//! Independent Component Analysis Imputer
#![allow(non_snake_case)]
#![allow(dead_code)]
//!
//! This module provides ICA-based imputation methods.
//!
//! # Note
//!
//! Not implemented in v0.1.0. `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,
};

/// Independent Component Analysis Imputer
///
/// Imputation using Independent Component Analysis to separate mixed signals
/// and uses ICA to unmix them, then imputes in the independent component space.
#[derive(Debug, Clone)]
pub struct ICAImputer<S = Untrained> {
    state: S,
    n_components: usize,
    algorithm: String,
    max_iter: usize,
    tol: f64,
    missing_values: f64,
    random_state: Option<u64>,
}

/// Trained state for ICAImputer
#[derive(Debug, Clone)]
pub struct ICAImputerTrained {
    components_: Array2<f64>,
    mixing_: Array2<f64>,
    mean_: Array1<f64>,
    n_features_in_: usize,
}

impl ICAImputer<Untrained> {
    /// Create a new ICAImputer instance
    pub fn new() -> Self {
        Self {
            state: Untrained,
            n_components: 2,
            algorithm: "fastica".to_string(),
            max_iter: 100,
            tol: 1e-6,
            missing_values: f64::NAN,
            random_state: None,
        }
    }

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

    /// Set the ICA algorithm
    pub fn algorithm(mut self, algorithm: String) -> Self {
        self.algorithm = algorithm;
        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
    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
    }
}

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

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

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

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

    #[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();

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

impl Transform<ArrayView2<'_, Float>, Array2<Float>> for ICAImputer<ICAImputerTrained> {
    #[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_samples, n_features);
        Err(SklearsError::NotImplemented(
            "ICAImputer::transform: not implemented in v0.1.0. Planned for v0.2.0.".to_string(),
        ))
    }
}
