//returns index of closest cluster centroid
public static int FindClosestClusterCenter(List <Centroid> clusterCenter, DocumentVector obj, string sim)
{
float[] similarityMeasure = new float[clusterCenter.Count()];
for (int i = 0; i < clusterCenter.Count(); i++)
{
if (sim == "Cosine")
{
similarityMeasure[i] = SimilarityMatrics.FindCosineSimilarity(clusterCenter[i].GroupedDocument[0].VectorSpace, obj.VectorSpace);
}
else
{
Wnlib.WNCommon.path = "C:\\Program Files\\WordNet\\3.0\\dict\\";
SentenceSimilarity semsim = new SentenceSimilarity();
similarityMeasure[i] = semsim.GetScore(clusterCenter[i].GroupedDocument[0].Content, obj.Content);
}
}
int index = 0;
float maxValue = similarityMeasure[0];
for (int i = 0; i < similarityMeasure.Count(); i++)
{
//if document is similar assign the document to the lowest index cluster center to avoid the long loop
if (similarityMeasure[i] > maxValue)
{
maxValue = similarityMeasure[i];
index = i;
}
}
return(index);
}
//returns index of closest cluster centroid
private static int FindClosestClusterCenter(List <Centroid> clusterCenter, DocumentVector obj)
{
float[] similarityMeasure = new float[clusterCenter.Count()];
for (int i = 0; i < clusterCenter.Count(); i++)
{
similarityMeasure[i] = SimilarityMatrics.FindCosineSimilarity(clusterCenter[i].GroupedDocument[0].VectorSpace, obj.VectorSpace);
}
int index = 0;
float maxValue = similarityMeasure[0];
for (int i = 0; i < similarityMeasure.Count(); i++)
{
//if document is similar assign the document to the lowest index cluster center to avoid the long loop
if (similarityMeasure[i] > maxValue)
{
maxValue = similarityMeasure[i];
index = i;
}
}
return(index);
}