Line | |
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1 | from abc import ABC |
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2 | |
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3 | from evolalg.base.step import Step |
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4 | import numpy as np |
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5 | |
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6 | |
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7 | class Dissimilarity(Step, ABC): |
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8 | |
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9 | def __init__(self, reduction="mean", output_field="dissim", *args, **kwargs): |
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10 | super(Dissimilarity, self).__init__(*args, **kwargs) |
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11 | |
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12 | self.output_field = output_field |
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13 | self.fn_reduce = None |
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14 | if reduction == "mean": |
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15 | self.fn_reduce = np.mean |
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16 | elif reduction == "max": |
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17 | self.fn_reduce = np.max |
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18 | elif reduction == "min": |
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19 | self.fn_reduce = np.min |
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20 | elif reduction == "sum": |
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21 | self.fn_reduce = np.sum |
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22 | elif reduction == "none" or reduction == None: |
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23 | self.fn_reduce = None |
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24 | else: |
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25 | raise ValueError("Unknown reduction type. Supported: mean, max, min, sum, none") |
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26 | |
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27 | def reduce(self, dissim_matrix): |
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28 | if self.fn_reduce is None: |
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29 | return dissim_matrix |
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30 | return self.fn_reduce(dissim_matrix, axis=1) |
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