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2964 lines (2427 loc) · 90.9 KB
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
/****************************************************************************************\
COPYRIGHT NOTICE
----------------
The code has been derived from libsvm library (version 2.6)
(http://www.csie.ntu.edu.tw/~cjlin/libsvm).
Here is the orignal copyright:
------------------------------------------------------------------------------------------
Copyright (c) 2000-2003 Chih-Chung Chang and Chih-Jen Lin
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
3. Neither name of copyright holders nor the names of its contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\****************************************************************************************/
using namespace cv;
#define CV_SVM_MIN_CACHE_SIZE (40 << 20) /* 40Mb */
#include <stdarg.h>
#include <ctype.h>
#if 1
typedef float Qfloat;
#define QFLOAT_TYPE CV_32F
#else
typedef double Qfloat;
#define QFLOAT_TYPE CV_64F
#endif
// Param Grid
bool CvParamGrid::check() const
{
bool ok = false;
CV_FUNCNAME( "CvParamGrid::check" );
__BEGIN__;
if( min_val > max_val )
CV_ERROR( CV_StsBadArg, "Lower bound of the grid must be less then the upper one" );
if( min_val < DBL_EPSILON )
CV_ERROR( CV_StsBadArg, "Lower bound of the grid must be positive" );
if( step < 1. + FLT_EPSILON )
CV_ERROR( CV_StsBadArg, "Grid step must greater then 1" );
ok = true;
__END__;
return ok;
}
CvParamGrid CvSVM::get_default_grid( int param_id )
{
CvParamGrid grid;
if( param_id == CvSVM::C )
{
grid.min_val = 0.1;
grid.max_val = 500;
grid.step = 5; // total iterations = 5
}
else if( param_id == CvSVM::GAMMA )
{
grid.min_val = 1e-5;
grid.max_val = 0.6;
grid.step = 15; // total iterations = 4
}
else if( param_id == CvSVM::P )
{
grid.min_val = 0.01;
grid.max_val = 100;
grid.step = 7; // total iterations = 4
}
else if( param_id == CvSVM::NU )
{
grid.min_val = 0.01;
grid.max_val = 0.2;
grid.step = 3; // total iterations = 3
}
else if( param_id == CvSVM::COEF )
{
grid.min_val = 0.1;
grid.max_val = 300;
grid.step = 14; // total iterations = 3
}
else if( param_id == CvSVM::DEGREE )
{
grid.min_val = 0.01;
grid.max_val = 4;
grid.step = 7; // total iterations = 3
}
else
cvError( CV_StsBadArg, "CvSVM::get_default_grid", "Invalid type of parameter "
"(use one of CvSVM::C, CvSVM::GAMMA et al.)", __FILE__, __LINE__ );
return grid;
}
// SVM training parameters
CvSVMParams::CvSVMParams() :
svm_type(CvSVM::C_SVC), kernel_type(CvSVM::RBF), degree(0),
gamma(1), coef0(0), C(1), nu(0), p(0), class_weights(0)
{
term_crit = cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 1000, FLT_EPSILON );
}
CvSVMParams::CvSVMParams( int _svm_type, int _kernel_type,
double _degree, double _gamma, double _coef0,
double _Con, double _nu, double _p,
CvMat* _class_weights, CvTermCriteria _term_crit ) :
svm_type(_svm_type), kernel_type(_kernel_type),
degree(_degree), gamma(_gamma), coef0(_coef0),
C(_Con), nu(_nu), p(_p), class_weights(_class_weights), term_crit(_term_crit)
{
}
/////////////////////////////////////// SVM kernel ///////////////////////////////////////
CvSVMKernel::CvSVMKernel()
{
