MatFunC
所属分类：数学计算
开发工具：matlab
文件大小：21KB
下载次数：9
上传日期：2012-10-25 20:52:31
上 传 者：
86955668
说明： 本算法包括最大似然估计，最小二乘估计，基于EM算法的多种混合高斯分布估计，EM算法测试实例，绘制每种分布的plot函数。非常有参考价值！
(The algorithm including maximum likelihood estimation, least squares estimation, based on the the many EM algorithm mixed Gaussian distribution is estimated, the EM algorithm test case to draw each distribution plot function. Reference value!)
文件列表:
MatFunC\Create\build_mix_2D_gaussian.m (1627, 2003-11-26)
MatFunC\Create\build_mix_gaussian.m (1068, 2003-11-24)
MatFunC\fit_maxwell_pdf.m (4798, 2003-11-16)
MatFunC\fit_mix_2D_gaussian.m (4276, 2003-11-26)
MatFunC\fit_mix_gaussian.m (2842, 2003-11-30)
MatFunC\fit_ML_laplace.m (3194, 2003-11-17)
MatFunC\fit_ML_log_normal.m (3430, 2003-11-17)
MatFunC\fit_ML_maxwell.m (3017, 2003-11-16)
MatFunC\fit_ML_normal.m (3606, 2004-04-28)
MatFunC\fit_ML_rayleigh.m (2867, 2003-11-16)
MatFunC\fit_rayleigh_pdf.m (4436, 2003-11-16)
MatFunC\Plot\plot_laplace.m (2088, 2003-11-17)
MatFunC\Plot\plot_log_normal.m (2257, 2003-11-17)
MatFunC\Plot\plot_maxwell.m (2105, 2003-11-16)
MatFunC\Plot\plot_mix_gaussian.m (4525, 2003-11-26)
MatFunC\Plot\plot_normal.m (2333, 2004-04-28)
MatFunC\Plot\plot_rayleigh.m (2032, 2003-11-16)
MatFunC\Create (0, 2004-04-28)
MatFunC\Plot (0, 2004-04-28)
MatFunC (0, 2004-04-28)
%
% This folder contains a collection of "fitting" functions.
% (Some has demo options - the third section)
% The GENERAL input to the functions should be samples of the distribution.
%
% for example, if we are to fit a normal distribution ('gaussian') with a mean "u" and varaince "sig"^2
% then the samples will distribute like:
% samples = randn(1,10000)*sig + u
%
%fitting with Least-Squares is done on the histogram of the samples.
% fitting with Maximum likelihood is done directly on the samples.
%
%
% Contents of this folder
% =======================
% 1) Maximum likelihood estimators
% 2) Least squares estimators
% 3) EM algorithm for estimation of multivariant gaussian distribution (mixed gaussians)
% 4) added folders: Create - which create samples for the EM algorithm test
% Plot - used to plot each of the distributions (parametric plot)
%
%
%
%
%
% Maximum likelihood estimators
% =============================
% fit_ML_maxwell - fit maxwellian distribution
% fit_ML_rayleigh - fit rayleigh distribution
% (which is for example: sqrt(abs(randn)^2+abs(randn)^2))
% fit_ML_laplace - fit laplace distribution
% fit_ML_log_normal- fit log-normal distribution
% fit_ML_normal - fit normal (gaussian) distribution
%
% NOTE: all estimators are efficient estimators. for this reason, the distribution
% might be written in a different way, for example, the "Rayleigh" distribution
% is given with a parameter "s" and not "s^2".
%
%
% least squares estimators
% =========================
% fit_maxwell_pdf - fits a given curve of a maxwellian distribution
% fit_rayleigh_pdf - fits a given curve of a rayleigh distribution
%
% NOTE: these fit function are used on a histogram output which is like a sampled
% distribution function. the given curve MUST be normalized, since the estimator
% is trying to fit a normalized distribution function.
%
%
%
%
% Multivariant Gaussian distribution
% ==================================
% for demo of 1D mixed-gaussian fitting, run: fit_mix_gaussian
% for demo of 2D mixed-gaussian fitting, run: fit_mix_2d_gaussian
%
% these routines fit and plot the results of the parameters of:
% random distribution of random amount of gaussians with random parameters
%
%
近期下载者：
相关文件：
收藏者：