Generate Lexicographic Series Efficiently In Python

I want to generate a lexicographic series of numbers such that for each number the sum of digits is a given constant. It is somewhat similar to 'subset sum problem'. For example if

Solution 1:

Very effective algorithm adapted from Jorg Arndt book "Matters Computational"
(Chapter 7.2 Co-lexicographic order for compositions into exactly k parts)

n = 4
k = 3

x = [0] * n
x[0] = k

while True:
    print(x)
    v = x[-1]
    if (k==v ):
        break
    x[-1] = 0
    j = -2
    while (0==x[j]):
        j -= 1
    x[j] -= 1
    x[j+1] = 1 + v

[3, 0, 0, 0]
[2, 1, 0, 0]
[2, 0, 1, 0]
[2, 0, 0, 1]
[1, 2, 0, 0]
[1, 1, 1, 0]
[1, 1, 0, 1]
[1, 0, 2, 0]
[1, 0, 1, 1]
[1, 0, 0, 2]
[0, 3, 0, 0]
[0, 2, 1, 0]
[0, 2, 0, 1]
[0, 1, 2, 0]
[0, 1, 1, 1]
[0, 1, 0, 2]
[0, 0, 3, 0]
[0, 0, 2, 1]
[0, 0, 1, 2]
[0, 0, 0, 3]

Number of compositions and time on seconds for plain Python (perhaps numpy arrays are faster) for n=100, and k = 2,3,4,5 (2.8 ghz Cel-1840)

2  5050 0.040000200271606445
3  171700 0.9900014400482178
4  4421275 20.02204465866089
5  91962520 372.03577995300293
I expect time  2 hours for 100/6 generation

Same with numpy arrays (x = np.zeros((n,), dtype=int)) gives worse results - but perhaps because I don't know how to use them properly

2  5050 0.07999992370605469
3  171700 2.390003204345703
4  4421275 54.74532389640808

Native code (this is Delphi, C/C++ compilers might optimize better) generates 100/6 in 21 seconds

3  171700  0.012
4  4421275  0.125
5  91962520  1.544
6  1609344100 20.748

Cannot go sleep until all measurements aren't done :)

MSVS VC++: 18 seconds! (O2 optimization)

5  91962520 1.466
6  1609344100 18.283

So 100 millions variants per second. A lot of time is wasted for checking of empty cells (because fill ratio is small). Speed described by Arndt is reached on higher k/n ratios and is about 300-500 millions variants per second:

n=25, k=15 25140840660 60.981  400 millions per second

Solution 2:

My recommendations:

1. Rewrite it as a generator utilizing yield, rather than a loop that concatenates a global variable on each iteration.
2. Keep a running sum instead of calculating the sum of some subset of the array representation of the number.
3. Operate on a single instance of your working number representation instead of splicing a copy of it to a temporary variable on each iteration.

Note no particular order is implied.

Solution 3:

I have a better solution using itertools as follows,

from itertools import product
n = 4 #number of elements
s = 3 #sum of elements
r = []
for x in range(n):
    r.append(x)
result = [p for p in product(r, repeat=n) if sum(p) == s]
print(len(result))
print(result)

I am saying this is better because it took 0.1 secs on my system, while your code with numpy took 0.2 secs.

But as far as n=100 and s=6, this code takes time to go through all the combinations, I think it will take days to compute the results.

Solution 4:

I found a solution using itertools as well (Source: https://bugs.python.org/msg144273). Code follows:

import itertools
import operator

def combinations_with_replacement(iterable, r):
    # combinations_with_replacement('ABC', 2) --> AA AB AC BB BC CC
    pool = tuple(iterable)
    n = len(pool)
    if not n and r:
        return
    indices = [0] * r
    yield tuple(pool[i] for i in indices)
    while True:
        for i in reversed(range(r)):
            if indices[i] != n - 1:
                break
        else:
            return
        indices[i:] = [indices[i] + 1] * (r - i)
        yield tuple(pool[i] for i in indices)

int_part = lambda n, k: (tuple(map(c.count, range(k))) for c in combinations_with_replacement(range(k), n))
for item in int_part(3,4): print(item)