Made changes to smc.py and sample_smc.py for pr#6114

Author: rowan-schaefer

docs/source/api/smc.rst

@@ -8,7 +8,8 @@ Sequential Monte Carlo
 
     sample_smc
 
-(smc_kernels)=
+.. _smc_kernels:
+
 SMC kernels
 -----------
 

pymc/smc/smc.py

@@ -43,7 +43,7 @@
 class SMC_KERNEL(ABC):
     """Base class for the Sequential Monte Carlo kernels.
 
-    To create a new kernel you should subclass from this.
+    To create a new SMC kernel you should subclass from this.
 
     Before sampling, the following methods are called once in order:
 
@@ -132,6 +132,7 @@ def __init__(
         threshold=0.5,
     ):
         """
+        Initialize the SMC_kernel class.
 
         Parameters
         ----------
@@ -149,6 +150,11 @@ def __init__(
             the higher the value of `threshold` the higher the number of stages. Defaults to 0.5.
             It should be between 0 and 1.
 
+        Attributes
+        ----------
+        self.var_info : dict
+            Dictionary that contains information about model variables shape and size.
+
         """
 
         self.draws = draws

pymc/variational/inference.py

@@ -49,12 +49,12 @@ class Inference:
 
     Parameters
     ----------
-    op: Operator class
-    approx: Approximation class or instance
-    tf: TestFunction instance
-    model: Model
+    op : Operator class    #:class:`~pymc.variational.operators`
+    approx : Approximation class or instance    #:class:`~pymc.variational.approximations`
+    tf : TestFunction instance  #?
+    model : Model
         PyMC Model
-    kwargs: kwargs passed to :class:`Operator`
+    kwargs : kwargs passed to :class:`Operator` #:class:`~pymc.variational.operators`, optional
     """
 
     def __init__(self, op, approx, tf, **kwargs):
@@ -96,18 +96,18 @@ def fit(self, n=10000, score=None, callbacks=None, progressbar=True, **kwargs):
 
         Parameters
         ----------
-        n: int
+        n : int
             number of iterations
-        score: bool
+        score : bool
             evaluate loss on each iteration or not
-        callbacks: list[function: (Approximation, losses, i) -> None]
+        callbacks : list[function: (Approximation, losses, i) -> None]
             calls provided functions after each iteration step
-        progressbar: bool
+        progressbar : bool
             whether to show progressbar or not
 
         Other Parameters
         ----------------
-        obj_n_mc: `int`
+        obj_n_mc: int
             Number of monte carlo samples used for approximation of objective gradients
         tf_n_mc: `int`
             Number of monte carlo samples used for approximation of test function gradients