1 function idx = findClosestCentroids(X, centroids) 2 %FINDCLOSESTCENTROIDS computes the centroid memberships for every example 3 % idx = FINDCLOSESTCENTROIDS (X, centroids) returns the closest centroids 4 % in idx for a dataset X where each row is a single example. idx = m x 1 5 % vector of centroid assignments (i.e. each entry in range [1..K]) 6 % 7 8 % Set K 9 K = size(centroids, 1); 10 11 % You need to return the following variables correctly. 12 idx = zeros(size(X,1), 1); 13 14 % ====================== YOUR CODE HERE ====================== 15 % Instructions: Go over every example, find its closest centroid, and store 16 % the index inside idx at the appropriate location. 17 % Concretely, idx(i) should contain the index of the centroid 18 % closest to example i. Hence, it should be a value in the 19 % range 1..K 20 % 21 % Note: You can use a for-loop over the examples to compute this. 22 % 23 24 25 for i=1:size(X,1) 26 for j =1:K 27 dis(j)=sum((centroids(j,:)-X(i,:)).^2,2);%sum 每行 28 end 29 [t,idx(i)]=min(dis);%t:最小值 idx ：最小值的索引 30 end 31 32 33 34 35 % ============================================================= 36 37 end
function centroids = computeCentroids(X, idx, K) %COMPUTECENTROIDS returns the new centroids by computing the means of the %data points assigned to each centroid. % centroids = COMPUTECENTROIDS(X, idx, K) returns the new centroids by % computing the means of the data points assigned to each centroid. It is % given a dataset X where each row is a single data point, a vector % idx of centroid assignments (i.e. each entry in range [1..K]) for each % example, and K, the number of centroids. You should return a matrix % centroids, where each row of centroids is the mean of the data points % assigned to it. % % Useful variables [m n] = size(X); % You need to return the following variables correctly. centroids = zeros(K, n); % ====================== YOUR CODE HERE ====================== % Instructions: Go over every centroid and compute mean of all points that % belong to it. Concretely, the row vector centroids(i, :) % should contain the mean of the data points assigned to % centroid i. % % Note: You can use a for-loop over the centroids to compute this. % for i=1:K s=sum(idx==i);%第I个中心所包含的点的个数 if(s~=0)%不为0 centroids(i,:)=mean( X(find(idx==i),:)); else centroids(i,:)=zeros(1,n); end % ============================================================= end
function centroids = kMeansInitCentroids(X, K) %KMEANSINITCENTROIDS This function initializes K centroids that are to be %used in K-Means on the dataset X % centroids = KMEANSINITCENTROIDS(X, K) returns K initial centroids to be % used with the K-Means on the dataset X % % You should return this values correctly centroids = zeros(K, size(X, 2)); % ====================== YOUR CODE HERE ====================== % Instructions: You should set centroids to randomly chosen examples from % the dataset X % randidx = randperm(size(X,1)); centroids = X(randidx(1:K),:); % ============================================================= end
function [U, S] = pca(X) %PCA Run principal component analysis on the dataset X % [U, S, X] = pca(X) computes eigenvectors of the covariance matrix of X % Returns the eigenvectors U, the eigenvalues (on diagonal) in S % % Useful values [m, n] = size(X); % You need to return the following variables correctly. U = zeros(n); S = zeros(n); % ====================== YOUR CODE HERE ====================== % Instructions: You should first compute the covariance matrix. Then, you % should use the "svd" function to compute the eigenvectors % and eigenvalues of the covariance matrix. % % Note: When computing the covariance matrix, remember to divide by m (the % number of examples). % sigma =1/m*X'*X; [U,S,V]=svd(sigma); % ========================================================================= end
function Z = projectData(X, U, K) %PROJECTDATA Computes the reduced data representation when projecting only %on to the top k eigenvectors % Z = projectData(X, U, K) computes the projection of % the normalized inputs X into the reduced dimensional space spanned by % the first K columns of U. It returns the projected examples in Z. % % You need to return the following variables correctly. Z = zeros(size(X, 1), K); % ====================== YOUR CODE HERE ====================== % Instructions: Compute the projection of the data using only the top K % eigenvectors in U (first K columns). % For the i-th example X(i,:), the projection on to the k-th % eigenvector is given as follows: % x = X(i, :)'; % projection_k = x' * U(:, k); % U_reduce = U(:,1:K); Z= X*U_reduce; % ============================================================= end
function X_rec = recoverData(Z, U, K) %RECOVERDATA Recovers an approximation of the original data when using the %projected data % X_rec = RECOVERDATA(Z, U, K) recovers an approximation the % original data that has been reduced to K dimensions. It returns the % approximate reconstruction in X_rec. % % You need to return the following variables correctly. X_rec = zeros(size(Z, 1), size(U, 1)); % ====================== YOUR CODE HERE ====================== % Instructions: Compute the approximation of the data by projecting back % onto the original space using the top K eigenvectors in U. % % For the i-th example Z(i,:), the (approximate) % recovered data for dimension j is given as follows: % v = Z(i, :)'; % recovered_j = v' * U(j, 1:K)'; % % Notice that U(j, 1:K) is a row vector. % U_reduce = U(:,1:K); X_rec = Z*U_reduce'; % ============================================================= end