mirror of
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2235 lines
82 KiB
ObjectPascal
2235 lines
82 KiB
ObjectPascal
// --------------------------------- OpenCV license.txt ---------------------------
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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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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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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// Project Delphi-OpenCV
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// **************************************************************************************************
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// Contributors:
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// Laentir Valetov
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// email:laex@bk.ru
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// Mikhail Grigorev
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// Email: sleuthhound@gmail.com
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// **************************************************************************************************
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// You may retrieve the latest version of this file at the GitHub,
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// located at git://github.com/Laex/Delphi-OpenCV.git
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// **************************************************************************************************
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// License:
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// The contents of this file are subject to the Mozilla Public License Version 1.1 (the "License");
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// you may not use this file except in compliance with the License. You may obtain a copy of the
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// License at http://www.mozilla.org/MPL/
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//
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// Software distributed under the License is distributed on an "AS IS" basis, WITHOUT WARRANTY OF
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// ANY KIND, either express or implied. See the License for the specific language governing rights
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// and limitations under the License.
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//
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// Alternatively, the contents of this file may be used under the terms of the
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// GNU Lesser General Public License (the "LGPL License"), in which case the
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// provisions of the LGPL License are applicable instead of those above.
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// If you wish to allow use of your version of this file only under the terms
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// of the LGPL License and not to allow others to use your version of this file
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// under the MPL, indicate your decision by deleting the provisions above and
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// replace them with the notice and other provisions required by the LGPL
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// License. If you do not delete the provisions above, a recipient may use
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// your version of this file under either the MPL or the LGPL License.
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//
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// For more information about the LGPL: http://www.gnu.org/copyleft/lesser.html
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// **************************************************************************************************
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// The Initial Developer of the Original Code:
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// OpenCV: open source computer vision library
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// Homepage: http://ocv.org
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// Online docs: http://docs.ocv.org
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// Q&A forum: http://answers.ocv.org
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// Dev zone: http://code.ocv.org
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// **************************************************************************************************
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// Original file:
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// opencv\modules\ml\include\opencv2\ml.hpp
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// *************************************************************************************************
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unit ocv.ml;
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{$POINTERMATH ON}
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interface
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Uses
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WinApi.Windows,
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ocv.core.types_c;
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/// ****************************************************************************************\
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// * Main struct definitions *
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// \****************************************************************************************/
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//
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/// * log(2*PI) */
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// #define CV_LOG2PI (1.8378770664093454835606594728112)
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//
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/// * columns of <trainData> matrix are training samples */
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// #define CV_COL_SAMPLE 0
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//
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/// * rows of <trainData> matrix are training samples */
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// #define CV_ROW_SAMPLE 1
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//
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// #define CV_IS_ROW_SAMPLE(flags) ((flags) & CV_ROW_SAMPLE)
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//
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// struct CvVectors
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// {
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// int type;
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// int dims, count;
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// CvVectors* next;
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// union
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// {
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// uchar** ptr;
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// float** fl;
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// double** db;
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// } data;
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// };
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//
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// #if 0
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/// * A structure, representing the lattice range of statmodel parameters.
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// It is used for optimizing statmodel parameters by cross-validation method.
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// The lattice is logarithmic, so <step> must be greater then 1. */
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// typedef struct CvParamLattice
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// {
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// double min_val;
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// double max_val;
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// double step;
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// }
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// CvParamLattice;
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//
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// CV_INLINE CvParamLattice cvParamLattice( double min_val, double max_val,
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// double log_step )
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// {
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// CvParamLattice pl;
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// pl.min_val = MIN( min_val, max_val );
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// pl.max_val = MAX( min_val, max_val );
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// pl.step = MAX( log_step, 1. );
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// return pl;
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// }
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//
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// CV_INLINE CvParamLattice cvDefaultParamLattice( void )
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// {
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// CvParamLattice pl = {0,0,0};
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// return pl;
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// }
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// #endif
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const
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/// * Variable type */
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CV_VAR_NUMERICAL = 0;
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CV_VAR_ORDERED = 0;
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CV_VAR_CATEGORICAL = 1;
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//
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// #define CV_TYPE_NAME_ML_SVM "opencv-ml-svm"
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// #define CV_TYPE_NAME_ML_KNN "opencv-ml-knn"
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// #define CV_TYPE_NAME_ML_NBAYES "opencv-ml-bayesian"
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// #define CV_TYPE_NAME_ML_EM "opencv-ml-em"
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// #define CV_TYPE_NAME_ML_BOOSTING "opencv-ml-boost-tree"
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// #define CV_TYPE_NAME_ML_TREE "opencv-ml-tree"
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// #define CV_TYPE_NAME_ML_ANN_MLP "opencv-ml-ann-mlp"
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// #define CV_TYPE_NAME_ML_CNN "opencv-ml-cnn"
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// #define CV_TYPE_NAME_ML_RTREES "opencv-ml-random-trees"
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// #define CV_TYPE_NAME_ML_ERTREES "opencv-ml-extremely-randomized-trees"
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// #define CV_TYPE_NAME_ML_GBT "opencv-ml-gradient-boosting-trees"
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//
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// #define CV_TRAIN_ERROR 0
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// #define CV_TEST_ERROR 1
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//
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// class CV_EXPORTS_W CvStatModel
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// {
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// public:
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// CvStatModel();
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// virtual ~CvStatModel();
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//
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// virtual void clear();
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//
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// CV_WRAP virtual void save( const char* filename, const char* name=0 ) const;
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// CV_WRAP virtual void load( const char* filename, const char* name=0 );
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//
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// virtual void write( CvFileStorage* storage, const char* name ) const;
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// virtual void read( CvFileStorage* storage, CvFileNode* node );
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//
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// protected:
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// const char* default_model_name;
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// };
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//
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/// ****************************************************************************************\
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// * Normal Bayes Classifier *
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// \****************************************************************************************/
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//
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/// * The structure, representing the grid range of statmodel parameters.
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// It is used for optimizing statmodel accuracy by varying model parameters,
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// the accuracy estimate being computed by cross-validation.
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// The grid is logarithmic, so <step> must be greater then 1. */
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//
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// class CvMLData;
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//
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// struct CV_EXPORTS_W_MAP CvParamGrid
