/// <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 PxFormula calcFormula(TrainingSet trainingSet)
{
int training_set_count = trainingSet.getCountOfRecords();
int size = trainingSet.getAllRecords().First().Length;
// calculate nu vector value
double[] nu = new double[size];
foreach (double[] feature_vector in trainingSet.getAllRecords())
{
nu = Matrix.Add(nu, feature_vector);
}
nu = Matrix.Divide(nu, training_set_count);
// calculate sigme matrix
double[,] sigma = new double[size, size];
foreach (double[] feature_vector in trainingSet.getAllRecords())
{
double[] x_nu = Matrix.Subtract(feature_vector, nu);
sigma = Matrix.Add(sigma, Matrix.Multiply(Matrix.ColumnVector(x_nu), Matrix.RowVector(x_nu)));
}
sigma = Matrix.Divide(sigma, training_set_count);
// 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 PxFormula calcFormula (TrainingSet trainingSet)
{
int training_set_count = trainingSet.getCountOfRecords();
int size = trainingSet.getAllRecords().First().Length;
// calculate nu vector value
double[] nu = new double[size];
foreach (double[] feature_vector in trainingSet.getAllRecords())
{
nu = Matrix.Add(nu, feature_vector);
}
nu = Matrix.Divide(nu,training_set_count);
// calculate sigme matrix
double[,] sigma = new double[size, size];
foreach (double[] feature_vector in trainingSet.getAllRecords())
{
double[] x_nu = Matrix.Subtract(feature_vector, nu);
sigma = Matrix.Add(sigma, Matrix.Multiply(Matrix.ColumnVector(x_nu), Matrix.RowVector(x_nu)));
}
sigma = Matrix.Divide(sigma, training_set_count);
// return PxFormula class object with setted nu and sigma values
return new PxFormula(nu, sigma);
}
