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52c727f09a
work_var_count and sample_count are both 32bit integer, if the product of work_var_count and sample_count is bigger than 2^31, the compiler will treat (work_var_count*sample_count) as a negative number. Force work_var_count as an unsigned 64bit integer to avoid this issue.
2163 lines
66 KiB
C++
2163 lines
66 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "old_ml_precomp.hpp"
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static inline double
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log_ratio( double val )
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{
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const double eps = 1e-5;
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val = MAX( val, eps );
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val = MIN( val, 1. - eps );
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return log( val/(1. - val) );
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}
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CvBoostParams::CvBoostParams()
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{
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boost_type = CvBoost::REAL;
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weak_count = 100;
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weight_trim_rate = 0.95;
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cv_folds = 0;
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max_depth = 1;
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}
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CvBoostParams::CvBoostParams( int _boost_type, int _weak_count,
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double _weight_trim_rate, int _max_depth,
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bool _use_surrogates, const float* _priors )
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{
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boost_type = _boost_type;
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weak_count = _weak_count;
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weight_trim_rate = _weight_trim_rate;
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split_criteria = CvBoost::DEFAULT;
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cv_folds = 0;
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max_depth = _max_depth;
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use_surrogates = _use_surrogates;
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priors = _priors;
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}
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///////////////////////////////// CvBoostTree ///////////////////////////////////
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CvBoostTree::CvBoostTree()
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{
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ensemble = 0;
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}
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CvBoostTree::~CvBoostTree()
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{
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clear();
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}
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void
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CvBoostTree::clear()
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{
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CvDTree::clear();
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ensemble = 0;
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}
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bool
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CvBoostTree::train( CvDTreeTrainData* _train_data,
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const CvMat* _subsample_idx, CvBoost* _ensemble )
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{
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clear();
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ensemble = _ensemble;
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data = _train_data;
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data->shared = true;
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return do_train( _subsample_idx );
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}
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bool
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CvBoostTree::train( const CvMat*, int, const CvMat*, const CvMat*,
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const CvMat*, const CvMat*, const CvMat*, CvDTreeParams )
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{
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assert(0);
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return false;
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}
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bool
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CvBoostTree::train( CvDTreeTrainData*, const CvMat* )
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{
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assert(0);
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return false;
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}
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void
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CvBoostTree::scale( double _scale )
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{
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CvDTreeNode* node = root;
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// traverse the tree and scale all the node values
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for(;;)
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{
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CvDTreeNode* parent;
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for(;;)
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{
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node->value *= _scale;
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if( !node->left )
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break;
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node = node->left;
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}
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for( parent = node->parent; parent && parent->right == node;
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node = parent, parent = parent->parent )
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;
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if( !parent )
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break;
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node = parent->right;
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}
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}
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void
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CvBoostTree::try_split_node( CvDTreeNode* node )
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{
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CvDTree::try_split_node( node );
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if( !node->left )
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{
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// if the node has not been split,
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// store the responses for the corresponding training samples
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double* weak_eval = ensemble->get_weak_response()->data.db;
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cv::AutoBuffer<int> inn_buf(node->sample_count);
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const int* labels = data->get_cv_labels( node, (int*)inn_buf );
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int i, count = node->sample_count;
