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SHOGUN
4.1.0
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SHOGUN
4.1.0
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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.
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 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 () |
| 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) |
| 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_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 CSGObject * | clone () |
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 |
| Parameter * | m_gradient_parameters |
| 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 void | load_serializable_pre () throw (ShogunException) |
| virtual void | load_serializable_post () throw (ShogunException) |
| virtual void | save_serializable_pre () throw (ShogunException) |
| virtual void | save_serializable_post () throw (ShogunException) |
Static Protected Member Functions | |
| static void * | 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 |
| CSVRLight | ( | ) |
default constructor
Definition at line 58 of file SVRLight.cpp.
constructor
| C | constant C |
| epsilon | epsilon |
| k | kernel |
| lab | labels |
Definition at line 52 of file SVRLight.cpp.
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virtual |
default destructor
Definition at line 64 of file SVRLight.cpp.
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inherited |
add to index
| index | index |
| elem | element at index |
Definition at line 978 of file SVMLight.cpp.
apply machine to data if data is not specified apply to the current features
| data | (test)data to be classified |
Definition at line 152 of file Machine.cpp.
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virtualinherited |
apply kernel machine to data for binary classification task
| data | (test)data to be classified |
Reimplemented from CMachine.
Reimplemented in CDomainAdaptationSVM.
Definition at line 248 of file KernelMachine.cpp.
apply get outputs
| data | features to compute outputs |
Definition at line 254 of file KernelMachine.cpp.
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staticinherited |
apply example helper, used in threads
| p | params of the thread |
Definition at line 424 of file KernelMachine.cpp.
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virtualinherited |
apply machine to data in means of latent problem
Reimplemented in CLinearLatentMachine.
Definition at line 232 of file Machine.cpp.
Applies a locked machine on a set of indices. Error if machine is not locked
| indices | index vector (of locked features) that is predicted |
Definition at line 187 of file Machine.cpp.
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virtualinherited |
Applies a locked machine on a set of indices. Error if machine is not locked. Binary case
| indices | index vector (of locked features) that is predicted |
Reimplemented from CMachine.
Definition at line 518 of file KernelMachine.cpp.
Applies a locked machine on a set of indices. Error if machine is not locked
| indices | index vector (of locked features) that is predicted |
Definition at line 531 of file KernelMachine.cpp.
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virtualinherited |
applies a locked machine on a set of indices for latent problems
Definition at line 266 of file Machine.cpp.
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virtualinherited |
applies a locked machine on a set of indices for multiclass problems
Definition at line 252 of file Machine.cpp.
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virtualinherited |
Applies a locked machine on a set of indices. Error if machine is not locked. Binary case
| indices | index vector (of locked features) that is predicted |
Reimplemented from CMachine.
Definition at line 524 of file KernelMachine.cpp.
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virtualinherited |
applies a locked machine on a set of indices for structured problems
Definition at line 259 of file Machine.cpp.
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virtualinherited |
apply machine to data in means of multiclass classification problem
Reimplemented in CNeuralNetwork, CCHAIDTree, CCARTree, CGaussianProcessClassification, CMulticlassMachine, CKNN, CC45ClassifierTree, CID3ClassifierTree, CDistanceMachine, CVwConditionalProbabilityTree, CGaussianNaiveBayes, CConditionalProbabilityTree, CMCLDA, CQDA, CRelaxedTree, and CBaggingMachine.
Definition at line 220 of file Machine.cpp.
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virtualinherited |
apply kernel machine to one example
| num | which example to apply to |
Reimplemented from CMachine.
Definition at line 405 of file KernelMachine.cpp.
|
virtualinherited |
apply kernel machine to data for regression task
| data | (test)data to be classified |
Reimplemented from CMachine.
Definition at line 242 of file KernelMachine.cpp.
|
virtualinherited |
apply machine to data in means of SO classification problem
Reimplemented in CLinearStructuredOutputMachine.
Definition at line 226 of file Machine.cpp.
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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.
| dict | dictionary of parameters to be built. |
Definition at line 597 of file SGObject.cpp.
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inherited |
calculate SVM model
| docs | docs |
| label | label |
| lin | lin |
| a | a |
| a_old | old a |
| c | c |
| working2dnum | working 2D num |
| active2dnum | active 2D num |
Definition at line 1249 of file SVMLight.cpp.
Definition at line 1685 of file SVMLight.cpp.
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inherited |
check optimality
| 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 |
Definition at line 1366 of file SVMLight.cpp.
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inherited |
|
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.
Definition at line 714 of file SGObject.cpp.
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inherited |
compute index
| binfeature | binary feature |
| range | range |
| index |
Definition at line 987 of file SVMLight.cpp.
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protectedvirtual |
compute kernel at given index
| i | index i |
| j | index j |
Reimplemented from CSVMLight.
