cleanup

.ipynb_checkpoints/README-checkpoint.md

@@ -1,27 +0,0 @@
-# Volt
-Public Implementation of 
-*Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes* [link] 
-
-by [Gregory Benton](https://g-benton.github.io/), [Wesley Maddox](https://wjmaddox.github.io), and [Andrew Gordon Wilson](https://cims.nyu.edu/~andrewgw/).
-
-Please cite our work if you find it useful:
-
-```
-@inproceedings{benton2022volatility,
-  title={olatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes},
-  author={Benton, Gregory and Maddox, Wesley and Wilson, Andrew Gordon Gordon},
-  booktitle={International Conference on Machine Learning},
-  year={2022},
-  organization={PMLR}
-}
-```
-
-![Overview of the Volt modeling pipeline](./figs/ret-vol-px.jpg)
-
-## Explanatory Notebook
-
-To see an overview of how to use Volt with synthetically generated code, see the `Example` notebook which walks through how the code is organized step by step.
-
-## Experiments
-
-The two core experimental settings from the paper involve modeling historical wind speeds and stock prices. The code to run these experiments with example commands is in the `experiments` folder.

.ipynb_checkpoints/setup-checkpoint.py

@@ -1,26 +0,0 @@
-from setuptools import setup
-import os
-import sys
-
-setup(
-    name='voltron',
-    version='alpha',
-    description=('Voltron Repo'),
-    author='Greg Benton',
-    author_email='greg.w.benton@gmail.com',
-    url='https://github.com/g-benton/voltron',
-    license='Apache-2.0',
-    packages=['voltron'],
-    install_requires=[
-    'matplotlib>=3.0.3',
-    'setuptools>=41.0.0',
-    'torch>=1.11.0',
-    'numpy>=1.16.2',
-    'gpytorch>=1.0.1',
-    ],
-    include_package_data=True,
-    classifiers=[
-        'Development Status :: 0',
-        'Intended Audience :: Science/Research',
-        'Programming Language :: Python :: 3.7'],
-)