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double value = node->value;
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for( i = 0; i < count; i++ )
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weak_eval[labels[i]] = value;
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}
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}
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double
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CvBoostTree::calc_node_dir( CvDTreeNode* node )
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{
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char* dir = (char*)data->direction->data.ptr;
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const double* weights = ensemble->get_subtree_weights()->data.db;
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int i, n = node->sample_count, vi = node->split->var_idx;
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double L, R;
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assert( !node->split->inversed );
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if( data->get_var_type(vi) >= 0 ) // split on categorical var
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{
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cv::AutoBuffer<int> inn_buf(n);
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const int* cat_labels = data->get_cat_var_data( node, vi, (int*)inn_buf );
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const int* subset = node->split->subset;
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double sum = 0, sum_abs = 0;
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for( i = 0; i < n; i++ )
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{
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int idx = ((cat_labels[i] == 65535) && data->is_buf_16u) ? -1 : cat_labels[i];
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double w = weights[i];
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int d = idx >= 0 ? CV_DTREE_CAT_DIR(idx,subset) : 0;
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sum += d*w; sum_abs += (d & 1)*w;
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dir[i] = (char)d;
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}
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R = (sum_abs + sum) * 0.5;
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L = (sum_abs - sum) * 0.5;
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}
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else // split on ordered var
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{
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cv::AutoBuffer<uchar> inn_buf(2*n*sizeof(int)+n*sizeof(float));
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float* values_buf = (float*)(uchar*)inn_buf;
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int* sorted_indices_buf = (int*)(values_buf + n);
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int* sample_indices_buf = sorted_indices_buf + n;
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const float* values = 0;
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const int* sorted_indices = 0;
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data->get_ord_var_data( node, vi, values_buf, sorted_indices_buf, &values, &sorted_indices, sample_indices_buf );
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int split_point = node->split->ord.split_point;
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int n1 = node->get_num_valid(vi);
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assert( 0 <= split_point && split_point < n1-1 );
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L = R = 0;
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for( i = 0; i <= split_point; i++ )
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{
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int idx = sorted_indices[i];
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double w = weights[idx];
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dir[idx] = (char)-1;
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L += w;
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}
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for( ; i < n1; i++ )
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{
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int idx = sorted_indices[i];
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double w = weights[idx];
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dir[idx] = (char)1;
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R += w;
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}
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for( ; i < n; i++ )
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dir[sorted_indices[i]] = (char)0;
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}
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node->maxlr = MAX( L, R );
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return node->split->quality/(L + R);
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}
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CvDTreeSplit*
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CvBoostTree::find_split_ord_class( CvDTreeNode* node, int vi, float init_quality,
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CvDTreeSplit* _split, uchar* _ext_buf )
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{
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const float epsilon = FLT_EPSILON*2;
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const double* weights = ensemble->get_subtree_weights()->data.db;
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int n = node->sample_count;
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int n1 = node->get_num_valid(vi);
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cv::AutoBuffer<uchar> inn_buf;
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if( !_ext_buf )
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inn_buf.allocate(n*(3*sizeof(int)+sizeof(float)));
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uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
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float* values_buf = (float*)ext_buf;
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int* sorted_indices_buf = (int*)(values_buf + n);
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int* sample_indices_buf = sorted_indices_buf + n;
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const float* values = 0;
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const int* sorted_indices = 0;
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data->get_ord_var_data( node, vi, values_buf, sorted_indices_buf, &values, &sorted_indices, sample_indices_buf );
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int* responses_buf = sorted_indices_buf + n;
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const int* responses = data->get_class_labels( node, responses_buf );
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const double* rcw0 = weights + n;
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double lcw[2] = {0,0}, rcw[2];
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int i, best_i = -1;
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double best_val = init_quality;
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int boost_type = ensemble->get_params().boost_type;
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int split_criteria = ensemble->get_params().split_criteria;
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rcw[0] = rcw0[0]; rcw[1] = rcw0[1];
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for( i = n1; i < n; i++ )
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{
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int idx = sorted_indices[i];
