def constant_point_coordinates_test(): """Test features.ConstantPointCoordinates.""" f = features.ConstantPointCoordinates(strokes=0, points_per_stroke=2, pen_down=False) g = features.ConstantPointCoordinates(strokes=0, points_per_stroke=200, pen_down=False) recording = testhelper.get_symbol_as_handwriting(292934) f._features_without_strokes(recording) g._features_without_strokes(recording) space_evenly = preprocessing.SpaceEvenly() space_evenly(recording) f._features_without_strokes(recording)
def test_simple_execution_test(): algorithms = [ preprocessing.RemoveDuplicateTime(), preprocessing.RemoveDots(), preprocessing.SpaceEvenly(), preprocessing.SpaceEvenlyPerStroke(), preprocessing.DouglasPeucker(), preprocessing.StrokeConnect(), preprocessing.DotReduction(), preprocessing.WildPointFilter(), preprocessing.WeightedAverageSmoothing(), ] for algorithm in algorithms: a = testhelper.get_symbol_as_handwriting(292934) algorithm(a)
def test_preprocessing_detection_test(): preprocessing_queue = [ {"ScaleAndShift": None}, {"StrokeConnect": None}, {"DouglasPeucker": [{"epsilon": 0.2}]}, {"SpaceEvenly": [{"number": 100}]}, ] correct = [ preprocessing.ScaleAndShift(), preprocessing.StrokeConnect(), preprocessing.DouglasPeucker(epsilon=0.2), preprocessing.SpaceEvenly(number=100), ] feature_list = preprocessing.get_preprocessing_queue(preprocessing_queue) # TODO: Not only compare lengths of lists but actual contents. assert len(feature_list) == len(correct)
def preprocessing_detection_test(): preprocessing_queue = [{ 'ScaleAndShift': None }, { 'StrokeConnect': None }, { 'DouglasPeucker': [{ 'epsilon': 0.2 }] }, { 'SpaceEvenly': [{ 'number': 100 }] }] correct = [ preprocessing.ScaleAndShift(), preprocessing.StrokeConnect(), preprocessing.DouglasPeucker(epsilon=0.2), preprocessing.SpaceEvenly(number=100) ] feature_list = preprocessing.get_preprocessing_queue(preprocessing_queue) # TODO: Not only compare lengths of lists but actual contents. nose.tools.assert_equal(len(feature_list), len(correct))