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coursera-ml-007-exercises
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neingeist 10 years ago
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ex7/projectData.m
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function Z = projectData(X, U, K) function Z = projectData(X, U, K)
%PROJECTDATA Computes the reduced data representation when projecting only %PROJECTDATA Computes the reduced data representation when projecting only
%on to the top k eigenvectors %on to the top k eigenvectors
% Z = projectData(X, U, K) computes the projection of % Z = projectData(X, U, K) computes the projection of
% the normalized inputs X into the reduced dimensional space spanned by % the normalized inputs X into the reduced dimensional space spanned by
% the first K columns of U. It returns the projected examples in Z. % the first K columns of U. It returns the projected examples in Z.
% %
% You need to return the following variables correctly. % You need to return the following variables correctly.
Z = zeros(size(X, 1), K); Z = zeros(size(X, 1), K);
% ====================== YOUR CODE HERE ====================== % ====================== YOUR CODE HERE ======================
% Instructions: Compute the projection of the data using only the top K % Instructions: Compute the projection of the data using only the top K
% eigenvectors in U (first K columns). % eigenvectors in U (first K columns).
% For the i-th example X(i,:), the projection on to the k-th % For the i-th example X(i,:), the projection on to the k-th
% eigenvector is given as follows: % eigenvector is given as follows:
% x = X(i, :)'; % x = X(i, :)';
% projection_k = x' * U(:, k); % projection_k = x' * U(:, k);
% %
U_reduce = U(:, 1:K);
m = size(X, 1);
% ============================================================= n = size(X, 2);
for i = 1:m
end x = X(i, :)';
z = x' * U_reduce;
Z(i, :) = z;
end
assert(size(Z) == [m K]);
% =============================================================
end