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// {
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// // SVM params type
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// enum { SVM_C=0, SVM_GAMMA=1, SVM_P=2, SVM_NU=3, SVM_COEF=4, SVM_DEGREE=5 };
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//
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// CvParamGrid()
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// {
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// min_val = max_val = step = 0;
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// }
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//
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// CvParamGrid( double min_val, double max_val, double log_step );
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// //CvParamGrid( int param_id );
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// bool check() const;
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//
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// CV_PROP_RW double min_val;
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// CV_PROP_RW double max_val;
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// CV_PROP_RW double step;
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// };
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//
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// inline CvParamGrid::CvParamGrid( double _min_val, double _max_val, double _log_step )
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// {
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// min_val = _min_val;
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// max_val = _max_val;
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// step = _log_step;
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// }
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//
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// class CV_EXPORTS_W CvNormalBayesClassifier : public CvStatModel
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// {
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// public:
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// CV_WRAP CvNormalBayesClassifier();
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// virtual ~CvNormalBayesClassifier();
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//
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// CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses,
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// const CvMat* varIdx=0, const CvMat* sampleIdx=0 );
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//
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// virtual bool train( const CvMat* trainData, const CvMat* responses,
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// const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false );
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//
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// virtual float predict( const CvMat* samples, CV_OUT CvMat* results=0 ) const;
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// CV_WRAP virtual void clear();
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//
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// CV_WRAP CvNormalBayesClassifier( const cv::Mat& trainData, const cv::Mat& responses,
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// const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat() );
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// CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
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// const cv::Mat& varIdx = cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
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// bool update=false );
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// CV_WRAP virtual float predict( const cv::Mat& samples, CV_OUT cv::Mat* results=0 ) const;
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//
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// virtual void write( CvFileStorage* storage, const char* name ) const;
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// virtual void read( CvFileStorage* storage, CvFileNode* node );
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//
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// protected:
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// int var_count, var_all;
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// CvMat* var_idx;
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// CvMat* cls_labels;
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// CvMat** count;
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// CvMat** sum;
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// CvMat** productsum;
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// CvMat** avg;
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// CvMat** inv_eigen_values;
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// CvMat** cov_rotate_mats;
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// CvMat* c;
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// };
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//
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//
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/// ****************************************************************************************\
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// * K-Nearest Neighbour Classifier *
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// \****************************************************************************************/
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Type
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TCvKNearest = class
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function train(const trainData: pCvMat; const responses: pCvMat; const sampleIdx: pCvMat = nil;
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is_regression: bool = false; maxK: Integer = 32; updateBase: bool = false): bool; virtual; stdcall; abstract;
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function find_nearest(const samples: pCvMat; k: Integer; results: pCvMat = nil; const neighbors: pSingle = nil;
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neighborResponses: pCvMat = nil; dist: pCvMat = nil): float; virtual; stdcall; abstract;
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end;
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// k Nearest Neighbors
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// class CV_EXPORTS_W CvKNearest : public CvStatModel
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// {
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// public:
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//
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// CV_WRAP CvKNearest();
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// virtual ~CvKNearest();
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//
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// CvKNearest( const CvMat* trainData, const CvMat* responses, const CvMat* sampleIdx=0, bool isRegression=false, int max_k=32 );
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//
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// virtual bool train( const CvMat* trainData, const CvMat* responses,
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// const CvMat* sampleIdx=0, bool is_regression=false,
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// int maxK=32, bool updateBase=false );
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//
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// virtual float find_nearest( const CvMat* samples, int k, CV_OUT CvMat* results=0,
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// const float** neighbors=0, CV_OUT CvMat* neighborResponses=0, CV_OUT CvMat* dist=0 ) const;
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//
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// CV_WRAP CvKNearest( const cv::Mat& trainData, const cv::Mat& responses,
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// const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false, int max_k=32 );
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//
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// CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
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// const cv::Mat& sampleIdx=cv::Mat(), bool isRegression=false,
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// int maxK=32, bool updateBase=false );
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//
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// virtual float find_nearest( const cv::Mat& samples, int k, cv::Mat* results=0,
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// const float** neighbors=0, cv::Mat* neighborResponses=0,
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// cv::Mat* dist=0 ) const;
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// CV_WRAP virtual float find_nearest( const cv::Mat& samples, int k, CV_OUT cv::Mat& results,
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// CV_OUT cv::Mat& neighborResponses, CV_OUT cv::Mat& dists) const;
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//
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// virtual void clear();
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// int get_max_k() const;
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// int get_var_count() const;
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// int get_sample_count() const;
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// bool is_regression() const;
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//
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// virtual float write_results( int k, int k1, int start, int end,
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// const float* neighbor_responses, const float* dist, CvMat* _results,
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// CvMat* _neighbor_responses, CvMat* _dist, Cv32suf* sort_buf ) const;
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//
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// virtual void find_neighbors_direct( const CvMat* _samples, int k, int start, int end,
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// float* neighbor_responses, const float** neighbors, float* dist ) const;
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//
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// protected:
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//
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// int max_k, var_count;
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// int total;
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// bool regression;
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// CvVectors* samples;
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// };
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//
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/// ****************************************************************************************\
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// * Support Vector Machines *
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// \****************************************************************************************/
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//
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/// / SVM training parameters
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// struct CV_EXPORTS_W_MAP CvSVMParams
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// {
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// CvSVMParams();
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// CvSVMParams( int svm_type, int kernel_type,
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// double degree, double gamma, double coef0,
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// double Cvalue, double nu, double p,
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// CvMat* class_weights, CvTermCriteria term_crit );
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//
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// CV_PROP_RW int svm_type;
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// CV_PROP_RW int kernel_type;
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// CV_PROP_RW double degree; // for poly
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// CV_PROP_RW double gamma; // for poly/rbf/sigmoid/chi2
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// CV_PROP_RW double coef0; // for poly/sigmoid
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//
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// CV_PROP_RW double C; // for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR
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// CV_PROP_RW double nu; // for CV_SVM_NU_SVC, CV_SVM_ONE_CLASS, and CV_SVM_NU_SVR
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// CV_PROP_RW double p; // for CV_SVM_EPS_SVR
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// CvMat* class_weights; // for CV_SVM_C_SVC
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// CV_PROP_RW CvTermCriteria term_crit; // termination criteria
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// };
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//
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//
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// struct CV_EXPORTS CvSVMKernel
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// {
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// typedef void (CvSVMKernel::*Calc)( int vec_count, int vec_size, const float** vecs,
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// const float* another, float* results );
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// CvSVMKernel();
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// CvSVMKernel( const CvSVMParams* params, Calc _calc_func );
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// virtual bool create( const CvSVMParams* params, Calc _calc_func );
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// virtual ~CvSVMKernel();
