function?results?=?knn_classify（train_data?test_data?num_neighbors?train_labels）
%?classify?test_data?using?KNN?classifier?established?from?train_data
%?Input:
%???????train_data??????-???????training?data?TRAIN_NUM?x?D?matrix
%???????test_data???????-???????testing?data??TEST_NUM?x?D?matrix
%???????num_neighbors???-???????number?of?nearest?neighbors
%???????train_labels????-???????training?labels?TRAIN_NUM?x?1?matrix
%?Output:
%???????results?????????-???????labels?for?testing?data?TEST_NUM?x?1?array



[TRAIN_NUM?D]?=?size（train_data）;
[TEST_NUM?D2]?=?size（test_data）;


results?=?zeros（TEST_NUM?1）;
for?i?=?1:TEST_NUM
????[dists?neighbors]?=?find_top_K_neighbors（train_data?test_data（i:）?num_neighbors）;
????results（i）?=?recog（train_labels（neighbors）?max（train_labels））;
end



%------------------------------subfunction--------------------------------%
function?[dists?neighbors]?=?find_top_K_neighbors（train_data?test_sample?K）
%?find?the?top?K?nearest?neighbors?in?the?train_data?for?test_sample
%?Input:
%???????train_data??????-???????training?data?N?x?D?matrix
%???????test_sample?????-???????test_sample?1?x?D?array
%???????K???????????????-???????number?of?neighbors
%?Output:
%???????dist????????????-???????least?K?distance
%???????neighbors???????-???????K?nearest?neighbors?1?x?K?array

[N?D]?=?size（train_data）;
[dummy?D2]?=?size（test_sample）;



test_matrix?=?repmat（test_sample?N?1）;????????%?N?by?D
dist_mat?=?（train_data?-?test_matrix）?.^?2;?????%?N?by?D
dist_array?=?sum（dist_mat‘）;????????????????????%?1?by?N
[dists?neighbors]?=?sort（dist_array）;
dists?=?dists（1:K）;
neighbors?=?neighbors（1:K）;


%--------------------------------subfunction------------------------------%
function?label?=?recog（neighbor_labels?num_class）
%?find?the?label?for?the?current?test?sample?with?neighbor_labels
%?Input:
%???????neighbor_labels?????????-???????labels?for?K?neighors?of?the?current?
%???????????????????????????????????????test?sample
%???????num_class???????????????-???????number?of?class
%?Output:
%???????label???????????????????-???????label?for?the?current?test?sample

num_neighbors?=?length（neighbor_labels）;
cnt_labels?=?zeros（num_class?1）;

for?i?=?1:num_neighbors
????cnt_labels（neighbor_labels（i））?=?cnt_labels（neighbor_labels（i））?+?1;
end

[dummy?label]?=?max（cnt_labels）;%Y是返回最大值的，I是返回最大值的位置的。

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2013-07-18?16:01??knn\
?????文件????????2448??2013-06-28?09:26??knn\knn_classify.m
?????文件????????2253??2013-07-18?15:47??knn\main.m