Same binary weight

本文介绍了一个算法问题——找到大于给定整数N的最小整数,该整数与N具有相同的二进制权重。文章通过具体实例展示了如何通过翻转特定位置的比特来实现这一目标。

Same binary weight

时间限制: 300 ms  |  内存限制: 65535 KB
难度: 3
描述

The binary weight of a positive  integer is the number of 1's in its binary representation.for example,the decmial number 1 has a binary weight of 1,and the decimal number 1717 (which is 11010110101 in binary) has a binary weight of 7.Give a positive integer N,return the smallest integer greater than N that has the same binary weight as N.N will be between 1 and 1000000000,inclusive,the result is guaranteed to fit in a signed 32-bit interget.

输入
The input has multicases and each case contains a integer N.
输出
For each case,output the smallest integer greater than N that has the same binary weight as N.
样例输入
1717
4
7
12
555555
样例输出
1718
8
11
17
555557

查看代码---运行号:252990----结果:Accepted

运行时间: 2012-10-07 13:53:19  |  运行人: huangyibiao
01. #include <iostream>
02. #include <bitset>
03.  
04. usingnamespacestd;
05.  
06. intmain()
07. {
08. unsignedlongn;
09.  
10. while(cin >> n)
11. {
12. bitset<32> b(n);
13. inti, index1, index0;
14.  
15. for(i = 0; i < b.size(); i++)
16. {
17. if(b.test(i))
18. {
19. index1 = i;//从最低位开始找第一个1的位置
20. break;
21. }
22. }
23. for(i = index1+1; i < b.size(); i++)
24. {
25. if(!b.test(i))
26. {
27. index0 = i;//从第一个1的位置开始找,第一个出现0的位置
28. break;
29. }
30. }
31. b.flip(index0);
32. b.flip(index0-1);
33. index0 --;
34.  
35. bitset<32> bit;
36. intj = 31;
37. for(i = b.size()-1; i > index0; i--)
38. bit[j--] = b[i];
39. for(i = index1-1; i >= 0; i--)
40. bit[j--] = b[i];
41. for(i = index0; i >= index1; i--)
42. {
43. bit[j--] = b[i];
44. }
45. cout << bit.to_ulong() << endl;
46. }
47. return0;
48. }

--------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) Cell In[11], line 4 2 print("\nStarting training...") 3 epochs = 50 ----> 4 train_losses, test_losses, train_accs, test_accs = train_model( 5 model, train_loader, test_loader, criterion, optimizer, epochs=epochs 6 ) Cell In[10], line 18, in train_model(model, train_loader, test_loader, criterion, optimizer, epochs) 15 optimizer.zero_grad() 16 outputs = model(inputs) # [batch_size, 3] ---> 18 loss = criterion(outputs, labels) # 计算 multi-label loss 19 loss.backward() 20 optimizer.step() File E:\python\interference_identification\.venv\Lib\site-packages\torch\nn\modules\module.py:1773, in Module._wrapped_call_impl(self, *args, **kwargs) 1771 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc] 1772 else: -> 1773 return self._call_impl(*args, **kwargs) File E:\python\interference_identification\.venv\Lib\site-packages\torch\nn\modules\module.py:1784, in Module._call_impl(self, *args, **kwargs) 1779 # If we don't have any hooks, we want to skip the rest of the logic in 1780 # this function, and just call forward. 1781 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 1782 or _global_backward_pre_hooks or _global_backward_hooks 1783 or _global_forward_hooks or _global_forward_pre_hooks): -> 1784 return forward_call(*args, **kwargs) 1786 result = None 1787 called_always_called_hooks = set() File E:\python\interference_identification\.venv\Lib\site-packages\torch\nn\modules\loss.py:828, in BCEWithLogitsLoss.forward(self, input, target) 827 def forward(self, input: Tensor, target: Tensor) -> Tensor: --> 828 return F.binary_cross_entropy_with_logits( 829 input, 830 target, 831 self.weight, 832 pos_weight=self.pos_weight, 833 reduction=self.reduction, 834 ) File E:\python\interference_identification\.venv\Lib\site-packages\torch\nn\functional.py:3597, in binary_cross_entropy_with_logits(input, target, weight, size_average, reduce, reduction, pos_weight) 3592 if not (target.size() == input.size()): 3593 raise ValueError( 3594 f"Target size ({target.size()}) must be the same as input size ({input.size()})" 3595 ) -> 3597 return torch.binary_cross_entropy_with_logits( 3598 input, target, weight, pos_weight, reduction_enum 3599 ) RuntimeError: result type Float can't be cast to the desired output type Long
最新发布
09-30
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