793 lines
21 KiB
C++
793 lines
21 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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#include <ctype.h>
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#define MISS_VAL FLT_MAX
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#define CV_VAR_MISS 0
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CvTrainTestSplit::CvTrainTestSplit()
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{
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train_sample_part_mode = CV_COUNT;
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train_sample_part.count = -1;
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mix = false;
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}
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CvTrainTestSplit::CvTrainTestSplit( int _train_sample_count, bool _mix )
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{
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train_sample_part_mode = CV_COUNT;
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train_sample_part.count = _train_sample_count;
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mix = _mix;
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}
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CvTrainTestSplit::CvTrainTestSplit( float _train_sample_portion, bool _mix )
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{
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train_sample_part_mode = CV_PORTION;
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train_sample_part.portion = _train_sample_portion;
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mix = _mix;
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}
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////////////////
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CvMLData::CvMLData()
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{
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values = missing = var_types = var_idx_mask = response_out = var_idx_out = var_types_out = 0;
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train_sample_idx = test_sample_idx = 0;
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header_lines_number = 0;
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sample_idx = 0;
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response_idx = -1;
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train_sample_count = -1;
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delimiter = ',';
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miss_ch = '?';
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//flt_separator = '.';
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rng = &cv::theRNG();
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}
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CvMLData::~CvMLData()
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{
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clear();
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}
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void CvMLData::free_train_test_idx()
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{
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cvReleaseMat( &train_sample_idx );
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cvReleaseMat( &test_sample_idx );
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sample_idx = 0;
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}
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void CvMLData::clear()
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{
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class_map.clear();
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cvReleaseMat( &values );
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cvReleaseMat( &missing );
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cvReleaseMat( &var_types );
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cvReleaseMat( &var_idx_mask );
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cvReleaseMat( &response_out );
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cvReleaseMat( &var_idx_out );
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cvReleaseMat( &var_types_out );
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free_train_test_idx();
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total_class_count = 0;
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response_idx = -1;
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train_sample_count = -1;
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}
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void CvMLData::set_header_lines_number( int idx )
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{
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header_lines_number = std::max(0, idx);
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}
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int CvMLData::get_header_lines_number() const
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{
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return header_lines_number;
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}
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static char *fgets_chomp(char *str, int n, FILE *stream)
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{
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char *head = fgets(str, n, stream);
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if( head )
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{
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for(char *tail = head + strlen(head) - 1; tail >= head; --tail)
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{
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if( *tail != '\r' && *tail != '\n' )
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break;
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*tail = '\0';
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}
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}
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return head;
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}
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int CvMLData::read_csv(const char* filename)
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{
