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This is the API documentation for the gdtest_docstring_math library.
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## Functions

Public functions


norm(vector: list) -> float

Compute the L2 (Euclidean) norm of a vector.

Returns the magnitude of the input vector, computed as
the square root of the sum of squared elements.

Parameters
----------
vector
    A list of numeric values representing a vector.

Returns
-------
float
    The L2 norm of the vector.

Notes
-----
Computes the L2 norm:

.. math::

    \|x\| = \sqrt{\sum_{i=1}^{n} x_i^2}

For a zero vector, the norm is 0.0. The computation uses
floating-point arithmetic, so results may have small
rounding errors for very large or very small values.

Examples
--------
>>> norm([3.0, 4.0])
5.0

>>> norm([1.0, 0.0, 0.0])
1.0

softmax(logits: list) -> list

Apply the softmax function to a list of logits.

Converts a vector of raw scores (logits) into a probability
distribution where each element is in (0, 1) and all
elements sum to 1.

Parameters
----------
logits
    A list of numeric values (raw scores).

Returns
-------
list
    A list of probabilities summing to 1.0.

Notes
-----
Applies the softmax function:

.. math::

    \sigma(z)_i = \frac{e^{z_i}}{\sum_{j=1}^{K} e^{z_j}}

For numerical stability, the implementation subtracts the
maximum logit value before exponentiation:

.. math::

    \sigma(z)_i = \frac{e^{z_i - \max(z)}}{\sum_{j=1}^{K} e^{z_j - \max(z)}}

This prevents overflow when logit values are large.

Examples
--------
>>> result = softmax([1.0, 2.0, 3.0])
>>> round(sum(result), 5)
1.0

>>> softmax([0.0, 0.0])
[0.5, 0.5]