SHOGUN  4.1.0
CC45ClassifierTree Class Reference

## Detailed Description

Class C45ClassifierTree implements the C4.5 algorithm for decision tree learning. The algorithm steps are briefy explained below :
.

function C4.5 (R: a set of non-categorical attributes, C: the categorical attribute, S: a training set):
returns a decision tree;
begin
If S consists of records all with the same value for the categorical attribute,
return a single node with that value;
If R is empty,
return a single node with as value the most frequent
of the values of the categorical attribute in C;
[note that then there will be errors, that is, records that will be improperly classified];
For each non-categorical attribute NC in R :
If NC is continuous then first convert it to nominal attribute by separating into 2 classes about a threshold
Find Gain of all attributes
Let D be the attribute with largest Gain(D,S) among attributes in R;
Let $${d_j| j=1,2, .., m}$$ be the values of attribute D;
Let $${S_j| j=1,2, .., m}$$ be the subsets of S consisting respectively of records with value $$d_j$$ for attribute D;
Return a tree with root labeled D and arcs labeled $$d_1, d_2, .., d_m$$ going respectively to the trees
C4.5(R-{D}, C, $$S_1$$), .., C4.5(R-{D}, C, $$S_m$$);
end C4.5;

Definition at line 75 of file C45ClassifierTree.h.

Inheritance diagram for CC45ClassifierTree:
[legend]

## Public Types

typedef CTreeMachineNode
< C45TreeNodeData
node_t

typedef CBinaryTreeMachineNode
< C45TreeNodeData
bnode_t

## Public Member Functions

CC45ClassifierTree ()

virtual ~CC45ClassifierTree ()

virtual const char * get_name () const

virtual CMulticlassLabelsapply_multiclass (CFeatures *data=NULL)

void prune_tree (CDenseFeatures< float64_t > *validation_data, CMulticlassLabels *validation_labels, float64_t epsilon=0.f)

SGVector< float64_tget_certainty_vector () const

void set_weights (SGVector< float64_t > w)

SGVector< float64_tget_weights () const

void clear_weights ()

void set_feature_types (SGVector< bool > ft)

SGVector< bool > get_feature_types () const

void clear_feature_types ()

void set_root (CTreeMachineNode< C45TreeNodeData > *root)

CTreeMachineNode
< C45TreeNodeData > *
get_root ()

CTreeMachineclone_tree ()

int32_t get_num_machines () const

virtual EProblemType get_machine_problem_type () const

virtual bool is_label_valid (CLabels *lab) const

virtual bool train (CFeatures *data=NULL)

virtual CLabelsapply (CFeatures *data=NULL)

virtual CBinaryLabelsapply_binary (CFeatures *data=NULL)

virtual CRegressionLabelsapply_regression (CFeatures *data=NULL)

virtual CStructuredLabelsapply_structured (CFeatures *data=NULL)

virtual CLatentLabelsapply_latent (CFeatures *data=NULL)

virtual void set_labels (CLabels *lab)

virtual CLabelsget_labels ()

void set_max_train_time (float64_t t)

float64_t get_max_train_time ()

virtual EMachineType get_classifier_type ()

void set_solver_type (ESolverType st)

ESolverType get_solver_type ()

virtual void set_store_model_features (bool store_model)

virtual bool train_locked (SGVector< index_t > indices)

virtual float64_t apply_one (int32_t i)

virtual CLabelsapply_locked (SGVector< index_t > indices)

virtual CBinaryLabelsapply_locked_binary (SGVector< index_t > indices)

virtual CRegressionLabelsapply_locked_regression (SGVector< index_t > indices)

virtual CMulticlassLabelsapply_locked_multiclass (SGVector< index_t > indices)

virtual CStructuredLabelsapply_locked_structured (SGVector< index_t > indices)

virtual CLatentLabelsapply_locked_latent (SGVector< index_t > indices)

virtual void data_lock (CLabels *labs, CFeatures *features)

virtual void post_lock (CLabels *labs, CFeatures *features)

virtual void data_unlock ()

virtual bool supports_locking () const

bool is_data_locked () const

virtual CSGObjectshallow_copy () const

virtual CSGObjectdeep_copy () const

virtual bool is_generic (EPrimitiveType *generic) const

template<class T >
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

void unset_generic ()

virtual void print_serializable (const char *prefix="")

virtual bool save_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())

virtual bool load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())

