/// <param name="args"> the command line arguments </param>
public static void Main(string[] args)
{
// test subsampling here too with some small dataset
//JAVA TO C# CONVERTER TODO TASK: Java wildcard generics are not converted to .NET:
//ORIGINAL LINE: org.neuroph.core.NeuralNetwork<?> nnet = org.neuroph.core.NeuralNetwork.createFromFile("MIcrNet1.nnet");
NeuralNetwork nnet = NeuralNetwork.createFromFile("MIcrNet1.nnet");
DataSet dataSet = DataSet.load("MICRData.tset");
// get class labels from output neurons
string[] classNames = new string[nnet.OutputsCount]; // = {"LeftHand", "RightHand", "Foot", "Rest"};
int i = 0;
foreach (Neuron n in nnet.OutputNeurons)
{
classNames[i] = n.Label;
i++;
}
CrossValidation crossval = new CrossValidation(nnet, dataSet, 5);
crossval.addEvaluator(new ClassifierEvaluator.MultiClass(classNames)); // add multi class here manualy to make it independent from data set
// data set should hav ecolumn names when loading/creating , not hardcocd
// crossval.setSampling(null);
crossval.run();
CrossValidationResult results = crossval.Result;
System.Console.WriteLine(results);
// razmisli kako da uzmes rezultate i kako da ih prikazes u Neuroph studio - vuci ih direktno iz evaluatora
// i kako da integrises ovo kroz training dialog - samo dodati opciju KFold u trening dijalogu
// tokom kfoldinga treba prikazivati gresku i desavanja tokom treninga - izlozi learning rule; napravi neki event listening za crossvalidaciju!!!
// svaku istreniranu mrezu sacuvati, amozda negde i rezultate testiranja
// potrebno je na kraju izracunati i srednju vrednost/statistikuu svih mera klasifikacije
// takodje napravi boostraping - da radi ovo isto samo sa drugim sampling algoritmom
// System.out.println("MeanSquare Error: " + crossval.getEvaluator(ErrorEvaluator.class).getResult());
//
// ClassificationEvaluator evaluator = crossval.getEvaluator(ClassificationEvaluator.MultiClass.class);
// ConfusionMatrix confusionMatrix = evaluator.getResult();
//
// System.out.println("Confusion Matrix: \r\n"+confusionMatrix.toString());
//
// System.out.println("Classification metrics: ");
//
// ClassificationMetrics[] metrics = ClassificationMetrics.createFromMatrix(confusionMatrix); // add all of these to result
// // createaverage statisticss from ClassificationMetrics
//
// for(ClassificationMetrics cm : metrics)
// System.out.println(cm.toString());
//
//
}
// kfolding is done here
// provide neural network and data set - thi is the main entry point for crossvalidation
public virtual void run()
{
// evaluation.addEvaluator(ClassificationEvaluator.createForDataSet(dataSet)); // this should be added elseewhere
// create subsets of the entire datasets that will be used for k-folding
List <DataSet> dataSets = sampling.sample(dataSet);
results = new CrossValidationResult();
//TODO Good place for parallelization. // But in order to make this possible NeuralNetwork must be cloneable or immutable
for (int i = 0; i < dataSets.Count; i++)
{
neuralNetwork.randomizeWeights(); // we shouldnt do this - we should clone the original network
dataSets[i].Label = dataSet.Label + "-subset-" + i;
neuralNetwork.learn(dataSets[i]); // train neural network with i-th data set fold
for (int j = 0; j < dataSets.Count; j++) // next do the testing with all other dataset folds
{
if (j == i)
{
continue; // dont use for testing the same dataset that was used for training
}
// testNetwork(neuralNetwork, dataSets.get(j));
EvaluationResult evaluationResult = evaluation.evaluateDataSet(neuralNetwork, dataSets[j]); // this method should return all evaluation results
results.addEvaluationResult(evaluationResult);
// results.add(result);
// get all the results from the single evaluation - for each evaluator Classifiaction and Error
// store it somewhere with neural network
// save evaluation results from multiple runs and then calculateaverages
// we should also save all these trained network along w ith their evaluation results or at least store them intor array...
// ne need to store evaluation results and neural network for each run
}
}
results.calculateStatistics();
}