clear();
}
void CvSVMKernel::clear()
{
params = 0;
calc_func = 0;
}
CvSVMKernel::~CvSVMKernel()
{
}
CvSVMKernel::CvSVMKernel( const CvSVMParams* _params, Calc _calc_func )
{
clear();
create( _params, _calc_func );
}
bool CvSVMKernel::create( const CvSVMParams* _params, Calc _calc_func )
{
clear();
params = _params;
calc_func = _calc_func;
if( !calc_func )
calc_func = params->kernel_type == CvSVM::RBF ? &CvSVMKernel::calc_rbf :
params->kernel_type == CvSVM::POLY ? &CvSVMKernel::calc_poly :
params->kernel_type == CvSVM::SIGMOID ? &CvSVMKernel::calc_sigmoid :
&CvSVMKernel::calc_linear;
return true;
}
void CvSVMKernel::calc_non_rbf_base( int vcount, int var_count, const float** vecs,
const float* another, Qfloat* results,
double alpha, double beta )
{
int j, k;
for( j = 0; j < vcount; j++ )
{
const float* sample = vecs[j];
double s = 0;
for( k = 0; k <= var_count - 4; k += 4 )
s += sample[k]*another[k] + sample[k+1]*another[k+1] +
sample[k+2]*another[k+2] + sample[k+3]*another[k+3];
for( ; k < var_count; k++ )
s += sample[k]*another[k];
results[j] = (Qfloat)(s*alpha + beta);
}
}
void CvSVMKernel::calc_linear( int vcount, int var_count, const float** vecs,
const float* another, Qfloat* results )
{
calc_non_rbf_base( vcount, var_count, vecs, another, results, 1, 0 );
}
void CvSVMKernel::calc_poly( int vcount, int var_count, const float** vecs,
const float* another, Qfloat* results )
{
CvMat R = cvMat( 1, vcount, QFLOAT_TYPE, results );
calc_non_rbf_base( vcount, var_count, vecs, another, results, params->gamma, params->coef0 );
if( vcount > 0 )
cvPow( &R, &R, params->degree );
}
void CvSVMKernel::calc_sigmoid( int vcount, int var_count, const float** vecs,
const float* another, Qfloat* results )
{
int j;
calc_non_rbf_base( vcount, var_count, vecs, another, results,
-2*params->gamma, -2*params->coef0 );
// TODO: speedup this
for( j = 0; j < vcount; j++ )
{
Qfloat t = results[j];
double e = exp(-fabs(t));
if( t > 0 )
results[j] = (Qfloat)((1. - e)/(1. + e));
else
results[j] = (Qfloat)((e - 1.)/(e + 1.));
}
}
void CvSVMKernel::calc_rbf( int vcount, int var_count, const float** vecs,
const float* another, Qfloat* results )
{
CvMat R = cvMat( 1, vcount, QFLOAT_TYPE, results );
double gamma = -params->gamma;
int j, k;
for( j = 0; j < vcount; j++ )
{
const float* sample = vecs[j];
double s = 0;
for( k = 0; k <= var_count - 4; k += 4 )
{
double t0 = sample[k] - another[k];
double t1 = sample[k+1] - another[k+1];
s += t0*t0 + t1*t1;
t0 = sample[k+2] - another[k+2];
t1 = sample[k+3] - another[k+3];
s += t0*t0 + t1*t1;
}
for( ; k < var_count; k++ )
{
double t0 = sample[k] - another[k];
s += t0*t0;
}
results[j] = (Qfloat)(s*gamma);
}
if( vcount > 0 )
cvExp( &R, &R );
}
void CvSVMKernel::calc( int vcount, int var_count, const float** vecs,
const float* another, Qfloat* results )
{
const Qfloat max_val = (Qfloat)(FLT_MAX*1e-3);
int j;
(this->*calc_func)( vcount, var_count, vecs, another, results );
for( j = 0; j < vcount; j++ )
{
if( results[j] > max_val )
results[j] = max_val;
}
}
// Generalized SMO+SVMlight algorithm
// Solves:
//
// min [0.5(\alpha^T Q \alpha) + b^T \alpha]
//
// y^T \alpha = \delta
// y_i = +1 or -1
// 0 <= alpha_i <= Cp for y_i = 1