DynArray< TParameter * > * load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="")

DynArray< TParameter * > * load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="")

void map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos)

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 ()

SGStringList< char > get_modelsel_names ()

void print_modsel_params ()

char * get_modsel_param_descr (const char *param_name)

index_t get_modsel_param_index (const char *param_name)

void build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict)

virtual void update_parameter_hash ()

virtual bool parameter_hash_changed ()

virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)

virtual CSGObjectclone ()

## Public Attributes

SGIOio

Parallelparallel

Versionversion

Parameterm_parameters

Parameterm_model_selection_parameters

ParameterMapm_parameter_map

uint32_t m_hash

## Static Public Attributes

static const float64_t MISSING =CMath::NOT_A_NUMBER

## Protected Member Functions

virtual bool train_machine (CFeatures *data=NULL)

virtual void store_model_features ()

virtual bool train_require_labels () const

virtual TParametermigrate (DynArray< TParameter * > *param_base, const SGParamInfo *target)

virtual void one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL)

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)

## Protected Attributes

CTreeMachineNode
< C45TreeNodeData > *
m_root

CDynamicObjectArraym_machines

float64_t m_max_train_time

CLabelsm_labels

ESolverType m_solver_type

bool m_store_model_features

bool m_data_locked

## Member Typedef Documentation

 typedef CBinaryTreeMachineNode bnode_t
inherited

bnode_t type- Tree node with max 2 possible children

Definition at line 55 of file TreeMachine.h.

 typedef CTreeMachineNode node_t
inherited

node_t type- Tree node with many possible children

Definition at line 52 of file TreeMachine.h.

## Constructor & Destructor Documentation

 CC45ClassifierTree ( )

constructor

Definition at line 40 of file C45ClassifierTree.cpp.

 ~CC45ClassifierTree ( )
virtual

destructor

Definition at line 46 of file C45ClassifierTree.cpp.

## Member Function Documentation

 CLabels * apply ( CFeatures * data = NULL )
virtualinherited

apply machine to data if data is not specified apply to the current features

Parameters
 data (test)data to be classified
Returns
classified labels

Definition at line 160 of file Machine.cpp.

 CBinaryLabels * apply_binary ( CFeatures * data = NULL )
virtualinherited

apply machine to data in means of binary classification problem

Definition at line 216 of file Machine.cpp.

 CLatentLabels * apply_latent ( CFeatures * data = NULL )
virtualinherited

apply machine to data in means of latent problem

Reimplemented in CLinearLatentMachine.

Definition at line 240 of file Machine.cpp.

 CLabels * apply_locked ( SGVector< index_t > indices )
virtualinherited

Applies a locked machine on a set of indices. Error if machine is not locked

Parameters
 indices index vector (of locked features) that is predicted

Definition at line 195 of file Machine.cpp.

 CBinaryLabels * apply_locked_binary ( SGVector< index_t > indices )
virtualinherited

applies a locked machine on a set of indices for binary problems

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 246 of file Machine.cpp.

 CLatentLabels * apply_locked_latent ( SGVector< index_t > indices )
virtualinherited

applies a locked machine on a set of indices for latent problems

Definition at line 274 of file Machine.cpp.

 CMulticlassLabels * apply_locked_multiclass ( SGVector< index_t > indices )
virtualinherited

applies a locked machine on a set of indices for multiclass problems

Definition at line 260 of file Machine.cpp.

 CRegressionLabels * apply_locked_regression ( SGVector< index_t > indices )
virtualinherited

applies a locked machine on a set of indices for regression problems

Reimplemented in CKernelMachine.

