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374 lines
20 KiB
C++
374 lines
20 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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#ifndef __ML_INTERNAL_H__
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#define __ML_INTERNAL_H__
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#if _MSC_VER >= 1200
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#pragma warning( disable: 4251 4514 4710 4711 4710 )
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#endif
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#include "opencv2/ml/ml.hpp"
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#include "opencv2/core/core_c.h"
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#include "opencv2/core/internal.hpp"
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#include <assert.h>
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#include <float.h>
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#include <limits.h>
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#include <math.h>
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#include <stdlib.h>
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#include <stdio.h>
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#include <string.h>
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#include <time.h>
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#define ML_IMPL CV_IMPL
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#define CV_MAT_ELEM_FLAG( mat, type, comp, vect, tflag ) \
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(( tflag == CV_ROW_SAMPLE ) \
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? (CV_MAT_ELEM( mat, type, comp, vect )) \
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: (CV_MAT_ELEM( mat, type, vect, comp )))
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/* Convert matrix to vector */
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#define ICV_MAT2VEC( mat, vdata, vstep, num ) \
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if( MIN( (mat).rows, (mat).cols ) != 1 ) \
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CV_ERROR( CV_StsBadArg, "" ); \
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(vdata) = ((mat).data.ptr); \
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if( (mat).rows == 1 ) \
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{ \
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(vstep) = CV_ELEM_SIZE( (mat).type ); \
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(num) = (mat).cols; \
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} \
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else \
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{ \
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(vstep) = (mat).step; \
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(num) = (mat).rows; \
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}
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/* get raw data */
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#define ICV_RAWDATA( mat, flags, rdata, sstep, cstep, m, n ) \
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(rdata) = (mat).data.ptr; \
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if( CV_IS_ROW_SAMPLE( flags ) ) \
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{ \
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(sstep) = (mat).step; \
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(cstep) = CV_ELEM_SIZE( (mat).type ); \
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(m) = (mat).rows; \
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(n) = (mat).cols; \
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} \
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else \
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{ \
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(cstep) = (mat).step; \
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(sstep) = CV_ELEM_SIZE( (mat).type ); \
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(n) = (mat).rows; \
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(m) = (mat).cols; \
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}
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#define ICV_IS_MAT_OF_TYPE( mat, mat_type) \
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(CV_IS_MAT( mat ) && CV_MAT_TYPE( mat->type ) == (mat_type) && \
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(mat)->cols > 0 && (mat)->rows > 0)
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/*
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uchar* data; int sstep, cstep; - trainData->data
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uchar* classes; int clstep; int ncl;- trainClasses
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uchar* tmask; int tmstep; int ntm; - typeMask
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uchar* missed;int msstep, mcstep; -missedMeasurements...
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int mm, mn; == m,n == size,dim
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uchar* sidx;int sistep; - sampleIdx
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uchar* cidx;int cistep; - compIdx
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int k, l; == n,m == dim,size (length of cidx, sidx)
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int m, n; == size,dim
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*/
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#define ICV_DECLARE_TRAIN_ARGS() \
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uchar* data; \
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int sstep, cstep; \
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uchar* classes; \
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int clstep; \
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int ncl; \
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uchar* tmask; \
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int tmstep; \
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int ntm; \
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uchar* missed; \
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int msstep, mcstep; \
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int mm, mn; \
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uchar* sidx; \
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int sistep; \
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uchar* cidx; \
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int cistep; \
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int k, l; \
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int m, n; \
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\
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data = classes = tmask = missed = sidx = cidx = NULL; \
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sstep = cstep = clstep = ncl = tmstep = ntm = msstep = mcstep = mm = mn = 0; \
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sistep = cistep = k = l = m = n = 0;
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#define ICV_TRAIN_DATA_REQUIRED( param, flags ) \
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if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) ) \
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{ \
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CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
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} \
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else \
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{ \
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ICV_RAWDATA( *(param), (flags), data, sstep, cstep, m, n ); \
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k = n; \
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l = m; \
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}
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#define ICV_TRAIN_CLASSES_REQUIRED( param ) \
