- #include<iostream>
- #include<string>
- #include<iomanip>
- #include<algorithm>
- using namespace std;
- #define MAXN 9999
- #define MAXSIZE 10
- #define DLEN 4
- class BigNum
- {
- private:
- int a[500]; //可以控制大数的位数
- int len; //大数长度
- public:
- BigNum(){ len = 1;memset(a,0,sizeof(a)); } //构造函数
- BigNum(const int); //将一个int类型的变量转化为大数
- BigNum(const char*); //将一个字符串类型的变量转化为大数
- BigNum(const BigNum &); //拷贝构造函数
- BigNum &operator=(const BigNum &); //重载赋值运算符,大数之间进行赋值运算
- friend istream& operator>>(istream&, BigNum&); //重载输入运算符
- friend ostream& operator<<(ostream&, BigNum&); //重载输出运算符
- BigNum operator+(const BigNum &) const; //重载加法运算符,两个大数之间的相加运算
- BigNum operator-(const BigNum &) const; //重载减法运算符,两个大数之间的相减运算
- BigNum operator*(const BigNum &) const; //重载乘法运算符,两个大数之间的相乘运算
- BigNum operator/(const int &) const; //重载除法运算符,大数对一个整数进行相除运算
- BigNum operator^(const int &) const; //大数的n次方运算
- int operator%(const int &) const; //大数对一个int类型的变量进行取模运算
- bool operator>(const BigNum & T)const; //大数和另一个大数的大小比较
- bool operator>(const int & t)const; //大数和一个int类型的变量的大小比较
- void print(); //输出大数
- };
- BigNum::BigNum(const int b) //将一个int类型的变量转化为大数
- {
- int c,d = b;
- len = 0;
- memset(a,0,sizeof(a));
- while(d > MAXN)
- {
- c = d - (d / (MAXN + 1)) * (MAXN + 1);
- d = d / (MAXN + 1);
- a[len++] = c;
- }
- a[len++] = d;
- }
- BigNum::BigNum(const char*s) //将一个字符串类型的变量转化为大数
- {
- int t,k,index,l,i;
- memset(a,0,sizeof(a));
- l=strlen(s);
- len=l/DLEN;
- if(l%DLEN)
- len++;
- index=0;
- for(i=l-1;i>=0;i-=DLEN)
- {
- t=0;
- k=i-DLEN+1;
- if(k<0)
- k=0;
- for(int j=k;j<=i;j++)
- t=t*10+s[j]-'0';
- a[index++]=t;
- }
- }
- BigNum::BigNum(const BigNum & T) : len(T.len) //拷贝构造函数
- {
- int i;
- memset(a,0,sizeof(a));
- for(i = 0 ; i < len ; i++)
- a[i] = T.a[i];
- }
- BigNum & BigNum::operator=(const BigNum & n) //重载赋值运算符,大数之间进行赋值运算
- {
- int i;
- len = n.len;
- memset(a,0,sizeof(a));
- for(i = 0 ; i < len ; i++)
- a[i] = n.a[i];
- return *this;
- }
- istream& operator>>(istream & in, BigNum & b) //重载输入运算符
- {
- char ch[MAXSIZE*4];
- int i = -1;
- in>>ch;
- int l=strlen(ch);
- int count=0,sum=0;
- for(i=l-1;i>=0;)
- {
- sum = 0;
- int t=1;
- for(int j=0;j<4&&i>=0;j++,i--,t*=10)
- {
- sum+=(ch[i]-'0')*t;
- }
- b.a[count]=sum;
- count++;
- }
- b.len =count++;
- return in;
- }
- ostream& operator<<(ostream& out, BigNum& b) //重载输出运算符
- {
- int i;
- cout << b.a[b.len - 1];
- for(i = b.len - 2 ; i >= 0 ; i--)
- {
- cout.width(DLEN);
- cout.fill('0');
- cout << b.a[i];
- }
- return out;
- }