Definition at line 253 of file Machine.cpp.

 CStructuredLabels * apply_locked_structured ( SGVector< index_t > indices )
virtualinherited

applies a locked machine on a set of indices for structured problems

Definition at line 267 of file Machine.cpp.

 CMulticlassLabels * apply_multiclass ( CFeatures * data = NULL )
virtual

classify data using C4.5 Tree

Parameters
 data data to be classified
Returns
MulticlassLabels corresponding to labels of various test vectors

Reimplemented from CMachine.

Definition at line 50 of file C45ClassifierTree.cpp.

 virtual float64_t apply_one ( int32_t i )
virtualinherited

applies to one vector

Definition at line 247 of file Machine.h.

 CRegressionLabels * apply_regression ( CFeatures * data = NULL )
virtualinherited

apply machine to data in means of regression problem

Definition at line 222 of file Machine.cpp.

 CStructuredLabels * apply_structured ( CFeatures * data = NULL )
virtualinherited

apply machine to data in means of SO classification problem

Reimplemented in CLinearStructuredOutputMachine.

Definition at line 234 of file Machine.cpp.

 void build_gradient_parameter_dictionary ( CMap< TParameter *, CSGObject * > * dict )
inherited

Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.

Parameters
 dict dictionary of parameters to be built.

Definition at line 1244 of file SGObject.cpp.

 void clear_feature_types ( )

clear feature types of various features

Definition at line 103 of file C45ClassifierTree.cpp.

 void clear_weights ( )

clear weights of data points

Definition at line 86 of file C45ClassifierTree.cpp.

 CSGObject * clone ( )
virtualinherited

Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.

Returns
an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

Definition at line 1361 of file SGObject.cpp.

 CTreeMachine* clone_tree ( )
inherited

clone tree

Returns
clone of entire tree

Definition at line 97 of file TreeMachine.h.

 void data_lock ( CLabels * labs, CFeatures * features )
virtualinherited

Locks the machine on given labels and data. After this call, only train_locked and apply_locked may be called

Only possible if supports_locking() returns true

Parameters
 labs labels used for locking features features used for locking

Reimplemented in CKernelMachine.

Definition at line 120 of file Machine.cpp.

 void data_unlock ( )
virtualinherited

Unlocks a locked machine and restores previous state

Reimplemented in CKernelMachine.

Definition at line 151 of file Machine.cpp.

 CSGObject * deep_copy ( ) const
virtualinherited

A deep copy. All the instance variables will also be copied.

Definition at line 200 of file SGObject.cpp.

 bool equals ( CSGObject * other, float64_t accuracy = 0.0, bool tolerant = false )
virtualinherited

Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!

May be overwritten but please do with care! Should not be necessary in most cases.

Parameters
 other object to compare with accuracy accuracy to use for comparison (optional) tolerant allows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 1265 of file SGObject.cpp.

 SGVector< float64_t > get_certainty_vector ( ) const

certainty of classification done by apply_multiclass. For each data point reaching a leaf node, it computes the ratio of weight of training data with same predicted label that reached that leaf node over the total weight of all training data points that reached the same leaf node

Returns
Vector of certainty values associated with data classified in apply_multiclass

Definition at line 70 of file C45ClassifierTree.cpp.

 EMachineType get_classifier_type ( )
virtualinherited

get classifier type

Returns
classifier type NONE

Definition at line 100 of file Machine.cpp.

 SGVector< bool > get_feature_types ( ) const

set feature types of various features

Returns
bool vector - true for nominal feature false for continuous feature type

Definition at line 98 of file C45ClassifierTree.cpp.

 SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 237 of file SGObject.cpp.

 Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 279 of file SGObject.cpp.

 Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 292 of file SGObject.cpp.

 CLabels * get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 84 of file Machine.cpp.

 EProblemType get_machine_problem_type ( ) const
virtualinherited

get problem type

Reimplemented from CMachine.

Reimplemented in CCHAIDTree, and CCARTree.

