Alex Rivera | Logout

How to improve performance of this code?

Asked 2010-11-28T07:27:43.433
43

Thanks to some help from people here, I was able to get my code for Tasmanian camels puzzle working. However, it is horribly slow (I think. I'm not sure because this is my first program in Python). The example run in the bottom of the code takes a long time to be solved in my machine:

dumrat@dumrat:~/programming/python$ time python camels.py
[['F', 'F', 'F', 'G', 'B', 'B', 'B'], ['F', 'F', 'G', 'F', 'B', 'B', 'B'],
 ['F', 'F', 'B', 'F', 'G', 'B', 'B'], ['F', 'F', 'B', 'F', 'B', 'G', 'B'],
 ['F', 'F', 'B', 'G', 'B', 'F', 'B'], ['F', 'G', 'B', 'F', 'B', 'F', 'B'],
 ['G', 'F', 'B', 'F', 'B', 'F', 'B'], ['B', 'F', 'G', 'F', 'B', 'F', 'B'],
 ['B', 'F', 'B', 'F', 'G', 'F', 'B'], ['B', 'F', 'B', 'F', 'B', 'F', 'G'],
 ['B', 'F', 'B', 'F', 'B', 'G', 'F'], ['B', 'F', 'B', 'G', 'B', 'F', 'F'],
 ['B', 'G', 'B', 'F', 'B', 'F', 'F'], ['B', 'B', 'G', 'F', 'B', 'F', 'F'],
 ['B', 'B', 'B', 'F', 'G', 'F', 'F']]

real    0m20.883s
user    0m20.549s
sys    0m0.020s

Here's the code:

import Queue

fCamel = 'F'
bCamel = 'B'
gap = 'G'

def solution(formation):
    return len([i for i in formation[formation.index(fCamel) + 1:]
                if i == bCamel]) == 0

def heuristic(formation):
    fCamels, score = 0, 0
    for i in formation:
        if i == fCamel:
            fCamels += 1;
        elif i == bCamel:
            score += fCamels;
        else:
            pass
    return score

def getneighbors (formation):
    igap = formation.index(gap)
    res = []
    # AB_CD --> A_BCD | ABC_D | B_ACD | ABD_C
    def genn(i,j):
        temp = list(formation)
        temp[i], temp[j] = temp[j], temp[i]
        res.append(temp)

    if(igap > 0):
        genn(igap, igap-1)
    if(igap > 1):
        genn(igap, igap-2)
    if igap < len(formation) - 1:
        genn(igap, igap+1)
    if igap < len(formation) - 2:
        genn(igap, igap+2)

    return res

class node:
    def __init__(self, a, g, p):
        self.arrangement =
python performance optimization time-complexity
Edit
Report

2 Answers

4

Replacing

class node:
    def __init__(self, a, g, p):
        self.arrangement = a
        self.g = g
        self.parent = p

with

node = collections.namedtuple('node', 'arrangement, g, parent')

dropped the times from around 340-600 msecs to 11.4 1.891 msecs on the input [fCamel, fCamel, gap, bCamel, bCamel]. It yielded the same output.

This obviously won't help with any algorithmic problems but as far as micro-optimizations go, it isn't bad.

1 I had the wrong input. There was an extra fCamel that was making it run slower.

answered 2010-11-28T08:56:32.233
3

Well, I can't really say quite where your algorithm is running astray, but I just went ahead and made my own. In the interest of doing the simplest thing that could possibly work, I used a bastardized version of Dijkstra's algorithm, where open nodes are visited in arbitrary order, without consideration of distance. This means I don't have to come up with a heuristic.

""" notation: a game state is a string containing angle
    brackets ('>' and '<') and blanks
 '>>> <<<'

 """

def get_moves(game):
    result = []
    lg = len(game)
    for i in range(lg):
        if game[i] == '>':
            if i < lg-1 and game[i+1] == ' ': # '> ' -> ' >'
                result.append(game[:i]+' >'+game[i+2:])
            if i < lg-2 and game[i+1] != ' ' and game[i+2] == ' ': # '>< ' -> ' <>'
                result.append(game[:i]+' '+game[i+1]+'>'+game[i+3:])
        if game[i] == '<':
            if i >= 1 and game[i-1] == ' ': # ' <' -> '< '
                result.append(game[:i-1]+'< '+game[i+1:])
            if i >= 2 and game[i-1] != ' ' and game[i-2] == ' ': # ' ><' -> '<> '
                result.append(game[:i-2]+'<'+game[i-1]+' '+game[i+1:])
    return result



def wander(start, stop):
    fringe = [start]
    paths = {}

    paths[start] = ()

    def visit(state):
      path = paths[state]
      moves = [move for move in get_moves(state) if move not in paths]
      for move in moves:
          paths[move] = paths[state] + (state,)
      fringe.extend(moves)

    while stop not in paths:
      visit(fringe.pop())

    print "still open: ", len(fringe)
    print "area covered: " , len(paths)
    return paths[stop] + (stop,)

if __name__ == "__main__":
    start = '>>>> <<<<'
    stop = '<<<< >>>>'
    print start, "   -->   ", stop
    pathway = wander(start,stop)
    print len(pathway), "moves: ",
answered 2010-11-28T11:20:52.740

Your Answer