modules/ml/src/svm.cpp

/* [<][>][^][v][top][bottom][index][help] */
This source file includes following definitions.
checkParamGrid
getType
calc_non_rbf_base
calc_linear
calc_poly
calc_sigmoid
calc_rbf
calc_intersec
calc_chi2
calc
sortSamplesByClasses
getDefaultGrid
ofs
clear
get_row_base
get_row_svc
get_row_one_class
get_row_svr
get_row
select_working_set
calc_rho
select_working_set_nu_svm
calc_rho_nu_svm
solve_c_svc
solve_nu_svc
solve_one_class
solve_eps_svr
solve_nu_svr
clear
getSupportVectors
CV_IMPL_PROPERTY
setKernel
setCustomKernel
checkParams
setParams
getSVCount
do_train
optimize_linear_svm
train
trainAuto
predict
getDecisionFunction
write_params
isTrained
isClassifier
getVarCount
getDefaultName
write
read_params
read
create
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#include "precomp.hpp"

#include <stdarg.h>
#include <ctype.h>

/****************************************************************************************\
                                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.
\****************************************************************************************/

namespace cv { namespace ml {

typedef float Qfloat;
const int QFLOAT_TYPE = DataDepth<Qfloat>::value;

// Param Grid
static void checkParamGrid(const ParamGrid& pg)
{
    if( pg.minVal > pg.maxVal )
        CV_Error( CV_StsBadArg, "Lower bound of the grid must be less then the upper one" );
    if( pg.minVal < DBL_EPSILON )
        CV_Error( CV_StsBadArg, "Lower bound of the grid must be positive" );
    if( pg.logStep < 1. + FLT_EPSILON )
        CV_Error( CV_StsBadArg, "Grid step must greater then 1" );
}

// SVM training parameters
struct SvmParams
{
    int         svmType;
    int         kernelType;
    double      gamma;
    double      coef0;
    double      degree;
    double      C;
    double      nu;
    double      p;
    Mat         classWeights;
    TermCriteria termCrit;

    SvmParams()
    {
        svmType = SVM::C_SVC;
        kernelType = SVM::RBF;
        degree = 0;
        gamma = 1;
        coef0 = 0;
        C = 1;
        nu = 0;
        p = 0;
        termCrit = TermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 1000, FLT_EPSILON );
    }

    SvmParams( int _svmType, int _kernelType,
            double _degree, double _gamma, double _coef0,
            double _Con, double _nu, double _p,
            const Mat& _classWeights, TermCriteria _termCrit )
    {
        svmType = _svmType;
        kernelType = _kernelType;
        degree = _degree;
        gamma = _gamma;
        coef0 = _coef0;
        C = _Con;
        nu = _nu;
        p = _p;
        classWeights = _classWeights;
        termCrit = _termCrit;
    }

};

/////////////////////////////////////// SVM kernel ///////////////////////////////////////
class SVMKernelImpl : public SVM::Kernel
{
public:
    SVMKernelImpl( const SvmParams& _params = SvmParams() )
    {
        params = _params;
    }

    int getType() const
    {
        return params.kernelType;
    }

    void 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*var_count];
            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 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 calc_poly( int vcount, int var_count, const float* vecs,
                    const float* another, Qfloat* results )
    {
        Mat R( 1, vcount, QFLOAT_TYPE, results );
        calc_non_rbf_base( vcount, var_count, vecs, another, results, params.gamma, params.coef0 );
        if( vcount > 0 )
            pow( R, params.degree, R );
    }

    void 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];
            Qfloat e = std::exp(-std::abs(t));
            if( t > 0 )
                results[j] = (Qfloat)((1. - e)/(1. + e));
            else
                results[j] = (Qfloat)((e - 1.)/(e + 1.));
        }
    }


    void calc_rbf( int vcount, int var_count, const float* vecs,
                   const float* another, Qfloat* results )
    {
        double gamma = -params.gamma;
        int j, k;

        for( j = 0; j < vcount; j++ )
        {
            const float* sample = &vecs[j*var_count];
            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 )
        {
            Mat R( 1, vcount, QFLOAT_TYPE, results );
            exp( R, R );
        }
    }

    /// Histogram intersection kernel
    void calc_intersec( int vcount, int var_count, const float* vecs,
                        const float* another, Qfloat* results )
    {
        int j, k;
        for( j = 0; j < vcount; j++ )
        {
            const float* sample = &vecs[j*var_count];
            double s = 0;
            for( k = 0; k <= var_count - 4; k += 4 )
                s += std::min(sample[k],another[k]) + std::min(sample[k+1],another[k+1]) +
                std::min(sample[k+2],another[k+2]) + std::min(sample[k+3],another[k+3]);
            for( ; k < var_count; k++ )
                s += std::min(sample[k],another[k]);
            results[j] = (Qfloat)(s);
        }
    }

    /// Exponential chi2 kernel
    void calc_chi2( int vcount, int var_count, const float* vecs,
                    const float* another, Qfloat* results )
    {
        Mat R( 1, vcount, QFLOAT_TYPE, results );
        double gamma = -params.gamma;
        int j, k;
        for( j = 0; j < vcount; j++ )
        {
            const float* sample = &vecs[j*var_count];
            double chi2 = 0;
            for(k = 0 ; k < var_count; k++ )
            {
                double d = sample[k]-another[k];
                double devisor = sample[k]+another[k];
                /// if devisor == 0, the Chi2 distance would be zero,
                // but calculation would rise an error because of deviding by zero
                if (devisor != 0)
                {
                    chi2 += d*d/devisor;
                }
            }
            results[j] = (Qfloat) (gamma*chi2);
        }
        if( vcount > 0 )
            exp( R, R );
    }

    void calc( int vcount, int var_count, const float* vecs,
               const float* another, Qfloat* results )
    {
        switch( params.kernelType )
        {
        case SVM::LINEAR:
            calc_linear(vcount, var_count, vecs, another, results);
            break;
        case SVM::RBF:
            calc_rbf(vcount, var_count, vecs, another, results);
            break;
        case SVM::POLY:
            calc_poly(vcount, var_count, vecs, another, results);
            break;
        case SVM::SIGMOID:
            calc_sigmoid(vcount, var_count, vecs, another, results);
            break;
        case SVM::CHI2:
            calc_chi2(vcount, var_count, vecs, another, results);
            break;
        case SVM::INTER:
            calc_intersec(vcount, var_count, vecs, another, results);
            break;
        default:
            CV_Error(CV_StsBadArg, "Unknown kernel type");
        }
        const Qfloat max_val = (Qfloat)(FLT_MAX*1e-3);
        for( int j = 0; j < vcount; j++ )
        {
            if( results[j] > max_val )
                results[j] = max_val;
        }
    }

    SvmParams params;
};



