public NNResult GetNearestNodes(float[] sample)
{
DataTable dt = GetDataTable();
double[,] results = NearestNeighbour.GetTwoNearestNeighbours(metric, numThreads, this.insertNodeCount, dt.NumDimensions, sample, dt.GetData());
int first_index = (int)results[0, 1];
int second_index = (int)results[1, 1];
NNResult nnresult = new NNResult();
nnresult.nearestNeighbour0 = (GraphNode)dt.GetDataObject(first_index);
nnresult.distance0 = results[0, 0];
nnresult.index0 = first_index;
nnresult.nearestNeightbour1 = (GraphNode)dt.GetDataObject(second_index);
nnresult.distance1 = results[1, 0];
nnresult.index1 = second_index;
return(nnresult);
}
public static List <int[]> PredictSamples(List <float[]> samples, List <int[]> nodeClusterIndex, List <float[]> positionsList, int maxThreads = 2, int metric = 1)
{
var datapointClusterIndex = new List <int[]>(samples.Count);
int numDims = positionsList[0].Length;
DataTable dt = new DataTable(numDims, positionsList);
float[][] positionsData = dt.GetData();
for (int sampleIndex = 0; sampleIndex < samples.Count; sampleIndex++)
{
float[] sample = samples[sampleIndex];
double[,] nnResults = NearestNeighbour.GetTwoNearestNeighbours(metric, maxThreads, positionsList.Count, numDims, sample, positionsData);
int firstNearestNeighbourNodeIndex = (int)nnResults[0, 1];
int clusterIndex = nodeClusterIndex[firstNearestNeighbourNodeIndex][1];
//datapointClusterIndex[sampleIndex] = new int[] { sampleIndex, clusterIndex };
int[] row = new int[] { sampleIndex, clusterIndex };
datapointClusterIndex.Add(row);
}
return(datapointClusterIndex);
}