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double w = weights[idx];
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rcw[responses[idx]] -= w;
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}
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if( split_criteria != CvBoost::GINI && split_criteria != CvBoost::MISCLASS )
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split_criteria = boost_type == CvBoost::DISCRETE ? CvBoost::MISCLASS : CvBoost::GINI;
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if( split_criteria == CvBoost::GINI )
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{
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double L = 0, R = rcw[0] + rcw[1];
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double lsum2 = 0, rsum2 = rcw[0]*rcw[0] + rcw[1]*rcw[1];
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for( i = 0; i < n1 - 1; i++ )
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{
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int idx = sorted_indices[i];
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double w = weights[idx], w2 = w*w;
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double lv, rv;
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idx = responses[idx];
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L += w; R -= w;
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lv = lcw[idx]; rv = rcw[idx];
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lsum2 += 2*lv*w + w2;
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rsum2 -= 2*rv*w - w2;
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lcw[idx] = lv + w; rcw[idx] = rv - w;
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if( values[i] + epsilon < values[i+1] )
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{
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double val = (lsum2*R + rsum2*L)/(L*R);
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if( best_val < val )
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{
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best_val = val;
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best_i = i;
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}
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}
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}
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}
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else
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{
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for( i = 0; i < n1 - 1; i++ )
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{
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int idx = sorted_indices[i];
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double w = weights[idx];
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idx = responses[idx];
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lcw[idx] += w;
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rcw[idx] -= w;
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if( values[i] + epsilon < values[i+1] )
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{
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double val = lcw[0] + rcw[1], val2 = lcw[1] + rcw[0];
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val = MAX(val, val2);
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if( best_val < val )
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{
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best_val = val;
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best_i = i;
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}
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}
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}
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}
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CvDTreeSplit* split = 0;
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if( best_i >= 0 )
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{
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split = _split ? _split : data->new_split_ord( 0, 0.0f, 0, 0, 0.0f );
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split->var_idx = vi;
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split->ord.c = (values[best_i] + values[best_i+1])*0.5f;
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split->ord.split_point = best_i;
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split->inversed = 0;
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split->quality = (float)best_val;
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}
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return split;
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}
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template<typename T>
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class LessThanPtr
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{
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public:
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bool operator()(T* a, T* b) const { return *a < *b; }
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};
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CvDTreeSplit*
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CvBoostTree::find_split_cat_class( CvDTreeNode* node, int vi, float init_quality, CvDTreeSplit* _split, uchar* _ext_buf )
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{
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int ci = data->get_var_type(vi);
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int n = node->sample_count;
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int mi = data->cat_count->data.i[ci];
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int base_size = (2*mi+3)*sizeof(double) + mi*sizeof(double*);
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cv::AutoBuffer<uchar> inn_buf((2*mi+3)*sizeof(double) + mi*sizeof(double*));
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if( !_ext_buf)
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inn_buf.allocate( base_size + 2*n*sizeof(int) );
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uchar* base_buf = (uchar*)inn_buf;
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uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
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int* cat_labels_buf = (int*)ext_buf;
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const int* cat_labels = data->get_cat_var_data(node, vi, cat_labels_buf);
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int* responses_buf = cat_labels_buf + n;
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const int* responses = data->get_class_labels(node, responses_buf);
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double lcw[2]={0,0}, rcw[2]={0,0};
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double* cjk = (double*)cv::alignPtr(base_buf,sizeof(double))+2;
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const double* weights = ensemble->get_subtree_weights()->data.db;
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double** dbl_ptr = (double**)(cjk + 2*mi);
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int i, j, k, idx;
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double L = 0, R;
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double best_val = init_quality;
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int best_subset = -1, subset_i;
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int boost_type = ensemble->get_params().boost_type;
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int split_criteria = ensemble->get_params().split_criteria;
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// init array of counters:
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// c_{jk} - number of samples that have vi-th input variable = j and response = k.
