final DistanceMeasure dist = new EuclideanDistance();
final LearningFactorFunction learning
= LearningFactorFunctionFactory.exponentialDecay(1, 0.1, 100);
final NeighbourhoodSizeFunction neighbourhood
= NeighbourhoodSizeFunctionFactory.exponentialDecay(3, 1, 100);
final UpdateAction update = new KohonenUpdateAction(dist, learning, neighbourhood);
// The following test ensures that, after one "update",
// 1. when the initial learning rate equal to 1, the best matching
// neuron's features are mapped to the input's features,
// 2. when the initial neighbourhood is larger than the network's size,
// all neuron's features get closer to the input's features.
final double[] features = new double[] { 0.3 };
final double[] distancesBefore = new double[netSize];
int count = 0;
for (Neuron n : net) {
distancesBefore[count++] = dist.compute(n.getFeatures(), features);
}
final Neuron bestBefore = MapUtils.findBest(features, net, dist);
// Initial distance from the best match is larger than zero.
Assert.assertTrue(dist.compute(bestBefore.getFeatures(), features) >= 0.2);
update.update(net, features);
final double[] distancesAfter = new double[netSize];
count = 0;
for (Neuron n : net) {
distancesAfter[count++] = dist.compute(n.getFeatures(), features);