EMD_Earth-Movers-Distance

MATLAB学习matlab6.0数学手册(MATLAB learning matlab6.0 Mathematics Handbook)

PSOt - a Particle Swarm Optimization Toolbox for use with Matlab. A Particle Swarm Optimization Toolbox (PSOt) for use with the Matlab scientific programming environment has been developed. PSO is introduced briefly and then the use of the toolbox is explained with some examples. A link to downloadable code is provided.

可以把mat格式的数据集转变为txt格式用来分类的文件。(You can format the data into a set of mat txt format used to classify documents.)

对农产品的销量进行预测，首先是归一化处理，再用LSSVM进行预测(To predict agricultural product sales, the first is normalized, and then LSSVM forecast)

高斯白噪声信道的仿真，自己写得，可以运行(Gauss　ｃｈａｎｎｅｌ)

Gauss消去法、Jacobi法、SOR法解线性方程组的源程序,可以方便得到解(code for solution of linear equations using Gauss elimination method、Jacobi method and SOR method)

说明：  混沌工具箱，用matlab编写的，该工具箱包括了混沌时间序列分析与预测的常用方法(Chaos toolbox matlab prepared to use the toolbox, including a chaotic time series analysis and forecast of the common methods)

ES UN PROGRAMA DE MARIO BROS

Automated registration dft

逐步判别分析的主函数 用于分类 [sel,c,c0,re,P]=StepclaassV2(data,[50 50 50],data,10) 输入变量 x为训练集.每行为一个样本，每列为一个变量. Class_x,为训练集的分类情况，一行，为各类样本数量,例如[5 6 9] Test为待分类样本. 输出变量：sel为选择的变量序号，c,c0为拟合出的判别函数.re为对Test的判别结果,P为其后验概率. author 王新 2012.4.8 例子Sample: load fisheriris x=meas Class_x=[50,50,50] Test=meas(1:5,:) Fa1=4 [sel,c,c0,re,P]=StepclaassV2(x,Class_x,Test,Fa1)( Stepwise discriminant analysis for the classification of the main function [sel, c, c0, re, P] = StepclaassV2 (data, [50 50 50], data, 10) input variables x as the training set, each conduct a sample of each as a variable. Class_x, the classification for the training set, a row, the number of samples for all types, for example, [5 6 9] Test sample to be classified. output variable: sel for the selected variables serial number, c, c0 is fitted discriminant function. re discrimination results for the Test, P its posterior probability. author Wang 2012.4.8 examples Sample: load fisheriris x = meas Class_x = [50,50,50] Test = meas (1:5, :) Fa1 = 4 [sel, c, c0, re, P] = StepclaassV2 (x, Class_x, Test, Fa1))