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const int M = 1000000;
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const char str_delimiter[3] = { ' ', delimiter, '\0' };
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FILE* file = 0;
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CvMemStorage* storage;
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CvSeq* seq;
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char *ptr;
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float* el_ptr;
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CvSeqReader reader;
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int cols_count = 0;
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uchar *var_types_ptr = 0;
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clear();
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file = fopen( filename, "rt" );
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if( !file )
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return -1;
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std::vector<char> _buf(M);
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char* buf = &_buf[0];
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// skip header lines
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for( int i = 0; i < header_lines_number; i++ )
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{
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if( fgets( buf, M, file ) == 0 )
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{
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fclose(file);
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return -1;
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}
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}
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// read the first data line and determine the number of variables
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if( !fgets_chomp( buf, M, file ))
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{
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fclose(file);
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return -1;
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}
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ptr = buf;
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while( *ptr == ' ' )
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ptr++;
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for( ; *ptr != '\0'; )
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{
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if(*ptr == delimiter || *ptr == ' ')
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{
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cols_count++;
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ptr++;
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while( *ptr == ' ' ) ptr++;
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}
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else
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ptr++;
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}
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cols_count++;
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if ( cols_count == 0)
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{
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fclose(file);
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return -1;
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}
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// create temporary memory storage to store the whole database
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el_ptr = new float[cols_count];
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storage = cvCreateMemStorage();
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seq = cvCreateSeq( 0, sizeof(*seq), cols_count*sizeof(float), storage );
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var_types = cvCreateMat( 1, cols_count, CV_8U );
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cvZero( var_types );
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var_types_ptr = var_types->data.ptr;
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for(;;)
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{
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char *token = NULL;
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int type;
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token = strtok(buf, str_delimiter);
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if (!token)
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break;
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for (int i = 0; i < cols_count-1; i++)
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{
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str_to_flt_elem( token, el_ptr[i], type);
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var_types_ptr[i] |= type;
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token = strtok(NULL, str_delimiter);
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if (!token)
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{
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fclose(file);
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delete [] el_ptr;
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return -1;
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}
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}
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str_to_flt_elem( token, el_ptr[cols_count-1], type);
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var_types_ptr[cols_count-1] |= type;
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cvSeqPush( seq, el_ptr );
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if( !fgets_chomp( buf, M, file ) )
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break;
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}
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fclose(file);
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values = cvCreateMat( seq->total, cols_count, CV_32FC1 );
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missing = cvCreateMat( seq->total, cols_count, CV_8U );
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var_idx_mask = cvCreateMat( 1, values->cols, CV_8UC1 );
