def test_ada_3d():
problem = ada_3d_problem()
diag_select("nothing")
print "problem:"
print problem
solver = GeometricSolver(problem)
print "drplan:"
print solver.dr
print "number of top-level rigids:",len(solver.dr.top_level())
result = solver.get_result()
print "result:"
print result
print "result is",result.flag, "with", len(result.solutions),"solutions"
check = True
if len(result.solutions) == 0:
check = False
diag_select(".*")
for sol in result.solutions:
print "solution:",sol
check = check and problem.verify(sol)
diag_select("nothing")
if check:
print "all solutions valid"
else:
print "INVALID"
def test(problem):
"""Test solver on a given problem"""
#diag_select(".*")
print "problem:"
print problem
print "Solving..."
solver = GeometricSolver(problem)
print "...done"
print "drplan:"
print solver.dr
print "top-level rigids:", list(solver.dr.top_level())
result = solver.get_result()
print "result:"
print result
print "result is", result.flag, "with", len(result.solutions), "solutions"
check = True
if len(result.solutions) == 0:
check = False
diag_select("(GeometricProblem.verify)|(satisfied)")
for sol in result.solutions:
print "solution:", sol
check = check and problem.verify(sol)
if check:
print "all solutions valid"
else:
print "INVALID"
def test(problem):
"""Test solver on a given problem"""
#diag_select(".*")
print "problem:"
print problem
print "Solving..."
solver = GeometricSolver(problem)
print "...done"
print "drplan:"
print solver.dr
print "top-level rigids:",list(solver.dr.top_level())
result = solver.get_result()
print "result:"
print result
print "result is",result.flag, "with", len(result.solutions),"solutions"
check = True
if len(result.solutions) == 0:
check = False
diag_select("(GeometricProblem.verify)|(satisfied)")
for sol in result.solutions:
print "solution:",sol
check = check and problem.verify(sol)
if check:
print "all solutions valid"
else:
print "INVALID"
def test(self):
"""Test solver on a given problem"""
problem = self.solver
# problem = fix3_problem_3d()
#diag_select(".*")
print("problem:")
print(problem)
print("Solving...")
solver = GeometricSolver(problem)
print("...done")
print("drplan:")
print(solver.dr)
print("top-level rigids:", list(solver.dr.top_level()))
result = solver.get_result()
print("result:")
print(result)
print("result is", result.flag, "with", len(result.solutions),
"solutions")
check = True
if len(result.solutions) == 0:
check = False
diag_select("(GeometricProblem.verify)|(satisfied)")
for sol in result.solutions:
print("solution:", sol)
check = check and problem.verify(sol)
if check:
print("all solutions valid")
else:
print("INVALID")
def stats_solving(minsize, maxsize, repeats):
print "times for solving problems from scratch"
print "size \t # \t time \t result"
for size in range(minsize, maxsize + 1):
for i in range(1, repeats + 1):
problem = random_triangular_problem_3D(size, 10.0, 0.0, 0.0)
t1 = time()
solver = GeometricSolver(problem)
result = solver.get_status()
t2 = time()
t = t2 - t1
print size, "\t", i, "\t", t, "\t", result
def stats_solving(minsize, maxsize, repeats):
print "times for solving problems from scratch"
print "size \t # \t time \t result"
for size in range(minsize,maxsize+1):
for i in range(1,repeats+1):
problem = random_triangular_problem_3D(size,10.0,0.0,0.0)
t1 = time()
solver = GeometricSolver(problem)
result = solver.get_status()
t2 = time()
t = t2-t1
print size,"\t",i,"\t",t,"\t",result
def stats_parametric(minsize, maxsize, repeats):
#diag_select("clsolver.remove")
print "times for parameteric updates (one constraint parameter)"
print "size \t # \t time \t result"
for size in range(minsize, maxsize + 1):
for i in range(1, repeats + 1):
problem = random_triangular_problem_3D(size, 10.0, 0.0, 0.0)
solver = GeometricSolver(problem)
constraint = random.choice(problem.cg.constraints())
t1 = time()
constraint.set_parameter(constraint.get_parameter())
result = solver.get_status()
t2 = time()
t = t2 - t1
print size, "\t", i, "\t", t, "\t", result
def stats_parametric(minsize, maxsize, repeats):
#diag_select("clsolver.remove")
print "times for parameteric updates (one constraint parameter)"
print "size \t # \t time \t result"
for size in range(minsize,maxsize+1):
for i in range(1,repeats+1):
problem = random_triangular_problem_3D(size,10.0,0.0,0.0)
solver = GeometricSolver(problem)
constraint = random.choice(problem.cg.constraints())
t1 = time()
constraint.set_parameter(constraint.get_parameter())
result = solver.get_status()
t2 = time()
t = t2-t1
print size,"\t",i,"\t",t,"\t",result
