//! Mini-batch Dictionary Learning algorithms

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

/// Mini-batch dictionary learning configuration
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct MiniBatchConfig {
    pub n_components: usize,
    pub batch_size: usize,
    pub max_iter: usize,
}

impl Default for MiniBatchConfig {
    fn default() -> Self {
        Self {
            n_components: 100,
            batch_size: 10,
            max_iter: 1000,
        }
    }
}

/// Mini-batch dictionary learning result
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct MiniBatchResult {
    pub components: Array2<Float>,
    pub n_iter: usize,
}

/// Mini-batch Dictionary Learning estimator
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct MiniBatchDictionaryLearning<State = Untrained> {
    config: MiniBatchConfig,
    state: std::marker::PhantomData<State>,
    /// Dictionary components - only available when trained
    components_: Option<Array2<Float>>,
}

impl MiniBatchDictionaryLearning<Untrained> {
    pub fn new(config: MiniBatchConfig) -> Self {
        Self {
            config,
            state: std::marker::PhantomData,
            components_: None,
        }
    }
}

impl MiniBatchDictionaryLearning<Trained> {
    /// Get the dictionary components
    pub fn components(&self) -> &Array2<Float> {
        self.components_.as_ref().expect("Model is trained")
    }
}

impl Estimator for MiniBatchDictionaryLearning<Untrained> {
    type Config = MiniBatchConfig;
    type Error = SklearsError;
    type Float = Float;

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

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

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

        Ok(MiniBatchDictionaryLearning {
            config: self.config,
            state: std::marker::PhantomData,
            components_: Some(components),
        })
    }
}

impl Transform<Array2<Float>, Array2<Float>> for MiniBatchDictionaryLearning<Trained> {
    fn transform(&self, x: &Array2<Float>) -> Result<Array2<Float>> {
        let (n_samples, _n_features) = x.dim();
        let codes = Array2::zeros((n_samples, self.config.n_components));
        Ok(codes)
    }
}

// Type alias for backward compatibility
pub type TrainedMiniBatchDictionaryLearning = MiniBatchDictionaryLearning<Trained>;
