Public Member Functions | Public Attributes | Protected Member Functions | Static Protected Member Functions | Protected Attributes

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 39 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)
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_max_iter (int32_t iter)
float64_t get_max_iter ()
SGVector< float64_t > get_radiuses ()
SGMatrix< float64_t > get_cluster_centers ()
int32_t get_dimensions ()
virtual const char * get_name () const
void set_distance (CDistance *d)
CDistance * get_distance ()
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 CMulticlassLabels * apply_multiclass (CFeatures *data=NULL)
virtual float64_t apply_one (int32_t num)
virtual bool train (CFeatures *data=NULL)
virtual CLabels * apply (CFeatures *data=NULL)
virtual CBinaryLabels * apply_binary (CFeatures *data=NULL)
virtual CRegressionLabels * apply_regression (CFeatures *data=NULL)
virtual CStructuredLabels * apply_structured (CFeatures *data=NULL)
virtual CLatentLabels * apply_latent (CFeatures *data=NULL)
virtual void set_labels (CLabels *lab)
virtual CLabels * get_labels ()
void set_max_train_time (float64_t t)
float64_t get_max_train_time ()
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 CLabels * apply_locked (SGVector< index_t > indices)
virtual CBinaryLabels * apply_locked_binary (SGVector< index_t > indices)
virtual CRegressionLabels * apply_locked_regression (SGVector< index_t > indices)
virtual CMulticlassLabels * apply_locked_multiclass (SGVector< index_t > indices)
virtual CStructuredLabels * apply_locked_structured (SGVector< index_t > indices)
virtual CLatentLabels * apply_locked_latent (SGVector< index_t > indices)
virtual void data_lock (CLabels *labs, CFeatures *features)
virtual void post_lock (CLabels *labs, CFeatures *features)
virtual void data_unlock ()
virtual bool supports_locking () const
bool is_data_locked () const
virtual EProblemType get_machine_problem_type () const
virtual CMachine * clone ()
virtual CSGObject * shallow_copy () const
virtual CSGObject * deep_copy () const
virtual bool is_generic (EPrimitiveType *generic) const
template<class T >
void set_generic ()
void unset_generic ()
virtual void print_serializable (const char *prefix="")
virtual bool save_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=VERSION_PARAMETER)
virtual bool load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=VERSION_PARAMETER)
DynArray< TParameter * > * load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="")
DynArray< TParameter * > * load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="")
void map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos)
void set_global_io (SGIO *io)
SGIO * get_global_io ()
void set_global_parallel (Parallel *parallel)
Parallel * get_global_parallel ()
void set_global_version (Version *version)
Version * get_global_version ()
SGStringList< char > get_modelsel_names ()
void print_modsel_params ()
char * get_modsel_param_descr (const char *param_name)
index_t get_modsel_param_index (const char *param_name)
void build_parameter_dictionary (CMap< TParameter *, CSGObject * > &dict)

Public Attributes

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

Protected Member Functions

void clustknb (bool use_old_mus, float64_t *mus_start)
virtual bool train_machine (CFeatures *data=NULL)
virtual void store_model_features ()
virtual bool train_require_labels () const
virtual bool is_label_valid (CLabels *lab) const
virtual TParameter * migrate (DynArray< TParameter * > *param_base, const SGParamInfo *target)
virtual void one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL)
virtual void load_serializable_pre () throw (ShogunException)
virtual void load_serializable_post () throw (ShogunException)
virtual void save_serializable_pre () throw (ShogunException)
virtual void save_serializable_post () throw (ShogunException)
virtual bool update_parameter_hash ()

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
 maximum number of iterations
int32_t k
 the k parameter in KMeans
int32_t dimensions
 number of dimensions
SGVector< float64_t > R
 radi of the clusters (size k)
CDistance * distance
float64_t m_max_train_time
CLabels * m_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 
)

constructor

Parameters:
k parameter k
d distance

Definition at line 35 of file KMeans.cpp.

