mirror of
https://github.com/ruvnet/RuView
synced 2026-08-11 20:41:44 +00:00
feat: vendor midstream and sublinear-time-solver libraries (#109)
Add ruvnet/midstream (AIMDS real-time inference) and ruvnet/sublinear-time-solver (sublinear optimization algorithms) as vendored dependencies under vendor/.
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rUv
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// WASM bindings for the fast sublinear-time solver
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#![cfg(feature = "wasm")]
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use wasm_bindgen::prelude::*;
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use serde::{Deserialize, Serialize};
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use crate::solver::{SolverOptions, ConvergenceMode};
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use crate::fast_solver::{FastCSRMatrix, FastConjugateGradient};
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use crate::utils::Precision;
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#[wasm_bindgen]
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extern "C" {
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#[wasm_bindgen(js_namespace = console)]
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fn log(s: &str);
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}
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macro_rules! console_log {
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($($t:tt)*) => (log(&format_args!($($t)*).to_string()))
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}
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#[wasm_bindgen]
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pub fn init_panic_hook() {
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#[cfg(feature = "console_error_panic_hook")]
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console_error_panic_hook::set_once();
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}
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#[derive(Serialize, Deserialize)]
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pub struct MatrixData {
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pub values: Vec<f64>,
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pub col_indices: Vec<u32>,
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pub row_ptr: Vec<u32>,
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pub rows: usize,
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pub cols: usize,
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}
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#[derive(Serialize, Deserialize)]
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pub struct SolverResult {
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pub solution: Vec<f64>,
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pub iterations: usize,
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pub residual: f64,
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pub converged: bool,
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pub compute_time_ms: f64,
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}
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#[wasm_bindgen]
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pub struct WasmSolver {
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tolerance: f64,
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max_iterations: usize,
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}
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#[wasm_bindgen]
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impl WasmSolver {
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#[wasm_bindgen(constructor)]
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pub fn new() -> Self {
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init_panic_hook();
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WasmSolver {
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tolerance: 1e-6,
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max_iterations: 1000,
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}
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}
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#[wasm_bindgen]
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pub fn set_tolerance(&mut self, tolerance: f64) {
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self.tolerance = tolerance;
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}
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#[wasm_bindgen]
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pub fn set_max_iterations(&mut self, max_iterations: usize) {
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self.max_iterations = max_iterations;
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}
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#[wasm_bindgen]
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pub fn solve_csr(&self, matrix_json: &str, vector_json: &str) -> Result<String, JsValue> {
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let start = js_sys::Date::now();
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// Parse input
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let matrix_data: MatrixData = serde_json::from_str(matrix_json)
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.map_err(|e| JsValue::from_str(&format!("Failed to parse matrix: {}", e)))?;
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let vector: Vec<f64> = serde_json::from_str(vector_json)
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.map_err(|e| JsValue::from_str(&format!("Failed to parse vector: {}", e)))?;
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// Create FastCSRMatrix
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let matrix = FastCSRMatrix {
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values: matrix_data.values,
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col_indices: matrix_data.col_indices,
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row_ptr: matrix_data.row_ptr,
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rows: matrix_data.rows,
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cols: matrix_data.cols,
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};
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// Check matrix dimensions
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if matrix.rows != vector.len() {
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return Err(JsValue::from_str(&format!(
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"Dimension mismatch: matrix has {} rows but vector has {} elements",
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matrix.rows,
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vector.len()
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)));
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}
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// Create solver
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let mut solver = FastConjugateGradient::new(&matrix, &vector);
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// Solve
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let solution = solver.solve_with_tolerance(self.tolerance, self.max_iterations);
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let compute_time_ms = js_sys::Date::now() - start;
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// Compute residual
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let mut residual_vec = vec![0.0; matrix.rows];
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matrix.multiply_vector(&solution, &mut residual_vec);
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let mut residual = 0.0;
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for i in 0..matrix.rows {
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let diff = residual_vec[i] - vector[i];
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residual += diff * diff;
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}
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residual = residual.sqrt();
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let result = SolverResult {
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solution,
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iterations: solver.iterations,
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residual,
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converged: residual < self.tolerance,
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compute_time_ms,
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};
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serde_json::to_string(&result)
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.map_err(|e| JsValue::from_str(&format!("Failed to serialize result: {}", e)))
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}
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#[wasm_bindgen]
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pub fn solve_dense(&self, matrix_json: &str, vector_json: &str) -> Result<String, JsValue> {
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let start = js_sys::Date::now();
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// Parse dense matrix
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let matrix_data: Vec<Vec<f64>> = serde_json::from_str(matrix_json)
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.map_err(|e| JsValue::from_str(&format!("Failed to parse matrix: {}", e)))?;
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let vector: Vec<f64> = serde_json::from_str(vector_json)
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.map_err(|e| JsValue::from_str(&format!("Failed to parse vector: {}", e)))?;
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let n = matrix_data.len();
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if n == 0 {
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return Err(JsValue::from_str("Empty matrix"));
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}
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// Convert dense to CSR
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let mut values = Vec::new();
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let mut col_indices = Vec::new();
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let mut row_ptr = vec![0];
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for row in &matrix_data {
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if row.len() != n {
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return Err(JsValue::from_str("Matrix must be square"));
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}
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for (j, &val) in row.iter().enumerate() {
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if val.abs() > 1e-10 {
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values.push(val);
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col_indices.push(j as u32);
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}
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}
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row_ptr.push(values.len() as u32);
