SHOGUN
4.0.0
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This class implements the Random Forests algorithm. In Random Forests algorithm, we train a number of randomized CART trees (see class CRandomCARTree) using the supplied training data. The number of trees to be trained is a parameter (called number of bags) controlled by the user. Test feature vectors are classified/regressed by combining the outputs of all these trained candidate trees using a combination rule (see class CCombinationRule). The feature for calculating out-of-box error is also provided to help determine the appropriate number of bags. The evaluatin criteria for calculating this out-of-box error is specified by the user (see class CEvaluation).
在文件 RandomForest.h 第 46 行定义.
Public 成员函数 | |
CRandomForest () | |
CRandomForest (int32_t num_rand_feats, int32_t num_bags=10) | |
CRandomForest (CFeatures *features, CLabels *labels, int32_t num_bags=10, int32_t num_rand_feats=0) | |
CRandomForest (CFeatures *features, CLabels *labels, SGVector< float64_t > weights, int32_t num_bags=10, int32_t num_rand_feats=0) | |
virtual | ~CRandomForest () |
virtual const char * | get_name () const |
virtual void | set_machine (CMachine *machine) |
void | set_weights (SGVector< float64_t > weights) |
SGVector< float64_t > | get_weights () const |
void | set_feature_types (SGVector< bool > ft) |
SGVector< bool > | get_feature_types () const |
virtual EProblemType | get_machine_problem_type () const |
void | set_machine_problem_type (EProblemType mode) |
void | set_num_random_features (int32_t rand_featsize) |
int32_t | get_num_random_features () const |
virtual CBinaryLabels * | apply_binary (CFeatures *data=NULL) |
virtual CMulticlassLabels * | apply_multiclass (CFeatures *data=NULL) |
virtual CRegressionLabels * | apply_regression (CFeatures *data=NULL) |
void | set_num_bags (int32_t num_bags) |
int32_t | get_num_bags () const |
virtual void | set_bag_size (int32_t bag_size) |
virtual int32_t | get_bag_size () const |
CMachine * | get_machine () const |
void | set_combination_rule (CCombinationRule *rule) |
CCombinationRule * | get_combination_rule () const |
virtual EMachineType | get_classifier_type () |
float64_t | get_oob_error (CEvaluation *eval) const |
virtual bool | train (CFeatures *data=NULL) |
virtual CLabels * | apply (CFeatures *data=NULL) |
virtual CStructuredLabels * | apply_structured (CFeatures *data=NULL) |
virtual CLatentLabels * | apply_latent (CFeatures *data=NULL) |
virtual void | set_labels (CLabels *lab) |
virtual CLabels * | get_labels () |
void | set_max_train_time (float64_t t) |
float64_t | get_max_train_time () |
void | set_solver_type (ESolverType st) |
ESolverType | get_solver_type () |
virtual void | set_store_model_features (bool store_model) |
virtual bool | train_locked (SGVector< index_t > indices) |
virtual float64_t | apply_one (int32_t i) |
virtual CLabels * | apply_locked (SGVector< index_t > indices) |
virtual CBinaryLabels * | apply_locked_binary (SGVector< index_t > indices) |
virtual CRegressionLabels * | apply_locked_regression (SGVector< index_t > indices) |
virtual CMulticlassLabels * | apply_locked_multiclass (SGVector< index_t > indices) |
virtual CStructuredLabels * | apply_locked_structured (SGVector< index_t > indices) |
virtual CLatentLabels * | apply_locked_latent (SGVector< index_t > indices) |
virtual void | data_lock (CLabels *labs, CFeatures *features) |
virtual void | post_lock (CLabels *labs, CFeatures *features) |
virtual void | data_unlock () |
virtual bool | supports_locking () const |
bool | is_data_locked () const |
virtual CSGObject * | shallow_copy () const |
virtual CSGObject * | deep_copy () const |
virtual bool | is_generic (EPrimitiveType *generic) const |
template<class T > | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
void | unset_generic () |
virtual void | print_serializable (const char *prefix="") |
virtual bool | save_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter()) |
virtual bool | load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter()) |
DynArray< TParameter * > * | load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="") |
DynArray< TParameter * > * | load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="") |
void | map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos) |
void | set_global_io (SGIO *io) |
SGIO * | get_global_io () |
void | set_global_parallel (Parallel *parallel) |
Parallel * | get_global_parallel () |
void | set_global_version (Version *version) |
Version * | get_global_version () |
SGStringList< char > | get_modelsel_names () |
void | print_modsel_params () |
char * | get_modsel_param_descr (const char *param_name) |
index_t | get_modsel_param_index (const char *param_name) |
void | build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict) |
virtual void | update_parameter_hash () |
virtual bool | parameter_hash_changed () |
virtual bool | equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false) |
virtual CSGObject * | clone () |
Public 属性 | |
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 成员函数 | |
virtual void | set_machine_parameters (CMachine *m, SGVector< index_t > idx) |
virtual bool | train_machine (CFeatures *data=NULL) |
SGVector< float64_t > | apply_get_outputs (CFeatures *data) |
void | register_parameters () |
CDynamicArray< index_t > * | get_oob_indices (const SGVector< index_t > &in_bag) |
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) |
Protected 属性 | |
CDynamicObjectArray * | m_bags |
CFeatures * | m_features |
CMachine * | m_machine |
int32_t | m_num_bags |
int32_t | m_bag_size |
CCombinationRule * | m_combination_rule |
SGVector< bool > | m_all_oob_idx |
CDynamicObjectArray * | m_oob_indices |
float64_t | m_max_train_time |
CLabels * | m_labels |
ESolverType | m_solver_type |
bool | m_store_model_features |
bool | m_data_locked |
CRandomForest | ( | ) |
constructor
在文件 RandomForest.cpp 第 36 行定义.
