/// <summary>
/// CalculateDistance is a function that claculates the distance between two elements of same size.
/// </summary>
/// <param name="FirstElement">first element</param>
/// <param name="SecondElement">second element</param>
/// <returns>
/// - Item 1: distance between the two elements
/// - Item 2: AnomalyDetectionResponse: a code and a message that state whether the function succeeded or encountered an error
/// when the function succeeds, it will return:
/// - Code: 0, "OK"</returns>
internal static Tuple <double, AnomalyDetectionResponse> CalculateDistance(double[] FirstElement, double[] SecondElement)
{
AnomalyDetectionResponse ADResponse;
try
{
if (FirstElement == null || SecondElement == null)
{
ADResponse = new AnomalyDetectionResponse(101, "Function <CalculateDistance>: At least one input is null");
return(Tuple.Create(-1.0, ADResponse));
}
if (FirstElement.Length != SecondElement.Length)
{
ADResponse = new AnomalyDetectionResponse(115, "Function <CalculateDistance>: Inputs have different dimensions");
return(Tuple.Create(-1.0, ADResponse));
}
double SquaredDistance = 0;
for (int i = 0; i < FirstElement.Length; i++)
{
SquaredDistance += Math.Pow(FirstElement[i] - SecondElement[i], 2);
}
ADResponse = new AnomalyDetectionResponse(0, "OK");
return(Tuple.Create(Math.Sqrt(SquaredDistance), ADResponse));
}
catch (Exception Ex)
{
ADResponse = new AnomalyDetectionResponse(400, "Function <CalculateDistance>: Unhandled exception:\t" + Ex.ToString());
return(Tuple.Create(-1.0, ADResponse));
}
}
/// <summary>
/// CalculateNearestCluster is a function that determines the nearest cluster and calculates the distance between those two clusters.
/// </summary>
/// <param name="Centroids">the centroids of the clusters</param>
/// <param name="SamplesInClusters">number of samples in each cluster</param>
/// <returns>Tuple of three Items:
/// - Item 1: contains the number of nearest cluster
/// - Item 2: contains the distance to the nearest cluster
/// - Item 3: AnomalyDetectionResponse: a code and a message that state whether the function succeeded or encountered an error
/// when the function succeeds, it will return:
/// - Code: 0, "OK"</returns>
private static Tuple <int[], double[], AnomalyDetectionResponse> CalculateNearestCluster(double[][] Centroids, int[] SamplesInClusters)
{
AnomalyDetectionResponse ADResponse;
int[] NearestClustersArray = new int[Centroids.Length];
double[] DistanceToNearestClusterArray = new double[Centroids.Length];
try
{
Tuple <double, AnomalyDetectionResponse> CDResponse;
for (int i = 0; i < Centroids.Length; i++)
{
//in case of empty cluster
if (SamplesInClusters[i] == 0)
{
NearestClustersArray[i] = -1;
DistanceToNearestClusterArray[i] = -1;
continue;
}
DistanceToNearestClusterArray[i] = double.MaxValue;
for (int j = 0; j < Centroids.Length; j++)
{
if (i == j || SamplesInClusters[j] == 0)
{
continue;
}
CDResponse = CalculateDistance(Centroids[i], Centroids[j]);
if (CDResponse.Item2.Code != 0)
{
NearestClustersArray = null;
DistanceToNearestClusterArray = null;
return(Tuple.Create(NearestClustersArray, DistanceToNearestClusterArray, CDResponse.Item2));
}
if (CDResponse.Item1 < DistanceToNearestClusterArray[i])
{
DistanceToNearestClusterArray[i] = CDResponse.Item1;
NearestClustersArray[i] = j;
}
}
}
ADResponse = new AnomalyDetectionResponse(0, "OK");
return(Tuple.Create(NearestClustersArray, DistanceToNearestClusterArray, ADResponse));
}
catch (Exception Ex)
{
NearestClustersArray = null;
DistanceToNearestClusterArray = null;
ADResponse = new AnomalyDetectionResponse(400, "Function <CalculateNearestCluster>: Unhandled exception:\t" + Ex.ToString());
return(Tuple.Create(NearestClustersArray, DistanceToNearestClusterArray, ADResponse));
}
}
/// <summary>
/// SamplesInCLusterNumber is a function that counts the number of samples of each cluster.
/// </summary>
/// <param name="RawData">data to be clustered</param>
/// <param name="DataToClusterMapping">contains the assigned cluster number for each sample of the RawData</param>
/// <param name="NumberOfClusters">the number of clusters</param>
/// <returns>Tuple of two Items:
/// - Item 1: contains the number of samples for each cluster
/// - Item 2: AnomalyDetectionResponse: a code and a message that state whether the function succeeded or encountered an error
/// when the function succeeds, it will return:
/// - Code: 0, "OK"</returns>
private static Tuple <int[], AnomalyDetectionResponse> SamplesInClusterNumber(double[][] RawData, int[] DataToClusterMapping, int NumberOfClusters)
{
AnomalyDetectionResponse ADResponse;
int[] ClusterSamplesCounter;
try
{
ClusterSamplesCounter = new int[NumberOfClusters];
for (int i = 0; i < DataToClusterMapping.Length; i++)
{
ClusterSamplesCounter[DataToClusterMapping[i]]++;
}
ADResponse = new AnomalyDetectionResponse(0, "OK");
return(Tuple.Create(ClusterSamplesCounter, ADResponse));
}
catch (Exception Ex)
{
ClusterSamplesCounter = null;
ADResponse = new AnomalyDetectionResponse(400, "Function <SamplesInClusterNumber>: Unhandled exception:\t" + Ex.ToString());
return(Tuple.Create(ClusterSamplesCounter, ADResponse));
}
}
/// <summary>
/// CreateClusteringResult is a function that generates the clustering results using several subfunctions.
