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CAveragedPerceptron Class Reference

Detailed Description

Class Averaged Perceptron implements the standard linear (online) algorithm. Averaged perceptron is the simple extension of Perceptron.

Given a maximum number of iterations (the standard averaged perceptron algorithm is not guaranteed to converge) and a fixed lerning rate, the result is a linear classifier.

See Also
CLinearMachine

Definition at line 30 of file AveragedPerceptron.h.

Inheritance diagram for CAveragedPerceptron:
Inheritance graph
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Public Member Functions

 CAveragedPerceptron ()
 CAveragedPerceptron (CDotFeatures *traindat, CLabels *trainlab)
virtual ~CAveragedPerceptron ()
virtual EClassifierType get_classifier_type ()
virtual bool train (CFeatures *data=NULL)
void set_learn_rate (float64_t r)
 set learn rate of gradient descent training algorithm
void set_max_iter (int32_t i)
 set maximum number of iterations
virtual const char * get_name () const
- Public Member Functions inherited from CLinearMachine
 CLinearMachine ()
virtual ~CLinearMachine ()
void get_w (float64_t *&dst_w, int32_t &dst_dims)
SGVector< float64_tget_w ()
void set_w (SGVector< float64_t > src_w)
void set_bias (float64_t b)
float64_t get_bias ()
virtual bool load (FILE *srcfile)
virtual bool save (FILE *dstfile)
virtual void set_features (CDotFeatures *feat)
virtual CLabelsapply ()
virtual CLabelsapply (CFeatures *data)
virtual float64_t apply (int32_t vec_idx)
 get output for example "vec_idx"
virtual CDotFeaturesget_features ()
- Public Member Functions inherited from CMachine
 CMachine ()
virtual ~CMachine ()
virtual void set_labels (CLabels *lab)
virtual CLabelsget_labels ()
virtual float64_t get_label (int32_t i)
void set_max_train_time (float64_t t)
float64_t get_max_train_time ()
void set_solver_type (ESolverType st)
ESolverType get_solver_type ()
virtual void set_store_model_features (bool store_model)
- 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)

Protected Attributes

float64_t learn_rate
int32_t max_iter
- Protected Attributes inherited from CLinearMachine
int32_t w_dim
float64_tw
float64_t bias
CDotFeaturesfeatures
- Protected Attributes inherited from CMachine
float64_t max_train_time
CLabelslabels
ESolverType solver_type
bool m_store_model_features

Additional Inherited Members

- Public Attributes inherited from CSGObject
SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
- Protected Member Functions inherited from CLinearMachine
virtual void store_model_features ()

Constructor & Destructor Documentation

default constructor

Definition at line 16 of file AveragedPerceptron.cpp.

CAveragedPerceptron ( CDotFeatures traindat,
CLabels trainlab 
)

constructor

Parameters
traindattraining features
trainlablabels for training features

Definition at line 21 of file AveragedPerceptron.cpp.

~CAveragedPerceptron ( )
virtual

Definition at line 28 of file AveragedPerceptron.cpp.

Member Function Documentation

virtual EClassifierType get_classifier_type ( )
virtual

get classifier type

Returns
classifier type AVERAGEDPERCEPTRON

Reimplemented from CMachine.

Definition at line 48 of file AveragedPerceptron.h.

virtual const char* get_name ( ) const
virtual
Returns
object name

Reimplemented from CLinearMachine.

Definition at line 73 of file AveragedPerceptron.h.

void set_learn_rate ( float64_t  r)

set learn rate of gradient descent training algorithm

Definition at line 61 of file AveragedPerceptron.h.

void set_max_iter ( int32_t  i)

set maximum number of iterations

Definition at line 67 of file AveragedPerceptron.h.

bool train ( CFeatures data = NULL)
virtual

train classifier

Parameters
datatraining data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data)
Returns
whether training was successful

Reimplemented from CMachine.

Definition at line 32 of file AveragedPerceptron.cpp.

Member Data Documentation

float64_t learn_rate
protected

learning rate

Definition at line 77 of file AveragedPerceptron.h.

int32_t max_iter
protected

maximum number of iterations

Definition at line 79 of file AveragedPerceptron.h.


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

SHOGUN Machine Learning Toolbox - Documentation