Author: Martin Royer

pecok is a python package containing a few clustering algorithms (unsupervised learning) for a given number of clusters. It is based on the PECOK paper (https://arxiv.org/abs/1606.05100)

The pecok package requires:

• python [>=3.5]
• numpy
• scikit-learn

You can find it on PyPI and install it with:

pip install pecok

Currently available algorithms are:

• pecok_clustering: the main clustering algorithm described in [Bunea, Giraud, Royer, Verzelen ('17)]

  Parameters:

  name	description
  obs	data matrix (n \times p), clustering is applied to the lines of obs.
  n_struct	number of structures to separate the data into.
  int_corr = 4	correction to be used, between 0 and 4. 0 means no correction, 4 is the correction from [Bunea, Giraud, Royer, Verzelen ('17)]. 1, 2 and 3 are more efficient proxy for the correction, we only recommend 2 and 3.
  rho = 5	bias-variance tradeoff parameter in ADMM.
  n_iter_max = -1	if positive, sets the stop condition for maximum number of iteration of the ADMM algorithm used to approximate the solution of the SDP.
  verbose = False	yields print for time and residuals value at ADMM stop time.

• kmeanz_clustering: efficient variant for main clustering algorithm, see my PhD thesis

  Parameters:

  name	description
  obs	data matrix (n \times p), clustering is applied to the lines of obs.
  n_struct	number of structures to separate the data into.
  int_corr = 4	correction to be used, between 0 and 4. 0 means no correction, 4 is the correction from [Bunea, Giraud, Royer, Verzelen ('17)]. 1, 2 and 3 are more efficient proxy for the correction, we only recommend 2 and 3.