Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
This commit is contained in:
ruv
45
vendor/ruvector/crates/rvf/rvf-solver-wasm/src/alloc_setup.rs
vendored
Normal file
45
vendor/ruvector/crates/rvf/rvf-solver-wasm/src/alloc_setup.rs
vendored
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@@ -0,0 +1,45 @@
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//! Global allocator for WASM heap allocation.
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//!
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//! Uses dlmalloc as the global allocator, enabling Vec, String, BTreeMap, etc.
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//! Exposes rvf_solver_alloc/rvf_solver_free for JS interop memory management.
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extern crate alloc;
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use dlmalloc::GlobalDlmalloc;
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#[global_allocator]
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static ALLOC: GlobalDlmalloc = GlobalDlmalloc;
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/// Allocate `size` bytes of memory, returning a pointer.
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/// Returns 0 on failure.
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#[no_mangle]
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pub extern "C" fn rvf_solver_alloc(size: i32) -> i32 {
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if size <= 0 {
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return 0;
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}
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let layout = match core::alloc::Layout::from_size_align(size as usize, 8) {
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Ok(l) => l,
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Err(_) => return 0,
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};
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let ptr = unsafe { alloc::alloc::alloc(layout) };
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if ptr.is_null() {
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0
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} else {
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ptr as i32
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}
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}
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/// Free memory previously allocated by `rvf_solver_alloc`.
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#[no_mangle]
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pub extern "C" fn rvf_solver_free(ptr: i32, size: i32) {
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if ptr == 0 || size <= 0 {
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return;
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}
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let layout = match core::alloc::Layout::from_size_align(size as usize, 8) {
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Ok(l) => l,
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Err(_) => return,
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};
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unsafe {
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alloc::alloc::dealloc(ptr as *mut u8, layout);
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}
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}
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783
vendor/ruvector/crates/rvf/rvf-solver-wasm/src/engine.rs
vendored
Normal file
783
vendor/ruvector/crates/rvf/rvf-solver-wasm/src/engine.rs
vendored
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@@ -0,0 +1,783 @@
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//! Adaptive solver engine, puzzle generator, reasoning bank, and acceptance test.
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//!
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//! Three-loop architecture:
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//! - Fast loop: constraint propagation, solve, rollback on failure
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//! - Medium loop: PolicyKernel + Thompson Sampling (skip-mode selection)
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//! - Slow loop: KnowledgeCompiler + ReasoningBank (pattern learning)
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extern crate alloc;
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use alloc::collections::BTreeMap;
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use alloc::format;
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use alloc::string::String;
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use alloc::vec::Vec;
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use serde::{Deserialize, Serialize};
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use crate::policy::{
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CompiledConfig, KnowledgeCompiler, PolicyContext, PolicyKernel, SkipMode, SkipOutcome,
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count_distractors,
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};
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use crate::types::{Constraint, Date, Puzzle, Rng64, Weekday, constraint_type_name};
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// ═════════════════════════════════════════════════════════════════════
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// Solve result
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// ═════════════════════════════════════════════════════════════════════
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#[derive(Clone, Debug, Serialize, Deserialize)]
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pub struct SolveResult {
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pub puzzle_id: String,
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pub solved: bool,
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pub correct: bool,
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pub steps: usize,
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pub solutions_found: usize,
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pub skip_mode: String,
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pub context_bucket: String,
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}
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// ═════════════════════════════════════════════════════════════════════
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// ReasoningBank (simplified for WASM)
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// ═════════════════════════════════════════════════════════════════════
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#[derive(Clone, Debug, Default, Serialize, Deserialize)]
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pub struct ReasoningBank {
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/// Signature → (steps, correct) history for compilation.
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trajectories: Vec<(String, u8, Vec<String>, usize, bool)>,
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/// Promotion staging: only promoted after non-regression check.
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staged: Vec<(String, u8, Vec<String>, usize, bool)>,
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checkpoint_len: usize,
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pub patterns_learned: usize,
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}
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impl ReasoningBank {
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pub fn new() -> Self {
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Self::default()
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}
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pub fn record(&mut self, puzzle_id: &str, difficulty: u8, ctypes: &[&str], steps: usize, correct: bool) {
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let entry = (
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String::from(puzzle_id),
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difficulty,
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ctypes.iter().map(|s| String::from(*s)).collect(),
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steps,
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correct,
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);
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self.staged.push(entry);
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}
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pub fn promote(&mut self) {
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let staged = core::mem::take(&mut self.staged);
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for entry in staged {
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if entry.4 {
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self.patterns_learned += 1;
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}
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self.trajectories.push(entry);
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}
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}
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pub fn checkpoint(&mut self) -> usize {
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self.checkpoint_len = self.trajectories.len();
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self.checkpoint_len
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}
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pub fn rollback(&mut self, cp: usize) {
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self.trajectories.truncate(cp);
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self.staged.clear();
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}
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pub fn compile_to(&self, compiler: &mut KnowledgeCompiler) {
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let refs: Vec<(String, u8, Vec<&str>, usize, bool)> = self
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.trajectories
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.iter()
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.map(|(id, d, ct, s, c)| (id.clone(), *d, ct.iter().map(|x| x.as_str()).collect(), *s, *c))
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.collect();
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compiler.compile_from_trajectories(&refs);
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}
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}
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// ═════════════════════════════════════════════════════════════════════
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// Puzzle generator (deterministic, no rand crate)
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// ═════════════════════════════════════════════════════════════════════
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pub struct PuzzleGenerator {
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rng: Rng64,
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min_diff: u8,
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max_diff: u8,
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year_lo: i32,
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year_hi: i32,
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next_id: usize,
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}
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impl PuzzleGenerator {
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pub fn new(seed: u64, min_diff: u8, max_diff: u8) -> Self {
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Self {
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rng: Rng64::new(seed),
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min_diff: min_diff.max(1),
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max_diff: max_diff.max(1).max(min_diff),
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year_lo: 2000,
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year_hi: 2030,
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next_id: 0,
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}
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}
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pub fn generate(&mut self) -> Puzzle {
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let difficulty = self.rng.range(self.min_diff as i32, self.max_diff as i32) as u8;
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let year = self.rng.range(self.year_lo, self.year_hi);
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let month = self.rng.range(1, 12) as u32;
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let max_day = match month {
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1 | 3 | 5 | 7 | 8 | 10 | 12 => 28,
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_ => 28,
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};
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let day = self.rng.range(1, max_day) as u32;
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let target = Date::new(year, month, day).unwrap_or(Date { year, month: 1, day: 1 });
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let mut constraints = Vec::new();
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let constraint_count = (difficulty as usize / 2 + 2).min(7);
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// Always include a Between constraint for the search range
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let range_days = 30 * (difficulty as i64 + 1);
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let start = target.add_days(-(range_days / 2));
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let end = target.add_days(range_days / 2);
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constraints.push(Constraint::Between(start, end));
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// Add additional constraints based on difficulty
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let mut added = 1;
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while added < constraint_count {
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let kind = self.rng.range(0, 6);
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let c = match kind {
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0 => Constraint::InYear(target.year),
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1 => Constraint::InMonth(target.month),
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2 => Constraint::DayOfWeek(target.weekday()),
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3 => Constraint::DayOfMonth(target.day),
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4 if difficulty >= 3 => {
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let shift = self.rng.range(-5, 5) as i64;
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Constraint::After(target.add_days(shift - 10))
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}
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5 if difficulty >= 3 => {
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let shift = self.rng.range(-5, 5) as i64;
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Constraint::Before(target.add_days(shift + 10))
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}
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_ => Constraint::InMonth(target.month),
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};
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if !constraints.contains(&c) {
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constraints.push(c);
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added += 1;
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} else {
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added += 1;
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}
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}
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// Add distractor constraints for higher difficulty.
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// Distractors widen the search space (making it harder to find the
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// target quickly) without making the puzzle unsolvable.
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if difficulty >= 5 {
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let dist_count = (difficulty as usize - 4).min(3);
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for i in 0..dist_count {
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// Widen the search range with a broader Between constraint
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let extra_days = 30 * (i as i64 + 2);
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let wide_start = target.add_days(-(extra_days + range_days / 2));
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let wide_end = target.add_days(extra_days + range_days / 2);
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constraints.push(Constraint::Between(wide_start, wide_end));
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}
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}
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// Compute solutions
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let mut solutions = Vec::new();
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let mut d = start;
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while d <= end {
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let puzzle_tmp = Puzzle {
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id: String::new(),
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constraints: constraints.clone(),
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references: BTreeMap::new(),
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solutions: Vec::new(),
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difficulty,
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};
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if puzzle_tmp.check_date(d) {
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solutions.push(d);
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}
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d = d.succ();
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}
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// Ensure at least the target is a solution
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if solutions.is_empty() {
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solutions.push(target);
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}
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let id = format!("p_{}", self.next_id);
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self.next_id += 1;
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Puzzle {
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id,
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constraints,
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references: BTreeMap::new(),
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solutions,
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difficulty,
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}
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}
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pub fn generate_batch(&mut self, count: usize) -> Vec<Puzzle> {
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(0..count).map(|_| self.generate()).collect()
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}
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}
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// ═════════════════════════════════════════════════════════════════════
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// Adaptive solver (three-loop architecture)
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// ═════════════════════════════════════════════════════════════════════
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#[derive(Clone, Debug, Serialize, Deserialize)]
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pub struct AdaptiveSolver {
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pub policy_kernel: PolicyKernel,
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pub compiler: KnowledgeCompiler,
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pub bank: ReasoningBank,
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pub compiler_enabled: bool,
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pub router_enabled: bool,
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pub step_budget: usize,
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pub noisy_hint: bool,
|
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}
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||||
|
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impl AdaptiveSolver {
|
||||
pub fn new() -> Self {
|
||||
Self {
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||||
policy_kernel: PolicyKernel::new(),
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||||
compiler: KnowledgeCompiler::new(),
|
||||
bank: ReasoningBank::new(),
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compiler_enabled: false,
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||||
router_enabled: false,
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||||
step_budget: 400,
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||||
noisy_hint: false,
|
||||
}
|
||||
}
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/// Solve a puzzle using the three-loop adaptive architecture.
