def choose_subset(self, select_subset, encodingFile, timeout=30, usc=False, solve_limit="umax,umax", clingoctl=None):
if clingo is None:
raise ImportError()
c = clingoctl
aset = [sys.maxsize, False, [], None, []]
def __on_model(model):
#if len(model.cost) == 0:
# return
logger.debug("better answer set found: %s %s %s", model, model.cost, model.optimality_proven)
aset[1] |= model.optimality_proven
opt = model.cost[0] if len(model.cost) > 0 else 0
if opt <= aset[0]:
if opt < aset[0]:
aset[2] = []
aset[0] = opt
answer_set = [safe_int(x) for x in str(model).translate(str.maketrans(dict.fromkeys("abs()"))).split(" ")]
# might get "fake" duplicates :(, with different model.optimality_proven
if answer_set not in aset[2][-1:]:
aset[2].append(answer_set)
with open(encodingFile,"r") as encoding:
encodingContent = "".join(encoding.readlines())
# FIXME: use mutable string
prog = encodingContent
if clingoctl is None:
c = clingo.Control()
if usc:
c.configuration.solver.opt_strategy = "usc,pmres,disjoint,stratify"
c.configuration.solver.opt_usc_shrink = "min"
c.configuration.solve.opt_mode = "opt"
# c.configuration.solve.models = 0
c.configuration.solve.solve_limit = solve_limit
for e in self._edges:
prog += "edge({0},{1}).\n".format(e[0], e[1])
for p in self._nodes:
prog += "p({0}).\n".format(p)
# subset (buckets) of proj to select upon
#for b in range(1, select_subset + 1, 1):
prog += "b({0}).\n".format(select_subset)
#print(prog)
aset[3] = c
c.add("prog{0}".format(select_subset), [], str(prog))
def solver(c, om):
c.ground([("prog{0}".format(select_subset), [])])
self.grounded = True
c.solve(on_model=om)
t = threading.Thread(target=solver, args=(c, __on_model))
t.start()
t.join(timeout)
self.timeout = t.is_alive()
c.interrupt()
t.join()
aset[1] |= c.statistics["summary"]["models"]["optimal"] > 0
aset[4] = c.statistics
return aset
def solve(
self,
solver_setup,
xml_files,
dump=None,
nmodels=0,
timers=False,
stats=False,
tests=False,
logic_programs=None,
facts_only=False,
):
"""Given two corpora, determine if they are compatible by way of
flattening header information into facts, and handing to a solver.
"""
# Ensure our files exist, and are provided in list form
if not isinstance(xml_files, list):
xml_files = [xml_files]
for path in xml_files:
if not os.path.exists(path):
sys.exit("%s does not exist." % path)
# logic programs to give to the solver
logic_programs = logic_programs or []
if not isinstance(logic_programs, list):
logic_programs = [logic_programs]
timer = Timer()
# Initialize the control object for the solver
self.control = clingo.Control()
self.control.configuration.solve.models = nmodels
self.control.configuration.asp.trans_ext = "all"
self.control.configuration.asp.eq = "5"
self.control.configuration.configuration = "tweety"
self.control.configuration.solve.parallel_mode = "2"
self.control.configuration.solver.opt_strategy = "usc,one"
# set up the problem -- this generates facts and rules
self.assumptions = []
with self.control.backend() as backend:
self.backend = backend
solver_setup.setup(self, xml_files, tests=tests)
if facts_only:
return
timer.phase("setup")
# read in the main ASP program and display logic -- these are
# handwritten, not generated, so we load them as resources
parent_dir = os.path.dirname(__file__)
import IPython
IPython.embed()
# self.control.load(os.path.join(parent_dir, 'compatible.lp'))
# self.control.load(os.path.join(parent_dir, "display.lp"))
timer.phase("load")
# Grounding is the first step in the solve -- it turns our facts
# and first-order logic rules into propositional logic.
self.control.ground([("base", [])])
timer.phase("ground")
# With a grounded program, we can run the solve.
result = Result()
models = [] # stable models if things go well
cores = [] # unsatisfiable cores if they do not
def on_model(model):
models.append((model.cost, model.symbols(shown=True, terms=True)))
solve_kwargs = {}
# Won't work after this, need to write files
# solve_kwargs = {
# "assumptions": self.assumptions,
# "on_model": on_model,
# "on_core": cores.append,
# }
# if clingo_cffi:
# solve_kwargs["on_unsat"] = cores.append
# solve_result = self.control.solve(**solve_kwargs)
# timer.phase("solve")
# once done, construct the solve result
# result.satisfiable = solve_result.satisfiable
# def stringify(x):
# if clingo_cffi:
# # Clingo w/ CFFI will throw an exception on failure
# try:
# return x.string
# except RuntimeError:
# return str(x)
# else:
# return x.string or str(x)
# if result.satisfiable:
# min_cost, best_model = min(models)
# tuples = [
# (sym.name, [stringify(a) for a in sym.arguments]) for sym in best_model
# ]
# result.answers.append((list(min_cost)))
# elif cores:
# symbols = dict((a.literal, a.symbol) for a in self.control.symbolic_atoms)
# for core in cores:
# core_symbols = []
# for atom in core:
# sym = symbols[atom]
# if sym.name == "rule":
# sym = sym.arguments[0].string
# core_symbols.append(sym)
# result.cores.append(core_symbols)
if timers:
timer.write()
print()
if stats:
print("Statistics:")
pprint.pprint(self.control.statistics)
return result
def largest_clique_asp(self, clingoctl=None, timeout=10, enum=True, usc=True, ground=False, prevent_k_hyperedge=3,
solve_limit="umax,umax"):
if clingo is None:
raise ImportError()
@curry
def __on_model(aset, model):
if len(model.cost) == 0:
return
# print model, model.cost, model.optimality_proven
aset[1] |= model.optimality_proven
opt = abs(model.cost[0])
if opt >= aset[0]:
if opt > aset[0]:
aset[2] = []
aset[0] = opt
answer_set = [safe_int(x) for x in str(model).translate(None, "u()").split(" ")]
# might get "fake" duplicates :(, with different model.optimality_proven
if answer_set not in aset[2][-1:]:
aset[2].append(answer_set)
prog = MutableString()
if clingoctl is None:
c = clingo.Control()
if usc:
c.configuration.solver.opt_strategy = "usc,pmres,disjoint,stratify"
c.configuration.solver.opt_usc_shrink = "min"
c.configuration.solve.opt_mode = "optN" if enum else "opt"
c.configuration.solve.models = 0
c.configuration.solve.solve_limit = solve_limit
guess = MutableString()
pos = 0
if len(self.__vertices) > 0:
guess += "{"
prog += "#maximize {"
for u in self.__vertices:
prog += " 1,u({0}):u({1}){2}".format(u, u, " }.\n" if pos == len(self.__vertices) - 1 else ";")
guess += " u({0}){1}".format(u, " }.\n" if pos == len(self.__vertices) - 1 else ";")
pos += 1
prog += "#show u/1.\n"
prog += guess
# has to be clique
if len(self.__edges) > 0:
if not ground:
prog += ":- u(Y1), u(Y2), not a(Y1, Y2), Y1 < Y2.\n"
prog += "a(Y1, Y2) :- e(X, Y1), e(X, Y2), Y1 < Y2.\n"
for k, e in self.__edges.iteritems():
for v in e:
prog += "e({0}, {1}).\n".format(k, v)
else:
adj = self.adj
for u in self.__vertices:
for v in self.__vertices:
if u < v and v not in adj[u]:
prog += ":- u({0}), u({1}).\n".format(u, v)
else:
c = clingoctl
aset = [0, False, [], c, []]
if len(self.__edges) == 0 or len(self.__vertices) == 0:
return aset
if not ground and len(self.__edges) > 0:
prog += ":- "
for i in xrange(0, prevent_k_hyperedge):
if i > 0:
prog += ", Y{0} < Y{1}, ".format(i - 1, i)
prog += "e(X, Y{0}), u(Y{0})".format(i)
prog += ".\n"
