#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using HeuristicLab.Common;

namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
  /// <summary>
  /// Calculates linear scaling parameters in one pass.
  /// The formulas to calculate the scaling parameters were taken from Scaled Symblic Regression by Maarten Keijzer.
  /// http://www.springerlink.com/content/x035121165125175/
  /// </summary>
  public class OnlineLinearScalingCalculator {
    private int n;
    private OnlineMeanAndVarianceCalculator yVarianceCalculator;
    private OnlineMeanAndVarianceCalculator tMeanCalculator;
    private OnlineCovarianceEvaluator ytCovarianceEvaluator;

    public double Alpha {
      get {
        if (n < 2) {
          return 0.0;
        } else {
          return tMeanCalculator.Mean - Beta * yVarianceCalculator.Mean;
        }
      }
    }

    public double Beta {
      get {
        if (n < 2 || yVarianceCalculator.PopulationVariance.IsAlmost(0.0)) {
          return 1;
        } else {
          return ytCovarianceEvaluator.Covariance / yVarianceCalculator.PopulationVariance;
        }
      }
    }

    public OnlineLinearScalingCalculator() {
      Reset();
    }

    public void Reset() {
      n = 0;
      yVarianceCalculator = new OnlineMeanAndVarianceCalculator();
      tMeanCalculator = new OnlineMeanAndVarianceCalculator();
      ytCovarianceEvaluator = new OnlineCovarianceEvaluator();

    }

    public void Add(double target, double x) {
      if (double.IsNaN(target) || double.IsInfinity(target) || double.IsNaN(x) || double.IsInfinity(x))
        throw new ArgumentException("Linear scaling is not defined for series containing NaN or infinity elements.");
      else {
        tMeanCalculator.Add(target);
        yVarianceCalculator.Add(x);
        ytCovarianceEvaluator.Add(x, target);

        n++;
      }
    }
  }
}