Mojo struct

DBSCAN

@memory_only
struct DBSCAN

A density based clustering method that expands clusters from samples that have more neighbors within a radius.

Aliases

• __del__is_trivial = False

Fields

• eps (Float32): The maximum distance between two samples for one to be considered as in the neighborhood of the other.
• min_samples (Int): The number of samples in a neighborhood for a point to be considered as a core point.
• metric (String): Metric to use for distance computation: Euclidean -> 'euc'; Manhattan -> 'man'.
• labels (List[Int])

Implemented traits

AnyType, ImplicitlyDestructible

Methods

__init__

fn __init__(out self, eps: Float32 = 1, min_samples: Int = 5, metric: String = "euc")

Args:

• eps (Float32)
• min_samples (Int)
• metric (String)
• self (Self)

Returns:

Self

Raises:

fit

fn fit(mut self, X: Matrix)

Perform clustering.

Args:

• self (Self)
• X (Matrix)

Raises:

fit_predict

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

Perform clustering and predict cluster indices.

Args:

• self (Self)
• X (Matrix)

Returns:

List: List of cluster indices.

Raises: