def johnson_algorithm(graph: DirectedGraph) -> list:
if graph.repr_type != RepresentationType.ADJACENCY_MATRIX:
graph.to_adjacency_matrix()
g = graph.repr
# add vertex to graph and add edges of value 0 from this vertex to the rest
g.append([0])
for i in range(len(g) - 2):
g[len(g) - 1].append(0)
for i in range(len(g)):
g[i].append(None)
graph.to_adjacency_list()
edges = bellman_ford(graph, len(g))
edges.pop(len(g))
graph.to_adjacency_matrix()
new_g = [[0 for x in range(len(g) - 1)] for y in range(len(g) - 1)]
for i in range(len(new_g)):
for j in range(len(new_g[i])):
if g[i][j] is not None:
new_g[i][j] = (g[i][j] + edges[i + 1] - edges[j + 1])
graph.repr.pop()
for i in range(len(graph.repr)):
graph.repr[i].pop()
graph_for_dijkstra = Graph()
data = get_graph_with_probability(len(graph.repr), 0.5)
graph_for_dijkstra.load_data(
data=data,
representation_type=RepresentationType.ADJACENCY_MATRIX_WITH_WEIGHTS)
for i in range(len(graph.repr)):
for j in range(len(graph.repr[i])):
graph_for_dijkstra.repr[i][j] = new_g[i][j]
dist_matrix = []
for s in range(len(graph_for_dijkstra.repr)):
print(f"For node [{s+1}]:")
from_point = find_shortest_path(
G=graph_for_dijkstra.get_weighted_adjacency_list(),
start=s + 1,
verbose=True)
dist_matrix.append([])
for node in from_point:
dist_matrix[s].append(from_point[node])
print()
return dist_matrix
import os, sys
currentdir = os.path.dirname(os.path.realpath(__file__))
parentdir = os.path.dirname(currentdir)
sys.path.append(parentdir)
from utils.Graph import Graph, RepresentationType
from utils.graph_generators import gen_random_conn_graph_weighted
from utils.graph_plotter import GraphPlotter
from algorithms.dijkstra import find_shortest_path
if __name__ == "__main__":
random_conn_graph = gen_random_conn_graph_weighted(10)
G = Graph()
G.load_data(
data=random_conn_graph,
representation_type=RepresentationType.ADJACENCY_MATRIX_WITH_WEIGHTS)
print(G)
GraphPlotter.plot_graph(G, draw_wages=True)
s = 1
print(50 * '-')
print("START: s = {0}".format(s))
print(
find_shortest_path(G=G.get_weighted_adjacency_list(),
start=s,
verbose=True))
# print(G.get_weighted_adjacency_list())
