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, 362 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/netlab3.3/demtrain.m b/sourcecodes/bnt-master/netlab3.3/demtrain.m
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+function demtrain(action);
+%DEMTRAIN Demonstrate training of MLP network.
+%
+%	Description
+%	DEMTRAIN brings up a simple GUI to show the training of an MLP
+%	network on classification and regression problems.  The user should
+%	load in a dataset (which should be in Netlab format: see  DATREAD),
+%	select the output activation function, the  number of cycles and
+%	hidden units and then train the network. The scaled conjugate
+%	gradient algorithm is used. A graph shows the evolution of the error:
+%	the value is shown  MAX(CEIL(ITERATIONS / 50), 5) cycles.
+%
+%	Once the network is trained, it is saved to the file MLPTRAIN.NET.
+%	The results can then be viewed as a confusion matrix (for
+%	classification problems) or a plot of output versus target (for
+%	regression problems).
+%
+%	See also
+%	CONFMAT, DATREAD, MLP, NETOPT, SCG
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% If run without parameters, initialise gui.
+if nargin<1,
+  action='initialise';
+end;
+
+% Global variable to reference GUI figure
+global DEMTRAIN_FIG
+% Global array to reference sub-figures for results plots
+global DEMTRAIN_RES_FIGS
+global NUM_DEMTRAIN_RES_FIGS
+
+if strcmp(action,'initialise'),
+
+  file = '';
+  path = '.';
+  
+  % Create FIGURE
+  fig = figure( ...
+	'Name', 'Netlab Demo', ...
+	'NumberTitle', 'off', ...
+	'Menubar', 'none', ...
+	'Color', [0.7529 0.7529 0.7529], ...
+	'Visible', 'on');
+  % Initialise the globals
+  DEMTRAIN_FIG = fig;
+  DEMTRAIN_RES_FIGS = 0;
+  NUM_DEMTRAIN_RES_FIGS = 0;
+
+  % Create GROUP for buttons
+  uicontrol(fig, ...
+	'Style', 'frame', ...
+	'Units', 'normalized', ...
+	'Position', [0.03 0.08 0.94 0.22], ...
+	'BackgroundColor', [0.5 0.5 0.5]);
+
+  % Create MAIN axis
+  hMain = axes( ...
+	'Units', 'normalized', ...
+	'Position', [0.10 0.5 0.80 0.40], ...
+	'XColor', [0 0 0], ...
+	'YColor', [0 0 0], ...
+	'Visible', 'on');
+
+  % Create static text for FILENAME and PATH
+  hFilename = uicontrol(fig, ...
+	'Style', 'text', ...
+	'Units', 'normalized', ...
+	'BackgroundColor', [0.7529 0.7529 0.7529], ...
+	'Position', [0.05 0.32 0.90 0.05], ...
+	'HorizontalAlignment', 'center', ...
+	'String', 'Please load data file.', ...
+	'Visible', 'on');
+  hPath = uicontrol(fig, ...
+	'Style', 'text', ...
+	'Units', 'normalized', ...
+	'BackgroundColor', [0.7529 0.7529 0.7529], ...
+	'Position', [0.05 0.37 0.90 0.05], ...
+	'HorizontalAlignment', 'center', ...
+	'String', '', ...
+	'Visible', 'on');
+
+  % Create NO OF HIDDEN UNITS slider and text
+  hSliderText = uicontrol(fig, ...
+	'Style', 'text', ...
+	'BackgroundColor', [0.5 0.5 0.5], ...
+	'Units', 'normalized', ...
+	'Position', [0.27 0.12 0.17 0.04], ...
+	'HorizontalAlignment', 'right', ...
+	'String', 'Hidden Units: 5');
+  hSlider = uicontrol(fig, ...
+	'Style', 'slider', ...
+	'Units', 'normalized', ...
+	'Position', [0.45 0.12 0.26 0.04], ...
+	'String', 'Slider', ...
+	'Min', 1, 'Max', 25, ...
+	'Value', 5, ...
+	'Callback', 'demtrain slider_moved');
+
+  % Create ITERATIONS slider and text
+  hIterationsText = uicontrol(fig, ...
+	'Style', 'text', ...
+	'BackgroundColor', [0.5 0.5 0.5], ...
+	'Units', 'normalized', ...
+	'Position', [0.27 0.21 0.17 0.04], ...
+	'HorizontalAlignment', 'right', ...
+	'String', 'Iterations: 50');
+  hIterations = uicontrol(fig, ...
+	'Style', 'slider', ...
+	'Units', 'normalized', ...
