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use rand::distributions::WeightedIndex;
use rand::prelude::*;
use rayon::prelude::*;
use solhop_types::{Clause, LBool, Lit, Solution};
use std::fs::File;
use std::io;
/// Magic numbers used by local search.
const C_MAKE: f32 = 0.5;
const C_BREAK: f32 = 3.7;
/// Scoring function type.
pub enum ScoreFnType {
/// Choose flip variable randomly.
Rand,
/// Use polynomial scoring function.
Poly,
/// Use exponential scoring function.
Exp,
/// Cutom scoring function.
Custom(Box<dyn Fn(i32, i32) -> f32>),
}
/// SLS Solver.
pub struct Solver {
num_vars: usize,
clauses: Vec<Clause>,
}
impl Solver {
/// Read formula in DIMACS format from STDIN.
pub fn new_from_stdin() -> Self {
Solver::new_from_buf_reader(&mut std::io::stdin().lock())
}
/// Read formula in DIMACS format from a file.
pub fn new_from_file(filename: &str) -> Self {
let file = File::open(filename).expect("File not found");
let mut reader = io::BufReader::new(file);
Solver::new_from_buf_reader(&mut reader)
}
/// Read formula in DIMACS format from buffer reader.
pub fn new_from_buf_reader<F>(reader: &mut F) -> Self
where
F: std::io::BufRead,
{
let parsed = solhop_types::dimacs::parse_dimacs_from_buf_reader(reader);
if let solhop_types::dimacs::Dimacs::Cnf { n_vars, clauses } = parsed {
Solver {
num_vars: n_vars,
clauses: clauses.into_iter().map(|lits| Clause { lits }).collect(),
}
} else {
panic!("Incorrect input format");
}
}
/// Returns the number of variables in the formula.
pub fn n_vars(&self) -> usize {
self.num_vars
}
/// Returns the number of clauses in the formula.
pub fn n_clauses(&self) -> usize {
self.clauses.len()
}
/// Add a clause to the formula.
pub fn add_clause(&mut self, lits: Vec<Lit>) {
self.clauses.push(Clause { lits });
}
/// Local Search based on probSAT. Tries for `max_tries` times
/// with `max_flips` flips in each try.
pub fn local_search(
&mut self,
max_tries: u32,
max_flips: u32,
score_fn_type: ScoreFnType,
parallel: bool,
) -> Solution {
let mut curr_model = vec![false; self.num_vars as usize];
let mut best_model = vec![false; self.num_vars as usize];
let mut best_n_unsat_clauses = self.clauses.len();
let mut clause_unsat = vec![1; self.clauses.len()];
let mut rng = thread_rng();
for _ in 0..max_tries {
Solver::gen_rand_model(
&mut curr_model,
&mut rng,
&vec![LBool::Undef; self.num_vars],
);
for _ in 0..max_flips {
let n_unsat_clauses = if parallel {
self.clauses
.par_iter()
.zip(clause_unsat.par_iter_mut())
.map(|(cl, cl_us)| {
let mut clause_unsat = 1;
for lit in &cl.lits {
let var = lit.var();
if lit.sign() != curr_model[var.index()] {
clause_unsat = 0;
break;
}
}
*cl_us = clause_unsat;
clause_unsat
})
.sum()
} else {
self.clauses
.iter()
.zip(clause_unsat.iter_mut())
.map(|(cl, cl_us)| {
let mut clause_unsat = 1;
for lit in &cl.lits {
let var = lit.var();
if lit.sign() != curr_model[var.index()] {
clause_unsat = 0;
break;
}
}
*cl_us = clause_unsat;
clause_unsat
})
.sum()
};
if n_unsat_clauses == 0 {
return Solution::Sat(curr_model.iter().copied().collect());
} else if n_unsat_clauses < best_n_unsat_clauses {
best_model.clone_from_slice(&curr_model);
best_n_unsat_clauses = n_unsat_clauses;
}
let dist = WeightedIndex::new(&clause_unsat).unwrap();
let selected_clause = dist.sample(&mut rng);
let Clause { lits: cl } = &self.clauses[selected_clause];
let mut scores = vec![0.0; self.num_vars as usize];
for x in cl {
let var_i = x.var();
curr_model[var_i.index()] = !curr_model[var_i.index()];
let (break_count, make_count) = if parallel {
self.clauses
.par_iter()
.zip(clause_unsat.par_iter())
.map(|(Clause { lits: cl }, cl_us)| {
let mut cl_unsat = 1;
for &lit in cl {
let var = lit.var();
if lit.sign() != curr_model[var.index()] {
cl_unsat = 0;
break;
}
}
if cl_unsat != *cl_us {
if cl_unsat == 1 {
// break_count += 1;
(1, 0)
} else {
// make_count += 1;
(0, 1)
}
} else {
(0, 0)
}
})
.reduce(|| (0, 0), |a, b| (a.0 + b.0, a.1 + b.1))
} else {
self.clauses
.iter()
.zip(clause_unsat.iter())
.map(|(Clause { lits: cl }, cl_us)| {
let mut cl_unsat = 1;
for &lit in cl {
let var = lit.var();
if lit.sign() != curr_model[var.index()] {
cl_unsat = 0;
break;
}
}
if cl_unsat != *cl_us {
if cl_unsat == 1 {
// break_count += 1;
(1, 0)
} else {
// make_count += 1;
(0, 1)
}
} else {
(0, 0)
}
})
.fold((0, 0), |a, b| (a.0 + b.0, a.1 + b.1))
};
curr_model[var_i.index()] = !curr_model[var_i.index()];
scores[var_i.index()] = match &score_fn_type {
ScoreFnType::Rand => 1.0,
ScoreFnType::Poly => 1.0 / (1.0 + break_count as f32).powf(C_BREAK),
ScoreFnType::Exp => C_MAKE.powi(make_count) / C_BREAK.powi(break_count),
ScoreFnType::Custom(f) => f(make_count, break_count),
};
}
let dist_var = WeightedIndex::new(&scores).unwrap();
let selected_var = dist_var.sample(&mut rng);
curr_model[selected_var] = !curr_model[selected_var];
}
}
Solution::Best(best_model.iter().copied().collect())
}
fn gen_rand_model<T>(model: &mut Vec<bool>, rng: &mut T, l_model: &[LBool])
where
T: rand::Rng,
{
for (i, v) in model.iter_mut().enumerate() {
match l_model[i] {
LBool::Undef => *v = 2 * rng.gen_range(0, 2) - 1 == 1,
LBool::True => *v = true,
LBool::False => *v = false,
}
}
}
}