scirs2_cluster/advanced/
transfer.rs

1//! Transfer learning clustering algorithms
2//!
3//! This module provides clustering algorithms that leverage knowledge from
4//! previously learned clustering tasks to improve performance on new, related
5//! clustering problems.
6
7use scirs2_core::ndarray::{Array1, Array2, ArrayView2};
8use scirs2_core::numeric::{Float, FromPrimitive};
9use serde::{Deserialize, Serialize};
10use std::collections::HashMap;
11use std::fmt::Debug;
12
13use crate::error::{ClusteringError, Result};
14
15/// Configuration for transfer learning clustering
16#[derive(Debug, Clone, Serialize, Deserialize)]
17pub struct TransferLearningConfig {
18    /// Source domain weight
19    pub source_weight: f64,
20    /// Target domain weight
21    pub target_weight: f64,
22    /// Number of adaptation iterations
23    pub adaptation_iterations: usize,
24    /// Learning rate for adaptation
25    pub adaptation_learning_rate: f64,
26    /// Feature alignment method
27    pub feature_alignment: FeatureAlignment,
28    /// Domain adaptation strength
29    pub domain_adaptation_strength: f64,
30    /// Enable adversarial training
31    pub adversarial_training: bool,
32    /// Maximum mismatch tolerance
33    pub max_mismatch_tolerance: f64,
34}
35
36impl Default for TransferLearningConfig {
37    fn default() -> Self {
38        Self {
39            source_weight: 0.7,
40            target_weight: 0.3,
41            adaptation_iterations: 50,
42            adaptation_learning_rate: 0.01,
43            feature_alignment: FeatureAlignment::Linear,
44            domain_adaptation_strength: 0.1,
45            adversarial_training: false,
46            max_mismatch_tolerance: 0.5,
47        }
48    }
49}
50
51/// Feature alignment methods for transfer learning
52#[derive(Debug, Clone, Serialize, Deserialize)]
53pub enum FeatureAlignment {
54    /// Linear transformation
55    Linear,
56    /// Non-linear neural network alignment
57    Neural { hidden_layers: Vec<usize> },
58    /// Canonical correlation analysis
59    CCA,
60    /// Maximum mean discrepancy
61    MMD,
62    /// Adversarial alignment
63    Adversarial { discriminator_layers: Vec<usize> },
64}
65
66/// Transfer learning clustering algorithm
67pub struct TransferLearningClustering<F: Float> {
68    config: TransferLearningConfig,
69    source_centroids: Option<Array2<F>>,
70    target_centroids: Option<Array2<F>>,
71    alignment_matrix: Option<Array2<F>>,
72    initialized: bool,
73}
74
75impl<F: Float + FromPrimitive + Debug> TransferLearningClustering<F> {
76    /// Create a new transfer learning clustering instance
77    pub fn new(config: TransferLearningConfig) -> Self {
78        Self {
79            config,
80            source_centroids: None,
81            target_centroids: None,
82            alignment_matrix: None,
83            initialized: false,
84        }
85    }
86
87    /// Fit using source domain knowledge and target domain data
88    pub fn fit(
89        &mut self,
90        source_data: ArrayView2<F>,
91        target_data: ArrayView2<F>,
92    ) -> Result<Array1<usize>> {
93        // Placeholder implementation - would contain full transfer learning algorithm
94        let n_samples = target_data.nrows();
95        let n_features = target_data.ncols();
96        let labels = Array1::from_shape_fn(n_samples, |i| i % 3);
97
98        // Initialize centroids (placeholder implementation)
99        self.source_centroids = Some(Array2::zeros((3, source_data.ncols())));
100        self.target_centroids = Some(Array2::zeros((3, n_features)));
101        self.initialized = true;
102        Ok(labels)
103    }
104
105    /// Predict cluster assignments for new target domain data
106    pub fn predict(&self, data: ArrayView2<F>) -> Result<Array1<usize>> {
107        if !self.initialized {
108            return Err(ClusteringError::InvalidInput(
109                "Model must be fitted before prediction".to_string(),
110            ));
111        }
112
113        // Placeholder implementation
114        let n_samples = data.nrows();
115        let labels = Array1::from_shape_fn(n_samples, |i| i % 3);
116        Ok(labels)
117    }
118
119    /// Get adapted target domain cluster centers
120    pub fn cluster_centers(&self) -> Option<&Array2<F>> {
121        self.target_centroids.as_ref()
122    }
123
124    /// Get feature alignment matrix
125    pub fn alignment_matrix(&self) -> Option<&Array2<F>> {
126        self.alignment_matrix.as_ref()
127    }
128}
129
130/// Convenience function for transfer learning clustering
131pub fn transfer_learning_clustering<F: Float + FromPrimitive + Debug + 'static>(
132    source_data: ArrayView2<F>,
133    target_data: ArrayView2<F>,
134    config: Option<TransferLearningConfig>,
135) -> Result<(Array2<F>, Array1<usize>)> {
136    let config = config.unwrap_or_default();
137    let mut clusterer = TransferLearningClustering::new(config);
138
139    let labels = clusterer.fit(source_data, target_data)?;
140    let centers = clusterer
141        .cluster_centers()
142        .ok_or_else(|| ClusteringError::InvalidInput("Failed to get cluster centers".to_string()))?
143        .clone();
144
145    Ok((centers, labels))
146}
147
148#[cfg(test)]
149mod tests {
150    use super::*;
151    use scirs2_core::ndarray::Array2;
152
153    #[test]
154    fn test_transfer_learning_config_default() {
155        let config = TransferLearningConfig::default();
156        assert_eq!(config.source_weight, 0.7);
157        assert_eq!(config.adaptation_iterations, 50);
158    }
159
160    #[test]
161    fn test_transfer_learning_clustering_creation() {
162        let config = TransferLearningConfig::default();
163        let clusterer = TransferLearningClustering::<f64>::new(config);
164        assert!(!clusterer.initialized);
165    }
166
167    #[test]
168    fn test_transfer_learning_clustering_placeholder() {
169        let source_data =
170            Array2::from_shape_vec((4, 2), (0..8).map(|x| x as f64).collect()).unwrap();
171        let target_data =
172            Array2::from_shape_vec((4, 2), (8..16).map(|x| x as f64).collect()).unwrap();
173        let result = transfer_learning_clustering(source_data.view(), target_data.view(), None);
174        assert!(result.is_ok());
175    }
176}
scirs2_cluster/advanced/transfer.rs

scirs2_cluster/advanced/
transfer.rs
