scirs2-cluster 0.4.2

//! Transfer learning clustering algorithms
//!
//! This module provides clustering algorithms that leverage knowledge from
//! previously learned clustering tasks to improve performance on new, related
//! clustering problems.

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

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

/// Configuration for transfer learning clustering
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TransferLearningConfig {
    /// Source domain weight
    pub source_weight: f64,
    /// Target domain weight
    pub target_weight: f64,
    /// Number of adaptation iterations
    pub adaptation_iterations: usize,
    /// Learning rate for adaptation
    pub adaptation_learning_rate: f64,
    /// Feature alignment method
    pub feature_alignment: FeatureAlignment,
    /// Domain adaptation strength
    pub domain_adaptation_strength: f64,
    /// Enable adversarial training
    pub adversarial_training: bool,
    /// Maximum mismatch tolerance
    pub max_mismatch_tolerance: f64,
}

impl Default for TransferLearningConfig {
    fn default() -> Self {
        Self {
            source_weight: 0.7,
            target_weight: 0.3,
            adaptation_iterations: 50,
            adaptation_learning_rate: 0.01,
            feature_alignment: FeatureAlignment::Linear,
            domain_adaptation_strength: 0.1,
            adversarial_training: false,
            max_mismatch_tolerance: 0.5,
        }
    }
}

/// Feature alignment methods for transfer learning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FeatureAlignment {
    /// Linear transformation
    Linear,
    /// Non-linear neural network alignment
    Neural { hidden_layers: Vec<usize> },
    /// Canonical correlation analysis
    CCA,
    /// Maximum mean discrepancy
    MMD,
    /// Adversarial alignment
    Adversarial { discriminator_layers: Vec<usize> },
}

/// Transfer learning clustering algorithm
pub struct TransferLearningClustering<F: Float> {
    config: TransferLearningConfig,
    source_centroids: Option<Array2<F>>,
    target_centroids: Option<Array2<F>>,
    alignment_matrix: Option<Array2<F>>,
    initialized: bool,
}

impl<F: Float + FromPrimitive + Debug> TransferLearningClustering<F> {
    /// Create a new transfer learning clustering instance
    pub fn new(config: TransferLearningConfig) -> Self {
        Self {
            config,
            source_centroids: None,
            target_centroids: None,
            alignment_matrix: None,
            initialized: false,
        }
    }

    /// Fit using source domain knowledge and target domain data
    pub fn fit(
        &mut self,
        source_data: ArrayView2<F>,
        target_data: ArrayView2<F>,
    ) -> Result<Array1<usize>> {
        // Placeholder implementation - would contain full transfer learning algorithm
        let n_samples = target_data.nrows();
        let n_features = target_data.ncols();
        let labels = Array1::from_shape_fn(n_samples, |i| i % 3);

        // Initialize centroids (placeholder implementation)
        self.source_centroids = Some(Array2::zeros((3, source_data.ncols())));
        self.target_centroids = Some(Array2::zeros((3, n_features)));
        self.initialized = true;
        Ok(labels)
    }

    /// Predict cluster assignments for new target domain data
    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(),
            ));
        }

        // Placeholder implementation
        let n_samples = data.nrows();
        let labels = Array1::from_shape_fn(n_samples, |i| i % 3);
        Ok(labels)
    }

    /// Get adapted target domain cluster centers
    pub fn cluster_centers(&self) -> Option<&Array2<F>> {
        self.target_centroids.as_ref()
    }

    /// Get feature alignment matrix
    pub fn alignment_matrix(&self) -> Option<&Array2<F>> {
        self.alignment_matrix.as_ref()
    }
}

/// Convenience function for transfer learning clustering
pub fn transfer_learning_clustering<F: Float + FromPrimitive + Debug + 'static>(
    source_data: ArrayView2<F>,
    target_data: ArrayView2<F>,
    config: Option<TransferLearningConfig>,
) -> Result<(Array2<F>, Array1<usize>)> {
    let config = config.unwrap_or_default();
    let mut clusterer = TransferLearningClustering::new(config);

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

    Ok((centers, labels))
}

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

    #[test]
    fn test_transfer_learning_config_default() {
        let config = TransferLearningConfig::default();
        assert_eq!(config.source_weight, 0.7);
        assert_eq!(config.adaptation_iterations, 50);
    }

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

    #[test]
    fn test_transfer_learning_clustering_placeholder() {
        let source_data = Array2::from_shape_vec((4, 2), (0..8).map(|x| x as f64).collect())
            .expect("Operation failed");
        let target_data = Array2::from_shape_vec((4, 2), (8..16).map(|x| x as f64).collect())
            .expect("Operation failed");
        let result = transfer_learning_clustering(source_data.view(), target_data.view(), None);
        assert!(result.is_ok());
    }
}