# numpy matrix vector multiplication

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]
```

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

, and `*`

will be treated like matrix multiplication.

## Other Solutions

Also know there are other options:

- As noted below, if using python3.5+ 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])
```

`numpy.inner`

functions the same way as`numpy.dot`

**for matrix-vector multiplication but behaves differently**for matrix-matrix and tensor multiplication (see Wikipedia regarding the differences between the inner product and dot product in general or see this SO answer regarding numpy's implementations).

```
>>> np.inner(a, b)
array([16, 6, 8])
# Beware using for matrix-matrix multiplication though!
>>> b = a.T
>>> np.dot(a, b)
array([[35, 9, 10],
[ 9, 3, 4],
[10, 4, 6]])
>>> np.inner(a, b)
array([[29, 12, 19],
[ 7, 4, 5],
[ 8, 5, 6]])
```

## Rarer options for edge cases

- If you have tensors (arrays of dimension greater than or equal to one), you can use
`numpy.tensordot`

with the optional argument`axes=1`

:

```
>>> np.tensordot(a, b, axes=1)
array([16, 6, 8])
```

**Don't use**if you have a matrix of complex numbers, as the matrix will be flattened to a 1D array, then it will try to find the complex conjugate dot product between your flattened matrix and vector (which will fail due to a size mismatch`numpy.vdot`

`n*m`

vs`n`

).

From: stackoverflow.com/q/21562986

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