SVM233::svm_parameter Struct Reference

#include <svm.h>

Public Member Functions
	svm_parameter ()
	svm_parameter (const svm_parameter &parameters)
	svm_parameter (KKStr &paramStr)
void	Gamma (double _gamma)
double	Gamma () const
void	KernalType (kkint32 _kernalType)
kkint32	KernalType () const
kkint32	MemoryConsumedEstimated () const
svm_parameter &	operator= (const svm_parameter &right)
void	ParseTabDelStr (const KKStr &_str)
void	ProcessSvmParameter (KKStr cmd, KKStr value, double valueNum, bool &parmUsed)
KKStr	ToCmdLineStr () const
KKStr	ToTabDelStr () const

Public Attributes
float	A
kkint32	boosting
double	C
double	cache_size
float	cBoost
double	coef0
double	confidence
double	degree
kkint32	dim
kkint32	dimSelect
double	eps
double *	featureWeight
double	gamma
kkint32	hist
kkint32	kernel_type
kkint32	nr_class
kkint32	nr_weight
double	nu
kkint32	numSVM
double	p
kkint32	probability
float	sample
kkint32	sampleSV
kkint32	shrinking
kkint32	svm_type
float	threshold
double *	weight
kkint32 *	weight_label

Detailed Description

Definition at line 65 of file svm.h.

Constructor & Destructor Documentation

svm_parameter::svm_parameter

(

)

Definition at line 730 of file svm.cpp.

References A, boosting, C, SVM233::C_SVC, cache_size, cBoost, coef0, confidence, degree, dim, dimSelect, eps, featureWeight, gamma, hist, kernel_type, nr_class, nr_weight, nu, numSVM, p, probability, SVM233::RBF, sample, sampleSV, shrinking, svm_type, threshold, weight, and weight_label.

Referenced by KKMLL::SVMparam::SVMparam().

                             :
   coef0           (0.0),
   degree          (3),
   gamma           (0.0),
   kernel_type     (RBF),
   svm_type        (C_SVC),

   /* these are for training only */
   C               (1),
   cache_size      (40.0),
   eps             (1e-3),
   nr_weight       (0),
   nu              (0.5),
   p               (0.1),
   probability     (0),
   shrinking       (1),
   weight          (NULL),
   weight_label    (NULL),

   A               (1.0f),
   boosting        (0),
   cBoost          (1.0),
   confidence      (0),
   dim             (0),
   dimSelect       (-1),
   featureWeight   (NULL),
   hist            (0),
   nr_class        (0),
   numSVM          (1),
   sample          (100),
   sampleSV        (0),
   threshold       (0.0f)
{
}

svm_parameter::svm_parameter

(

const svm_parameter &

parameters

)

Definition at line 768 of file svm.cpp.

References A, boosting, C, cache_size, cBoost, coef0, confidence, degree, dim, dimSelect, eps, featureWeight, gamma, hist, kernel_type, nr_class, nr_weight, nu, numSVM, p, probability, sample, sampleSV, shrinking, svm_type, threshold, weight, and weight_label.

Referenced by KKMLL::BinaryClassParms::BinaryClassParms(), and KKMLL::SVMparam::SVMparam().

  :
   coef0           (_param.coef0),
   degree          (_param.degree),
   gamma           (_param.gamma),
   kernel_type     (_param.kernel_type),
   svm_type        (_param.svm_type),

   /* these are for training only */
   C               (_param.C),
   cache_size      (_param.cache_size),
   eps             (_param.eps),
   nr_weight       (_param.nr_weight),
   nu              (_param.nu),
   p               (_param.p),
   probability     (_param.probability),
   shrinking       (_param.shrinking),
   weight          (NULL),
   weight_label    (NULL),

