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CMKLRegression Class Reference

Detailed Description

Multiple Kernel Learning for regression.

Performs support vector regression while learning kernel weights at the same time. Makes only sense if multiple kernels are used.

See also
CMKL

Definition at line 27 of file MKLRegression.h.

Inheritance diagram for CMKLRegression:
[legend]

Public Member Functions

 MACHINE_PROBLEM_TYPE (PT_REGRESSION)
 
 CMKLRegression (CSVM *s=NULL)
 
virtual ~CMKLRegression ()
 
virtual float64_t compute_sum_alpha ()
 
virtual const char * get_name () const
 
void set_constraint_generator (CSVM *s)
 
void set_svm (CSVM *s)
 
CSVMget_svm ()
 
void set_C_mkl (float64_t C)
 
void set_mkl_norm (float64_t norm)
 
void set_elasticnet_lambda (float64_t elasticnet_lambda)
 
void set_mkl_block_norm (float64_t q)
 
void set_interleaved_optimization_enabled (bool enable)
 
bool get_interleaved_optimization_enabled ()
 
float64_t compute_mkl_primal_objective ()
 
float64_t compute_elasticnet_dual_objective ()
 
void set_mkl_epsilon (float64_t eps)
 
float64_t get_mkl_epsilon ()
 
int32_t get_mkl_iterations ()
 
virtual bool perform_mkl_step (const float64_t *sumw, float64_t suma)
 
virtual void compute_sum_beta (float64_t *sumw)
 
 MACHINE_PROBLEM_TYPE (PT_BINARY)
 
void set_defaults (int32_t num_sv=0)
 
virtual SGVector< float64_tget_linear_term ()
 
virtual void set_linear_term (const SGVector< float64_t > linear_term)
 
bool load (FILE *svm_file)
 
bool save (FILE *svm_file)
 
void set_nu (float64_t nue)
 
void set_C (float64_t c_neg, float64_t c_pos)
 
void set_epsilon (float64_t eps)
 
void set_tube_epsilon (float64_t eps)
 
float64_t get_tube_epsilon ()
 
void set_qpsize (int32_t qps)
 
float64_t get_epsilon ()
 
float64_t get_nu ()
 
float64_t get_C1 ()
 
float64_t get_C2 ()
 
int32_t get_qpsize ()
 
void set_shrinking_enabled (bool enable)
 
bool get_shrinking_enabled ()
 
float64_t compute_svm_dual_objective ()
 
float64_t compute_svm_primal_objective ()
 
void set_objective (float64_t v)
 
float64_t get_objective ()
 
void set_callback_function (CMKL *m, bool(*cb)(CMKL *mkl, const float64_t *sumw, const float64_t suma))
 
void set_kernel (CKernel *k)
 
CKernelget_kernel ()
 
void set_batch_computation_enabled (bool enable)
 
bool get_batch_computation_enabled ()
 
void set_linadd_enabled (bool enable)
 
bool get_linadd_enabled ()
 
void set_bias_enabled (bool enable_bias)
 
bool get_bias_enabled ()
 
float64_t get_bias ()
 
void set_bias (float64_t bias)
 
int32_t get_support_vector (int32_t idx)
 
float64_t get_alpha (int32_t idx)
 
bool set_support_vector (int32_t idx, int32_t val)
 
bool set_alpha (int32_t idx, float64_t val)
 
int32_t get_num_support_vectors ()
 
void set_alphas (SGVector< float64_t > alphas)
 
void set_support_vectors (SGVector< int32_t > svs)
 
SGVector< int32_t > get_support_vectors ()
 
SGVector< float64_tget_alphas ()
 
bool create_new_model (int32_t num)
 
bool init_kernel_optimization ()
 
virtual CRegressionLabelsapply_regression (CFeatures *data=NULL)
 
virtual CBinaryLabelsapply_binary (CFeatures *data=NULL)
 
virtual float64_t apply_one (int32_t num)
 
virtual bool train_locked (SGVector< index_t > indices)
 
virtual CBinaryLabelsapply_locked_binary (SGVector< index_t > indices)
 
virtual CRegressionLabelsapply_locked_regression (SGVector< index_t > indices)
 
virtual SGVector< float64_tapply_locked_get_output (SGVector< index_t > indices)
 
virtual void data_lock (CLabels *labs, CFeatures *features=NULL)
 
virtual void data_unlock ()
 
virtual bool supports_locking () const
 
virtual bool train (CFeatures *data=NULL)
 
virtual CLabelsapply (CFeatures *data=NULL)
 
virtual CMulticlassLabelsapply_multiclass (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 CLabelsapply_locked (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 post_lock (CLabels *labs, CFeatures *features)
 
