Metadata-Version: 1.1
Name: ml3
Version: 1.2.0
Summary: TechYoung Machine Learning ToolKit
Home-page: http://ml3.techyoung.cn
Author: Jiao Shuai
Author-email: jiaoshuaihit@gmail.com
License: UNKNOWN
Description: # ML3
        -----
        
        Introduction
        ------------
        
        ML3是TechYoung课程辅助工具包.
        
        +-------------------------------+
        | ## Distribution               |
        +-------------------------------+
        | Run the following commands to |
        | register, build and upload    |
        | the package to PYPI.          |
        +-------------------------------+
        | python3 setup.py sdist upload |
        +-------------------------------+
        | The home page on PYPI is:     |
        | https://pypi.org/project/wcc/ |
        +-------------------------------+
        
        Install
        -------
        
        ::
        
            sudo pip3 install ml3
        
        --------------
        
        Usage
        -----
        
        After installation, run the following command:
        
        ::
        
            import ml3
        
        Methods:
        ~~~~~~~~
        
        plot.histplot(data, column\_name, \*\*kwargs):
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        -  ***data:*** dataframe
        
        -  ***column\_name:*** column name of dataframe, 例如 "hr\_mean"
        
        -  ***kwargs:*** "xmin", "xmax"
        
        plot.gmmplot(data, column\_names, k\_range, \*\*kwargs):
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        -  ***data:*** dataframe
        
        -  ***column\_names:*** list of columns name of dataframe, 例如
           ["hr\_mean", "hr\_std"]
        
        -  ***k\_range:*** the range of components (k), 例如 [2, 11] or (2, 11)
        
        -  ***kwargs:*** "xmin", "xmax"
        
        plot.kmeansplot(data, column\_names, k\_range, \*\*kwargs):
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        -  ***data:*** dataframe
        
        -  ***column\_names:*** list of columns name of dataframe, 例如
           ["hr\_mean", "hr\_std"]
        
        -  ***k\_range:*** the range of clusters (k), 例如 [2, 11] or (2, 11)
        
        -  ***kwargs:*** "xmin", "xmax"
        
        plot.metricplot(n\_clusters\_range, scores, scores2=[], \*\*kwargs):
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        -  ***n\_clusters\_range：*** tuple or list of range，例如 (2, 10)
        
        -  ***scores:*** list of score
        
        -  ***scores:*** list of score2 (option)
        
        -  ***kwargs:*** "x\_label", "y\_label"
        
        plot.errorbarplot(data, x, y=[], y2=[], \*\*kwargs):
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        -  ***data:*** dataframe
        
        -  ***x:*** x-axis column name，例如 "ctime"
        
        -  ***y:*** y column name，例如 ["hr\_mean", "hr\_std"]
        
        -  ***y2:*** y2 column name，例如 ["br\_mean", "br\_std"] (option)
        
        -  ***kwargs:*** "X\_LABEL", "Y\_LABEL", "TITLE", "LIMIT"
        
        plot.pcaplot(data, column\_names, \*\*kwargs):
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        -  ***data:*** dataframe
        
        -  ***column\_names:*** list of columns name of dataframe, 例如
           ["hr\_mean", "hr\_std"]
        
        -  ***kwargs:*** "n\_components"
        
        plot.tsenplot(data, column\_names, \*\*kwargs):
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        -  ***data:*** dataframe
        
        -  ***column\_names:*** list of columns name of dataframe, 例如
           ["hr\_mean", "hr\_std"]
        
        -  ***kwargs:*** "n\_components"
        
        seaborn.boxplot(x, y, \*\*kwargs):
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        此函数需要ml4进行对原始数据进行窗口化分类
        
        -  ***x:*** the UNIX timestamp list from ml4
        
        -  ***y:*** the data list from ml4
        
        -  ***kwargs:*** "X\_LABEL", "Y\_LABEL", "TITLE"
        
        seaborn.violinplot(x, y, \*\*kwargs):
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        此函数需要ml4进行对原始数据进行窗口化分类
        
        -  ***x:*** the UNIX timestamp list from ml4
        
        -  ***y:*** the data list from ml4
        
        -  ***kwargs:*** "X\_LABEL", "Y\_LABEL", "TITLE"
        
        Note
        ----
        
        版本里的1.2.4是旧的版本。1.2.5和以后的版本是用于函数计算的版本。
        1.2.5以及以后版本将去掉wcc自动框架.
        目录下的子目录：libwebp-0.4.1-linux-x86-64
        需要从网上下载，然后把里面的bin下的gif2webp放到/usr/bin里。这样就可以在wcc里调用了.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