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cvSet( var_idx_mask, cvRealScalar(1) );
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train_sample_count = seq->total;
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cvStartReadSeq( seq, &reader );
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for(int i = 0; i < seq->total; i++ )
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{
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const float* sdata = (float*)reader.ptr;
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float* ddata = values->data.fl + cols_count*i;
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uchar* dm = missing->data.ptr + cols_count*i;
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for( int j = 0; j < cols_count; j++ )
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{
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ddata[j] = sdata[j];
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dm[j] = ( fabs( MISS_VAL - sdata[j] ) <= FLT_EPSILON );
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}
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CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
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}
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if ( cvNorm( missing, 0, CV_L1 ) <= FLT_EPSILON )
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cvReleaseMat( &missing );
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cvReleaseMemStorage( &storage );
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delete []el_ptr;
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return 0;
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}
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const CvMat* CvMLData::get_values() const
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{
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return values;
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}
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const CvMat* CvMLData::get_missing() const
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{
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CV_FUNCNAME( "CvMLData::get_missing" );
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__BEGIN__;
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if ( !values )
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CV_ERROR( CV_StsInternal, "data is empty" );
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__END__;
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return missing;
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}
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const std::map<cv::String, int>& CvMLData::get_class_labels_map() const
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{
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return class_map;
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}
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void CvMLData::str_to_flt_elem( const char* token, float& flt_elem, int& type)
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{
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char* stopstring = NULL;
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flt_elem = (float)strtod( token, &stopstring );
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assert( stopstring );
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type = CV_VAR_ORDERED;
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if ( *stopstring == miss_ch && strlen(stopstring) == 1 ) // missed value
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{
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flt_elem = MISS_VAL;
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type = CV_VAR_MISS;
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}
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else
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{
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if ( (*stopstring != 0) && (*stopstring != '\n') && (strcmp(stopstring, "\r\n") != 0) ) // class label
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{
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int idx = class_map[token];
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if ( idx == 0)
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{
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total_class_count++;
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idx = total_class_count;
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class_map[token] = idx;
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}
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flt_elem = (float)idx;
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type = CV_VAR_CATEGORICAL;
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}
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}
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}
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void CvMLData::set_delimiter(char ch)
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{
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CV_FUNCNAME( "CvMLData::set_delimited" );
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__BEGIN__;
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if (ch == miss_ch /*|| ch == flt_separator*/)
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CV_ERROR(CV_StsBadArg, "delimited, miss_character and flt_separator must be different");
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delimiter = ch;
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__END__;
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}
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char CvMLData::get_delimiter() const
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{
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return delimiter;
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}
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void CvMLData::set_miss_ch(char ch)
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{
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CV_FUNCNAME( "CvMLData::set_miss_ch" );
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__BEGIN__;
