/*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 "old_ml_precomp.hpp" static inline double log_ratio( double val ) { const double eps = 1e-5; val = MAX( val, eps ); val = MIN( val, 1. - eps ); return log( val/(1. - val) ); } CvBoostParams::CvBoostParams() { boost_type = CvBoost::REAL; weak_count = 100; weight_trim_rate = 0.95; cv_folds = 0; max_depth = 1; } CvBoostParams::CvBoostParams( int _boost_type, int _weak_count, double _weight_trim_rate, int _max_depth, bool _use_surrogates, const float* _priors ) { boost_type = _boost_type; weak_count = _weak_count; weight_trim_rate = _weight_trim_rate; split_criteria = CvBoost::DEFAULT; cv_folds = 0; max_depth = _max_depth; use_surrogates = _use_surrogates; priors = _priors; } ///////////////////////////////// CvBoostTree /////////////////////////////////// CvBoostTree::CvBoostTree() { ensemble = 0; } CvBoostTree::~CvBoostTree() { clear(); } void CvBoostTree::clear() { CvDTree::clear(); ensemble = 0; } bool CvBoostTree::train( CvDTreeTrainData* _train_data, const CvMat* _subsample_idx, CvBoost* _ensemble ) { clear(); ensemble = _ensemble; data = _train_data; data->shared = true; return do_train( _subsample_idx ); } bool CvBoostTree::train( const CvMat*, int, const CvMat*, const CvMat*, const CvMat*, const CvMat*, const CvMat*, CvDTreeParams ) { assert(0); return false; } bool CvBoostTree::train( CvDTreeTrainData*, const CvMat* ) { assert(0); return false; } void CvBoostTree::scale( double _scale ) { CvDTreeNode* node = root; // traverse the tree and scale all the node values for(;;) { CvDTreeNode* parent; for(;;) { node->value *= _scale; 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; } } void CvBoostTree::try_split_node( CvDTreeNode* node ) { CvDTree::try_split_node( node ); if( !node->left ) { // if the node has not been split, // store the responses for the corresponding training samples double* weak_eval = ensemble->get_weak_response()->data.db; cv::AutoBuffer inn_buf(node->sample_count); const int* labels = data->get_cv_labels(node, inn_buf.data()); int i, count = node->sample_count; double value = node->value; for( i = 0; i < count; i++ ) weak_eval[labels[i]] = value; } } double CvBoostTree::calc_node_dir( CvDTreeNode* node ) { char* dir = (char*)data->direction->data.ptr; const double* weights = ensemble->get_subtree_weights()->data.db; 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 inn_buf(n); const int* cat_labels = data->get_cat_var_data(node, vi, inn_buf.data()); const int* subset = node->split->subset; double sum = 0, sum_abs = 0; for( i = 0; i < n; i++ ) { int idx = ((cat_labels[i] == 65535) && data->is_buf_16u) ? -1 : cat_labels[i]; double w = weights[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 { cv::AutoBuffer inn_buf(2*n*sizeof(int)+n*sizeof(float)); float* values_buf = (float*)inn_buf.data(); int* sorted_indices_buf = (int*)(values_buf + n); int* sample_indices_buf = sorted_indices_buf + n; const float* values = 0; const int* sorted_indices = 0; data->get_ord_var_data( node, vi, values_buf, sorted_indices_buf, &values, &sorted_indices, sample_indices_buf ); int split_point = node->split->ord.split_point; int n1 = node->get_num_valid(vi); assert( 0 <= split_point && split_point < n1-1 ); L = R = 0; for( i = 0; i <= split_point; i++ ) { int idx = sorted_indices[i]; double w = weights[idx]; dir[idx] = (char)-1; L += w; } for( ; i < n1; i++ ) { int idx = sorted_indices[i]; double w = weights[idx]; dir[idx] = (char)1; R += w; } for( ; i < n; i++ ) dir[sorted_indices[i]] = (char)0; } node->maxlr = MAX( L, R ); return node->split->quality/(L + R); } CvDTreeSplit* CvBoostTree::find_split_ord_class( CvDTreeNode* node, int vi, float init_quality, CvDTreeSplit* _split, uchar* _ext_buf ) { const float epsilon = FLT_EPSILON*2; const double* weights = ensemble->get_subtree_weights()->data.db; int n = node->sample_count; int n1 = node->get_num_valid(vi); cv::AutoBuffer inn_buf; if( !_ext_buf ) inn_buf.allocate(n*(3*sizeof(int)+sizeof(float))); uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data(); float* values_buf = (float*)ext_buf; int* sorted_indices_buf = (int*)(values_buf + n); int* sample_indices_buf = sorted_indices_buf + n; const float* values = 0; const int* sorted_indices = 0; data->get_ord_var_data( node, vi, values_buf, sorted_indices_buf, &values, &sorted_indices, sample_indices_buf ); int* responses_buf = sorted_indices_buf + n; const int* responses = data->get_class_labels( node, responses_buf ); const double* rcw0 = weights + n; double lcw[2] = {0,0}, rcw[2]; int i, best_i = -1; double best_val = init_quality; int boost_type = ensemble->get_params().boost_type; int split_criteria = ensemble->get_params().split_criteria; rcw[0] = rcw0[0]; rcw[1] = rcw0[1]; for( i = n1; i < n; i++ ) { int idx = sorted_indices[i]; double w = weights[idx]; rcw[responses[idx]] -= w; } if( split_criteria != CvBoost::GINI && split_criteria != CvBoost::MISCLASS ) split_criteria = boost_type == CvBoost::DISCRETE ? CvBoost::MISCLASS : CvBoost::GINI; if( split_criteria == CvBoost::GINI ) { double L = 0, R = rcw[0] + rcw[1]; double lsum2 = 0, rsum2 = rcw[0]*rcw[0] + rcw[1]*rcw[1]; for( i = 0; i < n1 - 1; i++ ) { int idx = sorted_indices[i]; double w = weights[idx], w2 = w*w; double lv, rv; idx = responses[idx]; L += w; R -= w; lv = lcw[idx]; rv = rcw[idx]; lsum2 += 2*lv*w + w2; rsum2 -= 2*rv*w - w2; lcw[idx] = lv + w; rcw[idx] = rv - w; if( values[i] + epsilon < values[i+1] ) { double val = (lsum2*R + rsum2*L)/(L*R); if( best_val < val ) { best_val = val; best_i = i; } } } } else { for( i = 0; i < n1 - 1; i++ ) { int idx = sorted_indices[i]; double w = weights[idx]; idx = responses[idx]; lcw[idx] += w; rcw[idx] -= w; if( values[i] + epsilon < values[i+1] ) { double val = lcw[0] + rcw[1], val2 = lcw[1] + rcw[0]; val = MAX(val, val2); if( best_val < val ) { best_val = val; best_i = i; } } } } CvDTreeSplit* split = 0; if( best_i >= 0 ) { split = _split ? _split : data->new_split_ord( 0, 0.0f, 0, 0, 0.0f ); split->var_idx = vi; split->ord.c = (values[best_i] + values[best_i+1])*0.5f; split->ord.split_point = best_i; split->inversed = 0; split->quality = (float)best_val; } return split; } template class LessThanPtr { public: bool operator()(T* a, T* b) const { return *a < *b; } }; CvDTreeSplit* CvBoostTree::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 mi = data->cat_count->data.i[ci]; int base_size = (2*mi+3)*sizeof(double) + mi*sizeof(double*); cv::AutoBuffer inn_buf((2*mi+3)*sizeof(double) + mi*sizeof(double*)); if( !