def test_normalize_features_two_feats2():
"""Test create_ffiles._normalize_features with two points."""
feature_list = [features.Width(), features.Height()]
prepared = [([123, 123], 1), ([100, 100], 1)]
is_traindata = True
out = create_ffiles._normalize_features(feature_list, prepared,
is_traindata)
# Mean: 111.5; Range: 23
assert out == [([0.5, 0.5], 1), ([-0.5, -0.5], 1)]
# Now the other set
prepared = [([111.5, 146], 1), ([146, 111.5], 1), ([54, 54], 1)]
is_traindata = False
out = create_ffiles._normalize_features(feature_list, prepared,
is_traindata)
assert out == [([0.0, 1.5], 1), ([1.5, 0.0], 1), ([-2.5, -2.5], 1)]
def test_normalize_features_two():
"""Test create_ffiles._normalize_features with two points."""
feature_list = [features.Width(), features.Height()]
prepared = [([123], 1), ([100], 1)]
is_traindata = True
out = create_ffiles._normalize_features(feature_list, prepared,
is_traindata)
# Mean: 111.5; Range: 23
assert out == [([0.5], 1), ([-0.5], 1)]
# Now the other set
prepared = [([111.5], 1), ([90], 1), ([180], 1)]
is_traindata = False
out = create_ffiles._normalize_features(feature_list, prepared,
is_traindata)
assert out == [([0.0], 1), ([-0.93478260869565222], 1),
([2.9782608695652173], 1)]
def normalize_features_one_test():
"""Test create_ffiles._normalize_features with one point."""
feature_list = [features.Width(), features.Height()]
prepared = [([123], 1)]
is_traindata = True
out = create_ffiles._normalize_features(feature_list, prepared,
is_traindata)
nose.tools.assert_equal(out, [([0.0], 1)])
def test_normalize_features_one():
"""Test create_ffiles._normalize_features with one point."""
feature_list = [features.Width(), features.Height()]
prepared = [([123], 1)]
is_traindata = True
out = create_ffiles._normalize_features(feature_list, prepared,
is_traindata)
assert out == [([0.0], 1)]
def test_normalize_features_two_classes():
"""Test create_ffiles._normalize_features with two classes."""
feature_list = [features.Width(), features.Height()]
prepared = [([123], 1), ([100], 1), ([500], 2)]
is_traindata = True
out = create_ffiles._normalize_features(feature_list, prepared,
is_traindata)
# Mean: 241; Range: 400
assert out == [([-0.295], 1), ([-0.3525], 1), ([0.6475], 2)]
def normalize_features_one_test():
"""Test create_ffiles._normalize_features with one point."""
feature_list = [features.Width(), features.Height()]
prepared = [([123], 1)]
is_traindata = True
out = create_ffiles._normalize_features(feature_list,
prepared,
is_traindata)
nose.tools.assert_equal(out, [([0.0], 1)])
def normalize_features_two_feats_test():
"""Test create_ffiles._normalize_features with two points."""
feature_list = [features.Width(), features.Height()]
prepared = [([123, 123], 1), ([100, 100], 1)]
is_traindata = True
out = create_ffiles._normalize_features(feature_list,
prepared,
is_traindata)
# Mean: 111.5; Range: 23
nose.tools.assert_equal(out, [([0.5, 0.5], 1), ([-0.5, -0.5], 1)])
# Now the other set
prepared = [([111.5, 111.5], 1), ([146, 146], 1), ([54, 54], 1)]
is_traindata = False
out = create_ffiles._normalize_features(feature_list,
prepared,
is_traindata)
nose.tools.assert_equal(out, [([0.0, 0.0], 1),
([1.5, 1.5], 1),
([-2.5, -2.5], 1)])
def normalize_features_two_test():
"""Test create_ffiles._normalize_features with two points."""
feature_list = [features.Width(), features.Height()]
prepared = [([123], 1), ([100], 1)]
is_traindata = True
out = create_ffiles._normalize_features(feature_list,
prepared,
is_traindata)
# Mean: 111.5; Range: 23
nose.tools.assert_equal(out, [([0.5], 1), ([-0.5], 1)])
# Now the other set
prepared = [([111.5], 1), ([90], 1), ([180], 1)]
is_traindata = False
out = create_ffiles._normalize_features(feature_list,
prepared,
is_traindata)
nose.tools.assert_equal(out, [([0.0], 1),
([-0.93478260869565222], 1),
([2.9782608695652173], 1)])
def normalize_features_two_classes_test():
"""Test create_ffiles._normalize_features with two classes."""
feature_list = [features.Width(), features.Height()]
prepared = [([123], 1), ([100], 1), ([500], 2)]
is_traindata = True
out = create_ffiles._normalize_features(feature_list,
prepared,
is_traindata)
# Mean: 241; Range: 400
nose.tools.assert_equal(out, [([-0.295], 1),
([-0.3525], 1),
([0.6475], 2)])