CRandomForest | ( | int32_t | num_rand_feats, |
int32_t | num_bags = 10 |
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constructor
num_rand_feats | number of attributes chosen randomly during node split in candidate trees |
num_bags | number of trees in forest |
在文件 RandomForest.cpp 第 42 行定义.
CRandomForest | ( | CFeatures * | features, |
CLabels * | labels, | ||
int32_t | num_bags = 10 , |
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int32_t | num_rand_feats = 0 |
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constructor
features | training features |
labels | training labels |
num_bags | number of trees in forest |
num_rand_feats | number of attributes chosen randomly during node split in candidate trees |
在文件 RandomForest.cpp 第 54 行定义.
CRandomForest | ( | CFeatures * | features, |
CLabels * | labels, | ||
SGVector< float64_t > | weights, | ||
int32_t | num_bags = 10 , |
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int32_t | num_rand_feats = 0 |
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) |
constructor
features | training features |
labels | training labels |
weights | weights of training feature vectors |
num_bags | number of trees in forest |
num_rand_feats | number of attributes chosen randomly during node split in candidate trees |
在文件 RandomForest.cpp 第 69 行定义.
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destructor
在文件 RandomForest.cpp 第 85 行定义.
apply machine to data if data is not specified apply to the current features
data | (test)data to be classified |
在文件 Machine.cpp 第 160 行定义.
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virtualinherited |
apply machine to data in means of binary classification problem
重载 CMachine .
在文件 BaggingMachine.cpp 第 45 行定义.
helper function for the apply_{regression,..} functions that computes the output
data | the data to compute the output for |
在文件 BaggingMachine.cpp 第 70 行定义.
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virtualinherited |
apply machine to data in means of latent problem
被 CLinearLatentMachine 重载.
在文件 Machine.cpp 第 240 行定义.
Applies a locked machine on a set of indices. Error if machine is not locked
indices | index vector (of locked features) that is predicted |
在文件 Machine.cpp 第 195 行定义.
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virtualinherited |
applies a locked machine on a set of indices for binary problems
被 CKernelMachine , 以及 CMultitaskLinearMachine 重载.
在文件 Machine.cpp 第 246 行定义.
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virtualinherited |
applies a locked machine on a set of indices for latent problems
在文件 Machine.cpp 第 274 行定义.
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virtualinherited |
applies a locked machine on a set of indices for multiclass problems
在文件 Machine.cpp 第 260 行定义.
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virtualinherited |
applies a locked machine on a set of indices for regression problems
被 CKernelMachine 重载.
在文件 Machine.cpp 第 253 行定义.
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virtualinherited |
applies a locked machine on a set of indices for structured problems
在文件 Machine.cpp 第 267 行定义.
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virtualinherited |
apply machine to data in means of multiclass classification problem
重载 CMachine .
在文件 BaggingMachine.cpp 第 53 行定义.
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virtualinherited |
applies to one vector
被 CKernelMachine, CRelaxedTree, CWDSVMOcas, COnlineLinearMachine, CLinearMachine, CMultitaskLinearMachine, CMulticlassMachine, CKNN, CDistanceMachine, CMultitaskLogisticRegression, CMultitaskLeastSquaresRegression, CScatterSVM, CGaussianNaiveBayes, CPluginEstimate , 以及 CFeatureBlockLogisticRegression 重载.
