pca_data_compress

matlab视频读取函数,用matlab处理视频文件,代码量较少.处理较容易.(matlab video read function with matlab processing video files, less the amount of code. deal easier.)

力矢量算法实现的画图程序，编写环境matlab， 3D 本人原创 (force-oriented graph drawing algrithm, 3D, computer science applied)

add sub bytes through incrypting image

MATLAB实现单摆在外力矩作用下的动画 源程序代码((MATLAB a simple pendulum animation torque under the action of outside source program code))

multivariable stepest decent method matlab code

Hypoplastic sand model

this code reduce the dimentional of feature space using combine sparse matrix+PCA for classification eeg signal. more detail exixst inside the code. this code tested and work properly

拉丁超立方体抽样，是一种初始化常用的方法，使得种群初始化更均匀(Latin hypercube sampling is a common method of initialization, which makes the population initialization more uniform)

这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。 调用最小风险贝叶斯分类器决策子函数时，根据先验概率，再根据自行给出的5*5的决策表，通过比较概率大小判断一个体重身高二维向量代表的人是男是女，放入决策数组中。 主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小风险贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到在一定先验概率条件下，决策表中不同决策的错误率的统计。 (This is a pattern recognition classifier minimum risk Bayes design .In reference to the case of routine , self- improvement in a certain a priori probability conditions, male , female and total error rate error rate statistics , into which each array . All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions . Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector The third step is the application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function . Bayesian classifier )

这是消除信号中噪声的程序，是国外编制的，比较适合初学。(This is the signal to eliminate noise procedures, the establishment abroad and are more suitable for a novice.)