   A                (_param.A),
   boosting         (_param.boosting),
   cBoost           ( _param.cBoost),
   confidence       (_param.confidence),
   dim              (_param.dim),
   dimSelect        (_param.dimSelect),
   featureWeight    (NULL),
   hist             (_param.hist),
   nr_class         (_param.nr_class),
   numSVM           (_param.numSVM),
   sample           (_param.sample),
   sampleSV         (_param.sampleSV),
   threshold        (_param.threshold)
  {
  if  (_param.weight_label)
  {
    weight_label = new kkint32[nr_weight];
    for  (kkint32 x = 0;  x < nr_weight;  x++)
      weight_label[x] = _param.weight_label[x];
  }

  if  (_param.weight)
  {
    weight = new double[nr_weight];
    for  (kkint32 x = 0;  x < nr_weight;  x++)
      weight[x] = _param.weight[x];
  }

  if  (_param.featureWeight)
  {
    featureWeight = new double[dim];
    for  (kkint32 x = 0;  x < dim;  x++)
      featureWeight[x] = _param.featureWeight[x];
  }
}  

svm_parameter::svm_parameter

(

KKStr &

paramStr

)

Definition at line 983 of file svm.cpp.

References A, boosting, C, SVM233::C_SVC, cache_size, cBoost, coef0, KKB::KKStr::Concat(), confidence, degree, dim, dimSelect, KKB::KKStr::Empty(), eps, KKB::KKStr::ExtractToken(), featureWeight, gamma, hist, kernel_type, KKB::KKStr::Len(), nr_class, nr_weight, nu, numSVM, KKB::KKStr::operator=(), KKB::KKStr::operator==(), KKB::KKStr::operator[](), p, probability, ProcessSvmParameter(), sample, sampleSV, shrinking, svm_type, threshold, KKB::KKStr::TrimLeft(), weight, and weight_label.

Referenced by KKMLL::BinaryClassParms::CreateFromTabDelStr().

                                              :
   coef0           (0.0),
   degree          (3),
   gamma           (0.0),
   kernel_type     (0),
   svm_type        (C_SVC),

   /* these are for training only */
   C               (1),
   cache_size      (40.0),
   eps             (1e-3),
   nr_weight       (0),
   nu              (0.5),
   p               (0.1),
   probability     (0),
   shrinking       (1),
   weight          (NULL),
   weight_label    (NULL),

  //luo add
   A               (1.0),
   boosting        (0),
   cBoost          (1.0),
   confidence      (0),
   dim             (0),
   dimSelect       (-1),
   featureWeight   (NULL),
   hist            (0),
   nr_class        (0),
   numSVM          (1),
   sample          (100),
   sampleSV        (0),
   threshold       (0.0f)

{
  KKStr  leftOverStr;

  KKStr  field (_paramStr.ExtractToken (" \t\n\r"));
  KKStr  value;

  double  valueNum;

  while  (!field.Empty ())
  {
    if  (field[0] != '-')  
    {
      leftOverStr << "  " << field;
    }
    else
    {
      _paramStr.TrimLeft (" \t\n\r");
      value == "";
      if  (_paramStr.Len () > 0)
      {
        if  (_paramStr[0] != '-')
          value = _paramStr.ExtractToken (" \t\n\r");
      }

      valueNum = atof (value.Str ()); 

      bool  parmUsed = false;

      if  (field.Len () > 2)
      {
        leftOverStr << "  " << field << " " << value;
      }
      else
      {
        ProcessSvmParameter (field, value, valueNum, parmUsed);
        if  (!parmUsed) 
        {
          leftOverStr << "  " << field << " " << value;
        }  
      }
    }
 
    field = _paramStr.ExtractToken (" \t\n\r");
  } 

  _paramStr = leftOverStr;
  
}  /* svm_parameter (KKStr  paramStr) */

Member Function Documentation

void SVM233::svm_parameter::Gamma ( double  _gamma )

inline

Definition at line 114 of file svm.h.

References gamma.

Referenced by KKMLL::SVMparam::Gamma().

{gamma       = _gamma;}

double SVM233::svm_parameter::Gamma ( ) const

inline

Definition at line 117 of file svm.h.

References gamma.

Referenced by KKMLL::SVMparam::Gamma().

{return  gamma;}

void SVM233::svm_parameter::KernalType ( kkint32  _kernalType )

inline

Definition at line 115 of file svm.h.

References kernel_type.

Referenced by KKMLL::SVMparam::KernalType().

{kernel_type = _kernalType;}

kkint32 SVM233::svm_parameter::KernalType ( ) const

inline

Definition at line 118 of file svm.h.

References kernel_type.

Referenced by KKMLL::SVMparam::KernalType().