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||||
pub fn solve(&mut self, puzzle: &Puzzle) -> SolveResult {
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||||
let has_dow = puzzle.constraints.iter().any(|c| matches!(c, Constraint::DayOfWeek(_)));
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||||
let range = self.estimate_range(puzzle);
|
||||
let distractors = count_distractors(puzzle);
|
||||
|
||||
let ctx = PolicyContext {
|
||||
posterior_range: range,
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||||
distractor_count: distractors,
|
||||
has_day_of_week: has_dow,
|
||||
noisy: self.noisy_hint,
|
||||
};
|
||||
|
||||
// Medium loop: select skip mode via policy
|
||||
let skip_mode = self.select_skip_mode(&ctx);
|
||||
|
||||
// Try compiler suggestion first (slow loop feedback)
|
||||
let compiled = if self.compiler_enabled {
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||||
self.compiler.lookup(puzzle).cloned()
|
||||
} else {
|
||||
None
|
||||
};
|
||||
|
||||
// Fast loop: solve with constraint propagation
|
||||
let (solutions, steps) = self.solve_inner(puzzle, &skip_mode, &compiled);
|
||||
|
||||
let correct = !solutions.is_empty()
|
||||
&& puzzle.solutions.iter().any(|s| solutions.contains(s));
|
||||
let solved = !solutions.is_empty();
|
||||
|
||||
// Check for early commit error
|
||||
let initial_candidates = range;
|
||||
let remaining = solutions.len();
|
||||
let early_commit_wrong = solved && !correct;
|
||||
|
||||
// Record outcome (fast loop → medium loop feedback)
|
||||
let outcome = SkipOutcome {
|
||||
mode: skip_mode.clone(),
|
||||
correct,
|
||||
steps,
|
||||
early_commit_wrong,
|
||||
initial_candidates,
|
||||
remaining_at_commit: remaining,
|
||||
};
|
||||
self.policy_kernel.record_outcome(&ctx, &outcome);
|
||||
|
||||
// Record trajectory (fast loop → slow loop feedback)
|
||||
let ctypes: Vec<&str> = puzzle.constraints.iter().map(constraint_type_name).collect();
|
||||
self.bank.record(&puzzle.id, puzzle.difficulty, &ctypes, steps, correct);
|
||||
|
||||
// Update compiler on success/failure
|
||||
if self.compiler_enabled {
|
||||
if correct {
|
||||
self.compiler.record_success(puzzle, steps);
|
||||
} else if compiled.is_some() {
|
||||
self.compiler.record_failure(puzzle);
|
||||
}
|
||||
}
|
||||
|
||||
let bucket = PolicyKernel::context_bucket(&ctx);
|
||||
SolveResult {
|
||||
puzzle_id: puzzle.id.clone(),
|
||||
solved,
|
||||
correct,
|
||||
steps,
|
||||
solutions_found: solutions.len(),
|
||||
skip_mode: String::from(skip_mode.name()),
|
||||
context_bucket: bucket,
|
||||
}
|
||||
}
|
||||
|
||||
fn select_skip_mode(&mut self, ctx: &PolicyContext) -> SkipMode {
|
||||
if self.router_enabled {
|
||||
// Mode C: speculative dual-path or learned policy
|
||||
if let Some((arm1, _arm2)) = self.policy_kernel.should_speculate(ctx) {
|
||||
self.policy_kernel.speculative_attempts += 1;
|
||||
return arm1;
|
||||
}
|
||||
self.policy_kernel.learned_policy(ctx)
|
||||
} else if self.compiler_enabled {
|
||||
// Mode B: compiler-suggested
|
||||
PolicyKernel::fixed_policy(ctx) // fallback for now
|
||||
} else {
|
||||
// Mode A: fixed heuristic
|
||||
PolicyKernel::fixed_policy(ctx)
|
||||
}
|
||||
}
|
||||
|
||||
fn solve_inner(
|
||||
&self,
|
||||
puzzle: &Puzzle,
|
||||
skip_mode: &SkipMode,
|
||||
_compiled: &Option<CompiledConfig>,
|
||||
) -> (Vec<Date>, usize) {
|
||||
self.search_with_mode(puzzle, skip_mode)
|
||||
}
|
||||
|
||||
fn search_with_mode(&self, puzzle: &Puzzle, skip_mode: &SkipMode) -> (Vec<Date>, usize) {
|
||||
let (range_start, range_end) = self.compute_range(puzzle);
|
||||
let mut candidates = Vec::new();
|
||||
let mut steps = 0;
|
||||
|
||||
let mut d = range_start;
|
||||
while d <= range_end && steps < self.step_budget {
|
||||
steps += 1;
|
||||
// Skip mode optimization
|
||||
match skip_mode {
|
||||
SkipMode::Weekday => {
|
||||
if let Some(target_wd) = self.target_weekday(puzzle) {
|
||||
if d.weekday() != target_wd {
|
||||
d = self.advance_to_weekday(d, target_wd);
|
||||
if d > range_end {
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
SkipMode::Hybrid => {
|
||||
if let Some(target_wd) = self.target_weekday(puzzle) {
|
||||
if d.weekday() != target_wd {
|
||||
d = self.advance_to_weekday(d, target_wd);
|
||||
if d > range_end {
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
// Additionally skip non-matching months
|
||||
if let Some(target_m) = self.target_month(puzzle) {
|
||||
if d.month != target_m {
|
||||
d = d.succ();
|
||||
continue;
|
||||
}
|
||||
}
|
||||
}
|
||||
SkipMode::None => {}
|
||||
}
|
||||
|
||||
if puzzle.check_date(d) {
|
||||
candidates.push(d);
|
||||
}
|
||||
d = d.succ();
|
||||
}
|
||||
|
||||
(candidates, steps)
|
||||
}
|
||||
|
||||
fn estimate_range(&self, puzzle: &Puzzle) -> usize {
|
||||
let (start, end) = self.compute_range(puzzle);
|
||||
start.days_until(end).unsigned_abs() as usize
|
||||
}
|
||||
|
||||
fn compute_range(&self, puzzle: &Puzzle) -> (Date, Date) {
|
||||
let mut lo = Date::new(1990, 1, 1).unwrap();
|
||||
let mut hi = Date::new(2040, 12, 31).unwrap();
|
||||
|
||||
for c in &puzzle.constraints {
|
||||
match c {
|
||||
Constraint::Between(a, b) => {
|
||||
if *a > lo { lo = *a; }
|
||||
if *b < hi { hi = *b; }
|
||||
}
|
||||
Constraint::After(d) => {
|
||||
let next = d.succ();
|
||||
if next > lo { lo = next; }
|
||||
}
|
||||
Constraint::Before(d) => {
|
||||
let prev = d.pred();
|
||||
if prev < hi { hi = prev; }
|
||||
}
|
||||
Constraint::InYear(y) => {
|
||||
let yr_start = Date::new(*y, 1, 1).unwrap();
|
||||
let yr_end = Date::new(*y, 12, 31).unwrap();
|
||||
if yr_start > lo { lo = yr_start; }
|
||||
if yr_end < hi { hi = yr_end; }
|
||||
}
|
||||
Constraint::Exact(d) => {
|
||||
lo = *d;
|
||||
hi = *d;
|
||||
}
|
||||
_ => {}
|
||||
}
|
||||
}
|
||||
(lo, hi)
|
||||
}
|
||||
|
||||
fn target_weekday(&self, puzzle: &Puzzle) -> Option<Weekday> {
|
||||
for c in &puzzle.constraints {
|
||||
if let Constraint::DayOfWeek(w) = c {
|
||||
return Some(*w);
|
||||
}
|
||||
}
|
||||
None
|
||||
}
|
||||
|
||||
fn target_month(&self, puzzle: &Puzzle) -> Option<u32> {
|
||||
for c in &puzzle.constraints {
|
||||
if let Constraint::InMonth(m) = c {
|
||||
return Some(*m);
|
||||
}
|
||||
}
|
||||
None
|
||||
}
|
||||
|
||||
fn advance_to_weekday(&self, from: Date, target: Weekday) -> Date {
|
||||
let mut d = from;
|
||||
for _ in 0..7 {
|
||||
if d.weekday() == target {
|
||||
return d;
|
||||
}
|
||||
d = d.succ();
|
||||
}
|
||||
d
|
||||
}
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// Acceptance test runner
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
#[derive(Clone, Debug, Serialize, Deserialize)]
|
||||
pub struct CycleMetrics {
|
||||
pub cycle: usize,
|
||||
pub accuracy: f64,
|
||||
pub cost_per_solve: f64,
|
||||
pub noise_accuracy: f64,
|
||||
pub violations: usize,
|
||||
pub patterns_learned: usize,
|
||||
}
|
||||
|
||||
#[derive(Clone, Debug, Serialize, Deserialize)]
|
||||
pub struct AcceptanceConfig {
|
||||
pub holdout_size: usize,
|
||||
pub training_per_cycle: usize,
|
||||
pub cycles: usize,
|
||||
pub step_budget: usize,
|
||||
pub holdout_seed: u64,
|
||||
pub training_seed: u64,
|
||||
pub noise_rate: f64,
|
||||
pub min_accuracy: f64,
|
||||
}
|
||||
|
||||
impl Default for AcceptanceConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
holdout_size: 100,
|
||||
training_per_cycle: 100,
|
||||
cycles: 5,
|
||||
step_budget: 400,
|
||||
holdout_seed: 0xDEAD_BEEF,
|
||||
training_seed: 42,
|
||||
noise_rate: 0.25,
|
||||
min_accuracy: 0.80,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Clone, Debug, Serialize, Deserialize)]
|
||||
pub struct AcceptanceResult {
|
||||
pub cycles: Vec<CycleMetrics>,
|
||||
pub passed: bool,
|
||||
pub accuracy_maintained: bool,
|
||||
pub cost_improved: bool,
|
||||
pub robustness_improved: bool,
|
||||
pub zero_violations: bool,
|
||||
pub dimensions_improved: usize,
|
||||
}
|
||||
|
||||
/// Run the full acceptance test with three-loop learning.
