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);
}
}
}
static void Main(string[] args)
{
VectorCollection <RawImage28x28> Dataset = null;
Knn <RawImage28x28> knn = null;
KmeansTrainResult <RawImage28x28> KmeansTrainResult = null;
Kmeans <RawImage28x28> kmeans = null;
//CheckFiles
if (File.Exists(MnistPixelFilePath) == false)
{
MnistPixelFilePath = Environment.CurrentDirectory + "\\train-images.idx3-ubyte";
}
if (File.Exists(MnistLabelFilePath) == false)
{
MnistLabelFilePath = Environment.CurrentDirectory + "\\train-labels.idx1-ubyte";
}
if (File.Exists(KmeansTrainResultFilePath) == false)
{
KmeansTrainResultFilePath = Environment.CurrentDirectory + "\\KmeansTrainResult.json";
}
//LoadFiles
if (File.Exists(MnistPixelFilePath) && File.Exists(MnistLabelFilePath))
{
var MnistDataSet = MnistDataSetLoader.LoadData(MnistPixelFilePath, MnistLabelFilePath);
Dataset = new VectorCollection <RawImage28x28>(MnistDataSet);
}
if (File.Exists(KmeansTrainResultFilePath))
{
KmeansTrainResult = new KmeansTrainResult <RawImage28x28>(KmeansTrainResultFilePath);
}
if (Dataset != null)
{
var knnTrainer = new KnnTrainer <RawImage28x28>(Dataset);
knn = new Knn <RawImage28x28>(knnTrainer.Train());
if (KmeansTrainResult == null)
{
Console.WriteLine("Can't find Kmeans train data.");
KmeansTrainResult = AskKmeansTrainData(false);
}
else if (KmeansTrainResult != null)
{
Console.WriteLine("Found Kmeans train data.");
Console.WriteLine("[U]se trained data or [t]rain again? [u/t]");
if (Console.ReadLine().ToLower().Contains('t'))
{
KmeansTrainResult = null;
KmeansTrainResult = AskKmeansTrainData(true);
}
}
}
else
{
Console.WriteLine("Can't find Knn train data, ignored");
Console.WriteLine("Press any key to continue.");
Console.ReadKey();
}
if (KmeansTrainResult != null)
{
kmeans = new Kmeans <RawImage28x28>(KmeansTrainResult);
}
if (kmeans == null && knn == null)
{
Console.WriteLine("Fatal error: Can not find any trained data.");
Console.WriteLine("Press any key to exit...");
Console.ReadKey();
return;
}
MainForm form = new MainForm(knn, kmeans);
Application.EnableVisualStyles();
Application.Run(form);
}
