<p align="center">
<a href="https://www.tinybig.org">
<img src="https://raw.githubusercontent.com/jwzhanggy/tinyBIG/main/docs/assets/img/tinybig.png" alt="function_data" style="max-width: 100%; height: auto;">
</a>
</p>
--------------------------------------------------------------------------------
### Introduction
`tinybig` is a Python library developed by the IFM Lab for deep function learning model designing and building.
* List of RPN Papers:
* RPN 1 (July 2024): https://arxiv.org/abs/2407.04819
* RPN 2 (November 2024):
* RPN 3 (In Development...)
* Official Website: https://www.tinybig.org/
* PyPI: https://pypi.org/project/tinybig/
* IFM Lab: https://www.ifmlab.org/index.html
* Project Description in Chinese:
* [RPN 1 项目中文介绍](docs/中文简介/RPN_1)
### Citation
If you find `tinybig` and RPN useful in your work, please cite the RPN paper as follows:
```
@article{Zhang2024RPN,
title={RPN: Reconciled Polynomial Network Towards Unifying PGMs, Kernel SVMs, MLP and KAN},
author={Jiawei Zhang},
year={2024},
eprint={2407.04819},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
### Installation
You can install `tinybig` either via `pip` or directly from the github source code.
#### Install via Pip
```shell
pip install tinybig
```
#### Install from Source
```shell
git clone https://github.com/jwzhanggy/tinyBIG.git
```
After entering the downloaded source code directory, tinybig can be installed with the following command:
```shell
python setup.py install
```
If you don't have `setuptools` installed locally, please consider to first install `setuptools`:
```shell
pip install setuptools
```
### Install Dependency
Please download the [requirements.txt](https://github.com/jwzhanggy/tinyBIG/blob/main/requirements.txt) file, and install all the dependency packages:
```shell
pip install -r requirements.txt
```
### Verification
If you have successfully installed both `tinybig` and the dependency packages, now you can use `tinybig` in your projects.
To ensure that `tinybig` was installed correctly, we can verify the installation by running the sample python code as follows:
```python
>>> import torch
>>> import tinybig as tb
>>> expansion_func = tb.expansion.taylor_expansion()
>>> expansion_func(torch.Tensor([[1, 2]]))
```
The output should be something like:
```python
tensor([[1., 2., 1., 2., 2., 4.]])
```
### Tutorials
| Tutorial ID | Tutorial Title | Last Update |
|:-------------------------------------------------------------------------------------:|:----------------------------------:|:---------------------:|
| [Tutorial 0](https://www.tinybig.org/guides/quick_start/) | Quickstart Tutorial | July 6, 2024 |
| [Tutorial 1](https://www.tinybig.org/tutorials/beginner/module/expansion_function/) | Data Expansion Functions | July 7, 2024 |
| Tutorial 2 | Extended and Nested Data Expansion | TBD |
### Examples
| Example ID | Example Title | Released Date |
|:---------------------------------------------------------------------:|:--------------------------------------------------:|:--------------:|
| [Example 0](https://www.tinybig.org/examples/text/kan/) | Failure of KAN on Sparse Data | July 9, 2024 |
| [Example 1](https://www.tinybig.org/examples/function/elementary/) | Elementary Function Approximation | July 7, 2024 |
| [Example 2](https://www.tinybig.org/examples/function/composite/) | Composite Function Approximation | July 8, 2024 |
| [Example 3](https://www.tinybig.org/examples/function/feynman/) | Feynman Function Approximation | July 8, 2024 |
| [Example 4](https://www.tinybig.org/examples/image/mnist/) | MNIST Classification with Identity Reconciliation | July 8, 2024 |
| [Example 5](https://www.tinybig.org/examples/image/mnist_dual_lphm/) | MNIST Classification with Dual LPHM Reconciliation | July 8, 2024 |
| [Example 6](https://www.tinybig.org/examples/image/cifar10/) | CIFAR10 Image Object Recognition | July 8, 2024 |
| [Example 7](https://www.tinybig.org/examples/text/imdb/) | IMDB Review Classification | July 9, 2024 |
| [Example 8](https://www.tinybig.org/examples/text/agnews/) | AGNews Topic Classification | July 9, 2024 |
| [Example 9](https://www.tinybig.org/examples/text/sst2/) | SST-2 Sentiment Classification | July 9, 2024 |
| [Example 10](https://www.tinybig.org/examples/tabular/iris/) | Iris Species Inference (Naive Probabilistic) | July 9, 2024 |
| [Example 11](https://www.tinybig.org/examples/tabular/diabetes/) | Diabetes Diagnosis (Comb. Probabilistic) | July 9, 2024 |
| [Example 12](https://www.tinybig.org/examples/tabular/banknote/) | Banknote Authentication (Comb. Probabilistic) | July 9, 2024 |
### Library Organizations
| Components | Descriptions |
|:----------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
| [`tinybig`](https://www.tinybig.org/documentations/tinybig/) | a deep function learning library like torch.nn, deeply integrated with autograd |