~CKMeans (  )  [virtual]

Definition at line 43 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 CPluginEstimate, CWDSVMOcas, CKernelMachine, CLinearMachine, COnlineLinearMachine, CDomainAdaptationSVM, and CDomainAdaptationSVMLinear.

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

Definition at line 197 of file Machine.cpp.

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

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

Reimplemented in CKernelMachine, CMultitaskCompositeMachine, 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:
num which 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 CWDSVMOcas, CKernelMachine, CLinearMachine, COnlineLinearMachine, and CGaussianProcessRegression.

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_parameter_dictionary ( CMap< TParameter *, CSGObject * > &  dict  )  [inherited]

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

Parameters:
dict dictionary of parameters to be built.

Definition at line 1201 of file SGObject.cpp.

virtual CMachine* clone (  )  [virtual, inherited]

clone

Reimplemented in CKernelMachine, and CLinearMachine.

Definition at line 294 of file Machine.h.

void clustknb ( bool  use_old_mus,
float64_t *  mus_start 
) [protected]

clustknb

Parameters:
use_old_mus if old mus shall be used
mus_start mus start

replace rhs feature vectors

set rhs to mus_start

update rhs

Definition at line 177 of file KMeans.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:
labs labels used for locking
features features 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 131 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:
result array of distance values
idx_a1 first feature vector a1 at idx_a1
idx_a2 last feature vector a2 at idx_a2
idx_b feature 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:
result array of distance values
idx_b1 first feature vector a1 at idx_b1
idx_b2 last feature vector a2 at idx_b2
idx_a feature vector a at idx_a

Definition at line 113 of file DistanceMachine.cpp.

virtual EMachineType get_classifier_type (  )  [virtual]

get classifier type

Returns:
classifier type KMEANS

Reimplemented from CMachine.

Definition at line 60 of file KMeans.h.

SGMatrix< float64_t > get_cluster_centers (  ) 

get centers

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

Definition at line 113 of file KMeans.cpp.

int32_t get_dimensions (  ) 

get dimensions

Returns:
number of dimensions

Definition at line 125 of file KMeans.cpp.

CDistance* get_distance (  )  [inherited]

get distance

Returns:
distance

Definition at line 56 of file DistanceMachine.h.

SGIO * get_global_io (  )  [inherited]

get the io object

Returns:
io object

Definition at line 224 of file SGObject.cpp.

Parallel * get_global_parallel (  )  [inherited]

get the parallel object

Returns:
parallel object

Definition at line 259 of file SGObject.cpp.

Version * get_global_version (  )  [inherited]

get the version object

Returns:
version object

Definition at line 272 of file SGObject.cpp.

int32_t get_k (  ) 

get k

Returns:
the parameter k

Definition at line 92 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 287 of file Machine.h.

float64_t get_max_iter (  ) 

get maximum number of iterations

Returns:
maximum number of iterations

Definition at line 103 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.

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

Definition at line 1108 of file SGObject.cpp.

char * get_modsel_param_descr ( const char *  param_name  )  [inherited]

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

Parameters:
param_name name of the parameter
Returns:
description of the parameter

Definition at line 1132 of file SGObject.cpp.

index_t get_modsel_param_index ( const char *  param_name  )  [inherited]

Returns index of model selection parameter with provided index

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

Definition at line 1145 of file SGObject.cpp.

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

Reimplemented from CDistanceMachine.

Definition at line 119 of file KMeans.h.

SGVector< float64_t > get_radiuses (  ) 

get radiuses

Returns:
radiuses

Definition at line 108 of file KMeans.cpp.

ESolverType get_solver_type (  )  [inherited]

get solver type

Returns:
solver

Definition at line 112 of file Machine.cpp.

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

Definition at line 284 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:
generic set to the type of the generic if returning TRUE
Returns:
TRUE if a class template.

