import numpy as np from Algorithms import Cluster from Database import DatabaseSimepar from Dissimilarity import DensityDistance from Database import DatabaseIris, TwoDimensionData # database = DatabaseIris() # a = ['flame.txt', 'jain.txt' ] # a = ['spiral.txt'] #a = ['Aggregation.txt' ,'Compound.txt' ,'D31.txt' ,'flame.txt' , 'jain.txt' , 'pathbased.txt' , 'R15.txt' , 'spiral.txt'] #a = ['flame.txt', 'pathbased.txt', 'spiral.txt', 'jain.txt', 'Compound.txt', 'R15.txt'] a = ['Aggregation.txt'] for f in a: print f fi = '../datasets/{}'.format(f) database = TwoDimensionData(fi, '\t') for rho in np.arange(1.0, 3.4, 0.2): dissimilarity = DensityDistance(rho=rho) cluster = Cluster(database, dissimilarity, P_size=15, K=3, max_iterations=2) # cluster.compute()
# a = ['Compound.txt' , 'flame.txt' ,'D31.txt', 'jain.txt' , 'pathbased.txt' , 'R15.txt' , 'spiral.txt'] #a = ['Aggregation.txt' ] # a = ['flame.txt', 'jain.txt'] #a = [('spiral.txt', 3)] #a = [('R15.txt', 15)] #a = [('Compound.txt', 6)] #a = [('pathbased.txt', 3)] a = [('flame.txt', 2)] results = [] for f, K in a: fi = '../datasets/{}'.format(f) database = TwoDimensionData(fi, '\t') base_name = os.path.basename(fi) name = os.path.splitext(base_name)[0] for rho in np.arange(1.0, 3.4, 0.2): # for rho in [2.8]: dissimilarity = DensityDistance(rho=rho) cluster = Cluster(database, dissimilarity, P_size=50, K=K, max_iterations=50) score, score_normalized = cluster.compute() results.append((name, K, rho, score, score_normalized)) for name, K, rho, score, score_normalized in results: print("{};{};{};{:.8f};{:.8f}".format(name, K, rho, score, score_normalized))
#!/usr/bin/python import numpy as np from Algorithms import Cluster from Database import DatabaseSimepar from Dissimilarity import DensityDistance from Database import DatabaseIris, TwoDimensionData # database = DatabaseIris() # a = ['Compound.txt' , 'flame.txt' ,'D31.txt', 'jain.txt' , 'pathbased.txt' , 'R15.txt' , 'spiral.txt'] a = ['D31.txt', 'Aggregation.txt'] for f in a: fi = '../datasets/{}'.format(f) database = TwoDimensionData(fi, '\t') for rho in np.arange(1.2, 3.8, 0.4): dissimilarity = DensityDistance(rho=rho) cluster = Cluster(database, dissimilarity, P_size=5, K=31, max_iterations=10) # cluster.compute()