The output of the nearest neighbor (1-NN) classification rule, g(x), depends on a given learning set S and on a distance function ρ(x,X). We show that transforming S_{N} into a set A_{N} whose patterns have a Hanan grid-like structure, results in the equivalence g(x) = g(x) that holds for any NN classifier with distance functions ‖x-X‖ and with any q ∈ (0,∞). Thanks to the equivalence, A can be used to learn g(x) to mimic a behavior of the classifier g(x) based on the original set S even when q is unknown (and varying). Possible application of the proposed framework (inspired also by a time-varying stimuli perception phenomenon) in autism spectrum disorder (ASD) therapeutic tools design is discussed.