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//
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// virtual void clear();
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// virtual void calc( int vcount, int n, const float** vecs, const float* another, float* results );
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//
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// const CvSVMParams* params;
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// Calc calc_func;
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//
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// virtual void calc_non_rbf_base( int vec_count, int vec_size, const float** vecs,
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// const float* another, float* results,
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// double alpha, double beta );
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// virtual void calc_intersec( int vcount, int var_count, const float** vecs,
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// const float* another, float* results );
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// virtual void calc_chi2( int vec_count, int vec_size, const float** vecs,
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// const float* another, float* results );
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// virtual void calc_linear( int vec_count, int vec_size, const float** vecs,
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// const float* another, float* results );
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// virtual void calc_rbf( int vec_count, int vec_size, const float** vecs,
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// const float* another, float* results );
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// virtual void calc_poly( int vec_count, int vec_size, const float** vecs,
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// const float* another, float* results );
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// virtual void calc_sigmoid( int vec_count, int vec_size, const float** vecs,
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// const float* another, float* results );
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// };
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//
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//
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// struct CvSVMKernelRow
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// {
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// CvSVMKernelRow* prev;
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// CvSVMKernelRow* next;
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// float* data;
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// };
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//
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//
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// struct CvSVMSolutionInfo
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// {
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// double obj;
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// double rho;
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// double upper_bound_p;
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// double upper_bound_n;
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// double r; // for Solver_NU
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// };
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//
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// class CV_EXPORTS CvSVMSolver
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// {
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// public:
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// typedef bool (CvSVMSolver::*SelectWorkingSet)( int& i, int& j );
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// typedef float* (CvSVMSolver::*GetRow)( int i, float* row, float* dst, bool existed );
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// typedef void (CvSVMSolver::*CalcRho)( double& rho, double& r );
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//
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// CvSVMSolver();
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//
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// CvSVMSolver( int count, int var_count, const float** samples, schar* y,
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// int alpha_count, double* alpha, double Cp, double Cn,
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// CvMemStorage* storage, CvSVMKernel* kernel, GetRow get_row,
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// SelectWorkingSet select_working_set, CalcRho calc_rho );
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// virtual bool create( int count, int var_count, const float** samples, schar* y,
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// int alpha_count, double* alpha, double Cp, double Cn,
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// CvMemStorage* storage, CvSVMKernel* kernel, GetRow get_row,
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// SelectWorkingSet select_working_set, CalcRho calc_rho );
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// virtual ~CvSVMSolver();
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//
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// virtual void clear();
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// virtual bool solve_generic( CvSVMSolutionInfo& si );
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//
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// virtual bool solve_c_svc( int count, int var_count, const float** samples, schar* y,
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// double Cp, double Cn, CvMemStorage* storage,
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// CvSVMKernel* kernel, double* alpha, CvSVMSolutionInfo& si );
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// virtual bool solve_nu_svc( int count, int var_count, const float** samples, schar* y,
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// CvMemStorage* storage, CvSVMKernel* kernel,
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// double* alpha, CvSVMSolutionInfo& si );
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// virtual bool solve_one_class( int count, int var_count, const float** samples,
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// CvMemStorage* storage, CvSVMKernel* kernel,
|
|
// double* alpha, CvSVMSolutionInfo& si );
|
|
//
|
|
// virtual bool solve_eps_svr( int count, int var_count, const float** samples, const float* y,
|
|
// CvMemStorage* storage, CvSVMKernel* kernel,
|
|
// double* alpha, CvSVMSolutionInfo& si );
|
|
//
|
|
// virtual bool solve_nu_svr( int count, int var_count, const float** samples, const float* y,
|
|
// CvMemStorage* storage, CvSVMKernel* kernel,
|
|
// double* alpha, CvSVMSolutionInfo& si );
|
|
//
|
|
// virtual float* get_row_base( int i, bool* _existed );
|
|
// virtual float* get_row( int i, float* dst );
|
|
//
|
|
// int sample_count;
|
|
// int var_count;
|
|
// int cache_size;
|
|
// int cache_line_size;
|
|
// const float** samples;
|
|
// const CvSVMParams* params;
|
|
// CvMemStorage* storage;
|
|
// CvSVMKernelRow lru_list;
|
|
// CvSVMKernelRow* rows;
|
|
//
|
|
// int alpha_count;
|
|
//
|
|
// double* G;
|
|
// double* alpha;
|
|
//
|
|
// // -1 - lower bound, 0 - free, 1 - upper bound
|
|
// schar* alpha_status;
|
|
//
|
|
// schar* y;
|
|
// double* b;
|
|
// float* buf[2];
|
|
// double eps;
|
|
// int max_iter;
|
|
// double C[2]; // C[0] == Cn, C[1] == Cp
|
|
// CvSVMKernel* kernel;
|
|
//
|
|
// SelectWorkingSet select_working_set_func;
|
|
// CalcRho calc_rho_func;
|
|
// GetRow get_row_func;
|
|
//
|
|
// virtual bool select_working_set( int& i, int& j );
|
|
// virtual bool select_working_set_nu_svm( int& i, int& j );
|
|
// virtual void calc_rho( double& rho, double& r );
|
|
// virtual void calc_rho_nu_svm( double& rho, double& r );
|
|
//
|
|
// virtual float* get_row_svc( int i, float* row, float* dst, bool existed );
|
|
// virtual float* get_row_one_class( int i, float* row, float* dst, bool existed );
|
|
// virtual float* get_row_svr( int i, float* row, float* dst, bool existed );
|
|
// };
|
|
//
|
|
//
|
|
// struct CvSVMDecisionFunc
|
|
// {
|
|
// double rho;
|
|
// int sv_count;
|
|
// double* alpha;
|
|
// int* sv_index;
|
|
// };
|
|
//
|
|
//
|
|
/// / SVM model
|
|
// class CV_EXPORTS_W CvSVM : public CvStatModel
|
|
// {
|
|
// public:
|
|
// // SVM type
|
|
// enum { C_SVC=100, NU_SVC=101, ONE_CLASS=102, EPS_SVR=103, NU_SVR=104 };
|
|
//
|
|
// // SVM kernel type
|
|
// enum { LINEAR=0, POLY=1, RBF=2, SIGMOID=3, CHI2=4, INTER=5 };
|
|
//
|
|
// // SVM params type
|
|
// enum { C=0, GAMMA=1, P=2, NU=3, COEF=4, DEGREE=5 };
|
|
//
|
|
// CV_WRAP CvSVM();
|
|
// virtual ~CvSVM();
|
|
//
|
|
// CvSVM( const CvMat* trainData, const CvMat* responses,
|
|
// const CvMat* varIdx=0, const CvMat* sampleIdx=0,
|
|
// CvSVMParams params=CvSVMParams() );
|
|
//
|
|
// virtual bool train( const CvMat* trainData, const CvMat* responses,
|
|
// const CvMat* varIdx=0, const CvMat* sampleIdx=0,
|
|
// CvSVMParams params=CvSVMParams() );
|
|
//
|
|
// virtual bool train_auto( const CvMat* trainData, const CvMat* responses,
|
|
// const CvMat* varIdx, const CvMat* sampleIdx, CvSVMParams params,
|
|
// int kfold = 10,
|
|
// CvParamGrid Cgrid = get_default_grid(CvSVM::C),
|
|
// CvParamGrid gammaGrid = get_default_grid(CvSVM::GAMMA),
|
|
// CvParamGrid pGrid = get_default_grid(CvSVM::P),
|
|
// CvParamGrid nuGrid = get_default_grid(CvSVM::NU),
|
|
// CvParamGrid coeffGrid = get_default_grid(CvSVM::COEF),
|
|
// CvParamGrid degreeGrid = get_default_grid(CvSVM::DEGREE),
|
|
// bool balanced=false );
|
|
//
|
|
// virtual float predict( const CvMat* sample, bool returnDFVal=false ) const;
|
|
// virtual float predict( const CvMat* samples, CV_OUT CvMat* results ) const;
|
|
//
|
|
// CV_WRAP CvSVM( const cv::Mat& trainData, const cv::Mat& responses,
|
|
// const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
|
|
// CvSVMParams params=CvSVMParams() );
|
|
//
|
|
// CV_WRAP virtual bool train( const cv::Mat& trainData, const cv::Mat& responses,
|
|
// const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
|
|
// CvSVMParams params=CvSVMParams() );
|
|
//
|
|
// CV_WRAP virtual bool train_auto( const cv::Mat& trainData, const cv::Mat& responses,
|
|
// const cv::Mat& varIdx, const cv::Mat& sampleIdx, CvSVMParams params,
|
|
// int k_fold = 10,
|
|
// CvParamGrid Cgrid = CvSVM::get_default_grid(CvSVM::C),
|
|
// CvParamGrid gammaGrid = CvSVM::get_default_grid(CvSVM::GAMMA),
|
|
// CvParamGrid pGrid = CvSVM::get_default_grid(CvSVM::P),
|
|
// CvParamGrid nuGrid = CvSVM::get_default_grid(CvSVM::NU),
|
|
// CvParamGrid coeffGrid = CvSVM::get_default_grid(CvSVM::COEF),
|
|
// CvParamGrid degreeGrid = CvSVM::get_default_grid(CvSVM::DEGREE),
|
|
// bool balanced=false);
|
|
// CV_WRAP virtual float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
|
|
// CV_WRAP_AS(predict_all) virtual void predict( cv::InputArray samples, cv::OutputArray results ) const;
|
|
//
|
|
// CV_WRAP virtual int get_support_vector_count() const;
|
|
// virtual const float* get_support_vector(int i) const;
|
|
// virtual CvSVMParams get_params() const { return params; };
|
|
// CV_WRAP virtual void clear();
|
|
//
|
|
// static CvParamGrid get_default_grid( int param_id );
|
|
//
|
|
// virtual void write( CvFileStorage* storage, const char* name ) const;
|
|
// virtual void read( CvFileStorage* storage, CvFileNode* node );
|
|
// CV_WRAP int get_var_count() const { return var_idx ? var_idx->cols : var_all; }
|
|
//
|
|
// protected:
|
|
//
|
|
// virtual bool set_params( const CvSVMParams& params );
|
|
// virtual bool train1( int sample_count, int var_count, const float** samples,
|
|
// const void* responses, double Cp, double Cn,
|
|
// CvMemStorage* _storage, double* alpha, double& rho );
|
|
// virtual bool do_train( int svm_type, int sample_count, int var_count, const float** samples,
|
|
// const CvMat* responses, CvMemStorage* _storage, double* alpha );
|
|
// virtual void create_kernel();
|
|
// virtual void create_solver();
|
|
//
|
|
// virtual float predict( const float* row_sample, int row_len, bool returnDFVal=false ) const;
|
|
//
|
|
// virtual void write_params( CvFileStorage* fs ) const;
|
|
// virtual void read_params( CvFileStorage* fs, CvFileNode* node );
|
|
//
|
|
// void optimize_linear_svm();
|
|
//
|
|
// CvSVMParams params;
|
|
// CvMat* class_labels;
|
|
// int var_all;
|
|
// float** sv;
|
|
// int sv_total;
|
|
// CvMat* var_idx;
|
|
// CvMat* class_weights;
|
|
// CvSVMDecisionFunc* decision_func;
|
|
// CvMemStorage* storage;
|
|
//
|
|
// CvSVMSolver* solver;
|
|
// CvSVMKernel* kernel;
|
|
// };
|
|
//
|
|
/// ****************************************************************************************\
|
|
// * Expectation - Maximization *
|
|
// \****************************************************************************************/
|
|
// namespace cv
|
|
// {
|
|
// class CV_EXPORTS_W EM : public Algorithm
|
|
// {
|
|
// public:
|
|
// // Type of covariation matrices
|
|
// enum {COV_MAT_SPHERICAL=0, COV_MAT_DIAGONAL=1, COV_MAT_GENERIC=2, COV_MAT_DEFAULT=COV_MAT_DIAGONAL};
|
|
//
|
|
// // Default parameters
|
|
// enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100};
|
|
//
|
|
// // The initial step
|
|
// enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
|
|
//
|
|
// CV_WRAP EM(int nclusters=EM::DEFAULT_NCLUSTERS, int covMatType=EM::COV_MAT_DIAGONAL,
|
|
// const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,
|
|
// EM::DEFAULT_MAX_ITERS, FLT_EPSILON));
|
|
//
|
|
// virtual ~EM();
|
|
// CV_WRAP virtual void clear();
|
|
//
|
|
// CV_WRAP virtual bool train(InputArray samples,
|
|
// OutputArray logLikelihoods=noArray(),
|
|
// OutputArray labels=noArray(),
|
|
// OutputArray probs=noArray());
|
|
//
|
|
// CV_WRAP virtual bool trainE(InputArray samples,
|
|
// InputArray means0,
|
|
// InputArray covs0=noArray(),
|
|
// InputArray weights0=noArray(),
|
|
// OutputArray logLikelihoods=noArray(),
|
|
// OutputArray labels=noArray(),
|
|
// OutputArray probs=noArray());
|
|
//
|
|
// CV_WRAP virtual bool trainM(InputArray samples,
|
|
// InputArray probs0,
|
|
// OutputArray logLikelihoods=noArray(),
|
|
// OutputArray labels=noArray(),
|
|
// OutputArray probs=noArray());
|
|
//
|
|
// CV_WRAP Vec2d predict(InputArray sample,
|
|
// OutputArray probs=noArray()) const;
|
|
//
|
|
// CV_WRAP bool isTrained() const;
|
|
//
|
|
// AlgorithmInfo* info() const;
|
|
// virtual void read(const FileNode& fn);
|
|
//
|
|
// protected:
|
|
//
|
|
// virtual void setTrainData(int startStep, const Mat& samples,
|
|
// const Mat* probs0,
|
|
// const Mat* means0,
|
|
// const std::vector<Mat>* covs0,
|
|
// const Mat* weights0);
|
|
//
|
|
// bool doTrain(int startStep,
|
|
// OutputArray logLikelihoods,
|
|
// OutputArray labels,
|
|
// OutputArray probs);
|
|
// virtual void eStep();
|
|
// virtual void mStep();
|
|
//
|
|
// void clusterTrainSamples();
|
|
// void decomposeCovs();
|
|
// void computeLogWeightDivDet();
|
|
//
|
|
// Vec2d computeProbabilities(const Mat& sample, Mat* probs) const;
|
|
//
|
|
// // all inner matrices have type CV_64FC1
|
|
// CV_PROP_RW int nclusters;
|
|
// CV_PROP_RW int covMatType;
|
|
// CV_PROP_RW int maxIters;
|
|
// CV_PROP_RW double epsilon;
|
|
//
|
|
// Mat trainSamples;
|
|
// Mat trainProbs;
|
|
// Mat trainLogLikelihoods;
|
|
// Mat trainLabels;
|
|
//
|
|
// CV_PROP Mat weights;
|
|
// CV_PROP Mat means;
|
|
// CV_PROP std::vector<Mat> covs;
|
|
//
|
|
// std::vector<Mat> covsEigenValues;
|
|
// std::vector<Mat> covsRotateMats;
|
|
// std::vector<Mat> invCovsEigenValues;
|
|
// Mat logWeightDivDet;
|
|
// };
|
|
// } // namespace cv
|
|
//
|
|
/// ****************************************************************************************\
|
|
// * Decision Tree *
|
|
// \****************************************************************************************/\
|
|
// struct CvPair16u32s
|
|
// {
|
|
// unsigned short* u;
|
|
// int* i;
|
|
// };
|
|
//
|
|
//
|
|
// #define CV_DTREE_CAT_DIR(idx,subset) \
|
|
// (2*((subset[(idx)>>5]&(1 << ((idx) & 31)))==0)-1)
|
|
Type
|
|
TOrd = record
|
|
c: float;
|
|
split_point: Integer;
|
|
end;
|
|
|
|
pCvDTreeSplit = ^TCvDTreeSplit;
|
|
|
|
TCvDTreeSplit = record // struct CvDTreeSplit
|
|
var_idx: Integer; // int var_idx;
|
|
condensed_idx: Integer; // int condensed_idx;
|
|
inversed: Integer; // int inversed;
|
|
quality: float; // float quality;
|
|
next: pCvDTreeSplit; // CvDTreeSplit* next;
|
|
case byte of
|
|
// union
|
|
// {
|
|
// int subset[2];
|
|
0:
|
|
(subset: array [0 .. 1] of Integer);
|
|
1:
|
|
(ord: TOrd);
|
|
// struct
|
|
// {
|
|
// float c;
|
|
// int split_point;
|
|
// }
|
|
// ord;
|
|
// };
|
|
end;
|
|
|
|
pCvDTreeNode = ^TCvDTreeNode;
|
|
|
|
TCvDTreeNode = record // struct CvDTreeNode
|
|
class_idx: Integer; // int class_idx;
|
|
Tn: Integer; // int Tn;
|
|
value: Double; // double value;
|
|
//
|
|
parent: pCvDTreeNode; // CvDTreeNode* parent;
|
|
left: pCvDTreeNode; // CvDTreeNode* left;
|
|
right: pCvDTreeNode; // CvDTreeNode* right;
|
|
//
|
|
split: pCvDTreeSplit; // CvDTreeSplit* split;
|
|
//
|
|
sample_count: Integer; // int sample_count;
|
|
depth: Integer; // int depth;
|
|
num_valid: pInteger; // int* num_valid;
|
|
offset: Integer; // int offset;
|
|
buf_idx: Integer; // int buf_idx;
|
|
maxlr: Double; // double maxlr;
|
|
//
|
|
// // global pruning data
|
|
complexity: Integer; // int complexity;
|
|
alpha: Double; // double alpha;
|
|
node_risk, tree_risk, tree_error: Double; // double node_risk, tree_risk, tree_error;
|
|
//
|
|
// // cross-validation pruning data
|
|
cv_Tn: pInteger; // int* cv_Tn;
|
|
cv_node_risk: pDouble; // double* cv_node_risk;
|
|
cv_node_error: pDouble; // double* cv_node_error;
|
|
//
|
|
// int get_num_valid(int vi) { return num_valid ? num_valid[vi] : sample_count; }
|
|
function get_num_valid(vi: Integer): Integer;
|
|
// void set_num_valid(int vi, int n) { if( num_valid ) num_valid[vi] = n; }
|
|
procedure set_num_valid(vi: Integer; n: Integer);
|
|
end;
|
|
|
|
TCvDTreeParams = record // struct CV_EXPORTS_W_MAP CvDTreeParams
|
|
max_categories: Integer; // CV_PROP_RW int max_categories ;
|
|
max_depth: Integer; // CV_PROP_RW int max_depth ;
|
|
min_sample_count: Integer; // CV_PROP_RW int min_sample_count ;
|
|
cv_folds: Integer; // CV_PROP_RW int cv_folds ;
|
|
use_surrogates: ByteBool; // CV_PROP_RW bool use_surrogates ;
|
|
use_1se_rule: ByteBool; // CV_PROP_RW bool use_1se_rule ;
|
|
truncate_pruned_tree: ByteBool; // CV_PROP_RW bool truncate_pruned_tree ;
|
|
regression_accuracy: float; // CV_PROP_RW float regression_accuracy ;
|
|
priors: pFloat; // const float* priors;
|
|
//
|
|
// CvDTreeParams();
|
|
{
|
|
CvDTreeParams( int max_depth, int min_sample_count, float regression_accuracy,
|
|
bool use_surrogates, int max_categories, int cv_folds,
|
|
bool use_1se_rule, bool truncate_pruned_tree, const float* priors );
|
|
}
|
|
end;
|
|
|
|
// struct CV_EXPORTS CvDTreeTrainData
|
|
// {
|
|
// CvDTreeTrainData();
|
|
// CvDTreeTrainData( const CvMat* trainData, int tflag,
|
|
// const CvMat* responses, const CvMat* varIdx=0,
|
|
// const CvMat* sampleIdx=0, const CvMat* varType=0,
|
|
// const CvMat* missingDataMask=0,
|
|
// const CvDTreeParams& params=CvDTreeParams(),
|
|
// bool _shared=false, bool _add_labels=false );
|
|
// virtual ~CvDTreeTrainData();
|
|
//
|
|
// virtual void set_data( const CvMat* trainData, int tflag,
|
|
// const CvMat* responses, const CvMat* varIdx=0,
|
|
// const CvMat* sampleIdx=0, const CvMat* varType=0,
|
|
// const CvMat* missingDataMask=0,
|
|
// const CvDTreeParams& params=CvDTreeParams(),
|
|
// bool _shared=false, bool _add_labels=false,
|
|
// bool _update_data=false );
|
|
// virtual void do_responses_copy();
|
|
//
|
|
// virtual void get_vectors( const CvMat* _subsample_idx,
|
|
// float* values, uchar* missing, float* responses, bool get_class_idx=false );
|
|
//
|
|
// virtual CvDTreeNode* subsample_data( const CvMat* _subsample_idx );
|
|
//
|
|
// virtual void write_params( CvFileStorage* fs ) const;
|
|
// virtual void read_params( CvFileStorage* fs, CvFileNode* node );
|
|
//
|
|
// // release all the data
|
|
// virtual void clear();
|
|
//
|
|
// int get_num_classes() const;
|
|
// int get_var_type(int vi) const;
|
|
// int get_work_var_count() const {return work_var_count;}
|
|
//
|
|
// virtual const float* get_ord_responses( CvDTreeNode* n, float* values_buf, int* sample_indices_buf );
|
|
// virtual const int* get_class_labels( CvDTreeNode* n, int* labels_buf );
|
|
// virtual const int* get_cv_labels( CvDTreeNode* n, int* labels_buf );
|
|
// virtual const int* get_sample_indices( CvDTreeNode* n, int* indices_buf );
|
|
// virtual const int* get_cat_var_data( CvDTreeNode* n, int vi, int* cat_values_buf );
|
|
// virtual void get_ord_var_data( CvDTreeNode* n, int vi, float* ord_values_buf, int* sorted_indices_buf,
|
|
// const float** ord_values, const int** sorted_indices, int* sample_indices_buf );
|
|
// virtual int get_child_buf_idx( CvDTreeNode* n );
|
|
//
|
|
// ////////////////////////////////////
|
|
//
|
|
// virtual bool set_params( const CvDTreeParams& params );
|
|
// virtual CvDTreeNode* new_node( CvDTreeNode* parent, int count,
|
|
// int storage_idx, int offset );
|
|
//
|
|
// virtual CvDTreeSplit* new_split_ord( int vi, float cmp_val,
|
|
// int split_point, int inversed, float quality );
|
|
// virtual CvDTreeSplit* new_split_cat( int vi, float quality );
|
|
// virtual void free_node_data( CvDTreeNode* node );
|
|
// virtual void free_train_data();
|
|
// virtual void free_node( CvDTreeNode* node );
|
|
//
|
|
// int sample_count, var_all, var_count, max_c_count;
|
|
// int ord_var_count, cat_var_count, work_var_count;
|
|
// bool have_labels, have_priors;
|
|
// bool is_classifier;
|
|
// int tflag;
|
|
//
|
|
// const CvMat* train_data;
|
|
// const CvMat* responses;
|
|
// CvMat* responses_copy; // used in Boosting
|
|
//
|
|
// int buf_count, buf_size; // buf_size is obsolete, please do not use it, use expression ((int64)buf->rows * (int64)buf->cols / buf_count) instead
|
|
// bool shared;
|
|
// int is_buf_16u;
|
|
//
|
|
// CvMat* cat_count;
|
|
// CvMat* cat_ofs;
|
|
// CvMat* cat_map;
|
|
//
|
|
// CvMat* counts;
|
|
// CvMat* buf;
|
|
// inline size_t get_length_subbuf() const
|
|
// {
|
|
// size_t res = (size_t)(work_var_count + 1) * (size_t)sample_count;
|
|
// return res;
|
|
// }
|
|
//
|
|
// CvMat* direction;
|
|
// CvMat* split_buf;
|
|
//
|
|
// CvMat* var_idx;
|
|
// CvMat* var_type; // i-th element =
|
|
// // k<0 - ordered
|
|
// // k>=0 - categorical, see k-th element of cat_* arrays
|
|
// CvMat* priors;
|
|
// CvMat* priors_mult;
|
|
//
|
|
// CvDTreeParams params;
|
|
//
|
|
// CvMemStorage* tree_storage;
|
|
// CvMemStorage* temp_storage;
|
|
//
|
|
// CvDTreeNode* data_root;
|
|
//
|
|
// CvSet* node_heap;
|
|
// CvSet* split_heap;
|
|
// CvSet* cv_heap;
|
|
// CvSet* nv_heap;
|
|
//
|
|
// cv::RNG* rng;
|
|
// };
|
|
//
|
|
// class CvDTree;
|
|
// class CvForestTree;
|
|
//
|
|
// namespace cv
|
|
// {
|
|
// struct DTreeBestSplitFinder;
|
|
// struct ForestTreeBestSplitFinder;
|
|
// }
|
|
//
|
|
// class CV_EXPORTS_W CvDTree : public CvStatModel
|
|
// {
|
|
// public:
|
|
// CV_WRAP CvDTree();
|
|
// virtual ~CvDTree();
|
|
//
|
|
// virtual bool train( const CvMat* trainData, int tflag,
|
|
// const CvMat* responses, const CvMat* varIdx=0,
|
|
// const CvMat* sampleIdx=0, const CvMat* varType=0,
|
|
// const CvMat* missingDataMask=0,
|
|
// CvDTreeParams params=CvDTreeParams() );
|
|
//
|
|
// virtual bool train( CvMLData* trainData, CvDTreeParams params=CvDTreeParams() );
|
|
//
|
|
// // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
|
|
// virtual float calc_error( CvMLData* trainData, int type, std::vector<float> *resp = 0 );
|
|
//
|
|
// virtual bool train( CvDTreeTrainData* trainData, const CvMat* subsampleIdx );
|
|
//
|
|
// virtual CvDTreeNode* predict( const CvMat* sample, const CvMat* missingDataMask=0,
|
|
// bool preprocessedInput=false ) const;
|
|
//
|
|
// CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
|
|
// const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
|
|
// const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
|
|
// const cv::Mat& missingDataMask=cv::Mat(),
|
|
// CvDTreeParams params=CvDTreeParams() );
|
|
//
|
|
// CV_WRAP virtual CvDTreeNode* predict( const cv::Mat& sample, const cv::Mat& missingDataMask=cv::Mat(),
|
|
// bool preprocessedInput=false ) const;
|
|
// CV_WRAP virtual cv::Mat getVarImportance();
|
|
//
|
|
// virtual const CvMat* get_var_importance();
|
|
// CV_WRAP virtual void clear();
|
|
//
|
|
// virtual void read( CvFileStorage* fs, CvFileNode* node );
|
|
// virtual void write( CvFileStorage* fs, const char* name ) const;
|
|
//
|
|
// // special read & write methods for trees in the tree ensembles
|
|
// virtual void read( CvFileStorage* fs, CvFileNode* node,
|
|
// CvDTreeTrainData* data );
|
|
// virtual void write( CvFileStorage* fs ) const;
|
|
//
|
|
// const CvDTreeNode* get_root() const;
|
|
// int get_pruned_tree_idx() const;
|
|
// CvDTreeTrainData* get_data();
|
|
//
|
|
// protected:
|
|
// friend struct cv::DTreeBestSplitFinder;
|
|
//
|
|
// virtual bool do_train( const CvMat* _subsample_idx );
|
|
//
|
|
// virtual void try_split_node( CvDTreeNode* n );
|
|
// virtual void split_node_data( CvDTreeNode* n );
|
|
// virtual CvDTreeSplit* find_best_split( CvDTreeNode* n );
|
|
// virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi,
|
|
// float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
|
|
// virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi,
|
|
// float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
|
|
// virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi,
|
|
// float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
|
|
// virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi,
|
|
// float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
|
|
// virtual CvDTreeSplit* find_surrogate_split_ord( CvDTreeNode* n, int vi, uchar* ext_buf = 0 );
|
|
// virtual CvDTreeSplit* find_surrogate_split_cat( CvDTreeNode* n, int vi, uchar* ext_buf = 0 );
|
|
// virtual double calc_node_dir( CvDTreeNode* node );
|
|
// virtual void complete_node_dir( CvDTreeNode* node );
|
|
// virtual void cluster_categories( const int* vectors, int vector_count,
|
|
// int var_count, int* sums, int k, int* cluster_labels );
|
|
//
|
|
// virtual void calc_node_value( CvDTreeNode* node );
|
|
//
|
|
// virtual void prune_cv();
|
|
// virtual double update_tree_rnc( int T, int fold );
|
|
// virtual int cut_tree( int T, int fold, double min_alpha );
|
|
// virtual void free_prune_data(bool cut_tree);
|
|
// virtual void free_tree();
|
|
//
|
|
// virtual void write_node( CvFileStorage* fs, CvDTreeNode* node ) const;
|
|
// virtual void write_split( CvFileStorage* fs, CvDTreeSplit* split ) const;
|
|
// virtual CvDTreeNode* read_node( CvFileStorage* fs, CvFileNode* node, CvDTreeNode* parent );
|
|
// virtual CvDTreeSplit* read_split( CvFileStorage* fs, CvFileNode* node );
|
|
// virtual void write_tree_nodes( CvFileStorage* fs ) const;
|
|
// virtual void read_tree_nodes( CvFileStorage* fs, CvFileNode* node );
|
|
//
|
|
// CvDTreeNode* root;
|
|
// CvMat* var_importance;
|
|
// CvDTreeTrainData* data;
|
|
//
|
|
// public:
|
|
// int pruned_tree_idx;
|
|
// };
|
|
//
|
|
//
|
|
/// ****************************************************************************************\
|
|
// * Random Trees Classifier *
|
|
// \****************************************************************************************/
|
|
//
|
|
// class CvRTrees;
|
|
//
|
|
// class CV_EXPORTS CvForestTree: public CvDTree
|
|
// {
|
|
// public:
|
|
// CvForestTree();
|
|
// virtual ~CvForestTree();
|
|
//
|
|
// virtual bool train( CvDTreeTrainData* trainData, const CvMat* _subsample_idx, CvRTrees* forest );
|
|
//
|
|
// virtual int get_var_count() const {return data ? data->var_count : 0;}
|
|
// virtual void read( CvFileStorage* fs, CvFileNode* node, CvRTrees* forest, CvDTreeTrainData* _data );
|
|
//
|
|
// /* dummy methods to avoid warnings: BEGIN */
|
|
// virtual bool train( const CvMat* trainData, int tflag,
|
|
// const CvMat* responses, const CvMat* varIdx=0,
|
|
// const CvMat* sampleIdx=0, const CvMat* varType=0,
|
|
