/// <summary>
/// Gets the vectors with a specific value count
/// </summary>
/// <param name="count">Count of element in the vector values</param>
/// <returns>Vector collection</returns>
public VectorCollection GetVectorsCountValues(int count)
{
var vc = new VectorCollection();
if (count > GetMaxCountValues())
{
return(vc);
}
foreach (var vec in this)
{
if (vec.Values.Count == count)
{
vc.Add(vec);
}
}
return(vc);
}
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();
}
}
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;
}
}
/// <summary>
/// Simplifies and combines the matrix
/// </summary>
/// <returns>Result group collection instance</returns>
public ResultGroupCollection Simplify()
{
var vectors = new VectorCollection();
foreach (var elm in Matrix.Keys)
{
var vect = vectors.GetVectorByKey(elm.Name);
if (vect == null)
{
vect = new Vector {
Key = elm.Name
};
vectors.Add(vect);
}
vect.Values.Add(Matrix[elm]);
}
var maxValues = vectors.GetMaxCountValues();
var resultGroups = new ResultGroupCollection();
while (maxValues > 0)
{
if (vectors.Count == 1)
{
var rgroup = new ResultGroup();
rgroup.Keys.Add(vectors[0].Key);
rgroup.Values.AddRange(vectors[0].Values);
resultGroups.Add(rgroup);
}
else
{
var rgroup = new ResultGroup();
for (var i = 0; i < vectors.Count; i++)
{
var vecX = vectors[i];
for (var j = i + 1; j < vectors.Count; j++)
{
var vecY = vectors[j];
var elementsCoincidentes = new List <string>();
var numIgualdades = 0;
foreach (var elmX in vecX.Values)
{
foreach (var elmY in vecY.Values)
{
if (elmX != elmY)
{
continue;
}
numIgualdades++;
if (!elementsCoincidentes.Contains(elmX))
{
elementsCoincidentes.Add(elmX);
}
}
}
if (numIgualdades == maxValues)
{
if (rgroup.Values.Count == 0)
{
rgroup.Values.AddRange(elementsCoincidentes);
rgroup.Keys.Add(vecX.Key);
rgroup.Keys.Add(vecY.Key);
}
else
{
var igualdadesgrupo = 0;
foreach (var elmX in rgroup.Values)
{
foreach (var elmY in elementsCoincidentes)
{
if (elmX == elmY)
{
igualdadesgrupo++;
}
}
}
if (igualdadesgrupo != maxValues)
{
if (rgroup.Keys.Count > 0)
{
resultGroups.Add(rgroup);
}
rgroup = new ResultGroup();
rgroup.Values.AddRange(elementsCoincidentes);
rgroup.Keys.Add(vecX.Key);
rgroup.Keys.Add(vecY.Key);
}
else if (igualdadesgrupo == maxValues)
{
var xAdded = false;
foreach (var key in rgroup.Keys)
{
if (key == vecX.Key)
{
xAdded = true;
}
}
if (!xAdded)
{
rgroup.Keys.Add(vecX.Key);
}
var yAdded = false;
foreach (var key in rgroup.Keys)
{
if (key == vecY.Key)
{
yAdded = true;
}
}
if (!yAdded)
{
rgroup.Keys.Add(vecY.Key);
}
}
}
}
else
{
if (maxValues == vecX.Values.Count && maxValues > vecY.Values.Count)
{
if (rgroup.Keys.Count > 0 && !resultGroups.ContainsRGroup(vecX))
{
resultGroups.Add(rgroup);
}
rgroup = new ResultGroup();
rgroup.Values.AddRange(vecX.Values);
rgroup.Keys.Add(vecX.Key);
}
if (maxValues == vecY.Values.Count && maxValues > vecX.Values.Count)
{
if (rgroup.Keys.Count > 0 && !resultGroups.ContainsRGroup(vecY))
{
resultGroups.Add(rgroup);
}
rgroup = new ResultGroup();
rgroup.Values.AddRange(vecY.Values);
rgroup.Keys.Add(vecY.Key);
}
}
}
}
if (rgroup.Keys.Count > 0 && rgroup.Values.Count > 0)
{
resultGroups.Add(rgroup);
}
}
maxValues--;
}
return(resultGroups);
}
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);
}