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if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) ) \
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{ \
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CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
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} \
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else \
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{ \
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ICV_MAT2VEC( *(param), classes, clstep, ncl ); \
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if( m != ncl ) \
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{ \
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CV_ERROR( CV_StsBadArg, "Unmatched sizes" ); \
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} \
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}
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#define ICV_ARG_NULL( param ) \
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if( (param) != NULL ) \
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{ \
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CV_ERROR( CV_StsBadArg, #param " parameter must be NULL" ); \
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}
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#define ICV_MISSED_MEASUREMENTS_OPTIONAL( param, flags ) \
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if( param ) \
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{ \
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if( !ICV_IS_MAT_OF_TYPE( param, CV_8UC1 ) ) \
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{ \
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CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
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} \
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else \
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{ \
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ICV_RAWDATA( *(param), (flags), missed, msstep, mcstep, mm, mn ); \
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if( mm != m || mn != n ) \
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{ \
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CV_ERROR( CV_StsBadArg, "Unmatched sizes" ); \
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} \
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} \
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}
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#define ICV_COMP_IDX_OPTIONAL( param ) \
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if( param ) \
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{ \
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if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) ) \
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{ \
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CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
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} \
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else \
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{ \
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ICV_MAT2VEC( *(param), cidx, cistep, k ); \
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if( k > n ) \
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CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
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} \
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}
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#define ICV_SAMPLE_IDX_OPTIONAL( param ) \
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if( param ) \
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{ \
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if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) ) \
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{ \
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CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
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} \
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else \
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{ \
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ICV_MAT2VEC( *sampleIdx, sidx, sistep, l ); \
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if( l > m ) \
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CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
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} \
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}
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/****************************************************************************************/
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#define ICV_CONVERT_FLOAT_ARRAY_TO_MATRICE( array, matrice ) \
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{ \
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CvMat a, b; \
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int dims = (matrice)->cols; \
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int nsamples = (matrice)->rows; \
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int type = CV_MAT_TYPE((matrice)->type); \
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int i, offset = dims; \
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\
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CV_ASSERT( type == CV_32FC1 || type == CV_64FC1 ); \
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offset *= ((type == CV_32FC1) ? sizeof(float) : sizeof(double));\
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\
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b = cvMat( 1, dims, CV_32FC1 ); \
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cvGetRow( matrice, &a, 0 ); \
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for( i = 0; i < nsamples; i++, a.data.ptr += offset ) \
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{ \
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b.data.fl = (float*)array[i]; \
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CV_CALL( cvConvert( &b, &a ) ); \
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} \
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}
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/****************************************************************************************\
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* Auxiliary functions declarations *
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\****************************************************************************************/
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/* Generates a set of classes centers in quantity <num_of_clusters> that are generated as
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uniform random vectors in parallelepiped, where <data> is concentrated. Vectors in
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<data> should have horizontal orientation. If <centers> != NULL, the function doesn't
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allocate any memory and stores generated centers in <centers>, returns <centers>.
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If <centers> == NULL, the function allocates memory and creates the matrice. Centers
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are supposed to be oriented horizontally. */
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CvMat* icvGenerateRandomClusterCenters( int seed,
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const CvMat* data,
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int num_of_clusters,
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CvMat* centers CV_DEFAULT(0));
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/* Fills the <labels> using <probs> by choosing the maximal probability. Outliers are
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fixed by <oulier_tresh> and have cluster label (-1). Function also controls that there
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weren't "empty" clusters by filling empty clusters with the maximal probability vector.