/// </summary>
/// <param name="RawData">data to be clustered</param>
/// <param name="DataToClusterMapping">contains the assigned cluster number for each sample of the RawData</param>
/// <param name="Centroids">the centroids of the clusters</param>
/// <param name="NumberOfClusters">the number of clusters</param>
/// <returns>Tuple of two Items:
/// - Item 1: contains the results for each cluster
/// - Item 2: AnomalyDetectionResponse: a code and a message that state whether the function succeeded or encountered an error
/// when the function succeeds, it will return:
/// - Code: 0, "OK"</returns>
public static Tuple <ClusteringResults[], AnomalyDetectionResponse> CreateClusteringResult(double[][] RawData, int[] DataToClusterMapping, double[][] Centroids, int NumberOfClusters)
{
ClusteringResults[] Results;
AnomalyDetectionResponse ADResponse;
try
{
//get how many samples are there in each cluster
Tuple <int[], AnomalyDetectionResponse> SICNResponse = SamplesInClusterNumber(RawData, DataToClusterMapping, NumberOfClusters);
if (SICNResponse.Item2.Code != 0)
{
Results = null;
return(Tuple.Create(Results, SICNResponse.Item2));
}
int[] ClusterSamplesCounter = SICNResponse.Item1;
Results = new ClusteringResults[NumberOfClusters];
for (int i = 0; i < NumberOfClusters; i++)
{
Results[i] = new ClusteringResults();
Results[i].Centroid = Centroids[i];
Results[i].ClusterData = new double[ClusterSamplesCounter[i]][];
Results[i].ClusterDataOriginalIndex = new int[ClusterSamplesCounter[i]];
//group the samples of the cluster
ADResponse = Results[i].AssignSamplesToClusters(RawData, DataToClusterMapping, i);
if (ADResponse.Code != 0)
{
Results = null;
return(Tuple.Create(Results, ADResponse));
}
ADResponse = Results[i].CalculateStatistics();
if (ADResponse.Code != 0)
{
Results = null;
return(Tuple.Create(Results, ADResponse));
}
}
//use functions to calculate the properties and statistics of each clusters.
Tuple <int[], double[], AnomalyDetectionResponse> CNCResponse = CalculateNearestCluster(Centroids, ClusterSamplesCounter);
if (CNCResponse.Item3.Code != 0)
{
Results = null;
return(Tuple.Create(Results, CNCResponse.Item3));
}
for (int i = 0; i < NumberOfClusters; i++)
{
Results[i].NearestCluster = CNCResponse.Item1[i];
Results[i].DistanceToNearestClusterCentroid = CNCResponse.Item2[i];
}
double[][] NearestForeignSampleInNearestClusterArray;
double[] DistanceToNearestForeignSampleInNearestClusterArray;
double[][] NearestForeignSampleArray;
double[] DistanceToNearestForeignSampleArray;
int[] ClusterOfNearestForeignSampleArray;
ADResponse = CalculateMoreStatistics(RawData, DataToClusterMapping, Centroids, CNCResponse.Item1, out NearestForeignSampleInNearestClusterArray, out DistanceToNearestForeignSampleInNearestClusterArray, out NearestForeignSampleArray, out DistanceToNearestForeignSampleArray, out ClusterOfNearestForeignSampleArray);
if (ADResponse.Code != 0)
{
Results = null;
return(Tuple.Create(Results, ADResponse));
}
for (int i = 0; i < NumberOfClusters; i++)
{
Results[i].NearestForeignSampleInNearestCluster = NearestForeignSampleInNearestClusterArray[i];
Results[i].DistanceToNearestForeignSampleInNearestCluster = DistanceToNearestForeignSampleInNearestClusterArray[i];
Results[i].NearestForeignSample = NearestForeignSampleArray[i];
Results[i].DistanceToNearestForeignSample = DistanceToNearestForeignSampleArray[i];
Results[i].ClusterOfNearestForeignSample = ClusterOfNearestForeignSampleArray[i];
}
ADResponse = new AnomalyDetectionResponse(0, "OK");
return(Tuple.Create(Results, ADResponse));
}
catch (Exception Ex)
{
Results = null;
ADResponse = new AnomalyDetectionResponse(400, "Function <CreateClusteringResult>: Unhandled exception:\t" + Ex.ToString());
return(Tuple.Create(Results, ADResponse));
}
}
