def __init__(self, file_name):
output_cap, server_fee, deploy_cost, cus_list, tol_demand, graph \
= read_graph(file_name)
self.p = server_fee
self.q = output_cap
self.o = deploy_cost
self.cus_list = cus_list
self.tol_demand = tol_demand
self.graph = graph
self.n = graph.number_of_nodes()
self.m = graph.number_of_edges()
self.level_num = len(output_cap)
self.cus_num = len(cus_list)
# Add "super source"
source = self.n
self.graph.add_node(source, demand=self.tol_demand)
for i in range(self.n):
self.graph.add_edge(source, i, cost=0, capacity=1000000)
self.x = {
} # 0-1 decision variables: x[j,k] means whether node j is deployed with a server of level k
self.f = {
} # Integer decision variables: f[i,j] means the flow on edge (i,j)
self.c = {} # c[i,j] means the unit cost of edge (i,j)
self.u = {} # u[i,j] means the capacity of edge (i,j)
self.b = {} # b[i] means the demand of node i
def check_allpaths(graph_filename, paths_filename):
g = read_graph(graph_filename)
for line_no, path in iter_allpaths(g, paths_filename):
success, error = check_path(g, path)
if success:
continue
print "%d: %s" % (line_no, error)
def __init__(self, file_name="./case_example_3/mid/case0.txt"):
output_cap, server_fee, deploy_cost, cus_list, total_demand, G = read_graph(file_name)
self.output_cap = output_cap
self.server_fee = server_fee
self.deploy_cost = deploy_cost
self.cus_list = cus_list
self.total_demand = total_demand
self.G = G
self.spl = nx.all_pairs_shortest_path_length(self.G)
self.compute_node_features()
def __init__(self, f):
self.open_cost, self.cus_list, self.tol_demand, self.graph = read_graph(
f)
self.n = self.graph.number_of_nodes()
self.m = self.graph.number_of_edges()
self.cus_num = len(self.cus_list)
# Add "super source"
source = self.n
self.graph.add_node(source, demand=self.tol_demand)
for i in range(self.n):
self.graph.add_edge(source, i, cost=0, capacity=1000000)
self.x = {
} # Open server decision variables: x[j] == 1 if node j is deployed with a server
self.f = {} # f[i,j] means the flow in edge (i,j) (Decision Variables)
self.c = {} # c[i,j] means the cost of edge (i,j)
self.u = {} # u[i,j] means the capacity of edge (i,j)
self.b = {} # b[i] means the demand of node i
import networkx as nx from read_graph import read_graph from common.pipeline import Pipeline from common.feature_generators import * # Loading PPI graph Graph = read_graph(directed=False, threshold=600) pipeline = Pipeline(NeighbouringConductance(range=3)) _ = pipeline.apply(Graph)
def configs_for_allpaths(graph_filename, paths_filename):
g = read_graph(graph_filename)
for line_no, path in iter_allpaths(g, paths_filename):
print "%d: %r %r" % (line_no, path, path_to_configs(g, path))
import networkx as nx from read_graph import read_graph from common.pipeline import Pipeline from common.feature_generators import * # Loading PPI graph Graph, node_names = read_graph(directed=False) pipeline = Pipeline(ExpectedDegree(default_recomputing=False, default_dump=True)) _ = pipeline.apply(Graph)
# 1. Помещаем все вершины в кучу, расставляя метки
# 2. Извлекаем вершину из кучи
# 3. Для каждого соседа:
# 3.1 Если расстояние от вершины до соседа + метка, меньше расстояния из кучи обновить расстояние в куче
# 4. Сохранить расстояния для вершины
from read_graph import read_graph
from dejkstra_simple import dejkstra_simple
from dejkstra import dejkstra
from print_pathes import print_pathes
import time
EXAMPLE_STR = 'examples/8000_0.5.txt'
N_TIMES = 10
input = read_graph(EXAMPLE_STR)
acc_simple = 0.0
acc_heaped = 0.0
i = 0
while i < N_TIMES:
i = i + 1
start_time_s = time.time()
pathes_s = dejkstra_simple(input)
acc_simple = acc_simple + (time.time() - start_time_s)
start_time = time.time()
pathes = dejkstra(input)
acc_heaped = acc_heaped + (time.time() - start_time)
print('Simple %s' % (acc_simple / N_TIMES))
_, node = queue.get()
neighbors = graph[node] # get node list connected to node
for edge in neighbors:
_, node_id, node_w = edge.a, edge.b, edge.w
# if not processed yet, update cost
if not self.processed.get(node_id):
queue.put((self.cost[node] + node_w, node_id))
# update cost if cost is less
if self.cost[node] + node_w < self.cost[node_id]:
self.cost[node_id] = self.cost[node] + node_w
self.from_[node_id] = node
# after update all its neighbors, mark node as processed
self.processed[node] = True
return self.cost
if __name__ == "__main__":
from read_graph import read_graph
filename = "/home/buxizhizhoum/1-Work/2-Codes/algorithm/graph/weighted_graph/test_graph.txt"
graph = read_graph(filename)
path_obj = ShortestPath(graph, start=0)
print(path_obj.find_path())
print(path_obj.from_)
# path_obj.show_path(end=3)
done_mask = end_moments == dt
done_works = chosen_works[done_mask]
all_done_works = done_works if len(all_done_works) == 0 \
else np.append(all_done_works, done_works, axis=0)
chosen_works = chosen_works[np.invert(done_mask)]
