scirs2_optimize/neuromorphic/
neural_ode_optimization.rs

1//! Neural ODE Optimization
2//!
3//! Implementation of neural ordinary differential equation-based optimization.
4
5use crate::error::OptimizeResult;
6use scirs2_core::ndarray::{Array1, ArrayView1};
7
8/// Neural ODE system for optimization
9#[derive(Debug, Clone)]
10pub struct NeuralODE {
11    /// Network parameters (weights)
12    pub weights: Array1<f64>,
13    /// System state
14    pub state: Array1<f64>,
15    /// Time step
16    pub dt: f64,
17}
18
19impl NeuralODE {
20    /// Create new Neural ODE system
21    pub fn new(state_size: usize, dt: f64) -> Self {
22        Self {
23            weights: Array1::from(vec![0.1; state_size * state_size]),
24            state: Array1::zeros(state_size),
25            dt,
26        }
27    }
28
29    /// Compute state derivative
30    pub fn compute_derivative(
31        &self,
32        state: &ArrayView1<f64>,
33        objective_gradient: &ArrayView1<f64>,
34    ) -> Array1<f64> {
35        let n = state.len();
36        let mut derivative = Array1::zeros(n);
37
38        // Neural dynamics: dx/dt = -Wx + u
39        // where W is weight matrix, x is state, u is input (objective gradient)
40        for i in 0..n {
41            for j in 0..n {
42                let weight_idx = i * n + j;
43                if weight_idx < self.weights.len() {
44                    derivative[i] -= self.weights[weight_idx] * state[j];
45                }
46            }
47
48            // Add objective gradient as driving input
49            if i < objective_gradient.len() {
50                derivative[i] += objective_gradient[i];
51            }
52        }
53
54        derivative
55    }
56
57    /// Integrate one step using Euler method
58    pub fn integrate_step(&mut self, objective_gradient: &ArrayView1<f64>) {
59        let derivative = self.compute_derivative(&self.state.view(), objective_gradient);
60
61        for i in 0..self.state.len() {
62            self.state[i] += self.dt * derivative[i];
63        }
64    }
65
66    /// Get current state as parameter estimate
67    pub fn get_parameters(&self) -> &Array1<f64> {
68        &self.state
69    }
70
71    /// Set initial state
72    pub fn set_initial_state(&mut self, initial_state: &ArrayView1<f64>) {
73        self.state = initial_state.to_owned();
74    }
75}
76
77/// Neural ODE-based optimization
78#[allow(dead_code)]
79pub fn neural_ode_optimize<F>(
80    objective: F,
81    initial_params: &ArrayView1<f64>,
82    num_steps: usize,
83    dt: f64,
84) -> OptimizeResult<Array1<f64>>
85where
86    F: Fn(&ArrayView1<f64>) -> f64,
87{
88    let mut neural_ode = NeuralODE::new(initial_params.len(), dt);
89    neural_ode.set_initial_state(initial_params);
90
91    for _step in 0..num_steps {
92        // Compute objective gradient
93        let current_params = neural_ode.get_parameters();
94        let gradient = compute_finite_difference_gradient(&objective, &current_params.view());
95
96        // Integrate Neural ODE
97        neural_ode.integrate_step(&(-1.0 * &gradient).view()); // Negative for minimization
98    }
99
100    Ok(neural_ode.get_parameters().clone())
101}
102
103/// Compute finite difference gradient
104#[allow(dead_code)]
105fn compute_finite_difference_gradient<F>(objective: &F, params: &ArrayView1<f64>) -> Array1<f64>
106where
107    F: Fn(&ArrayView1<f64>) -> f64,
108{
109    let n = params.len();
110    let mut gradient = Array1::zeros(n);
111    let h = 1e-6;
112    let f0 = objective(params);
113
114    for i in 0..n {
115        let mut params_plus = params.to_owned();
116        params_plus[i] += h;
117        let f_plus = objective(&params_plus.view());
118        gradient[i] = (f_plus - f0) / h;
119    }
120
121    gradient
122}
123
124#[allow(dead_code)]
125pub fn placeholder() {
126    // Placeholder function to prevent unused module warnings
127}
scirs2_optimize/neuromorphic/neural_ode_optimization.rs

scirs2_optimize/neuromorphic/
neural_ode_optimization.rs
