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1500 lines
70 KiB
1500 lines
70 KiB
/*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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/**
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@defgroup ml Machine Learning
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The Machine Learning Library (MLL) is a set of classes and functions for statistical
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classification, regression, and clustering of data.
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Most of the classification and regression algorithms are implemented as C++ classes. As the
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algorithms have different sets of features (like an ability to handle missing measurements or
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categorical input variables), there is a little common ground between the classes. This common
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ground is defined by the class cv::ml::StatModel that all the other ML classes are derived from.
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See detailed overview here: @ref ml_intro.
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*/
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namespace cv
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{
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namespace ml
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{
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//! @addtogroup ml
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//! @{
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/** @brief Variable types */
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enum VariableTypes
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{
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VAR_NUMERICAL =0, //!< same as VAR_ORDERED
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VAR_ORDERED =0, //!< ordered variables
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VAR_CATEGORICAL =1 //!< categorical variables
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};
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/** @brief %Error types */
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enum ErrorTypes
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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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/** @brief Sample types */
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enum SampleTypes
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{
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ROW_SAMPLE = 0, //!< each training sample is a row of samples
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COL_SAMPLE = 1 //!< each training sample occupies a column of samples
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};
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/** @brief The structure represents the logarithmic grid range of statmodel parameters.
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It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate
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being computed by cross-validation.
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*/
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class CV_EXPORTS ParamGrid
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{
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public:
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/** @brief Default constructor */
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ParamGrid();
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/** @brief Constructor with parameters */
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ParamGrid(double _minVal, double _maxVal, double _logStep);
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double minVal; //!< Minimum value of the statmodel parameter. Default value is 0.
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double maxVal; //!< Maximum value of the statmodel parameter. Default value is 0.
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/** @brief Logarithmic step for iterating the statmodel parameter.
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The grid determines the following iteration sequence of the statmodel parameter values:
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\f[(minVal, minVal*step, minVal*{step}^2, \dots, minVal*{logStep}^n),\f]
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where \f$n\f$ is the maximal index satisfying
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\f[\texttt{minVal} * \texttt{logStep} ^n < \texttt{maxVal}\f]
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The grid is logarithmic, so logStep must always be greater then 1. Default value is 1.
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*/
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double logStep;
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};
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/** @brief Class encapsulating training data.
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Please note that the class only specifies the interface of training data, but not implementation.
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All the statistical model classes in _ml_ module accepts Ptr\<TrainData\> as parameter. In other
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words, you can create your own class derived from TrainData and pass smart pointer to the instance
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of this class into StatModel::train.
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@sa @ref ml_intro_data
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*/
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class CV_EXPORTS_W 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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CV_WRAP virtual int getLayout() const = 0;
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CV_WRAP virtual int getNTrainSamples() const = 0;
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CV_WRAP virtual int getNTestSamples() const = 0;
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CV_WRAP virtual int getNSamples() const = 0;
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CV_WRAP virtual int getNVars() const = 0;
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CV_WRAP virtual int getNAllVars() const = 0;
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CV_WRAP virtual void getSample(InputArray varIdx, int sidx, float* buf) const = 0;
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CV_WRAP virtual Mat getSamples() const = 0;
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CV_WRAP virtual Mat getMissing() const = 0;
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/** @brief Returns matrix of train samples
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@param layout The requested layout. If it's different from the initial one, the matrix is
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transposed. See ml::SampleTypes.
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@param compressSamples if true, the function returns only the training samples (specified by
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sampleIdx)
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@param compressVars if true, the function returns the shorter training samples, containing only
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the active variables.
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In current implementation the function tries to avoid physical data copying and returns the
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matrix stored inside TrainData (unless the transposition or compression is needed).
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*/
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CV_WRAP 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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/** @brief Returns the vector of responses
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The function returns ordered or the original categorical responses. Usually it's used in
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regression algorithms.
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*/
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CV_WRAP virtual Mat getTrainResponses() const = 0;
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/** @brief Returns the vector of normalized categorical responses
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The function returns vector of responses. Each response is integer from `0` to `<number of
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classes>-1`. The actual label value can be retrieved then from the class label vector, see
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TrainData::getClassLabels.
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*/
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CV_WRAP virtual Mat getTrainNormCatResponses() const = 0;
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CV_WRAP virtual Mat getTestResponses() const = 0;
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CV_WRAP virtual Mat getTestNormCatResponses() const = 0;
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CV_WRAP virtual Mat getResponses() const = 0;
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CV_WRAP virtual Mat getNormCatResponses() const = 0;
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CV_WRAP virtual Mat getSampleWeights() const = 0;
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CV_WRAP virtual Mat getTrainSampleWeights() const = 0;
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CV_WRAP virtual Mat getTestSampleWeights() const = 0;
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CV_WRAP virtual Mat getVarIdx() const = 0;
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CV_WRAP virtual Mat getVarType() const = 0;
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CV_WRAP virtual int getResponseType() const = 0;
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CV_WRAP virtual Mat getTrainSampleIdx() const = 0;
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CV_WRAP virtual Mat getTestSampleIdx() const = 0;
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CV_WRAP 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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CV_WRAP virtual Mat getDefaultSubstValues() const = 0;
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CV_WRAP virtual int getCatCount(int vi) const = 0;
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/** @brief Returns the vector of class labels
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The function returns vector of unique labels occurred in the responses.
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*/
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CV_WRAP virtual Mat getClassLabels() const = 0;
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CV_WRAP virtual Mat getCatOfs() const = 0;
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CV_WRAP virtual Mat getCatMap() const = 0;
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/** @brief Splits the training data into the training and test parts
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@sa TrainData::setTrainTestSplitRatio
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*/
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CV_WRAP virtual void setTrainTestSplit(int count, bool shuffle=true) = 0;
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/** @brief Splits the training data into the training and test parts
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The function selects a subset of specified relative size and then returns it as the training
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set. If the function is not called, all the data is used for training. Please, note that for
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each of TrainData::getTrain\* there is corresponding TrainData::getTest\*, so that the test
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subset can be retrieved and processed as well.
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@sa TrainData::setTrainTestSplit
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*/
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CV_WRAP virtual void setTrainTestSplitRatio(double ratio, bool shuffle=true) = 0;
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CV_WRAP virtual void shuffleTrainTest() = 0;
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CV_WRAP static Mat getSubVector(const Mat& vec, const Mat& idx);
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/** @brief Reads the dataset from a .csv file and returns the ready-to-use training data.
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@param filename The input file name
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@param headerLineCount The number of lines in the beginning to skip; besides the header, the
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function also skips empty lines and lines staring with `#`
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@param responseStartIdx Index of the first output variable. If -1, the function considers the
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last variable as the response
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@param responseEndIdx Index of the last output variable + 1. If -1, then there is single
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response variable at responseStartIdx.
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@param varTypeSpec The optional text string that specifies the variables' types. It has the
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format `ord[n1-n2,n3,n4-n5,...]cat[n6,n7-n8,...]`. That is, variables from `n1 to n2`
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(inclusive range), `n3`, `n4 to n5` ... are considered ordered and `n6`, `n7 to n8` ... are
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considered as categorical. The range `[n1..n2] + [n3] + [n4..n5] + ... + [n6] + [n7..n8]`
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should cover all the variables. If varTypeSpec is not specified, then algorithm uses the
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following rules:
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- all input variables are considered ordered by default. If some column contains has non-
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numerical values, e.g. 'apple', 'pear', 'apple', 'apple', 'mango', the corresponding
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variable is considered categorical.
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- if there are several output variables, they are all considered as ordered. Error is
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reported when non-numerical values are used.
