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 test_heapsort(self):
# sequences of operations
pq = PQDict()
self.check_heap_invariant(pq)
self.check_index(pq)
items = generateData('int')
# push in a sequence of items
added_items = []
for dkey, pkey in items:
pq.additem(dkey, pkey)
self.check_heap_invariant(pq)
self.check_index(pq)
added_items.append( (dkey,pkey) )
# pop out all the items
popped_items = []
while pq:
dkey_pkey = pq.popitem()
self.check_heap_invariant(pq)
self.check_index(pq)
popped_items.append(dkey_pkey)
self.assertTrue(len(pq._heap)==0)
self.check_index(pq)
def __init__(self, id, dynamic_model, reactions, species, dynamic_compartments,
local_species_population, options=None):
""" Initialize an NRM submodel object
Args:
id (:obj:`str`): unique id of this dynamic NRM submodel
dynamic_model (:obj:`DynamicModel`): the aggregate state of a simulation
reactions (:obj:`list` of :obj:`Reaction`): the reactions modeled by this NRM submodel
species (:obj:`list` of :obj:`Species`): the species that participate in the reactions modeled
by this NRM submodel, with their initial concentrations
dynamic_compartments (:obj:`dict`): :obj:`DynamicCompartment`\ s, keyed by id, that contain
species which participate in reactions that this NRM submodel models, including
adjacent compartments used by its transfer reactions
local_species_population (:obj:`LocalSpeciesPopulation`): the store that maintains this
NRM submodel's species population
options (:obj:`dict`, optional): NRM submodel options
Raises:
:obj:`MultialgorithmError`: if the initial NRM wait exponential moving average is not positive
"""
super().__init__(id, dynamic_model, reactions, species, dynamic_compartments,
local_species_population)
self.random_state = RandomStateManager.instance()
self.execution_time_priority_queue = PQDict()
self.options = options
# to enable testing of uninitialized instances auto_initialize controls initialization here
# auto_initialize defaults to True
auto_initialize = True
if options is not None and 'auto_initialize' in options:
auto_initialize = options['auto_initialize']
if auto_initialize:
self.initialize()
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 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 __init__(self, oeos, a_id, a, a_max_fielded, b_id, b, b_max_fielded):
assert all(isinstance(oeo, Oeo) for oeo in oeos.values()), \
"oeos is not a dict of oeo_id:oeo"
assert isinstance(a_id, str), "'a_id' is not a string"
assert isinstance(a, set), "'a' is not a set of oeo_id"
assert isinstance(a_max_fielded, int), "'a_max_fielded' is not an int"
assert isinstance(b_id, str), "'b_id' is not a string"
assert isinstance(b, set), "'b' is not a set of oeo_id"
assert isinstance(b_max_fielded, int), "'b_max_fielded' is not an int"
# Throw error if each oeo_id is not unique between teams
if a & b:
raise ValueError(f"{a & b} oeo_id(s) not unique between teams")
self._oeo = oeos
self._a_id = a_id
self._a = a
self._b_id = b_id
self._b = b
self._turn_number = 0
self._field = Field(a_id, a_max_fielded, b_id, b_max_fielded)
self._pending_sim_events = PQDict()
self._processed_sim_events = []
move_set = set()
for o in itertools.chain(self._oeo.values()):
for move in o.moves:
move_set.add(move)
moves = Move.load_moves(move_set)
self._moves = moves
# self._speed_priority_list = None
self._setup_axel_events()
def dijkstra(graph, start):
p_queue = PQDict()
dist_to_start = {}
prev_vert = {}
# Fill the dictionaries with initial values
for vert in graph:
dist_to_start[vert] = 1000
prev_vert[vert] = None
# Initialize the priority queue dictionary
dist_to_start[start] = 0
for vert in graph:
p_queue[vert] = dist_to_start[vert] # starts at 1000
# BFS the priority queue dictionary
while len(p_queue) > 0:
current = p_queue.popitem()[0] # the first item in the tuple is the key
neighbors = graph[current].keys()
for vert in neighbors:
neighbor_dist = graph[current][vert]
new_distance = dist_to_start[current] + neighbor_dist
if new_distance < dist_to_start[vert]: # if smaller than the last dist_to_start replace
p_queue[vert] = new_distance
dist_to_start[vert] = new_distance
prev_vert[vert] = current
return prev_vert
def gillespie_nrm(tspan, c_0, c_poly, reactions, dep_graph):
t = tspan[0]
c = c_0.copy()
T = [t]
C = [c.copy()]
volume = compute_volume(c_0, c_poly)
# initialize scheduler
scheduler = PQDict()
for rxn in reactions:
tau = -log(rand()) / rxn.propensity(c, volume)
scheduler[rxn] = t + tau
# first event
rnext, tnext = scheduler.topitem()
t = tnext
C, T = fire_rxn(rnext, c, t, c_poly, C, T)
while t < tspan[1]:
# reschedule dependent reactions
for rxn in dep_graph[rnext]:
tau = -log(rand()) / rxn.propensity(c, volume)
scheduler[rxn] = t + tau
# fire the next one!
rnext, tnext = scheduler.topitem()
t = tnext
if (rnext.propensity(c, volume) > 0):
C, T = fire_rxn(rnext, c, t, c_poly, C, T)
else:
print(c_poly)
return array(T), array(C)
def test_swap_priority(self):
pq = PQDict(A=5, B=8, C=1)
pq.swap_priority('A', 'C')
self.check_index(pq)
self.assertEqual(pq['A'], 1)
self.assertEqual(pq['C'], 5)
self.assertEqual(pq.top(), 'A')
self.assertRaises(KeyError, pq.swap_priority, 'A', 'Z')
def test_topitem(self):
# empty
pq = PQDict()
self.assertRaises(KeyError, pq.top)
# non-empty
for num_items in range(1,30):
items = generateData('float', num_items)
pq = PQDict(items)
self.assertEqual(pq.topitem(), min(items, key=lambda x: x[1]))
def a_star(self, heuristic):
node = self.tree.create_node(state=State(self.wrigglers), pathCost=0)
node.heuristic = heuristic(node)
frontier = PQDict()
stateFrontier = {}
explored = {}
# Sacrifice memory to have a huge speed up being able to instantly check for state in frontier
stateFrontier[str(node.state)] = node.heuristic
frontier.additem(node._identifier, node.heuristic)
while(True):
if(len(frontier) == 0):
return None
nodeID = frontier.popitem()[0]
node = self.tree.get_node(nodeID)
nodeStateStr = str(node.state)
del stateFrontier[nodeStateStr]
if self.testGoal(node.state):
return node
explored[nodeStateStr] = -1 # we don't care what the hash matches
actions = self.getActions(node.state)
for action in actions:
child = self.childNode(node, action)
child.heuristic = heuristic(child)
childStr = str(child.state)
inExplored = False
inFrontier = False
if childStr in explored:
inExplored = True
bGreater = False
if childStr in stateFrontier:
if(stateFrontier[childStr] < child.heuristic + child.pathCost):
bGreater = True
inFrontier = True
if(not inExplored and not inFrontier):
stateFrontier[childStr] = child.heuristic
frontier.additem(child._identifier, child.heuristic + child.pathCost)
elif(bGreater):
bHappened = False
for key in frontier:
if(str(self.tree.get_node(key).state) == childStr):
bHappened = True
frontier.pop(key)
frontier.additem(child._identifier, child.heuristic + child.pathCost)
break
assert bHappened
def test_copy(self):
pq1 = PQDict(self.items)
pq2 = pq1.copy()
# equality by value
self.assertEqual(pq1, pq2)
dkey = random.choice(self.dkeys)
pq2[dkey] += 1
self.assertNotEqual(pq1[dkey], pq2[dkey])
self.assertNotEqual(pq1, pq2)
def test_replace_key(self):
pq = PQDict(A=5, B=8, C=1)
pq.replace_key('A', 'Alice')
pq.replace_key('B', 'Bob')
self.check_index(pq)
self.assertEqual(pq['Alice'], 5)
self.assertEqual(pq['Bob'], 8)
self.assertRaises(KeyError, pq.__getitem__, 'A')
self.assertRaises(KeyError, pq.__getitem__, 'B')
self.assertRaises(KeyError, pq.replace_key, 'C', 'Bob')
def primMST(G):
""" Return MST of the given undirected graph"""
vis = set()
tot_weight = 0
pq = PQDict()
Gprime = nx.Graph()
''' Add all nodes to PQDict with infinite distance'''
for node in G.nodes():
pq.additem(node, float("inf"))
curr = pq.pop() #Select initial node
vis.add(curr)
while len(pq) > 0:
for s, nod, wt in G.edges(curr, data=True):
if nod not in vis and wt['weight'] < pq[nod]:
pq.updateitem(nod, wt['weight'])
if len(pq) > 0:
top = pq.top()
source, destination, dist = [
data for data in sorted(G.edges(top, data=True),
key=lambda
(source, target, data): data['weight'])
if data[1] in vis
][0]
Gprime.add_edge(source, destination, weight=dist['weight'])
vis.add(top)
tot_weight += pq[top]
curr = pq.pop()
return Gprime, tot_weight
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 greedy_approx(G):
""" Return MST of the given undirected graph"""
vis = set()
tot_weight = 0
pq = PQDict()
path = []
'''Initialize Priority Queue which will help us find Farthest node after distance is calcualted from visited node'''
for node in G.nodes():
pq.additem(node, float("-inf"))
curr = pq.pop()
vis.add(curr)
path.append(curr)
while len(pq) > 0:
for s, nod, wt in G.edges(curr, data=True):
'''Distance calculation'''
if nod not in vis and -wt['weight'] > pq[nod]:
pq.updateitem(nod, -wt['weight'])
if len(pq) > 0:
''' Selection Step'''
top = pq.top()
vis.add(top)
curr = pq.pop()
''' Insertion Step'''
loc, cost = minCost(G, path, top)
'''Insert into the location found by minCost()'''
path.insert(loc, top)
tot_weight += cost
return path, tot_weight
def test_updateitem(self):
pq = PQDict(self.items)
dkey, pkey = random.choice(self.items)
# assign same value
pq.updateitem(dkey, pkey)
self.assertEqual(pq[dkey], pkey)
# assign new value
pq.updateitem(dkey, pkey + 1.0)
self.assertEqual(pq[dkey], pkey + 1.0)
# can only update existing dkeys
self.assertRaises(KeyError, pq.updateitem, 'does_not_exist', 99.0)
def test_update(self):
pq1 = PQDict(self.items)
pq2 = PQDict()
pq2['C'] = 3000
pq2['D'] = 4000
pq2['XYZ'] = 9000
pq1.update(pq2)
self.assertEqual(pq1['C'],3000) and \
self.assertEqual(pq1['D'],4000) and \
self.assertIn('XYZ',pq1) and \
self.assertEqual(pq1['XYZ'],9000)
def gillespie_nrm(tspan, initial_amounts, reactions, dep_graph):
"""
Implementation of the "Next-Reaction Method" variant of the Gillespie
stochastic simulation algorithm, described by Gibson and Bruck.
The main enhancements are:
- Use of dependency graph connecting the reaction channels to prevent
needless rescheduling of reactions that are unaffected by an event.
- Use of an indexed priority queue (pqdict) as a scheduler to achieve
O(log(M)) rescheduling on each iteration, where M is the number of
reactions, assuming the dependency graph is sparse.
The paper describes an additional modification to cut down on the amount of
random number generation which was not implemented here for simplicity.
"""
# initialize state
t = tspan[0]
x = initial_amounts
T = [t]
X = [x]
# initialize scheduler
scheduler = PQDict()
for rxn in reactions:
tau = -log(rand())/rxn.propensity(x)
scheduler[rxn] = t + tau
# first event
rnext, tnext = scheduler.topitem()
t = tnext
x += rnext.stoich
T.append( t )
X.append( x.copy() )
# main loop
while t < tspan[1]:
# reschedule
tau = -log(rand())/rnext.propensity(x)
scheduler[rnext] = t + tau
# reschedule dependent reactions
for rxn in dep_graph[rnext]:
tau = -log(rand())/rxn.propensity(x)
scheduler[rxn] = t + tau
# fire the next one!
rnext, tnext = scheduler.topitem()
t = tnext
x += rnext.stoich
T.append( t )
X.append( x.copy() )
return array(T), array(X)
def gillespie_nrm(tspan, initial_amounts, reactions, dep_graph):
"""
Implementation of the "Next-Reaction Method" variant of the Gillespie
stochastic simulation algorithm, described by Gibson and Bruck.
The main enhancements are:
- Use of dependency graph connecting the reaction channels to prevent
needless rescheduling of reactions that are unaffected by an event.
- Use of an indexed priority queue (pqdict) as a scheduler to achieve
O(log(M)) rescheduling on each iteration, where M is the number of
reactions, assuming the dependency graph is sparse.
The paper describes an additional modification to cut down on the amount of
random number generation which was not implemented here for simplicity.
"""
# initialize state
t = tspan[0]
x = initial_amounts
T = [t]
X = [x]
# initialize scheduler
scheduler = PQDict()
for rxn in reactions:
tau = -log(rand()) / rxn.propensity(x)
scheduler[rxn] = t + tau
# first event
rnext, tnext = scheduler.topitem()
t = tnext
x += rnext.stoich
T.append(t)
X.append(x.copy())
# main loop
while t < tspan[1]:
# reschedule
tau = -log(rand()) / rnext.propensity(x)
scheduler[rnext] = t + tau
# reschedule dependent reactions
for rxn in dep_graph[rnext]:
tau = -log(rand()) / rxn.propensity(x)
