Merge branch 'main' into Feat/flat-power-forgetting-curve

src/dataset.rs

@@ -169,7 +169,12 @@ pub fn filter_outlier(
 
         for (delta_t, sub_group) in sub_groups.iter().rev() {
             if has_been_removed + sub_group.len() > total / 20 {
-                filtered_items.extend_from_slice(sub_group);
+                // keep the group if it includes at least one item rated again (retention < 100%)
+                if sub_group.iter().any(|item| item.current().rating == 1) {
+                    filtered_items.extend_from_slice(sub_group);
+                } else {
+                    removed_pairs[rating as usize].insert(*delta_t);
+                }
             } else {
                 has_been_removed += sub_group.len();
                 removed_pairs[rating as usize].insert(*delta_t);

src/inference.rs

@@ -19,8 +19,8 @@ pub const FACTOR: f64 = 19f64 / 81f64;
 pub type Weights = [f32];
 
 pub static DEFAULT_WEIGHTS: [f32; 17] = [
-    0.5888, 1.4616, 3.8226, 14.1364, 4.9214, 1.0325, 0.8731, 0.0613, 1.57, 0.1395, 0.988, 2.212,
-    0.0658, 0.3439, 1.3098, 0.2837, 2.7766,
+    0.27, 0.74, 1.3, 5.52, 5.1, 1.02, 0.78, 0.06, 1.57, 0.14, 0.94, 2.16, 0.06, 0.31, 1.34, 0.21,
+    2.69,
 ];
 
 fn infer<B: Backend>(
@@ -503,7 +503,7 @@ mod tests {
             fsrs.memory_state_from_sm2(2.5, 10.0, 0.9).unwrap(),
             MemoryState {
                 stability: 9.999995,
-                difficulty: 6.8565593
+                difficulty: 6.6293178
             }
         );
         assert_eq!(

src/model.rs

@@ -278,7 +278,7 @@ mod tests {
         let stability = model.init_stability(rating);
         assert_eq!(
             stability.to_data(),
-            Data::from([0.5888, 1.4616, 3.8226, 14.1364, 0.5888, 1.4616])
+            Data::from([0.27, 0.74, 1.3, 5.52, 0.27, 0.74])
         )
     }
 
@@ -289,7 +289,7 @@ mod tests {
         let difficulty = model.init_difficulty(rating);
         assert_eq!(
             difficulty.to_data(),
-            Data::from([6.9864, 5.9539003, 4.9214, 3.8889, 6.9864, 5.9539003])
+            Data::from([7.14, 6.12, 5.1, 4.08, 7.14, 6.12])
         )
     }
 
@@ -317,13 +317,13 @@ mod tests {
         next_difficulty.clone().backward();
         assert_eq!(
             next_difficulty.to_data(),
-            Data::from([6.7462, 5.8731, 5.0, 4.1269])
+            Data::from([6.56, 5.7799997, 5.0, 4.2200003])
         );
         let next_difficulty = model.mean_reversion(next_difficulty);
         next_difficulty.clone().backward();
         assert_eq!(
             next_difficulty.to_data(),
-            Data::from([6.63434, 5.8147607, 4.995182, 4.175603])
+            Data::from([6.4723997, 5.7391996, 5.006, 4.2728004])
         )
     }
 
@@ -343,19 +343,19 @@ mod tests {
         s_recall.clone().backward();
         assert_eq!(
             s_recall.to_data(),
-            Data::from([24.938553, 15.710489, 57.993835, 185.87283])
+            Data::from([23.908455, 12.499619, 54.99991, 169.89117])
         );
         let s_forget = model.stability_after_failure(stability, difficulty, retention);
         s_forget.clone().backward();
         assert_eq!(
             s_forget.to_data(),
-            Data::from([2.1479936, 2.339425, 2.596607, 2.904485])
+            Data::from([1.8343093, 2.0118992, 2.245103, 2.5231054])
         );
         let next_stability = s_recall.mask_where(rating.clone().equal_elem(1), s_forget);
         next_stability.clone().backward();
         assert_eq!(
             next_stability.to_data(),
-            Data::from([2.1479936, 15.710489, 57.993835, 185.87283])
+            Data::from([1.8343093, 12.499619, 54.99991, 169.89117])
         )
     }
 

src/pre_training.rs

@@ -357,8 +357,7 @@ mod tests {
             ],
         )]);
         let actual = search_parameters(pretrainset, 0.9);
-        let expected = [(4, 1.4525769)].into_iter().collect();
-        assert_eq!(actual, expected);
+        Data::from([actual.get(&4).unwrap().clone()]).assert_approx_eq(&Data::from([1.2390649]), 4);
     }
 
     #[test]