- BigNum BigNum::operator+(const BigNum & T) const //两个大数之间的相加运算
- {
- BigNum t(*this);
- int i,big; //位数
- big = T.len > len ? T.len : len;
- for(i = 0 ; i < big ; i++)
- {
- t.a[i] +=T.a[i];
- if(t.a[i] > MAXN)
- {
- t.a[i + 1]++;
- t.a[i] -=MAXN+1;
- }
- }
- if(t.a[big] != 0)
- t.len = big + 1;
- else
- t.len = big;
- return t;
- }
- BigNum BigNum::operator-(const BigNum & T) const //两个大数之间的相减运算
- {
- int i,j,big;
- bool flag;
- BigNum t1,t2;
- if(*this>T)
- {
- t1=*this;
- t2=T;
- flag=0;
- }
- else
- {
- t1=T;
- t2=*this;
- flag=1;
- }
- big=t1.len;
- for(i = 0 ; i < big ; i++)
- {
- if(t1.a[i] < t2.a[i])
- {
- j = i + 1;
- while(t1.a[j] == 0)
- j++;
- t1.a[j--]--;
- while(j > i)
- t1.a[j--] += MAXN;
- t1.a[i] += MAXN + 1 - t2.a[i];
- }
- else
- t1.a[i] -= t2.a[i];
- }
- t1.len = big;
- while(t1.a[t1.len - 1] == 0 && t1.len > 1)
- {
- t1.len--;
- big--;
- }
- if(flag)
- t1.a[big-1]=0-t1.a[big-1];
- return t1;
- }
- BigNum BigNum::operator*(const BigNum & T) const //两个大数之间的相乘运算
- {
- BigNum ret;
- int i,j,up;
- int temp,temp1;
- for(i = 0 ; i < len ; i++)
- {
- up = 0;
- for(j = 0 ; j < T.len ; j++)
- {
- temp = a[i] * T.a[j] + ret.a[i + j] + up;
- if(temp > MAXN)
- {
- temp1 = temp - temp / (MAXN + 1) * (MAXN + 1);
- up = temp / (MAXN + 1);
- ret.a[i + j] = temp1;
- }
- else
- {
- up = 0;
- ret.a[i + j] = temp;
- }
- }
- if(up != 0)
- ret.a[i + j] = up;
- }
- ret.len = i + j;
- while(ret.a[ret.len - 1] == 0 && ret.len > 1)
- ret.len--;
- return ret;
- }
- BigNum BigNum::operator/(const int & b) const //大数对一个整数进行相除运算
- {
- BigNum ret;
- int i,down = 0;
- for(i = len - 1 ; i >= 0 ; i--)
- {
- ret.a[i] = (a[i] + down * (MAXN + 1)) / b;
- down = a[i] + down * (MAXN + 1) - ret.a[i] * b;
- }
- ret.len = len;
- while(ret.a[ret.len - 1] == 0 && ret.len > 1)
- ret.len--;
- return ret;
- }
- int BigNum::operator %(const int & b) const //大数对一个int类型的变量进行取模运算
- {
- int i,d=0;
- for (i = len-1; i>=0; i--)
- {
- d = ((d * (MAXN+1))% b + a[i])% b;
- }
- return d;
- }
- BigNum BigNum::operator^(const int & n) const //大数的n次方运算
- {
- BigNum t,ret(1);
- int i;
- if(n<0)
- exit(-1);
- if(n==0)
- return 1;
- if(n==1)
- return *this;
- int m=n;
- while(m>1)
- {
- t=*this;
- for( i=1;i<<1<=m;i<<=1)
- {
- t=t*t;
- }
- m-=i;
- ret=ret*t;
- if(m==1)
- ret=ret*(*this);
- }
- return ret;
- }
- bool BigNum::operator>(const BigNum & T) const //大数和另一个大数的大小比较
- {
- int ln;
- if(len > T.len)
- return true;
- else if(len == T.len)
- {
- ln = len - 1;
- while(a[ln] == T.a[ln] && ln >= 0)
