def do(searcher, problem):
p = search.InstrumentedProblem(problem)
goalNode = searcher(p)
return p, goalNode.path_cost
# This should be commented out for larger tasks
# print task
print(
"\n******************************START TEST******************************"
)
tic = time.time()
# Define a sub-class of the Problem class, make an instance for the task and call the search
# You should then set the variables:
# final_state - the final state at the end of the plan
# plan - a list of actions representing the plan
# cost - the cost of the plan
# Your planner_v2 here
p = search.InstrumentedProblem(PlanProblem(task))
# soln = search.astar_search(p, lambda n: 0)
soln = search.astar_search(p, lambda n: p.h_g(n))
# soln = search.astar_search(p, lambda n: p.hmax(n))
# soln = search.astar_search(p, lambda n: p.hsum(n))
# soln = search.astar_search(p, lambda n: p.hff(n))
print('search stats', p)
if soln is None:
print('no solution found')
exit()
plan = soln.solution()
cost = soln.path_cost
final_state = soln.state
toc = time.time()
print('Gathering data for depth ' + str(depth) + '...')
path_lengths[depth] = {
'BFS': [],
'IDS': [],
'A*-mis': [],
'A*-Man': []
}
state_counts[depth] = {
'BFS': [],
'IDS': [],
'A*-mis': [],
'A*-Man': []
}
for trial in range(trials):
puzzle = EightPuzzle(depth)
p = search.InstrumentedProblem(puzzle)
path_lengths[depth]['BFS'].append(
len(search.breadth_first_search(p).path()))
state_counts[depth]['BFS'].append(p.states)
p = search.InstrumentedProblem(puzzle)
path_lengths[depth]['IDS'].append(
len(search.iterative_deepening_search(p).path()))
state_counts[depth]['IDS'].append(p.states)
p = search.InstrumentedProblem(puzzle)
path_lengths[depth]['A*-mis'].append(
len(search.astar_search(p, misplaced).path()))
state_counts[depth]['A*-mis'].append(p.states)
p = search.InstrumentedProblem(puzzle)
path_lengths[depth]['A*-Man'].append(
len(search.astar_search(p, manhattan).path()))
state_counts[depth]['A*-Man'].append(p.states)
# print("new route:",new_route)
#self.state = new_route
return new_route
def cost(self, state):
# arvuta (või leia muul viisil) praeguse marsruudi kogupikkus. Ära unusta, et marsruut on suletud.
cost = 0
# print(self.matrix1[1][1])
for i in range(0, len(state) - 1):
# print(i,"cost",self.matrix1[state[i]][state[i+1]])
cost = cost + int(self.matrix1[i][i + 1])
# print(cost)
return cost
def value(self, state):
# kuna valmis otsingufunktsioonid arvavad, et mida suurem väärtus, seda parem, siis meie minimeerimisülesande TSP
# lahendamiseks tuleb teepikkusest pöördväärtus võtta.
return 1 / (self.cost(state) + 1)
p = search.InstrumentedProblem(TSP("gr17"))
g = search.simulated_annealing(p)
#g = search.simulated_annealing(p, search.exp_schedule(limit=10000))
#g = search.hill_climbing(p)
print("p", p)
print("g", g)
print(g.state)
print(p.cost(g.state))
