# Vector and matrix dot products, “np.outer”

## Contents

# Vector and matrix dot products, “np.outer”#

Our standard imports to start:

```
import numpy as np
import matplotlib.pyplot as plt
# Display array values to 6 digits of precision
np.set_printoptions(precision=6, suppress=True)
```

## Vector dot products#

If I have two vectors \(\vec{a}\) with elements \(a_0, a_1, ... a_{n-1}\), and \(\vec{b}\) with elements \(b_0, b_1, ... b_{n-1}\) then the dot product is defined as:

In code:

```
a = np.arange(5)
b = np.arange(10, 15)
np.dot(a, b)
```

```
130
```

```
# The same thing as
np.sum(a * b) # Elementwise multiplication
```

```
130
```

`dot`

is also a *method* of the NumPy array object, and using the method can
be neater and easier to read:

```
a.dot(b)
```

```
130
```

Better still, the matrix multiplication operator `@`

implies a dot product between two vectors:

```
a @ b
```

```
130
```

## Matrix dot products#

Matrix multiplication operates by taking dot products of the rows of the first array (matrix) with the columns of the second.

Let’s say I have a matrix \(\mathbf{X}\), and \(\vec{X_{i,:}}\) is row \(i\) in \(\mathbf{X}\). I have a matrix \(\mathbf{Y}\), and \(\vec{Y_{:,j}}\) is column \(j\) in \(\mathbf{Y}\). The output matrix \(\mathbf{Z} = \mathbf{X} \mathbf{Y}\) has entry \(Z_{i,j} = \vec{X_{i,:}} \cdot \vec{Y_{:, j}}\).

```
X = np.array([[0, 1, 2], [3, 4, 5]])
X
```

```
array([[0, 1, 2],
[3, 4, 5]])
```

```
Y = np.array([[7, 8], [9, 10], [11, 12]])
Y
```

```
array([[ 7, 8],
[ 9, 10],
[11, 12]])
```

```
X @ Y
```

```
array([[ 31, 34],
[112, 124]])
```

```
X[0, :] @ Y[:, 0]
```

```
31
```

```
X[1, :] @ Y[:, 0]
```

```
112
```

## The outer product#

We can use the rules of matrix multiplication for row vectors and column vectors.

A row vector is a 2D vector where the first dimension is length 1.

```
row_vector = np.array([[1, 3, 2]])
print(row_vector.shape)
row_vector
```

```
(1, 3)
```

```
array([[1, 3, 2]])
```

A column vector is a 2D vector where the second dimension is length 1.

```
col_vector = np.array([[2], [0], [1]])
print(col_vector.shape)
col_vector
```

```
(3, 1)
```

```
array([[2],
[0],
[1]])
```

We know what will happen if we matrix multiply the row vector and the column vector:

```
row_vector @ col_vector
```

```
array([[4]])
```

What happens when we matrix multiply the column vector by the row vector? We know this will work because we are multiplying a 3 by 1 array by a 1 by 3 array, so this should generate a 3 by 3 array:

```
col_vector @ row_vector
```

```
array([[2, 6, 4],
[0, 0, 0],
[1, 3, 2]])
```

This arises from the rules of matrix multiplication, except there is only one row * column pair making up each of the output elements:

```
print(col_vector[0] * row_vector)
print(col_vector[1] * row_vector)
print(col_vector[2] * row_vector)
```

```
[[2 6 4]]
[[0 0 0]]
[[1 3 2]]
```

This (M by 1) vector matrix multiply with a (1 by N) vector is also called the
*outer product* of two vectors. We can generate the same thing from 1D
vectors, by using the numpy `np.outer`

function:

```
np.outer(col_vector.ravel(), row_vector.ravel())
```

```
array([[2, 6, 4],
[0, 0, 0],
[1, 3, 2]])
```

## Dot, vectors and matrices#

Unlike MATLAB, Python has one-dimensional vectors. For example, if I slice a column out of a 2D array of shape (M, N), I do not get a column vector, shape (M, 1), I get a 1D vector, shape (M,):

```
X = np.array([[0, 1, 2],
[3, 4, 5]])
v = X[:, 0]
v
```

```
array([0, 3])
```

Because the 1D vector has lost the idea of being a column rather than a row in a matrix, it is no longer unambiguous what \(v \cdot \mathbf{X}\) means. It could mean a dot product of a row vector shape (1, M) with a matrix shape (M, N), which is valid – or a dot product of a row vector (M, 1) with a matrix shape (M, N), which is not valid.

If you pass a 1D vector into the `dot`

function or method, or use the `@`

matrix multiplier, NumPy assumes you mean it to be a row vector on the left,
and a column vector on the right, which is nearly always what you intended:

```
# 1D vector is row vector on the left hand side of dot / matrix multiply.
v @ X
```

```
array([ 9, 12, 15])
```

```
# 1D vector is column vector on the right hand side of dot / matrix multiply.
w = np.array([-1, 0, 1])
X @ w
```

```
array([2, 2])
```

Notice that, in both cases, `@`

returns a 1D result.

It sometimes helps to make a 1D vector into a 2D row or column vector, to make your intention explicit, and preserve the 2D shape of the output:

```
# Turn 1D vector into explicit row vector
row_v = np.reshape(v, (1, 2))
# @ now returns a row vector rather than a 1D vector
row_v @ X
```

```
array([[ 9, 12, 15]])
```