public CrossValidationResult <object> Validate(IClassifier classifier, TrainingData trainingData, int folds = 10)
{
var crossValidation = new CrossValidation(size: trainingData.Inputs.Length, folds: folds);
crossValidation.Fitting = delegate(int k, int[] indicesTrain, int[] indicesValidation)
{
var trainingInputs = trainingData.Inputs.Get(indicesTrain);
var trainingOutputs = trainingData.Outputs.Get(indicesTrain);
var validationInputs = trainingData.Inputs.Get(indicesValidation);
var validationOutputs = trainingData.Outputs.Get(indicesValidation);
var foldClassifier = classifier.CreateInstance(trainingData.FeatureDefaultsValueTypes, trainingData.FeatureGranularities);
foldClassifier.Train(trainingInputs, trainingOutputs);
var trainingPredicted = foldClassifier.Decide(trainingInputs);
var validationPredicted = foldClassifier.Decide(validationInputs);
double trainingError = new ZeroOneLoss(trainingOutputs).Loss(trainingPredicted);
double validationError = new ZeroOneLoss(validationOutputs).Loss(validationPredicted);
var confusionMatrix = new ConfusionMatrix(validationPredicted, validationOutputs, positiveValue: 1, negativeValue: 0);
Console.WriteLine($"{k}\t{trainingError}\t{validationError}\t{confusionMatrix.Accuracy}\t{confusionMatrix.TruePositives}\t{confusionMatrix.TrueNegatives}\t{confusionMatrix.FalsePositives}\t{confusionMatrix.FalseNegatives}\t{confusionMatrix.FalsePositiveRate}");
return(new CrossValidationValues(foldClassifier, trainingError, validationError));
};
var result = crossValidation.Compute();
return(result);
}