def test_fit_init_random(self):
"""Test fitting data into a GMM."""
gmm_learning = RiemannianEM(
metric=self.metric,
n_gaussians=self.n_gaussian,
initialisation_method=self.initialisation_method,
)
means, variances, coefficients = gmm_learning.fit(self.data)
self.assertTrue((coefficients < 1).all() and (coefficients > 0).all())
self.assertTrue((variances < 1).all() and (variances > 0).all())
self.assertTrue(self.space.belongs(means).all())
gmm_learning = RiemannianEM(
metric=self.metric,
n_gaussians=self.n_gaussian,
initialisation_method="random",
)
means, variances, coefficients = gmm_learning.fit(self.data)
self.assertTrue((coefficients < 1).all() and (coefficients > 0).all())
self.assertTrue((variances < 1).all() and (variances > 0).all())
self.assertTrue(self.space.belongs(means).all())
def expectation_maximisation_poincare_ball():
"""Apply EM algorithm on three random data clusters."""
dim = 2
n_samples = 5
cluster_1 = gs.random.uniform(low=0.2, high=0.6, size=(n_samples, dim))
cluster_2 = gs.random.uniform(low=-0.6, high=-0.2, size=(n_samples, dim))
cluster_3 = gs.random.uniform(low=-0.3, high=0, size=(n_samples, dim))
cluster_3[:, 0] = -cluster_3[:, 0]
data = gs.concatenate((cluster_1, cluster_2, cluster_3), axis=0)
n_clusters = 3
manifold = PoincareBall(dim=2)
metric = manifold.metric
EM = RiemannianEM(n_gaussians=n_clusters,
metric=metric,
initialisation_method='random')
means, variances, mixture_coefficients = EM.fit(data=data)
# Plot result
plot = plot_gaussian_mixture_distribution(data,
mixture_coefficients,
means,
variances,
plot_precision=100,
save_path='result.png',
metric=metric)
return plot
def test_fit_init_random_sphere(self):
"""Test fitting data into a GMM."""
space = Hypersphere(2)
gmm_learning = RiemannianEM(
metric=space.metric,
n_gaussians=2,
initialisation_method=self.initialisation_method,
)
means = space.random_uniform(2)
cluster_1 = space.random_von_mises_fisher(mu=means[0], kappa=20, n_samples=140)
cluster_2 = space.random_von_mises_fisher(mu=means[1], kappa=20, n_samples=140)
data = gs.concatenate((cluster_1, cluster_2), axis=0)
means, variances, coefficients = gmm_learning.fit(data)
self.assertTrue((coefficients < 1).all() and (coefficients > 0).all())
self.assertTrue((variances < 1).all() and (variances > 0).all())
self.assertTrue(space.belongs(means).all())