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676 lines
25 KiB
C++
676 lines
25 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Copyright (C) 2014, Itseez 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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//M*/
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#ifndef __OPENCV_ML_HPP__
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#define __OPENCV_ML_HPP__
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#ifdef __cplusplus
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# include "opencv2/core.hpp"
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#endif
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#ifdef __cplusplus
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#include <float.h>
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#include <map>
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#include <iostream>
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namespace cv
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{
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namespace ml
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{
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/* Variable type */
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enum
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{
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VAR_NUMERICAL =0,
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VAR_ORDERED =0,
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VAR_CATEGORICAL =1
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};
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enum
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{
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TEST_ERROR = 0,
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TRAIN_ERROR = 1
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};
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enum
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{
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ROW_SAMPLE = 0,
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COL_SAMPLE = 1
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};
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class CV_EXPORTS_W_MAP ParamGrid
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{
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public:
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ParamGrid();
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ParamGrid(double _minVal, double _maxVal, double _logStep);
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CV_PROP_RW double minVal;
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CV_PROP_RW double maxVal;
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CV_PROP_RW double logStep;
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};
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class CV_EXPORTS TrainData
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{
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public:
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static inline float missingValue() { return FLT_MAX; }
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virtual ~TrainData();
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virtual int getLayout() const = 0;
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virtual int getNTrainSamples() const = 0;
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virtual int getNTestSamples() const = 0;
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virtual int getNSamples() const = 0;
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virtual int getNVars() const = 0;
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virtual int getNAllVars() const = 0;
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virtual void getSample(InputArray varIdx, int sidx, float* buf) const = 0;
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virtual Mat getSamples() const = 0;
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virtual Mat getMissing() const = 0;
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virtual Mat getTrainSamples(int layout=ROW_SAMPLE,
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bool compressSamples=true,
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bool compressVars=true) const = 0;
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virtual Mat getTrainResponses() const = 0;
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virtual Mat getTrainNormCatResponses() const = 0;
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virtual Mat getTestResponses() const = 0;
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virtual Mat getTestNormCatResponses() const = 0;
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virtual Mat getResponses() const = 0;
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virtual Mat getNormCatResponses() const = 0;
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virtual Mat getSampleWeights() const = 0;
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virtual Mat getTrainSampleWeights() const = 0;
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virtual Mat getTestSampleWeights() const = 0;
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virtual Mat getVarIdx() const = 0;
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virtual Mat getVarType() const = 0;
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virtual int getResponseType() const = 0;
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virtual Mat getTrainSampleIdx() const = 0;
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virtual Mat getTestSampleIdx() const = 0;
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virtual void getValues(int vi, InputArray sidx, float* values) const = 0;
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virtual void getNormCatValues(int vi, InputArray sidx, int* values) const = 0;
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virtual Mat getDefaultSubstValues() const = 0;
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virtual int getCatCount(int vi) const = 0;
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virtual Mat getClassLabels() const = 0;
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virtual Mat getCatOfs() const = 0;
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virtual Mat getCatMap() const = 0;
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virtual void setTrainTestSplit(int count, bool shuffle=true) = 0;
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virtual void setTrainTestSplitRatio(double ratio, bool shuffle=true) = 0;
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virtual void shuffleTrainTest() = 0;
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static Mat getSubVector(const Mat& vec, const Mat& idx);
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static Ptr<TrainData> loadFromCSV(const String& filename,
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int headerLineCount,
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int responseStartIdx=-1,
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int responseEndIdx=-1,
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const String& varTypeSpec=String(),
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char delimiter=',',
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char missch='?');
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static Ptr<TrainData> create(InputArray samples, int layout, InputArray responses,
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InputArray varIdx=noArray(), InputArray sampleIdx=noArray(),
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InputArray sampleWeights=noArray(), InputArray varType=noArray());
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};
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class CV_EXPORTS_W StatModel : public Algorithm
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{
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public:
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enum { UPDATE_MODEL = 1, RAW_OUTPUT=1, COMPRESSED_INPUT=2, PREPROCESSED_INPUT=4 };
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virtual void clear();
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virtual int getVarCount() const = 0;
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virtual bool isTrained() const = 0;
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virtual bool isClassifier() const = 0;
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virtual bool train( const Ptr<TrainData>& trainData, int flags=0 );
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virtual bool train( InputArray samples, int layout, InputArray responses );
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virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const;
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virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
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template<typename _Tp> static Ptr<_Tp> load(const String& filename)
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{
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FileStorage fs(filename, FileStorage::READ);
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Ptr<_Tp> model = _Tp::create();
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model->read(fs.getFirstTopLevelNode());
