tensorflow_probability/python/distributions/von_mises.py
def von_mises_cdf(x, concentration):
  """Computes the cumulative density function (CDF) of von Mises distribution.

  Denote the density of vonMises(loc=0, concentration=concentration) by p(t).
  Note that p(t) is periodic, p(t) = p(t + 2 pi).
  The CDF at the point x is defined as int_{-pi}^x p(t) dt.
  Thus, when x in [-pi, pi], the CDF is in [0, 1]; when x is in [pi, 3pi], the
  CDF is in [1, 2], etc.

  The CDF is not available in closed form. Instead, we use the method [1]
  which uses either a series expansion or a Normal approximation, depending on
  the value of concentration.

  We also compute the derivative of the CDF w.r.t. both x and concentration.
  The derivative w.r.t. x is p(x), while the derivative w.r.t. concentration is
  computed
  using automatic differentiation. We use forward mode for the series case
  (which allows to save memory) and backward mode for the Normal approximation.

  Arguments:
    x: The point at which to evaluate the CDF.
    concentration: The concentration parameter of the von Mises distribution.

  Returns:
    The value of the CDF computed elementwise.

  References:
    [1] G. Hill "Algorithm 518: Incomplete Bessel Function I_0. The Von Mises
    Distribution." ACM Transactions on Mathematical Software, 1977
  """
  x = tf.convert_to_tensor(value=x)
  concentration = tf.convert_to_tensor(value=concentration)
  dtype = x.dtype

  # Map x to [-pi, pi].
  num_periods = tf.round(x / (2. * np.pi))
  x -= (2. * np.pi) * num_periods

  # We take the hyperparameters from Table I of [1], the row for D=8
  # decimal digits of accuracy. ck is the cut-off for concentration:
  # if concentration < ck,  the series expansion is used;
  # otherwise, the Normal approximation is used.
  ck = 10.5
  # The number of terms in the series expansion. [1] chooses it as a function
  # of concentration, n(concentration). This is hard to implement in TF.
  # Instead, we upper bound it over concentrations:
  #   num_terms = ceil ( max_{concentration <= ck} n(concentration) ).
  # The maximum is achieved for concentration = ck.
  num_terms = 20

  cdf_series, dcdf_dconcentration_series = _von_mises_cdf_series(
      x, concentration, num_terms, dtype)
  cdf_normal, dcdf_dconcentration_normal = _von_mises_cdf_normal(
      x, concentration, dtype)

  use_series = concentration < ck
  cdf = tf.where(use_series, cdf_series, cdf_normal)
  cdf += num_periods
  dcdf_dconcentration = tf.where(use_series, dcdf_dconcentration_series,
                                 dcdf_dconcentration_normal)

  def grad(dy):
    prob = tf.exp(concentration * (tf.cos(x) - 1.)) / (
        (2. * np.pi) * tf.math.bessel_i0e(concentration))
    return dy * prob, dy * dcdf_dconcentration

  return cdf, grad
def von_mises_cdf(x, concentration):
  """Computes the cumulative density function (CDF) of von Mises distribution.

  Denote the density of vonMises(loc=0, concentration=concentration) by p(t).
  Note that p(t) is periodic, p(t) = p(t + 2 pi).
  The CDF at the point x is defined as int_{-pi}^x p(t) dt.
  Thus, when x in [-pi, pi], the CDF is in [0, 1]; when x is in [pi, 3pi], the
  CDF is in [1, 2], etc.

  The CDF is not available in closed form. Instead, we use the method [1]
  which uses either a series expansion or a Normal approximation, depending on
  the value of concentration.

  We also compute the derivative of the CDF w.r.t. both x and concentration.
  The derivative w.r.t. x is p(x), while the derivative w.r.t. concentration is
  computed
  using automatic differentiation. We use forward mode for the series case
  (which allows to save memory) and backward mode for the Normal approximation.

  Arguments:
    x: The point at which to evaluate the CDF.
    concentration: The concentration parameter of the von Mises distribution.

  Returns:
    The value of the CDF computed elementwise.

  References:
    [1] G. Hill "Algorithm 518: Incomplete Bessel Function I_0. The Von Mises
    Distribution." ACM Transactions on Mathematical Software, 1977
  """
  x = tf.convert_to_tensor(value=x)
  concentration = tf.convert_to_tensor(value=concentration)
  dtype = x.dtype

  # Map x to [-pi, pi].
  num_periods = tf.round(x / (2. * np.pi))
  x -= (2. * np.pi) * num_periods

  # We take the hyperparameters from Table I of [1], the row for D=8
  # decimal digits of accuracy. ck is the cut-off for concentration:
  # if concentration < ck,  the series expansion is used;
  # otherwise, the Normal approximation is used.
  ck = 10.5
  # The number of terms in the series expansion. [1] chooses it as a function
  # of concentration, n(concentration). This is hard to implement in TF.
  # Instead, we upper bound it over concentrations:
  #   num_terms = ceil ( max_{concentration <= ck} n(concentration) ).
  # The maximum is achieved for concentration = ck.
  num_terms = 20

  cdf_series, dcdf_dconcentration_series = _von_mises_cdf_series(
      x, concentration, num_terms, dtype)
  cdf_normal, dcdf_dconcentration_normal = _von_mises_cdf_normal(
      x, concentration, dtype)

  use_series = concentration < ck
  cdf = tf.where(use_series, cdf_series, cdf_normal)
  cdf += num_periods
  dcdf_dconcentration = tf.where(use_series, dcdf_dconcentration_series,
                                 dcdf_dconcentration_normal)

  def grad(dy):
    prob = tf.exp(concentration * (tf.cos(x) - 1.)) / (
        (2. * np.pi) * tf.math.bessel_i0e(concentration))
    return dy * prob, dy * dcdf_dconcentration

  return cdf, grad
Computes the cumulative density function (CDF) of von Mises distribution.

  Denote the density of vonMises(loc=0, concentration=concentration) by p(t).
  Note that p(t) is periodic, p(t) = p(t + 2 pi).
  The CDF at the point x is defined as int_{-pi}^x p(t) dt.
  Thus, when x in [-pi, pi], the CDF is in [0, 1]; when x is in [pi, 3pi], the
  CDF is in [1, 2], etc.

  The CDF is not available in closed form. Instead, we use the method [1]
  which uses either a series expansion or a Normal approximation, depending on
  the value of concentration.

  We also compute the derivative of the CDF w.r.t. both x and concentration.
  The derivative w.r.t. x is p(x), while the derivative w.r.t. concentration is
  computed
  using automatic differentiation. We use forward mode for the series case
  (which allows to save memory) and backward mode for the Normal approximation.

  Arguments:
    x: The point at which to evaluate the CDF.
    concentration: The concentration parameter of the von Mises distribution.

  Returns:
    The value of the CDF computed elementwise.

  References:
    [1] G. Hill "Algorithm 518: Incomplete Bessel Function I_0. The Von Mises
    Distribution." ACM Transactions on Mathematical Software, 1977