def test_random_overconstrained(size):
# start with random well-constrained problem
problem = random_problem_2D(size, 0.0)
# add one random contraint
for i in range(100):
try:
add_random_constraint(problem, 0.5)
break
except:
pass
if i == 99:
raise StandardError, "could not add extra constraint"
# test
try:
drplanner = GeometricSolver(problem)
ntop = len(drplanner.dr.top_level())
if ntop > 1:
message = "underconstrained"
check = False
elif ntop == 0:
message = "no top level"
check = False
else: # ntop == 1
top = drplanner.dr.top_level()[0]
if not top.overconstrained:
message = "well-constrained"
check = False
else:
check = True
except Exception, e:
message = "error:", e
check = False
def test_fix(n):
"""Test fix constraints"""
#diag_select("drplan._search_triangle")
print "generate a random 2D problem"
problem = random_problem_2D(n)
print "create dr planner"
drplanner = GeometericSolver(problem)
print "number of top clusters:", len(drplanner.dr.top_level())
#print "top clusters:", map(str, drplanner.dr.top_level())
cons = problem.cg.constraints()
dists = filter(lambda d: isinstance(d, DistanceConstraint), cons)
con = random.choice(dists)
print "remove distance", con
problem.rem_constraint(con)
print "number of top clusters:", len(drplanner.dr.top_level())
#print "top clusters:", map(str, drplanner.dr.top_level())
print "replace with two fixes"
v1 = con.variables()[0]
v2 = con.variables()[1]
f1 = FixConstraint(v1, problem.get_point(v1))
f2 = FixConstraint(v2, problem.get_point(v2))
problem.add_constraint(f1)
problem.add_constraint(f2)
print "number of top clusters:", len(drplanner.dr.top_level())
def test_random_overconstrained(size):
# start with random well-constrained problem
problem = random_problem_2D(size, 0.0)
# add one random contraint
for i in range(100):
try:
add_random_constraint(problem, 0.5)
break
except:
pass
if i == 99:
raise StandardError, "could not add extra constraint"
# test
try:
drplanner = GeometricSolver(problem)
ntop = len(drplanner.dr.top_level())
if ntop > 1:
message = "underconstrained"
check = False
elif ntop == 0:
message = "no top level"
check = False
else: # ntop == 1
top = drplanner.dr.top_level()[0]
if not top.overconstrained:
message = "well-constrained"
check = False
else:
check = True
except Exception, e:
message = "error:",e
check = False
def test_fix(n):
"""Test fix constraints"""
#diag_select("drplan._search_triangle")
print "generate a random 2D problem"
problem = random_problem_2D(n)
print "create dr planner"
drplanner = GeometericSolver(problem)
print "number of top clusters:", len(drplanner.dr.top_level())
#print "top clusters:", map(str, drplanner.dr.top_level())
cons = problem.cg.constraints()
dists = filter(lambda d: isinstance(d, DistanceConstraint), cons)
con = random.choice(dists)
print "remove distance", con
problem.rem_constraint(con)
print "number of top clusters:", len(drplanner.dr.top_level())
#print "top clusters:", map(str, drplanner.dr.top_level())
print "replace with two fixes"
v1 = con.variables()[0]
v2 = con.variables()[1]
f1 = FixConstraint(v1, problem.get_point(v1))
f2 = FixConstraint(v2, problem.get_point(v2))
problem.add_constraint(f1)
problem.add_constraint(f2)
print "number of top clusters:", len(drplanner.dr.top_level())
def test_non_triangular(n):
problem = random_problem_2D(n)
print "before:"
print problem
randomize_angles(problem)
print "after:"
print problem
test(problem)
def test_non_triangular(n):
problem = random_problem_2D(n)
print "before:"
print problem
randomize_angles(problem)
print "after:"
print problem
test(problem)
def test_random_wellconstrained(size):
problem = random_problem_2D(size, 0.0)
try:
drplanner = GeometricSolver(problem)
ntop = len(drplanner.dr.top_level())
if ntop > 1:
message = "underconstrained"
check = False
elif ntop == 0:
message = "no top level"
check = False
else: # ntop == 1
top = drplanner.dr.top_level()[0]
if top.overconstrained:
message = "overconstrained"
check = False
else:
check = True
except Exception, e:
print "error in problem:", e
check = False
def test_random_wellconstrained(size):
problem = random_problem_2D(size, 0.0)
try:
drplanner = GeometricSolver(problem)
ntop = len(drplanner.dr.top_level())
if ntop > 1:
message = "underconstrained"
check = False
elif ntop == 0:
message = "no top level"
check = False
else: # ntop == 1
top = drplanner.dr.top_level()[0]
if top.overconstrained:
message = "overconstrained"
check = False
else:
check = True
except Exception, e:
print "error in problem:",e
check = False