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A python package for detecting gradual changepoint using Fuzzy Rough CP (roufCP)

Project 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.

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 : Kwiatkowski–Phillips–Schmidt–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

License

This code is licensed under MIT License.

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