double[] CombinedOldAndNewTargets()
{
double[] loadedTargets = ValfriStatic.Targets;
if (ValfriStatic.AddedPoints == null)
{
return(loadedTargets);
}
double[][] addedTargets = ListToMatrix(ValfriStatic.AddedPoints);
double[] combinedData = new double[loadedTargets.Length + addedTargets.Length];
int i = 0;
foreach (var point in loadedTargets)
{
combinedData[i++] = point;
}
foreach (var point in addedTargets)
{
combinedData[i++] = Knn.Classify(point, ValfriStatic.MatrixOfPoints, Dataset.NumberOfFeatures, Dataset.K);
}
return(combinedData);
}
private IEnumerable <Bitmap> DoRecognize(Bitmap b)
{
List <Bitmap> Result = new List <Bitmap>();
var NotClassifiedFeatures = FeatureExtractor.ExtractFeatures(b, null);
List <SURFFeature> FeatureRemain = new List <SURFFeature>();
Parallel.ForEach(NotClassifiedFeatures, (NotClassifiedFeature) =>
{
var KnnResult = knn.Classify(k, NotClassifiedFeature);
if (KnnResult.Confidence >= ConfidenceThreshold)
{
lock (FeatureRemain)
FeatureRemain.Add(NotClassifiedFeature);
}
});
var GroupedFeatureRemains = FeatureRemain.GroupBy(x => x.Tag);
Bitmap FinalResult = (Bitmap)b.Clone();
//Draw Features
FeaturesMarker featuresMarker = new FeaturesMarker(FeatureRemain.Select(x => x.Original));
Bitmap MarkedBitmap = featuresMarker.Apply(b);
//Draw bounding box
foreach (var GroupedFeatureRemain in GroupedFeatureRemains)
{
string Tag = GroupedFeatureRemain.Key;
Point2DGrouper point2DGrouper = new Point2DGrouper(MaximumPointGroupDistance)
{
MinimumGroupCount = MinimumPointGroupCount
};
var BoundingBoxes = point2DGrouper
.GetGrouped2DPoints(GroupedFeatureRemain.Select(f => f.GetLocation()))
.GroupBy(p => p.GroupID)
.Select(group => group.GetBoundingBox(MinimumPointGroupArea))
.Where(bbox => bbox != null);
var ObjectBox = BoundingBoxes.GetBoundingBox();
Brush brush = SelectBrush(Tag);
if (BoundingBoxes != null)
{
MarkedBitmap.DrawRect(BoundingBoxes.Select(boxes => boxes.ToRectangle()), brush, 3, "Bbox:" + Tag);
}
if (ObjectBox != null)
{
FinalResult.DrawRect(ObjectBox.ToRectangle(), brush, 3, "Object:" + Tag);
}
}
FinalResult.Tag = "Final Result";
MarkedBitmap.Tag = "Class Bounding Boxes";
Result.Add(FinalResult);
Result.Add(MarkedBitmap);
return(Result);
}
private void Classify_btn_Click(object sender, EventArgs e)
{
using (Bitmap ResizedInput = new Bitmap(InputPictureBox.Image, 28, 28))
{
RawImage28x28 NewInput = new RawImage28x28(ResizedInput, null);
NewInput.Print();
Knn?.Classify(Convert.ToInt32(Knn_KValue_Textbox.Text), NewInput);
Kmeans?.Classify(NewInput);
}
}
List <GameObject> CreatePies(List <List <double> > matrix, bool mark = false)
{
List <GameObject> pies = new List <GameObject>();
for (int i = 0; i < matrix.Count; i++)
{
GameObject pie = CreatePie(matrix[i].ToArray(), Knn.Classify(matrix[i].ToArray(), ValfriStatic.MatrixOfPoints, Dataset.NumberOfFeatures, Dataset.K), mark);
pies.Add(pie);
}
return(pies);
}
static void Main(string[] args)
{
var start = new APoint {
X = 0, Y = 0
};
var target = new APoint {
X = 5, Y = 4
};
//var DFSmatrix = Common.GetRandomMatrix(7, 7);
//var dfsMatrix = Algorithms.DFS(DFSmatrix, start, target);
//Console.WriteLine("DFS path: ");
//Common.PrettyPrint(dfsMatrix, 7, 7);
//Console.WriteLine("---------------------------");
//var BFSmatrix = Common.GetRandomMatrix(7, 7);
//var bfsMatrix = Algorithms.BFS(BFSmatrix, start, target);
//Console.WriteLine("BFS path: ");
//Common.PrettyPrint(bfsMatrix, 7, 7);
//var idsMatrix = Common.GetRandomMatrix(7, 7);
//IDS.Matrix = idsMatrix;
//IDS.Run(start, target);
//Console.WriteLine("IDS path: ");
//Common.PrettyPrint(idsMatrix, 7, 7);
//var aStarMatrix = Common.GetRandomMatrix(7, 7);
//InformedAlgs.Matrix = aStarMatrix;
//InformedAlgs.AStar(start, target);
//Console.WriteLine("A-Star path: ");
//Console.WriteLine(InformedAlgs.PathLength);
//Common.PrettyPrint(aStarMatrix, 7, 7);
//int n = 1000;
//QueensProblem queensProblem = new QueensProblem(n);
//queensProblem.Solve();
//queensProblem.GetSolution(n);
//var knapsack = new Knapsack();
//knapsack.InitializeItems();
//Generator generator = new Generator(knapsack);
//generator.Solve();
//Console.Read();
var knn = new Knn();
knn.InitializeSets();
int k = 12;
knn.Classify(k);
Console.WriteLine("Accuracy: " + knn.predictedAnswers * 100 / 20 + "%");
}
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 NewPrediction()
{
if (usedFeatures.Count > 0)
{
NewDataPoints[0][usedFeatures[0]] = newPred[0];
NewDataPoints[0][usedFeatures[1]] = newPred[1];
NewDataPoints[0][usedFeatures[2]] = newPred[2];
}
else
{
NewDataPoints[0][NewDataPoints[0].Keys.ElementAt(1)] = newPred[0];
NewDataPoints[0][NewDataPoints[0].Keys.ElementAt(2)] = newPred[1];
NewDataPoints[0][NewDataPoints[0].Keys.ElementAt(3)] = newPred[2];
}
var obj = CreateNewObject(NewDataPoints[0].Keys.Count() - 2, NewDataPoints[0]);
var classified = Knn.Classify(obj, DictToMatrix.DictionaryListToMatrix(ListOfPoints), GetNumberOfTargets(GetDataSetTargets()), K);
if (classified is - 1)
{
return;
}
public void Save()
{
List <double> point = scrollScript.GetInputs();
Debug.Log(point.Count);
GameObject pieInitiator = GameObject.Find("DataInitiator");
var pieInitiatorScript = (PieController)pieInitiator.GetComponent(typeof(PieController));
int target = Knn.Classify(point.ToArray(), ValfriStatic.MatrixOfPoints, Dataset.NumberOfFeatures, Dataset.K);
var canvas = GameObject.Find("Canvas");
var newPie = pieInitiatorScript.CreatePie(point.ToArray(), target);
var KPCA = KernalPca.Transform(point.ToArray());
Display.Pie(newPie, canvas);
Display.SetPosition(newPie, (float)KPCA[0], (float)KPCA[1], 0, true);
Delete();
}