{return  kernel_type;}

kkint32 svm_parameter::MemoryConsumedEstimated

(

)

const

Definition at line 886 of file svm.cpp.

References dim, featureWeight, nr_weight, and weight.

Referenced by KKMLL::BinaryClassParms::MemoryConsumedEstimated().

{
  kkint32  memoryConsumedEstimated = sizeof (svm_parameter);
  if  (weight)
    memoryConsumedEstimated += ((sizeof (double) + sizeof(kkint32)) * nr_weight);

  if  (featureWeight)
    memoryConsumedEstimated += (sizeof (double) * dim);

  return  memoryConsumedEstimated;
}

svm_parameter & svm_parameter::operator=

(

const svm_parameter &

right

)

Definition at line 828 of file svm.cpp.

References A, boosting, C, cache_size, cBoost, coef0, confidence, degree, dim, dimSelect, eps, featureWeight, gamma, hist, kernel_type, nr_class, nr_weight, nu, numSVM, p, probability, sample, sampleSV, shrinking, svm_type, threshold, weight, and weight_label.

Referenced by KKMLL::BinaryClassParms::Param(), and KKMLL::SVMparam::SVMparam().

{
  coef0           = right.coef0;
  degree          = right.degree;
  gamma           = right.gamma;
  kernel_type     = right.kernel_type;
  svm_type        = right.svm_type;

  cache_size      = right.cache_size;
  eps             = right.eps;
  C               = right.C;
  nr_weight       = right.nr_weight;
  weight_label    = NULL;
  weight          = NULL;
  nu              = right.nu;
  p               = right.p;
  shrinking       = right.shrinking;
  probability     = right.probability;
  numSVM          = right.numSVM;
  sampleSV        = right.sampleSV;
  hist            = right.hist;
  boosting        = right.boosting;
  cBoost          = right.cBoost;
  dimSelect       = right.dimSelect;
  dim             = right.dim;
  featureWeight   = NULL;
  confidence      = right.confidence;
  A               = right.A;
  nr_class        = right.nr_class;
  threshold       = right.threshold;
  sample          = right.sample;

  if  (right.weight_label)
  {
    weight_label = new kkint32[nr_weight];
    for  (kkint32 x = 0;  x < nr_weight;  x++)
      weight_label[x] = right.weight_label[x];
  }

  if  (right.weight)
  {
    weight = new double[nr_weight];
    for  (kkint32 x = 0;  x < nr_weight;  x++)
      weight[x] = right.weight[x];
  }

  if  (right.featureWeight)
  {
    featureWeight = new double[dim];
    for  (kkint32 x = 0;  x < dim;  x++)
      featureWeight[x] = right.featureWeight[x];
  }

  return  *this;
}  /* svm_parameter::operator= */

void svm_parameter::ParseTabDelStr

(

const KKStr &

_str

)

Definition at line 1176 of file svm.cpp.

References A, boosting, C, cache_size, cBoost, coef0, KKB::KKStr::Concat(), confidence, degree, dim, dimSelect, KKB::KKStr::Empty(), eps, KKB::KKStr::ExtractToken2(), KKB::KKStr::ExtractTokenDouble(), KKB::KKStr::ExtractTokenInt(), featureWeight, gamma, hist, kernel_type, nr_class, nr_weight, nu, numSVM, KKB::KKStr::operator==(), p, probability, sample, sampleSV, shrinking, svm_type, threshold, KKB::KKStr::ToDouble(), KKB::KKStr::ToFloat(), KKB::KKStr::ToInt(), weight, and weight_label.

{
  KKStr  str = _str;

  kkint32  x;


/*
  

  if  (featureWeight)
  {
    result << "featureWeight" << "\t";
    for  (kkint32 x = 0;  x < dim;  x++)
    {
      if  (x > 0)
        result << ",";
      result << featureWeight[x];
    }
    result << "\t";
  }
*/




  while  (!str.Empty ())
  {
    KKStr   field = str.ExtractToken2 ("\t");
    KKStr   value = str.ExtractToken2 ("\t");
    kkint32 valueI = value.ToInt    ();
    double  valueD = value.ToDouble ();
    float   valueF = value.ToFloat  ();

    if  (field == "svm_type")
      svm_type = valueI;

    else if  (field == "kernel_type")
      kernel_type = valueI;

    else if  (field == "degree")      
      degree = valueD;

    else if  (field == "gamma")
      gamma = valueD;

    else if  (field == "coef0")
      coef0 = valueD;
         
    else if  (field == "cache_size")
      cache_size = valueD;

    else if  (field == "eps")
      eps = valueD;

    else if  (field == "C")
      C = valueD;

    else if  (field == "nr_weight")
    {
      nr_weight = valueI;
      if  (nr_weight > 0)
      {
        delete[]  weight_label;
        weight_label = new kkint32[nr_weight];

