def NQueenDriver():
attempted = 0
hc_solved = 0
sa_solved = 0
saf_solved = 0
for i in range(1000):
attempted += 1
hc = hill_climbing(MyNQueen(8))
if -1 not in hc:
hc_solved += 1
print(f"hill_climbing solution: {hc}")
sa = simulated_annealing(MyNQueen(8))
if -1 not in sa:
sa_solved += 1
print(f"simulated annealing solution: {sa}")
saf = simulated_annealing_full(MyNQueen(8))
if -1 not in saf:
saf_solved += 1
print(f"Simulated annealing full solved: {saf}")
print(
f"N-Queen: Hill climbing (solved/attempted) {hc_solved} / {attempted}"
)
print(
f"N-Queen: Simulated annealing (solved/attempted) {sa_solved} / {attempted}"
)
print(
f"N-Queen: Simluated annealing full (solved/attempted) {saf_solved} / {attempted}"
)
def test_maxsat_hillclimbing_restarts(fn, restarts):
if VERBOSE_TESTING:
print 'Hillclimbing with random restarts...(' + str(HILL_CLIMBING_RESTARTS) + ')'
ms = MAXSAT(fn)
# print "Initial: ", ms.true_clauses(ms.initial)
t0 = time.time()
best = None
highest = 0
for x in range(HILL_CLIMBING_RESTARTS):
ms.initial = ms.init_variables(True) # randomize
sol = search.hill_climbing(ms)
if ms.true_clauses(sol) > highest:
highest = ms.true_clauses(sol)
best = sol[:]
td = time.time() - t0
if VERBOSE_TESTING:
print ms.true_clauses(ms.initial), '->', ms.true_clauses(best)
print "Time: %.2f" % td
return ms.true_clauses(best), round(td, DEC_PRECISION)
def test_rr_searches(restarts, initial_max):
hill_restart_solution = 0
hill_avg = []
annealing_restart_solution = 0
annealing_avg = []
for i in range(restarts):
# Get new starting point
internal_p = SineVariant(randrange(0, initial_max),
initial_max,
delta=1)
# Solve using hill-climbing + random restarts
current_hill_solution = hill_climbing(internal_p)
hill_avg.append(current_hill_solution)
if current_hill_solution > hill_restart_solution:
hill_restart_solution = current_hill_solution
# Solve the problem using simulated annealing + random restarts
current_annealing_solution = simulated_annealing(
internal_p, exp_schedule(k=30, lam=0.005, limit=1000))
annealing_avg.append(current_annealing_solution)
if current_annealing_solution > annealing_restart_solution:
annealing_restart_solution = current_annealing_solution
# Print results of each test
print('Hill-climbing (RR) solution x: ' +
str(hill_restart_solution) + '\t\tvalue: ' +
str(internal_p.value(hill_restart_solution)) +
'\taverage of runs: ' + str(sum(hill_avg) / len(hill_avg)))
print('Simulated annealing (RR) solution x: ' +
str(annealing_restart_solution) + '\t\tvalue: ' +
str(internal_p.value(annealing_restart_solution)) +
'\taverage of runs: ' + str(sum(annealing_avg) / len(annealing_avg)))
def compare_algorithms_for_this_world(world,
dist_dict,
attempts=30,
print_solution=False):
# using the initial as our baseline
best_hill_climber = world
best_annealing = world
# initializing averages
avg_hill_climber = 0
avg_annealing = 0
# our small world problem representation
tsp = TSP(world, dist_dict)
# try both algorithms over and over
for i in range(0, attempts):
hill_climber = hill_climbing(tsp)
annealing = simulated_annealing(
tsp, exp_schedule(k=20, lam=0.005, limit=1000))
# keep track of the best solutions
if tsp.value(hill_climber) > tsp.value(best_hill_climber):
best_hill_climber = hill_climber
if tsp.value(annealing) > tsp.value(best_annealing):
best_annealing = annealing
# maintain average values
avg_hill_climber += tsp.value(hill_climber)
avg_annealing += tsp.value(annealing)
# Calculate the average
avg_hill_climber /= attempts
avg_annealing /= attempts
# Ouptut: world statistics
print("World of size " + str(len(world)) + " with " + str(attempts) +
" per algorithm")
hill_path = str(best_hill_climber)
annealing_path = str(best_annealing)
if not print_solution:
hill_path = ""
annealing_path = ""
# Output: hill climbing results
print("Hill Climbing:\t" + hill_path + "\tbest: " +
str(tsp.value(best_hill_climber)) + "\tavg: " +
str(round(avg_hill_climber, 2)))
# Output: simulated annealing results
print("Annealing:\t" + annealing_path + "\tbest: " +
str(tsp.value(best_annealing)) + "\tavg: " +
str(round(avg_annealing, 2)))
def test_searches(searchspace):
