Machine Learning

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::SVM::Kernel
class	cv::ml::KNearest
	The class implements K-Nearest Neighbors model. More...
class	cv::ml::LogisticRegression
	Implements Logistic Regression classifier. More...
class	cv::ml::DTrees::Node
	The class represents a decision tree node. 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::DTrees::Split
	The class represents split in a decision tree. 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::TrainData
	Class encapsulating training data. More...

Enumerations
enum	{   cv::ml::EM::DEFAULT_NCLUSTERS =5,   cv::ml::EM::DEFAULT_MAX_ITERS =100 }
	Default parameters. More...
enum	{   cv::ml::EM::START_E_STEP =1,   cv::ml::EM::START_M_STEP =2,   cv::ml::EM::START_AUTO_STEP =0 }
	The initial step. More...
enum	cv::ml::ANN_MLP::ActivationFunctions {   cv::ml::ANN_MLP::IDENTITY = 0,   cv::ml::ANN_MLP::SIGMOID_SYM = 1,   cv::ml::ANN_MLP::GAUSSIAN = 2 }
enum	cv::ml::ErrorTypes {   cv::ml::TEST_ERROR = 0,   cv::ml::TRAIN_ERROR = 1 }
	Error types More...
enum	cv::ml::StatModel::Flags {   cv::ml::StatModel::UPDATE_MODEL = 1,   cv::ml::StatModel::RAW_OUTPUT =1,   cv::ml::StatModel::COMPRESSED_INPUT =2,   cv::ml::StatModel::PREPROCESSED_INPUT =4 }
enum	cv::ml::DTrees::Flags {   cv::ml::DTrees::PREDICT_AUTO =0,   cv::ml::DTrees::PREDICT_SUM =(1<<8),   cv::ml::DTrees::PREDICT_MAX_VOTE =(2<<8),   cv::ml::DTrees::PREDICT_MASK =(3<<8) }
enum	cv::ml::SVM::KernelTypes {   cv::ml::SVM::CUSTOM =-1,   cv::ml::SVM::LINEAR =0,   cv::ml::SVM::POLY =1,   cv::ml::SVM::RBF =2,   cv::ml::SVM::SIGMOID =3,   cv::ml::SVM::CHI2 =4,   cv::ml::SVM::INTER =5 }
	SVM kernel type More...
enum	cv::ml::LogisticRegression::Methods {   cv::ml::LogisticRegression::BATCH = 0,   cv::ml::LogisticRegression::MINI_BATCH = 1 }
	Training methods. More...
enum	cv::ml::SVM::ParamTypes {   cv::ml::SVM::C =0,   cv::ml::SVM::GAMMA =1,   cv::ml::SVM::P =2,   cv::ml::SVM::NU =3,   cv::ml::SVM::COEF =4,   cv::ml::SVM::DEGREE =5 }
	SVM params type More...
enum	cv::ml::LogisticRegression::RegKinds {   cv::ml::LogisticRegression::REG_DISABLE = -1,   cv::ml::LogisticRegression::REG_L1 = 0,   cv::ml::LogisticRegression::REG_L2 = 1 }
	Regularization kinds. More...
enum	cv::ml::SampleTypes {   cv::ml::ROW_SAMPLE = 0,   cv::ml::COL_SAMPLE = 1 }
	Sample types. More...
enum	cv::ml::ANN_MLP::TrainFlags {   cv::ml::ANN_MLP::UPDATE_WEIGHTS = 1,   cv::ml::ANN_MLP::NO_INPUT_SCALE = 2,   cv::ml::ANN_MLP::NO_OUTPUT_SCALE = 4 }
enum	cv::ml::ANN_MLP::TrainingMethods {   cv::ml::ANN_MLP::BACKPROP =0,   cv::ml::ANN_MLP::RPROP =1 }
enum	cv::ml::KNearest::Types {   cv::ml::KNearest::BRUTE_FORCE =1,   cv::ml::KNearest::KDTREE =2 }
	Implementations of KNearest algorithm. More...
enum	cv::ml::SVM::Types {   cv::ml::SVM::C_SVC =100,   cv::ml::SVM::NU_SVC =101,   cv::ml::SVM::ONE_CLASS =102,   cv::ml::SVM::EPS_SVR =103,   cv::ml::SVM::NU_SVR =104 }
	SVM type More...
enum	cv::ml::EM::Types {   cv::ml::EM::COV_MAT_SPHERICAL =0,   cv::ml::EM::COV_MAT_DIAGONAL =1,   cv::ml::EM::COV_MAT_GENERIC =2,   cv::ml::EM::COV_MAT_DEFAULT =COV_MAT_DIAGONAL }
	Type of covariation matrices. More...
enum	cv::ml::Boost::Types {   cv::ml::Boost::DISCRETE =0,   cv::ml::Boost::REAL =1,   cv::ml::Boost::LOGIT =2,   cv::ml::Boost::GENTLE =3 }
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

anonymous enum

Default parameters.

