deepof.utils.kleinberg

deepof.utils.kleinberg(offsets: list, s: float = 2.718281828459045, gamma: float = 1.0, n=None, T=None, k=None)

Apply Kleinberg’s algorithm (described in ‘Bursty and Hierarchical Structure in Streams’).

The algorithm models activity bursts in a time series as an infinite hidden Markov model.

Taken from pybursts (https://github.com/romain-fontugne/pybursts/blob/master/pybursts/pybursts.py) and adapted for dependency compatibility reasons.

Parameters:

  • offsets (list) – a list of time offsets (numeric)

  • s (float) – the base of the exponential distribution that is used for modeling the event frequencies

  • gamma (float) – coefficient for the transition costs between states

  • n – to have a fixed cost function (not dependent of the given offsets). Which is needed if you want to compare bursts for different inputs.

  • T – to have a fixed cost function (not dependent of the given offsets). Which is needed if you want to compare bursts for different inputs.

  • k – maximum burst level