Struct GMM 

pub struct GMM { /* private fields */ }

A Gaussian Mixture Model for multi-factor regime detection.

Implementations

impl GMM

pub fn new(means: Vec<Vec<f64>>, vars: Vec<Vec<f64>>, weights: Vec<f64>) -> Self

Creates a new GMM with pre-defined parameters.

pub fn with_components(k: usize, dims: usize) -> Self

Unfitted model with k components and dims dimensions (for .fit()).

pub fn components(&self) -> usize

pub fn dims(&self) -> usize

pub fn means(&self) -> &[Vec<f64>]

pub fn weights(&self) -> &[f64]

pub fn fit( &mut self, data: &[Vec<f64>], config: &GmmFitConfig, ) -> Result<GmmFitResult, GmmError>

Batch fit using EM (diagonal covariance).

Trait Implementations

impl Clone for GMM

fn clone(&self) -> GMM

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GMM

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for GMM

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Next<&[f64]> for GMM

type Output = MarketRegime

fn next(&mut self, x: &[f64]) -> Self::Output

Process the next input and return the updated output.

fn next_batch(&mut self, inputs: &[Input]) -> Vec<Self::Output>

where Input: Copy,

Process a batch of inputs eagerly. This allows implementations to override with vectorized logic, while falling back to scalar processing by default.

impl Serialize for GMM

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.