scheduler[rxn] = t + tau
# fire the next one!
rnext, tnext = scheduler.topitem()
t = tnext
x += rnext.stoich
T.append(t)
X.append(x.copy())
return array(T), array(X)
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 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 greedy_approx(G):
""" Return MST of the given undirected graph"""
vis = set()
tot_weight = 0
pq = PQDict()
path = []
'''Initialize Priority Queue which will help us find Farthest node after distance is calcualted from visited node'''
for node in G.nodes():
pq.additem(node, float("-inf"))
curr = pq.pop()
vis.add(curr)
path.append(curr)
while len(pq) > 0:
for s,nod, wt in G.edges(curr, data=True):
'''Distance calculation'''
if nod not in vis and -wt['weight'] > pq[nod]: pq.updateitem(nod, -wt['weight'])
if len(pq)>0:
''' Selection Step'''
top = pq.top()
vis.add(top)
curr = pq.pop()
''' Insertion Step'''
loc,cost = minCost(G,path,top)
'''Insert into the location found by minCost()'''
path.insert(loc, top)
tot_weight += cost
return path,tot_weight
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 primMST(G):
""" Return MST of the given undirected graph"""
vis = set()
tot_weight = 0
pq = PQDict()
Gprime = nx.Graph()
''' Add all nodes to PQDict with infinite distance'''
for node in G.nodes():
pq.additem(node, float("inf"))
curr = pq.pop() #Select initial node
vis.add(curr)
while len(pq) > 0:
for s,nod, wt in G.edges(curr, data=True):
if nod not in vis and wt['weight'] < pq[nod]: pq.updateitem(nod, wt['weight'])
if len(pq)>0:
top = pq.top()
source,destination, dist = [data for data in sorted(G.edges(top, data=True), key=lambda (source,target,data): data['weight']) if data[1] in vis][0]
Gprime.add_edge(source, destination, weight = dist['weight'])
vis.add(top)
tot_weight += pq[top]
curr = pq.pop()
return Gprime, tot_weight
def dijkstra(source):
distResults = [INF] * (N + 1)
distResults[source] = 0
dist = PQDict()
for i in range(1, N + 1):
dist[i] = distResults[i]
while dist:
v1, d1 = dist.popitem()
distResults[v1] = d1
if d1 == INF:
break
for v2 in range(1, N + 1):
if v2 in dist:
dist[v2] = min(dist[v2], graph[v1, v2] + d1)
return distResults
def greedy_approx(G):
vis = set()
pq = PQDict()
tot_length = 0
seq = []
start = 1
curr = start
vis.add(curr)
seq.append(curr)
while len(seq) != len(G.nodes()):
next = [
list for list in (
sorted(G.edges(curr, data=True),
key=lambda (source, target, data): data['weight']))
if list[1] not in vis
][0]
curr = next[1]
vis.add(curr)
seq.append(curr)
tot_length += next[2]['weight']
next = [
list
for list in (sorted(G.edges(curr, data=True),
key=lambda (source, target, data): data['weight']))
if list[1] == start
][0]
seq.append(next[1])
tot_length += next[2]['weight']
# print optimal, tot_length, tot_length/float(int(optimal))
return tot_length
# print greed()[1]
def apply_astar(self):
print "A star algorithm running to find the goal"
close = set()
open = set()
open.add(self.return_key(self._tiles))
parent = {}
g_score = defaultdict(lambda: math.inf)
g_score[self.return_key(self._tiles)] = 0
f_score = PQDict.minpq()
f_matric = {}
value = g_score[self.return_key(self._tiles)] + self.heuristic(self._tiles,self.end_index)
f_score[self.return_key(self._tiles)] = value
f_matric[self.return_key(self._tiles)] = self._tiles
while open:
temp_current_key = f_score.pop()
current = f_matric[temp_current_key]
if temp_current_key == self.return_key(self.end):
print "goal found"
self.construct_path(parent,moves,temp_current_key)
print "Number of closed states are ", len(close)
return 1
if temp_current_key in open:
open.discard(temp_current_key)
close.add(self.return_key(current))
l = self.cal_child_nodes(current)
moves = {}
for x in l:
temp_node = copy.copy(l[x][0])
if self.return_key(temp_node) in close:
continue
new_g_value = g_score[self.return_key(current)] + 1
if self.return_key(temp_node) not in open or new_g_value < g_score[self.return_key(temp_node)]:
parent[self.return_key(temp_node)] = [self.return_key(current),l[x][1]]
moves[self.return_key(temp_node)] = l[x][1]
g_score[self.return_key(temp_node)] = new_g_value
value_new = g_score[self.return_key(temp_node)] + self.heuristic(temp_node,self.end_index)
f_score[self.return_key(temp_node)] = value_new
if self.return_key(temp_node) not in open:
open.add(self.return_key(temp_node))
if self.return_key(temp_node) not in f_matric:
f_matric[self.return_key(temp_node)] = temp_node
return 0
def __init__(self):
self.query = input("Enter search query: ")
self.webpages_limit = input(
"Set total number of webpages to be crawled: ")
self.limit = input(
"Set limits on how many webpages be crawled from single site: ")
self.priority_queue = PQDict().maxpq()
self.queue = queue.Queue()
self.downloader = Downloader()
self.parser = Parser(self.query)
self.calculator = Calculator(self.query)
self.relevance = Relevance()
self.webpages_crawled = 0
self.logger = logging.getLogger(__name__)
self.visited_urls = set()
self.sites_times = {}
def run(self):
self.heapdict = PQDict()
self.data = {}
initx = np.linspace(0, 1, self.init_points)
n_points = self.init_points
if self.measure_at is not None:
xm = [(x - self.a) / (self.b - self.a) for x in self.measure_at]
initx = np.concatenate((initx, xm))
initx.sort()
n_points += len(self.measure_at)
#initdelta = (initx[1] - initx[0])/2.
inity = []
for x in initx:
xret = self.scale_to_x(x)
yret = self.f(xret)
yield (xret, yret)
inity.append(self.get_y(yret))
if self.yscale is None:
self.yscale = max(inity) - min(inity)
#self.scale_from_y = lambda y: (y-min(inity))/(max(inity)-min(inity))
self.scale_from_y = lambda y: (y) / self.yscale / self.scalefactor
inity = [self.scale_from_y(y) for y in inity]
for (i, x) in enumerate(initx[1:-1], 1):
d = [
inity[i], initx[i - 1], inity[i - 1], initx[i + 1],
inity[i + 1]
]
self.update_data(x, d)
while n_points < self.max_points:
try:
x = self.heapdict.top()
except KeyError:
return
else:
d = self.data[x]
for point in self.evalx(x, *d):
yield point
n_points += 2
def test_destructive_iteration(self):
for trial in range(100):
size = random.randrange(1,50)
items = generateData('float', size)
dkeys, pkeys = zip(*items)
if trial & 1: # Half of the time, heapify using the constructor
pq = PQDict(items)
else: # The rest of the time, insert items sequentially
pq = PQDict()
for dkey, pkey in items:
pq[dkey] = pkey
# NOTE: heapsort is NOT a stable sorting method, so dkeys with equal priority keys
# are not guaranteed to have the same order as in the original sequence.
pkeys_heapsorted = list(pq.iterprioritykeys())
self.assertEqual(pkeys_heapsorted, sorted(pkeys))
def test_fromkeys(self):
# assign same value to all
seq = ['foo', 'bar', 'baz']
pq = PQDict.fromkeys(seq)
for k in pq:
self.assertEqual(pq[k], float('inf'))
pq = PQDict.fromkeys(seq, 10)
for k in pq:
self.assertEqual(pq[k], 10)
pq = PQDict.fromkeys(seq, maxpq=True)
for k in pq:
self.assertEqual(pq[k], float('-inf'))
# use function to calculate pkeys
seq = [ (1,2), (1,2,3,4), ('foo', 'bar', 'baz') ]
pq = PQDict.fromkeys(seq, sort_by=len)
self.assertEqual(pq[1,2], 2)
self.assertEqual(pq['foo', 'bar', 'baz'], 3)
self.assertEqual(pq[1,2,3,4], 4)
def prim(G):
""" Return MST of the given undirected graph"""
sumWeight = 0
heap = PQDict()
for u in G:
heap[u] = float("inf")
flag = [False] * len(G)
heap[0] = 0
#parents is the MST
# parents = {}
# parents[0] = 0
while len(heap) != 0:
[u, value] = heap.popitem()
flag[u] = True
sumWeight += value
for v in G[u]:
if flag[v] is False:
# parents[v] = u
heap[v] = min(heap[v], G[u][v])
return sumWeight
def djp(grafo, n):
#Se determina el nodo inicial de forma aleatoria, mediante un random en entre el numero
#nodos
inicio = random.randint(0, n - 1)
print("Nodo inicial: ", inicio)
visitado = set()
camino_mst = []
#Se genera una cola de prioridad para generar el orden optimizado de los resultados de los
#caminos
cola_prioridad = PQDict()
actual = inicio
return recorre_djp(camino_mst, grafo, actual, cola_prioridad, visitado)
def test_updates_and_deletes(self):
pq = PQDict()
items = generateData('int')
dkeys, pkeys = zip(*items)
# heapify a sequence of items
pq = PQDict(items)
for oper in range(100):
if oper & 1: #update random item
dkey = random.choice(dkeys)
p_new = random.randrange(25)
pq[dkey] = p_new