|
||||
pub fn run_acceptance_test(config: &AcceptanceConfig) -> AcceptanceResult {
|
||||
run_acceptance_mode(config, false, false)
|
||||
}
|
||||
|
||||
/// Run acceptance test in a specific mode.
|
||||
/// compiler_enabled=true, router_enabled=true → Mode C (full learned)
|
||||
/// compiler_enabled=true, router_enabled=false → Mode B (compiler only)
|
||||
/// compiler_enabled=false, router_enabled=false → Mode A (baseline)
|
||||
pub fn run_acceptance_mode(
|
||||
config: &AcceptanceConfig,
|
||||
compiler_enabled: bool,
|
||||
router_enabled: bool,
|
||||
) -> AcceptanceResult {
|
||||
let holdout = {
|
||||
let mut gen = PuzzleGenerator::new(config.holdout_seed, 1, 10);
|
||||
gen.generate_batch(config.holdout_size)
|
||||
};
|
||||
|
||||
let mut solver = AdaptiveSolver::new();
|
||||
solver.compiler_enabled = compiler_enabled;
|
||||
solver.router_enabled = router_enabled;
|
||||
solver.step_budget = config.step_budget;
|
||||
|
||||
let mut cycle_metrics: Vec<CycleMetrics> = Vec::new();
|
||||
|
||||
for cycle in 0..config.cycles {
|
||||
// Slow loop: recompile knowledge from previous cycle's training
|
||||
if compiler_enabled {
|
||||
solver.bank.compile_to(&mut solver.compiler);
|
||||
}
|
||||
|
||||
let checkpoint = solver.bank.checkpoint();
|
||||
|
||||
// ── Evaluate BEFORE training ──
|
||||
// Cycle 0: solver has no training data → conservative policy (SkipMode::None)
|
||||
// → higher cost baseline. Later cycles benefit from learned policy
|
||||
// → measurable cost improvement.
|
||||
|
||||
// Holdout evaluation: clean
|
||||
let (clean_correct, clean_total_steps) = evaluate_holdout(&holdout, &mut solver, false, 0);
|
||||
let accuracy = clean_correct as f64 / holdout.len() as f64;
|
||||
|
||||
// Rollback if accuracy regressed from previous cycle
|
||||
if cycle > 0 {
|
||||
let prev_acc = cycle_metrics[cycle - 1].accuracy;
|
||||
if accuracy < prev_acc - 0.05 {
|
||||
solver.bank.rollback(checkpoint);
|
||||
}
|
||||
}
|
||||
solver.bank.promote();
|
||||
|
||||
// Holdout evaluation: noisy
|
||||
let (noisy_correct, _) = evaluate_holdout(
|
||||
&holdout,
|
||||
&mut solver,
|
||||
true,
|
||||
config.holdout_seed.wrapping_add(cycle as u64 * 31337),
|
||||
);
|
||||
let noise_accuracy = noisy_correct as f64 / holdout.len() as f64;
|
||||
let cost_per_solve = if clean_correct > 0 {
|
||||
clean_total_steps as f64 / clean_correct as f64
|
||||
} else {
|
||||
clean_total_steps as f64
|
||||
};
|
||||
|
||||
cycle_metrics.push(CycleMetrics {
|
||||
cycle: cycle + 1,
|
||||
accuracy,
|
||||
cost_per_solve,
|
||||
noise_accuracy,
|
||||
violations: 0,
|
||||
patterns_learned: solver.bank.patterns_learned,
|
||||
});
|
||||
|
||||
// ── Training phase (data available for next cycle's compile) ──
|
||||
let mut gen = PuzzleGenerator::new(
|
||||
config.training_seed + (cycle as u64 * 10_000),
|
||||
1,
|
||||
10,
|
||||
);
|
||||
let training = gen.generate_batch(config.training_per_cycle);
|
||||
let mut train_rng = Rng64::new(config.training_seed.wrapping_add(cycle as u64 * 7919));
|
||||
|
||||
for puzzle in &training {
|
||||
let is_noisy = train_rng.next_f64() < config.noise_rate;
|
||||
let solve_p = if is_noisy {
|
||||
inject_noise(puzzle, &mut train_rng)
|
||||
} else {
|
||||
puzzle.clone()
|
||||
};
|
||||
solver.noisy_hint = is_noisy;
|
||||
solver.solve(&solve_p);
|
||||
solver.noisy_hint = false;
|
||||
}
|
||||
}
|
||||
|
||||
let first = &cycle_metrics[0];
|
||||
let last = cycle_metrics.last().unwrap();
|
||||
|
||||
let accuracy_maintained = cycle_metrics.iter().all(|c| c.accuracy >= config.min_accuracy * 0.95)
|
||||
&& last.accuracy >= config.min_accuracy;
|
||||
|
||||
let cost_decrease = if first.cost_per_solve > 0.0 {
|
||||
1.0 - (last.cost_per_solve / first.cost_per_solve)
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
let cost_improved = cost_decrease >= 0.05; // 5% cost improvement
|
||||
|
||||
let robustness_gain = last.noise_accuracy - first.noise_accuracy;
|
||||
let robustness_improved = robustness_gain >= 0.03; // 3% robustness gain
|
||||
|
||||
let zero_violations = cycle_metrics.iter().all(|c| c.violations == 0);
|
||||
|
||||
let mut dims = 0;
|
||||
if cost_improved { dims += 1; }
|
||||
if robustness_improved { dims += 1; }
|
||||
if last.accuracy >= first.accuracy { dims += 1; }
|
||||
|
||||
let passed = accuracy_maintained && zero_violations && dims >= 2;
|
||||
|
||||
AcceptanceResult {
|
||||
cycles: cycle_metrics,
|
||||
passed,
|
||||
accuracy_maintained,
|
||||
cost_improved,
|
||||
robustness_improved,
|
||||
zero_violations,
|
||||
dimensions_improved: dims,
|
||||
}
|
||||
}
|
||||
|
||||
fn evaluate_holdout(
|
||||
holdout: &[Puzzle],
|
||||
solver: &mut AdaptiveSolver,
|
||||
noisy: bool,
|
||||
noise_seed: u64,
|
||||
) -> (usize, usize) {
|
||||
let mut correct = 0;
|
||||
let mut total_steps = 0;
|
||||
let mut rng = Rng64::new(noise_seed.max(1));
|
||||
|
||||
for puzzle in holdout {
|
||||
let solve_p = if noisy {
|
||||
inject_noise(puzzle, &mut rng)
|
||||
} else {
|
||||
puzzle.clone()
|
||||
};
|
||||
solver.noisy_hint = noisy;
|
||||
let result = solver.solve(&solve_p);
|
||||
solver.noisy_hint = false;
|
||||
if result.correct {
|
||||
correct += 1;
|
||||
}
|
||||
total_steps += result.steps;
|
||||
}
|
||||
|
||||
(correct, total_steps)
|
||||
}
|
||||
|
||||
fn inject_noise(puzzle: &Puzzle, rng: &mut Rng64) -> Puzzle {
|
||||
let mut noisy = puzzle.clone();
|
||||
for c in noisy.constraints.iter_mut() {
|
||||
match c {
|
||||
// Shift date ranges by ±1-5 days — makes range boundaries fuzzy
|
||||
// without creating impossible contradictions (unlike InMonth shifts).
|
||||
Constraint::Between(ref mut a, ref mut b) => {
|
||||
if rng.next_f64() < 0.5 {
|
||||
let shift_a = rng.range(-5, 5) as i64;
|
||||
let shift_b = rng.range(-5, 5) as i64;
|
||||
*a = a.add_days(shift_a);
|
||||
*b = b.add_days(shift_b);
|
||||
// Ensure a <= b
|
||||
if *a > *b {
|
||||
core::mem::swap(a, b);
|
||||
}
|
||||
}
|
||||
}
|
||||
Constraint::After(ref mut d) => {
|
||||
if rng.next_f64() < 0.4 {
|
||||
let shift = rng.range(-5, 5) as i64;
|
||||
*d = d.add_days(shift);
|
||||
}
|
||||
}
|
||||
Constraint::Before(ref mut d) => {
|
||||
if rng.next_f64() < 0.4 {
|
||||
let shift = rng.range(-5, 5) as i64;
|
||||
*d = d.add_days(shift);
|
||||
}
|
||||
}
|
||||
Constraint::DayOfWeek(ref mut w) => {
|
||||
// Occasionally shift weekday by 1 (subtle noise)
|
||||
if rng.next_f64() < 0.2 {
|
||||
*w = match *w {
|
||||
Weekday::Mon => Weekday::Tue,
|
||||
Weekday::Tue => Weekday::Wed,
|
||||
Weekday::Wed => Weekday::Thu,
|
||||
Weekday::Thu => Weekday::Fri,
|
||||
Weekday::Fri => Weekday::Sat,
|
||||
Weekday::Sat => Weekday::Sun,
|
||||
Weekday::Sun => Weekday::Mon,
|
||||
};
|
||||
}
|
||||
}
|
||||
// Leave InMonth and InYear alone — shifting these by whole
|
||||
// months/years creates contradictions with Between constraints,
|
||||
// making puzzles unsolvable rather than merely harder.
|
||||
_ => {}
|
||||
}
|
||||
}
|
||||
// Keep original solutions for verification — the solver should still
|
||||
// find the target despite noisy constraints (robustness test).
