/// <summary>
/// This method creates PxFormula class object and set in it calculated nu vector and sigma matrix values.
/// </summary>
/// <param name="trainingSet">This is TrainingSet class object. Must contain training set with ONLY normal records.</param>
/// <returns>PxFormula class object with setted nu vector and sigma matrix values.</returns>
private static PxFormula CalcFormula(TrainingSet trainingSet)
{
var trainingSetCount = trainingSet.GetCountOfRecords();
var size = trainingSet.GetAllRecords().First().Length;
// calculate nu vector value
var nu = new double[size];
nu = trainingSet.GetAllRecords().Aggregate(nu, (current, featureVector) => current.Add(featureVector));
nu = nu.Divide(trainingSetCount);
// calculate sigme matrix
var sigma = new double[size, size];
sigma = trainingSet.GetAllRecords().Select(featureVector => featureVector.Subtract(nu)).Aggregate(sigma, (current, xNu) => current.Add(Matrix.ColumnVector(xNu).Multiply(Matrix.RowVector(xNu))));
sigma = sigma.Divide(trainingSetCount);
// return PxFormula class object with setted nu and sigma values
return new PxFormula(nu, sigma);
}
/// <summary>
/// This method creates PxFormula class object and set in it calculated nu vector and sigma matrix values.
/// </summary>
/// <param name="trainingSet">This is TrainingSet class object. Must contain training set with ONLY normal records.</param>
/// <returns>PxFormula class object with setted nu vector and sigma matrix values.</returns>
private static PxFormula CalcFormula(TrainingSet trainingSet)
{
var trainingSetCount = trainingSet.GetCountOfRecords();
var size = trainingSet.GetAllRecords().First().Length;
// calculate nu vector value
var nu = new double[size];
nu = trainingSet.GetAllRecords().Aggregate(nu, (current, featureVector) => current.Add(featureVector));
nu = nu.Divide(trainingSetCount);
// calculate sigme matrix
var sigma = new double[size, size];
sigma = trainingSet.GetAllRecords().Select(featureVector => featureVector.Subtract(nu)).Aggregate(sigma, (current, xNu) => current.Add(Matrix.ColumnVector(xNu).Multiply(Matrix.RowVector(xNu))));
sigma = sigma.Divide(trainingSetCount);
// return PxFormula class object with setted nu and sigma values
return(new PxFormula(nu, sigma));
}
public void TrainingSetAdd()
{
var set = new TrainingSet(3);
set.AddRecord(new[] { 3.0, 4.0, 5.0 });
set.AddRecord(new[] { 3.0, 4.0, 5.0 });
set.AddRecord(new[] { 3.0, 4.0, 5.0 });
Assert.AreEqual(set.GetCountOfRecords(), 3);
try
{
set.AddRecord(new[] { 3.0, 4.0, 5.0, 6.0 });
}
catch (Exception)
{
Assert.Pass();
return;
}
Assert.Fail();
}
