SHOGUN
4.1.0
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Conditional Probability Tree, decide subtree by a random strategy.
Definition at line 23 of file RandomConditionalProbabilityTree.h.
Public Types | |
typedef CTreeMachineNode < ConditionalProbabilityTreeNodeData > | node_t |
typedef CBinaryTreeMachineNode < ConditionalProbabilityTreeNodeData > | bnode_t |
Public Attributes | |
SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
Parameter * | m_gradient_parameters |
uint32_t | m_hash |
Protected Member Functions | |
virtual bool | which_subtree (bnode_t *node, SGVector< float32_t > ex) |
virtual bool | train_require_labels () const |
virtual bool | train_machine (CFeatures *data) |
void | train_example (SGVector< float32_t > ex, int32_t label) |
void | train_path (SGVector< float32_t > ex, bnode_t *node) |
void | train_node (SGVector< float32_t > ex, float64_t label, bnode_t *node) |
float64_t | predict_node (SGVector< float32_t > ex, bnode_t *node) |
int32_t | create_machine (SGVector< float32_t > ex) |
virtual bool | which_subtree (bnode_t *node, SGVector< float32_t > ex)=0 |
void | compute_conditional_probabilities (SGVector< float32_t > ex) |
float64_t | accumulate_conditional_probability (bnode_t *leaf) |
virtual void | store_model_features () |
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 | |
int32_t | m_num_passes |
number of passes for online training More... | |
std::map< int32_t, bnode_t * > | m_leaves |
class => leaf mapping More... | |
CStreamingDenseFeatures < float32_t > * | m_feats |
online features More... | |
CTreeMachineNode < ConditionalProbabilityTreeNodeData > * | 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
Definition at line 55 of file TreeMachine.h.
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node_t type- Tree node with many possible children
Definition at line 52 of file TreeMachine.h.
constructor
Definition at line 27 of file RandomConditionalProbabilityTree.h.
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destructor
Definition at line 30 of file RandomConditionalProbabilityTree.h.
accumulate along the path to the root the conditional probability for a particular leaf node.
Definition at line 80 of file ConditionalProbabilityTree.cpp.
apply machine to data if data is not specified apply to the current features
data | (test)data to be classified |
Definition at line 152 of file Machine.cpp.
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apply machine to data in means of binary classification problem
Reimplemented in CKernelMachine, COnlineLinearMachine, CWDSVMOcas, CNeuralNetwork, CLinearMachine, CGaussianProcessClassification, CDomainAdaptationSVMLinear, CDomainAdaptationSVM, CPluginEstimate, and CBaggingMachine.
Definition at line 208 of file Machine.cpp.
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apply machine to data in means of latent problem
Reimplemented in CLinearLatentMachine.
Definition at line 232 of file Machine.cpp.
Applies a locked machine on a set of indices. Error if machine is not locked
indices | index vector (of locked features) that is predicted |
Definition at line 187 of file Machine.cpp.
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applies a locked machine on a set of indices for binary problems
Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
Definition at line 238 of file Machine.cpp.
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applies a locked machine on a set of indices for latent problems
Definition at line 266 of file Machine.cpp.
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applies a locked machine on a set of indices for multiclass problems
Definition at line 252 of file Machine.cpp.
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applies a locked machine on a set of indices for regression problems
Reimplemented in CKernelMachine.
Definition at line 245 of file Machine.cpp.
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applies a locked machine on a set of indices for structured problems
Definition at line 259 of file Machine.cpp.
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apply machine to data in means of multiclass classification problem
Reimplemented from CMachine.
Definition at line 21 of file ConditionalProbabilityTree.cpp.
apply machine one single example.
ex | a vector to be applied |
Definition at line 49 of file ConditionalProbabilityTree.cpp.
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applies to one vector
Reimplemented in CKernelMachine, CRelaxedTree, CWDSVMOcas, COnlineLinearMachine, CLinearMachine, CMultitaskLinearMachine, CMulticlassMachine, CKNN, CDistanceMachine, CMultitaskLogisticRegression, CMultitaskLeastSquaresRegression, CScatterSVM, CGaussianNaiveBayes, CPluginEstimate, and CFeatureBlockLogisticRegression.
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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 214 of file Machine.cpp.
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apply machine to data in means of SO classification problem
Reimplemented in CLinearStructuredOutputMachine.
