def test_dimensionality():
feature_list = [
(features.StrokeCount(), 1),
(
features.ConstantPointCoordinates(strokes=4,
points_per_stroke=20,
fill_empty_with=0),
160,
),
(
features.ConstantPointCoordinates(strokes=0,
points_per_stroke=20,
fill_empty_with=0),
60,
),
(
features.ConstantPointCoordinates(strokes=0,
points_per_stroke=20,
pen_down=False),
40,
),
(features.AspectRatio(), 1),
(features.Width(), 1),
(features.Height(), 1),
(features.Time(), 1),
(features.CenterOfMass(), 2),
]
for feat, dimension in feature_list:
assert feat.get_dimension() == dimension
def dimension_test():
l = [(features.ConstantPointCoordinates(), 160),
(features.FirstNPoints(), 162), # TODO: Check
(features.StrokeCount(), 1),
(features.Ink(), 1)
]
for alg, dimension in l:
nose.tools.assert_equal(alg.get_dimension(), dimension)
def repr_and_str_test():
l = [features.ConstantPointCoordinates(),
features.FirstNPoints(),
features.StrokeCount(),
features.Ink()
]
for alg in l:
str(alg)
repr(alg)
def test_dimension():
algorithms = [
(features.ConstantPointCoordinates(), 160),
(features.FirstNPoints(), 162), # TODO: Check
(features.StrokeCount(), 1),
(features.Ink(), 1),
]
for algorithm, dimension in algorithms:
assert algorithm.get_dimension() == dimension
def test_repr_and_str():
algorithms = [
features.ConstantPointCoordinates(),
features.FirstNPoints(),
features.StrokeCount(),
features.Ink(),
]
for algorithm in algorithms:
str(algorithm)
repr(algorithm)
def test_prepare_dataset():
"""Test create_ffiles.prepare_dataset."""
dataset = []
for i in range(200):
dataset.append({
"handwriting": th.get_symbol_as_handwriting(97705),
"formula_id": 42
})
# dataset[-1]['handwriting'].formula_id = 42
formula_id2index = {}
formula_id2index[42] = 1
feature_list = [features.StrokeCount()]
is_traindata = False
create_ffiles.prepare_dataset(dataset, formula_id2index, feature_list,
is_traindata)
def feature_detection_test():
l = [{'StrokeCount': None},
{'ConstantPointCoordinates':
[{'strokes': 4},
{'points_per_stroke': 81},
{'fill_empty_with': 0},
{'pen_down': False}]
}
]
correct = [features.StrokeCount(),
features.ConstantPointCoordinates(strokes=4,
points_per_stroke=81,
fill_empty_with=0,
pen_down=False)]
feature_list = features.get_features(l)
# TODO: Not only compare lengths of lists but actual contents.
nose.tools.assert_equal(len(feature_list), len(correct))
def features_detection_test():
feature_queue = [{
'StrokeCount': None
}, {
'ConstantPointCoordinates': [{
'strokes': 4,
'points_per_stroke': 20,
'fill_empty_with': 0
}]
}]
correct = [
features.StrokeCount(),
features.ConstantPointCoordinates(strokes=4,
points_per_stroke=20,
fill_empty_with=0)
]
feature_list = features.get_features(feature_queue)
# TODO: Not only compare lengths of lists but actual contents.
nose.tools.assert_equal(len(feature_list), len(correct))
def print_featurelist_test():
"""Test features.print_featurelist."""
feature_list = [
features.StrokeCount(),
features.ConstantPointCoordinates(strokes=4,
points_per_stroke=20,
fill_empty_with=0),
features.ConstantPointCoordinates(strokes=0,
points_per_stroke=20,
fill_empty_with=0),
features.ConstantPointCoordinates(strokes=0,
points_per_stroke=20,
pen_down=False),
features.AspectRatio(),
features.Width(),
features.Height(),
features.Time(),
features.CenterOfMass()
]
features.print_featurelist(feature_list)
def test_feature_detection():
queue = [
{"StrokeCount": None},
{
"ConstantPointCoordinates": [
{"strokes": 4},
{"points_per_stroke": 81},
{"fill_empty_with": 0},
{"pen_down": False},
]
},
]
correct = [
features.StrokeCount(),
features.ConstantPointCoordinates(
strokes=4, points_per_stroke=81, fill_empty_with=0, pen_down=False
),
]
feature_list = features.get_features(queue)
# TODO: Not only compare lengths of lists but actual contents.
assert len(feature_list) == len(correct)
def test_features_detection():
feature_queue = [
{
"StrokeCount": None
},
{
"ConstantPointCoordinates": [{
"strokes": 4,
"points_per_stroke": 20,
"fill_empty_with": 0
}]
},
]
correct = [
features.StrokeCount(),
features.ConstantPointCoordinates(strokes=4,
points_per_stroke=20,
fill_empty_with=0),
]
feature_list = features.get_features(feature_queue)
# TODO: Not only compare lengths of lists but actual contents.
assert len(feature_list) == len(correct)
def stroke_count_test():
feature_list = [features.StrokeCount()]
a = testhelper.get_symbol_as_handwriting(97705)
nose.tools.assert_equal(a.feature_extraction(feature_list), [1])
def test_stroke_count():
feature_list = [features.StrokeCount()]
a = testhelper.get_symbol_as_handwriting(97705)
assert a.feature_extraction(feature_list) == [1]