def test_discretize_in_multiple_points1(self):
def three_points_almost_equals(theroical_three_points,
actual_three_points):
def almost_equals(a, b):
return la.norm(
np.array(a.coordinates) - np.array(b.coordinates)) < 1e-10
res = True
for i in range(3):
res = res and almost_equals(theroical_three_points[i],
actual_three_points[i])
return res
theroical_three_points1 = list(map((lambda x: Point(self.discretisator3.change_base_u_to_e(x))),\
self.theorical_three_point_in_u31))
actual_three_points1 = self.discretisator3.discretize_in_multiple_points(Point(\
self.discretisator3.change_base_u_to_e(self.central_point_coord_in_base_u31)))
assert three_points_almost_equals(
theroical_three_points1,
actual_three_points1), str(actual_three_points1)
theroical_three_points2 = list(map((lambda x: Point(self.discretisator3.change_base_u_to_e(x))), \
self.theorical_three_point_in_u32))
actual_three_points2 = self.discretisator3.discretize_in_multiple_points(Point( \
self.discretisator3.change_base_u_to_e(self.central_point_coord_in_base_u32)))
assert three_points_almost_equals(
theroical_three_points2,
actual_three_points2), str(actual_three_points2)
def setUp(self):
"""Initialize tests."""
self.bf_tester = BloomFilterTester(10, 80)
self.bf_tester.feed([Point([0.5, 0.5])],
discretisator=RectangleDiscretisator(1))
self.discretized_points = [
Point([0.0, 0.0]),
Point([1.0, 0.0]),
Point([0.0, 1.0]),
Point([1.0, 1.0])
]
def setUp(self):
"""Initialize tests."""
self.bf_tester = BloomFilterTester(
10, 8000, 2, discretisator=CircleDiscretisator2D(5))
self.bf_tester.feed([Point([5, 5])])
self.discretized_points = [
Point([5, 5]),
Point([5, 6]),
Point([5, 4]),
Point([6, 5]),
Point([4, 5]),
Point([4, 7]),
Point([101, 90])
]
# Second test
dim = 2
delta = 5
feed_data = RandomDataGenerator(dim,
size_of_data_set=200,
domain=1000000)
feed_data.genarate()
self.similar_data = RandomDataGenerator(dim,
size_of_data_set=200,
domain=1000000)
self.similar_data.genarate_similar(delta, feed_data.get_points())
Cdiscretizor = CircleDiscretisator2D(10)
self.bf2 = BloomFilterTester(200, 3200, 2, discretisator=Cdiscretizor)
self.bf2.feed(feed_data.get_points())
def get_points(self):
# We test if the file exist
path_file = Path(self.file_path)
self.logger.info('We get the points from' + str(path_file))
if not path_file.is_file():
self.logger.error('The file does not exist')
raise Exception("The file does not exist" + str(path_file))
data_file = pd.read_csv(str(path_file))
line_counter = -1
try:
for i, row in enumerate(data_file.values):
line_counter += 1
coodinates = [
Decimal(int(elem)) for index, elem in enumerate(row)
if index != 0
]
# We get a dimension problem.
if len(coodinates) != self.dimension:
self.logger.error(
'The dimension is not correct, expected : ' +
str(len(coodinates)) + str(self.dimension))
raise Exception("The dimension of the point at line " +
str(line_counter) + " is not correct")
self.point_list.append(Point(coodinates))
except Exception as e:
self.logger.error("Probleme during reading line " +
str(line_counter))
raise e
return self.point_list
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 setUp(self):
"""Initialize tests."""
self.discretisator = RectangleDiscretisator(5)
""""For minimize"""
self.point_a = Point([7])
self.point_b = Point([5])
self.point_c = Point([10])
self.point_d = Point([1, 32])
self.point_e = Point([5, 10])
""""For discrette"""
self.point_f = Point([7])
self.point_g = Point([5])
self.point_h = Point([10])
self.point_i = Point([1, 32])
self.point_j = Point([5, 10])
def setUp(self):
"""Initialize tests."""
# For test_change_base_e_to_u
self.discretisator1 = CircleDiscretisator2D(10)
# For test_four_points_around
self.discretisator2 = CircleDiscretisator2D(10)
self.point_coord_in_base_u = [0.5, 0.5]
# For test_get_three_closer_points & test_discretize_in_multiple_points1
self.discretisator3 = CircleDiscretisator2D(10)
self.central_point_coord_in_base_u31 = [0.1, 0.1]
self.theorical_three_point_in_u31 = ([0.0, 0.0], [1.0,
0.0], [0.0, 1.0])
self.central_point_coord_in_base_u32 = [0.9, 0.9]
self.theorical_three_point_in_u32 = ([1.0, 0.0], [0.0,
1.0], [1.0, 1.0])
# For test_get_three_descretized_points_around2 & test_discretize_in_one_point
self.discretisator4 = CircleDiscretisator2D(10)
self.central_point41 = Point(
self.discretisator4.change_base_u_to_e([0.1, 0.1]))
self.central_point42 = Point(
self.discretisator4.change_base_u_to_e([0.9, 0.9]))
self.central_point43 = Point(
self.discretisator4.change_base_u_to_e([1.1, 0.1]))
self.central_point44 = Point(
self.discretisator4.change_base_u_to_e([0.1, 1.1]))
# Only for test_discretize_in_one_point
self.central_point45 = Point(
self.discretisator4.change_base_u_to_e([0.9, 0.1]))
self.central_point46 = Point(
self.discretisator4.change_base_u_to_e([0.1, 0.9]))
def discretise_point(self, point):
"""
Apply a combinatory ceil and floor to each coordinate of the given point in parameter, according to the lambda_error.
