32 lines
842 B
Python
32 lines
842 B
Python
"""ProtoTorch similarities."""
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import torch
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from .distances import euclidean_distance
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def gaussian(x, variance=1.0):
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return torch.exp(-(x * x) / (2 * variance))
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def euclidean_similarity(x, y, variance=1.0):
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distances = euclidean_distance(x, y)
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similarities = gaussian(distances, variance)
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return similarities
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def cosine_similarity(x, y):
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"""Compute the cosine similarity between :math:`x` and :math:`y`.
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Expected dimension of x is 2.
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Expected dimension of y is 2.
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"""
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x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
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norm_x = x.pow(2).sum(1).sqrt()
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norm_y = y.pow(2).sum(1).sqrt()
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norm_mat = norm_x.unsqueeze(-1) @ norm_y.unsqueeze(-1).T
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epsilon = torch.finfo(norm_mat.dtype).eps
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norm_mat.clamp_(min=epsilon)
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similarities = (x @ y.T) / norm_mat
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return similarities
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