Beispiel #1
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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
Beispiel #2
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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())
Beispiel #3
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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
Beispiel #4
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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())
Beispiel #5
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def test_non_triangular(n):
    problem = random_problem_2D(n)
    print "before:"
    print problem
    randomize_angles(problem)
    print "after:"
    print problem
    test(problem)
Beispiel #6
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def test_non_triangular(n):
    problem = random_problem_2D(n)
    print "before:"
    print problem
    randomize_angles(problem)
    print "after:"
    print problem
    test(problem)
Beispiel #7
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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
Beispiel #8
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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