user3272574 - 2 months ago 6x

Python Question

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, a (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]

Answer

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. Instead, you could try using `numpy.matrix`

, and `*`

will be treated like matrix multiplication.

Also know there are other options:

As noted below, if using python3 the

`@`

operator works as you'd expect:`>>> print(a @ b) array([16, 6, 8])`

If you want overkill, you can use

`numpy.einsum`

. The documentation will give you a flavor for how it works, but honestly, I didn't fully understand how to use it until reading this answer and just playing around with it on my own.`>>> np.einsum('ji,i->j', a, b) array([16, 6, 8])`

As of mid 2016 (numpy 1.10.1), you can try the experimental

`numpy.matmul`

, which works like`numpy.dot`

with two major exceptions: no scalar multiplication but it works with stacks of matrices.`>>> np.matmul(a, b) array([16, 6, 8])`

Source (Stackoverflow)

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