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If probs_sums != NULL, filles it with the sums of probabilities for each sample (it is
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useful for normalizing probabilities' matrice of FCM) */
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void icvFindClusterLabels( const CvMat* probs, float outlier_thresh, float r,
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const CvMat* labels );
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typedef struct CvSparseVecElem32f
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{
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int idx;
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float val;
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}
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CvSparseVecElem32f;
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/* Prepare training data and related parameters */
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#define CV_TRAIN_STATMODEL_DEFRAGMENT_TRAIN_DATA 1
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#define CV_TRAIN_STATMODEL_SAMPLES_AS_ROWS 2
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#define CV_TRAIN_STATMODEL_SAMPLES_AS_COLUMNS 4
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#define CV_TRAIN_STATMODEL_CATEGORICAL_RESPONSE 8
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#define CV_TRAIN_STATMODEL_ORDERED_RESPONSE 16
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#define CV_TRAIN_STATMODEL_RESPONSES_ON_OUTPUT 32
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#define CV_TRAIN_STATMODEL_ALWAYS_COPY_TRAIN_DATA 64
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#define CV_TRAIN_STATMODEL_SPARSE_AS_SPARSE 128
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int
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cvPrepareTrainData( const char* /*funcname*/,
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const CvMat* train_data, int tflag,
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const CvMat* responses, int response_type,
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const CvMat* var_idx,
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const CvMat* sample_idx,
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bool always_copy_data,
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const float*** out_train_samples,
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int* _sample_count,
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int* _var_count,
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int* _var_all,
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CvMat** out_responses,
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CvMat** out_response_map,
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CvMat** out_var_idx,
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CvMat** out_sample_idx=0 );
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void
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cvSortSamplesByClasses( const float** samples, const CvMat* classes,
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int* class_ranges, const uchar** mask CV_DEFAULT(0) );
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void
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cvCombineResponseMaps (CvMat* _responses,
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const CvMat* old_response_map,
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CvMat* new_response_map,
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CvMat** out_response_map);
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void
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cvPreparePredictData( const CvArr* sample, int dims_all, const CvMat* comp_idx,
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int class_count, const CvMat* prob, float** row_sample,
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int as_sparse CV_DEFAULT(0) );
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/* copies clustering [or batch "predict"] results
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(labels and/or centers and/or probs) back to the output arrays */
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void
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cvWritebackLabels( const CvMat* labels, CvMat* dst_labels,
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const CvMat* centers, CvMat* dst_centers,
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const CvMat* probs, CvMat* dst_probs,
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const CvMat* sample_idx, int samples_all,
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const CvMat* comp_idx, int dims_all );
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#define cvWritebackResponses cvWritebackLabels
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#define XML_FIELD_NAME "_name"
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CvFileNode* icvFileNodeGetChild(CvFileNode* father, const char* name);
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CvFileNode* icvFileNodeGetChildArrayElem(CvFileNode* father, const char* name,int index);
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CvFileNode* icvFileNodeGetNext(CvFileNode* n, const char* name);
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void cvCheckTrainData( const CvMat* train_data, int tflag,
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const CvMat* missing_mask,
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int* var_all, int* sample_all );
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CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, bool check_for_duplicates=false );
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CvMat* cvPreprocessVarType( const CvMat* type_mask, const CvMat* var_idx,
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int var_all, int* response_type );
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CvMat* cvPreprocessOrderedResponses( const CvMat* responses,
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const CvMat* sample_idx, int sample_all );
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CvMat* cvPreprocessCategoricalResponses( const CvMat* responses,
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const CvMat* sample_idx, int sample_all,
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CvMat** out_response_map, CvMat** class_counts=0 );
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const float** cvGetTrainSamples( const CvMat* train_data, int tflag,
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const CvMat* var_idx, const CvMat* sample_idx,
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int* _var_count, int* _sample_count,
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bool always_copy_data=false );
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namespace cv
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{
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struct DTreeBestSplitFinder
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{
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DTreeBestSplitFinder(){ tree = 0; node = 0; }
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DTreeBestSplitFinder( CvDTree* _tree, CvDTreeNode* _node);
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DTreeBestSplitFinder( const DTreeBestSplitFinder& finder, Split );
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virtual void operator()(const BlockedRange& range);
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void join( DTreeBestSplitFinder& rhs );
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Ptr<CvDTreeSplit> bestSplit;
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Ptr<CvDTreeSplit> split;
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int splitSize;
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CvDTree* tree;
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CvDTreeNode* node;
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};
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struct ForestTreeBestSplitFinder : DTreeBestSplitFinder
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{
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ForestTreeBestSplitFinder() : DTreeBestSplitFinder() {}
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ForestTreeBestSplitFinder( CvForestTree* _tree, CvDTreeNode* _node );
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ForestTreeBestSplitFinder( const ForestTreeBestSplitFinder& finder, Split );
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virtual void operator()(const BlockedRange& range);
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};
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}
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#endif /* __ML_H__ */
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