Finding the nearest neighbors

Nearest neighbors model refers to a general class of algorithms that aim to make a decision based on the number of nearest neighbors in the training dataset. Let's see how to find the nearest neighbors.

How to do it…

1. Create a new Python file, and import the following packages:

   import numpy as np
   import matplotlib.pyplot as plt
   from sklearn.neighbors import NearestNeighbors

2. Let's create some sample two-dimensional data:

   # Input data
   X = np.array([[1, 1], [1, 3], [2, 2], [2.5, 5], [3, 1], 
           [4, 2], [2, 3.5], [3, 3], [3.5, 4]])

3. Our goal is to find the three closest neighbors to any given point. Let's define this parameter:

   # Number of neighbors we want to find
   num_neighbors = 3

4. Let's define a random datapoint that's not present in the input data:

   # Input point
   input_point = [2.6, 1.7]

5. We need to see what this data looks like. Let's plot it, as follows:

   # Plot datapoints
   plt.figure()
   plt.scatter(X[:,0], X[:,1], marker='o', s=25, color='k')

6. In order to find the nearest...