Definition at line 32 of file BaseMulticlassMachine.cpp.

 float64_t get_max_train_time ( )
inherited

get maximum training time

Returns
maximum training time

Definition at line 95 of file Machine.cpp.

 SGStringList< char > get_modelsel_names ( )
inherited
Returns
vector of names of all parameters which are registered for model selection

Definition at line 1136 of file SGObject.cpp.

 char * get_modsel_param_descr ( const char * param_name )
inherited

Returns description of a given parameter string, if it exists. SG_ERROR otherwise

Parameters
 param_name name of the parameter
Returns
description of the parameter

Definition at line 1160 of file SGObject.cpp.

 index_t get_modsel_param_index ( const char * param_name )
inherited

Returns index of model selection parameter with provided index

Parameters
 param_name name of model selection parameter
Returns
index of model selection parameter with provided name, -1 if there is no such

Definition at line 1173 of file SGObject.cpp.

 virtual const char* get_name ( ) const
virtual

get name

Returns
class name C45ClassifierTree

Reimplemented from CTreeMachine< C45TreeNodeData >.

Definition at line 87 of file C45ClassifierTree.h.

 int32_t get_num_machines ( ) const
inherited

get number of machines

Returns
number of machines

Definition at line 27 of file BaseMulticlassMachine.cpp.

 CTreeMachineNode* get_root ( )
inherited

get root

Returns
root the root node of the tree

Definition at line 88 of file TreeMachine.h.

 ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 110 of file Machine.cpp.

 SGVector< float64_t > get_weights ( ) const

get weights of data points

Returns
vector of weights

Definition at line 81 of file C45ClassifierTree.cpp.

 bool is_data_locked ( ) const
inherited
Returns
whether this machine is locked

Definition at line 296 of file Machine.h.

 bool is_generic ( EPrimitiveType * generic ) const
virtualinherited

If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.

Parameters
 generic set to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 298 of file SGObject.cpp.

 bool is_label_valid ( CLabels * lab ) const
virtualinherited

check whether the labels is valid.

Parameters
 lab the labels being checked, guaranteed to be non-NULL

Reimplemented from CMachine.

Reimplemented in CCARTree, and CCHAIDTree.

Definition at line 37 of file BaseMulticlassMachine.cpp.

 DynArray< TParameter * > * load_all_file_parameters ( int32_t file_version, int32_t current_version, CSerializableFile * file, const char * prefix = "" )
inherited

maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)

Parameters
 file_version parameter version of the file current_version version from which mapping begins (you want to use Version::get_version_parameter() for this in most cases) file file to load from prefix prefix for members
Returns
(sorted) array of created TParameter instances with file data

Definition at line 705 of file SGObject.cpp.

 DynArray< TParameter * > * load_file_parameters ( const SGParamInfo * param_info, int32_t file_version, CSerializableFile * file, const char * prefix = "" )
inherited

loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned

Parameters
 param_info information of parameter file_version parameter version of the file, must be <= provided parameter version file file to load from prefix prefix for members
Returns
new array with TParameter instances with the attached data

Definition at line 546 of file SGObject.cpp.

 bool load_serializable ( CSerializableFile * file, const char * prefix = "", int32_t param_version = Version::get_version_parameter() )
virtualinherited

Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!

Parameters
 file where to load from prefix prefix for members param_version (optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 375 of file SGObject.cpp.

 void load_serializable_post ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 1063 of file SGObject.cpp.

 void load_serializable_pre ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 1058 of file SGObject.cpp.

 void map_parameters ( DynArray< TParameter * > * param_base, int32_t & base_version, DynArray< const SGParamInfo * > * target_param_infos )
inherited

Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match

Parameters
 param_base set of TParameter instances that are mapped to the provided target parameter infos base_version version of the parameter base target_param_infos set of SGParamInfo instances that specify the target parameter base

Definition at line 743 of file SGObject.cpp.

 TParameter * migrate ( DynArray< TParameter * > * param_base, const SGParamInfo * target )
protectedvirtualinherited

creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.

