Error using sparse Index into matrix must be positive.

Im converting a code from python to Matlab

% Python Code
% ksq and old_dim are dimensions e.g. 5, or 10 
C = np.arange(old_dim)
R = np.random.random_integers(ksq, size=old_dim) - 1
D = np.random.randint(2, size=old_dim) * 2 - 1
S = scipy.sparse.csr_matrix((D, (R, C)), shape=(ksq, old_dim))

The Matlab code i wrote.

C=1:old_dim;
R = randi(ksq,[1,old_dim]) - 1;
D = randi(2, [1,old_dim]) * 2 - 1;
S = sparse(R,C,D,ksq,old_dim);

When i run my matlab code, it tells me

Error using sparse Index into matrix must be positive.

My guess is when i have a zero in one of the variables R C D thats when it throws this error? and what could be a solution to this. thanks

[Adam]

Matlab indexes from 1 so just add one to any indexing you have in a 0-indexed language.

Answer 1

[Fabio Freschi]

Simply remove "-1" from the definition of R. randi returns pseudorandom integers between 1 and the first inupt. The entries of C will never be 0.

C = 1:old_dim;
R = randi(ksq,[1,old_dim]);
D = randi(2, [1,old_dim]) * 2 - 1;
S = sparse(R,C,D,ksq,old_dim);