Leetcode 239. Sliding Window Maximum

大话西游2单机版V8一键架设教程:5分钟搞定GM权限和仙玉修改(附完整物品代码表) 本文详细讲解了大话西游2单机版V8的一键架设教程,包括5分钟快速配置GM权限、仙玉修改及物品代码使用技巧。通过分步指导和服务端配置详解,帮助玩家轻松掌握游戏管理功能,重温经典回合制体验。附完整物品代码表,助你快速获取稀有道具和神兽。 阅读详情

Leetcode 239. Sliding Window Maximum

source url

题目描述

Given an array nums, there is a sliding window of size k which is moving from the very left of the array to the very right. You can only see the k numbers in the window. Each time the sliding window moves right by one position.

For example,
Given nums = [1,3,-1,-3,5,3,6,7], and k = 3.

Window position                        Max
---------------               -----
[1  3  -1] -3  5  3  6  7       3
 1 [3  -1  -3] 5  3  6  7       3
 1  3 [-1  -3  5] 3  6  7       5
 1  3  -1 [-3  5  3] 6  7       5
 1  3  -1  -3 [5  3  6] 7       6
 1  3  -1  -3  5 [3  6  7]      7

Therefore, return the max sliding window as [3,3,5,5,6,7].

输入:数组,滑动窗口大小
输出:滑动窗口中最大的数

思路

维护优先队列:维护一个优先队列,在窗口滑动时,剔除堆顶的无效元素,对于无效元素的定义是其下标不属于滑动窗口中;将新的元素加入堆中,同时输出堆顶元素。复杂度应该是 O(n2)

代码

class Solution {
public:
    vector<int> maxSlidingWindow(vector<int>& nums, int k) {
        vector<int> res;
        if(nums.empty()||nums.size()<k) return res;
        priority_queue<pair<int,int> > pq;
        //Init - insert k elements to heap
        for(int i =0;i<k;i++){
            pq.push(pair<int, int>(nums[i],i));
        }
        res.push_back(pq.top().first);

        //Check the heap top, pop, then insert
        for(int i=k;i<nums.size();i++){
            //invalid
            while(!pq.empty()&&pq.top().second<=i-k){
                pq.pop();
            }
            pq.push(pair<int,int>(nums[i],i));
            res.push_back(pq.top().first);
        }

        return res;
    }
};
缺陷检测-金属表面缺陷检测 1介绍 缺陷检测是工业视觉技术中最重要的组成部分。业务广泛,场景丰富,技术难点高、工作量大。针对不同的场景,选择不同的相机和 阅读详情

相关推荐

Unity Android构建报错:compileSdk 35兼容性五层定位法

Android构建中的compileSdk是编译时API目标版本,其升级并非孤立操作,而是触发JDK、Android Gradle Plugin(AGP)、NDK、targetSdk与Unity引擎版本之间强耦合校验的起点。当出现'using a newer Android Gradle plugin to use compileSdk = 35'类报错,本质是代际技术栈错配——例如AGP 7.1.3不识别compileSdk 35所需Java 17语法,或NDK 21与AGP 7.4的ABI规范冲突。该问

weixin_32496547的博客 569

LeetCode 239 Sliding Window Maximum

LeetCode 239 Sliding Window Maximum 思路 相当有趣的一道题,除了暴力外有三种解法 有序集合 使用multiset,初始化前k个数,然后每次查找-删除-添加-取最大值即可。C++里set multiset是有序的,虽然不支持随机访问,但是指定greater模板参数后就可以直接使用*set.begin()获得最大值了。 双端队列 维护一个双端队列,该队列总是保持如下特性 队列中任一位置前的元素总比该位置的大 队列最前端元素是最大值 队列最大为k 上述特性能够保证在O(1)

EricJeffrey的博客 322

ArcGIS-裁剪遥感影像及dem图像

6、选择【工具箱】-【数据管理工具】-【栅格】-【裁剪】。在【输入栅格】里选择要裁剪的dem,在【输出范围】里选择刚创建的裁剪范围。选择一个输出路径,其他参数保存默认。2、起一个名称,要素类型选择【面】。点击【编辑】选择与要裁剪的dem一致的地理坐标系。1、创建需要裁剪区域的面层shp,右击选择【新建】-【shapefile】3、点击编辑器,选择开始编辑。4、选择【裁剪范围】,点击确定。5、画出想要裁剪的范围并保存。

siwenaiyou的博客 1万+

【leetcode】239. Sliding Window Maximum

求滑动窗口括起来的数组中的最大值。 最简单的方法就是暴力法,但时间复杂度为O(nk),这种hard题暴力法不用想都知道是不能过的。 思路1: 使用双端队列。用双端队列作为存储空间,存储方法如下 从左往右依次遍历数组,如果队列为空,那么将当前元素的添加到队列的左边(index和元素是一一对应的,入栈index相当于入栈了元素) 如果当前队列不为空,且当前元素小于等于队列最右端元素,那么将当前元素...

