SHOGUN
4.1.0
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This class provides an interface to the LibLinear library for large- scale linear learning focusing on SVM [1]. This is the classification interface. For regression, see CLibLinearRegression. There is also an online version, see COnlineLibLinear.
LIBLINEAR is a linear SVM solver for data with millions of instances and features. It supports (for classification)
See the LIBLINEAR_SOLVER_TYPE enum for types of solvers.
[1] http://www.csie.ntu.edu.tw/~cjlin/liblinear/
Definition at line 61 of file LibLinear.h.
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 | |
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 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 | |
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 24 of file LibLinear.cpp.
CLibLinear | ( | LIBLINEAR_SOLVER_TYPE | liblinear_solver_type | ) |
constructor
liblinear_solver_type | liblinear_solver_type |
Definition at line 30 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 37 of file LibLinear.cpp.
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destructor
Definition at line 72 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 152 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 58 of file LinearMachine.cpp.
apply get outputs
data | features to compute outputs |
Reimplemented in CMultitaskLinearMachine, and CFeatureBlockLogisticRegression.
Definition at line 64 of file LinearMachine.cpp.
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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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applies a locked machine on a set of indices for binary problems
Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
Definition at line 238 of file Machine.cpp.
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applies a locked machine on a set of indices for latent problems
Definition at line 266 of file Machine.cpp.
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applies a locked machine on a set of indices for multiclass problems
Definition at line 252 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 245 of file Machine.cpp.
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applies a locked machine on a set of indices for structured problems
Definition at line 259 of file Machine.cpp.
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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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applies to one vector
Reimplemented from CMachine.
Reimplemented in CMultitaskLinearMachine, CMultitaskLogisticRegression, CMultitaskLeastSquaresRegression, and CFeatureBlockLogisticRegression.
Definition at line 47 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 52 of file LinearMachine.cpp.
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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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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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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.
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 112 of file Machine.cpp.
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Unlocks a locked machine and restores previous state
Reimplemented in CKernelMachine.
Definition at line 143 of file Machine.cpp.
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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.
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get bias
Reimplemented in CMultitaskLinearMachine.
Definition at line 101 of file LinearMachine.cpp.
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 109 of file LibLinear.h.
float64_t get_epsilon | ( | ) |
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LIBLINEAR_SOLVER_TYPE get_liblinear_solver_type | ( | ) |
Definition at line 91 of file LibLinear.h.
get the linear term for qp
Definition at line 1349 of file LibLinear.cpp.
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returns type of problem machine solves
Reimplemented in CNeuralNetwork, CRandomForest, CCHAIDTree, CCARTree, and CBaseMulticlassMachine.
int32_t get_max_iterations | ( | ) |
get the maximum number of iterations liblinear is allowed to do
Definition at line 158 of file LibLinear.h.
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Definition at line 498 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 522 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 535 of file SGObject.cpp.
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Reimplemented from CLinearMachine.
Reimplemented in CDomainAdaptationSVMLinear.
Definition at line 155 of file LibLinear.h.
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get w
Reimplemented in CMultitaskLinearMachine.
Definition at line 86 of file LinearMachine.cpp.
void init_linear_term | ( | ) |
set the linear term for qp
Definition at line 1357 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 296 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 CNeuralNetwork, CCARTree, CCHAIDTree, CGaussianProcessRegression, and CBaseMulticlassMachine.
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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 |
Definition at line 369 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 occurs. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 426 of file SGObject.cpp.
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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.
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Definition at line 262 of file SGObject.cpp.
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prints all parameter registered for model selection and their type
Definition at line 474 of file SGObject.cpp.
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prints registered parameters out
prefix | prefix for members |
Definition at line 308 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 |
Definition at line 314 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::SAVE_SERIALIZABLE_POST is called.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel.
Definition at line 436 of file SGObject.cpp.
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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.
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set bias
b | new bias |
Reimplemented in CMultitaskLinearMachine.
Definition at line 96 of file LinearMachine.cpp.
void set_bias_enabled | ( | bool | enable_bias | ) |
set if bias shall be enabled
enable_bias | if bias shall be enabled |
Definition at line 146 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 106 of file LinearMachine.cpp.
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Definition at line 41 of file SGObject.cpp.
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Definition at line 46 of file SGObject.cpp.
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Definition at line 51 of file SGObject.cpp.
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Definition at line 56 of file SGObject.cpp.
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Definition at line 61 of file SGObject.cpp.
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Definition at line 66 of file SGObject.cpp.
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Definition at line 71 of file SGObject.cpp.
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Definition at line 76 of file SGObject.cpp.
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Definition at line 81 of file SGObject.cpp.
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Definition at line 86 of file SGObject.cpp.
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Definition at line 91 of file SGObject.cpp.
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Definition at line 96 of file SGObject.cpp.
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Definition at line 101 of file SGObject.cpp.
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Definition at line 106 of file SGObject.cpp.
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Definition at line 111 of file SGObject.cpp.
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set generic type to T
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set the parallel object
parallel | parallel object to use |
Definition at line 241 of file SGObject.cpp.
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set the version object
version | version object to use |
Definition at line 283 of file SGObject.cpp.
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set labels
lab | labels |
Reimplemented in CNeuralNetwork, CGaussianProcessMachine, CCARTree, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.
Definition at line 65 of file Machine.cpp.
void set_liblinear_solver_type | ( | LIBLINEAR_SOLVER_TYPE | st | ) |
set the liblinear solver
st | the liblinear solver |
Definition at line 100 of file LibLinear.h.
set the linear term for qp
Definition at line 1332 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 164 of file LibLinear.h.
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set maximum training time
t | maximimum training time |
Definition at line 82 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 107 of file Machine.cpp.
set w
src_w | new w |
Reimplemented in CMultitaskLinearMachine.
Definition at line 91 of file LinearMachine.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 192 of file SGObject.cpp.
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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 119 of file LinearMachine.cpp.
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Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
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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, 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
NOT IMPLEMENTED
indices | index vector (of locked features) that is used for training |
Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
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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 76 of file LibLinear.cpp.
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returns whether machine require labels for training
Reimplemented in COnlineLinearMachine, 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 303 of file SGObject.cpp.
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Updates the hash of current parameter combination
Definition at line 248 of file SGObject.cpp.
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bias
Definition at line 160 of file LinearMachine.h.
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C1
Definition at line 204 of file LibLinear.h.
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C2
Definition at line 206 of file LibLinear.h.
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epsilon
Definition at line 210 of file LibLinear.h.
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features
Definition at line 162 of file LinearMachine.h.
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io
Definition at line 369 of file SGObject.h.
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solver type
Definition at line 218 of file LibLinear.h.
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parameters wrt which we can compute gradients
Definition at line 384 of file SGObject.h.
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Hash of parameter values
Definition at line 387 of file SGObject.h.
precomputed linear term
Definition at line 215 of file LibLinear.h.
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model selection parameters
Definition at line 381 of file SGObject.h.
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parameters
Definition at line 378 of file SGObject.h.
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maximum number of iterations
Definition at line 212 of file LibLinear.h.
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parallel
Definition at line 372 of file SGObject.h.
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if bias shall be used
Definition at line 208 of file LibLinear.h.
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version
Definition at line 375 of file SGObject.h.
w
Definition at line 158 of file LinearMachine.h.