对“视觉机器学习20讲配套仿真代码”的研究心得---KNN学习

*****************************************************************cvEucdist************************************************************************

% cvEucdist - Euclidean distance

%

% Synopsis

% [d] = cvEucdist(X, Y)

%

% Description

% cvEucdist calculates a squared euclidean distance between X and Y.

%

% Inputs ([]s are optional)

% (matrix) X D x N matrix where D is the dimension of vectors

% and N is the number of vectors.

% (matrix) [Y] D x P matrix where D is the dimension of vectors

% and P is the number of vectors.

% If Y is not given, the L2 norm of X is computed and

% 1 x N matrix (not N x 1) is returned.

%

% Outputs ([]s are optional)

% (matrix) d N x P matrix where d(n,p) represents the squared

% euclidean distance between X(:,n) and Y(:,p).

%

% Examples

% X = [1 2

% 1 2];

% Y = [1 2 3

% 1 2 3];

% d = cvEucdist(X, Y)

% % 0 2 8

% % 2 0 2

%

% See also

% cvMahaldist

% Authors

% Naotoshi Seo <sonots(at)sonots.com>

%

% License

% The program is free to use for non-commercial academic purposes,

% but for course works, you must understand what is going inside to use.

% The program can be used, modified, or re-distributed for any purposes

% if you or one of your group understand codes (the one must come to

% court if court cases occur.) Please contact the authors if you are

% interested in using the program without meeting the above conditions.

%

% Changes

% 06/2006 First Edition

function d = cvEucdist(X, Y)

if ~exist('Y', 'var') || isempty(Y)

%% Y = zeros(size(X, 1), 1);

U = ones(size(X, 1), 1);

d = abs(X'.^2*U).'; return;

end

V = ~isnan(X); X(~V) = 0; % V = ones(D, N);

%clear V;

U = ~isnan(Y); Y(~U) = 0; % U = ones(D, P);

%clear U;

%d = abs(X'.^2*U - 2*X'*Y + V'*Y.^2);

d1 = X'.^2*U;

d3 = V'*Y.^2;

d2 = X'*Y;

d = abs(d1-2*d2+d3);

% X = X';

% Y = Y';

% for i=1:size(X,1)

% for j=1:size(Y,1)

% d(i,j)=(norm(X(i,:)-Y(j,:)))^2; %计算每个测试样本与所有训练样本的欧氏距离

% end

% end

*******************************************************************cvKnn - K-Nearest Neighbor classification***************************************

% cvKnn - K-Nearest Neighbor classification

%

% Synopsis

% [Class] = cvKnn(X, Proto, ProtoClass, [K], [distFunc])

%

% Description

% K-Nearest Neighbor classification

%

% Inputs ([]s are optional)

% (matrix) X D x N matrix representing column classifiee vectors

% where D is the number of dimensions and N is the

% number of vectors.

% (matrix) Proto D x P matrix representing column prototype vectors

% where D is the number of dimensions and P is the

% number of vectors.

% (vector) ProtoClass

% 1 x P vector containing class lables for prototype

% vectors.

% (scalar) [K = 1] K-NN's K. Search K nearest neighbors.

% (func) [distFunc = @cvEucdist]

% A function handle for distance measure. The function

% must have two arguments for matrix X and Y. See

% cvEucdist.m (Euclidean distance) as a reference.

%

% Outputs ([]s are optional)

% (vector) Class 1 x N vector containing classified class labels

% for X. Class(n) is the class id for X(:,n).

% (matrix) [Rank] Available only for NN (K = 1) now.

% nClass x N vector containing ranking class labels

% for X. Rank(1,n) is the 1st candidate which is

% the same with Class(n), Rank(2,n) is the 2nd

% candidate, Rank(3,n) is the 3rd, and so on.

%

% See also

% cvEucdist, cvMahaldist

% Authors

% Naotoshi Seo <sonots(at)sonots.com>

%

% License

% The program is free to use for non-commercial academic purposes,

% but for course works, you must understand what is going inside to use.

% The program can be used, modified, or re-distributed for any purposes

% if you or one of your group understand codes (the one must come to

% court if court cases occur.) Please contact the authors if you are

% interested in using the program without meeting the above conditions.

%

% Changes

% 04/01/2005 First Edition

function [Class, Rank] = cvKnn(X, Proto, ProtoClass, K, distFunc)

if ~exist('K', 'var') || isempty(K)

K = 1;%默认为K = 1

end

if ~exist('distFunc', 'var') || isempty(distFunc)

distFunc = @cvEucdist;

end

if size(X, 1) ~= size(Proto, 1)

error('Dimensions of classifiee vectors and prototype vectors do not match.');

end

[D, N] = size(X);

% Calculate euclidean distances between classifiees and prototypes

d = distFunc(X, Proto);

if K == 1, % sort distances only if K>1

[mini, IndexProto] = min(d, [], 2); % 2 == row%每列的最小元素

Class = ProtoClass(IndexProto);

if nargout == 2, % instance indices in similarity descending order

[sorted, ind] = sort(d'); % PxN

RankIndex = ProtoClass(ind); %,e.g., [2 1 2 3 1 5 4 1 2]'

% conv into, e.g., [2 1 3 5 4]'

for n = 1:N

[ClassLabel, ind] = unique(RankIndex(:,n),'first');

[sorted, ind] = sort(ind);

Rank(:,n) = ClassLabel(ind);

end

end

else

[sorted, IndexProto] = sort(d'); % PxN

clear d；

% K closest

IndexProto = IndexProto(1:K,:);

KnnClass = ProtoClass(IndexProto);

% Find all class labels

ClassLabel = unique(ProtoClass);

nClass = length(ClassLabel);

for i = 1:nClass

ClassCounter(i,:) = sum(KnnClass == ClassLabel(i));

end

[maxi, winnerLabelIndex] = max(ClassCounter, [], 1); % 1 == col

% Future Work: Handle ties somehow

Class = ClassLabel(winnerLabelIndex);

end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%功能：演示KNN算法在计算机视觉中的应用

%实现如何利用KNN算法进行聚类；

%Modi: NUDT-VAP

%环境：Win7，Matlab2012b

%时间：2014-10-17

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function main

trainData = [

0.6213 0.5226 0.9797 0.9568 0.8801 0.8757 0.1730 0.2714 0.2523

0.7373 0.8939 0.6614 0.0118 0.1991 0.0648 0.2987 0.2844 0.4692

];

trainClass = [

1 1 1 2 2 2 3 3 3

];

testData = [

0.9883 0.5828 0.4235 0.5155 0.3340

0.4329 0.2259 0.5798 0.7604 0.5298

];

% main

testClass = cvKnn(testData, trainData, trainClass);

% plot prototype vectors

classLabel = unique(trainClass);

nClass = length(classLabel);

plotLabel = {'r*', 'g*', 'b*'};

figure;

for i=1:nClass

A = trainData(:, trainClass == classLabel(i));

plot(A(1,:), A(2,:), plotLabel{i});

hold on;

end

% plot classifiee vectors

plotLabel = {'ro', 'go', 'bo'};

for i=1:nClass

A = testData(:, testClass == classLabel(i));

plot(A(1,:), A(2,:), plotLabel{i});

hold on;

end

legend('1: prototype','2: prototype', '3: prototype', '1: classifiee', '2: classifiee', '3: classifiee', 'Location', 'NorthWest');

title('K nearest neighbor');

hold off;