        // value = weight label.
        for  (x = 0;  x < nr_weight;  x++)
        {
          weight_label[x] = value.ExtractTokenInt (",");
        }

        delete[]  weight;
        weight = new double [nr_weight];
        KKStr  weightStr = str.ExtractToken2 ("\t");
        for  (x = 0;  x < nr_weight;  x++)
        {
          weight[x] = weightStr.ExtractTokenDouble (",");
        }
      }
    }

    else if  (field == "nu")
      nu = valueD;

    else if  (field == "p")
      p = valueD;

    else if  (field == "shrinking")
      shrinking = valueI;

    else if  (field == "probability")
      probability = valueI;

    else if  (field == "numSVM")
      numSVM = valueI;

    else if  (field == "sampleSV")
      sampleSV = valueI;

    else if  (field == "hist")
      hist = valueI;

    else if  (field == "boosting")
      boosting = valueI;

    else if  (field == "cBoost")
      cBoost = valueF;

    else if  (field == "dimSelect")
      dimSelect = valueI;

    else if  (field == "dim")
      dim = valueI;

    else if  (field == "featureWeight")
    {
      delete[]  featureWeight;
      featureWeight = new double[dim];
      for  (x = 0;  x < dim;  x++)
      {
        featureWeight[x] = value.ExtractTokenDouble (",");
      }
    }

    else if  (field == "confidence" )
       confidence = valueD;

    else if  (field == "A")
       A = valueF;

    else if  (field == "nr_class")
      nr_class = valueI;

    else if  (field == "threshold")
      threshold  = valueF;

    else if  (field == "sample")
      sample = valueF;
  }
}  /* ParseTabDelStr */

void svm_parameter::ProcessSvmParameter	(	KKStr	cmd,
		KKStr	value,
		double	valueNum,
		bool &	parmUsed
	)

Definition at line 900 of file svm.cpp.

References A, boosting, C, cache_size, cBoost, coef0, confidence, degree, dimSelect, eps, gamma, hist, kernel_type, nr_weight, nu, numSVM, KKB::KKStr::operator==(), p, shrinking, KKB::KKStr::SubStrPart(), svm_type, threshold, KKB::KKStr::ToInt32(), KKB::KKStr::Upper(), weight, and weight_label.

Referenced by KKMLL::SVMparam::ParseCmdLineParameter(), and svm_parameter().

{
  parmUsed = true;

  cmd.Upper ();

  if  (cmd == "-A")
    numSVM  = (kkint32)valueNum;

  else if  (cmd == "-C")
    C       = valueNum;

  else if  (cmd == "-D")
    degree = valueNum;

  else if  (cmd == "-E")
    eps  = valueNum;

  else if  ((cmd == "-G")  ||  (cmd == "-GAMMA"))
    gamma = valueNum;

  else if  (cmd == "-H")
    shrinking = (kkint32)valueNum;

  else if  (cmd == "-I")
    threshold = (float)valueNum;

  else if  (cmd == "-J")
    hist  = (kkint32)valueNum;

  else if  (cmd == "-K")
    boosting = (kkint32)valueNum;

  else if  (cmd == "-L")
    cBoost = (float)valueNum;

  else if  (cmd == "-M")
    cache_size  = valueNum;

  else if  (cmd == "-N")
    nu          = valueNum;
    
  else if  (cmd == "-O")
    dimSelect   = (kkint32)valueNum;
    
  else if  (cmd == "-P")
    p           = valueNum;

  else if  (cmd == "-Q")
    confidence  = valueNum;

  else if  (cmd == "-R")
    coef0       = valueNum;
    
