Metadata-Version: 1.1
Name: ml3
Version: 1.2.4
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"
        
        plot.kalmanplot(data, column\_names, dim\_x=2, dim\_z=1, x=[], p=[], f=[], q=[], h=[], r=1, \*\*kwargs):
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        此函数所有默认变量设置仅可用于心率滤波，呼吸需修改初始预测矩阵x
        
        -  ***data:*** dataframe
        
        -  ***column\_names:*** list of columns name of rawdata dataframe, 例如
           ["hr"]
        
        -  ***dim\_x:*** the size of the state vector，状态空间维度
        
           -  默认为2
        
        -  ***dim\_z:*** the size of the measurement vector，观测矩阵维度
        
           -  默认为1
        
        -  ***x:*** filter state estimate，初始状态预测矩阵
        
           -  默认为[60, 0.1]，分别为心率和心率变化率
        
        -  ***p:*** covariance matrix，协方差矩阵
        
           -  默认为[[1, 0.1], [0.1,
              1]]，心率变化率和人的心率是一定的关系，根据运动状态或者濒死会有很明显的差别，选择0.1代表有一定关系，但是不关系大
        
        -  ***q:*** process
           uncertainty/noise，噪声矩阵，此矩阵不能为0，否则数据会异常
        
           -  默认为[[0.0001, 0], [0,
              0.0001]]，因为数据都是在cpu中进行，不会产生噪音
        
        -  ***r:*** measurement uncertainty/noise，测量误差
        
           -  默认为1，测量误差，医疗器械心率误差规定为+-1
        
        -  ***h:*** measurement function
        
           -  Sometimes certain states are measured, when others are not. For
              example, the first, third and fifth states of a five-dimensional
              state vector are measurable, while second and fourth states are
              not measurable H = [[1, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0,
              1]]
        
        -  ***f:*** state transistion matrix，状态转移矩阵
        
           -  默认为[[1, 0.5], [0, 1]]，此矩阵不能对称，否则会计算异常
        
        -  ***kwargs:*** None
        
        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"
        
        Example
        ~~~~~~~
        
        .. code:: python
        
            import ml3
            import ml4
            import pandas as pd
        
            data = pd.read_csv("feature.csv")
            # histogram
            ml3.plot.histplot(data, "hr_mean")
            # error bar
            ml3.plot.errorbarplot(data, "ctime", ["hr_mean", "hr_std"], ["br_mean", "br_std"])
            # single feature
            ml3.plot.kmeansplot(data, "hr_mean", (2, 10))
            ml3.plot.gmmplot(data, "hr_mean", (2, 10))
            # multiple features
            ml3.plot.gmmplot(data, ["hr_mean", "hr_std", "br_mean", "br_std", "mo_mean", "mo_std"], (2, 10))
            ml3.plot.kmeansplot(data, ["hr_mean", "hr_std", "br_mean", "br_std", "mo_mean", "mo_std"], (2, 10))
            # two scores metricplot
            scores = [110704, 75304, 60731, 52297, 45675, 41231, 37744, 35247, 33263]
            scores2 = [0.05, 0.09, 0.15, 0.2, 0.3, 0.5, 0.6, 0.9, 1]
            ml3.plot.metricplot((2, 11), scores, scores2)
            # boxplot and violoinplot
            x, y = ml4.ml4.getWindowData(data, "ctime", "hr")
            timeList = []
            for i in x:
                tmp = datetime.fromtimestamp(i)
                timeList.append(tmp.strftime("%H:%M"))
            ml3.seaborn.boxplot(timeList, y)
            ml3.seaborn.violinplot(timeList, y)
        
        
            data = pd.read_csv("rawdata.csv")
            ml3.plot.kalmanplot(data, ["hr"])
        
        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
