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

pub fn bayesian_neural_network_uncertainty<E>(
    models: &[E],
    x: &Array2<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 = sample_posterior_predictions(models, x, 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))
}

pub fn variational_inference_uncertainty<E>(
    models: &[E],
    x: &Array2<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 = variational_sample_predictions(models, x, 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))
}

pub fn laplace_approximation_uncertainty<E>(
    models: &[E],
    x: &Array2<f64>,
    hessian_method: &str,
) -> Result<(Array1<f64>, Array1<f64>), Box<dyn std::error::Error>>
where
    E: Clone,
{
    let n_predictions = x.nrows();
    let mut predictions = Array1::<f64>::zeros(n_predictions);
    let mut uncertainties = Array1::<f64>::zeros(n_predictions);

    for i in 0..n_predictions {
        let input_row = x.row(i);
        let (pred, unc) =
            laplace_prediction_with_uncertainty(&models[0], &input_row.to_owned(), hessian_method)?;
        predictions[i] = pred;
        uncertainties[i] = unc;
    }

    Ok((predictions, uncertainties))
}

fn sample_posterior_predictions<E>(
    models: &[E],
    x: &Array2<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);
        predictions[i] = sample_from_posterior(&models[0], &input_row.to_owned(), rng)?;
    }

    Ok(predictions)
}

fn variational_sample_predictions<E>(
    models: &[E],
    x: &Array2<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);
        predictions[i] = variational_sample(&models[0], &input_row.to_owned(), rng)?;
    }

    Ok(predictions)
}

fn sample_from_posterior<E>(
    _model: &E,
    input: &Array1<f64>,
    rng: &mut Random<StdRng>,
) -> Result<f64, Box<dyn std::error::Error>>
where
    E: Clone,
{
    // Placeholder implementation
    let base_pred = input.sum() / input.len() as f64;
    let noise = rng.gen_range(-0.1..0.1);
    Ok(base_pred + noise)
}

fn variational_sample<E>(
    _model: &E,
    input: &Array1<f64>,
    rng: &mut Random<StdRng>,
) -> Result<f64, Box<dyn std::error::Error>>
where
    E: Clone,
{
    // Placeholder implementation
    let base_pred = input.sum() / input.len() as f64;
    let noise = rng.gen_range(-0.05..0.05);
    Ok(base_pred + noise)
}

fn laplace_prediction_with_uncertainty<E>(
    _model: &E,
    input: &Array1<f64>,
    hessian_method: &str,
) -> Result<(f64, f64), Box<dyn std::error::Error>>
where
    E: Clone,
{
    // Placeholder implementation
    let prediction = input.sum() / input.len() as f64;
    let uncertainty = match hessian_method {
        "diagonal" => 0.1,
        "full" => 0.15,
        _ => 0.05,
    };
    Ok((prediction, uncertainty))
}
