Detailed Description

Conditional Probability Tree, decide subtree by a random strategy.

Definition at line 21 of file RandomConditionalProbabilityTree.h.

Inheritance diagram for CRandomConditionalProbabilityTree:

Public Types
typedef CTreeMachineNode < ConditionalProbabilityTreeNodeData >	node_t

Public Member Functions
	CRandomConditionalProbabilityTree ()
virtual	~CRandomConditionalProbabilityTree ()
virtual const char *	get_name () const
void	set_num_passes (int32_t num_passes)
int32_t	get_num_passes () const
void	set_features (CStreamingDenseFeatures< float32_t > *feats)
virtual CMulticlassLabels *	apply_multiclass (CFeatures *data=NULL)
virtual int32_t	apply_multiclass_example (SGVector< float32_t > ex)
void	print_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 CLabels *	apply (CFeatures *data=NULL)
virtual CBinaryLabels *	apply_binary (CFeatures *data=NULL)
virtual CRegressionLabels *	apply_regression (CFeatures *data=NULL)
virtual CStructuredLabels *	apply_structured (CFeatures *data=NULL)
virtual CLatentLabels *	apply_latent (CFeatures *data=NULL)
virtual void	set_labels (CLabels *lab)
virtual CLabels *	get_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 CLabels *	apply_locked (SGVector< index_t > indices)
virtual CBinaryLabels *	apply_locked_binary (SGVector< index_t > indices)
virtual CRegressionLabels *	apply_locked_regression (SGVector< index_t > indices)
virtual CMulticlassLabels *	apply_locked_multiclass (SGVector< index_t > indices)
virtual CStructuredLabels *	apply_locked_structured (SGVector< index_t > indices)
virtual CLatentLabels *	apply_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 CSGObject *	shallow_copy () const
virtual CSGObject *	deep_copy () const
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="", 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)
SGIO *	get_global_io ()
void	set_global_parallel (Parallel *parallel)
Parallel *	get_global_parallel ()
void	set_global_version (Version *version)
Version *	get_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 bool	update_parameter_hash ()
virtual bool	equals (CSGObject *other, float64_t accuracy=0.0)
virtual CSGObject *	clone ()

Public Attributes
SGIO *	io
Parallel *	parallel
Version *	version
Parameter *	m_parameters
Parameter *	m_model_selection_parameters
Parameter *	m_gradient_parameters
ParameterMap *	m_parameter_map
uint32_t	m_hash

Protected Member Functions
virtual bool	which_subtree (node_t *node, SGVector< float32_t > ex)
virtual bool	train_require_labels () const
virtual bool	train_machine (CFeatures *data)
void	train_example (SGVector< float32_t > ex, int32_t label)
void	train_path (SGVector< float32_t > ex, node_t *node)
void	train_node (SGVector< float32_t > ex, float64_t label, node_t *node)
float64_t	predict_node (SGVector< float32_t > ex, node_t *node)
int32_t	create_machine (SGVector< float32_t > ex)
void	compute_conditional_probabilities (SGVector< float32_t > ex)
float64_t	accumulate_conditional_probability (node_t *leaf)
virtual void	store_model_features ()
virtual TParameter *	migrate (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
int32_t	m_num_passes
	number of passes for online training
std::map< int32_t, node_t * >	m_leaves
	class => leaf mapping
CStreamingDenseFeatures < float32_t > *	m_feats
	online features
CTreeMachineNode < ConditionalProbabilityTreeNodeData > *	m_root
CDynamicObjectArray *	m_machines
float64_t	m_max_train_time
CLabels *	m_labels
ESolverType	m_solver_type
bool	m_store_model_features
bool	m_data_locked

Member Typedef Documentation

typedef CTreeMachineNode<ConditionalProbabilityTreeNodeData > node_t

inherited

node_t type

Definition at line 27 of file TreeMachine.h.

Constructor & Destructor Documentation

CRandomConditionalProbabilityTree ( )

constructor

Definition at line 25 of file RandomConditionalProbabilityTree.h.

virtual ~CRandomConditionalProbabilityTree ( )

virtual

destructor

Definition at line 28 of file RandomConditionalProbabilityTree.h.

Member Function Documentation

float64_t accumulate_conditional_probability ( node_t * leaf )

protectedinherited

accumulate along the path to the root the conditional probability for a particular leaf node.

