public void RemoteControllerFinished(RemoteCalculationsFinishedHandlerArgs args)
{
if (CalculationsRunning)
{
// evaluating function for best state of remote node - in case it's the best evaluation.
_function.Evaluate(((ParticleState)args.Result).Location);
}
}
public virtual void Transpose(IFitnessFunction <double[], double[]> function)
{
double[] newLocation;
var restart = _sinceLastImprovement == _iterationsToRestart;
if (restart)
{
newLocation = RandomGenerator.GetInstance().RandomVector(CurrentState.Location.Length, Bounds);
_sinceLastImprovement = 0;
}
else
{
newLocation = GetClampedLocation(CurrentState.Location.Select((x, i) => x + Velocity[i]).ToArray());
}
var newVal = function.Evaluate(newLocation);
var oldBest = PersonalBest;
CurrentState = new ParticleState(newLocation, newVal);
if (Optimization.IsBetter(newVal, PersonalBest.FitnessValue) < 0)
{
PersonalBest = CurrentState;
_sinceLastImprovement = 0;
}
else
{
_sinceLastImprovement++;
}
}
public static IParticle Create(PsoParticleType type,
int locationDim,
int fitnessDim,
IFitnessFunction<double[], double[]> function,
double restartEpsilon = double.MaxValue,
int iterationsToRestart = int.MaxValue,
DimensionBound[] bounds = null,
double[] initVelocity = null
)
{
IParticle particle = null;
var rand = RandomGenerator.GetInstance();
switch (type)
{
case PsoParticleType.Standard:
case PsoParticleType.FullyInformed:
particle = new StandardParticle(restartEpsilon, iterationsToRestart);
break;
case PsoParticleType.ChargedParticle:
particle = new ChargedParticle();
break;
case PsoParticleType.DummyParticle:
particle = new DummyParticle();
break;
}
var x = bounds != null ? rand.RandomVector(locationDim, bounds) : rand.RandomVector(locationDim);
particle.Init(new ParticleState(x, function.Evaluate(x)),
initVelocity ?? rand.RandomVector(locationDim, MinInitialVelocity, MaxInitialVelocity), bounds);
return particle;
}
public static IParticle Create(PsoParticleType type,
int locationDim,
int fitnessDim,
IFitnessFunction <double[], double[]> function,
double restartEpsilon = double.MaxValue,
int iterationsToRestart = int.MaxValue,
DimensionBound[] bounds = null,
double[] initVelocity = null
)
{
IParticle particle = null;
var rand = RandomGenerator.GetInstance();
switch (type)
{
case PsoParticleType.Standard:
case PsoParticleType.FullyInformed:
particle = new StandardParticle(restartEpsilon, iterationsToRestart);
break;
case PsoParticleType.ChargedParticle:
particle = new ChargedParticle();
break;
case PsoParticleType.DummyParticle:
particle = new DummyParticle();
break;
}
var x = bounds != null?rand.RandomVector(locationDim, bounds) : rand.RandomVector(locationDim);
particle.Init(new ParticleState(x, function.Evaluate(x)),
initVelocity ?? rand.RandomVector(locationDim, MinInitialVelocity, MaxInitialVelocity), bounds);
return(particle);
}
private static void Print(IPopulation <MathProgram> pop, uint generation,
IFitnessFunction <MathProgram> fitnessFunction, double diff)
{
var elem = pop.BestProgram;
Console.WriteLine(
$"Gen {generation:000}, pop: {pop.Count:000}, diff: {diff:0.00}, " +
$"fitness: {fitnessFunction.Evaluate(elem):0.00} | {elem}");
}
public void pruebaMutacion()
{
var cromosoma = new CocheCromosoma();
cromosoma.Generate();
Console.WriteLine(fitness.Evaluate(cromosoma));
for (int i = 0; i < 500; i++)
{
cromosoma.Mutate();
cromosoma.Evaluate(fitness);
Console.WriteLine(cromosoma.Fitness);
}
}
public void Run(PsoParameters psoParameters, IParticle[] proxyParticleServices = null)
{
_function = FunctionFactory.GetFitnessFunction(psoParameters.FunctionParameters);
var useGpu = psoParameters.GpuParameters.UseGpu && GpuController.AnySupportedGpu();
CudaParticle cudaParticle = null;
if (useGpu)
{
cudaParticle = PrepareCudaAlgorithm(psoParameters);
}
var particles = PrepareParticles(psoParameters, proxyParticleServices, cudaParticle);
RunningParameters = psoParameters;
