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CSVRLight Class Reference

Detailed Description

Class SVRLight, performs support vector regression using SVMLight.

The SVR solution can be expressed as

\[ f({\bf x})=\sum_{i=1}^{N} \alpha_i k({\bf x}, {\bf x_i})+b \]

where $\alpha$ and $b$ are determined in training, i.e. using a pre-specified kernel, a given tube-epsilon for the epsilon insensitive loss, the follwoing quadratic problem is minimized (using the chunking decomposition technique)

\begin{eqnarray*} \max_{{\bf \alpha},{\bf \alpha}^*} &-\frac{1}{2}\sum_{i,j=1}^N(\alpha_i-\alpha_i^*)(\alpha_j-\alpha_j^*){\bf x}_i^T {\bf x}_j -\sum_{i=1}^N(\alpha_i+\alpha_i^*)\epsilon - \sum_{i=1}^N(\alpha_i-\alpha_i^*)y_i\\ \mbox{wrt}:& {\bf \alpha},{\bf \alpha}^*\in{\bf R}^N\\ \mbox{s.t.}:& 0\leq \alpha_i,\alpha_i^*\leq C,\, \forall i=1\dots N\\ &\sum_{i=1}^N(\alpha_i-\alpha_i^*)y_i=0 \end{eqnarray*}

Note that the SV regression problem is reduced to the standard SV classification problem by introducing artificial labels $-y_i$ which leads to the epsilon insensitive loss constraints *

\begin{eqnarray*} {\bf w}^T{\bf x}_i+b-c_i-\xi_i\leq 0,&\, \forall i=1\dots N\\ -{\bf w}^T{\bf x}_i-b-c_i^*-\xi_i^*\leq 0,&\, \forall i=1\dots N \end{eqnarray*}

with $c_i=y_i+ \epsilon$ and $c_i^*=-y_i+ \epsilon$

This implementation supports multiple kernel learning, i.e. if a CCombinedKernel is used the weights $\beta$ in $ k_{combined}({\bf x}, {\bf x'}) = \sum_{m=0}^M \beta_m k_m({\bf x}, {\bf x'})$ can be determined in training (cf. Large Scale Multiple Kernel Learning Sonnenburg, Raetsch, Schaefer, Schoelkopf 2006).

linadd optimizations were implemented for kernels that support it (most string kernels and the linear kernel), which will result in significant speedups.

Definition at line 62 of file SVRLight.h.

Inheritance diagram for CSVRLight:
Inheritance graph
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List of all members.

