OpenCV
4.10.0
Open Source Computer Vision
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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.
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... | |
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... | |
Typedefs | |
typedef ANN_MLP | cv::ml::ANN_MLP_ANNEAL |
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. | |
void | cv::ml::randMVNormal (InputArray mean, InputArray cov, int nsamples, OutputArray samples) |
Generates sample from multivariate normal distribution. | |
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. | |
typedef ANN_MLP cv::ml::ANN_MLP_ANNEAL |
#include <opencv2/ml.hpp>
enum cv::ml::ErrorTypes |
#include <opencv2/ml.hpp>
Error types
Enumerator | |
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TEST_ERROR Python: cv.ml.TEST_ERROR | |
TRAIN_ERROR Python: cv.ml.TRAIN_ERROR |
enum cv::ml::SampleTypes |
#include <opencv2/ml.hpp>
Sample types.
Enumerator | |
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ROW_SAMPLE Python: cv.ml.ROW_SAMPLE | each training sample is a row of samples |
COL_SAMPLE Python: cv.ml.COL_SAMPLE | each training sample occupies a column of samples |
#include <opencv2/ml.hpp>
Variable types.
Enumerator | |
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VAR_NUMERICAL Python: cv.ml.VAR_NUMERICAL | same as VAR_ORDERED |
VAR_ORDERED Python: cv.ml.VAR_ORDERED | ordered variables |
VAR_CATEGORICAL Python: cv.ml.VAR_CATEGORICAL | categorical variables |
void cv::ml::createConcentricSpheresTestSet | ( | int | nsamples, |
int | nfeatures, | ||
int | nclasses, | ||
OutputArray | samples, | ||
OutputArray | responses | ||
) |
void cv::ml::randMVNormal | ( | InputArray | mean, |
InputArray | cov, | ||
int | nsamples, | ||
OutputArray | samples | ||
) |
#include <opencv2/ml.hpp>
Generates sample from multivariate normal distribution.
mean | an average row vector |
cov | symmetric covariation matrix |
nsamples | returned samples count |
samples | returned samples array |
int cv::ml::simulatedAnnealingSolver | ( | SimulatedAnnealingSolverSystem & | solverSystem, |
double | initialTemperature, | ||
double | finalTemperature, | ||
double | coolingRatio, | ||
size_t | iterationsPerStep, | ||
double * | lastTemperature = NULL , |
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cv::RNG & | rngEnergy = cv::theRNG() |
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) |
#include <opencv2/ml.hpp>
The class implements simulated annealing for optimization.
[147] for details
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) |