[SciPy-User] Normalizing a sparse matrix

Warren Weckesser warren.weckesser@enthought....
Sun Mar 20 05:37:21 CDT 2011

On Sun, Mar 20, 2011 at 2:06 AM, coolhead.pranay@gmail.com <
coolhead.pranay@gmail.com> wrote:

> Hi,
> I have a sparse matrix with nearly (300*10000) entries constructed out of
> 14000*14000 matrix...In each iteration after performing some operations on
> the sparse matrix(like multiply and dot) I have to divide each row of the
> corresponding dense matrix with the sum of its elements...
> Since sparse matrix format doesn't allow all the required matrix
> operation(divide) I tried to convert it to a dense format and then divide by
> the sum. But this raises MemoryError exception because 14000*14000 matrix
> doesn't fit memory..
> Can someone tell me how to normalize a sparse matrix ?

This will normalize the rows of R, a sparse matrix in CSR format:


# Normalize the rows of R.
row_sums = np.array(R.sum(axis=1))[:,0]
# OR: row_sums = R.dot(np.ones(R.shape[1]))
row_indices, col_indices = R.nonzero()
R.data /= row_sums[row_indices]


The attached code provides an example of that snippet in use.

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