def get_all_relevant_facts(
proposition,
assumptions=True,
context=global_assumptions,
use_known_facts=True,
iterations=oo,
):
# The relevant facts might introduce new keys, e.g., Q.zero(x*y) will
# introduce the keys Q.zero(x) and Q.zero(y), so we need to run it until
# we stop getting new things. Hopefully this strategy won't lead to an
# infinite loop in the future.
i = 0
relevant_facts = CNF()
exprs = None
all_exprs = set()
while exprs != set():
exprs, relevant_facts = get_relevant_facts(
proposition,
assumptions,
context,
exprs=exprs,
relevant_facts=relevant_facts,
)
all_exprs |= exprs
i += 1
if i >= iterations:
break
if use_known_facts:
known_facts_CNF = CNF()
known_facts_CNF.add_clauses(get_all_known_facts())
kf_encoded = EncodedCNF()
kf_encoded.from_cnf(known_facts_CNF)
def translate_literal(lit, delta):
if lit > 0:
return lit + delta
else:
return lit - delta
def translate_data(data, delta):
return [{translate_literal(i, delta)
for i in clause} for clause in data]
data = []
symbols = []
n_lit = len(kf_encoded.symbols)
for i, expr in enumerate(all_exprs):
symbols += [pred(expr) for pred in kf_encoded.symbols]
data += translate_data(kf_encoded.data, i * n_lit)
encoding = dict(list(zip(symbols, range(1, len(symbols) + 1))))
ctx = EncodedCNF(data, encoding)
else:
ctx = EncodedCNF()
ctx.add_from_cnf(relevant_facts)
return ctx
def get_all_relevant_facts(proposition,
assumptions,
context,
use_known_facts=True,
iterations=oo):
"""
Extract all relevant facts from *proposition* and *assumptions*.
This function extracts the facts by recursively calling
``get_relevant_facts()``. Extracted facts are converted to
``EncodedCNF`` and returned.
Parameters
==========
proposition : sympy.assumptions.cnf.CNF
CNF generated from proposition expression
assumptions : sympy.assumptions.cnf.CNF
CNF generated from assumption expression
context : sympy.assumptions.cnf.CNF
CNF generated from assumptions context
use_known_facts : bool, optional
If ``True``, facts from ``sympy.assumptions.ask_generated`` module
are encoded as well.
iterations : int, optional
Number of times that relevant facts are recursively extracted.
Default is infinite times until no new fact is found.
Returns
=======
sympy.assumptions.cnf.EncodedCNF
Examples
========
>>> from sympy import Q
>>> from sympy.assumptions.cnf import CNF
>>> from sympy.assumptions.satask import get_all_relevant_facts
>>> from sympy.abc import x, y
>>> props = CNF.from_prop(Q.nonzero(x*y))
>>> assump = CNF.from_prop(Q.nonzero(x))
>>> context = CNF.from_prop(Q.nonzero(y))
>>> get_all_relevant_facts(props, assump, context) #doctest: +SKIP
<sympy.assumptions.cnf.EncodedCNF at 0x7f09faa6ccd0>
"""
# The relevant facts might introduce new keys, e.g., Q.zero(x*y) will
# introduce the keys Q.zero(x) and Q.zero(y), so we need to run it until
# we stop getting new things. Hopefully this strategy won't lead to an
# infinite loop in the future.
i = 0
relevant_facts = CNF()
exprs = None
all_exprs = set()
while True:
if i == 0:
exprs = extract_predargs(proposition, assumptions, context)
all_exprs |= exprs
exprs, relevant_facts = get_relevant_facts(exprs, relevant_facts)
i += 1
if i >= iterations:
break
if not exprs:
break
if use_known_facts:
known_facts_CNF = CNF()
known_facts_CNF.add_clauses(get_all_known_facts())
kf_encoded = EncodedCNF()
kf_encoded.from_cnf(known_facts_CNF)
def translate_literal(lit, delta):
if lit > 0:
return lit + delta
else:
return lit - delta
def translate_data(data, delta):
return [{translate_literal(i, delta)
for i in clause} for clause in data]
data = []
symbols = []
n_lit = len(kf_encoded.symbols)
for i, expr in enumerate(all_exprs):
symbols += [pred(expr) for pred in kf_encoded.symbols]
data += translate_data(kf_encoded.data, i * n_lit)
encoding = dict(list(zip(symbols, range(1, len(symbols) + 1))))
ctx = EncodedCNF(data, encoding)
else:
ctx = EncodedCNF()
ctx.add_from_cnf(relevant_facts)
return ctx