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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.

To use mini-batch based training was see CKMeansMiniBatch

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

Definition at line 45 of file KMeans.h.

Inheritance diagram for CKMeans:
[legend]

Public Member Functions

 CKMeans ()
 
 CKMeans (int32_t k, CDistance *d, bool kmeanspp=false)
 
 CKMeans (int32_t k_i, CDistance *d_i, SGMatrix< float64_t > centers_i)
 
virtual ~CKMeans ()
 
virtual const char * get_name () const
 
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 void set_initial_centers (SGMatrix< float64_t > centers)
 
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 ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
void unset_generic ()
 
virtual void print_serializable (const char *prefix="")
 
virtual bool save_serializable (CSerializableFile *file, const char *prefix="")
 
virtual bool load_serializable (CSerializableFile *file, const char *prefix="")
 
void set_global_io (SGIO *io)
 
SGIOget_global_io ()
 
void set_global_parallel (Parallel *parallel)
 
Parallelget_global_parallel ()
 
void set_global_version (Version *version)
 
Versionget_global_version ()
 
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)
 
bool has (const std::string &name) const
 
template<typename T >
bool has (const Tag< T > &tag) const
 
template<typename T , typename U = void>
bool has (const std::string &name) const
 
template<typename T >
void set (const Tag< T > &_tag, const T &value)
 
template<typename T , typename U = void>
void set (const std::string &name, const T &value)
 
template<typename T >
get (const Tag< T > &_tag) const
 
template<typename T , typename U = void>
get (const std::string &name) const
 
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
 
uint32_t m_hash
 

Protected Member Functions

void initialize_training (CFeatures *data=NULL)
 
virtual void store_model_features ()
 
virtual bool train_require_labels () const
 
SGMatrix< float64_tkmeanspp ()
 
void init ()
 
void set_random_centers ()
 
void compute_cluster_variances ()
 
virtual bool is_label_valid (CLabels *lab) const
 
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)
 
template<typename T >
void register_param (Tag< T > &_tag, const T &value)
 
template<typename T >
void register_param (const std::string &name, const T &value)
 

Static Protected Member Functions

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

Protected Attributes

int32_t max_iter
 
bool fixed_centers
 
int32_t k
 
int32_t dimensions
 
SGVector< float64_tR
 
SGMatrix< float64_tmus_initial
 
bool use_kmeanspp
 
SGMatrix< float64_tmus
 
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 26 of file KMeans.cpp.

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

constructor

Parameters
kparameter k
ddistance
kmeansppSet to true for using KMeans++ (default false)

Definition at line 30 of file KMeans.cpp.

CKMeans ( int32_t  k_i,
CDistance d_i,
SGMatrix< float64_t centers_i 
)

constructor for supplying initial centers

Parameters
k_iparameter k
d_idistance
centers_iinitial centers for KMeans algorithm

Definition at line 34 of file KMeans.cpp.

~CKMeans ( )
virtual

Definition at line 38 of file KMeans.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 152 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, CNeuralNetwork, CLinearMachine, CGaussianProcessClassification, CDomainAdaptationSVMLinear, CDomainAdaptationSVM, CPluginEstimate, and CBaggingMachine.

Definition at line 208 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 232 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 187 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.

Definition at line 238 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 266 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 252 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 245 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 259 of file Machine.cpp.

CMulticlassLabels * apply_multiclass ( CFeatures data = NULL)
virtualinherited

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 208 of file DistanceMachine.cpp.

float64_t apply_one ( int32_t  num)
virtualinherited

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 234 of file DistanceMachine.cpp.

CRegressionLabels * apply_regression ( CFeatures data = NULL)
virtualinherited

apply machine to data in means of regression problem

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

Definition at line 214 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 226 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 630 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 747 of file SGObject.cpp.

void compute_cluster_variances ( )
protectedinherited

Definition at line 94 of file KMeansBase.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 112 of file Machine.cpp.

void data_unlock ( )
virtualinherited

Unlocks a locked machine and restores previous state

Reimplemented in CKernelMachine.

Definition at line 143 of file Machine.cpp.

CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 231 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 651 of file SGObject.cpp.

T get ( const Tag< T > &  _tag) const
inherited

Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
_tagname and type information of parameter
Returns
value of the parameter identified by the input tag

Definition at line 367 of file SGObject.h.

T get ( const std::string &  name) const
inherited

Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
namename of the parameter
Returns
value of the parameter corresponding to the input name and type

Definition at line 388 of file SGObject.h.

virtual EMachineType get_classifier_type ( )
virtualinherited

get classifier type

Returns
classifier type KMEANS

Reimplemented from CMachine.

Definition at line 60 of file KMeansBase.h.

