Foreign Exchange

本文介绍了一种使用快速排序和二分查找的方法来解决特定条件下的交换问题。通过将输入的每对数顺序交换并创建另一数据表,判断排序后的原始表与新表是否相同,从而判断交换是否可行。

题目要求:

    输入一串数对,每个数对的一个表示现在所在地方(可能是编号),第二个数表示要去的地方。如果恰好有需求可以交换的两个人,就可以执行交换。如果一组的所有成员都可以实现交换,就输出YES,否则输出NO。

分析:

    这道题许多答案都使用 ”快排+二分  ”的方法。稍微观察一下,我们可以发现,如果交换可以成立,显然有每2数对a,b满足:a.frist==b.second;b.frist==a.second。

    如果我们将输入的每对数,两者顺序交换,并创建另一个数据表,那么原来的表(排序后)和新的表(排序后)应该是相同的。直接比较两张表是不是相同即可,无需查找。

    下面代码使用vector,用set可能效率更高。
//Foreign Exchange
#include<iostream>
#include<vector>
#include<algorithm>
using namespace std;
int main()
{
    vector<pair<int, int> >table, duptable;
    pair<int, int> sign;
    int cnt;
    while (cin >> cnt, cnt)
    {
        table.clear();
        duptable.clear();
        while (cnt--)
        {
            cin >> sign.first >> sign.second;
            table.push_back(sign);
            swap(sign.first, sign.second);
            duptable.push_back(sign);
        }
        sort(table.begin(), table.end());
        sort(duptable.begin(), duptable.end());
        if (duptable == table)cout << "YES" << endl;
        else cout << "NO" << endl;
    }
    return 0;
}
(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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