/////////////////////////////////////////////////////////////////////////

static void sortSamplesByClasses( const Mat& _samples, const Mat& _responses,
                           vector<int>& sidx_all, vector<int>& class_ranges )
{
    int i, nsamples = _samples.rows;
    CV_Assert( _responses.isContinuous() && _responses.checkVector(1, CV_32S) == nsamples );

    setRangeVector(sidx_all, nsamples);

    const int* rptr = _responses.ptr<int>();
    std::sort(sidx_all.begin(), sidx_all.end(), cmp_lt_idx<int>(rptr));
    class_ranges.clear();
    class_ranges.push_back(0);

    for( i = 0; i < nsamples; i++ )
    {
        if( i == nsamples-1 || rptr[sidx_all[i]] != rptr[sidx_all[i+1]] )
            class_ranges.push_back(i+1);
    }
}

//////////////////////// SVM implementation //////////////////////////////

ParamGrid SVM::getDefaultGrid( int param_id )
{
    ParamGrid grid;
    if( param_id == SVM::C )
    {
        grid.minVal = 0.1;
        grid.maxVal = 500;
        grid.logStep = 5; // total iterations = 5
    }
    else if( param_id == SVM::GAMMA )
    {
        grid.minVal = 1e-5;
        grid.maxVal = 0.6;
        grid.logStep = 15; // total iterations = 4
    }
    else if( param_id == SVM::P )
    {
        grid.minVal = 0.01;
        grid.maxVal = 100;
        grid.logStep = 7; // total iterations = 4
    }
    else if( param_id == SVM::NU )
    {
        grid.minVal = 0.01;
        grid.maxVal = 0.2;
        grid.logStep = 3; // total iterations = 3
    }
    else if( param_id == SVM::COEF )
    {
        grid.minVal = 0.1;
        grid.maxVal = 300;
        grid.logStep = 14; // total iterations = 3
    }
    else if( param_id == SVM::DEGREE )
    {
        grid.minVal = 0.01;
        grid.maxVal = 4;
        grid.logStep = 7; // total iterations = 3
    }
    else
        cvError( CV_StsBadArg, "SVM::getDefaultGrid", "Invalid type of parameter "
                "(use one of SVM::C, SVM::GAMMA et al.)", __FILE__, __LINE__ );
    return grid;
}


class SVMImpl : public SVM
{
public:
    struct DecisionFunc
    {
        DecisionFunc(double _rho, int _ofs) : rho(_rho), ofs(_ofs) {}
        DecisionFunc() : rho(0.), ofs(0) {}
        double rho;
        int ofs;
    };

    // 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
    //
    class Solver
    {
    public:
        enum { MIN_CACHE_SIZE = (40 << 20) /* 40Mb */, MAX_CACHE_SIZE = (500 << 20) /* 500Mb */ };

        typedef bool (Solver::*SelectWorkingSet)( int& i, int& j );
        typedef Qfloat* (Solver::*GetRow)( int i, Qfloat* row, Qfloat* dst, bool existed );
        typedef void (Solver::*CalcRho)( double& rho, double& r );

        struct KernelRow
        {
            KernelRow() { idx = -1; prev = next = 0; }
            KernelRow(int _idx, int _prev, int _next) : idx(_idx), prev(_prev), next(_next) {}
            int idx;
            int prev;
            int next;
        };

        struct SolutionInfo
        {
            SolutionInfo() { obj = rho = upper_bound_p = upper_bound_n = r = 0; }
            double obj;
            double rho;
            double upper_bound_p;
            double upper_bound_n;
            double r;   // for Solver_NU
        };

        void clear()
        {
            alpha_vec = 0;
            select_working_set_func = 0;
            calc_rho_func = 0;
            get_row_func = 0;
            lru_cache.clear();
        }

        Solver( const Mat& _samples, const vector<schar>& _y,
                vector<double>& _alpha, const vector<double>& _b,
                double _Cp, double _Cn,
                const Ptr<SVM::Kernel>& _kernel, GetRow _get_row,
                SelectWorkingSet _select_working_set, CalcRho _calc_rho,
                TermCriteria _termCrit )
        {
            clear();

            samples = _samples;
            sample_count = samples.rows;
            var_count = samples.cols;

            y_vec = _y;
            alpha_vec = &_alpha;
            alpha_count = (int)alpha_vec->size();
            b_vec = _b;
            kernel = _kernel;

            C[0] = _Cn;
            C[1] = _Cp;
            eps = _termCrit.epsilon;
            max_iter = _termCrit.maxCount;

            G_vec.resize(alpha_count);
            alpha_status_vec.resize(alpha_count);
            buf[0].resize(sample_count*2);
            buf[1].resize(sample_count*2);

            select_working_set_func = _select_working_set;
            CV_Assert(select_working_set_func != 0);

            calc_rho_func = _calc_rho;
            CV_Assert(calc_rho_func != 0);

            get_row_func = _get_row;
            CV_Assert(get_row_func != 0);

            // assume that for large training sets ~25% of Q matrix is used
            int64 csize = (int64)sample_count*sample_count/4;
            csize = std::max(csize, (int64)(MIN_CACHE_SIZE/sizeof(Qfloat)) );
            csize = std::min(csize, (int64)(MAX_CACHE_SIZE/sizeof(Qfloat)) );
            max_cache_size = (int)((csize + sample_count-1)/sample_count);
            max_cache_size = std::min(std::max(max_cache_size, 1), sample_count);
            cache_size = 0;

            lru_cache.clear();
            lru_cache.resize(sample_count+1, KernelRow(-1, 0, 0));
            lru_first = lru_last = 0;
            lru_cache_data.create(max_cache_size, sample_count, QFLOAT_TYPE);
        }

        Qfloat* get_row_base( int i, bool* _existed )
        {
            int i1 = i < sample_count ? i : i - sample_count;
            KernelRow& kr = lru_cache[i1+1];
            if( _existed )
                *_existed = kr.idx >= 0;
            if( kr.idx < 0 )
            {
                if( cache_size < max_cache_size )
                {
                    kr.idx = cache_size;
                    cache_size++;
                    if (!lru_last)
                        lru_last = i1+1;
                }
                else
                {
                    KernelRow& last = lru_cache[lru_last];
                    kr.idx = last.idx;
                    last.idx = -1;
                    lru_cache[last.prev].next = 0;
                    lru_last = last.prev;
                    last.prev = 0;
                    last.next = 0;
                }
                kernel->calc( sample_count, var_count, samples.ptr<float>(),
                              samples.ptr<float>(i1), lru_cache_data.ptr<Qfloat>(kr.idx) );
            }
            else
            {
                if( kr.next )
                    lru_cache[kr.next].prev = kr.prev;
                else
                    lru_last = kr.prev;
                if( kr.prev )
                    lru_cache[kr.prev].next = kr.next;
                else
                    lru_first = kr.next;
            }
            if (lru_first)
                lru_cache[lru_first].prev = i1+1;
            kr.next = lru_first;
            kr.prev = 0;
            lru_first = i1+1;

            return lru_cache_data.ptr<Qfloat>(kr.idx);
        }

        Qfloat* get_row_svc( int i, Qfloat* row, Qfloat*, bool existed )
        {
            if( !existed )
            {
                const schar* _y = &y_vec[0];
                int j, len = 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;
        }

        Qfloat* get_row_one_class( int, Qfloat* row, Qfloat*, bool )
        {
            return row;
        }

