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附件中的m代码包括了一系列的拟合函数

% This folder contains a collection of "fitting" functions. <br>
% (Some has demo options - the third section)<br>
% The GENERAL input to the functions should be samples of the distribution.<br>
% <br>
% for example, if we are to fit a normal distribution ('gaussian') with a mean "u" and varaince "sig"^2<br>
% then the samples will distribute like:  <br>
%      samples = randn(1,10000)*sig + u<br>
% <br>
%fitting with Least-Squares is done on the histogram of the samples.<br>
% fitting with Maximum likelihood is done directly on the samples.<br>
%<br>
%<br>
% Contents of this folder<br>
% =======================<br>
% 1) Maximum likelihood estimators<br>
% 2) Least squares estimators<br>
% 3) EM algorithm for estimation of multivariant gaussian distribution (mixed gaussians)<br>
% 4) added folders: Create - which create samples for the EM algorithm test<br>
%                   Plot   - used to plot each of the distributions (parametric plot)<br>
%<br>
%<br>
%<br>
%<br>
%<br>
% Maximum likelihood estimators<br>
% =============================<br>
% fit_ML_maxwell   - fit maxwellian distribution<br>
% fit_ML_rayleigh  - fit rayleigh distribution<br>
%                      (which is for example: sqrt(abs(randn)^2+abs(randn)^2))<br>
% fit_ML_laplace   - fit laplace distribution<br>
% fit_ML_log_normal- fit log-normal distribution<br>
% fit_ML_normal    - fit normal (gaussian) distribution<br>
%<br>
% NOTE: all estimators are efficient estimators. for this reason, the distribution<br>
%       might be written in a different way, for example, the "Rayleigh" distribution<br>
%       is given with a parameter "s" and not "s^2".<br>
%<br>
%<br>
% least squares estimators<br>
% =========================<br>
% fit_maxwell_pdf  - fits a given curve of a maxwellian distribution<br>
% fit_rayleigh_pdf - fits a given curve of a rayleigh distribution<br>
%<br>
% NOTE: these fit function are used on a histogram output which is like a sampled <br>
%       distribution function. the given curve MUST be normalized, since the estimator<br>
%       is trying to fit a normalized distribution function.<br>
%<br>
%<br>
%<br>
%<br>
% Multivariant Gaussian distribution<br>
% ==================================<br>
% for demo of 1D mixed-gaussian fitting, run:  fit_mix_gaussian<br>
% for demo of 2D mixed-gaussian fitting, run:  fit_mix_2d_gaussian<br>
%<br>
% these routines fit and plot the results of the parameters of: <br>
% random distribution of random amount of gaussians with random parameters <br>
%  <br>
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            <td valign="top" width="25%"><b>File Id:</b></td>
            <td width="20%">4222</td>
            <td valign="top" width="25%"><b>Average rating:</b></td>
            <td width="29%">4.47</td>
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            <td valign="top"><b>Size:</b></td>
            <td>22 KB</td>
            <td valign="top"><b># of reviews:</b></td>
            <td>17</td>
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            <td valign="top">&nbsp;</td>
            <td valign="top"><b>Submitted:</b></td>
            <td>2003-12-05</td>
            <td valign="top"><b>Downloads:</b></td>
            <td>18419</td>
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            <td valign="top"><b>Subscribers:</b></td>
            <td>12</td>
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            <td>&nbsp;</td>
            <td valign="top"><b>Keywords:</b></td>
            <td colspan="3" valign="top">fit normal maxwell rayleigh log-normal laplace mixed-gaussian</td>
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                <td>&nbsp;</td>
                <td valign="top"><b>Zip file contents:</b></td>
                <td colspan="3" valign="top">build_mix_2D_gaussian.m,
build_mix_gaussian.m, fit_maxwell_pdf.m, fit_mix_2D_gaussian.m,
fit_mix_gaussian.m, fit_ML_laplace.m, fit_ML_log_normal.m,
fit_ML_maxwell.m, fit_ML_normal.m, fit_ML_rayleigh.m,
fit_rayleigh_pdf.m, plot_laplace.m, plot_log_normal.m, plot_maxwell.m,
plot_mix_gaussian.m, plot_normal.m, plot_rayleigh.m, readme.m</td>
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    <!--Acknowledgements -->
    
     <!--Acknowledgements -->

      









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        <td bgcolor="#dcdfe4"><b>Date</b>: 2007-12-23<br>
          <strong>From</strong>: mahdi&nbsp;poorakbari (mahdi.control) <br>
          <strong>Rating</strong>:
          
              <img src="loadFile.do_files/fullstar.gif" alt="" width="12" height="12" hspace="1"><img src="loadFile.do_files/fullstar.gif" alt="" width="12" height="12" hspace="1"><img src="loadFile.do_files/fullstar.gif" alt="" width="12" height="12" hspace="1"><img src="loadFile.do_files/fullstar.gif" alt="" width="12" height="12" hspace="1"><img src="loadFile.do_files/fullstar_grey.gif" alt="" width="12" height="12" hspace="1">
	  
          <br>
          
                <strong>Comments</strong>:   
          
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        <td width="9" bgcolor="#dcdfe4"></td>
        <td bgcolor="#dcdfe4"><b>Date</b>: 2007-12-13<br>
          <strong>From</strong>: Thomas&nbsp;Clark ([e-mail]) <br>
          <strong>Rating</strong>:
          
          N/A
          
          <br>
          
                <strong>Comments</strong>: This submission requires the statistics toolbox, in order to use the 2D gaussian fit (line 32, build_mix_2D_gaussian.m)  
          
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        <td bgcolor="#dcdfe4"><b>Date</b>: 2007-11-27<br>
          <strong>From</strong>: Todd&nbsp;Arnold  <br>
          <strong>Rating</strong>:
          
              <img src="loadFile.do_files/fullstar.gif" alt="" width="12" height="12" hspace="1"><img src="loadFile.do_files/fullstar.gif" alt="" width="12" height="12" hspace="1"><img src="loadFile.do_files/fullstar.gif" alt="" width="12" height="12" hspace="1"><img src="loadFile.do_files/fullstar.gif" alt="" width="12" height="12" hspace="1"><img src="loadFile.do_files/fullstar.gif" alt="" width="12" height="12" hspace="1">
	  
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                <strong>Comments</strong>: The provided files are an excellent source for EM based Matlab work.  
          
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