public static double Regressor(DataRow dataRow, DataSet trainData, int k)
{
int rowCount = trainData.DataRows.Count;
IndexAndDistance[] info = new IndexAndDistance[rowCount];
for (int i = 0; i < rowCount; i++)
{
IndexAndDistance curr = new IndexAndDistance();
double dist = Distance(dataRow, trainData.DataRows[i].AllValues.ToArray());
curr.idx = i;
curr.dist = dist;
info[i] = curr;
}
Array.Sort(info); //sortera distans
return(Regress(info, dataRow, trainData, k));
}
public static double WeightedRegressedClassify(DataRow dataRow, DataSet trainData, int k)
{
int rowCount = trainData.DataRows.Count;
IndexAndDistance[] info = new IndexAndDistance[rowCount];
for (int i = 0; i < rowCount; i++)
{
IndexAndDistance curr = new IndexAndDistance();
double dist = Distance(dataRow, trainData.DataRows[i].AllValues.ToArray());
curr.idx = i;
curr.dist = dist;
info[i] = curr;
}
Array.Sort(info); //sortera distans
double[] weights = MakeWeights(k, info);
return(WeightedRegressedVote(info, dataRow, trainData, weights, k));
}
