def test_fshape_scp_specific(self):
############################################################
from pykeops.numpy.shape_distance import FshapeScp
for k, t in itertools.product(['gaussian', 'cauchy'], self.type_to_test):
# Call cuda kernel
kgeom = k
ksig = 'gaussian'
ksphere = 'gaussian_oriented'
sigma_geom = 1.0
sigma_sig = 1.0
sigma_sphere = np.pi / 2
# Call cuda kernel
my_fshape_scp = FshapeScp(kernel_geom=kgeom, kernel_sig=ksig, kernel_sphere=ksphere, dtype=t)
gamma = my_fshape_scp(self.x.astype(t), self.y.astype(t),
self.f.astype(t), self.g.astype(t),
self.a.astype(t), self.b.astype(t),
sigma_geom=sigma_geom, sigma_sig=sigma_sig, sigma_sphere=sigma_sphere).ravel()
# Python version
areaa = np.linalg.norm(self.a, axis=1)
areab = np.linalg.norm(self.b, axis=1)
nalpha = self.a / areaa[:, np.newaxis]
nbeta = self.b / areab[:, np.newaxis]
gamma_py = np.sum((areaa[:, np.newaxis] * areab[np.newaxis, :])
* np_kernel(self.x, self.y, sigma_geom, kgeom)
* np_kernel(self.f, self.g, sigma_sig, ksig)
* np_kernel_sphere(nalpha, nbeta, sigma_sphere, ksphere), axis=1)
# compare output
print("ee")
self.assertTrue(np.allclose(gamma, gamma_py, atol=1e-6))
sigma_sig = 1.0
sigma_sphere = np.pi / 2
kgeom = 'gaussian'
ksig = 'gaussian'
ksphere = 'gaussian_oriented'
myconv = FshapeScp(kernel_geom=kgeom, kernel_sig=ksig, kernel_sphere=ksphere)
gamma = myconv(x,
y,
f,
g,
a,
b,
sigma_geom=sigma_geom,
sigma_sig=sigma_sig,
sigma_sphere=sigma_sphere).ravel()
areaa = np.linalg.norm(a, axis=1)
areab = np.linalg.norm(b, axis=1)
nalpha = a / areaa[:, np.newaxis]
nbeta = b / areab[:, np.newaxis]
gamma_py = np.sum(
(areaa[:, np.newaxis] * areab[np.newaxis, :]) *
np_kernel(x, y, sigma_geom, kgeom) * np_kernel(f, g, sigma_sig, ksig) *
np_kernel_sphere(nalpha, nbeta, sigma_sphere, ksphere),
axis=1)
# compare output
print(np.allclose(gamma, gamma_py, atol=1e-6))
