Trait pointcloud::Metric

pub trait Metric: 'static + Send + Sync + Debug {
    fn dense(x: &[f32], y: &[f32]) -> f32;
    fn sparse(x_ind: &[u32], x_val: &[f32], y_ind: &[u32], y_val: &[f32]) -> f32;
    fn norm(x: &[f32]) -> f32;
}

The trait that enables a metric

Required methods

fn dense(x: &[f32], y: &[f32]) -> f32

Dense calculation

fn sparse(x_ind: &[u32], x_val: &[f32], y_ind: &[u32], y_val: &[f32]) -> f32

Sparse calculation, we assume that the index slices are in accending order and that the values correspond to the indexes

fn norm(x: &[f32]) -> f32

The norm, dense(x,x)

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Implementors

impl Metric for CosineSim

fn dense(x: &[f32], y: &[f32]) -> f32

fn norm(_x: &[f32]) -> f32

fn sparse(x_ind: &[u32], x_val: &[f32], y_ind: &[u32], y_val: &[f32]) -> f32

impl Metric for L1

fn dense(x: &[f32], y: &[f32]) -> f32

fn norm(x: &[f32]) -> f32

fn sparse(x_ind: &[u32], x_val: &[f32], y_ind: &[u32], y_val: &[f32]) -> f32

impl Metric for L2

fn dense(x: &[f32], y: &[f32]) -> f32

fn norm(x: &[f32]) -> f32

fn sparse(x_ind: &[u32], x_val: &[f32], y_ind: &[u32], y_val: &[f32]) -> f32

impl Metric for Linfty

fn dense(x: &[f32], y: &[f32]) -> f32

fn norm(x: &[f32]) -> f32

fn sparse(x_ind: &[u32], x_val: &[f32], y_ind: &[u32], y_val: &[f32]) -> f32

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