public void TestClustering()
{
var hclust = new HierarchicalClustering();
var vals = TestUtils.ReadMatrix("Examples/clustering_array_copy_error.txt.gz");
var data = new FloatMatrixIndexer(vals);
var distance = new EuclideanDistance();
hclust.TreeClusterKmeans(data, MatrixAccess.Columns, distance, HierarchicalClusterLinkage.Average, false,
false, 1, 300, 1, 10,
(i) => { });
}
public void TestKmeansClusteringPreculusteringWithManyDuplicateRows()
{
HierarchicalClustering hclust = new HierarchicalClustering();
var values = new float[, ] {
{ 1, 2, 3, 1 }, { 2, 3, 4, 2 }, { 2, 3, 4, 2 }, { 2, 3, 4, 2 }, { 2, 3, 4, 2 }, { 3, 4, 5, 2 }
};
FloatMatrixIndexer data = new FloatMatrixIndexer(values);
var clusterNodes = hclust.TreeClusterKmeans(data, MatrixAccess.Rows, new EuclideanDistance(), HierarchicalClusterLinkage.Average, false, false, 1,
5, 1, 1000, i => { });
Assert.AreEqual(5, clusterNodes.Length);
}
public void TestKmeansClusteringPreculusteringWithDuplicateRows(
[ValueSource(nameof(_data))] float[,] values,
[ValueSource(nameof(_linkages))] HierarchicalClusterLinkage linkage,
[ValueSource(nameof(_distances))] IDistance distance)
{
HierarchicalClustering hclust = new HierarchicalClustering();
FloatMatrixIndexer data = new FloatMatrixIndexer(values);
var clusterNodes = hclust.TreeClusterKmeans(data, MatrixAccess.Columns, distance, linkage, false, false, 1,
2, 1, 1000, i => { });
Assert.AreEqual(3, clusterNodes.Length);
}