def get_dijkstra3(self, G, target):
Gk = G.reverse()
inf = float('inf')
D = {target: 0}
Que = PQDict(D)
P = {}
nodes = Gk.nodes
U = set(nodes)
while U:
#print('len U %d'%len(U))
#print('len Q %d'%len(Que))
if len(Que) == 0: break
(v, d) = Que.popitem()
D[v] = d
U.remove(v)
#if v == target: break
neigh = list(Gk.successors(v))
for u in neigh:
if u in U:
d = D[v] + Gk[v][u]['weight']
if d < Que.get(u, inf):
Que[u] = d
P[u] = v
return P
def djikstra(G, start):
'''
Djikstra's algorithm determines the length from `start` to every other
vertex in the graph.
The graph argument `G` should be a dict indexed by nodes. The value
of each item `G[v]` should also a dict indexed by predecessor nodes.
In other words, for any node `v`, `G[v]` is itself a dict, indexed
by the predecessors of `v`. For any directed edge `w -> v`, `G[v][w]`
is the length of the edge from `w` to `v`.
'''
inf = float('inf')
dist = {start: 0} # track shortest path distances from `start`
E = set([start]) # explored
U = set(G.keys()) - E # unexplored
while U: # unexplored nodes
D = PQDict() # frontier candidates
for u in U: # unexplored nodes
for v in G[u]: # neighbors of u
if v in E: # then u is a frontier node
l = dist[v] + G[u][v] # start -> v -> u
D[u] = min(l, D.get(u, inf)) # choose minimum for u
(x, d) = D.popitem() # node w/ min dist on frontier
dist[x] = d # assign djikstra greedy score
U.remove(x) # remove from unexplored
E.add(x) # add to explored
return dist # shortest path distances
def djikstra(G, start):
'''
Djikstra's algorithm determines the length from `start` to every other
vertex in the graph.
The graph argument `G` should be a dict indexed by nodes. The value
of each item `G[v]` should also a dict indexed by predecessor nodes.
In other words, for any node `v`, `G[v]` is itself a dict, indexed
by the predecessors of `v`. For any directed edge `w -> v`, `G[v][w]`
is the length of the edge from `w` to `v`.
'''
inf = float('inf')
dist = {start: 0} # track shortest path distances from `start`
E = set([start]) # explored
U = set(G.keys()) - E # unexplored
while U: # unexplored nodes
D = PQDict() # frontier candidates
for u in U: # unexplored nodes
for v in G[u]: # neighbors of u
if v in E: # then u is a frontier node
l = dist[v] + G[u][v] # start -> v -> u
D[u] = min(l, D.get(u, inf)) # choose minimum for u
(x, d) = D.popitem() # node w/ min dist on frontier
dist[x] = d # assign djikstra greedy score
U.remove(x) # remove from unexplored
E.add(x) # add to explored
return dist # shortest path distances
def dijkstra(graph, start, end):
inf = float('inf')
shortest_distances = {start: 0} # mapping of nodes to their dist from start
queue_sd = PQDict(shortest_distances) # priority queue for tracking min shortest path
predecessors = {} # mapping of nodes to their direct predecessors
unexplored = set(graph.keys()) # unexplored nodes
path = []
while unexplored: # nodes yet to explore
(minNode, minDistance) = queue_sd.popitem() # node w/ min dist d on frontier
shortest_distances[minNode] = minDistance # est dijkstra greedy score
unexplored.remove(minNode) # remove from unexplored
if minNode == end: break # end if goal already reached
# now consider the edges from minNode with an unexplored head -
# we may need to update the dist of unexplored successors
for neighbor in graph[minNode]: # successors to v
if neighbor in unexplored: # then neighbor is a frontier node
minDistance = shortest_distances[minNode] + graph[minNode][neighbor]
if minDistance < queue_sd.get(neighbor, inf):
queue_sd[neighbor] = minDistance
predecessors[neighbor] = minNode # set/update predecessor
currentNode = end
while currentNode != start:
try:
path.insert(0,currentNode)
currentNode = predecessors[currentNode]
except KeyError:
print('Path not reachable')
break
path.insert(0,start)
if shortest_distances[end] != inf:
return shortest_distances[end], path
def dijkstra(g, s):
vis = [False for i in range(len(g))]
dist = [float('inf') for i in range(len(g))]
prev = [None for i in range(len(g))]
dist[s] = 0
ipq = PQDict()
ipq.additem(s, 0)
while len(ipq) != 0:
index, minValue = ipq.popitem()
vis[index] = True
# mica optimizare, daca deja avem distanda mai buna pe alt drum fata de drumul direct dintre 2 noduri, pass
if dist[index] < minValue:
continue
for edge in g[index]:
# ia toate nodurile vecine nevizitate
if vis[edge[0]]:
continue
# le calculaeza distanta
newDist = dist[index] + edge[1]
# si o modifica doar daca este mai mica decat cea care era deja
if newDist < dist[edge[0]]:
dist[edge[0]] = newDist
# apoi modifica si in indexed priority queue, daca nu e, il adauga, daca e, ii updateaza valoarea
if ipq.get(edge[0]) is None:
ipq.additem(edge[0], newDist)
else:
ipq[edge[0]] = newDist
# si seteaza si tatal de fiecare data pentru a ramane ultimul adica cel cu costul cel mai mic
prev[edge[0]] = index
return dist, prev
def dijkstra(G, start, end=None):
'''
dijkstra's algorithm determines the length from `start` to every other
vertex in the graph.
