# Path: simpleai/search/utils.py
# class BoundedPriorityQueue(object):
#     def __init__(self, limit=None, *args):
#         self.limit = limit
#         self.queue = list()
# 
#     def __getitem__(self, val):
#         return self.queue[val]
# 
#     def __len__(self):
#         return len(self.queue)
# 
#     def append(self, x):
#         heapq.heappush(self.queue, x)
#         if self.limit and len(self.queue) > self.limit:
#             self.queue.remove(heapq.nlargest(1, self.queue)[0])
#             heapq.heapify(self.queue)
# 
#     def pop(self):
#         return heapq.heappop(self.queue)
# 
#     def extend(self, iterable):
#         for x in iterable:
#             self.append(x)
# 
#     def clear(self):
#         self.queue.clear()
# 
#     def remove(self, x):
#         self.queue.remove(x)
#         heapq.heapify(self.queue)
# 
#     def sorted(self):
#         return heapq.nsmallest(len(self.queue), self.queue)
# 
# class InverseTransformSampler(object):
#     def __init__(self, weights, objects):
#         assert weights and objects and len(weights) == len(objects)
#         self.objects = objects
#         tot = float(sum(weights))
#         if tot == 0:
#             tot = len(weights)
#             weights = [1 for x in weights]
#         accumulated = 0
#         self.probs = []
#         for w, x in izip(weights, objects):
#             p = w / tot
#             accumulated += p
#             self.probs.append(accumulated)
# 
#     def sample(self):
#         target = random.random()
#         i = 0
#         while i + 1 != len(self.probs) and target > self.probs[i]:
#             i += 1
#         return self.objects[i]
# 
# Path: simpleai/search/models.py
# class SearchNodeValueOrdered(SearchNode):
#     def __init__(self, *args, **kwargs):
#         super(SearchNodeValueOrdered, self).__init__(*args, **kwargs)
#         self.value = self.problem.value(self.state)
# 
#     def __lt__(self, other):
#         # value must work inverted, because heapq sorts 1-9
#         # and we need 9-1 sorting
#         return -self.value < -other.value


from simpleai.search.utils import BoundedPriorityQueue, InverseTransformSampler
from simpleai.search.models import SearchNodeValueOrdered
import math
import random
    If iterations_limit is specified, the algorithm will end after that
    number of iterations. Else, it will continue until it can't find a
    better node than the current one.
    Requires: SearchProblem.generate_random_state, SearchProblem.crossover,
    SearchProblem.mutate and SearchProblem.value.
    '''
    return _local_search(problem,
                         _create_genetic_expander(problem, mutation_chance),
                         iterations_limit=iterations_limit,
                         fringe_size=population_size,
                         random_initial_states=True,
                         stop_when_no_better=iterations_limit==0,
                         viewer=viewer)


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:

# Path: simpleai/search/utils.py
# class BoundedPriorityQueue(object):
#     def __init__(self, limit=None, *args):
#         self.limit = limit
#         self.queue = list()
# 
#     def __getitem__(self, val):
#         return self.queue[val]
# 
#     def __len__(self):
#         return len(self.queue)
# 
#     def append(self, x):
#         heapq.heappush(self.queue, x)
#         if self.limit and len(self.queue) > self.limit:
#             self.queue.remove(heapq.nlargest(1, self.queue)[0])
#             heapq.heapify(self.queue)
# 
#     def pop(self):
#         return heapq.heappop(self.queue)
# 
#     def extend(self, iterable):
#         for x in iterable:
#             self.append(x)
# 
#     def clear(self):
#         self.queue.clear()
# 
#     def remove(self, x):
#         self.queue.remove(x)
#         heapq.heapify(self.queue)
# 
#     def sorted(self):
#         return heapq.nsmallest(len(self.queue), self.queue)
# 
# class InverseTransformSampler(object):
#     def __init__(self, weights, objects):
#         assert weights and objects and len(weights) == len(objects)
#         self.objects = objects
#         tot = float(sum(weights))
#         if tot == 0:
#             tot = len(weights)
#             weights = [1 for x in weights]
#         accumulated = 0
#         self.probs = []
#         for w, x in izip(weights, objects):
#             p = w / tot
#             accumulated += p
#             self.probs.append(accumulated)
# 
#     def sample(self):
#         target = random.random()
#         i = 0
#         while i + 1 != len(self.probs) and target > self.probs[i]:
#             i += 1
#         return self.objects[i]
# 
# Path: simpleai/search/models.py
# class SearchNodeValueOrdered(SearchNode):
#     def __init__(self, *args, **kwargs):
#         super(SearchNodeValueOrdered, self).__init__(*args, **kwargs)
#         self.value = self.problem.value(self.state)
# 
#     def __lt__(self, other):
#         # value must work inverted, because heapq sorts 1-9
#         # and we need 9-1 sorting
#         return -self.value < -other.value


# Path: simpleai/search/local.py
from simpleai.search.utils import BoundedPriorityQueue, InverseTransformSampler
from simpleai.search.models import SearchNodeValueOrdered
import math
import random

    If iterations_limit is specified, the algorithm will end after that
    number of iterations. Else, it will continue until it can't find a
    better node than the current one.
    Requires: SearchProblem.generate_random_state, SearchProblem.crossover,
    SearchProblem.mutate and SearchProblem.value.
    '''
    return _local_search(problem,
                         _create_genetic_expander(problem, mutation_chance),
                         iterations_limit=iterations_limit,
                         fringe_size=population_size,
                         random_initial_states=True,
                         stop_when_no_better=iterations_limit==0,
                         viewer=viewer)


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,