self.assertTrue(pq[dkey]==p_new)
elif pq: #delete random item
dkey = random.choice(list(pq.keys()))
del pq[dkey]
self.assertTrue(dkey not in pq)
self.check_heap_invariant(pq)
self.check_index(pq)
def AStar(problem): #Give path of data file path = problem.datapath #Data data = list() data = readFunc(path) #Initialization of a node. node = Node() node.state = problem.initState node.pathcost = 0 #Frontier is a priority queue dict of tuples:(node.pathcost, node), with key:node.state #Frontier is ordered by path cost. frontier = PQDict() frontier.additem(node.state, (node.pathcost,node)) #Explored begins as the empty set. explored = Set() while True: if len(frontier) == 0: return 'No route exists' node = frontier.popitem()[1][1] #Chooses the lowest-cost node in frontier if problem.goalState == node.state: return solution(node,problem) explored.add(node.state) possibleActions = findPossibleActions(node.state,data) for action in range(len(possibleActions)): child = childNode(problem,node,possibleActions[action],data) if (child.state not in explored) and (child.state not in frontier): frontier.additem(child.state, (child.pathcost, child)) elif (child.state in frontier) and (frontier[child.state][1].pathcost > child.pathcost): frontier[child.state] = (child.pathcost, child)
def main():
# Main output dict
output = {'results': []}
# Read JSON from file
with open('input.json', 'r') as f:
inputjson = json.load(f)
profiles = inputjson['profiles']
for profile in profiles:
pq = PQDict()
no_of_profiles = 0
matches = []
# The output for this profile in JSON format
profile_output = {'profileId': profile['id'], 'matches': []}
for other_profile in profiles:
if other_profile['id'] == profile['id']:
# dont calculate against our own profile
continue
# Calculate match percentage with OKCupid's formula
match_score = math.sqrt(
satisfaction(profile, other_profile) *
satisfaction(other_profile, profile))
# Add the first ten matches to a min-heap (PQDict)
if len(pq) < 11:
pq.additem(other_profile['id'], match_score)
else:
if match_score > pq.popitem()[1]:
pq.additem(other_profile['id'], match_score)
else:
continue
for i in range(len(pq)):
key, value = pq.popitem()
temp = {'profileId': key, 'score': value}
matches.append(temp)
# Reverse the heap and store it in the output for that profile
profile_output['matches'] = matches[::-1]
output['results'].append(profile_output)
# Write out the output JSON to file
with open('output_optimized.json', 'w') as outf:
json.dump(output, outf, indent=1)
return 0
def prim(G, start):
"""Function recives a graph and a starting node, and returns a MST"""
stopN = G.number_of_nodes() - 1
current = start
closedSet = set()
pq = PQDict()
mst = []
while len(mst) < stopN:
# print " "
# print "Current node :", current
for node in G.neighbors(current):
if node not in closedSet and current not in closedSet:
# print " neigbors: ", node
if (current, node) not in pq and (node, current) not in pq:
w = G.edge[current][node]['weight']
pq.additem((current, node), w)
closedSet.add(current)
tup, wght = pq.popitem()
while (tup[1] in closedSet):
tup, wght = pq.popitem()
mst.append(tup)
current = tup[1]
return mst
def branchAndBound(G, cutoff, ftrace):
queue = PQDict()
bestSolution = INFINITY
bestTour = []
totalNodes = len(G.nodes())
startNode = G.nodes()[0]
nodeList = G.nodes()
nodeList.remove(startNode)
queue.additem(tuple([startNode]), lowerBound(G, [startNode]))
start_time = time.time()
while len(queue) != 0:
coveredNodes, lowerBoundCurrent = queue.popitem()
elapsed_time = time.time() - start_time
if elapsed_time > cutoff:
if bestSolution == INFINITY:
return [], -1
return bestTour, bestSolution
for neighbor in G.neighbors(coveredNodes[-1]):
if not neighbor in coveredNodes:
tempNodes = list(coveredNodes)
tempNodes.append(neighbor)
if len(tempNodes) == totalNodes:
cost = findCost(G, tempNodes) + G.get_edge_data(neighbor, startNode)["weight"]
if cost < bestSolution:
bestSolution = cost
bestTour = tempNodes
ftrace.write("{0:.2f}".format(elapsed_time * 1.0) + "," + str(bestSolution) + "\n")
else:
tempLowerBound = lowerBound(G, tempNodes)
if tempLowerBound < bestSolution:
queue.additem(tuple(tempNodes), tempLowerBound)
return bestTour, bestSolution
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 prim(self, start):
p_queue = PQDict()
prev_vert = {}
key = {}
for vert in self.graph:
prev_vert[vert] = None
key[vert] = 1000
key[start] = 0
for vert in self.graph:
p_queue[vert] = key[vert]
while p_queue:
current = p_queue.popitem()[0]
for vert in self.graph[current]:
if vert in p_queue and self.graph[current][vert] < key[vert]:
prev_vert[vert] = current
key[vert] = graph[current][vert]
p_queue[vert] = graph[current][vert]
return prev_vert
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 test_infpkey(self):
dkeys = ['A', 'B', 'C', 'D', 'E', 'F', 'G']
pkeys = [1, 2, 3, 4, 5, 6, 7]
pq = PQDict(zip(dkeys, pkeys))
pq.additem('top', -float('inf'))
pq.additem('bot', float('inf'))
dkeys_sorted = [key for key in pq.iterkeys()]
self.assertEqual(dkeys_sorted[0], 'top')
self.assertEqual(dkeys_sorted[-1], 'bot')
def plan(self, start_state, goal_state):
#PQ = pqdict()
V = {}
self.goal_state = goal_state
h0 = self.heuristic(start_state, goal_state)
n0 = node(start_state, None, None, 0, h0)
key0 = np.around(n0.state, decimals=1).tostring()
PQ = PQDict(key0=n0)
i = 0
while PQ and i < 100000:
current = PQ.popitem()[1]
#print '\n'
#print current.state[2]
if (self.state_is_equal(current.path, goal_state)):
path = self.reconstruct_path(current)
return (path, current.f)
V[np.around(current.state,
decimals=1).tostring()] = copy.deepcopy(current)
#get children
children = self.getChildren(
current) #do set parent, should return an array of nodes
for child in children:
i += 1
if i % 100 == 0:
print 'A* iteration ' + str(i)
child_key = np.around(child.state, decimals=1).tostring()
if child_key in V:
if child.f >= V[child_key].f:
continue
if (child_key in PQ):
existing_child = PQ[child_key]
if (child.g >= existing_child.g):
continue
else:
PQ.updateitem(child_key, child)
else:
#print child.state, current.state
#pdb.set_trace()
#if(child.state[2] < 0):
# pdb.set_trace()
PQ.additem(child_key, child)
print 'A* Failed'
return (None, None)
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
def main():
# Main output dict
output = {'results': []}
# Read JSON from file
with open('input.json', 'r') as f:
inputjson = json.load(f)
profiles = inputjson['profiles']
for profile in profiles:
pq = PQDict()
no_of_profiles = 0
matches=[]
# The output for this profile in JSON format
profile_output = {'profileId': profile['id'],
'matches': []}
for other_profile in profiles:
if other_profile['id'] == profile['id']:
# dont calculate against our own profile
continue
# Calculate match percentage with OKCupid's formula
match_score = math.sqrt(satisfaction(profile, other_profile) * satisfaction(other_profile, profile))
# Add the first ten matches to a min-heap (PQDict)
if len(pq) < 11:
pq.additem(other_profile['id'],match_score)
else:
if match_score > pq.popitem()[1]:
pq.additem(other_profile['id'],match_score)
else:
continue
for i in range(len(pq)):
key,value = pq.popitem()
temp = {'profileId': key,
'score': value}
matches.append(temp)
# Reverse the heap and store it in the output for that profile
profile_output['matches'] = matches[::-1]
output['results'].append(profile_output)
# Write out the output JSON to file
with open('output_optimized.json', 'w') as outf:
json.dump(output, outf, indent=1)
return 0
def primWeight(G):
""" Return MST of the given undirected graph"""
vis = set()
tot_weight = 0
pq = PQDict()
for node in G.nodes():
pq.additem(node, float("inf"))
curr = pq.pop()
vis.add(curr)
while len(pq) > 0:
for s, nod, wt in G.edges(curr, data=True):
if nod not in vis and wt['weight'] < pq[nod]:
pq.updateitem(nod, wt['weight'])
if len(pq) > 0:
top = pq.top()
vis.add(top)
tot_weight += pq[top]
curr = pq.pop()
return tot_weight
def run(self):
self.heapdict = PQDict()
self.data = {}
initx = np.linspace(0, 1, self.init_points)
n_points = self.init_points
if self.measure_at is not None:
xm = [(x-self.a)/(self.b-self.a) for x in self.measure_at]
initx = np.concatenate((initx, xm))
initx.sort()
n_points += len(self.measure_at)
#initdelta = (initx[1] - initx[0])/2.