// const CvMat* missingDataMask=0,
|
|
// CvDTreeParams params=CvDTreeParams() );
|
|
//
|
|
// virtual bool train( CvDTreeTrainData* trainData, const CvMat* _subsample_idx );
|
|
// virtual void read( CvFileStorage* fs, CvFileNode* node );
|
|
// virtual void read( CvFileStorage* fs, CvFileNode* node,
|
|
// CvDTreeTrainData* data );
|
|
// /* dummy methods to avoid warnings: END */
|
|
//
|
|
// protected:
|
|
// friend struct cv::ForestTreeBestSplitFinder;
|
|
//
|
|
// virtual CvDTreeSplit* find_best_split( CvDTreeNode* n );
|
|
// CvRTrees* forest;
|
|
// };
|
|
//
|
|
//
|
|
// struct CV_EXPORTS_W_MAP CvRTParams : public CvDTreeParams
|
|
// {
|
|
// //Parameters for the forest
|
|
// CV_PROP_RW bool calc_var_importance; // true <=> RF processes variable importance
|
|
// CV_PROP_RW int nactive_vars;
|
|
// CV_PROP_RW CvTermCriteria term_crit;
|
|
//
|
|
// CvRTParams();
|
|
// CvRTParams( int max_depth, int min_sample_count,
|
|
// float regression_accuracy, bool use_surrogates,
|
|
// int max_categories, const float* priors, bool calc_var_importance,
|
|
// int nactive_vars, int max_num_of_trees_in_the_forest,
|
|
// float forest_accuracy, int termcrit_type );
|
|
// };
|
|
//
|
|
//
|
|
// class CV_EXPORTS_W CvRTrees : public CvStatModel
|
|
// {
|
|
// public:
|
|
// CV_WRAP CvRTrees();
|
|
// virtual ~CvRTrees();
|
|
// virtual bool train( const CvMat* trainData, int tflag,
|
|
// const CvMat* responses, const CvMat* varIdx=0,
|
|
// const CvMat* sampleIdx=0, const CvMat* varType=0,
|
|
// const CvMat* missingDataMask=0,
|
|
// CvRTParams params=CvRTParams() );
|
|
//
|
|
// virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() );
|
|
// virtual float predict( const CvMat* sample, const CvMat* missing = 0 ) const;
|
|
// virtual float predict_prob( const CvMat* sample, const CvMat* missing = 0 ) const;
|
|
//
|
|
// CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
|
|
// const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
|
|
// const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
|
|
// const cv::Mat& missingDataMask=cv::Mat(),
|
|
// CvRTParams params=CvRTParams() );
|
|
// CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
|
|
// CV_WRAP virtual float predict_prob( const cv::Mat& sample, const cv::Mat& missing = cv::Mat() ) const;
|
|
// CV_WRAP virtual cv::Mat getVarImportance();
|
|
//
|
|
// CV_WRAP virtual void clear();
|
|
//
|
|
// virtual const CvMat* get_var_importance();
|
|
// virtual float get_proximity( const CvMat* sample1, const CvMat* sample2,
|
|
// const CvMat* missing1 = 0, const CvMat* missing2 = 0 ) const;
|
|
//
|
|
// virtual float calc_error( CvMLData* data, int type , std::vector<float>* resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
|
|
//
|
|
// virtual float get_train_error();
|
|
//
|
|
// virtual void read( CvFileStorage* fs, CvFileNode* node );
|
|
// virtual void write( CvFileStorage* fs, const char* name ) const;
|
|
//
|
|
// CvMat* get_active_var_mask();
|
|
// CvRNG* get_rng();
|
|
//
|
|
// int get_tree_count() const;
|
|
// CvForestTree* get_tree(int i) const;
|
|
//
|
|
// protected:
|
|
// virtual cv::String getName() const;
|
|
//
|
|
// virtual bool grow_forest( const CvTermCriteria term_crit );
|
|
//
|
|
// // array of the trees of the forest
|
|
// CvForestTree** trees;
|
|
// CvDTreeTrainData* data;
|
|
// int ntrees;
|
|
// int nclasses;
|
|
// double oob_error;
|
|
// CvMat* var_importance;
|
|
// int nsamples;
|
|
//
|
|
// cv::RNG* rng;
|
|
// CvMat* active_var_mask;
|
|
// };
|
|
//
|
|
/// ****************************************************************************************\
|
|
// * Extremely randomized trees Classifier *
|
|
// \****************************************************************************************/
|
|
// struct CV_EXPORTS CvERTreeTrainData : public CvDTreeTrainData
|
|
// {
|
|
// virtual void set_data( const CvMat* trainData, int tflag,
|
|
// const CvMat* responses, const CvMat* varIdx=0,
|
|
// const CvMat* sampleIdx=0, const CvMat* varType=0,
|
|
// const CvMat* missingDataMask=0,
|
|
// const CvDTreeParams& params=CvDTreeParams(),
|
|
// bool _shared=false, bool _add_labels=false,
|
|
// bool _update_data=false );
|
|
// virtual void get_ord_var_data( CvDTreeNode* n, int vi, float* ord_values_buf, int* missing_buf,
|
|
// const float** ord_values, const int** missing, int* sample_buf = 0 );
|
|
// virtual const int* get_sample_indices( CvDTreeNode* n, int* indices_buf );
|
|
// virtual const int* get_cv_labels( CvDTreeNode* n, int* labels_buf );
|
|
// virtual const int* get_cat_var_data( CvDTreeNode* n, int vi, int* cat_values_buf );
|
|
// virtual void get_vectors( const CvMat* _subsample_idx, float* values, uchar* missing,
|
|
// float* responses, bool get_class_idx=false );
|
|
// virtual CvDTreeNode* subsample_data( const CvMat* _subsample_idx );
|
|
// const CvMat* missing_mask;
|
|
// };
|
|
//
|
|
// class CV_EXPORTS CvForestERTree : public CvForestTree
|
|
// {
|
|
// protected:
|
|
// virtual double calc_node_dir( CvDTreeNode* node );
|
|
// virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi,
|
|
// float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
|
|
// virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi,
|
|
// float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
|
|
// virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi,
|
|
// float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
|
|
// virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi,
|
|
// float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
|
|
// virtual void split_node_data( CvDTreeNode* n );
|
|
// };
|
|
//
|
|
// class CV_EXPORTS_W CvERTrees : public CvRTrees
|
|
// {
|
|
// public:
|
|
// CV_WRAP CvERTrees();
|
|
// virtual ~CvERTrees();
|
|
// virtual bool train( const CvMat* trainData, int tflag,
|
|
// const CvMat* responses, const CvMat* varIdx=0,
|
|
// const CvMat* sampleIdx=0, const CvMat* varType=0,
|
|
// const CvMat* missingDataMask=0,
|
|
// CvRTParams params=CvRTParams());
|
|
// CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
|
|
// const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
|
|
// const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
|
|
// const cv::Mat& missingDataMask=cv::Mat(),
|
|
// CvRTParams params=CvRTParams());
|
|
// virtual bool train( CvMLData* data, CvRTParams params=CvRTParams() );
|
|
// protected:
|
|
// virtual cv::String getName() const;
|
|
// virtual bool grow_forest( const CvTermCriteria term_crit );
|
|
// };
|
|
//
|
|
//
|
|
/// ****************************************************************************************\
|
|
// * Boosted tree classifier *
|
|
// \****************************************************************************************/
|
|
//
|
|
// struct CV_EXPORTS_W_MAP CvBoostParams : public CvDTreeParams
|
|
// {
|
|
// CV_PROP_RW int boost_type;
|
|
// CV_PROP_RW int weak_count;
|
|
// CV_PROP_RW int split_criteria;
|
|
// CV_PROP_RW double weight_trim_rate;
|
|
//
|
|
// CvBoostParams();
|
|
// CvBoostParams( int boost_type, int weak_count, double weight_trim_rate,
|
|
// int max_depth, bool use_surrogates, const float* priors );
|
|
// };
|
|
//
|
|
//
|
|
// class CvBoost;
|
|
//
|
|
// class CV_EXPORTS CvBoostTree: public CvDTree
|
|
// {
|
|
// public:
|
|
// CvBoostTree();
|
|
// virtual ~CvBoostTree();
|
|
//
|
|
// virtual bool train( CvDTreeTrainData* trainData,
|
|
// const CvMat* subsample_idx, CvBoost* ensemble );
|
|
//
|
|
// virtual void scale( double s );
|
|
// virtual void read( CvFileStorage* fs, CvFileNode* node,
|
|
// CvBoost* ensemble, CvDTreeTrainData* _data );
|
|
// virtual void clear();
|
|
//
|
|
// /* dummy methods to avoid warnings: BEGIN */
|
|
// virtual bool train( const CvMat* trainData, int tflag,
|
|
// const CvMat* responses, const CvMat* varIdx=0,
|
|
// const CvMat* sampleIdx=0, const CvMat* varType=0,
|
|
// const CvMat* missingDataMask=0,
|
|
// CvDTreeParams params=CvDTreeParams() );
|
|
// virtual bool train( CvDTreeTrainData* trainData, const CvMat* _subsample_idx );
|
|
//
|
|
// virtual void read( CvFileStorage* fs, CvFileNode* node );
|
|
// virtual void read( CvFileStorage* fs, CvFileNode* node,
|
|
// CvDTreeTrainData* data );
|
|
// /* dummy methods to avoid warnings: END */
|
|
//
|
|
// protected:
|
|
//
|
|
// virtual void try_split_node( CvDTreeNode* n );
|
|
// virtual CvDTreeSplit* find_surrogate_split_ord( CvDTreeNode* n, int vi, uchar* ext_buf = 0 );
|
|
// virtual CvDTreeSplit* find_surrogate_split_cat( CvDTreeNode* n, int vi, uchar* ext_buf = 0 );
|
|
// virtual CvDTreeSplit* find_split_ord_class( CvDTreeNode* n, int vi,
|
|
// float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
|
|
// virtual CvDTreeSplit* find_split_cat_class( CvDTreeNode* n, int vi,
|
|
// float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
|
|
// virtual CvDTreeSplit* find_split_ord_reg( CvDTreeNode* n, int vi,
|
|
// float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
|
|
// virtual CvDTreeSplit* find_split_cat_reg( CvDTreeNode* n, int vi,
|
|
// float init_quality = 0, CvDTreeSplit* _split = 0, uchar* ext_buf = 0 );
|
|
// virtual void calc_node_value( CvDTreeNode* n );
|
|
// virtual double calc_node_dir( CvDTreeNode* n );
|
|
//
|
|
// CvBoost* ensemble;
|
|
// };
|
|
//
|
|
//
|
|
// class CV_EXPORTS_W CvBoost : public CvStatModel
|
|
// {
|
|
// public:
|
|
// // Boosting type
|
|
// enum { DISCRETE=0, REAL=1, LOGIT=2, GENTLE=3 };
|
|
//
|
|
// // Splitting criteria
|
|
// enum { DEFAULT=0, GINI=1, MISCLASS=3, SQERR=4 };
|
|
//
|
|
// CV_WRAP CvBoost();
|
|
// virtual ~CvBoost();
|
|
//
|
|
// CvBoost( const CvMat* trainData, int tflag,
|
|
// const CvMat* responses, const CvMat* varIdx=0,
|
|
// const CvMat* sampleIdx=0, const CvMat* varType=0,
|
|
// const CvMat* missingDataMask=0,
|
|
// CvBoostParams params=CvBoostParams() );
|
|
//
|
|
// virtual bool train( const CvMat* trainData, int tflag,
|
|
// const CvMat* responses, const CvMat* varIdx=0,
|
|
// const CvMat* sampleIdx=0, const CvMat* varType=0,
|
|
// const CvMat* missingDataMask=0,
|
|
// CvBoostParams params=CvBoostParams(),
|
|
// bool update=false );
|
|
//
|
|
// virtual bool train( CvMLData* data,
|
|
// CvBoostParams params=CvBoostParams(),
|
|
// bool update=false );
|
|
//
|
|
// virtual float predict( const CvMat* sample, const CvMat* missing=0,
|
|
// CvMat* weak_responses=0, CvSlice slice=CV_WHOLE_SEQ,
|
|
// bool raw_mode=false, bool return_sum=false ) const;
|
|
//
|
|
// CV_WRAP CvBoost( const cv::Mat& trainData, int tflag,
|
|
// const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
|
|
// const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
|
|
// const cv::Mat& missingDataMask=cv::Mat(),
|
|
// CvBoostParams params=CvBoostParams() );
|
|
//
|
|
// CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
|
|
// const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
|
|
// const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
|
|
// const cv::Mat& missingDataMask=cv::Mat(),
|
|
// CvBoostParams params=CvBoostParams(),
|
|
// bool update=false );
|
|
//
|
|
// CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(),
|
|
// const cv::Range& slice=cv::Range::all(), bool rawMode=false,
|
|
// bool returnSum=false ) const;
|
|
//
|
|
// virtual float calc_error( CvMLData* _data, int type , std::vector<float> *resp = 0 ); // type in {CV_TRAIN_ERROR, CV_TEST_ERROR}
|
|
//
|
|
// CV_WRAP virtual void prune( CvSlice slice );
|
|
//
|
|
// CV_WRAP virtual void clear();
|
|
//
|
|
// virtual void write( CvFileStorage* storage, const char* name ) const;
|
|
// virtual void read( CvFileStorage* storage, CvFileNode* node );
|
|
// virtual const CvMat* get_active_vars(bool absolute_idx=true);
|
|
//
|
|
// CvSeq* get_weak_predictors();
|
|
//
|
|
// CvMat* get_weights();
|
|
// CvMat* get_subtree_weights();
|
|
// CvMat* get_weak_response();
|
|
// const CvBoostParams& get_params() const;
|
|
// const CvDTreeTrainData* get_data() const;
|
|
//
|
|
// protected:
|
|
//
|
|
// virtual bool set_params( const CvBoostParams& params );
|
|
// virtual void update_weights( CvBoostTree* tree );
|
|
// virtual void trim_weights();
|
|
// virtual void write_params( CvFileStorage* fs ) const;
|
|
// virtual void read_params( CvFileStorage* fs, CvFileNode* node );
|
|
//
|
|
// virtual void initialize_weights(double (&p)[2]);
|
|
//
|
|
// CvDTreeTrainData* data;
|
|
// CvBoostParams params;
|
|
// CvSeq* weak;
|
|
//
|
|
// CvMat* active_vars;
|
|
// CvMat* active_vars_abs;
|
|
// bool have_active_cat_vars;
|
|
//
|
|
// CvMat* orig_response;
|
|
// CvMat* sum_response;
|
|
// CvMat* weak_eval;
|
|
// CvMat* subsample_mask;
|
|
// CvMat* weights;
|
|
// CvMat* subtree_weights;
|
|
// bool have_subsample;
|
|
// };
|
|
//
|
|
//
|
|
/// ****************************************************************************************\
|
|
// * Gradient Boosted Trees *
|
|
// \****************************************************************************************/
|
|
//
|
|
/// / DataType: STRUCT CvGBTreesParams
|
|
/// / Parameters of GBT (Gradient Boosted trees model), including single
|
|
/// / tree settings and ensemble parameters.
|
|
/// /
|
|
/// / weak_count - count of trees in the ensemble
|
|
/// / loss_function_type - loss function used for ensemble training
|
|
/// / subsample_portion - portion of whole training set used for
|
|
/// / every single tree training.
|
|
/// / subsample_portion value is in (0.0, 1.0].
|
|
/// / subsample_portion == 1.0 when whole dataset is
|
|
/// / used on each step. Count of sample used on each
|
|
/// / step is computed as
|
|
/// / int(total_samples_count * subsample_portion).
|
|
/// / shrinkage - regularization parameter.
|
|
/// / Each tree prediction is multiplied on shrinkage value.
|
|
//
|
|
//
|
|
// struct CV_EXPORTS_W_MAP CvGBTreesParams : public CvDTreeParams
|
|
// {
|
|
// CV_PROP_RW int weak_count;
|
|
// CV_PROP_RW int loss_function_type;
|
|
// CV_PROP_RW float subsample_portion;
|
|
// CV_PROP_RW float shrinkage;
|
|
//
|
|
// CvGBTreesParams();
|
|
// CvGBTreesParams( int loss_function_type, int weak_count, float shrinkage,
|
|
// float subsample_portion, int max_depth, bool use_surrogates );
|
|
// };
|
|
//
|
|
/// / DataType: CLASS CvGBTrees
|
|
/// / Gradient Boosting Trees (GBT) algorithm implementation.
