SHOGUN
v2.0.0
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MKLMulticlass is a class for L1-norm multiclass MKL.
It is based on the GMNPSVM Multiclass SVM. Its own parameters are the L2 norm weight change based MKL Its termination criterion set by void set_mkl_epsilon(float64_t eps ); and the maximal number of MKL iterations set by void set_max_num_mkliters(int32_t maxnum); It passes the regularization constants C1 and C2 to GMNPSVM.
Definition at line 33 of file MKLMulticlass.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 | |
CMKLMulticlass (const CMKLMulticlass &cm) | |
CMKLMulticlass | operator= (const CMKLMulticlass &cm) |
void | initlpsolver () |
void | initsvm () |
virtual bool | evaluatefinishcriterion (const int32_t numberofsilpiterations) |
void | addingweightsstep (const std::vector< float64_t > &curweights) |
float64_t | getsumofsignfreealphas () |
float64_t | getsquarenormofprimalcoefficients (const int32_t ind) |
virtual bool | train_machine (CFeatures *data=NULL) |
virtual const char * | get_name () const |
CSVM * | svm_proto () |
SGVector< int32_t > | svm_svs () |
virtual bool | init_machines_for_apply (CFeatures *data) |
virtual bool | is_acceptable_machine (CMachine *machine) |
virtual bool | init_machine_for_train (CFeatures *data) |
virtual bool | is_ready () |
virtual CMachine * | get_machine_from_trained (CMachine *machine) |
virtual int32_t | get_num_rhs_vectors () |
virtual void | add_machine_subset (SGVector< index_t > subset) |
virtual void | remove_machine_subset () |
void | init_strategy () |
void | clear_machines () |
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 | |
CGMNPSVM * | svm |
MKLMulticlassOptimizationBase * | lpw |
::std::vector< std::vector < float64_t > > | weightshistory |
float64_t | mkl_eps |
int32_t | max_num_mkl_iters |
float64_t | pnorm |
std::vector< float64_t > | normweightssquared |
float64_t | m_C |
CKernel * | m_kernel |
CMulticlassStrategy * | m_multiclass_strategy |
CMachine * | m_machine |
CDynamicObjectArray * | m_machines |
float64_t | m_max_train_time |
CLabels * | m_labels |
ESolverType | m_solver_type |
bool | m_store_model_features |
bool | m_data_locked |
CMKLMulticlass | ( | ) |
Class default Constructor
Definition at line 19 of file MKLMulticlass.cpp.
CMKLMulticlass | ( | float64_t | C, |
CKernel * | k, | ||
CLabels * | lab | ||
) |
Class Constructor commonly used in Shogun Toolbox
C | constant C |
k | kernel |
lab | labels |
Definition at line 30 of file MKLMulticlass.cpp.
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Class default Destructor
Definition at line 42 of file MKLMulticlass.cpp.
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Class Copy Constructor protected to avoid its usage
Definition at line 50 of file MKLMulticlass.cpp.
set subset to the features of the machine, deletes old one
subset | subset indices to set |
Implements CMulticlassMachine.
Definition at line 153 of file KernelMulticlassMachine.h.
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adds a constraint to the LP used in MKL
curweights | are the current MKL weights |
it uses void addingweightsstep( const std::vector<float64_t> & curweights); and float64_t getsumofsignfreealphas();
Definition at line 207 of file MKLMulticlass.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 machine to data in means of binary classification problem
Reimplemented in CKernelMachine, COnlineLinearMachine, CWDSVMOcas, CLinearMachine, CDomainAdaptationSVMLinear, CDomainAdaptationSVM, and CPluginEstimate.
Definition at line 218 of file Machine.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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classify all examples
Reimplemented from CMachine.
Reimplemented in CGaussianNaiveBayes, and CQDA.
Definition at line 92 of file MulticlassMachine.cpp.
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classify all examples with multiple output
Definition at line 146 of file MulticlassMachine.cpp.
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classify one example
vec_idx |
Reimplemented from CMachine.
Reimplemented in CGaussianNaiveBayes.
Definition at line 234 of file MulticlassMachine.cpp.
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apply machine to data in means of regression problem
Reimplemented in CKernelMachine, CWDSVMOcas, COnlineLinearMachine, CGaussianProcessRegression, and CLinearMachine.
Definition at line 224 of file Machine.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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clear machines
Reimplemented in CNativeMulticlassMachine.