        Qfloat* get_row_svr( int i, Qfloat* row, Qfloat* dst, bool )
        {
            int j, len = sample_count;
            Qfloat* dst_pos = dst;
            Qfloat* dst_neg = dst + len;
            if( i >= len )
                std::swap(dst_pos, dst_neg);

            for( j = 0; j < len; j++ )
            {
                Qfloat t = row[j];
                dst_pos[j] = t;
                dst_neg[j] = -t;
            }
            return dst;
        }

        Qfloat* 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 solve_generic( SolutionInfo& si )
        {
            const schar* y = &y_vec[0];
            double* alpha = &alpha_vec->at(0);
            schar* alpha_status = &alpha_status_vec[0];
            double* G = &G_vec[0];
            double* b = &b_vec[0];

            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][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][0] );
                Q_j = get_row( j, &buf[1][0] );

                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 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;

            const schar* y = &y_vec[0];
            const schar* alpha_status = &alpha_status_vec[0];
            const double* G = &G_vec[0];

            for( int 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 calc_rho( double& rho, double& r )
        {
            int nr_free = 0;
            double ub = DBL_MAX, lb = -DBL_MAX, sum_free = 0;
            const schar* y = &y_vec[0];
            const schar* alpha_status = &alpha_status_vec[0];
            const double* G = &G_vec[0];

            for( int 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 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;

            const schar* y = &y_vec[0];
            const schar* alpha_status = &alpha_status_vec[0];
            const double* G = &G_vec[0];

            for( int 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 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;

            const schar* y = &y_vec[0];
            const schar* alpha_status = &alpha_status_vec[0];
            const double* G = &G_vec[0];

            for( int 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;
                    }
                }
            }

            double r1 = nr_free1 > 0 ? sum_free1/nr_free1 : (ub1 + lb1)*0.5;
            double 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 ///////////////////////
        */
        static bool solve_c_svc( const Mat& _samples, const vector<schar>& _y,
                                 double _Cp, double _Cn, const Ptr<SVM::Kernel>& _kernel,
                                 vector<double>& _alpha, SolutionInfo& _si, TermCriteria termCrit )
        {
            int sample_count = _samples.rows;

            _alpha.assign(sample_count, 0.);
            vector<double> _b(sample_count, -1.);

            Solver solver( _samples, _y, _alpha, _b, _Cp, _Cn, _kernel,
                           &Solver::get_row_svc,
                           &Solver::select_working_set,
                           &Solver::calc_rho,
                           termCrit );

            if( !solver.solve_generic( _si ))
                return false;

            for( int i = 0; i < sample_count; i++ )
                _alpha[i] *= _y[i];

            return true;
        }


        static bool solve_nu_svc( const Mat& _samples, const vector<schar>& _y,
                                  double nu, const Ptr<SVM::Kernel>& _kernel,
                                  vector<double>& _alpha, SolutionInfo& _si,
                                  TermCriteria termCrit )
        {
            int sample_count = _samples.rows;

            _alpha.resize(sample_count);
            vector<double> _b(sample_count, 0.);

            double sum_pos = nu * sample_count * 0.5;
            double sum_neg = nu * sample_count * 0.5;

            for( int i = 0; i < sample_count; i++ )
            {
                double a;
                if( _y[i] > 0 )
                {
                    a = std::min(1.0, sum_pos);
                    sum_pos -= a;
                }
                else
                {
                    a = std::min(1.0, sum_neg);
                    sum_neg -= a;
                }
                _alpha[i] = a;
            }

            Solver solver( _samples, _y, _alpha, _b, 1., 1., _kernel,
                           &Solver::get_row_svc,
                           &Solver::select_working_set_nu_svm,
                           &Solver::calc_rho_nu_svm,
                           termCrit );

            if( !solver.solve_generic( _si ))
                return false;

            double inv_r = 1./_si.r;

            for( int i = 0; i < sample_count; i++ )
                _alpha[i] *= _y[i]*inv_r;

            _si.rho *= inv_r;
            _si.obj *= (inv_r*inv_r);
            _si.upper_bound_p = inv_r;
            _si.upper_bound_n = inv_r;

            return true;
        }

        static bool solve_one_class( const Mat& _samples, double nu,
                                     const Ptr<SVM::Kernel>& _kernel,
                                     vector<double>& _alpha, SolutionInfo& _si,
                                     TermCriteria termCrit )
        {
            int sample_count = _samples.rows;
            vector<schar> _y(sample_count, 1);
            vector<double> _b(sample_count, 0.);

            int i, n = cvRound( nu*sample_count );

            _alpha.resize(sample_count);
            for( i = 0; i < sample_count; i++ )
                _alpha[i] = i < n ? 1 : 0;

            if( n < sample_count )
                _alpha[n] = nu * sample_count - n;
            else
                _alpha[n-1] = nu * sample_count - (n-1);

            Solver solver( _samples, _y, _alpha, _b, 1., 1., _kernel,
                           &Solver::get_row_one_class,
                           &Solver::select_working_set,
                           &Solver::calc_rho,
                           termCrit );

            return solver.solve_generic(_si);
        }

        static bool solve_eps_svr( const Mat& _samples, const vector<float>& _yf,
                                   double p, double C, const Ptr<SVM::Kernel>& _kernel,
                                   vector<double>& _alpha, SolutionInfo& _si,
                                   TermCriteria termCrit )
        {
            int sample_count = _samples.rows;
            int alpha_count = sample_count*2;

            CV_Assert( (int)_yf.size() == sample_count );

            _alpha.assign(alpha_count, 0.);
            vector<schar> _y(alpha_count);
            vector<double> _b(alpha_count);

            for( int i = 0; i < sample_count; i++ )
            {
                _b[i] = p - _yf[i];
                _y[i] = 1;

                _b[i+sample_count] = p + _yf[i];
                _y[i+sample_count] = -1;
            }

            Solver solver( _samples, _y, _alpha, _b, C, C, _kernel,
                           &Solver::get_row_svr,
                           &Solver::select_working_set,
                           &Solver::calc_rho,
                           termCrit );

            if( !solver.solve_generic( _si ))
                return false;

            for( int i = 0; i < sample_count; i++ )
                _alpha[i] -= _alpha[i+sample_count];

            return true;
        }


        static bool solve_nu_svr( const Mat& _samples, const vector<float>& _yf,
                                  double nu, double C, const Ptr<SVM::Kernel>& _kernel,
                                  vector<double>& _alpha, SolutionInfo& _si,
                                  TermCriteria termCrit )
        {
            int sample_count = _samples.rows;
            int alpha_count = sample_count*2;
            double sum = C * nu * sample_count * 0.5;

            CV_Assert( (int)_yf.size() == sample_count );

            _alpha.resize(alpha_count);
            vector<schar> _y(alpha_count);
            vector<double> _b(alpha_count);

            for( int i = 0; i < sample_count; i++ )
            {
                _alpha[i] = _alpha[i + sample_count] = std::min(sum, C);
                sum -= _alpha[i];

                _b[i] = -_yf[i];
                _y[i] = 1;

