/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // Intel License Agreement // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of Intel Corporation may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "_ml.h" 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; CvDTreeTrainData::CvDTreeTrainData() { var_idx = var_type = cat_count = cat_ofs = cat_map = priors = priors_mult = counts = buf = direction = split_buf = 0; tree_storage = temp_storage = 0; clear(); } CvDTreeTrainData::CvDTreeTrainData( 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 ) { var_idx = var_type = cat_count = cat_ofs = cat_map = priors = priors_mult = counts = buf = direction = split_buf = 0; tree_storage = temp_storage = 0; set_data( _train_data, _tflag, _responses, _var_idx, _sample_idx, _var_type, _missing_mask, _params, _shared, _add_labels ); } CvDTreeTrainData::~CvDTreeTrainData() { clear(); } bool CvDTreeTrainData::set_params( const CvDTreeParams& _params ) { bool ok = false; CV_FUNCNAME( "CvDTreeTrainData::set_params" ); __BEGIN__; // set parameters params = _params; if( params.max_categories < 2 ) CV_ERROR( CV_StsOutOfRange, "params.max_categories should be >= 2" ); params.max_categories = MIN( params.max_categories, 15 ); if( params.max_depth < 0 ) CV_ERROR( CV_StsOutOfRange, "params.max_depth should be >= 0" ); params.max_depth = MIN( params.max_depth, 25 ); params.min_sample_count = MAX(params.min_sample_count,1); if( params.cv_folds < 0 ) CV_ERROR( CV_StsOutOfRange, "params.cv_folds should be =0 (the tree is not pruned) " "or n>0 (tree is pruned using n-fold cross-validation)" ); if( params.cv_folds == 1 ) params.cv_folds = 0; if( params.regression_accuracy < 0 ) CV_ERROR( CV_StsOutOfRange, "params.regression_accuracy should be >= 0" ); ok = true; __END__; return ok; } #define CV_CMP_NUM_PTR(a,b) (*(a) < *(b)) static CV_IMPLEMENT_QSORT_EX( icvSortIntPtr, int*, CV_CMP_NUM_PTR, int ) static CV_IMPLEMENT_QSORT_EX( icvSortDblPtr, double*, CV_CMP_NUM_PTR, int ) #define CV_CMP_PAIRS(a,b) ((a).val < (b).val) static CV_IMPLEMENT_QSORT_EX( icvSortPairs, CvPair32s32f, CV_CMP_PAIRS, int ) void CvDTreeTrainData::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_idx = 0; CvMat* var_type0 = 0; CvMat* tmp_map = 0; int** int_ptr = 0; CvDTreeTrainData* data = 0; CV_FUNCNAME( "CvDTreeTrainData::set_data" ); __BEGIN__; int sample_all = 0, r_type = 0, 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; char err[100]; const int *sidx = 0, *vidx = 0; if( _update_data && data_root ) { data = new CvDTreeTrainData( _train_data, _tflag, _responses, _var_idx, _sample_idx, _var_type, _missing_mask, _params, _shared, _add_labels ); // compare new and old train data if( !(data->var_count == var_count && cvNorm( data->var_type, var_type, CV_C ) < FLT_EPSILON && cvNorm( data->cat_count, cat_count, CV_C ) < FLT_EPSILON && cvNorm( data->cat_map, cat_map, CV_C ) < FLT_EPSILON) ) CV_ERROR( CV_StsBadArg, "The new training data must have the same types and the input and output variables " "and the same categories for categorical variables" ); cvReleaseMat( &priors ); cvReleaseMat( &priors_mult ); cvReleaseMat( &buf ); cvReleaseMat( &direction ); cvReleaseMat( &split_buf ); cvReleaseMemStorage( &temp_storage ); priors = data->priors; data->priors = 0; priors_mult = data->priors_mult; data->priors_mult = 0; buf = data->buf; data->buf = 0; buf_count = data->buf_count; buf_size = data->buf_size; sample_count = data->sample_count; direction = data->direction; data->direction = 0; split_buf = data->split_buf; data->split_buf = 0; temp_storage = data->temp_storage; data->temp_storage = 0; nv_heap = data->nv_heap; cv_heap = data->cv_heap; data_root = new_node( 0, sample_count, 0, 0 ); EXIT; } clear(); var_all = 0; rng = cvRNG(-1); CV_CALL( set_params( _params )); // check parameter types and sizes CV_CALL( cvCheckTrainData( _train_data, _tflag, _missing_mask, &var_all, &sample_all )); 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; } sample_count = sample_all; var_count = var_all; if( _sample_idx ) { CV_CALL( sample_idx = cvPreprocessIndexArray( _sample_idx, sample_all )); sidx = sample_idx->data.i; sample_count = sample_idx->rows + sample_idx->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" ); CV_CALL( var_type0 = cvPreprocessVarType( _var_type, var_idx, var_all, &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++ ) { var_type->data.i[vi] = var_type0->data.ptr[vi] == 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; buf_size = (ord_var_count + get_work_var_count())*sample_count + 2; shared = _shared; buf_count = shared ? 3 : 2; CV_CALL( buf = cvCreateMat( buf_count, buf_size, CV_32SC1 )); CV_CALL( cat_count = cvCreateMat( 1, cat_var_count+1, CV_32SC1 )); CV_CALL( cat_ofs = cvCreateMat( 1, cat_count->cols+1, CV_32SC1 )); CV_CALL( cat_map = cvCreateMat( 1, cat_count->cols*10 + 128, 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 = MAX( nv_size, (int)sizeof(CvSetElem) ); 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 )); CV_CALL( int_ptr = (int**)cvAlloc( sample_count*sizeof(int_ptr[0]) )); max_c_count = 1; // 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, *dst = get_cat_var_data( data_root, vi ); // 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++; } dst[i] = val; int_ptr[i] = dst + i; } // sort all the values, including the missing measurements // that should all move to the end icvSortIntPtr( int_ptr, sample_count, 0 ); //qsort( int_ptr, sample_count, sizeof(int_ptr[0]), icvCmpIntPtr ); c_count = num_valid > 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; // compact the class indices and build the map prev_label = ~*int_ptr[0]; c_count = -1; 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 { CvPair32s32f* dst = get_ord_var_data( data_root, vi ); 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++; } dst[i].i = i; dst[i].val = val; } icvSortPairs( dst, sample_count, 0 ); } else // special case: process ordered response, // it will be stored similarly to categorical vars (i.e. no pairs) { float* dst = get_ord_responses( data_root ); for( i = 0; i < sample_count; i++ ) { float val = ord_nan; int si = sidx ? sidx[i] : i; 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 out of range", i, vi, val ); CV_ERROR( CV_StsBadArg, err ); } dst[i] = val; } cat_count->data.i[cat_var_count] = 0; cat_ofs->data.i[cat_var_count] = total_c_count; num_valid = sample_count; } if( vi < var_count ) data_root->set_num_valid(vi, num_valid); } if( cv_n ) { int* dst = get_labels(data_root); CvRNG* r = &rng; for( i = vi = 0; i < sample_count; i++ ) { dst[i] = vi++; vi &= vi < cv_n ? -1 : 0; } for( i = 0; i < sample_count; i++ ) { int a = cvRandInt(r) % sample_count; int b = cvRandInt(r) % sample_count; CV_SWAP( dst[a], dst[b], vi ); } } 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; cvFree( &int_ptr ); cvReleaseMat( &sample_idx ); cvReleaseMat( &var_type0 ); cvReleaseMat( &tmp_map ); } CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx ) { CvDTreeNode* root = 0; CvMat* isubsample_idx = 0; CvMat* subsample_co = 0; CV_FUNCNAME( "CvDTreeTrainData::subsample_data" ); __BEGIN__; if( !data_root ) CV_ERROR( CV_StsError, "No training data has been set" ); if( _subsample_idx ) CV_CALL( isubsample_idx = cvPreprocessIndexArray( _subsample_idx, sample_count )); if( !isubsample_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 { int* sidx = isubsample_idx->data.i; // co - array of count/offset pairs (to handle duplicated values in _subsample_idx) int* co, cur_ofs = 0; int vi, i, total = data_root->sample_count; int count = isubsample_idx->rows + isubsample_idx->cols - 1; int work_var_count = get_work_var_count(); root = new_node( 0, count, 1, 0 ); CV_CALL( subsample_co = cvCreateMat( 1, total*2, CV_32SC1 )); cvZero( subsample_co ); co = subsample_co->data.i; for( i = 0; i < count; i++ ) co[sidx[i]*2]++; for( i = 0; i < total; i++ ) { if( co[i*2] ) { co[i*2+1] = cur_ofs; cur_ofs += co[i*2]; } else co[i*2+1] = -1; } for( vi = 0; vi < work_var_count; vi++ ) { int ci = get_var_type(vi); if( ci >= 0 || vi >= var_count ) { const int* src = get_cat_var_data( data_root, vi ); int* dst = get_cat_var_data( root, vi ); int num_valid = 0; for( i = 0; i < count; i++ ) { int val = src[sidx[i]]; dst[i] = val; num_valid += val >= 0; } if( vi < var_count ) root->set_num_valid(vi, num_valid); } else { const CvPair32s32f* src = get_ord_var_data( data_root, vi ); CvPair32s32f* dst = get_ord_var_data( root, vi ); int j = 0, idx, count_i; int num_valid = data_root->get_num_valid(vi); for( i = 0; i < num_valid; i++ ) { idx = src[i].i; count_i = co[idx*2]; if( count_i ) { float val = src[i].val; for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ ) { dst[j].val = val; dst[j].i = cur_ofs; } } } root->set_num_valid(vi, j); for( ; i < total; i++ ) { idx = src[i].i; count_i = co[idx*2]; if( count_i ) { float val = src[i].val; for( cur_ofs = co[idx*2+1]; count_i > 0; count_i--, j++, cur_ofs++ ) { dst[j].val = val; dst[j].i = cur_ofs; } } } } } } __END__; cvReleaseMat( &isubsample_idx ); cvReleaseMat( &subsample_co ); return root; } void CvDTreeTrainData::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_FUNCNAME( "CvDTreeTrainData::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; const int* src = get_cat_var_data(data_root, vi); 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 = val < 0; m += var_count; } } } else // ordered { float* dst = values + vi; uchar* m = missing ? missing + vi : 0; const CvPair32s32f* src = get_ord_var_data(data_root, vi); int count1 = data_root->get_num_valid(vi); for( i = 0; i < count1; i++ ) { int idx = src[i].i; int count_i = 1; if( co ) { count_i = co[idx*2]; cur_ofs = co[idx*2+1]; } else cur_ofs = idx*var_count; if( count_i ) { float val = src[i].val; for( ; count_i > 0; count_i--, cur_ofs += var_count ) { dst[cur_ofs] = val; if( m ) m[cur_ofs] = 0; } } } } } // copy responses if( responses ) { if( is_classifier ) { const int* src = get_class_labels(data_root); 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 { const float* src = get_ord_responses(data_root); for( i = 0; i < count; i++ ) { int idx = sidx ? sidx[i] : i; responses[i] = src[idx]; } } } __END__; cvReleaseMat( &subsample_idx ); cvReleaseMat( &subsample_co ); } CvDTreeNode* CvDTreeTrainData::new_node( CvDTreeNode* parent, int count, int storage_idx, int offset ) { CvDTreeNode* node = (CvDTreeNode*)cvSetNew( node_heap ); node->sample_count = count; node->depth = parent ? parent->depth + 1 : 0; node->parent = parent; node->left = node->right = 0; node->split = 0; node->value = 0; node->class_idx = 0; node->maxlr = 0.; node->buf_idx = storage_idx; node->offset = offset; if( nv_heap ) node->num_valid = (int*)cvSetNew( nv_heap ); else node->num_valid = 0; node->alpha = node->node_risk = node->tree_risk = node->tree_error = 0.; node->complexity = 0; if( params.cv_folds > 0 && cv_heap ) { int cv_n = params.cv_folds; node->Tn = INT_MAX; node->cv_Tn = (int*)cvSetNew( cv_heap ); node->cv_node_risk = (double*)cvAlignPtr(node->cv_Tn + cv_n, sizeof(double)); node->cv_node_error = node->cv_node_risk + cv_n; } else { node->Tn = 0; node->cv_Tn = 0; node->cv_node_risk = 0; node->cv_node_error = 0; } return node; } CvDTreeSplit* CvDTreeTrainData::new_split_ord( int vi, float cmp_val, int split_point, int inversed, float quality ) { CvDTreeSplit* split = (CvDTreeSplit*)cvSetNew( split_heap ); split->var_idx = vi; split->ord.c = cmp_val; split->ord.split_point = split_point; split->inversed = inversed; split->quality = quality; split->next = 0; return split; } CvDTreeSplit* CvDTreeTrainData::new_split_cat( int vi, float quality ) { CvDTreeSplit* split = (CvDTreeSplit*)cvSetNew( split_heap ); int i, n = (max_c_count + 31)/32; split->var_idx = vi; split->inversed = 0; split->quality = quality; for( i = 0; i < n; i++ ) split->subset[i] = 0; split->next = 0; return split; } void CvDTreeTrainData::free_node( CvDTreeNode* node ) { CvDTreeSplit* split = node->split; free_node_data( node ); while( split ) { CvDTreeSplit* next = split->next; cvSetRemoveByPtr( split_heap, split ); split = next; } node->split = 0; cvSetRemoveByPtr( node_heap, node ); } void CvDTreeTrainData::free_node_data( CvDTreeNode* node ) { if( node->num_valid ) { cvSetRemoveByPtr( nv_heap, node->num_valid ); node->num_valid = 0; } // do not free cv_* fields, as all the cross-validation related data is released at once. } void CvDTreeTrainData::free_train_data() { cvReleaseMat( &counts ); cvReleaseMat( &buf ); cvReleaseMat( &direction ); cvReleaseMat( &split_buf ); cvReleaseMemStorage( &temp_storage ); cv_heap = nv_heap = 0; } void CvDTreeTrainData::clear() { free_train_data(); cvReleaseMemStorage( &tree_storage ); cvReleaseMat( &var_idx ); cvReleaseMat( &var_type ); cvReleaseMat( &cat_count ); cvReleaseMat( &cat_ofs ); cvReleaseMat( &cat_map ); cvReleaseMat( &priors ); cvReleaseMat( &priors_mult ); node_heap = split_heap = 0; sample_count = var_all = var_count = max_c_count = ord_var_count = cat_var_count = 0; have_labels = have_priors = is_classifier = false; buf_count = buf_size = 0; shared = false; data_root = 0; rng = cvRNG(-1); } int CvDTreeTrainData::get_num_classes() const { return is_classifier ? cat_count->data.i[cat_var_count] : 0; } int CvDTreeTrainData::get_var_type(int vi) const { return var_type->data.i[vi]; } int CvDTreeTrainData::get_work_var_count() const { return var_count + 1 + (have_labels ? 1 : 0); } CvPair32s32f* CvDTreeTrainData::get_ord_var_data( CvDTreeNode* n, int vi ) { int oi = ~get_var_type(vi); assert( 0 <= oi && oi < ord_var_count ); return (CvPair32s32f*)(buf->data.i + n->buf_idx*buf->cols + n->offset + oi*n->sample_count*2); } int* CvDTreeTrainData::get_class_labels( CvDTreeNode* n ) { return get_cat_var_data( n, var_count ); } float* CvDTreeTrainData::get_ord_responses( CvDTreeNode* n ) { return (float*)get_cat_var_data( n, var_count ); } int* CvDTreeTrainData::get_labels( CvDTreeNode* n ) { return have_labels ? get_cat_var_data( n, var_count + 1 ) : 0; } int* CvDTreeTrainData::get_cat_var_data( CvDTreeNode* n, int vi ) { int ci = get_var_type(vi); assert( 0 <= ci && ci <= cat_var_count + 1 ); return buf->data.i + n->buf_idx*buf->cols + n->offset + (ord_var_count*2 + ci)*n->sample_count; } int CvDTreeTrainData::get_child_buf_idx( CvDTreeNode* n ) { int idx = n->buf_idx + 1; if( idx >= buf_count ) idx = shared ? 1 : 0; return idx; } void CvDTreeTrainData::write_params( CvFileStorage* fs ) { CV_FUNCNAME( "CvDTreeTrainData::write_params" ); __BEGIN__; int vi, vcount = var_count; cvWriteInt( fs, "is_classifier", is_classifier ? 1 : 0 ); cvWriteInt( fs, "var_all", var_all ); cvWriteInt( fs, "var_count", var_count ); cvWriteInt( fs, "ord_var_count", ord_var_count ); cvWriteInt( fs, "cat_var_count", cat_var_count ); cvStartWriteStruct( fs, "training_params", CV_NODE_MAP ); cvWriteInt( fs, "use_surrogates", params.use_surrogates ? 1 : 0 ); if( is_classifier ) { cvWriteInt( fs, "max_categories", params.max_categories ); } else { cvWriteReal( fs, "regression_accuracy", params.regression_accuracy ); } cvWriteInt( fs, "max_depth", params.max_depth ); cvWriteInt( fs, "min_sample_count", params.min_sample_count ); cvWriteInt( fs, "cross_validation_folds", params.cv_folds ); if( params.cv_folds > 1 ) { cvWriteInt( fs, "use_1se_rule", params.use_1se_rule ? 1 : 0 ); cvWriteInt( fs, "truncate_pruned_tree", params.truncate_pruned_tree ? 