def stats_incremental(minsize, maxsize, repeats):
#diag_select("clsolver.remove")
print "times for incremental re-solving (one constraint removed and re-added)"
print "size \t # \t time \t result"
for size in range(minsize, maxsize + 1):
for i in range(1, repeats + 1):
problem = random_triangular_problem_3D(size, 10.0, 0.0, 0.0)
solver = GeometricSolver(problem)
t1 = time()
constraint = random.choice(problem.cg.constraints())
problem.rem_constraint(constraint)
problem.add_constraint(constraint)
result = solver.get_status()
t2 = time()
t = t2 - t1
print size, "\t", i, "\t", t, "\t", result
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 stats_incremental(minsize, maxsize, repeats):
#diag_select("clsolver.remove")
print "times for incremental re-solving (one constraint removed and re-added)"
print "size \t # \t time \t result"
for size in range(minsize,maxsize+1):
for i in range(1,repeats+1):
problem = random_triangular_problem_3D(size,10.0,0.0,0.0)
solver = GeometricSolver(problem)
t1 = time()
constraint = random.choice(problem.cg.constraints())
problem.rem_constraint(constraint)
problem.add_constraint(constraint)
result = solver.get_status()
t2 = time()
t = t2-t1
print size,"\t",i,"\t",t,"\t",result
def selection_test():
problem = GeometricProblem(dimension=3,use_prototype=False)
problem.add_point('v1', vector([0.0, 0.0, 0.0]))
problem.add_point('v2', vector([1.0, 0.0, 0.0]))
problem.add_point('v3', vector([0.0, 1.0, 0.0]))
problem.add_point('v4', vector([0.5, 0.5, 1.0]))
problem.add_point('v5', vector([0.5, 0.5,-1.0]))
problem.add_constraint(DistanceConstraint('v1', 'v2', 10.0))
problem.add_constraint(DistanceConstraint('v1', 'v3', 10.0))
problem.add_constraint(DistanceConstraint('v2', 'v3', 10.0))
problem.add_constraint(DistanceConstraint('v1', 'v4', 10.0))
problem.add_constraint(DistanceConstraint('v2', 'v4', 10.0))
problem.add_constraint(DistanceConstraint('v3', 'v4', 10.0))
problem.add_constraint(DistanceConstraint('v1', 'v5', 10.0))
problem.add_constraint(DistanceConstraint('v2', 'v5', 10.0))
problem.add_constraint(DistanceConstraint('v3', 'v5', 10.0))
s1 = RightHandedConstraint('v1','v2','v4','v5')
# add selection con
problem.add_constraint(s1)
# solve
solver = GeometricSolver(problem)
print len(solver.get_solutions()), "solutions"
# remove and add constraint
print "removing selection-constraint"
problem.rem_constraint(s1)
# solve again
print len(solver.get_solutions()), "solutions"
# remove and add constraint
print "re-adding selection constraint"
problem.add_constraint(s1)
# solve again
print len(solver.get_solutions()), "solutions"
# remove distance
print "removing and re-adding distance v1-v5"
problem.rem_constraint(problem.get_distance("v1","v5"))
problem.add_constraint(DistanceConstraint('v1', 'v5', 10.0))
# solve again
print len(solver.get_solutions()), "solutions"
def test_mergehogs():
diag_select(".")
problem = GeometricProblem(dimension=2)
problem.add_point('x', vector([0.0, 0.0]))
problem.add_point('a', vector([1.0, 0.0]))
problem.add_point('b', vector([0.0, 1.0]))
problem.add_point('c', vector([-1.0, 0.0]))
problem.add_point('d', vector([0.0, -1.0]))
problem.add_constraint(AngleConstraint('a', 'x', 'b',
30.0 / 180 * math.pi))
problem.add_constraint(AngleConstraint('b', 'x', 'c',
30.0 / 180 * math.pi))
problem.add_constraint(AngleConstraint('c', 'x', 'd',
30.0 / 180 * math.pi))
solver = GeometricSolver(problem)
print solver.dr
for hog in solver.dr.hedgehogs():
conf = list(solver.mg.get(hog))[0]
print hog
print conf
print problem.verify(conf.map)
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_fix2(n):
"""Test fix constraints"""
# diag_select("drplan.*")
print "generate a random 2D problem"
problem = random_problem_2D(n)
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 "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 "create dr planner"
drplanner = GeometricSolver(problem)
print "number of top clusters:", len(drplanner.dr.top_level())
print "top clusters:", map(str, drplanner.dr.top_level())
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
#end try
if check == False:
print "--- problem ---"
print problem
print "--- diasgnostic messages ---"
diag_select("drplan")
drplanner = GeometricSolver(problem)
print "--- plan ---"
top = drplanner.dr.top_level()
print drplanner.dr
print "--- top level ---"
print len(top), "clusters:"
for cluster in drplanner.dr.top_level():
print cluster
print "--- conclusion ---"
print message
return False
else:
return True
def test_random_wellconstrained(size):