tensorflow_probability/python/internal/special_math.py
def _log_ndtr_asymptotic_series(x, series_order):
  """Calculates the asymptotic series used in log_ndtr."""
  npdt = dtype_util.as_numpy_dtype(x.dtype)
  if series_order <= 0:
    return npdt(1)
  x_2 = tf.square(x)
  even_sum = tf.zeros_like(x)
  odd_sum = tf.zeros_like(x)
  x_2n = x_2  # Start with x^{2*1} = x^{2*n} with n = 1.
  for n in range(1, series_order + 1):
    y = npdt(_double_factorial(2 * n - 1)) / x_2n
    if n % 2:
      odd_sum += y
    else:
      even_sum += y
    x_2n *= x_2
  return 1. + even_sum - odd_sum
def _log_ndtr_asymptotic_series(x, series_order):
  npdt = dtype_util.as_numpy_dtype(x.dtype)
  if series_order <= 0:
    return npdt(1)
  x_2 = tf.square(x)
  even_sum = tf.zeros_like(x)
  odd_sum = tf.zeros_like(x)
  x_2n = x_2  # Start with x^{2*1} = x^{2*n} with n = 1.
  for n in range(1, series_order + 1):
    y = npdt(_double_factorial(2 * n - 1)) / x_2n
    if n % 2:
      odd_sum += y
    else:
      even_sum += y
    x_2n *= x_2
  return 1. + even_sum - odd_sum
def _log_ndtr_asymptotic_series ( x , series_order ) : npdt = dtype_util . as_numpy_dtype ( x . dtype ) if series_order <= 0 : return npdt ( 1 ) x_2 = tf . square ( x ) even_sum = tf . zeros_like ( x ) odd_sum = tf . zeros_like ( x ) x_2n = x_2 # Start with x^{2*1} = x^{2*n} with n = 1. for n in range ( 1 , series_order + 1 ) : y = npdt ( _double_factorial ( 2 * n - 1 ) ) / x_2n if n % 2 : odd_sum += y else : even_sum += y x_2n *= x_2 return 1. + even_sum - odd_sum
Calculates the asymptotic series used in log_ndtr.
Calculates the asymptotic series used in log_ndtr .