Merge branch 'main' into add-exchange-heuristic-restarts

anti_clustering/_cluster_swap_heuristic.py

@@ -80,3 +80,12 @@ def _get_random_clusters(self, num_groups: int, num_elements: int) -> npt.NDArra
         )
 
         return cluster_assignment
+
+    def _calculate_objective(self, cluster_assignment: npt.NDArray[bool], distance_matrix: npt.NDArray[float]) -> float:
+        """
+        Calculate objective value
+        :param cluster_assignment: Cluster assignment matrix
+        :param distance_matrix: Cost matrix
+        :return: Objective value
+        """
+        return np.multiply(cluster_assignment, distance_matrix).sum()

anti_clustering/exchange_heuristic.py

@@ -19,7 +19,6 @@
 Psychological Methods, 26(2), 161–174. https://doi.org/10.1037/met0000301
 """
 
-import numpy as np
 import numpy.typing as npt
 from anti_clustering._cluster_swap_heuristic import ClusterSwapHeuristic
 
@@ -82,12 +81,3 @@ def _solve(self, distance_matrix: npt.NDArray[float], num_groups: int) -> npt.ND
         _, best_cluster_assignment = max(candidate_solutions, key=lambda x: x[0])
 
         return best_cluster_assignment
-
-    def _calculate_objective(self, cluster_assignment: npt.NDArray[bool], distance_matrix: npt.NDArray[float]) -> float:
-        """
-        Calculate objective value
-        :param cluster_assignment: Cluster assignment matrix
-        :param distance_matrix: Cost matrix
-        :return: Objective value
-        """
-        return np.multiply(cluster_assignment, distance_matrix).sum()

anti_clustering/naive_random_heuristic.py

@@ -24,8 +24,20 @@ class NaiveRandomHeuristicAntiClustering(ClusterSwapHeuristic):
     The naive randomized way of solving the anti-clustering problem.
     """
 
-    def __init__(self, verbose: bool = False, random_seed: int = None):
+    def __init__(self, verbose: bool = False, random_seed: int = None, iterations: int = 1000):
         super().__init__(verbose=verbose, random_seed=random_seed)
+        self.iterations = iterations
 
     def _solve(self, distance_matrix: npt.NDArray[float], num_groups: int) -> npt.NDArray[bool]:
-        return self._get_random_clusters(num_groups=num_groups, num_elements=len(distance_matrix))
+        best_candidate = self._get_random_clusters(num_groups=num_groups, num_elements=len(distance_matrix))
+        best_objective = self._calculate_objective(best_candidate, distance_matrix)
+
+        for _ in range(self.iterations):
+            candidate = self._get_random_clusters(num_groups=num_groups, num_elements=len(distance_matrix))
+            objective = self._calculate_objective(candidate, distance_matrix)
+
+            if objective > best_objective:
+                best_candidate = candidate
+                best_objective = objective
+
+        return best_candidate

anti_clustering/simulated_annealing_heuristic.py

@@ -16,7 +16,6 @@
 """
 
 import math
-import numpy as np
 import numpy.typing as npt
 from anti_clustering._cluster_swap_heuristic import ClusterSwapHeuristic
 
@@ -92,15 +91,6 @@ def _solve(self, distance_matrix: npt.NDArray[float], num_groups: int) -> npt.ND
 
         return best_cluster_assignment
 
-    def _calculate_objective(self, cluster_assignment: npt.NDArray[bool], distance_matrix: npt.NDArray[float]) -> float:
-        """
-        Calculate objective value
-        :param cluster_assignment: Cluster assignment
-        :param distance_matrix: Distance matrix
-        :return: Objective value
-        """
-        return np.multiply(cluster_assignment, distance_matrix).sum()
-
     def _accept(self, delta: float, temperature: float) -> bool:
         """
         Simulated annealing acceptance function. Notice d/t is used instead of -d/t because we are maximizing.