_algorithm = new PsoAlgorithm(psoParameters, _function, particles.ToArray());
_cudaReadyLock = new AutoResetEvent(false);
if (useGpu)
{
RunningCudaAlgorithm = Task <ParticleState> .Factory.StartNew(() =>
{
_cudaAlgorithm.Initialize();
_cudaReadyLock.Set();
var result = _cudaAlgorithm.Run(_cudaTokenSource.Token);
_function.Evaluate(result.Location);
_cudaAlgorithm.Dispose();
return(result);
}, _cudaTokenSource.Token);
}
else
{
_cudaReadyLock.Set();
}
RunningAlgorithm = Task <ParticleState> .Factory.StartNew(delegate
{
_cudaReadyLock.WaitOne();
_cudaReadyLock.Dispose();
return(StartAlgorithm(_tokenSource.Token));
}, _tokenSource.Token);
}
/// <summary>
/// Calculates the Fitness this genome based in the FitnessFunction implements
/// </summary>
/// <returns>Value of the Fitness Genome</returns>
public double Evaluate()
{
return(fitnessFunction.Evaluate(this));
}
/// <summary>
/// Evaluate chromosome with specified fitness function.
/// </summary>
///
/// <param name="function">Fitness function to use for evaluation of the chromosome.</param>
///
/// <remarks><para>Calculates chromosome's fitness using the specifed fitness function.</para></remarks>
///
public void Evaluate( IFitnessFunction function )
{
fitness = function.Evaluate( this );
}
/// <summary>
/// Evaluate chromosome with specified fitness function.
/// </summary>
/// <param name="function">Fitness function to use for evaluation of the chromosome.</param>
/// <remarks><para>Calculates chromosome's fitness using the specifed fitness function.</para></remarks>
public void Evaluate(IFitnessFunction function)
{
this.fitness = function.Evaluate((IChromosome)this);
}
public void Run(PsoParameters psoParameters, IParticle[] proxyParticleServices = null)
{
_function = FunctionFactory.GetFitnessFunction(psoParameters.FunctionParameters);
var useGpu = psoParameters.GpuParameters.UseGpu && GpuController.AnySupportedGpu();
CudaParticle cudaParticle = null;
if (useGpu)
cudaParticle = PrepareCudaAlgorithm(psoParameters);
var particles = PrepareParticles(psoParameters,proxyParticleServices,cudaParticle);
RunningParameters = psoParameters;
_algorithm = new PsoAlgorithm(psoParameters, _function, particles.ToArray());
_cudaReadyLock = new AutoResetEvent(false);
if (useGpu)
RunningCudaAlgorithm = Task<ParticleState>.Factory.StartNew(() =>
{
_cudaAlgorithm.Initialize();
_cudaReadyLock.Set();
var result = _cudaAlgorithm.Run(_cudaTokenSource.Token);
_function.Evaluate(result.Location);
_cudaAlgorithm.Dispose();
return result;
}, _cudaTokenSource.Token);
else
_cudaReadyLock.Set();
RunningAlgorithm = Task<ParticleState>.Factory.StartNew(delegate
{
_cudaReadyLock.WaitOne();
_cudaReadyLock.Dispose();
return StartAlgorithm(_tokenSource.Token);
}, _tokenSource.Token);
}
public void Evaluate(IFitnessFunction function)
{
m_fitness = function.Evaluate(this);
GenesEvaluated = this.Genes.ToList();
}
public virtual void Transpose(IFitnessFunction<double[], double[]> function)
{
double[] newLocation;
var restart = _sinceLastImprovement == _iterationsToRestart;
if (restart)
{
newLocation = RandomGenerator.GetInstance().RandomVector(CurrentState.Location.Length, Bounds);
_sinceLastImprovement = 0;
}
else
{
newLocation = GetClampedLocation(CurrentState.Location.Select((x, i) => x + Velocity[i]).ToArray());
}
var newVal = function.Evaluate(newLocation);
var oldBest = PersonalBest;
CurrentState = new ParticleState(newLocation, newVal);
if (Optimization.IsBetter(newVal, PersonalBest.FitnessValue) < 0)
{
PersonalBest = CurrentState;
_sinceLastImprovement = 0;
}
else
{
_sinceLastImprovement++;
}
}
/// <summary>
/// Simplifies the expression of the given <see cref="MathProgram" /> by returning a sub-combination whose fitness is
/// approximately equal to that of the original program by a margin of <paramref name="margin" />, and whose
/// expression is naturally simpler, i.e., has fewer sub-programs.
