Bugfix powered_exponential.py #279
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Based on the equivalence of this kernel with symmetric generalized gaussian densities (up to a difference in the power parametrization) and according to https://en.wikipedia.org/wiki/Generalized_normal_distribution#Connection_to_Positive-Definite_Functions the power parameter has to be in the interval (0, 1] for the kernel to be positive semidefinite. Adding a Sigmoid constraint to enforce that. Signed-off-by: Ingmar Schuster <ingmar.schuster@gmail.com>
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Test case had to be adapted Signed-off-by: Ingmar Schuster <ingmar.schuster@gmail.com>
thomaspinder
approved these changes
May 30, 2023
Signed-off-by: Thomas Pinder <tompinder@live.co.uk>
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poetry run pre-commit run --all-files --show-diff-on-failurebefore committing.Description
Based on the equivalence of this kernel with symmetric generalized gaussian densities (up to a difference in the power parametrization) and according to https://en.wikipedia.org/wiki/Generalized_normal_distribution#Connection_to_Positive-Definite_Functions the power parameter has to be in the interval (0, 1] for the kernel to be positive semidefinite. Adding a Sigmoid constraint to enforce that.
Issue Number: N/A