Definition at line 390 of file SVRLight.cpp.
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staticprotectedinherited |
|
inherited |
compute matrices for optimization
| 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 1176 of file SVMLight.cpp.
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inherited |
compute matrices for optimization in parallel
| 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 1044 of file SVMLight.cpp.
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virtual |
compute objective function
| a | a |
| lin | lin |
| c | c |
| eps | eps |
| label | label |
| totdoc | totdoc |
Reimplemented from CSVMLight.
Definition at line 337 of file SVRLight.cpp.
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inherited |
|
inherited |
|
inherited |
create new model
| num | number of alphas and support vectors in new model |
Definition at line 194 of file KernelMachine.cpp.
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
| labs | labels used for locking |
| features | features used for locking |
Reimplemented from CMachine.
Definition at line 623 of file KernelMachine.cpp.
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virtualinherited |
Unlocks a locked machine and restores previous state
Reimplemented from CMachine.
Definition at line 654 of file KernelMachine.cpp.
|
virtualinherited |
A deep copy. All the instance variables will also be copied.
Definition at line 198 of file SGObject.cpp.
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.
| other | object to compare with |
| accuracy | accuracy to use for comparison (optional) |
| tolerant | allows linient check on float equality (within accuracy) |
Definition at line 618 of file SGObject.cpp.
|
inherited |
get alpha at given index
| idx | index of alpha |
Definition at line 140 of file KernelMachine.cpp.
Definition at line 189 of file KernelMachine.cpp.
|
inherited |
check if batch computation is enabled
Definition at line 99 of file KernelMachine.cpp.
|
inherited |
|
inherited |
|
virtual |
get classifier type
Reimplemented from CSVMLight.
Definition at line 68 of file SVRLight.cpp.
|
inherited |
|
inherited |
|
inherited |
|
inherited |
|
inherited |
|
virtualinherited |
|
inherited |
check if linadd is enabled
Definition at line 109 of file KernelMachine.cpp.
|
protectedvirtualinherited |
|
virtualinherited |
returns type of problem machine solves
Reimplemented in CNeuralNetwork, CRandomForest, CCHAIDTree, CCARTree, and CBaseMulticlassMachine.
|
inherited |
|
inherited |
Definition at line 498 of file SGObject.cpp.
|
inherited |
Returns description of a given parameter string, if it exists. SG_ERROR otherwise
| param_name | name of the parameter |
Definition at line 522 of file SGObject.cpp.
|
inherited |
Returns index of model selection parameter with provided index
| param_name | name of model selection parameter |
Definition at line 535 of file SGObject.cpp.
|
virtual |
|
inherited |
get number of support vectors
Definition at line 169 of file KernelMachine.cpp.
|
inherited |
|
inherited |
|
inherited |
|
inherited |
|
inherited |
get support vector at given index
| idx | index of support vector |
Definition at line 134 of file KernelMachine.cpp.
|
inherited |
Definition at line 184 of file KernelMachine.cpp.
|
inherited |
|
inherited |
init SVM
Definition at line 139 of file SVMLight.cpp.
|
inherited |
initialise kernel optimisation
Definition at line 211 of file KernelMachine.cpp.
|
inherited |
init shrink state
| shrink_state | shrink state |
| totdoc | totdoc |
| maxhistory | maximum history |
Definition at line 1944 of file SVMLight.cpp.
|
inherited |
|
virtualinherited |
If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
| generic | set to the type of the generic if returning TRUE |
Definition at line 296 of file SGObject.cpp.
|
protectedvirtualinherited |
check whether the labels is valid.
Subclasses can override this to implement their check of label types.
| lab | the labels being checked, guaranteed to be non-NULL |
Reimplemented in CNeuralNetwork, CCARTree, CCHAIDTree, CGaussianProcessRegression, and CBaseMulticlassMachine.
|
inherited |
|
virtualinherited |
Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!
| file | where to load from |
| prefix | prefix for members |
Definition at line 369 of file SGObject.cpp.
|
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.
| ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 426 of file SGObject.cpp.
|
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.
| ShogunException | will be thrown if an error occurs. |
Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 421 of file SGObject.cpp.
|
inherited |
problem type
| MACHINE_PROBLEM_TYPE | ( | PT_REGRESSION | ) |
problem type
|
protectedinherited |
Definition at line 2396 of file SVMLight.cpp.
|
inherited |
optimise SVM
| 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 1007 of file SVMLight.cpp.
|
inherited |
optimize to convergence
| 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 |
Definition at line 546 of file SVMLight.cpp.
|
virtualinherited |
Definition at line 262 of file SGObject.cpp.
|
inherited |
prints all parameter registered for model selection and their type
Definition at line 474 of file SGObject.cpp.
|
virtualinherited |
prints registered parameters out
| prefix | prefix for members |
Definition at line 308 of file SGObject.cpp.