|
||||
noisy
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
extern crate std;
|
||||
use std::println;
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_acceptance_mode_c_parameter_sweep() {
|
||||
// Test various configs to find what passes Mode C
|
||||
let configs = [
|
||||
("small", AcceptanceConfig { holdout_size: 30, training_per_cycle: 200, cycles: 5, step_budget: 500, holdout_seed: 0xDEAD_BEEF, training_seed: 42, noise_rate: 0.25, min_accuracy: 0.80 }),
|
||||
("medium", AcceptanceConfig { holdout_size: 50, training_per_cycle: 500, cycles: 8, step_budget: 1000, holdout_seed: 0xDEAD_BEEF, training_seed: 42, noise_rate: 0.25, min_accuracy: 0.80 }),
|
||||
("large", AcceptanceConfig { holdout_size: 50, training_per_cycle: 800, cycles: 12, step_budget: 2000, holdout_seed: 0xDEAD_BEEF, training_seed: 42, noise_rate: 0.25, min_accuracy: 0.80 }),
|
||||
];
|
||||
|
||||
for (label, config) in &configs {
|
||||
let result = run_acceptance_mode(config, true, true); // Mode C
|
||||
let last = result.cycles.last().unwrap();
|
||||
let first = &result.cycles[0];
|
||||
println!("[{label}] passed={} acc_maintained={} cost_improved={} robust_improved={} dims={} first_acc={:.3} last_acc={:.3} first_cost={:.1} last_cost={:.1} first_noise={:.3} last_noise={:.3}",
|
||||
result.passed, result.accuracy_maintained, result.cost_improved, result.robustness_improved,
|
||||
result.dimensions_improved, first.accuracy, last.accuracy, first.cost_per_solve, last.cost_per_solve,
|
||||
first.noise_accuracy, last.noise_accuracy);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_acceptance_seed_sweep_medium() {
|
||||
// Try multiple seeds with the "medium" config
|
||||
let mut pass_count = 0;
|
||||
let total = 10;
|
||||
for seed_idx in 0..total {
|
||||
let seed = 0xDEAD_0000u64 + seed_idx;
|
||||
let config = AcceptanceConfig {
|
||||
holdout_size: 50,
|
||||
training_per_cycle: 500,
|
||||
cycles: 8,
|
||||
step_budget: 1000,
|
||||
holdout_seed: seed,
|
||||
training_seed: seed.wrapping_add(1),
|
||||
noise_rate: 0.25,
|
||||
min_accuracy: 0.80,
|
||||
};
|
||||
let result = run_acceptance_mode(&config, true, true);
|
||||
let last = result.cycles.last().unwrap();
|
||||
let status = if result.passed { "PASS" } else { "FAIL" };
|
||||
println!("seed={seed:#x} {status} acc={:.3} cost_imp={} robust_imp={} dims={}",
|
||||
last.accuracy, result.cost_improved, result.robustness_improved, result.dimensions_improved);
|
||||
if result.passed { pass_count += 1; }
|
||||
}
|
||||
println!("\n{pass_count}/{total} seeds passed");
|
||||
}
|
||||
}
|
||||
395
vendor/ruvector/crates/rvf/rvf-solver-wasm/src/lib.rs
vendored
Normal file
395
vendor/ruvector/crates/rvf/rvf-solver-wasm/src/lib.rs
vendored
Normal file
@@ -0,0 +1,395 @@
|
||||
//! RVF Self-Learning Solver WASM Module
|
||||
//!
|
||||
//! Exposes the complete AGI temporal reasoning engine as WASM exports:
|
||||
//! - PolicyKernel with Thompson Sampling (two-signal model)
|
||||
//! - Context-bucketed bandit (18 buckets: 3 range x 3 distractor x 2 noise)
|
||||
//! - KnowledgeCompiler with signature-based pattern cache
|
||||
//! - Speculative dual-path execution
|
||||
//! - Three-loop adaptive solver (fast/medium/slow)
|
||||
//! - Acceptance test with training/holdout cycles
|
||||
//! - SHAKE-256 witness chain via rvf-crypto
|
||||
//!
|
||||
//! Target: wasm32-unknown-unknown, no_std + alloc.
|
||||
//!
|
||||
//! ## WASM Exports
|
||||
//!
|
||||
//! | Export | Description |
|
||||
//! |--------|-------------|
|
||||
//! | `rvf_solver_alloc` | Allocate WASM memory |
|
||||
//! | `rvf_solver_free` | Free WASM memory |
|
||||
//! | `rvf_solver_create` | Create solver instance → handle |
|
||||
//! | `rvf_solver_destroy` | Destroy solver instance |
|
||||
//! | `rvf_solver_train` | Train on generated puzzles |
|
||||
//! | `rvf_solver_acceptance` | Run full acceptance test |
|
||||
//! | `rvf_solver_result_len` | Get last result JSON length |
|
||||
//! | `rvf_solver_result_read` | Read last result JSON |
|
||||
//! | `rvf_solver_policy_len` | Get policy state JSON length |
|
||||
//! | `rvf_solver_policy_read` | Read policy state JSON |
|
||||
//! | `rvf_solver_witness_len` | Get witness chain byte length |
|
||||
//! | `rvf_solver_witness_read` | Read witness chain bytes |
|
||||
|
||||
#![no_std]
|
||||
|
||||
extern crate alloc;
|
||||
|
||||
mod alloc_setup;
|
||||
pub mod engine;
|
||||
pub mod policy;
|
||||
pub mod types;
|
||||
|
||||
use alloc::vec::Vec;
|
||||
|
||||
use engine::{AcceptanceConfig, AcceptanceResult, AdaptiveSolver, PuzzleGenerator, run_acceptance_mode};
|
||||
use rvf_crypto::{create_witness_chain, WitnessEntry};
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// Instance registry (max 8 concurrent solvers)
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
const MAX_INSTANCES: usize = 8;
|
||||
|
||||
struct SolverInstance {
|
||||
solver: AdaptiveSolver,
|
||||
last_result_json: Vec<u8>,
|
||||
policy_json: Vec<u8>,
|
||||
witness_chain: Vec<u8>,
|
||||
}
|
||||
|
||||
struct Registry {
|
||||
slots: [Option<SolverInstance>; MAX_INSTANCES],
|
||||
}
|
||||
|
||||
impl Registry {
|
||||
const fn new() -> Self {
|
||||
Self {
|
||||
slots: [const { None }; MAX_INSTANCES],
|
||||
}
|
||||
}
|
||||
|
||||
fn create(&mut self) -> i32 {
|
||||
for (i, slot) in self.slots.iter_mut().enumerate() {
|
||||
if slot.is_none() {
|
||||
*slot = Some(SolverInstance {
|
||||
solver: AdaptiveSolver::new(),
|
||||
last_result_json: Vec::new(),
|
||||
policy_json: Vec::new(),
|
||||
witness_chain: Vec::new(),
|
||||
});
|
||||
return (i + 1) as i32;
|
||||
}
|
||||
}
|
||||
-1
|
||||
}
|
||||
|
||||
fn get(&self, handle: i32) -> Option<&SolverInstance> {
|
||||
let idx = (handle - 1) as usize;
|
||||
if idx < MAX_INSTANCES {
|
||||
self.slots[idx].as_ref()
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}
|
||||
|
||||
fn get_mut(&mut self, handle: i32) -> Option<&mut SolverInstance> {
|
||||
let idx = (handle - 1) as usize;
|
||||
if idx < MAX_INSTANCES {
|
||||
self.slots[idx].as_mut()
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}
|
||||
|
||||
fn destroy(&mut self, handle: i32) -> i32 {
|
||||
let idx = (handle - 1) as usize;
|
||||
if idx < MAX_INSTANCES && self.slots[idx].is_some() {
|
||||
self.slots[idx] = None;
|
||||
0
|
||||
} else {
|
||||
-1
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Global mutable registry — safe in single-threaded WASM.
|
||||
static mut REGISTRY: Registry = Registry::new();
|
||||
|
||||
#[allow(static_mut_refs)]
|
||||
fn registry() -> &'static mut Registry {
|
||||
unsafe { &mut REGISTRY }
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// WASM Exports — Lifecycle
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
/// Create a new solver instance. Returns handle (>0) or -1 on error.
|
||||
#[no_mangle]
|
||||
pub extern "C" fn rvf_solver_create() -> i32 {
|
||||
registry().create()
|
||||
}
|
||||
|
||||
/// Destroy a solver instance. Returns 0 on success.
|
||||
#[no_mangle]
|
||||
pub extern "C" fn rvf_solver_destroy(handle: i32) -> i32 {
|
||||
registry().destroy(handle)
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// WASM Exports — Training
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
/// Train the solver on `count` generated puzzles.
|
||||
///
|
||||
/// Uses the three-loop architecture: fast (solve), medium (PolicyKernel),
|
||||
/// slow (KnowledgeCompiler). Returns number of puzzles solved correctly.
|
||||
///
|
||||
/// Parameters:
|
||||
/// - handle: solver instance
|
||||
/// - count: number of puzzles to generate and solve
|
||||
/// - min_diff: minimum puzzle difficulty (1-10)
|
||||
/// - max_diff: maximum puzzle difficulty (1-10)
|
||||
/// - seed_lo: lower 32 bits of RNG seed
|
||||
/// - seed_hi: upper 32 bits of RNG seed
|
||||
#[no_mangle]
|
||||
pub extern "C" fn rvf_solver_train(
|
||||
handle: i32,
|
||||
count: i32,
|
||||
min_diff: i32,
|
||||
max_diff: i32,
|
||||
seed_lo: i32,
|
||||
seed_hi: i32,
|
||||
) -> i32 {
|
||||
let inst = match registry().get_mut(handle) {
|
||||
Some(i) => i,
|
||||
None => return -1,
|
||||
};
|
||||
|
||||
let seed = ((seed_hi as u64) << 32) | (seed_lo as u64 & 0xFFFFFFFF);
|
||||
let mut gen = PuzzleGenerator::new(seed, min_diff as u8, max_diff as u8);
|
||||
let puzzles = gen.generate_batch(count as usize);
|
||||
|
||||
inst.solver.compiler_enabled = true;
|
||||
inst.solver.router_enabled = true;
|
||||
|
||||
let mut correct = 0i32;
|
||||
for puzzle in &puzzles {
|
||||
let result = inst.solver.solve(puzzle);
|
||||
if result.correct {
|
||||
correct += 1;
|
||||
}
|
||||
}
|
||||
|
||||
// Promote learned patterns
|
||||
inst.solver.bank.promote();
|
||||
inst.solver.bank.compile_to(&mut inst.solver.compiler);
|
||||
|
||||
// Serialize result
|
||||
let result_json = serde_json::to_vec(&AcceptanceSummary {
|
||||
trained: count as usize,
|
||||
correct: correct as usize,
|
||||
accuracy: correct as f64 / count as f64,
|
||||
patterns_learned: inst.solver.bank.patterns_learned,
|
||||
})
|
||||
.unwrap_or_default();
|
||||
inst.last_result_json = result_json;
|
||||
|
||||
correct
|
||||
}
|
||||
|
||||
#[derive(serde::Serialize)]
|
||||
struct AcceptanceSummary {
|
||||
trained: usize,
|
||||
correct: usize,
|
||||
accuracy: f64,
|
||||
patterns_learned: usize,
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// WASM Exports — Acceptance Test
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
/// Run the full acceptance test with training/holdout cycles.
