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src/hssm/hssm.py
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| # # Need to determine what the output should look like | ||
| # if self.p_outlier.is_regression: | ||
| # raise NotImplementedError( | ||
| # "Regression for `p_outlier` is not implemented yet." | ||
| # ) |
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Keep commented code or ok to delete?
| "z": {"dist": "Weibull", "alpha": 1.2, "beta": 0.25}, | ||
| "t": {"dist": "HalfNormal", "sigma": 0.2}, | ||
| "sv": {"dist": "Weibull", "alpha": 1.5, "beta": "0.3"}, | ||
| "sz": {"dist": "Weibull", "alpha": 1.5, "beta": "0.3"}, | ||
| "st": {"dist": "Weibull", "alpha": 1.5, "beta": "0.3"}, | ||
| "sv": {"dist": "Weibull", "alpha": 1.5, "beta": 0.3}, | ||
| "sz": {"dist": "Weibull", "alpha": 1.5, "beta": 0.3}, | ||
| "st": {"dist": "Weibull", "alpha": 1.5, "beta": 0.3}, | ||
| "p_outlier": {"dist": "Weibull", "alpha": 1.5, "beta": 0.3}, |
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Why the switch to actual number types?
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To be honest I am confused how this became a string before. As far as I am concerned this should have always been float. @cpaniaguam @digicosmos86
digicosmos86
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LGTM! I think we also need to change some associated type annotations and for p_outliers in HSSM and other functions to make explicit that we do support regressions for it. It could be another PR though
adding in missing defaults for p-outlier regression, and a small associated tutorial.