def test_tour_length():
coords=[(0,0),(0,1),(1,0),(1,1)]
matrix=tsp.cartesian_matrix(coords)
assert 2 == tsp.tour_length(matrix,[0,1])
assert 2 == tsp.tour_length(matrix,[0,2])
assert (1+sqrt(2)+1) == tsp.tour_length(matrix,[0,1,2])
assert (1+sqrt(2)+1+sqrt(2)) == tsp.tour_length(matrix,[0,1,2,3])
def test_tour_length():
coords=[(0,0),(0,1),(1,0),(1,1)]
matrix=tsp.cartesian_matrix(coords)
assert 2 == tsp.tour_length(matrix,[0,1])
assert 2 == tsp.tour_length(matrix,[0,2])
assert (1+sqrt(2)+1) == tsp.tour_length(matrix,[0,1,2])
assert (1+sqrt(2)+1+sqrt(2)) == tsp.tour_length(matrix,[0,1,2,3])
def main():
size = 100
coords = tsp.random_coordinates(size)
distances = tsp.euclid_distances(coords)
evaluation = lambda t: tsp.tour_length(distances, t)
init_func = lambda: tsp.random_tour(len(coords))
result = tsp.tsp_hillclimb_restart_overall_iterations(init_func,evaluation,tsp.swapped_cities_generator,\
max_iterations=100000,restart_limit=None,verbose=0)
print("Best evaluation:", result['best_eval'])
print("Is local optimum:", result['local_optimum'])
print("Num of evaluations:", result['num_of_evaluations'])
print("Num of restarts:", result['num_of_restarts'])
tsp.draw_tour(coords,
result["best_tour"],
img_file="tour_best_swapping_cities.png")
size = 100
coords = tsp.random_coordinates(size)
distances = tsp.euclid_distances(coords)
evaluation = lambda t: tsp.tour_length(distances, t)
init_func = lambda: tsp.random_tour(len(coords))
result = tsp.tsp_hillclimb_restart_overall_iterations(init_func,evaluation,tsp.reversed_sections_generator,\
max_iterations=100000,restart_limit=None,verbose=0)
print("Best evaluation:", result['best_eval'])
print("Is local optimum:", result['local_optimum'])
print("Num of evaluations:", result['num_of_evaluations'])
print("Num of restarts:", result['num_of_restarts'])
tsp.draw_tour(coords,
result["best_tour"],
img_file="tour_best_reversing_sections.png")
def main():
size = 50
coords = tsp.random_coordinates(size)
distances = tsp.euclid_distances(coords)
evaluation = lambda t: tsp.tour_length(distances, t)
init_func = lambda: tsp.random_tour(len(coords))
result = tsp_sa.tsp_hillclimb_simulated_annealing_restart(init_func, evaluation, tsp.swapped_cities_generator,\
max_iterations=10000000, annealing_prob=tsp_sa.prob, annealing_temp=1.0, annealing_temp_decrease=0.95,\
restart_limit=10, verbose=1)
print("Best evaluation:", result['best_eval'])
print("Is local optimum:", result['local_optimum'])
print("Num of evaluations:", result['num_of_evaluations'])
print("Num of restarts:", result['num_of_restarts'])
tsp.draw_tour(coords,result["best_tour"],img_file="tour_best_sa_swapping_cities.png")
size = 50
coords = tsp.random_coordinates(size)
distances = tsp.euclid_distances(coords)
evaluation = lambda t: tsp.tour_length(distances, t)
init_func = lambda: tsp.random_tour(len(coords))
result = tsp_sa.tsp_hillclimb_simulated_annealing_restart(init_func, evaluation, tsp.reversed_sections_generator,\
max_iterations=10000000, annealing_prob=tsp_sa.prob, annealing_temp=1.0, annealing_temp_decrease=0.95,\
restart_limit=10, verbose=1)
print("Best evaluation:", result['best_eval'])
print("Is local optimum:", result['local_optimum'])
print("Num of evaluations:", result['num_of_evaluations'])
print("Num of restarts:", result['num_of_restarts'])
tsp.draw_tour(coords,result["best_tour"],img_file="tour_best_sa_reversing_sections.png")
def get_parameters():
global build_dict_thread, polyline_dict
cycles = int(request.form['cycles'])
algorithm = request.form['algorithm']
start_temp = float(request.form['start_temp'])
alpha = float(request.form['alpha'])
move_operator = request.form['move_operator']
start_city = int(request.form['start'])
mode = request.form['mode']
selected_cities = request.form['selected_cities']
