Package adams.data.binning.algorithm

Interface Summary

Interface	Description
BinningAlgorithm	Interface for binning algorithms.
BinningAlgorithmUser	Interface for classes that use a binning algorithm.
FixedBinWidthBinningAlgorithm	Interface for binning algorithms that require a fixed bin width.
FixedNumBinsBinningAlgorithm	Interface for binning algorithms that require to specify the number of bins.

Class Summary

Class	Description
AbstractBinningAlgorithm	Ancestor for binning algorithms.
AbstractEqualWidthBinningAlgorithm	Ancestor for algorithms that use bins with the same width.
DensityBinning	Performs density-based binning.
FreedmanDiaconisChoiceBinning	The Freedman-Diaconis' choice is based on the interquartile range.
FrequencyBinning	Performs frequency binning.
ManualBinning	Generates a predefined number of equal sized bins.
NoBinning	Performs no real binning, just places all items in one bin.
RiceRuleBinning	The Rice Rule is presented as a simple alternative to Sturges's rule.
ScottsNormalReferenceRuleBinning	Scott's normal reference rule is optimal for random samples of normally distributed data, in the sense that it minimizes the integrated mean squared error of the density estimate.
SquareRootChoiceBinning	Takes the square root of the number of data points in the sample used by Excel histograms and many others) and rounds to the next integer.
SturgesFormulaBinning	Sturges' formula is derived from a binomial distribution and implicitly assumes an approximately normal distribution.