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https://issues.apache.org/jira/browse/MAHOUT-6?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12571295#action_12571295
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Ted Dunning commented on MAHOUT-6:
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Here is my set of methods for Matrix1D. These are nearly identical to the
functions used by Colt with the addition of some convenience methods.
double get(int index);
void set(int index, double value);
Matrix1D plus(double x);
Matrix1D plus(Matrix1D x);
Matrix1D minus(Matrix1D x);
Matrix1D times(double x);
// should this be element-wise or dot ... I think element-wise
Matrix1D times(Matrix1D x);
double dot(Matrix1D x);
double zSum();
Matrix1D viewPart(int offset, int length);
Matrix1D copy();
double getQuick(int index);
void setQuick(int index, double value);
void getNonZeros(IntArrayList jx, DoubleArrayList values);
void foreachNonZero(IntDoubleFunction f);
int size();
int cardinality();
double aggregate(DoubleDoubleFunction aggregator, DoubleFunction map);
double aggregate(Matrix1D other, DoubleDoubleFunction aggregator,
DoubleDoubleFunction map);
Matrix1D assign(double[] values);
Matrix1D assign(double value);
Matrix1D assign(DoubleFunction function);
Matrix1D assign(Matrix1D other);
Matrix1D assign(Matrix1D y, DoubleDoubleFunction function);
NewMatrix1D assign(Matrix1D y, DoubleDoubleFunction function, IntArrayList
nonZeroIndexes);
boolean haveSharedCells(Matrix1D other);
Matrix1D like();
Matrix1D like(int n);
double[] toArray();
> Need a matrix implementation
> ----------------------------
>
> Key: MAHOUT-6
> URL: https://issues.apache.org/jira/browse/MAHOUT-6
> Project: Mahout
> Issue Type: New Feature
> Reporter: Ted Dunning
> Attachments: MAHOUT-6a.diff
>
>
> We need matrices for Mahout.
> An initial set of basic requirements includes:
> a) sparse and dense support are required
> b) row and column labels are important
> c) serialization for hadoop use is required
> d) reasonable floating point performance is required, but awesome FP is not
> e) the API should be simple enough to understand
> f) it should be easy to carve out sub-matrices for sending to different
> reducers
> g) a reasonable set of matrix operations should be supported, these should
> eventually include:
> simple matrix-matrix and matrix-vector and matrix-scalar linear algebra
> operations, A B, A + B, A v, A + x, v + x, u + v, dot(u, v)
> row and column sums
> generalized level 2 and 3 BLAS primitives, alpha A B + beta C and A u +
> beta v
> h) easy and efficient iteration constructs, especially for sparse matrices
> i) easy to extend with new implementations
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