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

code/python/assets/config.yaml

@@ -36,6 +36,10 @@ options:
     # show:
     #   - ALL-WEIGHTS
     #   - ALL-SUMS
+  genetic:
+    verbose: *ref_verbose
+  random-walk:
+    verbose: *ref_verbose
   euklid:
     verbose: *ref_verbose
   pollard-rho:

code/python/models/commands-schema.yaml

@@ -150,6 +150,51 @@ components:
             type: string
           default: []
     # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    # Algorithm: Random Walk
+    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    CommandRandomWalk:
+      description: |-
+        Instructions for execution of random walks to determine local extrema in a fitness landscape
+      type: object
+      required:
+        - name
+        - algorithm
+        - landscape
+        - optimise
+      properties:
+        name:
+          $ref: '#/components/schemas/EnumAlgorithmNames'
+        algorithm:
+          $ref: '#/components/schemas/EnumWalkMode'
+        landscape:
+          $ref: '#/components/schemas/DataTypeLandscapeGeometry'
+        optimise:
+          $ref: '#/components/schemas/EnumOptimiseMode'
+        annealing:
+          type: boolean
+          default: false
+    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    # Algorithm: Genetic Algorithm
+    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    CommandGenetic:
+      description: |-
+        Instructions for execution of the Genetic algorithm
+      type: object
+      required:
+        - name
+        - population
+      properties:
+        name:
+          $ref: '#/components/schemas/EnumAlgorithmNames'
+        population:
+          type: array
+          items:
+            type: array
+            items:
+              type: string
+          minItems: 2
+          # maxItems: 2 # FIXME: does not work!
+    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
     # Algorithm: Euklidean algorithm
     # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
     CommandEuklid:
@@ -190,6 +235,44 @@ components:
           default: 2
           minimum: 2
     # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    # Data-type Landscape Geometry, Landscape Values
+    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    DataTypeLandscapeGeometry:
+      description: |-
+        Structure for the geometry of a fitness landscape
+      type: object
+      required:
+        - neighbourhoods
+        - values
+      properties:
+        neighbourhoods:
+          $ref: '#/components/schemas/DataTypeLandscapeNeighbourhoods'
+        values:
+          $ref: '#/components/schemas/DataTypeLandscapeValues'
+    DataTypeLandscapeNeighbourhoods:
+      description: |-
+        Options for the definition of discrete neighbourhoods of a fitness landscape
+      type: object
+      required:
+        - metric
+      properties:
+        radius:
+          type: number
+          minimum: 1
+          default: 1
+        metric:
+          $ref: '#/components/schemas/EnumLandscapeMetric'
+    DataTypeLandscapeValues:
+      description: |-
+        A (potentially multi-dimensional) array of values for the fitness landscape.
+      oneOf:
+        - type: array
+          items:
+            type: number
+        - type: array
+          items:
+            $ref: '#/components/schemas/DataTypeLandscapeValues'
+    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
     # Enum Algorithm Names
     # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
     EnumAlgorithmNames:
@@ -201,6 +284,8 @@ components:
         - TSP
         - HIRSCHBERG
         - RUCKSACK
+        - RANDOM-WALK
+        - GENETIC
         - EUKLID
         - POLLARD-RHO
     # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -223,3 +308,29 @@ components:
       enum:
         - GREEDY
         - BRANCH-AND-BOUND
+    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    # Enum Type of walk mode for fitness walk algorithm
+    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    EnumWalkMode:
+      description: |-
+        Enumeration of walk mode for fitness walk algorithm
+        - `ADAPTIVE` - points uniformly randomly chosen from nbhd.
+        - `GRADIENT` - points uniformly randomly chosen amongst points in nbhd with steepest gradient.
+        - `METROPOLIS` - points uniformly randomly chosen from nbhd. or by entropy.
+      type: string
+      enum:
+        - ADAPTIVE
+        - GRADIENT
+        - METROPOLIS
+    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    # Enum for metric for neighbourhoods in fitness landscape
+    # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    EnumLandscapeMetric:
+      description: |-
+        Enumeration of mode for Rucksack problem
+        - `MAXIMUM` - `Q` is a neighbour of `P` <==> `max_i d(P_i, Q_i) <= r`
+        - `MANHATTAN` - `Q` is a neighbour of `P` <==> `sum_i d(P_i, Q_i) <= r`
+      type: string
+      enum:
+        - MAXIMUM
+        - MANHATTAN

code/python/models/config-schema.yaml

@@ -54,6 +54,8 @@ components:
         - tarjan
         - hirschberg
         - rucksack
+        - random-walk
+        - genetic
         - euklid
         - pollard-rho
       properties:
@@ -129,6 +131,22 @@ components:
               items:
                 $ref: '#/components/schemas/EnumRucksackShow'
               default: []
+        random-walk:
+          type: object
+          required:
+            - verbose
+          properties:
+            verbose:
+              type: boolean
+              default: false
+        genetic:
+          type: object
+          required:
+            - verbose
+          properties:
+            verbose:
+              type: boolean
+              default: false
         euklid:
           type: object
           required:

code/python/src/algorithms/genetic/__init__.py

@@ -0,0 +1,16 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# IMPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+from src.algorithms.genetic.algorithms import *;
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# EXPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+__all__ = [
+    'genetic_algorithm',
+];

code/python/src/algorithms/genetic/algorithms.py

@@ -0,0 +1,38 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# IMPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+from src.thirdparty.types import *;
+from src.thirdparty.maths import *;
+
+from models.generated.config import *;
+from src.core.log import *;
+from src.core.utils import *;
+from src.models.genetic import *;
+from src.algorithms.genetic.display import *;
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# EXPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+__all__ = [
+    'genetic_algorithm',
+];
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# METHOD genetic algorithm
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+def genetic_algorithm(
+    individual1: List[str],
+    individual2: List[str],
+    verbose: bool,
+):
+    '''
+    Führt den genetischen Algorithmus auf 2 Individuen aus.
+    '''
+    log_warn('Noch nicht implementiert!');
+    return;

code/python/src/algorithms/genetic/display.py

@@ -0,0 +1,30 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# IMPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+from src.thirdparty.code import *;
+from src.thirdparty.maths import *;
+from src.thirdparty.types import *;
+
+from src.core.log import *;
+from src.models.genetic import *;
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# EXPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+__all__ = [
+    'display_table',
+];
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# METHOD display table
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+def display_table(
+) -> str:
+    log_warn('Noch nicht implementiert!');
+    return '';

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

@@ -0,0 +1,18 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# IMPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+from src.algorithms.random_walk.algorithms import *;
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# EXPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+__all__ = [
+    'adaptive_walk_algorithm',
+    'gradient_walk_algorithm',
+    'metropolis_walk_algorithm',
+];

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

@@ -0,0 +1,75 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# IMPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+from src.thirdparty.types import *;
+from src.thirdparty.maths import *;
+
+from models.generated.config import *;
+from models.generated.commands import *;
+from src.core.log import *;
+from src.core.utils import *;
+from src.models.random_walk import *;
+from src.algorithms.random_walk.display import *;
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# EXPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+__all__ = [
+    'adaptive_walk_algorithm',
+    'gradient_walk_algorithm',
+    'metropolis_walk_algorithm',
+];
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# METHOD adaptive walk
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+def adaptive_walk_algorithm(
+    landscape: Landscape,
+    r: float,
+    optimise: EnumOptimiseMode,
+    verbose: bool,
+):
+    '''
+    Führt den Adapative-Walk Algorithmus aus, um ein lokales Minimum zu bestimmen.
+    '''
+    log_warn('Noch nicht implementiert!');
+    return;
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# METHOD gradient walk
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+def gradient_walk_algorithm(
+    landscape: Landscape,
+    r: float,
+    optimise: EnumOptimiseMode,
+    verbose: bool,
+):
+    '''
+    Führt den Gradient-Descent (bzw. Ascent) Algorithmus aus, um ein lokales Minimum zu bestimmen.
+    '''
+    log_warn('Noch nicht implementiert!');
+    return;
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# METHOD metropolis walk
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+def metropolis_walk_algorithm(
+    landscape: Landscape,
+    r: float,
+    annealing: bool,
+    optimise: EnumOptimiseMode,
+    verbose: bool,
+):
+    '''
+    Führt den Metropolis-Walk Algorithmus aus, um ein lokales Minimum zu bestimmen.
+    '''
+    log_warn('Noch nicht implementiert!');
+    return;

