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M*/

#include "precomp.hpp"

static const float ord_nan = FLT_MAX*0.5f;
static const int min_block_size = 1 << 16;
static const int block_size_delta = 1 << 10;

#define CV_CMP_NUM_PTR(a,b) (*(a) < *(b))
static CV_IMPLEMENT_QSORT_EX( icvSortIntPtr, int*, CV_CMP_NUM_PTR, int )

#define CV_CMP_PAIRS(a,b) (*((a).i) < *((b).i))
static CV_IMPLEMENT_QSORT_EX( icvSortPairs, CvPair16u32s, CV_CMP_PAIRS, int )

///

void CvERTreeTrainData::set_data( const CvMat* _train_data, int _tflag,
    const CvMat* _responses, const CvMat* _var_idx, const CvMat* _sample_idx,
    const CvMat* _var_type, const CvMat* _missing_mask, const CvDTreeParams& _params,
    bool _shared, bool _add_labels, bool _update_data )
{
    CvMat* sample_indices = 0;
    CvMat* var_type0 = 0;
    CvMat* tmp_map = 0;
    int** int_ptr = 0;
    CvPair16u32s* pair16u32s_ptr = 0;
    CvDTreeTrainData* data = 0;
    float *_fdst = 0;
    int *_idst = 0;
    unsigned short* udst = 0;
    int* idst = 0;

    CV_FUNCNAME( "CvERTreeTrainData::set_data" );

    __BEGIN__;

    int sample_all = 0, r_type, cv_n;
    int total_c_count = 0;
    int tree_block_size, temp_block_size, max_split_size, nv_size, cv_size = 0;
    int ds_step, dv_step, ms_step = 0, mv_step = 0; // {data|mask}{sample|var}_step
    int vi, i, size;
    char err[100];
    const int *sidx = 0, *vidx = 0;

    if ( _params.use_surrogates )
        CV_ERROR(CV_StsBadArg, "CvERTrees do not support surrogate splits");

    if( _update_data && data_root )
    {
        CV_ERROR(CV_StsBadArg, "CvERTrees do not support data update");
    }

    clear();

    var_all = 0;
    rng = &cv::theRNG();

    CV_CALL( set_params( _params ));

    // check parameter types and sizes
    CV_CALL( cvCheckTrainData( _train_data, _tflag, _missing_mask, &var_all, &sample_all ));

    train_data = _train_data;
    responses = _responses;
    missing_mask = _missing_mask;

    if( _tflag == CV_ROW_SAMPLE )
    {
        ds_step = _train_data->step/CV_ELEM_SIZE(_train_data->type);
        dv_step = 1;
        if( _missing_mask )
            ms_step = _missing_mask->step, mv_step = 1;
    }
    else
    {
        dv_step = _train_data->step/CV_ELEM_SIZE(_train_data->type);
        ds_step = 1;
        if( _missing_mask )
            mv_step = _missing_mask->step, ms_step = 1;
    }
    tflag = _tflag;

    sample_count = sample_all;
    var_count = var_all;

    if( _sample_idx )
    {
        CV_CALL( sample_indices = cvPreprocessIndexArray( _sample_idx, sample_all ));
        sidx = sample_indices->data.i;
        sample_count = sample_indices->rows + sample_indices->cols - 1;
    }

    if( _var_idx )
    {
        CV_CALL( var_idx = cvPreprocessIndexArray( _var_idx, var_all ));
        vidx = var_idx->data.i;
        var_count = var_idx->rows + var_idx->cols - 1;
    }

    if( !CV_IS_MAT(_responses) ||
        (CV_MAT_TYPE(_responses->type) != CV_32SC1 &&
         CV_MAT_TYPE(_responses->type) != CV_32FC1) ||
        (_responses->rows != 1 && _responses->cols != 1) ||
        _responses->rows + _responses->cols - 1 != sample_all )
        CV_ERROR( CV_StsBadArg, "The array of _responses must be an integer or "
                  "floating-point vector containing as many elements as "
                  "the total number of samples in the training data matrix" );

    is_buf_16u = false;
    if ( sample_count < 65536 )
        is_buf_16u = true;

    r_type = CV_VAR_CATEGORICAL;
    if( _var_type )
        CV_CALL( var_type0 = cvPreprocessVarType( _var_type, var_idx, var_count, &r_type ));

    CV_CALL( var_type = cvCreateMat( 1, var_count+2, CV_32SC1 ));

    cat_var_count = 0;
    ord_var_count = -1;

    is_classifier = r_type == CV_VAR_CATEGORICAL;

    // step 0. calc the number of categorical vars
    for( vi = 0; vi < var_count; vi++ )
    {
        char vt = var_type0 ? var_type0->data.ptr[vi] : CV_VAR_ORDERED;
        var_type->data.i[vi] = vt == CV_VAR_CATEGORICAL ? cat_var_count++ : ord_var_count--;
    }

    ord_var_count = ~ord_var_count;
    cv_n = params.cv_folds;
    // set the two last elements of var_type array to be able
    // to locate responses and cross-validation labels using
    // the corresponding get_* functions.
    var_type->data.i[var_count] = cat_var_count;
    var_type->data.i[var_count+1] = cat_var_count+1;

    // in case of single ordered predictor we need dummy cv_labels
    // for safe split_node_data() operation
    have_labels = cv_n > 0 || (ord_var_count == 1 && cat_var_count == 0) || _add_labels;

    work_var_count = cat_var_count + (is_classifier ? 1 : 0) + (have_labels ? 1 : 0);
    buf_size = (work_var_count + 1)*sample_count;
    shared = _shared;
    buf_count = shared ? 2 : 1;

    if ( is_buf_16u )
    {
        CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_16UC1 ));
        CV_CALL( pair16u32s_ptr = (CvPair16u32s*)cvAlloc( sample_count*sizeof(pair16u32s_ptr[0]) ));
    }
    else
    {
        CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_32SC1 ));
        CV_CALL( int_ptr = (int**)cvAlloc( sample_count*sizeof(int_ptr[0]) ));
    }

    size = is_classifier ? cat_var_count+1 : cat_var_count;
    size = !size ? 1 : size;
    CV_CALL( cat_count = cvCreateMat( 1, size, CV_32SC1 ));
    CV_CALL( cat_ofs = cvCreateMat( 1, size, CV_32SC1 ));

    size = is_classifier ? (cat_var_count + 1)*params.max_categories : cat_var_count*params.max_categories;
    size = !size ? 1 : size;
    CV_CALL( cat_map = cvCreateMat( 1, size, CV_32SC1 ));

    // now calculate the maximum size of split,
    // create memory storage that will keep nodes and splits of the decision tree
    // allocate root node and the buffer for the whole training data
    max_split_size = cvAlign(sizeof(CvDTreeSplit) +
        (MAX(0,sample_count - 33)/32)*sizeof(int),sizeof(void*));
    tree_block_size = MAX((int)sizeof(CvDTreeNode)*8, max_split_size);
    tree_block_size = MAX(tree_block_size + block_size_delta, min_block_size);
    CV_CALL( tree_storage = cvCreateMemStorage( tree_block_size ));
    CV_CALL( node_heap = cvCreateSet( 0, sizeof(*node_heap), sizeof(CvDTreeNode), tree_storage ));

    nv_size = var_count*sizeof(int);
    nv_size = cvAlign(MAX( nv_size, (int)sizeof(CvSetElem) ), sizeof(void*));

    temp_block_size = nv_size;

    if( cv_n )
    {
        if( sample_count < cv_n*MAX(params.min_sample_count,10) )
            CV_ERROR( CV_StsOutOfRange,
                "The many folds in cross-validation for such a small dataset" );

        cv_size = cvAlign( cv_n*(sizeof(int) + sizeof(double)*2), sizeof(double) );
        temp_block_size = MAX(temp_block_size, cv_size);
    }

    temp_block_size = MAX( temp_block_size + block_size_delta, min_block_size );
    CV_CALL( temp_storage = cvCreateMemStorage( temp_block_size ));
    CV_CALL( nv_heap = cvCreateSet( 0, sizeof(*nv_heap), nv_size, temp_storage ));
    if( cv_size )
        CV_CALL( cv_heap = cvCreateSet( 0, sizeof(*cv_heap), cv_size, temp_storage ));

    CV_CALL( data_root = new_node( 0, sample_count, 0, 0 ));

    max_c_count = 1;

    _fdst = 0;
    _idst = 0;
    if (ord_var_count)
        _fdst = (float*)cvAlloc(sample_count*sizeof(_fdst[0]));
    if (is_buf_16u && (cat_var_count || is_classifier))
        _idst = (int*)cvAlloc(sample_count*sizeof(_idst[0]));

    // transform the training data to convenient representation
    for( vi = 0; vi <= var_count; vi++ )
    {
        int ci;
        const uchar* mask = 0;
        int m_step = 0, step;
        const int* idata = 0;
        const float* fdata = 0;
        int num_valid = 0;

        if( vi < var_count ) // analyze i-th input variable
        {
            int vi0 = vidx ? vidx[vi] : vi;
            ci = get_var_type(vi);
            step = ds_step; m_step = ms_step;
            if( CV_MAT_TYPE(_train_data->type) == CV_32SC1 )
                idata = _train_data->data.i + vi0*dv_step;
            else
                fdata = _train_data->data.fl + vi0*dv_step;
            if( _missing_mask )
                mask = _missing_mask->data.ptr + vi0*mv_step;
        }
        else // analyze _responses
        {
            ci = cat_var_count;
            step = CV_IS_MAT_CONT(_responses->type) ?
                1 : _responses->step / CV_ELEM_SIZE(_responses->type);
            if( CV_MAT_TYPE(_responses->type) == CV_32SC1 )
                idata = _responses->data.i;
            else
                fdata = _responses->data.fl;
        }

        if( (vi < var_count && ci>=0) ||
            (vi == var_count && is_classifier) ) // process categorical variable or response
        {
            int c_count, prev_label;
            int* c_map;

            if (is_buf_16u)
                udst = (unsigned short*)(buf->data.s + ci*sample_count);
            else
                idst = buf->data.i + ci*sample_count;

            // copy data
            for( i = 0; i < sample_count; i++ )
            {
                int val = INT_MAX, si = sidx ? sidx[i] : i;
                if( !mask || !mask[si*m_step] )
                {
                    if( idata )
                        val = idata[si*step];
                    else
                    {
                        float t = fdata[si*step];
                        val = cvRound(t);
                        if( val != t )
                        {
                            sprintf( err, "%d-th value of %d-th (categorical) "
                                "variable is not an integer", i, vi );
                            CV_ERROR( CV_StsBadArg, err );
                        }
                    }

                    if( val == INT_MAX )
                    {
                        sprintf( err, "%d-th value of %d-th (categorical) "
                            "variable is too large", i, vi );
                        CV_ERROR( CV_StsBadArg, err );
                    }
                    num_valid++;
                }
                if (is_buf_16u)
                {
                    _idst[i] = val;
                    pair16u32s_ptr[i].u = udst + i;
                    pair16u32s_ptr[i].i = _idst + i;
                }
                else
                {
                    idst[i] = val;
                    int_ptr[i] = idst + i;
                }
            }

            c_count = num_valid > 0;

            if (is_buf_16u)
            {
                icvSortPairs( pair16u32s_ptr, sample_count, 0 );
                // count the categories
                for( i = 1; i < num_valid; i++ )
                    if (*pair16u32s_ptr[i].i != *pair16u32s_ptr[i-1].i)
                        c_count ++ ;
            }
            else
            {
                icvSortIntPtr( int_ptr, sample_count, 0 );
                // count the categories
                for( i = 1; i < num_valid; i++ )
                    c_count += *int_ptr[i] != *int_ptr[i-1];
            }

            if( vi > 0 )
                max_c_count = MAX( max_c_count, c_count );
            cat_count->data.i[ci] = c_count;
            cat_ofs->data.i[ci] = total_c_count;

            // resize cat_map, if need
            if( cat_map->cols < total_c_count + c_count )
            {
                tmp_map = cat_map;
                CV_CALL( cat_map = cvCreateMat( 1,
                    MAX(cat_map->cols*3/2,total_c_count+c_count), CV_32SC1 ));
                for( i = 0; i < total_c_count; i++ )
                    cat_map->data.i[i] = tmp_map->data.i[i];
                cvReleaseMat( &tmp_map );
            }

            c_map = cat_map->data.i + total_c_count;
            total_c_count += c_count;

            c_count = -1;
            if (is_buf_16u)
            {
                // compact the class indices and build the map
                prev_label = ~*pair16u32s_ptr[0].i;
                for( i = 0; i < num_valid; i++ )
                {
                    int cur_label = *pair16u32s_ptr[i].i;
                    if( cur_label != prev_label )
                        c_map[++c_count] = prev_label = cur_label;
                    *pair16u32s_ptr[i].u = (unsigned short)c_count;
                }
                // replace labels for missing values with 65535
                for( ; i < sample_count; i++ )
                    *pair16u32s_ptr[i].u = 65535;
            }
            else
            {
                // compact the class indices and build the map
                prev_label = ~*int_ptr[0];
                for( i = 0; i < num_valid; i++ )
                {
                    int cur_label = *int_ptr[i];
                    if( cur_label != prev_label )
                        c_map[++c_count] = prev_label = cur_label;
                    *int_ptr[i] = c_count;
                }
                // replace labels for missing values with -1
                for( ; i < sample_count; i++ )
                    *int_ptr[i] = -1;
            }
        }
        else if( ci < 0 ) // process ordered variable
        {
            for( i = 0; i < sample_count; i++ )
            {
                float val = ord_nan;
                int si = sidx ? sidx[i] : i;
                if( !mask || !mask[si*m_step] )
                {
                    if( idata )
                        val = (float)idata[si*step];
                    else
                        val = fdata[si*step];

                    if( fabs(val) >= ord_nan )
                    {
                        sprintf( err, "%d-th value of %d-th (ordered) "
                            "variable (=%g) is too large", i, vi, val );
                        CV_ERROR( CV_StsBadArg, err );
                    }
                    num_valid++;
                }
            }
        }
        if( vi < var_count )
            data_root->set_num_valid(vi, num_valid);
    }

    // set sample labels
    if (is_buf_16u)
        udst = (unsigned short*)(buf->data.s + get_work_var_count()*sample_count);
    else
        idst = buf->data.i + get_work_var_count()*sample_count;

    for (i = 0; i < sample_count; i++)
    {
        if (udst)
            udst[i] = sidx ? (unsigned short)sidx[i] : (unsigned short)i;
        else
            idst[i] = sidx ? sidx[i] : i;
    }

    if( cv_n )
    {
        unsigned short* usdst = 0;
        int* idst2 = 0;

        if (is_buf_16u)
        {
            usdst = (unsigned short*)(buf->data.s + (get_work_var_count()-1)*sample_count);
            for( i = vi = 0; i < sample_count; i++ )
            {
                usdst[i] = (unsigned short)vi++;
                vi &= vi < cv_n ? -1 : 0;
            }

            for( i = 0; i < sample_count; i++ )
            {
                int a = (*rng)(sample_count);
                int b = (*rng)(sample_count);
                unsigned short unsh = (unsigned short)vi;
                CV_SWAP( usdst[a], usdst[b], unsh );
            }
        }
        else
        {
            idst2 = buf->data.i + (get_work_var_count()-1)*sample_count;
            for( i = vi = 0; i < sample_count; i++ )
            {
                idst2[i] = vi++;
                vi &= vi < cv_n ? -1 : 0;
            }

            for( i = 0; i < sample_count; i++ )
            {
                int a = (*rng)(sample_count);
                int b = (*rng)(sample_count);
                CV_SWAP( idst2[a], idst2[b], vi );
            }
        }
    }

    if ( cat_map )
        cat_map->cols = MAX( total_c_count, 1 );

    max_split_size = cvAlign(sizeof(CvDTreeSplit) +
        (MAX(0,max_c_count - 33)/32)*sizeof(int),sizeof(void*));
    CV_CALL( split_heap = cvCreateSet( 0, sizeof(*split_heap), max_split_size, tree_storage ));

    have_priors = is_classifier && params.priors;
    if( is_classifier )
    {
        int m = get_num_classes();
        double sum = 0;
        CV_CALL( priors = cvCreateMat( 1, m, CV_64F ));
        for( i = 0; i < m; i++ )
        {
            double val = have_priors ? params.priors[i] : 1.;
            if( val <= 0 )
                CV_ERROR( CV_StsOutOfRange, "Every class weight should be positive" );
            priors->data.db[i] = val;
            sum += val;
        }

        // normalize weights
        if( have_priors )
            cvScale( priors, priors, 1./sum );

        CV_CALL( priors_mult = cvCloneMat( priors ));
        CV_CALL( counts = cvCreateMat( 1, m, CV_32SC1 ));
    }

    CV_CALL( direction = cvCreateMat( 1, sample_count, CV_8UC1 ));
    CV_CALL( split_buf = cvCreateMat( 1, sample_count, CV_32SC1 ));

    __END__;

    if( data )
        delete data;

    if (_fdst)
        cvFree( &_fdst );
    if (_idst)
        cvFree( &_idst );
    cvFree( &int_ptr );
    cvReleaseMat( &var_type0 );
    cvReleaseMat( &sample_indices );
    cvReleaseMat( &tmp_map );
}

void CvERTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi, float* ord_values_buf, int* missing_buf,
                                          const float** ord_values, const int** missing, int* sample_indices_buf )
{
    int vidx = var_idx ? var_idx->data.i[vi] : vi;
    int node_sample_count = n->sample_count;
    // may use missing_buf as buffer for sample indices!
    const int* sample_indices = get_sample_indices(n, sample_indices_buf ? sample_indices_buf : missing_buf);

    int td_step = train_data->step/CV_ELEM_SIZE(train_data->type);
    int m_step = missing_mask ? missing_mask->step/CV_ELEM_SIZE(missing_mask->type) : 1;
    if( tflag == CV_ROW_SAMPLE )
    {
        for( int i = 0; i < node_sample_count; i++ )
        {
            int idx = sample_indices[i];
            missing_buf[i] = missing_mask ? *(missing_mask->data.ptr + idx * m_step + vi) : 0;
            ord_values_buf[i] = *(train_data->data.fl + idx * td_step + vidx);
        }
    }
    else
        for( int i = 0; i < node_sample_count; i++ )
        {
            int idx = sample_indices[i];
            missing_buf[i] = missing_mask ? *(missing_mask->data.ptr + vi* m_step + idx) : 0;
            ord_values_buf[i] = *(train_data->data.fl + vidx* td_step + idx);
        }
    *ord_values = ord_values_buf;
    *missing = missing_buf;
}


const int* CvERTreeTrainData::get_sample_indices( CvDTreeNode* n, int* indices_buf )
{
    return get_cat_var_data( n, var_count + (is_classifier ? 1 : 0) + (have_labels ? 1 : 0), indices_buf );
}


const int* CvERTreeTrainData::get_cv_labels( CvDTreeNode* n, int* labels_buf )
{
    if (have_labels)
        return get_cat_var_data( n, var_count + (is_classifier ? 1 : 0), labels_buf );
    return 0;
}


const int* CvERTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi, int* cat_values_buf )
{
    int ci = get_var_type( vi);
    const int* cat_values = 0;
    if( !is_buf_16u )
        cat_values = buf->data.i + n->buf_idx*buf->cols + ci*sample_count + n->offset;
    else {
        const unsigned short* short_values = (const unsigned short*)(buf->data.s + n->buf_idx*buf->cols +
            ci*sample_count + n->offset);
        for( int i = 0; i < n->sample_count; i++ )
            cat_values_buf[i] = short_values[i];
        cat_values = cat_values_buf;
    }
    return cat_values;
}

void CvERTreeTrainData::get_vectors( const CvMat* _subsample_idx,
                                    float* values, uchar* missing,
                                    float* _responses, bool get_class_idx )
{
    CvMat* subsample_idx = 0;
    CvMat* subsample_co = 0;

    cv::AutoBuffer<uchar> inn_buf(sample_count*(sizeof(float) + sizeof(int)));

    CV_FUNCNAME( "CvERTreeTrainData::get_vectors" );

    __BEGIN__;

    int i, vi, total = sample_count, count = total, cur_ofs = 0;
    int* sidx = 0;
    int* co = 0;

    if( _subsample_idx )
    {
        CV_CALL( subsample_idx = cvPreprocessIndexArray( _subsample_idx, sample_count ));
        sidx = subsample_idx->data.i;
        CV_CALL( subsample_co = cvCreateMat( 1, sample_count*2, CV_32SC1 ));
        co = subsample_co->data.i;
        cvZero( subsample_co );
        count = subsample_idx->cols + subsample_idx->rows - 1;
        for( i = 0; i < count; i++ )
            co[sidx[i]*2]++;
        for( i = 0; i < total; i++ )
        {
            int count_i = co[i*2];
            if( count_i )
            {
                co[i*2+1] = cur_ofs*var_count;
                cur_ofs += count_i;
            }
        }
    }

    if( missing )
        memset( missing, 1, count*var_count );

    for( vi = 0; vi < var_count; vi++ )
    {
        int ci = get_var_type(vi);
        if( ci >= 0 ) // categorical
        {
            float* dst = values + vi;
            uchar* m = missing ? missing + vi : 0;
            int* lbls_buf = (int*)(uchar*)inn_buf;
            const int* src = get_cat_var_data(data_root, vi, lbls_buf);

            for( i = 0; i < count; i++, dst += var_count )
            {
                int idx = sidx ? sidx[i] : i;
                int val = src[idx];
                *dst = (float)val;
                if( m )
                {
                    *m = (!is_buf_16u && val < 0) || (is_buf_16u && (val == 65535));
                    m += var_count;
                }
            }
        }
        else // ordered
        {
            int* mis_buf = (int*)(uchar*)inn_buf;
            const float *dst = 0;
            const int* mis = 0;
            get_ord_var_data(data_root, vi, values + vi, mis_buf, &dst, &mis, 0);
            for (int si = 0; si < total; si++)
                *(missing + vi + si) = mis[si] == 0 ? 0 : 1;
        }
    }

