4 ms·

Quote: "Operations complete in under a millisecond, not GFLOPS performance, but certainly good enough for many applications". Then you have a scientist reading

• TeMPOraL 6y ago
  If these 3 lines of Python give you correct results... I keep wondering and never have this answered to my satisfaction: if so many people are just using floating point numbers in scientific calculations, and arguably even most scientists don't understand FP math any way better than the users of Android's Calculator app, then how many bad results are out there, that could be attributed to FP errors? Related: I don't understand why the default in scientific computing isn't some kind of scheme with error band propagation. What I mean is, when dealing with measurements, the result of 2.1 * 2.12345 isn't 4.459245, because the result exceeds the precision of the first factor. I'd like a common API that handles this, whether implicitly (through notation) or explicitly (through requiring you to state the precision of every number you use).
  • swiley 6y ago
    It seems strange that they don't use a rational type, those are much easier for "normal" people to reason about. What you want would maybe be better as probability distributions (maybe parameterized with rational numbers)
    • TeMPOraL 6y ago
      Probability distributions are a related concepts, and I'd love to work with them as first-class objects. But just carrying the confidence intervals around would be a huge win; even ignoring the underlying distribution, it matters whether your result is 1.23 ± 0.0001, or 1.23 ± 20.