def sqdist(self, x, y, c):
K = 1. / c
prod = self.minkowski_dot(x, y)
eps = {torch.float32: 1e-7, torch.float64: 1e-15}
theta = torch.clamp(-prod / K, min=1.0 + eps[x.dtype])
sqdist = K * arcosh(theta)**2
return torch.clamp(sqdist, max=50.0)
def sqdist(self, x, y, c):
K = 1. / c
prod = self.minkowski_dot(x, y)
theta = torch.clamp(-prod / K, min=1.0 + self.eps[x.dtype])
sqdist = K * arcosh(theta)**2
# clamp distance to avoid nans in Fermi-Dirac decoder
return torch.clamp(sqdist, max=50.0)
def logmap0(self, x, c):
K = 1. / c
sqrtK = K**0.5
d = x.size(-1) - 1
y = x.narrow(-1, 1, d).view(-1, d)
y_norm = torch.norm(y, p=2, dim=1, keepdim=True)
y_norm = torch.clamp(y_norm, min=self.min_norm)
res = torch.zeros_like(x)
theta = torch.clamp(x[:, 0:1] / sqrtK, min=1.0 + self.eps[x.dtype])
res[:, 1:] = sqrtK * arcosh(theta) * y / y_norm
return res
def induced_distance(self, x, y, c):
xy_inner = self.l_inner(x, y)
sqrt_c = c**0.5
return sqrt_c * arcosh(-xy_inner / c + self.eps[x.dtype])