optimizing project euler #83 solution

Just put this one in order someone wants to compare speed/size:

import sys
import heapq
import time

def dijkstra(G, start, end, start_cost = 0):
    q = [(start_cost, start, ())]  
    visited = set()      
    while True:
        cost, v1, path = heapq.heappop(q)
        if v1 not in visited:
            visited.add(v1)
            if v1 == end:
                return cost
            path = (v1, path)
            for (v2, cost2) in G[v1].iteritems():
                if v2 not in visited:
                    heapq.heappush(q, (cost + cost2, v2, path))

def matrix_to_graph(m):
    G = {}
    max_len = len(m)
    for x in range(max_len):
        for y in range(max_len):
            positions = [(x + 1, y), (x - 1, y), (x, y - 1), (x, y + 1)]
            for px, py in positions:
                if px >= 0 and px < max_len and py >= 0 and py < max_len:
                    src = '%d_%d' % (x, y)
                    dst = '%d_%d' % (px, py)
                    cost = m[px][py]
                    G.setdefault(src, {})[dst] = cost
    return G

def main():
    s = time.time()
    matrix = [[int(i) for i in j.split(',')] for j in open(sys.argv[1])]
    g = matrix_to_graph(matrix)
    d = len(matrix) - 1
    last_vertex = '%d_%d' % (d, d)
    cost = dijkstra(g, '0_0', last_vertex, matrix[0][0])
    print('Shortest path len = %d Time (s) = %f' % (cost, time.time()-s))

if __name__ == '__main__':
    main()

If you want to test your solution against bigger input cases, you can use the following code:

import random
import sys
N = int(sys.argv[1])
for line in [[random.randint(0, 1000) for _ in range(N)] for _ in range(N)]:
    print str(line)[1:-1]

Example: The matrix size 200x200 and 400x400:

gen.py 200 > 200.txt

results: dijkstra solution: 0.53 sec. original solution: 14.6 sec.

gen.py 400 > 400.txt

results: dijkstra solution: 1.95 sec. original solution: 109.6 sec.

import bisect
f = open('matrix.txt')
matrix = [[int(i) for i in j.split(',')] for j in f]

You could consider using the numpy library, it'll handle multidimensional arrays more neatly and efficiently.

def uniformCostSearch(startNode):

Python convention is to use underscore_with_letters for function names and parameters

    frontier = []
    frontier.append(startNode)

Use frontier = [startNode], it'll be a touch faster

    closedSet = set()
    while not len(frontier) == 0:

Use while frontier:, it does the same thing and due to less operations is going to be a bit faster. You should at least use != rather then not ==

        currentNode = frontier.pop(0)

Popping from the front of a list will be inefficient. Actually, what you really want is a priority queue, see python's deque module.

        if currentNode.x == 79 and currentNode.y == 79:
            return currentNode.priority
        else:
            if not (currentNode.x, currentNode.y) in closedSet:

Move the not over next to in I think it reads clearer. I might also use a 2D array of bools rather then a set here.

                closedSet.add((currentNode.x, currentNode.y))

There isn't really any reason to check if the node is in the set here, you can add the node multiple times. Just add it unconditionally.

        possibleMoves = currentNode.neighbors()
        for move in possibleMoves:

No reason to store it in a local, just combine the previous two lines

            if not (move.x, move.y) in closedSet:
                try:
                    index = frontier.index(move)
                    if move.priority < frontier[index].priority:
                        frontier.pop(index)
                        bisect.insort_left(frontier, move)

Move the last three lines into an else block. That'll make sure only your index line can actually throw things that'll get caught.

                except ValueError:
                    # move is not in frontier so just add it
                    bisect.insort_left(frontier, move)

You do this in either case, I'd move it after the try/except block. return -1

class Node:
    def __init__(self, x, y, priority=0):
        self.x = x
        self.y = y
        self.priority = priority

    def neighbors(self):
        tmp = [Node(self.x + 1, self.y), Node(self.x, self.y + 1),
               Node(self.x - 1, self.y), Node(self.x, self.y - 1),]
        childNodes = []
        for node in tmp:
            if node.x >= 0 and node.y >= 0 and node.x <= 79 and node.y <= 79:

I suggest moving 79 into a constant. I'd also do < 80 rather then <= 79.

                node.priority = self.priority + matrix[node.y][node.x]
                childNodes.append(node)

I'd use a yield here, and make this a generator

        return childNodes

    def __eq__(self, node):
        return self.x == node.x and self.y == node.y

This bothers me because multiple nodes will compare equal despite having different priorities.

    def __ne__(self, node):
        return not self.x == node.x and self.y == node.y

    def __cmp__(self, node):
        if self.priority < node.priority:
            return -1
        elif self.priority > node.priority:
            return 1
        else:
            return 0

This really bothers me because you aren't defining your comparisons in a consistent way.