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}
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let matrix = FastCSRMatrix {
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values,
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col_indices,
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row_ptr,
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rows: n,
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cols: n,
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};
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// Create solver
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let mut solver = FastConjugateGradient::new(&matrix, &vector);
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// Solve
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let solution = solver.solve_with_tolerance(self.tolerance, self.max_iterations);
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let compute_time_ms = js_sys::Date::now() - start;
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// Compute residual
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let mut residual_vec = vec![0.0; matrix.rows];
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matrix.multiply_vector(&solution, &mut residual_vec);
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let mut residual = 0.0;
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for i in 0..matrix.rows {
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let diff = residual_vec[i] - vector[i];
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residual += diff * diff;
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}
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residual = residual.sqrt();
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let result = SolverResult {
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solution,
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iterations: solver.iterations,
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residual,
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converged: residual < self.tolerance,
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compute_time_ms,
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};
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serde_json::to_string(&result)
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.map_err(|e| JsValue::from_str(&format!("Failed to serialize result: {}", e)))
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}
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#[wasm_bindgen]
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pub fn solve_neumann(&self, matrix_json: &str, vector_json: &str) -> Result<String, JsValue> {
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let start = js_sys::Date::now();
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// Parse input
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let matrix_data: MatrixData = serde_json::from_str(matrix_json)
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.map_err(|e| JsValue::from_str(&format!("Failed to parse matrix: {}", e)))?;
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let vector: Vec<f64> = serde_json::from_str(vector_json)
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.map_err(|e| JsValue::from_str(&format!("Failed to parse vector: {}", e)))?;
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// Create FastCSRMatrix
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let matrix = FastCSRMatrix {
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values: matrix_data.values,
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col_indices: matrix_data.col_indices,
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row_ptr: matrix_data.row_ptr,
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rows: matrix_data.rows,
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cols: matrix_data.cols,
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};
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// Use Neumann series solver
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let options = SolverOptions {
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tolerance: self.tolerance,
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max_iterations: self.max_iterations,
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convergence_mode: ConvergenceMode::Residual,
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enable_caching: true,
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cache_size: 100,
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enable_simd: true,
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enable_parallel: false,
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thread_count: 1,
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};
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// Solve using Neumann series
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let mut solution = vector.clone();
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let mut residual = vec![0.0; matrix.rows];
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let mut iterations = 0;
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for iter in 0..self.max_iterations {
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// Compute residual: r = b - Ax
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matrix.multiply_vector(&solution, &mut residual);
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for i in 0..matrix.rows {
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residual[i] = vector[i] - residual[i];
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}
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// Check convergence
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let residual_norm: f64 = residual.iter().map(|x| x * x).sum::<f64>().sqrt();
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if residual_norm < self.tolerance {
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iterations = iter + 1;
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break;
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}
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// Update solution: x = x + r
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for i in 0..matrix.rows {
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solution[i] += residual[i];
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}
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iterations = iter + 1;
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}
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let compute_time_ms = js_sys::Date::now() - start;
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// Final residual computation
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matrix.multiply_vector(&solution, &mut residual);
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let mut final_residual = 0.0;
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for i in 0..matrix.rows {
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let diff = residual[i] - vector[i];
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final_residual += diff * diff;
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}
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final_residual = final_residual.sqrt();
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let result = SolverResult {
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solution,
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iterations,
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residual: final_residual,
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converged: final_residual < self.tolerance,
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compute_time_ms,
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};
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serde_json::to_string(&result)
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.map_err(|e| JsValue::from_str(&format!("Failed to serialize result: {}", e)))
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}
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}
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#[wasm_bindgen]
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pub fn version() -> String {
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env!("CARGO_PKG_VERSION").to_string()
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}
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#[wasm_bindgen]
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pub fn create_test_matrix(n: usize) -> String {
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// Create a diagonally dominant test matrix
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let mut values = Vec::new();
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let mut col_indices = Vec::new();
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let mut row_ptr = vec![0];
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for i in 0..n {
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// Add off-diagonal elements
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if i > 0 {
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values.push(-1.0);
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col_indices.push((i - 1) as u32);
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}
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// Add diagonal element (make it dominant)
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values.push(4.0);
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col_indices.push(i as u32);
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if i < n - 1 {
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values.push(-1.0);
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col_indices.push((i + 1) as u32);
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}
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row_ptr.push(values.len() as u32);
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}
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let matrix_data = MatrixData {
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values,
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col_indices,
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row_ptr,
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rows: n,
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cols: n,
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};
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serde_json::to_string(&matrix_data).unwrap_or_else(|_| "{}".to_string())
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}
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#[wasm_bindgen]
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pub fn create_test_vector(n: usize) -> String {
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let vector: Vec<f64> = (0..n).map(|i| (i + 1) as f64).collect();
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serde_json::to_string(&vector).unwrap_or_else(|_| "[]".to_string())
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}
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