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, 65 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/mlpfwd.m b/sourcecodes/bnt-master/netlab3.3/mlpfwd.m
new file mode 100644
index 00000000..d470ac96
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+++ b/sourcecodes/bnt-master/netlab3.3/mlpfwd.m
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+function [y, z, a] = mlpfwd(net, x)
+%MLPFWD	Forward propagation through 2-layer network.
+%
+%	Description
+%	Y = MLPFWD(NET, X) takes a network data structure NET together with a
+%	matrix X of input vectors, and forward propagates the inputs through
+%	the network to generate a matrix Y of output vectors. Each row of X
+%	corresponds to one input vector and each row of Y corresponds to one
+%	output vector.
+%
+%	[Y, Z] = MLPFWD(NET, X) also generates a matrix Z of the hidden unit
+%	activations where each row corresponds to one pattern.
+%
+%	[Y, Z, A] = MLPFWD(NET, X) also returns a matrix A  giving the summed
+%	inputs to each output unit, where each row corresponds to one
+%	pattern.
+%
+%	See also
+%	MLP, MLPPAK, MLPUNPAK, MLPERR, MLPBKP, MLPGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+errstring = consist(net, 'mlp', x);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+ndata = size(x, 1);
+
+z = tanh(x*net.w1 + ones(ndata, 1)*net.b1);
+a = z*net.w2 + ones(ndata, 1)*net.b2;
+
+switch net.outfn
+
+  case 'linear'    % Linear outputs
+
+    y = a;
+
+  case 'logistic'  % Logistic outputs
+    % Prevent overflow and underflow: use same bounds as mlperr
+    % 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));
+
+  case 'softmax'   % Softmax outputs
+  
+    % Prevent overflow and underflow: use same bounds as glmerr
+    % Ensure that sum(exp(a), 2) does not overflow
+    maxcut = log(realmax) - log(net.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, net.nout));
+
+  otherwise
+    error(['Unknown activation function ', net.outfn]);  
+end