Kernel KNN函数

代码

knn.m

function [ y ] = knn( X, X_train, y_train, K)
%KNN K-Nearest Neighbors Algorithm.
%
%   INPUT：  X:          testing sample features, P-by-N_test matrix.
%            X_train:    training sample features, P-by-N matrix.
%            y_train:    training sampele labels, 1-by-N vector.
%            K:          take k in k-Nearest Neighbors.
%   OUTPUT:  y:          predicted labels, 1-by-N vector.
%Author: X-Lion
%Date:   20150807
[~,N_test] = size(X);predicted_label = zeros(1,N_test);
for i = 1:N_test% calculate the K nearest neighbors and the distance[dists,neighbors] = top_K_neighbors(X_train,y_train,X(:,i),K);% recognize the label of the test vector.predicted_label(i) = recog(y_train(neighbors),max(y_train));
endy = predicted_label;end

top_K_neighbors.m

function [ dists,neighbors ] = top_K_neighbors( X_train,y_train,X_test,K )
% TOP_K_NEIGHBORS
%
%   INPUT：  X_test:             the test vector with P-by-1.
%            X_train,y_train:    training data set.
%            K:                  is the K neighbor parameter.
%   OUTPUT:  dists,neighbors:    top K neighbors index and dist.
% Author: X-Lion
% Date:   20150807
[~,N_train] = size(X_train);
test_mat = repmat(X_test,1,N_train);
% The distance is the Euclid Distance.
dist_mat = (X_train-double(test_mat)).^2;
dist_array = sum(dist_mat);% Linear Kernel(k(ui,vj) = ui'*vj)
% for i = 1:N_train
%     Kxx = X_train(:,i)'*X_train(:,i);
%     Kxy = X_train(:,i)'*X_test;
%     Kyy = X_test'*X_test;
%     dist_array(i) = Kxx - 2 * Kxy + Kyy;
% end% Gaussian Kernel(k(ui,vj) = exp(-gama*||ui-vj||^2))
% Parameter:
%           g:gama
% g = 0.1;
% for i = 1:N_train
%     Kxx = exp(-g*norm(X_train(:,i)-X_train(:,i))^2);
%     Kxy = exp(-g*norm(X_train(:,i)-X_test)^2);
%     Kyy = exp(-g*norm(X_test-X_test)^2);
%     dist_array(i) = Kxx - 2 * Kxy + Kyy;
% end% Polynomial Kernel(k(ui,vj) = (gama*ui'*vj+coef)^d)
% Parameter:
%           g:gama
%           coef
%           d:degree
% g = 0.01;
% coef = 1;
% d = 2;
% for i = 1:N_train
%     Kxx = (g*X_train(:,i)'*X_train(:,i) + coef)^d;
%     Kxy = (g*X_train(:,i)'*X_test + coef)^d;
%     Kyy = (g*X_test'*X_test + coef)^d;
%     dist_array(i) = Kxx - 2 * Kxy + Kyy;
% end% Tanh Kernel(k(ui,vj) = tanh(gama*ui'*vj + coef))
% Parameter:
%           g:gama
%           coef
% g = 2;
% coef = 1;
% for i = 1:N_train
%     Kxx = tanh(g*X_train(:,i)'*X_train(:,i) + coef);
%     Kxy = tanh(g*X_train(:,i)'*X_test + coef);
%     Kyy = tanh(X_test'*X_test + coef);
%     dist_array(i) = Kxx - 2 * Kxy + Kyy;
% end%The neighbors are the index of top K nearest points.
[dists,neighbors] = sort(dist_array);
dists = dists(1:K);
neighbors = neighbors(1:K);
end

recog.m

function result = recog( K_labels,class_num )
%RECOG
%
%   INPUT：  X_test:             the test vector with P-by-1.
%            X_train,y_train:    training data set.
%            K:                  is the K neighbor parameter.
%   OUTPUT:  dists,neighbors:    top K neighbors index and dist.
%Author: X-Lion
%Date:   20150807
[~,K] = size(K_labels);
class_count = zeros(1,class_num+1);
for i = 1:Kclass_index = K_labels(i)+1;class_count(class_index) = class_count(class_index) + 1;
end
[~,result] = max(class_count);
result = result - 1;
end

参考资料

http://blog.csdn.net/rk2900/article/details/9080821

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Kernel KNN ( K-Nearest Neighbors )

Kernel KNN函数

代码

knn.m

top_K_neighbors.m

recog.m

参考资料

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