_ext_buf) inn_buf.allocate( base_size + 2*n*sizeof(int) ); uchar* base_buf = inn_buf.data(); uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size; int* cat_labels_buf = (int*)ext_buf; const int* cat_labels = data->get_cat_var_data(node, vi, cat_labels_buf); int* responses_buf = cat_labels_buf + n; const int* responses = data->get_class_labels(node, responses_buf); double lcw[2]={0,0}, rcw[2]={0,0}; double* cjk = (double*)cv::alignPtr(base_buf,sizeof(double))+2; const double* weights = ensemble->get_subtree_weights()->data.db; double** dbl_ptr = (double**)(cjk + 2*mi); int i, j, k, idx; double L = 0, R; double best_val = init_quality; int best_subset = -1, subset_i; int boost_type = ensemble->get_params().boost_type; int split_criteria = ensemble->get_params().split_criteria; // 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++ ) cjk[j*2] = cjk[j*2+1] = 0; for( i = 0; i < n; i++ ) { double w = weights[i]; j = ((cat_labels[i] == 65535) && data->is_buf_16u) ? -1 : cat_labels[i]; k = responses[i]; cjk[j*2 + k] += w; } for( j = 0; j < mi; j++ ) { rcw[0] += cjk[j*2]; rcw[1] += cjk[j*2+1]; dbl_ptr[j] = cjk + j*2 + 1; } R = rcw[0] + rcw[1]; if( split_criteria != CvBoost::GINI && split_criteria != CvBoost::MISCLASS ) split_criteria = boost_type == CvBoost::DISCRETE ? CvBoost::MISCLASS : CvBoost::GINI; // sort rows of c_jk by increasing c_j,1 // (i.e. by the weight of samples in j-th category that belong to class 1) std::sort(dbl_ptr, dbl_ptr + mi, LessThanPtr()); for( subset_i = 0; subset_i < mi-1; subset_i++ ) { idx = (int)(dbl_ptr[subset_i] - cjk)/2; const double* crow = cjk + idx*2; double w0 = crow[0], w1 = crow[1]; double weight = w0 + w1; if( weight < FLT_EPSILON ) continue; lcw[0] += w0; rcw[0] -= w0; lcw[1] += w1; rcw[1] -= w1; if( split_criteria == CvBoost::GINI ) { double lsum2 = lcw[0]*lcw[0] + lcw[1]*lcw[1]; double rsum2 = rcw[0]*rcw[0] + rcw[1]*rcw[1]; L += weight; R -= weight; if( L > FLT_EPSILON && R > FLT_EPSILON ) { double val = (lsum2*R + rsum2*L)/(L*R); if( best_val < val ) { best_val = val; best_subset = subset_i; } } } else { double val = lcw[0] + rcw[1]; double val2 = lcw[1] + rcw[0]; val = MAX(val, val2); if( best_val < val ) { best_val = val; best_subset = subset_i; } } } CvDTreeSplit* split = 0; if( best_subset >= 0 ) { split = _split ? _split : data->new_split_cat( 0, -1.0f); split->var_idx = vi; split->quality = (float)best_val; memset( split->subset, 0, (data->max_c_count + 31)/32 * sizeof(int)); for( i = 0; i <= best_subset; i++ ) { idx = (int)(dbl_ptr[i] - cjk) >> 1; split->subset[idx >> 5] |= 1 << (idx & 31); } } return split; } CvDTreeSplit* CvBoostTree::find_split_ord_reg( CvDTreeNode* node, int vi, float init_quality, CvDTreeSplit* _split, uchar* _ext_buf ) { const float epsilon = FLT_EPSILON*2; const double* weights = ensemble->get_subtree_weights()->data.db; int n = node->sample_count; int n1 = node->get_num_valid(vi); cv::AutoBuffer inn_buf; if( !_ext_buf ) inn_buf.allocate(2*n*(sizeof(int)+sizeof(float))); uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data(); float* values_buf = (float*)ext_buf; int* indices_buf = (int*)(values_buf + n); int* sample_indices_buf = indices_buf + n; const float* values = 0; const int* indices = 0; data->get_ord_var_data( node, vi, values_buf, indices_buf, &values, &indices, sample_indices_buf ); float* responses_buf = (float*)(indices_buf + n); const float* responses = data->get_ord_responses( node, responses_buf, sample_indices_buf ); int i, best_i = -1; double L = 0, R = weights[n]; double best_val = init_quality, lsum = 0, rsum = node->value*R; // compensate for missing values for( i = n1; i < n; i++ ) { int idx = indices[i]; double w = weights[idx]; rsum -= responses[idx]*w; R -= w; } // find the optimal split for( i = 0; i < n1 - 1; i++ ) { int idx = indices[i]; double w = weights[idx]; double t = responses[idx]*w; L += w; R -= w; lsum += t; rsum -= t; if( values[i] + epsilon < values[i+1] ) { double val = (lsum*lsum*R + rsum*rsum*L)/(L*R); if( best_val < val ) { best_val = val; best_i = i; } } } CvDTreeSplit* split = 0; if( best_i >= 0 ) { split = _split ? _split : data->new_split_ord( 0, 0.0f, 0, 0, 0.0f ); split->var_idx = vi; split->ord.c = (values[best_i] + values[best_i+1])*0.5f; split->ord.split_point = best_i; split->inversed = 0; split->quality = (float)best_val; } return split; } CvDTreeSplit* CvBoostTree::find_split_cat_reg( CvDTreeNode* node, int vi, float init_quality, CvDTreeSplit* _split, uchar* _ext_buf ) { const double* weights = ensemble->get_subtree_weights()->data.db; int ci = data->get_var_type(vi); int n = node->sample_count; int mi = data->cat_count->data.i[ci]; int base_size = (2*mi+3)*sizeof(double) + mi*sizeof(double*); cv::AutoBuffer inn_buf(base_size); if( !