We propose a penalization algorithm for functional linear regression models, where the coefficient function β is shrunk towards data-driven shape template γ. To best of our knowledge, we employ nonzero centered L2 penalty in novel manner, as center γ also optimized while being constrained to belong class piecewise functions Γ, by restricting its basis expansion. This indirect allows user contro...