Machine Learning

Detailed Description

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: Machine Learning Overview.

Classes
class	cv::ml::ANN_MLP
	Artificial Neural Networks - Multi-Layer Perceptrons. More...
class	cv::ml::Boost
	Boosted tree classifier derived from DTrees. More...
class	cv::ml::DTrees
	The class represents a single decision tree or a collection of decision trees. More...
class	cv::ml::EM
	The class implements the Expectation Maximization algorithm. More...
class	cv::ml::KNearest
	The class implements K-Nearest Neighbors model. More...
class	cv::ml::LogisticRegression
	Implements Logistic Regression classifier. More...
class	cv::ml::NormalBayesClassifier
	Bayes classifier for normally distributed data. More...
class	cv::ml::ParamGrid
	The structure represents the logarithmic grid range of statmodel parameters. More...
class	cv::ml::RTrees
	The class implements the random forest predictor. More...
struct	cv::ml::SimulatedAnnealingSolverSystem
	This class declares example interface for system state used in simulated annealing optimization algorithm. More...
class	cv::ml::StatModel
	Base class for statistical models in OpenCV ML. More...
class	cv::ml::SVM
	Support Vector Machines. More...
class	cv::ml::SVMSGD
	Stochastic Gradient Descent SVM classifier. More...
class	cv::ml::TrainData
	Class encapsulating training data. More...

Typedefs
typedef ANN_MLP	cv::ml::ANN_MLP_ANNEAL

Enumerations
enum	cv::ml::ErrorTypes {   cv::ml::TEST_ERROR = 0 ,   cv::ml::TRAIN_ERROR = 1 }
	Error types More...
enum	cv::ml::SampleTypes {   cv::ml::ROW_SAMPLE = 0 ,   cv::ml::COL_SAMPLE = 1 }
	Sample types. More...
enum	cv::ml::VariableTypes {   cv::ml::VAR_NUMERICAL =0 ,   cv::ml::VAR_ORDERED =0 ,   cv::ml::VAR_CATEGORICAL =1 }
	Variable types. More...

Functions
void	cv::ml::createConcentricSpheresTestSet (int nsamples, int nfeatures, int nclasses, OutputArray samples, OutputArray responses)
	Creates test set.
void	cv::ml::randMVNormal (InputArray mean, InputArray cov, int nsamples, OutputArray samples)
	Generates sample from multivariate normal distribution.
template<class SimulatedAnnealingSolverSystem >
int	cv::ml::simulatedAnnealingSolver (SimulatedAnnealingSolverSystem &solverSystem, double initialTemperature, double finalTemperature, double coolingRatio, size_t iterationsPerStep, double *lastTemperature=NULL, cv::RNG &rngEnergy=cv::theRNG())
	The class implements simulated annealing for optimization.

Typedef Documentation

ANN_MLP_ANNEAL

typedef ANN_MLP cv::ml::ANN_MLP_ANNEAL

#include <opencv2/ml.hpp>

Enumeration Type Documentation

ErrorTypes

enum cv::ml::ErrorTypes

#include <opencv2/ml.hpp>

Error types

Enumerator
TEST_ERROR  Python: cv.ml.TEST_ERROR
TRAIN_ERROR  Python: cv.ml.TRAIN_ERROR

SampleTypes

enum cv::ml::SampleTypes

#include <opencv2/ml.hpp>

Sample types.

Enumerator
ROW_SAMPLE  Python: cv.ml.ROW_SAMPLE	each training sample is a row of samples
COL_SAMPLE  Python: cv.ml.COL_SAMPLE	each training sample occupies a column of samples

VariableTypes

enum cv::ml::VariableTypes

#include <opencv2/ml.hpp>

Variable types.

Enumerator
VAR_NUMERICAL  Python: cv.ml.VAR_NUMERICAL	same as VAR_ORDERED
VAR_ORDERED  Python: cv.ml.VAR_ORDERED	ordered variables
VAR_CATEGORICAL  Python: cv.ml.VAR_CATEGORICAL	categorical variables

Function Documentation

createConcentricSpheresTestSet()

void cv::ml::createConcentricSpheresTestSet	(	int	nsamples,
		int	nfeatures,
		int	nclasses,
		OutputArray	samples,
		OutputArray	responses
	)

#include <opencv2/ml.hpp>

Creates test set.

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randMVNormal()

void cv::ml::randMVNormal	(	InputArray	mean,
		InputArray	cov,
		int	nsamples,
		OutputArray	samples
	)

#include <opencv2/ml.hpp>

Generates sample from multivariate normal distribution.

Parameters

mean	an average row vector
cov	symmetric covariation matrix
nsamples	returned samples count
samples	returned samples array

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simulatedAnnealingSolver()

template<class SimulatedAnnealingSolverSystem >

int cv::ml::simulatedAnnealingSolver	(	SimulatedAnnealingSolverSystem &	solverSystem,
		double	initialTemperature,
		double	finalTemperature,
		double	coolingRatio,
		size_t	iterationsPerStep,
		double *	lastTemperature = NULL,
		cv::RNG &	rngEnergy = cv::theRNG()
	)

#include <opencv2/ml.hpp>

The class implements simulated annealing for optimization.

[147] for details

Parameters

solverSystem	optimization system (see SimulatedAnnealingSolverSystem)
initialTemperature	initial temperature
finalTemperature	final temperature
coolingRatio	temperature step multiplies
iterationsPerStep	number of iterations per temperature changing step
lastTemperature	optional output for last used temperature
rngEnergy	specify custom random numbers generator (cv::theRNG() by default)

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