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20050906171710249
- 这个游戏不用多介绍了吧 不过适用机型只有SE 的K系列和S系列.W系列没测试过 经过本人K500C测试没问题 下载后直接将后缀名.zip改为.jar即可 -the game need not introduced but it only applies SE models of Series K and S series. W did not test series I read over E398 test no problem downloading dir
测试PSO算法的新的组合测试函数
- Novel Composition Test Functions for Numerical Global Optimization func_test.m is the main program, a basic PSO algorithm PSO_func.m is attached. SIS_novel_func.m is the function program,including six composition functions f=SIS_novel_func(x,f
pl/0
- /*PL/0编译系统C版本头文件pl0.h*/ /* typedef enum { false, true } bool; */ #define norw 13 /*关键字个数*/ #define txmax 100 /*名字表容量*/ #define nmax 14 /*number的最大位数*/ #define al 10
Senfore_DragDrop_v4.1
- Drag and Drop Component Suite Version 4.1 Field test 5, released 16-dec-2001 ?1997-2001 Angus Johnson & Anders Melander http://www.melander.dk/delphi/dragdrop/ ------------------------------------------- Table of Contents: ----------------------
BCS_
- 贝叶斯压缩感知的仿真程序，此程序使用LAPLACE先验，-This is a simple example to test the fast Laplace algorithm from the following paper: S. D. Babacan, R. Molina, A. K. Katsaggelos. bayesian Compressive Sensing using Laplace Priors
DM4
- 执行流程： 1. 用户输入参数：K的选择，训练数据，测试数据的路径； 2. 读取训练数据集和测试数据集文件，用ArffFileReader类读取并组织起InstanceSet数据结构； 3. 利用上面的相似度量标准，对每一个测试集中的Instance，计算与其最相似的K个训练集中的Instance，通过投票进行分类，将分类结果存储经Instance的成员变量targetGuess中； 4. 对分类结果进行度量，包括分类正确率，各种类别实例的Precision，Recall；Con
find_the_marble
- 利用动态规划算法解决猜测弹珠位置的问题Alice and Bob are playing a game. This game is played with several identical pots and one marble.-Alice and Bob are playing a game. This game is played with several identical pots and one marble. When the game starts, Alice puts th
Roystest
- ROYSTEST. Royston s Multivariate Normality Test. This file describe the Royston s multivariate normality test. ------------------------------------------------------------- Created by A. Trujillo-Ortiz, R. Hernandez-Walls, K. Barba-Rojo, an
m
- 该程序试图考察一组数据服从哪种分布（正态，指数或双边指数），并利用K-S检验对各种分布作了检验。-The program attempts to examine a set of data subject to which distribution (normal, exponential or bilateral index), and use KS test has been tested in a variety of distributions.
proj10-01
- 在试验中编写程序实现了K均值聚类算法,K均值聚类的原理是：在训练样本中找到C个聚类中心，每个聚类中心代表一个类的中心。然后将样本归类到与其最近的聚类中心的那一类。 C的选择是通过先验知识或经验选取的。聚类中心是通过算法迭代求得的。-In the test preparation process to achieve a K means clustering algorithm, K means clustering principle is: in the training samples to
kstest
- 非参数检验中K-S检验的matlab实现-Non-parametric test in the KS test matlab implementation
1245744552KrKDQA
- 1、 编写一个Java应用程序，文件名为Folder.java，编译后执行结果如下图所示，实现指定文件夹下目录和文件的文本形式的树状结构显示。要求： 1． 输入： java Folder，显示当前文件夹下的目录和文件的文本形式树状显示； 2． 输入： java Folder C:\test>，显示C:\test文件夹下的目录和文件的文本形式树状显示；（C:\test可以为任意文件夹路径名） 3． 输入： java Folder add aa，在当前文件夹下面添
AWGN-PDFaCDF
- 加性高斯白噪声信道的仿真。利用BOX-MULLER算法产生信道，画出PDF曲线。采用基于CDF的K-S TEST来检测所产生的高斯信道是否符合理论分布。-Additive white Gaussian noise channel emulation. BOX-MULLER algorithm to generate the channel, to draw PDF curve. Based on the CDF of KS the TEST to detect the generated Gau
Rayleigh-PDFaCDF
- 瑞利信道的仿真。画出PDF曲线。采用基于CDF的K-S TEST来检测所产生的瑞利信道是否符合理论分布。-Rayleigh channel simulation. Draw the PDF curve. CDF KS the TEST detection of Rayleigh channel is in line with the theoretical distribution.
ks
- KS2D1S K-S test in two dimensions, data vs. model (14.7) KS2D2S K-S test in two dimensions, data vs. data (14.7)-numerical recipes
program
- 动态K-S检验边缘分布的copula 分布估计算法- Marginal distribution in copula estimation of distribution algorithm based dynamic K-S test
program-two
- 静态K-S检验下的copula 分布估计算法边缘分布的研究- Marginal distribution in copula estimation of distribution algorithm based static K-S test
dna
- 分布函数的假设检验，K-S检验，计算K-S检验临界值Dn,a-Hypothesis testing, distribution function of K-S test, K-S test and Dn calculation of the critical value, a
KOLMOGOROV_TEST
- 基于matlab的K-S test算法实现，可供初学者参考和学习。(K-S test algorithm based Matlab, for beginners reference and learning.)
KS样本划分代码
- K-S，即kolmogorov检验法，亦称拟合优度检验法。用来检验给定的一组数据是否来自分布F=F0，原理是若H0成立，则max|v/n-F0(qj)|应该很小，用手算几乎在绝大多数情况下是不可能的，通常借助统计软件，如SAS，S+等(K-S, namely Kolmogorov test, also known as goodness of fit test. It is used to test whether a given set of data comes from the distr