inity = []
for x in initx:
xret = self.scale_to_x(x)
yret = self.f(xret)
yield (xret, yret)
inity.append(self.get_y(yret))
if self.yscale is None:
self.yscale = max(inity)-min(inity)
#self.scale_from_y = lambda y: (y-min(inity))/(max(inity)-min(inity))
self.scale_from_y = lambda y: (y) / self.yscale / self.scalefactor
inity = [self.scale_from_y(y) for y in inity]
for (i, x) in enumerate(initx[1:-1],1):
d = [inity[i],
initx[i-1], inity[i-1],
initx[i+1], inity[i+1]]
self.update_data(x, d)
while n_points < self.max_points:
try:
x = self.heapdict.top()
except KeyError:
return
else:
d = self.data[x]
for point in self.evalx(x, *d): yield point
n_points += 2
def primWeight(G):
""" Return MST of the given undirected graph"""
vis = set()
tot_weight = 0
pq = PQDict()
for node in G.nodes():
pq.additem(node, float("inf"))
curr = pq.pop()
vis.add(curr)
while len(pq) > 0:
for s,nod, wt in G.edges(curr, data=True):
if nod not in vis and wt['weight'] < pq[nod]: pq.updateitem(nod, wt['weight'])
if len(pq)>0:
top = pq.top()
vis.add(top)
tot_weight += pq[top]
curr = pq.pop()
return tot_weight
def plan(self, start_state, goal_state):
#PQ = pqdict()
V = {}
self.goal_state = goal_state
h0 = self.heuristic(start_state, goal_state)
n0 = node(start_state, None, None, 0, h0)
key0 = np.around(n0.state, decimals = 1).tostring()
PQ = PQDict(key0=n0)
i = 0
while PQ and i < 100000:
current = PQ.popitem()[1]
#print '\n'
#print current.state[2]
if(self.state_is_equal(current.path, goal_state)):
path = self.reconstruct_path(current)
return (path, current.f)
V[np.around(current.state, decimals = 1).tostring()] = copy.deepcopy(current)
#get children
children = self.getChildren(current)#do set parent, should return an array of nodes
for child in children:
i += 1
if i%100 == 0:
print 'A* iteration '+str(i)
child_key = np.around(child.state, decimals = 1).tostring()
if child_key in V:
if child.f >= V[child_key].f:
continue
if (child_key in PQ):
existing_child = PQ[child_key]
if(child.g >= existing_child.g):
continue
else:
PQ.updateitem(child_key, child)
else:
#print child.state, current.state
#pdb.set_trace()
#if(child.state[2] < 0):
# pdb.set_trace()
PQ.additem(child_key,child)
print 'A* Failed'
return (None, None)
def test_pop(self):
# pop selected item - return pkey
pq = PQDict(A=5, B=8, C=1)
pkey = pq.pop('B')
self.assertEqual(pkey, 8)
pq.pop('A')
pq.pop('C')
self.assertRaises(KeyError, pq.pop, 'A')
self.assertRaises(KeyError, pq.pop, 'does_not_exist')
# no args and empty - throws
self.assertRaises(KeyError, pq.pop) #pq is now empty
# no args - return top dkey
pq = PQDict(A=5, B=8, C=1)
self.assertEqual(pq.pop(), 'C')
def branchAndBound(G, cutoff, ftrace):
queue = PQDict()
bestSolution = INFINITY
bestTour = []
totalNodes = len(G.nodes())
startNode = G.nodes()[0]
nodeList = G.nodes()
nodeList.remove(startNode)
queue.additem(tuple([startNode]), lowerBound(G, [startNode]))
start_time = time.time()
while (len(queue) != 0):
# print count
# count+=1
coveredNodes, lowerBoundCurrent = queue.popitem()
# coveredNodes=list(coveredNodes)
elapsed_time = time.time() - start_time
if elapsed_time > cutoff:
if bestSolution == INFINITY:
return -1, []
return bestTour, bestSolution
for neighbor in G.neighbors(coveredNodes[-1]):
if not neighbor in coveredNodes:
tempNodes = list(coveredNodes)
tempNodes.append(neighbor)
if (len(tempNodes) == totalNodes):
cost = findCost(G, tempNodes) + G.get_edge_data(
neighbor, startNode)['weight']
if (cost < bestSolution):
bestSolution = cost
bestTour = tempNodes
ftrace.write("{0:.2f}".format(elapsed_time * 1.0) +
',' + str(bestSolution) + '\n')
else:
tempLowerBound = lowerBound(G, tempNodes)
if tempLowerBound < bestSolution:
queue.additem(tuple(tempNodes), tempLowerBound)
# else:
# print 'prune'
return bestTour, bestSolution
def prim(G: "networx Graph object", start: "1X2 Tuple(Node)") -> (list, list):
"""
Function recives a graph and a starting node,
and returns the MST and the history of the algorithm
"""
MST_len = G.number_of_nodes() - 1
current = start
visited = set()
# Priority Queue
#(keeps the item with the lowest value on the top -
#in this case the weight of an edge)
pq = PQDict()
# Minimal spamming tree
mst = []
steps = []
history = []
# While the MST has N - 1 edges (N is the total nodes)
while len(mst) < MST_len:
# Get the neighbors
# history.append((current, G.neighbors(current)))
for node in G.neighbors(current):
if node not in visited and current not in visited:
# Append the history
steps.append((current, node))
if (current, node) not in pq and (node, current) not in pq:
w = G.edge[current][node]['weight']
pq.additem((current, node), w)
# We have visited the node
visited.add(current)
# Tup is the edge "(X, Y)", wght is the cost
tup, wght = pq.popitem()
# Get the lowest edge if we haven't visited the node
while (tup[1] in visited):
tup, wght = pq.popitem()
# Append the edge to the minimum spanning tree
mst.append(tup)
history.append([tup, steps])
steps = list()
# Update the current node to the one we visit based on the minimal weight
current = tup[1]
return mst, history
def executa_prim(G, primeiro_no):
no_de_parada = G.number_of_nodes() - 1
atual = primeiro_no
visitados = set() # Tira os repetidos e é uma Hash
min_heap = PQDict() # É um min_heap
mst = []
peso_total = 0
pesos = []
while len(mst) < no_de_parada:
# print("Esse é o visitados", visitados)
# print("Esse é o min_heap: ", min_heap)
for noVizinho in G.neighbors(atual):
# print("Esse é o no: ", noVizinho)
# print("Esse é o atual: ", atual)
if noVizinho not in visitados and atual not in visitados:
if (atual,
noVizinho) not in min_heap and (noVizinho,
atual) not in min_heap:
peso_aresta = G[atual][noVizinho]['weight']
min_heap.additem((atual, noVizinho), peso_aresta)
visitados.add(atual)
aresta, peso = min_heap.popitem() # Tira a raiz
while (aresta[1] in visitados): # Aresta[1] é o vizinho
aresta, peso = min_heap.popitem(
