//! Bayesian imputation methods
#![allow(non_snake_case)]
//!
//! This module provides comprehensive Bayesian approaches to missing data imputation.
//!
//! # Note
//!
//! All Bayesian imputation methods in this module are stub implementations in v0.1.0.
//! Methods with `fit_transform` return `Err(NotImplemented)`. Data-only structs (priors,
//! diagnostics, samplers) are available for type definitions but lack functionality.
//! Full implementations are planned for v0.2.0.

use scirs2_core::ndarray::{Array1, Array2, ArrayView2};

/// Bayesian Linear Imputer
///
/// Imputes missing values using Bayesian linear regression with
/// conjugate priors on regression coefficients and noise variance.
///
/// # Note
///
/// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct BayesianLinearImputer {
    /// max_iter
    pub max_iter: usize,
    /// tol
    pub tol: f64,
    /// alpha
    pub alpha: f64,
    /// beta
    pub beta: f64,
}

impl Default for BayesianLinearImputer {
    fn default() -> Self {
        Self {
            max_iter: 1000,
            tol: 1e-6,
            alpha: 1.0,
            beta: 1.0,
        }
    }
}

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

    /// Fit the Bayesian linear model and impute 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("BayesianLinearImputer: not implemented in v0.1.0. Planned for v0.2.0.".to_string())
    }
}

/// Bayesian Logistic Imputer
///
/// Imputes missing binary/categorical values using Bayesian logistic regression
/// with appropriate link functions.
///
/// # Note
///
/// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct BayesianLogisticImputer {
    /// max_iter
    pub max_iter: usize,
    /// tol
    pub tol: f64,
}

impl Default for BayesianLogisticImputer {
    fn default() -> Self {
        Self {
            max_iter: 1000,
            tol: 1e-6,
        }
    }
}

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

    /// Fit the Bayesian logistic model and impute 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("BayesianLogisticImputer: not implemented in v0.1.0. Planned for v0.2.0.".to_string())
    }
}

/// Bayesian Multiple Imputer
///
/// Generates multiple imputed datasets using Bayesian posterior predictive
/// distributions, enabling proper uncertainty propagation (Rubin's rules).
///
/// # Note
///
/// Not implemented in v0.1.0. Returns `Err(NotImplemented)`. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct BayesianMultipleImputer {
    /// n_imputations
    pub n_imputations: usize,
    /// max_iter
    pub max_iter: usize,
}

impl Default for BayesianMultipleImputer {
    fn default() -> Self {
        Self {
            n_imputations: 5,
            max_iter: 1000,
        }
    }
}

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

    /// Fit the Bayesian multiple imputation model and impute 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("BayesianMultipleImputer: not implemented in v0.1.0. Planned for v0.2.0.".to_string())
    }
}

/// Hierarchical Bayesian Imputer
///
/// Uses hierarchical (multi-level) Bayesian models to impute missing values,
/// accounting for group-level structure in the data.
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct HierarchicalBayesianImputer {
    /// n_levels
    pub n_levels: usize,
    /// max_iter
    pub max_iter: usize,
}

impl Default for HierarchicalBayesianImputer {
    fn default() -> Self {
        Self {
            n_levels: 2,
            max_iter: 1000,
        }
    }
}

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

/// Variational Bayes Imputer
///
/// Uses variational inference to approximate the posterior distribution
/// over missing values, providing scalable Bayesian imputation.
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct VariationalBayesImputer {
    /// max_iter
    pub max_iter: usize,
    /// tol
    pub tol: f64,
}

impl Default for VariationalBayesImputer {
    fn default() -> Self {
        Self {
            max_iter: 1000,
            tol: 1e-6,
        }
    }
}

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

// Stub types for compatibility

/// Bayesian Model trait
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
pub trait BayesianModel: Send + Sync {
    fn log_likelihood(&self, X: &ArrayView2<f64>) -> f64;
    fn sample_posterior(&mut self, X: &ArrayView2<f64>) -> Result<(), String>;
}

