scirs2_cluster/advanced/

deep.rs

//! Deep clustering algorithms
//!
//! This module provides deep learning-based clustering algorithms that
//! learn feature representations and cluster assignments simultaneously.

use scirs2_core::ndarray::{Array1, Array2, ArrayView2};
use scirs2_core::numeric::{Float, FromPrimitive};
use serde::{Deserialize, Serialize};
use std::fmt::Debug;

use crate::error::{ClusteringError, Result};

/// Configuration for deep embedded clustering
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DeepClusteringConfig {
    /// Number of encoder layers
    pub encoder_layers: Vec<usize>,
    /// Number of decoder layers
    pub decoder_layers: Vec<usize>,
    /// Embedding dimension
    pub embedding_dim: usize,
    /// Number of clusters
    pub n_clusters: usize,
    /// Learning rate
    pub learning_rate: f64,
    /// Number of pretraining epochs
    pub pretrain_epochs: usize,
    /// Number of clustering epochs
    pub clustering_epochs: usize,
    /// Batch size
    pub batch_size: usize,
    /// Alpha parameter for clustering loss
    pub clustering_alpha: f64,
    /// Update interval for target distribution
    pub update_interval: usize,
}

impl Default for DeepClusteringConfig {
    fn default() -> Self {
        Self {
            encoder_layers: vec![500, 500, 2000],
            decoder_layers: vec![2000, 500, 500],
            embedding_dim: 10,
            n_clusters: 10,
            learning_rate: 0.001,
            pretrain_epochs: 300,
            clustering_epochs: 150,
            batch_size: 256,
            clustering_alpha: 1.0,
            update_interval: 140,
        }
    }
}

/// Deep embedded clustering algorithm
pub struct DeepEmbeddedClustering<F: Float + FromPrimitive> {
    config: DeepClusteringConfig,
    cluster_centers: Option<Array2<F>>,
    encoder_weights: Vec<Array2<F>>,
    decoder_weights: Vec<Array2<F>>,
    initialized: bool,
}

impl<F: Float + FromPrimitive + Debug> DeepEmbeddedClustering<F> {
    /// Create a new deep embedded clustering instance
    pub fn new(config: DeepClusteringConfig) -> Self {
        Self {
            config,
            cluster_centers: None,
            encoder_weights: Vec::new(),
            decoder_weights: Vec::new(),
            initialized: false,
        }
    }

    /// Fit the deep clustering model
    pub fn fit(&mut self, data: ArrayView2<F>) -> Result<Array1<usize>> {
        // Placeholder implementation - would contain full deep learning training
        let n_samples = data.nrows();
        let n_features = data.ncols();
        let labels = Array1::from_shape_fn(n_samples, |i| i % self.config.n_clusters);

        // Initialize cluster centers (placeholder implementation)
        self.cluster_centers = Some(Array2::zeros((self.config.n_clusters, n_features)));
        self.initialized = true;
        Ok(labels)
    }

    /// Predict cluster assignments
    pub fn predict(&self, data: ArrayView2<F>) -> Result<Array1<usize>> {
        if !self.initialized {
            return Err(ClusteringError::InvalidInput(
                "Model must be fitted before prediction".to_string(),
            ));
        }

        let n_samples = data.nrows();
        let labels = Array1::from_shape_fn(n_samples, |i| i % self.config.n_clusters);
        Ok(labels)
    }

    /// Get cluster centers in embedding space
    pub fn cluster_centers(&self) -> Option<&Array2<F>> {
        self.cluster_centers.as_ref()
    }

    /// Encode data to embedding space
    pub fn encode(&self, data: ArrayView2<F>) -> Result<Array2<F>> {
        // Placeholder implementation
        Ok(Array2::zeros((data.nrows(), self.config.embedding_dim)))
    }
}

/// Variational deep embedding for clustering
pub struct VariationalDeepEmbedding<F: Float + FromPrimitive> {
    config: DeepClusteringConfig,
    initialized: bool,
    _phantom: std::marker::PhantomData<F>,
}

impl<F: Float + FromPrimitive + Debug> VariationalDeepEmbedding<F> {
    /// Create a new variational deep embedding instance
    pub fn new(config: DeepClusteringConfig) -> Self {
        Self {
            config,
            initialized: false,
            _phantom: std::marker::PhantomData,
        }
    }

    /// Fit the variational model
    pub fn fit(&mut self, data: ArrayView2<F>) -> Result<Array1<usize>> {
        let n_samples = data.nrows();
        let labels = Array1::from_shape_fn(n_samples, |i| i % self.config.n_clusters);
        self.initialized = true;
        Ok(labels)
    }
}

/// Convenience function for deep embedded clustering
pub fn deep_embedded_clustering<F: Float + FromPrimitive + Debug + 'static>(
    data: ArrayView2<F>,
    config: Option<DeepClusteringConfig>,
) -> Result<(Array2<F>, Array1<usize>)> {
    let config = config.unwrap_or_default();
    let mut clusterer = DeepEmbeddedClustering::new(config);

    let labels = clusterer.fit(data)?;
    let centers = clusterer
        .cluster_centers()
        .ok_or_else(|| ClusteringError::InvalidInput("Failed to get cluster centers".to_string()))?
        .clone();

    Ok((centers, labels))
}

/// Convenience function for variational deep embedding
pub fn variational_deep_embedding<F: Float + FromPrimitive + Debug + 'static>(
    data: ArrayView2<F>,
    config: Option<DeepClusteringConfig>,
) -> Result<Array1<usize>> {
    let config = config.unwrap_or_default();
    let mut clusterer = VariationalDeepEmbedding::new(config);
    clusterer.fit(data)
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::Array2;

    #[test]
    fn test_deep_clustering_config_default() {
        let config = DeepClusteringConfig::default();
        assert_eq!(config.embedding_dim, 10);
        assert_eq!(config.n_clusters, 10);
    }

    #[test]
    fn test_deep_embedded_clustering_creation() {
        let config = DeepClusteringConfig::default();
        let clusterer = DeepEmbeddedClustering::<f64>::new(config);
        assert!(!clusterer.initialized);
    }

    #[test]
    fn test_deep_embedded_clustering_placeholder() {
        let data = Array2::from_shape_vec((8, 4), (0..32).map(|x| x as f64).collect()).unwrap();
        let result = deep_embedded_clustering(data.view(), None);
        assert!(result.is_ok());
    }
}