Struct bio::stats::hmm::univariate_continuous_emission::Model

pub struct Model<Dist: Continuous<f64, f64>> { /* private fields */ }

Implementation of a hmm::Model with emission values from univariate continuous distributions.

Log-scale probabilities are used for numeric stability.

Implementations

impl<Dist: Continuous<f64, f64>> Model<Dist>

pub fn new(
    transition: Array2<LogProb>,
    observation: Vec<Dist>,
    initial: Array1<LogProb>
) -> Result<Self, HMMError>

Construct new Hidden MarkovModel with the given transition, observation, and initial state matrices and vectors already in log-probability space.

pub fn with_prob(
    transition: &Array2<Prob>,
    observation: Vec<Dist>,
    initial: &Array1<Prob>
) -> Result<Self, HMMError>

Construct new Hidden MarkovModel with the given transition, observation, and initial state matrices and vectors already as Prob values.

pub fn with_float(
    transition: &Array2<f64>,
    observation: Vec<Dist>,
    initial: &Array1<f64>
) -> Result<Self, HMMError>

Construct new Hidden MarkovModel with the given transition, observation, and initial state matrices and vectors with probabilities as f64 values.

Trait Implementations

impl<Dist: Continuous<f64, f64>> Model<f64> for Model<Dist>

fn num_states(&self) -> usize

The number of states in the model.

fn states(&self) -> StateIter

Return iterator over the states of an HMM.

fn transitions(&self) -> StateTransitionIter

Returns an iterator of all transitions.

fn transition_prob(&self, from: State, to: State) -> LogProb

Transition probability between two states from and to.

fn initial_prob(&self, state: State) -> LogProb

Initial probability given the HMM state.

fn observation_prob(&self, state: State, observation: &f64) -> LogProb

Probability for the given observation in the given state.

fn transition_prob_idx(&self, from: State, to: State, _to_idx: usize) -> LogProb

Transition probability between two states from and to for observation with index _to_idx (index of to).