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 figureglobal DEMTRAIN_FIG% Global array to reference sub-figures for results plotsglobal DEMTRAIN_RES_FIGSglobal NUM_DEMTRAIN_RES_FIGSif 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  endelseif 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;