Colt 1.2.0

Uses of Interface
cern.colt.buffer.DoubleBufferConsumer

Packages that use DoubleBufferConsumer
cern.colt.buffer Fixed sized (non resizable) streaming buffers connected to a target objects to which data is automatically flushed upon buffer overflow. 
cern.colt.list Resizable lists holding objects or primitive data types such as int, double, etc. 
hep.aida.bin Multisets (bags) with efficient statistics operations defined upon; This package requires the Colt distribution. 
 

Uses of DoubleBufferConsumer in cern.colt.buffer
 

Classes in cern.colt.buffer that implement DoubleBufferConsumer
 class DoubleBuffer
          Fixed sized (non resizable) streaming buffer connected to a target DoubleBufferConsumer to which data is automatically flushed upon buffer overflow.
 

Constructors in cern.colt.buffer with parameters of type DoubleBufferConsumer
DoubleBuffer(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 DoubleBufferConsumer
 class AbstractDoubleList
          Abstract base class for resizable lists holding double elements; abstract.
 class DoubleArrayList
          Resizable list holding double elements; implemented with arrays.
 

Uses of DoubleBufferConsumer in hep.aida.bin
 

Classes in hep.aida.bin that implement DoubleBufferConsumer
 class AbstractBin1D
          Abstract base class for all 1-dimensional bins consumes double elements.
 class DynamicBin1D
          1-dimensional rebinnable bin holding double elements; Efficiently computes advanced statistics of data sequences.
 class 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.
 class 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.
 class StaticBin1D
          1-dimensional non-rebinnable bin consuming double elements; Efficiently computes basic statistics of data sequences.
 


Colt 1.2.0

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