Joe Verbout
opencv on mbed
Diff: opencv2/ml.hpp
- Revision:
- 0:ea44dc9ed014
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/opencv2/ml.hpp Thu Mar 31 21:16:38 2016 +0000
@@ -0,0 +1,1506 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+// By downloading, copying, installing or using the software you agree to this license.
+// If you do not agree to this license, do not download, install,
+// copy or use the software.
+//
+//
+// License Agreement
+// For Open Source Computer Vision Library
+//
+// Copyright (C) 2000, Intel Corporation, all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2014, Itseez Inc, all rights reserved.
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+//
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+// this list of conditions and the following disclaimer in the documentation
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+//
+// * The name of the copyright holders may not be used to endorse or promote products
+// derived from this software without specific prior written permission.
+//
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+// indirect, incidental, special, exemplary, or consequential damages
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+// and on any theory of liability, whether in contract, strict liability,
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+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_ML_HPP__
+#define __OPENCV_ML_HPP__
+
+#ifdef __cplusplus
+# include "opencv2/core.hpp"
+#endif
+
+#ifdef __cplusplus
+
+#include <float.h>
+#include <map>
+#include <iostream>
+
+/**
+ @defgroup ml Machine Learning
+
+ The Machine Learning Library (MLL) is a set of classes and functions for statistical
+ classification, regression, and clustering of data.
+
+ Most of the classification and regression algorithms are implemented as C++ classes. As the
+ algorithms have different sets of features (like an ability to handle missing measurements or
+ categorical input variables), there is a little common ground between the classes. This common
+ ground is defined by the class cv::ml::StatModel that all the other ML classes are derived from.
+
+ See detailed overview here: @ref ml_intro.
+ */
+
+namespace cv
+{
+
+namespace ml
+{
+
+//! @addtogroup ml
+//! @{
+
+/** @brief Variable types */
+enum VariableTypes
+{
+ VAR_NUMERICAL =0, //!< same as VAR_ORDERED
+ VAR_ORDERED =0, //!< ordered variables
+ VAR_CATEGORICAL =1 //!< categorical variables
+};
+
+/** @brief %Error types */
+enum ErrorTypes
+{
+ TEST_ERROR = 0,
+ TRAIN_ERROR = 1
+};
+
+/** @brief Sample types */
+enum SampleTypes
+{
+ ROW_SAMPLE = 0, //!< each training sample is a row of samples
+ COL_SAMPLE = 1 //!< each training sample occupies a column of samples
+};
+
+/** @brief The structure represents the logarithmic grid range of statmodel parameters.
+
+It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate
+being computed by cross-validation.
+ */
+class CV_EXPORTS ParamGrid
+{
+public:
+ /** @brief Default constructor */
+ ParamGrid();
+ /** @brief Constructor with parameters */
+ ParamGrid(double _minVal, double _maxVal, double _logStep);
+
+ double minVal; //!< Minimum value of the statmodel parameter. Default value is 0.
+ double maxVal; //!< Maximum value of the statmodel parameter. Default value is 0.
+ /** @brief Logarithmic step for iterating the statmodel parameter.
+
+ The grid determines the following iteration sequence of the statmodel parameter values:
+ \f[(minVal, minVal*step, minVal*{step}^2, \dots, minVal*{logStep}^n),\f]
+ where \f$n\f$ is the maximal index satisfying
+ \f[\texttt{minVal} * \texttt{logStep} ^n < \texttt{maxVal}\f]
+ The grid is logarithmic, so logStep must always be greater then 1. Default value is 1.
+ */
+ double logStep;
+};
+
+/** @brief Class encapsulating training data.
+
+Please note that the class only specifies the interface of training data, but not implementation.
+All the statistical model classes in _ml_ module accepts Ptr\<TrainData\> as parameter. In other
+words, you can create your own class derived from TrainData and pass smart pointer to the instance
+of this class into StatModel::train.
+
+@sa @ref ml_intro_data
+ */
+class CV_EXPORTS_W TrainData
+{
+public:
+ static inline float missingValue() { return FLT_MAX; }
+ virtual ~TrainData();
+
+ CV_WRAP virtual int getLayout() const = 0;
+ CV_WRAP virtual int getNTrainSamples() const = 0;
+ CV_WRAP virtual int getNTestSamples() const = 0;
+ CV_WRAP virtual int getNSamples() const = 0;
+ CV_WRAP virtual int getNVars() const = 0;
+ CV_WRAP virtual int getNAllVars() const = 0;
+
+ CV_WRAP virtual void getSample(InputArray varIdx, int sidx, float* buf) const = 0;
+ CV_WRAP virtual Mat getSamples() const = 0;
+ CV_WRAP virtual Mat getMissing() const = 0;
+
+ /** @brief Returns matrix of train samples
+
+ @param layout The requested layout. If it's different from the initial one, the matrix is
+ transposed. See ml::SampleTypes.
