lintsampler

Efficient random sampling via linear interpolation.

When you have a density function, but you would like to create a set of sample points from that density function, you can use linear interpolate sampling. Using the evaluation of the density at the two endpoints of 1D interval, or the four corners of a 2D rectangle, or generally the $2^k$ vertices of a $k$-dimensional hyperbox (or a series of such hyperboxes, e.g., the cells of a $k$-dimensional grid), linear interpolant sampling is a technique to draw random samples within the hyperbox. lintsampler provides a Python implementation of this.

See the documentation or our paper for further details.

Installation

Three ways of installing lintsampler:

• pip:

  pip install lintsampler

• conda:

  conda install -c conda-forge lintsampler

• Simply cloning this repository.

Quickstart Example

If you have a density function, such as this multi-modal 1d pdf with the bulk of the density between -7 and 7,

import numpy as np

def gmm_pdf(x):
    mu = np.array([-3.0, 0.5, 2.5])
    sig = np.array([1.0, 0.25, 0.75])
    w = np.array([0.4, 0.25, 0.35])
    return np.sum([w[i] * norm.pdf(x, mu[i], sig[i]) for i in range(3)], axis=0)

lintsampler can efficiently draw samples from it on some defined interval (here a 100-point grid between -7 and 7):

from lintsampler import LintSampler

grid = np.linspace(-7,7,100)
samples = LintSampler(grid,pdf=gmm_pdf).sample(N=10000)

Making a histogram of the resulting samples and comparing to the input density function shows good agreement -- and we can do even better by increasing the resolution.

See this page of the documentation for a more detailed explanation of this example.

Documentation

Complete documentation, including more example notebooks, is available at lintsampler.readthedocs.io/.

Attribution

If using lintsampler for a research publication, please cite our paper.

License

lintsampler is available under the MIT license. See the LICENSE file for specifics.