Support for bounds, initial guess, and threhold inputs #90
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| @@ -27,6 +27,11 @@ class ETP_SRI_LinearFitting(OsipiBase): | |||
| required_initial_guess_optional = False | |||
| accepted_dimensions = 1 | |||
| # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most? | |||
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| # Supported inputs in the standardized class | |||
| supported_bounds = False | |||
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I don't understand how these are used.
Are these now properties of the class that I can ask using self.supported_bounds? That would help with testing :)
| @@ -27,6 +27,11 @@ class PvH_KB_NKI_IVIMfit(OsipiBase): | |||
| required_initial_guess_optional =False | |||
| accepted_dimensions = 1 # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most? | |||
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| # Supported inputs in the standardized class | |||
| supported_bounds = False | |||
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Do we need to ask authors to double check whether these booleans are correct for their methods?
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Went through each standardized algorithm and checked/added support for bounds, initial guesses, and thresholds where applicable.
Additionally, the algorithms now have the following boolean attributes which can be used to filter algorithms in testing.
supported_bounds
supported_initial_guess
supported_thresholds