# Solve the problem using hill-climbing.
hill_solution = hill_climbing(searchspace)
print('Hill-climbing solution x: ' + str(hill_solution) +
'\t\tvalue: ' + str(p.value(hill_solution)))
# Solve the problem using simulated annealing.
annealing_solution = simulated_annealing(
searchspace, exp_schedule(k=30, lam=0.005, limit=1000))
print('Simulated annealing solution x: ' + str(annealing_solution) +
'\t\tvalue: ' + str(p.value(annealing_solution)))
def test_knapsack_hill_climbing_large_scale_3():
params = open_file("../dataset/large_scale/knapPI_1_500_1000_1")
problem = KnapsackProblem(params[0], params[1])
ins_problem = InstrumentedProblem(problem)
result = hill_climbing(ins_problem)
optimum = int((
open("../dataset/large_scale-optimum/knapPI_1_500_1000_1").readline()))
assert optimum == 28857
assert result.value <= 10000
assert result.value >= 9800
def run_local_search(runType, inFile, outFile=OUTFILE):
print >> outFile, '--------------------------------'
fi = FORBIDDEN_ISLAND(inFile)
if runType == 'SA':
print >> outFile, 'Simulated Annealing'
sTime = time.time()
rtn = search.simulated_annealing(fi, annealing_schedule())
print >> outFile, ' Time: {} secs'.format(time.time()-sTime)
print >> outFile, ' Value: {}'.format(fi.value(rtn.state))
print >> outFile, ' baseValues: {}'.format(rtn.state)
elif runType == 'GA':
print >> outFile, 'Genetic Algorithm'
sTime = time.time()
rtn = genetic_search(fi, fi.value)
print >> outFile, ' Time: {} secs'.format(time.time()-sTime)
print >> outFile, ' Value: {} / {}'.format(fi.value(rtn), fi.numClause)
elif runType == 'StA':
print >> outFile, 'Steepest ascent'
sTime = time.time()
rtn = search.hill_climbing(fi)
print >> outFile, ' Time: {} secs'.format(time.time()-sTime)
print >> outFile, ' Value: {} / {}'.format(fi.value(rtn), fi.numClause)
elif runType == 'StAR':
print >> outFile, 'Steepest ascent random restarts'
valueL = []
timeL = []
for r in range(20):
sTime = time.time()
fi.random_start()
rtn = search.hill_climbing(fi)
timeL.append(time.time()-sTime)
valueL.append(fi.value(rtn))
print >> outFile, ' Time: {} secs'.format(sum(timeL)/float(len(timeL)))
print >> outFile, ' Mean: {} / {}'.format(sum(valueL)/float(len(valueL)), fi.numClause)
print >> outFile, ' Min: {} / {}'.format(min(valueL), fi.numClause)
print >> outFile, ' Max: {} / {}'.format(max(valueL), fi.numClause)
def test_maxsat_hillclimbing(fn):
if VERBOSE_TESTING:
print 'Hillclimbing...'
