How can I convert double values into integers for indices to create a sparse matrix in MATLAB?

Question

I am using MATLAB to load a text file that I want to make a sparse matrix out of. The columns in the text file refer to the row indices and are double type. I need them to be integers to be able to use them as indices for rows and columns. I tried using uint8, int32 and int64 to convert them to integers to use them to build a sparse matrix as so:

??? Undefined function or method 'sparse' for input arguments of type 'int64'. Error in ==> make_network at 5

graph =sparse(int64(listedges(:,1)),int64(listedges(:,2)),ones(size(listedges,1),1));

How can I convert the text file entries loaded as double so as to be used by the sparse function?

Answer 1

There is no need for any conversion, keep the indices double:

r = round(listedges);
graph = sparse(r(:, 1), r(:, 2), ones(size(listedges, 1), 1));

Answer 2

There are two reasons why one might want to convert to int:
The first, because you have data type restrictions.
The second, your inputs may contain fractions and are un-fit to be used as integers.

If you want to convert because of the first reason - then there's no need to: Matlab works with double type by default and often treats doubles as ints (for example, when used as indices).

However, if you want to convert to integers becuase of the second reason (numbers may be fractionals), then you should use round(), ceil() or floor() - whatever suits your purpose best.

Answer 3

There is another very good reason ( and really the primary one..) why one may want to convert indices of any structure (array, matrix, etc.) to int.

If you ever program in any language other than Matlab, you would be familiar with wanting to save memory space, especially with large structures. Being able to address elements in such structures with indices other than double is key.

One major issue with Matlab is the inability to more finely control the size of multidimensional structures in this way. There are sparse matrix solutions, but those are not adequate for many cases. Cell arrays will preserve the data types upon access, however the storage for every element in the cell array is extremely wasteful in terms of storage (113 bytes for a single uint8 encapsulated in a cell).