SGMatrix< float64_t > get_cluster_centers ( )
inherited

get centers

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

Definition at line 237 of file KMeansBase.cpp.

int32_t get_dimensions ( )
inherited

get dimensions

Returns
number of dimensions

Definition at line 249 of file KMeansBase.cpp.

CDistance * get_distance ( ) const
inherited

get distance

Returns
distance

Definition at line 270 of file DistanceMachine.cpp.

bool get_fixed_centers ( )
inherited

get fixed centers

Returns
whether boolean centers are to be used

Definition at line 259 of file KMeansBase.cpp.

SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 268 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 310 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 323 of file SGObject.cpp.

int32_t get_k ( )
inherited

get k

Returns
the parameter k

Definition at line 216 of file KMeansBase.cpp.

CLabels * get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 76 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 299 of file Machine.h.

float64_t get_max_iter ( )
inherited

get maximum number of iterations

Returns
maximum number of iterations

Definition at line 227 of file KMeansBase.cpp.

float64_t get_max_train_time ( )
inherited

get maximum training time

Returns
maximum training time

Definition at line 87 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 531 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 555 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 568 of file SGObject.cpp.

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

Reimplemented from CKMeansBase.

Definition at line 70 of file KMeans.h.

SGVector< float64_t > get_radiuses ( )
inherited

get radiuses

Returns
radiuses

Definition at line 232 of file KMeansBase.cpp.

ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 102 of file Machine.cpp.

bool get_use_kmeanspp ( ) const
inherited

get use_kmeanspp attribute

Returns
use_kmeanspp If KMeans++ initialization is used

Definition at line 205 of file KMeansBase.cpp.

bool has ( const std::string &  name) const
inherited

Checks if object has a class parameter identified by a name.

Parameters
namename of the parameter
Returns
true if the parameter exists with the input name

Definition at line 289 of file SGObject.h.

bool has ( const Tag< T > &  tag) const
inherited

Checks if object has a class parameter identified by a Tag.

Parameters
tagtag of the parameter containing name and type information
Returns
true if the parameter exists with the input tag

Definition at line 301 of file SGObject.h.

bool has ( const std::string &  name) const
inherited

Checks if a type exists for a class parameter identified by a name.

Parameters
namename of the parameter
Returns
true if the parameter exists with the input name and type

Definition at line 312 of file SGObject.h.

void init ( )
protectedinherited

Definition at line 367 of file KMeansBase.cpp.

void initialize_training ( CFeatures data = NULL)
protectedinherited

Initialize training for KMeans algorithms

Definition at line 142 of file KMeansBase.cpp.

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

Definition at line 296 of file Machine.h.

bool is_generic ( EPrimitiveType *  generic) const
virtualinherited

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

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

Definition at line 329 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 348 of file Machine.h.

SGMatrix< float64_t > kmeanspp ( )
protectedinherited

K-Means++ algorithm to initialize cluster centers

Returns
initial cluster centers: matrix (k columns, dim rows)

Definition at line 276 of file KMeansBase.cpp.

bool load ( FILE *  srcfile)
virtualinherited

load distance machine from file

Parameters
srcfilefile to load from
Returns
if loading was successful

Definition at line 186 of file KMeansBase.cpp.

bool load_serializable ( CSerializableFile file,
const char *  prefix = "" 
)
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
Returns
TRUE if done, otherwise FALSE

Definition at line 402 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 459 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 454 of file SGObject.cpp.

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

Definition at line 295 of file SGObject.cpp.

virtual void post_lock ( CLabels labs,
CFeatures features 
)
virtualinherited

post lock

Definition at line 287 of file Machine.h.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 507 of file SGObject.cpp.

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

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 341 of file SGObject.cpp.

void register_param ( Tag< T > &  _tag,
const T &  value 
)
protectedinherited

Registers a class parameter which is identified by a tag. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
_tagname and type information of parameter
valuevalue of the parameter

Definition at line 439 of file SGObject.h.

void register_param ( const std::string &  name,
const T &  value 
)
protectedinherited

Registers a class parameter which is identified by a name. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 452 of file SGObject.h.

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)
virtualinherited

save distance machine to file

Parameters
dstfilefile to save to
Returns
if saving was successful

Definition at line 193 of file KMeansBase.cpp.

bool save_serializable ( CSerializableFile file,
const char *  prefix = "" 
)
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
Returns
TRUE if done, otherwise FALSE

Definition at line 347 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 469 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 464 of file SGObject.cpp.

void set ( const Tag< T > &  _tag,
const T &  value 
)
inherited

Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
_tagname and type information of parameter
valuevalue of the parameter

Definition at line 328 of file SGObject.h.

void set ( const std::string &  name,
const T &  value 
)
inherited

Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 354 of file SGObject.h.

void set_distance ( CDistance d)
inherited

set distance

Parameters
ddistance to set

Definition at line 263 of file DistanceMachine.cpp.

