aima-python/gui/tsp.py at master · dopecoder/aima-python · GitHub

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from tkinter import *

from tkinter import messagebox

import sys

import os.path

sys.path.append(os.path.join(os.path.dirname(__file__), '..'))

from search import *

import utils

import numpy as np

distances = {}

class TSP_problem(Problem):

""" subclass of Problem to define various functions """

def two_opt(self, state):

""" Neighbour generating function for Traveling Salesman Problem """

neighbour_state = state[:]

left = random.randint(0, len(neighbour_state) - 1)

right = random.randint(0, len(neighbour_state) - 1)

if left > right:

left, right = right, left

neighbour_state[left: right + 1] = reversed(neighbour_state[left: right + 1])

return neighbour_state

def actions(self, state):

""" action that can be excuted in given state """

return [self.two_opt]

def result(self, state, action):

""" result after applying the given action on the given state """

return action(state)

def path_cost(self, c, state1, action, state2):

""" total distance for the Traveling Salesman to be covered if in state2 """

cost = 0

for i in range(len(state2) - 1):

cost += distances[state2[i]][state2[i + 1]]

cost += distances[state2[0]][state2[-1]]

return cost

def value(self, state):

""" value of path cost given negative for the given state """

return -1 * self.path_cost(None, None, None, state)

class TSP_Gui():

""" Class to create gui of Traveling Salesman using simulated annealing where one can

select cities, change speed and temperature. Distances between cities are euclidean

distances between them.

"""

def __init__(self, root, all_cities):

self.root = root

self.vars = []

self.frame_locations = {}

self.calculate_canvas_size()

self.button_text = StringVar()

self.button_text.set("Start")

self.algo_var = StringVar()

self.all_cities = all_cities

self.frame_select_cities = Frame(self.root)

self.frame_select_cities.grid(row=1)

self.frame_canvas = Frame(self.root)

self.frame_canvas.grid(row=2)

Label(self.root, text="Map of Romania", font="Times 13 bold").grid(row=0, columnspan=10)

def create_checkboxes(self, side=LEFT, anchor=W):

""" To select cities which are to be a part of Traveling Salesman Problem """

row_number = 0

column_number = 0

for city in self.all_cities:

var = IntVar()

var.set(1)

Checkbutton(self.frame_select_cities, text=city, variable=var).grid(

row=row_number, column=column_number, sticky=W)

self.vars.append(var)

column_number += 1

if column_number == 10:

column_number = 0

row_number += 1

def create_buttons(self):

""" Create start and quit button """

Button(self.frame_select_cities, textvariable=self.button_text,

command=self.run_traveling_salesman).grid(row=5, column=4, sticky=E + W)

Button(self.frame_select_cities, text='Quit', command=self.on_closing).grid(

row=5, column=5, sticky=E + W)

def create_dropdown_menu(self):

""" Create dropdown menu for algorithm selection """

choices = {'Simulated Annealing', 'Genetic Algorithm', 'Hill Climbing'}

self.algo_var.set('Simulated Annealing')

dropdown_menu = OptionMenu(self.frame_select_cities, self.algo_var, *choices)

dropdown_menu.grid(row=4, column=4, columnspan=2, sticky=E + W)

dropdown_menu.config(width=19)

def run_traveling_salesman(self):

""" Choose selected citites """

cities = []

for i in range(len(self.vars)):

if self.vars[i].get() == 1:

cities.append(self.all_cities[i])

tsp_problem = TSP_problem(cities)

self.button_text.set("Reset")

self.create_canvas(tsp_problem)

def calculate_canvas_size(self):

""" Width and height for canvas """

minx, maxx = sys.maxsize, -1 * sys.maxsize

miny, maxy = sys.maxsize, -1 * sys.maxsize

for value in romania_map.locations.values():

minx = min(minx, value[0])

maxx = max(maxx, value[0])

miny = min(miny, value[1])

maxy = max(maxy, value[1])

# New locations squeezed to fit inside the map of romania

for name, coordinates in romania_map.locations.items():

self.frame_locations[name] = (coordinates[0] / 1.2 - minx +

150, coordinates[1] / 1.2 - miny + 165)

canvas_width = maxx - minx + 200

canvas_height = maxy - miny + 200

self.canvas_width = canvas_width

self.canvas_height = canvas_height

def create_canvas(self, problem):

