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CKNN Class Reference

Detailed Description

Class KNN, an implementation of the standard k-nearest neigbor classifier.

An example is classified to belong to the class of which the majority of the k closest examples belong to. Formally, kNN is described as

\[ label for x = \arg \max_{l} \sum_{i=1}^{k} [label of i-th example = l] \]

This class provides a capability to do weighted classfication using:

\[ label for x = \arg \max_{l} \sum_{i=1}^{k} [label of i-th example = l] q^{i}, \]

where \(|q|<1\).

To avoid ties, k should be an odd number. To define how close examples are k-NN requires a CDistance object to work with (e.g., CEuclideanDistance ).

Note that k-NN has zero training time but classification times increase dramatically with the number of examples. Also note that k-NN is capable of multi-class-classification. And finally, in case of k=1 classification will take less time with an special optimization provided.

Definition at line 57 of file KNN.h.

Inheritance diagram for CKNN:
Inheritance graph
[legend]

Public Member Functions

 CKNN ()
 CKNN (int32_t k, CDistance *d, CLabels *trainlab)
virtual ~CKNN ()
virtual EMachineType get_classifier_type ()
SGMatrix< index_tnearest_neighbors ()
virtual CMulticlassLabelsapply_multiclass (CFeatures *data=NULL)
virtual float64_t apply_one (int32_t vec_idx)
 get output for example "vec_idx"
SGMatrix< int32_t > classify_for_multiple_k ()
virtual bool load (FILE *srcfile)
virtual bool save (FILE *dstfile)
void set_k (int32_t k)
int32_t get_k ()
void set_q (float64_t q)
float64_t get_q ()
void set_use_covertree (bool use_covertree)
bool get_use_covertree () const
virtual const char * get_name () const
void set_distance (CDistance *d)
CDistanceget_distance () const
void distances_lhs (float64_t *result, int32_t idx_a1, int32_t idx_a2, int32_t idx_b)
void distances_rhs (float64_t *result, int32_t idx_b1, int32_t idx_b2, int32_t idx_a)
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 ()
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 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 EProblemType get_machine_problem_type () const
virtual CSGObjectshallow_copy () const
virtual CSGObjectdeep_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)
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
Parameterm_gradient_parameters
ParameterMapm_parameter_map
uint32_t m_hash

Protected Member Functions

virtual void store_model_features ()
virtual CMulticlassLabelsclassify_NN ()
void init_distance (CFeatures *data)
virtual bool train_machine (CFeatures *data=NULL)
virtual bool is_label_valid (CLabels *lab) const
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)

Static Protected Member Functions

static void * run_distance_thread_lhs (void *p)
static void * run_distance_thread_rhs (void *p)

Protected Attributes

int32_t m_k
 the k parameter in KNN
float64_t m_q
 parameter q of rank weighting
bool m_use_covertree
 parameter to enable cover tree support
int32_t m_num_classes
 number of classes (i.e. number of values labels can take)
int32_t m_min_label
 smallest label, i.e. -1
SGVector< int32_t > m_train_labels
CDistancedistance
float64_t m_max_train_time
CLabelsm_labels
ESolverType m_solver_type
bool m_store_model_features
bool m_data_locked

Constructor & Destructor Documentation

CKNN ( )

default constructor

Definition at line 28 of file KNN.cpp.

CKNN ( int32_t  k,
CDistance d,
CLabels trainlab 
)

constructor

Parameters
kk
ddistance
trainlablabels for training

Definition at line 34 of file KNN.cpp.

~CKNN ( )
virtual

Definition at line 68 of file KNN.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

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

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
indicesindex 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 objects

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

Reimplemented from CDistanceMachine.

Definition at line 153 of file KNN.cpp.

virtual float64_t apply_one ( int32_t  vec_idx)
virtual

get output for example "vec_idx"

Reimplemented from CDistanceMachine.

Definition at line 99 of file KNN.h.

CRegressionLabels * apply_regression ( CFeatures data = NULL)
virtualinherited

apply machine to data in means of regression problem

Reimplemented in CKernelMachine, CWDSVMOcas, COnlineLinearMachine, CNeuralNetwork, CCHAIDTree, CStochasticGBMachine, CCARTree, CLinearMachine, CGaussianProcessRegression, and CBaggingMachine.

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
dictdictionary of parameters to be built.

Definition at line 1243 of file SGObject.cpp.

SGMatrix< int32_t > classify_for_multiple_k ( )

classify all examples for 1...k

Definition at line 333 of file KNN.cpp.

