Struct bf16

pub struct bf16(/* private fields */);

A half-precision floating point number, called brain float.

Trait Implementations

impl ComplexProducts for bf16

fn dot(a: &[Self], b: &[Self]) -> Option<(f64, f64)>

Computes the dot product between two complex number vectors.

fn vdot(a: &[Self], b: &[Self]) -> Option<(f64, f64)>

Computes the Hermitian dot product (conjugate dot product) between two complex number vectors.

impl ProbabilitySimilarity for bf16

fn jensenshannon(a: &[Self], b: &[Self]) -> Option<f64>

Computes the Jensen-Shannon divergence between two probability distributions. The Jensen-Shannon divergence is a method of measuring the similarity between two probability distributions. It is based on the Kullback-Leibler divergence, but is symmetric and always has a finite value.

fn kullbackleibler(a: &[Self], b: &[Self]) -> Option<f64>

Computes the Kullback-Leibler divergence between two probability distributions. The Kullback-Leibler divergence is a measure of how one probability distribution diverges from a second, expected probability distribution.

impl SpatialSimilarity for bf16

fn cos(a: &[Self], b: &[Self]) -> Option<f64>

Computes the cosine similarity between two slices. The cosine similarity is a measure of similarity between two non-zero vectors of an dot product space that measures the cosine of the angle between them.

fn dot(a: &[Self], b: &[Self]) -> Option<f64>

Computes the inner product (also known as dot product) between two slices. The dot product is the sum of the products of the corresponding entries of the two sequences of numbers.

fn l2sq(a: &[Self], b: &[Self]) -> Option<f64>

Computes the squared Euclidean distance between two slices. The squared Euclidean distance is the sum of the squared differences between corresponding elements of the two slices.

fn sqeuclidean(a: &[Self], b: &[Self]) -> Option<f64>

Computes the squared Euclidean distance between two slices. The squared Euclidean distance is the sum of the squared differences between corresponding elements of the two slices.

fn inner(a: &[Self], b: &[Self]) -> Option<f64>

Computes the squared Euclidean distance between two slices. The squared Euclidean distance is the sum of the squared differences between corresponding elements of the two slices.

fn cosine(a: &[Self], b: &[Self]) -> Option<f64>

Computes the cosine similarity between two slices. The cosine similarity is a measure of similarity between two non-zero vectors of an dot product space that measures the cosine of the angle between them.