3 ms·

It is not about "cannot be done". Most high-level languages are Turing complete, hence every program in one can be written as some program in another. The real

by sn41 6y ago

It is not about "cannot be done". Most high-level languages are Turing complete, hence every program in one can be written as some program in another. The real clincher is expressiveness: shorter code is better, modular code is better, and languages which avoid unnecessary repetitiveness of boiler plate code is better. Different languages provide advantages along one or more of these parameters.

shawnz 6y ago
So can you give an example of something that can be expressed simply in APL/J/K that can't be expressed simply with numpy? And to be clear I don't think "less characters" makes the expression more simple. Maybe "less statements" or "less operators"
- smabie 6y ago
  Here's Conway's Game of Life by Arthur Whitney (creator of K): life:{3=a-x*4=a:2{+(0+':x)+1_x,0}/x} I'm guessing a numpy implementation would be between one and two orders of magnitude more verbose, even if you're just comparing the number of operations.
  - worc3131 6y ago
    It doesn't quite seem fair to compare numpy to one of the most famous K code golf's ever created, but here is my attempt, 140 characters compared to Arthur's 30, so 5 times longer. Not worry about padding the edges would cut it to 100. I don't think the gap is as wide as you think it is. def life(M): MP = np.pad(M,[(1,1),(1,1)]) N = sum(np.roll(MP,(i,j),(0,1)) for i in [1,0,-1] for j in [1,0,-1]) return (3==N-MP*(4==N))[1:-1,1:-1] Speedtest (presumably memory bound): 100 x 100 matrix, numpy 0.2ms, K 0.2ms 200 x 200 matrix, numpy 0.5ms, K 0.8ms 500 x 500 matrix, numpy 5.2ms, K 8.0ms 1000x1000 matrix, numpy 20.0ms, K 36.0ms 5000x5000 matrix, numpy 0.5s , K 1.2s Interestingly the K code is designed to return a boolean matrix, but operates much more slowly for me on a boolean matrix compared to an integer matrix, with the result that: q)\t klife M Takes 1.2 seconds whilst q)\t klife klife M Takes 24 seconds. So whilst the idea seems to be that klife is used with scan/over to run a number of iterations, it's actually a bad idea to run it more than once.

by sn41 6y ago

shawnz 6y ago

So can you give an example of something that can be expressed simply in APL/J/K that can't be expressed simply with numpy? And to be clear I don't think "less characters" makes the expression more simple. Maybe "less statements" or "less operators"

smabie 6y ago
Here's Conway's Game of Life by Arthur Whitney (creator of K): life:{3=a-x*4=a:2{+(0+':x)+1_x,0}/x} I'm guessing a numpy implementation would be between one and two orders of magnitude more verbose, even if you're just comparing the number of operations.
- worc3131 6y ago
  It doesn't quite seem fair to compare numpy to one of the most famous K code golf's ever created, but here is my attempt, 140 characters compared to Arthur's 30, so 5 times longer. Not worry about padding the edges would cut it to 100. I don't think the gap is as wide as you think it is. def life(M): MP = np.pad(M,[(1,1),(1,1)]) N = sum(np.roll(MP,(i,j),(0,1)) for i in [1,0,-1] for j in [1,0,-1]) return (3==N-MP*(4==N))[1:-1,1:-1] Speedtest (presumably memory bound): 100 x 100 matrix, numpy 0.2ms, K 0.2ms 200 x 200 matrix, numpy 0.5ms, K 0.8ms 500 x 500 matrix, numpy 5.2ms, K 8.0ms 1000x1000 matrix, numpy 20.0ms, K 36.0ms 5000x5000 matrix, numpy 0.5s , K 1.2s Interestingly the K code is designed to return a boolean matrix, but operates much more slowly for me on a boolean matrix compared to an integer matrix, with the result that: q)\t klife M Takes 1.2 seconds whilst q)\t klife klife M Takes 24 seconds. So whilst the idea seems to be that klife is used with scan/over to run a number of iterations, it's actually a bad idea to run it more than once.