ms = MAXSAT(fn)
# print "Initial: ", ms.true_clauses(ms.initial)
t0 = time.time()
solution = search.hill_climbing(ms)
td = time.time() - t0
if VERBOSE_TESTING:
print ms.true_clauses(ms.initial), '->', ms.true_clauses(solution)
print "Time: %.2f" % td
return ms.true_clauses(solution), round(td, DEC_PRECISION)
def EightPuzzleDriver():
hill_solved_count = 0
hill_attempted_count = 0
sa_attempted_count = 0
sa_solved_count = 0
saf_attempted_count = 0
saf_solved_count = 0
solution = (1, 2, 3, 4, 5, 6, 7, 8, 0)
for i in itertools.permutations(list("012345678")):
initial = tuple([int(j) for j in list(i)])
my_8_pz = MyEightPuzzle(initial)
hc = hill_climbing(my_8_pz)
hill_attempted_count += 1
if (solution == hc):
print(f"Hill climbing solved {initial} -> {state}")
hill_solved_count += 1
my_8_pz = MyEightPuzzle(initial)
sa = simulated_annealing(my_8_pz)
sa_attempted_count += 1
if (solution == sa):
print(f"Simulated annealing solved {initial} -> {state}")
sa_solved_count += 1
my_8_pz = MyEightPuzzle(initial)
saf = simulated_annealing_full(my_8_pz)
saf_attempted_count += 1
if (solution == saf):
print(f"Simulated annealing full solved {initial} -> {state}")
saf_solved_count += 1
if hill_attempted_count > 1000:
break
print(
f"8-puzzle: Hill climbing (solved / attempted): {hill_solved_count} / {hill_attempted_count}"
)
print(
f"8-puzzle: Simulated annealing (solved / attempted): {sa_solved_count} / {sa_attempted_count}"
)
print(
f"8-puzzle: Simulated annealing full (solved / attempted): {saf_solved_count} / {saf_attempted_count}"
)
import tours
import search
from itertools import permutations
from random import choice
reg_tours = tours.generate_instances(100)
reg_tours = [list(permutations(cities)) for cities in reg_tours]
optimal = search.brute_force(reg_tours)
# random tour
selected_tours = [list(choice(permus)) for permus in reg_tours]
search.random_tour(selected_tours)
# hill climbing with randomly selected starting points
min_costs = search.hill_climbing(selected_tours)
# count optimal tours and print out
num_optimal = sum(x == y for x, y in zip(min_costs, optimal))
print("OPTIMAL PATHS FOUND: " + str(num_optimal))
print()
'''
for tours with lots of cities
100 cities proved to take incredibly long,
50 gets the job done in a reasonable amount of time
'''
big_tours = tours.generate_instances(100, 50)
search.hill_climbing(big_tours)
def value(self, state):
mcovered = state.num_covered() # /(state.N*state.M)
over_covered = state.num_over_covered() / state.num_covered(
) if state.num_covered() > 0 else 0
antennas_used = len(state.antennas) / state.A
power_used = state.total_power() / state.P
return mcovered - over_covered - antennas_used - power_used
initial = state(2, 8, 120, 120)
problem = city_antenna(initial)
print("Initial sate")
print(initial.forbidden)
problem.initial.print_state()
sol = search.hill_climbing(problem)
print("")
print("Final sate")
sol.state.print_state()
print(sol.state.antennas)
print(sol.solution())
"""
a = state(9, 9, 12, 12, ())
print(a.add_antenna((2, (8,8))))
print(a.add_antenna((2, (0,0))))
print('Num antennas: ', len(a.antennas))
print('Antennas: ', a.antennas)
print('Covered squares: ', a.num_covered())
print('Total power: ', a.total_power())
print(a.forbidden)
idx += 1
assert (len(pos) == 2 or not pos)
return pos
def __str__(self):
return 'driver: {} Route: {}'.format(self.user['id'], self.travel)
"""
This is going to call the Hill Climbing algorithm
"""
state = State(n=100, m=50)
state.generate_random_problem()
print('Initial global distance: {}'.format(state.global_distance()))
hc = CO2(state)
print('Initial value: {}'.format(hc.value(state)))
print()
final = search.hill_climbing(hc)
print()
print('Final global distance: {}'.format(final.global_distance()))
print('Final value: {}'.format(hc.value(final)))
print()
print("Final state:")
print(final)
class TSP(Problem):
def __init__(self, instance):
self.city_count, self.matrix = read_txt_file(instance + ".txt")
self.initial = get_initial_route(self.matrix, self.city_count)
def actions(self, state):
return get_possible_pairs(self.city_count)
def result(self, state, action):
return two_opt_swap_route(state, *action)
def cost(self, state):
cost = 0
start = state[0]
for end in state[1:]:
num = self.matrix[start][end]
cost += num
start = end
last_to_home = self.matrix[state[-1]][state[0]]
return cost + last_to_home
def value(self, state):
return 1 / (self.cost(state) + 1)
p = search.InstrumentedProblem(TSP("gr48"))
g = search.hill_climbing(p)
print(g)
print(p.cost(g))
def value(self, x):
return math.fabs(x * math.sin(x))
if __name__ == '__main__':