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

@@ -0,0 +1,30 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# IMPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+from src.thirdparty.code import *;
+from src.thirdparty.maths import *;
+from src.thirdparty.types import *;
+
+from src.core.log import *;
+from src.models.random_walk import *;
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# EXPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+__all__ = [
+    'display_table',
+];
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# METHOD display table
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+def display_table(
+) -> str:
+    log_warn('Noch nicht implementiert!');
+    return '';

code/python/src/api.py

@@ -40,6 +40,10 @@ def run_command(command: Command) -> Result[CallResult, CallError]:
         return endpoint_hirschberg(command);
     elif isinstance(command, CommandRucksack):
         return endpoint_rucksack(command);
+    elif isinstance(command, CommandRandomWalk):
+        return endpoint_random_walk(command);
+    elif isinstance(command, CommandGenetic):
+        return endpoint_genetic(command);
     elif isinstance(command, CommandEuklid):
         return endpoint_euklid(command);
     elif isinstance(command, CommandPollard):

code/python/src/endpoints/__init__.py

@@ -9,6 +9,8 @@ from src.endpoints.ep_algorithm_hirschberg import *;
 from src.endpoints.ep_algorithm_tarjan import *;
 from src.endpoints.ep_algorithm_tsp import *;
 from src.endpoints.ep_algorithm_rucksack import *;
+from src.endpoints.ep_algorithm_genetic import *;
+from src.endpoints.ep_algorithm_random_walk import *;
 from src.endpoints.ep_algorithm_euklid import *;
 from src.endpoints.ep_algorithm_pollard_rho import *;
 
@@ -21,6 +23,8 @@ __all__ = [
     'endpoint_tarjan',
     'endpoint_tsp',
     'endpoint_rucksack',
+    'endpoint_random_walk',
+    'endpoint_genetic',
     'endpoint_euklid',
     'endpoint_pollard_rho',
 ];

code/python/src/endpoints/ep_algorithm_euklid.py

@@ -29,6 +29,6 @@ def endpoint_euklid(command: CommandEuklid) -> Result[CallResult, CallError]:
     result = euklidean_algorithm(
         a = command.numbers[0].__root__,
         b = command.numbers[1].__root__,
-        verbose = config.OPTIONS.tsp.verbose,
+        verbose = config.OPTIONS.euklid.verbose,
     );
     return Ok(CallResult(action_taken=True, message=result));

code/python/src/endpoints/ep_algorithm_genetic.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# IMPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+from src.thirdparty.code import *;
+
+from models.generated.commands import *;
+from src.core.calls import *;
+from src.setup import config;
+from src.algorithms.genetic import *;
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# EXPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+__all__ = [
+    'endpoint_genetic',
+];
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# ENDPOINT
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+@run_safely()
+def endpoint_genetic(command: CommandGenetic) -> Result[CallResult, CallError]:
+    result = genetic_algorithm(
+        individual1 = command.population[0],
+        individual2 = command.population[1],
+        verbose = config.OPTIONS.genetic.verbose,
+    );
+    return Ok(CallResult(action_taken=True, message=result));

