Dice

本文介绍了一种算法,用于解决两个特殊骰子通过翻转达到六个面完全相同所需的最少翻转次数的问题。采用广度优先搜索策略,并详细展示了实现代码。

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There are 2 special dices on the table. On each face of the dice, a distinct number was written. Consider a 1.a 2,a 3,a 4,a 5,a 6 to be numbers written on top face, bottom face, left face, right face, front face and back face of dice A. Similarly, consider b 1.b 2,b 3,b 4,b 5,b 6 to be numbers on specific faces of dice B. It’s guaranteed that all numbers written on dices are integers no smaller than 1 and no more than 6 while a i ≠ a j and b i ≠ b j for all i ≠ j. Specially, sum of numbers on opposite faces may not be 7. 

At the beginning, the two dices may face different(which means there exist some i, ai ≠ b i). Ddy wants to make the two dices look the same from all directions(which means for all i, a i = b i) only by the following four rotation operations.(Please read the picture for more information) 


Now Ddy wants to calculate the minimal steps that he has to take to achieve his goal. 

Input

There are multiple test cases. Please process till EOF. 

For each case, the first line consists of six integers a 1,a 2,a 3,a 4,a 5,a 6, representing the numbers on dice A. 

The second line consists of six integers b 1,b 2,b 3,b 4,b 5,b 6, representing the numbers on dice B.

Output

For each test case, print a line with a number representing the answer. If there’s no way to make two dices exactly the same, output -1.

Sample Input

1 2 3 4 5 6
1 2 3 4 5 6
1 2 3 4 5 6
1 2 5 6 4 3
1 2 3 4 5 6
1 4 2 5 3 6

Sample Output

0
3
-1

题意:两个筛子通过翻转变成六个面一样,最少翻转多少次。如果不能成功输出-1。

思路:前后不动进行左右翻转,左右不动进行上下翻转。利用广搜求最少次数。

#include<stdio.h>
#include<string.h>
#include<queue>
#include<math.h>
#include<algorithm>
using namespace std;
struct lou
{
    int a,b,c,d,e,f;
    int time;
};
int a1,b1,c1,d1,e1,f1,a2,b2,c2,d2,e2,f2;
int book[10][10][10][10][10][10];
void bfs()
{
    memset(book,0,sizeof(book));
    queue<lou>Q;
    lou p,q;
    p.a=a1;
    p.b=b1;
    p.c=c1;
    p.d=d1;
    p.e=e1;
    p.f=f1;
    book[a1][b1][c1][d1][e1][f1]=1;
    p.time=0;
    Q.push(p);
    while(!Q.empty())
    {
        p=Q.front();
        Q.pop();
        if(p.a==a2&&p.b==b2&&p.c==c2&&p.d==d2&&p.e==e2&&p.f==f2)
        {
            printf("%d\n",p.time);
            return;
        }
        int i;
        for(i=0; i<4; i++)
        {
            if(i==0)
            {
                q.a=p.d;
                q.b=p.c;
                q.c=p.a;
                q.d=p.b;
                q.e=p.e;
                q.f=p.f;
            }
            if(i==1)
            {
                q.a=p.c;
                q.b=p.d;
                q.c=p.b;
                q.d=p.a;
                q.e=p.e;
                q.f=p.f;
            }
            if(i==2)
            {
                q.a=p.e;
                q.b=p.f;
                q.c=p.c;
                q.d=p.d;
                q.e=p.b;
                q.f=p.a;
            }
            if(i==3)
            {
                q.a=p.f;
                q.b=p.e;
                q.c=p.c;
                q.d=p.d;
                q.e=p.a;
                q.f=p.b;
            }
            q.time=p.time+1;
            if(book[q.a][q.b][q.c][q.d][q.e][q.f]==0)
            {
                book[q.a][q.b][q.c][q.d][q.e][q.f]=1;
                Q.push(q);
            }
        }
    }
    printf("-1\n");
}

int main()
{
    while(~scanf("%d%d%d%d%d%d",&a1,&b1,&c1,&d1,&e1,&f1))
    {
        scanf("%d%d%d%d%d%d",&a2,&b2,&c2,&d2,&e2,&f2);
        bfs();
    }
    return 0;
}

 

03-13
### Soft Dice Loss Soft Dice Loss 是一种用于处理多分类分割问题的损失函数。该方法针对每个类别分别计算Dice系数并取平均作为最终的结果[^1]。 ```python import torch.nn.functional as F def soft_dice_loss(input, target): smooth = 1. input = F.softmax(input, dim=1) intersection = (input * target).sum() union = (input + target).sum() dice = (2. * intersection + smooth) / (union + smooth) return 1 - dice.mean() ``` 此损失函数特别适用于医学图像分析等领域,在这些场景下,不同类别的像素数量可能存在显著差异,而传统的交叉熵损失可能会偏向于占主导地位的大面积区域。 ### 使用 MedPy 库计算 Dice 相似系数 对于具体的二元分割任务,可以通过Python库`medpy`来快速获得两个集合之间的Dice相似系数[^2]: ```python from medpy.metric.binary import dc import numpy as np predict = np.random.randint(0, 4, size=(2, 3)) ground_truth = np.random.randint(0, 4, size=(2, 3)) dice_similarity_coefficient = dc(predict, ground_truth) print(f"Dice Similarity Coefficient is {dice_similarity_coefficient}") ``` 上述代码片段展示了如何定义预测结果与实际标签,并通过调用 `dc()` 函数获取两者间的Dice相似度得分。 ### DICE 模型概述 值得注意的是,“DICE”也指代另一种完全不同的概念——即基于因果推断技术构建的一种推荐系统模型。这种情况下,DICE旨在缓解因物品受欢迎程度而导致的偏差现象,而不是用来衡量重叠程度或者作为优化目标的一部分[^3]。
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