# Formulate a problem with a 2D hill function and a single maximum value.
maximum = 30
initial = randrange(0, maximum)
p = SineVariant(initial, maximum, delta=1.0)
print('Initial x: ' + str(p.initial) + '\t\tvalue: ' +
str(p.value(initial)))
# Solve the problem using hill-climbing.
hill_solution = hill_climbing(p)
print('Hill-climbing solution x: ' + str(hill_solution) +
'\tvalue: ' + str(p.value(hill_solution)))
# Solve the problem using simulated annealing.
annealing_solution = simulated_annealing(
p, exp_schedule(k=20, lam=0.005, limit=1000))
print('Simulated annealing solution x: ' + str(annealing_solution) +
'\tvalue: ' + str(p.value(annealing_solution)))
# Implements 100 random restarts, finding the average and best solutions for each algorithm
best_hill_solution = 0
best_annealing_solution = 0
sum_hill_solutions = 0
sum_annealing_solutions = 0
for i in range(100):
for i in range(2, len(state) - 1):
l[1] = l[i]
l[i] = second
action = list_to_string(l)
l[i] = l[1]
l[1] = second
actions.append(action)
return actions
def result(self, state, action):
state = action
return state
def path_cost(self, c, state1, action, state2):
return c + 1
def value(self, state):
l = list(state)
cost = 0
for i in range(0, len(l) - 1):
index_1 = self.nodes.index(l[i])
index_2 = self.nodes.index(l[i + 1])
cost += -1 * self.costs[index_1][index_2]
return cost
#result = astar_search(TSPProblem(nodes, costs))
#result = recursive_best_first_search(TSPProblem(nodes, costs))
result = hill_climbing(TSPProblem(nodes, costs, initial))
print(['-'.join(list(result))])
towns = 10
world = World(towns)
path = []
for i in range(towns):
path.append(i)
random.shuffle(path)
print(path)
tsp = TSP(world, path)
print('Initial x: ' + str(tsp.initial)
+ '\t\tvalue: ' + str(-tsp.value(path))
)
startHill = time.time()
