def runTest(self):
from reloop.languages.rlp.logkb import PyDatalogLogKb
#permute all parameters of the method for profit, but only once at a time
logkb = PyDatalogLogKb()
query_symbols = None
logical_query = None
coeff_expr = None
result = logkb.ask(query_symbols, logical_query, coeff_expr)
def runTest(self):
from reloop.languages.rlp.logkb import PyDatalogLogKb
#permute all parameters of the method for profit, but only once at a time
logkb = PyDatalogLogKb()
query_symbols = None
logical_query = None
coeff_expr = None
result = logkb.ask(query_symbols,logical_query,coeff_expr)
def setUp(self):
from reloop.languages.rlp.logkb import PyDatalogLogKb
from pyDatalog import pyDatalog
import random
from reloop.languages.rlp import numeric_predicate
self.logkb = PyDatalogLogKb()
self.predicate = numeric_predicate("test_predicate", 1)
self.float_test_data = random.random()
self.integer_test_data = random.randint(1, 100)
pyDatalog.assert_fact("test_predicate", 'b', self.integer_test_data)
pyDatalog.assert_fact("test_predicate", 'a', self.float_test_data)
def test_sudoku(self):
from pyDatalog import pyDatalog
from examples.RLP import sudoku_example
from reloop.languages.rlp.logkb import PyDatalogLogKb
for u in range(1, 10):
pyDatalog.assert_fact('num', u)
for u in range(1, 4):
pyDatalog.assert_fact('boxind', u)
pyDatalog.assert_fact('initial', 1, 1, 5)
pyDatalog.assert_fact('initial', 2, 1, 6)
pyDatalog.assert_fact('initial', 4, 1, 8)
pyDatalog.assert_fact('initial', 5, 1, 4)
pyDatalog.assert_fact('initial', 6, 1, 7)
pyDatalog.assert_fact('initial', 1, 2, 3)
pyDatalog.assert_fact('initial', 3, 2, 9)
pyDatalog.assert_fact('initial', 7, 2, 6)
pyDatalog.assert_fact('initial', 3, 3, 8)
pyDatalog.assert_fact('initial', 2, 4, 1)
pyDatalog.assert_fact('initial', 5, 4, 8)
pyDatalog.assert_fact('initial', 8, 4, 4)
pyDatalog.assert_fact('initial', 1, 5, 7)
pyDatalog.assert_fact('initial', 2, 5, 9)
pyDatalog.assert_fact('initial', 4, 5, 6)
pyDatalog.assert_fact('initial', 6, 5, 2)
pyDatalog.assert_fact('initial', 8, 5, 1)
pyDatalog.assert_fact('initial', 9, 5, 8)
pyDatalog.assert_fact('initial', 2, 6, 5)
pyDatalog.assert_fact('initial', 5, 6, 3)
pyDatalog.assert_fact('initial', 8, 6, 9)
pyDatalog.assert_fact('initial', 7, 7, 2)
pyDatalog.assert_fact('initial', 3, 8, 6)
pyDatalog.assert_fact('initial', 7, 8, 8)
pyDatalog.assert_fact('initial', 9, 8, 7)
pyDatalog.assert_fact('initial', 4, 9, 3)
pyDatalog.assert_fact('initial', 5, 9, 1)
pyDatalog.assert_fact('initial', 6, 9, 6)
pyDatalog.assert_fact('initial', 8, 9, 5)
pyDatalog.load("""
box(I, J, U, V) <= boxind(U) & boxind(V) & num(I) & num(J) & (I > (U-1)*3) & (I <= U*3) & (J > (V-1)*3) & (J <= V*3)
""")
logkb = PyDatalogLogKb()
grounder = BlockGrounder(logkb)
# Note: CVXOPT needs to be compiled with glpk support. See the CVXOPT documentation.
solver = CvxoptSolver(solver_solver='glpk')
model = sudoku_example.sudoku(grounder, solver)
self.assertEqual(model, 0, "ERROR : Sudoku couldn't be solved")
def test_pydatalog(self):
from pyDatalog import pyDatalog
from examples.RLP import maxflow_example
from reloop.languages.rlp.logkb import PyDatalogLogKb
pyDatalog.assert_fact('node', 'a')
pyDatalog.assert_fact('node', 'b')
pyDatalog.assert_fact('node', 'c')
pyDatalog.assert_fact('node', 'd')
pyDatalog.assert_fact('node', 'e')
pyDatalog.assert_fact('node', 'f')
pyDatalog.assert_fact('node', 'g')
pyDatalog.assert_fact('edge', 'a', 'b')
pyDatalog.assert_fact('edge', 'a', 'c')
pyDatalog.assert_fact('edge', 'b', 'd')
pyDatalog.assert_fact('edge', 'b', 'e')
pyDatalog.assert_fact('edge', 'c', 'd')
pyDatalog.assert_fact('edge', 'c', 'f')
pyDatalog.assert_fact('edge', 'd', 'e')
pyDatalog.assert_fact('edge', 'd', 'f')
pyDatalog.assert_fact('edge', 'e', 'g')
pyDatalog.assert_fact('edge', 'f', 'g')
pyDatalog.assert_fact('source', 'a')
pyDatalog.assert_fact('target', 'g')
pyDatalog.assert_fact('cost', 'a', 'b', 50)
pyDatalog.assert_fact('cost', 'a', 'c', 100)
pyDatalog.assert_fact('cost', 'b', 'd', 40)
pyDatalog.assert_fact('cost', 'b', 'e', 20)
pyDatalog.assert_fact('cost', 'c', 'd', 60)
pyDatalog.assert_fact('cost', 'c', 'f', 20)
pyDatalog.assert_fact('cost', 'd', 'e', 50)
pyDatalog.assert_fact('cost', 'd', 'f', 60)
pyDatalog.assert_fact('cost', 'e', 'g', 70)
pyDatalog.assert_fact('cost', 'f', 'g', 70)
logkb = PyDatalogLogKb()
grounder = BlockGrounder(logkb)
solver = CvxoptSolver()
model = maxflow_example.maxflow(grounder, solver)
self.assertEqual(model, 0, "PyDataLog Blockgrounding Failed")
numpy.set_printoptions(threshold=numpy.nan)
loadFacts("facts/simple_matrices.facts")
scenario(X)
for sname in X.data:
print(sname)
assertAll("activeScenario", [[sname]])
logfile = open("logs/{0}.blocklp.log".format(sname), "w", 0)
print >> logfile, "DOBSS BLOCK LP"
print >> logfile, "BEGIN GAME MATRIX"
print >> logfile, gameMatrix(sname, X, Y, V, W)
print >> logfile, "END GAME MATRIX"
analyzer = LiftingAnalysis(logfile,
dumpSingleMatrices=True,
dumpBlockMatrix=True)
solver = lambda: analyzer
logkb = PyDatalogLogKb()
grounder = BlockGrounder
solve_dobss_block(grounder, logkb, solver)
analyzer.subproblemLiftingAnalysis()
logfile.close()
retractAll("activeScenario", 1)