SHOGUN  4.1.0
CMKL Class Referenceabstract

## Detailed Description

Multiple Kernel Learning.

A support vector machine based method for use with multiple kernels. In Multiple Kernel Learning (MKL) in addition to the SVM $$\bf\alpha$$ and bias term $$b$$ the kernel weights $$\bf\beta$$ are estimated in training. The resulting kernel method can be stated as

$f({\bf x})=\sum_{i=0}^{N-1} \alpha_i \sum_{j=0}^M \beta_j k_j({\bf x}, {\bf x_i})+b .$

where $$N$$ is the number of training examples $$\alpha_i$$ are the weights assigned to each training example $$\beta_j$$ are the weights assigned to each sub-kernel $$k_j(x,x')$$ are sub-kernels and $$b$$ the bias.

Kernels have to be chosen a-priori. In MKL $$\alpha_i,\;\beta$$ and bias are determined by solving the following optimization program

\begin{eqnarray*} \mbox{min} && \gamma-\sum_{i=1}^N\alpha_i\\ \mbox{w.r.t.} && \gamma\in R, \alpha\in R^N \nonumber\\ \mbox{s.t.} && {\bf 0}\leq\alpha\leq{\bf 1}C,\;\;\sum_{i=1}^N \alpha_i y_i=0 \nonumber\\ && \frac{1}{2}\sum_{i,j=1}^N \alpha_i \alpha_j y_i y_j k_k({\bf x}_i,{\bf x}_j)\leq \gamma,\;\; \forall k=1,\ldots,K\nonumber\\ \end{eqnarray*}

here C is a pre-specified regularization parameter.

Within shogun this optimization problem is solved using semi-infinite programming. For 1-norm MKL using one of the two approaches described in

Soeren Sonnenburg, Gunnar Raetsch, Christin Schaefer, and Bernhard Schoelkopf. Large Scale Multiple Kernel Learning. Journal of Machine Learning Research, 7:1531-1565, July 2006.

The first approach (also called the wrapper algorithm) wraps around a single kernel SVMs, alternatingly solving for $$\alpha$$ and $$\beta$$. It is using a traditional SVM to generate new violated constraints and thus requires a single kernel SVM and any of the SVMs contained in shogun can be used. In the MKL step either a linear program is solved via glpk or cplex or analytically or a newton (for norms>1) step is performed.

The second much faster but also more memory demanding approach performing interleaved optimization, is integrated into the chunking-based SVMlight.

In addition sparsity of MKL can be controlled by the choice of the $$L_p$$-norm regularizing $$\beta$$ as described in

Marius Kloft, Ulf Brefeld, Soeren Sonnenburg, and Alexander Zien. Efficient and accurate lp-norm multiple kernel learning. In Advances in Neural Information Processing Systems 21. MIT Press, Cambridge, MA, 2009.

An alternative way to control the sparsity is the elastic-net regularization, which can be formulated into the following optimization problem:

\begin{eqnarray*} \mbox{min} && C\sum_{i=1}^N\ell\left(\sum_{k=1}^Kf_k(x_i)+b,y_i\right)+(1-\lambda)\left(\sum_{k=1}^K\|f_k\|_{\mathcal{H}_k}\right)^2+\lambda\sum_{k=1}^K\|f_k\|_{\mathcal{H}_k}^2\\ \mbox{w.r.t.} && f_1\in\mathcal{H}_1,f_2\in\mathcal{H}_2,\ldots,f_K\in\mathcal{H}_K,\,b\in R \nonumber\\ \end{eqnarray*}

where $$\ell$$ is a loss function. Here $$\lambda$$ controls the trade-off between the two regularization terms. $$\lambda=0$$ corresponds to $$L_1$$-MKL, whereas $$\lambda=1$$ corresponds to the uniform-weighted combination of kernels ( $$L_\infty$$-MKL). This approach was studied by Shawe-Taylor (2008) "Kernel Learning for Novelty Detection" (NIPS MKL Workshop 2008) and Tomioka & Suzuki (2009) "Sparsity-accuracy trade-off in MKL" (NIPS MKL Workshop 2009).

Definition at line 95 of file MKL.h.

