optimize mp creation, add parameter for phase 3

src/common.rs

@@ -24,6 +24,10 @@ pub(crate) struct Args {
     #[arg(short, long, default_value_t = 100)]
     pub(crate) min_difference: usize,
 
+    /// Number of patterns combined into a multipattern.
+    #[arg(long, default_value_t = 50)]
+    pub(crate) top_n: usize,
+
     /// Number of patterns combined into a multipattern.
     #[arg(short, long, default_value_t = 2)]
     pub(crate) patterns_combined: usize,

src/distinguishers.rs

@@ -260,18 +260,22 @@ impl Distinguisher for MultiPattern {
 }
 
 pub(crate) fn best_multi_pattern(data: &Data, patterns: &[Pattern], n: usize) -> MultiPattern {
-    let len_m1 = data.data.len() - 1;
     let mut best_mp: Option<MultiPattern> = None;
     let mut max_z = 0.0;
-    for ps in patterns.iter().combinations(n) {
-        let mut mp = MultiPattern::new(ps.iter().map(|x| x.to_owned().clone()).collect_vec());
-        mp.count = Some(
-            data.data
-                .par_iter()
-                .enumerate()
-                .map(|(i, blocks)| mp.evaluate(blocks, data.mask, i == len_m1))
-                .sum::<u32>() as usize,
-        );
+    let patterns_sets = patterns.iter().combinations(n).collect_vec();
+
+    let mut mps = patterns_sets
+        .par_iter()
+        .map(|ps| MultiPattern::new(ps.iter().map(|x| x.to_owned().clone()).collect_vec()))
+        .collect::<Vec<_>>();
+
+    let mut it = data.data.iter().peekable();
+    while let Some(blocks) = it.next() {
+        let is_last = it.peek().is_none();
+        mps.par_iter_mut()
+            .for_each(|mp| mp.increase_count(mp.evaluate(blocks, data.mask, is_last) as usize));
+    }
+    for mut mp in mps {
         let z = mp.z_score(data.num_of_blocks);
         if f64::abs(z) > f64::abs(max_z) {
             best_mp = Some(mp);

src/main.rs

@@ -112,10 +112,11 @@ fn parse_args(s: Vec<&str>) -> Args {
         block_size: s[1].trim().parse().unwrap(),
         k: s[2].trim().parse().unwrap(),
         min_difference: s[3].trim().parse().unwrap(),
-        patterns_combined: s[4].trim().parse().unwrap(),
-        base_pattern_size: s[5].trim().parse().unwrap(),
-        validation_and_testing_split: s[6].trim().parse().unwrap(),
-        hist: s[7].trim().parse().unwrap(),
+        top_n: s[4].trim().parse().unwrap(),
+        patterns_combined: s[5].trim().parse().unwrap(),
+        base_pattern_size: s[6].trim().parse().unwrap(),
+        validation_and_testing_split: s[7].trim().parse().unwrap(),
+        hist: s[8].trim().parse().unwrap(),
         config: false,
     }
 }