improve preview in float delta_t mode

src/fsrs_optimizer/fsrs_optimizer.py

@@ -1214,10 +1214,10 @@ def train(
             tqdm.write("\nTraining finished!")
         return plots
 
-    def preview(self, requestRetention: float, verbose=False):
+    def preview(self, requestRetention: float, verbose=False, n_steps=3):
         my_collection = Collection(self.w, self.float_delta_t)
         preview_text = "1:again, 2:hard, 3:good, 4:easy\n"
-        n_learning_steps = 3
+        n_learning_steps = n_steps if not self.float_delta_t else 0
         for first_rating in (1, 2, 3, 4):
             preview_text += f"\nfirst rating: {first_rating}\n"
             t_history = "0"
@@ -1242,8 +1242,14 @@ def preview(self, requestRetention: float, verbose=False):
                         )
                     )
                 left -= 1
-                next_t = next_interval(states[0], requestRetention) if left <= 0 else 0
-                t_history += f",{int(next_t)}"
+                next_t = (
+                    next_interval(
+                        states[0].detach().numpy(), requestRetention, self.float_delta_t
+                    )
+                    if left <= 0
+                    else 0
+                )
+                t_history += f",{next_t}"
                 d_history += f",{difficulty}"
                 s_history += f",{stability}"
                 r_history += f",3"
@@ -1254,13 +1260,22 @@ def preview(self, requestRetention: float, verbose=False):
                 + ",".join(
                     [
                         (
-                            f"{ivl}d"
-                            if ivl < 30
+                            f"{ivl:.4f}d"
+                            if ivl < 1 and ivl > 0
                             else (
-                                f"{ivl / 30:.1f}m" if ivl < 365 else f"{ivl / 365:.1f}y"
+                                f"{ivl:.1f}d"
+                                if ivl < 30
+                                else (
+                                    f"{ivl / 30:.1f}m"
+                                    if ivl < 365
+                                    else f"{ivl / 365:.1f}y"
+                                )
                             )
                         )
-                        for ivl in map(int, t_history.split(","))
+                        for ivl in map(
+                            int if not self.float_delta_t else float,
+                            t_history.split(","),
+                        )
                     ]
                 )
                 + "\n"
@@ -1295,8 +1310,10 @@ def preview_sequence(self, test_rating_sequence: str, requestRetention: float):
         for i in range(len(test_rating_sequence.split(","))):
             r_history = test_rating_sequence[: 2 * i + 1]
             states = my_collection.predict(t_history, r_history)
-            next_t = next_interval(states[0], requestRetention)
-            t_history += f",{int(next_t)}"
+            next_t = next_interval(
+                states[0].detach().numpy(), requestRetention, self.float_delta_t
+            )
+            t_history += f",{next_t}"
             difficulty = round(float(states[1]), 1)
             d_history += f",{difficulty}"
         preview_text = f"rating history: {test_rating_sequence}\n"
@@ -1305,13 +1322,20 @@ def preview_sequence(self, test_rating_sequence: str, requestRetention: float):
             + ",".join(
                 [
                     (
-                        f"{ivl}d"
-                        if ivl < 30
-                        else f"{ivl / 30:.1f}m"
-                        if ivl < 365
-                        else f"{ivl / 365:.1f}y"
+                        f"{ivl:.4f}d"
+                        if ivl < 1 and ivl > 0
+                        else (
+                            f"{ivl:.1f}d"
+                            if ivl < 30
+                            else (
+                                f"{ivl / 30:.1f}m" if ivl < 365 else f"{ivl / 365:.1f}y"
+                            )
+                        )
+                    )
+                    for ivl in map(
+                        int if not self.float_delta_t else float,
+                        t_history.split(","),
                     )
-                    for ivl in map(int, t_history.split(","))
                 ]
             )
             + "\n"

src/fsrs_optimizer/fsrs_simulator.py

@@ -12,9 +12,11 @@ def power_forgetting_curve(t, s):
     return (1 + FACTOR * t / s) ** DECAY
 
 
-def next_interval(s, r):
+def next_interval(s, r, float_ivl: bool = False):
     ivl = s / FACTOR * (r ** (1 / DECAY) - 1)
-    return np.maximum(1, np.round(ivl))
+    return (
+        np.maximum(1, np.round(ivl).astype(int)) if not float_ivl else np.round(ivl, 6)
+    )
 
 
 columns = [