The graph argument `G` should be a dict indexed by nodes. The value
of each item `G[v]` should also a dict indexed by successor nodes.
In other words, for any node `v`, `G[v]` is itself a dict, indexed
by the successors of `v`. For any directed edge `v -> w`, `G[v][w]`
is the length of the edge from `v` to `w`.
graph = {'a': {'b': 1},
'b': {'c': 2, 'b': 5},
'c': {'d': 1},
'd': {}}
Returns two dicts, `dist` and `pred`:
dist, pred = dijkstra(graph, start='a')
`dist` is a dict mapping each node to its shortest distance from the
specified starting node:
assert dist == {'a': 0, 'c': 3, 'b': 1, 'd': 4}
`pred` is a dict mapping each node to its predecessor node on the
shortest path from the specified starting node:
assert pred == {'b': 'a', 'c': 'b', 'd': 'c'}
'''
inf = float('inf')
D = {start: 0} # mapping of nodes to their dist from start p*
Q = PQDict(
D) # priority queue for tracking min shortest path Cin ????
P = {} # mapping of nodes to their direct predecessors 存边
U = set(G.keys()) # unexplored nodes
while U: # nodes yet to explore
(v,
d) = Q.popitem() # node w/ min dist d on frontier 找到Cln最小的l
D[v] = d # est dijkstra greedy score pl*
U.remove(v) # remove from unexplored 从U中删除l
if v == end: break #如果集合为空，则算法终止
# now consider the edges from v with an unexplored head -
# we may need to update the dist of unexplored successors
for w in G[v]: # successors to v
if w in U: # then w is a frontier node
d = D[v] + G[v][w] # dgs: dist of start -> v -> w
if d < Q.get(w, inf):
Q[w] = d # set/update dgs
P[w] = v # set/update predecessor
return D, P
def dijkstra(G, start, end=None):
'''
dijkstra's algorithm determines the length from `start` to every other
vertex in the graph.
The graph argument `G` should be a dict indexed by nodes. The value
of each item `G[v]` should also a dict indexed by successor nodes.
In other words, for any node `v`, `G[v]` is itself a dict, indexed
by the successors of `v`. For any directed edge `v -> w`, `G[v][w]`
is the length of the edge from `v` to `w`.
graph = {'a': {'b': 1},
'b': {'c': 2, 'b': 5},
'c': {'d': 1},
'd': {}}
Returns two dicts, `dist` and `pred`:
dist, pred = dijkstra(graph, start='a')
`dist` is a dict mapping each node to its shortest distance from the
specified starting node:
assert dist == {'a': 0, 'c': 3, 'b': 1, 'd': 4}
`pred` is a dict mapping each node to its predecessor node on the
shortest path from the specified starting node:
assert pred == {'b': 'a', 'c': 'b', 'd': 'c'}
'''
inf = float('inf')
D = {start: 0} # mapping of nodes to their dist from start
Q = PQDict(D) # priority queue for tracking min shortest path
P = {} # mapping of nodes to their direct predecessors
U = set(G.keys()) # unexplored nodes
while U: # nodes yet to explore
(v, d) = Q.popitem() # node w/ min dist d on frontier
D[v] = d # est dijkstra greedy score
U.remove(v) # remove from unexplored
if v == end: break
# now consider the edges from v with an unexplored head -
# we may need to update the dist of unexplored successors
for w in G[v]: # successors to v
if w in U: # then w is a frontier node
d = D[v] + G[v][w] # dgs: dist of start -> v -> w
if d < Q.get(w, inf):
Q[w] = d # set/update dgs
P[w] = v # set/update predecessor
return D, P
def dijkstra(G, src, dst=None):
inf = float('inf')
D = {src: 0} # distance
Q = PQDict(D) # priority queue
P = {} # predecessor
U = set(G.keys()) # unexplored nodes
while U: # still have unexplored
(v, d) = Q.popitem() # get node with least d
D[v] = d # add to D dict
U.remove(v) # node now explored
if v == dst: # reached destination
break
# now edges FROM v
for w in G[v]:
if w in U: # unvisited neighbour
tmpd = D[v] + G[v][w]
if tmpd < Q.get(w, inf): # return inf if not found
Q[w] = tmpd # update distance
P[w] = v # set predecessor as current
return D, P
def dijkstra(G, start, end=None):