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clone
Reimplemented in CKernelMachine, and CLinearMachine.
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create multiclass SVM. Appends the appropriate number of svm pointer (depending on multiclass strategy) to m_machines. All pointers are initialized with NULL.
num_classes | number of classes in SVM |
Definition at line 46 of file MulticlassSVM.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 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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checks MKL for convergence
numberofsilpiterations | is the number of currently done iterations |
Definition at line 131 of file MKLMulticlass.cpp.
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get batch computation option of base SVM
Definition at line 144 of file MulticlassSVM.h.
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get bias enabled options of base SVM
Definition at line 134 of file MulticlassSVM.h.
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get classifier type
Reimplemented from CMachine.
Definition at line 57 of file MKLMulticlass.h.
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get linadd option of base SVM
Definition at line 139 of file MulticlassSVM.h.
get linear term of base SVM
Definition at line 93 of file MulticlassSVM.h.
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get machine
num | index of machine to get |
Definition at line 71 of file MulticlassMachine.h.
construct kernel machine from given kernel machine
Implements CMulticlassMachine.
Definition at line 138 of file KernelMulticlassMachine.h.
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get problem type
Reimplemented from CMachine.
Definition at line 46 of file BaseMulticlassMachine.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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get the type of multiclass'ness
Definition at line 111 of file MulticlassMachine.h.
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Reimplemented from CKernelMulticlassMachine.
Definition at line 166 of file MKLMulticlass.h.
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get number of machines
Definition at line 40 of file BaseMulticlassMachine.h.
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return number of rhs feature vectors
Implements CMulticlassMachine.
Definition at line 144 of file KernelMulticlassMachine.h.
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get objective of base SVM
Definition at line 128 of file MulticlassSVM.h.
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returns rejection strategy
Definition at line 121 of file MulticlassMachine.h.
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get shrinking option of base SVM
Definition at line 123 of file MulticlassSVM.h.
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get output of i-th submachine for num-th vector
i | number of submachine |
num | number of feature vector |
Definition at line 79 of file MulticlassMachine.cpp.
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get outputs of i-th submachine
i | number of submachine |
Reimplemented in CDomainAdaptationMulticlassLibLinear.
Definition at line 70 of file MulticlassMachine.cpp.
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get SVM
num | which SVM to get |
Definition at line 74 of file MulticlassSVM.h.
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get tube epsilon of base SVM
Definition at line 98 of file MulticlassSVM.h.
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computes the second svm-dependent part used for generating MKL constraints
ind | is the index of the kernel for which to compute |
Definition at line 286 of file MKLMulticlass.cpp.
float64_t * getsubkernelweights | ( | int32_t & | numweights | ) |
returns MKL weights for the different kernels
numweights | is output parameter, is set to zero if no weights have been computed or to the number of MKL weights which is equal to the number of kernels |
Definition at line 404 of file MKLMulticlass.cpp.
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computes the first svm-dependent part used for generating MKL constraints it is
Definition at line 240 of file MKLMulticlass.cpp.
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init machine for training with kernel init
Implements CMulticlassMachine.
Definition at line 95 of file KernelMulticlassMachine.h.
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initializes machines (OvO, OvR) for apply
Reimplemented from CKernelMulticlassMachine.
Definition at line 71 of file MulticlassSVM.cpp.
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init strategy
Reimplemented in CNativeMulticlassMachine.
Definition at line 64 of file MulticlassMachine.cpp.
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performs some sanity checks (on the provided kernel), inits the GLPK-based LP solver
Definition at line 92 of file MKLMulticlass.cpp.
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inits the underlying Multiclass SVM
Definition at line 69 of file MKLMulticlass.cpp.
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is machine an SVM instance
Reimplemented from CMulticlassMachine.
Definition at line 224 of file MulticlassSVM.h.
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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.
lab | the labels being checked, guaranteed to be non-NULL |
Reimplemented from CMachine.
Definition at line 55 of file BaseMulticlassMachine.h.
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check kernel availability
Implements CMulticlassMachine.
Definition at line 129 of file KernelMulticlassMachine.h.
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load a Multiclass SVM from file
svm_file | the file handle |
Definition at line 109 of file MulticlassSVM.cpp.
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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.
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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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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.
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Class Assignment operator protected to avoid its usage
Definition at line 60 of file MKLMulticlass.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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deletes any subset set to the features of the machine
Implements CMulticlassMachine.
