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    Metadata details:
    filename: daisy.m
    path of file: ./repos/bspm/thirdparty/corr_toolbox_v2/LIBRA
    
    the code of the file until where you have to start completion: function result = daisy(x,vtype,metric)

%DAISY returns a row vector containing all the pairwise dissimilarities
% (distances) between observations in the dataset. The original
% variables may be of mixed types.
%
% The calculation of dissimilarities is explained in:
%   Kaufman, L. and Rousseeuw, P.J. (1990),
%   "Finding groups in data: An introduction to cluster analysis",
%   Wiley-Interscience: New York (Series in Applied Probability and
%   Statistics), ISBN 0-471-87876-6.
%
% Required input arguments:
%       x : Data matrix (rows = observations, columns = variables)
%   vtype : Variable type vector (length equals number of variables)
%           Possible values are 1  Asymmetric binary variable (0/1)
%                               2  Nominal variable (includes symmetric binary)
%                               3  Ordinal variable
%                               4  Interval variable
%  
% Optional input arguments:
%   metric : Metric to be used 
%            Possible values are 0: Mixed (not all interval variables, default)
%                                1: Euclidean (all interval variables, default)
%                                2: Manhattan
%                           
% I/O:
%   result=daisy(x,vtype,1)
%
% Example: 
%   load flower.mat
%   result=daisy(flower,[2 2 1 2 3 3 4]);
%
% The output of DAISY is a structure containing:
%   result.dys        : dissimilarities vector (read row by row from the
%                             lower dissimilarity matrix)
%   result.metric     : metric used
%   result.number     : number of observations

    
function result = daisy(x,vtype,metric)

%DAISY returns a row vector containing all the pairwise dissimilarities
% (distances) between observations in the dataset. The original
% variables may be of mixed types.
%
% The calculation of dissimilarities is explained in:
%   Kaufman, L. and Rousseeuw, P.J. (1990),
%   "Finding groups in data: An introduction to cluster analysis",
%   Wiley-Interscience: New York (Series in Applied Probability and
%   Statistics), ISBN 0-471-87876-6.
%
% Required input arguments:
%       x : Data matrix (rows = observations, columns = variables)
%   vtype : Variable type vector (length equals number of variables)
%           Possible values are 1  Asymmetric binary variable (0/1)
%                               2  Nominal variable (includes symmetric binary)
%                               3  Ordinal variable
%                               4  Interval variable
%  
% Optional input arguments:
%   metric : Metric to be used 
%            Possible values are 0: Mixed (not all interval variables, default)
%                                1: Euclidean (all interval variables, default)
%                                2: Manhattan
%                           
% I/O:
%   result=daisy(x,vtype,1)
%
% Example: 
%   load flower.mat
%   result=daisy(flower,[2 2 1 2 3 3 4]);
%
% The output of DAISY is a structure containing:
%   result.dys        : dissimilarities vector (read row by row from the
%                             lower dissimilarity matrix)
%   result.metric     : metric used
%   result.number     : number of observations
%
% This function is part of LIBRA: the Matlab Library for Robust Analysis,
% available at:
%              http://wis.kuleuven.be/stat/robust.html
%
% Written by Guy Brys and Wai Yan Kong (May 2006)

%Checking and filling in the inputs
if (nargin<2)
    error('Two input arguments required')
elseif (nargin<3)
    if (sum(vtype)~=4*size(x,2))
        metric = 'mixed';
        metri = 0;
    else
        metric = 'euclidean';
        metri = 1;
    end