// GET api/product/5
public IHttpActionResult GetKmeansData(int id)
{
KmeansData data = kmeansdata.Value.FirstOrDefault((p) => p.ID == id);
if (data == null)
{
return(NotFound());
}
return(Ok(data));
}
// POST api/product
/*
* public void KmeansDataAdd(KmeansData data) //post method
* {
*
* data.ID = ProductID;
* kmeansdata.Value.Add(data); //add the post product data to the product list
* ProductID++;
* //instead of adding product data to the static product list you can save data to the database.
* }
* /*/
public void KmeansDataCheck(KmeansData data) //post method
{
if (data.NumberOfClstrs != 0 && data.NumberOfClstrs != null)
{
numofclstrs = data.NumberOfClstrs;
// if (System.IO.File.Exists(@"F:\ProData\cluster.json"))// in case some files exist already delete them and upload the current one
// {
// System.IO.File.Delete(@"F:\ProData\cluster.json");
// System.IO.File.Delete(@"F:\ProData\Result\cluster.json");
// }
int numberofattributes = 2;
/// X,Y,Z components
double[][] result = MockApi.MockApi.CSVtoDoubleJaggedArray(@"F:\ProData\data.csv");
/// CSV to Array Conversion
if (System.IO.File.Exists(@"F:\ProData\data.csv"))
{
numberofattributes = result[0].Length;
}
/// Number of attributes are 3 for 3D and 2 for 2D
IAnomalyDetectionApi X = new AnomalyDetectionApi.AnomalyDetectionAPI(result, numofclstrs);
ClusteringSettings Y = new ClusteringSettings(result, 10, numofclstrs, numberofattributes, @"F:\ProData\cluster.json", Replace: true);
/// Set number of clusters, max iterations and give data in result
AnomalyDetectionResponse AB = X.ImportNewDataForClustering(Y);
/// CheckSample is a function that detect to which cluster the given sample belongs to.
ClusteringResults[] r;
/// All the Kmeans results needed for plotting
AnomalyDetectionResponse Z = X.GetResults(@"F:\ProData\cluster.json", out r);
/// Codes for Errors and Success
//if (PgaeLoadFlag == 1) //use this only for first time page load
//{
// if (r != null)// in case result contains some value we need to add results
// {
if (kmeansdata.IsValueCreated == true) //if already points are created we need to reinitiallize
{
checksample = kmeansdata.Value[0].CheckSampleResult;
kmeansdata = new Lazy <List <KmeansData> >();
}
int i = 0; //Counts number of Samples we have
for (int j = 0; j < numofclstrs; j++) //Runs for numberofclusters defined earlier
{
for (int k = 0; k < r[j].ClusterData.Length; k++) // add the data in web Api
{
if (numberofattributes == 3) // add data for 2D
{
kmeansdata.Value.Add(new KmeansData {
ID = i, ClustersDataLength = r[j].ClusterData.Length, TotalCentroid = r.Length, CentroidIdNumber = j, CentroidX = r[j].Centroid[0], CentroidY = r[j].Centroid[1], CentroidZ = r[j].Centroid[2], Xaxis = r[j].ClusterData[k][0], Yaxis = r[j].ClusterData[k][1], Zaxis = r[j].ClusterData[k][2], ClusterDataDistanceToCentroid = r[j].ClusterDataDistanceToCentroid[k], ClusterDataOriginalIndex = r[j].ClusterDataOriginalIndex[k], ClusterOfNearestForeignSample = r[j].ClusterOfNearestForeignSample, DistanceToNearestClusterCentroid = r[j].DistanceToNearestClusterCentroid, DistanceToNearestForeignSample = r[j].DistanceToNearestForeignSample, DistanceToNearestForeignSampleInNearestCluster = r[j].DistanceToNearestForeignSampleInNearestCluster, InClusterFarthestSampleX = r[j].InClusterFarthestSample[0], InClusterFarthestSampleY = r[j].InClusterFarthestSample[1], InClusterFarthestSampleZ = r[j].InClusterFarthestSample[2], InClusterFarthestSampleIndex = r[j].InClusterFarthestSampleIndex, InClusterMaxDistance = r[j].InClusterMaxDistance, MeanX = r[j].Mean[0], MeanY = r[j].Mean[1], MeanZ = r[j].Mean[2], NearestCluster = r[j].NearestCluster, NearestForeignSampleX = r[j].NearestForeignSample[0], NearestForeignSampleY = r[j].NearestForeignSample[1], NearestForeignSampleZ = r[j].NearestForeignSample[2], NearestForeignSampleInNearestClusterX = r[j].NearestForeignSampleInNearestCluster[0], NearestForeignSampleInNearestClusterY = r[j].NearestForeignSampleInNearestCluster[1], NearestForeignSampleInNearestClusterZ = r[j].NearestForeignSampleInNearestCluster[2], NumberOfAttribs = numberofattributes