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 ()
 

Static Public Member Functions

static bool perform_mkl_step_helper (CMKL *mkl, const float64_t *sumw, const float64_t suma)
 
static void * apply_helper (void *p)
 

Public Attributes

SGIOio
 
Parallelparallel
 
Versionversion
 
Parameterm_parameters
 
Parameterm_model_selection_parameters
 
Parameterm_gradient_parameters
 
uint32_t m_hash
 

Protected Member Functions

virtual void init_training ()
 
virtual EMachineType get_classifier_type ()
 
virtual float64_t compute_mkl_dual_objective ()
 
void perform_mkl_step (float64_t *beta, float64_t *old_beta, int num_kernels, int32_t *label, int32_t *active2dnum, float64_t *a, float64_t *lin, float64_t *sumw, int32_t &inner_iters)
 
virtual bool train_machine (CFeatures *data=NULL)
 
float64_t compute_optimal_betas_via_cplex (float64_t *beta, const float64_t *old_beta, int32_t num_kernels, const float64_t *sumw, float64_t suma, int32_t &inner_iters)
 
float64_t compute_optimal_betas_via_glpk (float64_t *beta, const float64_t *old_beta, int num_kernels, const float64_t *sumw, float64_t suma, int32_t &inner_iters)
 
float64_t compute_optimal_betas_elasticnet (float64_t *beta, const float64_t *old_beta, const int32_t num_kernels, const float64_t *sumw, const float64_t suma, const float64_t mkl_objective)
 
void elasticnet_transform (float64_t *beta, float64_t lmd, int32_t len)
 
void elasticnet_dual (float64_t *ff, float64_t *gg, float64_t *hh, const float64_t &del, const float64_t *nm, int32_t len, const float64_t &lambda)
 
float64_t compute_optimal_betas_directly (float64_t *beta, const float64_t *old_beta, const int32_t num_kernels, const float64_t *sumw, const float64_t suma, const float64_t mkl_objective)
 
float64_t compute_optimal_betas_block_norm (float64_t *beta, const float64_t *old_beta, const int32_t num_kernels, const float64_t *sumw, const float64_t suma, const float64_t mkl_objective)
 
float64_t compute_optimal_betas_newton (float64_t *beta, const float64_t *old_beta, int32_t num_kernels, const float64_t *sumw, float64_t suma, float64_t mkl_objective)
 
virtual bool converged ()
 
void init_solver ()
 
virtual float64_tget_linear_term_array ()
 
SGVector< float64_tapply_get_outputs (CFeatures *data)
 
virtual void store_model_features ()
 
virtual bool is_label_valid (CLabels *lab) const
 
virtual bool train_require_labels () 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)
 

Protected Attributes

CSVMsvm
 
float64_t C_mkl
 
float64_t mkl_norm
 
float64_t ent_lambda
 
float64_t mkl_block_norm
 
float64_tbeta_local
 
int32_t mkl_iterations
 
float64_t mkl_epsilon
 
bool interleaved_optimization
 
float64_tW
 
float64_t w_gap
 
float64_t rho
 
CTime training_time_clock
 
Unique< Selfself
 
bool lp_initialized
 
SGVector< float64_tm_linear_term
 
bool svm_loaded
 
float64_t epsilon
 
float64_t tube_epsilon
 
float64_t nu
 
float64_t C1
 
float64_t C2
 
float64_t objective
 
int32_t qpsize
 
bool use_shrinking
 
bool(* callback )(CMKL *mkl, const float64_t *sumw, const float64_t suma)
 
CMKLmkl
 
CKernelkernel
 
CCustomKernelm_custom_kernel
 
CKernelm_kernel_backup
 
bool use_batch_computation
 
bool use_linadd
 
bool use_bias
 
float64_t m_bias
 
SGVector< float64_tm_alpha
 
SGVector< int32_t > m_svs
 
float64_t m_max_train_time
 
CLabelsm_labels
 
ESolverType m_solver_type
 
bool m_store_model_features
 
bool m_data_locked
 

Constructor & Destructor Documentation

CMKLRegression ( CSVM s = NULL)

Constructor

Parameters
sSVM to use as constraint generator in MKL SILP

Definition at line 9 of file MKLRegression.cpp.