If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass

Parameters
 param_base set of TParameter instances to use for migration target parameter info for the resulting TParameter
Returns
a new TParameter instance with migrated data from the base of the type which is specified by the target parameter

Definition at line 950 of file SGObject.cpp.

 void one_to_one_migration_prepare ( DynArray< TParameter * > * param_base, const SGParamInfo * target, TParameter *& replacement, TParameter *& to_migrate, char * old_name = NULL )
protectedvirtualinherited

This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)

Parameters
 param_base set of TParameter instances to use for migration target parameter info for the resulting TParameter replacement (used as output) here the TParameter instance which is returned by migration is created into to_migrate the only source that is used for migration old_name with this parameter, a name change may be specified

Definition at line 890 of file SGObject.cpp.

 bool parameter_hash_changed ( )
virtualinherited
Returns
whether parameter combination has changed since last update

Definition at line 264 of file SGObject.cpp.

 virtual void post_lock ( CLabels * labs, CFeatures * features )
virtualinherited

post lock

Definition at line 287 of file Machine.h.

 void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 1112 of file SGObject.cpp.

 void print_serializable ( const char * prefix = "" )
virtualinherited

prints registered parameters out

Parameters
 prefix prefix for members

Definition at line 310 of file SGObject.cpp.

 void prune_tree ( CDenseFeatures< float64_t > * validation_data, CMulticlassLabels * validation_labels, float64_t epsilon = 0.f )

prune decision tree - uses reduced error pruning algorithm

At each node, starting from leaf nodes up to the root node, this algorithm checks if removing the subtree gives better results (or somewhat comparable results). If so, it replaces the subtree with a leaf node. The algorithm implemented is recursive which starts with the root node. At each node, it prunes its children first and then itself. As the algorithm goes down each level during recursion, it creates the new set of features by pushing subset into subset stack. While retracting, it pops these subsets to access previous state of feature matrix (see add_subset() and remove_subset() in Shogun documentation).

Parameters
 validation_data feature vectors from validation dataset validation_labels multiclass labels from validation dataset epsilon prune subtree even if there is epsilon loss in accuracy

Definition at line 62 of file C45ClassifierTree.cpp.

 bool save_serializable ( CSerializableFile * file, const char * prefix = "", int32_t param_version = Version::get_version_parameter() )
virtualinherited

Save this object to file.

Parameters
 file where to save the object; will be closed during returning if PREFIX is an empty string. prefix prefix for members param_version (optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 316 of file SGObject.cpp.

 void save_serializable_post ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 1073 of file SGObject.cpp.

 void save_serializable_pre ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 1068 of file SGObject.cpp.

 void set_feature_types ( SGVector< bool > ft )

set feature types of various features

Parameters
 ft bool vector true for nominal feature false for continuous feature type

Definition at line 92 of file C45ClassifierTree.cpp.

 void set_generic ( )
inherited

Definition at line 42 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 47 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 52 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 57 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 62 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 67 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 72 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 77 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 82 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 87 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 92 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 97 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 102 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 107 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 112 of file SGObject.cpp.

 void set_generic ( )
inherited

set generic type to T

 void set_global_io ( SGIO * io )
inherited

set the io object

Parameters
 io io object to use

Definition at line 230 of file SGObject.cpp.

 void set_global_parallel ( Parallel * parallel )
inherited

set the parallel object

Parameters
 parallel parallel object to use

Definition at line 243 of file SGObject.cpp.

 void set_global_version ( Version * version )
inherited

set the version object

Parameters
 version version object to use

Definition at line 285 of file SGObject.cpp.

 void set_labels ( CLabels * lab )
virtualinherited

set labels

Parameters
 lab labels

Reimplemented in CNeuralNetwork, CGaussianProcessMachine, CCARTree, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.