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- if there is a single output variable, then if its values are non-numerical or are all
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integers, then it's considered categorical. Otherwise, it's considered ordered.
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@param delimiter The character used to separate values in each line.
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@param missch The character used to specify missing measurements. It should not be a digit.
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Although it's a non-numerical value, it surely does not affect the decision of whether the
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variable ordered or categorical.
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*/
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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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/** @brief Creates training data from in-memory arrays.
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@param samples matrix of samples. It should have CV_32F type.
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@param layout see ml::SampleTypes.
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@param responses matrix of responses. If the responses are scalar, they should be stored as a
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single row or as a single column. The matrix should have type CV_32F or CV_32S (in the
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former case the responses are considered as ordered by default; in the latter case - as
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categorical)
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@param varIdx vector specifying which variables to use for training. It can be an integer vector
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(CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of
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active variables.
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@param sampleIdx vector specifying which samples to use for training. It can be an integer
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vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask
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of training samples.
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@param sampleWeights optional vector with weights for each sample. It should have CV_32F type.
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@param varType optional vector of type CV_8U and size `<number_of_variables_in_samples> +
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<number_of_variables_in_responses>`, containing types of each input and output variable. See
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ml::VariableTypes.
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*/
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CV_WRAP 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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/** @brief Base class for statistical models in OpenCV ML.
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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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/** Predict options */
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enum Flags {
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UPDATE_MODEL = 1,
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RAW_OUTPUT=1, //!< makes the method return the raw results (the sum), not the class label
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COMPRESSED_INPUT=2,
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PREPROCESSED_INPUT=4
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};
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/** @brief Returns the number of variables in training samples */
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CV_WRAP virtual int getVarCount() const = 0;
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CV_WRAP virtual bool empty() const;
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/** @brief Returns true if the model is trained */
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CV_WRAP virtual bool isTrained() const = 0;
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/** @brief Returns true if the model is classifier */
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CV_WRAP virtual bool isClassifier() const = 0;
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/** @brief Trains the statistical model
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@param trainData training data that can be loaded from file using TrainData::loadFromCSV or
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created with TrainData::create.
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@param flags optional flags, depending on the model. Some of the models can be updated with the
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new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).
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*/
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CV_WRAP virtual bool train( const Ptr<TrainData>& trainData, int flags=0 );
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/** @brief Trains the statistical model
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@param samples training samples
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@param layout See ml::SampleTypes.
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@param responses vector of responses associated with the training samples.
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*/
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CV_WRAP virtual bool train( InputArray samples, int layout, InputArray responses );
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/** @brief Computes error on the training or test dataset
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@param data the training data
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@param test if true, the error is computed over the test subset of the data, otherwise it's
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computed over the training subset of the data. Please note that if you loaded a completely
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different dataset to evaluate already trained classifier, you will probably want not to set
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the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so
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that the error is computed for the whole new set. Yes, this sounds a bit confusing.
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@param resp the optional output responses.
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The method uses StatModel::predict to compute the error. For regression models the error is
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computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).
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*/
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CV_WRAP virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const;
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/** @brief Predicts response(s) for the provided sample(s)
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@param samples The input samples, floating-point matrix
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@param results The optional output matrix of results.
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@param flags The optional flags, model-dependent. See cv::ml::StatModel::Flags.
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*/
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CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
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/** @brief Create and train model with default parameters
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The class must implement static `create()` method with no parameters or with all default parameter values
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*/
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template<typename _Tp> static Ptr<_Tp> train(const Ptr<TrainData>& data, int flags=0)
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{
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Ptr<_Tp> model = _Tp::create();
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return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>();
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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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/** @brief Bayes classifier for normally distributed data.
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@sa @ref ml_intro_bayes
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*/
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class CV_EXPORTS_W NormalBayesClassifier : public StatModel
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{
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public:
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/** @brief Predicts the response for sample(s).
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The method estimates the most probable classes for input vectors. Input vectors (one or more)
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are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one
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output vector outputs. The predicted class for a single input vector is returned by the method.
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The vector outputProbs contains the output probabilities corresponding to each element of
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result.
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*/
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CV_WRAP virtual float predictProb( InputArray inputs, OutputArray outputs,
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OutputArray outputProbs, int flags=0 ) const = 0;
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/** Creates empty model
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Use StatModel::train to train the model after creation. */
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CV_WRAP static Ptr<NormalBayesClassifier> create();
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};
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/****************************************************************************************\
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* K-Nearest Neighbour Classifier *
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\****************************************************************************************/
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/** @brief The class implements K-Nearest Neighbors model
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@sa @ref ml_intro_knn
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*/
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class CV_EXPORTS_W KNearest : public StatModel
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{
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public:
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/** Default number of neighbors to use in predict method. */
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/** @see setDefaultK */
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CV_WRAP virtual int getDefaultK() const = 0;
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/** @copybrief getDefaultK @see getDefaultK */
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CV_WRAP virtual void setDefaultK(int val) = 0;
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/** Whether classification or regression model should be trained. */
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/** @see setIsClassifier */
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CV_WRAP virtual bool getIsClassifier() const = 0;
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/** @copybrief getIsClassifier @see getIsClassifier */
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CV_WRAP virtual void setIsClassifier(bool val) = 0;
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/** Parameter for KDTree implementation. */
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/** @see setEmax */
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CV_WRAP virtual int getEmax() const = 0;
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/** @copybrief getEmax @see getEmax */
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CV_WRAP virtual void setEmax(int val) = 0;
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/** %Algorithm type, one of KNearest::Types. */
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/** @see setAlgorithmType */
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CV_WRAP virtual int getAlgorithmType() const = 0;
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/** @copybrief getAlgorithmType @see getAlgorithmType */
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CV_WRAP virtual void setAlgorithmType(int val) = 0;
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/** @brief Finds the neighbors and predicts responses for input vectors.
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@param samples Input samples stored by rows. It is a single-precision floating-point matrix of
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`<number_of_samples> * k` size.
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@param k Number of used nearest neighbors. Should be greater than 1.
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@param results Vector with results of prediction (regression or classification) for each input
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sample. It is a single-precision floating-point vector with `<number_of_samples>` elements.
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@param neighborResponses Optional output values for corresponding neighbors. It is a single-
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precision floating-point matrix of `<number_of_samples> * k` size.
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@param dist Optional output distances from the input vectors to the corresponding neighbors. It
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is a single-precision floating-point matrix of `<number_of_samples> * k` size.
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For each input vector (a row of the matrix samples), the method finds the k nearest neighbors.
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In case of regression, the predicted result is a mean value of the particular vector's neighbor
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responses. In case of classification, the class is determined by voting.
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For each input vector, the neighbors are sorted by their distances to the vector.
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In case of C++ interface you can use output pointers to empty matrices and the function will
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allocate memory itself.
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If only a single input vector is passed, all output matrices are optional and the predicted
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value is returned by the method.
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The function is parallelized with the TBB library.
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*/
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CV_WRAP 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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/** @brief Implementations of KNearest algorithm
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*/
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enum Types
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{
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BRUTE_FORCE=1,
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KDTREE=2
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};
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/** @brief Creates the empty model
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The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
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*/
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CV_WRAP static Ptr<KNearest> create();
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};
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/****************************************************************************************\
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* Support Vector Machines *
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\****************************************************************************************/
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/** @brief Support Vector Machines.
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@sa @ref ml_intro_svm
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*/
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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 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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/** Type of a %SVM formulation.
