def _inverse_log_det_jacobian(self, y):
with tf.control_dependencies(self._maybe_assert_valid_y(y)):
return (tf.math.square(tfp_math.erfcinv(y)) +
tf.math.log(self.scale) + 0.5 * np.log(np.pi) -
tf.math.log(tfp_math.erfcinv(y)))
def _inverse_log_det_jacobian(self, y):
with tf.control_dependencies(self._maybe_assert_valid_y(y)):
np_dtype = dtype_util.as_numpy_dtype(y.dtype)
return (tf.math.square(tfp_math.erfcinv(y)) +
tf.math.log(self.scale) + 0.5 * np_dtype(np.log(np.pi)) -
tf.math.log(tfp_math.erfcinv(y)))
def _inverse(self, y):
with tf.control_dependencies(self._maybe_assert_valid_y(y)):
return (self.loc - self.scale *
(np.log(2.) + 2. * tf.math.log(tfp_math.erfcinv(y))))
def _inverse(self, y):
with tf.control_dependencies(self._maybe_assert_valid_y(y)):
np_dtype = dtype_util.as_numpy_dtype(y.dtype)
return (
self.loc - self.scale *
(np.log(np_dtype(2.)) + 2. * tf.math.log(tfp_math.erfcinv(y))))