Coursera machine learning week 8 assignment answers – Andrew Ng

In this article, you will find Coursera machine learning week 8 assignment answers – Andrew Ng.  Use “Ctrl+F” To Find Any Questions or Answers. For Mobile Users, You Just Need To Click On Three dots In Your Browser & You Will Get A “Find” Option There. Use These Options to Get Any Random Questions Answer.

Try to solve all the assignments by yourself first, but if you get stuck somewhere then feel free to browse the code. Don’t just copy-paste the code for the sake of completion.  Even if you copy the code, make sure you understand the code first.

Coursera machine learning week 8 assignment answers
Coursera machine learning week 8 assignment answers

In this exercise, you will implement the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dimensional representation of face images. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.

Coursera machine learning week 8 assignment answers

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).
  %
  % DIMENSIONS:
  %    X = m x n
  Sigma = (1/m)*(X'*X); % n x n
  [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);
  %
  % DIMENSIONS:
  %    X = m x n
  %    U = n x n
  %    U_reduce = n x K
  %    K = scalar
  U_reduce = U(:,[1:K]);   % n x K
  Z = X * U_reduce;        % m x k
  % =============================================================
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.
  %
  % DIMENSIONS:
  %    Z = m x K
  %    U = n x n
  %    U_reduce = n x k
  %    K = scalar
  %    X_rec = m x n
  U_reduce = U(:,1:K);   % n x k
  X_rec = Z * U_reduce'; % m x n
  % =============================================================
end
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); % K x 1 == 3 x 1
  % You need to return the following variables correctly.
  idx = zeros(size(X,1), 1);  % m x 1 == 300 x 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.
  %
  % DIMENSIONS:
  %    centroids = K x no. of features = 3 x 2
  for i = 1:size(X,1)
      temp = zeros(K,1);
      for j = 1:K
          temp(j)=sqrt(sum((X(i,:)-centroids(j,:)).^2));
      end
      [~,idx(i)] = min(temp);
  end
  % =============================================================
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.
  %
  % DIMENSIONS:
  %    X =  m x n
  %    centroids = K x n
  %% %%%%%% WORKING: SOLUTION1 %%%%%%%%%
  % for i = 1:K
  %     idx_i = find(idx==i);       %indexes of all the input which belongs to cluster j
  %     centroids(i,:)=(1/length(idx_i))*sum(X(idx_i,:)); %calculating mean manually
  % end
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  %% %%%%%% WORKING: SOLUTION 2 %%%%%%%%
  for i = 1:K
      idx_i = find(idx==i);       %indexes of all the input which belongs to cluster j
      centroids(i,:) = mean(X(idx_i,:)); % calculating mean using built-in function
  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
  %
  % Randomly reorder the indices of examples
  randidx = randperm(size(X, 1));
  % Take the first K examples as centroids
  centroids = X(randidx(1:K), :);
  % =============================================================
end

Disclaimer:  Hopefully, this article will be useful for you to find all the Coursera machine learning week 8 assignment answers and grab some premium knowledge with less effort.

Finally, we are now, in the end, I just want to conclude some important message for you, Feel free to ask doubts in the comment section. I will try my best to answer it. If you find this helpful by any means like, comment, and share the post. Please share our posts on social media platforms and also suggest to your friends to Join Our Groups. Don’t forget to subscribe. This is the simplest way to encourage me to keep doing such work.

FAQs

Is Andrew Ng’s Machine Learning course good?
It is the Best Course for Supervised Machine Learning! Andrew Ng Sir has been like always has such important & difficult concepts of Supervised ML with such ease and great examples, Just amazing!

How do I get answers to coursera assignment?
Use “Ctrl+F” To Find Any Questions Answered. & For Mobile Users, You Just Need To Click On Three dots In Your Browser & You Will Get A “Find” Option There. Use These Options to Get Any Random Questions Answer.

How long does it take to finish coursera Machine Learning?
this specialization requires approximately 3 months with 75 hours of materials to complete, and I finished it in 3 weeks and spent an additional 1 week reviewing the whole course.

How do you submit assignments on Coursera Machine Learning?
Submit a programming assignment Open the assignment page for the assignment you want to submit. Read the assignment instructions and download any starter files. Finish the coding tasks in your local coding environment. Check the starter files and instructions when you need to. Reference

Leave a Comment