~CMKLRegression ( )
virtual

Destructor

Definition at line 22 of file MKLRegression.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 kernel machine to data for binary classification task

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

Reimplemented from CMachine.

Reimplemented in CDomainAdaptationSVM.

Definition at line 248 of file KernelMachine.cpp.

SGVector< float64_t > apply_get_outputs ( CFeatures data)
protectedinherited

apply get outputs

Parameters
datafeatures to compute outputs
Returns
outputs

Definition at line 254 of file KernelMachine.cpp.

void * apply_helper ( void *  p)
staticinherited

apply example helper, used in threads

Parameters
pparams of the thread
Returns
nothing really

Definition at line 424 of file KernelMachine.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. Error if machine is not locked. Binary case

Parameters
indicesindex vector (of locked features) that is predicted
Returns
resulting labels

Reimplemented from CMachine.

Definition at line 518 of file KernelMachine.cpp.

SGVector< float64_t > apply_locked_get_output ( 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
Returns
raw output of machine

Definition at line 531 of file KernelMachine.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. Error if machine is not locked. Binary case

Parameters
indicesindex vector (of locked features) that is predicted
Returns
resulting labels

Reimplemented from CMachine.

Definition at line 524 of file KernelMachine.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
float64_t apply_one ( int32_t  num)
virtualinherited

apply kernel machine to one example

Parameters
numwhich example to apply to
Returns
classified value

Reimplemented from CMachine.

Definition at line 405 of file KernelMachine.cpp.

CRegressionLabels * apply_regression ( CFeatures data = NULL)
virtualinherited

apply kernel machine to data for regression task

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

Reimplemented from CMachine.

Definition at line 242 of file KernelMachine.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.

float64_t compute_elasticnet_dual_objective ( )
inherited

compute ElasticnetMKL dual objective

Returns
computed dual objective

Definition at line 676 of file MKL.cpp.

float64_t compute_mkl_dual_objective ( )
protectedvirtual

compute mkl dual objective

Returns
computed dual objective

Reimplemented from CMKL.

Definition at line 39 of file MKLRegression.cpp.

float64_t compute_mkl_primal_objective ( )
inherited

compute mkl primal objective

Returns
computed mkl primal objective

Definition at line 177 of file MKL.h.

float64_t compute_optimal_betas_block_norm ( float64_t beta,
const float64_t old_beta,
const int32_t  num_kernels,
const float64_t sumw,
const float64_t  suma,
const float64_t  mkl_objective 
)
protectedinherited

given the alphas, compute the corresponding optimal betas

Parameters
betanew betas (kernel weights)
old_betaold betas (previous kernel weights)
num_kernelsnumber of kernels
sumw1/2*alpha'*K_j*alpha for each kernel j
suma(sum over alphas)
mkl_objectivethe current mkl objective
Returns
new objective value

Definition at line 751 of file MKL.cpp.

float64_t compute_optimal_betas_directly ( float64_t beta,
const float64_t old_beta,
const int32_t  num_kernels,
const float64_t sumw,
const float64_t  suma,
const float64_t  mkl_objective 
)
protectedinherited

given the alphas, compute the corresponding optimal betas

Parameters
betanew betas (kernel weights)
old_betaold betas (previous kernel weights)
num_kernelsnumber of kernels
sumw1/2*alpha'*K_j*alpha for each kernel j
suma(sum over alphas)
mkl_objectivethe current mkl objective
Returns
new objective value

Definition at line 787 of file MKL.cpp.

float64_t compute_optimal_betas_elasticnet ( float64_t beta,
const float64_t old_beta,
const int32_t  num_kernels,
const float64_t sumw,
const float64_t  suma,
const float64_t  mkl_objective 
)
protectedinherited

given the alphas, compute the corresponding optimal betas

Parameters
betanew betas (kernel weights)
old_betaold betas (previous kernel weights)
num_kernelsnumber of kernels
sumw1/2*alpha'*K_j*alpha for each kernel j
suma(sum over alphas)
mkl_objectivethe current mkl objective
Returns
new objective value

Definition at line 557 of file MKL.cpp.

