Bump up version; Fixes for docs (#102)

* Bump up version; Fixes for docs

* Fix accidental changes to pyproject.toml

* Fix docs

pgmax/bp/infer.py

@@ -25,7 +25,9 @@ def pass_var_to_fac_messages(
     Args:
         ftov_msgs: Array of shape (num_edge_state,). This holds all the flattened factor to variable
             messages.
-        Array of shape (num_var_states,) representing the flattened evidence for each variable
+        evidence: Array of shape (num_var_states,) representing the flattened evidence for each variable
+        var_states_for_edges: Array of shape (num_edge_states,)
+            Global variable state indices for each edge state
     Returns:
         Array of shape (num_edge_state,). This holds all the flattened variable to factor messages.
     """

pgmax/fg/graph.py

@@ -542,8 +542,7 @@ class FToVMessages:
         fg_state: Factor graph state
         value: Optionally specify initial value for ftov messages
 
-    Raises: ValueError if provided value does not match expected ftov messages
-        shape.
+    Raises: ValueError if provided value does not match expected ftov messages shape.
     """
 
     fg_state: FactorGraphState
@@ -790,13 +789,12 @@ def BP(bp_state: BPState, num_iters: int) -> Tuple[Callable, Callable, Callable]
         num_iters: Number of belief propagation iterations.
 
     Returns:
-        tuple containing
-
-        run_bp: Function for running belief propagation for num_iters.
-        Optionally takes as input log_potentials updates, ftov_msgs updates,
-        evidence updates, and damping factor, and outputs a BPArrays.
-        get_bp_state: Function to reconstruct the BPState from BPArrays.
-        get_beliefs: Function to calculate beliefs from BPArrays.
+        Tuple containing\n
+        \trun_bp: Function for running belief propagation for num_iters.\n
+        \t\tOptionally takes as input log_potentials updates, ftov_msgs updates,
+        \t\tevidence updates, and damping factor, and outputs a BPArrays.\n
+        \tget_bp_state: Function to reconstruct the BPState from BPArrays.\n
+        \tget_beliefs: Function to calculate beliefs from BPArrays.\n
     """
     max_msg_size = int(np.max(bp_state.fg_state.wiring.edges_num_states))
     num_val_configs = (

pgmax/fg/groups.py

@@ -1,3 +1,5 @@
+"""A module containing the classes for variable and factor groups in a Factor Graph."""
+
 import collections
 import itertools
 import typing

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pgmax"
-version = "0.2.0"
+version = "0.2.1"
 description = "Loopy belief propagation for factor graphs on discrete variables, in JAX!"
 authors = ["Stannis Zhou <[email protected]>", "Nishanth Kumar <[email protected]>", "Miguel Lazaro-Gredilla <[email protected]>", "Dileep George <[email protected]>"]
 maintainers = ["Stannis Zhou <[email protected]>", "Nishanth Kumar <[email protected]>"]