Public Member Functions

 MACHINE_PROBLEM_TYPE (PT_REGRESSION)
 CSVRLight ()
 CSVRLight (float64_t C, float64_t epsilon, CKernel *k, CLabels *lab)
virtual ~CSVRLight ()
virtual EMachineType get_classifier_type ()
void svr_learn ()
virtual float64_t compute_objective_function (float64_t *a, float64_t *lin, float64_t *c, float64_t *eps, int32_t *label, int32_t totdoc)
virtual void update_linear_component (int32_t *docs, int32_t *label, int32_t *active2dnum, float64_t *a, float64_t *a_old, int32_t *working2dnum, int32_t totdoc, float64_t *lin, float64_t *aicache, float64_t *c)
virtual void update_linear_component_mkl (int32_t *docs, int32_t *label, int32_t *active2dnum, float64_t *a, float64_t *a_old, int32_t *working2dnum, int32_t totdoc, float64_t *lin, float64_t *aicache, float64_t *c)
virtual void update_linear_component_mkl_linadd (int32_t *docs, int32_t *label, int32_t *active2dnum, float64_t *a, float64_t *a_old, int32_t *working2dnum, int32_t totdoc, float64_t *lin, float64_t *aicache, float64_t *c)
void call_mkl_callback (float64_t *a, int32_t *label, float64_t *lin, float64_t *c, int32_t totdoc)
virtual void reactivate_inactive_examples (int32_t *label, float64_t *a, SHRINK_STATE *shrink_state, float64_t *lin, float64_t *c, int32_t totdoc, int32_t iteration, int32_t *inconsistent, int32_t *docs, float64_t *aicache, float64_t *maxdiff)
virtual const char * get_name () const
void init ()
int32_t get_runtime ()
void svm_learn ()
int32_t optimize_to_convergence (int32_t *docs, int32_t *label, int32_t totdoc, SHRINK_STATE *shrink_state, int32_t *inconsistent, float64_t *a, float64_t *lin, float64_t *c, TIMING *timing_profile, float64_t *maxdiff, int32_t heldout, int32_t retrain)
void clear_index (int32_t *index)
void add_to_index (int32_t *index, int32_t elem)
int32_t compute_index (int32_t *binfeature, int32_t range, int32_t *index)
void optimize_svm (int32_t *docs, int32_t *label, int32_t *exclude_from_eq_const, float64_t eq_target, int32_t *chosen, int32_t *active2dnum, int32_t totdoc, int32_t *working2dnum, int32_t varnum, float64_t *a, float64_t *lin, float64_t *c, float64_t *aicache, QP *qp, float64_t *epsilon_crit_target)
void compute_matrices_for_optimization (int32_t *docs, int32_t *label, int32_t *exclude_from_eq_const, float64_t eq_target, int32_t *chosen, int32_t *active2dnum, int32_t *key, float64_t *a, float64_t *lin, float64_t *c, int32_t varnum, int32_t totdoc, float64_t *aicache, QP *qp)
void compute_matrices_for_optimization_parallel (int32_t *docs, int32_t *label, int32_t *exclude_from_eq_const, float64_t eq_target, int32_t *chosen, int32_t *active2dnum, int32_t *key, float64_t *a, float64_t *lin, float64_t *c, int32_t varnum, int32_t totdoc, float64_t *aicache, QP *qp)
int32_t calculate_svm_model (int32_t *docs, int32_t *label, float64_t *lin, float64_t *a, float64_t *a_old, float64_t *c, int32_t *working2dnum, int32_t *active2dnum)
int32_t check_optimality (int32_t *label, float64_t *a, float64_t *lin, float64_t *c, int32_t totdoc, float64_t *maxdiff, float64_t epsilon_crit_org, int32_t *misclassified, int32_t *inconsistent, int32_t *active2dnum, int32_t *last_suboptimal_at, int32_t iteration)
void update_linear_component_mkl (int32_t *docs, int32_t *label, int32_t *active2dnum, float64_t *a, float64_t *a_old, int32_t *working2dnum, int32_t totdoc, float64_t *lin, float64_t *aicache)
void update_linear_component_mkl_linadd (int32_t *docs, int32_t *label, int32_t *active2dnum, float64_t *a, float64_t *a_old, int32_t *working2dnum, int32_t totdoc, float64_t *lin, float64_t *aicache)
void call_mkl_callback (float64_t *a, int32_t *label, float64_t *lin)
int32_t select_next_qp_subproblem_grad (int32_t *label, float64_t *a, float64_t *lin, float64_t *c, int32_t totdoc, int32_t qp_size, int32_t *inconsistent, int32_t *active2dnum, int32_t *working2dnum, float64_t *selcrit, int32_t *select, int32_t cache_only, int32_t *key, int32_t *chosen)
int32_t select_next_qp_subproblem_rand (int32_t *label, float64_t *a, float64_t *lin, float64_t *c, int32_t totdoc, int32_t qp_size, int32_t *inconsistent, int32_t *active2dnum, int32_t *working2dnum, float64_t *selcrit, int32_t *select, int32_t *key, int32_t *chosen, int32_t iteration)
void select_top_n (float64_t *selcrit, int32_t range, int32_t *select, int32_t n)
void init_shrink_state (SHRINK_STATE *shrink_state, int32_t totdoc, int32_t maxhistory)
void shrink_state_cleanup (SHRINK_STATE *shrink_state)
int32_t shrink_problem (SHRINK_STATE *shrink_state, int32_t *active2dnum, int32_t *last_suboptimal_at, int32_t iteration, int32_t totdoc, int32_t minshrink, float64_t *a, int32_t *inconsistent, float64_t *c, float64_t *lin, int *label)
 MACHINE_PROBLEM_TYPE (PT_BINARY)
void set_defaults (int32_t num_sv=0)
virtual SGVector< float64_t > get_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)
CKernel * get_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_t > get_alphas ()
bool create_new_model (int32_t num)
bool init_kernel_optimization ()
virtual CRegressionLabels * apply_regression (CFeatures *data=NULL)
virtual CBinaryLabels * apply_binary (CFeatures *data=NULL)
virtual float64_t apply_one (int32_t num)
virtual bool train_locked (SGVector< index_t > indices)
virtual CBinaryLabels * apply_locked_binary (SGVector< index_t > indices)
virtual CRegressionLabels * apply_locked_regression (SGVector< index_t > indices)
virtual SGVector< float64_t > apply_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 CMachine * clone ()
virtual bool train (CFeatures *data=NULL)
virtual CLabels * apply (CFeatures *data=NULL)
virtual CMulticlassLabels * apply_multiclass (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 CLabels * apply_locked (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 post_lock (CLabels *labs, CFeatures *features)
bool is_data_locked () const
virtual EProblemType get_machine_problem_type () const
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)

Static Public Member Functions

static void * update_linear_component_mkl_linadd_helper (void *p)
static void * apply_helper (void *p)

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

int32_t regression_fix_index (int32_t i)
virtual float64_t compute_kernel (int32_t i, int32_t j)
virtual bool train_machine (CFeatures *data=NULL)
float64_t * optimize_qp (QP *qp, float64_t *epsilon_crit, int32_t nx, float64_t *threshold, int32_t &svm_maxqpsize)
virtual float64_t * get_linear_term_array ()
SGVector< float64_t > apply_get_outputs (CFeatures *data)
virtual void store_model_features ()
virtual bool is_label_valid (CLabels *lab) const
virtual bool train_require_labels () 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 * update_linear_component_linadd_helper (void *params)
static int32_t regression_fix_index2 (int32_t i, int32_t num_vectors)
static void * compute_kernel_helper (void *p)
static void * reactivate_inactive_examples_vanilla_helper (void *p)
static void * reactivate_inactive_examples_linadd_helper (void *p)

