#include <stdio.h>
#include <stdlib.h>
#define BUF_SIZE 1024
char buf[BUF_SIZE + 1];
int main(int argc, char * argv[])
{
if(argc != 3)
{
printf("Usage : %s <src_file> <dest_file>\n", argv[0]);
return EXIT_FAILURE;
}
FILE * in_fp;
FILE * out_fp;
char * in_file_name = argv[1];
char * out_file_name = argv[2];
size_t count;
if((in_fp = fopen(in_file_name, "rb")) == NULL)
{
perror("fopen");
return EXIT_SUCCESS;
}
if((out_fp = fopen(out_file_name, "wb+")) == NULL)
{
perror("fopen");
return EXIT_SUCCESS;
}
while((count = fread(buf, 1, BUF_SIZE, in_fp)) > 0)
{
if(fwrite(buf, 1, count, out_fp) != count)
{
perror("fwrite");
return EXIT_FAILURE;
}
}
if(fclose(in_fp) != 0)
{
perror("fclose");
return EXIT_FAILURE;
}
if(fclose(out_fp) != 0)
{
perror("fclose");
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
相关推荐
Linux下C语言实现文件拷贝
在Linux下用C语言实现文件拷贝的代码
C语言文件复制程序
#include <stdio.h> #include <sys/types.h> #include <sys/stat.h> #include <fcntl.h> #include <unistd.h> #include <string.h> int main() { char name[100]; pri...
c语言 文件复制
c语言 文件复制
c语言怎么判断编码,C语言中判断一个char*是不是utf8编码
C语言中判断一个char*是不是utf8编码C语言中判断一个char*是不是utf8编码里我修改了一下, 纯ASCII编码的字符串也返回true, 因为UTF8和ASCII兼容实例代码:int utf8_check(const char* str, size_t length) {size_t i;int nBytes;unsigned char chr;i = 0;nBytes = 0;whil...
igh ethercat 实时性测试
组备注std(us)max(us)a无锁核,无rt补丁8.38108b锁核+FIFO调度+内存锁定,无rt补丁0.9216c锁核+FIFO调度+内存锁定,+rt补丁0.9922可以看到 a组 使用ethercat不做其他优化,周期抖动非常大,且max能达到 100 多 us;通过 锁核+FIFO调度+内存锁定 进行优化,可以看到 抖动明显降低,且max 为 20 us 左右;
Unity游戏开发客户端面经——网络(初级)
Unity游戏开发客户端面经 之 网络 篇
C语言实现文件复制功能(包括文本文件和二进制文件)
文件的复制是常用的功能,要求写一段代码,让用户输入要复制的文件以及新建的文件,然后对文件进行复制。能够复制的文件包括文本文件和二进制文件,你可以复制1G的电影,也可以复制1Byte的txt文档。实现文件复制的主要思路是:开辟一个缓冲区,不断从原文件中读取内容到缓冲区,每读取完一次就将缓冲区中的内容写入到新建的文件,直到把原文件的内容读取完。这里有两个关键的问题需要解决:1) 开辟多大的缓冲区合适?...
Python实战:用MOOTDX库轻松抓取股票分时数据并绘制专业图表(附完整代码)
本文详细介绍了如何使用Python的MOOTDX库获取股票分时数据,并通过Pandas进行数据清洗与VWAP计算,最后利用Matplotlib绘制包含价格线、均线和成交量的专业分时图。文章提供了从环境搭建、数据获取到可视化呈现的完整代码,帮助读者快速构建本地化的股票分时数据分析工具。
用c语言复制程序,C语言复制文本文件
使用C语言编写一个简易的文本文件复制程序。一个打开的文本文件在内存中就是顺序存放的字符流,只要将打开的文件用 fgetc( ) 函数按字符序列顺序读取,并用 fputc( ) 函数依次输出到另一个打开的文件中,就可以实现文本文件的复制。代码清单 1:复制文件#include #include #define FILENAME_MAX 1024int main( ){char ch;FILE *sf...
