Class Hierarchy

Go to the graphical class hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:

[detail level 123]

CArchive	Defines a Pareto archive of programs
CFT::Pop::Archive
CFT::Opt::AutoBackProp
CFT::Parameters::BP
CFT::Opt::BP_NODE
▼CCBaggingMachine
Cshogun::CMyRandomForest	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)
▼CCLibLinear
Cshogun::CMyLibLinear
▼CCLinearMulticlassMachine
Cshogun::CMulticlassLogisticRegression	Multiclass logistic regression
Cshogun::CMyMulticlassLibLinear	Multiclass LibLinear wrapper. Uses Crammer-Singer formulation and gradient descent optimization algorithm implemented in the LibLinear library. Regularized bias support is added using stacking bias 'feature' to hyperplanes normal vectors
CFT::Sel::NSGA2::comparator_obj	Sort based on objective m
▼CCTreeMachine
►Cshogun::CMyCARTree
Cshogun::CMyRandomCARTree	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
CFT::Dat::Data	Data holding X, y, and Z data
CFT::Dat::DataRef
CFT::Eval::Evaluation	Evaluation mixin class for Feat
CFT::Feat	Main class for the Feat learner
CFT::Parameters::HC
CFT::Opt::HillClimb
CFT::Pop::Individual	Individual programs in the population
CFT::Util::Log_Stats
CFT::Util::Logger	Defines a multi level static logger for feat
CFT::Model::ML	Class that specifies the machine learning algorithm to pair with Feat
Cshogun::MyCARTreeNodeData	Structure to store data of a node of CART. This can be used as a template type in TreeMachineNode class. CART algorithm uses nodes of type CTreeMachineNode<CARTreeNodeData>
▼CFT::Pop::Op::Node	Represents nodes in a program
CFT::Pop::Op::NodeAnd
CFT::Pop::Op::NodeConstant
CFT::Pop::Op::NodeCount
►CFT::Pop::Op::NodeDx
CFT::Pop::Op::Node2dGaussian
CFT::Pop::Op::NodeAdd
CFT::Pop::Op::NodeCos
CFT::Pop::Op::NodeCube
CFT::Pop::Op::NodeDivide
CFT::Pop::Op::NodeExponent
CFT::Pop::Op::NodeExponential
CFT::Pop::Op::NodeGaussian
CFT::Pop::Op::NodeIf
CFT::Pop::Op::NodeIfThenElse
CFT::Pop::Op::NodeLog
CFT::Pop::Op::NodeLogit
CFT::Pop::Op::NodeMultiply
CFT::Pop::Op::NodeRelu
CFT::Pop::Op::NodeSin
CFT::Pop::Op::NodeSqrt
CFT::Pop::Op::NodeSquare
CFT::Pop::Op::NodeSubtract
CFT::Pop::Op::NodeTanh
CFT::Pop::Op::NodeEqual
CFT::Pop::Op::NodeFloat< T >
CFT::Pop::Op::NodeGEQ
CFT::Pop::Op::NodeGreaterThan
CFT::Pop::Op::NodeKurtosis
CFT::Pop::Op::NodeLEQ
CFT::Pop::Op::NodeLessThan
CFT::Pop::Op::NodeLongitudinal
CFT::Pop::Op::NodeMax
CFT::Pop::Op::NodeMean
CFT::Pop::Op::NodeMedian
CFT::Pop::Op::NodeMin
CFT::Pop::Op::NodeNot
CFT::Pop::Op::NodeOr
CFT::Pop::Op::NodeRecent
CFT::Pop::Op::NodeSign
CFT::Pop::Op::NodeSkew
CFT::Pop::Op::NodeSlope
CFT::Pop::Op::NodeStep
►CFT::Pop::Op::NodeTrain
CFT::Pop::Op::NodeFuzzyFixedSplit< T >
CFT::Pop::Op::NodeFuzzySplit< T >
CFT::Pop::Op::NodeSplit< T >
CFT::Pop::Op::NodeVar
CFT::Pop::Op::NodeVariable< T >
CFT::Pop::Op::NodeXor
CFT::Pop::Op::NodeMap
CFT::Util::Normalizer	Normalizes a matrix to unit variance, 0 mean centered
CFT::Parameters	Holds the hyperparameters for Feat
CFT::Pop::Population	Defines a population of programs and functions for constructing them
CFT::Util::Rnd	Defines a multi-core random number generator and its operators
CFT::Pop::Population::SameFitComplexity	Check for same fitness and complexity to filter uniqueness
CFT::Eval::Scorer	Scoring class for Feat
CFT::Sel::Selection	Interfaces with selection operators
▼CFT::Sel::SelectionOperator	Base class for selection operators
CFT::Sel::FairLexicase	FairLexicase selection operator
CFT::Sel::Lexicase	Lexicase selection operator
CFT::Sel::NSGA2
CFT::Sel::Offspring
CFT::Sel::Random
CFT::Sel::SimAnneal
CFT::Sel::Tournament
CFT::Sel::NSGA2::sort_n	Sort based on rank, breaking ties with crowding distance
CFT::Pop::Population::SortComplexity	Sort population in increasing complexity
CFT::Dat::Stack< type >	Template stack class which holds various stack types for feat
CFT::Dat::Stack< ArrayXb >
CFT::Dat::Stack< ArrayXf >
CFT::Dat::Stack< ArrayXi >
CFT::Dat::Stack< std::pair< vector< ArrayXf >, vector< ArrayXf > > >
CFT::Dat::Stack< string >
CFT::Dat::State	Various types of State actually used by feat
CFT::Util::Timer	Class for timing things
CFT::Dat::Trace	Used for tracing stack outputs for backprop algorithm
CFT::Vary::Variation
▼Cstd::vector< T >	STL class
CFT::Pop::NodeVector	Extension of a vector of unique pointers to nodes