Protected Attributes

int32_t num_vectors
MODEL * model
LEARN_PARM * learn_parm
int32_t verbosity
float64_t init_margin
int32_t init_iter
int32_t precision_violations
float64_t model_b
float64_t opt_precision
float64_t * primal
float64_t * dual
float64_t * W
int32_t count
float64_t mymaxdiff
bool use_kernel_cache
bool mkl_converged
SGVector< float64_t > m_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)
CMKL * mkl
CKernel * kernel
CCustomKernel * m_custom_kernel
CKernel * m_kernel_backup
bool use_batch_computation
bool use_linadd
bool use_bias
float64_t m_bias
SGVector< float64_t > m_alpha
SGVector< int32_t > m_svs
float64_t m_max_train_time
CLabels * m_labels
ESolverType m_solver_type
bool m_store_model_features
bool m_data_locked

Friends

class CMulticlassSVM

Constructor & Destructor Documentation

CSVRLight (  ) 

default constructor

Definition at line 58 of file SVRLight.cpp.

CSVRLight ( float64_t  C,
float64_t  epsilon,
CKernel *  k,
CLabels *  lab 
)

constructor

Parameters:
C constant C
epsilon epsilon
k kernel
lab labels

Definition at line 52 of file SVRLight.cpp.

~CSVRLight (  )  [virtual]

default destructor

Definition at line 64 of file SVRLight.cpp.

Member Function Documentation

void add_to_index ( int32_t *  index,
int32_t  elem 
) [inherited]

add to index

Parameters:
index index
elem element at index

Definition at line 977 of file SVMLight.cpp.

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 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 245 of file KernelMachine.cpp.

SGVector< float64_t > apply_get_outputs ( CFeatures *  data  )  [protected, inherited]

apply get outputs

Parameters:
data features to compute outputs
Returns:
outputs

Definition at line 251 of file KernelMachine.cpp.

void * apply_helper ( void *  p  )  [static, inherited]

apply example helper, used in threads

Parameters:
p params of the thread
Returns:
nothing really

Definition at line 421 of file KernelMachine.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. Error if machine is not locked. Binary case

Parameters:
indices index vector (of locked features) that is predicted
Returns:
resulting labels

Reimplemented from CMachine.

Definition at line 515 of file KernelMachine.cpp.

SGVector< float64_t > apply_locked_get_output ( 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
Returns:
raw output of machine

Definition at line 528 of file KernelMachine.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. Error if machine is not locked. Binary case

Parameters:
indices index vector (of locked features) that is predicted
Returns:
resulting labels

Reimplemented from CMachine.

Definition at line 521 of file KernelMachine.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]

apply machine to data in means of multiclass classification problem

Reimplemented in CDistanceMachine, CMulticlassMachine, CConjugateIndex, CGaussianNaiveBayes, CKNN, CQDA, CConditionalProbabilityTree, CRelaxedTree, and CVwConditionalProbabilityTree.

Definition at line 230 of file Machine.cpp.

float64_t apply_one ( int32_t  num  )  [virtual, inherited]

apply kernel machine to one example

Parameters:
num which example to apply to
Returns:
classified value

Reimplemented from CMachine.

Definition at line 402 of file KernelMachine.cpp.

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

apply kernel machine to data for regression task

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

Reimplemented from CMachine.

Definition at line 239 of file KernelMachine.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.

int32_t calculate_svm_model ( int32_t *  docs,
int32_t *  label,
float64_t *  lin,
float64_t *  a,
float64_t *  a_old,
float64_t *  c,
int32_t *  working2dnum,
int32_t *  active2dnum 
) [inherited]

calculate SVM model

Parameters:
docs docs
label label
lin lin
a a
a_old old a
c c
working2dnum working 2D num
active2dnum active 2D num
Returns:
something inty

Definition at line 1248 of file SVMLight.cpp.

void call_mkl_callback ( float64_t *  a,
int32_t *  label,
float64_t *  lin,
float64_t *  c,
int32_t  totdoc 
)

call mkl callback

Parameters:
a 
label 
lin 
c 
totdoc 

Definition at line 612 of file SVRLight.cpp.

void call_mkl_callback ( float64_t *  a,
int32_t *  label,
float64_t *  lin 
) [inherited]

Definition at line 1684 of file SVMLight.cpp.

int32_t check_optimality ( int32_t *  label,
float64_t *  a,
float64_t *  lin,
float64_t *  c,
int32_t  totdoc,
float64_t *  maxdiff,
float64_t  epsilon_crit_org,
int32_t *  misclassified,
int32_t *  inconsistent,
int32_t *  active2dnum,
int32_t *  last_suboptimal_at,
int32_t  iteration 
) [inherited]

check optimality

Parameters:
label label
a a
lin lin
c c
totdoc totdoc
maxdiff maximum diff
epsilon_crit_org epsilon crit org
misclassified misclassified
inconsistent inconsistent
active2dnum active 2D num
last_suboptimal_at last suboptimal at
iteration iteration
Returns:
something inty

Definition at line 1365 of file SVMLight.cpp.

void clear_index ( int32_t *  index  )  [inherited]

clear index

Parameters:
index index

Definition at line 971 of file SVMLight.cpp.

virtual CMachine* clone (  )  [virtual, inherited]

clone

Reimplemented from CMachine.

