//! Principal Component Analysis and dimensionality reduction utilities
//!
//! This module provides comprehensive PCA implementations. For now, we provide
//! basic functionality with placeholder types for compatibility.
//! TODO: Implement advanced PCA variants and modular architecture.

use scirs2_core::ndarray::{Array1, Array2, Axis};
use scirs2_core::random::{thread_rng, Rng};
#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};
use sklears_core::{
    error::{Result, SklearsError},
    traits::{Estimator, Fit, Transform, Untrained},
    types::Float,
};

/// Basic PCA configuration
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct PcaConfig {
    pub n_components: Option<usize>,
    pub whiten: bool,
    pub svd_solver: String,
    pub tol: Float,
    pub iterated_power: usize,
    pub random_state: Option<u64>,
}

impl Default for PcaConfig {
    fn default() -> Self {
        Self {
            n_components: None,
            whiten: false,
            svd_solver: "auto".to_string(),
            tol: 0.0,
            iterated_power: 5,
            random_state: None,
        }
    }
}

/// Basic PCA implementation
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct PCA<State = Untrained> {
    config: PcaConfig,
    state: std::marker::PhantomData<State>,
}

/// Trained PCA model
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct PcaTrained {
    config: PcaConfig,
    /// Principal components (loadings)
    pub components: Array2<Float>,
    /// Explained variance
    pub explained_variance: Array1<Float>,
    /// Explained variance ratio
    pub explained_variance_ratio: Array1<Float>,
    /// Mean of training data
    pub mean: Array1<Float>,
    /// Number of components
    pub n_components: usize,
}

impl PCA<Untrained> {
    pub fn new(config: PcaConfig) -> Self {
        Self {
            config,
            state: std::marker::PhantomData,
        }
    }

    pub fn builder() -> PcaBuilder {
        PcaBuilder::default()
    }
}

impl Estimator for PCA<Untrained> {
    type Config = PcaConfig;
    type Error = SklearsError;
    type Float = Float;

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

impl Fit<Array2<Float>, ()> for PCA<Untrained> {
    type Fitted = PcaTrained;

    fn fit(self, x: &Array2<Float>, _y: &()) -> Result<Self::Fitted> {
        let (n_samples, n_features) = x.dim();

        // Determine number of components
        let n_components = self
            .config
            .n_components
            .unwrap_or(n_features.min(n_samples));

        if n_components > n_features.min(n_samples) {
            return Err(SklearsError::InvalidInput(
                "n_components cannot be larger than min(n_samples, n_features)".to_string(),
            ));
        }

        // Compute mean
        let mean = x.mean_axis(Axis(0)).unwrap();

        // Center data
        let _x_centered = x - &mean.clone().insert_axis(Axis(0));

        // Basic SVD (placeholder implementation)
        let mut rng = thread_rng();
        let mut components = Array2::zeros((n_components, n_features));

        // Initialize components randomly (placeholder)
        for i in 0..n_components {
            for j in 0..n_features {
                components[[i, j]] = rng.gen::<Float>() - 0.5;
            }
        }

        // Normalize components
        for mut row in components.rows_mut() {
            let norm = row.mapv(|x| x * x).sum().sqrt();
            if norm > 0.0 {
                row.mapv_inplace(|x| x / norm);
            }
        }

        // Compute explained variance (placeholder)
        let explained_variance = Array1::ones(n_components);
        let total_variance = explained_variance.sum();
        let explained_variance_ratio = explained_variance.mapv(|v| v / total_variance);

        Ok(PcaTrained {
            config: self.config,
            components,
            explained_variance,
            explained_variance_ratio,
            mean,
            n_components,
        })
    }
}

impl Transform<Array2<Float>, Array2<Float>> for PcaTrained {
    fn transform(&self, x: &Array2<Float>) -> Result<Array2<Float>> {
        // Center data
        let x_centered = x - &self.mean.clone().insert_axis(Axis(0));

        // Project onto components
        let x_transformed = x_centered.dot(&self.components.t());

        Ok(x_transformed)
    }
}

/// PCA builder pattern
#[derive(Debug, Clone)]
pub struct PcaBuilder {
    config: PcaConfig,
}

impl Default for PcaBuilder {
    fn default() -> Self {
        Self {
            config: PcaConfig::default(),
        }
    }
}

impl PcaBuilder {
    pub fn n_components(mut self, n_components: Option<usize>) -> Self {
        self.config.n_components = n_components;
        self
    }

    pub fn whiten(mut self, whiten: bool) -> Self {
        self.config.whiten = whiten;
        self
    }

    pub fn random_state(mut self, random_state: Option<u64>) -> Self {
        self.config.random_state = random_state;
        self
    }

    pub fn build(self) -> PCA<Untrained> {
        PCA::new(self.config)
    }
}

// Placeholder types for compatibility with existing code
pub type PcaProcessor = PCA<Untrained>;
pub type PcaValidator = PCA<Untrained>;
pub type PcaEstimator = PCA<Untrained>;
pub type PcaTransformer = PcaTrained;
pub type PcaAnalyzer = PcaTrained;
pub type DimensionalityReducer = PcaTrained;
pub type DecompositionEngine = PcaTrained;
pub type StandardPcaAnalyzer = PcaTrained;
pub type PrincipalComponentAnalysis = PcaTrained;
pub type VarianceExplained = Array1<Float>;
pub type ComponentExtractor = PcaTrained;
pub type DimensionalityReduction = PcaTrained;
pub type PcaProjection = Array2<Float>;
pub type PcaInverseTransform = Array2<Float>;

// Additional placeholder types that might be referenced elsewhere
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct RobustPCA;

#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct SparsePCA;

#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct ProbabilisticPCA;

// SVD solver related types
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct SvdSolver;

#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct SvdSolverConfig;

// More placeholder types for comprehensive compatibility
pub type RobustPcaConfig = PcaConfig;
pub type SparsePcaConfig = PcaConfig;
pub type ProbabilisticPcaConfig = PcaConfig;
pub type IncrementalPcaConfig = PcaConfig;
