图的深度遍历，输出结果为（红色为键盘输入的数据，权值都置为1）： 输入顶点数和弧数:8 9 输入8个顶点. 输入顶点0:a 输入顶点1:b 输入顶点2:c 输入顶点3:d 输入顶点4:e 输入顶点5:f 输入顶点6:g 输入顶点7:h 输入9条弧. 输入弧0:a b 1 输入弧1:b d 1 输入弧2:b e 1 输入弧3:d h 1 输入弧4:e h 1 输入弧5:a c 1 输入弧6:c f 1 输入弧7:c g 1 输入弧8:f g 1 深度优先遍历: a b d h e c f g 程序结束.
标签：
上传时间： 2016-04-04
上传用户：lht618
在win2000sp4 + VM6基本稳定。 原理不多说了，自己看代码吧，我也早就发过了驱动的代码了，现在的就是一个完整的应用。希望能够对大家有一点帮助，但是不要用在不该用的场所。 使用方法将： dd1压缩包里面是驱动源码 console压缩包里面是控制台源码 hide.exe是最终产品 使用方法： 1、将hide.exe复制到系统目录 2、运行cmd 3、hide -h 查看帮助 hide -i 安装驱动 hide -u 卸载驱动 hide -f -a filename 添加一个隐藏文件
上传时间： 2013-12-12
上传用户：liglechongchong
On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
标签： demonstrates sequential Selection Bayesian
上传时间： 2016-04-07
上传用户：lindor
k-meansy算法源代码。This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given number of cluster centers using the K-means algorithm. Output is directed to the screen.
标签： code implementing directory algorithm
上传时间： 2016-04-07
上传用户：shawvi
华恒科技 HHCF5249-R3 技术手册 第一章 产品简介 第二章 软件系统 第三章 硬件系统 第四章 机械特性 第五章 底板的硬件设计 第六章 售后服务及技术支持 附录 附录A 初始化 附录B LINUX 常见术语 附录C 常用LINUX 命令 附录D GCC 与GDB 附录E MAKEFILE 附录F UCLINUX 系统分析 uClinux 简介 uClinux 小型化的做法 uClinux 的开发环境 uClinux 的内存管理 工具及内核 附录G 图形界面（GUI）接口函数API 附录H 参考资料
上传时间： 2013-12-24
上传用户：a6697238
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
标签： sequential reversible algorithm nstrates
上传时间： 2014-01-18
上传用户：康郎
This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
标签： reversible algorithm the nstrates
上传时间： 2014-01-08
上传用户：cuibaigao
The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar -xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
标签： algorithms problems Several trivial
上传时间： 2014-01-20
上传用户：royzhangsz
3.0V至5.5V、低功耗、1Mbps、真RS-232收发器，使用四只0.1µ F外部电容.
上传时间： 2016-04-17
上传用户：坏坏的华仔
void III_hufman_decode(struct Granule *gr,int part2_start, int freqline[SBLIMIT][SSLIMIT]) { unsigned int reg1, reg2,i unsigned int part3_length = part2_start + gr->part2_3_length unsigned used int h,*f=&freqline[0][0] if(gr->window_switching_flag && gr->block_type == 2) { /* short block regions */ reg1 = 36 reg2 = 576 } else { /* long block regions */ reg1 = sfBandIndex[fr_ps.header->sampling_frequency].l[gr->region0_count + 1] reg2 = sfBandIndex[fr_ps.header->sampling_frequency].l[gr->region0_count + gr->region1_count + 2] }
标签： III_hufman_decode int freqline Granule
上传时间： 2013-12-19
上传用户：jjj0202
