![Numpy Matrix Vector Multiplication [Duplicate]](https://docuprint.cloud68.co/uploads/numpy-matrix-vector-multiplication-duplicate_1200.jpg)
When I multiply two numpy arrays of sizes (n x n)*(n x 1), I get a matrix of size (n x n). Following normal matrix multiplication rules, an (n x 1) vector is expected, but I simply cannot find any information about how this is done in Python's Numpy module.
The thing is that I don't want to implement it manually to preserve the speed of the program.
Example code is shown below:
a = np.array([[5, 1, 3], [1, 1, 1], [1, 2, 1]])
b = np.array([1, 2, 3])
print a*b
>>
[[5 2 9]
[1 2 3]
[1 4 3]]
What I want is:
print a*b
>>
[16 6 8]
1 Answer
Simplest solution
Use numpy.dot or a.dot(b). See the documentation here.
>>> a = np.array([[ 5, 1 ,3],
[ 1, 1 ,1],
[ 1, 2 ,1]])
>>> b = np.array([1, 2, 3])
>>> print a.dot(b)
array([16, 6, 8])
This occurs because numpy arrays are not matrices, and the standard operations *, +, -, / work element-wise on arrays.
Note that while you can use numpy.matrix (as of early 2021) where * will be treated like standard matrix multiplication, numpy.matrix is deprecated and may be removed in future releases.. See the note in its documentation (reproduced below):
It is no longer recommended to use this class, even for linear algebra. Instead use regular arrays. The class may be removed in the future.
Thanks @HopeKing.
