111 lines
3.6 KiB
Python
111 lines
3.6 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# IMPORTS
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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from __future__ import annotations;
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from src.thirdparty.maths import *;
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from src.thirdparty.misc import *;
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from src.thirdparty.types import *;
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from models.generated.commands import *;
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# EXPORTS
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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__all__ = [
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'Landscape',
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];
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# METHOD fitness function -> Array
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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class Landscape():
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_fct: np.ndarray;
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_labels: list[str];
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_metric: EnumLandscapeMetric;
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_radius: float;
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_one_based: bool;
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def __init__(
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self,
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values: DataTypeLandscapeValues,
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labels: List[str],
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metric: EnumLandscapeMetric = EnumLandscapeMetric.maximum,
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one_based: bool = False,
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):
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self._fct = convert_to_nparray(values);
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assert len(labels) == self.dim, 'A label is required for each axis/dimension!';
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self._labels = labels;
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self._metric = metric;
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self._one_based = one_based;
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return;
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@property
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def shape(self) -> tuple:
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return self._fct.shape;
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@property
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def dim(self) -> int:
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return len(self._fct.shape);
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@property
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def coords_middle(self) -> tuple:
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return tuple(math.floor(s/2) for s in self.shape);
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def fitness(self, *x: int) -> float:
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return self._fct[x];
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def label(self, *x: int) -> str:
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if self._one_based:
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x = tuple(xx + 1 for xx in x);
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expr = ','.join([ f'{name}{xx}' for name, xx in zip(self._labels, x)]);
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if self.dim > 1:
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expr = f'({expr})';
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return expr;
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def neighbourhood(self, *x: int, r: float, strict: bool = False) -> List[tuple]:
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r = int(r);
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sides = [
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[ xx - j for j in range(1, r+1) if xx - j in range(s) ]
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+ ([ xx ] if xx in range(s) else [])
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+ [ xx + j for j in range(1, r+1) if xx + j in range(s) ]
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for xx, s in zip(x, self.shape)
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];
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match self._metric:
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case EnumLandscapeMetric.maximum:
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umg = list(itertools_product(*sides));
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case EnumLandscapeMetric.manhattan:
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umg = [
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(*x[:i], xx, *x[(i+1):])
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for i, side in enumerate(sides)
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for xx in side
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];
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case _:
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umg = [ x ];
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if strict:
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umg = [ p for p in umg if p != x ];
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return umg;
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# AUXILIARY METHODS
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def convert_to_array(values: DataTypeLandscapeValues) -> list:
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return [
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x if isinstance(x, float) else convert_to_array(x)
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for x in values.__root__
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];
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def convert_to_nparray(values: DataTypeLandscapeValues) -> np.ndarray:
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try:
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list_of_lists = convert_to_array(values);
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return np.asarray(list_of_lists, dtype=float);
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except:
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raise ValueError('Could not convert to a d-dimensional array! Ensure that the dimensions are consistent.');
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