If your matrix is sparse, then it seems that the currently method of storing it (as a full matrix in a text file) is very inefficient, and certainly makes loading it into MATLAB very hard. However, I suspect that as long as it is sparse enough, it can still be leaded into MATLAB as a sparse matrix.
The traditional way of doing this would be to load it all in at once, then convert to sparse representation. In your case, however, it would make sense to read in the text file, one line at a time, and convert to a MATLAB sparse matrix on-the-fly.
You can find out if this is possible by estimating the sparsity of your matrix, and using this to see if the whole thing could be loaded into MATLAB's memory as a sparse matrix.
Try something like: (untested code!)
% initialise sparse matrix
sparse_matrix = sparse(num_rows, num_cols);
row_num = 1;
fid = fopen(filename);
% read each line of text file in turn
while ~feof(fid)
this_line = fscanf(fid, '%f');
% add row to sparse matrix (note transpose, which I think is required)
sparse_matrix(row_num, :) = this_line';
row_num = row_num + 1;
end
fclose(fid)
% visualise using spy
spy(sparse_matrix)
Visualisation
With regards to visualisation: visualising a sparse matrix like this via a tool like imagesc
is possible, but I believe it may internally create the full matrix – maybe someone can confirm if this is true or not. If it does, then it's going to cause you memory problems.
All spy
is really doing is plotting in 2D the locations of the non-zero elements. You can fairly easily write your own spy function, which can have different coloured or sized points depending on the values at each location. See this answer for some examples.
Saving sparse matrices
As I say above, the method your matrix is saved as is pretty inefficient – for a matrix with 10% sparsity, around 95% of your text file will be a zero or a space. I don't know where this data has come from, but if you have any control over its creation (e.g. it comes from another program you have written) it would make much more sense to save only the non-zero elements in the format row_idx, col_idx, value
.
You can then use spconvert
to import the sparse matrix directly.