376. Wiggle Subsequence

本文介绍了一种寻找最长摆动子序列的有效算法。摆动序列是指一个数列中相邻两个数之间的差值严格交替正负的序列。文章通过示例说明了何为摆动序列,并给出了一段C++代码实现,该算法能够处理任意整数序列,返回最长摆动子序列的长度。

A sequence of numbers is called a wiggle sequence if the differences between successive numbers strictly alternate between positive and negative. The first difference (if one exists) may be either positive or negative. A sequence with fewer than two elements is trivially a wiggle sequence.

For example, [1,7,4,9,2,5] is a wiggle sequence because the differences (6,-3,5,-7,3) are alternately positive and negative. In contrast, [1,4,7,2,5] and [1,7,4,5,5] are not wiggle sequences, the first because its first two differences are positive and the second because its last difference is zero.

Given a sequence of integers, return the length of the longest subsequence that is a wiggle sequence. A subsequence is obtained by deleting some number of elements (eventually, also zero) from the original sequence, leaving the remaining elements in their original order.



class Solution {
public:
    int wiggleMaxLength(vector<int>& nums) {
        int n = nums.size();
    	if(n == 0) return 0;
        int s1[n],s2[n];
        s1[0] = 1;
        s2[0] = 1;
        for(int i = 1; i < n; i ++)
        {
        	if(nums[i] > nums[i-1])
        	{
        		s1[i] = s2[i - 1] + 1;
        		s2[i] = s2[i - 1];
        	}
        	else if(nums[i] < nums[i-1])
        	{
        		s2[i] = s1[i - 1] + 1;
        		s1[i] = s1[i - 1];        		
        	}
        	else
        	{
        		s1[i] = s1[i-1];
        		s2[i] = s2[i-1];
        	}
        }
        return max(s1[n-1],s2[n-1]);
    }
};

内容概要:本文介绍了ENVI Deep Learning V1.0的操作教程,重点讲解了如何利用ENVI软件进行深度学习模型的训练与应用,以实现遥感图像中特定目标(如集装箱)的自动提取。教程涵盖了从数据准备、标签图像创建、模型初始化与训练,到执行分类及结果优化的完整流程,并介绍了精度评价与通过ENVI Modeler实现一键化建模的方法。系统基于TensorFlow框架,采用ENVINet5(U-Net变体)架构,支持通过点、线、面ROI或分类图生成标签数据,适用于多/高光谱影像的单一类别特征提取。; 适合人群:具备遥感图像处理基础,熟悉ENVI软件操作,从事地理信息、测绘、环境监测等相关领域的技术人员或研究人员,尤其是希望将深度学习技术应用于遥感目标识别的初学者与实践者。; 使用场景及目标:①在遥感影像中自动识别和提取特定地物目标(如车辆、建筑、道路、集装箱等);②掌握ENVI环境下深度学习模型的训练流程与关键参数设置(如Patch Size、Epochs、Class Weight等);③通过模型调优与结果反馈提升分类精度,实现高效自动化信息提取。; 阅读建议:建议结合实际遥感项目边学边练,重点关注标签数据制作、模型参数配置与结果后处理环节,充分利用ENVI Modeler进行自动化建模与参数优化,同时注意软硬件环境(特别是NVIDIA GPU)的配置要求以保障训练效率。
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