shipinfenxi

流行学习英文文献，主要是局部线性嵌入方法的改进(Pop learning English literature, mainly locally linear embedding Improvement)

这是一个仿真qpsk信号通过不同信道模型（包括单径，多径，莱斯，瑞利平坦等信道）的性能的matlab程序。(This is a simulation qpsk signals through different channel model (including single-track, multi-track, Condoleezza Rice, Rayleigh and other flat Channel) Performance of Matlab procedures.)

function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers corresponding to each class. Version: 07/08/11 Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be) References: [1] J. B. MacQueen, "Some Methods for Classification and Analysis of MultiVariate Observations", in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, eds., vol. 1, UC Press, 1967, pp. 281-297. [2] D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of Careful Seeding", Technical Report 2006-13, Stanford InfoLab, 2006.

处理lasso regression。 使用stochastic Markov property。(Algorithm for solving the Lasso problem: 0.5* (y- X*beta) *(y- X*beta)+ lambda* ||beta||_1 where ||beta||_1 is the L_1 norm i.e., ||beta||_1 = sum(abs( beta )) We use the method proposed by Fu et. al based on single co-ordinate descent. For more details see GP s notes or the following paper: Penalized Regressions: The Bridge Versus the Lasso Wenjiang J. FU, Journal of Computational and Graphical Statistics, Volume 7, Number 3, Pages 397?416, 1998 )

基本粒子群优化算法Matlab源程序 主要用于对PSO的基本理解与思考 (Particle swarm optimization algorithm Matlab source)

simulation code for decode and forward

这是一个SVM的工具箱，很不错的代码，希望对大家有用(This is a SVM toolbox, very good code)

绘制序列及其傅里叶变换的幅值图，并求逆变换。一个matlab程序。(Draw sequences and Fourier amplitude figure, and pray for inverter change. A matlab application. )

drive design for induction machine

实现扩频系统。信源产生，扩频，基带成型后加噪声，解扩，低通。判决(Achieve spread spectrum system. Source production, spreading, baseband shaping, add noise, despreading, low-pass. Judgment)