//! Neural network-based imputation methods
#![allow(non_snake_case)]
//!
//! This module provides deep learning approaches to missing data imputation.
//!
//! # Note
//!
//! All neural imputation methods in this module are stub implementations in v0.1.0.
//! Each method returns `Err(NotImplemented)` when called. Full implementations are
//! planned for v0.2.0.

use scirs2_core::ndarray::{Array2, ArrayView2};

/// Autoencoder Imputer
///
/// Uses a denoising autoencoder architecture to learn latent representations
/// of the data and reconstruct missing values.
///
/// # Note
///
/// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct AutoencoderImputer {
    /// hidden_layers
    pub hidden_layers: Vec<usize>,
    /// activation
    pub activation: String,
    /// epochs
    pub epochs: usize,
    /// learning_rate
    pub learning_rate: f64,
}

impl Default for AutoencoderImputer {
    fn default() -> Self {
        Self {
            hidden_layers: vec![64, 32, 64],
            activation: "relu".to_string(),
            epochs: 100,
            learning_rate: 0.001,
        }
    }
}

impl AutoencoderImputer {
    pub fn new() -> Self {
        Self::default()
    }

    /// Fit the autoencoder to the data and transform it by imputing missing values.
    ///
    /// # Note
    ///
    /// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("AutoencoderImputer: not implemented in v0.1.0. Planned for v0.2.0.".to_string())
    }
}

/// Multi-Layer Perceptron Imputer
///
/// Uses a multi-layer perceptron to predict missing values from observed features.
///
/// # Note
///
/// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct MLPImputer {
    /// hidden_layers
    pub hidden_layers: Vec<usize>,
    /// activation
    pub activation: String,
    /// epochs
    pub epochs: usize,
    /// learning_rate
    pub learning_rate: f64,
}

impl Default for MLPImputer {
    fn default() -> Self {
        Self {
            hidden_layers: vec![100, 50],
            activation: "relu".to_string(),
            epochs: 100,
            learning_rate: 0.001,
        }
    }
}

impl MLPImputer {
    pub fn new() -> Self {
        Self::default()
    }

    /// Fit the MLP to the data and transform it by imputing missing values.
    ///
    /// # Note
    ///
    /// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("MLPImputer: not implemented in v0.1.0. Planned for v0.2.0.".to_string())
    }
}

/// Variational Autoencoder Imputer
///
/// Uses a variational autoencoder to model the data distribution and impute
/// missing values with uncertainty estimates via the learned latent space.
///
/// # Note
///
/// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct VAEImputer {
    /// latent_dim
    pub latent_dim: usize,
    /// hidden_layers
    pub hidden_layers: Vec<usize>,
    /// epochs
    pub epochs: usize,
    /// learning_rate
    pub learning_rate: f64,
    /// kl_weight
    pub kl_weight: f64,
}

impl Default for VAEImputer {
    fn default() -> Self {
        Self {
            latent_dim: 10,
            hidden_layers: vec![64, 32],
            epochs: 100,
            learning_rate: 0.001,
            kl_weight: 1.0,
        }
    }
}

impl VAEImputer {
    pub fn new() -> Self {
        Self::default()
    }

    /// Fit the VAE to the data and transform it by imputing missing values.
    ///
    /// # Note
    ///
    /// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("VAEImputer: not implemented in v0.1.0. Planned for v0.2.0.".to_string())
    }

    /// Predict imputed values along with uncertainty estimates.
    ///
    /// # Note
    ///
    /// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
    pub fn predict_with_uncertainty(
        &self,
        _X: &ArrayView2<f64>,
    ) -> Result<(Array2<f64>, Array2<f64>), String> {
        Err(
            "VAEImputer uncertainty prediction: not implemented in v0.1.0. Planned for v0.2.0."
                .to_string(),
        )
    }
}

/// Generative Adversarial Network Imputer
///
/// Uses a GAN framework (GAIN-style) where the generator imputes missing values
/// and the discriminator distinguishes real from imputed values.
///
/// # Note
///
/// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct GANImputer {
    /// generator_layers
    pub generator_layers: Vec<usize>,
    /// discriminator_layers
    pub discriminator_layers: Vec<usize>,
    /// epochs
    pub epochs: usize,
    /// learning_rate
    pub learning_rate: f64,
    /// noise_dim
    pub noise_dim: usize,
}

impl Default for GANImputer {
    fn default() -> Self {
        Self {
            generator_layers: vec![64, 128, 64],
            discriminator_layers: vec![64, 32, 1],
            epochs: 100,
            learning_rate: 0.001,
            noise_dim: 10,
        }
    }
}

impl GANImputer {
    pub fn new() -> Self {
        Self::default()
    }

    /// Fit the GAN to the data and transform it by imputing missing values.
    ///
    /// # Note
    ///
    /// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("GANImputer: not implemented in v0.1.0. Planned for v0.2.0.".to_string())
    }
}