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if (ch == delimiter/* || ch == flt_separator*/)
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CV_ERROR(CV_StsBadArg, "delimited, miss_character and flt_separator must be different");
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miss_ch = ch;
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__END__;
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}
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char CvMLData::get_miss_ch() const
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{
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return miss_ch;
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}
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void CvMLData::set_response_idx( int idx )
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{
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CV_FUNCNAME( "CvMLData::set_response_idx" );
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__BEGIN__;
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if ( !values )
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CV_ERROR( CV_StsInternal, "data is empty" );
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if ( idx >= values->cols)
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CV_ERROR( CV_StsBadArg, "idx value is not correct" );
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if ( response_idx >= 0 )
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chahge_var_idx( response_idx, true );
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if ( idx >= 0 )
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chahge_var_idx( idx, false );
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response_idx = idx;
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__END__;
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}
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int CvMLData::get_response_idx() const
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{
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CV_FUNCNAME( "CvMLData::get_response_idx" );
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__BEGIN__;
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if ( !values )
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CV_ERROR( CV_StsInternal, "data is empty" );
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__END__;
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return response_idx;
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}
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void CvMLData::change_var_type( int var_idx, int type )
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{
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CV_FUNCNAME( "CvMLData::change_var_type" );
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__BEGIN__;
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int var_count = 0;
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if ( !values )
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CV_ERROR( CV_StsInternal, "data is empty" );
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var_count = values->cols;
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if ( var_idx < 0 || var_idx >= var_count)
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CV_ERROR( CV_StsBadArg, "var_idx is not correct" );
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if ( type != CV_VAR_ORDERED && type != CV_VAR_CATEGORICAL)
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CV_ERROR( CV_StsBadArg, "type is not correct" );
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assert( var_types );
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if ( var_types->data.ptr[var_idx] == CV_VAR_CATEGORICAL && type == CV_VAR_ORDERED)
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CV_ERROR( CV_StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" );
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var_types->data.ptr[var_idx] = (uchar)type;
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__END__;
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return;
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}
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void CvMLData::set_var_types( const char* str )
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{
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CV_FUNCNAME( "CvMLData::set_var_types" );
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__BEGIN__;
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const char* ord = 0, *cat = 0;
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int var_count = 0, set_var_type_count = 0;
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if ( !values )
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CV_ERROR( CV_StsInternal, "data is empty" );
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var_count = values->cols;
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assert( var_types );
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ord = strstr( str, "ord" );
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cat = strstr( str, "cat" );
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if ( !ord && !cat )
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CV_ERROR( CV_StsBadArg, "types string is not correct" );
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if ( !ord && strlen(cat) == 3 ) // str == "cat"
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{
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cvSet( var_types, cvScalarAll(CV_VAR_CATEGORICAL) );
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return;
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}
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if ( !cat && strlen(ord) == 3 ) // str == "ord"
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{
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cvSet( var_types, cvScalarAll(CV_VAR_ORDERED) );
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return;
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}
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if ( ord ) // parse ord str
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{
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char* stopstring = NULL;
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if ( ord[3] != '[')
