Machine Learning

The Machine Learning Library (MLL) is a set of classes and functions for statistical classification, regression, and clustering of data. More...

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...
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...

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. More...
void	cv::ml::randMVNormal (InputArray mean, InputArray cov, int nsamples, OutputArray samples)
	Generates sample from multivariate normal distribution. More...

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.

Enumeration Type Documentation

enum cv::ml::ErrorTypes

#include <ml/include/opencv2/ml.hpp>

Error types

Enumerator
TEST_ERROR
TRAIN_ERROR

enum cv::ml::SampleTypes

#include <ml/include/opencv2/ml.hpp>

Sample types.

Enumerator
ROW_SAMPLE	each training sample is a row of samples
COL_SAMPLE	each training sample occupies a column of samples

enum cv::ml::VariableTypes

#include <ml/include/opencv2/ml.hpp>

Variable types.

Enumerator
VAR_NUMERICAL	same as VAR_ORDERED
VAR_ORDERED	ordered variables
VAR_CATEGORICAL	categorical variables

Function Documentation

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

#include <ml/include/opencv2/ml.hpp>

Creates test set.

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

#include <ml/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