def test_random():
"""
Test all approaches on random graph.
"""
num_of_nodes = 7
graph = Graph(num_of_nodes)
starting_node = next(iter(graph.graph))
print("{:-^100}".format(" TESTING RANDOM PATH "))
dfs = DFS(graph)
paths_dfs = dfs.depth_first_search(starting_node)
print("DEPTH FIRST SEARCH")
print("Number of Hamilton Cycles found:", len(paths_dfs))
print("Shortest path:", graph.get_shortest_path(paths_dfs), end="\n\n")
bfs = BFS(graph)
paths_bfs = bfs.breadth_first_search(starting_node)
print("BREADTH FIRST SEARCH")
print("Number of Hamilton Cycles found:", len(paths_bfs))
print("Shortest path:", graph.get_shortest_path(paths_bfs), end="\n\n")
greedy = Greedy(graph)
path_greedy = greedy.greedy_approach(starting_node)
print("GREEDY APPROACH")
print("Path length:",
(path_greedy, graph.calculate_path_length(path_greedy)),
end="\n\n")
a_star_custom = AStarCustom(graph)
path_a_star = a_star_custom.a_star(starting_node)
print("A* APPROACH")
print("Path length:",
(path_a_star, graph.calculate_path_length(path_a_star)),
end="\n")
print("{:-^100}".format(" END OF TEST "), end="\n\n")
def try_all_approaches(num_of_samples: int, num_of_nodes: int):
"""
Execute all approaches on the same graphs for given number of nodes and samples (the more samples the more
statistically accurate results).
Returns: Achieved path lengths and execution times for all approaches.
"""
graph = Graph(0)
dfs = DFS(graph)
dfs_path_lengths = []
dfs_execution_times = []
bfs = BFS(graph)
bfs_path_lengths = []
bfs_execution_times = []
greedy = Greedy(graph)
greedy_path_lengths = []
greedy_execution_times = []
a_star_custom = AStarCustom(graph)
a_star_path_lengths = []
a_star_execution_times = []
for i in range(a_star_custom.num_of_heuristics):
a_star_path_lengths.append([])
a_star_execution_times.append([])
for j in range(num_of_samples):
graph = Graph(num_of_nodes)
starting_node = next(iter(graph.graph))
start = time.time()
dfs.graph = graph
paths_dfs = dfs.depth_first_search(starting_node)
dfs_path_lengths.append(Graph.get_shortest_path(paths_dfs)[1])
execution_time = time.time() - start
dfs_execution_times.append(execution_time)
start = time.time()
bfs.graph = graph
paths_bfs = bfs.breadth_first_search(starting_node)
bfs_path_lengths.append(Graph.get_shortest_path(paths_bfs)[1])
execution_time = time.time() - start
bfs_execution_times.append(execution_time)
start = time.time()
greedy.graph = graph
path_greedy = greedy.greedy_approach(starting_node)
greedy_path_lengths.append(Graph.calculate_path_length(path_greedy))
execution_time = time.time() - start
greedy_execution_times.append(execution_time)
for i in range(a_star_custom.num_of_heuristics):
start = time.time()
a_star_custom.graph = graph
a_star_custom.heuristic_choice = i
path_a_star = a_star_custom.a_star(starting_node)
execution_time = time.time() - start
a_star_path_lengths[i].append(
Graph.calculate_path_length(path_a_star))
a_star_execution_times[i].append(execution_time)
dfs_avg_results = sum(dfs_path_lengths) / num_of_samples, sum(
dfs_execution_times) / num_of_nodes, "DFS Recurrent"
bfs_avg_results = sum(bfs_path_lengths) / num_of_samples, sum(
bfs_execution_times) / num_of_nodes, "BFS"
greedy_avg_results = sum(greedy_path_lengths) / num_of_samples, sum(
greedy_execution_times) / num_of_nodes, "Greedy"
a_star_avg_results = []
for i in range(a_star_custom.num_of_heuristics):
label = "A* heuristic " + str(i)
a_star_avg_results.append(
(sum(a_star_path_lengths[i]) / num_of_samples,
sum(a_star_execution_times[i]) / num_of_samples, label))
data = [dfs_avg_results, bfs_avg_results, greedy_avg_results]
data.extend(a_star_avg_results)
return data
