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CSquaredLoss Class Reference

Detailed Description

CSquaredLoss implements the squared loss function.

Definition at line 26 of file SquaredLoss.h.

Inheritance diagram for CSquaredLoss:
Inheritance graph
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Public Member Functions

 CSquaredLoss ()
 ~CSquaredLoss ()
virtual float64_t loss (float64_t prediction, float64_t label)
virtual float64_t first_derivative (float64_t prediction, float64_t label)
virtual float64_t second_derivative (float64_t prediction, float64_t label)
virtual float64_t get_update (float64_t prediction, float64_t label, float64_t eta_t, float64_t norm)
virtual float64_t get_square_grad (float64_t prediction, float64_t label)
virtual ELossType get_loss_type ()
virtual const char * get_name () const
- Public Member Functions inherited from CLossFunction
 CLossFunction ()
virtual ~CLossFunction ()
- Public Member Functions inherited from CSGObject
 CSGObject ()
 CSGObject (const CSGObject &orig)
virtual ~CSGObject ()
virtual bool is_generic (EPrimitiveType *generic) const
template<class T >
void set_generic ()
void unset_generic ()
virtual void print_serializable (const char *prefix="")
virtual bool save_serializable (CSerializableFile *file, const char *prefix="")
virtual bool load_serializable (CSerializableFile *file, const char *prefix="")
void set_global_io (SGIO *io)
SGIOget_global_io ()
void set_global_parallel (Parallel *parallel)
Parallelget_global_parallel ()
void set_global_version (Version *version)
Versionget_global_version ()
SGVector< char * > get_modelsel_names ()
char * get_modsel_param_descr (const char *param_name)
index_t get_modsel_param_index (const char *param_name)

Additional Inherited Members

- Public Attributes inherited from CSGObject
SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
- Protected Member Functions inherited from CSGObject
virtual void load_serializable_pre () throw (ShogunException)
virtual void load_serializable_post () throw (ShogunException)
virtual void save_serializable_pre () throw (ShogunException)
virtual void save_serializable_post () throw (ShogunException)

Constructor & Destructor Documentation

Constructor

Definition at line 32 of file SquaredLoss.h.

Destructor

Definition at line 37 of file SquaredLoss.h.

Member Function Documentation

float64_t first_derivative ( float64_t  prediction,
float64_t  label 
)
virtual

Get square of the gradient of the loss function

Parameters
predictionprediction
labellabel
Returns
square of gradient

Implements CLossFunction.

Definition at line 28 of file SquaredLoss.cpp.

virtual ELossType get_loss_type ( )
virtual

Return loss type

Returns
L_SQUAREDLOSS

Implements CLossFunction.

Definition at line 96 of file SquaredLoss.h.

virtual const char* get_name ( ) const
virtual

Return the name of the object

Returns
LossFunction

Reimplemented from CLossFunction.

Definition at line 98 of file SquaredLoss.h.

float64_t get_square_grad ( float64_t  prediction,
float64_t  label 
)
virtual

Get square of gradient, used for adaptive learning

Parameters
predictionprediction
labellabel
Returns
square of gradient

Implements CLossFunction.

Definition at line 51 of file SquaredLoss.cpp.

float64_t get_update ( float64_t  prediction,
float64_t  label,
float64_t  eta_t,
float64_t  norm 
)
virtual

Get importance aware weight update for this loss function

Parameters
predictionprediction
labellabel
eta_tlearning rate at update number t
normscale value
Returns
update

Implements CLossFunction.

Definition at line 38 of file SquaredLoss.cpp.

float64_t loss ( float64_t  prediction,
float64_t  label 
)
virtual

Get loss for an example

Parameters
predictionprediction
labellabel
Returns
loss

Implements CLossFunction.

Definition at line 21 of file SquaredLoss.cpp.

float64_t second_derivative ( float64_t  prediction,
float64_t  label 
)
virtual

Get second derivative of the loss function

Parameters
predictionprediction
labellabel
Returns
second derivative

Implements CLossFunction.

Definition at line 33 of file SquaredLoss.cpp.


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

SHOGUN Machine Learning Toolbox - Documentation