function out1 = plotroc(varargin)%PLOTROC Plot receiver operating characteristic.%% plotroc(targets,outputs) takes target data in 1-of-N form (each column% vector is all zeros with a single 1 indicating the class number), and% output data and generates a receiver operating characteristic plot.%% The best classifications will show the receiver operating line hugging% the left and top sides of the plots axis.%% plotroc(targets,1,outputs1,'name1',targets2,outputs2,names2,...)% generates a variable number of confusion plots in one figure.%% Here a pattern recognition network is trained and its accuracy plotted:%%   [x,t] = simpleclass_dataset;%   net = patternnet(10);%   net = train(net,x,t);%   y = net(x);%   plotroc(t,y);%% See also roc, plotconfusion, ploterrhist, plotregression.% Copyright 2010-2012 The MathWorks, Inc.%% =======================================================%  BOILERPLATE_START%  This code is the same for all Transfer Functions.  persistent INFO;  if isempty(INFO), INFO = get_info; end  if nargin == 0    fig = nnplots.find_training_plot(mfilename);    if nargout > 0      out1 = fig;    elseif ~isempty(fig)      figure(fig);    end    return;  end  in1 = varargin{1};  if ischar(in1)    switch in1      case 'info',        out1 = INFO;      case 'suitable'        [args,param] = nnparam.extract_param(varargin,INFO.defaultParam);        [net,tr,signals] = deal(args{2:end});        update_args = standard_args(net,tr,signals);        unsuitable = unsuitable_to_plot(param,update_args{:});        if nargout > 0          out1 = unsuitable;        elseif ~isempty(unsuitable)          for i=1:length(unsuitable)            disp(unsuitable{i});          end        end      case 'training_suitable'        [net,tr,signals,param] = deal(varargin{2:end});        update_args = training_args(net,tr,signals,param);        unsuitable = unsuitable_to_plot(param,update_args{:});        if nargout > 0          out1 = unsuitable;        elseif ~isempty(unsuitable)          for i=1:length(unsuitable)            disp(unsuitable{i});          end        end      case 'training'        [net,tr,signals,param] = deal(varargin{2:end});        update_args = training_args(net,tr,signals);        fig = nnplots.find_training_plot(mfilename);        if isempty(fig)          fig = figure('visible','off','tag',['TRAINING_' upper(mfilename)]);          plotData = setup_figure(fig,INFO,true);        else          plotData = get(fig,'userdata');        end        set_busy(fig);        unsuitable = unsuitable_to_plot(param,update_args{:});        if isempty(unsuitable)          set(0,'CurrentFigure',fig);          plotData = update_plot(param,fig,plotData,update_args{:});          update_training_title(fig,INFO,tr)          nnplots.enable_plot(plotData);        else          nnplots.disable_plot(plotData,unsuitable);        end        fig = unset_busy(fig,plotData);        if nargout > 0, out1 = fig; end      case 'close_request'        fig = nnplots.find_training_plot(mfilename);        if ~isempty(fig),close_request(fig); end      case 'check_param'        out1 = ''; % TODO      otherwise,        try          out1 = eval(['INFO.' in1]);        catch me, nnerr.throw(['Unrecognized first argument: ''' in1 ''''])        end    end  else    [args,param] = nnparam.extract_param(varargin,INFO.defaultParam);    update_args = standard_args(args{:});    if ischar(update_args)      nnerr.throw(update_args);    end    [plotData,fig] = setup_figure([],INFO,false);    unsuitable = unsuitable_to_plot(param,update_args{:});    if isempty(unsuitable)      plotData = update_plot(param,fig,plotData,update_args{:});      nnplots.enable_plot(plotData);    else      nnplots.disable_plot(plotData,unsuitable);    end    set(fig,'visible','on');    drawnow;    if nargout > 0, out1 = fig; end  endendfunction set_busy(fig)  set(fig,'userdata','BUSY');endfunction close_request(fig)  ud = get(fig,'userdata');  if ischar(ud)    set(fig,'userdata','CLOSE');  else    delete(fig);  end  drawnow;endfunction fig = unset_busy(fig,plotData)  ud = get(fig,'userdata');  if ischar(ud) && strcmp(ud,'CLOSE')    delete(fig);    fig = [];  else    set(fig,'userdata',plotData);  end  drawnow;endfunction tag = new_tag  tagnum = 1;  while true    tag = [upper(mfilename) num2str(tagnum)];    fig = nnplots.find_plot(tag);    if isempty(fig), return; end    tagnum = tagnum+1;  endendfunction [plotData,fig] = setup_figure(fig,info,isTraining)  PTFS = nnplots.title_font_size;  if isempty(fig)    fig = get(0,'CurrentFigure');    if isempty(fig) || strcmp(get(fig,'nextplot'),'new')      if isTraining        tag = ['TRAINING_' upper(mfilename)];      else        tag = new_tag;      end      fig = figure('visible','off','tag',tag);      if isTraining        set(fig,'CloseRequestFcn',[mfilename '(''close_request'')']);      end    else      clf(fig);      set(fig,'tag','');      set(fig,'tag',new_tag);    end  end  set(0,'CurrentFigure',fig);  ws = warning('off','MATLAB:Figure:SetPosition');  plotData = setup_plot(fig);  warning(ws);  if isTraining    set(fig,'nextplot','new');    update_training_title(fig,info,[]);  else    set(fig,'nextplot','replace');    set(fig,'name',[info.name ' (' mfilename ')']);  end  set(fig,'NumberTitle','off','toolbar','none');  plotData.CONTROL.text = uicontrol('parent',fig,'style','text',...    