【文件属性】:
文件名称:拟合函数--高斯,拉普拉斯,双高斯拟合
文件大小:24KB
文件格式:ZIP
更新时间:2014-12-27 13:59:00
mixture gaussian laplacian
% 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
【文件预览】:
license.txt
FitFunc
----readme.m(2KB)
----fit_ML_laplace.m(3KB)
----fit_ML_rayleigh.m(3KB)
----fit_ML_normal.m(4KB)
----fit_mix_gaussian.m(3KB)
----Plot()
--------plot_laplace.m(2KB)
--------plot_rayleigh.m(2KB)
--------plot_log_normal.m(2KB)
--------plot_maxwell.m(2KB)
--------plot_mix_gaussian.m(4KB)
--------plot_normal.m(2KB)
----fit_ML_maxwell.m(3KB)
----fit_maxwell_pdf.m(5KB)
----Create()
--------build_mix_2D_gaussian.m(2KB)
--------build_mix_gaussian.m(1KB)
----fit_rayleigh_pdf.m(4KB)
----fit_mix_2D_gaussian.m(4KB)
----fit_ML_log_normal.m(3KB)
网友评论
- 用来研究还不错
- 很基础 就是调用matlab自带的
- 非常基础的拟合函数,用来研究原理不错
- 用matlab自带函数的拼凑的
- 没什么特别的新意,用的也就是matlab自带函数的拼凑
- 不错 很详细