机器学习


class cv::ml::ANN_MLP
Artificial Neural Networks - Multi-Layer Perceptrons. 更多...
class cv::ml::Boost
Boosted tree classifier derived from DTrees . 更多...
class cv::ml::DTrees
The class represents a single decision tree or a collection of decision trees. 更多...
class cv::ml::EM
The class implements the Expectation Maximization algorithm. 更多...
class cv::ml::KNearest
The class implements K-Nearest Neighbors model. 更多...
class cv::ml::LogisticRegression
Implements Logistic Regression classifier. 更多...
class cv::ml::NormalBayesClassifier
Bayes classifier for normally distributed data. 更多...
class cv::ml::ParamGrid
The structure represents the logarithmic grid range of statmodel parameters. 更多...
class cv::ml::RTrees
The class implements the random forest predictor. 更多...
struct cv::ml::SimulatedAnnealingSolverSystem
This class declares example interface for system state used in simulated annealing optimization algorithm. 更多...
class cv::ml::StatModel
Base class for statistical models in OpenCV ML. 更多...
class cv::ml::SVM
Support Vector Machines. 更多...
class cv::ml::SVMSGD
Stochastic Gradient Descent SVM classifier. 更多...
class cv::ml::TrainData
Class encapsulating training data. 更多...

Typedefs

typedef ANN_MLP cv::ml::ANN_MLP_ANNEAL

枚举

enum cv::ml::ErrorTypes {
cv::ml::TEST_ERROR = 0,
cv::ml::TRAIN_ERROR = 1
}
Error types 更多...
enum cv::ml::SampleTypes {
cv::ml::ROW_SAMPLE = 0,
cv::ml::COL_SAMPLE = 1
}
Sample types. 更多...
enum cv::ml::VariableTypes {
cv::ml::VAR_NUMERICAL =0,
cv::ml::VAR_ORDERED =0,
cv::ml::VAR_CATEGORICAL =1
}
Variable types. 更多...

函数

void cv::ml::createConcentricSpheresTestSet (int nsamples, int nfeatures, int nclasses, OutputArray samples, OutputArray responses)
Creates test set. 更多...
void cv::ml::randMVNormal ( InputArray mean , InputArray cov, int nsamples, OutputArray samples)
Generates sample from multivariate normal distribution. 更多...
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. 更多...

详细描述

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 .

Typedef 文档编制

ANN_MLP_ANNEAL

枚举类型文档编制

ErrorTypes

#include < opencv2/ml.hpp >

Error types

枚举器
TEST_ERROR
Python: cv.ml.TEST_ERROR
TRAIN_ERROR
Python: cv.ml.TRAIN_ERROR

SampleTypes

#include < opencv2/ml.hpp >

Sample types.

枚举器
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

VariableTypes

#include < opencv2/ml.hpp >

Variable types.

枚举器
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

函数文档编制

createConcentricSpheresTestSet()

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

#include < opencv2/ml.hpp >

Creates test set.

randMVNormal()

void cv::ml::randMVNormal ( InputArray mean ,
InputArray cov ,
int nsamples ,
OutputArray samples
)

#include < opencv2/ml.hpp >

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 ()
)

#include < opencv2/ml.hpp >

The class implements simulated annealing for optimization.

[119] 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)