/// Bayesian Model Averaging
///
/// Combines predictions from multiple Bayesian models weighted by their
/// posterior model probabilities.
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone, Default)]
pub struct BayesianModelAveraging {
    /// models
    pub models: Vec<String>,
    /// weights
    pub weights: Vec<f64>,
}

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

/// Bayesian Model Averaging Results
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct BayesianModelAveragingResults {
    /// predictions
    pub predictions: Array2<f64>,
    /// weights
    pub weights: Array1<f64>,
    /// model_probabilities
    pub model_probabilities: Array1<f64>,
}

/// Convergence Diagnostics
///
/// Diagnostics for assessing MCMC convergence (R-hat, effective sample size).
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone, Default)]
pub struct ConvergenceDiagnostics {
    /// rhat
    pub rhat: Vec<f64>,
    /// ess
    pub ess: Vec<f64>,
    /// converged
    pub converged: bool,
}

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

/// Pooled Results for multiple imputation
///
/// Combines estimates across multiple imputed datasets using Rubin's rules.
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct PooledResults {
    /// estimates
    pub estimates: Array1<f64>,
    /// standard_errors
    pub standard_errors: Array1<f64>,
    /// degrees_of_freedom
    pub degrees_of_freedom: f64,
}

impl PooledResults {
    pub fn new(estimates: Array1<f64>) -> Self {
        let n = estimates.len();
        Self {
            estimates,
            standard_errors: Array1::zeros(n),
            degrees_of_freedom: 0.0,
        }
    }
}

/// Hierarchical Bayesian Sample
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct HierarchicalBayesianSample {
    /// global_parameters
    pub global_parameters: Array1<f64>,
    /// local_parameters
    pub local_parameters: Array2<f64>,
    /// hyperparameters
    pub hyperparameters: Array1<f64>,
}

/// Bayesian Regression Sample
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct BayesianRegressionSample {
    /// coefficients
    pub coefficients: Array1<f64>,
    /// intercept
    pub intercept: f64,
    /// sigma
    pub sigma: f64,
}

// Additional stub types that may be referenced in lib.rs

/// MCMC Sampler
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct MCMCsampler {
    /// n_samples
    pub n_samples: usize,
    /// burn_in
    pub burn_in: usize,
}

/// Gibbs Sampler
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct GibbsSampler {
    /// n_samples
    pub n_samples: usize,
}

/// Metropolis-Hastings Sampler
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct MetropolisHastingsSampler {
    /// n_samples
    pub n_samples: usize,
}

/// Prior Specification
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct PriorSpecification {
    /// prior_type
    pub prior_type: String,
    /// parameters
    pub parameters: Vec<f64>,
}

/// Conjugate Prior
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct ConjugatePrior {
    /// alpha
    pub alpha: f64,
    /// beta
    pub beta: f64,
}

/// Non-conjugate Prior
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct NonConjugatePrior {
    /// distribution
    pub distribution: String,
    /// parameters
    pub parameters: Vec<f64>,
}

/// Variational Parameters
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct VariationalParameters {
    /// mean
    pub mean: Array1<f64>,
    /// variance
    pub variance: Array1<f64>,
}

/// ELBO Components
///
/// # Note
///
/// Not implemented in v0.1.0. Planned for v0.2.0.
#[derive(Debug, Clone)]
pub struct ELBOComponents {
    /// log_likelihood
    pub log_likelihood: f64,
    /// kl_divergence
    pub kl_divergence: f64,
}

// Trained states (stubs)
pub type BayesianLinearImputerTrained = BayesianLinearImputer;
pub type BayesianLogisticImputerTrained = BayesianLogisticImputer;
pub type BayesianMultipleImputerTrained = BayesianMultipleImputer;
pub type HierarchicalBayesianImputerTrained = HierarchicalBayesianImputer;
pub type VariationalBayesImputerTrained = VariationalBayesImputer;
