fitting sum of gaussians into a data set (100 rows and 1 column)

2013 6 10
2 个回答
回答已采纳
5 次查看（30 天）

0 个投票

Getting difficulty in fitting and plotting a data set (100x1 matrix) by using gmdistribution.fit....i have to git a large number of sum of gaussians...but i guess it needs a matrix with mu and sigma values...can anyone help me in fitting this data into sum of gaussians and plotting it..i want a one dimensional plot... thanks

请先登录，再回答此问题。

 采纳的回答

Pourya Alinezhad
Pourya Alinezhad 2013-6-10

0 个投票

you can use the function "lsqcurvefit".first define Gaussian formula in a function mfile.then the lsqcurvefit function gives the fitted data .

3 个评论
显示 1更早的评论 隐藏 1更早的评论

Abhishek
Abhishek 2013-6-10
Thanks for you reply! Well wont it be inefficient if i use a large number of gaussians..say 50..?
Pourya Alinezhad
Pourya Alinezhad 2013-6-10
what is your understanding that "large number of gaussians"? if there is multiple qaussian functions with variable mu and sigma values you can do so for every of them and then interpolate the results to have one dimentional plot or take an average between the results.
Abhishek
Abhishek 2013-6-14
编辑：Abhishek 2013-6-14
I am using 15 gaussians. I have an one dimensional data set [101,1].its not fitting efficiently using lsqcurvefit
This is the command X = lsqcurvefit(@FUN,X0,x,y,[],[],options); where,
1)function ret= FUN(X,x) F=zeros(101,1); for i=1:15, F=F+ X(i,1)*exp((-(x-X(15+i,1))./X(30+i,1)).^2); end ret=F; return
2) X0=ones(3*15,1)/1;
3) x=[0:0.01:1]';
4) y=12*exp(-abs(x-0.00001)/.01)+8*exp(-abs(x-0.5)/.01)+12*exp(-abs(x-1)/.01); rmic=zeros(siz,20); h=[1:1:20]'; for i=1:siz;for j=1:20; rmic(i,j)=cos(2*pi*j*x(i));end;end; fore=rmic'*y;
5)options = optimset('TolX',1e-8,'Display','iter','MaxIter',1000000,'TolFun',1e-10,'MaxFunEvals',10000000,'DiffMaxChange',.1000,'LineSearchType','cubicpoly');
My data sets are : y (x): [ 11.9880 4.4190 1.6256 0.5980 0.2200 0.0809 0.0298 0.0110 0.0040 0.0015 0.0005 0.0002 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0004 0.0010 0.0027 0.0073 0.0198 0.0539 0.1465 0.3983 1.0827 2.9430 8.0000 2.9430 1.0827 0.3983 0.1465 0.0539 0.0198 0.0073 0.0027 0.0010 0.0004 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0002 0.0005 0.0015 0.0040 0.0109 0.0297 0.0809 0.2198 0.5974 1.6240 4.4146 12.0000]
and as mentioned, x=[0:0.01:1]';
I am not able to get the correct curve fit......is it the initial guess, X0.....or anything else.....please help me out...I am stuck on this..
thanks,

请先登录，再进行评论。

更多回答（1 个）

Alan Weiss
Alan Weiss 2013-6-14
编辑：Alan Weiss 2013-6-14

0 个投票

As clearly stated in the Input Arguments section of the documentation, you should not return the sum of squares, but should return a vector of values for your objective function. Making a sum of squares as you have done slows or stops convergence.
I am not sure that I understand your formulation completely, but perhaps you should check a similar sort of example in the documentation.
Alan Weiss
MATLAB mathematical toolbox documentation

1 个评论
显示 -1更早的评论 隐藏 -1更早的评论

Abhishek
Abhishek 2013-6-14
Well I am taking sum of Gaussian in the function...I mean is there any other way to use the sum of Gaussian to fit..

请先登录，再进行评论。

类别

在 帮助中心 和 File Exchange 中查找有关 Get Started with Curve Fitting Toolbox 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by