Suppose I have a matrix A of dimension Nx(N-1) in MATLAB, e.g.
N=5;
A=[1 2 3 4;
5 6 7 8;
9 10 11 12;
13 14 15 16;
You can do this with upper and lower triangular parts of the matrix (triu and tril).
Then it's a 1 line solution:
B = [tril(A,-1) zeros(N, 1)] + [zeros(N,1) triu(A)];
Edit: benchmark
This is a comparison of the loop method, the 2 methods in Sardar's answer, and my method above.
Benchmark code, using timeit for timing and directly lifting code from question and answers:
function benchie()
N = 1e4; A = rand(N,N-1); % Initialise large matrix
% Set up anonymous functions for input to timeit
s1 = @() sardar1(A,N); s2 = @() sardar2(A,N);
w = @() wolfie(A,N); u = @() user3285148(A,N);
% timings
timeit(s1), timeit(s2), timeit(w), timeit(u)
end
function sardar1(A, N) % using eye as an indexing matrix
B=double(~eye(N)); B(find(B))=A.'; B=B.';
end
function sardar2(A,N) % similar to sardar1, but avoiding slow operations
B=1-eye(N); B(logical(B))=A.'; B=B.';
end
function wolfie(A,N) % using triangular parts of the matrix
B = [tril(A,-1) zeros(N, 1)] + [zeros(N,1) triu(A)];
end
function user3285148(A, N) % original looping method
B = zeros(N,N); B(1,:) = [0 A(1,:)]; B(N,:) = [A(N,:) 0];
for j=2:N-1; B(j,:)= [A(j,1:j-1) 0 A(j,j:end)]; end
end
Results:
Conclusions:
find for large matrices is important, saving ~35% processing time between Sardar's methods.