Logical unification in Python, extensible via dispatch.
Using pip
:
pip install logical-unification
To install from source:
git clone [email protected]:pythological/unification.git
cd unification
pip install -r requirements.txt
Tests can be run with the provided Makefile
:
make check
unification
has built-in support for most Python data types:
>>> from unification import *
>>> unify(1, 1)
{}
>>> unify(1, 2)
False
>>> x = var('x')
>>> unify((1, x), (1, 2))
{~x: 2}
>>> unify((x, x), (1, 2))
False
Custom classes can be made "unifiable" with the unifiable
decorator:
@unifiable
class Account(object):
def __init__(self, id, name, balance):
self.id = id
self.name = name
self.balance = balance
>>> data = [Account(1, 'Alice', 100),
Account(2, 'Bob', 0),
Account(2, 'Charlie', 0),
Account(2, 'Denis', 400),
Account(2, 'Edith', 500)]
>>> id, name, balance = var('id'), var('name'), var('balance')
>>> [unify(Account(id, name, balance), acct) for acct in data]
[{~name: 'Alice', ~balance: 100, ~id: 1},
{~name: 'Bob', ~balance: 0, ~id: 2},
{~name: 'Charlie', ~balance: 0, ~id: 2},
{~name: 'Denis', ~balance: 400, ~id: 2},
{~name: 'Edith', ~balance: 500, ~id: 2}]
>>> [unify(Account(id, name, 0), acct) for acct in data]
[False,
{~name: 'Bob', ~id: 2},
{~name: 'Charlie', ~id: 2},
False,
False]
unification
also supports function dispatch through pattern matching:
>> from unification.match import *
>>> n = var('n')
@match(0)
def fib(n):
return 0
@match(1)
def fib(n):
return 1
@match(n)
def fib(n):
return fib(n - 1) + fib(n - 2)
>>> map(fib, [0, 1, 2, 3, 4, 5, 6, 7, 8, 0])
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
The pattern matching can be fairly complex:
>> name, amount = var('name'), var('amount')
@match({'status': 200, 'data': {'name': name, 'credit': amount}})
def respond(name, amount):
balance[name] += amount
@match({'status': 200, 'data': {'name': name, 'debit': amount}})
def respond(name, amount):
balance[name] -= amount
@match({'status': 404})
def respond():
print("Bad Request")
See the full example in the examples directory.
Unification stresses extensibility over performance, preliminary benchmarks show that this is 2-5x slower than straight tuple-based unification.
unification
's approach is reliable; although one caveat is set unification, which is challenging to do in general. It should work well in moderately complex cases, but it may break down under very complex ones.
This project is a fork of unification
.