# prog += ":- e(X, Y1), e(X, Y2), e(X, Y3), u(Y1), u(Y2), u(Y3), Y1 < Y2, Y2 < Y3."
else:
constr = set()
for e in self.__edges.values():
if len(e) <= 2 or len(e) < prevent_k_hyperedge:
continue
# prevent 3 elements from the same edge
sub = range(0, prevent_k_hyperedge)
while True: # sub[0] <= len(e) - prevent_k_hyperedge:
rule = []
pos = 0
# print sub, e
for v in sub:
rule.append(e[v])
pos += 1
rule = tuple(sorted(rule))
if rule not in constr:
constr.add(rule)
prog += ":- " + ", ".join(("u({0})".format(r) for r in rule)) + ".\n"
if sub[0] == len(e) - prevent_k_hyperedge:
break
# next position
for i in xrange(prevent_k_hyperedge - 1, -1, -1):
if sub[i] < len(e) + (i - prevent_k_hyperedge):
sub[i] += 1
for j in xrange(i + 1, prevent_k_hyperedge):
sub[j] = sub[i] + (j - i)
break
# print "XX, ", self.__edges, self.__vertices
# print(prog)
c.add("prog{0}".format(prevent_k_hyperedge), [], str(prog))
def solver(c, om):
c.ground([("prog{0}".format(prevent_k_hyperedge), [])])
# print "grounded"
c.solve(on_model=om)
t = threading.Thread(target=solver, args=(c, __on_model(aset)))
t.start()
t.join(timeout)
c.interrupt()
t.join()
aset[1] |= c.statistics["summary"]["models"]["optimal"] > 0
aset[4] = c.statistics
# print c.statistics
return aset
def solve_sudoku_ASP(sudoku, k):
"""
Function that solves given sudoku list with ASP encoding. Edges correspond
to undirected edge between cells in same row, column or k*k block.
"""
num_vertices = (k * k) * (k * k) # 81 for k = 3
# Nd array representation of sudoku
graph = np.array(sudoku).reshape(num_vertices, 1)
# Create 2d np array of vertice numbers of 0 until num_vertices-1 for edges
vertices = np.arange(num_vertices).reshape(k * k, k * k)
edges = get_edges(vertices, k)
vertices = vertices.reshape(num_vertices, 1)
# Number of possible values in sudoku which we refer to as numbers (nums/n)
num_nums = k * k
asp_code = ""
# Encode our facts as predicates vertex/1, value/2 which is possible values
# for each vertex and edges/2
for v in range(num_vertices):
asp_code += """vertex(v{}).\n""".format(v)
# If a cell contains u in input, cell in solution must contain u
if graph[v].item() != 0:
asp_code += """value(v{}, {}).\n""".format(v, graph[v].item())
else:
for n1 in range(1, num_nums + 1):
asp_code += """value(v{},{}).\n""".format(v, n1)
for (v1, v2) in edges:
asp_code += """edge(v{},v{}).\n""".format(v1, v2)
# Universily quantified variables; number for each vertex, already filled
# in value must also hold
for n1 in range(1, num_nums + 1):
asp_code += """num(V,{}) :- vertex(V), value(V, {})""".format(n1, n1)
for n2 in range(1, num_nums + 1):
if n1 != n2:
asp_code += """, not num(V,{})""".format(n2)
asp_code += """.\n"""
# Each two different cells in same row,col,block must contain diff values
asp_code += """:- edge(V1, V2), num(V1,N), num(V2,N).\n"""
# Only interested in the number predicate
asp_code += """#show num/2.\n"""
control = clingo.Control()
control.add("base", [], asp_code)
control.ground([("base", [])])
def on_model(model):
global output
output = model.symbols(shown=True)
control.configuration.solve.models = 1
answer = control.solve(on_model=on_model)
if answer.satisfiable:
# Fill in sudoku graph with answer
for i in range(len(output)):
v = int(str(output[i]).split(",")[0].split("v")[1])
n = int(str(output[i]).split(",")[-1][:-1])
graph[v] = n
graph = graph.reshape(k * k, k * k).tolist()
else:
graph = None
return graph
def main():
## Global variables
global models
global number_constraints
xor_constraints = 0
total_rounds = 0
current_state_key = 0
## Parse input data
args = parse_params()
## Check for input errors
check_input(args)
programs = []
for in_file in args.input:
try:
int(in_file)
except ValueError:
programs.append(in_file)
#print args
#print programs
## Create clingo object
round_num = 1
while True:
control = clingo.Control()
control.cleanup()
control.configuration.solve.models = 0
control.configuration.solve.opt_mode = "ignore"
if args.stats:
control.configuration.stats=1
for program in programs:
control.load(program)
control.ground([("base", [])])
if round_num == 1:
if len(programs) > 1:
print "Reading from %s ..."%programs[0]
else:
print "Reading from %s "%programs[0]
if args.incremental:
print "Incremental Solving..."
else:
print "Solving..."
## Initial solvinig... only solving call if not incremental or non iterative
control.register_propagator(Propagator(args.s))
solve_result = control.solve(None, lambda model: models.append(model.symbols(shown=True)))
## If incremental flag is on
if args.incremental and solve_result:
## Get info from round 1 if verbosity 0, 2 or 3
if args.incremental:
## Get xor constraints and solving rounds
xor_constraints = Propagator.number_constraints
total_rounds = Propagator.roundn
if args.verbose != 1:
print "round %s:"%total_rounds
if args.verbose == 0 or args.verbose == 2:
Models().print_state(Propagator.state)
if args.verbose == 0 or args.verbose == 3:
Models().print_aggregates(Propagator.state, Propagator.symbols)
## Get the last key from state
current_state_key = len(Propagator.state[0].keys())
## For round 2 or more
while True: ## While True until the search space is cut enough
if len(models) >= int(args.models)*2:
del models[:]
## Additional solve calls for incremental xor
solve_result = control.solve(None, lambda model: models.append(model.symbols(shown=True)))
## Get xor constraints and solving rounds
xor_constraints += Propagator.number_constraints
total_rounds = Propagator.roundn
## Update the last key from state
current_state_key += 1
if args.verbose != 1:
print ""
print "round %s:"%total_rounds
if args.verbose == 0 or args.verbose == 2:
last_state = {}
last_state[current_state_key] = Propagator.state[0][current_state_key]
Models().print_state([last_state])
if args.verbose == 0 or args.verbose == 3:
last_state = {}
last_state[current_state_key] = Propagator.state[0][current_state_key]
Models().print_aggregates([last_state], Propagator.symbols)
else:
break
if args.iterative == False:
#print "do not check sat!!!!!!!!"
