private static void RunExample2()
{
int numExpctedClusters = 7;
int numFeatures = 100;
int samples = 200;
DataSet dataSet = new DataSet(numExpctedClusters, numFeatures, samples, 2);
popc pop = new popc(dataSet);
if (pop.clusters.Count != numExpctedClusters)
{
throw new Exception("theory not confirmed");
}
DisplayClusters(pop.clusters);
}
private static void RunExample3()
{
// scenario 3
int numExpctedClusters = 7;
int numFeatures = 20;
int samplesPerCluser = 30;
DataSet dataSet = new DataSet(numExpctedClusters, numFeatures, samplesPerCluser, 3);
popc pop = new popc(dataSet);
DisplayClusters(pop.clusters);
//creates 7 clusters as expected
if (pop.clusters.Count != numExpctedClusters)
{
throw new Exception("theory not confirmed");
}
// results close expected 7
double vPOPC = popc.ComputeEval(pop.clusters, pop.countsAll);
if (Math.Abs(vPOPC - numExpctedClusters) > 0.1)
{
throw new Exception("theory not confirmed");
}
// pretend we know correct number of clusters
kmeans km = new kmeans(dataSet, pop.clusters.Count);
DisplayClusters(km.clusters);
// even with knowledge of amount of correct clusters
// we get score close to 0 due to k-means clusters
// concentrates on noisy features and does not find
// ideal clusters, hence score close to 0
double vKMEANS = popc.ComputeEval(km.clusters, pop.countsAll);
if (Math.Abs(vKMEANS - 0.0) > 0.1)
{
throw new Exception("theory not confirmed");
}
}
