SHOGUN
4.1.0
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This class implements randomized CART algorithm used in the tree growing process of candidate trees in Random Forests algorithm. The tree growing process is different from the original CART algorithm because of the input attributes which are considered for each node split. In randomized CART, a few (fixed number) attributes are randomly chosen from all available attributes while deciding the best split. This is unlike the original CART where all available attributes are considered while deciding the best split.
在文件 RandomCARTree.h 第 48 行定义.
Public 类型 | |
typedef CTreeMachineNode< CARTreeNodeData > | node_t |
typedef CBinaryTreeMachineNode< CARTreeNodeData > | bnode_t |
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 |
静态 Public 属性 | |
static const float64_t | MISSING =CMath::MAX_REAL_NUMBER |
static const float64_t | MIN_SPLIT_GAIN =1e-7 |
static const float64_t | EQ_DELTA =1e-7 |
Protected 属性 | |
float64_t | m_label_epsilon |
SGVector< bool > | m_nominal |
SGVector< float64_t > | m_weights |
bool | m_types_set |
bool | m_weights_set |
bool | m_apply_cv_pruning |
int32_t | m_folds |
EProblemType | m_mode |
CDynamicArray< float64_t > * | m_alphas |
int32_t | m_max_depth |
int32_t | m_min_node_size |
CTreeMachineNode< CARTreeNodeData > * | m_root |
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 |
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bnode_t type- Tree node with max 2 possible children
在文件 TreeMachine.h 第 55 行定义.
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node_t type- Tree node with many possible children
在文件 TreeMachine.h 第 52 行定义.
CRandomCARTree | ( | ) |
constructor
在文件 RandomCARTree.cpp 第 36 行定义.
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destructor
在文件 RandomCARTree.cpp 第 42 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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apply machine to data in means of binary classification problem
被 CKernelMachine, COnlineLinearMachine, CWDSVMOcas, CNeuralNetwork, CLinearMachine, CGaussianProcessClassification, CDomainAdaptationSVMLinear, CPluginEstimate , 以及 CBaggingMachine 重载.
在文件 Machine.cpp 第 216 行定义.
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uses current subtree to classify/regress data
feats | data to be classified/regressed |
current | root of current subtree |
在文件 CARTree.cpp 第 976 行定义.
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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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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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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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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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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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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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classify data using Classification Tree
data | data to be classified |
重载 CMachine .
在文件 CARTree.cpp 第 99 行定义.
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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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Get regression labels using Regression Tree
data | data whose regression output is needed |
重载 CMachine .
在文件 CARTree.cpp 第 111 行定义.
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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 1244 行定义.
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CARTtrain - recursive CART training method
data | training data |
weights | vector of weights of data points |
labels | labels of data points |
level | current tree depth |
在文件 CARTree.cpp 第 285 行定义.
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clear feature types of various features
在文件 CARTree.cpp 第 197 行定义.
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clear weights of data points
在文件 CARTree.cpp 第 180 行定义.
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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.
在文件 SGObject.cpp 第 1361 行定义.
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computes best attribute for CARTtrain
mat | data matrix |
weights | data weights |
labels_vec | data labels |
left | stores feature values for left transition |
right | stores feature values for right transition |
is_left_final | stores which feature vectors go to the left child |
num_missing | number of missing attributes |
count_left | stores number of feature values for left transition |
count_right | stores number of feature values for right transition |
重载 CCARTree .
在文件 RandomCARTree.cpp 第 52 行定义.
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computes error in classification/regression for classification it eveluates weight_missclassified/total_weight for regression it evaluates weighted sum of squared error/total_weight
labels | the labels whose error needs to be calculated |
reference | actual labels against which test labels are compared |
weights | weights associated with the labels |
在文件 CARTree.cpp 第 1198 行定义.
recursively cuts weakest link(s) in a tree
node | the root of subtree whose weakest link it cuts |
alpha | alpha value corresponding to weakest link |
在文件 CARTree.cpp 第 1305 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 1265 行定义.
recursively finds alpha corresponding to weakest link(s)
node | the root of subtree whose weakest link it finds |
在文件 CARTree.cpp 第 1284 行定义.
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recursively forms base case $ft_1$f tree from $ft_max$f during pruning
node | the root of current subtree |
在文件 CARTree.cpp 第 1335 行定义.
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returns gain in regression case
wleft | left child weight distribution |
wright | right child weights distribution |
wtotal | weight distribution in current node |
labels | regression labels |
在文件 CARTree.cpp 第 918 行定义.
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returns gain in Gini impurity measure
wleft | left child label distribution |
wright | right child label distribution |
wtotal | label distribution in current node |
在文件 CARTree.cpp 第 932 行定义.
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get classifier type
被 CLaRank, CDualLibQPBMSOSVM, CNeuralNetwork, CCCSOSVM, CLeastAngleRegression, CLDA, CKernelRidgeRegression, CLibLinearMTL, CBaggingMachine, CLibLinear, CGaussianProcessClassification, CKMeans, CLibSVR, CQDA, CGaussianNaiveBayes, CMCLDA, CLinearRidgeRegression, CKNN, CGaussianProcessRegression, CScatterSVM, CSGDQN, CSVMSGD, CSVMOcas, COnlineSVMSGD, CLeastSquaresRegression, CMKLRegression, CDomainAdaptationSVMLinear, CMKLMulticlass, CWDSVMOcas, CHierarchical, CMKLOneClass, CLibSVM, CStochasticSOSVM, CMKLClassification, CLPBoost, CPerceptron, CAveragedPerceptron, CFWSOSVM, CNewtonSVM, CLPM, CGMNPSVM, CSVMLin, CMulticlassLibSVM, CLibSVMOneClass, CMPDSVM, CGPBTSVM, CGNPPSVM , 以及 CCPLEXSVM 重载.