+	'Position', [0.45 0.21 0.26 0.04], ...
+	'String', 'Slider', ...
+	'Min', 10, 'Max', 500, ...
+	'Value', 50, ...
+	'Callback', 'demtrain iterations_moved');
+
+  % Create ACTIVATION FUNCTION popup and text
+  uicontrol(fig, ...
+	'Style', 'text', ...
+	'BackgroundColor', [0.5 0.5 0.5], ...
+	'Units', 'normalized', ...
+	'Position', [0.05 0.20 0.20 0.04], ...
+	'HorizontalAlignment', 'center', ...
+	'String', 'Activation Function:');
+  hPopup = uicontrol(fig, ...
+	'Style', 'popup', ...
+	'Units', 'normalized', ...
+	'Position' , [0.05 0.10 0.20 0.08], ...
+	'String', 'Linear|Logistic|Softmax', ...
+	'Callback', '');
+
+  % Create MENU
+  hMenu1 = uimenu('Label', 'Load Data file...', 'Callback', '');
+  uimenu(hMenu1, 'Label', 'Select training data file', ...
+	'Callback', 'demtrain get_ip_file');
+  hMenu2 = uimenu('Label', 'Show Results...', 'Callback', '');
+  uimenu(hMenu2, 'Label', 'Show classification results', ...
+	'Callback', 'demtrain classify');
+  uimenu(hMenu2, 'Label', 'Show regression results', ...
+	'Callback', 'demtrain predict');
+  
+  % Create START button
+  hStart = uicontrol(fig, ...
+	'Units', 'normalized', ...
+	'Position' , [0.75 0.2 0.20 0.08], ...
+	'String', 'Start Training', ...
+	'Enable', 'off',...
+	'Callback', 'demtrain start');
+
+  % Create CLOSE button
+  uicontrol(fig, ...
+	'Units', 'normalized', ...
+	'Position' , [0.75 0.1 0.20 0.08], ...
+	'String', 'Close', ...
+	'Callback', 'demtrain close');
+
+  % Save handles of important UI objects
+  hndlList = [hSlider hSliderText hFilename hPath hPopup ...
+      hIterations hIterationsText hStart];
+  set(fig, 'UserData', hndlList);
+  % Hide window from command line
+  set(fig, 'HandleVisibility', 'callback');
+
+  
+elseif strcmp(action, 'slider_moved'),
+
+  % Slider has been moved. 
+  
+  hndlList = get(gcf, 'UserData');
+  hSlider = hndlList(1);
+  hSliderText = hndlList(2);
+
+  val = get(hSlider, 'Value');
+  if rem(val, 1) < 0.5,  % Force up and down arrows to work!
+	val = ceil(val);
+  else
+	val = floor(val);
+  end;
+  set(hSlider, 'Value', val);
+  set(hSliderText, 'String', ['Hidden Units: ' int2str(val)]);
+
+  
+elseif strcmp(action, 'iterations_moved'),
+
+  % Slider has been moved. 
+  
+  hndlList = get(gcf, 'UserData');
+  hSlider = hndlList(6);
+  hSliderText = hndlList(7);
+
+  val = get(hSlider, 'Value');
+  set(hSliderText, 'String', ['Iterations: ' int2str(val)]);
+
+elseif strcmp(action, 'get_ip_file'),
+
+  % Get data file button pressed.
+  
+  hndlList = get(gcf, 'UserData');
+
+  [file, path] = uigetfile('*.dat', 'Get Data File', 50, 50);
+
+  if strcmp(file, '') | file == 0,
+    set(hndlList(3), 'String', 'No data file loaded.');
+    set(hndlList(4), 'String', '');
+  else
+    set(hndlList(3), 'String', file);
+    set(hndlList(4), 'String', path);
+  end;
+  
+  % Enable training button
+  set(hndlList(8), 'Enable', 'on');
+
+  set(gcf, 'UserData', hndlList);
+  
+elseif strcmp(action, 'start'),
+
+  % Start training
+  
+  % Get handles of and values from UI objects
+  hndlList = get(gcf, 'UserData');
+  hSlider = hndlList(1); % 				No of hidden units
+  hIterations = hndlList(6); 
+  iterations = get(hIterations, 'Value');
+  
+  hFilename = hndlList(3);	% 			Data file name
+  filename = get(hFilename, 'String');
+
+  hPath = hndlList(4);	% 				Data file path
+  path = get(hPath, 'String');
+
+  hPopup = hndlList(5);		% 			Activation function
+  if get(hPopup, 'Value') == 1,
+	act_fn = 'linear';
+  elseif get(hPopup, 'Value') == 2,
+	act_fn = 'logistic';
+  else
+	act_fn = 'softmax';
+  end;
+  nhidden = get(hSlider, 'Value');
+
+  % Check data file exists
+  if fopen([path '/' filename]) == -1,
+	errordlg('Training data file has not been selected.', 'Error');
+  else
+	% Load data file
+	[x,t,nin,nout,ndata] = datread([path filename]);
+	
+	% Call MLPTRAIN function repeatedly, while drawing training graph.
