# 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])
```
```     >>> 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`numpy.vdot` 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 `n*m` vs `n`).

From: stackoverflow.com/q/21562986