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return model->isTrained() ? model : Ptr<_Tp>();
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}
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template<typename _Tp> static Ptr<_Tp> train(const Ptr<TrainData>& data, const typename _Tp::Params& p, int flags=0)
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{
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Ptr<_Tp> model = _Tp::create(p);
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return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>();
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}
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template<typename _Tp> static Ptr<_Tp> train(InputArray samples, int layout, InputArray responses,
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const typename _Tp::Params& p, int flags=0)
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{
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Ptr<_Tp> model = _Tp::create(p);
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return !model.empty() && model->train(TrainData::create(samples, layout, responses), flags) ? model : Ptr<_Tp>();
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}
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virtual void save(const String& filename) const;
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virtual String getDefaultModelName() const = 0;
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};
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/****************************************************************************************\
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* Normal Bayes Classifier *
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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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class CV_EXPORTS_W NormalBayesClassifier : public StatModel
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{
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public:
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class CV_EXPORTS_W Params
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{
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public:
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Params();
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};
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virtual float predictProb( InputArray inputs, OutputArray outputs,
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OutputArray outputProbs, int flags=0 ) const = 0;
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virtual void setParams(const Params& params) = 0;
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virtual Params getParams() const = 0;
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static Ptr<NormalBayesClassifier> create(const Params& params=Params());
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};
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/****************************************************************************************\
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* K-Nearest Neighbour Classifier *
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\****************************************************************************************/
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// k Nearest Neighbors
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class CV_EXPORTS_W KNearest : public StatModel
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{
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public:
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class CV_EXPORTS_W_MAP Params
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{
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public:
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Params(int defaultK=10, bool isclassifier=true);
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CV_PROP_RW int defaultK;
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CV_PROP_RW bool isclassifier;
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};
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virtual void setParams(const Params& p) = 0;
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virtual Params getParams() const = 0;
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virtual float findNearest( InputArray samples, int k,
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OutputArray results,
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OutputArray neighborResponses=noArray(),
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OutputArray dist=noArray() ) const = 0;
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static Ptr<KNearest> create(const Params& params=Params());
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};
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/****************************************************************************************\
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* Support Vector Machines *
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\****************************************************************************************/
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// SVM model
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class CV_EXPORTS_W SVM : public StatModel
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{
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public:
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class CV_EXPORTS_W_MAP Params
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{
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public:
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Params();
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Params( 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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const Mat& classWeights, TermCriteria termCrit );
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CV_PROP_RW int svmType;
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CV_PROP_RW int kernelType;
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CV_PROP_RW double gamma, coef0, degree;
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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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CV_PROP_RW Mat classWeights; // for CV_SVM_C_SVC
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CV_PROP_RW TermCriteria termCrit; // termination criteria
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};
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class CV_EXPORTS Kernel : public Algorithm
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{
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public:
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virtual int getType() const = 0;
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virtual void calc( int vcount, int n, const float* vecs, const float* another, float* results ) = 0;
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};
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// SVM type
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enum { C_SVC=100, NU_SVC=101, ONE_CLASS=102, EPS_SVR=103, NU_SVR=104 };
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// SVM kernel type
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enum { CUSTOM=-1, LINEAR=0, POLY=1, RBF=2, SIGMOID=3, CHI2=4, INTER=5 };
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// SVM params type
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enum { C=0, GAMMA=1, P=2, NU=3, COEF=4, DEGREE=5 };
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virtual bool trainAuto( const Ptr<TrainData>& data, int kFold = 10,
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ParamGrid Cgrid = SVM::getDefaultGrid(SVM::C),
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ParamGrid gammaGrid = SVM::getDefaultGrid(SVM::GAMMA),
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ParamGrid pGrid = SVM::getDefaultGrid(SVM::P),
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ParamGrid nuGrid = SVM::getDefaultGrid(SVM::NU),
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ParamGrid coeffGrid = SVM::getDefaultGrid(SVM::COEF),
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ParamGrid degreeGrid = SVM::getDefaultGrid(SVM::DEGREE),
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bool balanced=false) = 0;
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CV_WRAP virtual Mat getSupportVectors() const = 0;
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virtual void setParams(const Params& p, const Ptr<Kernel>& customKernel=Ptr<Kernel>()) = 0;
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virtual Params getParams() const = 0;
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virtual Ptr<Kernel> getKernel() const = 0;
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virtual double getDecisionFunction(int i, OutputArray alpha, OutputArray svidx) const = 0;
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static ParamGrid getDefaultGrid( int param_id );
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static Ptr<SVM> create(const Params& p=Params(), const Ptr<Kernel>& customKernel=Ptr<Kernel>());
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};
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/****************************************************************************************\
