public void CalculateCentroidsAndSigmoids(List <Tuple <double[], double> > data)
{
var hiddenLayer = hiddenRadialBasisFunctionLayer;
List <double[]> centroidData = new List <double[]>();
foreach (var item in data)
{
centroidData.Add(item.Item1);
}
kMeansClustering kmeansClustering = new kMeansClustering(centroidData);
var clusters = kmeansClustering.ClusterWithCentroid(hiddenLayer.Neurons);
for (int i = 0; i < hiddenLayer.Neurons; i++)
{
foreach (var cluster in clusters[i])
{
hiddenLayer.Cetroids[i] = cluster.Item3;
var values = cluster.Item2;
var pNearestNeighbors = FindMinimal(centroidData, cluster.Item3, hiddenLayer.P);
var sigmasumme = 0.0;
foreach (var item in pNearestNeighbors)
{
sigmasumme += CalculateEuclidDistance(item.Item2, cluster.Item3);
}
var sigma = 0.0;
sigma = Math.Sqrt(1.0 / hiddenLayer.P * sigmasumme);
hiddenLayer.Sigmoids[i] = sigma;
}
}
}
public void kMeansTest()
{
List <double[]> centroidData = new List <double[]>();
foreach (var item in Enumerable.Range(0, 2))
{
foreach (var innerItem in Enumerable.Range(0, 2))
{
centroidData.Add(new double[] { item, innerItem });
}
}
kMeansClustering kmeansClustering = new kMeansClustering(centroidData);
var clusters = kmeansClustering.Cluster(2);
//var centroids = kmeansClustering.centroids;
//var clusters = kmeansClustering.ClusterWithCentroid(4);
}
public void kMeansIrisTest()
{
DataSetLoader dataSetLoader = new DataSetLoader();
Console.WriteLine(" Reading DataSet.. ");
var irises = dataSetLoader.SelectClusteringIrises();
kMeansClustering kmeansClustering = new kMeansClustering(irises, new MaximumMetric());
var clusters = kmeansClustering.Cluster(3);
var clusterCounter = 0;
Dictionary <int, int> clusterDictonary = new Dictionary <int, int>();
foreach (var cluster in clusters)
{
Debug.WriteLine(string.Format("Cluster {0} - Count {1}", clusterCounter, cluster.Count));
clusterDictonary.Add(clusterCounter, 0);
clusterCounter++;
}
var irisesTest = dataSetLoader.SelectClusteringIrises();
var trueCounter = 0;
var counter = 0;
foreach (var item in irisesTest)
{
var outputValue = kmeansClustering.CalculateClusterAffinity(item);
Debug.WriteLine(string.Format("Value {0} - Predicted {1}", item, outputValue));
clusterDictonary[outputValue]++;
counter++;
trueCounter++;
}
clusterCounter = 0;
foreach (var cluster in clusters)
{
var calculatedCluster = clusterDictonary[clusterCounter];
Debug.WriteLine(string.Format("Cluster {0} - Original Count {1} - Calculated Count {2}",
clusterCounter, cluster.Count, calculatedCluster));
clusterCounter++;
}
//var centroids = kmeansClustering.centroids;
//var clusters = kmeansClustering.ClusterWithCentroid(4);
}
