RapidLib  v2.0.0
A simple library for interactive machine learning
regression< T > Class Template Reference

#include <regression.h>

Inheritance diagram for regression< T >:
Inheritance graph
Collaboration diagram for regression< T >:
Collaboration graph

Public Member Functions

 regression ()
 
 regression (const std::vector< trainingExample< T > > &trainingSet)
 
 regression (const int &numInputs, const int &numOutputs)
 
 ~regression ()
 
bool train (const std::vector< trainingExample< T > > &trainingSet)
 
void setNumEpochs (const int &epochs)
 
std::vector< int > getNumHiddenLayers ()
 
void setNumHiddenLayers (const int &num_hidden_layers)
 
- Public Member Functions inherited from modelSet< T >
 modelSet ()
 
virtual ~modelSet ()
 
bool reset ()
 
std::vector< T > run (const std::vector< T > &inputVector)
 
std::string getJSON ()
 
void writeJSON (const std::string &filepath)
 
bool putJSON (const std::string &jsonMessage)
 
bool readJSON (const std::string &filepath)
 

Additional Inherited Members

- Protected Attributes inherited from modelSet< T >
std::vector< baseModel< T > * > myModelSet
 
int numInputs
 
std::vector< std::string > inputNames
 
int numOutputs
 
bool created
 

Detailed Description

template<typename T>
class regression< T >

Class for implementing a set of regression models.

This doesn't do anything modelSet can't do. But, it's simpler and more like wekinator.

Constructor & Destructor Documentation

§ regression() [1/3]

template<typename T >
regression< T >::regression ( )

with no arguments, just make an empty vector

§ regression() [2/3]

template<typename T >
regression< T >::regression ( const std::vector< trainingExample< T > > &  trainingSet)

create based on training set inputs and outputs

§ regression() [3/3]

template<typename T >
regression< T >::regression ( const int &  numInputs,
const int &  numOutputs 
)

create with proper models, but not trained

§ ~regression()

template<typename T >
regression< T >::~regression ( )
inline

destructor

Member Function Documentation

§ getNumHiddenLayers()

template<typename T >
std::vector< int > regression< T >::getNumHiddenLayers ( )

Check how many hidden layers are in each model. This feature is temporary, and will be replaced by a different design.

§ setNumEpochs()

template<typename T >
void regression< T >::setNumEpochs ( const int &  epochs)

Call before train, to set the number of training epochs

§ setNumHiddenLayers()

template<typename T >
void regression< T >::setNumHiddenLayers ( const int &  num_hidden_layers)

Set how many hidden layers are in all models. This feature is temporary, and will be replaced by a different design.

§ train()

template<typename T >
bool regression< T >::train ( const std::vector< trainingExample< T > > &  trainingSet)
virtual

Train on a specified set, causes creation if not created

Reimplemented from modelSet< T >.


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