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| 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/netlab3.3/glminit.m | |
| 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/netlab3.3/glminit.m')
| -rw-r--r-- | sourcecodes/bnt-master/netlab3.3/glminit.m | 36 |
1 files changed, 36 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/netlab3.3/glminit.m b/sourcecodes/bnt-master/netlab3.3/glminit.m new file mode 100644 index 00000000..3dc47bc7 --- /dev/null +++ b/sourcecodes/bnt-master/netlab3.3/glminit.m @@ -0,0 +1,36 @@ +function net = glminit(net, prior) +%GLMINIT Initialise the weights in a generalized linear model. +% +% Description +% +% NET = GLMINIT(NET, PRIOR) takes a generalized linear model NET and +% sets the weights and biases by sampling from a Gaussian distribution. +% If PRIOR is a scalar, then all of the parameters (weights and biases) +% are sampled from a single isotropic Gaussian with inverse variance +% equal to PRIOR. If PRIOR is a data structure similar to that in +% MLPPRIOR but for a single layer of weights, then the parameters are +% sampled from multiple Gaussians according to their groupings (defined +% by the INDEX field) with corresponding variances (defined by the +% ALPHA field). +% +% See also +% GLM, GLMPAK, GLMUNPAK, MLPINIT, MLPPRIOR +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, 'glm'); +if ~isempty(errstring); + error(errstring); +end +if isstruct(prior) + sig = 1./sqrt(prior.index*prior.alpha); + w = sig'.*randn(1, net.nwts); +elseif size(prior) == [1 1] + w = randn(1, net.nwts).*sqrt(1/prior); +else + error('prior must be a scalar or a structure'); +end + +net = glmunpak(net, w); + |