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* Expectation - Maximization *
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\****************************************************************************************/
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class CV_EXPORTS_W EM : public StatModel
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{
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public:
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// Type of covariation matrices
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enum {COV_MAT_SPHERICAL=0, COV_MAT_DIAGONAL=1, COV_MAT_GENERIC=2, COV_MAT_DEFAULT=COV_MAT_DIAGONAL};
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// Default parameters
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enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100};
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// The initial step
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enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
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class CV_EXPORTS_W_MAP Params
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{
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public:
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explicit Params(int nclusters=DEFAULT_NCLUSTERS, int covMatType=EM::COV_MAT_DIAGONAL,
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const TermCriteria& termCrit=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,
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EM::DEFAULT_MAX_ITERS, 1e-6));
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CV_PROP_RW int nclusters;
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CV_PROP_RW int covMatType;
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CV_PROP_RW TermCriteria termCrit;
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};
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virtual void setParams(const Params& p) = 0;
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virtual Params getParams() const = 0;
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virtual Mat getWeights() const = 0;
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virtual Mat getMeans() const = 0;
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virtual void getCovs(std::vector<Mat>& covs) const = 0;
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CV_WRAP virtual Vec2d predict2(InputArray sample, OutputArray probs) const = 0;
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virtual bool train( const Ptr<TrainData>& trainData, int flags=0 ) = 0;
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static Ptr<EM> train(InputArray samples,
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OutputArray logLikelihoods=noArray(),
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OutputArray labels=noArray(),
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OutputArray probs=noArray(),
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const Params& params=Params());
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static Ptr<EM> train_startWithE(InputArray samples, InputArray means0,
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InputArray covs0=noArray(),
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InputArray weights0=noArray(),
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OutputArray logLikelihoods=noArray(),
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OutputArray labels=noArray(),
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OutputArray probs=noArray(),
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const Params& params=Params());
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static Ptr<EM> train_startWithM(InputArray samples, InputArray probs0,
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OutputArray logLikelihoods=noArray(),
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OutputArray labels=noArray(),
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OutputArray probs=noArray(),
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const Params& params=Params());
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static Ptr<EM> create(const Params& params=Params());
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};
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/****************************************************************************************\
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* Decision Tree *
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\****************************************************************************************/
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class CV_EXPORTS_W DTrees : public StatModel
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{
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public:
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enum { PREDICT_AUTO=0, PREDICT_SUM=(1<<8), PREDICT_MAX_VOTE=(2<<8), PREDICT_MASK=(3<<8) };
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class CV_EXPORTS_W_MAP Params
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{
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public:
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Params();
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Params( int maxDepth, int minSampleCount,
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double regressionAccuracy, bool useSurrogates,
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int maxCategories, int CVFolds,
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bool use1SERule, bool truncatePrunedTree,
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const Mat& priors );
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CV_PROP_RW int maxCategories;
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CV_PROP_RW int maxDepth;
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CV_PROP_RW int minSampleCount;
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CV_PROP_RW int CVFolds;
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CV_PROP_RW bool useSurrogates;
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CV_PROP_RW bool use1SERule;
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CV_PROP_RW bool truncatePrunedTree;
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CV_PROP_RW float regressionAccuracy;
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CV_PROP_RW Mat priors;
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};
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class CV_EXPORTS Node
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{
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public:
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Node();
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double value;
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int classIdx;
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int parent;
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int left;
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int right;
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int defaultDir;
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int split;
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};
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class CV_EXPORTS Split
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{
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public:
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Split();
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int varIdx;
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bool inversed;
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float quality;
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int next;
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float c;
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int subsetOfs;
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};
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virtual void setDParams(const Params& p);
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virtual Params getDParams() const;
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virtual const std::vector<int>& getRoots() const = 0;
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virtual const std::vector<Node>& getNodes() const = 0;
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virtual const std::vector<Split>& getSplits() const = 0;
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virtual const std::vector<int>& getSubsets() const = 0;
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static Ptr<DTrees> create(const Params& params=Params());
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};
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/****************************************************************************************\
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* Random Trees Classifier *
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\****************************************************************************************/
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class CV_EXPORTS_W RTrees : public DTrees
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{
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public:
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class CV_EXPORTS_W_MAP Params : public DTrees::Params
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{
