Question

I have four numpy arrays like:

X1 = array([[1, 2], [2, 0]])

X2 = array([[3, 1], [2, 2]])

I1 = array([[1], [1]])

I2 = array([[1], [1]])

And I’m doing:

Y = array([I1, X1],
          [I2, X2]])

To get:

Y = array([[ 1,  1,  2],
           [ 1,  2,  0],
           [-1, -3, -1],
           [-1, -2, -2]])

Like this example, I have large matrices, where X1 and X2 are n x d matrices.

Is there an efficient way in Python whereby I can get the matrix Y?

Although I am aware of the iterative manner, I am searching for an efficient manner to accomplish the above mentioned.

Here, Y is an n x (d+1) matrix and I1 and I2 are identity matrices of the dimension n x 1.

Answer 1

You need numpy.bmat

In [4]: A = np.mat('1 ; 1 ')
In [5]: B = np.mat('2 2; 2 2')
In [6]: C = np.mat('3 ; 5')
In [7]: D = np.mat('7 8; 9 0')
In [8]: np.bmat([[A,B],[C,D]])
Out[8]: 
matrix([[1, 2, 2],
        [1, 2, 2],
        [3, 7, 8],
        [5, 9, 0]])