def test_predict_poincare_ball_distance(self):
"""Test the 'predict' class method using the Poincare ball distance."""
dim = 2
space = PoincareBall(dim=dim)
distance = space.metric.dist
training_dataset = gs.array([[1 / 2, 1 / 4], [1 / 2, 0],
[1 / 2, -1 / 4], [-1 / 2, 1 / 4],
[-1 / 2, 0], [-1 / 2, -1 / 4]])
labels = [0, 0, 0, 1, 1, 1]
kde = KernelDensityEstimationClassifier(distance=distance,
kernel='distance')
kde.fit(training_dataset, labels)
target_dataset = gs.array([[1 / 2, 1 / 5], [1 / 2, 0], [1 / 2, -1 / 5],
[-1 / 2, 1 / 5], [-1 / 2, 0],
[-1 / 2, -1 / 5]])
result = kde.predict(target_dataset)
expected = [0, 0, 0, 1, 1, 1]
self.assertAllClose(expected, result)
def test_predict_hypersphere_distance(self):
"""Test the 'predict' class method using the hypersphere distance."""
dim = 2
space = Hypersphere(dim=dim)
distance = space.metric.dist
training_dataset = gs.array([[1, 0, 0], [3**(1 / 2) / 2, 1 / 2, 0],
[3**(1 / 2) / 2, -1 / 2, 0], [0, 0, 1],
[0, 1 / 2, 3**(1 / 2) / 2],
[0, -1 / 2, 3**(1 / 2) / 2]])
labels = [0, 0, 0, 1, 1, 1]
kde = KernelDensityEstimationClassifier(distance=distance)
kde.fit(training_dataset, labels)
target_dataset = gs.array([[2**(1 / 2) / 2, 2**(1 / 2) / 2, 0],
[0, 1 / 2, -3**(1 / 2) / 2],
[0, -1 / 2, -3**(1 / 2) / 2],
[-3**(1 / 2) / 2, 1 / 2, 0],
[-3**(1 / 2) / 2, -1 / 2, 0],
[0, 2**(1 / 2) / 2, 2**(1 / 2) / 2]])
result = kde.predict(target_dataset)
expected = [0, 0, 0, 1, 1, 1]
self.assertAllClose(expected, result)
def test_predict_hyperboloid_distance(self):
"""Test the 'predict' class method using the hyperboloid distance."""
dim = 2
space = Hyperboloid(dim=dim)
distance = space.metric.dist
training_dataset_intrinsic = gs.array([
[1 / 2, 1 / 4],
[1 / 2, 0],
[1 / 2, -1 / 4],
[-1 / 2, 1 / 4],
[-1 / 2, 0],
[-1 / 2, -1 / 4],
])
training_dataset = space.change_coordinates_system(
training_dataset_intrinsic,
from_coordinates_system="intrinsic",
to_coordinates_system="extrinsic",
)
labels = [0, 0, 0, 1, 1, 1]
kde = KernelDensityEstimationClassifier(distance=distance,
kernel="distance")
kde.fit(training_dataset, labels)
target_dataset_intrinsic = gs.array([
[1 / 2, 1 / 5],
[1 / 2, 0],
[1 / 2, -1 / 5],
[-1 / 2, 1 / 5],
[-1 / 2, 0],
[-1 / 2, -1 / 5],
])
target_dataset = space.change_coordinates_system(
target_dataset_intrinsic,
from_coordinates_system="intrinsic",
to_coordinates_system="extrinsic",
)
result = kde.predict(target_dataset)
expected = [0, 0, 0, 1, 1, 1]
self.assertAllClose(expected, result)
def main():
"""Plot a Kernel Density Estimation Classification on the sphere."""
sphere = Hypersphere(dim=2)
sphere_distance = sphere.metric.dist
n_labels = 2
n_samples_per_dataset = 10
n_targets = 200
radius = np.inf
kernel = triangular_radial_kernel
bandwidth = 3
n_training_samples = n_labels * n_samples_per_dataset
dataset_1 = sphere.random_von_mises_fisher(
kappa=10,
n_samples=n_samples_per_dataset)
dataset_2 = - sphere.random_von_mises_fisher(
kappa=10,
n_samples=n_samples_per_dataset)
training_dataset = gs.concatenate((dataset_1, dataset_2), axis=0)
labels_dataset_1 = gs.zeros([n_samples_per_dataset], dtype=gs.int64)
labels_dataset_2 = gs.ones([n_samples_per_dataset], dtype=gs.int64)
labels = gs.concatenate((labels_dataset_1, labels_dataset_2))
target = sphere.random_uniform(n_samples=n_targets)
labels_colors = gs.zeros([n_labels, 3])
labels_colors[0, :] = gs.array([0, 0, 1])
labels_colors[1, :] = gs.array([1, 0, 0])
kde = KernelDensityEstimationClassifier(
radius=radius,
distance=sphere_distance,
kernel=kernel,
bandwidth=bandwidth,
outlier_label='most_frequent')
kde.fit(training_dataset, labels)
target_labels = kde.predict(target)
target_labels_proba = kde.predict_proba(target)
plt.figure(0)
ax = plt.subplot(111, projection='3d')
plt.title('Training set')
sphere_plot = visualization.Sphere()
sphere_plot.draw(ax=ax)
colors = gs.zeros([n_training_samples, 3])
for i_sample in range(n_training_samples):
colors[i_sample, :] = labels_colors[labels[i_sample], :]
sphere_plot.draw_points(ax=ax, points=training_dataset, c=colors)
plt.figure(1)
ax = plt.subplot(111, projection='3d')
plt.title('Classification')
sphere_plot = visualization.Sphere()
sphere_plot.draw(ax=ax)
colors = gs.zeros([n_targets, 3])
for i_target in range(n_targets):
colors[i_target, :] = labels_colors[target_labels[i_target], :]
sphere_plot.draw_points(ax=ax, points=target, c=colors)
plt.figure(2)
ax = plt.subplot(111, projection='3d')
plt.title('Probabilistic classification')
sphere_plot = visualization.Sphere()
sphere_plot.draw(ax=ax)
colors = target_labels_proba @ labels_colors
sphere_plot.draw_points(ax=ax, points=target, c=colors)
plt.show()