资源列表
排序选择:
[组合框控件] WINDOW
说明:图中两个窗口的实现类是从CControlBar派生出来的,我们并不需要从头到尾实现该类,因为Cristi Posea先生已经为我们实现了一个称为CSizingControlBar的类,而且做得相当完美!我们所要做的便是好好地利用该类,为了尽可能地简洁,笔者将CSizingControlBar类修改了一下并命名为CCoolBar,接下来我们将详细介绍如何利用该类实现我们所需的界面。-Figure realize two window type is derived from the CContro<廖国忠> 在 2026-01-21 上传 | 大小:25kb | 下载:0
[.net编程] FastSpring.NetV2.05-final
说明:这是一个非常好的软件框架,属于轻量级的国产化的AOP解决方案-This is a very good software fr a mework, are made of lightweight AOP solutions<袁春霞> 在 2026-01-21 上传 | 大小:15.25mb | 下载:0
[Windows编程] ci-example-src
说明:持续集成:软件质量改进和风险降低之道(第18届Jolt大奖提名图书)一书的示例代码-Continuous integration: improving software quality and reduce the risk of the Road (18th Jolt Award nominated book) 1 book, sample code<Kerry> 在 2026-01-21 上传 | 大小:7.46mb | 下载:0
[JSP源码/Java] AjaxPro
说明:这是一个非常好的开源ajax框架,可完全编译-This is a very good open source ajax fr a mework which can be fully compiled<袁春霞> 在 2026-01-21 上传 | 大小:135kb | 下载:0
[数学计算/工程计算] On-Line_MCMC_Bayesian_Model_Selection
说明: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 a<晨间> 在 2026-01-21 上传 | 大小:215kb | 下载:0
[数学计算/工程计算] Reversible_Jump_MCMC_Bayesian_Model_Selection
说明: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 paramete<晨间> 在 2026-01-21 上传 | 大小:340kb | 下载:0
[单片机(51,AVR,MSP430等)] 2005960377[1]
说明:51单片机C语言教程,讲解了8051下C语言开发的基本语法,给出了一些例子-51 Single-chip C Language Tutorial, 8051 explained under the C language to develop basic grammar, some examples are given<dulong> 在 2026-01-21 上传 | 大小:6.05mb | 下载:0
[matlab例程] MCMC_Unscented_Particle_Filter_demo
说明: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 Eri<晨间> 在 2026-01-21 上传 | 大小:57kb | 下载:0