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CKMeans Class Reference

Detailed Description

KMeans clustering, partitions the data into k (a-priori specified) clusters.

It minimizes

\[ \sum_{i=1}^k\sum_{x_j\in S_i} (x_j-\mu_i)^2 \]

where \(\mu_i\) are the cluster centers and \(S_i,\;i=1,\dots,k\) are the index sets of the clusters.

Beware that this algorithm obtains only a local optimum.

cf. http://en.wikipedia.org/wiki/K-means_algorithm

Definition at line 48 of file KMeans.h.

Inheritance diagram for CKMeans:
Inheritance graph
[legend]

List of all members.

Public Member Functions

 CKMeans ()
 CKMeans (int32_t k, CDistance *d, EKMeansMethod f)
 CKMeans (int32_t k, CDistance *d, bool kmeanspp=false, EKMeansMethod f=KMM_LLOYD)
 CKMeans (int32_t k_i, CDistance *d_i, SGMatrix< float64_t > centers_i, EKMeansMethod f=KMM_LLOYD)
virtual ~CKMeans ()
virtual EMachineType get_classifier_type ()
virtual bool load (FILE *srcfile)
virtual bool save (FILE *dstfile)
void set_k (int32_t p_k)
int32_t get_k ()
void set_use_kmeanspp (bool kmpp)
bool get_use_kmeanspp () const
void set_fixed_centers (bool fixed)
bool get_fixed_centers ()
void set_max_iter (int32_t iter)
float64_t get_max_iter ()
SGVector< float64_tget_radiuses ()
SGMatrix< float64_tget_cluster_centers ()
int32_t get_dimensions ()
virtual const char * get_name () const
virtual void set_initial_centers (SGMatrix< float64_t > centers)
void set_train_method (EKMeansMethod f)
EKMeansMethod get_train_method () const
void set_mbKMeans_batch_size (int32_t b)
int32_t get_mbKMeans_batch_size () const
void set_mbKMeans_iter (int32_t t)
int32_t get_mbKMeans_iter () const
void set_mbKMeans_params (int32_t b, int32_t t)
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 CMulticlassLabelsapply_multiclass (CFeatures *data=NULL)
virtual float64_t apply_one (int32_t num)
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 bool update_parameter_hash ()
virtual bool equals (CSGObject *other, float64_t accuracy=0.0)
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 bool is_label_valid (CLabels *lab) 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

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

CKMeans ( )

default constructor

Definition at line 29 of file KMeans.cpp.

CKMeans ( int32_t  k,
CDistance d,
EKMeansMethod  f 
)

constructor

Parameters:
kparameter k
ddistance
ftrain_method value

Definition at line 35 of file KMeans.cpp.

CKMeans ( int32_t  k,
CDistance d,
bool  kmeanspp = false,
EKMeansMethod  f = KMM_LLOYD 
)

constructor

Parameters:
kparameter k
ddistance
kmeanspptrue for using KMeans++ (default false)
ftrain_method value

Definition at line 44 of file KMeans.cpp.

CKMeans ( int32_t  k_i,
CDistance d_i,
SGMatrix< float64_t centers_i,
EKMeansMethod  f = KMM_LLOYD 
)

constructor for supplying initial centers

Parameters:
k_iparameter k
d_idistance
centers_iinitial centers for KMeans algorithm
ftrain_method value

Definition at line 54 of file KMeans.cpp.

~CKMeans ( ) [virtual]

Definition at line 64 of file KMeans.cpp.


Member Function Documentation

CLabels * apply ( CFeatures data = NULL) [virtual, inherited]

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) [virtual, inherited]

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) [virtual, inherited]

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) [virtual, inherited]

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 197 of file Machine.cpp.

CBinaryLabels * apply_locked_binary ( SGVector< index_t indices) [virtual, inherited]

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) [virtual, inherited]

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) [virtual, inherited]

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) [virtual, inherited]

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) [virtual, inherited]

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) [virtual, inherited]

Classify all provided features. Cluster index with smallest distance to to be classified element is returned

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

Reimplemented from CMachine.