Definition at line 288 of file KernelMachine.h.

int32_t compute_index ( int32_t *  binfeature,
int32_t  range,
int32_t *  index 
) [inherited]

compute index

Parameters:
binfeature binary feature
range range
index 
Returns:
something inty

Definition at line 986 of file SVMLight.cpp.

float64_t compute_kernel ( int32_t  i,
int32_t  j 
) [protected, virtual]

compute kernel at given index

Parameters:
i index i
j index j
Returns:
kernel value at i,j

Reimplemented from CSVMLight.

Definition at line 391 of file SVRLight.cpp.

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

helper for compute kernel

Parameters:
p p

Definition at line 109 of file SVMLight.cpp.

void compute_matrices_for_optimization ( int32_t *  docs,
int32_t *  label,
int32_t *  exclude_from_eq_const,
float64_t  eq_target,
int32_t *  chosen,
int32_t *  active2dnum,
int32_t *  key,
float64_t *  a,
float64_t *  lin,
float64_t *  c,
int32_t  varnum,
int32_t  totdoc,
float64_t *  aicache,
QP *  qp 
) [inherited]

compute matrices for optimization

Parameters:
docs docs
label label
exclude_from_eq_const exclude from eq const
eq_target eq target
chosen chosen
active2dnum active 2D num
key key
a a
lin lin
c c
varnum var num
totdoc totdoc
aicache ai cache
qp QP

Definition at line 1175 of file SVMLight.cpp.

void compute_matrices_for_optimization_parallel ( int32_t *  docs,
int32_t *  label,
int32_t *  exclude_from_eq_const,
float64_t  eq_target,
int32_t *  chosen,
int32_t *  active2dnum,
int32_t *  key,
float64_t *  a,
float64_t *  lin,
float64_t *  c,
int32_t  varnum,
int32_t  totdoc,
float64_t *  aicache,
QP *  qp 
) [inherited]

compute matrices for optimization in parallel

Parameters:
docs docs
label label
exclude_from_eq_const exclude from eq const
eq_target eq target
chosen chosen
active2dnum active 2D num
key key
a a
lin lin
c c
varnum var num
totdoc totdoc
aicache ai cache
qp QP

Definition at line 1043 of file SVMLight.cpp.

float64_t compute_objective_function ( float64_t *  a,
float64_t *  lin,
float64_t *  c,
float64_t *  eps,
int32_t *  label,
int32_t  totdoc 
) [virtual]

compute objective function

Parameters:
a a
lin lin
c c
eps eps
label label
totdoc totdoc

Reimplemented from CSVMLight.

Definition at line 338 of file SVRLight.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.

bool create_new_model ( int32_t  num  )  [inherited]

create new model

Parameters:
num number of alphas and support vectors in new model

Definition at line 191 of file KernelMachine.cpp.

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

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:
labs labels used for locking
features features used for locking

Reimplemented from CMachine.

Definition at line 620 of file KernelMachine.cpp.

void data_unlock (  )  [virtual, inherited]

Unlocks a locked machine and restores previous state

Reimplemented from CMachine.

Definition at line 649 of file KernelMachine.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.

float64_t get_alpha ( int32_t  idx  )  [inherited]

get alpha at given index

Parameters:
idx index of alpha
Returns:
alpha

Definition at line 137 of file KernelMachine.cpp.

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

Definition at line 186 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 96 of file KernelMachine.cpp.

float64_t get_bias (  )  [inherited]

get bias

Returns:
bias

Definition at line 121 of file KernelMachine.cpp.

bool get_bias_enabled (  )  [inherited]

get state of bias

Returns:
state of bias

Definition at line 116 of file KernelMachine.cpp.

float64_t get_C1 (  )  [inherited]

get C1

Returns:
C1

Definition at line 159 of file SVM.h.

float64_t get_C2 (  )  [inherited]

get C2

Returns:
C2

Definition at line 165 of file SVM.h.

EMachineType get_classifier_type (  )  [virtual]

get classifier type

Returns:
classifier type SVRLIGHT

Reimplemented from CSVMLight.

Definition at line 68 of file SVRLight.cpp.

float64_t get_epsilon (  )  [inherited]

get epsilon

Returns:
epsilon

Definition at line 147 of file SVM.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.

CKernel * get_kernel (  )  [inherited]

get kernel

Returns:
kernel

Definition at line 85 of file KernelMachine.cpp.

CLabels * get_labels (  )  [virtual, inherited]

get labels

Returns:
labels

Definition at line 86 of file Machine.cpp.

bool get_linadd_enabled (  )  [inherited]

check if linadd is enabled

Returns:
if linadd is enabled

Definition at line 106 of file KernelMachine.cpp.

SGVector< float64_t > get_linear_term (  )  [virtual, inherited]

get linear term

Returns:
the linear term

Definition at line 332 of file SVM.cpp.

float64_t * get_linear_term_array (  )  [protected, virtual, inherited]

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 [virtual, inherited]

returns type of problem machine solves

Reimplemented in CBaseMulticlassMachine.