+ @param compressSamples if true, the function returns only the training samples (specified by
+ sampleIdx)
+ @param compressVars if true, the function returns the shorter training samples, containing only
+ the active variables.
+
+ In current implementation the function tries to avoid physical data copying and returns the
+ matrix stored inside TrainData (unless the transposition or compression is needed).
+ */
+ CV_WRAP virtual Mat getTrainSamples(int layout=ROW_SAMPLE,
+ bool compressSamples=true,
+ bool compressVars=true) const = 0;
+
+ /** @brief Returns the vector of responses
+
+ The function returns ordered or the original categorical responses. Usually it's used in
+ regression algorithms.
+ */
+ CV_WRAP virtual Mat getTrainResponses() const = 0;
+
+ /** @brief Returns the vector of normalized categorical responses
+
+ The function returns vector of responses. Each response is integer from `0` to `<number of
+ classes>-1`. The actual label value can be retrieved then from the class label vector, see
+ TrainData::getClassLabels.
+ */
+ CV_WRAP virtual Mat getTrainNormCatResponses() const = 0;
+ CV_WRAP virtual Mat getTestResponses() const = 0;
+ CV_WRAP virtual Mat getTestNormCatResponses() const = 0;
+ CV_WRAP virtual Mat getResponses() const = 0;
+ CV_WRAP virtual Mat getNormCatResponses() const = 0;
+ CV_WRAP virtual Mat getSampleWeights() const = 0;
+ CV_WRAP virtual Mat getTrainSampleWeights() const = 0;
+ CV_WRAP virtual Mat getTestSampleWeights() const = 0;
+ CV_WRAP virtual Mat getVarIdx() const = 0;
+ CV_WRAP virtual Mat getVarType() const = 0;
+ CV_WRAP virtual int getResponseType() const = 0;
+ CV_WRAP virtual Mat getTrainSampleIdx() const = 0;
+ CV_WRAP virtual Mat getTestSampleIdx() const = 0;
+ CV_WRAP virtual void getValues(int vi, InputArray sidx, float* values) const = 0;
+ virtual void getNormCatValues(int vi, InputArray sidx, int* values) const = 0;
+ CV_WRAP virtual Mat getDefaultSubstValues() const = 0;
+
+ CV_WRAP virtual int getCatCount(int vi) const = 0;
+
+ /** @brief Returns the vector of class labels
+
+ The function returns vector of unique labels occurred in the responses.
+ */
+ CV_WRAP virtual Mat getClassLabels() const = 0;
+
+ CV_WRAP virtual Mat getCatOfs() const = 0;
+ CV_WRAP virtual Mat getCatMap() const = 0;
+
+ /** @brief Splits the training data into the training and test parts
+ @sa TrainData::setTrainTestSplitRatio
+ */
+ CV_WRAP virtual void setTrainTestSplit(int count, bool shuffle=true) = 0;
+
+ /** @brief Splits the training data into the training and test parts
+
+ The function selects a subset of specified relative size and then returns it as the training
+ set. If the function is not called, all the data is used for training. Please, note that for
+ each of TrainData::getTrain\* there is corresponding TrainData::getTest\*, so that the test
+ subset can be retrieved and processed as well.
+ @sa TrainData::setTrainTestSplit
+ */
+ CV_WRAP virtual void setTrainTestSplitRatio(double ratio, bool shuffle=true) = 0;
+ CV_WRAP virtual void shuffleTrainTest() = 0;
+
+ CV_WRAP static Mat getSubVector(const Mat& vec, const Mat& idx);
+
+ /** @brief Reads the dataset from a .csv file and returns the ready-to-use training data.
+
+ @param filename The input file name
+ @param headerLineCount The number of lines in the beginning to skip; besides the header, the
+ function also skips empty lines and lines staring with `#`
+ @param responseStartIdx Index of the first output variable. If -1, the function considers the
+ last variable as the response
+ @param responseEndIdx Index of the last output variable + 1. If -1, then there is single
+ response variable at responseStartIdx.
+ @param varTypeSpec The optional text string that specifies the variables' types. It has the
+ format `ord[n1-n2,n3,n4-n5,...]cat[n6,n7-n8,...]`. That is, variables from `n1 to n2`
+ (inclusive range), `n3`, `n4 to n5` ... are considered ordered and `n6`, `n7 to n8` ... are
+ considered as categorical. The range `[n1..n2] + [n3] + [n4..n5] + ... + [n6] + [n7..n8]`
+ should cover all the variables. If varTypeSpec is not specified, then algorithm uses the
+ following rules:
+ - all input variables are considered ordered by default. If some column contains has non-
+ numerical values, e.g. 'apple', 'pear', 'apple', 'apple', 'mango', the corresponding
+ variable is considered categorical.