很吵请安静 799

ANSYS Workbench对称分析进阶:手把手教你用Pre-Meshed Cyclic Region处理外部导入的复杂网格模型

本文详细介绍了ANSYS Workbench中Pre-Meshed Cyclic Region功能在处理外部导入复杂网格模型时的应用。通过循环对称分析技术,可显著减少计算资源消耗,特别适用于叶轮机械、齿轮组等周期性结构分析。文章深入解析了该功能的底层逻辑,并提供了从NASTRAN文件导入的离心压缩机叶轮案例,帮助工程师克服网格匹配难题。

weixin_28630185的博客 316

半导体行业周期分析:库存、需求与复苏路径深度解读

半导体行业作为现代科技产业的基石,其周期性波动受供需关系、库存水平和宏观经济等多重因素影响。从技术原理上看,行业景气度通常通过库存周转天数、晶圆厂产能利用率等核心指标进行监测,这些指标反映了产业链各环节的健康状况。在工程实践中,理解这些指标有助于企业进行库存管理和产能规划,从而提升运营效率。当前,半导体市场正经历结构性分化,其中**库存周转天数**和**晶圆厂产能利用率**成为判断市场拐点的关键。在应用场景上,汽车电子、人工智能等领域需求相对稳健,而消费电子则面临较大压力。本文聚焦于半导体行业的周期特征,探

weixin_29549793的博客 696

[Leetcode]_239 Sliding Window Maximum

/** * Index: 239 * Title: Sliding Window Maximum * Author: ltree98 **/ 题意 给定一个数组, 有一个长度为k的窗口从数组头部开始向后滑动,每次滑动1位; 输出每次窗口内最大值序列。 注意: k保证是合法的,即 k >= 1 且 k <= 数组长度 数组不会为空 测试样例中存在数组为空的情况 我...

继续激情,继续奋斗。 484

Sigrity SPEED2000 General SI Simulation模式如何进行信号仿真分析操作指导-DDR

本例是几个简单的板载DDR颗粒的结构,本例中使用General SI Simulation模式仿真DDR信号,结构拓扑如下,Addcmd,clk,cke都是T拓扑。Sigrity SPEED2000 General SI Simulation模式可以对信号进行仿真分析,仿真将电源视为理想电源,以下图为例进行说明。DQS的接收端模型设置为DQS_34_ODT60_1333。模式如何进行信号仿真分析操作指导-DDR。附带驱动和接收端的IBIS模型。

weixin_54787054的博客 513

【Leetcode】239. Sliding Window Maximum

这是一道很简单的单调队列的应用。单调队列简单的说,就是一种每次加元素的同时,要保持队列单调性的数据结构。本题基本思路是,维护一个双端队列,使得队列头就是当前滑动窗口的最大值,然后队列第二个数就是当前滑动窗口的次大数,以此类推。维护的方式是,每push一个数进去的时候,就把比该数小于等于的队列尾的数全pop出来,然后如果队首数字已经脱离了滑动窗口,也要pop出来。每次操作完后把队头的数加入最后结果,最后返回。,返回一个数组,每个数字代表原数组每。个数的滑动窗口的最大值。

数学、算法爱好者的博客 330

AI快速变现之路,AI视频创作

初期投入$3000(工具订阅+样片制作),重点攻克跨境电商和本地生活服务市场。关注2025年AIGC视频工具的API降价趋势(如Pika企业版折扣可达50%),建议与云服务商签订长期合作协议。

qq_29896351的博客 1983

【心电信号】基于多种滤波去除心电信号基线漂移含Matlab源码

1 简介 心电信号可以用来检测和诊断心脏疾病,心电信号在采集时经常受到呼吸活动、身体运动和皮肤与电极接触不良等影响,因此会产生基线漂移,基线漂移的存在会降低心电信号的质量。所以,在大多数心电信号处理中,包括心律失常的识别、心率变化分析和连续血压测量等,去除基线漂移成为了至关重要的一步。心电信号具有非平稳、非线性的特性,传统的去除心电信号中基线漂移的方法在对心电信号进行去噪时常因去噪过度或者不完全,容易造成大量非线性特征信息的丢失,从而破坏了心电信号本身的动力学特性,这给后续的心电信号信息分析带来了不利影响