                _b[i + sample_count] = _yf[i];
                _y[i + sample_count] = -1;
            }

            Solver solver( _samples, _y, _alpha, _b, 1., 1., _kernel,
                           &Solver::get_row_svr,
                           &Solver::select_working_set_nu_svm,
                           &Solver::calc_rho_nu_svm,
                           termCrit );

            if( !solver.solve_generic( _si ))
                return false;

            for( int i = 0; i < sample_count; i++ )
                _alpha[i] -= _alpha[i+sample_count];

            return true;
        }

        int sample_count;
        int var_count;
        int cache_size;
        int max_cache_size;
        Mat samples;
        SvmParams params;
        vector<KernelRow> lru_cache;
        int lru_first;
        int lru_last;
        Mat lru_cache_data;

        int alpha_count;

        vector<double> G_vec;
        vector<double>* alpha_vec;
        vector<schar> y_vec;
        // -1 - lower bound, 0 - free, 1 - upper bound
        vector<schar> alpha_status_vec;
        vector<double> b_vec;

        vector<Qfloat> buf[2];
        double eps;
        int max_iter;
        double C[2];  // C[0] == Cn, C[1] == Cp
        Ptr<SVM::Kernel> kernel;

        SelectWorkingSet select_working_set_func;
        CalcRho calc_rho_func;
        GetRow get_row_func;
    };

    //////////////////////////////////////////////////////////////////////////////////////////
    SVMImpl()
    {
        clear();
        checkParams();
    }

    ~SVMImpl()
    {
        clear();
    }

    void clear()
    {
        decision_func.clear();
        df_alpha.clear();
        df_index.clear();
        sv.release();
    }

    Mat getSupportVectors() const
    {
        return sv;
    }

    CV_IMPL_PROPERTY(int, Type, params.svmType)
    CV_IMPL_PROPERTY(double, Gamma, params.gamma)
    CV_IMPL_PROPERTY(double, Coef0, params.coef0)
    CV_IMPL_PROPERTY(double, Degree, params.degree)
    CV_IMPL_PROPERTY(double, C, params.C)
    CV_IMPL_PROPERTY(double, Nu, params.nu)
    CV_IMPL_PROPERTY(double, P, params.p)
    CV_IMPL_PROPERTY_S(cv::Mat, ClassWeights, params.classWeights)
    CV_IMPL_PROPERTY_S(cv::TermCriteria, TermCriteria, params.termCrit)

    int getKernelType() const
    {
        return params.kernelType;
    }

    void setKernel(int kernelType)
    {
        params.kernelType = kernelType;
        if (kernelType != CUSTOM)
            kernel = makePtr<SVMKernelImpl>(params);
    }

    void setCustomKernel(const Ptr<Kernel> &_kernel)
    {
        params.kernelType = CUSTOM;
        kernel = _kernel;
    }

    void checkParams()
    {
        int kernelType = params.kernelType;
        if (kernelType != CUSTOM)
        {
            if( kernelType != LINEAR && kernelType != POLY &&
                kernelType != SIGMOID && kernelType != RBF &&
                kernelType != INTER && kernelType != CHI2)
                CV_Error( CV_StsBadArg, "Unknown/unsupported kernel type" );

            if( kernelType == LINEAR )
                params.gamma = 1;
            else if( params.gamma <= 0 )
                CV_Error( CV_StsOutOfRange, "gamma parameter of the kernel must be positive" );

            if( kernelType != SIGMOID && kernelType != POLY )
                params.coef0 = 0;
            else if( params.coef0 < 0 )
                CV_Error( CV_StsOutOfRange, "The kernel parameter <coef0> must be positive or zero" );

            if( kernelType != POLY )
                params.degree = 0;
            else if( params.degree <= 0 )
                CV_Error( CV_StsOutOfRange, "The kernel parameter <degree> must be positive" );

            kernel = makePtr<SVMKernelImpl>(params);
        }
        else
        {
            if (!kernel)
                CV_Error( CV_StsBadArg, "Custom kernel is not set" );
        }

        int svmType = params.svmType;

        if( svmType != C_SVC && svmType != NU_SVC &&
            svmType != ONE_CLASS && svmType != EPS_SVR &&
            svmType != NU_SVR )
            CV_Error( CV_StsBadArg, "Unknown/unsupported SVM type" );

        if( svmType == ONE_CLASS || svmType == NU_SVC )
            params.C = 0;
        else if( params.C <= 0 )
            CV_Error( CV_StsOutOfRange, "The parameter C must be positive" );

        if( svmType == C_SVC || svmType == EPS_SVR )
            params.nu = 0;
        else if( params.nu <= 0 || params.nu >= 1 )
            CV_Error( CV_StsOutOfRange, "The parameter nu must be between 0 and 1" );

        if( svmType != EPS_SVR )
            params.p = 0;
        else if( params.p <= 0 )
            CV_Error( CV_StsOutOfRange, "The parameter p must be positive" );

        if( svmType != C_SVC )
            params.classWeights.release();

        if( !(params.termCrit.type & TermCriteria::EPS) )
            params.termCrit.epsilon = DBL_EPSILON;
        params.termCrit.epsilon = std::max(params.termCrit.epsilon, DBL_EPSILON);
        if( !(params.termCrit.type & TermCriteria::COUNT) )
            params.termCrit.maxCount = INT_MAX;
        params.termCrit.maxCount = std::max(params.termCrit.maxCount, 1);
    }

    void setParams( const SvmParams& _params)
    {
        params = _params;
        checkParams();
    }

    int getSVCount(int i) const
    {
        return (i < (int)(decision_func.size()-1) ? decision_func[i+1].ofs :
                (int)df_index.size()) - decision_func[i].ofs;
    }

    bool do_train( const Mat& _samples, const Mat& _responses )
    {
        int svmType = params.svmType;
        int i, j, k, sample_count = _samples.rows;
        vector<double> _alpha;
        Solver::SolutionInfo sinfo;

        CV_Assert( _samples.type() == CV_32F );
        var_count = _samples.cols;

        if( svmType == ONE_CLASS || svmType == EPS_SVR || svmType == NU_SVR )
        {
            int sv_count = 0;
            decision_func.clear();

            vector<float> _yf;
            if( !_responses.empty() )
                _responses.convertTo(_yf, CV_32F);

            bool ok =
            svmType == ONE_CLASS ? Solver::solve_one_class( _samples, params.nu, kernel, _alpha, sinfo, params.termCrit ) :
            svmType == EPS_SVR ? Solver::solve_eps_svr( _samples, _yf, params.p, params.C, kernel, _alpha, sinfo, params.termCrit ) :
            svmType == NU_SVR ? Solver::solve_nu_svr( _samples, _yf, params.nu, params.C, kernel, _alpha, sinfo, params.termCrit ) : false;

            if( !ok )
                return false;

            for( i = 0; i < sample_count; i++ )
                sv_count += fabs(_alpha[i]) > 0;

