public static int[] ClusterWithFuzzyART(double[,] trainingData, out int noOfCommittedF2Nodes)
{
int trnSetSize = trainingData.GetLength(0);
int IPSize = trainingData.GetLength(1);
int F2Size = trnSetSize;
int maxIterations = 100;
LoggedConsole.WriteLine("trnSetSize=" + trnSetSize + " IPSize=" + IPSize + " F2Size=" + F2Size);
//************************** INITIALISE PARAMETER VALUES *************************
double alpha = 0.57; //increasing alpha proliferates categories - 0.57 is good value
double beta = 0.8; //Beta=1.0 for fast learning/no momentum. Beta=0.0 for no change in weights
double rho = 0.9; //vigilance parameter - increasing rho proliferates categories
double theta = 0.1; //threshold for contrast enhancing - values < threshold := 0.0
int seed = 12345; //to seed random number generator
FuzzyART fuzzyART = new FuzzyART(IPSize, F2Size); //initialise FuzzyART class
fuzzyART.SetParameterValues(alpha, beta, rho, theta);
fuzzyART.WriteParameters();
fuzzyART.InitialiseArrays();
//fuzzyART.RepeatTrainNet(trainingData, maxIterations, 1, seed, 3);
//noOfCommittedF2Nodes = fuzzyART.CountCommittedF2Nodes();
var output = fuzzyART.TrainNet(trainingData, maxIterations, seed);
int iterNum = output.Item1;
noOfCommittedF2Nodes = output.Item2;
LoggedConsole.WriteLine("Training iterations=" + iterNum + ". Categories=" + noOfCommittedF2Nodes);
return(fuzzyART.inputCategory); //keepScore;
} //END of ClusterShapesWithFuzzyART.
} //END of ClusterShapesWithFuzzyART.
public static int[] RepeatClusterWithFuzzyART(double[,] trainingData, out int committedNodeCount)
{
if (trainingData == null)
{
LoggedConsole.WriteLine("WARNING: ClusterWithFuzzyART() PASSED NULL TRAINING DATA!");
committedNodeCount = 0;
return(null);
}
int trnSetSize = trainingData.GetLength(0);
int IPSize = trainingData.GetLength(1);
int F2Size = trnSetSize;
int numberOfRepeats = 1;
int maxIterations = 100;
LoggedConsole.WriteLine("trnSetSize=" + trnSetSize + " IPSize=" + IPSize + " F2Size=" + F2Size);
int[] noOfCommittedF2 = new int[numberOfRepeats]; // : array[1..MaxRepeatNo] of word;{# committed F2 units}
//int[] iterToConv = new int[numberOfRepeats]; // : array[1..MaxRepeatNo] of word;{# training iterations}
int code = 0; //used for getting error messages
//************************** INITIALISE PARAMETER VALUES *************************
//double alpha = 0.2; //increasing alpha proliferates categories - 0.57 is good value
//double beta = 0.5; //beta=1 for fast learning/no momentum. beta=0 for no change in weights
//double rho = 0.9; //vigilance parameter - increasing rho proliferates categories
//double theta = 0.05; //threshold for contrast enhancing
//double alpha = 0.2; //increasing alpha proliferates categories - 0.57 is good value
//double beta = 0.1; //beta=1 for fast learning/no momentum. beta=0 for no change in weights
//double rho = 0.9; //vigilance parameter - increasing rho proliferates categories
//double theta = 0.0; //threshold for contrast enhancing
double alpha = 0.2; //increasing alpha proliferates categories - 0.57 is good value
double beta = 0.2; //beta=1 for fast learning/no momentum. beta=0 for no change in weights
double rho = 0.8; //vigilance parameter - increasing rho proliferates categories
double theta = 0.0; //threshold for contrast enhancing
FuzzyART fuzzyART = new FuzzyART(IPSize, F2Size); //initialise FuzzyART class
fuzzyART.SetParameterValues(alpha, beta, rho, theta);
fuzzyART.WriteParameters();
//{********** DO REPEATS ***********}
for (int rep = 0; rep < numberOfRepeats; rep++)
{
//{********* RUN NET for ONE SET OF PARAMETERS for ALL ITERATIONS *********}
fuzzyART.InitialiseArrays();
int seed = 12345 * (rep + 1);
fuzzyART.TrainNet(trainingData, maxIterations, seed);
if (code != 0)
{
break;
}
noOfCommittedF2[rep] = fuzzyART.CountCommittedF2Nodes();
//ScoreTrainingResults (noOfCommittedF2[rep], noClasses, F2classLabel, F2classProb);
//wtsFpath = ART.ARTDir + ART.wtsFname + "s" + simul + rep + ART.wtsFExt;
//art2a.WriteWts(wtsFpath, F2classLabel, F2classProb);
//if (ART.DEBUG) LoggedConsole.WriteLine("wts= " + wtsFpath + " train set= " + trnSetFpath);
LoggedConsole.WriteLine("Number Of Committed F2 Nodes after rep" + rep + " = " + noOfCommittedF2[rep]);
} //end; {for rep = 1 to norepeats do} {***** END OF REPEATS *****}
committedNodeCount = noOfCommittedF2[0];
int[] keepScore = fuzzyART.inputCategory;
return(keepScore);
} //END of RepeatClusterWithFuzzyART.