1 : 0 ); } if( priors ) cvWrite( fs, "priors", priors ); cvEndWriteStruct( fs ); if( var_idx ) cvWrite( fs, "var_idx", var_idx ); cvStartWriteStruct( fs, "var_type", CV_NODE_SEQ+CV_NODE_FLOW ); for( vi = 0; vi < vcount; vi++ ) cvWriteInt( fs, 0, var_type->data.i[vi] >= 0 ); cvEndWriteStruct( fs ); if( cat_count && (cat_var_count > 0 || is_classifier) ) { CV_ASSERT( cat_count != 0 ); cvWrite( fs, "cat_count", cat_count ); cvWrite( fs, "cat_map", cat_map ); } __END__; } void CvDTreeTrainData::read_params( CvFileStorage* fs, CvFileNode* node ) { CV_FUNCNAME( "CvDTreeTrainData::read_params" ); __BEGIN__; CvFileNode *tparams_node, *vartype_node; CvSeqReader reader; int vi, max_split_size, tree_block_size; is_classifier = (cvReadIntByName( fs, node, "is_classifier" ) != 0); var_all = cvReadIntByName( fs, node, "var_all" ); var_count = cvReadIntByName( fs, node, "var_count", var_all ); cat_var_count = cvReadIntByName( fs, node, "cat_var_count" ); ord_var_count = cvReadIntByName( fs, node, "ord_var_count" ); tparams_node = cvGetFileNodeByName( fs, node, "training_params" ); if( tparams_node ) // training parameters are not necessary { params.use_surrogates = cvReadIntByName( fs, tparams_node, "use_surrogates", 1 ) != 0; if( is_classifier ) { params.max_categories = cvReadIntByName( fs, tparams_node, "max_categories" ); } else { params.regression_accuracy = (float)cvReadRealByName( fs, tparams_node, "regression_accuracy" ); } params.max_depth = cvReadIntByName( fs, tparams_node, "max_depth" ); params.min_sample_count = cvReadIntByName( fs, tparams_node, "min_sample_count" ); params.cv_folds = cvReadIntByName( fs, tparams_node, "cross_validation_folds" ); if( params.cv_folds > 1 ) { params.use_1se_rule = cvReadIntByName( fs, tparams_node, "use_1se_rule" ) != 0; params.truncate_pruned_tree = cvReadIntByName( fs, tparams_node, "truncate_pruned_tree" ) != 0; } priors = (CvMat*)cvReadByName( fs, tparams_node, "priors" ); if( priors ) { if( !CV_IS_MAT(priors) ) CV_ERROR( CV_StsParseError, "priors must stored as a matrix" ); priors_mult = cvCloneMat( priors ); } } CV_CALL( var_idx = (CvMat*)cvReadByName( fs, node, "var_idx" )); if( var_idx ) { if( !CV_IS_MAT(var_idx) || var_idx->cols != 1 && var_idx->rows != 1 || var_idx->cols + var_idx->rows - 1 != var_count || CV_MAT_TYPE(var_idx->type) != CV_32SC1 ) CV_ERROR( CV_StsParseError, "var_idx (if exist) must be valid 1d integer vector containing elements" ); for( vi = 0; vi < var_count; vi++ ) if( (unsigned)var_idx->data.i[vi] >= (unsigned)var_all ) CV_ERROR( CV_StsOutOfRange, "some of var_idx elements are out of range" ); } ////// read var type CV_CALL( var_type = cvCreateMat( 1, var_count + 2, CV_32SC1 )); cat_var_count = 0; ord_var_count = -1; vartype_node = cvGetFileNodeByName( fs, node, "var_type" ); if( vartype_node && CV_NODE_TYPE(vartype_node->tag) == CV_NODE_INT && var_count == 1 ) var_type->data.i[0] = vartype_node->data.i ? cat_var_count++ : ord_var_count--; else { if( !vartype_node || CV_NODE_TYPE(vartype_node->tag) != CV_NODE_SEQ || vartype_node->data.seq->total != var_count ) CV_ERROR( CV_StsParseError, "var_type must exist and be a sequence of 0's and 1's" ); cvStartReadSeq( vartype_node->data.seq, &reader ); for( vi = 0; vi < var_count; vi++ ) { CvFileNode* n = (CvFileNode*)reader.ptr; if( CV_NODE_TYPE(n->tag) != CV_NODE_INT || (n->data.i & ~1) ) CV_ERROR( CV_StsParseError, "var_type must exist and be a sequence of 0's and 1's" ); var_type->data.i[vi] = n->data.i ? cat_var_count++ : ord_var_count--; CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader ); } } var_type->data.i[var_count] = cat_var_count; ord_var_count = ~ord_var_count; if( cat_var_count != cat_var_count || ord_var_count != ord_var_count ) CV_ERROR( CV_StsParseError, "var_type is inconsistent with cat_var_count and ord_var_count" ); ////// if( cat_var_count > 0 || is_classifier ) { int ccount, total_c_count = 0; CV_CALL( cat_count = (CvMat*)cvReadByName( fs, node, "cat_count" )); CV_CALL( cat_map = (CvMat*)cvReadByName( fs, node, "cat_map" )); if( !CV_IS_MAT(cat_count) || !CV_IS_MAT(cat_map) || cat_count->cols != 1 && cat_count->rows != 1 || CV_MAT_TYPE(cat_count->type) != CV_32SC1 || cat_count->cols + cat_count->rows - 1 != cat_var_count + is_classifier || cat_map->cols != 1 && cat_map->rows != 1 || CV_MAT_TYPE(cat_map->type) != CV_32SC1 ) CV_ERROR( CV_StsParseError, "Both cat_count and cat_map must exist and be valid 1d integer vectors of an appropriate size" ); ccount = cat_var_count + is_classifier; CV_CALL( cat_ofs = cvCreateMat( 1, ccount + 1, CV_32SC1 )); cat_ofs->data.i[0] = 0; max_c_count = 1; for( vi = 0; vi < ccount; vi++ ) { int val = cat_count->data.i[vi]; if( val <= 0 ) CV_ERROR( CV_StsOutOfRange, "some of cat_count elements are out of range" ); max_c_count = MAX( max_c_count, val ); cat_ofs->data.i[vi+1] = total_c_count += val; } if( cat_map->cols + cat_map->rows - 1 != total_c_count ) CV_ERROR( CV_StsBadSize, "cat_map vector length is not equal to the total number of categories in all categorical vars" ); } max_split_size = cvAlign(sizeof(CvDTreeSplit) + (MAX(0,max_c_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[0]), sizeof(CvDTreeNode), tree_storage )); CV_CALL( split_heap = cvCreateSet( 0, sizeof(split_heap[0]), max_split_size, tree_storage )); __END__; } /////////////////////// Decision Tree ///////////////////////// CvDTree::CvDTree() { data = 0; var_importance = 0; default_model_name = "my_tree"; clear(); } void CvDTree::clear() { cvReleaseMat( &var_importance ); if( data ) { if( !data->shared ) delete data; else free_tree(); data = 0; } root = 0; pruned_tree_idx = -1; } CvDTree::~CvDTree() { clear(); } const CvDTreeNode* CvDTree::get_root() const { return root; } int CvDTree::get_pruned_tree_idx() const { return pruned_tree_idx; } CvDTreeTrainData* CvDTree::get_data() { return data; } bool CvDTree::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, CvDTreeParams _params ) { bool result = false; CV_FUNCNAME( "CvDTree::train" ); __BEGIN__; clear(); data = new CvDTreeTrainData( _train_data, _tflag, _responses, _var_idx, _sample_idx, _var_type, _missing_mask, _params, false ); CV_CALL( result = do_train(0)); __END__; return result; } bool CvDTree::train( CvDTreeTrainData* _data, const CvMat* _subsample_idx ) { bool result = false; CV_FUNCNAME( "CvDTree::train" ); __BEGIN__; clear(); data = _data; data->shared = true; CV_CALL( result = do_train(_subsample_idx)); __END__; return result; } bool CvDTree::do_train( const CvMat* _subsample_idx ) { bool result = false; CV_FUNCNAME( "CvDTree::do_train" ); __BEGIN__; root = data->subsample_data( _subsample_idx ); CV_CALL( try_split_node(root)); if( data->params.cv_folds > 0 ) CV_CALL( prune_cv()); if( !data->shared ) data->free_train_data(); result = true; __END__; return result; } void CvDTree::try_split_node( CvDTreeNode* node ) { CvDTreeSplit* best_split = 0; int i, n = node->sample_count, vi; bool can_split = true; double quality_scale; calc_node_value( node ); if( node->sample_count <= data->params.min_sample_count || node->depth >= data->params.max_depth ) can_split = false; if( can_split && data->is_classifier ) { // check if we have a "pure" node, // we assume that cls_count is filled by calc_node_value() int* cls_count = data->counts->data.i; int nz = 0, m = data->get_num_classes(); for( i = 0; i < m; i++ ) nz += cls_count[i] != 0; if( nz == 1 ) // there is only one class can_split = false; } else if( can_split ) { if( sqrt(node->node_risk)/n < data->params.regression_accuracy ) can_split = false; } if( can_split ) { best_split = find_best_split(node); // TODO: check the split quality ... node->split = best_split; } if( !can_split || !best_split ) { data->free_node_data(node); return; } quality_scale = calc_node_dir( node ); if( data->params.use_surrogates ) { // find all the surrogate splits // and sort them by their similarity to the primary one for( vi = 0; vi < data->var_count; vi++ ) { CvDTreeSplit* split; int ci = data->get_var_type(vi); if( vi == best_split->var_idx ) continue; if( ci >= 0 ) split = find_surrogate_split_cat( node, vi ); else split = find_surrogate_split_ord( node, vi ); if( split ) { // insert the split CvDTreeSplit* prev_split = node->split; split->quality = (float)(split->quality*quality_scale); while( prev_split->next && prev_split->next->quality > split->quality ) prev_split = prev_split->next; split->next = prev_split->next; prev_split->next = split; } } } split_node_data( node ); try_split_node( node->left ); try_split_node( node->right ); } // calculate direction (left(-1),right(1),missing(0)) // for each sample using the best split // the function returns scale coefficients for surrogate split quality factors. // the scale is applied to normalize surrogate split quality relatively to the // best (primary) split quality. That is, if a surrogate split is absolutely // identical to the primary split, its quality will be set to the maximum value = // quality of the primary split; otherwise, it will be lower. // besides, the function compute node->maxlr, // minimum possible quality (w/o considering the above mentioned scale) // for a surrogate split. Surrogate splits with quality less than node->maxlr // are not discarded. double CvDTree::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 { const int* labels = data->get_cat_var_data(node,vi); 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 ? 