float64_t compute_optimal_betas_newton ( float64_t beta,
const float64_t old_beta,
int32_t  num_kernels,
const float64_t sumw,
float64_t  suma,
float64_t  mkl_objective 
)
protectedinherited

given the alphas, compute the corresponding optimal betas

Parameters
betanew betas (kernel weights)
old_betaold betas (previous kernel weights)
num_kernelsnumber of kernels
sumw1/2*alpha'*K_j*alpha for each kernel j
suma(sum over alphas)
mkl_objectivethe current mkl objective
Returns
new objective value

Definition at line 876 of file MKL.cpp.

float64_t compute_optimal_betas_via_cplex ( float64_t beta,
const float64_t old_beta,
int32_t  num_kernels,
const float64_t sumw,
float64_t  suma,
int32_t &  inner_iters 
)
protectedinherited

given the alphas, compute the corresponding optimal betas using a lp for 1-norm mkl, a qcqp for 2-norm mkl and an iterated qcqp for general q-norm mkl.

Parameters
betanew betas (kernel weights)
old_betaold betas (previous kernel weights)
num_kernelsnumber of kernels
sumw1/2*alpha'*K_j*alpha for each kernel j
suma(sum over alphas)
inner_itersnumber of internal iterations (for statistics)
Returns
new objective value

Definition at line 1068 of file MKL.cpp.

float64_t compute_optimal_betas_via_glpk ( float64_t beta,
const float64_t old_beta,
int  num_kernels,
const float64_t sumw,
float64_t  suma,
int32_t &  inner_iters 
)
protectedinherited

given the alphas, compute the corresponding optimal betas using a lp for 1-norm mkl

Parameters
betanew betas (kernel weights)
old_betaold betas (previous kernel weights)
num_kernelsnumber of kernels
sumw1/2*alpha'*K_j*alpha for each kernel j
suma(sum over alphas)
inner_itersnumber of internal iterations (for statistics)
Returns
new objective value

Definition at line 1411 of file MKL.cpp.

float64_t compute_sum_alpha ( )
virtual

compute beta independent term from objective, e.g., in 2-class MKL sum_i alpha_i etc

Implements CMKL.

Definition at line 26 of file MKLRegression.cpp.

void compute_sum_beta ( float64_t sumw)
virtualinherited

compute 1/2*alpha'*K_j*alpha for each kernel j (beta dependent term from objective)

Parameters
sumwvector of size num_kernels to hold the result

Definition at line 1565 of file MKL.cpp.

float64_t compute_svm_dual_objective ( )
inherited

compute svm dual objective

Returns
computed dual objective

Definition at line 242 of file SVM.cpp.

float64_t compute_svm_primal_objective ( )
inherited

compute svm primal objective

Returns
computed svm primal objective

Definition at line 267 of file SVM.cpp.

virtual bool converged ( )
protectedvirtualinherited

check if mkl converged, i.e. 'gap' is below epsilon

Returns
whether mkl converged

Definition at line 394 of file MKL.h.

bool create_new_model ( int32_t  num)
inherited

create new model

Parameters
numnumber of alphas and support vectors in new model

Definition at line 194 of file KernelMachine.cpp.

void data_lock ( CLabels labs,
CFeatures features = NULL 
)
virtualinherited

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

Computes kernel matrix to speed up train/apply calls

Parameters
labslabels used for locking
featuresfeatures used for locking

Reimplemented from CMachine.

Definition at line 623 of file KernelMachine.cpp.

void data_unlock ( )
virtualinherited

Unlocks a locked machine and restores previous state

Reimplemented from CMachine.

Definition at line 654 of file KernelMachine.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 elasticnet_dual ( float64_t ff,
float64_t gg,
float64_t hh,
const float64_t del,
const float64_t nm,
int32_t  len,
const float64_t lambda 
)
protectedinherited

helper function to compute the elastic-net objective

Definition at line 649 of file MKL.cpp.

void elasticnet_transform ( float64_t beta,
float64_t  lmd,
int32_t  len 
)
protectedinherited

helper function to compute the elastic-net sub-kernel weights

Definition at line 335 of file MKL.h.

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.

float64_t get_alpha ( int32_t  idx)
inherited

get alpha at given index

Parameters
idxindex of alpha
Returns
alpha

Definition at line 140 of file KernelMachine.cpp.

