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diprism · Nov 6, 2024 · ef8612e · ef8612e
1 parent 53bf08b
commit ef8612e
Showing 1 changed file with 9 additions and 9 deletions.
diff --git a/fggs/sum_product.py b/fggs/sum_product.py
@@ -172,8 +172,8 @@ def J(fgg: FGG, x: MultiTensor, inputs: MultiTensor, semiring: Semiring,
                     #casts to from optional to satisfy mypy
                     prefix_product: PatternedTensor = cast(PatternedTensor, prefix_products[i - 1])
                     suffix_product: PatternedTensor = cast(PatternedTensor, suffix_products[i])
-                    prefix_output_node: list[Node] = cast(list[Node], prefix_output_nodes[i - 1])
-                    suffix_output_node: list[Node] = cast(list[Node], suffix_output_nodes[i])
+                    prefix_output_node: List[Node] = cast(List[Node], prefix_output_nodes[i - 1])
+                    suffix_output_node: List[Node] = cast(List[Node], suffix_output_nodes[i])
                     product = einsum([prefix_product, suffix_product], 
                                     [prefix_output_node, suffix_output_node],
                                     rule.rhs.ext + edge.nodes, semiring)
@@ -184,7 +184,7 @@ def J(fgg: FGG, x: MultiTensor, inputs: MultiTensor, semiring: Semiring,
                         J_inputs.add_single((n, edge.label), product)
     return Jx
 
-def compute_products(fgg: FGG, inputses: Iterable[MultiTensor], edges: list[Edge], rule_ext: list[Node], semiring: Semiring, direction: str) -> Tuple[list[Optional[PatternedTensor]], list[Optional[list[Node]]]]:
+def compute_products(fgg: FGG, inputses: Iterable[MultiTensor], edges: List[Edge], rule_ext: List[Node], semiring: Semiring, direction: str) -> Tuple[List[Optional[PatternedTensor]], List[Optional[List[Node]]]]:
     if direction == 'forward':
         future_nodes_slice: Iterable[Edge] = reversed(edges[1:])
         edge_loop_slice: Iterable[Edge] = edges[:-1]
@@ -206,10 +206,10 @@ def compute_products(fgg: FGG, inputses: Iterable[MultiTensor], edges: list[Edge
         future_nodes_lookup.append(future_nodes.copy()) 
     future_nodes_lookup.reverse()
 
-    products: list[Optional[PatternedTensor]] = []
-    output_nodes: list[Optional[list[Node]]] = [] 
+    products: List[Optional[PatternedTensor]] = []
+    output_nodes: List[Optional[List[Node]]] = [] 
     previous_weight: PatternedTensor = PatternedTensor.eye(1, semiring=semiring)
-    previous_output_nodes: list[Node] = []
+    previous_output_nodes: List[Node] = []
     for i, edge in enumerate(edge_loop_slice):
         for inputs in inputses:
             if edge.label in inputs:
@@ -233,9 +233,9 @@ def compute_products(fgg: FGG, inputses: Iterable[MultiTensor], edges: list[Edge
 
     return products, output_nodes
 
-def multiply_next_edge(fgg: FGG, previous_weight: PatternedTensor, previous_nodes: list[Node], 
-                       current_weight: PatternedTensor, current_nodes: list[Node], 
-                       ext: Iterable[Node], semiring: Semiring) -> Tuple[PatternedTensor, list[Node]]:
+def multiply_next_edge(fgg: FGG, previous_weight: PatternedTensor, previous_nodes: List[Node], 
+                       current_weight: PatternedTensor, current_nodes: List[Node], 
+                       ext: Iterable[Node], semiring: Semiring) -> Tuple[PatternedTensor, List[Node]]:
 
     indexing: List[Sequence[Node]] = [previous_nodes, current_nodes]
     tensors: List[PatternedTensor] = [previous_weight, current_weight]