Some modifications to the regression experiment.
This commit is contained in:
@@ -2,20 +2,25 @@
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use anyhow::Result;
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use rand::{thread_rng, Rng};
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// Our GP + compiler modules (adjust paths as needed)
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use offchain::compiler::ast::{UntypedAst, Push3Ast};
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use offchain::helpers::artifact::get_creation_code;
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use offchain::runner::revm_runner::{
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EvmRunner,
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Push3InterpreterInputs,
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Push3InterpreterOutputs
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};
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use offchain::gp::generate_spec::{ranmdom_code_fixed};
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use offchain::gp::mutation::mutate_by_index;
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use offchain::compiler::push3_describtor::make_sublist_descriptor;
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// Our GP operators
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use offchain::gp::generate_spec::ranmdom_code_fixed;
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use offchain::gp::mutation::{mutate_by_index, crossover_by_index};
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// Ethers + ABI
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use ethers::types::U256;
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use ethers::abi::{encode, Token};
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// 1) Generate (x, y) samples for f(x) = 3x^2 + x + 3
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/// 1) Generate (x, y) samples for f(x)=3x^2 + x + 3
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fn generate_samples() -> Vec<(i32, i32)> {
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let mut samples = Vec::new();
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for x in -5..=5 {
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@@ -25,8 +30,9 @@ fn generate_samples() -> Vec<(i32, i32)> {
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samples
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}
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// 2) Evaluate a single AST on a single input `x`, returning the integer
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// that ends up on top of the final int stack (or 0 if empty).
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/// 2) Evaluate a single AST on a single input `x` by
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/// pushing `x` onto the int stack, running in EVM,
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/// and reading the final top of the int stack.
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fn evaluate_ast_on_x(
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runner: &mut EvmRunner,
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ast: &UntypedAst,
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@@ -64,7 +70,7 @@ fn evaluate_ast_on_x(
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}
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}
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// 3) Evaluate an AST on all (x, y) samples => compute MSE
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/// 3) Evaluate an AST on all (x, y) samples => compute MSE
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fn evaluate_fitness(
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runner: &mut EvmRunner,
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ast: &UntypedAst,
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@@ -80,30 +86,33 @@ fn evaluate_fitness(
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}
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fn main() -> Result<()> {
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// A) Generate samples for f(x)=3x^2 + x + 3
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// 1) Generate samples for f(x)=3x^2 + x + 3
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let samples = generate_samples();
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// B) Create an ephemeral EVM runner (Push3Interpreter)
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// 2) Create ephemeral EVM runner for the interpreter
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let creation_hex_filename = "../onchain/out/Push3Interpreter.sol/Push3Interpreter.json";
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let creation_bytes = get_creation_code(creation_hex_filename)?;
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let mut runner = EvmRunner::new(creation_bytes)?;
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// C) Setup GP parameters
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let pop_size = 1000; // population size
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let max_points = 15; // max "size" for random code
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let generations = 100; // how many generations we run
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let early_stop_threshold = 1.0; // if error < this => we can stop
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// 3) GP parameters
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let pop_size = 1000; // must be divisible by 4 for our scheme
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let generations = 30;
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let early_stop_threshold = 1.0;
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let max_points = 15;
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assert_eq!(pop_size % 4, 0, "pop_size must be multiple of 4 for our scheme");
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let quarter = pop_size / 4;
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let mut rng = thread_rng();
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// D) Initialize random population
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// 4) Initialize population (random)
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let mut population: Vec<UntypedAst> = (0..pop_size)
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.map(|_| ranmdom_code_fixed(&mut rng, max_points))
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.collect();
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// E) Main GP Loop
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// 5) Main GP loop
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for gen in 0..generations {
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// 1) Evaluate each => store (ast, error)
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// (a) Evaluate
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let mut scored: Vec<(UntypedAst, f64)> = population.into_iter()
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.map(|ast| {
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let err = evaluate_fitness(&mut runner, &ast, &samples);
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@@ -111,59 +120,77 @@ fn main() -> Result<()> {
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})
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.collect();
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// 2) Sort ascending by error
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// (b) Sort ascending by error
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scored.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap());
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// 3) Print best error
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// (c) Print generation summary
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let best_err = scored[0].1;
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println!("Generation {gen}, best error = {best_err}");
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println!("\n=== Generation {gen} ===");
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println!("Best error = {best_err}");
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// 4) Check if best is below threshold => early stop
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// Print top 5 subjects in pretty format
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let top_n = 5.min(scored.len());
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println!("Top {top_n} subjects:");
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for i in 0..top_n {
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println!(
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" #{i}, err={}, AST:\n{:#?}",
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scored[i].1,
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scored[i].0
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);
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}
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// early stop?