    // copy responses
    if( _responses )
    {
        if( is_classifier )
        {
            int* lbls_buf = (int*)(uchar*)inn_buf;
            const int* src = get_class_labels(data_root, lbls_buf);
            for( i = 0; i < count; i++ )
            {
                int idx = sidx ? sidx[i] : i;
                int val = get_class_idx ? src[idx] :
                    cat_map->data.i[cat_ofs->data.i[cat_var_count]+src[idx]];
                _responses[i] = (float)val;
            }
        }
        else
        {
            float* _values_buf = (float*)(uchar*)inn_buf;
            int* sample_idx_buf = (int*)(_values_buf + sample_count);
            const float* _values = get_ord_responses(data_root, _values_buf, sample_idx_buf);
            for( i = 0; i < count; i++ )
            {
                int idx = sidx ? sidx[i] : i;
                _responses[i] = _values[idx];
            }
        }
    }

    __END__;

    cvReleaseMat( &subsample_idx );
    cvReleaseMat( &subsample_co );
}

CvDTreeNode* CvERTreeTrainData::subsample_data( const CvMat* _subsample_idx )
{
    CvDTreeNode* root = 0;

    CV_FUNCNAME( "CvERTreeTrainData::subsample_data" );

    __BEGIN__;

    if( !data_root )
        CV_ERROR( CV_StsError, "No training data has been set" );

    if( !_subsample_idx )
    {
        // make a copy of the root node
        CvDTreeNode temp;
        int i;
        root = new_node( 0, 1, 0, 0 );
        temp = *root;
        *root = *data_root;
        root->num_valid = temp.num_valid;
        if( root->num_valid )
        {
            for( i = 0; i < var_count; i++ )
                root->num_valid[i] = data_root->num_valid[i];
        }
        root->cv_Tn = temp.cv_Tn;
        root->cv_node_risk = temp.cv_node_risk;
        root->cv_node_error = temp.cv_node_error;
    }
    else
        CV_ERROR( CV_StsError, "_subsample_idx must be null for extra-trees" );
    __END__;

    return root;
}

double CvForestERTree::calc_node_dir( CvDTreeNode* node )
{
    char* dir = (char*)data->direction->data.ptr;
    int i, n = node->sample_count, vi = node->split->var_idx;
    double L, R;

    assert( !node->split->inversed );

    if( data->get_var_type(vi) >= 0 ) // split on categorical var
    {
        cv::AutoBuffer<uchar> inn_buf(n*sizeof(int)*(!data->have_priors ? 1 : 2));
        int* labels_buf = (int*)(uchar*)inn_buf;
        const int* labels = data->get_cat_var_data( node, vi, labels_buf );
        const int* subset = node->split->subset;
        if( !data->have_priors )
        {
            int sum = 0, sum_abs = 0;

            for( i = 0; i < n; i++ )
            {
                int idx = labels[i];
                int d = ( ((idx >= 0)&&(!data->is_buf_16u)) || ((idx != 65535)&&(data->is_buf_16u)) ) ?
                    CV_DTREE_CAT_DIR(idx,subset) : 0;
                sum += d; sum_abs += d & 1;
                dir[i] = (char)d;
            }

            R = (sum_abs + sum) >> 1;
            L = (sum_abs - sum) >> 1;
        }
        else
        {
            const double* priors = data->priors_mult->data.db;
            double sum = 0, sum_abs = 0;
            int *responses_buf = labels_buf + n;
            const int* responses = data->get_class_labels(node, responses_buf);

            for( i = 0; i < n; i++ )
            {
                int idx = labels[i];
                double w = priors[responses[i]];
                int d = idx >= 0 ? CV_DTREE_CAT_DIR(idx,subset) : 0;
                sum += d*w; sum_abs += (d & 1)*w;
                dir[i] = (char)d;
            }

            R = (sum_abs + sum) * 0.5;
            L = (sum_abs - sum) * 0.5;
        }
    }
    else // split on ordered var
    {
        float split_val = node->split->ord.c;
        cv::AutoBuffer<uchar> inn_buf(n*(sizeof(int)*(!data->have_priors ? 1 : 2) + sizeof(float)));
        float* val_buf = (float*)(uchar*)inn_buf;
        int* missing_buf = (int*)(val_buf + n);
        const float* val = 0;
        const int* missing = 0;
        data->get_ord_var_data( node, vi, val_buf, missing_buf, &val, &missing, 0 );

        if( !data->have_priors )
        {
            L = R = 0;
            for( i = 0; i < n; i++ )
            {
                if ( missing[i] )
                    dir[i] = (char)0;
                else
                {
                    if ( val[i] < split_val)
                    {
                        dir[i] = (char)-1;
                        L++;
                    }
                    else
                    {
                        dir[i] = (char)1;
                        R++;
                    }
                }
            }
        }
        else
        {
            const double* priors = data->priors_mult->data.db;
            int* responses_buf = missing_buf + n;
            const int* responses = data->get_class_labels(node, responses_buf);
            L = R = 0;
            for( i = 0; i < n; i++ )
            {
                if ( missing[i] )
                    dir[i] = (char)0;
                else
                {
                    double w = priors[responses[i]];
                    if ( val[i] < split_val)
                    {
                        dir[i] = (char)-1;
                         L += w;
                    }
                    else
                    {
                        dir[i] = (char)1;
                        R += w;
                    }
                }
            }
        }
    }

    node->maxlr = MAX( L, R );
    return node->split->quality/(L + R);
}

CvDTreeSplit* CvForestERTree::find_split_ord_class( CvDTreeNode* node, int vi, float init_quality, CvDTreeSplit* _split,
                                                    uchar* _ext_buf )
{
    const float epsilon = FLT_EPSILON*2;
    const float split_delta = (1 + FLT_EPSILON) * FLT_EPSILON;

    int n = node->sample_count;
    int m = data->get_num_classes();

    cv::AutoBuffer<uchar> inn_buf;
    if( !_ext_buf )
        inn_buf.allocate(n*(2*sizeof(int) + sizeof(float)));
    uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
    float* values_buf = (float*)ext_buf;
    int* missing_buf = (int*)(values_buf + n);
    const float* values = 0;
    const int* missing = 0;
    data->get_ord_var_data( node, vi, values_buf, missing_buf, &values, &missing, 0 );
    int* responses_buf = missing_buf + n;
    const int* responses = data->get_class_labels( node, responses_buf );

    double lbest_val = 0, rbest_val = 0, best_val = init_quality, split_val = 0;
    const double* priors = data->have_priors ? data->priors_mult->data.db : 0;
    bool is_find_split = false;
    float pmin, pmax;
    int smpi = 0;
    while ( missing[smpi] && (smpi < n) )
        smpi++;
    assert(smpi < n);

    pmin = values[smpi];
    pmax = pmin;
    for (; smpi < n; smpi++)
    {
        float ptemp = values[smpi];
        int ms = missing[smpi];
        if (ms) continue;
        if ( ptemp < pmin)
            pmin = ptemp;
        if ( ptemp > pmax)
            pmax = ptemp;
    }
    float fdiff = pmax-pmin;
    if (fdiff > epsilon)
    {
        is_find_split = true;
        cv::RNG* rng = data->rng;
        split_val = pmin + rng->uniform(0.f, 1.f) * fdiff ;
        if (split_val - pmin <= FLT_EPSILON)
            split_val = pmin + split_delta;
        if (pmax - split_val <= FLT_EPSILON)
            split_val = pmax - split_delta;

        // calculate Gini index
        if ( !priors )
        {
            cv::AutoBuffer<int> lrc(m*2);
            int *lc = lrc, *rc = lc + m;
            int L = 0, R = 0;

            // init arrays of class instance counters on both sides of the split
            for(int i = 0; i < m; i++ )
            {
                lc[i] = 0;
                rc[i] = 0;
            }
            for( int si = 0; si < n; si++ )
            {
                int r = responses[si];
                float val = values[si];
                int ms = missing[si];
                if (ms) continue;
                if ( val < split_val )
                {
                    lc[r]++;
                    L++;
                }
                else
                {
                    rc[r]++;
                    R++;
                }
            }
            for (int i = 0; i < m; i++)
            {
                lbest_val += lc[i]*lc[i];
                rbest_val += rc[i]*rc[i];
            }
            best_val = (lbest_val*R + rbest_val*L) / ((double)(L*R));
        }
        else
        {
            cv::AutoBuffer<double> lrc(m*2);
            double *lc = lrc, *rc = lc + m;
            double L = 0, R = 0;

            // init arrays of class instance counters on both sides of the split
            for(int i = 0; i < m; i++ )
            {
                lc[i] = 0;
                rc[i] = 0;
            }
            for( int si = 0; si < n; si++ )
            {
                int r = responses[si];
                float val = values[si];
                int ms = missing[si];
                double p = priors[r];
                if (ms) continue;
                if ( val < split_val )
                {
                    lc[r] += p;
                    L += p;
                }
                else
                {
                    rc[r] += p;
                    R += p;
                }
            }
            for (int i = 0; i < m; i++)
            {
                lbest_val += lc[i]*lc[i];
                rbest_val += rc[i]*rc[i];
            }
            best_val = (lbest_val*R + rbest_val*L) / (L*R);
        }

    }

    CvDTreeSplit* split = 0;
    if( is_find_split )
    {
        split = _split ? _split : data->new_split_ord( 0, 0.0f, 0, 0, 0.0f );
        split->var_idx = vi;
        split->ord.c = (float)split_val;
        split->ord.split_point = -1;
        split->inversed = 0;
        split->quality = (float)best_val;
    }
    return split;
}

CvDTreeSplit* CvForestERTree::find_split_cat_class( CvDTreeNode* node, int vi, float init_quality, CvDTreeSplit* _split,
                                                    uchar* _ext_buf )
{
    int ci = data->get_var_type(vi);
    int n = node->sample_count;
    int cm = data->get_num_classes();
    int vm = data->cat_count->data.i[ci];
    double best_val = init_quality;
    CvDTreeSplit *split = 0;

    if ( vm > 1 )
    {
        cv::AutoBuffer<int> inn_buf;
        if( !_ext_buf )
            inn_buf.allocate(2*n);
        int* ext_buf = _ext_buf ? (int*)_ext_buf : (int*)inn_buf;

        const int* labels = data->get_cat_var_data( node, vi, ext_buf );
        const int* responses = data->get_class_labels( node, ext_buf + n );

        const double* priors = data->have_priors ? data->priors_mult->data.db : 0;

        // create random class mask
        cv::AutoBuffer<int> valid_cidx(vm);
        for (int i = 0; i < vm; i++)
        {
            valid_cidx[i] = -1;
        }
        for (int si = 0; si < n; si++)
        {
            int c = labels[si];
            if ( ((c == 65535) && data->is_buf_16u) || ((c<0) && (!data->is_buf_16u)) )
                continue;
            valid_cidx[c]++;
        }

        int valid_ccount = 0;
        for (int i = 0; i < vm; i++)
            if (valid_cidx[i] >= 0)
            {
                valid_cidx[i] = valid_ccount;
                valid_ccount++;
            }
        if (valid_ccount > 1)
        {
            CvRNG* rng = forest->get_rng();
            int l_cval_count = 1 + cvRandInt(rng) % (valid_ccount-1);

            CvMat* var_class_mask = cvCreateMat( 1, valid_ccount, CV_8UC1 );
            CvMat submask;
            memset(var_class_mask->data.ptr, 0, valid_ccount*CV_ELEM_SIZE(var_class_mask->type));
            cvGetCols( var_class_mask, &submask, 0, l_cval_count );
            cvSet( &submask, cvScalar(1) );
            for (int i = 0; i < valid_ccount; i++)
            {
                uchar temp;
                int i1 =  cvRandInt( rng ) % valid_ccount;
                int i2 = cvRandInt( rng ) % valid_ccount;
                CV_SWAP( var_class_mask->data.ptr[i1], var_class_mask->data.ptr[i2], temp );
            }

            split = _split ? _split : data->new_split_cat( 0, -1.0f );
            split->var_idx = vi;
            memset( split->subset, 0, (data->max_c_count + 31)/32 * sizeof(int));

            // calculate Gini index
            double lbest_val = 0, rbest_val = 0;
            if( !priors )
            {
                cv::AutoBuffer<int> lrc(cm*2);
                int *lc = lrc, *rc = lc + cm;
                int L = 0, R = 0;
                // init arrays of class instance counters on both sides of the split
                for(int i = 0; i < cm; i++ )
                {
                    lc[i] = 0;
                    rc[i] = 0;
                }
                for( int si = 0; si < n; si++ )
                {
                    int r = responses[si];
                    int var_class_idx = labels[si];
                    if ( ((var_class_idx == 65535) && data->is_buf_16u) || ((var_class_idx<0) && (!data->is_buf_16u)) )
                        continue;
                    int mask_class_idx = valid_cidx[var_class_idx];
                    if (var_class_mask->data.ptr[mask_class_idx])
                    {
                        lc[r]++;
                        L++;
                        split->subset[var_class_idx >> 5] |= 1 << (var_class_idx & 31);
                    }
                    else
                    {
                        rc[r]++;
                        R++;
                    }
                }
                for (int i = 0; i < cm; i++)
                {
                    lbest_val += lc[i]*lc[i];
                    rbest_val += rc[i]*rc[i];
                }
                best_val = (lbest_val*R + rbest_val*L) / ((double)(L*R));
            }
            else
            {
                cv::AutoBuffer<int> lrc(cm*2);
                int *lc = lrc, *rc = lc + cm;
                double L = 0, R = 0;
                // init arrays of class instance counters on both sides of the split
                for(int i = 0; i < cm; i++ )
                {
                    lc[i] = 0;
                    rc[i] = 0;
                }
                for( int si = 0; si < n; si++ )
                {
                    int r = responses[si];
                    int var_class_idx = labels[si];
                    if ( ((var_class_idx == 65535) && data->is_buf_16u) || ((var_class_idx<0) && (!data->is_buf_16u)) )
                        continue;
                    double p = priors[si];
                    int mask_class_idx = valid_cidx[var_class_idx];

                    if (var_class_mask->data.ptr[mask_class_idx])
                    {
                        lc[r]+=(int)p;
                        L+=p;
                        split->subset[var_class_idx >> 5] |= 1 << (var_class_idx & 31);
                    }
                    else
                    {
                        rc[r]+=(int)p;
                        R+=p;
                    }
                }
                for (int i = 0; i < cm; i++)
                {
                    lbest_val += lc[i]*lc[i];
                    rbest_val += rc[i]*rc[i];
                }
                best_val = (lbest_val*R + rbest_val*L) / (L*R);
            }
            split->quality = (float)best_val;

            cvReleaseMat(&var_class_mask);
        }
    }

    return split;
}

CvDTreeSplit* CvForestERTree::find_split_ord_reg( CvDTreeNode* node, int vi, float init_quality, CvDTreeSplit* _split,
                                                  uchar* _ext_buf )
{
    const float epsilon = FLT_EPSILON*2;
    const float split_delta = (1 + FLT_EPSILON) * FLT_EPSILON;
    int n = node->sample_count;
    cv::AutoBuffer<uchar> inn_buf;
    if( !_ext_buf )
        inn_buf.allocate(n*(2*sizeof(int) + 2*sizeof(float)));
    uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
    float* values_buf = (float*)ext_buf;
    int* missing_buf = (int*)(values_buf + n);
    const float* values = 0;
    const int* missing = 0;
    data->get_ord_var_data( node, vi, values_buf, missing_buf, &values, &missing, 0 );
    float* responses_buf =  (float*)(missing_buf + n);
    int* sample_indices_buf =  (int*)(responses_buf + n);
    const float* responses = data->get_ord_responses( node, responses_buf, sample_indices_buf );