Definition at line 73 of file Machine.cpp.

 void set_max_train_time ( float64_t t )
inherited

set maximum training time

Parameters
 t maximimum training time

Definition at line 90 of file Machine.cpp.

 void set_root ( CTreeMachineNode< C45TreeNodeData > * root )
inherited

set root

Parameters
 root the root node of the tree

Definition at line 78 of file TreeMachine.h.

 void set_solver_type ( ESolverType st )
inherited

set solver type

Parameters
 st solver type

Definition at line 105 of file Machine.cpp.

 void set_store_model_features ( bool store_model )
virtualinherited

Setter for store-model-features-after-training flag

Parameters
 store_model whether model should be stored after training

Definition at line 115 of file Machine.cpp.

 void set_weights ( SGVector< float64_t > w )

set weights of data points

Parameters
 w vector of weights

Definition at line 75 of file C45ClassifierTree.cpp.

 CSGObject * shallow_copy ( ) const
virtualinherited

A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.

Reimplemented in CGaussianKernel.

Definition at line 194 of file SGObject.cpp.

 virtual void store_model_features ( )
protectedvirtualinherited

enable unlocked cross-validation - no model features to store

Reimplemented from CMachine.

Definition at line 152 of file TreeMachine.h.

 virtual bool supports_locking ( ) const
virtualinherited
Returns
whether this machine supports locking

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 293 of file Machine.h.

 bool train ( CFeatures * data = NULL )
virtualinherited

train machine

Parameters
 data training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data). If flag is set, model features will be stored after training.
Returns
whether training was successful

Reimplemented in CRelaxedTree, CAutoencoder, CSGDQN, and COnlineSVMSGD.

Definition at line 47 of file Machine.cpp.

 virtual bool train_locked ( SGVector< index_t > indices )
virtualinherited

Trains a locked machine on a set of indices. Error if machine is not locked

NOT IMPLEMENTED

Parameters
 indices index vector (of locked features) that is used for training
Returns
whether training was successful

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 239 of file Machine.h.

 bool train_machine ( CFeatures * data = NULL )
protectedvirtual

train machine - build C4.5 Tree from training data

Parameters
 data training data

Reimplemented from CMachine.

Definition at line 109 of file C45ClassifierTree.cpp.

 virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

Definition at line 354 of file Machine.h.

 void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 305 of file SGObject.cpp.

 void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 250 of file SGObject.cpp.

## Member Data Documentation

 SGIO* io
inherited

io

Definition at line 496 of file SGObject.h.

 bool m_data_locked
protectedinherited

whether data is locked

Definition at line 370 of file Machine.h.

inherited

parameters wrt which we can compute gradients

Definition at line 511 of file SGObject.h.

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 517 of file SGObject.h.

 CLabels* m_labels
protectedinherited

labels

Definition at line 361 of file Machine.h.

 CDynamicObjectArray* m_machines
protectedinherited

machines

Definition at line 56 of file BaseMulticlassMachine.h.

 float64_t m_max_train_time
protectedinherited

maximum training time

Definition at line 358 of file Machine.h.

 Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 508 of file SGObject.h.

 ParameterMap* m_parameter_map
inherited

map for different parameter versions

Definition at line 514 of file SGObject.h.

 Parameter* m_parameters
inherited

parameters

Definition at line 505 of file SGObject.h.

 CTreeMachineNode* m_root
protectedinherited

tree root

Definition at line 156 of file TreeMachine.h.

 ESolverType m_solver_type
protectedinherited

solver type

Definition at line 364 of file Machine.h.

 bool m_store_model_features
protectedinherited

whether model features should be stored after training

Definition at line 367 of file Machine.h.

 const float64_t MISSING =CMath::NOT_A_NUMBER
static

denotes that a feature in a vector is missing MISSING = NOT_A_NUMBER

Definition at line 213 of file C45ClassifierTree.h.

 Parallel* parallel
inherited

parallel

Definition at line 499 of file SGObject.h.

 Version* version
inherited

version

Definition at line 502 of file SGObject.h.

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

SHOGUN Machine Learning Toolbox - Documentation