SHOGUN
v2.0.0
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Class LibSVR, performs support vector regression using LibSVM.
The SVR solution can be expressed as
where and 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 sequential minimal decomposition (SMO))
Note that the SV regression problem is reduced to the standard SV classification problem by introducing artificial labels which leads to the epsilon insensitive loss constraints *
with and
Static Public Member Functions | |
static void * | apply_helper (void *p) |
Public Attributes | |
SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
ParameterMap * | m_parameter_map |
uint32_t | m_hash |
Protected Member Functions | |
virtual bool | train_machine (CFeatures *data=NULL) |
virtual float64_t * | get_linear_term_array () |
SGVector< float64_t > | apply_get_outputs (CFeatures *data) |
virtual void | store_model_features () |
virtual bool | is_label_valid (CLabels *lab) const |
virtual bool | train_require_labels () const |
virtual TParameter * | migrate (DynArray< TParameter * > *param_base, const SGParamInfo *target) |
virtual void | one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL) |
virtual void | load_serializable_pre () throw (ShogunException) |
virtual void | load_serializable_post () throw (ShogunException) |
virtual void | save_serializable_pre () throw (ShogunException) |
virtual void | save_serializable_post () throw (ShogunException) |
virtual bool | update_parameter_hash () |
Protected Attributes | |
svm_problem | problem |
svm_parameter | param |
struct svm_model * | model |
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 |
CLibSVR | ( | ) |
default constructor
Definition at line 17 of file LibSVR.cpp.
constructor
C | constant C |
epsilon | tube epsilon |
k | kernel |
lab | labels |
Definition at line 23 of file LibSVR.cpp.
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virtual |
Definition at line 34 of file LibSVR.cpp.
apply machine to data if data is not specified apply to the current features
data | (test)data to be classified |
Definition at line 162 of file Machine.cpp.
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apply kernel machine to data for binary classification task
data | (test)data to be classified |
Reimplemented from CMachine.
Reimplemented in CDomainAdaptationSVM.
Definition at line 245 of file KernelMachine.cpp.
apply get outputs
data | features to compute outputs |
Definition at line 251 of file KernelMachine.cpp.
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apply example helper, used in threads
p | params of the thread |
Definition at line 421 of file KernelMachine.cpp.
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apply machine to data in means of latent problem
Reimplemented in CLinearLatentMachine.
Definition at line 242 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 197 of file Machine.cpp.
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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 515 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 528 of file KernelMachine.cpp.
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applies a locked machine on a set of indices for latent problems
Definition at line 276 of file Machine.cpp.
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applies a locked machine on a set of indices for multiclass problems
Definition at line 262 of file Machine.cpp.
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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 521 of file KernelMachine.cpp.
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applies a locked machine on a set of indices for structured problems
Definition at line 269 of file Machine.cpp.
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apply machine to data in means of multiclass classification problem
Reimplemented in CDistanceMachine, CMulticlassMachine, CKNN, CVwConditionalProbabilityTree, CGaussianNaiveBayes, CConjugateIndex, CConditionalProbabilityTree, CQDA, and CRelaxedTree.
Definition at line 230 of file Machine.cpp.
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apply kernel machine to one example
num | which example to apply to |
Reimplemented from CMachine.
Definition at line 402 of file KernelMachine.cpp.
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apply kernel machine to data for regression task
data | (test)data to be classified |
Reimplemented from CMachine.
Definition at line 239 of file KernelMachine.cpp.
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apply machine to data in means of SO classification problem
Reimplemented in CLinearStructuredOutputMachine.
Definition at line 236 of file Machine.cpp.
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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 1204 of file SGObject.cpp.
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create new model
num | number of alphas and support vectors in new model |
Definition at line 191 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 620 of file KernelMachine.cpp.
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Unlocks a locked machine and restores previous state
Reimplemented from CMachine.
Definition at line 649 of file KernelMachine.cpp.
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A deep copy. All the instance variables will also be copied.
Definition at line 131 of file SGObject.h.
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get alpha at given index
idx | index of alpha |
Definition at line 137 of file KernelMachine.cpp.
Definition at line 186 of file KernelMachine.cpp.
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check if batch computation is enabled
Definition at line 96 of file KernelMachine.cpp.
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get classifier type
Reimplemented from CMachine.
Definition at line 39 of file LibSVR.cpp.
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check if linadd is enabled
Definition at line 106 of file KernelMachine.cpp.
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returns type of problem machine solves
Reimplemented in CBaseMulticlassMachine.
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Definition at line 1108 of file SGObject.cpp.
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Returns description of a given parameter string, if it exists. SG_ERROR otherwise
param_name | name of the parameter |
Definition at line 1132 of file SGObject.cpp.
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Returns index of model selection parameter with provided index
param_name | name of model selection parameter |
Definition at line 1145 of file SGObject.cpp.
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get number of support vectors
Definition at line 166 of file KernelMachine.cpp.
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get support vector at given index
idx | index of support vector |
Definition at line 131 of file KernelMachine.cpp.
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Definition at line 181 of file KernelMachine.cpp.
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initialise kernel optimisation
Definition at line 208 of file KernelMachine.cpp.
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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 278 of file SGObject.cpp.
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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 CBaseMulticlassMachine.
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maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)
file_version | parameter version of the file |
current_version | version from which mapping begins (you want to use VERSION_PARAMETER for this in most cases) |
file | file to load from |
prefix | prefix for members |
Definition at line 679 of file SGObject.cpp.
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loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned
param_info | information of parameter |
file_version | parameter version of the file, must be <= provided parameter version |
file | file to load from |
prefix | prefix for members |
Definition at line 523 of file SGObject.cpp.
