%MISVAL Fixed mapping handling the missing values in a dataset
%
% B = MISVAL(A,VAL)
% B = A*MISVAL([],VAL)
% B = A*MISVAL(VAL)
%
% INPUT
% A Dataset, containing NaNs (missing values)
% VAL String with substitution option
% or value used for substitution
%
% B Dataset with NaNs substituted
%
% DESCRIPTION
%
% The following values for VAL are possible:
% 'remove' remove objects (rows) that contain missing values (default)
% 'f-remove' remove features (columns) that contain missing values
% 'mean' fill the entries with the mean of their features
% 'c-mean' fill the entries with the class mean of their features
% <value> fill the entries with a fixed constant
%
% SEE ALSO (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% DATASETS
% Copyright: D.M.J. Tax, D.M.J.Tax@37steps.com
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands
function [x,msg] = misval(varargin)
mapname = 'Missing values';
argin = shiftargin(varargin,{'scalar','char'});
argin = setdefaults(argin,[],[]);
if mapping_task(argin,'definition')
x = define_mapping(argin,'fixed',mapname);
elseif mapping_task(argin,'training') % Compute mapping.
[x,val] = deal(argin{:});
x = prdataset(x);
[m,k,c] = getsize(x);
% Where are the offenders?
I = isnan(x);
% If there are missing values, go:
if any(I(:))
switch val
case {'remove' 'delete'}
J = find(sum(I,2)==0);
x = x(J,:);
msg = 'Objects with missing values have been removed.';
case {'f-remove' 'f-delete'}
J = find(sum(I,1)==0);
x = x(:,J);
msg = 'Features with missing values have been removed.';
case 'mean'
for i=1:k
J = ~I(:,i);
if any(I(:,i)) %is there a missing value in this feature?
if ~any(J)
error('Missing value cannot be filled: all values are NaN.');
end
mn = mean(x(J,i));
x(find(I(:,i)),i) = mn;
end
end
msg = 'Missing values have been replaced by the feature mean.';
case 'c-mean'
for j=1:c
L = findnlab(x,j);
for i=1:k
J = ~I(L,i);
if any(I(L,i)) %is there a missing value in this feature for this class?
if ~any(J)
error('Missing value cannot be filled: all values are NaN.');
end
mn = mean(x(J,i));
x(find(I(:,i)),i) = mn;
end
end
end
msg = 'Missing values have been replaced by the class feature mean.';
otherwise
if isstr(val)
error('unknown option')
end
if ~isa(val,'double')
error('Missing values can only be filled by scalars.');
end
x(I) = val;
msg = sprintf('Missing values have been replaced by %f.',val);
end
end
end
return