"data/segmentation_all.txt": 4, "data/ecoli.txt": 8, "data/appendicitis.txt": 7}
optics_radius = {"data/aggregation.txt": 1.40, "data/flame.txt": 1.3, "data/DS850.txt": 0.4, "data/R15.txt": 0.55,
"data/D31.txt": 0.95, "data/dim512.txt": 0.36, "data/iris.txt": 0.12, "data/wdbc.txt": 0.49,
"data/seeds.txt": 0.24, "data/segmentation_all.txt": 0.15, "data/ecoli.txt": 0.26,
"data/appendicitis.txt": 0.3}
optics_num = {"data/aggregation.txt": 6, "data/flame.txt": 7, "data/DS850.txt": 8, "data/R15.txt": 11,
"data/D31.txt": 34, "data/dim512.txt": 22, "data/iris.txt": 4, "data/wdbc.txt": 50, "data/seeds.txt": 15,
"data/segmentation_all.txt": 1, "data/ecoli.txt": 40, "data/appendicitis.txt": 10}
# 画图的图尺寸
range_dict = {"data/aggregation.txt": [[2, 38], [0, 35]], "data/flame.txt": [[-2, 16], [12, 30]],
"data/R15.txt": [[2, 18], [2, 18]], "data/D31.txt": [[0, 35], [0, 35]],
"data/DS850.txt": [[-1, 5], [-0.5, 6.5]]}
filename, data_set_type = PublicFunctions.select_file()
datapoints, labels_true = PublicFunctions.readRawDataFromFile(filename) # Read data from file
if data_set_type == '2' or filename == "data/dim512.txt":
min_max_scaler = preprocessing.MinMaxScaler()
datapoints = min_max_scaler.fit_transform(datapoints)
choice = input("""select algorithm:
SCA2-------------1
SCA--------------2
K means----------3
HAC--------------4
DBSCAN-----------5
OPTICS-----------6
....>>>""")
times = 1 if choice not in ["1", "2", "3"] else 20
result = []
mean = []
var = []