| [`tinybig.expansion`](https://www.tinybig.org/documentations/expansion/) | a library providing the "data expansion functions" for multi-modal data effective expansions |
| [`tinybig.reconciliation`](https://www.tinybig.org/documentations/reconciliation/) | a library providing the "parameter reconciliation functions" for parameter efficient learning |
| [`tinybig.remainder`](https://www.tinybig.org/documentations/remainder/) | a library providing the "remainder functions" for complementary information addition |
| [`tinybig.module`](https://www.tinybig.org/documentations/module/) | a library providing the basic building blocks for RPN model designing and implementation |
| [`tinybig.model`](https://www.tinybig.org/documentations/model/) | a library providing the RPN models for addressing various deep function learning tasks |
| [`tinybig.config`](https://www.tinybig.org/documentations/config/) | a library providing model component instantiation from textual configuration descriptions |
| [`tinybig.learner`](https://www.tinybig.org/documentations/learner/) | a library providing the learners that can be used for RPN model training and testing |
| [`tinybig.data`](https://www.tinybig.org/documentations/data/) | a library providing multi-modal datasets for solving various deep function learning tasks |
| [`tinybig.output`](https://www.tinybig.org/documentations/output/) | a library providing the processing method interfaces for output processing, saving and loading |
| [`tinybig.metric`](https://www.tinybig.org/documentations/metric/) | a library providing the metrics that can be used for RPN model performance evaluation |
| [`tinybig.util`](https://www.tinybig.org/documentations/util/) | a library of utility functions for RPN model design, implementation and learning |
### License & Copyright
Copyright © 2024 [IFM Lab](https://www.ifmlab.org/). All rights reserved.
* `tinybig` source code is published under the terms of the MIT License.
* `tinybig`'s documentation and the RPN papers are licensed under a Creative Commons Attribution-Share Alike 4.0 Unported License ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)).
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"description": "<p align=\"center\">\n <a href=\"https://www.tinybig.org\">\n <img src=\"https://raw.githubusercontent.com/jwzhanggy/tinyBIG/main/docs/assets/img/tinybig.png\" alt=\"function_data\" style=\"max-width: 100%; height: auto;\">\n </a>\n</p>\n\n--------------------------------------------------------------------------------\n\n### Introduction\n\n`tinybig` is a Python library developed by the IFM Lab for deep function learning model designing and building.\n\n* List of RPN Papers: \n * RPN 1 (July 2024): https://arxiv.org/abs/2407.04819\n * RPN 2 (November 2024): \n * RPN 3 (In Development...)\n* Official Website: https://www.tinybig.org/\n* PyPI: https://pypi.org/project/tinybig/\n* IFM Lab: https://www.ifmlab.org/index.html\n* Project Description in Chinese: \n * [RPN 1 \u9879\u76ee\u4e2d\u6587\u4ecb\u7ecd](docs/\u4e2d\u6587\u7b80\u4ecb/RPN_1)\n\n### Citation\n\nIf you find `tinybig` and RPN useful in your work, please cite the RPN paper as follows:\n```\n@article{Zhang2024RPN,\n title={RPN: Reconciled Polynomial Network Towards Unifying PGMs, Kernel SVMs, MLP and KAN},\n author={Jiawei Zhang},\n year={2024},\n eprint={2407.04819},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n```\n\n### Installation\n\nYou can install `tinybig` either via `pip` or directly from the github source code.\n\n#### Install via Pip\n\n```shell\npip install tinybig\n```\n\n#### Install from Source\n\n```shell\ngit clone https://github.com/jwzhanggy/tinyBIG.git\n```\n\nAfter entering the downloaded source code directory, tinybig can be installed with the following command:\n\n```shell\npython setup.py install\n```\n\nIf you don't have `setuptools` installed locally, please consider to first install `setuptools`:\n```shell\npip install setuptools \n```\n\n### Install Dependency\n\nPlease download the [requirements.txt](https://github.com/jwzhanggy/tinyBIG/blob/main/requirements.txt) file, and install all the dependency packages:\n```shell\npip install -r requirements.txt\n```\n\n### Verification\n\nIf you have successfully installed both `tinybig` and the dependency packages, now you can use `tinybig` in your projects.\n\nTo ensure that `tinybig` was installed correctly, we can verify the installation by running the sample python code as follows:\n\n```python\n>>> import torch\n>>> import tinybig as tb\n>>> expansion_func = tb.expansion.taylor_expansion()\n>>> expansion_func(torch.Tensor([[1, 2]]))\n```\nThe output should be something like:\n```python\ntensor([[1., 2., 1., 2., 2., 4.]])