|
virtual |
reactivate inactive examples
| 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 666 of file SVRLight.cpp.
|
staticprotectedinherited |
helper for reactivate inactive examples linadd
| p | p |
Definition at line 2037 of file SVMLight.cpp.
|
staticprotectedinherited |
helper for reactivate inactive examples vanilla
| p | p |
Definition at line 2060 of file SVMLight.cpp.
|
protected |
|
staticprotected |
regression fix index2
| i | i |
| num_vectors | number of vectors |
Definition at line 381 of file SVRLight.cpp.
|
inherited |
|
virtualinherited |
Save this object to file.
| file | where to save the object; will be closed during returning if PREFIX is an empty string. |
| prefix | prefix for members |
Definition at line 314 of file SGObject.cpp.
|
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.
| ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel.
Definition at line 436 of file SGObject.cpp.
|
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.
| ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 431 of file SGObject.cpp.
|
inherited |
select next qp subproblem grad
| 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 |
Definition at line 1746 of file SVMLight.cpp.
|
inherited |
select next qp subproblem rand
| 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 |
Definition at line 1838 of file SVMLight.cpp.
|
inherited |
select top n
| selcrit | selcrit |
| range | range |
| select | select |
| n | n |
Definition at line 1908 of file SVMLight.cpp.
|
inherited |
set alpha at given index to given value
| idx | index of alpha vector |
| val | new value of alpha vector |
Definition at line 159 of file KernelMachine.cpp.
set alphas to given values
| alphas | float vector with all alphas to set |
Definition at line 174 of file KernelMachine.cpp.
|
inherited |
set batch computation enabled
| enable | if batch computation shall be enabled |
Definition at line 94 of file KernelMachine.cpp.
|
inherited |
|
inherited |
set state of bias
| enable_bias | if bias shall be enabled |
Definition at line 114 of file KernelMachine.cpp.
|
inherited |
|
inherited |
|
inherited |
Definition at line 41 of file SGObject.cpp.
|
inherited |
Definition at line 46 of file SGObject.cpp.
|
inherited |
Definition at line 51 of file SGObject.cpp.
|
inherited |
Definition at line 56 of file SGObject.cpp.
|
inherited |
Definition at line 61 of file SGObject.cpp.
|
inherited |
Definition at line 66 of file SGObject.cpp.
|
inherited |
Definition at line 71 of file SGObject.cpp.
|
inherited |
Definition at line 76 of file SGObject.cpp.
|
inherited |
Definition at line 81 of file SGObject.cpp.
|
inherited |
Definition at line 86 of file SGObject.cpp.
|
inherited |
Definition at line 91 of file SGObject.cpp.
|
inherited |
Definition at line 96 of file SGObject.cpp.
|
inherited |
Definition at line 101 of file SGObject.cpp.
|
inherited |
Definition at line 106 of file SGObject.cpp.
|
inherited |
Definition at line 111 of file SGObject.cpp.
|
inherited |
set generic type to T
|
inherited |
|
inherited |
set the parallel object
| parallel | parallel object to use |
Definition at line 241 of file SGObject.cpp.
|
inherited |
set the version object
| version | version object to use |
Definition at line 283 of file SGObject.cpp.
|
inherited |
|
virtualinherited |
set labels
| lab | labels |
Reimplemented in CNeuralNetwork, CGaussianProcessMachine, CCARTree, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.
Definition at line 65 of file Machine.cpp.
|
inherited |
set linadd enabled
| enable | if linadd shall be enabled |
Definition at line 104 of file KernelMachine.cpp.
|
inherited |
set maximum training time
| t | maximimum training time |
Definition at line 82 of file Machine.cpp.
|
inherited |
|
inherited |
|
inherited |
|
inherited |
|
inherited |
|
virtualinherited |
Setter for store-model-features-after-training flag
| store_model | whether model should be stored after training |
Definition at line 107 of file Machine.cpp.
|
inherited |
set support vector at given index to given value
| idx | index of support vector |
| val | new value of support vector |
Definition at line 149 of file KernelMachine.cpp.
|
inherited |
set support vectors to given values
| svs | integer vector with all support vectors indexes to set |
Definition at line 179 of file KernelMachine.cpp.
|
inherited |
|
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.
|
inherited |
shrink problem
| 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 |
Definition at line 1976 of file SVMLight.cpp.
|
inherited |
cleanup shrink state
| shrink_state | shrink state |
Definition at line 1965 of file SVMLight.cpp.
|
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.
|
virtualinherited |
Reimplemented from CMachine.
Definition at line 699 of file KernelMachine.cpp.
|
inherited |
learn SVM
Definition at line 301 of file SVMLight.cpp.
| void svr_learn | ( | ) |
SVR learn
Definition at line 155 of file SVRLight.cpp.
|
virtualinherited |
train machine
| 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. |
Reimplemented in CRelaxedTree, CAutoencoder, CSGDQN, and COnlineSVMSGD.
