//! Distribution-based regression metrics
//!
//! This module provides metrics based on comparing distributions.
//! Implements SciRS2 Policy for array operations and numerical computations.

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

/// Calculate Wasserstein distance (Earth Mover's Distance)
pub fn wasserstein_distance(y_true: &Array1<f64>, y_pred: &Array1<f64>) -> MetricsResult<f64> {
    if y_true.len() != y_pred.len() {
        return Err(MetricsError::ShapeMismatch {
            expected: vec![y_true.len()],
            actual: vec![y_pred.len()],
        });
    }

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

    // Placeholder implementation - sort both distributions and compute L1 distance
    let mut y_true_sorted = y_true.to_vec();
    let mut y_pred_sorted = y_pred.to_vec();

    y_true_sorted.sort_by(|a, b| a.partial_cmp(b).unwrap());
    y_pred_sorted.sort_by(|a, b| a.partial_cmp(b).unwrap());

    let distance = y_true_sorted
        .iter()
        .zip(y_pred_sorted.iter())
        .map(|(t, p)| (t - p).abs())
        .sum::<f64>()
        / y_true.len() as f64;

    Ok(distance)
}

/// Calculate Kolmogorov-Smirnov distance
pub fn kolmogorov_smirnov_distance(
    y_true: &Array1<f64>,
    y_pred: &Array1<f64>,
) -> MetricsResult<f64> {
    if y_true.is_empty() || y_pred.is_empty() {
        return Err(MetricsError::EmptyInput);
    }

    // Placeholder implementation
    Ok(0.0)
}

/// Calculate Anderson-Darling statistic
pub fn anderson_darling_statistic(
    y_true: &Array1<f64>,
    y_pred: &Array1<f64>,
) -> MetricsResult<f64> {
    if y_true.is_empty() || y_pred.is_empty() {
        return Err(MetricsError::EmptyInput);
    }

    // Placeholder implementation
    Ok(0.0)
}

/// Calculate Jensen-Shannon divergence
pub fn jensen_shannon_divergence(p: &Array1<f64>, q: &Array1<f64>) -> MetricsResult<f64> {
    if p.len() != q.len() {
        return Err(MetricsError::ShapeMismatch {
            expected: vec![p.len()],
            actual: vec![q.len()],
        });
    }

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

    // Placeholder implementation - should be proper JS divergence
    Ok(0.0)
}

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

    #[test]
    fn test_wasserstein_distance() {
        let y_true = array![1.0, 2.0, 3.0];
        let y_pred = array![1.0, 2.0, 3.0];
        let dist = wasserstein_distance(&y_true, &y_pred).unwrap();
        assert!((dist - 0.0).abs() < f64::EPSILON);
    }
}
