سورس کد الگوریتم knn
 

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پیاپیش از همکاری شما صمیمانه سپاس گذارم.اگر کد کوتاه تری دارید ممنون میشوم بنویسید.در ضمن خطایی که این الگوریتم پایینی می دهد این چنین است:
Undefined function or variable 'trlabel'. چگونه این خطا را باید برطرف کنم؟؟یا در صورت امکان کد دیگری از knn بریپایم بفرستید
%================================================= ====================
% Filename: knn.m
%
% Version: 0.9.0 (testing)
% Author: LI, Wei <kuantkid<at>gmail.com>
%
% Modified at: Mon Sep 3 11:59:05 2012
% Created at: Mon Sep 3 11:47:55 2012
%
% Description: Kmeans with
% 1. parallel(TODO)/chunking distance calculatation
% for large dataset
% 2. simple posterior estimation using distance
% 3. class weight assignment
% Input:
% train: n_features x n_data
% trlabel: training labels, should be 1 - n_label
% probe: n_features x n_data
% k: number of k_nearest neighbor
% opt: options
% cache_num: number of chunks to be used
% trans : transform the score from squared distance using any rbf fuction, standard knn can have trans = @(x) ones(size(x));
% pad : using the pad+ nearest neighbor, useful when doing cross validation
%
% Copyright Wei LI (@kuantkid) Released Under Create Common BY-SA
%================================================= ====================
function [label, voting, prob_est] = knn(train, trlabel ,probe, k ,opt)
% make sure that label is 1 - numel(unique(trlabel))
if ~exist('opt','var')
opt = struct();
end

if ~isfield(opt,'tran')
opt.tran = @(x, sigma) exp(-x ./ (2*sigma) );
end

if ~isfield(opt,'cache_num')
opt.cache_num = 1;
end

if ~isfield(opt,'pad')
opt.pad = 0;
end

% probe may be too big
n_tr = size(train, 2);
dim = size(train, 1);
n_pr = size(probe, 2);

u_label = unique(trlabel);
n_label = length(u_label);

if ~isfield(opt,'labelweight')
opt.labelweight = ones(1, n_label);
end
opt.labelweight = opt.labelweight(:)';

ll = sparse(1:n_tr, trlabel, 1, n_tr, n_label, n_tr);

k_group = kgroup(1:n_pr, opt.cache_num);

result = zeros(n_pr, n_label);
for i = 1:opt.cache_num
fprintf('Batch %d of %d', i, opt.cache_num);
% calculate the training set
% TIP: we can use the distance to get the ranking then apply the exp
M = (sqdist(train, probe(:,k_group{i})));
[D,M] = sort(M,1,'ascend');
% construct a ll matrix, by the labels from the first
M = (M((1:k) + opt.pad,:));
D = opt.tran(D((1:k) + opt.pad,:), median(M(:)));

% convert M into labels
M = trlabel(M(:));
% tVoter = full(sparse(repmat(k_group{i}(:)', [1 k]) ,M(:), D(:) , k * numel(k_group{i}) ,n_label));
tVoter = full(sparse(1: k * numel(k_group{i}) ,M(:), D(:) , k * numel(k_group{i}) ,n_label));
% fold the k-trials into the first dimension, the n_probe into the
% second and n_label into the third
tVoter = reshape(tVoter,[k numel(k_group{i}) n_label]);

% using the label weight.
tSize = size(tVoter);
tVoter = reshape(bsxfun(@times,...
reshape(tVoter,[prod(tSize(1:end-1)) tSize(end)]),...
opt.labelweight),...
tSize);

tVoter = squeeze(sum(tVoter, 1));

% pack into the result
result(k_group{i},:) = tVoter;

% memory clear
clear tVoter M D
end

% calculate the label, largest affinity
% result should be a n_probe x n_label matrix, distance is transformed
[~,l] = max(result,[],2);
label = u_label(l(:,1));

if(nargout >= 2)
voting = result;
end

% calculate probability output
if(nargout >= 3)
prob_est = bsxfun(@times, voting,1./sum(voting,2));
end

end

function [M] = sqdist(X1, X2)
M = bsxfun(@plus, sum(X1 .* X1, 1)', (-2) * X1' * X2);
M = bsxfun(@plus, sum(X2 .* X2, 1), M);
end

function [grp] = kgroup(idx, k, opt)
% make the indexes into K-Disjoint Groups
if ~exist('opt','var')
opt.rand = false;
end

if isfield(opt,'rand')
opt.rand = false;
end

n = length(idx);

if(opt.rand)
ridx = randperm(length(idx));
idx = idx(ridx);
end

% the last one is the largest.
grp = cell(1,k);
gnum = floor(n / k);

% grp indexes
for i = 1:(k-1)
grp{i} = idx( (1 + (i-1) * gnum) : i * gnum);
end

% the kth group
grp{k} = idx((gnum * (k-1)+1): n);
end