// 0 <= alpha_i <= Cn for y_i = -1
//
// Given:
//
// Q, b, y, Cp, Cn, and an initial feasible point \alpha
// l is the size of vectors and matrices
// eps is the stopping criterion
//
// solution will be put in \alpha, objective value will be put in obj
//
void CvSVMSolver::clear()
{
G = 0;
alpha = 0;
y = 0;
b = 0;
buf[0] = buf[1] = 0;
cvReleaseMemStorage( &storage );
kernel = 0;
select_working_set_func = 0;
calc_rho_func = 0;
rows = 0;
samples = 0;
get_row_func = 0;
}
CvSVMSolver::CvSVMSolver()
{
storage = 0;
clear();
}
CvSVMSolver::~CvSVMSolver()
{
clear();
}
CvSVMSolver::CvSVMSolver( int _sample_count, int _var_count, const float** _samples, schar* _y,
int _alpha_count, double* _alpha, double _Cp, double _Cn,
CvMemStorage* _storage, CvSVMKernel* _kernel, GetRow _get_row,
SelectWorkingSet _select_working_set, CalcRho _calc_rho )
{
storage = 0;
create( _sample_count, _var_count, _samples, _y, _alpha_count, _alpha, _Cp, _Cn,
_storage, _kernel, _get_row, _select_working_set, _calc_rho );
}
bool CvSVMSolver::create( int _sample_count, int _var_count, const float** _samples, schar* _y,
int _alpha_count, double* _alpha, double _Cp, double _Cn,
CvMemStorage* _storage, CvSVMKernel* _kernel, GetRow _get_row,
SelectWorkingSet _select_working_set, CalcRho _calc_rho )
{
bool ok = false;
int i, svm_type;
CV_FUNCNAME( "CvSVMSolver::create" );
__BEGIN__;
int rows_hdr_size;
clear();
sample_count = _sample_count;
var_count = _var_count;
samples = _samples;
y = _y;
alpha_count = _alpha_count;
alpha = _alpha;
kernel = _kernel;
C[0] = _Cn;
C[1] = _Cp;
eps = kernel->params->term_crit.epsilon;
max_iter = kernel->params->term_crit.max_iter;
storage = cvCreateChildMemStorage( _storage );
b = (double*)cvMemStorageAlloc( storage, alpha_count*sizeof(b[0]));
alpha_status = (schar*)cvMemStorageAlloc( storage, alpha_count*sizeof(alpha_status[0]));
G = (double*)cvMemStorageAlloc( storage, alpha_count*sizeof(G[0]));
for( i = 0; i < 2; i++ )
buf[i] = (Qfloat*)cvMemStorageAlloc( storage, sample_count*2*sizeof(buf[i][0]) );
svm_type = kernel->params->svm_type;
select_working_set_func = _select_working_set;
if( !select_working_set_func )
select_working_set_func = svm_type == CvSVM::NU_SVC || svm_type == CvSVM::NU_SVR ?
&CvSVMSolver::select_working_set_nu_svm : &CvSVMSolver::select_working_set;
calc_rho_func = _calc_rho;
if( !calc_rho_func )
calc_rho_func = svm_type == CvSVM::NU_SVC || svm_type == CvSVM::NU_SVR ?
&CvSVMSolver::calc_rho_nu_svm : &CvSVMSolver::calc_rho;
get_row_func = _get_row;
if( !get_row_func )
get_row_func = params->svm_type == CvSVM::EPS_SVR ||
params->svm_type == CvSVM::NU_SVR ? &CvSVMSolver::get_row_svr :
params->svm_type == CvSVM::C_SVC ||
params->svm_type == CvSVM::NU_SVC ? &CvSVMSolver::get_row_svc :
&CvSVMSolver::get_row_one_class;
cache_line_size = sample_count*sizeof(Qfloat);
// cache size = max(num_of_samples^2*sizeof(Qfloat)*0.25, 64Kb)
// (assuming that for large training sets ~25% of Q matrix is used)
cache_size = MAX( cache_line_size*sample_count/4, CV_SVM_MIN_CACHE_SIZE );
// the size of Q matrix row headers
rows_hdr_size = sample_count*sizeof(rows[0]);
if( rows_hdr_size > storage->block_size )
CV_ERROR( CV_StsOutOfRange, "Too small storage block size" );
lru_list.prev = lru_list.next = &lru_list;
rows = (CvSVMKernelRow*)cvMemStorageAlloc( storage, rows_hdr_size );
memset( rows, 0, rows_hdr_size );
ok = true;
__END__;
return ok;
}
float* CvSVMSolver::get_row_base( int i, bool* _existed )
{
int i1 = i < sample_count ? i : i - sample_count;
CvSVMKernelRow* row = rows + i1;
bool existed = row->data != 0;
Qfloat* data;
if( existed || cache_size <= 0 )
{
CvSVMKernelRow* del_row = existed ? row : lru_list.prev;
data = del_row->data;
assert( data != 0 );
// delete row from the LRU list
del_row->data = 0;
del_row->prev->next = del_row->next;
del_row->next->prev = del_row->prev;
}
else
{
data = (Qfloat*)cvMemStorageAlloc( storage, cache_line_size );
cache_size -= cache_line_size;
}
// insert row into the LRU list
row->data = data;
row->prev = &lru_list;
row->next = lru_list.next;
row->prev->next = row->next->prev = row;
if( !existed )
{
kernel->calc( sample_count, var_count, samples, samples[i1], row->data );
}
if( _existed )
*_existed = existed;
return row->data;
}
float* CvSVMSolver::get_row_svc( int i, float* row, float*, bool existed )
{
if( !existed )
{
const schar* _y = y;
int j, len = sample_count;
assert( _y && i < sample_count );
if( _y[i] > 0 )
{
for( j = 0; j < len; j++ )
row[j] = _y[j]*row[j];
}
else
{
for( j = 0; j < len; j++ )
row[j] = -_y[j]*row[j];
}
}
return row;
}
float* CvSVMSolver::get_row_one_class( int, float* row, float*, bool )
{
return row;
}
float* CvSVMSolver::get_row_svr( int i, float* row, float* dst, bool )
{
int j, len = sample_count;
Qfloat* dst_pos = dst;
Qfloat* dst_neg = dst + len;
if( i >= len )
{
Qfloat* temp;
CV_SWAP( dst_pos, dst_neg, temp );
}
for( j = 0; j < len; j++ )
{
Qfloat t = row[j];
dst_pos[j] = t;
dst_neg[j] = -t;
}
return dst;
}
float* CvSVMSolver::get_row( int i, float* dst )
{
bool existed = false;
float* row = get_row_base( i, &existed );
return (this->*get_row_func)( i, row, dst, existed );
}
#undef is_upper_bound
#define is_upper_bound(i) (alpha_status[i] > 0)
#undef is_lower_bound
#define is_lower_bound(i) (alpha_status[i] < 0)
#undef is_free
#define is_free(i) (alpha_status[i] == 0)
#undef get_C
#define get_C(i) (C[y[i]>0])
#undef update_alpha_status
#define update_alpha_status(i) \
alpha_status[i] = (schar)(alpha[i] >= get_C(i) ? 1 : alpha[i] <= 0 ? -1 : 0)
#undef reconstruct_gradient
#define reconstruct_gradient() /* empty for now */
bool CvSVMSolver::solve_generic( CvSVMSolutionInfo& si )
{
int iter = 0;
int i, j, k;
// 1. initialize gradient and alpha status
for( i = 0; i < alpha_count; i++ )
{
update_alpha_status(i);
G[i] = b[i];
if( fabs(G[i]) > 1e200 )
return false;
}
for( i = 0; i < alpha_count; i++ )
{
if( !is_lower_bound(i) )
{
const Qfloat *Q_i = get_row( i, buf[0] );
double alpha_i = alpha[i];
for( j = 0; j < alpha_count; j++ )
G[j] += alpha_i*Q_i[j];
}
}
// 2. optimization loop
for(;;)
{
const Qfloat *Q_i, *Q_j;
double C_i, C_j;
double old_alpha_i, old_alpha_j, alpha_i, alpha_j;
double delta_alpha_i, delta_alpha_j;
#ifdef _DEBUG
for( i = 0; i < alpha_count; i++ )
{
if( fabs(G[i]) > 1e+300 )
return false;
if( fabs(alpha[i]) > 1e16 )
return false;
}
#endif
