SciSharp

NumSharp's differential-fuzz pipeline — the NumPy 2.4.2 "oracle" that proves every NDIter-backed op is BIT-IDENTICAL to NumPy across the input space. Use this whenever you add or change an np.* op and need fuzz coverage, regenerate the committed corpus, wire…

NumSharp's NumPy-vs-NumSharp performance harness — the op/dtype/N matrix (C# BenchmarkDotNet vs a warm NumPy process) plus five appended subsystems (nditer, layout, operand, cast, fusion), all in the NPY/NS convention. Use this whenever you add a benchmark…