public void FindAll()
{
Console.WriteLine();
Console.WriteLine();
int errorCount = 0;
foreach (var item in TrainingSet)
{
double min = double.MaxValue;
WeatherModel origin = null;
double diff = 0;
foreach (var elem in DataSet)
{
diff = WeatherModel.ToNormal(item, elem);
if (diff < min)
{
min = diff;
origin = elem;
}
}
if (origin == null)
{
continue;
}
if (origin.RRR == 0 && item.RRR != 0 || origin.RRR != 0 && item.RRR == 0)
{
errorCount++;
}
Console.SetCursorPosition(0, Console.CursorTop - 2);
Console.WriteLine($"ErrorCount {errorCount} of total {TrainingSet.Count}");
Console.WriteLine($"Prediction precision {(int)((1 - (float)errorCount / TrainingSet.Count) * 100)}" + "%");
}
}
public static double ToNormal(WeatherModel item1, WeatherModel item2)
{
return
(Math.Pow(item1.T - item2.T, 2) +
Math.Pow(item1.Po - item2.Po, 2) +
Math.Pow(item1.P - item2.P, 2) +
Math.Pow(item1.Pa - item2.Pa, 2) +
Math.Pow(item1.U - item2.U, 2) +
Math.Pow(item1.Ef - item2.Ef, 2) +
Math.Pow(item1.Td - item2.Td, 2) +
Math.Pow(item1.RRR - item2.RRR, 2));
}
private WeatherModel findMin(WeatherModel item, ref List <WeatherModel> dataSet)
{
double min = double.MaxValue;
WeatherModel origin = null;
foreach (var elem in dataSet)
{
var diff = WeatherModel.ToNormal(item, elem);
if (diff < min)
{
min = diff;
origin = elem;
}
}
return(origin);
}