            CV_Assert(sv_count != 0);

            sv.create(sv_count, _samples.cols, CV_32F);
            df_alpha.resize(sv_count);
            df_index.resize(sv_count);

            for( i = k = 0; i < sample_count; i++ )
            {
                if( std::abs(_alpha[i]) > 0 )
                {
                    _samples.row(i).copyTo(sv.row(k));
                    df_alpha[k] = _alpha[i];
                    df_index[k] = k;
                    k++;
                }
            }

            decision_func.push_back(DecisionFunc(sinfo.rho, 0));
        }
        else
        {
            int class_count = (int)class_labels.total();
            vector<int> svidx, sidx, sidx_all, sv_tab(sample_count, 0);
            Mat temp_samples, class_weights;
            vector<int> class_ranges;
            vector<schar> temp_y;
            double nu = params.nu;
            CV_Assert( svmType == C_SVC || svmType == NU_SVC );

            if( svmType == C_SVC && !params.classWeights.empty() )
            {
                const Mat cw = params.classWeights;

                if( (cw.cols != 1 && cw.rows != 1) ||
                    (int)cw.total() != class_count ||
                    (cw.type() != CV_32F && cw.type() != CV_64F) )
                    CV_Error( CV_StsBadArg, "params.class_weights must be 1d floating-point vector "
                        "containing as many elements as the number of classes" );

                cw.convertTo(class_weights, CV_64F, params.C);
                //normalize(cw, class_weights, params.C, 0, NORM_L1, CV_64F);
            }

            decision_func.clear();
            df_alpha.clear();
            df_index.clear();

            sortSamplesByClasses( _samples, _responses, sidx_all, class_ranges );

            //check that while cross-validation there were the samples from all the classes
            if( class_ranges[class_count] <= 0 )
                CV_Error( CV_StsBadArg, "While cross-validation one or more of the classes have "
                "been fell out of the sample. Try to enlarge <Params::k_fold>" );

            if( svmType == NU_SVC )
            {
                // check if nu is feasible
                for( i = 0; i < class_count; i++ )
                {
                    int ci = class_ranges[i+1] - class_ranges[i];
                    for( j = i+1; j< class_count; j++ )
                    {
                        int cj = class_ranges[j+1] - class_ranges[j];
                        if( nu*(ci + cj)*0.5 > std::min( ci, cj ) )
                            // TODO: add some diagnostic
                            return false;
                    }
                }
            }

            size_t samplesize = _samples.cols*_samples.elemSize();

            // train n*(n-1)/2 classifiers
            for( i = 0; i < class_count; i++ )
            {
                for( j = i+1; j < class_count; j++ )
                {
                    int si = class_ranges[i], ci = class_ranges[i+1] - si;
                    int sj = class_ranges[j], cj = class_ranges[j+1] - sj;
                    double Cp = params.C, Cn = Cp;

                    temp_samples.create(ci + cj, _samples.cols, _samples.type());
                    sidx.resize(ci + cj);
                    temp_y.resize(ci + cj);

                    // form input for the binary classification problem
                    for( k = 0; k < ci+cj; k++ )
                    {
                        int idx = k < ci ? si+k : sj+k-ci;
                        memcpy(temp_samples.ptr(k), _samples.ptr(sidx_all[idx]), samplesize);
                        sidx[k] = sidx_all[idx];
                        temp_y[k] = k < ci ? 1 : -1;
                    }

                    if( !class_weights.empty() )
                    {
                        Cp = class_weights.at<double>(i);
                        Cn = class_weights.at<double>(j);
                    }

                    DecisionFunc df;
                    bool ok = params.svmType == C_SVC ?
                                Solver::solve_c_svc( temp_samples, temp_y, Cp, Cn,
                                                     kernel, _alpha, sinfo, params.termCrit ) :
                              params.svmType == NU_SVC ?
                                Solver::solve_nu_svc( temp_samples, temp_y, params.nu,
                                                      kernel, _alpha, sinfo, params.termCrit ) :
                              false;
                    if( !ok )
                        return false;
                    df.rho = sinfo.rho;
                    df.ofs = (int)df_index.size();
                    decision_func.push_back(df);

                    for( k = 0; k < ci + cj; k++ )
                    {
                        if( std::abs(_alpha[k]) > 0 )
                        {
                            int idx = k < ci ? si+k : sj+k-ci;
                            sv_tab[sidx_all[idx]] = 1;
                            df_index.push_back(sidx_all[idx]);
                            df_alpha.push_back(_alpha[k]);
                        }
                    }
                }
            }

            // allocate support vectors and initialize sv_tab
            for( i = 0, k = 0; i < sample_count; i++ )
            {
                if( sv_tab[i] )
                    sv_tab[i] = ++k;
            }

            int sv_total = k;
            sv.create(sv_total, _samples.cols, _samples.type());

            for( i = 0; i < sample_count; i++ )
            {
                if( !sv_tab[i] )
                    continue;
                memcpy(sv.ptr(sv_tab[i]-1), _samples.ptr(i), samplesize);
            }

            // set sv pointers
            int n = (int)df_index.size();
            for( i = 0; i < n; i++ )
            {
                CV_Assert( sv_tab[df_index[i]] > 0 );
                df_index[i] = sv_tab[df_index[i]] - 1;
            }
        }

        optimize_linear_svm();
        return true;
    }

    void optimize_linear_svm()
    {
        // we optimize only linear SVM: compress all the support vectors into one.
        if( params.kernelType != LINEAR )
            return;

        int i, df_count = (int)decision_func.size();

        for( i = 0; i < df_count; i++ )
        {
            if( getSVCount(i) != 1 )
                break;
        }

        // if every decision functions uses a single support vector;
        // it's already compressed. skip it then.
        if( i == df_count )
            return;

        AutoBuffer<double> vbuf(var_count);
        double* v = vbuf;
        Mat new_sv(df_count, var_count, CV_32F);

        vector<DecisionFunc> new_df;

        for( i = 0; i < df_count; i++ )
        {
            float* dst = new_sv.ptr<float>(i);
            memset(v, 0, var_count*sizeof(v[0]));
            int j, k, sv_count = getSVCount(i);
            const DecisionFunc& df = decision_func[i];
            const int* sv_index = &df_index[df.ofs];
            const double* sv_alpha = &df_alpha[df.ofs];
            for( j = 0; j < sv_count; j++ )
            {
                const float* src = sv.ptr<float>(sv_index[j]);
                double a = sv_alpha[j];
                for( k = 0; k < var_count; k++ )
                    v[k] += src[k]*a;
            }
            for( k = 0; k < var_count; k++ )
                dst[k] = (float)v[k];
            new_df.push_back(DecisionFunc(df.rho, i));
        }

        setRangeVector(df_index, df_count);
        df_alpha.assign(df_count, 1.);
        std::swap(sv, new_sv);
        std::swap(decision_func, new_df);
    }

    bool train( const Ptr<TrainData>& data, int )
    {
        clear();

        checkParams();

        int svmType = params.svmType;
        Mat samples = data->getTrainSamples();
        Mat responses;

        if( svmType == C_SVC || svmType == NU_SVC )
        {
            responses = data->getTrainNormCatResponses();
            if( responses.empty() )
                CV_Error(CV_StsBadArg, "in the case of classification problem the responses must be categorical; "
                                       "either specify varType when creating TrainData, or pass integer responses");
            class_labels = data->getClassLabels();
        }
        else
            responses = data->getTrainResponses();

        if( !do_train( samples, responses ))
        {
            clear();
            return false;
        }

        return true;
    }

    bool trainAuto( const Ptr<TrainData>& data, int k_fold,
                    ParamGrid C_grid, ParamGrid gamma_grid, ParamGrid p_grid,
                    ParamGrid nu_grid, ParamGrid coef_grid, ParamGrid degree_grid,
                    bool balanced )
    {
        checkParams();

        int svmType = params.svmType;
        RNG rng((uint64)-1);

        if( svmType == ONE_CLASS )
            // current implementation of "auto" svm does not support the 1-class case.
            return train( data, 0 );

        clear();

