def solve(num_variables, clauses, selection_heuristic):
"""
Uses the dpll algorithm to determine if the formula is satisfiable or
unsatisfiable
Returns a tuple with the following formats:
- If the formula is satisfiable
(True, [None, truth_value1, truth_vaue2, ...] )
- If the formula is unsatisfiable
(False, frozenset() )
"""
# Dictionary with clauses classified by literals
litclauses = datautil.classifyClausesByLiteral(clauses)
# Amount of times each clause appears in the formula.
# At the beginning every clause appears only once but after some
# branching some clauses can appear more than once
ctimes = { c : 1 for c in clauses }
# We use an struct to have less parameters
cdata = ClausesData(clauses, ctimes, litclauses)
variables, interpretation = getVarsAndFirstIntp(num_variables, cdata)
return _solve(variables, cdata, interpretation, selection_heuristic)
def solve(num_variables, clauses, selection_heuristic):
"""
Uses the dpll algorithm to determine if the formula is satisfiable or
unsatisfiable
Returns a tuple with the following formats:
- If the formula is satisfiable
(True, [None, truth_value1, truth_vaue2, ...] )
- If the formula is unsatisfiable
(False, frozenset() )
"""
# Dictionary with clauses classified by literals
litclauses = datautil.classifyClausesByLiteral(clauses)
# Amount of times each clause appears in the formula.
# At the beginning every clause appears only once but after some
# branching some clauses can appear more than once
ctimes = {c: 1 for c in clauses}
# We use an struct to have less parameters
cdata = ClausesData(clauses, ctimes, litclauses)
variables, interpretation = getVarsAndFirstIntp(num_variables, cdata)
return _solve(variables, cdata, interpretation, selection_heuristic)
def solve(num_variables, clauses, selection_heuristic):
"""
Uses the dp algorithm to determine if the formula is satisfiable or
unsatisfiable
Returns a tuple with the following formats:
- If the formula is satisfiable
(True, frozenset() )
- If the formula is unsatisfiable
(False, frozenset(<core_clause1>, <core_clause2>, ...) )
"""
litclauses = datautil.classifyClausesByLiteral(clauses)
variables = range(1, num_variables + 1)
reason = {} # Clause "parents"
while variables:
if unitPropagation(variables, clauses, litclauses, reason):
return (False, generateCoreFromEmtpyClause(reason))
# All clauses and variables wasted without finding the emtpy clause
if not clauses or not variables:
return (True, frozenset())
# Select variable
var = selection_heuristic(variables, litclauses)
variables.remove(var)
# Ignore variables that don't belong to any clause
if not litclauses.has_key(var) and not litclauses.has_key(var):
continue
if resolution(var, clauses, litclauses, reason):
return (False, generateCoreFromEmtpyClause(reason))
return (True, frozenset())
def solve(num_variables, clauses, selection_heuristic):
"""
Uses the dp algorithm to determine if the formula is satisfiable or
unsatisfiable
Returns a tuple with the following formats:
- If the formula is satisfiable
(True, frozenset() )
- If the formula is unsatisfiable
(False, frozenset(<core_clause1>, <core_clause2>, ...) )
"""
litclauses = datautil.classifyClausesByLiteral(clauses)
variables = range(1, num_variables+1)
reason = {} # Clause "parents"
while variables:
if unitPropagation(variables, clauses, litclauses, reason):
return (False, generateCoreFromEmtpyClause(reason))
# All clauses and variables wasted without finding the emtpy clause
if not clauses or not variables:
return (True, frozenset())
# Select variable
var = selection_heuristic(variables, litclauses)
variables.remove(var)
# Ignore variables that don't belong to any clause
if not litclauses.has_key(var) and not litclauses.has_key(var):
continue
if resolution(var, clauses, litclauses, reason):
return (False, generateCoreFromEmtpyClause(reason))
return (True, frozenset())