code/python/src/endpoints/ep_algorithm_random_walk.py

@@ -27,15 +27,28 @@ __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
             );
@@ -43,6 +56,7 @@ 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
             );
@@ -50,6 +64,8 @@ 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

@@ -27,16 +27,23 @@ __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
@@ -47,14 +54,27 @@ 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: