A Python port of the 'burst detection' algorithm by Kleinberg, originally implemented in R
This is a Python port of the R implementation of 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.
pip install pybursts
import pybursts offsets = [4, 17, 23, 27, 33, 35, 37, 76, 77, 82, 84, 88, 90, 92] print pybursts.kleinberg(offsets, s=2, gamma=0.1)
- offsets: a list of time offsets (numeric)
- s: the base of the exponential distribution that is used for modeling the event frequencies
- gamma: coefficient for the transition costs between states
An array of intervals in which a burst of activity was detected. The first column denotes the level within the hierarchy; the second column the start value of the interval; the third column the end value. The first row is always the top-level activity (the complete interval from start to finish).
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size pybursts-0.1.1.tar.gz (1.8 kB)||File type Source||Python version None||Upload date||Hashes View|