source: branches/Scheduling/HeuristicLab.ExtLibs/HeuristicLab.LibSVM/1.6.3/LibSVM-1.6.3/Kernel.cs @ 6409

using System;

namespace SVM {
  internal interface IQMatrix {
    float[] GetQ(int column, int len);
    float[] GetQD();
    void SwapIndex(int i, int j);
  }

  internal abstract class Kernel : IQMatrix {
    private Node[][] _x;
    private double[] _xSquare;

    private KernelType _kernelType;
    private int _degree;
    private double _gamma;
    private double _coef0;

    public abstract float[] GetQ(int column, int len);
    public abstract float[] GetQD();

    public virtual void SwapIndex(int i, int j) {
      _x.SwapIndex(i, j);

      if (_xSquare != null) {
        _xSquare.SwapIndex(i, j);
      }
    }

    private static double powi(double value, int times) {
      double tmp = value, ret = 1.0;

      for (int t = times; t > 0; t /= 2) {
        if (t % 2 == 1) ret *= tmp;
        tmp = tmp * tmp;
      }
      return ret;
    }

    public double KernelFunction(int i, int j) {
      switch (_kernelType) {
        case KernelType.LINEAR:
          return dot(_x[i], _x[j]);
        case KernelType.POLY:
          return powi(_gamma * dot(_x[i], _x[j]) + _coef0, _degree);
        case KernelType.RBF:
          return Math.Exp(-_gamma * (_xSquare[i] + _xSquare[j] - 2 * dot(_x[i], _x[j])));
        case KernelType.SIGMOID:
          return Math.Tanh(_gamma * dot(_x[i], _x[j]) + _coef0);
        case KernelType.PRECOMPUTED:
          return _x[i][(int)(_x[j][0].Value)].Value;
        default:
          return 0;
      }
    }

    public Kernel(int l, Node[][] x_, Parameter param) {
      _kernelType = param.KernelType;
      _degree = param.Degree;
      _gamma = param.Gamma;
      _coef0 = param.Coefficient0;

      _x = (Node[][])x_.Clone();

      if (_kernelType == KernelType.RBF) {
        _xSquare = new double[l];
        for (int i = 0; i < l; i++)
          _xSquare[i] = dot(_x[i], _x[i]);
      } else _xSquare = null;
    }

    private static double dot(Node[] xNodes, Node[] yNodes) {
      double sum = 0;
      int xlen = xNodes.Length;
      int ylen = yNodes.Length;
      int i = 0;
      int j = 0;
      Node x = xNodes[0];
      Node y = yNodes[0];
      while (true) {
        if (x._index == y._index) {
          sum += x._value * y._value;
          i++;
          j++;
          if (i < xlen && j < ylen) {
            x = xNodes[i];
            y = yNodes[j];
          } else if (i < xlen) {
            x = xNodes[i];
            break;
          } else if (j < ylen) {
            y = yNodes[j];
            break;
          } else break;
        } else {
          if (x._index > y._index) {
            ++j;
            if (j < ylen)
              y = yNodes[j];
            else break;
          } else {
            ++i;
            if (i < xlen)
              x = xNodes[i];
            else break;
          }
        }
      }
      return sum;
    }

    private static double computeSquaredDistance(Node[] xNodes, Node[] yNodes) {
      Node x = xNodes[0];
      Node y = yNodes[0];
      int xLength = xNodes.Length;
      int yLength = yNodes.Length;
      int xIndex = 0;
      int yIndex = 0;
      double sum = 0;

      while (true) {
        if (x._index == y._index) {
          double d = x._value - y._value;
          sum += d * d;
          xIndex++;
          yIndex++;
          if (xIndex < xLength && yIndex < yLength) {
            x = xNodes[xIndex];
            y = yNodes[yIndex];
          } else if (xIndex < xLength) {
            x = xNodes[xIndex];
            break;
          } else if (yIndex < yLength) {
            y = yNodes[yIndex];
            break;
          } else break;
        } else if (x._index > y._index) {
          sum += y._value * y._value;
          if (++yIndex < yLength)
            y = yNodes[yIndex];
          else break;
        } else {
          sum += x._value * x._value;
          if (++xIndex < xLength)
            x = xNodes[xIndex];
          else break;
        }
      }

      for (; xIndex < xLength; xIndex++) {
        double d = xNodes[xIndex]._value;
        sum += d * d;
      }

      for (; yIndex < yLength; yIndex++) {
        double d = yNodes[yIndex]._value;
        sum += d * d;
      }

      return sum;
    }

    public static double KernelFunction(Node[] x, Node[] y, Parameter param) {
      switch (param.KernelType) {
        case KernelType.LINEAR:
          return dot(x, y);
        case KernelType.POLY:
          return powi(param.Degree * dot(x, y) + param.Coefficient0, param.Degree);
        case KernelType.RBF: {
            double sum = computeSquaredDistance(x, y);
            return Math.Exp(-param.Gamma * sum);
          }
        case KernelType.SIGMOID:
          return Math.Tanh(param.Gamma * dot(x, y) + param.Coefficient0);
        case KernelType.PRECOMPUTED:
          return x[(int)(y[0].Value)].Value;
        default:
          return 0;
      }
    }
  }
}