+++ /dev/null
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-// By downloading, copying, installing or using the software you agree to this license.
-// If you do not agree to this license, do not download, install,
-// copy or use the software.
-//
-//
-// Intel License Agreement
-//
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-// * Redistribution's of source code must retain the above copyright notice,
-// this list of conditions and the following disclaimer.
-//
-// * Redistribution's in binary form must reproduce the above copyright notice,
-// this list of conditions and the following disclaimer in the documentation
-// and/or other materials provided with the distribution.
-//
-// * The name of Intel Corporation may not be used to endorse or promote products
-// derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#include "_ml.h"
-
-CvForestTree::CvForestTree()
-{
- forest = NULL;
-}
-
-
-CvForestTree::~CvForestTree()
-{
- clear();
-}
-
-
-bool CvForestTree::train( CvDTreeTrainData* _data,
- const CvMat* _subsample_idx,
- CvRTrees* _forest )
-{
- bool result = false;
-
- CV_FUNCNAME( "CvForestTree::train" );
-
- __BEGIN__;
-
-
- clear();
- forest = _forest;
-
- data = _data;
- data->shared = true;
- CV_CALL(result = do_train(_subsample_idx));
-
- __END__;
-
- return result;
-}
-
-
-bool
-CvForestTree::train( const CvMat*, int, const CvMat*, const CvMat*,
- const CvMat*, const CvMat*, const CvMat*, CvDTreeParams )
-{
- assert(0);
- return false;
-}
-
-
-bool
-CvForestTree::train( CvDTreeTrainData*, const CvMat* )
-{
- assert(0);
- return false;
-}
-
-
-CvDTreeSplit* CvForestTree::find_best_split( CvDTreeNode* node )
-{
- int vi;
- CvDTreeSplit *best_split = 0, *split = 0, *t;
-
- CV_FUNCNAME("CvForestTree::find_best_split");
- __BEGIN__;
-
- CvMat* active_var_mask = 0;
- if( forest )
- {
- int var_count;
- CvRNG* rng = forest->get_rng();
-
- active_var_mask = forest->get_active_var_mask();
- var_count = active_var_mask->cols;
-
- CV_ASSERT( var_count == data->var_count );
-
- for( vi = 0; vi < var_count; vi++ )
- {
- uchar temp;
- int i1 = cvRandInt(rng) % var_count;
- int i2 = cvRandInt(rng) % var_count;
- CV_SWAP( active_var_mask->data.ptr[i1],
- active_var_mask->data.ptr[i2], temp );
- }
- }
- for( vi = 0; vi < data->var_count; vi++ )
- {
- int ci = data->var_type->data.i[vi];
- if( node->num_valid[vi] <= 1
- || (active_var_mask && !active_var_mask->data.ptr[vi]) )
- continue;
-
- if( data->is_classifier )
- {
- if( ci >= 0 )
- split = find_split_cat_class( node, vi );
- else
- split = find_split_ord_class( node, vi );
- }
- else
- {
- if( ci >= 0 )
- split = find_split_cat_reg( node, vi );
- else
- split = find_split_ord_reg( node, vi );
- }
-
- if( split )
- {
- if( !best_split || best_split->quality < split->quality )
- CV_SWAP( best_split, split, t );
- if( split )
- cvSetRemoveByPtr( data->split_heap, split );
- }
- }
-
- __END__;
-
- return best_split;
-}
-
-
-void CvForestTree::read( CvFileStorage* fs, CvFileNode* fnode, CvRTrees* _forest, CvDTreeTrainData* _data )
-{
- CvDTree::read( fs, fnode, _data );
- forest = _forest;
-}
-
-
-void CvForestTree::read( CvFileStorage*, CvFileNode* )
-{
- assert(0);
-}
-
-void CvForestTree::read( CvFileStorage* _fs, CvFileNode* _node,
- CvDTreeTrainData* _data )
-{
- CvDTree::read( _fs, _node, _data );
-}
-
-
-//////////////////////////////////////////////////////////////////////////////////////////
