def test_predict_proba(self):
"""Test the 'predict_proba' class method."""
training_dataset = gs.array([[0], [1], [2], [3]])
labels = [0, 0, 1, 1]
neigh = KNearestNeighborsClassifier(n_neighbors=self.n_neighbors,
distance=self.distance)
neigh.fit(training_dataset, labels)
result = neigh.predict_proba([[0.9]])
expected = gs.array([[2 / 3, 1 / 3]])
self.assertAllClose(expected, result, atol=gs.atol)
def test_predict(self):
"""Test the 'predict' class method."""
training_dataset = gs.array([[0], [1], [2], [3]])
labels = [0, 0, 1, 1]
neigh = KNearestNeighborsClassifier(n_neighbors=self.n_neighbors,
metric=self.distance)
neigh.fit(training_dataset, labels)
result = neigh.predict([[1.1]])
expected = gs.array([0])
self.assertAllClose(expected, result)
def main():
"""Plot the result of a KNN classification on the sphere."""
sphere = Hypersphere(dim=2)
sphere_distance = sphere.metric.dist
n_labels = 2
n_samples_per_dataset = 10
n_targets = 200
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)
neigh = KNearestNeighborsClassifier(n_neighbors=2,
distance=sphere_distance)
neigh.fit(training_dataset, labels)
target_labels = neigh.predict(target)
plt.figure(0)
ax = plt.subplot(111, projection="3d")
plt.title("Training set")
sphere_plot = visualization.Sphere()
sphere_plot.draw(ax=ax)
for i_label in range(n_labels):
points_label_i = training_dataset[labels == i_label, ...]
sphere_plot.draw_points(ax=ax, points=points_label_i)
plt.figure(1)
ax = plt.subplot(111, projection="3d")
plt.title("Classification")
sphere_plot = visualization.Sphere()
sphere_plot.draw(ax=ax)
for i_label in range(n_labels):
target_points_label_i = target[target_labels == i_label, ...]
sphere_plot.draw_points(ax=ax, points=target_points_label_i)
plt.show()
labels = np.array(labels)
pca = PCA(n_components=2)
features = pca.fit_transform(features)
scaler = StandardScaler()
features = scaler.fit_transform(features) * 3
features = np.concatenate((features, np.zeros_like(features[:, 0:1])), axis=1)
sphere = Hypersphere(dim=2)
transformer = ToTangentSpace(sphere)
base_point = np.array([0, 0, 1])
s_points = transformer.inverse_transform(features, base_point)
classifier = KNearestNeighborsClassifier()
classifier.fit(s_points, labels)
f_labels = []
def loss_f(x):
label = classifier.predict(x[np.newaxis, :])
f_labels.append(label)
return label == 0
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
plot_sphere = Sphere()
n_points = 10000
f_points = plot_sphere.fibonnaci_points(n_points).swapaxes(0, 1)
plot_sphere.plot_heatmap(ax=ax, n_points=n_points, scalar_function=loss_f)