Matrices can be created with the mat
function. This function does not make a copy if the input is already a matrix
or an ndarray
. Calling this function is equivalent to calling matrix(data, copy=False)
. We will also demonstrate transposing and inverting matrices.
mat
function with the following string to create a matrix:A = np.mat('1 2 3; 4 5 6; 7 8 9') print "Creation from string", A
The matrix output should be the following matrix:
Creation from string [[1 2 3] [4 5 6] [7 8 9]]
T
attribute, as follows:print "transpose A", A.T
The following is the transposed matrix:
transpose A [[1 4 7] [2 5 8] [3 6 9]]
I
attribute, as follows:print "Inverse A", A.I
The inverse matrix is printed as follows (be warned that this is a O(n3)
operation):
Inverse A [[ -4.50359963e+15 9.00719925e+15 -4.50359963e+15] [ 9.00719925e+15 -1.80143985e+16 9.00719925e+15] [ -4.50359963e+15 9.00719925e+15 -4.50359963e+15]]
print "Creation from array", np.mat(np.arange(9).reshape(3, 3))
The newly-created array is printed as follows:
Creation from array [[0 1 2] [3 4 5] [6 7 8]]
We created matrices with the mat
function. We transposed the matrices with the T
attribute and inverted them with the I
attribute (see matrixcreation.py
):
import numpy as np A = np.mat('1 2 3; 4 5 6; 7 8 9') print "Creation from string", A print "transpose A", A.T print "Inverse A", A.I print "Check Inverse", A * A.I print "Creation from array", np.mat(np.arange(9).reshape(3, 3))