tensorflow_probability/python/internal/special_math.py
def log_cdf_laplace(x, name="log_cdf_laplace"):
  """Log Laplace distribution function.

  This function calculates `Log[L(x)]`, where `L(x)` is the cumulative
  distribution function of the Laplace distribution, i.e.

  ```L(x) := 0.5 * int_{-infty}^x e^{-|t|} dt```

  For numerical accuracy, `L(x)` is computed in different ways depending on `x`,

  ```
  x <= 0:
    Log[L(x)] = Log[0.5] + x, which is exact

  0 < x:
    Log[L(x)] = Log[1 - 0.5 * e^{-x}], which is exact
  ```

  Args:
    x: `Tensor` of type `float32`, `float64`.
    name: Python string. A name for the operation (default="log_ndtr").

  Returns:
    `Tensor` with `dtype=x.dtype`.

  Raises:
    TypeError: if `x.dtype` is not handled.
  """

  with tf.name_scope(name):
    x = tf.convert_to_tensor(value=x, name="x")

    # For x < 0, L(x) = 0.5 * exp{x} exactly, so Log[L(x)] = log(0.5) + x.
    lower_solution = -np.log(2.) + x

    # safe_exp_neg_x = exp{-x} for x > 0, but is
    # bounded above by 1, which avoids
    #   log[1 - 1] = -inf for x = log(1/2), AND
    #   exp{-x} --> inf, for x << -1
    safe_exp_neg_x = tf.exp(-tf.abs(x))

    # log1p(z) = log(1 + z) approx z for |z| << 1. This approxmation is used
    # internally by log1p, rather than being done explicitly here.
    upper_solution = tf.math.log1p(-0.5 * safe_exp_neg_x)

    return tf.where(x < 0., lower_solution, upper_solution)
def log_cdf_laplace(x, name="log_cdf_laplace"):
  with tf.name_scope(name):
    x = tf.convert_to_tensor(value=x, name="x")

    # For x < 0, L(x) = 0.5 * exp{x} exactly, so Log[L(x)] = log(0.5) + x.
    lower_solution = -np.log(2.) + x

    # safe_exp_neg_x = exp{-x} for x > 0, but is
    # bounded above by 1, which avoids
    #   log[1 - 1] = -inf for x = log(1/2), AND
    #   exp{-x} --> inf, for x << -1
    safe_exp_neg_x = tf.exp(-tf.abs(x))

    # log1p(z) = log(1 + z) approx z for |z| << 1. This approxmation is used
    # internally by log1p, rather than being done explicitly here.
    upper_solution = tf.math.log1p(-0.5 * safe_exp_neg_x)

    return tf.where(x < 0., lower_solution, upper_solution)
def log_cdf_laplace ( x , name = "log_cdf_laplace" ) : with tf . name_scope ( name ) : x = tf . convert_to_tensor ( value = x , name = "x" ) # For x < 0, L(x) = 0.5 * exp{x} exactly, so Log[L(x)] = log(0.5) + x. lower_solution = - np . log ( 2. ) + x # safe_exp_neg_x = exp{-x} for x > 0, but is # bounded above by 1, which avoids #   log[1 - 1] = -inf for x = log(1/2), AND #   exp{-x} --> inf, for x << -1 safe_exp_neg_x = tf . exp ( - tf . abs ( x ) ) # log1p(z) = log(1 + z) approx z for |z| << 1. This approxmation is used # internally by log1p, rather than being done explicitly here. upper_solution = tf . math . log1p ( - 0.5 * safe_exp_neg_x ) return tf . where ( x < 0. , lower_solution , upper_solution )
Log Laplace distribution function.

  This function calculates `Log[L(x)]`, where `L(x)` is the cumulative
  distribution function of the Laplace distribution, i.e.

  ```L(x) := 0.5 * int_{-infty}^x e^{-|t|} dt```

  For numerical accuracy, `L(x)` is computed in different ways depending on `x`,

  ```
  x <= 0:
    Log[L(x)] = Log[0.5] + x, which is exact

  0 < x:
    Log[L(x)] = Log[1 - 0.5 * e^{-x}], which is exact
  ```

  Args:
    x: `Tensor` of type `float32`, `float64`.
    name: Python string. A name for the operation (default="log_ndtr").

  Returns:
    `Tensor` with `dtype=x.dtype`.

  Raises:
    TypeError: if `x.dtype` is not handled.
Log Laplace distribution function .