Add Detailed Documentation for ZeroSumNormal Distribution

pymc/distributions/transforms.py

@@ -107,7 +107,8 @@ def backward(self, value, *inputs):
     def forward(self, value, *inputs):
         y = pt.zeros(value.shape)
         y = pt.set_subtensor(y[..., 0], value[..., 0])
-        y = pt.set_subtensor(y[..., 1:], pt.log(value[..., 1:] - value[..., :-1]))
+        log_value = pt.log(value[..., 1:] - value[..., :-1])
+        y = pt.set_subtensor(y[..., 1:], log_value)
         return y
 
     def log_jac_det(self, value, *inputs):
@@ -116,8 +117,9 @@ def log_jac_det(self, value, *inputs):
 
 class SumTo1(Transform):
     """
-    Transforms K - 1 dimensional simplex space (k values in [0,1] and that sum to 1) to a K - 1 vector of values in [0,1]
-    This Transformation operates on the last dimension of the input tensor.
+    Transforms K - 1 dimensional simplex space (k values in [0,1] and that
+    sum to 1) to a K - 1 vector of values in [0,1]. This Transformation
+    operates on the last dimension of the input tensor.
     """
 
     name = "sumto1"
@@ -140,7 +142,8 @@ def log_jac_det(self, value, *inputs):
 
 class CholeskyCovPacked(Transform):
     """
-    Transforms the diagonal elements of the LKJCholeskyCov distribution to be on the
+    Transforms the diagonal elements of
+    the LKJCholeskyCov distribution to be on the
     log scale
     """
 
@@ -157,10 +160,14 @@ def __init__(self, n):
         self.diag_idxs = pt.arange(1, n + 1).cumsum() - 1
 
     def backward(self, value, *inputs):
-        return pt.set_subtensor(value[..., self.diag_idxs], pt.exp(value[..., self.diag_idxs]))
+        diag_values = value[..., self.diag_idxs]
+        exp_values = pt.exp(diag_values)
+        return pt.set_subtensor(value[..., self.diag_idxs], exp_values)
 
     def forward(self, value, *inputs):
-        return pt.set_subtensor(value[..., self.diag_idxs], pt.log(value[..., self.diag_idxs]))
+        diag_values = value[..., self.diag_idxs]
+        log_values = pt.log(diag_values)
+        return pt.set_subtensor(value[..., self.diag_idxs], log_values)
 
     def log_jac_det(self, value, *inputs):
         return pt.sum(value[..., self.diag_idxs], axis=-1)
@@ -180,8 +187,9 @@ def log_jac_det(self, value, *inputs):
 
 
 class Interval(IntervalTransform):
-    """Wrapper around  :class:`pymc.logprob.transforms.IntervalTransform` for use in the
-    ``transform`` argument of a random variable.
+    """
+    Wrapper around :class:`pymc.logprob.transforms.IntervalTransform` for use
+    in the ``transform`` argument of a random variable.
 
     Parameters
     ----------
@@ -192,15 +200,15 @@ class Interval(IntervalTransform):
         Upper bound of the interval transform. Must be a constant finite value.
         By default (``upper=None``), the interval is not bounded above.
     bounds_fn : callable, optional
-        Alternative to lower and upper. Must return a tuple of lower and upper bounds
-        as a symbolic function of the respective distribution inputs. If one of lower or
-        upper is ``None``, the interval is unbounded on that edge.
-
-        .. warning:: Expressions returned by `bounds_fn` should depend only on the
-            distribution inputs or other constants. Expressions that depend on nonlocal
-            variables, such as other distributions defined in the model context will
-            likely break sampling.
+        Alternative to lower and upper. Must return a tuple of lower and upper
+        bounds as a symbolic function of the respective distribution inputs. If
+        one of lower or upper is ``None``,the interval is unbounded on
+        that edge.
 
+        .. warning:: Expressions returned by `bounds_fn` should depend only on
+            the distribution inputs or other constants. Expressions that depend
+            on nonlocal variables, such as other distributions defined in the
+            model context will likely break sampling.
 
     Examples
     --------
@@ -220,10 +228,14 @@ def get_bounds(rng, size, mu, sigma):
             return 0, None
 
         with pm.Model():
-            interval = pm.distributions.transforms.Interval(bounds_fn=get_bounds)
+            interval = pm.distributions.transforms.Interval(
+                bounds_fn=get_bounds
+            )
+
             x = pm.Normal("x", transform=interval)
 
-    Create a lower-bounded interval transform that depends on a distribution parameter
+    Create a lower-bounded interval transform that depends on a
+    distribution parameter
 
     .. code-block:: python
 
@@ -267,10 +279,47 @@ class ZeroSumTransform(Transform):
     """
     Constrains any random samples to sum to zero along the user-provided ``zerosum_axes``.
 
+    This transform is useful when modeling distributions where the sum of certain dimensions
+    must be zero, such as in some types of constrained latent variable models or in certain
+    types of signal processing applications.
+
     Parameters
     ----------
-    zerosum_axes : list of ints
-        Must be a list of integers (positive or negative).
+    zerosum_axes : list of int
+        List of integers specifying the axes along which the random samples should sum to zero.
+        Positive integers indicate dimensions in the standard order, while negative integers
+        can be used to reference dimensions from the end of the shape.
+
+    Examples
+    --------
+    Suppose you want to ensure that the last dimension of a tensor sums to zero. You can use
+    `ZeroSumTransform` as follows:
+
+    .. code-block:: python
+
+        import pymc as pm
+
+        with pm.Model() as model:
+            # Create a 2D variable with the last axis constrained to sum to zero
+            x = pm.Normal("x", shape=(10, 5), transform=pm.distributions.transforms.ZeroSumTransform(zerosum_axes=[-1]))
+
+    Methods
+    -------
+    forward(value, *rv_inputs)
+        Transforms the input tensor to ensure that the specified axes sum to zero.
+
+    backward(value, *rv_inputs)
+        Computes the inverse transform to convert back to the original space where the sum was zero.
+
+    log_jac_det(value, *rv_inputs)
+        Returns the log Jacobian determinant of the transform. For this transform, it is zero.
+
+    Notes
+    -----
+    The `extend_axis` and `extend_axis_rev` methods are used internally to handle the transformation:
+    - `extend_axis`: Extends the axis by adding an additional element to ensure zero-sum constraint.
+    - `extend_axis_rev`: Reverses the extension operation applied by `extend_axis`.
+
     """
 
     name = "zerosum"