SVMModel.cpp File Reference

#include "FirstIncludes.h"
#include <stdio.h>
#include <string>
#include <iostream>
#include <fstream>
#include <math.h>
#include <vector>
#include <sstream>
#include <iomanip>
#include <set>
#include "MemoryDebug.h"
#include "GlobalGoalKeeper.h"
#include "KKBaseTypes.h"
#include "KKException.h"
#include "KKStr.h"
#include "OSservices.h"
#include "RunLog.h"
#include "XmlStream.h"
#include "SVMModel.h"
#include "KKMLLTypes.h"
#include "BinaryClassParms.h"
#include "FeatureEncoder.h"
#include "FeatureNumList.h"
#include "FeatureVector.h"
#include "SvmWrapper.h"

Go to the source code of this file.

Namespaces
	KKMLL
	Namespace for all K^2 Machine Learning code.

Functions
double	AdjProb (double prob)
template<class T >
void	GetMaxIndex (T *vote, kkint32 voteLength, kkint32 &maxIndex1, kkint32 &maxIndex2)
bool	KKMLL::PairCompareOperator (ProbNamePair l, ProbNamePair r)

Function Documentation

double AdjProb

(

double

prob

)

Definition at line 105 of file SVMModel.cpp.

Referenced by KKMLL::SVMModel::FindWorstSupportVectors(), and KKMLL::SVMModel::FindWorstSupportVectors2().

{
  if  (prob < 0.001)
    prob = 0.001;
  else if  (prob > 0.999)
    prob = 0.999;
  return  prob;
}

template<class T >

void GetMaxIndex	(	T *	vote,
		kkint32	voteLength,
		kkint32 &	maxIndex1,
		kkint32 &	maxIndex2
	)

Definition at line 39 of file SVMModel.cpp.

{
  T max1     = vote[0];
  maxIndex1  = 0;

  T  max2    = 0;
  maxIndex2  = -1;

  for  (kkint32 i = 1;  i < voteLength;  i++)
  {
    if  (vote[i] > max1)
    {
      max2      = max1;
      maxIndex2 = maxIndex1;
      max1      = vote[i];
      maxIndex1 = i;
    }

    else if  (maxIndex2 < 0)
    {
      max2      = vote[i];
      maxIndex2 = i;
    }

    else if  (vote[i] > max2)
    {
      max2 = vote[i];
      maxIndex2 = i;
    }
  }

  return;
}  /* GetMaxIndex */