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| from typing import Optional |
|
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| from torch import Tensor |
| from typing_extensions import Literal |
|
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| from torchmetrics.functional.pairwise.helpers import _check_input, _reduce_distance_matrix |
| from torchmetrics.utilities.compute import _safe_matmul |
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|
|
| def _pairwise_linear_similarity_update( |
| x: Tensor, y: Optional[Tensor] = None, zero_diagonal: Optional[bool] = None |
| ) -> Tensor: |
| """Calculate the pairwise linear similarity matrix. |
| |
| Args: |
| x: tensor of shape ``[N,d]`` |
| y: tensor of shape ``[M,d]`` |
| zero_diagonal: determines if the diagonal of the distance matrix should be set to zero |
| |
| """ |
| x, y, zero_diagonal = _check_input(x, y, zero_diagonal) |
|
|
| distance = _safe_matmul(x, y) |
| if zero_diagonal: |
| distance.fill_diagonal_(0) |
| return distance |
|
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|
|
| def pairwise_linear_similarity( |
| x: Tensor, |
| y: Optional[Tensor] = None, |
| reduction: Literal["mean", "sum", "none", None] = None, |
| zero_diagonal: Optional[bool] = None, |
| ) -> Tensor: |
| r"""Calculate pairwise linear similarity. |
| |
| .. math:: |
| s_{lin}(x,y) = <x,y> = \sum_{d=1}^D x_d \cdot y_d |
| |
| If both :math:`x` and :math:`y` are passed in, the calculation will be performed pairwise between |
| the rows of :math:`x` and :math:`y`. |
| If only :math:`x` is passed in, the calculation will be performed between the rows of :math:`x`. |
| |
| Args: |
| x: Tensor with shape ``[N, d]`` |
| y: Tensor with shape ``[M, d]``, optional |
| reduction: reduction to apply along the last dimension. Choose between `'mean'`, `'sum'` |
| (applied along column dimension) or `'none'`, `None` for no reduction |
| zero_diagonal: if the diagonal of the distance matrix should be set to 0. If only `x` is given |
| this defaults to `True` else if `y` is also given it defaults to `False` |
| |
| Returns: |
| A ``[N,N]`` matrix of distances if only ``x`` is given, else a ``[N,M]`` matrix |
| |
| Example: |
| >>> import torch |
| >>> from torchmetrics.functional.pairwise import pairwise_linear_similarity |
| >>> x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32) |
| >>> y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32) |
| >>> pairwise_linear_similarity(x, y) |
| tensor([[ 2., 7.], |
| [ 3., 11.], |
| [ 5., 18.]]) |
| >>> pairwise_linear_similarity(x) |
| tensor([[ 0., 21., 34.], |
| [21., 0., 55.], |
| [34., 55., 0.]]) |
| |
| """ |
| distance = _pairwise_linear_similarity_update(x, y, zero_diagonal) |
| return _reduce_distance_matrix(distance, reduction) |
|
|
