scirs2_optimize/neuromorphic/
event_driven.rs

1//! Event-Driven Optimization
2//!
3//! Implementation of event-driven optimization algorithms for neuromorphic computing.
4
5use scirs2_core::error::CoreResult as Result;
6use scirs2_core::ndarray::{Array1, ArrayView1};
7use std::cmp::Ordering;
8use std::collections::BinaryHeap;
9
10/// Event in the optimization process
11#[derive(Debug, Clone)]
12pub struct OptimizationEvent {
13    /// Time of the event
14    pub time: f64,
15    /// Type of event
16    pub event_type: EventType,
17    /// Associated data
18    pub data: Array1<f64>,
19}
20
21/// Types of optimization events
22#[derive(Debug, Clone)]
23pub enum EventType {
24    /// Parameter update event
25    ParameterUpdate,
26    /// Gradient computation event
27    GradientComputation,
28    /// Objective evaluation event
29    ObjectiveEvaluation,
30}
31
32impl PartialEq for OptimizationEvent {
33    fn eq(&self, other: &Self) -> bool {
34        self.time == other.time
35    }
36}
37
38impl Eq for OptimizationEvent {}
39
40impl PartialOrd for OptimizationEvent {
41    fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
42        // Reverse ordering for min-heap behavior
43        other.time.partial_cmp(&self.time)
44    }
45}
46
47impl Ord for OptimizationEvent {
48    fn cmp(&self, other: &Self) -> Ordering {
49        self.partial_cmp(other).unwrap_or(Ordering::Equal)
50    }
51}
52
53/// Event-driven optimization scheduler
54#[derive(Debug, Clone)]
55pub struct EventDrivenOptimizer {
56    /// Event queue
57    pub event_queue: BinaryHeap<OptimizationEvent>,
58    /// Current time
59    pub current_time: f64,
60    /// Current parameters
61    pub parameters: Array1<f64>,
62}
63
64impl EventDrivenOptimizer {
65    /// Create new event-driven optimizer
66    pub fn new(initial_params: Array1<f64>) -> Self {
67        Self {
68            event_queue: BinaryHeap::new(),
69            current_time: 0.0,
70            parameters: initial_params,
71        }
72    }
73
74    /// Schedule an event
75    pub fn schedule_event(&mut self, event: OptimizationEvent) {
76        self.event_queue.push(event);
77    }
78
79    /// Process next event
80    pub fn process_next_event<F>(&mut self, objective: &F) -> Result<bool>
81    where
82        F: Fn(&ArrayView1<f64>) -> f64,
83    {
84        if let Some(event) = self.event_queue.pop() {
85            self.current_time = event.time;
86
87            match event.event_type {
88                EventType::ParameterUpdate => {
89                    // Update parameters with event data
90                    for (i, &update) in event.data.iter().enumerate() {
91                        if i < self.parameters.len() {
92                            self.parameters[i] += update;
93                        }
94                    }
95                }
96                EventType::GradientComputation => {
97                    // Compute gradient (simplified)
98                    let _gradient = self.compute_finite_difference_gradient(objective);
99                }
100                EventType::ObjectiveEvaluation => {
101                    let _obj_val = objective(&self.parameters.view());
102                }
103            }
104
105            Ok(true)
106        } else {
107            Ok(false)
108        }
109    }
110
111    /// Compute finite difference gradient
112    fn compute_finite_difference_gradient<F>(&self, objective: &F) -> Array1<f64>
113    where
114        F: Fn(&ArrayView1<f64>) -> f64,
115    {
116        let n = self.parameters.len();
117        let mut gradient = Array1::zeros(n);
118        let h = 1e-6;
119        let f0 = objective(&self.parameters.view());
120
121        for i in 0..n {
122            let mut params_plus = self.parameters.clone();
123            params_plus[i] += h;
124            let f_plus = objective(&params_plus.view());
125            gradient[i] = (f_plus - f0) / h;
126        }
127
128        gradient
129    }
130}
131
132/// Event-driven optimization function
133#[allow(dead_code)]
134pub fn event_driven_optimize<F>(
135    objective: F,
136    initial_params: &ArrayView1<f64>,
137    max_events: usize,
138) -> Result<Array1<f64>>
139where
140    F: Fn(&ArrayView1<f64>) -> f64,
141{
142    let mut optimizer = EventDrivenOptimizer::new(initial_params.to_owned());
143
144    // Schedule initial events
145    for i in 0..10 {
146        let event = OptimizationEvent {
147            time: i as f64 * 0.1,
148            event_type: EventType::ParameterUpdate,
149            data: Array1::from(vec![0.01; initial_params.len()]),
150        };
151        optimizer.schedule_event(event);
152    }
153
154    // Process events
155    for _ in 0..max_events {
156        if !optimizer.process_next_event(&objective)? {
157            break;
158        }
159    }
160
161    Ok(optimizer.parameters)
162}
163
164#[allow(dead_code)]
165pub fn placeholder() {
166    // Placeholder function to prevent unused module warnings
167}
scirs2_optimize/neuromorphic/event_driven.rs

scirs2_optimize/neuromorphic/
event_driven.rs