Inheritance diagram for CMKL:
[legend]

## Public Member Functions

CMKL (CSVM *s=NULL)

virtual ~CMKL ()

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 ()

virtual float64_t compute_mkl_dual_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 float64_t compute_sum_alpha ()=0

virtual void compute_sum_beta (float64_t *sumw)

virtual const char * get_name () const

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 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_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 ()

virtual EMachineType get_classifier_type ()

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)

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

uint32_t m_hash

## Protected Member Functions

virtual bool train_machine (CFeatures *data=NULL)

virtual void init_training ()=0

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)

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 ()

bool init_cplex ()

void set_qnorm_constraints (float64_t *beta, int32_t num_kernels)

bool cleanup_cplex ()

bool init_glpk ()

bool cleanup_glpk ()

bool check_glp_status (glp_prob *lp)

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)

## 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

CPXENVptr env

CPXLPptr lp_cplex

glp_prob * lp_glpk

glp_smcp * lp_glpk_parm

bool lp_initialized

SGVector< float64_tm_linear_term

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_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

 CMKL ( CSVM * s = NULL )

Constructor

Parameters
 s SVM to use as constraint generator in MKL SIP

Definition at line 22 of file MKL.cpp.

 ~CMKL ( )
virtual

Destructor

Definition at line 41 of file MKL.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.

Definition at line 248 of file KernelMachine.cpp.

 SGVector< float64_t > apply_get_outputs ( CFeatures * data )
protectedinherited

apply get outputs

Parameters
 data features 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
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
 indices index 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
 indices index 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
 indices index 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
 indices index 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

apply machine to data in means of multiclass classification problem

Definition at line 220 of file Machine.cpp.

 float64_t apply_one ( int32_t num )
virtualinherited

apply kernel machine to one example

Parameters
 num which 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
 dict dictionary of parameters to be built.

Definition at line 597 of file SGObject.cpp.

 bool check_glp_status ( glp_prob * lp )
protected

check glpk error status

Returns
if in good status

Definition at line 179 of file MKL.cpp.

 bool cleanup_cplex ( )
protected

cleanup cplex

Returns
if cleanup was successful

Definition at line 119 of file MKL.cpp.

 bool cleanup_glpk ( )
protected

cleanup glpk

Returns
if cleanup was successful

Definition at line 169 of file MKL.cpp.

 CSGObject * clone ( )
virtualinherited

Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.

Returns
an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

Definition at line 714 of file SGObject.cpp.

 float64_t compute_elasticnet_dual_objective ( )

compute ElasticnetMKL dual objective

Returns
computed dual objective

Definition at line 591 of file MKL.cpp.

 float64_t compute_mkl_dual_objective ( )
virtual

compute mkl dual objective

Returns
computed dual objective

Reimplemented in CMKLRegression.

Definition at line 1525 of file MKL.cpp.

 float64_t compute_mkl_primal_objective ( )

compute mkl primal objective

Returns
computed mkl primal objective

Definition at line 187 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 )
protected

given the alphas, compute the corresponding optimal betas

Parameters
 beta new betas (kernel weights) old_beta old betas (previous kernel weights) num_kernels number of kernels sumw 1/2*alpha'*K_j*alpha for each kernel j suma (sum over alphas) mkl_objective the current mkl objective
Returns
new objective value

Definition at line 666 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 )
protected

given the alphas, compute the corresponding optimal betas

Parameters
 beta new betas (kernel weights) old_beta old betas (previous kernel weights) num_kernels number of kernels sumw 1/2*alpha'*K_j*alpha for each kernel j suma (sum over alphas) mkl_objective the current mkl objective
Returns
new objective value

Definition at line 702 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 )
protected

given the alphas, compute the corresponding optimal betas

Parameters
 beta new betas (kernel weights) old_beta old betas (previous kernel weights) num_kernels number of kernels sumw 1/2*alpha'*K_j*alpha for each kernel j suma (sum over alphas) mkl_objective the current mkl objective
Returns
new objective value

Definition at line 472 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 )
protected

given the alphas, compute the corresponding optimal betas

Parameters
 beta new betas (kernel weights) old_beta old betas (previous kernel weights) num_kernels number of kernels sumw 1/2*alpha'*K_j*alpha for each kernel j suma (sum over alphas) mkl_objective the current mkl objective
Returns
new objective value

Definition at line 791 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 )
protected

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
 beta new betas (kernel weights) old_beta old betas (previous kernel weights) num_kernels number of kernels sumw 1/2*alpha'*K_j*alpha for each kernel j suma (sum over alphas) inner_iters number of internal iterations (for statistics)
Returns
new objective value

Definition at line 983 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 )
protected

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

Parameters
 beta new betas (kernel weights) old_beta old betas (previous kernel weights) num_kernels number of kernels sumw 1/2*alpha'*K_j*alpha for each kernel j suma (sum over alphas) inner_iters number of internal iterations (for statistics)
Returns
new objective value

Definition at line 1326 of file MKL.cpp.

 virtual float64_t compute_sum_alpha ( )
pure virtual

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

Implemented in CMKLRegression, CMKLOneClass, and CMKLClassification.

 void compute_sum_beta ( float64_t * sumw )
virtual

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

Parameters
 sumw vector of size num_kernels to hold the result

Definition at line 1480 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 ( )
protectedvirtual

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

Returns
whether mkl converged

Definition at line 404 of file MKL.h.

 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 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
 labs labels used for locking features features 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 198 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 )
protected

helper function to compute the elastic-net objective

Definition at line 564 of file MKL.cpp.

 void elasticnet_transform ( float64_t * beta, float64_t lmd, int32_t len )
protected

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

Definition at line 345 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
 other object to compare with accuracy accuracy to use for comparison (optional) tolerant allows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 618 of file SGObject.cpp.

 float64_t get_alpha ( int32_t idx )
inherited

get alpha at given index

Parameters
 idx index 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.

 EMachineType get_classifier_type ( )
virtualinherited

get classifier type

Returns
classifier type NONE

Definition at line 92 of file Machine.cpp.

 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 235 of file SGObject.cpp.

 Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 277 of file SGObject.cpp.

 Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 290 of file SGObject.cpp.

 bool get_interleaved_optimization_enabled ( )

get state of optimization (interleaved or wrapper)

Returns
true if interleaved optimization is used; wrapper otherwise

Definition at line 178 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.

inherited

Returns

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 ( )

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

Returns
epsilon for weights

Definition at line 215 of file MKL.h.

 int32_t get_mkl_iterations ( )

get number of MKL iterations

Returns
mkl_iterations

Definition at line 221 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 498 of file SGObject.cpp.

 char * get_modsel_param_descr ( const char * param_name )
inherited

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

Parameters
 param_name name of the parameter
Returns
description of the parameter

Definition at line 522 of file SGObject.cpp.

 index_t get_modsel_param_index ( const char * param_name )
inherited

Returns index of model selection parameter with provided index

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

Definition at line 535 of file SGObject.cpp.

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

Reimplemented from CSVM.

Reimplemented in CMKLRegression, CMKLOneClass, and CMKLClassification.

Definition at line 260 of file MKL.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
 idx index 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 ( )

get SVM that is used as constraint generator in MKL SIP

Returns
svm

Definition at line 132 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 init_cplex ( )
protected

init cplex

Returns
if init was successful

Definition at line 70 of file MKL.cpp.

 bool init_glpk ( )
protected

init glpk

Returns
if init was successful

Definition at line 155 of file MKL.cpp.

 bool init_kernel_optimization ( )
inherited

initialise kernel optimisation

Returns
if operation was successful

Definition at line 211 of file KernelMachine.cpp.

 void init_solver ( )
protected

initialize solver such as glpk or cplex

Definition at line 52 of file MKL.cpp.

 virtual void init_training ( )
protectedpure virtual

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

Implemented in CMKLRegression, CMKLOneClass, and CMKLClassification.

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

Definition at line 296 of file SGObject.cpp.

 virtual bool is_label_valid ( CLabels * lab ) const
protectedvirtualinherited

check whether the labels is valid.

Subclasses can override this to implement their check of label types.

Parameters
 lab the 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

Parameters
 svm_file the 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
 file where to load from prefix prefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 369 of file SGObject.cpp.

 void load_serializable_post ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 426 of file SGObject.cpp.

 void load_serializable_pre ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 421 of file SGObject.cpp.

