Exemplo n.º 1
0
        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();
            }
        }
Exemplo n.º 2
0
        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;
            }
        }