Fix/consider short-term params when clipping PLS (#150)

* Fix/consider short-term params when clipping PLS

* update simulator

* update unit tests

* bump version

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "FSRS-Optimizer"
-version = "5.4.1"
+version = "5.4.2"
 readme = "README.md"
 dependencies = [
     "matplotlib>=3.7.0",

src/fsrs_optimizer/fsrs_optimizer.py

@@ -90,13 +90,15 @@ def stability_after_success(
         return new_s
 
     def stability_after_failure(self, state: Tensor, r: Tensor) -> Tensor:
+        old_s = state[:, 0]
         new_s = (
             self.w[11]
             * torch.pow(state[:, 1], -self.w[12])
-            * (torch.pow(state[:, 0] + 1, self.w[13]) - 1)
+            * (torch.pow(old_s + 1, self.w[13]) - 1)
             * torch.exp((1 - r) * self.w[14])
         )
-        return torch.minimum(new_s, state[:, 0])
+        new_minimum_s = old_s / torch.exp(self.w[17] * self.w[18])
+        return torch.minimum(new_s, new_minimum_s)
 
     def stability_short_term(self, state: Tensor, rating: Tensor) -> Tensor:
         new_s = state[:, 0] * torch.exp(self.w[17] * (rating - 3 + self.w[18]))

src/fsrs_optimizer/fsrs_simulator.py

@@ -106,7 +106,7 @@ def stability_after_failure(s, r, d):
                 * np.power(d, -w[12])
                 * (np.power(s + 1, w[13]) - 1)
                 * np.exp((1 - r) * w[14]),
-                s,
+                s / np.exp(w[17] * w[18]),
             ),
         )
 

tests/model_test.py

@@ -65,7 +65,7 @@ def test_forward(self):
         difficulty = state[:, 1]
         assert torch.allclose(
             stability,
-            torch.tensor([0.2619, 1.7073, 5.8691, 25.0123, 0.3403, 2.1482]),
+            torch.tensor([0.2619, 1.7074, 5.8691, 25.0124, 0.2859, 2.1482]),
             atol=1e-4,
         )
         assert torch.allclose(

tests/simulator_test.py

@@ -11,7 +11,7 @@ def test_simulate(self):
             cost_per_day,
             revlogs,
         ) = simulate(w=DEFAULT_PARAMETER, request_retention=0.9)
-        assert memorized_cnt_per_day[-1] == 5875.025236206539
+        assert memorized_cnt_per_day[-1] == 5880.482440745369
 
     def test_optimal_retention(self):
         default_params = {