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for( j = -1; j < mi; j++ )
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cjk[j*2] = cjk[j*2+1] = 0;
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for( i = 0; i < n; i++ )
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{
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double w = weights[i];
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j = ((cat_labels[i] == 65535) && data->is_buf_16u) ? -1 : cat_labels[i];
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k = responses[i];
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cjk[j*2 + k] += w;
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}
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for( j = 0; j < mi; j++ )
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{
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rcw[0] += cjk[j*2];
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rcw[1] += cjk[j*2+1];
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dbl_ptr[j] = cjk + j*2 + 1;
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}
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R = rcw[0] + rcw[1];
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if( split_criteria != CvBoost::GINI && split_criteria != CvBoost::MISCLASS )
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split_criteria = boost_type == CvBoost::DISCRETE ? CvBoost::MISCLASS : CvBoost::GINI;
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// sort rows of c_jk by increasing c_j,1
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// (i.e. by the weight of samples in j-th category that belong to class 1)
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std::sort(dbl_ptr, dbl_ptr + mi, LessThanPtr<double>());
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for( subset_i = 0; subset_i < mi-1; subset_i++ )
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{
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idx = (int)(dbl_ptr[subset_i] - cjk)/2;
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const double* crow = cjk + idx*2;
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double w0 = crow[0], w1 = crow[1];
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double weight = w0 + w1;
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if( weight < FLT_EPSILON )
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continue;
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lcw[0] += w0; rcw[0] -= w0;
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lcw[1] += w1; rcw[1] -= w1;
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if( split_criteria == CvBoost::GINI )
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{
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double lsum2 = lcw[0]*lcw[0] + lcw[1]*lcw[1];
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double rsum2 = rcw[0]*rcw[0] + rcw[1]*rcw[1];
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L += weight;
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R -= weight;
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if( L > FLT_EPSILON && R > FLT_EPSILON )
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{
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double val = (lsum2*R + rsum2*L)/(L*R);
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if( best_val < val )
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{
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best_val = val;
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best_subset = subset_i;
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}
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}
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}
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else
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{
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double val = lcw[0] + rcw[1];
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double val2 = lcw[1] + rcw[0];
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val = MAX(val, val2);
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if( best_val < val )
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{
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best_val = val;
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best_subset = subset_i;
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}
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}
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}
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CvDTreeSplit* split = 0;
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if( best_subset >= 0 )
