code/python/models/commands-schema.yaml

@@ -170,21 +170,9 @@ components:
           $ref: '#/components/schemas/DataTypeLandscapeGeometry'
         optimise:
           $ref: '#/components/schemas/EnumOptimiseMode'
-        coords-init:
-          description: Initial co-ordinates to start the algorithm.
-          type: array
-          items:
-            type: integer
-          minItems: 1
-        temperature-init:
-          type: float
-          default: 1.
         annealing:
           type: boolean
           default: false
-        one-based:
-          type: boolean
-          default: false
     # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
     # Algorithm: Genetic Algorithm
     # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -255,16 +243,10 @@ components:
       type: object
       required:
         - neighbourhoods
-        - labels
         - values
       properties:
         neighbourhoods:
           $ref: '#/components/schemas/DataTypeLandscapeNeighbourhoods'
-        labels:
-          type: array
-          items:
-            type: string
-          minItems: 1
         values:
           $ref: '#/components/schemas/DataTypeLandscapeValues'
     DataTypeLandscapeNeighbourhoods:

code/python/src/algorithms/random_walk/algorithms.py

@@ -32,50 +32,14 @@ __all__ = [
 def adaptive_walk_algorithm(
     landscape: Landscape,
     r: float,
-    coords_init: tuple,
     optimise: EnumOptimiseMode,
     verbose: bool,
 ):
     '''
     Führt den Adapative-Walk Algorithmus aus, um ein lokales Minimum zu bestimmen.
     '''
-
+    log_warn('Noch nicht implementiert!');
-    # lege Fitness- und Umgebungsfunktionen fest:
+    return;
-    match optimise:
-        case EnumOptimiseMode.max:
-            f = lambda x: -landscape.fitness(*x);
-        case _:
-            f = lambda x: landscape.fitness(*x);
-    nbhd = lambda x: landscape.neighbourhood(*x, r=r, strict=True);
-    label = lambda x: landscape.label(*x);
-
-    # initialisiere
-    x = coords_init;
-    fx = f(x);
-    fy = fx;
-    N = nbhd(x);
-
-    # führe walk aus:
-    while True:
-        # Wähle zufälligen Punkt und berechne fitness-Wert:
-        y = uniform_random_choice(N);
-        fy = f(y);
-
-        # Nur dann aktualisieren, wenn sich f-Wert verbessert:
-        if fy < fx:
-            # Punkt + Umgebung + f-Wert aktualisieren
-            x = y;
-            fx = fy;
-            N = nbhd(x);
-        else:
-            # Nichts machen!
-            pass;
-
-        # Nur dann (erfolgreich) abbrechen, wenn f-Wert lokal Min:
-        if fx <= min([f(y) for y in N], default=fx):
-            break;
-
-    return x;
 
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 # METHOD gradient walk
@@ -84,56 +48,14 @@ def adaptive_walk_algorithm(
 def gradient_walk_algorithm(
     landscape: Landscape,
     r: float,
-    coords_init: tuple,
     optimise: EnumOptimiseMode,
     verbose: bool,
 ):
     '''
     Führt den Gradient-Descent (bzw. Ascent) Algorithmus aus, um ein lokales Minimum zu bestimmen.
     '''
-
+    log_warn('Noch nicht implementiert!');
-    # lege Fitness- und Umgebungsfunktionen fest:
+    return;
-    match optimise:
-        case EnumOptimiseMode.max:
-            f = lambda x: -landscape.fitness(*x);
-        case _:
-            f = lambda x: landscape.fitness(*x);
-    nbhd = lambda x: landscape.neighbourhood(*x, r=r, strict=True);
-    label = lambda x: landscape.label(*x);
-
-    # initialisiere
-    x = coords_init;
-    fx = landscape.fitness(*x);
-    fy = fx;
-    N = nbhd(x);
-    f_values = [f(y) for y in N];
-    fmin = min(f_values);
-    Z = [y for y, fy in zip(N, f_values) if fy == fmin];
-
-    # führe walk aus:
-    while True:
-        # Wähle zufälligen Punkt mit steilstem Abstieg und berechne fitness-Wert:
-        y = uniform_random_choice(Z);
-        fy = fmin;
-
-        # Nur dann aktualisieren, wenn sich f-Wert verbessert:
-        if fy < fx:
-            # Punkt + Umgebung + f-Wert aktualisieren
-            x = y;
-            fx = fy;
-            N = nbhd(y);
-            f_values = [f(y) for y in N];
-            fmin = min(f_values);
-            Z = [y for y, fy in zip(N, f_values) if fy == fmin];
-        else:
-            # Nichts machen!
-            pass;
-
-        # Nur dann (erfolgreich) abbrechen, wenn f-Wert lokal Min:
-        if fx <= min([f(y) for y in N], default=fx):
-            break;
-
-    return x;
 
