E. Riding in a Lift(Codeforces Round #274)

本文探讨了一个特定的算法问题:在一栋有n层楼的大楼里,从a层出发,在不经过b层的情况下,进行k次楼层间移动的不同路径数量。通过动态规划的方法解决了这一问题,并给出了详细的实现代码。
E. Riding in a Lift
time limit per test
2 seconds
memory limit per test
256 megabytes
input
standard input
output
standard output

Imagine that you are in a building that has exactly n floors. You can move between the floors in a lift. Let's number the floors from bottom to top with integers from 1 to n. Now you're on the floor number a. You are very bored, so you want to take the lift. Floor number b has a secret lab, the entry is forbidden. However, you already are in the mood and decide to make k consecutive trips in the lift.

Let us suppose that at the moment you are on the floor number x (initially, you were on floor a). For another trip between floors you choose some floor with number y (y ≠ x) and the lift travels to this floor. As you cannot visit floor b with the secret lab, you decided that the distance from the current floor x to the chosen y must be strictly less than the distance from the current floor x to floor b with the secret lab. Formally, it means that the following inequation must fulfill: |x - y| < |x - b|. After the lift successfully transports you to floor y, you write down number y in your notepad.

Your task is to find the number of distinct number sequences that you could have written in the notebook as the result of k trips in the lift. As the sought number of trips can be rather large, find the remainder after dividing the number by 1000000007 (109 + 7).

Input

The first line of the input contains four space-separated integers nabk (2 ≤ n ≤ 50001 ≤ k ≤ 50001 ≤ a, b ≤ na ≠ b).

Output

Print a single integer — the remainder after dividing the sought number of sequences by 1000000007 (109 + 7).

Sample test(s)
input
5 2 4 1
output
2
input
5 2 4 2
output
2
input
5 3 4 1
output
0
Note

Two sequences p1, p2, ..., pk and q1, q2, ..., qk are distinct, if there is such integer j (1 ≤ j ≤ k), that pj ≠ qj.

Notes to the samples:

  1. In the first sample after the first trip you are either on floor 1, or on floor 3, because |1 - 2| < |2 - 4| and |3 - 2| < |2 - 4|.
  2. In the second sample there are two possible sequences: (1, 2)(1, 3). You cannot choose floor 3 for the first trip because in this case no floor can be the floor for the second trip.

  1. In the third sample there are no sought sequences, because you cannot choose the floor for the first trip.


上次的cf今天才补题o(╯□╰)o,给n层楼。在a层開始,不能在b层停,且当在x层去y层时。|x - y| < |x - b|,求运行k
 
次的方案数。

有两种情况,dp[i][j],i为第i次,j为当前停的层数。


 当a<b时,此时全部的x不会超过b,当第i次停在j层。第i-1次肯定在[0,(b+j-1)/2],左端点不难想到,右端点推导过程:

设第i-1次停在x层。则第i层全部大于x小于b的点都能够取。我们仅仅考虑小于x的点。则x-j<=b-x-1,

整理得:   x<=(b+j-1)/2; 所以转移方程为:dp[i][j]=(sum[i-1][(j+b-1)/2]-dp[i-1][j]+mod)%mod;

当a>b时,同理得dp[i][j]=((sum[i-1][n]-sum[i-1][(j+b)/2]+mod)%mod-dp[i-1][j]+mod)%mod;

代码:
#include <iostream>
#include <cstdio>
#include <algorithm>
#include <cstring>
using namespace std;
const int maxn=5000+100;
const int mod=1000000000+7;
int dp[maxn][maxn];
int sum[maxn][maxn];
int n;
void getsum(int x)
{
    for(int i=1;i<=n;i++)
    {
    sum[x][i]=(sum[x][i-1]+dp[x][i])%mod;
  //  printf("%I64d\n",sum[x][i]);
    }
}
int main()
{
    int a,b,k;
    scanf("%d%d%d%d",&n,&a,&b,&k);
    memset(dp,0,sizeof(dp));
    memset(sum,0,sizeof(sum));
    dp[0][a]=1;
    if(a<b)
    {
        getsum(0);
       for(int i=1;i<=k;i++)
       {
        for(int j=1;j<b;j++)
        {
           dp[i][j]=(sum[i-1][(j+b-1)/2]-dp[i-1][j]+mod)%mod;
          // printf("%I64d ",dp[i][j]);
        }
      // printf("\n");
        getsum(i);
       }
    }
    else
    {
        getsum(0);
        for(int i=1;i<=k;i++)
        {
            for(int j=b+1;j<=n;j++)
            {
                //printf("%d %d\n",sum[i-1])
                dp[i][j]=((sum[i-1][n]-sum[i-1][(j+b)/2]+mod)%mod-dp[i-1][j]+mod)%mod;
               // printf("%d ",dp[i][j]);
            }
            getsum(i);
        }
    }
    long long ans=0;
    for(int i=1;i<=n;i++)
    {
    ans=(ans+dp[k][i])%mod;
    //printf("%d ",dp[k][i]);
    }
    printf("%I64d\n",ans);
    return 0;
}


为下面的代码 每行代码都 添加中文注释 , 每个函数都进行详细注释 , 代码的算法原理也写在注释中 , 原来的英文注释翻译成中文注释 : ```from sentence_transformers.cross_encoder import CrossEncoder # 1. Load a pretrained CrossEncoder model model = CrossEncoder("cross-encoder/stsb-distilroberta-base") # We want to compute the similarity between the query sentence... query = "A man is eating pasta." # ... and all sentences in the corpus corpus = [ "A man is eating food.", "A man is eating a piece of bread.", "The girl is carrying a baby.", "A man is riding a horse.", "A woman is playing violin.", "Two men pushed carts through the woods.", "A man is riding a white horse on an enclosed ground.", "A monkey is playing drums.", "A cheetah is running behind its prey.", ] # 2. We rank all sentences in the corpus for the query ranks = model.rank(query, corpus) # Print the scores print("Query: ", query) for rank in ranks: print(f"{rank[&#39;score&#39;]:.2f}\t{corpus[rank[&#39;corpus_id&#39;]]}") """ Query: A man is eating pasta. 0.67 A man is eating food. 0.34 A man is eating a piece of bread. 0.08 A man is riding a horse. 0.07 A man is riding a white horse on an enclosed ground. 0.01 The girl is carrying a baby. 0.01 Two men pushed carts through the woods. 0.01 A monkey is playing drums. 0.01 A woman is playing violin. 0.01 A cheetah is running behind its prey. """ # 3. Alternatively, you can also manually compute the score between two sentences import numpy as np sentence_combinations = [[query, sentence] for sentence in corpus] scores = model.predict(sentence_combinations) # Sort the scores in decreasing order to get the corpus indices ranked_indices = np.argsort(scores)[::-1] print("Scores:", scores) print("Indices:", ranked_indices) """ Scores: [0.6732372, 0.34102544, 0.00542465, 0.07569341, 0.00525378, 0.00536814, 0.06676237, 0.00534825, 0.00516717] Indices: [0 1 3 6 2 5 7 4 8] """```
03-10
考虑可再生能源出力不确定性的商业园区用户需求响应策略(Matlab代码实现)内容概要:本文围绕“考虑可再生能源出力不确定性的商业园区用户需求响应策略”展开,结合Matlab代码实现,研究在可再生能源(如风电、光伏)出力具有不确定性的背景下,商业园区如何制定有效的需求响应策略以优化能源调度和提升系统经济性。文中可能涉及不确定性建模(如场景生成与缩减)、优化模型构建(如随机规划、鲁棒优化)以及需求响应机制设计(如价格型、激励型),并通过Matlab仿真验证所提策略的有效性。此外,文档还列举了大量相关的电力系统、综合能源系统优化调度案例与代码资源,涵盖微电网调度、储能配置、负荷预测等多个方向,形成一个完整的科研支持体系。; 适合人群:具备一定电力系统、优化理论和Matlab编程基础的研究生、科研人员及从事能源系统规划与运行的工程技术人员。; 使用场景及目标:①学习如何建模可再生能源的不确定性并应用于需求响应优化;②掌握使用Matlab进行商业园区能源系统仿真与优化调度的方法;③复现论文结果或开展相关课题研究,提升科研效率与创新能力。; 阅读建议:建议结合文中提供的Matlab代码实例,逐步理解模型构建与求解过程,重点关注不确定性处理方法与需求响应机制的设计逻辑,同时可参考文档中列出的其他资源进行扩展学习与交叉验证。
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