SHOGUN
4.1.0
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Class GaussianNaiveBayes, a Gaussian Naive Bayes classifier.
This classifier assumes that a posteriori conditional probabilities are gaussian pdfs. For each vector gaussian naive bayes chooses the class C with maximal
\[ P(c) \prod_{i} P(x_i|c) \]
Definition at line 37 of file GaussianNaiveBayes.h.
Public Attributes | |
SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
Parameter * | m_gradient_parameters |
ParameterMap * | m_parameter_map |
uint32_t | m_hash |
Protected Member Functions | |
virtual bool | train_machine (CFeatures *data=NULL) |
void | init_strategy () |
void | clear_machines () |
virtual bool | init_machine_for_train (CFeatures *data) |
virtual bool | init_machines_for_apply (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 () |
virtual bool | is_acceptable_machine (CMachine *machine) |
virtual void | store_model_features () |
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) |
Protected Attributes | |
CDotFeatures * | m_features |
features for training or classifying More... | |
int32_t | m_min_label |
minimal label More... | |
int32_t | m_num_classes |
number of different classes (labels) More... | |
int32_t | m_dim |
dimensionality of feature space More... | |
SGMatrix< float64_t > | m_means |
means for normal distributions of features More... | |
SGMatrix< float64_t > | m_variances |
variances for normal distributions of features More... | |
SGVector< float64_t > | m_label_prob |
a priori probabilities of labels More... | |
SGVector< float64_t > | m_rates |
label rates More... | |
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 |
default constructor
Definition at line 21 of file GaussianNaiveBayes.cpp.
CGaussianNaiveBayes | ( | CFeatures * | train_examples, |
CLabels * | train_labels | ||
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constructor
train_examples | train examples |
train_labels | labels corresponding to train_examples |
Definition at line 28 of file GaussianNaiveBayes.cpp.
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virtual |
destructor
Definition at line 42 of file GaussianNaiveBayes.cpp.
set subset to the features of the machine, deletes old one
subset | subset indices to set |
Implements CMulticlassMachine.
Definition at line 68 of file NativeMulticlassMachine.h.
apply machine to data if data is not specified apply to the current features
data | (test)data to be classified |
Definition at line 160 of file Machine.cpp.
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virtualinherited |
apply machine to data in means of binary classification problem
Reimplemented in CKernelMachine, COnlineLinearMachine, CWDSVMOcas, CNeuralNetwork, CLinearMachine, CGaussianProcessClassification, CDomainAdaptationSVMLinear, CPluginEstimate, and CBaggingMachine.
Definition at line 216 of file Machine.cpp.
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virtualinherited |
apply machine to data in means of latent problem
Reimplemented in CLinearLatentMachine.
Definition at line 240 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 195 of file Machine.cpp.
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virtualinherited |
applies a locked machine on a set of indices for binary problems
Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
Definition at line 246 of file Machine.cpp.
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virtualinherited |
applies a locked machine on a set of indices for latent problems
Definition at line 274 of file Machine.cpp.
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virtualinherited |
applies a locked machine on a set of indices for multiclass problems
Definition at line 260 of file Machine.cpp.
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virtualinherited |
applies a locked machine on a set of indices for regression problems
Reimplemented in CKernelMachine.
Definition at line 253 of file Machine.cpp.
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virtualinherited |
applies a locked machine on a set of indices for structured problems
Definition at line 267 of file Machine.cpp.
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virtual |
classify specified examples
data | examples to be classified |
Reimplemented from CMulticlassMachine.
Definition at line 175 of file GaussianNaiveBayes.cpp.
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virtualinherited |
classify all examples with multiple output
Definition at line 195 of file MulticlassMachine.cpp.
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virtual |
classifiy specified example
idx | example index |
Reimplemented from CMulticlassMachine.
Definition at line 199 of file GaussianNaiveBayes.cpp.
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virtualinherited |
apply machine to data in means of regression problem
Reimplemented in CKernelMachine, CWDSVMOcas, COnlineLinearMachine, CNeuralNetwork, CCHAIDTree, CStochasticGBMachine, CCARTree, CLinearMachine, CGaussianProcessRegression, and CBaggingMachine.
Definition at line 222 of file Machine.cpp.
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virtualinherited |
apply machine to data in means of SO classification problem
Reimplemented in CLinearStructuredOutputMachine.
Definition at line 234 of file Machine.cpp.
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inherited |
Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.
dict | dictionary of parameters to be built. |
Definition at line 1244 of file SGObject.cpp.
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protectedinherited |
clear machines
Definition at line 47 of file NativeMulticlassMachine.h.
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virtualinherited |
Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.
Definition at line 1361 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 120 of file Machine.cpp.
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virtualinherited |
Unlocks a locked machine and restores previous state
Reimplemented in CKernelMachine.
Definition at line 151 of file Machine.cpp.
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virtualinherited |
A deep copy. All the instance variables will also be copied.
Definition at line 200 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 1265 of file SGObject.cpp.
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virtual |
get classifier type
Reimplemented from CMachine.
Definition at line 89 of file GaussianNaiveBayes.h.
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get features for classify
Definition at line 47 of file GaussianNaiveBayes.cpp.
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inherited |
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inherited |
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virtualinherited |
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get machine
num | index of machine to get |
Definition at line 74 of file MulticlassMachine.h.
obtain machine from trained one
Implements CMulticlassMachine.
Definition at line 59 of file NativeMulticlassMachine.h.
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virtualinherited |
get problem type
Reimplemented from CMachine.
Reimplemented in CCHAIDTree, and CCARTree.
Definition at line 32 of file BaseMulticlassMachine.cpp.
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inherited |
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inherited |
Definition at line 1136 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 1160 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 1173 of file SGObject.cpp.
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get the type of multiclass'ness
Definition at line 114 of file MulticlassMachine.h.
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get name
Reimplemented from CNativeMulticlassMachine.
Definition at line 84 of file GaussianNaiveBayes.h.
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get number of machines
Definition at line 27 of file BaseMulticlassMachine.cpp.
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protectedvirtualinherited |
get num rhs vectors
Implements CMulticlassMachine.
Definition at line 62 of file NativeMulticlassMachine.h.
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get prob output heuristic of multiclass strategy
Definition at line 145 of file MulticlassMachine.h.
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returns rejection strategy
Definition at line 124 of file MulticlassMachine.h.
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virtualinherited |
get output of i-th submachine for num-th vector
i | number of submachine |
num | number of feature vector |
Definition at line 80 of file MulticlassMachine.cpp.
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virtualinherited |
get outputs of i-th submachine
i | number of submachine |
Reimplemented in CDomainAdaptationMulticlassLibLinear.
Definition at line 71 of file MulticlassMachine.cpp.
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protectedvirtualinherited |
abstract init machine for training method
Implements CMulticlassMachine.
Definition at line 50 of file NativeMulticlassMachine.h.
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protectedvirtualinherited |
abstract init machines for applying method
Implements CMulticlassMachine.
Definition at line 53 of file NativeMulticlassMachine.h.
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protectedinherited |
init strategy
Definition at line 44 of file NativeMulticlassMachine.h.
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protectedvirtualinherited |
whether the machine is acceptable in set_machine
Reimplemented from CMulticlassMachine.
Definition at line 74 of file NativeMulticlassMachine.h.
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inherited |
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virtualinherited |
If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
generic | set to the type of the generic if returning TRUE |
Definition at line 298 of file SGObject.cpp.
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virtualinherited |
check whether the labels is valid.
lab | the labels being checked, guaranteed to be non-NULL |
Reimplemented from CMachine.
Reimplemented in CCARTree, and CCHAIDTree.
Definition at line 37 of file BaseMulticlassMachine.cpp.
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protectedvirtualinherited |
check whether machine is ready
Implements CMulticlassMachine.
Definition at line 56 of file NativeMulticlassMachine.h.
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inherited |
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::get_version_parameter() for this in most cases) |
file | file to load from |
prefix | prefix for members |
Definition at line 705 of file SGObject.cpp.
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inherited |
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 546 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) |
Definition at line 375 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 1063 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 1058 of file SGObject.cpp.
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inherited |
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 743 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 950 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 890 of file SGObject.cpp.
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virtualinherited |
Definition at line 264 of file SGObject.cpp.
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inherited |
prints all parameter registered for model selection and their type
Definition at line 1112 of file SGObject.cpp.
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virtualinherited |
prints registered parameters out
prefix | prefix for members |
Definition at line 310 of file SGObject.cpp.
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protectedvirtualinherited |
deletes any subset set to the features of the machine
Implements CMulticlassMachine.
Definition at line 71 of file NativeMulticlassMachine.h.
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virtualinherited |
Save this object to file.
file | where to save the object; will be closed during returning if PREFIX is an empty string. |
prefix | prefix for members |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
Definition at line 316 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 1073 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 1068 of file SGObject.cpp.
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virtual |
set features for classify
features | features to be set |
Definition at line 53 of file GaussianNaiveBayes.cpp.
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Definition at line 42 of file SGObject.cpp.
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Definition at line 47 of file SGObject.cpp.
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Definition at line 52 of file SGObject.cpp.
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Definition at line 57 of file SGObject.cpp.
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Definition at line 62 of file SGObject.cpp.