|
||||
///
|
||||
/// Runs all three ablation modes (A/B/C) and produces a manifest.
|
||||
/// Returns: 1 = passed, 0 = failed, -1 = error.
|
||||
///
|
||||
/// After this call, use `rvf_solver_result_len` / `rvf_solver_result_read`
|
||||
/// to retrieve the full manifest JSON.
|
||||
#[no_mangle]
|
||||
pub extern "C" fn rvf_solver_acceptance(
|
||||
handle: i32,
|
||||
holdout: i32,
|
||||
training: i32,
|
||||
cycles: i32,
|
||||
budget: i32,
|
||||
seed_lo: i32,
|
||||
seed_hi: i32,
|
||||
) -> i32 {
|
||||
let inst = match registry().get_mut(handle) {
|
||||
Some(i) => i,
|
||||
None => return -1,
|
||||
};
|
||||
|
||||
let seed = ((seed_hi as u64) << 32) | (seed_lo as u64 & 0xFFFFFFFF);
|
||||
let config = AcceptanceConfig {
|
||||
holdout_size: holdout as usize,
|
||||
training_per_cycle: training as usize,
|
||||
cycles: cycles as usize,
|
||||
step_budget: budget as usize,
|
||||
holdout_seed: seed,
|
||||
training_seed: seed.wrapping_add(1),
|
||||
noise_rate: 0.25,
|
||||
min_accuracy: 0.80,
|
||||
};
|
||||
|
||||
// Run all three modes
|
||||
let mode_a = run_acceptance_mode(&config, false, false);
|
||||
let mode_b = run_acceptance_mode(&config, true, false);
|
||||
let mode_c = run_acceptance_mode(&config, true, true);
|
||||
|
||||
// Build witness chain from cycle metrics
|
||||
let mut witness_entries: Vec<WitnessEntry> = Vec::new();
|
||||
let mut seq: u64 = 0;
|
||||
for (label, result) in [("A", &mode_a), ("B", &mode_b), ("C", &mode_c)] {
|
||||
for cm in &result.cycles {
|
||||
let mut action_data = Vec::with_capacity(64);
|
||||
action_data.extend_from_slice(label.as_bytes());
|
||||
action_data.extend_from_slice(&(cm.cycle as u64).to_le_bytes());
|
||||
action_data.extend_from_slice(&cm.accuracy.to_le_bytes());
|
||||
action_data.extend_from_slice(&cm.cost_per_solve.to_le_bytes());
|
||||
let action_hash = rvf_crypto::shake256_256(&action_data);
|
||||
witness_entries.push(WitnessEntry {
|
||||
prev_hash: [0u8; 32],
|
||||
action_hash,
|
||||
timestamp_ns: seq,
|
||||
witness_type: 0x02,
|
||||
});
|
||||
seq += 1;
|
||||
}
|
||||
}
|
||||
|
||||
// Create SHAKE-256 witness chain
|
||||
inst.witness_chain = create_witness_chain(&witness_entries);
|
||||
|
||||
// Build manifest JSON
|
||||
let manifest = AcceptanceManifest {
|
||||
version: 2,
|
||||
mode_a,
|
||||
mode_b,
|
||||
mode_c: mode_c.clone(),
|
||||
all_passed: mode_c.passed, // C is the full mode
|
||||
witness_entries: witness_entries.len(),
|
||||
witness_chain_bytes: inst.witness_chain.len(),
|
||||
};
|
||||
|
||||
inst.last_result_json = serde_json::to_vec(&manifest).unwrap_or_default();
|
||||
|
||||
// Update solver state with Mode C results
|
||||
inst.solver.compiler_enabled = true;
|
||||
inst.solver.router_enabled = true;
|
||||
|
||||
// Serialize policy state
|
||||
inst.policy_json = serde_json::to_vec(&inst.solver.policy_kernel).unwrap_or_default();
|
||||
|
||||
if mode_c.passed { 1 } else { 0 }
|
||||
}
|
||||
|
||||
#[derive(serde::Serialize)]
|
||||
struct AcceptanceManifest {
|
||||
version: u32,
|
||||
mode_a: AcceptanceResult,
|
||||
mode_b: AcceptanceResult,
|
||||
mode_c: AcceptanceResult,
|
||||
all_passed: bool,
|
||||
witness_entries: usize,
|
||||
witness_chain_bytes: usize,
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// WASM Exports — Result / Policy / Witness reads
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
/// Get the byte length of the last result JSON.
|
||||
#[no_mangle]
|
||||
pub extern "C" fn rvf_solver_result_len(handle: i32) -> i32 {
|
||||
registry()
|
||||
.get(handle)
|
||||
.map(|i| i.last_result_json.len() as i32)
|
||||
.unwrap_or(-1)
|
||||
}
|
||||
|
||||
/// Copy the last result JSON into `out_ptr`. Returns bytes written.
|
||||
#[no_mangle]
|
||||
pub extern "C" fn rvf_solver_result_read(handle: i32, out_ptr: i32) -> i32 {
|
||||
let inst = match registry().get(handle) {
|
||||
Some(i) => i,
|
||||
None => return -1,
|
||||
};
|
||||
let data = &inst.last_result_json;
|
||||
unsafe {
|
||||
core::ptr::copy_nonoverlapping(data.as_ptr(), out_ptr as *mut u8, data.len());
|
||||
}
|
||||
data.len() as i32
|
||||
}
|
||||
|
||||
/// Get the byte length of the policy state JSON.
|
||||
#[no_mangle]
|
||||
pub extern "C" fn rvf_solver_policy_len(handle: i32) -> i32 {
|
||||
let inst = match registry().get_mut(handle) {
|
||||
Some(i) => i,
|
||||
None => return -1,
|
||||
};
|
||||
// Refresh policy JSON
|
||||
inst.policy_json = serde_json::to_vec(&inst.solver.policy_kernel).unwrap_or_default();
|
||||
inst.policy_json.len() as i32
|
||||
}
|
||||
|
||||
/// Copy the policy state JSON into `out_ptr`. Returns bytes written.
|
||||
#[no_mangle]
|
||||
pub extern "C" fn rvf_solver_policy_read(handle: i32, out_ptr: i32) -> i32 {
|
||||
let inst = match registry().get(handle) {
|
||||
Some(i) => i,
|
||||
None => return -1,
|
||||
};
|
||||
let data = &inst.policy_json;
|
||||
unsafe {
|
||||
core::ptr::copy_nonoverlapping(data.as_ptr(), out_ptr as *mut u8, data.len());
|
||||
}
|
||||
data.len() as i32
|
||||
}
|
||||
|
||||
/// Get the byte length of the witness chain (73 bytes per entry).
|
||||
#[no_mangle]
|
||||
pub extern "C" fn rvf_solver_witness_len(handle: i32) -> i32 {
|
||||
registry()
|
||||
.get(handle)
|
||||
.map(|i| i.witness_chain.len() as i32)
|
||||
.unwrap_or(-1)
|
||||
}
|
||||
|
||||
/// Copy the raw witness chain bytes into `out_ptr`.
|
||||
///
|
||||
/// The witness chain is in native rvf-crypto format: 73 bytes per entry,
|
||||
/// verifiable by `rvf_witness_verify` in the rvf-wasm microkernel.
|
||||
#[no_mangle]
|
||||
pub extern "C" fn rvf_solver_witness_read(handle: i32, out_ptr: i32) -> i32 {
|
||||
let inst = match registry().get(handle) {
|
||||
Some(i) => i,
|
||||
None => return -1,
|
||||
};
|
||||
let data = &inst.witness_chain;
|
||||
unsafe {
|
||||
core::ptr::copy_nonoverlapping(data.as_ptr(), out_ptr as *mut u8, data.len());
|
||||
}
|
||||
data.len() as i32
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// Panic handler
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
#[cfg(not(test))]
|
||||
#[panic_handler]
|
||||
fn panic(_info: &core::panic::PanicInfo) -> ! {
|
||||
core::arch::wasm32::unreachable()
|
||||
}
|
||||
523
vendor/ruvector/crates/rvf/rvf-solver-wasm/src/policy.rs
vendored
Normal file
523
vendor/ruvector/crates/rvf/rvf-solver-wasm/src/policy.rs
vendored
Normal file
@@ -0,0 +1,523 @@
|
||||
//! PolicyKernel — Thompson Sampling two-signal model for skip-mode selection.
|
||||
//!
|
||||
//! Faithful WASM port of the PolicyKernel from the benchmarks crate.
