public void AnomalyDetectionAlgorithmAccuracy()
{
var algObject = new AnomalyDetectionAlgorithm();
algObject.SetAccuracy(null);
Assert.AreEqual(algObject.GetAccuracy(), null);
}
/// <summary>
/// Train and run anomaly detection algorithm for passes students.
/// </summary>
/// <param name="studentsAndMarks">all students, that we want to check</param>
/// <param name="trainingSet1">Training set</param>
/// <param name="trainingSet2Normal">Cross validation set with only normal records</param>
/// <param name="trainingSet2Anomalies">Cross validation set with inly anomalies records</param>
/// <returns></returns>
public static IEnumerable <KeyValuePair <KeyValuePair <User, double[]>, bool> > RunAlg(IEnumerable <KeyValuePair <User, double[]> > studentsAndMarks, TrainingSet trainingSet1, TrainingSet trainingSet2Normal, TrainingSet trainingSet2Anomalies)
{
var train = new AnomalyDetectionAlgorithm();
var formula = train.TrainAlgorithm(trainingSet1, trainingSet2Normal, trainingSet2Anomalies);
return((from studentAndMark in studentsAndMarks
let isAnomaly = formula.IsAnomaly(studentAndMark.Value)
select new KeyValuePair <KeyValuePair <User, double[]>, bool>(studentAndMark, isAnomaly)).ToList());
}
// For demo
public static IEnumerable<KeyValuePair<KeyValuePair<User, double[]>, bool>> runAlg(IEnumerable<KeyValuePair<User, double[]>> studentsAndMarks, TrainingSet trainingSet1, TrainingSet trainingSet2Normal, TrainingSet trainingSet2Anomalies)
{
AnomalyDetectionAlgorithm train = new AnomalyDetectionAlgorithm();
var formula = train.trainAlgorithm(trainingSet1, trainingSet2Normal, trainingSet2Anomalies);
List<KeyValuePair<KeyValuePair<User, double[]>, bool>> res = new List<KeyValuePair<KeyValuePair<User, double[]>, bool>>();
foreach (KeyValuePair<User, double[]> studentAndMark in studentsAndMarks)
{
bool isAnomaly = formula.isAnomaly(studentAndMark.Value);
res.Add(new KeyValuePair<KeyValuePair<User, double[]>, bool>(studentAndMark, isAnomaly));
}
return res;
}
// For demo
public static IEnumerable <KeyValuePair <KeyValuePair <User, double[]>, bool> > runAlg(IEnumerable <KeyValuePair <User, double[]> > studentsAndMarks, TrainingSet trainingSet1, TrainingSet trainingSet2Normal, TrainingSet trainingSet2Anomalies)
{
AnomalyDetectionAlgorithm train = new AnomalyDetectionAlgorithm();
var formula = train.trainAlgorithm(trainingSet1, trainingSet2Normal, trainingSet2Anomalies);
List <KeyValuePair <KeyValuePair <User, double[]>, bool> > res = new List <KeyValuePair <KeyValuePair <User, double[]>, bool> >();
foreach (KeyValuePair <User, double[]> studentAndMark in studentsAndMarks)
{
bool isAnomaly = formula.isAnomaly(studentAndMark.Value);
res.Add(new KeyValuePair <KeyValuePair <User, double[]>, bool>(studentAndMark, isAnomaly));
}
return(res);
}
/// <summary>
/// Train and run anomaly detection algorithm for passes students.
/// </summary>
/// <param name="studentsAndMarks">all students, that we want to check</param>
/// <param name="trainingSet1">Training set</param>
/// <param name="trainingSet2Normal">Cross validation set with only normal records</param>
/// <param name="trainingSet2Anomalies">Cross validation set with inly anomalies records</param>
/// <returns></returns>
public static IEnumerable<KeyValuePair<KeyValuePair<User, double[]>, bool>> RunAlg(IEnumerable<KeyValuePair<User, double[]>> studentsAndMarks, TrainingSet trainingSet1, TrainingSet trainingSet2Normal, TrainingSet trainingSet2Anomalies)
{
var train = new AnomalyDetectionAlgorithm();
var formula = train.TrainAlgorithm(trainingSet1, trainingSet2Normal, trainingSet2Anomalies);
return (from studentAndMark in studentsAndMarks
let isAnomaly = formula.IsAnomaly(studentAndMark.Value)
select new KeyValuePair<KeyValuePair<User, double[]>, bool>(studentAndMark, isAnomaly)).ToList();
}