""" creating map with cities """

map_canvas = Canvas(self.frame_canvas, width=self.canvas_width, height=self.canvas_height)

map_canvas.grid(row=3, columnspan=10)

current = Node(problem.initial)

map_canvas.delete("all")

self.romania_image = PhotoImage(file="../images/romania_map.png")

map_canvas.create_image(self.canvas_width / 2, self.canvas_height / 2,

image=self.romania_image)

cities = current.state

for city in cities:

x = self.frame_locations[city][0]

y = self.frame_locations[city][1]

map_canvas.create_oval(x - 3, y - 3, x + 3, y + 3,

fill="red", outline="red")

map_canvas.create_text(x - 15, y - 10, text=city)

self.cost = StringVar()

Label(self.frame_canvas, textvariable=self.cost, relief="sunken").grid(

row=2, columnspan=10)

self.speed = IntVar()

speed_scale = Scale(self.frame_canvas, from_=500, to=1, orient=HORIZONTAL,

variable=self.speed, label="Speed ----> ", showvalue=0, font="Times 11",

relief="sunken", cursor="gumby")

speed_scale.grid(row=1, columnspan=5, sticky=N + S + E + W)

if self.algo_var.get() == 'Simulated Annealing':

self.temperature = IntVar()

temperature_scale = Scale(self.frame_canvas, from_=100, to=0, orient=HORIZONTAL,

length=200, variable=self.temperature, label="Temperature ---->",

font="Times 11", relief="sunken", showvalue=0, cursor="gumby")

temperature_scale.grid(row=1, column=5, columnspan=5, sticky=N + S + E + W)

self.simulated_annealing_with_tunable_T(problem, map_canvas)

elif self.algo_var.get() == 'Genetic Algorithm':

self.mutation_rate = DoubleVar()

self.mutation_rate.set(0.05)

mutation_rate_scale = Scale(self.frame_canvas, from_=0, to=1, orient=HORIZONTAL,

length=200, variable=self.mutation_rate, label='Mutation Rate ---->',

font='Times 11', relief='sunken', showvalue=0, cursor='gumby', resolution=0.001)

mutation_rate_scale.grid(row=1, column=5, columnspan=5, sticky='nsew')

self.genetic_algorithm(problem, map_canvas)

elif self.algo_var.get() == 'Hill Climbing':

self.no_of_neighbors = IntVar()

self.no_of_neighbors.set(100)

no_of_neighbors_scale = Scale(self.frame_canvas, from_=10, to=1000, orient=HORIZONTAL,

length=200, variable=self.no_of_neighbors, label='Number of neighbors ---->',

font='Times 11',relief='sunken', showvalue=0, cursor='gumby')

no_of_neighbors_scale.grid(row=1, column=5, columnspan=5, sticky='nsew')

self.hill_climbing(problem, map_canvas)

def exp_schedule(k=100, lam=0.03, limit=1000):

""" One possible schedule function for simulated annealing """

return lambda t: (k * math.exp(-lam * t) if t < limit else 0)

def simulated_annealing_with_tunable_T(self, problem, map_canvas, schedule=exp_schedule()):

""" Simulated annealing where temperature is taken as user input """

current = Node(problem.initial)

while(1):

T = schedule(self.temperature.get())

if T == 0:

return current.state

neighbors = current.expand(problem)

if not neighbors:

return current.state

next = random.choice(neighbors)

delta_e = problem.value(next.state) - problem.value(current.state)

if delta_e > 0 or probability(math.exp(delta_e / T)):

map_canvas.delete("poly")

current = next

self.cost.set("Cost = " + str('%0.3f' % (-1 * problem.value(current.state))))

points = []

for city in current.state:

points.append(self.frame_locations[city][0])

points.append(self.frame_locations[city][1])

map_canvas.create_polygon(points, outline='red', width=3, fill='', tag="poly")

map_canvas.update()

map_canvas.after(self.speed.get())

def genetic_algorithm(self, problem, map_canvas):

""" Genetic Algorithm modified for the given problem """

def init_population(pop_number, gene_pool, state_length):

""" initialize population """

population = []

for i in range(pop_number):

population.append(utils.shuffled(gene_pool))

return population

def recombine(state_a, state_b):