Reimplemented in CKNN.

Definition at line 207 of file DistanceMachine.cpp.

float64_t apply_one ( int32_t  num) [virtual, inherited]

Apply machine to one example. Cluster index with smallest distance to to be classified element is returned

Parameters:
numwhich example to apply machine to
Returns:
cluster label nearest to example

Reimplemented from CMachine.

Reimplemented in CKNN.

Definition at line 233 of file DistanceMachine.cpp.

CRegressionLabels * apply_regression ( CFeatures data = NULL) [virtual, inherited]

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) [virtual, inherited]

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:
dictdictionary of parameters to be built.

Definition at line 1156 of file SGObject.cpp.

CSGObject * clone ( ) [virtual, inherited]

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 1273 of file SGObject.cpp.

void data_lock ( CLabels labs,
CFeatures features 
) [virtual, inherited]

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 122 of file Machine.cpp.

void data_unlock ( ) [virtual, inherited]

Unlocks a locked machine and restores previous state

Reimplemented in CKernelMachine.

Definition at line 153 of file Machine.cpp.

virtual CSGObject* deep_copy ( ) const [virtual, inherited]

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

Definition at line 126 of file SGObject.h.

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 51 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 113 of file DistanceMachine.cpp.

bool equals ( CSGObject other,
float64_t  accuracy = 0.0 
) [virtual, inherited]

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

Definition at line 1177 of file SGObject.cpp.

virtual EMachineType get_classifier_type ( ) [virtual]

get classifier type

Returns:
classifier type KMEANS

Reimplemented from CMachine.

Definition at line 87 of file KMeans.h.

get centers

Returns:
cluster centers or empty matrix if no radiuses are there (not trained yet)

Definition at line 370 of file KMeans.cpp.

int32_t get_dimensions ( )

get dimensions

Returns:
number of dimensions

Definition at line 382 of file KMeans.cpp.

CDistance * get_distance ( ) const [inherited]

get distance

Returns:
distance

Definition at line 269 of file DistanceMachine.cpp.

get fixed centers

Returns:
whether boolean centers are to be used

Definition at line 392 of file KMeans.cpp.

SGIO * get_global_io ( ) [inherited]

get the io object

Returns:
io object

Definition at line 174 of file SGObject.cpp.

Parallel * get_global_parallel ( ) [inherited]

get the parallel object

Returns:
parallel object

Definition at line 209 of file SGObject.cpp.

Version * get_global_version ( ) [inherited]

get the version object

Returns:
version object

Definition at line 222 of file SGObject.cpp.

int32_t get_k ( )

get k

Returns:
the parameter k

Definition at line 309 of file KMeans.cpp.

CLabels * get_labels ( ) [virtual, inherited]

get labels

Returns:
labels

Definition at line 86 of file Machine.cpp.

virtual EProblemType get_machine_problem_type ( ) const [virtual, inherited]

returns type of problem machine solves

Reimplemented in CBaseMulticlassMachine.

Definition at line 292 of file Machine.h.

get maximum number of iterations

Returns:
maximum number of iterations

Definition at line 320 of file KMeans.cpp.

float64_t get_max_train_time ( ) [inherited]

get maximum training time

Returns:
maximum training time

Definition at line 97 of file Machine.cpp.

int32_t get_mbKMeans_batch_size ( ) const

get batch size for mini-batch KMeans

Returns:
batch size

Definition at line 341 of file KMeans.cpp.

int32_t get_mbKMeans_iter ( ) const

get no. of iterations for mini-batch KMeans

Returns:
no. of iterations

Definition at line 352 of file KMeans.cpp.

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

Definition at line 1060 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 1084 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 1097 of file SGObject.cpp.

virtual const char* get_name ( ) const [virtual]
Returns:
object name

Reimplemented from CDistanceMachine.

Definition at line 170 of file KMeans.h.

get radiuses

Returns:
radiuses

Definition at line 365 of file KMeans.cpp.