_ext_buf ) inn_buf.allocate(base_size + n*(2*sizeof(int) + sizeof(float))); uchar* base_buf = inn_buf.data(); uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size; int* cat_labels_buf = (int*)ext_buf; const int* cat_labels = data->get_cat_var_data(node, vi, cat_labels_buf); float* responses_buf = (float*)(cat_labels_buf + n); int* sample_indices_buf = (int*)(responses_buf + n); const float* responses = data->get_ord_responses(node, responses_buf, sample_indices_buf); double* sum = (double*)cv::alignPtr(base_buf,sizeof(double)) + 1; double* counts = sum + mi + 1; double** sum_ptr = (double**)(counts + mi); double L = 0, R = 0, best_val = init_quality, lsum = 0, rsum = 0; int i, 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 = ((cat_labels[i] == 65535) && data->is_buf_16u) ? -1 : cat_labels[i]; double w = weights[i]; double s = sum[idx] + responses[i]*w; double nc = counts[idx] + w; 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] = fabs(counts[i]) > DBL_EPSILON ? sum[i]/counts[i] : 0; sum_ptr[i] = sum + i; } std::sort(sum_ptr, sum_ptr + mi, LessThanPtr()); // revert back to unnormalized sums // (there should be a very little loss in 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); double ni = counts[idx]; if( ni > FLT_EPSILON ) { double s = sum[idx]; lsum += s; L += ni; rsum -= s; R -= ni; if( L > FLT_EPSILON && R > FLT_EPSILON ) { double val = (lsum*lsum*R + rsum*rsum*L)/(L*R); if( best_val < val ) { best_val = val; best_subset = subset_i; } } } } CvDTreeSplit* split = 0; if( best_subset >= 0 ) { split = _split ? _split : data->new_split_cat( 0, -1.0f); split->var_idx = vi; split->quality = (float)best_val; memset( split->subset, 0, (data->max_c_count + 31)/32 * sizeof(int)); for( i = 0; i <= best_subset; i++ ) { int idx = (int)(sum_ptr[i] - sum); split->subset[idx >> 5] |= 1 << (idx & 31); } } return split; } CvDTreeSplit* CvBoostTree::find_surrogate_split_ord( CvDTreeNode* node, int vi, uchar* _ext_buf ) { const float epsilon = FLT_EPSILON*2; int n = node->sample_count; cv::AutoBuffer inn_buf; if( !_ext_buf ) inn_buf.allocate(n*(2*sizeof(int)+sizeof(float))); uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data(); float* values_buf = (float*)ext_buf; int* indices_buf = (int*)(values_buf + n); int* sample_indices_buf = indices_buf + n; const float* values = 0; const int* indices = 0; data->get_ord_var_data( node, vi, values_buf, indices_buf, &values, &indices, sample_indices_buf ); const double* weights = ensemble->get_subtree_weights()->data.db; 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; double LL = 0, RL = 0, LR, RR; double worst_val = node->maxlr; double sum = 0, sum_abs = 0; best_val = worst_val; for( i = 0; i < n1; i++ ) { int idx = indices[i]; double w = weights[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 = indices[i]; double w = weights[idx]; int d = dir[idx]; if( d < 0 ) { LL += w; LR -= w; if( LL + RR > best_val && values[i] + epsilon < values[i+1] ) { best_val = LL + RR; best_i = i; best_inversed = 0; } } else if( d > 0 ) { RL += w; RR -= w; if( RL + LR > best_val && values[i] + epsilon < values[i+1] ) { best_val = RL + LR; best_i = i; best_inversed = 1; } } } return best_i >= 0 && best_val > node->maxlr ? data->new_split_ord( vi, (values[best_i] + values[best_i+1])*0.5f, best_i, best_inversed, (float)best_val ) : 0; } CvDTreeSplit* CvBoostTree::find_surrogate_split_cat( CvDTreeNode* node, int vi, uchar* _ext_buf ) { const char* dir = (char*)data->direction->data.ptr; const double* weights = ensemble->get_subtree_weights()->data.db; int n = node->sample_count; int i, mi = data->cat_count->data.i[data->get_var_type(vi)]; int base_size = (2*mi+3)*sizeof(double); cv::AutoBuffer inn_buf(base_size); if( !_ext_buf ) inn_buf.allocate(base_size + n*sizeof(int)); uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data(); int* cat_labels_buf = (int*)ext_buf; const int* cat_labels = data->get_cat_var_data(node, vi, cat_labels_buf); // 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 ); double best_val = 0; double* lc = (double*)cv::alignPtr(cat_labels_buf + n, sizeof(double)) + 1; double* rc = lc + mi + 1; for( i = -1; i < mi; i++ ) lc[i] = rc[i] = 0; // 1. for each category calculate the weight of samples // sent to the left (lc) and to the right (rc) by the primary split for( i = 0; i < n; i++ ) { int idx = ((cat_labels[i] == 65535) && data->is_buf_16u) ? -1 : cat_labels[i]; double w = weights[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; } else best_val += rval; } split->quality = (float)best_val; if( split->quality <= node->maxlr ) cvSetRemoveByPtr( data->split_heap, split ), split = 0; return split; } void CvBoostTree::calc_node_value( CvDTreeNode* node ) { int i, n = node->sample_count; const double* weights = ensemble->get_weights()->data.db; cv::AutoBuffer inn_buf(n*(sizeof(int) + ( data->is_classifier ? sizeof(int) : sizeof(int) + sizeof(float)))); int* labels_buf = (int*)inn_buf.data(); const int* labels = data->get_cv_labels(node, labels_buf); double* subtree_weights = ensemble->get_subtree_weights()->data.db; double rcw[2] = {0,0}; int boost_type = ensemble->get_params().boost_type; if( data->is_classifier ) { int* _responses_buf = labels_buf + n; const int* _responses = data->get_class_labels(node, _responses_buf); int m = data->get_num_classes(); int* cls_count = data->counts->data.i; for( int k = 0; k < m; k++ ) cls_count[k] = 0; for( i = 0; i < n; i++ ) { int idx = labels[i]; double w = weights[idx]; int r = _responses[i]; rcw[r] += w; cls_count[r]++; subtree_weights[i] = w; } node->class_idx = rcw[1] > rcw[0]; if( boost_type == CvBoost::DISCRETE ) { // ignore cat_map for responses, and use {-1,1}, // as the whole ensemble response is computes as sign(sum_i(weak_response_i) node->value = node->class_idx*2 - 1; } else { double p = rcw[1]/(rcw[0] + rcw[1]); assert( boost_type == CvBoost::REAL ); // store log-ratio of the probability node->value = 0.5*log_ratio(p); } } 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) double sum = 0, sum2 = 0, iw; float* values_buf = (float*)(labels_buf + n); int* sample_indices_buf = (int*)(values_buf + n); const float* values = data->get_ord_responses(node, values_buf, sample_indices_buf); for( i = 0; i < n; i++ ) { int idx = labels[i]; double w = weights[idx]/*priors[values[i] > 