self._usecolsTuple = tuple(range(0, len(self._headItemList)))
时间: 2023-11-23 21:07:46 浏览: 26
这是一个用于初始化 `_usecolsTuple` 的元组。它的作用是将 `_headItemList` 的索引范围转换为一个元组,以便在后续的操作中使用。`range(0, len(self._headItemList))` 生成从 0 到 `self._headItemList` 长度减一的整数序列,然后通过 `tuple()` 转换为元组。这样做是为了确保 `_usecolsTuple` 的值是不可变的,以防止被意外修改。
相关问题
#256个block memory_size = 256 #pid进程号 class Process: def __init__(self, pid, block, duration): self.__block = block self.__duration = duration self.__pid = pid self.__memory = None @property def pid(self): return self.__pid @property def block(self): return self.__block @property def duration(self): return self.__duration def set_memory(self, memory_start, memory_end): self.__memory = (memory_start, memory_end) def get_memory(self): return self.__memory class MemoryAllocator: def __init__(self, memory_size): self.__memory_blocks = [None] * memory_size def memory_view(self): '''return the array of the use of memory blocks.''' return tuple(self.__memory_blocks) def allocate_memory(self, block_start, length, process): for block_id in range(block_start, block_start+length): assert self.__memory_blocks[block_id] is None, 'tend to allocate occupied blocks' self.__memory_blocks[block_id] = process process.set_memory(block_start, length) def free_memory(self, process): assert process.get_memory() is not None, 'process should already hold memory blocks' block_start, length = process.get_memory() for block_id in range(block_start, block_start+length): assert self.__memory_blocks[block_id] == process, 'the orresponding memory blocks should be assigned to the process' self.__memory_blocks[block_id] = None
这段代码实现了一个简单的内存分配器和进程类。其中,内存分配器使用一个数组来表示内存块的使用情况,每个进程对象都包含一个指向内存块的引用。
这段代码的实现还可以进行一些改进:
1. 内存分配器可以使用更高效的数据结构,比如位图,来表示内存块的使用情况。
2. 进程类可以添加一个方法来释放内存,而不是在内存分配器中实现。
3. 内存分配器可以添加一个方法来查找可用的内存块,而不是在进程类中实现。
4. 进程类可以将内存块的起始位置和长度作为属性,而不是在set_memory方法中设置。
下面是改进后的代码:
```
class Process:
def __init__(self, pid, duration):
self.__pid = pid
self.__duration = duration
self.__memory_start = None
self.__memory_length = None
@property
def pid(self):
return self.__pid
@property
def duration(self):
return self.__duration
@property
def memory_start(self):
return self.__memory_start
@property
def memory_length(self):
return self.__memory_length
def set_memory(self, start, length):
self.__memory_start = start
self.__memory_length = length
def release_memory(self):
self.__memory_start = None
self.__memory_length = None
class MemoryAllocator:
def __init__(self, memory_size):
self.__memory_blocks = [False] * memory_size
def find_free_memory(self, length):
for i in range(len(self.__memory_blocks) - length + 1):
if all(not self.__memory_blocks[j] for j in range(i, i + length)):
return i
return None
def allocate_memory(self, process):
length = process.duration
start = self.find_free_memory(length)
assert start is not None, 'allocation failed'
for i in range(start, start + length):
self.__memory_blocks[i] = True
process.set_memory(start, length)
def free_memory(self, process):
start = process.memory_start
length = process.memory_length
for i in range(start, start + length):
self.__memory_blocks[i] = False
process.release_memory()
```
改进后的代码更加清晰和易于维护,也更加高效。
class AbstractGreedyAndPrune(): def __init__(self, aoi: AoI, uavs_tours: dict, max_rounds: int, debug: bool = True): self.aoi = aoi self.max_rounds = max_rounds self.debug = debug self.graph = aoi.graph self.nnodes = self.aoi.n_targets self.uavs = list(uavs_tours.keys()) self.nuavs = len(self.uavs) self.uavs_tours = {i: uavs_tours[self.uavs[i]] for i in range(self.nuavs)} self.__check_depots() self.reachable_points = self.__reachable_points() def __pruning(self, mr_solution: MultiRoundSolution) -> MultiRoundSolution: return utility.pruning_multiroundsolution(mr_solution) def solution(self) -> MultiRoundSolution: mrs_builder = MultiRoundSolutionBuilder(self.aoi) for uav in self.uavs: mrs_builder.add_drone(uav) residual_ntours_to_assign = {i : self.max_rounds for i in range(self.nuavs)} tour_to_assign = self.max_rounds * self.nuavs visited_points = set() while not self.greedy_stop_condition(visited_points, tour_to_assign): itd_uav, ind_tour = self.local_optimal_choice(visited_points, residual_ntours_to_assign) residual_ntours_to_assign[itd_uav] -= 1 tour_to_assign -= 1 opt_tour = self.uavs_tours[itd_uav][ind_tour] visited_points |= set(opt_tour.targets_indexes) # update visited points mrs_builder.append_tour(self.uavs[itd_uav], opt_tour) return self.__pruning(mrs_builder.build()) class CumulativeGreedyCoverage(AbstractGreedyAndPrune): choice_dict = {} for ind_uav in range(self.nuavs): uav_residual_rounds = residual_ntours_to_assign[ind_uav] if uav_residual_rounds > 0: uav_tours = self.uavs_tours[ind_uav] for ind_tour in range(len(uav_tours)): tour = uav_tours[ind_tour] quality_tour = self.evaluate_tour(tour, uav_residual_rounds, visited_points) choice_dict[quality_tour] = (ind_uav, ind_tour) best_value = max(choice_dict, key=int) return choice_dict[best_value] def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set): new_points = (set(tour.targets_indexes) - visited_points) return round_count * len(new_points) 如何改写上述程序,使其能返回所有已经探索过的目标点visited_points的数量,请用代码表示
可以在 `solution()` 方法中添加一个变量来记录已经探索过的目标点数量,然后在每次更新 `visited_points` 后更新这个变量。下面是修改后的代码:
```
class AbstractGreedyAndPrune():
def __init__(self, aoi: AoI, uavs_tours: dict, max_rounds: int, debug: bool = True):
self.aoi = aoi
self.max_rounds = max_rounds
self.debug = debug
self.graph = aoi.graph
self.nnodes = self.aoi.n_targets
self.uavs = list(uavs_tours.keys())
self.nuavs = len(self.uavs)
self.uavs_tours = {i: uavs_tours[self.uavs[i]] for i in range(self.nuavs)}
self.__check_depots()
self.reachable_points = self.__reachable_points()
def __pruning(self, mr_solution: MultiRoundSolution) -> MultiRoundSolution:
return utility.pruning_multiroundsolution(mr_solution)
def solution(self) -> Tuple[MultiRoundSolution, int]:
mrs_builder = MultiRoundSolutionBuilder(self.aoi)
for uav in self.uavs:
mrs_builder.add_drone(uav)
residual_ntours_to_assign = {i : self.max_rounds for i in range(self.nuavs)}
tour_to_assign = self.max_rounds * self.nuavs
visited_points = set()
explored_points = 0
while not self.greedy_stop_condition(visited_points, tour_to_assign):
itd_uav, ind_tour = self.local_optimal_choice(visited_points, residual_ntours_to_assign)
residual_ntours_to_assign[itd_uav] -= 1
tour_to_assign -= 1
opt_tour = self.uavs_tours[itd_uav][ind_tour]
new_points = set(opt_tour.targets_indexes) - visited_points
explored_points += len(new_points)
visited_points |= new_points # update visited points
mrs_builder.append_tour(self.uavs[itd_uav], opt_tour)
return self.__pruning(mrs_builder.build()), explored_points
class CumulativeGreedyCoverage(AbstractGreedyAndPrune):
def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set):
new_points = set(tour.targets_indexes) - visited_points
return round_count * len(new_points)
def local_optimal_choice(self, visited_points, residual_ntours_to_assign):
choice_dict = {}
for ind_uav in range(self.nuavs):
uav_residual_rounds = residual_ntours_to_assign[ind_uav]
if uav_residual_rounds > 0:
uav_tours = self.uavs_tours[ind_uav]
for ind_tour in range(len(uav_tours)):
tour = uav_tours[ind_tour]
quality_tour = self.evaluate_tour(tour, uav_residual_rounds, visited_points)
choice_dict[quality_tour] = (ind_uav, ind_tour)
best_value = max(choice_dict, key=int)
return choice_dict[best_value]