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pythonarraysnumpymatrix
noclew
noclew

Reputation: 542

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.

Upvotes: 0

Views: 118

Answers (2)

Mark Hannel
Mark Hannel

Reputation: 795

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, 4]])

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

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

Upvotes: 2

travelingbones
travelingbones

Reputation: 8418

np.dot is probably what you're looking for?

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

b = np.array([[1,10], [1, 10]])

np.dot(a,b)

Out[6]:
array([[  3,  30],
       [  7,  70],
       [ 11, 110]])

Upvotes: 1

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