Definition at line 278 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:
lab the labels being checked, guaranteed to be non-NULL

Reimplemented in CBaseMulticlassMachine.

Definition at line 343 of file Machine.h.

bool load ( FILE *  srcfile  )  [virtual]

load distance machine from file

Parameters:
srcfile file to load from
Returns:
if loading was successful

Definition at line 71 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_version parameter version of the file
current_version version from which mapping begins (you want to use VERSION_PARAMETER for this in most cases)
file file to load from
prefix prefix for members
Returns:
(sorted) array of created TParameter instances with file data

Definition at line 679 of file SGObject.cpp.

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

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

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

Definition at line 523 of file SGObject.cpp.

bool load_serializable ( CSerializableFile *  file,
const char *  prefix = "",
int32_t  param_version = 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:
file where to load from
prefix prefix for members
param_version (optional) a parameter version different to (this is mainly for testing, better do not use)
Returns:
TRUE if done, otherwise FALSE

Reimplemented in CModelSelectionParameters.

Definition at line 354 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:
ShogunException Will be thrown if an error occurres.

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

Definition at line 1033 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:
ShogunException Will be thrown if an error occurres.

Definition at line 1028 of file SGObject.cpp.

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

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

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

Definition at line 717 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_base set of TParameter instances to use for migration
target parameter info for the resulting TParameter
Returns:
a new TParameter instance with migrated data from the base of the type which is specified by the target parameter

Definition at line 923 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_base set of TParameter instances to use for migration
target parameter info for the resulting TParameter
replacement (used as output) here the TParameter instance which is returned by migration is created into
to_migrate the only source that is used for migration
old_name with this parameter, a name change may be specified

Definition at line 864 of file SGObject.cpp.

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

post lock

Reimplemented in CMultitaskCompositeMachine, and CMultitaskLinearMachine.

Definition at line 275 of file Machine.h.

void print_modsel_params (  )  [inherited]

prints all parameter registered for model selection and their type

Definition at line 1084 of file SGObject.cpp.

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

prints registered parameters out

Parameters:
prefix prefix for members

Definition at line 290 of file SGObject.cpp.

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

pthread function for compute distance values

Parameters:
p thread parameter

Definition at line 175 of file DistanceMachine.cpp.

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

pthread function for compute distance values

Parameters:
p thread parameter

Definition at line 191 of file DistanceMachine.cpp.

bool save ( FILE *  dstfile  )  [virtual]

save distance machine to file

Parameters:
dstfile file to save to
Returns:
if saving was successful

Definition at line 78 of file KMeans.cpp.

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

Save this object to file.

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

Reimplemented in CModelSelectionParameters.

Definition at line 296 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:
ShogunException Will be thrown if an error occurres.

Reimplemented in CKernel.

Definition at line 1043 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:
ShogunException Will be thrown if an error occurres.

Reimplemented in CKernel.

Definition at line 1038 of file SGObject.cpp.

void set_distance ( CDistance *  d  )  [inherited]

set distance

Parameters:
d distance to set

Definition at line 45 of file DistanceMachine.h.

void set_generic< floatmax_t > (  )  [inherited]

set generic type to T

void set_global_io ( SGIO *  io  )  [inherited]

set the io object

Parameters:
io io object to use

Definition at line 217 of file SGObject.cpp.

void set_global_parallel ( Parallel *  parallel  )  [inherited]

set the parallel object

Parameters:
parallel parallel object to use

Definition at line 230 of file SGObject.cpp.

void set_global_version ( Version *  version  )  [inherited]

set the version object

Parameters:
version version object to use

Definition at line 265 of file SGObject.cpp.

void set_k ( int32_t  p_k  ) 

set k

Parameters:
p_k new k

Definition at line 86 of file KMeans.cpp.

void set_labels ( CLabels *  lab  )  [virtual, inherited]

set labels

Parameters:
lab labels

Reimplemented in CMulticlassMachine, and CRelaxedTree.

