/**
* Calculates the fitness of the given solution by setting the neural network weights to the
* solution and evaluating the training set in order to calculate the MSE (which is minimized).
*
* @param solution the weights representing a solution.
* @return a new MinimisationFitness wrapping the MSE training error.
*/
@Override
protected Fitness calculateFitness(Type solution) {
if (trainingSet == null) {
this.initialise();
}
int currentIteration = AbstractAlgorithm.get().getIterations();
if (currentIteration != previousShuffleIteration) {
try {
shuffler.operate(trainingSet);
} catch (CIlibIOException exception) {
exception.printStackTrace();
}
}
neuralNetwork.getArchitecture().accept(solutionConversionStrategy.interpretSolution(solution));
double errorTraining = 0.0;
OutputErrorVisitor visitor = new OutputErrorVisitor();
Vector error = null;
for (StandardPattern pattern : trainingSet) {
Vector output = neuralNetwork.evaluatePattern(pattern);
visitor.setInput(pattern);
neuralNetwork.getArchitecture().accept(visitor);
error = visitor.getOutput();
for (Numeric real : error) {
errorTraining += real.doubleValue() * real.doubleValue();
}
}
errorTraining /= trainingSet.getNumRows() * error.size();
return objective.evaluate(errorTraining);
}
/**
* @param x
* @return
*/
private int length(Vector x) {
int count = 0;
for (Numeric n : x) {
if (n.booleanValue()) {
count++;
}
}
return count;
}
@Override
public void performIteration(PSO algorithm) {
delegate.performIteration(algorithm);
Topology<Particle> topology = algorithm.getTopology();
// calculate vAvg
Vector avgV =
Vectors.mean(
Lists.transform(
topology,
new Function<Particle, Vector>() {
@Override
public Vector apply(Particle f) {
return (Vector) f.getVelocity();
}
}));
Vector.Builder builder = Vector.newBuilder();
for (Numeric n : avgV) {
if (Math.abs(n.doubleValue()) > vMax.getParameter()) {
builder.add(vMax.getParameter());
} else {
builder.add(n);
}
}
avgV = builder.build();
// mutation
Particle gBest = Topologies.getBestEntity(topology, new SocialBestFitnessComparator());
Particle mutated = gBest.getClone();
Vector pos = (Vector) gBest.getBestPosition();
final Bounds bounds = pos.boundsOf(0);
pos =
pos.plus(
avgV.multiply(
new Supplier<Number>() {
@Override
public Number get() {
return distribution.getRandomNumber() * bounds.getRange()
+ bounds.getLowerBound();
}
}));
mutated.setCandidateSolution(pos);
mutated.calculateFitness();
if (gBest.getBestFitness().compareTo(mutated.getFitness()) < 0) {
gBest.getProperties().put(EntityType.Particle.BEST_FITNESS, mutated.getBestFitness());
gBest.getProperties().put(EntityType.Particle.BEST_POSITION, mutated.getBestPosition());
}
}
private String getSubSequence(Vector x, String target) {
StringBuilder builder = new StringBuilder();
for (int i = 0; i < x.size(); i++) {
Numeric n = x.get(i);
if (n.booleanValue()) {
builder.append(target.charAt(i));
}
}
return builder.toString();
}
/** Test velocity clamping. */
@Test
public void testClamping() {
Particle particle = createParticle(Vector.of(0.0));
Particle nBest = createParticle(Vector.of(1.0));
particle.setNeighbourhoodBest(nBest);
nBest.setNeighbourhoodBest(nBest);
ClampingVelocityProvider velocityProvider = new ClampingVelocityProvider();
velocityProvider.setVMax(new ConstantControlParameter(0.5));
Vector velocity = velocityProvider.get(particle);
for (Numeric number : velocity) {
Assert.assertTrue(Double.compare(number.doubleValue(), 0.5) <= 0);
Assert.assertTrue(Double.compare(number.doubleValue(), -0.5) >= 0);
}
}
private void updateInertia(PSO pso) {
int dimension = pso.getTopology().last().getDimension();
if (inertiaWeight.size() < dimension) {
Vector.Builder builder = Vector.newBuilder();
builder.repeat(dimension, Real.valueOf(initialInertiaWeight.getParameter()));
inertiaWeight = builder.build();
}
Vector.Builder builder = Vector.newBuilder();
for (int i = 0; i < dimension; i++) {
builder.add(
Math.sqrt(
Math.pow(absoluteAverageVelocityVector.doubleValueOf(i), 2)
+ Math.pow(averageSpeedVector.doubleValueOf(i), 2)));
}
Vector d = builder.build(); // get the degree of convergence vector
double max_d = 0;
for (Numeric component : d) {
if (component.doubleValue() > max_d) {
max_d = component.doubleValue();
}
}
if (max_d != 0) {
Vector.Builder builder2 = Vector.newBuilder();
for (Numeric component : d) {
builder2.add(max_d / (max_d + component.doubleValue()));
}
Vector w = builder2.build();
/*double sum_w = 0;
for(Numeric component : w) {
sum_w += component.doubleValue();
}
/*
Vector.Builder builder3 = Vector.newBuilder();
for(Numeric component : w) {
builder3.add(Math.pow(dimension * component.doubleValue() / sum_w, pwr.getParameter()));
} */
/*
for(Numeric component : w) {
//builder3.add(component.doubleValue() - w_mean / w_stdDiv);
builder3.add(component.doubleValue() * initialInertiaWeight.getParameter());
}
for(int i = 0; i < inertiaWeight.size(); i++) {
builder3.add(w.doubleValueOf(i) * inertiaWeight.doubleValueOf(i));
}
*/
/*
Vector m = builder3.build();
double sum_m = 0;
for (Numeric num : m) {
sum_m += num.doubleValue();
}
double m_mean = sum_m / (double) dimension;
double sum_diff_squared = 0;
for(Numeric component : m) {
sum_diff_squared += Math.pow(component.doubleValue() - m_mean, 2);
}
double m_stdDiv = Math.sqrt(sum_diff_squared / (double) dimension);
*/
// System.out.println("VEL: StdDiv of M: " + m_stdDiv + ", mean of M: " + m_mean);
for (int i = 0; i < inertiaWeight.size(); i++) {
inertiaWeight.setReal(
i,
(1 - filter.getParameter()) * w.doubleValueOf(i)
+ filter.getParameter()
* inertiaWeight.doubleValueOf(i)); // w.doubleValueOf(i));//;
}
}
}