CMulticlassLabels * classify_NN ( )
protectedvirtual

classify all examples with nearest neighbor (k=1)

Returns
classified labels

Definition at line 288 of file KNN.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 1360 of file SGObject.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
labslabels used for locking
featuresfeatures 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.

void distances_lhs ( float64_t result,
int32_t  idx_a1,
int32_t  idx_a2,
int32_t  idx_b 
)
inherited

get distance functions for lhs feature vectors going from a1 to a2 and rhs feature vector b

Parameters
resultarray of distance values
idx_a1first feature vector a1 at idx_a1
idx_a2last feature vector a2 at idx_a2
idx_bfeature vector b at idx_b

Definition at line 52 of file DistanceMachine.cpp.

void distances_rhs ( float64_t result,
int32_t  idx_b1,
int32_t  idx_b2,
int32_t  idx_a 
)
inherited

get distance functions for rhs feature vectors going from b1 to b2 and lhs feature vector a

Parameters
resultarray of distance values
idx_b1first feature vector a1 at idx_b1
idx_b2last feature vector a2 at idx_b2
idx_afeature vector a at idx_a

Definition at line 114 of file DistanceMachine.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
otherobject to compare with
accuracyaccuracy to use for comparison (optional)
tolerantallows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 1264 of file SGObject.cpp.

virtual EMachineType get_classifier_type ( )
virtual

get classifier type

Returns
classifier type KNN

Reimplemented from CMachine.

Definition at line 78 of file KNN.h.

CDistance * get_distance ( ) const
inherited

get distance

Returns
distance

Definition at line 270 of file DistanceMachine.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 278 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 291 of file SGObject.cpp.

int32_t get_k ( )

get k

Returns
value of k

Definition at line 138 of file KNN.h.

CLabels * get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 84 of file Machine.cpp.

virtual EProblemType get_machine_problem_type ( ) const
virtualinherited

returns type of problem machine solves

Reimplemented in CNeuralNetwork, CRandomForest, CCHAIDTree, CCARTree, and CBaseMulticlassMachine.

Definition at line 297 of file Machine.h.

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 1135 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_namename of the parameter
Returns
description of the parameter

Definition at line 1159 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_namename of model selection parameter
Returns
index of model selection parameter with provided name, -1 if there is no such

Definition at line 1172 of file SGObject.cpp.

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

Reimplemented from CDistanceMachine.

Definition at line 171 of file KNN.h.

float64_t get_q ( )

get q

Returns
q parameter

Definition at line 155 of file KNN.h.

ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 110 of file Machine.cpp.

bool get_use_covertree ( ) const

get whether to use cover trees for fast KNN

Returns
use_covertree parameter

Definition at line 168 of file KNN.h.

void init_distance ( CFeatures data)
protected

init distances to test examples

Parameters
datatest examples

Definition at line 422 of file KNN.cpp.

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

Definition at line 294 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
genericset to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 297 of file SGObject.cpp.

virtual bool is_label_valid ( CLabels lab) const
protectedvirtualinherited

check whether the labels is valid.

Subclasses can override this to implement their check of label types.

Parameters
labthe labels being checked, guaranteed to be non-NULL

Reimplemented in CNeuralNetwork, CCARTree, CCHAIDTree, CGaussianProcessRegression, and CBaseMulticlassMachine.

Definition at line 346 of file Machine.h.

bool load ( FILE *  srcfile)
virtual

load from file

Parameters
srcfilefile to load from
Returns
if loading was successful

Definition at line 436 of file KNN.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_versionparameter version of the file
current_versionversion from which mapping begins (you want to use Version::get_version_parameter() for this in most cases)
filefile to load from
prefixprefix for members
Returns
(sorted) array of created TParameter instances with file data

Definition at line 704 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_infoinformation of parameter
file_versionparameter version of the file, must be <= provided parameter version
filefile to load from
prefixprefix for members
Returns
new array with TParameter instances with the attached data

Definition at line 545 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
filewhere to load from
prefixprefix 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 374 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
ShogunExceptionwill be thrown if an error occurs.

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

Definition at line 1062 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
ShogunExceptionwill be thrown if an error occurs.

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

Definition at line 1057 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_baseset of TParameter instances that are mapped to the provided target parameter infos
base_versionversion of the parameter base
target_param_infosset of SGParamInfo instances that specify the target parameter base

Definition at line 742 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_baseset of TParameter instances to use for migration
targetparameter 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 949 of file SGObject.cpp.

SGMatrix< index_t > nearest_neighbors ( )

for each example in the rhs features of the distance member, find the m_k nearest neighbors among the vectors in the lhs features

Returns
matrix with indices to the nearest neighbors, the dimensions of the matrix are k rows and n columns, where n is the number of feature vectors in rhs; among the nearest neighbors, the closest are in the first row, and the furthest in the last one

Definition at line 109 of file KNN.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_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
replacement(used as output) here the TParameter instance which is returned by migration is created into
to_migratethe only source that is used for migration
old_namewith this parameter, a name change may be specified

Definition at line 889 of file SGObject.cpp.