  else if  (cmd == "-S")
    svm_type    = (kkint32)valueNum;

  else if  (cmd == "-T")
    kernel_type = (kkint32)valueNum;
    
  else if  (cmd == "-U")
    A           = (float)valueNum;

  else if  (cmd == "-W")
  {
    ++nr_weight;
    weight_label = KKB::kkReallocateArray (weight_label, nr_weight - 1, nr_weight);
    weight       = KKB::kkReallocateArray (weight,       nr_weight - 1, nr_weight);
    weight_label [nr_weight - 1] = cmd.SubStrPart (2).ToInt32 ();
    weight       [nr_weight - 1] = valueNum;
  }

  else
    parmUsed = false;

}  /* ProcessSvmParameter */

KKStr svm_parameter::ToCmdLineStr

(

)

const

Definition at line 1069 of file svm.cpp.

References A, boosting, C, cache_size, cBoost, coef0, KKB::KKStr::Concat(), confidence, degree, dimSelect, eps, gamma, hist, kernel_type, KKB::KKStr::KKStr(), nu, numSVM, p, shrinking, svm_type, and threshold.

Referenced by KKMLL::ModelParamOldSVM::SvmParamToString(), KKMLL::SVMparam::SvmParamToString(), and KKMLL::BinaryClassParms::ToTabDelString().

{
  KKStr cmdStr (200); // Initialized char* allocation to 200

  cmdStr << "-a " << numSVM         << "  "
         //<< "-b " << sample         << "  "  
         << "-c " << C              << "  "
         << "-d " << degree         << "  "
         << "-e " << eps            << "  "
         << "-g " << gamma          << "  "
         << "-h " << shrinking      << "  "
         << "-i " << threshold      << "  "
         << "-j " << hist           << "  "
         << "-k " << boosting       << "  "
         << "-l " << cBoost         << "  "
         << "-m " << cache_size     << "  "
         << "-n " << nu             << "  ";

  if  (dimSelect >= 0)
    cmdStr  << "-o " << dimSelect   << "  ";

  cmdStr << "-p " << p              << "  "
         << "-q " << confidence     << "  "
         << "-r " << coef0          << "  "
         << "-s " << svm_type       << "  "
         << "-t " << kernel_type    << "  "
         << "-u " << A              << "  ";

  return  cmdStr;
}  /* ToCmdLineStr */

KKStr svm_parameter::ToTabDelStr

(

)

const

Definition at line 1104 of file svm.cpp.

References A, boosting, C, cache_size, cBoost, coef0, KKB::KKStr::Concat(), confidence, degree, dim, dimSelect, eps, featureWeight, gamma, hist, kernel_type, KKB::KKStr::KKStr(), nr_class, nr_weight, nu, numSVM, p, probability, sample, sampleSV, shrinking, svm_type, threshold, weight, and weight_label.

Referenced by SVM233::SvmModel233::WriteXML(), and KKMLL::SVMparam::WriteXML().

{
  kkint32  x;

  KKStr  result (300);

  result << "svm_type"    << "\t"  << svm_type       << "\t"
         << "kernel_type" << "\t"  << kernel_type    << "\t"
         << "degree"      << "\t"  << degree         << "\t"
         << "gamma"       << "\t"  << gamma          << "\t"
         << "coef0"       << "\t"  << coef0          << "\t"
         << "cache_size"  << "\t"  << cache_size     << "\t"
         << "eps"         << "\t"  << eps            << "\t"
         << "C"           << "\t"  << C              << "\t";

  result << "nr_weight"   << "\t"  << nr_weight      << "\t";
  if  (nr_weight > 0)
  {
    for  (x = 0;  x < nr_weight;  x++)
    {
      if  (x > 0)  result << ",";
      result << weight_label[x];
    }
    result << "\t";

    for  (x = 0;  x < nr_weight;  x++)
    {
      if  (x > 0)  result << ",";
      result << weight[x];
    }
    result << "\t";
  }