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public:
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Params();
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Params( int maxDepth, int minSampleCount,
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double regressionAccuracy, bool useSurrogates,
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int maxCategories, const Mat& priors,
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bool calcVarImportance, int nactiveVars,
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TermCriteria termCrit );
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CV_PROP_RW bool calcVarImportance; // true <=> RF processes variable importance
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CV_PROP_RW int nactiveVars;
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CV_PROP_RW TermCriteria termCrit;
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};
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virtual void setRParams(const Params& p) = 0;
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virtual Params getRParams() const = 0;
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virtual Mat getVarImportance() const = 0;
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static Ptr<RTrees> create(const Params& params=Params());
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};
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/****************************************************************************************\
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* Boosted tree classifier *
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\****************************************************************************************/
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class CV_EXPORTS_W Boost : public DTrees
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{
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public:
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class CV_EXPORTS_W_MAP Params : public DTrees::Params
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{
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public:
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CV_PROP_RW int boostType;
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CV_PROP_RW int weakCount;
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CV_PROP_RW double weightTrimRate;
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Params();
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Params( int boostType, int weakCount, double weightTrimRate,
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int maxDepth, bool useSurrogates, const Mat& priors );
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};
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// Boosting type
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enum { DISCRETE=0, REAL=1, LOGIT=2, GENTLE=3 };
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virtual Params getBParams() const = 0;
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virtual void setBParams(const Params& p) = 0;
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static Ptr<Boost> create(const Params& params=Params());
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};
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/****************************************************************************************\
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* Gradient Boosted Trees *
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\****************************************************************************************/
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/*class CV_EXPORTS_W GBTrees : public DTrees
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{
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public:
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struct CV_EXPORTS_W_MAP Params : public DTrees::Params
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{
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CV_PROP_RW int weakCount;
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CV_PROP_RW int lossFunctionType;
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CV_PROP_RW float subsamplePortion;
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CV_PROP_RW float shrinkage;
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Params();
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Params( int lossFunctionType, int weakCount, float shrinkage,
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float subsamplePortion, int maxDepth, bool useSurrogates );
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};
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enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS};
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virtual void setK(int k) = 0;
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virtual float predictSerial( InputArray samples,
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OutputArray weakResponses, int flags) const = 0;
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static Ptr<GBTrees> create(const Params& p);
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};*/
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/****************************************************************************************\
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* Artificial Neural Networks (ANN) *
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\****************************************************************************************/
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/////////////////////////////////// Multi-Layer Perceptrons //////////////////////////////
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class CV_EXPORTS_W ANN_MLP : public StatModel
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{
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public:
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struct CV_EXPORTS_W_MAP Params
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{
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Params();
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Params( const Mat& layerSizes, int activateFunc, double fparam1, double fparam2,
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TermCriteria termCrit, int trainMethod, double param1, double param2=0 );
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enum { BACKPROP=0, RPROP=1 };
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CV_PROP_RW Mat layerSizes;
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CV_PROP_RW int activateFunc;
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CV_PROP_RW double fparam1;
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CV_PROP_RW double fparam2;
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CV_PROP_RW TermCriteria termCrit;
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CV_PROP_RW int trainMethod;
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// backpropagation parameters
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CV_PROP_RW double bpDWScale, bpMomentScale;
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// rprop parameters
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CV_PROP_RW double rpDW0, rpDWPlus, rpDWMinus, rpDWMin, rpDWMax;
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};
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// possible activation functions
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enum { IDENTITY = 0, SIGMOID_SYM = 1, GAUSSIAN = 2 };
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// available training flags
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enum { UPDATE_WEIGHTS = 1, NO_INPUT_SCALE = 2, NO_OUTPUT_SCALE = 4 };
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virtual Mat getWeights(int layerIdx) const = 0;
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virtual void setParams(const Params& p) = 0;
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virtual Params getParams() const = 0;
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|
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static Ptr<ANN_MLP> create(const Params& params=Params());
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};
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/*!
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Fast Nearest Neighbor Search Class.
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The class implements D. Lowe BBF (Best-Bin-First) algorithm for the last
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approximate (or accurate) nearest neighbor search in multi-dimensional spaces.
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First, a set of vectors is passed to KDTree::KDTree() constructor
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or KDTree::build() method, where it is reordered.
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Then arbitrary vectors can be passed to KDTree::findNearest() methods, which
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find the K nearest neighbors among the vectors from the initial set.
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|
The user can balance between the speed and accuracy of the search by varying Emax
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|
parameter, which is the number of leaves that the algorithm checks.