if( (this->*select_working_set_func)( i, j ) != 0 || iter++ >= max_iter )
break;
Q_i = get_row( i, buf[0] );
Q_j = get_row( j, buf[1] );
C_i = get_C(i);
C_j = get_C(j);
alpha_i = old_alpha_i = alpha[i];
alpha_j = old_alpha_j = alpha[j];
if( y[i] != y[j] )
{
double denom = Q_i[i]+Q_j[j]+2*Q_i[j];
double delta = (-G[i]-G[j])/MAX(fabs(denom),FLT_EPSILON);
double diff = alpha_i - alpha_j;
alpha_i += delta;
alpha_j += delta;
if( diff > 0 && alpha_j < 0 )
{
alpha_j = 0;
alpha_i = diff;
}
else if( diff <= 0 && alpha_i < 0 )
{
alpha_i = 0;
alpha_j = -diff;
}
if( diff > C_i - C_j && alpha_i > C_i )
{
alpha_i = C_i;
alpha_j = C_i - diff;
}
else if( diff <= C_i - C_j && alpha_j > C_j )
{
alpha_j = C_j;
alpha_i = C_j + diff;
}
}
else
{
double denom = Q_i[i]+Q_j[j]-2*Q_i[j];
double delta = (G[i]-G[j])/MAX(fabs(denom),FLT_EPSILON);
double sum = alpha_i + alpha_j;
alpha_i -= delta;
alpha_j += delta;
if( sum > C_i && alpha_i > C_i )
{
alpha_i = C_i;
alpha_j = sum - C_i;
}
else if( sum <= C_i && alpha_j < 0)
{
alpha_j = 0;
alpha_i = sum;
}
if( sum > C_j && alpha_j > C_j )
{
alpha_j = C_j;
alpha_i = sum - C_j;
}
else if( sum <= C_j && alpha_i < 0 )
{
alpha_i = 0;
alpha_j = sum;
}
}
// update alpha
alpha[i] = alpha_i;
alpha[j] = alpha_j;
update_alpha_status(i);
update_alpha_status(j);
// update G
delta_alpha_i = alpha_i - old_alpha_i;
delta_alpha_j = alpha_j - old_alpha_j;
for( k = 0; k < alpha_count; k++ )
G[k] += Q_i[k]*delta_alpha_i + Q_j[k]*delta_alpha_j;
}
// calculate rho
(this->*calc_rho_func)( si.rho, si.r );
// calculate objective value
for( i = 0, si.obj = 0; i < alpha_count; i++ )
si.obj += alpha[i] * (G[i] + b[i]);
si.obj *= 0.5;
si.upper_bound_p = C[1];
si.upper_bound_n = C[0];
return true;
}
// return 1 if already optimal, return 0 otherwise
bool
CvSVMSolver::select_working_set( int& out_i, int& out_j )
{
// return i,j which maximize -grad(f)^T d , under constraint
// if alpha_i == C, d != +1
// if alpha_i == 0, d != -1
double Gmax1 = -DBL_MAX; // max { -grad(f)_i * d | y_i*d = +1 }
int Gmax1_idx = -1;
double Gmax2 = -DBL_MAX; // max { -grad(f)_i * d | y_i*d = -1 }
int Gmax2_idx = -1;
int i;
for( i = 0; i < alpha_count; i++ )
{
double t;
if( y[i] > 0 ) // y = +1
{
if( !is_upper_bound(i) && (t = -G[i]) > Gmax1 ) // d = +1
{
Gmax1 = t;
Gmax1_idx = i;
}
if( !is_lower_bound(i) && (t = G[i]) > Gmax2 ) // d = -1
{
Gmax2 = t;
Gmax2_idx = i;
}
}
else // y = -1
{
if( !is_upper_bound(i) && (t = -G[i]) > Gmax2 ) // d = +1
{
Gmax2 = t;
Gmax2_idx = i;
}
if( !is_lower_bound(i) && (t = G[i]) > Gmax1 ) // d = -1
{
Gmax1 = t;
Gmax1_idx = i;
}
}
}
out_i = Gmax1_idx;
out_j = Gmax2_idx;
return Gmax1 + Gmax2 < eps;
}
void
CvSVMSolver::calc_rho( double& rho, double& r )
{
int i, nr_free = 0;
double ub = DBL_MAX, lb = -DBL_MAX, sum_free = 0;
for( i = 0; i < alpha_count; i++ )
{
double yG = y[i]*G[i];
if( is_lower_bound(i) )
{
if( y[i] > 0 )
ub = MIN(ub,yG);
else
lb = MAX(lb,yG);
}
else if( is_upper_bound(i) )
{
if( y[i] < 0)
ub = MIN(ub,yG);
else
lb = MAX(lb,yG);
}
else
{
++nr_free;
sum_free += yG;
}
}
rho = nr_free > 0 ? sum_free/nr_free : (ub + lb)*0.5;
r = 0;
}
bool
CvSVMSolver::select_working_set_nu_svm( int& out_i, int& out_j )
{
// return i,j which maximize -grad(f)^T d , under constraint