Enumerator
DEFAULT_NCLUSTERS
DEFAULT_MAX_ITERS

anonymous enum

The initial step.

Enumerator
START_E_STEP
START_M_STEP
START_AUTO_STEP

enum cv::ml::ANN_MLP::ActivationFunctions

possible activation functions

Enumerator
IDENTITY	Identity function: \(f(x)=x\)
SIGMOID_SYM	Symmetrical sigmoid: \(f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x}\) 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].
GAUSSIAN	Gaussian function: \(f(x)=\beta e^{-\alpha x*x}\)

enum cv::ml::ErrorTypes

Error types

Enumerator
TEST_ERROR
TRAIN_ERROR

enum cv::ml::StatModel::Flags

Predict options

Enumerator
UPDATE_MODEL
RAW_OUTPUT	makes the method return the raw results (the sum), not the class label
COMPRESSED_INPUT
PREPROCESSED_INPUT

enum cv::ml::DTrees::Flags

Predict options

Enumerator
PREDICT_AUTO
PREDICT_SUM
PREDICT_MAX_VOTE
PREDICT_MASK

enum cv::ml::SVM::KernelTypes

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.

image

Enumerator
CUSTOM	Returned by SVM::getKernelType in case when custom kernel has been set
LINEAR	Linear kernel. No mapping is done, linear discrimination (or regression) is done in the original feature space. It is the fastest option. \(K(x_i, x_j) = x_i^T x_j\).
POLY	Polynomial kernel: \(K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\).
RBF	Radial basis function (RBF), a good choice in most cases. \(K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0\).
SIGMOID	Sigmoid kernel: \(K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\).
CHI2	Exponential Chi2 kernel, similar to the RBF kernel: \(K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\).
INTER	Histogram intersection kernel. A fast kernel. \(K(x_i, x_j) = min(x_i,x_j)\).

enum cv::ml::LogisticRegression::Methods

Training methods.

Enumerator
BATCH
MINI_BATCH	Set MiniBatchSize to a positive integer when using this method.

enum cv::ml::SVM::ParamTypes

SVM params type

Enumerator
C
GAMMA
P
NU
COEF
DEGREE

enum cv::ml::LogisticRegression::RegKinds

Regularization kinds.

Enumerator
REG_DISABLE	Regularization disabled.
REG_L1	L1 norm
REG_L2	L2 norm

enum cv::ml::SampleTypes

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::ANN_MLP::TrainFlags

Train options

Enumerator
UPDATE_WEIGHTS	Update the network weights, rather than compute them from scratch. In the latter case the weights are initialized using the Nguyen-Widrow algorithm.
NO_INPUT_SCALE	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_OUTPUT_SCALE	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.

enum cv::ml::ANN_MLP::TrainingMethods

Available training methods

Enumerator
BACKPROP	The back-propagation algorithm.
RPROP	The RPROP algorithm. See [117] for details.

enum cv::ml::KNearest::Types

Implementations of KNearest algorithm.

Enumerator
BRUTE_FORCE
KDTREE

enum cv::ml::SVM::Types

SVM type

Enumerator
C_SVC	C-Support Vector Classification. n-class classification (n \(\geq\) 2), allows imperfect separation of classes with penalty multiplier C for outliers.
NU_SVC	\(\nu\)-Support Vector Classification. n-class classification with possible imperfect separation. Parameter \(\nu\) (in the range 0..1, the larger the value, the smoother the decision boundary) is used instead of C.
ONE_CLASS	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.
EPS_SVR	\(\epsilon\)-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.
NU_SVR	\(\nu\)-Support Vector Regression. \(\nu\) is used instead of p. See [26] for details.

enum cv::ml::EM::Types

Type of covariation matrices.

Enumerator
COV_MAT_SPHERICAL	A scaled identity matrix \(\mu_k * I\). There is the only parameter \(\mu_k\) 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_DIAGONAL	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_GENERIC	A symmetric positively defined matrix. The number of free parameters in each matrix is about \(d^2/2\). 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_DEFAULT

enum cv::ml::Boost::Types

Boosting type. Gentle AdaBoost and Real AdaBoost are often the preferable choices.

Enumerator
DISCRETE	Discrete AdaBoost.
REAL	Real AdaBoost. It is a technique that utilizes confidence-rated predictions and works well with categorical data.
LOGIT	LogitBoost. It can produce good regression fits.
GENTLE	Gentle AdaBoost. It puts less weight on outlier data points and for that reason is often good with regression data.

enum cv::ml::VariableTypes

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
	)

Creates test set.

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

Generates sample from multivariate normal distribution.

Parameters

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