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This is why the big rational data type is so useful on occasion. When you just want to get one calculation exactly right, and performance doesn't matter so muc
by CydeWeys 6y ago
This is why the big rational data type is so useful on occasion. When you just want to get one calculation exactly right, and performance doesn't matter so much because you aren't doing huge numbers of repeated operations (as in ML), use a big rational with its arbitrary precision numerator and denominator and you don't need to worry about even epsilon error.
- bonoboTP 6y agoExcept if you run exp, log, sqrt, sin, cos etc. Then you immediately leave the rationals.
- CydeWeys 6y agoYes, it definitely depends on what you're doing with the numbers, but personally I don't recall ever having needed trigonometric functions in my decade plus working life doing standard line of business type stuff.
- marketingPro 6y agoI can't tell if you are dismissing this, but as an FYI trig is used in Engineering.
- CydeWeys 6y agoI said "it definitely depends on what you're doing with the numbers". That's not a dismissal; I'm simply pointing out that neither I nor many if not most other software engineers use trig on a frequent basis. I'm well aware that it's used in actual engineering, but that's just not what most of us here do.
- visarga 6y ago> because you aren't doing huge numbers of repeated operations (as in ML) In ML precision doesn't count so much. It's often possible that we train on 32bits and deploy the model on 16bits. In fact stochasticity is useful and we add it in by dropout or other things. You can drop any connection (or millions of them) in a neural net and get almost the same result. There have been papers that showed that reducing the number of bits used to represent weights might even improve the network because it has a regularising effect. The real brain is also stochastic.