[REF] Move example, polish rst

curvlinops/trace/hutchinson.py

@@ -17,12 +17,26 @@ class HutchinsonTraceEstimator:
       matrix for laplacian smoothing splines. Communication in Statistics---Simulation
       and Computation.
 
+    Example:
+        >>> from numpy import trace, mean
+        >>> from numpy.random import rand, seed
+        >>> seed(0) # make deterministic
+        >>> A = rand(10, 10)
+        >>> tr_A = trace(A) # exact trace as reference
+        >>> estimator = HutchinsonTraceEstimator(A)
+        >>> # one- and multi-sample approximations
+        >>> tr_A_low_precision = estimator.sample()
+        >>> tr_A_high_precision = mean([estimator.sample() for _ in range(1_000)])
+        >>> assert abs(tr_A - tr_A_low_precision) > abs(tr_A - tr_A_high_precision)
+        >>> tr_A, tr_A_low_precision, tr_A_high_precision
+        (4.457529730942303, 6.679568384120655, 4.388630875995861)
+
     Attributes:
-        SUPPORTED_SAMPLINGS: Dictionary mapping supported distributions to their
+        SUPPORTED_DISTRIBUTIONS: Dictionary mapping supported distributions to their
             sampling functions.
     """
 
-    SUPPORTED_SAMPLINGS: Dict[str, Callable[[int], ndarray]] = {
+    SUPPORTED_DISTRIBUTIONS: Dict[str, Callable[[int], ndarray]] = {
         "rademacher": rademacher,
         "normal": normal,
     }
@@ -48,38 +62,24 @@ def sample(self, distribution: str = "rademacher") -> float:
 
         Args:
             distribution: Distribution of the vector along which the linear operator
-                will be evaluated. Either `'rademacher'` or `'normal'`.
-                Default is `'rademacher'`.
+                will be evaluated. Either ``'rademacher'`` or ``'normal'``.
+                Default is ``'rademacher'``.
 
         Returns:
             Sample from the trace estimator.
 
         Raises:
             ValueError: If the distribution is not supported.
-
-        Example:
-            >>> from numpy import trace, mean
-            >>> from numpy.random import rand, seed
-            >>> seed(0) # make deterministic
-            >>> A = rand(10, 10)
-            >>> tr_A = trace(A) # exact trace as reference
-            >>> estimator = HutchinsonTraceEstimator(A)
-            >>> # one- and multi-sample approximations
-            >>> tr_A_low_precision = estimator.sample()
-            >>> tr_A_high_precision = mean([estimator.sample() for _ in range(1_000)])
-            >>> assert abs(tr_A - tr_A_low_precision) > abs(tr_A - tr_A_high_precision)
-            >>> tr_A, tr_A_low_precision, tr_A_high_precision
-            (4.457529730942303, 6.679568384120655, 4.388630875995861)
         """
         dim = self._A.shape[1]
 
-        if distribution not in self.SUPPORTED_SAMPLINGS:
+        if distribution not in self.SUPPORTED_DISTRIBUTIONS:
             raise ValueError(
                 f"Unsupported distribution '{distribution}'. "
-                f"Supported distributions are {list(self.SUPPORTED_SAMPLINGS)}."
+                f"Supported distributions are {list(self.SUPPORTED_DISTRIBUTIONS)}."
             )
 
-        v = self.SUPPORTED_SAMPLINGS[distribution](dim)
+        v = self.SUPPORTED_DISTRIBUTIONS[distribution](dim)
         Av = self._A @ v
 
         return dot(v, Av)