added docs for standardize and one_hot

Author: eodole

bayesflow/adapters/transforms/one_hot.py

@@ -9,6 +9,10 @@
 
 @serializable(package="bayesflow.adapters")
 class OneHot(ElementwiseTransform):
+    """
+    Changes data to be one-hot encoded. 
+    """
+
     def __init__(self, num_classes: int):
         super().__init__()
         self.num_classes = num_classes

bayesflow/adapters/transforms/standardize.py

@@ -10,6 +10,21 @@
 
 @serializable(package="bayesflow.adapters")
 class Standardize(ElementwiseTransform):
+    """
+    Transform that when applied standardizes data using typical z-score standardization i.e. for some unstandardized data 
+    x the standardized version z  would be
+
+    z = (x - mean(x))/std(x)
+
+    Parameters: 
+    mean: integer or float used to specify a mean if known but will be estimated from data when not provided
+    std: integer or float used to specify a standard devation if known but will be estimated from data when not provided
+    axis: integer representing a specific axis along which standardization should take place. By default
+        standardization happens individually for each dimension
+    momentum: float in (0,1) specifying the momentum during training
+
+    """
+
     def __init__(
         self,
         mean: int | float | np.ndarray = None,