void set_fixed_centers ( bool  fixed)
inherited

set fixed centers

Parameters
fixedtrue if fixed cluster centers are intended

Definition at line 254 of file KMeansBase.cpp.

void set_generic ( )
inherited

Definition at line 74 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 79 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 84 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 89 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 94 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 99 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 104 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 109 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 114 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 119 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 124 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 129 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 134 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 139 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 144 of file SGObject.cpp.

void set_generic ( )
inherited

set generic type to T

void set_global_io ( SGIO io)
inherited

set the io object

Parameters
ioio object to use

Definition at line 261 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 274 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 316 of file SGObject.cpp.

void set_initial_centers ( SGMatrix< float64_t centers)
virtualinherited

set the initial cluster centers

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

Definition at line 56 of file KMeansBase.cpp.

void set_k ( int32_t  p_k)
inherited

set k

Parameters
p_knew k

Definition at line 210 of file KMeansBase.cpp.

void set_labels ( CLabels lab)
virtualinherited

set labels

Parameters
lablabels

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

Definition at line 65 of file Machine.cpp.

void set_max_iter ( int32_t  iter)
inherited

set maximum number of iterations

Parameters
iterthe new maximum

Definition at line 221 of file KMeansBase.cpp.

void set_max_train_time ( float64_t  t)
inherited

set maximum training time

Parameters
tmaximimum training time

Definition at line 82 of file Machine.cpp.

void set_random_centers ( )
protectedinherited

Algorithm to initialize random cluster centers

Returns
initial cluster centers: matrix (k columns, dim rows)

Definition at line 68 of file KMeansBase.cpp.

void set_solver_type ( ESolverType  st)
inherited

set solver type

Parameters
stsolver type

Definition at line 97 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 107 of file Machine.cpp.

void set_use_kmeanspp ( bool  kmpp)
inherited

set use_kmeanspp attribute

Parameters
kmppSet true/false to use/not use KMeans++ initialization

Definition at line 200 of file KMeansBase.cpp.

CSGObject * shallow_copy ( ) const
virtualinherited

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

Reimplemented in CGaussianKernel.

Definition at line 225 of file SGObject.cpp.

void store_model_features ( )
protectedvirtualinherited

Ensures cluster centers are in lhs of underlying distance

Reimplemented from CDistanceMachine.

Definition at line 264 of file KMeansBase.cpp.

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

Reimplemented in CKernelMachine.

Definition at line 293 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, CLinearMachine, CSGDQN, and COnlineSVMSGD.

Definition at line 39 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.

Definition at line 239 of file Machine.h.

virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

Reimplemented from CMachine.

Definition at line 158 of file KMeansBase.h.

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 336 of file SGObject.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 281 of file SGObject.cpp.

Member Data Documentation

int32_t dimensions
protectedinherited

Number of dimensions

Definition at line 187 of file KMeansBase.h.

CDistance* distance
protectedinherited

the distance

Definition at line 130 of file DistanceMachine.h.

bool fixed_centers
protectedinherited

If cluster centers are to be kept fixed

Definition at line 181 of file KMeansBase.h.

SGIO* io
inherited

io

Definition at line 537 of file SGObject.h.

int32_t k
protectedinherited

The k parameter in KMeans

Definition at line 184 of file KMeansBase.h.

bool m_data_locked
protectedinherited

whether data is locked

Definition at line 370 of file Machine.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 552 of file SGObject.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 555 of file SGObject.h.

CLabels* m_labels
protectedinherited

labels

Definition at line 361 of file Machine.h.

float64_t m_max_train_time
protectedinherited

maximum training time

Definition at line 358 of file Machine.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 549 of file SGObject.h.

Parameter* m_parameters
inherited

parameters

Definition at line 546 of file SGObject.h.

ESolverType m_solver_type
protectedinherited

solver type

Definition at line 364 of file Machine.h.

bool m_store_model_features
protectedinherited

whether model features should be stored after training

Definition at line 367 of file Machine.h.

int32_t max_iter
protectedinherited

Maximum number of iterations

Definition at line 178 of file KMeansBase.h.

SGMatrix<float64_t> mus
protectedinherited

Cluster centers

Definition at line 199 of file KMeansBase.h.

SGMatrix<float64_t> mus_initial
protectedinherited

Initial centers supplied

Definition at line 193 of file KMeansBase.h.

Parallel* parallel
inherited

parallel

Definition at line 540 of file SGObject.h.

SGVector<float64_t> R
protectedinherited

Radi of the clusters (size k)

Definition at line 190 of file KMeansBase.h.

bool use_kmeanspp
protectedinherited

Flag to check if kmeans++ has to be used

Definition at line 196 of file KMeansBase.h.

Version* version
inherited

version

Definition at line 543 of file SGObject.h.


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

SHOGUN Machine Learning Toolbox - Documentation