Exemple #1
0
        public void Train(int numberOfEpochs, DataGetter dataGetter)
        {
            InputData    = new List <double>();
            ExpectedData = new List <double>();
            ErrorX       = new List <double>();
            ErrorY       = new List <double>();
            InputData    = dataGetter.getInputData();
            ExpectedData = dataGetter.getExpectedData();
            double error = 0;

            for (int i = 0; i < numberOfEpochs; i++)
            {
                error = 0;



                /* WYBOR SASIADUJACYCH NEURONOW DLA KAZDEGO NEURONU ABY ZAKTUALIZOWAC SIGME */
                foreach (var radialNeuron in HiddenLayer)
                {
                    var tempRadialNeurons = HiddenLayer.OrderBy(n => n.EuclideanDistance(radialNeuron)).Take(5).Skip(1).ToList();       //gdyby cos nie dzialalo warto to przeanaliozwac
                    radialNeuron.UpdateSigma(tempRadialNeurons);
                }

                for (int j = 0; j < InputData.Count; j++)
                {
                    /* FORWARD PROPAGATION - RADIAL NEURONS */
                    HiddenLayer.ForEach(rn => rn.GaussianFunction(InputData[j]));

                    /* FORWARD PROPAGATION - NEURONS */
                    OutputLayer.ForEach(n => n.CalculateOutputValue(HiddenLayer));

                    /* BACKPROPAGATION - NEURONS */
                    OutputLayer.ForEach(n => n.CalculateGradient(ExpectedData[j]));
                    OutputLayer.ForEach(n => n.UpdateWeights(LearnRate, Momentum, HiddenLayer));
                    error += MSE(ExpectedData[j]);
                }
                error = 1.0 * error / ExpectedData.Count;
                ErrorX.Add(i);
                ErrorY.Add(error);
                //if(i % (numberOfEpochs / 10.0) == 0)
                //    Console.WriteLine(error);
            }

            Console.WriteLine("Blad sredniokwadratowy aproksymacji: " + error + "\n");
        }
Exemple #2
0
        public List <double> Test(DataGetter dataGetter)
        {
            InputData    = new List <double>();
            ExpectedData = new List <double>();
            InputData    = dataGetter.getInputData();
            ExpectedData = dataGetter.getExpectedData();
            double        error = 0;
            List <double> vs    = new List <double>();

            for (int i = 0; i < dataGetter.getInputData().Count; i++)
            {
                HiddenLayer.ForEach(rn => rn.GaussianFunction(dataGetter.getInputData()[i]));
                OutputLayer.ForEach(n => n.CalculateOutputValue(HiddenLayer));
                vs.Add(OutputLayer[0].OutputValue);
                error += MSE(ExpectedData[i]);
            }
            error = 1.0 * error / ExpectedData.Count;
            Console.WriteLine("Blad sredniokwadratowy aproksymacji: " + error);
            return(vs);
        }