0]*/; double t = values[i]; rcw[0] += w; subtree_weights[i] = w; sum += t*w; sum2 += t*t*w; } iw = 1./rcw[0]; node->value = sum*iw; node->node_risk = sum2 - (sum*iw)*sum; // renormalize the risk, as in try_split_node the unweighted formula // sqrt(risk)/n is used, rather than sqrt(risk)/sum(weights_i) node->node_risk *= n*iw*n*iw; } // store summary weights subtree_weights[n] = rcw[0]; subtree_weights[n+1] = rcw[1]; } void CvBoostTree::read( const cv::FileNode& fnode, CvBoost* _ensemble, CvDTreeTrainData* _data ) { CvDTree::read( fnode, _data ); ensemble = _ensemble; } void CvBoostTree::read( cv::FileNode& ) { assert(0); } void CvBoostTree::read( cv::FileNode& _node, CvDTreeTrainData* _data ) { CvDTree::read( _node, _data ); } /////////////////////////////////// CvBoost ///////////////////////////////////// CvBoost::CvBoost() { data = 0; weak = 0; default_model_name = "my_boost_tree"; active_vars = active_vars_abs = orig_response = sum_response = weak_eval = subsample_mask = weights = subtree_weights = 0; have_active_cat_vars = have_subsample = false; clear(); } void CvBoost::prune( CvSlice slice ) { if( weak && weak->total > 0 ) { CvSeqReader reader; int i, count = cvSliceLength( slice, weak ); cvStartReadSeq( weak, &reader ); cvSetSeqReaderPos( &reader, slice.start_index ); for( i = 0; i < count; i++ ) { CvBoostTree* w; CV_READ_SEQ_ELEM( w, reader ); delete w; } cvSeqRemoveSlice( weak, slice ); } } void CvBoost::clear() { if( weak ) { prune( CV_WHOLE_SEQ ); cvReleaseMemStorage( &weak->storage ); } if( data ) delete data; weak = 0; data = 0; cvReleaseMat( &active_vars ); cvReleaseMat( &active_vars_abs ); cvReleaseMat( &orig_response ); cvReleaseMat( &sum_response ); cvReleaseMat( &weak_eval ); cvReleaseMat( &subsample_mask ); cvReleaseMat( &weights ); cvReleaseMat( &subtree_weights ); have_subsample = false; } CvBoost::~CvBoost() { clear(); } CvBoost::CvBoost( 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, CvBoostParams _params ) { weak = 0; data = 0; default_model_name = "my_boost_tree"; active_vars = active_vars_abs = orig_response = sum_response = weak_eval = subsample_mask = weights = subtree_weights = 0; train( _train_data, _tflag, _responses, _var_idx, _sample_idx, _var_type, _missing_mask, _params ); } bool CvBoost::set_params( const CvBoostParams& _params ) { bool ok = false; CV_FUNCNAME( "CvBoost::set_params" ); __BEGIN__; params = _params; if( params.boost_type != DISCRETE && params.boost_type != REAL && params.boost_type != LOGIT && params.boost_type != GENTLE ) CV_ERROR( cv::Error::StsBadArg, "Unknown/unsupported boosting type" ); params.weak_count = MAX( params.weak_count, 1 ); params.weight_trim_rate = MAX( params.weight_trim_rate, 0. ); params.weight_trim_rate = MIN( params.weight_trim_rate, 1. ); if( params.weight_trim_rate < FLT_EPSILON ) params.weight_trim_rate = 1.f; if( params.boost_type == DISCRETE && params.split_criteria != GINI && params.split_criteria != MISCLASS ) params.split_criteria = MISCLASS; if( params.boost_type == REAL && params.split_criteria != GINI && params.split_criteria != MISCLASS ) params.split_criteria = GINI; if( (params.boost_type == LOGIT || params.boost_type == GENTLE) && params.split_criteria != SQERR ) params.split_criteria = SQERR; ok = true; __END__; return ok; } bool CvBoost::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, CvBoostParams _params, bool _update ) { bool ok = false; CvMemStorage* storage = 0; CV_FUNCNAME( "CvBoost::train" ); __BEGIN__; int i; set_params( _params ); cvReleaseMat( &active_vars ); cvReleaseMat( &active_vars_abs ); if( !_update || !data ) { clear(); data = new CvDTreeTrainData( _train_data, _tflag, _responses, _var_idx, _sample_idx, _var_type, _missing_mask, _params, true, true ); if( data->get_num_classes() != 2 ) CV_ERROR( cv::Error::StsNotImplemented, "Boosted trees can only be used for 2-class classification." ); CV_CALL( storage = cvCreateMemStorage() ); weak = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvBoostTree*), storage ); storage = 0; } else { data->set_data( _train_data, _tflag, _responses, _var_idx, _sample_idx, _var_type, _missing_mask, _params, true, true, true ); } if ( (_params.boost_type == LOGIT) || (_params.boost_type == GENTLE) ) data->do_responses_copy(); update_weights( 0 ); for( i = 0; i < params.weak_count; i++ ) { CvBoostTree* tree = new CvBoostTree; if( !tree->train( data, subsample_mask, this ) ) { delete tree; break; } //cvCheckArr( get_weak_response()); cvSeqPush( weak, &tree ); update_weights( tree ); trim_weights(); if( cvCountNonZero(subsample_mask) == 0 ) break; } if(weak->total > 0) { get_active_vars(); // recompute active_vars* maps and condensed_idx's in the splits. data->is_classifier = true; data->free_train_data(); ok = true; } else clear(); __END__; return ok; } bool CvBoost::train( CvMLData* _data, CvBoostParams _params, bool update ) { bool result = false; CV_FUNCNAME( "CvBoost::train" ); __BEGIN__; const CvMat* values = _data->get_values(); const CvMat* response = _data->get_responses(); const CvMat* missing = _data->get_missing(); const CvMat* var_types = _data->get_var_types(); const CvMat* train_sidx = _data->get_train_sample_idx(); const CvMat* var_idx = _data->get_var_idx(); CV_CALL( result = train( values, CV_ROW_SAMPLE, response, var_idx, train_sidx, var_types, missing, _params, update ) ); __END__; return result; } void CvBoost::initialize_weights(double (&p)[2]) { p[0] = 1.; p[1] = 1.; } void CvBoost::update_weights( CvBoostTree* tree ) { CV_FUNCNAME( "CvBoost::update_weights" ); __BEGIN__; int i, n = data->sample_count; double sumw = 0.; int step = 0; float* fdata = 0; int *sample_idx_buf; const int* sample_idx = 0; cv::AutoBuffer inn_buf; size_t _buf_size = (params.boost_type == LOGIT) || (params.boost_type == GENTLE) ? (size_t)(data->sample_count)*sizeof(int) : 0; if( !tree ) _buf_size += n*sizeof(int); else { if( have_subsample ) _buf_size += data->get_length_subbuf()*(sizeof(float)+sizeof(uchar)); } inn_buf.allocate(_buf_size); uchar* cur_buf_pos = inn_buf.data(); if ( (params.boost_type == LOGIT) || (params.boost_type == GENTLE) ) { step = CV_IS_MAT_CONT(data->responses_copy->type) ? 