def test_mean_riemann():
covmats = CovMats.random(10, 10)
old_dist = mean_riemann(covmats.numpy_array)
covmats.reset_covmats_fields()
new_dist = Mean.euclidean(covmats)
return _get_state(old_dist, new_dist, "mean riemann")
def test_mean_riemann():
covmats = CovMats.random(10, 10)
old_dist = mean_riemann(covmats.numpy_array)
covmats.reset_covmats_fields()
new_dist = Mean.euclidean(covmats)
return _get_state(old_dist, new_dist, "mean riemann")
def compute(matrice_order, loop_number):
tmp = numpy.random.rand(matrice_order, 2 * matrice_order)
a = numpy.dot(tmp, tmp.T) / 1000
l = [a]
covmats = numpy.array(l)
for i in range(0, loop_number):
mean = mean_riemann(covmats)
l += [geodesic_riemann(covmats[j, :, :], mean) for j in range(covmats.shape[0])]
covmats = numpy.array(l)
def compute(matrice_order, loop_number):
tmp = numpy.random.rand(matrice_order, 2 * matrice_order)
a = numpy.dot(tmp, tmp.T) / 1000
l = [a]
covmats = numpy.array(l)
for i in range(0, loop_number):
mean = mean_riemann(covmats)
l += [
geodesic_riemann(covmats[j, :, :], mean)
for j in range(covmats.shape[0])
]
covmats = numpy.array(l)