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|
The larger parameter values yield more accurate results at the expense of lower processing speed.
|
|
|
|
\code
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|
KDTree T(points, false);
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|
const int K = 3, Emax = INT_MAX;
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|
int idx[K];
|
|
float dist[K];
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|
T.findNearest(query_vec, K, Emax, idx, 0, dist);
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|
CV_Assert(dist[0] <= dist[1] && dist[1] <= dist[2]);
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|
\endcode
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|
*/
|
|
class CV_EXPORTS_W KDTree
|
|
{
|
|
public:
|
|
/*!
|
|
The node of the search tree.
|
|
*/
|
|
struct Node
|
|
{
|
|
Node() : idx(-1), left(-1), right(-1), boundary(0.f) {}
|
|
Node(int _idx, int _left, int _right, float _boundary)
|
|
: idx(_idx), left(_left), right(_right), boundary(_boundary) {}
|
|
|
|
//! split dimension; >=0 for nodes (dim), < 0 for leaves (index of the point)
|
|
int idx;
|
|
//! node indices of the left and the right branches
|
|
int left, right;
|
|
//! go to the left if query_vec[node.idx]<=node.boundary, otherwise go to the right
|
|
float boundary;
|
|
};
|
|
|
|
//! the default constructor
|
|
CV_WRAP KDTree();
|
|
//! the full constructor that builds the search tree
|
|
CV_WRAP KDTree(InputArray points, bool copyAndReorderPoints = false);
|
|
//! the full constructor that builds the search tree
|
|
CV_WRAP KDTree(InputArray points, InputArray _labels,
|
|
bool copyAndReorderPoints = false);
|
|
//! builds the search tree
|
|
CV_WRAP void build(InputArray points, bool copyAndReorderPoints = false);
|
|
//! builds the search tree
|
|
CV_WRAP void build(InputArray points, InputArray labels,
|
|
bool copyAndReorderPoints = false);
|
|
//! finds the K nearest neighbors of "vec" while looking at Emax (at most) leaves
|
|
CV_WRAP int findNearest(InputArray vec, int K, int Emax,
|
|
OutputArray neighborsIdx,
|
|
OutputArray neighbors = noArray(),
|
|
OutputArray dist = noArray(),
|
|
OutputArray labels = noArray()) const;
|
|
//! finds all the points from the initial set that belong to the specified box
|
|
CV_WRAP void findOrthoRange(InputArray minBounds,
|
|
InputArray maxBounds,
|
|
OutputArray neighborsIdx,
|
|
OutputArray neighbors = noArray(),
|
|
OutputArray labels = noArray()) const;
|
|
//! returns vectors with the specified indices
|
|
CV_WRAP void getPoints(InputArray idx, OutputArray pts,
|
|
OutputArray labels = noArray()) const;
|
|
//! return a vector with the specified index
|
|
const float* getPoint(int ptidx, int* label = 0) const;
|
|
//! returns the search space dimensionality
|
|
CV_WRAP int dims() const;
|
|
|
|
std::vector<Node> nodes; //!< all the tree nodes
|
|
CV_PROP Mat points; //!< all the points. It can be a reordered copy of the input vector set or the original vector set.
|
|
CV_PROP std::vector<int> labels; //!< the parallel array of labels.
|
|
CV_PROP int maxDepth; //!< maximum depth of the search tree. Do not modify it
|
|
CV_PROP_RW int normType; //!< type of the distance (cv::NORM_L1 or cv::NORM_L2) used for search. Initially set to cv::NORM_L2, but you can modify it
|
|
};
|
|
|
|
/****************************************************************************************\
|
|
* Auxilary functions declarations *
|
|
\****************************************************************************************/
|
|
|
|
/* Generates <sample> from multivariate normal distribution, where <mean> - is an
|
|
average row vector, <cov> - symmetric covariation matrix */
|
|
CV_EXPORTS void randMVNormal( InputArray mean, InputArray cov, int nsamples, OutputArray samples);
|
|
|
|
/* Generates sample from gaussian mixture distribution */
|
|
CV_EXPORTS void randGaussMixture( InputArray means, InputArray covs, InputArray weights,
|
|
int nsamples, OutputArray samples, OutputArray sampClasses );
|
|
|
|
/* creates test set */
|
|
CV_EXPORTS void createConcentricSpheresTestSet( int nsamples, int nfeatures, int nclasses,
|
|
OutputArray samples, OutputArray responses);
|
|
|
|
}
|
|
}
|
|
|
|
#endif // __cplusplus
|
|
#endif // __OPENCV_ML_HPP__
|
|
|
|
/* End of file. */
|