scirs2_optimize/reinforcement_learning/
meta_learning.rs

1//! Meta-Learning for Optimization
2//!
3//! Learning to optimize across different problem classes and domains.
4
5use crate::error::OptimizeResult;
6use crate::result::OptimizeResults;
7use scirs2_core::ndarray::{Array1, ArrayView1};
8// Unused import
9// use scirs2_core::error::CoreResult;
10use std::collections::HashMap;
11
12/// Meta-learning optimizer that learns across multiple tasks
13#[derive(Debug, Clone)]
14pub struct MetaLearningOptimizer {
15    /// Task-specific learned parameters
16    pub task_parameters: HashMap<String, Array1<f64>>,
17    /// Meta-parameters learned across tasks
18    pub meta_parameters: Array1<f64>,
19    /// Learning rate for meta-updates
20    pub meta_learning_rate: f64,
21    /// Task counter
22    pub task_count: usize,
23}
24
25impl MetaLearningOptimizer {
26    /// Create new meta-learning optimizer
27    pub fn new(_param_size: usize, meta_learning_rate: f64) -> Self {
28        Self {
29            task_parameters: HashMap::new(),
30            meta_parameters: Array1::zeros(_param_size),
31            meta_learning_rate,
32            task_count: 0,
33        }
34    }
35
36    /// Learn on a single task
37    pub fn learn_task<F>(
38        &mut self,
39        task_id: String,
40        objective: &F,
41        initial_params: &ArrayView1<f64>,
42        num_steps: usize,
43    ) -> OptimizeResult<Array1<f64>>
44    where
45        F: Fn(&ArrayView1<f64>) -> f64,
46    {
47        // Initialize task parameters with meta-parameters
48        let mut task_params = if let Some(existing) = self.task_parameters.get(&task_id) {
49            existing.clone()
50        } else {
51            &self.meta_parameters + initial_params
52        };
53
54        // Task-specific optimization (simplified gradient descent)
55        for _step in 0..num_steps {
56            let current_obj = objective(&task_params.view());
57
58            // Finite difference gradient
59            let mut gradient = Array1::zeros(task_params.len());
60            let h = 1e-6;
61
62            for i in 0..task_params.len() {
63                let mut params_plus = task_params.clone();
64                params_plus[i] += h;
65                let obj_plus = objective(&params_plus.view());
66                gradient[i] = (obj_plus - current_obj) / h;
67            }
68
69            // Update task parameters
70            task_params = &task_params - &(0.01 * &gradient);
71        }
72
73        // Store task parameters
74        self.task_parameters.insert(task_id, task_params.clone());
75        self.task_count += 1;
76
77        Ok(task_params)
78    }
79
80    /// Update meta-parameters based on task experiences
81    pub fn update_meta_parameters(&mut self) {
82        if self.task_parameters.is_empty() {
83            return;
84        }
85
86        // Simple meta-update: average of all task parameters
87        let mut sum = Array1::zeros(self.meta_parameters.len());
88        for task_params in self.task_parameters.values() {
89            sum = &sum + task_params;
90        }
91
92        let average = &sum / self.task_parameters.len() as f64;
93
94        // Update meta-parameters with momentum
95        self.meta_parameters = &((1.0 - self.meta_learning_rate) * &self.meta_parameters)
96            + &(self.meta_learning_rate * &average);
97    }
98
99    /// Optimize new task using learned meta-parameters
100    pub fn optimize_new_task<F>(
101        &mut self,
102        objective: &F,
103        initial_params: &ArrayView1<f64>,
104        num_steps: usize,
105    ) -> OptimizeResult<OptimizeResults<f64>>
106    where
107        F: Fn(&ArrayView1<f64>) -> f64,
108    {
109        let task_id = format!("task_{}", self.task_count);
110        let result_params = self.learn_task(task_id, objective, initial_params, num_steps)?;
111
112        // Update meta-parameters
113        self.update_meta_parameters();
114
115        Ok(OptimizeResults::<f64> {
116            x: result_params.clone(),
117            fun: objective(&result_params.view()),
118            success: true,
119            nit: num_steps,
120            message: "Meta-learning optimization completed".to_string(),
121            jac: None,
122            hess: None,
123            constr: None,
124            nfev: num_steps * (self.task_count + 1), // Steps * tasks
125            njev: 0,
126            nhev: 0,
127            maxcv: 0,
128            status: 0,
129        })
130    }
131}
132
133/// Meta-learning optimization function
134#[allow(dead_code)]
135pub fn meta_learning_optimize<F>(
136    objective: F,
137    initial_params: &ArrayView1<f64>,
138    num_tasks: usize,
139    steps_per_task: usize,
140) -> OptimizeResult<OptimizeResults<f64>>
141where
142    F: Fn(&ArrayView1<f64>) -> f64,
143{
144    let mut meta_optimizer = MetaLearningOptimizer::new(initial_params.len(), 0.1);
145
146    // Train on multiple similar _tasks (variations of the objective)
147    for task_idx in 0..num_tasks {
148        let task_id = format!("training_task_{}", task_idx);
149
150        // Create variation of the objective (simple shift)
151        let shift = (task_idx as f64 - num_tasks as f64 * 0.5) * 0.1;
152        let task_objective = |x: &ArrayView1<f64>| objective(x) + shift;
153
154        meta_optimizer.learn_task(task_id, &task_objective, initial_params, steps_per_task)?;
155        meta_optimizer.update_meta_parameters();
156    }
157
158    // Optimize on the original objective
159    meta_optimizer.optimize_new_task(&objective, initial_params, steps_per_task)
160}
161
162#[allow(dead_code)]
163pub fn placeholder() {}
scirs2_optimize/reinforcement_learning/meta_learning.rs

scirs2_optimize/reinforcement_learning/
meta_learning.rs
