def test_simetry_of_aligment(self):
"""Check registration using inverse composition"""
warping = elastic_registration_warping(self.unimodal_samples,
self.template)
inverse = invert_warping(warping)
register = self.template_rep.compose(inverse)
distances = np.diag(metric(self.unimodal_samples, register))
np.testing.assert_allclose(distances, 0, atol=12e-3)
def test_invert_warping(self):
inverse = invert_warping(self.polynomial)
# Check if identity
id = self.polynomial.compose(inverse)
np.testing.assert_array_almost_equal([self.time, self.time],
id.data_matrix[..., 0],
decimal=3)
def test_simmetry_of_aligment(self):
"""Check registration using inverse composition"""
reg = ElasticRegistration(template=self.template)
reg.fit_transform(self.unimodal_samples)
warping = reg.warping_
inverse = invert_warping(warping)
register = self.template_rep.compose(inverse)
distances = np.diag(metric(self.unimodal_samples, register))
np.testing.assert_allclose(distances, 0, atol=12e-3)
t = np.linspace(0, 1)
fig.axes[0].plot(t, t, linestyle='--')
# Legend
fig.axes[0].legend(['$\\gamma$', '$\\gamma_{id}$'])
fig
##############################################################################
# The transformation necessary to align :math:`g` to :math:`f` will be the
# inverse of the original warping function, :math:`\gamma^{-1}`.
# This fact is a consequence of the use of the Fisher-Rao metric as energy
# function.
#
warping_inverse = invert_warping(warping)
fig = fd.plot(label='$f$')
g.compose(warping_inverse).plot(fig=fig, color='C1', linestyle='--')
# Legend
fig.axes[0].legend(['$f$', '$g$', '$g \\circ \\gamma^{-1} $'])
##############################################################################
# The amount of deformation used in the registration can be controlled by
# using a variation of the metric with a penalty term
# :math:`\lambda \mathcal{R}(\gamma)` wich will reduce the elasticity of the
# metric.
#
# The following figure shows the original curves and the result to the