// 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 );
}