增加等待时间算法

test.py

-import networkx as nx
-import numpy as np
-import matplotlib.pyplot as plt
-# G = nx.Graph(name= 'dispatch_topo')
-# # print(G.graph)
-# point = np.array([1,2,3,'a','b','c','m','n'])
-# for i in point:
-#     G.add_node(i)
-# # print(G.graph)
-# G.add_edge(1,'m',weight = 100)
-# G.add_edge(2,'m',weight = 80)
-# G.add_edge('a','m', weight = 120)
-# G.add_edge('n','m',weight = 200)
-# G.add_edge('b','m',weight = 200)
-# G.add_edge('c','m',weight = 210)
-# print(G.edges(data = True))
-# nx.draw(G,node_color = 'red')
-# plt.show()
-
-import matplotlib.pyplot as plt
-import networkx as nx
-import numpy as np
-
-G = nx.Graph()
-
-# G.add_edge('a', 'b', weight=0.6)
-# G.add_edge('a', 'c', weight=0.2)
-# G.add_edge('c', 'd', weight=0.1)
-# G.add_edge('c', 'e', weight=0.7)
-# G.add_edge('c', 'f', weight=0.9)
-# G.add_edge('a', 'd', weight=0.3)
-G.add_edge(1,'m',length = 100)
-G.add_edge(2,'m',length = 80)
-G.add_edge('a','m', length = 120)
-G.add_edge('n','m',length = 10)
-G.add_edge('b','n',length = 100)
-G.add_edge('h','n',length = 10)
-G.add_edge('b','h',length = 80)
-G.add_edge('c','n',length = 210)
-
-# elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] > 0.5]
-# esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d['weight'] <= 0.5]
-#
-# pos = nx.spring_layout(G)  # positions for all nodes
-#
-# # nodes
-# nx.draw_networkx_nodes(G, pos, node_size=700)
-#
-# # edges
-# nx.draw_networkx_edges(G, pos, edgelist=elarge,
-#                        width=6)
-# nx.draw_networkx_edges(G, pos, edgelist=esmall,
-#                        width=6, alpha=0.5, edge_color='b', style='dashed')
-#
-# # labels
-# nx.draw_networkx_labels(G, pos, font_size=20, font_family='sans-serif')
-# plt.axis('off')
-# plt.savefig("weight.jpg")
-pos=nx.shell_layout(G)
-nx.draw(G,pos,with_labels=True, node_color='red', edge_color='blue', font_size = 18,width = 5,node_size=600, alpha=0.5 )
-# pylab.title('Self_Define Net',fontsize=15)
-plt.show()
-
-# print('dijkstra方法寻找最短路径：')
-# path = nx.dijkstra_path(G, source='a', target='b', weight='length')
-# print('节点a到b的路径：', path)
-G_node = np.array(G.edges)
-GG = G.number_of_edges()
-print (G_node[0][1], type(G_node[0][1]))
-print(GG,type(GG))
-
-# G1 = nx.Graph()
-# print(np.array(G1.nodes))
-
-
-
-# G2 = nx.Graph()  # 创建：空的 无向图
-# G2.add_weighted_edges_from([(1,2,2),(1,3,8),(1,4,1),
-#                             (2,3,6),(2,5,1),
-#                             (3,4,7),(3,5,5),(3,6,1),(3,7,2),
-#                             (4,7,9),
-#                             (5,6,3),(5,8,2),(5,9,9),
-#                             (6,7,4),(6,9,6),
-#                             (7,9,3),(7,10,1),
-#                             (8,9,7),(8,11,9),
-#                             (9,10,1),(9,11,2),
-#                             (10,11,4)])  # 向图中添加多条赋权边: (node1,node2,weight)
-#
-