Definition at line 287 of file Machine.h.

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 CSVMLight.

Definition at line 193 of file SVRLight.h.

float64_t get_nu (  )  [inherited]

get nu

Returns:
nu

Definition at line 153 of file SVM.h.

int32_t get_num_support_vectors (  )  [inherited]

get number of support vectors

Returns:
number of support vectors

Definition at line 166 of file KernelMachine.cpp.

float64_t get_objective (  )  [inherited]

get objective

Returns:
objective

Definition at line 216 of file SVM.h.

int32_t get_qpsize (  )  [inherited]

get qpsize

Returns:
qpsize

Definition at line 171 of file SVM.h.

int32_t get_runtime (  )  [inherited]

get runtime

Returns:
runtime

Definition at line 288 of file SVMLight.cpp.

bool get_shrinking_enabled (  )  [inherited]

get state of shrinking

Returns:
if shrinking is enabled

Definition at line 186 of file SVM.h.

ESolverType get_solver_type (  )  [inherited]

get solver type

Returns:
solver

Definition at line 112 of file Machine.cpp.

int32_t get_support_vector ( int32_t  idx  )  [inherited]

get support vector at given index

Parameters:
idx index of support vector
Returns:
support vector

Definition at line 131 of file KernelMachine.cpp.

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

Definition at line 181 of file KernelMachine.cpp.

float64_t get_tube_epsilon (  )  [inherited]

get tube epsilon

Returns:
tube epsilon

Definition at line 135 of file SVM.h.

void init (  )  [inherited]

init SVM

Reimplemented from CKernelMachine.

Definition at line 133 of file SVMLight.cpp.

bool init_kernel_optimization (  )  [inherited]

initialise kernel optimisation

Returns:
if operation was successful

Definition at line 208 of file KernelMachine.cpp.

void init_shrink_state ( SHRINK_STATE *  shrink_state,
int32_t  totdoc,
int32_t  maxhistory 
) [inherited]

init shrink state

Parameters:
shrink_state shrink state
totdoc totdoc
maxhistory maximum history

Definition at line 1943 of file SVMLight.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 *  svm_file  )  [inherited]

load a SVM from file

Parameters:
svm_file the file handle

Definition at line 90 of file SVM.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.

MACHINE_PROBLEM_TYPE ( PT_BINARY   )  [inherited]

problem type

MACHINE_PROBLEM_TYPE ( PT_REGRESSION   ) 

problem type

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.

float64_t * optimize_qp ( QP *  qp,
float64_t *  epsilon_crit,
int32_t  nx,
float64_t *  threshold,
int32_t &  svm_maxqpsize 
) [protected, inherited]

Definition at line 2395 of file SVMLight.cpp.

void optimize_svm ( int32_t *  docs,
int32_t *  label,
int32_t *  exclude_from_eq_const,
float64_t  eq_target,
int32_t *  chosen,
int32_t *  active2dnum,
int32_t  totdoc,
int32_t *  working2dnum,
int32_t  varnum,
float64_t *  a,
float64_t *  lin,
float64_t *  c,
float64_t *  aicache,
QP *  qp,
float64_t *  epsilon_crit_target 
) [inherited]

optimise SVM

Parameters:
docs docs
label label
exclude_from_eq_const exclude from eq const
eq_target eq target
chosen chosen
active2dnum active 2D num
totdoc totdoc
working2dnum working 2D num
varnum var num
a a
lin lin
c c
aicache ai cache
qp QP
epsilon_crit_target epsilon crit target

Definition at line 1006 of file SVMLight.cpp.

int32_t optimize_to_convergence ( int32_t *  docs,
int32_t *  label,
int32_t  totdoc,
SHRINK_STATE *  shrink_state,
int32_t *  inconsistent,
float64_t *  a,
float64_t *  lin,
float64_t *  c,
TIMING *  timing_profile,
float64_t *  maxdiff,
int32_t  heldout,
int32_t  retrain 
) [inherited]

optimize to convergence

Parameters:
docs the docs
label the label
totdoc the totdoc
shrink_state shrink state
inconsistent inconsistent
a a
lin lin
c c
timing_profile timing profile
maxdiff maximum diff
heldout held out
retrain retrain
Returns:
something inty

Definition at line 543 of file SVMLight.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 reactivate_inactive_examples ( int32_t *  label,
float64_t *  a,
SHRINK_STATE *  shrink_state,
float64_t *  lin,
float64_t *  c,
int32_t  totdoc,
int32_t  iteration,
int32_t *  inconsistent,
int32_t *  docs,
float64_t *  aicache,
float64_t *  maxdiff 
) [virtual]

reactivate inactive examples

Parameters:
label label
a a
shrink_state shrink state
lin lin
c c
totdoc totdoc
iteration iteration
inconsistent inconsistent
docs docs
aicache ai cache
maxdiff maxdiff

Reimplemented from CSVMLight.

Definition at line 667 of file SVRLight.cpp.

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

helper for reactivate inactive examples linadd

Parameters:
p p

Definition at line 2036 of file SVMLight.cpp.

