class PointTest(unittest.TestCase):
"""Test case used for tests my Point class."""
def setUp(self):
"""Initialize tests."""
self.point_a = Point([1,2])
self.point_b = Point([4,6])
def test_distance(self):
"""Test of function distance."""
dist = self.point_a.distance(self.point_b)
self.assertEqual(5, dist)
def test_zero_distance(self):
"""Test of function distance."""
dist = self.point_a.distance(self.point_a)
self.assertEqual(0, dist)
def genarate_similar(self, delta, data_set, save_file_name=None):
"""
This method genrate the data and store it into the object attribute point_list. Each point generated in
list_point is at least at <delta> from any point in <data_set>.
:param delta: (float) the minimum distance between the point <point> to the <data_set>
:param data_set: The data set in form of list of object Point
:param save_file_name: name of the file in which we will store the generate data for next tests.
if this parameter if not registered the data will be not save.
:return: Nothing
"""
number_of_vector = 0
number_of_test = 0
array_vector = []
while number_of_vector < self.size_of_data_set:
index = randint(0, len(data_set) - 1)
vct = list(data_set[index].coordinates)
for i in range(len(vct)):
vct[i] += uniform(0, 1) * delta
point = Point(vct)
if point.distance(data_set[index]) < delta:
self.point_list.append(point)
array_vector.append(vct)
number_of_vector += 1
number_of_test += 1
if number_of_test >= 1000 * len(data_set):
logger.error("infinit loop to construct similar")
assert (False)
if save_file_name:
try:
os.remove(
os.path.join(os.getcwd(), DATA_FOLDER, save_file_name))
except OSError:
pass
pd.DataFrame(array_vector).to_csv(os.path.join(
os.getcwd(), DATA_FOLDER, save_file_name),
encoding='utf-8')