Namespace List

Here is a list of all namespaces with brief descriptions:

[detail level 1234]

NEigen
▼NFT	Main Feat namespace
▼NDat	Namespace containing Data structures used in Feat
CData	Data holding X, y, and Z data
CDataRef
CStack	Template stack class which holds various stack types for feat
CState	Various types of State actually used by feat
CTrace	Used for tracing stack outputs for backprop algorithm
▼NEval	Namespace containing various Evaluation classes used in Feat
CEvaluation	Evaluation mixin class for Feat
CScorer	Scoring class for Feat
▼NModel	Namespace containing ML methods used in Feat
CML	Class that specifies the machine learning algorithm to pair with Feat
▼NOpt	Namespace for backprop classes in Feat
CBP_NODE
CAutoBackProp
CHillClimb
▼NPop	Namespace containing population representations used in Feat
►NOp	Namespace representing various operations on population individuals used in Feat
CNodeMap
CNode2dGaussian
CNodeAdd
CNodeCos
CNodeCube
CNodeDivide
CNodeExponent
CNodeExponential
CNodeFloat
CNodeGaussian
CNodeLog
CNodeLogit
CNodeMultiply
CNodeRelu
CNodeSign
CNodeSin
CNodeSqrt
CNodeSquare
CNodeStep
CNodeSubtract
CNodeTanh
CNodeIf
CNodeIfThenElse
CNodeFuzzyFixedSplit
CNodeFuzzySplit
CNodeSplit
CNodeAnd
CNodeEqual
CNodeGEQ
CNodeGreaterThan
CNodeLEQ
CNodeLessThan
CNodeNot
CNodeOr
CNodeXor
CNodeCount
CNodeKurtosis
CNodeLongitudinal
CNodeMax
CNodeMean
CNodeMedian
CNodeMin
CNodeRecent
CNodeSkew
CNodeSlope
CNodeVar
CNodeDx
CNodeTrain
CNode	Represents nodes in a program
CNodeConstant
CNodeVariable
CArchive
CIndividual	Individual programs in the population
CNodeVector	Extension of a vector of unique pointers to nodes
►CPopulation	Defines a population of programs and functions for constructing them
CSameFitComplexity	Check for same fitness and complexity to filter uniqueness
CSortComplexity	Sort population in increasing complexity
▼NSel	Namespace containing Selection methods for best individuals used in Feat
CFairLexicase	FairLexicase selection operator
CLexicase	Lexicase selection operator
►CNSGA2
Ccomparator_obj	Sort based on objective m
Csort_n	Sort based on rank, breaking ties with crowding distance
COffspring
CRandom
CSelection	Interfaces with selection operators
CSelectionOperator	Base class for selection operators
CSimAnneal
CTournament
▼NUtil	Namespace containing various utility functions used in Feat
CLogger	Defines a multi level static logger for feat
CRnd	Defines a multi-core random number generator and its operators
CTimer	Class for timing things
CNormalizer	Normalizes a matrix to unit variance, 0 mean centered
CLog_Stats
▼NVary	Namespace containing various variation methods for cross and mutation in Feat
CVariation
CFeat	Main class for the Feat learner
▼CParameters	Holds the hyperparameters for Feat
CBP
CHC
▼Nshogun
CCMulticlassLogisticRegression	Multiclass logistic regression
CCMyCARTree
CMyCARTreeNodeData	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>
CCMyLibLinear
CCMyMulticlassLibLinear	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
CCMyRandomCARTree	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
CCMyRandomForest	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)