return_list = selected_cities.split(',')
id_list = []
current_score=[]
animation_coords = []
nodes2 = []
animation_steps = []
#Extract city ids and names into lists then look up coordinates and build
#a dictionary with id as the key and a tuple of coordinates as the value
for i in range(len(return_list)):
if i%2 == 0:
id_list.append(int(return_list[i]))
for id in id_list:
nodes2.append(model.session.query(model.City).filter_by(id = id).one())
coords = tsp.read_coords_db(nodes2)
i=0
coord_dict = {}
for id in id_list:
coord_dict[id]=coords[i]
i+= 1
#Build the distance matrix. The distance matrix is of the form
#{(i, j): dist, ... (i,j): dist}
if mode == "as_the_crow_flies":
matrix = tsp.distance_matrix2(coords, id_list)
elif mode == "roads":
matrix = tsp.road_matrix(polyline_dict, id_list)
elif mode == "airline":
matrix = tsp.air_matrix(coords, id_list)
else:
return "Error"
#Choose our algorithm
init_function =lambda: tsp.init_random_tour(id_list)#returns shuffled
objective_function=lambda tour: -tsp.tour_length(matrix,tour) #note negation
move_dict={"swapped_cities": tsp.swapped_cities, "reversed_sections": tsp.reversed_sections}
if algorithm == "hillclimb":
result = tsp.hillclimb(init_function, move_dict[move_operator],
objective_function, cycles)
num_evaluations, best_score, best, animation_steps, current_score = result
elif algorithm == "hill_restart":
result = tsp.hillclimb_and_restart(init_function, move_dict[move_operator],
objective_function, cycles)
num_evaluations, best_score, best, animation_steps, current_score = result
elif algorithm == "nearest":
result = tsp.greedy(matrix, start_city)
num_evaluations, best_score, best = result
elif algorithm == "annealing":
result = tsp.anneal(init_function, move_dict[move_operator], objective_function,
cycles,start_temp,alpha)
num_evaluations, best_score, best, animation_steps, current_score = result
else:
print "error"
return "error"
tour_coords = tsp.drawtour_on_map2(coord_dict, best)
#coordinates for each step
for i in range(len(animation_steps)):
animation_coords.append(tsp.drawtour_on_map2(coord_dict, animation_steps[i]))
tour_cities = convert_tour_to_city(best)
if mode == "roads" and not build_dict_thread.isAlive():
poly_list, data = poly_line_tour2(best)
poly_animation_steps = polyline_animation_steps2(animation_steps)
else:
poly_list = []
poly_animation_steps = []
results = {"iterations" : num_evaluations, "best_score" : best_score, "route" : best,
"tour_coords": tour_coords, "tour_cities": tour_cities, "animation_coords": animation_coords,
"current_score": current_score, "poly_list": poly_list, "poly_animation_steps": poly_animation_steps}
data = json.dumps(results)
return data
import tsp
import profile
coords=tsp.read_coords(file('city500.txt'))
init_function=lambda: tsp.init_random_tour(len(coords))
matrix=tsp.cartesian_matrix(coords)
objective_function=lambda tour: -tsp.tour_length(matrix,tour)
move_operator=tsp.reversed_sections
max_iterations=1000
#profile.run('tsp.run_hillclimb(init_function,move_operator,objective_function,max_iterations)')
profile.run('tsp.run_evolve(init_function,move_operator,objective_function,max_iterations)')
def length_ratio(cities): #The ratio of the tour lengths for nn_tsp and alltours_tsp algorithms return tsp.tour_length(nn_tsp(cities)) / tsp.tour_length(tsp.alltours_tsp(cities))
def length_ratio(cities):
#The ratio of the tour lengths for nn_tsp and alltours_tsp algorithms
return tsp.tour_length(nn_tsp(cities)) / tsp.tour_length(
tsp.alltours_tsp(cities))
x = ch[t][0]
y = ch[t][1]
if xOld:
imd.line((xOld, yOld, x, y), fill = (0, 0, 0), width = 4)
xOld = x
yOld = y
imd.line((xOld, yOld, ch[first][0], ch[first][1]), fill = (0, 0, 0), \
width = 4)
def plotCities(imd, ch, s = 5):
"""For a given city hash, draws the points onto a img draw object.
