public static void Main(string[] args)
{
// create data set from csv file
MultiLayerPerceptron neuralNet = (MultiLayerPerceptron)NeuralNetwork.createFromFile("irisNet.nnet");
DataSet dataSet = DataSet.createFromFile("data_sets/iris_data_normalised.txt", 4, 3, ",");
string[] classNames = new string[] { "Virginica", "Setosa", "Versicolor" };
CrossValidation crossval = new CrossValidation(neuralNet, dataSet, 5);
crossval.addEvaluator(new ClassifierEvaluator.MultiClass(classNames));
crossval.run();
CrossValidationResult results = crossval.Result;
Console.WriteLine(results);
}
/// <param name="dataSet"> training set used for error estimation </param>
/// <returns> neural network model with optimized architecture for provided data set </returns>
public virtual NeuralNetwork createOptimalModel(DataSet dataSet)
{
List <int> neurons = new List <int>();
neurons.Add(minNeuronsPerLayer);
findArchitectures(1, minNeuronsPerLayer, neurons);
LOG.info("Total [{}] different network topologies found", allArchitectures.Count);
foreach (List <int> architecture in allArchitectures)
{
architecture.Insert(0, dataSet.InputSize);
architecture.Add(dataSet.OutputSize);
LOG.info("Architecture: [{}]", architecture);
MultiLayerPerceptron network = new MultiLayerPerceptron(architecture);
LearningListener listener = new LearningListener(10, learningRule.MaxIterations);
learningRule.addListener(listener);
network.LearningRule = learningRule;
errorEstimationMethod = new CrossValidation(network, dataSet, 10);
errorEstimationMethod.run();
// FIX
var evaluator = errorEstimationMethod.getEvaluator <ClassifierEvaluator.MultiClass>(typeof(ClassifierEvaluator.MultiClass));
ClassificationMetrics[] result = ClassificationMetrics.createFromMatrix(evaluator.Result);
// nadji onaj sa najmanjim f measure
if (optimalResult == null || optimalResult.FMeasure < result[0].FMeasure)
{
LOG.info("Architecture [{}] became optimal architecture with metrics {}", architecture, result);
optimalResult = result[0];
optimalClassifier = network;
optimalArchitecure = architecture;
}
LOG.info("#################################################################");
}
LOG.info("Optimal Architecture: {}", optimalArchitecure);
return(optimalClassifier);
}