-// Random trees //
-//////////////////////////////////////////////////////////////////////////////////////////
-
-CvRTrees::CvRTrees()
-{
- nclasses = 0;
- oob_error = 0;
- ntrees = 0;
- trees = NULL;
- data = NULL;
- active_var_mask = NULL;
- var_importance = NULL;
- rng = cvRNG(0xffffffff);
- default_model_name = "my_random_trees";
-}
-
-
-void CvRTrees::clear()
-{
- int k;
- for( k = 0; k < ntrees; k++ )
- delete trees[k];
- cvFree( &trees );
-
- delete data;
- data = 0;
-
- cvReleaseMat( &active_var_mask );
- cvReleaseMat( &var_importance );
- ntrees = 0;
-}
-
-
-CvRTrees::~CvRTrees()
-{
- clear();
-}
-
-
-CvMat* CvRTrees::get_active_var_mask()
-{
- return active_var_mask;
-}
-
-
-CvRNG* CvRTrees::get_rng()
-{
- return &rng;
-}
-
-bool CvRTrees::train( const CvMat* _train_data, int _tflag,
- const CvMat* _responses, const CvMat* _var_idx,
- const CvMat* _sample_idx, const CvMat* _var_type,
- const CvMat* _missing_mask, CvRTParams params )
-{
- bool result = false;
-
- CV_FUNCNAME("CvRTrees::train");
- __BEGIN__;
-
- int var_count = 0;
-
- clear();
-
- CvDTreeParams tree_params( params.max_depth, params.min_sample_count,
- params.regression_accuracy, params.use_surrogates, params.max_categories,
- params.cv_folds, params.use_1se_rule, false, params.priors );
-
- data = new CvDTreeTrainData();
- CV_CALL(data->set_data( _train_data, _tflag, _responses, _var_idx,
- _sample_idx, _var_type, _missing_mask, tree_params, true));
-
- var_count = data->var_count;
- if( params.nactive_vars > var_count )
- params.nactive_vars = var_count;
- else if( params.nactive_vars == 0 )
- params.nactive_vars = (int)sqrt((double)var_count);
- else if( params.nactive_vars < 0 )
- CV_ERROR( CV_StsBadArg, "<nactive_vars> must be non-negative" );
- params.term_crit = cvCheckTermCriteria( params.term_crit, 0.1, 1000 );
-
- // Create mask of active variables at the tree nodes
- CV_CALL(active_var_mask = cvCreateMat( 1, var_count, CV_8UC1 ));
- if( params.calc_var_importance )
- {
- CV_CALL(var_importance = cvCreateMat( 1, var_count, CV_32FC1 ));
- cvZero(var_importance);
- }
- { // initialize active variables mask
- CvMat submask1, submask2;
- cvGetCols( active_var_mask, &submask1, 0, params.nactive_vars );
- cvGetCols( active_var_mask, &submask2, params.nactive_vars, var_count );
- cvSet( &submask1, cvScalar(1) );
- cvZero( &submask2 );
- }
-
- CV_CALL(result = grow_forest( params.term_crit ));
-
- result = true;
-
- __END__;
-
- return result;
-}
-
-
-bool CvRTrees::grow_forest( const CvTermCriteria term_crit )
-{
- bool result = false;
-
- CvMat* sample_idx_mask_for_tree = 0;
- CvMat* sample_idx_for_tree = 0;
-
- CvMat* oob_sample_votes = 0;
- CvMat* oob_responses = 0;
-
- float* oob_samples_perm_ptr= 0;
-
- float* samples_ptr = 0;
- uchar* missing_ptr = 0;
- float* true_resp_ptr = 0;
-
- CV_FUNCNAME("CvRTrees::grow_forest");
- __BEGIN__;
-
- const int max_ntrees = term_crit.max_iter;
- const double max_oob_err = term_crit.epsilon;
-
- const int dims = data->var_count;
- float maximal_response = 0;
-
- // oob_predictions_sum[i] = sum of predicted values for the i-th sample
- // oob_num_of_predictions[i] = number of summands
- // (number of predictions for the i-th sample)
- // initialize these variable to avoid warning C4701
- CvMat oob_predictions_sum = cvMat( 1, 1, CV_32FC1 );
- CvMat oob_num_of_predictions = cvMat( 1, 1, CV_32FC1 );
-
- nsamples = data->sample_count;