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{
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split = _split ? _split : data->new_split_cat( 0, -1.0f);
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split->var_idx = vi;
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split->quality = (float)best_val;
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memset( split->subset, 0, (data->max_c_count + 31)/32 * sizeof(int));
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for( i = 0; i <= best_subset; i++ )
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{
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idx = (int)(dbl_ptr[i] - cjk) >> 1;
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split->subset[idx >> 5] |= 1 << (idx & 31);
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}
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}
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return split;
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}
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CvDTreeSplit*
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CvBoostTree::find_split_ord_reg( CvDTreeNode* node, int vi, float init_quality, CvDTreeSplit* _split, uchar* _ext_buf )
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{
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const float epsilon = FLT_EPSILON*2;
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const double* weights = ensemble->get_subtree_weights()->data.db;
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int n = node->sample_count;
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int n1 = node->get_num_valid(vi);
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cv::AutoBuffer<uchar> inn_buf;
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if( !_ext_buf )
|
|
inn_buf.allocate(2*n*(sizeof(int)+sizeof(float)));
|
|
uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
|
|
|
|
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<uchar> inn_buf(base_size);
|
|
if( !_ext_buf )
|
|
inn_buf.allocate(base_size + n*(2*sizeof(int) + sizeof(float)));
|
|
uchar* base_buf = (uchar*)inn_buf;
|
|
uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
|
|
|
|
int* 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<double>());
|
|
|
|
// 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<uchar> inn_buf;
|
|
if( !_ext_buf )
|
|
inn_buf.allocate(n*(2*sizeof(int)+sizeof(float)));
|
|
uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
|
|
float* values_buf = (float*)ext_buf;
|
|
int* 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<uchar> inn_buf(base_size);
|
|
if( !_ext_buf )
|
|
inn_buf.allocate(base_size + n*sizeof(int));
|
|
uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
|
|
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<uchar> inn_buf(n*(sizeof(int) + ( data->is_classifier ? sizeof(int) : sizeof(int) + sizeof(float))));
|
|
int* labels_buf = (int*)(uchar*)inn_buf;
|
|
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 - <node_value>)^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( CvFileStorage* fs, CvFileNode* fnode, CvBoost* _ensemble, CvDTreeTrainData* _data )
|
|
{
|
|
CvDTree::read( fs, fnode, _data );
|
|
ensemble = _ensemble;
|
|
}
|
|
|
|
void CvBoostTree::read( CvFileStorage*, CvFileNode* )
|
|
{
|
|
assert(0);
|
|
}
|
|
|
|
void CvBoostTree::read( CvFileStorage* _fs, CvFileNode* _node,
|
|
CvDTreeTrainData* _data )
|
|
{
|
|
CvDTree::read( _fs, _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_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_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<uchar> 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 = (uchar*)inn_buf;
|
|
|
|
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_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_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_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_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_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<float>();
|
|
dst_mask = missing.ptr<uchar>();
|
|
|
|
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_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_StsBadArg, "In raw mode the input vectors must be continuous" );
|
|
}
|
|
|
|
cvStartReadSeq( weak, &reader );
|
|
cvSetSeqReaderPos( &reader, slice.start_index );
|
|
|
|
sample_data = sample.ptr<float>();
|
|
|
|
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<uchar>() : 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<float> *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( CvFileStorage* 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 )
