//! Information-theoretic regression metrics
//!
//! This module provides information-theoretic metrics for regression evaluation.
//! Implements SciRS2 Policy for array operations and numerical computations.

use crate::{MetricsError, MetricsResult};
use scirs2_core::ndarray::Array1;

/// Calculate mutual information score
pub fn mutual_information_score(x: &Array1<f64>, y: &Array1<f64>) -> MetricsResult<f64> {
    if x.len() != y.len() {
        return Err(MetricsError::ShapeMismatch {
            expected: vec![x.len()],
            actual: vec![y.len()],
        });
    }

    if x.is_empty() {
        return Err(MetricsError::EmptyInput);
    }

    // Placeholder implementation
    Ok(0.0)
}

/// Calculate normalized mutual information
pub fn normalized_mutual_information(x: &Array1<f64>, y: &Array1<f64>) -> MetricsResult<f64> {
    if x.len() != y.len() {
        return Err(MetricsError::ShapeMismatch {
            expected: vec![x.len()],
            actual: vec![y.len()],
        });
    }

    if x.is_empty() {
        return Err(MetricsError::EmptyInput);
    }

    // Placeholder implementation
    Ok(0.0)
}

/// Calculate adjusted mutual information
pub fn adjusted_mutual_information(x: &Array1<f64>, y: &Array1<f64>) -> MetricsResult<f64> {
    if x.len() != y.len() {
        return Err(MetricsError::ShapeMismatch {
            expected: vec![x.len()],
            actual: vec![y.len()],
        });
    }

    if x.is_empty() {
        return Err(MetricsError::EmptyInput);
    }

    // Placeholder implementation
    Ok(0.0)
}

/// Calculate variation of information
pub fn variation_of_information(x: &Array1<f64>, y: &Array1<f64>) -> MetricsResult<f64> {
    if x.len() != y.len() {
        return Err(MetricsError::ShapeMismatch {
            expected: vec![x.len()],
            actual: vec![y.len()],
        });
    }

    if x.is_empty() {
        return Err(MetricsError::EmptyInput);
    }

    // Placeholder implementation
    Ok(0.0)
}

#[allow(non_snake_case)]
#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::array;

    #[test]
    fn test_mutual_information_score() {
        let x = array![1.0, 2.0, 3.0];
        let y = array![1.0, 2.0, 3.0];
        let mi = mutual_information_score(&x, &y).unwrap();
        assert!(mi >= 0.0);
    }
}
