568. Maximum Vacation Days

本文介绍了一个算法问题,即如何在给定的城市间飞行矩阵和各城市每周可休假天数矩阵的情况下,规划行程以最大化K周内的休假天数。通过递归深度优先搜索策略实现了这一目标。

LeetCode wants to give one of its best employees the option to travel among N cities to collect algorithm problems. But all work and no play makes Jack a dull boy, you could take vacations in some particular cities and weeks. Your job is to schedule the traveling to maximize the number of vacation days you could take, but there are certain rules and restrictions you need to follow.

Rules and restrictions:

  1. You can only travel among N cities, represented by indexes from 0 to N-1. Initially, you are in the city indexed 0 on Monday.
  2. The cities are connected by flights. The flights are represented as a N*N matrix (not necessary symmetrical), called flightsrepresenting the airline status from the city i to the city j. If there is no flight from the city i to the city j, flights[i][j] = 0; Otherwise, flights[i][j] = 1. Also, flights[i][i] = 0 for all i.
  3. You totally have K weeks (each week has 7 days) to travel. You can only take flights at most once per day and can only take flights on each week's Monday morning. Since flight time is so short, we don't consider the impact of flight time.
  4. For each city, you can only have restricted vacation days in different weeks, given an N*K matrix called days representing this relationship. For the value of days[i][j], it represents the maximum days you could take vacation in the city i in the week j.

 

You're given the flights matrix and days matrix, and you need to output the maximum vacation days you could take during K weeks.

Example 1:

Input:flights = [[0,1,1],[1,0,1],[1,1,0]], days = [[1,3,1],[6,0,3],[3,3,3]]
Output: 12
Explanation: 
Ans = 6 + 3 + 3 = 12.
One of the best strategies is: 1st week : fly from city 0 to city 1 on Monday, and play 6 days and work 1 day.
(Although you start at city 0, we could also fly to and start at other cities since it is Monday.) 2nd week : fly from city 1 to city 2 on Monday, and play 3 days and work 4 days. 3rd week : stay at city 2, and play 3 days and work 4 days.

 

Example 2:

Input:flights = [[0,0,0],[0,0,0],[0,0,0]], days = [[1,1,1],[7,7,7],[7,7,7]]
Output: 3
Explanation: 
Ans = 1 + 1 + 1 = 3.
Since there is no flights enable you to move to another city, you have to stay at city 0 for the whole 3 weeks.
For each week, you only have one day to play and six days to work.
So the maximum number of vacation days is 3.

 

Example 3:

Input:flights = [[0,1,1],[1,0,1],[1,1,0]], days = [[7,0,0],[0,7,0],[0,0,7]]
Output: 21
Explanation:
Ans = 7 + 7 + 7 = 21
One of the best strategies is: 1st week : stay at city 0, and play 7 days. 2nd week : fly from city 0 to city 1 on Monday, and play 7 days. 3rd week : fly from city 1 to city 2 on Monday, and play 7 days.

 

class Solution {
public:
    int maxVacationDays(vector<vector<int>>& flights, vector<vector<int>>& days) {
           int N = days.size();
           int K = days[0].size();
           vector<vector<int>> dp(N,vector<int>(K));
           return dfs(0,0,dp,flights,days);
    }
    
    int dfs(int city,int week,vector<vector<int>>&dp,vector<vector<int>>& flights, vector<vector<int>>& days)
    {
         int N = days.size();
         int K = days[0].size();
         if(week>=K) return 0;
         if(dp[city][week]) return dp[city][week];
         int res = 0;
         for(int dest = 0;dest<N;dest++)
         {
             if(city==dest||flights[city][dest])
             {
                 res = max(res,days[dest][week]+dfs(dest,week+1,dp,flights,days));
             }
         }
         dp[city][week] = res;
        return res;
    }
    
    
};

 

转载于:https://www.cnblogs.com/jxr041100/p/7885798.html

### 使用 `np.maximum` 的方法及其示例 `np.maximum` 是 NumPy 庳库中的一个函数,用于逐元素比较两个数组并返回较大值。如果输入的是标量,则会将其广播到与另一个数组相同的形状。 以下是关于 `np.maximum` 的详细介绍以及一些实际使用的例子: #### 基本语法 ```python numpy.maximum(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) ``` - **参数说明**: - `x1`, `x2`: 输入的两个数组或标量。 - `/`: 表明位置参数结束的位置(Python 3.8 及以上版本支持)。 - `out`: 存储结果的 ndarray 或元组。 - `where`: 条件布尔掩码,指定哪些位置执行操作[^5]。 #### 示例代码 ##### 示例 1: 比较两个标量 ```python import numpy as np a = 5 b = 7 result = np.maximum(a, b) print(f"The larger value is {result}") ``` 此代码片段将打印较大的数值 `7`。 ##### 示例 2: 对一维数组应用最大值计算 ```python array1 = np.array([1, 3, 5]) array2 = np.array([2, 2, 6]) max_array = np.maximum(array1, array2) print(max_array) # 输出 [2 3 6] ``` 这里展示了如何对两个相同大小的一维数组进行逐元素的最大值运算[^5]。 ##### 示例 3: 广播机制下的最大值计算 当其中一个输入是一个标量时,NumPy 将自动扩展它以匹配另一方的维度。 ```python scalar_value = 4 vector = np.array([1, 9, 3]) broadcasted_max = np.maximum(scalar_value, vector) print(broadcasted_max) # 输出 [4 9 4] ``` 上述实例中,标量 `4` 被广播至长度为三的向量 `[4, 4, 4]` 后再做逐项对比[^5]。 #### 实际应用场景 在机器学习领域,有时需要防止某些变量变得过小以至于接近零从而影响梯度下降效果;此时可以利用 `np.maximum` 设定下限阈值来规避此类风险。例如,在激活函数 Softplus 中就可能涉及类似的逻辑处理[^6]。 ```python def softplus(x): return np.log(np.exp(-np.minimum(0, x)) + np.exp(np.maximum(0, x))) test_input = np.array([-1., 0., 1.]) output = softplus(test_input) print(output) # 结果应近似于 [0.31326166 0.69314718 1.3132616 ] ```
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