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if best_err < early_stop_threshold {
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println!("Best error < {early_stop_threshold}, stopping early!");
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// we can keep 'scored' for final if we want
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population = scored.into_iter().map(|(a, _)| a).collect();
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break;
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}
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// 5) Keep best half
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let half = scored.len() / 2;
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let survivors = &scored[..half];
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// (d) Reproduction => build new population
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let mut new_pop = Vec::with_capacity(pop_size);
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// 6) Refill population: half mutated, half fresh random
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let mut new_pop = Vec::with_capacity(scored.len());
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// a) keep survivors
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for &(ref ast, _) in survivors {
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new_pop.push(ast.clone());
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// keep best quarter
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for i in 0..quarter {
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new_pop.push(scored[i].0.clone());
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}
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// b) how many to add?
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let remainder_size = pop_size - new_pop.len();
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// we do half mutated, half new random
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let mutated_count = remainder_size / 2;
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let random_count = remainder_size - mutated_count;
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// c) add mutated
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for _ in 0..mutated_count {
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let idx = rng.gen_range(0..half);
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let mutated = mutate_by_index(&survivors[idx].0, &mut rng, max_points);
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new_pop.push(mutated);
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}
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// d) add brand-new random
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for _ in 0..random_count {
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// add random quarter
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for _ in 0..quarter {
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let fresh = ranmdom_code_fixed(&mut rng, max_points);
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new_pop.push(fresh);
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}
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// store for next generation
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// crossover quarter
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let top_half_count = scored.len() / 2;
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let crossover_pairs = quarter / 2; // each yields 2 kids => quarter total
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for _ in 0..crossover_pairs {
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let idx1 = rng.gen_range(0..top_half_count);
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let idx2 = rng.gen_range(0..top_half_count);
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let (child1, child2) = crossover_by_index(
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&scored[idx1].0,
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&scored[idx2].0,
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&mut rng
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);
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new_pop.push(child1);
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new_pop.push(child2);
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}
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// mutate quarter
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while new_pop.len() < pop_size {
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let idx = rng.gen_range(0..top_half_count);
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let mutated = mutate_by_index(&scored[idx].0, &mut rng, max_points);
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new_pop.push(mutated);
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}
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population = new_pop;
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}
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// F) Final Print
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// 6) Final population => we can evaluate & print
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println!("\n=== Final Population ===");
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for (i, ast) in population.iter().enumerate() {
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let err = evaluate_fitness(&mut runner, ast, &samples);
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println!("Subject #{i}, err={err}, ast={:?}", ast);
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println!("Subject #{i}, err={err}, AST:\n{:#?}", ast);
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}
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Ok(())
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@@ -47,7 +47,7 @@ impl InstructionSet {
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InstructionAtom::Opcode(op) => UntypedAst::Instruction(op.clone()),
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InstructionAtom::EphemeralInt => {
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// For ephemeral int, produce a random literal in some range
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let val = rng.gen_range(-10..10);
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let val = rng.gen_range(-100..100);
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UntypedAst::IntLiteral(val)
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}
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}
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@@ -84,3 +84,64 @@ pub fn mutate_by_index(
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replace_subtree(original, chosen_path, new_subtree)
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}
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/// Return the subtree of `original` at `path`,
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/// cloning it as a `UntypedAst`.
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/// If `path` is empty => returns the entire `original`.
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pub fn get_subtree(original: &UntypedAst, path: &[usize]) -> UntypedAst {
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if path.is_empty() {
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// The entire AST is the subtree
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return original.clone();
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}
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match original {
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UntypedAst::Sublist(children) => {
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let (first_idx, tail_path) = (path[0], &path[1..]);
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if first_idx >= children.len() {
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// If path index is out of range, just clone the entire AST or
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// do something fallback
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return original.clone();
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}
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get_subtree(&children[first_idx], tail_path)
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}
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UntypedAst::IntLiteral(_) | UntypedAst::Instruction(_) => {
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// If path is non-empty but this is a leaf,
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// fallback: just return the leaf.
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original.clone()
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}
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}
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}
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/// Perform a subtree crossover between two ASTs.
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/// 1) We pick a random node in `a` and a random node in `b`,
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/// 2) We swap those subtrees,
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/// 3) Return the two new ASTs.
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///
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/// Example usage:
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/// ```rust
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/// let (childA, childB) = crossover_by_index(&parentA, &parentB, &mut rng);
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/// ```
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pub fn crossover_by_index(
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a: &UntypedAst,
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b: &UntypedAst,
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rng: &mut impl Rng,
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) -> (UntypedAst, UntypedAst) {
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// 1) enumerate nodes in a
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let paths_a = enum_nodes_dfs(a);
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let idx_a = rng.gen_range(0..paths_a.len());
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let chosen_a = &paths_a[idx_a];
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// 2) enumerate nodes in b
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let paths_b = enum_nodes_dfs(b);
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let idx_b = rng.gen_range(0..paths_b.len());
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let chosen_b = &paths_b[idx_b];
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// 3) get subtree from a, from b
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let subtree_a = get_subtree(a, chosen_a);
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let subtree_b = get_subtree(b, chosen_b);
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// 4) replace them
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let new_a = replace_subtree(a, chosen_a, subtree_b);
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let new_b = replace_subtree(b, chosen_b, subtree_a);
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(new_a, new_b)
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}
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