Mojo struct

KMeans

@memory_only
struct KMeans

K-Means clustering.

Aliases

• __del__is_trivial = False

Fields

• K (Int): The number of clusters to form as well as the number of centroids to generate.
• init (String): Method for initialization -> 'kmeans++', 'random'.
• max_iters (Int): Maximum number of iterations of the k-means algorithm for a single run.
• converge (String): The converge method: Change in centroids <= tol -> 'centroid'; Change in inertia <= tol -> 'inertia'; Exact change in labels -> 'label'.
• tol (Float32): Relative tolerance value.
• labels (List[Int])
• centroids (Matrix)
• inertia (Float32): Sum of squared distances of samples to their closest cluster center.
• X (Matrix)

Implemented traits

AnyType, ImplicitlyDestructible

Methods

__init__

fn __init__(out self, K: Int = 5, init: String = "kmeans++", max_iters: Int = 100, converge: String = "centroid", tol: Float32 = 1.0E-4, random_state: Int = 42)

Args:

• K (Int)
• init (String)
• max_iters (Int)
• converge (String)
• tol (Float32)
• random_state (Int)
• self (Self)

Returns:

Self

fit

fn fit(mut self, X: Matrix)

Compute cluster centers and cluster index for each sample.

Args:

• self (Self)
• X (Matrix)

Raises:

fit_predict

fn fit_predict(mut self, X: Matrix) -> List[Int]

Compute cluster centers and predict cluster index for each sample.

Args:

• self (Self)
• X (Matrix)

Returns:

List: List of cluster indices.

Raises: