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刷题有感：
Random random=new Random();//定义随机类
int a=random.nextInt(n);生成一个随机数a，a的范围为【0，n）左闭右开
刷题有感：
动态方程自己写，写错了
不用使用dp，使用a，b多次交换
String s=str.substring(i-2,i);//左闭右开，取子串
String中compareTo方法：
字符串前面部分的每个字符完全一样，返回：后面两个字符串长度差；
字符串前面部分的每个字符存在不一样，返回：出现不一样的字符 ASCII 码的差值。
字符串的每个字符完全一样，返回 0；
解决鸿蒙/荣耀设备GMS应用调试中的“Play保护机制认证”报错
本文详细解析了在鸿蒙或荣耀设备上调试GMS应用时，遇到“Play保护机制认证”报错的根本原因与解决方案。核心在于获取设备的GMS Device ID，并通过Google官方渠道完成临时注册，从而绕过认证限制。文章提供了无需Root和ADB查询两种具体方法，并分享了授权后的故障排查与实战经验，帮助开发者高效解决问题。
2021.1.8寒假打卡Day5
寒假打卡 Day 5 数据结构 队列???? 规则：先进先出（和我们食堂里排队一样，先来的人先轮到打饭 食堂好像不用排队 ） 操作： enqueue(x); //在队列末尾添加元素x dequeue(); //从队列开头取出元素 isEmpty(); //检查队列是否为空 isFull(); //检查队列是否已满 Queue???? There are n processes in a queue. Each process has namei and timei. The round-ro
GD32H7 SPI驱动实战：手把手教你用SPI3连接外部Flash（W25Q128）并实现读写
本文详细介绍了基于GD32H7系列MCU的SPI3接口驱动W25Q128外部Flash的全流程开发指南。从硬件设计、引脚配置到SPI3外设深度配置，再到底层通信函数实现和高级功能优化，提供了完整的解决方案。特别针对高速读取、DMA传输和信号完整性等关键环节进行了优化，确保系统在-40℃~85℃范围内稳定工作。
2021.1.4寒假打卡Day1
寒假打卡Day1 (说是说Day1，其实是Day2补写的(/ω＼)) 总之今天很水，划了一天的水。。。 problem 1: Way Too Long Words Sometimes some words like “localization” or “internationalization” are so long that writing them many times in one text is quite tiresome. Let’s consider a word too long, if
CKA考试实战指南--从备考到通关的完整攻略
本文提供了一份全面的CKA考试实战指南，涵盖从备考规划、资源选择到高效练习环境搭建的全过程。重点解析了考试核心内容，特别是占分30%的故障排除难点，并分享了集群升级、etcd备份等高频考点的突破技巧与临场实战策略，帮助考生系统掌握Kubernetes实操技能，顺利通过认证。
第3版•系统集成项目管理工程师（中级） │ 第12章 执行过程组 ✦ 管理沟通
🥏第3版•系统集成项目管理工程师（中级） │ 第12章 执行过程组 ✦ 管理沟通
2021.1.9寒假打卡Day6
哈尔滨学院2021ACM冬令营Day1 E - ACboy needs your help again! ACboy was kidnapped!! he miss his mother very much and is very scare now.You can’t image how dark the room he was put into is, so poor ???? . As a smart ACMer, you want to get ACboy out of the monster’s
【雕爷学编程】Arduino智慧交通之RF24通信实现车队之间的实时通信和协调
通过这些示例，车队领导可以发送指令给车队成员，车队成员可以接收指令并执行相应的操作，同时车队成员也可以发送自己的位置信息给车队领导或其他成员，以实现车队之间的实时通信和协调。例如，在高速公路上，车队中的车辆可以根据前方车辆的速度和位置信息，自动调整自身的速度和距离，以保持车队的整体稳定和安全。Arduino智慧交通系统利用RF24通信模块实现车队之间的实时通信和协调，通过无线通信技术，车辆之间可以实时传输数据和指令，以实现车队的协同行驶和交通优化。通过数据处理和分析，可以生成实时的交通信息和统计数据。
