// add statistics here?
// calculate avg, max, min, variation, std, for
// should we also calculate stats for each class???
/// <summary>
/// Calculate average over all classes and datasets
/// </summary>
public virtual void calculateStatistics()
{
average = new ClassificationMetrics.Stats();
// ClassificationMetrics.Stats max = new ClassificationMetrics.Stats();
double count = 0;
List <string> classLabels = new List <string>();
// avg max min variation std
foreach (EvaluationResult er in results)
{
ClassificationMetrics[] ccm = er.ClassificationMetricses;
foreach (ClassificationMetrics cm in ccm)
{
average.accuracy += cm.Accuracy;
average.precision += cm.Precision;
average.recall += cm.Recall;
average.fScore += cm.FMeasure;
average.mserror += er.MeanSquareError;
if (!classLabels.Contains(cm.ClassLabel))
{
classLabels.Add(cm.ClassLabel);
}
}
count++;
}
count = count * classLabels.Count; // * classes count
average.accuracy = average.accuracy / count;
average.precision = average.precision / count;
average.recall = average.recall / count;
average.fScore = average.fScore / count;
average.mserror = average.mserror / count;
}
public static void Main(string[] args)
{
Evaluation evaluation = new Evaluation();
evaluation.addEvaluator(new ErrorEvaluator(new MeanSquaredError()));
string[] classNames = new string[] { "Virginica", "Setosa", "Versicolor" };
MultiLayerPerceptron neuralNet = (MultiLayerPerceptron)NeuralNetwork.createFromFile("irisNet.nnet");
DataSet dataSet = DataSet.createFromFile("data_sets/iris_data_normalised.txt", 4, 3, ",");
evaluation.addEvaluator(new ClassifierEvaluator.MultiClass(classNames));
evaluation.evaluateDataSet(neuralNet, dataSet);
ClassifierEvaluator evaluator = evaluation.getEvaluator(typeof(ClassifierEvaluator.MultiClass));
ConfusionMatrix confusionMatrix = evaluator.Result;
Console.WriteLine("Confusion matrrix:\r\n");
Console.WriteLine(confusionMatrix.ToString() + "\r\n\r\n");
Console.WriteLine("Classification metrics\r\n");
ClassificationMetrics[] metrics = ClassificationMetrics.createFromMatrix(confusionMatrix);
ClassificationMetrics.Stats average = ClassificationMetrics.average(metrics);
foreach (ClassificationMetrics cm in metrics)
{
Console.WriteLine(cm.ToString() + "\r\n");
}
Console.WriteLine(average.ToString());
}