SGVector< float64_t > get_alphas ( )
inherited
Returns
vector of alphas

Definition at line 189 of file KernelMachine.cpp.

bool get_batch_computation_enabled ( )
inherited

check if batch computation is enabled

Returns
if batch computation is enabled

Definition at line 99 of file KernelMachine.cpp.

float64_t get_bias ( )
inherited

get bias

Returns
bias

Definition at line 124 of file KernelMachine.cpp.

bool get_bias_enabled ( )
inherited

get state of bias

Returns
state of bias

Definition at line 119 of file KernelMachine.cpp.

float64_t get_C1 ( )
inherited

get C1

Returns
C1

Definition at line 161 of file SVM.h.

float64_t get_C2 ( )
inherited

get C2

Returns
C2

Definition at line 167 of file SVM.h.

virtual EMachineType get_classifier_type ( )
protectedvirtual

get classifier type

Returns
classifier type MKL_REGRESSION

Reimplemented from CMachine.

Definition at line 61 of file MKLRegression.h.

float64_t get_epsilon ( )
inherited

get epsilon

Returns
epsilon

Definition at line 149 of file SVM.h.

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.

bool get_interleaved_optimization_enabled ( )
inherited

get state of optimization (interleaved or wrapper)

Returns
true if interleaved optimization is used; wrapper otherwise

Definition at line 168 of file MKL.h.

CKernel * get_kernel ( )
inherited

get kernel

Returns
kernel

Definition at line 88 of file KernelMachine.cpp.

CLabels * get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 76 of file Machine.cpp.

bool get_linadd_enabled ( )
inherited

check if linadd is enabled

Returns
if linadd is enabled

Definition at line 109 of file KernelMachine.cpp.

SGVector< float64_t > get_linear_term ( )
virtualinherited

get linear term

Returns
the linear term

Definition at line 332 of file SVM.cpp.

float64_t * get_linear_term_array ( )
protectedvirtualinherited

get linear term copy as dynamic array

Returns
linear term copied to a dynamic array

Definition at line 302 of file SVM.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_train_time ( )
inherited

get maximum training time

Returns
maximum training time

Definition at line 87 of file Machine.cpp.

float64_t get_mkl_epsilon ( )
inherited

get mkl epsilon for weights (optimization accuracy for kernel weights)

Returns
epsilon for weights

Definition at line 205 of file MKL.h.

int32_t get_mkl_iterations ( )
inherited

get number of MKL iterations

Returns
mkl_iterations

Definition at line 211 of file MKL.h.

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 CMKL.

Definition at line 49 of file MKLRegression.h.

float64_t get_nu ( )
inherited

get nu

Returns
nu

Definition at line 155 of file SVM.h.

int32_t get_num_support_vectors ( )
inherited

get number of support vectors

Returns
number of support vectors

Definition at line 169 of file KernelMachine.cpp.

float64_t get_objective ( )
inherited

get objective

Returns
objective

Definition at line 218 of file SVM.h.

int32_t get_qpsize ( )
inherited

get qpsize

Returns
qpsize

Definition at line 173 of file SVM.h.

bool get_shrinking_enabled ( )
inherited

get state of shrinking

Returns
if shrinking is enabled

Definition at line 188 of file SVM.h.

ESolverType get_solver_type ( )
inherited

get solver type

Returns
solver

Definition at line 102 of file Machine.cpp.

int32_t get_support_vector ( int32_t  idx)
inherited

get support vector at given index

Parameters
idxindex of support vector
Returns
support vector

Definition at line 134 of file KernelMachine.cpp.

SGVector< int32_t > get_support_vectors ( )
inherited
Returns
all support vectors

Definition at line 184 of file KernelMachine.cpp.

CSVM* get_svm ( )
inherited

get SVM that is used as constraint generator in MKL SIP

Returns
svm

Definition at line 122 of file MKL.h.

float64_t get_tube_epsilon ( )
inherited

get tube epsilon

Returns
tube epsilon

Definition at line 137 of file SVM.h.

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.

bool init_kernel_optimization ( )
inherited

initialise kernel optimisation

Returns
if operation was successful

Definition at line 211 of file KernelMachine.cpp.

void init_solver ( )
protectedinherited

initialize solver such as glpk or cplex

Definition at line 265 of file MKL.cpp.

void init_training ( )
protectedvirtual

check run before starting training (to e.g. check if labeling is two-class labeling in classification case

Implements CMKL.

Definition at line 45 of file MKLRegression.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.

bool load ( FILE *  svm_file)
inherited

load a SVM from file

Parameters
svm_filethe file handle

Definition at line 90 of file SVM.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.