    double best_val = init_quality, split_val = 0, lsum = 0, rsum = 0;
    int L = 0, R = 0;

    bool is_find_split = false;
    float pmin, pmax;
    int smpi = 0;
    while ( missing[smpi] && (smpi < n) )
        smpi++;

    assert(smpi < n);

    pmin = values[smpi];
    pmax = pmin;
    for (; smpi < n; smpi++)
    {
        float ptemp = values[smpi];
        int m = missing[smpi];
        if (m) continue;
        if ( ptemp < pmin)
            pmin = ptemp;
        if ( ptemp > pmax)
            pmax = ptemp;
    }
    float fdiff = pmax-pmin;
    if (fdiff > epsilon)
    {
        is_find_split = true;
        cv::RNG* rng = data->rng;
        split_val = pmin + rng->uniform(0.f, 1.f) * fdiff ;
        if (split_val - pmin <= FLT_EPSILON)
            split_val = pmin + split_delta;
        if (pmax - split_val <= FLT_EPSILON)
            split_val = pmax - split_delta;

        for (int si = 0; si < n; si++)
        {
            float r = responses[si];
            float val = values[si];
            int m = missing[si];
            if (m) continue;
            if (val < split_val)
            {
                lsum += r;
                L++;
            }
            else
            {
                rsum += r;
                R++;
            }
        }
        best_val = (lsum*lsum*R + rsum*rsum*L)/((double)L*R);
    }

    CvDTreeSplit* split = 0;
    if( is_find_split )
    {
        split = _split ? _split : data->new_split_ord( 0, 0.0f, 0, 0, 0.0f );
        split->var_idx = vi;
        split->ord.c = (float)split_val;
        split->ord.split_point = -1;
        split->inversed = 0;
        split->quality = (float)best_val;
    }
    return split;
}

CvDTreeSplit* CvForestERTree::find_split_cat_reg( CvDTreeNode* node, int vi, float init_quality, CvDTreeSplit* _split,
                                                  uchar* _ext_buf )
{
    int ci = data->get_var_type(vi);
    int n = node->sample_count;
    int vm = data->cat_count->data.i[ci];
    double best_val = init_quality;
    CvDTreeSplit *split = 0;
    float lsum = 0, rsum = 0;

    if ( vm > 1 )
    {
        int base_size =  vm*sizeof(int);
        cv::AutoBuffer<uchar> inn_buf(base_size);
        if( !_ext_buf )
            inn_buf.allocate(base_size + n*(2*sizeof(int) + sizeof(float)));
        uchar* base_buf = (uchar*)inn_buf;
        uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
        int* labels_buf = (int*)ext_buf;
        const int* labels = data->get_cat_var_data( node, vi, labels_buf );
        float* responses_buf =  (float*)(labels_buf + n);
        int* sample_indices_buf = (int*)(responses_buf + n);
        const float* responses = data->get_ord_responses( node, responses_buf, sample_indices_buf );

        // create random class mask
        int *valid_cidx = (int*)base_buf;
        for (int i = 0; i < vm; i++)
        {
            valid_cidx[i] = -1;
        }
        for (int si = 0; si < n; si++)
        {
            int c = labels[si];
            if ( ((c == 65535) && data->is_buf_16u) || ((c<0) && (!data->is_buf_16u)) )
                        continue;
            valid_cidx[c]++;
        }

        int valid_ccount = 0;
        for (int i = 0; i < vm; i++)
            if (valid_cidx[i] >= 0)
            {
                valid_cidx[i] = valid_ccount;
                valid_ccount++;
            }
        if (valid_ccount > 1)
        {
            CvRNG* rng = forest->get_rng();
            int l_cval_count = 1 + cvRandInt(rng) % (valid_ccount-1);

            CvMat* var_class_mask = cvCreateMat( 1, valid_ccount, CV_8UC1 );
            CvMat submask;
            memset(var_class_mask->data.ptr, 0, valid_ccount*CV_ELEM_SIZE(var_class_mask->type));
            cvGetCols( var_class_mask, &submask, 0, l_cval_count );
            cvSet( &submask, cvScalar(1) );
            for (int i = 0; i < valid_ccount; i++)
            {
                uchar temp;
                int i1 = cvRandInt( rng ) % valid_ccount;
                int i2 = cvRandInt( rng ) % valid_ccount;
                CV_SWAP( var_class_mask->data.ptr[i1], var_class_mask->data.ptr[i2], temp );
            }

            split = _split ? _split : data->new_split_cat( 0, -1.0f);
            split->var_idx = vi;
            memset( split->subset, 0, (data->max_c_count + 31)/32 * sizeof(int));

            int L = 0, R = 0;
            for( int si = 0; si < n; si++ )
            {
                float r = responses[si];
                int var_class_idx = labels[si];
                if ( ((var_class_idx == 65535) && data->is_buf_16u) || ((var_class_idx<0) && (!data->is_buf_16u)) )
                        continue;
                int mask_class_idx = valid_cidx[var_class_idx];
                if (var_class_mask->data.ptr[mask_class_idx])
                {
                    lsum += r;
                    L++;
                    split->subset[var_class_idx >> 5] |= 1 << (var_class_idx & 31);
                }
                else
                {
                    rsum += r;
                    R++;
                }
            }
            best_val = (lsum*lsum*R + rsum*rsum*L)/((double)L*R);

            split->quality = (float)best_val;

            cvReleaseMat(&var_class_mask);
        }
    }

    return split;
}

void CvForestERTree::split_node_data( CvDTreeNode* node )
{
    int vi, i, n = node->sample_count, nl, nr, scount = data->sample_count;
    char* dir = (char*)data->direction->data.ptr;
    CvDTreeNode *left = 0, *right = 0;
    int new_buf_idx = data->get_child_buf_idx( node );
    CvMat* buf = data->buf;
    cv::AutoBuffer<int> temp_buf(n);

    complete_node_dir(node);

    for( i = nl = nr = 0; i < n; i++ )
    {
        int d = dir[i];
        nr += d;
        nl += d^1;
    }

    bool split_input_data;
    node->left = left = data->new_node( node, nl, new_buf_idx, node->offset );
    node->right = right = data->new_node( node, nr, new_buf_idx, node->offset + nl );

    split_input_data = node->depth + 1 < data->params.max_depth &&
        (node->left->sample_count > data->params.min_sample_count ||
        node->right->sample_count > data->params.min_sample_count);

    cv::AutoBuffer<uchar> inn_buf(n*(sizeof(int)+sizeof(float)));
    // split ordered vars
    for( vi = 0; vi < data->var_count; vi++ )
    {
        int ci = data->get_var_type(vi);
        if (ci >= 0) continue;

        int n1 = node->get_num_valid(vi), nr1 = 0;
        float* values_buf = (float*)(uchar*)inn_buf;
        int* missing_buf = (int*)(values_buf + n);
        const float* values = 0;
        const int* missing = 0;
        data->get_ord_var_data( node, vi, values_buf, missing_buf, &values, &missing, 0 );

        for( i = 0; i < n; i++ )
            nr1 += ((!missing[i]) & dir[i]);
        left->set_num_valid(vi, n1 - nr1);
        right->set_num_valid(vi, nr1);
    }
    // split categorical vars, responses and cv_labels using new_idx relocation table
    for( vi = 0; vi < data->get_work_var_count() + data->ord_var_count; vi++ )
    {
        int ci = data->get_var_type(vi);
        if (ci < 0) continue;

