rhmm 0.0.2

A rust implementation of hidden markov models.
Documentation 
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//! Baum-Welch algorithm implementation

use crate::errors::Result;
use crate::utils::normalize_vector;
use ndarray::{Array1, Array2};
// Forward and backward algorithms are used by callers, not directly here

/// Baum-Welch algorithm for HMM parameter estimation
///
/// The Baum-Welch algorithm is an Expectation-Maximization (EM) algorithm
/// for finding the maximum likelihood estimates of the parameters of an HMM.
///
/// # Arguments
///
/// * `observations` - Observation sequence
/// * `start_prob` - Initial state probabilities (will be updated)
/// * `transition_matrix` - State transition matrix (will be updated)
/// * `emission_probs` - Emission probabilities (will be updated)
/// * `max_iter` - Maximum number of iterations
/// * `tol` - Convergence tolerance
///
/// # Returns
///
/// Number of iterations performed
pub fn baum_welch(
    _observations: &Array2<f64>,
    _start_prob: &mut Array1<f64>,
    _transition_matrix: &mut Array2<f64>,
    _emission_probs: &mut Array2<f64>,
    max_iter: usize,
    _tol: f64,
) -> Result<usize> {
    // TODO: Implement full Baum-Welch algorithm
    // This is a placeholder that returns immediately

    // The full implementation would:
    // 1. E-step: Compute forward and backward probabilities
    // 2. Compute gamma (state occupation probabilities)
    // 3. Compute xi (state transition probabilities)
    // 4. M-step: Update parameters based on gamma and xi
    // 5. Check convergence and repeat

    Ok(max_iter)
}

/// Compute gamma values (state occupation probabilities)
///
/// # Arguments
///
/// * `alpha` - Forward probabilities
/// * `beta` - Backward probabilities
///
/// # Returns
///
/// Gamma values of shape (n_samples, n_states)
pub fn compute_gamma(alpha: &Array2<f64>, beta: &Array2<f64>) -> Result<Array2<f64>> {
    let n_samples = alpha.nrows();
    let n_states = alpha.ncols();
    let mut gamma = Array2::zeros((n_samples, n_states));

    for t in 0..n_samples {
        for i in 0..n_states {
            gamma[[t, i]] = alpha[[t, i]] * beta[[t, i]];
        }
        // Normalize using utility function
        let row = gamma.row(t).to_owned();
        let normalized_row = normalize_vector(row);
        for i in 0..n_states {
            gamma[[t, i]] = normalized_row[i];
        }
    }

    Ok(gamma)
}