'units','normalized','position',[0 0 1 1],'fontsize',PTFS,...    'fontweight','bold','foreground',[0.7 0 0]);  set(fig,'userdata',plotData);endfunction update_training_title(fig,info,tr)  if isempty(tr)    epochs = '0';    stop = '';  else    epochs = num2str(tr.num_epochs);    if isempty(tr.stop)      stop = '';    else      stop = [', ' tr.stop];    end  end  set(fig,'name',['Neural Network Training ' ...    info.name ' (' mfilename '), Epoch ' epochs stop]);end%  BOILERPLATE_END%% =======================================================function info = get_info  info = nnfcnPlot(mfilename,'Receiver Operating Characteristic',7.0,[]);endfunction args = training_args(net,tr,data)  yall  = nncalc.y(net,data.X,data.Xi,data.Ai);  y = {yall};  t = {gmultiply(data.train.mask,data.T)};  names = {'Training'};  if ~isempty(data.val.enabled)    y = [y {yall}];    t = [t {gmultiply(data.val.mask,data.T)}];    names = [names {'Validation'}];  end  if ~isempty(data.test.enabled)    y = [y {yall}];    t = [t {gmultiply(data.test.mask,data.T)}];    names = [names {'Test'}];  end  if length(t) >= 2    t = [t {data.T}];    y = [y {yall}];    names = [names {'All'}];  end  args = {t y names};endfunction args = standard_args(varargin)  if nargin < 2    args = 'Not enough input arguments.';  elseif (nargin > 2) && (rem(nargin,3) ~= 0)    args = 'Incorrect number of input arguments.';  elseif nargin == 2    % (t,y)    t = { nntype.data('format',varargin{1}) };    y = { nntype.data('format',varargin{2}) };    names = {''};    args = {t y names};  else    % (t1,y1,name1,...)    % TODO - Check data is consistent    count = nargin/3;    t = cell(1,count);    y = cell(1,count);    names = cell(1,count);    for i=1:count      t{i} = nntype.data('format',varargin{i*3-2});      y{i} = nntype.data('format',varargin{i*3-1});      names{i} = varargin{i*3};    end    param.outputIndex = 1;    args = {t y names};  endendfunction plotData = setup_plot(fig)  plotData.numSignals = 0;endfunction fail = unsuitable_to_plot(param,t,y,names)  fail = '';  t1 = t{1};  if numsamples(t1) == 0    fail = 'The target data has no samples to plot.';  elseif numtimesteps(t1) == 0    fail = 'The target data has no timesteps to plot.';  elseif sum(numelements(t1)) == 0    fail = 'The target data has no elements to plot.';  endendfunction plotData = update_plot(param,fig,plotData,tt,yy,names)  t = tt{1};  numSignals = length(names);  numClasses = size(t{1},1);  % Rebuild figure  if (plotData.numSignals ~= numSignals) || (plotData.numClasses ~= numClasses)    set(fig,'nextplot','replace');    plotData.numSignals = numSignals;    plotData.numClasses = numClasses;    plotData.axes = zeros(1,numSignals);    colors = nncolor.ncolors(numClasses);    plotcols = ceil(sqrt(numSignals));    plotrows = ceil(numSignals/plotcols);    for plotrow=1:plotrows      for plotcol=1:plotcols        i = (plotrow-1)*plotcols+plotcol;        if (i<=numSignals)          a = subplot(plotrows,plotcols,i);          cla(a)          set(a,'dataaspectratio',[1 1 1]);          set(a,'xlim',[0 1]);          set(a,'ylim',[0 1]);          hold on          axisdata = [];          axisdata.lines = zeros(1,numClasses);          for j=1:numClasses            c = colors(j,:);            line([0 1],[0 1],'linewidth',2,'color',[1 1 1]*0.8);            axisdata.lines(j) = line([0 1],[0 1],'linewidth',2,'Color',c);          end          if ~isempty(names{1})            titleStr = [names{i} ' ROC'];          else            titleStr = 'ROC';          end          title(a,titleStr);          xlabel(a,'False Positive Rate');          ylabel(a,'True Positive Rate');          plotData.axes(i) = a;          set(a,'userdata',axisdata);          if (i==1) && (numClasses > 1)            labels = cell(1,numClasses);            for ii=1:numClasses, labels{ii} = ['Class ' num2str(ii)]; end            legend(axisdata.lines,labels{:})          end        end      end    end    screenSize = get(0,'ScreenSize');    screenSize = screenSize(3:4);    windowSize = 700 * [1 (plotrows/plotcols)];    pos = [(screenSize-windowSize)/2 windowSize];    set(fig,'position',pos);  end  % Update details  for i=1:numSignals    y = yy{i}; if iscell(y), y = cell2mat(y); end    t = tt{i}; if iscell(t), t = cell2mat(t); end    [tpr,fpr] = roc(t,y);    if ~iscell(tpr)      tpr = {tpr};      fpr = {fpr};    end    a = plotData.axes(i);    axisdata = get(a,'userdata');    for j=1:numClasses      set(axisdata.lines(j),'xdata',[0 fpr{j} 1],'ydata',[0 tpr{j} 1]);    end  endend