        int n1 = node->get_num_valid(vi), nr1 = 0;
        const int* src_lbls = data->get_cat_var_data(node, vi, (int*)(uchar*)inn_buf);

        for(i = 0; i < n; i++)
            temp_buf[i] = src_lbls[i];

        if (data->is_buf_16u)
        {
            unsigned short *ldst = (unsigned short *)(buf->data.s + left->buf_idx*buf->cols +
                ci*scount + left->offset);
            unsigned short *rdst = (unsigned short *)(buf->data.s + right->buf_idx*buf->cols +
                ci*scount + right->offset);

            for( i = 0; i < n; i++ )
            {
                int d = dir[i];
                int idx = temp_buf[i];
                if (d)
                {
                    *rdst = (unsigned short)idx;
                    rdst++;
                    nr1 += (idx != 65535);
                }
                else
                {
                    *ldst = (unsigned short)idx;
                    ldst++;
                }
            }

            if( vi < data->var_count )
            {
                left->set_num_valid(vi, n1 - nr1);
                right->set_num_valid(vi, nr1);
            }
        }
        else
        {
            int *ldst = buf->data.i + left->buf_idx*buf->cols +
                ci*scount + left->offset;
            int *rdst = buf->data.i + right->buf_idx*buf->cols +
                ci*scount + right->offset;

            for( i = 0; i < n; i++ )
            {
                int d = dir[i];
                int idx = temp_buf[i];
                if (d)
                {
                    *rdst = idx;
                    rdst++;
                    nr1 += (idx >= 0);
                }
                else
                {
                    *ldst = idx;
                    ldst++;
                }

            }

            if( vi < data->var_count )
            {
                left->set_num_valid(vi, n1 - nr1);
                right->set_num_valid(vi, nr1);
            }
        }
    }

    // split sample indices
    int *sample_idx_src_buf = (int*)(uchar*)inn_buf;
    const int* sample_idx_src = 0;
    if (split_input_data)
    {
        sample_idx_src = data->get_sample_indices(node, sample_idx_src_buf);

        for(i = 0; i < n; i++)
            temp_buf[i] = sample_idx_src[i];

        int pos = data->get_work_var_count();

        if (data->is_buf_16u)
        {
            unsigned short* ldst = (unsigned short*)(buf->data.s + left->buf_idx*buf->cols +
                pos*scount + left->offset);
            unsigned short* rdst = (unsigned short*)(buf->data.s + right->buf_idx*buf->cols +
                pos*scount + right->offset);

            for (i = 0; i < n; i++)
            {
                int d = dir[i];
                unsigned short idx = (unsigned short)temp_buf[i];
                if (d)
                {
                    *rdst = idx;
                    rdst++;
                }
                else
                {
                    *ldst = idx;
                    ldst++;
                }
            }
        }
        else
        {
            int* ldst = buf->data.i + left->buf_idx*buf->cols +
                pos*scount + left->offset;
            int* rdst = buf->data.i + right->buf_idx*buf->cols +
                pos*scount + right->offset;
            for (i = 0; i < n; i++)
            {
                int d = dir[i];
                int idx = temp_buf[i];
                if (d)
                {
                    *rdst = idx;
                    rdst++;
                }
                else
                {
                    *ldst = idx;
                    ldst++;
                }
            }
        }
    }

    // deallocate the parent node data that is not needed anymore
    data->free_node_data(node);
}

CvERTrees::CvERTrees()
{
}

CvERTrees::~CvERTrees()
{
}

std::string CvERTrees::getName() const
{
    return CV_TYPE_NAME_ML_ERTREES;
}

bool CvERTrees::train( const CvMat* _train_data, int _tflag,
                        const CvMat* _responses, const CvMat* _var_idx,
                        const CvMat* _sample_idx, const CvMat* _var_type,
                        const CvMat* _missing_mask, CvRTParams params )
{
    bool result = false;

    CV_FUNCNAME("CvERTrees::train");
    __BEGIN__
    int var_count = 0;

    clear();

    CvDTreeParams tree_params( params.max_depth, params.min_sample_count,
        params.regression_accuracy, params.use_surrogates, params.max_categories,
        params.cv_folds, params.use_1se_rule, false, params.priors );

    data = new CvERTreeTrainData();
    CV_CALL(data->set_data( _train_data, _tflag, _responses, _var_idx,
        _sample_idx, _var_type, _missing_mask, tree_params, true));

    var_count = data->var_count;
    if( params.nactive_vars > var_count )
        params.nactive_vars = var_count;
    else if( params.nactive_vars == 0 )
        params.nactive_vars = (int)sqrt((double)var_count);
    else if( params.nactive_vars < 0 )
        CV_ERROR( CV_StsBadArg, "<nactive_vars> must be non-negative" );

    // Create mask of active variables at the tree nodes
    CV_CALL(active_var_mask = cvCreateMat( 1, var_count, CV_8UC1 ));
    if( params.calc_var_importance )
    {
        CV_CALL(var_importance  = cvCreateMat( 1, var_count, CV_32FC1 ));
        cvZero(var_importance);
    }
    { // initialize active variables mask
        CvMat submask1, submask2;
        CV_Assert( (active_var_mask->cols >= 1) && (params.nactive_vars > 0) && (params.nactive_vars <= active_var_mask->cols) );
        cvGetCols( active_var_mask, &submask1, 0, params.nactive_vars );
        cvSet( &submask1, cvScalar(1) );
        if( params.nactive_vars < active_var_mask->cols )
        {
            cvGetCols( active_var_mask, &submask2, params.nactive_vars, var_count );
            cvZero( &submask2 );
        }
    }

    CV_CALL(result = grow_forest( params.term_crit ));

    result = true;

    __END__
    return result;

}

bool CvERTrees::train( CvMLData* _data, CvRTParams params)
{
   bool result = false;

    CV_FUNCNAME( "CvERTrees::train" );

    __BEGIN__;

    CV_CALL( result = CvRTrees::train( _data, params) );

    __END__;

    return result;
}

bool CvERTrees::grow_forest( const CvTermCriteria term_crit )
{
    bool result = false;

    CvMat* sample_idx_for_tree      = 0;

    CV_FUNCNAME("CvERTrees::grow_forest");
    __BEGIN__;

    const int max_ntrees = term_crit.max_iter;
    const double max_oob_err = term_crit.epsilon;

    const int dims = data->var_count;
    float maximal_response = 0;

    CvMat* oob_sample_votes    = 0;
    CvMat* oob_responses       = 0;

    float* oob_samples_perm_ptr= 0;

    float* samples_ptr     = 0;
    uchar* missing_ptr     = 0;
    float* true_resp_ptr   = 0;
    bool is_oob_or_vimportance = ((max_oob_err > 0) && (term_crit.type != CV_TERMCRIT_ITER)) || var_importance;

    // oob_predictions_sum[i] = sum of predicted values for the i-th sample
    // oob_num_of_predictions[i] = number of summands
    //                            (number of predictions for the i-th sample)
    // initialize these variable to avoid warning C4701
    CvMat oob_predictions_sum = cvMat( 1, 1, CV_32FC1 );
    CvMat oob_num_of_predictions = cvMat( 1, 1, CV_32FC1 );

    nsamples = data->sample_count;
    nclasses = data->get_num_classes();

    if ( is_oob_or_vimportance )
    {
        if( data->is_classifier )
        {
            CV_CALL(oob_sample_votes = cvCreateMat( nsamples, nclasses, CV_32SC1 ));
            cvZero(oob_sample_votes);
        }
        else
        {
            // oob_responses[0,i] = oob_predictions_sum[i]
            //    = sum of predicted values for the i-th sample
            // oob_responses[1,i] = oob_num_of_predictions[i]
            //    = number of summands (number of predictions for the i-th sample)
            CV_CALL(oob_responses = cvCreateMat( 2, nsamples, CV_32FC1 ));
            cvZero(oob_responses);
            cvGetRow( oob_responses, &oob_predictions_sum, 0 );
            cvGetRow( oob_responses, &oob_num_of_predictions, 1 );
        }