        CV_Assert( k_fold >= 2 );

        // All the parameters except, possibly, <coef0> are positive.
        // <coef0> is nonnegative
        #define CHECK_GRID(grid, param) \
        if( grid.logStep <= 1 ) \
        { \
            grid.minVal = grid.maxVal = params.param; \
            grid.logStep = 10; \
        } \
        else \
            checkParamGrid(grid)

        CHECK_GRID(C_grid, C);
        CHECK_GRID(gamma_grid, gamma);
        CHECK_GRID(p_grid, p);
        CHECK_GRID(nu_grid, nu);
        CHECK_GRID(coef_grid, coef0);
        CHECK_GRID(degree_grid, degree);

        // these parameters are not used:
        if( params.kernelType != POLY )
            degree_grid.minVal = degree_grid.maxVal = params.degree;
        if( params.kernelType == LINEAR )
            gamma_grid.minVal = gamma_grid.maxVal = params.gamma;
        if( params.kernelType != POLY && params.kernelType != SIGMOID )
            coef_grid.minVal = coef_grid.maxVal = params.coef0;
        if( svmType == NU_SVC || svmType == ONE_CLASS )
            C_grid.minVal = C_grid.maxVal = params.C;
        if( svmType == C_SVC || svmType == EPS_SVR )
            nu_grid.minVal = nu_grid.maxVal = params.nu;
        if( svmType != EPS_SVR )
            p_grid.minVal = p_grid.maxVal = params.p;

        Mat samples = data->getTrainSamples();
        Mat responses;
        bool is_classification = false;
        int class_count = (int)class_labels.total();

        if( svmType == C_SVC || svmType == NU_SVC )
        {
            responses = data->getTrainNormCatResponses();
            class_labels = data->getClassLabels();
            class_count = (int)class_labels.total();
            is_classification = true;

            vector<int> temp_class_labels;
            setRangeVector(temp_class_labels, class_count);

            // temporarily replace class labels with 0, 1, ..., NCLASSES-1
            Mat(temp_class_labels).copyTo(class_labels);
        }
        else
            responses = data->getTrainResponses();

        CV_Assert(samples.type() == CV_32F);

        int sample_count = samples.rows;
        var_count = samples.cols;
        size_t sample_size = var_count*samples.elemSize();

        vector<int> sidx;
        setRangeVector(sidx, sample_count);

        int i, j, k;

        // randomly permute training samples
        for( i = 0; i < sample_count; i++ )
        {
            int i1 = rng.uniform(0, sample_count);
            int i2 = rng.uniform(0, sample_count);
            std::swap(sidx[i1], sidx[i2]);
        }

        if( is_classification && class_count == 2 && balanced )
        {
            // reshuffle the training set in such a way that
            // instances of each class are divided more or less evenly
            // between the k_fold parts.
            vector<int> sidx0, sidx1;

            for( i = 0; i < sample_count; i++ )
            {
                if( responses.at<int>(sidx[i]) == 0 )
                    sidx0.push_back(sidx[i]);
                else
                    sidx1.push_back(sidx[i]);
            }

            int n0 = (int)sidx0.size(), n1 = (int)sidx1.size();
            int a0 = 0, a1 = 0;
            sidx.clear();
            for( k = 0; k < k_fold; k++ )
            {
                int b0 = ((k+1)*n0 + k_fold/2)/k_fold, b1 = ((k+1)*n1 + k_fold/2)/k_fold;
                int a = (int)sidx.size(), b = a + (b0 - a0) + (b1 - a1);
                for( i = a0; i < b0; i++ )
                    sidx.push_back(sidx0[i]);
                for( i = a1; i < b1; i++ )
                    sidx.push_back(sidx1[i]);
                for( i = 0; i < (b - a); i++ )
                {
                    int i1 = rng.uniform(a, b);
                    int i2 = rng.uniform(a, b);
                    std::swap(sidx[i1], sidx[i2]);
                }
                a0 = b0; a1 = b1;
            }
        }

        int test_sample_count = (sample_count + k_fold/2)/k_fold;
        int train_sample_count = sample_count - test_sample_count;

        SvmParams best_params = params;
        double min_error = FLT_MAX;

        int rtype = responses.type();

        Mat temp_train_samples(train_sample_count, var_count, CV_32F);
        Mat temp_test_samples(test_sample_count, var_count, CV_32F);
        Mat temp_train_responses(train_sample_count, 1, rtype);
        Mat temp_test_responses;

        // If grid.minVal == grid.maxVal, this will allow one and only one pass through the loop with params.var = grid.minVal.
        #define FOR_IN_GRID(var, grid) \
            for( params.var = grid.minVal; params.var == grid.minVal || params.var < grid.maxVal; params.var = (grid.minVal == grid.maxVal) ? grid.maxVal + 1 : params.var * grid.logStep )

        FOR_IN_GRID(C, C_grid)
        FOR_IN_GRID(gamma, gamma_grid)
        FOR_IN_GRID(p, p_grid)
        FOR_IN_GRID(nu, nu_grid)
        FOR_IN_GRID(coef0, coef_grid)
        FOR_IN_GRID(degree, degree_grid)
        {
            // make sure we updated the kernel and other parameters
            setParams(params);

            double error = 0;
            for( k = 0; k < k_fold; k++ )
            {
                int start = (k*sample_count + k_fold/2)/k_fold;
                for( i = 0; i < train_sample_count; i++ )
                {
                    j = sidx[(i+start)%sample_count];
                    memcpy(temp_train_samples.ptr(i), samples.ptr(j), sample_size);
                    if( is_classification )
                        temp_train_responses.at<int>(i) = responses.at<int>(j);
                    else if( !responses.empty() )
                        temp_train_responses.at<float>(i) = responses.at<float>(j);
                }