qq_59747472的博客 2209

LeetCode 239. Sliding Window Maximum 题解

本题是单调队列的经典应用。使用双端队列维护单调递减序列队首是当前窗口的最大值及时移除不在窗口内和较小的元素。

cannonjinx的博客 2657

AIGC游戏测试自动化:AI如何提升游戏QA效率

随着游戏产业的蓬勃发展,游戏的复杂度和规模不断增加,传统的游戏测试方式面临着巨大的挑战。本文章的目的在于深入探讨AIGC技术在游戏测试自动化中的应用,分析AI如何提升游戏QA的效率。范围涵盖了从核心概念到实际应用的各个方面,包括算法原理、数学模型、项目实战案例以及未来发展趋势等。本文将按照以下结构进行阐述:首先介绍核心概念与联系,明确AIGC和游戏测试自动化的相关概念和关系;接着讲解核心算法原理和具体操作步骤,通过Python代码进行详细说明;然后介绍数学模型和公式,并举例说明;

AI 原生应用开发的博客 1357

leetcode 239: Sliding Window Maximum

leetcode 239: Sliding Window Maximum python c++ java

西施豆腐渣 1万+

NTscan扫描工具

ntscan是一个扫描器,可以扫描没有密码和弱密码的共享,先到配置文件中填写猜测的用户名和密码,扫描过程中右侧出现的是扫描日志。

锂电池充电器电路设计 里面有电路

锂电池充电器电路设计 里面有电路 3.7V升压到5V电路设计。

LeetCode 239. Sliding Window Maximum

LeetCode 239. Sliding Window Maximum (Hard) 主要知识点:sliding window、deque、动态规划;优先级:5

温迪今天刷题了吗 354

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

LeetCode //C - 239. Sliding Window Maximum

【代码】LeetCode //C - 239. Sliding Window Maximum。

Made in Code 1108

efm32 入门.pdf

本PDF共计22页,介绍的是操作驱动API,下载与否自己斟酌。

Leetcode 239. Sliding Window Maximum (python&c++)

Leetcode 239. Sliding Window Maximum题目:解法:deque实现的单调队列 题目: 解法:deque实现的单调队列 关于单调队列的解释:https://medium.com/algorithms-and-leetcode/monotonic-queue-explained-with-leetcode-problems-7db7c530c1d6 举个例子来说明单调队列,现在有个递减的单调队列[6,4,3]: 新元素为2,队列变为[6,4,3,2] 新元素为5,队列变为[6

qq_37821701的博客 471

[Leetcode 239, Hard] Sliding Window Maximum

[Leetcode 239, Hard] Sliding Window Maximum

算法的实践 591

Leetcode 239. Sliding Window Maximum(python)

Leetcode 239. Sliding Window Maximum题目解法1:brutal force解法2:利用单调递减双向队列解法3:动态规划,构建左右最大数组 题目 Given an array nums, there is a sliding window of size k which is moving from the very left of the array to the...

qq_37821701的博客 622

[leetcode] 239. Sliding Window Maximum 解题报告

题目链接: https://leetcode.com/problems/sliding-window-maximum/ Given an array nums, there is a sliding window of size k which is moving from the very left of the array to the very right. Y

小榕流光的专栏 1364

[leetcode 239]Sliding Window Maximum

Given an array nums, there is a sliding window of size k which is moving from the very left of the array to the very right. You can only see the k numbers in the window. Each time the sliding window

ER_Plough的专栏 844

LeetCode239:Sliding Window Maximum

Given an array nums, there is a sliding window of size k which is moving from the very left of the array to the very right. You can only see the k numbers in the window. Each time the sliding window m...

卷卷萌的博客 301

leetcode 239 Sliding Window Maximum

Given an array nums, there is a sliding window of size k which is moving from the very left of the array to the very right. You can only see the k numbers in the window. Each time the sliding window...

LOVETEDA的博客 352

Leetcode-239. Sliding Window Maximum

leetcode

技术与生活齐头并进 1032

LeetCode: 239. Sliding Window Maximum

LeetCode: 239. Sliding Window Maximum 题目描述 Given an array nums, there is a sliding window of size k which is moving from the very left of the array to the very right. You can only see the k numbers in...

杨领well的专栏 142

Leetcode 239 Sliding Window Maximum

1. 问题描述  给定一个整数数组和一个大小为k的滑动窗口,滑动窗口每次从左到右移动一个数字,返回每次滑动窗口中的最大值。   2. 方法与思路  Method 1:最朴素的解法就是将窗口在数组上进行滑动,每滑动一次求一下窗口的最值。时间复杂度O(nk)O(nk)。   Method 2:还有就是使用平衡二叉树。       I. 取出数组前kk个元素,构建平衡二叉树。  II.

Jeanphorn的专栏 3169
上一篇: Leetcode 236. Lowest Common Ancestor of a Binary Tree
下一篇: Leetcode 113. Path Sum II
vincentCheungM
博客等级 码龄10年 4粉丝 23原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值