OpenCV: Machine Learning

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OpenCV 3.0.0

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::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::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

enum cv::ml::ErrorTypes

Error types

Enumerator
TEST_ERROR
TRAIN_ERROR

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

enum cv::ml::VariableTypes

Variable types.

Enumerator
VAR_NUMERICAL	same as VAR_ORDERED
VAR_ORDERED	ordered variables
VAR_CATEGORICAL	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.

Parameters: mean an average row vector

cov symmetric covariation matrix

nsamples returned samples count

samples returned samples array

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