+	figure(DEMTRAIN_FIG);
+	hold on;
+	
+	title('Training - please wait.');
+	
+	% Create net and find initial error
+	net = mlp(size(x, 2), nhidden, size(t, 2), act_fn);
+	% Initialise network with inverse variance of 10
+	net = mlpinit(net, 10);
+	error = mlperr(net, x, t);
+	% Work out reporting step: should be sufficiently big to let training
+	% algorithm have a chance
+	step = max(ceil(iterations / 50), 5);
+
+	% Refresh and rescale axis.
+	cla;
+	max = error;
+	min = max/10;
+	set(gca, 'YScale', 'log');
+	ylabel('log Error');
+	xlabel('No. iterations');
+	axis([0 iterations min max+1]);
+	iold = 0;
+	errold = error;
+	% Plot circle to show error of last iteration
+	% Setting erase mode to none prevents screen flashing during 
+	% training
+	plot(0, error, 'ro', 'EraseMode', 'none');
+	hold on
+	drawnow; % Force redraw
+	for i = step-1:step:iterations,
+	  [net, error] = mlptrain(net, x, t, step);
+	  % Plot line from last point to new point.
+	  line([iold i], [errold error], 'Color', 'r', 'EraseMode', 'none');
+	  iold = i;
+	  errold = error;
+	  
+	  % If new point off scale, redraw axes.
+	  if error > max,
+	    max = error;
+	    axis([0 iterations min max+1]);
+	  end;
+	  if error < min
+	    min = error/10;
+	    axis([0 iterations min max+1]);
+	  end
+	  % Plot circle to show error of last iteration
+	  plot(i, error, 'ro', 'EraseMode', 'none');
+	  drawnow; % Force redraw
+	end;
+	save mlptrain.net net
+	zoom on;
+
+	title(['Training complete. Final error=', num2str(error)]);
+	
+  end;
+
+elseif strcmp(action, 'close'),
+  
+  % Close all the figures we have created
+  close(DEMTRAIN_FIG);
+  for n = 1:NUM_DEMTRAIN_RES_FIGS
+    if ishandle(DEMTRAIN_RES_FIGS(n))
+      close(DEMTRAIN_RES_FIGS(n));
+    end
+  end
+
+elseif strcmp(action, 'classify'),
+  
+  if fopen('mlptrain.net') == -1,
+	errordlg('You have not yet trained the network.', 'Error');
+  else
+  
+	hndlList = get(gcf, 'UserData');
+	filename = get(hndlList(3), 'String');
+	path = get(hndlList(4), 'String');
+	[x,t,nin,nout,ndata] = datread([path filename]);
+	load mlptrain.net net -mat
+	y = mlpfwd(net, x);
+	
+	% Save results figure so that it can be closed later
+	NUM_DEMTRAIN_RES_FIGS = NUM_DEMTRAIN_RES_FIGS + 1;
+	DEMTRAIN_RES_FIGS(NUM_DEMTRAIN_RES_FIGS)=conffig(y,t);
+	
+  end;
+
+elseif strcmp(action, 'predict'),
+  
+  if fopen('mlptrain.net') == -1,
+	errordlg('You have not yet trained the network.', 'Error');
+  else
+  
+	hndlList = get(gcf, 'UserData');
+	filename = get(hndlList(3), 'String');
+	path = get(hndlList(4), 'String');
+	[x,t,nin,nout,ndata] = datread([path filename]);
+	load mlptrain.net net -mat
+	y = mlpfwd(net, x);
+	
+	for i = 1:size(y,2),
+	  % Save results figure so that it can be closed later
+	  NUM_DEMTRAIN_RES_FIGS = NUM_DEMTRAIN_RES_FIGS + 1;
+	  DEMTRAIN_RES_FIGS(NUM_DEMTRAIN_RES_FIGS) = figure;
+	  hold on;
+	  title(['Output no ' num2str(i)]);
+	  plot([0 1], [0 1], 'r:');
+	  plot(y(:,i),t(:,i), 'o');
+	  hold off;
+	end;
+  end;
+	
+end;
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