def test_exception_when_it_cant_reach_k(self):
"""Check for exception when it doesn't reach K clusters"""
dataset = testloader._load_local('20_2_1000_r_1.5_035')
num_clusters = 20
with self.assertRaises(InitialisationException):
ikminit_c.generate(dataset.data, num_clusters)
"""Temp bootstrap file to run K&A"""
import pathhack
from datasets import testloader
from initialisations import yuan2004 as alg
from preprocessors import stddise
from sklearn.cluster import KMeans
# dataset = testloader.load_fossil()
# dataset = testloader.load_iris()
# Didn't complete on Ceres
dataset = testloader._load_local('2_2_1000_u_1_048')
data = dataset.data
num_clusters = 2
centroids = alg.generate(data, num_clusters)
print(centroids)
"""
est = KMeans(n_clusters=num_clusters, init=centroids, n_init=1)
est.fit(dataset.data)
print(est.cluster_centers_)
"""
"""Temp bootstrap file to run Hand 2005"""
import pathhack
from datasets import testloader
from initialisations import hand2005 as alg
dataset = testloader._load_local('20_1000_1000_u_1_005')
num_clusters = 20
centroids = alg.generate(dataset.data, num_clusters)
print(centroids)
./avilatr/exceptions-000.csv
./ecoli/exceptions-000.csv
./glass/exceptions-000.csv
./letterrec/exceptions-000.csv
./optdigits/exceptions-000.csv
./pendigits/exceptions-000.csv
./wineq_red/exceptions-000.csv
"""
# Didn't initially complete on Ceres
# dataset = testloader._load_local('20_2_1000_r_1.5_035')
# num_clusters = 20
# Exceptions on Ceres
# dataset = testloader._load_local('wineq_red')
# num_clusters = 6
dataset = testloader._load_local('iris')
num_clusters = 3
data = dataset.data
centroids = alg.generate(data, num_clusters)
print(centroids)
est = KMeans(n_clusters=num_clusters, init=centroids, n_init=1)
est.fit(dataset.data)
print(est.cluster_centers_)
"""Temp bootstrap file to run K&A"""
import pathhack
from datasets import testloader
from initialisations import khanahmad2004 as ka
# dataset = testloader.load_fossil()
# dataset = testloader.load_iris()
# The normalised fossil generated by their Java
dataset = testloader._load_local('fossil_ccia')
# Fossil and Iris both have 3
num_clusters = 3
centroids = ka.generate(dataset.data, num_clusters)
print(centroids)
"""Temp bootstrap file to run ikmeans"""
import pathhack
from datasets import testloader
from initialisations import ikmeans_card as alg
from preprocessors import stddise
# from sklearn.cluster import KMeans
# Seems to hang on Ceres
dataset = testloader._load_local('wbco')
num_clusters = 2
data = dataset.data
centroids = alg.generate(data, num_clusters)
print(centroids)
"""
est = KMeans(n_clusters=num_clusters, init=centroids, n_init=1)
est.fit(dataset.data)
print(est.cluster_centers_)
"""