diff options
| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
|---|---|---|
| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/gmminit.htm | |
| parent | 7cc31810d53176e805532b2789955f4eedbce6bb (diff) | |
| download | BNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz | |
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02
Diffstat (limited to 'sourcecodes/bnt-master/nethelp3.3/gmminit.htm')
| -rw-r--r-- | sourcecodes/bnt-master/nethelp3.3/gmminit.htm | 65 |
1 files changed, 65 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/nethelp3.3/gmminit.htm b/sourcecodes/bnt-master/nethelp3.3/gmminit.htm new file mode 100644 index 00000000..3108da74 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/gmminit.htm @@ -0,0 +1,65 @@ +<html> +<head> +<title> +Netlab Reference Manual gmminit +</title> +</head> +<body> +<H1> gmminit +</H1> +<h2> +Purpose +</h2> +Initialises Gaussian mixture model from data + +<p><h2> +Synopsis +</h2> +<PRE> + +mix = gmminit(mix, x, options) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>mix = gmminit(mix, x, options)</CODE> uses a dataset <CODE>x</CODE> +to initialise the parameters of a Gaussian mixture +model defined by the data structure <CODE>mix</CODE>. The k-means algorithm +is used to determine the centres. The priors are computed from the +proportion of examples belonging to each cluster. +The covariance matrices are calculated as the sample covariance of the +points associated with (i.e. closest to) the corresponding centres. +For a mixture of PPCA model, the PPCA decomposition is calculated +for the points closest to a given centre. +This initialisation can be used as the starting point for training the +model using the EM algorithm. + +<p><h2> +Example +</h2> +<PRE> + +mix = gmm(3, 2); +options = foptions; +options(14) = 5; +mix = gmminit(mix, data, options); +</PRE> + +This code sets up a Gaussian mixture model with 3 centres in 2 dimensions, and +then initialises the parameters from the data set <CODE>data</CODE> with 5 iterations +of the k means algorithm. + +<p><h2> +See Also +</h2> +<CODE><a href="gmm.htm">gmm</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file |