Xilinx ERNIC RoCEv2 FPGA实现与QP队列深度解析
本文深入解析Xilinx ERNIC RoCEv2在FPGA上的实现,重点剖析QP队列对的管理机制与深度优化策略。通过Verilog代码示例和工程实战,详细介绍了如何实现高吞吐量、低延迟的RDMA硬件卸载方案,为金融交易和数据中心应用提供性能优化指导。
Python与Wind量化接口实战:从配置到数据提取全流程解析
本文详细解析了Python与Wind量化接口的实战应用,从环境配置到数据提取全流程。通过Anaconda管理Python环境,安装WindPy接口,实现历史行情、截面数据和实时行情的查询与优化。文章还提供了高级功能如批量查询、数据缓存策略及实战案例,帮助开发者高效构建量化分析框架。
深究C语言6.文件操作
文件操作看起来高大上,解决起来其实也是轻轻松松的。come on,我们来干掉它!
PVE(Proxmox VE)添加VMware Esxi并迁移虚拟机
跨平台迁移虚拟机:从VMware虚拟化平台迁移虚拟机到PVE平台。
C语言编程序,实现文本文件的复制
问题描述 C语言编程序,实现文本文件的复制 将source.txt文件复制到target.txt文件中。文件内容如下: When you are old and grey and full of sleep, 当你老了,头发花白,睡意沉沉, And nodding by the fire,take down this book, 倦坐在炉边,取下这本书来, And slowly read,and ...
SAP Fiori 会话超时全解析:从 Launchpad 到 Identity Authentication 的统一策略
SAP Fiori会话超时机制解析与配置策略 本文深入探讨SAP Fiori项目中的会话超时机制,分析其对系统安全和用户体验的双重影响。文章系统拆解了SAP Fiori Launchpad与SAP Cloud Identity Services之间的会话超时工作原理,重点介绍了两种超时机制:服务器驱动的超时(通过SESSION_TIMEOUT_STOP_DATA_REFRESH参数控制)和UI驱动的超时(通过SESSION_TIMEOUT_INTERVAL_IN_MINUTES参数控制)。文中结合制造业产线
车牌识别系统
随这图形图像技术的发展,现在的车牌识别技术准确率越来越高,识别速度越来越快。无论何种形式的车牌识别系统,它们都是由触发、图像采集、图像识别模块、辅助光源和通信模块组成的。车牌识别系统涉及光学、电器、电子控制、数字图像处理、计算视觉、人工智能等多项技术。触发模块负责在车辆到达合适位置时,给出触发信号,控制抓拍。辅助光源提供辅助照明,保证系统在不同的光照条件下都能拍摄到高质量的图像。图像预处理程序对抓拍的图像进行处理,去除噪声,并进行参数调整。然后通过车牌定位、字符识别,最后将识别结果输出。 牌照自动识别是一
c语言实现文件内容复制
实现过程: (1)打开vc++ 6.0开发环境,新建一个c源文件,并输入c源文件的名称 (2)引用头文件,代码如下#include<stdio.h> (3)使用while循环从被复制的文件中复制文件并复制到另一个文件。 (4)使用main函数作为入门函数,代码如下 main() { FILE *in,*out; char ch,infile[50],outfile[50]; print...
