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- use rand::distributions::WeightedIndex;
- use rand::prelude::Distribution;
- use rand::Rng;
- use std::cell::{RefCell, RefMut};
- use thread_local::ThreadLocal;
- use crate::params::*;
- use crate::poly::*;
- use std::f64::consts::PI;
- pub const NUM_WIDTHS: usize = 8;
- pub struct DiscreteGaussian<T: Rng + Send> {
- choices: Vec<i64>,
- dist: WeightedIndex<f64>,
- rng: ThreadLocal<RefCell<T>>,
- rnggen: fn() -> T,
- }
- impl<T: Rng + Send> DiscreteGaussian<T> {
- pub fn init(params: &Params, rnggen: fn() -> T) -> Self {
- let max_val = (params.noise_width * (NUM_WIDTHS as f64)).ceil() as i64;
- let mut choices = Vec::new();
- let mut table = vec![0f64; 0];
- for i in -max_val..max_val + 1 {
- let p_val = f64::exp(-PI * f64::powi(i as f64, 2) / f64::powi(params.noise_width, 2));
- choices.push(i);
- table.push(p_val);
- }
- let dist = WeightedIndex::new(&table).unwrap();
- Self { choices, dist, rng: ThreadLocal::new(), rnggen }
- }
- // FIXME: not constant-time
- fn sample_from_members(choices: &Vec<i64>, dist: &WeightedIndex<f64>,
- rng: &mut T) -> i64 {
- choices[dist.sample(rng)]
- }
- pub fn get_rng(&self) -> RefMut<T> {
- self.rng.get_or(|| RefCell::new((self.rnggen)())).borrow_mut()
- }
- #[cfg(test)]
- fn sample(&self) -> i64 {
- let mut rng = self.get_rng();
- Self::sample_from_members(&self.choices, &self.dist, &mut *rng)
- }
- pub fn sample_matrix(&self, p: &mut PolyMatrixRaw) {
- let modulus = p.get_params().modulus;
- let choices = &self.choices;
- let dist = &self.dist;
- let rng = &mut *self.get_rng();
- for r in 0..p.rows {
- for c in 0..p.cols {
- let poly = p.get_poly_mut(r, c);
- for z in 0..poly.len() {
- let mut s = Self::sample_from_members(choices, dist, rng);
- s += modulus as i64;
- s %= modulus as i64; // FIXME: not constant time
- poly[z] = s as u64;
- }
- }
- }
- }
- }
- #[cfg(test)]
- mod test {
- use rand::SeedableRng;
- use rand_chacha::ChaCha20Rng;
- use super::*;
- use crate::util::*;
- #[test]
- fn dg_seems_okay() {
- let params = get_test_params();
- let dg = DiscreteGaussian::init(¶ms, ChaCha20Rng::from_entropy);
- let mut v = Vec::new();
- let trials = 10000;
- let mut sum = 0;
- for _ in 0..trials {
- let val = dg.sample();
- v.push(val);
- sum += val;
- }
- let mean = sum as f64 / trials as f64;
- let std_dev = params.noise_width / f64::sqrt(2f64 * std::f64::consts::PI);
- let std_dev_of_mean = std_dev / f64::sqrt(trials as f64);
- assert!(f64::abs(mean) < std_dev_of_mean * 5f64);
- }
- }
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