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Definition at line 67 of file SGObject.cpp.
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Definition at line 72 of file SGObject.cpp.
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Definition at line 77 of file SGObject.cpp.
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Definition at line 82 of file SGObject.cpp.
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Definition at line 87 of file SGObject.cpp.
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Definition at line 92 of file SGObject.cpp.
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Definition at line 97 of file SGObject.cpp.
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Definition at line 102 of file SGObject.cpp.
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Definition at line 107 of file SGObject.cpp.
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inherited |
Definition at line 112 of file SGObject.cpp.
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inherited |
set generic type to T
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inherited |
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inherited |
set the parallel object
parallel | parallel object to use |
Definition at line 243 of file SGObject.cpp.
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set the version object
version | version object to use |
Definition at line 285 of file SGObject.cpp.
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virtualinherited |
set labels
lab | labels |
Reimplemented from CMachine.
Definition at line 52 of file MulticlassMachine.cpp.
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inherited |
set machine
num | index of machine |
machine | machine to set |
Definition at line 59 of file MulticlassMachine.h.
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inherited |
set maximum training time
t | maximimum training time |
Definition at line 90 of file Machine.cpp.
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set prob output heuristic of multiclass strategy
prob_heuris | type of probability heuristic |
Definition at line 153 of file MulticlassMachine.h.
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inherited |
sets rejection strategy
rejection_strategy | rejection strategy to be set |
Definition at line 133 of file MulticlassMachine.h.
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inherited |
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virtualinherited |
Setter for store-model-features-after-training flag
store_model | whether model should be stored after training |
Definition at line 115 of file Machine.cpp.
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virtualinherited |
A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
Reimplemented in CGaussianKernel.
Definition at line 194 of file SGObject.cpp.
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protectedvirtualinherited |
Stores feature data of underlying model. After this method has been called, it is possible to change the machine's feature data and call apply(), which is then performed on the training feature data that is part of the machine's model.
Base method, has to be implemented in order to allow cross-validation and model selection.
NOT IMPLEMENTED! Has to be done in subclasses
Reimplemented in CKernelMachine, CKNN, CLinearMulticlassMachine, CTreeMachine< T >, CTreeMachine< ConditionalProbabilityTreeNodeData >, CTreeMachine< RelaxedTreeNodeData >, CTreeMachine< id3TreeNodeData >, CTreeMachine< VwConditionalProbabilityTreeNodeData >, CTreeMachine< CARTreeNodeData >, CTreeMachine< C45TreeNodeData >, CTreeMachine< CHAIDTreeNodeData >, CTreeMachine< NbodyTreeNodeData >, CLinearMachine, CGaussianProcessMachine, CHierarchical, CDistanceMachine, CKernelMulticlassMachine, and CLinearStructuredOutputMachine.
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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 47 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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protectedvirtual |
train classifier
data | train examples |
Reimplemented from CMulticlassMachine.
Definition at line 63 of file GaussianNaiveBayes.cpp.
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protectedvirtualinherited |
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 305 of file SGObject.cpp.
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virtualinherited |
Updates the hash of current parameter combination
Definition at line 250 of file SGObject.cpp.
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io
Definition at line 496 of file SGObject.h.
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protectedinherited |
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protected |
dimensionality of feature space
Definition at line 111 of file GaussianNaiveBayes.h.
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features for training or classifying
Definition at line 102 of file GaussianNaiveBayes.h.
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parameters wrt which we can compute gradients
Definition at line 511 of file SGObject.h.
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Hash of parameter values
Definition at line 517 of file SGObject.h.
a priori probabilities of labels
Definition at line 120 of file GaussianNaiveBayes.h.
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machine
Definition at line 208 of file MulticlassMachine.h.
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machines
Definition at line 56 of file BaseMulticlassMachine.h.
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means for normal distributions of features
Definition at line 114 of file GaussianNaiveBayes.h.
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protected |
minimal label
Definition at line 105 of file GaussianNaiveBayes.h.
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model selection parameters
Definition at line 508 of file SGObject.h.
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type of multiclass strategy
Definition at line 205 of file MulticlassMachine.h.
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protected |
number of different classes (labels)
Definition at line 108 of file GaussianNaiveBayes.h.
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map for different parameter versions
Definition at line 514 of file SGObject.h.
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parameters
Definition at line 505 of file SGObject.h.
label rates
Definition at line 123 of file GaussianNaiveBayes.h.
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protectedinherited |
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protectedinherited |
variances for normal distributions of features
Definition at line 117 of file GaussianNaiveBayes.h.
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parallel
Definition at line 499 of file SGObject.h.
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version
Definition at line 502 of file SGObject.h.