/// </summary>
/// <returns>An program corresponding to a simplification of the given program.</returns>
/// <param name="program">The program we want to simplify.</param>
/// <param name="fitnessFunction">The fitness function used to determine equivalence.</param>
/// <param name="margin">
/// The acceptable difference between the fitness of the given program and that of a simplified program for them to be
/// considered equivalent.
/// </param>
public static ITreeProgram <double> Simplify(
this MathProgram program, IFitnessFunction <MathProgram> fitnessFunction, double margin = DEFAULT_MARGIN)
{
// gets original program's fitness and count
var simplified = program;
var fitness = fitnessFunction.Evaluate(program);
var length = program.Length;
// first replaces all sub-programs by infinity and NaN recursively
var consts = new HashSet <Constant>
{
new Constant(double.NegativeInfinity),
new Constant(double.NaN),
new Constant(double.PositiveInfinity)
};
bool simplificationFound;
do
{
simplificationFound = false;
var simpLength = simplified.Length;
for (var i = 0u; i < simpLength && !simplificationFound; i++)
{
if (consts.Contains(simplified.ProgramAt(i)))
{
continue;
}
foreach (var constant in consts)
{
var prog = (MathProgram)simplified.Replace(i, constant);
var fit = fitnessFunction.Evaluate(prog);
var progLength = prog.Length;
if (Math.Abs(fit - fitness) < margin && progLength <= length)
{
simplified = prog;
length = progLength;
simplificationFound = true;
break;
}
}
}
} while (simplificationFound);
// then gets all sub-combinations of the simplified program
var alternatives = simplified.GetSubCombinations();
foreach (MathProgram subComb in alternatives)
{
var subFit = fitnessFunction.Evaluate(subComb);
var subCombLength = subComb.Length;
//checks their fitness and count
if (Math.Abs(subFit - fitness) < margin && subCombLength < length)
{
simplified = subComb;
length = subCombLength;
}
}
return(simplified);
}
public void Evaluate(IFitnessFunction function, int objectiveIndex)
{
_objectives[objectiveIndex] = function.Evaluate(this);
}
public void EvaluateChromosome(GPChromosome c)
{
c.fitness = fitnessFunction.Evaluate(c.expressionTree, functionSet);
}
/// <summary>
/// Evaluate chromosome with specified fitness function.
/// </summary>
///
/// <param name="function">Fitness function to use for evaluation of the chromosome.</param>
///
/// <remarks><para>Calculates chromosome's fitness using the specifed fitness function.</para></remarks>
///
public void Evaluate(IFitnessFunction function)
{
_objectives[0] = function.Evaluate(this);
}
/// <summary>
/// Evaluate chromosome with specified fitness function.
/// </summary>
///
/// <param name="function">Fitness function to use for evaluation of the chromosome.</param>
///
/// <remarks><para>Calculates chromosome's fitness using the specifed fitness function.</para></remarks>
///
public void Evaluate( IFitnessFunction function )
{
fitness = function.Evaluate( this );
}
public virtual void Evaluate(IFitnessFunction function)
{
Fitness = function.Evaluate(this);
}
