@Test
public void value() {
PSO pso = new PSO();
MeasuredStoppingCondition maximumIterations =
new MeasuredStoppingCondition(new Iterations(), new Maximum(), 100);
pso.addStoppingCondition(maximumIterations);
for (int i = 0; i < 10; i++) pso.performIteration();
PercentageComplete percentageComplete = new PercentageComplete();
Assert.assertEquals(0.1, percentageComplete.getValue(pso).doubleValue(), 0.001);
}
@Test
public void algorithmExecution() {
NNDataTrainingProblem problem = new NNDataTrainingProblem();
problem.getDataTableBuilder().setDataReader(new ARFFFileReader());
problem.getDataTableBuilder().setSourceURL("library/src/test/resources/datasets/iris.arff");
problem.setTrainingSetPercentage(0.7);
problem.setGeneralizationSetPercentage(0.3);
problem
.getNeuralNetwork()
.getArchitecture()
.setArchitectureBuilder(new CascadeArchitectureBuilder());
problem.getNeuralNetwork().setOperationVisitor(new CascadeVisitor());
problem
.getNeuralNetwork()
.getArchitecture()
.getArchitectureBuilder()
.addLayer(new LayerConfiguration(4));
problem
.getNeuralNetwork()
.getArchitecture()
.getArchitectureBuilder()
.addLayer(new LayerConfiguration(0));
problem
.getNeuralNetwork()
.getArchitecture()
.getArchitectureBuilder()
.addLayer(new LayerConfiguration(1));
problem
.getNeuralNetwork()
.getArchitecture()
.getArchitectureBuilder()
.getLayerBuilder()
.setDomain("R(-3:3)");
problem.initialise();
PSO pso = new PSO();
pso.getInitialisationStrategy().setEntityType(new DynamicParticle());
pso.addStoppingCondition(new MeasuredStoppingCondition());
pso.setOptimisationProblem(problem);
pso.performInitialisation();
CascadeNetworkExpansionReactionStrategy reaction =
new CascadeNetworkExpansionReactionStrategy();
Assert.assertEquals(5, ((Vector) pso.getBestSolution().getPosition()).size());
Assert.assertEquals(5, problem.getNeuralNetwork().getWeights().size());
for (int i = 0; i < Topologies.getBestEntity(pso.getTopology()).getDimension(); ++i) {
((Vector) Topologies.getBestEntity(pso.getTopology()).getPosition())
.set(i, Real.valueOf(0.0));
((Vector) Topologies.getBestEntity(pso.getTopology()).getVelocity())
.set(i, Real.valueOf(0.0));
((Vector) Topologies.getBestEntity(pso.getTopology()).getBestPosition())
.set(i, Real.valueOf(0.0));
}
reaction.performReaction(pso);
Assert.assertEquals(11, ((Vector) pso.getBestSolution().getPosition()).size());
Assert.assertEquals(11, problem.getNeuralNetwork().getWeights().size());
Assert.assertEquals(
Vector.of(
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getPosition());
Assert.assertEquals(
Vector.of(
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getVelocity());
Assert.assertEquals(
Vector.of(
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getBestPosition());
for (int i = 0; i < Topologies.getBestEntity(pso.getTopology()).getDimension(); ++i) {
((Vector) Topologies.getBestEntity(pso.getTopology()).getPosition())
.set(i, Real.valueOf(0.0));
((Vector) Topologies.getBestEntity(pso.getTopology()).getVelocity())
.set(i, Real.valueOf(0.0));
((Vector) Topologies.getBestEntity(pso.getTopology()).getBestPosition())
.set(i, Real.valueOf(0.0));
}
reaction.performReaction(pso);
Assert.assertEquals(18, ((Vector) pso.getBestSolution().getPosition()).size());
Assert.assertEquals(18, problem.getNeuralNetwork().getWeights().size());
Assert.assertEquals(
Vector.of(
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getPosition());
Assert.assertEquals(
Vector.of(
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getVelocity());
Assert.assertEquals(
Vector.of(
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getBestPosition());
for (int i = 0; i < Topologies.getBestEntity(pso.getTopology()).getDimension(); ++i) {
((Vector) Topologies.getBestEntity(pso.getTopology()).getPosition())
.set(i, Real.valueOf(0.0));
((Vector) Topologies.getBestEntity(pso.getTopology()).getVelocity())
.set(i, Real.valueOf(0.0));
((Vector) Topologies.getBestEntity(pso.getTopology()).getBestPosition())
.set(i, Real.valueOf(0.0));
}
reaction.performReaction(pso);
Assert.assertEquals(26, ((Vector) pso.getBestSolution().getPosition()).size());
Assert.assertEquals(26, problem.getNeuralNetwork().getWeights().size());
Assert.assertEquals(
Vector.of(
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getPosition());
Assert.assertEquals(
Vector.of(
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getVelocity());
Assert.assertEquals(
Vector.of(
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
Double.NaN,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
Double.NaN),
(Vector) Topologies.getBestEntity(pso.getTopology()).getBestPosition());
}