nl.append(max(lot, key=lambda x: x[1])) lot.remove(nl[-1])解释这两句代码
时间: 2024-04-28 10:25:30 浏览: 42
这两句代码是 Python 代码,其中 `nl` 和 `lot` 都是列表(list)类型的变量。
第一句代码 `nl.append(max(lot, key=lambda x: x[1]))` 的作用是:
- `max(lot, key=lambda x: x[1])` 表示在 `lot` 列表中,根据每个元素的第二个值(即下标为1的值)进行比较,找到一个值最大的元素。
- `nl.append()` 表示将找到的最大元素添加到 `nl` 列表的末尾。
第二句代码 `lot.remove(nl[-1])` 的作用是:
- `nl[-1]` 表示 `nl` 列表的最后一个元素。
- `lot.remove()` 表示从 `lot` 列表中删除一个元素,这个元素就是 `nl` 列表的最后一个元素。也就是说,从 `lot` 列表中删除了一个值最大的元素。
综上,这两句代码的作用是在 `lot` 列表中寻找一个值最大的元素,将其添加到 `nl` 列表的末尾,并且从 `lot` 列表中删除这个元素。
相关问题
修改以下代码,使他避开障碍物:def Astar(Map): s = Point(Map.start, None, None) openlist = [s] closedlist = [] s.get_f(Map.goal) while openlist: n = openlist.pop(0) if n.position[0] == Map.goal[0] and n.position[1] == Map.goal[1]: return n else: closedlist.append(n) subp_list = Point.EXPAND(n, Map) for subp in subp_list: subp.get_f(Map.goal) for openp in openlist: if openp.position[0] == subp.position[0] and openp.position[1] == subp.position[1]: if subp.f < openp.f: openlist.remove(openp) openlist.append(subp) for closedp in closedlist: if closedp.position[0] == subp.position[0] and closedp.position[1] == subp.position[1]: if subp.f < closedp.f: closedlist.remove(closedp) openlist.append(subp) openlist.append(subp) openlist.sort(key= lambda x:x.f)
Sure, here's the modified code that avoids obstacles using A* algorithm:
```
def Astar(Map):
s = Point(Map.start, None, None)
openlist = [s]
closedlist = []
s.get_f(Map.goal)
while openlist:
n = openlist.pop(0)
if n.position[0] == Map.goal[0] and n.position[1] == Map.goal[1]:
return n
else:
closedlist.append(n)
subp_list = Point.EXPAND(n, Map)
for subp in subp_list:
if Map.is_obstacle(subp.position[0], subp.position[1]):
continue
subp.get_f(Map.goal)
for openp in openlist:
if openp.position[0] == subp.position[0] and openp.position[1] == subp.position[1]:
if subp.f < openp.f:
openlist.remove(openp)
openlist.append(subp)
break
else:
for closedp in closedlist:
if closedp.position[0] == subp.position[0] and closedp.position[1] == subp.position[1]:
if subp.f < closedp.f:
closedlist.remove(closedp)
openlist.append(subp)
break
else:
openlist.append(subp)
openlist.sort(key=lambda x: x.f)
```
The modification is done by adding a check for obstacles in the sub-points list and skipping those points, as well as updating the open and closed lists based on the new condition.
inter_n=10 try_n=1 study_data=[] for i in range(try_n): n=neuralNetwork(input_nodes,hidden_nodes,output_nodes,learning_rate) temp=[] for j in range(inter_n): n.train_all() temp.append((n.test(),n.w_in_hid,n.w_hid_out)) temp.sort(key=lambda x:x[0],reverse=True) study_data.append(temp[0]) study_data.sort(key=lambda x:x[0],reverse=True) sc,w_in_hid,w_hid_out=study_data[0] print("max:"+str(sc))
There seems to be a syntax error in the code. The line 'print("max:" str(sc))' should have a comma to separate the two arguments being printed. Here is the corrected code:
inter_n=10
try_n=1
study_data=[]
for i in range(try_n):
n=neuralNetwork(input_nodes,hidden_nodes,output_nodes,learning_rate)
temp=[]
for j in range(inter_n):
n.train_all()
temp.append((n.test(),n.w_in_hid,n.w_hid_out))
temp.sort(key=lambda x:x[0],reverse=True)
study_data.append(temp[0])
study_data.sort(key=lambda x:x[0],reverse=True)
sc,w_in_hid,w_hid_out=study_data[0]
print("max:", sc)