- ln--;
- if(ln >= 0 && a[ln] > T.a[ln])
- return true;
- else
- return false;
- }
- else
- return false;
- }
- bool BigNum::operator >(const int & t) const //大数和一个int类型的变量的大小比较
- {
- BigNum b(t);
- return *this>b;
- }
- void BigNum::print() //输出大数
- {
- int i;
- cout << a[len - 1];
- for(i = len - 2 ; i >= 0 ; i--)
- {
- cout.width(DLEN);
- cout.fill('0');
- cout << a[i];
- }
- cout << endl;
- }
- int main(void)
- {
- int i,n;
- BigNum x[101]; //定义大数的对象数组
- x[0]=1;
- for(i=1;i<101;i++)
- x[i]=x[i-1]*(4*i-2)/(i+1);
- while(scanf("%d",&n)==1 && n!=-1)
- {
- x[n].print();
- }
- }
相关推荐
比较字符串大小C++源程序
比较字符串的大小。要求编写一完整程序。 从键盘接收5个字符串,输出最大和最小的字符串到显示屏。 c++6.0运行 字符串输入时,每个字符串以回车结束
c++利用函数重载实现求两个整数,两个实数,三个字符的最大值
c++利用函数重载实现求两个整数,两个实数,三个字符的最大值
C++ 重载运算符+==-用于字符串的比较
C++ ,VS2010平台编辑,下载后可以直接使用,模拟打分及求平均分的过程
c++ - 大整数除法(重载运算符)
用vector存储大整数并重载运算符
iOS自动化之打包-xcodebuild命令行打包.app并安装到ios模拟器
xcode xcodebuild xcrun ios模拟器 iOS UI自动化测试 ios自动打包安装到ios模拟器
行列式详解:从定义到应用
本文系统介绍了行列式的核心概念和应用。首先从二阶、三阶行列式入手,逐步扩展到n阶行列式的定义,讲解了余子式和代数余子式的概念。其次,详细阐述了行列式的重要性质,包括转置不变性、行列交换变号等基本性质以及乘积性质等推论。在计算方法方面,介绍了按行展开法、化上三角矩阵法等实用技巧,并列举了特殊行列式的计算案例。最后重点讨论了行列式在克拉默法则、矩阵可逆性判断、几何体积计算及线性相关性判断等领域的应用。文章强调理解行列式的几何意义和灵活运用性质的重要性,并给出了学习建议和常见误区提示。行列式作为连接代数与几何的桥
大整数求和(运算符重载)
BigInt类表示不超过100位的无符号大整数。试重载>>,<<和+,以支持无符号大整数的输入、输出与求和(假设结果仍是一个不超过100位的无符号大整数)。 重载面向BigInt类对象的运算符: >> << + 裁判测试程序样例: #include <iostream> #include <string> using namespace std; /* 请在这里填写答案 */ int main(){ Big..
DrissionPage无头模式破盾记:实战绕过CloudFlare 5秒验证
本文详细介绍了如何使用DrissionPage无头模式绕过CloudFlare 5秒盾验证,提升爬虫效率。通过关键参数配置、服务器环境问题处理及高级绕过技巧,如动态UA轮换和鼠标移动模拟,帮助开发者有效应对反爬机制。实战案例展示了如何结合最新Chrome UA和参数设置实现稳定数据采集。
C++大数模板
分别使用C++中的运算符重载的方法来实现大数之间的数学运算,包括加法、减法、乘法、除法、n次方、取模、大小比较、赋值以及输入流、输出流的重载。。 并且使用这个大数模板,顺利AC了HDOJ上的1134这个题目的Catalan数计数问题。。点击打开链接大数模板的代码如
YOLOv12改进策略【卷积层】| 引入注意力卷积模块RFAConv,关注感受野空间特征 助力yolov12精度提升
RFAConv:创新空间注意力和标准卷积运算感受野空间特征是针对卷积核设计的,根据卷积核大小动态生成。以3×3卷积核为例,它由非重叠滑动窗口组成,每个窗口代表一个感受野滑块。
WinCC 7.5 读取 SQL server2016 中的表格数据
环境 win10 SQL Server 2016 WinCC V7.5 SP2 1 安装SQL Server 2016 2 选择服务器名称 .\WINCC 3 建数据库
c++大数模板
分别使用C++中的运算符重载的方法来实现大数之间的数学运算,包括加法、减法、乘法、除法、n次方、取模、大小比较、赋值以及输入流、输出流的重载。。 #include <iostream> #include <cstring> #include <iomanip> #include <algorithm> #include <cstdio>...
SAP自动化-ME51N批量创建采购申请
2、需要管理员授权RZ11脚本录制回放功能。1、仅限于正对SAP日常工作的批量处理。3、数据模板按实际需求更新。物料编码,工厂,数量。
c++ 大数类 大数模板
转自:http://blog.youkuaiyun.com/hackbuteer1/article/details/6595881 分别使用C++中的运算符重载的方法来实现大数之间的数学运算,包括加法、减法、乘法、除法、n次方、取模、大小比较、赋值以及输入流、输出流的重载。。 并且使用这个大数模板,顺利AC了HDOJ上的1134这个题目的Catalan数计数问题。。http://acm.
