OpenCV  3.0.0
Open Source Computer Vision
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Machine Learning


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::TrainData
 Class encapsulating training data. More...


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...


void cv::ml::createConcentricSpheresTestSet (int nsamples, int nfeatures, int nclasses, OutputArray samples, OutputArray responses)
 Creates test set. More...
void cv::ml::randGaussMixture (InputArray means, InputArray covs, InputArray weights, int nsamples, OutputArray samples, OutputArray sampClasses)
 Generates sample from gaussian mixture distribution. 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

Error types


Sample types.


each training sample is a row of samples


each training sample occupies a column of samples

Variable types.




ordered variables


categorical variables

Function Documentation

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

Creates test set.

void cv::ml::randGaussMixture ( InputArray  means,
InputArray  covs,
InputArray  weights,
int  nsamples,
OutputArray  samples,
OutputArray  sampClasses 

Generates sample from gaussian mixture distribution.

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

Generates sample from multivariate normal distribution.

meanan average row vector
covsymmetric covariation matrix
nsamplesreturned samples count
samplesreturned samples array