Metadata-Version: 2.1
Name: roufcp
Version: 0.1.1
Summary: A python package for detecting gradual changepoint using Fuzzy Rough CP (roufCP)
Home-page: https://github.com/subroy13/roufcp
Author: Subhrajyoty Roy
Author-email: subhrajyotyroy@gmail.com
License: MIT
Description: # roufcp - Rough Fuzzy Changepoint Detection
        
        Gradual Change-Point Detection Library based on Rough Fuzzy Changepoint Detection algorithm `roufcp`.
        
        The package is available in [PyPI](https://pypi.org/project/roufcp/).
        
        ## Usage
        
        ```
        >> import numpy as np
        >> from roufcp import roufCP
        >> X = np.concatenate([np.ones(20) * 5, np.zeros(20), np.ones(20) * 10]) + np.random.randn(60)
        >> roufCP(delta = 3, w = 3).fit(X, moving_window = 10, k = 2)
        ```
        
        Try `help(roufCP)` for detailed documentation.
        
        `roufCP` is a class for Rough Fuzzy Changepoint Detection with the following attributes and functions.
        
        * Attributes
            - `delta` : `int`, The fuzzyness parameter, typically between 5-100
            - `w` : `int`, The roughness parameter, typically between 5-100
        
        * Methods
            - `fit_from_regularity_measure(X, regularity_measure, k)` :
                fit the data X with help of the regularity measure and output the estimated changepoints
        
            - `fit(X, moving_window, method, k)`: fit the data X with given regularity measures and output the estimated changepoints. The method argument defaults to kstest, available options are;
              - `meandiff` : Two sample mean difference
              - `ttest` : Two sample t test statistic
              - `kstest` : Two sample Kolmogorov test statistic
              - `mannwhitney` : Two sample Mann Whitney U statistic
              - `anderson-darling` : Two sample Anderson Darling test statistic
              - `adf` : Augmented Dickey Fuller test of stationarity with linear trend
              - `kpss` : Kwiatkowskiâ€“Phillipsâ€“Schmidtâ€“Shin (KPSS) test of stationarity with linear trend 
            
            - `hypothesis_test(cp_list, cp_entropy, mu, sigma, a_delta)`:
                Performs hypothesis testing of the null hypothesis that there is no changepoint in the data, against the alternative that there is changepoint at the specified indices, and outputs the p-value
            
        
        
        ## Authors & Contributors
        
        * Subhrajyoty Roy - https://subroy13.github.io/
        * Ritwik Bhaduri - https://github.com/Ritwik-Bhaduri
        * Sankar Kumar Pal - https://www.isical.ac.in/~sankar/
        
        
        ## License
        
        This code is licensed under MIT License.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.0
Description-Content-Type: text/markdown