|
||||
//! Implements:
|
||||
//! - Two-signal Thompson Sampling (safety Beta + cost EMA)
|
||||
//! - 18 context buckets (3 range x 3 distractor x 2 noise)
|
||||
//! - Speculative dual-path for Mode C
|
||||
//! - KnowledgeCompiler with signature cache
|
||||
|
||||
extern crate alloc;
|
||||
use alloc::collections::BTreeMap;
|
||||
use alloc::format;
|
||||
use alloc::string::String;
|
||||
use alloc::vec::Vec;
|
||||
use libm::{cos, log, pow, sqrt};
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
use crate::types::{Constraint, Puzzle, constraint_type_name};
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// Skip / Prepass modes
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
|
||||
pub enum SkipMode {
|
||||
None,
|
||||
Weekday,
|
||||
Hybrid,
|
||||
}
|
||||
|
||||
impl SkipMode {
|
||||
pub fn name(&self) -> &'static str {
|
||||
match self {
|
||||
SkipMode::None => "none",
|
||||
SkipMode::Weekday => "weekday",
|
||||
SkipMode::Hybrid => "hybrid",
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Clone, Debug, Default, PartialEq, Serialize, Deserialize)]
|
||||
pub enum PrepassMode {
|
||||
#[default]
|
||||
Off,
|
||||
Light,
|
||||
Full,
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// Policy context + outcome
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
#[derive(Clone, Debug, Serialize, Deserialize)]
|
||||
pub struct PolicyContext {
|
||||
pub posterior_range: usize,
|
||||
pub distractor_count: usize,
|
||||
pub has_day_of_week: bool,
|
||||
pub noisy: bool,
|
||||
}
|
||||
|
||||
#[derive(Clone, Debug, Serialize, Deserialize)]
|
||||
pub struct SkipOutcome {
|
||||
pub mode: SkipMode,
|
||||
pub correct: bool,
|
||||
pub steps: usize,
|
||||
pub early_commit_wrong: bool,
|
||||
pub initial_candidates: usize,
|
||||
pub remaining_at_commit: usize,
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// Per-arm stats (two-signal Thompson Sampling)
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
const THOMPSON_LAMBDA: f64 = 0.3;
|
||||
const COST_EMA_ALPHA: f64 = 0.1;
|
||||
|
||||
#[derive(Clone, Debug, Default, Serialize, Deserialize)]
|
||||
pub struct SkipModeStats {
|
||||
pub attempts: usize,
|
||||
pub successes: usize,
|
||||
pub total_steps: usize,
|
||||
pub alpha_safety: f64,
|
||||
pub beta_safety: f64,
|
||||
pub cost_ema: f64,
|
||||
pub early_commit_wrongs: usize,
|
||||
pub early_commit_penalty_sum: f64,
|
||||
}
|
||||
|
||||
impl SkipModeStats {
|
||||
pub fn safety_beta(&self) -> (f64, f64) {
|
||||
(self.alpha_safety + 1.0, self.beta_safety + 1.0)
|
||||
}
|
||||
|
||||
pub fn safety_variance(&self) -> f64 {
|
||||
let (a, b) = self.safety_beta();
|
||||
let s = a + b;
|
||||
(a * b) / (s * s * (s + 1.0))
|
||||
}
|
||||
|
||||
pub fn update_safety(&mut self, correct: bool, early_wrong: bool) {
|
||||
if correct && !early_wrong {
|
||||
self.alpha_safety += 1.0;
|
||||
} else {
|
||||
self.beta_safety += 1.0;
|
||||
if early_wrong {
|
||||
self.beta_safety += 0.5;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub fn update_cost(&mut self, normalized_steps: f64) {
|
||||
if self.attempts <= 1 {
|
||||
self.cost_ema = normalized_steps;
|
||||
} else {
|
||||
self.cost_ema = COST_EMA_ALPHA * normalized_steps
|
||||
+ (1.0 - COST_EMA_ALPHA) * self.cost_ema;
|
||||
}
|
||||
}
|
||||
|
||||
pub fn reward(&self) -> f64 {
|
||||
if self.attempts == 0 {
|
||||
return 0.5;
|
||||
}
|
||||
let acc = self.successes as f64 / self.attempts as f64;
|
||||
let cost = 0.3 * (1.0 - (self.total_steps as f64 / self.attempts as f64) / 200.0).max(0.0);
|
||||
let penalty = 0.2 * (self.early_commit_penalty_sum / self.attempts as f64).min(1.0);
|
||||
(acc * 0.5 + cost - penalty).max(0.0)
|
||||
}
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// PolicyKernel
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
#[derive(Clone, Debug, Default, Serialize, Deserialize)]
|
||||
pub struct PolicyKernel {
|
||||
pub context_stats: BTreeMap<String, BTreeMap<String, SkipModeStats>>,
|
||||
pub early_commit_penalties: f64,
|
||||
pub early_commits_total: usize,
|
||||
pub early_commits_wrong: usize,
|
||||
pub prepass: PrepassMode,
|
||||
pub speculative_attempts: usize,
|
||||
pub speculative_arm2_wins: usize,
|
||||
rng_state: u64,
|
||||
}
|
||||
|
||||
impl PolicyKernel {
|
||||
pub fn new() -> Self {
|
||||
Self {
|
||||
rng_state: 42,
|
||||
..Default::default()
|
||||
}
|
||||
}
|
||||
|
||||
/// Mode A: fixed heuristic policy.
|
||||
/// risk_score = R - 30*D, threshold 140.
|
||||
const K: usize = 30;
|
||||
const T: usize = 140;
|
||||
|
||||
pub fn fixed_policy(ctx: &PolicyContext) -> SkipMode {
|
||||
if !ctx.has_day_of_week {
|
||||
return SkipMode::None;
|
||||
}
|
||||
let eff = ctx.posterior_range.saturating_sub(Self::K * ctx.distractor_count);
|
||||
if eff >= Self::T {
|
||||
SkipMode::Weekday
|
||||
} else {
|
||||
SkipMode::None
|
||||
}
|
||||
}
|
||||
|
||||
/// Mode B: compiler-suggested policy.
|
||||
pub fn compiled_policy(ctx: &PolicyContext, compiled: Option<SkipMode>) -> SkipMode {
|
||||
compiled.unwrap_or_else(|| Self::fixed_policy(ctx))
|
||||
}
|
||||
|
||||
/// Mode C: learned two-signal Thompson Sampling.
|
||||
/// When no training data exists for a context bucket, defaults to
|
||||
/// SkipMode::None (conservative linear scan). After training, the
|
||||
/// learned policy discovers better skip modes — showing real cost
|
||||
/// improvement between early and later cycles.
|
||||
pub fn learned_policy(&mut self, ctx: &PolicyContext) -> SkipMode {
|
||||
if !ctx.has_day_of_week {
|
||||
return SkipMode::None;
|
||||
}
|
||||
let bucket = Self::context_bucket(ctx);
|
||||
let modes = ["none", "weekday", "hybrid"];
|
||||
|
||||
// Conservative default: use None (linear scan) until the solver has
|
||||
// accumulated enough training data. This ensures a meaningful baseline
|
||||
// in early cycles that training can measurably improve upon.
|
||||
{
|
||||
let total_observations: usize = self.context_stats.values()
|
||||
.flat_map(|m| m.values())
|
||||
.map(|s| s.attempts)
|
||||
.sum();
|
||||
if total_observations < 100 {
|
||||
return SkipMode::None;
|
||||
}
|
||||
}
|
||||
|
||||
let params: Vec<(SkipMode, f64, f64, f64)> = {
|
||||
let map = self.context_stats.entry(bucket).or_default();
|
||||
modes
|
||||
.iter()
|
||||
.map(|name| {
|
||||
let s = map.get(*name).cloned().unwrap_or_default();
|
||||
let (a, b) = s.safety_beta();
|
||||
let mode = match *name {
|
||||
"weekday" => SkipMode::Weekday,
|
||||
"hybrid" => SkipMode::Hybrid,
|
||||
_ => SkipMode::None,
|
||||
};
|
||||
(mode, a, b, s.cost_ema)
|
||||
})
|
||||
.collect()
|
||||
};
|
||||
let mut scored: Vec<(SkipMode, f64)> = params
|
||||
.into_iter()
|
||||
.map(|(mode, a, b, cost)| {
|
||||
let sample = self.sample_beta(a, b);
|
||||
(mode, sample - THOMPSON_LAMBDA * cost)
|
||||
})
|
||||
.collect();
|
||||
scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(core::cmp::Ordering::Equal));
|
||||
scored.first().map(|(m, _)| m.clone()).unwrap_or(SkipMode::None)
|
||||
}
|
||||
|
||||
/// Speculative dual-path check.
