Enum DistanceCalculationMetric

pub enum DistanceCalculationMetric {
    Euclidean,
    Manhattan,
    Minkowski,
}

Represents different distance calculation methods used in various machine learning algorithms.

This enum defines common distance metrics that can be used in clustering algorithms, nearest neighbor searches, and other applications where distance between points is relevant.

Variants

• Euclidean - Euclidean distance (L2 norm), calculated as the square root of the sum of squared differences between corresponding coordinates.
• Manhattan - Manhattan distance (L1 norm), calculated as the sum of absolute differences between corresponding coordinates.
• Minkowski - A generalized metric that includes both Euclidean and Manhattan distances as special cases. Requires an additional parameter p (not implemented in this enum).

Variants

Euclidean

Euclidean distance (L2 norm) - the straight-line distance between two points.

Manhattan

Manhattan distance (L1 norm) - the sum of absolute differences between coordinates.

Minkowski

Minkowski distance - a generalized metric that includes both Euclidean and Manhattan distances.

Trait Implementations

impl Clone for DistanceCalculationMetric

fn clone(&self) -> DistanceCalculationMetric

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for DistanceCalculationMetric

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for DistanceCalculationMetric

fn eq(&self, other: &DistanceCalculationMetric) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for DistanceCalculationMetric