Library for folding (or reducing) over a Reduced Ordered Binary Decision Diagram.
Project description
Fold-BDD
Library for folding (or reducing) over a Reduced Ordered Binary Decision Diagram.
Table of Contents
Installation
If you just need to use fold_bdd
, you can just run:
$ pip install fold-bdd
For developers, note that this project uses the poetry python package/dependency management tool. Please familarize yourself with it and then run:
$ poetry install
Usage
The fold-bdd
library supports two types of folds:
-
Folding over the DAG of a
BDD
starting at the root and then recursively merging the low and high branches until theTrue
/False
leaves. This is simply a compressed variant of a post-order traversal. -
Folding over a path in the DAG, starting at the root and moving the the corresponding leaf (left fold).
In both cases, local context such as the levels of the parent and child nodes are passed in.
As input, each of these take in a bdd, from the dd library and function for accumulating or merging.
The following example illustrates how to use fold_bdd
to count the
number of solutions to a predicate using post_order
and evaluate a
path using fold_path
.
Create ROBDD
# Create BDD.
from dd.cudd import BDD
manager = BDD()
manager.declare('x', 'y')
manager.reorder({'x': 1, 'y': 0})
manger.configure(reordering=False)
bexpr = manager.add_expr('x | y')
Post-Order Examples
from fold_bdd import post_order
Count Number of Nodes in BDD
def merge1(ctx, low=None, high=None):
return 1 if low is None else low + high
def dag_size(bexpr):
return post_order(bexpr, merge1)
assert bexpr.dag_size == dag_size(bexpr)
Fold Path Examples
Count nodes along path.
def merge(ctx, val, acc):
return acc + 1
def count_nodes(bexpr, vals):
return fold_path(merge, bexpr, vals, initial=0)
assert count_nodes(bexpr, (False, False)) == 3
assert count_nodes(bexpr, (True, False)) == 2
Context Object Attributes
The Context
object contains exposes attributes
node: Hashable
# Reference to Node in ROBDD.node_val: Union[str, bool]
# Node name or leaf value.negated: bool
# Is the edge to prev node negated.first_lvl: int
# Level of first decision in ROBDD.max_lvl: int
# How many decision variables are there.curr_lvl: int
# Which decision is this.low_lvl: Optional[int]
# Which decision does the False edge point to. None if leaf.high_lvl: Optional[int]
# Which decision does the True edge point to. None if leaf.is_leaf: bool
# Is the current node a leaf.skipped: int
# How many decisions were skipped on edge to this node.
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