+ - if there are several output variables, they are all considered as ordered. Error is
+ reported when non-numerical values are used.
+ - if there is a single output variable, then if its values are non-numerical or are all
+ integers, then it's considered categorical. Otherwise, it's considered ordered.
+ @param delimiter The character used to separate values in each line.
+ @param missch The character used to specify missing measurements. It should not be a digit.
+ Although it's a non-numerical value, it surely does not affect the decision of whether the
+ variable ordered or categorical.
+ @note If the dataset only contains input variables and no responses, use responseStartIdx = -2
+ and responseEndIdx = 0. The output variables vector will just contain zeros.
+ */
+ static Ptr<TrainData> loadFromCSV(const String& filename,
+ int headerLineCount,
+ int responseStartIdx=-1,
+ int responseEndIdx=-1,
+ const String& varTypeSpec=String(),
+ char delimiter=',',
+ char missch='?');
+
+ /** @brief Creates training data from in-memory arrays.
+
+ @param samples matrix of samples. It should have CV_32F type.
+ @param layout see ml::SampleTypes.
+ @param responses matrix of responses. If the responses are scalar, they should be stored as a
+ single row or as a single column. The matrix should have type CV_32F or CV_32S (in the
+ former case the responses are considered as ordered by default; in the latter case - as
+ categorical)
+ @param varIdx vector specifying which variables to use for training. It can be an integer vector
+ (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of
+ active variables.
+ @param sampleIdx vector specifying which samples to use for training. It can be an integer
+ vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask
+ of training samples.
+ @param sampleWeights optional vector with weights for each sample. It should have CV_32F type.
+ @param varType optional vector of type CV_8U and size `<number_of_variables_in_samples> +
+ <number_of_variables_in_responses>`, containing types of each input and output variable. See
+ ml::VariableTypes.
+ */
+ CV_WRAP static Ptr<TrainData> create(InputArray samples, int layout, InputArray responses,
+ InputArray varIdx=noArray(), InputArray sampleIdx=noArray(),
+ InputArray sampleWeights=noArray(), InputArray varType=noArray());
+};
+
+/** @brief Base class for statistical models in OpenCV ML.
+ */
+class CV_EXPORTS_W StatModel : public Algorithm
+{
+public:
+ /** Predict options */
+ enum Flags {
+ UPDATE_MODEL = 1,
+ RAW_OUTPUT=1, //!< makes the method return the raw results (the sum), not the class label
+ COMPRESSED_INPUT=2,
+ PREPROCESSED_INPUT=4
+ };
+
+ /** @brief Returns the number of variables in training samples */
+ CV_WRAP virtual int getVarCount() const = 0;
+
+ CV_WRAP virtual bool empty() const;
+
+ /** @brief Returns true if the model is trained */
+ CV_WRAP virtual bool isTrained() const = 0;
+ /** @brief Returns true if the model is classifier */
+ CV_WRAP virtual bool isClassifier() const = 0;
+
+ /** @brief Trains the statistical model
+
+ @param trainData training data that can be loaded from file using TrainData::loadFromCSV or
+ created with TrainData::create.
+ @param flags optional flags, depending on the model. Some of the models can be updated with the
+ new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).
+ */
+ CV_WRAP virtual bool train( const Ptr<TrainData>& trainData, int flags=0 );
+
+ /** @brief Trains the statistical model
+
+ @param samples training samples
+ @param layout See ml::SampleTypes.
+ @param responses vector of responses associated with the training samples.
+ */
+ CV_WRAP virtual bool train( InputArray samples, int layout, InputArray responses );
+
+ /** @brief Computes error on the training or test dataset
+
+ @param data the training data
+ @param test if true, the error is computed over the test subset of the data, otherwise it's
+ computed over the training subset of the data. Please note that if you loaded a completely
+ different dataset to evaluate already trained classifier, you will probably want not to set
+ the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so
+ that the error is computed for the whole new set. Yes, this sounds a bit confusing.
+ @param resp the optional output responses.
+
+ The method uses StatModel::predict to compute the error. For regression models the error is
+ computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).
+ */
+ CV_WRAP virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const;
+
+ /** @brief Predicts response(s) for the provided sample(s)
+
+ @param samples The input samples, floating-point matrix
+ @param results The optional output matrix of results.
+ @param flags The optional flags, model-dependent. See cv::ml::StatModel::Flags.
