def run_algorithm_dijkstra_apsp(self, values):
graph = Graph.import_values(values)
t = time()
dist = Dijkstra_apsp(graph)
time_seconds = time() - t
return self.export_algorithm(dist, time_seconds)
def run_algorithm_dijkstra(self, values, source):
graph = Graph.import_values(values)
t = time()
dist = Dijkstra(int(source), graph)
time_seconds = time() - t
return self.export_algorithm([dist], time_seconds)
def run_algorithm_floyd_warshall(self, values):
graph = Graph.import_values(values)
t = time()
dist = Floyd_Warshall(graph)
time_seconds = time() - t
return self.export_algorithm(dist, time_seconds)
def run_algorithm_quinca(self, values, dist):
graph = Graph.import_values(values)
matrix_distances = self.import_matrix(dist)
t = time()
dist = Quinca(graph, matrix_distances)
time_seconds = time() - t
return self.export_algorithm(dist, time_seconds)
def run_algorithm_knnb_node_incremental(self, values, dist):
graph = Graph.import_values(values)
matrix_distances = self.import_matrix(dist)
t = time()
dist = KNNB_Node_Incremental(graph, matrix_distances)
time_seconds = time() - t
return self.export_algorithm(dist, time_seconds)
def run_algorithm_rr_bfs_truncated(self, values, dist):
graph = Graph.import_values(values)
matrix_distances = self.import_matrix(dist)
t = time()
dist = Bfs_Truncated_With_Sources(graph, matrix_distances)
time_seconds = time() - t
return self.export_algorithm(dist, time_seconds)
def run_algorithm_forest(self, values, dist):
graph = Graph.import_values(values)
matrix_distances = self.import_matrix(dist)
t = time()
dist = Forest_apsp(graph, np.array(matrix_distances))
time_seconds = time() - t
return self.export_algorithm(dist, time_seconds)
def run_algorithm_abm(self, values, dist):
graph = Graph.import_values(values)
matrix_distances = self.import_matrix(dist)
t = time()
dist = ABM_Update(graph, np.array(matrix_distances))
time_seconds = time() - t
print("TIME ABM: ", time_seconds * 1000)
return self.export_algorithm(dist, time_seconds)
def run_algorithm_quinca(self, values, dist):
graph = Graph.import_values(values)
matrix_distances = self.import_matrix(dist)
t = time()
dist = Quinca(graph, np.array(matrix_distances))
time_seconds = time() - t
print("TIME QUINCA: ", time_seconds * 1000)
return self.export_algorithm(dist, time_seconds)
def dynamic_update_random_edge(self, values):
graph = Graph.import_values(values)
graph.edge_update_random()
return self.export(graph)
def dynamic_decreasing_random_edge(self, values):
graph = Graph.import_values(values)
graph.dynamic_decreasing_random_edge()
return self.export(graph)
def dynamic_incremental_random_edge(self, values):
graph = Graph.import_values(values)
graph.dynamic_incremental_random_edge()
return self.export(graph)
def __init__(self, values):
self.attempt = 30
self.graph = Graph.import_values(values)
def __init__(self, values=None):
self.attempt = 30
self.graph = Graph()
if values is not None:
self.graph = Graph.import_values(values)
