def four_neighbors(matrix): """ Generate four neighbor matrices for a given matrix """ shape = matrix.shape neighbor_up = matrix[(range(1, shape[0]), range(shape[1]))] neighbor_down = matrix[(range(shape[0] - 1), range(shape[1]))] neighbor_left = matrix[(range(shape[0]), range(1, shape[1]))] neighbor_right = matrix[(range(shape[0]), range(shape[1] - 1))] return neighbor_up, neighbor_down, neighbor_left, neighbor_right优化这段代码
时间: 2023-06-02 19:07:03 浏览: 58
可以使用numpy中的函数处理这段代码,让其更加简洁高效:
import numpy as np
def four_neighbors(matrix):
"""
Generate four neighbor matrices for a given matrix
"""
neighbor_up = matrix[1:, :]
neighbor_down = matrix[:-1, :]
neighbor_left = matrix[:, 1:]
neighbor_right = matrix[:, :-1]
return neighbor_up, neighbor_down, neighbor_left, neighbor_right
使用numpy中的切片方法,可以快速生成四个邻居矩阵,同时避免使用range函数导致的不必要计算。
相关问题
def __init__(self, n_neighbors=5): self.n_neighbors = n_neighbors
这段代码是KNN算法的初始化方法,其中`n_neighbors`是KNN算法中的一个参数,表示选择最近的多少个邻居进行分类。
在这个初始化方法中,`n_neighbors`被赋予一个默认值5。如果在创建KNN对象时没有提供`n_neighbors`的值,则默认为5。
例如,可以这样创建一个KNN对象并设置`n_neighbors`为3:
```python
knn = KNN(n_neighbors=3)
```
这样创建的KNN对象将使用3个最近邻居进行分类。
希望这能回答你的问题!
校正以下代码的语法错误 def encode_edge(self, mode, node_history, node_history_st, edge_type, neighbors, neighbors_edge_value, first_history_indices, batch_size): max_hl = self.hyperparams['maximum_history_length'] max_neighbors = 0 for neighbor_states in neighbors: max_neighbors = max(max_neighbors, len(neighbor_states)) edge_states_list = list() # list of [#of neighbors, max_ht, state_dim] for i, neighbor_states in enumerate(neighbors): # Get neighbors for timestep in batch if len(neighbor_states) == 0: # There are no neighbors for edge type # TODO necessary? neighbor_state_length = int( np.sum([len(entity_dims) for entity_dims in self.state[edge_type[1]].values()]) ) edge_states_list.append(torch.zeros((1, max_hl + 1, neighbor_state_length), device=self.device)) else: edge_states_list.append(torch.stack(neighbor_states, dim=0).to(self.device)) # if self.hyperparams['edge_state_combine_method'] == 'sum': # Used in Structural-RNN to combine edges as well. op_applied_edge_states_list = list() for neighbors_state in edge_states_list: op_applied_edge_states_list.append(torch.sum(neighbors_state, dim=0))#torch.sum combined_neighbors = torch.stack(op_applied_edge_states_list, dim=0) # 获取combined_neighbors的第一个维度,代表邻接边的总数 combined_neighbors_0 = combined_neighbors.shape[0] # 创建全零矩阵s_next,形状为[batch_size, max_neighbors, combined_neighbors_0] s_next = torch.zeros((batch_size, max_neighbors, combined_neighbors_0), device=self.device) # 为s_next矩阵中每一行赋值为对应的combined_neighbors # for b in range(batch_size): # s_next[b, :len(neighbors[b]), :] = combined_neighbors[first_history_indices[b]] for i in range(batch_size): s_next[0, i, :] = batch_size[:] for i in range(max_neighbors): s_next[1, i, :] = max_neighbors[i, :] for i in range(combined_neighbors.shape[0]): s_next[2, i, :] = combined_neighbors
代码中缺少冒号,不是合法的 Python 代码。正确的代码应该是这样的:
```
def encode_edge(self, mode, node_history, node_history_st, edge_type, neighbors, neighbors_edge_value, first_history_indices, batch_size):
max_hl = self.hyperparams['maximum_history_length']
max_neighbors = 0
for neighbor_states in neighbors:
max_neighbors = max(max_neighbors, len(neighbor_states))
edge_states_list = list()
```
相关推荐
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)