OpenCV  3.3.0-rc
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::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::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...
 

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

Error types

Enumerator
TEST_ERROR 
TRAIN_ERROR 

§ 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

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