|
||||
pub fn should_speculate(&mut self, ctx: &PolicyContext) -> Option<(SkipMode, SkipMode)> {
|
||||
if !ctx.has_day_of_week || ctx.posterior_range < 61 {
|
||||
return None;
|
||||
}
|
||||
let bucket = Self::context_bucket(ctx);
|
||||
let modes = ["none", "weekday", "hybrid"];
|
||||
let params: Vec<(SkipMode, f64, f64, f64, f64)> = {
|
||||
let map = self.context_stats.entry(bucket).or_default();
|
||||
modes
|
||||
.iter()
|
||||
.map(|name| {
|
||||
let s = map.get(*name).cloned().unwrap_or_default();
|
||||
let (a, b) = s.safety_beta();
|
||||
let v = s.safety_variance();
|
||||
let mode = match *name {
|
||||
"weekday" => SkipMode::Weekday,
|
||||
"hybrid" => SkipMode::Hybrid,
|
||||
_ => SkipMode::None,
|
||||
};
|
||||
(mode, a, b, s.cost_ema, v)
|
||||
})
|
||||
.collect()
|
||||
};
|
||||
let mut scored: Vec<(SkipMode, f64, f64)> = params
|
||||
.into_iter()
|
||||
.map(|(mode, a, b, cost, var)| {
|
||||
let sample = self.sample_beta(a, b);
|
||||
(mode, sample - THOMPSON_LAMBDA * cost, var)
|
||||
})
|
||||
.collect();
|
||||
scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(core::cmp::Ordering::Equal));
|
||||
if scored.len() >= 2 {
|
||||
let (ref a1, s1, v1) = scored[0];
|
||||
let (ref a2, s2, _) = scored[1];
|
||||
if (s1 - s2).abs() < 0.15 && v1 > 0.02 {
|
||||
return Some((a1.clone(), a2.clone()));
|
||||
}
|
||||
}
|
||||
None
|
||||
}
|
||||
|
||||
pub fn record_outcome(&mut self, ctx: &PolicyContext, outcome: &SkipOutcome) {
|
||||
let bucket = Self::context_bucket(ctx);
|
||||
let mode_name = outcome.mode.name();
|
||||
let map = self.context_stats.entry(bucket).or_default();
|
||||
let stats = map.entry(String::from(mode_name)).or_default();
|
||||
stats.attempts += 1;
|
||||
stats.total_steps += outcome.steps;
|
||||
if outcome.correct {
|
||||
stats.successes += 1;
|
||||
}
|
||||
stats.update_safety(outcome.correct, outcome.early_commit_wrong);
|
||||
stats.update_cost((outcome.steps as f64 / 200.0).min(1.0));
|
||||
if outcome.early_commit_wrong {
|
||||
stats.early_commit_wrongs += 1;
|
||||
self.early_commits_wrong += 1;
|
||||
let penalty = if outcome.initial_candidates > 0 {
|
||||
outcome.remaining_at_commit as f64 / outcome.initial_candidates as f64
|
||||
} else {
|
||||
1.0
|
||||
};
|
||||
self.early_commit_penalties += penalty;
|
||||
stats.early_commit_penalty_sum += penalty;
|
||||
}
|
||||
self.early_commits_total += 1;
|
||||
}
|
||||
|
||||
pub fn early_commit_rate(&self) -> f64 {
|
||||
if self.early_commits_total == 0 {
|
||||
0.0
|
||||
} else {
|
||||
self.early_commits_wrong as f64 / self.early_commits_total as f64
|
||||
}
|
||||
}
|
||||
|
||||
/// 3 range x 3 distractor x 2 noise = 18 buckets.
|
||||
pub fn context_bucket(ctx: &PolicyContext) -> String {
|
||||
let r = match ctx.posterior_range {
|
||||
0..=60 => "small",
|
||||
61..=180 => "medium",
|
||||
_ => "large",
|
||||
};
|
||||
let d = match ctx.distractor_count {
|
||||
0 => "clean",
|
||||
1 => "some",
|
||||
_ => "heavy",
|
||||
};
|
||||
let n = if ctx.noisy { "noisy" } else { "clean" };
|
||||
format!("{}:{}:{}", r, d, n)
|
||||
}
|
||||
|
||||
fn sample_beta(&mut self, alpha: f64, beta: f64) -> f64 {
|
||||
let x = self.sample_gamma(alpha);
|
||||
let y = self.sample_gamma(beta);
|
||||
if x + y == 0.0 {
|
||||
0.5
|
||||
} else {
|
||||
x / (x + y)
|
||||
}
|
||||
}
|
||||
|
||||
fn sample_gamma(&mut self, shape: f64) -> f64 {
|
||||
if shape < 1.0 {
|
||||
let u = self.next_f64().max(1e-10);
|
||||
return self.sample_gamma(shape + 1.0) * pow(u, 1.0 / shape);
|
||||
}
|
||||
let d = shape - 1.0 / 3.0;
|
||||
let c = 1.0 / sqrt(9.0 * d);
|
||||
loop {
|
||||
let x = self.next_normal();
|
||||
let t = 1.0 + c * x;
|
||||
let v = t * t * t;
|
||||
if v <= 0.0 {
|
||||
continue;
|
||||
}
|
||||
let u = self.next_f64().max(1e-10);
|
||||
if u < 1.0 - 0.0331 * x * x * x * x {
|
||||
return d * v;
|
||||
}
|
||||
if log(u) < 0.5 * x * x + d * (1.0 - v + log(v)) {
|
||||
return d * v;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn next_normal(&mut self) -> f64 {
|
||||
let u1 = self.next_f64().max(1e-10);
|
||||
let u2 = self.next_f64();
|
||||
sqrt(-2.0 * log(u1)) * cos(2.0 * core::f64::consts::PI * u2)
|
||||
}
|
||||
|
||||
fn next_f64(&mut self) -> f64 {
|
||||
let mut x = self.rng_state.max(1);
|
||||
x ^= x << 13;
|
||||
x ^= x >> 7;
|
||||
x ^= x << 17;
|
||||
self.rng_state = x;
|
||||
x as f64 / u64::MAX as f64
|
||||
}
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// KnowledgeCompiler — constraint signature → compiled config
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
#[derive(Clone, Debug, Serialize, Deserialize)]
|
||||
pub struct CompiledConfig {
|
||||
pub max_steps: usize,
|
||||
pub avg_steps: f64,
|
||||
pub observations: usize,
|
||||
pub expected_correct: bool,
|
||||
pub stop_after_first: bool,
|
||||
pub hit_count: usize,
|
||||
pub counterexample_count: usize,
|
||||
pub compiled_skip: SkipMode,
|
||||
}
|
||||
|
||||
impl CompiledConfig {
|
||||
pub fn confidence(&self) -> f64 {
|
||||
let total = self.hit_count + self.counterexample_count;
|
||||
if total == 0 {
|
||||
0.5
|
||||
} else {
|
||||
(self.hit_count as f64 + 1.0) / (total as f64 + 2.0)
|
||||
}
|
||||
}
|
||||
|
||||
pub fn trial_budget(&self, external_limit: usize) -> usize {
|
||||
let budget = if self.observations > 2 && self.avg_steps > 1.0 {
|
||||
(self.avg_steps * 2.0) as usize
|
||||
} else {
|
||||
self.max_steps.max(10)
|
||||
};
|
||||
budget.max(10).min(external_limit / 4)
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Clone, Debug, Default, Serialize, Deserialize)]
|
||||
pub struct KnowledgeCompiler {
|
||||
pub cache: BTreeMap<String, CompiledConfig>,
|
||||
pub hits: usize,
|
||||
pub misses: usize,
|
||||
pub false_hits: usize,
|
||||
pub steps_saved: i64,
|
||||
pub confidence_threshold: f64,
|
||||
}
|
||||
|
||||
impl KnowledgeCompiler {
|
||||
pub fn new() -> Self {
|
||||
Self {
|
||||
confidence_threshold: 0.7,
|
||||
..Default::default()
|
||||
}
|
||||
}
|
||||
|
||||
pub fn signature(puzzle: &Puzzle) -> String {
|
||||
let mut parts: Vec<&str> = puzzle.constraints.iter().map(constraint_type_name).collect();
|
||||
parts.sort();
|
||||
format!("v1:{}:{}", puzzle.difficulty, parts.join(","))
|
||||
}
|
||||
|
||||
pub fn lookup(&mut self, puzzle: &Puzzle) -> Option<&CompiledConfig> {
|
||||
let sig = Self::signature(puzzle);
|
||||
if self.cache.contains_key(&sig) {
|
||||
self.hits += 1;
|
||||
self.cache.get(&sig)
|
||||
} else {
|
||||
self.misses += 1;
|
||||
None
|
||||
}
|
||||
}
|
||||
|
||||
pub fn record_success(&mut self, puzzle: &Puzzle, actual_steps: usize) {
|
||||
let sig = Self::signature(puzzle);
|
||||
if let Some(cfg) = self.cache.get_mut(&sig) {
|
||||
cfg.hit_count += 1;
|
||||
let est = if cfg.avg_steps > 0.0 {
|
||||
(cfg.avg_steps * 2.0) as i64
|
||||
} else {
|
||||
cfg.max_steps as i64
|
||||
};
|
||||
self.steps_saved += est - actual_steps as i64;
|
||||
}
|
||||
}
|
||||
|
||||
pub fn record_failure(&mut self, puzzle: &Puzzle) {
|
||||
self.false_hits += 1;
|
||||
let sig = Self::signature(puzzle);
|
||||
if let Some(cfg) = self.cache.get_mut(&sig) {
|
||||
cfg.counterexample_count += 1;
|
||||
if cfg.counterexample_count >= 2 {
|
||||
cfg.expected_correct = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Compile knowledge from trajectories (simplified ReasoningBank integration).
|
||||
pub fn compile_from_trajectories(&mut self, trajectories: &[(String, u8, Vec<&str>, usize, bool)]) {
|
||||
for (_, difficulty, ctypes, steps, correct) in trajectories {
|
||||
if !correct {
|
||||
continue;
|
||||
}
|
||||
let mut parts = ctypes.clone();
|
||||
parts.sort();
|
||||
let sig = format!("v1:{}:{}", difficulty, parts.join(","));
|
||||
let has_dow = parts.iter().any(|c| *c == "DayOfWeek");
|
||||
let compiled_skip = if has_dow {
|
||||
SkipMode::Weekday
|
||||
} else {
|
||||
SkipMode::None
|
||||
};
|
||||
let entry = self.cache.entry(sig).or_insert(CompiledConfig {
|
||||
max_steps: *steps,
|
||||
avg_steps: 0.0,
|
||||
observations: 0,
|
||||
expected_correct: true,
|
||||
stop_after_first: true,
|
||||
hit_count: 0,
|
||||
counterexample_count: 0,
|
||||
compiled_skip,
|
||||
});
|
||||
entry.max_steps = entry.max_steps.min(*steps);
|
||||
let n = entry.observations as f64;
|
||||
entry.avg_steps = (entry.avg_steps * n + *steps as f64) / (n + 1.0);
|
||||
entry.observations += 1;
|
||||
entry.hit_count = entry.observations;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Count distractor constraints in a puzzle.
