Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

[detail level 123]

▼Nml
▼NClustering	Methods and classes for clustering algorithms
CCentroidsInitialiser	Chooses initial locations of centroids
CClosestCentroid	Initialises centroids and then assigns the responsibility for each point to its closest centroid
CForgy	Chooses random points as new centroids
CKMeans	Naive K-means clustering method
CKPP	Implements the K++ algorithm
CModel	Abstract clustering model
CRandomPartition	Assigns points to clusters randomly and then returns cluster means
CResponsibilitiesInitialiser	Chooses initial component responsibilities
▼NDecisionTrees	Helper functions and classes for decision trees
CLeafNode	Terminal node, which returns a constant prediction value for features which ended up on it
CNode	Tree node. Nodes are split (non-terminal) or leaf (terminal)
CSplitNode	Non-terminal node, which splits data depending on a threshold value of some feature
▼NKernels	Methods and classes for working with kernels
CDifferentiableKernel	Abstract differentiable R^D kernel interface
CDifferentiableRadialBasisFunction	Differentiable radial basis function kernel
CDifferentiableRBFKernel	Differentiable radial basis function kernel
CDoubleDifferentiableKernel	Abstract double differentiable R^D kernel interface
CDoubleDifferentiableRadialBasisFunction	Double differentiable radial basis function kernel
CGaussianRBF	Gaussian radial basis function
CKernel	Abstract R^D kernel interface
CRadialBasisFunction	Radial basis function
CRBFKernel	Radial basis function kernel
▼NLinearRegression	Linear regression algorithms
CLassoRegressionResult	Result of a multivariate Lasso regression with intercept.
CMultivariateOLSResult	Result of multivariate Ordinary Least Squares regression.
CRecursiveMultivariateOLS	Recursive multivariate Ordinary Least Squares
CRegularisedRegressionResult	Result of a multivariate regularised regression with intercept
CResult	Result of linear regression
CRidgeRegressionResult	Result of a multivariate ridge regression with intercept.
CUnivariateOLSResult	Result of 1D Ordinary Least Squares regression (with or without intercept)
CAbstractLogisticRegression	Abstract implementation, sharing the common parameters and stopping criterion
CBallTree	Ball tree: an efficient tree structure for nearest-neighbour search in R^D space
CConjugateGradientLogisticRegression	Conjugate gradient logistic regression, as described in Sec. 4 of Thomas P. Minka, "A comparison of numerical optimizers for logistic regression"
CDecisionTree	Decision tree
CEM	Gaussian Expectation-Maximisation algorithm
▼CLogisticRegression	Binomial logistic regression algorithm
CResult	Result of binomial logistic regression