\n```\n\n### Tutorials\n\n| Tutorial ID | Tutorial Title | Last Update |\n|:-------------------------------------------------------------------------------------:|:----------------------------------:|:---------------------:|\n| [Tutorial 0](https://www.tinybig.org/guides/quick_start/) | Quickstart Tutorial | July 6, 2024 |\n| [Tutorial 1](https://www.tinybig.org/tutorials/beginner/module/expansion_function/) | Data Expansion Functions | July 7, 2024 |\n| Tutorial 2 | Extended and Nested Data Expansion | TBD |\n\n### Examples\n\n| Example ID | Example Title | Released Date |\n|:---------------------------------------------------------------------:|:--------------------------------------------------:|:--------------:|\n| [Example 0](https://www.tinybig.org/examples/text/kan/) | Failure of KAN on Sparse Data | July 9, 2024 |\n| [Example 1](https://www.tinybig.org/examples/function/elementary/) | Elementary Function Approximation | July 7, 2024 |\n| [Example 2](https://www.tinybig.org/examples/function/composite/) | Composite Function Approximation | July 8, 2024 |\n| [Example 3](https://www.tinybig.org/examples/function/feynman/) | Feynman Function Approximation | July 8, 2024 |\n| [Example 4](https://www.tinybig.org/examples/image/mnist/) | MNIST Classification with Identity Reconciliation | July 8, 2024 |\n| [Example 5](https://www.tinybig.org/examples/image/mnist_dual_lphm/) | MNIST Classification with Dual LPHM Reconciliation | July 8, 2024 |\n| [Example 6](https://www.tinybig.org/examples/image/cifar10/) | CIFAR10 Image Object Recognition | July 8, 2024 |\n| [Example 7](https://www.tinybig.org/examples/text/imdb/) | IMDB Review Classification | July 9, 2024 |\n| [Example 8](https://www.tinybig.org/examples/text/agnews/) | AGNews Topic Classification | July 9, 2024 |\n| [Example 9](https://www.tinybig.org/examples/text/sst2/) | SST-2 Sentiment Classification | July 9, 2024 |\n| [Example 10](https://www.tinybig.org/examples/tabular/iris/) | Iris Species Inference (Naive Probabilistic) | July 9, 2024 |\n| [Example 11](https://www.tinybig.org/examples/tabular/diabetes/) | Diabetes Diagnosis (Comb. Probabilistic) | July 9, 2024 |\n| [Example 12](https://www.tinybig.org/examples/tabular/banknote/) | Banknote Authentication (Comb. Probabilistic)\t | July 9, 2024 |\n\n### Library Organizations\n\n| Components | Descriptions |\n|:----------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|\n| [`tinybig`](https://www.tinybig.org/documentations/tinybig/) | a deep function learning library like torch.nn, deeply integrated with autograd |\n| [`tinybig.expansion`](https://www.tinybig.org/documentations/expansion/) | a library providing the \"data expansion functions\" for multi-modal data effective expansions |\n| [`tinybig.reconciliation`](https://www.tinybig.org/documentations/reconciliation/) | a library providing the \"parameter reconciliation functions\" for parameter efficient learning |\n| [`tinybig.remainder`](https://www.tinybig.org/documentations/remainder/) | a library providing the \"remainder functions\" for complementary information addition |\n| [`tinybig.module`](https://www.tinybig.org/documentations/module/) | a library providing the basic building blocks for RPN model designing and implementation |\n| [`tinybig.model`](https://www.tinybig.org/documentations/model/) | a library providing the RPN models for addressing various deep function learning tasks |\n| [`tinybig.config`](https://www.tinybig.org/documentations/config/) | a library providing model component instantiation from textual configuration descriptions |\n| [`tinybig.learner`](https://www.tinybig.org/documentations/learner/) | a library providing the learners that can be used for RPN model training and testing |\n| [`tinybig.data`](https://www.tinybig.org/documentations/data/) | a library providing multi-modal datasets for solving various deep function learning tasks |\n| [`tinybig.output`](https://www.tinybig.org/documentations/output/) | a library providing the processing method interfaces for output processing, saving and loading |\n| [`tinybig.metric`](https://www.tinybig.org/documentations/metric/) | a library providing the metrics that can be used for RPN model performance evaluation |\n| [`tinybig.util`](https://www.tinybig.org/documentations/util/) | a library of utility functions for RPN model design, implementation and learning | \n\n\n### License & Copyright\n\nCopyright \u00a9 2024 [IFM Lab](https://www.ifmlab.org/). All rights reserved.\n\n* `tinybig` source code is published under the terms of the MIT License. \n* `tinybig`'s documentation and the RPN papers are licensed under a Creative Commons Attribution-Share Alike 4.0 Unported License ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)). \n\n",
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