Mojo struct

KMeans

@memory_only
struct KMeans

K-Means clustering.

Aliases

• MODEL_ID = 5

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'.
• n_centroid_init (Int): The number of candidate centroids to be initialized.
• 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_mean (Matrix)

Implemented traits

AnyType, Copyable, ImplicitlyDestructible, Movable

Methods

__init__

def __init__(out self, k: Int = 5, init: String = "kmeans++", n_centroid_init: Int = 1, max_iters: Int = 100, converge: String = "centroid", tol: Float32 = 1.0E-4, random_state: Int = 0)

Args:

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

Returns:

Self

fit

def fit(mut self, X: Matrix)

Compute cluster centers and cluster index for each sample.

Args:

• self (Self)
• X (Matrix)

Raises:

predict

def predict(self, X: Matrix) -> List[Int]

Predict cluster index for each sample.

Args:

• self (Self)
• X (Matrix)

Returns:

List: List of cluster indices.

Raises:

fit_predict

def 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:

save

def save(self, path: String)

Save model data necessary for prediction to the specified path.

Args:

• self (Self)
• path (String)

Raises:

load

@staticmethod
def load(path: String) -> Self

Load a saved model from the specified path for prediction.

Args:

• path (String)

Returns:

Self

Raises:

centroids

def centroids(self) -> Matrix

Args:

• self (Self)

Returns:

Matrix

Raises: