568 - Just the Facts

本文介绍了一种高效计算任意大整数阶乘后第一个非零数字的方法。通过优化算法,避免了直接计算阶乘所带来的巨大数值运算问题,特别适用于编程竞赛中涉及大数运算的题目。


  Just the Facts 

The expression N!, read as ``N factorial," denotes the product of the first N positive integers, where N is nonnegative. So, for example,

NN!
01
11
22
36
424
5120
103628800

For this problem, you are to write a program that can compute the last non-zero digit of any factorial for ($0 \le N \le 10000$). For example, if your program is asked to compute the last nonzero digit of 5!, your program should produce ``2" because 5! = 120, and 2 is the last nonzero digit of 120.

Input 

Input to the program is a series of nonnegative integers not exceeding 10000, each on its own line with no other letters, digits or spaces. For each integer N, you should read the value and compute the last nonzero digit of N!.

Output 

For each integer input, the program should print exactly one line of output. Each line of output should contain the value N, right-justified in columns 1 through 5 with leading blanks, not leading zeroes. Columns 6 - 9 must contain `` -> " (space hyphen greater space). Column 10 must contain the single last non-zero digit of N!.

Sample Input 

1
2
26
125
3125
9999

Sample Output 

    1 -> 1
    2 -> 2
   26 -> 4
  125 -> 8
 3125 -> 2
 9999 -> 8


题意:给你n,让你求出n!从各位开始的第一个非零的数

这道题如果直接暴力计算,必然会超。因为只要求要个位,所以其实可以稍做处理,把一些必然无关紧要的给省略了。

还有就是,不能每次都只保存个位。因为有可能个位乘以n之后,进位之后就不对了。

#include<iostream>
#include<cstdio>
#include<cmath>
#include<cstring>
using namespace std;
int a[10010]= {0};
int main ()
{
    int i,n,fa=1;
    a[0]=1;
    for (i=1; i<=10000; i++)
    {
        a[i]=a[i-1]*i;
        while(a[i]%10==0)
            a[i]=a[i]/10;//这里就是把0给去掉
        a[i]=a[i]%100000;//这里要取多几位
    }
    while(cin>>n)
        printf("%5d -> %d\n",n,a[n]%10);
    return 0;
}


Compared with homogeneous network-based methods, het- erogeneous network-based treatment is closer to reality, due to the different kinds of entities with various kinds of relations [22– 24]. In recent years, knowledge graph (KG) has been utilized for data integration and federation [11, 17]. It allows the knowledge graph embedding (KGE) model to excel in the link prediction tasks [18, 19]. For example, Dai et al. provided a method using Wasser- stein adversarial autoencoder-based KGE, which can solve the problem of vanishing gradient on the discrete representation and exploit autoencoder to generate high-quality negative samples [20]. The SumGNN model proposed by Yu et al. succeeds in inte- grating external information of KG by combining high-quality fea- tures and multi-channel knowledge of the sub-graph [21]. Lin et al. proposed KGNN to predict DDI only based on triple facts of KG [66]. Although these methods have used KG information, only focusing on the triple facts or simple data fusion can limit performance and inductive capability [69]. Su et al. successively proposed two DDIs prediction methods [55, 56]. The first one is an end-to-end model called KG2ECapsule based on the biomedical knowledge graph (BKG), which can generate high-quality negative samples and make predictions through feature recursively propagating. Another one learns both drug attributes and triple facts based on attention to extract global representation and obtains good performance. However, these methods also have limited ability or ignore the merging of information from multiple perspectives. Apart from the above, the single perspective has many limitations, such as the need to ensure the integrity of related descriptions, just as network-based methods cannot process new nodes [65]. So, the methods only based on network are not inductive, causing limited generalization [69]. However, it can be alleviated by fully using the intrinsic property of the drug seen as local information, such as chemical structure (CS) [40]. And a handful of existing frameworks can effectively integrate multi-information without losing induction [69]. Thus, there is a necessity for us to propose an effective model to fully learn and fuse the local and global infor- mation for improving performance of DDI identification through multiple information complementing.是什么意思
06-11
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