/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // Intel License Agreement // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of Intel Corporation may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "old_ml_precomp.hpp" #include <ctype.h> #define MISS_VAL FLT_MAX #define CV_VAR_MISS 0 CvTrainTestSplit::CvTrainTestSplit() { train_sample_part_mode = CV_COUNT; train_sample_part.count = -1; mix = false; } CvTrainTestSplit::CvTrainTestSplit( int _train_sample_count, bool _mix ) { train_sample_part_mode = CV_COUNT; train_sample_part.count = _train_sample_count; mix = _mix; } CvTrainTestSplit::CvTrainTestSplit( float _train_sample_portion, bool _mix ) { train_sample_part_mode = CV_PORTION; train_sample_part.portion = _train_sample_portion; mix = _mix; } //////////////// CvMLData::CvMLData() { values = missing = var_types = var_idx_mask = response_out = var_idx_out = var_types_out = 0; train_sample_idx = test_sample_idx = 0; header_lines_number = 0; sample_idx = 0; response_idx = -1; train_sample_count = -1; delimiter = ','; miss_ch = '?'; //flt_separator = '.'; rng = &cv::theRNG(); } CvMLData::~CvMLData() { clear(); } void CvMLData::free_train_test_idx() { cvReleaseMat( &train_sample_idx ); cvReleaseMat( &test_sample_idx ); sample_idx = 0; } void CvMLData::clear() { class_map.clear(); cvReleaseMat( &values ); cvReleaseMat( &missing ); cvReleaseMat( &var_types ); cvReleaseMat( &var_idx_mask ); cvReleaseMat( &response_out ); cvReleaseMat( &var_idx_out ); cvReleaseMat( &var_types_out ); free_train_test_idx(); total_class_count = 0; response_idx = -1; train_sample_count = -1; } void CvMLData::set_header_lines_number( int idx ) { header_lines_number = std::max(0, idx); } int CvMLData::get_header_lines_number() const { return header_lines_number; } static char *fgets_chomp(char *str, int n, FILE *stream) { char *head = fgets(str, n, stream); if( head ) { for(char *tail = head + strlen(head) - 1; tail >= head; --tail) { if( *tail != '\r' && *tail != '\n' ) break; *tail = '\0'; } } return head; } int CvMLData::read_csv(const char* filename) { const int M = 1000000; const char str_delimiter[3] = { ' ', delimiter, '\0' }; FILE* file = 0; CvMemStorage* storage; CvSeq* seq; char *ptr; float* el_ptr; CvSeqReader reader; int cols_count = 0; uchar *var_types_ptr = 0; clear(); file = fopen( filename, "rt" ); if( !file ) return -1; std::vector<char> _buf(M); char* buf = &_buf[0]; // skip header lines for( int i = 0; i < header_lines_number; i++ ) { if( fgets( buf, M, file ) == 0 ) { fclose(file); return -1; } } // read the first data line and determine the number of variables if( !fgets_chomp( buf, M, file )) { fclose(file); return -1; } ptr = buf; while( *ptr == ' ' ) ptr++; for( ; *ptr != '\0'; ) { if(*ptr == delimiter || *ptr == ' ') { cols_count++; ptr++; while( *ptr == ' ' ) ptr++; } else ptr++; } cols_count++; if ( cols_count == 0) { fclose(file); return -1; } // create temporary memory storage to store the whole database el_ptr = new float[cols_count]; storage = cvCreateMemStorage(); seq = cvCreateSeq( 0, sizeof(*seq), cols_count*sizeof(float), storage ); var_types = cvCreateMat( 1, cols_count, CV_8U ); cvZero( var_types ); var_types_ptr = var_types->data.ptr; for(;;) { char *token = NULL; int type; token = strtok(buf, str_delimiter); if (!token) break; for (int i = 0; i < cols_count-1; i++) { str_to_flt_elem( token, el_ptr[i], type); var_types_ptr[i] |= type; token = strtok(NULL, str_delimiter); if (!token) { fclose(file); delete [] el_ptr; return -1; } } str_to_flt_elem( token, el_ptr[cols_count-1], type); var_types_ptr[cols_count-1] |= type; cvSeqPush( seq, el_ptr ); if( !fgets_chomp( buf, M, file ) ) break; } fclose(file); values = cvCreateMat( seq->total, cols_count, CV_32FC1 ); missing = cvCreateMat( seq->total, cols_count, CV_8U ); var_idx_mask = cvCreateMat( 1, values->cols, CV_8UC1 ); cvSet( var_idx_mask, cvRealScalar(1) ); train_sample_count = seq->total; cvStartReadSeq( seq, &reader ); for(int i = 0; i < seq->total; i++ ) { const float* sdata = (float*)reader.ptr; float* ddata = values->data.fl + cols_count*i; uchar* dm = missing->data.ptr + cols_count*i; for( int j = 0; j < cols_count; j++ ) { ddata[j] = sdata[j]; dm[j] = ( fabs( MISS_VAL - sdata[j] ) <= FLT_EPSILON ); } CV_NEXT_SEQ_ELEM( seq->elem_size, reader ); } if ( cvNorm( missing, 0, CV_L1 ) <= FLT_EPSILON ) cvReleaseMat( &missing ); cvReleaseMemStorage( &storage ); delete []el_ptr; return 0; } const CvMat* CvMLData::get_values() const { return values; } const CvMat* CvMLData::get_missing() const { CV_FUNCNAME( "CvMLData::get_missing" ); __BEGIN__; if ( !values ) CV_ERROR( CV_StsInternal, "data is empty" ); __END__; return missing; } const std::map<cv::String, int>& CvMLData::get_class_labels_map() const { return class_map; } void CvMLData::str_to_flt_elem( const char* token, float& flt_elem, int& type) { char* stopstring = NULL; flt_elem = (float)strtod( token, &stopstring ); assert( stopstring ); type = CV_VAR_ORDERED; if ( *stopstring == miss_ch && strlen(stopstring) == 1 ) // missed value { flt_elem = MISS_VAL; type = CV_VAR_MISS; } else { if ( (*stopstring != 0) && (*stopstring != '\n') && (strcmp(stopstring, "\r\n") != 0) ) // class label { int idx = class_map[token]; if ( idx == 0) { total_class_count++; idx = total_class_count; class_map[token] = idx; } flt_elem = (float)idx; type = CV_VAR_CATEGORICAL; } } } void CvMLData::set_delimiter(char ch) { CV_FUNCNAME( "CvMLData::set_delimited" ); __BEGIN__; if (ch == miss_ch /*|| ch == flt_separator*/) CV_ERROR(CV_StsBadArg, "delimited, miss_character and flt_separator must be different"); delimiter = ch; __END__; } char CvMLData::get_delimiter() const { return delimiter; } void CvMLData::set_miss_ch(char ch) { CV_FUNCNAME( "CvMLData::set_miss_ch" ); __BEGIN__; if (ch == delimiter/* || ch == flt_separator*/) CV_ERROR(CV_StsBadArg, "delimited, miss_character and flt_separator must be different"); miss_ch = ch; __END__; } char CvMLData::get_miss_ch() const { return miss_ch; } void CvMLData::set_response_idx( int idx ) { CV_FUNCNAME( "CvMLData::set_response_idx" ); __BEGIN__; if ( !values ) CV_ERROR( CV_StsInternal, "data is empty" ); if ( idx >= values->cols) CV_ERROR( CV_StsBadArg, "idx value is not correct" ); if ( response_idx >= 0 ) chahge_var_idx( response_idx, true ); if ( idx >= 0 ) chahge_var_idx( idx, false ); response_idx = idx; __END__; } int CvMLData::get_response_idx() const { CV_FUNCNAME( "CvMLData::get_response_idx" ); __BEGIN__; if ( !values ) CV_ERROR( CV_StsInternal, "data is empty" ); __END__; return response_idx; } void CvMLData::change_var_type( int var_idx, int type ) { CV_FUNCNAME( "CvMLData::change_var_type" ); __BEGIN__; int var_count = 0; if ( !values ) CV_ERROR( CV_StsInternal, "data is empty" ); var_count = values->cols; if ( var_idx < 0 || var_idx >= var_count) CV_ERROR( CV_StsBadArg, "var_idx