                // Train SVM on <train_size> samples
                if( !do_train( temp_train_samples, temp_train_responses ))
                    continue;

                for( i = 0; i < train_sample_count; i++ )
                {
                    j = sidx[(i+start+train_sample_count) % sample_count];
                    memcpy(temp_train_samples.ptr(i), samples.ptr(j), sample_size);
                }

                predict(temp_test_samples, temp_test_responses, 0);
                for( i = 0; i < test_sample_count; i++ )
                {
                    float val = temp_test_responses.at<float>(i);
                    j = sidx[(i+start+train_sample_count) % sample_count];
                    if( is_classification )
                        error += (float)(val != responses.at<int>(j));
                    else
                    {
                        val -= responses.at<float>(j);
                        error += val*val;
                    }
                }
            }
            if( min_error > error )
            {
                min_error   = error;
                best_params = params;
            }
        }

        params = best_params;
        return do_train( samples, responses );
    }

    struct PredictBody : ParallelLoopBody
    {
        PredictBody( const SVMImpl* _svm, const Mat& _samples, Mat& _results, bool _returnDFVal )
        {
            svm = _svm;
            results = &_results;
            samples = &_samples;
            returnDFVal = _returnDFVal;
        }

        void operator()( const Range& range ) const
        {
            int svmType = svm->params.svmType;
            int sv_total = svm->sv.rows;
            int class_count = !svm->class_labels.empty() ? (int)svm->class_labels.total() : svmType == ONE_CLASS ? 1 : 0;

            AutoBuffer<float> _buffer(sv_total + (class_count+1)*2);
            float* buffer = _buffer;

            int i, j, dfi, k, si;

            if( svmType == EPS_SVR || svmType == NU_SVR || svmType == ONE_CLASS )
            {
                for( si = range.start; si < range.end; si++ )
                {
                    const float* row_sample = samples->ptr<float>(si);
                    svm->kernel->calc( sv_total, svm->var_count, svm->sv.ptr<float>(), row_sample, buffer );

                    const SVMImpl::DecisionFunc* df = &svm->decision_func[0];
                    double sum = -df->rho;
                    for( i = 0; i < sv_total; i++ )
                        sum += buffer[i]*svm->df_alpha[i];
                    float result = svm->params.svmType == ONE_CLASS && !returnDFVal ? (float)(sum > 0) : (float)sum;
                    results->at<float>(si) = result;
                }
            }
            else if( svmType == C_SVC || svmType == NU_SVC )
            {
                int* vote = (int*)(buffer + sv_total);

                for( si = range.start; si < range.end; si++ )
                {
                    svm->kernel->calc( sv_total, svm->var_count, svm->sv.ptr<float>(),
                                       samples->ptr<float>(si), buffer );
                    double sum = 0.;

                    memset( vote, 0, class_count*sizeof(vote[0]));

                    for( i = dfi = 0; i < class_count; i++ )
                    {
                        for( j = i+1; j < class_count; j++, dfi++ )
                        {
                            const DecisionFunc& df = svm->decision_func[dfi];
                            sum = -df.rho;
                            int sv_count = svm->getSVCount(dfi);
                            const double* alpha = &svm->df_alpha[df.ofs];
                            const int* sv_index = &svm->df_index[df.ofs];
                            for( k = 0; k < sv_count; k++ )
                                sum += alpha[k]*buffer[sv_index[k]];

                            vote[sum > 0 ? i : j]++;
                        }
                    }

                    for( i = 1, k = 0; i < class_count; i++ )
                    {
                        if( vote[i] > vote[k] )
                            k = i;
                    }
                    float result = returnDFVal && class_count == 2 ?
                        (float)sum : (float)(svm->class_labels.at<int>(k));
                    results->at<float>(si) = result;
                }
            }
            else
                CV_Error( CV_StsBadArg, "INTERNAL ERROR: Unknown SVM type, "
                         "the SVM structure is probably corrupted" );
        }

        const SVMImpl* svm;
        const Mat* samples;
        Mat* results;
        bool returnDFVal;
    };

    float predict( InputArray _samples, OutputArray _results, int flags ) const
    {
        float result = 0;
        Mat samples = _samples.getMat(), results;
        int nsamples = samples.rows;
        bool returnDFVal = (flags & RAW_OUTPUT) != 0;

        CV_Assert( samples.cols == var_count && samples.type() == CV_32F );

        if( _results.needed() )
        {
            _results.create( nsamples, 1, samples.type() );
            results = _results.getMat();
        }
        else
        {
            CV_Assert( nsamples == 1 );
            results = Mat(1, 1, CV_32F, &result);
        }

        PredictBody invoker(this, samples, results, returnDFVal);
        if( nsamples < 10 )
            invoker(Range(0, nsamples));
        else
            parallel_for_(Range(0, nsamples), invoker);
        return result;
    }

    double getDecisionFunction(int i, OutputArray _alpha, OutputArray _svidx ) const
    {
        CV_Assert( 0 <= i && i < (int)decision_func.size());
        const DecisionFunc& df = decision_func[i];
        int count = getSVCount(i);
        Mat(1, count, CV_64F, (double*)&df_alpha[df.ofs]).copyTo(_alpha);
        Mat(1, count, CV_32S, (int*)&df_index[df.ofs]).copyTo(_svidx);
        return df.rho;
    }

    void write_params( FileStorage& fs ) const
    {
        int svmType = params.svmType;
        int kernelType = params.kernelType;

        String svm_type_str =
            svmType == C_SVC ? "C_SVC" :
            svmType == NU_SVC ? "NU_SVC" :
            svmType == ONE_CLASS ? "ONE_CLASS" :
            svmType == EPS_SVR ? "EPS_SVR" :
            svmType == NU_SVR ? "NU_SVR" : format("Uknown_%d", svmType);
        String kernel_type_str =
            kernelType == LINEAR ? "LINEAR" :
            kernelType == POLY ? "POLY" :
            kernelType == RBF ? "RBF" :
            kernelType == SIGMOID ? "SIGMOID" :
            kernelType == CHI2 ? "CHI2" :
            kernelType == INTER ? "INTER" : format("Unknown_%d", kernelType);

        fs << "svmType" << svm_type_str;

        // save kernel
        fs << "kernel" << "{" << "type" << kernel_type_str;

        if( kernelType == POLY )
            fs << "degree" << params.degree;

        if( kernelType != LINEAR )
            fs << "gamma" << params.gamma;

        if( kernelType == POLY || kernelType == SIGMOID )
            fs << "coef0" << params.coef0;

        fs << "}";

        if( svmType == C_SVC || svmType == EPS_SVR || svmType == NU_SVR )
            fs << "C" << params.C;

        if( svmType == NU_SVC || svmType == ONE_CLASS || svmType == NU_SVR )
            fs << "nu" << params.nu;

        if( svmType == EPS_SVR )
            fs << "p" << params.p;

        fs << "term_criteria" << "{:";
        if( params.termCrit.type & TermCriteria::EPS )
            fs << "epsilon" << params.termCrit.epsilon;
        if( params.termCrit.type & TermCriteria::COUNT )
            fs << "iterations" << params.termCrit.maxCount;
        fs << "}";
    }

    bool isTrained() const
    {
        return !sv.empty();
    }

    bool isClassifier() const
    {
        return params.svmType == C_SVC || params.svmType == NU_SVC || params.svmType == ONE_CLASS;
    }

    int getVarCount() const
    {
        return var_count;
    }

    String getDefaultName() const
    {
        return "opencv_ml_svm";
    }

    void write( FileStorage& fs ) const
    {
        int class_count = !class_labels.empty() ? (int)class_labels.total() :
                          params.svmType == ONE_CLASS ? 1 : 0;
        if( !isTrained() )
            CV_Error( CV_StsParseError, "SVM model data is invalid, check sv_count, var_* and class_count tags" );

        write_params( fs );

        fs << "var_count" << var_count;

        if( class_count > 0 )
        {
            fs << "class_count" << class_count;

            if( !class_labels.empty() )
                fs << "class_labels" << class_labels;

            if( !params.classWeights.empty() )
                fs << "class_weights" << params.classWeights;
        }

