True, it's inefficient, but it's good for profiling Python interpreters, since it does a lot of work.
It's also fast enough for low n (up to 8). Why write more code when you can get your result with less? Maybe the time he's saved with this approach can now be used writing a deduplicator (to cull out mirrored and rotated solutions).
Maybe the point of the article was not only to show how fast PyPy is, but how convenient itertools.permutation() is. You can't show the convenience of itertools by not using it.
However... You know what? When I was asked to write a n-queens solver recently, I also wrote a backtracker. It just doesn't mean I did it better than this guy.
I know the point of this article is not to give the fastest n-queens algorithm. But what the heck; here is an iterative backtracking solution. On my machine, this code finds all solutions for the 10x10 board in about 1.2 sec, while the code from the article takes just under 18 sec. Using CPy 2.6.5, just like the article. Have not tried PyPy. Output is essentially the same format, except that my generator spits out lists instead of tuples.
Yes, it's twice as long and harder to read. Improvements, anyone?
def hit_last(p):
x = p[-1]
i = len(p)-1
for j in range(i):
y = p[j]
if x == y or i - j == abs(x - y):
return True
return False
def n_queen(n):
p = [0]
while True: # Begin iter w/ board good, except maybe last Q
full = len(p) == n
hit = hit_last(p)
if full or hit: # Will we backtrack?
if not hit: # Found solution?
yield p
while len(p) > 0 and p[-1] == n-1:
p.pop()
if len(p) == 0:
return
p[-1] += 1
else:
p.append(0)
Don't worry, PyPy beasts this as well, at ``n_queens(12)`` on my machine:
alex@alex-gaynor-laptop:/tmp$ time python queens.py
real 0m16.837s
user 0m16.820s
sys 0m0.000s
alex@alex-gaynor-laptop:/tmp$ time pypy queens.py
real 0m2.548s
user 0m2.530s
sys 0m0.010s
It's also fast enough for low n (up to 8). Why write more code when you can get your result with less? Maybe the time he's saved with this approach can now be used writing a deduplicator (to cull out mirrored and rotated solutions).
Maybe the point of the article was not only to show how fast PyPy is, but how convenient itertools.permutation() is. You can't show the convenience of itertools by not using it.
However... You know what? When I was asked to write a n-queens solver recently, I also wrote a backtracker. It just doesn't mean I did it better than this guy.