/usr/include/shogun/machine/BaggingMachine.h is in libshogun-dev 3.2.0-7.5.
This file is owned by root:root, with mode 0o644.
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* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2013 Viktor Gal
* Copyright (C) 2013 Viktor Gal
*/
#ifndef BAGGINGMACHINE_H
#define BAGGINGMACHINE_H
#include <shogun/machine/Machine.h>
#include <shogun/ensemble/CombinationRule.h>
#include <shogun/evaluation/Evaluation.h>
namespace shogun
{
/**
* @brief: Bagging algorithm
* i.e. bootstrap aggregating
*/
class CBaggingMachine : public CMachine
{
public:
/** default ctor */
CBaggingMachine();
/**
* constructor
*
* @param features training features
* @param labels training labels
*/
CBaggingMachine(CFeatures* features, CLabels* labels);
virtual ~CBaggingMachine();
virtual CBinaryLabels* apply_binary(CFeatures* data=NULL);
virtual CMulticlassLabels* apply_multiclass(CFeatures* data=NULL);
virtual CRegressionLabels* apply_regression(CFeatures* data=NULL);
/**
* Set number of bags/machine to create
*
* @param num_bags number of bags
*/
void set_num_bags(int32_t num_bags);
/**
* Get number of bags/machines
*
* @return number of bags
*/
int32_t get_num_bags() const;
/**
* Set number of feature vectors to use
* for each bag/machine
*
* @param bag_size number of vectors to use for a bag
*/
void set_bag_size(int32_t bag_size);
/**
* Get number of feature vectors that are use
* for training each bag/machine
*
* @return number of vectors used for training for each bag.
*/
int32_t get_bag_size() const;
/**
* Get machine for bagging
*
* @return machine that is being used in bagging
*/
CMachine* get_machine() const;
/**
* Set machine to use in bagging
*
* @param machine the machine to use for bagging
*/
void set_machine(CMachine* machine);
/**
* Set the combination rule to use for aggregating the classification
* results
*
* @param rule combination rule
*/
void set_combination_rule(CCombinationRule* rule);
/**
* Get the combination rule that is used for aggregating the results
*
* @return CCombinationRule
*/
CCombinationRule* get_combination_rule() const;
/** get classifier type
*
* @return classifier type CT_BAGGING
*/
virtual EMachineType get_classifier_type() { return CT_BAGGING; }
/** get out-of-bag error
* CombinationRule is used for combining the predictions.
*
* @param eval Evaluation method to use for calculating the error
* @return out-of-bag error.
*/
float64_t get_oob_error(CEvaluation* eval) const;
/** name **/
virtual const char* get_name() const { return "BaggingMachine"; }
protected:
virtual bool train_machine(CFeatures* data=NULL);
/** helper function for the apply_{regression,..} functions that
* computes the output
*
* @param data the data to compute the output for
* @return predictions
*/
SGVector<float64_t> apply_get_outputs(CFeatures* data);
private:
void register_parameters();
void init();
/**
* get the vector of indices for feature vectors that are out of bag
*
* @param in_bag vector of indices that are in bag.
* NOTE: in_bag is a randomly generated with replacement
* @return
*/
CDynamicArray<index_t>* get_oob_indices(const SGVector<index_t>& in_bag);
void clear_oob_indicies();
private:
/** bags array */
CDynamicObjectArray* m_bags;
/** features to train on */
CFeatures* m_features;
/** machine to use for bagging */
CMachine* m_machine;
/** number of bags to create */
int32_t m_num_bags;
/** number of vectors to use from the training features */
int32_t m_bag_size;
/** combination rule to use */
CCombinationRule* m_combination_rule;
/** indices of all feature vectors that are out of bag */
SGVector<bool> m_all_oob_idx;
/** array of oob indices */
CDynamicObjectArray* m_oob_indices;
};
}
#endif /* BAGGINGMACHINE_H */
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