Fix/consider short-term params when clipping PLS

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]))