public void ClusteringLanguageTest()
{
var netMLString =
"create clustering kmetroids euclidmetric ";
NetMLParser netMLParser = new NetMLParser();
var result = netMLParser.Parse(netMLString);
NetMLCreator netMLCreator = new NetMLCreator(result);
DataSetLoader dataSetLoader = new DataSetLoader();
var irises = dataSetLoader.SelectClusteringIrises();
netMLCreator.Create(irises);
var clusters = netMLCreator.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 = netMLCreator.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++;
}
}
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);
}
