def test_experiment_with_unibucket_1(self):
dim = 50
vector_count = 100
vectors = numpy.random.randn(dim, vector_count)
unibucket = UniBucket('testHash')
nearest = NearestFilter(10)
engine = Engine(dim, lshashes=[unibucket], vector_filters=[nearest])
exp = RecallPrecisionExperiment(10, vectors)
result = exp.perform_experiment([engine])
# Both recall and precision must be one in this case
self.assertEqual(result[0][0], 1.0)
self.assertEqual(result[0][1], 1.0)
def test_experiment_with_unibucket_3(self):
dim = 50
vector_count = 100
vectors = numpy.random.randn(dim, vector_count)
unibucket = UniBucket('testHash')
nearest = NearestFilter(5)
engine = Engine(dim, lshashes=[unibucket], vector_filters=[nearest])
exp = RecallPrecisionExperiment(10, vectors)
result = exp.perform_experiment([engine])
# In this case recall is only 0.5
# because the engine returns 5 nearest, but
# the experiment looks for 10 nearest.
self.assertEqual(result[0][0], 0.5)
self.assertEqual(result[0][1], 1.0)
def test_experiment_with_list_2(self):
dim = 50
vector_count = 100
vectors = []
for index in range(vector_count):
vectors.append(numpy.random.randn(dim))
unibucket = UniBucket('testHash')
nearest = NearestFilter(10)
engine = Engine(dim, lshashes=[unibucket], vector_filters=[nearest])
exp = RecallPrecisionExperiment(5, vectors)
result = exp.perform_experiment([engine])
# In this case precision is only 0.5
# because the engine returns 10 nearest, but
# the experiment only looks for 5 nearest.
self.assertEqual(result[0][0], 1.0)
self.assertEqual(result[0][1], 0.5)
def test_delete_vector_with_provided_value(self):
engine = Engine(self.dim, lshashes=[UniBucket('testHash')])
self.fill_engine(engine)
engine.delete_vector(self.removed_value, self.removed_vector)
self.check_delete(engine)
def test_delete_vector_multiple_hash(self):
hashes = [UniBucket('name_hash_%d' % k) for k in range(10)]
engine = Engine(self.dim, lshashes=hashes)
self.fill_engine(engine)
engine.delete_vector(self.removed_value)
self.check_delete(engine)
def test_delete_vector_single_hash(self):
engine = Engine(self.dim, lshashes=[UniBucket('testHash')])
self.fill_engine(engine)
engine.delete_vector(self.removed_value)
self.check_delete(engine)