User-extendable Abstract RealMatrix implementation
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Key: MATH-231
URL: https://issues.apache.org/jira/browse/MATH-231
Project: Commons Math
Issue Type: New Feature
Affects Versions: 2.1
Environment: Any
Reporter: Sujit Pal
Priority: Minor
Many algorithms are described using Matrices. In some situations, the Matrices
for applications are too large to fit into memory, so it makes sense to use
custom implementations that are backed by a sparse map or list, or backed by
Lucene indexes or database tables, etc. This allows the algorithm
implementations (in the application) to be clean and mimic the algorithm
itself, making it easy to understand and debug.
However, the only way to make a custom implementation currently is to implement
RealMatrix, and to implement all its methods, or to extend and override methods
in RealMatrixImpl. The former involves a knowledge of matrix operations, which
is not guaranteed for people who are business programmers (which may be the
reason they are using commons-math in the first place). The latter involves
looking at the existing code and making the overrides where needed, again
something that is not expected from a commons-math user. A simpler way would be
to have something generic mostly built, except the getEntry() and setEntry()
methods which would be declared abstract and hence meant to be overriden, and
all the other methods to be pre-built based on getEntry() and setEntry().
This would make the creation of custom matrices very flexible, and would also
remove the burden of providing "common matrix impls" from the commons-math
project, since clients can very simply create custom versions of their own.
I suggest the following:
<<RealMatrix.java>> <-- RealMatrixImpl
<-- (AbstractRealMatrixImpl)
The SparseRealMatrixImpl I contributed in MATH-230 could be a user-defined
custom subclass of AbstractRealMatrixImpl and not have to belong to the
commons-math project at all. Or it could be included as an example if there is
enough need.
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