public KMeansClusteringSolution(KMeansClusteringModel model, IClusteringProblemData problemData)
: base(model, problemData)
{
double trainingIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TrainingIndices);
double testIntraClusterSumOfSquares = KMeansClusteringUtil.CalculateIntraClusterSumOfSquares(model, problemData.Dataset, problemData.TestIndices);
this.Add(new Result(TrainingIntraClusterSumOfSquaresResultName, "The sum of squared distances of points of the training partition to the cluster center (is minimized by k-Means).", new DoubleValue(trainingIntraClusterSumOfSquares)));
this.Add(new Result(TestIntraClusterSumOfSquaresResultName, "The sum of squared distances of points of the test partition to the cluster center (is minimized by k-Means).", new DoubleValue(testIntraClusterSumOfSquares)));
}
public IEnumerable <int> GetClusterValues(IDataset dataset, IEnumerable <int> rows)
{
return(KMeansClusteringUtil.FindClosestCenters(centers, dataset, allowedInputVariables, rows));
}