Foreign Exchange

Your non-profit organization (iCORE - international Confederation of Revolver Enthusiasts) coordinates a very successful foreign student exchange program. Over the last few years, demand has sky-rocketed and now you need assistance with your task. The program your organization runs works as follows: All candidates are asked for their original location and the location they would like to go to. The program works out only if every student has a suitable exchange partner. In other words, if a student wants to go from A to B, there must be another student who wants to go from B to A. This was an easy task when there were only about 50 candidates, however now there are up to 500000 candidates!

Input

The input file contains multiple cases. Each test case will consist of a line containing n – the number of candidates (1 ≤ n ≤ 500000), followed by n lines representing the exchange information for each candidate. Each of these lines will contain 2 integers, separated by a single space, representing the candidate’s original location and the candidate’s target location respectively. Locations will be represented by nonnegative integer numbers. You may assume that no candidate will have his or her original location being the same as his or her target location as this would fall into the domestic exchange program. The input is terminated by a case where n = 0; this case should not be processed.

Output

For each test case, print ‘YES’ on a single line if there is a way for the exchange program to work out, otherwise print ‘NO’.

Sample Input

10

1 2

2 1

3 4

4 3

100 200

200 100

57 2

2 57

1 2

2 1

10

1 2

3 4

5 6

7 8

9 10

11 12

13 14

15 16

17 18

19 20

0

Sample Output

YES

NO

题解:交换地址后看是否数目比配。

#include<cstdio>
#include<cstring>
#include<string>
#include<iostream>
#include<algorithm>
#include<map>
using namespace std;
struct node
{
    int a,b;
    node(int x,int y)
    {
        a=x,b=y;
    }
    friend bool operator <(node a,node b) //用友元函数重载<
    {
        if(a.a!=b.a)
            return a.a>b.a;
        return a.b>b.b;
    }
};
int main()
{
    int n;
    while(scanf("%d",&n),n)
    {
        map<node,int> m;
        for(int i=0; i<n; i++)
        {
            int a,b;
            scanf("%d%d",&a,&b);
            node x(a,b);
            m[x]++;
        }
        map<node,int>::iterator it;
        for(it=m.begin(); it!=m.end(); it++)
        {
            node x=it->first;
            swap(x.a,x.b);
            if( it->second!=m[x] ) break;  //交换地址后不比配就跳出循环
        }
        if(it==m.end()) printf("YES\n");
        else printf("NO\n");
    }
    return 0;
}
题目链接:https://vjudge.net/contest/169852#problem/D

(a) The daily log return of the exchange rate can be calculated using the following formula: log return = ln(price[t]) - ln(price[t-1]) where price[t] represents the exchange rate at time t and price[t-1] represents the exchange rate at time t-1. Using the data in the file d-exuseu.txt, we can calculate the daily log returns as follows (assuming the data is stored in a variable called "exchange_rate"): ```python import numpy as np log_returns = np.log(exchange_rate[1:]) - np.log(exchange_rate[:-1]) ``` The first element of "exchange_rate" is excluded from the calculation because there is no previous price to compare it to. (b) The sample mean, standard deviation, skewness, excess kurtosis, minimum, and maximum of the log returns can be calculated using the following code: ```python mean = np.mean(log_returns) std_dev = np.std(log_returns) skewness = stats.skew(log_returns) kurtosis = stats.kurtosis(log_returns, fisher=False) minimum = np.min(log_returns) maximum = np.max(log_returns) print("Sample mean:", mean) print("Standard deviation:", std_dev) print("Skewness:", skewness) print("Excess kurtosis:", kurtosis - 3) # convert to excess kurtosis print("Minimum:", minimum) print("Maximum:", maximum) ``` This code requires the "scipy.stats" module to be imported at the beginning of the script. The output will show the sample mean, standard deviation, skewness, excess kurtosis, minimum, and maximum of the log returns. (c) To obtain a density plot of the daily log returns, we can use the following code: ```python import matplotlib.pyplot as plt plt.hist(log_returns, bins=50, density=True) plt.xlabel("Daily log return") plt.ylabel("Density") plt.show() ``` This code will create a histogram of the log returns with 50 bins and normalize it to create a density plot. The output will show the density plot of the log returns. (d) To test the hypothesis H0 : µ = 0 versus Ha : µ ̸= 0, where µ denotes the mean of the daily log return of Dollar-Euro exchange rate, we can use a t-test. The null hypothesis states that the mean log return is equal to zero, while the alternative hypothesis states that the mean log return is not equal to zero. ```python from scipy.stats import ttest_1samp t_stat, p_value = ttest_1samp(log_returns, 0) print("t-statistic:", t_stat) print("p-value:", p_value) ``` This code uses the "ttest_1samp" function from the "scipy.stats" module to calculate the t-statistic and the p-value. The output will show the t-statistic and the p-value of the test. If the p-value is less than the significance level (e.g., 0.05), we can reject the null hypothesis and conclude that the mean log return is significantly different from zero.
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