rapidmix::machineLearning< MachineLearningModule > Class Template Reference

Host class for machine learning algorithms. More...

#include <machineLearning.h>

Inheritance diagram for rapidmix::machineLearning< MachineLearningModule >:

Collaboration diagram for rapidmix::machineLearning< MachineLearningModule >:

Public Member Functions
	machineLearning ()
template<class T >
	machineLearning (T type)
bool	train (const trainingData &newTrainingData)
	This function becomes specialized in the implementation. More...
std::vector< double >	run (const std::vector< double > &inputVector)
std::string	run (const std::vector< double > &inputVector, const std::string &label)
std::string	run (const std::vector< std::vector< double > > &inputSeries)
bool	reset ()
template<>
bool	train (const trainingData &newTrainingData)
template<>
bool	train (const trainingData &newTrainingData)
template<>
bool	train (const trainingData &newTrainingData)
template<>
bool	train (const trainingData &newTrainingData)
template<>
std::string	run (const std::vector< double > &inputVector, const std::string &label)
template<>
std::string	run (const std::vector< std::vector< double > > &inputSeries)

Detailed Description

template<typename MachineLearningModule>
class rapidmix::machineLearning< MachineLearningModule >

Host class for machine learning algorithms.

Constructor & Destructor Documentation

machineLearning() [1/2]

template<typename MachineLearningModule >

rapidmix::machineLearning< MachineLearningModule >::machineLearning ( )

inline

machineLearning() [2/2]

template<typename MachineLearningModule >

template<class T >

rapidmix::machineLearning< MachineLearningModule >::machineLearning ( T  type )

inline

Member Function Documentation

reset()

template<typename MachineLearningModule >

bool rapidmix::machineLearning< MachineLearningModule >::reset ( )

inline

run() [1/5]

template<typename MachineLearningModule >

std::vector<double> rapidmix::machineLearning< MachineLearningModule >::run ( const std::vector< double > &  inputVector )

inline

run() [2/5]

template<typename MachineLearningModule >

std::string rapidmix::machineLearning< MachineLearningModule >::run	(	const std::vector< double > &	inputVector,
		const std::string &	label
	)

run() [3/5]

template<typename MachineLearningModule >

std::string rapidmix::machineLearning< MachineLearningModule >::run

(

const std::vector< std::vector< double > > &

inputSeries

)

run() [4/5]

template<>

std::string rapidmix::machineLearning< classification< double > >::run	(	const std::vector< double > &	inputVector,
		const std::string &	label
	)

run() [5/5]

template<>

std::string rapidmix::machineLearning< seriesClassification< double > >::run

(

const std::vector< std::vector< double > > &

inputSeries

)

train() [1/5]

template<>

bool rapidmix::machineLearning< classification< double > >::train

(

const trainingData &

newTrainingData

)

train() [2/5]

template<>

bool rapidmix::machineLearning< regression< double > >::train

(

const trainingData &

newTrainingData

)

train() [3/5]

template<>

bool rapidmix::machineLearning< seriesClassification< double > >::train

(

const trainingData &

newTrainingData

)

train() [4/5]

template<class MachineLearningModule >

bool rapidmix::machineLearning< MachineLearningModule >::train

(

const trainingData &

newTrainingData

)

This function becomes specialized in the implementation.

Generic train.

train() [5/5]

template<>

bool rapidmix::machineLearning< rapidGVF >::train

(

const trainingData &

newTrainingData

)

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The documentation for this class was generated from the following files:

• src/machineLearning/machineLearning.h
• src/machineLearning/machineLearning.cpp
• src/machineLearning/rapidXMM/rapidXMM.cpp