2021.1.11寒假打卡Day8
哈尔滨学院2021ACM冬令营Day2（20级） The puzzle Problem Description Kayaking is playing a puzzle game containing n different blocks. He marks the blocks with integers from 1 to n, which show the blocks’ original positions. Each time he can exchange two blocks and he w
论文分栏前后内容不连续？教你word如何删除分节符
问题 当我们在写论文的时候，很多期刊的模板都是两栏的格式，但有的时候我们插入的图像尺寸可能太大，两栏格式放不下，因此需要将其以一栏的格式出入，如下： 但当我们把图片删除后，再将其转换成两栏格式，前后内容就出现不连续的情况： 正常情况下应该是如下： 正确处理方式 里面有个分页符，把分页符去掉，或者把你的文字全部复制到记事本里，然后重新复制回word就可以取消原有的格式了。 删除分节符的方法（以word 2019为例） -点击菜单栏上的“视图”，点击“大纲视图”。 可以看到word文档以整列的形式展开：
操作系统 | 知识梳理 | 复习（上）
操作系统复习
2021.1.7寒假打卡Day4
寒假打卡 Day 4 直入主题，瞧瞧昨天的遗留问题： Shell Sort???? Shell Sort is a generalization of Insertion Sort to arrange a list of n elements A. insertionSort(A, n, g) for i = g to n-1 v = A[i] j = i - g while j >= 0 && A[j] &
Allegro如何改变线宽操作指导
用Allegro做pcb设计的时候，改变走线线宽是非常常用的功能，如下图。线宽目前是12mil，需要把线宽改成15mil。如何改变线宽操作指导。
【寒假打卡 DAY1】三位数反转
文章目录前言一、三位数反转二、思考——如果个位是0，反转后应该输出吗？今日Tips 前言 这里开始记录寒假学习算法以及数据结构的日常。 提示：以下是本篇文章正文内容，下面案例可供参考 一、三位数反转 输入一个三位数，分离出它的百位、十位和个位，反转后输出。 样例输入： 123 样例输出： 321 #include<stdio.h> int main() { int n; scanf("%d",&n); printf("%d%d%d",n%10,n%100/10,n/100);.
WINCC7.3破解文件
WINCC7.3硬件够破解文件，有需要的下载！WINCC7.3硬件够破解文件，有需要的下载！
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")
2021.1.6寒假打卡Day3
寒假打卡Day3 今天开始看的是 这本书???? d6tc 我就截取看到的重点做了些笔记 （绝对不是因为懒(/ω＼)） 算法的效率 复杂度 分为两方面： Time Complexity Space Complexity 在竞赛中，题目限制通常为 1s ，即大约108 次操作，因此我们通常遇到的都是时间复杂度过高的问题。 表示法：Big-Oh-Notation 诸如O(n),O(n2)···O(g(n))，g(n)可以认为是1s内的操作次数 e.g. 给出m个数ai (i=1,2,···,m)，找出最
win10 跳过U8环境检测的IE WebControls.rar
win10 安装u8的时候 提示IE WebControls 不通过，这个工具可以欺骗u8的检查。 跳过U8环境检测的IE WebControls。提示导入失败后，重新检测通过。
2021.1.5寒假打卡Day2
寒假打卡Day2 (今天是今天写的(/ω＼)) 今天看的是递归哦 求两个数的最大公因数 这题因为太简单所以没有oj有，那就没有链接啦（bushi #include <iostream> using namespace std; int gcd(int a,int b){ if(a%b==0) return b; else return gcd(b,a%b); } int main(){ int a,b; cin>>a>>b; cout<<gc
jdk-8u144-linux-x64.tar.gz.
