class Bagging

This class represents a Trainer that implements the well-known Bagging algorithm (Breiman, 1996).

Inheritance:

Public Fields

WeightedSumMachine* w_machine

This machine performs the combination.

int n_trainers

The number of trainers in the bagging

int** unselected_examples

for each trainer, keep the indices of examples not used during training

int** selected_examples

for each trainer, keep the indices of examples used during training

int* n_unselected_examples

for each trainer, keep the number of examples not used during training

int* is_selected_examples

for each trainer, keep the number of examples used during training

Public Methods

Bagging(WeightedSumMachine* w_machine_, DataSet* data_)

virtual void bootstrapData(int* selected, int* is_selected)

create a boostrap of the data and put in in selected

Inherited from Trainer:

Public Methods

virtual void train(List* measurers)

virtual void test(List* measurers)

virtual void testExample(List* measurers, int t)

virtual void crossValidate(int k_fold, List* train_measurers, List* test_measurers, List* cross_valid_measurers=NULL)

virtual void loadFILE(FILE* file)

virtual void saveFILE(FILE* file)

Inherited from Object:

Public Methods

virtual void init()

void addOption(const char* name, int size, void* ptr, const char* help="", bool is_allowed_after_init=false)

void addIOption(const char* name, int* ptr, int init_value, const char* help="", bool is_allowed_after_init=false)

void addROption(const char* name, real* ptr, real init_value, const char* help="", bool is_allowed_after_init=false)

void addBOption(const char* name, bool* ptr, bool init_value, const char* help="", bool is_allowed_after_init=false)

void setOption(const char* name, void* ptr)

void setIOption(const char* name, int option)

void setROption(const char* name, real option)

void setBOption(const char* name, bool option)

void load(const char* filename)

void save(const char* filename)

Documentation

This class represents a Trainer that implements the well-known Bagging algorithm (Breiman, 1996). A "bagger" contains a series of trainers, each trained on a bootstrap of the original dataset. The output of the bagging is then the average of the output of each trainer.

It is implemented using a WeightedSumMachine that performs the combination.

WeightedSumMachine* w_machine

This machine performs the combination. It contains many trainers.

int n_trainers

The number of trainers in the bagging

int** unselected_examples

for each trainer, keep the indices of examples not used during training

int** selected_examples

for each trainer, keep the indices of examples used during training

int* n_unselected_examples

for each trainer, keep the number of examples not used during training

int* is_selected_examples

for each trainer, keep the number of examples used during training

Bagging(WeightedSumMachine* w_machine_, DataSet* data_)

virtual void bootstrapData(int* selected, int* is_selected)

create a boostrap of the data and put in in selected

This class has no child classes.

Author:

Samy Bengio (bengio@idiap.ch)

See Also:

WeightedSumMachine

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