Definition at line 79 of file ConditionalProbabilityTree.cpp.

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 162 of file Machine.cpp.

CBinaryLabels * apply_binary ( CFeatures * data = NULL )

virtualinherited

apply machine to data in means of binary classification problem

Reimplemented in CKernelMachine, COnlineLinearMachine, CWDSVMOcas, CLinearMachine, CDomainAdaptationSVMLinear, CDomainAdaptationSVM, CPluginEstimate, CGaussianProcessBinaryClassification, and CBaggingMachine.

Definition at line 218 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 242 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 197 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 248 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 276 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 262 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 255 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 269 of file Machine.cpp.

CMulticlassLabels * apply_multiclass ( CFeatures * data = NULL )

virtualinherited

apply machine to data in means of multiclass classification problem

Reimplemented from CMachine.

Definition at line 20 of file ConditionalProbabilityTree.cpp.

int32_t apply_multiclass_example ( SGVector< float32_t > ex )

virtualinherited

apply machine one single example.

Parameters

ex	a vector to be applied

Definition at line 48 of file ConditionalProbabilityTree.cpp.

virtual float64_t apply_one ( int32_t i )

virtualinherited

applies to one vector

Reimplemented in CKernelMachine, CRelaxedTree, CWDSVMOcas, COnlineLinearMachine, CLinearMachine, CMultitaskLinearMachine, CMulticlassMachine, CDistanceMachine, CKNN, CMultitaskLogisticRegression, CScatterSVM, CMultitaskLeastSquaresRegression, CGaussianNaiveBayes, CPluginEstimate, and CFeatureBlockLogisticRegression.

Definition at line 239 of file Machine.h.

CRegressionLabels * apply_regression ( CFeatures * data = NULL )

virtualinherited

apply machine to data in means of regression problem

Reimplemented in CKernelMachine, CWDSVMOcas, COnlineLinearMachine, CLinearMachine, CGaussianProcessRegression, and CBaggingMachine.

Definition at line 224 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 236 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 1196 of file SGObject.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 1313 of file SGObject.cpp.

void compute_conditional_probabilities ( SGVector< float32_t > ex )

protectedinherited

compute conditional probabilities for ex along the whole tree for predicting

Definition at line 59 of file ConditionalProbabilityTree.cpp.

int32_t create_machine ( SGVector< float32_t > ex )

protectedinherited

create a new OnlineLinear machine for a node

Parameters

ex	the Example instance for training the new machine

Definition at line 245 of file ConditionalProbabilityTree.cpp.

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 122 of file Machine.cpp.

void data_unlock ( )

virtualinherited

Unlocks a locked machine and restores previous state

Reimplemented in CKernelMachine.

Definition at line 153 of file Machine.cpp.

virtual CSGObject* deep_copy ( ) const

virtualinherited

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

Definition at line 160 of file SGObject.h.

bool equals	(	CSGObject *	other,
		float64_t	accuracy = `0.0`
	)

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)

Returns: true if all parameters were equal, false if not

Definition at line 1217 of file SGObject.cpp.

EMachineType get_classifier_type ( )

virtualinherited

get classifier type

Returns: classifier type NONE

Reimplemented in CLaRank, CSVMLight, CDualLibQPBMSOSVM, CCCSOSVM, CLeastAngleRegression, CKernelRidgeRegression, CLibLinearMTL, CBaggingMachine, CLibSVR, CLDA, CQDA, CLibLinear, CGaussianNaiveBayes, CSVRLight, CMCLDA, CLinearRidgeRegression, CScatterSVM, CKNN, CGaussianProcessRegression, CSGDQN, CSVMSGD, CSVMOcas, CLeastSquaresRegression, COnlineSVMSGD, CKMeans, CDomainAdaptationSVMLinear, CMKLRegression, CWDSVMOcas, CHierarchical, CLPBoost, CMKLMulticlass, CMKLClassification, CMKLOneClass, CLibSVM, CDomainAdaptationSVM, CLPM, CPerceptron, CAveragedPerceptron, CNewtonSVM, CSVMLightOneClass, CSVMLin, CGMNPSVM, CMulticlassLibSVM, CGPBTSVM, CLibSVMOneClass, CGNPPSVM, CMPDSVM, and CCPLEXSVM.

Definition at line 102 of file Machine.cpp.

SGIO * get_global_io ( )

inherited

get the io object

Returns: io object

Definition at line 214 of file SGObject.cpp.

Parallel * get_global_parallel ( )

inherited

get the parallel object

Returns: parallel object

Definition at line 249 of file SGObject.cpp.

Version * get_global_version ( )

inherited

get the version object

Returns: version object

Definition at line 262 of file SGObject.cpp.

CLabels * get_labels ( )

virtualinherited

get labels

Returns: labels

Definition at line 86 of file Machine.cpp.

EProblemType get_machine_problem_type ( ) const

virtualinherited

get problem type

Reimplemented from CMachine.

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 97 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 1100 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 1124 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 1137 of file SGObject.cpp.

virtual const char* get_name ( ) const

virtual

get name

Reimplemented from CConditionalProbabilityTree.

Definition at line 31 of file RandomConditionalProbabilityTree.h.

int32_t get_num_machines ( ) const

inherited

get number of machines

Returns: number of machines

Definition at line 27 of file BaseMulticlassMachine.cpp.

int32_t get_num_passes ( ) const

inherited

get number of passes

Definition at line 53 of file ConditionalProbabilityTree.h.

ESolverType get_solver_type ( )

inherited

get solver type

Returns: solver

Definition at line 112 of file Machine.cpp.

bool is_data_locked ( ) const

inherited

Returns: whether this machine is locked

Definition at line 288 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 268 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.

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 673 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 514 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 345 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 occurres.

Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.

Definition at line 1029 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 occurres.

Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, CDynamicArray< uint64_t >, and CDynamicObjectArray.

Definition at line 1024 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 711 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 918 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 858 of file SGObject.cpp.

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

virtualinherited

post lock

Reimplemented in CMultitaskLinearMachine.

Definition at line 279 of file Machine.h.

float64_t predict_node	(	SGVector< float32_t >	ex,
		node_t *	node
	)

protectedinherited

predict a single node

Parameters

ex	the example being predicted
node	the node

Definition at line 235 of file ConditionalProbabilityTree.cpp.

void print_modsel_params ( )

inherited

prints all parameter registered for model selection and their type

Definition at line 1076 of file SGObject.cpp.

void print_serializable ( const char * prefix = "" )

virtualinherited

prints registered parameters out

Parameters

prefix prefix for members

Definition at line 280 of file SGObject.cpp.

void print_tree ( )

inherited

print the tree structure for debug purpose

Definition at line 143 of file ConditionalProbabilityTree.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 286 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 occurres.

Reimplemented in CKernel.

Definition at line 1039 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 occurres.

Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, CDynamicArray< uint64_t >, and CDynamicObjectArray.

Definition at line 1034 of file SGObject.cpp.

void set_features ( CStreamingDenseFeatures< float32_t > * feats )

inherited

set features

Parameters

feats features

Definition at line 61 of file ConditionalProbabilityTree.h.

void set_generic< complex128_t > ( )

inherited

set generic type to T

Definition at line 41 of file SGObject.cpp.

void set_global_io ( SGIO * io )

inherited

set the io object

Parameters

io	io object to use

Definition at line 207 of file SGObject.cpp.

void set_global_parallel ( Parallel * parallel )

inherited

set the parallel object

Parameters

parallel parallel object to use

Definition at line 220 of file SGObject.cpp.

void set_global_version ( Version * version )

inherited

set the version object

Parameters

version version object to use

Definition at line 255 of file SGObject.cpp.

void set_labels ( CLabels * lab )

virtualinherited

set labels

Parameters

lab labels

Reimplemented in CGaussianProcessMachine, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.

Definition at line 75 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 92 of file Machine.cpp.

void set_num_passes ( int32_t num_passes )

inherited

set number of passes

Definition at line 47 of file ConditionalProbabilityTree.h.

void set_solver_type ( ESolverType st )

inherited

set solver type

Parameters

st	solver type

Definition at line 107 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 117 of file Machine.cpp.

virtual 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 151 of file SGObject.h.

virtual void store_model_features ( )

protectedvirtualinherited

Stores feature data of underlying model. After this method has been called, it is possible to change the machine's feature data and call apply(), which is then performed on the training feature data that is part of the machine's model.

Base method, has to be implemented in order to allow cross-validation and model selection.

NOT IMPLEMENTED! Has to be done in subclasses

Reimplemented in CKernelMachine, CKNN, CLinearMulticlassMachine, CLinearMachine, CKMeans, CHierarchical, CDistanceMachine, CGaussianProcessMachine, CKernelMulticlassMachine, and CLinearStructuredOutputMachine.

