixed_shape,
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equal_int_shape,
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)
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def squared_euclidean_distance(x, y):
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@ -261,5 +264,86 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
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return diss.permute([1, 0, 2]).squeeze(-1)
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class KernelDistance:
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r"""Kernel Distance
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Distance based on a kernel function.
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"""
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def __init__(self, kernel_fn):
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self.kernel_fn = kernel_fn
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def __call__(self, x_batch: torch.Tensor, y_batch: torch.Tensor):
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return self._single_call(x_batch, y_batch)
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def _single_call(self, x, y):
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remove_dims = []
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if len(x.shape) == 1:
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x = x.unsqueeze(0)
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remove_dims.append(0)
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if len(y.shape) == 1:
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y = y.unsqueeze(0)
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remove_dims.append(-1)
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output = self.kernel_fn(x, x).diag().unsqueeze(1) - 2 * self.kernel_fn(
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x, y) + self.kernel_fn(y, y).diag()
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for dim in remove_dims:
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output.squeeze_(dim)
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return torch.sqrt(output)
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class BatchKernelDistance:
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r"""Kernel Distance
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Distance based on a kernel function.
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"""
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def __init__(self, kernel_fn):
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self.kernel_fn = kernel_fn
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def __call__(self, x_batch: torch.Tensor, y_batch: torch.Tensor):
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remove_dims = 0
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# Extend Single inputs
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if len(x_batch.shape) == 1:
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x_batch = x_batch.unsqueeze(0)
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remove_dims += 1
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if len(y_batch.shape) == 1:
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y_batch = y_batch.unsqueeze(0)
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remove_dims += 1
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# Loop over batches
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output = torch.FloatTensor(len(x_batch), len(y_batch))
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for i, x in enumerate(x_batch):
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for j, y in enumerate(y_batch):
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output[i][j] = self._single_call(x, y)
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for _ in range(remove_dims):
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output.squeeze_(0)
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return output
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def _single_call(self, x, y):
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kappa_xx = self.kernel_fn(x, x)
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kappa_xy = self.kernel_fn(x, y)
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kappa_yy = self.kernel_fn(y, y)
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squared_distance = kappa_xx - 2 * kappa_xy + kappa_yy
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return torch.sqrt(squared_distance)
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class SquaredKernelDistance(KernelDistance):
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r"""Squared Kernel Distance
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Kernel distance without final squareroot.
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"""
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def single_call(self, x, y):
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kappa_xx = self.kernel_fn(x, x)
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kappa_xy = self.kernel_fn(x, y)
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kappa_yy = self.kernel_fn(y, y)
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return kappa_xx - 2 * kappa_xy + kappa_yy
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# Aliases
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sed = squared_euclidean_distance
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sed = squared_euclidean_distance
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28
prototorch/functions/kernels.py
Normal file
28
prototorch/functions/kernels.py
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@ -0,0 +1,28 @@
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"""
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Experimental Kernels
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"""
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import torch
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class ExplicitKernel:
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def __init__(self, projection=torch.nn.Identity()):
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self.projection = projection
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def __call__(self, x, y):
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return self.projection(x) @ self.projection(y).T
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class RadialBasisFunctionKernel:
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def __init__(self, sigma) -> None:
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self.s2 = sigma * sigma
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def __call__(self, x, y):
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remove_dim = False
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if len(x.shape) > 1:
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x = x.unsqueeze(1)
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remove_dim = True
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output = torch.exp(-torch.sum((x - y)**2, dim=-1) / (2 * self.s2))
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if remove_dim:
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output = output.squeeze(1)
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return output
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@ -1,8 +1,7 @@
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import torch
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from torch import nn
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from prototorch.functions.distances import (euclidean_distance_matrix,
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tangent_distance)
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from prototorch.functions.distances import euclidean_distance_matrix, tangent_distance
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from prototorch.functions.helper import _check_shapes, _int_and_mixed_shape
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from prototorch.functions.normalization import orthogonalization
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from prototorch.modules.prototypes import Prototypes1D
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@ -5,8 +5,13 @@ import unittest
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import numpy as np
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import torch
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from prototorch.functions import (activations, competitions, distances,
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initializers, losses)
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from prototorch.functions import (
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activations,
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competitions,
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distances,
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initializers,
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losses,
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)
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class TestActivations(unittest.TestCase):
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98
tests/test_kernels.py
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98
tests/test_kernels.py
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@ -0,0 +1,98 @@
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"""ProtoTorch kernels test suite."""
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import unittest
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import numpy as np
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import torch
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from prototorch.functions.distances import KernelDistance
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from prototorch.functions.kernels import ExplicitKernel, RadialBasisFunctionKernel
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class TestExplicitKernel(unittest.TestCase):
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def setUp(self):
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self.single_x = torch.randn(1024)
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self.single_y = torch.randn(1024)
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self.batch_x = torch.randn(32, 1024)
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self.batch_y = torch.randn(32, 1024)
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def test_single_values(self):
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kernel = ExplicitKernel()
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self.assertEqual(
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kernel(self.single_x, self.single_y).shape, torch.Size([]))
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def test_single_batch(self):
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kernel = ExplicitKernel()
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self.assertEqual(
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kernel(self.single_x, self.batch_y).shape, torch.Size([32]))
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def test_batch_single(self):
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kernel = ExplicitKernel()
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self.assertEqual(
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kernel(self.batch_x, self.single_y).shape, torch.Size([32]))
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def test_batch_values(self):
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kernel = ExplicitKernel()
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self.assertEqual(
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kernel(self.batch_x, self.batch_y).shape, torch.Size([32, 32]))
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class TestRadialBasisFunctionKernel(unittest.TestCase):
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def setUp(self):
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self.single_x = torch.randn(1024)
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self.single_y = torch.randn(1024)
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self.batch_x = torch.randn(32, 1024)
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self.batch_y = torch.randn(32, 1024)
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def test_single_values(self):
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kernel = RadialBasisFunctionKernel(1)
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self.assertEqual(
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kernel(self.single_x, self.single_y).shape, torch.Size([]))
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def test_single_batch(self):
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kernel = RadialBasisFunctionKernel(1)
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self.assertEqual(
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kernel(self.single_x, self.batch_y).shape, torch.Size([32]))
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def test_batch_single(self):
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kernel = RadialBasisFunctionKernel(1)
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self.assertEqual(
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kernel(self.batch_x, self.single_y).shape, torch.Size([32]))
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def test_batch_values(self):
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kernel = RadialBasisFunctionKernel(1)
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self.assertEqual(
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kernel(self.batch_x, self.batch_y).shape, torch.Size([32, 32]))
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class TestKernelDistance(unittest.TestCase):
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def setUp(self):
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self.single_x = torch.randn(1024)
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self.single_y = torch.randn(1024)
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self.batch_x = torch.randn(32, 1024)
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self.batch_y = torch.randn(32, 1024)
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self.kernel = ExplicitKernel()
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def test_single_values(self):
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distance = KernelDistance(self.kernel)
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self.assertEqual(
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distance(self.single_x, self.single_y).shape, torch.Size([]))
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def test_single_batch(self):
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distance = KernelDistance(self.kernel)
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self.assertEqual(
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distance(self.single_x, self.batch_y).shape, torch.Size([32]))
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def test_batch_single(self):
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distance = KernelDistance(self.kernel)
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self.assertEqual(
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distance(self.batch_x, self.single_y).shape, torch.Size([32]))
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def test_batch_values(self):
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distance = KernelDistance(self.kernel)
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self.assertEqual(
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distance(self.batch_x, self.batch_y).shape, torch.Size([32, 32]))
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