def _local_search(problem, fringe_expander, iterations_limit=0, fringe_size=1,
random_initial_states=False):
fringe = BoundedPriorityQueue(fringe_size)
if random_initial_states:
for _ in xrange(fringe_size):
s = problem.generate_random_state()
fringe.append(SearchNodeValueOrdered(state=s, problem=problem))
else:
fringe.append(SearchNodeValueOrdered(state=problem.initial_state,
problem=problem))
iteration = 0
run = True
best = None
while run:
old_best = fringe[0]
fringe_expander(fringe, iteration)
best = fringe[0]
iteration += 1
if iterations_limit and iteration >= iterations_limit:
run = False
elif old_best.value() >= best.value():
run = False
return best
def greedy(problem, graph_search=False, viewer=None):
'''
Greedy search.
If graph_search=True, will avoid exploring repeated states.
Requires: SearchProblem.actions, SearchProblem.result,
SearchProblem.is_goal, SearchProblem.cost, and SearchProblem.heuristic.
'''
return _search(problem,
BoundedPriorityQueue(),
graph_search=graph_search,
node_factory=SearchNodeHeuristicOrdered,
graph_replace_when_better=True,
viewer=viewer)
def uniform_cost(problem, graph_search=False, viewer=None):
'''
Uniform cost search.
If graph_search=True, will avoid exploring repeated states.
Requires: SearchProblem.actions, SearchProblem.result,
SearchProblem.is_goal, and SearchProblem.cost.
'''
return _search(problem,
BoundedPriorityQueue(),
graph_search=graph_search,
node_factory=SearchNodeCostOrdered,
graph_replace_when_better=True,
viewer=viewer)
def astar(problem, graph_search=False, viewer=None):
'''
A* search.
If graph_search=True, will avoid exploring repeated states.
Requires: SearchProblem.actions, SearchProblem.result,
SearchProblem.is_goal, SearchProblem.cost, and SearchProblem.heuristic.
'''
print ("I am in astar() now in traditional")
return _search(problem,
BoundedPriorityQueue(), # this is defined in the utils
graph_search=graph_search,
node_factory=SearchNodeStarOrdered, # this is defined in the models. This is a class name so an object of this class is created as it is passed as an argument to a function
graph_replace_when_better=True,
viewer=viewer)
def beam_search(problem, beam_size=100):
fringe = BoundedPriorityQueue(beam_size)
fringe.append(SearchNodeValueOrdered(state=problem.initial_state,
problem=problem))
while fringe:
successors = BoundedPriorityQueue(beam_size)
for node in fringe:
if problem.is_goal(node.state):
return node
successors.extend(node.expand())
fringe = successors
def _local_search(problem,
fringe_expander,
iterations_limit=0,
fringe_size=1,
random_initial_states=False,
stop_when_no_better=True,
viewer=None):
'''
Basic algorithm for all local search algorithms.
'''
if viewer:
viewer.event('started')
fringe = BoundedPriorityQueue(fringe_size)
if random_initial_states:
for _ in range(fringe_size):
s = problem.generate_random_state()
fringe.append(SearchNodeValueOrdered(state=s, problem=problem))
else:
fringe.append(
SearchNodeValueOrdered(state=problem.initial_state,
problem=problem))
finish_reason = ''
iteration = 0
run = True
best = None
while run:
if viewer:
viewer.event('new_iteration', list(fringe))
old_best = fringe[0]
fringe_expander(fringe, iteration, viewer)
best = fringe[0]
iteration += 1
if iterations_limit and iteration >= iterations_limit:
run = False
finish_reason = 'reaching iteration limit'
elif old_best.value >= best.value and stop_when_no_better:
run = False
finish_reason = 'not being able to improve solution'
if viewer:
viewer.event('finished', fringe, best,
'returned after %s' % finish_reason)
return best