code/python/src/endpoints/ep_algorithm_pollard_rho.py

@@ -29,6 +29,6 @@ def endpoint_pollard_rho(command: CommandPollard) -> Result[CallResult, CallErro
     result = pollard_rho_algorithm(
         n = command.number,
         x_init = command.x_init,
-        verbose = config.OPTIONS.tsp.verbose,
+        verbose = config.OPTIONS.pollard_rho.verbose,
     );
     return Ok(CallResult(action_taken=True, message=result));

code/python/src/endpoints/ep_algorithm_random_walk.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# IMPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+from src.thirdparty.code import *;
+
+from models.generated.commands import *;
+from src.core.calls import *;
+from src.setup import config;
+from src.models.random_walk import *;
+from src.algorithms.random_walk import *;
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# EXPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+__all__ = [
+    'endpoint_random_walk',
+];
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# ENDPOINT
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+@run_safely()
+def endpoint_random_walk(command: CommandRandomWalk) -> Result[CallResult, CallError]:
+    landscape = Landscape(
+        values = command.landscape.values,
+        metric = command.landscape.neighbourhoods.metric,
+    );
+    match command.algorithm:
+        case EnumWalkMode.adaptive:
+            result = adaptive_walk_algorithm(
+                landscape = landscape,
+                r = command.landscape.neighbourhoods.radius,
+                optimise = command.optimise,
+                verbose = config.OPTIONS.random_walk.verbose
+            );
+        case EnumWalkMode.gradient:
+            result = gradient_walk_algorithm(
+                landscape = landscape,
+                r = command.landscape.neighbourhoods.radius,
+                optimise = command.optimise,
+                verbose = config.OPTIONS.random_walk.verbose
+            );
+        case EnumWalkMode.metropolis:
+            result = metropolis_walk_algorithm(
+                landscape = landscape,
+                r = command.landscape.neighbourhoods.radius,
+                annealing = command.annealing,
+                optimise = command.optimise,
+                verbose = config.OPTIONS.random_walk.verbose
+            );
+        case _ as alg:
+            raise Exception(f'No algorithm implemented for {alg.value}.');
+    return Ok(CallResult(action_taken=True, message=result));

code/python/src/endpoints/ep_algorithm_rucksack.py

@@ -50,5 +50,5 @@ def endpoint_rucksack(command: CommandRucksack) -> Result[CallResult, CallError]
                 verbose = config.OPTIONS.rucksack.verbose,
             );
         case _ as alg:
-            raise Exception(f'No algorithm implemented for {alg.value}.')
+            raise Exception(f'No algorithm implemented for {alg.value}.');
     return Ok(CallResult(action_taken=True, message=result));

code/python/src/models/config/commands.py

@@ -44,6 +44,10 @@ def interpret_command(command: Command) -> Command:
             return CommandHirschberg(**command.dict());
         case EnumAlgorithmNames.rucksack:
             return CommandRucksack(**command.dict());
+        case EnumAlgorithmNames.random_walk:
+            return CommandRandomWalk(**command.dict());
+        case EnumAlgorithmNames.genetic:
+            return CommandGenetic(**command.dict());
         case EnumAlgorithmNames.euklid:
             return CommandEuklid(**command.dict());
         case EnumAlgorithmNames.pollard_rho:

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

@@ -0,0 +1,16 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# IMPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+from src.models.random_walk.landscape import *;
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# EXPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+__all__ = [
+    'Landscape',
+];

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

@@ -0,0 +1,90 @@
+#!/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.');

code/python/src/thirdparty/misc.py

@@ -7,6 +7,7 @@
 
 from datetime import datetime;
 from datetime import timedelta;
+from itertools import product as itertools_product;
 import lorem;
 import re;
 from textwrap import dedent;
@@ -16,9 +17,10 @@ from textwrap import dedent;
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 __all__ = [
-    'lorem',
     'datetime',
     'timedelta',
+    'itertools_product',
+    'lorem',
     're',
     'dedent',
 ];