Friday, 6 September 2013

F# for Numerics

A version of our F# for Numerics library optimized for .NET 4.x has been released. New features include:

  • Linear least-squares regression (curve fitting).
  • 2D convex hulls.
  • Delaunay triangulation.
  • k-means algorithm for machine learning with example data.
  • Longest common subsequence and Levenshtein edit distance.
  • Root finding.
  • Parallel aggregates: mapReduce, reduce, minBy, maxBy, tryPick, tryFindIndex, exists and forall.

Existing customers can download the latest .NET assembly and an F# script providing extensive worked examples for free.

As always, the new features are designed for ease of use and integrate seamlessly with our F# for Visualization library. Convex hull:

Delaunay triangulation:
 Linear least squares best fit of a quadratic through a sine wave:


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