""" recombine two problem states """

start = random.randint(0, len(state_a) - 1)

end = random.randint(start + 1, len(state_a))

new_state = state_a[start:end]

for city in state_b:

if city not in new_state:

new_state.append(city)

return new_state

def mutate(state, mutation_rate):

""" mutate problem states """

if random.uniform(0, 1) < mutation_rate:

sample = random.sample(range(len(state)), 2)

state[sample[0]], state[sample[1]] = state[sample[1]], state[sample[0]]

return state

def fitness_fn(state):

""" calculate fitness of a particular state """

fitness = problem.value(state)

return int((5600 + fitness) ** 2)

current = Node(problem.initial)

population = init_population(100, current.state, len(current.state))

all_time_best = current.state

while(1):

population = [mutate(recombine(*select(2, population, fitness_fn)), self.mutation_rate.get()) for i in range(len(population))]

current_best = utils.argmax(population, key=fitness_fn)

if fitness_fn(current_best) > fitness_fn(all_time_best):

all_time_best = current_best

self.cost.set("Cost = " + str('%0.3f' % (-1 * problem.value(all_time_best))))

map_canvas.delete('poly')

points = []

for city in current_best:

points.append(self.frame_locations[city][0])

points.append(self.frame_locations[city][1])

map_canvas.create_polygon(points, outline='red', width=1, fill='', tag='poly')

best_points = []

for city in all_time_best:

best_points.append(self.frame_locations[city][0])

best_points.append(self.frame_locations[city][1])

map_canvas.create_polygon(best_points, outline='red', width=3, fill='', tag='poly')

map_canvas.update()

map_canvas.after(self.speed.get())

def hill_climbing(self, problem, map_canvas):

""" hill climbing where number of neighbors is taken as user input """

def find_neighbors(state, number_of_neighbors=100):

""" finds neighbors using two_opt method """

neighbors = []

for i in range(number_of_neighbors):

new_state = problem.two_opt(state)

neighbors.append(Node(new_state))

state = new_state

return neighbors

current = Node(problem.initial)

while(1):

neighbors = find_neighbors(current.state, self.no_of_neighbors.get())

neighbor = utils.argmax_random_tie(neighbors, key=lambda node: problem.value(node.state))

map_canvas.delete('poly')

points = []

for city in current.state:

points.append(self.frame_locations[city][0])

points.append(self.frame_locations[city][1])

map_canvas.create_polygon(points, outline='red', width=3, fill='', tag='poly')

neighbor_points = []

for city in neighbor.state:

neighbor_points.append(self.frame_locations[city][0])

neighbor_points.append(self.frame_locations[city][1])

map_canvas.create_polygon(neighbor_points, outline='red', width=1, fill='', tag='poly')

map_canvas.update()

map_canvas.after(self.speed.get())

if problem.value(neighbor.state) > problem.value(current.state):

current.state = neighbor.state

self.cost.set("Cost = " + str('%0.3f' % (-1 * problem.value(current.state))))

def on_closing(self):

if messagebox.askokcancel('Quit', 'Do you want to quit?'):

self.root.destroy()

def main():

all_cities = []

for city in romania_map.locations.keys():

distances[city] = {}

all_cities.append(city)

all_cities.sort()

# distances['city1']['city2'] contains euclidean distance between their coordinates

for name_1, coordinates_1 in romania_map.locations.items():

for name_2, coordinates_2 in romania_map.locations.items():

distances[name_1][name_2] = np.linalg.norm(

[coordinates_1[0] - coordinates_2[0], coordinates_1[1] - coordinates_2[1]])

distances[name_2][name_1] = np.linalg.norm(

[coordinates_1[0] - coordinates_2[0], coordinates_1[1] - coordinates_2[1]])

root = Tk()

root.title("Traveling Salesman Problem")

cities_selection_panel = TSP_Gui(root, all_cities)

cities_selection_panel.create_checkboxes()

cities_selection_panel.create_buttons()

cities_selection_panel.create_dropdown_menu()

root.protocol('WM_DELETE_WINDOW', cities_selection_panel.on_closing)

root.mainloop()

if __name__ == '__main__':

main()

aima-python/gui/tsp.py at master · dopecoder/aima-python · GitHub

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