) # Vai tirando a raiz até encontrar um vizinho não visitado
# print("Essa é a aresta: ", aresta)
# print("Esse é o peso: ", peso)
peso_total += peso
pesos.append(peso)
mst.append(aresta)
atual = aresta[1]
return mst, peso_total, pesos
def rout(self, start, desti, edge_desty, vedge_desty, nodes_order, U, G2,
points, speed_dict):
path = []
Q, P_hat = [], []
neigh = list(G2.successors(start))
print('neigh %d' % len(neigh))
start_ax = points[start]
desti_ax = points[desti]
s_d_arr = [desti_ax[0] - start_ax[0], desti_ax[1] - start_ax[1]]
all_expire = 0.0
def get_weight(p_hat):
w_p_hat = {}
if p_hat in edge_desty:
w_p_hat = edge_desty[p_hat]
elif p_hat in speed_dict:
w_p_hat = speed_dict[p_hat]
elif p_hat in vedge_desty:
w_p_hat = vedge_desty[p_hat]
return w_p_hat
def get_maxP(w_p_, vv):
p_max = 0.0
for pk in w_p_:
w_pk = w_p_[pk]
pk_ = int(int(pk) / self.sigma)
if int(pk) % self.sigma == 0: pk_ += 1
p_max += float(w_pk) * U[vv][pk_]
return p_max
has_visit = set()
has_visit.add(start)
Que = PQDict.maxpq()
Q = {}
for vi in neigh:
if vi in has_visit: continue
else: has_visit.add(vi)
p_hat = start + '-' + vi
w_p_hat = get_weight(p_hat)
w_min = min([float(l) for l in w_p_hat.keys()])
p_order = nodes_order[vi] #p_hat]
tcost1 = time.time()
inx_min = np.argwhere(U[vi] > 0)
if len(inx_min) == 0:
#print('u 0 vd: %s %s'%(vi, desti))
continue
inx_min = inx_min[0][0]
all_expire += time.time() - tcost1
cost_time = w_min + inx_min * self.sigma
if cost_time <= self.T:
tcost1 = time.time()
p_max = get_maxP(w_p_hat, vi)
all_expire += time.time() - tcost1
Que[p_hat] = p_max
Q[p_hat] = (p_max, w_p_hat, cost_time)
#print('len Q %d'%len(Q))
QQ = {}
p_best_p, flag = 'none', False
p_max_m, p_best_cost, p_w_p = -1, -1, -1
if len(Q) == 0: return 'none1', -1, -1, -1, all_expire, -1
all_rounds = 0
while len(Q) != 0:
(p_hat, pqv) = Que.popitem()
all_rounds += 1
(p_max, w_p_hat, cost_time) = Q[p_hat]
del Q[p_hat]
a = p_hat.rfind('-')
v_l = p_hat[a + 1:]
if v_l == desti:
p_best_p = p_hat
p_max_m = p_max
p_best_cost = cost_time
p_w_p = w_p_hat
flag = True
break
neigh = list(G2.successors(v_l))
cost_sv = min([float(l) for l in w_p_hat.keys()])
vd_d_arr = [
points[desti][0] - points[v_l][0],
points[desti][1] - points[v_l][1]
]
for u in neigh:
if u == desti:
vu = v_l + '-' + u
w_vu = get_weight(vu)
if len(w_vu) == 0: cost_vu = 0
else: cost_vu = min([float(l) for l in w_vu.keys()])
tcost1 = time.time()
inx_min = np.argwhere(U[u] > 0)
inx_min = inx_min[0][0]
p_best_p = p_hat + ';' + vu
p_w_p = self.conv(w_p_hat, w_vu)
p_max_m = get_maxP(w_p_hat, u)
all_expire += time.time() - tcost1
p_best_cost = cost_sv + cost_vu + inx_min * self.sigma
flag = True
break
if u in has_visit:
#print('u1 %s, vd %s'%(u, desti))
continue
else:
has_visit.add(u)
if u in p_hat:
#print('u2 %s, vd %s'%(u, desti))
continue
vu = v_l + '-' + u
w_vu = get_weight(vu)
if len(w_vu) == 0:
#print('vu %s, vd %s'%(vu, desti))
continue
cost_vu = min([float(l) for l in w_vu.keys()])
p_order = nodes_order[u] #p_hat]
tcost1 = time.time()
inx_min = np.argwhere(U[u] > 0)
if len(inx_min) == 0:
#print('inx vu %s, vd %s'%(vu, desti))
continue
inx_min = inx_min[0][0]
all_expire += time.time() - tcost1
cost_time = cost_sv + cost_vu + inx_min * self.sigma
if cost_time <= self.T:
p_hat_p = p_hat + ';' + vu
w_p_hat_p = self.conv(w_p_hat, w_vu)
tcost1 = time.time()
p_hat_max = get_maxP(w_p_hat, u)
all_expire += time.time() - tcost1
QQ[p_hat_p] = (p_hat_max, w_p_hat_p, cost_time)
if flag: break
if len(Q) == 0:
Q = copy.deepcopy(QQ)
for qqk in QQ:
Que[qqk] = QQ[qqk][0]
QQ = {}
return p_best_p, p_max_m, p_best_cost, p_w_p, all_expire, all_rounds
class Battle(object):
"""
Fight a battle between two teams of oeo
"""
_turn_stage_map = {
8: '02',
7: '03',
6: '04',
5: '05',
4: '06',
3: '07',
2: '08',
1: '09',
0: '10',
-1: '11',
-2: '12',
-3: '13',
-4: '14',
-5: '15',
-6: '16',
-7: '17'
}
_turn_spi_map = {
0: '01',
1: '02',
2: '03',
3: '04',
4: '05',
5: '06',
6: '07',
7: '08',
8: '09',
9: '10',
10: '11',
11: '12'
}
def __init__(self, oeos, a_id, a, a_max_fielded, b_id, b, b_max_fielded):
assert all(isinstance(oeo, Oeo) for oeo in oeos.values()), \
"oeos is not a dict of oeo_id:oeo"
assert isinstance(a_id, str), "'a_id' is not a string"
assert isinstance(a, set), "'a' is not a set of oeo_id"
assert isinstance(a_max_fielded, int), "'a_max_fielded' is not an int"
assert isinstance(b_id, str), "'b_id' is not a string"
assert isinstance(b, set), "'b' is not a set of oeo_id"
assert isinstance(b_max_fielded, int), "'b_max_fielded' is not an int"
# Throw error if each oeo_id is not unique between teams
if a & b:
raise ValueError(f"{a & b} oeo_id(s) not unique between teams")
self._oeo = oeos
self._a_id = a_id
self._a = a
self._b_id = b_id
self._b = b
self._turn_number = 0
self._field = Field(a_id, a_max_fielded, b_id, b_max_fielded)
self._pending_sim_events = PQDict()
self._processed_sim_events = []
move_set = set()
for o in itertools.chain(self._oeo.values()):
for move in o.moves:
move_set.add(move)
moves = Move.load_moves(move_set)
self._moves = moves
# self._speed_priority_list = None
self._setup_axel_events()
@property
def teams(self):
return {self._a_id: self._a, self._b_id: self._b}
@property
def field(self):
return self._field
def _setup_axel_events(self):
"""
Initialise axel events
"""
# sim_output_message(msg)
self.sim_output_message = Event()
# event_choose_deployments(team_id, non_fielded_team, empty_positions)
# non_fielded_team: oeo from the team who are not on the field
# but are conscious
# empty_positions: empty positions on the field into which an oeo
# could be deployed
self.event_choose_deployments = Event()
# event_choose_actions(team_id, oeo_requiring_actions)
# oeo_requiring_actions: oeo that are on the field and need to select
# an action for the current turn
self.event_choose_actions = Event()
def run(self):
"""
Run the battle
:return: id of the victor
:rtype: str
"""
logger.info(f"{self._a_id} vs {self._b_id}...")