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See SVM::Types. Default value is SVM::C_SVC. */
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/** @see setType */
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CV_WRAP virtual int getType() const = 0;
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/** @copybrief getType @see getType */
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CV_WRAP virtual void setType(int val) = 0;
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/** Parameter \f$\gamma\f$ of a kernel function.
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For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1. */
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/** @see setGamma */
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CV_WRAP virtual double getGamma() const = 0;
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/** @copybrief getGamma @see getGamma */
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CV_WRAP virtual void setGamma(double val) = 0;
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/** Parameter _coef0_ of a kernel function.
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For SVM::POLY or SVM::SIGMOID. Default value is 0.*/
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/** @see setCoef0 */
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CV_WRAP virtual double getCoef0() const = 0;
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/** @copybrief getCoef0 @see getCoef0 */
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CV_WRAP virtual void setCoef0(double val) = 0;
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/** Parameter _degree_ of a kernel function.
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For SVM::POLY. Default value is 0. */
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/** @see setDegree */
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CV_WRAP virtual double getDegree() const = 0;
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/** @copybrief getDegree @see getDegree */
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CV_WRAP virtual void setDegree(double val) = 0;
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/** Parameter _C_ of a %SVM optimization problem.
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For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0. */
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/** @see setC */
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CV_WRAP virtual double getC() const = 0;
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/** @copybrief getC @see getC */
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CV_WRAP virtual void setC(double val) = 0;
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/** Parameter \f$\nu\f$ of a %SVM optimization problem.
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For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0. */
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/** @see setNu */
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CV_WRAP virtual double getNu() const = 0;
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/** @copybrief getNu @see getNu */
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CV_WRAP virtual void setNu(double val) = 0;
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/** Parameter \f$\epsilon\f$ of a %SVM optimization problem.
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For SVM::EPS_SVR. Default value is 0. */
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/** @see setP */
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CV_WRAP virtual double getP() const = 0;
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/** @copybrief getP @see getP */
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CV_WRAP virtual void setP(double val) = 0;
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/** Optional weights in the SVM::C_SVC problem, assigned to particular classes.
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They are multiplied by _C_ so the parameter _C_ of class _i_ becomes `classWeights(i) * C`. Thus
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these weights affect the misclassification penalty for different classes. The larger weight,
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the larger penalty on misclassification of data from the corresponding class. Default value is
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empty Mat. */
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/** @see setClassWeights */
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CV_WRAP virtual cv::Mat getClassWeights() const = 0;
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/** @copybrief getClassWeights @see getClassWeights */
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CV_WRAP virtual void setClassWeights(const cv::Mat &val) = 0;
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/** Termination criteria of the iterative %SVM training procedure which solves a partial
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case of constrained quadratic optimization problem.
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You can specify tolerance and/or the maximum number of iterations. Default value is
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`TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON )`; */
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/** @see setTermCriteria */
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CV_WRAP virtual cv::TermCriteria getTermCriteria() const = 0;
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/** @copybrief getTermCriteria @see getTermCriteria */
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CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0;
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/** Type of a %SVM kernel.
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See SVM::KernelTypes. Default value is SVM::RBF. */
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CV_WRAP virtual int getKernelType() const = 0;
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/** Initialize with one of predefined kernels.
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See SVM::KernelTypes. */
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CV_WRAP virtual void setKernel(int kernelType) = 0;
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/** Initialize with custom kernel.
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See SVM::Kernel class for implementation details */
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virtual void setCustomKernel(const Ptr<Kernel> &_kernel) = 0;
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//! %SVM type
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enum Types {
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/** C-Support Vector Classification. n-class classification (n \f$\geq\f$ 2), allows
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imperfect separation of classes with penalty multiplier C for outliers. */
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C_SVC=100,
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/** \f$\nu\f$-Support Vector Classification. n-class classification with possible
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imperfect separation. Parameter \f$\nu\f$ (in the range 0..1, the larger the value, the smoother
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the decision boundary) is used instead of C. */
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NU_SVC=101,
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/** Distribution Estimation (One-class %SVM). All the training data are from
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the same class, %SVM builds a boundary that separates the class from the rest of the feature
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space. */
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ONE_CLASS=102,
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/** \f$\epsilon\f$-Support Vector Regression. The distance between feature vectors
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from the training set and the fitting hyper-plane must be less than p. For outliers the
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penalty multiplier C is used. */
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EPS_SVR=103,
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/** \f$\nu\f$-Support Vector Regression. \f$\nu\f$ is used instead of p.
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See @cite LibSVM for details. */
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NU_SVR=104
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};
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/** @brief %SVM kernel type
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A comparison of different kernels on the following 2D test case with four classes. Four
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SVM::C_SVC SVMs have been trained (one against rest) with auto_train. Evaluation on three
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different kernels (SVM::CHI2, SVM::INTER, SVM::RBF). The color depicts the class with max score.
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Bright means max-score \> 0, dark means max-score \< 0.
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*/
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enum KernelTypes {
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/** Returned by SVM::getKernelType in case when custom kernel has been set */
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CUSTOM=-1,
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/** Linear kernel. No mapping is done, linear discrimination (or regression) is
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done in the original feature space. It is the fastest option. \f$K(x_i, x_j) = x_i^T x_j\f$. */
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LINEAR=0,
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/** Polynomial kernel:
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\f$K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\f$. */
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POLY=1,
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/** Radial basis function (RBF), a good choice in most cases.
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\f$K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0\f$. */
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RBF=2,
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/** Sigmoid kernel: \f$K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\f$. */
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SIGMOID=3,
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/** Exponential Chi2 kernel, similar to the RBF kernel:
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\f$K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\f$. */
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CHI2=4,
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/** Histogram intersection kernel. A fast kernel. \f$K(x_i, x_j) = min(x_i,x_j)\f$. */
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INTER=5
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};
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//! %SVM params type
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enum ParamTypes {
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C=0,
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GAMMA=1,
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P=2,
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NU=3,
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COEF=4,
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DEGREE=5
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};
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/** @brief Trains an %SVM with optimal parameters.
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@param data the training data that can be constructed using TrainData::create or
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TrainData::loadFromCSV.
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@param kFold Cross-validation parameter. The training set is divided into kFold subsets. One
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subset is used to test the model, the others form the train set. So, the %SVM algorithm is
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executed kFold times.
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@param Cgrid grid for C
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@param gammaGrid grid for gamma
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@param pGrid grid for p
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@param nuGrid grid for nu
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@param coeffGrid grid for coeff
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@param degreeGrid grid for degree
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@param balanced If true and the problem is 2-class classification then the method creates more
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balanced cross-validation subsets that is proportions between classes in subsets are close
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to such proportion in the whole train dataset.
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The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
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nu, coef0, degree. Parameters are considered optimal when the cross-validation
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estimate of the test set error is minimal.
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If there is no need to optimize a parameter, the corresponding grid step should be set to any
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value less than or equal to 1. For example, to avoid optimization in gamma, set `gammaGrid.step
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= 0`, `gammaGrid.minVal`, `gamma_grid.maxVal` as arbitrary numbers. In this case, the value
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`Gamma` is taken for gamma.
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And, finally, if the optimization in a parameter is required but the corresponding grid is
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unknown, you may call the function SVM::getDefaultGrid. To generate a grid, for example, for
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gamma, call `SVM::getDefaultGrid(SVM::GAMMA)`.
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This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
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regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
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the usual %SVM with parameters specified in params is executed.
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*/
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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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/** @brief Retrieves all the support vectors
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The method returns all the support vector as floating-point matrix, where support vectors are
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stored as matrix rows.
