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The trick is that by default rounding happens using banker's rounding. Programming languages use this because this is what CPUs use. When you want to round your way, you need an extra digit and round manually:

    def tax_f4(amt, rate):
      tax = round(amt * rate * 1000)
      return tax // 10 + (tax % 10 > 4)





That works for 10% of $21.15, giving the desired 212.

However, for 10.14% of $21.15, it gives 215, but it should be 214. Another example is 3.5% of $60.70, for which it gives 213 but correct is 212.


You're right. My remainder calculation in my code snippet is incorrect. It should've been a floating point remainder instead.

    import math
    
    def tax_f5(amt, rate):
        t = amt * rate * 1000
        return round(t) // 10 + ((math.fmod(t, 10.0) - 5.0) > -1e-7)
But then since there's now an epsilon, it raises the question of how many digits of precision the tax rates typically need. This is indeed a difficult problem.

Some exhaustive testing on all amounts from $0.01 through $999.99 in $0.01 increments and all taxes from 0.01% through 99.99% in increments of 0.01% show that this is the minimum that does the trick (switching to C from Python for speed):

  unsigned long tax = (unsigned long)round(amt * rate * 1000000);
  return tax/(10000) + (fmod(tax, (double)(10000)) - (double)(5000) > -1e-5 ? 1 : 0);
(Yes, I see that I goofed in translation your code to C and typed -1e-5 instead of -1e-7. It looks like the results are the same with -1e-7).

I also tested that up through $9999.99 with taxes up to 12%, and no problems.

Adding another 0 to the 1000000, the two 10000's, and the 5000 works. And another, and another. Past that it starts to fail, but not the simple off-by-one failures you get when you don't use enough digits. These are way way off, so I'm guessing its running into some new class of problem. I haven't looked to see what that is yet.




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