Accord.Neuro.Learning.PerceptronLearning.RunEpoch C# (CSharp) Method

        public double RunEpoch( double[][] input, double[][] output )
        {
            double error = 0.0;

            // run learning procedure for all samples
            for ( int i = 0, n = input.Length; i < n; i++ )
            {
                error += Run( input[i], output[i] );
            }

            // return summary error
            return error;
        }
    }

        // Worker thread
        void SearchSolution()
        {
            // prepare learning data
            double[][] input = new double[samples][];
            double[][] output = new double[samples][];

            for (int i = 0; i < samples; i++)
            {
                input[i] = new double[variables];
                output[i] = new double[1];

                // copy input
                for (int j = 0; j < variables; j++)
                    input[i][j] = data[i, j];
                // copy output
                output[i][0] = classes[i];
            }

            // create perceptron
            ActivationNetwork network = new ActivationNetwork(new ThresholdFunction(), variables, 1);
            ActivationNeuron neuron = network.Layers[0].Neurons[0] as ActivationNeuron;
            // create teacher
            PerceptronLearning teacher = new PerceptronLearning(network);
            // set learning rate
            teacher.LearningRate = learningRate;

            // iterations
            int iteration = 1;

            // statistic files
            StreamWriter errorsFile = null;
            StreamWriter weightsFile = null;

            try
            {
                // check if we need to save statistics to files
                if (saveStatisticsToFiles)
                {
                    // open files
                    errorsFile = File.CreateText("errors.csv");
                    weightsFile = File.CreateText("weights.csv");
                }

                // erros list
                ArrayList errorsList = new ArrayList();

                // loop
                while (!needToStop)
                {
                    // save current weights
                    if (weightsFile != null)
                    {
                        for (int i = 0; i < variables; i++)
                        {
                            weightsFile.Write(neuron.Weights[i] + ",");
                        }
                        weightsFile.WriteLine(neuron.Threshold);
                    }

                    // run epoch of learning procedure
                    double error = teacher.RunEpoch(input, output);
                    errorsList.Add(error);

                    // show current iteration
                    SetText(iterationsBox, iteration.ToString());

                    // save current error
                    if (errorsFile != null)
                    {
                        errorsFile.WriteLine(error);
                    }

                    // show classifier in the case of 2 dimensional data
                    if ((neuron.InputsCount == 2) && (neuron.Weights[1] != 0))
                    {
                        double k = -neuron.Weights[0] / neuron.Weights[1];
                        double b = -neuron.Threshold / neuron.Weights[1];

                        double[,] classifier = new double[2, 2] {
							{ chart.RangeX.Min, chart.RangeX.Min * k + b },
							{ chart.RangeX.Max, chart.RangeX.Max * k + b }
																};
                        // update chart
                        chart.UpdateDataSeries("classifier", classifier);
                    }

                    // stop if no error
                    if (error == 0)
                        break;

                    iteration++;
                }

                // show perceptron's weights
                ListViewItem item = null;

                ClearList(weightsList);
                for (int i = 0; i < variables; i++)
                {
                    item = AddListItem(weightsList, string.Format("Weight {0}", i + 1));
                    AddListSubitem(item, neuron.Weights[i].ToString("F6"));
                }
                item = AddListItem(weightsList, "Threshold");
                AddListSubitem(item, neuron.Threshold.ToString("F6"));

                // show error's dynamics
                double[,] errors = new double[errorsList.Count, 2];

                for (int i = 0, n = errorsList.Count; i < n; i++)
                {
                    errors[i, 0] = i;
                    errors[i, 1] = (double)errorsList[i];
                }

                errorChart.RangeX = new Range(0, errorsList.Count - 1);
                errorChart.RangeY = new Range(0, samples);
                errorChart.UpdateDataSeries("error", errors);
            }
            catch (IOException)
            {
                MessageBox.Show("Failed writing file", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
            }
            finally
            {
                // close files
                if (errorsFile != null)
                    errorsFile.Close();
                if (weightsFile != null)
                    weightsFile.Close();
            }

            // enable settings controls
            EnableControls(true);
        }