// if alpha_i == C, d != +1
// if alpha_i == 0, d != -1
double Gmax1 = -DBL_MAX; // max { -grad(f)_i * d | y_i = +1, d = +1 }
int Gmax1_idx = -1;
double Gmax2 = -DBL_MAX; // max { -grad(f)_i * d | y_i = +1, d = -1 }
int Gmax2_idx = -1;
double Gmax3 = -DBL_MAX; // max { -grad(f)_i * d | y_i = -1, d = +1 }
int Gmax3_idx = -1;
double Gmax4 = -DBL_MAX; // max { -grad(f)_i * d | y_i = -1, d = -1 }
int Gmax4_idx = -1;
int i;
for( i = 0; i < alpha_count; i++ )
{
double t;
if( y[i] > 0 ) // y == +1
{
if( !is_upper_bound(i) && (t = -G[i]) > Gmax1 ) // d = +1
{
Gmax1 = t;
Gmax1_idx = i;
}
if( !is_lower_bound(i) && (t = G[i]) > Gmax2 ) // d = -1
{
Gmax2 = t;
Gmax2_idx = i;
}
}
else // y == -1
{
if( !is_upper_bound(i) && (t = -G[i]) > Gmax3 ) // d = +1
{
Gmax3 = t;
Gmax3_idx = i;
}
if( !is_lower_bound(i) && (t = G[i]) > Gmax4 ) // d = -1
{
Gmax4 = t;
Gmax4_idx = i;
}
}
}
if( MAX(Gmax1 + Gmax2, Gmax3 + Gmax4) < eps )
return 1;
if( Gmax1 + Gmax2 > Gmax3 + Gmax4 )
{
out_i = Gmax1_idx;
out_j = Gmax2_idx;
}
else
{
out_i = Gmax3_idx;
out_j = Gmax4_idx;
}
return 0;
}
void
CvSVMSolver::calc_rho_nu_svm( double& rho, double& r )
{
int nr_free1 = 0, nr_free2 = 0;
double ub1 = DBL_MAX, ub2 = DBL_MAX;
double lb1 = -DBL_MAX, lb2 = -DBL_MAX;
double sum_free1 = 0, sum_free2 = 0;
double r1, r2;
int i;
for( i = 0; i < alpha_count; i++ )
{
double G_i = G[i];
if( y[i] > 0 )
{
if( is_lower_bound(i) )
ub1 = MIN( ub1, G_i );
else if( is_upper_bound(i) )
lb1 = MAX( lb1, G_i );
else
{
++nr_free1;
sum_free1 += G_i;
}
}
else
{
if( is_lower_bound(i) )
ub2 = MIN( ub2, G_i );
else if( is_upper_bound(i) )
lb2 = MAX( lb2, G_i );
else
{
++nr_free2;
sum_free2 += G_i;
}
}
}
r1 = nr_free1 > 0 ? sum_free1/nr_free1 : (ub1 + lb1)*0.5;
r2 = nr_free2 > 0 ? sum_free2/nr_free2 : (ub2 + lb2)*0.5;
rho = (r1 - r2)*0.5;
r = (r1 + r2)*0.5;
}
/*
///////////////////////// construct and solve various formulations ///////////////////////
*/
bool CvSVMSolver::solve_c_svc( int _sample_count, int _var_count, const float** _samples, schar* _y,
double _Cp, double _Cn, CvMemStorage* _storage,
CvSVMKernel* _kernel, double* _alpha, CvSVMSolutionInfo& _si )
{
int i;
if( !create( _sample_count, _var_count, _samples, _y, _sample_count,
_alpha, _Cp, _Cn, _storage, _kernel, &CvSVMSolver::get_row_svc,
&CvSVMSolver::select_working_set, &CvSVMSolver::calc_rho ))
return false;
for( i = 0; i < sample_count; i++ )
{
alpha[i] = 0;
b[i] = -1;
}
if( !solve_generic( _si ))
return false;
for( i = 0; i < sample_count; i++ )
alpha[i] *= y[i];
return true;
}
bool CvSVMSolver::solve_nu_svc( int _sample_count, int _var_count, const float** _samples, schar* _y,
CvMemStorage* _storage, CvSVMKernel* _kernel,
double* _alpha, CvSVMSolutionInfo& _si )
{
int i;
double sum_pos, sum_neg, inv_r;
if( !create( _sample_count, _var_count, _samples, _y, _sample_count,
_alpha, 1., 1., _storage, _kernel, &CvSVMSolver::get_row_svc,
&CvSVMSolver::select_working_set_nu_svm, &CvSVMSolver::calc_rho_nu_svm ))
return false;
sum_pos = kernel->params->nu * sample_count * 0.5;
sum_neg = kernel->params->nu * sample_count * 0.5;
for( i = 0; i < sample_count; i++ )
{
if( y[i] > 0 )
{
alpha[i] = MIN(1.0, sum_pos);