Mastering OpenCV 4 with Python

Understanding k-nearest neighbors

The knn_introduction.py script carries out a simple introduction to kNN, where a set of points are randomly created and assigned a label (0 or 1). Label 0 will represent red triangles, while label 1 will represent blue squares. We will use the kNN algorithm to classify a sample point based on the k nearest neighbors.

Hence, the first step is to create both the set of points with the corresponding label and the sample point to classify:

# The data is composed of 16 points:data = np.random.randint(0, 100, (16, 2)).astype(np.float32)# We create the labels (0: red, 1: blue) for each of the 16 points:labels = np.random.randint(0, 2, (16, 1)).astype(np.float32)# Create the sample point to be classified:sample = ...