ZOJ 3827 Information Entropy 水题-布布扣-bubuko.com

Information Entropy

Time Limit: 1 Sec

Memory Limit: 256 MB

题目连接

http://acm.zju.edu.cn/onlinejudge/showProblem.do?problemCode=3827

Description

Information Theory is one of the most popular courses in Marjar University. In this course, there is an important chapter about information entropy.

Entropy is the average amount of information contained in each message received. Here, a message stands for an event, or a sample or a character drawn from a distribution or a data stream. Entropy thus characterizes our uncertainty about our source of information. The source is also characterized by the probability distribution of the samples drawn from it. The idea here is that the less likely an event is, the more information it provides when it occurs.

Generally, "entropy" stands for "disorder" or uncertainty. The entropy we talk about here was introduced by Claude E. Shannon in his 1948 paper "A Mathematical Theory of Communication". We also call it Shannon entropy or information entropy to distinguish from other occurrences of the term, which appears in various parts of physics in different forms.

Named after Boltzmann‘s H-theorem, Shannon defined the entropy Η (Greek letter Η, η) of a discrete random variable X with possible values {x₁, x₂, ..., x_n} and probability mass function P(X) as:

H (X) = E (- ln (P (x)))

Here E is the expected value operator. When taken from a finite sample, the entropy can explicitly be written as

H (X) = - \sum i = 1 n P (x i) log b (P (x i))

Where b is the base of the logarithm used. Common values of b are 2, Euler‘s number e, and 10. The unit of entropy is bit for b = 2, natfor b = e, and dit (or digit) for b = 10 respectively.

In the case of P(x_i) = 0 for some i, the value of the corresponding summand 0 log_b(0) is taken to be a well-known limit:

0 log b (0) = lim p \to 0 + p log b (p)

Your task is to calculate the entropy of a finite sample with N values.

Input

There are multiple test cases. The first line of input contains an integer T indicating the number of test cases. For each test case:

The first line contains an integer N (1 <= N <= 100) and a string S. The string S is one of "bit", "nat" or "dit", indicating the unit of entropy.

In the next line, there are N non-negative integers P₁, P₂, .., P_N. P_i means the probability of the i-th value in percentage and the sum of P_iwill be 100.

HINT

题意

给你n个数，然后让你算XXX东西……

然后把公式给你了

题解：

题目已经把公式给你了，当x=0的时候，就直接跳过就好了……

代码：

//qscqesze
#pragma comment(linker, "/STACK:1024000000,1024000000")
#include <cstdio>
#include <cmath>
#include <cstring>
#include <ctime>
#include <iostream>
#include <algorithm>
#include <set>
#include <bitset>
#include <vector>
#include <sstream>
#include <queue>
#include <typeinfo>
#include <fstream>
#include <map>
#include <stack>
typedef long long ll;
using namespace std;
//freopen("D.in","r",stdin);
//freopen("D.out","w",stdout);
#define sspeed ios_base::sync_with_stdio(0);cin.tie(0)
#define maxn 100006
#define mod 1000000007
#define eps 1e-9
#define e exp(1.0)
#define PI acos(-1)
const double EP  = 1E-10 ;
int Num;
//const int inf=0x7fffffff;
const ll inf=999999999;
inline ll read()
{
    ll x=0,f=1;char ch=getchar();
    while(ch<‘0‘||ch>‘9‘){if(ch==‘-‘)f=-1;ch=getchar();}
    while(ch>=‘0‘&&ch<=‘9‘){x=x*10+ch-‘0‘;ch=getchar();}
    return x*f;
}
//*************************************************************************************

int main()
{
    int t=read();
    while(t--)
    {
        int n=read();string s;
        cin>>s;
        double b;
        if(s=="bit")b=2.0;
        if(s=="nat")b=e;
        if(s=="dit")b=10.0;
        double ans = 0;
        for(int i=1;i<=n;i++)
        {
            double x;scanf("%lf",&x);
            x/=100.0;
            if(x)
                ans += -(x*log(x)/log(b));
        }
        printf("%.14f\n",ans);
    }
}

原文：http://www.cnblogs.com/qscqesze/p/4836588.html

ZOJ 3827 Information Entropy 水题

Information Entropy

题目连接

Description

Input

Output

Sample Input

HINT