sklears-decomposition 0.2.0

//! Online Dictionary Learning algorithms for streaming data

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

/// Online dictionary learning algorithms
#[derive(Debug, Clone, Copy)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub enum OnlineDictLearningAlgorithm {
    /// Online Gradient Descent
    SGD,
    /// Online K-SVD
    OnlineKSVD,
    /// Adaptive Dictionary Learning
    Adaptive,
}

/// Online Dictionary Learning configuration
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct OnlineDictLearningConfig {
    /// Number of dictionary atoms
    pub n_components: usize,
    /// Learning rate
    pub learning_rate: Float,
    /// Batch size for mini-batches
    pub batch_size: usize,
    /// Algorithm variant
    pub algorithm: OnlineDictLearningAlgorithm,
}

impl Default for OnlineDictLearningConfig {
    fn default() -> Self {
        Self {
            n_components: 100,
            learning_rate: 0.01,
            batch_size: 10,
            algorithm: OnlineDictLearningAlgorithm::SGD,
        }
    }
}

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

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

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

    /// Get the number of samples seen
    pub fn n_samples_seen(&self) -> usize {
        self.n_samples_seen_.expect("Model is trained")
    }
}

impl Estimator for OnlineDictionaryLearning<Untrained> {
    type Config = OnlineDictLearningConfig;
    type Error = SklearsError;
    type Float = Float;

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

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

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

        // Initialize dictionary randomly
        let mut rng = thread_rng();
        let mut components = Array2::zeros((self.config.n_components, n_features));

        for i in 0..self.config.n_components {
            for j in 0..n_features {
                components[[i, j]] = rng.random::<Float>() - 0.5;
            }
        }

        Ok(OnlineDictionaryLearning {
            config: self.config,
            state: std::marker::PhantomData,
            components_: Some(components),
            n_samples_seen_: Some(x.nrows()),
        })
    }
}

impl Transform<Array2<Float>, Array2<Float>> for OnlineDictionaryLearning<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)
    }
}

// Algorithm-specific implementations (placeholders)
pub struct OnlineGradientDescent;
pub struct OnlineKSvd;
pub struct AdaptiveDictionaryLearning;

// Type alias for backward compatibility
pub type TrainedOnlineDictionaryLearning = OnlineDictionaryLearning<Trained>;