AI漫剧全套万能提示词合集
今天给大家整理一套已经跑通爆款漫剧的万能提示词库!覆盖:豆包剧本人设词 + 分镜生成词 + 即梦绘图词 + 动态优化词 + 防翻车固定词直接复制粘贴,新手也能做出稳定、高清、统一、有质感的AI漫剧!你是资深短视频漫剧编剧,擅长制作60秒竖屏爆款AI漫剧,请为我创作一集完整漫剧内容。要求:开头3秒高能抓眼,中段有冲突转折,结尾留强悬念钩子,节奏紧凑、适合短视频流量。首先生成固定统一人物人设:包含脸型、五官、发色、发型、穿搭、身材、气质、画风、光影细节,要求足够精细,保证全程角色不变脸。
ARM系统控制和管理接口System Control and Management Interface
本文档描述了一个可扩展的独立于操作系统的软件接口,用于执行各种系统控制和管理任务,包括电源和性能管理。 本文档描述了系统控制和管理接口(SCMI),它是一组操作系统无关的软件接口,用于系统管理。
C++ 大数运算模板
该模板可以算加,减,乘,除基本运算,其中加法只能是大数减小数。 #include <iostream> #include <cstdio> #include <cstdlib> #include <cstring> #include <string> #include <algorithm> us...
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")
[转]C++大数模板 卡特兰数
http://blog.youkuaiyun.com/hackbuteer1/article/details/6595881 分别使用C++中的运算符重载的方法来实现大数之间的数学运算,包括加法、减法、乘法、除法、n次方、取模、大小比较、赋值以及输入流、输出流的重载。。 并且使用这个大数模板,顺利AC了HDOJ上的1134这个题目的Catalan数计数问题。。http://a
lwip-2.0.3
lwip是瑞典计算机科学院(SICS)的Adam Dunkels 开发的一个小型开源的TCP/IP协议栈。实现的重点是在保持TCP协议主要功能的基础上减少对RAM 的占用。
Outlook Express 6 (OE6) for Win7/Win8/Win10
英文版 Outlook Express6.0, 兼容Win7/Win10/WinXP,解压就可用。
C++复习——函数模板和函数的重载
前言:慢慢复习路!!! 0×01:函数的模板 定义:C++允许在同一个域中用一个函数名定义多个函数,这些函数的参数个数、参数类型不相同。用一个函数名实现不同的功能,就是函数的重载。 例一: #include <iostream> using namespace std; int max(int a,int b,int c) //求3个整数中的最大者 { if (b>a) a=b; if (c>a) a=c; return a; } float max(flo
STM32F407中文手册
STM32f407中文手册 本参考手册面向应用开发人员,提供有关使用 STM32F405xx/07xx、STM32F415xx/17xx、 STM32F42xxx 和 STM32F43xxx 微控制器存储器与外设的完整信息。STM32F405xx/07xx、STM32F415xx/17xx、STM32F42xxx 和 STM32F43xxx 构成一个微 控制器系列,各产品具有不同的存储器大小、封装和外设。有关订购信息以及器件的机械与电气特性,请参见数据手册。有关 ARM Cortex™-M4F 内核的信息,请参见《Cortex™-M4F 技术参考手册》。
【大数问题】C++大数问题模板
#define MAXN 9999 #define MAXSIZE 10 #define DLEN 4 class BigNum { private: int a[1500]; //可以控制大数的位数 int len; //大数长度 public: BigNum() { len = 1; memset(a, 0, sizeof(a));
大数模板 poj3982
1. 这个模板不是自己写的,转载的别人转载的,还没学完c++的我,想写也没有那能力。 这个模板我用在了POJ的一道题上,传送门--POJ3982 一般大数的题,都可用这个模板解决,只需要修改主函数就好了,但是如果不能独立写出来的话,不相当于白搭吗,所以我学完c++后会手写出模板的!! 注意,这个大数模板只适用于不太大的模拟,几万位,肯定会爆内存的,后续会补上功能更强大的
C++ 大数类 大数模板
转自:http://blog.youkuaiyun.com/vsooda/article/details/8543351 分别使用C++中的运算符重载的方法来实现大数之间的数学运算,包括加法、减法、乘法、除法、n次方、取模、大小比较、赋值以及输入流、输出流的重载。。 圣光降临~ 例题是HDU 1134 大数卡特兰 #include #include #include #includ
4960



被折叠的 条评论
为什么被折叠?