Definition at line 226 of file Machine.cpp.
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Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.
dict | dictionary of parameters to be built. |
Definition at line 597 of file SGObject.cpp.
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Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.
Definition at line 714 of file SGObject.cpp.
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compute conditional probabilities for ex along the whole tree for predicting
Definition at line 60 of file ConditionalProbabilityTree.cpp.
create a new OnlineLinear machine for a node
ex | the Example instance for training the new machine |
Definition at line 248 of file ConditionalProbabilityTree.cpp.
Locks the machine on given labels and data. After this call, only train_locked and apply_locked may be called
Only possible if supports_locking() returns true
labs | labels used for locking |
features | features used for locking |
Reimplemented in CKernelMachine.
Definition at line 112 of file Machine.cpp.
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Unlocks a locked machine and restores previous state
Reimplemented in CKernelMachine.
Definition at line 143 of file Machine.cpp.
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A deep copy. All the instance variables will also be copied.
Definition at line 198 of file SGObject.cpp.
Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!
May be overwritten but please do with care! Should not be necessary in most cases.
other | object to compare with |
accuracy | accuracy to use for comparison (optional) |
tolerant | allows linient check on float equality (within accuracy) |
Definition at line 618 of file SGObject.cpp.
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get classifier type
Reimplemented in CLaRank, CSVMLight, CDualLibQPBMSOSVM, CNeuralNetwork, CCCSOSVM, CLeastAngleRegression, CLDA, CKernelRidgeRegression, CLibLinearMTL, CBaggingMachine, CLibLinear, CGaussianProcessClassification, CKMeans, CLibSVR, CQDA, CGaussianNaiveBayes, CSVRLight, CMCLDA, CLinearRidgeRegression, CKNN, CScatterSVM, CGaussianProcessRegression, CSGDQN, CSVMSGD, CSVMOcas, COnlineSVMSGD, CLeastSquaresRegression, CMKLRegression, CDomainAdaptationSVMLinear, CMKLMulticlass, CWDSVMOcas, CHierarchical, CMKLOneClass, CLibSVM, CStochasticSOSVM, CMKLClassification, CDomainAdaptationSVM, CLPBoost, CPerceptron, CAveragedPerceptron, CFWSOSVM, CNewtonSVM, CLPM, CGMNPSVM, CSVMLightOneClass, CSVMLin, CMulticlassLibSVM, CLibSVMOneClass, CMPDSVM, CGPBTSVM, CGNPPSVM, and CCPLEXSVM.
Definition at line 92 of file Machine.cpp.
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get problem type
Reimplemented from CMachine.
Reimplemented in CCHAIDTree, and CCARTree.
Definition at line 32 of file BaseMulticlassMachine.cpp.
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Definition at line 498 of file SGObject.cpp.
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Returns description of a given parameter string, if it exists. SG_ERROR otherwise
param_name | name of the parameter |
Definition at line 522 of file SGObject.cpp.
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Returns index of model selection parameter with provided index
param_name | name of model selection parameter |
Definition at line 535 of file SGObject.cpp.
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get name
Reimplemented from CConditionalProbabilityTree.
Definition at line 33 of file RandomConditionalProbabilityTree.h.
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get number of machines
Definition at line 27 of file BaseMulticlassMachine.cpp.
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get number of passes
Definition at line 55 of file ConditionalProbabilityTree.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 296 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.
Reimplemented in CCARTree, and CCHAIDTree.
Definition at line 37 of file BaseMulticlassMachine.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 |
Definition at line 369 of file SGObject.cpp.
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protectedvirtualinherited |
Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 426 of file SGObject.cpp.
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Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 421 of file SGObject.cpp.
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Definition at line 262 of file SGObject.cpp.
predict a single node
ex | the example being predicted |
node | the node |
Definition at line 237 of file ConditionalProbabilityTree.cpp.
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prints all parameter registered for model selection and their type
Definition at line 474 of file SGObject.cpp.
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prints registered parameters out
prefix | prefix for members |
Definition at line 308 of file SGObject.cpp.
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print the tree structure for debug purpose
Definition at line 144 of file ConditionalProbabilityTree.cpp.
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Save this object to file.
file | where to save the object; will be closed during returning if PREFIX is an empty string. |
prefix | prefix for members |
Definition at line 314 of file SGObject.cpp.
