Add NdArray Mean function#15
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Jun 6, 2026
…cs (Phase 1 F3a) NumSharp promoted every integer input to float64 for the float-producing unary ufuncs (sqrt/cbrt/exp/log/trig/...), regardless of input width. NumPy NEP50 uses WIDTH-BASED promotion: bool/int8/uint8 -> float16, int16/uint16 -> float32, int32/uint32/int64/uint64 -> float64 (float/complex preserved). FUZZ_FINDINGS #7. Fix: new DefaultEngine.ResolveUnaryFloatReturnType implements the width-based rule; the 20 transcendental Default.<Op>.cs files (ACos/ASin/ATan/Cbrt/Cos/Cosh/Deg2Rad/ Exp/Exp2/Expm1/Log/Log10/Log1p/Log2/Rad2Deg/Sin/Sinh/Sqrt/Tan/Tanh) now call it instead of ResolveUnaryReturnType (which widened to float64 via GetComputingType). The int->Half / int->Single unary kernels already existed, so this is a pure result-dtype routing change; int32/int64 are unaffected (already float64). The dtype-PRESERVING ufuncs (square/floor/ceil/trunc/round/reciprocal) are intentionally left on the old resolver pending F3b (they need integer identity / x*x / int-reciprocal kernels to preserve the integer dtype). Verification: * 364 of 494 unary dtype divergences clear bit-exact; the remaining 130 are the F3b-pending preserve-dtype ops, now scoped in MisalignedRegistry so a transcendental promotion regression fails the gate (not silently excused). * Half/Single transcendental values stay within 2 ULP of NumPy's float16/float32 libm (documented algorithm difference, same class as complex-divide ULP). * DtypeCoverageTests.Sqrt_IntegerDtypes updated: it asserted the old uniform float64 (codified the bug); now asserts the NumPy width-based dtype per input. * Full net10.0 suite green: 9422 passed / 0 failed; FuzzMatrix 17/17. Clears the transcendental half of FUZZ_FINDINGS #7 (and #15 dtype for those ops).
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