|
|
/// /
|
|
/// / data - training dataset
|
|
/// / params - parameters of the CvGBTrees
|
|
/// / weak - array[0..(class_count-1)] of CvSeq
|
|
/// / for storing tree ensembles
|
|
/// / orig_response - original responses of the training set samples
|
|
/// / sum_response - predicitons of the current model on the training dataset.
|
|
/// / this matrix is updated on every iteration.
|
|
/// / sum_response_tmp - predicitons of the model on the training set on the next
|
|
/// / step. On every iteration values of sum_responses_tmp are
|
|
/// / computed via sum_responses values. When the current
|
|
/// / step is complete sum_response values become equal to
|
|
/// / sum_responses_tmp.
|
|
/// / sampleIdx - indices of samples used for training the ensemble.
|
|
/// / CvGBTrees training procedure takes a set of samples
|
|
/// / (train_data) and a set of responses (responses).
|
|
/// / Only pairs (train_data[i], responses[i]), where i is
|
|
/// / in sample_idx are used for training the ensemble.
|
|
/// / subsample_train - indices of samples used for training a single decision
|
|
/// / tree on the current step. This indices are countered
|
|
/// / relatively to the sample_idx, so that pairs
|
|
/// / (train_data[sample_idx[i]], responses[sample_idx[i]])
|
|
/// / are used for training a decision tree.
|
|
/// / Training set is randomly splited
|
|
/// / in two parts (subsample_train and subsample_test)
|
|
/// / on every iteration accordingly to the portion parameter.
|
|
/// / subsample_test - relative indices of samples from the training set,
|
|
/// / which are not used for training a tree on the current
|
|
/// / step.
|
|
/// / missing - mask of the missing values in the training set. This
|
|
/// / matrix has the same size as train_data. 1 - missing
|
|
/// / value, 0 - not a missing value.
|
|
/// / class_labels - output class labels map.
|
|
/// / rng - random number generator. Used for spliting the
|
|
/// / training set.
|
|
/// / class_count - count of output classes.
|
|
/// / class_count == 1 in the case of regression,
|
|
/// / and > 1 in the case of classification.
|
|
/// / delta - Huber loss function parameter.
|
|
/// / base_value - start point of the gradient descent procedure.
|
|
/// / model prediction is
|
|
/// / f(x) = f_0 + sum_{i=1..weak_count-1}(f_i(x)), where
|
|
/// / f_0 is the base value.
|
|
//
|
|
//
|
|
//
|
|
// class CV_EXPORTS_W CvGBTrees : public CvStatModel
|
|
// {
|
|
// public:
|
|
//
|
|
// /*
|
|
// // DataType: ENUM
|
|
// // Loss functions implemented in CvGBTrees.
|
|
// //
|
|
// // SQUARED_LOSS
|
|
// // problem: regression
|
|
// // loss = (x - x')^2
|
|
// //
|
|
// // ABSOLUTE_LOSS
|
|
// // problem: regression
|
|
// // loss = abs(x - x')
|
|
// //
|
|
// // HUBER_LOSS
|
|
// // problem: regression
|
|
// // loss = delta*( abs(x - x') - delta/2), if abs(x - x') > delta
|
|
// // 1/2*(x - x')^2, if abs(x - x') <= delta,
|
|
// // where delta is the alpha-quantile of pseudo responses from
|
|
// // the training set.
|
|
// //
|
|
// // DEVIANCE_LOSS
|
|
// // problem: classification
|
|
// //
|
|
// */
|
|
// enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS};
|
|
//
|
|
//
|
|
// /*
|
|
// // Default constructor. Creates a model only (without training).
|
|
// // Should be followed by one form of the train(...) function.
|
|
// //
|
|
// // API
|
|
// // CvGBTrees();
|
|
//
|
|
// // INPUT
|
|
// // OUTPUT
|
|
// // RESULT
|
|
// */
|
|
// CV_WRAP CvGBTrees();
|
|
//
|
|
//
|
|
// /*
|
|
// // Full form constructor. Creates a gradient boosting model and does the
|
|
// // train.
|
|
// //
|
|
// // API
|
|
// // CvGBTrees( const CvMat* trainData, int tflag,
|
|
// const CvMat* responses, const CvMat* varIdx=0,
|
|
// const CvMat* sampleIdx=0, const CvMat* varType=0,
|
|
// const CvMat* missingDataMask=0,
|
|
// CvGBTreesParams params=CvGBTreesParams() );
|
|
//
|
|
// // INPUT
|
|
// // trainData - a set of input feature vectors.
|
|
// // size of matrix is
|
|
// // <count of samples> x <variables count>
|
|
// // or <variables count> x <count of samples>
|
|
// // depending on the tflag parameter.
|
|
// // matrix values are float.
|
|
// // tflag - a flag showing how do samples stored in the
|
|
// // trainData matrix row by row (tflag=CV_ROW_SAMPLE)
|
|
// // or column by column (tflag=CV_COL_SAMPLE).
|
|
// // responses - a vector of responses corresponding to the samples
|
|
// // in trainData.
|
|
// // varIdx - indices of used variables. zero value means that all
|
|
// // variables are active.
|
|
// // sampleIdx - indices of used samples. zero value means that all
|
|
// // samples from trainData are in the training set.
|
|
// // varType - vector of <variables count> length. gives every
|
|
// // variable type CV_VAR_CATEGORICAL or CV_VAR_ORDERED.
|
|
// // varType = 0 means all variables are numerical.
|
|
// // missingDataMask - a mask of misiing values in trainData.
|
|
// // missingDataMask = 0 means that there are no missing
|
|
// // values.
|
|
// // params - parameters of GTB algorithm.
|
|
// // OUTPUT
|
|
// // RESULT
|
|
// */
|
|
// CvGBTrees( const CvMat* trainData, int tflag,
|
|
// const CvMat* responses, const CvMat* varIdx=0,
|
|
// const CvMat* sampleIdx=0, const CvMat* varType=0,
|
|
// const CvMat* missingDataMask=0,
|
|
// CvGBTreesParams params=CvGBTreesParams() );
|
|
//
|
|
//
|
|
// /*
|
|
// // Destructor.
|
|
// */
|
|
// virtual ~CvGBTrees();
|
|
//
|
|
//
|
|
// /*
|
|
// // Gradient tree boosting model training
|
|
// //
|
|
// // API
|
|
// // virtual bool train( const CvMat* trainData, int tflag,
|
|
// const CvMat* responses, const CvMat* varIdx=0,
|
|
// const CvMat* sampleIdx=0, const CvMat* varType=0,
|
|
// const CvMat* missingDataMask=0,
|
|
// CvGBTreesParams params=CvGBTreesParams(),
|
|
// bool update=false );
|
|
//
|
|
// // INPUT
|
|
// // trainData - a set of input feature vectors.
|
|
// // size of matrix is
|
|
// // <count of samples> x <variables count>
|
|
// // or <variables count> x <count of samples>
|
|
// // depending on the tflag parameter.
|
|
// // matrix values are float.
|
|
// // tflag - a flag showing how do samples stored in the
|
|
// // trainData matrix row by row (tflag=CV_ROW_SAMPLE)
|
|
// // or column by column (tflag=CV_COL_SAMPLE).
|
|
// // responses - a vector of responses corresponding to the samples
|
|
// // in trainData.
|
|
// // varIdx - indices of used variables. zero value means that all
|
|
// // variables are active.
|
|
// // sampleIdx - indices of used samples. zero value means that all
|
|
// // samples from trainData are in the training set.
|
|
// // varType - vector of <variables count> length. gives every
|
|
// // variable type CV_VAR_CATEGORICAL or CV_VAR_ORDERED.
|
|
// // varType = 0 means all variables are numerical.
|
|
// // missingDataMask - a mask of misiing values in trainData.
|
|
// // missingDataMask = 0 means that there are no missing
|
|
// // values.
|
|
// // params - parameters of GTB algorithm.
|
|
// // update - is not supported now. (!)
|
|
// // OUTPUT
|
|
// // RESULT
|
|
// // Error state.
|
|
// */
|
|
// virtual bool train( const CvMat* trainData, int tflag,
|
|
// const CvMat* responses, const CvMat* varIdx=0,
|
|
// const CvMat* sampleIdx=0, const CvMat* varType=0,
|
|
// const CvMat* missingDataMask=0,
|
|
// CvGBTreesParams params=CvGBTreesParams(),
|
|
// bool update=false );
|
|
//
|
|
//
|
|
// /*
|
|
// // Gradient tree boosting model training
|
|
// //
|
|
// // API
|
|
// // virtual bool train( CvMLData* data,
|
|
// CvGBTreesParams params=CvGBTreesParams(),
|
|
// bool update=false ) {return false;};
|
|
//
|
|
// // INPUT
|
|
// // data - training set.
|
|
// // params - parameters of GTB algorithm.
|
|
// // update - is not supported now. (!)
|
|
// // OUTPUT
|
|
// // RESULT
|
|
// // Error state.
|
|
// */
|
|
// virtual bool train( CvMLData* data,
|
|
// CvGBTreesParams params=CvGBTreesParams(),
|
|
// bool update=false );
|
|
//
|
|
//
|
|
// /*
|
|
// // Response value prediction
|
|
// //
|
|
// // API
|
|
// // virtual float predict_serial( const CvMat* sample, const CvMat* missing=0,
|
|
// CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ,
|
|
// int k=-1 ) const;
|
|
//
|
|
// // INPUT
|
|
// // sample - input sample of the same type as in the training set.
|
|
// // missing - missing values mask. missing=0 if there are no
|
|
// // missing values in sample vector.
|
|
// // weak_responses - predictions of all of the trees.
|
|
// // not implemented (!)
|
|
// // slice - part of the ensemble used for prediction.
|
|
// // slice = CV_WHOLE_SEQ when all trees are used.
|
|
// // k - number of ensemble used.
|
|
// // k is in {-1,0,1,..,<count of output classes-1>}.
|
|
// // in the case of classification problem
|
|
// // <count of output classes-1> ensembles are built.
|
|
// // If k = -1 ordinary prediction is the result,
|
|
// // otherwise function gives the prediction of the
|
|
// // k-th ensemble only.
|
|
// // OUTPUT
|
|
// // RESULT
|
|
// // Predicted value.
|
|
// */
|
|
// virtual float predict_serial( const CvMat* sample, const CvMat* missing=0,
|
|
// CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ,
|
|
// int k=-1 ) const;
|
|
//
|
|
// /*
|
|
// // Response value prediction.
|
|
// // Parallel version (in the case of TBB existence)
|
|
// //
|
|
// // API
|
|
// // virtual float predict( const CvMat* sample, const CvMat* missing=0,
|
|
// CvMat* weak_responses=0, CvSlice slice = CV_WHOLE_SEQ,
|
|
// int k=-1 ) const;
|
|
//
|
|
// // INPUT
|
|
// // sample - input sample of the same type as in the training set.
|
|
// // missing - missing values mask. missing=0 if there are no
|
|
// // missing values in sample vector.
|
|
// // weak_responses - predictions of all of the trees.
|
|
// // not implemented (!)
|
|
// // slice - part of the ensemble used for prediction.
|
|
// // slice = CV_WHOLE_SEQ when all trees are used.
|
|
// // k - number of ensemble used.
|
|
// // k is in {-1,0,1,..,<count of output classes-1>}.
|
|
// // in the case of classification problem
|
|
// // <count of output classes-1> ensembles are built.
|
|
// // If k = -1 ordinary prediction is the result,
|
|
// // otherwise function gives the prediction of the
|
|
// // k-th ensemble only.
|
|
// // OUTPUT
|
|
// // RESULT
|
|
// // Predicted value.
|
|
// */
|
|
// virtual float predict( const CvMat* sample, const CvMat* missing=0,
|
|
// CvMat* weakResponses=0, CvSlice slice = CV_WHOLE_SEQ,
|
|
// int k=-1 ) const;
|
|
//
|
|
// /*
|
|
// // Deletes all the data.