在文件 Machine.cpp 第 100 行定义.
int32_t get_feature_subset_size | ( | ) | const |
get number of random features to choose in each node split
在文件 RandomCARTree.h 第 72 行定义.
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set feature types of various features
在文件 CARTree.cpp 第 192 行定义.
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get problem type - multiclass classification or regression
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在文件 SGObject.cpp 第 1136 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 1160 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 1173 行定义.
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get number of subsets used for cross validation
在文件 CARTree.cpp 第 203 行定义.
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modify labels for compute_best_attribute
labels_vec | labels vector |
n_ulabels | stores number of unique labels |
在文件 CARTree.cpp 第 462 行定义.
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returns Gini impurity of a node
weighted_lab_classes | vector of weights associated with various labels |
total_weight | stores the total weight of all classes |
在文件 CARTree.cpp 第 944 行定义.
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handles missing values for a chosen continuous surrogate attribute
m | training data matrix |
missing_vecs | column indices of vectors with missing attribute in data matrix |
association_index | stores the final lambda values used to address members of missing_vecs |
intersect_vecs | column indices of vectors with known values for the best attribute as well as the chosen surrogate |
is_left | whether a vector goes into left child |
weights | weights of training data vectors |
p | min(p_l,p_r) in the lambda formula |
attr | surrogate attribute chosen for split |
在文件 CARTree.cpp 第 781 行定义.
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handles missing values for a chosen nominal surrogate attribute
m | training data matrix |
missing_vecs | column indices of vectors with missing attribute in data matrix |
association_index | stores the final lambda values used to address members of missing_vecs |
intersect_vecs | column indices of vectors with known values for the best attribute as well as the chosen surrogate |
is_left | whether a vector goes into left child |
weights | weights of training data vectors |
p | min(p_l,p_r) in the lambda formula |
attr | surrogate attribute chosen for split |
在文件 CARTree.cpp 第 838 行定义.
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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 |
在文件 SGObject.cpp 第 298 行定义.
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whether labels supplied are valid for current problem type
lab | labels supplied |
在文件 CARTree.cpp 第 89 行定义.
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returns least squares deviation
labels | regression labels |
weights | weights of regression data points |
total_weight | stores sum of weights in weights vector |
在文件 CARTree.cpp 第 958 行定义.
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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 705 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 546 行定义.
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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) |
在文件 SGObject.cpp 第 375 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 1063 行定义.
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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 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 1058 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 743 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 950 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 890 行定义.
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在文件 SGObject.cpp 第 264 行定义.
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prints all parameter registered for model selection and their type
在文件 SGObject.cpp 第 1112 行定义.
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prune by cross validation
data | training data |
folds | the integer V for V-fold cross validation |
在文件 CARTree.cpp 第 1059 行定义.
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cost-complexity pruning
tree | the tree to be pruned |
在文件 CARTree.cpp 第 1236 行定义.
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uses test dataset to choose best pruned subtree
feats | test data to be used |
gnd_truth | test labels |
weights | weights of data points |
在文件 CARTree.cpp 第 123 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 316 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::SAVE_SERIALIZABLE_POST is called.
ShogunException | will be thrown if an error occurs. |
被 CKernel 重载.
在文件 SGObject.cpp 第 1073 行定义.
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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 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 1068 行定义.
void set_feature_subset_size | ( | int32_t | size | ) |
set number of random features to choose in each node split
size | subset size |
在文件 RandomCARTree.cpp 第 46 行定义.
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set feature types of various features
ft | bool vector true for nominal feature false for continuous feature type |
在文件 CARTree.cpp 第 186 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 - automagically switch machine problem type based on type of labels supplied
lab | labels |
重载 CMachine .
在文件 CARTree.cpp 第 70 行定义.
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set problem type - multiclass classification or regression
mode | EProblemType PT_MULTICLASS or PT_REGRESSION |
在文件 CARTree.cpp 第 84 行定义.
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set number of subsets for cross validation
folds | number of folds used in cross validation |
在文件 CARTree.cpp 第 208 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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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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enable unlocked cross-validation - no model features to store
重载 CMachine .
在文件 TreeMachine.h 第 152 行定义.
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被 CKernelMachine , 以及 CMultitaskLinearMachine 重载.
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handles missing values through surrogate splits
data | training data matrix |
weights | vector of weights of data points |
nm_left | whether a data point is put into left child (available for only data points with non-missing attribute attr) |
attr | best attribute chosen for split |
在文件 CARTree.cpp 第 706 行定义.
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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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train machine - build CART from training data
data | training data |
重载 CMachine .
在文件 CARTree.cpp 第 242 行定义.
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returns whether machine require labels for training
被 COnlineLinearMachine, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree , 以及 CLibSVMOneClass 重载.
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unset generic type
this has to be called in classes specializing a template class
在文件 SGObject.cpp 第 305 行定义.
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Updates the hash of current parameter combination
在文件 SGObject.cpp 第 250 行定义.
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io
在文件 SGObject.h 第 496 行定义.
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parameters wrt which we can compute gradients
在文件 SGObject.h 第 511 行定义.
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Hash of parameter values
在文件 SGObject.h 第 517 行定义.
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machines
在文件 BaseMulticlassMachine.h 第 56 行定义.
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model selection parameters
在文件 SGObject.h 第 508 行定义.
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map for different parameter versions
在文件 SGObject.h 第 514 行定义.
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parameters
在文件 SGObject.h 第 505 行定义.
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tree root
在文件 TreeMachine.h 第 156 行定义.
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parallel
在文件 SGObject.h 第 499 行定义.
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version
在文件 SGObject.h 第 502 行定义.