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*/
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CV_WRAP virtual Mat getSupportVectors() const = 0;
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/** @brief Retrieves the decision function
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@param i the index of the decision function. If the problem solved is regression, 1-class or
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2-class classification, then there will be just one decision function and the index should
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always be 0. Otherwise, in the case of N-class classification, there will be \f$N(N-1)/2\f$
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decision functions.
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@param alpha the optional output vector for weights, corresponding to different support vectors.
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In the case of linear %SVM all the alpha's will be 1's.
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@param svidx the optional output vector of indices of support vectors within the matrix of
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support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear
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%SVM each decision function consists of a single "compressed" support vector.
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The method returns rho parameter of the decision function, a scalar subtracted from the weighted
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sum of kernel responses.
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*/
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CV_WRAP virtual double getDecisionFunction(int i, OutputArray alpha, OutputArray svidx) const = 0;
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/** @brief Generates a grid for %SVM parameters.
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@param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is
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generated for the parameter with this ID.
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The function generates a grid for the specified parameter of the %SVM algorithm. The grid may be
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passed to the function SVM::trainAuto.
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*/
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static ParamGrid getDefaultGrid( int param_id );
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/** Creates empty model.
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Use StatModel::train to train the model. Since %SVM has several parameters, you may want to
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find the best parameters for your problem, it can be done with SVM::trainAuto. */
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CV_WRAP static Ptr<SVM> create();
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};
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/****************************************************************************************\
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* Expectation - Maximization *
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\****************************************************************************************/
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/** @brief The class implements the Expectation Maximization algorithm.
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@sa @ref ml_intro_em
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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 Types {
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/** A scaled identity matrix \f$\mu_k * I\f$. There is the only
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parameter \f$\mu_k\f$ to be estimated for each matrix. The option may be used in special cases,
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when the constraint is relevant, or as a first step in the optimization (for example in case
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when the data is preprocessed with PCA). The results of such preliminary estimation may be
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passed again to the optimization procedure, this time with
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covMatType=EM::COV_MAT_DIAGONAL. */
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COV_MAT_SPHERICAL=0,
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/** A diagonal matrix with positive diagonal elements. The number of
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free parameters is d for each matrix. This is most commonly used option yielding good
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estimation results. */
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COV_MAT_DIAGONAL=1,
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/** A symmetric positively defined matrix. The number of free
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parameters in each matrix is about \f$d^2/2\f$. It is not recommended to use this option, unless
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there is pretty accurate initial estimation of the parameters and/or a huge number of
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training samples. */
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COV_MAT_GENERIC=2,
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COV_MAT_DEFAULT=COV_MAT_DIAGONAL
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};
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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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/** The number of mixture components in the Gaussian mixture model.
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Default value of the parameter is EM::DEFAULT_NCLUSTERS=5. Some of %EM implementation could
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determine the optimal number of mixtures within a specified value range, but that is not the
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case in ML yet. */
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/** @see setClustersNumber */
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CV_WRAP virtual int getClustersNumber() const = 0;
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/** @copybrief getClustersNumber @see getClustersNumber */
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CV_WRAP virtual void setClustersNumber(int val) = 0;
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/** Constraint on covariance matrices which defines type of matrices.
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See EM::Types. */
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/** @see setCovarianceMatrixType */
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CV_WRAP virtual int getCovarianceMatrixType() const = 0;
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/** @copybrief getCovarianceMatrixType @see getCovarianceMatrixType */
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CV_WRAP virtual void setCovarianceMatrixType(int val) = 0;
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/** The termination criteria of the %EM algorithm.
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The %EM algorithm can be terminated by the number of iterations termCrit.maxCount (number of
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M-steps) or when relative change of likelihood logarithm is less than termCrit.epsilon. Default
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maximum number of iterations is EM::DEFAULT_MAX_ITERS=100. */
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/** @see setTermCriteria */
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CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
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/** @copybrief getTermCriteria @see getTermCriteria */
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CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0;
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/** @brief Returns weights of the mixtures
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Returns vector with the number of elements equal to the number of mixtures.
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*/
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CV_WRAP virtual Mat getWeights() const = 0;
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/** @brief Returns the cluster centers (means of the Gaussian mixture)
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Returns matrix with the number of rows equal to the number of mixtures and number of columns
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equal to the space dimensionality.
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*/
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CV_WRAP virtual Mat getMeans() const = 0;
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/** @brief Returns covariation matrices
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Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures,
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each matrix is a square floating-point matrix NxN, where N is the space dimensionality.
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*/
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virtual void getCovs(std::vector<Mat>& covs) const = 0;
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/** @brief Returns a likelihood logarithm value and an index of the most probable mixture component
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for the given sample.
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@param sample A sample for classification. It should be a one-channel matrix of
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\f$1 \times dims\f$ or \f$dims \times 1\f$ size.
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@param probs Optional output matrix that contains posterior probabilities of each component
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given the sample. It has \f$1 \times nclusters\f$ size and CV_64FC1 type.
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The method returns a two-element double vector. Zero element is a likelihood logarithm value for
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the sample. First element is an index of the most probable mixture component for the given
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sample.
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*/
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CV_WRAP CV_WRAP virtual Vec2d predict2(InputArray sample, OutputArray probs) const = 0;
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/** @brief Estimate the Gaussian mixture parameters from a samples set.
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This variation starts with Expectation step. Initial values of the model parameters will be
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estimated by the k-means algorithm.
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Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take
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responses (class labels or function values) as input. Instead, it computes the *Maximum
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Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the
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parameters inside the structure: \f$p_{i,k}\f$ in probs, \f$a_k\f$ in means , \f$S_k\f$ in
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covs[k], \f$\pi_k\f$ in weights , and optionally computes the output "class label" for each
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sample: \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most
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probable mixture component for each sample).
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The trained model can be used further for prediction, just like any other classifier. The
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trained model is similar to the NormalBayesClassifier.
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@param samples Samples from which the Gaussian mixture model will be estimated. It should be a
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one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
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it will be converted to the inner matrix of such type for the further computing.
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@param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
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each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
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@param labels The optional output "class label" for each sample:
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\f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
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mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
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@param probs The optional output matrix that contains posterior probabilities of each Gaussian
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mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
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CV_64FC1 type.
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*/
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CV_WRAP virtual bool trainEM(InputArray samples,
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OutputArray logLikelihoods=noArray(),
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OutputArray labels=noArray(),
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OutputArray probs=noArray()) = 0;
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|
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/** @brief Estimate the Gaussian mixture parameters from a samples set.
|
|
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|
This variation starts with Expectation step. You need to provide initial means \f$a_k\f$ of
|
|
mixture components. Optionally you can pass initial weights \f$\pi_k\f$ and covariance matrices
|
|
\f$S_k\f$ of mixture components.
|
|
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@param samples Samples from which the Gaussian mixture model will be estimated. It should be a
|
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one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
|
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it will be converted to the inner matrix of such type for the further computing.
|
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@param means0 Initial means \f$a_k\f$ of mixture components. It is a one-channel matrix of
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\f$nclusters \times dims\f$ size. If the matrix does not have CV_64F type it will be
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converted to the inner matrix of such type for the further computing.