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

helper for reactivate inactive examples vanilla

Parameters:
p p

Definition at line 2059 of file SVMLight.cpp.

int32_t regression_fix_index ( int32_t  i  )  [protected]

regression fix index

Parameters:
i i
Returns:
fix index

Definition at line 374 of file SVRLight.cpp.

int32_t regression_fix_index2 ( int32_t  i,
int32_t  num_vectors 
) [static, protected]

regression fix index2

Parameters:
i i
num_vectors number of vectors
Returns:
fix index

Definition at line 382 of file SVRLight.cpp.

bool save ( FILE *  svm_file  )  [inherited]

write a SVM to a file

Parameters:
svm_file the file handle

Definition at line 206 of file SVM.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.

int32_t select_next_qp_subproblem_grad ( int32_t *  label,
float64_t *  a,
float64_t *  lin,
float64_t *  c,
int32_t  totdoc,
int32_t  qp_size,
int32_t *  inconsistent,
int32_t *  active2dnum,
int32_t *  working2dnum,
float64_t *  selcrit,
int32_t *  select,
int32_t  cache_only,
int32_t *  key,
int32_t *  chosen 
) [inherited]

select next qp subproblem grad

Parameters:
label label
a a
lin lin
c c
totdoc totdoc
qp_size size of qp
inconsistent inconsistent
active2dnum active 2D num
working2dnum working 2D num
selcrit selcrit
select select
cache_only cache only
key key
chosen chosen
Returns:
something inty

Definition at line 1745 of file SVMLight.cpp.

int32_t select_next_qp_subproblem_rand ( int32_t *  label,
float64_t *  a,
float64_t *  lin,
float64_t *  c,
int32_t  totdoc,
int32_t  qp_size,
int32_t *  inconsistent,
int32_t *  active2dnum,
int32_t *  working2dnum,
float64_t *  selcrit,
int32_t *  select,
int32_t *  key,
int32_t *  chosen,
int32_t  iteration 
) [inherited]

select next qp subproblem rand

Parameters:
label label
a a
lin lin
c c
totdoc totdoc
qp_size size of qp
inconsistent inconsistent
active2dnum active 2D num
working2dnum working 2D num
selcrit selcrit
select select
key key
chosen chosen
iteration iteration
Returns:
something inty

Definition at line 1837 of file SVMLight.cpp.

void select_top_n ( float64_t *  selcrit,
int32_t  range,
int32_t *  select,
int32_t  n 
) [inherited]

select top n

Parameters:
selcrit selcrit
range range
select select
n n

Definition at line 1907 of file SVMLight.cpp.

bool set_alpha ( int32_t  idx,
float64_t  val 
) [inherited]

set alpha at given index to given value

Parameters:
idx index of alpha vector
val new value of alpha vector
Returns:
if operation was successful

Definition at line 156 of file KernelMachine.cpp.

void set_alphas ( SGVector< float64_t >  alphas  )  [inherited]

set alphas to given values

Parameters:
alphas float vector with all alphas to set

Definition at line 171 of file KernelMachine.cpp.

void set_batch_computation_enabled ( bool  enable  )  [inherited]

set batch computation enabled

Parameters:
enable if batch computation shall be enabled

Definition at line 91 of file KernelMachine.cpp.

void set_bias ( float64_t  bias  )  [inherited]

set bias to given value

Parameters:
bias new bias

Definition at line 126 of file KernelMachine.cpp.

void set_bias_enabled ( bool  enable_bias  )  [inherited]

set state of bias

Parameters:
enable_bias if bias shall be enabled

Definition at line 111 of file KernelMachine.cpp.

void set_C ( float64_t  c_neg,
float64_t  c_pos 
) [inherited]

set C

Parameters:
c_neg new C constant for negatively labeled examples
c_pos new C constant for positively labeled examples

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

Definition at line 116 of file SVM.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:
m pointer to mkl object
cb callback function

Definition at line 232 of file SVM.cpp.

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_epsilon ( float64_t  eps  )  [inherited]

set epsilon

Parameters:
eps new epsilon

Definition at line 123 of file SVM.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_kernel ( CKernel *  k  )  [inherited]

set kernel

Parameters:
k kernel

Definition at line 78 of file KernelMachine.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_linadd_enabled ( bool  enable  )  [inherited]

set linadd enabled

Parameters:
enable if linadd shall be enabled

Definition at line 101 of file KernelMachine.cpp.

void set_linear_term ( const SGVector< float64_t >  linear_term  )  [virtual, inherited]

set linear term of the QP

Parameters:
linear_term the linear term

Definition at line 314 of file SVM.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_nu ( float64_t  nue  )  [inherited]

set nu

Parameters:
nue new nu

Definition at line 105 of file SVM.h.

void set_objective ( float64_t  v  )  [inherited]

set objective

Parameters:
v objective

Definition at line 207 of file SVM.h.

void set_qpsize ( int32_t  qps  )  [inherited]

set qpsize

Parameters:
qps new qpsize

Definition at line 141 of file SVM.h.

void set_shrinking_enabled ( bool  enable  )  [inherited]

set state of shrinking

Parameters:
enable if shrinking will be enabled

Definition at line 177 of file SVM.h.

