Module opencv::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: [ml_intro].

Modules

• prelude

Structs

• DTrees_Node
  The class represents a decision tree node.
• DTrees_Split
  The class represents split in a decision tree.
• ParamGrid
  The structure represents the logarithmic grid range of statmodel parameters.

Enums

• ANN_MLP_ActivationFunctions
  possible activation functions
• ANN_MLP_TrainFlags
  Train options
• ANN_MLP_TrainingMethods
  Available training methods
• Boost_Types
  Boosting type. Gentle AdaBoost and Real AdaBoost are often the preferable choices.
• DTrees_Flags
  Predict options
• EM_Types
  Type of covariation matrices
• ErrorTypes
  %Error types
• KNearest_Types
  Implementations of KNearest algorithm
• LogisticRegression_Methods
  Training methods
• LogisticRegression_RegKinds
  Regularization kinds
• SVMSGD_MarginType
  Margin type.
• SVMSGD_SvmsgdType
  SVMSGD type. ASGD is often the preferable choice.
• SVM_KernelTypes
  %SVM kernel type
• SVM_ParamTypes
  %SVM params type
• SVM_Types
  %SVM type
• SampleTypes
  Sample types
• StatModel_Flags
  Predict options
• VariableTypes
  Variable types

Constants

• COL_SAMPLE
  each training sample occupies a column of samples
• EM_DEFAULT_MAX_ITERS
• EM_DEFAULT_NCLUSTERS
• EM_START_AUTO_STEP
• EM_START_E_STEP
• EM_START_M_STEP
• ROW_SAMPLE
  each training sample is a row of samples
• TEST_ERROR
• TRAIN_ERROR
• VAR_CATEGORICAL
  categorical variables
• VAR_NUMERICAL
  same as VAR_ORDERED
• VAR_ORDERED
  ordered variables

Traits

• ANN_MLP
  Artificial Neural Networks - Multi-Layer Perceptrons.
• ANN_MLPConst
  Constant methods for crate::ml::ANN_MLP
• Boost
  Boosted tree classifier derived from DTrees
• BoostConst
  Constant methods for crate::ml::Boost
• DTrees
  The class represents a single decision tree or a collection of decision trees.
• DTreesConst
  Constant methods for crate::ml::DTrees
• DTrees_NodeTrait
  Mutable methods for crate::ml::DTrees_Node
• DTrees_NodeTraitConst
  Constant methods for crate::ml::DTrees_Node
• DTrees_SplitTrait
  Mutable methods for crate::ml::DTrees_Split
• DTrees_SplitTraitConst
  Constant methods for crate::ml::DTrees_Split
• EM
  The class implements the Expectation Maximization algorithm.
• EMConst
  Constant methods for crate::ml::EM
• KNearest
  The class implements K-Nearest Neighbors model
• KNearestConst
  Constant methods for crate::ml::KNearest
• LogisticRegression
  Implements Logistic Regression classifier.
• LogisticRegressionConst
  Constant methods for crate::ml::LogisticRegression
• NormalBayesClassifier
  Bayes classifier for normally distributed data.
• NormalBayesClassifierConst
  Constant methods for crate::ml::NormalBayesClassifier
• ParamGridTrait
  Mutable methods for crate::ml::ParamGrid
• ParamGridTraitConst
  Constant methods for crate::ml::ParamGrid
• RTrees
  The class implements the random forest predictor.
• RTreesConst
  Constant methods for crate::ml::RTrees
• SVM
  Support Vector Machines.
• SVMConst
  Constant methods for crate::ml::SVM
• SVMSGD
  ! Stochastic Gradient Descent SVM classifier
• SVMSGDConst
  Constant methods for crate::ml::SVMSGD
• SVM_Kernel
• SVM_KernelConst
  Constant methods for crate::ml::SVM_Kernel
• StatModel
  Base class for statistical models in OpenCV ML.
• StatModelConst
  Constant methods for crate::ml::StatModel
• TrainData
  Class encapsulating training data.
• TrainDataConst
  Constant methods for crate::ml::TrainData

Functions

• create_concentric_spheres_test_set
  Creates test set
• rand_mv_normal
  Generates sample from multivariate normal distribution

Type Definitions

• ANN_MLP_ANNEAL