def gathgeva_test(clusters, noise=10):
q = 100 * clusters
xs = datagen_2d.generate_2d_gathgeva_dataset(clusters, noise=noise, q=q)
scatter_2d_data(xs)
clusters = [GathGeva.GGCluster(1000, 2) for k in range(clusters)]
fc = FuzzyClustring.FuzzyClassifier(xs, clusters, m=2)
fc.fit(delta=.001,
increase_iteration=20,
increase_factor=1.2,
plot_level=2,
verbose_level=0,
verbose_iteration=100)
print fc.C
fc.scatter_clusters_data()
def linear_test(clusters, noise=10):
q = 50 * clusters
xs = datagen_2d.generate_2d_line_dataset(clusters, noise=noise, q=q)
scatter_2d_data(xs)
clusters = [Linear.LinearCluster(1000, 2) for k in range(clusters)]
fc = FuzzyClustring.FuzzyClassifier(xs, clusters, m=2)
fc.fit(delta=.0001,
increase_iteration=30,
increase_factor=1.1,
plot_level=2,
verbose_level=0,
verbose_iteration=100)
print fc.C
fc.scatter_clusters_data()
def cmean_test(clusters, noise=10, data=None):
if data is None:
q = 100 * clusters
xs = datagen_2d.generate_2d_cmeans_dataset(clusters, noise=noise, q=q)
scatter_2d_data(xs)
else:
xs = data
clusters = [CMean.CMeanCluster(1000, 2) for k in range(clusters)]
fc = FuzzyClustring.FuzzyClassifier(xs, clusters, m=2)
fc.fit(delta=.001,
increase_iteration=20,
increase_factor=1.2,
plot_level=2,
verbose_level=0,
verbose_iteration=100)
print fc.C
fc.scatter_clusters_data()
def elliptical_test(clusters, ellipce_type=2, noise=10):
q = 50 * clusters
xs = datagen_2d.generate_2d_ellipse_dataset(clusters, noise=noise, q=q)
scatter_2d_data(xs)
if ellipce_type == 1:
clusters = [
Elliptical.EllipticalCluster(1000, 2) for k in range(clusters)
]
elif ellipce_type == 2:
clusters = [
Elliptical.EllipticalCluster2(1000, 2) for k in range(clusters)
]
fc = FuzzyClustring.FuzzyClassifier(xs, clusters, m=2)
fc.fit(delta=.001,
increase_iteration=30,
increase_factor=1.2,
plot_level=2,
verbose_level=0,
verbose_iteration=100)
print fc.C
fc.scatter_clusters_data()