- nclasses = data->get_num_classes();
-
- trees = (CvForestTree**)cvAlloc( sizeof(trees[0])*max_ntrees );
- memset( trees, 0, sizeof(trees[0])*max_ntrees );
-
- if( data->is_classifier )
- {
- CV_CALL(oob_sample_votes = cvCreateMat( nsamples, nclasses, CV_32SC1 ));
- cvZero(oob_sample_votes);
- }
- else
- {
- // oob_responses[0,i] = oob_predictions_sum[i]
- // = sum of predicted values for the i-th sample
- // oob_responses[1,i] = oob_num_of_predictions[i]
- // = number of summands (number of predictions for the i-th sample)
- CV_CALL(oob_responses = cvCreateMat( 2, nsamples, CV_32FC1 ));
- cvZero(oob_responses);
- cvGetRow( oob_responses, &oob_predictions_sum, 0 );
- cvGetRow( oob_responses, &oob_num_of_predictions, 1 );
- }
- CV_CALL(sample_idx_mask_for_tree = cvCreateMat( 1, nsamples, CV_8UC1 ));
- CV_CALL(sample_idx_for_tree = cvCreateMat( 1, nsamples, CV_32SC1 ));
- CV_CALL(oob_samples_perm_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims ));
- CV_CALL(samples_ptr = (float*)cvAlloc( sizeof(float)*nsamples*dims ));
- CV_CALL(missing_ptr = (uchar*)cvAlloc( sizeof(uchar)*nsamples*dims ));
- CV_CALL(true_resp_ptr = (float*)cvAlloc( sizeof(float)*nsamples ));
-
- CV_CALL(data->get_vectors( 0, samples_ptr, missing_ptr, true_resp_ptr ));
- {
- double minval, maxval;
- CvMat responses = cvMat(1, nsamples, CV_32FC1, true_resp_ptr);
- cvMinMaxLoc( &responses, &minval, &maxval );
- maximal_response = (float)MAX( MAX( fabs(minval), fabs(maxval) ), 0 );
- }
-
- ntrees = 0;
- while( ntrees < max_ntrees )
- {
- int i, oob_samples_count = 0;
- double ncorrect_responses = 0; // used for estimation of variable importance
- CvMat sample, missing;
- CvForestTree* tree = 0;
-
- cvZero( sample_idx_mask_for_tree );
- for( i = 0; i < nsamples; i++ ) //form sample for creation one tree
- {
- int idx = cvRandInt( &rng ) % nsamples;
- sample_idx_for_tree->data.i[i] = idx;
- sample_idx_mask_for_tree->data.ptr[idx] = 0xFF;
- }
-
- trees[ntrees] = new CvForestTree();
- tree = trees[ntrees];
- CV_CALL(tree->train( data, sample_idx_for_tree, this ));
-
- // form array of OOB samples indices and get these samples
- sample = cvMat( 1, dims, CV_32FC1, samples_ptr );
- missing = cvMat( 1, dims, CV_8UC1, missing_ptr );
-
- oob_error = 0;
- for( i = 0; i < nsamples; i++,
- sample.data.fl += dims, missing.data.ptr += dims )
- {
- CvDTreeNode* predicted_node = 0;
- // check if the sample is OOB
- if( sample_idx_mask_for_tree->data.ptr[i] )
- continue;
-
- // predict oob samples
- if( !predicted_node )
- CV_CALL(predicted_node = tree->predict(&sample, &missing, true));
-
- if( !data->is_classifier ) //regression
- {
- double avg_resp, resp = predicted_node->value;
- oob_predictions_sum.data.fl[i] += (float)resp;
- oob_num_of_predictions.data.fl[i] += 1;
-
- // compute oob error
- avg_resp = oob_predictions_sum.data.fl[i]/oob_num_of_predictions.data.fl[i];
- avg_resp -= true_resp_ptr[i];
- oob_error += avg_resp*avg_resp;
- resp = (resp - true_resp_ptr[i])/maximal_response;
- ncorrect_responses += exp( -resp*resp );
- }
- else //classification
- {
- double prdct_resp;
- CvPoint max_loc;
- CvMat votes;
-
- cvGetRow(oob_sample_votes, &votes, i);
- votes.data.i[predicted_node->class_idx]++;
-
- // compute oob error
- cvMinMaxLoc( &votes, 0, 0, 0, &max_loc );
-
- prdct_resp = data->cat_map->data.i[max_loc.x];
- oob_error += (fabs(prdct_resp - true_resp_ptr[i]) < FLT_EPSILON) ? 0 : 1;
-
