#include <regression.h>
Public Member Functions | |
regressionTemplate () | |
regressionTemplate (const std::vector< trainingExampleTemplate< T > > &trainingSet) | |
regressionTemplate (const int &numInputs, const int &numOutputs) | |
~regressionTemplate () | |
bool | train (const std::vector< trainingExampleTemplate< T > > &trainingSet) |
void | setNumEpochs (const int &epochs) |
std::vector< int > | getNumHiddenLayers () |
void | setNumHiddenLayers (const int &num_hidden_layers) |
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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 | |
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std::vector< baseModel< T > * > | myModelSet |
int | numInputs |
std::vector< std::string > | inputNames |
int | numOutputs |
bool | created |
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.
regressionTemplate< T >::regressionTemplate | ( | ) |
with no arguments, just make an empty vector
regressionTemplate< T >::regressionTemplate | ( | const std::vector< trainingExampleTemplate< T > > & | trainingSet | ) |
create based on training set inputs and outputs
regressionTemplate< T >::regressionTemplate | ( | const int & | numInputs, |
const int & | numOutputs | ||
) |
create with proper models, but not trained
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inline |
destructor
std::vector< int > regressionTemplate< T >::getNumHiddenLayers | ( | ) |
Check how many hidden layers are in each model. This feature is temporary, and will be replaced by a different design.
void regressionTemplate< T >::setNumEpochs | ( | const int & | epochs | ) |
Call before train, to set the number of training epochs
void regressionTemplate< 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.
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virtual |
Train on a specified set, causes creation if not created
Reimplemented from modelSet< T >.