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can someone explain this code?

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Srika
Srika 2016-4-6
关闭： Walter Roberson 2016-4-6
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

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