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

Definition at line 263 of file SGObject.cpp.

virtual void post_lock ( CLabels labs,
CFeatures features 
)
virtualinherited

post lock

Reimplemented in CMultitaskLinearMachine.

Definition at line 285 of file Machine.h.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 1111 of file SGObject.cpp.

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

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 309 of file SGObject.cpp.

void * run_distance_thread_lhs ( void *  p)
staticprotectedinherited

thread function for computing distance values

Parameters
pthread parameter

Definition at line 176 of file DistanceMachine.cpp.

void * run_distance_thread_rhs ( void *  p)
staticprotectedinherited

thread function for computing distance values

Parameters
pthread parameter

Definition at line 192 of file DistanceMachine.cpp.

bool save ( FILE *  dstfile)
virtual

save to file

Parameters
dstfilefile to save to
Returns
if saving was successful

Definition at line 443 of file KNN.cpp.

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

Save this object to file.

Parameters
filewhere to save the object; will be closed during returning if PREFIX is an empty string.
prefixprefix 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 315 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
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 1072 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
ShogunExceptionwill be thrown if an error occurs.

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

Definition at line 1067 of file SGObject.cpp.

void set_distance ( CDistance d)
inherited

set distance

Parameters
ddistance to set

Definition at line 263 of file DistanceMachine.cpp.

void set_generic< complex128_t > ( )
inherited

set generic type to T

Definition at line 42 of file SGObject.cpp.

void set_global_io ( SGIO io)
inherited

set the io object

Parameters
ioio object to use

Definition at line 230 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 243 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 284 of file SGObject.cpp.

void set_k ( int32_t  k)

set k

Parameters
kk to be set

Definition at line 128 of file KNN.h.

void set_labels ( CLabels lab)
virtualinherited

set labels

Parameters
lablabels

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
tmaximimum training time

Definition at line 90 of file Machine.cpp.

void set_q ( float64_t  q)

set q

Parameters
qvalue

Definition at line 146 of file KNN.h.

void set_solver_type ( ESolverType  st)
inherited

set solver type

Parameters
stsolver 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_modelwhether model should be stored after training

Definition at line 115 of file Machine.cpp.

void set_use_covertree ( bool  use_covertree)

set whether to use cover trees for fast KNN

Parameters
use_covertree

Definition at line 160 of file KNN.h.

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.

void store_model_features ( )
protectedvirtual

Stores feature data of underlying model.

Replaces lhs and rhs of underlying distance with copies of themselves

Reimplemented from CDistanceMachine.

Definition at line 450 of file KNN.cpp.

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

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 291 of file Machine.h.

bool train ( CFeatures data = NULL)
virtualinherited

train machine

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

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 237 of file Machine.h.

bool train_machine ( CFeatures data = NULL)
protectedvirtual

train k-NN 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 72 of file KNN.cpp.

virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

Reimplemented in COnlineLinearMachine, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree, and CLibSVMOneClass.

Definition at line 352 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 304 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

CDistance* distance
protectedinherited

the distance

Definition at line 130 of file DistanceMachine.h.

SGIO* io
inherited

io

Definition at line 496 of file SGObject.h.

bool m_data_locked
protectedinherited

whether data is locked

Definition at line 368 of file Machine.h.

Parameter* m_gradient_parameters
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.

int32_t m_k
protected

the k parameter in KNN

Definition at line 233 of file KNN.h.

CLabels* m_labels
protectedinherited

labels

Definition at line 359 of file Machine.h.

float64_t m_max_train_time
protectedinherited

maximum training time

Definition at line 356 of file Machine.h.

int32_t m_min_label
protected

smallest label, i.e. -1

Definition at line 245 of file KNN.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 508 of file SGObject.h.

int32_t m_num_classes
protected

number of classes (i.e. number of values labels can take)

Definition at line 242 of file KNN.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.

float64_t m_q
protected

parameter q of rank weighting

Definition at line 236 of file KNN.h.

ESolverType m_solver_type
protectedinherited

solver type

Definition at line 362 of file Machine.h.

bool m_store_model_features
protectedinherited

whether model features should be stored after training

Definition at line 365 of file Machine.h.

SGVector<int32_t> m_train_labels
protected

the actual trainlabels

Definition at line 248 of file KNN.h.

bool m_use_covertree
protected

parameter to enable cover tree support

Definition at line 239 of file KNN.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