  result << "nu"          << "\t" << nu           << "\t"
         << "p"           << "\t" << p            << "\t"
         << "shrinking"   << "\t" << shrinking    << "\t"
         << "probability" << "\t" << probability  << "\t"
         << "numSVM"      << "\t" << numSVM       << "\t"
         << "sampleSV"    << "\t" << sampleSV     << "\t"
         << "hist"        << "\t" << hist         << "\t"
         << "boosting"    << "\t" << boosting     << "\t"
         << "cBoost"      << "\t" << cBoost       << "\t"
         << "dimSelect"   << "\t" << dimSelect    << "\t"
         << "dim"         << "\t" << dim          << "\t";

  if  (featureWeight)
  {
    result << "featureWeight" << "\t";
    for  (kkint32 x = 0;  x < dim;  x++)
    {
      if  (x > 0)
        result << ",";
      result << featureWeight[x];
    }
    result << "\t";
  }

  result << "confidence" << "\t"  << confidence << "\t"
         << "A"          << "\t"  << A          << "\t"
         << "nr_class"   << "\t"  << nr_class   << "\t"
         << "threshold"  << "\t"  << threshold  << "\t"
         << "sample"     << "\t"  << sample     << "\t";

  return  result;
}  /* ToTabDelStr */

Member Data Documentation

float SVM233::svm_parameter::A

Definition at line 97 of file svm.h.

Referenced by KKMLL::SVMparam::A_Param(), KKMLL::BinaryClassParms::AParam(), KKMLL::SVMModel::FindWorstSupportVectors(), KKMLL::SVMModel::FindWorstSupportVectors2(), operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), ToCmdLineStr(), and ToTabDelStr().

kkint32 SVM233::svm_parameter::boosting

Definition at line 98 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), KKMLL::SvmTrainModel(), ToCmdLineStr(), and ToTabDelStr().

double SVM233::svm_parameter::C

Definition at line 84 of file svm.h.

Referenced by KKMLL::BinaryClassParms::C(), KKMLL::ModelParamOldSVM::C_Param(), KKMLL::SVMparam::C_Param(), operator=(), ParseTabDelStr(), ProcessSvmParameter(), SVM233::solve_c_svc(), SVM233::solve_epsilon_svr(), SVM233::solve_nu_svr(), svm_parameter(), ToCmdLineStr(), and ToTabDelStr().

double SVM233::svm_parameter::cache_size

Definition at line 85 of file svm.h.

Referenced by SVM233::ONE_CLASS_Q::ONE_CLASS_Q(), operator=(), ParseTabDelStr(), ProcessSvmParameter(), SVM233::SVC_Q::SVC_Q(), svm_parameter(), SVM233::SVR_Q::SVR_Q(), ToCmdLineStr(), and ToTabDelStr().

float SVM233::svm_parameter::cBoost

Definition at line 99 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), ToCmdLineStr(), and ToTabDelStr().

double SVM233::svm_parameter::coef0

Definition at line 77 of file svm.h.

Referenced by SVM233::Kernel::k_function(), SVM233::Kernel::k_function_subspace(), SVM233::Kernel::Kernel(), operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), ToCmdLineStr(), and ToTabDelStr().

double SVM233::svm_parameter::confidence

Definition at line 100 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), ToCmdLineStr(), and ToTabDelStr().

double SVM233::svm_parameter::degree

Definition at line 78 of file svm.h.

Referenced by SVM233::Kernel::k_function(), SVM233::Kernel::k_function_subspace(), SVM233::Kernel::Kernel(), operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), ToCmdLineStr(), and ToTabDelStr().

kkint32 SVM233::svm_parameter::dim

Definition at line 101 of file svm.h.

Referenced by SVM233::Kernel::Kernel(), MemoryConsumedEstimated(), operator=(), ParseTabDelStr(), svm_parameter(), and ToTabDelStr().

kkint32 SVM233::svm_parameter::dimSelect

Definition at line 102 of file svm.h.

Referenced by SVM233::Kernel::Kernel(), operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), SVM233::svm_predictTwoClasses(), SVM233::svm_train_one(), KKMLL::SvmTrainModel(), ToCmdLineStr(), and ToTabDelStr().

double SVM233::svm_parameter::eps

Definition at line 86 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), ProcessSvmParameter(), SVM233::solve_c_svc(), SVM233::solve_epsilon_svr(), SVM233::solve_nu_svc(), SVM233::solve_nu_svr(), SVM233::solve_one_class(), svm_parameter(), ToCmdLineStr(), and ToTabDelStr().

double* SVM233::svm_parameter::featureWeight

Definition at line 103 of file svm.h.

Referenced by MemoryConsumedEstimated(), operator=(), ParseTabDelStr(), svm_parameter(), SVM233::svm_train_one(), and ToTabDelStr().

double SVM233::svm_parameter::gamma

Definition at line 79 of file svm.h.

Referenced by KKMLL::BinaryClassParms::Gamma(), Gamma(), SVM233::Kernel::k_function(), SVM233::Kernel::k_function_subspace(), SVM233::Kernel::Kernel(), operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), ToCmdLineStr(), and ToTabDelStr().

kkint32 SVM233::svm_parameter::hist

Definition at line 104 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), ToCmdLineStr(), and ToTabDelStr().

kkint32 SVM233::svm_parameter::kernel_type

Definition at line 80 of file svm.h.

Referenced by SVM233::Kernel::k_function(), SVM233::Kernel::k_function_subspace(), KernalType(), SVM233::Kernel::Kernel(), operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), ToCmdLineStr(), and ToTabDelStr().

kkint32 SVM233::svm_parameter::nr_class

Definition at line 105 of file svm.h.

Referenced by KKMLL::SVMModel::FindWorstSupportVectors2(), operator=(), ParseTabDelStr(), svm_parameter(), and ToTabDelStr().

kkint32 SVM233::svm_parameter::nr_weight

Definition at line 87 of file svm.h.

Referenced by MemoryConsumedEstimated(), operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), and ToTabDelStr().

double SVM233::svm_parameter::nu

Definition at line 88 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), ProcessSvmParameter(), SVM233::solve_nu_svc(), SVM233::solve_nu_svr(), SVM233::solve_one_class(), svm_parameter(), ToCmdLineStr(), and ToTabDelStr().

kkint32 SVM233::svm_parameter::numSVM

Definition at line 106 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), KKMLL::SvmTrainModel(), ToCmdLineStr(), and ToTabDelStr().

double SVM233::svm_parameter::p

Definition at line 89 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), ProcessSvmParameter(), SVM233::solve_epsilon_svr(), svm_parameter(), ToCmdLineStr(), and ToTabDelStr().

kkint32 SVM233::svm_parameter::probability

Definition at line 90 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), svm_parameter(), and ToTabDelStr().

float SVM233::svm_parameter::sample

Definition at line 107 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), svm_parameter(), KKMLL::SvmTrainModel(), and ToTabDelStr().

kkint32 SVM233::svm_parameter::sampleSV

Definition at line 108 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), svm_parameter(), KKMLL::SvmTrainModel(), and ToTabDelStr().

kkint32 SVM233::svm_parameter::shrinking

Definition at line 91 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), ProcessSvmParameter(), SVM233::solve_c_svc(), SVM233::solve_epsilon_svr(), SVM233::solve_nu_svc(), SVM233::solve_nu_svr(), SVM233::solve_one_class(), svm_parameter(), ToCmdLineStr(), and ToTabDelStr().

kkint32 SVM233::svm_parameter::svm_type

Definition at line 81 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), ProcessSvmParameter(), SVM233::svm_BuildProbFromTwoClassModel(), SVM233::svm_GetSupportVectorStatistics(), SVM233::svm_margin(), svm_parameter(), SVM233::svm_predictTwoClasses(), SVM233::svm_train_one(), ToCmdLineStr(), and ToTabDelStr().

float SVM233::svm_parameter::threshold

Definition at line 109 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), ToCmdLineStr(), and ToTabDelStr().

double* SVM233::svm_parameter::weight

Definition at line 92 of file svm.h.

Referenced by MemoryConsumedEstimated(), operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), and ToTabDelStr().

kkint32* SVM233::svm_parameter::weight_label

Definition at line 93 of file svm.h.

Referenced by operator=(), ParseTabDelStr(), ProcessSvmParameter(), svm_parameter(), and ToTabDelStr().

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The documentation for this struct was generated from the following files:

• C:/Users/Kurt/GitHub/KSquareLibraries/KKMachineLearning/svm.h
• C:/Users/Kurt/GitHub/KSquareLibraries/KKMachineLearning/svm.cpp