break
elif args.iterative == True:
if solve_result.satisfiable:
print "total rounds: %s"%round_num
break
else: round_num+=1
if args.incremental and args.verbose != 1:
print ""
print "----------------------------------------------------------"
print "----------------------------------------------------------"
if args.incremental:
print "total rounds: %s"%total_rounds
print "total xor constraints used: %s"%xor_constraints
else:
print "xor constraints: %s"%Propagator.number_constraints
state = Propagator.state
symbols = Propagator.symbols
if args.verbose == 0:
Models().print_state(state)
Models().print_aggregates(state, symbols)
print ""
Models().print_results(control, solve_result, args, models)
elif args.verbose == 1:
Models().print_results(control, solve_result, args, models)
elif args.verbose == 2:
Models().print_state(state)
print ""
Models().print_results(control, solve_result, args, models)
elif args.verbose == 3:
Models().print_aggregates(state, symbols)
print ""
Models().print_results(control, solve_result, args, models)
if args.stats:
Models().print_stats(control)
import sys
import clingo
import copy
# options
files, options, max_models = [], [], 1
for i in sys.argv[1:]:
if i[0] == "-":
options.append(i)
elif i.isdigit():
max_models = i
else:
files.append(i)
# guess
guess = clingo.Control(options + ["0"])
for i in files:
guess.load(i)
guess.ground([("base", [])])
# check
# check = copy.deepcopy(guess)?
check = clingo.Control(["0", "--enum-mode=cautious"])
for i in files:
check.load(i)
check.ground([("base", [])])
# gather knowledge predicates and atoms
k_preds, k_atoms = set(), []
for atom in guess.symbolic_atoms.by_signature("k", 1):
k_preds.add((atom.symbol.arguments[0].name,
def run_with(self, filenames:[str]=(), inline:str='',
programs:iter=(['base', ()],), options:list=[]):
import clingo
ctl = clingo.Control(options)
main = Main(propagators=self, programs=programs, inline=inline, generator=True)
return main(ctl)
def load_model(self, model_path=None):
ctl = clingo.Control(message_limit=0)
#ctl.configuration.configuration = 'tweety'
ctl.load(model_path)
self.model = ctl
def reset_grounder(self):
self._str_model = ''
self._control = clingo.Control()
if self._parser_widget is not None:
self._parser_widget.update()
def __init__(self, arguments=[], **kwargs):
self.cmdline_arguments = arguments
self.input = ""
self.control = clingo.Control(arguments, **kwargs)
self.__simple = True
for root, dirs, files in os.walk(directory):
split_root = root.split("/")
if len(split_root)!=7:
continue
env = split_root[2]
app = split_root[3]
cons = split_root[5]
instance = split_root[6]
automata_file_name =f"{app}_automata.lp"
if not automata_file_name in files:
print(f"No automata file for {root}")
continue
if app=="telingo":
continue
file_path = os.path.join(root, automata_file_name)
ctl = clingo.Control([0])
ctl.load(file_path)
ctl.load("../formula_to_automaton/size.lp")
ctl.ground([('base',[])])
ctl.solve(on_model= lambda m: save(env,app,cons,instance,m))
# print(dirs)
# print("")
# for filename in files:
# print(os.path.join(root, filename))
for env,val in results.items():
file_name_csv = f'results/tables/{env}/size.csv'.format(env)
file_name_tex_csv = f'results/tables/{env}/size.tex'.format(env)
os.makedirs(os.path.dirname(f'results/tables/{env}/size.csv'), exist_ok=True)
def solve(self):
""" Creating the program object """
program_types = clingo.Control([])
program_dynamic = clingo.Control([])
""" We want to obtain ALL possible stable models """
program_types.configuration.solve.models = 0
program_dynamic.configuration.solve.models = 0
""" We initialize data structures """
action_list = []
fluent_list = []
initial_states = []
""" Reading from the input file """
try:
program_types.load(self.file_name)
except RuntimeError:
return
try:
program_dynamic.load(self.file_name)
except RuntimeError:
raise RuntimeError
""" We obtain fluents, actions and the initial state """
try:
program_types.ground([('initial', []), ('types', [0]),
('static', [])])
except RuntimeError:
raise RuntimeError
models = program_types.solve(yield_=True)
for element in models:
initial_state = []
for atom in element.symbols(atoms=True):
if 'action' in str(atom):
action_list.append(
clingo.Function(
TFG.src.main.model.util.atomutils.obtain_dynamic(
str(atom))))
elif 'fluent' in str(atom):
fluent_list.append(
clingo.Function(
TFG.src.main.model.util.atomutils.obtain_dynamic(
str(atom))))
elif ((clingo.Function(str(atom))) in fluent_list) or (
(clingo.Function(str(atom))) in action_list):
initial_state.append(clingo.Function(str(atom)))
initial_states.append(initial_state)
if len(initial_states) > 1:
print('Warning: several answer sets obtained as initial state')
""" We add as external atoms all the possible fluents and actions """
for atom in fluent_list:
program_dynamic.add('fluents', [],
"#external" + " " + str(atom) + ".")
for atom in action_list:
program_dynamic.add('actions', [],
"#external" + " " + str(atom) + ".")
