def dist(self, x, y, keepdims=False):
sqrt_k = tf.math.sqrt(tf.cast(self.k, x.dtype))
x_y = self._mobius_add(-x, y)
norm_x_y = tf.linalg.norm(x_y, axis=-1, keepdims=keepdims)
eps = utils.get_eps(x)
tanh = tf.clip_by_value(sqrt_k * norm_x_y, -1.0 + eps, 1.0 - eps)
return 2 * tf.math.atanh(tanh) / sqrt_k
def log(self, x, y):
sqrt_k = tf.math.sqrt(tf.cast(self.k, x.dtype))
x_y = self._mobius_add(-x, y)
norm_x_y = tf.linalg.norm(x_y, axis=-1, keepdims=True)
eps = utils.get_eps(x)
tanh = tf.clip_by_value(sqrt_k * norm_x_y, -1.0 + eps, 1.0 - eps)
lambda_x = self._lambda(x, keepdims=True)
return 2 * (x_y / norm_x_y) * tf.math.atanh(tanh) / (sqrt_k * lambda_x)
def projx(self, x):
sqrt_k = tf.math.sqrt(tf.cast(self.k, x.dtype))
norm = tf.linalg.norm(x, axis=-1, keepdims=True)
return tf.where(
sqrt_k * norm < tf.ones_like(norm),
x,
x / (sqrt_k * norm + 10 * utils.get_eps(x)),
)
def _mobius_scal_mul(self, x, r):
"""Compute the Möbius scalar multiplication of :math:`x \in
\mathcal{D}^{n}_{k} \ {0}` by :math:`r`
:math:`x \otimes r = (1/\sqrt{k})\tanh(r
\atanh(\sqrt{k}||x||))\frac{x}{||x||}`
"""
sqrt_k = tf.math.sqrt(tf.cast(self.k, x.dtype))
norm_x = tf.linalg.norm(x, axis=-1, keepdims=True)
eps = utils.get_eps(x)
tan = tf.clip_by_value(sqrt_k * norm_x, -1.0 + eps, 1.0 - eps)
return (1 / sqrt_k) * tf.math.tanh(r * tf.math.atanh(tan)) * x / norm_x
def _diff_power(self, x, d, power):
e, v = tf.linalg.eigh(x)
v_t = utils.transposem(v)
e = tf.expand_dims(e, -2)
if power == "log":
pow_e = tf.math.log(e)
elif power == "exp":
pow_e = tf.math.exp(e)
s = utils.transposem(tf.ones_like(e)) @ e
pow_s = utils.transposem(tf.ones_like(pow_e)) @ pow_e
denom = utils.transposem(s) - s
numer = utils.transposem(pow_s) - pow_s
abs_denom = tf.math.abs(denom)
eps = utils.get_eps(x)
if power == "log":
numer = tf.where(abs_denom < eps, tf.ones_like(numer), numer)
denom = tf.where(abs_denom < eps, utils.transposem(s), denom)
elif power == "exp":
numer = tf.where(abs_denom < eps, utils.transposem(pow_s), numer)
denom = tf.where(abs_denom < eps, tf.ones_like(denom), denom)
t = v_t @ d @ v * numer / denom
return v @ t @ v_t
def from_poincare(self, x, k):
"""Inverse of the diffeomorphism to the Poincaré ball"""
k = tf.cast(k, x.dtype)
x_sq_norm = tf.reduce_sum(x * x, axis=-1, keepdims=True)
y = tf.math.sqrt(k) * tf.concat([1 + x_sq_norm, 2 * x], axis=-1)
return y / (1.0 - x_sq_norm + utils.get_eps(x))
def norm(self, x, u, keepdims=False):
inner = self.inner(x, u, u, keepdims=keepdims)
return tf.math.sqrt(tf.maximum(inner, utils.get_eps(x)))
def _check_vector_on_tangent(self, x, u, atol, rtol):
inner = self.inner(x, x, u)
rtol = 100 * utils.get_eps(x) if rtol is None else rtol
return utils.allclose(inner, tf.zeros_like(inner), atol, rtol)
def log(self, x, y):
u = self.proju(x, y - x)
norm_u = self.norm(x, u, keepdims=True)
dist = self.dist(x, y, keepdims=True)
log = u * dist / norm_u
return tf.where(dist > utils.get_eps(x), log, u)
def exp(self, x, u):
norm_u = self.norm(x, u, keepdims=True)
exp = x * tf.math.cos(norm_u) + u * tf.math.sin(norm_u) / norm_u
retr = self.projx(x + u)
return tf.where(norm_u > utils.get_eps(x), exp, retr)
def norm(self, x, u, keepdims=False):
norm_u = tf.linalg.norm(u, axis=-1, keepdims=keepdims)
return tf.maximum(norm_u, utils.get_eps(x))
