classicML: 简单易用的经典机器学习框架

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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')

更多示例代码点击

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

Metadata

Release files for classicML-python 0.9

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for classicML-python 0.9
File	Size	Uploaded
classicML-python-0.9.tar.gz	62.6 kB	Jun 30, 2022	Details

Built distribution (wheel)

Table of built distributions (wheels) for classicML-python 0.9
File	Interpreter	ABI	Platform	Reset
classicML_python-0.9-py3-none-any.whl 93.0 kB Jun 30, 2022	Python 3	none	any	Details

Total release size: 155.6 kB

Release files / classicML-python-0.9.tar.gz

Download URL	classicML-python-0.9.tar.gz
Size	62.6 kB
Tags	Source
SHA-256 checksum How to use checksums	`9aa38239bfaa393f21f65fb064c91545546037354dc06b03f37b0a073daf588d`
BLAKE2b-256 checksum How to use checksums	`64832ba31e91e6c8035946420783c0025d8dd0aa58b6c2529c8dfef411e58a11`
Upload date	Jun 30, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.0 CPython/3.9.12`

Release files / classicML_python-0.9-py3-none-any.whl

Download URL	classicML_python-0.9-py3-none-any.whl
Size	93.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`81fcd243fe2faeeba31a3066eff9964b0828387e6038df24cb49b90b75c03f83`
BLAKE2b-256 checksum How to use checksums	`f7dd3c3a46760369397b1474913fab907011f12021ca73a08011430319b326ca`
Upload date	Jun 30, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.0 CPython/3.9.12`