//private void DoWidths(double[][] centroids)
//{
// // this version uses a common width which is dmax / sqrt(2*numHidden)
// double maxDist = double.MinValue;
// for (int i = 0; i < centroids.Length - 1; ++i)
// {
// for (int j = i + 1; j < centroids.Length; ++j)
// {
// double dist = EuclideanDist(centroids[i], centroids[j], centroids[i].Length);
// if (dist > maxDist)
// maxDist = dist;
// }
// }
// double width = maxDist / Math.Sqrt(2.0 * numHidden);
// Console.WriteLine("The common width is: " + width.ToString("F4"));
// for (int i = 0; i < this.widths.Length; ++i) // all widths the same
// this.widths[i] = width;
//}
private double[] DoWeights(double[][] trainData, int maxIterations)
{
// use PSO to find weights and bias values that produce a RBF network
// that best matches training data
int numberParticles = trainData.Length / 3;
int Dim = (numHidden * numOutput) + numOutput; // dimensions is num weights + num biases
double minX = -10.0; // implicitly assumes data has been normalizzed
double maxX = 10.0;
double minV = minX;
double maxV = maxX;
Particle[] swarm = new Particle[numberParticles];
double[] bestGlobalPosition = new double[Dim]; // best solution found by any particle in the swarm. implicit initialization to all 0.0
double smallesttGlobalError = double.MaxValue; // smaller values better
// initialize swarm
for (int i = 0; i < swarm.Length; ++i) // initialize each Particle in the swarm
{
double[] randomPosition = new double[Dim];
for (int j = 0; j < randomPosition.Length; ++j)
{
double lo = minX;
double hi = maxX;
randomPosition[j] = (hi - lo) * rnd.NextDouble() + lo; //
}
double err = MeanSquaredError(trainData, randomPosition); // error associated with the random position/solution
double[] randomVelocity = new double[Dim];
for (int j = 0; j < randomVelocity.Length; ++j)
{
double lo = -1.0 * Math.Abs(maxV - minV);
double hi = Math.Abs(maxV - minV);
randomVelocity[j] = (hi - lo) * rnd.NextDouble() + lo;
}
swarm[i] = new Particle(randomPosition, err, randomVelocity, randomPosition, err);
// does current Particle have global best position/solution?
if (swarm[i].error < smallesttGlobalError)
{
smallesttGlobalError = swarm[i].error;
swarm[i].position.CopyTo(bestGlobalPosition, 0);
}
} // initialization
// main PSO algorithm
// compute new velocity -> compute new position -> check if new best
double w = 0.729; // inertia weight
double c1 = 1.49445; // cognitive/local weight
double c2 = 1.49445; // social/global weight
double r1, r2; // cognitive and social randomizations
int[] sequence = new int[numberParticles]; // process particles in random order
for (int i = 0; i < sequence.Length; ++i)
{
sequence[i] = i;
}
int iteration = 0;
while (iteration < maxIterations)
{
if (smallesttGlobalError < 0.060)
{
break; // early exit (MSE)
}
double[] newVelocity = new double[Dim]; // step 1
double[] newPosition = new double[Dim]; // step 2
double newError; // step 3
Shuffle(sequence); // move particles in random sequence
for (int pi = 0; pi < swarm.Length; ++pi) // each Particle (index)
{
int i = sequence[pi];
Particle currP = swarm[i]; // for coding convenience
// 1. compute new velocity
for (int j = 0; j < currP.velocity.Length; ++j) // each x value of the velocity
{
r1 = rnd.NextDouble();
r2 = rnd.NextDouble();
// velocity depends on old velocity, best position of parrticle, and
// best position of any particle
newVelocity[j] = (w * currP.velocity[j]) +
(c1 * r1 * (currP.bestPosition[j] - currP.position[j])) +
(c2 * r2 * (bestGlobalPosition[j] - currP.position[j]));
if (newVelocity[j] < minV)
{
newVelocity[j] = minV;
}
else if (newVelocity[j] > maxV)
{
newVelocity[j] = maxV; // crude way to keep velocity in range
}
}
newVelocity.CopyTo(currP.velocity, 0);
// 2. use new velocity to compute new position
for (int j = 0; j < currP.position.Length; ++j)
{
newPosition[j] = currP.position[j] + newVelocity[j]; // compute new position
if (newPosition[j] < minX)
{
newPosition[j] = minX;
}
else if (newPosition[j] > maxX)
{
newPosition[j] = maxX;
}
}
newPosition.CopyTo(currP.position, 0);
// 3. use new position to compute new error
// consider cross-entropy error instead of MSE
newError = MeanSquaredError(trainData, newPosition); // makes next check a bit cleaner
currP.error = newError;
if (newError < currP.smallestError) // new particle best?
{
newPosition.CopyTo(currP.bestPosition, 0);
currP.smallestError = newError;
}
if (newError < smallesttGlobalError) // new global best?
{
newPosition.CopyTo(bestGlobalPosition, 0);
smallesttGlobalError = newError;
}
// consider using weight decay, particle death here
} // each Particle
++iteration;
} // while (main PSO processing loop)
//Console.WriteLine("\n\nFinal training MSE = " + smallesttGlobalError.ToString("F4") + "\n\n");
// copy best weights found into RBF network, and also return them
this.SetWeights(bestGlobalPosition);
double[] returnResult = new double[(numHidden * numOutput) + numOutput];
Array.Copy(bestGlobalPosition, returnResult, bestGlobalPosition.Length);
Console.WriteLine("The best weights and bias values found are:\n");
Helpers.ShowVector(bestGlobalPosition, 3, 10, true);
return(returnResult);
} // DoWeights
