public FilterResult PrtFltr_MkwskSum(
int max_iter,
double epsilon,
SistemaObstaculos S,
List <PointD> arm,
PointD obj,
int samples,
double media_gausiana)
{
int it;
double distance;
var parametros_gausiana = new GausianParameters(media_gausiana, 0, 0, 1);
var chaint = new Chain(arm.ToArray());
var particles = new Particles(chaint, samples, parametros_gausiana.ToArray(), true);
Particle CHresult = pik(max_iter, epsilon, obj, particles, S, out it, out distance);
PointD[] chainResult_points = CHresult.Cadena.Chain2positions();
var fr = new FilterResult
{
ChainResult = CHresult,
ChainResult_points = chainResult_points,
Iterations = it,
Distance = distance
};
return(fr);
}
private static Particles PF(PointD goal, Particles particles, SistemaObstaculos S, out int bestIndex)
{
double wSum = 0;
double resampling = 0;
var p = new Particles();
for (int i = 0; i < particles.Particulas.Count; i++)
{
//sampling
//for (int j = 0; j < particles.Particulas[i].Cadena.Size; j++ )
//p.Particulas[i].Cadena =
Chain c = Chain.ChainQuTEMSampling(particles.Particulas[i].Gausian, particles.Particulas[i].Cadena); //???????
//compute weight
//p.Particulas[i].Gausian =
double[] gausian = particles.Particulas[i].Gausian;
double weight = Particle.WeightFunction(c, goal, S);
//p.Particulas[i].W =
double w = particles.Particulas[i].W * weight;
//weights cumsum
var particle = new Particle(c, gausian, w);
p.Particulas.Add(particle);
wSum = wSum + w;
//computing resamplig condition
resampling = resampling + Math.Pow(w, 2);
}
//normalize weights
foreach (Particle t in p.Particulas)
{
t.W = t.W / wSum;
}
// best chain
double max = 0;
int imax = 0;
for (int i = 0; i < p.Particulas.Count; i++)
{
//ww = particles(i).w
if (p.Particulas[i].W > max)
{
max = p.Particulas[i].W;
imax = i;
}
}
bestIndex = imax;
if (p.Particulas[bestIndex].W == 0)
{
bestIndex = 0;
}
//if necessary, resampling
//Effective Sample Size, Tutorial PF, 15 year later
// if ((1/resampling) <= (size(p, 1)/2))
// 'resampling'
return(Particles.Remostrejar(p));
}
//public double WeightFunction(PointD goal, SistemaObstaculos S)
//{
// // euclideanDistance
// PointD ee = this.Cadena.ChainEndEffectorPosition();
// //Si ms de un end effector, multiplicar
// //w = exp((-1)*(euclidianDistance(ee, goal))*imageDistance(image, chain));
// double w = Math.Exp((-1)*MathUtils.euclidianDistance(ee, goal));
// //el peso será cero si entra en contacto con algun obstáculo
// w = w * S.S_Touch(this.Cadena);
// return w;
//}
public static double WeightFunction(Chain chain, PointD goal, SistemaObstaculos S)
{
// euclideanDistance
PointD ee = chain.ChainEndEffectorPosition();
//Si ms de un end effector, multiplicar
//w = exp((-1)*(euclidianDistance(ee, goal))*imageDistance(image, chain));
double w = Math.Exp((-1) * MathUtils.euclidianDistance(ee, goal));
//el peso será cero si entra en contacto con algun obstáculo
w = w * S.S_Touch(chain);
return(w);
}
private Particle pik(int max_iter, double epsilon, PointD goal, Particles particles, SistemaObstaculos S, out int it, out double distance)
{
//chainResult = chain;
Particle chainResult = particles.Particulas[0];
PointD ee = chainResult.Cadena.ChainEndEffectorPosition();
it = 0;
while (it < max_iter && MathUtils.euclidianDistance(ee, goal) > epsilon)
{
int I;
particles = PF(goal, particles, S, out I);
ee = particles.Particulas[I].Cadena.ChainEndEffectorPosition();
chainResult = particles.Particulas[I];
it = it + 1;
}
distance = MathUtils.euclidianDistance(ee, goal);
return(chainResult);
}