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)])