FittFunc
所属分类：matlab编程
开发工具：matlab
文件大小：21KB
下载次数：758
上传日期：2008-07-05 21:31:21
上 传 者：
ANN_LCP
说明： 多种概率分布的拟合函数集合
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！
(Fitting a wide range of probability distribution functions including the collection of this algorithm maximum likelihood estimation, least squares estimation, based on the EM algorithm estimate a mixture Gaussian distribution, EM algorithm for the test examples, each mapping the distribution of the plot function. Have reference value is!)
文件列表:
FitFunc\Create\build_mix_2D_gaussian.m (1627, 2003-11-26)
FitFunc\Create\build_mix_gaussian.m (1068, 2003-11-24)
FitFunc\fit_maxwell_pdf.m (4798, 2003-11-16)
FitFunc\fit_mix_2D_gaussian.m (4276, 2003-11-26)
FitFunc\fit_mix_gaussian.m (2842, 2003-11-30)
FitFunc\fit_ML_laplace.m (3194, 2003-11-17)
FitFunc\fit_ML_log_normal.m (3430, 2003-11-17)
FitFunc\fit_ML_maxwell.m (3017, 2003-11-16)
FitFunc\fit_ML_normal.m (3606, 2004-04-28)
FitFunc\fit_ML_rayleigh.m (2867, 2003-11-16)
FitFunc\fit_rayleigh_pdf.m (4436, 2003-11-16)
FitFunc\Plot\plot_laplace.m (2088, 2003-11-17)
FitFunc\Plot\plot_log_normal.m (2257, 2003-11-17)
FitFunc\Plot\plot_maxwell.m (2105, 2003-11-16)
FitFunc\Plot\plot_mix_gaussian.m (4525, 2003-11-26)
FitFunc\Plot\plot_normal.m (2333, 2004-04-28)
FitFunc\Plot\plot_rayleigh.m (2032, 2003-11-16)
FitFunc\Create (0, 2008-07-05)
FitFunc\Plot (0, 2008-07-05)
FitFunc (0, 2008-07-05)
%
% 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
%
%
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