"""
for x,y in ch.values():
imd.ellipse((x - s, y - s, x + s, y + s), \
fill = (0, 0, 0))
if __name__ == "__main__":
cl = loadCityFile("./files/cityPixels.txt")
tour = loadTour("tour.txt")
cities = fileio.readCities("./files/cities.txt")
tourNum = loadTourNum(tour, cities)
dist = fileio.readDistances("./files/distMiles.txt")
plotTour(tour, cl)
print "Tour length:" + str(tsp.tour_length(dist,tourNum))
def get_parameters():
global build_dict_thread, polyline_dict
cycles = int(request.form['cycles'])
algorithm = request.form['algorithm']
start_temp = float(request.form['start_temp'])
alpha = float(request.form['alpha'])
move_operator = request.form['move_operator']
start_city = int(request.form['start'])
mode = request.form['mode']
selected_cities = request.form['selected_cities']
return_list = selected_cities.split(',')
id_list = []
current_score = []
animation_coords = []
nodes2 = []
animation_steps = []
#Extract city ids and names into lists then look up coordinates and build
#a dictionary with id as the key and a tuple of coordinates as the value
for i in range(len(return_list)):
if i % 2 == 0:
id_list.append(int(return_list[i]))
for id in id_list:
nodes2.append(model.session.query(model.City).filter_by(id=id).one())
coords = tsp.read_coords_db(nodes2)
i = 0
coord_dict = {}
for id in id_list:
coord_dict[id] = coords[i]
i += 1
#Build the distance matrix. The distance matrix is of the form
#{(i, j): dist, ... (i,j): dist}
if mode == "as_the_crow_flies":
matrix = tsp.distance_matrix2(coords, id_list)
elif mode == "roads":
matrix = tsp.road_matrix(polyline_dict, id_list)
elif mode == "airline":
matrix = tsp.air_matrix(coords, id_list)
else:
return "Error"
#Choose our algorithm
init_function = lambda: tsp.init_random_tour(id_list) #returns shuffled
objective_function = lambda tour: -tsp.tour_length(matrix, tour
) #note negation
move_dict = {
"swapped_cities": tsp.swapped_cities,
"reversed_sections": tsp.reversed_sections
}
if algorithm == "hillclimb":
result = tsp.hillclimb(init_function, move_dict[move_operator],
objective_function, cycles)
num_evaluations, best_score, best, animation_steps, current_score = result
elif algorithm == "hill_restart":
result = tsp.hillclimb_and_restart(init_function,
move_dict[move_operator],
objective_function, cycles)
num_evaluations, best_score, best, animation_steps, current_score = result
elif algorithm == "nearest":
result = tsp.greedy(matrix, start_city)
num_evaluations, best_score, best = result
elif algorithm == "annealing":
result = tsp.anneal(init_function, move_dict[move_operator],
objective_function, cycles, start_temp, alpha)
num_evaluations, best_score, best, animation_steps, current_score = result
else:
print "error"
return "error"
tour_coords = tsp.drawtour_on_map2(coord_dict, best)
#coordinates for each step
for i in range(len(animation_steps)):
animation_coords.append(
tsp.drawtour_on_map2(coord_dict, animation_steps[i]))
tour_cities = convert_tour_to_city(best)
if mode == "roads" and not build_dict_thread.isAlive():
poly_list, data = poly_line_tour2(best)
poly_animation_steps = polyline_animation_steps2(animation_steps)
else:
poly_list = []
poly_animation_steps = []
results = {
"iterations": num_evaluations,
"best_score": best_score,
"route": best,
"tour_coords": tour_coords,
"tour_cities": tour_cities,
"animation_coords": animation_coords,
"current_score": current_score,
"poly_list": poly_list,
"poly_animation_steps": poly_animation_steps
}
data = json.dumps(results)
return data