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@param covs0 The vector of initial covariance matrices \f$S_k\f$ of mixture components. Each of
|
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covariance matrices is a one-channel matrix of \f$dims \times dims\f$ size. If the matrices
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do not have CV_64F type they will be converted to the inner matrices of such type for the
|
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further computing.
|
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@param weights0 Initial weights \f$\pi_k\f$ of mixture components. It should be a one-channel
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floating-point matrix with \f$1 \times nclusters\f$ or \f$nclusters \times 1\f$ size.
|
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@param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
|
|
each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
|
|
@param labels The optional output "class label" for each sample:
|
|
\f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
|
|
mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
|
|
@param probs The optional output matrix that contains posterior probabilities of each Gaussian
|
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mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
|
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CV_64FC1 type.
|
|
*/
|
|
CV_WRAP virtual bool trainE(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(),
|
|
OutputArray labels=noArray(),
|
|
OutputArray probs=noArray()) = 0;
|
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|
|
/** @brief Estimate the Gaussian mixture parameters from a samples set.
|
|
|
|
This variation starts with Maximization step. You need to provide initial probabilities
|
|
\f$p_{i,k}\f$ to use this option.
|
|
|
|
@param samples Samples from which the Gaussian mixture model will be estimated. It should be a
|
|
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
|
|
it will be converted to the inner matrix of such type for the further computing.
|
|
@param probs0
|
|
@param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
|
|
each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
|
|
@param labels The optional output "class label" for each sample:
|
|
\f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
|
|
mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
|
|
@param probs The optional output matrix that contains posterior probabilities of each Gaussian
|
|
mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
|
|
CV_64FC1 type.
|
|
*/
|
|
CV_WRAP virtual bool trainM(InputArray samples, InputArray probs0,
|
|
OutputArray logLikelihoods=noArray(),
|
|
OutputArray labels=noArray(),
|
|
OutputArray probs=noArray()) = 0;
|
|
|
|
/** Creates empty %EM model.
|
|
The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you
|
|
can use one of the EM::train\* methods or load it from file using Algorithm::load\<EM\>(filename).
|
|
*/
|
|
CV_WRAP static Ptr<EM> create();
|
|
};
|
|
|
|
/****************************************************************************************\
|
|
* Decision Tree *
|
|
\****************************************************************************************/
|
|
|
|
/** @brief The class represents a single decision tree or a collection of decision trees.
|
|
|
|
The current public interface of the class allows user to train only a single decision tree, however
|
|
the class is capable of storing multiple decision trees and using them for prediction (by summing
|
|
responses or using a voting schemes), and the derived from DTrees classes (such as RTrees and Boost)
|
|
use this capability to implement decision tree ensembles.
|
|
|
|
@sa @ref ml_intro_trees
|
|
*/
|
|
class CV_EXPORTS_W DTrees : public StatModel
|
|
{
|
|
public:
|
|
/** Predict options */
|
|
enum Flags { PREDICT_AUTO=0, PREDICT_SUM=(1<<8), PREDICT_MAX_VOTE=(2<<8), PREDICT_MASK=(3<<8) };
|
|
|
|
/** Cluster possible values of a categorical variable into K\<=maxCategories clusters to
|
|
find a suboptimal split.
|
|
If a discrete variable, on which the training procedure tries to make a split, takes more than
|
|
maxCategories values, the precise best subset estimation may take a very long time because the
|
|
algorithm is exponential. Instead, many decision trees engines (including our implementation)
|
|
try to find sub-optimal split in this case by clustering all the samples into maxCategories
|
|
clusters that is some categories are merged together. The clustering is applied only in n \>
|
|
2-class classification problems for categorical variables with N \> max_categories possible
|
|
values. In case of regression and 2-class classification the optimal split can be found
|
|
efficiently without employing clustering, thus the parameter is not used in these cases.
|
|
Default value is 10.*/
|
|
/** @see setMaxCategories */
|
|
CV_WRAP virtual int getMaxCategories() const = 0;
|
|
/** @copybrief getMaxCategories @see getMaxCategories */
|
|
CV_WRAP virtual void setMaxCategories(int val) = 0;
|
|
|
|
/** The maximum possible depth of the tree.
|
|
That is the training algorithms attempts to split a node while its depth is less than maxDepth.
|
|
The root node has zero depth. The actual depth may be smaller if the other termination criteria
|
|
are met (see the outline of the training procedure @ref ml_intro_trees "here"), and/or if the
|
|
tree is pruned. Default value is INT_MAX.*/
|
|
/** @see setMaxDepth */
|
|
CV_WRAP virtual int getMaxDepth() const = 0;
|
|
/** @copybrief getMaxDepth @see getMaxDepth */
|
|
CV_WRAP virtual void setMaxDepth(int val) = 0;
|
|
|
|
/** If the number of samples in a node is less than this parameter then the node will not be split.
|
|
|
|
Default value is 10.*/
|
|
/** @see setMinSampleCount */
|
|
CV_WRAP virtual int getMinSampleCount() const = 0;
|
|
/** @copybrief getMinSampleCount @see getMinSampleCount */
|
|
CV_WRAP virtual void setMinSampleCount(int val) = 0;
|
|
|
|
/** If CVFolds \> 1 then algorithms prunes the built decision tree using K-fold
|
|
cross-validation procedure where K is equal to CVFolds.
|
|
Default value is 10.*/
|
|
/** @see setCVFolds */
|
|
CV_WRAP virtual int getCVFolds() const = 0;
|
|
/** @copybrief getCVFolds @see getCVFolds */
|
|
CV_WRAP virtual void setCVFolds(int val) = 0;
|
|
|
|
/** If true then surrogate splits will be built.
|
|
These splits allow to work with missing data and compute variable importance correctly.
|
|
Default value is false.
|
|
@note currently it's not implemented.*/
|
|
/** @see setUseSurrogates */
|
|
CV_WRAP virtual bool getUseSurrogates() const = 0;
|
|
/** @copybrief getUseSurrogates @see getUseSurrogates */
|
|
CV_WRAP virtual void setUseSurrogates(bool val) = 0;
|
|
|
|
/** If true then a pruning will be harsher.
|
|
This will make a tree more compact and more resistant to the training data noise but a bit less
|
|
accurate. Default value is true.*/
|
|
/** @see setUse1SERule */
|
|
CV_WRAP virtual bool getUse1SERule() const = 0;
|
|
/** @copybrief getUse1SERule @see getUse1SERule */
|
|
CV_WRAP virtual void setUse1SERule(bool val) = 0;
|
|
|
|
/** If true then pruned branches are physically removed from the tree.
|
|
Otherwise they are retained and it is possible to get results from the original unpruned (or
|
|
pruned less aggressively) tree. Default value is true.*/
|
|
/** @see setTruncatePrunedTree */
|
|
CV_WRAP virtual bool getTruncatePrunedTree() const = 0;
|
|
/** @copybrief getTruncatePrunedTree @see getTruncatePrunedTree */
|
|
CV_WRAP virtual void setTruncatePrunedTree(bool val) = 0;
|
|
|
|
/** Termination criteria for regression trees.
|
|
If all absolute differences between an estimated value in a node and values of train samples
|
|
in this node are less than this parameter then the node will not be split further. Default
|
|
value is 0.01f*/
|
|
/** @see setRegressionAccuracy */
|
|
CV_WRAP virtual float getRegressionAccuracy() const = 0;
|
|
/** @copybrief getRegressionAccuracy @see getRegressionAccuracy */
|
|
CV_WRAP virtual void setRegressionAccuracy(float val) = 0;
|
|
|
|
/** @brief The array of a priori class probabilities, sorted by the class label value.
|
|
|
|
The parameter can be used to tune the decision tree preferences toward a certain class. For
|
|
example, if you want to detect some rare anomaly occurrence, the training base will likely
|
|
contain much more normal cases than anomalies, so a very good classification performance
|
|
will be achieved just by considering every case as normal. To avoid this, the priors can be
|
|
specified, where the anomaly probability is artificially increased (up to 0.5 or even
|
|
greater), so the weight of the misclassified anomalies becomes much bigger, and the tree is
|
|
adjusted properly.