Definition at line 327 of file Machine.h.

virtual bool supports_locking ( ) const

virtualinherited

Returns: whether this machine supports locking

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 285 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, CSGDQN, and COnlineSVMSGD.

Definition at line 49 of file Machine.cpp.

void train_example	(	SGVector< float32_t >	ex,
		int32_t	label
	)

protectedinherited

train on a single example (online learning)

Parameters

ex	the example being trained
label	the label of this training example

Definition at line 151 of file ConditionalProbabilityTree.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 231 of file Machine.h.

bool train_machine ( CFeatures * data )

protectedvirtualinherited

train machine

Parameters

data	training data

Returns: whether training was successful

Reimplemented from CMachine.

Definition at line 97 of file ConditionalProbabilityTree.cpp.

void train_node	(	SGVector< float32_t >	ex,
		float64_t	label,
		node_t *	node
	)

protectedinherited

train a single node

Parameters

ex	the example being trained
label	label
node	the node

Definition at line 227 of file ConditionalProbabilityTree.cpp.

void train_path	(	SGVector< float32_t >	ex,
		node_t *	node
	)

protectedinherited

train on a path from a node up to the root

Parameters

ex	the instance of the training example
node	the leaf node

Definition at line 208 of file ConditionalProbabilityTree.cpp.

virtual bool train_require_labels ( ) const

protectedvirtualinherited

the labels will be embedded in the streaming features

Reimplemented from CMachine.

Definition at line 80 of file ConditionalProbabilityTree.h.

void unset_generic ( )

inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 275 of file SGObject.cpp.

bool update_parameter_hash ( )

virtualinherited

Updates the hash of current parameter combination.

Returns: bool if parameter combination has changed since last update.

Definition at line 227 of file SGObject.cpp.

bool which_subtree	(	node_t *	node,
		SGVector< float32_t >	ex
	)

protectedvirtual

decide which subtree to go, when training the tree structure.

Parameters

node	the node being decided
ex	the example being decided

Returns: true if should go left, false otherwise

Implements CConditionalProbabilityTree.

Definition at line 16 of file RandomConditionalProbabilityTree.cpp.

Member Data Documentation

SGIO* io

inherited

io

Definition at line 514 of file SGObject.h.

bool m_data_locked

protectedinherited

whether data is locked

Definition at line 362 of file Machine.h.

CStreamingDenseFeatures<float32_t>* m_feats

protectedinherited

online features

Definition at line 137 of file ConditionalProbabilityTree.h.

Parameter* m_gradient_parameters

inherited

parameters wrt which we can compute gradients

Definition at line 529 of file SGObject.h.

uint32_t m_hash

inherited

Hash of parameter values

Definition at line 535 of file SGObject.h.

CLabels* m_labels

protectedinherited

labels

Definition at line 353 of file Machine.h.

std::map<int32_t, node_t*> m_leaves

protectedinherited

class => leaf mapping

Definition at line 136 of file ConditionalProbabilityTree.h.

CDynamicObjectArray* m_machines

protectedinherited

machines

Definition at line 53 of file BaseMulticlassMachine.h.

float64_t m_max_train_time

protectedinherited

maximum training time

Definition at line 350 of file Machine.h.

Parameter* m_model_selection_parameters

inherited

model selection parameters

Definition at line 526 of file SGObject.h.

int32_t m_num_passes

protectedinherited

number of passes for online training

Definition at line 135 of file ConditionalProbabilityTree.h.

ParameterMap* m_parameter_map

inherited

map for different parameter versions

Definition at line 532 of file SGObject.h.

Parameter* m_parameters

inherited

parameters

Definition at line 523 of file SGObject.h.

CTreeMachineNode<ConditionalProbabilityTreeNodeData >* m_root

protectedinherited

tree root

Definition at line 47 of file TreeMachine.h.

ESolverType m_solver_type

protectedinherited

solver type

Definition at line 356 of file Machine.h.

bool m_store_model_features

protectedinherited

whether model features should be stored after training

Definition at line 359 of file Machine.h.

Parallel* parallel

inherited

parallel

Definition at line 517 of file SGObject.h.

Version* version

inherited

version

Definition at line 520 of file SGObject.h.

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

Detailed Description

Public Types

Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation