7 ms·

> what if we just generate all products from the set of numbers 2:n and exclude those as "not prime" from all the numbers up to n? It's fun to translate terse

> what if we just generate all products from the set of numbers 2:n and exclude those as "not prime" from all the numbers up to n? It's fun to translate terse APL to somewhat terse numpy. The result still can be very compact and you can parse it easily if you're used to looking at numpy: s = arange(2, 50); p = outer(s, s).ravel(); sorted(set(s) - set(p))

jonocarroll 3y ago
What's interesting there is that numpy is inspired (more than a little) by APL and aims to bring that 'array' thinking to python. I agree that thinking in this 'array' way helps to better construct a solution in any language, so I'm leaning towards 'designing' with APL glyphs, even if that's not the language I'm implementing the thing in.
- nerdponx 3y ago
  If it takes any inspiration from APL, it would be mostly indirect, via Matlab.
  - jonocarroll 3y ago
    I've seen the connection made here https://dev.to/bakerjd99/numpy-another-iverson-ghost-9mc https://dev.to/bakerjd99/numpy-another-iverson-ghost-9mc though the link to the source quote is broken. In all fairness, Matlab is itself inspired by APL.
bruturis 3y ago
Analogous code in J, /:~ s -. p [ p =: s*/s [ s=: 2+i.48 An exercise for numpy, test that GCD(x,y) * LCM(x,y) = x*y using 1000 random numbers in the range 0..99 for x e y. test =: (* = *. * +.) & ? *./ test~ 1000 # 100
- cl3misch 3y ago
  Thanks, that was fun. def d(x): N = arange(1, x + 1); return N[x % N == 0] def m(x, n): return x * arange(1, n + 1) def gcd(x, y): return max(set(d(x)) & set(d(y))) def lcm(x, y): return min(set(m(x, y)) & set(m(y, x))) def test(x, y): return gcd(x, y) * lcm(x, y) == x * y all([test(x, y) for (x, y) in randint(1, 100, (1000, 2))]) # True I am not a good golfer. Now I want to look at the codegolf stackexchange for this...
  - deleted 3y ago
    [deleted]
- bear8642 3y ago
  Equivalently in APL: ⎕io←0 ⍝ to match J test ← (× = ∨ × ∧) ∘ ? ∧⌿ test⍨ 1000⍴100

jonocarroll 3y ago

What's interesting there is that numpy is inspired (more than a little) by APL and aims to bring that 'array' thinking to python. I agree that thinking in this 'array' way helps to better construct a solution in any language, so I'm leaning towards 'designing' with APL glyphs, even if that's not the language I'm implementing the thing in.

nerdponx 3y ago
If it takes any inspiration from APL, it would be mostly indirect, via Matlab.
- jonocarroll 3y ago
  I've seen the connection made here https://dev.to/bakerjd99/numpy-another-iverson-ghost-9mc https://dev.to/bakerjd99/numpy-another-iverson-ghost-9mc though the link to the source quote is broken. In all fairness, Matlab is itself inspired by APL.

bruturis 3y ago

Analogous code in J, /:~ s -. p [ p =: s*/s [ s=: 2+i.48 An exercise for numpy, test that GCD(x,y) * LCM(x,y) = x*y using 1000 random numbers in the range 0..99 for x e y. test =: (* = *. * +.) & ? *./ test~ 1000 # 100

cl3misch 3y ago
Thanks, that was fun. def d(x): N = arange(1, x + 1); return N[x % N == 0] def m(x, n): return x * arange(1, n + 1) def gcd(x, y): return max(set(d(x)) & set(d(y))) def lcm(x, y): return min(set(m(x, y)) & set(m(y, x))) def test(x, y): return gcd(x, y) * lcm(x, y) == x * y all([test(x, y) for (x, y) in randint(1, 100, (1000, 2))]) # True I am not a good golfer. Now I want to look at the codegolf stackexchange for this...
- deleted 3y ago
  [deleted]
bear8642 3y ago
Equivalently in APL: ⎕io←0 ⍝ to match J test ← (× = ∨ × ∧) ∘ ? ∧⌿ test⍨ 1000⍴100