try:
program_dynamic.ground([('fluents', []), ('actions', []),
('dynamic', [1]), ('static', []),
('final', [1])])
except RuntimeError:
raise RuntimeError
""" Delegates to its search algorithm """
self.search_algorithm.add_domain(program_dynamic)
self.search_algorithm.search(initial_states[0], self.fringe,
action_list, fluent_list, self.heuristic,
self.bfs, self.states)
def learn_ini(self):
buffer = []
rule_index = 1
for line in self.aspProgram.splitlines():
if line.startswith('@getWeight'):
self.weightsDic[str(rule_index)] = {
'wIndex': rule_index,
'weight': 0,
'n': 0,
'nii': 0,
'atomName': str(line.split(' ')[1]).split('(')[0],
'gradient': [],
}
self.weightsDic[str(rule_index)]['weight'] += self.init_weight
buffer.append(
str(self.weightsDic[str(rule_index)]['weight']) + ' ' +
''.join(line.split(' ')[1:]))
else:
buffer.append(line)
if "".join(line.split()) != "":
rule_index += 1
for line in buffer:
self.progranWithoutPlaceholder += line + "\n"
# Done on creating weight file, and initializing dictionary
content = processor.lpmln_to_asp_parser(self.progranWithoutPlaceholder)
finalout = processor.asp_domain_2_asp_parser(content)
warn_option = "--warn=none"
thread_option = "-t 4"
options = [warn_option, thread_option]
sampling_control = clingo.Control(options)
sampling_control.add("base", [], finalout)
sampling_control.ground([("base", [])])
symbols = [atom.symbol for atom in sampling_control.symbolic_atoms]
for s in symbols:
for key, value in self.weightsDic.items():
if s.name == value['atomName']:
value['n'] += 1
xorSampler = xor_constraint_drawer.xorSampler(
self.xorMode, [finalout, self.evidence], self.clingoOptions)
models = xorSampler.startDrawSample()
if len(models) > 1:
# randomly generate a index from models
randomIndex = random.randint(0, len(models) - 1)
model = models[randomIndex]
else:
model = models[0]
finalModels = []
finalModels.append(model)
for model in finalModels:
for atom in model:
if str(atom.name) == "unsat":
idx = eval(str(atom.arguments[0]))
for key, value in self.weightsDic.items():
if value['wIndex'] == idx:
value['nii'] += 1
def solve_sudoku_ASP(sudoku, k):
# First we create a list containing all the edges for all vertices in the sudoku
length = len(sudoku)
num_vertices = length**2
matrix = np.arange(num_vertices).reshape(length, length)
# edges = {'squares':[], 'rows':[], 'columns':[]}
edges = []
sudoku = np.array(sudoku).reshape(length * length)
# The loop below fills the edges list with all edges in the sudoku
for i in range(length):
for j in range(length):
# specify the current value i,j as the left-hand value for the edge tuple
left = int(matrix[i][j] + 1)
# iterate over values in the square
col = j // k
row = i // k
rows = matrix[(row * k):(row * k + k)]
box = [x[(col * k):(col * k + k)] for x in rows]
for v in range(k):
for w in range(k):
right = int(box[v][w] + 1)
# make sure that we're not assigning the current value as the right-hand vertix
if (row * k + v, col * k + w) != (i, j):
if (right, left) not in edges:
edges.append((left, right))
# iterative over cells in row i,
for g in range(length):
right = int(matrix[i][g] + 1)
if (i, g) != (i, j):
if (right, left) not in edges:
edges.append((left, right))
# iterate over cells in column j,
for c in range(length):
right = int(matrix[c][j] + 1)
if (c, j) != (i, j):
if (right, left) not in edges:
# edges['columns'].append((left, right))
edges.append((left, right))
# We encode the graph in ASP
asp_code = ""
for i in range(1, num_vertices + 1):
asp_code += '\n\tvertex(v{}).'.format(i)
for (i, j) in edges:
asp_code += '\n\tedge(v{},v{}).'.format(i, j)
asp_code += '\n\t'
for value in range(1, length + 1):
rule = 'value(V,{}) :- vertex(V)'.format(value)
for i in range(1, length + 1):
if i != value:
rule += ', not value(V,{})'.format(i)
rule += '.'
asp_code += """{}\n\t""".format(rule)
# for value in range(1, length+1):
# rule = 'value(V,{}) :- vertex(V), not color (V,2), not color (V,3)'
# print_answer_sets("""
# choice(1..{}).
# choose(X,a) :- not choose(X,b), choice(X).
# choose(X,b) :- not choose(X,a), choice(X).
# """.format(k))
for i in range(length**2):
# if i == :
# break
if sudoku[i] != 0:
val = sudoku[i]
asp_code += '\n\tvalue(v{},{}) :- vertex(v{}).'.format(
i + 1, val, i + 1)
asp_code += """
:- edge(V1,V2), value(V1,C), value(V2,C).
"""
# asp_code += """
# value(v1,2) :- vertex(v1).
# """
asp_code += """
#show value/2.
"""
# asp_code_2 = ''
#
# asp_code_2 += """
# vertex(v1).
# vertex(v2).
# vertex(v3).
# vertex(v4).
# edge(v1,v2).
# edge(v1,v3).
# edge(v2,v3).
# edge(v2,v4).
# edge(v3,v4).
# """;
#
# asp_code_2 += """
# color(V,1) :- vertex(V), not color(V,2), not color(V,3).
# color(V,2) :- vertex(V), not color(V,1), not color(V,3).
# color(V,3) :- vertex(V), not color(V,1), not color(V,2).
# """
#
# asp_code_2 += """
# :- edge(V1,V2), color(V1,C), color(V2,C).
# """
#
# asp_code_2 += """
# #show color/2.
# """
#
# print(asp_code)
#
# print(asp_code_2)
# print_answer_sets(asp_code)
control = clingo.Control()
control.add("base", [], asp_code)
control.ground([("base", [])])
def on_model(model):
model.symbols(shown=False)
control.configuration.solve.models = 1
answer = control.solve(on_model=on_model)
if answer.satisfiable:
solution = []
with control.solve(yield_=True) as handle:
for model in handle:
# solution.append(model.symbols(shown=True))
for atom in model.symbols(shown=True):
solution.append(str(atom))
solution.sort(key=lambda x: int(x.split(',')[0].split('(v')[-1]))
result = np.ones(length**2, dtype=int)
for i, vertex in enumerate(solution):
res = vertex[:-1].split(',')[-1]
res = int(res)
result[i] = res
result = result.reshape(length, length).tolist()
return result
else:
return None
def main():
## Parse input data
args = parse_params()
## Check for input errors
check_input(args)
# Input data
## STFT parameters
sr = 44100.0 # Sample Rate
N = args.samples # FFT size or Number of Samples
M = N # Window size
H = int(M / 64) # Hop size
B = args.erb # ERB Bands
low_lim = 20 # centre freq. of lowest filter
high_lim = sr / 2 # centre freq. of highest filter
threshold = args.essential_threshold # Essential bands
analyze = args.analyze # Extract audio features or use instances in dir
s = args.s
q = args.q
benchmark = args.benchmarks
solve = not benchmark
## ASP variables
project = args.project #project name
models = [] # answer sets
tracks = []
tracks_duration = []
## Read wav files from project
print("Reading tracks...")
for fl in os.listdir("projects/%s/" % project):
if fl.endswith(".wav"):
tracks.append(os.path.splitext(fl)[0])
if analyze:
t = "projects/%s/%s.wav" % (project, os.path.splitext(fl)[0])
with contextlib.closing(wave.open(t, 'r')) as f:
frames = f.getnframes()
rate = f.getframerate()
duration = frames / float(rate)
tracks_duration.append(duration)
if analyze:
duration = int(round(max(tracks_duration)))
print("Mixdown duration (secs): %s" % duration)
## create tracks instance lp
## get audio features and create each track_name.lp file
file_params = "S%s_B%s_ET%s_MF%s" % (N, B, args.essential_threshold,
args.masking_factor)
## Create clingo object and load instances
## Add arguments
if solve:
clingo_args = [
"--sign-def=rnd", "--sign-fix", "--rand-freq=1",
"--seed=%s" % random.randint(0, 32767), "--restart-on-model",
"--enum-mode=record"
]
control = clingo.Control(clingo_args)
## Load eq.lp
control.load("lp/eq.lp")
## for each track
track_number = 1
frequencies = []
spectrums = []
erbs = []
filters = []
for track in tracks:
if analyze:
print(" Building instance: %s" % track)
## Get spectrum and signal size
spectrum, len_signal = af.get_spectrum(
"projects/%s/%s" % (project, track), sr, N, M, H)
# Equivalent Rectangular Bandwidth
erb_bands, bandwidths, frequencies, center_fr, filters = af.get_erb_bands(
spectrum, len_signal, sr, B, low_lim, high_lim)
# Save data for plotting
spectrums.append(spectrum)
erbs.append(erb_bands)
# ASP instances
instance = "projects/%s/%s.lp" % (project, track)
if analyze: ## If not benchmark
file = open(instance, "w")
af.build_asp_instance(file, track_number, instance, erb_bands,
threshold)
file.close()
if solve:
control.load("projects/%s/%s.lp" % (project, track))
track_number += 1
# Build mixdown graphics
if analyze:
print("Building graphics...")