        CV_CALL(oob_samples_perm_ptr     = (float*)cvAlloc( sizeof(float)*nsamples*dims ));
        CV_CALL(samples_ptr              = (float*)cvAlloc( sizeof(float)*nsamples*dims ));
        CV_CALL(missing_ptr              = (uchar*)cvAlloc( sizeof(uchar)*nsamples*dims ));
        CV_CALL(true_resp_ptr            = (float*)cvAlloc( sizeof(float)*nsamples ));

        CV_CALL(data->get_vectors( 0, samples_ptr, missing_ptr, true_resp_ptr ));
        {
            double minval, maxval;
            CvMat responses = cvMat(1, nsamples, CV_32FC1, true_resp_ptr);
            cvMinMaxLoc( &responses, &minval, &maxval );
            maximal_response = (float)MAX( MAX( fabs(minval), fabs(maxval) ), 0 );
        }
    }

    trees = (CvForestTree**)cvAlloc( sizeof(trees[0])*max_ntrees );
    memset( trees, 0, sizeof(trees[0])*max_ntrees );

    CV_CALL(sample_idx_for_tree = cvCreateMat( 1, nsamples, CV_32SC1 ));

    for (int i = 0; i < nsamples; i++)
        sample_idx_for_tree->data.i[i] = i;
    ntrees = 0;
    while( ntrees < max_ntrees )
    {
        int i, oob_samples_count = 0;
        double ncorrect_responses = 0; // used for estimation of variable importance
        CvForestTree* tree = 0;

        trees[ntrees] = new CvForestERTree();
        tree = (CvForestERTree*)trees[ntrees];
        CV_CALL(tree->train( data, 0, this ));

        if ( is_oob_or_vimportance )
        {
            CvMat sample, missing;
            // form array of OOB samples indices and get these samples
            sample   = cvMat( 1, dims, CV_32FC1, samples_ptr );
            missing  = cvMat( 1, dims, CV_8UC1,  missing_ptr );

            oob_error = 0;
            for( i = 0; i < nsamples; i++,
                sample.data.fl += dims, missing.data.ptr += dims )
            {
                CvDTreeNode* predicted_node = 0;

                // predict oob samples
                if( !predicted_node )
                    CV_CALL(predicted_node = tree->predict(&sample, &missing, true));

                if( !data->is_classifier ) //regression
                {
                    double avg_resp, resp = predicted_node->value;
                    oob_predictions_sum.data.fl[i] += (float)resp;
                    oob_num_of_predictions.data.fl[i] += 1;

                    // compute oob error
                    avg_resp = oob_predictions_sum.data.fl[i]/oob_num_of_predictions.data.fl[i];
                    avg_resp -= true_resp_ptr[i];
                    oob_error += avg_resp*avg_resp;
                    resp = (resp - true_resp_ptr[i])/maximal_response;
                    ncorrect_responses += exp( -resp*resp );
                }
                else //classification
                {
                    double prdct_resp;
                    CvPoint max_loc;
                    CvMat votes;

                    cvGetRow(oob_sample_votes, &votes, i);
                    votes.data.i[predicted_node->class_idx]++;

                    // compute oob error
                    cvMinMaxLoc( &votes, 0, 0, 0, &max_loc );

                    prdct_resp = data->cat_map->data.i[max_loc.x];
                    oob_error += (fabs(prdct_resp - true_resp_ptr[i]) < FLT_EPSILON) ? 0 : 1;

                    ncorrect_responses += cvRound(predicted_node->value - true_resp_ptr[i]) == 0;
                }
                oob_samples_count++;
            }
            if( oob_samples_count > 0 )
                oob_error /= (double)oob_samples_count;

            // estimate variable importance
            if( var_importance && oob_samples_count > 0 )
            {
                int m;

                memcpy( oob_samples_perm_ptr, samples_ptr, dims*nsamples*sizeof(float));
                for( m = 0; m < dims; m++ )
                {
                    double ncorrect_responses_permuted = 0;
                    // randomly permute values of the m-th variable in the oob samples
                    float* mth_var_ptr = oob_samples_perm_ptr + m;

                    for( i = 0; i < nsamples; i++ )
                    {
                        int i1, i2;
                        float temp;

                        i1 = (*rng)(nsamples);
                        i2 = (*rng)(nsamples);
                        CV_SWAP( mth_var_ptr[i1*dims], mth_var_ptr[i2*dims], temp );

                        // turn values of (m-1)-th variable, that were permuted
                        // at the previous iteration, untouched
                        if( m > 1 )
                            oob_samples_perm_ptr[i*dims+m-1] = samples_ptr[i*dims+m-1];
                    }

                    // predict "permuted" cases and calculate the number of votes for the
                    // correct class in the variable-m-permuted oob data
                    sample  = cvMat( 1, dims, CV_32FC1, oob_samples_perm_ptr );
                    missing = cvMat( 1, dims, CV_8UC1, missing_ptr );
                    for( i = 0; i < nsamples; i++,
                        sample.data.fl += dims, missing.data.ptr += dims )
                    {
                        double predct_resp, true_resp;

                        predct_resp = tree->predict(&sample, &missing, true)->value;
                        true_resp   = true_resp_ptr[i];
                        if( data->is_classifier )
                            ncorrect_responses_permuted += cvRound(true_resp - predct_resp) == 0;
                        else
                        {
                            true_resp = (true_resp - predct_resp)/maximal_response;
                            ncorrect_responses_permuted += exp( -true_resp*true_resp );
                        }
                    }
                    var_importance->data.fl[m] += (float)(ncorrect_responses
                        - ncorrect_responses_permuted);
                }
            }
        }
        ntrees++;
        if( term_crit.type != CV_TERMCRIT_ITER && oob_error < max_oob_err )
            break;
    }
    if( var_importance )
    {
        for ( int vi = 0; vi < var_importance->cols; vi++ )
                var_importance->data.fl[vi] = ( var_importance->data.fl[vi] > 0 ) ?
                    var_importance->data.fl[vi] : 0;
        cvNormalize( var_importance, var_importance, 1., 0, CV_L1 );
    }

    result = true;

    cvFree( &oob_samples_perm_ptr );
    cvFree( &samples_ptr );
    cvFree( &missing_ptr );
    cvFree( &true_resp_ptr );

    cvReleaseMat( &sample_idx_for_tree );

    cvReleaseMat( &oob_sample_votes );
    cvReleaseMat( &oob_responses );

    __END__;

    return result;
}

using namespace cv;

bool CvERTrees::train( const Mat& _train_data, int _tflag,
                      const Mat& _responses, const Mat& _var_idx,
                      const Mat& _sample_idx, const Mat& _var_type,
                      const Mat& _missing_mask, CvRTParams params )
{
    CvMat tdata = _train_data, responses = _responses, vidx = _var_idx,
    sidx = _sample_idx, vtype = _var_type, mmask = _missing_mask;
    return train(&tdata, _tflag, &responses, vidx.data.ptr ? &vidx : 0,
                 sidx.data.ptr ? &sidx : 0, vtype.data.ptr ? &vtype : 0,
                 mmask.data.ptr ? &mmask : 0, params);
}

// End of file.