1 : data->responses_copy->step / CV_ELEM_SIZE(data->responses_copy->type); fdata = data->responses_copy->data.fl; sample_idx_buf = (int*)cur_buf_pos; cur_buf_pos = (uchar*)(sample_idx_buf + data->sample_count); sample_idx = data->get_sample_indices( data->data_root, sample_idx_buf ); } CvMat* dtree_data_buf = data->buf; size_t length_buf_row = data->get_length_subbuf(); if( !tree ) // before training the first tree, initialize weights and other parameters { int* class_labels_buf = (int*)cur_buf_pos; cur_buf_pos = (uchar*)(class_labels_buf + n); const int* class_labels = data->get_class_labels(data->data_root, class_labels_buf); // in case of logitboost and gentle adaboost each weak tree is a regression tree, // so we need to convert class labels to floating-point values double w0 = 1./ n; double p[2] = { 1., 1. }; initialize_weights(p); cvReleaseMat( &orig_response ); cvReleaseMat( &sum_response ); cvReleaseMat( &weak_eval ); cvReleaseMat( &subsample_mask ); cvReleaseMat( &weights ); cvReleaseMat( &subtree_weights ); CV_CALL( orig_response = cvCreateMat( 1, n, CV_32S )); CV_CALL( weak_eval = cvCreateMat( 1, n, CV_64F )); CV_CALL( subsample_mask = cvCreateMat( 1, n, CV_8U )); CV_CALL( weights = cvCreateMat( 1, n, CV_64F )); CV_CALL( subtree_weights = cvCreateMat( 1, n + 2, CV_64F )); if( data->have_priors ) { // compute weight scale for each class from their prior probabilities int c1 = 0; for( i = 0; i < n; i++ ) c1 += class_labels[i]; p[0] = data->priors->data.db[0]*(c1 < n ? 1./(n - c1) : 0.); p[1] = data->priors->data.db[1]*(c1 > 0 ? 1./c1 : 0.); p[0] /= p[0] + p[1]; p[1] = 1. - p[0]; } if (data->is_buf_16u) { unsigned short* labels = (unsigned short*)(dtree_data_buf->data.s + data->data_root->buf_idx*length_buf_row + data->data_root->offset + (size_t)(data->work_var_count-1)*data->sample_count); for( i = 0; i < n; i++ ) { // save original categorical responses {0,1}, convert them to {-1,1} orig_response->data.i[i] = class_labels[i]*2 - 1; // make all the samples active at start. // later, in trim_weights() deactivate/reactive again some, if need subsample_mask->data.ptr[i] = (uchar)1; // make all the initial weights the same. weights->data.db[i] = w0*p[class_labels[i]]; // set the labels to find (from within weak tree learning proc) // the particular sample weight, and where to store the response. labels[i] = (unsigned short)i; } } else { int* labels = dtree_data_buf->data.i + data->data_root->buf_idx*length_buf_row + data->data_root->offset + (size_t)(data->work_var_count-1)*data->sample_count; for( i = 0; i < n; i++ ) { // save original categorical responses {0,1}, convert them to {-1,1} orig_response->data.i[i] = class_labels[i]*2 - 1; // make all the samples active at start. // later, in trim_weights() deactivate/reactive again some, if need subsample_mask->data.ptr[i] = (uchar)1; // make all the initial weights the same. weights->data.db[i] = w0*p[class_labels[i]]; // set the labels to find (from within weak tree learning proc) // the particular sample weight, and where to store the response. labels[i] = i; } } if( params.boost_type == LOGIT ) { CV_CALL( sum_response = cvCreateMat( 1, n, CV_64F )); for( i = 0; i < n; i++ ) { sum_response->data.db[i] = 0; fdata[sample_idx[i]*step] = orig_response->data.i[i] > 0 ? 2.f : -2.f; } // in case of logitboost each weak tree is a regression tree. // the target function values are recalculated for each of the trees data->is_classifier = false; } else if( params.boost_type == GENTLE ) { for( i = 0; i < n; i++ ) fdata[sample_idx[i]*step] = (float)orig_response->data.i[i]; data->is_classifier = false; } } else { // at this moment, for all the samples that participated in the training of the most // recent weak classifier we know the responses. For other samples we need to compute them if( have_subsample ) { float* values = (float*)cur_buf_pos; cur_buf_pos = (uchar*)(values + data->get_length_subbuf()); uchar* missing = cur_buf_pos; cur_buf_pos = missing + data->get_length_subbuf() * (size_t)CV_ELEM_SIZE(data->buf->type); CvMat _sample, _mask; // invert the subsample mask cvXorS( subsample_mask, cvScalar(1.), subsample_mask ); data->get_vectors( subsample_mask, values, missing, 0 ); _sample = cvMat( 1, data->var_count, CV_32F ); _mask = cvMat( 1, data->var_count, CV_8U ); // run tree through all the non-processed samples for( i = 0; i < n; i++ ) if( subsample_mask->data.ptr[i] ) { _sample.data.fl = values; _mask.data.ptr = missing; values += _sample.cols; missing += _mask.cols; weak_eval->data.db[i] = tree->predict( &_sample, &_mask, true )->value; } } // now update weights and other parameters for each type of boosting if( params.boost_type == DISCRETE ) { // Discrete AdaBoost: // weak_eval[i] (=f(x_i)) is in {-1,1} // err = sum(w_i*(f(x_i) != y_i))/sum(w_i) // C = log((1-err)/err) // w_i *= exp(C*(f(x_i) != y_i)) double C, err = 0.; double scale[] = { 1., 0. }; for( i = 0; i < n; i++ ) { double w = weights->data.db[i]; sumw += w; err += w*(weak_eval->data.db[i] != orig_response->data.i[i]); } if( sumw != 0 ) err /= sumw; C = err = -log_ratio( err ); scale[1] = exp(err); sumw = 0; for( i = 0; i < n; i++ ) { double w = weights->data.db[i]* scale[weak_eval->data.db[i] != orig_response->data.i[i]]; sumw += w; weights->data.db[i] = w; } tree->scale( C ); } else if( params.boost_type == REAL ) { // Real AdaBoost: // weak_eval[i] = f(x_i) = 0.5*log(p(x_i)/(1-p(x_i))), p(x_i)=P(y=1|x_i) // w_i *= exp(-y_i*f(x_i)) for( i = 0; i < n; i++ ) weak_eval->data.db[i] *= -orig_response->data.i[i]; cvExp( weak_eval, weak_eval ); for( i = 0; i < n; i++ ) { double w = weights->data.db[i]*weak_eval->data.db[i]; sumw += w; weights->data.db[i] = w; } } else if( params.boost_type == LOGIT ) { // LogitBoost: // weak_eval[i] = f(x_i) in [-z_max,z_max] // sum_response = F(x_i). // F(x_i) += 0.5*f(x_i) // p(x_i) = exp(F(x_i))/(exp(F(x_i)) + exp(-F(x_i))=1/(1+exp(-2*F(x_i))) // reuse weak_eval: weak_eval[i] <- p(x_i) // w_i = p(x_i)*1(1 - p(x_i)) // z_i = ((y_i+1)/2 - p(x_i))/(p(x_i)*(1 - p(x_i))) // store z_i to the data->data_root as the new target responses const double lb_weight_thresh = FLT_EPSILON; const double lb_z_max = 10.; /*float* responses_buf = data->get_resp_float_buf(); const float* responses = 0; data->get_ord_responses(data->data_root, responses_buf, &responses);*/ /*if( weak->total == 7 ) putchar('*');*/ for( i = 0; i < n; i++ ) { double s = sum_response->data.db[i] + 0.5*weak_eval->data.db[i]; sum_response->data.db[i] = s; weak_eval->data.db[i] = -2*s; } cvExp( weak_eval, weak_eval ); for( i = 0; i < n; i++ ) { double p = 1./(1. + weak_eval->data.db[i]); double w = p*(1 - p), z; w = MAX( w, lb_weight_thresh ); weights->data.db[i] = w; sumw += w; if( orig_response->data.i[i] > 0 ) { z = 1./p; fdata[sample_idx[i]*step] = (float)MIN(z, lb_z_max); } else { z = 1./(1-p); fdata[sample_idx[i]*step] = (float)-MIN(z, lb_z_max); } } } else { // Gentle AdaBoost: // weak_eval[i] = f(x_i) in [-1,1] // w_i *= exp(-y_i*f(x_i)) assert( params.boost_type == GENTLE ); for( i = 0; i < n; i++ ) weak_eval->data.db[i] *= -orig_response->data.i[i]; cvExp( weak_eval, weak_eval ); for( i = 0; i < n; i++ ) { double w = weights->data.db[i] * weak_eval->data.db[i]; weights->data.db[i] = w; sumw += w; } } } // renormalize weights if( sumw > FLT_EPSILON ) { sumw = 1./sumw; for( i = 0; i < n; ++i ) weights->data.db[i] *= sumw; } __END__; } void CvBoost::trim_weights() { //CV_FUNCNAME( "CvBoost::trim_weights" ); __BEGIN__; int i, count = data->sample_count, nz_count = 0; double sum, threshold; if( params.weight_trim_rate <= 0. || params.weight_trim_rate >= 1. ) EXIT; // use weak_eval as temporary buffer for sorted weights cvCopy( weights, weak_eval ); std::sort(weak_eval->data.db, weak_eval->data.db + count); // as weight trimming occurs immediately after updating the weights, // where they are renormalized, we assume that the weight sum = 1. sum = 1. - params.weight_trim_rate; for( i = 0; i < count; i++ ) { double w = weak_eval->data.db[i]; if( sum <= 0 ) break; sum -= w; } threshold = i < count ? weak_eval->data.db[i] : DBL_MAX; for( i = 0; i < count; i++ ) { double w = weights->data.db[i]; int f = w >= threshold; subsample_mask->data.ptr[i] = (uchar)f; nz_count += f; } have_subsample = nz_count < count; __END__; } const CvMat* CvBoost::get_active_vars( bool absolute_idx ) { CvMat* mask = 0; CvMat* inv_map = 0; CvMat* result = 0; CV_FUNCNAME( "CvBoost::get_active_vars" ); __BEGIN__; if( !weak ) CV_ERROR( cv::Error::StsError, "The boosted tree ensemble has not been trained yet" ); if( !active_vars || !active_vars_abs ) { CvSeqReader reader; int i, j, nactive_vars; CvBoostTree* wtree; const CvDTreeNode* node; assert(!active_vars && !active_vars_abs); mask = cvCreateMat( 1, data->var_count, CV_8U ); inv_map = cvCreateMat( 1, data->var_count, CV_32S ); cvZero( mask ); cvSet( inv_map, cvScalar(-1) ); // first pass: compute the mask of used variables cvStartReadSeq( weak, &reader ); for( i = 0; i < weak->total; i++ ) { CV_READ_SEQ_ELEM(wtree, reader); node = wtree->get_root(); assert( node != 0 ); for(;;) { const CvDTreeNode* parent; for(;;) { CvDTreeSplit* split = node->split; for( ; split != 0; split = split->next ) mask->data.ptr[split->var_idx] = 1; 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; } } nactive_vars = cvCountNonZero(mask); //if ( nactive_vars > 0 ) { active_vars = cvCreateMat( 1, nactive_vars, CV_32S ); active_vars_abs = cvCreateMat( 1, nactive_vars, CV_32S ); have_active_cat_vars = false; for( i = j = 0; i < data->var_count; i++ ) { if( mask->data.ptr[i] ) { active_vars->data.i[j] = i; active_vars_abs->data.i[j] = data->var_idx ? data->var_idx->data.i[i] : i; inv_map->data.i[i] = j; if( data->var_type->data.i[i] >= 0 ) have_active_cat_vars = true; j++; } } // second pass: now compute the condensed indices cvStartReadSeq( weak, &reader ); for( i = 0; i < weak->total; i++ ) { CV_READ_SEQ_ELEM(wtree, reader); node = wtree->get_root(); for(;;) { const CvDTreeNode* parent; for(;;) { CvDTreeSplit* split = node->split; for( ; split != 0; split = split->next ) { split->condensed_idx = inv_map->data.i[split->var_idx]; assert( split->condensed_idx >= 0 ); } 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; } } } } result = absolute_idx ? active_vars_abs : active_vars; __END__; cvReleaseMat( &mask ); cvReleaseMat( &inv_map ); return result; } float CvBoost::predict( const CvMat* _sample, const CvMat* _missing, CvMat* weak_responses, CvSlice slice, bool raw_mode, bool return_sum ) const { float value = -FLT_MAX; CvSeqReader reader; double sum = 0; int wstep = 0; const float* sample_data; if( !weak ) CV_Error( cv::Error::StsError, "The boosted tree ensemble 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 && !raw_mode) || (active_vars && _sample->cols + _sample->rows - 1 != active_vars->cols && raw_mode) ) CV_Error( cv::Error::StsBadArg, "the input sample must be 1d floating-point vector with the same " "number of elements as the total number of variables or " "as the number of variables used for training" ); if( _missing ) { if( !CV_IS_MAT(_missing) || !CV_IS_MASK_ARR(_missing) || !CV_ARE_SIZES_EQ(_missing, _sample) ) CV_Error( cv::Error::StsBadArg, "the missing data mask must be 8-bit vector of the same size as input sample" ); } int i, weak_count = cvSliceLength( slice, weak ); if( weak_count >= weak->total ) { weak_count = weak->total; slice.start_index = 0; } if( weak_responses ) { if( !CV_IS_MAT(weak_responses) || CV_MAT_TYPE(weak_responses->type) != CV_32FC1 || (weak_responses->cols != 1 && weak_responses->rows != 1) || weak_responses->cols + weak_responses->rows - 1 != weak_count ) CV_Error( cv::Error::StsBadArg, "The output matrix of weak classifier responses must be valid " "floating-point vector of the same number of components as the length of input slice" ); wstep = CV_IS_MAT_CONT(weak_responses->type) ? 