af.build_graphics(frequencies, spectrums, "projects/%s" % (project),
project, erbs, B, filters, tracks, False)
## Number of mixes
if solve:
control.configuration.solve.models = args.mixes
## Add masking factor
control.add(
"p", [],
"#const masking_factor = %s." % int(args.masking_factor * 100))
## Ground
print("Grounding...")
control.ground([("base", [])])
control.ground([("p", [])])
## Solve
if s >= 0:
print("Propagator Registered for Sampling")
control.register_propagator(Propagator(s, q))
print("Solving...")
solve_result = control.solve(
None, lambda model: models.append(model.symbols(shown=True)))
if str(solve_result) == "SAT":
print("%s, Exhausted: %s" % (solve_result, solve_result.exhausted))
print(models)
print("")
results_path = "%s/results/" % ("projects/%s" % (project))
dir = os.path.dirname(results_path)
if not os.path.exists(dir):
os.makedirs(dir)
file = open("%s/plan_%s.txt" % (results_path, file_params), "w")
for answer in models:
answer_number = models.index(answer) + 1
print("Answer: %s" % answer_number)
#Plan
file.write("Answer: %s \n" % answer_number)
eqs = af.parse_answer_sets_to_plan(file, tracks, answer,
center_fr, bandwidths)
file.write("\n")
#Csound
csound_file = "Answer_%s_mixdown_%s.csd" % (answer_number,
file_params)
file_csd = open("%s/%s" % (results_path, csound_file), "w")
csd.create_header(file_csd, results_path, csound_file)
for i in range(len(tracks)):
if (i + 1) in eqs:
## Create csound instrument with EQs
csd.create_instrument(file_csd, i + 1, eqs[(i + 1)])
else:
## Create csound instrument without EQ
csd.create_instrument(file_csd, i + 1, None)
# Csound Bridge between Orchestra and Scores
csd.create_bridge(file_csd)
# Csound Orchestra
for i in range(len(tracks)):
csd.create_orchestra(file_csd, (i + 1), tracks[i],
duration)
# Csound Footer
csd.create_footer(file_csd)
# Close file
file_csd.close()
# Render csound files
#if args.normalize:
# print("normalize tracks")
csd.render(results_path, csound_file)
print("")
file.close()
else:
print(
"No masking detected for the given values masking-factor and/or essential-threshold"
)
if benchmark:
## Create a single file instance with the masking factor constant and with all the sub instances of the project
print("Generating Benchmarks")
# ctr(I, K):- ctr(I-1, K), not_item(I).
# :- ctr(u, K), K > m.
# :- ctr(u, K), K < n.
# ctr(0, 0).
# """
###################################
asp_code += """binary(1..10).
{ choice(X): binary(X)}.
#show choice/1.\n
"""
print(asp_code)
control = clingo.Control()
control.add("base", [], asp_code)
control.ground([("base", [])])
def on_model(model):
print(model.symbols(shown=True))
control.configuration.solve.models = 0
answer = control.solve(on_model=on_model)
if answer.satisfiable:
print("satisfiable:")
else:
print("unsatisfiable")
def __init__(self, args):
self.control = clingo.Control()
self.args = args
self.numberOfMachines = 0
self.numberOfJobs = 0
def execute(pcontext, rewritten, facts, plugins, config):
# prepare contexts that are for this program but not yet specific for a clasp solver process
# (multiple clasp solvers are used for finding compatible sets and for checking FLP property)
# preparing clasp context which does not hold concrete clasp information yet
# (such information is added during propagation)
ccontext = ClaspContext()
# preparing evaluator for external atoms which needs to know the clasp context
#eaeval = EAtomEvaluator(ccontext)
eaeval = CachedEAtomEvaluator(ccontext)
# find names of external atoms that advertises to do checks on a partial assignment
partial_evaluation_eatoms = [ eatomname for eatomname, info in dlvhex.eatoms.items() if info.props.provides_partial ]
# XXX we could filter here to reduce this set or we could decide to do no partial evaluation at all or we could do this differently for FLP checker and Compatible Set finder
should_do_partial_evaluation_on = partial_evaluation_eatoms
propagatorFactory = lambda name: ClingoPropagator(name, pcontext, ccontext, eaeval, should_do_partial_evaluation_on)
if config.flpcheck == 'explicit':
flp_checker_factory = flp.ExplicitFLPChecker
else:
assert(config.flpcheck == 'none')
flp_checker_factory = flp.DummyFLPChecker
flpchecker = flp_checker_factory(propagatorFactory)
# TODO get settings from commandline
cmdlineargs = []
if config.number != 1:
cmdlineargs.append(str(config.number))
# just in case we need optimization
cmdlineargs.append('--opt-mode=optN')
cmdlineargs.append('--opt-strategy=usc,9')
logging.info('sending nonground program to clingo control')
cc = clingo.Control(cmdlineargs)
sendprog = shp.shallowprint(rewritten)
try:
logging.debug('sending program ===\n'+sendprog+'\n===')
cc.add('base', (), sendprog)
except:
raise Exception("error sending program ===\n"+sendprog+"\n=== to clingo:\n"+traceback.format_exc())
# preparing context for instantiation
# (this class is specific to the gringo API)
logging.info('grounding with gringo context')
ccc = GringoContext(eaeval)
flpchecker.attach(cc)
cc.ground([('base',())], ccc)
logging.info('preparing for search')
# name of this propagator CSF = compatible set finder
checkprop = propagatorFactory('CSF')
cc.register_propagator(checkprop)
mr = ModelReceiver(facts, config, flpchecker)
logging.info('starting search')
cc.solve(on_model=mr)
# TODO return code for unsat/sat/opt?
return 0
('fixed', len(ba.label) + 4, ba),
('fixed', len(bb.label) + 4, bb), sf
], 1)
f = urwid.Frame(urwid.Filler(c.display), None, b, 'footer')
palette = [
('red', 'black', 'light red'),
('green', 'black', 'light green'),
('blue', 'black', 'light blue'),
]
self.loop = urwid.MainLoop(f, palette)
self.loop.run()
c = clingo.Control()
c.add("check", ["k"], "#external query(k).")
for f in sys.argv[1:]:
c.load(f)
def make_on_model(field, stone, move, target):
def on_model(m):
for atom in m.symbols(atoms=True):
if atom.name == "field" and len(atom.arguments) == 2:
x, y = [n.number for n in atom.arguments]
field.append((x, y))
elif atom.name == "stone" and len(atom.arguments) == 5:
s, d, x, y, l = [(n.number if n.type
== clingo.SymbolType.Number else str(n))
for n in atom.arguments]
def solve_planning_problem_using_ASP(planning_problem, t_max):
"""
Parameters:
planning_problem (PlanningProblem): Planning problem for which a shortest plan is to be found.
t_max (int): The upper bound on the length of plans to consider.