# Solve the problem using hill-climbing.
hill_solution = hill_climbing(tsp)
endHill = time.time()
print('Hill-climbing solution x: ' + str(hill_solution)
+ '\tvalue: ' + str(-tsp.value(hill_solution))
)
print("Time taken was", round(endHill - startHill, 7))
startAnneal = time.time()
annealing_solution = simulated_annealing(
tsp,
exp_schedule(k=20, lam=0.005, limit=1000)
)
endAnneal = time.time()
print('Simulated annealing solution x: ' + str(annealing_solution)
+ '\tvalue: ' + str(-tsp.value(annealing_solution))
)
possibilities = list(node.state.possible_values(i, j))
return len(possibilities) - 1
return 0
choices = {
'depth_first': depth_first_tree_search,
# 'best_first_uniform': uniform_cost_tree_search,
'best_first_h1': lambda x: best_first_tree_search(x, h1),
'best_first_h2': lambda x: best_first_tree_search(x, h2),
'best_first_greedy_h1': lambda x: best_first_greedy_tree_search(x, h1),
'best_first_greedy_h2': lambda x: best_first_greedy_tree_search(x, h2),
'best_first_h3': lambda x: best_first_tree_search(x, h3),
'best_first_greedy_h3': lambda x: best_first_greedy_tree_search(x, h3),
'best_first_graph_h1': lambda x: best_first_graph_search(x, h1),
'best_first_graph_h2': lambda x: best_first_graph_search(x, h2),
'hill_climbing': lambda x: hill_climbing(x),
'annealing': lambda x: simulated_annealing(x, schedule=sim)
}
# print(sys.argv)
searchers = sys.argv[1:]
lewisProblems = map(LewisSudokuProblem, sudokus)
naiveProblems = map(SudokuProblem, sudokus)
#csv header
print("problem,searcher,explored,states,tests,solution,value")
for (i, (p1, p2)) in enumerate(zip(lewisProblems, naiveProblems)):
#print("Initial state:")
for s in searchers:
if s in ['hill_climbing', 'annealing']:
ip = InstrumentedProblem(p1)
eforie=(562, 293),
fagaras=(305, 449),
giurgiu=(375, 270),
hirsova=(534, 350),
iasi=(473, 506),
lugoj=(165, 379),
mehadia=(168, 339),
neamt=(406, 537),
oradea=(131, 571),
pitesti=(320, 368),
rimnicuvilcea=(233, 410),
sibiu=(207, 457),
timisoara=(94, 410),
urziceni=(456, 350),
vaslui=(509, 444),
zerind=(108, 531))
initial1 = random.choice(list(locations.keys()))
problem1 = TSP(locations, initial1)
initial2 = random.choice(list(locations.keys()))
problem2 = TSP(locations, initial2)
hill_solution = hill_climbing(problem1)
print('\nHill-climbing solution x: ' + '\tvalue: ' +
str(math.fabs(problem1.value(hill_solution))) + "\n\n")
annealing_solution = simulated_annealing(
problem2, exp_schedule(k=20, lam=0.005, limit=15000))
print('\nAnnealing solution x: ' + '\tvalue: ' +
str(math.fabs(problem2.value(annealing_solution))) + "\n\n")
for k in vertexstring[i + 1:]:
vertex_dict[k] = rnd.randint(1, maxdist)
graph_dict[vertexstring[i]] = vertex_dict
return graph_dict
if __name__ == '__main__':
# invalid_it = ['C','D','B','A']
# valid_it = ['C','A','B','D']
# simpleTSP = TSP(invalid_it, simple_map)
# print(hill_climbing(simpleTSP))
graph_dict = gen_graph(vertexstring='ABCDEFGHIJKLMNOPQRSTUVWXYZ')
print(graph_dict)
myGraph = UndirectedGraph(graph_dict)
myTSP = TSP(get_city_list(myGraph), myGraph)
values = []
for _ in range(1000):
hill_climbing_solution = hill_climbing(myTSP)
values.append(myTSP.value(hill_climbing_solution))
# print(hill_climbing_solution)
# print(myTSP.value(hill_climbing_solution))
print(max(values))
annealing_solution = simulated_annealing(myTSP, exp_schedule(k=20, lam=0.005, limit=100000))
print(annealing_solution)
print(myTSP.value(annealing_solution))
def value_function(b, n, d):
return math.pow(b, d + 1) - (n + 1) * b + n
city_num = 20
start = generate_random_init(city_num)
print(start)
tsp = TSPHillClimbingProblem(start,
cities=generate_random_cities(city_num, 100))
#print(tsp.actions(start))
# compare the execution time
start_time = time.time()
path = hill_climbing(tsp)
print(path)
print("Execution time for hill-climbing: {}".format(time.time() - start_time))
xs = []
ys = []
for i in range(len(path)):
xs.append(tsp.cities[path[i]][0])
ys.append(tsp.cities[path[i]][1])
xs.append(tsp.cities[path[0]][0])
ys.append(tsp.cities[path[0]][1])
plt.plot(xs, ys)
plt.axis([-1, 100. + 1, -1, 100 + 1])
plt.show()
import search
from utils import (is_in, argmin, argmax, argmax_random_tie, probability,
weighted_sampler, memoize, print_table, open_data, Stack,
FIFOQueue, PriorityQueue, name, distance, vector_add)
eight_puzzle = search.EightPuzzle((1, 2, 3, 4, 5, 7, 8, 6, 0))
nqueens = search.NQueensProblem(8)
romania_problem = search.GraphProblem('Arad', 'Craiova', search.romania_map)
prob = search.PeakFindingProblem(
(0, 0), [[0, 5, 30, 20], [10, 5, 45, 20], [6, 9, 18, 22]
]) #N,W,S,E considered to find peak
print(search.hill_climbing(prob))
if debug:
# Debug statement.
print("\nDistances between pairs of cities: A --> B and B --> A")
for key, value in cityDistancesUnique.items():
print("Key is: " + str(key))
print("Value is: " + str(value))
################################################################################################
# Initialize the Traveling Salesman Problem.
travel = Salesman(cityDistancesUnique, cities)
# Solve the problem using hill climbing.
hillStartTime = time.time()
hill_solution = hill_climbing(travel)
hillEndTime = time.time()
print('Hill-climbing:')
print('\tSolution: ' + str(hill_solution))
print('\tValue: ' + str(travel.value(hill_solution)))
print('\tTime to find solution using hill-climbing: ' +
str(hillEndTime - hillStartTime))
# Solve the problem using simulated annealing.
annealStartTime = time.time()
annealing_solution = simulated_annealing(
travel, exp_schedule(k=20, lam=0.005, limit=10000))
annealEndTime = time.time()
print('Simulated annealing:')
print('\tSolution: ' + str(annealing_solution))
print('\tValue: ' + str(travel.value(annealing_solution)))
def main():
args = parser.parse_args()
bounds, ghosts, pacman, goal = mapPositions(args.layout)
print('Barreiras:', bounds)
print('Fantasmas:', ghosts)
print('Pacman:', pacman)
print('Gol:', goal)
print()
#Problema e algoritmos
problem = PacmanProblem(obstacles=bounds | ghosts,
initial=pacman,
goal=goal)
gfsProblem = greedy_best_first_search(problem)
astarProblem = astar_search(problem)
bfsProblem = breadth_first_graph_search(problem)
dfsProblem = depth_first_graph_search(problem)
hcProblem = hill_climbing(problem)
print('Greedy Best First Search:')
print('Caminho:', gfsProblem.path())
print('Gol:', gfsProblem)
print('A* Search:')
print('Caminho:', astarProblem.path())
print('Solução:', astarProblem.solution())
print('Estados:', path_states(astarProblem))
print('Gol:', astarProblem)
print('Breadth-First Search:')
print('Caminho:', bfsProblem.path())
print('Solução:', bfsProblem.solution())
print('Estados:', path_states(bfsProblem))
print('Gol:', dfsProblem)
print('Depth-First Search:')
print('Caminho:', dfsProblem.path())
print('Solução:', dfsProblem.solution())
print('Estados:', path_states(dfsProblem))
print('Gol:', dfsProblem)
print('Hill Climbing:')
print('Caminho:', hcProblem.path())
print('Solução:', hcProblem.solution())
print('Estados:', path_states(hcProblem))
print('Gol:', dfsProblem)
print()
print('Gerando saídas...')
