def setUp(self):
self.dataArray1 = np.array([[0, 0, 0], [18, 18, 21], [4, 11, 9],
[22, 0, 25], [23, 1, 29], [24, 2, 26],
[5, 8, 10], [20, 19, 18], [10, 15, 15],
[3, 13, 11], [19, 20, 19], [21, 19, 20]])
self.data1 = read_database(self.dataArray1)
self.data_base_sort_with_ref_point_1 = distance_from_ref_point(
self.data1)
self.dataArray2 = np.array([[30, 30, 30], [18, 18, 21], [4, 11, 9],
[22, 0, 25], [23, 1, 29], [24, 2, 26],
[5, 8, 10], [20, 19, 18], [10, 15, 15],
[3, 13, 11], [19, 20, 19], [21, 19, 20]])
self.data2 = read_database(self.dataArray2)
self.minPts = 3
self.eps = 4
def algorythm_swdbscan(minPts, eps, data):
max_cluster_label_1 = -1
max_cluster_label_2 = -1
dataBase = read_database(data)
grid_clustering(dataBase, minPts, eps, 0)
grid_clustering(dataBase, minPts, eps, 1)
for p in dataBase:
if p.label[1] == "UNDEFINED":
p.label[1] = -1
if p.label[0] == "UNDEFINED":
p.label[0] = -1
for i in range(0, len(dataBase)):
if dataBase[i].label[0] > max_cluster_label_1:
max_cluster_label_1 = dataBase[i].label[0]
if dataBase[i].label[1] > max_cluster_label_2:
max_cluster_label_2 = dataBase[i].label[1]
max_cluster_label_1 = max_cluster_label_1 + 1
max_cluster_label_2 = max_cluster_label_2 + 1
matrix_M = get_matrix_M(max_cluster_label_1, max_cluster_label_2, dataBase)
smaller_matrix_M = concatenate_rows(matrix_M, max_cluster_label_1,
max_cluster_label_2, minPts)
compute_ending_clusters(smaller_matrix_M, max_cluster_label_2)
for p in dataBase:
if p.label[2] == "UNDEFINED":
p.label[2] = -1
return dataBase
def algorythm_dbscan(minPts, eps, data, label_number):
dataBase = read_database(data)
algorythm_dbscan_without_read(minPts, eps, dataBase, label_number, 0)
return dataBase