pyepo.data.shortestpath

Synthetic data for shortest path problem

Functions

genData(→ tuple[numpy.ndarray, numpy.ndarray])

Generate synthetic feature-cost pairs for the shortest path problem.

Module Contents

pyepo.data.shortestpath.genData(num_data: int, num_features: int, grid: tuple[int, int], deg: int = 1, noise_width: float = 0, seed: int = 135) → tuple[numpy.ndarray, numpy.ndarray]

Generate synthetic feature-cost pairs for the shortest path problem.

Features are sampled from a standard multivariate Gaussian \(\mathcal{N}(0, \mathbf{I})\). A random Bernoulli(0.5) matrix \(\mathcal{B}\) maps each feature vector into the edge-cost coefficients via a polynomial of degree deg, scaled by multiplicative uniform noise of half-width noise_width.

Parameters:

  • num_data – number of data points

  • num_features – dimension of features

  • grid – size of grid network

  • deg – polynomial degree of the feature-to-cost mapping

  • noise_width – half-width of the multiplicative uniform noise

  • seed – random seed (default 135 for reproducibility)

Returns:

data features (np.ndarray), costs (np.ndarray)

Return type:

tuple