Uses of Package
hep.aida.bin

Packages that use hep.aida.bin

cern.colt.matrix.doublealgo	Double matrix algorithms such as print formatting, sorting, partitioning and statistics.
hep.aida.bin	Multisets (bags) with efficient statistics operations defined upon; This package requires the Colt distribution.

Classes in hep.aida.bin used by cern.colt.matrix.doublealgo

BinFunction1D
Interface that represents a function object: a function that takes two bins as arguments and returns a single value.

DynamicBin1D
1-dimensional rebinnable bin holding double elements; Efficiently computes advanced statistics of data sequences.

Classes in hep.aida.bin used by hep.aida.bin

AbstractBin
Abstract base class for all arbitrary-dimensional bins consumes double elements.

AbstractBin1D
Abstract base class for all 1-dimensional bins consumes double elements.

BinBinFunction1D
Interface that represents a function object: a function that takes two bins as arguments and returns a single value.

BinFunction1D
Interface that represents a function object: a function that takes two bins as arguments and returns a single value.

BinFunctions1D
Function objects computing dynamic bin aggregations; to be passed to generic methods.

DynamicBin1D
1-dimensional rebinnable bin holding double elements; Efficiently computes advanced statistics of data sequences.

MightyStaticBin1D
Static and the same as its superclass, except that it can do more: Additionally computes moments of arbitrary integer order, harmonic mean, geometric mean, etc.

QuantileBin1D
1-dimensional non-rebinnable bin holding double elements with scalable quantile operations defined upon; Using little main memory, quickly computes approximate quantiles over very large data sequences with and even without a-priori knowledge of the number of elements to be filled; Conceptually a strongly lossily compressed multiset (or bag); Guarantees to respect the worst case approximation error specified upon instance construction.

StaticBin1D
1-dimensional non-rebinnable bin consuming double elements; Efficiently computes basic statistics of data sequences.