private ICandidateFactory <MpiSysConfig> CreatePopulationInitialiser(
IClonableObjectiveEvaluator <MpiSysConfig> objectiveEvaluator,
MpiSysConfig templateParameterSet,
MpiSysConfig[] seedsPopulation,
string initialisationOption = "")
{
Log.Debug("Root: creating population initialiser");
var options = initialisationOption.Split(':');
var seeds = new List <MpiSysConfig> {
templateParameterSet
};
if (seedsPopulation != null)
{
seeds.AddRange(seedsPopulation);
}
var rng = new BasicRngFactory(SceParameterDefinition.RandomizationSeed);
ICandidateFactory <MpiSysConfig> result = CreatePopulationInitialiser(
templateParameterSet,
rng,
objectiveEvaluator,
options);
if (result is BestOfSampling <MpiSysConfig> )
{
return(result);
}
else
{
return(new SeededSamplingFactory <MpiSysConfig>(result, seeds));
}
}
private ShuffledComplexEvolution <MpiSysConfig> CreateShuffledComplexEvolution(
IClonableObjectiveEvaluator <MpiSysConfig> objectiveEvaluator,
IDictionary <string, string> dict,
ICandidateFactory <MpiSysConfig> populationInitializer,
ILoggerMh logger)
{
Log.Debug("Root: creating SCE optimiser");
ShuffledComplexEvolution <MpiSysConfig> optimEngine = new ShuffledComplexEvolution <MpiSysConfig>(
objectiveEvaluator,
populationInitializer,
CreateTerminationCriterion(),
SceParameterDefinition.Parameters.P,
SceParameterDefinition.Parameters.M,
SceParameterDefinition.Parameters.Q,
SceParameterDefinition.Parameters.Alpha,
SceParameterDefinition.Parameters.Beta,
SceParameterDefinition.Parameters.NumShuffle,
new BasicRngFactory(SceParameterDefinition.RandomizationSeed + 1000),
new DefaultFitnessAssignment(),
dict,
trapezoidalPdfParam: SceParameterDefinition.Parameters.TrapezoidalDensityParameter,
options: sceOptions,
pmin: SceParameterDefinition.Parameters.Pmin)
{
Logger = logger
};
return(optimEngine);
}
public BestOfSampling(ICandidateFactory <TSysConfig> innerSamplingFactory, int poolSize, int bestPoints,
IObjectiveEvaluator <TSysConfig> evaluator, IFitnessAssignment <double> fitAssignment = null)
{
if (fitAssignment == null)
{
fitAssignment = new DefaultFitnessAssignment();
}
if (poolSize < bestPoints)
{
throw new ArgumentOutOfRangeException("poolSize", poolSize, String.Format("poolSize must be >= bestPoints({0})", bestPoints));
}
this.HcFactory = (IHyperCubeOperationsFactory)innerSamplingFactory;
this.innerSamplingFactory = innerSamplingFactory;
var tmp = new IObjectiveScores[poolSize];
for (int i = 0; i < poolSize; i++)
{
tmp[i] = evaluator.EvaluateScore(innerSamplingFactory.CreateRandomCandidate());
}
var points = fitAssignment.AssignFitness(tmp);
Array.Sort(points);
candidates = new FitnessAssignedScores <double> [bestPoints];
for (int i = 0; i < bestPoints; i++)
{
candidates[i] = points[i];
}
}
public UniformRandomSampling(IClonableObjectiveEvaluator <T> evaluator,
ICandidateFactory <T> populationInializer,
int numShuffle, IDictionary <string, string> tags = null)
{
this.evaluator = evaluator;
this.populationInitializer = populationInializer;
this.numShuffle = numShuffle;
this.logTags = tags;
}
/// <summary>
/// Executes the shuffled complex evolution for nesting within the rosenbrock optimiser.
/// </summary>
/// <param name="objectiveEvaluator"> The compound obj calculator. </param>
/// <param name="dict"> The dict. </param>
/// <param name="populationInitializer"> The population initializer. </param>
/// <param name="logger"> The logger. </param>
/// <returns> </returns>
private MpiSysConfig ExecuteShuffledComplexForRosen(
IClonableObjectiveEvaluator <MpiSysConfig> objectiveEvaluator,
IDictionary <string, string> dict,
ICandidateFactory <MpiSysConfig> populationInitializer,
ILoggerMh logger)
{
Log.Debug("Root: Executing SCE optimiser");
var optimEngine = CreateShuffledComplexEvolution(objectiveEvaluator, dict, populationInitializer, logger);
return(BestParameterSet(optimEngine.Evolve(), new DefaultFitnessAssignment()));
}
private IEvolutionEngine <MpiSysConfig> CreateSceRosenEngine(
IClonableObjectiveEvaluator <MpiSysConfig> objectiveEvaluator,
IDictionary <string, string> dict,
ICandidateFactory <MpiSysConfig> populationInitializer,
ILoggerMh logger)
{
Log.Debug("Root: Creating Rosenbrock + SCE optimiser");
Func <IOptimizationResults <MpiSysConfig> > rosenFunc = () => ExecuteRosenbrock(objectiveEvaluator, dict, populationInitializer, logger);
return(new ChainOptimizations <MpiSysConfig>("Sce+Rosen", rosenFunc));
}
private IEvolutionEngine <MpiSysConfig> CreateEngine(
IClonableObjectiveEvaluator <MpiSysConfig> objectiveEvaluator,
IDictionary <string, string> dict,
ICandidateFactory <MpiSysConfig> populationInitializer,
ILoggerMh logger)
{
switch (OptimisationMethod)
{
default:
case OptimisationMethods.Sce:
return(CreateShuffledComplexEvolution(objectiveEvaluator, dict, populationInitializer, logger));
case OptimisationMethods.RosenSce:
return(CreateSceRosenEngine(objectiveEvaluator, dict, populationInitializer, logger));
}
}
private IOptimizationResults <MpiSysConfig> ExecuteRosenbrock(
IClonableObjectiveEvaluator <MpiSysConfig> objectiveEvaluator,
IDictionary <string, string> dict,
ICandidateFactory <MpiSysConfig> populationInitializer,
ILoggerMh logger)
{
Log.Debug("Root: Executing Rosenbrock optimiser");
var engine = new RosenbrockOptimizer <MpiSysConfig, double>(
objectiveEvaluator,
ExecuteShuffledComplexForRosen(objectiveEvaluator, dict, populationInitializer, logger),
new RosenbrockOptimizer <MpiSysConfig, double> .RosenbrockOptimizerIterationTermination(RosenbrockIterations),
logTags: dict)
{
Logger = logger, AlgebraProvider = new TimeAlgebraProvider()
};
return(engine.Evolve());
}
public SeededSamplingFactory(ICandidateFactory <TSysConfig> uniformRandomSamplingFactory, IEnumerable <TSysConfig> seeds)
{
this.HcFactory = (IHyperCubeOperationsFactory)uniformRandomSamplingFactory;
this.uniformRandomSamplingFactory = uniformRandomSamplingFactory;
this.seeds = seeds == null ? null : seeds.ToArray();
}