- ncorrect_responses += cvRound(predicted_node->value - true_resp_ptr[i]) == 0;
- }
- oob_samples_count++;
- }
- if( oob_samples_count > 0 )
- oob_error /= (double)oob_samples_count;
-
- // estimate variable importance
- if( var_importance && oob_samples_count > 0 )
- {
- int m;
-
- memcpy( oob_samples_perm_ptr, samples_ptr, dims*nsamples*sizeof(float));
- for( m = 0; m < dims; m++ )
- {
- double ncorrect_responses_permuted = 0;
- // randomly permute values of the m-th variable in the oob samples
- float* mth_var_ptr = oob_samples_perm_ptr + m;
-
- for( i = 0; i < nsamples; i++ )
- {
- int i1, i2;
- float temp;
-
- if( sample_idx_mask_for_tree->data.ptr[i] ) //the sample is not OOB
- continue;
- i1 = cvRandInt( &rng ) % nsamples;
- i2 = cvRandInt( &rng ) % nsamples;
- CV_SWAP( mth_var_ptr[i1*dims], mth_var_ptr[i2*dims], temp );
-
- // turn values of (m-1)-th variable, that were permuted
- // at the previous iteration, untouched
- if( m > 1 )
- oob_samples_perm_ptr[i*dims+m-1] = samples_ptr[i*dims+m-1];
- }
-
- // predict "permuted" cases and calculate the number of votes for the
- // correct class in the variable-m-permuted oob data
- sample = cvMat( 1, dims, CV_32FC1, oob_samples_perm_ptr );
- missing = cvMat( 1, dims, CV_8UC1, missing_ptr );
- for( i = 0; i < nsamples; i++,
- sample.data.fl += dims, missing.data.ptr += dims )
- {
- double predct_resp, true_resp;
-
- if( sample_idx_mask_for_tree->data.ptr[i] ) //the sample is not OOB
- continue;
-
- predct_resp = tree->predict(&sample, &missing, true)->value;
- true_resp = true_resp_ptr[i];
- if( data->is_classifier )
- ncorrect_responses_permuted += cvRound(true_resp - predct_resp) == 0;
- else
- {
- true_resp = (true_resp - predct_resp)/maximal_response;
- ncorrect_responses_permuted += exp( -true_resp*true_resp );
- }
- }
- var_importance->data.fl[m] += (float)(ncorrect_responses
- - ncorrect_responses_permuted);
- }
- }
- ntrees++;
- if( term_crit.type != CV_TERMCRIT_ITER && oob_error < max_oob_err )
- break;
- }
- if( var_importance )
- CV_CALL(cvConvertScale( var_importance, var_importance, 1./ntrees/nsamples ));
-
- result = true;
-
- __END__;
-
- cvReleaseMat( &sample_idx_mask_for_tree );
- cvReleaseMat( &sample_idx_for_tree );
- cvReleaseMat( &oob_sample_votes );
- cvReleaseMat( &oob_responses );
-
- cvFree( &oob_samples_perm_ptr );
- cvFree( &samples_ptr );
- cvFree( &missing_ptr );
- cvFree( &true_resp_ptr );
-
- return result;
-}
-
-
-const CvMat* CvRTrees::get_var_importance()
-{
- return var_importance;
-}
-
-
-float CvRTrees::get_proximity( const CvMat* sample1, const CvMat* sample2,
- const CvMat* missing1, const CvMat* missing2 ) const
-{
- float result = 0;
-
- CV_FUNCNAME( "CvRTrees::get_proximity" );
-
- __BEGIN__;
-
- int i;
- for( i = 0; i < ntrees; i++ )
- result += trees[i]->predict( sample1, missing1 ) ==
- trees[i]->predict( sample2, missing2 ) ? 1 : 0;
- result = result/(float)ntrees;
-
- __END__;
-
- return result;
-}
-
-
-float CvRTrees::predict( const CvMat* sample, const CvMat* missing ) const
-{
- double result = -1;
-
- CV_FUNCNAME("CvRTrees::predict");
- __BEGIN__;
-
- int k;
-
- if( nclasses > 0 ) //classification
- {
- int max_nvotes = 0;
- int* votes = (int*)alloca( sizeof(int)*nclasses );
- memset( votes, 0, sizeof(*votes)*nclasses );
- for( k = 0; k < ntrees; k++ )
- {
- CvDTreeNode* predicted_node = trees[k]->predict( sample, missing );
- int nvotes;
- int class_idx = predicted_node->class_idx;