+ */
+ CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
+
+ /** @brief Create and train model with default parameters
+
+ The class must implement static `create()` method with no parameters or with all default parameter values
+ */
+ template<typename _Tp> static Ptr<_Tp> train(const Ptr<TrainData>& data, int flags=0)
+ {
+ Ptr<_Tp> model = _Tp::create();
+ return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>();
+ }
+};
+
+/****************************************************************************************\
+* Normal Bayes Classifier *
+\****************************************************************************************/
+
+/** @brief Bayes classifier for normally distributed data.
+
+@sa @ref ml_intro_bayes
+ */
+class CV_EXPORTS_W NormalBayesClassifier : public StatModel
+{
+public:
+ /** @brief Predicts the response for sample(s).
+
+ The method estimates the most probable classes for input vectors. Input vectors (one or more)
+ are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one
+ output vector outputs. The predicted class for a single input vector is returned by the method.
+ The vector outputProbs contains the output probabilities corresponding to each element of
+ result.
+ */
+ CV_WRAP virtual float predictProb( InputArray inputs, OutputArray outputs,
+ OutputArray outputProbs, int flags=0 ) const = 0;
+
+ /** Creates empty model
+ Use StatModel::train to train the model after creation. */
+ CV_WRAP static Ptr<NormalBayesClassifier> create();
+};
+
+/****************************************************************************************\
+* K-Nearest Neighbour Classifier *
+\****************************************************************************************/
+
+/** @brief The class implements K-Nearest Neighbors model
+
+@sa @ref ml_intro_knn
+ */
+class CV_EXPORTS_W KNearest : public StatModel
+{
+public:
+
+ /** Default number of neighbors to use in predict method. */
+ /** @see setDefaultK */
+ CV_WRAP virtual int getDefaultK() const = 0;
+ /** @copybrief getDefaultK @see getDefaultK */
+ CV_WRAP virtual void setDefaultK(int val) = 0;
+
+ /** Whether classification or regression model should be trained. */
+ /** @see setIsClassifier */
+ CV_WRAP virtual bool getIsClassifier() const = 0;
+ /** @copybrief getIsClassifier @see getIsClassifier */
+ CV_WRAP virtual void setIsClassifier(bool val) = 0;
+
+ /** Parameter for KDTree implementation. */
+ /** @see setEmax */
+ CV_WRAP virtual int getEmax() const = 0;
+ /** @copybrief getEmax @see getEmax */
+ CV_WRAP virtual void setEmax(int val) = 0;
+
+ /** %Algorithm type, one of KNearest::Types. */
+ /** @see setAlgorithmType */
+ CV_WRAP virtual int getAlgorithmType() const = 0;
+ /** @copybrief getAlgorithmType @see getAlgorithmType */
+ CV_WRAP virtual void setAlgorithmType(int val) = 0;
+
+ /** @brief Finds the neighbors and predicts responses for input vectors.
+
+ @param samples Input samples stored by rows. It is a single-precision floating-point matrix of
+ `<number_of_samples> * k` size.
+ @param k Number of used nearest neighbors. Should be greater than 1.
+ @param results Vector with results of prediction (regression or classification) for each input
+ sample. It is a single-precision floating-point vector with `<number_of_samples>` elements.
+ @param neighborResponses Optional output values for corresponding neighbors. It is a single-
+ precision floating-point matrix of `<number_of_samples> * k` size.
+ @param dist Optional output distances from the input vectors to the corresponding neighbors. It
+ is a single-precision floating-point matrix of `<number_of_samples> * k` size.
+
+ For each input vector (a row of the matrix samples), the method finds the k nearest neighbors.
+ In case of regression, the predicted result is a mean value of the particular vector's neighbor
+ responses. In case of classification, the class is determined by voting.
+
+ For each input vector, the neighbors are sorted by their distances to the vector.
+
+ In case of C++ interface you can use output pointers to empty matrices and the function will
+ allocate memory itself.
+
+ If only a single input vector is passed, all output matrices are optional and the predicted
+ value is returned by the method.
+
+ The function is parallelized with the TBB library.
+ */
+ CV_WRAP virtual float findNearest( InputArray samples, int k,
+ OutputArray results,
+ OutputArray neighborResponses=noArray(),
+ OutputArray dist=noArray() ) const = 0;
+
+ /** @brief Implementations of KNearest algorithm
+ */
+ enum Types
+ {
+ BRUTE_FORCE=1,
+ KDTREE=2
+ };
+
+ /** @brief Creates the empty model
+
+ The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
+ */
+ CV_WRAP static Ptr<KNearest> create();
+};
+
+/****************************************************************************************\
+* Support Vector Machines *
+\****************************************************************************************/
+
+/** @brief Support Vector Machines.
