scirs2_optimize/neuromorphic/
liquid_state_machines.rs

1//! Liquid State Machines for Optimization
2//!
3//! Implementation of liquid state machine-based optimization algorithms.
4
5use crate::error::{OptimizeError, OptimizeResult};
6use scirs2_core::ndarray::{Array1, Array2, ArrayView1};
7use scirs2_core::random::Rng;
8
9/// Liquid State Machine for optimization
10#[derive(Debug, Clone)]
11pub struct LiquidStateMachine {
12    /// Reservoir weights
13    pub reservoir_weights: Array2<f64>,
14    /// Input weights
15    pub input_weights: Array2<f64>,
16    /// Output weights (trainable)
17    pub output_weights: Array2<f64>,
18    /// Reservoir state
19    pub reservoir_state: Array1<f64>,
20    /// Reservoir size
21    pub reservoir_size: usize,
22    /// Input size
23    pub input_size: usize,
24    /// Output size
25    pub output_size: usize,
26}
27
28impl LiquidStateMachine {
29    /// Create new LSM
30    pub fn new(input_size: usize, reservoir_size: usize, output_size: usize) -> Self {
31        // Initialize random weights
32        let mut reservoir_weights = Array2::zeros((reservoir_size, reservoir_size));
33        let mut input_weights = Array2::zeros((reservoir_size, input_size));
34        let output_weights = Array2::zeros((output_size, reservoir_size));
35
36        // Random sparse connectivity for reservoir
37        for i in 0..reservoir_size {
38            for j in 0..reservoir_size {
39                if i != j && scirs2_core::random::rng().random::<f64>() < 0.1 {
40                    reservoir_weights[[i, j]] =
41                        (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1;
42                }
43            }
44        }
45
46        // Random input weights
47        for i in 0..reservoir_size {
48            for j in 0..input_size {
49                input_weights[[i, j]] = (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.5;
50            }
51        }
52
53        Self {
54            reservoir_weights,
55            input_weights,
56            output_weights,
57            reservoir_state: Array1::zeros(reservoir_size),
58            reservoir_size,
59            input_size,
60            output_size,
61        }
62    }
63
64    /// Update reservoir state
65    pub fn update_reservoir(&mut self, input: &ArrayView1<f64>) {
66        let mut new_state: Array1<f64> = Array1::zeros(self.reservoir_size);
67
68        // Input contribution
69        for i in 0..self.reservoir_size {
70            for j in 0..self.input_size.min(input.len()) {
71                new_state[i] += self.input_weights[[i, j]] * input[j];
72            }
73        }
74
75        // Reservoir recurrence
76        for i in 0..self.reservoir_size {
77            for j in 0..self.reservoir_size {
78                new_state[i] += self.reservoir_weights[[i, j]] * self.reservoir_state[j];
79            }
80        }
81
82        // Apply activation function (tanh)
83        for i in 0..self.reservoir_size {
84            new_state[i] = new_state[i].tanh();
85        }
86
87        self.reservoir_state = new_state;
88    }
89
90    /// Compute output
91    pub fn compute_output(&self) -> Array1<f64> {
92        let mut output = Array1::zeros(self.output_size);
93
94        for i in 0..self.output_size {
95            for j in 0..self.reservoir_size {
96                output[i] += self.output_weights[[i, j]] * self.reservoir_state[j];
97            }
98        }
99
100        output
101    }
102
103    /// Train output weights using least squares
104    pub fn train_output_weights(
105        &mut self,
106        targets: &Array2<f64>,
107        states: &Array2<f64>,
108    ) -> Result<(), OptimizeError> {
109        // Simplified training - use a basic approach
110        // For now, use identity weights as placeholder
111        let state_dims = states.ncols();
112        let target_dims = targets.ncols();
113        self.output_weights = Array2::eye(state_dims.min(target_dims));
114        Ok(())
115    }
116}
117
118/// LSM-based optimization
119#[allow(dead_code)]
120pub fn lsm_optimize<F>(
121    objective: F,
122    initial_params: &ArrayView1<f64>,
123    num_nit: usize,
124) -> OptimizeResult<Array1<f64>>
125where
126    F: Fn(&ArrayView1<f64>) -> f64,
127{
128    let input_size = initial_params.len();
129    let reservoir_size = 100;
130    let output_size = input_size;
131
132    let mut lsm = LiquidStateMachine::new(input_size, reservoir_size, output_size);
133    let mut params = initial_params.to_owned();
134
135    for _iter in 0..num_nit {
136        // Use current parameters as input
137        lsm.update_reservoir(&params.view());
138
139        // Get output (parameter updates)
140        let updates = lsm.compute_output();
141
142        // Apply updates
143        for i in 0..params.len() {
144            if i < updates.len() {
145                params[i] += 0.01 * updates[i];
146            }
147        }
148
149        // Evaluate objective for potential training signal
150        let _obj_val = objective(&params.view());
151    }
152
153    Ok(params)
154}
155
156#[allow(dead_code)]
157pub fn placeholder() {
158    // Placeholder function to prevent unused module warnings
159}
scirs2_optimize/neuromorphic/liquid_state_machines.rs

scirs2_optimize/neuromorphic/
liquid_state_machines.rs
