def test_log_and_exp_power_affine(self):
base_point = gs.array([[5., 0., 0.], [0., 7., 2.], [0., 2., 8.]])
point = gs.array([[9., 0., 0.], [0., 5., 0.], [0., 0., 1.]])
metric = SPDMetricAffine(3, power_affine=.5)
log = metric.log(point, base_point)
result = metric.exp(log, base_point)
expected = point
self.assertAllClose(result, expected)
def test_estimate_spd_two_samples(self):
space = SPDMatrices(3)
metric = SPDMetricAffine(3)
point = space.random_point(2)
mean = FrechetMean(metric)
mean.fit(point)
result = mean.estimate_
expected = metric.exp(metric.log(point[0], point[1]) / 2, point[1])
self.assertAllClose(expected, result)
def test_log_and_exp_power_affine(self):
"""Test of SPDMetricAffine.log and exp methods with power!=1."""
base_point = gs.array([[5.0, 0.0, 0.0], [0.0, 7.0, 2.0], [0.0, 2.0, 8.0]])
point = gs.array([[9.0, 0.0, 0.0], [0.0, 5.0, 0.0], [0.0, 0.0, 1.0]])
metric = SPDMetricAffine(3, power_affine=0.5)
log = metric.log(point, base_point)
result = metric.exp(log, base_point)
expected = point
self.assertAllClose(result, expected)
def test_log(self, n, power_affine, point, base_point, expected):
metric = SPDMetricAffine(n, power_affine)
self.assertAllClose(
metric.log(gs.array(point), gs.array(base_point)), gs.array(expected)
)