Then most people shouldn’t be writing code, I don’t know what else to tell you, this is probably one of the first thing you learn about FP arithmetic, and any decent compiler/linter should warn you about that.
Not sure, what blog post you’re talking about, but there’s only really three things you can be doing wrong:
Tons of cloning.
Running your application from a debug build rather than release build.
All the usual things one can be doing wrong in any programming language. Like, I imagine real-world raytracing is done on the GPU and uses highly optimized algorithms. I doubt a blog post would dive into those depths. And well, any kind of graphics programming is extremely slow, if you don’t issue the exact right incantations that the GPU manufacturer optimized for.
Meh. Imo anyone comparing an integer to a float and not expecting one of them to be implicitly casted to the other’s type will create that issue for themselves when doing the same thing with an explicit cast.
But how far should that be taken should 8 == 8 return false because one is an unsigned int and the other is signed? Or 0.0 == 0.0 where they are floats and doubles? You can make a case for those due to a strong type system but it would go against most peoples idea of what equality is.
If bits aren’t same then i dont want it to tell me they are the same. And python just has one implementation for int and float.
I like python cos everything’s an object i dont want different types of objects to evaluate the same they are fundamentally different objects is that not what u would expect?
Even in python you can have control of what types of numbers are used under the hood with numpy arrays (and chances are if you are using floats in any quantity you want to be using numpy). I would be very surprised if array([1,2,3], dtype=uint8) == array([1,2,3], dtype=int16) gave [False, False, False]. In general I think == for numbers should give mathematical equivalence, with the understanding that comparing floats is highly likely to give false negatives unless you are extremely careful with what you are comparing.
More Fortran than C, but its the same for any language doing those sorts of array mathematics, they will be calling compiled versions of blas and lapack. Numpy builds up low level highly optimised compiled functions into a coherant python ecosystem. A numpy array is a C array with some metadata sure, but a python list is also just a C array of pointers to pyobjects.
Do we have a js type situation here
Probably more like the old precision problem. It ecists in C/C++ too and it’s just how fliats and ints work.
I dont think comparisons should be doing type conversion if i compare a float to an int i want it to say false cos types are different.
I don’t think that’s how most programmers expect it to work at all.
However most people would also expect 0.1+0.2==0.3 to return true, so what do I know.
Floating point is something most of us ignore until it bites us in the ass. And then we never trust it again.
Then most people shouldn’t be writing code, I don’t know what else to tell you, this is probably one of the first thing you learn about FP arithmetic, and any decent compiler/linter should warn you about that.
Thats why i recon its good to keep u aware of it. Mind u i find its often fine as long as my ide and chagpt know what type it is im usually fine.
I do kinda like the rigidity of types tho. Proper Python type hints are a godsend.
Way too late for that. Every language I know makes some kind of auto conversion for numeric comparisons… and sometimes for strings as well.
I know of Rust, which is pedantic enough to not allow comparing integers to floats directly.
In certain situations, it even disallows making assumptions about equality and ordering between floats.
I still cant properly manage my head around the rust object borrowing. My ray tracer implementation from that blog on ray tracing was slow as shiiiit.
Not sure, what blog post you’re talking about, but there’s only really three things you can be doing wrong:
That makes sense, but then you’d just have people converting the int to a float manually and run into the exact same issues.
I mean honestly its expected u should check floats similarity not equivalence.
Agreed. But the less experienced programmers I know are surprisingly naive about this.
Yeah its gonna be one of those problems chatgpt ain’t gonna help
They wouldn’t be running into an issue, but creating one, that’s different
Meh. Imo anyone comparing an integer to a float and not expecting one of them to be implicitly casted to the other’s type will create that issue for themselves when doing the same thing with an explicit cast.
What I meant is, the former can be a genuine mistake, the latter is a conscious (probably uneducated) decision
But how far should that be taken should 8 == 8 return false because one is an unsigned int and the other is signed? Or 0.0 == 0.0 where they are floats and doubles? You can make a case for those due to a strong type system but it would go against most peoples idea of what equality is.
If bits aren’t same then i dont want it to tell me they are the same. And python just has one implementation for int and float.
I like python cos everything’s an object i dont want different types of objects to evaluate the same they are fundamentally different objects is that not what u would expect?
Even in python you can have control of what types of numbers are used under the hood with numpy arrays (and chances are if you are using floats in any quantity you want to be using numpy). I would be very surprised if
array([1,2,3], dtype=uint8) == array([1,2,3], dtype=int16)gave[False, False, False]. In general I think==for numbers should give mathematical equivalence, with the understanding that comparing floats is highly likely to give false negatives unless you are extremely careful with what you are comparing.Numpys more or less a math wrapper for c isnt it?
More Fortran than C, but its the same for any language doing those sorts of array mathematics, they will be calling compiled versions of blas and lapack. Numpy builds up low level highly optimised compiled functions into a coherant python ecosystem. A numpy array is a C array with some metadata sure, but a python list is also just a C array of pointers to pyobjects.
Idiots downvoting you (c/technology…) but this how e.g. Haskell and rust handle that, and probably most strongly typed languages