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, 92 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/mlphint.m b/sourcecodes/bnt-master/netlab3.3/mlphint.m
new file mode 100644
index 00000000..3546dfd3
--- /dev/null
+++ b/sourcecodes/bnt-master/netlab3.3/mlphint.m
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+function [h1, h2] = mlphint(net);
+%MLPHINT Plot Hinton diagram for 2-layer feed-forward network.
+%
+%	Description
+%
+%	MLPHINT(NET) takes a network structure NET and plots the Hinton
+%	diagram comprised of two figure windows, one displaying the first-
+%	layer weights and biases, and one displaying the second-layer weights
+%	and biases.
+%
+%	[H1, H2] = MLPHINT(NET) also returns handles H1 and  H2 to the
+%	figures which can be used, for instance, to delete the  figures when
+%	they are no longer needed.
+%
+%	To print the figure correctly, you should call SET(H,
+%	'INVERTHARDCOPY', 'ON') before printing.
+%
+%	See also
+%	DEMHINT, HINTMAT, MLP, MLPPAK, MLPUNPAK
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Set scale to be up to 0.9 of maximum absolute weight value, where scale
+% defined so that area of box proportional to weight value.
+
+% Use no more than 640x480 pixels
+xmax = 640; ymax = 480;
+
+% Offset bottom left hand corner
+x01 = 40; y01 = 40;
+x02 = 80; y02 = 80;
+
+% Need to allow 5 pixels border for window frame: but 30 at top
+border = 5;
+top_border = 30;
+
+ymax = ymax - top_border;
+xmax = xmax - border;
+
+% First layer
+
+wb1 = [net.w1; net.b1];
+[xvals, yvals, color] = hintmat(wb1');
+% Try to preserve aspect ratio approximately
+if (8*net.nhidden < 6*(net.nin + 1))
+  delx = xmax; dely = xmax*net.nhidden/(net.nin + 1);
+else
+  delx = ymax*(net.nin + 1)/net.nhidden; dely = ymax;
+end
+
+h1 = figure('Color', [0.5 0.5 0.5], ...
+  'Name', 'Hinton diagram: first-layer weights and biases', ...
+  'NumberTitle', 'off', ...
+  'Colormap', [0 0 0; 1 1 1], ...
+  'Units', 'pixels', ...
+  'Position', [x01 y01 delx dely]);
+set(gca, 'Visible', 'off', 'Position', [0 0 1 1]);
+hold on
+
+cmap = [0 0 0; 1 1 1];
+colors(1, :, :) = cmap(color, :);
+patch(xvals', yvals', colors, 'Edgecolor', 'none');
+axis equal;
+xpos = net.nin;
+line([xpos xpos], [0 net.nhidden], 'color', 'red', 'linewidth', 3);
+
+% Second layer
+
+wb2 = [net.w2; net.b2];
+[xvals, yvals, color] = hintmat(wb2');
+if (8*net.nout < 6*(net.nhidden + 1))
+  delx = xmax; dely = xmax*net.nout/(net.nhidden + 1);
+else
+  delx = ymax*(net.nhidden + 1)/net.nout; dely = ymax;
+end
+
+h2 = figure('Color', [0.5 0.5 0.5], ...
+  'Name', 'Hinton diagram: second-layer weights and biases', ...
+  'NumberTitle', 'off', ...
+  'Colormap', [0 0 0; 1 1 1], ...  
+  'Units', 'pixels', ...
+  'Position', [x02 y02 delx dely]);
+set(gca, 'Visible', 'off', 'Position', [0 0 1 1]);
+
+hold on
+colors2(1, :, :) = cmap(color, :);
+patch(xvals', yvals', colors2, 'Edgecolor', 'none');
+axis equal;
+xpos = net.nhidden;
+line([xpos xpos], [0 net.nout], 'color', 'red', 'linewidth', 3);
+