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()