package com.heu.wsq.leetcode.inoffer;
/**
* 剑指 Offer 04. 二维数组中的查找
* @author wsq
* @date 2021/5/27
* 在一个 n * m 的二维数组中,每一行都按照从左到右递增的顺序排序,
* 每一列都按照从上到下递增的顺序排序。请完成一个高效的函数,
* 输入这样的一个二维数组和一个整数,判断数组中是否含有该整数。
*
* 示例:
* 现有矩阵 matrix 如下:
* [
* [1, 4, 7, 11, 15],
* [2, 5, 8, 12, 19],
* [3, 6, 9, 16, 22],
* [10, 13, 14, 17, 24],
* [18, 21, 23, 26, 30]
* ]
* 给定 target = 5,返回 true。
* 给定 target = 20,返回 false。
*
* 链接:https://leetcode-cn.com/problems/er-wei-shu-zu-zhong-de-cha-zhao-lcof
*/
public class FindNumberIn2DArray {
/**
* 通过分析数组元素值的分布特征:
* 1. 每一行元素是递增的
* 2. 每一列元素也是递增的
* 因此我们需要找到一个位置,在该位置,它有减少和增加两种选择,这样的位置可以选择左下角和右上角
* 这里我们选择右上角作为起点进行遍历
* @param matrix
* @param target
* @return
*/
public boolean findNumberIn2DArray(int[][] matrix, int target) {
if(matrix == null || matrix.length == 0 || matrix[0].length == 0){
return false;
}
int m = matrix.length, n = matrix[0].length;
int i = 0, j = n-1;
while(i < m && j >= 0){
if(matrix[i][j] == target){
return true;
}else if(matrix[i][j] < target){
i++;
}else {
j--;
}
}
return false;
}
}
相关推荐
基于C#的ASP.NET开发的党史学习网站课设
开展党史学习教育,就是要以史为镜、以史明志,把苦难辉煌的过去、日新月异的现在、光明宏大的未来贯通起来,在鉴往知来中砥砺前行,在乱云飞渡中把准航向,在凝心聚力中团结奋斗,共同创造新的历史伟业。登陆按钮与数据库进行连接,逻辑实现是如图3.1.3所示,自己定义一个SQL查询语句,将账号、密码放入SQL查询语句中,用sqlDataReader.HasRows语句进行逐一比较,找出结果,将用户名存入session并调用cookies,如果结果存在就关闭数据库,进行数据跳转,并弹出登陆成功的提示框,并关闭数据库。
LeetCode 之 剑指 Offer 04. 二维数组中的查找(Java)
LeetCode 之 剑指 Offer 04. 二维数组中的查找(Java),在一个 n * m 的二维数组中,每一行都按照从左到右递增的顺序排序,每一列都按照从上到下递增的顺序排序。请完成一个高效的函数,输入这样的一个二维数组和一个整数,判断数组中是否含有该整数。本文简要分析解题思路,提供暴力遍历和规律性删减法。
【图形学】Loop细分算法及半边结构实现(C++)
文章目录Loop 细分算法介绍增加新顶点更新旧顶点算法实现半边结构半边结构的表示方法半边结构的构造方法创建所有顶点创建所有面及半边半边结构的使用 Loop 细分 顾名思义,网格细分是将粗糙网格精细化的过程,如下动图。Loop细分是众多网格细分算法的一种,Loop细分仅仅对三角形网格模型有效。值得注意的是,虽然叫Loop细分,但是不能理解为“循环”细分,叫这个名字是因为作者的名字是这个而已。 下面是一个粗糙的兔子模型1000个面,进行3次细分 算法介绍 Loop细分算法主要分两步进行 增加新顶点:增加
剑指offer04——二维数组中的查找
在一个 n * m的二维数组中,每一行都按照从左到右递增的顺序排序,每一列都按照从上到下递增的顺序排序。请完成一个高效的函数,输入这样的一个二维数组和一个整数,判断数组中是否含有该整数。
【HCIP-AI 进阶之路(一)】零成本白嫖算力,7天速通华为人工智能资深开发者认证
《HCIP-AIEIDeveloper认证备考指南》摘要:本文深度解析华为HCIP-AI(H13-331)认证考试要点,涵盖考试大纲、评分标准及备考策略。重点剖析昇腾AI全栈架构,包括达芬奇架构核心计算单元(Cube/Vector/Scalar Unit)和四大层级关系(芯片-CANN-框架-应用)。针对无硬件资源的开发者,提供MindSpore CPU版、ModelArts免费算力和沙箱实验室三种零成本实践方案。文章强调该认证对国企/华为生态企业求职者的价值,并指出多选题是主要失分点。下篇将聚焦计算机视觉
STM32H743玩转以太网:不用FreeRTOS,手把手搞定LAN8742和DHCP(附MPU配置避坑指南)
本文详细介绍了在STM32H743裸机环境下实现以太网通信的完整方案,重点讲解了LAN8742 PHY芯片的配置和DHCP自动获取IP的实现。通过CubeMX配置、内存布局优化和MPU设置,避开了H7系列常见的Cache陷阱,提供了无操作系统环境下的高效网络通信解决方案。
9. leetCode--剑指Offer04二维数组中的查找
剑指Offer04二维数组中的查找 题目连接:https://leetcode-cn.com/problems/er-wei-shu-zu-zhong-de-cha-zhao-lcof/ 题目类型: 简单矩阵 1. 问题描述 在一个 n * m 的二维数组中,每一行都按照从左到右递增的顺序排序,每一列都按照从上到下递增的顺序排序。请完成一个函数,输入这样的一个二维数组和一个整数,判断数组中是否含有该整数。 示例: //现有矩阵 matrix 如下: [ [1, 4, 7, 11,
