Cracking the coding interview--Q3

本文探讨了如何利用单一数组实现三个栈、设计带有min功能的栈、创建SetOfStacks数据结构、解决汉诺塔问题、用两个栈实现队列及对栈进行排序的方法。这些实现展示了高效的数据结构操作技巧。

Chapter 3 | Stacks and Queues

3.1 Describe how you could use a single array to implement three stacks.

分段呗- -

3.2 How would you design a stack which, in addition to push and pop, also has a function min which returns the minimum element? Push, pop and min should all operate in O(1) time.

一倍冗余数据,分段可以减少冗余数据

3.3 Imagine a (literal) stack of plates. If the stack gets too high, it might topple. Therefore, in real life, we would likely start a new stack when the previous stack exceeds some threshold. Implement a data structure SetOfStacks that mimics this. SetOfStacks should be composed of several stacks, and should create a new stack once the previous one exceeds capacity. SetOfStacks.push() and SetOfStacks.pop() should behave identically to a single stack (that is, pop() should return the same values as it would if there were just a single stack). FOLLOW UP Implement a function popAt(int index) which performs a pop operation on a specific sub-stack.

直接malloc个新的 何难。。需要考虑边界条件,中间子栈是空的。

3.4 In the classic problem of the Towers of Hanoi, you have 3 rods and N disks of different sizes which can slide onto any tower. The puzzle starts with disks sorted in ascending order of size from top to bottom (e.g., each disk sits on top of an even larger one). You have the following constraints: (A) Only one disk can be moved at a time. (B) A disk is slid off the top of one rod onto the next rod. © A disk can only be placed on top of a larger disk. Write a program to move the disks from the first rod to the last using Stacks.

http://blog.youkuaiyun.com/kkkkkxiaofei/article/details/8333644

3.5 Implement a MyQueue class which implements a queue using two stacks.

如原解,似乎效率很低

3.6 Write a program to sort a stack in ascending order. You should not make any assump- tions about how the stack is implemented. The following are the only functions that should be used to write this program: push | pop | peek | isEmpty.

额外结构排序。插入排序


MATLAB代码实现了一个基于多种智能优化算法优化RBF神经网络的回归预测模型,其核心是通过智能优化算法自动寻找最优的RBF扩展参数(spread),以提升预测精度。 1.主要功能 多算法优化RBF网络:使用多种智能优化算法优化RBF神经网络的核心参数spread。 回归预测:对输入特征进行回归预测,适用于连续值输出问题。 性能对比:对比不同优化算法在训练集和测试集上的预测性能,绘制适应度曲线、预测对比图、误差指标柱状图等。 2.算法步骤 数据准备:导入数据,随机打乱,划分训练集和测试集(默认7:3)。 数据归一化:使用mapminmax将输入和输出归一化到[0,1]区间。 标准RBF建模:使用固定spread=100建立基准RBF模型。 智能优化循环: 调用优化算法(从指定文件夹中读取算法文件)优化spread参数。 使用优化后的spread重新训练RBF网络。 评估预测结果,保存性能指标。 结果可视化: 绘制适应度曲线、训练集/测试集预测对比图。 绘制误差指标(MAE、RMSE、MAPE、MBE)柱状图。 十种智能优化算法分别是: GWO:灰狼算法 HBA:蜜獾算法 IAO:改进天鹰优化算法,改进①:Tent混沌映射种群初始化,改进②:自适应权重 MFO:飞蛾扑火算法 MPA:海洋捕食者算法 NGO:北方苍鹰算法 OOA:鱼鹰优化算法 RTH:红尾鹰算法 WOA:鲸鱼算法 ZOA:斑马算法
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