Machine Learning with Swift

Implementing k-means in Swift

Similar to the KNN from the previous chapter, we'll have a structure to represent an algorithm and keep all its hyperparameters:

struct KMeans { 
  public let k: Int 

The standard k-means algorithm was designed to be used only with Euclidean distance:

internal let distanceMetric = Euclidean.distance 

We need several arrays to store different kinds of data during the clustering.

Storage for samples:

internal var data: [[Double]] = [] 

Coordinates of centroids:

public var centroids: [[Double]] = [] 

An array that matches each sample to its cluster. It should be of the same length as the data, and for every sample, it stores an index of centroid in the centroids array:

private(set) var clusters: [Int] = [] 

Within-cluster ...