class Trainer

Trainer.

Inheritance:


Public Methods

[more] Trainer(Machine* machine_, DataSet* data_)
[more]virtual void train(List* measurers) = 0
Train the machine.
[more]virtual void test(List* measurers)
Test the machine.
[more]virtual void testExample(List* measurers, int t)
Test on one example.
[more]virtual void crossValidate(int k_fold, List* train_measurers, List* test_measurers, List* cross_valid_measurers=NULL)
K-fold cross-validation.
[more]virtual void loadFILE(FILE* file)
Load the parameters of the machine and the dataset
[more]virtual void saveFILE(FILE* file)
Save the parameters of the machine and the dataset


Inherited from Object:

Public Methods

ovirtual void init()
ovoid addOption(const char* name, int size, void* ptr, const char* help="", bool is_allowed_after_init=false)
ovoid addIOption(const char* name, int* ptr, int init_value, const char* help="", bool is_allowed_after_init=false)
ovoid addROption(const char* name, real* ptr, real init_value, const char* help="", bool is_allowed_after_init=false)
ovoid addBOption(const char* name, bool* ptr, bool init_value, const char* help="", bool is_allowed_after_init=false)
ovoid setOption(const char* name, void* ptr)
ovoid setIOption(const char* name, int option)
ovoid setROption(const char* name, real option)
ovoid setBOption(const char* name, bool option)
ovoid load(const char* filename)
ovoid save(const char* filename)


Documentation

Trainer.

A trainer takes a Machine and a DataSet, and is able to train this machine on this dataset.

o Trainer(Machine* machine_, DataSet* data_)

ovirtual void train(List* measurers) = 0
Train the machine. The Trainer has to call the measurers when it want.

ovirtual void test(List* measurers)
Test the machine. This method call all the measurers, for all the examples of their associated dataset. It's already written...

ovirtual void testExample(List* measurers, int t)
Test on one example. It supposes that all the measurers have the same dataset, and call the measurers for the example t of this dataset.

ovirtual void crossValidate(int k_fold, List* train_measurers, List* test_measurers, List* cross_valid_measurers=NULL)
K-fold cross-validation. Do a K-fold cross-validation on data.

ovirtual void loadFILE(FILE* file)
Load the parameters of the machine and the dataset

ovirtual void saveFILE(FILE* file)
Save the parameters of the machine and the dataset


Direct child classes:
QCTrainer
NPTrainer
GMTrainer
EMTrainer
Boosting
BayesClassifier
Bagging

Alphabetic index HTML hierarchy of classes or Java



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