Uses of Interface
cern.colt.buffer.DoubleBufferConsumer
Packages that use DoubleBufferConsumer
Package
Description
Fixed sized (non resizable) streaming buffers connected to a target objects to which data is automatically flushed upon buffer overflow.
Resizable lists holding objects or primitive data types such as int,
double, etc.
Multisets (bags) with efficient statistics operations defined upon; This package
requires the Colt distribution.
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Uses of DoubleBufferConsumer in cern.colt.buffer
Classes in cern.colt.buffer that implement DoubleBufferConsumerModifier and TypeClassDescriptionclass
Fixed sized (non resizable) streaming buffer connected to a target DoubleBufferConsumer to which data is automatically flushed upon buffer overflow.Fields in cern.colt.buffer declared as DoubleBufferConsumerConstructors in cern.colt.buffer with parameters of type DoubleBufferConsumerModifierConstructorDescriptionDoubleBuffer
(DoubleBufferConsumer target, int capacity) Constructs and returns a new buffer with the given target. -
Uses of DoubleBufferConsumer in cern.colt.list
Classes in cern.colt.list that implement DoubleBufferConsumerModifier and TypeClassDescriptionclass
Abstract base class for resizable lists holdingdouble
elements; abstract.class
Resizable list holdingdouble
elements; implemented with arrays. -
Uses of DoubleBufferConsumer in hep.aida.bin
Classes in hep.aida.bin that implement DoubleBufferConsumerModifier and TypeClassDescriptionclass
Abstract base class for all 1-dimensional bins consumes double elements.class
1-dimensional rebinnable bin holding double elements; Efficiently computes advanced statistics of data sequences.class
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.class
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.class
1-dimensional non-rebinnable bin consuming double elements; Efficiently computes basic statistics of data sequences.