| 1 | from abc import ABC |
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| 2 | |
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| 3 | import numpy as np |
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| 4 | |
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| 5 | from evolalg.base.frams_step import FramsStep |
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| 6 | from evolalg.dissimilarity.dissimilarity import Dissimilarity |
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| 7 | |
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| 8 | |
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| 9 | class FramsDissimilarity(FramsStep): |
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| 10 | |
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| 11 | def __init__(self, frams_lib, reduction="mean", output_field="dissim", *args, **kwargs): |
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| 12 | super(FramsDissimilarity, self).__init__(frams_lib, *args, **kwargs) |
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| 13 | |
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| 14 | self.output_field = output_field |
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| 15 | self.fn_reduce = None |
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| 16 | if reduction == "mean": |
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| 17 | self.fn_reduce = np.mean |
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| 18 | elif reduction == "max": |
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| 19 | self.fn_reduce = np.max |
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| 20 | elif reduction == "min": |
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| 21 | self.fn_reduce = np.min |
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| 22 | elif reduction == "sum": |
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| 23 | self.fn_reduce = np.sum |
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| 24 | elif reduction == "none" or reduction is None: |
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| 25 | self.fn_reduce = None |
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| 26 | else: |
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| 27 | raise ValueError("Unknown reduction type. Supported: mean, max, min, sum, none") |
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| 28 | |
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| 29 | def reduce(self, dissim_matrix): |
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| 30 | if self.fn_reduce is None: |
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| 31 | return dissim_matrix |
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| 32 | return self.fn_reduce(dissim_matrix, axis=1) |
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| 33 | |
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| 34 | def call(self, population): |
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| 35 | super(FramsDissimilarity, self).call(population) |
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| 36 | if len(population) == 0: |
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| 37 | return [] |
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| 38 | dissim_matrix = self.frams.dissimilarity([_.genotype for _ in population]) |
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| 39 | dissim = self.reduce(dissim_matrix) |
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| 40 | for d,ind in zip(dissim, population): |
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| 41 | setattr(ind, self.output_field, d) |
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| 42 | return population |
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