Definition at line 39 of file Machine.cpp.
Trains a locked machine on a set of indices. Error if machine is not locked
| indices | index vector (of locked features) that is used for training |
Reimplemented from CMachine.
Definition at line 482 of file KernelMachine.cpp.
|
protectedvirtual |
train regression
| data | training data (parameter can be avoided if distance or kernel-based regressors are used and distance/kernels are initialized with train data) |
Reimplemented from CSVMLight.
Definition at line 73 of file SVRLight.cpp.
|
protectedvirtualinherited |
returns whether machine require labels for training
Reimplemented in COnlineLinearMachine, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree, and CLibSVMOneClass.
|
inherited |
unset generic type
this has to be called in classes specializing a template class
Definition at line 303 of file SGObject.cpp.
|
virtual |
update linear component
| 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 397 of file SVRLight.cpp.
|
staticprotected |
thread helper for update linear component linadd
| params |
Definition at line 361 of file SVRLight.cpp.
|
virtual |
update linear component MKL
| 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 495 of file SVRLight.cpp.
|
inherited |
update linear component MKL
| 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 1524 of file SVMLight.cpp.
|
virtual |
update linear component MKL linadd
| 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 568 of file SVRLight.cpp.
|
inherited |
update linear component MKL
| 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 1598 of file SVMLight.cpp.
|
staticinherited |
helper for update linear component MKL linadd
| p | p |
Definition at line 1672 of file SVMLight.cpp.
|
virtualinherited |
Updates the hash of current parameter combination
Definition at line 248 of file SGObject.cpp.
|
protectedinherited |
number of iteration
Definition at line 681 of file SVMLight.h.
|
protectedinherited |
dual
Definition at line 672 of file SVMLight.h.
|
protectedinherited |
init iter
Definition at line 662 of file SVMLight.h.
|
protectedinherited |
init margin
Definition at line 660 of file SVMLight.h.
|
inherited |
io
Definition at line 369 of file SGObject.h.
|
protectedinherited |
kernel
Definition at line 311 of file KernelMachine.h.
|
protectedinherited |
learn parameters
Definition at line 655 of file SVMLight.h.
coefficients alpha
Definition at line 332 of file KernelMachine.h.
|
protectedinherited |
bias term b
Definition at line 329 of file KernelMachine.h.
|
protectedinherited |
is filled with pre-computed custom kernel on data lock
Definition at line 314 of file KernelMachine.h.
|
protectedinherited |
|
inherited |
parameters wrt which we can compute gradients
Definition at line 384 of file SGObject.h.
|
inherited |
Hash of parameter values
Definition at line 387 of file SGObject.h.
|
protectedinherited |
old kernel is stored here on data lock
Definition at line 317 of file KernelMachine.h.
|
protectedinherited |
|
inherited |
model selection parameters
Definition at line 381 of file SGObject.h.
|
inherited |
parameters
Definition at line 378 of file SGObject.h.
|
protectedinherited |
|
protectedinherited |
|
protectedinherited |
array of ``support vectors'' (indices of feature objects)
Definition at line 335 of file KernelMachine.h.
|
protectedinherited |
|
protectedinherited |
mkl converged
Definition at line 687 of file SVMLight.h.
|
protectedinherited |
model
Definition at line 653 of file SVMLight.h.
|
protectedinherited |
model b
Definition at line 666 of file SVMLight.h.
|
protectedinherited |
current alpha gap
Definition at line 683 of file SVMLight.h.
|
protected |
number of train elements
Definition at line 237 of file SVRLight.h.
|
protectedinherited |
opt precision
Definition at line 668 of file SVMLight.h.
|
inherited |
parallel
Definition at line 372 of file SGObject.h.
|
protectedinherited |
precision violations
Definition at line 664 of file SVMLight.h.
|
protectedinherited |
primal
Definition at line 670 of file SVMLight.h.
|
protectedinherited |
|
protectedinherited |
if batch computation is enabled
Definition at line 320 of file KernelMachine.h.
|
protectedinherited |
if bias shall be used
Definition at line 326 of file KernelMachine.h.
|
protectedinherited |
if kernel cache is used
Definition at line 685 of file SVMLight.h.
|
protectedinherited |
if linadd is enabled
Definition at line 323 of file KernelMachine.h.
|
protectedinherited |
|
protectedinherited |
verbosity level (0-4)
Definition at line 657 of file SVMLight.h.
|
inherited |
version
Definition at line 375 of file SGObject.h.
|
protectedinherited |
Matrix that stores the contribution by each kernel for each example (for current alphas)
Definition at line 679 of file SVMLight.h.