Documentation of fitdistc

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Function Synopsis

fitdistc(OBJ_F, VLUB, FitDistOpt, Dummy, P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13,P14,P15)

Help text

 FITness DISTance Correlation computation

 This function computes the fitness-distance correlation
 of a given objective function or using saved data.
 A fitness distance scatter plot is generated.

 Syntax:  fitdistc(OBJ_F, VLUB, FitDistOpt, Dummy, P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13,P14,P15)

 Input parameters:
    OBJ_F     - String containing the name of the objective function
    VLUB      - (optional) Matrix containing boundaries of variables
                of objective function
                if VLUB is empty, feval(OBJ_F, [],1) is used to
                determine the boundaries to use
                if OBJ_F is empty, VLUB contains the full name of
                a text data file containing objective values (first 
                column) and variables of corresponding individuals
                (second - last column), each row corresponds to
                objective value and variables of one individual
    FitDistOpt- (optional) Vektor containing options of fitness
                distance calculation
                FitDistOpt(1): Number of variables (dimensions) to use
                               if omitted, number of variables is
                               determined from size of VLUB
                FitDistOpt(2): 0: Use points generated by an evenly
                                  spaced grid
                               1: Use randomly generated points
                               if number of variables (dimensions) is
                               greater than 6, randomly created points
                               are used
                FitDistOpt(3): Maximal number of points to use
    Dummy     - not used at the moment
    P1-P15    - Additional parameters directly forwarded to the objective
                function

 Output parameter:
    no output parameter

 Examples:
 % Compute fdc of objfun1 using default parameters
 >> fitdistc('objfun1')

 % Compute fdc of objfun1 in 5 dimensions, use 10000 evenly spaced points
 >> fitdistc('objfun1', [], [5, 0, 10000])

 See also: geamain, resplot, compdiv

Cross-Reference Information

Listing of function fitdistc

%  Author:  Hartmut Pohlheim
%  History: 26.03.97    file created


function  fitdistc(OBJ_F, VLUB, FitDistOpt, Dummy, P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13,P14,P15)

   % Set standard input parameters
   FitDistOptStandard = [10, 1, 2000];      % FitDistNvar = 10, PointsRand = 1, NPointsAll = 16

   % Check input parameters
   if nargin < 1, help fitdistc; end
   if isempty(OBJ_F), READFILE = 1; else READFILE = 0; end
   
   if nargin < 2, VLUB = []; end
   if nargin < 3, FitDistOpt = []; end
   if isnan(FitDistOpt), FitDistOpt = []; end
   if length(FitDistOpt) > length(FitDistOptStandard), error(' Too many parameter in FitDistOpt'); end

   if READFILE == 0,
      if isempty(VLUB), VLUB = feval(OBJ_F, [], 1); 
      elseif size(VLUB, 1) ~= 2, error('Size of boundaries matrix is wrong (should  be two rows) !'); end
      
      FitDistOptStandard(1) = size(VLUB, 2);
      FitDistOptIntern = FitDistOptStandard; FitDistOptIntern(1:length(FitDistOpt)) = FitDistOpt;
      FitDistNvar = FitDistOptIntern(1);
      PointsRand  = FitDistOptIntern(2);
      NPointsAll  = FitDistOptIntern(3);

      if isnan(FitDistNvar), FitDistNvar = FitDistOptStandard(1);
      elseif FitDistNvar < 1, error('Parameter for number of variables must be greater 0 !'); end

      if isnan(PointsRand), PointsRand = FitDistOptStandard(2);
      elseif ~any(PointsRand == [0, 1]), error('Parameter for PointsRand must be 0 or 1 !'); end

      if isnan(NPointsAll), NPointsAll = FitDistOptStandard(3);
      elseif NPointsAll <= 10, error('Use more points for fitness-distance calculation !'); end

      % Set VLUB to correct size
      SelIx = rem([1:FitDistNvar]-1, size(VLUB,2))+1;
      VLUB = VLUB(:, SelIx);

   elseif READFILE == 1,
      if isempty(VLUB), error('Name of file with data is needed (parameter 2) !'); end
      FileName = VLUB;
      [Head, Data] = hdrload(FileName);
      ObjV = Data(:,1);
      Chrom = Data(:,2:size(Data,2));
      Nvar = size(Chrom,2);
   end
   

