noclew - 1 year ago 93
Python Question

# Numpy matrix(array) rationale

I am so confused about Numpy array. Let's say I have two Numpy arrays.

a = np.array([[1,2], [3,4], [5,6]])
b = np.array([[1,10], [1, 10]])

My interpretations of
a
and
b
are 3x2 and 2x2 matrices, i.e,

a = 1 2 b = 1 10
3 4 1 10
5 6

Then, I thought it should be fine to do
a * b
since it is a multiplication of 3x2 and 2x2 matrices. However, it was not possible and I had to use
a.dot(b)
.

Given this fact, I think my intepretation of Numpy array is not right. Can anyone let me know how I should think of Numpy array? I know that I can do
a*b
if I convert
a
and
b
into np.matrix. However, looking at other's code, it seems that people are just fine to use Numpy array as matrix, so I wonder how I should understand Numpy array in terms of matrix.

Answer Source

For numpy arrays, the * operator is used for element by element multiplication of arrays. This is only well defined if both arrays have the same dimensions. To illuminate *-multiplication, note that element by element multiplication with the identity matrix will not return the same matrix

>>> I = np.array([[1,0],[0,1]])
>>> B = np.array([[1,2],[3,4]])
>>> I*B
array([[ 1,  0],
[ 0, 10]])

Using the numpy function dot(a,b) produces the typical matrix multiplication.

>>> dot(I,B)
array([[ 1, 2],
[ 3, 4]])
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