First matlab approach for linear implementation (V0)

.gitignore

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+# project
+bin/
+out/
+mat/
+mtx/
+
+# hpc
+exclude
+hpc_auth_sync.sh
+
+# eclipse
+.project
+.cproject
+.settings/
+
+# matlab
+*.m~
+

homework_1/matlab/dist2.m

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+function [D2] = dist2(X, Y)
+% Calculates the squares of the distances of X and Y
+%
+% X: A mxd array with m d-dimentional points
+% Y: A nxd array with n d-dimentional points
+% d: Must be the same
+
+    [~, d1] = size(X);
+    [~, d2] = size(Y);
+    if d1 ~= d2
+        error('X,Y column dimensions must match');
+    end
+    %D2 = sqrt((X.^2)*ones(d,m) -2*X*Y' + ones(n,d)*(Y.^2)');
+    D2 = max(sum(X.^2, 2) - 2 * X*Y' + sum(Y.^2, 2)', 0);
+    D2 = sqrt(D2);
+    
+end
+

homework_1/matlab/knnsearch2.m

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+function [idx, dst] = knnsearch2(C, Q, k)
+% C: Is a mxd matrix (Corpus)
+% Q: Is a nxd matrix (Query)
+% k: The number of nearest neighbors needded
+
+    % Calculate the distance matrix between C and Q
+    % D is an m x n matrix where each element D(i, j) is the distance 
+    % between the i-th point in C and the j-th point in Q.
+    % k is the number of nearest neighbors to find.
+    D = dist2(C, Q);
+
+    % Find the k-nearest neighbors for each query point in Q
+    % [~,n] = size(D);
+    % for j = 1:n        
+    %    [dst(:, j), idx(:, j)] = mink(D(:, j), k);
+    % end
+    [dst, idx] = mink(D, k, 1);  % mink along dimension 1 for each query point
+    
+    % Transpose the output to match the knnsearch format
+    idx = idx';  % Make idx an n x k matrix
+    dst = dst';  % Make dst an n x k matrix
+
+end
+

homework_1/matlab/test.m

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+%
+%
+%
+
+C = rand(40000,4);
+Q = rand(4000,4);
+
+disp ('C-Q');
+disp ('build-in')
+tic; [i1, d1] = knnsearch(C, Q, 'k', 4); toc
+disp ('mine')
+tic; [i2, d2] = knnsearch2(C, Q, 4); toc
+sum(i1-i2)
+sum(d1-d2)
+
+disp (' ');
+disp (' ');
+disp ('C-C');
+disp ('build-in')
+tic; [i1, d1] = knnsearch(C, C, 'k', 4); toc
+disp ('mine')
+tic; [i2, d2] = knnsearch2(C, C, 4); toc
+sum(i1-i2)
+sum(d1-d2)