class FixedSizeHeap: def __init__(self, max_size, key = isMoreThan): self.max_size = max_size self.heap = [] 其中isMoreThan是一个函数,这种类该怎么调用
时间: 2024-04-12 12:32:30 浏览: 12
要调用这个类,你需要先实例化一个对象。在实例化时,你可以选择传递一个自定义的函数作为参数key,或者使用默认值isMoreThan。
下面是一个示例代码:
```python
def isMoreThan(a, b):
return a > b
heap = FixedSizeHeap(10, key=isMoreThan)
```
在这个示例中,我们创建了一个FixedSizeHeap对象,并将max_size设置为10,同时传递了自定义的isMoreThan函数作为key参数。
请注意,这个类的完整代码还没有给出。这只是一个示例,你可能需要根据自己的需求进行适当修改。
相关问题
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]
class Transformer(nn.Module): def __init__(self, vocab_size: int, max_seq_len: int, embed_dim: int, hidden_dim: int, n_layer: int, n_head: int, ff_dim: int, embed_drop: float, hidden_drop: float): super().__init__() self.tok_embedding = nn.Embedding(vocab_size, embed_dim) self.pos_embedding = nn.Embedding(max_seq_len, embed_dim) layer = nn.TransformerEncoderLayer( d_model=hidden_dim, nhead=n_head, dim_feedforward=ff_dim, dropout=hidden_drop) self.encoder = nn.TransformerEncoder(layer, num_layers=n_layer) self.embed_dropout = nn.Dropout(embed_drop) self.linear1 = nn.Linear(embed_dim, hidden_dim) self.linear2 = nn.Linear(hidden_dim, embed_dim) def encode(self, x, mask): x = x.transpose(0, 1) x = self.encoder(x, src_key_padding_mask=mask) x = x.transpose(0, 1) return x
这是一段使用 PyTorch 实现的 Transformer 模型的代码,用于自然语言处理任务中的序列建模,例如文本分类、机器翻译等。
该模型的输入是一个词汇表大小为 `vocab_size`,最大序列长度为 `max_seq_len` 的词嵌入(embedding)矩阵,其中每个词嵌入的维度为 `embed_dim`。模型使用了 `n_layer` 层 TransformerEncoderLayer,每个 EncoderLayer 中包含了 `n_head` 个注意力头(self-attention)。每个 EncoderLayer 的隐藏层大小为 `hidden_dim`,Feedforward 层的大小为 `ff_dim`,并在每个 EncoderLayer 后应用了一个 `hidden_drop` 的 Dropout。在模型的输入层和第一个 EncoderLayer 之间,使用了一个 `embed_drop` 的 Dropout。
在 forward 方法中,输入的 `x` 是一个形状为 `(batch_size, seq_len)` 的整数张量,表示一个批次中的多个序列。`mask` 是一个形状为 `(batch_size, seq_len)` 的布尔型张量,用于指示哪些位置是填充值,需要被屏蔽。在 encode 方法中,模型首先将输入的 `x` 转置为 `(seq_len, batch_size)` 的形状,然后将其输入到 TransformerEncoder 中进行编码。最后,将编码结果再次转置为 `(batch_size, seq_len)` 的形状并返回。
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