MACHINE_PROBLEM_TYPE ( PT_REGRESSION  )

problem type

MACHINE_PROBLEM_TYPE ( PT_BINARY  )
inherited

problem type

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

Definition at line 295 of file SGObject.cpp.

bool perform_mkl_step ( const float64_t sumw,
float64_t  suma 
)
virtualinherited

perform single mkl iteration

given sum of alphas, objectives for current alphas for each kernel and current kernel weighting compute the corresponding optimal kernel weighting (all via get/set_subkernel_weights in CCombinedKernel)

Parameters
sumwvector of 1/2*alpha'*K_j*alpha for each kernel j
sumascalar sum_i alpha_i etc.

Definition at line 488 of file MKL.cpp.

void perform_mkl_step ( float64_t beta,
float64_t old_beta,
int  num_kernels,
int32_t *  label,
int32_t *  active2dnum,
float64_t a,
float64_t lin,
float64_t sumw,
int32_t &  inner_iters 
)
protectedinherited

perform single mkl iteration

given the alphas, compute the corresponding optimal betas

Parameters
betanew betas (kernel weights)
old_betaold betas (previous kernel weights)
num_kernelsnumber of kernels
label(from svmlight label)
active2dnum(from svmlight active2dnum)
a(from svmlight alphas)
lin(from svmlight linear components)
sumw1/2*alpha'*K_j*alpha for each kernel j
inner_itersnumber of required internal iterations
static bool perform_mkl_step_helper ( CMKL mkl,
const float64_t sumw,
const float64_t  suma 
)
staticinherited

callback helper function calling perform_mkl_step

Parameters
mklMKL object
sumwvector of 1/2*alpha'*K_j*alpha for each kernel j
sumascalar sum_i alpha_i etc.

Definition at line 231 of file MKL.h.

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.

bool save ( FILE *  svm_file)
inherited

write a SVM to a file

Parameters
svm_filethe file handle

Definition at line 206 of file SVM.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.

bool set_alpha ( int32_t  idx,
float64_t  val 
)
inherited

set alpha at given index to given value

Parameters
idxindex of alpha vector
valnew value of alpha vector
Returns
if operation was successful

Definition at line 159 of file KernelMachine.cpp.

void set_alphas ( SGVector< float64_t alphas)
inherited

set alphas to given values

Parameters
alphasfloat vector with all alphas to set

Definition at line 174 of file KernelMachine.cpp.

void set_batch_computation_enabled ( bool  enable)
inherited

set batch computation enabled

Parameters
enableif batch computation shall be enabled

Definition at line 94 of file KernelMachine.cpp.

void set_bias ( float64_t  bias)
inherited

set bias to given value

Parameters
biasnew bias

Definition at line 129 of file KernelMachine.cpp.

void set_bias_enabled ( bool  enable_bias)
inherited

set state of bias

Parameters
enable_biasif bias shall be enabled

Definition at line 114 of file KernelMachine.cpp.

void set_C ( float64_t  c_neg,
float64_t  c_pos 
)
inherited

set C

Parameters
c_negnew C constant for negatively labeled examples
c_posnew C constant for positively labeled examples

Note that not all SVMs support this (however at least CLibSVM and CSVMLight do)

Definition at line 118 of file SVM.h.

void set_C_mkl ( float64_t  C)
inherited

set C mkl

Parameters
Cnew C_mkl

Definition at line 132 of file MKL.h.

void set_callback_function ( CMKL m,
bool(*)(CMKL *mkl, const float64_t *sumw, const float64_t suma)  cb 
)
inherited

set callback function svm optimizers may call when they have a new (small) set of alphas

Parameters
mpointer to mkl object
cbcallback function

Definition at line 232 of file SVM.cpp.

void set_constraint_generator ( CSVM s)
inherited

SVM to use as constraint generator in MKL SIP

Parameters
ssvm

Definition at line 102 of file MKL.h.

void set_defaults ( int32_t  num_sv = 0)
inherited

set default values for members a SVM object

Definition at line 48 of file SVM.cpp.

void set_elasticnet_lambda ( float64_t  elasticnet_lambda)
inherited

set elasticnet lambda

Parameters
elasticnet_lambdanew elastic net lambda (must be 0<=lambda<=1) lambda=0: L1-MKL lambda=1: Linfinity-MKL

Definition at line 467 of file MKL.cpp.

void set_epsilon ( float64_t  eps)
inherited

set epsilon

Parameters
epsnew epsilon

Definition at line 125 of file SVM.h.