+
+@sa @ref ml_intro_svm
+ */
+class CV_EXPORTS_W SVM : public StatModel
+{
+public:
+
+ class CV_EXPORTS Kernel : public Algorithm
+ {
+ public:
+ virtual int getType() const = 0;
+ virtual void calc( int vcount, int n, const float* vecs, const float* another, float* results ) = 0;
+ };
+
+ /** Type of a %SVM formulation.
+ See SVM::Types. Default value is SVM::C_SVC. */
+ /** @see setType */
+ CV_WRAP virtual int getType() const = 0;
+ /** @copybrief getType @see getType */
+ CV_WRAP virtual void setType(int val) = 0;
+
+ /** Parameter \f$\gamma\f$ of a kernel function.
+ For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1. */
+ /** @see setGamma */
+ CV_WRAP virtual double getGamma() const = 0;
+ /** @copybrief getGamma @see getGamma */
+ CV_WRAP virtual void setGamma(double val) = 0;
+
+ /** Parameter _coef0_ of a kernel function.
+ For SVM::POLY or SVM::SIGMOID. Default value is 0.*/
+ /** @see setCoef0 */
+ CV_WRAP virtual double getCoef0() const = 0;
+ /** @copybrief getCoef0 @see getCoef0 */
+ CV_WRAP virtual void setCoef0(double val) = 0;
+
+ /** Parameter _degree_ of a kernel function.
+ For SVM::POLY. Default value is 0. */
+ /** @see setDegree */
+ CV_WRAP virtual double getDegree() const = 0;
+ /** @copybrief getDegree @see getDegree */
+ CV_WRAP virtual void setDegree(double val) = 0;
+
+ /** Parameter _C_ of a %SVM optimization problem.
+ For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0. */
+ /** @see setC */
+ CV_WRAP virtual double getC() const = 0;
+ /** @copybrief getC @see getC */
+ CV_WRAP virtual void setC(double val) = 0;
+
+ /** Parameter \f$\nu\f$ of a %SVM optimization problem.
+ For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0. */
+ /** @see setNu */
+ CV_WRAP virtual double getNu() const = 0;
+ /** @copybrief getNu @see getNu */
+ CV_WRAP virtual void setNu(double val) = 0;
+
+ /** Parameter \f$\epsilon\f$ of a %SVM optimization problem.
+ For SVM::EPS_SVR. Default value is 0. */
+ /** @see setP */
+ CV_WRAP virtual double getP() const = 0;
+ /** @copybrief getP @see getP */
+ CV_WRAP virtual void setP(double val) = 0;
+
+ /** Optional weights in the SVM::C_SVC problem, assigned to particular classes.
+ They are multiplied by _C_ so the parameter _C_ of class _i_ becomes `classWeights(i) * C`. Thus
+ these weights affect the misclassification penalty for different classes. The larger weight,
+ the larger penalty on misclassification of data from the corresponding class. Default value is
+ empty Mat. */
+ /** @see setClassWeights */
+ CV_WRAP virtual cv::Mat getClassWeights() const = 0;
+ /** @copybrief getClassWeights @see getClassWeights */
+ CV_WRAP virtual void setClassWeights(const cv::Mat &val) = 0;
+
+ /** Termination criteria of the iterative %SVM training procedure which solves a partial
+ case of constrained quadratic optimization problem.
+ You can specify tolerance and/or the maximum number of iterations. Default value is
+ `TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON )`; */
+ /** @see setTermCriteria */
+ CV_WRAP virtual cv::TermCriteria getTermCriteria() const = 0;
+ /** @copybrief getTermCriteria @see getTermCriteria */
+ CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0;
+
+ /** Type of a %SVM kernel.
+ See SVM::KernelTypes. Default value is SVM::RBF. */
+ CV_WRAP virtual int getKernelType() const = 0;
+
+ /** Initialize with one of predefined kernels.
+ See SVM::KernelTypes. */
+ CV_WRAP virtual void setKernel(int kernelType) = 0;
+
+ /** Initialize with custom kernel.
+ See SVM::Kernel class for implementation details */
+ virtual void setCustomKernel(const Ptr<Kernel> &_kernel) = 0;
+
+ //! %SVM type
+ enum Types {
+ /** C-Support Vector Classification. n-class classification (n \f$\geq\f$ 2), allows
+ imperfect separation of classes with penalty multiplier C for outliers. */
+ C_SVC=100,
+ /** \f$\nu\f$-Support Vector Classification. n-class classification with possible
+ imperfect separation. Parameter \f$\nu\f$ (in the range 0..1, the larger the value, the smoother
+ the decision boundary) is used instead of C. */
+ NU_SVC=101,
+ /** Distribution Estimation (One-class %SVM). All the training data are from
+ the same class, %SVM builds a boundary that separates the class from the rest of the feature
+ space. */
+ ONE_CLASS=102,
+ /** \f$\epsilon\f$-Support Vector Regression. The distance between feature vectors
+ from the training set and the fitting hyper-plane must be less than p. For outliers the
+ penalty multiplier C is used. */
+ EPS_SVR=103,
+ /** \f$\nu\f$-Support Vector Regression. \f$\nu\f$ is used instead of p.
