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1 <html> | |
2 <head> | |
3 <title> | |
4 Netlab Reference Manual demgmm4 | |
5 </title> | |
6 </head> | |
7 <body> | |
8 <H1> demgmm4 | |
9 </H1> | |
10 <h2> | |
11 Purpose | |
12 </h2> | |
13 Demonstrate density modelling with a Gaussian mixture model. | |
14 | |
15 <p><h2> | |
16 Synopsis | |
17 </h2> | |
18 <PRE> | |
19 demgmm4</PRE> | |
20 | |
21 | |
22 <p><h2> | |
23 Description | |
24 </h2> | |
25 | |
26 The problem consists of modelling data generated | |
27 by a mixture of three Gaussians in 2 dimensions with a mixture model | |
28 using full covariance matrices. The priors are 0.3, 0.5 and 0.2; the | |
29 centres are (2, 3.5), (0, 0) and (0,2); the variances are (0.16, 0.64) | |
30 axis aligned, (0.25, 1) rotated by 30 degrees and the identity | |
31 matrix. The first figure contains a scatter plot of the data. | |
32 | |
33 <p>A Gaussian mixture model with three components is trained using EM. The | |
34 parameter vector is printed before training and after training. The user | |
35 should press any key to continue at these points. The parameter vector | |
36 consists of priors (the column), and centres (given as (x, y) pairs as | |
37 the next two columns). The covariance matrices are printed separately. | |
38 | |
39 <p>The second figure is a 3 dimensional view of the density function, | |
40 while the third shows the axes of the 1-standard deviation ellipses | |
41 for the three components of the mixture model. | |
42 | |
43 <p><h2> | |
44 See Also | |
45 </h2> | |
46 <CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmminit.htm">gmminit</a></CODE>, <CODE><a href="gmmem.htm">gmmem</a></CODE>, <CODE><a href="gmmprob.htm">gmmprob</a></CODE>, <CODE><a href="gmmunpak.htm">gmmunpak</a></CODE><hr> | |
47 <b>Pages:</b> | |
48 <a href="index.htm">Index</a> | |
49 <hr> | |
50 <p>Copyright (c) Ian T Nabney (1996-9) | |
51 | |
52 | |
53 </body> | |
54 </html> |