https://github.com/axsk/pccaplus.jl

Julia implementation of PCCA+
https://github.com/axsk/pccaplus.jl

julia package pcca zib

Last synced: 2 days ago
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Julia implementation of PCCA+

• Host: GitHub
• URL: https://github.com/axsk/pccaplus.jl
• Owner: axsk
• License: mit
• Created: 2021-11-03T16:47:46.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2025-02-15T11:18:02.000Z (over 1 year ago)
• Last Synced: 2025-02-20T21:18:09.407Z (over 1 year ago)
• Topics: julia, package, pcca, zib
• Language: Julia
• Homepage:
• Size: 130 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.bib

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README

          
# PCCAPlus

[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://axsk.github.io/PCCAPlus.jl/stable)

[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://axsk.github.io/PCCAPlus.jl/dev)

[![Build Status](https://github.com/axsk/PCCAPlus.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/axsk/PCCAPlus.jl/actions/workflows/CI.yml?query=branch%3Amain)

[![Coverage](https://codecov.io/gh/axsk/PCCAPlus.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/axsk/PCCAPlus.jl)

A [KISS](https://en.wikipedia.org/wiki/KISS_principle) style implementation of PCCA+ (Robust Perron Cluster Analysis) [1,2] with support for non-reversible systems [3].

For a similar python implementation see also the [cmdtools](https://github.com/zib-cmd/cmdtools/) package.

## Basic usage

```julia

using PCCAPlus

P=rand(10,10)

P = P ./ sum(P, dims=2) # row stochastic matrix

# basic PCCA+ clustering with 2 clusters (using no weighting and the ISA initial guess only)

chi = pcca(P, 2)

using KrylovKit

using SparseArrays

P = sprand(100,100, 0.1)

P = P ./ sum(P, dims=2) # sparse row stochastic matrix

# solve the PCCA+ problem weighted with the stationary density 

# and optimize for crispness, using the KrylovKit.jl eigensolver

chi = pcca(P, 2; pi=:stationary, optimize=true, solver=KrylovSolver())

```

For sparse matrix support, add either the `ArnoldiMethod.jl` or `KrylovKit.jl` and pass the corresponding `ArnoldiSolver()` or `KrylovSolver()` as a solver.

## References

1. [2006, M. Weber: Meshless Methods in Conformation Dynamics](https://opus4.kobv.de/opus4-zib/frontdoor/deliver/index/docId/1023/file/promotionweber.pdf)

2. [2013, S. Röblitz, M. Weber: Fuzzy Spectral Clustering by PCCA+](https://doi.org/10.1007/s11634-013-0134-6)

3. [2018, K. Fackeldey, A. Sikorski, M. Weber: Spectral Clustering for Non-Reversible Markov Chains](https://doi.org/10.1007/s40314-018-0697-0)