大中型超市收银系统(可直接运行)
光步前台收银系统系统功能:商品销售、商品退货、挂单、收银员权限、会员销售、单品取消、整单取消、商品查询、脱机销售、脱机数据上传,基本资料下载,开钱箱、小票打印、收款结算,支持多币种付款方式(现金、储值卡、礼券、微信等),支持多种促销。运行环境:.net 4.0登陆信息用户名:1001 密码:123456内置商品信息,商品编码可输入00001000 - 00010000,测试效果相关功能键F5 结算 F9挂单 F11商品查询 F8退货 Insert单品取消 Del整单取消 F12开钱箱F2 会员 Ctrl+Z退出系统 Home 锁屏 相关源码待公开
C语言复制二进制文件
#include <stdio.h> #include<stdlib.h> #define APACITY 2048 int main(){ FILE * input= fopen("D:\\movie.mkv","rb"); FILE * output= fopen("D:\\电影.mkv","wb"); if(input==NULL){
IEM模型,地表粗糙度模拟
利用微波反演土壤水分时候,利用IEM模型可以实现对随机地表的模拟
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox(带书签),MATLAB统计学习工具
【C语言刷题】文件操作章节配套练习(选择题 + 编程题)
本篇为《维生素C语言》系列配套练习题,挑选了一些相对来说比较实用的练习,有助于巩固学习的知识。题目附带了答案和解析可供参考,顺便附上文件操作教学链接,可供复习:
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")
C 语言 文件复制
C语言文件复制问题 转自:http://hi.baidu.com/weni23/item/68e0b2db408b72fccb0c3911 文件复制,首先要知道被复制的文件的大小,可以用 fseek 将文件指针定位到文件尾部SEEK_END,再用 ftell 来取得文件大小,注意,这个返回值单位是 字节 ,即 char 的大小,还有就是别忘了ftell之后再次用 fseek
C语言学习笔记——(六)文件
注:编码工具是CLion+Cygwin64 文件的读写 fopen函数,以特定模式打开指定文件。 模式:r、w、rb、wb、rw。 读文件的时候,如果指定文件不存在会报错。 写文件的时候,如果指定文件不存在会创建文件。 fclose函数,文件操作完成后,要调用此函数关闭文件,释放资源。 读文件 ...
linux c语言拷贝u盘,用C语言编写linux cp 拷贝命令
(注:若转载本文请注明出处!)对于初学者,学习文件操作参考作用,希望我的一些时间能给到大家一点帮助。代码粗糙,但还是涉及到了很到指针移动操作,初学者可以参考,看代码,不多解释。linux环境假如有/home/hello.tar.gz#gcc filecp.c -o filecp#./filecp /home/hello.tar.gz#cd/home/#./filecp hello.tar.gz ...
文件复制,文件加密,文件长度
文件复制 #include <stdio.h> int copyFile(const char *srcfile,const char *destfile){ FILE *fpsrc = fopen(srcfile,"r"); if(fpsrc == NULL){ //access函数可以判断一个文件是否存在 printf("fopen %s failed!\n",srcfile); return -1; } FILE *fpdest = fopen(destfile,
文件选择输出 c语言,C语言文件输入和输出操作的学习心得(四)
概述C语言文件输入和输出操作的学习心得(一)C语言文件输入和输出操作的学习心得(二)C语言文件输入和输出操作的学习心得(三)在以上三篇文章中介绍了C语言文件输入和输出的一些基本操作,相信大家阅读以后会对C语言文件操作的知识有一定的了解。本文将通过一个实例向大家演示如何运用前面三篇文章讨论过的函数。该实例的目的为通过C语言对一个文本进行复制,并新建一个文本用来存放复制的字符。事不宜迟,我们立即上码。...
【C语言】文件操作
持续学习&持续更新中… 学习态度:守破离 【C语言】文件操作打开文件fgetc、fputc读写文本文件fgets、fputs读写文本文件二进制(fread、fwrite)读写文件参考 打开文件 FILE * fopen(char * file_name, char * mode); // 失败返回NULL 注意:如果读写的是二进制文件,则还要在后面加上“b”,比如“rb”、“r+b”等。 注意:打开文件后需要手动关闭文件。 int fclose(FILE * fp); // 失败返回EOF(.
IO学习系列之使用多线程复制同一个文件内容
【代码】IO学习系列之使用多线程复制同一个文件内容。
933



被折叠的 条评论
为什么被折叠?



