SHOGUN
v2.0.0
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class to implement LibLinear
Definition at line 47 of file LibLinear.h.
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 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 | |
float64_t | C1 |
float64_t | C2 |
bool | use_bias |
float64_t | epsilon |
int32_t | max_iterations |
SGVector< float64_t > | m_linear_term |
LIBLINEAR_SOLVER_TYPE | liblinear_solver_type |
SGVector< float64_t > | w |
float64_t | bias |
CDotFeatures * | features |
float64_t | m_max_train_time |
CLabels * | m_labels |
ESolverType | m_solver_type |
bool | m_store_model_features |
bool | m_data_locked |
CLibLinear | ( | ) |
default constructor
Definition at line 25 of file LibLinear.cpp.
CLibLinear | ( | LIBLINEAR_SOLVER_TYPE | liblinear_solver_type | ) |
constructor
liblinear_solver_type | liblinear_solver_type |
Definition at line 31 of file LibLinear.cpp.
CLibLinear | ( | float64_t | C, |
CDotFeatures * | traindat, | ||
CLabels * | trainlab | ||
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constructor (using L2R_L1LOSS_SVC_DUAL as default)
C | constant C |
traindat | training features |
trainlab | training labels |
Definition at line 38 of file LibLinear.cpp.
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destructor
Definition at line 73 of file LibLinear.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 linear machine to data for binary classification problem
data | (test)data to be classified |
Reimplemented from CMachine.
Reimplemented in CDomainAdaptationSVMLinear.
Definition at line 57 of file LinearMachine.cpp.
apply get outputs
data | features to compute outputs |
Reimplemented in CMultitaskLinearMachine, and CFeatureBlockLogisticRegression.
Definition at line 63 of file LinearMachine.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 for binary problems
Reimplemented in CKernelMachine, CMultitaskLinearMachine, and CMultitaskCompositeMachine.
Definition at line 248 of file Machine.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 for regression problems
Reimplemented in CKernelMachine.
Definition at line 255 of file Machine.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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applies to one vector
Reimplemented from CMachine.
Reimplemented in CMultitaskLinearMachine, CMultitaskLogisticRegression, CMultitaskLeastSquaresRegression, and CFeatureBlockLogisticRegression.
Definition at line 46 of file LinearMachine.cpp.
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apply linear machine to data for regression problem
data | (test)data to be classified |
Reimplemented from CMachine.
Definition at line 51 of file LinearMachine.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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Locks the machine on given labels and data. After this call, only train_locked and apply_locked may be called
Only possible if supports_locking() returns true
labs | labels used for locking |
features | features used for locking |
Reimplemented in CKernelMachine.
Definition at line 122 of file Machine.cpp.
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Unlocks a locked machine and restores previous state
Reimplemented in CKernelMachine.
Definition at line 153 of file Machine.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 bias
Reimplemented in CMultitaskLinearMachine.
Definition at line 104 of file LinearMachine.h.
bool get_bias_enabled | ( | ) |
float64_t get_C1 | ( | ) |
float64_t get_C2 | ( | ) |
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get classifier type
Reimplemented from CMachine.
Reimplemented in CDomainAdaptationSVMLinear.
Definition at line 88 of file LibLinear.h.
float64_t get_epsilon | ( | ) |
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LIBLINEAR_SOLVER_TYPE get_liblinear_solver_type | ( | ) |
Definition at line 74 of file LibLinear.h.
get the linear term for qp
Definition at line 1347 of file LibLinear.cpp.
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returns type of problem machine solves
Reimplemented in CBaseMulticlassMachine.
int32_t get_max_iterations | ( | ) |
get the maximum number of iterations liblinear is allowed to do
Definition at line 137 of file LibLinear.h.
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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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Reimplemented from CLinearMachine.
Reimplemented in CDomainAdaptationSVMLinear.
Definition at line 134 of file LibLinear.h.
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get w
Reimplemented in CMultitaskLinearMachine.
Definition at line 77 of file LinearMachine.h.
void init_linear_term | ( | ) |
set the linear term for qp
Definition at line 1355 of file LibLinear.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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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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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.
MACHINE_PROBLEM_TYPE | ( | PT_BINARY | ) |
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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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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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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 bias
b | new bias |
Reimplemented in CMultitaskLinearMachine.
Definition at line 95 of file LinearMachine.h.
void set_bias_enabled | ( | bool | enable_bias | ) |
set if bias shall be enabled
enable_bias | if bias shall be enabled |
Definition at line 125 of file LibLinear.h.
void set_epsilon | ( | float64_t | eps | ) |
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set features
feat | features to set |
Reimplemented in CLDA, CLPBoost, and CLPM.
Definition at line 113 of file LinearMachine.h.
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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.
void set_liblinear_solver_type | ( | LIBLINEAR_SOLVER_TYPE | st | ) |
Definition at line 79 of file LibLinear.h.
set the linear term for qp
Definition at line 1330 of file LibLinear.cpp.
void set_max_iterations | ( | int32_t | max_iter = 1000 | ) |
set the maximum number of iterations liblinear is allowed to do
Definition at line 143 of file LibLinear.h.
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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.
set w
src_w | new w |
Reimplemented in CMultitaskLinearMachine.
Definition at line 86 of file LinearMachine.h.
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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 underlying model. Does nothing because Linear machines store the normal vector of the separating hyperplane and therefore the model anyway
Reimplemented from CMachine.
Definition at line 171 of file LinearMachine.h.
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Reimplemented in CKernelMachine, CMultitaskLinearMachine, and CMultitaskCompositeMachine.
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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
NOT IMPLEMENTED
indices | index vector (of locked features) that is used for training |
Reimplemented in CKernelMachine, CMultitaskLinearMachine, and CMultitaskCompositeMachine.
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train linear SVM classifier
data | training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data) |
Reimplemented from CMachine.
Reimplemented in CDomainAdaptationSVMLinear.
Definition at line 77 of file LibLinear.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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bias
Definition at line 181 of file LinearMachine.h.
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C1
Definition at line 183 of file LibLinear.h.
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C2
Definition at line 185 of file LibLinear.h.
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epsilon
Definition at line 189 of file LibLinear.h.
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features
Definition at line 183 of file LinearMachine.h.
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io
Definition at line 462 of file SGObject.h.
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solver type
Definition at line 197 of file LibLinear.h.
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Hash of parameter values
Definition at line 480 of file SGObject.h.
precomputed linear term
Definition at line 194 of file LibLinear.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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maximum number of iterations
Definition at line 191 of file LibLinear.h.
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parallel
Definition at line 465 of file SGObject.h.
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if bias shall be used
Definition at line 187 of file LibLinear.h.
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version
Definition at line 468 of file SGObject.h.
w
Definition at line 179 of file LinearMachine.h.