Define a Vector – Linear Algebra

Lesson 8 Chapter 3

Let's talk about how we define a vector with Numpy. Assume we would like to define a column vector with has a size of k \times 1. Take a careful look to the code below and the shape of the arrays:

# Import Numpy library
import numpy as np

# Rank-1 array
v = np.array([0,8])
print('Shape: ', v.shape)

# Rank-2 array (row vector)
v = np.array([[0,8]])
print('Shape: ', v.shape)

# Rank-2 array (column vector)
v = np.array([[0],[8]])
print('Shape: ', v.shape)

In the above code, we defined the same arrays in terms of numeric values with different ranks and shape. At line 7, we defined a rank-1 array (has only one dimension). At 11, we defined a rank-2 array which is a row vector (1 row and multiple columns). For defining vectors, the preference is how we did at line 15 which results in a rank-2 array and a column vector (multiple rows and 1 column). The output of the above code is as below:

Shape:  (2,)
Shape:  (1, 2)
Shape:  (2, 1)

Remember we do NOT usually need to define vectors as we did in lines 11 or 15. That approach seemed to be a little bit complicated using all those sorts of nested Python lists! Now let's do it the easy way:

import numpy as np

# Rank-1 array
v = np.array([0,8])
print('Shape: ', v.shape)

# Rank-1 array (row vector)
row_v = v.reshape(1,-1)
print('Shape: ', row_v.shape)

# Rank-1 array (column vector)
column_v = v.reshape(-1,1)
print('Shape: ', column_v.shape)

What I did above? (1) I used "-1" as it indicates all rows (columns). (2) I used the Numpy "reshape" method which simply changes the shape of the array to the desired shape (details later in this tutorial). (3) I used "1" indexing which indicates one!

Let me explain the line 8 of the above code for further illustration. (1) "-1" is the total columns which are the total elements of the vector \mathbf{v}, equals 2. (2) Numpy "reshape" method changes the \mathbf{v} shape to (1,2) which means the new vector (row_v) has 2 columns and only one row! It is worth to emphasize row_v is a row vector as it only has one row.

NOTE: In simple words, (1) (1,-1) means put only one row and place all elements in columns and (-1,1) means put only one column and place all elements in rows. Check the below figure.

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