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

1 files changed, 66 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/mdngrad.m b/sourcecodes/bnt-master/netlab3.3/mdngrad.m
new file mode 100644
index 00000000..6a0b6ea8
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+++ b/sourcecodes/bnt-master/netlab3.3/mdngrad.m
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+function g = mdngrad(net, x, t)
+%MDNGRAD Evaluate gradient of error function for Mixture Density Network.
+%
+%	Description
+%	 G = MDNGRAD(NET, X, T) takes a mixture density network data
+%	structure NET, a matrix X of input vectors and a matrix T of target
+%	vectors, and evaluates the gradient G of the error function with
+%	respect to the network weights. The error function is negative log
+%	likelihood of the target data.  Each row of X corresponds to one
+%	input vector and each row of T corresponds to one target vector.
+%
+%	See also
+%	MDN, MDNFWD, MDNERR, MDNPROB, MLPBKP
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+%	David J Evans (1998)
+
+% Check arguments for consistency
+errstring = consist(net, 'mdn', x, t);
+if ~isempty(errstring)
+  error(errstring);
+end
+
+[mixparams, y, z] = mdnfwd(net, x);
+
+% Compute gradients at MLP outputs: put the answer in deltas
+ncentres = net.mdnmixes.ncentres;
+dim_target = net.mdnmixes.dim_target;
+nmixparams = net.mdnmixes.nparams;
+ntarget = size(t, 1);
+deltas = zeros(ntarget, net.mlp.nout);
+e = ones(ncentres, 1);
+f = ones(1, dim_target);
+
+post = mdnpost(mixparams, t);
+
+% Calculate prior derivatives
+deltas(:,1:ncentres)  = mixparams.mixcoeffs - post;
+
+% Calculate centre derivatives
+long_t = kron(ones(1, ncentres), t);
+centre_err = mixparams.centres - long_t;
+
+% Get the post to match each u_jk:
+% this array will be (ntarget, (ncentres*dim_target))
+long_post = kron(ones(dim_target, 1), post);
+long_post = reshape(long_post, ntarget, (ncentres*dim_target));
+
+% Get the variance to match each u_jk:
+var = mixparams.covars;
+var = kron(ones(dim_target, 1), var);
+var = reshape(var, ntarget, (ncentres*dim_target));
+
+% Compute centre deltas
+deltas(:, (ncentres+1):(ncentres*(1+dim_target))) = ...
+                       (centre_err.*long_post)./var;
+
+% Compute variance deltas
+dist2             = mdndist2(mixparams, t);
+c                 = dim_target*ones(ntarget, ncentres);
+deltas(:, (ncentres*(1+dim_target)+1):nmixparams) = ...
+                      post.*((dist2./mixparams.covars)-c)./(-2);
+
+% Now back-propagate deltas through MLP
+g = mlpbkp(net.mlp, x, z, deltas);