rhmm 0.0.2

A rust implementation of hidden markov models.
Documentation 
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//! Gaussian Mixture Model Hidden Markov Model

use crate::base::{CovarianceType, HiddenMarkovModel};
use crate::errors::{HmmError, Result};
use crate::utils::validate_observations;
use ndarray::{Array1, Array2};

/// Gaussian Mixture Model Hidden Markov Model
///
/// A Hidden Markov Model with Gaussian Mixture Model emission distributions.
#[derive(Debug, Clone)]
pub struct GMMHMM {
    n_states: usize,
    n_features: usize,
    n_mix: usize,
    #[allow(dead_code)]
    covariance_type: CovarianceType,
    is_fitted: bool,
}

impl GMMHMM {
    /// Create a new GMM HMM
    ///
    /// # Arguments
    ///
    /// * `n_states` - Number of hidden states
    /// * `n_mix` - Number of mixture components per state
    pub fn new(n_states: usize, n_mix: usize) -> Self {
        Self {
            n_states,
            n_features: 0,
            n_mix,
            covariance_type: CovarianceType::default(),
            is_fitted: false,
        }
    }

    /// Get the number of mixture components
    pub fn n_mix(&self) -> usize {
        self.n_mix
    }
}

impl HiddenMarkovModel for GMMHMM {
    fn n_states(&self) -> usize {
        self.n_states
    }

    fn n_features(&self) -> usize {
        self.n_features
    }

    fn fit(&mut self, observations: &Array2<f64>, _lengths: Option<&[usize]>) -> Result<()> {
        if observations.nrows() == 0 || observations.ncols() == 0 {
            return Err(HmmError::InvalidParameter(
                "Observations cannot be empty".to_string(),
            ));
        }

        self.n_features = observations.ncols();

        // Validate observations if n_features was already set
        if self.n_features > 0 {
            validate_observations(observations, self.n_features)?;
        }

        self.is_fitted = true;

        // TODO: Implement GMM-HMM training
        Ok(())
    }

    fn predict(&self, observations: &Array2<f64>) -> Result<Array1<usize>> {
        if !self.is_fitted {
            return Err(HmmError::ModelNotFitted(
                "Model must be fitted before prediction".to_string(),
            ));
        }

        // TODO: Implement Viterbi for GMM-HMM
        Ok(Array1::zeros(observations.nrows()))
    }

    fn score(&self, _observations: &Array2<f64>) -> Result<f64> {
        if !self.is_fitted {
            return Err(HmmError::ModelNotFitted(
                "Model must be fitted before scoring".to_string(),
            ));
        }

        // TODO: Implement forward algorithm for GMM-HMM
        Ok(0.0)
    }

    fn sample(&self, n_samples: usize) -> Result<(Array2<f64>, Array1<usize>)> {
        if !self.is_fitted {
            return Err(HmmError::ModelNotFitted(
                "Model must be fitted before sampling".to_string(),
            ));
        }

        // TODO: Implement sampling for GMM-HMM
        let observations = Array2::zeros((n_samples, self.n_features));
        let states = Array1::zeros(n_samples);
        Ok((observations, states))
    }
}