斯坦福大学(吴恩达) 机器学习课后习题详解 第八周 编程题 k-means and PCA

编程作业下载地址：https://download.csdn.net/download/wwangfabei1989/10318165

1. PCA.m

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).
%




[U,S,V] = svd(1/m*X'*X);








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


end

2. projectData.m

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);
%


Ureduce = U(:,1:K);
Z = X * Ureduce;




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


end

3.recoverData.m 

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.
%               


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


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


end

4.findClosestCentroids.m 

function idx = findClosestCentroids(X, centroids)
%FINDCLOSESTCENTROIDS computes the centroid memberships for every example
%   idx = FINDCLOSESTCENTROIDS (X, centroids) returns the closest centroids
%   in idx for a dataset X where each row is a single example. idx = m x 1 
%   vector of centroid assignments (i.e. each entry in range [1..K])
%


% Set K
K = size(centroids, 1);


% You need to return the following variables correctly.
idx = zeros(size(X,1), 1);


% ====================== YOUR CODE HERE ======================
% Instructions: Go over every example, find its closest centroid, and store
%               the index inside idx at the appropriate location.
%               Concretely, idx(i) should contain the index of the centroid
%               closest to example i. Hence, it should be a value in the 
%               range 1..K
%
% Note: You can use a for-loop over the examples to compute this.
%


for i=1:size(X,1)%获取训练集数量
  temp=(X(i,:)-centroids(1,:));%
  offset=temp*(temp)';%总的差值平方和
  idx(i)=1;
  for j=2:K  %遍历所有的质心点（去除第一个）
     temp_1=(X(i,:)-centroids(j,:));
     offset_1=temp_1*(temp_1)';
     if offset>offset_1%如果新的质心比较近，则 取新的
       idx(i)=j;
       offset=offset_1;
     end;
   end;
end;








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


end

 

5.computeCentroids.m 

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
  count=0;
  sum=zeros(1,n);
  for j=1:m
    if idx(j)==i
      sum=sum+X(j,:);
      count=count+1;
    end;
   end;
  centroids(i,:)= sum./count;
end;












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




end

 

6.kMeansInitCentroids.m

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

 

知乎： https://zhuanlan.zhihu.com/albertwang

微信公众号：AI-Research-Studio

​ ​​

下面是赞赏码