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 # METHOD metropolis walk
@@ -142,8 +64,6 @@ def gradient_walk_algorithm(
 def metropolis_walk_algorithm(
     landscape: Landscape,
     r: float,
-    coords_init: tuple,
-    T: float,
     annealing: bool,
     optimise: EnumOptimiseMode,
     verbose: bool,
@@ -151,56 +71,5 @@ def metropolis_walk_algorithm(
     '''
     Führt den Metropolis-Walk Algorithmus aus, um ein lokales Minimum zu bestimmen.
     '''
-
+    log_warn('Noch nicht implementiert!');
-    # lege Fitness- und Umgebungsfunktionen fest:
+    return;
-    match optimise:
-        case EnumOptimiseMode.max:
-            f = lambda x: -landscape.fitness(*x);
-        case _:
-            f = lambda x: landscape.fitness(*x);
-    nbhd = lambda x: landscape.neighbourhood(*x, r=r, strict=True);
-    label = lambda x: landscape.label(*x);
-
-    # initialisiere
-    x = coords_init;
-    fx = f(x);
-    fy = fx;
-    nbhd_x = nbhd(x);
-
-    # führe walk aus:
-    k = 0;
-    while True:
-        # Wähle zufälligen Punkt und berechne fitness-Wert:
-        y = uniform_random_choice(nbhd_x);
-        r = uniform(0,1);
-        fy = f(y);
-
-        # Nur dann aktualisieren, wenn sich f-Wert verbessert:
-        if fy < fx or r < math.exp(-(fy-fx)/T):
-            # Punkt + Umgebung + f-Wert aktualisieren
-            x = y;
-            fx = fy;
-            nbhd_x = nbhd(x);
-        else:
-            # Nichts machen!
-            pass;
-
-        # »Temperatur« ggf. abkühlen:
-        if annealing:
-            T = cool_temperature(T, k);
-
-        # Nur dann (erfolgreich) abbrechen, wenn f-Wert lokal Min:
-        if fx <= min([f(y) for y in nbhd_x], default=fx):
-            break;
-
-        k += 1;
-
-    return x;
-
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-# AUXILIARY METHODS
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-def cool_temperature(T: float, k: int, const: float = 1.) -> float:
-    harm = const*(k + 1);
-    return T/(1 + T/harm);

code/python/src/endpoints/ep_algorithm_random_walk.py

@@ -27,28 +27,15 @@ __all__ = [
 
 @run_safely()
 def endpoint_random_walk(command: CommandRandomWalk) -> Result[CallResult, CallError]:
-    # Compute landscape (fitness fct + topology) + initial co-ordinates:
-    one_based = command.one_based;
     landscape = Landscape(
         values = command.landscape.values,
-        labels = command.landscape.labels,
         metric = command.landscape.neighbourhoods.metric,
-        one_based = one_based,
     );
-    if isinstance(command.coords_init, list):
-        coords_init = tuple(command.coords_init);
-        if one_based:
-            coords_init = tuple(xx - 1 for xx in coords_init);
-        assert len(coords_init) == landscape.dim, 'Dimension of initial co-ordinations inconsistent with landscape!';
-    else:
-        coords_init = landscape.coords_middle;
-
     match command.algorithm:
         case EnumWalkMode.adaptive:
             result = adaptive_walk_algorithm(
                 landscape = landscape,
                 r = command.landscape.neighbourhoods.radius,
-                coords_init = coords_init,
                 optimise = command.optimise,
                 verbose = config.OPTIONS.random_walk.verbose
             );
@@ -56,7 +43,6 @@ def endpoint_random_walk(command: CommandRandomWalk) -> Result[CallResult, CallE
             result = gradient_walk_algorithm(
                 landscape = landscape,
                 r = command.landscape.neighbourhoods.radius,
-                coords_init = coords_init,
                 optimise = command.optimise,
                 verbose = config.OPTIONS.random_walk.verbose
             );
@@ -64,8 +50,6 @@ def endpoint_random_walk(command: CommandRandomWalk) -> Result[CallResult, CallE
             result = metropolis_walk_algorithm(
                 landscape = landscape,
                 r = command.landscape.neighbourhoods.radius,
-                coords_init = coords_init,
-                T = command.temperature_init,
                 annealing = command.annealing,
                 optimise = command.optimise,
                 verbose = config.OPTIONS.random_walk.verbose

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

@@ -27,23 +27,16 @@ __all__ = [
 
 class Landscape():
     _fct: np.ndarray;
-    _labels: list[str];
     _metric: EnumLandscapeMetric;
     _radius: float;
-    _one_based: bool;
 
     def __init__(
         self,
         values: DataTypeLandscapeValues,
-        labels: List[str],
         metric: EnumLandscapeMetric = EnumLandscapeMetric.maximum,
-        one_based: bool = False,
     ):
         self._fct = convert_to_nparray(values);
-        assert len(labels) == self.dim, 'A label is required for each axis/dimension!';
-        self._labels = labels;
         self._metric = metric;
-        self._one_based = one_based;
         return;
 
     @property
@@ -54,27 +47,14 @@ class Landscape():
     def dim(self) -> int:
         return len(self._fct.shape);
 
-    @property
-    def coords_middle(self) -> tuple:
-        return tuple(math.floor(s/2) for s in self.shape);
-
     def fitness(self, *x: int) -> float:
         return self._fct[x];
 
-    def label(self, *x: int) -> str:
-        if self._one_based:
-            x = tuple(xx + 1 for xx in x);
-        expr = ','.join([ f'{name}{xx}' for name, xx in zip(self._labels, x)]);
-        if self.dim > 1:
-            expr = f'({expr})';
-        return expr;
-
     def neighbourhood(self, *x: int, r: float, strict: bool = False) -> List[tuple]:
-        r = int(r);
         sides = [
-            [ xx - j for j in range(1, r+1) if xx - j in range(s) ]
+            [ 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) ]
+            + [ 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:

code/python/src/thirdparty/maths.py

@@ -10,8 +10,6 @@ import math;
 import numpy as np;
 import pandas as pd;
 import random;
-from random import uniform;
-from random import choice as uniform_random_choice;
 from tabulate import tabulate;
 
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -24,7 +22,5 @@ __all__ = [
     'np',
     'pd',
     'random',
-    'uniform',
-    'uniform_random_choice',
     'tabulate',
 ];