A differentiable measure of shared mutual information via overlapping exclusions in event (measure) spaces for discrete variables.
Computes a pointwise partial information decomposition (PPID) for multiple sources (up to 4 sources) and one target via the I_sx meausre. Pointwise means that every realization (point) in the distribution gets its own PID. In essence, it returns the PPID of , the local mutual information -- for each realization -- and the PID of its average
.
For more details, check the preprint:
- A. Makkeh, A. Gutnecht, M. Wibral, Introducting A differentiable measure of pointwise shared information; Phys Rev E 103, 032149
Note that SxPID is also embbeded in the Information dynamics toolkit xl (IDTxl) where you can use IDTxl's build-in functions to analyse the node dynamics of networks from multivariate time series data using SxPID.
- Download or clone the repository from GitHub
- unpack it
- run (from the folder containing SxPID's setup.py file) the following
pip install . or the editable mode pip install -e .
The example file demo/demo_and_gate.py has detailed explanation on how to run the code, in particular the main function Sxpid.pid() to compute the partial information decomposition.