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45 lines
1.5 KiB
45 lines
1.5 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from dtaidistance import dtw_ndim
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__all__ = [
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'dtw_distance',
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]
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def dtw_distance(xs: np.ndarray, ys: np.ndarray) -> float:
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"""Dynamic Time Warping.
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This function keeps a compact matrix, not the full warping paths matrix.
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Uses dynamic programming to compute:
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Examples:
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.. code-block:: python
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wps[i, j] = (s1[i]-s2[j])**2 + min(
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wps[i-1, j ] + penalty, // vertical / insertion / expansion
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wps[i , j-1] + penalty, // horizontal / deletion / compression
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wps[i-1, j-1]) // diagonal / match
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dtw = sqrt(wps[-1, -1])
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Args:
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xs (np.ndarray): ref sequence, [T,D]
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ys (np.ndarray): hyp sequence, [T,D]
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Returns:
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float: dtw distance
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"""
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return dtw_ndim.distance(xs, ys)
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