semvec/pypi-packages huggingface dataset:
multivar-horner 3.1.0
python package implementing a multivariate Horner scheme for efficiently evaluating multivariate polynomials
License: MIT
Requires Python: >=3.8,<4
Keywords: mathematics,polynomials,evaluation,multivariate,horner-scheme

===============
multivar_horner
===============


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``multivar_horner`` is a python package implementing a multivariate
`Horner scheme ("Horner's method", "Horner's rule") <https://en.wikipedia.org/wiki/Horner%27s_method>`__
for efficiently evaluating multivariate polynomials.


Quick Guide:

.. code-block:: console

    pip install multivar_horner


For efficiency this package is compiling the instructions required for polynomial evaluation to C by default.
If you don't have a C compiler (``gcc`` or ``cc``) installed you also need to install numba for using an alternative method:

.. code-block:: console

    pip install multivar_horner[numba]


Simple example:

.. code-block:: python

    import numpy as np
    from multivar_horner import HornerMultivarPolynomial

    # input parameters defining the polynomial
    #   p(x) = 5.0 + 1.0 x_1^3 x_2^1 + 2.0 x_1^2 x_3^1 + 3.0 x_1^1 x_2^1 x_3^1
    coefficients = np.array([[5.0], [1.0], [2.0], [3.0]], dtype=np.float64)
    exponents = np.array([[0, 0, 0], [3, 1, 0], [2, 0, 1], [1, 1, 1]], dtype=np.uint32)

    # [#ops=7] p(x) = x_1 (x_1 (x_1 (1.0 x_2) + 2.0 x_3) + 3.0 x_2 x_3) + 5.0
    horner_polynomial = HornerMultivarPolynomial(coefficients, exponents)
    x = np.array([-2.0, 3.0, 1.0], dtype=np.float64)
    p_x = horner_polynomial(x)


For more refer to the `documentation <https://multivar_horner.readthedocs.io/en/latest/>`__.


Also see:
`GitHub <https://github.com/jannikmi/multivar_horner>`__,
`PyPI <https://pypi.python.org/pypi/multivar_horner/>`__,
`arXiv paper <https://arxiv.org/abs/2007.13152>`__