def main():
random_state = 111
X, y = make_circles(n_samples=1000, random_state=random_state, noise=0.07)
window = (X > 0).all(axis=1)
X_window = X[window]
y_window = y[window]
spk = SuperK(k=5)
spk.load_data(X_window, y_window)
figsize = (5, 5)
vox_genpts = spk.voxelize()
spk.genpts = np.vstack([class_genpts for _, class_genpts in vox_genpts])
spk.genpts_labels = np.hstack([
np.full(len(class_genpts), class_inx)
for class_inx, class_genpts in vox_genpts
])
plot_boundaries(spk, "voxelized", figsize)
em_genpts = spk.apply_em(vox_genpts)
spk.genpts = np.vstack([class_genpts for _, class_genpts in em_genpts])
spk.genpts_labels = np.hstack([
np.full(len(class_genpts), class_inx)
for class_inx, class_genpts in em_genpts
])
plot_boundaries(spk, "after_em", figsize)
spk.genpts, spk.genpts_labels = spk.merge_and_label(em_genpts)
plot_boundaries(spk, "relabeled", figsize)
spk.correct()
plot_boundaries(spk, "corrected", figsize)
def main():
random_state = 111
X, y = make_moons(n_samples=1000, random_state=random_state, noise=0.15)
figsize = (4, 2.75)
spk = SuperKlust(k=2, kmns_n_init=3)
spk.fit(X, y)
plot_boundaries(spk, "moons_1", figsize)
spk = SuperKlust(k=4, kmns_n_init=3)
spk.fit(X, y)
plot_boundaries(spk, "moons_2", figsize)
spk = SuperKlust(k=6, kmns_n_init=3)
spk.fit(X, y)
plot_boundaries(spk, "moons_3", figsize)
def main():
random_state = 111
X, y = make_circles(n_samples=1000, random_state=random_state, noise=0.05)
figsize = (4, 4.5)
spk = SuperK(k=10)
spk.fit(X, y)
plot_boundaries(spk, "circles_1", figsize)
spk = SuperK(k=20)
spk.fit(X, y)
plot_boundaries(spk, "circles_2", figsize)
spk = SuperK(k=30)
spk.fit(X, y)
plot_boundaries(spk, "circles_3", figsize)
def main():
random_state = 6
X, y = make_classification(n_samples=1000,
n_classes=3,
n_features=2,
n_redundant=0,
n_informative=2,
n_clusters_per_class=1,
random_state=random_state)
figsize = (4, 5.25)
spk = SuperK(k=2)
spk.fit(X, y)
plot_boundaries(spk, "random_1", figsize)
spk = SuperK(k=5)
spk.fit(X, y)
plot_boundaries(spk, "random_2", figsize)
spk = SuperK(k=8)
spk.fit(X, y)
plot_boundaries(spk, "random_3", figsize)