Returns:
(list(Expr)): A list of expressions (each of which specifies a ground action) that composes
a shortest plan for planning_problem (if some plan of length at most t_max exists),
and None otherwise.
"""
def encode_init_ASP(planning_problem, t_max):
"""
Method for encoding the initial statements into ASP
input: planning problem in PDDL
returns: planning problem in ASP encoding
"""
#encode t_max time steps
asp_code = ""
asp_code += "#const tMax={}.\n".format(t_max - 1)
asp_code += "time(0..tMax).\n"
#encode initial statements as state(T, init(x,y)) for T=0
#swapcase used to switch caps and lowercase for different representation
#of variables and constants in DPPL vs ASP
for init in planning_problem.initial:
asp_code += "state(0, {}).\n".format(init.__repr__().swapcase())
return asp_code
def encode_actions_ASP(planning_problem):
"""
Method for encoding the actions into ASP
input: planning problem in PDDL
returns: planning problem in ASP encoding
"""
asp_code = ""
for action in planning_problem.actions:
available_action = "available(Time, {}) :- not goal_saved(Time), time(Time)".format(
action.__repr__().swapcase())
if not action.precond:
available_action = available_action
else:
for prec in action.precond:
if str(
prec
)[0] == '~': #check if a negation is present to alter rule to not state
prec = expr(str(prec)[1:])
available_action += ", not state(Time, {})".format(
prec.__repr__().swapcase())
else:
available_action += ", state(Time, {})".format(
prec.__repr__().swapcase())
fluent = "state(Time, S) :- time(Time), state(Time-1, S), chosen(Time-1, {})".format(
action.__repr__().swapcase())
effect = ""
for effects in action.effect:
if str(
effects
)[0] == '~': #check if a negation is present to alter rule to not same
fluent += ", S!={}".format(str(effects)[1:].swapcase())
else:
effect += "state(Time, {}) :- time(Time), chosen(Time-1, {}). \n".format(
effects.__repr__().swapcase(),
action.__repr__().swapcase())
available_rule = "1{chosen(Time, A) : available(Time, A)}1 :- time(Time), available(Time, _). \n"
asp_code += effect + fluent + ". \n" + available_action + ". \n" + available_rule
return asp_code
def encode_goals_ASP(planning_problem):
"""
Method for encoding the goals into ASP
input: planning problem in PDDL
returns: planning problem in ASP encoding
"""
asp_code = ""
goallist = []
for goal in planning_problem.goals:
if str(
goal
)[0] == '~': #check if a negation is present to alter rule to not state
goal = expr(str(goal)[1:])
goallist += [
"not state(Time, {})".format(goal.__repr__().swapcase())
]
else:
goallist += [
"state(Time, {})".format(goal.__repr__().swapcase())
]
#add all goal rules, constraints and optimizations
asp_code += "goal(Time) :- " + "{}. \n".format(', '.join(goallist))
asp_code += "goal_saved(Time) :- goal(Time). \n"
asp_code += "goal_saved(Time) :- time(Time), goal_saved(Time-1). \n"
#constraint
asp_code += ":- time(Time), not goal(_). \n"
#optimization statement
asp_code += "#minimize{Time : goal(Time)}. \n"
#show the chosen in answer set
asp_code += '#show chosen/2. \n'
return asp_code
def return_plan(sorted_model):
"""
Method to extract the planning from the model
Probably not the best way to do it but could not fix it otherwise
"""
plan_length = len(sorted_model)
#used to order list according to T in chose(T, A)
plan = [None] * plan_length
for plans in sorted_model:
#find all words in the plan
words = re.findall(r'\w+', plans)
#extract action
action = words[2].swapcase()
#extract arguments
arguments = [word.swapcase() for word in words[3:]]
plan[int(words[1])] = action + "({})".format(', '.join(arguments))
return plan
def on_model(model):
"""
check if optimal model, if optimal model,
sort results and return planning
"""
if model.optimality_proven == True:
model = [str(atom) for atom in model.symbols(shown=True)]
model.sort()
global planning
planning = return_plan(model)
#create the ASP encoding
asp_code = ""
asp_code += encode_init_ASP(planning_problem, t_max)
asp_code += encode_actions_ASP(planning_problem)
asp_code += encode_goals_ASP(planning_problem)
#solve the ASP encoding
control = clingo.Control()
control.add("base", [], asp_code)
control.ground([("base", [])])
#optimization statements, show 1 model
control.configuration.solve.opt_mode = "optN"
control.configuration.solve.models = 1
#need this to get planning back, must be another way to extract from clingo.model
answer = control.solve(on_model=on_model)
#return None if no plan else return planning
return planning
def __init__(self, args):
self.control = clingo.Control()
self.args = args
self.horizon = 0
self.objects = 0
self.end = None
def test_control_model_integration(self):
spu = SymbolPredicateUnifier()
@spu.register
class Afact(Predicate):
num1 = IntegerField()
num2 = IntegerField()
str1 = StringField()
@spu.register
class Bfact(Predicate):
num1 = IntegerField()
str1 = StringField()
af1 = Afact(1, 10, "bbb")
af2 = Afact(2, 20, "aaa")
af3 = Afact(3, 20, "aaa")
bf1 = Bfact(1, "aaa")
bf2 = Bfact(2, "bbb")
fb1 = FactBase()
fb1.add([af1, af2, af3, bf1, bf2])
fb2 = None
def on_model(model):
nonlocal fb2
self.assertTrue(model.contains(af1))
self.assertTrue(model.contains(af1.raw))
self.assertTrue(model.model_.contains(af1.raw))
fb2 = model.facts(spu, atoms=True)
# Check that the known attributes behave the same as the real model
self.assertEqual(model.cost, model.model_.cost)
self.assertEqual(model.number, model.model_.number)
self.assertEqual(model.optimality_proven,
model.model_.optimality_proven)
self.assertEqual(model.thread_id, model.model_.thread_id)
self.assertEqual(model.type, model.model_.type)
