Stump Class Reference

Decision stump. More...

#include <stump.h>

Inheritance diagram for Stump:

[legend]Collaboration diagram for Stump:

[legend]List of all members.

Public Member Functions
	Stump (UINT n_in=0)
	Stump (std::istream &is)
virtual const id_t &	id () const
virtual Stump *	create () const
	Create a new object using the default constructor.
virtual Stump *	clone () const
	Create a new object by replicating itself.
UINT	index () const
REAL	threshold () const
bool	direction () const
bool	soft_threshold () const
void	use_soft_threshold (bool s=true)
virtual bool	support_weighted_data () const
	Whether the learning model/algorithm supports unequally weighted data.
virtual void	train ()
	Train with preset data set and sample weight.
virtual Output	operator() (const Input &) const
Static Public Member Functions
static REAL	train_1d (const std::vector< REAL > &, const std::vector< REAL > &, REAL, bool &, bool &, REAL &, REAL &)
	Find the optimal threshold and direction (prefer the middle thresholds).
static REAL	train_1d (const std::vector< REAL > &, const std::vector< REAL > &)
	Find the optimal threshold for positive direction.
Protected Member Functions
virtual bool	serialize (std::ostream &, ver_list &) const
virtual bool	unserialize (std::istream &, ver_list &, const id_t &=NIL_ID)

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Detailed Description

Decision stump.

Todo:

Documentation

Definition at line 18 of file stump.h.

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Constructor & Destructor Documentation

Stump (  UINT  n_in = 0  )  [inline, explicit]

Definition at line 25 of file stump.h. Referenced by Stump::clone(), and Stump::create().

Stump (  std::istream &  is  )  [inline, explicit]

Definition at line 27 of file stump.h.

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Member Function Documentation

virtual Stump* clone (   )  const [inline, virtual]

Create a new object by replicating itself. Returns: A pointer to the new copy. The code for a derived class Derived is always return new Derived(*this); Though seemingly redundant, it helps to copy an object without knowing the real type of the object. See also: C++ FAQ Lite 20.6 Implements LearnModel. Definition at line 31 of file stump.h. References Stump::Stump().

virtual Stump* create (   )  const [inline, virtual]

Create a new object using the default constructor. The code for a derived class Derived is always return new Derived(); Implements LearnModel. Definition at line 30 of file stump.h. References Stump::Stump().

bool direction (   )  const [inline]

Definition at line 35 of file stump.h.

virtual const id_t& id (   )  const [virtual]

Returns: Class ID string (class name) Implements Object.

UINT index (   )  const [inline]

Definition at line 33 of file stump.h.

Output operator() (  const Input &   )  const [virtual]

Implements LearnModel. Definition at line 194 of file stump.cpp. References LearnModel::n_input().

bool serialize (  std::ostream &  , ver_list &  )  const [protected, virtual]

Reimplemented from LearnModel. Definition at line 16 of file stump.cpp. References SERIALIZE_PARENT.

bool soft_threshold (   )  const [inline]

Definition at line 36 of file stump.h.

virtual bool support_weighted_data (   )  const [inline, virtual]

Whether the learning model/algorithm supports unequally weighted data. Returns: true if supporting; false otherwise. The default is false, just for safety. See also: set_train_data() Reimplemented from LearnModel. Definition at line 39 of file stump.h.

REAL threshold (   )  const [inline]

Definition at line 34 of file stump.h.

void train (   )  [virtual]

Train with preset data set and sample weight. Implements LearnModel. Definition at line 160 of file stump.cpp. References LearnModel::_n_in, LearnModel::n_samples, LearnModel::ptd, LearnModel::ptw, LearnModel::set_dimensions(), and Stump::train_1d().

REAL train_1d (  const std::vector< REAL > &  , const std::vector< REAL > &  )  [static]

Find the optimal threshold for positive direction. Definition at line 43 of file stump.cpp. References INFINITESIMAL, and INFINITY.

REAL train_1d (  const std::vector< REAL > &  , const std::vector< REAL > &  , REAL  , bool &  , bool &  , REAL &  , REAL &  )  [static]

Find the optimal threshold and direction (prefer the middle thresholds). Definition at line 82 of file stump.cpp. References INFINITESIMAL. Referenced by Stump::train().

bool unserialize (  std::istream &  , ver_list &  , const id_t &  = NIL_ID )  [protected, virtual]

Reimplemented from LearnModel. Definition at line 22 of file stump.cpp. References LearnModel::_n_in, LearnModel::_n_out, Object::NIL_ID, and UNSERIALIZE_PARENT.

void use_soft_threshold (  bool  s = true  )  [inline]

Definition at line 37 of file stump.h.

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

• stump.h
• stump.cpp

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