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.

bool set_support_vector ( int32_t  idx,
int32_t  val 
) [inherited]

set support vector at given index to given value

Parameters:
idx index of support vector
val new value of support vector
Returns:
if operation was successful

Definition at line 146 of file KernelMachine.cpp.

void set_support_vectors ( SGVector< int32_t >  svs  )  [inherited]

set support vectors to given values

Parameters:
svs integer vector with all support vectors indexes to set

Definition at line 176 of file KernelMachine.cpp.

void set_tube_epsilon ( float64_t  eps  )  [inherited]

set tube epsilon

Parameters:
eps new tube epsilon

Definition at line 129 of file SVM.h.

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.

int32_t shrink_problem ( SHRINK_STATE *  shrink_state,
int32_t *  active2dnum,
int32_t *  last_suboptimal_at,
int32_t  iteration,
int32_t  totdoc,
int32_t  minshrink,
float64_t *  a,
int32_t *  inconsistent,
float64_t *  c,
float64_t *  lin,
int *  label 
) [inherited]

shrink problem

Parameters:
shrink_state shrink state
active2dnum active 2D num
last_suboptimal_at last suboptimal at
iteration iteration
totdoc totdoc
minshrink minimal shrink
a a
inconsistent inconsistent
c c
lin lin
label label
Returns:
something inty

Definition at line 1975 of file SVMLight.cpp.

void shrink_state_cleanup ( SHRINK_STATE *  shrink_state  )  [inherited]

cleanup shrink state

Parameters:
shrink_state shrink state

Definition at line 1964 of file SVMLight.cpp.

void store_model_features (  )  [protected, virtual, inherited]

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 450 of file KernelMachine.cpp.

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

Reimplemented from CMachine.

Definition at line 285 of file KernelMachine.h.

void svm_learn (  )  [inherited]

learn SVM

Definition at line 296 of file SVMLight.cpp.

void svr_learn (  ) 

SVR learn

Definition at line 155 of file SVRLight.cpp.

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.

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

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

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

Reimplemented from CMachine.

Definition at line 479 of file KernelMachine.cpp.

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

train regression

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

Reimplemented from CSVMLight.

Definition at line 73 of file SVRLight.cpp.

virtual bool train_require_labels (  )  const [protected, virtual, inherited]

returns whether machine require labels for training

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

Definition at line 349 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 285 of file SGObject.cpp.

void update_linear_component ( int32_t *  docs,
int32_t *  label,
int32_t *  active2dnum,
float64_t *  a,
float64_t *  a_old,
int32_t *  working2dnum,
int32_t  totdoc,
float64_t *  lin,
float64_t *  aicache,
float64_t *  c 
) [virtual]

update linear component

Parameters:
docs docs
label label
active2dnum active2dnum
a a
a_old a old
working2dnum working2dnum
totdoc totdoc
lin lin
aicache ai cache
c c

Reimplemented from CSVMLight.

Definition at line 398 of file SVRLight.cpp.

void * update_linear_component_linadd_helper ( void *  params  )  [static, protected]

thread helper for update linear component linadd

Parameters:
params 

Reimplemented from CSVMLight.

Definition at line 362 of file SVRLight.cpp.

void update_linear_component_mkl ( int32_t *  docs,
int32_t *  label,
int32_t *  active2dnum,
float64_t *  a,
float64_t *  a_old,
int32_t *  working2dnum,
int32_t  totdoc,
float64_t *  lin,
float64_t *  aicache,
float64_t *  c 
) [virtual]

update linear component MKL

Parameters:
docs docs
label label
active2dnum active2dnum
a a
a_old a old
working2dnum working2dnum
totdoc totdoc
lin lin
aicache ai cache
c c

Definition at line 496 of file SVRLight.cpp.

void update_linear_component_mkl ( int32_t *  docs,
int32_t *  label,
int32_t *  active2dnum,
float64_t *  a,
float64_t *  a_old,
int32_t *  working2dnum,
int32_t  totdoc,
float64_t *  lin,
float64_t *  aicache 
) [inherited]

update linear component MKL

Parameters:
docs docs
label label
active2dnum active 2D num
a a
a_old old a
working2dnum working 2D num
totdoc totdoc
lin lin
aicache ai cache

Definition at line 1523 of file SVMLight.cpp.

void update_linear_component_mkl_linadd ( int32_t *  docs,
int32_t *  label,
int32_t *  active2dnum,
float64_t *  a,
float64_t *  a_old,
int32_t *  working2dnum,
int32_t  totdoc,
float64_t *  lin,
float64_t *  aicache,
float64_t *  c 
) [virtual]

update linear component MKL linadd

Parameters:
docs docs
label label
active2dnum active2dnum
a a
a_old a old
working2dnum working2dnum
totdoc totdoc
lin lin
aicache ai cache
c c

Definition at line 569 of file SVRLight.cpp.

void update_linear_component_mkl_linadd ( int32_t *  docs,
int32_t *  label,
int32_t *  active2dnum,
float64_t *  a,
float64_t *  a_old,
int32_t *  working2dnum,
int32_t  totdoc,
float64_t *  lin,
float64_t *  aicache 
) [inherited]

update linear component MKL

Parameters:
docs docs
label label
active2dnum active 2D num
a a
a_old old a
working2dnum working 2D num
totdoc totdoc
lin lin
aicache ai cache

Definition at line 1597 of file SVMLight.cpp.

void * update_linear_component_mkl_linadd_helper ( void *  p  )  [static, inherited]

helper for update linear component MKL linadd

Parameters:
p p

Definition at line 1671 of file SVMLight.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.

