How to create this matrix from numpy array?

Question

So I want to create the sparse matrix as below from the numpy array matrix as usual:

from scipy import sparse
I = np.array([0,1,2, 0,1,2, 0,1,2])
J = np.array([0,0,0,1,1,1,2,2,2])
DataElement = np.array([2,1,2,1,0,1,2,1,2])
A = sparse.coo_matrix((DataElement,(I,J)),shape=(3,3))
print(A.toarray()) ## This is what I expect to see.

My attempt with numpy is:

import numpy as np

U = np.empty((3,3,), order = "F")
U[:] = np.nan

## Initialize 
U[0,0] = 2
U[2,0] = 2
U[0,2] = 2
U[2,2] = 2

for j in range(0,3):
    ## Slice columns first: 
    if (j !=0 and j!= 2):
        for i in range(0,3):
            ## slice rows: 
            if (i != 0 and i != 2):
                  U[i,j] = 0
            else: 
                U[i,j] = 1

Chris · Accepted Answer · 2020-10-03 03:23:18Z

2

One way using numpy.add.at:

arr = np.zeros((3,3), int)
np.add.at(arr, (I, J), DataElement)
print(arr)

Output:

array([[2, 1, 2],
       [1, 0, 1],
       [2, 1, 2]])

answered Oct 3, 2020 at 3:23

Chris

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Mikhail Genkin · Accepted Answer · 2020-10-03 03:27:29Z

0

There are several ways of manual filling of the arrays.

First, you can explicitly define each entry:

U = np.array([[2,1,2],[1,0,1],[2,1,2]],order='F')

Or you can initialize an array with nans and then define each element by subscribing them:

U = np.empty((3,3,), order = "F")
U[:] = np.nan
U[0,0],U[0,1],U[0,2]=2,1,2
U[1,0],U[1,1],U[1,2]=1,0,1
U[2,0],U[2,1],U[2,2]=2,1,2

Finally, if there is a pattern, one can slice and define multiple values at once:

U[:,0]=[2,1,2]
U[:,1]=U[:,0]-1
U[:,2]=U[:,0]

In your attempt, you simply miss some of the entries, and they remain nans.

answered Oct 3, 2020 at 3:27

Mikhail Genkin

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1 Comment

Dong Le Over a year ago

Thank you!! I just figured it out with the for-loop one.

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