# Note: the SolveControl object returned is created dynamically on
# each call so will be different for both calls. So test that the
# symbolic_atoms property is the same.
sas1 = set(model.context.symbolic_atoms)
sas2 = set(model.model_.context.symbolic_atoms)
self.assertEqual(len(sas1), len(sas2))
# Test that clorm.clingo.Model produces the correct string
self.assertEqual(str(model), str(model.model_))
self.assertEqual(repr(model), repr(model.model_))
# Use the orignal clingo.Control object so that we can test the wrapper call
ctrlX_ = oclingo.Control()
ctrl = cclingo.Control(control_=ctrlX_)
ctrl.add_facts(fb1)
ctrl.ground([("base", [])])
ctrl.solve(on_model=on_model)
# Check that the known control attributes behave the same as the real control
cfg1 = ctrl.configuration
cfg2 = ctrl.control_.configuration
self.assertEqual(len(cfg1), len(cfg2))
self.assertEqual(set(cfg1.keys), set(cfg2.keys))
sas1 = set(ctrl.symbolic_atoms)
sas2 = set(ctrl.control_.symbolic_atoms)
self.assertEqual(len(sas1), len(sas2))
self.assertEqual(ctrl.is_conflicting, ctrl.control_.is_conflicting)
stat1 = ctrl.statistics
stat2 = ctrl.control_.statistics
self.assertEqual(len(stat1), len(stat2))
tas1 = ctrl.theory_atoms
tas2 = ctrl.control_.theory_atoms
self.assertEqual(len(list(tas1)), len(list(tas2)))
# _control_add_facts works with both a list of facts (either
# clorm.Predicate or clingo.Symbol instances) and a FactBase
ctrl2 = Control()
ctrl2.add_facts([af1, af2, af3.raw, bf1.raw, bf2])
safact1 = fb2.select(Afact).where(Afact.num1 == ph1_)
safact2 = fb2.select(Afact).where(Afact.num1 < ph1_)
self.assertEqual(safact1.get_unique(1), af1)
self.assertEqual(safact1.get_unique(2), af2)
self.assertEqual(safact1.get_unique(3), af3)
self.assertEqual(set(list(safact2.get(1))), set([]))
self.assertEqual(set(list(safact2.get(2))), set([af1]))
self.assertEqual(set(list(safact2.get(3))), set([af1, af2]))
self.assertEqual(
fb2.select(Bfact).where(Bfact.str1 == "aaa").get_unique(), bf1)
self.assertEqual(
fb2.select(Bfact).where(Bfact.str1 == "bbb").get_unique(), bf2)
import clingo
def mop(m: clingo.Model):
av = set()
for symbol in m.symbols(shown=True):
# We expect atoms of the form `currentExecutable(move(X, Y)`
# but we are only interested in the first argument `move(X, Y)`
#av.add(str(symbol.arguments[0]))
sym = str(symbol)
av.add(sym)
print(sorted(av))
def mip(m: clingo.Model):
print(m)
def mep(m: clingo.Model):
mip(m)
mop(m)
ctr = clingo.Control()
ctr.load("SimpleClingo.lp")
#ctr.load("frozenLake4x4.lp")
ctr.ground([("base", [])])
ctr.solve(on_model=mep)
def test_control_and_model_wrapper(self):
# Test a control wrapper of a wrapper
ctrl0 = oclingo.Control()
ctrl1 = cclingo.Control(control_=ctrl0)
ctrl2 = cclingo.Control(control_=ctrl1)
ctrl2.ground([("base", [])])
with ctrl2.solve(yield_=True) as sh:
tmp = [m.facts(unifier=[], atoms=True) for m in sh]
self.assertEqual(len(tmp), 1)
self.assertEqual(len(tmp[0]), 0)
# Test a model wrapper
ctrl = oclingo.Control()
ctrl.ground([("base", [])])
with ctrl.solve(yield_=True) as sh:
for n, m_ in enumerate(sh):
self.assertEqual(n, 0)
m = cclingo.Model(model=m_, unifier=[])
self.assertEqual(len(m.facts()), 0)
# Test wrapping a bad object - missing attributes and functions
# Missing ground function
with self.assertRaises(AttributeError) as ctx:
class Bad(object):
def solve(self):
pass
bad = Bad()
ctrl = cclingo.Control(control_=bad)
check_errmsg("'Bad' object has no attribute 'ground'", ctx)
# Missing solve function
with self.assertRaises(AttributeError) as ctx:
class Bad(object):
def ground(self):
pass
bad = Bad()
ctrl = cclingo.Control(control_=bad)
check_errmsg("'Bad' object has no attribute 'solve'", ctx)
# Ground is an attribute but not a function
with self.assertRaises(AttributeError) as ctx:
class Bad(object):
def __init__(self):
self.ground = 4
def solve(self):
pass
bad = Bad()
ctrl = cclingo.Control(control_=bad)
check_errmsg(("Wrapped object of type '{}' does not have a "
"function 'ground()'").format(type(bad)), ctx)
# Solve is an attribute but not a function
with self.assertRaises(AttributeError) as ctx:
class Bad(object):
def __init__(self):
self.solve = 4
def ground(self):
pass
bad = Bad()
ctrl = cclingo.Control(control_=bad)
check_errmsg(("Wrapped object of type '{}' does not have a "
"function 'solve()'").format(type(bad)), ctx)
# Model wrapper with no "symbols" function or attribute
with self.assertRaises(AttributeError) as ctx:
class Bad(object):
def __init__(self):
self.symbols = 2
bad = Bad()
m = cclingo.Model(model=bad)
check_errmsg(("Wrapped object of type '{}' does not have a "
"function 'symbols()'").format(type(bad)), ctx)
with self.assertRaises(AttributeError) as ctx:
class Bad(object):
def __init__(self):
pass
bad = Bad()
m = cclingo.Model(model=bad)
check_errmsg("'Bad' object has no attribute 'symbols'", ctx)
def learn(self,
list_of_obs,
num_of_samples=50,
lr=0.01,
thres=0.0001,
max_iter=None,
num_pretrain=5):
time_init = time.time()
check_continue = True
# Step 1: Parameter Pre-training: we pretrain the parameters
# so that it's easier to generate sample stable models
assert type(num_pretrain) is int
if num_pretrain >= 1:
if self.args.verbosity >= 6:
print(
"\n#######################################################\nParameter Pre-training for {} iterations...\n#######################################################"
.format(num_pretrain))
for iteration in range(num_pretrain):
if self.args.verbosity >= 6:
print(
"\n#### Iteration {} for Pre-Training ####\nGenerating 1 stable model for each observation...\n"
.format(iteration + 1))
dif = lr * self.gradients_multi_obs_by_one_sample(list_of_obs)
self.parameters = (np.array(self.parameters) + dif).tolist()
self.normalize_probs()
if self.args.verbosity >= 6:
print("After {} seconds of training (in total)".format(
time.time() - time_init))
print("Current parameters: {}".format(self.parameters))
# For each observation, decide which observation uses learn exact or learn sample
learn_obs_decider = []
for obs in list_of_obs:
crl = clingo.Control(['0', "--warn=none"])
crl.add("base", [], self.pi_prime + obs)
crl.ground([("base", [])])
with crl.solve(yield_=True) as handle:
counter = 0
for m in handle:
counter += 1
if counter == 2500:
break
if counter == 2500:
learn_obs_decider.append('sample')
else:
learn_obs_decider.append('exact')
if self.args.verbosity >= 6:
print("Leraning from Observations Decider: ", learn_obs_decider)
self.all_SM_under_obs = {}
for i, method in enumerate(learn_obs_decider):
if method == 'exact':
self.all_SM_under_obs[i] = self.find_all_SM_under_obs(
list_of_obs[i])
iteration = 1
while check_continue:
if self.args.verbosity >= 4:
print("\n#### Iteration {} ####".format(iteration))
old_parameters = np.array(self.parameters)
check_continue = False
total_dif = np.array([[0.0 for item in l]
for l in self.parameters])
for i, obs in enumerate(list_of_obs):
if learn_obs_decider[i] == 'sample':
dif = lr * self.gradients_multi_obs_by_sampling(
[obs], num=num_of_samples)
else:
dif = lr * self.gradients_one_obs(i)
for ruleIdx, list_of_bools in enumerate(self.learnable):
# 1st, we turn each gradient into [-0.2, 0.2]
for atomIdx, b in enumerate(list_of_bools):
if b == True:
if dif[ruleIdx][atomIdx] > 0.2:
dif[ruleIdx][atomIdx] = 0.2
elif dif[ruleIdx][atomIdx] < -0.2:
dif[ruleIdx][atomIdx] = -0.2
total_dif += dif
self.parameters = (np.array(self.parameters) + total_dif).tolist()
self.normalize_probs()
if self.args.verbosity >= 6:
print("After {} seconds of training (in total)".format(
time.time() - time_init))
print("Current parameters: {}".format(self.parameters))
# we termintate if the change of the parameters is lower than thres
dif = np.array(self.parameters) - old_parameters
dif = abs(max(dif.min(), dif.max(), key=abs))
if self.args.verbosity >= 6:
print("Max change on probabilities: {}".format(dif))
iteration += 1
if dif > thres:
check_continue = True
if max_iter is not None:
if iteration > max_iter:
check_continue = False
if self.args.verbosity >= 4:
print("Final parameters: {}".format(self.parameters))
self.write_final_program()
def solve_sudoku_ASP(sudoku: List[List[int]], k: int) -> Union[List[List[int]], None]:
"""Solve a k-by-k block Sudoku puzzle by encoding it as an answer set programming (ASP) problem
and using an ASP solver
:param sudoku: A Sudoku puzzle represented as a list of lists. Values to be filled in are set to zero (0)
:type sudoku: List[List[int]]
:param k: Size of the Sudoku puzzle (a grid of k x k blocks)
:type k: int
:return: The solved sudoku puzzles as a list of lists or None if no solution is found.
