An easy-to-use ML framework
Project description
classicML: 简单易用的经典机器学习框架
classicML 是一个用 Python 和 C++ 混编的机器学习项目,您既可以使用纯 Python 版本进行学习,也可以使用CC标准版进行实验和探索自定义功能。它既实现了Python的简单易用快速上手,又实现了C++的高效性能。classicML的设计目标是简单易用,快速入门,高扩展性和编程风格简洁。更多信息请访问文档网站。
多后端支持
classicML 本身是一个Python项目,但是机器学习中涉及到的复杂的矩阵运算对于Python有点儿捉襟见肘,因此我们提供了使用C++后端的加速版本。为了保证兼容性,classicML默认使用Python后端,现在全部算法支持了使用C++作为后端进行加速,如果您需要使用标准版的classicML,只需在开头使用这条语句切换后端。
import os
os.environ['CLASSICML_ENGINE'] = 'CC'
精度控制
目前,classicML 正在对全部算法支持32位和64位切换精度,使用32位的精度可以获得更快的运行速度和更小固化模型。
import os
os.environ['CLASSICML_PRECISION'] = '32-bit'
第一个机器学习程序
使用线性判别分析进行二分类
- 下载示例数据集
wget https://github.com/sun1638650145/classicML/blob/master/datasets/西瓜数据集alpha.csv
- 运行下面的代码
import classicML as cml
DATASET_PATH = '/path/to/西瓜数据集alpha.csv'
# 读取数据
ds = cml.data.Dataset()
ds.from_csv(DATASET_PATH)
# 生成模型
model = cml.models.LDA()
# 训练模型
model.fit(ds.x, ds.y)
# 可视化模型
cml.plots.plot_lda(model, ds.x, ds.y, '密度', '含糖率')
感谢Jetbrains Open Source对项目的支持
v0.9 预览
- 这个版本将增加
cml.models.cluster
模块, 并添加几种聚类算法 - 将在
v1.0
之前添加100%的类型注释
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