Error using zeros Maximum variable size allowed by the program is exceeded.
Error in knnclassify>distfun (line 216) D = zeros(n,size(Test,1));
Code:
function class = knnclassify(sample, TRAIN, group, K, distance,tieBreaker)
%KNNCLASSIFY K-Nearest Neighbor Classifier.
% CLASS = KNNCLASSIFY(SAMPLE,TRAINING,GROUP) classifies each row of the
% data in SAMPLE into one of the groups in TRAINING using the nearest
% neighbour method. SAMPLE and TRAINING must be matrices with the same
% number of columns. GROUP is a grouping variable for TRAINING. Its
% unique values define groups, and each element defines the group to
% which the corresponding row of TRAINING belongs. GROUP can be a
% numeric vector, a string array, or a cell array of strings. TRAINING
% and GROUP must have the same number of rows. CLASSIFY treats NaNs or
% empty strings in GROUP as missing values, and ignores the corresponding
% rows of TRAINING. CLASS indicates which group each row of SAMPLE has
% been assigned to, and is of the same type as GROUP. % % CLASS = KNNCLASSIFY(SAMPLE,TRAINING,GROUP,K) allows you to specify K,
% the number of nearest neighbors used in the classification. The default
% is 1. % % CLASS = KNNCLASSIFY(SAMPLE,TRAINING,GROUP,K,DISTANCE) allows you to
% select the distance metric. Choices are:
% {'sqEuclidean'} - Squared Euclidean distance
% 'cityblock' - Sum of absolute differences, a.k.a. L1
% 'cosine' - One minus the cosine of the included angle
% between points (treated as vectors)
% 'correlation' - One minus the sample correlation between
% points (treated as sequences of values)
% 'Hamming' - Percentage of bits that differ (only
% suitable for binary data) % % CLASS = KNNCLASSIFY(SAMPLE,TRAINING,GROUP,K,DISTANCE,TIEBREAK) allows
% you to specify the method used to break a tie in the number of nearest
% neighbors. Options are 'random', which selects a random tiebreaker, and
% 'nearest' which uses the nearest neighbor amongst the tied groups to
% break the tie.
% % Examples: % % % training data: two normal components
% training = [mvnrnd([ 1 1], eye(2), 100); ...
% mvnrnd([-1 -1], 2*eye(2), 100)];
% group = [repmat(1,100,1); repmat(2,100,1)];
% gscatter(training(:,1),training(:,2),group);hold on; % % % % some random sample data
% sample = unifrnd(-5, 5, 100, 2);
% % classify the sample using the nearest neighbor classification
% c = knnclassify(sample, training, group); % % gscatter(sample(:,1),sample(:,2),c,'mc'); hold on;
% c3 = knnclassify(sample, training, group, 3);
% gscatter(sample(:,1),sample(:,2),c3,'mc','o');
% Copyright 1993-2002 The MathWorks, Inc.
% $Revision: $ $Date: 2002/04/11 14:24:07 $
% References:
% [1]
if nargin < 3
error('Requires at least three arguments.');
end
% grp2idx sorts a numeric grouping var ascending, and a string grouping
% var by order of first occurrence
[gindex,groups] = grp2idx(group);
nans = find(isnan(gindex));
if length(nans) > 0
TRAIN(nans,:) = [];
gindex(nans) = [];
end
ngroups = length(groups);
gsize = hist(gindex,1:ngroups);
[n,d] = size(TRAIN);
if size(gindex,1) ~= n
error('The length of GROUP must equal the number of rows in TRAINING.');
elseif size(sample,2) ~= d
error('SAMPLE and TRAINING must have the same number of columns.');
end
m = size(sample,1);
if nargin < 4
elseif ~isnumeric(K)
error('K must be numeric.');
end
if nargin < 5 isempty(distance)
distance = 'sqeuclidean';
end
if ischar(distance)
distNames = {'sqeuclidean','cityblock','cosine','correlation','hamming'};
i = strmatch(lower(distance), distNames);
if length(i) > 1
error('stats:knn:AmbiguousDistance', ...
'Ambiguous ''distance'' parameter value: %s.', distance);
elseif isempty(i)
error('stats:knn:UnknownDistance', ...
'Unknown ''distance'' parameter value: %s.', distance);
end
distance = distNames{i};
else
error('stats:knn:InvalidDistance', ...
'The ''distance'' parameter value must be a string.');
end
if nargin < 6
elseif ischar(tieBreaker)
tieNames = {'random','nearest','farthest'};
i = strmatch(lower(tieBreaker), tieNames);
if length(i) > 1
error('stats:knn:AmbiguousDistance', ...
'Ambiguous ''distance'' parameter value: %s.', distance);
elseif isempty(i)
error('stats:knn:UnknownDistance', ...
'Unknown ''distance'' parameter value: %s.', distance);
end
tieBreaker = tieNames{i};
else
error('stats:knn:InvalidDistance', ...
'The ''distance'' parameter value must be a string.');
end
% Calculate the distances from all points in the training set to all points
% in the test set.
dists = distfun(sample,TRAIN,distance);
% find the K nearest
if K >1
[dSorted,dIndex] = sort(dists,2);
dIndex = dIndex(:,1:K);
classes = gindex(dIndex);
if size(classes,2) == 1
classes = classes';
end
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,class] = max(counts,[],2);
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 tieBreaker
case 'random'
tb = randsample(numTies,1);
class(checkRows(i)) = choice(tb);
case 'nearest'
for inner = 1:K
if ismember(classes(checkRows(i),inner),find(ties))
class(checkRows(i)) = classes(checkRows(i),inner);
break
end
end
case 'farthest'
for inner = K:-1:1
if ismember(classes(checkRows(i),inner),find(ties))
class(checkRows(i)) = classes(checkRows(i),inner);
break
end
end
end
end
end
else
[dSorted,dIndex] = min(dists,[],2);
class = gindex(dIndex);
end
% Convert back to original grouping variable
if isnumeric(group)
groups = str2num(char(groups));
class = groups(class);
elseif ischar(group)
groups = char(groups);
class = groups(class,:);
else %if iscellstr(group)
end
function D = distfun(Train, Test, dist)
%DISTFUN Calculate distances from training points to test points.
[n,p] = size(Train);
D = zeros(n,size(Test,1));
numTest = size(Test,1);
switch dist
case 'sqeuclidean'
for i = 1:numTest
D(:,i) = sum((Train - Test(repmat(i,n,1),:)).^2, 2);
end
case 'cityblock'
for i = 1:numTest
D(:,i) = sum(abs(Train - Test(repmat(i,n,1),:)), 2);
end
case {'cosine','correlation'}
normTrain = sqrt(sum(Train.^2, 2));
normTest = sqrt(sum(Test.^2, 2));
normData = sqrt(sum([Train;Test].^2, 2));
Train = Train ./ normTrain(:,ones(1,size(Train,2)));
if any(normData < eps)
error('stats:knn:ZeroTestentroid', ...
'Zero cluster centroid created at iteration %d.',iter);
end
for i = 1:numTest
D(:,i) = 1 - (Train * Test(i,:)') ./ normTest(i);
end
case 'hamming'
if ~all(ismember(X(:),[0 1]))
error('Non-binary data cannot be clustered using Hamming distance.');
end
for i = 1:numTest
D(:,i) = sum(abs(Train - Test(repmat(i,n,1),:)), 2) / p;
end
end
