def main():
sphere = Hypersphere(dimension=2)
data = sphere.random_von_mises_fisher(kappa=10, n_samples=1000)
n_clusters = 4
clustering = OnlineKMeans(metric=sphere.metric, n_clusters=n_clusters)
clustering = clustering.fit(data)
plt.figure(0)
ax = plt.subplot(111, projection="3d")
visualization.plot(points=clustering.cluster_centers_,
ax=ax,
space='S2',
c='r')
plt.show()
plt.figure(1)
ax = plt.subplot(111, projection="3d")
sphere_plot = visualization.Sphere()
sphere_plot.draw(ax=ax)
for i in range(n_clusters):
cluster = data[clustering.labels_ == i, :]
sphere_plot.draw_points(ax=ax, points=cluster)
plt.show()
def test_fit(self):
X = self.data
clustering = OnlineKMeans(metric=self.metric, n_clusters=1,
n_repetitions=10)
clustering.fit(X)
center = clustering.cluster_centers_
mean = self.metric.mean(X)
result = self.metric.dist(center, mean)
expected = 0.
self.assertAllClose(expected, result, atol=TOLERANCE)
def test_predict(self):
X = self.data
clustering = OnlineKMeans(
metric=self.metric, n_clusters=3, n_repetitions=1)
clustering.fit(X)
point = self.data[0, :]
prediction = clustering.predict(point)
result = prediction
expected = clustering.labels_[0]
self.assertAllClose(expected, result)
def test_fit(self):
X = self.data
clustering = OnlineKMeans(metric=self.metric,
n_clusters=1,
n_repetitions=10)
clustering.fit(X)
center = clustering.cluster_centers_
mean = FrechetMean(metric=self.metric, lr=1.)
mean.fit(X)
result = self.metric.dist(center, mean.estimate_)
expected = 0.
self.assertAllClose(expected, result, atol=1e-3)
def main():
circle = Hypersphere(dimension=1)
data = circle.random_uniform(n_samples=1000)
n_clusters = 5
clustering = OnlineKMeans(metric=circle.metric, n_clusters=n_clusters)
clustering = clustering.fit(data)
plt.figure(0)
visualization.plot(points=clustering.cluster_centers_, space='S1',
color='red')
plt.show()
plt.figure(1)
ax = plt.axes()
circle_plot = visualization.Circle()
circle_plot.draw(ax=ax)
for i in range(n_clusters):
cluster = data[clustering.labels_ == i, :]
circle_plot.draw_points(ax=ax, points=cluster)
plt.show()