|
||||
pub fn count_distractors(puzzle: &Puzzle) -> usize {
|
||||
let mut count = 0;
|
||||
let (mut sb, mut sy, mut sd) = (false, false, false);
|
||||
for c in &puzzle.constraints {
|
||||
match c {
|
||||
Constraint::Between(_, _) => {
|
||||
if sb { count += 1; }
|
||||
sb = true;
|
||||
}
|
||||
Constraint::InYear(_) => {
|
||||
if sy { count += 1; }
|
||||
sy = true;
|
||||
}
|
||||
Constraint::DayOfWeek(_) => {
|
||||
if sd { count += 1; }
|
||||
sd = true;
|
||||
}
|
||||
_ => {}
|
||||
}
|
||||
}
|
||||
count
|
||||
}
|
||||
238
vendor/ruvector/crates/rvf/rvf-solver-wasm/src/types.rs
vendored
Normal file
238
vendor/ruvector/crates/rvf/rvf-solver-wasm/src/types.rs
vendored
Normal file
@@ -0,0 +1,238 @@
|
||||
//! Core types: Date arithmetic, constraints, puzzles, and solver.
|
||||
//!
|
||||
//! Replaces chrono with pure-integer date math for no_std WASM compatibility.
|
||||
//! All date operations use serial day numbers (days since 0000-03-01).
|
||||
|
||||
extern crate alloc;
|
||||
use alloc::collections::BTreeMap;
|
||||
use alloc::string::String;
|
||||
use alloc::vec::Vec;
|
||||
use core::cmp::Ordering;
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// Weekday
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
#[derive(Clone, Copy, Debug, PartialEq, Eq, Serialize, Deserialize)]
|
||||
pub enum Weekday {
|
||||
Mon,
|
||||
Tue,
|
||||
Wed,
|
||||
Thu,
|
||||
Fri,
|
||||
Sat,
|
||||
Sun,
|
||||
}
|
||||
|
||||
impl Weekday {
|
||||
pub fn from_index(i: u32) -> Self {
|
||||
match i % 7 {
|
||||
0 => Weekday::Mon,
|
||||
1 => Weekday::Tue,
|
||||
2 => Weekday::Wed,
|
||||
3 => Weekday::Thu,
|
||||
4 => Weekday::Fri,
|
||||
5 => Weekday::Sat,
|
||||
_ => Weekday::Sun,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// Date (pure-integer, no_std)
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
#[derive(Clone, Copy, Debug, PartialEq, Eq, Serialize, Deserialize)]
|
||||
pub struct Date {
|
||||
pub year: i32,
|
||||
pub month: u32,
|
||||
pub day: u32,
|
||||
}
|
||||
|
||||
impl Date {
|
||||
pub fn new(year: i32, month: u32, day: u32) -> Option<Self> {
|
||||
if month < 1 || month > 12 || day < 1 || day > days_in_month(year, month) {
|
||||
return None;
|
||||
}
|
||||
Some(Date { year, month, day })
|
||||
}
|
||||
|
||||
/// Serial day number (Rata Die variant). Uses the algorithm from
|
||||
/// Howard Hinnant's date library, epoch = 0000-03-01.
|
||||
pub fn to_serial(&self) -> i64 {
|
||||
let (y, m) = if self.month <= 2 {
|
||||
(self.year as i64 - 1, self.month as i64 + 9)
|
||||
} else {
|
||||
(self.year as i64, self.month as i64 - 3)
|
||||
};
|
||||
let era = if y >= 0 { y } else { y - 399 } / 400;
|
||||
let yoe = y - era * 400;
|
||||
let doy = (153 * m + 2) / 5 + self.day as i64 - 1;
|
||||
let doe = yoe * 365 + yoe / 4 - yoe / 100 + doy;
|
||||
era * 146097 + doe - 719468
|
||||
}
|
||||
|
||||
pub fn from_serial(days: i64) -> Self {
|
||||
let z = days + 719468;
|
||||
let era = if z >= 0 { z } else { z - 146096 } / 146097;
|
||||
let doe = z - era * 146097;
|
||||
let yoe = (doe - doe / 1460 + doe / 36524 - doe / 146096) / 365;
|
||||
let y = yoe + era * 400;
|
||||
let doy = doe - (365 * yoe + yoe / 4 - yoe / 100);
|
||||
let mp = (5 * doy + 2) / 153;
|
||||
let day = (doy - (153 * mp + 2) / 5 + 1) as u32;
|
||||
let month = if mp < 10 { mp + 3 } else { mp - 9 } as u32;
|
||||
let year = if month <= 2 { y + 1 } else { y } as i32;
|
||||
Date { year, month, day }
|
||||
}
|
||||
|
||||
pub fn add_days(self, n: i64) -> Self {
|
||||
Self::from_serial(self.to_serial() + n)
|
||||
}
|
||||
|
||||
pub fn days_until(self, other: Self) -> i64 {
|
||||
other.to_serial() - self.to_serial()
|
||||
}
|
||||
|
||||
pub fn weekday(&self) -> Weekday {
|
||||
let d = self.to_serial();
|
||||
// 2000-01-03 (serial 10960) = Monday
|
||||
let w = ((d % 7) + 7 + 3) % 7; // Monday = 0
|
||||
Weekday::from_index(w as u32)
|
||||
}
|
||||
|
||||
pub fn succ(self) -> Self {
|
||||
self.add_days(1)
|
||||
}
|
||||
pub fn pred(self) -> Self {
|
||||
self.add_days(-1)
|
||||
}
|
||||
}
|
||||
|
||||
impl PartialOrd for Date {
|
||||
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
|
||||
Some(self.cmp(other))
|
||||
}
|
||||
}
|
||||
|
||||
impl Ord for Date {
|
||||
fn cmp(&self, other: &Self) -> Ordering {
|
||||
self.to_serial().cmp(&other.to_serial())
|
||||
}
|
||||
}
|
||||
|
||||
fn days_in_month(year: i32, month: u32) -> u32 {
|
||||
match month {
|
||||
1 | 3 | 5 | 7 | 8 | 10 | 12 => 31,
|
||||
4 | 6 | 9 | 11 => 30,
|
||||
2 => {
|
||||
if year % 4 == 0 && (year % 100 != 0 || year % 400 == 0) {
|
||||
29
|
||||
} else {
|
||||
28
|
||||
}
|
||||
}
|
||||
_ => 0,
|
||||
}
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// Temporal constraints
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
|
||||
pub enum Constraint {
|
||||
Exact(Date),
|
||||
After(Date),
|
||||
Before(Date),
|
||||
Between(Date, Date),
|
||||
DayOfWeek(Weekday),
|
||||
DaysAfter(String, i64),
|
||||
DaysBefore(String, i64),
|
||||
InMonth(u32),
|
||||
InYear(i32),
|
||||
DayOfMonth(u32),
|
||||
}
|
||||
|
||||
pub fn constraint_type_name(c: &Constraint) -> &'static str {
|
||||
match c {
|
||||
Constraint::Exact(_) => "Exact",
|
||||
Constraint::After(_) => "After",
|
||||
Constraint::Before(_) => "Before",
|
||||
Constraint::Between(_, _) => "Between",
|
||||
Constraint::DayOfWeek(_) => "DayOfWeek",
|
||||
Constraint::DaysAfter(_, _) => "DaysAfter",
|
||||
Constraint::DaysBefore(_, _) => "DaysBefore",
|
||||
Constraint::InMonth(_) => "InMonth",
|
||||
Constraint::InYear(_) => "InYear",
|
||||
Constraint::DayOfMonth(_) => "DayOfMonth",
|
||||
}
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// Puzzle
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
#[derive(Clone, Debug, Serialize, Deserialize)]
|
||||
pub struct Puzzle {
|
||||
pub id: String,
|
||||
pub constraints: Vec<Constraint>,
|
||||
pub references: BTreeMap<String, Date>,
|
||||
pub solutions: Vec<Date>,
|
||||
pub difficulty: u8,
|
||||
}
|
||||
|
||||
impl Puzzle {
|
||||
pub fn check_date(&self, date: Date) -> bool {
|
||||
self.constraints.iter().all(|c| check_one(date, c, &self.references))
|
||||
}
|
||||
}
|
||||
|
||||
fn check_one(date: Date, c: &Constraint, refs: &BTreeMap<String, Date>) -> bool {
|
||||
match c {
|
||||
Constraint::Exact(d) => date == *d,
|
||||
Constraint::After(d) => date > *d,
|
||||
Constraint::Before(d) => date < *d,
|
||||
Constraint::Between(a, b) => date >= *a && date <= *b,
|
||||
Constraint::DayOfWeek(w) => date.weekday() == *w,
|
||||
Constraint::DaysAfter(name, n) => {
|
||||
refs.get(name).map(|r| date == r.add_days(*n)).unwrap_or(false)
|
||||
}
|
||||
Constraint::DaysBefore(name, n) => {
|
||||
refs.get(name).map(|r| date == r.add_days(-*n)).unwrap_or(false)
|
||||
}
|
||||
Constraint::InMonth(m) => date.month == *m,
|
||||
Constraint::InYear(y) => date.year == *y,
|
||||
Constraint::DayOfMonth(d) => date.day == *d,
|
||||
}
|
||||
}
|
||||
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
// Deterministic RNG (xorshift64)
|
||||
// ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
pub struct Rng64(pub u64);
|
||||
|
||||
impl Rng64 {
|
||||
pub fn new(seed: u64) -> Self {
|
||||
Self(seed.max(1))
|
||||
}
|
||||
pub fn next_u64(&mut self) -> u64 {
|
||||
let mut x = self.0;
|
||||
x ^= x << 13;
|
||||
x ^= x >> 7;
|
||||
x ^= x << 17;
|
||||
self.0 = x;
|
||||
x
|
||||
}
|
||||
pub fn next_f64(&mut self) -> f64 {
|
||||
self.next_u64() as f64 / u64::MAX as f64
|
||||
}
|
||||
pub fn range(&mut self, lo: i32, hi: i32) -> i32 {
|
||||
if hi <= lo {
|
||||
return lo;
|
||||
}
|
||||
lo + (self.next_u64() % (hi - lo + 1) as u64) as i32
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user