semvec/pypi-packages huggingface dataset:
kedm-cuda11x 0.9.3
A high-performance implementation of the Empirical Dynamic Modeling (EDM) framework
Requires Python: >=3.8

# kEDM

[![build](https://github.com/keichi/kEDM/workflows/build/badge.svg)](https://github.com/keichi/kEDM/actions?query=workflow%3Abuild) [![Documentation Status](https://readthedocs.org/projects/kedm/badge/?version=latest)](https://kedm.readthedocs.io/en/latest/?badge=latest) [![PyPI version](https://badge.fury.io/py/kedm.svg)](https://badge.fury.io/py/kedm)

kEDM (Kokkos-EDM) is a high-performance implementation of the [Empirical
Dynamical Modeling (EDM)](https://sugiharalab.github.io/EDM_Documentation/)
framework. The goal of kEDM is to provide an optimized and parallelized
implementation of EDM algorithms for high-end CPUs and GPUs, while ensuring
compatibility with the original reference implementation
([cppEDM](https://github.com/SugiharaLab/cppEDM)).

Following EDM algorithms are currently implemented in kEDM:

- Simplex projection [1]
- Sequential Locally Weighted Global Linear Maps (S-Map) [2]
- Convergent Cross Mapping (CCM) [3]

## Installation

CPU (Linux and macOS)

```
pip3 install kedm
```

NVIDIA GPU (CUDA 11.2 or later)

```
pip3 install kedm-11x
```

NVIDIA GPU (CUDA 12.0 or later)

```
pip3 install kedm-12x
```

## Citing

Please cite the following papers if you find kEDM useful:

- Keichi Takahashi, Kohei Ichikawa, Joseph Park, Gerald M. Pao, “Scalable Empirical Dynamic Modeling
  with Parallel Computing and Approximate k-NN Search,” IEEE Access, vol. 11, pp. 68171–68183,
  Jun. 2023. [10.1109/ACCESS.2023.3289836](https://doi.org/10.1109/ACCESS.2023.3289836)
- Keichi Takahashi, Wassapon Watanakeesuntorn, Kohei Ichikawa, Joseph Park,
  Ryousei Takano, Jason Haga, George Sugihara, Gerald M. Pao, "kEDM: A
  Performance-portable Implementation of Empirical Dynamical Modeling," Practice
  & Experience in Advanced Research Computing (PEARC 2021), Jul. 2021.
  [10.1145/3437359.3465571](https://doi.org/10.1145/3437359.3465571)

## References

1. George Sugihara, Robert May, "Nonlinear forecasting as a way of
   distinguishing chaos from measurement error in time series," Nature, vol.
   344, pp. 734–741,  1990. [10.1038/344734a0](https://doi.org/10.1038/344734a0)
2. George Sugihara, "Nonlinear forecasting for the classification of natural
   time series. Philosophical Transactions," Physical Sciences and Engineering,
   vol. 348, no. 1688, pp. 477–495, 1994.
   [10.1098/rsta.1994.0106](https://doi.org/10.1098/rsta.1994.0106)
3. George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Michael
   Fogarty, Stephan Munch, "Detecting Causality in Complex Ecosystems,"
   Science, vol. 338, pp. 496–500, 2012.
   [10.1126/science.1227079](https://doi.org/10.1126/science.1227079)