Machine Learning 第七波编程作业——K-means Clustering and Principal Component Analysis
2013-06-18 12:16
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仅列出核心代码：
1.findClosestCentroids.m
1.findClosestCentroids.m
m = size(X, 1);
len = zeros(K, 1);
for i = 1:m
for j = 1:K
len(j) = norm(X(i, :) - centroids(j, :))^2;
end
[~, idx(i)] = min(len);
end
2.computeCentroids.mlen = zeros(K, 1);
for i = 1:m
for j = 1:K
len(j) = norm(X(i, :) - centroids(j, :))^2;
end
[~, idx(i)] = min(len);
end
for k = 1:K
ind = find(idx == k);
centroids(k, :) = mean(X(ind, :));
end
3.pca.mind = find(idx == k);
centroids(k, :) = mean(X(ind, :));
end
Sigma = X'*X/m;
[U,S,~] = svd(Sigma);
4.projectData.m[U,S,~] = svd(Sigma);
Z = X * U(:, 1:K);
5.recoverData.m X_rec = Z * U(:, 1:K)';
课程地址：https://www.coursera.org/course/ml
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