def test_tangent_pca_error(self):
X = self.X
tpca = TangentPCA(self.metric, n_components=self.n_components)
tpca.fit(X)
X_diff_size = gs.ones((self.n_samples, gs.shape(X)[1] + 1))
self.assertRaises(ValueError, tpca.transform, X_diff_size)
def test_fit_fit_transform_matrix(self):
X = self.spd.random_point(n_samples=5)
tpca = TangentPCA(metric=self.spd_metric)
expected = tpca.fit_transform(X)
result = tpca.fit(X).transform(X)
self.assertAllClose(result, expected)
def main():
"""Perform tangent PCA at the mean on H2."""
fig = plt.figure(figsize=(15, 5))
hyperbolic_plane = Hyperboloid(dim=2)
data = hyperbolic_plane.random_uniform(n_samples=140)
mean = FrechetMean(metric=hyperbolic_plane.metric)
mean.fit(data)
mean_estimate = mean.estimate_
tpca = TangentPCA(metric=hyperbolic_plane.metric, n_components=2)
tpca = tpca.fit(data, base_point=mean_estimate)
tangent_projected_data = tpca.transform(data)
geodesic_0 = hyperbolic_plane.metric.geodesic(
initial_point=mean_estimate, initial_tangent_vec=tpca.components_[0])
geodesic_1 = hyperbolic_plane.metric.geodesic(
initial_point=mean_estimate, initial_tangent_vec=tpca.components_[1])
n_steps = 100
t = np.linspace(-1, 1, n_steps)
geodesic_points_0 = geodesic_0(t)
geodesic_points_1 = geodesic_1(t)
logging.info(
'Coordinates of the Log of the first 5 data points at the mean, '
'projected on the principal components:')
logging.info('\n{}'.format(tangent_projected_data[:5]))
ax_var = fig.add_subplot(121)
xticks = np.arange(1, 2 + 1, 1)
ax_var.xaxis.set_ticks(xticks)
ax_var.set_title('Explained variance')
ax_var.set_xlabel('Number of Principal Components')
ax_var.set_ylim((0, 1))
ax_var.plot(xticks, tpca.explained_variance_ratio_)
ax = fig.add_subplot(122)
visualization.plot(mean_estimate,
ax,
space='H2_poincare_disk',
color='darkgreen',
s=10)
visualization.plot(geodesic_points_0,
ax,
space='H2_poincare_disk',
linewidth=2)
visualization.plot(geodesic_points_1,
ax,
space='H2_poincare_disk',
linewidth=2)
visualization.plot(data,
ax,
space='H2_poincare_disk',
color='black',
alpha=0.7)
plt.show()
def test_fit_mle(self):
X = self.X
tpca = TangentPCA(self.metric, n_components='mle')
tpca.fit(X)
self.assertEqual(tpca.n_features_, gs.shape(X)[1])
def test_fit_inverse_transform_vector(self):
tpca = TangentPCA(metric=self.metric, point_type='vector')
tangent_projected_data = tpca.fit_transform(self.X)
result = tpca.inverse_transform(tangent_projected_data)
expected = self.X
self.assertAllClose(result, expected)
def test_fit_transform_vector(self):
expected = 2
tpca = TangentPCA(metric=self.metric, n_components=expected)
tangent_projected_data = tpca.fit_transform(self.X)
result = tangent_projected_data.shape[-1]
self.assertAllClose(result, expected)