A simple deeplearning kit, implementing the module concept of SES Conda
Project description
SES Conda 2.1.0 自述文件/README
中英双语 | Bilingual
SES Conda是一个轻量深度学习套件,利用EricasZ的思路,实现简单的数据推断。
SES Conda is a lightweight deeplearning toolkit, implementing simple data prediction following the idea of EricasZ
1. 30 秒上手 / 30-Second Start
# 深度学习回归 / Deep regression
from sesconda.dlkit import RegressionKit
X, y = np.loadtxt('X.txt'), np.loadtxt('y.txt')
model_fn = RegressionKit('numpy', X_path='X.npy', y_path='y.npy').run()
print(model_fn([[0.5, 1.2]]))
# 经典多项式拟合 / Classic polynomial fit
from sesconda import expo
e = expo()
e.train([(0, 1), (1, 2.7), (2, 7.4)])
e.display()
2. 功能总览 / Features Overview
| 模块 / Module | 能力 / Capability | 适用场景 / Use-Case |
|---|---|---|
| RegressionKit | 任意非线性黑盒逼近 / Universal nonlinear approximation | 大数据、未知解析式 / Big data & unknown formula |
| expo | 最优多项式分段拟合 / Optimal polynomial segment fit | 小样本、快速可视化 / Small sample & quick plot |
3. API 速查 / Quick API
3.1 RegressionKit(深度学习 / DL)
kit = RegressionKit(source, **kwargs)
| 数据源 / Source | 示例 / Example |
|---|---|
| csv | RegressionKit('csv', path='data.csv', x_cols=[0,1], y_col=2) |
| numpy | RegressionKit('numpy', X_path='X.npy', y_path='y.npy') |
| sklearn | RegressionKit('sklearn', name='california') |
| function | RegressionKit('func', func=lambda x: np.sin(x)) |
可调关键参数 / Key args
model_fn = kit.run(
hidden_dims=(256, 256, 128),
epochs=800,
patience=50,
lr=1e-3,
batch_size=128,
standardize=True
)
3.2 expo(经典 / Classic)
e = expo()
e.train([(x1, y1), (x2, y2), ...]) # 支持批量 / Batch input
e.display() # 自动选阶 & 绘图 / Auto degree & plot
4. 更新日志 / Changelog
| 版本 / Version | 日期 / Date | 亮点 / Highlights |
|---|---|---|
| 2.1.0 | 2025-08-13 | 并入 dlkit,新增 RegressionKit / Integrated dlkit, added RegressionKit |
| 2.0.1 | 2025-07-xx | Bug 修复 / Bug fixes |
| 2.0.0-dev | 2025-06-xx | scikit-learn 情感分析优化 / Sentiment analysis improvement |
| 1.x | … | 早期多项式与关系解析 / Early polynomial & relation parse |
5. 安装 / Installation
pip install -U ses-conda
依赖 / Requirements
torch ≥ 1.9, scikit-learn ≥ 1.3, numpy, matplotlib, pandas
6. 许可证 / License
MIT © 2024-End of the Time EricasZ & 街角的猫_wjz
详见 LICENSE.txt / See LICENSE.txt for details.
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