|
|
|
|
You can also think about this parameter as weights of prediction categories which determine
|
|
relative weights that you give to misclassification. That is, if the weight of the first
|
|
category is 1 and the weight of the second category is 10, then each mistake in predicting
|
|
the second category is equivalent to making 10 mistakes in predicting the first category.
|
|
Default value is empty Mat.*/
|
|
/** @see setPriors */
|
|
CV_WRAP virtual cv::Mat getPriors() const = 0;
|
|
/** @copybrief getPriors @see getPriors */
|
|
CV_WRAP virtual void setPriors(const cv::Mat &val) = 0;
|
|
|
|
/** @brief The class represents a decision tree node.
|
|
*/
|
|
class CV_EXPORTS Node
|
|
{
|
|
public:
|
|
Node();
|
|
double value; //!< Value at the node: a class label in case of classification or estimated
|
|
//!< function value in case of regression.
|
|
int classIdx; //!< Class index normalized to 0..class_count-1 range and assigned to the
|
|
//!< node. It is used internally in classification trees and tree ensembles.
|
|
int parent; //!< Index of the parent node
|
|
int left; //!< Index of the left child node
|
|
int right; //!< Index of right child node
|
|
int defaultDir; //!< Default direction where to go (-1: left or +1: right). It helps in the
|
|
//!< case of missing values.
|
|
int split; //!< Index of the first split
|
|
};
|
|
|
|
/** @brief The class represents split in a decision tree.
|
|
*/
|
|
class CV_EXPORTS Split
|
|
{
|
|
public:
|
|
Split();
|
|
int varIdx; //!< Index of variable on which the split is created.
|
|
bool inversed; //!< If true, then the inverse split rule is used (i.e. left and right
|
|
//!< branches are exchanged in the rule expressions below).
|
|
float quality; //!< The split quality, a positive number. It is used to choose the best split.
|
|
int next; //!< Index of the next split in the list of splits for the node
|
|
float c; /**< The threshold value in case of split on an ordered variable.
|
|
The rule is:
|
|
@code{.none}
|
|
if var_value < c
|
|
then next_node <- left
|
|
else next_node <- right
|
|
@endcode */
|
|
int subsetOfs; /**< Offset of the bitset used by the split on a categorical variable.
|
|
The rule is:
|
|
@code{.none}
|
|
if bitset[var_value] == 1
|
|
then next_node <- left
|
|
else next_node <- right
|
|
@endcode */
|
|
};
|
|
|
|
/** @brief Returns indices of root nodes
|
|
*/
|
|
virtual const std::vector<int>& getRoots() const = 0;
|
|
/** @brief Returns all the nodes
|
|
|
|
all the node indices are indices in the returned vector
|
|
*/
|
|
virtual const std::vector<Node>& getNodes() const = 0;
|
|
/** @brief Returns all the splits
|
|
|
|
all the split indices are indices in the returned vector
|
|
*/
|
|
virtual const std::vector<Split>& getSplits() const = 0;
|
|
/** @brief Returns all the bitsets for categorical splits
|
|
|
|
Split::subsetOfs is an offset in the returned vector
|
|
*/
|
|
virtual const std::vector<int>& getSubsets() const = 0;
|
|
|
|
/** @brief Creates the empty model
|
|
|
|
The static method creates empty decision tree with the specified parameters. It should be then
|
|
trained using train method (see StatModel::train). Alternatively, you can load the model from
|
|
file using Algorithm::load\<DTrees\>(filename).
|
|
*/
|
|
CV_WRAP static Ptr<DTrees> create();
|
|
};
|
|
|
|
/****************************************************************************************\
|
|
* Random Trees Classifier *
|
|
\****************************************************************************************/
|
|
|
|
/** @brief The class implements the random forest predictor.
|
|
|
|
@sa @ref ml_intro_rtrees
|
|
*/
|
|
class CV_EXPORTS_W RTrees : public DTrees
|
|
{
|
|
public:
|
|
|
|
/** If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance.
|
|
Default value is false.*/
|
|
/** @see setCalculateVarImportance */
|
|
CV_WRAP virtual bool getCalculateVarImportance() const = 0;
|
|
/** @copybrief getCalculateVarImportance @see getCalculateVarImportance */
|
|
CV_WRAP virtual void setCalculateVarImportance(bool val) = 0;
|
|
|
|
/** The size of the randomly selected subset of features at each tree node and that are used
|
|
to find the best split(s).
|
|
If you set it to 0 then the size will be set to the square root of the total number of
|
|
features. Default value is 0.*/
|
|
/** @see setActiveVarCount */
|
|
CV_WRAP virtual int getActiveVarCount() const = 0;
|
|
/** @copybrief getActiveVarCount @see getActiveVarCount */
|
|
CV_WRAP virtual void setActiveVarCount(int val) = 0;
|
|
|
|
/** The termination criteria that specifies when the training algorithm stops.
|
|
Either when the specified number of trees is trained and added to the ensemble or when
|
|
sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the
|
|
better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes
|
|
pass a certain number of trees. Also to keep in mind, the number of tree increases the
|
|
prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS +
|
|
TermCriteria::EPS, 50, 0.1)*/
|
|
/** @see setTermCriteria */
|
|
CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
|
|
/** @copybrief getTermCriteria @see getTermCriteria */
|
|
CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0;
|
|
|
|
/** Returns the variable importance array.
|
|
The method returns the variable importance vector, computed at the training stage when
|
|
CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is
|
|
returned.
|
|
*/
|
|
CV_WRAP virtual Mat getVarImportance() const = 0;
|
|
|
|
/** Creates the empty model.
|
|
Use StatModel::train to train the model, StatModel::train to create and train the model,
|
|
Algorithm::load to load the pre-trained model.
|
|
*/
|
|
CV_WRAP static Ptr<RTrees> create();
|
|
};
|
|
|
|
/****************************************************************************************\
|
|
* Boosted tree classifier *
|
|
\****************************************************************************************/
|
|
|
|
/** @brief Boosted tree classifier derived from DTrees
|
|
|
|
@sa @ref ml_intro_boost
|
|
*/
|
|
class CV_EXPORTS_W Boost : public DTrees
|
|
{
|
|
public:
|
|
/** Type of the boosting algorithm.
|
|
See Boost::Types. Default value is Boost::REAL. */
|
|
/** @see setBoostType */
|
|
CV_WRAP virtual int getBoostType() const = 0;
|
|
/** @copybrief getBoostType @see getBoostType */
|
|
CV_WRAP virtual void setBoostType(int val) = 0;
|
|
|
|
/** The number of weak classifiers.
|
|
Default value is 100. */
|
|
/** @see setWeakCount */
|
|
CV_WRAP virtual int getWeakCount() const = 0;
|
|
/** @copybrief getWeakCount @see getWeakCount */
|
|
CV_WRAP virtual void setWeakCount(int val) = 0;
|
|
|
|
/** A threshold between 0 and 1 used to save computational time.
|
|
Samples with summary weight \f$\leq 1 - weight_trim_rate\f$ do not participate in the *next*
|
|
iteration of training. Set this parameter to 0 to turn off this functionality. Default value is 0.95.*/
|
|
/** @see setWeightTrimRate */
|
|
CV_WRAP virtual double getWeightTrimRate() const = 0;
|
|
/** @copybrief getWeightTrimRate @see getWeightTrimRate */
|
|
CV_WRAP virtual void setWeightTrimRate(double val) = 0;
|
|
|
|
/** Boosting type.