Friends And Related Function Documentation

friend class CMulticlassSVM [friend, inherited]

Definition at line 272 of file SVM.h.

Member Data Documentation

float64_t C1 [protected, inherited]

C1 regularization const

Definition at line 255 of file SVM.h.

float64_t C2 [protected, inherited]

C2

Definition at line 257 of file SVM.h.

bool(* callback)(CMKL *mkl, const float64_t *sumw, const float64_t suma) [protected, inherited]

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

Definition at line 267 of file SVM.h.

int32_t count [protected, inherited]

number of iteration

Definition at line 687 of file SVMLight.h.

float64_t* dual [protected, inherited]

dual

Definition at line 678 of file SVMLight.h.

float64_t epsilon [protected, inherited]

epsilon

Definition at line 249 of file SVM.h.

int32_t init_iter [protected, inherited]

init iter

Definition at line 668 of file SVMLight.h.

float64_t init_margin [protected, inherited]

init margin

Definition at line 666 of file SVMLight.h.

SGIO* io [inherited]

io

Definition at line 462 of file SGObject.h.

CKernel* kernel [protected, inherited]

kernel

Definition at line 316 of file KernelMachine.h.

LEARN_PARM* learn_parm [protected, inherited]

learn parameters

Definition at line 661 of file SVMLight.h.

SGVector<float64_t> m_alpha [protected, inherited]

coefficients alpha

Definition at line 337 of file KernelMachine.h.

float64_t m_bias [protected, inherited]

bias term b

Definition at line 334 of file KernelMachine.h.

CCustomKernel* m_custom_kernel [protected, inherited]

is filled with pre-computed custom kernel on data lock

Definition at line 319 of file KernelMachine.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.

CKernel* m_kernel_backup [protected, inherited]

old kernel is stored here on data lock

Definition at line 322 of file KernelMachine.h.

CLabels* m_labels [protected, inherited]

labels

Definition at line 356 of file Machine.h.

SGVector<float64_t> m_linear_term [protected, inherited]

linear term in qp

Definition at line 244 of file SVM.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.

SGVector<int32_t> m_svs [protected, inherited]

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

Definition at line 340 of file KernelMachine.h.

CMKL* mkl [protected, inherited]

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

Definition at line 270 of file SVM.h.

bool mkl_converged [protected, inherited]

mkl converged

Definition at line 693 of file SVMLight.h.

MODEL* model [protected, inherited]

model

Definition at line 659 of file SVMLight.h.

float64_t model_b [protected, inherited]

model b

Definition at line 672 of file SVMLight.h.

float64_t mymaxdiff [protected, inherited]

current alpha gap

Definition at line 689 of file SVMLight.h.

float64_t nu [protected, inherited]

nu

Definition at line 253 of file SVM.h.

int32_t num_vectors [protected]

number of train elements

Definition at line 237 of file SVRLight.h.

float64_t objective [protected, inherited]

objective

Definition at line 259 of file SVM.h.

float64_t opt_precision [protected, inherited]

opt precision

Definition at line 674 of file SVMLight.h.

Parallel* parallel [inherited]

parallel

Definition at line 465 of file SGObject.h.

int32_t precision_violations [protected, inherited]

precision violations

Definition at line 670 of file SVMLight.h.

float64_t* primal [protected, inherited]

primal

Definition at line 676 of file SVMLight.h.

int32_t qpsize [protected, inherited]

qpsize

Definition at line 261 of file SVM.h.

bool svm_loaded [protected, inherited]

if SVM is loaded

Definition at line 247 of file SVM.h.

float64_t tube_epsilon [protected, inherited]

tube epsilon for support vector regression

Definition at line 251 of file SVM.h.

bool use_batch_computation [protected, inherited]

if batch computation is enabled

Definition at line 325 of file KernelMachine.h.

bool use_bias [protected, inherited]

if bias shall be used

Definition at line 331 of file KernelMachine.h.

bool use_kernel_cache [protected, inherited]

if kernel cache is used

Definition at line 691 of file SVMLight.h.

bool use_linadd [protected, inherited]

if linadd is enabled

Definition at line 328 of file KernelMachine.h.

bool use_shrinking [protected, inherited]

if shrinking shall be used

Definition at line 263 of file SVM.h.

int32_t verbosity [protected, inherited]

verbosity level (0-4)

Definition at line 663 of file SVMLight.h.

Version* version [inherited]

version

Definition at line 468 of file SGObject.h.

float64_t* W [protected, inherited]

Matrix that stores the contribution by each kernel for each example (for current alphas)

Definition at line 685 of file SVMLight.h.

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