ajm*_*tin 7 python memory iteration recursion stack
我已经看了一些像Heapy这样的常用工具来衡量每种遍历技术使用了多少内存,但我不知道他们是否给了我正确的结果.这是给出上下文的一些代码.
代码只是测量图中唯一节点的数量.提供了两种遍历技术.count_bfs和count_dfs
import sys
from guppy import hpy
class Graph:
def __init__(self, key):
self.key = key #unique id for a vertex
self.connections = []
self.visited = False
def count_bfs(start):
parents = [start]
children = []
count = 0
while parents:
for ind in parents:
if not ind.visited:
count += 1
ind.visited = True
for child in ind.connections:
children.append(child)
parents = children
children = []
return count
def count_dfs(start):
if not start.visited:
start.visited = True
else:
return 0
n = 1
for connection in start.connections:
n += count_dfs(connection)
return n
def construct(file, s=1):
"""Constructs a Graph using the adjacency matrix given in the file
:param file: path to the file with the matrix
:param s: starting node key. Defaults to 1
:return start vertex of the graph
"""
d = {}
f = open(file,'rU')
size = int(f.readline())
for x in xrange(1,size+1):
d[x] = Graph(x)
start = d[s]
for i in xrange(0,size):
l = map(lambda x: int(x), f.readline().split())
node = l[0]
for child in l[1:]:
d[node].connections.append(d[child])
return start
if __name__ == "__main__":
s = construct(sys.argv[1])
#h = hpy()
print(count_bfs(s))
#print h.heap()
s = construct(sys.argv[1])
#h = hpy()
print(count_dfs(s))
#print h.heap()
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我想知道两种遍历技术中总内存利用率的不同因素是什么.count_dfs和count_bfs?dfs由于为每个函数调用创建了一个新堆栈,因此可能有一种直觉可能很昂贵.如何测量每种遍历技术中的总内存分配?
(评论)hpy陈述是否给出了所需的衡量标准?
带连接的示例文件:
4
1 2 3
2 1 3
3 4
4
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这是一个Python问题,使用多少堆栈空间可能比多少总内存更重要。Cpython 的下限为 1000 帧,因为它与 c 调用堆栈共享其调用堆栈,而 c 调用堆栈在大多数地方又限制为 1 MB 的量级。因此,当递归深度无界时,您几乎*总是更喜欢迭代解决方案而不是递归解决方案。
* python 的其他实现可能没有此限制。cpython 和 pypy 的无堆栈变体具有这个确切的属性