public static void KnnMain()
{
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)");
NodesMaxValueSet = Convert.ToInt32(Console.ReadLine());
Console.WriteLine("Input node count:(Default:150)");
NodesSet = Convert.ToInt32(Console.ReadLine());
Console.WriteLine("Input groups count for random points to divid into:(Default:3, Min:1)");
DataGroupCount = Convert.ToInt32(Console.ReadLine());
Console.WriteLine("Input fluctuation ratio:(Default:0.5, Min:0.1)");
FluctuationRatio = Convert.ToSingle(Console.ReadLine());
Console.WriteLine("Input new node count:(Default:10)");
NewNodesCount = Convert.ToInt32(Console.ReadLine());
Console.WriteLine("Input k set:(Default:7)");
k = Convert.ToInt32(Console.ReadLine());
}
//Gen grouped random nodes
Random2DPoints.OnGeneratePointGroups += Random2DPoints_OnGeneratePointGroups;
var ClassifiedPoints = Random2DPoints.GenerateRandomPointsGroup(NodesMaxValueSet, NodesSet, DataGroupCount, FluctuationRatio);
var Dataset = new VectorCollection <Point2D>(ClassifiedPoints);
Dataset.Print();
var knnTrainer = new KnnTrainer <Point2D>(Dataset);
Knn <Point2D> knn = new Knn <Point2D>(knnTrainer.Train());
knn.OnClassify += Knn_OnClassify;
while (Retry)
{
var NewNodes = Random2DPoints.GenerateRandomPoints(NodesMaxValueSet, NewNodesCount);
foreach (var NewNode in NewNodes)
{
knn.Classify(k, NewNode);
}
InputCommand();
}
}
static void Main(string[] args)
{
KnnTrainResult <SURFFeature> TrainedFeatures = new KnnTrainResult <SURFFeature>();
SURFFeatureExtractor FeatureExtractor = new SURFFeatureExtractor(threshold, octaves, initial)
{
ExtractNegativeOnly = true, MinimumScale = minimumScale
};
//Training
foreach (var SamplePathTag_Pair in SamplePathTag_Pairs)
{
string Path = SamplePathTag_Pair.Key;
string Tag = SamplePathTag_Pair.Value;
BitmapReader TestSampleBitmapReader = new BitmapReader(Path);
var Bitmaps = TestSampleBitmapReader.GetBitmaps();
Console.WriteLine("Extract features from tag:{0}, path:{1}", Tag, Path);
var features = FeatureExtractor.ExtractFeatures(Bitmaps, Tag);
Console.WriteLine("Features extract ,Count:{0}", features.Count());
var knnTrainer = new KnnTrainer <SURFFeature>(features);
var knnTrainResult = knnTrainer.Train();
TrainedFeatures.AddRange(knnTrainResult);
foreach (var b in Bitmaps)
{
b.Dispose();
}
GC.Collect();
GC.WaitForPendingFinalizers();
}
Application.EnableVisualStyles();
Application.Run(new MainForm(FeatureExtractor, TrainedFeatures));
}
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);
}