v0.3.2 addition of alpha-dpp sampler

New version following #62

@danielecc contributed with an implementation of a fast exact Finite DPP sampling algorithm which does not require looking at all items and applies to both DPPs and k-DPPs.

Sampling from a k-DPP without looking at all items
Daniele Calandriello, Michal Derezinski, Michal Valko, NeurIPS, 2020.

v0.3.1 Reduce dependencies, addition of some continuous DPP samplers

@danielecc simplified installation instructions to work with minimal dependencies: numpy, scipy, matplotlib.
See also README for more details.
Additional dependencies:

• zonotope for the zonotope MCMC based sampler using cvxopt,
• trees for uniform spanning tree samplers using networkx,
• docs for the documentation using sphinxcontrib-bibtexand sphinx_rtd_theme,

can be installed locally after cloning the repo.

@guilgautier contributed with (see also /notebooks):

• an exact sampler for multivariate Jacobi ensembles used to do Monte Carlo integration
  On two ways to use determinantal point processes for Monte Carlo integration
  G. Gautier, R. Bardenet, M.Valko, NeurIPS, 2019.

• a Markov chain based sampler for beta-ensembles with polynomial potential
  Fast sampling from beta-ensembles
  G. Gautier, R. Bardenet, M.Valko, arXiv preprint, 2020.

v0.3.0 Addition of vfx sampler for finite DPPs

@danielecc contributed with an implementation of the vfx sampler, the associated documentation and tests.

In practice

DPP = FiniteDPP('likelihood', **{'L_eval_X_data': (eval_L, X_data)})
DPP.sample_exact(mode='vfx')

See the corresponding NeurIPS 2019 paper of Derezinski, Calandriello, and Valko Exact sampling of determinantal point processes with sublinear time preprocessing

v0.2.0 Resubmission to MLOSS

Since the last submission, we have put efforts on:

• the coverage rate 14% -> 90%
  • Travis build + 90% coverage
  • Coveralls
• the documentation with better explanations, illustrations, docstrings
  • Documentation build
• the implementation of the multivariate Jacobi Ensemble, used for Monte Carlo integration

Companion paper:

• arXiv v2
• GitHub

Submission to MLOSS

We feel the project mature enough to be released on PyPI and to be submitted to the special MLOSS track of JMLR. The last version of the corresponding companion paper can be found at DPPy_paper.