happened_events = get_available_events(matrix, all_done_works)
available_works = get_available_works(
matrix, happened_events,
np.append(all_done_works, chosen_works, axis=0))
new_works = choose_works(matrix, available_works, reserve_time,
workers_number - len(chosen_works), type)
chosen_works = chosen_works if len(new_works) == 0 else np.append(
chosen_works, new_works, axis=0)
end_moments = end_moments[np.invert(done_mask)] - dt
end_moments = np.append(end_moments, get_duration(new_works, matrix))
works_story.append(chosen_works)
t_story.append(t)
print_result_latex(available_works, chosen_works, done_works,
end_moments, happened_events, matrix, t)
plot_diagram(t_story, works_story, workers_number)
if __name__ == '__main__':
matrix, early_moments, late_moments, reserve_time = read_graph(
'input_data', True)
solve(np.array(matrix), np.array(reserve_time), 3, 3)
for distance in range(1, max_distance + 1):
# 计算结果输出到以下文件中
distance_output_file_name = 'distances/threshold=%d_distance=%d.txt' % (
weight_threshold, distance)
distance_files[distance] = open(distance_output_file_name, 'w')
for uid in graph:
closer_uid_set = set()
last_uid_set = set([uid])
for distance in range(1, max_distance + 1):
next_uid_set = set()
for uid1 in last_uid_set:
for uid2 in graph[uid1]:
if uid2 != uid and uid2 not in closer_uid_set:
next_uid_set.add(uid2)
for uid_next in next_uid_set:
if uid < uid_next:
distance_files[distance].write('%s\t%s\n' %
(uid, uid_next))
closer_uid_set.update(next_uid_set)
last_uid_set = next_uid_set
for distance in range(1, max_distance + 1):
distance_files[distance].close()
if __name__ == '__main__':
graph = read_graph("10_graph.txt", 30)
cal_distances(graph, 30, 6)
for weight_threshold in range(10, 81, 5):
graph = read_graph("10_graph.txt", weight_threshold)
cal_distances(graph, weight_threshold, 3)
from common.feature_generators import Degree
from common.feature_generators import ClosenessCentrality
from common.feature_generators import BetweennessCentrality
from common.feature_generators import HITS
from common.feature_generators import PageRank
from common.feature_generators import Log10Wrapper
from common.feature_generators import NormalizeWrapper
from common.feature_generators import NeighbouringConductance
from common.feature_generators import FeatureSelector
from common.feature_generators import ExternalFeature
from validation import compute_correlations
from prediction import get_labels, train_model, get_and_save_metrics
# Loading PPI graph
print("\n######### Loading Graph #########")
Graph = read_graph(directed=False)
print("Loaded graph:\n\t{} nodes\n\t{} edges".format(Graph.number_of_nodes(),
Graph.number_of_edges()))
#########################
# Computing node features
#########################
print("\n######### Computing/retrieving node features #########")
# The pipeline object takes as an argument the sequence of feature generator objects we want
pipeline = Pipeline(
Log10Wrapper(Degree(default_dump=True, default_recomputing=False))(),
Log10Wrapper(ExpectedDegree(default_dump=True,
default_recomputing=False))(),
ClusteringCoefficient(), ClosenessCentrality(), BetweennessCentrality(),
from read_graph import read_graph
from centrality import centrality
from build_heap import build_heap
from down_heap import down_heapify
def order_centrality(G):
ctrs = {}
for actor in G.keys():
ctrs[actor] = centrality(G, actor)
# print ctrs
elems = ctrs.values()
G = build_heap(elems)
min = 0
for i in range(1):
min = G[0]
G[0] = G[len(G)-1]
G.pop()
down_heapify(G, 0)
return min
#test
G = read_graph('file.tsv')
print order_centrality(G)
cur_configs = new_configs
return sum(cur_configs.values())
def time(g, count=100):
""" Benchmark the algorithm. """
result = timeit.Timer(stmt=lambda:count_paths(g)).timeit(number=count)
return "%s seconds for %s iterations, giving %s seconds per iteration" % (result, count, result/count)
if __name__ == "__main__":
f = sys.stdin
timing = None
if len(sys.argv) > 1:
f = open(sys.argv[1])
if len(sys.argv) > 2:
timing = int(sys.argv[2])
g = read_graph(f)
if timing:
print time(g, timing)
else:
print count_paths(g)
import networkx as nx
import time
from read_graph import read_graph
file_name = "./case_example/low/case0.txt"
#file_name = "./case_example/high/topo2400.txt"
output_cap, server_fee, deploy_cost, cus_list, total_demand, G = read_graph(
file_name)
for i in range(G.number_of_nodes()):
G.node[i]['demand'] = -G.node[i]['demand']
# Super sink
sink = G.number_of_nodes()
G.add_node(sink, demand=total_demand)
for i in range(G.number_of_nodes() - 1):
if G.node[i]['demand'] != 0:
G.add_edge(i, sink, capacity=G.node[i]['demand'], cost=0)
for i in range(G.number_of_nodes() - 1):
G.node[i]['demand'] = 0
for i in range(G.number_of_nodes() - 1):
G.add_node(i, demand=-total_demand)
start_time = time.clock()
nx.max_flow_min_cost(G, i, sink, weight='cost')
#nx.capacity_scaling(G, weight='cost')
end_time = time.clock()