scirs2_cluster/advanced/
reinforcement.rs

1//! Reinforcement learning-based clustering algorithms
2//!
3//! This module provides clustering algorithms that use reinforcement learning
4//! principles to adaptively improve clustering performance through reward-based
5//! learning mechanisms.
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 reinforcement learning-based clustering
16#[derive(Debug, Clone, Serialize, Deserialize)]
17pub struct RLClusteringConfig {
18    /// Learning rate for Q-learning
19    pub learning_rate: f64,
20    /// Discount factor for future rewards
21    pub discount_factor: f64,
22    /// Exploration rate (epsilon in epsilon-greedy)
23    pub exploration_rate: f64,
24    /// Decay rate for exploration
25    pub exploration_decay: f64,
26    /// Number of episodes for training
27    pub n_episodes: usize,
28    /// Maximum steps per episode
29    pub max_steps_per_episode: usize,
30    /// Initial cluster count
31    pub initial_clusters: usize,
32    /// Maximum allowed clusters
33    pub max_clusters: usize,
34    /// Reward function type
35    pub reward_function: RewardFunction,
36}
37
38impl Default for RLClusteringConfig {
39    fn default() -> Self {
40        Self {
41            learning_rate: 0.1,
42            discount_factor: 0.95,
43            exploration_rate: 0.1,
44            exploration_decay: 0.995,
45            n_episodes: 100,
46            max_steps_per_episode: 1000,
47            initial_clusters: 3,
48            max_clusters: 20,
49            reward_function: RewardFunction::Silhouette,
50        }
51    }
52}
53
54/// Reward function types for RL clustering
55#[derive(Debug, Clone, Serialize, Deserialize)]
56pub enum RewardFunction {
57    /// Silhouette score based reward
58    Silhouette,
59    /// Davies-Bouldin index based reward
60    DaviesBouldin,
61    /// Calinski-Harabasz index based reward
62    CalinskiHarabasz,
63    /// Custom reward function
64    Custom { parameters: HashMap<String, f64> },
65}
66
67/// Reinforcement learning clustering algorithm
68pub struct RLClustering<F: Float> {
69    config: RLClusteringConfig,
70    q_table: HashMap<String, HashMap<String, f64>>,
71    current_clusters: Vec<Array1<F>>,
72    initialized: bool,
73}
74
75impl<F: Float + FromPrimitive + Debug> RLClustering<F> {
76    /// Create a new RL clustering instance
77    pub fn new(config: RLClusteringConfig) -> Self {
78        Self {
79            config,
80            q_table: HashMap::new(),
81            current_clusters: Vec::new(),
82            initialized: false,
83        }
84    }
85
86    /// Train the RL agent and perform clustering
87    pub fn fit(&mut self, data: ArrayView2<F>) -> Result<Array1<usize>> {
88        // Placeholder implementation - would contain full RL training loop
89        let n_samples = data.nrows();
90        let labels = Array1::from_shape_fn(n_samples, |i| i % self.config.initial_clusters);
91        self.initialized = true;
92        Ok(labels)
93    }
94
95    /// Predict cluster assignments for new data
96    pub fn predict(&self, data: ArrayView2<F>) -> Result<Array1<usize>> {
97        if !self.initialized {
98            return Err(ClusteringError::InvalidInput(
99                "Model must be fitted before prediction".to_string(),
100            ));
101        }
102
103        // Placeholder implementation
104        let n_samples = data.nrows();
105        let labels = Array1::from_shape_fn(n_samples, |i| i % self.config.initial_clusters);
106        Ok(labels)
107    }
108
109    /// Get current cluster centers
110    pub fn cluster_centers(&self) -> Option<Array2<F>> {
111        if !self.initialized {
112            return None;
113        }
114
115        // Placeholder implementation
116        Some(Array2::zeros((self.config.initial_clusters, 2)))
117    }
118}
119
120/// Convenience function for RL-based clustering
121pub fn rl_clustering<F: Float + FromPrimitive + Debug>(
122    data: ArrayView2<F>,
123    config: Option<RLClusteringConfig>,
124) -> Result<(Array2<F>, Array1<usize>)> {
125    let config = config.unwrap_or_default();
126    let mut clusterer = RLClustering::new(config);
127
128    let labels = clusterer.fit(data)?;
129    let centers = clusterer.cluster_centers().ok_or_else(|| {
130        ClusteringError::InvalidInput("Failed to get cluster centers".to_string())
131    })?;
132
133    Ok((centers, labels))
134}
135
136#[cfg(test)]
137mod tests {
138    use super::*;
139    use scirs2_core::ndarray::Array2;
140
141    #[test]
142    fn test_rl_clustering_config_default() {
143        let config = RLClusteringConfig::default();
144        assert_eq!(config.learning_rate, 0.1);
145        assert_eq!(config.n_episodes, 100);
146    }
147
148    #[test]
149    fn test_rl_clustering_creation() {
150        let config = RLClusteringConfig::default();
151        let clusterer = RLClustering::<f64>::new(config);
152        assert!(!clusterer.initialized);
153    }
154
155    #[test]
156    fn test_rl_clustering_placeholder() {
157        let data = Array2::from_shape_vec((6, 2), (0..12).map(|x| x as f64).collect()).unwrap();
158        let result = rl_clustering(data.view(), None);
159        assert!(result.is_ok());
160    }
161}
scirs2_cluster/advanced/reinforcement.rs

scirs2_cluster/advanced/
reinforcement.rs
