can someone explain this code?

function outClass = bayesien(sample, TRAIN, group, K, distance,rule)
bioinfochecknargin(nargin,3,mfilename)

[gindex,groups] = grp2idx(group);
nans = find(isnan(gindex));
if ~isempty(nans)
    TRAIN(nans,:) = [];
    gindex(nans) = [];
end
ngroups = length(groups);

[n,d] = size(TRAIN);
if size(gindex,1) ~= n
    error(message('bioinfo:knnclassify:BadGroupLength'));
elseif size(sample,2) ~= d
    error(message('bioinfo:knnclassify:SampleTrainingSizeMismatch'));
end
m = size(sample,1);

if nargin < 4
    K = 1;
elseif ~isnumeric(K)
    error(message('bioinfo:knnclassify:KNotNumeric'));
end
if ~isscalar(K)
    error(message('bioinfo:knnclassify:KNotScalar'));
end

if K<1
    error(message('bioinfo:knnclassify:KLessThanOne'));
end

if isnan(K)
    error(message('bioinfo:knnclassify:KNaN'));
end

if nargin < 5 || isempty(distance)
    distance  = 'euclidean';
elseif ischar(distance)
    distNames = {'euclidean','cityblock','cosine','correlation','hamming'};
    i = find(strncmpi(distance, distNames,numel(distance)));
    if length(i) > 1
        error(message('bioinfo:knnclassify:AmbiguousDistance', distance));
    elseif isempty(i)
        error(message('bioinfo:knnclassify:UnknownDistance', distance));
    end
    distance = distNames{i};
else
    error(message('bioinfo:knnclassify:InvalidDistance'));
end

if nargin < 6
    rule = 'nearest';
elseif ischar(rule)

      % lots of testers misspelled consensus.
      if strncmpi(rule,'conc',4)
          rule(4) = 's';
      end
      ruleNames = {'random','nearest','farthest','consensus'};
      i = find(strncmpi(rule, ruleNames,numel(rule)));
      % %   May need this if we add more rules and introduce the possibility of
      % %   ambiguity.
      %     if length(i) > 1
      %         error('bioinfo:knnclassify:AmbiguousRule', ...
      %             'Ambiguous ''Rule'' parameter value:  %s.', rule);
      %     else
      if isempty(i)
          error(message('bioinfo:knnclassify:UnknownRule', rule));
      end
      rule = ruleNames{i};
      %     end
  else
      error(message('bioinfo:knnclassify:InvalidRule'));
  end

% Calculate the distances from all points in the training set to all points
% in the test set.

if strncmpi(distance,'hamming',3)
        if ~all(ismember(sample(:),[0 1]))||~all(ismember(TRAIN(:),[0 1]))
            error(message('bioinfo:knnclassify:HammingNonBinary'));
        end
end
dIndex = knnsearch(TRAIN,sample,'distance', distance,'K',K);
% find the K nearest

if K >1
    classes = gindex(dIndex);
    % special case when we have one sample(test) point -- this gets turned into a
    % column vector, so we have to turn it back into a row vector.
    if size(classes,2) == 1
        classes = classes';
    end
    % count the occurrences of the classes

      counts = zeros(m,ngroups);
      for outer = 1:m
          for inner = 1:K
              counts(outer,classes(outer,inner)) = counts(outer,classes(outer,inner)) + 1;
          end
      end

      [L,outClass] = max(counts,[],2);

      % Deal with consensus rule
      if strcmp(rule,'consensus')
          noconsensus = (L~=K);

          if any(noconsensus)
              outClass(noconsensus) = ngroups+1;
              if isnumeric(group) || islogical(group)
                  groups(end+1) = {'NaN'};
              else
                  groups(end+1) = {''};
              end
          end
      else    % we need to check case where L <= K/2 for possible ties
          checkRows = find(L<=(K/2));

          for i = 1:numel(checkRows)
              ties = counts(checkRows(i),:) == L(checkRows(i));
              numTies = sum(ties);
              if numTies > 1
                  choice = find(ties);
                  switch rule
                      case 'random'
                          % random tie break

                          tb = randsample(numTies,1);
                          outClass(checkRows(i)) = choice(tb);
                      case 'nearest'
                          % find the use the closest element of the equal groups
                          % to break the tie
                          for inner = 1:K
                              if ismember(classes(checkRows(i),inner),choice)
                                  outClass(checkRows(i)) = classes(checkRows(i),inner);
                                  break
                              end
                          end
                      case 'farthest'
                          % find the use the closest element of the equal groups
                          % to break the tie
                          for inner = K:-1:1
                              if ismember(classes(checkRows(i),inner),choice)
                                  outClass(checkRows(i)) = classes(checkRows(i),inner);
                                  break
                              end
                          end
                  end
              end
          end
      end

else
    outClass = gindex(dIndex);
end

% Convert back to original grouping variable
if isa(group,'categorical')
    labels = getlabels(group);
    if isa(group,'nominal')
        groups = nominal(groups,[],labels);
    else
        groups = ordinal(groups,[],getlabels(group));
    end
    outClass = groups(outClass);
elseif isnumeric(group) || islogical(group)
    groups = str2num(char(groups)); %#ok
    outClass = groups(outClass);
elseif ischar(group)
    groups = char(groups);
    outClass = groups(outClass,:);
else %if iscellstr(group)
    outClass = groups(outClass);
end