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_interleaved_optimization_enabled ( bool  enable)
inherited

set state of optimization (interleaved or wrapper)

Parameters
enableif true interleaved optimization is used; wrapper otherwise

Definition at line 159 of file MKL.h.

void set_kernel ( CKernel k)
inherited

set kernel

Parameters
kkernel

Definition at line 81 of file KernelMachine.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_linadd_enabled ( bool  enable)
inherited

set linadd enabled

Parameters
enableif linadd shall be enabled

Definition at line 104 of file KernelMachine.cpp.

void set_linear_term ( const SGVector< float64_t linear_term)
virtualinherited

set linear term of the QP

Parameters
linear_termthe linear term

Definition at line 314 of file SVM.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_mkl_block_norm ( float64_t  q)
inherited

set block norm q (used in block norm mkl)

Parameters
qmixed norm (1<=q<=inf)

Definition at line 480 of file MKL.cpp.

void set_mkl_epsilon ( float64_t  eps)
inherited

set mkl epsilon (optimization accuracy for kernel weights)

Parameters
epsnew weight_epsilon

Definition at line 199 of file MKL.h.

void set_mkl_norm ( float64_t  norm)
inherited

set mkl norm

Parameters
normnew mkl norm (must be greater equal 1)

Definition at line 458 of file MKL.cpp.

void set_nu ( float64_t  nue)
inherited

set nu

Parameters
nuenew nu

Definition at line 107 of file SVM.h.

void set_objective ( float64_t  v)
inherited

set objective

Parameters
vobjective

Definition at line 209 of file SVM.h.

void set_qpsize ( int32_t  qps)
inherited

set qpsize

Parameters
qpsnew qpsize

Definition at line 143 of file SVM.h.

void set_shrinking_enabled ( bool  enable)
inherited

set state of shrinking

Parameters
enableif shrinking will be enabled

Definition at line 179 of file SVM.h.

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.

bool set_support_vector ( int32_t  idx,
int32_t  val 
)
inherited

set support vector at given index to given value

Parameters
idxindex of support vector
valnew value of support vector
Returns
if operation was successful

Definition at line 149 of file KernelMachine.cpp.

void set_support_vectors ( SGVector< int32_t >  svs)
inherited

set support vectors to given values

Parameters
svsinteger vector with all support vectors indexes to set

Definition at line 179 of file KernelMachine.cpp.

void set_svm ( CSVM s)
inherited

SVM to use as constraint generator in MKL SIP

Parameters
ssvm

Definition at line 111 of file MKL.h.

void set_tube_epsilon ( float64_t  eps)
inherited

set tube epsilon

Parameters
epsnew tube epsilon

Definition at line 131 of file SVM.h.

CSGObject * shallow_copy ( ) const
virtualinherited

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

Reimplemented in CGaussianKernel.

Definition at line 225 of file SGObject.cpp.

void store_model_features ( )
protectedvirtualinherited

Stores feature data of the SV indices and sets it to the lhs of the underlying kernel. Then, all SV indices are set to identity.

May be overwritten by subclasses in case the model should be stored differently.

Reimplemented from CMachine.

Definition at line 453 of file KernelMachine.cpp.

bool supports_locking ( ) const
virtualinherited
Returns
whether machine supports locking

Reimplemented from CMachine.

Definition at line 699 of file KernelMachine.cpp.

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.

bool train_locked ( SGVector< index_t indices)
virtualinherited

Trains a locked machine on a set of indices. Error if machine is not locked

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

Reimplemented from CMachine.

Definition at line 482 of file KernelMachine.cpp.

bool train_machine ( CFeatures data = NULL)
protectedvirtualinherited

train MKL classifier

Parameters
datatraining data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data)
Returns
whether training was successful

Reimplemented from CMachine.

