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comparison trafficintelligence/tests/prediction.txt @ 1287:76f5693b530c

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updated tests for numpy 2
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Sat, 20 Jul 2024 20:35:21 -0400
parents a095d4fbb2ea
children
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1284:8e30c9a6ac6f 1287:76f5693b530c
24 >>> acceleration = lambda: random.uniform(-0.5,0.5) 24 >>> acceleration = lambda: random.uniform(-0.5,0.5)
25 >>> steering = lambda: random.uniform(-0.1,0.1) 25 >>> steering = lambda: random.uniform(-0.1,0.1)
26 >>> et = PredictedTrajectoryRandomControl(moving.Point(0,0),moving.Point(1,1), acceleration, steering, maxSpeed = 2) 26 >>> et = PredictedTrajectoryRandomControl(moving.Point(0,0),moving.Point(1,1), acceleration, steering, maxSpeed = 2)
27 >>> p = et.predictPosition(500) 27 >>> p = et.predictPosition(500)
28 >>> max(et.getPredictedSpeeds()) <= 2. 28 >>> max(et.getPredictedSpeeds()) <= 2.
29 True 29 np.True_
30 30
31 >>> p = moving.Point(3,4) 31 >>> p = moving.Point(3,4)
32 >>> sp = SafetyPoint(p, 0.1, 0) 32 >>> sp = SafetyPoint(p, 0.1, 0)
33 >>> print(sp) 33 >>> print(sp)
34 3 4 0.1 0 34 3 4 0.1 0
51 51
52 >>> proto = storage.loadTrajectoriesFromSqlite('../samples/laurier.sqlite', 'feature', [1204])[0] 52 >>> proto = storage.loadTrajectoriesFromSqlite('../samples/laurier.sqlite', 'feature', [1204])[0]
53 >>> proto.getPositions().computeCumulativeDistances() 53 >>> proto.getPositions().computeCumulativeDistances()
54 >>> et = PredictedTrajectoryPrototype(proto.getPositionAt(10)+moving.Point(0.5, 0.5), proto.getVelocityAt(10)*0.9, proto, True) 54 >>> et = PredictedTrajectoryPrototype(proto.getPositionAt(10)+moving.Point(0.5, 0.5), proto.getVelocityAt(10)*0.9, proto, True)
55 >>> absolute(et.initialSpeed - proto.getVelocityAt(10).norm2()*0.9) < 1e-5 55 >>> absolute(et.initialSpeed - proto.getVelocityAt(10).norm2()*0.9) < 1e-5
56 True 56 np.True_
57 >>> for t in range(int(proto.length())): x=et.predictPosition(t) 57 >>> for t in range(int(proto.length())): x=et.predictPosition(t)
58 >>> traj = et.getPredictedTrajectory() 58 >>> traj = et.getPredictedTrajectory()
59 >>> traj.computeCumulativeDistances() 59 >>> traj.computeCumulativeDistances()
60 >>> absolute(array(traj.distances).mean() - et.initialSpeed < 1e-3) 60 >>> absolute(array(traj.distances).mean() - et.initialSpeed < 1e-3)
61 True 61 np.True_
62 62
63 >>> et = PredictedTrajectoryPrototype(proto.getPositionAt(10)+moving.Point(0.6, 0.6), proto.getVelocityAt(10)*0.7, proto, False) 63 >>> et = PredictedTrajectoryPrototype(proto.getPositionAt(10)+moving.Point(0.6, 0.6), proto.getVelocityAt(10)*0.7, proto, False)
64 >>> absolute(et.initialSpeed - proto.getVelocityAt(10).norm2()*0.7) < 1e-5 64 >>> absolute(et.initialSpeed - proto.getVelocityAt(10).norm2()*0.7) < 1e-5
65 True 65 np.True_
66 >>> proto = moving.MovingObject.generate(1, moving.Point(-5.,0.), moving.Point(1.,0.), moving.TimeInterval(0,10)) 66 >>> proto = moving.MovingObject.generate(1, moving.Point(-5.,0.), moving.Point(1.,0.), moving.TimeInterval(0,10))
67 >>> et = PredictedTrajectoryPrototype(proto.getPositionAt(0)+moving.Point(0., 1.), proto.getVelocityAt(0)*0.5, proto, False) 67 >>> et = PredictedTrajectoryPrototype(proto.getPositionAt(0)+moving.Point(0., 1.), proto.getVelocityAt(0)*0.5, proto, False)
68 >>> for t in range(int(proto.length()/0.5)): x=et.predictPosition(t) 68 >>> for t in range(int(proto.length()/0.5)): x=et.predictPosition(t)
69 >>> et.predictPosition(10) # doctest:+ELLIPSIS 69 >>> et.predictPosition(10) # doctest:+ELLIPSIS
70 (0.0...,1.0...) 70 (0.0...,1.0...)