 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 262 of file SGObject.cpp.

 bool perform_mkl_step ( const float64_t * sumw, float64_t suma )
virtual

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
 sumw vector of 1/2*alpha'*K_j*alpha for each kernel j suma scalar sum_i alpha_i etc.

Definition at line 403 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 )
protected

perform single mkl iteration

given the alphas, compute the corresponding optimal betas

Parameters
 beta new betas (kernel weights) old_beta old betas (previous kernel weights) num_kernels number of kernels label (from svmlight label) active2dnum (from svmlight active2dnum) a (from svmlight alphas) lin (from svmlight linear components) sumw 1/2*alpha'*K_j*alpha for each kernel j inner_iters number of required internal iterations
 static bool perform_mkl_step_helper ( CMKL * mkl, const float64_t * sumw, const float64_t suma )
static

callback helper function calling perform_mkl_step

Parameters
 mkl MKL object sumw vector of 1/2*alpha'*K_j*alpha for each kernel j suma scalar sum_i alpha_i etc.

Definition at line 241 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 474 of file SGObject.cpp.

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

prints registered parameters out

Parameters
 prefix prefix for members

Definition at line 308 of file SGObject.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 = "" )
virtualinherited

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
Returns
TRUE if done, otherwise FALSE

Definition at line 314 of file SGObject.cpp.

 void save_serializable_post ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 436 of file SGObject.cpp.

 void save_serializable_pre ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 431 of file SGObject.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 159 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 174 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 94 of file KernelMachine.cpp.

 void set_bias ( float64_t bias )
inherited

set bias to given value

Parameters
 bias new bias

Definition at line 129 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 114 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 118 of file SVM.h.

 void set_C_mkl ( float64_t C )

set C mkl

Parameters
 C new C_mkl

Definition at line 142 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
 m pointer to mkl object cb callback function

Definition at line 232 of file SVM.cpp.

 void set_constraint_generator ( CSVM * s )

SVM to use as constraint generator in MKL SIP

Parameters
 s svm

Definition at line 112 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 )

set elasticnet lambda

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

Definition at line 382 of file MKL.cpp.

 void set_epsilon ( float64_t eps )
inherited

set epsilon

Parameters
 eps new epsilon

Definition at line 125 of file SVM.h.

 void set_generic ( )
inherited

Definition at line 41 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 46 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 51 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 56 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 61 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 66 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 71 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 76 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 81 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 86 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 91 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 96 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 101 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 106 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 111 of file SGObject.cpp.

 void set_generic ( )
inherited

set generic type to T

 void set_global_io ( SGIO * io )
inherited

set the io object

Parameters
 io io object to use

Definition at line 228 of file SGObject.cpp.

 void set_global_parallel ( Parallel * parallel )
inherited

set the parallel object

Parameters
 parallel parallel object to use

Definition at line 241 of file SGObject.cpp.

 void set_global_version ( Version * version )
inherited

set the version object

Parameters
 version version object to use

Definition at line 283 of file SGObject.cpp.

 void set_interleaved_optimization_enabled ( bool enable )

set state of optimization (interleaved or wrapper)

Parameters
 enable if true interleaved optimization is used; wrapper otherwise

Definition at line 169 of file MKL.h.

 void set_kernel ( CKernel * k )
inherited

set kernel

Parameters
 k kernel

Definition at line 81 of file KernelMachine.cpp.

 void set_labels ( CLabels * lab )
virtualinherited

set labels

Parameters
 lab labels

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

Definition at line 65 of file Machine.cpp.

 void set_linadd_enabled ( bool enable )
inherited

Parameters
 enable if 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_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 82 of file Machine.cpp.

 void set_mkl_block_norm ( float64_t q )

set block norm q (used in block norm mkl)

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

Definition at line 395 of file MKL.cpp.

 void set_mkl_epsilon ( float64_t eps )

set mkl epsilon (optimization accuracy for kernel weights)

Parameters
 eps new weight_epsilon

Definition at line 209 of file MKL.h.

 void set_mkl_norm ( float64_t norm )

set mkl norm

Parameters
 norm new mkl norm (must be greater equal 1)

Definition at line 373 of file MKL.cpp.

 void set_nu ( float64_t nue )
inherited

set nu

Parameters
 nue new nu

Definition at line 107 of file SVM.h.