+ See @cite LibSVM for details. */
+ NU_SVR=104
+ };
+
+ /** @brief %SVM kernel type
+
+ A comparison of different kernels on the following 2D test case with four classes. Four
+ SVM::C_SVC SVMs have been trained (one against rest) with auto_train. Evaluation on three
+ different kernels (SVM::CHI2, SVM::INTER, SVM::RBF). The color depicts the class with max score.
+ Bright means max-score \> 0, dark means max-score \< 0.
+ 
+ */
+ enum KernelTypes {
+ /** Returned by SVM::getKernelType in case when custom kernel has been set */
+ CUSTOM=-1,
+ /** Linear kernel. No mapping is done, linear discrimination (or regression) is
+ done in the original feature space. It is the fastest option. \f$K(x_i, x_j) = x_i^T x_j\f$. */
+ LINEAR=0,
+ /** Polynomial kernel:
+ \f$K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\f$. */
+ POLY=1,
+ /** Radial basis function (RBF), a good choice in most cases.
+ \f$K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0\f$. */
+ RBF=2,
+ /** Sigmoid kernel: \f$K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\f$. */
+ SIGMOID=3,
+ /** Exponential Chi2 kernel, similar to the RBF kernel:
+ \f$K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\f$. */
+ CHI2=4,
+ /** Histogram intersection kernel. A fast kernel. \f$K(x_i, x_j) = min(x_i,x_j)\f$. */
+ INTER=5
+ };
+
+ //! %SVM params type
+ enum ParamTypes {
+ C=0,
+ GAMMA=1,
+ P=2,
+ NU=3,
+ COEF=4,
+ DEGREE=5
+ };
+
+ /** @brief Trains an %SVM with optimal parameters.
+
+ @param data the training data that can be constructed using TrainData::create or
+ TrainData::loadFromCSV.
+ @param kFold Cross-validation parameter. The training set is divided into kFold subsets. One
+ subset is used to test the model, the others form the train set. So, the %SVM algorithm is
+ executed kFold times.
+ @param Cgrid grid for C
+ @param gammaGrid grid for gamma
+ @param pGrid grid for p
+ @param nuGrid grid for nu
+ @param coeffGrid grid for coeff
+ @param degreeGrid grid for degree
+ @param balanced If true and the problem is 2-class classification then the method creates more
+ balanced cross-validation subsets that is proportions between classes in subsets are close
+ to such proportion in the whole train dataset.
+
+ The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
+ nu, coef0, degree. Parameters are considered optimal when the cross-validation
+ estimate of the test set error is minimal.
+
+ If there is no need to optimize a parameter, the corresponding grid step should be set to any
+ value less than or equal to 1. For example, to avoid optimization in gamma, set `gammaGrid.step
+ = 0`, `gammaGrid.minVal`, `gamma_grid.maxVal` as arbitrary numbers. In this case, the value
+ `Gamma` is taken for gamma.
+
+ And, finally, if the optimization in a parameter is required but the corresponding grid is
+ unknown, you may call the function SVM::getDefaultGrid. To generate a grid, for example, for
+ gamma, call `SVM::getDefaultGrid(SVM::GAMMA)`.
+
+ This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
+ regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
+ the usual %SVM with parameters specified in params is executed.
+ */
+ virtual bool trainAuto( const Ptr<TrainData>& data, int kFold = 10,
+ ParamGrid Cgrid = SVM::getDefaultGrid(SVM::C),
+ ParamGrid gammaGrid = SVM::getDefaultGrid(SVM::GAMMA),
+ ParamGrid pGrid = SVM::getDefaultGrid(SVM::P),
+ ParamGrid nuGrid = SVM::getDefaultGrid(SVM::NU),
+ ParamGrid coeffGrid = SVM::getDefaultGrid(SVM::COEF),
+ ParamGrid degreeGrid = SVM::getDefaultGrid(SVM::DEGREE),
+ bool balanced=false) = 0;
+
+ /** @brief Retrieves all the support vectors
+
+ The method returns all the support vectors as a floating-point matrix, where support vectors are
+ stored as matrix rows.
+ */
+ CV_WRAP virtual Mat getSupportVectors() const = 0;
+
+ /** @brief Retrieves all the uncompressed support vectors of a linear %SVM
+
+ The method returns all the uncompressed support vectors of a linear %SVM that the compressed
+ support vector, used for prediction, was derived from. They are returned in a floating-point
+ matrix, where the support vectors are stored as matrix rows.
