woche12 > master: code py - leere EPs für walks + genetic hinzugefügt (stubs)

code/python/src/models/random_walk/landscape.py

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