Adding a vector to matrix rows in numpy

Question

Is there a fast way in numpy to add a vector to every row or column of a matrix.

Lately, I have been tiling the vector to the size of the matrix, which can use a lot of memory. For example

    mat=np.arange(15)
    mat.shape=(5,3)

    vec=np.ones(3)
    mat+=np.tile(vec, (5,1))

The other way I can think of is using a python loop, but loops are slow:

    for i in xrange(len(mat)):
        mat[i,:]+=vec

Is there a fast way to do this in numpy without resorting to C extensions?

It would be nice to be able to virtually tile a vector, like a more flexible version of broadcasting. Or to be able to iterate an operation row-wise or column-wise, which you may almost be able to do with some of the ufunc methods.

Could you give another example? The one you've given would give the same answer just with mat + vec, so I'm not sure exactly what you're after. [Incidentally, this is an array, not a matrix.] — DSM
by matrix, I mean a 2-d array (a matrix in the mathematical sense) — user1149913
I want to add the same 1-d array to every row of the 2d array — user1149913
In numpy, a matrix is different from a 2d array. For example, multiplication is matrix multiplication on matrix objects but elementwise on array objects, so it's a good idea to keep them distinct. — DSM

JoshAdel JoshAdel · Accepted Answer · 2012-08-15T14:37:31

For adding a 1d array to every row, broadcasting already takes care of things for you:

mat += vec

However more generally you can use np.newaxis to coerce the array into a broadcastable form. For example:

mat + np.ones(3)[np.newaxis,:]

While not necessary for adding the array to every row, this is necessary to do the same for column-wise addition:

mat + np.ones(5)[:,np.newaxis]

EDIT: as Sebastian mentions, for row addition, mat + vec already handles the broadcasting correctly. It is also faster than using np.newaxis. I've edited my original answer to make this clear.

Adding a vector to matrix rows in numpy

1 Answers