:rtype: Union[List[List[int]], None]
"""
###
### Solution Notes
###
### Inspiration from the following references:
### - Chapter 3 of Answer Set Solving in Practice
### - 'block' helper function retrieved from the blog of Enrico Höschler:
### https://ddmler.github.io/asp/2018/07/10/answer-set-programming-sudoku-solver.html
###
### sudoku(R, C, V) denotes a sudoku cell at a row index (R) and column index (C) filled with value (V)
###
### There are n == k**2 such rows, columns, and values
###
### Rows and columns are indexed at zero (0). Values start at one (1)
###
### Constraints:
### - Cardinality and Choice: Each cell must have exactly one value in the domain [1,n]
### - Integrity: No cell in the same row may share a value
### - Integrity: No cell in the same column may share a value
### - Integrity: No cell in the same block may share a value
solved_sudoku = copy(sudoku)
grid_size = k**2
# Build a string where each line is the fact corresponding to a pre-filled cell
facts = ''
for row_index, column_index in get_cell_indices(k):
value = solved_sudoku[row_index][column_index]
if value != 0:
facts += f'sudoku({row_index}, {column_index}, {value}).\n'
# As noted above, this was inspired by Enrico Höschler's blog
# The rule applies if two cells are in the same block
same_block_rule = 'block(R1, C1, R2, C2) :- row(R1), row(R2), column(C1), column(C2), R1/k == R2/k, C1/k == C2/k.'
# Constraints
cell_uniqueness = '1{sudoku(R, C, V): value(V)}1 :- row(R) , column(C).'
row_integrity = ':- sudoku(R, C1, V), sudoku(R, C2, V), C1 != C2.'
column_uniqueness = ':- sudoku(R1, C, V), sudoku(R2, C, V), R1 != R2.'
block_uniqueness = ':- sudoku(R1, C1, V), sudoku(R2, C2, V), block(R1, C1, R2, C2), R1 != R2, C1 != C2.'
asp_program = f"""
#const k = {k}.
#const n = {grid_size}.
row(0..n-1).
column(0..n-1).
value(1..n).
{facts}
{same_block_rule}
{cell_uniqueness}
{row_integrity}
{column_uniqueness}
{block_uniqueness}
"""
# Fill in the puzzle once an answer set is found
def on_model(model):
for atom in model.symbols(atoms=True):
if atom.name == 'sudoku':
row_index, column_index, value = atom.arguments
solved_sudoku[row_index.number][column_index.number] = value.number
# Encode and solve
control = clingo.Control()
control.add("base", [], asp_program)
control.ground([("base", [])])
control.configuration.solve.models = 1 # we only need one feasible solution
answer = control.solve(on_model=on_model)
if not answer.satisfiable:
return None
return solved_sudoku
if self.trace:
print(" NEXT: ", self.trace[0]["state"],
self.trace[0]["thread_id"])
self.cv.notify_all()
self.cv.release()
def propagate(self, control, changes):
self.run_trace(control, control.thread_id, "propagate")
def check(self, control):
self.run_trace(control, control.thread_id, "check")
if __name__ == "__main__":
ctl = clingo.Control(["-t3", "0"])
ctl.register_propagator(Retracer())
print("============ STEP 1 ============")
ctl.add(
"step1", [], """\
#theory cp {
sum_term { };
&minimize/0 : sum_term, directive
}.
&minimize { x }.
""")
ctl.ground([("step1", [])])
n = 0
for m in ctl.solve(yield_=True):
#!/usr/bin/env python
import clingo
def om(m):
print "Model: " + str(m.atoms())
#init
prg=clingo.Control("0")
prg.add("base",[],"{a}. b :- a. #external base_e.")
prg.ground([("base",[])])
prg.assign_external(clingo.Function("base_e"), True)
print prg.solve(om, None)
#assert new_e1
print "?- assert new_e1."
prg.add("ext1",[],"#external new_e1.")
prg.ground([("ext1",[])])
prg.assign_external(clingo.Function("new_e1"), True)
print prg.solve(om, None)
#assert new_e2.
print "?- assert new_e2."
prg.add("ext2",[],"#external new_e2.")
prg.ground([("ext2",[])])
prg.assign_external(clingo.Function("new_e2"), True)
print prg.solve(om, None)
#retract new_1.
print "?- retract new_1."
prg.release_external(clingo.Function("new_e1"))
import clingo
cc = clingo.Control([])
cc.load("./simple.lp")
solution = cc.solve()
print(str(solution))
import sys
def inject_one(val):
return clingo.create_num(val.num() + val.num())
def inject_two(val):
return [val, clingo.create_num(val.num() + val.num())]
def on_model(m):
print m
c = clingo.Control(sys.argv)
c.add(
"base", [],
"#external c. a :- c. a :- not b. b :- not a. r(@inject_one(2)). r(@inject_two(3)). #program p(k). q(k)."
)
c.ground([("base", []), ("p", [clingo.create_num(17)])], sys.modules[__name__])
print c.solve(on_model=on_model)
vals = [
clingo.create_id("p"),
clingo.create_str("q"),
clingo.create_num(42), clingo.Sup, clingo.Inf
]
vals.append(clingo.create_fun("f", vals, True))
vals.append(clingo.create_fun("", vals))
print vals