+ */
+ CV_WRAP Mat getUncompressedSupportVectors() const;
+
+ /** @brief Retrieves the decision function
+
+ @param i the index of the decision function. If the problem solved is regression, 1-class or
+ 2-class classification, then there will be just one decision function and the index should
+ always be 0. Otherwise, in the case of N-class classification, there will be \f$N(N-1)/2\f$
+ decision functions.
+ @param alpha the optional output vector for weights, corresponding to different support vectors.
+ In the case of linear %SVM all the alpha's will be 1's.
+ @param svidx the optional output vector of indices of support vectors within the matrix of
+ support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear
+ %SVM each decision function consists of a single "compressed" support vector.
+
+ The method returns rho parameter of the decision function, a scalar subtracted from the weighted
+ sum of kernel responses.
+ */
+ CV_WRAP virtual double getDecisionFunction(int i, OutputArray alpha, OutputArray svidx) const = 0;
+
+ /** @brief Generates a grid for %SVM parameters.
+
+ @param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is
+ generated for the parameter with this ID.
+
+ The function generates a grid for the specified parameter of the %SVM algorithm. The grid may be
+ passed to the function SVM::trainAuto.
+ */
+ static ParamGrid getDefaultGrid( int param_id );
+
+ /** Creates empty model.
+ Use StatModel::train to train the model. Since %SVM has several parameters, you may want to
+ find the best parameters for your problem, it can be done with SVM::trainAuto. */
+ CV_WRAP static Ptr<SVM> create();
+};
+
+/****************************************************************************************\
+* Expectation - Maximization *
+\****************************************************************************************/
+
+/** @brief The class implements the Expectation Maximization algorithm.
+
+@sa @ref ml_intro_em
+ */
+class CV_EXPORTS_W EM : public StatModel
+{
+public:
+ //! Type of covariation matrices
+ enum Types {
+ /** A scaled identity matrix \f$\mu_k * I\f$. There is the only
+ parameter \f$\mu_k\f$ to be estimated for each matrix. The option may be used in special cases,
+ when the constraint is relevant, or as a first step in the optimization (for example in case
+ when the data is preprocessed with PCA). The results of such preliminary estimation may be
+ passed again to the optimization procedure, this time with
+ covMatType=EM::COV_MAT_DIAGONAL. */
+ COV_MAT_SPHERICAL=0,
+ /** A diagonal matrix with positive diagonal elements. The number of
+ free parameters is d for each matrix. This is most commonly used option yielding good
+ estimation results. */
+ COV_MAT_DIAGONAL=1,
+ /** A symmetric positively defined matrix. The number of free
+ parameters in each matrix is about \f$d^2/2\f$. It is not recommended to use this option, unless
+ there is pretty accurate initial estimation of the parameters and/or a huge number of
+ training samples. */
+ COV_MAT_GENERIC=2,
+ COV_MAT_DEFAULT=COV_MAT_DIAGONAL
+ };
+
+ //! Default parameters
+ enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100};
+
+ //! The initial step
+ enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
+
+ /** The number of mixture components in the Gaussian mixture model.
+ Default value of the parameter is EM::DEFAULT_NCLUSTERS=5. Some of %EM implementation could
+ determine the optimal number of mixtures within a specified value range, but that is not the
+ case in ML yet. */
+ /** @see setClustersNumber */
+ CV_WRAP virtual int getClustersNumber() const = 0;
+ /** @copybrief getClustersNumber @see getClustersNumber */
+ CV_WRAP virtual void setClustersNumber(int val) = 0;
+
+ /** Constraint on covariance matrices which defines type of matrices.
+ See EM::Types. */
+ /** @see setCovarianceMatrixType */
+ CV_WRAP virtual int getCovarianceMatrixType() const = 0;
+ /** @copybrief getCovarianceMatrixType @see getCovarianceMatrixType */
+ CV_WRAP virtual void setCovarianceMatrixType(int val) = 0;
+
+ /** The termination criteria of the %EM algorithm.
+ The %EM algorithm can be terminated by the number of iterations termCrit.maxCount (number of
+ M-steps) or when relative change of likelihood logarithm is less than termCrit.epsilon. Default
+ maximum number of iterations is EM::DEFAULT_MAX_ITERS=100. */
+ /** @see setTermCriteria */
+ CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
+ /** @copybrief getTermCriteria @see getTermCriteria */
+ CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0;
+
+ /** @brief Returns weights of the mixtures
+
+ Returns vector with the number of elements equal to the number of mixtures.
+ */
+ CV_WRAP virtual Mat getWeights() const = 0;
+ /** @brief Returns the cluster centers (means of the Gaussian mixture)
+
+ Returns matrix with the number of rows equal to the number of mixtures and number of columns
+ equal to the space dimensionality.
+ */
+ CV_WRAP virtual Mat getMeans() const = 0;
+ /** @brief Returns covariation matrices
+
+ Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures,
+ each matrix is a square floating-point matrix NxN, where N is the space dimensionality.
+ */
+ CV_WRAP virtual void getCovs(CV_OUT std::vector<Mat>& covs) const = 0;
+
+ /** @brief Returns a likelihood logarithm value and an index of the most probable mixture component
+ for the given sample.
+
+ @param sample A sample for classification. It should be a one-channel matrix of
+ \f$1 \times dims\f$ or \f$dims \times 1\f$ size.
+ @param probs Optional output matrix that contains posterior probabilities of each component
+ given the sample. It has \f$1 \times nclusters\f$ size and CV_64FC1 type.
+
+ The method returns a two-element double vector. Zero element is a likelihood logarithm value for
+ the sample. First element is an index of the most probable mixture component for the given
+ sample.
+ */
+ CV_WRAP virtual Vec2d predict2(InputArray sample, OutputArray probs) const = 0;
+
+ /** @brief Estimate the Gaussian mixture parameters from a samples set.
+
+ This variation starts with Expectation step. Initial values of the model parameters will be
+ estimated by the k-means algorithm.
+
+ Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take
+ responses (class labels or function values) as input. Instead, it computes the *Maximum
+ Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the
+ parameters inside the structure: \f$p_{i,k}\f$ in probs, \f$a_k\f$ in means , \f$S_k\f$ in
+ covs[k], \f$\pi_k\f$ in weights , and optionally computes the 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).
+
+ The trained model can be used further for prediction, just like any other classifier. The
+ trained model is similar to the NormalBayesClassifier.
+
+ @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 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 trainEM(InputArray samples,
+ OutputArray logLikelihoods=noArray(),
+ OutputArray labels=noArray(),
+ OutputArray probs=noArray()) = 0;
+
+ /** @brief Estimate the Gaussian mixture parameters from a samples set.
+
+ 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.
+
+ @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 means0 Initial means \f$a_k\f$ of mixture components. It is a one-channel matrix of
+ \f$nclusters \times dims\f$ size. 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 covs0 The vector of initial covariance matrices \f$S_k\f$ of mixture components. Each of
+ covariance matrices is a one-channel matrix of \f$dims \times dims\f$ size. If the matrices
+ do not have CV_64F type they will be converted to the inner matrices of such type for the
+ further computing.
+ @param weights0 Initial weights \f$\pi_k\f$ of mixture components. It should be a one-channel
+ floating-point matrix with \f$1 \times nclusters\f$ or \f$nclusters \times 1\f$ size.
+ @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 trainE(InputArray samples, InputArray means0,
+ InputArray covs0=noArray(),
+ InputArray weights0=noArray(),
+ OutputArray logLikelihoods=noArray(),
+ OutputArray labels=noArray(),
+ OutputArray probs=noArray()) = 0;
+
+ /** @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;
+
+ /** RPROP: Update-values lower limit \f$\Delta_{min}\f$.
+ It must be positive. Default value is FLT_EPSILON.*/
+ /** @see setRpropDWMin */
+ CV_WRAP virtual double getRpropDWMin() const = 0;
+ /** @copybrief getRpropDWMin @see getRpropDWMin */
+ CV_WRAP virtual void setRpropDWMin(double val) = 0;
+
+ /** RPROP: Update-values upper limit \f$\Delta_{max}\f$.
+ It must be \>1. Default value is 50.*/
+ /** @see setRpropDWMax */
+ CV_WRAP virtual double getRpropDWMax() const = 0;
+ /** @copybrief getRpropDWMax @see getRpropDWMax */
+ CV_WRAP virtual void setRpropDWMax(double val) = 0;
+
+ /** possible activation functions */
+ enum ActivationFunctions {
+ /** Identity function: \f$f(x)=x\f$ */
+ IDENTITY = 0,
+ /** Symmetrical sigmoid: \f$f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x}\f$
+ @note
+ 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
+ will range from [-1.7159, 1.7159], instead of [0,1].*/
+ SIGMOID_SYM = 1,
+ /** Gaussian function: \f$f(x)=\beta e^{-\alpha x*x}\f$ */
+ GAUSSIAN = 2
+ };
+
+ /** Train options */
+ enum TrainFlags {
+ /** 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,
+ /** 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. */
+ NO_INPUT_SCALE = 2,
+ /** 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. */
+ 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 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. */
+