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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 |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Reimplemented in CModelSelectionParameters.
Definition at line 354 of file SGObject.cpp.
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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 occurres. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CANOVAKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 1033 of file SGObject.cpp.
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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 occurres. |
Definition at line 1028 of file SGObject.cpp.
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problem type
MACHINE_PROBLEM_TYPE | ( | PT_REGRESSION | ) |
problem type
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Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match
param_base | set of TParameter instances that are mapped to the provided target parameter infos |
base_version | version of the parameter base |
target_param_infos | set of SGParamInfo instances that specify the target parameter base |
Definition at line 717 of file SGObject.cpp.
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protectedvirtualinherited |
creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.
If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass
param_base | set of TParameter instances to use for migration |
target | parameter info for the resulting TParameter |
Definition at line 923 of file SGObject.cpp.
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protectedvirtualinherited |
This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)
param_base | set of TParameter instances to use for migration |
target | parameter info for the resulting TParameter |
replacement | (used as output) here the TParameter instance which is returned by migration is created into |
to_migrate | the only source that is used for migration |
old_name | with this parameter, a name change may be specified |
Definition at line 864 of file SGObject.cpp.
post lock
Reimplemented in CMultitaskLinearMachine, and CMultitaskCompositeMachine.
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prints all parameter registered for model selection and their type
Definition at line 1084 of file SGObject.cpp.
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prints registered parameters out
prefix | prefix for members |
Definition at line 290 of file SGObject.cpp.
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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 |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Reimplemented in CModelSelectionParameters.
Definition at line 296 of file SGObject.cpp.
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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 occurres. |
Reimplemented in CKernel.
Definition at line 1043 of file SGObject.cpp.
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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 occurres. |
Reimplemented in CKernel.
Definition at line 1038 of file SGObject.cpp.
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set alpha at given index to given value
idx | index of alpha vector |
val | new value of alpha vector |
Definition at line 156 of file KernelMachine.cpp.
set alphas to given values
alphas | float vector with all alphas to set |
Definition at line 171 of file KernelMachine.cpp.
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set batch computation enabled
enable | if batch computation shall be enabled |
Definition at line 91 of file KernelMachine.cpp.
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set state of bias
enable_bias | if bias shall be enabled |
Definition at line 111 of file KernelMachine.cpp.
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set generic type to T
Definition at line 41 of file SGObject.cpp.
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set the parallel object
parallel | parallel object to use |
Definition at line 230 of file SGObject.cpp.
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set the version object
version | version object to use |
Definition at line 265 of file SGObject.cpp.
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set labels
lab | labels |
Reimplemented in CRelaxedTree, and CMulticlassMachine.
Definition at line 75 of file Machine.cpp.
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set linadd enabled
enable | if linadd shall be enabled |
Definition at line 101 of file KernelMachine.cpp.
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set maximum training time
t | maximimum training time |
Definition at line 92 of file Machine.cpp.
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Setter for store-model-features-after-training flag
store_model | whether model should be stored after training |
Definition at line 117 of file Machine.cpp.
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set support vector at given index to given value
idx | index of support vector |
val | new value of support vector |
Definition at line 146 of file KernelMachine.cpp.
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set support vectors to given values
svs | integer vector with all support vectors indexes to set |
Definition at line 176 of file KernelMachine.cpp.
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A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
Reimplemented in CGaussianKernel.
Definition at line 122 of file SGObject.h.
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Stores feature data of the SV indices and sets it to the lhs of the underlying kernel. Then, all SV indices are set to identity.
May be overwritten by subclasses in case the model should be stored differently.
Reimplemented from CMachine.
Definition at line 450 of file KernelMachine.cpp.
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Reimplemented from CMachine.
Definition at line 285 of file KernelMachine.h.
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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, CSGDQN, and COnlineSVMSGD.
Definition at line 49 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 479 of file KernelMachine.cpp.
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train regression
data | training data (parameter can be avoided if distance or kernel-based regressor are used and distance/kernels are initialized with train data) |
Reimplemented from CMachine.
Definition at line 44 of file LibSVR.cpp.
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returns whether machine require labels for training
Reimplemented in COnlineLinearMachine, CKMeans, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree, and CLibSVMOneClass.
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unset generic type
this has to be called in classes specializing a template class
Definition at line 285 of file SGObject.cpp.
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Updates the hash of current parameter combination.
Definition at line 237 of file SGObject.cpp.
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io
Definition at line 462 of file SGObject.h.
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kernel
Definition at line 316 of file KernelMachine.h.
coefficients alpha
Definition at line 337 of file KernelMachine.h.
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bias term b
Definition at line 334 of file KernelMachine.h.
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is filled with pre-computed custom kernel on data lock
Definition at line 319 of file KernelMachine.h.
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Hash of parameter values
Definition at line 480 of file SGObject.h.
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old kernel is stored here on data lock
Definition at line 322 of file KernelMachine.h.
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model selection parameters
Definition at line 474 of file SGObject.h.
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map for different parameter versions
Definition at line 477 of file SGObject.h.
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parameters
Definition at line 471 of file SGObject.h.
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array of ``support vectors'' (indices of feature objects)
Definition at line 340 of file KernelMachine.h.
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parallel
Definition at line 465 of file SGObject.h.
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if batch computation is enabled
Definition at line 325 of file KernelMachine.h.
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if bias shall be used
Definition at line 331 of file KernelMachine.h.
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if linadd is enabled
Definition at line 328 of file KernelMachine.h.
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version
Definition at line 468 of file SGObject.h.