matrix multiplication of arrays in python
I feel a bit silly asking this, but I can't seem to find the answer
Using arrays in Numpy I want to multiply a 3X1 array by 1X3 array and get a 3X3 array as a results, but because dot function always treats the first element as a column vector and the second as a row vector I can' seem to get it to work, I have to therefore use matrices.
A=array([1,2,3])
print "Amat=",dot(A,A)
print "A2mat=",dot(A.transpose(),A)
print "A3mat=",dot(A,A.transpose())
u2=mat([ux,uy,uz])
print "u2mat=", u2.transpose()*u2
And the outputs:
Amat= 14
A2开发者_运维知识库mat= 14
A3mat= 14
u2mat=
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 1.]]
np.outer is a builtin to do that:
A = array([1,2,3])
print( "outer:", np.outer( A, A ))
(transpose
doesn't work because A.T
is exactly the same as A for 1d arrays:
print( A.shape, A.T.shape, A[:,np.newaxis].shape )
>>> ( (3,), (3,), (3, 1) )
)
Added: np.add.outer
adds pairs of elements --
np.outer
is much like np.multiply.outer
. And
np.ufunc.outer (A, B) combines pairs with any
binary ufunc.
>>> A=np.array([1,2,3])
>>> A[:,np.newaxis]
array([[1],
[2],
[3]])
>>> A[np.newaxis,:]
array([[1, 2, 3]])
>>> np.dot(A[:,np.newaxis],A[np.newaxis,:])
array([[1, 2, 3],
[2, 4, 6],
[3, 6, 9]])
well one way to obtain this is to work with the matrix
class/type instead.
import numpy as np
A = np.matrix([1,2,3])
B = A.T #transpose of A
>>> B*A
>>> matrix([[1, 2, 3],
[2, 4, 6],
[3, 6, 9]])
the objects belonging to the matrix class behave pretty much the same as the arrays. Actually arrays and matrices are mutually interchangeable.
精彩评论