// 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); }
// Worker thread void SearchSolution( ) { // initialize input and output values double[][] input = null; double[][] output = null; if ( sigmoidType == 0 ) { // unipolar data input = new double[4][] { new double[] {0, 0}, new double[] {0, 1}, new double[] {1, 0}, new double[] {1, 1} }; output = new double[4][] { new double[] {0}, new double[] {1}, new double[] {1}, new double[] {0} }; } else { // bipolar data input = new double[4][] { new double[] {-1, -1}, new double[] {-1, 1}, new double[] { 1, -1}, new double[] { 1, 1} }; output = new double[4][] { new double[] {-1}, new double[] { 1}, new double[] { 1}, new double[] {-1} }; } // create neural network ActivationNetwork network = new ActivationNetwork( ( sigmoidType == 0 ) ? (IActivationFunction) new SigmoidFunction( sigmoidAlphaValue ) : (IActivationFunction) new BipolarSigmoidFunction( sigmoidAlphaValue ), 2, 2, 1 ); // create teacher var teacher = new ParallelResilientBackpropagationLearning(network); // set learning rate and momentum teacher.Reset(initialStep); // iterations int iteration = 0; // statistic files StreamWriter errorsFile = null; try { // check if we need to save statistics to files if ( saveStatisticsToFiles ) { // open files errorsFile = File.CreateText( "errors.csv" ); } // erros list ArrayList errorsList = new ArrayList( ); // loop while ( !needToStop ) { // run epoch of learning procedure double error = teacher.RunEpoch( input, output ); errorsList.Add( error ); // save current error if ( errorsFile != null ) { errorsFile.WriteLine( error ); } // show current iteration & error SetText( currentIterationBox, iteration.ToString( ) ); SetText( currentErrorBox, error.ToString( ) ); iteration++; // check if we need to stop if ( error <= learningErrorLimit ) break; } // 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.UpdateDataSeries( "error", errors ); } catch ( IOException ) { MessageBox.Show( "Failed writing file", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error ); } finally { // close files if ( errorsFile != null ) errorsFile.Close( ); } // enable settings controls EnableControls( true ); }