//! Sparse Imputation methods
#![allow(non_snake_case)]
#![allow(dead_code)]
//!
//! This module provides sparse imputation methods for high-dimensional data.
//!
//! # Note
//!
//! `CompressedSensingImputer` is a stub in v0.1.0. Its `fit` and `transform`
//! return `Err(SklearsError::NotImplemented)`. Planned for v0.2.0.

use scirs2_core::ndarray::{Array1, Array2, ArrayView2};
use sklears_core::{
    error::{Result as SklResult, SklearsError},
    traits::{Estimator, Fit, Transform, Untrained},
    types::Float,
};

/// Sparse Imputation methods for high-dimensional data
///
/// Imputation designed for high-dimensional sparse data where most values are zero
/// or missing. Uses compressed sensing and sparse coding techniques.
#[derive(Debug, Clone)]
pub struct SparseImputer<S = Untrained> {
    state: S,
    sparsity_level: f64,
    regularization: f64,
    max_iter: usize,
    tol: f64,
    missing_values: f64,
    random_state: Option<u64>,
}

/// Trained state for SparseImputer
#[derive(Debug, Clone)]
pub struct SparseImputerTrained {
    dictionary_: Array2<f64>,
    sparse_codes_: Array2<f64>,
    mean_: Array1<f64>,
    n_features_in_: usize,
    n_components_: usize,
}

impl SparseImputer<Untrained> {
    /// Create a new SparseImputer instance
    pub fn new() -> Self {
        Self {
            state: Untrained,
            sparsity_level: 0.5,
            regularization: 0.1,
            max_iter: 100,
            tol: 1e-6,
            missing_values: f64::NAN,
            random_state: None,
        }
    }

    /// Set the expected sparsity level
    pub fn sparsity_level(mut self, sparsity_level: f64) -> Self {
        self.sparsity_level = sparsity_level.clamp(0.0, 1.0);
        self
    }

    /// Set the L1 regularization parameter
    pub fn regularization(mut self, regularization: f64) -> Self {
        self.regularization = regularization;
        self
    }

    /// Set the maximum number of iterations
    pub fn max_iter(mut self, max_iter: usize) -> Self {
        self.max_iter = max_iter;
        self
    }

    /// Set the tolerance for convergence
    pub fn tol(mut self, tol: f64) -> Self {
        self.tol = tol;
        self
    }

    /// Set the missing values placeholder
    pub fn missing_values(mut self, missing_values: f64) -> Self {
        self.missing_values = missing_values;
        self
    }

    /// Set the random state
    pub fn random_state(mut self, random_state: Option<u64>) -> Self {
        self.random_state = random_state;
        self
    }

    fn is_missing(&self, value: f64) -> bool {
        if self.missing_values.is_nan() {
            value.is_nan()
        } else {
            (value - self.missing_values).abs() < f64::EPSILON
        }
    }
}

impl Default for SparseImputer<Untrained> {
    fn default() -> Self {
        Self::new()
    }
}

impl Estimator for SparseImputer<Untrained> {
    type Config = ();
    type Error = SklearsError;
    type Float = Float;

    fn config(&self) -> &Self::Config {
        &()
    }
}

impl Fit<ArrayView2<'_, Float>, ()> for SparseImputer<Untrained> {
    type Fitted = SparseImputer<SparseImputerTrained>;

    #[allow(non_snake_case)]
    fn fit(self, X: &ArrayView2<'_, Float>, _y: &()) -> SklResult<Self::Fitted> {
        let X = X.mapv(|x| x);
        let (n_samples, n_features) = X.dim();

        // Simplified implementation - in practice would implement sparse coding
        let mean = Array1::zeros(n_features);
        let dictionary = Array2::zeros((n_features, n_features.min(100)));
        let sparse_codes = Array2::zeros((n_samples, n_features.min(100)));

