拟合函数--高斯，拉普拉斯，双高斯拟合
% 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 1
文件下载
资源详情
[{"title":"（ 19 个子文件 24KB ） 拟合函数--高斯，拉普拉斯，双高斯拟合","children":[{"title":"license.txt <span style='color:#111;'> 1.30KB </span>","children":null,"spread":false},{"title":"FitFunc","children":[{"title":"readme.m <span style='color:#111;'> 2.30KB </span>","children":null,"spread":false},{"title":"fit_ML_laplace.m <span style='color:#111;'> 3.12KB </span>","children":null,"spread":false},{"title":"fit_ML_rayleigh.m <span style='color:#111;'> 2.80KB </span>","children":null,"spread":false},{"title":"fit_ML_normal.m <span style='color:#111;'> 3.52KB </span>","children":null,"spread":false},{"title":"fit_mix_gaussian.m <span style='color:#111;'> 2.78KB </span>","children":null,"spread":false},{"title":"Plot","children":[{"title":"plot_laplace.m <span style='color:#111;'> 2.04KB </span>","children":null,"spread":false},{"title":"plot_rayleigh.m <span style='color:#111;'> 1.98KB </span>","children":null,"spread":false},{"title":"plot_log_normal.m <span style='color:#111;'> 2.20KB </span>","children":null,"spread":false},{"title":"plot_maxwell.m <span style='color:#111;'> 2.06KB </span>","children":null,"spread":false},{"title":"plot_mix_gaussian.m <span style='color:#111;'> 4.42KB </span>","children":null,"spread":false},{"title":"plot_normal.m <span style='color:#111;'> 2.28KB </span>","children":null,"spread":false}],"spread":true},{"title":"fit_ML_maxwell.m <span style='color:#111;'> 2.95KB </span>","children":null,"spread":false},{"title":"fit_maxwell_pdf.m <span style='color:#111;'> 4.69KB </span>","children":null,"spread":false},{"title":"Create","children":[{"title":"build_mix_2D_gaussian.m <span style='color:#111;'> 1.59KB </span>","children":null,"spread":false},{"title":"build_mix_gaussian.m <span style='color:#111;'> 1.04KB </span>","children":null,"spread":false}],"spread":true},{"title":"fit_rayleigh_pdf.m <span style='color:#111;'> 4.33KB </span>","children":null,"spread":false},{"title":"fit_mix_2D_gaussian.m <span style='color:#111;'> 4.18KB </span>","children":null,"spread":false},{"title":"fit_ML_log_normal.m <span style='color:#111;'> 3.35KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]
评论信息
-
-
-
lppl :用来研究还不错2016-11-24 -
-
lenciTT :很基础 就是调用matlab自带的2014-12-15
-
-
ceachother :非常基础的拟合函数，用来研究原理不错2013-12-18
-
-
taoyuan2309ah123 :用matlab自带函数的拼凑的2013-06-24
-
-
wingyoung :没什么特别的新意，用的也就是matlab自带函数的拼凑2013-05-08
-
其他资源
- afx.h头文件
- MATLAB程序设计与应用(刘卫国编)课后实验答案
- 软件开发毕业论文
- 软件在线更新/升级模块源代码 vb.net (vs2010)
- 中国河流GIS数据
- 基于PCA算法的人脸识别matlab程序
- 基于变分水平集的图像分割
- Ubuntu下VIM及相关插件的安装（附带教程）
- CST2018各模块基本教程
- 开关电源Boost(升压型斩波器)仿真电路.pdf
- java实现的棋盘算法
- hudson.model.UpdateCenter.xml
- html5调用本地摄相头拍照上传相片
- 7000sds1.2.3.zip
- FastReport 6.5.5 Enterprise 源码.7z
- 知识图谱研究进展-漆桂林.pdf
- Javascript网页特效（3）拉幕效果
- cmake-2.8.11.2
- one-wire-ts-input-src-20111026.tar.gz
- 基于 JSP+Struts2医院挂号系统
- candence14.2原理图库schlib和封装库pcblib
- SIFT算法实现图像自动配准
- LogiReg_data.txt 梯度下降 逻辑回归 成绩与录取 数据集
免责申明
【只为小站】的资源来自网友分享，仅供学习研究，请务必在下载后24小时内给予删除，不得用于其他任何用途，否则后果自负。基于互联网的特殊性，【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查；无论 【只为小站】 经营者是否已进行审查，用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明
本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明