public static void KmeansMain()
{
Console.WriteLine("Using demo setup?[y/n]");
string str = Console.ReadLine();
if (str.ToLower().Contains("y") == false)
{
Console.WriteLine("Input node maximum value:(Default:10000 , Min:0)");
NodesMaxValueSet = Convert.ToInt32(Console.ReadLine());
Console.WriteLine("Input node count:(Default:10000 , Min:0)");
NodesSet = Convert.ToInt32(Console.ReadLine());
Console.WriteLine("Input groups count for random points to divid into:(Default:3 , Min:0)");
DataGroupCount = Convert.ToInt32(Console.ReadLine());
Console.WriteLine("Input fluctuation ratio:(Default:0.18 , Min:0 , Max:0.5)");
FluctuationRatio = Convert.ToSingle(Console.ReadLine());
Console.WriteLine("Input k:( Default:3 , Min:1)");
k = Convert.ToInt32(Console.ReadLine());
Console.WriteLine("Input iteration limit:(Default:100)");
IterationLimit = Convert.ToInt32(Console.ReadLine());
Console.WriteLine("Input convergence distance:(Default:5)");
ConvDistance = Convert.ToInt32(Console.ReadLine());
}
//Gen grouped random nodes
Random2DPoints.OnGeneratePointGroups += Random2DPoints_OnGeneratePointGroups;
var RandomPoints = Random2DPoints.GenerateRandomPointsGroup(NodesMaxValueSet, NodesSet, DataGroupCount, FluctuationRatio);
var Dataset = new VectorCollection <Point2D>(RandomPoints);
Dataset.Print();
while (Retry)
{
KmeansTrainer <Point2D> kmeansTrainer = new KmeansTrainer <Point2D>(Dataset, k, ConvDistance, IterationLimit);
kmeansTrainer.OnIteration += Kmeans_OnIteration;
var TrainResult = kmeansTrainer.Train();
//Kmeans<Point2D> kmeans = new Kmeans<Point2D>(TrainResult);
InputCommand();
kmeansTrainer.OnIteration -= Kmeans_OnIteration;
}
}
private static KmeansTrainResult <RawImage28x28> AskKmeansTrainData(bool SkipConfirm)
{
while (true)
{
string input;
if (SkipConfirm)
{
input = "t";
}
else
{
Console.WriteLine("Input 't' to start training, 'i' to ignore.");
input = Console.ReadLine();
}
if (input.ToLower().Contains('t'))
{
Console.WriteLine("Input ConvDistance:(Default:3)");
float ConvDistance = Convert.ToSingle(Console.ReadLine());
Console.WriteLine("Input iteration limit:(Default:200)");
int IterationLimit = Convert.ToInt32(Console.ReadLine());
Console.WriteLine("Training......");
var MnistDataSet = MnistDataSetLoader.LoadData(MnistPixelFilePath, MnistLabelFilePath);
KmeansTrainer <RawImage28x28> kmeansTrainer = new KmeansTrainer <RawImage28x28>(MnistDataSet, 10, ConvDistance, IterationLimit);
var TrainResult = kmeansTrainer.Train();
Console.WriteLine("Save trained result?[y/n]");
if (Console.ReadLine().ToLower().Contains('y'))
{
TrainResult.SaveToFile(KmeansTrainResultFilePath);
Console.WriteLine("Saved at {0}", KmeansTrainResultFilePath);
}
return(TrainResult);
}
else if (input.ToLower().Contains("i"))
{
return(null);
}
}
}
