Codeforces Round#194(Div.2) A Candy Bag

本文介绍了一个解决兄弟分糖果问题的算法,通过合理分配糖果数量,确保每个兄弟获得相同数量的糖果。该算法适用于偶数个兄弟的情况,并在特定输入下提供输出实例,演示了解决方案的有效性。

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A. Candy Bags
time limit per test
1 second
memory limit per test
256 megabytes
input
standard input
output
standard output

Gerald has n younger brothers and their number happens to be even. One day he bought n2 candy bags. One bag has one candy, one bag has two candies, one bag has three candies and so on. In fact, for each integer k from 1 to n2 he has exactly one bag with kcandies.

Help him give n bags of candies to each brother so that all brothers got the same number of candies.

Input

The single line contains a single integer n (n is even, 2 ≤ n ≤ 100) — the number of Gerald's brothers.

Output

Let's assume that Gerald indexes his brothers with numbers from 1 to n. You need to print n lines, on the i-th line print n integers — the numbers of candies in the bags for the i-th brother. Naturally, all these numbers should be distinct and be within limits from 1 to n2. You can print the numbers in the lines in any order.

It is guaranteed that the solution exists at the given limits.

Sample test(s)
input
2
output
1 4
2 3
Note

The sample shows Gerald's actions if he has two brothers. In this case, his bags contain 1, 2, 3 and 4 candies. He can give the bags with 1 and 4 candies to one brother and the bags with 2 and 3 to the other brother.



这个题想不进去的觉得难,想进去了就好水啊。。

直接贴图,看了图你就明白了:

六个人的时候:


八个人的时候:


所以说我就不多解释了,直接上代码:

#include <stdio.h>
int main()
{
    int n,a[10005],i;
    for(i=1;i<=10000;i++)
    {
        a[i]=i;
    }
    while(scanf("%d",&n)!=EOF)
    {
        int m=n*n,x,falt=0,sum,j;
        for(i=1;i<=n;i++)
        {
            x=i,falt=0,sum=0;
            for(j=1;j<=i;j++)
            {
                if(falt==0)
                {
                    printf("%d",x);
                    falt=1;
                    sum+=x;
                }
                else
                {
                    printf(" %d",x);
                    sum+=x;
                }
                x+=n-1;
            }
            x=n*(i+1);
            for(j=i+1;j<=n;j++)
            {
                if(x>n*n)
                {
                    break;
                }
                printf(" %d",x);
                sum+=x;
                x+=n-1;
            }
            printf(" \n",sum);
        }
    }
    return 0;
}


### 使用Transformer模型进行图像分类的方法 #### 方法概述 为了使Transformer能够应用于图像分类任务,一种有效的方式是将图像分割成固定大小的小块(patches),这些小块被线性映射为向量,并加上位置编码以保留空间信息[^2]。 #### 数据预处理 在准备输入数据的过程中,原始图片会被切分成多个不重叠的patch。假设一张尺寸为\(H \times W\)的RGB图像是要处理的对象,则可以按照设定好的宽度和高度参数来划分该图像。例如,对于分辨率为\(224\times 224\)像素的图像,如果选择每边切成16个部分的话,那么最终会得到\((224/16)^2=196\)个小方格作为单独的特征表示单元。之后,每一个这样的补丁都会通过一个简单的全连接层转换成为维度固定的嵌入向量。 ```python import torch from torchvision import transforms def preprocess_image(image_path, patch_size=16): transform = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), # 假设目标分辨率是224x224 transforms.ToTensor(), ]) image = Image.open(image_path).convert('RGB') tensor = transform(image) patches = [] for i in range(tensor.shape[-2] // patch_size): # 高度方向上的循环 row_patches = [] for j in range(tensor.shape[-1] // patch_size): # 宽度方向上的循环 patch = tensor[:, :, i*patch_size:(i+1)*patch_size, j*patch_size:(j+1)*patch_size].flatten() row_patches.append(patch) patches.extend(row_patches) return torch.stack(patches) ``` #### 构建Transformer架构 构建Vision Transformer (ViT),通常包括以下几个组成部分: - **Patch Embedding Layer**: 将每个图像块转化为低维向量; - **Positional Encoding Layer**: 添加绝对或相对位置信息给上述获得的向量序列; - **Multiple Layers of Self-Attention and Feed Forward Networks**: 多层自注意机制与前馈神经网络交替堆叠而成的核心模块; 最后,在顶层附加一个全局平均池化层(Global Average Pooling)以及一个多类别Softmax回归器用于预测类标签。 ```python class VisionTransformer(nn.Module): def __init__(self, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=False, drop_rate=0.): super().__init__() self.patch_embed = PatchEmbed(embed_dim=embed_dim) self.pos_embed = nn.Parameter(torch.zeros(1, self.patch_embed.num_patches + 1, embed_dim)) self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) dpr = [drop_rate for _ in range(depth)] self.blocks = nn.Sequential(*[ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=dpr[i], ) for i in range(depth)]) self.norm = nn.LayerNorm(embed_dim) self.head = nn.Linear(embed_dim, num_classes) def forward(self, x): B = x.shape[0] cls_tokens = self.cls_token.expand(B, -1, -1) x = self.patch_embed(x) x = torch.cat((cls_tokens, x), dim=1) x += self.pos_embed x = self.blocks(x) x = self.norm(x) return self.head(x[:, 0]) ```
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