告别龟速!实测Copernicus Dataspace下载Sentinel-2数据的3个提速技巧(附Workspace批量操作)
本文分享了在Copernicus Dataspace平台高效下载Sentinel-2遥感数据的3个实用技巧,包括网络传输层优化、浏览器与API调优以及Workspace批量操作。通过这些方法,用户可将下载速度提升300%以上,特别适合科研人员和团队处理大规模数据需求。
LeetCode(python):剑指 Offer 04. 二维数组中的查找
LeetCode(python):剑指 Offer 04. 二维数组中的查找 1. 题目:剑指 Offer 04. 二维数组中的查找 2. 题目链接:https://leetcode-cn.com/problems/er-wei-shu-zu-zhong-de-cha-zhao-lcof/ 3. 题目内容 在一个 n * m 的二维数组中,每一行都按照从左到右递增的顺序排序,每一列都按照从上到下递增的顺序排序。请完成一个函数,输入这样的一个二维数组和一个整数,判断数组中是否含有该整数。 4. 输入输出示例
matlab2021a版本条件下安装spm
SPM (Statistical Parametric Mapping),中文名叫统计参数映射。统计参数映射是指构建和评估空间扩展的统计过程,用于测试关于功能成像数据的假设。这些想法已经在名为SPM的免费开源软件中得到了实例化。SPM软件包用于脑成像数据序列分析。目前的版本可以分析fMRI, PET, SPECT, EEG和MEG。 可能由于matlab版本更新的问题,按照之前教程所写的安装方法会出现错误,踩了一些坑,但好在最终解决了。。。 1、下载链接,如下 SPM12 Software -...
Unity 佳能SDK 及数据获取
3. 把CameraControl 下面只要是绿色的 .cs 文件都复制到Unity 中。1. 填写信息跟官方申请SDK,大概1-2个工作日会邮件回复你。2. 将SDK这两个文件放到 Unity Plugins文件夹。
【剑指offer】剑指 Offer 04. 二维数组中的查找
目录标题算法汇总题目思路代码1.从左下角2.暴力法 算法汇总 以下是所有算法汇总,包括GitHub源码地址链接:力扣算法练习汇总(持续更新…) 题目 剑指 Offer 04. 二维数组中的查找 思路 抓住几个关键词有序(升序); 代码 1.从左下角 class Solution { public boolean findNumberIn2DArray(int[][] matrix, int target) { // 二维数组的行 int row = matrix
泰凌微8258入门教程 基础篇①——Bluetooth® SIG Mesh 快速上手
Bluetooth® SIG Mesh Quick Start
剑指 Offer 04. 二维数组中的查找(中等)
剑指 Offer 04. 二维数组中的查找 在一个 n * m 的二维数组中,每一行都按照从左到右递增的顺序排序,每一列都按照从上到下递增的顺序排序。请完成一个高效的函数,输入这样的一个二维数组和一个整数,判断数组中是否含有该整数。 示例: 现有矩阵 matrix 如下: [ [1, 4, 7, 11, 15], [2, 5, 8, 12, 19], [3, 6, 9, 16, 22], [10, 13, 14, 17, 24], [18, 21, 23.
TMS_WebGMaps_1.9.8.0_Full_Source
TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder
SIMATIC S7-1200, S7-1500 PID 控制[手册]
SIMATIC S7-1200, S7-1500 PID 控制[手册]
LeetCode刷题记录:剑指 Offer 04. 二维数组中的查找
剑指 Offer 04. 二维数组中的查找 题目描述: 在一个 n * m 的二维数组中,每一行都按照从左到右递增的顺序排序,每一列都按照从上到下递增的顺序排序。请完成一个函数,输入这样的一个二维数组和一个整数,判断数组中是否含有该整数。 示例: 现有矩阵 matrix 如下: [ [1, 4, 7, 11, 15], [2, 5, 8, 12, 19], [3, 6, 9, 16, 22], [10, 13, 14, 17, 24], [18, 21, 23, 26, 30] ] 给定
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。
剑指 Offer 04. 二维数组中的查找 LeetCode Java
剑指 Offer 04. 二维数组中的查找 难度中等682收藏分享切换为英文接收动态反馈 在一个 n * m 的二维数组中,每一行都按照从左到右递增的顺序排序,每一列都按照从上到下递增的顺序排序。请完成一个高效的函数,输入这样的一个二维数组和一个整数,判断数组中是否含有该整数。 示例: 现有矩阵 matrix 如下: [ [1, 4, 7, 11, 15], [2, 5, 8, 12, 19], [3, 6, 9, 16, 22], [10, 13, 14,
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');```[](https://bitdeli.com/free "Bitdeli Badge")
【剑指offer 04.二维数组中的查找】
剑指offer 04.二维数组中的查找
剑指offer04:二维数组中的查找 python
剑指offer04:二维数组中的查找 python题目描述解法 题目描述 解法 从左下角开始遍历,如果值比较小,那么右移,如果值大,那么上移。 class Solution: def findNumberIn2DArray(self, matrix: List[List[int]], target: int) -> bool: j = 0 i = len(matrix)-1 if i < 0: return Fa
974



被折叠的 条评论
为什么被折叠?