|
|
// //
|
|
// // API
|
|
// // virtual void clear();
|
|
//
|
|
// // INPUT
|
|
// // OUTPUT
|
|
// // delete data, weak, orig_response, sum_response,
|
|
// // weak_eval, subsample_train, subsample_test,
|
|
// // sample_idx, missing, lass_labels
|
|
// // delta = 0.0
|
|
// // RESULT
|
|
// */
|
|
// CV_WRAP virtual void clear();
|
|
//
|
|
// /*
|
|
// // Compute error on the train/test set.
|
|
// //
|
|
// // API
|
|
// // virtual float calc_error( CvMLData* _data, int type,
|
|
// // std::vector<float> *resp = 0 );
|
|
// //
|
|
// // INPUT
|
|
// // data - dataset
|
|
// // type - defines which error is to compute: train (CV_TRAIN_ERROR) or
|
|
// // test (CV_TEST_ERROR).
|
|
// // OUTPUT
|
|
// // resp - vector of predicitons
|
|
// // RESULT
|
|
// // Error value.
|
|
// */
|
|
// virtual float calc_error( CvMLData* _data, int type,
|
|
// std::vector<float> *resp = 0 );
|
|
//
|
|
// /*
|
|
// //
|
|
// // Write parameters of the gtb model and data. Write learned model.
|
|
// //
|
|
// // API
|
|
// // virtual void write( CvFileStorage* fs, const char* name ) const;
|
|
// //
|
|
// // INPUT
|
|
// // fs - file storage to read parameters from.
|
|
// // name - model name.
|
|
// // OUTPUT
|
|
// // RESULT
|
|
// */
|
|
// virtual void write( CvFileStorage* fs, const char* name ) const;
|
|
//
|
|
//
|
|
// /*
|
|
// //
|
|
// // Read parameters of the gtb model and data. Read learned model.
|
|
// //
|
|
// // API
|
|
// // virtual void read( CvFileStorage* fs, CvFileNode* node );
|
|
// //
|
|
// // INPUT
|
|
// // fs - file storage to read parameters from.
|
|
// // node - file node.
|
|
// // OUTPUT
|
|
// // RESULT
|
|
// */
|
|
// virtual void read( CvFileStorage* fs, CvFileNode* node );
|
|
//
|
|
//
|
|
// // new-style C++ interface
|
|
// CV_WRAP CvGBTrees( const cv::Mat& trainData, int tflag,
|
|
// const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
|
|
// const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
|
|
// const cv::Mat& missingDataMask=cv::Mat(),
|
|
// CvGBTreesParams params=CvGBTreesParams() );
|
|
//
|
|
// CV_WRAP virtual bool train( const cv::Mat& trainData, int tflag,
|
|
// const cv::Mat& responses, const cv::Mat& varIdx=cv::Mat(),
|
|
// const cv::Mat& sampleIdx=cv::Mat(), const cv::Mat& varType=cv::Mat(),
|
|
// const cv::Mat& missingDataMask=cv::Mat(),
|
|
// CvGBTreesParams params=CvGBTreesParams(),
|
|
// bool update=false );
|
|
//
|
|
// CV_WRAP virtual float predict( const cv::Mat& sample, const cv::Mat& missing=cv::Mat(),
|
|
// const cv::Range& slice = cv::Range::all(),
|
|
// int k=-1 ) const;
|
|
//
|
|
// protected:
|
|
//
|
|
// /*
|
|
// // Compute the gradient vector components.
|
|
// //
|
|
// // API
|
|
// // virtual void find_gradient( const int k = 0);
|
|
//
|
|
// // INPUT
|
|
// // k - used for classification problem, determining current
|
|
// // tree ensemble.
|
|
// // OUTPUT
|
|
// // changes components of data->responses
|
|
// // which correspond to samples used for training
|
|
// // on the current step.
|
|
// // RESULT
|
|
// */
|
|
// virtual void find_gradient( const int k = 0);
|
|
//
|
|
//
|
|
// /*
|
|
// //
|
|
// // Change values in tree leaves according to the used loss function.
|
|
// //
|
|
// // API
|
|
// // virtual void change_values(CvDTree* tree, const int k = 0);
|
|
// //
|
|
// // INPUT
|
|
// // tree - decision tree to change.
|
|
// // k - used for classification problem, determining current
|
|
// // tree ensemble.
|
|
// // OUTPUT
|
|
// // changes 'value' fields of the trees' leaves.
|
|
// // changes sum_response_tmp.
|
|
// // RESULT
|
|
// */
|
|
// virtual void change_values(CvDTree* tree, const int k = 0);
|
|
//
|
|
//
|
|
// /*
|
|
// //
|
|
// // Find optimal constant prediction value according to the used loss
|
|
// // function.
|
|
// // The goal is to find a constant which gives the minimal summary loss
|
|
// // on the _Idx samples.
|
|
// //
|
|
// // API
|
|
// // virtual float find_optimal_value( const CvMat* _Idx );
|
|
// //
|
|
// // INPUT
|
|
// // _Idx - indices of the samples from the training set.
|
|
// // OUTPUT
|
|
// // RESULT
|
|
// // optimal constant value.
|
|
// */
|
|
// virtual float find_optimal_value( const CvMat* _Idx );
|
|
//
|
|
//
|
|
// /*
|
|
// //
|
|
// // Randomly split the whole training set in two parts according
|
|
// // to params.portion.
|
|
// //
|
|
// // API
|
|
// // virtual void do_subsample();
|
|
// //
|
|
// // INPUT
|
|
// // OUTPUT
|
|
// // subsample_train - indices of samples used for training
|
|
// // subsample_test - indices of samples used for test
|
|
// // RESULT
|
|
// */
|
|
// virtual void do_subsample();
|
|
//
|
|
//
|
|
// /*
|
|
// //
|
|
// // Internal recursive function giving an array of subtree tree leaves.
|
|
// //
|
|
// // API
|
|
// // void leaves_get( CvDTreeNode** leaves, int& count, CvDTreeNode* node );
|
|
// //
|
|
// // INPUT
|
|
// // node - current leaf.
|
|
// // OUTPUT
|
|
// // count - count of leaves in the subtree.
|
|
// // leaves - array of pointers to leaves.
|
|
// // RESULT
|
|
// */
|
|
// void leaves_get( CvDTreeNode** leaves, int& count, CvDTreeNode* node );
|
|
//
|
|
//
|
|
// /*
|
|
// //
|
|
// // Get leaves of the tree.
|
|
// //
|
|
// // API
|
|
// // CvDTreeNode** GetLeaves( const CvDTree* dtree, int& len );
|
|
// //
|
|
// // INPUT
|
|
// // dtree - decision tree.
|
|
// // OUTPUT
|
|
// // len - count of the leaves.
|
|
// // RESULT
|
|
// // CvDTreeNode** - array of pointers to leaves.
|
|
// */
|
|
// CvDTreeNode** GetLeaves( const CvDTree* dtree, int& len );
|
|
//
|
|
//
|
|
// /*
|
|
// //
|
|
// // Is it a regression or a classification.
|
|
// //
|
|
// // API
|
|
// // bool problem_type();
|
|
// //
|
|
// // INPUT
|
|
// // OUTPUT
|
|
// // RESULT
|
|
// // false if it is a classification problem,
|
|
// // true - if regression.
|
|
// */
|
|
// virtual bool problem_type() const;
|
|
//
|
|
//
|
|
// /*
|
|
// //
|
|
// // Write parameters of the gtb model.
|
|
// //
|
|
// // API
|
|
// // virtual void write_params( CvFileStorage* fs ) const;
|
|
// //
|
|
// // INPUT
|
|
// // fs - file storage to write parameters to.
|
|
// // OUTPUT
|
|
// // RESULT
|
|
// */
|
|
// virtual void write_params( CvFileStorage* fs ) const;
|
|
//
|
|
//
|
|
// /*
|
|
// //
|
|
// // Read parameters of the gtb model and data.
|
|
// //
|
|
// // API
|
|
// // virtual void read_params( CvFileStorage* fs );
|
|
// //
|
|
// // INPUT
|
|
// // fs - file storage to read parameters from.
|
|
// // OUTPUT
|
|
// // params - parameters of the gtb model.
|
|
// // data - contains information about the structure
|
|
// // of the data set (count of variables,
|
|
// // their types, etc.).
|
|
// // class_labels - output class labels map.
|
|
// // RESULT
|
|
// */
|
|
// virtual void read_params( CvFileStorage* fs, CvFileNode* fnode );
|
|
// int get_len(const CvMat* mat) const;
|
|
//
|
|
//
|
|
// CvDTreeTrainData* data;
|
|
// CvGBTreesParams params;
|
|
//
|
|
// CvSeq** weak;
|
|
// CvMat* orig_response;
|
|
// CvMat* sum_response;
|
|
// CvMat* sum_response_tmp;
|
|
// CvMat* sample_idx;
|
|
// CvMat* subsample_train;
|
|
// CvMat* subsample_test;
|
|
// CvMat* missing;
|
|
// CvMat* class_labels;
|
|
//
|
|
// cv::RNG* rng;
|
|
//
|
|
// int class_count;
|
|
// float delta;
|
|
// float base_value;
|
|
//
|
|
// };
|
|
//
|
|
//
|
|
//
|
|
/// ****************************************************************************************\
|
|
// * Artificial Neural Networks (ANN) *
|
|
// \****************************************************************************************/
|
|
//
|
|
/// ////////////////////////////////// Multi-Layer Perceptrons //////////////////////////////
|
|
//
|
|
// struct CV_EXPORTS_W_MAP CvANN_MLP_TrainParams
|
|
// {
|
|
// CvANN_MLP_TrainParams();
|
|
// CvANN_MLP_TrainParams( CvTermCriteria term_crit, int train_method,
|
|
// double param1, double param2=0 );
|
|
// ~CvANN_MLP_TrainParams();
|
|
//
|
|
// enum { BACKPROP=0, RPROP=1 };
|
|
//
|
|
// CV_PROP_RW CvTermCriteria term_crit;
|
|
// CV_PROP_RW int train_method;
|
|
//
|
|
// // backpropagation parameters
|
|
// CV_PROP_RW double bp_dw_scale, bp_moment_scale;
|
|
//
|
|
// // rprop parameters
|
|
// CV_PROP_RW double rp_dw0, rp_dw_plus, rp_dw_minus, rp_dw_min, rp_dw_max;
|
|
// };
|
|
//
|
|
//
|
|
// class CV_EXPORTS_W CvANN_MLP : public CvStatModel
|
|
// {
|
|
// public:
|
|
// CV_WRAP CvANN_MLP();
|
|
// CvANN_MLP( const CvMat* layerSizes,
|
|
// int activateFunc=CvANN_MLP::SIGMOID_SYM,
|
|
// double fparam1=0, double fparam2=0 );
|
|
//
|
|
// virtual ~CvANN_MLP();
|
|
//
|
|
// virtual void create( const CvMat* layerSizes,
|
|
// int activateFunc=CvANN_MLP::SIGMOID_SYM,
|
|
// double fparam1=0, double fparam2=0 );
|
|
//
|
|
// virtual int train( const CvMat* inputs, const CvMat* outputs,
|
|
// const CvMat* sampleWeights, const CvMat* sampleIdx=0,
|
|
// CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(),
|
|
// int flags=0 );
|
|
// virtual float predict( const CvMat* inputs, CV_OUT CvMat* outputs ) const;
|
|
//
|
|
// CV_WRAP CvANN_MLP( const cv::Mat& layerSizes,
|
|
// int activateFunc=CvANN_MLP::SIGMOID_SYM,
|
|
// double fparam1=0, double fparam2=0 );
|
|
//
|
|
// CV_WRAP virtual void create( const cv::Mat& layerSizes,
|
|
// int activateFunc=CvANN_MLP::SIGMOID_SYM,
|
|
// double fparam1=0, double fparam2=0 );
|
|
//
|
|
// CV_WRAP virtual int train( const cv::Mat& inputs, const cv::Mat& outputs,