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protectedvirtualinherited |
Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel.
Definition at line 436 of file SGObject.cpp.
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protectedvirtualinherited |
Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 431 of file SGObject.cpp.
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Definition at line 41 of file SGObject.cpp.
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Definition at line 46 of file SGObject.cpp.
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Definition at line 51 of file SGObject.cpp.
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Definition at line 56 of file SGObject.cpp.
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Definition at line 61 of file SGObject.cpp.
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Definition at line 66 of file SGObject.cpp.
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Definition at line 71 of file SGObject.cpp.
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Definition at line 76 of file SGObject.cpp.
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Definition at line 81 of file SGObject.cpp.
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Definition at line 86 of file SGObject.cpp.
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Definition at line 91 of file SGObject.cpp.
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Definition at line 96 of file SGObject.cpp.
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Definition at line 101 of file SGObject.cpp.
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Definition at line 106 of file SGObject.cpp.
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Definition at line 111 of file SGObject.cpp.
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set generic type to T
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set the parallel object
parallel | parallel object to use |
Definition at line 241 of file SGObject.cpp.
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set the version object
version | version object to use |
Definition at line 283 of file SGObject.cpp.
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set labels
lab | labels |
Reimplemented in CNeuralNetwork, CGaussianProcessMachine, CCARTree, CStructuredOutputMachine, CRelaxedTree, and CMulticlassMachine.
Definition at line 65 of file Machine.cpp.
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set maximum training time
t | maximimum training time |
Definition at line 82 of file Machine.cpp.
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set number of passes
Definition at line 49 of file ConditionalProbabilityTree.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 107 of file Machine.cpp.
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A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
Reimplemented in CGaussianKernel.
Definition at line 192 of file SGObject.cpp.
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enable unlocked cross-validation - no model features to store
Reimplemented from CMachine.
Definition at line 152 of file TreeMachine.h.
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Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
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train machine
data | training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data). If flag is set, model features will be stored after training. |
Reimplemented in CRelaxedTree, CAutoencoder, CSGDQN, and COnlineSVMSGD.
Definition at line 39 of file Machine.cpp.
train on a single example (online learning)
ex | the example being trained |
label | the label of this training example |
Definition at line 152 of file ConditionalProbabilityTree.cpp.
Trains a locked machine on a set of indices. Error if machine is not locked
NOT IMPLEMENTED
indices | index vector (of locked features) that is used for training |
Reimplemented in CKernelMachine, and CMultitaskLinearMachine.
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train machine
data | training data |
Reimplemented from CMachine.
Definition at line 98 of file ConditionalProbabilityTree.cpp.
train a single node
ex | the example being trained |
label | label |
node | the node |
Definition at line 228 of file ConditionalProbabilityTree.cpp.
train on a path from a node up to the root
ex | the instance of the training example |
node | the leaf node |
Definition at line 209 of file ConditionalProbabilityTree.cpp.
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the labels will be embedded in the streaming features
Reimplemented from CMachine.
Definition at line 82 of file ConditionalProbabilityTree.h.
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unset generic type
this has to be called in classes specializing a template class
Definition at line 303 of file SGObject.cpp.
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Updates the hash of current parameter combination
Definition at line 248 of file SGObject.cpp.
decide which subtree to go, when training the tree structure.
node | the node being decided |
ex | the example being decided |
Definition at line 16 of file RandomConditionalProbabilityTree.cpp.
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decide which subtree to go, when training the tree structure.
node | the node being decided |
ex | the example being decided |
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io
Definition at line 369 of file SGObject.h.
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online features
Definition at line 139 of file ConditionalProbabilityTree.h.
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parameters wrt which we can compute gradients
Definition at line 384 of file SGObject.h.
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Hash of parameter values
Definition at line 387 of file SGObject.h.
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class => leaf mapping
Definition at line 138 of file ConditionalProbabilityTree.h.
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machines
Definition at line 56 of file BaseMulticlassMachine.h.
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model selection parameters
Definition at line 381 of file SGObject.h.
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number of passes for online training
Definition at line 137 of file ConditionalProbabilityTree.h.
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parameters
Definition at line 378 of file SGObject.h.
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tree root
Definition at line 156 of file TreeMachine.h.
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parallel
Definition at line 372 of file SGObject.h.
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version
Definition at line 375 of file SGObject.h.