is not correct" ); if ( type != CV_VAR_ORDERED && type != CV_VAR_CATEGORICAL) CV_ERROR( CV_StsBadArg, "type is not correct" ); assert( var_types ); if ( var_types->data.ptr[var_idx] == CV_VAR_CATEGORICAL && type == CV_VAR_ORDERED) CV_ERROR( CV_StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" ); var_types->data.ptr[var_idx] = (uchar)type; __END__; return; } void CvMLData::set_var_types( const char* str ) { CV_FUNCNAME( "CvMLData::set_var_types" ); __BEGIN__; const char* ord = 0, *cat = 0; int var_count = 0, set_var_type_count = 0; if ( !values ) CV_ERROR( CV_StsInternal, "data is empty" ); var_count = values->cols; assert( var_types ); ord = strstr( str, "ord" ); cat = strstr( str, "cat" ); if ( !ord && !cat ) CV_ERROR( CV_StsBadArg, "types string is not correct" ); if ( !ord && strlen(cat) == 3 ) // str == "cat" { cvSet( var_types, cvScalarAll(CV_VAR_CATEGORICAL) ); return; } if ( !cat && strlen(ord) == 3 ) // str == "ord" { cvSet( var_types, cvScalarAll(CV_VAR_ORDERED) ); return; } if ( ord ) // parse ord str { char* stopstring = NULL; if ( ord[3] != '[') CV_ERROR( CV_StsBadArg, "types string is not correct" ); ord += 4; // pass "ord[" do { int b1 = (int)strtod( ord, &stopstring ); if ( *stopstring == 0 || (*stopstring != ',' && *stopstring != ']' && *stopstring != '-') ) CV_ERROR( CV_StsBadArg, "types string is not correct" ); ord = stopstring + 1; if ( (stopstring[0] == ',') || (stopstring[0] == ']')) { if ( var_types->data.ptr[b1] == CV_VAR_CATEGORICAL) CV_ERROR( CV_StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" ); var_types->data.ptr[b1] = CV_VAR_ORDERED; set_var_type_count++; } else { if ( stopstring[0] == '-') { int b2 = (int)strtod( ord, &stopstring); if ( (*stopstring == 0) || (*stopstring != ',' && *stopstring != ']') ) CV_ERROR( CV_StsBadArg, "types string is not correct" ); ord = stopstring + 1; for (int i = b1; i <= b2; i++) { if ( var_types->data.ptr[i] == CV_VAR_CATEGORICAL) CV_ERROR( CV_StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" ); var_types->data.ptr[i] = CV_VAR_ORDERED; } set_var_type_count += b2 - b1 + 1; } else CV_ERROR( CV_StsBadArg, "types string is not correct" ); } } while (*stopstring != ']'); if ( stopstring[1] != '\0' && stopstring[1] != ',') CV_ERROR( CV_StsBadArg, "types string is not correct" ); } if ( cat ) // parse cat str { char* stopstring = NULL; if ( cat[3] != '[') CV_ERROR( CV_StsBadArg, "types string is not correct" ); cat += 4; // pass "cat[" do { int b1 = (int)strtod( cat, &stopstring ); if ( *stopstring == 0 || (*stopstring != ',' && *stopstring != ']' && *stopstring != '-') ) CV_ERROR( CV_StsBadArg, "types string is not correct" ); cat = stopstring + 1; if ( (stopstring[0] == ',') || (stopstring[0] == ']')) { var_types->data.ptr[b1] = CV_VAR_CATEGORICAL; set_var_type_count++; } else { if ( stopstring[0] == '-') { int b2 = (int)strtod( cat, &stopstring); if ( (*stopstring == 0) || (*stopstring != ',' && *stopstring != ']') ) CV_ERROR( CV_StsBadArg, "types string is not correct" ); cat = stopstring + 1; for (int i = b1; i <= b2; i++) var_types->data.ptr[i] = CV_VAR_CATEGORICAL; set_var_type_count += b2 - b1 + 1; } else CV_ERROR( CV_StsBadArg, "types string is not correct" ); } } while (*stopstring != ']'); if ( stopstring[1] != '\0' && stopstring[1] != ',') CV_ERROR( CV_StsBadArg, "types string is not correct" ); } 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. */