/// Normalizing Flow Imputer
///
/// Uses normalizing flows to model complex data distributions and impute
/// missing values by sampling from the learned distribution.
///
/// # Note
///
/// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct NormalizingFlowImputer {
    /// n_flows
    pub n_flows: usize,
    /// hidden_dim
    pub hidden_dim: usize,
    /// epochs
    pub epochs: usize,
    /// learning_rate
    pub learning_rate: f64,
}

impl Default for NormalizingFlowImputer {
    fn default() -> Self {
        Self {
            n_flows: 8,
            hidden_dim: 64,
            epochs: 100,
            learning_rate: 0.001,
        }
    }
}

impl NormalizingFlowImputer {
    pub fn new() -> Self {
        Self::default()
    }

    /// Fit the normalizing flow model to the data and transform it by imputing missing values.
    ///
    /// # Note
    ///
    /// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("NormalizingFlowImputer: not implemented in v0.1.0. Planned for v0.2.0.".to_string())
    }
}

/// Diffusion Model Imputer
///
/// Uses score-based diffusion models to iteratively denoise and impute
/// missing values through a learned reverse diffusion process.
///
/// # Note
///
/// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct DiffusionImputer {
    /// n_timesteps
    pub n_timesteps: usize,
    /// hidden_dim
    pub hidden_dim: usize,
    /// epochs
    pub epochs: usize,
    /// learning_rate
    pub learning_rate: f64,
}

impl Default for DiffusionImputer {
    fn default() -> Self {
        Self {
            n_timesteps: 1000,
            hidden_dim: 128,
            epochs: 100,
            learning_rate: 0.001,
        }
    }
}

impl DiffusionImputer {
    pub fn new() -> Self {
        Self::default()
    }

    /// Fit the diffusion model to the data and transform it by imputing missing values.
    ///
    /// # Note
    ///
    /// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("DiffusionImputer: not implemented in v0.1.0. Planned for v0.2.0.".to_string())
    }
}

/// Neural ODE Imputer
///
/// Uses neural ordinary differential equations to model continuous-time
/// dynamics of the data and impute missing values.
///
/// # Note
///
/// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct NeuralODEImputer {
    /// hidden_dim
    pub hidden_dim: usize,
    /// n_layers
    pub n_layers: usize,
    /// time_steps
    pub time_steps: Vec<f64>,
    /// solver
    pub solver: String,
}

impl Default for NeuralODEImputer {
    fn default() -> Self {
        Self {
            hidden_dim: 64,
            n_layers: 3,
            time_steps: vec![0.0, 1.0],
            solver: "dopri5".to_string(),
        }
    }
}

impl NeuralODEImputer {
    pub fn new() -> Self {
        Self::default()
    }

    /// Fit the Neural ODE model to the data and transform it by imputing missing values.
    ///
    /// # Note
    ///
    /// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("NeuralODEImputer: not implemented in v0.1.0. Planned for v0.2.0.".to_string())
    }
}

// Additional stub types that may be referenced

/// Transformer Imputer
///
/// Uses self-attention mechanisms to capture complex dependencies between
/// features for imputation.
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct TransformerImputer {
    /// n_heads
    pub n_heads: usize,
    /// n_layers
    pub n_layers: usize,
    /// hidden_dim
    pub hidden_dim: usize,
}

impl Default for TransformerImputer {
    fn default() -> Self {
        Self {
            n_heads: 8,
            n_layers: 6,
            hidden_dim: 512,
        }
    }
}

/// RNN Imputer
///
/// Uses recurrent neural networks to model sequential dependencies
/// for imputation of sequential/temporal data.
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct RNNImputer {
    /// hidden_dim
    pub hidden_dim: usize,
    /// n_layers
    pub n_layers: usize,
    /// cell_type
    pub cell_type: String,
}

impl Default for RNNImputer {
    fn default() -> Self {
        Self {
            hidden_dim: 64,
            n_layers: 2,
            cell_type: "lstm".to_string(),
        }
    }
}

/// LSTM Imputer
///
/// Uses Long Short-Term Memory networks for imputation, particularly
/// suited for time-series data with long-range dependencies.
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct LSTMImputer {
    /// hidden_dim
    pub hidden_dim: usize,
    /// n_layers
    pub n_layers: usize,
    /// bidirectional
    pub bidirectional: bool,
}

impl Default for LSTMImputer {
    fn default() -> Self {
        Self {
            hidden_dim: 64,
            n_layers: 2,
            bidirectional: false,
        }
    }
}

/// Attention Mechanism
///
/// Attention mechanism component for use in neural imputation models.
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct AttentionMechanism {
    /// attention_type
    pub attention_type: String,
    /// n_heads
    pub n_heads: usize,
}

/// Sequential Imputation
///
/// Handles sequential/ordered imputation using sliding windows.
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct SequentialImputation {
    /// sequence_length
    pub sequence_length: usize,
    /// overlap
    pub overlap: usize,
}

// Trained states (type aliases for simplicity)
pub type AutoencoderImputerTrained = AutoencoderImputer;
pub type MLPImputerTrained = MLPImputer;
pub type VAEImputerTrained = VAEImputer;
pub type GANImputerTrained = GANImputer;
pub type NormalizingFlowImputerTrained = NormalizingFlowImputer;
pub type DiffusionImputerTrained = DiffusionImputer;
pub type NeuralODEImputerTrained = NeuralODEImputer;