ESolverType get_solver_type ( ) [inherited]

get solver type

Returns:
solver

Definition at line 112 of file Machine.cpp.

get training method

Returns:
training method used - minibatch or lloyd

Definition at line 330 of file KMeans.cpp.

bool get_use_kmeanspp ( ) const

get use_kmeanspp attribute

Returns:
use_kmeanspp true=>use KMeans++ false=>don't use KMeans++

Definition at line 298 of file KMeans.cpp.

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

Definition at line 289 of file Machine.h.

bool is_generic ( EPrimitiveType *  generic) const [virtual, inherited]

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 228 of file SGObject.cpp.

virtual bool is_label_valid ( CLabels lab) const [protected, virtual, inherited]

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 CGaussianProcessBinaryClassification, CGaussianProcessRegression, and CBaseMulticlassMachine.

Definition at line 341 of file Machine.h.

bool load ( FILE *  srcfile) [virtual]

load distance machine from file

Parameters:
srcfilefile to load from
Returns:
if loading was successful

Definition at line 279 of file KMeans.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 633 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 474 of file SGObject.cpp.

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

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 305 of file SGObject.cpp.

void load_serializable_post ( ) throw (ShogunException) [protected, virtual, inherited]

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 occurres.

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

Definition at line 989 of file SGObject.cpp.

void load_serializable_pre ( ) throw (ShogunException) [protected, virtual, inherited]

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 occurres.

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

Definition at line 984 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 671 of file SGObject.cpp.

TParameter * migrate ( DynArray< TParameter * > *  param_base,
const SGParamInfo target 
) [protected, virtual, inherited]

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 878 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 
) [protected, virtual, inherited]

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 818 of file SGObject.cpp.

virtual void post_lock ( CLabels labs,
CFeatures features 
) [virtual, inherited]

post lock

Reimplemented in CMultitaskLinearMachine.

Definition at line 280 of file Machine.h.

void print_modsel_params ( ) [inherited]

prints all parameter registered for model selection and their type

Definition at line 1036 of file SGObject.cpp.

void print_serializable ( const char *  prefix = "") [virtual, inherited]

prints registered parameters out

Parameters:
prefixprefix for members

Definition at line 240 of file SGObject.cpp.

void * run_distance_thread_lhs ( void *  p) [static, protected, inherited]

thread function for computing distance values

Parameters:
pthread parameter

Definition at line 175 of file DistanceMachine.cpp.

void * run_distance_thread_rhs ( void *  p) [static, protected, inherited]

thread function for computing distance values

Parameters:
pthread parameter

Definition at line 191 of file DistanceMachine.cpp.

bool save ( FILE *  dstfile) [virtual]

save distance machine to file

Parameters:
dstfilefile to save to
Returns:
if saving was successful

Definition at line 286 of file KMeans.cpp.

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

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 246 of file SGObject.cpp.

void save_serializable_post ( ) throw (ShogunException) [protected, virtual, inherited]

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 occurres.

Reimplemented in CKernel.

Definition at line 999 of file SGObject.cpp.

void save_serializable_pre ( ) throw (ShogunException) [protected, virtual, inherited]

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 occurres.

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

Definition at line 994 of file SGObject.cpp.

void set_distance ( CDistance d) [inherited]

set distance

Parameters:
ddistance to set

Definition at line 262 of file DistanceMachine.cpp.

void set_fixed_centers ( bool  fixed)

set fixed centers

Parameters:
fixedtrue if fixed cluster centers are intended

Definition at line 387 of file KMeans.cpp.

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:
ioio object to use

Definition at line 167 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel) [inherited]

set the parallel object

Parameters:
parallelparallel object to use

Definition at line 180 of file SGObject.cpp.

void set_global_version ( Version version) [inherited]

set the version object

Parameters:
versionversion object to use

Definition at line 215 of file SGObject.cpp.

void set_initial_centers ( SGMatrix< float64_t centers) [virtual]

set the initial cluster centers

Parameters:
centersmatrix with cluster centers (k colums, dim rows)

Definition at line 68 of file KMeans.cpp.

void set_k ( int32_t  p_k)

set k

Parameters:
p_knew k

Definition at line 303 of file KMeans.cpp.

void set_labels ( CLabels lab) [virtual, inherited]

set labels

Parameters:
lablabels

Reimplemented in CGaussianProcessMachine, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.