ta = {oeo_id: self._oeo[oeo_id] for oeo_id in self._a}
tb = {oeo_id: self._oeo[oeo_id] for oeo_id in self._b}
logger.debug(f"{self._a_id}'s team:\n{ta}")
logger.debug(f"{self._b_id}'s team:\n{tb}")
# Add the BEGIN_TURN SimEvent for turn 1
self._pending_sim_events.additem(SimEvent(SimEventType.BeginTurn), 1)
victor = None
# While there are pending sim events, loop until we break when a
# battle end condition is met
while self._pending_sim_events:
# Remove any oeo with 0 HP from the field
self._remove_unconscious_oeo()
# Check teams: if all oeo in the battle are unconscious,
# then end battle as a draw
# if all oeo in team A are unconscious,
# then end battle as win for team B
# if all oeo in team B are unconscious,
# then end battle as win for team A
if not any(oeo.conscious for oeo in self._oeo.values()):
logger.info("All oeo on both sides of the battle are "
"unconscious, the battle is a draw")
victor = "DRAW"
break
elif not any(self._oeo[oeo_id].conscious for oeo_id in self._a):
logger.info(f"{self._a_id}'s team are unconscious, "
f"{self._b_id} wins the battle")
victor = self._b_id
break
elif not any(self._oeo[oeo_id].conscious for oeo_id in self._b):
logger.info(f"{self._b_id}'s team are unconscious, "
f"{self._a_id} wins the battle")
victor = self._a_id
break
# Let both sides choose oeo to deploy
self._choose_deployments()
logger.debug(f"{self._a_id}'s side: {self._field[self._a_id]}")
logger.debug(f"{self._b_id}'s side: {self._field[self._b_id]}")
# Check both sides of the field:
# if team A side is empty, then end as win for team B
# if team B side is empty, then end as win for team A
if self._field[self._a_id].is_empty():
logger.info(f"{self._a_id} yields, "
f"{self._b_id} wins the battle")
victor = self._b_id
break
elif self._field[self._b_id].is_empty():
logger.info(f"{self._b_id} yields, "
f"{self._a_id} wins the battle")
victor = self._a_id
break
# Pop the next event to be processed, add it to the
# processed events list, and process it
event, event_priority = self._pending_sim_events.popitem()
event_complete = 0
event_type = event.event_type
if event_type is SimEventType.BeginTurn:
event_complete = self._process_begin_turn()
elif event_type is SimEventType.UseMove:
event_complete = self._process_use_move(**event.data)
elif event_type is SimEventType.UseItem:
# event_complete = self._process_use_item
pass
elif event_type is SimEventType.Switch:
# event_complete = self._process_switch_oeo
pass
elif event_type is SimEventType.Run:
# event_complete = self._process_run
pass
else:
raise ValueError(f"Invalid event_type: {event}")
self._processed_sim_events.append(
(event_priority, event, event_complete))
logger.debug(f"Pending events: {self._pending_sim_events}")
logger.info(f"Processed events: {self._processed_sim_events}")
return victor
def _process_begin_turn(self):
# Increment the turn number and add the BeginTurn SimEvent
# for the next turn
self._turn_number += 1
logger.debug(f"Processing BeginTurn({self._turn_number}) SimEvent")
self._pending_sim_events.additem(SimEvent(SimEventType.BeginTurn),
self._turn_number + 1)
# TODO: Update status conditions - burn, poison, landing from flight,
# then remove unconscious oeo from field
# Choose the actions for the oeo on the field this turn, calculate the
# order in which the actions should occur, and add them to the pending
# sim events priority queue
self._choose_actions()
return 1
def _process_use_move(self, user_id, move_id, target_id):
logger.debug("Processing UseMove SimEvent")
user, move, target = self._oeo[user_id], self._moves[move_id], \
self._oeo[target_id]
user_is_fielded = self._is_fielded(user_id)
target_is_fielded = self._is_fielded(target_id)
if user_is_fielded and target_is_fielded:
logger.info(f"{user_id} attacks {target_id} using {move_id}")
df_id = getattr(move, "df_id", "Standard")
damage_function = get_damage_function(df_id)
damage = damage_function(user, move, target)
hp = target.current_hp
target.current_hp -= damage
logger.info(f"{target_id}'s HP = {hp}-{damage} "
f"= {target.current_hp}")
return 1
else:
logger.debug(f"User on field = {user_is_fielded}, "
f"Target on field = {target_is_fielded}")
return -1
def _process_use_item(self, item, target):
logger.debug("Processing UseItem SimEvent")
return 0
def _process_switch(self, user, target):
logger.debug("Processing Switch SimEvent")
return 0
def _process_run(self, user, run_type):
logger.debug("Processing Run SimEvent")
return 0
def _calculate_event_priority(self, turn, priority, speed_priority):
"""
:param turn: the turn in which the event is to be actioned
:param priority: the stage of the turn in which the event is \
to be actioned
:param speed_priority: the speed_priority of the oeo undertaking \
the event
:return: the priority of the event to be actioned
"""
return float(f"{turn}.{self._turn_stage_map[priority]}"
f"{self._turn_spi_map[speed_priority]}")
def _remove_unconscious_oeo(self):
"""
Withdraw unconscious oeo from the field
"""
for team_id in [self._a_id, self._b_id]:
oeo_to_remove = [
oeo_id for oeo_id in self._field[team_id].fielded
if self._oeo[oeo_id].conscious is False
]
for oeo_id in oeo_to_remove:
self._field.withdraw(team_id, oeo_id)
def _is_fielded(self, oeo_id):
"""
:param oeo_id:
:return: True if oeo_id is on the field else False
"""
if (oeo_id in self._field[self._a_id]) or \
(oeo_id in self._field[self._b_id]):
return True
else:
return False
def _choose_deployments(self):
"""
Choose and make deployments to the field
"""
for team_id in [self._a_id, self._b_id]:
empty_positions = self._field[team_id].empty_positions
logger.debug(f"Empty positions on {team_id}'s side: "
f"{empty_positions}")
if empty_positions:
fielded = self._field[team_id].fielded
benched = [
oeo_id for oeo_id in self.teams[team_id]
if oeo_id not in fielded and self._oeo[oeo_id].conscious
]
logger.debug(f"Benched on {team_id}'s side: {benched}")
if benched:
deployments = self._poll_deployments(
team_id, benched, empty_positions)
logger.debug(f"{team_id}'s oeo to deploy: {deployments}")
for position, oeo_id in deployments.items():
self._field.deploy(team_id, oeo_id, position)
def _poll_deployments(self, team_id, non_fielded_team, empty_positions):
"""
Polls for deployments for team_id
:return: dict of position:oeo_id
"""
logger.debug(f"Polling for deployments from {team_id}")
results = self.event_choose_deployments(team_id,
list(non_fielded_team),
empty_positions)
flag, result, handler = results[0]
if flag:
for position, oeo_id in result.items():
# Ensure that each oeo is only deployed to one
# field position at most
if list(result.values()).count(oeo_id) > 1:
raise Exception(f"{oeo_id} can not be deployed to more "
"than one field position")
# Ensure 0 >= position < len(self._field[team_id])
if position < 0 or position >= len(self._field[team_id]):
raise Exception(f"Position {position} is out of bounds"
f"(0-{len(self._field[team_id]) - 1})")
# Ensure oeo_id is in self.team[team_id]
if oeo_id not in self.teams[team_id]:
raise Exception(f"{oeo_id} is not in {team_id}'s team")
return result
else:
raise Exception("Exception in choose_deployments handler") \
from result
