function out1 = plotroc(varargin)

%PLOTROC Plot receiver operating characteristic.

%

% <a href="matlab:doc plotroc">plotroc</a>(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.

%

% <a href="matlab:doc plotroc">plotroc</a>(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] = <a href="matlab:doc simpleclass_dataset">simpleclass_dataset</a>;

%   net = <a href="matlab:doc patternnet">patternnet</a>(10);

%   net = <a href="matlab:doc train">train</a>(net,x,t);

%   y = net(x);

%   <a href="matlab:doc plotroc">plotroc</a>(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

  end

end

function set_busy(fig)

  set(fig,'userdata','BUSY');

end

function close_request(fig)

  ud = get(fig,'userdata');

  if ischar(ud)

    set(fig,'userdata','CLOSE');

  else

    delete(fig);

  end

  drawnow;

end

function 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;

end

function 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;

  end

end

function [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);

end

function 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,[]);

end

function 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};

end

function 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};

  end

end

function plotData = setup_plot(fig)

  plotData.numSignals = 0;

end

function 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.';

  end

end

function 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

  end

end