use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::rand_prelude::StdRng;
use scirs2_core::random::Random;

pub fn monte_carlo_dropout_uncertainty<E>(
    models: &[E],
    x: &Array2<f64>,
    dropout_rate: f64,
    n_samples: usize,
    rng: &mut Random<StdRng>,
) -> Result<(Array1<f64>, Array1<f64>), Box<dyn std::error::Error>>
where
    E: Clone,
{
    let n_predictions = x.nrows();
    let mut all_predictions = Array2::<f64>::zeros((n_samples, n_predictions));

    for sample_idx in 0..n_samples {
        let predictions = simulate_dropout_predictions(models, x, dropout_rate, rng)?;
        for (i, pred) in predictions.iter().enumerate() {
            all_predictions[[sample_idx, i]] = *pred;
        }
    }

    let mean_predictions = all_predictions
        .mean_axis(scirs2_core::ndarray::Axis(0))
        .unwrap();
    let uncertainties = all_predictions.var_axis(scirs2_core::ndarray::Axis(0), 0.0);

    Ok((mean_predictions, uncertainties))
}

fn simulate_dropout_predictions<E>(
    models: &[E],
    x: &Array2<f64>,
    dropout_rate: f64,
    rng: &mut Random<StdRng>,
) -> Result<Array1<f64>, Box<dyn std::error::Error>>
where
    E: Clone,
{
    let n_predictions = x.nrows();
    let mut predictions = Array1::<f64>::zeros(n_predictions);

    for i in 0..n_predictions {
        let input_row = x.row(i);

        let mut masked_input = input_row.to_owned();
        for j in 0..masked_input.len() {
            if rng.gen_range(0.0..1.0) < dropout_rate {
                masked_input[j] = 0.0;
            } else {
                masked_input[j] /= 1.0 - dropout_rate;
            }
        }

        predictions[i] = simulate_model_prediction(&models[0], &masked_input)?;
    }

    Ok(predictions)
}

fn simulate_model_prediction<E>(
    _model: &E,
    input: &Array1<f64>,
) -> Result<f64, Box<dyn std::error::Error>>
where
    E: Clone,
{
    // Placeholder implementation - in real use, this would call the actual model
    let sum = input.sum();
    Ok(sum / input.len() as f64)
}
