OpenCV: Machine Learning

| OpenCV 3.4.2 Open Source Computer Vision

Classes | Enumerations | Functions
Machine Learning

## Classes
class | cv::ml::ANN_MLP
| Artificial Neural Networks - Multi-Layer Perceptrons. More...
class | cv::ml::ANN_MLP_ANNEAL
| 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...

## 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...
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. 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

## ◆ ErrorTypes

enum cv::ml::ErrorTypes

Error types

Enumerator
TEST_ERROR |
TRAIN_ERROR |

## ◆ SampleTypes

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

## ◆ VariableTypes

enum cv::ml::VariableTypes

Variable types.

Enumerator
VAR_NUMERICAL | same as VAR_ORDERED
VAR_ORDERED | ordered variables
VAR_CATEGORICAL | categorical variables

## Function Documentation

## ◆ createConcentricSpheresTestSet()

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

Creates test set.

## ◆ randMVNormal()

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

## ◆ 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()`
| ) |  |

The class implements simulated annealing for optimization.

[Kirkpatrick83] 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)