def _choose_actions(self):
"""
Choose and schedule actions for oeo on the field
"""
# Create the speed_priority_list for this turn
oeo_against_speed = [(oeo_id, oeo.speed)
for oeo_id, oeo in self._oeo.items()]
oeo_against_speed.sort(key=itemgetter(1), reverse=True)
logger.debug(f"Speed Priority List: {oeo_against_speed}")
speed_priority_list = [t[0] for t in oeo_against_speed]
a_fielded = self._field[self._a_id].fielded
b_fielded = self._field[self._b_id].fielded
# Create action_map dictionary of oeo_id to action:None for
# fielded oeo and update it from future_action dictionary
action_map = {
oeo_id: None
for oeo_id in itertools.chain(a_fielded, b_fielded)
}
# todo: Update action_map from future_actions dictionary
logger.debug(f"Initial action map for turn {self._turn_number}: "
f"{action_map}")
# Call event_choose_actions for each team for oeo that do not have
# an action to perform (action is None)
a_oeo_requiring_actions = [
oeo_id for oeo_id, action in action_map.items()
if (oeo_id in a_fielded) and (action is None)
]
a_oeo_requiring_actions.sort(key=lambda x: self._oeo[x].speed,
reverse=True)
b_oeo_requiring_actions = [
oeo_id for oeo_id, action in action_map.items()
if (oeo_id in b_fielded) and (action is None)
]
b_oeo_requiring_actions.sort(key=lambda x: self._oeo[x].speed,
reverse=True)
logger.debug(f"{self._a_id}'s oeo requiring actions: "
f"{a_oeo_requiring_actions}")
logger.debug(f"{self._b_id}'s oeo requiring actions: "
f"{b_oeo_requiring_actions}")
a_actions = self._poll_actions(self._a_id, a_oeo_requiring_actions)
b_actions = self._poll_actions(self._b_id, b_oeo_requiring_actions)
logger.info(f"{self._a_id}'s actions chosen: {a_actions}")
logger.info(f"{self._b_id}'s actions chosen: {b_actions}")
# For each {oeo_id: action} in dictionary returned by the event
# handlers, add it to the action map if the oeo does not already have
# an action for this turn in the action map
for oeo_id, action in itertools.chain(a_actions.items(),
b_actions.items()):
if action_map[oeo_id] is not None:
raise Exception(f"{oeo_id} already had an action "
"for this turn")
action_map[oeo_id] = action
logger.debug(f"Final action map for turn {self._turn_number}: "
f"{action_map}")
# For each {oeo_id: action} in the action map add the SimEvent for
# the action to the pending sim events queue
for oeo_id, action in action_map.items():
if action:
if action.event_type == SimEventType.UseMove:
target = action.data["target_id"]
move_id = action.data["move_id"]
move_priority = self._moves[move_id].priority
oeo_priority = speed_priority_list.index(oeo_id)
s = SimEvent(SimEventType.UseMove,
user_id=oeo_id,
target_id=target,
move_id=move_id)
ep = self._calculate_event_priority(
self._turn_number, move_priority, oeo_priority)
self._pending_sim_events.additem(s, ep)
if action.event_type == SimEventType.UseItem:
pass
def _poll_actions(self, team_id, oeo_requiring_actions):
"""
Polls for actions from team_id
:return: dict of oeo_id:action
"""
logger.debug(f"Polling for actions from {team_id}")
results = self.event_choose_actions(team_id, oeo_requiring_actions)
flag, result, handler = results[0]
if flag:
for oeo_id, action in result.items():
# Ensure oeo is on the field
if not self._is_fielded(oeo_id):
raise Exception(f"{oeo_id} is not on the field")
# Ensure oeo is in team_id
if oeo_id not in self.teams[team_id]:
raise Exception(f"{oeo_id} is not on {team_id}'s side")
# Ensure action is SimEvent
if not isinstance(action, SimEvent):
raise Exception("Action is not a SimEvent")
# Ensure action.event_type is UseMove, UseItem, Switch or Run
if action.event_type not in [
SimEventType.UseMove, SimEventType.UseItem,
SimEventType.Switch, SimEventType.Run
]:
raise Exception("Action event type not UseMove, UseItem, "
"Switch or Run")
return result
else:
raise Exception("Exception in choose_actions handler") from result
def rout(self, start, desti, edge_desty, vedge_desty, nodes_order, U, G,
points, pred):
path = []
Q, P_hat = [], []
neigh = list(G.successors(start))
print('neigh %d' % len(neigh))
start_ax = points[start]
desti_ax = points[desti]
s_d_arr = [desti_ax[0] - start_ax[0], desti_ax[1] - start_ax[1]]
all_expire2 = 0.0
def get_weight(p_hat):
w_p_hat = {}
if p_hat in edge_desty:
w_p_hat = edge_desty[p_hat]
elif p_hat in vedge_desty:
w_p_hat = vedge_desty[p_hat]
return w_p_hat
def get_maxP3(w_p_, vv, vi_getmin):
p_max = 0.0
for pk in w_p_:
w_pk = w_p_[pk]
pk_ = int(int(pk) / self.sigma)
if int(pk) % self.sigma == 0: pk_ += 1
if (1.0 * self.T - float(pk)) > vi_getmin:
p_max += float(w_pk)
return p_max
def get_vigetmin(vv):
start1 = time.time()
v = vv
path_ = [v]
while v != desti:
v = pred[v]
path_.append(v)
spath = path_[0]
vi_getmin = 0.0
for epath in path_[1:]:
key = spath + '-' + epath
vi_getmin += min(abs(float(l)) for l in edge_desty[key].keys())
spath = epath
expire_time = time.time() - start1
return vi_getmin, expire_time
has_visit = set()
has_visit.add(start)
QQue = PQDict.maxpq()
Q = {}
for vi in neigh:
if vi in has_visit: continue
else: has_visit.add(vi)
p_hat = start + '-' + vi
w_p_hat = get_weight(p_hat)
w_min = min([float(l) for l in w_p_hat.keys()])
p_order = nodes_order[vi] #p_hat]
vi_getmin, ex_p = get_vigetmin(vi)
all_expire2 += ex_p
cost_time = w_min + vi_getmin
if cost_time <= self.T:
p_max = max(list(w_p_hat.values()))
QQue[p_hat] = p_max
Q[p_hat] = (p_max, w_p_hat, cost_time)
print('len Q %d' % len(Q))
QQ = {}
p_best_p, flag = 'none', False
p_max_m, p_best_cost, p_w_p = -1, -1, -1
if len(Q) == 0: return 'none1', -1, -1, -1, all_expire2, -1
all_rounds = 0
while len(Q) != 0:
(p_hat, pqv) = QQue.popitem()
all_rounds += 1
(p_max, w_p_hat, cost_time) = Q[p_hat]
del Q[p_hat]
a = p_hat.rfind('-')
v_l = p_hat[a + 1:]
if v_l == desti:
p_best_p = p_hat
p_max_m = p_max
p_best_cost = cost_time
p_w_p = w_p_hat
flag = True
break
neigh = list(G.successors(v_l))
cost_sv = min([float(l) for l in w_p_hat.keys()])
vd_d_arr = [
points[desti][0] - points[v_l][0],
points[desti][1] - points[v_l][1]
]
for u in neigh:
if u == desti:
vu = v_l + '-' + u
w_vu = get_weight(vu)
if len(w_vu) == 0: cost_vu = 0
else: cost_vu = min([float(l) for l in w_vu.keys()])
vi_getmin, ex_p = get_vigetmin(u)
all_expire2 += ex_p
p_best_p = p_hat + ';' + vu
p_w_p = self.conv(w_p_hat, w_vu)
p_max_m = max(list(p_w_p.values()))
p_best_cost = cost_sv + cost_vu + vi_getmin #inx_min*self.sigma
flag = True
break
if u in has_visit:
#print('u1 %s'%u)
continue
else:
has_visit.add(u)
if u in p_hat:
#print('u2 %s'%u)
continue
vu = v_l + '-' + u
w_vu = get_weight(vu)
if len(w_vu) == 0:
#print('vu %s'%vu)
continue
cost_vu = min([float(l) for l in w_vu.keys()])
p_order = nodes_order[u] #p_hat]
vi_getmin, ex_p = get_vigetmin(u)
all_expire2 += ex_p
cost_time = cost_sv + cost_vu + vi_getmin #inx_min*self.sigma
if cost_time <= self.T:
p_hat_p = p_hat + ';' + vu
w_p_hat_p = self.conv(w_p_hat, w_vu)
p_hat_max = max(list(w_p_hat_p.values()))
QQ[p_hat_p] = (p_hat_max, w_p_hat_p, cost_time)
if flag: break
if len(Q) == 0:
Q = copy.deepcopy(QQ)
for qqk in QQ:
QQue[qqk] = QQ[qqk][0]
QQ = {}
return p_best_p, p_max_m, p_best_cost, p_w_p, all_expire2, all_rounds