});
}
if (numberofattributes == 2) // add data for 3D
{
kmeansdata.Value.Add(new KmeansData {
ID = i, ClustersDataLength = r[j].ClusterData.Length, TotalCentroid = r.Length, CentroidIdNumber = j, CentroidX = r[j].Centroid[0], CentroidY = r[j].Centroid[1], Xaxis = r[j].ClusterData[k][0], Yaxis = r[j].ClusterData[k][1], ClusterDataDistanceToCentroid = r[j].ClusterDataDistanceToCentroid[k], ClusterDataOriginalIndex = r[j].ClusterDataOriginalIndex[k], ClusterOfNearestForeignSample = r[j].ClusterOfNearestForeignSample, DistanceToNearestClusterCentroid = r[j].DistanceToNearestClusterCentroid, DistanceToNearestForeignSample = r[j].DistanceToNearestForeignSample, DistanceToNearestForeignSampleInNearestCluster = r[j].DistanceToNearestForeignSampleInNearestCluster, InClusterFarthestSampleX = r[j].InClusterFarthestSample[0], InClusterFarthestSampleY = r[j].InClusterFarthestSample[1], InClusterFarthestSampleIndex = r[j].InClusterFarthestSampleIndex, InClusterMaxDistance = r[j].InClusterMaxDistance, MeanX = r[j].Mean[0], MeanY = r[j].Mean[1], NearestCluster = r[j].NearestCluster, NearestForeignSampleX = r[j].NearestForeignSample[0], NearestForeignSampleY = r[j].NearestForeignSample[1], NearestForeignSampleInNearestClusterX = r[j].NearestForeignSampleInNearestCluster[0], NearestForeignSampleInNearestClusterY = r[j].NearestForeignSampleInNearestCluster[1], NumberOfAttribs = numberofattributes
});
}
i++;
}
}
// PgaeLoadFlag++;
//}
kmeansdata.Value[0].CheckSampleResult = checksample;
}
// }
else
{
numofclstrs = 3;
int numberofclusters = 3;
/// desired number of clusters
int numberofattributes = 3;
/// X,Y,Z components
double[][] result = MockApi.MockApi.CSVtoDoubleJaggedArray(@"F:\ProData\data.csv");
/// CSV to Array Conversion
if (System.IO.File.Exists(@"F:\ProData\data.csv"))
{
numberofattributes = result[0].Length;
}
if (numberofattributes == data.ClustersDataLength)
{
/// Number of attributes are 3 for 3D and 2 for 2D
IAnomalyDetectionApi X = new AnomalyDetectionApi.AnomalyDetectionAPI(result, numofclstrs);
ClusteringSettings Y = new ClusteringSettings(result, 10, numofclstrs, numberofattributes, @"F:\ProData\cluster.json");
/// Set number of clusters, max iterations and give data in result
X.ImportNewDataForClustering(Y);
/// CheckSample is a function that detect to which cluster the given sample belongs to.
ClusteringResults[] r;
/// All the Kmeans results needed for plotting
AnomalyDetectionResponse Z = X.GetResults(@"F:\ProData\cluster.json", out r);
if (numberofattributes == 3)
{
double[] sample = new double[3] {
data.XaxisCheck, data.YaxisCheck, data.ZaxisCheck
};
CheckingSampleSettings A = new CheckingSampleSettings(@"F:\ProData\cluster.json", sample, 10);
int N;
Z = X.CheckSample(A, out N);
data.CheckSampleResult = Z.Message;
kmeansdata.Value[0].CheckSampleResult = Z.Message;
}
if (numberofattributes == 2)
{
double[] sample = new double[2] {
data.XaxisCheck, data.YaxisCheck
};
CheckingSampleSettings A = new CheckingSampleSettings(@"F:\ProData\cluster.json", sample, 10);
int N;
Z = X.CheckSample(A, out N);
data.CheckSampleResult = Z.Message;
kmeansdata.Value[0].CheckSampleResult = Z.Message;
}
}
else
{
kmeansdata.Value[0].CheckSampleResult = "Your Data attributes are invalid ! Please Enter number of Cordinates according to the File Uploaded !";
}
}
//kmeansdata.Value.Add.();
//data.XaxisCheck = ProductID;
//kmeansdata.Value.Add(data); //add the post product data to the product list
//ProductID++;
//instead of adding product data to the static product list you can save data to the database.
}
