def check_evaluate(id_dp, dp):
""" Test for PrimitiveDP:evaluate() """
print('Testing %s: %s' % (id_dp, dp))
F = dp.get_fun_space()
R = dp.get_res_space()
UR = UpperSets(R)
LF = LowerSets(F)
M = dp.get_imp_space()
# We get a random m
m0 = M.witness()
try:
lf, ur = dp.evaluate(m0)
except NotSolvableNeedsApprox:
return
UR.belongs(ur)
LF.belongs(lf)
if not lf.maximals or not ur.minimals:
msg = 'The point m0 gave empty lf, ur.'
raise_desc(Exception,
msg,
lf=lf,
ur=ur,
m0=M.format(m0),
M=M,
dp=dp.repr_long())
# take one possible feasible pair
_f = list(lf.maximals)[0]
_r = list(ur.minimals)[0]
def eval_constant_lowersetfromcollection(op, context):
x = eval_constant(op.value, context)
v = x.value
u = x.unit
S = u.S
maximals = poset_minima(v.elements, S.leq)
value = LowerSet(maximals, S)
unit = LowerSets(S)
if do_extra_checks():
unit.belongs(value)
vu = ValueWithUnits(value, unit)
return vu
def eval_constant_lowersetfromcollection(op, context):
x = eval_constant(op.value, context)
v = x.value
u = x.unit
S = u.S
maximals = poset_minima(v.elements, S.leq)
value = LowerSet(maximals, S)
unit = LowerSets(S)
if do_extra_checks():
unit.belongs(value)
vu = ValueWithUnits(value, unit)
return vu
def solve_r(self, r):
l2 = self.dp2.solve_r(r)
if do_extra_checks():
F2 = self.dp2.get_fun_space()
LF2 = LowerSets(F2)
LF2.belongs(l2)
maxs = set([])
# todo: express as operation on antichains
for l in l2.maximals:
v = self.dp1.solve_r(l)
maxs.update(v.maximals)
F = self.get_fun_space()
maximals = poset_maxima(maxs, F.leq)
lf = LowerSet(maximals, F)
return lf
def dual01_chain(id_dp, dp):
try:
with primitive_dp_test(id_dp, dp):
print('Starting testing with %r' % id_dp)
# get a chain of resources
F = dp.get_fun_space()
R = dp.get_res_space()
# try to solve
try:
dp.solve(F.witness())
dp.solve_r(R.witness())
except NotSolvableNeedsApprox:
print('NotSolvableNeedsApprox - doing lower bound ')
n = 5
dpL, dpU = get_dp_bounds(dp, nl=n, nu=n)
dual01_chain(id_dp+'_L%s'%n, dpL)
dual01_chain(id_dp+'_U%s'%n, dpU)
return
LF = LowerSets(F)
UR = UpperSets(R)
rchain = R.get_test_chain(n=8)
poset_check_chain(R, rchain)
try:
lfchain = list(map(dp.solve_r, rchain))
for lf in lfchain:
LF.belongs(lf)
except NotSolvableNeedsApprox as e:
print('skipping because %s' % e)
return
try:
poset_check_chain(LF, list(reversed(lfchain)))
except ValueError as e:
msg = 'The results of solve_r() are not a chain.'
raise_wrapped(Exception, e, msg, chain=rchain, lfchain=lfchain)
# now, for each functionality f,
# we know that the corresponding resource should be feasible
for lf, r in zip(lfchain, rchain):
print('')
print('r: %s' % R.format(r))
print('lf = h*(r) = %s' % LF.format(lf))
for f in lf.maximals:
print(' f = %s' % F.format(f))
f_ur = dp.solve(f)
print(' f_ur = h(f) = %s' % UR.format(f_ur))
try:
f_ur.belongs(r)
except NotBelongs as e:
try:
Rcomp.tolerate_numerical_errors = True
f_ur.belongs(r)
logger.info('In this case, there was a numerical error')
logger.info('Rcomp.tolerate_numerical_errors = True solved the problem')
except:
msg = ''
raise_wrapped(AssertionError, e, msg,
lf=lf, r=r, f_ur=f_ur,
r_repr=r.__repr__(),
f_ur_minimals=f_ur.minimals.__repr__())
finally:
Rcomp.tolerate_numerical_errors = False
def dual01_chain(id_dp, dp):
try:
with primitive_dp_test(id_dp, dp):
print('Starting testing with %r' % id_dp)
# get a chain of resources
F = dp.get_fun_space()
R = dp.get_res_space()
# try to solve
try:
dp.solve(F.witness())
dp.solve_r(R.witness())
except NotSolvableNeedsApprox:
print('NotSolvableNeedsApprox - doing lower bound ')
n = 5
dpL, dpU = get_dp_bounds(dp, nl=n, nu=n)
dual01_chain(id_dp+'_L%s'%n, dpL)
dual01_chain(id_dp+'_U%s'%n, dpU)
return
LF = LowerSets(F)
UR = UpperSets(R)
rchain = R.get_test_chain(n=8)
poset_check_chain(R, rchain)
try:
lfchain = list(map(dp.solve_r, rchain))
for lf in lfchain:
LF.belongs(lf)
except NotSolvableNeedsApprox as e:
print('skipping because %s' % e)
return
try:
poset_check_chain(LF, list(reversed(lfchain)))
except ValueError as e:
msg = 'The results of solve_r() are not a chain.'
raise_wrapped(Exception, e, msg, chain=rchain, lfchain=lfchain)
# now, for each functionality f,
# we know that the corresponding resource should be feasible
for lf, r in zip(lfchain, rchain):
print('')
print('r: %s' % R.format(r))
print('lf = h*(r) = %s' % LF.format(lf))
for f in lf.maximals:
print(' f = %s' % F.format(f))
f_ur = dp.solve(f)
print(' f_ur = h(f) = %s' % UR.format(f_ur))
try:
f_ur.belongs(r)
except NotBelongs as e:
try:
Rcomp.tolerate_numerical_errors = True
f_ur.belongs(r)
logger.info('In this case, there was a numerical error')
logger.info('Rcomp.tolerate_numerical_errors = True solved the problem')
except:
msg = ''
raise_wrapped(AssertionError, e, msg,
lf=lf, r=r, f_ur=f_ur,
r_repr=r.__repr__(),
f_ur_minimals=f_ur.minimals.__repr__())
finally:
Rcomp.tolerate_numerical_errors = False