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virtualinherited |
apply machine to data in means of SO classification problem
被 CLinearStructuredOutputMachine 重载.
在文件 Machine.cpp 第 234 行定义.
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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. |
在文件 SGObject.cpp 第 1243 行定义.
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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.
在文件 SGObject.cpp 第 1360 行定义.
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 |
被 CKernelMachine 重载.
在文件 Machine.cpp 第 120 行定义.
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Unlocks a locked machine and restores previous state
被 CKernelMachine 重载.
在文件 Machine.cpp 第 151 行定义.
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A deep copy. All the instance variables will also be copied.
在文件 SGObject.cpp 第 200 行定义.
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) |
在文件 SGObject.cpp 第 1264 行定义.
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Get number of feature vectors that are use for training each bag/machine
在文件 BaggingMachine.cpp 第 214 行定义.
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Get the combination rule that is used for aggregating the results
在文件 BaggingMachine.cpp 第 252 行定义.
SGVector< bool > get_feature_types | ( | ) | const |
get feature types of various features
在文件 RandomForest.cpp 第 110 行定义.
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get problem type - multiclass classification or regression
重载 CMachine .
在文件 RandomForest.cpp 第 116 行定义.
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在文件 SGObject.cpp 第 1135 行定义.
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Returns description of a given parameter string, if it exists. SG_ERROR otherwise
param_name | name of the parameter |
在文件 SGObject.cpp 第 1159 行定义.
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Returns index of model selection parameter with provided index
param_name | name of model selection parameter |
在文件 SGObject.cpp 第 1172 行定义.
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int32_t get_num_random_features | ( | ) | const |
get number of random features to be chosen during node splits
在文件 RandomForest.cpp 第 136 行定义.
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get out-of-bag error CombinationRule is used for combining the predictions.
eval | Evaluation method to use for calculating the error |
在文件 BaggingMachine.cpp 第 258 行定义.
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protectedinherited |
get the vector of indices for feature vectors that are out of bag
in_bag | vector of indices that are in bag. NOTE: in_bag is a randomly generated with replacement |
在文件 BaggingMachine.cpp 第 338 行定义.
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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 |
在文件 SGObject.cpp 第 297 行定义.
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protectedvirtualinherited |
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 |
被 CNeuralNetwork, CCARTree, CCHAIDTree, CGaussianProcessRegression , 以及 CBaseMulticlassMachine 重载.
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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::get_version_parameter() for this in most cases) |
file | file to load from |
prefix | prefix for members |
在文件 SGObject.cpp 第 704 行定义.
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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 |
在文件 SGObject.cpp 第 545 行定义.
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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) |
在文件 SGObject.cpp 第 374 行定义.
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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 occurs. |
被 CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel , 以及 CExponentialKernel 重载.
在文件 SGObject.cpp 第 1062 行定义.
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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. |
被 CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.
在文件 SGObject.cpp 第 1057 行定义.
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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 |
在文件 SGObject.cpp 第 742 行定义.
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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 |
在文件 SGObject.cpp 第 949 行定义.
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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 |
在文件 SGObject.cpp 第 889 行定义.
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在文件 SGObject.cpp 第 263 行定义.
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prints all parameter registered for model selection and their type
在文件 SGObject.cpp 第 1111 行定义.
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Register paramaters
在文件 BaggingMachine.cpp 第 184 行定义.
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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) |
在文件 SGObject.cpp 第 315 行定义.
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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. |
被 CKernel 重载.
在文件 SGObject.cpp 第 1072 行定义.
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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. |
被 CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.
在文件 SGObject.cpp 第 1067 行定义.
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Set number of feature vectors to use for each bag/machine
bag_size | number of vectors to use for a bag |
在文件 BaggingMachine.cpp 第 209 行定义.
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Set the combination rule to use for aggregating the classification results
rule | combination rule |
在文件 BaggingMachine.cpp 第 245 行定义.
void set_feature_types | ( | SGVector< bool > | ft | ) |
set feature types of various features
ft | bool vector true for nominal feature false for continuous feature type |
在文件 RandomForest.cpp 第 104 行定义.
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在文件 SGObject.cpp 第 42 行定义.
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在文件 SGObject.cpp 第 47 行定义.
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在文件 SGObject.cpp 第 52 行定义.
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在文件 SGObject.cpp 第 57 行定义.
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在文件 SGObject.cpp 第 62 行定义.
