Creating a sparse matrix from numpy array

Question

I need to create a matrix with values from a numpy array. The values should be distributed over the matrix lines according to an array of indices.

Like this:

>>> values
array([ 0.73620381,  0.61843002,  0.33604769,  0.72344274,  0.48943796])
>>> inds
array([0, 1, 2, 3, 2])
>>> m = np.zeros((4, 5))
>>> for i, (index, value) in enumerate(zip(inds, values)):
        m[index, i] = value
>>> m
array([[ 0.73620381,  0.        ,  0.        ,  0.        ,  0.        ],
       [ 0.        ,  0.61843002,  0.        ,  0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.33604769,  0.        ,  0.48943796],
       [ 0.        ,  0.        ,  0.        ,  0.72344274,  0.        ]])

I'd like to know if there is a vectorized way to do it, i.e., without a loop. Any suggestions?

Glorfindel · Accepted Answer · 2023-01-03 21:00:34Z

4

Here's how you could do it with fancy indexing:

>>> values
array([ 0.73620381,  0.61843002,  0.33604769,  0.72344274,  0.48943796])
>>> inds
array([0, 1, 2, 3, 2])
>>> mshape = (4,5)
>>> m = np.zeros(mshape)
>>> m[inds,np.arange(mshape[1])] = values
>>> m
array([[ 0.73620381,  0.        ,  0.        ,  0.        ,  0.        ],
       [ 0.        ,  0.61843002,  0.        ,  0.        ,  0.        ],
       [ 0.        ,  0.        ,  0.33604769,  0.        ,  0.48943796],
       [ 0.        ,  0.        ,  0.        ,  0.72344274,  0.        ]])

edited Jan 3, 2023 at 21:00

Glorfindel

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answered Feb 2, 2013 at 21:51

John Vinyard

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2 Comments

DSM Over a year ago

[Looks in his command history to see why this didn't work for him, given that it was the first thing he tried.. realizes that he's an idiot.. deletes his answer and upvotes this. IOW, a typical afternoon.]

John Vinyard Over a year ago

@DSM, we've all been there. FWIW, your numpy/scipy answers are always stellar.

hpaulj · Accepted Answer · 2013-08-31 05:40:29Z

2

Your values and inds arrays can be used as input to a scipy.sparse constructor (similar to sparse in Matlab).

from scipy import sparse
values = np.array([ 0.73620381,  0.61843002,  0.33604769,  0.72344274,  0.48943796])
inds=np.array([0,1,2,3,2])
index = np.arange(5)
m=sparse.csc_matrix((values,(inds,index)),shape=(4,5))
m.todense()  # produces a matrix or
m.toarray()

answered Aug 31, 2013 at 5:40

hpaulj

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