def test_calculation_identical(self):
vector1 = [0, 1]
vector2 = [0, 1]
euclidean_distance = EuclideanDistance()
assert euclidean_distance.compute(vector1, vector2) == 0
def test_better_than_noise(self):
embedding_file = self._random_test_file()
with open(embedding_file, "w+") as file_output:
print("2 2\n" +
"paris 0.0 3.0\n" +
"berlin 1.0 0.0", file=file_output)
embedding = EmbeddingFileParser.create_from_file(embedding_file)
assert embedding.squared_euclidean_noise == 10.0
euclidean_distance = EuclideanDistance()
assert euclidean_distance.is_better_than_noise(9.99, embedding)
assert not euclidean_distance.is_better_than_noise(10.0, embedding)
assert not euclidean_distance.is_better_than_noise(10.01, embedding)
def __init__(self, name, test_set):
super(EuclideanOutlierDetectionTask,
self).__init__(name, test_set, EuclideanDistance())
def test_equality(self):
assert EuclideanDistance() == EuclideanDistance()
assert EuclideanDistance() is not None
assert not EuclideanDistance() == CosineSimilarity()
def test_batch_compute(self):
euclidean_distance = EuclideanDistance()
vectors = [[0.0, 3.0], [1.0, 0.0]]
result = euclidean_distance.batch_compute(vectors)
assert result == 10.0
def __init__(self, name, test_set):
super(EuclideanNeighborhoodTask, self).__init__(name, test_set, EuclideanDistance())
def __init__(self, name, test_set):
super(EuclideanSimilarityTask, self).__init__(name, test_set, EuclideanDistance())
def _calculate_squared_euclidean_noise(vectors):
return EuclideanDistance().batch_compute(vectors)