Definition at line 159 of file KernelMulticlassMachine.h.
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write a Multiclass SVM to a file
svm_file | the file handle |
Definition at line 257 of file MulticlassSVM.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 batch computation option
enable | whether batch computation should be enabled |
Definition at line 205 of file MulticlassSVM.h.
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set bias option
enable_bias | whether bias should be enabled |
Definition at line 195 of file MulticlassSVM.h.
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set C parameters
C | set regularization parameter |
Definition at line 160 of file MulticlassSVM.h.
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set default number of support vectors
num_sv | number of support vectors |
Definition at line 150 of file MulticlassSVM.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 from CMachine.
Definition at line 51 of file MulticlassMachine.cpp.
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set linadd option
enable | whether linadd should be enabled |
Definition at line 200 of file MulticlassSVM.h.
set linear term
linear_term | linear term vector |
Definition at line 155 of file MulticlassSVM.h.
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set machine
num | index of machine |
machine | machine to set |
Definition at line 56 of file MulticlassMachine.h.
void set_max_num_mkliters | ( | int32_t | maxnum | ) |
sets maximal number of MKL iterations
maxnum | is the desired maximal number of MKL iterations; when it is reached the MKL terminates irrespective of the MKL progress set it to a nonpositive value in order to turn it off |
Definition at line 425 of file MKLMulticlass.cpp.
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set maximum training time
t | maximimum training time |
Definition at line 92 of file Machine.cpp.
void set_mkl_epsilon | ( | float64_t | eps | ) |
sets MKL termination threshold
eps | is the desired threshold value the termination criterion is the L2 norm between the current MKL weights and their counterpart from the previous iteration |
Definition at line 420 of file MKLMulticlass.cpp.
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sets rejection strategy
rejection_strategy | rejection strategy to be set |
Definition at line 130 of file MulticlassMachine.h.
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set shrinking option
enable | whether shrinking should be enabled |
Definition at line 185 of file MulticlassSVM.h.
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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 SVM
num | number to set |
svm | SVM to set |
Definition at line 61 of file MulticlassSVM.cpp.
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set tube epsilon value
eps | tube epsilon value |
Definition at line 175 of file MulticlassSVM.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.
Need to store the SVs for all sub-machines. We make a union of the SVs for all sub-machines, store the union and adjust the sub-machines to index into the union.
Reimplemented from CMachine.
Definition at line 17 of file KernelMulticlassMachine.cpp.
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Reimplemented in CKernelMachine, CMultitaskLinearMachine, and CMultitaskCompositeMachine.
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casts m_machine to SVM
Definition at line 210 of file MulticlassSVM.h.
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returns support vectors
Definition at line 215 of file MulticlassSVM.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
NOT IMPLEMENTED
indices | index vector (of locked features) that is used for training |
Reimplemented in CKernelMachine, CMultitaskLinearMachine, and CMultitaskCompositeMachine.
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train Multiclass MKL 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 CMulticlassMachine.
Definition at line 322 of file MKLMulticlass.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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the solver wrapper
Definition at line 177 of file MKLMulticlass.h.
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C regularization constant
Definition at line 239 of file MulticlassSVM.h.
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Hash of parameter values
Definition at line 480 of file SGObject.h.
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kernel
Definition at line 167 of file KernelMulticlassMachine.h.
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machine
Definition at line 191 of file MulticlassMachine.h.
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machines
Definition at line 62 of file BaseMulticlassMachine.h.
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model selection parameters
Definition at line 474 of file SGObject.h.
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type of multiclass strategy
Definition at line 188 of file MulticlassMachine.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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maximal number of MKL iterations is set by void set_max_num_mkliters(int32_t maxnum);
Definition at line 190 of file MKLMulticlass.h.
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MKL termination threshold is set void set_mkl_epsilon(float64_t eps );
Definition at line 186 of file MKLMulticlass.h.
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stores the term | w_l |^2 ~~~ "alpha o Y K_l Y o alpha"
Definition at line 198 of file MKLMulticlass.h.
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parallel
Definition at line 465 of file SGObject.h.
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MKL norm >=1
Definition at line 194 of file MKLMulticlass.h.
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the multiclass svm for fixed MKL weights
Definition at line 173 of file MKLMulticlass.h.
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version
Definition at line 468 of file SGObject.h.
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stores the last two mkl iteration weights
Definition at line 181 of file MKLMulticlass.h.