private static double GetInfluence(SOMAttractionFunction attractionFunction, double distSquared, double searchRadius)
{
const double SIGMA = .28; // this gives peak negative at x=.5 (roughly), and near 0 at x=1
const double SIGMASQUARED = SIGMA * SIGMA;
switch (attractionFunction)
{
case SOMAttractionFunction.Guassian_ORIG:
// This is a guassian, but I wasn't scaling to search radius very well
double twiceSearchRadiusSquared = 2d * (searchRadius * searchRadius);
return(Math.Exp(-distSquared / twiceSearchRadiusSquared)); // this will reduce the learning rate as distance increases (guassian dropoff)
case SOMAttractionFunction.Guassian:
//https://en.wikipedia.org/wiki/Gaussian_function
double percentSearchRadius1 = Math.Sqrt(distSquared) / searchRadius;
percentSearchRadius1 *= 2;
percentSearchRadius1 *= percentSearchRadius1;
return(Math.Exp(-percentSearchRadius1));
case SOMAttractionFunction.MexicanHat:
//https://en.wikipedia.org/wiki/Mexican_hat_wavelet
double percentSearchRadius2 = Math.Sqrt(distSquared) / searchRadius;
percentSearchRadius2 *= percentSearchRadius2; // it needs to be squared
double left = (1 - (percentSearchRadius2 / SIGMASQUARED));
double right = percentSearchRadius2 / (2 * SIGMASQUARED);
right = Math.Exp(-right);
return(left * right);
default:
throw new ApplicationException("Unknown SOMAttractionFunction: " + attractionFunction.ToString());
}
}
public SOMRules(int numNodes, int numIterations, double initialRadiusPercent, double learningRate, SOMAttractionFunction attractionFunction = SOMAttractionFunction.Guassian)
{
this.NumNodes = numNodes;
this.NumIterations = numIterations;
this.InitialRadiusPercent = initialRadiusPercent;
this.LearningRate = learningRate;
this.AttractionFunction = attractionFunction;
}