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 int* responses = data->get_class_labels(node); const double* priors = data->priors_mult->data.db; double sum = 0, sum_abs = 0; 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 { const CvPair32s32f* sorted = data->get_ord_var_data(node,vi); int split_point = node->split->ord.split_point; int n1 = node->get_num_valid(vi); assert( 0 <= split_point && split_point < n1-1 ); if( !data->have_priors ) { for( i = 0; i <= split_point; i++ ) dir[sorted[i].i] = (char)-1; for( ; i < n1; i++ ) dir[sorted[i].i] = (char)1; for( ; i < n; i++ ) dir[sorted[i].i] = (char)0; L = split_point-1; R = n1 - split_point + 1; } else { const int* responses = data->get_class_labels(node); const double* priors = data->priors_mult->data.db; L = R = 0; for( i = 0; i <= split_point; i++ ) { int idx = sorted[i].i; double w = priors[responses[idx]]; dir[idx] = (char)-1; L += w; } for( ; i < n1; i++ ) { int idx = sorted[i].i; double w = priors[responses[idx]]; dir[idx] = (char)1; R += w; } for( ; i < n; i++ ) dir[sorted[i].i] = (char)0; } } node->maxlr = MAX( L, R ); return node->split->quality/(L + R); } CvDTreeSplit* CvDTree::find_best_split( CvDTreeNode* node ) { int vi; CvDTreeSplit *best_split = 0, *split = 0, *t; for( vi = 0; vi < data->var_count; vi++ ) { int ci = data->get_var_type(vi); if( node->get_num_valid(vi) <= 1 ) continue; if( data->is_classifier ) { if( ci >= 0 ) split = find_split_cat_class( node, vi ); else split = find_split_ord_class( node, vi ); } else { if( ci >= 0 ) split = find_split_cat_reg( node, vi ); else split = find_split_ord_reg( node, vi ); } if( split ) { if( !best_split || best_split->quality < split->quality ) CV_SWAP( best_split, split, t ); if( split ) cvSetRemoveByPtr( data->split_heap, split ); } } return best_split; } CvDTreeSplit* CvDTree::find_split_ord_class( CvDTreeNode* node, int vi ) { const float epsilon = FLT_EPSILON*2; const CvPair32s32f* sorted = data->get_ord_var_data(node, vi); const int* responses = data->get_class_labels(node); int n = node->sample_count; int n1 = node->get_num_valid(vi); int m = data->get_num_classes(); const int* rc0 = data->counts->data.i; int* lc = (int*)cvStackAlloc(m*sizeof(lc[0])); int* rc = (int*)cvStackAlloc(m*sizeof(rc[0])); int i, best_i = -1; double lsum2 = 0, rsum2 = 0, best_val = 0; const double* priors = data->have_priors ? data->priors_mult->data.db : 0; // init arrays of class instance counters on both sides of the split for( i = 0; i < m; i++ ) { lc[i] = 0; rc[i] = rc0[i]; } // compensate for missing values for( i = n1; i < n; i++ ) rc[responses[sorted[i].i]]--; if( !priors ) { int L = 0, R = n1; for( i = 0; i < m; i++ ) rsum2 += (double)rc[i]*rc[i]; for( i = 0; i < n1 - 1; i++ ) { int idx = responses[sorted[i].i]; int lv, rv; L++; R--; lv = lc[idx]; rv = rc[idx]; lsum2 += lv*2 + 1; rsum2 -= rv*2 - 1; lc[idx] = lv + 1; rc[idx] = rv - 1; if( sorted[i].val + epsilon < sorted[i+1].val ) { double val = (lsum2*R + rsum2*L)/((double)L*R); if( best_val < val ) { best_val = val; best_i = i; } } } } else { double L = 0, R = 0; for( i = 0; i < m; i++ ) { double wv = rc[i]*priors[i]; R += wv; rsum2 += wv*wv; } for( i = 0; i < n1 - 1; i++ ) { int idx = responses[sorted[i].i]; int lv, rv; double p = priors[idx], p2 = p*p; L += p; R -= p; lv = lc[idx]; rv = rc[idx]; lsum2 += p2*(lv*2 + 1); rsum2 -= p2*(rv*2 - 1); lc[idx] = lv + 1; rc[idx] = rv - 1; if( sorted[i].val + epsilon < sorted[i+1].val ) { double val = (lsum2*R + rsum2*L)/((double)L*R); if( best_val < val ) { best_val = val; best_i = i; } } } } return best_i >= 0 ? data->new_split_ord( vi, (sorted[best_i].val + sorted[best_i+1].val)*0.5f, best_i, 0, (float)best_val ) : 0; } void CvDTree::cluster_categories( const int* vectors, int n, int m, int* csums, int k, int* labels ) { // TODO: consider adding priors (class weights) and sample weights to the clustering algorithm int iters = 0, max_iters = 100; int i, j, idx; double* buf = (double*)cvStackAlloc( (n + k)*sizeof(buf[0]) ); double *v_weights = buf, *c_weights = buf + k; bool modified = true; CvRNG* r = &data->rng; // assign labels randomly for( i = idx = 0; i < n; i++ ) { int sum = 0; const int* v = vectors + i*m; labels[i] = idx++; idx &= idx < k ? -1 : 0; // compute weight of each vector for( j = 0; j < m; j++ ) sum += v[j]; v_weights[i] = sum ? 1./sum : 0.; } for( i = 0; i < n; i++ ) { int i1 = cvRandInt(r) % n; int i2 = cvRandInt(r) % n; CV_SWAP( labels[i1], labels[i2], j ); } for( iters = 0; iters <= max_iters; iters++ ) { // calculate csums for( i = 0; i < k; i++ ) { for( j = 0; j < m; j++ ) csums[i*m + j] = 0; } for( i = 0; i < n; i++ ) { const int* v = vectors + i*m; int* s = csums + labels[i]*m; for( j = 0; j < m; j++ ) s[j] += v[j]; } // exit the loop here, when we have up-to-date csums if( iters == max_iters || !modified ) break; modified = false; // calculate weight of each cluster for( i = 0; i < k; i++ ) { const int* s = csums + i*m; int sum = 0; for( j = 0; j < m; j++ ) sum += s[j]; c_weights[i] = sum ? 1./sum : 0; } // now for each vector determine the closest cluster for( i = 0; i < n; i++ ) { const int* v = vectors + i*m; double alpha = v_weights[i]; double min_dist2 = DBL_MAX; int min_idx = -1; for( idx = 0; idx < k; idx++ ) { const int* s = csums + idx*m; double dist2 = 0., beta = c_weights[idx]; for( j = 0; j < m; j++ ) { double t = v[j]*alpha - s[j]*beta; dist2 += t*t; } if( min_dist2 > dist2 ) { min_dist2 = dist2; min_idx = idx; } } if( min_idx != labels[i] ) modified = true; labels[i] = min_idx; } } } CvDTreeSplit* CvDTree::find_split_cat_class( CvDTreeNode* node, int vi ) { CvDTreeSplit* split; const int* labels = data->get_cat_var_data(node, vi); const int* responses = data->get_class_labels(node); int ci = data->get_var_type(vi); int n = node->sample_count; int m = data->get_num_classes(); int _mi = data->cat_count->data.i[ci], mi = _mi; int* lc = (int*)cvStackAlloc(m*sizeof(lc[0])); int* rc = (int*)cvStackAlloc(m*sizeof(rc[0])); int* _cjk = (int*)cvStackAlloc(m*(mi+1)*sizeof(_cjk[0]))+m, *cjk = _cjk; double* c_weights = (double*)cvStackAlloc( mi*sizeof(c_weights[0]) ); int* cluster_labels = 0; int** int_ptr = 0; int i, j, k, idx; double L = 0, R = 0; double best_val = 0; int prevcode = 0, best_subset = -1, subset_i, subset_n, subtract = 0; const double* priors = data->priors_mult->data.db; // init array of counters: // c_{jk} - number of samples that have vi-th input variable = j and response = k. for( j = -1; j < mi; j++ ) for( k = 0; k < m; k++ ) cjk[j*m + k] = 0; for( i = 0; i < n; i++ ) { j = labels[i]; k = responses[i]; cjk[j*m + k]++; } if( m > 2 ) { if( mi > data->params.max_categories ) { mi = MIN(data->params.max_categories, n); cjk += _mi*m; cluster_labels = (int*)cvStackAlloc(mi*sizeof(cluster_labels[0])); cluster_categories( _cjk, _mi, m, cjk, mi, cluster_labels ); } subset_i = 1; subset_n = 1 << mi; } else { assert( m == 2 ); int_ptr = (int**)cvStackAlloc( mi*sizeof(int_ptr[0]) ); for( j = 0; j < mi; j++ ) int_ptr[j] = cjk + j*2 + 1; icvSortIntPtr( int_ptr, mi, 0 ); subset_i = 0; subset_n = mi; } for( k = 0; k < m; k++ ) { int sum = 0; for( j = 0; j < mi; j++ ) sum += cjk[j*m + k]; rc[k] = sum; lc[k] = 0; } for( j = 0; j < mi; j++ ) { double sum = 0; for( k = 0; k < m; k++ ) sum += cjk[j*m + k]*priors[k]; c_weights[j] = sum; R += c_weights[j]; } for( ; subset_i < subset_n; subset_i++ ) { double weight; int* crow; double lsum2 = 0, rsum2 = 0; if( m == 2 ) idx = (int)(int_ptr[subset_i] - cjk)/2; else { int graycode = (subset_i>>1)^subset_i; int diff = graycode ^ prevcode; // determine index of the changed bit. Cv32suf u; idx = diff >= (1 << 16) ? 