K-Means算法相關(guān)：

findClosestCentroids：

function idx = findClosestCentroids(X, centroids)

K = size(centroids, 1);

idx = zeros(size(X,1), 1);

% ====================== YOUR CODE HERE ======================

m = size(X, 1);

for i = 1:m

? distance_vec = zeros(K, 1);

? for k = 1:K

? ? distance_vec(k) = sum((X(i, :) - centroids(k, :)) .^ 2);

? endfor

? [min_dist, min_idx] = min(distance_vec);

? idx(i) = min_idx;

endfor

% =============================================================

end

computeCentroids：

function centroids = computeCentroids(X, idx, K)

[m n] = size(X);

centroids = zeros(K, n);

% ====================== YOUR CODE HERE ======================

total_vec = zeros(K, n);

count_vec = zeros(K, 1);

for i = 1:m

? k = idx(i);

? total_vec(k, :) += X(i, :);

? count_vec(k) += 1;

endfor

for k = 1:K

? centroids(k, :) = total_vec(k, :) / count_vec(k);

endfor

% =============================================================

end

PCA主成分析法相關(guān)：

pca：

function [U, S] = pca(X)

[m, n] = size(X);

U = zeros(n);

S = zeros(n);

% ====================== YOUR CODE HERE ======================

Sigma = (1 / m) * (X' * X);

[U, S, V] = svd(Sigma);

% =========================================================================

end

projectData：

function Z = projectData(X, U, K)

Z = zeros(size(X, 1), K);

% ====================== YOUR CODE HERE ======================

U_reduce = U(:, 1:K);

Z = X * U_reduce;

% =============================================================

end

recoverData：

function X_rec = recoverData(Z, U, K)

X_rec = zeros(size(Z, 1), size(U, 1));

% ====================== YOUR CODE HERE ======================

X_rec = Z * U(:, 1:K)';

% =============================================================

end