Cosine distance between two vectors is defined as:
It is often used as evaluate the similarity of two vectors, the bigger the value is, the more similar between these two vectors.
Cosine distance is also can be defined as:
The smaller θ, the more similar x and y.
In this tutorial, we will introduce how to calculate the cosine distance between two vectors using numpy, you can refer to our example to learn how to do.
import numpy as np
Create two vectors
vector_1 = np.array([1, 5, 1, 4, 0, 0, 0, 0, 0]) vector_2 = np.array([2, 4, 1, 1, 1, 1, 0, 0, 0])
Calculate cosine distance
def cos_sim(a, b): """Takes 2 vectors a, b and returns the cosine similarity """ dot_product = np.dot(a, b) # x.y norm_a = np.linalg.norm(a) #|x| norm_b = np.linalg.norm(b) #|y| return dot_product / (norm_a * norm_b)
How to use?
The output is: