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(2024), An Efficient Algorithm for Exact Segmentation of Large Compositional and Categorical Time Series. Stat, 13: e70012. https://doi.org/10.1002/sta4.70012\n\n\n\n## Python\n\nTo install the Python package, run in a terminal\n\n```bash\npython -m pip install git+https://github.com/deepcharles/compositionaldust.git\n```\n\n## Usage\n\nCreate a 3D signal with Dirichlet distributed components. \nThe parameter of the Dirichlet distribution is piecewise constant.\n\n```python\nfrom compositionaldust import generate_signal\n\nsignal, bkps_true = generate_signal(n_samples=1_000, n_dims=3)\n\nprint(f\"The true change-points are {bkps_true}.\")\n```\n\nWe can now use our method to estimate the true change-point positions.\nThe value of penalty controls the number of change-points that will be estimated: large values will detect few changes, and vice-versa.\n\n```python\nfrom compositionaldust import get_bkps\n\nbkps_pred = get_bkps(signal=signal, penalty=1)\n\nprint(f\"The predicted change-points are {bkps_pred}.\")\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepcharles%2Fcompositionaldust","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeepcharles%2Fcompositionaldust","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepcharles%2Fcompositionaldust/lists"}