1 : weak_responses->step/sizeof(float); } int var_count = active_vars->cols; const int* vtype = data->var_type->data.i; const int* cmap = data->cat_map->data.i; const int* cofs = data->cat_ofs->data.i; cv::Mat sample = cv::cvarrToMat(_sample); cv::Mat missing; if(!_missing) missing = cv::cvarrToMat(_missing); // if need, preprocess the input vector if( !raw_mode ) { int sstep, mstep = 0; const float* src_sample; const uchar* src_mask = 0; float* dst_sample; uchar* dst_mask; const int* vidx = active_vars->data.i; const int* vidx_abs = active_vars_abs->data.i; bool have_mask = _missing != 0; sample = cv::Mat(1, var_count, CV_32FC1); missing = cv::Mat(1, var_count, CV_8UC1); dst_sample = sample.ptr(); dst_mask = missing.ptr(); src_sample = _sample->data.fl; sstep = CV_IS_MAT_CONT(_sample->type) ? 1 : _sample->step/sizeof(src_sample[0]); if( _missing ) { src_mask = _missing->data.ptr; mstep = CV_IS_MAT_CONT(_missing->type) ? 1 : _missing->step; } for( i = 0; i < var_count; i++ ) { int idx = vidx[i], idx_abs = vidx_abs[i]; float val = src_sample[idx_abs*sstep]; int ci = vtype[idx]; uchar m = src_mask ? src_mask[idx_abs*mstep] : (uchar)0; if( ci >= 0 ) { int a = cofs[ci], b = (ci+1 >= data->cat_ofs->cols) ? data->cat_map->cols : cofs[ci+1], c = a; int ival = cvRound(val); if ( (ival != val) && (!m) ) CV_Error( cv::Error::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] ) { m = 1; have_mask = true; } else { val = (float)(c - cofs[ci]); } } dst_sample[i] = val; dst_mask[i] = m; } if( !have_mask ) missing.release(); } else { if( !CV_IS_MAT_CONT(_sample->type & (_missing ? _missing->type : -1)) ) CV_Error( cv::Error::StsBadArg, "In raw mode the input vectors must be continuous" ); } cvStartReadSeq( weak, &reader ); cvSetSeqReaderPos( &reader, slice.start_index ); sample_data = sample.ptr(); if( !have_active_cat_vars && missing.empty() && !weak_responses ) { for( i = 0; i < weak_count; i++ ) { CvBoostTree* wtree; const CvDTreeNode* node; CV_READ_SEQ_ELEM( wtree, reader ); node = wtree->get_root(); while( node->left ) { CvDTreeSplit* split = node->split; int vi = split->condensed_idx; float val = sample_data[vi]; int dir = val <= split->ord.c ? -1 : 1; if( split->inversed ) dir = -dir; node = dir < 0 ? node->left : node->right; } sum += node->value; } } else { const int* avars = active_vars->data.i; const uchar* m = !missing.empty() ? missing.ptr() : 0; // full-featured version for( i = 0; i < weak_count; i++ ) { CvBoostTree* wtree; const CvDTreeNode* node; CV_READ_SEQ_ELEM( wtree, reader ); node = wtree->get_root(); while( node->left ) { const CvDTreeSplit* split = node->split; int dir = 0; for( ; !dir && split != 0; split = split->next ) { int vi = split->condensed_idx; int ci = vtype[avars[vi]]; float val = sample_data[vi]; if( m && m[vi] ) continue; if( ci < 0 ) // ordered dir = val <= split->ord.c ? -1 : 1; else // categorical { int c = cvRound(val); dir = CV_DTREE_CAT_DIR(c, split->subset); } if( split->inversed ) dir = -dir; } if( !dir ) { int diff = node->right->sample_count - node->left->sample_count; dir = diff < 0 ? -1 : 1; } node = dir < 0 ? node->left : node->right; } if( weak_responses ) weak_responses->data.fl[i*wstep] = (float)node->value; sum += node->value; } } if( return_sum ) value = (float)sum; else { int cls_idx = sum >= 0; if( raw_mode ) value = (float)cls_idx; else value = (float)cmap[cofs[vtype[data->var_count]] + cls_idx]; } return value; } float CvBoost::calc_error( CvMLData* _data, int type, std::vector *resp ) { float err = 0; const CvMat* values = _data->get_values(); const CvMat* response = _data->get_responses(); const CvMat* missing = _data->get_missing(); const CvMat* sample_idx = (type == CV_TEST_ERROR) ? _data->get_test_sample_idx() : _data->get_train_sample_idx(); const CvMat* var_types = _data->get_var_types(); int* sidx = sample_idx ? sample_idx->data.i : 0; int r_step = CV_IS_MAT_CONT(response->type) ? 1 : response->step / CV_ELEM_SIZE(response->type); bool is_classifier = var_types->data.ptr[var_types->cols-1] == CV_VAR_CATEGORICAL; int sample_count = sample_idx ? sample_idx->cols : 0; sample_count = (type == CV_TRAIN_ERROR && sample_count == 0) ? values->rows : sample_count; float* pred_resp = 0; if( resp && (sample_count > 0) ) { resp->resize( sample_count ); pred_resp = &((*resp)[0]); } if ( is_classifier ) { for( int i = 0; i < sample_count; i++ ) { CvMat sample, miss; int si = sidx ? sidx[i] : i; cvGetRow( values, &sample, si ); if( missing ) cvGetRow( missing, &miss, si ); float r = (float)predict( &sample, missing ? &miss : 0 ); if( pred_resp ) pred_resp[i] = r; int d = fabs((double)r - response->data.fl[si*r_step]) <= FLT_EPSILON ? 