generlize to k rotations

reasoning_gym/algorithmic/rotate_matrix.py

@@ -1,4 +1,4 @@
-"""Rotate a square matrix by 90 degrees clockwise.
+"""Rotate a square matrix clockwise.
 
 A popular Leetcode problem:
 https://leetcode.com/problems/rotate-image/description/
@@ -11,11 +11,11 @@ from typing import Optional
 
 from ..factory import ProceduralDataset, register_dataset
 
-QUESTION_TEMPLATE = """Given a square matrix, your job is to rotate it by 90 degrees clockwise.
+QUESTION_TEMPLATE = """Given a square matrix, your job is to rotate it clockwise.
 
 Example:
 
-Input:
+Input: Rotate the matrix below by 90 degrees clockwise:
 1 2 3
 4 5 6
 7 8 9
@@ -25,7 +25,7 @@ Output:
 8 5 2
 9 6 3
 
-Rotate the matrix below by 90 degrees clockwise:
+Rotate the matrix below by {degrees} degrees clockwise:
 {matrix}
 """
 
@@ -35,6 +35,7 @@ class RotateMatrixConfig:
     """Configuration for Rotate Matrix dataset generation"""
 
     max_n: int = 10  # Maximum size of the matrix
+    max_rotations: int = 4  # Maximum number of rotations (90 degrees each)
 
     size: int = 500  # Virtual dataset size
     seed: Optional[int] = None
@@ -42,6 +43,7 @@ class RotateMatrixConfig:
     def validate(self):
         """Validate configuration parameters"""
         assert 1 <= self.max_n, "max_n must be at least 1"
+        assert 0 <= self.max_rotations, "max_rotations must be at least 0"
 
 
 class RotateMatrixDataset(ProceduralDataset):
@@ -58,19 +60,21 @@ class RotateMatrixDataset(ProceduralDataset):
         matrix = [numbers[i * n : (i + 1) * n] for i in range(n)]
         return matrix
 
-    def _get_rotated(self, matrix: list[list[int]]) -> list[list[int]]:
+    def _get_rotated(self, matrix: list[list[int]], num_rotations: int) -> list[list[int]]:
         """Rotate the matrix by 90 degrees clockwise"""
+        num_rotations %= 4
         n = len(matrix)
         output = deepcopy(matrix)
 
-        for l in range(n // 2):
-            for i in range(l, n - 1 - l):
-                (output[l][i], output[i][n - 1 - l], output[n - 1 - l][n - 1 - i], output[n - 1 - i][l]) = (
-                    matrix[n - 1 - i][l],
-                    matrix[l][i],
-                    matrix[i][n - 1 - l],
-                    matrix[n - 1 - l][n - 1 - i],
-                )
+        for _ in range(num_rotations):
+            for l in range(n // 2):
+                for i in range(l, n - 1 - l):
+                    (output[l][i], output[i][n - 1 - l], output[n - 1 - l][n - 1 - i], output[n - 1 - i][l]) = (
+                        output[n - 1 - i][l],
+                        output[l][i],
+                        output[i][n - 1 - l],
+                        output[n - 1 - l][n - 1 - i],
+                    )
 
         return output
 
@@ -83,14 +87,16 @@ class RotateMatrixDataset(ProceduralDataset):
         rng = Random(self.seed + idx)
 
         matrix = self._get_matrix(rng)
+        num_rotations = rng.randint(0, self.config.max_rotations)
         matrix_str = self._matrix_to_str(matrix)
-        answer = self._get_rotated(matrix)
+
+        answer = self._get_rotated(matrix, num_rotations)
         answer_str = self._matrix_to_str(answer)
 
         return {
-            "question": QUESTION_TEMPLATE.format(matrix=matrix_str),
+            "question": QUESTION_TEMPLATE.format(matrix=matrix_str, degrees=num_rotations * 90),
             "answer": answer_str,
-            "metadata": {"matrix": matrix, "solution": answer},
+            "metadata": {"matrix": matrix, "num_rotations": num_rotations, "solution": answer},
         }