|
|
Gentle AdaBoost and Real AdaBoost are often the preferable choices. */
|
|
enum Types {
|
|
DISCRETE=0, //!< Discrete AdaBoost.
|
|
REAL=1, //!< Real AdaBoost. It is a technique that utilizes confidence-rated predictions
|
|
//!< and works well with categorical data.
|
|
LOGIT=2, //!< LogitBoost. It can produce good regression fits.
|
|
GENTLE=3 //!< Gentle AdaBoost. It puts less weight on outlier data points and for that
|
|
//!<reason is often good with regression data.
|
|
};
|
|
|
|
/** Creates the empty model.
|
|
Use StatModel::train to train the model, Algorithm::load\<Boost\>(filename) to load the pre-trained model. */
|
|
CV_WRAP static Ptr<Boost> create();
|
|
};
|
|
|
|
/****************************************************************************************\
|
|
* Gradient Boosted Trees *
|
|
\****************************************************************************************/
|
|
|
|
/*class CV_EXPORTS_W GBTrees : public DTrees
|
|
{
|
|
public:
|
|
struct CV_EXPORTS_W_MAP Params : public DTrees::Params
|
|
{
|
|
CV_PROP_RW int weakCount;
|
|
CV_PROP_RW int lossFunctionType;
|
|
CV_PROP_RW float subsamplePortion;
|
|
CV_PROP_RW float shrinkage;
|
|
|
|
Params();
|
|
Params( int lossFunctionType, int weakCount, float shrinkage,
|
|
float subsamplePortion, int maxDepth, bool useSurrogates );
|
|
};
|
|
|
|
enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS};
|
|
|
|
virtual void setK(int k) = 0;
|
|
|
|
virtual float predictSerial( InputArray samples,
|
|
OutputArray weakResponses, int flags) const = 0;
|
|
|
|
static Ptr<GBTrees> create(const Params& p);
|
|
};*/
|
|
|
|
/****************************************************************************************\
|
|
* Artificial Neural Networks (ANN) *
|
|
\****************************************************************************************/
|
|
|
|
/////////////////////////////////// Multi-Layer Perceptrons //////////////////////////////
|
|
|
|
/** @brief Artificial Neural Networks - Multi-Layer Perceptrons.
|
|
|
|
Unlike many other models in ML that are constructed and trained at once, in the MLP model these
|
|
steps are separated. First, a network with the specified topology is created using the non-default
|
|
constructor or the method ANN_MLP::create. All the weights are set to zeros. Then, the network is
|
|
trained using a set of input and output vectors. The training procedure can be repeated more than
|
|
once, that is, the weights can be adjusted based on the new training data.
|
|
|
|
Additional flags for StatModel::train are available: ANN_MLP::TrainFlags.
|
|
|
|
@sa @ref ml_intro_ann
|
|
*/
|
|
class CV_EXPORTS_W ANN_MLP : public StatModel
|
|
{
|
|
public:
|
|
/** Available training methods */
|
|
enum TrainingMethods {
|
|
BACKPROP=0, //!< The back-propagation algorithm.
|
|
RPROP=1 //!< The RPROP algorithm. See @cite RPROP93 for details.
|
|
};
|
|
|
|
/** Sets training method and common parameters.
|
|
@param method Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.
|
|
@param param1 passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP
|
|
@param param2 passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP.
|
|
*/
|
|
CV_WRAP virtual void setTrainMethod(int method, double param1 = 0, double param2 = 0) = 0;
|
|
|
|
/** Returns current training method */
|
|
CV_WRAP virtual int getTrainMethod() const = 0;
|
|
|
|
/** Initialize the activation function for each neuron.
|
|
Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.
|
|
@param type The type of activation function. See ANN_MLP::ActivationFunctions.
|
|
@param param1 The first parameter of the activation function, \f$\alpha\f$. Default value is 0.
|
|
@param param2 The second parameter of the activation function, \f$\beta\f$. Default value is 0.
|
|
*/
|
|
CV_WRAP virtual void setActivationFunction(int type, double param1 = 0, double param2 = 0) = 0;
|
|
|
|
/** Integer vector specifying the number of neurons in each layer including the input and output layers.
|
|
The very first element specifies the number of elements in the input layer.
|
|
The last element - number of elements in the output layer. Default value is empty Mat.
|
|
@sa getLayerSizes */
|
|
CV_WRAP virtual void setLayerSizes(InputArray _layer_sizes) = 0;
|
|
|
|
/** Integer vector specifying the number of neurons in each layer including the input and output layers.
|
|
The very first element specifies the number of elements in the input layer.
|
|
The last element - number of elements in the output layer.
|
|
@sa setLayerSizes */
|
|
CV_WRAP virtual cv::Mat getLayerSizes() const = 0;
|
|
|
|
/** Termination criteria of the training algorithm.
|
|
You can specify the maximum number of iterations (maxCount) and/or how much the error could
|
|
change between the iterations to make the algorithm continue (epsilon). Default value is
|
|
TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 0.01).*/
|
|
/** @see setTermCriteria */
|
|
CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
|
|
/** @copybrief getTermCriteria @see getTermCriteria */
|
|
CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0;
|
|
|
|
/** BPROP: Strength of the weight gradient term.
|
|
The recommended value is about 0.1. Default value is 0.1.*/
|
|
/** @see setBackpropWeightScale */
|
|
CV_WRAP virtual double getBackpropWeightScale() const = 0;
|
|
/** @copybrief getBackpropWeightScale @see getBackpropWeightScale */
|
|
CV_WRAP virtual void setBackpropWeightScale(double val) = 0;
|
|
|
|
/** BPROP: Strength of the momentum term (the difference between weights on the 2 previous iterations).
|
|
This parameter provides some inertia to smooth the random fluctuations of the weights. It can
|
|
vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough.
|
|
Default value is 0.1.*/
|
|
/** @see setBackpropMomentumScale */
|
|
CV_WRAP virtual double getBackpropMomentumScale() const = 0;
|
|
/** @copybrief getBackpropMomentumScale @see getBackpropMomentumScale */
|
|
CV_WRAP virtual void setBackpropMomentumScale(double val) = 0;
|
|
|
|
/** RPROP: Initial value \f$\Delta_0\f$ of update-values \f$\Delta_{ij}\f$.
|
|
Default value is 0.1.*/
|
|
/** @see setRpropDW0 */
|
|
CV_WRAP virtual double getRpropDW0() const = 0;
|
|
/** @copybrief getRpropDW0 @see getRpropDW0 */
|
|
CV_WRAP virtual void setRpropDW0(double val) = 0;
|
|
|
|
/** RPROP: Increase factor \f$\eta^+\f$.
|
|
It must be \>1. Default value is 1.2.*/
|
|
/** @see setRpropDWPlus */
|
|
CV_WRAP virtual double getRpropDWPlus() const = 0;
|
|
/** @copybrief getRpropDWPlus @see getRpropDWPlus */
|
|
CV_WRAP virtual void setRpropDWPlus(double val) = 0;
|
|
|
|
/** RPROP: Decrease factor \f$\eta^-\f$.
|
|
It must be \<1. Default value is 0.5.*/
|
|
/** @see setRpropDWMinus */
|
|
CV_WRAP virtual double getRpropDWMinus() const = 0;
|
|
/** @copybrief getRpropDWMinus @see getRpropDWMinus */
|
|
CV_WRAP virtual void setRpropDWMinus(double val) = 0;
|
|
|
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/** RPROP: Update-values lower limit \f$\Delta_{min}\f$.
