def pruning_test():
# Setup Team Object
team_edm = Team()
lines = list()
lines.append(Line(0, 1.701))
lines.append(Line(1, 0.658))
lines.append(Line(2, 0.299))
lines.append(Line(3, 0.433))
team_edm.set_team("EDM", lines=lines,
start_line_index=0) # Edmonton Oilers
path = Path()
path.team = team_edm
values_best = []
for i in range(30):
temp_path = path.copy()
optimal_path, num_visited = process(team_edm[team_edm.curr_line_index],
temp_path, 1, i)
values_best.append(num_visited)
path.team[path.team.curr_line_index].toi = 0
values_worst = []
for i in range(30):
temp_path = path.copy()
optimal_path, num_visited = process(team_edm[team_edm.curr_line_index],
temp_path, 1, i)
values_worst.append(num_visited)
theoretical_values = [theoretical_nodes(i) for i in range(30)]
print(theoretical_values)
print(values_best)
print(values_worst)
def main():
"""
Sets up and processes team, EXAMPLE;
:return: NONE
"""
# Setup Team Object
team_edm = Team()
lines = list()
lines.append(Line(0, 1.701))
lines.append(Line(1, 0.658))
lines.append(Line(2, 0.299))
lines.append(Line(3, 0.433))
team_edm.set_team("EDM", lines=lines,
start_line_index=0) # Edmonton Oilers
print(team_edm)
# Setup Path Object
path = Path()
path.team = team_edm
schedule = []
num_intervals = PERIOD_LENGTH // INTERVAL
start = time.time()
for i in range(num_intervals - 1):
max_depth = MAX_DEPTH
if num_intervals - i < MAX_DEPTH:
max_depth = num_intervals - i
# start = time.time()
# find optimal path from curr_line for the next (MAX_DEPTH * INTERVAL) seconds
temp_path = path.copy()
optimal_path, num_visited = process(team_edm[team_edm.curr_line_index],
temp_path, 1, max_depth)
# print("\n\n", path.team, "\n\n")
path.team.update(optimal_path[1], INTERVAL)
schedule.append(optimal_path[1].line_num)
elapsed_time = time.time() - start
t_nodes = theoretical_nodes(max_depth)
# print("Optimal ", optimal_path)
#
print("Progress: {p:3.0f}% @ t: {e:5.2f}".format(
p=i / (num_intervals - 1) * 100, e=elapsed_time))
# print("Look Ahead: ", LOOK_AHEAD)
# print("Depth: ", max_depth)
# print("Visited: ", num_visited)
# print("Theoretical: ", t_nodes)
# print("Removal Rate: ", "{0:0.5f}".format((t_nodes - num_visited)/t_nodes))
# print("Speed Up: ", "{0:4.2f}".format(t_nodes/num_visited))
#
# print("\nTime: ", elapsed_time)
print(schedule)
path = collection.pop()
# get its last node
node = path.last
# get its neighbors
raw_neighbors = create_pages(node.links)
# get its unvisited neighbors
neighbors = list(
filter(lambda x: not (x in collection.visited), raw_neighbors))
# for each neighbor
for n in neighbors:
# copy the base path so we can create a new path for each neighbor
new_path = path.copy()
# create a path for the neighbor
new_path.add(n)
# let the collection decide whether to add it or not
collection.add(new_path)
# find the next min path
collection.set_min()
end = time.time()
paths_out()
sitemap_out()
dead_out()