0 : 1; err += d; } err = sample_count ? err / (float)sample_count * 100 : -FLT_MAX; } else { for( int i = 0; i < sample_count; i++ ) { CvMat sample, miss; int si = sidx ? sidx[i] : i; cvGetRow( values, &sample, si ); if( missing ) cvGetRow( missing, &miss, si ); float r = (float)predict( &sample, missing ? &miss : 0 ); if( pred_resp ) pred_resp[i] = r; float d = r - response->data.fl[si*r_step]; err += d*d; } err = sample_count ? err / (float)sample_count : -FLT_MAX; } return err; } void CvBoost::write_params( cv::FileStorage& fs ) const { const char* boost_type_str = params.boost_type == DISCRETE ? "DiscreteAdaboost" : params.boost_type == REAL ? "RealAdaboost" : params.boost_type == LOGIT ? "LogitBoost" : params.boost_type == GENTLE ? "GentleAdaboost" : 0; const char* split_crit_str = params.split_criteria == DEFAULT ? "Default" : params.split_criteria == GINI ? "Gini" : params.boost_type == MISCLASS ? "Misclassification" : params.boost_type == SQERR ? "SquaredErr" : 0; if( boost_type_str ) fs.write( "boosting_type", boost_type_str ); else fs.write( "boosting_type", params.boost_type ); if( split_crit_str ) fs.write( "splitting_criteria", split_crit_str ); else fs.write( "splitting_criteria", params.split_criteria ); fs.write( "ntrees", weak->total ); fs.write( "weight_trimming_rate", params.weight_trim_rate ); data->write_params( fs ); } void CvBoost::read_params( cv::FileNode& fnode ) { CV_FUNCNAME( "CvBoost::read_params" ); __BEGIN__; if( fnode.empty() || !fnode.isMap() ) return; data = new CvDTreeTrainData(); data->read_params( fnode ); data->shared = true; params.max_depth = data->params.max_depth; params.min_sample_count = data->params.min_sample_count; params.max_categories = data->params.max_categories; params.priors = data->params.priors; params.regression_accuracy = data->params.regression_accuracy; params.use_surrogates = data->params.use_surrogates; cv::FileNode temp = fnode[ "boosting_type" ]; if( temp.empty() ) return; if ( temp.isString() ) { std::string boost_type_str = temp; params.boost_type = (boost_type_str == "DiscreteAdaboost") ? DISCRETE : (boost_type_str == "RealAdaboost") ? REAL : (boost_type_str == "LogitBoost") ? LOGIT : (boost_type_str == "GentleAdaboost") ? GENTLE : -1; } else params.boost_type = temp.empty() ? -1 : (int)temp; if( params.boost_type < DISCRETE || params.boost_type > GENTLE ) CV_ERROR( cv::Error::StsBadArg, "Unknown boosting type" ); temp = fnode[ "splitting_criteria" ]; if( !temp.empty() && temp.isString() ) { std::string split_crit_str = temp; params.split_criteria = ( split_crit_str == "Default" ) ? DEFAULT : ( split_crit_str == "Gini" ) ? GINI : ( split_crit_str == "Misclassification" ) ? MISCLASS : ( split_crit_str == "SquaredErr" ) ? SQERR : -1; } else params.split_criteria = temp.empty() ? -1 : (int) temp; if( params.split_criteria < DEFAULT || params.boost_type > SQERR ) CV_ERROR( cv::Error::StsBadArg, "Unknown boosting type" ); params.weak_count = (int) fnode[ "ntrees" ]; params.weight_trim_rate = (double)fnode["weight_trimming_rate"]; __END__; } void CvBoost::read( cv::FileNode& node ) { CV_FUNCNAME( "CvBoost::read" ); __BEGIN__; cv::FileNodeIterator reader; cv::FileNode trees_fnode; CvMemStorage* storage; int ntrees; clear(); read_params( node ); if( !data ) EXIT; trees_fnode = node[ "trees" ]; if( trees_fnode.empty() || !trees_fnode.isSeq() ) CV_ERROR( cv::Error::StsParseError, " tag is missing" ); reader = trees_fnode.begin(); ntrees = (int) trees_fnode.size(); if( ntrees != params.weak_count ) CV_ERROR( cv::Error::StsUnmatchedSizes, "The number of trees stored does not match tag value" ); CV_CALL( storage = cvCreateMemStorage() ); weak = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvBoostTree*), storage ); for( int i = 0; i < ntrees; i++ ) { CvBoostTree* tree = new CvBoostTree(); tree->read( *reader, this, data ); reader++; cvSeqPush( weak, &tree ); } get_active_vars(); __END__; } void CvBoost::write( cv::FileStorage& fs, const char* name ) const { CV_FUNCNAME( "CvBoost::write" ); __BEGIN__; CvSeqReader reader; int i; fs.startWriteStruct( name, cv::FileNode::MAP, CV_TYPE_NAME_ML_BOOSTING ); if( !weak ) CV_ERROR( cv::Error::StsBadArg, "The classifier has not been trained yet" ); write_params( fs ); fs.startWriteStruct( "trees", cv::FileNode::SEQ ); cvStartReadSeq(weak, &reader); for( i = 0; i < weak->total; i++ ) { CvBoostTree* tree; CV_READ_SEQ_ELEM( tree, reader ); fs.startWriteStruct( 0, cv::FileNode::MAP ); tree->write( fs ); fs.endWriteStruct(); } fs.endWriteStruct(); fs.endWriteStruct(); __END__; } CvMat* CvBoost::get_weights() { return weights; } CvMat* CvBoost::get_subtree_weights() { return subtree_weights; } CvMat* CvBoost::get_weak_response() { return weak_eval; } const CvBoostParams& CvBoost::get_params() const { return params; } CvSeq* CvBoost::get_weak_predictors() { return weak; } const CvDTreeTrainData* CvBoost::get_data() const { return data; } using namespace cv; CvBoost::CvBoost( 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, CvBoostParams _params ) { weak = 0; data = 0; default_model_name = "my_boost_tree"; active_vars = active_vars_abs = orig_response = sum_response = weak_eval = subsample_mask = weights = subtree_weights = 0; train( _train_data, _tflag, _responses, _var_idx, _sample_idx, _var_type, _missing_mask, _params ); } bool CvBoost::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, CvBoostParams _params, bool _update ) { train_data_hdr = cvMat(_train_data); train_data_mat = _train_data; responses_hdr = cvMat(_responses); responses_mat = _responses; CvMat vidx = cvMat(_var_idx), sidx = cvMat(_sample_idx), vtype = cvMat(_var_type), mmask = cvMat(_missing_mask); return train(&train_data_hdr, _tflag, &responses_hdr, vidx.data.ptr ? &vidx : 0, sidx.data.ptr ? &sidx : 0, vtype.data.ptr ? &vtype : 0, mmask.data.ptr ? &mmask : 0, _params, _update); } float CvBoost::predict( const Mat& _sample, const Mat& _missing, const Range& slice, bool raw_mode, bool return_sum ) const { CvMat sample = cvMat(_sample), mmask = cvMat(_missing); /*if( weak_responses ) { int weak_count = cvSliceLength( slice, weak ); if( weak_count >= weak->total ) { weak_count = weak->total; slice.start_index = 0; } if( !(weak_responses->data && weak_responses->type() == CV_32FC1 && (weak_responses->cols == 1 || weak_responses->rows == 1) && weak_responses->cols + weak_responses->rows - 1 == weak_count) ) weak_responses->create(weak_count, 1, CV_32FC1); pwr = &(wr = *weak_responses); }*/ return predict(&sample, _missing.empty() ? 0 : &mmask, 0, slice == Range::all() ? CV_WHOLE_SEQ : cvSlice(slice.start, slice.end), raw_mode, return_sum); } /* End of file. */