16 : 0; u.f = (float)(((diff >> 16) | diff) & 65535); idx += (u.i >> 23) - 127; subtract = graycode < prevcode; prevcode = graycode; } crow = cjk + idx*m; weight = c_weights[idx]; if( weight < FLT_EPSILON ) continue; if( !subtract ) { for( k = 0; k < m; k++ ) { int t = crow[k]; int lval = lc[k] + t; int rval = rc[k] - t; double p = priors[k], p2 = p*p; lsum2 += p2*lval*lval; rsum2 += p2*rval*rval; lc[k] = lval; rc[k] = rval; } L += weight; R -= weight; } else { for( k = 0; k < m; k++ ) { int t = crow[k]; int lval = lc[k] - t; int rval = rc[k] + t; double p = priors[k], p2 = p*p; lsum2 += p2*lval*lval; rsum2 += p2*rval*rval; lc[k] = lval; rc[k] = rval; } L -= weight; R += weight; } if( L > FLT_EPSILON && R > FLT_EPSILON ) { double val = (lsum2*R + rsum2*L)/((double)L*R); if( best_val < val ) { best_val = val; best_subset = subset_i; } } } if( best_subset < 0 ) return 0; split = data->new_split_cat( vi, (float)best_val ); if( m == 2 ) { for( i = 0; i <= best_subset; i++ ) { idx = (int)(int_ptr[i] - cjk) >> 1; split->subset[idx >> 5] |= 1 << (idx & 31); } } else { for( i = 0; i < _mi; i++ ) { idx = cluster_labels ? cluster_labels[i] : i; if( best_subset & (1 << idx) ) split->subset[i >> 5] |= 1 << (i & 31); } } return split; } CvDTreeSplit* CvDTree::find_split_ord_reg( CvDTreeNode* node, int vi ) { const float epsilon = FLT_EPSILON*2; const CvPair32s32f* sorted = data->get_ord_var_data(node, vi); const float* responses = data->get_ord_responses(node); int n = node->sample_count; int n1 = node->get_num_valid(vi); int i, best_i = -1; double best_val = 0, lsum = 0, rsum = node->value*n; int L = 0, R = n1; // compensate for missing values for( i = n1; i < n; i++ ) rsum -= responses[sorted[i].i]; // find the optimal split for( i = 0; i < n1 - 1; i++ ) { float t = responses[sorted[i].i]; L++; R--; lsum += t; rsum -= t; if( sorted[i].val + epsilon < sorted[i+1].val ) { double val = (lsum*lsum*R + rsum*rsum*L)/((double)L*R); if( best_val < val ) { best_val = val; best_i = i; } } } return best_i >= 0 ? data->new_split_ord( vi, (sorted[best_i].val + sorted[best_i+1].val)*0.5f, best_i, 0, (float)best_val ) : 0; } CvDTreeSplit* CvDTree::find_split_cat_reg( CvDTreeNode* node, int vi ) { CvDTreeSplit* split; const int* labels = data->get_cat_var_data(node, vi); const float* responses = data->get_ord_responses(node); int ci = data->get_var_type(vi); int n = node->sample_count; int mi = data->cat_count->data.i[ci]; double* sum = (double*)cvStackAlloc( (mi+1)*sizeof(sum[0]) ) + 1; int* counts = (int*)cvStackAlloc( (mi+1)*sizeof(counts[0]) ) + 1; double** sum_ptr = 0; int i, L = 0, R = 0; double best_val = 0, lsum = 0, rsum = 0; int best_subset = -1, subset_i; for( i = -1; i < mi; i++ ) sum[i] = counts[i] = 0; // calculate sum response and weight of each category of the input var for( i = 0; i < n; i++ ) { int idx = labels[i]; double s = sum[idx] + responses[i]; int nc = counts[idx] + 1; sum[idx] = s; counts[idx] = nc; } // calculate average response in each category for( i = 0; i < mi; i++ ) { R += counts[i]; rsum += sum[i]; sum[i] /= MAX(counts[i],1); sum_ptr[i] = sum + i; } icvSortDblPtr( sum_ptr, mi, 0 ); // revert back to unnormalized sums // (there should be a very little loss of accuracy) for( i = 0; i < mi; i++ ) sum[i] *= counts[i]; for( subset_i = 0; subset_i < mi-1; subset_i++ ) { int idx = (int)(sum_ptr[subset_i] - sum); int ni = counts[idx]; if( ni ) { double s = sum[idx]; lsum += s; L += ni; rsum -= s; R -= ni; if( L && R ) { double val = (lsum*lsum*R + rsum*rsum*L)/((double)L*R); if( best_val < val ) { best_val = val; best_subset = subset_i; } } } } if( best_subset < 0 ) return 0; split = data->new_split_cat( vi, (float)best_val ); for( i = 0; i <= best_subset; i++ ) { int idx = (int)(sum_ptr[i] - sum); split->subset[idx >> 5] |= 1 << (idx & 31); } return split; } CvDTreeSplit* CvDTree::find_surrogate_split_ord( CvDTreeNode* node, int vi ) { const float epsilon = FLT_EPSILON*2; const CvPair32s32f* sorted = data->get_ord_var_data(node, vi); const char* dir = (char*)data->direction->data.ptr; int n1 = node->get_num_valid(vi); // LL - number of samples that both the primary and the surrogate splits send to the left // LR - ... primary split sends to the left and the surrogate split sends to the right // RL - ... primary split sends to the right and the surrogate split sends to the left // RR - ... both send to the right int i, best_i = -1, best_inversed = 0; double best_val; if( !data->have_priors ) { int LL = 0, RL = 0, LR, RR; int worst_val = cvFloor(node->maxlr), _best_val = worst_val; int sum = 0, sum_abs = 0; for( i = 0; i < n1; i++ ) { int d = dir[sorted[i].i]; sum += d; sum_abs += d & 1; } // sum_abs = R + L; sum = R - L RR = (sum_abs + sum) >> 1; LR = (sum_abs - sum) >> 1; // initially all the samples are sent to the right by the surrogate split, // LR of them are sent to the left by primary split, and RR - to the right. // now iteratively compute LL, LR, RL and RR for every possible surrogate split value. for( i = 0; i < n1 - 1; i++ ) { int d = dir[sorted[i].i]; if( d < 0 ) { LL++; LR--; if( LL + RR > _best_val && sorted[i].val + epsilon < sorted[i+1].val ) { best_val = LL + RR; best_i = i; best_inversed = 0; } } else if( d > 0 ) { RL++; RR--; if( RL + LR > _best_val && sorted[i].val + epsilon < sorted[i+1].val ) { best_val = RL + LR; best_i = i; best_inversed = 1; } } } best_val = _best_val; } else { double LL = 0, RL = 0, LR, RR; double worst_val = node->maxlr; double sum = 0, sum_abs = 0; const double* priors = data->priors_mult->data.db; const int* responses = data->get_class_labels(node); best_val = worst_val; for( i = 0; i < n1; i++ ) { int idx = sorted[i].i; double w = priors[responses[idx]]; int d = dir[idx]; sum += d*w; sum_abs += (d & 1)*w; } // sum_abs = R + L; sum = R - L RR = (sum_abs + sum)*0.5; LR = (sum_abs - sum)*0.5; // initially all the samples are sent to the right by the surrogate split, // LR of them are sent to the left by primary split, and RR - to the right. // now iteratively compute LL, LR, RL and RR for every possible surrogate split value. for( i = 0; i < n1 - 1; i++ ) { int idx = sorted[i].i; double w = priors[responses[idx]]; int d = dir[idx]; if( d < 0 ) { LL += w; LR -= w; if( LL + RR > best_val && sorted[i].val + epsilon < sorted[i+1].val ) { best_val = LL + RR; best_i = i; best_inversed = 0; } } else if( d > 0 ) { RL += w; RR -= w; if( RL + LR > best_val && sorted[i].val + epsilon < sorted[i+1].val ) { best_val = RL + LR; best_i = i; best_inversed = 1; } } } } return best_i >= 0 && best_val > node->maxlr ? data->new_split_ord( vi, (sorted[best_i].val + sorted[best_i+1].val)*0.5f, best_i, best_inversed, (float)best_val ) : 0; } CvDTreeSplit* CvDTree::find_surrogate_split_cat( CvDTreeNode* node, int vi ) { const int* labels = data->get_cat_var_data(node, vi); const char* dir = (char*)data->direction->data.ptr; int n = node->sample_count; // LL - number of samples that both the primary and the surrogate splits send to the left // LR - ... primary split sends to the left and the surrogate split sends to the right // RL - ... primary split sends to the right and the surrogate split sends to the left // RR - ... both send to the right CvDTreeSplit* split = data->new_split_cat( vi, 0 ); int i, mi = data->cat_count->data.i[data->get_var_type(vi)], l_win = 0; double best_val = 0; double* lc = (double*)cvStackAlloc( (mi+1)*2*sizeof(lc[0]) ) + 1; double* rc = lc + mi + 1; for( i = -1; i < mi; i++ ) lc[i] = rc[i] = 0; // for each category calculate the weight of samples // sent to the left (lc) and to the right (rc) by the primary split if( !data->have_priors ) { int* _lc = (int*)cvStackAlloc((mi+2)*2*sizeof(_lc[0])) + 1; int* _rc = _lc + mi + 1; for( i = -1; i < mi; i++ ) _lc[i] = _rc[i] = 0; for( i = 0; i < n; i++ ) { int idx = labels[i]; int d = dir[i]; int sum = _lc[idx] + d; int sum_abs = _rc[idx] + (d & 1); _lc[idx] = sum; _rc[idx] = sum_abs; } for( i = 0; i < mi; i++ ) { int sum = _lc[i]; int sum_abs = _rc[i]; lc[i] = (sum_abs - sum) >> 1; rc[i] = (sum_abs + sum) >> 1; } } else { const double* priors = data->priors_mult->data.db; const int* responses = data->get_class_labels(node); for( i = 0; i < n; i++ ) { int idx = labels[i]; double w = priors[responses[i]]; int d = dir[i]; double sum = lc[idx] + d*w; double sum_abs = rc[idx] + (d & 1)*w; lc[idx] = sum; rc[idx] = sum_abs; } for( i = 0; i < mi; i++ ) { double sum = lc[i]; double sum_abs = rc[i]; lc[i] = (sum_abs - sum) * 0.5; rc[i] = (sum_abs + sum) * 0.5; } } // 2. now form the split. // in each category send all the samples to the same direction as majority for( i = 0; i < mi; i++ ) { double lval = lc[i], rval = rc[i]; if( lval > rval ) { split->subset[i >> 5] |= 1 << (i & 31); best_val += lval; l_win++; } else best_val += rval; } split->quality = (float)best_val; if( split->quality <= node->maxlr || l_win == 0 || l_win == mi ) cvSetRemoveByPtr( data->split_heap, split ), split = 0; return split; } void CvDTree::calc_node_value( CvDTreeNode* node ) { int i, j, k, n = node->sample_count, cv_n = data->params.cv_folds; const int* cv_labels = data->get_labels(node); if( data->is_classifier ) { // in case of classification tree: // * node value is the label of the class that has the largest weight in the node. // * node risk is the weighted number of misclassified samples, // * j-th cross-validation fold value and risk are calculated as above, // but using the samples with cv_labels(*)!