The inverse of a matrix A in linear algebra is the matrix A
-1, which when multiplied with the original matrix, is equal to the identity matrix I. This can be written, as A* A
-1
= I.
The inv function in the numpy.linalg package can do this for us. Let's invert an example matrix. To invert matrices, perform the following steps:
We will create the example matrix with the
matfunction that we used in the previous chapters.A = np.mat("0 1 2;1 0 3;4 -3 8") print "A\n", AThe
Amatrix is printed as follows:A [[ 0 1 2] [ 1 0 3] [ 4 -3 8]]
Now, we can see the
invfunction in action, using which we will invert the matrix.inverse = np.linalg.inv(A) print "inverse of A\n", inverse
The inverse matrix is shown as follows:
inverse of A [[-4.5 7. -1.5] [-2. 4. -1. ] [ 1.5 -2. 0.5]]
Let...