def clusterPopulation(self):
"""Clusters the given population into k clusters using balanced k-leader-means clustering."""
# TODO: POSSIBLE CHANGE = CALCULATE OPTIMAL k VALUE
# The first leader is the solution with maximum value in an arbitrary objective.
leaders = [self.population[0]]
for solution in self.population:
if solution.fitness[0] > leaders[0].fitness[0]:
leaders[0] = solution
# The solution with the largest nearest-leader distance is chosen as the next leader,
# repeated k - 1 times to obtain k leaders.
for _ in range(self.amountOfClusters - 1):
nearestLeaderDistance = {}
for solution in self.population:
if solution not in leaders:
nearestLeaderDistance[solution] = util.euclidianDistance(
solution.fitness, leaders[0].fitness)
for leader in leaders:
leaderDistance = util.euclidianDistance(
solution.fitness, leader.fitness)
if leaderDistance < nearestLeaderDistance[solution]:
nearestLeaderDistance[solution] = leaderDistance
leader = max(nearestLeaderDistance, key=nearestLeaderDistance.get)
leaders.append(leader)
# k-means clustering is performed with k leaders as the initial cluster means.
clusters = []
for leader in leaders:
mean = leader.fitness
cluster = Cluster(mean, self.problem)
clusters.append(cluster)
# Perform k-means clustering until all clusters are unchanged.
while True in [cluster.changed for cluster in clusters]:
for solution in self.population:
nearestCluster = clusters[0]
nearestClusterDistance = util.euclidianDistance(
solution.fitness, nearestCluster.mean)
for cluster in clusters:
clusterDistance = util.euclidianDistance(
solution.fitness, cluster.mean)
if clusterDistance < nearestClusterDistance:
nearestCluster = cluster
nearestClusterDistance = clusterDistance
nearestCluster.append(solution)
for cluster in clusters:
cluster.computeMean()
cluster.clear()
# Expand the clusters with the closest c solutions.
c = int(2 / self.amountOfClusters * self.populationSize)
for cluster in clusters:
distance = {}
for solution in self.population:
distance[solution] = util.euclidianDistance(
solution.fitness, cluster.mean)
for _ in range(c):
if len(distance) > 0:
solution = min(distance, key=distance.get)
del distance[solution]
cluster.append(solution)
self.clusters = clusters