=j. // * j-th cross-validation fold error is calculated as the weighted number of // misclassified samples with cv_labels(*)==j. // compute the number of instances of each class int* cls_count = data->counts->data.i; const int* responses = data->get_class_labels(node); int m = data->get_num_classes(); int* cv_cls_count = (int*)cvStackAlloc(m*cv_n*sizeof(cv_cls_count[0])); double max_val = -1, total_weight = 0; int max_k = -1; double* priors = data->priors_mult->data.db; for( k = 0; k < m; k++ ) cls_count[k] = 0; if( cv_n == 0 ) { for( i = 0; i < n; i++ ) cls_count[responses[i]]++; } else { for( j = 0; j < cv_n; j++ ) for( k = 0; k < m; k++ ) cv_cls_count[j*m + k] = 0; for( i = 0; i < n; i++ ) { j = cv_labels[i]; k = responses[i]; cv_cls_count[j*m + k]++; } for( j = 0; j < cv_n; j++ ) for( k = 0; k < m; k++ ) cls_count[k] += cv_cls_count[j*m + k]; } if( data->have_priors && node->parent == 0 ) { // compute priors_mult from priors, take the sample ratio into account. double sum = 0; for( k = 0; k < m; k++ ) { int n_k = cls_count[k]; priors[k] = data->priors->data.db[k]*(n_k ? 1./n_k : 0.); sum += priors[k]; } sum = 1./sum; for( k = 0; k < m; k++ ) priors[k] *= sum; } for( k = 0; k < m; k++ ) { double val = cls_count[k]*priors[k]; total_weight += val; if( max_val < val ) { max_val = val; max_k = k; } } node->class_idx = max_k; node->value = data->cat_map->data.i[ data->cat_ofs->data.i[data->cat_var_count] + max_k]; node->node_risk = total_weight - max_val; for( j = 0; j < cv_n; j++ ) { double sum_k = 0, sum = 0, max_val_k = 0; max_val = -1; max_k = -1; for( k = 0; k < m; k++ ) { double w = priors[k]; double val_k = cv_cls_count[j*m + k]*w; double val = cls_count[k]*w - val_k; sum_k += val_k; sum += val; if( max_val < val ) { max_val = val; max_val_k = val_k; max_k = k; } } node->cv_Tn[j] = INT_MAX; node->cv_node_risk[j] = sum - max_val; node->cv_node_error[j] = sum_k - max_val_k; } } else { // in case of regression tree: // * node value is 1/n*sum_i(Y_i), where Y_i is i-th response, // n is the number of samples in the node. // * node risk is the sum of squared errors: sum_i((Y_i - )^2) // * j-th cross-validation fold value and risk are calculated as above, // but using the samples with cv_labels(*)!=j. // * j-th cross-validation fold error is calculated // using samples with cv_labels(*)==j as the test subset: // error_j = sum_(i,cv_labels(i)==j)((Y_i - )^2), // where node_value_j is the node value calculated // as described in the previous bullet, and summation is done // over the samples with cv_labels(*)==j. double sum = 0, sum2 = 0; const float* values = data->get_ord_responses(node); double *cv_sum = 0, *cv_sum2 = 0; int* cv_count = 0; if( cv_n == 0 ) { for( i = 0; i < n; i++ ) { double t = values[i]; sum += t; sum2 += t*t; } } else { cv_sum = (double*)cvStackAlloc( cv_n*sizeof(cv_sum[0]) ); cv_sum2 = (double*)cvStackAlloc( cv_n*sizeof(cv_sum2[0]) ); cv_count = (int*)cvStackAlloc( cv_n*sizeof(cv_count[0]) ); for( j = 0; j < cv_n; j++ ) { cv_sum[j] = cv_sum2[j] = 0.; cv_count[j] = 0; } for( i = 0; i < n; i++ ) { j = cv_labels[i]; double t = values[i]; double s = cv_sum[j] + t; double s2 = cv_sum2[j] + t*t; int nc = cv_count[j] + 1; cv_sum[j] = s; cv_sum2[j] = s2; cv_count[j] = nc; } for( j = 0; j < cv_n; j++ ) { sum += cv_sum[j]; sum2 += cv_sum2[j]; } } node->node_risk = sum2 - (sum/n)*sum; node->value = sum/n; for( j = 0; j < cv_n; j++ ) { double s = cv_sum[j], si = sum - s; double s2 = cv_sum2[j], s2i = sum2 - s2; int c = cv_count[j], ci = n - c; double r = si/MAX(ci,1); node->cv_node_risk[j] = s2i - r*r*ci; node->cv_node_error[j] = s2 - 2*r*s + c*r*r; node->cv_Tn[j] = INT_MAX; } } } void CvDTree::complete_node_dir( CvDTreeNode* node ) { int vi, i, n = node->sample_count, nl, nr, d0 = 0, d1 = -1; int nz = n - node->get_num_valid(node->split->var_idx); char* dir = (char*)data->direction->data.ptr; // try to complete direction using surrogate splits if( nz && data->params.use_surrogates ) { CvDTreeSplit* split = node->split->next; for( ; split != 0 && nz; split = split->next ) { int inversed_mask = split->inversed ? -1 : 0; vi = split->var_idx; if( data->get_var_type(vi) >= 0 ) // split on categorical var { const int* labels = data->get_cat_var_data(node, vi); const int* subset = split->subset; for( i = 0; i < n; i++ ) { int idx; if( !dir[i] && (idx = labels[i]) >= 0 ) { int d = CV_DTREE_CAT_DIR(idx,subset); dir[i] = (char)((d ^ inversed_mask) - inversed_mask); if( --nz ) break; } } } else // split on ordered var { const CvPair32s32f* sorted = data->get_ord_var_data(node, vi); int split_point = split->ord.split_point; int n1 = node->get_num_valid(vi); assert( 0 <= split_point && split_point < n-1 ); for( i = 0; i < n1; i++ ) { int idx = sorted[i].i; if( !dir[idx] ) { int d = i <= split_point ? -1 : 1; dir[idx] = (char)((d ^ inversed_mask) - inversed_mask); if( --nz ) break; } } } } } // find the default direction for the rest if( nz ) { for( i = nr = 0; i < n; i++ ) nr += dir[i] > 0; nl = n - nr - nz; d0 = nl > nr ? -1 : nr > nl; } // make sure that every sample is directed either to the left or to the right for( i = 0; i < n; i++ ) { int d = dir[i]; if( !d ) { d = d0; if( !d ) d = d1, d1 = -d1; } d = d > 0; dir[i] = (char)d; // remap (-1,1) to (0,1) } } void CvDTree::split_node_data( CvDTreeNode* node ) { int vi, i, n = node->sample_count, nl, nr; char* dir = (char*)data->direction->data.ptr; CvDTreeNode *left = 0, *right = 0; int* new_idx = data->split_buf->data.i; int new_buf_idx = data->get_child_buf_idx( node ); int work_var_count = data->get_work_var_count(); // speedup things a little, especially for tree ensembles with a lots of small trees: // do not physically split the input data between the left and right child nodes // when we are not going to split them further, // as calc_node_value() does not requires input features anyway. bool split_input_data; complete_node_dir(node); for( i = nl = nr = 0; i < n; i++ ) { int d = dir[i]; // initialize new indices for splitting ordered variables new_idx[i] = (nl & (d-1)) | (nr & -d); // d ? ri : li nr += d; nl += d^1; } 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 + (data->ord_var_count + work_var_count)*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); // split ordered variables, keep both halves sorted. for( vi = 0; vi < data->var_count; vi++ ) { int ci = data->get_var_type(vi); int n1 = node->get_num_valid(vi); CvPair32s32f *src, *ldst0, *rdst0, *ldst, *rdst; CvPair32s32f tl, tr; if( ci >= 0 || !split_input_data ) continue; src = data->get_ord_var_data(node, vi); ldst0 = ldst = data->get_ord_var_data(left, vi); rdst0 = rdst = data->get_ord_var_data(right, vi); tl = ldst0[nl]; tr = rdst0[nr]; // split sorted for( i = 0; i < n1; i++ ) { int idx = src[i].i; float val = src[i].val; int d = dir[idx]; idx = new_idx[idx]; ldst->i = rdst->i = idx; ldst->val = rdst->val = val; ldst += d^1; rdst += d; } left->set_num_valid(vi, (int)(ldst - ldst0)); right->set_num_valid(vi, (int)(rdst - rdst0)); // split missing for( ; i < n; i++ ) { int idx = src[i].i; int d = dir[idx]; idx = new_idx[idx]; ldst->i = rdst->i = idx; ldst->val = rdst->val = ord_nan; ldst += d^1; rdst += d; } ldst0[nl] = tl; rdst0[nr] = tr; } // split categorical vars, responses and cv_labels using new_idx relocation table for( vi = 0; vi < work_var_count; vi++ ) { int ci = data->get_var_type(vi); int n1 = node->get_num_valid(vi), nr1 = 0; int *src, *ldst0, *rdst0, *ldst, *rdst; int tl, tr; if( ci < 0 || (vi < data->var_count && !split_input_data) ) continue; src = data->get_cat_var_data(node, vi); ldst0 = ldst = data->get_cat_var_data(left, vi); rdst0 = rdst = data->get_cat_var_data(right, vi); tl = ldst0[nl]; tr = rdst0[nr]; for( i = 0; i < n; i++ ) { int d = dir[i]; int val = src[i]; *ldst = *rdst = val; ldst += d^1; rdst += d; nr1 += (val >= 0)&d; } if( vi < data->var_count ) { left->set_num_valid(vi, n1 - nr1); right->set_num_valid(vi, nr1); } ldst0[nl] = tl; rdst0[nr] = tr; } // deallocate the parent node data that is not needed anymore data->free_node_data(node); } void CvDTree::prune_cv() { CvMat* ab = 0; CvMat* temp = 0; CvMat* err_jk = 0; // 1. build tree sequence for each cv fold, calculate error_{Tj,beta_k}. // 2. choose the best tree index (if need, apply 1SE rule). // 3. store the best index and cut the branches. CV_FUNCNAME( "CvDTree::prune_cv" ); __BEGIN__; int ti, j, tree_count = 0, cv_n = data->params.cv_folds, n = root->sample_count; // currently, 1SE for regression is not implemented bool use_1se = data->params.use_1se_rule != 0 && data->is_classifier; double* err; double min_err = 0, min_err_se = 0; int min_idx = -1; CV_CALL( ab = cvCreateMat( 1, 256, CV_64F )); // build the main tree sequence, calculate alpha's for(;;tree_count++) { double min_alpha = update_tree_rnc(tree_count, -1); if( cut_tree(tree_count, -1, min_alpha) ) break; if( ab->cols <= tree_count ) { CV_CALL( temp = cvCreateMat( 1, ab->cols*3/2, CV_64F )); for( ti = 0; ti < ab->cols; ti++ ) temp->data.db[ti] = ab->data.db[ti]; cvReleaseMat( &ab ); ab = temp; temp = 0; } ab->data.db[tree_count] = min_alpha; } ab->data.db[0] = 0.; if( tree_count > 0 ) { for( ti = 1; ti < tree_count-1; ti++ ) ab->data.db[ti] = sqrt(ab->data.db[ti]*ab->data.db[ti+1]); ab->data.db[tree_count-1] = DBL_MAX*0.5; CV_CALL( err_jk = cvCreateMat( cv_n, tree_count, CV_64F )); err = err_jk->data.db; for( j = 0; j < cv_n; j++ ) { int tj = 0, tk = 0; for( ; tk < tree_count; tj++ ) { double min_alpha = update_tree_rnc(tj, j); if( cut_tree(tj, j, min_alpha) ) min_alpha = DBL_MAX; for( ; tk < tree_count; tk++ ) { if( ab->data.db[tk] > min_alpha ) break; err[j*tree_count + tk] = root->tree_error; } } } for( ti = 0; ti < tree_count; ti++ ) { double sum_err = 0; for( j = 0; j < cv_n; j++ ) sum_err += err[j*tree_count + ti]; if( ti == 0 || sum_err < min_err ) { min_err = sum_err; min_idx = ti; if( use_1se ) min_err_se = sqrt( sum_err*(n - sum_err) ); } else if( sum_err < min_err + min_err_se ) min_idx = ti; } } pruned_tree_idx = min_idx; free_prune_data(data->params.truncate_pruned_tree != 0); __END__; cvReleaseMat( &err_jk ); cvReleaseMat( &ab ); cvReleaseMat( &temp ); } double CvDTree::update_tree_rnc( int T, int fold ) { CvDTreeNode* node = root; double min_alpha = DBL_MAX; for(;;) { CvDTreeNode* parent; for(;;) { int t = fold >= 0 ? node->cv_Tn[fold] : node->Tn; if( t <= T || !node->left ) { node->complexity = 1; node->tree_risk = node->node_risk; node->tree_error = 0.; if( fold >= 0 ) { node->tree_risk = node->cv_node_risk[fold]; node->tree_error = node->cv_node_error[fold]; } break; } node = node->left; } for( parent = node->parent; parent && parent->right == node; node = parent, parent = parent->parent ) { parent->complexity += node->complexity; parent->tree_risk += node->tree_risk; parent->tree_error += node->tree_error; parent->alpha = ((fold >= 0 ? parent->cv_node_risk[fold] : parent->node_risk) - parent->tree_risk)/(parent->complexity - 1); min_alpha = MIN( min_alpha, parent->alpha ); } if( !parent ) break; parent->complexity = node->complexity; parent->tree_risk = node->tree_risk; parent->tree_error = node->tree_error; node = parent->right; } return min_alpha; } int CvDTree::cut_tree( int T, int fold, double min_alpha ) { CvDTreeNode* node = root; if( !node->left ) return 1; for(;;) { CvDTreeNode* parent; for(;;) { int t = fold >= 0 ? node->cv_Tn[fold] : node->Tn; if( t <= T || !node->left ) break; if( node->alpha <= min_alpha + FLT_EPSILON ) { if( fold >= 0 ) node->cv_Tn[fold] = T; else node->Tn = T; if( node == root ) return 1; break; } node = node->left; } for( parent = node->parent; parent && parent->right == node; node = parent, parent = parent->parent ) ; if( !parent ) break; node = parent->right; } return 0; } void CvDTree::free_prune_data(bool cut_tree) { CvDTreeNode* node = root; for(;;) { CvDTreeNode* parent; for(;;) { // do not call cvSetRemoveByPtr( cv_heap, node->cv_Tn ) // as we will clear the whole cross-validation heap at the end node->cv_Tn = 0; node->cv_node_error = node->cv_node_risk = 0; if( !node->left ) break; node = node->left; } for( parent = node->parent; parent && parent->right == node; node = parent, parent = parent->parent ) { if( cut_tree && parent->Tn <= pruned_tree_idx ) { data->free_node( parent->left ); data->free_node( parent->right ); parent->left = parent->right = 0; } } if( !parent ) break; node = parent->right; } if( data->cv_heap ) cvClearSet( data->cv_heap ); } void CvDTree::free_tree() { if( root && data && data->shared ) { pruned_tree_idx = INT_MIN; free_prune_data(true); data->free_node(root); root = 0; } } CvDTreeNode* CvDTree::predict( const CvMat* _sample, const CvMat* _missing, bool preprocessed_input ) const { CvDTreeNode* result = 0; int* catbuf = 0; CV_FUNCNAME( "CvDTree::predict" ); __BEGIN__; int i, step, mstep = 0; const float* sample; const uchar* m = 0; CvDTreeNode* node = root; const int* vtype; const int* vidx; const int* cmap; const int* cofs; if( !node ) CV_ERROR( CV_StsError, "The tree has not been trained yet" ); if( !CV_IS_MAT(_sample) || CV_MAT_TYPE(_sample->type) != CV_32FC1 || _sample->cols != 1 && _sample->rows != 1 || _sample->cols + _sample->rows - 1 != data->var_all && !preprocessed_input || _sample->cols + _sample->rows - 1 != data->var_count && preprocessed_input ) CV_ERROR( CV_StsBadArg, "the input sample must be 1d floating-point vector with the same " "number of elements as the total number of variables used for training" ); sample = _sample->data.fl; step = CV_IS_MAT_CONT(_sample->type) ? 1 : _sample->step/sizeof(sample[0]); if( data->cat_count && !preprocessed_input ) // cache for categorical variables { int n = data->cat_count->cols; catbuf = (int*)cvStackAlloc(n*sizeof(catbuf[0])); for( i = 0; i < n; i++ ) catbuf[i] = -1; } if( _missing ) { if( !CV_IS_MAT(_missing) || !CV_IS_MASK_ARR(_missing) || !CV_ARE_SIZES_EQ(_missing, _sample) ) CV_ERROR( CV_StsBadArg, "the missing data mask must be 8-bit vector of the same size as input sample" ); m = _missing->data.ptr; mstep = CV_IS_MAT_CONT(_missing->type) ? 1 : _missing->step/sizeof(m[0]); } vtype = data->var_type->data.i; vidx = data->var_idx && !preprocessed_input ? data->var_idx->data.i : 0; cmap = data->cat_map ? data->cat_map->data.i : 0; cofs = data->cat_ofs ? data->cat_ofs->data.i : 0; while( node->Tn > pruned_tree_idx && node->left ) { CvDTreeSplit* split = node->split; int dir = 0; for( ; !dir && split != 0; split = split->next ) { int vi = split->var_idx; int ci = vtype[vi]; i = vidx ? vidx[vi] : vi; float val = sample[i*step]; if( m && m[i*mstep] ) continue; if( ci < 0 ) // ordered dir = val <= split->ord.c ? -1 : 1; else // categorical { int c; if( preprocessed_input ) c = cvRound(val); else { c = catbuf[ci]; if( c < 0 ) { int a = c = cofs[ci]; int b = cofs[ci+1]; int ival = cvRound(val); if( ival != val ) CV_ERROR( CV_StsBadArg, "one of input categorical variable is not an integer" ); while( a < b ) { c = (a + b) >> 1; if( ival < cmap[c] ) b = c; else if( ival > cmap[c] ) a = c+1; else break; } if( c < 0 || ival != cmap[c] ) continue; catbuf[ci] = c -= cofs[ci]; } } dir = CV_DTREE_CAT_DIR(c, split->subset); } if( split->inversed ) dir = -dir; } if( !dir ) { double diff = node->right->sample_count - node->left->sample_count; dir = diff < 0 ? -1 : 1; } node = dir < 0 ? node->left : node->right; } result = node; __END__; return result; } const CvMat* CvDTree::get_var_importance() { if( !var_importance ) { CvDTreeNode* node = root; double* importance; if( !node ) return 0; var_importance = cvCreateMat( 1, data->var_count, CV_64F ); cvZero( var_importance ); importance = var_importance->data.db; for(;;) { CvDTreeNode* parent; for( ;; node = node->left ) { CvDTreeSplit* split = node->split; if( !node->left || node->Tn <= pruned_tree_idx ) break; for( ; split != 0; split = split->next ) importance[split->var_idx] += split->quality; } for( parent = node->parent; parent && parent->right == node; node = parent, parent = parent->parent ) ; if( !parent ) break; node = parent->right; } cvNormalize( var_importance, var_importance, 1., 0, CV_L1 ); } return var_importance; } void CvDTree::write_split( CvFileStorage* fs, CvDTreeSplit* split ) { int ci; cvStartWriteStruct( fs, 0, CV_NODE_MAP + CV_NODE_FLOW ); cvWriteInt( fs, "var", split->var_idx ); cvWriteReal( fs, "quality", split->quality ); ci = data->get_var_type(split->var_idx); if( ci >= 0 ) // split on a categorical var { int i, n = data->cat_count->data.i[ci], to_right = 0, default_dir; for( i = 0; i < n; i++ ) to_right += CV_DTREE_CAT_DIR(i,split->subset) > 0; // ad-hoc rule when to use inverse categorical split notation // to achieve more compact and clear representation default_dir = to_right <= 1 || to_right <= MIN(3, n/2) || to_right <= n/3 ? -1 : 1; cvStartWriteStruct( fs, default_dir*(split->inversed ? -1 : 1) > 0 ? "in" : "not_in", CV_NODE_SEQ+CV_NODE_FLOW ); for( i = 0; i < n; i++ ) { int dir = CV_DTREE_CAT_DIR(i,split->subset); if( dir*default_dir < 0 ) cvWriteInt( fs, 0, i ); } cvEndWriteStruct( fs ); } else cvWriteReal( fs, !split->inversed ? "le" : "gt", split->ord.c ); cvEndWriteStruct( fs ); } void CvDTree::write_node( CvFileStorage* fs, CvDTreeNode* node ) { CvDTreeSplit* split; cvStartWriteStruct( fs, 0, CV_NODE_MAP ); cvWriteInt( fs, "depth", node->depth ); cvWriteInt( fs, "sample_count", node->sample_count ); cvWriteReal( fs, "value", node->value ); if( data->is_classifier ) cvWriteInt( fs, "norm_class_idx", node->class_idx ); cvWriteInt( fs, "Tn", node->Tn ); cvWriteInt( fs, "complexity", node->complexity ); cvWriteReal( fs, "alpha", node->alpha ); cvWriteReal( fs, "node_risk", node->node_risk ); cvWriteReal( fs, "tree_risk", node->tree_risk ); cvWriteReal( fs, "tree_error", node->tree_error ); if( node->left ) { cvStartWriteStruct( fs, "splits", CV_NODE_SEQ ); for( split = node->split; split != 0; split = split->next ) write_split( fs, split ); cvEndWriteStruct( fs ); } cvEndWriteStruct( fs ); } void CvDTree::write_tree_nodes( CvFileStorage* fs ) { //CV_FUNCNAME( "CvDTree::write_tree_nodes" ); __BEGIN__; CvDTreeNode* node = root; // traverse the tree and save all the nodes in depth-first order for(;;) { CvDTreeNode* parent; for(;;) { write_node( fs, node ); if( !node->left ) break; node = node->left; } for( parent = node->parent; parent && parent->right == node; node = parent, parent = parent->parent ) ; if( !parent ) break; node = parent->right; } __END__; } void CvDTree::write( CvFileStorage* fs, const char* name ) { //CV_FUNCNAME( "CvDTree::write" ); __BEGIN__; cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_ML_TREE ); get_var_importance(); data->write_params( fs ); if( var_importance ) cvWrite( fs, "var_importance", var_importance ); write( fs ); cvEndWriteStruct( fs ); __END__; } void CvDTree::write( CvFileStorage* fs ) { //CV_FUNCNAME( "CvDTree::write" ); __BEGIN__; cvWriteInt( fs, "best_tree_idx", pruned_tree_idx ); cvStartWriteStruct( fs, "nodes", CV_NODE_SEQ ); write_tree_nodes( fs ); cvEndWriteStruct( fs ); __END__; } CvDTreeSplit* CvDTree::read_split( CvFileStorage* fs, CvFileNode* fnode ) { CvDTreeSplit* split = 0; CV_FUNCNAME( "CvDTree::read_split" ); __BEGIN__; int vi, ci; if( !fnode || CV_NODE_TYPE(fnode->tag) != CV_NODE_MAP ) CV_ERROR( CV_StsParseError, "some of the splits are not stored properly" ); vi = cvReadIntByName( fs, fnode, "var", -1 ); if( (unsigned)vi >= (unsigned)data->var_count ) CV_ERROR( CV_StsOutOfRange, "Split variable index is out of range" ); ci = data->get_var_type(vi); if( ci >= 0 ) // split on categorical var { int i, n = data->cat_count->data.i[ci], inversed = 0, val; CvSeqReader reader; CvFileNode* inseq; split = data->new_split_cat( vi, 0 ); inseq = cvGetFileNodeByName( fs, fnode, "in" ); if( !inseq ) { inseq = cvGetFileNodeByName( fs, fnode, "not_in" ); inversed = 1; } if( !inseq || (CV_NODE_TYPE(inseq->tag) != CV_NODE_SEQ && CV_NODE_TYPE(inseq->tag) != CV_NODE_INT)) CV_ERROR( CV_StsParseError, "Either 'in' or 'not_in' tags should be inside a categorical split data" ); if( CV_NODE_TYPE(inseq->tag) == CV_NODE_INT ) { val = inseq->data.i; if( (unsigned)val >= (unsigned)n ) CV_ERROR( CV_StsOutOfRange, "some of in/not_in elements are out of range" ); split->subset[val >> 5] |= 1 << (val & 31); } else { cvStartReadSeq( inseq->data.seq, &reader ); for( i = 0; i < reader.seq->total; i++ ) { CvFileNode* inode = (CvFileNode*)reader.ptr; val = inode->data.i; if( CV_NODE_TYPE(inode->tag) != CV_NODE_INT || (unsigned)val >= (unsigned)n ) CV_ERROR( CV_StsOutOfRange, "some of in/not_in elements are out of range" ); split->subset[val >> 5] |= 1 << (val & 31); CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader ); } } // for categorical splits we do not use inversed splits, // instead we inverse the variable set in the split if( inversed ) for( i = 0; i < (n + 31) >> 5; i++ ) split->subset[i] ^= -1; } else { CvFileNode* cmp_node; split = data->new_split_ord( vi, 0, 0, 0, 0 ); cmp_node = cvGetFileNodeByName( fs, fnode, "le" ); if( !cmp_node ) { cmp_node = cvGetFileNodeByName( fs, fnode, "gt" ); split->inversed = 1; } split->ord.c = (float)cvReadReal( cmp_node ); } split->quality = (float)cvReadRealByName( fs, fnode, "quality" ); __END__; return split; } CvDTreeNode* CvDTree::read_node( CvFileStorage* fs, CvFileNode* fnode, CvDTreeNode* parent ) { CvDTreeNode* node = 0; CV_FUNCNAME( "CvDTree::read_node" ); __BEGIN__; CvFileNode* splits; int i, depth; if( !fnode || CV_NODE_TYPE(fnode->tag) != CV_NODE_MAP ) CV_ERROR( CV_StsParseError, "some of the tree elements are not stored properly" ); CV_CALL( node = data->new_node( parent, 0, 0, 0 )); depth = cvReadIntByName( fs, fnode, "depth", -1 ); if( depth != node->depth ) CV_ERROR( CV_StsParseError, "incorrect node depth" ); node->sample_count = cvReadIntByName( fs, fnode, "sample_count" ); node->value = cvReadRealByName( fs, fnode, "value" ); if( data->is_classifier ) node->class_idx = cvReadIntByName( fs, fnode, "norm_class_idx" ); node->Tn = cvReadIntByName( fs, fnode, "Tn" ); node->complexity = cvReadIntByName( fs, fnode, "complexity" ); node->alpha = cvReadRealByName( fs, fnode, "alpha" ); node->node_risk = cvReadRealByName( fs, fnode, "node_risk" ); node->tree_risk = cvReadRealByName( fs, fnode, "tree_risk" ); node->tree_error = cvReadRealByName( fs, fnode, "tree_error" ); splits = cvGetFileNodeByName( fs, fnode, "splits" ); if( splits ) { CvSeqReader reader; CvDTreeSplit* last_split = 0; if( CV_NODE_TYPE(splits->tag) != CV_NODE_SEQ ) CV_ERROR( CV_StsParseError, "splits tag must stored as a sequence" ); cvStartReadSeq( splits->data.seq, &reader ); for( i = 0; i < reader.seq->total; i++ ) { CvDTreeSplit* split; CV_CALL( split = read_split( fs, (CvFileNode*)reader.ptr )); if( !last_split ) node->split = last_split = split; else last_split = last_split->next = split; CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader ); } } __END__; return node; } void CvDTree::read_tree_nodes( CvFileStorage* fs, CvFileNode* fnode ) { CV_FUNCNAME( "CvDTree::read_tree_nodes" ); __BEGIN__; CvSeqReader reader; CvDTreeNode _root; CvDTreeNode* parent = &_root; int i; parent->left = parent->right = parent->parent = 0; cvStartReadSeq( fnode->data.seq, &reader ); for( i = 0; i < reader.seq->total; i++ ) { CvDTreeNode* node; CV_CALL( node = read_node( fs, (CvFileNode*)reader.ptr, parent != &_root ? parent : 0 )); if( !parent->left ) parent->left = node; else parent->right = node; if( node->split ) parent = node; else { while( parent && parent->right ) parent = parent->parent; } CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader ); } root = _root.left; __END__; } void CvDTree::read( CvFileStorage* fs, CvFileNode* fnode ) { CvDTreeTrainData* _data = new CvDTreeTrainData(); _data->read_params( fs, fnode ); read( fs, fnode, _data ); get_var_importance(); } // a special entry point for reading weak decision trees from the tree ensembles void CvDTree::read( CvFileStorage* fs, CvFileNode* node, CvDTreeTrainData* _data ) { CV_FUNCNAME( "CvDTree::read" ); __BEGIN__; CvFileNode* tree_nodes; clear(); data = _data; tree_nodes = cvGetFileNodeByName( fs, node, "nodes" ); if( !tree_nodes || CV_NODE_TYPE(tree_nodes->tag) != CV_NODE_SEQ ) CV_ERROR( CV_StsParseError, "nodes tag is missing" ); pruned_tree_idx = cvReadIntByName( fs, node, "best_tree_idx", -1 ); read_tree_nodes( fs, tree_nodes ); __END__; } /* End of file. */