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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.

The option of using mini-batch based training was added to this class. See EKMeansMethod

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

Definition at line 57 of file KMeans.h.

Inheritance diagram for CKMeans:
Inheritance graph
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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 ()
 
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)
 
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

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)
 

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)
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, CWDSVMOcas, 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, and CMultitaskLinearMachine.

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, CWDSVMOcas, COnlineLinearMachine, CNeuralNetwork, CCHAIDTree, CStochasticGBMachine, CCARTree, CLinearMachine, 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 597 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 714 of file SGObject.cpp.

void data_lock ( CLabels labs,
CFeatures features 
)
virtualinherited

Locks the machine on given labels and data. After this call, only train_locked and apply_locked may be called

Only possible if supports_locking() returns true

Parameters
labslabels used for locking
featuresfeatures used for locking

Reimplemented in CKernelMachine.

Definition at line 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 198 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 618 of file SGObject.cpp.

virtual EMachineType get_classifier_type ( )
virtual

get classifier type

Returns
classifier type KMEANS

Reimplemented from CMachine.

Definition at line 96 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 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 270 of file DistanceMachine.cpp.

bool get_fixed_centers ( )

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 235 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 277 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 290 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 ( )
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 ( )

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 87 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 498 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 522 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 535 of file SGObject.cpp.

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

Reimplemented from CDistanceMachine.

Definition at line 179 of file KMeans.h.

SGVector< float64_t > get_radiuses ( )

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 102 of file Machine.cpp.

EKMeansMethod get_train_method ( ) const

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 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 296 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.

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.

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 369 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 426 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 421 of file SGObject.cpp.

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

Definition at line 262 of file SGObject.cpp.

virtual void post_lock ( CLabels labs,
CFeatures features 
)
virtualinherited

post lock

Reimplemented in CMultitaskLinearMachine.

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 474 of file SGObject.cpp.

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

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 308 of file SGObject.cpp.

void * run_distance_thread_lhs ( void *  p)
staticprotectedinherited

thread function for computing distance values

Parameters
pthread parameter

Definition at line 176 of file DistanceMachine.cpp.

void * run_distance_thread_rhs ( void *  p)
staticprotectedinherited

thread function for computing distance values

Parameters
pthread parameter

Definition at line 192 of file DistanceMachine.cpp.

bool save ( FILE *  dstfile)
virtual

save 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 = "" 
)
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 314 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 436 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 431 of file SGObject.cpp.

void set_distance ( CDistance d)
inherited

set distance

Parameters
ddistance to set

Definition at line 263 of file DistanceMachine.cpp.

void set_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 ( )
inherited

Definition at line 41 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 46 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 51 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 56 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 61 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 66 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 71 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 76 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 81 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 86 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 91 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 96 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 101 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 106 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 111 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 228 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 241 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 283 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)
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)

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 82 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 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_train_method ( EKMeansMethod  f)

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.

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 192 of file SGObject.cpp.

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

Reimplemented in CKernelMachine, and CMultitaskLinearMachine.

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, 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, and CMultitaskLinearMachine.

Definition at line 239 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 303 of file SGObject.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 248 of file SGObject.cpp.

Member Data Documentation

CDistance* distance
protectedinherited

the distance

Definition at line 130 of file DistanceMachine.h.

SGIO* io
inherited

io

Definition at line 369 of file SGObject.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 384 of file SGObject.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 387 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 381 of file SGObject.h.

Parameter* m_parameters
inherited

parameters

Definition at line 378 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.

Parallel* parallel
inherited

parallel

Definition at line 372 of file SGObject.h.

Version* version
inherited

version

Definition at line 375 of file SGObject.h.


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

SHOGUN Machine Learning Toolbox - Documentation