/// <summary>
/// Runs this sample
/// </summary>
//JAVA TO C# CONVERTER WARNING: Method 'throws' clauses are not available in .NET:
//ORIGINAL LINE: public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException
public static void Main(string[] args)
{
// create neural network
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(2, 3, 1);
// use file provided in org.neuroph.sample.data package
string inputFileName = typeof(FileIOSample).getResource("data/xor_data.txt").File;
// create file input adapter using specifed file
FileInputAdapter fileIn = new FileInputAdapter(inputFileName);
// create file output adapter using specified file name
FileOutputAdapter fileOut = new FileOutputAdapter("some_output_file.txt");
double[] input; // input buffer used for reading network input from file
// read network input using input adapter
while ((input = fileIn.readInput()) != null)
{
// feed neywork with input
neuralNet.Input = input;
// calculate network ...
neuralNet.calculate();
// .. and get network output
double[] output = neuralNet.Output;
// write network output using output adapter
fileOut.writeOutput(output);
}
// close input and output files
fileIn.close();
fileOut.close();
// Also note that shorter way for this is using org.neuroph.util.io.IOHelper class
}
public MatrixMultiLayerPerceptron(MultiLayerPerceptron sourceNetwork)
{
this.sourceNetwork = sourceNetwork;
// copy layers, input and output neurons
createMatrixLayers();
this.LearningRule = new MatrixMomentumBackpropagation();
}
public static void Main(string[] args)
{
DataSet irisDataSet = loadDataSet();
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(4, 15, 3);
configureLearningRule(neuralNet);
neuralNet.learn(irisDataSet);
Evaluation.runFullEvaluation(neuralNet, irisDataSet);
}
public static void Main(string[] args)
{
DataSet trainingSet = new DataSet(2, 1);
trainingSet.addRow(new DataSetRow(new double[] { 0, 0 }, new double[] { 0 }));
trainingSet.addRow(new DataSetRow(new double[] { 0, 1 }, new double[] { 1 }));
trainingSet.addRow(new DataSetRow(new double[] { 1, 0 }, new double[] { 1 }));
trainingSet.addRow(new DataSetRow(new double[] { 1, 1 }, new double[] { 0 }));
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, 2, 3, 1);
neuralNet.learn(trainingSet);
Evaluation.runFullEvaluation(neuralNet, trainingSet);
}
private static void configureLearningRule(MultiLayerPerceptron neuralNet)
{
neuralNet.LearningRule.LearningRate = 0.02;
neuralNet.LearningRule.MaxError = 0.01;
neuralNet.LearningRule.ErrorFunction = new MeanSquaredError();
}