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CV_ERROR( CV_StsBadArg, "types string is not correct" );
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ord += 4; // pass "ord["
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do
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{
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int b1 = (int)strtod( ord, &stopstring );
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if ( *stopstring == 0 || (*stopstring != ',' && *stopstring != ']' && *stopstring != '-') )
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CV_ERROR( CV_StsBadArg, "types string is not correct" );
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ord = stopstring + 1;
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if ( (stopstring[0] == ',') || (stopstring[0] == ']'))
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{
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if ( var_types->data.ptr[b1] == CV_VAR_CATEGORICAL)
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CV_ERROR( CV_StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" );
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var_types->data.ptr[b1] = CV_VAR_ORDERED;
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set_var_type_count++;
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}
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else
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{
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if ( stopstring[0] == '-')
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{
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int b2 = (int)strtod( ord, &stopstring);
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if ( (*stopstring == 0) || (*stopstring != ',' && *stopstring != ']') )
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CV_ERROR( CV_StsBadArg, "types string is not correct" );
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ord = stopstring + 1;
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for (int i = b1; i <= b2; i++)
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{
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if ( var_types->data.ptr[i] == CV_VAR_CATEGORICAL)
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CV_ERROR( CV_StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" );
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var_types->data.ptr[i] = CV_VAR_ORDERED;
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}
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set_var_type_count += b2 - b1 + 1;
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}
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else
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CV_ERROR( CV_StsBadArg, "types string is not correct" );
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}
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}
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while (*stopstring != ']');
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if ( stopstring[1] != '\0' && stopstring[1] != ',')
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CV_ERROR( CV_StsBadArg, "types string is not correct" );
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}
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if ( cat ) // parse cat str
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{
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char* stopstring = NULL;
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if ( cat[3] != '[')
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CV_ERROR( CV_StsBadArg, "types string is not correct" );
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cat += 4; // pass "cat["
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do
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{
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int b1 = (int)strtod( cat, &stopstring );
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if ( *stopstring == 0 || (*stopstring != ',' && *stopstring != ']' && *stopstring != '-') )
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CV_ERROR( CV_StsBadArg, "types string is not correct" );
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cat = stopstring + 1;
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if ( (stopstring[0] == ',') || (stopstring[0] == ']'))
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{
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var_types->data.ptr[b1] = CV_VAR_CATEGORICAL;
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set_var_type_count++;
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}
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else
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{
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if ( stopstring[0] == '-')
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{
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int b2 = (int)strtod( cat, &stopstring);
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if ( (*stopstring == 0) || (*stopstring != ',' && *stopstring != ']') )
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CV_ERROR( CV_StsBadArg, "types string is not correct" );
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cat = stopstring + 1;
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for (int i = b1; i <= b2; i++)
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var_types->data.ptr[i] = CV_VAR_CATEGORICAL;
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set_var_type_count += b2 - b1 + 1;
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}
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else
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CV_ERROR( CV_StsBadArg, "types string is not correct" );
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}
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}
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while (*stopstring != ']');
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if ( stopstring[1] != '\0' && stopstring[1] != ',')
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CV_ERROR( CV_StsBadArg, "types string is not correct" );
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}
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if (set_var_type_count != var_count)