   % Read data from file
   if READFILE == 0,
      Nvar = size(VLUB,2);
      if Nvar > 6, PointsRand = 1; end
      if PointsRand == 0,
         % Create matrix with rastered test cases (values of variables)
         % each row contains the values of one test case
         NPointsGrid = max(3, floor(NPointsAll.^(1/Nvar)));
         PointsGrid = linspace(VLUB(1,1), VLUB(2,1), NPointsGrid);
         for irun = 1:Nvar-1,
            PointsGrid2 = expandm(PointsGrid, [1, NPointsGrid]);
            PointsDim = linspace(VLUB(1,irun+1), VLUB(2,irun+1), NPointsGrid);
            PointsGrid = [PointsGrid2; repmat(PointsDim, [1, NPointsGrid.^irun])];
         end
         PointsAll = PointsGrid';
         Noise = (VLUB(2,:)-VLUB(1,:))./NPointsGrid./5;
         Noise = repmat(Noise, [size(PointsAll,1),1]) .* rand(size(PointsAll));
         PointsAll = PointsAll + Noise;

      elseif PointsRand == 1,
         % Create matrix with randomly distributed test cases
         % each row contains the values (of variables) of one test case
         PointsRand = rand(NPointsAll, Nvar);
         PointsRand = PointsRand .* repmat(VLUB(2,:)-VLUB(1,:), [NPointsAll, 1]);
         PointsRand = PointsRand + repmat(VLUB(1,:), [NPointsAll, 1]);
         PointsAll = PointsRand;
      end
      
      % Build string of objective function
      genevalstr = ['ObjV = ' OBJ_F '(Chrom'];
      if ~any(OBJ_F < 48),
         % Add additional parameters
         for i = 1:nargin-4, genevalstr = [genevalstr, ',P', int2str(i)]; end
         genevalstr = [genevalstr, ');'];
      end

      % Compute objective values
      Chrom = PointsAll; eval(genevalstr);
      
      % Save data to text file
      Data = [ObjV Chrom];
      % save tefidi_1.txt Data -ascii -tabs

   end

   % Calculate best individual
      [BestObjV, BestChromPos] = min(ObjV(:,1));
      BestChrom = Chrom(BestChromPos, :);
   
   % fitness-distance calculation
      [fdccoeff, Fitnesses, Distances] = compdiv('fdc', ObjV(:,1), Chrom, BestChrom);

   % look for figure, set name, paperposition, position and all standard settings
      UserDataString = 'geatbx_fitdist_plot';
      figfitdist = findobj('UserData', UserDataString);
      if isempty(figfitdist), figfitdist = figure('UserData', UserDataString, 'NumberTitle', 'Off'); end
      if isempty(OBJ_F), Name = VLUB; else Name = feval(OBJ_F, [], 2); end
      FigName = sprintf('FitDist: %s %s (Points=%d)', Name, OBJ_F, size(Chrom,1));
      set(figfitdist, 'Name', FigName);

      % Set PaperPosition for saving of figure
      PaperPos = [0.5, 2.5, 8, 7];
      set(figfitdist, 'PaperPosition', PaperPos);
      % Set all standard settings defined in plotstd   
      plotstd(figfitdist);
      figure(figfitdist);

   % Plot of fitness distance scatter plot
      plot(Distances, Fitnesses, '.');
      DiffDistances = 0.05 * (max(Distances) - min(Distances))*(1-eps);
      DiffFitnesses = 0.05 * (max(Fitnesses) - min(Fitnesses))*(1-eps);
      set(gca, 'XLim', [min(Distances)-DiffDistances, max(Distances)+DiffDistances]);
      set(gca, 'YLim', [min(Fitnesses)-DiffFitnesses, max(Fitnesses)+DiffFitnesses]);
      Lan = 'g';
      TextAdd = sprintf('%s: Dim = %d', Name, size(Chrom,2));
      TitleAdd = sprintf(': %.4g (%s)', fdccoeff, TextAdd);
      if Lan == 'g', title(sprintf('Fitneß-Distanz-Korrelation%s', TitleAdd));
      else           title(sprintf('Fitness Distance Correlation%s', TitleAdd)); end
      if Lan == 'g', xlabel('Distanz zum besten Individuum');  ylabel('Zielfunktionswert der Individuen (Fitneß)');
      else           xlabel('distance to best individual'); ylabel('objective value of individuals (fitness)'); end

      drawnow;


% End of function