Definition at line 75 of file Machine.cpp.

void set_max_iter ( int32_t  iter  ) 

set maximum number of iterations

Parameters:
iter the new maximum

Definition at line 97 of file KMeans.cpp.

void set_max_train_time ( float64_t  t  )  [inherited]

set maximum training time

Parameters:
t maximimum training time

Definition at line 92 of file Machine.cpp.

void set_solver_type ( ESolverType  st  )  [inherited]

set solver type

Parameters:
st solver type

Definition at line 107 of file Machine.cpp.

void set_store_model_features ( bool  store_model  )  [virtual, inherited]

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

Parameters:
store_model whether model should be stored after training

Definition at line 117 of file Machine.cpp.

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

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

Reimplemented in CGaussianKernel.

Definition at line 122 of file SGObject.h.

void store_model_features (  )  [protected, virtual]

Ensures cluster centers are in lhs of underlying distance

Reimplemented from CDistanceMachine.

Definition at line 462 of file KMeans.cpp.

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

Reimplemented in CKernelMachine, CMultitaskCompositeMachine, and CMultitaskLinearMachine.

Definition at line 281 of file Machine.h.

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

train machine

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

Reimplemented in COnlineSVMSGD, CSGDQN, and CRelaxedTree.

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

Reimplemented in CKernelMachine, CMultitaskCompositeMachine, and CMultitaskLinearMachine.

Definition at line 227 of file Machine.h.

bool train_machine ( CFeatures *  data = NULL  )  [protected, virtual]

train k-means

Parameters:
data training 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 47 of file KMeans.cpp.

virtual bool train_require_labels (  )  const [protected, virtual]

returns whether machine require labels for training

Reimplemented from CMachine.

Definition at line 142 of file KMeans.h.

void unset_generic (  )  [inherited]

unset generic type

this has to be called in classes specializing a template class

Definition at line 285 of file SGObject.cpp.

bool update_parameter_hash (  )  [protected, virtual, inherited]

Updates the hash of current parameter combination.

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

Definition at line 237 of file SGObject.cpp.

Member Data Documentation

int32_t dimensions [protected]

number of dimensions

Definition at line 155 of file KMeans.h.

CDistance* distance [protected, inherited]

the distance

Definition at line 140 of file DistanceMachine.h.

SGIO* io [inherited]

io

Definition at line 462 of file SGObject.h.

int32_t k [protected]

the k parameter in KMeans

Definition at line 152 of file KMeans.h.

bool m_data_locked [protected, inherited]

whether data is locked

Definition at line 365 of file Machine.h.

uint32_t m_hash [inherited]

Hash of parameter values

Definition at line 480 of file SGObject.h.

CLabels* m_labels [protected, inherited]

labels

Definition at line 356 of file Machine.h.

float64_t m_max_train_time [protected, inherited]

maximum training time

Definition at line 353 of file Machine.h.

Parameter* m_model_selection_parameters [inherited]

model selection parameters

Definition at line 474 of file SGObject.h.

ParameterMap* m_parameter_map [inherited]

map for different parameter versions

Definition at line 477 of file SGObject.h.

Parameter* m_parameters [inherited]

parameters

Definition at line 471 of file SGObject.h.

ESolverType m_solver_type [protected, inherited]

solver type

Definition at line 359 of file Machine.h.

bool m_store_model_features [protected, inherited]

whether model features should be stored after training

Definition at line 362 of file Machine.h.

int32_t max_iter [protected]

maximum number of iterations

Definition at line 149 of file KMeans.h.

Parallel* parallel [inherited]

parallel

Definition at line 465 of file SGObject.h.

SGVector<float64_t> R [protected]

radi of the clusters (size k)

Definition at line 158 of file KMeans.h.

Version* version [inherited]

version

Definition at line 468 of file SGObject.h.

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