private int Compare(DataWeightedKNearestNeighborsModel data1, DataWeightedKNearestNeighborsModel data2)
{
int compare = data1.Distance.CompareTo(data2.Distance);
// CompareTo() method
return(compare);
}
public string run(List <Data> listInputData, Data predictData, int K)
{
double maxTemperature, minTemperature;
if (findMaxTemperature(listInputData, out maxTemperature) &&
fineMinTemperature(listInputData, out minTemperature))
{
List <DataWeightedKNearestNeighborsModel> listData = new List <DataWeightedKNearestNeighborsModel>();
foreach (Data inputdata in listInputData)
{
DataWeightedKNearestNeighborsModel weightKNNData = new DataWeightedKNearestNeighborsModel();
weightKNNData.Eat = inputdata.Eat;
weightKNNData.Sick = inputdata.Sick;
weightKNNData.Temperature = inputdata.Temperature;
weightKNNData.Normalized = NormalizeAttributes(maxTemperature, minTemperature, inputdata.Temperature);
double predicNomorized = NormalizeAttributes(maxTemperature, minTemperature, predictData.Temperature);
weightKNNData.Distance = EuclideanDistance(weightKNNData.Eat, weightKNNData.Normalized, predictData.Eat, predicNomorized);
listData.Add(weightKNNData);
}
List <DataWeightedKNearestNeighborsModel> listWeightKNNData = getKElement(K, listData);
calWeight(listWeightKNNData);
double[] predicArr;
if (predicData(listWeightKNNData, out predicArr))
{
if (predicArr[(int)GroupIndex.Y] > predicArr[(int)GroupIndex.N])
{
return("Y");
}
else
{
return("N");
}
}
}
return("cannot run");
}
