https://github.com/gpavanb1/nodefit
Fit time-series data with a Neural Differential Equation!
https://github.com/gpavanb1/nodefit
neural-network neural-ode numerical-methods python pytorch
Last synced: 9 months ago
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Fit time-series data with a Neural Differential Equation!
- Host: GitHub
- URL: https://github.com/gpavanb1/nodefit
- Owner: gpavanb1
- License: mit
- Created: 2023-12-12T18:10:25.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-18T13:39:10.000Z (about 2 years ago)
- Last Synced: 2025-04-09T04:46:39.051Z (9 months ago)
- Topics: neural-network, neural-ode, numerical-methods, python, pytorch
- Language: Python
- Homepage:
- Size: 135 KB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# NODEFit
[](https://pepy.tech/project/nodefit)
Fit time-series data with a Neural Differential Equation!
This repository contains time-series data fit capabilities using both Neural Ordinary Differential Equations and Neural Stochastic Differential Equations
GPU support is packaged as part of [PyTorch](https://pytorch.org/)
## How to install and execute?
Tested on Python 3.9
Just run
```
pip install nodefit
```
The following program illustrates a basic example
```python
import numpy as np
import torch.nn as nn
from nodefit.constants import DEVICE
from nodefit.neural_ode import NeuralODE
from nodefit.neural_sde import NeuralSDE
###
# DEFINE NETWORKS
###
# Neural ODE parameters
ndim, drift_nhidden, diffusion_nhidden = 2, 10, 2
drift_nn = nn.Sequential(
nn.Linear(ndim+1, drift_nhidden),
nn.Sigmoid(),
nn.Linear(drift_nhidden, ndim)
).double().to(DEVICE)
diffusion_nn = nn.Sequential(
nn.Linear(ndim+1, diffusion_nhidden),
nn.Sigmoid(),
nn.Linear(diffusion_nhidden, ndim)
).double().to(DEVICE)
###
# PROVIDE DATA
###
# Training between data for 0 and 5 seconds
t = np.linspace(0, 5, 10)
# Provide data as list of lists with starting condition
data = np.array([[...]])
###
# FIT USING NEURALODE
###
print('Performing fit using Neural ODE...')
neural_ode = NeuralODE(drift_nn, t, data)
neural_ode.train(2000)
# # Extrapolate the training data to 10 seconds
extra_data = neural_ode.extrapolate(10)
neural_ode.plot(extra_data)
###
# FIT USING NEURALSDE
###
print('Performing fit using Neural SDE...')
neural_sde = NeuralSDE(drift_nn, diffusion_nn, t, data)
neural_sde.train(1)
# # Extrapolate the training data to 10 seconds
extra_data = neural_sde.extrapolate(10)
neural_sde.plot(extra_data)
```
## Sample Output
## Whom to contact?
Please direct your queries to [gpavanb1](http://github.com/gpavanb1)
for any questions.
## Acknowledgements
This package would not be possible without the supporting packages - [torchdiffeq](https://github.com/rtqichen/torchdiffeq) and [torchsde](https://github.com/google-research/torchsde)