def main():
abstract_dissimilarity = CityBlock()
vector1 = [
7876206.000000, 2.721705, 2.482324, 510.317183, 1310.820459,
907.367521, 594.858896
]
vector2 = [
7537091.000000, 3.262750, 3.004038, 518.909965, 1310.309662,
1107.230769, 733.423313
]
vector3 = [
7421256.000000, 3.462628, 3.211842, 524.057260, 1306.746288,
1179.025641, 786.895706
]
vector4 = [
7284190.000000, 3.630403, 3.402637, 523.408172, 1303.025129,
1241.683761, 837.233129
]
dense_vector1 = PointsVector(vector1, 1, "")
dense_vector2 = PointsVector(vector2, 2, "")
dense_vector3 = PointsVector(vector3, 3, "")
dense_vector4 = PointsVector(vector4, 4, "")
print(abstract_dissimilarity.calculate(dense_vector1, dense_vector2))
print(abstract_dissimilarity.calculate(dense_vector1, dense_vector3))
print(abstract_dissimilarity.calculate(dense_vector1, dense_vector4))
print(abstract_dissimilarity.calculate(dense_vector2, dense_vector3))
print(abstract_dissimilarity.calculate(dense_vector2, dense_vector4))
print(abstract_dissimilarity.calculate(dense_vector3, dense_vector4))
def traverse_aabstrees(self, node1, node2, max_depth):
dense_vector1 = PointsVector(node1.moments, "", "")
dense_vector2 = PointsVector(node2.moments, "", "")
local_value = 0 + self.dissimilarity_function.calculate(dense_vector1, dense_vector2)
self.local_similarity.append(local_value)
if node1.depth <= max_depth:
if node1.child1 is not None and node2.child1 is not None:
self.traverse_aabstrees(node1.child1, node2.child1, max_depth)
if node1.child2 is not None and node2.child2 is not None:
self.traverse_aabstrees(node1.child2, node2.child2, max_depth)
def traverse_quadtrees(self, node1, node2, max_depth):
dense_vector1 = PointsVector(node1.moments, "", "")
dense_vector2 = PointsVector(node2.moments, "", "")
local_value = self.dissimilarity_function.calculate(dense_vector1, dense_vector2) # * node1.weight
self.local_similarity.append(local_value)
if node1.depth <= max_depth:
if node1.topLeftChild is not None and node2.topLeftChild is not None:
self.traverse_quadtrees(node1.topLeftChild, node2.topLeftChild, max_depth)
if node1.topRightChild is not None and node2.topRightChild is not None:
self.traverse_quadtrees(node1.topRightChild, node2.topRightChild, max_depth)
if node1.botLeftChild is not None and node2.botLeftChild is not None:
self.traverse_quadtrees(node1.botLeftChild, node2.botLeftChild, max_depth)
if node1.botRightChild is not None and node2.botRightChild is not None:
self.traverse_quadtrees(node1.botRightChild, node2.botRightChild, max_depth)
def load(self, file_name):
self.file_name = file_name
file = open(file_name, "r")
count = 0
index = 1
self.attributes = []
self.labels = []
self.ids = []
self.instances = []
for line in file:
count += 1
if count == 1:
option = line[-1:]
if option == 'Y' or option == 'y':
self.class_flag = True
else:
self.class_flag = False
elif count == 2:
number_instances = line
elif count == 3:
number_attributes = line
elif count == 4:
self.attributes = [x.strip() for x in line.split(';')]
else:
line_array = [x.strip() for x in line.split(';')]
self.labels.append(line_array[0])
self.ids.append(index)
if self.class_flag:
dense_vector = PointsVector(
line_array[1:(len(line_array) - 1)], line_array[0],
line_array[-1])
self.instances.append(dense_vector)
else:
dense_vector = PointsVector(line_array[1:], line_array[0],
1.0)
self.instances.append(dense_vector)
index += 1
def calculate_dismatrix_matmatrix_point_by_point(self):
self.njdismatrix = DistanceMatrix()
self.njdismatrix.labels = self.matmatrix.list_labels
self.njdismatrix.ids = []
self.njdismatrix.classes = np.ones(len(self.matmatrix.list_matrices))
self.njdismatrix.file_name = ""
self.njdismatrix.nr_elements = len(self.matmatrix.list_matrices)
self.njdismatrix.max_distance = float("-inf")
self.njdismatrix.min_distance = float("inf")
self.njdismatrix.matrix = []
self.matmatrix.list_representations = []
min_depth = 10000
for i in range(0, self.njdismatrix.nr_elements -1):
row = [0] * (i+1)
self.njdismatrix.matrix.append(row)
for i in range(0, self.njdismatrix.nr_elements-1):
for j in range(0, i + 1):
matrix1 = self.matmatrix.list_matrices[i+1]
matrix2 = self.matmatrix.list_matrices[j]
vector1 = np.ravel(matrix1)
vector2 = np.ravel(matrix2)
dense_vector1 = PointsVector(vector1, "", "")
dense_vector2 = PointsVector(vector2, "", "")
value = self.dissimilarity_function.calculate(dense_vector1, dense_vector2)
value = round(float(value), 6)
if (i + 1) < j:
self.njdismatrix.matrix[j - 1][i + 1] = value
else:
self.njdismatrix.matrix[i][j] = value
if value > self.njdismatrix.max_distance and value >= 0.0:
self.njdismatrix.max_distance = value
if 0.0 <= value < self.njdismatrix.min_distance:
self.njdismatrix.min_distance = value