pyepo.data.knapsack
===================

.. py:module:: pyepo.data.knapsack

.. autoapi-nested-parse::

   Synthetic data for knapsack problem



Functions
---------

.. autoapisummary::

   pyepo.data.knapsack.genData


Module Contents
---------------

.. py:function:: genData(num_data: int, num_features: int, num_items: int, dim: int = 1, deg: int = 1, noise_width: float = 0, seed: int = 135) -> tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]

   Generate synthetic feature-cost pairs for the multi-dimensional knapsack.

   Item weights are fixed across instances; only the value (cost) of each
   item depends on features. Features are sampled from a standard Gaussian
   :math:`\mathcal{N}(0, \mathbf{I})`, mapped through a random Bernoulli(0.5)
   matrix :math:`\mathcal{B}` and a polynomial of degree ``deg``, then
   scaled by multiplicative uniform noise of half-width ``noise_width`` and
   rounded up to the nearest integer.

   :param num_data: number of data points
   :param num_features: dimension of features
   :param num_items: number of items
   :param dim: dimension of multi-dimensional knapsack
   :param deg: polynomial degree of the feature-to-cost mapping
   :param noise_width: half-width of the multiplicative uniform noise
   :param seed: random state seed (default 135 for reproducibility)

   :returns: weights of items (np.ndarray), data features (np.ndarray), costs (np.ndarray)
   :rtype: tuple