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, 62 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/mlperr.m b/sourcecodes/bnt-master/netlab3.3/mlperr.m
new file mode 100644
index 00000000..7e3efe47
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+++ b/sourcecodes/bnt-master/netlab3.3/mlperr.m
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+function [e, edata, eprior] = mlperr(net, x, t)
+%MLPERR	Evaluate error function for 2-layer network.
+%
+%	Description
+%	E = MLPERR(NET, X, T) takes a network data structure NET together
+%	with a matrix X of input vectors and a matrix T of target vectors,
+%	and evaluates the error function E. The choice of error function
+%	corresponds to the output unit activation function. Each row of X
+%	corresponds to one input vector and each row of T corresponds to one
+%	target vector.
+%
+%	[E, EDATA, EPRIOR] = MLPERR(NET, X, T) additionally returns the data
+%	and prior components of the error, assuming a zero mean Gaussian
+%	prior on the weights with inverse variance parameters ALPHA and BETA
+%	taken from the network data structure NET.
+%
+%	See also
+%	MLP, MLPPAK, MLPUNPAK, MLPFWD, MLPBKP, MLPGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+errstring = consist(net, 'mlp', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+[y, z, a] = mlpfwd(net, x);
+
+switch net.outfn
+
+  case 'linear'        % Linear outputs
+    edata = 0.5*sum(sum((y - t).^2));
+
+  case 'logistic'      % Logistic outputs
+    % Ensure that log(1-y) is computable: need exp(a) > eps
+    maxcut = -log(eps);
+    % Ensure that log(y) is computable
+    mincut = -log(1/realmin - 1);
+    a = min(a, maxcut);
+    a = max(a, mincut);
+    y = 1./(1 + exp(-a));
+    edata = - sum(sum(t.*log(y) + (1 - t).*log(1 - y)));
+
+  case 'softmax'       % Softmax outputs
+    nout = size(a,2);
+    % Ensure that sum(exp(a), 2) does not overflow
+    maxcut = log(realmax) - log(nout);
+    % Ensure that exp(a) > 0
+    mincut = log(realmin);
+    a = min(a, maxcut);
+    a = max(a, mincut);
+    temp = exp(a);
+    y = temp./(sum(temp, 2)*ones(1,nout));
+    % Ensure that log(y) is computable
+    y(y<realmin) = realmin;
+    edata = - sum(sum(t.*log(y)));
+
+  otherwise
+    error(['Unknown activation function ', net.outfn]);  
+end
+[e, edata, eprior] = errbayes(net, edata);