RapidLib  v0.1.2
A simple library for interactive machine learning
regression Class Reference

#include <regression.h>

Inheritance diagram for regression:
Inheritance graph
Collaboration diagram for regression:
Collaboration graph

Public Member Functions

 regression ()
 
 regression (const std::vector< trainingExample > &trainingSet)
 
 regression (const int &numInputs, const int &numOutputs)
 
 ~regression ()
 
bool train (const std::vector< trainingExample > &trainingSet)
 
- Public Member Functions inherited from modelSet
 modelSet ()
 
virtual ~modelSet ()
 
bool initialize ()
 
std::vector< double > process (const std::vector< double > &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
std::vector< baseModel * > myModelSet
 
int numInputs
 
std::vector< std::string > inputNames
 
int numOutputs
 
bool created
 

Detailed Description

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]

regression::regression ( )

with no arguments, just make an empty vector

§ regression() [2/3]

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

create based on training set inputs and outputs

§ regression() [3/3]

regression::regression ( const int &  numInputs,
const int &  numOutputs 
)

create with proper models, but not trained

§ ~regression()

regression::~regression ( )
inline

destructor

Member Function Documentation

§ train()

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

Train on a specified set, causes creation if not created

Reimplemented from modelSet.


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