        // write the joint collection of support vectors
        int i, sv_total = sv.rows;
        fs << "sv_total" << sv_total;
        fs << "support_vectors" << "[";
        for( i = 0; i < sv_total; i++ )
        {
            fs << "[:";
            fs.writeRaw("f", sv.ptr(i), sv.cols*sv.elemSize());
            fs << "]";
        }
        fs << "]";

        // write decision functions
        int df_count = (int)decision_func.size();

        fs << "decision_functions" << "[";
        for( i = 0; i < df_count; i++ )
        {
            const DecisionFunc& df = decision_func[i];
            int sv_count = getSVCount(i);
            fs << "{" << "sv_count" << sv_count
               << "rho" << df.rho
               << "alpha" << "[:";
            fs.writeRaw("d", (const uchar*)&df_alpha[df.ofs], sv_count*sizeof(df_alpha[0]));
            fs << "]";
            if( class_count > 2 )
            {
                fs << "index" << "[:";
                fs.writeRaw("i", (const uchar*)&df_index[df.ofs], sv_count*sizeof(df_index[0]));
                fs << "]";
            }
            else
                CV_Assert( sv_count == sv_total );
            fs << "}";
        }
        fs << "]";
    }

    void read_params( const FileNode& fn )
    {
        SvmParams _params;

        // check for old naming
        String svm_type_str = (String)(fn["svm_type"].empty() ? fn["svmType"] : fn["svm_type"]);
        int svmType =
            svm_type_str == "C_SVC" ? C_SVC :
            svm_type_str == "NU_SVC" ? NU_SVC :
            svm_type_str == "ONE_CLASS" ? ONE_CLASS :
            svm_type_str == "EPS_SVR" ? EPS_SVR :
            svm_type_str == "NU_SVR" ? NU_SVR : -1;

        if( svmType < 0 )
            CV_Error( CV_StsParseError, "Missing of invalid SVM type" );

        FileNode kernel_node = fn["kernel"];
        if( kernel_node.empty() )
            CV_Error( CV_StsParseError, "SVM kernel tag is not found" );

        String kernel_type_str = (String)kernel_node["type"];
        int kernelType =
            kernel_type_str == "LINEAR" ? LINEAR :
            kernel_type_str == "POLY" ? POLY :
            kernel_type_str == "RBF" ? RBF :
            kernel_type_str == "SIGMOID" ? SIGMOID :
            kernel_type_str == "CHI2" ? CHI2 :
            kernel_type_str == "INTER" ? INTER : CUSTOM;

        if( kernelType == CUSTOM )
            CV_Error( CV_StsParseError, "Invalid SVM kernel type (or custom kernel)" );

        _params.svmType = svmType;
        _params.kernelType = kernelType;
        _params.degree = (double)kernel_node["degree"];
        _params.gamma = (double)kernel_node["gamma"];
        _params.coef0 = (double)kernel_node["coef0"];

        _params.C = (double)fn["C"];
        _params.nu = (double)fn["nu"];
        _params.p = (double)fn["p"];
        _params.classWeights = Mat();

        FileNode tcnode = fn["term_criteria"];
        if( !tcnode.empty() )
        {
            _params.termCrit.epsilon = (double)tcnode["epsilon"];
            _params.termCrit.maxCount = (int)tcnode["iterations"];
            _params.termCrit.type = (_params.termCrit.epsilon > 0 ? TermCriteria::EPS : 0) +
                                   (_params.termCrit.maxCount > 0 ? TermCriteria::COUNT : 0);
        }
        else
            _params.termCrit = TermCriteria( TermCriteria::EPS + TermCriteria::COUNT, 1000, FLT_EPSILON );

        setParams( _params );
    }

    void read( const FileNode& fn )
    {
        clear();

        // read SVM parameters
        read_params( fn );

        // and top-level data
        int i, sv_total = (int)fn["sv_total"];
        var_count = (int)fn["var_count"];
        int class_count = (int)fn["class_count"];

        if( sv_total <= 0 || var_count <= 0 )
            CV_Error( CV_StsParseError, "SVM model data is invalid, check sv_count, var_* and class_count tags" );

        FileNode m = fn["class_labels"];
        if( !m.empty() )
            m >> class_labels;
        m = fn["class_weights"];
        if( !m.empty() )
            m >> params.classWeights;

        if( class_count > 1 && (class_labels.empty() || (int)class_labels.total() != class_count))
            CV_Error( CV_StsParseError, "Array of class labels is missing or invalid" );

        // read support vectors
        FileNode sv_node = fn["support_vectors"];

        CV_Assert((int)sv_node.size() == sv_total);
        sv.create(sv_total, var_count, CV_32F);

        FileNodeIterator sv_it = sv_node.begin();
        for( i = 0; i < sv_total; i++, ++sv_it )
        {
            (*sv_it).readRaw("f", sv.ptr(i), var_count*sv.elemSize());
        }

        // read decision functions
        int df_count = class_count > 1 ? class_count*(class_count-1)/2 : 1;
        FileNode df_node = fn["decision_functions"];

        CV_Assert((int)df_node.size() == df_count);

        FileNodeIterator df_it = df_node.begin();
        for( i = 0; i < df_count; i++, ++df_it )
        {
            FileNode dfi = *df_it;
            DecisionFunc df;
            int sv_count = (int)dfi["sv_count"];
            int ofs = (int)df_index.size();
            df.rho = (double)dfi["rho"];
            df.ofs = ofs;
            df_index.resize(ofs + sv_count);
            df_alpha.resize(ofs + sv_count);
            dfi["alpha"].readRaw("d", (uchar*)&df_alpha[ofs], sv_count*sizeof(df_alpha[0]));
            if( class_count > 2 )
                dfi["index"].readRaw("i", (uchar*)&df_index[ofs], sv_count*sizeof(df_index[0]));
            decision_func.push_back(df);
        }
        if( class_count <= 2 )
            setRangeVector(df_index, sv_total);
        if( (int)fn["optimize_linear"] != 0 )
            optimize_linear_svm();
    }

    SvmParams params;
    Mat class_labels;
    int var_count;
    Mat sv;
    vector<DecisionFunc> decision_func;
    vector<double> df_alpha;
    vector<int> df_index;

    Ptr<Kernel> kernel;
};


Ptr<SVM> SVM::create()
{
    return makePtr<SVMImpl>();
}

}
}

/* End of file. */
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root/modules/ml/src/svm.cpp

DEFINITIONS
