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https://issues.apache.org/jira/browse/MATH-312?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Phil Steitz reopened MATH-312:
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Still TODO:
1) add unit tests
2) remove *mapTo and *mapToSelf from OpenMapRealVector
3) fix javadocs
4) remove support for non-zero default values in OpenMapRealVector
> RealVector interface could use some iterators (dense and sparse) and generic
> map() and collect() methods.
> ---------------------------------------------------------------------------------------------------------
>
> Key: MATH-312
> URL: https://issues.apache.org/jira/browse/MATH-312
> Project: Commons Math
> Issue Type: New Feature
> Affects Versions: 2.0
> Environment: all
> Reporter: Jake Mannix
> Fix For: 2.1
>
> Attachments: MATH-312.patch, MATH-312.patch
>
>
> As discussed on the [math] list, there are other projects out there which
> would love to get a chance to standardize on using commons-math for things
> like linear algebra primitives, as it would build a common base to build
> upon. But to do that, some well-known and used techniques for dealing with
> vectors, for one thing, are missing. Most glaringly is the treatment of
> sparse vectors: giving no Iterator for non-default values means external
> clients lose the advantage of sparseness - only internal methods can skip
> around.
> Extending the RealVector interface with sparse (and dense) iterator methods
> would fix this:
> {code}
> double getDefaultValue();
> Iterator<RealVector.Entry> iterator();
> Iterator<RealVector.Entry> nonDefaultIterator();
> {code}
> but there is another way to deal with vector data as well: instead of passing
> iterators around, and worrying about all the lovely ConcurrentModification
> and unsupported "remove" methods (which aren't the end of the world), we can
> instead expose generic map functions:
> {code}
> RealVector map(UnivariateRealFunction f);
> RealVector mapToSelf(UnivariateRealFunction f);
> {code}
> where RealVector mapToSelf(UnivariateRealFunction), which applies the
> function to the vector's entries (checking whether the function preserves the
> default value up front allows it to chose between the sparse or dense
> iterator), and map just applies mapToSelf to a copy.
> This doesn't exhaust all possible places where Iterators could be used
> helpfully (there's also combining two vectors together via a
> {code}map(BinaryRealFunction, RealVector other){code} which could be
> specialized nonlinear forms of addition or subtraction, and {code}double
> collect(UnivariateRealFunction, BinaryRealFunction){code} which uses the
> iterates over all of the entries, applying the first unary function to each
> entry, and then applying the binary function to combine this value with the
> previous accumulated value - with "pow(2)", and "+" as the two functions, you
> get L2 norm, with "abs()" and "+", you get L1 norm, etc...)
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