|
|
CV_ERROR( CV_StsBadArg, "types string is not correct" );
|
|
|
|
__END__;
|
|
}
|
|
|
|
const CvMat* CvMLData::get_var_types()
|
|
{
|
|
CV_FUNCNAME( "CvMLData::get_var_types" );
|
|
__BEGIN__;
|
|
|
|
uchar *var_types_out_ptr = 0;
|
|
int avcount, vt_size;
|
|
if ( !values )
|
|
CV_ERROR( CV_StsInternal, "data is empty" );
|
|
|
|
assert( var_idx_mask );
|
|
|
|
avcount = cvFloor( cvNorm( var_idx_mask, 0, CV_L1 ) );
|
|
vt_size = avcount + (response_idx >= 0);
|
|
|
|
if ( avcount == values->cols || (avcount == values->cols-1 && response_idx == values->cols-1) )
|
|
return var_types;
|
|
|
|
if ( !var_types_out || ( var_types_out && var_types_out->cols != vt_size ) )
|
|
{
|
|
cvReleaseMat( &var_types_out );
|
|
var_types_out = cvCreateMat( 1, vt_size, CV_8UC1 );
|
|
}
|
|
|
|
var_types_out_ptr = var_types_out->data.ptr;
|
|
for( int i = 0; i < var_types->cols; i++)
|
|
{
|
|
if (i == response_idx || !var_idx_mask->data.ptr[i]) continue;
|
|
*var_types_out_ptr = var_types->data.ptr[i];
|
|
var_types_out_ptr++;
|
|
}
|
|
if ( response_idx >= 0 )
|
|
*var_types_out_ptr = var_types->data.ptr[response_idx];
|
|
|
|
__END__;
|
|
|
|
return var_types_out;
|
|
}
|
|
|
|
int CvMLData::get_var_type( int var_idx ) const
|
|
{
|
|
return var_types->data.ptr[var_idx];
|
|
}
|
|
|
|
const CvMat* CvMLData::get_responses()
|
|
{
|
|
CV_FUNCNAME( "CvMLData::get_responses_ptr" );
|
|
__BEGIN__;
|
|
|
|
int var_count = 0;
|
|
|
|
if ( !values )
|
|
CV_ERROR( CV_StsInternal, "data is empty" );
|
|
var_count = values->cols;
|
|
|
|
if ( response_idx < 0 || response_idx >= var_count )
|
|
return 0;
|
|
if ( !response_out )
|
|
response_out = cvCreateMatHeader( values->rows, 1, CV_32FC1 );
|
|
else
|
|
cvInitMatHeader( response_out, values->rows, 1, CV_32FC1);
|
|
cvGetCol( values, response_out, response_idx );
|
|
|
|
__END__;
|
|
|
|
return response_out;
|
|
}
|
|
|
|
void CvMLData::set_train_test_split( const CvTrainTestSplit * spl)
|
|
{
|
|
CV_FUNCNAME( "CvMLData::set_division" );
|
|
__BEGIN__;
|
|
|
|
int sample_count = 0;
|
|
|
|
if ( !values )
|
|
CV_ERROR( CV_StsInternal, "data is empty" );
|
|
|
|
sample_count = values->rows;
|
|
|
|
float train_sample_portion;
|
|
|
|
if (spl->train_sample_part_mode == CV_COUNT)
|
|
{
|
|
train_sample_count = spl->train_sample_part.count;
|
|
if (train_sample_count > sample_count)
|
|
CV_ERROR( CV_StsBadArg, "train samples count is not correct" );
|
|
train_sample_count = train_sample_count<=0 ? sample_count : train_sample_count;
|
|
}
|
|
else // dtype.train_sample_part_mode == CV_PORTION
|
|
{
|
|
train_sample_portion = spl->train_sample_part.portion;
|
|
if ( train_sample_portion > 1)
|
|
CV_ERROR( CV_StsBadArg, "train samples count is not correct" );
|
|
train_sample_portion = train_sample_portion <= FLT_EPSILON ||
|
|
1 - train_sample_portion <= FLT_EPSILON ? 1 : train_sample_portion;
|
|
train_sample_count = std::max(1, cvFloor( train_sample_portion * sample_count ));
|
|
}
|
|
|
|
if ( train_sample_count == sample_count )
|
|
{
|
|
free_train_test_idx();
|
|
return;
|
|
}
|
|
|
|
if ( train_sample_idx && train_sample_idx->cols != train_sample_count )
|
|
free_train_test_idx();
|
|
|
|
if ( !sample_idx)
|
|
{
|
|
int test_sample_count = sample_count- train_sample_count;
|
|
sample_idx = (int*)cvAlloc( sample_count * sizeof(sample_idx[0]) );
|
|
for (int i = 0; i < sample_count; i++ )
|
|
sample_idx[i] = i;
|
|
train_sample_idx = cvCreateMatHeader( 1, train_sample_count, CV_32SC1 );
|
|
*train_sample_idx = cvMat( 1, train_sample_count, CV_32SC1, &sample_idx[0] );
|
|
|
|
CV_Assert(test_sample_count > 0);
|
|
test_sample_idx = cvCreateMatHeader( 1, test_sample_count, CV_32SC1 );
|
|
*test_sample_idx = cvMat( 1, test_sample_count, CV_32SC1, &sample_idx[train_sample_count] );
|
|
}
|
|
|
|
mix = spl->mix;
|
|
if ( mix )
|
|
mix_train_and_test_idx();
|
|
|
|
__END__;
|
|
}
|
|
|
|
const CvMat* CvMLData::get_train_sample_idx() const
|
|
{
|
|
CV_FUNCNAME( "CvMLData::get_train_sample_idx" );
|
|
__BEGIN__;
|
|
|
|
if ( !values )
|
|
CV_ERROR( CV_StsInternal, "data is empty" );
|
|
__END__;
|
|
|
|
return train_sample_idx;
|
|
}
|
|
|
|
const CvMat* CvMLData::get_test_sample_idx() const
|
|
{
|
|
CV_FUNCNAME( "CvMLData::get_test_sample_idx" );
|
|
__BEGIN__;
|
|
|
|
if ( !values )
|
|
CV_ERROR( CV_StsInternal, "data is empty" );
|
|
__END__;
|
|
|
|
return test_sample_idx;
|
|
}
|
|
|
|
void CvMLData::mix_train_and_test_idx()
|
|
{
|
|
CV_FUNCNAME( "CvMLData::mix_train_and_test_idx" );
|
|
__BEGIN__;
|
|
|
|
if ( !values )
|
|
CV_ERROR( CV_StsInternal, "data is empty" );
|
|
__END__;
|
|
|
|
if ( !sample_idx)
|
|
return;
|
|
|
|
if ( train_sample_count > 0 && train_sample_count < values->rows )
|
|
{
|
|
int n = values->rows;
|
|
for (int i = 0; i < n; i++)
|
|
{
|
|
int a = (*rng)(n);
|
|
int b = (*rng)(n);
|
|
int t;
|
|
CV_SWAP( sample_idx[a], sample_idx[b], t );
|
|
}
|
|
}
|
|
}
|
|
|
|
const CvMat* CvMLData::get_var_idx()
|
|
{
|
|
CV_FUNCNAME( "CvMLData::get_var_idx" );
|
|
__BEGIN__;
|
|
|
|
int avcount = 0;
|
|
|
|
if ( !values )
|
|
CV_ERROR( CV_StsInternal, "data is empty" );
|
|
|
|
assert( var_idx_mask );
|
|
|
|
avcount = cvFloor( cvNorm( var_idx_mask, 0, CV_L1 ) );
|
|
int* vidx;
|
|
|
|
if ( avcount == values->cols )
|
|
return 0;
|
|
|
|
if ( !var_idx_out || ( var_idx_out && var_idx_out->cols != avcount ) )
|
|
{
|
|
cvReleaseMat( &var_idx_out );
|
|
var_idx_out = cvCreateMat( 1, avcount, CV_32SC1);
|
|
if ( response_idx >=0 )
|
|
var_idx_mask->data.ptr[response_idx] = 0;
|
|
}
|
|
|
|
vidx = var_idx_out->data.i;
|
|
|
|
for(int i = 0; i < var_idx_mask->cols; i++)
|
|
if ( var_idx_mask->data.ptr[i] )
|
|
{
|
|
*vidx = i;
|
|
vidx++;
|
|
}
|
|
|
|
__END__;
|
|
|
|
return var_idx_out;
|
|
}
|
|
|
|
void CvMLData::chahge_var_idx( int vi, bool state )
|
|
{
|
|
change_var_idx( vi, state );
|
|
}
|
|
|
|
void CvMLData::change_var_idx( int vi, bool state )
|
|
{
|
|
CV_FUNCNAME( "CvMLData::change_var_idx" );
|
|
__BEGIN__;
|
|
|
|
int var_count = 0;
|
|
|
|
if ( !values )
|
|
CV_ERROR( CV_StsInternal, "data is empty" );
|
|
|
|
var_count = values->cols;
|
|
|
|
if ( vi < 0 || vi >= var_count)
|
|
CV_ERROR( CV_StsBadArg, "variable index is not correct" );
|
|
|
|
assert( var_idx_mask );
|
|
var_idx_mask->data.ptr[vi] = state;
|
|
|
|
__END__;
|
|
}
|
|
|
|
/* End of file. */
|