|
|
cvWriteString( fs, "boosting_type", boost_type_str );
|
|
else
|
|
cvWriteInt( fs, "boosting_type", params.boost_type );
|
|
|
|
if( split_crit_str )
|
|
cvWriteString( fs, "splitting_criteria", split_crit_str );
|
|
else
|
|
cvWriteInt( fs, "splitting_criteria", params.split_criteria );
|
|
|
|
cvWriteInt( fs, "ntrees", weak->total );
|
|
cvWriteReal( fs, "weight_trimming_rate", params.weight_trim_rate );
|
|
|
|
data->write_params( fs );
|
|
}
|
|
|
|
|
|
void CvBoost::read_params( CvFileStorage* fs, CvFileNode* fnode )
|
|
{
|
|
CV_FUNCNAME( "CvBoost::read_params" );
|
|
|
|
__BEGIN__;
|
|
|
|
CvFileNode* temp;
|
|
|
|
if( !fnode || !CV_NODE_IS_MAP(fnode->tag) )
|
|
return;
|
|
|
|
data = new CvDTreeTrainData();
|
|
CV_CALL( data->read_params(fs, 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;
|
|
|
|
temp = cvGetFileNodeByName( fs, fnode, "boosting_type" );
|
|
if( !temp )
|
|
return;
|
|
|
|
if( temp && CV_NODE_IS_STRING(temp->tag) )
|
|
{
|
|
const char* boost_type_str = cvReadString( temp, "" );
|
|
params.boost_type = strcmp( boost_type_str, "DiscreteAdaboost" ) == 0 ? DISCRETE :
|
|
strcmp( boost_type_str, "RealAdaboost" ) == 0 ? REAL :
|
|
strcmp( boost_type_str, "LogitBoost" ) == 0 ? LOGIT :
|
|
strcmp( boost_type_str, "GentleAdaboost" ) == 0 ? GENTLE : -1;
|
|
}
|
|
else
|
|
params.boost_type = cvReadInt( temp, -1 );
|
|
|
|
if( params.boost_type < DISCRETE || params.boost_type > GENTLE )
|
|
CV_ERROR( CV_StsBadArg, "Unknown boosting type" );
|
|
|
|
temp = cvGetFileNodeByName( fs, fnode, "splitting_criteria" );
|
|
if( temp && CV_NODE_IS_STRING(temp->tag) )
|
|
{
|
|
const char* split_crit_str = cvReadString( temp, "" );
|
|
params.split_criteria = strcmp( split_crit_str, "Default" ) == 0 ? DEFAULT :
|
|
strcmp( split_crit_str, "Gini" ) == 0 ? GINI :
|
|
strcmp( split_crit_str, "Misclassification" ) == 0 ? MISCLASS :
|
|
strcmp( split_crit_str, "SquaredErr" ) == 0 ? SQERR : -1;
|
|
}
|
|
else
|
|
params.split_criteria = cvReadInt( temp, -1 );
|
|
|
|
if( params.split_criteria < DEFAULT || params.boost_type > SQERR )
|
|
CV_ERROR( CV_StsBadArg, "Unknown boosting type" );
|
|
|
|
params.weak_count = cvReadIntByName( fs, fnode, "ntrees" );
|
|
params.weight_trim_rate = cvReadRealByName( fs, fnode, "weight_trimming_rate", 0. );
|
|
|
|
__END__;
|
|
}
|
|
|
|
|
|
|
|
void
|
|
CvBoost::read( CvFileStorage* fs, CvFileNode* node )
|
|
{
|
|
CV_FUNCNAME( "CvBoost::read" );
|
|
|
|
__BEGIN__;
|
|
|
|
CvSeqReader reader;
|
|
CvFileNode* trees_fnode;
|
|
CvMemStorage* storage;
|
|
int i, ntrees;
|
|
|
|
clear();
|
|
read_params( fs, node );
|
|
|
|
if( !data )
|
|
EXIT;
|
|
|
|
trees_fnode = cvGetFileNodeByName( fs, node, "trees" );
|
|
if( !trees_fnode || !CV_NODE_IS_SEQ(trees_fnode->tag) )
|
|
CV_ERROR( CV_StsParseError, "<trees> tag is missing" );
|
|
|
|
cvStartReadSeq( trees_fnode->data.seq, &reader );
|
|
ntrees = trees_fnode->data.seq->total;
|
|
|
|
if( ntrees != params.weak_count )
|
|
CV_ERROR( CV_StsUnmatchedSizes,
|
|
"The number of trees stored does not match <ntrees> tag value" );
|
|
|
|
CV_CALL( storage = cvCreateMemStorage() );
|
|
weak = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvBoostTree*), storage );
|
|
|
|
for( i = 0; i < ntrees; i++ )
|
|
{
|
|
CvBoostTree* tree = new CvBoostTree();
|
|
CV_CALL(tree->read( fs, (CvFileNode*)reader.ptr, this, data ));
|
|
CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
|
|
cvSeqPush( weak, &tree );
|
|
}
|
|
get_active_vars();
|
|
|
|
__END__;
|
|
}
|
|
|
|
|
|
void
|
|
CvBoost::write( CvFileStorage* fs, const char* name ) const
|
|
{
|
|
CV_FUNCNAME( "CvBoost::write" );
|
|
|
|
__BEGIN__;
|
|
|
|
CvSeqReader reader;
|
|
int i;
|
|
|
|
cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_ML_BOOSTING );
|
|
|
|
if( !weak )
|
|
CV_ERROR( CV_StsBadArg, "The classifier has not been trained yet" );
|
|
|
|
write_params( fs );
|
|
cvStartWriteStruct( fs, "trees", CV_NODE_SEQ );
|
|
|
|
cvStartReadSeq( weak, &reader );
|
|
|
|
for( i = 0; i < weak->total; i++ )
|
|
{
|
|
CvBoostTree* tree;
|
|
CV_READ_SEQ_ELEM( tree, reader );
|
|
cvStartWriteStruct( fs, 0, CV_NODE_MAP );
|
|
tree->write( fs );
|
|
cvEndWriteStruct( fs );
|
|
}
|
|
|
|
cvEndWriteStruct( fs );
|
|
cvEndWriteStruct( fs );
|
|
|
|
__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 = _train_data;
|
|
train_data_mat = _train_data;
|
|
responses_hdr = _responses;
|
|
responses_mat = _responses;
|
|
|
|
CvMat vidx = _var_idx, sidx = _sample_idx, vtype = _var_type, mmask = _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 = _sample, mmask = _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. */
|