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在文件 SGObject.cpp 第 67 行定义.
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在文件 SGObject.cpp 第 72 行定义.
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在文件 SGObject.cpp 第 77 行定义.
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在文件 SGObject.cpp 第 82 行定义.
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在文件 SGObject.cpp 第 87 行定义.
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在文件 SGObject.cpp 第 92 行定义.
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在文件 SGObject.cpp 第 97 行定义.
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在文件 SGObject.cpp 第 102 行定义.
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在文件 SGObject.cpp 第 107 行定义.
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在文件 SGObject.cpp 第 112 行定义.
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set generic type to T
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set labels
lab | labels |
被 CNeuralNetwork, CGaussianProcessMachine, CCARTree, CStructuredOutputMachine, CRelaxedTree , 以及 CMulticlassMachine 重载.
在文件 Machine.cpp 第 73 行定义.
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machine is set to modified CART(RandomCART) and cannot be changed
machine | the machine to use for bagging |
重载 CBaggingMachine .
在文件 RandomForest.cpp 第 89 行定义.
sets parameters of CARTree - sets machine labels and weights here
m | machine |
idx | indices of training vectors chosen in current bag |
重载 CBaggingMachine .
在文件 RandomForest.cpp 第 142 行定义.
void set_machine_problem_type | ( | EProblemType | mode | ) |
set problem type - multiclass classification or regression
mode | EProblemType PT_MULTICLASS or PT_REGRESSION |
在文件 RandomForest.cpp 第 122 行定义.
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void set_num_random_features | ( | int32_t | rand_featsize | ) |
set number of random features to be chosen during node splits
rand_featsize | number of randomly chosen features during each node split |
在文件 RandomForest.cpp 第 128 行定义.
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Setter for store-model-features-after-training flag
store_model | whether model should be stored after training |
在文件 Machine.cpp 第 115 行定义.
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virtualinherited |
A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
被 CGaussianKernel 重载.
在文件 SGObject.cpp 第 194 行定义.
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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
被 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 , 以及 CLinearStructuredOutputMachine 重载.
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被 CKernelMachine , 以及 CMultitaskLinearMachine 重载.
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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. |
被 CRelaxedTree, CAutoencoder, CSGDQN , 以及 COnlineSVMSGD 重载.
在文件 Machine.cpp 第 47 行定义.
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 |
被 CKernelMachine , 以及 CMultitaskLinearMachine 重载.
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protectedvirtualinherited |
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) |
NOT IMPLEMENTED!
重载 CMachine .
在文件 BaggingMachine.cpp 第 104 行定义.
|
protectedvirtualinherited |
returns whether machine require labels for training
被 COnlineLinearMachine, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree , 以及 CLibSVMOneClass 重载.
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inherited |
unset generic type
this has to be called in classes specializing a template class
在文件 SGObject.cpp 第 304 行定义.
|
virtualinherited |
Updates the hash of current parameter combination
在文件 SGObject.cpp 第 250 行定义.
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inherited |
io
在文件 SGObject.h 第 496 行定义.
|
protectedinherited |
indices of all feature vectors that are out of bag
在文件 BaggingMachine.h 第 177 行定义.
|
protectedinherited |
number of vectors to use from the training features
在文件 BaggingMachine.h 第 171 行定义.
|
protectedinherited |
bags array
在文件 BaggingMachine.h 第 159 行定义.
|
protectedinherited |
combination rule to use
在文件 BaggingMachine.h 第 174 行定义.
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protectedinherited |
features to train on
在文件 BaggingMachine.h 第 162 行定义.
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inherited |
parameters wrt which we can compute gradients
在文件 SGObject.h 第 511 行定义.
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inherited |
Hash of parameter values
在文件 SGObject.h 第 517 行定义.
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protectedinherited |
machine to use for bagging
在文件 BaggingMachine.h 第 165 行定义.
|
inherited |
model selection parameters
在文件 SGObject.h 第 508 行定义.
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protectedinherited |
number of bags to create
在文件 BaggingMachine.h 第 168 行定义.
|
protectedinherited |
array of oob indices
在文件 BaggingMachine.h 第 180 行定义.
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inherited |
map for different parameter versions
在文件 SGObject.h 第 514 行定义.
|
inherited |
parameters
在文件 SGObject.h 第 505 行定义.
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protectedinherited |
|
protectedinherited |
|
inherited |
parallel
在文件 SGObject.h 第 499 行定义.
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inherited |
version
在文件 SGObject.h 第 502 行定义.