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The DataBag type currently offers only an abstraction for parallel collections (DataBag[A]). While this allows expression of basic dataflows, certain classes of algorithms (for example, machine learning) might benefit from a direct linear algebra abstraction.
As a first part of this effort, I suggest to define two core API traits:
Matrix[A : Numeric],
Vector[A : Numeric],
with empty methods (def op(...): T = ???).
We can use the approach suggested by @joroKr21 in #50 and implement the operators as macros.
The
DataBag
type currently offers only an abstraction for parallel collections (DataBag[A]
). While this allows expression of basic dataflows, certain classes of algorithms (for example, machine learning) might benefit from a direct linear algebra abstraction.As a first part of this effort, I suggest to define two core API traits:
Matrix[A : Numeric]
,Vector[A : Numeric]
,with empty methods (
def op(...): T = ???
).We can use the approach suggested by @joroKr21 in #50 and implement the operators as macros.
@fschueler, @FelixNeutatz: do you want to take a stab on this with me next week?
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