Definition at line 75 of file Machine.cpp.

void set_max_iter ( int32_t  iter)

set maximum number of iterations

Parameters:
iterthe new maximum

Definition at line 314 of file KMeans.cpp.

void set_max_train_time ( float64_t  t) [inherited]

set maximum training time

Parameters:
tmaximimum training time

Definition at line 92 of file Machine.cpp.

void set_mbKMeans_batch_size ( int32_t  b)

set batch size for mini-batch KMeans

Parameters:
bbatch size int32_t(greater than 0)

Definition at line 335 of file KMeans.cpp.

void set_mbKMeans_iter ( int32_t  t)

set no. of iterations for mini-batch KMeans

Parameters:
tno. of iterations int32_t(greater than 0)

Definition at line 346 of file KMeans.cpp.

void set_mbKMeans_params ( int32_t  b,
int32_t  t 
)

set batch size and no. of iteration for mini-batch KMeans

Parameters:
bbatch size
tno. of iterations

Definition at line 357 of file KMeans.cpp.

void set_solver_type ( ESolverType  st) [inherited]

set solver type

Parameters:
stsolver type

Definition at line 107 of file Machine.cpp.

void set_store_model_features ( bool  store_model) [virtual, inherited]

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

Parameters:
store_modelwhether model should be stored after training

Definition at line 117 of file Machine.cpp.

set training method

Parameters:
fminibatch if mini-batch KMeans

Definition at line 325 of file KMeans.cpp.

void set_use_kmeanspp ( bool  kmpp)

set use_kmeanspp attribute

Parameters:
kmpptrue=>use KMeans++ false=>don't use KMeans++

Definition at line 293 of file KMeans.cpp.

virtual CSGObject* shallow_copy ( ) const [virtual, inherited]

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

Reimplemented in CGaussianKernel.

Definition at line 117 of file SGObject.h.

virtual bool supports_locking ( ) const [virtual, inherited]
Returns:
whether this machine supports locking

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

Definition at line 286 of file Machine.h.

bool train ( CFeatures data = NULL) [virtual, inherited]

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, CSGDQN, and COnlineSVMSGD.

Definition at line 49 of file Machine.cpp.

virtual bool train_locked ( SGVector< index_t indices) [virtual, inherited]

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 232 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 235 of file SGObject.cpp.

bool update_parameter_hash ( ) [virtual, inherited]

Updates the hash of current parameter combination.

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

Definition at line 187 of file SGObject.cpp.


Member Data Documentation

CDistance* distance [protected, inherited]

the distance

Definition at line 133 of file DistanceMachine.h.

SGIO* io [inherited]

io

Definition at line 473 of file SGObject.h.

bool m_data_locked [protected, inherited]

whether data is locked

Definition at line 363 of file Machine.h.

parameters wrt which we can compute gradients

Definition at line 488 of file SGObject.h.

uint32_t m_hash [inherited]

Hash of parameter values

Definition at line 494 of file SGObject.h.

CLabels* m_labels [protected, inherited]

labels

Definition at line 354 of file Machine.h.

float64_t m_max_train_time [protected, inherited]

maximum training time

Definition at line 351 of file Machine.h.

model selection parameters

Definition at line 485 of file SGObject.h.

map for different parameter versions

Definition at line 491 of file SGObject.h.

Parameter* m_parameters [inherited]

parameters

Definition at line 482 of file SGObject.h.

ESolverType m_solver_type [protected, inherited]

solver type

Definition at line 357 of file Machine.h.

bool m_store_model_features [protected, inherited]

whether model features should be stored after training

Definition at line 360 of file Machine.h.

Parallel* parallel [inherited]

parallel

Definition at line 476 of file SGObject.h.

Version* version [inherited]

version

Definition at line 479 of file SGObject.h.


The documentation for this class was generated from the following files:
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SHOGUN Machine Learning Toolbox - Documentation