HyperSINDy, a package to discover RDEs from time series data.
Project description
HyperSINDy
This repository is the official implementation of HyperSINDy, first introduced in HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations.
Requirements
All the requirements are contained in the environment.yml file\ To install the requirements, run:
conda env create -f environment.yml
Then, activate the conda environment:
conda activate hypersindy
Or, with some conda environment that can run Python 3.9, you can manually install the dependencies:
conda install scipy seaborn tensorboard matplotlib scikit-learn pandas jupyterlab pip
pip3 install pysindy torch==1.12.0 torchvision
Installation
After installing dependencies, you can install hypersindy:
pip3 install hypersindy
Example use case
See example.py as well. `` python3
from hypersindy.library import Library from hypersindy.net import Net from hypersindy.trainer import Trainer from hypersindy.dataset import SyntheticDataset from hypersindy.utils import set_random_seed
set_random_seed(0) device = 2 x_dim = 3 z_dim = 6 data_path = 'x_train.npy'
library = Library(x_dim) net = Net(library, z_dim).to(device) dataset = SyntheticDataset(library, fpath=data_path) trainer = Trainer(net, library, "runs/1", "runs/1.pt", device=device) trainer.train(dataset)
equations results ``
Results can be viewed in tensorboard.
tensorboard --logdir="runs"
Paper
To reproduce results from the paper, go to the paper folder and view the README there.
cd paper
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