 void set_objective ( float64_t v )
inherited

set objective

Parameters
 v objective

Definition at line 209 of file SVM.h.

 void set_qnorm_constraints ( float64_t * beta, int32_t num_kernels )
protected

set qnorm mkl constraints

Definition at line 1575 of file MKL.cpp.

 void set_qpsize ( int32_t qps )
inherited

set qpsize

Parameters
 qps new qpsize

Definition at line 143 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 179 of file SVM.h.

 void set_solver_type ( ESolverType st )
inherited

set solver type

Parameters
 st solver 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_model whether 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
 idx index of support vector val new 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
 svs integer vector with all support vectors indexes to set

Definition at line 179 of file KernelMachine.cpp.

 void set_svm ( CSVM * s )

SVM to use as constraint generator in MKL SIP

Parameters
 s svm

Definition at line 121 of file MKL.h.

 void set_tube_epsilon ( float64_t eps )
inherited

set tube epsilon

Parameters
 eps new 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 192 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
 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 CRelaxedTree, CAutoencoder, 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
 indices index 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 )
protectedvirtual

train MKL classifier

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

Reimplemented from CMachine.

Definition at line 197 of file MKL.cpp.

 virtual bool train_require_labels ( ) const
protectedvirtualinherited

returns whether machine require labels for training

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 303 of file SGObject.cpp.

 void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 248 of file SGObject.cpp.

## Member Data Documentation

 float64_t* beta_local
protected

sub-kernel weights on the L1-term of ElasticnetMKL

Definition at line 468 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
protected

C_mkl

Definition at line 453 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
protected

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

Definition at line 461 of file MKL.h.

 CPXENVptr env
protected

env

Definition at line 489 of file MKL.h.

 float64_t epsilon
protectedinherited

epsilon

Definition at line 251 of file SVM.h.

 bool interleaved_optimization
protected

whether to use mkl wrapper or interleaved opt.

Definition at line 474 of file MKL.h.

 SGIO* io
inherited

io

Definition at line 369 of file SGObject.h.

 CKernel* kernel
protectedinherited

kernel

Definition at line 311 of file KernelMachine.h.

 CPXLPptr lp_cplex
protected

lp

Definition at line 491 of file MKL.h.

 glp_prob* lp_glpk
protected

lp

Definition at line 496 of file MKL.h.

 glp_smcp* lp_glpk_parm
protected

lp parameters

Definition at line 499 of file MKL.h.

 bool lp_initialized
protected

if lp is initialized

Definition at line 502 of file MKL.h.

 SGVector 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.

inherited

parameters wrt which we can compute gradients

Definition at line 384 of file SGObject.h.

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 387 of file SGObject.h.

 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 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 381 of file SGObject.h.

 Parameter* m_parameters
inherited

parameters

Definition at line 378 of file SGObject.h.

 ESolverType m_solver_type
protectedinherited

solver type

Definition at line 364 of file Machine.h.

 bool m_store_model_features
protectedinherited

whether model features should be stored after training

Definition at line 367 of file Machine.h.

 SGVector 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
protected

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

Definition at line 465 of file MKL.h.

 float64_t mkl_epsilon
protected

mkl_epsilon for multiple kernel learning

Definition at line 472 of file MKL.h.

 int32_t mkl_iterations
protected

number of mkl steps

Definition at line 470 of file MKL.h.

 float64_t mkl_norm
protected

norm used in mkl must be > 0

Definition at line 455 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 372 of file SGObject.h.

 int32_t qpsize
protectedinherited

qpsize

Definition at line 263 of file SVM.h.

 float64_t rho
protected

objective after mkl iterations

Definition at line 482 of file MKL.h.

 CSVM* svm
protected

wrapper SVM

Definition at line 451 of file MKL.h.

protectedinherited

Definition at line 249 of file SVM.h.

 CTime training_time_clock
protected

measures training time for use with get_max_train_time()

Definition at line 485 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.

protectedinherited

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 375 of file SGObject.h.

 float64_t* W
protected

partial objectives (one per kernel)

Definition at line 477 of file MKL.h.

 float64_t w_gap
protected

gap between iterations

Definition at line 480 of file MKL.h.

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

SHOGUN Machine Learning Toolbox - Documentation