Return a list of points that enclose the given point in parameter, in function of the dimension of the point and the lambda_error
Args :
:param point: coordinates which have to be maximised
"""
#deepcopy the point given in parmeter to prevent instruction to modify it and be able able to use it again without modification
point_c = deepcopy(point)
results = []
results.append(Point(self.minimisePoint(point_c.coordinates)))
self.discretise_recursive(point_c.coordinates, point.coordinates, 0,
results)
return results
def four_points_around(self, point_coord_in_base_u):
"""
:param point_coord_in_base_u:
:return: tuple of for point that are around the point represented by the coordonate : point_coord_in_base_u
"""
floored_x_coord = math.floor(point_coord_in_base_u[0])
floored_y_coord = math.floor(point_coord_in_base_u[1])
down_left = Point(
list(
np.transpose(
np.dot(self.transition_matrix_u_to_e,
np.array([[floored_x_coord],
[floored_y_coord]])))[0]))
down_right = Point(
list(
np.transpose(
np.dot(
self.transition_matrix_u_to_e,
np.array([[floored_x_coord + 1],
[floored_y_coord]])))[0]))
up_left = Point(
list(
np.transpose(
np.dot(
self.transition_matrix_u_to_e,
np.array([[floored_x_coord],
[floored_y_coord + 1]])))[0]))
up_right = Point(
list(
np.transpose(
np.dot(
self.transition_matrix_u_to_e,
np.array([[floored_x_coord + 1],
[floored_y_coord + 1]])))[0]))
return (down_left, down_right, up_left, up_right)
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')
def discretise_recursive(self, point, original_points, starting_index,
results):
"""
shouldn't be called directly, call for discretise
"""
for i in range(starting_index, len(point)):
if point[i] != original_points[i]:
#the original value of point should be the same for each passage in the loop so deepcopy
point_c = point[:]
if self.method_type == Constants.DIS_DOUBLE:
point_c[i] += self.lambda_error
else:
point_c[i] += 2 * self.lambda_error
results.append(Point(point_c))
self.discretise_recursive(point_c, original_points, i + 1,
results)
def test_discretize_in_one_point(self):
def almost_equals(a, b):
return la.norm(np.array(a.coordinates) -
np.array(b.coordinates)) < 1e-10
closest_dis_point1 = self.discretisator4.discretize_in_one_point(
self.central_point41)
closest_dis_point2 = self.discretisator4.discretize_in_one_point(
self.central_point42)
closest_dis_point3 = self.discretisator4.discretize_in_one_point(
self.central_point45)
closest_dis_point4 = self.discretisator4.discretize_in_one_point(
self.central_point46)
assert almost_equals(closest_dis_point1, Point(
[0, 0])), str(closest_dis_point1) + " " + str(Point([0, 0]))
assert almost_equals(closest_dis_point2, Point(self.discretisator4.change_base_u_to_e([1, 1]))),\
str(closest_dis_point2) + " " + str(Point([1, 1]))
assert almost_equals(closest_dis_point3, Point(self.discretisator4.change_base_u_to_e([1, 0]))), \
str(closest_dis_point3) + " " + str(Point([1, 0]))
assert almost_equals(closest_dis_point4, Point(self.discretisator4.change_base_u_to_e([0, 1]))), \
str(closest_dis_point4) + " " + str(Point([0, 1]))
def genarate(self, save_file_name=None):
"""
This method genrate the data and store it into the object attribute point_list.
: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.
"""
array_vector = np.random.randint(self.domain,
size=(self.size_of_data_set,
self.dimension))
for vct in array_vector:
self.point_list.append(Point(list(vct)))
if save_file_name:
try:
os.remove(
os.path.join(os.getcwd(), DATA_FOLDER, save_file_name))
except OSError:
pass
vector_data_frame_ = pd.DataFrame(array_vector)
vector_data_frame_.to_csv(os.path.join(os.getcwd(), DATA_FOLDER,
save_file_name),
encoding='utf-8')
def genarate_falses(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:
vct = np.random.randint(self.domain,
size=(self.size_of_data_set,
self.dimension)).tolist()[0]
point = Point(vct)
if self.distant_enough(point, delta, data_set):
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("infinite loop to construct different")
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')
return array_vector
def test_four_points_around(self):
def almost_equals(a, b):
return la.norm(np.array(a.coordinates) -
np.array(b.coordinates)) < 1e-10
assert almost_equals(self.discretisator2.four_points_around(self.point_coord_in_base_u)[0], Point([0.0, 0.0])),\
self.discretisator2.four_points_around(self.point_coord_in_base_u)[0].to_string()
assert almost_equals(self.discretisator2.four_points_around(self.point_coord_in_base_u)[1], Point(list(\
self.discretisator2.u1))), self.discretisator2.four_points_around(self.point_coord_in_base_u)[1].\
to_string()
assert almost_equals(self.discretisator2.four_points_around(self.point_coord_in_base_u)[2], Point(list(\
self.discretisator2.u2))), self.discretisator2.four_points_around(self.point_coord_in_base_u)[2]. \
to_string()
assert almost_equals(self.discretisator2.four_points_around(self.point_coord_in_base_u)[3], Point(list( \
self.discretisator2.u2+self.discretisator2.u1))), self.discretisator2.four_points_around(self.\
point_coord_in_base_u)[3].to_string()
def setUp(self):
"""Initialize tests."""
self.point_a = Point([1,2])
self.point_b = Point([4,6])