|
|
// const cv::Mat& sampleWeights, const cv::Mat& sampleIdx=cv::Mat(),
|
|
// CvANN_MLP_TrainParams params = CvANN_MLP_TrainParams(),
|
|
// int flags=0 );
|
|
//
|
|
// CV_WRAP virtual float predict( const cv::Mat& inputs, CV_OUT cv::Mat& outputs ) const;
|
|
//
|
|
// CV_WRAP virtual void clear();
|
|
//
|
|
// // possible activation functions
|
|
// enum { IDENTITY = 0, SIGMOID_SYM = 1, GAUSSIAN = 2 };
|
|
//
|
|
// // available training flags
|
|
// enum { UPDATE_WEIGHTS = 1, NO_INPUT_SCALE = 2, NO_OUTPUT_SCALE = 4 };
|
|
//
|
|
// virtual void read( CvFileStorage* fs, CvFileNode* node );
|
|
// virtual void write( CvFileStorage* storage, const char* name ) const;
|
|
//
|
|
// int get_layer_count() { return layer_sizes ? layer_sizes->cols : 0; }
|
|
// const CvMat* get_layer_sizes() { return layer_sizes; }
|
|
// double* get_weights(int layer)
|
|
// {
|
|
// return layer_sizes && weights &&
|
|
// (unsigned)layer <= (unsigned)layer_sizes->cols ? weights[layer] : 0;
|
|
// }
|
|
//
|
|
// virtual void calc_activ_func_deriv( CvMat* xf, CvMat* deriv, const double* bias ) const;
|
|
//
|
|
// protected:
|
|
//
|
|
// virtual bool prepare_to_train( const CvMat* _inputs, const CvMat* _outputs,
|
|
// const CvMat* _sample_weights, const CvMat* sampleIdx,
|
|
// CvVectors* _ivecs, CvVectors* _ovecs, double** _sw, int _flags );
|
|
//
|
|
// // sequential random backpropagation
|
|
// virtual int train_backprop( CvVectors _ivecs, CvVectors _ovecs, const double* _sw );
|
|
//
|
|
// // RPROP algorithm
|
|
// virtual int train_rprop( CvVectors _ivecs, CvVectors _ovecs, const double* _sw );
|
|
//
|
|
// virtual void calc_activ_func( CvMat* xf, const double* bias ) const;
|
|
// virtual void set_activ_func( int _activ_func=SIGMOID_SYM,
|
|
// double _f_param1=0, double _f_param2=0 );
|
|
// virtual void init_weights();
|
|
// virtual void scale_input( const CvMat* _src, CvMat* _dst ) const;
|
|
// virtual void scale_output( const CvMat* _src, CvMat* _dst ) const;
|
|
// virtual void calc_input_scale( const CvVectors* vecs, int flags );
|
|
// virtual void calc_output_scale( const CvVectors* vecs, int flags );
|
|
//
|
|
// virtual void write_params( CvFileStorage* fs ) const;
|
|
// virtual void read_params( CvFileStorage* fs, CvFileNode* node );
|
|
//
|
|
// CvMat* layer_sizes;
|
|
// CvMat* wbuf;
|
|
// CvMat* sample_weights;
|
|
// double** weights;
|
|
// double f_param1, f_param2;
|
|
// double min_val, max_val, min_val1, max_val1;
|
|
// int activ_func;
|
|
// int max_count, max_buf_sz;
|
|
// CvANN_MLP_TrainParams params;
|
|
// cv::RNG* rng;
|
|
// };
|
|
//
|
|
/// ****************************************************************************************\
|
|
// * Auxilary functions declarations *
|
|
// \****************************************************************************************/
|
|
//
|
|
/// * Generates <sample> from multivariate normal distribution, where <mean> - is an
|
|
// average row vector, <cov> - symmetric covariation matrix */
|
|
// CVAPI(void) cvRandMVNormal( CvMat* mean, CvMat* cov, CvMat* sample,
|
|
// CvRNG* rng CV_DEFAULT(0) );
|
|
//
|
|
/// * Generates sample from gaussian mixture distribution */
|
|
// CVAPI(void) cvRandGaussMixture( CvMat* means[],
|
|
// CvMat* covs[],
|
|
// float weights[],
|
|
// int clsnum,
|
|
// CvMat* sample,
|
|
// CvMat* sampClasses CV_DEFAULT(0) );
|
|
//
|
|
// #define CV_TS_CONCENTRIC_SPHERES 0
|
|
//
|
|
/// * creates test set */
|
|
// CVAPI(void) cvCreateTestSet( int type, CvMat** samples,
|
|
// int num_samples,
|
|
// int num_features,
|
|
// CvMat** responses,
|
|
// int num_classes, ... );
|
|
//
|
|
/// ****************************************************************************************\
|
|
// * Data *
|
|
// \****************************************************************************************/
|
|
//
|
|
// #define CV_COUNT 0
|
|
// #define CV_PORTION 1
|
|
//
|
|
// struct CV_EXPORTS CvTrainTestSplit
|
|
// {
|
|
// CvTrainTestSplit();
|
|
// CvTrainTestSplit( int train_sample_count, bool mix = true);
|
|
// CvTrainTestSplit( float train_sample_portion, bool mix = true);
|
|
//
|
|
// union
|
|
// {
|
|
// int count;
|
|
// float portion;
|
|
// } train_sample_part;
|
|
// int train_sample_part_mode;
|
|
//
|
|
// bool mix;
|
|
// };
|
|
//
|
|
// class CV_EXPORTS CvMLData
|
|
// {
|
|
// public:
|
|
// CvMLData();
|
|
// virtual ~CvMLData();
|
|
//
|
|
// // returns:
|
|
// // 0 - OK
|
|
// // -1 - file can not be opened or is not correct
|
|
// int read_csv( const char* filename );
|
|
//
|
|
// const CvMat* get_values() const;
|
|
// const CvMat* get_responses();
|
|
// const CvMat* get_missing() const;
|
|
//
|
|
// void set_header_lines_number( int n );
|
|
// int get_header_lines_number() const;
|
|
//
|
|
// void set_response_idx( int idx ); // old response become predictors, new response_idx = idx
|
|
// // if idx < 0 there will be no response
|
|
// int get_response_idx() const;
|
|
//
|
|
// void set_train_test_split( const CvTrainTestSplit * spl );
|
|
// const CvMat* get_train_sample_idx() const;
|
|
// const CvMat* get_test_sample_idx() const;
|
|
// void mix_train_and_test_idx();
|
|
//
|
|
// const CvMat* get_var_idx();
|
|
// void chahge_var_idx( int vi, bool state ); // misspelled (saved for back compitability),
|
|
// // use change_var_idx
|
|
// void change_var_idx( int vi, bool state ); // state == true to set vi-variable as predictor
|
|
//
|
|
// const CvMat* get_var_types();
|
|
// int get_var_type( int var_idx ) const;
|
|
// // following 2 methods enable to change vars type
|
|
// // use these methods to assign CV_VAR_CATEGORICAL type for categorical variable
|
|
// // with numerical labels; in the other cases var types are correctly determined automatically
|
|
// void set_var_types( const char* str ); // str examples:
|
|
// // "ord[0-17],cat[18]", "ord[0,2,4,10-12], cat[1,3,5-9,13,14]",
|
|
// // "cat", "ord" (all vars are categorical/ordered)
|
|
// void change_var_type( int var_idx, int type); // type in { CV_VAR_ORDERED, CV_VAR_CATEGORICAL }
|
|
//
|
|
// void set_delimiter( char ch );
|
|
// char get_delimiter() const;
|
|
//
|
|
// void set_miss_ch( char ch );
|
|
// char get_miss_ch() const;
|
|
//
|
|
// const std::map<cv::String, int>& get_class_labels_map() const;
|
|
//
|
|
// protected:
|
|
// virtual void clear();
|
|
//
|
|
// void str_to_flt_elem( const char* token, float& flt_elem, int& type);
|
|
// void free_train_test_idx();
|
|
//
|
|
// char delimiter;
|
|
// char miss_ch;
|
|
// //char flt_separator;
|
|
//
|
|
// CvMat* values;
|
|
// CvMat* missing;
|
|
// CvMat* var_types;
|
|
// CvMat* var_idx_mask;
|
|
//
|
|
// CvMat* response_out; // header
|
|
// CvMat* var_idx_out; // mat
|
|
// CvMat* var_types_out; // mat
|
|
//
|
|
// int header_lines_number;
|
|
//
|
|
// int response_idx;
|
|
//
|
|
// int train_sample_count;
|
|
// bool mix;
|
|
//
|
|
// int total_class_count;
|
|
// std::map<cv::String, int> class_map;
|
|
//
|
|
// CvMat* train_sample_idx;
|
|
// CvMat* test_sample_idx;
|
|
// int* sample_idx; // data of train_sample_idx and test_sample_idx
|
|
//
|
|
// cv::RNG* rng;
|
|
// };
|
|
//
|
|
//
|
|
// namespace cv
|
|
// {
|
|
//
|
|
// typedef CvStatModel StatModel;
|
|
// typedef CvParamGrid ParamGrid;
|
|
// typedef CvNormalBayesClassifier NormalBayesClassifier;
|
|
// typedef CvKNearest KNearest;
|
|
// typedef CvSVMParams SVMParams;
|
|
// typedef CvSVMKernel SVMKernel;
|
|
// typedef CvSVMSolver SVMSolver;
|
|
// typedef CvSVM SVM;
|
|
// typedef CvDTreeParams DTreeParams;
|
|
// typedef CvMLData TrainData;
|
|
// typedef CvDTree DecisionTree;
|
|
// typedef CvForestTree ForestTree;
|
|
// typedef CvRTParams RandomTreeParams;
|
|
// typedef CvRTrees RandomTrees;
|
|
// typedef CvERTreeTrainData ERTreeTRainData;
|
|
// typedef CvForestERTree ERTree;
|
|
// typedef CvERTrees ERTrees;
|
|
// typedef CvBoostParams BoostParams;
|
|
// typedef CvBoostTree BoostTree;
|
|
// typedef CvBoost Boost;
|
|
// typedef CvANN_MLP_TrainParams ANN_MLP_TrainParams;
|
|
// typedef CvANN_MLP NeuralNet_MLP;
|
|
// typedef CvGBTreesParams GradientBoostingTreeParams;
|
|
// typedef CvGBTrees GradientBoostingTrees;
|
|
//
|
|
// template<> CV_EXPORTS void Ptr<CvDTreeSplit>::delete_obj();
|
|
//
|
|
// CV_EXPORTS bool initModule_ml(void);
|
|
// }
|
|
|
|
function CreateCvKNearest: TCvKNearest; stdcall; overload;
|
|
function CreateCvKNearest(const trainData: pCvMat; const responses: pCvMat; const sampleIdx: pCvMat = nil; isRegression: bool = false; max_k: Integer = 32)
|
|
: TCvKNearest; stdcall; overload;
|
|
procedure ReleaseCvKNearest(ex: TCvKNearest); stdcall;
|
|
|
|
implementation
|
|
|
|
uses ocv.lib;
|
|
|
|
function CreateCvKNearest: TCvKNearest; stdcall; external opencv_classes_lib name 'CreateCvKNearest';
|
|
function CreateCvKNearest(const trainData: pCvMat; const responses: pCvMat; const sampleIdx: pCvMat = nil; isRegression: bool = false; max_k: Integer = 32)
|
|
: TCvKNearest; stdcall; external opencv_classes_lib name 'CreateCvKNearestTR';
|
|
procedure ReleaseCvKNearest; stdcall; external opencv_classes_lib;
|
|
|
|
{ TCvDTreeNode }
|
|
|
|
function TCvDTreeNode.get_num_valid(vi: Integer): Integer;
|
|
begin
|
|
if num_valid = nil then
|
|
Result := sample_count
|
|
else
|
|
Result := num_valid[vi];
|
|
end;
|
|
|
|
procedure TCvDTreeNode.set_num_valid(vi, n: Integer);
|
|
begin
|
|
if num_valid <> nil then
|
|
num_valid[vi] := n;
|
|
end;
|
|
|
|
end.
|