def test_inner_product(self, n, tangent_vec_a, tangent_vec_b, base_point,
expected):
metric = SPDMetricLogEuclidean(n)
result = metric.inner_product(gs.array(tangent_vec_a),
gs.array(tangent_vec_b),
gs.array(base_point))
self.assertAllClose(result, gs.array(expected))
def setup_method(self):
"""Set up the test"""
self.n = 3
self.spd_cov_n = (self.n * (self.n + 1)) // 2
self.samples = 5
self.spd = SPDMatrices(self.n)
self.log_euclidean = SPDMetricLogEuclidean(self.n)
self.affine_invariant = SPDMetricAffine(self.n)
self.euclidean = Euclidean(self.n)
def setUp(self):
warnings.simplefilter('ignore', category=ImportWarning)
gs.random.seed(1234)
self.n = 3
self.space = SPDMatrices(n=self.n)
self.metric_affine = SPDMetricAffine(n=self.n)
self.metric_procrustes = SPDMetricProcrustes(n=self.n)
self.metric_euclidean = SPDMetricEuclidean(n=self.n)
self.metric_logeuclidean = SPDMetricLogEuclidean(n=self.n)
self.n_samples = 4
def setUp(self):
"""Set up the test."""
warnings.simplefilter('ignore', category=ImportWarning)
gs.random.seed(1234)
self.n = 3
self.space = SPDMatrices(n=self.n)
self.metric_affine = SPDMetricAffine(n=self.n)
self.metric_bureswasserstein = SPDMetricBuresWasserstein(n=self.n)
self.metric_euclidean = SPDMetricEuclidean(n=self.n)
self.metric_logeuclidean = SPDMetricLogEuclidean(n=self.n)
self.n_samples = 4
def setUp_alt(self, n=3, n_samples=4):
"""Set up the test, flexible parameters."""
warnings.simplefilter('ignore', category=ImportWarning)
gs.random.seed(1234)
self.n = n
self.space = SPDMatrices(n=self.n)
self.metric_affine = SPDMetricAffine(n=self.n)
self.metric_procrustes = SPDMetricProcrustes(n=self.n)
self.metric_euclidean = SPDMetricEuclidean(n=self.n)
self.metric_logeuclidean = SPDMetricLogEuclidean(n=self.n)
self.n_samples = n_samples
def __init__(self, manifold, mean, cov):
n = mean.shape[-1]
metric = manifold.metric
if metric is None:
manifold.metric = SPDMetricLogEuclidean(n)
else:
if not isinstance(metric,
SPDMetricLogEuclidean) and not isinstance(
metric, SPDMetricAffine):
raise ValueError("Invalid Metric, "
"Should be of type SPDMetricLogEuclidean"
"or SPDMetricAffine")
self.manifold = manifold
self.mean = mean
self.cov = cov
def test_log(self, n, point, base_point, expected):
metric = SPDMetricLogEuclidean(n)
result = metric.log(gs.array(point), gs.array(base_point))
self.assertAllClose(result, gs.array(expected))
def test_exp(self, n, tangent_vec, base_point, expected):
metric = SPDMetricLogEuclidean(n)
result = metric.exp(gs.array(tangent_vec), gs.array(base_point))
self.assertAllClose(result, gs.array(expected))
def test_dist(self, n, point_a, point_b, expected):
metric = SPDMetricLogEuclidean(n)
result = metric.dist(gs.array(point_a), gs.array(point_b))
self.assertAllClose(result, gs.array(expected))