该资源为搭建Linux系统Java环境用，资源的后缀为tar.gz格式的压缩包
2021.1.12寒假打卡Day9
P3613 【深基15.例2】寄包柜 题目描述 超市里有 n (n ≤ 105) 个寄包柜。每个寄包柜格子数量不一，第 i 个寄包柜有 ai (ai ≤ 105) 个格子，不过我们并不知道各个 ai 的值。对于每个寄包柜，格子编号从 1 开始，一直到 ai 。 现在有 q (q ≤ 105) 次操作： 1 i j k：在第 i 个柜子的第 j 个格子存入物品 k (0 ≤ k ≤ 109)。当 k=0k=0 时说明清空该格子。 2 i j：查询第 i 个柜子的第 j 个格子中的物品是什么，保证查询的柜子
ACwing寒假每日一题2022打卡 Day 1
原题链接： 2058. 笨拙的手指 - AcWing题库高质量的算法题库https://www.acwing.com/problem/content/2060/思路： 由题意，二进制中改变一位，三进制中改变一位，两者改完后恰好是同一个十进制数，直接暴力枚举二进制和三进制的每一位改变情况再变成十进制找重复就行了，注意是枚举二进制和三进制，不要枚举十进制分解，不然n=1e9超时 Code： #include<iostream> #include<cstring> #incl
2021.1.17寒假打卡Day13
A - 小心FST! Mia Hossain is a very good competitive programmer & spends his most of the time in programming contest. His wife is very much interested in competitive programming. In her free time she sits beside him & curiously watches how he solves p
寒假零基础打卡day1
今日所学 #include<stdio.h> int main() { printf("hellow world\n"); return 0; } //%c打印字符格式的数据 //%d打印整形十进制数据 //char 字符数据类型 //short 短整型 //int 整型 //long 长整型 //long long 更长的整形 ...
2022/1/18 寒假学习打卡 Day4
安装虚拟机/stegsolve安装/web之[极客大挑战]EasySQL2019/Misc之你竟然赶我走/Misc之N种方法解决
2021.1.20寒假打卡Day16
A - Wizard of Orz There are n digital panels placed in a straight line. Each panel can show any digit from 0 to 9. Initially, all panels show 0. Every second, the digit shown by each panel increases by 1. In other words, at the end of every second, a panel
2021.1.10寒假打卡Day7
哈尔滨学院2021ACM冬令营训练赛（一） A. Short Substrings Alice guesses the strings that Bob made for her. At first, Bob came up with the secret string a consisting of lowercase English letters. The string a has a length of 2 or more characters. Then, from string a he bui
2021.1.28寒假打卡Day21
求n个数的最小公倍数 #include<iostream> using namespace std; int gcd(int a,int b){ int r=a%b; if(r==0) return b; else return gcd(b,r); } int nlcm(int a[],int b){ while(b!=1){ a[b-2]=a[b-1]*a[b-2]/gcd(a[b-1],a[b-2]); --b; }
2021.1.18寒假打卡Day14
洛谷题单 动态规划1 P1434 [SHOI2002]滑雪 Michael 喜欢滑雪。这并不奇怪，因为滑雪的确很刺激。可是为了获得速度，滑的区域必须向下倾斜，而且当你滑到坡底，你不得不再次走上坡或者等待升降机来载你。Michael 想知道在一个区域中最长的滑坡。区域由一个二维数组给出。数组的每个数字代表点的高度。下面是一个例子： 1 2 3 4 5 16 17 18 19 6 15 24 25 20 7 14 23 22 21 8 13 12
2021.1.19寒假打卡Day15
P1241 括号序列 定义如下规则序列(字符串)： 空序列是规则序列； 如果S是规则序列，那么(S)和[S]也是规则序列； 如果A和B都是规则序列，那么AB也是规则序列。 例如，下面的字符串都是规则序列： ()，[]，(())，([])，()[]，()[()] 而以下几个则不是： (，[，]，)(，())，([()jh15024551551@96371 现在，给你一些由(，)，[，]构成的序列，你要做的，是补全该括号序列，即扫描一遍原序列，对每一个右括号，找到在它左边最靠近它的左括号匹配，如
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