test3.py

-from path_plan.topo_graph import *
-import networkx as nx
-import matplotlib.pyplot as plt
-
-
-# from traffic_flow.traffic_flow_planner import *
-
-# from path_plan.path_planner_2 import *
-# from dispatcher import Dispatcher, PreSchedule
-topo = Topo()
-topo.generate_topo_graph()
-# path_planner = PathPlanner(topo)
-# a, b = path_planner.path_cost_generate("ef89f721-5134-3ef1-91e2-95b4e5004675", "e5d0ac4e-c134-3ef1-901b-867980c8453e", False)
-# print(a,b)
-load_G = topo.get_load_G()
-unload_G = topo.get_unload_G()
-print(unload_G.edges(data=True))
-print(load_G.edges(data=True))
-# alpha = 1
-# beta = 1
-# source_area = "e44052d4-1134-6602-ea64-f14d05ecfb3f"
-# target_area = "659ca4d5-2134-6602-ed05-1deddfca63f9"
-# unload_source_node = str(session_postgre.query(DiggingWorkArea).filter_by(Id=source_area).first().ExitNodeId)
-# unload_end_node = str(session_postgre.query(DumpArea).filter_by(Id=target_area).first().EntranceNodeId)
-# # _, park_path = nx.single_source_dijkstra(unload_G, source=source_node, target=source_node, weight="real_distance")
-# # path = park_path
-# # for i in range(len(path)-1):
-# #     pair_now = [path[i], path[i+1]]
-# #     # edge_lane = nx.get_edge_attributes(load_G,'lane')
-# #     data = dict(unload_G[path[i]][path[i+1]]['lane'])
-# #     for u,v in data.items():
-# #         data[u] = [v[0],beta*3]
-# #     val = list(data.values())
-# #     unload_G[path[i]][path[i+1]]['locked_distance'] = sum([beta * sum(i[0] for i in val) + alpha * sum(i[-1] for i in val)])
-#
-# unload_cost = nx.dijkstra_path_length(unload_G, source=unload_source_node, target=unload_end_node,
-#                                        weight="locked_distance")
-# print(unload_cost)
-# # print([path[i], path[i+1]], unload_G[path[i]][path[i+1]]['locked_distance'])
-# # for i in range(len(park_path) - 1):
-# #     data = dict(unload_G[park_path[i]][park_path[i + 1]]['lane'])
-# #     for u, v in data.items():
-# #         # u_lane_cost = lane_cost_generate(u)
-# #         data[u] = [v[0], beta *3]
-# #     val = list(data.values())
-# #     # unload_G[park_path[i]][park_path[i + 1]]['locked_distance'] = sum(beta * sum(i[0] for i in val) + alpha * sum(i[-1] for i in val))
-# #     unload_G[park_path[i]][park_path[i + 1]]['locked_distance'] = sum( beta * sum(i[0] for i in val))
-# #     # unload_G[park_path[i]][park_path[i + 1]]['locked_distance'] = sum(beta * sum(i[0] for i in val) + alpha * sum(i[-1] for i in val))
-# #     unload_G[park_path[i]][park_path[i + 1]]['locked_distance'] = sum( beta * sum(i[0] for i in val))
-#
-# # print(load_G.edges(data=True))
-# print(unload_G.edges(data=True))
-# # distance = nx.dijkstra_path_length(load_G, source="81be6a9e-8134-3ef2-141e-4bb045b8d097", target= "1c85bc2c-9134-6281-1265-fd19d0dcbd52", weight="locked_distance")
-# # print(distance)
-# # print(distance,path)
-# # aaa = topo.get_load_source_node("7b9c8e89-7134-63ac-9c44-f183674b090c")
-# # print(aaa)
-# # path,_, _= topo.get_load_target_node_real("7b9c8e89-7134-63ac-9c44-f183674b090c",None)
-# # print(path)
-# # updata_edge = {}
-# # for i in range(len(path)-1):
-# #     pair_now = [path[i], path[i+1]]
-# #     # edge_lane = nx.get_edge_attributes(load_G,'lane')
-# #     data = dict(load_G[path[i]][path[i+1]]['lane'])
-# #     for u,v in data.items():
-# #         data[u] = [v[0],v[0]]
-# #     load_G[path[i]][path[i+1]]['locked_distance'] = sum(i[-1] for i in list(data.values()))
-# #     print([path[i], path[i+1]], load_G[path[i]][path[i+1]]['locked_distance'])
-#
-#
-
-
-
-# unload_G = topo.get_un  data=True))
-pos = nx.shell_layout(load_G)
-nx.draw(load_G, pos, with_labels=True, node_color='red', edge_color='blue', font_size=18, width=5, node_size=600,
-        alpha=0.5)
-plt.show()
-
-pos = nx.shell_layout(unload_G)
-nx.draw(unload_G, pos, with_labels=True, node_color='red', edge_color='blue', font_size=18, width=5, node_size=600,
-        alpha=0.5)
-plt.show()
-# unload_G = topo.get_unload_G()
-# print(unload_G.edges(data=True))
-#
-# edge_arrtibutes = dict(nx.get_edge_attributes(unload_G,'lane'))
-# print(edge_arrtibutes)
-# print(type(edge_arrtibutes))
-# print(edge_arrtibutes.values())
-# print(type(edge_arrtibutes.values()))
-# pos = nx.shell_layout(load_G)
-# nx.draw(load_G, pos, with_labels=True, node_color='red', edge_color='blue', font_size=18, width=5, node_size=600,
-#         alpha=0.5)
-# plt.show()
-
-# a = "-rw-r--r-- 1  cs9315  22439  Feb 18 2021"
-# print(len(a))
-
-# end= time.process_time()
-# print('Running time: %s Seconds'%(end-start))

test4.py

-import copy
-import numpy as np
-
-# # Question 1 optimisation problem
-A = np.array([[1., 2., 1., -1.],
-             [-1., 1., 0., 2.],
-             [0., -1., -2., 1.]])
-b = np.array([[3.], [2.], [-2.]])
-x = np.array([[1.], [1.], [1.], [1.]])
-k = 0
-alpha = 0.1
-gamma = 0.2
-
-def gradient_step_1(A, b, x, gamma):
-    updated_x = np.array(A.T.dot((A.dot(x) - b)) + gamma*x)
-    return np.round(updated_x,4)
-
-print_form = []
-
-def edit_form(print_form,x):
-    print_form.pop(0)
-    x_k = copy.copy(x)
-    print_form.append([k, x_k])
-
-while True:
-    if k > 0 and k <= 5:
-        x_k= copy.copy(x)
-        print_form.append((k,x_k))
-    if k == 6:
-        for item in print_form:
-            print(f"k = {item[0]}, x({item[0]}) = {item[1]}")
-    if k > 6:
-        edit_form(print_form,x)
-    k += 1
-    x -= alpha*gradient_step_1(A, b, x, gamma)
-
-    if np.linalg.norm(gradient_step_1(A, b, x, gamma), ord=2)<0.001:
-        edit_form(print_form, x)
-        k += 1
-        x -= alpha * gradient_step_1(A, b, x, gamma)
-        edit_form(print_form, x)
-        for item in print_form:
-            print(f"k = {item[0]}, x({item[0]}) = {item[1]}")
-        break
-
-
-
-# x = np.array([[1.123], [1.123], [1.123], [1.123]])
-# x =np.array([[-4.59627504],
-#  [8.95686683],
-#  [-9.76586351],
-#  [ 8.87335576]])
-# print(np.around(x,2))
-# for i in range(10):
-#     x = A.T.dot((A.dot(x) - b)) + gamma*x
-#     print(np.round(x,4))