        Ok(SparseImputer {
            state: SparseImputerTrained {
                dictionary_: dictionary,
                sparse_codes_: sparse_codes,
                mean_: mean,
                n_features_in_: n_features,
                n_components_: n_features.min(100),
            },
            sparsity_level: self.sparsity_level,
            regularization: self.regularization,
            max_iter: self.max_iter,
            tol: self.tol,
            missing_values: self.missing_values,
            random_state: self.random_state,
        })
    }
}

impl Transform<ArrayView2<'_, Float>, Array2<Float>> for SparseImputer<SparseImputerTrained> {
    #[allow(non_snake_case)]
    fn transform(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>> {
        let X = X.mapv(|x| x);
        let (n_samples, n_features) = X.dim();

        if n_features != self.state.n_features_in_ {
            return Err(SklearsError::InvalidInput(format!(
                "Number of features {} does not match training features {}",
                n_features, self.state.n_features_in_
            )));
        }

        // Simplified implementation - just fill missing values with zeros
        let mut X_imputed = X.clone();
        for i in 0..n_samples {
            for j in 0..n_features {
                if self.is_missing(X[[i, j]]) {
                    X_imputed[[i, j]] = 0.0; // Sparse assumption
                }
            }
        }

        Ok(X_imputed.mapv(|x| x as Float))
    }
}

impl SparseImputer<SparseImputerTrained> {
    fn is_missing(&self, value: f64) -> bool {
        if self.missing_values.is_nan() {
            value.is_nan()
        } else {
            (value - self.missing_values).abs() < f64::EPSILON
        }
    }
}

/// Compressed Sensing Imputer for high-dimensional sparse data
///
/// Uses compressed sensing principles to reconstruct missing values
/// in high-dimensional sparse datasets.
#[derive(Debug, Clone)]
pub struct CompressedSensingImputer<S = Untrained> {
    state: S,
    measurement_ratio: f64,
    regularization: f64,
    max_iter: usize,
    tol: f64,
    missing_values: f64,
}

/// Trained state for CompressedSensingImputer
#[derive(Debug, Clone)]
pub struct CompressedSensingImputerTrained {
    measurement_matrix_: Array2<f64>,
    n_features_in_: usize,
}

impl CompressedSensingImputer<Untrained> {
    /// Create a new CompressedSensingImputer instance
    pub fn new() -> Self {
        Self {
            state: Untrained,
            measurement_ratio: 0.3,
            regularization: 0.1,
            max_iter: 1000,
            tol: 1e-4,
            missing_values: f64::NAN,
        }
    }
}

impl Default for CompressedSensingImputer<Untrained> {
    fn default() -> Self {
        Self::new()
    }
}

impl Estimator for CompressedSensingImputer<Untrained> {
    type Config = ();
    type Error = SklearsError;
    type Float = Float;

    fn config(&self) -> &Self::Config {
        &()
    }
}

impl Fit<ArrayView2<'_, Float>, ()> for CompressedSensingImputer<Untrained> {
    type Fitted = CompressedSensingImputer<CompressedSensingImputerTrained>;

    #[allow(non_snake_case)]
    fn fit(self, X: &ArrayView2<'_, Float>, _y: &()) -> SklResult<Self::Fitted> {
        let X = X.mapv(|x| x);
        let (_, n_features) = X.dim();

        // Not implemented in v0.1.0. Planned for v0.2.0.
        let _ = n_features;
        Err(SklearsError::NotImplemented(
            "CompressedSensingImputer::fit: not implemented in v0.1.0. Planned for v0.2.0."
                .to_string(),
        ))
    }
}

impl Transform<ArrayView2<'_, Float>, Array2<Float>>
    for CompressedSensingImputer<CompressedSensingImputerTrained>
{
    #[allow(non_snake_case)]
    fn transform(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>> {
        let X = X.mapv(|x| x);
        let (_n_samples, n_features) = X.dim();

        if n_features != self.state.n_features_in_ {
            return Err(SklearsError::InvalidInput(format!(
                "Number of features {} does not match training features {}",
                n_features, self.state.n_features_in_
            )));
        }

        // Not implemented in v0.1.0. Planned for v0.2.0.
        let _ = n_features;
        Err(SklearsError::NotImplemented(
            "CompressedSensingImputer::transform: not implemented in v0.1.0. Planned for v0.2.0."
                .to_string(),
        ))
    }
}

impl CompressedSensingImputer<CompressedSensingImputerTrained> {
    fn is_missing(&self, value: f64) -> bool {
        if self.missing_values.is_nan() {
            value.is_nan()
        } else {
            (value - self.missing_values).abs() < f64::EPSILON
        }
    }
}