generateOutput(
set(gfsProblem.solution()) - {pacman, goal},
gfsProblem.explored - {pacman, goal}, args.layout, 'gfs')
generateOutput(
set(astarProblem.solution()) - {pacman, goal},
astarProblem.explored - {pacman, goal}, args.layout, 'astar')
generateOutput(
set(bfsProblem.solution()) - {pacman, goal},
bfsProblem.explored - {pacman, goal}, args.layout, 'bfs')
generateOutput(
set(dfsProblem.solution()) - {pacman, goal},
dfsProblem.explored - {pacman, goal}, args.layout, 'dfs')
generateOutput(
set(hcProblem.solution()) - {pacman, goal},
hcProblem.explored - {pacman, goal}, args.layout, 'hc')
print()
print('Desempenho:')
report([
greedy_best_first_search, astar_search, breadth_first_graph_search,
depth_first_graph_search, hill_climbing
], [problem])
def value(self, x):
return math.fabs(x * math.sin(x))
if __name__ == '__main__':
# Formulate a problem with a 2D hill function and a single maximum value.
maximum = 30
initial = randrange(0, maximum)
p = SineVariant(initial, maximum, delta=1.1)
print('Initial x: ' + str(p.initial) + '\t\tvalue: ' +
str(p.value(initial)))
# Solve the problem using hill-climbing.
hill_solution = hill_climbing(p)
annealing_solution = simulated_annealing(
p, exp_schedule(k=20, lam=0.005, limit=1000))
hill_restarts = hill_solution
hill_value_accumulator = 0
hill_x_accumulator = 0
annealing_restarts = annealing_solution
annealing_value_accumulator = 0
annealing_x_accumulator = 0
restarts = 100
for i in range(restarts):
initial = randrange(0, maximum)
p = SineVariant(initial, maximum, delta=1.1)
class TSP2(Problem):
def __init__(self, n, initial):
self.n = n
self.initial = initial
def actions(self, state):
actions = []
for column in range(self.n):
for row in [x for x in range(self.n) if x != state[column]]:
actions.append([column, row])
for i in range(1,10):
return actions
def result(self, state, move):
"""Makes the given move on a copy of the given state."""
new_state = state[:]
new_state[move[0]] = move[1]
return new_state
def goal_test(self, state):
"""Check to see if there are no conflicts."""
return not any(self.conflicted(state, state[col], col)
for col in range(len(state)))
def conflicted(self, state, row, col):
"""Check to see if placing a queen at (row, col) would conflict with
anything.
"""
return any(self.conflict(row, col, state[c], c)
for c in range(col))
def conflict(self, row1, col1, row2, col2):
"""Check to see if two queens placed in (row1, col1) and (row2, col2)
respectively would conflict with each other.
"""
return (row1 == row2 # # same row
or col1 == col2 # # same column
or row1 - col1 == row2 - col2 # # same \ diagonal
or row1 + col1 == row2 + col2) # # same / diagonal
def value(self, state):
"""This method computes a value of given state based on the number of
conflicting pairs of queens. It doesn't follow AIMA-Python's NQueens
gradient-descent formulation; instead, it counts the number of
non-conflicting pairs (e.g., top score: 28 for a 8-queen problem;
worst score: 0).
"""
# Start with the highest possible score (n combined 2).
value = math.factorial(self.n) / (2 * math.factorial(self.n - 2))
# Loop through all pairs, subtracting one for every conflicted pair.
for pair in itertools.combinations(range(self.n), 2):
if self.conflict(state[pair[0]], pair[0], state[pair[1]], pair[1]):
value -= 1
return value
if __name__ == '__main__':
# Set the board size.
n = 8
# Initialize the board with all queens in the first row.
board = [0] * n
print('Start: ' + str(board))
# Initialize the NQueens problem
p = TSP2(n, board)
print('Value: ' + str(p.value(board)))
# Solve the problem using hill climbing.
hill_solution = hill_climbing(p)
print('Hill-climbing:')
print('\tSolution: ' + str(hill_solution))
print('\tValue: ' + str(p.value(hill_solution)))
print('\tGoal? ' + str(p.goal_test(hill_solution)))
# Solve the problem using simulated annealing.
annealing_solution = simulated_annealing(
p,
exp_schedule(k=20, lam=0.005, limit=10000)
)
print('Simulated annealing:')
print('\tSolution: ' + str(annealing_solution))
print('\tValue: ' + str(p.value(annealing_solution)))
print('\tGoal? ' + str(p.goal_test(annealing_solution)))