Accord.Neuro.Learning.ParallelResilientBackpropagationLearning.Reset C# (CSharp) Method

        public void Reset(double rate)
        {
            Parallel.For(0, weightsUpdates.Length, i =>
            {
                for (int j = 0; j < weightsUpdates[i].Length; j++)
                    for (int k = 0; k < weightsUpdates[i][j].Length; k++)
                        weightsUpdates[i][j][k] = rate;

                for (int j = 0; j < thresholdsUpdates[i].Length; j++)
                    thresholdsUpdates[i][j] = rate;
            });
        }

        // Worker thread
        void SearchSolution()
        {
            // number of learning samples
            int samples = data.Length - predictionSize - windowSize;
            // data transformation factor
            double factor = 1.7 / chart.RangeY.Length;
            double yMin = chart.RangeY.Min;
            // prepare learning data
            double[][] input = new double[samples][];
            double[][] output = new double[samples][];

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

                // set input
                for (int j = 0; j < windowSize; j++)
                {
                    input[i][j] = (data[i + j] - yMin) * factor - 0.85;
                }
                // set output
                output[i][0] = (data[i + windowSize] - yMin) * factor - 0.85;
            }

            // create multi-layer neural network
            ActivationNetwork network = new ActivationNetwork(
                new BipolarSigmoidFunction(sigmoidAlphaValue),
                windowSize, windowSize * 2, 1);

            // create teacher
            var teacher = new ParallelResilientBackpropagationLearning(network);

            teacher.Reset(initialStep);

            // run at least one backpropagation epoch
            //teacher2.RunEpoch(input, output);

            // iterations
            int iteration = 1;

            // solution array
            int solutionSize = data.Length - windowSize;
            double[,] solution = new double[solutionSize, 2];
            double[] networkInput = new double[windowSize];

            // calculate X values to be used with solution function
            for (int j = 0; j < solutionSize; j++)
            {
                solution[j, 0] = j + windowSize;
            }

            // loop
            while (!needToStop)
            {
                // run epoch of learning procedure
                double error = teacher.RunEpoch(input, output) / samples;

                // calculate solution and learning and prediction errors
                double learningError = 0.0;
                double predictionError = 0.0;
                // go through all the data
                for (int i = 0, n = data.Length - windowSize; i < n; i++)
                {
                    // put values from current window as network's input
                    for (int j = 0; j < windowSize; j++)
                    {
                        networkInput[j] = (data[i + j] - yMin) * factor - 0.85;
                    }

                    // evalue the function
                    solution[i, 1] = (network.Compute(networkInput)[0] + 0.85) / factor + yMin;

                    // calculate prediction error
                    if (i >= n - predictionSize)
                    {
                        predictionError += Math.Abs(solution[i, 1] - data[windowSize + i]);
                    }
                    else
                    {
                        learningError += Math.Abs(solution[i, 1] - data[windowSize + i]);
                    }
                }
                // update solution on the chart
                chart.UpdateDataSeries("solution", solution);

                // set current iteration's info
                SetText(currentIterationBox, iteration.ToString());
                SetText(currentLearningErrorBox, learningError.ToString("F3"));
                SetText(currentPredictionErrorBox, predictionError.ToString("F3"));

                // increase current iteration
                iteration++;

                // check if we need to stop
                if ((iterations != 0) && (iteration > iterations))
                    break;
            }

            // show new solution
            for (int j = windowSize, k = 0, n = data.Length; j < n; j++, k++)
            {
                AddSubItem(dataList, j, solution[k, 1].ToString());
            }

            // enable settings controls
            EnableControls(true);
        }