inf = float('inf')
D = {start: 0} # mapping of nodes to their dist from start
Q = PQDict(D) # priority queue for tracking min shortest path
P = {} # mapping of nodes to their direct predecessors
U = set(G.keys()) # unexplored nodes
while U: # nodes yet to explore
(v, d) = Q.popitem() # node w/ min dist d on frontier
D[v] = d # est dijkstra greedy score
U.remove(v) # remove from unexplored
if v == end: break
# now consider the edges from v with an unexplored head -
# we may need to update the dist of unexplored successors
for w in G[v]: # successors to v
if w in U: # then w is a frontier node
d = int(D[v]) + int(G[v][w]) # dgs: dist of start -> v -> w
if d < Q.get(w, inf):
Q[w] = d # set/update dgs
P[w] = v # set/update predecessor
return D, P
def dijkstra(self, src, dst):
"""dijkstras algorithm used to calculate the shortest path between two nodes a Priority queue is used in this implementation"""
inf = float('inf')
D = {src: 0}
Q = PQDict(D)
P = {}
U = set(self.router_list)
while U:
(v, d) = Q.popitem()
D[v] = d
U.remove(v)
if str(v) == dst:
break
for w in self.router_list[v]:
if w in U:
d = D[v] + self.router_list[v][w]
if d < Q.get(w, inf):
Q[w] = d
P[w] = v
return D, P
class Storage:
"""
Kademlia storage implementation.
Three responsibilities:
- Storing data
- Listing old keys to refresh
- Keeping a record of data popularity and evicting unpopular data when
a storage limit is reached.
"""
implements(kademlia.storage.IStorage)
max_len = 5000
def __init__(self, args, ttl=604800, time=time):
self.args = args
self.time = time
# linked
self.popularity_queue = PQDict()
self.age_dict = OrderedDict()
# separate
self.future_popularity_queue = PQDict()
self.step = ttl
def cull(self):
if len(self.popularity_queue) > self.max_len:
key = self.popularity_queue.pop()
if self.args.verbose:
log_info('Dropping key {} (over count {})'.format(binascii.hexlify(key), self.max_len))
del self.age_dict[key]
if len(self.future_popularity_queue) > self.max_len:
key = self.future_popularity_queue.pop()
if self.args.verbose:
log_info('Dropping future key {} (over count {})'.format(binascii.hexlify(key), self.max_len))
def inc_popularity(self, key):
current = self.popularity_queue.get(key)
if current is not None:
self.popularity_queue[key] = current + self.step
else:
current = self.future_popularity_queue.get(key, self.time.time())
self.future_popularity_queue[key] = current + self.step
def _tripleIterable(self):
ikeys = self.age_dict.iterkeys()
ibirthday = imap(operator.itemgetter(0), self.age_dict.itervalues())
ivalues = imap(operator.itemgetter(1), self.age_dict.itervalues())
return izip(ikeys, ibirthday, ivalues)
# interface methods below
def __setitem__(self, key, value):
age, oldvalue = self.age_dict.get(key) or self.time.time(), None
if not validate(self.args, key, value, oldvalue)[0]:
return
if oldvalue is not None:
self.age_dict[key] = (age, value)
else:
age = self.future_popularity_queue.pop(key, self.time.time())
self.age_dict[key] = (self.time.time(), value)
self.popularity_queue[key] = age
self.cull()
def __getitem__(self, key):
self.inc_popularity(key)
self.cull()
return self.age_dict[key][1]
def get(self, key, default=None):
self.inc_popularity(key)
self.cull()
if key in self.age_dict:
return self.age_dict[key][1]
return default
def iteritemsOlderThan(self, secondsOld):
minBirthday = self.time.time() - secondsOld
zipped = self._tripleIterable()
matches = takewhile(lambda r: minBirthday >= r[1], zipped)
return imap(operator.itemgetter(0, 2), matches)
def iteritems(self):
self.cull()
return self.age_dict.iteritems()
def test_get(self):
pq = PQDict(self.items)
self.assertEqual(pq.get('A'), 5)
self.assertEqual(pq.get('A', None), 5)
self.assertIs(pq.get('does_not_exist', None), None)
self.assertRaises(KeyError, pq.get('does_not_exist'))