- CV_ASSERT( 0 <= class_idx && class_idx < nclasses );
-
- nvotes = ++votes[class_idx];
- if( nvotes > max_nvotes )
- {
- max_nvotes = nvotes;
- result = predicted_node->value;
- }
- }
- }
- else // regression
- {
- result = 0;
- for( k = 0; k < ntrees; k++ )
- result += trees[k]->predict( sample, missing )->value;
- result /= (double)ntrees;
- }
-
- __END__;
-
- return (float)result;
-}
-
-
-void CvRTrees::write( CvFileStorage* fs, const char* name )
-{
- CV_FUNCNAME( "CvRTrees::write" );
-
- __BEGIN__;
-
- int k;
-
- if( ntrees < 1 || !trees || nsamples < 1 )
- CV_ERROR( CV_StsBadArg, "Invalid CvRTrees object" );
-
- cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_ML_RTREES );
-
- cvWriteInt( fs, "nclasses", nclasses );
- cvWriteInt( fs, "nsamples", nsamples );
- cvWriteInt( fs, "nactive_vars", (int)cvSum(active_var_mask).val[0] );
- cvWriteReal( fs, "oob_error", oob_error );
-
- if( var_importance )
- cvWrite( fs, "var_importance", var_importance );
-
- cvWriteInt( fs, "ntrees", ntrees );
-
- CV_CALL(data->write_params( fs ));
-
- cvStartWriteStruct( fs, "trees", CV_NODE_SEQ );
-
- for( k = 0; k < ntrees; k++ )
- {
- cvStartWriteStruct( fs, 0, CV_NODE_MAP );
- CV_CALL( trees[k]->write( fs ));
- cvEndWriteStruct( fs );
- }
-
- cvEndWriteStruct( fs ); //trees
- cvEndWriteStruct( fs ); //CV_TYPE_NAME_ML_RTREES
-
- __END__;
-}
-
-
-void CvRTrees::read( CvFileStorage* fs, CvFileNode* fnode )
-{
- CV_FUNCNAME( "CvRTrees::read" );
-
- __BEGIN__;
-
- int nactive_vars, var_count, k;
- CvSeqReader reader;
- CvFileNode* trees_fnode = 0;
-
- clear();
-
- nclasses = cvReadIntByName( fs, fnode, "nclasses", -1 );
- nsamples = cvReadIntByName( fs, fnode, "nsamples" );
- nactive_vars = cvReadIntByName( fs, fnode, "nactive_vars", -1 );
- oob_error = cvReadRealByName(fs, fnode, "oob_error", -1 );
- ntrees = cvReadIntByName( fs, fnode, "ntrees", -1 );
-
- var_importance = (CvMat*)cvReadByName( fs, fnode, "var_importance" );
-
- if( nclasses < 0 || nsamples <= 0 || nactive_vars < 0 || oob_error < 0 || ntrees <= 0)
- CV_ERROR( CV_StsParseError, "Some <nclasses>, <nsamples>, <var_count>, "
- "<nactive_vars>, <oob_error>, <ntrees> of tags are missing" );
-
- rng = CvRNG( -1 );
-
- trees = (CvForestTree**)cvAlloc( sizeof(trees[0])*ntrees );
- memset( trees, 0, sizeof(trees[0])*ntrees );
-
- data = new CvDTreeTrainData();
- data->read_params( fs, fnode );
- data->shared = true;
-
- trees_fnode = cvGetFileNodeByName( fs, fnode, "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 );
- if( reader.seq->total != ntrees )
- CV_ERROR( CV_StsParseError,
- "<ntrees> is not equal to the number of trees saved in file" );
-
- for( k = 0; k < ntrees; k++ )
- {
- trees[k] = new CvForestTree();
- CV_CALL(trees[k]->read( fs, (CvFileNode*)reader.ptr, this, data ));
- CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
- }
-
- var_count = data->var_count;
- CV_CALL(active_var_mask = cvCreateMat( 1, var_count, CV_8UC1 ));
- {
- // initialize active variables mask
- CvMat submask1, submask2;
- cvGetCols( active_var_mask, &submask1, 0, nactive_vars );
- cvGetCols( active_var_mask, &submask2, nactive_vars, var_count );
- cvSet( &submask1, cvScalar(1) );
- cvZero( &submask2 );
- }
-
- __END__;
-}
-
-
-int CvRTrees::get_tree_count() const
-{
- return ntrees;
-}
-
-CvForestTree* CvRTrees::get_tree(int i) const
-{
- return (unsigned)i < (unsigned)ntrees ? trees[i] : 0;
-}
-
-// End of file.
projects / opencv / blobdiff