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It must be positive. Default value is FLT_EPSILON.*/
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/** @see setRpropDWMin */
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CV_WRAP virtual double getRpropDWMin() const = 0;
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/** @copybrief getRpropDWMin @see getRpropDWMin */
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CV_WRAP virtual void setRpropDWMin(double val) = 0;
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|
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/** RPROP: Update-values upper limit \f$\Delta_{max}\f$.
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It must be \>1. Default value is 50.*/
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/** @see setRpropDWMax */
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CV_WRAP virtual double getRpropDWMax() const = 0;
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/** @copybrief getRpropDWMax @see getRpropDWMax */
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CV_WRAP virtual void setRpropDWMax(double val) = 0;
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|
|
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/** possible activation functions */
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|
enum ActivationFunctions {
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/** Identity function: \f$f(x)=x\f$ */
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IDENTITY = 0,
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/** Symmetrical sigmoid: \f$f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x}\f$
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|
@note
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|
If you are using the default sigmoid activation function with the default parameter values
|
|
fparam1=0 and fparam2=0 then the function used is y = 1.7159\*tanh(2/3 \* x), so the output
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|
will range from [-1.7159, 1.7159], instead of [0,1].*/
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SIGMOID_SYM = 1,
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/** Gaussian function: \f$f(x)=\beta e^{-\alpha x*x}\f$ */
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|
GAUSSIAN = 2
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};
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|
|
|
/** Train options */
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|
enum TrainFlags {
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|
/** Update the network weights, rather than compute them from scratch. In the latter case
|
|
the weights are initialized using the Nguyen-Widrow algorithm. */
|
|
UPDATE_WEIGHTS = 1,
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|
/** Do not normalize the input vectors. If this flag is not set, the training algorithm
|
|
normalizes each input feature independently, shifting its mean value to 0 and making the
|
|
standard deviation equal to 1. If the network is assumed to be updated frequently, the new
|
|
training data could be much different from original one. In this case, you should take care
|
|
of proper normalization. */
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|
NO_INPUT_SCALE = 2,
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|
/** Do not normalize the output vectors. If the flag is not set, the training algorithm
|
|
normalizes each output feature independently, by transforming it to the certain range
|
|
depending on the used activation function. */
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|
NO_OUTPUT_SCALE = 4
|
|
};
|
|
|
|
CV_WRAP virtual Mat getWeights(int layerIdx) const = 0;
|
|
|
|
/** @brief Creates empty model
|
|
|
|
Use StatModel::train to train the model, Algorithm::load\<ANN_MLP\>(filename) to load the pre-trained model.
|
|
Note that the train method has optional flags: ANN_MLP::TrainFlags.
|
|
*/
|
|
CV_WRAP static Ptr<ANN_MLP> create();
|
|
};
|
|
|
|
/****************************************************************************************\
|
|
* Logistic Regression *
|
|
\****************************************************************************************/
|
|
|
|
/** @brief Implements Logistic Regression classifier.
|
|
|
|
@sa @ref ml_intro_lr
|
|
*/
|
|
class CV_EXPORTS_W LogisticRegression : public StatModel
|
|
{
|
|
public:
|
|
|
|
/** Learning rate. */
|
|
/** @see setLearningRate */
|
|
CV_WRAP virtual double getLearningRate() const = 0;
|
|
/** @copybrief getLearningRate @see getLearningRate */
|
|
CV_WRAP virtual void setLearningRate(double val) = 0;
|
|
|
|
/** Number of iterations. */
|
|
/** @see setIterations */
|
|
CV_WRAP virtual int getIterations() const = 0;
|
|
/** @copybrief getIterations @see getIterations */
|
|
CV_WRAP virtual void setIterations(int val) = 0;
|
|
|
|
/** Kind of regularization to be applied. See LogisticRegression::RegKinds. */
|
|
/** @see setRegularization */
|
|
CV_WRAP virtual int getRegularization() const = 0;
|
|
/** @copybrief getRegularization @see getRegularization */
|
|
CV_WRAP virtual void setRegularization(int val) = 0;
|
|
|
|
/** Kind of training method used. See LogisticRegression::Methods. */
|
|
/** @see setTrainMethod */
|
|
CV_WRAP virtual int getTrainMethod() const = 0;
|
|
/** @copybrief getTrainMethod @see getTrainMethod */
|
|
CV_WRAP virtual void setTrainMethod(int val) = 0;
|
|
|
|
/** Specifies the number of training samples taken in each step of Mini-Batch Gradient
|
|
Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It
|
|
has to take values less than the total number of training samples. */
|
|
/** @see setMiniBatchSize */
|
|
CV_WRAP virtual int getMiniBatchSize() const = 0;
|
|
/** @copybrief getMiniBatchSize @see getMiniBatchSize */
|
|
CV_WRAP virtual void setMiniBatchSize(int val) = 0;
|
|
|
|
/** Termination criteria of the algorithm. */
|
|
/** @see setTermCriteria */
|
|
CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
|
|
/** @copybrief getTermCriteria @see getTermCriteria */
|
|
CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0;
|
|
|
|
//! Regularization kinds
|
|
enum RegKinds {
|
|
REG_DISABLE = -1, //!< Regularization disabled
|
|
REG_L1 = 0, //!< %L1 norm
|
|
REG_L2 = 1 //!< %L2 norm
|
|
};
|
|
|
|
//! Training methods
|
|
enum Methods {
|
|
BATCH = 0,
|
|
MINI_BATCH = 1 //!< Set MiniBatchSize to a positive integer when using this method.
|
|
};
|
|
|
|
/** @brief Predicts responses for input samples and returns a float type.
|
|
|
|
@param samples The input data for the prediction algorithm. Matrix [m x n], where each row
|
|
contains variables (features) of one object being classified. Should have data type CV_32F.
|
|
@param results Predicted labels as a column matrix of type CV_32S.
|
|
@param flags Not used.
|
|
*/
|
|
CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
|
|
|
|
/** @brief This function returns the trained paramters arranged across rows.
|
|
|
|
For a two class classifcation problem, it returns a row matrix. It returns learnt paramters of
|
|
the Logistic Regression as a matrix of type CV_32F.
|
|
*/
|
|
CV_WRAP virtual Mat get_learnt_thetas() const = 0;
|
|
|
|
/** @brief Creates empty model.
|
|
|
|
Creates Logistic Regression model with parameters given.
|
|
*/
|
|
CV_WRAP static Ptr<LogisticRegression> create();
|
|
};
|
|
|
|
/****************************************************************************************\
|
|
* Auxilary functions declarations *
|
|
\****************************************************************************************/
|
|
|
|
/** @brief Generates _sample_ from multivariate normal distribution
|
|
|
|
@param mean an average row vector
|
|
@param cov symmetric covariation matrix
|
|
@param nsamples returned samples count
|
|
@param samples returned samples array
|
|
*/
|
|
CV_EXPORTS void randMVNormal( InputArray mean, InputArray cov, int nsamples, OutputArray samples);
|
|
|
|
/** @brief Generates sample from gaussian mixture distribution */
|
|
CV_EXPORTS void randGaussMixture( InputArray means, InputArray covs, InputArray weights,
|
|
int nsamples, OutputArray samples, OutputArray sampClasses );
|
|
|
|
/** @brief Creates test set */
|
|
CV_EXPORTS void createConcentricSpheresTestSet( int nsamples, int nfeatures, int nclasses,
|
|
OutputArray samples, OutputArray responses);
|
|
|
|
//! @} ml
|
|
|
|
}
|
|
}
|
|
|
|
#endif // __cplusplus
|
|
#endif // __OPENCV_ML_HPP__
|
|
|
|
/* End of file. */
|