Definition at line 282 of file MKL.cpp.

virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

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

Definition at line 354 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 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

float64_t* beta_local
protectedinherited

sub-kernel weights on the L1-term of ElasticnetMKL

Definition at line 421 of file MKL.h.

float64_t C1
protectedinherited

C1 regularization const

Definition at line 257 of file SVM.h.

float64_t C2
protectedinherited

C2

Definition at line 259 of file SVM.h.

float64_t C_mkl
protectedinherited

C_mkl

Definition at line 406 of file MKL.h.

bool(* callback)(CMKL *mkl, const float64_t *sumw, const float64_t suma)
protectedinherited

callback function svm optimizers may call when they have a new (small) set of alphas

Definition at line 269 of file SVM.h.

float64_t ent_lambda
protectedinherited

Sparsity trade-off parameter used in ElasticnetMKL must be 0<=lambda<=1 lambda=0: L1-MKL lambda=1: Linfinity-MKL

Definition at line 414 of file MKL.h.

float64_t epsilon
protectedinherited

epsilon

Definition at line 251 of file SVM.h.

bool interleaved_optimization
protectedinherited

whether to use mkl wrapper or interleaved opt.

Definition at line 427 of file MKL.h.

SGIO* io
inherited

io

Definition at line 537 of file SGObject.h.

CKernel* kernel
protectedinherited

kernel

Definition at line 311 of file KernelMachine.h.

bool lp_initialized
protectedinherited

if lp is initialized

Definition at line 445 of file MKL.h.

SGVector<float64_t> m_alpha
protectedinherited

coefficients alpha

Definition at line 332 of file KernelMachine.h.

float64_t m_bias
protectedinherited

bias term b

Definition at line 329 of file KernelMachine.h.

CCustomKernel* m_custom_kernel
protectedinherited

is filled with pre-computed custom kernel on data lock

Definition at line 314 of file KernelMachine.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.

CKernel* m_kernel_backup
protectedinherited

old kernel is stored here on data lock

Definition at line 317 of file KernelMachine.h.

CLabels* m_labels
protectedinherited

labels

Definition at line 361 of file Machine.h.

SGVector<float64_t> m_linear_term
protectedinherited

linear term in qp

Definition at line 246 of file SVM.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.

SGVector<int32_t> m_svs
protectedinherited

array of ``support vectors'' (indices of feature objects)

Definition at line 335 of file KernelMachine.h.

CMKL* mkl
protectedinherited

mkl object that svm optimizers need to pass when calling the callback function

Definition at line 272 of file SVM.h.

float64_t mkl_block_norm
protectedinherited

Sparsity trade-off parameter used in block norm MKL should be 1 <= mkl_block_norm <= inf

Definition at line 418 of file MKL.h.

float64_t mkl_epsilon
protectedinherited

mkl_epsilon for multiple kernel learning

Definition at line 425 of file MKL.h.

int32_t mkl_iterations
protectedinherited

number of mkl steps

Definition at line 423 of file MKL.h.

float64_t mkl_norm
protectedinherited

norm used in mkl must be > 0

Definition at line 408 of file MKL.h.

float64_t nu
protectedinherited

nu

Definition at line 255 of file SVM.h.

float64_t objective
protectedinherited

objective

Definition at line 261 of file SVM.h.

Parallel* parallel
inherited

parallel

Definition at line 540 of file SGObject.h.

int32_t qpsize
protectedinherited

qpsize

Definition at line 263 of file SVM.h.

float64_t rho
protectedinherited

objective after mkl iterations

Definition at line 435 of file MKL.h.

Unique<Self> self
protectedinherited

Definition at line 441 of file MKL.h.

CSVM* svm
protectedinherited

wrapper SVM

Definition at line 404 of file MKL.h.

bool svm_loaded
protectedinherited

if SVM is loaded

Definition at line 249 of file SVM.h.

CTime training_time_clock
protectedinherited

measures training time for use with get_max_train_time()

Definition at line 438 of file MKL.h.

float64_t tube_epsilon
protectedinherited

tube epsilon for support vector regression

Definition at line 253 of file SVM.h.

bool use_batch_computation
protectedinherited

if batch computation is enabled

Definition at line 320 of file KernelMachine.h.

bool use_bias
protectedinherited

if bias shall be used

Definition at line 326 of file KernelMachine.h.

bool use_linadd
protectedinherited

if linadd is enabled

Definition at line 323 of file KernelMachine.h.

bool use_shrinking
protectedinherited

if shrinking shall be used

Definition at line 265 of file SVM.h.

Version* version
inherited

version

Definition at line 543 of file SGObject.h.

float64_t* W
protectedinherited

partial objectives (one per kernel)

Definition at line 430 of file MKL.h.

float64_t w_gap
protectedinherited

gap between iterations

Definition at line 433 of file MKL.h.


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

SHOGUN Machine Learning Toolbox - Documentation